From b0375498719a62adf92b1648f42e66d665c3bb46 Mon Sep 17 00:00:00 2001 From: zhouxin Date: Fri, 25 Apr 2025 11:00:58 +0800 Subject: [PATCH 1/2] Fix mlu pir test --- backends/mlu/tests/CMakeLists.txt | 4 ++-- backends/mlu/tests/unittests/test_multinomial_op_mlu.py | 6 +++++- .../tools/dockerfile/Dockerfile.mlu.kylinv10.gcc82.py310 | 1 + .../tools/dockerfile/Dockerfile.mlu.ubuntu20.gcc84.py310 | 1 + 4 files changed, 9 insertions(+), 3 deletions(-) diff --git a/backends/mlu/tests/CMakeLists.txt b/backends/mlu/tests/CMakeLists.txt index 5b44e3b60..f77e6bcba 100644 --- a/backends/mlu/tests/CMakeLists.txt +++ b/backends/mlu/tests/CMakeLists.txt @@ -22,7 +22,7 @@ function(py_test_modules TARGET_NAME) add_test( NAME ${TARGET_NAME} COMMAND - ${CMAKE_COMMAND} -E env + ${CMAKE_COMMAND} -E env FLAGS_use_stream_safe_cuda_allocator=false CUSTOM_DEVICE_ROOT=${CMAKE_BINARY_DIR}/python/paddle_custom_device/ PYTHONPATH=${PYTHON_SOURCE_DIR}:${PYTHON_SOURCE_DIR}/tests:$ENV{PYTHONPATH} ${py_test_modules_ENVS} python ${PYTHON_SOURCE_DIR}/tools/test_runner.py @@ -37,7 +37,7 @@ endfunction() add_test( NAME test_LeNet_MNIST COMMAND - ${CMAKE_COMMAND} -E env + ${CMAKE_COMMAND} -E env FLAGS_use_stream_safe_cuda_allocator=false CUSTOM_DEVICE_ROOT=${CMAKE_BINARY_DIR}/python/paddle_custom_device/ PYTHONPATH=${PYTHON_SOURCE_DIR}:${PYTHON_SOURCE_DIR}/tests:$ENV{PYTHONPATH} python test_LeNet_MNIST.py diff --git a/backends/mlu/tests/unittests/test_multinomial_op_mlu.py b/backends/mlu/tests/unittests/test_multinomial_op_mlu.py index 2170103b7..2cb249147 100644 --- a/backends/mlu/tests/unittests/test_multinomial_op_mlu.py +++ b/backends/mlu/tests/unittests/test_multinomial_op_mlu.py @@ -169,7 +169,11 @@ def test_static(self): train_program = base.Program() with base.program_guard(train_program, startup_program): x = paddle.static.data("x", shape=[4], dtype="float32") - out = paddle.multinomial(x, num_samples=100000, replacement=True) + outs = [ + paddle.multinomial(x, num_samples=100000, replacement=True) + for _ in range(10) + ] + out = paddle.concat(outs, axis=0) place = base.CustomPlace("mlu", 0) exe = base.Executor(place) diff --git a/backends/mlu/tools/dockerfile/Dockerfile.mlu.kylinv10.gcc82.py310 b/backends/mlu/tools/dockerfile/Dockerfile.mlu.kylinv10.gcc82.py310 index 20f270762..78e215256 100644 --- a/backends/mlu/tools/dockerfile/Dockerfile.mlu.kylinv10.gcc82.py310 +++ b/backends/mlu/tools/dockerfile/Dockerfile.mlu.kylinv10.gcc82.py310 @@ -41,6 +41,7 @@ ENV FLAGS_use_stride_kernel=0 ENV FLAGS_allocator_strategy=auto_growth ENV CNCL_MEM_POOL_MULTI_CLIQUE_ENABLE=1 ENV PADDLE_XCCL_BACKEND=mlu +ENV FLAGS_use_stream_safe_cuda_allocator=false # yum and pip clean RUN yum clean all && \ diff --git a/backends/mlu/tools/dockerfile/Dockerfile.mlu.ubuntu20.gcc84.py310 b/backends/mlu/tools/dockerfile/Dockerfile.mlu.ubuntu20.gcc84.py310 index 46b9c6422..c3afa3c22 100644 --- a/backends/mlu/tools/dockerfile/Dockerfile.mlu.ubuntu20.gcc84.py310 +++ b/backends/mlu/tools/dockerfile/Dockerfile.mlu.ubuntu20.gcc84.py310 @@ -83,6 +83,7 @@ ENV FLAGS_use_stride_kernel=0 ENV FLAGS_allocator_strategy=auto_growth ENV CNCL_MEM_POOL_MULTI_CLIQUE_ENABLE=1 ENV PADDLE_XCCL_BACKEND=mlu +ENV FLAGS_use_stream_safe_cuda_allocator=false # Clean RUN apt-get clean -y From 6a8a7bf5e29ed848632886bbc5a8fd3fa64fae87 Mon Sep 17 00:00:00 2001 From: zhouxin Date: Mon, 28 Apr 2025 14:16:31 +0800 Subject: [PATCH 2/2] stash env --- .env | 16 + backends/mlu/kernels/funcs/mlu_baseop.cc | 3 + backends/mlu/kernels/memcpy_kernel.cc | 40 + backends/mlu/kernels/multinomial_kernel.cc | 63 +- backends/mlu/tests/CMakeLists.txt | 2 +- .../unittests/test_multinomial_op_mlu.py | 52 +- cmake/paddle.cmake | 2 +- fixIf/IR.log | 1249 ++ fixIf/InstructionExe.log | 267 + fixIf/VLOG0.log | 10003 ++++++++++++++++ mycheck.py | 35 + 11 files changed, 11701 insertions(+), 31 deletions(-) create mode 100644 .env create mode 100644 fixIf/IR.log create mode 100644 fixIf/InstructionExe.log create mode 100644 fixIf/VLOG0.log create mode 100644 mycheck.py diff --git a/.env b/.env new file mode 100644 index 000000000..892651c05 --- /dev/null +++ b/.env @@ -0,0 +1,16 @@ +PYTHONPATH=/work/PaddleX/PaddleCustomDevice/python:/work/PaddleX/PaddleCustomDevice/python/tests +FLAGS_enable_pir_api=1 +# FLAGS_print_ir=True +# GLOG_v=6 +PADDLE_PDX_DEBUG=True +PADDLE_PDX_DISABLE_DEV_MODEL_WL=True +FLAGS_json_format_model=1 +FLAGS_fast_eager_deletion_mode=0 +FLAGS_use_stream_safe_cuda_allocator=false + +# FLAGS_new_executor_serial_run=1 +# FLAGS_call_stack_level=2 + + +# DEBUG_WAIT_AFTER_YIELD=0 +# DEBUG_WAIT_BEFORE_YIELD=0 \ No newline at end of file diff --git a/backends/mlu/kernels/funcs/mlu_baseop.cc b/backends/mlu/kernels/funcs/mlu_baseop.cc index 4b0d5244c..dea644253 100644 --- a/backends/mlu/kernels/funcs/mlu_baseop.cc +++ b/backends/mlu/kernels/funcs/mlu_baseop.cc @@ -5985,6 +5985,9 @@ NormalizeDesc::~NormalizeDesc() { workspace_size, output_desc, out)); + cnrtQueue_t queue; + PADDLE_ENFORCE_MLU_SUCCESS(cnnlGetQueue(handle, &queue)); + PADDLE_ENFORCE_MLU_SUCCESS(cnrtQueueSync(queue)); } /* static */ void MLUOP::OpYoloBox(const Context& ctx, diff --git a/backends/mlu/kernels/memcpy_kernel.cc b/backends/mlu/kernels/memcpy_kernel.cc index 0507e4339..d8275237d 100644 --- a/backends/mlu/kernels/memcpy_kernel.cc +++ b/backends/mlu/kernels/memcpy_kernel.cc @@ -12,11 +12,49 @@ // See the License for the specific language governing permissions and // limitations under the License. +#include #include "kernels/funcs/mlu_baseop.h" #include "kernels/funcs/mlu_funcs.h" namespace custom_kernel { +using phi::CPUPlace; +using phi::DenseTensor; +const int64_t SAMPLE_MAX = 4; +template +void printInfo(const Context &dev_ctx, const DenseTensor &x, const std::string& name, bool frequency=false, bool shoud_sleep=false) { + std::cout << "========================== START PRINT " << name << " ==========================" << std::endl; + std::cout << "numel: " + << x.numel() + << std::endl; + std::cout << "place: " + << x.place() + << std::endl; + + if constexpr (std::is_same_v || std::is_same_v) { + const T* data_p = static_cast(x.data()); + if(frequency){ + std::vector frequency(SAMPLE_MAX, 0); + for (int i = 0; i < x.numel(); ++i) { + if (data_p[i] >= 0 && data_p[i] < SAMPLE_MAX) { + frequency[data_p[i]]++; + } else { + std::cout << "FOUND INVALID SAMPLE!" << std::endl; + return ; + } + } + std::cout << "frequency: " << std::endl; + for (int i = 0; i < SAMPLE_MAX; ++i) { + std::cout <(frequency[i]) / x.numel() << "\t"; + } + std::cout << std::endl; + } + } + + std::cout<< "========================== END PRINT " << name << " ==========================" << std::endl << std::endl; + if(shoud_sleep) + std::this_thread::sleep_for(std::chrono::milliseconds(5000)); +} template void MemcpyKernel(const Context& dev_ctx, const phi::DenseTensor& x, @@ -53,7 +91,9 @@ void MemcpyD2HKernel(const Context& dev_ctx, const phi::DenseTensor& x, int dst_place_type, phi::DenseTensor* out) { + std::cout << "Begin MemcpyD2HKernel" << std::endl; TensorCopy(dev_ctx, x, false, out, phi::CPUPlace()); + // printInfo(dev_ctx, *out, "Memcpy out", true, false); } template diff --git a/backends/mlu/kernels/multinomial_kernel.cc b/backends/mlu/kernels/multinomial_kernel.cc index 905e2717a..c9a019523 100644 --- a/backends/mlu/kernels/multinomial_kernel.cc +++ b/backends/mlu/kernels/multinomial_kernel.cc @@ -12,16 +12,68 @@ // See the License for the specific language governing permissions and // limitations under the License. +#include +#include +#include +#include +#include #include "kernels/funcs/mlu_funcs.h" +#include "paddle/phi/core/dense_tensor.h" namespace custom_kernel { +using phi::CPUPlace; +using phi::DenseTensor; +const int64_t SAMPLE_MAX = 4; +template +void printInfo(const Context &dev_ctx, const DenseTensor &x, const std::string& name, bool frequency=false, bool shoud_sleep=false) { + std::cout << "========================== START PRINT " << name << " ==========================" << std::endl; + std::cout << "numel: " + << x.numel() + << std::endl; + std::cout << "place: " + << x.place() + << std::endl; + + phi::DenseTensor tensor_tmp; + phi::Copy( + dev_ctx, + x, + CPUPlace(), + true, + &tensor_tmp); + if constexpr (std::is_same_v || std::is_same_v) { + T* data_p = static_cast(tensor_tmp.data()); + + if(frequency){ + std::vector frequency(SAMPLE_MAX, 0); + for (int i = 0; i < x.numel(); ++i) { + if (data_p[i] >= 0 && data_p[i] < SAMPLE_MAX) { + frequency[data_p[i]]++; + } else { + std::cout << "FOUND INVALID SAMPLE!" << std::endl; + return ; + } + } + std::cout << "frequency: " << std::endl; + for (int i = 0; i < SAMPLE_MAX; ++i) { + std::cout <(frequency[i]) / x.numel() << "\t"; + } + std::cout << std::endl; + } + } + + std::cout<< "========================== END PRINT " << name << " ==========================" << std::endl << std::endl; + if(shoud_sleep) + std::this_thread::sleep_for(std::chrono::milliseconds(5000)); +} template -void MultinomialKernel(const Context& dev_ctx, - const phi::DenseTensor& x, - const phi::Scalar& num, +void MultinomialKernel(const Context &dev_ctx, + const phi::DenseTensor &x, + const phi::Scalar &num, bool replacement, - phi::DenseTensor* out) { + phi::DenseTensor *out) { + // std::this_thread::sleep_for(std::chrono::milliseconds(2000)); dev_ctx.template Alloc(out); MLUCnnlTensorDesc desc_x(x); MLUCnnlTensorDesc desc_out(*out); @@ -39,6 +91,9 @@ void MultinomialKernel(const Context& dev_ctx, GetBasePtr(&generator_desc->get_state()), desc_out.get(), GetBasePtr(out)); + std::cout << "End MultinomialKernel" << std::endl; + // printInfo(dev_ctx, x, "x"); + // printInfo(dev_ctx, *out, "out", true, false); } } // namespace custom_kernel diff --git a/backends/mlu/tests/CMakeLists.txt b/backends/mlu/tests/CMakeLists.txt index f77e6bcba..04d5e9e5f 100644 --- a/backends/mlu/tests/CMakeLists.txt +++ b/backends/mlu/tests/CMakeLists.txt @@ -22,7 +22,7 @@ function(py_test_modules TARGET_NAME) add_test( NAME ${TARGET_NAME} COMMAND - ${CMAKE_COMMAND} -E env FLAGS_use_stream_safe_cuda_allocator=false + ${CMAKE_COMMAND} -E env CUSTOM_DEVICE_ROOT=${CMAKE_BINARY_DIR}/python/paddle_custom_device/ PYTHONPATH=${PYTHON_SOURCE_DIR}:${PYTHON_SOURCE_DIR}/tests:$ENV{PYTHONPATH} ${py_test_modules_ENVS} python ${PYTHON_SOURCE_DIR}/tools/test_runner.py diff --git a/backends/mlu/tests/unittests/test_multinomial_op_mlu.py b/backends/mlu/tests/unittests/test_multinomial_op_mlu.py index 2cb249147..feae48d3a 100644 --- a/backends/mlu/tests/unittests/test_multinomial_op_mlu.py +++ b/backends/mlu/tests/unittests/test_multinomial_op_mlu.py @@ -22,7 +22,7 @@ import numpy as np paddle.enable_static() - +paddle.seed(1000) def sample_output_one_dimension(out, dim): # count numbers of different categories @@ -164,30 +164,32 @@ def test_dygraph4(self): paddle.enable_static() def test_static(self): - paddle.set_device("mlu:0") - startup_program = base.Program() - train_program = base.Program() - with base.program_guard(train_program, startup_program): - x = paddle.static.data("x", shape=[4], dtype="float32") - outs = [ - paddle.multinomial(x, num_samples=100000, replacement=True) - for _ in range(10) - ] - out = paddle.concat(outs, axis=0) - - place = base.CustomPlace("mlu", 0) - exe = base.Executor(place) - - exe.run(startup_program) - x_np = np.random.rand(4).astype("float32") - out = exe.run(train_program, feed={"x": x_np}, fetch_list=[out]) - - sample_prob = sample_output_one_dimension(out, 4) - prob = x_np / x_np.sum(axis=-1, keepdims=True) - self.assertTrue( - np.allclose(sample_prob, prob, rtol=0, atol=0.01), - "sample_prob: " + str(sample_prob) + "\nprob: " + str(prob), - ) + for _ in range(10000): + print(f"start {_}") + paddle.set_device("mlu:0") + startup_program = base.Program() + train_program = base.Program() + with base.program_guard(train_program, startup_program): + x = paddle.static.data("x", shape=[4], dtype="float32") + outs = [ + paddle.multinomial(x, num_samples=100000, replacement=True) + for _ in range(10) + ] + out = paddle.concat(outs, axis=0) + + place = base.CustomPlace("mlu", 0) + exe = base.Executor(place) + + exe.run(startup_program) + x_np = np.random.rand(4).astype("float32") + out = exe.run(train_program, feed={"x": x_np}, fetch_list=[out]) + + sample_prob = sample_output_one_dimension(out, 4) + prob = x_np / x_np.sum(axis=-1, keepdims=True) + self.assertTrue( + np.allclose(sample_prob, prob, rtol=0, atol=0.01), + "sample_prob: " + str(sample_prob) + "\nprob: " + str(prob), + ) class TestMultinomialFP16Op(OpTest): diff --git a/cmake/paddle.cmake b/cmake/paddle.cmake index 140bb5acf..f5efa01e8 100644 --- a/cmake/paddle.cmake +++ b/cmake/paddle.cmake @@ -70,7 +70,7 @@ endif() # submodule Paddle first set(paddle_submodule $ENV{paddle_submodule}) -if(paddle_submodule) +if(NOT paddle_submodule) get_filename_component(REPO_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../../" ABSOLUTE) get_filename_component(PADDLE_SOURCE_DIR "${REPO_SOURCE_DIR}/Paddle" ABSOLUTE) diff --git a/fixIf/IR.log b/fixIf/IR.log new file mode 100644 index 000000000..f0eae0441 --- /dev/null +++ b/fixIf/IR.log @@ -0,0 +1,1249 @@ +IR after lowering = { + (%0) = "builtin.constant" [id:2016] () {origin_id:2014,persistable:[true],value:"constant_folding@_174521851658966282064"} : () -> cpu_tensor<3xi64> + (%1) = "builtin.constant" [id:2017] () {origin_id:2005,persistable:[true],value:"constant_folding@_174521851657900260063"} : () -> cpu_tensor + (%2) = "builtin.parameter" [id:2018] () {origin_id:1996,parameter_name:"constant_folding@_174521851656800928162",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor + (%3) = "builtin.constant" [id:2019] () {origin_id:1987,persistable:[true],value:"constant_folding@_174521851655704932161"} : () -> cpu_tensor<1xf32> + (%4) = "builtin.constant" [id:2020] () {origin_id:1978,persistable:[true],value:"constant_folding@_174521851654203266160"} : () -> cpu_tensor + (%5) = "builtin.constant" [id:2021] () {origin_id:1956,persistable:[true],value:"constant_folding@_174521851650320418258"} : () -> cpu_tensor<1xi32> + (%6) = "builtin.constant" [id:2022] () {origin_id:1947,persistable:[true],value:"constant_folding@_174521851648933759257"} : () -> cpu_tensor + (%7) = "builtin.constant" [id:2023] () {origin_id:1925,persistable:[true],value:"constant_folding@_174521851646524997255"} : () -> cpu_tensor + (%8) = "builtin.constant" [id:2024] () {origin_id:1912,persistable:[true],value:"constant_folding@_174521851645254726254"} : () -> cpu_tensor + (%9) = "builtin.constant" [id:2025] () {origin_id:1899,persistable:[true],value:"constant_folding@_174521851644141361253"} : () -> cpu_tensor + (%10) = "builtin.constant" [id:2026] () {origin_id:1890,persistable:[true],value:"constant_folding@_174521851643067642352"} : () -> cpu_tensor<0xi64> + (%11) = "builtin.parameter" [id:2027] () {origin_id:1881,parameter_name:"constant_folding@_174521851641085011351",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor + (%12) = "builtin.constant" [id:2028] () {origin_id:1872,persistable:[true],value:"constant_folding@_174521851639742309350"} : () -> cpu_tensor<1xf32> + (%13) = "builtin.constant" [id:2029] () {origin_id:1863,persistable:[true],value:"constant_folding@_174521851637647249349"} : () -> cpu_tensor + (%14) = "builtin.parameter" [id:2030] () {origin_id:1854,parameter_name:"constant_folding@_174521851636292209448",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor<1x256x1x1xf32> + (%15) = "builtin.constant" [id:2031] () {origin_id:1832,persistable:[true],value:"constant_folding@_174521851634116350446"} : () -> cpu_tensor<0xi64> + (%16) = "builtin.parameter" [id:2032] () {origin_id:1823,parameter_name:"constant_folding@_174521851632977055445",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor<1x1x1xf32> + (%17) = "builtin.constant" [id:2033] () {origin_id:1814,persistable:[true],value:"constant_folding@_174521851631400097444"} : () -> cpu_tensor + (%18) = "builtin.constant" [id:2034] () {origin_id:1805,persistable:[true],value:"constant_folding@_174521851630301719443"} : () -> cpu_tensor<1xf32> + (%19) = "builtin.constant" [id:2035] () {origin_id:1796,persistable:[true],value:"constant_folding@_174521851628333812542"} : () -> cpu_tensor<1xf32> + (%20) = "builtin.constant" [id:2036] () {origin_id:1784,persistable:[true],value:"constant_folding@_174521851622731044541"} : () -> cpu_tensor + (%21) = "builtin.constant" [id:2037] () {origin_id:1771,persistable:[true],value:"constant_folding@_174521851621460025640"} : () -> cpu_tensor + (%22) = "builtin.constant" [id:2038] () {origin_id:1758,persistable:[true],value:"constant_folding@_174521851620358852639"} : () -> cpu_tensor<0xi64> + (%23) = "builtin.constant" [id:2039] () {origin_id:1749,persistable:[true],value:"constant_folding@_174521851619271450638"} : () -> cpu_tensor<1xi32> + (%24) = "builtin.constant" [id:2040] () {origin_id:1740,persistable:[true],value:"constant_folding@_174521851618188492637"} : () -> cpu_tensor<1xf32> + (%25) = "builtin.parameter" [id:2041] () {origin_id:1731,parameter_name:"constant_folding@_174521851617051507636",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor + (%26) = "builtin.constant" [id:2042] () {origin_id:1722,persistable:[true],value:"constant_folding@_174521851615926908635"} : () -> cpu_tensor<1xf32> + (%27) = "builtin.parameter" [id:2043] () {origin_id:1713,parameter_name:"constant_folding@_174521851614630018734",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor<1xi32> + (%28) = "builtin.parameter" [id:2044] () {origin_id:1693,parameter_name:"constant_folding@_174521851612167306732",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor<1x6xf32> + (%29) = "builtin.parameter" [id:2045] () {origin_id:1673,parameter_name:"constant_folding@_174521851609906802730",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor + (%30) = "builtin.constant" [id:2046] () {origin_id:1664,persistable:[true],value:"constant_folding@_174521851608824093729"} : () -> cpu_tensor + (%31) = "builtin.constant" [id:2047] () {origin_id:1655,persistable:[true],value:"constant_folding@_174521851607746359828"} : () -> cpu_tensor + (%32) = "builtin.constant" [id:2048] () {origin_id:1646,persistable:[true],value:"constant_folding@_174521851606653388827"} : () -> cpu_tensor<1xf32> + (%33) = "builtin.constant" [id:2049] () {origin_id:1637,persistable:[true],value:"constant_folding@_174521851605576262826"} : () -> cpu_tensor<1xf32> + (%34) = "builtin.constant" [id:2050] () {origin_id:1628,persistable:[true],value:"constant_folding@_174521851604483521825"} : () -> cpu_tensor<1xf32> + (%35) = "builtin.constant" [id:2051] () {origin_id:1619,persistable:[true],value:"constant_folding@_174521851603390216824"} : () -> cpu_tensor<1xf32> + (%36) = "builtin.constant" [id:2052] () {origin_id:1610,persistable:[true],value:"constant_folding@_174521851602328666823"} : () -> cpu_tensor<1xi64> + (%37) = "builtin.constant" [id:2053] () {origin_id:1601,persistable:[true],value:"constant_folding@_174521851601250522922"} : () -> cpu_tensor<1xf32> + (%38) = "builtin.constant" [id:2054] () {origin_id:1592,persistable:[true],value:"constant_folding@_174521851600176357921"} : () -> cpu_tensor<2xi64> + (%39) = "builtin.constant" [id:2055] () {origin_id:1583,persistable:[true],value:"constant_folding@_174521851599109053920"} : () -> cpu_tensor<2xi64> + (%40) = "builtin.parameter" [id:2056] () {origin_id:1574,parameter_name:"constant_folding@_174521851597940077919",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor + (%41) = "builtin.constant" [id:2057] () {origin_id:1565,persistable:[true],value:"constant_folding@_174521851596837382918"} : () -> cpu_tensor<1xi64> + (%42) = "builtin.constant" [id:2058] () {origin_id:1556,persistable:[true],value:"constant_folding@_174521851595733295917"} : () -> cpu_tensor<1xi64> + (%43) = "builtin.constant" [id:2059] () {origin_id:1547,persistable:[true],value:"constant_folding@_174521851594646348916"} : () -> cpu_tensor<1xi64> + (%44) = "builtin.constant" [id:2060] () {origin_id:1538,persistable:[true],value:"constant_folding@_174521851593553109915"} : () -> cpu_tensor<1xi64> + (%45) = "builtin.constant" [id:2061] () {origin_id:1529,persistable:[true],value:"constant_folding@_174521851592463330014"} : () -> cpu_tensor<2xi64> + (%46) = "builtin.parameter" [id:2062] () {origin_id:1520,parameter_name:"constant_folding@_174521851591135695013",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor<1x15x4xf32> + (%47) = "builtin.constant" [id:2063] () {origin_id:1498,persistable:[true],value:"constant_folding@_174521851588962779011"} : () -> cpu_tensor<3xi64> + (%48) = "builtin.constant" [id:2064] () {origin_id:1489,persistable:[true],value:"constant_folding@_174521851587867296010"} : () -> cpu_tensor<1xi64> + (%49) = "builtin.constant" [id:2065] () {origin_id:1480,persistable:[true],value:"constant_folding@_17452185158660622909"} : () -> cpu_tensor<1xf32> + (%50) = "builtin.constant" [id:2066] () {origin_id:1471,persistable:[true],value:"constant_folding@_17452185158546554518"} : () -> cpu_tensor<1xi64> + (%51) = "builtin.constant" [id:2067] () {origin_id:1462,persistable:[true],value:"constant_folding@_17452185158410167217"} : () -> cpu_tensor<1xi64> + (%52) = "builtin.constant" [id:2068] () {origin_id:1453,persistable:[true],value:"constant_folding@_17452185158298218916"} : () -> cpu_tensor<1xi64> + (%53) = "builtin.parameter" [id:2069] () {origin_id:1444,parameter_name:"constant_folding@_17452185158158873515",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor<1x60x1x1xf32> + (%54) = "builtin.parameter" [id:2070] () {origin_id:1431,parameter_name:"constant_folding@_17452185158019830114",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor<1x15x1x1xf32> + (%55) = "builtin.parameter" [id:2071] () {origin_id:1418,parameter_name:"constant_folding@_17452185157878266523",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor<1x1024x1x1xf32> + (%56) = "builtin.parameter" [id:2072] () {origin_id:1405,parameter_name:"constant_folding@_17452185157732596322",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor<1x80x1x1xf32> + (%57) = "builtin.constant" [id:2073] () {origin_id:1383,persistable:[true],value:"constant_folding@_17452185157441333520"} : () -> cpu_tensor<2xi64> + (%58) = "builtin.parameter" [id:2074] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:8,parameter_name:"conv2d_56.w_0_deepcopy_280",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<80x256x1x1xf32> + (%59) = "builtin.parameter" [id:2075] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:10,parameter_name:"conv2d_transpose_0.w_0_deepcopy_278",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<2048x256x2x2xf32> + (%60) = "builtin.parameter" [id:2076] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:11,parameter_name:"linear_1.b_0_deepcopy_277",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<320xf32> + (%61) = "builtin.parameter" [id:2077] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:12,parameter_name:"linear_1.w_0_deepcopy_276",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<2048x320xf32> + (%62) = "builtin.parameter" [id:2078] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:13,parameter_name:"linear_0.b_0_deepcopy_275",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<81xf32> + (%63) = "builtin.parameter" [id:2079] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:14,parameter_name:"linear_0.w_0_deepcopy_274",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<2048x81xf32> + (%64) = "builtin.parameter" [id:2080] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:15,parameter_name:"batch_norm2d_52.w_2_deepcopy_273",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%65) = "builtin.parameter" [id:2081] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:16,parameter_name:"batch_norm2d_52.w_1_deepcopy_272",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%66) = "builtin.parameter" [id:2082] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:17,parameter_name:"batch_norm2d_52.b_0_deepcopy_271",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%67) = "builtin.parameter" [id:2083] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:18,parameter_name:"batch_norm2d_52.w_0_deepcopy_270",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%68) = "builtin.parameter" [id:2084] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:19,parameter_name:"conv2d_55.w_0_deepcopy_269",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<2048x512x1x1xf32> + (%69) = "builtin.parameter" [id:2085] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:20,parameter_name:"batch_norm2d_51.w_2_deepcopy_268",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%70) = "builtin.parameter" [id:2086] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:21,parameter_name:"batch_norm2d_51.w_1_deepcopy_267",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%71) = "builtin.parameter" [id:2087] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:22,parameter_name:"batch_norm2d_51.b_0_deepcopy_266",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%72) = "builtin.parameter" [id:2088] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:23,parameter_name:"batch_norm2d_51.w_0_deepcopy_265",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%73) = "builtin.parameter" [id:2089] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:24,parameter_name:"conv2d_54.w_0_deepcopy_264",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x512x3x3xf32> + (%74) = "builtin.parameter" [id:2090] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:25,parameter_name:"batch_norm2d_50.w_2_deepcopy_263",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%75) = "builtin.parameter" [id:2091] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:26,parameter_name:"batch_norm2d_50.w_1_deepcopy_262",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%76) = "builtin.parameter" [id:2092] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:27,parameter_name:"batch_norm2d_50.b_0_deepcopy_261",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%77) = "builtin.parameter" [id:2093] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:28,parameter_name:"batch_norm2d_50.w_0_deepcopy_260",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%78) = "builtin.parameter" [id:2094] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:29,parameter_name:"conv2d_53.w_0_deepcopy_259",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x2048x1x1xf32> + (%79) = "builtin.parameter" [id:2095] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:30,parameter_name:"batch_norm2d_49.w_2_deepcopy_258",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%80) = "builtin.parameter" [id:2096] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:31,parameter_name:"batch_norm2d_49.w_1_deepcopy_257",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%81) = "builtin.parameter" [id:2097] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:32,parameter_name:"batch_norm2d_49.b_0_deepcopy_256",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%82) = "builtin.parameter" [id:2098] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:33,parameter_name:"batch_norm2d_49.w_0_deepcopy_255",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%83) = "builtin.parameter" [id:2099] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:34,parameter_name:"conv2d_52.w_0_deepcopy_254",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<2048x512x1x1xf32> + (%84) = "builtin.parameter" [id:2100] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:35,parameter_name:"batch_norm2d_48.w_2_deepcopy_253",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%85) = "builtin.parameter" [id:2101] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:36,parameter_name:"batch_norm2d_48.w_1_deepcopy_252",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%86) = "builtin.parameter" [id:2102] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:37,parameter_name:"batch_norm2d_48.b_0_deepcopy_251",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%87) = "builtin.parameter" [id:2103] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:38,parameter_name:"batch_norm2d_48.w_0_deepcopy_250",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%88) = "builtin.parameter" [id:2104] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:39,parameter_name:"conv2d_51.w_0_deepcopy_249",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x512x3x3xf32> + (%89) = "builtin.parameter" [id:2105] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:40,parameter_name:"batch_norm2d_47.w_2_deepcopy_248",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%90) = "builtin.parameter" [id:2106] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:41,parameter_name:"batch_norm2d_47.w_1_deepcopy_247",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%91) = "builtin.parameter" [id:2107] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:42,parameter_name:"batch_norm2d_47.b_0_deepcopy_246",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%92) = "builtin.parameter" [id:2108] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:43,parameter_name:"batch_norm2d_47.w_0_deepcopy_245",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%93) = "builtin.parameter" [id:2109] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:44,parameter_name:"conv2d_50.w_0_deepcopy_244",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x2048x1x1xf32> + (%94) = "builtin.parameter" [id:2110] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:45,parameter_name:"batch_norm2d_46.w_2_deepcopy_243",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%95) = "builtin.parameter" [id:2111] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:46,parameter_name:"batch_norm2d_46.w_1_deepcopy_242",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%96) = "builtin.parameter" [id:2112] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:47,parameter_name:"batch_norm2d_46.b_0_deepcopy_241",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%97) = "builtin.parameter" [id:2113] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:48,parameter_name:"batch_norm2d_46.w_0_deepcopy_240",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%98) = "builtin.parameter" [id:2114] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:49,parameter_name:"conv2d_49.w_0_deepcopy_239",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<2048x1024x1x1xf32> + (%99) = "builtin.parameter" [id:2115] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:50,parameter_name:"batch_norm2d_45.w_2_deepcopy_238",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%100) = "builtin.parameter" [id:2116] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:51,parameter_name:"batch_norm2d_45.w_1_deepcopy_237",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%101) = "builtin.parameter" [id:2117] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:52,parameter_name:"batch_norm2d_45.b_0_deepcopy_236",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%102) = "builtin.parameter" [id:2118] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:53,parameter_name:"batch_norm2d_45.w_0_deepcopy_235",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%103) = "builtin.parameter" [id:2119] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:54,parameter_name:"conv2d_48.w_0_deepcopy_234",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<2048x512x1x1xf32> + (%104) = "builtin.parameter" [id:2120] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:55,parameter_name:"batch_norm2d_44.w_2_deepcopy_233",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%105) = "builtin.parameter" [id:2121] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:56,parameter_name:"batch_norm2d_44.w_1_deepcopy_232",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%106) = "builtin.parameter" [id:2122] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:57,parameter_name:"batch_norm2d_44.b_0_deepcopy_231",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%107) = "builtin.parameter" [id:2123] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:58,parameter_name:"batch_norm2d_44.w_0_deepcopy_230",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%108) = "builtin.parameter" [id:2124] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:59,parameter_name:"conv2d_47.w_0_deepcopy_229",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x512x3x3xf32> + (%109) = "builtin.parameter" [id:2125] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:60,parameter_name:"batch_norm2d_43.w_2_deepcopy_228",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%110) = "builtin.parameter" [id:2126] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:61,parameter_name:"batch_norm2d_43.w_1_deepcopy_227",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%111) = "builtin.parameter" [id:2127] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:62,parameter_name:"batch_norm2d_43.b_0_deepcopy_226",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%112) = "builtin.parameter" [id:2128] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:63,parameter_name:"batch_norm2d_43.w_0_deepcopy_225",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%113) = "builtin.parameter" [id:2129] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:64,parameter_name:"conv2d_46.w_0_deepcopy_224",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x1024x1x1xf32> + (%114) = "builtin.parameter" [id:2130] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:66,parameter_name:"conv2d_45.w_0_deepcopy_222",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<60x1024x1x1xf32> + (%115) = "builtin.parameter" [id:2131] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:68,parameter_name:"conv2d_44.w_0_deepcopy_220",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<15x1024x1x1xf32> + (%116) = "builtin.parameter" [id:2132] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:70,parameter_name:"conv2d_43.w_0_deepcopy_218",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<1024x1024x3x3xf32> + (%117) = "builtin.parameter" [id:2133] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:71,parameter_name:"batch_norm2d_42.w_2_deepcopy_216",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%118) = "builtin.parameter" [id:2134] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:72,parameter_name:"batch_norm2d_42.w_1_deepcopy_215",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%119) = "builtin.parameter" [id:2135] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:73,parameter_name:"batch_norm2d_42.b_0_deepcopy_214",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%120) = "builtin.parameter" [id:2136] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:74,parameter_name:"batch_norm2d_42.w_0_deepcopy_213",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%121) = "builtin.parameter" [id:2137] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:75,parameter_name:"conv2d_42.w_0_deepcopy_212",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<1024x256x1x1xf32> + (%122) = "builtin.parameter" [id:2138] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:76,parameter_name:"batch_norm2d_41.w_2_deepcopy_211",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%123) = "builtin.parameter" [id:2139] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:77,parameter_name:"batch_norm2d_41.w_1_deepcopy_210",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%124) = "builtin.parameter" [id:2140] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:78,parameter_name:"batch_norm2d_41.b_0_deepcopy_209",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%125) = "builtin.parameter" [id:2141] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:79,parameter_name:"batch_norm2d_41.w_0_deepcopy_208",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%126) = "builtin.parameter" [id:2142] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:80,parameter_name:"conv2d_41.w_0_deepcopy_207",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<256x256x3x3xf32> + (%127) = "builtin.parameter" [id:2143] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:81,parameter_name:"batch_norm2d_40.w_2_deepcopy_206",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%128) = "builtin.parameter" [id:2144] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:82,parameter_name:"batch_norm2d_40.w_1_deepcopy_205",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%129) = "builtin.parameter" [id:2145] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:83,parameter_name:"batch_norm2d_40.b_0_deepcopy_204",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%130) = "builtin.parameter" [id:2146] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:84,parameter_name:"batch_norm2d_40.w_0_deepcopy_203",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%131) = "builtin.parameter" [id:2147] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:85,parameter_name:"conv2d_40.w_0_deepcopy_202",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<256x1024x1x1xf32> + (%132) = "builtin.parameter" [id:2148] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:86,parameter_name:"batch_norm2d_39.w_2_deepcopy_201",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%133) = "builtin.parameter" [id:2149] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:87,parameter_name:"batch_norm2d_39.w_1_deepcopy_200",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%134) = "builtin.parameter" [id:2150] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:88,parameter_name:"batch_norm2d_39.b_0_deepcopy_199",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%135) = "builtin.parameter" [id:2151] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:89,parameter_name:"batch_norm2d_39.w_0_deepcopy_198",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%136) = "builtin.parameter" [id:2152] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:90,parameter_name:"conv2d_39.w_0_deepcopy_197",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<1024x256x1x1xf32> + (%137) = "builtin.parameter" [id:2153] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:91,parameter_name:"batch_norm2d_38.w_2_deepcopy_196",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%138) = "builtin.parameter" [id:2154] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:92,parameter_name:"batch_norm2d_38.w_1_deepcopy_195",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%139) = "builtin.parameter" [id:2155] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:93,parameter_name:"batch_norm2d_38.b_0_deepcopy_194",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%140) = "builtin.parameter" [id:2156] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:94,parameter_name:"batch_norm2d_38.w_0_deepcopy_193",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%141) = "builtin.parameter" [id:2157] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:95,parameter_name:"conv2d_38.w_0_deepcopy_192",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<256x256x3x3xf32> + (%142) = "builtin.parameter" [id:2158] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:96,parameter_name:"batch_norm2d_37.w_2_deepcopy_191",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%143) = "builtin.parameter" [id:2159] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:97,parameter_name:"batch_norm2d_37.w_1_deepcopy_190",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%144) = "builtin.parameter" [id:2160] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:98,parameter_name:"batch_norm2d_37.b_0_deepcopy_189",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%145) = "builtin.parameter" [id:2161] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:99,parameter_name:"batch_norm2d_37.w_0_deepcopy_188",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%146) = "builtin.parameter" [id:2162] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:100,parameter_name:"conv2d_37.w_0_deepcopy_187",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<256x1024x1x1xf32> + (%147) = "builtin.parameter" [id:2163] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:101,parameter_name:"batch_norm2d_36.w_2_deepcopy_186",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%148) = "builtin.parameter" [id:2164] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:102,parameter_name:"batch_norm2d_36.w_1_deepcopy_185",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%149) = "builtin.parameter" [id:2165] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:103,parameter_name:"batch_norm2d_36.b_0_deepcopy_184",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%150) = "builtin.parameter" [id:2166] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:104,parameter_name:"batch_norm2d_36.w_0_deepcopy_183",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%151) = "builtin.parameter" [id:2167] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:105,parameter_name:"conv2d_36.w_0_deepcopy_182",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<1024x256x1x1xf32> + (%152) = "builtin.parameter" [id:2168] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:106,parameter_name:"batch_norm2d_35.w_2_deepcopy_181",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%153) = "builtin.parameter" [id:2169] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:107,parameter_name:"batch_norm2d_35.w_1_deepcopy_180",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%154) = "builtin.parameter" [id:2170] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:108,parameter_name:"batch_norm2d_35.b_0_deepcopy_179",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%155) = "builtin.parameter" [id:2171] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:109,parameter_name:"batch_norm2d_35.w_0_deepcopy_178",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%156) = "builtin.parameter" [id:2172] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:110,parameter_name:"conv2d_35.w_0_deepcopy_177",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<256x256x3x3xf32> + (%157) = "builtin.parameter" [id:2173] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:111,parameter_name:"batch_norm2d_34.w_2_deepcopy_176",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%158) = "builtin.parameter" [id:2174] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:112,parameter_name:"batch_norm2d_34.w_1_deepcopy_175",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%159) = "builtin.parameter" [id:2175] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:113,parameter_name:"batch_norm2d_34.b_0_deepcopy_174",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%160) = "builtin.parameter" [id:2176] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:114,parameter_name:"batch_norm2d_34.w_0_deepcopy_173",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%161) = "builtin.parameter" [id:2177] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:115,parameter_name:"conv2d_34.w_0_deepcopy_172",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<256x1024x1x1xf32> + (%162) = "builtin.parameter" [id:2178] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:116,parameter_name:"batch_norm2d_33.w_2_deepcopy_171",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%163) = "builtin.parameter" [id:2179] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:117,parameter_name:"batch_norm2d_33.w_1_deepcopy_170",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%164) = "builtin.parameter" [id:2180] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:118,parameter_name:"batch_norm2d_33.b_0_deepcopy_169",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%165) = "builtin.parameter" [id:2181] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:119,parameter_name:"batch_norm2d_33.w_0_deepcopy_168",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%166) = "builtin.parameter" [id:2182] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:120,parameter_name:"conv2d_33.w_0_deepcopy_167",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<1024x256x1x1xf32> + (%167) = "builtin.parameter" [id:2183] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:121,parameter_name:"batch_norm2d_32.w_2_deepcopy_166",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%168) = "builtin.parameter" [id:2184] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:122,parameter_name:"batch_norm2d_32.w_1_deepcopy_165",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%169) = "builtin.parameter" [id:2185] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:123,parameter_name:"batch_norm2d_32.b_0_deepcopy_164",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%170) = "builtin.parameter" [id:2186] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:124,parameter_name:"batch_norm2d_32.w_0_deepcopy_163",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%171) = "builtin.parameter" [id:2187] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:125,parameter_name:"conv2d_32.w_0_deepcopy_162",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<256x256x3x3xf32> + (%172) = "builtin.parameter" [id:2188] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:126,parameter_name:"batch_norm2d_31.w_2_deepcopy_161",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%173) = "builtin.parameter" [id:2189] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:127,parameter_name:"batch_norm2d_31.w_1_deepcopy_160",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%174) = "builtin.parameter" [id:2190] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:128,parameter_name:"batch_norm2d_31.b_0_deepcopy_159",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%175) = "builtin.parameter" [id:2191] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:129,parameter_name:"batch_norm2d_31.w_0_deepcopy_158",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%176) = "builtin.parameter" [id:2192] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:130,parameter_name:"conv2d_31.w_0_deepcopy_157",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<256x1024x1x1xf32> + (%177) = "builtin.parameter" [id:2193] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:131,parameter_name:"batch_norm2d_30.w_2_deepcopy_156",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%178) = "builtin.parameter" [id:2194] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:132,parameter_name:"batch_norm2d_30.w_1_deepcopy_155",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%179) = "builtin.parameter" [id:2195] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:133,parameter_name:"batch_norm2d_30.b_0_deepcopy_154",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%180) = "builtin.parameter" [id:2196] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:134,parameter_name:"batch_norm2d_30.w_0_deepcopy_153",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%181) = "builtin.parameter" [id:2197] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:135,parameter_name:"conv2d_30.w_0_deepcopy_152",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<1024x256x1x1xf32> + (%182) = "builtin.parameter" [id:2198] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:136,parameter_name:"batch_norm2d_29.w_2_deepcopy_151",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%183) = "builtin.parameter" [id:2199] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:137,parameter_name:"batch_norm2d_29.w_1_deepcopy_150",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%184) = "builtin.parameter" [id:2200] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:138,parameter_name:"batch_norm2d_29.b_0_deepcopy_149",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%185) = "builtin.parameter" [id:2201] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:139,parameter_name:"batch_norm2d_29.w_0_deepcopy_148",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%186) = "builtin.parameter" [id:2202] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:140,parameter_name:"conv2d_29.w_0_deepcopy_147",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<256x256x3x3xf32> + (%187) = "builtin.parameter" [id:2203] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:141,parameter_name:"batch_norm2d_28.w_2_deepcopy_146",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%188) = "builtin.parameter" [id:2204] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:142,parameter_name:"batch_norm2d_28.w_1_deepcopy_145",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%189) = "builtin.parameter" [id:2205] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:143,parameter_name:"batch_norm2d_28.b_0_deepcopy_144",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%190) = "builtin.parameter" [id:2206] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:144,parameter_name:"batch_norm2d_28.w_0_deepcopy_143",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%191) = "builtin.parameter" [id:2207] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:145,parameter_name:"conv2d_28.w_0_deepcopy_142",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<256x1024x1x1xf32> + (%192) = "builtin.parameter" [id:2208] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:146,parameter_name:"batch_norm2d_27.w_2_deepcopy_141",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%193) = "builtin.parameter" [id:2209] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:147,parameter_name:"batch_norm2d_27.w_1_deepcopy_140",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%194) = "builtin.parameter" [id:2210] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:148,parameter_name:"batch_norm2d_27.b_0_deepcopy_139",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%195) = "builtin.parameter" [id:2211] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:149,parameter_name:"batch_norm2d_27.w_0_deepcopy_138",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%196) = "builtin.parameter" [id:2212] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:150,parameter_name:"conv2d_27.w_0_deepcopy_137",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<1024x512x1x1xf32> + (%197) = "builtin.parameter" [id:2213] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:151,parameter_name:"batch_norm2d_26.w_2_deepcopy_136",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%198) = "builtin.parameter" [id:2214] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:152,parameter_name:"batch_norm2d_26.w_1_deepcopy_135",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%199) = "builtin.parameter" [id:2215] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:153,parameter_name:"batch_norm2d_26.b_0_deepcopy_134",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%200) = "builtin.parameter" [id:2216] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:154,parameter_name:"batch_norm2d_26.w_0_deepcopy_133",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%201) = "builtin.parameter" [id:2217] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:155,parameter_name:"conv2d_26.w_0_deepcopy_132",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<1024x256x1x1xf32> + (%202) = "builtin.parameter" [id:2218] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:156,parameter_name:"batch_norm2d_25.w_2_deepcopy_131",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%203) = "builtin.parameter" [id:2219] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:157,parameter_name:"batch_norm2d_25.w_1_deepcopy_130",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%204) = "builtin.parameter" [id:2220] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:158,parameter_name:"batch_norm2d_25.b_0_deepcopy_129",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%205) = "builtin.parameter" [id:2221] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:159,parameter_name:"batch_norm2d_25.w_0_deepcopy_128",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%206) = "builtin.parameter" [id:2222] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:160,parameter_name:"conv2d_25.w_0_deepcopy_127",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<256x256x3x3xf32> + (%207) = "builtin.parameter" [id:2223] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:161,parameter_name:"batch_norm2d_24.w_2_deepcopy_126",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%208) = "builtin.parameter" [id:2224] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:162,parameter_name:"batch_norm2d_24.w_1_deepcopy_125",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%209) = "builtin.parameter" [id:2225] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:163,parameter_name:"batch_norm2d_24.b_0_deepcopy_124",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%210) = "builtin.parameter" [id:2226] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:164,parameter_name:"batch_norm2d_24.w_0_deepcopy_123",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%211) = "builtin.parameter" [id:2227] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:165,parameter_name:"conv2d_24.w_0_deepcopy_122",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<256x512x1x1xf32> + (%212) = "builtin.parameter" [id:2228] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:166,parameter_name:"batch_norm2d_23.w_2_deepcopy_121",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%213) = "builtin.parameter" [id:2229] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:167,parameter_name:"batch_norm2d_23.w_1_deepcopy_120",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%214) = "builtin.parameter" [id:2230] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:168,parameter_name:"batch_norm2d_23.b_0_deepcopy_119",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%215) = "builtin.parameter" [id:2231] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:169,parameter_name:"batch_norm2d_23.w_0_deepcopy_118",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%216) = "builtin.parameter" [id:2232] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:170,parameter_name:"conv2d_23.w_0_deepcopy_117",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x128x1x1xf32> + (%217) = "builtin.parameter" [id:2233] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:171,parameter_name:"batch_norm2d_22.w_2_deepcopy_116",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%218) = "builtin.parameter" [id:2234] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:172,parameter_name:"batch_norm2d_22.w_1_deepcopy_115",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%219) = "builtin.parameter" [id:2235] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:173,parameter_name:"batch_norm2d_22.b_0_deepcopy_114",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%220) = "builtin.parameter" [id:2236] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:174,parameter_name:"batch_norm2d_22.w_0_deepcopy_113",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%221) = "builtin.parameter" [id:2237] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:175,parameter_name:"conv2d_22.w_0_deepcopy_112",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<128x128x3x3xf32> + (%222) = "builtin.parameter" [id:2238] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:176,parameter_name:"batch_norm2d_21.w_2_deepcopy_111",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%223) = "builtin.parameter" [id:2239] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:177,parameter_name:"batch_norm2d_21.w_1_deepcopy_110",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%224) = "builtin.parameter" [id:2240] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:178,parameter_name:"batch_norm2d_21.b_0_deepcopy_109",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%225) = "builtin.parameter" [id:2241] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:179,parameter_name:"batch_norm2d_21.w_0_deepcopy_108",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%226) = "builtin.parameter" [id:2242] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:180,parameter_name:"conv2d_21.w_0_deepcopy_107",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<128x512x1x1xf32> + (%227) = "builtin.parameter" [id:2243] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:181,parameter_name:"batch_norm2d_20.w_2_deepcopy_106",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%228) = "builtin.parameter" [id:2244] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:182,parameter_name:"batch_norm2d_20.w_1_deepcopy_105",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%229) = "builtin.parameter" [id:2245] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:183,parameter_name:"batch_norm2d_20.b_0_deepcopy_104",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%230) = "builtin.parameter" [id:2246] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:184,parameter_name:"batch_norm2d_20.w_0_deepcopy_103",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%231) = "builtin.parameter" [id:2247] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:185,parameter_name:"conv2d_20.w_0_deepcopy_102",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x128x1x1xf32> + (%232) = "builtin.parameter" [id:2248] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:186,parameter_name:"batch_norm2d_19.w_2_deepcopy_101",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%233) = "builtin.parameter" [id:2249] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:187,parameter_name:"batch_norm2d_19.w_1_deepcopy_100",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%234) = "builtin.parameter" [id:2250] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:188,parameter_name:"batch_norm2d_19.b_0_deepcopy_99",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%235) = "builtin.parameter" [id:2251] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:189,parameter_name:"batch_norm2d_19.w_0_deepcopy_98",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%236) = "builtin.parameter" [id:2252] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:190,parameter_name:"conv2d_19.w_0_deepcopy_97",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<128x128x3x3xf32> + (%237) = "builtin.parameter" [id:2253] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:191,parameter_name:"batch_norm2d_18.w_2_deepcopy_96",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%238) = "builtin.parameter" [id:2254] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:192,parameter_name:"batch_norm2d_18.w_1_deepcopy_95",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%239) = "builtin.parameter" [id:2255] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:193,parameter_name:"batch_norm2d_18.b_0_deepcopy_94",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%240) = "builtin.parameter" [id:2256] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:194,parameter_name:"batch_norm2d_18.w_0_deepcopy_93",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%241) = "builtin.parameter" [id:2257] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:195,parameter_name:"conv2d_18.w_0_deepcopy_92",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<128x512x1x1xf32> + (%242) = "builtin.parameter" [id:2258] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:196,parameter_name:"batch_norm2d_17.w_2_deepcopy_91",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%243) = "builtin.parameter" [id:2259] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:197,parameter_name:"batch_norm2d_17.w_1_deepcopy_90",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%244) = "builtin.parameter" [id:2260] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:198,parameter_name:"batch_norm2d_17.b_0_deepcopy_89",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%245) = "builtin.parameter" [id:2261] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:199,parameter_name:"batch_norm2d_17.w_0_deepcopy_88",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%246) = "builtin.parameter" [id:2262] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:200,parameter_name:"conv2d_17.w_0_deepcopy_87",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x128x1x1xf32> + (%247) = "builtin.parameter" [id:2263] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:201,parameter_name:"batch_norm2d_16.w_2_deepcopy_86",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%248) = "builtin.parameter" [id:2264] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:202,parameter_name:"batch_norm2d_16.w_1_deepcopy_85",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%249) = "builtin.parameter" [id:2265] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:203,parameter_name:"batch_norm2d_16.b_0_deepcopy_84",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%250) = "builtin.parameter" [id:2266] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:204,parameter_name:"batch_norm2d_16.w_0_deepcopy_83",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%251) = "builtin.parameter" [id:2267] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:205,parameter_name:"conv2d_16.w_0_deepcopy_82",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<128x128x3x3xf32> + (%252) = "builtin.parameter" [id:2268] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:206,parameter_name:"batch_norm2d_15.w_2_deepcopy_81",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%253) = "builtin.parameter" [id:2269] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:207,parameter_name:"batch_norm2d_15.w_1_deepcopy_80",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%254) = "builtin.parameter" [id:2270] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:208,parameter_name:"batch_norm2d_15.b_0_deepcopy_79",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%255) = "builtin.parameter" [id:2271] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:209,parameter_name:"batch_norm2d_15.w_0_deepcopy_78",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%256) = "builtin.parameter" [id:2272] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:210,parameter_name:"conv2d_15.w_0_deepcopy_77",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<128x512x1x1xf32> + (%257) = "builtin.parameter" [id:2273] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:211,parameter_name:"batch_norm2d_14.w_2_deepcopy_76",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%258) = "builtin.parameter" [id:2274] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:212,parameter_name:"batch_norm2d_14.w_1_deepcopy_75",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%259) = "builtin.parameter" [id:2275] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:213,parameter_name:"batch_norm2d_14.b_0_deepcopy_74",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%260) = "builtin.parameter" [id:2276] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:214,parameter_name:"batch_norm2d_14.w_0_deepcopy_73",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%261) = "builtin.parameter" [id:2277] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:215,parameter_name:"conv2d_14.w_0_deepcopy_72",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x256x1x1xf32> + (%262) = "builtin.parameter" [id:2278] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:216,parameter_name:"batch_norm2d_13.w_2_deepcopy_71",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%263) = "builtin.parameter" [id:2279] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:217,parameter_name:"batch_norm2d_13.w_1_deepcopy_70",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%264) = "builtin.parameter" [id:2280] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:218,parameter_name:"batch_norm2d_13.b_0_deepcopy_69",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%265) = "builtin.parameter" [id:2281] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:219,parameter_name:"batch_norm2d_13.w_0_deepcopy_68",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%266) = "builtin.parameter" [id:2282] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:220,parameter_name:"conv2d_13.w_0_deepcopy_67",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x128x1x1xf32> + (%267) = "builtin.parameter" [id:2283] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:221,parameter_name:"batch_norm2d_12.w_2_deepcopy_66",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%268) = "builtin.parameter" [id:2284] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:222,parameter_name:"batch_norm2d_12.w_1_deepcopy_65",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%269) = "builtin.parameter" [id:2285] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:223,parameter_name:"batch_norm2d_12.b_0_deepcopy_64",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%270) = "builtin.parameter" [id:2286] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:224,parameter_name:"batch_norm2d_12.w_0_deepcopy_63",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%271) = "builtin.parameter" [id:2287] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:225,parameter_name:"conv2d_12.w_0_deepcopy_62",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<128x128x3x3xf32> + (%272) = "builtin.parameter" [id:2288] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:226,parameter_name:"batch_norm2d_11.w_2_deepcopy_61",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%273) = "builtin.parameter" [id:2289] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:227,parameter_name:"batch_norm2d_11.w_1_deepcopy_60",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%274) = "builtin.parameter" [id:2290] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:228,parameter_name:"batch_norm2d_11.b_0_deepcopy_59",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%275) = "builtin.parameter" [id:2291] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:229,parameter_name:"batch_norm2d_11.w_0_deepcopy_58",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%276) = "builtin.parameter" [id:2292] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:230,parameter_name:"conv2d_11.w_0_deepcopy_57",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<128x256x1x1xf32> + (%277) = "builtin.parameter" [id:2293] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:231,parameter_name:"batch_norm2d_10.w_2_deepcopy_56",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%278) = "builtin.parameter" [id:2294] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:232,parameter_name:"batch_norm2d_10.w_1_deepcopy_55",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%279) = "builtin.parameter" [id:2295] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:233,parameter_name:"batch_norm2d_10.b_0_deepcopy_54",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%280) = "builtin.parameter" [id:2296] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:234,parameter_name:"batch_norm2d_10.w_0_deepcopy_53",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%281) = "builtin.parameter" [id:2297] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:235,parameter_name:"conv2d_10.w_0_deepcopy_52",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256x64x1x1xf32> + (%282) = "builtin.parameter" [id:2298] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:236,parameter_name:"batch_norm2d_9.w_2_deepcopy_51",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%283) = "builtin.parameter" [id:2299] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:237,parameter_name:"batch_norm2d_9.w_1_deepcopy_50",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%284) = "builtin.parameter" [id:2300] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:238,parameter_name:"batch_norm2d_9.b_0_deepcopy_49",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%285) = "builtin.parameter" [id:2301] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:239,parameter_name:"batch_norm2d_9.w_0_deepcopy_48",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%286) = "builtin.parameter" [id:2302] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:240,parameter_name:"conv2d_9.w_0_deepcopy_47",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64x64x3x3xf32> + (%287) = "builtin.parameter" [id:2303] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:241,parameter_name:"batch_norm2d_8.w_2_deepcopy_46",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%288) = "builtin.parameter" [id:2304] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:242,parameter_name:"batch_norm2d_8.w_1_deepcopy_45",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%289) = "builtin.parameter" [id:2305] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:243,parameter_name:"batch_norm2d_8.b_0_deepcopy_44",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%290) = "builtin.parameter" [id:2306] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:244,parameter_name:"batch_norm2d_8.w_0_deepcopy_43",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%291) = "builtin.parameter" [id:2307] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:245,parameter_name:"conv2d_8.w_0_deepcopy_42",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64x256x1x1xf32> + (%292) = "builtin.parameter" [id:2308] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:246,parameter_name:"batch_norm2d_7.w_2_deepcopy_41",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%293) = "builtin.parameter" [id:2309] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:247,parameter_name:"batch_norm2d_7.w_1_deepcopy_40",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%294) = "builtin.parameter" [id:2310] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:248,parameter_name:"batch_norm2d_7.b_0_deepcopy_39",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%295) = "builtin.parameter" [id:2311] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:249,parameter_name:"batch_norm2d_7.w_0_deepcopy_38",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%296) = "builtin.parameter" [id:2312] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:250,parameter_name:"conv2d_7.w_0_deepcopy_37",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256x64x1x1xf32> + (%297) = "builtin.parameter" [id:2313] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:251,parameter_name:"batch_norm2d_6.w_2_deepcopy_36",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%298) = "builtin.parameter" [id:2314] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:252,parameter_name:"batch_norm2d_6.w_1_deepcopy_35",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%299) = "builtin.parameter" [id:2315] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:253,parameter_name:"batch_norm2d_6.b_0_deepcopy_34",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%300) = "builtin.parameter" [id:2316] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:254,parameter_name:"batch_norm2d_6.w_0_deepcopy_33",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%301) = "builtin.parameter" [id:2317] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:255,parameter_name:"conv2d_6.w_0_deepcopy_32",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64x64x3x3xf32> + (%302) = "builtin.parameter" [id:2318] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:256,parameter_name:"batch_norm2d_5.w_2_deepcopy_31",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%303) = "builtin.parameter" [id:2319] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:257,parameter_name:"batch_norm2d_5.w_1_deepcopy_30",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%304) = "builtin.parameter" [id:2320] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:258,parameter_name:"batch_norm2d_5.b_0_deepcopy_29",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%305) = "builtin.parameter" [id:2321] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:259,parameter_name:"batch_norm2d_5.w_0_deepcopy_28",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%306) = "builtin.parameter" [id:2322] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:260,parameter_name:"conv2d_5.w_0_deepcopy_27",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64x256x1x1xf32> + (%307) = "builtin.parameter" [id:2323] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:261,parameter_name:"batch_norm2d_4.w_2_deepcopy_26",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%308) = "builtin.parameter" [id:2324] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:262,parameter_name:"batch_norm2d_4.w_1_deepcopy_25",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%309) = "builtin.parameter" [id:2325] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:263,parameter_name:"batch_norm2d_4.b_0_deepcopy_24",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%310) = "builtin.parameter" [id:2326] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:264,parameter_name:"batch_norm2d_4.w_0_deepcopy_23",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%311) = "builtin.parameter" [id:2327] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:265,parameter_name:"conv2d_4.w_0_deepcopy_22",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256x64x1x1xf32> + (%312) = "builtin.parameter" [id:2328] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:266,parameter_name:"batch_norm2d_3.w_2_deepcopy_21",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%313) = "builtin.parameter" [id:2329] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:267,parameter_name:"batch_norm2d_3.w_1_deepcopy_20",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%314) = "builtin.parameter" [id:2330] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:268,parameter_name:"batch_norm2d_3.b_0_deepcopy_19",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%315) = "builtin.parameter" [id:2331] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:269,parameter_name:"batch_norm2d_3.w_0_deepcopy_18",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%316) = "builtin.parameter" [id:2332] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:270,parameter_name:"conv2d_3.w_0_deepcopy_17",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256x64x1x1xf32> + (%317) = "builtin.parameter" [id:2333] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:271,parameter_name:"batch_norm2d_2.w_2_deepcopy_16",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%318) = "builtin.parameter" [id:2334] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:272,parameter_name:"batch_norm2d_2.w_1_deepcopy_15",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%319) = "builtin.parameter" [id:2335] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:273,parameter_name:"batch_norm2d_2.b_0_deepcopy_14",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%320) = "builtin.parameter" [id:2336] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:274,parameter_name:"batch_norm2d_2.w_0_deepcopy_13",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%321) = "builtin.parameter" [id:2337] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:275,parameter_name:"conv2d_2.w_0_deepcopy_12",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64x64x3x3xf32> + (%322) = "builtin.parameter" [id:2338] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:276,parameter_name:"batch_norm2d_1.w_2_deepcopy_11",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%323) = "builtin.parameter" [id:2339] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:277,parameter_name:"batch_norm2d_1.w_1_deepcopy_10",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%324) = "builtin.parameter" [id:2340] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:278,parameter_name:"batch_norm2d_1.b_0_deepcopy_9",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%325) = "builtin.parameter" [id:2341] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:279,parameter_name:"batch_norm2d_1.w_0_deepcopy_8",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%326) = "builtin.parameter" [id:2342] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:280,parameter_name:"conv2d_1.w_0_deepcopy_7",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64x64x1x1xf32> + (%327) = "builtin.parameter" [id:2343] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:281,parameter_name:"batch_norm2d_0.w_2_deepcopy_6",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%328) = "builtin.parameter" [id:2344] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:282,parameter_name:"batch_norm2d_0.w_1_deepcopy_5",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%329) = "builtin.parameter" [id:2345] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:283,parameter_name:"batch_norm2d_0.b_0_deepcopy_4",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%330) = "builtin.parameter" [id:2346] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:284,parameter_name:"batch_norm2d_0.w_0_deepcopy_3",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%331) = "builtin.parameter" [id:2347] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:285,parameter_name:"conv2d_0.w_0_deepcopy_2",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64x3x7x7xf32> + (%332) = "data(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"data",name:"im_shape",op_name:"pd_op.data",origin_id:2348,place:Place(undefined:0),shape:[-1,2],stop_gradient:[false]} : () -> undefined_tensor<-1x2xf32> + (%333) = "shadow_feed(phi_kernel)" (%332) {dst_place_type:1,kernel_key:,kernel_name:"shadow_feed",op_name:"pd_op.shadow_feed",origin_id:2349} : (undefined_tensor<-1x2xf32>) -> custom_device_tensor<-1x2xf32> + (%334) = "data(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"data",name:"image",op_name:"pd_op.data",origin_id:2350,place:Place(undefined:0),shape:[-1,3,-1,-1],stop_gradient:[false]} : () -> undefined_tensor<-1x3x-1x-1xf32> + (%335) = "shadow_feed(phi_kernel)" (%334) {dst_place_type:1,kernel_key:,kernel_name:"shadow_feed",op_name:"pd_op.shadow_feed",origin_id:2351} : (undefined_tensor<-1x3x-1x-1xf32>) -> custom_device_tensor<-1x3x-1x-1xf32> + (%336) = "data(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"data",name:"scale_factor",op_name:"pd_op.data",origin_id:2352,place:Place(undefined:0),shape:[-1,2],stop_gradient:[false]} : () -> undefined_tensor<-1x2xf32> + (%337) = "shadow_feed(phi_kernel)" (%336) {dst_place_type:1,kernel_key:,kernel_name:"shadow_feed",op_name:"pd_op.shadow_feed",origin_id:2353} : (undefined_tensor<-1x2xf32>) -> custom_device_tensor<-1x2xf32> + (%338) = "conv2d(phi_kernel)" (%335, %331) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2354,padding_algorithm:"EXPLICIT",paddings:[3,3],stop_gradient:[false],strides:[2,2],struct_name:"/ResNet/Sequential/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x3x-1x-1xf32>, custom_device_tensor<64x3x7x7xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%339, %340, %341, %342, %343, %344) = "batch_norm_(phi_kernel)" (%338, %328, %327, %330, %329) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2355,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Sequential/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>) -> custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<-1xu8> + (%345) = "relu(phi_kernel)" (%339) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2356,stop_gradient:[false],struct_name:"/ResNet/Sequential/ConvNormLayer/"} : (custom_device_tensor<-1x64x-1x-1xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%346) = "transpose(phi_kernel)" (%345) {kernel_key:,kernel_name:"transpose",op_name:"pd_op.transpose",origin_id:2357,perm:[0,2,3,1],source:"transfer_layout_pass",stop_gradient:[false]} : (custom_device_tensor<-1x64x-1x-1xf32>) -> custom_device_tensor<-1x-1x-1x64xf32> + (%347) = "pool2d(phi_kernel)" (%346, %57) {adaptive:false,ceil_mode:false,data_format:"NHWC",exclusive:true,global_pooling:false,kernel_key:,kernel_name:"pool2d",op_name:"pd_op.pool2d",origin_id:2358,padding_algorithm:"EXPLICIT",paddings:[1,1],pooling_type:"max",stop_gradient:[false],strides:[2,2],struct_name:"/ResNet/"} : (custom_device_tensor<-1x-1x-1x64xf32>, cpu_tensor<2xi64>) -> custom_device_tensor<-1x-1x-1x64xf32> + (%348) = "transpose(phi_kernel)" (%347) {kernel_key:,kernel_name:"transpose",op_name:"pd_op.transpose",origin_id:2359,perm:[0,3,1,2],source:"transfer_layout_pass",stop_gradient:[false]} : (custom_device_tensor<-1x-1x-1x64xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%349) = "conv2d(phi_kernel)" (%348, %326) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2360,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64x64x1x1xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%350, %351, %352, %353, %354, %355) = "batch_norm_(phi_kernel)" (%349, %323, %322, %325, %324) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2361,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>) -> custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<-1xu8> + (%356) = "relu(phi_kernel)" (%350) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2362,stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer/"} : (custom_device_tensor<-1x64x-1x-1xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%357) = "conv2d(phi_kernel)" (%356, %321) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2363,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64x64x3x3xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%358, %359, %360, %361, %362, %363) = "batch_norm_(phi_kernel)" (%357, %318, %317, %320, %319) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2364,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>) -> custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<-1xu8> + (%364) = "relu(phi_kernel)" (%358) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2365,stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer_1/"} : (custom_device_tensor<-1x64x-1x-1xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%365) = "conv2d(phi_kernel)" (%364, %316) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2366,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<256x64x1x1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%366, %367, %368, %369, %370, %371) = "batch_norm_(phi_kernel)" (%365, %313, %312, %315, %314) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2367,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%372) = "conv2d(phi_kernel)" (%348, %311) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2368,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer_3/Conv2D/"} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<256x64x1x1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%373, %374, %375, %376, %377, %378) = "batch_norm_(phi_kernel)" (%372, %308, %307, %310, %309) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2369,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer_3/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%379) = "add(phi_kernel)" (%366, %373) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2370,stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%380) = "relu(phi_kernel)" (%379) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2371,stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%381) = "conv2d(phi_kernel)" (%380, %306) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2372,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck_1/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<64x256x1x1xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%382, %383, %384, %385, %386, %387) = "batch_norm_(phi_kernel)" (%381, %303, %302, %305, %304) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2373,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck_1/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>) -> custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<-1xu8> + (%388) = "relu(phi_kernel)" (%382) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2374,stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck_1/ConvNormLayer/"} : (custom_device_tensor<-1x64x-1x-1xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%389) = "conv2d(phi_kernel)" (%388, %301) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2375,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck_1/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64x64x3x3xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%390, %391, %392, %393, %394, %395) = "batch_norm_(phi_kernel)" (%389, %298, %297, %300, %299) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2376,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck_1/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>) -> custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<-1xu8> + (%396) = "relu(phi_kernel)" (%390) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2377,stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck_1/ConvNormLayer_1/"} : (custom_device_tensor<-1x64x-1x-1xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%397) = "conv2d(phi_kernel)" (%396, %296) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2378,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck_1/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<256x64x1x1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%398, %399, %400, %401, %402, %403) = "batch_norm_(phi_kernel)" (%397, %293, %292, %295, %294) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2379,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck_1/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%404) = "add(phi_kernel)" (%398, %380) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2380,stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck_1/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%405) = "relu(phi_kernel)" (%404) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2381,stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck_1/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%406) = "conv2d(phi_kernel)" (%405, %291) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2382,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck_2/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<64x256x1x1xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%407, %408, %409, %410, %411, %412) = "batch_norm_(phi_kernel)" (%406, %288, %287, %290, %289) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2383,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck_2/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>) -> custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<-1xu8> + (%413) = "relu(phi_kernel)" (%407) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2384,stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck_2/ConvNormLayer/"} : (custom_device_tensor<-1x64x-1x-1xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%414) = "conv2d(phi_kernel)" (%413, %286) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2385,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck_2/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64x64x3x3xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%415, %416, %417, %418, %419, %420) = "batch_norm_(phi_kernel)" (%414, %283, %282, %285, %284) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2386,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck_2/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>) -> custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<-1xu8> + (%421) = "relu(phi_kernel)" (%415) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2387,stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck_2/ConvNormLayer_1/"} : (custom_device_tensor<-1x64x-1x-1xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%422) = "conv2d(phi_kernel)" (%421, %281) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2388,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck_2/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<256x64x1x1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%423, %424, %425, %426, %427, %428) = "batch_norm_(phi_kernel)" (%422, %278, %277, %280, %279) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2389,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck_2/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%429) = "add(phi_kernel)" (%423, %405) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2390,stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck_2/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%430) = "relu(phi_kernel)" (%429) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2391,stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck_2/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%431) = "conv2d(phi_kernel)" (%430, %276) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2392,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<128x256x1x1xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%432, %433, %434, %435, %436, %437) = "batch_norm_(phi_kernel)" (%431, %273, %272, %275, %274) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2393,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>) -> custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<-1xu8> + (%438) = "relu(phi_kernel)" (%432) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2394,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer/"} : (custom_device_tensor<-1x128x-1x-1xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%439) = "conv2d(phi_kernel)" (%438, %271) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2395,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[2,2],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128x128x3x3xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%440, %441, %442, %443, %444, %445) = "batch_norm_(phi_kernel)" (%439, %268, %267, %270, %269) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2396,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>) -> custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<-1xu8> + (%446) = "relu(phi_kernel)" (%440) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2397,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer_1/"} : (custom_device_tensor<-1x128x-1x-1xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%447) = "conv2d(phi_kernel)" (%446, %266) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2398,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<512x128x1x1xf32>) -> custom_device_tensor<-1x512x-1x-1xf32> + (%448, %449, %450, %451, %452, %453) = "batch_norm_(phi_kernel)" (%447, %263, %262, %265, %264) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2399,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%454) = "conv2d(phi_kernel)" (%430, %261) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2400,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[2,2],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer_3/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<512x256x1x1xf32>) -> custom_device_tensor<-1x512x-1x-1xf32> + (%455, %456, %457, %458, %459, %460) = "batch_norm_(phi_kernel)" (%454, %258, %257, %260, %259) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2401,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer_3/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%461) = "add(phi_kernel)" (%448, %455) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2402,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck/"} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<-1x512x-1x-1xf32>) -> custom_device_tensor<-1x512x-1x-1xf32> + (%462) = "relu(phi_kernel)" (%461) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2403,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck/"} : (custom_device_tensor<-1x512x-1x-1xf32>) -> custom_device_tensor<-1x512x-1x-1xf32> + (%463) = "conv2d(phi_kernel)" (%462, %256) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2404,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck_1/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<128x512x1x1xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%464, %465, %466, %467, %468, %469) = "batch_norm_(phi_kernel)" (%463, %253, %252, %255, %254) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2405,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck_1/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>) -> custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<-1xu8> + (%470) = "relu(phi_kernel)" (%464) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2406,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_1/ConvNormLayer/"} : (custom_device_tensor<-1x128x-1x-1xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%471) = "conv2d(phi_kernel)" (%470, %251) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2407,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck_1/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128x128x3x3xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%472, %473, %474, %475, %476, %477) = "batch_norm_(phi_kernel)" (%471, %248, %247, %250, %249) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2408,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck_1/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>) -> custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<-1xu8> + (%478) = "relu(phi_kernel)" (%472) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2409,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_1/ConvNormLayer_1/"} : (custom_device_tensor<-1x128x-1x-1xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%479) = "conv2d(phi_kernel)" (%478, %246) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2410,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck_1/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<512x128x1x1xf32>) -> custom_device_tensor<-1x512x-1x-1xf32> + (%480, %481, %482, %483, %484, %485) = "batch_norm_(phi_kernel)" (%479, %243, %242, %245, %244) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2411,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck_1/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%486) = "add(phi_kernel)" (%480, %462) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2412,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_1/"} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<-1x512x-1x-1xf32>) -> custom_device_tensor<-1x512x-1x-1xf32> + (%487) = "relu(phi_kernel)" (%486) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2413,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_1/"} : (custom_device_tensor<-1x512x-1x-1xf32>) -> custom_device_tensor<-1x512x-1x-1xf32> + (%488) = "conv2d(phi_kernel)" (%487, %241) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2414,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck_2/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<128x512x1x1xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%489, %490, %491, %492, %493, %494) = "batch_norm_(phi_kernel)" (%488, %238, %237, %240, %239) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2415,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck_2/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>) -> custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<-1xu8> + (%495) = "relu(phi_kernel)" (%489) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2416,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_2/ConvNormLayer/"} : (custom_device_tensor<-1x128x-1x-1xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%496) = "conv2d(phi_kernel)" (%495, %236) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2417,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck_2/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128x128x3x3xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%497, %498, %499, %500, %501, %502) = "batch_norm_(phi_kernel)" (%496, %233, %232, %235, %234) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2418,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck_2/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>) -> custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<-1xu8> + (%503) = "relu(phi_kernel)" (%497) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2419,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_2/ConvNormLayer_1/"} : (custom_device_tensor<-1x128x-1x-1xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%504) = "conv2d(phi_kernel)" (%503, %231) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2420,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck_2/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<512x128x1x1xf32>) -> custom_device_tensor<-1x512x-1x-1xf32> + (%505, %506, %507, %508, %509, %510) = "batch_norm_(phi_kernel)" (%504, %228, %227, %230, %229) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2421,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck_2/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%511) = "add(phi_kernel)" (%505, %487) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2422,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_2/"} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<-1x512x-1x-1xf32>) -> custom_device_tensor<-1x512x-1x-1xf32> + (%512) = "relu(phi_kernel)" (%511) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2423,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_2/"} : (custom_device_tensor<-1x512x-1x-1xf32>) -> custom_device_tensor<-1x512x-1x-1xf32> + (%513) = "conv2d(phi_kernel)" (%512, %226) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2424,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck_3/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<128x512x1x1xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%514, %515, %516, %517, %518, %519) = "batch_norm_(phi_kernel)" (%513, %223, %222, %225, %224) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2425,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck_3/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>) -> custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<-1xu8> + (%520) = "relu(phi_kernel)" (%514) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2426,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_3/ConvNormLayer/"} : (custom_device_tensor<-1x128x-1x-1xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%521) = "conv2d(phi_kernel)" (%520, %221) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2427,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck_3/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128x128x3x3xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%522, %523, %524, %525, %526, %527) = "batch_norm_(phi_kernel)" (%521, %218, %217, %220, %219) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2428,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck_3/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>) -> custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<-1xu8> + (%528) = "relu(phi_kernel)" (%522) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2429,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_3/ConvNormLayer_1/"} : (custom_device_tensor<-1x128x-1x-1xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%529) = "conv2d(phi_kernel)" (%528, %216) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2430,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck_3/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<512x128x1x1xf32>) -> custom_device_tensor<-1x512x-1x-1xf32> + (%530, %531, %532, %533, %534, %535) = "batch_norm_(phi_kernel)" (%529, %213, %212, %215, %214) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2431,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck_3/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%536) = "add(phi_kernel)" (%530, %512) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2432,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_3/"} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<-1x512x-1x-1xf32>) -> custom_device_tensor<-1x512x-1x-1xf32> + (%537) = "relu(phi_kernel)" (%536) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2433,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_3/"} : (custom_device_tensor<-1x512x-1x-1xf32>) -> custom_device_tensor<-1x512x-1x-1xf32> + (%538) = "conv2d(phi_kernel)" (%537, %211) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2434,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<256x512x1x1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%539, %540, %541, %542, %543, %544) = "batch_norm_(phi_kernel)" (%538, %208, %207, %210, %209) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2435,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%545) = "relu(phi_kernel)" (%539) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2436,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%546) = "conv2d(phi_kernel)" (%545, %206) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2437,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[2,2],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256x256x3x3xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%547, %548, %549, %550, %551, %552) = "batch_norm_(phi_kernel)" (%546, %203, %202, %205, %204) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2438,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%553) = "relu(phi_kernel)" (%547) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2439,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer_1/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%554) = "conv2d(phi_kernel)" (%553, %201) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2440,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<1024x256x1x1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%555, %556, %557, %558, %559, %560) = "batch_norm_(phi_kernel)" (%554, %198, %197, %200, %199) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2441,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<-1xu8> + (%561) = "conv2d(phi_kernel)" (%537, %196) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2442,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[2,2],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer_3/Conv2D/"} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<1024x512x1x1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%562, %563, %564, %565, %566, %567) = "batch_norm_(phi_kernel)" (%561, %193, %192, %195, %194) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2443,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer_3/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<-1xu8> + (%568) = "add(phi_kernel)" (%555, %562) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2444,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<-1x1024x-1x-1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%569) = "relu(phi_kernel)" (%568) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2445,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck/"} : (custom_device_tensor<-1x1024x-1x-1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%570) = "conv2d(phi_kernel)" (%569, %191) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2446,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_1/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<256x1024x1x1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%571, %572, %573, %574, %575, %576) = "batch_norm_(phi_kernel)" (%570, %188, %187, %190, %189) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2447,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_1/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%577) = "relu(phi_kernel)" (%571) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2448,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_1/ConvNormLayer/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%578) = "conv2d(phi_kernel)" (%577, %186) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2449,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_1/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256x256x3x3xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%579, %580, %581, %582, %583, %584) = "batch_norm_(phi_kernel)" (%578, %183, %182, %185, %184) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2450,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_1/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%585) = "relu(phi_kernel)" (%579) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2451,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_1/ConvNormLayer_1/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%586) = "conv2d(phi_kernel)" (%585, %181) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2452,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_1/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<1024x256x1x1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%587, %588, %589, %590, %591, %592) = "batch_norm_(phi_kernel)" (%586, %178, %177, %180, %179) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2453,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_1/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<-1xu8> + (%593) = "add(phi_kernel)" (%587, %569) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2454,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_1/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<-1x1024x-1x-1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%594) = "relu(phi_kernel)" (%593) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2455,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_1/"} : (custom_device_tensor<-1x1024x-1x-1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%595) = "conv2d(phi_kernel)" (%594, %176) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2456,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_2/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<256x1024x1x1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%596, %597, %598, %599, %600, %601) = "batch_norm_(phi_kernel)" (%595, %173, %172, %175, %174) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2457,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_2/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%602) = "relu(phi_kernel)" (%596) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2458,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_2/ConvNormLayer/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%603) = "conv2d(phi_kernel)" (%602, %171) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2459,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_2/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256x256x3x3xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%604, %605, %606, %607, %608, %609) = "batch_norm_(phi_kernel)" (%603, %168, %167, %170, %169) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2460,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_2/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%610) = "relu(phi_kernel)" (%604) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2461,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_2/ConvNormLayer_1/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%611) = "conv2d(phi_kernel)" (%610, %166) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2462,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_2/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<1024x256x1x1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%612, %613, %614, %615, %616, %617) = "batch_norm_(phi_kernel)" (%611, %163, %162, %165, %164) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2463,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_2/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<-1xu8> + (%618) = "add(phi_kernel)" (%612, %594) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2464,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_2/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<-1x1024x-1x-1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%619) = "relu(phi_kernel)" (%618) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2465,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_2/"} : (custom_device_tensor<-1x1024x-1x-1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%620) = "conv2d(phi_kernel)" (%619, %161) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2466,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_3/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<256x1024x1x1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%621, %622, %623, %624, %625, %626) = "batch_norm_(phi_kernel)" (%620, %158, %157, %160, %159) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2467,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_3/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%627) = "relu(phi_kernel)" (%621) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2468,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_3/ConvNormLayer/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%628) = "conv2d(phi_kernel)" (%627, %156) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2469,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_3/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256x256x3x3xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%629, %630, %631, %632, %633, %634) = "batch_norm_(phi_kernel)" (%628, %153, %152, %155, %154) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2470,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_3/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%635) = "relu(phi_kernel)" (%629) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2471,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_3/ConvNormLayer_1/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%636) = "conv2d(phi_kernel)" (%635, %151) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2472,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_3/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<1024x256x1x1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%637, %638, %639, %640, %641, %642) = "batch_norm_(phi_kernel)" (%636, %148, %147, %150, %149) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2473,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_3/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<-1xu8> + (%643) = "add(phi_kernel)" (%637, %619) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2474,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_3/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<-1x1024x-1x-1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%644) = "relu(phi_kernel)" (%643) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2475,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_3/"} : (custom_device_tensor<-1x1024x-1x-1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%645) = "conv2d(phi_kernel)" (%644, %146) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2476,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_4/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<256x1024x1x1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%646, %647, %648, %649, %650, %651) = "batch_norm_(phi_kernel)" (%645, %143, %142, %145, %144) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2477,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_4/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%652) = "relu(phi_kernel)" (%646) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2478,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_4/ConvNormLayer/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%653) = "conv2d(phi_kernel)" (%652, %141) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2479,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_4/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256x256x3x3xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%654, %655, %656, %657, %658, %659) = "batch_norm_(phi_kernel)" (%653, %138, %137, %140, %139) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2480,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_4/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%660) = "relu(phi_kernel)" (%654) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2481,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_4/ConvNormLayer_1/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%661) = "conv2d(phi_kernel)" (%660, %136) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2482,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_4/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<1024x256x1x1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%662, %663, %664, %665, %666, %667) = "batch_norm_(phi_kernel)" (%661, %133, %132, %135, %134) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2483,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_4/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<-1xu8> + (%668) = "add(phi_kernel)" (%662, %644) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2484,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_4/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<-1x1024x-1x-1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%669) = "relu(phi_kernel)" (%668) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2485,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_4/"} : (custom_device_tensor<-1x1024x-1x-1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%670) = "conv2d(phi_kernel)" (%669, %131) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2486,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_5/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<256x1024x1x1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%671, %672, %673, %674, %675, %676) = "batch_norm_(phi_kernel)" (%670, %128, %127, %130, %129) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2487,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_5/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%677) = "relu(phi_kernel)" (%671) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2488,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_5/ConvNormLayer/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%678) = "conv2d(phi_kernel)" (%677, %126) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2489,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_5/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256x256x3x3xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%679, %680, %681, %682, %683, %684) = "batch_norm_(phi_kernel)" (%678, %123, %122, %125, %124) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2490,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_5/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%685) = "relu(phi_kernel)" (%679) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2491,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_5/ConvNormLayer_1/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%686) = "conv2d(phi_kernel)" (%685, %121) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2492,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_5/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<1024x256x1x1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%687, %688, %689, %690, %691, %692) = "batch_norm_(phi_kernel)" (%686, %118, %117, %120, %119) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2493,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_5/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<-1xu8> + (%693) = "add(phi_kernel)" (%687, %669) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2494,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_5/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<-1x1024x-1x-1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%694) = "relu(phi_kernel)" (%693) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2495,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_5/"} : (custom_device_tensor<-1x1024x-1x-1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%695) = "conv2d(phi_kernel)" (%694, %116) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2496,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/RPNHead/RPNFeat/Conv2D/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024x1024x3x3xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%696) = "add(phi_kernel)" (%695, %55) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2497,stop_gradient:[false],struct_name:"/RPNHead/RPNFeat/Conv2D/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1x1024x1x1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%697) = "relu(phi_kernel)" (%696) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2498,stop_gradient:[false],struct_name:"/RPNHead/RPNFeat/"} : (custom_device_tensor<-1x1024x-1x-1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%698) = "conv2d(phi_kernel)" (%697, %115) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2499,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/RPNHead/Conv2D/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<15x1024x1x1xf32>) -> custom_device_tensor<-1x15x-1x-1xf32> + (%699) = "add(phi_kernel)" (%698, %54) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2500,stop_gradient:[false],struct_name:"/RPNHead/Conv2D/"} : (custom_device_tensor<-1x15x-1x-1xf32>, custom_device_tensor<1x15x1x1xf32>) -> custom_device_tensor<-1x15x-1x-1xf32> + (%700) = "conv2d(phi_kernel)" (%697, %114) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2501,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/RPNHead/Conv2D_1/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<60x1024x1x1xf32>) -> custom_device_tensor<-1x60x-1x-1xf32> + (%701) = "add(phi_kernel)" (%700, %53) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2502,stop_gradient:[false],struct_name:"/RPNHead/Conv2D_1/"} : (custom_device_tensor<-1x60x-1x-1xf32>, custom_device_tensor<1x60x1x1xf32>) -> custom_device_tensor<-1x60x-1x-1xf32> + (%702) = "shape64(phi_kernel)" (%697) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2503,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (custom_device_tensor<-1x1024x-1x-1xf32>) -> cpu_tensor<4xi64> + (%703) = "slice(phi_kernel)" (%702, %52, %51) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2504,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (cpu_tensor<4xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%704) = "slice(phi_kernel)" (%702, %51, %50) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2505,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (cpu_tensor<4xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%705) = "scale(phi_kernel)" (%704, %49) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2506,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%706) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2507,place:Place(cpu),shape:[1],stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/",value:0} : () -> cpu_tensor<1xf32> + (%707) = "cast(phi_kernel)" (%705) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:2508,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (cpu_tensor) -> cpu_tensor + (%708) = "arange(phi_kernel)" (%706, %707, %49) {dtype:float32,kernel_key:,kernel_name:"arange",op_name:"pd_op.arange",origin_id:2509,place:Place(undefined:0),stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (cpu_tensor<1xf32>, cpu_tensor, cpu_tensor<1xf32>) -> custom_device_tensor<-1xf32> + (%709) = "scale(phi_kernel)" (%703, %49) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2510,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%710) = "cast(phi_kernel)" (%709) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:2511,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (cpu_tensor) -> cpu_tensor + (%711) = "arange(phi_kernel)" (%706, %710, %49) {dtype:float32,kernel_key:,kernel_name:"arange",op_name:"pd_op.arange",origin_id:2512,place:Place(undefined:0),stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (cpu_tensor<1xf32>, cpu_tensor, cpu_tensor<1xf32>) -> custom_device_tensor<-1xf32> + (%712) = "builtin.combine" [id:2513] (%711, %708) {origin_id:462,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (custom_device_tensor<-1xf32>, custom_device_tensor<-1xf32>) -> vec[custom_device_tensor<-1xf32>,custom_device_tensor<-1xf32>] + (%713) = "meshgrid(phi_kernel)" (%712) {kernel_key:,kernel_name:"meshgrid",op_name:"pd_op.meshgrid",origin_id:2514,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (vec[custom_device_tensor<-1xf32>,custom_device_tensor<-1xf32>]) -> vec[custom_device_tensor<-1x-1xf32>,custom_device_tensor<-1x-1xf32>] + (%714, %715) = "builtin.split" [id:2515] (%713) {origin_id:464,stop_gradient:[true,true],struct_name:"/RPNHead/AnchorGenerator/"} : (vec[custom_device_tensor<-1x-1xf32>,custom_device_tensor<-1x-1xf32>]) -> custom_device_tensor<-1x-1xf32>, custom_device_tensor<-1x-1xf32> + (%716) = "reshape(phi_kernel)" (%715, %48) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:2516,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (custom_device_tensor<-1x-1xf32>, cpu_tensor<1xi64>) -> custom_device_tensor<-1xf32> + (%717) = "reshape(phi_kernel)" (%714, %48) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:2517,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (custom_device_tensor<-1x-1xf32>, cpu_tensor<1xi64>) -> custom_device_tensor<-1xf32> + (%718) = "builtin.combine" [id:2518] (%716, %717, %716, %717) {origin_id:469,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (custom_device_tensor<-1xf32>, custom_device_tensor<-1xf32>, custom_device_tensor<-1xf32>, custom_device_tensor<-1xf32>) -> vec[custom_device_tensor<-1xf32>,custom_device_tensor<-1xf32>,custom_device_tensor<-1xf32>,custom_device_tensor<-1xf32>] + (%719) = "stack(phi_kernel)" (%718) {axis:1,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2519,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (vec[custom_device_tensor<-1xf32>,custom_device_tensor<-1xf32>,custom_device_tensor<-1xf32>,custom_device_tensor<-1xf32>]) -> custom_device_tensor<-1x4xf32> + (%720) = "reshape(phi_kernel)" (%719, %47) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:2520,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (custom_device_tensor<-1x4xf32>, cpu_tensor<3xi64>) -> custom_device_tensor<-1x1x4xf32> + (%721) = "add(phi_kernel)" (%720, %46) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2521,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (custom_device_tensor<-1x1x4xf32>, custom_device_tensor<1x15x4xf32>) -> custom_device_tensor<-1x15x4xf32> + (%722) = "reshape(phi_kernel)" (%721, %45) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:2522,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (custom_device_tensor<-1x15x4xf32>, cpu_tensor<2xi64>) -> custom_device_tensor<-1x4xf32> + (%723) = "shape64(phi_kernel)" (%333) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2523,stop_gradient:[true],struct_name:"/RPNHead/"} : (custom_device_tensor<-1x2xf32>) -> cpu_tensor<2xi64> + (%724) = "slice(phi_kernel)" (%723, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2524,stop_gradient:[true],struct_name:"/RPNHead/"} : (cpu_tensor<2xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%725) = "arange(phi_kernel)" (%42, %724, %41) {dtype:int64,kernel_key:,kernel_name:"arange",op_name:"pd_op.arange",origin_id:2525,place:Place(undefined:0),stop_gradient:[true],struct_name:"/RPNHead/"} : (cpu_tensor<1xi64>, cpu_tensor, cpu_tensor<1xi64>) -> custom_device_tensor<-1xi64> + (%726) = "shape64(phi_kernel)" (%725) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2526,stop_gradient:[true],struct_name:"/RPNHead/"} : (custom_device_tensor<-1xi64>) -> cpu_tensor<1xi64> + (%727) = "slice(phi_kernel)" (%726, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2527,stop_gradient:[true],struct_name:"/RPNHead/"} : (cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%728) = "create_array(phi_kernel)" () {dtype:Undefined,kernel_key:,kernel_name:"create_array",op_name:"pd_op.create_array",origin_id:2528,stop_gradient:[true],struct_name:"/RPNHead/"} : () -> cpu_tensor_array + (%729) = "create_array(phi_kernel)" () {dtype:Undefined,kernel_key:,kernel_name:"create_array",op_name:"pd_op.create_array",origin_id:2529,stop_gradient:[true],struct_name:"/RPNHead/"} : () -> cpu_tensor_array + (%730) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2530,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/RPNHead/",value:0} : () -> custom_device_tensor + (%731) = "memcpy_h2d(phi_kernel)" (%727) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2531} : (cpu_tensor) -> custom_device_tensor + (%732) = "less_than(phi_kernel)" (%730, %731) {kernel_key:,kernel_name:"less_than",op_name:"pd_op.less_than",origin_id:2532,stop_gradient:[true],struct_name:"/RPNHead/"} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%733) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2533,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/RPNHead/",value:0} : () -> custom_device_tensor<-1x4xf32> + (%734) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2534,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/RPNHead/",value:0} : () -> custom_device_tensor + (%735) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2535,place:Place(undefined:0),shape:[],stop_gradient:[false],struct_name:"/RPNHead/",value:0} : () -> custom_device_tensor<-1x60x-1x-1xf32> + (%736) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2536,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/RPNHead/",value:0} : () -> custom_device_tensor<-1x4xf32> + (%737) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2537,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/RPNHead/",value:0} : () -> custom_device_tensor<-1xf32> + (%738) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2538,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/RPNHead/",value:0} : () -> custom_device_tensor<-1x1xf32> + (%739) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2539,place:Place(undefined:0),shape:[],stop_gradient:[false],struct_name:"/RPNHead/",value:0} : () -> custom_device_tensor<-1x15x-1x-1xf32> + (%740) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2540,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/RPNHead/",value:0} : () -> custom_device_tensor<-1xf32> + (%741) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2541,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/RPNHead/",value:0} : () -> custom_device_tensor<-1x4xf32> + (%742, %743, %744, %745, %746, %747, %748, %749, %750, %751) = "pd_op.while" [id:2542] (cond=%732, inputs=%730, %733, %734, %735, %736, %737, %738, %739, %740, %741) { + ^%arg_0 {stop_gradient:true}, %arg_1 {stop_gradient:true}, %arg_2 {stop_gradient:true}, %arg_3 {stop_gradient:false}, %arg_4 {stop_gradient:true}, %arg_5 {stop_gradient:true}, %arg_6 {stop_gradient:true}, %arg_7 {stop_gradient:false}, %arg_8 {stop_gradient:true}, %arg_9 {stop_gradient:true} + (%752) = "memcpy_d2h(phi_kernel)" (%arg_0) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2543} : (custom_device_tensor) -> cpu_tensor + (%753) = "scale(phi_kernel)" (%752, %19) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2544,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%754) = "builtin.combine" [id:2545] (%arg_0) {origin_id:505,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%755) = "stack(phi_kernel)" (%754) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2546,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%756) = "builtin.combine" [id:2547] (%753) {origin_id:507,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%757) = "stack(phi_kernel)" (%756) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2548,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%758) = "slice(phi_kernel)" (%725, %755, %757) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2549,stop_gradient:[true]} : (custom_device_tensor<-1xi64>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor + (%759) = "memcpy_d2h(phi_kernel)" (%758) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2550} : (custom_device_tensor) -> cpu_tensor + (%760) = "scale(phi_kernel)" (%759, %19) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2551,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%761) = "builtin.combine" [id:2552] (%758) {origin_id:512,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%762) = "stack(phi_kernel)" (%761) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2553,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%763) = "builtin.combine" [id:2554] (%760) {origin_id:514,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%764) = "stack(phi_kernel)" (%763) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2555,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%765) = "slice(phi_kernel)" (%699, %762, %764) {axes:[0],decrease_axis:[],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2556,stop_gradient:[false]} : (custom_device_tensor<-1x15x-1x-1xf32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x15x-1x-1xf32> + (%766) = "memcpy_d2h(phi_kernel)" (%758) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2557} : (custom_device_tensor) -> cpu_tensor + (%767) = "scale(phi_kernel)" (%766, %19) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2558,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%768) = "builtin.combine" [id:2559] (%758) {origin_id:519,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%769) = "stack(phi_kernel)" (%768) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2560,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%770) = "builtin.combine" [id:2561] (%767) {origin_id:521,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%771) = "stack(phi_kernel)" (%770) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2562,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%772) = "slice(phi_kernel)" (%701, %769, %771) {axes:[0],decrease_axis:[],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2563,stop_gradient:[false]} : (custom_device_tensor<-1x60x-1x-1xf32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x60x-1x-1xf32> + (%773) = "memcpy_d2h(phi_kernel)" (%758) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2564} : (custom_device_tensor) -> cpu_tensor + (%774) = "scale(phi_kernel)" (%773, %19) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2565,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%775) = "builtin.combine" [id:2566] (%758) {origin_id:526,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%776) = "stack(phi_kernel)" (%775) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2567,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%777) = "builtin.combine" [id:2568] (%774) {origin_id:528,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%778) = "stack(phi_kernel)" (%777) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2569,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%779) = "slice(phi_kernel)" (%333, %776, %778) {axes:[0],decrease_axis:[],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2570,stop_gradient:[false]} : (custom_device_tensor<-1x2xf32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x2xf32> + (%780) = "full_like(phi_kernel)" (%722, %19) {dtype:float32,kernel_key:,kernel_name:"full_like",op_name:"pd_op.full_like",origin_id:2571,place:Place(undefined:0),stop_gradient:[true]} : (custom_device_tensor<-1x4xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x4xf32> + (%781) = "memcpy_d2h(phi_kernel)" (%765) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2572} : (custom_device_tensor<-1x15x-1x-1xf32>) -> cpu_tensor<-1x15x-1x-1xf32> + (%782) = "memcpy_d2h(phi_kernel)" (%772) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2573} : (custom_device_tensor<-1x60x-1x-1xf32>) -> cpu_tensor<-1x60x-1x-1xf32> + (%783) = "memcpy_d2h(phi_kernel)" (%779) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2574} : (custom_device_tensor<-1x2xf32>) -> cpu_tensor<-1x2xf32> + (%784) = "memcpy_d2h(phi_kernel)" (%722) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2575} : (custom_device_tensor<-1x4xf32>) -> cpu_tensor<-1x4xf32> + (%785) = "memcpy_d2h(phi_kernel)" (%780) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2576} : (custom_device_tensor<-1x4xf32>) -> cpu_tensor<-1x4xf32> + (%786, %787, %788) = "generate_proposals(phi_kernel)" (%781, %782, %783, %784, %785) {eta:1,kernel_key:,kernel_name:"generate_proposals",min_size:0,nms_thresh:0.7,op_name:"pd_op.generate_proposals",origin_id:2577,pixel_offset:false,post_nms_top_n:1000,pre_nms_top_n:6000,stop_gradient:[true,true,true]} : (cpu_tensor<-1x15x-1x-1xf32>, cpu_tensor<-1x60x-1x-1xf32>, cpu_tensor<-1x2xf32>, cpu_tensor<-1x4xf32>, cpu_tensor<-1x4xf32>) -> cpu_tensor<-1x4xf32>, cpu_tensor<-1x1xf32>, cpu_tensor<-1xf32> + (%789) = "flatten(phi_kernel)" (%787) {kernel_key:,kernel_name:"flatten",op_name:"pd_op.flatten",origin_id:2578,start_axis:0,stop_axis:1,stop_gradient:[true]} : (cpu_tensor<-1x1xf32>) -> cpu_tensor<-1xf32> + (%790) = "array_length(phi_kernel)" (%729) {kernel_key:,kernel_name:"array_length",op_name:"pd_op.array_length",origin_id:2579} : (cpu_tensor_array) -> cpu_tensor<1xi64> + (%791) = "array_write_(phi_kernel)" (%729, %786, %790) {is_inplace:true,kernel_key:,kernel_name:"array_write",op_name:"pd_op.array_write_",origin_id:2580} : (cpu_tensor_array, cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>) -> cpu_tensor_array + (%792) = "shape64(phi_kernel)" (%786) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2581,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>) -> cpu_tensor<2xi64> + (%793) = "slice(phi_kernel)" (%792, %44, %43) {axes:[0],decrease_axis:[],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2582,stop_gradient:[true]} : (cpu_tensor<2xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<1xi64> + (%794) = "array_length(phi_kernel)" (%728) {kernel_key:,kernel_name:"array_length",op_name:"pd_op.array_length",origin_id:2583} : (cpu_tensor_array) -> cpu_tensor<1xi64> + (%795) = "array_write_(phi_kernel)" (%728, %793, %794) {is_inplace:true,kernel_key:,kernel_name:"array_write",op_name:"pd_op.array_write_",origin_id:2584} : (cpu_tensor_array, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor_array + (%796) = "memcpy_d2h(phi_kernel)" (%arg_0) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2585} : (custom_device_tensor) -> cpu_tensor + (%797) = "scale(phi_kernel)" (%796, %19) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2586,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%798) = "less_than(phi_kernel)" (%797, %727) {kernel_key:,kernel_name:"less_than",op_name:"pd_op.less_than",origin_id:2587,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> cpu_tensor + (%799) = "memcpy_h2d(phi_kernel)" (%798) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2588} : (cpu_tensor) -> custom_device_tensor + (%800) = "memcpy_h2d(phi_kernel)" (%797) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2589} : (cpu_tensor) -> custom_device_tensor + (%801) = "memcpy_h2d(phi_kernel)" (%786) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2590} : (cpu_tensor<-1x4xf32>) -> custom_device_tensor<-1x4xf32> + (%802) = "memcpy_h2d(phi_kernel)" (%788) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2591} : (cpu_tensor<-1xf32>) -> custom_device_tensor<-1xf32> + (%803) = "memcpy_h2d(phi_kernel)" (%787) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2592} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%804) = "memcpy_h2d(phi_kernel)" (%789) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2593} : (cpu_tensor<-1xf32>) -> custom_device_tensor<-1xf32> + (%805) = "memcpy_h2d(phi_kernel)" (%786) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2594} : (cpu_tensor<-1x4xf32>) -> custom_device_tensor<-1x4xf32> + () = "cf.yield" [id:2595] (%799, %800, %722, %758, %701, %801, %802, %803, %699, %804, %805) {origin_id:546} : (custom_device_tensor, custom_device_tensor, custom_device_tensor<-1x4xf32>, custom_device_tensor, custom_device_tensor<-1x60x-1x-1xf32>, custom_device_tensor<-1x4xf32>, custom_device_tensor<-1xf32>, custom_device_tensor<-1x1xf32>, custom_device_tensor<-1x15x-1x-1xf32>, custom_device_tensor<-1xf32>, custom_device_tensor<-1x4xf32>) -> + } + (%806, %807) = "array_to_tensor(phi_kernel)" (%728) {axis:0,kernel_key:,kernel_name:"array_to_tensor",op_name:"pd_op.array_to_tensor",origin_id:2596,stop_gradient:[true,true],struct_name:"/RPNHead/",use_stack:false} : (cpu_tensor_array) -> cpu_tensor<-1xi64>, cpu_tensor<-1xi32> + (%808) = "array_length(phi_kernel)" (%729) {kernel_key:,kernel_name:"array_length",op_name:"pd_op.array_length",origin_id:2597,struct_name:"/BBoxHead/RoIAlign/"} : (cpu_tensor_array) -> cpu_tensor<1xi64> + (%809) = "memcpy_h2d(phi_kernel)" (%808) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2598} : (cpu_tensor<1xi64>) -> custom_device_tensor<1xi64> + (%810) = "greater_than(phi_kernel)" (%809, %40) {kernel_key:,kernel_name:"greater_than",op_name:"pd_op.greater_than",origin_id:2599,stop_gradient:[true],struct_name:"/BBoxHead/RoIAlign/"} : (custom_device_tensor<1xi64>, custom_device_tensor) -> custom_device_tensor<1xb> + (%811) = "pd_op.if" [id:2600] (%810) {} -> custom_device_tensor<-1x4xf32> { + (%812, %813) = "array_to_tensor(phi_kernel)" (%729) {axis:0,kernel_key:,kernel_name:"array_to_tensor",op_name:"pd_op.array_to_tensor",origin_id:2601,stop_gradient:[true,true],use_stack:false} : (cpu_tensor_array) -> cpu_tensor<-1x4xf32>, cpu_tensor<-1xi32> + (%814) = "memcpy_h2d(phi_kernel)" (%812) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2602} : (cpu_tensor<-1x4xf32>) -> custom_device_tensor<-1x4xf32> + () = "cf.yield" [id:2603] (%814) {origin_id:553} : (custom_device_tensor<-1x4xf32>) -> + } else { + (%815) = "slice_array_dense(phi_kernel)" (%729, %44) {kernel_key:,kernel_name:"slice_array_dense",op_name:"pd_op.slice_array_dense",origin_id:2604,stop_gradient:[true]} : (cpu_tensor_array, cpu_tensor<1xi64>) -> cpu_tensor<-1x4xf32> + (%816) = "memcpy_h2d(phi_kernel)" (%815) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2605} : (cpu_tensor<-1x4xf32>) -> custom_device_tensor<-1x4xf32> + () = "cf.yield" [id:2606] (%816) {origin_id:556} : (custom_device_tensor<-1x4xf32>) -> + } + (%817) = "cast(phi_kernel)" (%806) {dtype:int32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:2607,stop_gradient:[true],struct_name:"/BBoxHead/RoIAlign/"} : (cpu_tensor<-1xi64>) -> cpu_tensor<-1xi32> + (%818) = "memcpy_h2d(phi_kernel)" (%817) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2608} : (cpu_tensor<-1xi32>) -> custom_device_tensor<-1xi32> + (%819) = "roi_align(phi_kernel)" (%694, %811, %818) {aligned:true,kernel_key:,kernel_name:"roi_align",op_name:"pd_op.roi_align",origin_id:2609,pooled_height:14,pooled_width:14,sampling_ratio:-1,spatial_scale:0.0625,stop_gradient:[false],struct_name:"/BBoxHead/RoIAlign/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<-1x4xf32>, custom_device_tensor<-1xi32>) -> custom_device_tensor<-1x1024x14x14xf32> + (%820) = "conv2d(phi_kernel)" (%819, %113) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2610,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x1024x14x14xf32>, custom_device_tensor<512x1024x1x1xf32>) -> custom_device_tensor<-1x512x14x14xf32> + (%821, %822, %823, %824, %825, %826) = "batch_norm_(phi_kernel)" (%820, %110, %109, %112, %111) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2611,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x14x14xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x14x14xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%827) = "relu(phi_kernel)" (%821) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2612,stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer/"} : (custom_device_tensor<-1x512x14x14xf32>) -> custom_device_tensor<-1x512x14x14xf32> + (%828) = "conv2d(phi_kernel)" (%827, %108) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2613,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[2,2],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x512x14x14xf32>, custom_device_tensor<512x512x3x3xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%829, %830, %831, %832, %833, %834) = "batch_norm_(phi_kernel)" (%828, %105, %104, %107, %106) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2614,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%835) = "relu(phi_kernel)" (%829) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2615,stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer_1/"} : (custom_device_tensor<-1x512x7x7xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%836) = "conv2d(phi_kernel)" (%835, %103) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2616,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<2048x512x1x1xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%837, %838, %839, %840, %841, %842) = "batch_norm_(phi_kernel)" (%836, %100, %99, %102, %101) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2617,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>) -> custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<-1xu8> + (%843) = "conv2d(phi_kernel)" (%819, %98) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2618,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[2,2],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer_3/Conv2D/"} : (custom_device_tensor<-1x1024x14x14xf32>, custom_device_tensor<2048x1024x1x1xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%844, %845, %846, %847, %848, %849) = "batch_norm_(phi_kernel)" (%843, %95, %94, %97, %96) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2619,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer_3/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>) -> custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<-1xu8> + (%850) = "add(phi_kernel)" (%837, %844) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2620,stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/"} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%851) = "relu(phi_kernel)" (%850) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2621,stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/"} : (custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%852) = "conv2d(phi_kernel)" (%851, %93) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2622,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<512x2048x1x1xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%853, %854, %855, %856, %857, %858) = "batch_norm_(phi_kernel)" (%852, %90, %89, %92, %91) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2623,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%859) = "relu(phi_kernel)" (%853) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2624,stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/ConvNormLayer/"} : (custom_device_tensor<-1x512x7x7xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%860) = "conv2d(phi_kernel)" (%859, %88) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2625,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512x512x3x3xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%861, %862, %863, %864, %865, %866) = "batch_norm_(phi_kernel)" (%860, %85, %84, %87, %86) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2626,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%867) = "relu(phi_kernel)" (%861) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2627,stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/ConvNormLayer_1/"} : (custom_device_tensor<-1x512x7x7xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%868) = "conv2d(phi_kernel)" (%867, %83) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2628,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<2048x512x1x1xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%869, %870, %871, %872, %873, %874) = "batch_norm_(phi_kernel)" (%868, %80, %79, %82, %81) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2629,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>) -> custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<-1xu8> + (%875) = "add(phi_kernel)" (%869, %851) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2630,stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/"} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%876) = "relu(phi_kernel)" (%875) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2631,stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/"} : (custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%877) = "conv2d(phi_kernel)" (%876, %78) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2632,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<512x2048x1x1xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%878, %879, %880, %881, %882, %883) = "batch_norm_(phi_kernel)" (%877, %75, %74, %77, %76) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2633,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%884) = "relu(phi_kernel)" (%878) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2634,stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/ConvNormLayer/"} : (custom_device_tensor<-1x512x7x7xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%885) = "conv2d(phi_kernel)" (%884, %73) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2635,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512x512x3x3xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%886, %887, %888, %889, %890, %891) = "batch_norm_(phi_kernel)" (%885, %70, %69, %72, %71) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2636,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%892) = "relu(phi_kernel)" (%886) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2637,stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/ConvNormLayer_1/"} : (custom_device_tensor<-1x512x7x7xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%893) = "conv2d(phi_kernel)" (%892, %68) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2638,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<2048x512x1x1xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%894, %895, %896, %897, %898, %899) = "batch_norm_(phi_kernel)" (%893, %65, %64, %67, %66) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2639,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>) -> custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<-1xu8> + (%900) = "add(phi_kernel)" (%894, %876) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2640,stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/"} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%901) = "relu(phi_kernel)" (%900) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2641,stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/"} : (custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%902) = "pool2d(phi_kernel)" (%901, %39) {adaptive:true,ceil_mode:false,data_format:"NCHW",exclusive:true,global_pooling:false,kernel_key:,kernel_name:"pool2d",op_name:"pd_op.pool2d",origin_id:2642,padding_algorithm:"EXPLICIT",paddings:[0,0],pooling_type:"avg",stop_gradient:[false],strides:[1,1],struct_name:"/BBoxHead/"} : (custom_device_tensor<-1x2048x7x7xf32>, cpu_tensor<2xi64>) -> custom_device_tensor<-1x2048x1x1xf32> + (%903) = "squeeze(phi_kernel)" (%902, %38) {kernel_key:,kernel_name:"squeeze",op_name:"pd_op.squeeze",origin_id:2643,stop_gradient:[false],struct_name:"/BBoxHead/"} : (custom_device_tensor<-1x2048x1x1xf32>, cpu_tensor<2xi64>) -> custom_device_tensor<-1x2048xf32> + (%904) = "matmul(phi_kernel)" (%903, %63) {kernel_key:,kernel_name:"matmul",op_name:"pd_op.matmul",origin_id:2644,stop_gradient:[false],struct_name:"/BBoxHead/Linear/",transpose_x:false,transpose_y:false} : (custom_device_tensor<-1x2048xf32>, custom_device_tensor<2048x81xf32>) -> custom_device_tensor<-1x81xf32> + (%905) = "add(phi_kernel)" (%904, %62) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2645,stop_gradient:[false],struct_name:"/BBoxHead/Linear/"} : (custom_device_tensor<-1x81xf32>, custom_device_tensor<81xf32>) -> custom_device_tensor<-1x81xf32> + (%906) = "matmul(phi_kernel)" (%903, %61) {kernel_key:,kernel_name:"matmul",op_name:"pd_op.matmul",origin_id:2646,stop_gradient:[false],struct_name:"/BBoxHead/Linear_1/",transpose_x:false,transpose_y:false} : (custom_device_tensor<-1x2048xf32>, custom_device_tensor<2048x320xf32>) -> custom_device_tensor<-1x320xf32> + (%907) = "add(phi_kernel)" (%906, %60) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2647,stop_gradient:[false],struct_name:"/BBoxHead/Linear_1/"} : (custom_device_tensor<-1x320xf32>, custom_device_tensor<320xf32>) -> custom_device_tensor<-1x320xf32> + (%908) = "softmax(phi_kernel)" (%905) {axis:-1,kernel_key:,kernel_name:"softmax",op_name:"pd_op.softmax",origin_id:2648,stop_gradient:[false],struct_name:"/BBoxHead/"} : (custom_device_tensor<-1x81xf32>) -> custom_device_tensor<-1x81xf32> + (%909) = "slice(phi_kernel)" (%723, %44, %43) {axes:[0],decrease_axis:[],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2649,stop_gradient:[true]} : (cpu_tensor<2xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<1xi64> + (%910) = "arange(phi_kernel)" (%42, %909, %41) {dtype:int64,kernel_key:,kernel_name:"arange",op_name:"pd_op.arange",origin_id:2650,place:Place(undefined:0),stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1xi64> + (%911) = "shape64(phi_kernel)" (%910) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2651,stop_gradient:[true]} : (custom_device_tensor<-1xi64>) -> cpu_tensor<1xi64> + (%912) = "slice(phi_kernel)" (%911, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2652,stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%913) = "create_array(phi_kernel)" () {dtype:Undefined,kernel_key:,kernel_name:"create_array",op_name:"pd_op.create_array",origin_id:2653,stop_gradient:[true]} : () -> cpu_tensor_array + (%914) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2654,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%915) = "memcpy_h2d(phi_kernel)" (%912) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2655} : (cpu_tensor) -> custom_device_tensor + (%916) = "less_than(phi_kernel)" (%914, %915) {kernel_key:,kernel_name:"less_than",op_name:"pd_op.less_than",origin_id:2656,stop_gradient:[true]} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%917) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2657,place:Place(undefined:0),shape:[],stop_gradient:[false],value:0} : () -> custom_device_tensor<-1x2xf32> + (%918) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2658,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%919) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2659,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%920, %921, %922, %923) = "pd_op.while" [id:2660] (cond=%916, inputs=%914, %917, %918, %919) { + ^%arg_10 {stop_gradient:true}, %arg_11 {stop_gradient:false}, %arg_12 {stop_gradient:true}, %arg_13 {stop_gradient:true} + (%924) = "memcpy_d2h(phi_kernel)" (%arg_10) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2661} : (custom_device_tensor) -> cpu_tensor + (%925) = "scale(phi_kernel)" (%924, %18) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2662,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%926) = "builtin.combine" [id:2663] (%arg_10) {origin_id:620,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%927) = "stack(phi_kernel)" (%926) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2664,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%928) = "builtin.combine" [id:2665] (%925) {origin_id:622,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%929) = "stack(phi_kernel)" (%928) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2666,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%930) = "slice(phi_kernel)" (%910, %927, %929) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2667,stop_gradient:[true]} : (custom_device_tensor<-1xi64>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor + (%931) = "memcpy_d2h(phi_kernel)" (%930) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2668} : (custom_device_tensor) -> cpu_tensor + (%932) = "scale(phi_kernel)" (%931, %18) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2669,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%933) = "builtin.combine" [id:2670] (%930) {origin_id:627,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%934) = "stack(phi_kernel)" (%933) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2671,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%935) = "builtin.combine" [id:2672] (%932) {origin_id:629,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%936) = "stack(phi_kernel)" (%935) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2673,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%937) = "slice(phi_kernel)" (%806, %934, %936) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2674,stop_gradient:[true]} : (cpu_tensor<-1xi64>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%938) = "memcpy_d2h(phi_kernel)" (%930) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2675} : (custom_device_tensor) -> cpu_tensor + (%939) = "scale(phi_kernel)" (%938, %18) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2676,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%940) = "builtin.combine" [id:2677] (%930) {origin_id:634,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%941) = "stack(phi_kernel)" (%940) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2678,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%942) = "builtin.combine" [id:2679] (%939) {origin_id:636,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%943) = "stack(phi_kernel)" (%942) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2680,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%944) = "slice(phi_kernel)" (%333, %941, %943) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2681,stop_gradient:[false]} : (custom_device_tensor<-1x2xf32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<2xf32> + (%945) = "builtin.combine" [id:2682] (%937, %17) {origin_id:640,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor] + (%946) = "stack(phi_kernel)" (%945) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2683,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor]) -> cpu_tensor<2xi64> + (%947) = "expand(phi_kernel)" (%944, %946) {kernel_key:,kernel_name:"expand",op_name:"pd_op.expand",origin_id:2684,stop_gradient:[false]} : (custom_device_tensor<2xf32>, cpu_tensor<2xi64>) -> custom_device_tensor<-1x2xf32> + (%948) = "array_length(phi_kernel)" (%913) {kernel_key:,kernel_name:"array_length",op_name:"pd_op.array_length",origin_id:2685} : (cpu_tensor_array) -> cpu_tensor<1xi64> + (%949) = "memcpy_d2h(phi_kernel)" (%947) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2686} : (custom_device_tensor<-1x2xf32>) -> cpu_tensor<-1x2xf32> + (%950) = "array_write_(phi_kernel)" (%913, %949, %948) {is_inplace:true,kernel_key:,kernel_name:"array_write",op_name:"pd_op.array_write_",origin_id:2687} : (cpu_tensor_array, cpu_tensor<-1x2xf32>, cpu_tensor<1xi64>) -> cpu_tensor_array + (%951) = "memcpy_d2h(phi_kernel)" (%arg_10) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2688} : (custom_device_tensor) -> cpu_tensor + (%952) = "scale(phi_kernel)" (%951, %18) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2689,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%953) = "less_than(phi_kernel)" (%952, %912) {kernel_key:,kernel_name:"less_than",op_name:"pd_op.less_than",origin_id:2690,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> cpu_tensor + (%954) = "memcpy_h2d(phi_kernel)" (%953) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2691} : (cpu_tensor) -> custom_device_tensor + (%955) = "memcpy_h2d(phi_kernel)" (%952) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2692} : (cpu_tensor) -> custom_device_tensor + (%956) = "memcpy_h2d(phi_kernel)" (%937) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2693} : (cpu_tensor) -> custom_device_tensor + () = "cf.yield" [id:2694] (%954, %955, %947, %930, %956) {origin_id:648} : (custom_device_tensor, custom_device_tensor, custom_device_tensor<-1x2xf32>, custom_device_tensor, custom_device_tensor) -> + } + (%957, %958) = "array_to_tensor(phi_kernel)" (%913) {axis:0,kernel_key:,kernel_name:"array_to_tensor",op_name:"pd_op.array_to_tensor",origin_id:2695,stop_gradient:[true,true],use_stack:false} : (cpu_tensor_array) -> cpu_tensor<-1x2xf32>, cpu_tensor<-1xi32> + (%959, %960) = "array_to_tensor(phi_kernel)" (%729) {axis:0,kernel_key:,kernel_name:"array_to_tensor",op_name:"pd_op.array_to_tensor",origin_id:2696,stop_gradient:[true,true],use_stack:false} : (cpu_tensor_array) -> cpu_tensor<-1x4xf32>, cpu_tensor<-1xi32> + (%961) = "slice(phi_kernel)" (%959, %52, %51) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2697,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%962) = "slice(phi_kernel)" (%959, %44, %43) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2698,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%963) = "subtract(phi_kernel)" (%961, %962) {kernel_key:,kernel_name:"subtract",op_name:"pd_op.subtract",origin_id:2699,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%964) = "slice(phi_kernel)" (%959, %51, %50) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2700,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%965) = "slice(phi_kernel)" (%959, %43, %52) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2701,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%966) = "subtract(phi_kernel)" (%964, %965) {kernel_key:,kernel_name:"subtract",op_name:"pd_op.subtract",origin_id:2702,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%967) = "slice(phi_kernel)" (%959, %44, %43) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2703,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%968) = "scale(phi_kernel)" (%963, %37) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2704,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<1xf32>) -> cpu_tensor<-1xf32> + (%969) = "add(phi_kernel)" (%967, %968) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2705,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%970) = "slice(phi_kernel)" (%959, %43, %52) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2706,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%971) = "scale(phi_kernel)" (%966, %37) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2707,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<1xf32>) -> cpu_tensor<-1xf32> + (%972) = "add(phi_kernel)" (%970, %971) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2708,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%973) = "strided_slice(phi_kernel)" (%907, %44, %36, %50) {axes:[1],kernel_key:,kernel_name:"strided_slice",op_name:"pd_op.strided_slice",origin_id:2709,stop_gradient:[false]} : (custom_device_tensor<-1x320xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x80xf32> + (%974) = "scale(phi_kernel)" (%973, %35) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2710,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x80xf32> + (%975) = "strided_slice(phi_kernel)" (%907, %43, %36, %50) {axes:[1],kernel_key:,kernel_name:"strided_slice",op_name:"pd_op.strided_slice",origin_id:2711,stop_gradient:[false]} : (custom_device_tensor<-1x320xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x80xf32> + (%976) = "scale(phi_kernel)" (%975, %35) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2712,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x80xf32> + (%977) = "strided_slice(phi_kernel)" (%907, %52, %36, %50) {axes:[1],kernel_key:,kernel_name:"strided_slice",op_name:"pd_op.strided_slice",origin_id:2713,stop_gradient:[false]} : (custom_device_tensor<-1x320xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x80xf32> + (%978) = "scale(phi_kernel)" (%977, %34) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2714,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x80xf32> + (%979) = "strided_slice(phi_kernel)" (%907, %51, %36, %50) {axes:[1],kernel_key:,kernel_name:"strided_slice",op_name:"pd_op.strided_slice",origin_id:2715,stop_gradient:[false]} : (custom_device_tensor<-1x320xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x80xf32> + (%980) = "scale(phi_kernel)" (%979, %34) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2716,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x80xf32> + (%981) = "clip(phi_kernel)" (%978, %33, %32) {kernel_key:,kernel_name:"clip",op_name:"pd_op.clip",origin_id:2717,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, cpu_tensor<1xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x80xf32> + (%982) = "clip(phi_kernel)" (%980, %33, %32) {kernel_key:,kernel_name:"clip",op_name:"pd_op.clip",origin_id:2718,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, cpu_tensor<1xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x80xf32> + (%983) = "unsqueeze(phi_kernel)" (%963, %43) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:2719,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<1xi64>) -> cpu_tensor<-1x1xf32> + (%984) = "memcpy_h2d(phi_kernel)" (%983) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2720} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%985) = "multiply(phi_kernel)" (%974, %984) {kernel_key:,kernel_name:"multiply",op_name:"pd_op.multiply",origin_id:2721,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%986) = "unsqueeze(phi_kernel)" (%969, %43) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:2722,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<1xi64>) -> cpu_tensor<-1x1xf32> + (%987) = "memcpy_h2d(phi_kernel)" (%986) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2723} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%988) = "add(phi_kernel)" (%985, %987) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2724,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%989) = "unsqueeze(phi_kernel)" (%966, %43) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:2725,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<1xi64>) -> cpu_tensor<-1x1xf32> + (%990) = "memcpy_h2d(phi_kernel)" (%989) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2726} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%991) = "multiply(phi_kernel)" (%976, %990) {kernel_key:,kernel_name:"multiply",op_name:"pd_op.multiply",origin_id:2727,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%992) = "unsqueeze(phi_kernel)" (%972, %43) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:2728,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<1xi64>) -> cpu_tensor<-1x1xf32> + (%993) = "memcpy_h2d(phi_kernel)" (%992) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2729} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%994) = "add(phi_kernel)" (%991, %993) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2730,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%995) = "exp(phi_kernel)" (%981) {kernel_key:,kernel_name:"exp",op_name:"pd_op.exp",origin_id:2731,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>) -> custom_device_tensor<-1x80xf32> + (%996) = "unsqueeze(phi_kernel)" (%963, %43) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:2732,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<1xi64>) -> cpu_tensor<-1x1xf32> + (%997) = "memcpy_h2d(phi_kernel)" (%996) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2733} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%998) = "multiply(phi_kernel)" (%995, %997) {kernel_key:,kernel_name:"multiply",op_name:"pd_op.multiply",origin_id:2734,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%999) = "exp(phi_kernel)" (%982) {kernel_key:,kernel_name:"exp",op_name:"pd_op.exp",origin_id:2735,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>) -> custom_device_tensor<-1x80xf32> + (%1000) = "unsqueeze(phi_kernel)" (%966, %43) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:2736,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<1xi64>) -> cpu_tensor<-1x1xf32> + (%1001) = "memcpy_h2d(phi_kernel)" (%1000) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2737} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1002) = "multiply(phi_kernel)" (%999, %1001) {kernel_key:,kernel_name:"multiply",op_name:"pd_op.multiply",origin_id:2738,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%1003) = "scale(phi_kernel)" (%998, %37) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2739,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x80xf32> + (%1004) = "subtract(phi_kernel)" (%988, %1003) {kernel_key:,kernel_name:"subtract",op_name:"pd_op.subtract",origin_id:2740,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x80xf32>) -> custom_device_tensor<-1x80xf32> + (%1005) = "scale(phi_kernel)" (%1002, %37) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2741,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x80xf32> + (%1006) = "subtract(phi_kernel)" (%994, %1005) {kernel_key:,kernel_name:"subtract",op_name:"pd_op.subtract",origin_id:2742,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x80xf32>) -> custom_device_tensor<-1x80xf32> + (%1007) = "scale(phi_kernel)" (%998, %37) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2743,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x80xf32> + (%1008) = "add(phi_kernel)" (%988, %1007) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2744,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x80xf32>) -> custom_device_tensor<-1x80xf32> + (%1009) = "scale(phi_kernel)" (%1002, %37) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2745,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x80xf32> + (%1010) = "add(phi_kernel)" (%994, %1009) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2746,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x80xf32>) -> custom_device_tensor<-1x80xf32> + (%1011) = "builtin.combine" [id:2747] (%1004, %1006, %1008, %1010) {origin_id:739,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x80xf32>) -> vec[custom_device_tensor<-1x80xf32>,custom_device_tensor<-1x80xf32>,custom_device_tensor<-1x80xf32>,custom_device_tensor<-1x80xf32>] + (%1012) = "stack(phi_kernel)" (%1011) {axis:-1,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2748,stop_gradient:[false]} : (vec[custom_device_tensor<-1x80xf32>,custom_device_tensor<-1x80xf32>,custom_device_tensor<-1x80xf32>,custom_device_tensor<-1x80xf32>]) -> custom_device_tensor<-1x80x4xf32> + (%1013) = "slice(phi_kernel)" (%908, %44, %48) {axes:[1],decrease_axis:[],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2749,stop_gradient:[false]} : (custom_device_tensor<-1x81xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x80xf32> + (%1014) = "shape64(phi_kernel)" (%1012) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2750,stop_gradient:[true]} : (custom_device_tensor<-1x80x4xf32>) -> cpu_tensor<3xi64> + (%1015) = "slice(phi_kernel)" (%1014, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2751,stop_gradient:[true]} : (cpu_tensor<3xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1016) = "builtin.combine" [id:2752] (%1015, %31, %30) {origin_id:750,stop_gradient:[true]} : (cpu_tensor, cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor,cpu_tensor] + (%1017) = "stack(phi_kernel)" (%1016) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2753,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor,cpu_tensor]) -> cpu_tensor<3xi64> + (%1018) = "expand(phi_kernel)" (%1012, %1017) {kernel_key:,kernel_name:"expand",op_name:"pd_op.expand",origin_id:2754,stop_gradient:[false]} : (custom_device_tensor<-1x80x4xf32>, cpu_tensor<3xi64>) -> custom_device_tensor<-1x80x4xf32> + (%1019) = "slice(phi_kernel)" (%957, %44, %43) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2755,stop_gradient:[true]} : (cpu_tensor<-1x2xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%1020) = "unsqueeze(phi_kernel)" (%1019, %43) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:2756,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<1xi64>) -> cpu_tensor<-1x1xf32> + (%1021) = "slice(phi_kernel)" (%957, %43, %52) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2757,stop_gradient:[true]} : (cpu_tensor<-1x2xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%1022) = "unsqueeze(phi_kernel)" (%1021, %43) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:2758,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<1xi64>) -> cpu_tensor<-1x1xf32> + (%1023) = "full_like(phi_kernel)" (%1020, %706) {dtype:float32,kernel_key:,kernel_name:"full_like",op_name:"pd_op.full_like",origin_id:2759,place:Place(undefined:0),stop_gradient:[true]} : (cpu_tensor<-1x1xf32>, cpu_tensor<1xf32>) -> cpu_tensor<-1x1xf32> + (%1024) = "slice(phi_kernel)" (%1018, %44, %43) {axes:[2],decrease_axis:[2],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2760,stop_gradient:[false]} : (custom_device_tensor<-1x80x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x80xf32> + (%1025) = "memcpy_h2d(phi_kernel)" (%1022) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2761} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1026) = "minimum(phi_kernel)" (%1024, %1025) {kernel_key:,kernel_name:"minimum",op_name:"pd_op.minimum",origin_id:2762,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%1027) = "memcpy_h2d(phi_kernel)" (%1023) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2763} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1028) = "maximum(phi_kernel)" (%1026, %1027) {kernel_key:,kernel_name:"maximum",op_name:"pd_op.maximum",origin_id:2764,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%1029) = "slice(phi_kernel)" (%1018, %43, %52) {axes:[2],decrease_axis:[2],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2765,stop_gradient:[false]} : (custom_device_tensor<-1x80x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x80xf32> + (%1030) = "memcpy_h2d(phi_kernel)" (%1020) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2766} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1031) = "minimum(phi_kernel)" (%1029, %1030) {kernel_key:,kernel_name:"minimum",op_name:"pd_op.minimum",origin_id:2767,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%1032) = "memcpy_h2d(phi_kernel)" (%1023) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2768} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1033) = "maximum(phi_kernel)" (%1031, %1032) {kernel_key:,kernel_name:"maximum",op_name:"pd_op.maximum",origin_id:2769,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%1034) = "slice(phi_kernel)" (%1018, %52, %51) {axes:[2],decrease_axis:[2],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2770,stop_gradient:[false]} : (custom_device_tensor<-1x80x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x80xf32> + (%1035) = "memcpy_h2d(phi_kernel)" (%1022) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2771} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1036) = "minimum(phi_kernel)" (%1034, %1035) {kernel_key:,kernel_name:"minimum",op_name:"pd_op.minimum",origin_id:2772,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%1037) = "memcpy_h2d(phi_kernel)" (%1023) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2773} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1038) = "maximum(phi_kernel)" (%1036, %1037) {kernel_key:,kernel_name:"maximum",op_name:"pd_op.maximum",origin_id:2774,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%1039) = "slice(phi_kernel)" (%1018, %51, %50) {axes:[2],decrease_axis:[2],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2775,stop_gradient:[false]} : (custom_device_tensor<-1x80x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x80xf32> + (%1040) = "memcpy_h2d(phi_kernel)" (%1020) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2776} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1041) = "minimum(phi_kernel)" (%1039, %1040) {kernel_key:,kernel_name:"minimum",op_name:"pd_op.minimum",origin_id:2777,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%1042) = "memcpy_h2d(phi_kernel)" (%1023) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2778} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1043) = "maximum(phi_kernel)" (%1041, %1042) {kernel_key:,kernel_name:"maximum",op_name:"pd_op.maximum",origin_id:2779,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%1044) = "builtin.combine" [id:2780] (%1028, %1033, %1038, %1043) {origin_id:785,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x80xf32>) -> vec[custom_device_tensor<-1x80xf32>,custom_device_tensor<-1x80xf32>,custom_device_tensor<-1x80xf32>,custom_device_tensor<-1x80xf32>] + (%1045) = "stack(phi_kernel)" (%1044) {axis:-1,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2781,stop_gradient:[false]} : (vec[custom_device_tensor<-1x80xf32>,custom_device_tensor<-1x80xf32>,custom_device_tensor<-1x80xf32>,custom_device_tensor<-1x80xf32>]) -> custom_device_tensor<-1x80x4xf32> + (%1046) = "memcpy_d2h(phi_kernel)" (%1045) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2782} : (custom_device_tensor<-1x80x4xf32>) -> cpu_tensor<-1x80x4xf32> + (%1047) = "memcpy_d2h(phi_kernel)" (%1013) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2783} : (custom_device_tensor<-1x80xf32>) -> cpu_tensor<-1x80xf32> + (%1048, %1049, %1050) = "multiclass_nms3(phi_kernel)" (%1046, %1047, %806) {background_label:80,keep_top_k:100,kernel_key:,kernel_name:"multiclass_nms3",nms_eta:1,nms_threshold:0.5,nms_top_k:-1,normalized:true,op_name:"pd_op.multiclass_nms3",origin_id:2784,score_threshold:0.05,stop_gradient:[false,false,false]} : (cpu_tensor<-1x80x4xf32>, cpu_tensor<-1x80xf32>, cpu_tensor<-1xi64>) -> cpu_tensor<-1x6xf32>, cpu_tensor<-1x1xi32>, cpu_tensor<-1xi32> + (%1051) = "shape64(phi_kernel)" (%1048) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2785,stop_gradient:[true],struct_name:"/MaskHead/"} : (cpu_tensor<-1x6xf32>) -> cpu_tensor<2xi64> + (%1052) = "slice(phi_kernel)" (%1051, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2786,stop_gradient:[true],struct_name:"/MaskHead/"} : (cpu_tensor<2xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1053) = "memcpy_h2d(phi_kernel)" (%1052) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2787} : (cpu_tensor) -> custom_device_tensor + (%1054) = "equal(phi_kernel)" (%1053, %29) {kernel_key:,kernel_name:"equal",op_name:"pd_op.equal",origin_id:2788,stop_gradient:[true],struct_name:"/MaskHead/"} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%1055) = "pd_op.if" [id:2789] (%1054) {} -> custom_device_tensor<-1x-1x-1xf32> { + () = "cf.yield" [id:2790] (%16) {origin_id:796} : (custom_device_tensor<1x1x1xf32>) -> + } else { + (%1056) = "slice(phi_kernel)" (%1048, %52, %36) {axes:[1],decrease_axis:[],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2791,stop_gradient:[false]} : (cpu_tensor<-1x6xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1x4xf32> + (%1057) = "slice(phi_kernel)" (%1048, %44, %43) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2792,stop_gradient:[false]} : (cpu_tensor<-1x6xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%1058) = "cast(phi_kernel)" (%1057) {dtype:int32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:2793,stop_gradient:[false]} : (cpu_tensor<-1xf32>) -> cpu_tensor<-1xi32> + (%1059) = "memcpy_h2d(phi_kernel)" (%1056) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2794} : (cpu_tensor<-1x4xf32>) -> custom_device_tensor<-1x4xf32> + (%1060) = "memcpy_h2d(phi_kernel)" (%1050) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2795} : (cpu_tensor<-1xi32>) -> custom_device_tensor<-1xi32> + (%1061) = "roi_align(phi_kernel)" (%694, %1059, %1060) {aligned:true,kernel_key:,kernel_name:"roi_align",op_name:"pd_op.roi_align",origin_id:2796,pooled_height:14,pooled_width:14,sampling_ratio:-1,spatial_scale:0.0625,stop_gradient:[false]} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<-1x4xf32>, custom_device_tensor<-1xi32>) -> custom_device_tensor<-1x1024x14x14xf32> + (%1062) = "conv2d(phi_kernel)" (%1061, %113) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2797,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (custom_device_tensor<-1x1024x14x14xf32>, custom_device_tensor<512x1024x1x1xf32>) -> custom_device_tensor<-1x512x14x14xf32> + (%1063, %1064, %1065, %1066, %1067, %1068) = "batch_norm_(phi_kernel)" (%1062, %110, %109, %112, %111) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2798,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x14x14xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x14x14xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%1069) = "relu(phi_kernel)" (%1063) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2799,stop_gradient:[false]} : (custom_device_tensor<-1x512x14x14xf32>) -> custom_device_tensor<-1x512x14x14xf32> + (%1070) = "conv2d(phi_kernel)" (%1069, %108) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2800,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[2,2]} : (custom_device_tensor<-1x512x14x14xf32>, custom_device_tensor<512x512x3x3xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%1071, %1072, %1073, %1074, %1075, %1076) = "batch_norm_(phi_kernel)" (%1070, %105, %104, %107, %106) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2801,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%1077) = "relu(phi_kernel)" (%1071) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2802,stop_gradient:[false]} : (custom_device_tensor<-1x512x7x7xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%1078) = "conv2d(phi_kernel)" (%1077, %103) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2803,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<2048x512x1x1xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%1079, %1080, %1081, %1082, %1083, %1084) = "batch_norm_(phi_kernel)" (%1078, %100, %99, %102, %101) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2804,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>) -> custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<-1xu8> + (%1085) = "conv2d(phi_kernel)" (%1061, %98) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2805,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[2,2]} : (custom_device_tensor<-1x1024x14x14xf32>, custom_device_tensor<2048x1024x1x1xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%1086, %1087, %1088, %1089, %1090, %1091) = "batch_norm_(phi_kernel)" (%1085, %95, %94, %97, %96) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2806,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>) -> custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<-1xu8> + (%1092) = "add(phi_kernel)" (%1079, %1086) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2807,stop_gradient:[false]} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%1093) = "relu(phi_kernel)" (%1092) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2808,stop_gradient:[false]} : (custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%1094) = "conv2d(phi_kernel)" (%1093, %93) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2809,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<512x2048x1x1xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%1095, %1096, %1097, %1098, %1099, %1100) = "batch_norm_(phi_kernel)" (%1094, %90, %89, %92, %91) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2810,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%1101) = "relu(phi_kernel)" (%1095) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2811,stop_gradient:[false]} : (custom_device_tensor<-1x512x7x7xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%1102) = "conv2d(phi_kernel)" (%1101, %88) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2812,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1]} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512x512x3x3xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%1103, %1104, %1105, %1106, %1107, %1108) = "batch_norm_(phi_kernel)" (%1102, %85, %84, %87, %86) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2813,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%1109) = "relu(phi_kernel)" (%1103) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2814,stop_gradient:[false]} : (custom_device_tensor<-1x512x7x7xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%1110) = "conv2d(phi_kernel)" (%1109, %83) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2815,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<2048x512x1x1xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%1111, %1112, %1113, %1114, %1115, %1116) = "batch_norm_(phi_kernel)" (%1110, %80, %79, %82, %81) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2816,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>) -> custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<-1xu8> + (%1117) = "add(phi_kernel)" (%1111, %1093) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2817,stop_gradient:[false]} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%1118) = "relu(phi_kernel)" (%1117) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2818,stop_gradient:[false]} : (custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%1119) = "conv2d(phi_kernel)" (%1118, %78) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2819,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<512x2048x1x1xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%1120, %1121, %1122, %1123, %1124, %1125) = "batch_norm_(phi_kernel)" (%1119, %75, %74, %77, %76) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2820,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%1126) = "relu(phi_kernel)" (%1120) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2821,stop_gradient:[false]} : (custom_device_tensor<-1x512x7x7xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%1127) = "conv2d(phi_kernel)" (%1126, %73) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2822,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1]} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512x512x3x3xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%1128, %1129, %1130, %1131, %1132, %1133) = "batch_norm_(phi_kernel)" (%1127, %70, %69, %72, %71) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2823,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%1134) = "relu(phi_kernel)" (%1128) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2824,stop_gradient:[false]} : (custom_device_tensor<-1x512x7x7xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%1135) = "conv2d(phi_kernel)" (%1134, %68) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2825,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<2048x512x1x1xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%1136, %1137, %1138, %1139, %1140, %1141) = "batch_norm_(phi_kernel)" (%1135, %65, %64, %67, %66) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2826,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>) -> custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<-1xu8> + (%1142) = "add(phi_kernel)" (%1136, %1118) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2827,stop_gradient:[false]} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%1143) = "relu(phi_kernel)" (%1142) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2828,stop_gradient:[false]} : (custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%1144) = "conv2d_transpose(phi_kernel)" (%1143, %59, %15) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d_transpose",op_name:"pd_op.conv2d_transpose",origin_id:2829,output_padding:[],padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[2,2]} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048x256x2x2xf32>, cpu_tensor<0xi64>) -> custom_device_tensor<-1x256x14x14xf32> + (%1145) = "add(phi_kernel)" (%1144, %14) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2830,stop_gradient:[false]} : (custom_device_tensor<-1x256x14x14xf32>, custom_device_tensor<1x256x1x1xf32>) -> custom_device_tensor<-1x256x14x14xf32> + (%1146) = "relu(phi_kernel)" (%1145) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2831,stop_gradient:[false]} : (custom_device_tensor<-1x256x14x14xf32>) -> custom_device_tensor<-1x256x14x14xf32> + (%1147) = "conv2d(phi_kernel)" (%1146, %58) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2832,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (custom_device_tensor<-1x256x14x14xf32>, custom_device_tensor<80x256x1x1xf32>) -> custom_device_tensor<-1x80x14x14xf32> + (%1148) = "add(phi_kernel)" (%1147, %56) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2833,stop_gradient:[false]} : (custom_device_tensor<-1x80x14x14xf32>, custom_device_tensor<1x80x1x1xf32>) -> custom_device_tensor<-1x80x14x14xf32> + (%1149) = "shape64(phi_kernel)" (%1148) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2834,stop_gradient:[true]} : (custom_device_tensor<-1x80x14x14xf32>) -> cpu_tensor<4xi64> + (%1150) = "slice(phi_kernel)" (%1149, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2835,stop_gradient:[true]} : (cpu_tensor<4xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1151) = "arange(phi_kernel)" (%42, %1150, %41) {dtype:int64,kernel_key:,kernel_name:"arange",op_name:"pd_op.arange",origin_id:2836,place:Place(undefined:0),stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor, cpu_tensor<1xi64>) -> custom_device_tensor<-1xi64> + (%1152) = "cast(phi_kernel)" (%1151) {dtype:int32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:2837,stop_gradient:[true]} : (custom_device_tensor<-1xi64>) -> custom_device_tensor<-1xi32> + (%1153) = "builtin.combine" [id:2838] (%1152, %1058) {origin_id:855,stop_gradient:[false]} : (custom_device_tensor<-1xi32>, cpu_tensor<-1xi32>) -> vec[custom_device_tensor<-1xi32>,cpu_tensor<-1xi32>] + (%1154) = "memcpy_d2h(phi_kernel)" (%1152) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2839} : (custom_device_tensor<-1xi32>) -> cpu_tensor<-1xi32> + (%1155) = "builtin.combine" [id:2840] (%1154, %1058) {origin_id:2840} : (cpu_tensor<-1xi32>, cpu_tensor<-1xi32>) -> vec[cpu_tensor<-1xi32>,cpu_tensor<-1xi32>] + (%1156) = "broadcast_tensors(phi_kernel)" (%1155) {kernel_key:,kernel_name:"broadcast_tensors",op_name:"pd_op.broadcast_tensors",origin_id:2841,stop_gradient:[false]} : (vec[cpu_tensor<-1xi32>,cpu_tensor<-1xi32>]) -> vec[cpu_tensor<-1xi32>,cpu_tensor<-1xi32>] + (%1157, %1158) = "builtin.split" [id:2842] (%1156) {origin_id:857,stop_gradient:[false,false]} : (vec[cpu_tensor<-1xi32>,cpu_tensor<-1xi32>]) -> cpu_tensor<-1xi32>, cpu_tensor<-1xi32> + (%1159) = "builtin.combine" [id:2843] (%1157, %1158) {origin_id:858,stop_gradient:[false]} : (cpu_tensor<-1xi32>, cpu_tensor<-1xi32>) -> vec[cpu_tensor<-1xi32>,cpu_tensor<-1xi32>] + (%1160) = "stack(phi_kernel)" (%1159) {axis:-1,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2844,stop_gradient:[false]} : (vec[cpu_tensor<-1xi32>,cpu_tensor<-1xi32>]) -> cpu_tensor<-1x2xi32> + (%1161) = "memcpy_h2d(phi_kernel)" (%1160) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2845} : (cpu_tensor<-1x2xi32>) -> custom_device_tensor<-1x2xi32> + (%1162) = "gather_nd(phi_kernel)" (%1148, %1161) {kernel_key:,kernel_name:"gather_nd",op_name:"pd_op.gather_nd",origin_id:2846,stop_gradient:[false]} : (custom_device_tensor<-1x80x14x14xf32>, custom_device_tensor<-1x2xi32>) -> custom_device_tensor<-1x14x14xf32> + (%1163) = "shape64(phi_kernel)" (%1152) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2847,stop_gradient:[true]} : (custom_device_tensor<-1xi32>) -> cpu_tensor<1xi64> + (%1164) = "slice(phi_kernel)" (%1163, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2848,stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1165) = "builtin.combine" [id:2849] (%1164, %13, %13) {origin_id:871,stop_gradient:[true]} : (cpu_tensor, cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor,cpu_tensor] + (%1166) = "(phi_kernel)" (%1165) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2850,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor,cpu_tensor]) -> cpu_tensor<3xi64> + (%1167) = "reshape(phi_kernel)" (%1162, %1166) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:2851,stop_gradient:[false]} : (custom_device_tensor<-1x14x14xf32>, cpu_tensor<3xi64>) -> custom_device_tensor<-1x14x14xf32> + (%1168) = "sigmoid(phi_kernel)" (%1167) {kernel_key:,kernel_name:"sigmoid",op_name:"pd_op.sigmoid",origin_id:2852,stop_gradient:[false]} : (custom_device_tensor<-1x14x14xf32>) -> custom_device_tensor<-1x14x14xf32> + () = "cf.yield" [id:2853] (%1168) {origin_id:875} : (custom_device_tensor<-1x14x14xf32>) -> + } + (%1169) = "shape64(phi_kernel)" (%1050) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2854,stop_gradient:[true]} : (cpu_tensor<-1xi32>) -> cpu_tensor<1xi64> + (%1170) = "slice(phi_kernel)" (%1169, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2855,stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1171) = "arange(phi_kernel)" (%42, %1170, %41) {dtype:int64,kernel_key:,kernel_name:"arange",op_name:"pd_op.arange",origin_id:2856,place:Place(undefined:0),stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor, cpu_tensor<1xi64>) -> custom_device_tensor<-1xi64> + (%1172) = "shape64(phi_kernel)" (%1171) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2857,stop_gradient:[true]} : (custom_device_tensor<-1xi64>) -> cpu_tensor<1xi64> + (%1173) = "slice(phi_kernel)" (%1172, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2858,stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1174) = "create_array(phi_kernel)" () {dtype:Undefined,kernel_key:,kernel_name:"create_array",op_name:"pd_op.create_array",origin_id:2859,stop_gradient:[true]} : () -> cpu_tensor_array + (%1175) = "create_array(phi_kernel)" () {dtype:Undefined,kernel_key:,kernel_name:"create_array",op_name:"pd_op.create_array",origin_id:2860,stop_gradient:[true]} : () -> cpu_tensor_array + (%1176) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2861,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%1177) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2862,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%1178) = "memcpy_h2d(phi_kernel)" (%1173) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2863} : (cpu_tensor) -> custom_device_tensor + (%1179) = "less_than(phi_kernel)" (%1176, %1178) {kernel_key:,kernel_name:"less_than",op_name:"pd_op.less_than",origin_id:2864,stop_gradient:[true]} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%1180) = "full(phi_kernel)" () {dtype:int32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2865,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor<-1xi32> + (%1181) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2866,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor<-1x6xf32> + (%1182) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2867,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%1183, %1184, %1185, %1186, %1187) = "pd_op.while" [id:2868] (cond=%1179, inputs=%1176, %1177, %1180, %1181, %1182) { + ^%arg_14 {stop_gradient:true}, %arg_15 {stop_gradient:true}, %arg_16 {stop_gradient:true}, %arg_17 {stop_gradient:true}, %arg_18 {stop_gradient:true} + (%1188) = "memcpy_d2h(phi_kernel)" (%arg_14) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2869} : (custom_device_tensor) -> cpu_tensor + (%1189) = "scale(phi_kernel)" (%1188, %12) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2870,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1190) = "builtin.combine" [id:2871] (%arg_14) {origin_id:902,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1191) = "stack(phi_kernel)" (%1190) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2872,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1192) = "builtin.combine" [id:2873] (%1189) {origin_id:904,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1193) = "stack(phi_kernel)" (%1192) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2874,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1194) = "slice(phi_kernel)" (%1171, %1191, %1193) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2875,stop_gradient:[true]} : (custom_device_tensor<-1xi64>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor + (%1195) = "memcpy_d2h(phi_kernel)" (%1194) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2876} : (custom_device_tensor) -> cpu_tensor + (%1196) = "scale(phi_kernel)" (%1195, %12) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2877,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1197) = "builtin.combine" [id:2878] (%1194) {origin_id:909,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1198) = "stack(phi_kernel)" (%1197) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2879,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1199) = "builtin.combine" [id:2880] (%1196) {origin_id:911,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1200) = "stack(phi_kernel)" (%1199) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2881,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1201) = "slice(phi_kernel)" (%1050, %1198, %1200) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2882,stop_gradient:[false]} : (cpu_tensor<-1xi32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1202) = "memcpy_h2d(phi_kernel)" (%1201) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2883} : (cpu_tensor) -> custom_device_tensor + (%1203) = "equal(phi_kernel)" (%1202, %11) {kernel_key:,kernel_name:"equal",op_name:"pd_op.equal",origin_id:2884,stop_gradient:[true]} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%1204, %1205, %1206) = "pd_op.if" [id:2885] (%1203) {} -> custom_device_tensor<-1xi32>, custom_device_tensor<-1x6xf32>, custom_device_tensor { + () = "cf.yield" [id:2886] (%27, %28, %arg_15) {origin_id:917} : (custom_device_tensor<1xi32>, custom_device_tensor<1x6xf32>, custom_device_tensor) -> + } else { + (%1207) = "memcpy_d2h(phi_kernel)" (%1194) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2887} : (custom_device_tensor) -> cpu_tensor + (%1208) = "scale(phi_kernel)" (%1207, %12) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2888,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1209) = "builtin.combine" [id:2889] (%1194) {origin_id:920,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1210) = "stack(phi_kernel)" (%1209) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2890,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1211) = "builtin.combine" [id:2891] (%1208) {origin_id:922,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1212) = "stack(phi_kernel)" (%1211) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2892,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1213) = "slice(phi_kernel)" (%1050, %1210, %1212) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2893,stop_gradient:[false]} : (cpu_tensor<-1xi32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1214) = "cast(phi_kernel)" (%1213) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:2894,stop_gradient:[false]} : (cpu_tensor) -> cpu_tensor + (%1215) = "memcpy_h2d(phi_kernel)" (%1214) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2895} : (cpu_tensor) -> custom_device_tensor + (%1216) = "add(phi_kernel)" (%arg_15, %1215) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2896,stop_gradient:[true]} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%1217) = "builtin.combine" [id:2897] (%arg_15) {origin_id:927,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1218) = "stack(phi_kernel)" (%1217) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2898,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1219) = "builtin.combine" [id:2899] (%1216) {origin_id:929,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1220) = "stack(phi_kernel)" (%1219) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2900,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1221) = "slice(phi_kernel)" (%1048, %1218, %1220) {axes:[0],decrease_axis:[],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2901,stop_gradient:[false]} : (cpu_tensor<-1x6xf32>, custom_device_tensor<1xi64>, custom_device_tensor<1xi64>) -> cpu_tensor<-1x6xf32> + (%1222) = "memcpy_d2h(phi_kernel)" (%1194) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2902} : (custom_device_tensor) -> cpu_tensor + (%1223) = "scale(phi_kernel)" (%1222, %12) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2903,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1224) = "builtin.combine" [id:2904] (%1194) {origin_id:934,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1225) = "stack(phi_kernel)" (%1224) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2905,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1226) = "builtin.combine" [id:2906] (%1223) {origin_id:936,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1227) = "stack(phi_kernel)" (%1226) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2907,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1228) = "slice(phi_kernel)" (%1050, %1225, %1227) {axes:[0],decrease_axis:[],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2908,stop_gradient:[false]} : (cpu_tensor<-1xi32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xi32> + (%1229) = "memcpy_d2h(phi_kernel)" (%1194) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2909} : (custom_device_tensor) -> cpu_tensor + (%1230) = "scale(phi_kernel)" (%1229, %12) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2910,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1231) = "builtin.combine" [id:2911] (%1194) {origin_id:941,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1232) = "stack(phi_kernel)" (%1231) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2912,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1233) = "builtin.combine" [id:2913] (%1230) {origin_id:943,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1234) = "stack(phi_kernel)" (%1233) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2914,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1235) = "slice(phi_kernel)" (%1050, %1232, %1234) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2915,stop_gradient:[false]} : (cpu_tensor<-1xi32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1236) = "cast(phi_kernel)" (%1235) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:2916,stop_gradient:[false]} : (cpu_tensor) -> cpu_tensor + (%1237) = "memcpy_h2d(phi_kernel)" (%1236) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2917} : (cpu_tensor) -> custom_device_tensor + (%1238) = "add(phi_kernel)" (%arg_15, %1237) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2918,stop_gradient:[false]} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%1239) = "memcpy_h2d(phi_kernel)" (%1228) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2919} : (cpu_tensor<-1xi32>) -> custom_device_tensor<-1xi32> + (%1240) = "memcpy_h2d(phi_kernel)" (%1221) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2920} : (cpu_tensor<-1x6xf32>) -> custom_device_tensor<-1x6xf32> + () = "cf.yield" [id:2921] (%1239, %1240, %1238) {origin_id:948} : (custom_device_tensor<-1xi32>, custom_device_tensor<-1x6xf32>, custom_device_tensor) -> + } + (%1241) = "array_length(phi_kernel)" (%1174) {kernel_key:,kernel_name:"array_length",op_name:"pd_op.array_length",origin_id:2922} : (cpu_tensor_array) -> cpu_tensor<1xi64> + (%1242) = "memcpy_d2h(phi_kernel)" (%1205) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2923} : (custom_device_tensor<-1x6xf32>) -> cpu_tensor<-1x6xf32> + (%1243) = "array_write_(phi_kernel)" (%1174, %1242, %1241) {is_inplace:true,kernel_key:,kernel_name:"array_write",op_name:"pd_op.array_write_",origin_id:2924} : (cpu_tensor_array, cpu_tensor<-1x6xf32>, cpu_tensor<1xi64>) -> cpu_tensor_array + (%1244) = "array_length(phi_kernel)" (%1175) {kernel_key:,kernel_name:"array_length",op_name:"pd_op.array_length",origin_id:2925} : (cpu_tensor_array) -> cpu_tensor<1xi64> + (%1245) = "memcpy_d2h(phi_kernel)" (%1204) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2926} : (custom_device_tensor<-1xi32>) -> cpu_tensor<-1xi32> + (%1246) = "array_write_(phi_kernel)" (%1175, %1245, %1244) {is_inplace:true,kernel_key:,kernel_name:"array_write",op_name:"pd_op.array_write_",origin_id:2927} : (cpu_tensor_array, cpu_tensor<-1xi32>, cpu_tensor<1xi64>) -> cpu_tensor_array + (%1247) = "memcpy_d2h(phi_kernel)" (%arg_14) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2928} : (custom_device_tensor) -> cpu_tensor + (%1248) = "scale(phi_kernel)" (%1247, %12) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2929,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1249) = "less_than(phi_kernel)" (%1248, %1173) {kernel_key:,kernel_name:"less_than",op_name:"pd_op.less_than",origin_id:2930,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> cpu_tensor + (%1250) = "memcpy_h2d(phi_kernel)" (%1249) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2931} : (cpu_tensor) -> custom_device_tensor + (%1251) = "memcpy_h2d(phi_kernel)" (%1248) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2932} : (cpu_tensor) -> custom_device_tensor + () = "cf.yield" [id:2933] (%1250, %1251, %1206, %1204, %1205, %1194) {origin_id:956} : (custom_device_tensor, custom_device_tensor, custom_device_tensor, custom_device_tensor<-1xi32>, custom_device_tensor<-1x6xf32>, custom_device_tensor) -> + } + (%1252, %1253) = "array_to_tensor(phi_kernel)" (%1174) {axis:0,kernel_key:,kernel_name:"array_to_tensor",op_name:"pd_op.array_to_tensor",origin_id:2934,stop_gradient:[true,true],use_stack:false} : (cpu_tensor_array) -> cpu_tensor<-1x6xf32>, cpu_tensor<-1xi32> + (%1254, %1255) = "array_to_tensor(phi_kernel)" (%1175) {axis:0,kernel_key:,kernel_name:"array_to_tensor",op_name:"pd_op.array_to_tensor",origin_id:2935,stop_gradient:[true,true],use_stack:false} : (cpu_tensor_array) -> cpu_tensor<-1xi32>, cpu_tensor<-1xi32> + (%1256) = "divide(phi_kernel)" (%333, %337) {kernel_key:,kernel_name:"divide",op_name:"pd_op.divide",origin_id:2936,stop_gradient:[false]} : (custom_device_tensor<-1x2xf32>, custom_device_tensor<-1x2xf32>) -> custom_device_tensor<-1x2xf32> + (%1257) = "scale(phi_kernel)" (%1256, %26) {bias:0.5,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2937,stop_gradient:[false]} : (custom_device_tensor<-1x2xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x2xf32> + (%1258) = "floor(phi_kernel)" (%1257) {kernel_key:,kernel_name:"floor",op_name:"pd_op.floor",origin_id:2938,stop_gradient:[false]} : (custom_device_tensor<-1x2xf32>) -> custom_device_tensor<-1x2xf32> + (%1259) = "shape64(phi_kernel)" (%1254) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2939,stop_gradient:[true]} : (cpu_tensor<-1xi32>) -> cpu_tensor<1xi64> + (%1260) = "slice(phi_kernel)" (%1259, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2940,stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1261) = "arange(phi_kernel)" (%42, %1260, %41) {dtype:int64,kernel_key:,kernel_name:"arange",op_name:"pd_op.arange",origin_id:2941,place:Place(undefined:0),stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor, cpu_tensor<1xi64>) -> custom_device_tensor<-1xi64> + (%1262) = "shape64(phi_kernel)" (%1261) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2942,stop_gradient:[true]} : (custom_device_tensor<-1xi64>) -> cpu_tensor<1xi64> + (%1263) = "slice(phi_kernel)" (%1262, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2943,stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1264) = "create_array(phi_kernel)" () {dtype:Undefined,kernel_key:,kernel_name:"create_array",op_name:"pd_op.create_array",origin_id:2944,stop_gradient:[true]} : () -> cpu_tensor_array + (%1265) = "create_array(phi_kernel)" () {dtype:Undefined,kernel_key:,kernel_name:"create_array",op_name:"pd_op.create_array",origin_id:2945,stop_gradient:[true]} : () -> cpu_tensor_array + (%1266) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2946,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%1267) = "memcpy_h2d(phi_kernel)" (%1263) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2947} : (cpu_tensor) -> custom_device_tensor + (%1268) = "less_than(phi_kernel)" (%1266, %1267) {kernel_key:,kernel_name:"less_than",op_name:"pd_op.less_than",origin_id:2948,stop_gradient:[true]} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%1269) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2949,place:Place(undefined:0),shape:[],stop_gradient:[false],value:0} : () -> custom_device_tensor<-1x4xf32> + (%1270) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2950,place:Place(undefined:0),shape:[],stop_gradient:[false],value:0} : () -> custom_device_tensor<-1x2xf32> + (%1271) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2951,place:Place(undefined:0),shape:[],stop_gradient:[false],value:0} : () -> custom_device_tensor<4xf32> + (%1272) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2952,place:Place(undefined:0),shape:[],stop_gradient:[false],value:0} : () -> custom_device_tensor<1xf32> + (%1273) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2953,place:Place(undefined:0),shape:[],stop_gradient:[false],value:0} : () -> custom_device_tensor<1xf32> + (%1274, %1275, %1276, %1277, %1278, %1279, %1280) = "pd_op.while" [id:2954] (cond=%1268, inputs=%1266, %1187, %1269, %1270, %1271, %1272, %1273) { + ^%arg_19 {stop_gradient:true}, %arg_20 {stop_gradient:true}, %arg_21 {stop_gradient:false}, %arg_22 {stop_gradient:false}, %arg_23 {stop_gradient:false}, %arg_24 {stop_gradient:false}, %arg_25 {stop_gradient:false} + (%1281) = "memcpy_d2h(phi_kernel)" (%arg_19) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2955} : (custom_device_tensor) -> cpu_tensor + (%1282) = "scale(phi_kernel)" (%1281, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2956,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1283) = "builtin.combine" [id:2957] (%arg_19) {origin_id:986,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1284) = "stack(phi_kernel)" (%1283) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2958,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1285) = "builtin.combine" [id:2959] (%1282) {origin_id:988,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1286) = "stack(phi_kernel)" (%1285) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2960,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1287) = "slice(phi_kernel)" (%1261, %1284, %1286) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2961,stop_gradient:[true]} : (custom_device_tensor<-1xi64>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor + (%1288) = "memcpy_d2h(phi_kernel)" (%1287) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2962} : (custom_device_tensor) -> cpu_tensor + (%1289) = "scale(phi_kernel)" (%1288, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2963,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1290) = "builtin.combine" [id:2964] (%1287) {origin_id:993,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1291) = "stack(phi_kernel)" (%1290) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2965,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1292) = "builtin.combine" [id:2966] (%1289) {origin_id:995,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1293) = "stack(phi_kernel)" (%1292) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2967,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1294) = "slice(phi_kernel)" (%1258, %1291, %1293) {axes:[0],decrease_axis:[],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2968,stop_gradient:[false]} : (custom_device_tensor<-1x2xf32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x2xf32> + (%1295) = "memcpy_d2h(phi_kernel)" (%1287) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2969} : (custom_device_tensor) -> cpu_tensor + (%1296) = "scale(phi_kernel)" (%1295, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2970,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1297) = "builtin.combine" [id:2971] (%1287) {origin_id:1000,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1298) = "stack(phi_kernel)" (%1297) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2972,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1299) = "builtin.combine" [id:2973] (%1296) {origin_id:1002,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1300) = "stack(phi_kernel)" (%1299) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2974,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1301) = "slice(phi_kernel)" (%1254, %1298, %1300) {axes:[0],decrease_axis:[],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2975,stop_gradient:[true]} : (cpu_tensor<-1xi32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xi32> + (%1302) = "cast(phi_kernel)" (%1301) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:2976,stop_gradient:[true]} : (cpu_tensor<-1xi32>) -> cpu_tensor<-1xi64> + (%1303) = "reshape(phi_kernel)" (%1302, %10) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:2977,stop_gradient:[true]} : (cpu_tensor<-1xi64>, cpu_tensor<0xi64>) -> cpu_tensor + (%1304) = "builtin.combine" [id:2978] (%1303, %9) {origin_id:1009,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor] + (%1305) = "stack(phi_kernel)" (%1304) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2979,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor]) -> cpu_tensor<2xi64> + (%1306) = "expand(phi_kernel)" (%1294, %1305) {kernel_key:,kernel_name:"expand",op_name:"pd_op.expand",origin_id:2980,stop_gradient:[false]} : (custom_device_tensor<-1x2xf32>, cpu_tensor<2xi64>) -> custom_device_tensor<-1x2xf32> + (%1307) = "memcpy_d2h(phi_kernel)" (%1287) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2981} : (custom_device_tensor) -> cpu_tensor + (%1308) = "scale(phi_kernel)" (%1307, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2982,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1309) = "builtin.combine" [id:2983] (%1287, %8) {origin_id:1020,stop_gradient:[true]} : (custom_device_tensor, cpu_tensor) -> vec[custom_device_tensor,cpu_tensor] + (%1310) = "memcpy_h2d(phi_kernel)" (%8) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2984,persistable:[true]} : (cpu_tensor) -> custom_device_tensor + (%1311) = "builtin.combine" [id:2985] (%1287, %1310) {origin_id:2985} : (custom_device_tensor, custom_device_tensor) -> vec[custom_device_tensor,custom_device_tensor] + (%1312) = "stack(phi_kernel)" (%1311) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2986,stop_gradient:[true]} : (vec[custom_device_tensor,custom_device_tensor]) -> custom_device_tensor<2xi64> + (%1313) = "builtin.combine" [id:2987] (%1308, %7) {origin_id:1022,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor] + (%1314) = "stack(phi_kernel)" (%1313) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2988,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor]) -> cpu_tensor<2xi64> + (%1315) = "slice(phi_kernel)" (%337, %1312, %1314) {axes:[0,1],decrease_axis:[0,1],infer_flags:[-1,1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2989,stop_gradient:[false]} : (custom_device_tensor<-1x2xf32>, custom_device_tensor<2xi64>, cpu_tensor<2xi64>) -> custom_device_tensor + (%1316) = "memcpy_d2h(phi_kernel)" (%1287) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2990} : (custom_device_tensor) -> cpu_tensor + (%1317) = "scale(phi_kernel)" (%1316, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2991,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1318) = "builtin.combine" [id:2992] (%1287, %7) {origin_id:1033,stop_gradient:[true]} : (custom_device_tensor, cpu_tensor) -> vec[custom_device_tensor,cpu_tensor] + (%1319) = "memcpy_h2d(phi_kernel)" (%7) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2993,persistable:[true]} : (cpu_tensor) -> custom_device_tensor + (%1320) = "builtin.combine" [id:2994] (%1287, %1319) {origin_id:2994} : (custom_device_tensor, custom_device_tensor) -> vec[custom_device_tensor,custom_device_tensor] + (%1321) = "stack(phi_kernel)" (%1320) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2995,stop_gradient:[true]} : (vec[custom_device_tensor,custom_device_tensor]) -> custom_device_tensor<2xi64> + (%1322) = "builtin.combine" [id:2996] (%1317, %6) {origin_id:1035,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor] + (%1323) = "stack(phi_kernel)" (%1322) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2997,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor]) -> cpu_tensor<2xi64> + (%1324) = "slice(phi_kernel)" (%337, %1321, %1323) {axes:[0,1],decrease_axis:[0,1],infer_flags:[-1,1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2998,stop_gradient:[false]} : (custom_device_tensor<-1x2xf32>, custom_device_tensor<2xi64>, cpu_tensor<2xi64>) -> custom_device_tensor + (%1325) = "unsqueeze(phi_kernel)" (%1315, %44) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:2999,stop_gradient:[false]} : (custom_device_tensor, cpu_tensor<1xi64>) -> custom_device_tensor<1xf32> + (%1326) = "unsqueeze(phi_kernel)" (%1324, %44) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:3000,stop_gradient:[false]} : (custom_device_tensor, cpu_tensor<1xi64>) -> custom_device_tensor<1xf32> + (%1327) = "builtin.combine" [id:3001] (%1326, %1325, %1326, %1325) {origin_id:1043,stop_gradient:[false]} : (custom_device_tensor<1xf32>, custom_device_tensor<1xf32>, custom_device_tensor<1xf32>, custom_device_tensor<1xf32>) -> vec[custom_device_tensor<1xf32>,custom_device_tensor<1xf32>,custom_device_tensor<1xf32>,custom_device_tensor<1xf32>] + (%1328) = "concat(phi_kernel)" (%1327, %5) {kernel_key:,kernel_name:"concat",op_name:"pd_op.concat",origin_id:3002,stop_gradient:[false]} : (vec[custom_device_tensor<1xf32>,custom_device_tensor<1xf32>,custom_device_tensor<1xf32>,custom_device_tensor<1xf32>], cpu_tensor<1xi32>) -> custom_device_tensor<4xf32> + (%1329) = "memcpy_d2h(phi_kernel)" (%1287) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3003} : (custom_device_tensor) -> cpu_tensor + (%1330) = "scale(phi_kernel)" (%1329, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3004,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1331) = "builtin.combine" [id:3005] (%1287) {origin_id:1047,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1332) = "stack(phi_kernel)" (%1331) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3006,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1333) = "builtin.combine" [id:3007] (%1330) {origin_id:1049,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1334) = "stack(phi_kernel)" (%1333) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3008,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1335) = "slice(phi_kernel)" (%1254, %1332, %1334) {axes:[0],decrease_axis:[],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3009,stop_gradient:[true]} : (cpu_tensor<-1xi32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xi32> + (%1336) = "cast(phi_kernel)" (%1335) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3010,stop_gradient:[true]} : (cpu_tensor<-1xi32>) -> cpu_tensor<-1xi64> + (%1337) = "reshape(phi_kernel)" (%1336, %10) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:3011,stop_gradient:[true]} : (cpu_tensor<-1xi64>, cpu_tensor<0xi64>) -> cpu_tensor + (%1338) = "builtin.combine" [id:3012] (%1337, %30) {origin_id:1056,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor] + (%1339) = "stack(phi_kernel)" (%1338) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3013,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor]) -> cpu_tensor<2xi64> + (%1340) = "expand(phi_kernel)" (%1328, %1339) {kernel_key:,kernel_name:"expand",op_name:"pd_op.expand",origin_id:3014,stop_gradient:[false]} : (custom_device_tensor<4xf32>, cpu_tensor<2xi64>) -> custom_device_tensor<-1x4xf32> + (%1341) = "array_length(phi_kernel)" (%1264) {kernel_key:,kernel_name:"array_length",op_name:"pd_op.array_length",origin_id:3015} : (cpu_tensor_array) -> cpu_tensor<1xi64> + (%1342) = "memcpy_d2h(phi_kernel)" (%1306) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3016} : (custom_device_tensor<-1x2xf32>) -> cpu_tensor<-1x2xf32> + (%1343) = "array_write_(phi_kernel)" (%1264, %1342, %1341) {is_inplace:true,kernel_key:,kernel_name:"array_write",op_name:"pd_op.array_write_",origin_id:3017} : (cpu_tensor_array, cpu_tensor<-1x2xf32>, cpu_tensor<1xi64>) -> cpu_tensor_array + (%1344) = "array_length(phi_kernel)" (%1265) {kernel_key:,kernel_name:"array_length",op_name:"pd_op.array_length",origin_id:3018} : (cpu_tensor_array) -> cpu_tensor<1xi64> + (%1345) = "memcpy_d2h(phi_kernel)" (%1340) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3019} : (custom_device_tensor<-1x4xf32>) -> cpu_tensor<-1x4xf32> + (%1346) = "array_write_(phi_kernel)" (%1265, %1345, %1344) {is_inplace:true,kernel_key:,kernel_name:"array_write",op_name:"pd_op.array_write_",origin_id:3020} : (cpu_tensor_array, cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>) -> cpu_tensor_array + (%1347) = "memcpy_d2h(phi_kernel)" (%arg_19) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3021} : (custom_device_tensor) -> cpu_tensor + (%1348) = "scale(phi_kernel)" (%1347, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3022,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1349) = "less_than(phi_kernel)" (%1348, %1263) {kernel_key:,kernel_name:"less_than",op_name:"pd_op.less_than",origin_id:3023,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> cpu_tensor + (%1350) = "memcpy_h2d(phi_kernel)" (%1349) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3024} : (cpu_tensor) -> custom_device_tensor + (%1351) = "memcpy_h2d(phi_kernel)" (%1348) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3025} : (cpu_tensor) -> custom_device_tensor + () = "cf.yield" [id:3026] (%1350, %1351, %1287, %1340, %1306, %1328, %1326, %1325) {origin_id:1066} : (custom_device_tensor, custom_device_tensor, custom_device_tensor, custom_device_tensor<-1x4xf32>, custom_device_tensor<-1x2xf32>, custom_device_tensor<4xf32>, custom_device_tensor<1xf32>, custom_device_tensor<1xf32>) -> + } + (%1352, %1353) = "array_to_tensor(phi_kernel)" (%1264) {axis:0,kernel_key:,kernel_name:"array_to_tensor",op_name:"pd_op.array_to_tensor",origin_id:3027,stop_gradient:[true,true],use_stack:false} : (cpu_tensor_array) -> cpu_tensor<-1x2xf32>, cpu_tensor<-1xi32> + (%1354, %1355) = "array_to_tensor(phi_kernel)" (%1265) {axis:0,kernel_key:,kernel_name:"array_to_tensor",op_name:"pd_op.array_to_tensor",origin_id:3028,stop_gradient:[true,true],use_stack:false} : (cpu_tensor_array) -> cpu_tensor<-1x4xf32>, cpu_tensor<-1xi32> + (%1356) = "slice(phi_kernel)" (%1252, %44, %43) {axes:[1],decrease_axis:[],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3029,stop_gradient:[true]} : (cpu_tensor<-1x6xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1x1xf32> + (%1357) = "slice(phi_kernel)" (%1252, %43, %52) {axes:[1],decrease_axis:[],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3030,stop_gradient:[true]} : (cpu_tensor<-1x6xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1x1xf32> + (%1358) = "slice(phi_kernel)" (%1252, %52, %36) {axes:[1],decrease_axis:[],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3031,stop_gradient:[true]} : (cpu_tensor<-1x6xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1x4xf32> + (%1359) = "divide(phi_kernel)" (%1358, %1354) {kernel_key:,kernel_name:"divide",op_name:"pd_op.divide",origin_id:3032,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<-1x4xf32>) -> cpu_tensor<-1x4xf32> + (%1360) = "slice(phi_kernel)" (%1352, %44, %43) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3033,stop_gradient:[true]} : (cpu_tensor<-1x2xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%1361) = "slice(phi_kernel)" (%1352, %43, %52) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3034,stop_gradient:[true]} : (cpu_tensor<-1x2xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%1362) = "full_like(phi_kernel)" (%1360, %706) {dtype:float32,kernel_key:,kernel_name:"full_like",op_name:"pd_op.full_like",origin_id:3035,place:Place(undefined:0),stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<1xf32>) -> cpu_tensor<-1xf32> + (%1363) = "slice(phi_kernel)" (%1359, %44, %43) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3036,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%1364) = "minimum(phi_kernel)" (%1363, %1361) {kernel_key:,kernel_name:"minimum",op_name:"pd_op.minimum",origin_id:3037,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%1365) = "maximum(phi_kernel)" (%1364, %1362) {kernel_key:,kernel_name:"maximum",op_name:"pd_op.maximum",origin_id:3038,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%1366) = "slice(phi_kernel)" (%1359, %43, %52) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3039,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%1367) = "minimum(phi_kernel)" (%1366, %1360) {kernel_key:,kernel_name:"minimum",op_name:"pd_op.minimum",origin_id:3040,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%1368) = "maximum(phi_kernel)" (%1367, %1362) {kernel_key:,kernel_name:"maximum",op_name:"pd_op.maximum",origin_id:3041,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%1369) = "slice(phi_kernel)" (%1359, %52, %51) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3042,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%1370) = "minimum(phi_kernel)" (%1369, %1361) {kernel_key:,kernel_name:"minimum",op_name:"pd_op.minimum",origin_id:3043,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%1371) = "maximum(phi_kernel)" (%1370, %1362) {kernel_key:,kernel_name:"maximum",op_name:"pd_op.maximum",origin_id:3044,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%1372) = "slice(phi_kernel)" (%1359, %51, %50) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3045,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%1373) = "minimum(phi_kernel)" (%1372, %1360) {kernel_key:,kernel_name:"minimum",op_name:"pd_op.minimum",origin_id:3046,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%1374) = "maximum(phi_kernel)" (%1373, %1362) {kernel_key:,kernel_name:"maximum",op_name:"pd_op.maximum",origin_id:3047,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%1375) = "builtin.combine" [id:3048] (%1365, %1368, %1371, %1374) {origin_id:1107,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>, cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> vec[cpu_tensor<-1xf32>,cpu_tensor<-1xf32>,cpu_tensor<-1xf32>,cpu_tensor<-1xf32>] + (%1376) = "stack(phi_kernel)" (%1375) {axis:-1,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3049,stop_gradient:[true]} : (vec[cpu_tensor<-1xf32>,cpu_tensor<-1xf32>,cpu_tensor<-1xf32>,cpu_tensor<-1xf32>]) -> cpu_tensor<-1x4xf32> + (%1377) = "slice(phi_kernel)" (%1376, %52, %51) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3050,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%1378) = "slice(phi_kernel)" (%1376, %44, %43) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3051,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%1379) = "subtract(phi_kernel)" (%1377, %1378) {kernel_key:,kernel_name:"subtract",op_name:"pd_op.subtract",origin_id:3052,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%1380) = "slice(phi_kernel)" (%1376, %51, %50) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3053,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%1381) = "slice(phi_kernel)" (%1376, %43, %52) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3054,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%1382) = "subtract(phi_kernel)" (%1380, %1381) {kernel_key:,kernel_name:"subtract",op_name:"pd_op.subtract",origin_id:3055,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%1383) = "memcpy_h2d(phi_kernel)" (%1382) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3056} : (cpu_tensor<-1xf32>) -> custom_device_tensor<-1xf32> + (%1384) = "greater_than(phi_kernel)" (%1383, %25) {kernel_key:,kernel_name:"greater_than",op_name:"pd_op.greater_than",origin_id:3057,stop_gradient:[true]} : (custom_device_tensor<-1xf32>, custom_device_tensor) -> custom_device_tensor<-1xb> + (%1385) = "memcpy_h2d(phi_kernel)" (%1379) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3058} : (cpu_tensor<-1xf32>) -> custom_device_tensor<-1xf32> + (%1386) = "greater_than(phi_kernel)" (%1385, %25) {kernel_key:,kernel_name:"greater_than",op_name:"pd_op.greater_than",origin_id:3059,stop_gradient:[true]} : (custom_device_tensor<-1xf32>, custom_device_tensor) -> custom_device_tensor<-1xb> + (%1387) = "logical_and(phi_kernel)" (%1384, %1386) {kernel_key:,kernel_name:"logical_and",op_name:"pd_op.logical_and",origin_id:3060,stop_gradient:[true]} : (custom_device_tensor<-1xb>, custom_device_tensor<-1xb>) -> custom_device_tensor<-1xb> + (%1388) = "unsqueeze(phi_kernel)" (%1387, %43) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:3061,stop_gradient:[true]} : (custom_device_tensor<-1xb>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x1xb> + (%1389) = "full_like(phi_kernel)" (%1356, %26) {dtype:float32,kernel_key:,kernel_name:"full_like",op_name:"pd_op.full_like",origin_id:3062,place:Place(undefined:0),stop_gradient:[true]} : (cpu_tensor<-1x1xf32>, cpu_tensor<1xf32>) -> cpu_tensor<-1x1xf32> + (%1390) = "scale(phi_kernel)" (%1389, %24) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3063,stop_gradient:[true]} : (cpu_tensor<-1x1xf32>, cpu_tensor<1xf32>) -> cpu_tensor<-1x1xf32> + (%1391) = "memcpy_h2d(phi_kernel)" (%1356) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3064} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1392) = "memcpy_h2d(phi_kernel)" (%1390) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3065} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1393) = "where(phi_kernel)" (%1388, %1391, %1392) {kernel_key:,kernel_name:"where",op_name:"pd_op.where",origin_id:3066,stop_gradient:[true]} : (custom_device_tensor<-1x1xb>, custom_device_tensor<-1x1xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1394) = "builtin.combine" [id:3067] (%1393, %1357, %1376) {origin_id:1136,stop_gradient:[true]} : (custom_device_tensor<-1x1xf32>, cpu_tensor<-1x1xf32>, cpu_tensor<-1x4xf32>) -> vec[custom_device_tensor<-1x1xf32>,cpu_tensor<-1x1xf32>,cpu_tensor<-1x4xf32>] + (%1395) = "memcpy_h2d(phi_kernel)" (%1357) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3068} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1396) = "memcpy_h2d(phi_kernel)" (%1376) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3069} : (cpu_tensor<-1x4xf32>) -> custom_device_tensor<-1x4xf32> + (%1397) = "builtin.combine" [id:3070] (%1393, %1395, %1396) {origin_id:3070} : (custom_device_tensor<-1x1xf32>, custom_device_tensor<-1x1xf32>, custom_device_tensor<-1x4xf32>) -> vec[custom_device_tensor<-1x1xf32>,custom_device_tensor<-1x1xf32>,custom_device_tensor<-1x4xf32>] + (%1398) = "concat(phi_kernel)" (%1397, %23) {kernel_key:,kernel_name:"concat",op_name:"pd_op.concat",origin_id:3071,stop_gradient:[true]} : (vec[custom_device_tensor<-1x1xf32>,custom_device_tensor<-1x1xf32>,custom_device_tensor<-1x4xf32>], cpu_tensor<1xi32>) -> custom_device_tensor<-1x6xf32> + (%1399) = "shape64(phi_kernel)" (%1055) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:3072,stop_gradient:[true]} : (custom_device_tensor<-1x-1x-1xf32>) -> cpu_tensor<3xi64> + (%1400) = "slice(phi_kernel)" (%1399, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3073,stop_gradient:[true]} : (cpu_tensor<3xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1401) = "cast(phi_kernel)" (%1352) {dtype:int32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3074,stop_gradient:[true]} : (cpu_tensor<-1x2xf32>) -> cpu_tensor<-1x2xi32> + (%1402) = "slice(phi_kernel)" (%1401, %44, %43) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3075,stop_gradient:[true]} : (cpu_tensor<-1x2xi32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xi32> + (%1403) = "max(phi_kernel)" (%1402, %22) {keepdim:false,kernel_key:,kernel_name:"max",op_name:"pd_op.max",origin_id:3076,stop_gradient:[true]} : (cpu_tensor<-1xi32>, cpu_tensor<0xi64>) -> cpu_tensor + (%1404) = "slice(phi_kernel)" (%1401, %43, %52) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3077,stop_gradient:[true]} : (cpu_tensor<-1x2xi32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xi32> + (%1405) = "max(phi_kernel)" (%1404, %22) {keepdim:false,kernel_key:,kernel_name:"max",op_name:"pd_op.max",origin_id:3078,stop_gradient:[true]} : (cpu_tensor<-1xi32>, cpu_tensor<0xi64>) -> cpu_tensor + (%1406) = "cast(phi_kernel)" (%1403) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3079,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + (%1407) = "cast(phi_kernel)" (%1405) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3080,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + (%1408) = "builtin.combine" [id:3081] (%1400, %1406, %1407) {origin_id:1155,stop_gradient:[true]} : (cpu_tensor, cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor,cpu_tensor] + (%1409) = "stack(phi_kernel)" (%1408) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3082,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor,cpu_tensor]) -> cpu_tensor<3xi64> + (%1410) = "full_with_tensor(phi_kernel)" (%706, %1409) {dtype:int32,kernel_key:,kernel_name:"full_with_tensor",op_name:"pd_op.full_with_tensor",origin_id:3083,stop_gradient:[true]} : (cpu_tensor<1xf32>, cpu_tensor<3xi64>) -> custom_device_tensor<-1x-1x-1xi32> + (%1411) = "memcpy_d2h(phi_kernel)" (%1410) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3084} : (custom_device_tensor<-1x-1x-1xi32>) -> cpu_tensor<-1x-1x-1xi32> + (%1412) = "scale(phi_kernel)" (%1411, %26) {bias:-1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3085,stop_gradient:[true]} : (cpu_tensor<-1x-1x-1xi32>, cpu_tensor<1xf32>) -> cpu_tensor<-1x-1x-1xi32> + (%1413) = "shape64(phi_kernel)" (%1254) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:3086,stop_gradient:[true]} : (cpu_tensor<-1xi32>) -> cpu_tensor<1xi64> + (%1414) = "slice(phi_kernel)" (%1413, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3087,stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1415) = "arange(phi_kernel)" (%42, %1414, %41) {dtype:int64,kernel_key:,kernel_name:"arange",op_name:"pd_op.arange",origin_id:3088,place:Place(undefined:0),stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor, cpu_tensor<1xi64>) -> custom_device_tensor<-1xi64> + (%1416) = "shape64(phi_kernel)" (%1415) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:3089,stop_gradient:[true]} : (custom_device_tensor<-1xi64>) -> cpu_tensor<1xi64> + (%1417) = "slice(phi_kernel)" (%1416, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3090,stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1418) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:3091,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%1419) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:3092,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%1420) = "memcpy_h2d(phi_kernel)" (%1417) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3093} : (cpu_tensor) -> custom_device_tensor + (%1421) = "less_than(phi_kernel)" (%1418, %1420) {kernel_key:,kernel_name:"less_than",op_name:"pd_op.less_than",origin_id:3094,stop_gradient:[true]} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%1422) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:3095,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor<-1x6xf32> + (%1423) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:3096,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%1424) = "full(phi_kernel)" () {dtype:int32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:3097,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%1425) = "full(phi_kernel)" () {dtype:int32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:3098,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%1426) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:3099,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor<-1x-1x-1xf32> + (%1427) = "full(phi_kernel)" () {dtype:int32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:3100,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor<-1x-1x-1xi32> + (%1428, %1429, %1430, %1431, %1432, %1433, %1434, %1435, %1436) = "pd_op.while" [id:3101] (cond=%1421, inputs=%1418, %1419, %1412, %1422, %1423, %1424, %1425, %1426, %1427) { + ^%arg_26 {stop_gradient:true}, %arg_27 {stop_gradient:true}, %arg_28 {stop_gradient:true}, %arg_29 {stop_gradient:true}, %arg_30 {stop_gradient:true}, %arg_31 {stop_gradient:true}, %arg_32 {stop_gradient:true}, %arg_33 {stop_gradient:true}, %arg_34 {stop_gradient:true} + (%1437) = "memcpy_d2h(phi_kernel)" (%arg_26) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3102} : (custom_device_tensor) -> cpu_tensor + (%1438) = "scale(phi_kernel)" (%1437, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3103,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1439) = "builtin.combine" [id:3104] (%arg_26) {origin_id:1184,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1440) = "stack(phi_kernel)" (%1439) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3105,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1441) = "builtin.combine" [id:3106] (%1438) {origin_id:1186,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1442) = "stack(phi_kernel)" (%1441) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3107,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1443) = "slice(phi_kernel)" (%1415, %1440, %1442) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3108,stop_gradient:[true]} : (custom_device_tensor<-1xi64>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor + (%1444) = "memcpy_d2h(phi_kernel)" (%1443) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3109} : (custom_device_tensor) -> cpu_tensor + (%1445) = "scale(phi_kernel)" (%1444, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3110,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1446) = "builtin.combine" [id:3111] (%1443) {origin_id:1191,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1447) = "stack(phi_kernel)" (%1446) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3112,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1448) = "builtin.combine" [id:3113] (%1445) {origin_id:1193,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1449) = "stack(phi_kernel)" (%1448) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3114,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1450) = "slice(phi_kernel)" (%1254, %1447, %1449) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3115,stop_gradient:[true]} : (cpu_tensor<-1xi32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1451) = "cast(phi_kernel)" (%1450) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3116,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + (%1452) = "memcpy_h2d(phi_kernel)" (%1451) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3117} : (cpu_tensor) -> custom_device_tensor + (%1453) = "add(phi_kernel)" (%arg_27, %1452) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:3118,stop_gradient:[true]} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%1454) = "builtin.combine" [id:3119] (%arg_27) {origin_id:1198,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1455) = "stack(phi_kernel)" (%1454) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3120,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1456) = "builtin.combine" [id:3121] (%1453) {origin_id:1200,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1457) = "stack(phi_kernel)" (%1456) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3122,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1458) = "slice(phi_kernel)" (%1398, %1455, %1457) {axes:[0],decrease_axis:[],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3123,stop_gradient:[true]} : (custom_device_tensor<-1x6xf32>, custom_device_tensor<1xi64>, custom_device_tensor<1xi64>) -> custom_device_tensor<-1x6xf32> + (%1459) = "memcpy_d2h(phi_kernel)" (%1443) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3124} : (custom_device_tensor) -> cpu_tensor + (%1460) = "scale(phi_kernel)" (%1459, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3125,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1461) = "builtin.combine" [id:3126] (%1443) {origin_id:1205,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1462) = "stack(phi_kernel)" (%1461) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3127,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1463) = "builtin.combine" [id:3128] (%1460) {origin_id:1207,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1464) = "stack(phi_kernel)" (%1463) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3129,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1465) = "slice(phi_kernel)" (%1254, %1462, %1464) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3130,stop_gradient:[true]} : (cpu_tensor<-1xi32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1466) = "cast(phi_kernel)" (%1465) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3131,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + (%1467) = "memcpy_h2d(phi_kernel)" (%1466) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3132} : (cpu_tensor) -> custom_device_tensor + (%1468) = "add(phi_kernel)" (%arg_27, %1467) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:3133,stop_gradient:[true]} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%1469) = "builtin.combine" [id:3134] (%1468) {origin_id:1214,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1470) = "stack(phi_kernel)" (%1469) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3135,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1471) = "slice(phi_kernel)" (%1055, %1455, %1470) {axes:[0],decrease_axis:[],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3136,stop_gradient:[true]} : (custom_device_tensor<-1x-1x-1xf32>, custom_device_tensor<1xi64>, custom_device_tensor<1xi64>) -> custom_device_tensor<-1x-1x-1xf32> + (%1472) = "memcpy_d2h(phi_kernel)" (%1443) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3137} : (custom_device_tensor) -> cpu_tensor + (%1473) = "scale(phi_kernel)" (%1472, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3138,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1474) = "builtin.combine" [id:3139] (%1443, %21) {origin_id:1225,stop_gradient:[true]} : (custom_device_tensor, cpu_tensor) -> vec[custom_device_tensor,cpu_tensor] + (%1475) = "memcpy_h2d(phi_kernel)" (%21) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3140,persistable:[true]} : (cpu_tensor) -> custom_device_tensor + (%1476) = "builtin.combine" [id:3141] (%1443, %1475) {origin_id:3141} : (custom_device_tensor, custom_device_tensor) -> vec[custom_device_tensor,custom_device_tensor] + (%1477) = "stack(phi_kernel)" (%1476) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3142,stop_gradient:[true]} : (vec[custom_device_tensor,custom_device_tensor]) -> custom_device_tensor<2xi64> + (%1478) = "builtin.combine" [id:3143] (%1473, %20) {origin_id:1227,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor] + (%1479) = "stack(phi_kernel)" (%1478) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3144,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor]) -> cpu_tensor<2xi64> + (%1480) = "slice(phi_kernel)" (%1401, %1477, %1479) {axes:[0,1],decrease_axis:[0,1],infer_flags:[-1,1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3145,stop_gradient:[true]} : (cpu_tensor<-1x2xi32>, custom_device_tensor<2xi64>, cpu_tensor<2xi64>) -> cpu_tensor + (%1481) = "memcpy_d2h(phi_kernel)" (%1443) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3146} : (custom_device_tensor) -> cpu_tensor + (%1482) = "scale(phi_kernel)" (%1481, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3147,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1483) = "builtin.combine" [id:3148] (%1443, %20) {origin_id:1238,stop_gradient:[true]} : (custom_device_tensor, cpu_tensor) -> vec[custom_device_tensor,cpu_tensor] + (%1484) = "memcpy_h2d(phi_kernel)" (%20) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3149,persistable:[true]} : (cpu_tensor) -> custom_device_tensor + (%1485) = "builtin.combine" [id:3150] (%1443, %1484) {origin_id:3150} : (custom_device_tensor, custom_device_tensor) -> vec[custom_device_tensor,custom_device_tensor] + (%1486) = "stack(phi_kernel)" (%1485) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3151,stop_gradient:[true]} : (vec[custom_device_tensor,custom_device_tensor]) -> custom_device_tensor<2xi64> + (%1487) = "builtin.combine" [id:3152] (%1482, %4) {origin_id:1240,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor] + (%1488) = "stack(phi_kernel)" (%1487) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3153,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor]) -> cpu_tensor<2xi64> + (%1489) = "slice(phi_kernel)" (%1401, %1486, %1488) {axes:[0,1],decrease_axis:[0,1],infer_flags:[-1,1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3154,stop_gradient:[true]} : (cpu_tensor<-1x2xi32>, custom_device_tensor<2xi64>, cpu_tensor<2xi64>) -> cpu_tensor + (%1490) = "unsqueeze(phi_kernel)" (%1471, %43) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:3155,stop_gradient:[true]} : (custom_device_tensor<-1x-1x-1xf32>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x1x-1x-1xf32> + (%1491) = "slice(phi_kernel)" (%1458, %52, %36) {axes:[1],decrease_axis:[],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3156,stop_gradient:[true]} : (custom_device_tensor<-1x6xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x4xf32> + (%1492) = "split_with_num(phi_kernel)" (%1491, %23) {kernel_key:,kernel_name:"split_with_num",num:4,op_name:"pd_op.split_with_num",origin_id:3157,stop_gradient:[true]} : (custom_device_tensor<-1x4xf32>, cpu_tensor<1xi32>) -> vec[custom_device_tensor<-1x1xf32>,custom_device_tensor<-1x1xf32>,custom_device_tensor<-1x1xf32>,custom_device_tensor<-1x1xf32>] + (%1493, %1494, %1495, %1496) = "builtin.split" [id:3158] (%1492) {origin_id:1250,stop_gradient:[true,true,true,true]} : (vec[custom_device_tensor<-1x1xf32>,custom_device_tensor<-1x1xf32>,custom_device_tensor<-1x1xf32>,custom_device_tensor<-1x1xf32>]) -> custom_device_tensor<-1x1xf32>, custom_device_tensor<-1x1xf32>, custom_device_tensor<-1x1xf32>, custom_device_tensor<-1x1xf32> + (%1497) = "shape64(phi_kernel)" (%1490) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:3159,stop_gradient:[true]} : (custom_device_tensor<-1x1x-1x-1xf32>) -> cpu_tensor<4xi64> + (%1498) = "slice(phi_kernel)" (%1497, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3160,stop_gradient:[true]} : (cpu_tensor<4xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1499) = "cast(phi_kernel)" (%1480) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3161,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + (%1500) = "arange(phi_kernel)" (%42, %1499, %41) {dtype:int64,kernel_key:,kernel_name:"arange",op_name:"pd_op.arange",origin_id:3162,place:Place(undefined:0),stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor, cpu_tensor<1xi64>) -> custom_device_tensor<-1xi64> + (%1501) = "cast(phi_kernel)" (%1500) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3163,stop_gradient:[true]} : (custom_device_tensor<-1xi64>) -> custom_device_tensor<-1xf32> + (%1502) = "scale(phi_kernel)" (%1501, %26) {bias:0.5,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3164,stop_gradient:[true]} : (custom_device_tensor<-1xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1xf32> + (%1503) = "cast(phi_kernel)" (%1489) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3165,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + (%1504) = "arange(phi_kernel)" (%42, %1503, %41) {dtype:int64,kernel_key:,kernel_name:"arange",op_name:"pd_op.arange",origin_id:3166,place:Place(undefined:0),stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor, cpu_tensor<1xi64>) -> custom_device_tensor<-1xi64> + (%1505) = "cast(phi_kernel)" (%1504) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3167,stop_gradient:[true]} : (custom_device_tensor<-1xi64>) -> custom_device_tensor<-1xf32> + (%1506) = "scale(phi_kernel)" (%1505, %26) {bias:0.5,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3168,stop_gradient:[true]} : (custom_device_tensor<-1xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1xf32> + (%1507) = "subtract(phi_kernel)" (%1502, %1494) {kernel_key:,kernel_name:"subtract",op_name:"pd_op.subtract",origin_id:3169,stop_gradient:[true]} : (custom_device_tensor<-1xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x-1xf32> + (%1508) = "subtract(phi_kernel)" (%1496, %1494) {kernel_key:,kernel_name:"subtract",op_name:"pd_op.subtract",origin_id:3170,stop_gradient:[true]} : (custom_device_tensor<-1x1xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1509) = "divide(phi_kernel)" (%1507, %1508) {kernel_key:,kernel_name:"divide",op_name:"pd_op.divide",origin_id:3171,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x-1xf32> + (%1510) = "scale(phi_kernel)" (%1509, %3) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3172,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x-1xf32> + (%1511) = "scale(phi_kernel)" (%1510, %26) {bias:-1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3173,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x-1xf32> + (%1512) = "subtract(phi_kernel)" (%1506, %1493) {kernel_key:,kernel_name:"subtract",op_name:"pd_op.subtract",origin_id:3174,stop_gradient:[true]} : (custom_device_tensor<-1xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x-1xf32> + (%1513) = "subtract(phi_kernel)" (%1495, %1493) {kernel_key:,kernel_name:"subtract",op_name:"pd_op.subtract",origin_id:3175,stop_gradient:[true]} : (custom_device_tensor<-1x1xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1514) = "divide(phi_kernel)" (%1512, %1513) {kernel_key:,kernel_name:"divide",op_name:"pd_op.divide",origin_id:3176,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x-1xf32> + (%1515) = "scale(phi_kernel)" (%1514, %3) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3177,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x-1xf32> + (%1516) = "scale(phi_kernel)" (%1515, %26) {bias:-1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3178,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x-1xf32> + (%1517) = "unsqueeze(phi_kernel)" (%1516, %43) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:3179,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x1x-1xf32> + (%1518) = "shape64(phi_kernel)" (%1511) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:3180,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>) -> cpu_tensor<2xi64> + (%1519) = "slice(phi_kernel)" (%1518, %43, %52) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3181,stop_gradient:[true]} : (cpu_tensor<2xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1520) = "shape64(phi_kernel)" (%1516) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:3182,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>) -> cpu_tensor<2xi64> + (%1521) = "slice(phi_kernel)" (%1520, %43, %52) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3183,stop_gradient:[true]} : (cpu_tensor<2xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1522) = "builtin.combine" [id:3184] (%1498, %1519, %1521) {origin_id:1305,stop_gradient:[true]} : (cpu_tensor, cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor,cpu_tensor] + (%1523) = "stack(phi_kernel)" (%1522) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3185,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor,cpu_tensor]) -> cpu_tensor<3xi64> + (%1524) = "expand(phi_kernel)" (%1517, %1523) {kernel_key:,kernel_name:"expand",op_name:"pd_op.expand",origin_id:3186,stop_gradient:[true]} : (custom_device_tensor<-1x1x-1xf32>, cpu_tensor<3xi64>) -> custom_device_tensor<-1x-1x-1xf32> + (%1525) = "unsqueeze(phi_kernel)" (%1511, %52) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:3187,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x-1x1xf32> + (%1526) = "shape64(phi_kernel)" (%1511) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:3188,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>) -> cpu_tensor<2xi64> + (%1527) = "slice(phi_kernel)" (%1526, %43, %52) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3189,stop_gradient:[true]} : (cpu_tensor<2xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1528) = "shape64(phi_kernel)" (%1516) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:3190,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>) -> cpu_tensor<2xi64> + (%1529) = "slice(phi_kernel)" (%1528, %43, %52) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3191,stop_gradient:[true]} : (cpu_tensor<2xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1530) = "builtin.combine" [id:3192] (%1498, %1527, %1529) {origin_id:1324,stop_gradient:[true]} : (cpu_tensor, cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor,cpu_tensor] + (%1531) = "stack(phi_kernel)" (%1530) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3193,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor,cpu_tensor]) -> cpu_tensor<3xi64> + (%1532) = "expand(phi_kernel)" (%1525, %1531) {kernel_key:,kernel_name:"expand",op_name:"pd_op.expand",origin_id:3194,stop_gradient:[true]} : (custom_device_tensor<-1x-1x1xf32>, cpu_tensor<3xi64>) -> custom_device_tensor<-1x-1x-1xf32> + (%1533) = "builtin.combine" [id:3195] (%1524, %1532) {origin_id:1327,stop_gradient:[true]} : (custom_device_tensor<-1x-1x-1xf32>, custom_device_tensor<-1x-1x-1xf32>) -> vec[custom_device_tensor<-1x-1x-1xf32>,custom_device_tensor<-1x-1x-1xf32>] + (%1534) = "stack(phi_kernel)" (%1533) {axis:3,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3196,stop_gradient:[true]} : (vec[custom_device_tensor<-1x-1x-1xf32>,custom_device_tensor<-1x-1x-1xf32>]) -> custom_device_tensor<-1x-1x-1x2xf32> + (%1535) = "grid_sample(phi_kernel)" (%1490, %1534) {align_corners:false,kernel_key:,kernel_name:"grid_sample",mode:"bilinear",op_name:"pd_op.grid_sample",origin_id:3197,padding_mode:"zeros",stop_gradient:[true]} : (custom_device_tensor<-1x1x-1x-1xf32>, custom_device_tensor<-1x-1x-1x2xf32>) -> custom_device_tensor<-1x1x-1x-1xf32> + (%1536) = "slice(phi_kernel)" (%1535, %44, %43) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3198,stop_gradient:[true]} : (custom_device_tensor<-1x1x-1x-1xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x-1x-1xf32> + (%1537) = "greater_equal(phi_kernel)" (%1536, %2) {kernel_key:,kernel_name:"greater_equal",op_name:"pd_op.greater_equal",origin_id:3199,stop_gradient:[true]} : (custom_device_tensor<-1x-1x-1xf32>, custom_device_tensor) -> custom_device_tensor<-1x-1x-1xb> + (%1538) = "cast(phi_kernel)" (%1537) {dtype:int32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3200,stop_gradient:[true]} : (custom_device_tensor<-1x-1x-1xb>) -> custom_device_tensor<-1x-1x-1xi32> + (%1539) = "memcpy_d2h(phi_kernel)" (%1443) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3201} : (custom_device_tensor) -> cpu_tensor + (%1540) = "scale(phi_kernel)" (%1539, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3202,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1541) = "builtin.combine" [id:3203] (%1443) {origin_id:1338,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1542) = "stack(phi_kernel)" (%1541) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3204,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1543) = "builtin.combine" [id:3205] (%1540) {origin_id:1340,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1544) = "stack(phi_kernel)" (%1543) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3206,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1545) = "slice(phi_kernel)" (%1254, %1542, %1544) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3207,stop_gradient:[true]} : (cpu_tensor<-1xi32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1546) = "cast(phi_kernel)" (%1545) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3208,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + (%1547) = "memcpy_h2d(phi_kernel)" (%1546) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3209} : (cpu_tensor) -> custom_device_tensor + (%1548) = "add(phi_kernel)" (%arg_27, %1547) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:3210,stop_gradient:[true]} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%1549) = "cast(phi_kernel)" (%1480) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3211,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + (%1550) = "cast(phi_kernel)" (%1489) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3212,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + (%1551) = "builtin.combine" [id:3213] (%arg_27, %1, %1) {origin_id:1351,stop_gradient:[true]} : (custom_device_tensor, cpu_tensor, cpu_tensor) -> vec[custom_device_tensor,cpu_tensor,cpu_tensor] + (%1552) = "memcpy_h2d(phi_kernel)" (%1) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3214,persistable:[true]} : (cpu_tensor) -> custom_device_tensor + (%1553) = "memcpy_h2d(phi_kernel)" (%1) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3215,persistable:[true]} : (cpu_tensor) -> custom_device_tensor + (%1554) = "builtin.combine" [id:3216] (%arg_27, %1552, %1553) {origin_id:3216} : (custom_device_tensor, custom_device_tensor, custom_device_tensor) -> vec[custom_device_tensor,custom_device_tensor,custom_device_tensor] + (%1555) = "stack(phi_kernel)" (%1554) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3217,stop_gradient:[true]} : (vec[custom_device_tensor,custom_device_tensor,custom_device_tensor]) -> custom_device_tensor<3xi64> + (%1556) = "builtin.combine" [id:3218] (%1548, %1549, %1550) {origin_id:1353,stop_gradient:[true]} : (custom_device_tensor, cpu_tensor, cpu_tensor) -> vec[custom_device_tensor,cpu_tensor,cpu_tensor] + (%1557) = "memcpy_h2d(phi_kernel)" (%1549) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3219} : (cpu_tensor) -> custom_device_tensor + (%1558) = "memcpy_h2d(phi_kernel)" (%1550) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3220} : (cpu_tensor) -> custom_device_tensor + (%1559) = "builtin.combine" [id:3221] (%1548, %1557, %1558) {origin_id:3221} : (custom_device_tensor, custom_device_tensor, custom_device_tensor) -> vec[custom_device_tensor,custom_device_tensor,custom_device_tensor] + (%1560) = "stack(phi_kernel)" (%1559) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3222,stop_gradient:[true]} : (vec[custom_device_tensor,custom_device_tensor,custom_device_tensor]) -> custom_device_tensor<3xi64> + (%1561) = "memcpy_h2d(phi_kernel)" (%arg_28) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3223} : (cpu_tensor<-1x-1x-1xi32>) -> custom_device_tensor<-1x-1x-1xi32> + (%1562) = "set_value_with_tensor_(phi_kernel)" (%1561, %1538, %1555, %1560, %0) {axes:[0,1,2],decrease_axes:[],is_inplace:true,kernel_key:,kernel_name:"set_value_with_tensor",none_axes:[],op_name:"pd_op.set_value_with_tensor_",origin_id:3224,stop_gradient:[true]} : (custom_device_tensor<-1x-1x-1xi32>, custom_device_tensor<-1x-1x-1xi32>, custom_device_tensor<3xi64>, custom_device_tensor<3xi64>, cpu_tensor<3xi64>) -> custom_device_tensor<-1x-1x-1xi32> + (%1563) = "memcpy_d2h(phi_kernel)" (%1443) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3225} : (custom_device_tensor) -> cpu_tensor + (%1564) = "scale(phi_kernel)" (%1563, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3226,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1565) = "builtin.combine" [id:3227] (%1443) {origin_id:1359,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1566) = "stack(phi_kernel)" (%1565) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3228,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1567) = "builtin.combine" [id:3229] (%1564) {origin_id:1361,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1568) = "stack(phi_kernel)" (%1567) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3230,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1569) = "slice(phi_kernel)" (%1254, %1566, %1568) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3231,stop_gradient:[true]} : (cpu_tensor<-1xi32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1570) = "cast(phi_kernel)" (%1569) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3232,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + (%1571) = "memcpy_h2d(phi_kernel)" (%1570) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3233} : (cpu_tensor) -> custom_device_tensor + (%1572) = "add(phi_kernel)" (%arg_27, %1571) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:3234,stop_gradient:[true]} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%1573) = "memcpy_d2h(phi_kernel)" (%arg_26) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3235} : (custom_device_tensor) -> cpu_tensor + (%1574) = "scale(phi_kernel)" (%1573, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3236,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1575) = "less_than(phi_kernel)" (%1574, %1417) {kernel_key:,kernel_name:"less_than",op_name:"pd_op.less_than",origin_id:3237,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> cpu_tensor + (%1576) = "memcpy_h2d(phi_kernel)" (%1575) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3238} : (cpu_tensor) -> custom_device_tensor + (%1577) = "memcpy_h2d(phi_kernel)" (%1574) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3239} : (cpu_tensor) -> custom_device_tensor + (%1578) = "memcpy_d2h(phi_kernel)" (%1562) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3240} : (custom_device_tensor<-1x-1x-1xi32>) -> cpu_tensor<-1x-1x-1xi32> + (%1579) = "memcpy_h2d(phi_kernel)" (%1480) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3241} : (cpu_tensor) -> custom_device_tensor + (%1580) = "memcpy_h2d(phi_kernel)" (%1489) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3242} : (cpu_tensor) -> custom_device_tensor + () = "cf.yield" [id:3243] (%1576, %1577, %1572, %1578, %1458, %1443, %1579, %1580, %1471, %1538) {origin_id:1369} : (custom_device_tensor, custom_device_tensor, custom_device_tensor, cpu_tensor<-1x-1x-1xi32>, custom_device_tensor<-1x6xf32>, custom_device_tensor, custom_device_tensor, custom_device_tensor, custom_device_tensor<-1x-1x-1xf32>, custom_device_tensor<-1x-1x-1xi32>) -> + } + () = "builtin.shadow_output" [id:3244] (%1398) {origin_id:1785,output_name:"fetch_name_0"} : (custom_device_tensor<-1x6xf32>) -> + () = "builtin.shadow_output" [id:3245] (%1254) {origin_id:1786,output_name:"fetch_name_1"} : (cpu_tensor<-1xi32>) -> + () = "builtin.shadow_output" [id:3246] (%1430) {origin_id:1787,output_name:"fetch_name_2"} : (cpu_tensor<-1x-1x-1xi32>) -> +} \ No newline at end of file diff --git a/fixIf/InstructionExe.log b/fixIf/InstructionExe.log new file mode 100644 index 000000000..0808ebddf --- /dev/null +++ b/fixIf/InstructionExe.log @@ -0,0 +1,267 @@ + +Begin run op pd_op.slice kernel in Place(cpu) +End run op pd_op.slice kernel. +Begin run op pd_op.slice kernel in Place(cpu) +End run op pd_op.slice kernel. +Begin run op pd_op.cast kernel in Place(cpu) +End run op pd_op.cast kernel. +Begin run op pd_op.memcpy_h2d kernel in Place(mlu:0) +End run op pd_op.memcpy_h2d kernel. +Begin run op pd_op.memcpy_h2d kernel in Place(mlu:0) +End run op pd_op.memcpy_h2d kernel. +Begin run op pd_op.roi_align kernel in Place(mlu:0) +End run op pd_op.roi_align kernel. +Begin run op pd_op.conv2d kernel in Place(mlu:0) +End run op pd_op.conv2d kernel. +Begin run op pd_op.batch_norm_ kernel in Place(mlu:0) +End run op pd_op.batch_norm_ kernel. +Begin run op pd_op.relu_ kernel in Place(mlu:0) +End run op pd_op.relu_ kernel. +Begin run op pd_op.conv2d kernel in Place(mlu:0) +End run op pd_op.conv2d kernel. +Begin run op pd_op.batch_norm_ kernel in Place(mlu:0) +End run op pd_op.batch_norm_ kernel. +Begin run op pd_op.relu_ kernel in Place(mlu:0) +End run op pd_op.relu_ kernel. +Begin run op pd_op.conv2d kernel in Place(mlu:0) +End run op pd_op.conv2d kernel. +Begin run op pd_op.batch_norm_ kernel in Place(mlu:0) +End run op pd_op.batch_norm_ kernel. +Begin run op pd_op.conv2d kernel in Place(mlu:0) +End run op pd_op.conv2d kernel. +Begin run op pd_op.batch_norm_ kernel in Place(mlu:0) +End run op pd_op.batch_norm_ kernel. +Begin run op pd_op.add_ kernel in Place(mlu:0) +End run op pd_op.add_ kernel. +Begin run op pd_op.relu_ kernel in Place(mlu:0) +End run op pd_op.relu_ kernel. +Begin run op pd_op.conv2d kernel in Place(mlu:0) +End run op pd_op.conv2d kernel. +Begin run op pd_op.batch_norm_ kernel in Place(mlu:0) +End run op pd_op.batch_norm_ kernel. +Begin run op pd_op.relu_ kernel in Place(mlu:0) +End run op pd_op.relu_ kernel. +Begin run op pd_op.conv2d kernel in Place(mlu:0) +End run op pd_op.conv2d kernel. +Begin run op pd_op.batch_norm_ kernel in Place(mlu:0) +End run op pd_op.batch_norm_ kernel. +Begin run op pd_op.relu_ kernel in Place(mlu:0) +End run op pd_op.relu_ kernel. +Begin run op pd_op.conv2d kernel in Place(mlu:0) +End run op pd_op.conv2d kernel. +Begin run op pd_op.batch_norm_ kernel in Place(mlu:0) +End run op pd_op.batch_norm_ kernel. +Begin run op pd_op.add_ kernel in Place(mlu:0) +End run op pd_op.add_ kernel. +Begin run op pd_op.relu_ kernel in Place(mlu:0) +End run op pd_op.relu_ kernel. +Begin run op pd_op.conv2d kernel in Place(mlu:0) +End run op pd_op.conv2d kernel. +Begin run op pd_op.batch_norm_ kernel in Place(mlu:0) +End run op pd_op.batch_norm_ kernel. +Begin run op pd_op.relu_ kernel in Place(mlu:0) +End run op pd_op.relu_ kernel. +Begin run op pd_op.conv2d kernel in Place(mlu:0) +End run op pd_op.conv2d kernel. +Begin run op pd_op.batch_norm_ kernel in Place(mlu:0) +End run op pd_op.batch_norm_ kernel. +Begin run op pd_op.relu_ kernel in Place(mlu:0) +End run op pd_op.relu_ kernel. +Begin run op pd_op.conv2d kernel in Place(mlu:0) +End run op pd_op.conv2d kernel. +Begin run op pd_op.batch_norm_ kernel in Place(mlu:0) +End run op pd_op.batch_norm_ kernel. +Begin run op pd_op.add_ kernel in Place(mlu:0) +End run op pd_op.add_ kernel. +Begin run op pd_op.relu_ kernel in Place(mlu:0) +End run op pd_op.relu_ kernel. +Begin run op pd_op.conv2d_transpose kernel in Place(mlu:0) +End run op pd_op.conv2d_transpose kernel. +Begin run op pd_op.add_ kernel in Place(mlu:0) +End run op pd_op.add_ kernel. +Begin run op pd_op.relu_ kernel in Place(mlu:0) +End run op pd_op.relu_ kernel. +Begin run op pd_op.conv2d kernel in Place(mlu:0) +End run op pd_op.conv2d kernel. +Begin run op pd_op.add_ kernel in Place(mlu:0) +End run op pd_op.add_ kernel. +Begin run op pd_op.shape64 kernel in Place(mlu:0) +End run op pd_op.shape64 kernel. +Begin run op pd_op.slice kernel in Place(cpu) +End run op pd_op.slice kernel. +Begin run op pd_op.arange kernel in Place(mlu:0) +End run op pd_op.arange kernel. +Begin run op pd_op.cast kernel in Place(mlu:0) +End run op pd_op.cast kernel. +Begin run op pd_op.memcpy_d2h kernel in Place(mlu:0) +End run op pd_op.memcpy_d2h kernel. +Begin run op pd_op.broadcast_tensors kernel in Place(cpu) +End run op pd_op.broadcast_tensors kernel. +Begin run op pd_op.stack kernel in Place(cpu) +End run op pd_op.stack kernel. +Begin run op pd_op.memcpy_h2d kernel in Place(mlu:0) +End run op pd_op.memcpy_h2d kernel. +Begin run op pd_op.gather_nd kernel in Place(mlu:0) +End run op pd_op.gather_nd kernel. +Begin run op pd_op.shape64 kernel in Place(mlu:0) +End run op pd_op.shape64 kernel. +Begin run op pd_op.slice kernel in Place(cpu) +End run op pd_op.slice kernel. +Begin run op pd_op.stack kernel in Place(cpu) +End run op pd_op.stack kernel. +Begin run op pd_op.reshape_ kernel in Place(mlu:0) +End run op pd_op.reshape_ kernel. +Begin run op pd_op.sigmoid_ kernel in Place(mlu:0) +End run op pd_op.sigmoid_ kernel. +2025-04-21 14:49:05.097072: [cnrtError] [17444] [Card: 0] Error occurred during calling 'cnQueueSync' in CNDrv interface. +2025-04-21 14:49:05.097128: [cnrtError] [17444] [Card: 0] Return value is 100124, CN_INVOKE_ERROR_ADDRESS_SPACE. +2025-04-21 14:49:05.097136: [cnrtError] [17444] [Card: 0] cnrtQueueSync: MLU queue sync failed. +I0421 14:49:05.097790 17444 op_call_stack.cc:127] MLU CNRT error(632015), cnrtErrorCndrvFuncCall: failed to call the driver-api function. (at /paddle/backends/mlu/runtime/runtime.cc:229) +W0421 14:49:05.097824 17444 pir_interpreter.cc:1990] Instruction OP id: 341, Ir OP id is null, if_instruction raises an EnforceNotMet exception common::enforce::EnforceNotMet +I0421 14:49:05.097908 17444 pir_interpreter.cc:1712] Exception caught EnforceNotMet +Traceback (most recent call last): + File "/work/PaddleX/PaddleCustomDevice/../paddlex/utils/result_saver.py", line 28, in wrap + result = func(self, *args, **kwargs) + File "/work/PaddleX/PaddleCustomDevice/../paddlex/engine.py", line 49, in run + for res in self._model.predict(): + File "/work/PaddleX/PaddleCustomDevice/../paddlex/model.py", line 131, in predict + yield from predictor(**predict_kwargs) + File "/work/PaddleX/PaddleCustomDevice/../paddlex/inference/models/base/predictor/base_predictor.py", line 213, in __call__ + yield from self.apply(input, **kwargs) + File "/work/PaddleX/PaddleCustomDevice/../paddlex/inference/models/base/predictor/base_predictor.py", line 269, in apply + prediction = self.process(batch_data, **kwargs) + File "/work/PaddleX/PaddleCustomDevice/../paddlex/inference/models/instance_segmentation/predictor.py", line 130, in process + batch_preds = self.infer(batch_inputs) + File "/work/PaddleX/PaddleCustomDevice/../paddlex/inference/models/common/static_infer.py", line 334, in __call__ + pred = self.infer(x) + File "/work/PaddleX/PaddleCustomDevice/../paddlex/inference/models/common/static_infer.py", line 291, in __call__ + self.predictor.run() +paddle.base.libpaddle.EnforceNotMet: In user code: + + + MLU CNRT error(632015), cnrtErrorCndrvFuncCall: failed to call the driver-api function. (at /paddle/backends/mlu/runtime/runtime.cc:229) + [operator < pd_op.if > error] + + +I0421 14:04:07.625958 5615 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "memcpy_d2h(phi_kernel)" (%arg_0 {stop_gradient:true}) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3102} : (custom_device_tensor) -> cpu_tensor + (%1) = "scale_(phi_kernel)" (%0, %2) {bias:1,bias_after_scale:true,is_inplace:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale_",origin_id:3103,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%3) = "builtin.combine" [id:3104] (%arg_0) {origin_id:1184,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%4) = "stack(phi_kernel)" (%3) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3105,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%5) = "builtin.combine" [id:3106] (%1) {origin_id:1186,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%6) = "stack(phi_kernel)" (%5) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3107,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%7) = "slice(phi_kernel)" (%8, %4, %6) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3108,stop_gradient:[true]} : (custom_device_tensor<-1xi64>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor + (%9) = "memcpy_d2h(phi_kernel)" (%7) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3109} : (custom_device_tensor) -> cpu_tensor + (%10) = "scale_(phi_kernel)" (%9, %2) {bias:1,bias_after_scale:true,is_inplace:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale_",origin_id:3110,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%11) = "builtin.combine" [id:3111] (%7) {origin_id:1191,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%12) = "stack(phi_kernel)" (%11) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3112,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%13) = "builtin.combine" [id:3113] (%10) {origin_id:1193,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%14) = "stack(phi_kernel)" (%13) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3114,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%15) = "slice(phi_kernel)" (%16, %12, %14) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3115,stop_gradient:[true]} : (cpu_tensor<-1xi32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%17) = "cast(phi_kernel)" (%15) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3116,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + (%18) = "memcpy_h2d(phi_kernel)" (%17) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3117} : (cpu_tensor) -> custom_device_tensor + (%19) = "add(phi_kernel)" (%arg_1 {stop_gradient:true}, %18) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:3118,stop_gradient:[true]} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%20) = "builtin.combine" [id:3119] (%arg_1) {origin_id:1198,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%21) = "stack(phi_kernel)" (%20) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3120,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%22) = "builtin.combine" [id:3121] (%19) {origin_id:1200,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%23) = "stack(phi_kernel)" (%22) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3122,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%24) = "slice(phi_kernel)" (%25, %21, %23) {axes:[0],decrease_axis:[],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3123,stop_gradient:[true]} : (custom_device_tensor<-1x6xf32>, custom_device_tensor<1xi64>, custom_device_tensor<1xi64>) -> custom_device_tensor<-1x6xf32> + (%26) = "memcpy_d2h(phi_kernel)" (%7) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3124} : (custom_device_tensor) -> cpu_tensor + (%27) = "scale_(phi_kernel)" (%26, %2) {bias:1,bias_after_scale:true,is_inplace:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale_",origin_id:3125,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%28) = "builtin.combine" [id:3126] (%7) {origin_id:1205,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%29) = "stack(phi_kernel)" (%28) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3127,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%30) = "builtin.combine" [id:3128] (%27) {origin_id:1207,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%31) = "stack(phi_kernel)" (%30) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3129,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%32) = "slice(phi_kernel)" (%16, %29, %31) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3130,stop_gradient:[true]} : (cpu_tensor<-1xi32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%33) = "cast(phi_kernel)" (%32) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3131,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + (%34) = "memcpy_h2d(phi_kernel)" (%33) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3132} : (cpu_tensor) -> custom_device_tensor + (%35) = "add(phi_kernel)" (%arg_1, %34) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:3133,stop_gradient:[true]} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%36) = "builtin.combine" [id:3134] (%35) {origin_id:1214,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%37) = "stack(phi_kernel)" (%36) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3135,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%38) = "slice(phi_kernel)" (%39, %21, %37) {axes:[0],decrease_axis:[],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3136,stop_gradient:[true]} : (custom_device_tensor<-1x-1x-1xf32>, custom_device_tensor<1xi64>, custom_device_tensor<1xi64>) -> custom_device_tensor<-1x-1x-1xf32> + (%40) = "memcpy_d2h(phi_kernel)" (%7) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3137} : (custom_device_tensor) -> cpu_tensor + (%41) = "scale_(phi_kernel)" (%40, %2) {bias:1,bias_after_scale:true,is_inplace:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale_",origin_id:3138,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%42) = "builtin.combine" [id:3139] (%7, %43) {origin_id:1225,stop_gradient:[true]} : (custom_device_tensor, cpu_tensor) -> vec[custom_device_tensor,cpu_tensor] + (%44) = "memcpy_h2d(phi_kernel)" (%43) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3140,persistable:[true]} : (cpu_tensor) -> custom_device_tensor + (%45) = "builtin.combine" [id:3141] (%7, %44) {origin_id:3141} : (custom_device_tensor, custom_device_tensor) -> vec[custom_device_tensor,custom_device_tensor] + (%46) = "stack(phi_kernel)" (%45) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3142,stop_gradient:[true]} : (vec[custom_device_tensor,custom_device_tensor]) -> custom_device_tensor<2xi64> + (%47) = "builtin.combine" [id:3143] (%41, %48) {origin_id:1227,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor] + (%49) = "stack(phi_kernel)" (%47) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3144,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor]) -> cpu_tensor<2xi64> + (%50) = "slice(phi_kernel)" (%51, %46, %49) {axes:[0,1],decrease_axis:[0,1],infer_flags:[-1,1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3145,stop_gradient:[true]} : (cpu_tensor<-1x2xi32>, custom_device_tensor<2xi64>, cpu_tensor<2xi64>) -> cpu_tensor + (%52) = "memcpy_d2h(phi_kernel)" (%7) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3146} : (custom_device_tensor) -> cpu_tensor + (%53) = "scale_(phi_kernel)" (%52, %2) {bias:1,bias_after_scale:true,is_inplace:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale_",origin_id:3147,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%54) = "builtin.combine" [id:3148] (%7, %48) {origin_id:1238,stop_gradient:[true]} : (custom_device_tensor, cpu_tensor) -> vec[custom_device_tensor,cpu_tensor] + (%55) = "memcpy_h2d(phi_kernel)" (%48) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3149,persistable:[true]} : (cpu_tensor) -> custom_device_tensor + (%56) = "builtin.combine" [id:3150] (%7, %55) {origin_id:3150} : (custom_device_tensor, custom_device_tensor) -> vec[custom_device_tensor,custom_device_tensor] + (%57) = "stack(phi_kernel)" (%56) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3151,stop_gradient:[true]} : (vec[custom_device_tensor,custom_device_tensor]) -> custom_device_tensor<2xi64> + (%58) = "builtin.combine" [id:3152] (%53, %59) {origin_id:1240,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor] + (%60) = "stack(phi_kernel)" (%58) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3153,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor]) -> cpu_tensor<2xi64> + (%61) = "slice(phi_kernel)" (%51, %57, %60) {axes:[0,1],decrease_axis:[0,1],infer_flags:[-1,1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3154,stop_gradient:[true]} : (cpu_tensor<-1x2xi32>, custom_device_tensor<2xi64>, cpu_tensor<2xi64>) -> cpu_tensor + (%62) = "unsqueeze(phi_kernel)" (%38, %63) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:3155,stop_gradient:[true]} : (custom_device_tensor<-1x-1x-1xf32>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x1x-1x-1xf32> + (%64) = "slice(phi_kernel)" (%24, %65, %66) {axes:[1],decrease_axis:[],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3156,stop_gradient:[true]} : (custom_device_tensor<-1x6xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x4xf32> + (%67) = "split_with_num(phi_kernel)" (%64, %68) {kernel_key:,kernel_name:"split_with_num",num:4,op_name:"pd_op.split_with_num",origin_id:3157,stop_gradient:[true]} : (custom_device_tensor<-1x4xf32>, cpu_tensor<1xi32>) -> vec[custom_device_tensor<-1x1xf32>,custom_device_tensor<-1x1xf32>,custom_device_tensor<-1x1xf32>,custom_device_tensor<-1x1xf32>] + (%69, %70, %71, %72) = "builtin.split" [id:3158] (%67) {origin_id:1250,stop_gradient:[true,true,true,true]} : (vec[custom_device_tensor<-1x1xf32>,custom_device_tensor<-1x1xf32>,custom_device_tensor<-1x1xf32>,custom_device_tensor<-1x1xf32>]) -> custom_device_tensor<-1x1xf32>, custom_device_tensor<-1x1xf32>, custom_device_tensor<-1x1xf32>, custom_device_tensor<-1x1xf32> + (%73) = "shape64(phi_kernel)" (%62) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:3159,stop_gradient:[true]} : (custom_device_tensor<-1x1x-1x-1xf32>) -> cpu_tensor<4xi64> + (%74) = "slice(phi_kernel)" (%73, %75, %63) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3160,stop_gradient:[true]} : (cpu_tensor<4xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%76) = "cast(phi_kernel)" (%50) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3161,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + (%77) = "arange(phi_kernel)" (%78, %76, %79) {dtype:int64,kernel_key:,kernel_name:"arange",op_name:"pd_op.arange",origin_id:3162,place:Place(undefined:0),stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor, cpu_tensor<1xi64>) -> custom_device_tensor<-1xi64> + (%80) = "cast(phi_kernel)" (%77) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3163,stop_gradient:[true]} : (custom_device_tensor<-1xi64>) -> custom_device_tensor<-1xf32> + (%81) = "scale(phi_kernel)" (%80, %2) {bias:0.5,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3164,stop_gradient:[true]} : (custom_device_tensor<-1xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1xf32> + (%82) = "cast(phi_kernel)" (%61) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3165,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + (%83) = "arange(phi_kernel)" (%78, %82, %79) {dtype:int64,kernel_key:,kernel_name:"arange",op_name:"pd_op.arange",origin_id:3166,place:Place(undefined:0),stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor, cpu_tensor<1xi64>) -> custom_device_tensor<-1xi64> + (%84) = "cast(phi_kernel)" (%83) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3167,stop_gradient:[true]} : (custom_device_tensor<-1xi64>) -> custom_device_tensor<-1xf32> + (%85) = "scale(phi_kernel)" (%84, %2) {bias:0.5,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3168,stop_gradient:[true]} : (custom_device_tensor<-1xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1xf32> + (%86) = "subtract(phi_kernel)" (%81, %70) {kernel_key:,kernel_name:"subtract",op_name:"pd_op.subtract",origin_id:3169,stop_gradient:[true]} : (custom_device_tensor<-1xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x-1xf32> + (%87) = "subtract_(phi_kernel)" (%72, %70) {is_inplace:true,kernel_key:,kernel_name:"subtract",op_name:"pd_op.subtract_",origin_id:3170,stop_gradient:[true]} : (custom_device_tensor<-1x1xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%88) = "divide(phi_kernel)" (%86, %87) {kernel_key:,kernel_name:"divide",op_name:"pd_op.divide",origin_id:3171,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x-1xf32> + (%89) = "scale(phi_kernel)" (%88, %90) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3172,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x-1xf32> + (%91) = "scale(phi_kernel)" (%89, %2) {bias:-1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3173,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x-1xf32> + (%92) = "subtract(phi_kernel)" (%85, %69) {kernel_key:,kernel_name:"subtract",op_name:"pd_op.subtract",origin_id:3174,stop_gradient:[true]} : (custom_device_tensor<-1xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x-1xf32> + (%93) = "subtract_(phi_kernel)" (%71, %69) {is_inplace:true,kernel_key:,kernel_name:"subtract",op_name:"pd_op.subtract_",origin_id:3175,stop_gradient:[true]} : (custom_device_tensor<-1x1xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%94) = "divide(phi_kernel)" (%92, %93) {kernel_key:,kernel_name:"divide",op_name:"pd_op.divide",origin_id:3176,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x-1xf32> + (%95) = "scale(phi_kernel)" (%94, %90) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3177,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x-1xf32> + (%96) = "scale(phi_kernel)" (%95, %2) {bias:-1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3178,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x-1xf32> + (%97) = "unsqueeze_(phi_kernel)" (%96, %63) {is_inplace:true,kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze_",origin_id:3179,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x1x-1xf32> + (%98) = "shape64(phi_kernel)" (%91) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:3180,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>) -> cpu_tensor<2xi64> + (%99) = "slice(phi_kernel)" (%98, %63, %65) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3181,stop_gradient:[true]} : (cpu_tensor<2xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%100) = "shape64(phi_kernel)" (%96) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:3182,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>) -> cpu_tensor<2xi64> + (%101) = "slice(phi_kernel)" (%100, %63, %65) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3183,stop_gradient:[true]} : (cpu_tensor<2xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%102) = "builtin.combine" [id:3184] (%74, %99, %101) {origin_id:1305,stop_gradient:[true]} : (cpu_tensor, cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor,cpu_tensor] + (%103) = "stack(phi_kernel)" (%102) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3185,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor,cpu_tensor]) -> cpu_tensor<3xi64> + (%104) = "expand(phi_kernel)" (%97, %103) {kernel_key:,kernel_name:"expand",op_name:"pd_op.expand",origin_id:3186,stop_gradient:[true]} : (custom_device_tensor<-1x1x-1xf32>, cpu_tensor<3xi64>) -> custom_device_tensor<-1x-1x-1xf32> + (%105) = "unsqueeze_(phi_kernel)" (%91, %65) {is_inplace:true,kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze_",origin_id:3187,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x-1x1xf32> + (%106) = "shape64(phi_kernel)" (%91) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:3188,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>) -> cpu_tensor<2xi64> + (%107) = "slice(phi_kernel)" (%106, %63, %65) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3189,stop_gradient:[true]} : (cpu_tensor<2xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%108) = "shape64(phi_kernel)" (%96) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:3190,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>) -> cpu_tensor<2xi64> + (%109) = "slice(phi_kernel)" (%108, %63, %65) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3191,stop_gradient:[true]} : (cpu_tensor<2xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%110) = "builtin.combine" [id:3192] (%74, %107, %109) {origin_id:1324,stop_gradient:[true]} : (cpu_tensor, cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor,cpu_tensor] + (%111) = "stack(phi_kernel)" (%110) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3193,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor,cpu_tensor]) -> cpu_tensor<3xi64> + (%112) = "expand(phi_kernel)" (%105, %111) {kernel_key:,kernel_name:"expand",op_name:"pd_op.expand",origin_id:3194,stop_gradient:[true]} : (custom_device_tensor<-1x-1x1xf32>, cpu_tensor<3xi64>) -> custom_device_tensor<-1x-1x-1xf32> + (%113) = "builtin.combine" [id:3195] (%104, %112) {origin_id:1327,stop_gradient:[true]} : (custom_device_tensor<-1x-1x-1xf32>, custom_device_tensor<-1x-1x-1xf32>) -> vec[custom_device_tensor<-1x-1x-1xf32>,custom_device_tensor<-1x-1x-1xf32>] + (%114) = "stack(phi_kernel)" (%113) {axis:3,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3196,stop_gradient:[true]} : (vec[custom_device_tensor<-1x-1x-1xf32>,custom_device_tensor<-1x-1x-1xf32>]) -> custom_device_tensor<-1x-1x-1x2xf32> + (%115) = "grid_sample(phi_kernel)" (%62, %114) {align_corners:false,kernel_key:,kernel_name:"grid_sample",mode:"bilinear",op_name:"pd_op.grid_sample",origin_id:3197,padding_mode:"zeros",stop_gradient:[true]} : (custom_device_tensor<-1x1x-1x-1xf32>, custom_device_tensor<-1x-1x-1x2xf32>) -> custom_device_tensor<-1x1x-1x-1xf32> + (%116) = "slice(phi_kernel)" (%115, %75, %63) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3198,stop_gradient:[true]} : (custom_device_tensor<-1x1x-1x-1xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x-1x-1xf32> + (%117) = "greater_equal(phi_kernel)" (%116, %118) {kernel_key:,kernel_name:"greater_equal",op_name:"pd_op.greater_equal",origin_id:3199,stop_gradient:[true]} : (custom_device_tensor<-1x-1x-1xf32>, custom_device_tensor) -> custom_device_tensor<-1x-1x-1xb> + (%119) = "cast(phi_kernel)" (%117) {dtype:int32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3200,stop_gradient:[true]} : (custom_device_tensor<-1x-1x-1xb>) -> custom_device_tensor<-1x-1x-1xi32> + (%120) = "memcpy_d2h(phi_kernel)" (%7) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3201} : (custom_device_tensor) -> cpu_tensor + (%121) = "scale_(phi_kernel)" (%120, %2) {bias:1,bias_after_scale:true,is_inplace:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale_",origin_id:3202,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%122) = "builtin.combine" [id:3203] (%7) {origin_id:1338,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%123) = "stack(phi_kernel)" (%122) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3204,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%124) = "builtin.combine" [id:3205] (%121) {origin_id:1340,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%125) = "stack(phi_kernel)" (%124) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3206,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%126) = "slice(phi_kernel)" (%16, %123, %125) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3207,stop_gradient:[true]} : (cpu_tensor<-1xi32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%127) = "cast(phi_kernel)" (%126) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3208,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + (%128) = "memcpy_h2d(phi_kernel)" (%127) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3209} : (cpu_tensor) -> custom_device_tensor + (%129) = "add(phi_kernel)" (%arg_1, %128) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:3210,stop_gradient:[true]} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%130) = "cast(phi_kernel)" (%50) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3211,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + (%131) = "cast(phi_kernel)" (%61) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3212,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + (%132) = "builtin.combine" [id:3213] (%arg_1, %133, %133) {origin_id:1351,stop_gradient:[true]} : (custom_device_tensor, cpu_tensor, cpu_tensor) -> vec[custom_device_tensor,cpu_tensor,cpu_tensor] + (%134) = "memcpy_h2d(phi_kernel)" (%133) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3214,persistable:[true]} : (cpu_tensor) -> custom_device_tensor + (%135) = "memcpy_h2d(phi_kernel)" (%133) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3215,persistable:[true]} : (cpu_tensor) -> custom_device_tensor + (%136) = "builtin.combine" [id:3216] (%arg_1, %134, %135) {origin_id:3216} : (custom_device_tensor, custom_device_tensor, custom_device_tensor) -> vec[custom_device_tensor,custom_device_tensor,custom_device_tensor] + (%137) = "stack(phi_kernel)" (%136) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3217,stop_gradient:[true]} : (vec[custom_device_tensor,custom_device_tensor,custom_device_tensor]) -> custom_device_tensor<3xi64> + (%138) = "builtin.combine" [id:3218] (%129, %130, %131) {origin_id:1353,stop_gradient:[true]} : (custom_device_tensor, cpu_tensor, cpu_tensor) -> vec[custom_device_tensor,cpu_tensor,cpu_tensor] + (%139) = "memcpy_h2d(phi_kernel)" (%130) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3219} : (cpu_tensor) -> custom_device_tensor + (%140) = "memcpy_h2d(phi_kernel)" (%131) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3220} : (cpu_tensor) -> custom_device_tensor + (%141) = "builtin.combine" [id:3221] ( \ No newline at end of file diff --git a/fixIf/VLOG0.log b/fixIf/VLOG0.log new file mode 100644 index 000000000..0bdbed978 --- /dev/null +++ b/fixIf/VLOG0.log @@ -0,0 +1,10003 @@ +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1571,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/BBoxHead/RoIAlign/",value:1} : () -> custom_device_tensor + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1572,stop_gradient:[true]} : (custom_device_tensor) -> custom_device_tensor + () = "builtin.shadow_output" [id:1573] (%1) {origin_id:1569,output_name:"constant_folding@_174513116811615103119"} : (custom_device_tensor) -> +} + +I0420 14:39:28.120246 115867 pir_interpreter.cc:1569] value info of interpretercore 0xcf78040 +value -> var_name -> id -> variable* +0xd05f850 -> constant_folding@_174513116811615103119 -> 1 -> 0xcf90680 +0xcf0e810 -> 0xcf780401745131168119932841_inner_var_0 -> 0 -> 0xcf03a30 +I0420 14:39:28.123003 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.123885 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.124491 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1571,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/BBoxHead/RoIAlign/",value:1} : () -> custom_device_tensor + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1572,stop_gradient:[true]} : (custom_device_tensor) -> custom_device_tensor + () = "builtin.shadow_output" [id:1573] (%1) {origin_id:1569,output_name:"constant_folding@_174513116811615103119"} : (custom_device_tensor) -> +} +I0420 14:39:28.124516 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xcf780401745131168119932841_inner_var_0 -> 0xcf03a30 +1 -> constant_folding@_174513116811615103119 -> 0xcf90680 + +I0420 14:39:28.124532 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.125103 116681 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.125335 116682 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.125615 116683 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.126622 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xcf781e0) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full_int_array" [id:1576] () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:"/BBoxHead/",value:[1,1]} : () -> tensor<2xi64> + (%1) = "pd_op.cast" [id:1577] (%0) {dtype:int64,stop_gradient:[true]} : (tensor<2xi64>) -> tensor<2xi64> + () = "builtin.shadow_output" [id:1578] (%1) {output_name:"constant_folding@_174513116813053746120"} : (tensor<2xi64>) -> +} + +IR after lowering = { + (%0) = "full_int_array(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full_int_array",op_name:"pd_op.full_int_array",origin_id:1580,place:Place(cpu),stop_gradient:[true],struct_name:"/BBoxHead/",value:[1,1]} : () -> cpu_tensor<2xi64> + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1581,stop_gradient:[true]} : (cpu_tensor<2xi64>) -> cpu_tensor<2xi64> + () = "builtin.shadow_output" [id:1582] (%1) {origin_id:1578,output_name:"constant_folding@_174513116813053746120"} : (cpu_tensor<2xi64>) -> +} + +I0420 14:39:28.134368 115867 pir_interpreter.cc:1569] value info of interpretercore 0xcf78040 +value -> var_name -> id -> variable* +0xd05f850 -> constant_folding@_174513116813053746120 -> 1 -> 0xcf61980 +0xed55190 -> 0xcf780401745131168134050701_inner_var_0 -> 0 -> 0xee49670 +I0420 14:39:28.136972 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.137852 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.138397 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full_int_array(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full_int_array",op_name:"pd_op.full_int_array",origin_id:1580,place:Place(cpu),stop_gradient:[true],struct_name:"/BBoxHead/",value:[1,1]} : () -> cpu_tensor<2xi64> + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1581,stop_gradient:[true]} : (cpu_tensor<2xi64>) -> cpu_tensor<2xi64> + () = "builtin.shadow_output" [id:1582] (%1) {origin_id:1578,output_name:"constant_folding@_174513116813053746120"} : (cpu_tensor<2xi64>) -> +} +I0420 14:39:28.138422 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full_int_array +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xcf780401745131168134050701_inner_var_0 -> 0xee49670 +1 -> constant_folding@_174513116813053746120 -> 0xcf61980 + +I0420 14:39:28.138432 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.139029 116684 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.139256 116685 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.139511 116686 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.140163 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xcf781e0) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full_int_array" [id:1585] () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:"/BBoxHead/",value:[2,3]} : () -> tensor<2xi64> + (%1) = "pd_op.cast" [id:1586] (%0) {dtype:int64,stop_gradient:[true]} : (tensor<2xi64>) -> tensor<2xi64> + () = "builtin.shadow_output" [id:1587] (%1) {output_name:"constant_folding@_174513116814471704121"} : (tensor<2xi64>) -> +} + +IR after lowering = { + (%0) = "full_int_array(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full_int_array",op_name:"pd_op.full_int_array",origin_id:1589,place:Place(cpu),stop_gradient:[true],struct_name:"/BBoxHead/",value:[2,3]} : () -> cpu_tensor<2xi64> + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1590,stop_gradient:[true]} : (cpu_tensor<2xi64>) -> cpu_tensor<2xi64> + () = "builtin.shadow_output" [id:1591] (%1) {origin_id:1587,output_name:"constant_folding@_174513116814471704121"} : (cpu_tensor<2xi64>) -> +} + +I0420 14:39:28.148530 115867 pir_interpreter.cc:1569] value info of interpretercore 0xcf78040 +value -> var_name -> id -> variable* +0xd05f850 -> constant_folding@_174513116814471704121 -> 1 -> 0xd05fe30 +0xca58110 -> 0xcf780401745131168148203940_inner_var_0 -> 0 -> 0xcf282c0 +I0420 14:39:28.151113 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.151989 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.152545 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full_int_array(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full_int_array",op_name:"pd_op.full_int_array",origin_id:1589,place:Place(cpu),stop_gradient:[true],struct_name:"/BBoxHead/",value:[2,3]} : () -> cpu_tensor<2xi64> + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1590,stop_gradient:[true]} : (cpu_tensor<2xi64>) -> cpu_tensor<2xi64> + () = "builtin.shadow_output" [id:1591] (%1) {origin_id:1587,output_name:"constant_folding@_174513116814471704121"} : (cpu_tensor<2xi64>) -> +} +I0420 14:39:28.152572 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full_int_array +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xcf780401745131168148203940_inner_var_0 -> 0xcf282c0 +1 -> constant_folding@_174513116814471704121 -> 0xd05fe30 + +I0420 14:39:28.152583 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.153131 116687 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.153365 116688 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.153622 116689 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.154260 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xcf781e0) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full" [id:1594] () {dtype:float32,place:Place(cpu),shape:[1],stop_gradient:[true],value:0.5} : () -> tensor<1xf32> + (%1) = "pd_op.cast" [id:1595] (%0) {dtype:float32,stop_gradient:[true]} : (tensor<1xf32>) -> tensor<1xf32> + () = "builtin.shadow_output" [id:1596] (%1) {output_name:"constant_folding@_174513116815886735022"} : (tensor<1xf32>) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1598,place:Place(cpu),shape:[1],stop_gradient:[true],value:0.5} : () -> cpu_tensor<1xf32> + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1599,stop_gradient:[true]} : (cpu_tensor<1xf32>) -> cpu_tensor<1xf32> + () = "builtin.shadow_output" [id:1600] (%1) {origin_id:1596,output_name:"constant_folding@_174513116815886735022"} : (cpu_tensor<1xf32>) -> +} + +I0420 14:39:28.162811 115867 pir_interpreter.cc:1569] value info of interpretercore 0xcf78040 +value -> var_name -> id -> variable* +0xd05f850 -> constant_folding@_174513116815886735022 -> 1 -> 0xcf7aeb0 +0xcf09b90 -> 0xcf780401745131168162473920_inner_var_0 -> 0 -> 0xcf20e00 +I0420 14:39:28.165460 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.166354 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.166942 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1598,place:Place(cpu),shape:[1],stop_gradient:[true],value:0.5} : () -> cpu_tensor<1xf32> + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1599,stop_gradient:[true]} : (cpu_tensor<1xf32>) -> cpu_tensor<1xf32> + () = "builtin.shadow_output" [id:1600] (%1) {origin_id:1596,output_name:"constant_folding@_174513116815886735022"} : (cpu_tensor<1xf32>) -> +} +I0420 14:39:28.166971 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xcf780401745131168162473920_inner_var_0 -> 0xcf20e00 +1 -> constant_folding@_174513116815886735022 -> 0xcf7aeb0 + +I0420 14:39:28.166987 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.167537 116690 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.167769 116691 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.168020 116692 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.168720 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xcf781e0) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full_int_array" [id:1603] () {dtype:int64,place:Place(cpu),stop_gradient:[true],value:[2147483647]} : () -> tensor<1xi64> + (%1) = "pd_op.cast" [id:1604] (%0) {dtype:int64,stop_gradient:[true]} : (tensor<1xi64>) -> tensor<1xi64> + () = "builtin.shadow_output" [id:1605] (%1) {output_name:"constant_folding@_174513116817322536023"} : (tensor<1xi64>) -> +} + +IR after lowering = { + (%0) = "full_int_array(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full_int_array",op_name:"pd_op.full_int_array",origin_id:1607,place:Place(cpu),stop_gradient:[true],value:[2147483647]} : () -> cpu_tensor<1xi64> + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1608,stop_gradient:[true]} : (cpu_tensor<1xi64>) -> cpu_tensor<1xi64> + () = "builtin.shadow_output" [id:1609] (%1) {origin_id:1605,output_name:"constant_folding@_174513116817322536023"} : (cpu_tensor<1xi64>) -> +} + +I0420 14:39:28.177121 115867 pir_interpreter.cc:1569] value info of interpretercore 0xcf78040 +value -> var_name -> id -> variable* +0xd05f5f0 -> constant_folding@_174513116817322536023 -> 1 -> 0xce89990 +0xcb0e190 -> 0xcf780401745131168176795410_inner_var_0 -> 0 -> 0xcf8f9d0 +I0420 14:39:28.179738 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.180627 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.181178 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full_int_array(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full_int_array",op_name:"pd_op.full_int_array",origin_id:1607,place:Place(cpu),stop_gradient:[true],value:[2147483647]} : () -> cpu_tensor<1xi64> + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1608,stop_gradient:[true]} : (cpu_tensor<1xi64>) -> cpu_tensor<1xi64> + () = "builtin.shadow_output" [id:1609] (%1) {origin_id:1605,output_name:"constant_folding@_174513116817322536023"} : (cpu_tensor<1xi64>) -> +} +I0420 14:39:28.181209 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full_int_array +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xcf780401745131168176795410_inner_var_0 -> 0xcf8f9d0 +1 -> constant_folding@_174513116817322536023 -> 0xce89990 + +I0420 14:39:28.181224 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.181752 116693 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.181991 116694 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.182237 116695 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.182891 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xcf781e0) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full" [id:1612] () {dtype:float32,place:Place(cpu),shape:[1],stop_gradient:[true],value:0.1} : () -> tensor<1xf32> + (%1) = "pd_op.cast" [id:1613] (%0) {dtype:float32,stop_gradient:[true]} : (tensor<1xf32>) -> tensor<1xf32> + () = "builtin.shadow_output" [id:1614] (%1) {output_name:"constant_folding@_174513116818740431024"} : (tensor<1xf32>) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1616,place:Place(cpu),shape:[1],stop_gradient:[true],value:0.1} : () -> cpu_tensor<1xf32> + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1617,stop_gradient:[true]} : (cpu_tensor<1xf32>) -> cpu_tensor<1xf32> + () = "builtin.shadow_output" [id:1618] (%1) {origin_id:1614,output_name:"constant_folding@_174513116818740431024"} : (cpu_tensor<1xf32>) -> +} + +I0420 14:39:28.191421 115867 pir_interpreter.cc:1569] value info of interpretercore 0xcf78040 +value -> var_name -> id -> variable* +0xd05f5f0 -> constant_folding@_174513116818740431024 -> 1 -> 0xce98460 +0xcf08990 -> 0xcf780401745131168191097890_inner_var_0 -> 0 -> 0xcf8ced0 +I0420 14:39:28.194113 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.195016 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.195627 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1616,place:Place(cpu),shape:[1],stop_gradient:[true],value:0.1} : () -> cpu_tensor<1xf32> + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1617,stop_gradient:[true]} : (cpu_tensor<1xf32>) -> cpu_tensor<1xf32> + () = "builtin.shadow_output" [id:1618] (%1) {origin_id:1614,output_name:"constant_folding@_174513116818740431024"} : (cpu_tensor<1xf32>) -> +} +I0420 14:39:28.195654 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xcf780401745131168191097890_inner_var_0 -> 0xcf8ced0 +1 -> constant_folding@_174513116818740431024 -> 0xce98460 + +I0420 14:39:28.195674 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.196255 116696 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.196514 116697 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.196764 116698 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.197446 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xcf781e0) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full" [id:1621] () {dtype:float32,place:Place(cpu),shape:[1],stop_gradient:[true],value:0.2} : () -> tensor<1xf32> + (%1) = "pd_op.cast" [id:1622] (%0) {dtype:float32,stop_gradient:[true]} : (tensor<1xf32>) -> tensor<1xf32> + () = "builtin.shadow_output" [id:1623] (%1) {output_name:"constant_folding@_174513116820193818025"} : (tensor<1xf32>) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1625,place:Place(cpu),shape:[1],stop_gradient:[true],value:0.2} : () -> cpu_tensor<1xf32> + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1626,stop_gradient:[true]} : (cpu_tensor<1xf32>) -> cpu_tensor<1xf32> + () = "builtin.shadow_output" [id:1627] (%1) {origin_id:1623,output_name:"constant_folding@_174513116820193818025"} : (cpu_tensor<1xf32>) -> +} + +I0420 14:39:28.205763 115867 pir_interpreter.cc:1569] value info of interpretercore 0xcf78040 +value -> var_name -> id -> variable* +0xd05f850 -> constant_folding@_174513116820193818025 -> 1 -> 0xce87a10 +0xed55190 -> 0xcf780401745131168205427500_inner_var_0 -> 0 -> 0xcf0ed40 +I0420 14:39:28.208367 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.209244 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.209826 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1625,place:Place(cpu),shape:[1],stop_gradient:[true],value:0.2} : () -> cpu_tensor<1xf32> + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1626,stop_gradient:[true]} : (cpu_tensor<1xf32>) -> cpu_tensor<1xf32> + () = "builtin.shadow_output" [id:1627] (%1) {origin_id:1623,output_name:"constant_folding@_174513116820193818025"} : (cpu_tensor<1xf32>) -> +} +I0420 14:39:28.209856 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xcf780401745131168205427500_inner_var_0 -> 0xcf0ed40 +1 -> constant_folding@_174513116820193818025 -> 0xce87a10 + +I0420 14:39:28.209873 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.210403 116699 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.210779 116700 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.210995 116701 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.211691 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xcf781e0) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full" [id:1630] () {dtype:float32,place:Place(cpu),shape:[1],stop_gradient:[true],value:-3.40282e+38} : () -> tensor<1xf32> + (%1) = "pd_op.cast" [id:1631] (%0) {dtype:float32,stop_gradient:[true]} : (tensor<1xf32>) -> tensor<1xf32> + () = "builtin.shadow_output" [id:1632] (%1) {output_name:"constant_folding@_174513116821619066926"} : (tensor<1xf32>) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1634,place:Place(cpu),shape:[1],stop_gradient:[true],value:-3.40282e+38} : () -> cpu_tensor<1xf32> + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1635,stop_gradient:[true]} : (cpu_tensor<1xf32>) -> cpu_tensor<1xf32> + () = "builtin.shadow_output" [id:1636] (%1) {origin_id:1632,output_name:"constant_folding@_174513116821619066926"} : (cpu_tensor<1xf32>) -> +} + +I0420 14:39:28.220219 115867 pir_interpreter.cc:1569] value info of interpretercore 0xcff2340 +value -> var_name -> id -> variable* +0xd05f5f0 -> constant_folding@_174513116821619066926 -> 1 -> 0xcff2a20 +0xed1b810 -> 0xcff23401745131168219898399_inner_var_0 -> 0 -> 0xcf2e8f0 +I0420 14:39:28.222887 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.223786 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.224380 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1634,place:Place(cpu),shape:[1],stop_gradient:[true],value:-3.40282e+38} : () -> cpu_tensor<1xf32> + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1635,stop_gradient:[true]} : (cpu_tensor<1xf32>) -> cpu_tensor<1xf32> + () = "builtin.shadow_output" [id:1636] (%1) {origin_id:1632,output_name:"constant_folding@_174513116821619066926"} : (cpu_tensor<1xf32>) -> +} +I0420 14:39:28.224406 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xcff23401745131168219898399_inner_var_0 -> 0xcf2e8f0 +1 -> constant_folding@_174513116821619066926 -> 0xcff2a20 + +I0420 14:39:28.224418 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.225008 116702 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.225190 116703 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.225431 116704 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.226120 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xcff24e0) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full" [id:1639] () {dtype:float32,place:Place(cpu),shape:[1],stop_gradient:[true],value:4.13517} : () -> tensor<1xf32> + (%1) = "pd_op.cast" [id:1640] (%0) {dtype:float32,stop_gradient:[true]} : (tensor<1xf32>) -> tensor<1xf32> + () = "builtin.shadow_output" [id:1641] (%1) {output_name:"constant_folding@_174513116823069183927"} : (tensor<1xf32>) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1643,place:Place(cpu),shape:[1],stop_gradient:[true],value:4.13517} : () -> cpu_tensor<1xf32> + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1644,stop_gradient:[true]} : (cpu_tensor<1xf32>) -> cpu_tensor<1xf32> + () = "builtin.shadow_output" [id:1645] (%1) {origin_id:1641,output_name:"constant_folding@_174513116823069183927"} : (cpu_tensor<1xf32>) -> +} + +I0420 14:39:28.234530 115867 pir_interpreter.cc:1569] value info of interpretercore 0xcff2340 +value -> var_name -> id -> variable* +0xd05f5f0 -> constant_folding@_174513116823069183927 -> 1 -> 0xcf64350 +0xca58110 -> 0xcff23401745131168234201529_inner_var_0 -> 0 -> 0xcf0d160 +I0420 14:39:28.237144 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.238030 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.238603 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1643,place:Place(cpu),shape:[1],stop_gradient:[true],value:4.13517} : () -> cpu_tensor<1xf32> + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1644,stop_gradient:[true]} : (cpu_tensor<1xf32>) -> cpu_tensor<1xf32> + () = "builtin.shadow_output" [id:1645] (%1) {origin_id:1641,output_name:"constant_folding@_174513116823069183927"} : (cpu_tensor<1xf32>) -> +} +I0420 14:39:28.238623 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xcff23401745131168234201529_inner_var_0 -> 0xcf0d160 +1 -> constant_folding@_174513116823069183927 -> 0xcf64350 + +I0420 14:39:28.238632 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.239173 116705 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.239403 116706 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.239634 116707 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.240325 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xcff24e0) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full" [id:1648] () {dtype:int64,place:Place(cpu),shape:[],stop_gradient:[true],value:80} : () -> tensor + (%1) = "pd_op.cast" [id:1649] (%0) {dtype:int64,stop_gradient:[true]} : (tensor) -> tensor + () = "builtin.shadow_output" [id:1650] (%1) {output_name:"constant_folding@_174513116824495087928"} : (tensor) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1652,place:Place(cpu),shape:[],stop_gradient:[true],value:80} : () -> cpu_tensor + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1653,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + () = "builtin.shadow_output" [id:1654] (%1) {origin_id:1650,output_name:"constant_folding@_174513116824495087928"} : (cpu_tensor) -> +} + +I0420 14:39:28.248802 115867 pir_interpreter.cc:1569] value info of interpretercore 0xcff2340 +value -> var_name -> id -> variable* +0xd05f5f0 -> constant_folding@_174513116824495087928 -> 1 -> 0xcf3ae90 +0xcb0e190 -> 0xcff23401745131168248463219_inner_var_0 -> 0 -> 0xce87c00 +I0420 14:39:28.251400 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.252283 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.252867 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1652,place:Place(cpu),shape:[],stop_gradient:[true],value:80} : () -> cpu_tensor + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1653,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + () = "builtin.shadow_output" [id:1654] (%1) {origin_id:1650,output_name:"constant_folding@_174513116824495087928"} : (cpu_tensor) -> +} +I0420 14:39:28.252892 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xcff23401745131168248463219_inner_var_0 -> 0xce87c00 +1 -> constant_folding@_174513116824495087928 -> 0xcf3ae90 + +I0420 14:39:28.252902 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.253441 116708 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.253679 116709 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.253964 116710 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.254667 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xcff24e0) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full" [id:1657] () {dtype:int64,place:Place(cpu),shape:[],stop_gradient:[true],value:4} : () -> tensor + (%1) = "pd_op.cast" [id:1658] (%0) {dtype:int64,stop_gradient:[true]} : (tensor) -> tensor + () = "builtin.shadow_output" [id:1659] (%1) {output_name:"constant_folding@_174513116825929047929"} : (tensor) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1661,place:Place(cpu),shape:[],stop_gradient:[true],value:4} : () -> cpu_tensor + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1662,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + () = "builtin.shadow_output" [id:1663] (%1) {origin_id:1659,output_name:"constant_folding@_174513116825929047929"} : (cpu_tensor) -> +} + +I0420 14:39:28.263127 115867 pir_interpreter.cc:1569] value info of interpretercore 0xcff2340 +value -> var_name -> id -> variable* +0xd05f5f0 -> constant_folding@_174513116825929047929 -> 1 -> 0xcf7c3f0 +0xcf08990 -> 0xcff23401745131168262811959_inner_var_0 -> 0 -> 0xcf51020 +I0420 14:39:28.268133 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.269033 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.269613 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1661,place:Place(cpu),shape:[],stop_gradient:[true],value:4} : () -> cpu_tensor + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1662,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + () = "builtin.shadow_output" [id:1663] (%1) {origin_id:1659,output_name:"constant_folding@_174513116825929047929"} : (cpu_tensor) -> +} +I0420 14:39:28.269639 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xcff23401745131168262811959_inner_var_0 -> 0xcf51020 +1 -> constant_folding@_174513116825929047929 -> 0xcf7c3f0 + +I0420 14:39:28.269649 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.270166 116711 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.270393 116712 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.270628 116713 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.271327 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xcff24e0) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full" [id:1666] () {dtype:int64,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/MaskHead/",value:0} : () -> tensor + (%1) = "pd_op.cast" [id:1667] (%0) {dtype:int64,stop_gradient:[true]} : (tensor) -> tensor + () = "builtin.shadow_output" [id:1668] (%1) {output_name:"constant_folding@_174513116827593559930"} : (tensor) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1670,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/MaskHead/",value:0} : () -> custom_device_tensor + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1671,stop_gradient:[true]} : (custom_device_tensor) -> custom_device_tensor + () = "builtin.shadow_output" [id:1672] (%1) {origin_id:1668,output_name:"constant_folding@_174513116827593559930"} : (custom_device_tensor) -> +} + +I0420 14:39:28.279981 115867 pir_interpreter.cc:1569] value info of interpretercore 0xcff2340 +value -> var_name -> id -> variable* +0xd05f5f0 -> constant_folding@_174513116827593559930 -> 1 -> 0xcf437e0 +0xcdb7cb0 -> 0xcff23401745131168279668479_inner_var_0 -> 0 -> 0xce87650 +I0420 14:39:28.282733 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.283623 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.284214 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1670,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/MaskHead/",value:0} : () -> custom_device_tensor + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1671,stop_gradient:[true]} : (custom_device_tensor) -> custom_device_tensor + () = "builtin.shadow_output" [id:1672] (%1) {origin_id:1668,output_name:"constant_folding@_174513116827593559930"} : (custom_device_tensor) -> +} +I0420 14:39:28.284236 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xcff23401745131168279668479_inner_var_0 -> 0xce87650 +1 -> constant_folding@_174513116827593559930 -> 0xcf437e0 + +I0420 14:39:28.284246 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.284827 116714 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.285113 116715 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.285601 116716 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.287103 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xcff24e0) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full" [id:1675] () {dtype:float32,place:Place(undefined:0),shape:[1,6],stop_gradient:[true],value:0} : () -> tensor<1x6xf32> + (%1) = "pd_op.cast" [id:1676] (%0) {dtype:float32,stop_gradient:[true]} : (tensor<1x6xf32>) -> tensor<1x6xf32> + () = "builtin.shadow_output" [id:1677] (%1) {output_name:"constant_folding@_174513116829170639831"} : (tensor<1x6xf32>) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1679,place:Place(undefined:0),shape:[1,6],stop_gradient:[true],value:0} : () -> custom_device_tensor<1x6xf32> + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1680,stop_gradient:[true]} : (custom_device_tensor<1x6xf32>) -> custom_device_tensor<1x6xf32> + () = "builtin.shadow_output" [id:1681] (%1) {origin_id:1677,output_name:"constant_folding@_174513116829170639831"} : (custom_device_tensor<1x6xf32>) -> +} + +I0420 14:39:28.295744 115867 pir_interpreter.cc:1569] value info of interpretercore 0xcff2340 +value -> var_name -> id -> variable* +0xd05f5f0 -> constant_folding@_174513116829170639831 -> 1 -> 0xcf1b9b0 +0xcff2290 -> 0xcff23401745131168295422118_inner_var_0 -> 0 -> 0xcf1a490 +I0420 14:39:28.298472 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.299355 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.299952 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1679,place:Place(undefined:0),shape:[1,6],stop_gradient:[true],value:0} : () -> custom_device_tensor<1x6xf32> + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1680,stop_gradient:[true]} : (custom_device_tensor<1x6xf32>) -> custom_device_tensor<1x6xf32> + () = "builtin.shadow_output" [id:1681] (%1) {origin_id:1677,output_name:"constant_folding@_174513116829170639831"} : (custom_device_tensor<1x6xf32>) -> +} +I0420 14:39:28.299975 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xcff23401745131168295422118_inner_var_0 -> 0xcf1a490 +1 -> constant_folding@_174513116829170639831 -> 0xcf1b9b0 + +I0420 14:39:28.299983 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.300563 116717 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.300773 116718 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.301046 116719 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.301927 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xcff24e0) got event_name: TaskCompletion +IR before lowering = { + (%0) = "builtin.parameter" [id:1684] () {parameter_name:"constant_folding@_174513116829170639831",stop_gradient:[false]} : () -> tensor<1x6xf32> + (%1) = "pd_op.assign_value_" [id:1685] (%0) {dtype:float32,place:Place(undefined:0),shape:[1,6],stop_gradient:[true],values:[0,0,0,0,1,1]} : (tensor<1x6xf32>) -> tensor<1x6xf32> + (%2) = "pd_op.cast" [id:1686] (%1) {dtype:float32,stop_gradient:[true]} : (tensor<1x6xf32>) -> tensor<1x6xf32> + () = "builtin.shadow_output" [id:1687] (%2) {output_name:"constant_folding@_174513116830651898832"} : (tensor<1x6xf32>) -> +} + +IR after lowering = { + (%0) = "builtin.parameter" [id:1689] () {origin_id:1684,parameter_name:"constant_folding@_174513116829170639831",stop_gradient:[false]} : () -> custom_device_tensor<1x6xf32> + (%1) = "assign_value_(phi_kernel)" (%0) {dtype:float32,is_inplace:true,kernel_key:,kernel_name:"assign_value",op_name:"pd_op.assign_value_",origin_id:1690,place:Place(undefined:0),shape:[1,6],stop_gradient:[true],values:[0,0,0,0,1,1]} : (custom_device_tensor<1x6xf32>) -> custom_device_tensor<1x6xf32> + (%2) = "cast(phi_kernel)" (%1) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1691,stop_gradient:[true]} : (custom_device_tensor<1x6xf32>) -> custom_device_tensor<1x6xf32> + () = "builtin.shadow_output" [id:1692] (%2) {origin_id:1687,output_name:"constant_folding@_174513116830651898832"} : (custom_device_tensor<1x6xf32>) -> +} + +I0420 14:39:28.311492 115867 pir_interpreter.cc:1569] value info of interpretercore 0xcff2340 +value -> var_name -> id -> variable* +0xcf85170 -> constant_folding@_174513116830651898832 -> 1 -> 0xed1a750 +0xcf4ea30 -> constant_folding@_174513116829170639831 -> 0 -> 0xcf1b9b0 +0xce89790 -> constant_folding@_174513116829170639831 -> 0 -> 0xcf1b9b0 +I0420 14:39:28.314860 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.315768 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.316494 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "builtin.parameter" [id:1689] () {origin_id:1684,parameter_name:"constant_folding@_174513116829170639831",stop_gradient:[false]} : () -> custom_device_tensor<1x6xf32> + (%1) = "assign_value_(phi_kernel)" (%0) {dtype:float32,is_inplace:true,kernel_key:,kernel_name:"assign_value",op_name:"pd_op.assign_value_",origin_id:1690,place:Place(undefined:0),shape:[1,6],stop_gradient:[true],values:[0,0,0,0,1,1]} : (custom_device_tensor<1x6xf32>) -> custom_device_tensor<1x6xf32> + (%2) = "cast(phi_kernel)" (%1) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1691,stop_gradient:[true]} : (custom_device_tensor<1x6xf32>) -> custom_device_tensor<1x6xf32> + () = "builtin.shadow_output" [id:1692] (%2) {origin_id:1687,output_name:"constant_folding@_174513116830651898832"} : (custom_device_tensor<1x6xf32>) -> +} +I0420 14:39:28.316534 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.assign_value_ ( 0 ) +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> constant_folding@_174513116829170639831 -> 0xcf1b9b0 +1 -> constant_folding@_174513116830651898832 -> 0xed1a750 + +I0420 14:39:28.316550 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.317090 116720 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.317339 116721 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.317584 116722 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.318317 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xcff24e0) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full" [id:1695] () {dtype:int32,place:Place(undefined:0),shape:[1],stop_gradient:[true],value:0} : () -> tensor<1xi32> + (%1) = "pd_op.cast" [id:1696] (%0) {dtype:int32,stop_gradient:[true]} : (tensor<1xi32>) -> tensor<1xi32> + () = "builtin.shadow_output" [id:1697] (%1) {output_name:"constant_folding@_174513116832231632833"} : (tensor<1xi32>) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:int32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1699,place:Place(undefined:0),shape:[1],stop_gradient:[true],value:0} : () -> custom_device_tensor<1xi32> + (%1) = "cast(phi_kernel)" (%0) {dtype:int32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1700,stop_gradient:[true]} : (custom_device_tensor<1xi32>) -> custom_device_tensor<1xi32> + () = "builtin.shadow_output" [id:1701] (%1) {origin_id:1697,output_name:"constant_folding@_174513116832231632833"} : (custom_device_tensor<1xi32>) -> +} + +I0420 14:39:28.326445 115867 pir_interpreter.cc:1569] value info of interpretercore 0xcff2340 +value -> var_name -> id -> variable* +0xd05f850 -> constant_folding@_174513116832231632833 -> 1 -> 0xcf3a6e0 +0xce89220 -> 0xcff23401745131168326130778_inner_var_0 -> 0 -> 0xce95670 +I0420 14:39:28.329192 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.330082 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.330688 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:int32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1699,place:Place(undefined:0),shape:[1],stop_gradient:[true],value:0} : () -> custom_device_tensor<1xi32> + (%1) = "cast(phi_kernel)" (%0) {dtype:int32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1700,stop_gradient:[true]} : (custom_device_tensor<1xi32>) -> custom_device_tensor<1xi32> + () = "builtin.shadow_output" [id:1701] (%1) {origin_id:1697,output_name:"constant_folding@_174513116832231632833"} : (custom_device_tensor<1xi32>) -> +} +I0420 14:39:28.330709 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xcff23401745131168326130778_inner_var_0 -> 0xce95670 +1 -> constant_folding@_174513116832231632833 -> 0xcf3a6e0 + +I0420 14:39:28.330717 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.331261 116723 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.331498 116724 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.331792 116725 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.332456 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xcff24e0) got event_name: TaskCompletion +IR before lowering = { + (%0) = "builtin.parameter" [id:1704] () {parameter_name:"constant_folding@_174513116832231632833",stop_gradient:[false]} : () -> tensor<1xi32> + (%1) = "pd_op.assign_value_" [id:1705] (%0) {dtype:int32,place:Place(undefined:0),shape:[1],stop_gradient:[true],values:[1]} : (tensor<1xi32>) -> tensor<1xi32> + (%2) = "pd_op.cast" [id:1706] (%1) {dtype:int32,stop_gradient:[true]} : (tensor<1xi32>) -> tensor<1xi32> + () = "builtin.shadow_output" [id:1707] (%2) {output_name:"constant_folding@_174513116833656139834"} : (tensor<1xi32>) -> +} + +IR after lowering = { + (%0) = "builtin.parameter" [id:1709] () {origin_id:1704,parameter_name:"constant_folding@_174513116832231632833",stop_gradient:[false]} : () -> custom_device_tensor<1xi32> + (%1) = "assign_value_(phi_kernel)" (%0) {dtype:int32,is_inplace:true,kernel_key:,kernel_name:"assign_value",op_name:"pd_op.assign_value_",origin_id:1710,place:Place(undefined:0),shape:[1],stop_gradient:[true],values:[1]} : (custom_device_tensor<1xi32>) -> custom_device_tensor<1xi32> + (%2) = "cast(phi_kernel)" (%1) {dtype:int32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1711,stop_gradient:[true]} : (custom_device_tensor<1xi32>) -> custom_device_tensor<1xi32> + () = "builtin.shadow_output" [id:1712] (%2) {origin_id:1707,output_name:"constant_folding@_174513116833656139834"} : (custom_device_tensor<1xi32>) -> +} + +I0420 14:39:28.341424 115867 pir_interpreter.cc:1569] value info of interpretercore 0xcff2340 +value -> var_name -> id -> variable* +0xcf4ea30 -> constant_folding@_174513116833656139834 -> 1 -> 0xcf419a0 +0xd05f5f0 -> constant_folding@_174513116832231632833 -> 0 -> 0xcf3a6e0 +0xcb0e190 -> constant_folding@_174513116832231632833 -> 0 -> 0xcf3a6e0 +I0420 14:39:28.344732 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.345635 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.346349 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "builtin.parameter" [id:1709] () {origin_id:1704,parameter_name:"constant_folding@_174513116832231632833",stop_gradient:[false]} : () -> custom_device_tensor<1xi32> + (%1) = "assign_value_(phi_kernel)" (%0) {dtype:int32,is_inplace:true,kernel_key:,kernel_name:"assign_value",op_name:"pd_op.assign_value_",origin_id:1710,place:Place(undefined:0),shape:[1],stop_gradient:[true],values:[1]} : (custom_device_tensor<1xi32>) -> custom_device_tensor<1xi32> + (%2) = "cast(phi_kernel)" (%1) {dtype:int32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1711,stop_gradient:[true]} : (custom_device_tensor<1xi32>) -> custom_device_tensor<1xi32> + () = "builtin.shadow_output" [id:1712] (%2) {origin_id:1707,output_name:"constant_folding@_174513116833656139834"} : (custom_device_tensor<1xi32>) -> +} +I0420 14:39:28.346372 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.assign_value_ ( 0 ) +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> constant_folding@_174513116832231632833 -> 0xcf3a6e0 +1 -> constant_folding@_174513116833656139834 -> 0xcf419a0 + +I0420 14:39:28.346380 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.346930 116726 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.347164 116727 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.347393 116728 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.348043 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xcff24e0) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full" [id:1715] () {dtype:float32,place:Place(cpu),shape:[1],stop_gradient:[true],value:1} : () -> tensor<1xf32> + (%1) = "pd_op.cast" [id:1716] (%0) {dtype:float32,stop_gradient:[true]} : (tensor<1xf32>) -> tensor<1xf32> + () = "builtin.shadow_output" [id:1717] (%1) {output_name:"constant_folding@_174513116835231166835"} : (tensor<1xf32>) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1719,place:Place(cpu),shape:[1],stop_gradient:[true],value:1} : () -> cpu_tensor<1xf32> + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1720,stop_gradient:[true]} : (cpu_tensor<1xf32>) -> cpu_tensor<1xf32> + () = "builtin.shadow_output" [id:1721] (%1) {origin_id:1717,output_name:"constant_folding@_174513116835231166835"} : (cpu_tensor<1xf32>) -> +} + +I0420 14:39:28.356158 115867 pir_interpreter.cc:1569] value info of interpretercore 0xcff2340 +value -> var_name -> id -> variable* +0xcf17480 -> constant_folding@_174513116835231166835 -> 1 -> 0xcf0c190 +0xce95710 -> 0xcff23401745131168355840248_inner_var_0 -> 0 -> 0xcf0c130 +I0420 14:39:28.358776 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.359668 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.360227 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1719,place:Place(cpu),shape:[1],stop_gradient:[true],value:1} : () -> cpu_tensor<1xf32> + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1720,stop_gradient:[true]} : (cpu_tensor<1xf32>) -> cpu_tensor<1xf32> + () = "builtin.shadow_output" [id:1721] (%1) {origin_id:1717,output_name:"constant_folding@_174513116835231166835"} : (cpu_tensor<1xf32>) -> +} +I0420 14:39:28.360248 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xcff23401745131168355840248_inner_var_0 -> 0xcf0c130 +1 -> constant_folding@_174513116835231166835 -> 0xcf0c190 + +I0420 14:39:28.360256 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.360791 116729 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.361040 116730 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.361263 116731 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.361963 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xcff24e0) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full" [id:1724] () {dtype:float32,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> tensor + (%1) = "pd_op.cast" [id:1725] (%0) {dtype:float32,stop_gradient:[true]} : (tensor) -> tensor + () = "builtin.shadow_output" [id:1726] (%1) {output_name:"constant_folding@_174513116836671179736"} : (tensor) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1728,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1729,stop_gradient:[true]} : (custom_device_tensor) -> custom_device_tensor + () = "builtin.shadow_output" [id:1730] (%1) {origin_id:1726,output_name:"constant_folding@_174513116836671179736"} : (custom_device_tensor) -> +} + +I0420 14:39:28.370744 115867 pir_interpreter.cc:1569] value info of interpretercore 0xcff2340 +value -> var_name -> id -> variable* +0xcf17ee0 -> constant_folding@_174513116836671179736 -> 1 -> 0xcf806d0 +0xee46d00 -> 0xcff23401745131168370417287_inner_var_0 -> 0 -> 0xcf7e090 +I0420 14:39:28.373477 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.374363 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.374962 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1728,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1729,stop_gradient:[true]} : (custom_device_tensor) -> custom_device_tensor + () = "builtin.shadow_output" [id:1730] (%1) {origin_id:1726,output_name:"constant_folding@_174513116836671179736"} : (custom_device_tensor) -> +} +I0420 14:39:28.374989 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xcff23401745131168370417287_inner_var_0 -> 0xcf7e090 +1 -> constant_folding@_174513116836671179736 -> 0xcf806d0 + +I0420 14:39:28.375002 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.375530 116732 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.375783 116733 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.376070 116734 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.376505 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xcff24e0) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full" [id:1733] () {dtype:float32,place:Place(cpu),shape:[1],stop_gradient:[true],value:-1} : () -> tensor<1xf32> + (%1) = "pd_op.cast" [id:1734] (%0) {dtype:float32,stop_gradient:[true]} : (tensor<1xf32>) -> tensor<1xf32> + () = "builtin.shadow_output" [id:1735] (%1) {output_name:"constant_folding@_174513116838085921737"} : (tensor<1xf32>) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1737,place:Place(cpu),shape:[1],stop_gradient:[true],value:-1} : () -> cpu_tensor<1xf32> + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1738,stop_gradient:[true]} : (cpu_tensor<1xf32>) -> cpu_tensor<1xf32> + () = "builtin.shadow_output" [id:1739] (%1) {origin_id:1735,output_name:"constant_folding@_174513116838085921737"} : (cpu_tensor<1xf32>) -> +} + +I0420 14:39:28.388237 115867 pir_interpreter.cc:1569] value info of interpretercore 0xcff2340 +value -> var_name -> id -> variable* +0xcf17ee0 -> constant_folding@_174513116838085921737 -> 1 -> 0xcf83ea0 +0xce89690 -> 0xcff23401745131168387912157_inner_var_0 -> 0 -> 0xcf7c270 +I0420 14:39:28.390892 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.391790 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.392366 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1737,place:Place(cpu),shape:[1],stop_gradient:[true],value:-1} : () -> cpu_tensor<1xf32> + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1738,stop_gradient:[true]} : (cpu_tensor<1xf32>) -> cpu_tensor<1xf32> + () = "builtin.shadow_output" [id:1739] (%1) {origin_id:1735,output_name:"constant_folding@_174513116838085921737"} : (cpu_tensor<1xf32>) -> +} +I0420 14:39:28.392395 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xcff23401745131168387912157_inner_var_0 -> 0xcf7c270 +1 -> constant_folding@_174513116838085921737 -> 0xcf83ea0 + +I0420 14:39:28.392411 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.392887 116735 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.393061 116736 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.393304 116737 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.394007 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xcff24e0) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full" [id:1742] () {dtype:int32,place:Place(cpu),shape:[1],stop_gradient:[true],value:1} : () -> tensor<1xi32> + (%1) = "pd_op.cast" [id:1743] (%0) {dtype:int32,stop_gradient:[true]} : (tensor<1xi32>) -> tensor<1xi32> + () = "builtin.shadow_output" [id:1744] (%1) {output_name:"constant_folding@_174513116839857308738"} : (tensor<1xi32>) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:int32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1746,place:Place(cpu),shape:[1],stop_gradient:[true],value:1} : () -> cpu_tensor<1xi32> + (%1) = "cast(phi_kernel)" (%0) {dtype:int32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1747,stop_gradient:[true]} : (cpu_tensor<1xi32>) -> cpu_tensor<1xi32> + () = "builtin.shadow_output" [id:1748] (%1) {origin_id:1744,output_name:"constant_folding@_174513116839857308738"} : (cpu_tensor<1xi32>) -> +} + +I0420 14:39:28.402474 115867 pir_interpreter.cc:1569] value info of interpretercore 0xcff2340 +value -> var_name -> id -> variable* +0xcf17480 -> constant_folding@_174513116839857308738 -> 1 -> 0xcdb8950 +0xca58110 -> 0xcff23401745131168402155097_inner_var_0 -> 0 -> 0xce87fd0 +I0420 14:39:28.405098 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.405987 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.406565 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:int32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1746,place:Place(cpu),shape:[1],stop_gradient:[true],value:1} : () -> cpu_tensor<1xi32> + (%1) = "cast(phi_kernel)" (%0) {dtype:int32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1747,stop_gradient:[true]} : (cpu_tensor<1xi32>) -> cpu_tensor<1xi32> + () = "builtin.shadow_output" [id:1748] (%1) {origin_id:1744,output_name:"constant_folding@_174513116839857308738"} : (cpu_tensor<1xi32>) -> +} +I0420 14:39:28.406589 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xcff23401745131168402155097_inner_var_0 -> 0xce87fd0 +1 -> constant_folding@_174513116839857308738 -> 0xcdb8950 + +I0420 14:39:28.406601 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.407012 116738 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.407290 116739 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.407563 116740 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.408334 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xcff24e0) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full_int_array" [id:1751] () {dtype:int64,place:Place(cpu),stop_gradient:[true],value:[]} : () -> tensor<0xi64> + (%1) = "pd_op.cast" [id:1752] (%0) {dtype:int64,stop_gradient:[true]} : (tensor<0xi64>) -> tensor<0xi64> + () = "builtin.shadow_output" [id:1753] (%1) {output_name:"constant_folding@_174513116841278885739"} : (tensor<0xi64>) -> +} + +IR after lowering = { + (%0) = "full_int_array(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full_int_array",op_name:"pd_op.full_int_array",origin_id:1755,place:Place(cpu),stop_gradient:[true],value:[]} : () -> cpu_tensor<0xi64> + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1756,stop_gradient:[true]} : (cpu_tensor<0xi64>) -> cpu_tensor<0xi64> + () = "builtin.shadow_output" [id:1757] (%1) {origin_id:1753,output_name:"constant_folding@_174513116841278885739"} : (cpu_tensor<0xi64>) -> +} + +I0420 14:39:28.416734 115867 pir_interpreter.cc:1569] value info of interpretercore 0xce86750 +value -> var_name -> id -> variable* +0xcf17480 -> constant_folding@_174513116841278885739 -> 1 -> 0xcdb8970 +0xcacee10 -> 0xce867501745131168416403587_inner_var_0 -> 0 -> 0xcf28260 +I0420 14:39:28.419281 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.420164 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.420708 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full_int_array(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full_int_array",op_name:"pd_op.full_int_array",origin_id:1755,place:Place(cpu),stop_gradient:[true],value:[]} : () -> cpu_tensor<0xi64> + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1756,stop_gradient:[true]} : (cpu_tensor<0xi64>) -> cpu_tensor<0xi64> + () = "builtin.shadow_output" [id:1757] (%1) {origin_id:1753,output_name:"constant_folding@_174513116841278885739"} : (cpu_tensor<0xi64>) -> +} +I0420 14:39:28.420732 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full_int_array +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xce867501745131168416403587_inner_var_0 -> 0xcf28260 +1 -> constant_folding@_174513116841278885739 -> 0xcdb8970 + +I0420 14:39:28.420745 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.421200 116741 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.421448 116742 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.421762 116743 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.422103 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xce868f0) got event_name: TaskCompletion +IR before lowering = { + (%0) = "builtin.constant" [id:1760] () {persistable:[true],value:"constant_folding@_174513115176548053017"} : () -> tensor<1xi64> + (%1) = "builtin.constant" [id:1761] () {persistable:[true],value:"constant_folding@_174513116841278885739"} : () -> tensor<0xi64> + (%2) = "pd_op.reshape" [id:1762] (%0, %1) {stop_gradient:[true]} : (tensor<1xi64>, tensor<0xi64>) -> tensor + (%3) = "pd_op.cast" [id:1763] (%2) {dtype:int64,stop_gradient:[true]} : (tensor) -> tensor + () = "builtin.shadow_output" [id:1764] (%3) {output_name:"constant_folding@_174513116842705804740"} : (tensor) -> +} + +IR after lowering = { + (%0) = "builtin.constant" [id:1766] () {origin_id:1760,persistable:[true],value:"constant_folding@_174513115176548053017"} : () -> cpu_tensor<1xi64> + (%1) = "builtin.constant" [id:1767] () {origin_id:1761,persistable:[true],value:"constant_folding@_174513116841278885739"} : () -> cpu_tensor<0xi64> + (%2) = "reshape(phi_kernel)" (%0, %1) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:1768,stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor<0xi64>) -> cpu_tensor + (%3) = "cast(phi_kernel)" (%2) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1769,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + () = "builtin.shadow_output" [id:1770] (%3) {origin_id:1764,output_name:"constant_folding@_174513116842705804740"} : (cpu_tensor) -> +} + +I0420 14:39:28.431789 115867 pir_interpreter.cc:1569] value info of interpretercore 0xce86750 +value -> var_name -> id -> variable* +0xcf17480 -> constant_folding@_174513116842705804740 -> 3 -> 0xcf92430 +0xcdb8380 -> constant_folding@_174513116841278885739 -> 1 -> 0xcdb8970 +0xcdb4210 -> 0xce867501745131168431394687_inner_var_2 -> 2 -> 0xcf41230 +0xce89690 -> constant_folding@_174513115176548053017 -> 0 -> 0xcf8c2c0 +I0420 14:39:28.434737 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.435693 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.436409 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "builtin.constant" [id:1766] () {origin_id:1760,persistable:[true],value:"constant_folding@_174513115176548053017"} : () -> cpu_tensor<1xi64> + (%1) = "builtin.constant" [id:1767] () {origin_id:1761,persistable:[true],value:"constant_folding@_174513116841278885739"} : () -> cpu_tensor<0xi64> + (%2) = "reshape(phi_kernel)" (%0, %1) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:1768,stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor<0xi64>) -> cpu_tensor + (%3) = "cast(phi_kernel)" (%2) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1769,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + () = "builtin.shadow_output" [id:1770] (%3) {origin_id:1764,output_name:"constant_folding@_174513116842705804740"} : (cpu_tensor) -> +} +I0420 14:39:28.436442 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 2 ) = pd_op.reshape ( 1 ) ( 0 ) +1: ( 3 ) = pd_op.cast ( 2 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> constant_folding@_174513115176548053017 -> 0xcf8c2c0 +1 -> constant_folding@_174513116841278885739 -> 0xcdb8970 +2 -> 0xce867501745131168431394687_inner_var_2 -> 0xcf41230 +3 -> constant_folding@_174513116842705804740 -> 0xcf92430 + +I0420 14:39:28.436453 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.436897 116744 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.437068 116745 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.437294 116746 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.437815 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xce868f0) got event_name: TaskCompletion +IR before lowering = { + (%0) = "builtin.constant" [id:1773] () {persistable:[true],value:"constant_folding@_174513115258237754718"} : () -> tensor<1xi64> + (%1) = "builtin.constant" [id:1774] () {persistable:[true],value:"constant_folding@_174513116841278885739"} : () -> tensor<0xi64> + (%2) = "pd_op.reshape" [id:1775] (%0, %1) {stop_gradient:[true]} : (tensor<1xi64>, tensor<0xi64>) -> tensor + (%3) = "pd_op.cast" [id:1776] (%2) {dtype:int64,stop_gradient:[true]} : (tensor) -> tensor + () = "builtin.shadow_output" [id:1777] (%3) {output_name:"constant_folding@_174513116844224746641"} : (tensor) -> +} + +IR after lowering = { + (%0) = "builtin.constant" [id:1779] () {origin_id:1773,persistable:[true],value:"constant_folding@_174513115258237754718"} : () -> cpu_tensor<1xi64> + (%1) = "builtin.constant" [id:1780] () {origin_id:1774,persistable:[true],value:"constant_folding@_174513116841278885739"} : () -> cpu_tensor<0xi64> + (%2) = "reshape(phi_kernel)" (%0, %1) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:1781,stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor<0xi64>) -> cpu_tensor + (%3) = "cast(phi_kernel)" (%2) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1782,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + () = "builtin.shadow_output" [id:1783] (%3) {origin_id:1777,output_name:"constant_folding@_174513116844224746641"} : (cpu_tensor) -> +} + +I0420 14:39:28.446902 115867 pir_interpreter.cc:1569] value info of interpretercore 0xce86750 +value -> var_name -> id -> variable* +0xcf17480 -> constant_folding@_174513116844224746641 -> 3 -> 0xcdb72f0 +0xcdb4210 -> 0xce867501745131168446507456_inner_var_2 -> 2 -> 0xd068f50 +0xca58110 -> constant_folding@_174513116841278885739 -> 1 -> 0xcdb8970 +0xcfbe780 -> constant_folding@_174513115258237754718 -> 0 -> 0xcfce240 +I0420 14:39:28.449767 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.450708 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.451412 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "builtin.constant" [id:1779] () {origin_id:1773,persistable:[true],value:"constant_folding@_174513115258237754718"} : () -> cpu_tensor<1xi64> + (%1) = "builtin.constant" [id:1780] () {origin_id:1774,persistable:[true],value:"constant_folding@_174513116841278885739"} : () -> cpu_tensor<0xi64> + (%2) = "reshape(phi_kernel)" (%0, %1) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:1781,stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor<0xi64>) -> cpu_tensor + (%3) = "cast(phi_kernel)" (%2) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1782,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + () = "builtin.shadow_output" [id:1783] (%3) {origin_id:1777,output_name:"constant_folding@_174513116844224746641"} : (cpu_tensor) -> +} +I0420 14:39:28.451442 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 2 ) = pd_op.reshape ( 1 ) ( 0 ) +1: ( 3 ) = pd_op.cast ( 2 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> constant_folding@_174513115258237754718 -> 0xcfce240 +1 -> constant_folding@_174513116841278885739 -> 0xcdb8970 +2 -> 0xce867501745131168446507456_inner_var_2 -> 0xd068f50 +3 -> constant_folding@_174513116844224746641 -> 0xcdb72f0 + +I0420 14:39:28.451458 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.451930 116747 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.452105 116748 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.452314 116749 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.452752 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xce868f0) got event_name: TaskCompletion +I0420 14:39:28.459409 115867 print_statistics.cc:44] --- detected [42, 749] subgraphs! +--- Running PIR pass [dead_code_elimination_pass] +I0420 14:39:28.475628 115867 print_statistics.cc:50] --- detected [19] subgraphs! +--- Running PIR pass [replace_fetch_with_shadow_output_pass] +I0420 14:39:28.487242 115867 print_statistics.cc:50] --- detected [3] subgraphs! +--- Running PIR pass [constant_folding_pass] +IR before lowering = { + (%0) = "pd_op.full" [id:1789] () {dtype:float32,place:Place(cpu),shape:[1],stop_gradient:[true],value:1} : () -> tensor<1xf32> + (%1) = "pd_op.cast" [id:1790] (%0) {dtype:float32,stop_gradient:[true]} : (tensor<1xf32>) -> tensor<1xf32> + () = "builtin.shadow_output" [id:1791] (%1) {output_name:"constant_folding@_174513116850011146642"} : (tensor<1xf32>) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1793,place:Place(cpu),shape:[1],stop_gradient:[true],value:1} : () -> cpu_tensor<1xf32> + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1794,stop_gradient:[true]} : (cpu_tensor<1xf32>) -> cpu_tensor<1xf32> + () = "builtin.shadow_output" [id:1795] (%1) {origin_id:1791,output_name:"constant_folding@_174513116850011146642"} : (cpu_tensor<1xf32>) -> +} + +I0420 14:39:28.504009 115867 pir_interpreter.cc:1569] value info of interpretercore 0xd04abc0 +value -> var_name -> id -> variable* +0xcf17480 -> constant_folding@_174513116850011146642 -> 1 -> 0xcf2d600 +0xed1b1e0 -> 0xd04abc01745131168503682435_inner_var_0 -> 0 -> 0xcff2770 +I0420 14:39:28.506650 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.507539 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.508102 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1793,place:Place(cpu),shape:[1],stop_gradient:[true],value:1} : () -> cpu_tensor<1xf32> + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1794,stop_gradient:[true]} : (cpu_tensor<1xf32>) -> cpu_tensor<1xf32> + () = "builtin.shadow_output" [id:1795] (%1) {origin_id:1791,output_name:"constant_folding@_174513116850011146642"} : (cpu_tensor<1xf32>) -> +} +I0420 14:39:28.508127 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xd04abc01745131168503682435_inner_var_0 -> 0xcff2770 +1 -> constant_folding@_174513116850011146642 -> 0xcf2d600 + +I0420 14:39:28.508136 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.508647 116750 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.508883 116751 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.509150 116752 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.509593 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xd04ad60) got event_name: TaskCompletion +I0420 14:39:28.513999 115867 print_statistics.cc:44] --- detected [1, 37] subgraphs! +--- Running PIR pass [dead_code_elimination_pass] +--- Running PIR pass [constant_folding_pass] +I0420 14:39:28.518666 115867 print_statistics.cc:44] --- detected [0, 4] subgraphs! +--- Running PIR pass [dead_code_elimination_pass] +--- Running PIR pass [constant_folding_pass] +IR before lowering = { + (%0) = "pd_op.full" [id:1798] () {dtype:float32,place:Place(cpu),shape:[1],stop_gradient:[true],value:1} : () -> tensor<1xf32> + (%1) = "pd_op.cast" [id:1799] (%0) {dtype:float32,stop_gradient:[true]} : (tensor<1xf32>) -> tensor<1xf32> + () = "builtin.shadow_output" [id:1800] (%1) {output_name:"constant_folding@_174513116852268476543"} : (tensor<1xf32>) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1802,place:Place(cpu),shape:[1],stop_gradient:[true],value:1} : () -> cpu_tensor<1xf32> + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1803,stop_gradient:[true]} : (cpu_tensor<1xf32>) -> cpu_tensor<1xf32> + () = "builtin.shadow_output" [id:1804] (%1) {origin_id:1800,output_name:"constant_folding@_174513116852268476543"} : (cpu_tensor<1xf32>) -> +} + +I0420 14:39:28.526566 115867 pir_interpreter.cc:1569] value info of interpretercore 0xd04abc0 +value -> var_name -> id -> variable* +0xcf17ee0 -> constant_folding@_174513116852268476543 -> 1 -> 0xcfbe9a0 +0xca58110 -> 0xd04abc01745131168526234015_inner_var_0 -> 0 -> 0xcf50a30 +I0420 14:39:28.529173 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.530069 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.530668 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1802,place:Place(cpu),shape:[1],stop_gradient:[true],value:1} : () -> cpu_tensor<1xf32> + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1803,stop_gradient:[true]} : (cpu_tensor<1xf32>) -> cpu_tensor<1xf32> + () = "builtin.shadow_output" [id:1804] (%1) {origin_id:1800,output_name:"constant_folding@_174513116852268476543"} : (cpu_tensor<1xf32>) -> +} +I0420 14:39:28.530692 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xd04abc01745131168526234015_inner_var_0 -> 0xcf50a30 +1 -> constant_folding@_174513116852268476543 -> 0xcfbe9a0 + +I0420 14:39:28.530702 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.531199 116753 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.531396 116754 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.531668 116755 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.532070 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xd04ad60) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full" [id:1807] () {dtype:int64,place:Place(cpu),shape:[],stop_gradient:[true],value:2} : () -> tensor + (%1) = "pd_op.cast" [id:1808] (%0) {dtype:int64,stop_gradient:[true]} : (tensor) -> tensor + () = "builtin.shadow_output" [id:1809] (%1) {output_name:"constant_folding@_174513116853662943544"} : (tensor) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1811,place:Place(cpu),shape:[],stop_gradient:[true],value:2} : () -> cpu_tensor + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1812,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + () = "builtin.shadow_output" [id:1813] (%1) {origin_id:1809,output_name:"constant_folding@_174513116853662943544"} : (cpu_tensor) -> +} + +I0420 14:39:28.540498 115867 pir_interpreter.cc:1569] value info of interpretercore 0xd04abc0 +value -> var_name -> id -> variable* +0xcf17480 -> constant_folding@_174513116853662943544 -> 1 -> 0xcdb8c50 +0xcfbe780 -> 0xd04abc01745131168540170505_inner_var_0 -> 0 -> 0xd069ff0 +I0420 14:39:28.543136 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.544029 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.544613 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1811,place:Place(cpu),shape:[],stop_gradient:[true],value:2} : () -> cpu_tensor + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1812,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + () = "builtin.shadow_output" [id:1813] (%1) {origin_id:1809,output_name:"constant_folding@_174513116853662943544"} : (cpu_tensor) -> +} +I0420 14:39:28.544634 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xd04abc01745131168540170505_inner_var_0 -> 0xd069ff0 +1 -> constant_folding@_174513116853662943544 -> 0xcdb8c50 + +I0420 14:39:28.544642 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.545172 116756 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.545410 116757 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.545673 116758 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.546370 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xd04ad60) got event_name: TaskCompletion +I0420 14:39:28.550750 115867 print_statistics.cc:44] --- detected [2, 28] subgraphs! +--- Running PIR pass [dead_code_elimination_pass] +--- Running PIR pass [constant_folding_pass] +IR before lowering = { + (%0) = "pd_op.full" [id:1816] () {dtype:float32,place:Place(undefined:0),shape:[1,1,1],stop_gradient:[true],value:-1} : () -> tensor<1x1x1xf32> + (%1) = "pd_op.cast" [id:1817] (%0) {dtype:float32,stop_gradient:[true]} : (tensor<1x1x1xf32>) -> tensor<1x1x1xf32> + () = "builtin.shadow_output" [id:1818] (%1) {output_name:"constant_folding@_174513116855594796545"} : (tensor<1x1x1xf32>) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1820,place:Place(undefined:0),shape:[1,1,1],stop_gradient:[true],value:-1} : () -> custom_device_tensor<1x1x1xf32> + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1821,stop_gradient:[true]} : (custom_device_tensor<1x1x1xf32>) -> custom_device_tensor<1x1x1xf32> + () = "builtin.shadow_output" [id:1822] (%1) {origin_id:1818,output_name:"constant_folding@_174513116855594796545"} : (custom_device_tensor<1x1x1xf32>) -> +} + +I0420 14:39:28.560210 115867 pir_interpreter.cc:1569] value info of interpretercore 0xd04abc0 +value -> var_name -> id -> variable* +0xcf17480 -> constant_folding@_174513116855594796545 -> 1 -> 0xcf11a20 +0xcdb8380 -> 0xd04abc01745131168559886035_inner_var_0 -> 0 -> 0xd05b3f0 +I0420 14:39:28.563006 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.563926 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.564549 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1820,place:Place(undefined:0),shape:[1,1,1],stop_gradient:[true],value:-1} : () -> custom_device_tensor<1x1x1xf32> + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1821,stop_gradient:[true]} : (custom_device_tensor<1x1x1xf32>) -> custom_device_tensor<1x1x1xf32> + () = "builtin.shadow_output" [id:1822] (%1) {origin_id:1818,output_name:"constant_folding@_174513116855594796545"} : (custom_device_tensor<1x1x1xf32>) -> +} +I0420 14:39:28.564572 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xd04abc01745131168559886035_inner_var_0 -> 0xd05b3f0 +1 -> constant_folding@_174513116855594796545 -> 0xcf11a20 + +I0420 14:39:28.564582 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.565168 116759 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.565394 116760 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.565629 116761 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.566431 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xd04ad60) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full_int_array" [id:1825] () {dtype:int64,place:Place(cpu),stop_gradient:[true],value:[]} : () -> tensor<0xi64> + (%1) = "pd_op.cast" [id:1826] (%0) {dtype:int64,stop_gradient:[true]} : (tensor<0xi64>) -> tensor<0xi64> + () = "builtin.shadow_output" [id:1827] (%1) {output_name:"constant_folding@_174513116857122500446"} : (tensor<0xi64>) -> +} + +IR after lowering = { + (%0) = "full_int_array(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full_int_array",op_name:"pd_op.full_int_array",origin_id:1829,place:Place(cpu),stop_gradient:[true],value:[]} : () -> cpu_tensor<0xi64> + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1830,stop_gradient:[true]} : (cpu_tensor<0xi64>) -> cpu_tensor<0xi64> + () = "builtin.shadow_output" [id:1831] (%1) {origin_id:1827,output_name:"constant_folding@_174513116857122500446"} : (cpu_tensor<0xi64>) -> +} + +I0420 14:39:28.575073 115867 pir_interpreter.cc:1569] value info of interpretercore 0xd04abc0 +value -> var_name -> id -> variable* +0xcf17480 -> constant_folding@_174513116857122500446 -> 1 -> 0xd0606f0 +0xce89790 -> 0xd04abc01745131168574758314_inner_var_0 -> 0 -> 0xcf0ab40 +I0420 14:39:28.577623 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.578492 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.579035 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full_int_array(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full_int_array",op_name:"pd_op.full_int_array",origin_id:1829,place:Place(cpu),stop_gradient:[true],value:[]} : () -> cpu_tensor<0xi64> + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1830,stop_gradient:[true]} : (cpu_tensor<0xi64>) -> cpu_tensor<0xi64> + () = "builtin.shadow_output" [id:1831] (%1) {origin_id:1827,output_name:"constant_folding@_174513116857122500446"} : (cpu_tensor<0xi64>) -> +} +I0420 14:39:28.579057 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full_int_array +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xd04abc01745131168574758314_inner_var_0 -> 0xcf0ab40 +1 -> constant_folding@_174513116857122500446 -> 0xd0606f0 + +I0420 14:39:28.579067 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.579499 116762 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.579766 116763 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.580021 116764 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.580351 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xd04ad60) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full_int_array" [id:1834] () {dtype:int64,place:Place(cpu),stop_gradient:[true],value:[1,256,1,1]} : () -> tensor<4xi64> + (%1) = "pd_op.cast" [id:1835] (%0) {dtype:int64,stop_gradient:[true]} : (tensor<4xi64>) -> tensor<4xi64> + () = "builtin.shadow_output" [id:1836] (%1) {output_name:"constant_folding@_174513116858507737447"} : (tensor<4xi64>) -> +} + +IR after lowering = { + (%0) = "full_int_array(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full_int_array",op_name:"pd_op.full_int_array",origin_id:1838,place:Place(cpu),stop_gradient:[true],value:[1,256,1,1]} : () -> cpu_tensor<4xi64> + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1839,stop_gradient:[true]} : (cpu_tensor<4xi64>) -> cpu_tensor<4xi64> + () = "builtin.shadow_output" [id:1840] (%1) {origin_id:1836,output_name:"constant_folding@_174513116858507737447"} : (cpu_tensor<4xi64>) -> +} + +I0420 14:39:28.588860 115867 pir_interpreter.cc:1569] value info of interpretercore 0xd04abc0 +value -> var_name -> id -> variable* +0xcf17480 -> constant_folding@_174513116858507737447 -> 1 -> 0xcf90a10 +0xd070000 -> 0xd04abc01745131168588539494_inner_var_0 -> 0 -> 0xcf400e0 +I0420 14:39:28.591434 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.592314 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.592862 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full_int_array(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full_int_array",op_name:"pd_op.full_int_array",origin_id:1838,place:Place(cpu),stop_gradient:[true],value:[1,256,1,1]} : () -> cpu_tensor<4xi64> + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1839,stop_gradient:[true]} : (cpu_tensor<4xi64>) -> cpu_tensor<4xi64> + () = "builtin.shadow_output" [id:1840] (%1) {origin_id:1836,output_name:"constant_folding@_174513116858507737447"} : (cpu_tensor<4xi64>) -> +} +I0420 14:39:28.592882 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full_int_array +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xd04abc01745131168588539494_inner_var_0 -> 0xcf400e0 +1 -> constant_folding@_174513116858507737447 -> 0xcf90a10 + +I0420 14:39:28.592890 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.593330 116765 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.593569 116766 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.593811 116767 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.594447 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xd04ad60) got event_name: TaskCompletion +IR before lowering = { + (%0) = "builtin.parameter" [id:1843] () {parameter_name:"conv2d_transpose_0.b_0_deepcopy_279",stop_gradient:[false]} : () -> tensor<256xf32> + (%1) = "builtin.constant" [id:1844] () {value:"constant_folding@_174513116858507737447"} : () -> tensor<4xi64> + (%2) = "pd_op.reshape" [id:1845] (%0, %1) {stop_gradient:[false]} : (tensor<256xf32>, tensor<4xi64>) -> tensor<1x256x1x1xf32> + (%3) = "pd_op.cast" [id:1846] (%2) {dtype:float32,stop_gradient:[false]} : (tensor<1x256x1x1xf32>) -> tensor<1x256x1x1xf32> + () = "builtin.shadow_output" [id:1847] (%3) {output_name:"constant_folding@_174513116859911589448"} : (tensor<1x256x1x1xf32>) -> +} + +IR after lowering = { + (%0) = "builtin.parameter" [id:1849] () {origin_id:1843,parameter_name:"conv2d_transpose_0.b_0_deepcopy_279",stop_gradient:[false]} : () -> custom_device_tensor<256xf32> + (%1) = "builtin.constant" [id:1850] () {origin_id:1844,value:"constant_folding@_174513116858507737447"} : () -> cpu_tensor<4xi64> + (%2) = "reshape(phi_kernel)" (%0, %1) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:1851,stop_gradient:[false]} : (custom_device_tensor<256xf32>, cpu_tensor<4xi64>) -> custom_device_tensor<1x256x1x1xf32> + (%3) = "cast(phi_kernel)" (%2) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1852,stop_gradient:[false]} : (custom_device_tensor<1x256x1x1xf32>) -> custom_device_tensor<1x256x1x1xf32> + () = "builtin.shadow_output" [id:1853] (%3) {origin_id:1847,output_name:"constant_folding@_174513116859911589448"} : (custom_device_tensor<1x256x1x1xf32>) -> +} + +I0420 14:39:28.603942 115867 pir_interpreter.cc:1569] value info of interpretercore 0xd04abc0 +value -> var_name -> id -> variable* +0xcf17480 -> constant_folding@_174513116859911589448 -> 3 -> 0xcf41cc0 +0xcdb4210 -> 0xd04abc01745131168603571424_inner_var_2 -> 2 -> 0xc3866c0 +0xed1b1e0 -> constant_folding@_174513116858507737447 -> 1 -> 0xcf90a10 +0xcfbeb10 -> conv2d_transpose_0.b_0_deepcopy_279 -> 0 -> 0xcf383c0 +I0420 14:39:28.606962 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.607925 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.608688 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "builtin.parameter" [id:1849] () {origin_id:1843,parameter_name:"conv2d_transpose_0.b_0_deepcopy_279",stop_gradient:[false]} : () -> custom_device_tensor<256xf32> + (%1) = "builtin.constant" [id:1850] () {origin_id:1844,value:"constant_folding@_174513116858507737447"} : () -> cpu_tensor<4xi64> + (%2) = "reshape(phi_kernel)" (%0, %1) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:1851,stop_gradient:[false]} : (custom_device_tensor<256xf32>, cpu_tensor<4xi64>) -> custom_device_tensor<1x256x1x1xf32> + (%3) = "cast(phi_kernel)" (%2) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1852,stop_gradient:[false]} : (custom_device_tensor<1x256x1x1xf32>) -> custom_device_tensor<1x256x1x1xf32> + () = "builtin.shadow_output" [id:1853] (%3) {origin_id:1847,output_name:"constant_folding@_174513116859911589448"} : (custom_device_tensor<1x256x1x1xf32>) -> +} +I0420 14:39:28.608714 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 2 ) = pd_op.reshape ( 1 ) ( 0 ) +1: ( 3 ) = pd_op.cast ( 2 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> conv2d_transpose_0.b_0_deepcopy_279 -> 0xcf383c0 +1 -> constant_folding@_174513116858507737447 -> 0xcf90a10 +2 -> 0xd04abc01745131168603571424_inner_var_2 -> 0xc3866c0 +3 -> constant_folding@_174513116859911589448 -> 0xcf41cc0 + +I0420 14:39:28.608722 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.609119 116768 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.609333 116769 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.609660 116770 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.611086 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xd04ad60) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full" [id:1856] () {dtype:int64,place:Place(cpu),shape:[],stop_gradient:[true],value:14} : () -> tensor + (%1) = "pd_op.cast" [id:1857] (%0) {dtype:int64,stop_gradient:[true]} : (tensor) -> tensor + () = "builtin.shadow_output" [id:1858] (%1) {output_name:"constant_folding@_174513116861634597449"} : (tensor) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1860,place:Place(cpu),shape:[],stop_gradient:[true],value:14} : () -> cpu_tensor + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1861,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + () = "builtin.shadow_output" [id:1862] (%1) {origin_id:1858,output_name:"constant_folding@_174513116861634597449"} : (cpu_tensor) -> +} + +I0420 14:39:28.620172 115867 pir_interpreter.cc:1569] value info of interpretercore 0xd04abc0 +value -> var_name -> id -> variable* +0xcf17480 -> constant_folding@_174513116861634597449 -> 1 -> 0xcf09130 +0xcfcd2b0 -> 0xd04abc01745131168619855774_inner_var_0 -> 0 -> 0xcf41ce0 +I0420 14:39:28.622773 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.623662 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.624218 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1860,place:Place(cpu),shape:[],stop_gradient:[true],value:14} : () -> cpu_tensor + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1861,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + () = "builtin.shadow_output" [id:1862] (%1) {origin_id:1858,output_name:"constant_folding@_174513116861634597449"} : (cpu_tensor) -> +} +I0420 14:39:28.624239 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xd04abc01745131168619855774_inner_var_0 -> 0xcf41ce0 +1 -> constant_folding@_174513116861634597449 -> 0xcf09130 + +I0420 14:39:28.624248 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.624727 116772 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.624969 116773 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.625233 116774 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.625937 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xd04ad60) got event_name: TaskCompletion +I0420 14:39:28.630326 115867 print_statistics.cc:44] --- detected [5, 64] subgraphs! +--- Running PIR pass [dead_code_elimination_pass] +I0420 14:39:28.638031 115867 print_statistics.cc:50] --- detected [2] subgraphs! +--- Running PIR pass [constant_folding_pass] +IR before lowering = { + (%0) = "pd_op.full" [id:1865] () {dtype:float32,place:Place(cpu),shape:[1],stop_gradient:[true],value:1} : () -> tensor<1xf32> + (%1) = "pd_op.cast" [id:1866] (%0) {dtype:float32,stop_gradient:[true]} : (tensor<1xf32>) -> tensor<1xf32> + () = "builtin.shadow_output" [id:1867] (%1) {output_name:"constant_folding@_174513116864033493350"} : (tensor<1xf32>) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1869,place:Place(cpu),shape:[1],stop_gradient:[true],value:1} : () -> cpu_tensor<1xf32> + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1870,stop_gradient:[true]} : (cpu_tensor<1xf32>) -> cpu_tensor<1xf32> + () = "builtin.shadow_output" [id:1871] (%1) {origin_id:1867,output_name:"constant_folding@_174513116864033493350"} : (cpu_tensor<1xf32>) -> +} + +I0420 14:39:28.644203 115867 pir_interpreter.cc:1569] value info of interpretercore 0xd04abc0 +value -> var_name -> id -> variable* +0xcf17480 -> constant_folding@_174513116864033493350 -> 1 -> 0xcf3a750 +0xce89790 -> 0xd04abc01745131168643880653_inner_var_0 -> 0 -> 0xcf933b0 +I0420 14:39:28.646833 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.647719 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.648278 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1869,place:Place(cpu),shape:[1],stop_gradient:[true],value:1} : () -> cpu_tensor<1xf32> + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1870,stop_gradient:[true]} : (cpu_tensor<1xf32>) -> cpu_tensor<1xf32> + () = "builtin.shadow_output" [id:1871] (%1) {origin_id:1867,output_name:"constant_folding@_174513116864033493350"} : (cpu_tensor<1xf32>) -> +} +I0420 14:39:28.648303 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xd04abc01745131168643880653_inner_var_0 -> 0xcf933b0 +1 -> constant_folding@_174513116864033493350 -> 0xcf3a750 + +I0420 14:39:28.648321 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.648757 116775 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.649029 116776 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.649327 116777 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.649917 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xd04ad60) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full" [id:1874] () {dtype:int32,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> tensor + (%1) = "pd_op.cast" [id:1875] (%0) {dtype:int32,stop_gradient:[true]} : (tensor) -> tensor + () = "builtin.shadow_output" [id:1876] (%1) {output_name:"constant_folding@_174513116865440940351"} : (tensor) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:int32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1878,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%1) = "cast(phi_kernel)" (%0) {dtype:int32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1879,stop_gradient:[true]} : (custom_device_tensor) -> custom_device_tensor + () = "builtin.shadow_output" [id:1880] (%1) {origin_id:1876,output_name:"constant_folding@_174513116865440940351"} : (custom_device_tensor) -> +} + +I0420 14:39:28.658413 115867 pir_interpreter.cc:1569] value info of interpretercore 0xd04abc0 +value -> var_name -> id -> variable* +0xcf17ee0 -> constant_folding@_174513116865440940351 -> 1 -> 0xcf3edb0 +0xed1b1e0 -> 0xd04abc01745131168658102963_inner_var_0 -> 0 -> 0xce86b30 +I0420 14:39:28.661141 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.662039 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.662632 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:int32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1878,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%1) = "cast(phi_kernel)" (%0) {dtype:int32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1879,stop_gradient:[true]} : (custom_device_tensor) -> custom_device_tensor + () = "builtin.shadow_output" [id:1880] (%1) {origin_id:1876,output_name:"constant_folding@_174513116865440940351"} : (custom_device_tensor) -> +} +I0420 14:39:28.662653 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xd04abc01745131168658102963_inner_var_0 -> 0xce86b30 +1 -> constant_folding@_174513116865440940351 -> 0xcf3edb0 + +I0420 14:39:28.662662 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.663130 116778 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.663376 116779 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.663683 116780 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.664259 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xd04ad60) got event_name: TaskCompletion +I0420 14:39:28.668228 115867 print_statistics.cc:44] --- detected [2, 23] subgraphs! +--- Running PIR pass [dead_code_elimination_pass] +--- Running PIR pass [constant_folding_pass] +I0420 14:39:28.672364 115867 print_statistics.cc:44] --- detected [0, 29] subgraphs! +--- Running PIR pass [dead_code_elimination_pass] +--- Running PIR pass [constant_folding_pass] +IR before lowering = { + (%0) = "pd_op.full_int_array" [id:1883] () {dtype:int64,place:Place(cpu),stop_gradient:[true],value:[]} : () -> tensor<0xi64> + (%1) = "pd_op.cast" [id:1884] (%0) {dtype:int64,stop_gradient:[true]} : (tensor<0xi64>) -> tensor<0xi64> + () = "builtin.shadow_output" [id:1885] (%1) {output_name:"constant_folding@_174513116867667180352"} : (tensor<0xi64>) -> +} + +IR after lowering = { + (%0) = "full_int_array(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full_int_array",op_name:"pd_op.full_int_array",origin_id:1887,place:Place(cpu),stop_gradient:[true],value:[]} : () -> cpu_tensor<0xi64> + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1888,stop_gradient:[true]} : (cpu_tensor<0xi64>) -> cpu_tensor<0xi64> + () = "builtin.shadow_output" [id:1889] (%1) {origin_id:1885,output_name:"constant_folding@_174513116867667180352"} : (cpu_tensor<0xi64>) -> +} + +I0420 14:39:28.680467 115867 pir_interpreter.cc:1569] value info of interpretercore 0xd04b540 +value -> var_name -> id -> variable* +0xcf17ee0 -> constant_folding@_174513116867667180352 -> 1 -> 0xee46cc0 +0xcfcddf0 -> 0xd04b5401745131168680141953_inner_var_0 -> 0 -> 0xcedcf80 +I0420 14:39:28.683022 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.683916 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.684453 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full_int_array(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full_int_array",op_name:"pd_op.full_int_array",origin_id:1887,place:Place(cpu),stop_gradient:[true],value:[]} : () -> cpu_tensor<0xi64> + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1888,stop_gradient:[true]} : (cpu_tensor<0xi64>) -> cpu_tensor<0xi64> + () = "builtin.shadow_output" [id:1889] (%1) {origin_id:1885,output_name:"constant_folding@_174513116867667180352"} : (cpu_tensor<0xi64>) -> +} +I0420 14:39:28.684475 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full_int_array +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xd04b5401745131168680141953_inner_var_0 -> 0xcedcf80 +1 -> constant_folding@_174513116867667180352 -> 0xee46cc0 + +I0420 14:39:28.684489 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.685078 116781 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.685286 116782 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.685534 116783 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.685875 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xd04b6e0) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full" [id:1892] () {dtype:int64,place:Place(cpu),shape:[],stop_gradient:[true],value:2} : () -> tensor + (%1) = "pd_op.cast" [id:1893] (%0) {dtype:int64,stop_gradient:[true]} : (tensor) -> tensor + () = "builtin.shadow_output" [id:1894] (%1) {output_name:"constant_folding@_174513116869046783353"} : (tensor) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1896,place:Place(cpu),shape:[],stop_gradient:[true],value:2} : () -> cpu_tensor + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1897,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + () = "builtin.shadow_output" [id:1898] (%1) {origin_id:1894,output_name:"constant_folding@_174513116869046783353"} : (cpu_tensor) -> +} + +I0420 14:39:28.694417 115867 pir_interpreter.cc:1569] value info of interpretercore 0xd04b540 +value -> var_name -> id -> variable* +0xcf17ee0 -> constant_folding@_174513116869046783353 -> 1 -> 0xcf2b9b0 +0xcfbeb10 -> 0xd04b5401745131168694086343_inner_var_0 -> 0 -> 0xd04a9b0 +I0420 14:39:28.697058 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.697959 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.698542 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1896,place:Place(cpu),shape:[],stop_gradient:[true],value:2} : () -> cpu_tensor + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1897,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + () = "builtin.shadow_output" [id:1898] (%1) {origin_id:1894,output_name:"constant_folding@_174513116869046783353"} : (cpu_tensor) -> +} +I0420 14:39:28.698567 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xd04b5401745131168694086343_inner_var_0 -> 0xd04a9b0 +1 -> constant_folding@_174513116869046783353 -> 0xcf2b9b0 + +I0420 14:39:28.698582 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.699090 116784 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.699335 116785 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.699581 116786 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.700276 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xd04b6e0) got event_name: TaskCompletion +IR before lowering = { + (%0) = "builtin.constant" [id:1901] () {persistable:[true],value:"constant_folding@_174513115176548053017"} : () -> tensor<1xi64> + (%1) = "builtin.constant" [id:1902] () {persistable:[true],value:"constant_folding@_174513116867667180352"} : () -> tensor<0xi64> + (%2) = "pd_op.reshape" [id:1903] (%0, %1) {stop_gradient:[true]} : (tensor<1xi64>, tensor<0xi64>) -> tensor + (%3) = "pd_op.cast" [id:1904] (%2) {dtype:int64,stop_gradient:[true]} : (tensor) -> tensor + () = "builtin.shadow_output" [id:1905] (%3) {output_name:"constant_folding@_174513116870501220354"} : (tensor) -> +} + +IR after lowering = { + (%0) = "builtin.constant" [id:1907] () {origin_id:1901,persistable:[true],value:"constant_folding@_174513115176548053017"} : () -> cpu_tensor<1xi64> + (%1) = "builtin.constant" [id:1908] () {origin_id:1902,persistable:[true],value:"constant_folding@_174513116867667180352"} : () -> cpu_tensor<0xi64> + (%2) = "reshape(phi_kernel)" (%0, %1) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:1909,stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor<0xi64>) -> cpu_tensor + (%3) = "cast(phi_kernel)" (%2) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1910,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + () = "builtin.shadow_output" [id:1911] (%3) {origin_id:1905,output_name:"constant_folding@_174513116870501220354"} : (cpu_tensor) -> +} + +I0420 14:39:28.709960 115867 pir_interpreter.cc:1569] value info of interpretercore 0xd04b540 +value -> var_name -> id -> variable* +0xcf17ee0 -> constant_folding@_174513116870501220354 -> 3 -> 0xcea7bc0 +0xcdb4210 -> 0xd04b5401745131168709574412_inner_var_2 -> 2 -> 0xcfcca10 +0xd04bb90 -> constant_folding@_174513116867667180352 -> 1 -> 0xee46cc0 +0xcfcddf0 -> constant_folding@_174513115176548053017 -> 0 -> 0xcf8c2c0 +I0420 14:39:28.712875 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.713845 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.714594 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "builtin.constant" [id:1907] () {origin_id:1901,persistable:[true],value:"constant_folding@_174513115176548053017"} : () -> cpu_tensor<1xi64> + (%1) = "builtin.constant" [id:1908] () {origin_id:1902,persistable:[true],value:"constant_folding@_174513116867667180352"} : () -> cpu_tensor<0xi64> + (%2) = "reshape(phi_kernel)" (%0, %1) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:1909,stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor<0xi64>) -> cpu_tensor + (%3) = "cast(phi_kernel)" (%2) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1910,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + () = "builtin.shadow_output" [id:1911] (%3) {origin_id:1905,output_name:"constant_folding@_174513116870501220354"} : (cpu_tensor) -> +} +I0420 14:39:28.714620 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 2 ) = pd_op.reshape ( 1 ) ( 0 ) +1: ( 3 ) = pd_op.cast ( 2 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> constant_folding@_174513115176548053017 -> 0xcf8c2c0 +1 -> constant_folding@_174513116867667180352 -> 0xee46cc0 +2 -> 0xd04b5401745131168709574412_inner_var_2 -> 0xcfcca10 +3 -> constant_folding@_174513116870501220354 -> 0xcea7bc0 + +I0420 14:39:28.714640 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.715222 116787 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.715433 116788 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.715667 116789 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.716346 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xd04b6e0) got event_name: TaskCompletion +IR before lowering = { + (%0) = "builtin.constant" [id:1914] () {persistable:[true],value:"constant_folding@_174513115258237754718"} : () -> tensor<1xi64> + (%1) = "builtin.constant" [id:1915] () {persistable:[true],value:"constant_folding@_174513116867667180352"} : () -> tensor<0xi64> + (%2) = "pd_op.reshape" [id:1916] (%0, %1) {stop_gradient:[true]} : (tensor<1xi64>, tensor<0xi64>) -> tensor + (%3) = "pd_op.cast" [id:1917] (%2) {dtype:int64,stop_gradient:[true]} : (tensor) -> tensor + () = "builtin.shadow_output" [id:1918] (%3) {output_name:"constant_folding@_174513116872098526255"} : (tensor) -> +} + +IR after lowering = { + (%0) = "builtin.constant" [id:1920] () {origin_id:1914,persistable:[true],value:"constant_folding@_174513115258237754718"} : () -> cpu_tensor<1xi64> + (%1) = "builtin.constant" [id:1921] () {origin_id:1915,persistable:[true],value:"constant_folding@_174513116867667180352"} : () -> cpu_tensor<0xi64> + (%2) = "reshape(phi_kernel)" (%0, %1) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:1922,stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor<0xi64>) -> cpu_tensor + (%3) = "cast(phi_kernel)" (%2) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1923,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + () = "builtin.shadow_output" [id:1924] (%3) {origin_id:1918,output_name:"constant_folding@_174513116872098526255"} : (cpu_tensor) -> +} + +I0420 14:39:28.725729 115867 pir_interpreter.cc:1569] value info of interpretercore 0xca57080 +value -> var_name -> id -> variable* +0xcf17ee0 -> constant_folding@_174513116872098526255 -> 3 -> 0xd061370 +0xcdb4210 -> 0xca570801745131168725332812_inner_var_2 -> 2 -> 0xce97620 +0xcfc8c80 -> constant_folding@_174513116867667180352 -> 1 -> 0xee46cc0 +0xce88b90 -> constant_folding@_174513115258237754718 -> 0 -> 0xcfce240 +I0420 14:39:28.728608 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.729560 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.730269 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "builtin.constant" [id:1920] () {origin_id:1914,persistable:[true],value:"constant_folding@_174513115258237754718"} : () -> cpu_tensor<1xi64> + (%1) = "builtin.constant" [id:1921] () {origin_id:1915,persistable:[true],value:"constant_folding@_174513116867667180352"} : () -> cpu_tensor<0xi64> + (%2) = "reshape(phi_kernel)" (%0, %1) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:1922,stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor<0xi64>) -> cpu_tensor + (%3) = "cast(phi_kernel)" (%2) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1923,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + () = "builtin.shadow_output" [id:1924] (%3) {origin_id:1918,output_name:"constant_folding@_174513116872098526255"} : (cpu_tensor) -> +} +I0420 14:39:28.730296 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 2 ) = pd_op.reshape ( 1 ) ( 0 ) +1: ( 3 ) = pd_op.cast ( 2 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> constant_folding@_174513115258237754718 -> 0xcfce240 +1 -> constant_folding@_174513116867667180352 -> 0xee46cc0 +2 -> 0xca570801745131168725332812_inner_var_2 -> 0xce97620 +3 -> constant_folding@_174513116872098526255 -> 0xd061370 + +I0420 14:39:28.730309 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.730872 116790 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.731083 116791 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.731412 116792 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.732167 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xca57220) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full" [id:1927] () {dtype:int64,place:Place(cpu),shape:[1],stop_gradient:[true],value:2} : () -> tensor<1xi64> + (%1) = "pd_op.cast" [id:1928] (%0) {dtype:int64,stop_gradient:[true]} : (tensor<1xi64>) -> tensor<1xi64> + () = "builtin.shadow_output" [id:1929] (%1) {output_name:"constant_folding@_174513116873667365256"} : (tensor<1xi64>) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1931,place:Place(cpu),shape:[1],stop_gradient:[true],value:2} : () -> cpu_tensor<1xi64> + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1932,stop_gradient:[true]} : (cpu_tensor<1xi64>) -> cpu_tensor<1xi64> + () = "builtin.shadow_output" [id:1933] (%1) {origin_id:1929,output_name:"constant_folding@_174513116873667365256"} : (cpu_tensor<1xi64>) -> +} + +I0420 14:39:28.740510 115867 pir_interpreter.cc:1569] value info of interpretercore 0xca57080 +value -> var_name -> id -> variable* +0xcf17ee0 -> constant_folding@_174513116873667365256 -> 1 -> 0xce99ed0 +0xd066b80 -> 0xca570801745131168740194282_inner_var_0 -> 0 -> 0xd04bcb0 +I0420 14:39:28.743127 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.744014 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.744587 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1931,place:Place(cpu),shape:[1],stop_gradient:[true],value:2} : () -> cpu_tensor<1xi64> + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1932,stop_gradient:[true]} : (cpu_tensor<1xi64>) -> cpu_tensor<1xi64> + () = "builtin.shadow_output" [id:1933] (%1) {origin_id:1929,output_name:"constant_folding@_174513116873667365256"} : (cpu_tensor<1xi64>) -> +} +I0420 14:39:28.744616 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xca570801745131168740194282_inner_var_0 -> 0xd04bcb0 +1 -> constant_folding@_174513116873667365256 -> 0xce99ed0 + +I0420 14:39:28.744632 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.745146 116793 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.745343 116794 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.745657 116795 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.746340 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xca57220) got event_name: TaskCompletion +IR before lowering = { + (%0) = "builtin.parameter" [id:1936] () {parameter_name:"constant_folding@_174513116873667365256",stop_gradient:[false]} : () -> tensor<1xi64> + (%1) = "builtin.constant" [id:1937] () {persistable:[true],value:"constant_folding@_174513116867667180352"} : () -> tensor<0xi64> + (%2) = "pd_op.reshape" [id:1938] (%0, %1) {stop_gradient:[true]} : (tensor<1xi64>, tensor<0xi64>) -> tensor + (%3) = "pd_op.cast" [id:1939] (%2) {dtype:int64,stop_gradient:[true]} : (tensor) -> tensor + () = "builtin.shadow_output" [id:1940] (%3) {output_name:"constant_folding@_174513116875198908257"} : (tensor) -> +} + +IR after lowering = { + (%0) = "builtin.parameter" [id:1942] () {origin_id:1936,parameter_name:"constant_folding@_174513116873667365256",stop_gradient:[false]} : () -> custom_device_tensor<1xi64> + (%1) = "builtin.constant" [id:1943] () {origin_id:1937,persistable:[true],value:"constant_folding@_174513116867667180352"} : () -> cpu_tensor<0xi64> + (%2) = "reshape(phi_kernel)" (%0, %1) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:1944,stop_gradient:[true]} : (custom_device_tensor<1xi64>, cpu_tensor<0xi64>) -> custom_device_tensor + (%3) = "cast(phi_kernel)" (%2) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1945,stop_gradient:[true]} : (custom_device_tensor) -> custom_device_tensor + () = "builtin.shadow_output" [id:1946] (%3) {origin_id:1940,output_name:"constant_folding@_174513116875198908257"} : (custom_device_tensor) -> +} + +I0420 14:39:28.756913 115867 pir_interpreter.cc:1569] value info of interpretercore 0xca57080 +value -> var_name -> id -> variable* +0xcf17ee0 -> constant_folding@_174513116875198908257 -> 3 -> 0xc386660 +0xcdb4210 -> 0xca570801745131168756542782_inner_var_2 -> 2 -> 0xcb0e880 +0xcfc8c80 -> constant_folding@_174513116867667180352 -> 1 -> 0xee46cc0 +0xce88b90 -> constant_folding@_174513116873667365256 -> 0 -> 0xce99ed0 +I0420 14:39:28.759913 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.760864 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.761607 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "builtin.parameter" [id:1942] () {origin_id:1936,parameter_name:"constant_folding@_174513116873667365256",stop_gradient:[false]} : () -> custom_device_tensor<1xi64> + (%1) = "builtin.constant" [id:1943] () {origin_id:1937,persistable:[true],value:"constant_folding@_174513116867667180352"} : () -> cpu_tensor<0xi64> + (%2) = "reshape(phi_kernel)" (%0, %1) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:1944,stop_gradient:[true]} : (custom_device_tensor<1xi64>, cpu_tensor<0xi64>) -> custom_device_tensor + (%3) = "cast(phi_kernel)" (%2) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1945,stop_gradient:[true]} : (custom_device_tensor) -> custom_device_tensor + () = "builtin.shadow_output" [id:1946] (%3) {origin_id:1940,output_name:"constant_folding@_174513116875198908257"} : (custom_device_tensor) -> +} +I0420 14:39:28.761637 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 2 ) = pd_op.reshape ( 1 ) ( 0 ) +1: ( 3 ) = pd_op.cast ( 2 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> constant_folding@_174513116873667365256 -> 0xce99ed0 +1 -> constant_folding@_174513116867667180352 -> 0xee46cc0 +2 -> 0xca570801745131168756542782_inner_var_2 -> 0xcb0e880 +3 -> constant_folding@_174513116875198908257 -> 0xc386660 + +I0420 14:39:28.761660 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.762199 116797 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.762413 116798 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.762660 116799 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.763804 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xca57220) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full" [id:1949] () {dtype:int32,place:Place(cpu),shape:[1],stop_gradient:[true],value:0} : () -> tensor<1xi32> + (%1) = "pd_op.cast" [id:1950] (%0) {dtype:int32,stop_gradient:[true]} : (tensor<1xi32>) -> tensor<1xi32> + () = "builtin.shadow_output" [id:1951] (%1) {output_name:"constant_folding@_174513116876880248258"} : (tensor<1xi32>) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:int32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1953,place:Place(cpu),shape:[1],stop_gradient:[true],value:0} : () -> cpu_tensor<1xi32> + (%1) = "cast(phi_kernel)" (%0) {dtype:int32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1954,stop_gradient:[true]} : (cpu_tensor<1xi32>) -> cpu_tensor<1xi32> + () = "builtin.shadow_output" [id:1955] (%1) {origin_id:1951,output_name:"constant_folding@_174513116876880248258"} : (cpu_tensor<1xi32>) -> +} + +I0420 14:39:28.772692 115867 pir_interpreter.cc:1569] value info of interpretercore 0xee72020 +value -> var_name -> id -> variable* +0xcf17ee0 -> constant_folding@_174513116876880248258 -> 1 -> 0xee71fe0 +0xce97710 -> 0xee720201745131168772354082_inner_var_0 -> 0 -> 0xcf8e2e0 +I0420 14:39:28.775295 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.776194 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.776773 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:int32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1953,place:Place(cpu),shape:[1],stop_gradient:[true],value:0} : () -> cpu_tensor<1xi32> + (%1) = "cast(phi_kernel)" (%0) {dtype:int32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1954,stop_gradient:[true]} : (cpu_tensor<1xi32>) -> cpu_tensor<1xi32> + () = "builtin.shadow_output" [id:1955] (%1) {origin_id:1951,output_name:"constant_folding@_174513116876880248258"} : (cpu_tensor<1xi32>) -> +} +I0420 14:39:28.776795 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xee720201745131168772354082_inner_var_0 -> 0xcf8e2e0 +1 -> constant_folding@_174513116876880248258 -> 0xee71fe0 + +I0420 14:39:28.776806 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.777369 116802 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.777594 116803 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.777842 116804 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.778560 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xee721c0) got event_name: TaskCompletion +I0420 14:39:28.783015 115867 print_statistics.cc:44] --- detected [7, 64] subgraphs! +--- Running PIR pass [dead_code_elimination_pass] +I0420 14:39:28.790596 115867 print_statistics.cc:50] --- detected [1] subgraphs! +--- Running PIR pass [constant_folding_pass] +IR before lowering = { + (%0) = "pd_op.full" [id:1958] () {dtype:int64,place:Place(cpu),shape:[1],stop_gradient:[true],value:2} : () -> tensor<1xi64> + (%1) = "pd_op.cast" [id:1959] (%0) {dtype:int64,stop_gradient:[true]} : (tensor<1xi64>) -> tensor<1xi64> + () = "builtin.shadow_output" [id:1960] (%1) {output_name:"constant_folding@_174513116879348731159"} : (tensor<1xi64>) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1962,place:Place(cpu),shape:[1],stop_gradient:[true],value:2} : () -> cpu_tensor<1xi64> + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1963,stop_gradient:[true]} : (cpu_tensor<1xi64>) -> cpu_tensor<1xi64> + () = "builtin.shadow_output" [id:1964] (%1) {origin_id:1960,output_name:"constant_folding@_174513116879348731159"} : (cpu_tensor<1xi64>) -> +} + +I0420 14:39:28.797361 115867 pir_interpreter.cc:1569] value info of interpretercore 0xee72020 +value -> var_name -> id -> variable* +0xcf17ee0 -> constant_folding@_174513116879348731159 -> 1 -> 0xce86f60 +0xcfc8c80 -> 0xee720201745131168797041471_inner_var_0 -> 0 -> 0xce99d00 +I0420 14:39:28.799966 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.800853 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.801414 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1962,place:Place(cpu),shape:[1],stop_gradient:[true],value:2} : () -> cpu_tensor<1xi64> + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1963,stop_gradient:[true]} : (cpu_tensor<1xi64>) -> cpu_tensor<1xi64> + () = "builtin.shadow_output" [id:1964] (%1) {origin_id:1960,output_name:"constant_folding@_174513116879348731159"} : (cpu_tensor<1xi64>) -> +} +I0420 14:39:28.801438 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xee720201745131168797041471_inner_var_0 -> 0xce99d00 +1 -> constant_folding@_174513116879348731159 -> 0xce86f60 + +I0420 14:39:28.801448 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.802000 116805 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.802237 116806 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.802517 116807 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.803215 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xee721c0) got event_name: TaskCompletion +IR before lowering = { + (%0) = "builtin.parameter" [id:1967] () {parameter_name:"constant_folding@_174513116879348731159",stop_gradient:[false]} : () -> tensor<1xi64> + (%1) = "builtin.constant" [id:1968] () {persistable:[true],value:"constant_folding@_174513116841278885739"} : () -> tensor<0xi64> + (%2) = "pd_op.reshape" [id:1969] (%0, %1) {stop_gradient:[true]} : (tensor<1xi64>, tensor<0xi64>) -> tensor + (%3) = "pd_op.cast" [id:1970] (%2) {dtype:int64,stop_gradient:[true]} : (tensor) -> tensor + () = "builtin.shadow_output" [id:1971] (%3) {output_name:"constant_folding@_174513116880873974160"} : (tensor) -> +} + +IR after lowering = { + (%0) = "builtin.parameter" [id:1973] () {origin_id:1967,parameter_name:"constant_folding@_174513116879348731159",stop_gradient:[false]} : () -> custom_device_tensor<1xi64> + (%1) = "builtin.constant" [id:1974] () {origin_id:1968,persistable:[true],value:"constant_folding@_174513116841278885739"} : () -> cpu_tensor<0xi64> + (%2) = "reshape(phi_kernel)" (%0, %1) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:1975,stop_gradient:[true]} : (custom_device_tensor<1xi64>, cpu_tensor<0xi64>) -> custom_device_tensor + (%3) = "cast(phi_kernel)" (%2) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1976,stop_gradient:[true]} : (custom_device_tensor) -> custom_device_tensor + () = "builtin.shadow_output" [id:1977] (%3) {origin_id:1971,output_name:"constant_folding@_174513116880873974160"} : (custom_device_tensor) -> +} + +I0420 14:39:28.813663 115867 pir_interpreter.cc:1569] value info of interpretercore 0xee72020 +value -> var_name -> id -> variable* +0xcf17ee0 -> constant_folding@_174513116880873974160 -> 3 -> 0xee71f60 +0xcdb4210 -> 0xee720201745131168813273751_inner_var_2 -> 2 -> 0xca57bf0 +0xd04a880 -> constant_folding@_174513116841278885739 -> 1 -> 0xcdb8970 +0xcf7fa10 -> constant_folding@_174513116879348731159 -> 0 -> 0xce86f60 +I0420 14:39:28.816653 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.817617 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.818352 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "builtin.parameter" [id:1973] () {origin_id:1967,parameter_name:"constant_folding@_174513116879348731159",stop_gradient:[false]} : () -> custom_device_tensor<1xi64> + (%1) = "builtin.constant" [id:1974] () {origin_id:1968,persistable:[true],value:"constant_folding@_174513116841278885739"} : () -> cpu_tensor<0xi64> + (%2) = "reshape(phi_kernel)" (%0, %1) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:1975,stop_gradient:[true]} : (custom_device_tensor<1xi64>, cpu_tensor<0xi64>) -> custom_device_tensor + (%3) = "cast(phi_kernel)" (%2) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1976,stop_gradient:[true]} : (custom_device_tensor) -> custom_device_tensor + () = "builtin.shadow_output" [id:1977] (%3) {origin_id:1971,output_name:"constant_folding@_174513116880873974160"} : (custom_device_tensor) -> +} +I0420 14:39:28.818383 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 2 ) = pd_op.reshape ( 1 ) ( 0 ) +1: ( 3 ) = pd_op.cast ( 2 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> constant_folding@_174513116879348731159 -> 0xce86f60 +1 -> constant_folding@_174513116841278885739 -> 0xcdb8970 +2 -> 0xee720201745131168813273751_inner_var_2 -> 0xca57bf0 +3 -> constant_folding@_174513116880873974160 -> 0xee71f60 + +I0420 14:39:28.818393 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.818970 116809 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.819169 116810 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.819411 116811 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.820703 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xee721c0) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full" [id:1980] () {dtype:float32,place:Place(cpu),shape:[1],stop_gradient:[true],value:2} : () -> tensor<1xf32> + (%1) = "pd_op.cast" [id:1981] (%0) {dtype:float32,stop_gradient:[true]} : (tensor<1xf32>) -> tensor<1xf32> + () = "builtin.shadow_output" [id:1982] (%1) {output_name:"constant_folding@_174513116882724144161"} : (tensor<1xf32>) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1984,place:Place(cpu),shape:[1],stop_gradient:[true],value:2} : () -> cpu_tensor<1xf32> + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1985,stop_gradient:[true]} : (cpu_tensor<1xf32>) -> cpu_tensor<1xf32> + () = "builtin.shadow_output" [id:1986] (%1) {origin_id:1982,output_name:"constant_folding@_174513116882724144161"} : (cpu_tensor<1xf32>) -> +} + +I0420 14:39:28.831087 115867 pir_interpreter.cc:1569] value info of interpretercore 0xcaa3760 +value -> var_name -> id -> variable* +0xcf17ee0 -> constant_folding@_174513116882724144161 -> 1 -> 0xcdb8360 +0xce88b90 -> 0xcaa37601745131168830770311_inner_var_0 -> 0 -> 0xd04b0b0 +I0420 14:39:28.833702 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.834595 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.835153 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1984,place:Place(cpu),shape:[1],stop_gradient:[true],value:2} : () -> cpu_tensor<1xf32> + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1985,stop_gradient:[true]} : (cpu_tensor<1xf32>) -> cpu_tensor<1xf32> + () = "builtin.shadow_output" [id:1986] (%1) {origin_id:1982,output_name:"constant_folding@_174513116882724144161"} : (cpu_tensor<1xf32>) -> +} +I0420 14:39:28.835179 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xcaa37601745131168830770311_inner_var_0 -> 0xd04b0b0 +1 -> constant_folding@_174513116882724144161 -> 0xcdb8360 + +I0420 14:39:28.835188 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.835749 116814 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.835978 116815 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.836277 116816 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.836987 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xcaa3900) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full" [id:1989] () {dtype:float32,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0.5} : () -> tensor + (%1) = "pd_op.cast" [id:1990] (%0) {dtype:float32,stop_gradient:[true]} : (tensor) -> tensor + () = "builtin.shadow_output" [id:1991] (%1) {output_name:"constant_folding@_174513116884162097162"} : (tensor) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1993,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0.5} : () -> custom_device_tensor + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1994,stop_gradient:[true]} : (custom_device_tensor) -> custom_device_tensor + () = "builtin.shadow_output" [id:1995] (%1) {origin_id:1991,output_name:"constant_folding@_174513116884162097162"} : (custom_device_tensor) -> +} + +I0420 14:39:28.845646 115867 pir_interpreter.cc:1569] value info of interpretercore 0xcaa3760 +value -> var_name -> id -> variable* +0xcf17480 -> constant_folding@_174513116884162097162 -> 1 -> 0xcf7caa0 +0xee71e10 -> 0xcaa37601745131168845312001_inner_var_0 -> 0 -> 0xcf54520 +I0420 14:39:28.848357 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.849254 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.849844 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:1993,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0.5} : () -> custom_device_tensor + (%1) = "cast(phi_kernel)" (%0) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:1994,stop_gradient:[true]} : (custom_device_tensor) -> custom_device_tensor + () = "builtin.shadow_output" [id:1995] (%1) {origin_id:1991,output_name:"constant_folding@_174513116884162097162"} : (custom_device_tensor) -> +} +I0420 14:39:28.849871 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xcaa37601745131168845312001_inner_var_0 -> 0xcf54520 +1 -> constant_folding@_174513116884162097162 -> 0xcf7caa0 + +I0420 14:39:28.849881 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.850422 116817 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.850656 116818 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.850940 116819 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.851732 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xcaa3900) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full" [id:1998] () {dtype:int64,place:Place(cpu),shape:[],stop_gradient:[true],value:0} : () -> tensor + (%1) = "pd_op.cast" [id:1999] (%0) {dtype:int64,stop_gradient:[true]} : (tensor) -> tensor + () = "builtin.shadow_output" [id:2000] (%1) {output_name:"constant_folding@_174513116885637196163"} : (tensor) -> +} + +IR after lowering = { + (%0) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2002,place:Place(cpu),shape:[],stop_gradient:[true],value:0} : () -> cpu_tensor + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:2003,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + () = "builtin.shadow_output" [id:2004] (%1) {origin_id:2000,output_name:"constant_folding@_174513116885637196163"} : (cpu_tensor) -> +} + +I0420 14:39:28.860250 115867 pir_interpreter.cc:1569] value info of interpretercore 0xcaa3760 +value -> var_name -> id -> variable* +0xcf17480 -> constant_folding@_174513116885637196163 -> 1 -> 0xd04b8a0 +0xd04a880 -> 0xcaa37601745131168859929860_inner_var_0 -> 0 -> 0xee71dd0 +I0420 14:39:28.862867 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.863770 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.864331 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2002,place:Place(cpu),shape:[],stop_gradient:[true],value:0} : () -> cpu_tensor + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:2003,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + () = "builtin.shadow_output" [id:2004] (%1) {origin_id:2000,output_name:"constant_folding@_174513116885637196163"} : (cpu_tensor) -> +} +I0420 14:39:28.864355 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xcaa37601745131168859929860_inner_var_0 -> 0xee71dd0 +1 -> constant_folding@_174513116885637196163 -> 0xd04b8a0 + +I0420 14:39:28.864365 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.864926 116820 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.865120 116821 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.865363 116822 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.866055 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xcaa3900) got event_name: TaskCompletion +IR before lowering = { + (%0) = "pd_op.full_int_array" [id:2007] () {dtype:int64,place:Place(cpu),stop_gradient:[true],value:[1,1,1]} : () -> tensor<3xi64> + (%1) = "pd_op.cast" [id:2008] (%0) {dtype:int64,stop_gradient:[true]} : (tensor<3xi64>) -> tensor<3xi64> + () = "builtin.shadow_output" [id:2009] (%1) {output_name:"constant_folding@_174513116887061917064"} : (tensor<3xi64>) -> +} + +IR after lowering = { + (%0) = "full_int_array(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full_int_array",op_name:"pd_op.full_int_array",origin_id:2011,place:Place(cpu),stop_gradient:[true],value:[1,1,1]} : () -> cpu_tensor<3xi64> + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:2012,stop_gradient:[true]} : (cpu_tensor<3xi64>) -> cpu_tensor<3xi64> + () = "builtin.shadow_output" [id:2013] (%1) {origin_id:2009,output_name:"constant_folding@_174513116887061917064"} : (cpu_tensor<3xi64>) -> +} + +I0420 14:39:28.874408 115867 pir_interpreter.cc:1569] value info of interpretercore 0xcaa3760 +value -> var_name -> id -> variable* +0xcf17480 -> constant_folding@_174513116887061917064 -> 1 -> 0xd062b30 +0xce97710 -> 0xcaa37601745131168874092350_inner_var_0 -> 0 -> 0xcfa8ad0 +I0420 14:39:28.876995 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:28.877888 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:28.878425 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "full_int_array(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full_int_array",op_name:"pd_op.full_int_array",origin_id:2011,place:Place(cpu),stop_gradient:[true],value:[1,1,1]} : () -> cpu_tensor<3xi64> + (%1) = "cast(phi_kernel)" (%0) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:2012,stop_gradient:[true]} : (cpu_tensor<3xi64>) -> cpu_tensor<3xi64> + () = "builtin.shadow_output" [id:2013] (%1) {origin_id:2009,output_name:"constant_folding@_174513116887061917064"} : (cpu_tensor<3xi64>) -> +} +I0420 14:39:28.878448 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 0 ) = pd_op.full_int_array +1: ( 1 ) = pd_op.cast ( 0 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> 0xcaa37601745131168874092350_inner_var_0 -> 0xcfa8ad0 +1 -> constant_folding@_174513116887061917064 -> 0xd062b30 + +I0420 14:39:28.878455 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 + +I0420 14:39:28.879014 116823 nonblocking_threadpool.h:251] HostTasks_thread_0 started +I0420 14:39:28.879254 116824 nonblocking_threadpool.h:251] HostTasks_thread_1 started +I0420 14:39:28.879513 116825 nonblocking_threadpool.h:251] DeviceKernelLaunch_thread_0 started +I0420 14:39:28.880066 115867 pir_interpreter.cc:1791] main_thread_blocker_(0xcaa3900) got event_name: TaskCompletion +I0420 14:39:28.884510 115867 print_statistics.cc:44] --- detected [6, 120] subgraphs! +--- Running PIR pass [dead_code_elimination_pass] +I0420 14:39:28.892474 115867 print_statistics.cc:50] --- detected [1] subgraphs! +IR before lowering = { + (%0) = "builtin.constant" [id:2014] () {persistable:[true],value:"constant_folding@_174513116887061917064"} : () -> tensor<3xi64> + (%1) = "builtin.constant" [id:2005] () {persistable:[true],value:"constant_folding@_174513116885637196163"} : () -> tensor + (%2) = "builtin.parameter" [id:1996] () {parameter_name:"constant_folding@_174513116884162097162",persistable:[true],stop_gradient:[false]} : () -> tensor + (%3) = "builtin.constant" [id:1987] () {persistable:[true],value:"constant_folding@_174513116882724144161"} : () -> tensor<1xf32> + (%4) = "builtin.constant" [id:1978] () {persistable:[true],value:"constant_folding@_174513116880873974160"} : () -> tensor + (%5) = "builtin.constant" [id:1956] () {persistable:[true],value:"constant_folding@_174513116876880248258"} : () -> tensor<1xi32> + (%6) = "builtin.constant" [id:1947] () {persistable:[true],value:"constant_folding@_174513116875198908257"} : () -> tensor + (%7) = "builtin.constant" [id:1925] () {persistable:[true],value:"constant_folding@_174513116872098526255"} : () -> tensor + (%8) = "builtin.constant" [id:1912] () {persistable:[true],value:"constant_folding@_174513116870501220354"} : () -> tensor + (%9) = "builtin.constant" [id:1899] () {persistable:[true],value:"constant_folding@_174513116869046783353"} : () -> tensor + (%10) = "builtin.constant" [id:1890] () {persistable:[true],value:"constant_folding@_174513116867667180352"} : () -> tensor<0xi64> + (%11) = "builtin.parameter" [id:1881] () {parameter_name:"constant_folding@_174513116865440940351",persistable:[true],stop_gradient:[false]} : () -> tensor + (%12) = "builtin.constant" [id:1872] () {persistable:[true],value:"constant_folding@_174513116864033493350"} : () -> tensor<1xf32> + (%13) = "builtin.constant" [id:1863] () {persistable:[true],value:"constant_folding@_174513116861634597449"} : () -> tensor + (%14) = "builtin.parameter" [id:1854] () {parameter_name:"constant_folding@_174513116859911589448",persistable:[true],stop_gradient:[false]} : () -> tensor<1x256x1x1xf32> + (%15) = "builtin.constant" [id:1832] () {persistable:[true],value:"constant_folding@_174513116857122500446"} : () -> tensor<0xi64> + (%16) = "builtin.parameter" [id:1823] () {parameter_name:"constant_folding@_174513116855594796545",persistable:[true],stop_gradient:[false]} : () -> tensor<1x1x1xf32> + (%17) = "builtin.constant" [id:1814] () {persistable:[true],value:"constant_folding@_174513116853662943544"} : () -> tensor + (%18) = "builtin.constant" [id:1805] () {persistable:[true],value:"constant_folding@_174513116852268476543"} : () -> tensor<1xf32> + (%19) = "builtin.constant" [id:1796] () {persistable:[true],value:"constant_folding@_174513116850011146642"} : () -> tensor<1xf32> + (%20) = "builtin.constant" [id:1784] () {persistable:[true],value:"constant_folding@_174513116844224746641"} : () -> tensor + (%21) = "builtin.constant" [id:1771] () {persistable:[true],value:"constant_folding@_174513116842705804740"} : () -> tensor + (%22) = "builtin.constant" [id:1758] () {persistable:[true],value:"constant_folding@_174513116841278885739"} : () -> tensor<0xi64> + (%23) = "builtin.constant" [id:1749] () {persistable:[true],value:"constant_folding@_174513116839857308738"} : () -> tensor<1xi32> + (%24) = "builtin.constant" [id:1740] () {persistable:[true],value:"constant_folding@_174513116838085921737"} : () -> tensor<1xf32> + (%25) = "builtin.parameter" [id:1731] () {parameter_name:"constant_folding@_174513116836671179736",persistable:[true],stop_gradient:[false]} : () -> tensor + (%26) = "builtin.constant" [id:1722] () {persistable:[true],value:"constant_folding@_174513116835231166835"} : () -> tensor<1xf32> + (%27) = "builtin.parameter" [id:1713] () {parameter_name:"constant_folding@_174513116833656139834",persistable:[true],stop_gradient:[false]} : () -> tensor<1xi32> + (%28) = "builtin.parameter" [id:1693] () {parameter_name:"constant_folding@_174513116830651898832",persistable:[true],stop_gradient:[false]} : () -> tensor<1x6xf32> + (%29) = "builtin.parameter" [id:1673] () {parameter_name:"constant_folding@_174513116827593559930",persistable:[true],stop_gradient:[false]} : () -> tensor + (%30) = "builtin.constant" [id:1664] () {persistable:[true],value:"constant_folding@_174513116825929047929"} : () -> tensor + (%31) = "builtin.constant" [id:1655] () {persistable:[true],value:"constant_folding@_174513116824495087928"} : () -> tensor + (%32) = "builtin.constant" [id:1646] () {persistable:[true],value:"constant_folding@_174513116823069183927"} : () -> tensor<1xf32> + (%33) = "builtin.constant" [id:1637] () {persistable:[true],value:"constant_folding@_174513116821619066926"} : () -> tensor<1xf32> + (%34) = "builtin.constant" [id:1628] () {persistable:[true],value:"constant_folding@_174513116820193818025"} : () -> tensor<1xf32> + (%35) = "builtin.constant" [id:1619] () {persistable:[true],value:"constant_folding@_174513116818740431024"} : () -> tensor<1xf32> + (%36) = "builtin.constant" [id:1610] () {persistable:[true],value:"constant_folding@_174513116817322536023"} : () -> tensor<1xi64> + (%37) = "builtin.constant" [id:1601] () {persistable:[true],value:"constant_folding@_174513116815886735022"} : () -> tensor<1xf32> + (%38) = "builtin.constant" [id:1592] () {persistable:[true],value:"constant_folding@_174513116814471704121"} : () -> tensor<2xi64> + (%39) = "builtin.constant" [id:1583] () {persistable:[true],value:"constant_folding@_174513116813053746120"} : () -> tensor<2xi64> + (%40) = "builtin.parameter" [id:1574] () {parameter_name:"constant_folding@_174513116811615103119",persistable:[true],stop_gradient:[false]} : () -> tensor + (%41) = "builtin.constant" [id:1565] () {persistable:[true],value:"constant_folding@_174513115258237754718"} : () -> tensor<1xi64> + (%42) = "builtin.constant" [id:1556] () {persistable:[true],value:"constant_folding@_174513115176548053017"} : () -> tensor<1xi64> + (%43) = "builtin.constant" [id:1547] () {persistable:[true],value:"constant_folding@_174513115110199844916"} : () -> tensor<1xi64> + (%44) = "builtin.constant" [id:1538] () {persistable:[true],value:"constant_folding@_174513115013851471515"} : () -> tensor<1xi64> + (%45) = "builtin.constant" [id:1529] () {persistable:[true],value:"constant_folding@_174513114941937302314"} : () -> tensor<2xi64> + (%46) = "builtin.parameter" [id:1520] () {parameter_name:"constant_folding@_174513114857650263913",persistable:[true],stop_gradient:[false]} : () -> tensor<1x15x4xf32> + (%47) = "builtin.constant" [id:1498] () {persistable:[true],value:"constant_folding@_174513114736420657211"} : () -> tensor<3xi64> + (%48) = "builtin.constant" [id:1489] () {persistable:[true],value:"constant_folding@_174513114667774590410"} : () -> tensor<1xi64> + (%49) = "builtin.constant" [id:1480] () {persistable:[true],value:"constant_folding@_17451311460158745859"} : () -> tensor<1xf32> + (%50) = "builtin.constant" [id:1471] () {persistable:[true],value:"constant_folding@_17451311452954254518"} : () -> tensor<1xi64> + (%51) = "builtin.constant" [id:1462] () {persistable:[true],value:"constant_folding@_17451311445836590377"} : () -> tensor<1xi64> + (%52) = "builtin.constant" [id:1453] () {persistable:[true],value:"constant_folding@_17451311436834671136"} : () -> tensor<1xi64> + (%53) = "builtin.parameter" [id:1444] () {parameter_name:"constant_folding@_17451311430626338225",persistable:[true],stop_gradient:[false]} : () -> tensor<1x60x1x1xf32> + (%54) = "builtin.parameter" [id:1431] () {parameter_name:"constant_folding@_17451311422710820934",persistable:[true],stop_gradient:[false]} : () -> tensor<1x15x1x1xf32> + (%55) = "builtin.parameter" [id:1418] () {parameter_name:"constant_folding@_17451311415972965563",persistable:[true],stop_gradient:[false]} : () -> tensor<1x1024x1x1xf32> + (%56) = "builtin.parameter" [id:1405] () {parameter_name:"constant_folding@_17451311399980650772",persistable:[true],stop_gradient:[false]} : () -> tensor<1x80x1x1xf32> + (%57) = "builtin.constant" [id:1383] () {persistable:[true],value:"constant_folding@_17451311284029886470"} : () -> tensor<2xi64> + (%58) = "builtin.parameter" [id:8] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_56.w_0_deepcopy_280",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<80x256x1x1xf32> + (%59) = "builtin.parameter" [id:10] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_transpose_0.w_0_deepcopy_278",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<2048x256x2x2xf32> + (%60) = "builtin.parameter" [id:11] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"linear_1.b_0_deepcopy_277",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<320xf32> + (%61) = "builtin.parameter" [id:12] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"linear_1.w_0_deepcopy_276",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<2048x320xf32> + (%62) = "builtin.parameter" [id:13] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"linear_0.b_0_deepcopy_275",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<81xf32> + (%63) = "builtin.parameter" [id:14] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"linear_0.w_0_deepcopy_274",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<2048x81xf32> + (%64) = "builtin.parameter" [id:15] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_52.w_2_deepcopy_273",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<2048xf32> + (%65) = "builtin.parameter" [id:16] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_52.w_1_deepcopy_272",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<2048xf32> + (%66) = "builtin.parameter" [id:17] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_52.b_0_deepcopy_271",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<2048xf32> + (%67) = "builtin.parameter" [id:18] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_52.w_0_deepcopy_270",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<2048xf32> + (%68) = "builtin.parameter" [id:19] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_55.w_0_deepcopy_269",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<2048x512x1x1xf32> + (%69) = "builtin.parameter" [id:20] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_51.w_2_deepcopy_268",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%70) = "builtin.parameter" [id:21] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_51.w_1_deepcopy_267",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%71) = "builtin.parameter" [id:22] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_51.b_0_deepcopy_266",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%72) = "builtin.parameter" [id:23] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_51.w_0_deepcopy_265",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%73) = "builtin.parameter" [id:24] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_54.w_0_deepcopy_264",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<512x512x3x3xf32> + (%74) = "builtin.parameter" [id:25] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_50.w_2_deepcopy_263",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%75) = "builtin.parameter" [id:26] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_50.w_1_deepcopy_262",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%76) = "builtin.parameter" [id:27] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_50.b_0_deepcopy_261",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%77) = "builtin.parameter" [id:28] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_50.w_0_deepcopy_260",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%78) = "builtin.parameter" [id:29] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_53.w_0_deepcopy_259",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<512x2048x1x1xf32> + (%79) = "builtin.parameter" [id:30] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_49.w_2_deepcopy_258",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<2048xf32> + (%80) = "builtin.parameter" [id:31] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_49.w_1_deepcopy_257",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<2048xf32> + (%81) = "builtin.parameter" [id:32] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_49.b_0_deepcopy_256",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<2048xf32> + (%82) = "builtin.parameter" [id:33] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_49.w_0_deepcopy_255",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<2048xf32> + (%83) = "builtin.parameter" [id:34] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_52.w_0_deepcopy_254",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<2048x512x1x1xf32> + (%84) = "builtin.parameter" [id:35] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_48.w_2_deepcopy_253",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%85) = "builtin.parameter" [id:36] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_48.w_1_deepcopy_252",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%86) = "builtin.parameter" [id:37] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_48.b_0_deepcopy_251",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%87) = "builtin.parameter" [id:38] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_48.w_0_deepcopy_250",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%88) = "builtin.parameter" [id:39] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_51.w_0_deepcopy_249",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<512x512x3x3xf32> + (%89) = "builtin.parameter" [id:40] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_47.w_2_deepcopy_248",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%90) = "builtin.parameter" [id:41] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_47.w_1_deepcopy_247",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%91) = "builtin.parameter" [id:42] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_47.b_0_deepcopy_246",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%92) = "builtin.parameter" [id:43] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_47.w_0_deepcopy_245",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%93) = "builtin.parameter" [id:44] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_50.w_0_deepcopy_244",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<512x2048x1x1xf32> + (%94) = "builtin.parameter" [id:45] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_46.w_2_deepcopy_243",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<2048xf32> + (%95) = "builtin.parameter" [id:46] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_46.w_1_deepcopy_242",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<2048xf32> + (%96) = "builtin.parameter" [id:47] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_46.b_0_deepcopy_241",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<2048xf32> + (%97) = "builtin.parameter" [id:48] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_46.w_0_deepcopy_240",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<2048xf32> + (%98) = "builtin.parameter" [id:49] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_49.w_0_deepcopy_239",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<2048x1024x1x1xf32> + (%99) = "builtin.parameter" [id:50] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_45.w_2_deepcopy_238",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<2048xf32> + (%100) = "builtin.parameter" [id:51] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_45.w_1_deepcopy_237",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<2048xf32> + (%101) = "builtin.parameter" [id:52] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_45.b_0_deepcopy_236",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<2048xf32> + (%102) = "builtin.parameter" [id:53] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_45.w_0_deepcopy_235",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<2048xf32> + (%103) = "builtin.parameter" [id:54] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_48.w_0_deepcopy_234",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<2048x512x1x1xf32> + (%104) = "builtin.parameter" [id:55] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_44.w_2_deepcopy_233",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%105) = "builtin.parameter" [id:56] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_44.w_1_deepcopy_232",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%106) = "builtin.parameter" [id:57] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_44.b_0_deepcopy_231",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%107) = "builtin.parameter" [id:58] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_44.w_0_deepcopy_230",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%108) = "builtin.parameter" [id:59] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_47.w_0_deepcopy_229",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<512x512x3x3xf32> + (%109) = "builtin.parameter" [id:60] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_43.w_2_deepcopy_228",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%110) = "builtin.parameter" [id:61] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_43.w_1_deepcopy_227",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%111) = "builtin.parameter" [id:62] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_43.b_0_deepcopy_226",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%112) = "builtin.parameter" [id:63] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_43.w_0_deepcopy_225",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%113) = "builtin.parameter" [id:64] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_46.w_0_deepcopy_224",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<512x1024x1x1xf32> + (%114) = "builtin.parameter" [id:66] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_45.w_0_deepcopy_222",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<60x1024x1x1xf32> + (%115) = "builtin.parameter" [id:68] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_44.w_0_deepcopy_220",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<15x1024x1x1xf32> + (%116) = "builtin.parameter" [id:70] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_43.w_0_deepcopy_218",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<1024x1024x3x3xf32> + (%117) = "builtin.parameter" [id:71] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_42.w_2_deepcopy_216",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%118) = "builtin.parameter" [id:72] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_42.w_1_deepcopy_215",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%119) = "builtin.parameter" [id:73] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_42.b_0_deepcopy_214",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%120) = "builtin.parameter" [id:74] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_42.w_0_deepcopy_213",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%121) = "builtin.parameter" [id:75] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_42.w_0_deepcopy_212",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<1024x256x1x1xf32> + (%122) = "builtin.parameter" [id:76] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_41.w_2_deepcopy_211",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%123) = "builtin.parameter" [id:77] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_41.w_1_deepcopy_210",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%124) = "builtin.parameter" [id:78] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_41.b_0_deepcopy_209",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%125) = "builtin.parameter" [id:79] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_41.w_0_deepcopy_208",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%126) = "builtin.parameter" [id:80] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_41.w_0_deepcopy_207",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<256x256x3x3xf32> + (%127) = "builtin.parameter" [id:81] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_40.w_2_deepcopy_206",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%128) = "builtin.parameter" [id:82] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_40.w_1_deepcopy_205",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%129) = "builtin.parameter" [id:83] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_40.b_0_deepcopy_204",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%130) = "builtin.parameter" [id:84] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_40.w_0_deepcopy_203",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%131) = "builtin.parameter" [id:85] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_40.w_0_deepcopy_202",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<256x1024x1x1xf32> + (%132) = "builtin.parameter" [id:86] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_39.w_2_deepcopy_201",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%133) = "builtin.parameter" [id:87] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_39.w_1_deepcopy_200",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%134) = "builtin.parameter" [id:88] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_39.b_0_deepcopy_199",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%135) = "builtin.parameter" [id:89] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_39.w_0_deepcopy_198",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%136) = "builtin.parameter" [id:90] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_39.w_0_deepcopy_197",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<1024x256x1x1xf32> + (%137) = "builtin.parameter" [id:91] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_38.w_2_deepcopy_196",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%138) = "builtin.parameter" [id:92] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_38.w_1_deepcopy_195",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%139) = "builtin.parameter" [id:93] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_38.b_0_deepcopy_194",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%140) = "builtin.parameter" [id:94] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_38.w_0_deepcopy_193",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%141) = "builtin.parameter" [id:95] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_38.w_0_deepcopy_192",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<256x256x3x3xf32> + (%142) = "builtin.parameter" [id:96] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_37.w_2_deepcopy_191",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%143) = "builtin.parameter" [id:97] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_37.w_1_deepcopy_190",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%144) = "builtin.parameter" [id:98] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_37.b_0_deepcopy_189",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%145) = "builtin.parameter" [id:99] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_37.w_0_deepcopy_188",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%146) = "builtin.parameter" [id:100] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_37.w_0_deepcopy_187",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<256x1024x1x1xf32> + (%147) = "builtin.parameter" [id:101] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_36.w_2_deepcopy_186",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%148) = "builtin.parameter" [id:102] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_36.w_1_deepcopy_185",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%149) = "builtin.parameter" [id:103] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_36.b_0_deepcopy_184",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%150) = "builtin.parameter" [id:104] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_36.w_0_deepcopy_183",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%151) = "builtin.parameter" [id:105] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_36.w_0_deepcopy_182",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<1024x256x1x1xf32> + (%152) = "builtin.parameter" [id:106] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_35.w_2_deepcopy_181",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%153) = "builtin.parameter" [id:107] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_35.w_1_deepcopy_180",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%154) = "builtin.parameter" [id:108] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_35.b_0_deepcopy_179",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%155) = "builtin.parameter" [id:109] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_35.w_0_deepcopy_178",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%156) = "builtin.parameter" [id:110] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_35.w_0_deepcopy_177",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<256x256x3x3xf32> + (%157) = "builtin.parameter" [id:111] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_34.w_2_deepcopy_176",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%158) = "builtin.parameter" [id:112] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_34.w_1_deepcopy_175",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%159) = "builtin.parameter" [id:113] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_34.b_0_deepcopy_174",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%160) = "builtin.parameter" [id:114] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_34.w_0_deepcopy_173",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%161) = "builtin.parameter" [id:115] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_34.w_0_deepcopy_172",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<256x1024x1x1xf32> + (%162) = "builtin.parameter" [id:116] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_33.w_2_deepcopy_171",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%163) = "builtin.parameter" [id:117] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_33.w_1_deepcopy_170",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%164) = "builtin.parameter" [id:118] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_33.b_0_deepcopy_169",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%165) = "builtin.parameter" [id:119] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_33.w_0_deepcopy_168",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%166) = "builtin.parameter" [id:120] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_33.w_0_deepcopy_167",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<1024x256x1x1xf32> + (%167) = "builtin.parameter" [id:121] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_32.w_2_deepcopy_166",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%168) = "builtin.parameter" [id:122] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_32.w_1_deepcopy_165",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%169) = "builtin.parameter" [id:123] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_32.b_0_deepcopy_164",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%170) = "builtin.parameter" [id:124] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_32.w_0_deepcopy_163",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%171) = "builtin.parameter" [id:125] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_32.w_0_deepcopy_162",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<256x256x3x3xf32> + (%172) = "builtin.parameter" [id:126] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_31.w_2_deepcopy_161",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%173) = "builtin.parameter" [id:127] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_31.w_1_deepcopy_160",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%174) = "builtin.parameter" [id:128] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_31.b_0_deepcopy_159",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%175) = "builtin.parameter" [id:129] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_31.w_0_deepcopy_158",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%176) = "builtin.parameter" [id:130] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_31.w_0_deepcopy_157",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<256x1024x1x1xf32> + (%177) = "builtin.parameter" [id:131] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_30.w_2_deepcopy_156",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%178) = "builtin.parameter" [id:132] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_30.w_1_deepcopy_155",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%179) = "builtin.parameter" [id:133] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_30.b_0_deepcopy_154",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%180) = "builtin.parameter" [id:134] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_30.w_0_deepcopy_153",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%181) = "builtin.parameter" [id:135] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_30.w_0_deepcopy_152",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<1024x256x1x1xf32> + (%182) = "builtin.parameter" [id:136] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_29.w_2_deepcopy_151",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%183) = "builtin.parameter" [id:137] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_29.w_1_deepcopy_150",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%184) = "builtin.parameter" [id:138] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_29.b_0_deepcopy_149",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%185) = "builtin.parameter" [id:139] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_29.w_0_deepcopy_148",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%186) = "builtin.parameter" [id:140] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_29.w_0_deepcopy_147",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<256x256x3x3xf32> + (%187) = "builtin.parameter" [id:141] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_28.w_2_deepcopy_146",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%188) = "builtin.parameter" [id:142] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_28.w_1_deepcopy_145",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%189) = "builtin.parameter" [id:143] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_28.b_0_deepcopy_144",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%190) = "builtin.parameter" [id:144] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_28.w_0_deepcopy_143",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%191) = "builtin.parameter" [id:145] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_28.w_0_deepcopy_142",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<256x1024x1x1xf32> + (%192) = "builtin.parameter" [id:146] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_27.w_2_deepcopy_141",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%193) = "builtin.parameter" [id:147] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_27.w_1_deepcopy_140",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%194) = "builtin.parameter" [id:148] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_27.b_0_deepcopy_139",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%195) = "builtin.parameter" [id:149] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_27.w_0_deepcopy_138",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%196) = "builtin.parameter" [id:150] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_27.w_0_deepcopy_137",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<1024x512x1x1xf32> + (%197) = "builtin.parameter" [id:151] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_26.w_2_deepcopy_136",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%198) = "builtin.parameter" [id:152] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_26.w_1_deepcopy_135",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%199) = "builtin.parameter" [id:153] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_26.b_0_deepcopy_134",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%200) = "builtin.parameter" [id:154] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_26.w_0_deepcopy_133",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<1024xf32> + (%201) = "builtin.parameter" [id:155] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_26.w_0_deepcopy_132",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<1024x256x1x1xf32> + (%202) = "builtin.parameter" [id:156] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_25.w_2_deepcopy_131",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%203) = "builtin.parameter" [id:157] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_25.w_1_deepcopy_130",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%204) = "builtin.parameter" [id:158] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_25.b_0_deepcopy_129",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%205) = "builtin.parameter" [id:159] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_25.w_0_deepcopy_128",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%206) = "builtin.parameter" [id:160] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_25.w_0_deepcopy_127",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<256x256x3x3xf32> + (%207) = "builtin.parameter" [id:161] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_24.w_2_deepcopy_126",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%208) = "builtin.parameter" [id:162] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_24.w_1_deepcopy_125",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%209) = "builtin.parameter" [id:163] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_24.b_0_deepcopy_124",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%210) = "builtin.parameter" [id:164] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_24.w_0_deepcopy_123",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%211) = "builtin.parameter" [id:165] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_24.w_0_deepcopy_122",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<256x512x1x1xf32> + (%212) = "builtin.parameter" [id:166] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_23.w_2_deepcopy_121",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%213) = "builtin.parameter" [id:167] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_23.w_1_deepcopy_120",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%214) = "builtin.parameter" [id:168] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_23.b_0_deepcopy_119",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%215) = "builtin.parameter" [id:169] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_23.w_0_deepcopy_118",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%216) = "builtin.parameter" [id:170] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_23.w_0_deepcopy_117",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<512x128x1x1xf32> + (%217) = "builtin.parameter" [id:171] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_22.w_2_deepcopy_116",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%218) = "builtin.parameter" [id:172] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_22.w_1_deepcopy_115",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%219) = "builtin.parameter" [id:173] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_22.b_0_deepcopy_114",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%220) = "builtin.parameter" [id:174] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_22.w_0_deepcopy_113",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%221) = "builtin.parameter" [id:175] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_22.w_0_deepcopy_112",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<128x128x3x3xf32> + (%222) = "builtin.parameter" [id:176] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_21.w_2_deepcopy_111",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%223) = "builtin.parameter" [id:177] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_21.w_1_deepcopy_110",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%224) = "builtin.parameter" [id:178] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_21.b_0_deepcopy_109",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%225) = "builtin.parameter" [id:179] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_21.w_0_deepcopy_108",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%226) = "builtin.parameter" [id:180] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_21.w_0_deepcopy_107",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<128x512x1x1xf32> + (%227) = "builtin.parameter" [id:181] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_20.w_2_deepcopy_106",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%228) = "builtin.parameter" [id:182] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_20.w_1_deepcopy_105",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%229) = "builtin.parameter" [id:183] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_20.b_0_deepcopy_104",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%230) = "builtin.parameter" [id:184] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_20.w_0_deepcopy_103",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%231) = "builtin.parameter" [id:185] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_20.w_0_deepcopy_102",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<512x128x1x1xf32> + (%232) = "builtin.parameter" [id:186] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_19.w_2_deepcopy_101",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%233) = "builtin.parameter" [id:187] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_19.w_1_deepcopy_100",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%234) = "builtin.parameter" [id:188] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_19.b_0_deepcopy_99",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%235) = "builtin.parameter" [id:189] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_19.w_0_deepcopy_98",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%236) = "builtin.parameter" [id:190] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_19.w_0_deepcopy_97",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<128x128x3x3xf32> + (%237) = "builtin.parameter" [id:191] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_18.w_2_deepcopy_96",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%238) = "builtin.parameter" [id:192] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_18.w_1_deepcopy_95",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%239) = "builtin.parameter" [id:193] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_18.b_0_deepcopy_94",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%240) = "builtin.parameter" [id:194] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_18.w_0_deepcopy_93",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%241) = "builtin.parameter" [id:195] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_18.w_0_deepcopy_92",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<128x512x1x1xf32> + (%242) = "builtin.parameter" [id:196] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_17.w_2_deepcopy_91",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%243) = "builtin.parameter" [id:197] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_17.w_1_deepcopy_90",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%244) = "builtin.parameter" [id:198] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_17.b_0_deepcopy_89",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%245) = "builtin.parameter" [id:199] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_17.w_0_deepcopy_88",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%246) = "builtin.parameter" [id:200] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_17.w_0_deepcopy_87",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<512x128x1x1xf32> + (%247) = "builtin.parameter" [id:201] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_16.w_2_deepcopy_86",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%248) = "builtin.parameter" [id:202] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_16.w_1_deepcopy_85",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%249) = "builtin.parameter" [id:203] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_16.b_0_deepcopy_84",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%250) = "builtin.parameter" [id:204] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_16.w_0_deepcopy_83",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%251) = "builtin.parameter" [id:205] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_16.w_0_deepcopy_82",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<128x128x3x3xf32> + (%252) = "builtin.parameter" [id:206] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_15.w_2_deepcopy_81",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%253) = "builtin.parameter" [id:207] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_15.w_1_deepcopy_80",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%254) = "builtin.parameter" [id:208] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_15.b_0_deepcopy_79",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%255) = "builtin.parameter" [id:209] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_15.w_0_deepcopy_78",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%256) = "builtin.parameter" [id:210] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_15.w_0_deepcopy_77",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<128x512x1x1xf32> + (%257) = "builtin.parameter" [id:211] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_14.w_2_deepcopy_76",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%258) = "builtin.parameter" [id:212] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_14.w_1_deepcopy_75",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%259) = "builtin.parameter" [id:213] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_14.b_0_deepcopy_74",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%260) = "builtin.parameter" [id:214] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_14.w_0_deepcopy_73",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%261) = "builtin.parameter" [id:215] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_14.w_0_deepcopy_72",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<512x256x1x1xf32> + (%262) = "builtin.parameter" [id:216] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_13.w_2_deepcopy_71",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%263) = "builtin.parameter" [id:217] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_13.w_1_deepcopy_70",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%264) = "builtin.parameter" [id:218] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_13.b_0_deepcopy_69",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%265) = "builtin.parameter" [id:219] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_13.w_0_deepcopy_68",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<512xf32> + (%266) = "builtin.parameter" [id:220] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_13.w_0_deepcopy_67",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<512x128x1x1xf32> + (%267) = "builtin.parameter" [id:221] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_12.w_2_deepcopy_66",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%268) = "builtin.parameter" [id:222] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_12.w_1_deepcopy_65",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%269) = "builtin.parameter" [id:223] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_12.b_0_deepcopy_64",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%270) = "builtin.parameter" [id:224] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_12.w_0_deepcopy_63",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%271) = "builtin.parameter" [id:225] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_12.w_0_deepcopy_62",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<128x128x3x3xf32> + (%272) = "builtin.parameter" [id:226] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_11.w_2_deepcopy_61",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%273) = "builtin.parameter" [id:227] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_11.w_1_deepcopy_60",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%274) = "builtin.parameter" [id:228] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_11.b_0_deepcopy_59",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%275) = "builtin.parameter" [id:229] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_11.w_0_deepcopy_58",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<128xf32> + (%276) = "builtin.parameter" [id:230] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_11.w_0_deepcopy_57",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<128x256x1x1xf32> + (%277) = "builtin.parameter" [id:231] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_10.w_2_deepcopy_56",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%278) = "builtin.parameter" [id:232] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_10.w_1_deepcopy_55",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%279) = "builtin.parameter" [id:233] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_10.b_0_deepcopy_54",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%280) = "builtin.parameter" [id:234] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_10.w_0_deepcopy_53",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%281) = "builtin.parameter" [id:235] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_10.w_0_deepcopy_52",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256x64x1x1xf32> + (%282) = "builtin.parameter" [id:236] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_9.w_2_deepcopy_51",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%283) = "builtin.parameter" [id:237] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_9.w_1_deepcopy_50",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%284) = "builtin.parameter" [id:238] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_9.b_0_deepcopy_49",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%285) = "builtin.parameter" [id:239] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_9.w_0_deepcopy_48",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%286) = "builtin.parameter" [id:240] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_9.w_0_deepcopy_47",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64x64x3x3xf32> + (%287) = "builtin.parameter" [id:241] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_8.w_2_deepcopy_46",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%288) = "builtin.parameter" [id:242] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_8.w_1_deepcopy_45",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%289) = "builtin.parameter" [id:243] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_8.b_0_deepcopy_44",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%290) = "builtin.parameter" [id:244] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_8.w_0_deepcopy_43",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%291) = "builtin.parameter" [id:245] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_8.w_0_deepcopy_42",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64x256x1x1xf32> + (%292) = "builtin.parameter" [id:246] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_7.w_2_deepcopy_41",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%293) = "builtin.parameter" [id:247] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_7.w_1_deepcopy_40",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%294) = "builtin.parameter" [id:248] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_7.b_0_deepcopy_39",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%295) = "builtin.parameter" [id:249] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_7.w_0_deepcopy_38",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%296) = "builtin.parameter" [id:250] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_7.w_0_deepcopy_37",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256x64x1x1xf32> + (%297) = "builtin.parameter" [id:251] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_6.w_2_deepcopy_36",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%298) = "builtin.parameter" [id:252] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_6.w_1_deepcopy_35",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%299) = "builtin.parameter" [id:253] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_6.b_0_deepcopy_34",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%300) = "builtin.parameter" [id:254] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_6.w_0_deepcopy_33",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%301) = "builtin.parameter" [id:255] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_6.w_0_deepcopy_32",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64x64x3x3xf32> + (%302) = "builtin.parameter" [id:256] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_5.w_2_deepcopy_31",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%303) = "builtin.parameter" [id:257] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_5.w_1_deepcopy_30",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%304) = "builtin.parameter" [id:258] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_5.b_0_deepcopy_29",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%305) = "builtin.parameter" [id:259] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_5.w_0_deepcopy_28",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%306) = "builtin.parameter" [id:260] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_5.w_0_deepcopy_27",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64x256x1x1xf32> + (%307) = "builtin.parameter" [id:261] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_4.w_2_deepcopy_26",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%308) = "builtin.parameter" [id:262] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_4.w_1_deepcopy_25",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%309) = "builtin.parameter" [id:263] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_4.b_0_deepcopy_24",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%310) = "builtin.parameter" [id:264] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_4.w_0_deepcopy_23",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%311) = "builtin.parameter" [id:265] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_4.w_0_deepcopy_22",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256x64x1x1xf32> + (%312) = "builtin.parameter" [id:266] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_3.w_2_deepcopy_21",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%313) = "builtin.parameter" [id:267] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_3.w_1_deepcopy_20",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%314) = "builtin.parameter" [id:268] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_3.b_0_deepcopy_19",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%315) = "builtin.parameter" [id:269] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_3.w_0_deepcopy_18",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256xf32> + (%316) = "builtin.parameter" [id:270] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_3.w_0_deepcopy_17",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<256x64x1x1xf32> + (%317) = "builtin.parameter" [id:271] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_2.w_2_deepcopy_16",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%318) = "builtin.parameter" [id:272] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_2.w_1_deepcopy_15",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%319) = "builtin.parameter" [id:273] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_2.b_0_deepcopy_14",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%320) = "builtin.parameter" [id:274] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_2.w_0_deepcopy_13",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%321) = "builtin.parameter" [id:275] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_2.w_0_deepcopy_12",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64x64x3x3xf32> + (%322) = "builtin.parameter" [id:276] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_1.w_2_deepcopy_11",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%323) = "builtin.parameter" [id:277] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_1.w_1_deepcopy_10",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%324) = "builtin.parameter" [id:278] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_1.b_0_deepcopy_9",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%325) = "builtin.parameter" [id:279] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_1.w_0_deepcopy_8",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%326) = "builtin.parameter" [id:280] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_1.w_0_deepcopy_7",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64x64x1x1xf32> + (%327) = "builtin.parameter" [id:281] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_0.w_2_deepcopy_6",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%328) = "builtin.parameter" [id:282] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_0.w_1_deepcopy_5",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%329) = "builtin.parameter" [id:283] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_0.b_0_deepcopy_4",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%330) = "builtin.parameter" [id:284] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"batch_norm2d_0.w_0_deepcopy_3",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64xf32> + (%331) = "builtin.parameter" [id:285] () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:"conv2d_0.w_0_deepcopy_2",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> tensor<64x3x7x7xf32> + (%332) = "pd_op.data" [id:286] () {dtype:float32,name:"im_shape",place:Place(undefined:0),shape:[-1,2],stop_gradient:[false]} : () -> tensor<-1x2xf32> + (%333) = "pd_op.data" [id:287] () {dtype:float32,name:"image",place:Place(undefined:0),shape:[-1,3,-1,-1],stop_gradient:[false]} : () -> tensor<-1x3x-1x-1xf32> + (%334) = "pd_op.data" [id:288] () {dtype:float32,name:"scale_factor",place:Place(undefined:0),shape:[-1,2],stop_gradient:[false]} : () -> tensor<-1x2xf32> + (%335) = "pd_op.conv2d" [id:289] (%333, %331) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[3,3],stop_gradient:[false],strides:[2,2],struct_name:"/ResNet/Sequential/ConvNormLayer/Conv2D/"} : (tensor<-1x3x-1x-1xf32>, tensor<64x3x7x7xf32>) -> tensor<-1x64x-1x-1xf32> + (%336, %337, %338, %339, %340, %341) = "pd_op.batch_norm_" [id:290] (%335, %328, %327, %330, %329) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Sequential/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x64x-1x-1xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>) -> tensor<-1x64x-1x-1xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<-1xu8> + (%342) = "pd_op.relu" [id:291] (%336) {stop_gradient:[false],struct_name:"/ResNet/Sequential/ConvNormLayer/"} : (tensor<-1x64x-1x-1xf32>) -> tensor<-1x64x-1x-1xf32> + (%343) = "pd_op.transpose" [id:1373] (%342) {perm:[0,2,3,1],source:"transfer_layout_pass",stop_gradient:[false]} : (tensor<-1x64x-1x-1xf32>) -> tensor<-1x-1x-1x64xf32> + (%344) = "pd_op.pool2d" [id:293] (%343, %57) {adaptive:false,ceil_mode:false,data_format:"NHWC",exclusive:true,global_pooling:false,padding_algorithm:"EXPLICIT",paddings:[1,1],pooling_type:"max",stop_gradient:[false],strides:[2,2],struct_name:"/ResNet/"} : (tensor<-1x-1x-1x64xf32>, tensor<2xi64>) -> tensor<-1x-1x-1x64xf32> + (%345) = "pd_op.transpose" [id:1374] (%344) {perm:[0,3,1,2],source:"transfer_layout_pass",stop_gradient:[false]} : (tensor<-1x-1x-1x64xf32>) -> tensor<-1x64x-1x-1xf32> + (%346) = "pd_op.conv2d" [id:294] (%345, %326) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer/Conv2D/"} : (tensor<-1x64x-1x-1xf32>, tensor<64x64x1x1xf32>) -> tensor<-1x64x-1x-1xf32> + (%347, %348, %349, %350, %351, %352) = "pd_op.batch_norm_" [id:295] (%346, %323, %322, %325, %324) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x64x-1x-1xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>) -> tensor<-1x64x-1x-1xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<-1xu8> + (%353) = "pd_op.relu" [id:296] (%347) {stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer/"} : (tensor<-1x64x-1x-1xf32>) -> tensor<-1x64x-1x-1xf32> + (%354) = "pd_op.conv2d" [id:297] (%353, %321) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer_1/Conv2D/"} : (tensor<-1x64x-1x-1xf32>, tensor<64x64x3x3xf32>) -> tensor<-1x64x-1x-1xf32> + (%355, %356, %357, %358, %359, %360) = "pd_op.batch_norm_" [id:298] (%354, %318, %317, %320, %319) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x64x-1x-1xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>) -> tensor<-1x64x-1x-1xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<-1xu8> + (%361) = "pd_op.relu" [id:299] (%355) {stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer_1/"} : (tensor<-1x64x-1x-1xf32>) -> tensor<-1x64x-1x-1xf32> + (%362) = "pd_op.conv2d" [id:300] (%361, %316) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer_2/Conv2D/"} : (tensor<-1x64x-1x-1xf32>, tensor<256x64x1x1xf32>) -> tensor<-1x256x-1x-1xf32> + (%363, %364, %365, %366, %367, %368) = "pd_op.batch_norm_" [id:301] (%362, %313, %312, %315, %314) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<-1xu8> + (%369) = "pd_op.conv2d" [id:302] (%345, %311) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer_3/Conv2D/"} : (tensor<-1x64x-1x-1xf32>, tensor<256x64x1x1xf32>) -> tensor<-1x256x-1x-1xf32> + (%370, %371, %372, %373, %374, %375) = "pd_op.batch_norm_" [id:303] (%369, %308, %307, %310, %309) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer_3/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<-1xu8> + (%376) = "pd_op.add" [id:304] (%363, %370) {stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck/"} : (tensor<-1x256x-1x-1xf32>, tensor<-1x256x-1x-1xf32>) -> tensor<-1x256x-1x-1xf32> + (%377) = "pd_op.relu" [id:305] (%376) {stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck/"} : (tensor<-1x256x-1x-1xf32>) -> tensor<-1x256x-1x-1xf32> + (%378) = "pd_op.conv2d" [id:306] (%377, %306) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck_1/ConvNormLayer/Conv2D/"} : (tensor<-1x256x-1x-1xf32>, tensor<64x256x1x1xf32>) -> tensor<-1x64x-1x-1xf32> + (%379, %380, %381, %382, %383, %384) = "pd_op.batch_norm_" [id:307] (%378, %303, %302, %305, %304) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck_1/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x64x-1x-1xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>) -> tensor<-1x64x-1x-1xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<-1xu8> + (%385) = "pd_op.relu" [id:308] (%379) {stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck_1/ConvNormLayer/"} : (tensor<-1x64x-1x-1xf32>) -> tensor<-1x64x-1x-1xf32> + (%386) = "pd_op.conv2d" [id:309] (%385, %301) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck_1/ConvNormLayer_1/Conv2D/"} : (tensor<-1x64x-1x-1xf32>, tensor<64x64x3x3xf32>) -> tensor<-1x64x-1x-1xf32> + (%387, %388, %389, %390, %391, %392) = "pd_op.batch_norm_" [id:310] (%386, %298, %297, %300, %299) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck_1/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x64x-1x-1xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>) -> tensor<-1x64x-1x-1xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<-1xu8> + (%393) = "pd_op.relu" [id:311] (%387) {stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck_1/ConvNormLayer_1/"} : (tensor<-1x64x-1x-1xf32>) -> tensor<-1x64x-1x-1xf32> + (%394) = "pd_op.conv2d" [id:312] (%393, %296) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck_1/ConvNormLayer_2/Conv2D/"} : (tensor<-1x64x-1x-1xf32>, tensor<256x64x1x1xf32>) -> tensor<-1x256x-1x-1xf32> + (%395, %396, %397, %398, %399, %400) = "pd_op.batch_norm_" [id:313] (%394, %293, %292, %295, %294) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck_1/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<-1xu8> + (%401) = "pd_op.add" [id:314] (%395, %377) {stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck_1/"} : (tensor<-1x256x-1x-1xf32>, tensor<-1x256x-1x-1xf32>) -> tensor<-1x256x-1x-1xf32> + (%402) = "pd_op.relu" [id:315] (%401) {stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck_1/"} : (tensor<-1x256x-1x-1xf32>) -> tensor<-1x256x-1x-1xf32> + (%403) = "pd_op.conv2d" [id:316] (%402, %291) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck_2/ConvNormLayer/Conv2D/"} : (tensor<-1x256x-1x-1xf32>, tensor<64x256x1x1xf32>) -> tensor<-1x64x-1x-1xf32> + (%404, %405, %406, %407, %408, %409) = "pd_op.batch_norm_" [id:317] (%403, %288, %287, %290, %289) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck_2/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x64x-1x-1xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>) -> tensor<-1x64x-1x-1xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<-1xu8> + (%410) = "pd_op.relu" [id:318] (%404) {stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck_2/ConvNormLayer/"} : (tensor<-1x64x-1x-1xf32>) -> tensor<-1x64x-1x-1xf32> + (%411) = "pd_op.conv2d" [id:319] (%410, %286) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck_2/ConvNormLayer_1/Conv2D/"} : (tensor<-1x64x-1x-1xf32>, tensor<64x64x3x3xf32>) -> tensor<-1x64x-1x-1xf32> + (%412, %413, %414, %415, %416, %417) = "pd_op.batch_norm_" [id:320] (%411, %283, %282, %285, %284) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck_2/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x64x-1x-1xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>) -> tensor<-1x64x-1x-1xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<-1xu8> + (%418) = "pd_op.relu" [id:321] (%412) {stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck_2/ConvNormLayer_1/"} : (tensor<-1x64x-1x-1xf32>) -> tensor<-1x64x-1x-1xf32> + (%419) = "pd_op.conv2d" [id:322] (%418, %281) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck_2/ConvNormLayer_2/Conv2D/"} : (tensor<-1x64x-1x-1xf32>, tensor<256x64x1x1xf32>) -> tensor<-1x256x-1x-1xf32> + (%420, %421, %422, %423, %424, %425) = "pd_op.batch_norm_" [id:323] (%419, %278, %277, %280, %279) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck_2/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<-1xu8> + (%426) = "pd_op.add" [id:324] (%420, %402) {stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck_2/"} : (tensor<-1x256x-1x-1xf32>, tensor<-1x256x-1x-1xf32>) -> tensor<-1x256x-1x-1xf32> + (%427) = "pd_op.relu" [id:325] (%426) {stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck_2/"} : (tensor<-1x256x-1x-1xf32>) -> tensor<-1x256x-1x-1xf32> + (%428) = "pd_op.conv2d" [id:326] (%427, %276) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer/Conv2D/"} : (tensor<-1x256x-1x-1xf32>, tensor<128x256x1x1xf32>) -> tensor<-1x128x-1x-1xf32> + (%429, %430, %431, %432, %433, %434) = "pd_op.batch_norm_" [id:327] (%428, %273, %272, %275, %274) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x128x-1x-1xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>) -> tensor<-1x128x-1x-1xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<-1xu8> + (%435) = "pd_op.relu" [id:328] (%429) {stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer/"} : (tensor<-1x128x-1x-1xf32>) -> tensor<-1x128x-1x-1xf32> + (%436) = "pd_op.conv2d" [id:329] (%435, %271) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[2,2],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer_1/Conv2D/"} : (tensor<-1x128x-1x-1xf32>, tensor<128x128x3x3xf32>) -> tensor<-1x128x-1x-1xf32> + (%437, %438, %439, %440, %441, %442) = "pd_op.batch_norm_" [id:330] (%436, %268, %267, %270, %269) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x128x-1x-1xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>) -> tensor<-1x128x-1x-1xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<-1xu8> + (%443) = "pd_op.relu" [id:331] (%437) {stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer_1/"} : (tensor<-1x128x-1x-1xf32>) -> tensor<-1x128x-1x-1xf32> + (%444) = "pd_op.conv2d" [id:332] (%443, %266) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer_2/Conv2D/"} : (tensor<-1x128x-1x-1xf32>, tensor<512x128x1x1xf32>) -> tensor<-1x512x-1x-1xf32> + (%445, %446, %447, %448, %449, %450) = "pd_op.batch_norm_" [id:333] (%444, %263, %262, %265, %264) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x512x-1x-1xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<-1x512x-1x-1xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<-1xu8> + (%451) = "pd_op.conv2d" [id:334] (%427, %261) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[2,2],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer_3/Conv2D/"} : (tensor<-1x256x-1x-1xf32>, tensor<512x256x1x1xf32>) -> tensor<-1x512x-1x-1xf32> + (%452, %453, %454, %455, %456, %457) = "pd_op.batch_norm_" [id:335] (%451, %258, %257, %260, %259) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer_3/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x512x-1x-1xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<-1x512x-1x-1xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<-1xu8> + (%458) = "pd_op.add" [id:336] (%445, %452) {stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck/"} : (tensor<-1x512x-1x-1xf32>, tensor<-1x512x-1x-1xf32>) -> tensor<-1x512x-1x-1xf32> + (%459) = "pd_op.relu" [id:337] (%458) {stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck/"} : (tensor<-1x512x-1x-1xf32>) -> tensor<-1x512x-1x-1xf32> + (%460) = "pd_op.conv2d" [id:338] (%459, %256) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck_1/ConvNormLayer/Conv2D/"} : (tensor<-1x512x-1x-1xf32>, tensor<128x512x1x1xf32>) -> tensor<-1x128x-1x-1xf32> + (%461, %462, %463, %464, %465, %466) = "pd_op.batch_norm_" [id:339] (%460, %253, %252, %255, %254) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck_1/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x128x-1x-1xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>) -> tensor<-1x128x-1x-1xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<-1xu8> + (%467) = "pd_op.relu" [id:340] (%461) {stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_1/ConvNormLayer/"} : (tensor<-1x128x-1x-1xf32>) -> tensor<-1x128x-1x-1xf32> + (%468) = "pd_op.conv2d" [id:341] (%467, %251) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck_1/ConvNormLayer_1/Conv2D/"} : (tensor<-1x128x-1x-1xf32>, tensor<128x128x3x3xf32>) -> tensor<-1x128x-1x-1xf32> + (%469, %470, %471, %472, %473, %474) = "pd_op.batch_norm_" [id:342] (%468, %248, %247, %250, %249) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck_1/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x128x-1x-1xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>) -> tensor<-1x128x-1x-1xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<-1xu8> + (%475) = "pd_op.relu" [id:343] (%469) {stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_1/ConvNormLayer_1/"} : (tensor<-1x128x-1x-1xf32>) -> tensor<-1x128x-1x-1xf32> + (%476) = "pd_op.conv2d" [id:344] (%475, %246) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck_1/ConvNormLayer_2/Conv2D/"} : (tensor<-1x128x-1x-1xf32>, tensor<512x128x1x1xf32>) -> tensor<-1x512x-1x-1xf32> + (%477, %478, %479, %480, %481, %482) = "pd_op.batch_norm_" [id:345] (%476, %243, %242, %245, %244) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck_1/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x512x-1x-1xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<-1x512x-1x-1xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<-1xu8> + (%483) = "pd_op.add" [id:346] (%477, %459) {stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_1/"} : (tensor<-1x512x-1x-1xf32>, tensor<-1x512x-1x-1xf32>) -> tensor<-1x512x-1x-1xf32> + (%484) = "pd_op.relu" [id:347] (%483) {stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_1/"} : (tensor<-1x512x-1x-1xf32>) -> tensor<-1x512x-1x-1xf32> + (%485) = "pd_op.conv2d" [id:348] (%484, %241) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck_2/ConvNormLayer/Conv2D/"} : (tensor<-1x512x-1x-1xf32>, tensor<128x512x1x1xf32>) -> tensor<-1x128x-1x-1xf32> + (%486, %487, %488, %489, %490, %491) = "pd_op.batch_norm_" [id:349] (%485, %238, %237, %240, %239) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck_2/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x128x-1x-1xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>) -> tensor<-1x128x-1x-1xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<-1xu8> + (%492) = "pd_op.relu" [id:350] (%486) {stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_2/ConvNormLayer/"} : (tensor<-1x128x-1x-1xf32>) -> tensor<-1x128x-1x-1xf32> + (%493) = "pd_op.conv2d" [id:351] (%492, %236) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck_2/ConvNormLayer_1/Conv2D/"} : (tensor<-1x128x-1x-1xf32>, tensor<128x128x3x3xf32>) -> tensor<-1x128x-1x-1xf32> + (%494, %495, %496, %497, %498, %499) = "pd_op.batch_norm_" [id:352] (%493, %233, %232, %235, %234) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck_2/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x128x-1x-1xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>) -> tensor<-1x128x-1x-1xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<-1xu8> + (%500) = "pd_op.relu" [id:353] (%494) {stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_2/ConvNormLayer_1/"} : (tensor<-1x128x-1x-1xf32>) -> tensor<-1x128x-1x-1xf32> + (%501) = "pd_op.conv2d" [id:354] (%500, %231) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck_2/ConvNormLayer_2/Conv2D/"} : (tensor<-1x128x-1x-1xf32>, tensor<512x128x1x1xf32>) -> tensor<-1x512x-1x-1xf32> + (%502, %503, %504, %505, %506, %507) = "pd_op.batch_norm_" [id:355] (%501, %228, %227, %230, %229) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck_2/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x512x-1x-1xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<-1x512x-1x-1xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<-1xu8> + (%508) = "pd_op.add" [id:356] (%502, %484) {stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_2/"} : (tensor<-1x512x-1x-1xf32>, tensor<-1x512x-1x-1xf32>) -> tensor<-1x512x-1x-1xf32> + (%509) = "pd_op.relu" [id:357] (%508) {stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_2/"} : (tensor<-1x512x-1x-1xf32>) -> tensor<-1x512x-1x-1xf32> + (%510) = "pd_op.conv2d" [id:358] (%509, %226) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck_3/ConvNormLayer/Conv2D/"} : (tensor<-1x512x-1x-1xf32>, tensor<128x512x1x1xf32>) -> tensor<-1x128x-1x-1xf32> + (%511, %512, %513, %514, %515, %516) = "pd_op.batch_norm_" [id:359] (%510, %223, %222, %225, %224) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck_3/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x128x-1x-1xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>) -> tensor<-1x128x-1x-1xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<-1xu8> + (%517) = "pd_op.relu" [id:360] (%511) {stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_3/ConvNormLayer/"} : (tensor<-1x128x-1x-1xf32>) -> tensor<-1x128x-1x-1xf32> + (%518) = "pd_op.conv2d" [id:361] (%517, %221) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck_3/ConvNormLayer_1/Conv2D/"} : (tensor<-1x128x-1x-1xf32>, tensor<128x128x3x3xf32>) -> tensor<-1x128x-1x-1xf32> + (%519, %520, %521, %522, %523, %524) = "pd_op.batch_norm_" [id:362] (%518, %218, %217, %220, %219) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck_3/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x128x-1x-1xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>) -> tensor<-1x128x-1x-1xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<-1xu8> + (%525) = "pd_op.relu" [id:363] (%519) {stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_3/ConvNormLayer_1/"} : (tensor<-1x128x-1x-1xf32>) -> tensor<-1x128x-1x-1xf32> + (%526) = "pd_op.conv2d" [id:364] (%525, %216) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck_3/ConvNormLayer_2/Conv2D/"} : (tensor<-1x128x-1x-1xf32>, tensor<512x128x1x1xf32>) -> tensor<-1x512x-1x-1xf32> + (%527, %528, %529, %530, %531, %532) = "pd_op.batch_norm_" [id:365] (%526, %213, %212, %215, %214) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck_3/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x512x-1x-1xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<-1x512x-1x-1xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<-1xu8> + (%533) = "pd_op.add" [id:366] (%527, %509) {stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_3/"} : (tensor<-1x512x-1x-1xf32>, tensor<-1x512x-1x-1xf32>) -> tensor<-1x512x-1x-1xf32> + (%534) = "pd_op.relu" [id:367] (%533) {stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_3/"} : (tensor<-1x512x-1x-1xf32>) -> tensor<-1x512x-1x-1xf32> + (%535) = "pd_op.conv2d" [id:368] (%534, %211) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer/Conv2D/"} : (tensor<-1x512x-1x-1xf32>, tensor<256x512x1x1xf32>) -> tensor<-1x256x-1x-1xf32> + (%536, %537, %538, %539, %540, %541) = "pd_op.batch_norm_" [id:369] (%535, %208, %207, %210, %209) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<-1xu8> + (%542) = "pd_op.relu" [id:370] (%536) {stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer/"} : (tensor<-1x256x-1x-1xf32>) -> tensor<-1x256x-1x-1xf32> + (%543) = "pd_op.conv2d" [id:371] (%542, %206) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[2,2],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer_1/Conv2D/"} : (tensor<-1x256x-1x-1xf32>, tensor<256x256x3x3xf32>) -> tensor<-1x256x-1x-1xf32> + (%544, %545, %546, %547, %548, %549) = "pd_op.batch_norm_" [id:372] (%543, %203, %202, %205, %204) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<-1xu8> + (%550) = "pd_op.relu" [id:373] (%544) {stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer_1/"} : (tensor<-1x256x-1x-1xf32>) -> tensor<-1x256x-1x-1xf32> + (%551) = "pd_op.conv2d" [id:374] (%550, %201) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer_2/Conv2D/"} : (tensor<-1x256x-1x-1xf32>, tensor<1024x256x1x1xf32>) -> tensor<-1x1024x-1x-1xf32> + (%552, %553, %554, %555, %556, %557) = "pd_op.batch_norm_" [id:375] (%551, %198, %197, %200, %199) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x1024x-1x-1xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>) -> tensor<-1x1024x-1x-1xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<-1xu8> + (%558) = "pd_op.conv2d" [id:376] (%534, %196) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[2,2],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer_3/Conv2D/"} : (tensor<-1x512x-1x-1xf32>, tensor<1024x512x1x1xf32>) -> tensor<-1x1024x-1x-1xf32> + (%559, %560, %561, %562, %563, %564) = "pd_op.batch_norm_" [id:377] (%558, %193, %192, %195, %194) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer_3/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x1024x-1x-1xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>) -> tensor<-1x1024x-1x-1xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<-1xu8> + (%565) = "pd_op.add" [id:378] (%552, %559) {stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck/"} : (tensor<-1x1024x-1x-1xf32>, tensor<-1x1024x-1x-1xf32>) -> tensor<-1x1024x-1x-1xf32> + (%566) = "pd_op.relu" [id:379] (%565) {stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck/"} : (tensor<-1x1024x-1x-1xf32>) -> tensor<-1x1024x-1x-1xf32> + (%567) = "pd_op.conv2d" [id:380] (%566, %191) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_1/ConvNormLayer/Conv2D/"} : (tensor<-1x1024x-1x-1xf32>, tensor<256x1024x1x1xf32>) -> tensor<-1x256x-1x-1xf32> + (%568, %569, %570, %571, %572, %573) = "pd_op.batch_norm_" [id:381] (%567, %188, %187, %190, %189) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_1/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<-1xu8> + (%574) = "pd_op.relu" [id:382] (%568) {stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_1/ConvNormLayer/"} : (tensor<-1x256x-1x-1xf32>) -> tensor<-1x256x-1x-1xf32> + (%575) = "pd_op.conv2d" [id:383] (%574, %186) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_1/ConvNormLayer_1/Conv2D/"} : (tensor<-1x256x-1x-1xf32>, tensor<256x256x3x3xf32>) -> tensor<-1x256x-1x-1xf32> + (%576, %577, %578, %579, %580, %581) = "pd_op.batch_norm_" [id:384] (%575, %183, %182, %185, %184) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_1/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<-1xu8> + (%582) = "pd_op.relu" [id:385] (%576) {stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_1/ConvNormLayer_1/"} : (tensor<-1x256x-1x-1xf32>) -> tensor<-1x256x-1x-1xf32> + (%583) = "pd_op.conv2d" [id:386] (%582, %181) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_1/ConvNormLayer_2/Conv2D/"} : (tensor<-1x256x-1x-1xf32>, tensor<1024x256x1x1xf32>) -> tensor<-1x1024x-1x-1xf32> + (%584, %585, %586, %587, %588, %589) = "pd_op.batch_norm_" [id:387] (%583, %178, %177, %180, %179) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_1/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x1024x-1x-1xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>) -> tensor<-1x1024x-1x-1xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<-1xu8> + (%590) = "pd_op.add" [id:388] (%584, %566) {stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_1/"} : (tensor<-1x1024x-1x-1xf32>, tensor<-1x1024x-1x-1xf32>) -> tensor<-1x1024x-1x-1xf32> + (%591) = "pd_op.relu" [id:389] (%590) {stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_1/"} : (tensor<-1x1024x-1x-1xf32>) -> tensor<-1x1024x-1x-1xf32> + (%592) = "pd_op.conv2d" [id:390] (%591, %176) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_2/ConvNormLayer/Conv2D/"} : (tensor<-1x1024x-1x-1xf32>, tensor<256x1024x1x1xf32>) -> tensor<-1x256x-1x-1xf32> + (%593, %594, %595, %596, %597, %598) = "pd_op.batch_norm_" [id:391] (%592, %173, %172, %175, %174) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_2/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<-1xu8> + (%599) = "pd_op.relu" [id:392] (%593) {stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_2/ConvNormLayer/"} : (tensor<-1x256x-1x-1xf32>) -> tensor<-1x256x-1x-1xf32> + (%600) = "pd_op.conv2d" [id:393] (%599, %171) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_2/ConvNormLayer_1/Conv2D/"} : (tensor<-1x256x-1x-1xf32>, tensor<256x256x3x3xf32>) -> tensor<-1x256x-1x-1xf32> + (%601, %602, %603, %604, %605, %606) = "pd_op.batch_norm_" [id:394] (%600, %168, %167, %170, %169) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_2/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<-1xu8> + (%607) = "pd_op.relu" [id:395] (%601) {stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_2/ConvNormLayer_1/"} : (tensor<-1x256x-1x-1xf32>) -> tensor<-1x256x-1x-1xf32> + (%608) = "pd_op.conv2d" [id:396] (%607, %166) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_2/ConvNormLayer_2/Conv2D/"} : (tensor<-1x256x-1x-1xf32>, tensor<1024x256x1x1xf32>) -> tensor<-1x1024x-1x-1xf32> + (%609, %610, %611, %612, %613, %614) = "pd_op.batch_norm_" [id:397] (%608, %163, %162, %165, %164) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_2/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x1024x-1x-1xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>) -> tensor<-1x1024x-1x-1xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<-1xu8> + (%615) = "pd_op.add" [id:398] (%609, %591) {stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_2/"} : (tensor<-1x1024x-1x-1xf32>, tensor<-1x1024x-1x-1xf32>) -> tensor<-1x1024x-1x-1xf32> + (%616) = "pd_op.relu" [id:399] (%615) {stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_2/"} : (tensor<-1x1024x-1x-1xf32>) -> tensor<-1x1024x-1x-1xf32> + (%617) = "pd_op.conv2d" [id:400] (%616, %161) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_3/ConvNormLayer/Conv2D/"} : (tensor<-1x1024x-1x-1xf32>, tensor<256x1024x1x1xf32>) -> tensor<-1x256x-1x-1xf32> + (%618, %619, %620, %621, %622, %623) = "pd_op.batch_norm_" [id:401] (%617, %158, %157, %160, %159) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_3/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<-1xu8> + (%624) = "pd_op.relu" [id:402] (%618) {stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_3/ConvNormLayer/"} : (tensor<-1x256x-1x-1xf32>) -> tensor<-1x256x-1x-1xf32> + (%625) = "pd_op.conv2d" [id:403] (%624, %156) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_3/ConvNormLayer_1/Conv2D/"} : (tensor<-1x256x-1x-1xf32>, tensor<256x256x3x3xf32>) -> tensor<-1x256x-1x-1xf32> + (%626, %627, %628, %629, %630, %631) = "pd_op.batch_norm_" [id:404] (%625, %153, %152, %155, %154) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_3/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<-1xu8> + (%632) = "pd_op.relu" [id:405] (%626) {stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_3/ConvNormLayer_1/"} : (tensor<-1x256x-1x-1xf32>) -> tensor<-1x256x-1x-1xf32> + (%633) = "pd_op.conv2d" [id:406] (%632, %151) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_3/ConvNormLayer_2/Conv2D/"} : (tensor<-1x256x-1x-1xf32>, tensor<1024x256x1x1xf32>) -> tensor<-1x1024x-1x-1xf32> + (%634, %635, %636, %637, %638, %639) = "pd_op.batch_norm_" [id:407] (%633, %148, %147, %150, %149) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_3/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x1024x-1x-1xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>) -> tensor<-1x1024x-1x-1xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<-1xu8> + (%640) = "pd_op.add" [id:408] (%634, %616) {stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_3/"} : (tensor<-1x1024x-1x-1xf32>, tensor<-1x1024x-1x-1xf32>) -> tensor<-1x1024x-1x-1xf32> + (%641) = "pd_op.relu" [id:409] (%640) {stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_3/"} : (tensor<-1x1024x-1x-1xf32>) -> tensor<-1x1024x-1x-1xf32> + (%642) = "pd_op.conv2d" [id:410] (%641, %146) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_4/ConvNormLayer/Conv2D/"} : (tensor<-1x1024x-1x-1xf32>, tensor<256x1024x1x1xf32>) -> tensor<-1x256x-1x-1xf32> + (%643, %644, %645, %646, %647, %648) = "pd_op.batch_norm_" [id:411] (%642, %143, %142, %145, %144) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_4/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<-1xu8> + (%649) = "pd_op.relu" [id:412] (%643) {stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_4/ConvNormLayer/"} : (tensor<-1x256x-1x-1xf32>) -> tensor<-1x256x-1x-1xf32> + (%650) = "pd_op.conv2d" [id:413] (%649, %141) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_4/ConvNormLayer_1/Conv2D/"} : (tensor<-1x256x-1x-1xf32>, tensor<256x256x3x3xf32>) -> tensor<-1x256x-1x-1xf32> + (%651, %652, %653, %654, %655, %656) = "pd_op.batch_norm_" [id:414] (%650, %138, %137, %140, %139) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_4/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<-1xu8> + (%657) = "pd_op.relu" [id:415] (%651) {stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_4/ConvNormLayer_1/"} : (tensor<-1x256x-1x-1xf32>) -> tensor<-1x256x-1x-1xf32> + (%658) = "pd_op.conv2d" [id:416] (%657, %136) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_4/ConvNormLayer_2/Conv2D/"} : (tensor<-1x256x-1x-1xf32>, tensor<1024x256x1x1xf32>) -> tensor<-1x1024x-1x-1xf32> + (%659, %660, %661, %662, %663, %664) = "pd_op.batch_norm_" [id:417] (%658, %133, %132, %135, %134) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_4/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x1024x-1x-1xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>) -> tensor<-1x1024x-1x-1xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<-1xu8> + (%665) = "pd_op.add" [id:418] (%659, %641) {stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_4/"} : (tensor<-1x1024x-1x-1xf32>, tensor<-1x1024x-1x-1xf32>) -> tensor<-1x1024x-1x-1xf32> + (%666) = "pd_op.relu" [id:419] (%665) {stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_4/"} : (tensor<-1x1024x-1x-1xf32>) -> tensor<-1x1024x-1x-1xf32> + (%667) = "pd_op.conv2d" [id:420] (%666, %131) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_5/ConvNormLayer/Conv2D/"} : (tensor<-1x1024x-1x-1xf32>, tensor<256x1024x1x1xf32>) -> tensor<-1x256x-1x-1xf32> + (%668, %669, %670, %671, %672, %673) = "pd_op.batch_norm_" [id:421] (%667, %128, %127, %130, %129) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_5/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<-1xu8> + (%674) = "pd_op.relu" [id:422] (%668) {stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_5/ConvNormLayer/"} : (tensor<-1x256x-1x-1xf32>) -> tensor<-1x256x-1x-1xf32> + (%675) = "pd_op.conv2d" [id:423] (%674, %126) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_5/ConvNormLayer_1/Conv2D/"} : (tensor<-1x256x-1x-1xf32>, tensor<256x256x3x3xf32>) -> tensor<-1x256x-1x-1xf32> + (%676, %677, %678, %679, %680, %681) = "pd_op.batch_norm_" [id:424] (%675, %123, %122, %125, %124) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_5/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<-1x256x-1x-1xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<-1xu8> + (%682) = "pd_op.relu" [id:425] (%676) {stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_5/ConvNormLayer_1/"} : (tensor<-1x256x-1x-1xf32>) -> tensor<-1x256x-1x-1xf32> + (%683) = "pd_op.conv2d" [id:426] (%682, %121) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_5/ConvNormLayer_2/Conv2D/"} : (tensor<-1x256x-1x-1xf32>, tensor<1024x256x1x1xf32>) -> tensor<-1x1024x-1x-1xf32> + (%684, %685, %686, %687, %688, %689) = "pd_op.batch_norm_" [id:427] (%683, %118, %117, %120, %119) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_5/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x1024x-1x-1xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>) -> tensor<-1x1024x-1x-1xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<-1xu8> + (%690) = "pd_op.add" [id:428] (%684, %666) {stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_5/"} : (tensor<-1x1024x-1x-1xf32>, tensor<-1x1024x-1x-1xf32>) -> tensor<-1x1024x-1x-1xf32> + (%691) = "pd_op.relu" [id:429] (%690) {stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_5/"} : (tensor<-1x1024x-1x-1xf32>) -> tensor<-1x1024x-1x-1xf32> + (%692) = "pd_op.conv2d" [id:430] (%691, %116) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/RPNHead/RPNFeat/Conv2D/"} : (tensor<-1x1024x-1x-1xf32>, tensor<1024x1024x3x3xf32>) -> tensor<-1x1024x-1x-1xf32> + (%693) = "pd_op.add" [id:433] (%692, %55) {stop_gradient:[false],struct_name:"/RPNHead/RPNFeat/Conv2D/"} : (tensor<-1x1024x-1x-1xf32>, tensor<1x1024x1x1xf32>) -> tensor<-1x1024x-1x-1xf32> + (%694) = "pd_op.relu" [id:434] (%693) {stop_gradient:[false],struct_name:"/RPNHead/RPNFeat/"} : (tensor<-1x1024x-1x-1xf32>) -> tensor<-1x1024x-1x-1xf32> + (%695) = "pd_op.conv2d" [id:435] (%694, %115) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/RPNHead/Conv2D/"} : (tensor<-1x1024x-1x-1xf32>, tensor<15x1024x1x1xf32>) -> tensor<-1x15x-1x-1xf32> + (%696) = "pd_op.add" [id:438] (%695, %54) {stop_gradient:[false],struct_name:"/RPNHead/Conv2D/"} : (tensor<-1x15x-1x-1xf32>, tensor<1x15x1x1xf32>) -> tensor<-1x15x-1x-1xf32> + (%697) = "pd_op.conv2d" [id:439] (%694, %114) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/RPNHead/Conv2D_1/"} : (tensor<-1x1024x-1x-1xf32>, tensor<60x1024x1x1xf32>) -> tensor<-1x60x-1x-1xf32> + (%698) = "pd_op.add" [id:442] (%697, %53) {stop_gradient:[false],struct_name:"/RPNHead/Conv2D_1/"} : (tensor<-1x60x-1x-1xf32>, tensor<1x60x1x1xf32>) -> tensor<-1x60x-1x-1xf32> + (%699) = "pd_op.shape64" [id:443] (%694) {stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (tensor<-1x1024x-1x-1xf32>) -> tensor<4xi64> + (%700) = "pd_op.slice" [id:446] (%699, %52, %51) {axes:[0],decrease_axis:[0],infer_flags:[1],stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (tensor<4xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%701) = "pd_op.slice" [id:449] (%699, %51, %50) {axes:[0],decrease_axis:[0],infer_flags:[1],stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (tensor<4xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%702) = "pd_op.scale" [id:451] (%701, %49) {bias:0,bias_after_scale:true,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (tensor, tensor<1xf32>) -> tensor + (%703) = "pd_op.full" [id:452] () {dtype:float32,place:Place(cpu),shape:[1],stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/",value:0} : () -> tensor<1xf32> + (%704) = "pd_op.cast" [id:453] (%702) {dtype:float32,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (tensor) -> tensor + (%705) = "pd_op.arange" [id:455] (%703, %704, %49) {dtype:float32,place:Place(undefined:0),stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (tensor<1xf32>, tensor, tensor<1xf32>) -> tensor<-1xf32> + (%706) = "pd_op.scale" [id:457] (%700, %49) {bias:0,bias_after_scale:true,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (tensor, tensor<1xf32>) -> tensor + (%707) = "pd_op.cast" [id:459] (%706) {dtype:float32,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (tensor) -> tensor + (%708) = "pd_op.arange" [id:461] (%703, %707, %49) {dtype:float32,place:Place(undefined:0),stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (tensor<1xf32>, tensor, tensor<1xf32>) -> tensor<-1xf32> + (%709) = "builtin.combine" [id:462] (%708, %705) {stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (tensor<-1xf32>, tensor<-1xf32>) -> vec[tensor<-1xf32>,tensor<-1xf32>] + (%710) = "pd_op.meshgrid" [id:463] (%709) {stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (vec[tensor<-1xf32>,tensor<-1xf32>]) -> vec[tensor<-1x-1xf32>,tensor<-1x-1xf32>] + (%711, %712) = "builtin.split" [id:464] (%710) {stop_gradient:[true,true],struct_name:"/RPNHead/AnchorGenerator/"} : (vec[tensor<-1x-1xf32>,tensor<-1x-1xf32>]) -> tensor<-1x-1xf32>, tensor<-1x-1xf32> + (%713) = "pd_op.reshape" [id:466] (%712, %48) {stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (tensor<-1x-1xf32>, tensor<1xi64>) -> tensor<-1xf32> + (%714) = "pd_op.reshape" [id:468] (%711, %48) {stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (tensor<-1x-1xf32>, tensor<1xi64>) -> tensor<-1xf32> + (%715) = "builtin.combine" [id:469] (%713, %714, %713, %714) {stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (tensor<-1xf32>, tensor<-1xf32>, tensor<-1xf32>, tensor<-1xf32>) -> vec[tensor<-1xf32>,tensor<-1xf32>,tensor<-1xf32>,tensor<-1xf32>] + (%716) = "pd_op.stack" [id:470] (%715) {axis:1,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (vec[tensor<-1xf32>,tensor<-1xf32>,tensor<-1xf32>,tensor<-1xf32>]) -> tensor<-1x4xf32> + (%717) = "pd_op.reshape" [id:472] (%716, %47) {stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (tensor<-1x4xf32>, tensor<3xi64>) -> tensor<-1x1x4xf32> + (%718) = "pd_op.add" [id:475] (%717, %46) {stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (tensor<-1x1x4xf32>, tensor<1x15x4xf32>) -> tensor<-1x15x4xf32> + (%719) = "pd_op.reshape" [id:477] (%718, %45) {stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (tensor<-1x15x4xf32>, tensor<2xi64>) -> tensor<-1x4xf32> + (%720) = "pd_op.shape64" [id:478] (%332) {stop_gradient:[true],struct_name:"/RPNHead/"} : (tensor<-1x2xf32>) -> tensor<2xi64> + (%721) = "pd_op.slice" [id:481] (%720, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],stop_gradient:[true],struct_name:"/RPNHead/"} : (tensor<2xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%722) = "pd_op.arange" [id:484] (%42, %721, %41) {dtype:int64,place:Place(undefined:0),stop_gradient:[true],struct_name:"/RPNHead/"} : (tensor<1xi64>, tensor, tensor<1xi64>) -> tensor<-1xi64> + (%723) = "pd_op.shape64" [id:485] (%722) {stop_gradient:[true],struct_name:"/RPNHead/"} : (tensor<-1xi64>) -> tensor<1xi64> + (%724) = "pd_op.slice" [id:488] (%723, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],stop_gradient:[true],struct_name:"/RPNHead/"} : (tensor<1xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%725) = "pd_op.create_array" [id:489] () {dtype:Undefined,stop_gradient:[true],struct_name:"/RPNHead/"} : () -> pd_op.tensor_array + (%726) = "pd_op.create_array" [id:490] () {dtype:Undefined,stop_gradient:[true],struct_name:"/RPNHead/"} : () -> pd_op.tensor_array + (%727) = "pd_op.full" [id:491] () {dtype:int64,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/RPNHead/",value:0} : () -> tensor + (%728) = "pd_op.less_than" [id:492] (%727, %724) {stop_gradient:[true],struct_name:"/RPNHead/"} : (tensor, tensor) -> tensor + (%729) = "pd_op.full" [id:493] () {dtype:float32,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/RPNHead/",value:0} : () -> tensor<-1x4xf32> + (%730) = "pd_op.full" [id:494] () {dtype:int64,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/RPNHead/",value:0} : () -> tensor + (%731) = "pd_op.full" [id:495] () {dtype:float32,place:Place(undefined:0),shape:[],stop_gradient:[false],struct_name:"/RPNHead/",value:0} : () -> tensor<-1x60x-1x-1xf32> + (%732) = "pd_op.full" [id:496] () {dtype:float32,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/RPNHead/",value:0} : () -> tensor<-1x4xf32> + (%733) = "pd_op.full" [id:497] () {dtype:float32,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/RPNHead/",value:0} : () -> tensor<-1xf32> + (%734) = "pd_op.full" [id:498] () {dtype:float32,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/RPNHead/",value:0} : () -> tensor<-1x1xf32> + (%735) = "pd_op.full" [id:499] () {dtype:float32,place:Place(undefined:0),shape:[],stop_gradient:[false],struct_name:"/RPNHead/",value:0} : () -> tensor<-1x15x-1x-1xf32> + (%736) = "pd_op.full" [id:500] () {dtype:float32,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/RPNHead/",value:0} : () -> tensor<-1xf32> + (%737) = "pd_op.full" [id:501] () {dtype:float32,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/RPNHead/",value:0} : () -> tensor<-1x4xf32> + (%738, %739, %740, %741, %742, %743, %744, %745, %746, %747) = "pd_op.while" [id:502] (cond=%728, inputs=%727, %729, %730, %731, %732, %733, %734, %735, %736, %737) { + ^%arg_0 {}, %arg_1 {}, %arg_2 {}, %arg_3 {}, %arg_4 {}, %arg_5 {}, %arg_6 {}, %arg_7 {}, %arg_8 {}, %arg_9 {} + (%748) = "pd_op.scale" [id:504] (%arg_0, %19) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%749) = "builtin.combine" [id:505] (%arg_0) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%750) = "pd_op.stack" [id:506] (%749) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%751) = "builtin.combine" [id:507] (%748) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%752) = "pd_op.stack" [id:508] (%751) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%753) = "pd_op.slice" [id:509] (%722, %750, %752) {axes:[0],decrease_axis:[0],infer_flags:[-1],stop_gradient:[true]} : (tensor<-1xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%754) = "pd_op.scale" [id:511] (%753, %19) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%755) = "builtin.combine" [id:512] (%753) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%756) = "pd_op.stack" [id:513] (%755) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%757) = "builtin.combine" [id:514] (%754) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%758) = "pd_op.stack" [id:515] (%757) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%759) = "pd_op.slice" [id:516] (%696, %756, %758) {axes:[0],decrease_axis:[],infer_flags:[-1],stop_gradient:[false]} : (tensor<-1x15x-1x-1xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1x15x-1x-1xf32> + (%760) = "pd_op.scale" [id:518] (%753, %19) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%761) = "builtin.combine" [id:519] (%753) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%762) = "pd_op.stack" [id:520] (%761) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%763) = "builtin.combine" [id:521] (%760) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%764) = "pd_op.stack" [id:522] (%763) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%765) = "pd_op.slice" [id:523] (%698, %762, %764) {axes:[0],decrease_axis:[],infer_flags:[-1],stop_gradient:[false]} : (tensor<-1x60x-1x-1xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1x60x-1x-1xf32> + (%766) = "pd_op.scale" [id:525] (%753, %19) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%767) = "builtin.combine" [id:526] (%753) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%768) = "pd_op.stack" [id:527] (%767) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%769) = "builtin.combine" [id:528] (%766) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%770) = "pd_op.stack" [id:529] (%769) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%771) = "pd_op.slice" [id:530] (%332, %768, %770) {axes:[0],decrease_axis:[],infer_flags:[-1],stop_gradient:[false]} : (tensor<-1x2xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1x2xf32> + (%772) = "pd_op.full_like" [id:532] (%719, %19) {dtype:float32,place:Place(undefined:0),stop_gradient:[true]} : (tensor<-1x4xf32>, tensor<1xf32>) -> tensor<-1x4xf32> + (%773, %774, %775) = "pd_op.generate_proposals" [id:533] (%759, %765, %771, %719, %772) {eta:1,min_size:0,nms_thresh:0.7,pixel_offset:false,post_nms_top_n:1000,pre_nms_top_n:6000,stop_gradient:[true,true,true]} : (tensor<-1x15x-1x-1xf32>, tensor<-1x60x-1x-1xf32>, tensor<-1x2xf32>, tensor<-1x4xf32>, tensor<-1x4xf32>) -> tensor<-1x4xf32>, tensor<-1x1xf32>, tensor<-1xf32> + (%776) = "pd_op.flatten" [id:534] (%774) {start_axis:0,stop_axis:1,stop_gradient:[true]} : (tensor<-1x1xf32>) -> tensor<-1xf32> + (%777) = "pd_op.array_length" [id:535] (%726) {} : (pd_op.tensor_array) -> tensor<1xi64> + (%778) = "pd_op.array_write_" [id:536] (%726, %773, %777) {} : (pd_op.tensor_array, tensor<-1x4xf32>, tensor<1xi64>) -> pd_op.tensor_array + (%779) = "pd_op.shape64" [id:537] (%773) {stop_gradient:[true]} : (tensor<-1x4xf32>) -> tensor<2xi64> + (%780) = "pd_op.slice" [id:540] (%779, %44, %43) {axes:[0],decrease_axis:[],infer_flags:[1],stop_gradient:[true]} : (tensor<2xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor<1xi64> + (%781) = "pd_op.array_length" [id:541] (%725) {} : (pd_op.tensor_array) -> tensor<1xi64> + (%782) = "pd_op.array_write_" [id:542] (%725, %780, %781) {} : (pd_op.tensor_array, tensor<1xi64>, tensor<1xi64>) -> pd_op.tensor_array + (%783) = "pd_op.scale" [id:544] (%arg_0, %19) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%784) = "pd_op.less_than" [id:545] (%783, %724) {stop_gradient:[true]} : (tensor, tensor) -> tensor + () = "cf.yield" [id:546] (%784, %783, %719, %753, %698, %773, %775, %774, %696, %776, %773) {} : (tensor, tensor, tensor<-1x4xf32>, tensor, tensor<-1x60x-1x-1xf32>, tensor<-1x4xf32>, tensor<-1xf32>, tensor<-1x1xf32>, tensor<-1x15x-1x-1xf32>, tensor<-1xf32>, tensor<-1x4xf32>) -> + } + (%785, %786) = "pd_op.array_to_tensor" [id:547] (%725) {axis:0,stop_gradient:[true,true],struct_name:"/RPNHead/",use_stack:false} : (pd_op.tensor_array) -> tensor<-1xi64>, tensor<-1xi32> + (%787) = "pd_op.array_length" [id:548] (%726) {struct_name:"/BBoxHead/RoIAlign/"} : (pd_op.tensor_array) -> tensor<1xi64> + (%788) = "pd_op.greater_than" [id:550] (%787, %40) {stop_gradient:[true],struct_name:"/BBoxHead/RoIAlign/"} : (tensor<1xi64>, tensor) -> tensor<1xb> + (%789) = "pd_op.if" [id:551] (%788) {stop_gradient:[true],struct_name:"/BBoxHead/RoIAlign/"} -> tensor<-1x4xf32> { + (%790, %791) = "pd_op.array_to_tensor" [id:552] (%726) {axis:0,stop_gradient:[true,true],use_stack:false} : (pd_op.tensor_array) -> tensor<-1x4xf32>, tensor<-1xi32> + () = "cf.yield" [id:553] (%790) {} : (tensor<-1x4xf32>) -> + } else { + (%792) = "pd_op.slice_array_dense" [id:555] (%726, %44) {stop_gradient:[true]} : (pd_op.tensor_array, tensor<1xi64>) -> tensor<-1x4xf32> + () = "cf.yield" [id:556] (%792) {} : (tensor<-1x4xf32>) -> + } + (%793) = "pd_op.cast" [id:557] (%785) {dtype:int32,stop_gradient:[true],struct_name:"/BBoxHead/RoIAlign/"} : (tensor<-1xi64>) -> tensor<-1xi32> + (%794) = "pd_op.roi_align" [id:558] (%691, %789, %793) {aligned:true,pooled_height:14,pooled_width:14,sampling_ratio:-1,spatial_scale:0.0625,stop_gradient:[false],struct_name:"/BBoxHead/RoIAlign/"} : (tensor<-1x1024x-1x-1xf32>, tensor<-1x4xf32>, tensor<-1xi32>) -> tensor<-1x1024x14x14xf32> + (%795) = "pd_op.conv2d" [id:559] (%794, %113) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer/Conv2D/"} : (tensor<-1x1024x14x14xf32>, tensor<512x1024x1x1xf32>) -> tensor<-1x512x14x14xf32> + (%796, %797, %798, %799, %800, %801) = "pd_op.batch_norm_" [id:560] (%795, %110, %109, %112, %111) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x512x14x14xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<-1x512x14x14xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<-1xu8> + (%802) = "pd_op.relu" [id:561] (%796) {stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer/"} : (tensor<-1x512x14x14xf32>) -> tensor<-1x512x14x14xf32> + (%803) = "pd_op.conv2d" [id:562] (%802, %108) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[2,2],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer_1/Conv2D/"} : (tensor<-1x512x14x14xf32>, tensor<512x512x3x3xf32>) -> tensor<-1x512x7x7xf32> + (%804, %805, %806, %807, %808, %809) = "pd_op.batch_norm_" [id:563] (%803, %105, %104, %107, %106) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x512x7x7xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<-1x512x7x7xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<-1xu8> + (%810) = "pd_op.relu" [id:564] (%804) {stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer_1/"} : (tensor<-1x512x7x7xf32>) -> tensor<-1x512x7x7xf32> + (%811) = "pd_op.conv2d" [id:565] (%810, %103) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer_2/Conv2D/"} : (tensor<-1x512x7x7xf32>, tensor<2048x512x1x1xf32>) -> tensor<-1x2048x7x7xf32> + (%812, %813, %814, %815, %816, %817) = "pd_op.batch_norm_" [id:566] (%811, %100, %99, %102, %101) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x2048x7x7xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>) -> tensor<-1x2048x7x7xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<-1xu8> + (%818) = "pd_op.conv2d" [id:567] (%794, %98) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[2,2],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer_3/Conv2D/"} : (tensor<-1x1024x14x14xf32>, tensor<2048x1024x1x1xf32>) -> tensor<-1x2048x7x7xf32> + (%819, %820, %821, %822, %823, %824) = "pd_op.batch_norm_" [id:568] (%818, %95, %94, %97, %96) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer_3/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x2048x7x7xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>) -> tensor<-1x2048x7x7xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<-1xu8> + (%825) = "pd_op.add" [id:569] (%812, %819) {stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/"} : (tensor<-1x2048x7x7xf32>, tensor<-1x2048x7x7xf32>) -> tensor<-1x2048x7x7xf32> + (%826) = "pd_op.relu" [id:570] (%825) {stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/"} : (tensor<-1x2048x7x7xf32>) -> tensor<-1x2048x7x7xf32> + (%827) = "pd_op.conv2d" [id:571] (%826, %93) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/ConvNormLayer/Conv2D/"} : (tensor<-1x2048x7x7xf32>, tensor<512x2048x1x1xf32>) -> tensor<-1x512x7x7xf32> + (%828, %829, %830, %831, %832, %833) = "pd_op.batch_norm_" [id:572] (%827, %90, %89, %92, %91) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x512x7x7xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<-1x512x7x7xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<-1xu8> + (%834) = "pd_op.relu" [id:573] (%828) {stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/ConvNormLayer/"} : (tensor<-1x512x7x7xf32>) -> tensor<-1x512x7x7xf32> + (%835) = "pd_op.conv2d" [id:574] (%834, %88) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/ConvNormLayer_1/Conv2D/"} : (tensor<-1x512x7x7xf32>, tensor<512x512x3x3xf32>) -> tensor<-1x512x7x7xf32> + (%836, %837, %838, %839, %840, %841) = "pd_op.batch_norm_" [id:575] (%835, %85, %84, %87, %86) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x512x7x7xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<-1x512x7x7xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<-1xu8> + (%842) = "pd_op.relu" [id:576] (%836) {stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/ConvNormLayer_1/"} : (tensor<-1x512x7x7xf32>) -> tensor<-1x512x7x7xf32> + (%843) = "pd_op.conv2d" [id:577] (%842, %83) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/ConvNormLayer_2/Conv2D/"} : (tensor<-1x512x7x7xf32>, tensor<2048x512x1x1xf32>) -> tensor<-1x2048x7x7xf32> + (%844, %845, %846, %847, %848, %849) = "pd_op.batch_norm_" [id:578] (%843, %80, %79, %82, %81) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x2048x7x7xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>) -> tensor<-1x2048x7x7xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<-1xu8> + (%850) = "pd_op.add" [id:579] (%844, %826) {stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/"} : (tensor<-1x2048x7x7xf32>, tensor<-1x2048x7x7xf32>) -> tensor<-1x2048x7x7xf32> + (%851) = "pd_op.relu" [id:580] (%850) {stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/"} : (tensor<-1x2048x7x7xf32>) -> tensor<-1x2048x7x7xf32> + (%852) = "pd_op.conv2d" [id:581] (%851, %78) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/ConvNormLayer/Conv2D/"} : (tensor<-1x2048x7x7xf32>, tensor<512x2048x1x1xf32>) -> tensor<-1x512x7x7xf32> + (%853, %854, %855, %856, %857, %858) = "pd_op.batch_norm_" [id:582] (%852, %75, %74, %77, %76) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x512x7x7xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<-1x512x7x7xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<-1xu8> + (%859) = "pd_op.relu" [id:583] (%853) {stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/ConvNormLayer/"} : (tensor<-1x512x7x7xf32>) -> tensor<-1x512x7x7xf32> + (%860) = "pd_op.conv2d" [id:584] (%859, %73) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/ConvNormLayer_1/Conv2D/"} : (tensor<-1x512x7x7xf32>, tensor<512x512x3x3xf32>) -> tensor<-1x512x7x7xf32> + (%861, %862, %863, %864, %865, %866) = "pd_op.batch_norm_" [id:585] (%860, %70, %69, %72, %71) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x512x7x7xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<-1x512x7x7xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<-1xu8> + (%867) = "pd_op.relu" [id:586] (%861) {stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/ConvNormLayer_1/"} : (tensor<-1x512x7x7xf32>) -> tensor<-1x512x7x7xf32> + (%868) = "pd_op.conv2d" [id:587] (%867, %68) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/ConvNormLayer_2/Conv2D/"} : (tensor<-1x512x7x7xf32>, tensor<2048x512x1x1xf32>) -> tensor<-1x2048x7x7xf32> + (%869, %870, %871, %872, %873, %874) = "pd_op.batch_norm_" [id:588] (%868, %65, %64, %67, %66) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (tensor<-1x2048x7x7xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>) -> tensor<-1x2048x7x7xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<-1xu8> + (%875) = "pd_op.add" [id:589] (%869, %851) {stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/"} : (tensor<-1x2048x7x7xf32>, tensor<-1x2048x7x7xf32>) -> tensor<-1x2048x7x7xf32> + (%876) = "pd_op.relu" [id:590] (%875) {stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/"} : (tensor<-1x2048x7x7xf32>) -> tensor<-1x2048x7x7xf32> + (%877) = "pd_op.pool2d" [id:592] (%876, %39) {adaptive:true,ceil_mode:false,data_format:"NCHW",exclusive:true,global_pooling:false,padding_algorithm:"EXPLICIT",paddings:[0,0],pooling_type:"avg",stop_gradient:[false],strides:[1,1],struct_name:"/BBoxHead/"} : (tensor<-1x2048x7x7xf32>, tensor<2xi64>) -> tensor<-1x2048x1x1xf32> + (%878) = "pd_op.squeeze" [id:594] (%877, %38) {stop_gradient:[false],struct_name:"/BBoxHead/"} : (tensor<-1x2048x1x1xf32>, tensor<2xi64>) -> tensor<-1x2048xf32> + (%879) = "pd_op.matmul" [id:595] (%878, %63) {stop_gradient:[false],struct_name:"/BBoxHead/Linear/",transpose_x:false,transpose_y:false} : (tensor<-1x2048xf32>, tensor<2048x81xf32>) -> tensor<-1x81xf32> + (%880) = "pd_op.add" [id:596] (%879, %62) {stop_gradient:[false],struct_name:"/BBoxHead/Linear/"} : (tensor<-1x81xf32>, tensor<81xf32>) -> tensor<-1x81xf32> + (%881) = "pd_op.matmul" [id:597] (%878, %61) {stop_gradient:[false],struct_name:"/BBoxHead/Linear_1/",transpose_x:false,transpose_y:false} : (tensor<-1x2048xf32>, tensor<2048x320xf32>) -> tensor<-1x320xf32> + (%882) = "pd_op.add" [id:598] (%881, %60) {stop_gradient:[false],struct_name:"/BBoxHead/Linear_1/"} : (tensor<-1x320xf32>, tensor<320xf32>) -> tensor<-1x320xf32> + (%883) = "pd_op.softmax" [id:599] (%880) {axis:-1,stop_gradient:[false],struct_name:"/BBoxHead/"} : (tensor<-1x81xf32>) -> tensor<-1x81xf32> + (%884) = "pd_op.slice" [id:603] (%720, %44, %43) {axes:[0],decrease_axis:[],infer_flags:[1],stop_gradient:[true]} : (tensor<2xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor<1xi64> + (%885) = "pd_op.arange" [id:606] (%42, %884, %41) {dtype:int64,place:Place(undefined:0),stop_gradient:[true]} : (tensor<1xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1xi64> + (%886) = "pd_op.shape64" [id:607] (%885) {stop_gradient:[true]} : (tensor<-1xi64>) -> tensor<1xi64> + (%887) = "pd_op.slice" [id:610] (%886, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],stop_gradient:[true]} : (tensor<1xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%888) = "pd_op.create_array" [id:611] () {dtype:Undefined,stop_gradient:[true]} : () -> pd_op.tensor_array + (%889) = "pd_op.full" [id:612] () {dtype:int64,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> tensor + (%890) = "pd_op.less_than" [id:613] (%889, %887) {stop_gradient:[true]} : (tensor, tensor) -> tensor + (%891) = "pd_op.full" [id:614] () {dtype:float32,place:Place(undefined:0),shape:[],stop_gradient:[false],value:0} : () -> tensor<-1x2xf32> + (%892) = "pd_op.full" [id:615] () {dtype:int64,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> tensor + (%893) = "pd_op.full" [id:616] () {dtype:int64,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> tensor + (%894, %895, %896, %897) = "pd_op.while" [id:617] (cond=%890, inputs=%889, %891, %892, %893) { + ^%arg_10 {}, %arg_11 {}, %arg_12 {}, %arg_13 {} + (%898) = "pd_op.scale" [id:619] (%arg_10, %18) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%899) = "builtin.combine" [id:620] (%arg_10) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%900) = "pd_op.stack" [id:621] (%899) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%901) = "builtin.combine" [id:622] (%898) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%902) = "pd_op.stack" [id:623] (%901) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%903) = "pd_op.slice" [id:624] (%885, %900, %902) {axes:[0],decrease_axis:[0],infer_flags:[-1],stop_gradient:[true]} : (tensor<-1xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%904) = "pd_op.scale" [id:626] (%903, %18) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%905) = "builtin.combine" [id:627] (%903) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%906) = "pd_op.stack" [id:628] (%905) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%907) = "builtin.combine" [id:629] (%904) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%908) = "pd_op.stack" [id:630] (%907) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%909) = "pd_op.slice" [id:631] (%785, %906, %908) {axes:[0],decrease_axis:[0],infer_flags:[-1],stop_gradient:[true]} : (tensor<-1xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%910) = "pd_op.scale" [id:633] (%903, %18) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%911) = "builtin.combine" [id:634] (%903) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%912) = "pd_op.stack" [id:635] (%911) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%913) = "builtin.combine" [id:636] (%910) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%914) = "pd_op.stack" [id:637] (%913) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%915) = "pd_op.slice" [id:638] (%332, %912, %914) {axes:[0],decrease_axis:[0],infer_flags:[-1],stop_gradient:[false]} : (tensor<-1x2xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<2xf32> + (%916) = "builtin.combine" [id:640] (%909, %17) {stop_gradient:[true]} : (tensor, tensor) -> vec[tensor,tensor] + (%917) = "pd_op.stack" [id:641] (%916) {axis:0,stop_gradient:[true]} : (vec[tensor,tensor]) -> tensor<2xi64> + (%918) = "pd_op.expand" [id:642] (%915, %917) {stop_gradient:[false]} : (tensor<2xf32>, tensor<2xi64>) -> tensor<-1x2xf32> + (%919) = "pd_op.array_length" [id:643] (%888) {} : (pd_op.tensor_array) -> tensor<1xi64> + (%920) = "pd_op.array_write_" [id:644] (%888, %918, %919) {} : (pd_op.tensor_array, tensor<-1x2xf32>, tensor<1xi64>) -> pd_op.tensor_array + (%921) = "pd_op.scale" [id:646] (%arg_10, %18) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%922) = "pd_op.less_than" [id:647] (%921, %887) {stop_gradient:[true]} : (tensor, tensor) -> tensor + () = "cf.yield" [id:648] (%922, %921, %918, %903, %909) {} : (tensor, tensor, tensor<-1x2xf32>, tensor, tensor) -> + } + (%923, %924) = "pd_op.array_to_tensor" [id:649] (%888) {axis:0,stop_gradient:[true,true],use_stack:false} : (pd_op.tensor_array) -> tensor<-1x2xf32>, tensor<-1xi32> + (%925, %926) = "pd_op.array_to_tensor" [id:650] (%726) {axis:0,stop_gradient:[true,true],use_stack:false} : (pd_op.tensor_array) -> tensor<-1x4xf32>, tensor<-1xi32> + (%927) = "pd_op.slice" [id:653] (%925, %52, %51) {axes:[1],decrease_axis:[1],infer_flags:[1],stop_gradient:[true]} : (tensor<-1x4xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1xf32> + (%928) = "pd_op.slice" [id:656] (%925, %44, %43) {axes:[1],decrease_axis:[1],infer_flags:[1],stop_gradient:[true]} : (tensor<-1x4xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1xf32> + (%929) = "pd_op.subtract" [id:657] (%927, %928) {stop_gradient:[true]} : (tensor<-1xf32>, tensor<-1xf32>) -> tensor<-1xf32> + (%930) = "pd_op.slice" [id:660] (%925, %51, %50) {axes:[1],decrease_axis:[1],infer_flags:[1],stop_gradient:[true]} : (tensor<-1x4xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1xf32> + (%931) = "pd_op.slice" [id:663] (%925, %43, %52) {axes:[1],decrease_axis:[1],infer_flags:[1],stop_gradient:[true]} : (tensor<-1x4xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1xf32> + (%932) = "pd_op.subtract" [id:664] (%930, %931) {stop_gradient:[true]} : (tensor<-1xf32>, tensor<-1xf32>) -> tensor<-1xf32> + (%933) = "pd_op.slice" [id:667] (%925, %44, %43) {axes:[1],decrease_axis:[1],infer_flags:[1],stop_gradient:[true]} : (tensor<-1x4xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1xf32> + (%934) = "pd_op.scale" [id:669] (%929, %37) {bias:0,bias_after_scale:true,stop_gradient:[true]} : (tensor<-1xf32>, tensor<1xf32>) -> tensor<-1xf32> + (%935) = "pd_op.add" [id:670] (%933, %934) {stop_gradient:[true]} : (tensor<-1xf32>, tensor<-1xf32>) -> tensor<-1xf32> + (%936) = "pd_op.slice" [id:673] (%925, %43, %52) {axes:[1],decrease_axis:[1],infer_flags:[1],stop_gradient:[true]} : (tensor<-1x4xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1xf32> + (%937) = "pd_op.scale" [id:675] (%932, %37) {bias:0,bias_after_scale:true,stop_gradient:[true]} : (tensor<-1xf32>, tensor<1xf32>) -> tensor<-1xf32> + (%938) = "pd_op.add" [id:676] (%936, %937) {stop_gradient:[true]} : (tensor<-1xf32>, tensor<-1xf32>) -> tensor<-1xf32> + (%939) = "pd_op.strided_slice" [id:680] (%882, %44, %36, %50) {axes:[1],stop_gradient:[false]} : (tensor<-1x320xf32>, tensor<1xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1x80xf32> + (%940) = "pd_op.scale" [id:682] (%939, %35) {bias:0,bias_after_scale:true,stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<1xf32>) -> tensor<-1x80xf32> + (%941) = "pd_op.strided_slice" [id:686] (%882, %43, %36, %50) {axes:[1],stop_gradient:[false]} : (tensor<-1x320xf32>, tensor<1xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1x80xf32> + (%942) = "pd_op.scale" [id:688] (%941, %35) {bias:0,bias_after_scale:true,stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<1xf32>) -> tensor<-1x80xf32> + (%943) = "pd_op.strided_slice" [id:692] (%882, %52, %36, %50) {axes:[1],stop_gradient:[false]} : (tensor<-1x320xf32>, tensor<1xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1x80xf32> + (%944) = "pd_op.scale" [id:694] (%943, %34) {bias:0,bias_after_scale:true,stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<1xf32>) -> tensor<-1x80xf32> + (%945) = "pd_op.strided_slice" [id:698] (%882, %51, %36, %50) {axes:[1],stop_gradient:[false]} : (tensor<-1x320xf32>, tensor<1xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1x80xf32> + (%946) = "pd_op.scale" [id:700] (%945, %34) {bias:0,bias_after_scale:true,stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<1xf32>) -> tensor<-1x80xf32> + (%947) = "pd_op.clip" [id:703] (%944, %33, %32) {stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<-1x80xf32> + (%948) = "pd_op.clip" [id:706] (%946, %33, %32) {stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<-1x80xf32> + (%949) = "pd_op.unsqueeze" [id:708] (%929, %43) {stop_gradient:[true]} : (tensor<-1xf32>, tensor<1xi64>) -> tensor<-1x1xf32> + (%950) = "pd_op.multiply" [id:709] (%940, %949) {stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<-1x1xf32>) -> tensor<-1x80xf32> + (%951) = "pd_op.unsqueeze" [id:711] (%935, %43) {stop_gradient:[true]} : (tensor<-1xf32>, tensor<1xi64>) -> tensor<-1x1xf32> + (%952) = "pd_op.add" [id:712] (%950, %951) {stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<-1x1xf32>) -> tensor<-1x80xf32> + (%953) = "pd_op.unsqueeze" [id:714] (%932, %43) {stop_gradient:[true]} : (tensor<-1xf32>, tensor<1xi64>) -> tensor<-1x1xf32> + (%954) = "pd_op.multiply" [id:715] (%942, %953) {stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<-1x1xf32>) -> tensor<-1x80xf32> + (%955) = "pd_op.unsqueeze" [id:717] (%938, %43) {stop_gradient:[true]} : (tensor<-1xf32>, tensor<1xi64>) -> tensor<-1x1xf32> + (%956) = "pd_op.add" [id:718] (%954, %955) {stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<-1x1xf32>) -> tensor<-1x80xf32> + (%957) = "pd_op.exp" [id:719] (%947) {stop_gradient:[false]} : (tensor<-1x80xf32>) -> tensor<-1x80xf32> + (%958) = "pd_op.unsqueeze" [id:721] (%929, %43) {stop_gradient:[true]} : (tensor<-1xf32>, tensor<1xi64>) -> tensor<-1x1xf32> + (%959) = "pd_op.multiply" [id:722] (%957, %958) {stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<-1x1xf32>) -> tensor<-1x80xf32> + (%960) = "pd_op.exp" [id:723] (%948) {stop_gradient:[false]} : (tensor<-1x80xf32>) -> tensor<-1x80xf32> + (%961) = "pd_op.unsqueeze" [id:725] (%932, %43) {stop_gradient:[true]} : (tensor<-1xf32>, tensor<1xi64>) -> tensor<-1x1xf32> + (%962) = "pd_op.multiply" [id:726] (%960, %961) {stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<-1x1xf32>) -> tensor<-1x80xf32> + (%963) = "pd_op.scale" [id:728] (%959, %37) {bias:0,bias_after_scale:true,stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<1xf32>) -> tensor<-1x80xf32> + (%964) = "pd_op.subtract" [id:729] (%952, %963) {stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<-1x80xf32>) -> tensor<-1x80xf32> + (%965) = "pd_op.scale" [id:731] (%962, %37) {bias:0,bias_after_scale:true,stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<1xf32>) -> tensor<-1x80xf32> + (%966) = "pd_op.subtract" [id:732] (%956, %965) {stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<-1x80xf32>) -> tensor<-1x80xf32> + (%967) = "pd_op.scale" [id:734] (%959, %37) {bias:0,bias_after_scale:true,stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<1xf32>) -> tensor<-1x80xf32> + (%968) = "pd_op.add" [id:735] (%952, %967) {stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<-1x80xf32>) -> tensor<-1x80xf32> + (%969) = "pd_op.scale" [id:737] (%962, %37) {bias:0,bias_after_scale:true,stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<1xf32>) -> tensor<-1x80xf32> + (%970) = "pd_op.add" [id:738] (%956, %969) {stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<-1x80xf32>) -> tensor<-1x80xf32> + (%971) = "builtin.combine" [id:739] (%964, %966, %968, %970) {stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<-1x80xf32>, tensor<-1x80xf32>, tensor<-1x80xf32>) -> vec[tensor<-1x80xf32>,tensor<-1x80xf32>,tensor<-1x80xf32>,tensor<-1x80xf32>] + (%972) = "pd_op.stack" [id:740] (%971) {axis:-1,stop_gradient:[false]} : (vec[tensor<-1x80xf32>,tensor<-1x80xf32>,tensor<-1x80xf32>,tensor<-1x80xf32>]) -> tensor<-1x80x4xf32> + (%973) = "pd_op.slice" [id:743] (%883, %44, %48) {axes:[1],decrease_axis:[],infer_flags:[1],stop_gradient:[false]} : (tensor<-1x81xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1x80xf32> + (%974) = "pd_op.shape64" [id:744] (%972) {stop_gradient:[true]} : (tensor<-1x80x4xf32>) -> tensor<3xi64> + (%975) = "pd_op.slice" [id:747] (%974, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],stop_gradient:[true]} : (tensor<3xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%976) = "builtin.combine" [id:750] (%975, %31, %30) {stop_gradient:[true]} : (tensor, tensor, tensor) -> vec[tensor,tensor,tensor] + (%977) = "pd_op.stack" [id:751] (%976) {axis:0,stop_gradient:[true]} : (vec[tensor,tensor,tensor]) -> tensor<3xi64> + (%978) = "pd_op.expand" [id:752] (%972, %977) {stop_gradient:[false]} : (tensor<-1x80x4xf32>, tensor<3xi64>) -> tensor<-1x80x4xf32> + (%979) = "pd_op.slice" [id:755] (%923, %44, %43) {axes:[1],decrease_axis:[1],infer_flags:[1],stop_gradient:[true]} : (tensor<-1x2xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1xf32> + (%980) = "pd_op.unsqueeze" [id:757] (%979, %43) {stop_gradient:[true]} : (tensor<-1xf32>, tensor<1xi64>) -> tensor<-1x1xf32> + (%981) = "pd_op.slice" [id:760] (%923, %43, %52) {axes:[1],decrease_axis:[1],infer_flags:[1],stop_gradient:[true]} : (tensor<-1x2xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1xf32> + (%982) = "pd_op.unsqueeze" [id:762] (%981, %43) {stop_gradient:[true]} : (tensor<-1xf32>, tensor<1xi64>) -> tensor<-1x1xf32> + (%983) = "pd_op.full_like" [id:764] (%980, %703) {dtype:float32,place:Place(undefined:0),stop_gradient:[true]} : (tensor<-1x1xf32>, tensor<1xf32>) -> tensor<-1x1xf32> + (%984) = "pd_op.slice" [id:767] (%978, %44, %43) {axes:[2],decrease_axis:[2],infer_flags:[1],stop_gradient:[false]} : (tensor<-1x80x4xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1x80xf32> + (%985) = "pd_op.minimum" [id:768] (%984, %982) {stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<-1x1xf32>) -> tensor<-1x80xf32> + (%986) = "pd_op.maximum" [id:769] (%985, %983) {stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<-1x1xf32>) -> tensor<-1x80xf32> + (%987) = "pd_op.slice" [id:772] (%978, %43, %52) {axes:[2],decrease_axis:[2],infer_flags:[1],stop_gradient:[false]} : (tensor<-1x80x4xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1x80xf32> + (%988) = "pd_op.minimum" [id:773] (%987, %980) {stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<-1x1xf32>) -> tensor<-1x80xf32> + (%989) = "pd_op.maximum" [id:774] (%988, %983) {stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<-1x1xf32>) -> tensor<-1x80xf32> + (%990) = "pd_op.slice" [id:777] (%978, %52, %51) {axes:[2],decrease_axis:[2],infer_flags:[1],stop_gradient:[false]} : (tensor<-1x80x4xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1x80xf32> + (%991) = "pd_op.minimum" [id:778] (%990, %982) {stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<-1x1xf32>) -> tensor<-1x80xf32> + (%992) = "pd_op.maximum" [id:779] (%991, %983) {stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<-1x1xf32>) -> tensor<-1x80xf32> + (%993) = "pd_op.slice" [id:782] (%978, %51, %50) {axes:[2],decrease_axis:[2],infer_flags:[1],stop_gradient:[false]} : (tensor<-1x80x4xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1x80xf32> + (%994) = "pd_op.minimum" [id:783] (%993, %980) {stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<-1x1xf32>) -> tensor<-1x80xf32> + (%995) = "pd_op.maximum" [id:784] (%994, %983) {stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<-1x1xf32>) -> tensor<-1x80xf32> + (%996) = "builtin.combine" [id:785] (%986, %989, %992, %995) {stop_gradient:[false]} : (tensor<-1x80xf32>, tensor<-1x80xf32>, tensor<-1x80xf32>, tensor<-1x80xf32>) -> vec[tensor<-1x80xf32>,tensor<-1x80xf32>,tensor<-1x80xf32>,tensor<-1x80xf32>] + (%997) = "pd_op.stack" [id:786] (%996) {axis:-1,stop_gradient:[false]} : (vec[tensor<-1x80xf32>,tensor<-1x80xf32>,tensor<-1x80xf32>,tensor<-1x80xf32>]) -> tensor<-1x80x4xf32> + (%998, %999, %1000) = "pd_op.multiclass_nms3" [id:787] (%997, %973, %785) {background_label:80,keep_top_k:100,nms_eta:1,nms_threshold:0.5,nms_top_k:-1,normalized:true,score_threshold:0.05,stop_gradient:[false,false,false]} : (tensor<-1x80x4xf32>, tensor<-1x80xf32>, tensor<-1xi64>) -> tensor<-1x6xf32>, tensor<-1x1xi32>, tensor<-1xi32> + (%1001) = "pd_op.shape64" [id:788] (%998) {stop_gradient:[true],struct_name:"/MaskHead/"} : (tensor<-1x6xf32>) -> tensor<2xi64> + (%1002) = "pd_op.slice" [id:791] (%1001, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],stop_gradient:[true],struct_name:"/MaskHead/"} : (tensor<2xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%1003) = "pd_op.equal" [id:793] (%1002, %29) {stop_gradient:[true],struct_name:"/MaskHead/"} : (tensor, tensor) -> tensor + (%1004) = "pd_op.if" [id:794] (%1003) {stop_gradient:[true],struct_name:"/MaskHead/"} -> tensor<-1x-1x-1xf32> { + () = "cf.yield" [id:796] (%16) {} : (tensor<1x1x1xf32>) -> + } else { + (%1005) = "pd_op.slice" [id:799] (%998, %52, %36) {axes:[1],decrease_axis:[],infer_flags:[1],stop_gradient:[false]} : (tensor<-1x6xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1x4xf32> + (%1006) = "pd_op.slice" [id:802] (%998, %44, %43) {axes:[1],decrease_axis:[1],infer_flags:[1],stop_gradient:[false]} : (tensor<-1x6xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1xf32> + (%1007) = "pd_op.cast" [id:803] (%1006) {dtype:int32,stop_gradient:[false]} : (tensor<-1xf32>) -> tensor<-1xi32> + (%1008) = "pd_op.roi_align" [id:804] (%691, %1005, %1000) {aligned:true,pooled_height:14,pooled_width:14,sampling_ratio:-1,spatial_scale:0.0625,stop_gradient:[false]} : (tensor<-1x1024x-1x-1xf32>, tensor<-1x4xf32>, tensor<-1xi32>) -> tensor<-1x1024x14x14xf32> + (%1009) = "pd_op.conv2d" [id:805] (%1008, %113) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (tensor<-1x1024x14x14xf32>, tensor<512x1024x1x1xf32>) -> tensor<-1x512x14x14xf32> + (%1010, %1011, %1012, %1013, %1014, %1015) = "pd_op.batch_norm_" [id:806] (%1009, %110, %109, %112, %111) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (tensor<-1x512x14x14xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<-1x512x14x14xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<-1xu8> + (%1016) = "pd_op.relu" [id:807] (%1010) {stop_gradient:[false]} : (tensor<-1x512x14x14xf32>) -> tensor<-1x512x14x14xf32> + (%1017) = "pd_op.conv2d" [id:808] (%1016, %108) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[2,2]} : (tensor<-1x512x14x14xf32>, tensor<512x512x3x3xf32>) -> tensor<-1x512x7x7xf32> + (%1018, %1019, %1020, %1021, %1022, %1023) = "pd_op.batch_norm_" [id:809] (%1017, %105, %104, %107, %106) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (tensor<-1x512x7x7xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<-1x512x7x7xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<-1xu8> + (%1024) = "pd_op.relu" [id:810] (%1018) {stop_gradient:[false]} : (tensor<-1x512x7x7xf32>) -> tensor<-1x512x7x7xf32> + (%1025) = "pd_op.conv2d" [id:811] (%1024, %103) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (tensor<-1x512x7x7xf32>, tensor<2048x512x1x1xf32>) -> tensor<-1x2048x7x7xf32> + (%1026, %1027, %1028, %1029, %1030, %1031) = "pd_op.batch_norm_" [id:812] (%1025, %100, %99, %102, %101) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (tensor<-1x2048x7x7xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>) -> tensor<-1x2048x7x7xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<-1xu8> + (%1032) = "pd_op.conv2d" [id:813] (%1008, %98) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[2,2]} : (tensor<-1x1024x14x14xf32>, tensor<2048x1024x1x1xf32>) -> tensor<-1x2048x7x7xf32> + (%1033, %1034, %1035, %1036, %1037, %1038) = "pd_op.batch_norm_" [id:814] (%1032, %95, %94, %97, %96) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (tensor<-1x2048x7x7xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>) -> tensor<-1x2048x7x7xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<-1xu8> + (%1039) = "pd_op.add" [id:815] (%1026, %1033) {stop_gradient:[false]} : (tensor<-1x2048x7x7xf32>, tensor<-1x2048x7x7xf32>) -> tensor<-1x2048x7x7xf32> + (%1040) = "pd_op.relu" [id:816] (%1039) {stop_gradient:[false]} : (tensor<-1x2048x7x7xf32>) -> tensor<-1x2048x7x7xf32> + (%1041) = "pd_op.conv2d" [id:817] (%1040, %93) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (tensor<-1x2048x7x7xf32>, tensor<512x2048x1x1xf32>) -> tensor<-1x512x7x7xf32> + (%1042, %1043, %1044, %1045, %1046, %1047) = "pd_op.batch_norm_" [id:818] (%1041, %90, %89, %92, %91) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (tensor<-1x512x7x7xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<-1x512x7x7xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<-1xu8> + (%1048) = "pd_op.relu" [id:819] (%1042) {stop_gradient:[false]} : (tensor<-1x512x7x7xf32>) -> tensor<-1x512x7x7xf32> + (%1049) = "pd_op.conv2d" [id:820] (%1048, %88) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1]} : (tensor<-1x512x7x7xf32>, tensor<512x512x3x3xf32>) -> tensor<-1x512x7x7xf32> + (%1050, %1051, %1052, %1053, %1054, %1055) = "pd_op.batch_norm_" [id:821] (%1049, %85, %84, %87, %86) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (tensor<-1x512x7x7xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<-1x512x7x7xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<-1xu8> + (%1056) = "pd_op.relu" [id:822] (%1050) {stop_gradient:[false]} : (tensor<-1x512x7x7xf32>) -> tensor<-1x512x7x7xf32> + (%1057) = "pd_op.conv2d" [id:823] (%1056, %83) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (tensor<-1x512x7x7xf32>, tensor<2048x512x1x1xf32>) -> tensor<-1x2048x7x7xf32> + (%1058, %1059, %1060, %1061, %1062, %1063) = "pd_op.batch_norm_" [id:824] (%1057, %80, %79, %82, %81) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (tensor<-1x2048x7x7xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>) -> tensor<-1x2048x7x7xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<-1xu8> + (%1064) = "pd_op.add" [id:825] (%1058, %1040) {stop_gradient:[false]} : (tensor<-1x2048x7x7xf32>, tensor<-1x2048x7x7xf32>) -> tensor<-1x2048x7x7xf32> + (%1065) = "pd_op.relu" [id:826] (%1064) {stop_gradient:[false]} : (tensor<-1x2048x7x7xf32>) -> tensor<-1x2048x7x7xf32> + (%1066) = "pd_op.conv2d" [id:827] (%1065, %78) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (tensor<-1x2048x7x7xf32>, tensor<512x2048x1x1xf32>) -> tensor<-1x512x7x7xf32> + (%1067, %1068, %1069, %1070, %1071, %1072) = "pd_op.batch_norm_" [id:828] (%1066, %75, %74, %77, %76) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (tensor<-1x512x7x7xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<-1x512x7x7xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<-1xu8> + (%1073) = "pd_op.relu" [id:829] (%1067) {stop_gradient:[false]} : (tensor<-1x512x7x7xf32>) -> tensor<-1x512x7x7xf32> + (%1074) = "pd_op.conv2d" [id:830] (%1073, %73) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1]} : (tensor<-1x512x7x7xf32>, tensor<512x512x3x3xf32>) -> tensor<-1x512x7x7xf32> + (%1075, %1076, %1077, %1078, %1079, %1080) = "pd_op.batch_norm_" [id:831] (%1074, %70, %69, %72, %71) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (tensor<-1x512x7x7xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<-1x512x7x7xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<-1xu8> + (%1081) = "pd_op.relu" [id:832] (%1075) {stop_gradient:[false]} : (tensor<-1x512x7x7xf32>) -> tensor<-1x512x7x7xf32> + (%1082) = "pd_op.conv2d" [id:833] (%1081, %68) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (tensor<-1x512x7x7xf32>, tensor<2048x512x1x1xf32>) -> tensor<-1x2048x7x7xf32> + (%1083, %1084, %1085, %1086, %1087, %1088) = "pd_op.batch_norm_" [id:834] (%1082, %65, %64, %67, %66) {data_format:"NCHW",epsilon:1e-05,is_test:true,momentum:0.9,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (tensor<-1x2048x7x7xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>) -> tensor<-1x2048x7x7xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<-1xu8> + (%1089) = "pd_op.add" [id:835] (%1083, %1065) {stop_gradient:[false]} : (tensor<-1x2048x7x7xf32>, tensor<-1x2048x7x7xf32>) -> tensor<-1x2048x7x7xf32> + (%1090) = "pd_op.relu" [id:836] (%1089) {stop_gradient:[false]} : (tensor<-1x2048x7x7xf32>) -> tensor<-1x2048x7x7xf32> + (%1091) = "pd_op.conv2d_transpose" [id:838] (%1090, %59, %15) {data_format:"NCHW",dilations:[1,1],groups:1,output_padding:[],padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[2,2]} : (tensor<-1x2048x7x7xf32>, tensor<2048x256x2x2xf32>, tensor<0xi64>) -> tensor<-1x256x14x14xf32> + (%1092) = "pd_op.add" [id:841] (%1091, %14) {stop_gradient:[false]} : (tensor<-1x256x14x14xf32>, tensor<1x256x1x1xf32>) -> tensor<-1x256x14x14xf32> + (%1093) = "pd_op.relu" [id:842] (%1092) {stop_gradient:[false]} : (tensor<-1x256x14x14xf32>) -> tensor<-1x256x14x14xf32> + (%1094) = "pd_op.conv2d" [id:843] (%1093, %58) {data_format:"NCHW",dilations:[1,1],groups:1,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (tensor<-1x256x14x14xf32>, tensor<80x256x1x1xf32>) -> tensor<-1x80x14x14xf32> + (%1095) = "pd_op.add" [id:846] (%1094, %56) {stop_gradient:[false]} : (tensor<-1x80x14x14xf32>, tensor<1x80x1x1xf32>) -> tensor<-1x80x14x14xf32> + (%1096) = "pd_op.shape64" [id:847] (%1095) {stop_gradient:[true]} : (tensor<-1x80x14x14xf32>) -> tensor<4xi64> + (%1097) = "pd_op.slice" [id:850] (%1096, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],stop_gradient:[true]} : (tensor<4xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%1098) = "pd_op.arange" [id:853] (%42, %1097, %41) {dtype:int64,place:Place(undefined:0),stop_gradient:[true]} : (tensor<1xi64>, tensor, tensor<1xi64>) -> tensor<-1xi64> + (%1099) = "pd_op.cast" [id:854] (%1098) {dtype:int32,stop_gradient:[true]} : (tensor<-1xi64>) -> tensor<-1xi32> + (%1100) = "builtin.combine" [id:855] (%1099, %1007) {stop_gradient:[false]} : (tensor<-1xi32>, tensor<-1xi32>) -> vec[tensor<-1xi32>,tensor<-1xi32>] + (%1101) = "pd_op.broadcast_tensors" [id:856] (%1100) {stop_gradient:[false]} : (vec[tensor<-1xi32>,tensor<-1xi32>]) -> vec[tensor<-1xi32>,tensor<-1xi32>] + (%1102, %1103) = "builtin.split" [id:857] (%1101) {stop_gradient:[false,false]} : (vec[tensor<-1xi32>,tensor<-1xi32>]) -> tensor<-1xi32>, tensor<-1xi32> + (%1104) = "builtin.combine" [id:858] (%1102, %1103) {stop_gradient:[false]} : (tensor<-1xi32>, tensor<-1xi32>) -> vec[tensor<-1xi32>,tensor<-1xi32>] + (%1105) = "pd_op.stack" [id:859] (%1104) {axis:-1,stop_gradient:[false]} : (vec[tensor<-1xi32>,tensor<-1xi32>]) -> tensor<-1x2xi32> + (%1106) = "pd_op.gather_nd" [id:860] (%1095, %1105) {stop_gradient:[false]} : (tensor<-1x80x14x14xf32>, tensor<-1x2xi32>) -> tensor<-1x14x14xf32> + (%1107) = "pd_op.shape64" [id:865] (%1099) {stop_gradient:[true]} : (tensor<-1xi32>) -> tensor<1xi64> + (%1108) = "pd_op.slice" [id:868] (%1107, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],stop_gradient:[true]} : (tensor<1xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%1109) = "builtin.combine" [id:871] (%1108, %13, %13) {stop_gradient:[true]} : (tensor, tensor, tensor) -> vec[tensor,tensor,tensor] + (%1110) = "pd_op.stack" [id:872] (%1109) {axis:0,stop_gradient:[true]} : (vec[tensor,tensor,tensor]) -> tensor<3xi64> + (%1111) = "pd_op.reshape" [id:873] (%1106, %1110) {stop_gradient:[false]} : (tensor<-1x14x14xf32>, tensor<3xi64>) -> tensor<-1x14x14xf32> + (%1112) = "pd_op.sigmoid" [id:874] (%1111) {stop_gradient:[false]} : (tensor<-1x14x14xf32>) -> tensor<-1x14x14xf32> + () = "cf.yield" [id:875] (%1112) {} : (tensor<-1x14x14xf32>) -> + } + (%1113) = "pd_op.shape64" [id:880] (%1000) {stop_gradient:[true]} : (tensor<-1xi32>) -> tensor<1xi64> + (%1114) = "pd_op.slice" [id:883] (%1113, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],stop_gradient:[true]} : (tensor<1xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%1115) = "pd_op.arange" [id:886] (%42, %1114, %41) {dtype:int64,place:Place(undefined:0),stop_gradient:[true]} : (tensor<1xi64>, tensor, tensor<1xi64>) -> tensor<-1xi64> + (%1116) = "pd_op.shape64" [id:887] (%1115) {stop_gradient:[true]} : (tensor<-1xi64>) -> tensor<1xi64> + (%1117) = "pd_op.slice" [id:890] (%1116, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],stop_gradient:[true]} : (tensor<1xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%1118) = "pd_op.create_array" [id:891] () {dtype:Undefined,stop_gradient:[true]} : () -> pd_op.tensor_array + (%1119) = "pd_op.create_array" [id:892] () {dtype:Undefined,stop_gradient:[true]} : () -> pd_op.tensor_array + (%1120) = "pd_op.full" [id:893] () {dtype:int64,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> tensor + (%1121) = "pd_op.full" [id:894] () {dtype:int64,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> tensor + (%1122) = "pd_op.less_than" [id:895] (%1120, %1117) {stop_gradient:[true]} : (tensor, tensor) -> tensor + (%1123) = "pd_op.full" [id:896] () {dtype:int32,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> tensor<-1xi32> + (%1124) = "pd_op.full" [id:897] () {dtype:float32,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> tensor<-1x6xf32> + (%1125) = "pd_op.full" [id:898] () {dtype:int64,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> tensor + (%1126, %1127, %1128, %1129, %1130) = "pd_op.while" [id:899] (cond=%1122, inputs=%1120, %1121, %1123, %1124, %1125) { + ^%arg_14 {}, %arg_15 {}, %arg_16 {}, %arg_17 {}, %arg_18 {} + (%1131) = "pd_op.scale" [id:901] (%arg_14, %12) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%1132) = "builtin.combine" [id:902] (%arg_14) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1133) = "pd_op.stack" [id:903] (%1132) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1134) = "builtin.combine" [id:904] (%1131) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1135) = "pd_op.stack" [id:905] (%1134) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1136) = "pd_op.slice" [id:906] (%1115, %1133, %1135) {axes:[0],decrease_axis:[0],infer_flags:[-1],stop_gradient:[true]} : (tensor<-1xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%1137) = "pd_op.scale" [id:908] (%1136, %12) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%1138) = "builtin.combine" [id:909] (%1136) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1139) = "pd_op.stack" [id:910] (%1138) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1140) = "builtin.combine" [id:911] (%1137) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1141) = "pd_op.stack" [id:912] (%1140) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1142) = "pd_op.slice" [id:913] (%1000, %1139, %1141) {axes:[0],decrease_axis:[0],infer_flags:[-1],stop_gradient:[false]} : (tensor<-1xi32>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%1143) = "pd_op.equal" [id:915] (%1142, %11) {stop_gradient:[true]} : (tensor, tensor) -> tensor + (%1144, %1145, %1146) = "pd_op.if" [id:916] (%1143) {stop_gradient:[true,true,true]} -> tensor<-1xi32>, tensor<-1x6xf32>, tensor { + () = "cf.yield" [id:917] (%27, %28, %arg_15) {} : (tensor<1xi32>, tensor<1x6xf32>, tensor) -> + } else { + (%1147) = "pd_op.scale" [id:919] (%1136, %12) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%1148) = "builtin.combine" [id:920] (%1136) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1149) = "pd_op.stack" [id:921] (%1148) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1150) = "builtin.combine" [id:922] (%1147) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1151) = "pd_op.stack" [id:923] (%1150) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1152) = "pd_op.slice" [id:924] (%1000, %1149, %1151) {axes:[0],decrease_axis:[0],infer_flags:[-1],stop_gradient:[false]} : (tensor<-1xi32>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%1153) = "pd_op.cast" [id:925] (%1152) {dtype:int64,stop_gradient:[false]} : (tensor) -> tensor + (%1154) = "pd_op.add" [id:926] (%arg_15, %1153) {stop_gradient:[true]} : (tensor, tensor) -> tensor + (%1155) = "builtin.combine" [id:927] (%arg_15) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1156) = "pd_op.stack" [id:928] (%1155) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1157) = "builtin.combine" [id:929] (%1154) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1158) = "pd_op.stack" [id:930] (%1157) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1159) = "pd_op.slice" [id:931] (%998, %1156, %1158) {axes:[0],decrease_axis:[],infer_flags:[-1],stop_gradient:[false]} : (tensor<-1x6xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1x6xf32> + (%1160) = "pd_op.scale" [id:933] (%1136, %12) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%1161) = "builtin.combine" [id:934] (%1136) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1162) = "pd_op.stack" [id:935] (%1161) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1163) = "builtin.combine" [id:936] (%1160) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1164) = "pd_op.stack" [id:937] (%1163) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1165) = "pd_op.slice" [id:938] (%1000, %1162, %1164) {axes:[0],decrease_axis:[],infer_flags:[-1],stop_gradient:[false]} : (tensor<-1xi32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1xi32> + (%1166) = "pd_op.scale" [id:940] (%1136, %12) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%1167) = "builtin.combine" [id:941] (%1136) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1168) = "pd_op.stack" [id:942] (%1167) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1169) = "builtin.combine" [id:943] (%1166) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1170) = "pd_op.stack" [id:944] (%1169) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1171) = "pd_op.slice" [id:945] (%1000, %1168, %1170) {axes:[0],decrease_axis:[0],infer_flags:[-1],stop_gradient:[false]} : (tensor<-1xi32>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%1172) = "pd_op.cast" [id:946] (%1171) {dtype:int64,stop_gradient:[false]} : (tensor) -> tensor + (%1173) = "pd_op.add" [id:947] (%arg_15, %1172) {stop_gradient:[false]} : (tensor, tensor) -> tensor + () = "cf.yield" [id:948] (%1165, %1159, %1173) {} : (tensor<-1xi32>, tensor<-1x6xf32>, tensor) -> + } + (%1174) = "pd_op.array_length" [id:949] (%1118) {} : (pd_op.tensor_array) -> tensor<1xi64> + (%1175) = "pd_op.array_write_" [id:950] (%1118, %1145, %1174) {} : (pd_op.tensor_array, tensor<-1x6xf32>, tensor<1xi64>) -> pd_op.tensor_array + (%1176) = "pd_op.array_length" [id:951] (%1119) {} : (pd_op.tensor_array) -> tensor<1xi64> + (%1177) = "pd_op.array_write_" [id:952] (%1119, %1144, %1176) {} : (pd_op.tensor_array, tensor<-1xi32>, tensor<1xi64>) -> pd_op.tensor_array + (%1178) = "pd_op.scale" [id:954] (%arg_14, %12) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%1179) = "pd_op.less_than" [id:955] (%1178, %1117) {stop_gradient:[true]} : (tensor, tensor) -> tensor + () = "cf.yield" [id:956] (%1179, %1178, %1146, %1144, %1145, %1136) {} : (tensor, tensor, tensor, tensor<-1xi32>, tensor<-1x6xf32>, tensor) -> + } + (%1180, %1181) = "pd_op.array_to_tensor" [id:957] (%1118) {axis:0,stop_gradient:[true,true],use_stack:false} : (pd_op.tensor_array) -> tensor<-1x6xf32>, tensor<-1xi32> + (%1182, %1183) = "pd_op.array_to_tensor" [id:958] (%1119) {axis:0,stop_gradient:[true,true],use_stack:false} : (pd_op.tensor_array) -> tensor<-1xi32>, tensor<-1xi32> + (%1184) = "pd_op.divide" [id:959] (%332, %334) {stop_gradient:[false]} : (tensor<-1x2xf32>, tensor<-1x2xf32>) -> tensor<-1x2xf32> + (%1185) = "pd_op.scale" [id:961] (%1184, %26) {bias:0.5,bias_after_scale:true,stop_gradient:[false]} : (tensor<-1x2xf32>, tensor<1xf32>) -> tensor<-1x2xf32> + (%1186) = "pd_op.floor" [id:962] (%1185) {stop_gradient:[false]} : (tensor<-1x2xf32>) -> tensor<-1x2xf32> + (%1187) = "pd_op.shape64" [id:963] (%1182) {stop_gradient:[true]} : (tensor<-1xi32>) -> tensor<1xi64> + (%1188) = "pd_op.slice" [id:966] (%1187, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],stop_gradient:[true]} : (tensor<1xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%1189) = "pd_op.arange" [id:969] (%42, %1188, %41) {dtype:int64,place:Place(undefined:0),stop_gradient:[true]} : (tensor<1xi64>, tensor, tensor<1xi64>) -> tensor<-1xi64> + (%1190) = "pd_op.shape64" [id:970] (%1189) {stop_gradient:[true]} : (tensor<-1xi64>) -> tensor<1xi64> + (%1191) = "pd_op.slice" [id:973] (%1190, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],stop_gradient:[true]} : (tensor<1xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%1192) = "pd_op.create_array" [id:974] () {dtype:Undefined,stop_gradient:[true]} : () -> pd_op.tensor_array + (%1193) = "pd_op.create_array" [id:975] () {dtype:Undefined,stop_gradient:[true]} : () -> pd_op.tensor_array + (%1194) = "pd_op.full" [id:976] () {dtype:int64,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> tensor + (%1195) = "pd_op.less_than" [id:977] (%1194, %1191) {stop_gradient:[true]} : (tensor, tensor) -> tensor + (%1196) = "pd_op.full" [id:978] () {dtype:float32,place:Place(undefined:0),shape:[],stop_gradient:[false],value:0} : () -> tensor<-1x4xf32> + (%1197) = "pd_op.full" [id:979] () {dtype:float32,place:Place(undefined:0),shape:[],stop_gradient:[false],value:0} : () -> tensor<-1x2xf32> + (%1198) = "pd_op.full" [id:980] () {dtype:float32,place:Place(undefined:0),shape:[],stop_gradient:[false],value:0} : () -> tensor<4xf32> + (%1199) = "pd_op.full" [id:981] () {dtype:float32,place:Place(undefined:0),shape:[],stop_gradient:[false],value:0} : () -> tensor<1xf32> + (%1200) = "pd_op.full" [id:982] () {dtype:float32,place:Place(undefined:0),shape:[],stop_gradient:[false],value:0} : () -> tensor<1xf32> + (%1201, %1202, %1203, %1204, %1205, %1206, %1207) = "pd_op.while" [id:983] (cond=%1195, inputs=%1194, %1130, %1196, %1197, %1198, %1199, %1200) { + ^%arg_19 {}, %arg_20 {}, %arg_21 {}, %arg_22 {}, %arg_23 {}, %arg_24 {}, %arg_25 {} + (%1208) = "pd_op.scale" [id:985] (%arg_19, %26) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%1209) = "builtin.combine" [id:986] (%arg_19) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1210) = "pd_op.stack" [id:987] (%1209) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1211) = "builtin.combine" [id:988] (%1208) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1212) = "pd_op.stack" [id:989] (%1211) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1213) = "pd_op.slice" [id:990] (%1189, %1210, %1212) {axes:[0],decrease_axis:[0],infer_flags:[-1],stop_gradient:[true]} : (tensor<-1xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%1214) = "pd_op.scale" [id:992] (%1213, %26) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%1215) = "builtin.combine" [id:993] (%1213) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1216) = "pd_op.stack" [id:994] (%1215) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1217) = "builtin.combine" [id:995] (%1214) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1218) = "pd_op.stack" [id:996] (%1217) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1219) = "pd_op.slice" [id:997] (%1186, %1216, %1218) {axes:[0],decrease_axis:[],infer_flags:[-1],stop_gradient:[false]} : (tensor<-1x2xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1x2xf32> + (%1220) = "pd_op.scale" [id:999] (%1213, %26) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%1221) = "builtin.combine" [id:1000] (%1213) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1222) = "pd_op.stack" [id:1001] (%1221) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1223) = "builtin.combine" [id:1002] (%1220) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1224) = "pd_op.stack" [id:1003] (%1223) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1225) = "pd_op.slice" [id:1004] (%1182, %1222, %1224) {axes:[0],decrease_axis:[],infer_flags:[-1],stop_gradient:[true]} : (tensor<-1xi32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1xi32> + (%1226) = "pd_op.cast" [id:1005] (%1225) {dtype:int64,stop_gradient:[true]} : (tensor<-1xi32>) -> tensor<-1xi64> + (%1227) = "pd_op.reshape" [id:1007] (%1226, %10) {stop_gradient:[true]} : (tensor<-1xi64>, tensor<0xi64>) -> tensor + (%1228) = "builtin.combine" [id:1009] (%1227, %9) {stop_gradient:[true]} : (tensor, tensor) -> vec[tensor,tensor] + (%1229) = "pd_op.stack" [id:1010] (%1228) {axis:0,stop_gradient:[true]} : (vec[tensor,tensor]) -> tensor<2xi64> + (%1230) = "pd_op.expand" [id:1011] (%1219, %1229) {stop_gradient:[false]} : (tensor<-1x2xf32>, tensor<2xi64>) -> tensor<-1x2xf32> + (%1231) = "pd_op.scale" [id:1013] (%1213, %26) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%1232) = "builtin.combine" [id:1020] (%1213, %8) {stop_gradient:[true]} : (tensor, tensor) -> vec[tensor,tensor] + (%1233) = "pd_op.stack" [id:1021] (%1232) {axis:0,stop_gradient:[true]} : (vec[tensor,tensor]) -> tensor<2xi64> + (%1234) = "builtin.combine" [id:1022] (%1231, %7) {stop_gradient:[true]} : (tensor, tensor) -> vec[tensor,tensor] + (%1235) = "pd_op.stack" [id:1023] (%1234) {axis:0,stop_gradient:[true]} : (vec[tensor,tensor]) -> tensor<2xi64> + (%1236) = "pd_op.slice" [id:1024] (%334, %1233, %1235) {axes:[0,1],decrease_axis:[0,1],infer_flags:[-1,1],stop_gradient:[false]} : (tensor<-1x2xf32>, tensor<2xi64>, tensor<2xi64>) -> tensor + (%1237) = "pd_op.scale" [id:1026] (%1213, %26) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%1238) = "builtin.combine" [id:1033] (%1213, %7) {stop_gradient:[true]} : (tensor, tensor) -> vec[tensor,tensor] + (%1239) = "pd_op.stack" [id:1034] (%1238) {axis:0,stop_gradient:[true]} : (vec[tensor,tensor]) -> tensor<2xi64> + (%1240) = "builtin.combine" [id:1035] (%1237, %6) {stop_gradient:[true]} : (tensor, tensor) -> vec[tensor,tensor] + (%1241) = "pd_op.stack" [id:1036] (%1240) {axis:0,stop_gradient:[true]} : (vec[tensor,tensor]) -> tensor<2xi64> + (%1242) = "pd_op.slice" [id:1037] (%334, %1239, %1241) {axes:[0,1],decrease_axis:[0,1],infer_flags:[-1,1],stop_gradient:[false]} : (tensor<-1x2xf32>, tensor<2xi64>, tensor<2xi64>) -> tensor + (%1243) = "pd_op.unsqueeze" [id:1039] (%1236, %44) {stop_gradient:[false]} : (tensor, tensor<1xi64>) -> tensor<1xf32> + (%1244) = "pd_op.unsqueeze" [id:1041] (%1242, %44) {stop_gradient:[false]} : (tensor, tensor<1xi64>) -> tensor<1xf32> + (%1245) = "builtin.combine" [id:1043] (%1244, %1243, %1244, %1243) {stop_gradient:[false]} : (tensor<1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> vec[tensor<1xf32>,tensor<1xf32>,tensor<1xf32>,tensor<1xf32>] + (%1246) = "pd_op.concat" [id:1044] (%1245, %5) {stop_gradient:[false]} : (vec[tensor<1xf32>,tensor<1xf32>,tensor<1xf32>,tensor<1xf32>], tensor<1xi32>) -> tensor<4xf32> + (%1247) = "pd_op.scale" [id:1046] (%1213, %26) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%1248) = "builtin.combine" [id:1047] (%1213) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1249) = "pd_op.stack" [id:1048] (%1248) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1250) = "builtin.combine" [id:1049] (%1247) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1251) = "pd_op.stack" [id:1050] (%1250) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1252) = "pd_op.slice" [id:1051] (%1182, %1249, %1251) {axes:[0],decrease_axis:[],infer_flags:[-1],stop_gradient:[true]} : (tensor<-1xi32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1xi32> + (%1253) = "pd_op.cast" [id:1052] (%1252) {dtype:int64,stop_gradient:[true]} : (tensor<-1xi32>) -> tensor<-1xi64> + (%1254) = "pd_op.reshape" [id:1054] (%1253, %10) {stop_gradient:[true]} : (tensor<-1xi64>, tensor<0xi64>) -> tensor + (%1255) = "builtin.combine" [id:1056] (%1254, %30) {stop_gradient:[true]} : (tensor, tensor) -> vec[tensor,tensor] + (%1256) = "pd_op.stack" [id:1057] (%1255) {axis:0,stop_gradient:[true]} : (vec[tensor,tensor]) -> tensor<2xi64> + (%1257) = "pd_op.expand" [id:1058] (%1246, %1256) {stop_gradient:[false]} : (tensor<4xf32>, tensor<2xi64>) -> tensor<-1x4xf32> + (%1258) = "pd_op.array_length" [id:1059] (%1192) {} : (pd_op.tensor_array) -> tensor<1xi64> + (%1259) = "pd_op.array_write_" [id:1060] (%1192, %1230, %1258) {} : (pd_op.tensor_array, tensor<-1x2xf32>, tensor<1xi64>) -> pd_op.tensor_array + (%1260) = "pd_op.array_length" [id:1061] (%1193) {} : (pd_op.tensor_array) -> tensor<1xi64> + (%1261) = "pd_op.array_write_" [id:1062] (%1193, %1257, %1260) {} : (pd_op.tensor_array, tensor<-1x4xf32>, tensor<1xi64>) -> pd_op.tensor_array + (%1262) = "pd_op.scale" [id:1064] (%arg_19, %26) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%1263) = "pd_op.less_than" [id:1065] (%1262, %1191) {stop_gradient:[true]} : (tensor, tensor) -> tensor + () = "cf.yield" [id:1066] (%1263, %1262, %1213, %1257, %1230, %1246, %1244, %1243) {} : (tensor, tensor, tensor, tensor<-1x4xf32>, tensor<-1x2xf32>, tensor<4xf32>, tensor<1xf32>, tensor<1xf32>) -> + } + (%1264, %1265) = "pd_op.array_to_tensor" [id:1067] (%1192) {axis:0,stop_gradient:[true,true],use_stack:false} : (pd_op.tensor_array) -> tensor<-1x2xf32>, tensor<-1xi32> + (%1266, %1267) = "pd_op.array_to_tensor" [id:1068] (%1193) {axis:0,stop_gradient:[true,true],use_stack:false} : (pd_op.tensor_array) -> tensor<-1x4xf32>, tensor<-1xi32> + (%1268) = "pd_op.slice" [id:1071] (%1180, %44, %43) {axes:[1],decrease_axis:[],infer_flags:[1],stop_gradient:[true]} : (tensor<-1x6xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1x1xf32> + (%1269) = "pd_op.slice" [id:1074] (%1180, %43, %52) {axes:[1],decrease_axis:[],infer_flags:[1],stop_gradient:[true]} : (tensor<-1x6xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1x1xf32> + (%1270) = "pd_op.slice" [id:1077] (%1180, %52, %36) {axes:[1],decrease_axis:[],infer_flags:[1],stop_gradient:[true]} : (tensor<-1x6xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1x4xf32> + (%1271) = "pd_op.divide" [id:1078] (%1270, %1266) {stop_gradient:[true]} : (tensor<-1x4xf32>, tensor<-1x4xf32>) -> tensor<-1x4xf32> + (%1272) = "pd_op.slice" [id:1081] (%1264, %44, %43) {axes:[1],decrease_axis:[1],infer_flags:[1],stop_gradient:[true]} : (tensor<-1x2xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1xf32> + (%1273) = "pd_op.slice" [id:1084] (%1264, %43, %52) {axes:[1],decrease_axis:[1],infer_flags:[1],stop_gradient:[true]} : (tensor<-1x2xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1xf32> + (%1274) = "pd_op.full_like" [id:1086] (%1272, %703) {dtype:float32,place:Place(undefined:0),stop_gradient:[true]} : (tensor<-1xf32>, tensor<1xf32>) -> tensor<-1xf32> + (%1275) = "pd_op.slice" [id:1089] (%1271, %44, %43) {axes:[1],decrease_axis:[1],infer_flags:[1],stop_gradient:[true]} : (tensor<-1x4xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1xf32> + (%1276) = "pd_op.minimum" [id:1090] (%1275, %1273) {stop_gradient:[true]} : (tensor<-1xf32>, tensor<-1xf32>) -> tensor<-1xf32> + (%1277) = "pd_op.maximum" [id:1091] (%1276, %1274) {stop_gradient:[true]} : (tensor<-1xf32>, tensor<-1xf32>) -> tensor<-1xf32> + (%1278) = "pd_op.slice" [id:1094] (%1271, %43, %52) {axes:[1],decrease_axis:[1],infer_flags:[1],stop_gradient:[true]} : (tensor<-1x4xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1xf32> + (%1279) = "pd_op.minimum" [id:1095] (%1278, %1272) {stop_gradient:[true]} : (tensor<-1xf32>, tensor<-1xf32>) -> tensor<-1xf32> + (%1280) = "pd_op.maximum" [id:1096] (%1279, %1274) {stop_gradient:[true]} : (tensor<-1xf32>, tensor<-1xf32>) -> tensor<-1xf32> + (%1281) = "pd_op.slice" [id:1099] (%1271, %52, %51) {axes:[1],decrease_axis:[1],infer_flags:[1],stop_gradient:[true]} : (tensor<-1x4xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1xf32> + (%1282) = "pd_op.minimum" [id:1100] (%1281, %1273) {stop_gradient:[true]} : (tensor<-1xf32>, tensor<-1xf32>) -> tensor<-1xf32> + (%1283) = "pd_op.maximum" [id:1101] (%1282, %1274) {stop_gradient:[true]} : (tensor<-1xf32>, tensor<-1xf32>) -> tensor<-1xf32> + (%1284) = "pd_op.slice" [id:1104] (%1271, %51, %50) {axes:[1],decrease_axis:[1],infer_flags:[1],stop_gradient:[true]} : (tensor<-1x4xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1xf32> + (%1285) = "pd_op.minimum" [id:1105] (%1284, %1272) {stop_gradient:[true]} : (tensor<-1xf32>, tensor<-1xf32>) -> tensor<-1xf32> + (%1286) = "pd_op.maximum" [id:1106] (%1285, %1274) {stop_gradient:[true]} : (tensor<-1xf32>, tensor<-1xf32>) -> tensor<-1xf32> + (%1287) = "builtin.combine" [id:1107] (%1277, %1280, %1283, %1286) {stop_gradient:[true]} : (tensor<-1xf32>, tensor<-1xf32>, tensor<-1xf32>, tensor<-1xf32>) -> vec[tensor<-1xf32>,tensor<-1xf32>,tensor<-1xf32>,tensor<-1xf32>] + (%1288) = "pd_op.stack" [id:1108] (%1287) {axis:-1,stop_gradient:[true]} : (vec[tensor<-1xf32>,tensor<-1xf32>,tensor<-1xf32>,tensor<-1xf32>]) -> tensor<-1x4xf32> + (%1289) = "pd_op.slice" [id:1111] (%1288, %52, %51) {axes:[1],decrease_axis:[1],infer_flags:[1],stop_gradient:[true]} : (tensor<-1x4xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1xf32> + (%1290) = "pd_op.slice" [id:1114] (%1288, %44, %43) {axes:[1],decrease_axis:[1],infer_flags:[1],stop_gradient:[true]} : (tensor<-1x4xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1xf32> + (%1291) = "pd_op.subtract" [id:1115] (%1289, %1290) {stop_gradient:[true]} : (tensor<-1xf32>, tensor<-1xf32>) -> tensor<-1xf32> + (%1292) = "pd_op.slice" [id:1118] (%1288, %51, %50) {axes:[1],decrease_axis:[1],infer_flags:[1],stop_gradient:[true]} : (tensor<-1x4xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1xf32> + (%1293) = "pd_op.slice" [id:1121] (%1288, %43, %52) {axes:[1],decrease_axis:[1],infer_flags:[1],stop_gradient:[true]} : (tensor<-1x4xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1xf32> + (%1294) = "pd_op.subtract" [id:1122] (%1292, %1293) {stop_gradient:[true]} : (tensor<-1xf32>, tensor<-1xf32>) -> tensor<-1xf32> + (%1295) = "pd_op.greater_than" [id:1124] (%1294, %25) {stop_gradient:[true]} : (tensor<-1xf32>, tensor) -> tensor<-1xb> + (%1296) = "pd_op.greater_than" [id:1126] (%1291, %25) {stop_gradient:[true]} : (tensor<-1xf32>, tensor) -> tensor<-1xb> + (%1297) = "pd_op.logical_and" [id:1127] (%1295, %1296) {stop_gradient:[true]} : (tensor<-1xb>, tensor<-1xb>) -> tensor<-1xb> + (%1298) = "pd_op.unsqueeze" [id:1129] (%1297, %43) {stop_gradient:[true]} : (tensor<-1xb>, tensor<1xi64>) -> tensor<-1x1xb> + (%1299) = "pd_op.full_like" [id:1131] (%1268, %26) {dtype:float32,place:Place(undefined:0),stop_gradient:[true]} : (tensor<-1x1xf32>, tensor<1xf32>) -> tensor<-1x1xf32> + (%1300) = "pd_op.scale" [id:1133] (%1299, %24) {bias:0,bias_after_scale:true,stop_gradient:[true]} : (tensor<-1x1xf32>, tensor<1xf32>) -> tensor<-1x1xf32> + (%1301) = "pd_op.where" [id:1134] (%1298, %1268, %1300) {stop_gradient:[true]} : (tensor<-1x1xb>, tensor<-1x1xf32>, tensor<-1x1xf32>) -> tensor<-1x1xf32> + (%1302) = "builtin.combine" [id:1136] (%1301, %1269, %1288) {stop_gradient:[true]} : (tensor<-1x1xf32>, tensor<-1x1xf32>, tensor<-1x4xf32>) -> vec[tensor<-1x1xf32>,tensor<-1x1xf32>,tensor<-1x4xf32>] + (%1303) = "pd_op.concat" [id:1137] (%1302, %23) {stop_gradient:[true]} : (vec[tensor<-1x1xf32>,tensor<-1x1xf32>,tensor<-1x4xf32>], tensor<1xi32>) -> tensor<-1x6xf32> + (%1304) = "pd_op.shape64" [id:1138] (%1004) {stop_gradient:[true]} : (tensor<-1x-1x-1xf32>) -> tensor<3xi64> + (%1305) = "pd_op.slice" [id:1141] (%1304, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],stop_gradient:[true]} : (tensor<3xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%1306) = "pd_op.cast" [id:1142] (%1264) {dtype:int32,stop_gradient:[true]} : (tensor<-1x2xf32>) -> tensor<-1x2xi32> + (%1307) = "pd_op.slice" [id:1145] (%1306, %44, %43) {axes:[1],decrease_axis:[1],infer_flags:[1],stop_gradient:[true]} : (tensor<-1x2xi32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1xi32> + (%1308) = "pd_op.max" [id:1147] (%1307, %22) {keepdim:false,stop_gradient:[true]} : (tensor<-1xi32>, tensor<0xi64>) -> tensor + (%1309) = "pd_op.slice" [id:1150] (%1306, %43, %52) {axes:[1],decrease_axis:[1],infer_flags:[1],stop_gradient:[true]} : (tensor<-1x2xi32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1xi32> + (%1310) = "pd_op.max" [id:1152] (%1309, %22) {keepdim:false,stop_gradient:[true]} : (tensor<-1xi32>, tensor<0xi64>) -> tensor + (%1311) = "pd_op.cast" [id:1153] (%1308) {dtype:int64,stop_gradient:[true]} : (tensor) -> tensor + (%1312) = "pd_op.cast" [id:1154] (%1310) {dtype:int64,stop_gradient:[true]} : (tensor) -> tensor + (%1313) = "builtin.combine" [id:1155] (%1305, %1311, %1312) {stop_gradient:[true]} : (tensor, tensor, tensor) -> vec[tensor,tensor,tensor] + (%1314) = "pd_op.stack" [id:1156] (%1313) {axis:0,stop_gradient:[true]} : (vec[tensor,tensor,tensor]) -> tensor<3xi64> + (%1315) = "pd_op.full_with_tensor" [id:1158] (%703, %1314) {dtype:int32,stop_gradient:[true]} : (tensor<1xf32>, tensor<3xi64>) -> tensor<-1x-1x-1xi32> + (%1316) = "pd_op.scale" [id:1160] (%1315, %26) {bias:-1,bias_after_scale:true,stop_gradient:[true]} : (tensor<-1x-1x-1xi32>, tensor<1xf32>) -> tensor<-1x-1x-1xi32> + (%1317) = "pd_op.shape64" [id:1161] (%1182) {stop_gradient:[true]} : (tensor<-1xi32>) -> tensor<1xi64> + (%1318) = "pd_op.slice" [id:1164] (%1317, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],stop_gradient:[true]} : (tensor<1xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%1319) = "pd_op.arange" [id:1167] (%42, %1318, %41) {dtype:int64,place:Place(undefined:0),stop_gradient:[true]} : (tensor<1xi64>, tensor, tensor<1xi64>) -> tensor<-1xi64> + (%1320) = "pd_op.shape64" [id:1168] (%1319) {stop_gradient:[true]} : (tensor<-1xi64>) -> tensor<1xi64> + (%1321) = "pd_op.slice" [id:1171] (%1320, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],stop_gradient:[true]} : (tensor<1xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%1322) = "pd_op.full" [id:1172] () {dtype:int64,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> tensor + (%1323) = "pd_op.full" [id:1173] () {dtype:int64,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> tensor + (%1324) = "pd_op.less_than" [id:1174] (%1322, %1321) {stop_gradient:[true]} : (tensor, tensor) -> tensor + (%1325) = "pd_op.full" [id:1175] () {dtype:float32,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> tensor<-1x6xf32> + (%1326) = "pd_op.full" [id:1176] () {dtype:int64,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> tensor + (%1327) = "pd_op.full" [id:1177] () {dtype:int32,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> tensor + (%1328) = "pd_op.full" [id:1178] () {dtype:int32,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> tensor + (%1329) = "pd_op.full" [id:1179] () {dtype:float32,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> tensor<-1x-1x-1xf32> + (%1330) = "pd_op.full" [id:1180] () {dtype:int32,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> tensor<-1x-1x-1xi32> + (%1331, %1332, %1333, %1334, %1335, %1336, %1337, %1338, %1339) = "pd_op.while" [id:1181] (cond=%1324, inputs=%1322, %1323, %1316, %1325, %1326, %1327, %1328, %1329, %1330) { + ^%arg_26 {}, %arg_27 {}, %arg_28 {}, %arg_29 {}, %arg_30 {}, %arg_31 {}, %arg_32 {}, %arg_33 {}, %arg_34 {} + (%1340) = "pd_op.scale" [id:1183] (%arg_26, %26) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%1341) = "builtin.combine" [id:1184] (%arg_26) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1342) = "pd_op.stack" [id:1185] (%1341) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1343) = "builtin.combine" [id:1186] (%1340) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1344) = "pd_op.stack" [id:1187] (%1343) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1345) = "pd_op.slice" [id:1188] (%1319, %1342, %1344) {axes:[0],decrease_axis:[0],infer_flags:[-1],stop_gradient:[true]} : (tensor<-1xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%1346) = "pd_op.scale" [id:1190] (%1345, %26) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%1347) = "builtin.combine" [id:1191] (%1345) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1348) = "pd_op.stack" [id:1192] (%1347) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1349) = "builtin.combine" [id:1193] (%1346) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1350) = "pd_op.stack" [id:1194] (%1349) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1351) = "pd_op.slice" [id:1195] (%1182, %1348, %1350) {axes:[0],decrease_axis:[0],infer_flags:[-1],stop_gradient:[true]} : (tensor<-1xi32>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%1352) = "pd_op.cast" [id:1196] (%1351) {dtype:int64,stop_gradient:[true]} : (tensor) -> tensor + (%1353) = "pd_op.add" [id:1197] (%arg_27, %1352) {stop_gradient:[true]} : (tensor, tensor) -> tensor + (%1354) = "builtin.combine" [id:1198] (%arg_27) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1355) = "pd_op.stack" [id:1199] (%1354) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1356) = "builtin.combine" [id:1200] (%1353) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1357) = "pd_op.stack" [id:1201] (%1356) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1358) = "pd_op.slice" [id:1202] (%1303, %1355, %1357) {axes:[0],decrease_axis:[],infer_flags:[-1],stop_gradient:[true]} : (tensor<-1x6xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1x6xf32> + (%1359) = "pd_op.scale" [id:1204] (%1345, %26) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%1360) = "builtin.combine" [id:1205] (%1345) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1361) = "pd_op.stack" [id:1206] (%1360) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1362) = "builtin.combine" [id:1207] (%1359) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1363) = "pd_op.stack" [id:1208] (%1362) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1364) = "pd_op.slice" [id:1209] (%1182, %1361, %1363) {axes:[0],decrease_axis:[0],infer_flags:[-1],stop_gradient:[true]} : (tensor<-1xi32>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%1365) = "pd_op.cast" [id:1210] (%1364) {dtype:int64,stop_gradient:[true]} : (tensor) -> tensor + (%1366) = "pd_op.add" [id:1211] (%arg_27, %1365) {stop_gradient:[true]} : (tensor, tensor) -> tensor + (%1367) = "builtin.combine" [id:1214] (%1366) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1368) = "pd_op.stack" [id:1215] (%1367) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1369) = "pd_op.slice" [id:1216] (%1004, %1355, %1368) {axes:[0],decrease_axis:[],infer_flags:[-1],stop_gradient:[true]} : (tensor<-1x-1x-1xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1x-1x-1xf32> + (%1370) = "pd_op.scale" [id:1218] (%1345, %26) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%1371) = "builtin.combine" [id:1225] (%1345, %21) {stop_gradient:[true]} : (tensor, tensor) -> vec[tensor,tensor] + (%1372) = "pd_op.stack" [id:1226] (%1371) {axis:0,stop_gradient:[true]} : (vec[tensor,tensor]) -> tensor<2xi64> + (%1373) = "builtin.combine" [id:1227] (%1370, %20) {stop_gradient:[true]} : (tensor, tensor) -> vec[tensor,tensor] + (%1374) = "pd_op.stack" [id:1228] (%1373) {axis:0,stop_gradient:[true]} : (vec[tensor,tensor]) -> tensor<2xi64> + (%1375) = "pd_op.slice" [id:1229] (%1306, %1372, %1374) {axes:[0,1],decrease_axis:[0,1],infer_flags:[-1,1],stop_gradient:[true]} : (tensor<-1x2xi32>, tensor<2xi64>, tensor<2xi64>) -> tensor + (%1376) = "pd_op.scale" [id:1231] (%1345, %26) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%1377) = "builtin.combine" [id:1238] (%1345, %20) {stop_gradient:[true]} : (tensor, tensor) -> vec[tensor,tensor] + (%1378) = "pd_op.stack" [id:1239] (%1377) {axis:0,stop_gradient:[true]} : (vec[tensor,tensor]) -> tensor<2xi64> + (%1379) = "builtin.combine" [id:1240] (%1376, %4) {stop_gradient:[true]} : (tensor, tensor) -> vec[tensor,tensor] + (%1380) = "pd_op.stack" [id:1241] (%1379) {axis:0,stop_gradient:[true]} : (vec[tensor,tensor]) -> tensor<2xi64> + (%1381) = "pd_op.slice" [id:1242] (%1306, %1378, %1380) {axes:[0,1],decrease_axis:[0,1],infer_flags:[-1,1],stop_gradient:[true]} : (tensor<-1x2xi32>, tensor<2xi64>, tensor<2xi64>) -> tensor + (%1382) = "pd_op.unsqueeze" [id:1244] (%1369, %43) {stop_gradient:[true]} : (tensor<-1x-1x-1xf32>, tensor<1xi64>) -> tensor<-1x1x-1x-1xf32> + (%1383) = "pd_op.slice" [id:1247] (%1358, %52, %36) {axes:[1],decrease_axis:[],infer_flags:[1],stop_gradient:[true]} : (tensor<-1x6xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1x4xf32> + (%1384) = "pd_op.split_with_num" [id:1249] (%1383, %23) {num:4,stop_gradient:[true]} : (tensor<-1x4xf32>, tensor<1xi32>) -> vec[tensor<-1x1xf32>,tensor<-1x1xf32>,tensor<-1x1xf32>,tensor<-1x1xf32>] + (%1385, %1386, %1387, %1388) = "builtin.split" [id:1250] (%1384) {stop_gradient:[true,true,true,true]} : (vec[tensor<-1x1xf32>,tensor<-1x1xf32>,tensor<-1x1xf32>,tensor<-1x1xf32>]) -> tensor<-1x1xf32>, tensor<-1x1xf32>, tensor<-1x1xf32>, tensor<-1x1xf32> + (%1389) = "pd_op.shape64" [id:1251] (%1382) {stop_gradient:[true]} : (tensor<-1x1x-1x-1xf32>) -> tensor<4xi64> + (%1390) = "pd_op.slice" [id:1254] (%1389, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],stop_gradient:[true]} : (tensor<4xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%1391) = "pd_op.cast" [id:1262] (%1375) {dtype:int64,stop_gradient:[true]} : (tensor) -> tensor + (%1392) = "pd_op.arange" [id:1264] (%42, %1391, %41) {dtype:int64,place:Place(undefined:0),stop_gradient:[true]} : (tensor<1xi64>, tensor, tensor<1xi64>) -> tensor<-1xi64> + (%1393) = "pd_op.cast" [id:1265] (%1392) {dtype:float32,stop_gradient:[true]} : (tensor<-1xi64>) -> tensor<-1xf32> + (%1394) = "pd_op.scale" [id:1267] (%1393, %26) {bias:0.5,bias_after_scale:true,stop_gradient:[true]} : (tensor<-1xf32>, tensor<1xf32>) -> tensor<-1xf32> + (%1395) = "pd_op.cast" [id:1269] (%1381) {dtype:int64,stop_gradient:[true]} : (tensor) -> tensor + (%1396) = "pd_op.arange" [id:1271] (%42, %1395, %41) {dtype:int64,place:Place(undefined:0),stop_gradient:[true]} : (tensor<1xi64>, tensor, tensor<1xi64>) -> tensor<-1xi64> + (%1397) = "pd_op.cast" [id:1272] (%1396) {dtype:float32,stop_gradient:[true]} : (tensor<-1xi64>) -> tensor<-1xf32> + (%1398) = "pd_op.scale" [id:1274] (%1397, %26) {bias:0.5,bias_after_scale:true,stop_gradient:[true]} : (tensor<-1xf32>, tensor<1xf32>) -> tensor<-1xf32> + (%1399) = "pd_op.subtract" [id:1275] (%1394, %1386) {stop_gradient:[true]} : (tensor<-1xf32>, tensor<-1x1xf32>) -> tensor<-1x-1xf32> + (%1400) = "pd_op.subtract" [id:1276] (%1388, %1386) {stop_gradient:[true]} : (tensor<-1x1xf32>, tensor<-1x1xf32>) -> tensor<-1x1xf32> + (%1401) = "pd_op.divide" [id:1277] (%1399, %1400) {stop_gradient:[true]} : (tensor<-1x-1xf32>, tensor<-1x1xf32>) -> tensor<-1x-1xf32> + (%1402) = "pd_op.scale" [id:1279] (%1401, %3) {bias:0,bias_after_scale:true,stop_gradient:[true]} : (tensor<-1x-1xf32>, tensor<1xf32>) -> tensor<-1x-1xf32> + (%1403) = "pd_op.scale" [id:1281] (%1402, %26) {bias:-1,bias_after_scale:true,stop_gradient:[true]} : (tensor<-1x-1xf32>, tensor<1xf32>) -> tensor<-1x-1xf32> + (%1404) = "pd_op.subtract" [id:1282] (%1398, %1385) {stop_gradient:[true]} : (tensor<-1xf32>, tensor<-1x1xf32>) -> tensor<-1x-1xf32> + (%1405) = "pd_op.subtract" [id:1283] (%1387, %1385) {stop_gradient:[true]} : (tensor<-1x1xf32>, tensor<-1x1xf32>) -> tensor<-1x1xf32> + (%1406) = "pd_op.divide" [id:1284] (%1404, %1405) {stop_gradient:[true]} : (tensor<-1x-1xf32>, tensor<-1x1xf32>) -> tensor<-1x-1xf32> + (%1407) = "pd_op.scale" [id:1286] (%1406, %3) {bias:0,bias_after_scale:true,stop_gradient:[true]} : (tensor<-1x-1xf32>, tensor<1xf32>) -> tensor<-1x-1xf32> + (%1408) = "pd_op.scale" [id:1288] (%1407, %26) {bias:-1,bias_after_scale:true,stop_gradient:[true]} : (tensor<-1x-1xf32>, tensor<1xf32>) -> tensor<-1x-1xf32> + (%1409) = "pd_op.unsqueeze" [id:1290] (%1408, %43) {stop_gradient:[true]} : (tensor<-1x-1xf32>, tensor<1xi64>) -> tensor<-1x1x-1xf32> + (%1410) = "pd_op.shape64" [id:1291] (%1403) {stop_gradient:[true]} : (tensor<-1x-1xf32>) -> tensor<2xi64> + (%1411) = "pd_op.slice" [id:1297] (%1410, %43, %52) {axes:[0],decrease_axis:[0],infer_flags:[1],stop_gradient:[true]} : (tensor<2xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%1412) = "pd_op.shape64" [id:1298] (%1408) {stop_gradient:[true]} : (tensor<-1x-1xf32>) -> tensor<2xi64> + (%1413) = "pd_op.slice" [id:1304] (%1412, %43, %52) {axes:[0],decrease_axis:[0],infer_flags:[1],stop_gradient:[true]} : (tensor<2xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%1414) = "builtin.combine" [id:1305] (%1390, %1411, %1413) {stop_gradient:[true]} : (tensor, tensor, tensor) -> vec[tensor,tensor,tensor] + (%1415) = "pd_op.stack" [id:1306] (%1414) {axis:0,stop_gradient:[true]} : (vec[tensor,tensor,tensor]) -> tensor<3xi64> + (%1416) = "pd_op.expand" [id:1307] (%1409, %1415) {stop_gradient:[true]} : (tensor<-1x1x-1xf32>, tensor<3xi64>) -> tensor<-1x-1x-1xf32> + (%1417) = "pd_op.unsqueeze" [id:1309] (%1403, %52) {stop_gradient:[true]} : (tensor<-1x-1xf32>, tensor<1xi64>) -> tensor<-1x-1x1xf32> + (%1418) = "pd_op.shape64" [id:1310] (%1403) {stop_gradient:[true]} : (tensor<-1x-1xf32>) -> tensor<2xi64> + (%1419) = "pd_op.slice" [id:1316] (%1418, %43, %52) {axes:[0],decrease_axis:[0],infer_flags:[1],stop_gradient:[true]} : (tensor<2xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%1420) = "pd_op.shape64" [id:1317] (%1408) {stop_gradient:[true]} : (tensor<-1x-1xf32>) -> tensor<2xi64> + (%1421) = "pd_op.slice" [id:1323] (%1420, %43, %52) {axes:[0],decrease_axis:[0],infer_flags:[1],stop_gradient:[true]} : (tensor<2xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%1422) = "builtin.combine" [id:1324] (%1390, %1419, %1421) {stop_gradient:[true]} : (tensor, tensor, tensor) -> vec[tensor,tensor,tensor] + (%1423) = "pd_op.stack" [id:1325] (%1422) {axis:0,stop_gradient:[true]} : (vec[tensor,tensor,tensor]) -> tensor<3xi64> + (%1424) = "pd_op.expand" [id:1326] (%1417, %1423) {stop_gradient:[true]} : (tensor<-1x-1x1xf32>, tensor<3xi64>) -> tensor<-1x-1x-1xf32> + (%1425) = "builtin.combine" [id:1327] (%1416, %1424) {stop_gradient:[true]} : (tensor<-1x-1x-1xf32>, tensor<-1x-1x-1xf32>) -> vec[tensor<-1x-1x-1xf32>,tensor<-1x-1x-1xf32>] + (%1426) = "pd_op.stack" [id:1328] (%1425) {axis:3,stop_gradient:[true]} : (vec[tensor<-1x-1x-1xf32>,tensor<-1x-1x-1xf32>]) -> tensor<-1x-1x-1x2xf32> + (%1427) = "pd_op.grid_sample" [id:1329] (%1382, %1426) {align_corners:false,mode:"bilinear",padding_mode:"zeros",stop_gradient:[true]} : (tensor<-1x1x-1x-1xf32>, tensor<-1x-1x-1x2xf32>) -> tensor<-1x1x-1x-1xf32> + (%1428) = "pd_op.slice" [id:1332] (%1427, %44, %43) {axes:[1],decrease_axis:[1],infer_flags:[1],stop_gradient:[true]} : (tensor<-1x1x-1x-1xf32>, tensor<1xi64>, tensor<1xi64>) -> tensor<-1x-1x-1xf32> + (%1429) = "pd_op.greater_equal" [id:1334] (%1428, %2) {stop_gradient:[true]} : (tensor<-1x-1x-1xf32>, tensor) -> tensor<-1x-1x-1xb> + (%1430) = "pd_op.cast" [id:1335] (%1429) {dtype:int32,stop_gradient:[true]} : (tensor<-1x-1x-1xb>) -> tensor<-1x-1x-1xi32> + (%1431) = "pd_op.scale" [id:1337] (%1345, %26) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%1432) = "builtin.combine" [id:1338] (%1345) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1433) = "pd_op.stack" [id:1339] (%1432) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1434) = "builtin.combine" [id:1340] (%1431) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1435) = "pd_op.stack" [id:1341] (%1434) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1436) = "pd_op.slice" [id:1342] (%1182, %1433, %1435) {axes:[0],decrease_axis:[0],infer_flags:[-1],stop_gradient:[true]} : (tensor<-1xi32>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%1437) = "pd_op.cast" [id:1343] (%1436) {dtype:int64,stop_gradient:[true]} : (tensor) -> tensor + (%1438) = "pd_op.add" [id:1344] (%arg_27, %1437) {stop_gradient:[true]} : (tensor, tensor) -> tensor + (%1439) = "pd_op.cast" [id:1349] (%1375) {dtype:int64,stop_gradient:[true]} : (tensor) -> tensor + (%1440) = "pd_op.cast" [id:1350] (%1381) {dtype:int64,stop_gradient:[true]} : (tensor) -> tensor + (%1441) = "builtin.combine" [id:1351] (%arg_27, %1, %1) {stop_gradient:[true]} : (tensor, tensor, tensor) -> vec[tensor,tensor,tensor] + (%1442) = "pd_op.stack" [id:1352] (%1441) {axis:0,stop_gradient:[true]} : (vec[tensor,tensor,tensor]) -> tensor<3xi64> + (%1443) = "builtin.combine" [id:1353] (%1438, %1439, %1440) {stop_gradient:[true]} : (tensor, tensor, tensor) -> vec[tensor,tensor,tensor] + (%1444) = "pd_op.stack" [id:1354] (%1443) {axis:0,stop_gradient:[true]} : (vec[tensor,tensor,tensor]) -> tensor<3xi64> + (%1445) = "pd_op.set_value_with_tensor_" [id:1356] (%arg_28, %1430, %1442, %1444, %0) {axes:[0,1,2],decrease_axes:[],none_axes:[],stop_gradient:[true]} : (tensor<-1x-1x-1xi32>, tensor<-1x-1x-1xi32>, tensor<3xi64>, tensor<3xi64>, tensor<3xi64>) -> tensor<-1x-1x-1xi32> + (%1446) = "pd_op.scale" [id:1358] (%1345, %26) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%1447) = "builtin.combine" [id:1359] (%1345) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1448) = "pd_op.stack" [id:1360] (%1447) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1449) = "builtin.combine" [id:1361] (%1446) {stop_gradient:[true]} : (tensor) -> vec[tensor] + (%1450) = "pd_op.stack" [id:1362] (%1449) {axis:0,stop_gradient:[true]} : (vec[tensor]) -> tensor<1xi64> + (%1451) = "pd_op.slice" [id:1363] (%1182, %1448, %1450) {axes:[0],decrease_axis:[0],infer_flags:[-1],stop_gradient:[true]} : (tensor<-1xi32>, tensor<1xi64>, tensor<1xi64>) -> tensor + (%1452) = "pd_op.cast" [id:1364] (%1451) {dtype:int64,stop_gradient:[true]} : (tensor) -> tensor + (%1453) = "pd_op.add" [id:1365] (%arg_27, %1452) {stop_gradient:[true]} : (tensor, tensor) -> tensor + (%1454) = "pd_op.scale" [id:1367] (%arg_26, %26) {bias:1,bias_after_scale:true,stop_gradient:[true]} : (tensor, tensor<1xf32>) -> tensor + (%1455) = "pd_op.less_than" [id:1368] (%1454, %1321) {stop_gradient:[true]} : (tensor, tensor) -> tensor + () = "cf.yield" [id:1369] (%1455, %1454, %1453, %1445, %1358, %1345, %1375, %1381, %1369, %1430) {} : (tensor, tensor, tensor, tensor<-1x-1x-1xi32>, tensor<-1x6xf32>, tensor, tensor, tensor, tensor<-1x-1x-1xf32>, tensor<-1x-1x-1xi32>) -> + } + () = "builtin.shadow_output" [id:1785] (%1303) {output_name:"fetch_name_0"} : (tensor<-1x6xf32>) -> + () = "builtin.shadow_output" [id:1786] (%1182) {output_name:"fetch_name_1"} : (tensor<-1xi32>) -> + () = "builtin.shadow_output" [id:1787] (%1333) {output_name:"fetch_name_2"} : (tensor<-1x-1x-1xi32>) -> +} + +IR after lowering = { + (%0) = "builtin.constant" [id:2016] () {origin_id:2014,persistable:[true],value:"constant_folding@_174513116887061917064"} : () -> cpu_tensor<3xi64> + (%1) = "builtin.constant" [id:2017] () {origin_id:2005,persistable:[true],value:"constant_folding@_174513116885637196163"} : () -> cpu_tensor + (%2) = "builtin.parameter" [id:2018] () {origin_id:1996,parameter_name:"constant_folding@_174513116884162097162",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor + (%3) = "builtin.constant" [id:2019] () {origin_id:1987,persistable:[true],value:"constant_folding@_174513116882724144161"} : () -> cpu_tensor<1xf32> + (%4) = "builtin.constant" [id:2020] () {origin_id:1978,persistable:[true],value:"constant_folding@_174513116880873974160"} : () -> cpu_tensor + (%5) = "builtin.constant" [id:2021] () {origin_id:1956,persistable:[true],value:"constant_folding@_174513116876880248258"} : () -> cpu_tensor<1xi32> + (%6) = "builtin.constant" [id:2022] () {origin_id:1947,persistable:[true],value:"constant_folding@_174513116875198908257"} : () -> cpu_tensor + (%7) = "builtin.constant" [id:2023] () {origin_id:1925,persistable:[true],value:"constant_folding@_174513116872098526255"} : () -> cpu_tensor + (%8) = "builtin.constant" [id:2024] () {origin_id:1912,persistable:[true],value:"constant_folding@_174513116870501220354"} : () -> cpu_tensor + (%9) = "builtin.constant" [id:2025] () {origin_id:1899,persistable:[true],value:"constant_folding@_174513116869046783353"} : () -> cpu_tensor + (%10) = "builtin.constant" [id:2026] () {origin_id:1890,persistable:[true],value:"constant_folding@_174513116867667180352"} : () -> cpu_tensor<0xi64> + (%11) = "builtin.parameter" [id:2027] () {origin_id:1881,parameter_name:"constant_folding@_174513116865440940351",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor + (%12) = "builtin.constant" [id:2028] () {origin_id:1872,persistable:[true],value:"constant_folding@_174513116864033493350"} : () -> cpu_tensor<1xf32> + (%13) = "builtin.constant" [id:2029] () {origin_id:1863,persistable:[true],value:"constant_folding@_174513116861634597449"} : () -> cpu_tensor + (%14) = "builtin.parameter" [id:2030] () {origin_id:1854,parameter_name:"constant_folding@_174513116859911589448",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor<1x256x1x1xf32> + (%15) = "builtin.constant" [id:2031] () {origin_id:1832,persistable:[true],value:"constant_folding@_174513116857122500446"} : () -> cpu_tensor<0xi64> + (%16) = "builtin.parameter" [id:2032] () {origin_id:1823,parameter_name:"constant_folding@_174513116855594796545",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor<1x1x1xf32> + (%17) = "builtin.constant" [id:2033] () {origin_id:1814,persistable:[true],value:"constant_folding@_174513116853662943544"} : () -> cpu_tensor + (%18) = "builtin.constant" [id:2034] () {origin_id:1805,persistable:[true],value:"constant_folding@_174513116852268476543"} : () -> cpu_tensor<1xf32> + (%19) = "builtin.constant" [id:2035] () {origin_id:1796,persistable:[true],value:"constant_folding@_174513116850011146642"} : () -> cpu_tensor<1xf32> + (%20) = "builtin.constant" [id:2036] () {origin_id:1784,persistable:[true],value:"constant_folding@_174513116844224746641"} : () -> cpu_tensor + (%21) = "builtin.constant" [id:2037] () {origin_id:1771,persistable:[true],value:"constant_folding@_174513116842705804740"} : () -> cpu_tensor + (%22) = "builtin.constant" [id:2038] () {origin_id:1758,persistable:[true],value:"constant_folding@_174513116841278885739"} : () -> cpu_tensor<0xi64> + (%23) = "builtin.constant" [id:2039] () {origin_id:1749,persistable:[true],value:"constant_folding@_174513116839857308738"} : () -> cpu_tensor<1xi32> + (%24) = "builtin.constant" [id:2040] () {origin_id:1740,persistable:[true],value:"constant_folding@_174513116838085921737"} : () -> cpu_tensor<1xf32> + (%25) = "builtin.parameter" [id:2041] () {origin_id:1731,parameter_name:"constant_folding@_174513116836671179736",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor + (%26) = "builtin.constant" [id:2042] () {origin_id:1722,persistable:[true],value:"constant_folding@_174513116835231166835"} : () -> cpu_tensor<1xf32> + (%27) = "builtin.parameter" [id:2043] () {origin_id:1713,parameter_name:"constant_folding@_174513116833656139834",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor<1xi32> + (%28) = "builtin.parameter" [id:2044] () {origin_id:1693,parameter_name:"constant_folding@_174513116830651898832",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor<1x6xf32> + (%29) = "builtin.parameter" [id:2045] () {origin_id:1673,parameter_name:"constant_folding@_174513116827593559930",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor + (%30) = "builtin.constant" [id:2046] () {origin_id:1664,persistable:[true],value:"constant_folding@_174513116825929047929"} : () -> cpu_tensor + (%31) = "builtin.constant" [id:2047] () {origin_id:1655,persistable:[true],value:"constant_folding@_174513116824495087928"} : () -> cpu_tensor + (%32) = "builtin.constant" [id:2048] () {origin_id:1646,persistable:[true],value:"constant_folding@_174513116823069183927"} : () -> cpu_tensor<1xf32> + (%33) = "builtin.constant" [id:2049] () {origin_id:1637,persistable:[true],value:"constant_folding@_174513116821619066926"} : () -> cpu_tensor<1xf32> + (%34) = "builtin.constant" [id:2050] () {origin_id:1628,persistable:[true],value:"constant_folding@_174513116820193818025"} : () -> cpu_tensor<1xf32> + (%35) = "builtin.constant" [id:2051] () {origin_id:1619,persistable:[true],value:"constant_folding@_174513116818740431024"} : () -> cpu_tensor<1xf32> + (%36) = "builtin.constant" [id:2052] () {origin_id:1610,persistable:[true],value:"constant_folding@_174513116817322536023"} : () -> cpu_tensor<1xi64> + (%37) = "builtin.constant" [id:2053] () {origin_id:1601,persistable:[true],value:"constant_folding@_174513116815886735022"} : () -> cpu_tensor<1xf32> + (%38) = "builtin.constant" [id:2054] () {origin_id:1592,persistable:[true],value:"constant_folding@_174513116814471704121"} : () -> cpu_tensor<2xi64> + (%39) = "builtin.constant" [id:2055] () {origin_id:1583,persistable:[true],value:"constant_folding@_174513116813053746120"} : () -> cpu_tensor<2xi64> + (%40) = "builtin.parameter" [id:2056] () {origin_id:1574,parameter_name:"constant_folding@_174513116811615103119",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor + (%41) = "builtin.constant" [id:2057] () {origin_id:1565,persistable:[true],value:"constant_folding@_174513115258237754718"} : () -> cpu_tensor<1xi64> + (%42) = "builtin.constant" [id:2058] () {origin_id:1556,persistable:[true],value:"constant_folding@_174513115176548053017"} : () -> cpu_tensor<1xi64> + (%43) = "builtin.constant" [id:2059] () {origin_id:1547,persistable:[true],value:"constant_folding@_174513115110199844916"} : () -> cpu_tensor<1xi64> + (%44) = "builtin.constant" [id:2060] () {origin_id:1538,persistable:[true],value:"constant_folding@_174513115013851471515"} : () -> cpu_tensor<1xi64> + (%45) = "builtin.constant" [id:2061] () {origin_id:1529,persistable:[true],value:"constant_folding@_174513114941937302314"} : () -> cpu_tensor<2xi64> + (%46) = "builtin.parameter" [id:2062] () {origin_id:1520,parameter_name:"constant_folding@_174513114857650263913",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor<1x15x4xf32> + (%47) = "builtin.constant" [id:2063] () {origin_id:1498,persistable:[true],value:"constant_folding@_174513114736420657211"} : () -> cpu_tensor<3xi64> + (%48) = "builtin.constant" [id:2064] () {origin_id:1489,persistable:[true],value:"constant_folding@_174513114667774590410"} : () -> cpu_tensor<1xi64> + (%49) = "builtin.constant" [id:2065] () {origin_id:1480,persistable:[true],value:"constant_folding@_17451311460158745859"} : () -> cpu_tensor<1xf32> + (%50) = "builtin.constant" [id:2066] () {origin_id:1471,persistable:[true],value:"constant_folding@_17451311452954254518"} : () -> cpu_tensor<1xi64> + (%51) = "builtin.constant" [id:2067] () {origin_id:1462,persistable:[true],value:"constant_folding@_17451311445836590377"} : () -> cpu_tensor<1xi64> + (%52) = "builtin.constant" [id:2068] () {origin_id:1453,persistable:[true],value:"constant_folding@_17451311436834671136"} : () -> cpu_tensor<1xi64> + (%53) = "builtin.parameter" [id:2069] () {origin_id:1444,parameter_name:"constant_folding@_17451311430626338225",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor<1x60x1x1xf32> + (%54) = "builtin.parameter" [id:2070] () {origin_id:1431,parameter_name:"constant_folding@_17451311422710820934",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor<1x15x1x1xf32> + (%55) = "builtin.parameter" [id:2071] () {origin_id:1418,parameter_name:"constant_folding@_17451311415972965563",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor<1x1024x1x1xf32> + (%56) = "builtin.parameter" [id:2072] () {origin_id:1405,parameter_name:"constant_folding@_17451311399980650772",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor<1x80x1x1xf32> + (%57) = "builtin.constant" [id:2073] () {origin_id:1383,persistable:[true],value:"constant_folding@_17451311284029886470"} : () -> cpu_tensor<2xi64> + (%58) = "builtin.parameter" [id:2074] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:8,parameter_name:"conv2d_56.w_0_deepcopy_280",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<80x256x1x1xf32> + (%59) = "builtin.parameter" [id:2075] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:10,parameter_name:"conv2d_transpose_0.w_0_deepcopy_278",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<2048x256x2x2xf32> + (%60) = "builtin.parameter" [id:2076] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:11,parameter_name:"linear_1.b_0_deepcopy_277",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<320xf32> + (%61) = "builtin.parameter" [id:2077] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:12,parameter_name:"linear_1.w_0_deepcopy_276",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<2048x320xf32> + (%62) = "builtin.parameter" [id:2078] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:13,parameter_name:"linear_0.b_0_deepcopy_275",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<81xf32> + (%63) = "builtin.parameter" [id:2079] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:14,parameter_name:"linear_0.w_0_deepcopy_274",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<2048x81xf32> + (%64) = "builtin.parameter" [id:2080] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:15,parameter_name:"batch_norm2d_52.w_2_deepcopy_273",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%65) = "builtin.parameter" [id:2081] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:16,parameter_name:"batch_norm2d_52.w_1_deepcopy_272",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%66) = "builtin.parameter" [id:2082] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:17,parameter_name:"batch_norm2d_52.b_0_deepcopy_271",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%67) = "builtin.parameter" [id:2083] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:18,parameter_name:"batch_norm2d_52.w_0_deepcopy_270",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%68) = "builtin.parameter" [id:2084] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:19,parameter_name:"conv2d_55.w_0_deepcopy_269",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<2048x512x1x1xf32> + (%69) = "builtin.parameter" [id:2085] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:20,parameter_name:"batch_norm2d_51.w_2_deepcopy_268",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%70) = "builtin.parameter" [id:2086] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:21,parameter_name:"batch_norm2d_51.w_1_deepcopy_267",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%71) = "builtin.parameter" [id:2087] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:22,parameter_name:"batch_norm2d_51.b_0_deepcopy_266",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%72) = "builtin.parameter" [id:2088] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:23,parameter_name:"batch_norm2d_51.w_0_deepcopy_265",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%73) = "builtin.parameter" [id:2089] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:24,parameter_name:"conv2d_54.w_0_deepcopy_264",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x512x3x3xf32> + (%74) = "builtin.parameter" [id:2090] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:25,parameter_name:"batch_norm2d_50.w_2_deepcopy_263",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%75) = "builtin.parameter" [id:2091] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:26,parameter_name:"batch_norm2d_50.w_1_deepcopy_262",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%76) = "builtin.parameter" [id:2092] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:27,parameter_name:"batch_norm2d_50.b_0_deepcopy_261",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%77) = "builtin.parameter" [id:2093] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:28,parameter_name:"batch_norm2d_50.w_0_deepcopy_260",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%78) = "builtin.parameter" [id:2094] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:29,parameter_name:"conv2d_53.w_0_deepcopy_259",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x2048x1x1xf32> + (%79) = "builtin.parameter" [id:2095] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:30,parameter_name:"batch_norm2d_49.w_2_deepcopy_258",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%80) = "builtin.parameter" [id:2096] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:31,parameter_name:"batch_norm2d_49.w_1_deepcopy_257",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%81) = "builtin.parameter" [id:2097] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:32,parameter_name:"batch_norm2d_49.b_0_deepcopy_256",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%82) = "builtin.parameter" [id:2098] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:33,parameter_name:"batch_norm2d_49.w_0_deepcopy_255",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%83) = "builtin.parameter" [id:2099] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:34,parameter_name:"conv2d_52.w_0_deepcopy_254",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<2048x512x1x1xf32> + (%84) = "builtin.parameter" [id:2100] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:35,parameter_name:"batch_norm2d_48.w_2_deepcopy_253",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%85) = "builtin.parameter" [id:2101] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:36,parameter_name:"batch_norm2d_48.w_1_deepcopy_252",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%86) = "builtin.parameter" [id:2102] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:37,parameter_name:"batch_norm2d_48.b_0_deepcopy_251",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%87) = "builtin.parameter" [id:2103] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:38,parameter_name:"batch_norm2d_48.w_0_deepcopy_250",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%88) = "builtin.parameter" [id:2104] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:39,parameter_name:"conv2d_51.w_0_deepcopy_249",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x512x3x3xf32> + (%89) = "builtin.parameter" [id:2105] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:40,parameter_name:"batch_norm2d_47.w_2_deepcopy_248",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%90) = "builtin.parameter" [id:2106] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:41,parameter_name:"batch_norm2d_47.w_1_deepcopy_247",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%91) = "builtin.parameter" [id:2107] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:42,parameter_name:"batch_norm2d_47.b_0_deepcopy_246",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%92) = "builtin.parameter" [id:2108] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:43,parameter_name:"batch_norm2d_47.w_0_deepcopy_245",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%93) = "builtin.parameter" [id:2109] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:44,parameter_name:"conv2d_50.w_0_deepcopy_244",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x2048x1x1xf32> + (%94) = "builtin.parameter" [id:2110] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:45,parameter_name:"batch_norm2d_46.w_2_deepcopy_243",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%95) = "builtin.parameter" [id:2111] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:46,parameter_name:"batch_norm2d_46.w_1_deepcopy_242",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%96) = "builtin.parameter" [id:2112] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:47,parameter_name:"batch_norm2d_46.b_0_deepcopy_241",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%97) = "builtin.parameter" [id:2113] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:48,parameter_name:"batch_norm2d_46.w_0_deepcopy_240",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%98) = "builtin.parameter" [id:2114] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:49,parameter_name:"conv2d_49.w_0_deepcopy_239",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<2048x1024x1x1xf32> + (%99) = "builtin.parameter" [id:2115] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:50,parameter_name:"batch_norm2d_45.w_2_deepcopy_238",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%100) = "builtin.parameter" [id:2116] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:51,parameter_name:"batch_norm2d_45.w_1_deepcopy_237",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%101) = "builtin.parameter" [id:2117] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:52,parameter_name:"batch_norm2d_45.b_0_deepcopy_236",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%102) = "builtin.parameter" [id:2118] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:53,parameter_name:"batch_norm2d_45.w_0_deepcopy_235",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%103) = "builtin.parameter" [id:2119] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:54,parameter_name:"conv2d_48.w_0_deepcopy_234",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<2048x512x1x1xf32> + (%104) = "builtin.parameter" [id:2120] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:55,parameter_name:"batch_norm2d_44.w_2_deepcopy_233",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%105) = "builtin.parameter" [id:2121] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:56,parameter_name:"batch_norm2d_44.w_1_deepcopy_232",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%106) = "builtin.parameter" [id:2122] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:57,parameter_name:"batch_norm2d_44.b_0_deepcopy_231",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%107) = "builtin.parameter" [id:2123] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:58,parameter_name:"batch_norm2d_44.w_0_deepcopy_230",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%108) = "builtin.parameter" [id:2124] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:59,parameter_name:"conv2d_47.w_0_deepcopy_229",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x512x3x3xf32> + (%109) = "builtin.parameter" [id:2125] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:60,parameter_name:"batch_norm2d_43.w_2_deepcopy_228",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%110) = "builtin.parameter" [id:2126] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:61,parameter_name:"batch_norm2d_43.w_1_deepcopy_227",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%111) = "builtin.parameter" [id:2127] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:62,parameter_name:"batch_norm2d_43.b_0_deepcopy_226",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%112) = "builtin.parameter" [id:2128] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:63,parameter_name:"batch_norm2d_43.w_0_deepcopy_225",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%113) = "builtin.parameter" [id:2129] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:64,parameter_name:"conv2d_46.w_0_deepcopy_224",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x1024x1x1xf32> + (%114) = "builtin.parameter" [id:2130] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:66,parameter_name:"conv2d_45.w_0_deepcopy_222",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<60x1024x1x1xf32> + (%115) = "builtin.parameter" [id:2131] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:68,parameter_name:"conv2d_44.w_0_deepcopy_220",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<15x1024x1x1xf32> + (%116) = "builtin.parameter" [id:2132] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:70,parameter_name:"conv2d_43.w_0_deepcopy_218",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<1024x1024x3x3xf32> + (%117) = "builtin.parameter" [id:2133] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:71,parameter_name:"batch_norm2d_42.w_2_deepcopy_216",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%118) = "builtin.parameter" [id:2134] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:72,parameter_name:"batch_norm2d_42.w_1_deepcopy_215",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%119) = "builtin.parameter" [id:2135] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:73,parameter_name:"batch_norm2d_42.b_0_deepcopy_214",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%120) = "builtin.parameter" [id:2136] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:74,parameter_name:"batch_norm2d_42.w_0_deepcopy_213",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%121) = "builtin.parameter" [id:2137] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:75,parameter_name:"conv2d_42.w_0_deepcopy_212",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<1024x256x1x1xf32> + (%122) = "builtin.parameter" [id:2138] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:76,parameter_name:"batch_norm2d_41.w_2_deepcopy_211",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%123) = "builtin.parameter" [id:2139] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:77,parameter_name:"batch_norm2d_41.w_1_deepcopy_210",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%124) = "builtin.parameter" [id:2140] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:78,parameter_name:"batch_norm2d_41.b_0_deepcopy_209",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%125) = "builtin.parameter" [id:2141] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:79,parameter_name:"batch_norm2d_41.w_0_deepcopy_208",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%126) = "builtin.parameter" [id:2142] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:80,parameter_name:"conv2d_41.w_0_deepcopy_207",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<256x256x3x3xf32> + (%127) = "builtin.parameter" [id:2143] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:81,parameter_name:"batch_norm2d_40.w_2_deepcopy_206",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%128) = "builtin.parameter" [id:2144] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:82,parameter_name:"batch_norm2d_40.w_1_deepcopy_205",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%129) = "builtin.parameter" [id:2145] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:83,parameter_name:"batch_norm2d_40.b_0_deepcopy_204",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%130) = "builtin.parameter" [id:2146] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:84,parameter_name:"batch_norm2d_40.w_0_deepcopy_203",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%131) = "builtin.parameter" [id:2147] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:85,parameter_name:"conv2d_40.w_0_deepcopy_202",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<256x1024x1x1xf32> + (%132) = "builtin.parameter" [id:2148] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:86,parameter_name:"batch_norm2d_39.w_2_deepcopy_201",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%133) = "builtin.parameter" [id:2149] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:87,parameter_name:"batch_norm2d_39.w_1_deepcopy_200",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%134) = "builtin.parameter" [id:2150] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:88,parameter_name:"batch_norm2d_39.b_0_deepcopy_199",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%135) = "builtin.parameter" [id:2151] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:89,parameter_name:"batch_norm2d_39.w_0_deepcopy_198",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%136) = "builtin.parameter" [id:2152] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:90,parameter_name:"conv2d_39.w_0_deepcopy_197",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<1024x256x1x1xf32> + (%137) = "builtin.parameter" [id:2153] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:91,parameter_name:"batch_norm2d_38.w_2_deepcopy_196",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%138) = "builtin.parameter" [id:2154] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:92,parameter_name:"batch_norm2d_38.w_1_deepcopy_195",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%139) = "builtin.parameter" [id:2155] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:93,parameter_name:"batch_norm2d_38.b_0_deepcopy_194",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%140) = "builtin.parameter" [id:2156] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:94,parameter_name:"batch_norm2d_38.w_0_deepcopy_193",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%141) = "builtin.parameter" [id:2157] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:95,parameter_name:"conv2d_38.w_0_deepcopy_192",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<256x256x3x3xf32> + (%142) = "builtin.parameter" [id:2158] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:96,parameter_name:"batch_norm2d_37.w_2_deepcopy_191",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%143) = "builtin.parameter" [id:2159] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:97,parameter_name:"batch_norm2d_37.w_1_deepcopy_190",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%144) = "builtin.parameter" [id:2160] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:98,parameter_name:"batch_norm2d_37.b_0_deepcopy_189",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%145) = "builtin.parameter" [id:2161] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:99,parameter_name:"batch_norm2d_37.w_0_deepcopy_188",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%146) = "builtin.parameter" [id:2162] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:100,parameter_name:"conv2d_37.w_0_deepcopy_187",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<256x1024x1x1xf32> + (%147) = "builtin.parameter" [id:2163] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:101,parameter_name:"batch_norm2d_36.w_2_deepcopy_186",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%148) = "builtin.parameter" [id:2164] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:102,parameter_name:"batch_norm2d_36.w_1_deepcopy_185",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%149) = "builtin.parameter" [id:2165] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:103,parameter_name:"batch_norm2d_36.b_0_deepcopy_184",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%150) = "builtin.parameter" [id:2166] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:104,parameter_name:"batch_norm2d_36.w_0_deepcopy_183",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%151) = "builtin.parameter" [id:2167] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:105,parameter_name:"conv2d_36.w_0_deepcopy_182",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<1024x256x1x1xf32> + (%152) = "builtin.parameter" [id:2168] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:106,parameter_name:"batch_norm2d_35.w_2_deepcopy_181",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%153) = "builtin.parameter" [id:2169] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:107,parameter_name:"batch_norm2d_35.w_1_deepcopy_180",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%154) = "builtin.parameter" [id:2170] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:108,parameter_name:"batch_norm2d_35.b_0_deepcopy_179",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%155) = "builtin.parameter" [id:2171] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:109,parameter_name:"batch_norm2d_35.w_0_deepcopy_178",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%156) = "builtin.parameter" [id:2172] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:110,parameter_name:"conv2d_35.w_0_deepcopy_177",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<256x256x3x3xf32> + (%157) = "builtin.parameter" [id:2173] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:111,parameter_name:"batch_norm2d_34.w_2_deepcopy_176",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%158) = "builtin.parameter" [id:2174] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:112,parameter_name:"batch_norm2d_34.w_1_deepcopy_175",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%159) = "builtin.parameter" [id:2175] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:113,parameter_name:"batch_norm2d_34.b_0_deepcopy_174",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%160) = "builtin.parameter" [id:2176] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:114,parameter_name:"batch_norm2d_34.w_0_deepcopy_173",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%161) = "builtin.parameter" [id:2177] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:115,parameter_name:"conv2d_34.w_0_deepcopy_172",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<256x1024x1x1xf32> + (%162) = "builtin.parameter" [id:2178] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:116,parameter_name:"batch_norm2d_33.w_2_deepcopy_171",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%163) = "builtin.parameter" [id:2179] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:117,parameter_name:"batch_norm2d_33.w_1_deepcopy_170",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%164) = "builtin.parameter" [id:2180] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:118,parameter_name:"batch_norm2d_33.b_0_deepcopy_169",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%165) = "builtin.parameter" [id:2181] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:119,parameter_name:"batch_norm2d_33.w_0_deepcopy_168",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%166) = "builtin.parameter" [id:2182] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:120,parameter_name:"conv2d_33.w_0_deepcopy_167",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<1024x256x1x1xf32> + (%167) = "builtin.parameter" [id:2183] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:121,parameter_name:"batch_norm2d_32.w_2_deepcopy_166",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%168) = "builtin.parameter" [id:2184] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:122,parameter_name:"batch_norm2d_32.w_1_deepcopy_165",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%169) = "builtin.parameter" [id:2185] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:123,parameter_name:"batch_norm2d_32.b_0_deepcopy_164",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%170) = "builtin.parameter" [id:2186] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:124,parameter_name:"batch_norm2d_32.w_0_deepcopy_163",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%171) = "builtin.parameter" [id:2187] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:125,parameter_name:"conv2d_32.w_0_deepcopy_162",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<256x256x3x3xf32> + (%172) = "builtin.parameter" [id:2188] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:126,parameter_name:"batch_norm2d_31.w_2_deepcopy_161",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%173) = "builtin.parameter" [id:2189] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:127,parameter_name:"batch_norm2d_31.w_1_deepcopy_160",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%174) = "builtin.parameter" [id:2190] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:128,parameter_name:"batch_norm2d_31.b_0_deepcopy_159",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%175) = "builtin.parameter" [id:2191] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:129,parameter_name:"batch_norm2d_31.w_0_deepcopy_158",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%176) = "builtin.parameter" [id:2192] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:130,parameter_name:"conv2d_31.w_0_deepcopy_157",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<256x1024x1x1xf32> + (%177) = "builtin.parameter" [id:2193] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:131,parameter_name:"batch_norm2d_30.w_2_deepcopy_156",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%178) = "builtin.parameter" [id:2194] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:132,parameter_name:"batch_norm2d_30.w_1_deepcopy_155",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%179) = "builtin.parameter" [id:2195] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:133,parameter_name:"batch_norm2d_30.b_0_deepcopy_154",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%180) = "builtin.parameter" [id:2196] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:134,parameter_name:"batch_norm2d_30.w_0_deepcopy_153",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%181) = "builtin.parameter" [id:2197] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:135,parameter_name:"conv2d_30.w_0_deepcopy_152",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<1024x256x1x1xf32> + (%182) = "builtin.parameter" [id:2198] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:136,parameter_name:"batch_norm2d_29.w_2_deepcopy_151",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%183) = "builtin.parameter" [id:2199] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:137,parameter_name:"batch_norm2d_29.w_1_deepcopy_150",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%184) = "builtin.parameter" [id:2200] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:138,parameter_name:"batch_norm2d_29.b_0_deepcopy_149",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%185) = "builtin.parameter" [id:2201] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:139,parameter_name:"batch_norm2d_29.w_0_deepcopy_148",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%186) = "builtin.parameter" [id:2202] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:140,parameter_name:"conv2d_29.w_0_deepcopy_147",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<256x256x3x3xf32> + (%187) = "builtin.parameter" [id:2203] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:141,parameter_name:"batch_norm2d_28.w_2_deepcopy_146",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%188) = "builtin.parameter" [id:2204] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:142,parameter_name:"batch_norm2d_28.w_1_deepcopy_145",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%189) = "builtin.parameter" [id:2205] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:143,parameter_name:"batch_norm2d_28.b_0_deepcopy_144",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%190) = "builtin.parameter" [id:2206] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:144,parameter_name:"batch_norm2d_28.w_0_deepcopy_143",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%191) = "builtin.parameter" [id:2207] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:145,parameter_name:"conv2d_28.w_0_deepcopy_142",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<256x1024x1x1xf32> + (%192) = "builtin.parameter" [id:2208] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:146,parameter_name:"batch_norm2d_27.w_2_deepcopy_141",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%193) = "builtin.parameter" [id:2209] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:147,parameter_name:"batch_norm2d_27.w_1_deepcopy_140",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%194) = "builtin.parameter" [id:2210] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:148,parameter_name:"batch_norm2d_27.b_0_deepcopy_139",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%195) = "builtin.parameter" [id:2211] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:149,parameter_name:"batch_norm2d_27.w_0_deepcopy_138",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%196) = "builtin.parameter" [id:2212] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:150,parameter_name:"conv2d_27.w_0_deepcopy_137",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<1024x512x1x1xf32> + (%197) = "builtin.parameter" [id:2213] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:151,parameter_name:"batch_norm2d_26.w_2_deepcopy_136",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%198) = "builtin.parameter" [id:2214] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:152,parameter_name:"batch_norm2d_26.w_1_deepcopy_135",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%199) = "builtin.parameter" [id:2215] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:153,parameter_name:"batch_norm2d_26.b_0_deepcopy_134",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%200) = "builtin.parameter" [id:2216] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:154,parameter_name:"batch_norm2d_26.w_0_deepcopy_133",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%201) = "builtin.parameter" [id:2217] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:155,parameter_name:"conv2d_26.w_0_deepcopy_132",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<1024x256x1x1xf32> + (%202) = "builtin.parameter" [id:2218] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:156,parameter_name:"batch_norm2d_25.w_2_deepcopy_131",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%203) = "builtin.parameter" [id:2219] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:157,parameter_name:"batch_norm2d_25.w_1_deepcopy_130",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%204) = "builtin.parameter" [id:2220] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:158,parameter_name:"batch_norm2d_25.b_0_deepcopy_129",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%205) = "builtin.parameter" [id:2221] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:159,parameter_name:"batch_norm2d_25.w_0_deepcopy_128",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%206) = "builtin.parameter" [id:2222] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:160,parameter_name:"conv2d_25.w_0_deepcopy_127",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<256x256x3x3xf32> + (%207) = "builtin.parameter" [id:2223] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:161,parameter_name:"batch_norm2d_24.w_2_deepcopy_126",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%208) = "builtin.parameter" [id:2224] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:162,parameter_name:"batch_norm2d_24.w_1_deepcopy_125",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%209) = "builtin.parameter" [id:2225] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:163,parameter_name:"batch_norm2d_24.b_0_deepcopy_124",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%210) = "builtin.parameter" [id:2226] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:164,parameter_name:"batch_norm2d_24.w_0_deepcopy_123",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%211) = "builtin.parameter" [id:2227] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:165,parameter_name:"conv2d_24.w_0_deepcopy_122",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<256x512x1x1xf32> + (%212) = "builtin.parameter" [id:2228] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:166,parameter_name:"batch_norm2d_23.w_2_deepcopy_121",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%213) = "builtin.parameter" [id:2229] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:167,parameter_name:"batch_norm2d_23.w_1_deepcopy_120",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%214) = "builtin.parameter" [id:2230] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:168,parameter_name:"batch_norm2d_23.b_0_deepcopy_119",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%215) = "builtin.parameter" [id:2231] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:169,parameter_name:"batch_norm2d_23.w_0_deepcopy_118",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%216) = "builtin.parameter" [id:2232] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:170,parameter_name:"conv2d_23.w_0_deepcopy_117",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x128x1x1xf32> + (%217) = "builtin.parameter" [id:2233] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:171,parameter_name:"batch_norm2d_22.w_2_deepcopy_116",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%218) = "builtin.parameter" [id:2234] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:172,parameter_name:"batch_norm2d_22.w_1_deepcopy_115",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%219) = "builtin.parameter" [id:2235] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:173,parameter_name:"batch_norm2d_22.b_0_deepcopy_114",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%220) = "builtin.parameter" [id:2236] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:174,parameter_name:"batch_norm2d_22.w_0_deepcopy_113",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%221) = "builtin.parameter" [id:2237] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:175,parameter_name:"conv2d_22.w_0_deepcopy_112",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<128x128x3x3xf32> + (%222) = "builtin.parameter" [id:2238] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:176,parameter_name:"batch_norm2d_21.w_2_deepcopy_111",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%223) = "builtin.parameter" [id:2239] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:177,parameter_name:"batch_norm2d_21.w_1_deepcopy_110",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%224) = "builtin.parameter" [id:2240] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:178,parameter_name:"batch_norm2d_21.b_0_deepcopy_109",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%225) = "builtin.parameter" [id:2241] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:179,parameter_name:"batch_norm2d_21.w_0_deepcopy_108",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%226) = "builtin.parameter" [id:2242] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:180,parameter_name:"conv2d_21.w_0_deepcopy_107",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<128x512x1x1xf32> + (%227) = "builtin.parameter" [id:2243] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:181,parameter_name:"batch_norm2d_20.w_2_deepcopy_106",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%228) = "builtin.parameter" [id:2244] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:182,parameter_name:"batch_norm2d_20.w_1_deepcopy_105",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%229) = "builtin.parameter" [id:2245] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:183,parameter_name:"batch_norm2d_20.b_0_deepcopy_104",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%230) = "builtin.parameter" [id:2246] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:184,parameter_name:"batch_norm2d_20.w_0_deepcopy_103",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%231) = "builtin.parameter" [id:2247] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:185,parameter_name:"conv2d_20.w_0_deepcopy_102",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x128x1x1xf32> + (%232) = "builtin.parameter" [id:2248] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:186,parameter_name:"batch_norm2d_19.w_2_deepcopy_101",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%233) = "builtin.parameter" [id:2249] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:187,parameter_name:"batch_norm2d_19.w_1_deepcopy_100",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%234) = "builtin.parameter" [id:2250] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:188,parameter_name:"batch_norm2d_19.b_0_deepcopy_99",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%235) = "builtin.parameter" [id:2251] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:189,parameter_name:"batch_norm2d_19.w_0_deepcopy_98",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%236) = "builtin.parameter" [id:2252] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:190,parameter_name:"conv2d_19.w_0_deepcopy_97",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<128x128x3x3xf32> + (%237) = "builtin.parameter" [id:2253] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:191,parameter_name:"batch_norm2d_18.w_2_deepcopy_96",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%238) = "builtin.parameter" [id:2254] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:192,parameter_name:"batch_norm2d_18.w_1_deepcopy_95",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%239) = "builtin.parameter" [id:2255] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:193,parameter_name:"batch_norm2d_18.b_0_deepcopy_94",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%240) = "builtin.parameter" [id:2256] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:194,parameter_name:"batch_norm2d_18.w_0_deepcopy_93",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%241) = "builtin.parameter" [id:2257] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:195,parameter_name:"conv2d_18.w_0_deepcopy_92",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<128x512x1x1xf32> + (%242) = "builtin.parameter" [id:2258] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:196,parameter_name:"batch_norm2d_17.w_2_deepcopy_91",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%243) = "builtin.parameter" [id:2259] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:197,parameter_name:"batch_norm2d_17.w_1_deepcopy_90",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%244) = "builtin.parameter" [id:2260] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:198,parameter_name:"batch_norm2d_17.b_0_deepcopy_89",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%245) = "builtin.parameter" [id:2261] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:199,parameter_name:"batch_norm2d_17.w_0_deepcopy_88",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%246) = "builtin.parameter" [id:2262] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:200,parameter_name:"conv2d_17.w_0_deepcopy_87",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x128x1x1xf32> + (%247) = "builtin.parameter" [id:2263] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:201,parameter_name:"batch_norm2d_16.w_2_deepcopy_86",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%248) = "builtin.parameter" [id:2264] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:202,parameter_name:"batch_norm2d_16.w_1_deepcopy_85",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%249) = "builtin.parameter" [id:2265] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:203,parameter_name:"batch_norm2d_16.b_0_deepcopy_84",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%250) = "builtin.parameter" [id:2266] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:204,parameter_name:"batch_norm2d_16.w_0_deepcopy_83",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%251) = "builtin.parameter" [id:2267] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:205,parameter_name:"conv2d_16.w_0_deepcopy_82",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<128x128x3x3xf32> + (%252) = "builtin.parameter" [id:2268] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:206,parameter_name:"batch_norm2d_15.w_2_deepcopy_81",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%253) = "builtin.parameter" [id:2269] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:207,parameter_name:"batch_norm2d_15.w_1_deepcopy_80",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%254) = "builtin.parameter" [id:2270] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:208,parameter_name:"batch_norm2d_15.b_0_deepcopy_79",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%255) = "builtin.parameter" [id:2271] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:209,parameter_name:"batch_norm2d_15.w_0_deepcopy_78",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%256) = "builtin.parameter" [id:2272] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:210,parameter_name:"conv2d_15.w_0_deepcopy_77",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<128x512x1x1xf32> + (%257) = "builtin.parameter" [id:2273] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:211,parameter_name:"batch_norm2d_14.w_2_deepcopy_76",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%258) = "builtin.parameter" [id:2274] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:212,parameter_name:"batch_norm2d_14.w_1_deepcopy_75",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%259) = "builtin.parameter" [id:2275] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:213,parameter_name:"batch_norm2d_14.b_0_deepcopy_74",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%260) = "builtin.parameter" [id:2276] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:214,parameter_name:"batch_norm2d_14.w_0_deepcopy_73",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%261) = "builtin.parameter" [id:2277] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:215,parameter_name:"conv2d_14.w_0_deepcopy_72",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x256x1x1xf32> + (%262) = "builtin.parameter" [id:2278] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:216,parameter_name:"batch_norm2d_13.w_2_deepcopy_71",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%263) = "builtin.parameter" [id:2279] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:217,parameter_name:"batch_norm2d_13.w_1_deepcopy_70",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%264) = "builtin.parameter" [id:2280] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:218,parameter_name:"batch_norm2d_13.b_0_deepcopy_69",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%265) = "builtin.parameter" [id:2281] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:219,parameter_name:"batch_norm2d_13.w_0_deepcopy_68",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%266) = "builtin.parameter" [id:2282] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:220,parameter_name:"conv2d_13.w_0_deepcopy_67",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x128x1x1xf32> + (%267) = "builtin.parameter" [id:2283] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:221,parameter_name:"batch_norm2d_12.w_2_deepcopy_66",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%268) = "builtin.parameter" [id:2284] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:222,parameter_name:"batch_norm2d_12.w_1_deepcopy_65",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%269) = "builtin.parameter" [id:2285] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:223,parameter_name:"batch_norm2d_12.b_0_deepcopy_64",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%270) = "builtin.parameter" [id:2286] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:224,parameter_name:"batch_norm2d_12.w_0_deepcopy_63",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%271) = "builtin.parameter" [id:2287] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:225,parameter_name:"conv2d_12.w_0_deepcopy_62",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<128x128x3x3xf32> + (%272) = "builtin.parameter" [id:2288] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:226,parameter_name:"batch_norm2d_11.w_2_deepcopy_61",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%273) = "builtin.parameter" [id:2289] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:227,parameter_name:"batch_norm2d_11.w_1_deepcopy_60",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%274) = "builtin.parameter" [id:2290] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:228,parameter_name:"batch_norm2d_11.b_0_deepcopy_59",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%275) = "builtin.parameter" [id:2291] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:229,parameter_name:"batch_norm2d_11.w_0_deepcopy_58",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<128xf32> + (%276) = "builtin.parameter" [id:2292] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:230,parameter_name:"conv2d_11.w_0_deepcopy_57",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<128x256x1x1xf32> + (%277) = "builtin.parameter" [id:2293] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:231,parameter_name:"batch_norm2d_10.w_2_deepcopy_56",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%278) = "builtin.parameter" [id:2294] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:232,parameter_name:"batch_norm2d_10.w_1_deepcopy_55",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%279) = "builtin.parameter" [id:2295] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:233,parameter_name:"batch_norm2d_10.b_0_deepcopy_54",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%280) = "builtin.parameter" [id:2296] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:234,parameter_name:"batch_norm2d_10.w_0_deepcopy_53",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%281) = "builtin.parameter" [id:2297] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:235,parameter_name:"conv2d_10.w_0_deepcopy_52",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256x64x1x1xf32> + (%282) = "builtin.parameter" [id:2298] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:236,parameter_name:"batch_norm2d_9.w_2_deepcopy_51",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%283) = "builtin.parameter" [id:2299] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:237,parameter_name:"batch_norm2d_9.w_1_deepcopy_50",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%284) = "builtin.parameter" [id:2300] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:238,parameter_name:"batch_norm2d_9.b_0_deepcopy_49",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%285) = "builtin.parameter" [id:2301] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:239,parameter_name:"batch_norm2d_9.w_0_deepcopy_48",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%286) = "builtin.parameter" [id:2302] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:240,parameter_name:"conv2d_9.w_0_deepcopy_47",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64x64x3x3xf32> + (%287) = "builtin.parameter" [id:2303] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:241,parameter_name:"batch_norm2d_8.w_2_deepcopy_46",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%288) = "builtin.parameter" [id:2304] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:242,parameter_name:"batch_norm2d_8.w_1_deepcopy_45",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%289) = "builtin.parameter" [id:2305] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:243,parameter_name:"batch_norm2d_8.b_0_deepcopy_44",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%290) = "builtin.parameter" [id:2306] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:244,parameter_name:"batch_norm2d_8.w_0_deepcopy_43",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%291) = "builtin.parameter" [id:2307] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:245,parameter_name:"conv2d_8.w_0_deepcopy_42",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64x256x1x1xf32> + (%292) = "builtin.parameter" [id:2308] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:246,parameter_name:"batch_norm2d_7.w_2_deepcopy_41",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%293) = "builtin.parameter" [id:2309] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:247,parameter_name:"batch_norm2d_7.w_1_deepcopy_40",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%294) = "builtin.parameter" [id:2310] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:248,parameter_name:"batch_norm2d_7.b_0_deepcopy_39",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%295) = "builtin.parameter" [id:2311] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:249,parameter_name:"batch_norm2d_7.w_0_deepcopy_38",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%296) = "builtin.parameter" [id:2312] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:250,parameter_name:"conv2d_7.w_0_deepcopy_37",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256x64x1x1xf32> + (%297) = "builtin.parameter" [id:2313] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:251,parameter_name:"batch_norm2d_6.w_2_deepcopy_36",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%298) = "builtin.parameter" [id:2314] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:252,parameter_name:"batch_norm2d_6.w_1_deepcopy_35",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%299) = "builtin.parameter" [id:2315] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:253,parameter_name:"batch_norm2d_6.b_0_deepcopy_34",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%300) = "builtin.parameter" [id:2316] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:254,parameter_name:"batch_norm2d_6.w_0_deepcopy_33",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%301) = "builtin.parameter" [id:2317] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:255,parameter_name:"conv2d_6.w_0_deepcopy_32",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64x64x3x3xf32> + (%302) = "builtin.parameter" [id:2318] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:256,parameter_name:"batch_norm2d_5.w_2_deepcopy_31",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%303) = "builtin.parameter" [id:2319] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:257,parameter_name:"batch_norm2d_5.w_1_deepcopy_30",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%304) = "builtin.parameter" [id:2320] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:258,parameter_name:"batch_norm2d_5.b_0_deepcopy_29",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%305) = "builtin.parameter" [id:2321] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:259,parameter_name:"batch_norm2d_5.w_0_deepcopy_28",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%306) = "builtin.parameter" [id:2322] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:260,parameter_name:"conv2d_5.w_0_deepcopy_27",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64x256x1x1xf32> + (%307) = "builtin.parameter" [id:2323] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:261,parameter_name:"batch_norm2d_4.w_2_deepcopy_26",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%308) = "builtin.parameter" [id:2324] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:262,parameter_name:"batch_norm2d_4.w_1_deepcopy_25",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%309) = "builtin.parameter" [id:2325] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:263,parameter_name:"batch_norm2d_4.b_0_deepcopy_24",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%310) = "builtin.parameter" [id:2326] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:264,parameter_name:"batch_norm2d_4.w_0_deepcopy_23",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%311) = "builtin.parameter" [id:2327] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:265,parameter_name:"conv2d_4.w_0_deepcopy_22",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256x64x1x1xf32> + (%312) = "builtin.parameter" [id:2328] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:266,parameter_name:"batch_norm2d_3.w_2_deepcopy_21",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%313) = "builtin.parameter" [id:2329] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:267,parameter_name:"batch_norm2d_3.w_1_deepcopy_20",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%314) = "builtin.parameter" [id:2330] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:268,parameter_name:"batch_norm2d_3.b_0_deepcopy_19",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%315) = "builtin.parameter" [id:2331] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:269,parameter_name:"batch_norm2d_3.w_0_deepcopy_18",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%316) = "builtin.parameter" [id:2332] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:270,parameter_name:"conv2d_3.w_0_deepcopy_17",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256x64x1x1xf32> + (%317) = "builtin.parameter" [id:2333] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:271,parameter_name:"batch_norm2d_2.w_2_deepcopy_16",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%318) = "builtin.parameter" [id:2334] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:272,parameter_name:"batch_norm2d_2.w_1_deepcopy_15",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%319) = "builtin.parameter" [id:2335] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:273,parameter_name:"batch_norm2d_2.b_0_deepcopy_14",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%320) = "builtin.parameter" [id:2336] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:274,parameter_name:"batch_norm2d_2.w_0_deepcopy_13",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%321) = "builtin.parameter" [id:2337] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:275,parameter_name:"conv2d_2.w_0_deepcopy_12",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64x64x3x3xf32> + (%322) = "builtin.parameter" [id:2338] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:276,parameter_name:"batch_norm2d_1.w_2_deepcopy_11",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%323) = "builtin.parameter" [id:2339] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:277,parameter_name:"batch_norm2d_1.w_1_deepcopy_10",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%324) = "builtin.parameter" [id:2340] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:278,parameter_name:"batch_norm2d_1.b_0_deepcopy_9",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%325) = "builtin.parameter" [id:2341] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:279,parameter_name:"batch_norm2d_1.w_0_deepcopy_8",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%326) = "builtin.parameter" [id:2342] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:280,parameter_name:"conv2d_1.w_0_deepcopy_7",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64x64x1x1xf32> + (%327) = "builtin.parameter" [id:2343] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:281,parameter_name:"batch_norm2d_0.w_2_deepcopy_6",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%328) = "builtin.parameter" [id:2344] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:282,parameter_name:"batch_norm2d_0.w_1_deepcopy_5",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%329) = "builtin.parameter" [id:2345] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:283,parameter_name:"batch_norm2d_0.b_0_deepcopy_4",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%330) = "builtin.parameter" [id:2346] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:284,parameter_name:"batch_norm2d_0.w_0_deepcopy_3",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64xf32> + (%331) = "builtin.parameter" [id:2347] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:285,parameter_name:"conv2d_0.w_0_deepcopy_2",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<64x3x7x7xf32> + (%332) = "data(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"data",name:"im_shape",op_name:"pd_op.data",origin_id:2348,place:Place(undefined:0),shape:[-1,2],stop_gradient:[false]} : () -> undefined_tensor<-1x2xf32> + (%333) = "shadow_feed(phi_kernel)" (%332) {dst_place_type:1,kernel_key:,kernel_name:"shadow_feed",op_name:"pd_op.shadow_feed",origin_id:2349} : (undefined_tensor<-1x2xf32>) -> custom_device_tensor<-1x2xf32> + (%334) = "data(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"data",name:"image",op_name:"pd_op.data",origin_id:2350,place:Place(undefined:0),shape:[-1,3,-1,-1],stop_gradient:[false]} : () -> undefined_tensor<-1x3x-1x-1xf32> + (%335) = "shadow_feed(phi_kernel)" (%334) {dst_place_type:1,kernel_key:,kernel_name:"shadow_feed",op_name:"pd_op.shadow_feed",origin_id:2351} : (undefined_tensor<-1x3x-1x-1xf32>) -> custom_device_tensor<-1x3x-1x-1xf32> + (%336) = "data(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"data",name:"scale_factor",op_name:"pd_op.data",origin_id:2352,place:Place(undefined:0),shape:[-1,2],stop_gradient:[false]} : () -> undefined_tensor<-1x2xf32> + (%337) = "shadow_feed(phi_kernel)" (%336) {dst_place_type:1,kernel_key:,kernel_name:"shadow_feed",op_name:"pd_op.shadow_feed",origin_id:2353} : (undefined_tensor<-1x2xf32>) -> custom_device_tensor<-1x2xf32> + (%338) = "conv2d(phi_kernel)" (%335, %331) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2354,padding_algorithm:"EXPLICIT",paddings:[3,3],stop_gradient:[false],strides:[2,2],struct_name:"/ResNet/Sequential/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x3x-1x-1xf32>, custom_device_tensor<64x3x7x7xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%339, %340, %341, %342, %343, %344) = "batch_norm_(phi_kernel)" (%338, %328, %327, %330, %329) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2355,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Sequential/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>) -> custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<-1xu8> + (%345) = "relu(phi_kernel)" (%339) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2356,stop_gradient:[false],struct_name:"/ResNet/Sequential/ConvNormLayer/"} : (custom_device_tensor<-1x64x-1x-1xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%346) = "transpose(phi_kernel)" (%345) {kernel_key:,kernel_name:"transpose",op_name:"pd_op.transpose",origin_id:2357,perm:[0,2,3,1],source:"transfer_layout_pass",stop_gradient:[false]} : (custom_device_tensor<-1x64x-1x-1xf32>) -> custom_device_tensor<-1x-1x-1x64xf32> + (%347) = "pool2d(phi_kernel)" (%346, %57) {adaptive:false,ceil_mode:false,data_format:"NHWC",exclusive:true,global_pooling:false,kernel_key:,kernel_name:"pool2d",op_name:"pd_op.pool2d",origin_id:2358,padding_algorithm:"EXPLICIT",paddings:[1,1],pooling_type:"max",stop_gradient:[false],strides:[2,2],struct_name:"/ResNet/"} : (custom_device_tensor<-1x-1x-1x64xf32>, cpu_tensor<2xi64>) -> custom_device_tensor<-1x-1x-1x64xf32> + (%348) = "transpose(phi_kernel)" (%347) {kernel_key:,kernel_name:"transpose",op_name:"pd_op.transpose",origin_id:2359,perm:[0,3,1,2],source:"transfer_layout_pass",stop_gradient:[false]} : (custom_device_tensor<-1x-1x-1x64xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%349) = "conv2d(phi_kernel)" (%348, %326) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2360,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64x64x1x1xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%350, %351, %352, %353, %354, %355) = "batch_norm_(phi_kernel)" (%349, %323, %322, %325, %324) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2361,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>) -> custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<-1xu8> + (%356) = "relu(phi_kernel)" (%350) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2362,stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer/"} : (custom_device_tensor<-1x64x-1x-1xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%357) = "conv2d(phi_kernel)" (%356, %321) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2363,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64x64x3x3xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%358, %359, %360, %361, %362, %363) = "batch_norm_(phi_kernel)" (%357, %318, %317, %320, %319) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2364,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>) -> custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<-1xu8> + (%364) = "relu(phi_kernel)" (%358) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2365,stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer_1/"} : (custom_device_tensor<-1x64x-1x-1xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%365) = "conv2d(phi_kernel)" (%364, %316) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2366,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<256x64x1x1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%366, %367, %368, %369, %370, %371) = "batch_norm_(phi_kernel)" (%365, %313, %312, %315, %314) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2367,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%372) = "conv2d(phi_kernel)" (%348, %311) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2368,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer_3/Conv2D/"} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<256x64x1x1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%373, %374, %375, %376, %377, %378) = "batch_norm_(phi_kernel)" (%372, %308, %307, %310, %309) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2369,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck/ConvNormLayer_3/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%379) = "add(phi_kernel)" (%366, %373) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2370,stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%380) = "relu(phi_kernel)" (%379) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2371,stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%381) = "conv2d(phi_kernel)" (%380, %306) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2372,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck_1/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<64x256x1x1xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%382, %383, %384, %385, %386, %387) = "batch_norm_(phi_kernel)" (%381, %303, %302, %305, %304) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2373,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck_1/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>) -> custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<-1xu8> + (%388) = "relu(phi_kernel)" (%382) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2374,stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck_1/ConvNormLayer/"} : (custom_device_tensor<-1x64x-1x-1xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%389) = "conv2d(phi_kernel)" (%388, %301) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2375,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck_1/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64x64x3x3xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%390, %391, %392, %393, %394, %395) = "batch_norm_(phi_kernel)" (%389, %298, %297, %300, %299) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2376,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck_1/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>) -> custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<-1xu8> + (%396) = "relu(phi_kernel)" (%390) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2377,stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck_1/ConvNormLayer_1/"} : (custom_device_tensor<-1x64x-1x-1xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%397) = "conv2d(phi_kernel)" (%396, %296) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2378,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck_1/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<256x64x1x1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%398, %399, %400, %401, %402, %403) = "batch_norm_(phi_kernel)" (%397, %293, %292, %295, %294) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2379,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck_1/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%404) = "add(phi_kernel)" (%398, %380) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2380,stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck_1/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%405) = "relu(phi_kernel)" (%404) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2381,stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck_1/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%406) = "conv2d(phi_kernel)" (%405, %291) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2382,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck_2/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<64x256x1x1xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%407, %408, %409, %410, %411, %412) = "batch_norm_(phi_kernel)" (%406, %288, %287, %290, %289) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2383,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck_2/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>) -> custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<-1xu8> + (%413) = "relu(phi_kernel)" (%407) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2384,stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck_2/ConvNormLayer/"} : (custom_device_tensor<-1x64x-1x-1xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%414) = "conv2d(phi_kernel)" (%413, %286) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2385,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck_2/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64x64x3x3xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%415, %416, %417, %418, %419, %420) = "batch_norm_(phi_kernel)" (%414, %283, %282, %285, %284) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2386,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck_2/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>) -> custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<64xf32>, custom_device_tensor<-1xu8> + (%421) = "relu(phi_kernel)" (%415) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2387,stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck_2/ConvNormLayer_1/"} : (custom_device_tensor<-1x64x-1x-1xf32>) -> custom_device_tensor<-1x64x-1x-1xf32> + (%422) = "conv2d(phi_kernel)" (%421, %281) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2388,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks/BottleNeck_2/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x64x-1x-1xf32>, custom_device_tensor<256x64x1x1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%423, %424, %425, %426, %427, %428) = "batch_norm_(phi_kernel)" (%422, %278, %277, %280, %279) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2389,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks/BottleNeck_2/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%429) = "add(phi_kernel)" (%423, %405) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2390,stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck_2/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%430) = "relu(phi_kernel)" (%429) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2391,stop_gradient:[false],struct_name:"/ResNet/Blocks/BottleNeck_2/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%431) = "conv2d(phi_kernel)" (%430, %276) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2392,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<128x256x1x1xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%432, %433, %434, %435, %436, %437) = "batch_norm_(phi_kernel)" (%431, %273, %272, %275, %274) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2393,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>) -> custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<-1xu8> + (%438) = "relu(phi_kernel)" (%432) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2394,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer/"} : (custom_device_tensor<-1x128x-1x-1xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%439) = "conv2d(phi_kernel)" (%438, %271) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2395,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[2,2],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128x128x3x3xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%440, %441, %442, %443, %444, %445) = "batch_norm_(phi_kernel)" (%439, %268, %267, %270, %269) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2396,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>) -> custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<-1xu8> + (%446) = "relu(phi_kernel)" (%440) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2397,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer_1/"} : (custom_device_tensor<-1x128x-1x-1xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%447) = "conv2d(phi_kernel)" (%446, %266) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2398,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<512x128x1x1xf32>) -> custom_device_tensor<-1x512x-1x-1xf32> + (%448, %449, %450, %451, %452, %453) = "batch_norm_(phi_kernel)" (%447, %263, %262, %265, %264) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2399,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%454) = "conv2d(phi_kernel)" (%430, %261) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2400,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[2,2],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer_3/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<512x256x1x1xf32>) -> custom_device_tensor<-1x512x-1x-1xf32> + (%455, %456, %457, %458, %459, %460) = "batch_norm_(phi_kernel)" (%454, %258, %257, %260, %259) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2401,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck/ConvNormLayer_3/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%461) = "add(phi_kernel)" (%448, %455) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2402,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck/"} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<-1x512x-1x-1xf32>) -> custom_device_tensor<-1x512x-1x-1xf32> + (%462) = "relu(phi_kernel)" (%461) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2403,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck/"} : (custom_device_tensor<-1x512x-1x-1xf32>) -> custom_device_tensor<-1x512x-1x-1xf32> + (%463) = "conv2d(phi_kernel)" (%462, %256) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2404,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck_1/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<128x512x1x1xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%464, %465, %466, %467, %468, %469) = "batch_norm_(phi_kernel)" (%463, %253, %252, %255, %254) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2405,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck_1/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>) -> custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<-1xu8> + (%470) = "relu(phi_kernel)" (%464) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2406,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_1/ConvNormLayer/"} : (custom_device_tensor<-1x128x-1x-1xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%471) = "conv2d(phi_kernel)" (%470, %251) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2407,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck_1/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128x128x3x3xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%472, %473, %474, %475, %476, %477) = "batch_norm_(phi_kernel)" (%471, %248, %247, %250, %249) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2408,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck_1/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>) -> custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<-1xu8> + (%478) = "relu(phi_kernel)" (%472) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2409,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_1/ConvNormLayer_1/"} : (custom_device_tensor<-1x128x-1x-1xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%479) = "conv2d(phi_kernel)" (%478, %246) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2410,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck_1/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<512x128x1x1xf32>) -> custom_device_tensor<-1x512x-1x-1xf32> + (%480, %481, %482, %483, %484, %485) = "batch_norm_(phi_kernel)" (%479, %243, %242, %245, %244) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2411,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck_1/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%486) = "add(phi_kernel)" (%480, %462) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2412,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_1/"} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<-1x512x-1x-1xf32>) -> custom_device_tensor<-1x512x-1x-1xf32> + (%487) = "relu(phi_kernel)" (%486) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2413,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_1/"} : (custom_device_tensor<-1x512x-1x-1xf32>) -> custom_device_tensor<-1x512x-1x-1xf32> + (%488) = "conv2d(phi_kernel)" (%487, %241) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2414,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck_2/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<128x512x1x1xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%489, %490, %491, %492, %493, %494) = "batch_norm_(phi_kernel)" (%488, %238, %237, %240, %239) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2415,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck_2/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>) -> custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<-1xu8> + (%495) = "relu(phi_kernel)" (%489) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2416,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_2/ConvNormLayer/"} : (custom_device_tensor<-1x128x-1x-1xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%496) = "conv2d(phi_kernel)" (%495, %236) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2417,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck_2/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128x128x3x3xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%497, %498, %499, %500, %501, %502) = "batch_norm_(phi_kernel)" (%496, %233, %232, %235, %234) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2418,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck_2/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>) -> custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<-1xu8> + (%503) = "relu(phi_kernel)" (%497) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2419,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_2/ConvNormLayer_1/"} : (custom_device_tensor<-1x128x-1x-1xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%504) = "conv2d(phi_kernel)" (%503, %231) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2420,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck_2/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<512x128x1x1xf32>) -> custom_device_tensor<-1x512x-1x-1xf32> + (%505, %506, %507, %508, %509, %510) = "batch_norm_(phi_kernel)" (%504, %228, %227, %230, %229) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2421,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck_2/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%511) = "add(phi_kernel)" (%505, %487) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2422,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_2/"} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<-1x512x-1x-1xf32>) -> custom_device_tensor<-1x512x-1x-1xf32> + (%512) = "relu(phi_kernel)" (%511) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2423,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_2/"} : (custom_device_tensor<-1x512x-1x-1xf32>) -> custom_device_tensor<-1x512x-1x-1xf32> + (%513) = "conv2d(phi_kernel)" (%512, %226) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2424,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck_3/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<128x512x1x1xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%514, %515, %516, %517, %518, %519) = "batch_norm_(phi_kernel)" (%513, %223, %222, %225, %224) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2425,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck_3/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>) -> custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<-1xu8> + (%520) = "relu(phi_kernel)" (%514) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2426,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_3/ConvNormLayer/"} : (custom_device_tensor<-1x128x-1x-1xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%521) = "conv2d(phi_kernel)" (%520, %221) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2427,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck_3/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128x128x3x3xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%522, %523, %524, %525, %526, %527) = "batch_norm_(phi_kernel)" (%521, %218, %217, %220, %219) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2428,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck_3/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>) -> custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<128xf32>, custom_device_tensor<-1xu8> + (%528) = "relu(phi_kernel)" (%522) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2429,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_3/ConvNormLayer_1/"} : (custom_device_tensor<-1x128x-1x-1xf32>) -> custom_device_tensor<-1x128x-1x-1xf32> + (%529) = "conv2d(phi_kernel)" (%528, %216) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2430,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_1/BottleNeck_3/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x128x-1x-1xf32>, custom_device_tensor<512x128x1x1xf32>) -> custom_device_tensor<-1x512x-1x-1xf32> + (%530, %531, %532, %533, %534, %535) = "batch_norm_(phi_kernel)" (%529, %213, %212, %215, %214) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2431,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_1/BottleNeck_3/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%536) = "add(phi_kernel)" (%530, %512) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2432,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_3/"} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<-1x512x-1x-1xf32>) -> custom_device_tensor<-1x512x-1x-1xf32> + (%537) = "relu(phi_kernel)" (%536) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2433,stop_gradient:[false],struct_name:"/ResNet/Blocks_1/BottleNeck_3/"} : (custom_device_tensor<-1x512x-1x-1xf32>) -> custom_device_tensor<-1x512x-1x-1xf32> + (%538) = "conv2d(phi_kernel)" (%537, %211) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2434,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<256x512x1x1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%539, %540, %541, %542, %543, %544) = "batch_norm_(phi_kernel)" (%538, %208, %207, %210, %209) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2435,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%545) = "relu(phi_kernel)" (%539) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2436,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%546) = "conv2d(phi_kernel)" (%545, %206) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2437,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[2,2],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256x256x3x3xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%547, %548, %549, %550, %551, %552) = "batch_norm_(phi_kernel)" (%546, %203, %202, %205, %204) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2438,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%553) = "relu(phi_kernel)" (%547) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2439,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer_1/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%554) = "conv2d(phi_kernel)" (%553, %201) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2440,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<1024x256x1x1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%555, %556, %557, %558, %559, %560) = "batch_norm_(phi_kernel)" (%554, %198, %197, %200, %199) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2441,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<-1xu8> + (%561) = "conv2d(phi_kernel)" (%537, %196) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2442,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[2,2],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer_3/Conv2D/"} : (custom_device_tensor<-1x512x-1x-1xf32>, custom_device_tensor<1024x512x1x1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%562, %563, %564, %565, %566, %567) = "batch_norm_(phi_kernel)" (%561, %193, %192, %195, %194) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2443,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck/ConvNormLayer_3/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<-1xu8> + (%568) = "add(phi_kernel)" (%555, %562) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2444,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<-1x1024x-1x-1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%569) = "relu(phi_kernel)" (%568) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2445,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck/"} : (custom_device_tensor<-1x1024x-1x-1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%570) = "conv2d(phi_kernel)" (%569, %191) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2446,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_1/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<256x1024x1x1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%571, %572, %573, %574, %575, %576) = "batch_norm_(phi_kernel)" (%570, %188, %187, %190, %189) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2447,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_1/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%577) = "relu(phi_kernel)" (%571) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2448,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_1/ConvNormLayer/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%578) = "conv2d(phi_kernel)" (%577, %186) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2449,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_1/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256x256x3x3xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%579, %580, %581, %582, %583, %584) = "batch_norm_(phi_kernel)" (%578, %183, %182, %185, %184) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2450,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_1/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%585) = "relu(phi_kernel)" (%579) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2451,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_1/ConvNormLayer_1/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%586) = "conv2d(phi_kernel)" (%585, %181) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2452,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_1/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<1024x256x1x1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%587, %588, %589, %590, %591, %592) = "batch_norm_(phi_kernel)" (%586, %178, %177, %180, %179) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2453,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_1/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<-1xu8> + (%593) = "add(phi_kernel)" (%587, %569) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2454,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_1/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<-1x1024x-1x-1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%594) = "relu(phi_kernel)" (%593) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2455,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_1/"} : (custom_device_tensor<-1x1024x-1x-1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%595) = "conv2d(phi_kernel)" (%594, %176) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2456,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_2/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<256x1024x1x1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%596, %597, %598, %599, %600, %601) = "batch_norm_(phi_kernel)" (%595, %173, %172, %175, %174) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2457,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_2/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%602) = "relu(phi_kernel)" (%596) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2458,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_2/ConvNormLayer/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%603) = "conv2d(phi_kernel)" (%602, %171) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2459,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_2/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256x256x3x3xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%604, %605, %606, %607, %608, %609) = "batch_norm_(phi_kernel)" (%603, %168, %167, %170, %169) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2460,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_2/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%610) = "relu(phi_kernel)" (%604) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2461,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_2/ConvNormLayer_1/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%611) = "conv2d(phi_kernel)" (%610, %166) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2462,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_2/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<1024x256x1x1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%612, %613, %614, %615, %616, %617) = "batch_norm_(phi_kernel)" (%611, %163, %162, %165, %164) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2463,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_2/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<-1xu8> + (%618) = "add(phi_kernel)" (%612, %594) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2464,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_2/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<-1x1024x-1x-1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%619) = "relu(phi_kernel)" (%618) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2465,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_2/"} : (custom_device_tensor<-1x1024x-1x-1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%620) = "conv2d(phi_kernel)" (%619, %161) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2466,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_3/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<256x1024x1x1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%621, %622, %623, %624, %625, %626) = "batch_norm_(phi_kernel)" (%620, %158, %157, %160, %159) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2467,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_3/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%627) = "relu(phi_kernel)" (%621) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2468,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_3/ConvNormLayer/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%628) = "conv2d(phi_kernel)" (%627, %156) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2469,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_3/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256x256x3x3xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%629, %630, %631, %632, %633, %634) = "batch_norm_(phi_kernel)" (%628, %153, %152, %155, %154) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2470,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_3/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%635) = "relu(phi_kernel)" (%629) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2471,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_3/ConvNormLayer_1/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%636) = "conv2d(phi_kernel)" (%635, %151) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2472,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_3/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<1024x256x1x1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%637, %638, %639, %640, %641, %642) = "batch_norm_(phi_kernel)" (%636, %148, %147, %150, %149) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2473,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_3/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<-1xu8> + (%643) = "add(phi_kernel)" (%637, %619) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2474,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_3/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<-1x1024x-1x-1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%644) = "relu(phi_kernel)" (%643) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2475,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_3/"} : (custom_device_tensor<-1x1024x-1x-1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%645) = "conv2d(phi_kernel)" (%644, %146) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2476,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_4/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<256x1024x1x1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%646, %647, %648, %649, %650, %651) = "batch_norm_(phi_kernel)" (%645, %143, %142, %145, %144) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2477,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_4/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%652) = "relu(phi_kernel)" (%646) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2478,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_4/ConvNormLayer/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%653) = "conv2d(phi_kernel)" (%652, %141) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2479,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_4/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256x256x3x3xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%654, %655, %656, %657, %658, %659) = "batch_norm_(phi_kernel)" (%653, %138, %137, %140, %139) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2480,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_4/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%660) = "relu(phi_kernel)" (%654) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2481,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_4/ConvNormLayer_1/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%661) = "conv2d(phi_kernel)" (%660, %136) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2482,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_4/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<1024x256x1x1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%662, %663, %664, %665, %666, %667) = "batch_norm_(phi_kernel)" (%661, %133, %132, %135, %134) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2483,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_4/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<-1xu8> + (%668) = "add(phi_kernel)" (%662, %644) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2484,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_4/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<-1x1024x-1x-1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%669) = "relu(phi_kernel)" (%668) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2485,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_4/"} : (custom_device_tensor<-1x1024x-1x-1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%670) = "conv2d(phi_kernel)" (%669, %131) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2486,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_5/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<256x1024x1x1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%671, %672, %673, %674, %675, %676) = "batch_norm_(phi_kernel)" (%670, %128, %127, %130, %129) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2487,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_5/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%677) = "relu(phi_kernel)" (%671) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2488,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_5/ConvNormLayer/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%678) = "conv2d(phi_kernel)" (%677, %126) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2489,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_5/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256x256x3x3xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%679, %680, %681, %682, %683, %684) = "batch_norm_(phi_kernel)" (%678, %123, %122, %125, %124) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2490,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_5/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>) -> custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<256xf32>, custom_device_tensor<-1xu8> + (%685) = "relu(phi_kernel)" (%679) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2491,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_5/ConvNormLayer_1/"} : (custom_device_tensor<-1x256x-1x-1xf32>) -> custom_device_tensor<-1x256x-1x-1xf32> + (%686) = "conv2d(phi_kernel)" (%685, %121) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2492,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/ResNet/Blocks_2/BottleNeck_5/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x256x-1x-1xf32>, custom_device_tensor<1024x256x1x1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%687, %688, %689, %690, %691, %692) = "batch_norm_(phi_kernel)" (%686, %118, %117, %120, %119) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2493,stop_gradient:[false,false,false,false,false,false],struct_name:"/ResNet/Blocks_2/BottleNeck_5/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<1024xf32>, custom_device_tensor<-1xu8> + (%693) = "add(phi_kernel)" (%687, %669) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2494,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_5/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<-1x1024x-1x-1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%694) = "relu(phi_kernel)" (%693) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2495,stop_gradient:[false],struct_name:"/ResNet/Blocks_2/BottleNeck_5/"} : (custom_device_tensor<-1x1024x-1x-1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%695) = "conv2d(phi_kernel)" (%694, %116) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2496,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/RPNHead/RPNFeat/Conv2D/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1024x1024x3x3xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%696) = "add(phi_kernel)" (%695, %55) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2497,stop_gradient:[false],struct_name:"/RPNHead/RPNFeat/Conv2D/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<1x1024x1x1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%697) = "relu(phi_kernel)" (%696) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2498,stop_gradient:[false],struct_name:"/RPNHead/RPNFeat/"} : (custom_device_tensor<-1x1024x-1x-1xf32>) -> custom_device_tensor<-1x1024x-1x-1xf32> + (%698) = "conv2d(phi_kernel)" (%697, %115) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2499,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/RPNHead/Conv2D/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<15x1024x1x1xf32>) -> custom_device_tensor<-1x15x-1x-1xf32> + (%699) = "add(phi_kernel)" (%698, %54) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2500,stop_gradient:[false],struct_name:"/RPNHead/Conv2D/"} : (custom_device_tensor<-1x15x-1x-1xf32>, custom_device_tensor<1x15x1x1xf32>) -> custom_device_tensor<-1x15x-1x-1xf32> + (%700) = "conv2d(phi_kernel)" (%697, %114) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2501,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/RPNHead/Conv2D_1/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<60x1024x1x1xf32>) -> custom_device_tensor<-1x60x-1x-1xf32> + (%701) = "add(phi_kernel)" (%700, %53) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2502,stop_gradient:[false],struct_name:"/RPNHead/Conv2D_1/"} : (custom_device_tensor<-1x60x-1x-1xf32>, custom_device_tensor<1x60x1x1xf32>) -> custom_device_tensor<-1x60x-1x-1xf32> + (%702) = "shape64(phi_kernel)" (%697) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2503,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (custom_device_tensor<-1x1024x-1x-1xf32>) -> cpu_tensor<4xi64> + (%703) = "slice(phi_kernel)" (%702, %52, %51) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2504,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (cpu_tensor<4xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%704) = "slice(phi_kernel)" (%702, %51, %50) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2505,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (cpu_tensor<4xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%705) = "scale(phi_kernel)" (%704, %49) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2506,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%706) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2507,place:Place(cpu),shape:[1],stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/",value:0} : () -> cpu_tensor<1xf32> + (%707) = "cast(phi_kernel)" (%705) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:2508,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (cpu_tensor) -> cpu_tensor + (%708) = "arange(phi_kernel)" (%706, %707, %49) {dtype:float32,kernel_key:,kernel_name:"arange",op_name:"pd_op.arange",origin_id:2509,place:Place(undefined:0),stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (cpu_tensor<1xf32>, cpu_tensor, cpu_tensor<1xf32>) -> custom_device_tensor<-1xf32> + (%709) = "scale(phi_kernel)" (%703, %49) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2510,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%710) = "cast(phi_kernel)" (%709) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:2511,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (cpu_tensor) -> cpu_tensor + (%711) = "arange(phi_kernel)" (%706, %710, %49) {dtype:float32,kernel_key:,kernel_name:"arange",op_name:"pd_op.arange",origin_id:2512,place:Place(undefined:0),stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (cpu_tensor<1xf32>, cpu_tensor, cpu_tensor<1xf32>) -> custom_device_tensor<-1xf32> + (%712) = "builtin.combine" [id:2513] (%711, %708) {origin_id:462,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (custom_device_tensor<-1xf32>, custom_device_tensor<-1xf32>) -> vec[custom_device_tensor<-1xf32>,custom_device_tensor<-1xf32>] + (%713) = "meshgrid(phi_kernel)" (%712) {kernel_key:,kernel_name:"meshgrid",op_name:"pd_op.meshgrid",origin_id:2514,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (vec[custom_device_tensor<-1xf32>,custom_device_tensor<-1xf32>]) -> vec[custom_device_tensor<-1x-1xf32>,custom_device_tensor<-1x-1xf32>] + (%714, %715) = "builtin.split" [id:2515] (%713) {origin_id:464,stop_gradient:[true,true],struct_name:"/RPNHead/AnchorGenerator/"} : (vec[custom_device_tensor<-1x-1xf32>,custom_device_tensor<-1x-1xf32>]) -> custom_device_tensor<-1x-1xf32>, custom_device_tensor<-1x-1xf32> + (%716) = "reshape(phi_kernel)" (%715, %48) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:2516,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (custom_device_tensor<-1x-1xf32>, cpu_tensor<1xi64>) -> custom_device_tensor<-1xf32> + (%717) = "reshape(phi_kernel)" (%714, %48) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:2517,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (custom_device_tensor<-1x-1xf32>, cpu_tensor<1xi64>) -> custom_device_tensor<-1xf32> + (%718) = "builtin.combine" [id:2518] (%716, %717, %716, %717) {origin_id:469,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (custom_device_tensor<-1xf32>, custom_device_tensor<-1xf32>, custom_device_tensor<-1xf32>, custom_device_tensor<-1xf32>) -> vec[custom_device_tensor<-1xf32>,custom_device_tensor<-1xf32>,custom_device_tensor<-1xf32>,custom_device_tensor<-1xf32>] + (%719) = "stack(phi_kernel)" (%718) {axis:1,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2519,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (vec[custom_device_tensor<-1xf32>,custom_device_tensor<-1xf32>,custom_device_tensor<-1xf32>,custom_device_tensor<-1xf32>]) -> custom_device_tensor<-1x4xf32> + (%720) = "reshape(phi_kernel)" (%719, %47) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:2520,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (custom_device_tensor<-1x4xf32>, cpu_tensor<3xi64>) -> custom_device_tensor<-1x1x4xf32> + (%721) = "add(phi_kernel)" (%720, %46) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2521,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (custom_device_tensor<-1x1x4xf32>, custom_device_tensor<1x15x4xf32>) -> custom_device_tensor<-1x15x4xf32> + (%722) = "reshape(phi_kernel)" (%721, %45) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:2522,stop_gradient:[true],struct_name:"/RPNHead/AnchorGenerator/"} : (custom_device_tensor<-1x15x4xf32>, cpu_tensor<2xi64>) -> custom_device_tensor<-1x4xf32> + (%723) = "shape64(phi_kernel)" (%333) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2523,stop_gradient:[true],struct_name:"/RPNHead/"} : (custom_device_tensor<-1x2xf32>) -> cpu_tensor<2xi64> + (%724) = "slice(phi_kernel)" (%723, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2524,stop_gradient:[true],struct_name:"/RPNHead/"} : (cpu_tensor<2xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%725) = "arange(phi_kernel)" (%42, %724, %41) {dtype:int64,kernel_key:,kernel_name:"arange",op_name:"pd_op.arange",origin_id:2525,place:Place(undefined:0),stop_gradient:[true],struct_name:"/RPNHead/"} : (cpu_tensor<1xi64>, cpu_tensor, cpu_tensor<1xi64>) -> custom_device_tensor<-1xi64> + (%726) = "shape64(phi_kernel)" (%725) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2526,stop_gradient:[true],struct_name:"/RPNHead/"} : (custom_device_tensor<-1xi64>) -> cpu_tensor<1xi64> + (%727) = "slice(phi_kernel)" (%726, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2527,stop_gradient:[true],struct_name:"/RPNHead/"} : (cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%728) = "create_array(phi_kernel)" () {dtype:Undefined,kernel_key:,kernel_name:"create_array",op_name:"pd_op.create_array",origin_id:2528,stop_gradient:[true],struct_name:"/RPNHead/"} : () -> cpu_tensor_array + (%729) = "create_array(phi_kernel)" () {dtype:Undefined,kernel_key:,kernel_name:"create_array",op_name:"pd_op.create_array",origin_id:2529,stop_gradient:[true],struct_name:"/RPNHead/"} : () -> cpu_tensor_array + (%730) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2530,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/RPNHead/",value:0} : () -> custom_device_tensor + (%731) = "memcpy_h2d(phi_kernel)" (%727) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2531} : (cpu_tensor) -> custom_device_tensor + (%732) = "less_than(phi_kernel)" (%730, %731) {kernel_key:,kernel_name:"less_than",op_name:"pd_op.less_than",origin_id:2532,stop_gradient:[true],struct_name:"/RPNHead/"} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%733) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2533,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/RPNHead/",value:0} : () -> custom_device_tensor<-1x4xf32> + (%734) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2534,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/RPNHead/",value:0} : () -> custom_device_tensor + (%735) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2535,place:Place(undefined:0),shape:[],stop_gradient:[false],struct_name:"/RPNHead/",value:0} : () -> custom_device_tensor<-1x60x-1x-1xf32> + (%736) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2536,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/RPNHead/",value:0} : () -> custom_device_tensor<-1x4xf32> + (%737) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2537,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/RPNHead/",value:0} : () -> custom_device_tensor<-1xf32> + (%738) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2538,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/RPNHead/",value:0} : () -> custom_device_tensor<-1x1xf32> + (%739) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2539,place:Place(undefined:0),shape:[],stop_gradient:[false],struct_name:"/RPNHead/",value:0} : () -> custom_device_tensor<-1x15x-1x-1xf32> + (%740) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2540,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/RPNHead/",value:0} : () -> custom_device_tensor<-1xf32> + (%741) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2541,place:Place(undefined:0),shape:[],stop_gradient:[true],struct_name:"/RPNHead/",value:0} : () -> custom_device_tensor<-1x4xf32> + (%742, %743, %744, %745, %746, %747, %748, %749, %750, %751) = "pd_op.while" [id:2542] (cond=%732, inputs=%730, %733, %734, %735, %736, %737, %738, %739, %740, %741) { + ^%arg_0 {stop_gradient:true}, %arg_1 {stop_gradient:true}, %arg_2 {stop_gradient:true}, %arg_3 {stop_gradient:false}, %arg_4 {stop_gradient:true}, %arg_5 {stop_gradient:true}, %arg_6 {stop_gradient:true}, %arg_7 {stop_gradient:false}, %arg_8 {stop_gradient:true}, %arg_9 {stop_gradient:true} + (%752) = "memcpy_d2h(phi_kernel)" (%arg_0) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2543} : (custom_device_tensor) -> cpu_tensor + (%753) = "scale(phi_kernel)" (%752, %19) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2544,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%754) = "builtin.combine" [id:2545] (%arg_0) {origin_id:505,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%755) = "stack(phi_kernel)" (%754) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2546,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%756) = "builtin.combine" [id:2547] (%753) {origin_id:507,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%757) = "stack(phi_kernel)" (%756) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2548,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%758) = "slice(phi_kernel)" (%725, %755, %757) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2549,stop_gradient:[true]} : (custom_device_tensor<-1xi64>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor + (%759) = "memcpy_d2h(phi_kernel)" (%758) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2550} : (custom_device_tensor) -> cpu_tensor + (%760) = "scale(phi_kernel)" (%759, %19) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2551,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%761) = "builtin.combine" [id:2552] (%758) {origin_id:512,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%762) = "stack(phi_kernel)" (%761) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2553,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%763) = "builtin.combine" [id:2554] (%760) {origin_id:514,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%764) = "stack(phi_kernel)" (%763) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2555,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%765) = "slice(phi_kernel)" (%699, %762, %764) {axes:[0],decrease_axis:[],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2556,stop_gradient:[false]} : (custom_device_tensor<-1x15x-1x-1xf32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x15x-1x-1xf32> + (%766) = "memcpy_d2h(phi_kernel)" (%758) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2557} : (custom_device_tensor) -> cpu_tensor + (%767) = "scale(phi_kernel)" (%766, %19) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2558,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%768) = "builtin.combine" [id:2559] (%758) {origin_id:519,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%769) = "stack(phi_kernel)" (%768) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2560,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%770) = "builtin.combine" [id:2561] (%767) {origin_id:521,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%771) = "stack(phi_kernel)" (%770) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2562,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%772) = "slice(phi_kernel)" (%701, %769, %771) {axes:[0],decrease_axis:[],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2563,stop_gradient:[false]} : (custom_device_tensor<-1x60x-1x-1xf32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x60x-1x-1xf32> + (%773) = "memcpy_d2h(phi_kernel)" (%758) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2564} : (custom_device_tensor) -> cpu_tensor + (%774) = "scale(phi_kernel)" (%773, %19) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2565,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%775) = "builtin.combine" [id:2566] (%758) {origin_id:526,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%776) = "stack(phi_kernel)" (%775) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2567,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%777) = "builtin.combine" [id:2568] (%774) {origin_id:528,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%778) = "stack(phi_kernel)" (%777) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2569,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%779) = "slice(phi_kernel)" (%333, %776, %778) {axes:[0],decrease_axis:[],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2570,stop_gradient:[false]} : (custom_device_tensor<-1x2xf32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x2xf32> + (%780) = "full_like(phi_kernel)" (%722, %19) {dtype:float32,kernel_key:,kernel_name:"full_like",op_name:"pd_op.full_like",origin_id:2571,place:Place(undefined:0),stop_gradient:[true]} : (custom_device_tensor<-1x4xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x4xf32> + (%781) = "memcpy_d2h(phi_kernel)" (%765) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2572} : (custom_device_tensor<-1x15x-1x-1xf32>) -> cpu_tensor<-1x15x-1x-1xf32> + (%782) = "memcpy_d2h(phi_kernel)" (%772) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2573} : (custom_device_tensor<-1x60x-1x-1xf32>) -> cpu_tensor<-1x60x-1x-1xf32> + (%783) = "memcpy_d2h(phi_kernel)" (%779) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2574} : (custom_device_tensor<-1x2xf32>) -> cpu_tensor<-1x2xf32> + (%784) = "memcpy_d2h(phi_kernel)" (%722) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2575} : (custom_device_tensor<-1x4xf32>) -> cpu_tensor<-1x4xf32> + (%785) = "memcpy_d2h(phi_kernel)" (%780) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2576} : (custom_device_tensor<-1x4xf32>) -> cpu_tensor<-1x4xf32> + (%786, %787, %788) = "generate_proposals(phi_kernel)" (%781, %782, %783, %784, %785) {eta:1,kernel_key:,kernel_name:"generate_proposals",min_size:0,nms_thresh:0.7,op_name:"pd_op.generate_proposals",origin_id:2577,pixel_offset:false,post_nms_top_n:1000,pre_nms_top_n:6000,stop_gradient:[true,true,true]} : (cpu_tensor<-1x15x-1x-1xf32>, cpu_tensor<-1x60x-1x-1xf32>, cpu_tensor<-1x2xf32>, cpu_tensor<-1x4xf32>, cpu_tensor<-1x4xf32>) -> cpu_tensor<-1x4xf32>, cpu_tensor<-1x1xf32>, cpu_tensor<-1xf32> + (%789) = "flatten(phi_kernel)" (%787) {kernel_key:,kernel_name:"flatten",op_name:"pd_op.flatten",origin_id:2578,start_axis:0,stop_axis:1,stop_gradient:[true]} : (cpu_tensor<-1x1xf32>) -> cpu_tensor<-1xf32> + (%790) = "array_length(phi_kernel)" (%729) {kernel_key:,kernel_name:"array_length",op_name:"pd_op.array_length",origin_id:2579} : (cpu_tensor_array) -> cpu_tensor<1xi64> + (%791) = "array_write_(phi_kernel)" (%729, %786, %790) {is_inplace:true,kernel_key:,kernel_name:"array_write",op_name:"pd_op.array_write_",origin_id:2580} : (cpu_tensor_array, cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>) -> cpu_tensor_array + (%792) = "shape64(phi_kernel)" (%786) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2581,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>) -> cpu_tensor<2xi64> + (%793) = "slice(phi_kernel)" (%792, %44, %43) {axes:[0],decrease_axis:[],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2582,stop_gradient:[true]} : (cpu_tensor<2xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<1xi64> + (%794) = "array_length(phi_kernel)" (%728) {kernel_key:,kernel_name:"array_length",op_name:"pd_op.array_length",origin_id:2583} : (cpu_tensor_array) -> cpu_tensor<1xi64> + (%795) = "array_write_(phi_kernel)" (%728, %793, %794) {is_inplace:true,kernel_key:,kernel_name:"array_write",op_name:"pd_op.array_write_",origin_id:2584} : (cpu_tensor_array, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor_array + (%796) = "memcpy_d2h(phi_kernel)" (%arg_0) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2585} : (custom_device_tensor) -> cpu_tensor + (%797) = "scale(phi_kernel)" (%796, %19) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2586,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%798) = "less_than(phi_kernel)" (%797, %727) {kernel_key:,kernel_name:"less_than",op_name:"pd_op.less_than",origin_id:2587,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> cpu_tensor + (%799) = "memcpy_h2d(phi_kernel)" (%798) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2588} : (cpu_tensor) -> custom_device_tensor + (%800) = "memcpy_h2d(phi_kernel)" (%797) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2589} : (cpu_tensor) -> custom_device_tensor + (%801) = "memcpy_h2d(phi_kernel)" (%786) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2590} : (cpu_tensor<-1x4xf32>) -> custom_device_tensor<-1x4xf32> + (%802) = "memcpy_h2d(phi_kernel)" (%788) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2591} : (cpu_tensor<-1xf32>) -> custom_device_tensor<-1xf32> + (%803) = "memcpy_h2d(phi_kernel)" (%787) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2592} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%804) = "memcpy_h2d(phi_kernel)" (%789) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2593} : (cpu_tensor<-1xf32>) -> custom_device_tensor<-1xf32> + (%805) = "memcpy_h2d(phi_kernel)" (%786) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2594} : (cpu_tensor<-1x4xf32>) -> custom_device_tensor<-1x4xf32> + () = "cf.yield" [id:2595] (%799, %800, %722, %758, %701, %801, %802, %803, %699, %804, %805) {origin_id:546} : (custom_device_tensor, custom_device_tensor, custom_device_tensor<-1x4xf32>, custom_device_tensor, custom_device_tensor<-1x60x-1x-1xf32>, custom_device_tensor<-1x4xf32>, custom_device_tensor<-1xf32>, custom_device_tensor<-1x1xf32>, custom_device_tensor<-1x15x-1x-1xf32>, custom_device_tensor<-1xf32>, custom_device_tensor<-1x4xf32>) -> + } + (%806, %807) = "array_to_tensor(phi_kernel)" (%728) {axis:0,kernel_key:,kernel_name:"array_to_tensor",op_name:"pd_op.array_to_tensor",origin_id:2596,stop_gradient:[true,true],struct_name:"/RPNHead/",use_stack:false} : (cpu_tensor_array) -> cpu_tensor<-1xi64>, cpu_tensor<-1xi32> + (%808) = "array_length(phi_kernel)" (%729) {kernel_key:,kernel_name:"array_length",op_name:"pd_op.array_length",origin_id:2597,struct_name:"/BBoxHead/RoIAlign/"} : (cpu_tensor_array) -> cpu_tensor<1xi64> + (%809) = "memcpy_h2d(phi_kernel)" (%808) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2598} : (cpu_tensor<1xi64>) -> custom_device_tensor<1xi64> + (%810) = "greater_than(phi_kernel)" (%809, %40) {kernel_key:,kernel_name:"greater_than",op_name:"pd_op.greater_than",origin_id:2599,stop_gradient:[true],struct_name:"/BBoxHead/RoIAlign/"} : (custom_device_tensor<1xi64>, custom_device_tensor) -> custom_device_tensor<1xb> + (%811) = "pd_op.if" [id:2600] (%810) {} -> custom_device_tensor<-1x4xf32> { + (%812, %813) = "array_to_tensor(phi_kernel)" (%729) {axis:0,kernel_key:,kernel_name:"array_to_tensor",op_name:"pd_op.array_to_tensor",origin_id:2601,stop_gradient:[true,true],use_stack:false} : (cpu_tensor_array) -> cpu_tensor<-1x4xf32>, cpu_tensor<-1xi32> + (%814) = "memcpy_h2d(phi_kernel)" (%812) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2602} : (cpu_tensor<-1x4xf32>) -> custom_device_tensor<-1x4xf32> + () = "cf.yield" [id:2603] (%814) {origin_id:553} : (custom_device_tensor<-1x4xf32>) -> + } else { + (%815) = "slice_array_dense(phi_kernel)" (%729, %44) {kernel_key:,kernel_name:"slice_array_dense",op_name:"pd_op.slice_array_dense",origin_id:2604,stop_gradient:[true]} : (cpu_tensor_array, cpu_tensor<1xi64>) -> cpu_tensor<-1x4xf32> + (%816) = "memcpy_h2d(phi_kernel)" (%815) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2605} : (cpu_tensor<-1x4xf32>) -> custom_device_tensor<-1x4xf32> + () = "cf.yield" [id:2606] (%∂) {origin_id:556} : (custom_device_tensor<-1x4xf32>) -> + } + (%817) = "cast(phi_kernel)" (%806) {dtype:int32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:2607,stop_gradient:[true],struct_name:"/BBoxHead/RoIAlign/"} : (cpu_tensor<-1xi64>) -> cpu_tensor<-1xi32> + (%818) = "memcpy_h2d(phi_kernel)" (%817) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2608} : (cpu_tensor<-1xi32>) -> custom_device_tensor<-1xi32> + (%819) = "roi_align(phi_kernel)" (%694, %811, %818) {aligned:true,kernel_key:,kernel_name:"roi_align",op_name:"pd_op.roi_align",origin_id:2609,pooled_height:14,pooled_width:14,sampling_ratio:-1,spatial_scale:0.0625,stop_gradient:[false],struct_name:"/BBoxHead/RoIAlign/"} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<-1x4xf32>, custom_device_tensor<-1xi32>) -> custom_device_tensor<-1x1024x14x14xf32> + (%820) = "conv2d(phi_kernel)" (%819, %113) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2610,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x1024x14x14xf32>, custom_device_tensor<512x1024x1x1xf32>) -> custom_device_tensor<-1x512x14x14xf32> + (%821, %822, %823, %824, %825, %826) = "batch_norm_(phi_kernel)" (%820, %110, %109, %112, %111) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2611,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x14x14xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x14x14xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%827) = "relu(phi_kernel)" (%821) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2612,stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer/"} : (custom_device_tensor<-1x512x14x14xf32>) -> custom_device_tensor<-1x512x14x14xf32> + (%828) = "conv2d(phi_kernel)" (%827, %108) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2613,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[2,2],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x512x14x14xf32>, custom_device_tensor<512x512x3x3xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%829, %830, %831, %832, %833, %834) = "batch_norm_(phi_kernel)" (%828, %105, %104, %107, %106) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2614,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%835) = "relu(phi_kernel)" (%829) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2615,stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer_1/"} : (custom_device_tensor<-1x512x7x7xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%836) = "conv2d(phi_kernel)" (%835, %103) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2616,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<2048x512x1x1xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%837, %838, %839, %840, %841, %842) = "batch_norm_(phi_kernel)" (%836, %100, %99, %102, %101) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2617,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>) -> custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<-1xu8> + (%843) = "conv2d(phi_kernel)" (%819, %98) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2618,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[2,2],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer_3/Conv2D/"} : (custom_device_tensor<-1x1024x14x14xf32>, custom_device_tensor<2048x1024x1x1xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%844, %845, %846, %847, %848, %849) = "batch_norm_(phi_kernel)" (%843, %95, %94, %97, %96) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2619,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/ConvNormLayer_3/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>) -> custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<-1xu8> + (%850) = "add(phi_kernel)" (%837, %844) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2620,stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/"} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%851) = "relu(phi_kernel)" (%850) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2621,stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck/"} : (custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%852) = "conv2d(phi_kernel)" (%851, %93) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2622,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<512x2048x1x1xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%853, %854, %855, %856, %857, %858) = "batch_norm_(phi_kernel)" (%852, %90, %89, %92, %91) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2623,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%859) = "relu(phi_kernel)" (%853) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2624,stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/ConvNormLayer/"} : (custom_device_tensor<-1x512x7x7xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%860) = "conv2d(phi_kernel)" (%859, %88) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2625,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512x512x3x3xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%861, %862, %863, %864, %865, %866) = "batch_norm_(phi_kernel)" (%860, %85, %84, %87, %86) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2626,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%867) = "relu(phi_kernel)" (%861) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2627,stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/ConvNormLayer_1/"} : (custom_device_tensor<-1x512x7x7xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%868) = "conv2d(phi_kernel)" (%867, %83) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2628,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<2048x512x1x1xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%869, %870, %871, %872, %873, %874) = "batch_norm_(phi_kernel)" (%868, %80, %79, %82, %81) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2629,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>) -> custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<-1xu8> + (%875) = "add(phi_kernel)" (%869, %851) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2630,stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/"} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%876) = "relu(phi_kernel)" (%875) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2631,stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_1/"} : (custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%877) = "conv2d(phi_kernel)" (%876, %78) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2632,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/ConvNormLayer/Conv2D/"} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<512x2048x1x1xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%878, %879, %880, %881, %882, %883) = "batch_norm_(phi_kernel)" (%877, %75, %74, %77, %76) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2633,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/ConvNormLayer/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%884) = "relu(phi_kernel)" (%878) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2634,stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/ConvNormLayer/"} : (custom_device_tensor<-1x512x7x7xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%885) = "conv2d(phi_kernel)" (%884, %73) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2635,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/ConvNormLayer_1/Conv2D/"} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512x512x3x3xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%886, %887, %888, %889, %890, %891) = "batch_norm_(phi_kernel)" (%885, %70, %69, %72, %71) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2636,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/ConvNormLayer_1/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%892) = "relu(phi_kernel)" (%886) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2637,stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/ConvNormLayer_1/"} : (custom_device_tensor<-1x512x7x7xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%893) = "conv2d(phi_kernel)" (%892, %68) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2638,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/ConvNormLayer_2/Conv2D/"} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<2048x512x1x1xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%894, %895, %896, %897, %898, %899) = "batch_norm_(phi_kernel)" (%893, %65, %64, %67, %66) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2639,stop_gradient:[false,false,false,false,false,false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/ConvNormLayer_2/BatchNorm2D/",trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>) -> custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<-1xu8> + (%900) = "add(phi_kernel)" (%894, %876) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2640,stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/"} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%901) = "relu(phi_kernel)" (%900) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2641,stop_gradient:[false],struct_name:"/BBoxHead/Res5Head/Blocks/BottleNeck_2/"} : (custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%902) = "pool2d(phi_kernel)" (%901, %39) {adaptive:true,ceil_mode:false,data_format:"NCHW",exclusive:true,global_pooling:false,kernel_key:,kernel_name:"pool2d",op_name:"pd_op.pool2d",origin_id:2642,padding_algorithm:"EXPLICIT",paddings:[0,0],pooling_type:"avg",stop_gradient:[false],strides:[1,1],struct_name:"/BBoxHead/"} : (custom_device_tensor<-1x2048x7x7xf32>, cpu_tensor<2xi64>) -> custom_device_tensor<-1x2048x1x1xf32> + (%903) = "squeeze(phi_kernel)" (%902, %38) {kernel_key:,kernel_name:"squeeze",op_name:"pd_op.squeeze",origin_id:2643,stop_gradient:[false],struct_name:"/BBoxHead/"} : (custom_device_tensor<-1x2048x1x1xf32>, cpu_tensor<2xi64>) -> custom_device_tensor<-1x2048xf32> + (%904) = "matmul(phi_kernel)" (%903, %63) {kernel_key:,kernel_name:"matmul",op_name:"pd_op.matmul",origin_id:2644,stop_gradient:[false],struct_name:"/BBoxHead/Linear/",transpose_x:false,transpose_y:false} : (custom_device_tensor<-1x2048xf32>, custom_device_tensor<2048x81xf32>) -> custom_device_tensor<-1x81xf32> + (%905) = "add(phi_kernel)" (%904, %62) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2645,stop_gradient:[false],struct_name:"/BBoxHead/Linear/"} : (custom_device_tensor<-1x81xf32>, custom_device_tensor<81xf32>) -> custom_device_tensor<-1x81xf32> + (%906) = "matmul(phi_kernel)" (%903, %61) {kernel_key:,kernel_name:"matmul",op_name:"pd_op.matmul",origin_id:2646,stop_gradient:[false],struct_name:"/BBoxHead/Linear_1/",transpose_x:false,transpose_y:false} : (custom_device_tensor<-1x2048xf32>, custom_device_tensor<2048x320xf32>) -> custom_device_tensor<-1x320xf32> + (%907) = "add(phi_kernel)" (%906, %60) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2647,stop_gradient:[false],struct_name:"/BBoxHead/Linear_1/"} : (custom_device_tensor<-1x320xf32>, custom_device_tensor<320xf32>) -> custom_device_tensor<-1x320xf32> + (%908) = "softmax(phi_kernel)" (%905) {axis:-1,kernel_key:,kernel_name:"softmax",op_name:"pd_op.softmax",origin_id:2648,stop_gradient:[false],struct_name:"/BBoxHead/"} : (custom_device_tensor<-1x81xf32>) -> custom_device_tensor<-1x81xf32> + (%909) = "slice(phi_kernel)" (%723, %44, %43) {axes:[0],decrease_axis:[],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2649,stop_gradient:[true]} : (cpu_tensor<2xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<1xi64> + (%910) = "arange(phi_kernel)" (%42, %909, %41) {dtype:int64,kernel_key:,kernel_name:"arange",op_name:"pd_op.arange",origin_id:2650,place:Place(undefined:0),stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1xi64> + (%911) = "shape64(phi_kernel)" (%910) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2651,stop_gradient:[true]} : (custom_device_tensor<-1xi64>) -> cpu_tensor<1xi64> + (%912) = "slice(phi_kernel)" (%911, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2652,stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%913) = "create_array(phi_kernel)" () {dtype:Undefined,kernel_key:,kernel_name:"create_array",op_name:"pd_op.create_array",origin_id:2653,stop_gradient:[true]} : () -> cpu_tensor_array + (%914) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2654,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%915) = "memcpy_h2d(phi_kernel)" (%912) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2655} : (cpu_tensor) -> custom_device_tensor + (%916) = "less_than(phi_kernel)" (%914, %915) {kernel_key:,kernel_name:"less_than",op_name:"pd_op.less_than",origin_id:2656,stop_gradient:[true]} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%917) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2657,place:Place(undefined:0),shape:[],stop_gradient:[false],value:0} : () -> custom_device_tensor<-1x2xf32> + (%918) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2658,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%919) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2659,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%920, %921, %922, %923) = "pd_op.while" [id:2660] (cond=%916, inputs=%914, %917, %918, %919) { + ^%arg_10 {stop_gradient:true}, %arg_11 {stop_gradient:false}, %arg_12 {stop_gradient:true}, %arg_13 {stop_gradient:true} + (%924) = "memcpy_d2h(phi_kernel)" (%arg_10) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2661} : (custom_device_tensor) -> cpu_tensor + (%925) = "scale(phi_kernel)" (%924, %18) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2662,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%926) = "builtin.combine" [id:2663] (%arg_10) {origin_id:620,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%927) = "stack(phi_kernel)" (%926) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2664,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%928) = "builtin.combine" [id:2665] (%925) {origin_id:622,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%929) = "stack(phi_kernel)" (%928) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2666,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%930) = "slice(phi_kernel)" (%910, %927, %929) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2667,stop_gradient:[true]} : (custom_device_tensor<-1xi64>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor + (%931) = "memcpy_d2h(phi_kernel)" (%930) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2668} : (custom_device_tensor) -> cpu_tensor + (%932) = "scale(phi_kernel)" (%931, %18) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2669,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%933) = "builtin.combine" [id:2670] (%930) {origin_id:627,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%934) = "stack(phi_kernel)" (%933) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2671,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%935) = "builtin.combine" [id:2672] (%932) {origin_id:629,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%936) = "stack(phi_kernel)" (%935) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2673,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%937) = "slice(phi_kernel)" (%806, %934, %936) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2674,stop_gradient:[true]} : (cpu_tensor<-1xi64>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%938) = "memcpy_d2h(phi_kernel)" (%930) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2675} : (custom_device_tensor) -> cpu_tensor + (%939) = "scale(phi_kernel)" (%938, %18) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2676,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%940) = "builtin.combine" [id:2677] (%930) {origin_id:634,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%941) = "stack(phi_kernel)" (%940) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2678,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%942) = "builtin.combine" [id:2679] (%939) {origin_id:636,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%943) = "stack(phi_kernel)" (%942) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2680,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%944) = "slice(phi_kernel)" (%333, %941, %943) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2681,stop_gradient:[false]} : (custom_device_tensor<-1x2xf32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<2xf32> + (%945) = "builtin.combine" [id:2682] (%937, %17) {origin_id:640,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor] + (%946) = "stack(phi_kernel)" (%945) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2683,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor]) -> cpu_tensor<2xi64> + (%947) = "expand(phi_kernel)" (%944, %946) {kernel_key:,kernel_name:"expand",op_name:"pd_op.expand",origin_id:2684,stop_gradient:[false]} : (custom_device_tensor<2xf32>, cpu_tensor<2xi64>) -> custom_device_tensor<-1x2xf32> + (%948) = "array_length(phi_kernel)" (%913) {kernel_key:,kernel_name:"array_length",op_name:"pd_op.array_length",origin_id:2685} : (cpu_tensor_array) -> cpu_tensor<1xi64> + (%949) = "memcpy_d2h(phi_kernel)" (%947) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2686} : (custom_device_tensor<-1x2xf32>) -> cpu_tensor<-1x2xf32> + (%950) = "array_write_(phi_kernel)" (%913, %949, %948) {is_inplace:true,kernel_key:,kernel_name:"array_write",op_name:"pd_op.array_write_",origin_id:2687} : (cpu_tensor_array, cpu_tensor<-1x2xf32>, cpu_tensor<1xi64>) -> cpu_tensor_array + (%951) = "memcpy_d2h(phi_kernel)" (%arg_10) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2688} : (custom_device_tensor) -> cpu_tensor + (%952) = "scale(phi_kernel)" (%951, %18) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2689,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%953) = "less_than(phi_kernel)" (%952, %912) {kernel_key:,kernel_name:"less_than",op_name:"pd_op.less_than",origin_id:2690,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> cpu_tensor + (%954) = "memcpy_h2d(phi_kernel)" (%953) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2691} : (cpu_tensor) -> custom_device_tensor + (%955) = "memcpy_h2d(phi_kernel)" (%952) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2692} : (cpu_tensor) -> custom_device_tensor + (%956) = "memcpy_h2d(phi_kernel)" (%937) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2693} : (cpu_tensor) -> custom_device_tensor + () = "cf.yield" [id:2694] (%954, %955, %947, %930, %956) {origin_id:648} : (custom_device_tensor, custom_device_tensor, custom_device_tensor<-1x2xf32>, custom_device_tensor, custom_device_tensor) -> + } + (%957, %958) = "array_to_tensor(phi_kernel)" (%913) {axis:0,kernel_key:,kernel_name:"array_to_tensor",op_name:"pd_op.array_to_tensor",origin_id:2695,stop_gradient:[true,true],use_stack:false} : (cpu_tensor_array) -> cpu_tensor<-1x2xf32>, cpu_tensor<-1xi32> + (%959, %960) = "array_to_tensor(phi_kernel)" (%729) {axis:0,kernel_key:,kernel_name:"array_to_tensor",op_name:"pd_op.array_to_tensor",origin_id:2696,stop_gradient:[true,true],use_stack:false} : (cpu_tensor_array) -> cpu_tensor<-1x4xf32>, cpu_tensor<-1xi32> + (%961) = "slice(phi_kernel)" (%959, %52, %51) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2697,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%962) = "slice(phi_kernel)" (%959, %44, %43) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2698,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%963) = "subtract(phi_kernel)" (%961, %962) {kernel_key:,kernel_name:"subtract",op_name:"pd_op.subtract",origin_id:2699,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%964) = "slice(phi_kernel)" (%959, %51, %50) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2700,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%965) = "slice(phi_kernel)" (%959, %43, %52) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2701,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%966) = "subtract(phi_kernel)" (%964, %965) {kernel_key:,kernel_name:"subtract",op_name:"pd_op.subtract",origin_id:2702,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%967) = "slice(phi_kernel)" (%959, %44, %43) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2703,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%968) = "scale(phi_kernel)" (%963, %37) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2704,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<1xf32>) -> cpu_tensor<-1xf32> + (%969) = "add(phi_kernel)" (%967, %968) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2705,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%970) = "slice(phi_kernel)" (%959, %43, %52) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2706,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%971) = "scale(phi_kernel)" (%966, %37) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2707,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<1xf32>) -> cpu_tensor<-1xf32> + (%972) = "add(phi_kernel)" (%970, %971) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2708,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%973) = "strided_slice(phi_kernel)" (%907, %44, %36, %50) {axes:[1],kernel_key:,kernel_name:"strided_slice",op_name:"pd_op.strided_slice",origin_id:2709,stop_gradient:[false]} : (custom_device_tensor<-1x320xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x80xf32> + (%974) = "scale(phi_kernel)" (%973, %35) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2710,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x80xf32> + (%975) = "strided_slice(phi_kernel)" (%907, %43, %36, %50) {axes:[1],kernel_key:,kernel_name:"strided_slice",op_name:"pd_op.strided_slice",origin_id:2711,stop_gradient:[false]} : (custom_device_tensor<-1x320xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x80xf32> + (%976) = "scale(phi_kernel)" (%975, %35) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2712,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x80xf32> + (%977) = "strided_slice(phi_kernel)" (%907, %52, %36, %50) {axes:[1],kernel_key:,kernel_name:"strided_slice",op_name:"pd_op.strided_slice",origin_id:2713,stop_gradient:[false]} : (custom_device_tensor<-1x320xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x80xf32> + (%978) = "scale(phi_kernel)" (%977, %34) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2714,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x80xf32> + (%979) = "strided_slice(phi_kernel)" (%907, %51, %36, %50) {axes:[1],kernel_key:,kernel_name:"strided_slice",op_name:"pd_op.strided_slice",origin_id:2715,stop_gradient:[false]} : (custom_device_tensor<-1x320xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x80xf32> + (%980) = "scale(phi_kernel)" (%979, %34) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2716,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x80xf32> + (%981) = "clip(phi_kernel)" (%978, %33, %32) {kernel_key:,kernel_name:"clip",op_name:"pd_op.clip",origin_id:2717,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, cpu_tensor<1xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x80xf32> + (%982) = "clip(phi_kernel)" (%980, %33, %32) {kernel_key:,kernel_name:"clip",op_name:"pd_op.clip",origin_id:2718,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, cpu_tensor<1xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x80xf32> + (%983) = "unsqueeze(phi_kernel)" (%963, %43) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:2719,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<1xi64>) -> cpu_tensor<-1x1xf32> + (%984) = "memcpy_h2d(phi_kernel)" (%983) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2720} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%985) = "multiply(phi_kernel)" (%974, %984) {kernel_key:,kernel_name:"multiply",op_name:"pd_op.multiply",origin_id:2721,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%986) = "unsqueeze(phi_kernel)" (%969, %43) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:2722,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<1xi64>) -> cpu_tensor<-1x1xf32> + (%987) = "memcpy_h2d(phi_kernel)" (%986) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2723} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%988) = "add(phi_kernel)" (%985, %987) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2724,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%989) = "unsqueeze(phi_kernel)" (%966, %43) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:2725,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<1xi64>) -> cpu_tensor<-1x1xf32> + (%990) = "memcpy_h2d(phi_kernel)" (%989) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2726} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%991) = "multiply(phi_kernel)" (%976, %990) {kernel_key:,kernel_name:"multiply",op_name:"pd_op.multiply",origin_id:2727,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%992) = "unsqueeze(phi_kernel)" (%972, %43) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:2728,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<1xi64>) -> cpu_tensor<-1x1xf32> + (%993) = "memcpy_h2d(phi_kernel)" (%992) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2729} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%994) = "add(phi_kernel)" (%991, %993) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2730,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%995) = "exp(phi_kernel)" (%981) {kernel_key:,kernel_name:"exp",op_name:"pd_op.exp",origin_id:2731,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>) -> custom_device_tensor<-1x80xf32> + (%996) = "unsqueeze(phi_kernel)" (%963, %43) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:2732,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<1xi64>) -> cpu_tensor<-1x1xf32> + (%997) = "memcpy_h2d(phi_kernel)" (%996) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2733} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%998) = "multiply(phi_kernel)" (%995, %997) {kernel_key:,kernel_name:"multiply",op_name:"pd_op.multiply",origin_id:2734,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%999) = "exp(phi_kernel)" (%982) {kernel_key:,kernel_name:"exp",op_name:"pd_op.exp",origin_id:2735,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>) -> custom_device_tensor<-1x80xf32> + (%1000) = "unsqueeze(phi_kernel)" (%966, %43) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:2736,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<1xi64>) -> cpu_tensor<-1x1xf32> + (%1001) = "memcpy_h2d(phi_kernel)" (%1000) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2737} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1002) = "multiply(phi_kernel)" (%999, %1001) {kernel_key:,kernel_name:"multiply",op_name:"pd_op.multiply",origin_id:2738,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%1003) = "scale(phi_kernel)" (%998, %37) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2739,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x80xf32> + (%1004) = "subtract(phi_kernel)" (%988, %1003) {kernel_key:,kernel_name:"subtract",op_name:"pd_op.subtract",origin_id:2740,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x80xf32>) -> custom_device_tensor<-1x80xf32> + (%1005) = "scale(phi_kernel)" (%1002, %37) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2741,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x80xf32> + (%1006) = "subtract(phi_kernel)" (%994, %1005) {kernel_key:,kernel_name:"subtract",op_name:"pd_op.subtract",origin_id:2742,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x80xf32>) -> custom_device_tensor<-1x80xf32> + (%1007) = "scale(phi_kernel)" (%998, %37) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2743,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x80xf32> + (%1008) = "add(phi_kernel)" (%988, %1007) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2744,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x80xf32>) -> custom_device_tensor<-1x80xf32> + (%1009) = "scale(phi_kernel)" (%1002, %37) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2745,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x80xf32> + (%1010) = "add(phi_kernel)" (%994, %1009) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2746,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x80xf32>) -> custom_device_tensor<-1x80xf32> + (%1011) = "builtin.combine" [id:2747] (%1004, %1006, %1008, %1010) {origin_id:739,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x80xf32>) -> vec[custom_device_tensor<-1x80xf32>,custom_device_tensor<-1x80xf32>,custom_device_tensor<-1x80xf32>,custom_device_tensor<-1x80xf32>] + (%1012) = "stack(phi_kernel)" (%1011) {axis:-1,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2748,stop_gradient:[false]} : (vec[custom_device_tensor<-1x80xf32>,custom_device_tensor<-1x80xf32>,custom_device_tensor<-1x80xf32>,custom_device_tensor<-1x80xf32>]) -> custom_device_tensor<-1x80x4xf32> + (%1013) = "slice(phi_kernel)" (%908, %44, %48) {axes:[1],decrease_axis:[],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2749,stop_gradient:[false]} : (custom_device_tensor<-1x81xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x80xf32> + (%1014) = "shape64(phi_kernel)" (%1012) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2750,stop_gradient:[true]} : (custom_device_tensor<-1x80x4xf32>) -> cpu_tensor<3xi64> + (%1015) = "slice(phi_kernel)" (%1014, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2751,stop_gradient:[true]} : (cpu_tensor<3xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1016) = "builtin.combine" [id:2752] (%1015, %31, %30) {origin_id:750,stop_gradient:[true]} : (cpu_tensor, cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor,cpu_tensor] + (%1017) = "stack(phi_kernel)" (%1016) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2753,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor,cpu_tensor]) -> cpu_tensor<3xi64> + (%1018) = "expand(phi_kernel)" (%1012, %1017) {kernel_key:,kernel_name:"expand",op_name:"pd_op.expand",origin_id:2754,stop_gradient:[false]} : (custom_device_tensor<-1x80x4xf32>, cpu_tensor<3xi64>) -> custom_device_tensor<-1x80x4xf32> + (%1019) = "slice(phi_kernel)" (%957, %44, %43) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2755,stop_gradient:[true]} : (cpu_tensor<-1x2xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%1020) = "unsqueeze(phi_kernel)" (%1019, %43) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:2756,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<1xi64>) -> cpu_tensor<-1x1xf32> + (%1021) = "slice(phi_kernel)" (%957, %43, %52) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2757,stop_gradient:[true]} : (cpu_tensor<-1x2xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%1022) = "unsqueeze(phi_kernel)" (%1021, %43) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:2758,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<1xi64>) -> cpu_tensor<-1x1xf32> + (%1023) = "full_like(phi_kernel)" (%1020, %706) {dtype:float32,kernel_key:,kernel_name:"full_like",op_name:"pd_op.full_like",origin_id:2759,place:Place(undefined:0),stop_gradient:[true]} : (cpu_tensor<-1x1xf32>, cpu_tensor<1xf32>) -> cpu_tensor<-1x1xf32> + (%1024) = "slice(phi_kernel)" (%1018, %44, %43) {axes:[2],decrease_axis:[2],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2760,stop_gradient:[false]} : (custom_device_tensor<-1x80x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x80xf32> + (%1025) = "memcpy_h2d(phi_kernel)" (%1022) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2761} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1026) = "minimum(phi_kernel)" (%1024, %1025) {kernel_key:,kernel_name:"minimum",op_name:"pd_op.minimum",origin_id:2762,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%1027) = "memcpy_h2d(phi_kernel)" (%1023) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2763} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1028) = "maximum(phi_kernel)" (%1026, %1027) {kernel_key:,kernel_name:"maximum",op_name:"pd_op.maximum",origin_id:2764,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%1029) = "slice(phi_kernel)" (%1018, %43, %52) {axes:[2],decrease_axis:[2],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2765,stop_gradient:[false]} : (custom_device_tensor<-1x80x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x80xf32> + (%1030) = "memcpy_h2d(phi_kernel)" (%1020) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2766} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1031) = "minimum(phi_kernel)" (%1029, %1030) {kernel_key:,kernel_name:"minimum",op_name:"pd_op.minimum",origin_id:2767,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%1032) = "memcpy_h2d(phi_kernel)" (%1023) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2768} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1033) = "maximum(phi_kernel)" (%1031, %1032) {kernel_key:,kernel_name:"maximum",op_name:"pd_op.maximum",origin_id:2769,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%1034) = "slice(phi_kernel)" (%1018, %52, %51) {axes:[2],decrease_axis:[2],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2770,stop_gradient:[false]} : (custom_device_tensor<-1x80x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x80xf32> + (%1035) = "memcpy_h2d(phi_kernel)" (%1022) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2771} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1036) = "minimum(phi_kernel)" (%1034, %1035) {kernel_key:,kernel_name:"minimum",op_name:"pd_op.minimum",origin_id:2772,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%1037) = "memcpy_h2d(phi_kernel)" (%1023) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2773} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1038) = "maximum(phi_kernel)" (%1036, %1037) {kernel_key:,kernel_name:"maximum",op_name:"pd_op.maximum",origin_id:2774,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%1039) = "slice(phi_kernel)" (%1018, %51, %50) {axes:[2],decrease_axis:[2],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2775,stop_gradient:[false]} : (custom_device_tensor<-1x80x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x80xf32> + (%1040) = "memcpy_h2d(phi_kernel)" (%1020) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2776} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1041) = "minimum(phi_kernel)" (%1039, %1040) {kernel_key:,kernel_name:"minimum",op_name:"pd_op.minimum",origin_id:2777,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%1042) = "memcpy_h2d(phi_kernel)" (%1023) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2778} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1043) = "maximum(phi_kernel)" (%1041, %1042) {kernel_key:,kernel_name:"maximum",op_name:"pd_op.maximum",origin_id:2779,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x80xf32> + (%1044) = "builtin.combine" [id:2780] (%1028, %1033, %1038, %1043) {origin_id:785,stop_gradient:[false]} : (custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x80xf32>, custom_device_tensor<-1x80xf32>) -> vec[custom_device_tensor<-1x80xf32>,custom_device_tensor<-1x80xf32>,custom_device_tensor<-1x80xf32>,custom_device_tensor<-1x80xf32>] + (%1045) = "stack(phi_kernel)" (%1044) {axis:-1,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2781,stop_gradient:[false]} : (vec[custom_device_tensor<-1x80xf32>,custom_device_tensor<-1x80xf32>,custom_device_tensor<-1x80xf32>,custom_device_tensor<-1x80xf32>]) -> custom_device_tensor<-1x80x4xf32> + (%1046) = "memcpy_d2h(phi_kernel)" (%1045) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2782} : (custom_device_tensor<-1x80x4xf32>) -> cpu_tensor<-1x80x4xf32> + (%1047) = "memcpy_d2h(phi_kernel)" (%1013) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2783} : (custom_device_tensor<-1x80xf32>) -> cpu_tensor<-1x80xf32> + (%1048, %1049, %1050) = "multiclass_nms3(phi_kernel)" (%1046, %1047, %806) {background_label:80,keep_top_k:100,kernel_key:,kernel_name:"multiclass_nms3",nms_eta:1,nms_threshold:0.5,nms_top_k:-1,normalized:true,op_name:"pd_op.multiclass_nms3",origin_id:2784,score_threshold:0.05,stop_gradient:[false,false,false]} : (cpu_tensor<-1x80x4xf32>, cpu_tensor<-1x80xf32>, cpu_tensor<-1xi64>) -> cpu_tensor<-1x6xf32>, cpu_tensor<-1x1xi32>, cpu_tensor<-1xi32> + (%1051) = "shape64(phi_kernel)" (%1048) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2785,stop_gradient:[true],struct_name:"/MaskHead/"} : (cpu_tensor<-1x6xf32>) -> cpu_tensor<2xi64> + (%1052) = "slice(phi_kernel)" (%1051, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2786,stop_gradient:[true],struct_name:"/MaskHead/"} : (cpu_tensor<2xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1053) = "memcpy_h2d(phi_kernel)" (%1052) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2787} : (cpu_tensor) -> custom_device_tensor + (%1054) = "equal(phi_kernel)" (%1053, %29) {kernel_key:,kernel_name:"equal",op_name:"pd_op.equal",origin_id:2788,stop_gradient:[true],struct_name:"/MaskHead/"} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%1055) = "pd_op.if" [id:2789] (%1054) {} -> custom_device_tensor<-1x-1x-1xf32> { + () = "cf.yield" [id:2790] (%16) {origin_id:796} : (custom_device_tensor<1x1x1xf32>) -> + } else { + (%1056) = "slice(phi_kernel)" (%1048, %52, %36) {axes:[1],decrease_axis:[],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2791,stop_gradient:[false]} : (cpu_tensor<-1x6xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1x4xf32> + (%1057) = "slice(phi_kernel)" (%1048, %44, %43) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2792,stop_gradient:[false]} : (cpu_tensor<-1x6xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%1058) = "cast(phi_kernel)" (%1057) {dtype:int32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:2793,stop_gradient:[false]} : (cpu_tensor<-1xf32>) -> cpu_tensor<-1xi32> + (%1059) = "memcpy_h2d(phi_kernel)" (%1056) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2794} : (cpu_tensor<-1x4xf32>) -> custom_device_tensor<-1x4xf32> + (%1060) = "memcpy_h2d(phi_kernel)" (%1050) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2795} : (cpu_tensor<-1xi32>) -> custom_device_tensor<-1xi32> + (%1061) = "roi_align(phi_kernel)" (%694, %1059, %1060) {aligned:true,kernel_key:,kernel_name:"roi_align",op_name:"pd_op.roi_align",origin_id:2796,pooled_height:14,pooled_width:14,sampling_ratio:-1,spatial_scale:0.0625,stop_gradient:[false]} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<-1x4xf32>, custom_device_tensor<-1xi32>) -> custom_device_tensor<-1x1024x14x14xf32> + (%1062) = "conv2d(phi_kernel)" (%1061, %113) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2797,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (custom_device_tensor<-1x1024x14x14xf32>, custom_device_tensor<512x1024x1x1xf32>) -> custom_device_tensor<-1x512x14x14xf32> + (%1063, %1064, %1065, %1066, %1067, %1068) = "batch_norm_(phi_kernel)" (%1062, %110, %109, %112, %111) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2798,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x14x14xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x14x14xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%1069) = "relu(phi_kernel)" (%1063) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2799,stop_gradient:[false]} : (custom_device_tensor<-1x512x14x14xf32>) -> custom_device_tensor<-1x512x14x14xf32> + (%1070) = "conv2d(phi_kernel)" (%1069, %108) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2800,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[2,2]} : (custom_device_tensor<-1x512x14x14xf32>, custom_device_tensor<512x512x3x3xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%1071, %1072, %1073, %1074, %1075, %1076) = "batch_norm_(phi_kernel)" (%1070, %105, %104, %107, %106) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2801,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%1077) = "relu(phi_kernel)" (%1071) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2802,stop_gradient:[false]} : (custom_device_tensor<-1x512x7x7xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%1078) = "conv2d(phi_kernel)" (%1077, %103) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2803,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<2048x512x1x1xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%1079, %1080, %1081, %1082, %1083, %1084) = "batch_norm_(phi_kernel)" (%1078, %100, %99, %102, %101) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2804,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>) -> custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<-1xu8> + (%1085) = "conv2d(phi_kernel)" (%1061, %98) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2805,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[2,2]} : (custom_device_tensor<-1x1024x14x14xf32>, custom_device_tensor<2048x1024x1x1xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%1086, %1087, %1088, %1089, %1090, %1091) = "batch_norm_(phi_kernel)" (%1085, %95, %94, %97, %96) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2806,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>) -> custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<-1xu8> + (%1092) = "add(phi_kernel)" (%1079, %1086) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2807,stop_gradient:[false]} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%1093) = "relu(phi_kernel)" (%1092) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2808,stop_gradient:[false]} : (custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%1094) = "conv2d(phi_kernel)" (%1093, %93) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2809,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<512x2048x1x1xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%1095, %1096, %1097, %1098, %1099, %1100) = "batch_norm_(phi_kernel)" (%1094, %90, %89, %92, %91) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2810,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%1101) = "relu(phi_kernel)" (%1095) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2811,stop_gradient:[false]} : (custom_device_tensor<-1x512x7x7xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%1102) = "conv2d(phi_kernel)" (%1101, %88) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2812,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1]} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512x512x3x3xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%1103, %1104, %1105, %1106, %1107, %1108) = "batch_norm_(phi_kernel)" (%1102, %85, %84, %87, %86) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2813,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%1109) = "relu(phi_kernel)" (%1103) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2814,stop_gradient:[false]} : (custom_device_tensor<-1x512x7x7xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%1110) = "conv2d(phi_kernel)" (%1109, %83) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2815,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<2048x512x1x1xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%1111, %1112, %1113, %1114, %1115, %1116) = "batch_norm_(phi_kernel)" (%1110, %80, %79, %82, %81) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2816,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>) -> custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<-1xu8> + (%1117) = "add(phi_kernel)" (%1111, %1093) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2817,stop_gradient:[false]} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%1118) = "relu(phi_kernel)" (%1117) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2818,stop_gradient:[false]} : (custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%1119) = "conv2d(phi_kernel)" (%1118, %78) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2819,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<512x2048x1x1xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%1120, %1121, %1122, %1123, %1124, %1125) = "batch_norm_(phi_kernel)" (%1119, %75, %74, %77, %76) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2820,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%1126) = "relu(phi_kernel)" (%1120) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2821,stop_gradient:[false]} : (custom_device_tensor<-1x512x7x7xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%1127) = "conv2d(phi_kernel)" (%1126, %73) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2822,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1]} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512x512x3x3xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%1128, %1129, %1130, %1131, %1132, %1133) = "batch_norm_(phi_kernel)" (%1127, %70, %69, %72, %71) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2823,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%1134) = "relu(phi_kernel)" (%1128) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2824,stop_gradient:[false]} : (custom_device_tensor<-1x512x7x7xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%1135) = "conv2d(phi_kernel)" (%1134, %68) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2825,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<2048x512x1x1xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%1136, %1137, %1138, %1139, %1140, %1141) = "batch_norm_(phi_kernel)" (%1135, %65, %64, %67, %66) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2826,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>) -> custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<-1xu8> + (%1142) = "add(phi_kernel)" (%1136, %1118) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2827,stop_gradient:[false]} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%1143) = "relu(phi_kernel)" (%1142) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2828,stop_gradient:[false]} : (custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%1144) = "conv2d_transpose(phi_kernel)" (%1143, %59, %15) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d_transpose",op_name:"pd_op.conv2d_transpose",origin_id:2829,output_padding:[],padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[2,2]} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048x256x2x2xf32>, cpu_tensor<0xi64>) -> custom_device_tensor<-1x256x14x14xf32> + (%1145) = "add(phi_kernel)" (%1144, %14) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2830,stop_gradient:[false]} : (custom_device_tensor<-1x256x14x14xf32>, custom_device_tensor<1x256x1x1xf32>) -> custom_device_tensor<-1x256x14x14xf32> + (%1146) = "relu(phi_kernel)" (%1145) {kernel_key:,kernel_name:"relu",op_name:"pd_op.relu",origin_id:2831,stop_gradient:[false]} : (custom_device_tensor<-1x256x14x14xf32>) -> custom_device_tensor<-1x256x14x14xf32> + (%1147) = "conv2d(phi_kernel)" (%1146, %58) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2832,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (custom_device_tensor<-1x256x14x14xf32>, custom_device_tensor<80x256x1x1xf32>) -> custom_device_tensor<-1x80x14x14xf32> + (%1148) = "add(phi_kernel)" (%1147, %56) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2833,stop_gradient:[false]} : (custom_device_tensor<-1x80x14x14xf32>, custom_device_tensor<1x80x1x1xf32>) -> custom_device_tensor<-1x80x14x14xf32> + (%1149) = "shape64(phi_kernel)" (%1148) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2834,stop_gradient:[true]} : (custom_device_tensor<-1x80x14x14xf32>) -> cpu_tensor<4xi64> + (%1150) = "slice(phi_kernel)" (%1149, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2835,stop_gradient:[true]} : (cpu_tensor<4xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1151) = "arange(phi_kernel)" (%42, %1150, %41) {dtype:int64,kernel_key:,kernel_name:"arange",op_name:"pd_op.arange",origin_id:2836,place:Place(undefined:0),stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor, cpu_tensor<1xi64>) -> custom_device_tensor<-1xi64> + (%1152) = "cast(phi_kernel)" (%1151) {dtype:int32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:2837,stop_gradient:[true]} : (custom_device_tensor<-1xi64>) -> custom_device_tensor<-1xi32> + (%1153) = "builtin.combine" [id:2838] (%1152, %1058) {origin_id:855,stop_gradient:[false]} : (custom_device_tensor<-1xi32>, cpu_tensor<-1xi32>) -> vec[custom_device_tensor<-1xi32>,cpu_tensor<-1xi32>] + (%1154) = "memcpy_d2h(phi_kernel)" (%1152) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2839} : (custom_device_tensor<-1xi32>) -> cpu_tensor<-1xi32> + (%1155) = "builtin.combine" [id:2840] (%1154, %1058) {origin_id:2840} : (cpu_tensor<-1xi32>, cpu_tensor<-1xi32>) -> vec[cpu_tensor<-1xi32>,cpu_tensor<-1xi32>] + (%1156) = "broadcast_tensors(phi_kernel)" (%1155) {kernel_key:,kernel_name:"broadcast_tensors",op_name:"pd_op.broadcast_tensors",origin_id:2841,stop_gradient:[false]} : (vec[cpu_tensor<-1xi32>,cpu_tensor<-1xi32>]) -> vec[cpu_tensor<-1xi32>,cpu_tensor<-1xi32>] + (%1157, %1158) = "builtin.split" [id:2842] (%1156) {origin_id:857,stop_gradient:[false,false]} : (vec[cpu_tensor<-1xi32>,cpu_tensor<-1xi32>]) -> cpu_tensor<-1xi32>, cpu_tensor<-1xi32> + (%1159) = "builtin.combine" [id:2843] (%1157, %1158) {origin_id:858,stop_gradient:[false]} : (cpu_tensor<-1xi32>, cpu_tensor<-1xi32>) -> vec[cpu_tensor<-1xi32>,cpu_tensor<-1xi32>] + (%1160) = "stack(phi_kernel)" (%1159) {axis:-1,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2844,stop_gradient:[false]} : (vec[cpu_tensor<-1xi32>,cpu_tensor<-1xi32>]) -> cpu_tensor<-1x2xi32> + (%1161) = "memcpy_h2d(phi_kernel)" (%1160) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2845} : (cpu_tensor<-1x2xi32>) -> custom_device_tensor<-1x2xi32> + (%1162) = "gather_nd(phi_kernel)" (%1148, %1161) {kernel_key:,kernel_name:"gather_nd",op_name:"pd_op.gather_nd",origin_id:2846,stop_gradient:[false]} : (custom_device_tensor<-1x80x14x14xf32>, custom_device_tensor<-1x2xi32>) -> custom_device_tensor<-1x14x14xf32> + (%1163) = "shape64(phi_kernel)" (%1152) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2847,stop_gradient:[true]} : (custom_device_tensor<-1xi32>) -> cpu_tensor<1xi64> + (%1164) = "slice(phi_kernel)" (%1163, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2848,stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1165) = "builtin.combine" [id:2849] (%1164, %13, %13) {origin_id:871,stop_gradient:[true]} : (cpu_tensor, cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor,cpu_tensor] + (%1166) = "stack(phi_kernel)" (%1165) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2850,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor,cpu_tensor]) -> cpu_tensor<3xi64> + (%1167) = "reshape(phi_kernel)" (%1162, %1166) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:2851,stop_gradient:[false]} : (custom_device_tensor<-1x14x14xf32>, cpu_tensor<3xi64>) -> custom_device_tensor<-1x14x14xf32> + (%1168) = "sigmoid(phi_kernel)" (%1167) {kernel_key:,kernel_name:"sigmoid",op_name:"pd_op.sigmoid",origin_id:2852,stop_gradient:[false]} : (custom_device_tensor<-1x14x14xf32>) -> custom_device_tensor<-1x14x14xf32> + () = "cf.yield" [id:2853] (%1168) {origin_id:875} : (custom_device_tensor<-1x14x14xf32>) -> + } + (%1169) = "shape64(phi_kernel)" (%1050) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2854,stop_gradient:[true]} : (cpu_tensor<-1xi32>) -> cpu_tensor<1xi64> + (%1170) = "slice(phi_kernel)" (%1169, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2855,stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1171) = "arange(phi_kernel)" (%42, %1170, %41) {dtype:int64,kernel_key:,kernel_name:"arange",op_name:"pd_op.arange",origin_id:2856,place:Place(undefined:0),stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor, cpu_tensor<1xi64>) -> custom_device_tensor<-1xi64> + (%1172) = "shape64(phi_kernel)" (%1171) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2857,stop_gradient:[true]} : (custom_device_tensor<-1xi64>) -> cpu_tensor<1xi64> + (%1173) = "slice(phi_kernel)" (%1172, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2858,stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1174) = "create_array(phi_kernel)" () {dtype:Undefined,kernel_key:,kernel_name:"create_array",op_name:"pd_op.create_array",origin_id:2859,stop_gradient:[true]} : () -> cpu_tensor_array + (%1175) = "create_array(phi_kernel)" () {dtype:Undefined,kernel_key:,kernel_name:"create_array",op_name:"pd_op.create_array",origin_id:2860,stop_gradient:[true]} : () -> cpu_tensor_array + (%1176) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2861,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%1177) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2862,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%1178) = "memcpy_h2d(phi_kernel)" (%1173) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2863} : (cpu_tensor) -> custom_device_tensor + (%1179) = "less_than(phi_kernel)" (%1176, %1178) {kernel_key:,kernel_name:"less_than",op_name:"pd_op.less_than",origin_id:2864,stop_gradient:[true]} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%1180) = "full(phi_kernel)" () {dtype:int32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2865,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor<-1xi32> + (%1181) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2866,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor<-1x6xf32> + (%1182) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2867,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%1183, %1184, %1185, %1186, %1187) = "pd_op.while" [id:2868] (cond=%1179, inputs=%1176, %1177, %1180, %1181, %1182) { + ^%arg_14 {stop_gradient:true}, %arg_15 {stop_gradient:true}, %arg_16 {stop_gradient:true}, %arg_17 {stop_gradient:true}, %arg_18 {stop_gradient:true} + (%1188) = "memcpy_d2h(phi_kernel)" (%arg_14) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2869} : (custom_device_tensor) -> cpu_tensor + (%1189) = "scale(phi_kernel)" (%1188, %12) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2870,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1190) = "builtin.combine" [id:2871] (%arg_14) {origin_id:902,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1191) = "stack(phi_kernel)" (%1190) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2872,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1192) = "builtin.combine" [id:2873] (%1189) {origin_id:904,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1193) = "stack(phi_kernel)" (%1192) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2874,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1194) = "slice(phi_kernel)" (%1171, %1191, %1193) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2875,stop_gradient:[true]} : (custom_device_tensor<-1xi64>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor + (%1195) = "memcpy_d2h(phi_kernel)" (%1194) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2876} : (custom_device_tensor) -> cpu_tensor + (%1196) = "scale(phi_kernel)" (%1195, %12) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2877,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1197) = "builtin.combine" [id:2878] (%1194) {origin_id:909,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1198) = "stack(phi_kernel)" (%1197) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2879,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1199) = "builtin.combine" [id:2880] (%1196) {origin_id:911,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1200) = "stack(phi_kernel)" (%1199) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2881,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1201) = "slice(phi_kernel)" (%1050, %1198, %1200) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2882,stop_gradient:[false]} : (cpu_tensor<-1xi32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1202) = "memcpy_h2d(phi_kernel)" (%1201) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2883} : (cpu_tensor) -> custom_device_tensor + (%1203) = "equal(phi_kernel)" (%1202, %11) {kernel_key:,kernel_name:"equal",op_name:"pd_op.equal",origin_id:2884,stop_gradient:[true]} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%1204, %1205, %1206) = "pd_op.if" [id:2885] (%1203) {} -> custom_device_tensor<-1xi32>, custom_device_tensor<-1x6xf32>, custom_device_tensor { + () = "cf.yield" [id:2886] (%27, %28, %arg_15) {origin_id:917} : (custom_device_tensor<1xi32>, custom_device_tensor<1x6xf32>, custom_device_tensor) -> + } else { + (%1207) = "memcpy_d2h(phi_kernel)" (%1194) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2887} : (custom_device_tensor) -> cpu_tensor + (%1208) = "scale(phi_kernel)" (%1207, %12) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2888,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1209) = "builtin.combine" [id:2889] (%1194) {origin_id:920,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1210) = "stack(phi_kernel)" (%1209) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2890,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1211) = "builtin.combine" [id:2891] (%1208) {origin_id:922,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1212) = "stack(phi_kernel)" (%1211) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2892,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1213) = "slice(phi_kernel)" (%1050, %1210, %1212) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2893,stop_gradient:[false]} : (cpu_tensor<-1xi32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1214) = "cast(phi_kernel)" (%1213) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:2894,stop_gradient:[false]} : (cpu_tensor) -> cpu_tensor + (%1215) = "memcpy_h2d(phi_kernel)" (%1214) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2895} : (cpu_tensor) -> custom_device_tensor + (%1216) = "add(phi_kernel)" (%arg_15, %1215) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2896,stop_gradient:[true]} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%1217) = "builtin.combine" [id:2897] (%arg_15) {origin_id:927,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1218) = "stack(phi_kernel)" (%1217) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2898,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1219) = "builtin.combine" [id:2899] (%1216) {origin_id:929,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1220) = "stack(phi_kernel)" (%1219) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2900,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1221) = "slice(phi_kernel)" (%1048, %1218, %1220) {axes:[0],decrease_axis:[],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2901,stop_gradient:[false]} : (cpu_tensor<-1x6xf32>, custom_device_tensor<1xi64>, custom_device_tensor<1xi64>) -> cpu_tensor<-1x6xf32> + (%1222) = "memcpy_d2h(phi_kernel)" (%1194) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2902} : (custom_device_tensor) -> cpu_tensor + (%1223) = "scale(phi_kernel)" (%1222, %12) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2903,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1224) = "builtin.combine" [id:2904] (%1194) {origin_id:934,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1225) = "stack(phi_kernel)" (%1224) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2905,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1226) = "builtin.combine" [id:2906] (%1223) {origin_id:936,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1227) = "stack(phi_kernel)" (%1226) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2907,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1228) = "slice(phi_kernel)" (%1050, %1225, %1227) {axes:[0],decrease_axis:[],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2908,stop_gradient:[false]} : (cpu_tensor<-1xi32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xi32> + (%1229) = "memcpy_d2h(phi_kernel)" (%1194) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2909} : (custom_device_tensor) -> cpu_tensor + (%1230) = "scale(phi_kernel)" (%1229, %12) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2910,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1231) = "builtin.combine" [id:2911] (%1194) {origin_id:941,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1232) = "stack(phi_kernel)" (%1231) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2912,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1233) = "builtin.combine" [id:2913] (%1230) {origin_id:943,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1234) = "stack(phi_kernel)" (%1233) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2914,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1235) = "slice(phi_kernel)" (%1050, %1232, %1234) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2915,stop_gradient:[false]} : (cpu_tensor<-1xi32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1236) = "cast(phi_kernel)" (%1235) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:2916,stop_gradient:[false]} : (cpu_tensor) -> cpu_tensor + (%1237) = "memcpy_h2d(phi_kernel)" (%1236) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2917} : (cpu_tensor) -> custom_device_tensor + (%1238) = "add(phi_kernel)" (%arg_15, %1237) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:2918,stop_gradient:[false]} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%1239) = "memcpy_h2d(phi_kernel)" (%1228) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2919} : (cpu_tensor<-1xi32>) -> custom_device_tensor<-1xi32> + (%1240) = "memcpy_h2d(phi_kernel)" (%1221) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2920} : (cpu_tensor<-1x6xf32>) -> custom_device_tensor<-1x6xf32> + () = "cf.yield" [id:2921] (%1239, %1240, %1238) {origin_id:948} : (custom_device_tensor<-1xi32>, custom_device_tensor<-1x6xf32>, custom_device_tensor) -> + } + (%1241) = "array_length(phi_kernel)" (%1174) {kernel_key:,kernel_name:"array_length",op_name:"pd_op.array_length",origin_id:2922} : (cpu_tensor_array) -> cpu_tensor<1xi64> + (%1242) = "memcpy_d2h(phi_kernel)" (%1205) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2923} : (custom_device_tensor<-1x6xf32>) -> cpu_tensor<-1x6xf32> + (%1243) = "array_write_(phi_kernel)" (%1174, %1242, %1241) {is_inplace:true,kernel_key:,kernel_name:"array_write",op_name:"pd_op.array_write_",origin_id:2924} : (cpu_tensor_array, cpu_tensor<-1x6xf32>, cpu_tensor<1xi64>) -> cpu_tensor_array + (%1244) = "array_length(phi_kernel)" (%1175) {kernel_key:,kernel_name:"array_length",op_name:"pd_op.array_length",origin_id:2925} : (cpu_tensor_array) -> cpu_tensor<1xi64> + (%1245) = "memcpy_d2h(phi_kernel)" (%1204) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2926} : (custom_device_tensor<-1xi32>) -> cpu_tensor<-1xi32> + (%1246) = "array_write_(phi_kernel)" (%1175, %1245, %1244) {is_inplace:true,kernel_key:,kernel_name:"array_write",op_name:"pd_op.array_write_",origin_id:2927} : (cpu_tensor_array, cpu_tensor<-1xi32>, cpu_tensor<1xi64>) -> cpu_tensor_array + (%1247) = "memcpy_d2h(phi_kernel)" (%arg_14) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2928} : (custom_device_tensor) -> cpu_tensor + (%1248) = "scale(phi_kernel)" (%1247, %12) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2929,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1249) = "less_than(phi_kernel)" (%1248, %1173) {kernel_key:,kernel_name:"less_than",op_name:"pd_op.less_than",origin_id:2930,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> cpu_tensor + (%1250) = "memcpy_h2d(phi_kernel)" (%1249) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2931} : (cpu_tensor) -> custom_device_tensor + (%1251) = "memcpy_h2d(phi_kernel)" (%1248) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2932} : (cpu_tensor) -> custom_device_tensor + () = "cf.yield" [id:2933] (%1250, %1251, %1206, %1204, %1205, %1194) {origin_id:956} : (custom_device_tensor, custom_device_tensor, custom_device_tensor, custom_device_tensor<-1xi32>, custom_device_tensor<-1x6xf32>, custom_device_tensor) -> + } + (%1252, %1253) = "array_to_tensor(phi_kernel)" (%1174) {axis:0,kernel_key:,kernel_name:"array_to_tensor",op_name:"pd_op.array_to_tensor",origin_id:2934,stop_gradient:[true,true],use_stack:false} : (cpu_tensor_array) -> cpu_tensor<-1x6xf32>, cpu_tensor<-1xi32> + (%1254, %1255) = "array_to_tensor(phi_kernel)" (%1175) {axis:0,kernel_key:,kernel_name:"array_to_tensor",op_name:"pd_op.array_to_tensor",origin_id:2935,stop_gradient:[true,true],use_stack:false} : (cpu_tensor_array) -> cpu_tensor<-1xi32>, cpu_tensor<-1xi32> + (%1256) = "divide(phi_kernel)" (%333, %337) {kernel_key:,kernel_name:"divide",op_name:"pd_op.divide",origin_id:2936,stop_gradient:[false]} : (custom_device_tensor<-1x2xf32>, custom_device_tensor<-1x2xf32>) -> custom_device_tensor<-1x2xf32> + (%1257) = "scale(phi_kernel)" (%1256, %26) {bias:0.5,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2937,stop_gradient:[false]} : (custom_device_tensor<-1x2xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x2xf32> + (%1258) = "floor(phi_kernel)" (%1257) {kernel_key:,kernel_name:"floor",op_name:"pd_op.floor",origin_id:2938,stop_gradient:[false]} : (custom_device_tensor<-1x2xf32>) -> custom_device_tensor<-1x2xf32> + (%1259) = "shape64(phi_kernel)" (%1254) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2939,stop_gradient:[true]} : (cpu_tensor<-1xi32>) -> cpu_tensor<1xi64> + (%1260) = "slice(phi_kernel)" (%1259, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2940,stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1261) = "arange(phi_kernel)" (%42, %1260, %41) {dtype:int64,kernel_key:,kernel_name:"arange",op_name:"pd_op.arange",origin_id:2941,place:Place(undefined:0),stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor, cpu_tensor<1xi64>) -> custom_device_tensor<-1xi64> + (%1262) = "shape64(phi_kernel)" (%1261) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2942,stop_gradient:[true]} : (custom_device_tensor<-1xi64>) -> cpu_tensor<1xi64> + (%1263) = "slice(phi_kernel)" (%1262, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2943,stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1264) = "create_array(phi_kernel)" () {dtype:Undefined,kernel_key:,kernel_name:"create_array",op_name:"pd_op.create_array",origin_id:2944,stop_gradient:[true]} : () -> cpu_tensor_array + (%1265) = "create_array(phi_kernel)" () {dtype:Undefined,kernel_key:,kernel_name:"create_array",op_name:"pd_op.create_array",origin_id:2945,stop_gradient:[true]} : () -> cpu_tensor_array + (%1266) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2946,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%1267) = "memcpy_h2d(phi_kernel)" (%1263) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2947} : (cpu_tensor) -> custom_device_tensor + (%1268) = "less_than(phi_kernel)" (%1266, %1267) {kernel_key:,kernel_name:"less_than",op_name:"pd_op.less_than",origin_id:2948,stop_gradient:[true]} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%1269) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2949,place:Place(undefined:0),shape:[],stop_gradient:[false],value:0} : () -> custom_device_tensor<-1x4xf32> + (%1270) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2950,place:Place(undefined:0),shape:[],stop_gradient:[false],value:0} : () -> custom_device_tensor<-1x2xf32> + (%1271) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2951,place:Place(undefined:0),shape:[],stop_gradient:[false],value:0} : () -> custom_device_tensor<4xf32> + (%1272) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2952,place:Place(undefined:0),shape:[],stop_gradient:[false],value:0} : () -> custom_device_tensor<1xf32> + (%1273) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:2953,place:Place(undefined:0),shape:[],stop_gradient:[false],value:0} : () -> custom_device_tensor<1xf32> + (%1274, %1275, %1276, %1277, %1278, %1279, %1280) = "pd_op.while" [id:2954] (cond=%1268, inputs=%1266, %1187, %1269, %1270, %1271, %1272, %1273) { + ^%arg_19 {stop_gradient:true}, %arg_20 {stop_gradient:true}, %arg_21 {stop_gradient:false}, %arg_22 {stop_gradient:false}, %arg_23 {stop_gradient:false}, %arg_24 {stop_gradient:false}, %arg_25 {stop_gradient:false} + (%1281) = "memcpy_d2h(phi_kernel)" (%arg_19) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2955} : (custom_device_tensor) -> cpu_tensor + (%1282) = "scale(phi_kernel)" (%1281, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2956,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1283) = "builtin.combine" [id:2957] (%arg_19) {origin_id:986,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1284) = "stack(phi_kernel)" (%1283) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2958,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1285) = "builtin.combine" [id:2959] (%1282) {origin_id:988,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1286) = "stack(phi_kernel)" (%1285) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2960,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1287) = "slice(phi_kernel)" (%1261, %1284, %1286) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2961,stop_gradient:[true]} : (custom_device_tensor<-1xi64>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor + (%1288) = "memcpy_d2h(phi_kernel)" (%1287) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2962} : (custom_device_tensor) -> cpu_tensor + (%1289) = "scale(phi_kernel)" (%1288, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2963,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1290) = "builtin.combine" [id:2964] (%1287) {origin_id:993,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1291) = "stack(phi_kernel)" (%1290) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2965,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1292) = "builtin.combine" [id:2966] (%1289) {origin_id:995,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1293) = "stack(phi_kernel)" (%1292) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2967,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1294) = "slice(phi_kernel)" (%1258, %1291, %1293) {axes:[0],decrease_axis:[],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2968,stop_gradient:[false]} : (custom_device_tensor<-1x2xf32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x2xf32> + (%1295) = "memcpy_d2h(phi_kernel)" (%1287) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2969} : (custom_device_tensor) -> cpu_tensor + (%1296) = "scale(phi_kernel)" (%1295, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2970,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1297) = "builtin.combine" [id:2971] (%1287) {origin_id:1000,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1298) = "stack(phi_kernel)" (%1297) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2972,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1299) = "builtin.combine" [id:2973] (%1296) {origin_id:1002,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1300) = "stack(phi_kernel)" (%1299) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2974,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1301) = "slice(phi_kernel)" (%1254, %1298, %1300) {axes:[0],decrease_axis:[],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2975,stop_gradient:[true]} : (cpu_tensor<-1xi32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xi32> + (%1302) = "cast(phi_kernel)" (%1301) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:2976,stop_gradient:[true]} : (cpu_tensor<-1xi32>) -> cpu_tensor<-1xi64> + (%1303) = "reshape(phi_kernel)" (%1302, %10) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:2977,stop_gradient:[true]} : (cpu_tensor<-1xi64>, cpu_tensor<0xi64>) -> cpu_tensor + (%1304) = "builtin.combine" [id:2978] (%1303, %9) {origin_id:1009,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor] + (%1305) = "stack(phi_kernel)" (%1304) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2979,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor]) -> cpu_tensor<2xi64> + (%1306) = "expand(phi_kernel)" (%1294, %1305) {kernel_key:,kernel_name:"expand",op_name:"pd_op.expand",origin_id:2980,stop_gradient:[false]} : (custom_device_tensor<-1x2xf32>, cpu_tensor<2xi64>) -> custom_device_tensor<-1x2xf32> + (%1307) = "memcpy_d2h(phi_kernel)" (%1287) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2981} : (custom_device_tensor) -> cpu_tensor + (%1308) = "scale(phi_kernel)" (%1307, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2982,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1309) = "builtin.combine" [id:2983] (%1287, %8) {origin_id:1020,stop_gradient:[true]} : (custom_device_tensor, cpu_tensor) -> vec[custom_device_tensor,cpu_tensor] + (%1310) = "memcpy_h2d(phi_kernel)" (%8) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2984,persistable:[true]} : (cpu_tensor) -> custom_device_tensor + (%1311) = "builtin.combine" [id:2985] (%1287, %1310) {origin_id:2985} : (custom_device_tensor, custom_device_tensor) -> vec[custom_device_tensor,custom_device_tensor] + (%1312) = "stack(phi_kernel)" (%1311) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2986,stop_gradient:[true]} : (vec[custom_device_tensor,custom_device_tensor]) -> custom_device_tensor<2xi64> + (%1313) = "builtin.combine" [id:2987] (%1308, %7) {origin_id:1022,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor] + (%1314) = "stack(phi_kernel)" (%1313) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2988,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor]) -> cpu_tensor<2xi64> + (%1315) = "slice(phi_kernel)" (%337, %1312, %1314) {axes:[0,1],decrease_axis:[0,1],infer_flags:[-1,1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2989,stop_gradient:[false]} : (custom_device_tensor<-1x2xf32>, custom_device_tensor<2xi64>, cpu_tensor<2xi64>) -> custom_device_tensor + (%1316) = "memcpy_d2h(phi_kernel)" (%1287) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2990} : (custom_device_tensor) -> cpu_tensor + (%1317) = "scale(phi_kernel)" (%1316, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:2991,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1318) = "builtin.combine" [id:2992] (%1287, %7) {origin_id:1033,stop_gradient:[true]} : (custom_device_tensor, cpu_tensor) -> vec[custom_device_tensor,cpu_tensor] + (%1319) = "memcpy_h2d(phi_kernel)" (%7) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2993,persistable:[true]} : (cpu_tensor) -> custom_device_tensor + (%1320) = "builtin.combine" [id:2994] (%1287, %1319) {origin_id:2994} : (custom_device_tensor, custom_device_tensor) -> vec[custom_device_tensor,custom_device_tensor] + (%1321) = "stack(phi_kernel)" (%1320) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2995,stop_gradient:[true]} : (vec[custom_device_tensor,custom_device_tensor]) -> custom_device_tensor<2xi64> + (%1322) = "builtin.combine" [id:2996] (%1317, %6) {origin_id:1035,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor] + (%1323) = "stack(phi_kernel)" (%1322) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2997,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor]) -> cpu_tensor<2xi64> + (%1324) = "slice(phi_kernel)" (%337, %1321, %1323) {axes:[0,1],decrease_axis:[0,1],infer_flags:[-1,1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2998,stop_gradient:[false]} : (custom_device_tensor<-1x2xf32>, custom_device_tensor<2xi64>, cpu_tensor<2xi64>) -> custom_device_tensor + (%1325) = "unsqueeze(phi_kernel)" (%1315, %44) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:2999,stop_gradient:[false]} : (custom_device_tensor, cpu_tensor<1xi64>) -> custom_device_tensor<1xf32> + (%1326) = "unsqueeze(phi_kernel)" (%1324, %44) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:3000,stop_gradient:[false]} : (custom_device_tensor, cpu_tensor<1xi64>) -> custom_device_tensor<1xf32> + (%1327) = "builtin.combine" [id:3001] (%1326, %1325, %1326, %1325) {origin_id:1043,stop_gradient:[false]} : (custom_device_tensor<1xf32>, custom_device_tensor<1xf32>, custom_device_tensor<1xf32>, custom_device_tensor<1xf32>) -> vec[custom_device_tensor<1xf32>,custom_device_tensor<1xf32>,custom_device_tensor<1xf32>,custom_device_tensor<1xf32>] + (%1328) = "concat(phi_kernel)" (%1327, %5) {kernel_key:,kernel_name:"concat",op_name:"pd_op.concat",origin_id:3002,stop_gradient:[false]} : (vec[custom_device_tensor<1xf32>,custom_device_tensor<1xf32>,custom_device_tensor<1xf32>,custom_device_tensor<1xf32>], cpu_tensor<1xi32>) -> custom_device_tensor<4xf32> + (%1329) = "memcpy_d2h(phi_kernel)" (%1287) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3003} : (custom_device_tensor) -> cpu_tensor + (%1330) = "scale(phi_kernel)" (%1329, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3004,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1331) = "builtin.combine" [id:3005] (%1287) {origin_id:1047,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1332) = "stack(phi_kernel)" (%1331) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3006,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1333) = "builtin.combine" [id:3007] (%1330) {origin_id:1049,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1334) = "stack(phi_kernel)" (%1333) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3008,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1335) = "slice(phi_kernel)" (%1254, %1332, %1334) {axes:[0],decrease_axis:[],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3009,stop_gradient:[true]} : (cpu_tensor<-1xi32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xi32> + (%1336) = "cast(phi_kernel)" (%1335) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3010,stop_gradient:[true]} : (cpu_tensor<-1xi32>) -> cpu_tensor<-1xi64> + (%1337) = "reshape(phi_kernel)" (%1336, %10) {kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape",origin_id:3011,stop_gradient:[true]} : (cpu_tensor<-1xi64>, cpu_tensor<0xi64>) -> cpu_tensor + (%1338) = "builtin.combine" [id:3012] (%1337, %30) {origin_id:1056,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor] + (%1339) = "stack(phi_kernel)" (%1338) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3013,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor]) -> cpu_tensor<2xi64> + (%1340) = "expand(phi_kernel)" (%1328, %1339) {kernel_key:,kernel_name:"expand",op_name:"pd_op.expand",origin_id:3014,stop_gradient:[false]} : (custom_device_tensor<4xf32>, cpu_tensor<2xi64>) -> custom_device_tensor<-1x4xf32> + (%1341) = "array_length(phi_kernel)" (%1264) {kernel_key:,kernel_name:"array_length",op_name:"pd_op.array_length",origin_id:3015} : (cpu_tensor_array) -> cpu_tensor<1xi64> + (%1342) = "memcpy_d2h(phi_kernel)" (%1306) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3016} : (custom_device_tensor<-1x2xf32>) -> cpu_tensor<-1x2xf32> + (%1343) = "array_write_(phi_kernel)" (%1264, %1342, %1341) {is_inplace:true,kernel_key:,kernel_name:"array_write",op_name:"pd_op.array_write_",origin_id:3017} : (cpu_tensor_array, cpu_tensor<-1x2xf32>, cpu_tensor<1xi64>) -> cpu_tensor_array + (%1344) = "array_length(phi_kernel)" (%1265) {kernel_key:,kernel_name:"array_length",op_name:"pd_op.array_length",origin_id:3018} : (cpu_tensor_array) -> cpu_tensor<1xi64> + (%1345) = "memcpy_d2h(phi_kernel)" (%1340) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3019} : (custom_device_tensor<-1x4xf32>) -> cpu_tensor<-1x4xf32> + (%1346) = "array_write_(phi_kernel)" (%1265, %1345, %1344) {is_inplace:true,kernel_key:,kernel_name:"array_write",op_name:"pd_op.array_write_",origin_id:3020} : (cpu_tensor_array, cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>) -> cpu_tensor_array + (%1347) = "memcpy_d2h(phi_kernel)" (%arg_19) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3021} : (custom_device_tensor) -> cpu_tensor + (%1348) = "scale(phi_kernel)" (%1347, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3022,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1349) = "less_than(phi_kernel)" (%1348, %1263) {kernel_key:,kernel_name:"less_than",op_name:"pd_op.less_than",origin_id:3023,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> cpu_tensor + (%1350) = "memcpy_h2d(phi_kernel)" (%1349) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3024} : (cpu_tensor) -> custom_device_tensor + (%1351) = "memcpy_h2d(phi_kernel)" (%1348) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3025} : (cpu_tensor) -> custom_device_tensor + () = "cf.yield" [id:3026] (%1350, %1351, %1287, %1340, %1306, %1328, %1326, %1325) {origin_id:1066} : (custom_device_tensor, custom_device_tensor, custom_device_tensor, custom_device_tensor<-1x4xf32>, custom_device_tensor<-1x2xf32>, custom_device_tensor<4xf32>, custom_device_tensor<1xf32>, custom_device_tensor<1xf32>) -> + } + (%1352, %1353) = "array_to_tensor(phi_kernel)" (%1264) {axis:0,kernel_key:,kernel_name:"array_to_tensor",op_name:"pd_op.array_to_tensor",origin_id:3027,stop_gradient:[true,true],use_stack:false} : (cpu_tensor_array) -> cpu_tensor<-1x2xf32>, cpu_tensor<-1xi32> + (%1354, %1355) = "array_to_tensor(phi_kernel)" (%1265) {axis:0,kernel_key:,kernel_name:"array_to_tensor",op_name:"pd_op.array_to_tensor",origin_id:3028,stop_gradient:[true,true],use_stack:false} : (cpu_tensor_array) -> cpu_tensor<-1x4xf32>, cpu_tensor<-1xi32> + (%1356) = "slice(phi_kernel)" (%1252, %44, %43) {axes:[1],decrease_axis:[],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3029,stop_gradient:[true]} : (cpu_tensor<-1x6xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1x1xf32> + (%1357) = "slice(phi_kernel)" (%1252, %43, %52) {axes:[1],decrease_axis:[],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3030,stop_gradient:[true]} : (cpu_tensor<-1x6xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1x1xf32> + (%1358) = "slice(phi_kernel)" (%1252, %52, %36) {axes:[1],decrease_axis:[],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3031,stop_gradient:[true]} : (cpu_tensor<-1x6xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1x4xf32> + (%1359) = "divide(phi_kernel)" (%1358, %1354) {kernel_key:,kernel_name:"divide",op_name:"pd_op.divide",origin_id:3032,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<-1x4xf32>) -> cpu_tensor<-1x4xf32> + (%1360) = "slice(phi_kernel)" (%1352, %44, %43) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3033,stop_gradient:[true]} : (cpu_tensor<-1x2xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%1361) = "slice(phi_kernel)" (%1352, %43, %52) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3034,stop_gradient:[true]} : (cpu_tensor<-1x2xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%1362) = "full_like(phi_kernel)" (%1360, %706) {dtype:float32,kernel_key:,kernel_name:"full_like",op_name:"pd_op.full_like",origin_id:3035,place:Place(undefined:0),stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<1xf32>) -> cpu_tensor<-1xf32> + (%1363) = "slice(phi_kernel)" (%1359, %44, %43) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3036,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%1364) = "minimum(phi_kernel)" (%1363, %1361) {kernel_key:,kernel_name:"minimum",op_name:"pd_op.minimum",origin_id:3037,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%1365) = "maximum(phi_kernel)" (%1364, %1362) {kernel_key:,kernel_name:"maximum",op_name:"pd_op.maximum",origin_id:3038,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%1366) = "slice(phi_kernel)" (%1359, %43, %52) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3039,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%1367) = "minimum(phi_kernel)" (%1366, %1360) {kernel_key:,kernel_name:"minimum",op_name:"pd_op.minimum",origin_id:3040,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%1368) = "maximum(phi_kernel)" (%1367, %1362) {kernel_key:,kernel_name:"maximum",op_name:"pd_op.maximum",origin_id:3041,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%1369) = "slice(phi_kernel)" (%1359, %52, %51) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3042,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%1370) = "minimum(phi_kernel)" (%1369, %1361) {kernel_key:,kernel_name:"minimum",op_name:"pd_op.minimum",origin_id:3043,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%1371) = "maximum(phi_kernel)" (%1370, %1362) {kernel_key:,kernel_name:"maximum",op_name:"pd_op.maximum",origin_id:3044,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%1372) = "slice(phi_kernel)" (%1359, %51, %50) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3045,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%1373) = "minimum(phi_kernel)" (%1372, %1360) {kernel_key:,kernel_name:"minimum",op_name:"pd_op.minimum",origin_id:3046,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%1374) = "maximum(phi_kernel)" (%1373, %1362) {kernel_key:,kernel_name:"maximum",op_name:"pd_op.maximum",origin_id:3047,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%1375) = "builtin.combine" [id:3048] (%1365, %1368, %1371, %1374) {origin_id:1107,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>, cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> vec[cpu_tensor<-1xf32>,cpu_tensor<-1xf32>,cpu_tensor<-1xf32>,cpu_tensor<-1xf32>] + (%1376) = "stack(phi_kernel)" (%1375) {axis:-1,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3049,stop_gradient:[true]} : (vec[cpu_tensor<-1xf32>,cpu_tensor<-1xf32>,cpu_tensor<-1xf32>,cpu_tensor<-1xf32>]) -> cpu_tensor<-1x4xf32> + (%1377) = "slice(phi_kernel)" (%1376, %52, %51) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3050,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%1378) = "slice(phi_kernel)" (%1376, %44, %43) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3051,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%1379) = "subtract(phi_kernel)" (%1377, %1378) {kernel_key:,kernel_name:"subtract",op_name:"pd_op.subtract",origin_id:3052,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%1380) = "slice(phi_kernel)" (%1376, %51, %50) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3053,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%1381) = "slice(phi_kernel)" (%1376, %43, %52) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3054,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%1382) = "subtract(phi_kernel)" (%1380, %1381) {kernel_key:,kernel_name:"subtract",op_name:"pd_op.subtract",origin_id:3055,stop_gradient:[true]} : (cpu_tensor<-1xf32>, cpu_tensor<-1xf32>) -> cpu_tensor<-1xf32> + (%1383) = "memcpy_h2d(phi_kernel)" (%1382) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3056} : (cpu_tensor<-1xf32>) -> custom_device_tensor<-1xf32> + (%1384) = "greater_than(phi_kernel)" (%1383, %25) {kernel_key:,kernel_name:"greater_than",op_name:"pd_op.greater_than",origin_id:3057,stop_gradient:[true]} : (custom_device_tensor<-1xf32>, custom_device_tensor) -> custom_device_tensor<-1xb> + (%1385) = "memcpy_h2d(phi_kernel)" (%1379) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3058} : (cpu_tensor<-1xf32>) -> custom_device_tensor<-1xf32> + (%1386) = "greater_than(phi_kernel)" (%1385, %25) {kernel_key:,kernel_name:"greater_than",op_name:"pd_op.greater_than",origin_id:3059,stop_gradient:[true]} : (custom_device_tensor<-1xf32>, custom_device_tensor) -> custom_device_tensor<-1xb> + (%1387) = "logical_and(phi_kernel)" (%1384, %1386) {kernel_key:,kernel_name:"logical_and",op_name:"pd_op.logical_and",origin_id:3060,stop_gradient:[true]} : (custom_device_tensor<-1xb>, custom_device_tensor<-1xb>) -> custom_device_tensor<-1xb> + (%1388) = "unsqueeze(phi_kernel)" (%1387, %43) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:3061,stop_gradient:[true]} : (custom_device_tensor<-1xb>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x1xb> + (%1389) = "full_like(phi_kernel)" (%1356, %26) {dtype:float32,kernel_key:,kernel_name:"full_like",op_name:"pd_op.full_like",origin_id:3062,place:Place(undefined:0),stop_gradient:[true]} : (cpu_tensor<-1x1xf32>, cpu_tensor<1xf32>) -> cpu_tensor<-1x1xf32> + (%1390) = "scale(phi_kernel)" (%1389, %24) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3063,stop_gradient:[true]} : (cpu_tensor<-1x1xf32>, cpu_tensor<1xf32>) -> cpu_tensor<-1x1xf32> + (%1391) = "memcpy_h2d(phi_kernel)" (%1356) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3064} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1392) = "memcpy_h2d(phi_kernel)" (%1390) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3065} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1393) = "where(phi_kernel)" (%1388, %1391, %1392) {kernel_key:,kernel_name:"where",op_name:"pd_op.where",origin_id:3066,stop_gradient:[true]} : (custom_device_tensor<-1x1xb>, custom_device_tensor<-1x1xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1394) = "builtin.combine" [id:3067] (%1393, %1357, %1376) {origin_id:1136,stop_gradient:[true]} : (custom_device_tensor<-1x1xf32>, cpu_tensor<-1x1xf32>, cpu_tensor<-1x4xf32>) -> vec[custom_device_tensor<-1x1xf32>,cpu_tensor<-1x1xf32>,cpu_tensor<-1x4xf32>] + (%1395) = "memcpy_h2d(phi_kernel)" (%1357) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3068} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1396) = "memcpy_h2d(phi_kernel)" (%1376) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3069} : (cpu_tensor<-1x4xf32>) -> custom_device_tensor<-1x4xf32> + (%1397) = "builtin.combine" [id:3070] (%1393, %1395, %1396) {origin_id:3070} : (custom_device_tensor<-1x1xf32>, custom_device_tensor<-1x1xf32>, custom_device_tensor<-1x4xf32>) -> vec[custom_device_tensor<-1x1xf32>,custom_device_tensor<-1x1xf32>,custom_device_tensor<-1x4xf32>] + (%1398) = "concat(phi_kernel)" (%1397, %23) {kernel_key:,kernel_name:"concat",op_name:"pd_op.concat",origin_id:3071,stop_gradient:[true]} : (vec[custom_device_tensor<-1x1xf32>,custom_device_tensor<-1x1xf32>,custom_device_tensor<-1x4xf32>], cpu_tensor<1xi32>) -> custom_device_tensor<-1x6xf32> + (%1399) = "shape64(phi_kernel)" (%1055) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:3072,stop_gradient:[true]} : (custom_device_tensor<-1x-1x-1xf32>) -> cpu_tensor<3xi64> + (%1400) = "slice(phi_kernel)" (%1399, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3073,stop_gradient:[true]} : (cpu_tensor<3xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1401) = "cast(phi_kernel)" (%1352) {dtype:int32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3074,stop_gradient:[true]} : (cpu_tensor<-1x2xf32>) -> cpu_tensor<-1x2xi32> + (%1402) = "slice(phi_kernel)" (%1401, %44, %43) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3075,stop_gradient:[true]} : (cpu_tensor<-1x2xi32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xi32> + (%1403) = "max(phi_kernel)" (%1402, %22) {keepdim:false,kernel_key:,kernel_name:"max",op_name:"pd_op.max",origin_id:3076,stop_gradient:[true]} : (cpu_tensor<-1xi32>, cpu_tensor<0xi64>) -> cpu_tensor + (%1404) = "slice(phi_kernel)" (%1401, %43, %52) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3077,stop_gradient:[true]} : (cpu_tensor<-1x2xi32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xi32> + (%1405) = "max(phi_kernel)" (%1404, %22) {keepdim:false,kernel_key:,kernel_name:"max",op_name:"pd_op.max",origin_id:3078,stop_gradient:[true]} : (cpu_tensor<-1xi32>, cpu_tensor<0xi64>) -> cpu_tensor + (%1406) = "cast(phi_kernel)" (%1403) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3079,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + (%1407) = "cast(phi_kernel)" (%1405) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3080,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + (%1408) = "builtin.combine" [id:3081] (%1400, %1406, %1407) {origin_id:1155,stop_gradient:[true]} : (cpu_tensor, cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor,cpu_tensor] + (%1409) = "stack(phi_kernel)" (%1408) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3082,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor,cpu_tensor]) -> cpu_tensor<3xi64> + (%1410) = "full_with_tensor(phi_kernel)" (%706, %1409) {dtype:int32,kernel_key:,kernel_name:"full_with_tensor",op_name:"pd_op.full_with_tensor",origin_id:3083,stop_gradient:[true]} : (cpu_tensor<1xf32>, cpu_tensor<3xi64>) -> custom_device_tensor<-1x-1x-1xi32> + (%1411) = "memcpy_d2h(phi_kernel)" (%1410) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3084} : (custom_device_tensor<-1x-1x-1xi32>) -> cpu_tensor<-1x-1x-1xi32> + (%1412) = "scale(phi_kernel)" (%1411, %26) {bias:-1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3085,stop_gradient:[true]} : (cpu_tensor<-1x-1x-1xi32>, cpu_tensor<1xf32>) -> cpu_tensor<-1x-1x-1xi32> + (%1413) = "shape64(phi_kernel)" (%1254) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:3086,stop_gradient:[true]} : (cpu_tensor<-1xi32>) -> cpu_tensor<1xi64> + (%1414) = "slice(phi_kernel)" (%1413, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3087,stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1415) = "arange(phi_kernel)" (%42, %1414, %41) {dtype:int64,kernel_key:,kernel_name:"arange",op_name:"pd_op.arange",origin_id:3088,place:Place(undefined:0),stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor, cpu_tensor<1xi64>) -> custom_device_tensor<-1xi64> + (%1416) = "shape64(phi_kernel)" (%1415) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:3089,stop_gradient:[true]} : (custom_device_tensor<-1xi64>) -> cpu_tensor<1xi64> + (%1417) = "slice(phi_kernel)" (%1416, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3090,stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1418) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:3091,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%1419) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:3092,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%1420) = "memcpy_h2d(phi_kernel)" (%1417) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3093} : (cpu_tensor) -> custom_device_tensor + (%1421) = "less_than(phi_kernel)" (%1418, %1420) {kernel_key:,kernel_name:"less_than",op_name:"pd_op.less_than",origin_id:3094,stop_gradient:[true]} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%1422) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:3095,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor<-1x6xf32> + (%1423) = "full(phi_kernel)" () {dtype:int64,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:3096,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%1424) = "full(phi_kernel)" () {dtype:int32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:3097,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%1425) = "full(phi_kernel)" () {dtype:int32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:3098,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor + (%1426) = "full(phi_kernel)" () {dtype:float32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:3099,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor<-1x-1x-1xf32> + (%1427) = "full(phi_kernel)" () {dtype:int32,kernel_key:,kernel_name:"full",op_name:"pd_op.full",origin_id:3100,place:Place(undefined:0),shape:[],stop_gradient:[true],value:0} : () -> custom_device_tensor<-1x-1x-1xi32> + (%1428, %1429, %1430, %1431, %1432, %1433, %1434, %1435, %1436) = "pd_op.while" [id:3101] (cond=%1421, inputs=%1418, %1419, %1412, %1422, %1423, %1424, %1425, %1426, %1427) { + ^%arg_26 {stop_gradient:true}, %arg_27 {stop_gradient:true}, %arg_28 {stop_gradient:true}, %arg_29 {stop_gradient:true}, %arg_30 {stop_gradient:true}, %arg_31 {stop_gradient:true}, %arg_32 {stop_gradient:true}, %arg_33 {stop_gradient:true}, %arg_34 {stop_gradient:true} + (%1437) = "memcpy_d2h(phi_kernel)" (%arg_26) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3102} : (custom_device_tensor) -> cpu_tensor + (%1438) = "scale(phi_kernel)" (%1437, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3103,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1439) = "builtin.combine" [id:3104] (%arg_26) {origin_id:1184,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1440) = "stack(phi_kernel)" (%1439) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3105,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1441) = "builtin.combine" [id:3106] (%1438) {origin_id:1186,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1442) = "stack(phi_kernel)" (%1441) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3107,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1443) = "slice(phi_kernel)" (%1415, %1440, %1442) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3108,stop_gradient:[true]} : (custom_device_tensor<-1xi64>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor + (%1444) = "memcpy_d2h(phi_kernel)" (%1443) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3109} : (custom_device_tensor) -> cpu_tensor + (%1445) = "scale(phi_kernel)" (%1444, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3110,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1446) = "builtin.combine" [id:3111] (%1443) {origin_id:1191,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1447) = "stack(phi_kernel)" (%1446) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3112,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1448) = "builtin.combine" [id:3113] (%1445) {origin_id:1193,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1449) = "stack(phi_kernel)" (%1448) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3114,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1450) = "slice(phi_kernel)" (%1254, %1447, %1449) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3115,stop_gradient:[true]} : (cpu_tensor<-1xi32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1451) = "cast(phi_kernel)" (%1450) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3116,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + (%1452) = "memcpy_h2d(phi_kernel)" (%1451) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3117} : (cpu_tensor) -> custom_device_tensor + (%1453) = "add(phi_kernel)" (%arg_27, %1452) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:3118,stop_gradient:[true]} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%1454) = "builtin.combine" [id:3119] (%arg_27) {origin_id:1198,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1455) = "stack(phi_kernel)" (%1454) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3120,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1456) = "builtin.combine" [id:3121] (%1453) {origin_id:1200,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1457) = "stack(phi_kernel)" (%1456) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3122,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1458) = "slice(phi_kernel)" (%1398, %1455, %1457) {axes:[0],decrease_axis:[],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3123,stop_gradient:[true]} : (custom_device_tensor<-1x6xf32>, custom_device_tensor<1xi64>, custom_device_tensor<1xi64>) -> custom_device_tensor<-1x6xf32> + (%1459) = "memcpy_d2h(phi_kernel)" (%1443) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3124} : (custom_device_tensor) -> cpu_tensor + (%1460) = "scale(phi_kernel)" (%1459, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3125,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1461) = "builtin.combine" [id:3126] (%1443) {origin_id:1205,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1462) = "stack(phi_kernel)" (%1461) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3127,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1463) = "builtin.combine" [id:3128] (%1460) {origin_id:1207,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1464) = "stack(phi_kernel)" (%1463) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3129,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1465) = "slice(phi_kernel)" (%1254, %1462, %1464) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3130,stop_gradient:[true]} : (cpu_tensor<-1xi32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1466) = "cast(phi_kernel)" (%1465) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3131,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + (%1467) = "memcpy_h2d(phi_kernel)" (%1466) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3132} : (cpu_tensor) -> custom_device_tensor + (%1468) = "add(phi_kernel)" (%arg_27, %1467) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:3133,stop_gradient:[true]} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%1469) = "builtin.combine" [id:3134] (%1468) {origin_id:1214,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1470) = "stack(phi_kernel)" (%1469) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3135,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1471) = "slice(phi_kernel)" (%1055, %1455, %1470) {axes:[0],decrease_axis:[],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3136,stop_gradient:[true]} : (custom_device_tensor<-1x-1x-1xf32>, custom_device_tensor<1xi64>, custom_device_tensor<1xi64>) -> custom_device_tensor<-1x-1x-1xf32> + (%1472) = "memcpy_d2h(phi_kernel)" (%1443) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3137} : (custom_device_tensor) -> cpu_tensor + (%1473) = "scale(phi_kernel)" (%1472, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3138,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1474) = "builtin.combine" [id:3139] (%1443, %21) {origin_id:1225,stop_gradient:[true]} : (custom_device_tensor, cpu_tensor) -> vec[custom_device_tensor,cpu_tensor] + (%1475) = "memcpy_h2d(phi_kernel)" (%21) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3140,persistable:[true]} : (cpu_tensor) -> custom_device_tensor + (%1476) = "builtin.combine" [id:3141] (%1443, %1475) {origin_id:3141} : (custom_device_tensor, custom_device_tensor) -> vec[custom_device_tensor,custom_device_tensor] + (%1477) = "stack(phi_kernel)" (%1476) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3142,stop_gradient:[true]} : (vec[custom_device_tensor,custom_device_tensor]) -> custom_device_tensor<2xi64> + (%1478) = "builtin.combine" [id:3143] (%1473, %20) {origin_id:1227,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor] + (%1479) = "stack(phi_kernel)" (%1478) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3144,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor]) -> cpu_tensor<2xi64> + (%1480) = "slice(phi_kernel)" (%1401, %1477, %1479) {axes:[0,1],decrease_axis:[0,1],infer_flags:[-1,1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3145,stop_gradient:[true]} : (cpu_tensor<-1x2xi32>, custom_device_tensor<2xi64>, cpu_tensor<2xi64>) -> cpu_tensor + (%1481) = "memcpy_d2h(phi_kernel)" (%1443) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3146} : (custom_device_tensor) -> cpu_tensor + (%1482) = "scale(phi_kernel)" (%1481, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3147,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1483) = "builtin.combine" [id:3148] (%1443, %20) {origin_id:1238,stop_gradient:[true]} : (custom_device_tensor, cpu_tensor) -> vec[custom_device_tensor,cpu_tensor] + (%1484) = "memcpy_h2d(phi_kernel)" (%20) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3149,persistable:[true]} : (cpu_tensor) -> custom_device_tensor + (%1485) = "builtin.combine" [id:3150] (%1443, %1484) {origin_id:3150} : (custom_device_tensor, custom_device_tensor) -> vec[custom_device_tensor,custom_device_tensor] + (%1486) = "stack(phi_kernel)" (%1485) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3151,stop_gradient:[true]} : (vec[custom_device_tensor,custom_device_tensor]) -> custom_device_tensor<2xi64> + (%1487) = "builtin.combine" [id:3152] (%1482, %4) {origin_id:1240,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor] + (%1488) = "stack(phi_kernel)" (%1487) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3153,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor]) -> cpu_tensor<2xi64> + (%1489) = "slice(phi_kernel)" (%1401, %1486, %1488) {axes:[0,1],decrease_axis:[0,1],infer_flags:[-1,1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3154,stop_gradient:[true]} : (cpu_tensor<-1x2xi32>, custom_device_tensor<2xi64>, cpu_tensor<2xi64>) -> cpu_tensor + (%1490) = "unsqueeze(phi_kernel)" (%1471, %43) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:3155,stop_gradient:[true]} : (custom_device_tensor<-1x-1x-1xf32>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x1x-1x-1xf32> + (%1491) = "slice(phi_kernel)" (%1458, %52, %36) {axes:[1],decrease_axis:[],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3156,stop_gradient:[true]} : (custom_device_tensor<-1x6xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x4xf32> + (%1492) = "split_with_num(phi_kernel)" (%1491, %23) {kernel_key:,kernel_name:"split_with_num",num:4,op_name:"pd_op.split_with_num",origin_id:3157,stop_gradient:[true]} : (custom_device_tensor<-1x4xf32>, cpu_tensor<1xi32>) -> vec[custom_device_tensor<-1x1xf32>,custom_device_tensor<-1x1xf32>,custom_device_tensor<-1x1xf32>,custom_device_tensor<-1x1xf32>] + (%1493, %1494, %1495, %1496) = "builtin.split" [id:3158] (%1492) {origin_id:1250,stop_gradient:[true,true,true,true]} : (vec[custom_device_tensor<-1x1xf32>,custom_device_tensor<-1x1xf32>,custom_device_tensor<-1x1xf32>,custom_device_tensor<-1x1xf32>]) -> custom_device_tensor<-1x1xf32>, custom_device_tensor<-1x1xf32>, custom_device_tensor<-1x1xf32>, custom_device_tensor<-1x1xf32> + (%1497) = "shape64(phi_kernel)" (%1490) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:3159,stop_gradient:[true]} : (custom_device_tensor<-1x1x-1x-1xf32>) -> cpu_tensor<4xi64> + (%1498) = "slice(phi_kernel)" (%1497, %44, %43) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3160,stop_gradient:[true]} : (cpu_tensor<4xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1499) = "cast(phi_kernel)" (%1480) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3161,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + (%1500) = "arange(phi_kernel)" (%42, %1499, %41) {dtype:int64,kernel_key:,kernel_name:"arange",op_name:"pd_op.arange",origin_id:3162,place:Place(undefined:0),stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor, cpu_tensor<1xi64>) -> custom_device_tensor<-1xi64> + (%1501) = "cast(phi_kernel)" (%1500) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3163,stop_gradient:[true]} : (custom_device_tensor<-1xi64>) -> custom_device_tensor<-1xf32> + (%1502) = "scale(phi_kernel)" (%1501, %26) {bias:0.5,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3164,stop_gradient:[true]} : (custom_device_tensor<-1xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1xf32> + (%1503) = "cast(phi_kernel)" (%1489) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3165,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + (%1504) = "arange(phi_kernel)" (%42, %1503, %41) {dtype:int64,kernel_key:,kernel_name:"arange",op_name:"pd_op.arange",origin_id:3166,place:Place(undefined:0),stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor, cpu_tensor<1xi64>) -> custom_device_tensor<-1xi64> + (%1505) = "cast(phi_kernel)" (%1504) {dtype:float32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3167,stop_gradient:[true]} : (custom_device_tensor<-1xi64>) -> custom_device_tensor<-1xf32> + (%1506) = "scale(phi_kernel)" (%1505, %26) {bias:0.5,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3168,stop_gradient:[true]} : (custom_device_tensor<-1xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1xf32> + (%1507) = "subtract(phi_kernel)" (%1502, %1494) {kernel_key:,kernel_name:"subtract",op_name:"pd_op.subtract",origin_id:3169,stop_gradient:[true]} : (custom_device_tensor<-1xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x-1xf32> + (%1508) = "subtract(phi_kernel)" (%1496, %1494) {kernel_key:,kernel_name:"subtract",op_name:"pd_op.subtract",origin_id:3170,stop_gradient:[true]} : (custom_device_tensor<-1x1xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1509) = "divide(phi_kernel)" (%1507, %1508) {kernel_key:,kernel_name:"divide",op_name:"pd_op.divide",origin_id:3171,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x-1xf32> + (%1510) = "scale(phi_kernel)" (%1509, %3) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3172,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x-1xf32> + (%1511) = "scale(phi_kernel)" (%1510, %26) {bias:-1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3173,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x-1xf32> + (%1512) = "subtract(phi_kernel)" (%1506, %1493) {kernel_key:,kernel_name:"subtract",op_name:"pd_op.subtract",origin_id:3174,stop_gradient:[true]} : (custom_device_tensor<-1xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x-1xf32> + (%1513) = "subtract(phi_kernel)" (%1495, %1493) {kernel_key:,kernel_name:"subtract",op_name:"pd_op.subtract",origin_id:3175,stop_gradient:[true]} : (custom_device_tensor<-1x1xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%1514) = "divide(phi_kernel)" (%1512, %1513) {kernel_key:,kernel_name:"divide",op_name:"pd_op.divide",origin_id:3176,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>, custom_device_tensor<-1x1xf32>) -> custom_device_tensor<-1x-1xf32> + (%1515) = "scale(phi_kernel)" (%1514, %3) {bias:0,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3177,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x-1xf32> + (%1516) = "scale(phi_kernel)" (%1515, %26) {bias:-1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3178,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x-1xf32> + (%1517) = "unsqueeze(phi_kernel)" (%1516, %43) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:3179,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x1x-1xf32> + (%1518) = "shape64(phi_kernel)" (%1511) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:3180,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>) -> cpu_tensor<2xi64> + (%1519) = "slice(phi_kernel)" (%1518, %43, %52) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3181,stop_gradient:[true]} : (cpu_tensor<2xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1520) = "shape64(phi_kernel)" (%1516) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:3182,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>) -> cpu_tensor<2xi64> + (%1521) = "slice(phi_kernel)" (%1520, %43, %52) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3183,stop_gradient:[true]} : (cpu_tensor<2xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1522) = "builtin.combine" [id:3184] (%1498, %1519, %1521) {origin_id:1305,stop_gradient:[true]} : (cpu_tensor, cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor,cpu_tensor] + (%1523) = "stack(phi_kernel)" (%1522) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3185,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor,cpu_tensor]) -> cpu_tensor<3xi64> + (%1524) = "expand(phi_kernel)" (%1517, %1523) {kernel_key:,kernel_name:"expand",op_name:"pd_op.expand",origin_id:3186,stop_gradient:[true]} : (custom_device_tensor<-1x1x-1xf32>, cpu_tensor<3xi64>) -> custom_device_tensor<-1x-1x-1xf32> + (%1525) = "unsqueeze(phi_kernel)" (%1511, %52) {kernel_key:,kernel_name:"unsqueeze",op_name:"pd_op.unsqueeze",origin_id:3187,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x-1x1xf32> + (%1526) = "shape64(phi_kernel)" (%1511) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:3188,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>) -> cpu_tensor<2xi64> + (%1527) = "slice(phi_kernel)" (%1526, %43, %52) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3189,stop_gradient:[true]} : (cpu_tensor<2xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1528) = "shape64(phi_kernel)" (%1516) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:3190,stop_gradient:[true]} : (custom_device_tensor<-1x-1xf32>) -> cpu_tensor<2xi64> + (%1529) = "slice(phi_kernel)" (%1528, %43, %52) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3191,stop_gradient:[true]} : (cpu_tensor<2xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1530) = "builtin.combine" [id:3192] (%1498, %1527, %1529) {origin_id:1324,stop_gradient:[true]} : (cpu_tensor, cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor,cpu_tensor] + (%1531) = "stack(phi_kernel)" (%1530) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3193,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor,cpu_tensor]) -> cpu_tensor<3xi64> + (%1532) = "expand(phi_kernel)" (%1525, %1531) {kernel_key:,kernel_name:"expand",op_name:"pd_op.expand",origin_id:3194,stop_gradient:[true]} : (custom_device_tensor<-1x-1x1xf32>, cpu_tensor<3xi64>) -> custom_device_tensor<-1x-1x-1xf32> + (%1533) = "builtin.combine" [id:3195] (%1524, %1532) {origin_id:1327,stop_gradient:[true]} : (custom_device_tensor<-1x-1x-1xf32>, custom_device_tensor<-1x-1x-1xf32>) -> vec[custom_device_tensor<-1x-1x-1xf32>,custom_device_tensor<-1x-1x-1xf32>] + (%1534) = "stack(phi_kernel)" (%1533) {axis:3,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3196,stop_gradient:[true]} : (vec[custom_device_tensor<-1x-1x-1xf32>,custom_device_tensor<-1x-1x-1xf32>]) -> custom_device_tensor<-1x-1x-1x2xf32> + (%1535) = "grid_sample(phi_kernel)" (%1490, %1534) {align_corners:false,kernel_key:,kernel_name:"grid_sample",mode:"bilinear",op_name:"pd_op.grid_sample",origin_id:3197,padding_mode:"zeros",stop_gradient:[true]} : (custom_device_tensor<-1x1x-1x-1xf32>, custom_device_tensor<-1x-1x-1x2xf32>) -> custom_device_tensor<-1x1x-1x-1xf32> + (%1536) = "slice(phi_kernel)" (%1535, %44, %43) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3198,stop_gradient:[true]} : (custom_device_tensor<-1x1x-1x-1xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x-1x-1xf32> + (%1537) = "greater_equal(phi_kernel)" (%1536, %2) {kernel_key:,kernel_name:"greater_equal",op_name:"pd_op.greater_equal",origin_id:3199,stop_gradient:[true]} : (custom_device_tensor<-1x-1x-1xf32>, custom_device_tensor) -> custom_device_tensor<-1x-1x-1xb> + (%1538) = "cast(phi_kernel)" (%1537) {dtype:int32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3200,stop_gradient:[true]} : (custom_device_tensor<-1x-1x-1xb>) -> custom_device_tensor<-1x-1x-1xi32> + (%1539) = "memcpy_d2h(phi_kernel)" (%1443) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3201} : (custom_device_tensor) -> cpu_tensor + (%1540) = "scale(phi_kernel)" (%1539, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3202,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1541) = "builtin.combine" [id:3203] (%1443) {origin_id:1338,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1542) = "stack(phi_kernel)" (%1541) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3204,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1543) = "builtin.combine" [id:3205] (%1540) {origin_id:1340,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1544) = "stack(phi_kernel)" (%1543) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3206,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1545) = "slice(phi_kernel)" (%1254, %1542, %1544) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3207,stop_gradient:[true]} : (cpu_tensor<-1xi32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1546) = "cast(phi_kernel)" (%1545) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3208,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + (%1547) = "memcpy_h2d(phi_kernel)" (%1546) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3209} : (cpu_tensor) -> custom_device_tensor + (%1548) = "add(phi_kernel)" (%arg_27, %1547) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:3210,stop_gradient:[true]} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%1549) = "cast(phi_kernel)" (%1480) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3211,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + (%1550) = "cast(phi_kernel)" (%1489) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3212,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + (%1551) = "builtin.combine" [id:3213] (%arg_27, %1, %1) {origin_id:1351,stop_gradient:[true]} : (custom_device_tensor, cpu_tensor, cpu_tensor) -> vec[custom_device_tensor,cpu_tensor,cpu_tensor] + (%1552) = "memcpy_h2d(phi_kernel)" (%1) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3214,persistable:[true]} : (cpu_tensor) -> custom_device_tensor + (%1553) = "memcpy_h2d(phi_kernel)" (%1) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3215,persistable:[true]} : (cpu_tensor) -> custom_device_tensor + (%1554) = "builtin.combine" [id:3216] (%arg_27, %1552, %1553) {origin_id:3216} : (custom_device_tensor, custom_device_tensor, custom_device_tensor) -> vec[custom_device_tensor,custom_device_tensor,custom_device_tensor] + (%1555) = "stack(phi_kernel)" (%1554) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3217,stop_gradient:[true]} : (vec[custom_device_tensor,custom_device_tensor,custom_device_tensor]) -> custom_device_tensor<3xi64> + (%1556) = "builtin.combine" [id:3218] (%1548, %1549, %1550) {origin_id:1353,stop_gradient:[true]} : (custom_device_tensor, cpu_tensor, cpu_tensor) -> vec[custom_device_tensor,cpu_tensor,cpu_tensor] + (%1557) = "memcpy_h2d(phi_kernel)" (%1549) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3219} : (cpu_tensor) -> custom_device_tensor + (%1558) = "memcpy_h2d(phi_kernel)" (%1550) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3220} : (cpu_tensor) -> custom_device_tensor + (%1559) = "builtin.combine" [id:3221] (%1548, %1557, %1558) {origin_id:3221} : (custom_device_tensor, custom_device_tensor, custom_device_tensor) -> vec[custom_device_tensor,custom_device_tensor,custom_device_tensor] + (%1560) = "stack(phi_kernel)" (%1559) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3222,stop_gradient:[true]} : (vec[custom_device_tensor,custom_device_tensor,custom_device_tensor]) -> custom_device_tensor<3xi64> + (%1561) = "memcpy_h2d(phi_kernel)" (%arg_28) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3223} : (cpu_tensor<-1x-1x-1xi32>) -> custom_device_tensor<-1x-1x-1xi32> + (%1562) = "set_value_with_tensor_(phi_kernel)" (%1561, %1538, %1555, %1560, %0) {axes:[0,1,2],decrease_axes:[],is_inplace:true,kernel_key:,kernel_name:"set_value_with_tensor",none_axes:[],op_name:"pd_op.set_value_with_tensor_",origin_id:3224,stop_gradient:[true]} : (custom_device_tensor<-1x-1x-1xi32>, custom_device_tensor<-1x-1x-1xi32>, custom_device_tensor<3xi64>, custom_device_tensor<3xi64>, cpu_tensor<3xi64>) -> custom_device_tensor<-1x-1x-1xi32> + (%1563) = "memcpy_d2h(phi_kernel)" (%1443) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3225} : (custom_device_tensor) -> cpu_tensor + (%1564) = "scale(phi_kernel)" (%1563, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3226,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1565) = "builtin.combine" [id:3227] (%1443) {origin_id:1359,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%1566) = "stack(phi_kernel)" (%1565) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3228,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%1567) = "builtin.combine" [id:3229] (%1564) {origin_id:1361,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%1568) = "stack(phi_kernel)" (%1567) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:3230,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%1569) = "slice(phi_kernel)" (%1254, %1566, %1568) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:3231,stop_gradient:[true]} : (cpu_tensor<-1xi32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%1570) = "cast(phi_kernel)" (%1569) {dtype:int64,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:3232,stop_gradient:[true]} : (cpu_tensor) -> cpu_tensor + (%1571) = "memcpy_h2d(phi_kernel)" (%1570) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3233} : (cpu_tensor) -> custom_device_tensor + (%1572) = "add(phi_kernel)" (%arg_27, %1571) {kernel_key:,kernel_name:"add",op_name:"pd_op.add",origin_id:3234,stop_gradient:[true]} : (custom_device_tensor, custom_device_tensor) -> custom_device_tensor + (%1573) = "memcpy_d2h(phi_kernel)" (%arg_26) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3235} : (custom_device_tensor) -> cpu_tensor + (%1574) = "scale(phi_kernel)" (%1573, %26) {bias:1,bias_after_scale:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale",origin_id:3236,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%1575) = "less_than(phi_kernel)" (%1574, %1417) {kernel_key:,kernel_name:"less_than",op_name:"pd_op.less_than",origin_id:3237,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> cpu_tensor + (%1576) = "memcpy_h2d(phi_kernel)" (%1575) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3238} : (cpu_tensor) -> custom_device_tensor + (%1577) = "memcpy_h2d(phi_kernel)" (%1574) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3239} : (cpu_tensor) -> custom_device_tensor + (%1578) = "memcpy_d2h(phi_kernel)" (%1562) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:3240} : (custom_device_tensor<-1x-1x-1xi32>) -> cpu_tensor<-1x-1x-1xi32> + (%1579) = "memcpy_h2d(phi_kernel)" (%1480) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3241} : (cpu_tensor) -> custom_device_tensor + (%1580) = "memcpy_h2d(phi_kernel)" (%1489) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:3242} : (cpu_tensor) -> custom_device_tensor + () = "cf.yield" [id:3243] (%1576, %1577, %1572, %1578, %1458, %1443, %1579, %1580, %1471, %1538) {origin_id:1369} : (custom_device_tensor, custom_device_tensor, custom_device_tensor, cpu_tensor<-1x-1x-1xi32>, custom_device_tensor<-1x6xf32>, custom_device_tensor, custom_device_tensor, custom_device_tensor, custom_device_tensor<-1x-1x-1xf32>, custom_device_tensor<-1x-1x-1xi32>) -> + } + () = "builtin.shadow_output" [id:3244] (%1398) {origin_id:1785,output_name:"fetch_name_0"} : (custom_device_tensor<-1x6xf32>) -> + () = "builtin.shadow_output" [id:3245] (%1254) {origin_id:1786,output_name:"fetch_name_1"} : (cpu_tensor<-1xi32>) -> + () = "builtin.shadow_output" [id:3246] (%1430) {origin_id:1787,output_name:"fetch_name_2"} : (cpu_tensor<-1x-1x-1xi32>) -> +} + +--- Running PIR pass [remove_shadow_feed_pass] +I0420 14:39:30.019955 115867 print_statistics.cc:50] --- detected [3] subgraphs! +--- Running PIR pass [inplace_pass] +I0420 14:39:31.039285 115867 print_statistics.cc:50] --- detected [52] subgraphs! +--- Running PIR pass [remove_shadow_feed_pass] +--- Running PIR pass [inplace_pass] +I0420 14:39:31.090761 115867 print_statistics.cc:50] --- detected [5] subgraphs! +--- Running PIR pass [remove_shadow_feed_pass] +--- Running PIR pass [inplace_pass] +--- Running PIR pass [remove_shadow_feed_pass] +--- Running PIR pass [inplace_pass] +I0420 14:39:31.120358 115867 print_statistics.cc:50] --- detected [4] subgraphs! +--- Running PIR pass [remove_shadow_feed_pass] +--- Running PIR pass [inplace_pass] +I0420 14:39:31.198530 115867 print_statistics.cc:50] --- detected [17] subgraphs! +--- Running PIR pass [remove_shadow_feed_pass] +--- Running PIR pass [inplace_pass] +I0420 14:39:31.228188 115867 print_statistics.cc:50] --- detected [3] subgraphs! +--- Running PIR pass [remove_shadow_feed_pass] +--- Running PIR pass [inplace_pass] +I0420 14:39:31.253866 115867 print_statistics.cc:50] --- detected [3] subgraphs! +--- Running PIR pass [remove_shadow_feed_pass] +--- Running PIR pass [inplace_pass] +I0420 14:39:31.306921 115867 print_statistics.cc:50] --- detected [11] subgraphs! +--- Running PIR pass [remove_shadow_feed_pass] +--- Running PIR pass [inplace_pass] +I0420 14:39:31.431869 115867 print_statistics.cc:50] --- detected [12] subgraphs! +I0420 14:39:31.434250 115867 analysis_predictor.cc:1214] ======= pir optimization completed ======= +I0420 14:39:31.511039 115867 helper.h:475] Init predictor : [cpu current allocated memory: 0.000320435MB], [cpu current reserved memory: 0.000320435MB], [cpu peak allocated memory: 137.39MB], [cpu peak reserved memory: 137.39MB] +Connecting to https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_instance_segmentation_004.png ... +Downloading general_instance_segmentation_004.png ... + +[ ] 0.60% +[=============================== ] 62.51% +[==================================================] 100.00% +I0420 14:39:31.982393 115867 helper.h:475] before run : [cpu current allocated memory: 0.000320435MB], [cpu current reserved memory: 0.000320435MB], [cpu peak allocated memory: 137.39MB], [cpu peak reserved memory: 137.39MB] +I0420 14:39:31.985543 115867 pir_interpreter.cc:1569] value info of interpretercore 0xcf59f40 +value -> var_name -> id -> variable* +0xc4bce10 -> 0xcf59f401745131171435049990_inner_var_1093 -> 1093 -> 0xee7ac30 +0xc4bce30 -> 0xcf59f401745131171435049990_inner_var_1092 -> 1092 -> 0xee7a810 +0xc4bce70 -> 0xcf59f401745131171435049990_inner_var_1090 -> 1090 -> 0xee79fd0 +0xc4bce88 -> 0xcf59f401745131171435049990_inner_var_1089 -> 1089 -> 0xee79bb0 +0xc4bcea0 -> 0xcf59f401745131171435049990_inner_var_1088 -> 1088 -> 0xee79790 +0xc4bceb8 -> fetch_name_2 -> 1087 -> 0xee79370 +0xca8d4b0 -> 0xcf59f401745131171435049990_inner_var_1082 -> 1082 -> 0xee77ef0 +0xca8d060 -> 0xcf59f401745131171435049990_inner_var_1081 -> 1081 -> 0xee77ad0 +0xca8cc10 -> 0xcf59f401745131171435049990_inner_var_1080 -> 1080 -> 0xee776b0 +0xca8b670 -> 0xcf59f401745131171435049990_inner_var_1074 -> 1074 -> 0xee75df0 +0xccca970 -> 0xcf59f401745131171435049990_inner_var_1069 -> 1069 -> 0xcfbd680 +0xccca1d0 -> 0xcf59f401745131171435049990_inner_var_1067 -> 1067 -> 0xcfbca10 +0xccc7780 -> 0xcf59f401745131171435049990_inner_var_1066 -> 1066 -> 0xcfbc9f0 +0xccc76b0 -> 0xcf59f401745131171435049990_inner_var_1064 -> 1064 -> 0xcfbc2e0 +0xccc75e0 -> 0xcf59f401745131171435049990_inner_var_1063 -> 1063 -> 0xcfbbec0 +0xccc9550 -> 0xcf59f401745131171435049990_inner_var_1062 -> 1062 -> 0xcfbbaa0 +0xccc8d10 -> 0xcf59f401745131171435049990_inner_var_1060 -> 1060 -> 0xcfbb260 +0xccc8660 -> 0xcf59f401745131171435049990_inner_var_1059 -> 1059 -> 0xcfbae40 +0xccc7510 -> 0xcf59f401745131171435049990_inner_var_1058 -> 1058 -> 0xcfbaa20 +0xccc7e10 -> 0xcf59f401745131171435049990_inner_var_1057 -> 1057 -> 0xcfba600 +0xccc6ed0 -> fetch_name_0 -> 1055 -> 0xcaabf30 +0xccc2730 -> 0xcf59f401745131171435049990_inner_var_1049 -> 1049 -> 0xcfbe6a0 +0xccc5820 -> 0xcf59f401745131171435049990_inner_var_1046 -> 1046 -> 0xcfb51a0 +0xccc4e10 -> 0xcf59f401745131171435049990_inner_var_1044 -> 1044 -> 0xcfb4940 +0xccc4890 -> 0xcf59f401745131171435049990_inner_var_1043 -> 1043 -> 0xcfb4520 +0xccc4410 -> 0xcf59f401745131171435049990_inner_var_1041 -> 1041 -> 0xcfb3ce0 +0xca8c7c0 -> 0xcf59f401745131171435049990_inner_var_1079 -> 1079 -> 0xee77290 +0xca83d20 -> 0xcf59f401745131171435049990_inner_var_1040 -> 1040 -> 0xcf78df0 +0xccc38a0 -> 0xcf59f401745131171435049990_inner_var_1037 -> 1037 -> 0xcfb3080 +0xccc19b0 -> 0xcf59f401745131171435049990_inner_var_1029 -> 1029 -> 0xcaad590 +0xccbf440 -> 0xcf59f401745131171435049990_inner_var_1019 -> 1019 -> 0xcbeef60 +0xccbea10 -> 0xcf59f401745131171435049990_inner_var_1017 -> 1017 -> 0xcbedfe0 +0xccbe210 -> 0xcf59f401745131171435049990_inner_var_1015 -> 1015 -> 0xcbedbc0 +0xd03b830 -> 0xcf59f401745131171435049990_inner_var_1008 -> 1008 -> 0xcbebec0 +0xd03b8b0 -> 0xcf59f401745131171435049990_inner_var_1003 -> 1003 -> 0xcbeaa20 +0xd03b8c8 -> 0xcf59f401745131171435049990_inner_var_1002 -> 1002 -> 0xcbe8940 +0xd03b180 -> 0xcf59f401745131171435049990_inner_var_1001 -> 1001 -> 0xcbea200 +0xd03ad00 -> 0xcf59f401745131171435049990_inner_var_1000 -> 1000 -> 0xcbe9de0 +0xccddec0 -> 0xcf59f401745131171435049990_inner_var_993 -> 993 -> 0xcbe7f80 +0xd037ee0 -> 0xcf59f401745131171435049990_inner_var_988 -> 988 -> 0xcbe55a0 +0xd035b10 -> 0xcf59f401745131171435049990_inner_var_987 -> 987 -> 0xcbe6260 +0xd036f20 -> 0xcf59f401745131171435049990_inner_var_985 -> 985 -> 0xcbe62a0 +0xca8c270 -> 0xcf59f401745131171435049990_inner_var_1078 -> 1078 -> 0xee76e70 +0xccb2520 -> 0xcf59f401745131171435049990_inner_var_979 -> 979 -> 0xcbe4920 +0xccb2550 -> 0xcf59f401745131171435049990_inner_var_977 -> 977 -> 0xcbe40e0 +0xccb2568 -> 0xcf59f401745131171435049990_inner_var_976 -> 976 -> 0xcbe3cc0 +0xcca0e10 -> 0xcf59f401745131171435049990_inner_var_974 -> 974 -> 0xcbe34a0 +0xca5f9b0 -> 0xcf59f401745131171435049990_inner_var_970 -> 970 -> 0xcbe1870 +0xca61eb0 -> 0xcf59f401745131171435049990_inner_var_965 -> 965 -> 0xcbe1180 +0xca68510 -> 0xcf59f401745131171435049990_inner_var_962 -> 962 -> 0xcbe0520 +0xca5f8e0 -> 0xcf59f401745131171435049990_inner_var_961 -> 961 -> 0xca5f6f0 +0xce93150 -> 0xcf59f401745131171435049990_inner_var_960 -> 960 -> 0xca5ea90 +0xccdfb30 -> 0xcf59f401745131171435049990_inner_var_959 -> 959 -> 0xca5eeb0 +0xccdefc0 -> 0xcf59f401745131171435049990_inner_var_956 -> 956 -> 0xca5e250 +0xccb7010 -> 0xcf59f401745131171435049990_inner_var_955 -> 955 -> 0xca5de30 +0xccb7040 -> 0xcf59f401745131171435049990_inner_var_953 -> 953 -> 0xca5d1d0 +0xccde4c0 -> 0xcf59f401745131171435049990_inner_var_952 -> 952 -> 0xceea310 +0xcccae30 -> 0xcf59f401745131171435049990_inner_var_1073 -> 1073 -> 0xee759d0 +0xccdc370 -> 0xcf59f401745131171435049990_inner_var_950 -> 950 -> 0xcfc2b30 +0xce92b70 -> 0xcf59f401745131171435049990_inner_var_949 -> 949 -> 0xcf017f0 +0xccdb370 -> 0xcf59f401745131171435049990_inner_var_947 -> 947 -> 0xcee32f0 +0xccdc060 -> 0xcf59f401745131171435049990_inner_var_943 -> 943 -> 0xcef8160 +0xccd7c10 -> 0xcf59f401745131171435049990_inner_var_937 -> 937 -> 0xced4800 +0xccd6fd0 -> 0xcf59f401745131171435049990_inner_var_935 -> 935 -> 0xcecbfe0 +0xccda790 -> 0xcf59f401745131171435049990_inner_var_934 -> 934 -> 0xced8b80 +0xccc3500 -> 0xcf59f401745131171435049990_inner_var_1036 -> 1036 -> 0xcfb2c60 +0xce92d70 -> 0xcf59f401745131171435049990_inner_var_932 -> 932 -> 0xcecf430 +0xce92e40 -> 0xcf59f401745131171435049990_inner_var_930 -> 930 -> 0xceac320 +0xd037740 -> 0xcf59f401745131171435049990_inner_var_990 -> 990 -> 0xcbe77e0 +0xccd9610 -> 0xcf59f401745131171435049990_inner_var_929 -> 929 -> 0xcec7570 +0xccd8e00 -> 0xcf59f401745131171435049990_inner_var_927 -> 927 -> 0xcebe990 +0xccd8970 -> 0xcf59f401745131171435049990_inner_var_926 -> 926 -> 0xcebc860 +0xccd8600 -> 0xcf59f401745131171435049990_inner_var_925 -> 925 -> 0xcec3310 +0xce92ca0 -> 0xcf59f401745131171435049990_inner_var_922 -> 922 -> 0xceb1250 +0xce93040 -> 0xcf59f401745131171435049990_inner_var_921 -> 921 -> 0xceb7a40 +0xccd72b0 -> 0xcf59f401745131171435049990_inner_var_920 -> 920 -> 0xceb6370 +0xccdac50 -> 0xcf59f401745131171435049990_inner_var_936 -> 936 -> 0xcf105d0 +0xce904e0 -> 0xcf59f401745131171435049990_inner_var_919 -> 919 -> 0xcea2d80 +0xce934a0 -> 0xcf59f401745131171435049990_inner_var_918 -> 918 -> 0xcea6ce0 +0xce8b970 -> 0xcf59f401745131171435049990_inner_var_916 -> 916 -> 0xcea2620 +0xd03b898 -> 0xcf59f401745131171435049990_inner_var_1004 -> 1004 -> 0xcbeae40 +0xd0395f0 -> 0xcf59f401745131171435049990_inner_var_994 -> 994 -> 0xcbe8520 +0xce90d50 -> 0xcf59f401745131171435049990_inner_var_909 -> 909 -> 0xce9bd00 +0xce909b0 -> 0xcf59f401745131171435049990_inner_var_908 -> 908 -> 0xcf11580 +0xce8f6b0 -> 0xcf59f401745131171435049990_inner_var_904 -> 904 -> 0xcf7ad50 +0xce8f3d0 -> 0xcf59f401745131171435049990_inner_var_903 -> 903 -> 0xcf48420 +0xce8ed50 -> 0xcf59f401745131171435049990_inner_var_902 -> 902 -> 0xcf17fd0 +0xce8e8e0 -> 0xcf59f401745131171435049990_inner_var_900 -> 900 -> 0xcaa2690 +0xce8e600 -> 0xcf59f401745131171435049990_inner_var_899 -> 899 -> 0xcfc3f20 +0xc4bced0 -> 0xcf59f401745131171435049990_inner_var_1086 -> 1086 -> 0xee78f50 +0xce8d810 -> 0xcf59f401745131171435049990_inner_var_894 -> 894 -> 0xcf07c60 +0xce8cf50 -> 0xcf59f401745131171435049990_inner_var_891 -> 891 -> 0xceb85d0 +0xce8cbf0 -> 0xcf59f401745131171435049990_inner_var_890 -> 890 -> 0xcf15cd0 +0xccc1e10 -> 0xcf59f401745131171435049990_inner_var_1030 -> 1030 -> 0xcaad9b0 +0xcaba090 -> 0xcf59f401745131171435049990_inner_var_889 -> 889 -> 0xcf35d30 +0xccb2d90 -> 0xcf59f401745131171435049990_inner_var_886 -> 886 -> 0xcf83140 +0xccbfe10 -> 0xcf59f401745131171435049990_inner_var_1022 -> 1022 -> 0xcbefad0 +0xce8b130 -> 0xcf59f401745131171435049990_inner_var_884 -> 884 -> 0xcf82600 +0xce8b5d0 -> 0xcf59f401745131171435049990_inner_var_883 -> 883 -> 0xcaf0b00 +0xce8a8c0 -> 0xcf59f401745131171435049990_inner_var_882 -> 882 -> 0xcee18b0 +0xcf74e30 -> 0xcf59f401745131171435049990_inner_var_880 -> 880 -> 0xceed1c0 +0xca56690 -> 0xcf59f401745131171435049990_inner_var_878 -> 878 -> 0xcf7a830 +0xce89ca0 -> 0xcf59f401745131171435049990_inner_var_877 -> 877 -> 0xca98d00 +0xcabd4c0 -> 0xcf59f401745131171435049990_inner_var_874 -> 874 -> 0xcee6180 +0xcabc430 -> 0xcf59f401745131171435049990_inner_var_870 -> 870 -> 0xca841f0 +0xcabbc20 -> 0xcf59f401745131171435049990_inner_var_868 -> 868 -> 0xccacef0 +0xccc30a0 -> 0xcf59f401745131171435049990_inner_var_1035 -> 1035 -> 0xceda8f0 +0xcabb320 -> 0xcf59f401745131171435049990_inner_var_866 -> 866 -> 0xee8cac0 +0xcabab48 -> 0xcf59f401745131171435049990_inner_var_864 -> 864 -> 0xcc99a90 +0xc4bce50 -> 0xcf59f401745131171435049990_inner_var_1091 -> 1091 -> 0xee7a3f0 +0xcaba6f0 -> 0xcf59f401745131171435049990_inner_var_863 -> 863 -> 0xcef8a10 +0xcaba708 -> 0xcf59f401745131171435049990_inner_var_862 -> 862 -> 0xcfbe720 +0xccbd010 -> 0xcf59f401745131171435049990_inner_var_861 -> 861 -> 0xcf78ad0 +0xccbd028 -> 0xcf59f401745131171435049990_inner_var_860 -> 860 -> 0xcf53e00 +0xc4bcee8 -> 0xcf59f401745131171435049990_inner_var_1085 -> 1085 -> 0xee76a50 +0xcaec4a0 -> 0xcf59f401745131171435049990_inner_var_856 -> 856 -> 0xcf1baf0 +0xcc951b0 -> 0xcf59f401745131171435049990_inner_var_852 -> 852 -> 0xcf28a70 +0xccb2ea0 -> 0xcf59f401745131171435049990_inner_var_850 -> 850 -> 0xcf0e480 +0xccb27b0 -> 0xcf59f401745131171435049990_inner_var_847 -> 847 -> 0xcf378c0 +0xccc6970 -> 0xcf59f401745131171435049990_inner_var_1053 -> 1053 -> 0xcaab510 +0xcaea110 -> 0xcf59f401745131171435049990_inner_var_845 -> 845 -> 0xcf156e0 +0xccdbd20 -> 0xcf59f401745131171435049990_inner_var_941 -> 941 -> 0xcee3b70 +0xcae9a30 -> 0xcf59f401745131171435049990_inner_var_843 -> 843 -> 0xee86de0 +0xcae8f00 -> 0xcf59f401745131171435049990_inner_var_841 -> 841 -> 0xcdbbda0 +0xcae9030 -> 0xcf59f401745131171435049990_inner_var_840 -> 840 -> 0xccb5c80 +0xcae57f0 -> 0xcf59f401745131171435049990_inner_var_839 -> 839 -> 0xcf6fa60 +0xcae7f98 -> 0xcf59f401745131171435049990_inner_var_836 -> 836 -> 0xcca6740 +0xcae6e98 -> 0xcf59f401745131171435049990_inner_var_828 -> 828 -> 0xce8a480 +0xd03b850 -> 0xcf59f401745131171435049990_inner_var_1007 -> 1007 -> 0xcbebaa0 +0xcae6ee0 -> 0xcf59f401745131171435049990_inner_var_825 -> 825 -> 0xccde190 +0xd03b868 -> 0xcf59f401745131171435049990_inner_var_1006 -> 1006 -> 0xcbeb680 +0xcae6ef8 -> 0xcf59f401745131171435049990_inner_var_824 -> 824 -> 0xd034610 +0xd0369b0 -> 0xcf59f401745131171435049990_inner_var_984 -> 984 -> 0xcbe5de0 +0xcae6590 -> 0xcf59f401745131171435049990_inner_var_823 -> 823 -> 0xcf1af20 +0xcae5df0 -> 0xcf59f401745131171435049990_inner_var_819 -> 819 -> 0xca54b70 +0xcae5e20 -> 0xcf59f401745131171435049990_inner_var_817 -> 817 -> 0xcf16e80 +0xcae5e38 -> 0xcf59f401745131171435049990_inner_var_816 -> 816 -> 0xca57ab0 +0xcae9610 -> 0xcf59f401745131171435049990_inner_var_853 -> 853 -> 0xcefff10 +0xcae50c0 -> 0xcf59f401745131171435049990_inner_var_815 -> 815 -> 0xcf19b60 +0xee8c030 -> 0xcf59f401745131171435049990_inner_var_814 -> 814 -> 0xcf16450 +0xcae4960 -> 0xcf59f401745131171435049990_inner_var_810 -> 810 -> 0xcf131f0 +0xcae4978 -> 0xcf59f401745131171435049990_inner_var_809 -> 809 -> 0xcf1bdf0 +0xcae4990 -> 0xcf59f401745131171435049990_inner_var_808 -> 808 -> 0xcf28470 +0xee8c430 -> 0xcf59f401745131171435049990_inner_var_806 -> 806 -> 0xcfc6ca0 +0xd038b80 -> 0xcf59f401745131171435049990_inner_var_991 -> 991 -> 0xcbe7bb0 +0xee8bb80 -> 0xcf59f401745131171435049990_inner_var_800 -> 800 -> 0xcefcab0 +0xee8bb98 -> 0xcf59f401745131171435049990_inner_var_799 -> 799 -> 0xee73140 +0xee89e90 -> 0xcf59f401745131171435049990_inner_var_790 -> 790 -> 0xca78980 +0xee85d40 -> 0xcf59f401745131171435049990_inner_var_789 -> 789 -> 0xcae6ce0 +0xee89500 -> 0xcf59f401745131171435049990_inner_var_787 -> 787 -> 0xcaa0680 +0xcaec8f0 -> 0xcf59f401745131171435049990_inner_var_857 -> 857 -> 0xcf17520 +0xee89518 -> 0xcf59f401745131171435049990_inner_var_786 -> 786 -> 0xcf35750 +0xee89530 -> 0xcf59f401745131171435049990_inner_var_785 -> 785 -> 0xcdbbe60 +0xee89548 -> 0xcf59f401745131171435049990_inner_var_784 -> 784 -> 0xee883c0 +0xee89560 -> 0xcf59f401745131171435049990_inner_var_783 -> 783 -> 0xca52390 +0xee89578 -> 0xcf59f401745131171435049990_inner_var_782 -> 782 -> 0xcfbe560 +0xee86e90 -> 0xcf59f401745131171435049990_inner_var_781 -> 781 -> 0xccb97f0 +0xee88710 -> 0xcf59f401745131171435049990_inner_var_776 -> 776 -> 0xcf22a30 +0xee88728 -> 0xcf59f401745131171435049990_inner_var_775 -> 775 -> 0xcabd0a0 +0xee87a30 -> 0xcf59f401745131171435049990_inner_var_774 -> 774 -> 0xccd2c20 +0xee87478 -> 0xcf59f401745131171435049990_inner_var_771 -> 771 -> 0xced6a50 +0xee874a8 -> 0xcf59f401745131171435049990_inner_var_769 -> 769 -> 0xca6eea0 +0xccbe670 -> 0xcf59f401745131171435049990_inner_var_1016 -> 1016 -> 0xcbee150 +0xcabae40 -> 0xcf59f401745131171435049990_inner_var_895 -> 895 -> 0xd067330 +0xee874d8 -> 0xcf59f401745131171435049990_inner_var_767 -> 767 -> 0xd04d780 +0xee86388 -> 0xcf59f401745131171435049990_inner_var_759 -> 759 -> 0xc988150 +0xccb3d30 -> 0xcf59f401745131171435049990_inner_var_758 -> 758 -> 0xee48330 +0xccb2fb0 -> 0xcf59f401745131171435049990_inner_var_756 -> 756 -> 0xca65620 +0xccb1e80 -> 0xcf59f401745131171435049990_inner_var_755 -> 755 -> 0xccbf1b0 +0xccb0a60 -> 0xcf59f401745131171435049990_inner_var_753 -> 753 -> 0xca81b10 +0xcabb720 -> 0xcf59f401745131171435049990_inner_var_867 -> 867 -> 0x3be6bd0 +0xd051040 -> 0xcf59f401745131171435049990_inner_var_751 -> 751 -> 0xcfce090 +0xccdb860 -> 0xcf59f401745131171435049990_inner_var_939 -> 939 -> 0xceef440 +0xccb0340 -> 0xcf59f401745131171435049990_inner_var_750 -> 750 -> 0xcf6e0b0 +0xccb0358 -> 0xcf59f401745131171435049990_inner_var_749 -> 749 -> 0xced2ea0 +0xca9bb80 -> 0xcf59f401745131171435049990_inner_var_748 -> 748 -> 0xceca5e0 +0xd039fa0 -> 0xcf59f401745131171435049990_inner_var_997 -> 997 -> 0xcbe9180 +0xca9bbe0 -> 0xcf59f401745131171435049990_inner_var_745 -> 745 -> 0xcf98e10 +0xca9bc78 -> 0xcf59f401745131171435049990_inner_var_739 -> 739 -> 0xca95ba0 +0xcae4930 -> 0xcf59f401745131171435049990_inner_var_812 -> 812 -> 0xca6ad40 +0xca9aee0 -> 0xcf59f401745131171435049990_inner_var_738 -> 738 -> 0xccc6500 +0xca9aa50 -> 0xcf59f401745131171435049990_inner_var_737 -> 737 -> 0xcf6d020 +0xca9a5c0 -> 0xcf59f401745131171435049990_inner_var_736 -> 736 -> 0xced39a0 +0xca99c50 -> 0xcf59f401745131171435049990_inner_var_734 -> 734 -> 0xd039050 +0xce8d450 -> 0xcf59f401745131171435049990_inner_var_893 -> 893 -> 0xcf04fb0 +0xcf77330 -> 0xcf59f401745131171435049990_inner_var_729 -> 729 -> 0xced2af0 +0xcf74af0 -> 0xcf59f401745131171435049990_inner_var_728 -> 728 -> 0xca846e0 +0xcf76ce0 -> 0xcf59f401745131171435049990_inner_var_727 -> 727 -> 0xd0549d0 +0xcf760e0 -> 0xcf59f401745131171435049990_inner_var_724 -> 724 -> 0xcf27be0 +0xcf74bc0 -> 0xcf59f401745131171435049990_inner_var_723 -> 723 -> 0xca57c30 +0xcf74520 -> 0xcf59f401745131171435049990_inner_var_719 -> 719 -> 0xca81ea0 +0xd047360 -> 0xcf59f401745131171435049990_inner_var_716 -> 716 -> 0xcecb900 +0xcae5dc0 -> 0xcf59f401745131171435049990_inner_var_821 -> 821 -> 0xcf29870 +0xcf732d0 -> 0xcf59f401745131171435049990_inner_var_714 -> 714 -> 0xca90a80 +0xd049690 -> 0xcf59f401745131171435049990_inner_var_709 -> 709 -> 0xcf01cf0 +0xcb0e8c0 -> 0xcf59f401745131171435049990_inner_var_708 -> 708 -> 0xd0375b0 +0xce91930 -> 0xcf59f401745131171435049990_inner_var_912 -> 912 -> 0xce9fd90 +0xd0471c0 -> 0xcf59f401745131171435049990_inner_var_707 -> 707 -> 0xcef8d90 +0xca8a9a0 -> 0xcf59f401745131171435049990_inner_var_1071 -> 1071 -> 0xcfbdec0 +0xd047290 -> 0xcf59f401745131171435049990_inner_var_704 -> 704 -> 0xcee99f0 +0xcdba950 -> 0xcf59f401745131171435049990_inner_var_703 -> 703 -> 0xce95310 +0xcdbb530 -> 0xcf59f401745131171435049990_inner_var_700 -> 700 -> 0xd033b90 +0xd046aa0 -> 0xcf59f401745131171435049990_inner_var_697 -> 697 -> 0xcf30f60 +0xd046680 -> 0xcf59f401745131171435049990_inner_var_696 -> 696 -> 0xcf039f0 +0xd045980 -> 0xcf59f401745131171435049990_inner_var_693 -> 693 -> 0xed1b7a0 +0xcae7fb0 -> 0xcf59f401745131171435049990_inner_var_835 -> 835 -> 0xc3a1720 +0xcdbfc30 -> 0xcf59f401745131171435049990_inner_var_690 -> 690 -> 0xca95690 +0xcdbf860 -> 0xcf59f401745131171435049990_inner_var_689 -> 689 -> 0xcabba50 +0xcdbe7a0 -> 0xcf59f401745131171435049990_inner_var_681 -> 681 -> 0xcea5ea0 +0xcdbe7b8 -> 0xcf59f401745131171435049990_inner_var_680 -> 680 -> 0xca9c030 +0xcdbe7d0 -> 0xcf59f401745131171435049990_inner_var_679 -> 679 -> 0xcea3640 +0xccc7930 -> 0xcf59f401745131171435049990_inner_var_1065 -> 1065 -> 0xcaabf50 +0xcdbe800 -> 0xcf59f401745131171435049990_inner_var_677 -> 677 -> 0xcec8c10 +0xcdbc7c0 -> 0xcf59f401745131171435049990_inner_var_674 -> 674 -> 0xcc9c7b0 +0xce91170 -> 0xcf59f401745131171435049990_inner_var_910 -> 910 -> 0xcf715a0 +0xcdbcd20 -> 0xcf59f401745131171435049990_inner_var_667 -> 667 -> 0xcf1dc10 +0xcdbc150 -> 0xcf59f401745131171435049990_inner_var_664 -> 664 -> 0xcf027a0 +0xcdbc168 -> 0xcf59f401745131171435049990_inner_var_663 -> 663 -> 0xcecf4b0 +0xcdbc180 -> 0xcf59f401745131171435049990_inner_var_662 -> 662 -> 0xcae2480 +0xcae4948 -> 0xcf59f401745131171435049990_inner_var_811 -> 811 -> 0xcf34430 +0xd055540 -> 0xcf59f401745131171435049990_inner_var_657 -> 657 -> 0xcf2c6b0 +0xca9bc00 -> 0xcf59f401745131171435049990_inner_var_744 -> 744 -> 0xcf27c80 +0xd056248 -> 0xcf59f401745131171435049990_inner_var_655 -> 655 -> 0xca8f450 +0xca9bc30 -> 0xcf59f401745131171435049990_inner_var_742 -> 742 -> 0xd03ab70 +0xd056278 -> 0xcf59f401745131171435049990_inner_var_653 -> 653 -> 0xd03da00 +0xca9bc48 -> 0xcf59f401745131171435049990_inner_var_741 -> 741 -> 0xca910c0 +0xd056290 -> 0xcf59f401745131171435049990_inner_var_652 -> 652 -> 0xcf38de0 +0xca9bc60 -> 0xcf59f401745131171435049990_inner_var_740 -> 740 -> 0xcfcad00 +0xd0562a8 -> 0xcf59f401745131171435049990_inner_var_651 -> 651 -> 0xcea71c0 +0xccbd058 -> 0xcf59f401745131171435049990_inner_var_858 -> 858 -> 0xcaa5610 +0xd055940 -> 0xcf59f401745131171435049990_inner_var_650 -> 650 -> 0xca621e0 +0xccd7a00 -> 0xcf59f401745131171435049990_inner_var_923 -> 923 -> 0xcebdad0 +0xd054060 -> 0xcf59f401745131171435049990_inner_var_649 -> 649 -> 0xd041750 +0xd0551a0 -> 0xcf59f401745131171435049990_inner_var_646 -> 646 -> 0xcf39480 +0xccc0e20 -> 0xcf59f401745131171435049990_inner_var_1026 -> 1026 -> 0xcaaca20 +0xd0551b8 -> 0xcf59f401745131171435049990_inner_var_645 -> 645 -> 0xcf73560 +0xd0551d0 -> 0xcf59f401745131171435049990_inner_var_644 -> 644 -> 0xcee4230 +0xd0551e8 -> 0xcf59f401745131171435049990_inner_var_643 -> 643 -> 0xcefff70 +0xced6910 -> 0xcf59f401745131171435049990_inner_var_726 -> 726 -> 0xca82ce0 +0xd053c90 -> 0xcf59f401745131171435049990_inner_var_639 -> 639 -> 0xccc0a20 +0xccc83b0 -> 0xcf59f401745131171435049990_inner_var_1068 -> 1068 -> 0xcfbce40 +0xd053ca8 -> 0xcf59f401745131171435049990_inner_var_638 -> 638 -> 0xcef7de0 +0xd053cc0 -> 0xcf59f401745131171435049990_inner_var_637 -> 637 -> 0xced4c00 +0xd053cf0 -> 0xcf59f401745131171435049990_inner_var_635 -> 635 -> 0xcf41960 +0xccbd860 -> 0xcf59f401745131171435049990_inner_var_1013 -> 1013 -> 0xcbed380 +0xca520b0 -> 0xcf59f401745131171435049990_inner_var_632 -> 632 -> 0xcec0d50 +0xd052ad0 -> 0xcf59f401745131171435049990_inner_var_631 -> 631 -> 0xceb7710 +0xccc71f0 -> 0xcf59f401745131171435049990_inner_var_1056 -> 1056 -> 0xcaac2e0 +0xd052ae8 -> 0xcf59f401745131171435049990_inner_var_630 -> 630 -> 0xce8d6a0 +0xee8bb20 -> 0xcf59f401745131171435049990_inner_var_804 -> 804 -> 0xccd05a0 +0xd052b00 -> 0xcf59f401745131171435049990_inner_var_629 -> 629 -> 0xccbed40 +0xee8bb38 -> 0xcf59f401745131171435049990_inner_var_803 -> 803 -> 0xcf00390 +0xd052b18 -> 0xcf59f401745131171435049990_inner_var_628 -> 628 -> 0xcec27d0 +0xee8bb68 -> 0xcf59f401745131171435049990_inner_var_801 -> 801 -> 0xcefbce0 +0xd052b48 -> 0xcf59f401745131171435049990_inner_var_626 -> 626 -> 0xd076d00 +0xd051ea0 -> 0xcf59f401745131171435049990_inner_var_625 -> 625 -> 0xcad50a0 +0xce8dd10 -> 0xcf59f401745131171435049990_inner_var_896 -> 896 -> 0xcf0cdf0 +0xcae6190 -> 0xcf59f401745131171435049990_inner_var_830 -> 830 -> 0xccc0b50 +0xca52cf0 -> 0xcf59f401745131171435049990_inner_var_624 -> 624 -> 0xceaedf0 +0xca53c10 -> 0xcf59f401745131171435049990_inner_var_623 -> 623 -> 0xcee6000 +0xca53c28 -> 0xcf59f401745131171435049990_inner_var_622 -> 622 -> 0xcf1c190 +0xca53c40 -> 0xcf59f401745131171435049990_inner_var_621 -> 621 -> 0xcf6ce80 +0xca53c88 -> 0xcf59f401745131171435049990_inner_var_618 -> 618 -> 0xceb8980 +0xd048050 -> 0xcf59f401745131171435049990_inner_var_702 -> 702 -> 0xd0344d0 +0xca53250 -> 0xcf59f401745131171435049990_inner_var_617 -> 617 -> 0xccd1710 +0xca4fe20 -> 0xcf59f401745131171435049990_inner_var_616 -> 616 -> 0xca8f7e0 +0xca52680 -> 0xcf59f401745131171435049990_inner_var_614 -> 614 -> 0xcdbd150 +0xca52698 -> 0xcf59f401745131171435049990_inner_var_613 -> 613 -> 0xcaddfc0 +0xca526c8 -> 0xcf59f401745131171435049990_inner_var_611 -> 611 -> 0xcf5b640 +0xca84410 -> 0xcf59f401745131171435049990_inner_var_1010 -> 1010 -> 0xcbec720 +0xca526f8 -> 0xcf59f401745131171435049990_inner_var_609 -> 609 -> 0xca98d50 +0xca84850 -> 0xcf59f401745131171435049990_inner_var_1012 -> 1012 -> 0xcbecf60 +0xca50890 -> 0xcf59f401745131171435049990_inner_var_607 -> 607 -> 0xcf15d30 +0xccc1280 -> 0xcf59f401745131171435049990_inner_var_1027 -> 1027 -> 0xcaacdf0 +0xca51580 -> 0xcf59f401745131171435049990_inner_var_606 -> 606 -> 0xee71db0 +0xca51598 -> 0xcf59f401745131171435049990_inner_var_605 -> 605 -> 0xcad27d0 +0xd0533a0 -> 0xcf59f401745131171435049990_inner_var_633 -> 633 -> 0xcee5560 +0xca515b0 -> 0xcf59f401745131171435049990_inner_var_604 -> 604 -> 0xccb23e0 +0xca515c8 -> 0xcf59f401745131171435049990_inner_var_603 -> 603 -> 0xcefd140 +0xd049fe0 -> 0xcf59f401745131171435049990_inner_var_713 -> 713 -> 0xce9bd80 +0xca50c90 -> 0xcf59f401745131171435049990_inner_var_600 -> 600 -> 0xceb8250 +0xca50398 -> 0xcf59f401745131171435049990_inner_var_597 -> 597 -> 0xccb5920 +0xee45cc0 -> 0xcf59f401745131171435049990_inner_var_773 -> 773 -> 0xca6a0e0 +0xca503b0 -> 0xcf59f401745131171435049990_inner_var_596 -> 596 -> 0xcf1fa00 +0xca503f8 -> 0xcf59f401745131171435049990_inner_var_593 -> 593 -> 0xcea7e20 +0xcdba7f0 -> 0xcf59f401745131171435049990_inner_var_992 -> 992 -> 0xcbe6240 +0xca4f6d0 -> 0xcf59f401745131171435049990_inner_var_592 -> 592 -> 0xced6750 +0xccdc930 -> 0xcf59f401745131171435049990_inner_var_944 -> 944 -> 0xceff540 +0xcaa0bf0 -> 0xcf59f401745131171435049990_inner_var_591 -> 591 -> 0xd060cb0 +0xcaa18f8 -> 0xcf59f401745131171435049990_inner_var_588 -> 588 -> 0xed1b2d0 +0xcaa1910 -> 0xcf59f401745131171435049990_inner_var_587 -> 587 -> 0xcf22ec0 +0xcaa1928 -> 0xcf59f401745131171435049990_inner_var_586 -> 586 -> 0xcab6ec0 +0xcaa1940 -> 0xcf59f401745131171435049990_inner_var_585 -> 585 -> 0xca8d7c0 +0xcaa1958 -> 0xcf59f401745131171435049990_inner_var_584 -> 584 -> 0xced5ae0 +0xca9fb30 -> 0xcf59f401745131171435049990_inner_var_582 -> 582 -> 0xccbdb90 +0xcae7fc8 -> 0xcf59f401745131171435049990_inner_var_834 -> 834 -> 0xca616f0 +0xcaa0820 -> 0xcf59f401745131171435049990_inner_var_581 -> 581 -> 0xcca6620 +0xcae7fe0 -> 0xcf59f401745131171435049990_inner_var_833 -> 833 -> 0xca93340 +0xcaa0838 -> 0xcf59f401745131171435049990_inner_var_580 -> 580 -> 0xccc5af0 +0xcae7ff8 -> 0xcf59f401745131171435049990_inner_var_832 -> 832 -> 0xccd79a0 +0xcaa0850 -> 0xcf59f401745131171435049990_inner_var_579 -> 579 -> 0xca6bd30 +0xee45f30 -> 0xcf59f401745131171435049990_inner_var_754 -> 754 -> 0xd05a7c0 +0xcaa0868 -> 0xcf59f401745131171435049990_inner_var_578 -> 578 -> 0xd0415b0 +0xd0361a0 -> 0xcf59f401745131171435049990_inner_var_981 -> 981 -> 0xcbe5180 +0xcaa0880 -> 0xcf59f401745131171435049990_inner_var_577 -> 577 -> 0xca8c400 +0xd0361b8 -> 0xcf59f401745131171435049990_inner_var_980 -> 980 -> 0xcbe4d40 +0xccda330 -> 0xcf59f401745131171435049990_inner_var_942 -> 942 -> 0xceebb60 +0xcaa0898 -> 0xcf59f401745131171435049990_inner_var_576 -> 576 -> 0xcefc390 +0xca9ff30 -> 0xcf59f401745131171435049990_inner_var_575 -> 575 -> 0xcedf140 +0xca9eda0 -> 0xcf59f401745131171435049990_inner_var_567 -> 567 -> 0xd048710 +0xcadf3d0 -> 0xcf59f401745131171435049990_inner_var_566 -> 566 -> 0xcebf980 +0xca9e1b0 -> 0xcf59f401745131171435049990_inner_var_564 -> 564 -> 0xcf7ffc0 +0xca9e1e0 -> 0xcf59f401745131171435049990_inner_var_562 -> 562 -> 0xcf9a7b0 +0xca9e1f8 -> 0xcf59f401745131171435049990_inner_var_561 -> 561 -> 0xee3fb80 +0xccd9f50 -> 0xcf59f401745131171435049990_inner_var_933 -> 933 -> 0xceb9ec0 +0xca9e210 -> 0xcf59f401745131171435049990_inner_var_560 -> 560 -> 0xccc9660 +0xca9e228 -> 0xcf59f401745131171435049990_inner_var_559 -> 559 -> 0xcef0070 +0xd053d08 -> 0xcf59f401745131171435049990_inner_var_634 -> 634 -> 0xcfa0f80 +0xcae3110 -> 0xcf59f401745131171435049990_inner_var_558 -> 558 -> 0xcabcf60 +0xcae2c00 -> 0xcf59f401745131171435049990_inner_var_557 -> 557 -> 0xca60560 +0xcae2c30 -> 0xcf59f401745131171435049990_inner_var_555 -> 555 -> 0xcce0c70 +0xcae2c78 -> 0xcf59f401745131171435049990_inner_var_552 -> 552 -> 0xcca1c90 +0xcaf0090 -> 0xcf59f401745131171435049990_inner_var_550 -> 550 -> 0xcebdb50 +0xd049dc0 -> 0xcf59f401745131171435049990_inner_var_712 -> 712 -> 0xee40740 +0xcaf0f00 -> 0xcf59f401745131171435049990_inner_var_549 -> 549 -> 0xca9d030 +0xcaf0f30 -> 0xcf59f401745131171435049990_inner_var_547 -> 547 -> 0xce9cd30 +0xcaf0f60 -> 0xcf59f401745131171435049990_inner_var_545 -> 545 -> 0xee747c0 +0xcaea780 -> 0xcf59f401745131171435049990_inner_var_849 -> 849 -> 0xed1b940 +0xcdbdeb0 -> 0xcf59f401745131171435049990_inner_var_675 -> 675 -> 0xcebaab0 +0xcaf0f78 -> 0xcf59f401745131171435049990_inner_var_544 -> 544 -> 0xcc9f700 +0xca68ee0 -> 0xcf59f401745131171435049990_inner_var_963 -> 963 -> 0xcbe0940 +0xccdd130 -> 0xcf59f401745131171435049990_inner_var_948 -> 948 -> 0xcfc20c0 +0xcf73e00 -> 0xcf59f401745131171435049990_inner_var_717 -> 717 -> 0xcead7d0 +0xccad6c0 -> 0xcf59f401745131171435049990_inner_var_542 -> 542 -> 0xcef52c0 +0xee87490 -> 0xcf59f401745131171435049990_inner_var_770 -> 770 -> 0xcf097c0 +0xcaf9b00 -> batch_norm2d_21.b_0_deepcopy_109 -> 224 -> 0xcf23400 +0xcaf9730 -> batch_norm2d_21.w_1_deepcopy_110 -> 223 -> 0xcf9c4b0 +0xcaf9360 -> batch_norm2d_21.w_2_deepcopy_111 -> 222 -> 0xd05c7d0 +0xccce5c0 -> 0xcf59f401745131171435049990_inner_var_484 -> 484 -> 0xccdbe30 +0xcaf5a20 -> batch_norm2d_16.w_1_deepcopy_85 -> 248 -> 0xcfa6d50 +0xcaf2df0 -> batch_norm2d_15.w_1_deepcopy_80 -> 253 -> 0xcfcce30 +0xccd3ba0 -> batch_norm2d_18.w_0_deepcopy_93 -> 240 -> 0xcfa68e0 +0xccd9180 -> 0xcf59f401745131171435049990_inner_var_928 -> 928 -> 0xcec9a50 +0xcaf83d0 -> batch_norm2d_22.w_1_deepcopy_115 -> 218 -> 0xd05e730 +0xcaf2650 -> conv2d_16.w_0_deepcopy_82 -> 251 -> 0xcf536b0 +0xccd3400 -> batch_norm2d_18.w_1_deepcopy_95 -> 238 -> 0xcf84390 +0xca51a50 -> 0xcf59f401745131171435049990_inner_var_608 -> 608 -> 0xcf1d860 +0xcae2c48 -> 0xcf59f401745131171435049990_inner_var_554 -> 554 -> 0xcad4be0 +0xcaf6430 -> conv2d_21.w_0_deepcopy_107 -> 226 -> 0xcf41b60 +0xcaf8f90 -> conv2d_22.w_0_deepcopy_112 -> 221 -> 0xcfa5f90 +0xcdbc620 -> 0xcf59f401745131171435049990_inner_var_665 -> 665 -> 0xca78840 +0xccce1f0 -> 0xcf59f401745131171435049990_inner_var_474 -> 474 -> 0xcf36750 +0xccb2580 -> 0xcf59f401745131171435049990_inner_var_975 -> 975 -> 0xcbe2420 +0xcaf87a0 -> batch_norm2d_22.b_0_deepcopy_114 -> 219 -> 0xcf3fa10 +0xd049dd8 -> 0xcf59f401745131171435049990_inner_var_711 -> 711 -> 0xced67d0 +0xcaf0f18 -> 0xcf59f401745131171435049990_inner_var_548 -> 548 -> 0xca83730 +0xcaf31c0 -> batch_norm2d_15.b_0_deepcopy_79 -> 254 -> 0xcfcb200 +0xccd3f70 -> conv2d_18.w_0_deepcopy_92 -> 241 -> 0xcf22d20 +0xcdb5f70 -> batch_norm2d_22.w_2_deepcopy_116 -> 217 -> 0xd05f3f0 +0xee72b90 -> constant_folding@_174513116882724144161 -> 3 -> 0xcdb8360 +0xcdb4bc0 -> batch_norm2d_23.w_2_deepcopy_121 -> 212 -> 0xd062570 +0xcdc1f10 -> batch_norm2d_24.b_0_deepcopy_124 -> 209 -> 0xd062fb0 +0xca76600 -> conv2d_52.w_0_deepcopy_254 -> 83 -> 0xcfc1c10 +0xccc6720 -> 0xcf59f401745131171435049990_inner_var_1052 -> 1052 -> 0xcaab530 +0xca9e1c8 -> 0xcf59f401745131171435049990_inner_var_563 -> 563 -> 0xd03c730 +0xcdc1b40 -> batch_norm2d_24.w_1_deepcopy_125 -> 208 -> 0xd065590 +0xcdc13a0 -> conv2d_25.w_0_deepcopy_127 -> 206 -> 0xcf91990 +0xcdc0fd0 -> batch_norm2d_25.w_0_deepcopy_128 -> 205 -> 0xd067f70 +0xd0588f0 -> 0xcf59f401745131171435049990_inner_var_368 -> 368 -> 0xccd76c0 +0xcdc0830 -> batch_norm2d_25.w_1_deepcopy_130 -> 203 -> 0xd069110 +0xcdc04a0 -> batch_norm2d_25.w_2_deepcopy_131 -> 202 -> 0xd069fd0 +0xcdb8e48 -> 0xcf59f401745131171435049990_inner_var_340 -> 340 -> 0xd0606b0 +0xce92490 -> 0xcf59f401745131171435049990_inner_var_915 -> 915 -> 0xceac080 +0xcc9bc50 -> conv2d_26.w_0_deepcopy_132 -> 201 -> 0xcf71280 +0xd04e850 -> batch_norm2d_46.w_0_deepcopy_240 -> 97 -> 0xcf93910 +0xc80d3d0 -> batch_norm2d_36.w_0_deepcopy_183 -> 150 -> 0xd065350 +0xee8cd20 -> 0xcf59f401745131171435049990_inner_var_813 -> 813 -> 0xcee8540 +0xee8aa78 -> 0xcf59f401745131171435049990_inner_var_795 -> 795 -> 0xcf124f0 +0xcc98998 -> 0xcf59f401745131171435049990_inner_var_397 -> 397 -> 0xceee010 +0xcc9b0e0 -> batch_norm2d_26.w_1_deepcopy_135 -> 198 -> 0xd06c980 +0xcfad320 -> batch_norm2d_41.b_0_deepcopy_209 -> 124 -> 0xcf8ca40 +0xee74908 -> 0xcf59f401745131171435049990_inner_var_379 -> 379 -> 0xcec7d50 +0xd0412b0 -> constant_folding@_174513116833656139834 -> 27 -> 0xcf419a0 +0xd03a860 -> 0xcf59f401745131171435049990_inner_var_999 -> 999 -> 0xcbe99c0 +0xccb7a08 -> 0xcf59f401745131171435049990_inner_var_429 -> 429 -> 0xcab60d0 +0xcc9cbc0 -> batch_norm2d_28.b_0_deepcopy_144 -> 189 -> 0xd071b20 +0xcf6d200 -> constant_folding@_17451311436834671136 -> 52 -> 0xcf820a0 +0xca9e840 -> 0xcf59f401745131171435049990_inner_var_574 -> 574 -> 0xca8f060 +0xcaf5280 -> conv2d_17.w_0_deepcopy_87 -> 246 -> 0xcf17680 +0xee73358 -> 0xcf59f401745131171435049990_inner_var_370 -> 370 -> 0xce97c00 +0xca84428 -> 0xcf59f401745131171435049990_inner_var_1009 -> 1009 -> 0xcbec2e0 +0xcfab5d0 -> batch_norm2d_30.b_0_deepcopy_154 -> 179 -> 0xcf28de0 +0xee45a10 -> 0xcf59f401745131171435049990_inner_var_967 -> 967 -> 0xcbe15a0 +0xcaf4ae0 -> batch_norm2d_17.b_0_deepcopy_89 -> 244 -> 0xcfa8c60 +0xcc9d730 -> batch_norm2d_27.w_2_deepcopy_141 -> 192 -> 0xd06fa30 +0xd047430 -> 0xcf59f401745131171435049990_inner_var_710 -> 710 -> 0xbbe8160 +0xcaee570 -> batch_norm2d_10.b_0_deepcopy_54 -> 279 -> 0xcf51350 +0xccb4b20 -> 0xcf59f401745131171435049990_inn +I0420 14:39:33.226711 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:33.491245 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:33.665498 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "builtin.constant" [id:2016] () {origin_id:2014,persistable:[true],value:"constant_folding@_174513116887061917064"} : () -> cpu_tensor<3xi64> + (%1) = "builtin.constant" [id:2017] () {origin_id:2005,persistable:[true],value:"constant_folding@_174513116885637196163"} : () -> cpu_tensor + (%2) = "builtin.parameter" [id:2018] () {origin_id:1996,parameter_name:"constant_folding@_174513116884162097162",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor + (%3) = "builtin.constant" [id:2019] () {origin_id:1987,persistable:[true],value:"constant_folding@_174513116882724144161"} : () -> cpu_tensor<1xf32> + (%4) = "builtin.constant" [id:2020] () {origin_id:1978,persistable:[true],value:"constant_folding@_174513116880873974160"} : () -> cpu_tensor + (%5) = "builtin.constant" [id:2021] () {origin_id:1956,persistable:[true],value:"constant_folding@_174513116876880248258"} : () -> cpu_tensor<1xi32> + (%6) = "builtin.constant" [id:2022] () {origin_id:1947,persistable:[true],value:"constant_folding@_174513116875198908257"} : () -> cpu_tensor + (%7) = "builtin.constant" [id:2023] () {origin_id:1925,persistable:[true],value:"constant_folding@_174513116872098526255"} : () -> cpu_tensor + (%8) = "builtin.constant" [id:2024] () {origin_id:1912,persistable:[true],value:"constant_folding@_174513116870501220354"} : () -> cpu_tensor + (%9) = "builtin.constant" [id:2025] () {origin_id:1899,persistable:[true],value:"constant_folding@_174513116869046783353"} : () -> cpu_tensor + (%10) = "builtin.constant" [id:2026] () {origin_id:1890,persistable:[true],value:"constant_folding@_174513116867667180352"} : () -> cpu_tensor<0xi64> + (%11) = "builtin.parameter" [id:2027] () {origin_id:1881,parameter_name:"constant_folding@_174513116865440940351",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor + (%12) = "builtin.constant" [id:2028] () {origin_id:1872,persistable:[true],value:"constant_folding@_174513116864033493350"} : () -> cpu_tensor<1xf32> + (%13) = "builtin.constant" [id:2029] () {origin_id:1863,persistable:[true],value:"constant_folding@_174513116861634597449"} : () -> cpu_tensor + (%14) = "builtin.parameter" [id:2030] () {origin_id:1854,parameter_name:"constant_folding@_174513116859911589448",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor<1x256x1x1xf32> + (%15) = "builtin.constant" [id:2031] () {origin_id:1832,persistable:[true],value:"constant_folding@_174513116857122500446"} : () -> cpu_tensor<0xi64> + (%16) = "builtin.parameter" [id:2032] () {origin_id:1823,parameter_name:"constant_folding@_174513116855594796545",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor<1x1x1xf32> + (%17) = "builtin.constant" [id:2033] () {origin_id:1814,persistable:[true],value:"constant_folding@_174513116853662943544"} : () -> cpu_tensor + (%18) = "builtin.constant" [id:2034] () {origin_id:1805,persistable:[true],value:"constant_folding@_174513116852268476543"} : () -> cpu_tensor<1xf32> + (%19) = "builtin.constant" [id:2035] () {origin_id:1796,persistable:[true],value:"constant_folding@_174513116850011146642"} : () -> cpu_tensor<1xf32> + (%20) = "builtin.constant" [id:2036] () {origin_id:1784,persistable:[true],value:"constant_folding@_174513116844224746641"} : () -> cpu_tensor + (%21) = "builtin.constant" [id:2037] () {origin_id:1771,persistable:[true],value:"constant_folding@_174513116842705804740"} : () -> cpu_tensor + (%22) = "builtin.constant" [id:2038] () {origin_id:1758,persistable:[true],value:"constant_folding@_174513116841278885739"} : () -> cpu_tensor<0xi64> + (%23) = "builtin.constant" [id:2039] () {origin_id:1749,persistable:[true],value:"constant_folding@_174513116839857308738"} : () -> cpu_tensor<1xi32> + (%24) = "builtin.constant" [id:2040] () {origin_id:1740,persistable:[true],value:"constant_folding@_174513116838085921737"} : () -> cpu_tensor<1xf32> + (%25) = "builtin.parameter" [id:2041] () {origin_id:1731,parameter_name:"constant_folding@_174513116836671179736",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor + (%26) = "builtin.constant" [id:2042] () {origin_id:1722,persistable:[true],value:"constant_folding@_174513116835231166835"} : () -> cpu_tensor<1xf32> + (%27) = "builtin.parameter" [id:2043] () {origin_id:1713,parameter_name:"constant_folding@_174513116833656139834",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor<1xi32> + (%28) = "builtin.parameter" [id:2044] () {origin_id:1693,parameter_name:"constant_folding@_174513116830651898832",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor<1x6xf32> + (%29) = "builtin.parameter" [id:2045] () {origin_id:1673,parameter_name:"constant_folding@_174513116827593559930",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor + (%30) = "builtin.constant" [id:2046] () {origin_id:1664,persistable:[true],value:"constant_folding@_174513116825929047929"} : () -> cpu_tensor + (%31) = "builtin.constant" [id:2047] () {origin_id:1655,persistable:[true],value:"constant_folding@_174513116824495087928"} : () -> cpu_tensor + (%32) = "builtin.constant" [id:2048] () {origin_id:1646,persistable:[true],value:"constant_folding@_174513116823069183927"} : () -> cpu_tensor<1xf32> + (%33) = "builtin.constant" [id:2049] () {origin_id:1637,persistable:[true],value:"constant_folding@_174513116821619066926"} : () -> cpu_tensor<1xf32> + (%34) = "builtin.constant" [id:2050] () {origin_id:1628,persistable:[true],value:"constant_folding@_174513116820193818025"} : () -> cpu_tensor<1xf32> + (%35) = "builtin.constant" [id:2051] () {origin_id:1619,persistable:[true],value:"constant_folding@_174513116818740431024"} : () -> cpu_tensor<1xf32> + (%36) = "builtin.constant" [id:2052] () {origin_id:1610,persistable:[true],value:"constant_folding@_174513116817322536023"} : () -> cpu_tensor<1xi64> + (%37) = "builtin.constant" [id:2053] () {origin_id:1601,persistable:[true],value:"constant_folding@_174513116815886735022"} : () -> cpu_tensor<1xf32> + (%38) = "builtin.constant" [id:2054] () {origin_id:1592,persistable:[true],value:"constant_folding@_174513116814471704121"} : () -> cpu_tensor<2xi64> + (%39) = "builtin.constant" [id:2055] () {origin_id:1583,persistable:[true],value:"constant_folding@_174513116813053746120"} : () -> cpu_tensor<2xi64> + (%40) = "builtin.parameter" [id:2056] () {origin_id:1574,parameter_name:"constant_folding@_174513116811615103119",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor + (%41) = "builtin.constant" [id:2057] () {origin_id:1565,persistable:[true],value:"constant_folding@_174513115258237754718"} : () -> cpu_tensor<1xi64> + (%42) = "builtin.constant" [id:2058] () {origin_id:1556,persistable:[true],value:"constant_folding@_174513115176548053017"} : () -> cpu_tensor<1xi64> + (%43) = "builtin.constant" [id:2059] () {origin_id:1547,persistable:[true],value:"constant_folding@_174513115110199844916"} : () -> cpu_tensor<1xi64> + (%44) = "builtin.constant" [id:2060] () {origin_id:1538,persistable:[true],value:"constant_folding@_174513115013851471515"} : () -> cpu_tensor<1xi64> + (%45) = "builtin.constant" [id:2061] () {origin_id:1529,persistable:[true],value:"constant_folding@_174513114941937302314"} : () -> cpu_tensor<2xi64> + (%46) = "builtin.parameter" [id:2062] () {origin_id:1520,parameter_name:"constant_folding@_174513114857650263913",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor<1x15x4xf32> + (%47) = "builtin.constant" [id:2063] () {origin_id:1498,persistable:[true],value:"constant_folding@_174513114736420657211"} : () -> cpu_tensor<3xi64> + (%48) = "builtin.constant" [id:2064] () {origin_id:1489,persistable:[true],value:"constant_folding@_174513114667774590410"} : () -> cpu_tensor<1xi64> + (%49) = "builtin.constant" [id:2065] () {origin_id:1480,persistable:[true],value:"constant_folding@_17451311460158745859"} : () -> cpu_tensor<1xf32> + (%50) = "builtin.constant" [id:2066] () {origin_id:1471,persistable:[true],value:"constant_folding@_17451311452954254518"} : () -> cpu_tensor<1xi64> + (%51) = "builtin.constant" [id:2067] () {origin_id:1462,persistable:[true],value:"constant_folding@_17451311445836590377"} : () -> cpu_tensor<1xi64> + (%52) = "builtin.constant" [id:2068] () {origin_id:1453,persistable:[true],value:"constant_folding@_17451311436834671136"} : () -> cpu_tensor<1xi64> + (%53) = "builtin.parameter" [id:2069] () {origin_id:1444,parameter_name:"constant_folding@_17451311430626338225",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor<1x60x1x1xf32> + (%54) = "builtin.parameter" [id:2070] () {origin_id:1431,parameter_name:"constant_folding@_17451311422710820934",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor<1x15x1x1xf32> + (%55) = "builtin.parameter" [id:2071] () {origin_id:1418,parameter_name:"constant_folding@_17451311415972965563",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor<1x1024x1x1xf32> + (%56) = "builtin.parameter" [id:2072] () {origin_id:1405,parameter_name:"constant_folding@_17451311399980650772",persistable:[true],stop_gradient:[false]} : () -> custom_device_tensor<1x80x1x1xf32> + (%57) = "builtin.constant" [id:2073] () {origin_id:1383,persistable:[true],value:"constant_folding@_17451311284029886470"} : () -> cpu_tensor<2xi64> + (%58) = "builtin.parameter" [id:2074] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:8,parameter_name:"conv2d_56.w_0_deepcopy_280",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<80x256x1x1xf32> + (%59) = "builtin.parameter" [id:2075] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:10,parameter_name:"conv2d_transpose_0.w_0_deepcopy_278",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<2048x256x2x2xf32> + (%60) = "builtin.parameter" [id:2076] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:11,parameter_name:"linear_1.b_0_deepcopy_277",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<320xf32> + (%61) = "builtin.parameter" [id:2077] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:12,parameter_name:"linear_1.w_0_deepcopy_276",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<2048x320xf32> + (%62) = "builtin.parameter" [id:2078] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:13,parameter_name:"linear_0.b_0_deepcopy_275",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<81xf32> + (%63) = "builtin.parameter" [id:2079] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:14,parameter_name:"linear_0.w_0_deepcopy_274",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<2048x81xf32> + (%64) = "builtin.parameter" [id:2080] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:15,parameter_name:"batch_norm2d_52.w_2_deepcopy_273",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%65) = "builtin.parameter" [id:2081] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:16,parameter_name:"batch_norm2d_52.w_1_deepcopy_272",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%66) = "builtin.parameter" [id:2082] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:17,parameter_name:"batch_norm2d_52.b_0_deepcopy_271",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%67) = "builtin.parameter" [id:2083] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:18,parameter_name:"batch_norm2d_52.w_0_deepcopy_270",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%68) = "builtin.parameter" [id:2084] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:19,parameter_name:"conv2d_55.w_0_deepcopy_269",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<2048x512x1x1xf32> + (%69) = "builtin.parameter" [id:2085] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:20,parameter_name:"batch_norm2d_51.w_2_deepcopy_268",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%70) = "builtin.parameter" [id:2086] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:21,parameter_name:"batch_norm2d_51.w_1_deepcopy_267",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%71) = "builtin.parameter" [id:2087] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:22,parameter_name:"batch_norm2d_51.b_0_deepcopy_266",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%72) = "builtin.parameter" [id:2088] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:23,parameter_name:"batch_norm2d_51.w_0_deepcopy_265",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%73) = "builtin.parameter" [id:2089] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:24,parameter_name:"conv2d_54.w_0_deepcopy_264",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x512x3x3xf32> + (%74) = "builtin.parameter" [id:2090] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:25,parameter_name:"batch_norm2d_50.w_2_deepcopy_263",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%75) = "builtin.parameter" [id:2091] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:26,parameter_name:"batch_norm2d_50.w_1_deepcopy_262",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%76) = "builtin.parameter" [id:2092] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:27,parameter_name:"batch_norm2d_50.b_0_deepcopy_261",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%77) = "builtin.parameter" [id:2093] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:28,parameter_name:"batch_norm2d_50.w_0_deepcopy_260",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%78) = "builtin.parameter" [id:2094] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:29,parameter_name:"conv2d_53.w_0_deepcopy_259",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x2048x1x1xf32> + (%79) = "builtin.parameter" [id:2095] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:30,parameter_name:"batch_norm2d_49.w_2_deepcopy_258",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%80) = "builtin.parameter" [id:2096] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:31,parameter_name:"batch_norm2d_49.w_1_deepcopy_257",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%81) = "builtin.parameter" [id:2097] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:32,parameter_name:"batch_norm2d_49.b_0_deepcopy_256",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%82) = "builtin.parameter" [id:2098] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:33,parameter_name:"batch_norm2d_49.w_0_deepcopy_255",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%83) = "builtin.parameter" [id:2099] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:34,parameter_name:"conv2d_52.w_0_deepcopy_254",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<2048x512x1x1xf32> + (%84) = "builtin.parameter" [id:2100] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:35,parameter_name:"batch_norm2d_48.w_2_deepcopy_253",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%85) = "builtin.parameter" [id:2101] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:36,parameter_name:"batch_norm2d_48.w_1_deepcopy_252",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%86) = "builtin.parameter" [id:2102] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:37,parameter_name:"batch_norm2d_48.b_0_deepcopy_251",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%87) = "builtin.parameter" [id:2103] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:38,parameter_name:"batch_norm2d_48.w_0_deepcopy_250",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%88) = "builtin.parameter" [id:2104] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:39,parameter_name:"conv2d_51.w_0_deepcopy_249",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x512x3x3xf32> + (%89) = "builtin.parameter" [id:2105] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:40,parameter_name:"batch_norm2d_47.w_2_deepcopy_248",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%90) = "builtin.parameter" [id:2106] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:41,parameter_name:"batch_norm2d_47.w_1_deepcopy_247",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%91) = "builtin.parameter" [id:2107] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:42,parameter_name:"batch_norm2d_47.b_0_deepcopy_246",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%92) = "builtin.parameter" [id:2108] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:43,parameter_name:"batch_norm2d_47.w_0_deepcopy_245",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%93) = "builtin.parameter" [id:2109] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:44,parameter_name:"conv2d_50.w_0_deepcopy_244",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x2048x1x1xf32> + (%94) = "builtin.parameter" [id:2110] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:45,parameter_name:"batch_norm2d_46.w_2_deepcopy_243",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%95) = "builtin.parameter" [id:2111] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:46,parameter_name:"batch_norm2d_46.w_1_deepcopy_242",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%96) = "builtin.parameter" [id:2112] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:47,parameter_name:"batch_norm2d_46.b_0_deepcopy_241",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%97) = "builtin.parameter" [id:2113] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:48,parameter_name:"batch_norm2d_46.w_0_deepcopy_240",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%98) = "builtin.parameter" [id:2114] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:49,parameter_name:"conv2d_49.w_0_deepcopy_239",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<2048x1024x1x1xf32> + (%99) = "builtin.parameter" [id:2115] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:50,parameter_name:"batch_norm2d_45.w_2_deepcopy_238",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%100) = "builtin.parameter" [id:2116] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:51,parameter_name:"batch_norm2d_45.w_1_deepcopy_237",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%101) = "builtin.parameter" [id:2117] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:52,parameter_name:"batch_norm2d_45.b_0_deepcopy_236",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%102) = "builtin.parameter" [id:2118] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:53,parameter_name:"batch_norm2d_45.w_0_deepcopy_235",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<2048xf32> + (%103) = "builtin.parameter" [id:2119] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:54,parameter_name:"conv2d_48.w_0_deepcopy_234",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<2048x512x1x1xf32> + (%104) = "builtin.parameter" [id:2120] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:55,parameter_name:"batch_norm2d_44.w_2_deepcopy_233",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%105) = "builtin.parameter" [id:2121] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:56,parameter_name:"batch_norm2d_44.w_1_deepcopy_232",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%106) = "builtin.parameter" [id:2122] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:57,parameter_name:"batch_norm2d_44.b_0_deepcopy_231",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%107) = "builtin.parameter" [id:2123] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:58,parameter_name:"batch_norm2d_44.w_0_deepcopy_230",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%108) = "builtin.parameter" [id:2124] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:59,parameter_name:"conv2d_47.w_0_deepcopy_229",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x512x3x3xf32> + (%109) = "builtin.parameter" [id:2125] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:60,parameter_name:"batch_norm2d_43.w_2_deepcopy_228",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%110) = "builtin.parameter" [id:2126] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:61,parameter_name:"batch_norm2d_43.w_1_deepcopy_227",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%111) = "builtin.parameter" [id:2127] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:62,parameter_name:"batch_norm2d_43.b_0_deepcopy_226",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%112) = "builtin.parameter" [id:2128] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:63,parameter_name:"batch_norm2d_43.w_0_deepcopy_225",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<512xf32> + (%113) = "builtin.parameter" [id:2129] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:64,parameter_name:"conv2d_46.w_0_deepcopy_224",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<512x1024x1x1xf32> + (%114) = "builtin.parameter" [id:2130] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:66,parameter_name:"conv2d_45.w_0_deepcopy_222",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<60x1024x1x1xf32> + (%115) = "builtin.parameter" [id:2131] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:68,parameter_name:"conv2d_44.w_0_deepcopy_220",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<15x1024x1x1xf32> + (%116) = "builtin.parameter" [id:2132] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:70,parameter_name:"conv2d_43.w_0_deepcopy_218",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<1024x1024x3x3xf32> + (%117) = "builtin.parameter" [id:2133] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:71,parameter_name:"batch_norm2d_42.w_2_deepcopy_216",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%118) = "builtin.parameter" [id:2134] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:72,parameter_name:"batch_norm2d_42.w_1_deepcopy_215",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%119) = "builtin.parameter" [id:2135] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:73,parameter_name:"batch_norm2d_42.b_0_deepcopy_214",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%120) = "builtin.parameter" [id:2136] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:74,parameter_name:"batch_norm2d_42.w_0_deepcopy_213",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%121) = "builtin.parameter" [id:2137] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:75,parameter_name:"conv2d_42.w_0_deepcopy_212",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<1024x256x1x1xf32> + (%122) = "builtin.parameter" [id:2138] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:76,parameter_name:"batch_norm2d_41.w_2_deepcopy_211",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%123) = "builtin.parameter" [id:2139] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:77,parameter_name:"batch_norm2d_41.w_1_deepcopy_210",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%124) = "builtin.parameter" [id:2140] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:78,parameter_name:"batch_norm2d_41.b_0_deepcopy_209",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%125) = "builtin.parameter" [id:2141] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:79,parameter_name:"batch_norm2d_41.w_0_deepcopy_208",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%126) = "builtin.parameter" [id:2142] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:80,parameter_name:"conv2d_41.w_0_deepcopy_207",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<256x256x3x3xf32> + (%127) = "builtin.parameter" [id:2143] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:81,parameter_name:"batch_norm2d_40.w_2_deepcopy_206",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%128) = "builtin.parameter" [id:2144] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:82,parameter_name:"batch_norm2d_40.w_1_deepcopy_205",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%129) = "builtin.parameter" [id:2145] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:83,parameter_name:"batch_norm2d_40.b_0_deepcopy_204",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%130) = "builtin.parameter" [id:2146] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:84,parameter_name:"batch_norm2d_40.w_0_deepcopy_203",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<256xf32> + (%131) = "builtin.parameter" [id:2147] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:85,parameter_name:"conv2d_40.w_0_deepcopy_202",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> custom_device_tensor<256x1024x1x1xf32> + (%132) = "builtin.parameter" [id:2148] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:86,parameter_name:"batch_norm2d_39.w_2_deepcopy_201",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%133) = "builtin.parameter" [id:2149] () {is_distributed:[false],is_parameter:[true],need_clip:[true],origin_id:87,parameter_name:"batch_norm2d_39.w_1_deepcopy_200",persistable:[true],stop_gradient:[true],trainable:[false]} : () -> custom_device_tensor<1024xf32> + (%134) = "builtin.parame +I0420 14:39:33.667184 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 335 ) = pd_op.conv2d ( 331 ) ( 333 ) +1: ( 327 ) ( 328 ) ( 341 ) ( 340 ) ( 339 ) ( 338 ) ( 337 ) ( 336 ) = pd_op.batch_norm_ ( 329 ) ( 330 ) ( 327 ) ( 328 ) ( 335 ) +2: ( 342 ) = pd_op.relu ( 336 ) +3: ( 343 ) = pd_op.transpose ( 342 ) +4: ( 344 ) = pd_op.pool2d ( 57 ) ( 343 ) +5: ( 345 ) = pd_op.transpose ( 344 ) +6: ( 346 ) = pd_op.conv2d ( 326 ) ( 345 ) +7: ( 323 ) ( 352 ) ( 351 ) ( 350 ) ( 322 ) ( 349 ) ( 348 ) ( 347 ) = pd_op.batch_norm_ ( 324 ) ( 322 ) ( 323 ) ( 325 ) ( 346 ) +8: ( 353 ) = pd_op.relu ( 347 ) +9: ( 354 ) = pd_op.conv2d ( 321 ) ( 353 ) +10: ( 318 ) ( 360 ) ( 317 ) ( 359 ) ( 358 ) ( 357 ) ( 356 ) ( 355 ) = pd_op.batch_norm_ ( 319 ) ( 317 ) ( 320 ) ( 318 ) ( 354 ) +11: ( 361 ) = pd_op.relu ( 355 ) +12: ( 362 ) = pd_op.conv2d ( 316 ) ( 361 ) +13: ( 312 ) ( 368 ) ( 367 ) ( 366 ) ( 365 ) ( 313 ) ( 364 ) ( 363 ) = pd_op.batch_norm_ ( 314 ) ( 315 ) ( 312 ) ( 313 ) ( 362 ) +14: ( 369 ) = pd_op.conv2d ( 311 ) ( 345 ) +15: ( 307 ) ( 308 ) ( 375 ) ( 374 ) ( 373 ) ( 372 ) ( 371 ) ( 370 ) = pd_op.batch_norm_ ( 309 ) ( 310 ) ( 308 ) ( 307 ) ( 369 ) +16: ( 376 ) = pd_op.add ( 370 ) ( 363 ) +17: ( 377 ) = pd_op.relu ( 376 ) +18: ( 378 ) = pd_op.conv2d ( 306 ) ( 377 ) +19: ( 302 ) ( 384 ) ( 383 ) ( 382 ) ( 303 ) ( 381 ) ( 380 ) ( 379 ) = pd_op.batch_norm_ ( 304 ) ( 305 ) ( 302 ) ( 303 ) ( 378 ) +20: ( 385 ) = pd_op.relu ( 379 ) +21: ( 386 ) = pd_op.conv2d ( 301 ) ( 385 ) +22: ( 297 ) ( 392 ) ( 391 ) ( 390 ) ( 389 ) ( 298 ) ( 388 ) ( 387 ) = pd_op.batch_norm_ ( 299 ) ( 300 ) ( 297 ) ( 298 ) ( 386 ) +23: ( 393 ) = pd_op.relu ( 387 ) +24: ( 394 ) = pd_op.conv2d ( 296 ) ( 393 ) +25: ( 293 ) ( 400 ) ( 399 ) ( 398 ) ( 397 ) ( 396 ) ( 292 ) ( 395 ) = pd_op.batch_norm_ ( 294 ) ( 295 ) ( 293 ) ( 292 ) ( 394 ) +26: ( 401 ) = pd_op.add ( 377 ) ( 395 ) +27: ( 402 ) = pd_op.relu ( 401 ) +28: ( 403 ) = pd_op.conv2d ( 291 ) ( 402 ) +29: ( 288 ) ( 409 ) ( 408 ) ( 287 ) ( 407 ) ( 406 ) ( 405 ) ( 404 ) = pd_op.batch_norm_ ( 289 ) ( 290 ) ( 287 ) ( 288 ) ( 403 ) +30: ( 410 ) = pd_op.relu ( 404 ) +31: ( 411 ) = pd_op.conv2d ( 286 ) ( 410 ) +32: ( 283 ) ( 417 ) ( 282 ) ( 416 ) ( 415 ) ( 414 ) ( 413 ) ( 412 ) = pd_op.batch_norm_ ( 284 ) ( 285 ) ( 282 ) ( 283 ) ( 411 ) +33: ( 418 ) = pd_op.relu ( 412 ) +34: ( 419 ) = pd_op.conv2d ( 281 ) ( 418 ) +35: ( 278 ) ( 425 ) ( 424 ) ( 423 ) ( 422 ) ( 421 ) ( 277 ) ( 420 ) = pd_op.batch_norm_ ( 279 ) ( 280 ) ( 277 ) ( 278 ) ( 419 ) +36: ( 426 ) = pd_op.add ( 402 ) ( 420 ) +37: ( 427 ) = pd_op.relu ( 426 ) +38: ( 428 ) = pd_op.conv2d ( 276 ) ( 427 ) +39: ( 272 ) ( 434 ) ( 433 ) ( 273 ) ( 432 ) ( 431 ) ( 430 ) ( 429 ) = pd_op.batch_norm_ ( 274 ) ( 272 ) ( 273 ) ( 275 ) ( 428 ) +40: ( 435 ) = pd_op.relu ( 429 ) +41: ( 436 ) = pd_op.conv2d ( 271 ) ( 435 ) +42: ( 267 ) ( 442 ) ( 441 ) ( 440 ) ( 439 ) ( 438 ) ( 268 ) ( 437 ) = pd_op.batch_norm_ ( 269 ) ( 270 ) ( 267 ) ( 268 ) ( 436 ) +43: ( 443 ) = pd_op.relu ( 437 ) +44: ( 444 ) = pd_op.conv2d ( 266 ) ( 443 ) +45: ( 262 ) ( 450 ) ( 449 ) ( 263 ) ( 448 ) ( 447 ) ( 446 ) ( 445 ) = pd_op.batch_norm_ ( 264 ) ( 262 ) ( 263 ) ( 265 ) ( 444 ) +46: ( 451 ) = pd_op.conv2d ( 261 ) ( 427 ) +47: ( 257 ) ( 258 ) ( 457 ) ( 456 ) ( 455 ) ( 454 ) ( 453 ) ( 452 ) = pd_op.batch_norm_ ( 259 ) ( 260 ) ( 257 ) ( 258 ) ( 451 ) +48: ( 458 ) = pd_op.add ( 452 ) ( 445 ) +49: ( 459 ) = pd_op.relu ( 458 ) +50: ( 460 ) = pd_op.conv2d ( 256 ) ( 459 ) +51: ( 252 ) ( 253 ) ( 466 ) ( 465 ) ( 464 ) ( 463 ) ( 462 ) ( 461 ) = pd_op.batch_norm_ ( 254 ) ( 255 ) ( 252 ) ( 253 ) ( 460 ) +52: ( 467 ) = pd_op.relu ( 461 ) +53: ( 468 ) = pd_op.conv2d ( 251 ) ( 467 ) +54: ( 247 ) ( 474 ) ( 248 ) ( 473 ) ( 472 ) ( 471 ) ( 470 ) ( 469 ) = pd_op.batch_norm_ ( 249 ) ( 250 ) ( 247 ) ( 248 ) ( 468 ) +55: ( 475 ) = pd_op.relu ( 469 ) +56: ( 476 ) = pd_op.conv2d ( 246 ) ( 475 ) +57: ( 243 ) ( 482 ) ( 481 ) ( 480 ) ( 242 ) ( 479 ) ( 478 ) ( 477 ) = pd_op.batch_norm_ ( 244 ) ( 245 ) ( 242 ) ( 243 ) ( 476 ) +58: ( 483 ) = pd_op.add ( 459 ) ( 477 ) +59: ( 484 ) = pd_op.relu ( 483 ) +60: ( 485 ) = pd_op.conv2d ( 241 ) ( 484 ) +61: ( 237 ) ( 491 ) ( 490 ) ( 489 ) ( 488 ) ( 487 ) ( 238 ) ( 486 ) = pd_op.batch_norm_ ( 239 ) ( 240 ) ( 237 ) ( 238 ) ( 485 ) +62: ( 492 ) = pd_op.relu ( 486 ) +63: ( 493 ) = pd_op.conv2d ( 236 ) ( 492 ) +64: ( 232 ) ( 499 ) ( 233 ) ( 498 ) ( 497 ) ( 496 ) ( 495 ) ( 494 ) = pd_op.batch_norm_ ( 234 ) ( 235 ) ( 232 ) ( 233 ) ( 493 ) +65: ( 500 ) = pd_op.relu ( 494 ) +66: ( 501 ) = pd_op.conv2d ( 231 ) ( 500 ) +67: ( 228 ) ( 507 ) ( 506 ) ( 505 ) ( 227 ) ( 504 ) ( 503 ) ( 502 ) = pd_op.batch_norm_ ( 229 ) ( 230 ) ( 227 ) ( 228 ) ( 501 ) +68: ( 508 ) = pd_op.add ( 484 ) ( 502 ) +69: ( 509 ) = pd_op.relu ( 508 ) +70: ( 510 ) = pd_op.conv2d ( 226 ) ( 509 ) +71: ( 222 ) ( 223 ) ( 516 ) ( 515 ) ( 514 ) ( 513 ) ( 512 ) ( 511 ) = pd_op.batch_norm_ ( 224 ) ( 225 ) ( 222 ) ( 223 ) ( 510 ) +72: ( 517 ) = pd_op.relu ( 511 ) +73: ( 518 ) = pd_op.conv2d ( 221 ) ( 517 ) +74: ( 217 ) ( 218 ) ( 524 ) ( 523 ) ( 522 ) ( 521 ) ( 520 ) ( 519 ) = pd_op.batch_norm_ ( 219 ) ( 220 ) ( 217 ) ( 218 ) ( 518 ) +75: ( 525 ) = pd_op.relu ( 519 ) +76: ( 526 ) = pd_op.conv2d ( 216 ) ( 525 ) +77: ( 212 ) ( 532 ) ( 213 ) ( 531 ) ( 530 ) ( 529 ) ( 528 ) ( 527 ) = pd_op.batch_norm_ ( 214 ) ( 212 ) ( 213 ) ( 215 ) ( 526 ) +78: ( 533 ) = pd_op.add ( 509 ) ( 527 ) +79: ( 534 ) = pd_op.relu ( 533 ) +80: ( 535 ) = pd_op.conv2d ( 211 ) ( 534 ) +81: ( 207 ) ( 541 ) ( 540 ) ( 539 ) ( 208 ) ( 538 ) ( 537 ) ( 536 ) = pd_op.batch_norm_ ( 209 ) ( 210 ) ( 207 ) ( 208 ) ( 535 ) +82: ( 542 ) = pd_op.relu ( 536 ) +83: ( 543 ) = pd_op.conv2d ( 206 ) ( 542 ) +84: ( 202 ) ( 549 ) ( 548 ) ( 547 ) ( 546 ) ( 545 ) ( 203 ) ( 544 ) = pd_op.batch_norm_ ( 204 ) ( 205 ) ( 202 ) ( 203 ) ( 543 ) +85: ( 550 ) = pd_op.relu ( 544 ) +86: ( 551 ) = pd_op.conv2d ( 201 ) ( 550 ) +87: ( 197 ) ( 557 ) ( 198 ) ( 556 ) ( 555 ) ( 554 ) ( 553 ) ( 552 ) = pd_op.batch_norm_ ( 199 ) ( 200 ) ( 197 ) ( 198 ) ( 551 ) +88: ( 558 ) = pd_op.conv2d ( 196 ) ( 534 ) +89: ( 192 ) ( 564 ) ( 563 ) ( 193 ) ( 562 ) ( 561 ) ( 560 ) ( 559 ) = pd_op.batch_norm_ ( 194 ) ( 195 ) ( 192 ) ( 193 ) ( 558 ) +90: ( 565 ) = pd_op.add ( 559 ) ( 552 ) +91: ( 566 ) = pd_op.relu ( 565 ) +92: ( 567 ) = pd_op.conv2d ( 191 ) ( 566 ) +93: ( 187 ) ( 573 ) ( 572 ) ( 571 ) ( 570 ) ( 569 ) ( 188 ) ( 568 ) = pd_op.batch_norm_ ( 189 ) ( 190 ) ( 187 ) ( 188 ) ( 567 ) +94: ( 574 ) = pd_op.relu ( 568 ) +95: ( 575 ) = pd_op.conv2d ( 186 ) ( 574 ) +96: ( 182 ) ( 183 ) ( 581 ) ( 580 ) ( 579 ) ( 578 ) ( 577 ) ( 576 ) = pd_op.batch_norm_ ( 184 ) ( 185 ) ( 182 ) ( 183 ) ( 575 ) +97: ( 582 ) = pd_op.relu ( 576 ) +98: ( 583 ) = pd_op.conv2d ( 181 ) ( 582 ) +99: ( 177 ) ( 178 ) ( 589 ) ( 588 ) ( 587 ) ( 586 ) ( 585 ) ( 584 ) = pd_op.batch_norm_ ( 179 ) ( 177 ) ( 178 ) ( 180 ) ( 583 ) +100: ( 590 ) = pd_op.add ( 566 ) ( 584 ) +101: ( 591 ) = pd_op.relu ( 590 ) +102: ( 592 ) = pd_op.conv2d ( 176 ) ( 591 ) +103: ( 598 ) ( 597 ) ( 596 ) ( 172 ) ( 173 ) ( 595 ) ( 594 ) ( 593 ) = pd_op.batch_norm_ ( 174 ) ( 175 ) ( 172 ) ( 173 ) ( 592 ) +104: ( 599 ) = pd_op.relu ( 593 ) +105: ( 600 ) = pd_op.conv2d ( 171 ) ( 599 ) +106: ( 168 ) ( 606 ) ( 605 ) ( 604 ) ( 603 ) ( 167 ) ( 602 ) ( 601 ) = pd_op.batch_norm_ ( 169 ) ( 170 ) ( 167 ) ( 168 ) ( 600 ) +107: ( 607 ) = pd_op.relu ( 601 ) +108: ( 608 ) = pd_op.conv2d ( 166 ) ( 607 ) +109: ( 162 ) ( 163 ) ( 614 ) ( 613 ) ( 612 ) ( 611 ) ( 610 ) ( 609 ) = pd_op.batch_norm_ ( 164 ) ( 165 ) ( 162 ) ( 163 ) ( 608 ) +110: ( 615 ) = pd_op.add ( 591 ) ( 609 ) +111: ( 616 ) = pd_op.relu ( 615 ) +112: ( 617 ) = pd_op.conv2d ( 161 ) ( 616 ) +113: ( 157 ) ( 623 ) ( 622 ) ( 621 ) ( 620 ) ( 619 ) ( 158 ) ( 618 ) = pd_op.batch_norm_ ( 159 ) ( 160 ) ( 157 ) ( 158 ) ( 617 ) +114: ( 624 ) = pd_op.relu ( 618 ) +115: ( 625 ) = pd_op.conv2d ( 156 ) ( 624 ) +116: ( 153 ) ( 631 ) ( 630 ) ( 152 ) ( 629 ) ( 628 ) ( 627 ) ( 626 ) = pd_op.batch_norm_ ( 154 ) ( 155 ) ( 152 ) ( 153 ) ( 625 ) +117: ( 632 ) = pd_op.relu ( 626 ) +118: ( 633 ) = pd_op.conv2d ( 151 ) ( 632 ) +119: ( 147 ) ( 148 ) ( 639 ) ( 638 ) ( 637 ) ( 636 ) ( 635 ) ( 634 ) = pd_op.batch_norm_ ( 149 ) ( 147 ) ( 150 ) ( 148 ) ( 633 ) +120: ( 640 ) = pd_op.add ( 616 ) ( 634 ) +121: ( 641 ) = pd_op.relu ( 640 ) +122: ( 642 ) = pd_op.conv2d ( 146 ) ( 641 ) +123: ( 143 ) ( 648 ) ( 647 ) ( 646 ) ( 645 ) ( 644 ) ( 142 ) ( 643 ) = pd_op.batch_norm_ ( 144 ) ( 145 ) ( 142 ) ( 143 ) ( 642 ) +124: ( 649 ) = pd_op.relu ( 643 ) +125: ( 650 ) = pd_op.conv2d ( 141 ) ( 649 ) +126: ( 138 ) ( 656 ) ( 655 ) ( 654 ) ( 653 ) ( 652 ) ( 137 ) ( 651 ) = pd_op.batch_norm_ ( 139 ) ( 137 ) ( 140 ) ( 138 ) ( 650 ) +127: ( 657 ) = pd_op.relu ( 651 ) +128: ( 658 ) = pd_op.conv2d ( 136 ) ( 657 ) +129: ( 132 ) ( 133 ) ( 664 ) ( 663 ) ( 662 ) ( 661 ) ( 660 ) ( 659 ) = pd_op.batch_norm_ ( 134 ) ( 135 ) ( 132 ) ( 133 ) ( 658 ) +130: ( 665 ) = pd_op.add ( 641 ) ( 659 ) +131: ( 666 ) = pd_op.relu ( 665 ) +132: ( 667 ) = pd_op.conv2d ( 131 ) ( 666 ) +133: ( 673 ) ( 672 ) ( 128 ) ( 671 ) ( 127 ) ( 670 ) ( 669 ) ( 668 ) = pd_op.batch_norm_ ( 129 ) ( 130 ) ( 127 ) ( 128 ) ( 667 ) +134: ( 674 ) = pd_op.relu ( 668 ) +135: ( 675 ) = pd_op.conv2d ( 126 ) ( 674 ) +136: ( 122 ) ( 123 ) ( 681 ) ( 680 ) ( 679 ) ( 678 ) ( 677 ) ( 676 ) = pd_op.batch_norm_ ( 124 ) ( 125 ) ( 122 ) ( 123 ) ( 675 ) +137: ( 682 ) = pd_op.relu ( 676 ) +138: ( 683 ) = pd_op.conv2d ( 121 ) ( 682 ) +139: ( 117 ) ( 118 ) ( 689 ) ( 688 ) ( 687 ) ( 686 ) ( 685 ) ( 684 ) = pd_op.batch_norm_ ( 120 ) ( 117 ) ( 118 ) ( 119 ) ( 683 ) +140: ( 690 ) = pd_op.add ( 666 ) ( 684 ) +141: ( 691 ) = pd_op.relu ( 690 ) +142: ( 692 ) = pd_op.conv2d ( 116 ) ( 691 ) +143: ( 693 ) = pd_op.add ( 55 ) ( 692 ) +144: ( 694 ) = pd_op.relu ( 693 ) +145: ( 695 ) = pd_op.conv2d ( 115 ) ( 694 ) +146: ( 696 ) = pd_op.add ( 54 ) ( 695 ) +147: ( 697 ) = pd_op.conv2d ( 114 ) ( 694 ) +148: ( 698 ) = pd_op.add ( 53 ) ( 697 ) +149: ( 699 ) = pd_op.shape64 ( 694 ) +150: ( 700 ) = pd_op.slice ( 51 ) ( 52 ) ( 699 ) +151: ( 701 ) = pd_op.slice ( 50 ) ( 51 ) ( 699 ) +152: ( 701 ) ( 702 ) = pd_op.scale_ ( 49 ) ( 701 ) +153: ( 703 ) = pd_op.full +154: ( 704 ) = pd_op.cast ( 702 ) +155: ( 705 ) = pd_op.arange ( 49 ) ( 704 ) ( 703 ) +156: ( 700 ) ( 706 ) = pd_op.scale_ ( 49 ) ( 700 ) +157: ( 707 ) = pd_op.cast ( 706 ) +158: ( 708 ) = pd_op.arange ( 49 ) ( 707 ) ( 703 ) +159: ( 709 708 705 ) = builtin_combine_instruction ( 705 ) ( 708 ) +160: ( 710 711 712 ) = pd_op.meshgrid ( 709 708 705 ) +161: ( 712 ) ( 713 ) = pd_op.reshape_ ( 48 ) ( 712 ) +162: ( 711 ) ( 714 ) = pd_op.reshape_ ( 48 ) ( 711 ) +163: ( 715 713 714 713 714 ) = builtin_combine_instruction ( 714 ) ( 713 ) +164: ( 716 ) = pd_op.stack ( 715 713 714 713 714 ) +165: ( 716 ) ( 717 ) = pd_op.reshape_ ( 47 ) ( 716 ) +166: ( 718 ) = pd_op.add ( 46 ) ( 717 ) +167: ( 718 ) ( 719 ) = pd_op.reshape_ ( 45 ) ( 718 ) +168: ( 720 ) = pd_op.shape64 ( 332 ) +169: ( 721 ) = pd_op.slice ( 43 ) ( 44 ) ( 720 ) +170: ( 722 ) = pd_op.arange ( 721 ) ( 41 ) ( 42 ) +171: ( 723 ) = pd_op.shape64 ( 722 ) +172: ( 724 ) = pd_op.slice ( 43 ) ( 44 ) ( 723 ) +173: ( 725 ) = pd_op.create_array +174: ( 726 ) = pd_op.create_array +175: ( 727 ) = pd_op.full +176: ( 728 ) = pd_op.memcpy_h2d ( 724 ) +177: ( 729 ) = pd_op.less_than ( 728 ) ( 727 ) +178: ( 730 ) = pd_op.full +179: ( 731 ) = pd_op.full +180: ( 732 ) = pd_op.full +181: ( 733 ) = pd_op.full +182: ( 734 ) = pd_op.full +183: ( 735 ) = pd_op.full +184: ( 736 ) = pd_op.full +185: ( 737 ) = pd_op.full +186: ( 738 ) = pd_op.full +187: ( 725 ) ( 726 ) ( 748 ) ( 747 ) ( 746 ) ( 744 ) ( 743 ) ( 742 ) ( 745 ) ( 741 ) ( 740 ) ( 739 ) = while_instruction ( 724 ) ( 332 ) ( 725 ) ( 19 ) ( 722 ) ( 726 ) ( 729 ) ( 733 ) ( 727 ) ( 736 ) ( 730 ) ( 731 ) ( 732 ) ( 735 ) ( 734 ) ( 719 ) ( 737 ) ( 698 ) ( 738 ) ( 696 ) ( 44 ) ( 43 ) +188: ( 750 ) ( 749 ) = pd_op.array_to_tensor ( 725 ) +189: ( 751 ) = pd_op.array_length ( 726 ) +190: ( 752 ) = pd_op.memcpy_h2d ( 751 ) +191: ( 753 ) = pd_op.greater_than ( 40 ) ( 752 ) +192: ( 754 ) = if_instruction ( 44 ) ( 726 ) ( 753 ) +193: ( 755 ) = pd_op.cast ( 749 ) +194: ( 756 ) = pd_op.memcpy_h2d ( 755 ) +195: ( 757 ) = pd_op.roi_align ( 756 ) ( 754 ) ( 691 ) +196: ( 758 ) = pd_op.conv2d ( 113 ) ( 757 ) +197: ( 109 ) ( 110 ) ( 764 ) ( 763 ) ( 762 ) ( 761 ) ( 760 ) ( 759 ) = pd_op.batch_norm_ ( 112 ) ( 109 ) ( 110 ) ( 111 ) ( 758 ) +198: ( 759 ) ( 765 ) = pd_op.relu_ ( 759 ) +199: ( 766 ) = pd_op.conv2d ( 108 ) ( 765 ) +200: ( 772 ) ( 771 ) ( 770 ) ( 104 ) ( 769 ) ( 768 ) ( 105 ) ( 767 ) = pd_op.batch_norm_ ( 106 ) ( 107 ) ( 104 ) ( 105 ) ( 766 ) +201: ( 767 ) ( 773 ) = pd_op.relu_ ( 767 ) +202: ( 774 ) = pd_op.conv2d ( 103 ) ( 773 ) +203: ( 99 ) ( 100 ) ( 780 ) ( 779 ) ( 778 ) ( 777 ) ( 776 ) ( 775 ) = pd_op.batch_norm_ ( 101 ) ( 102 ) ( 99 ) ( 100 ) ( 774 ) +204: ( 781 ) = pd_op.conv2d ( 98 ) ( 757 ) +205: ( 787 ) ( 94 ) ( 786 ) ( 785 ) ( 95 ) ( 784 ) ( 783 ) ( 782 ) = pd_op.batch_norm_ ( 96 ) ( 97 ) ( 94 ) ( 95 ) ( 781 ) +206: ( 775 ) ( 788 ) = pd_op.add_ ( 782 ) ( 775 ) +207: ( 788 ) ( 789 ) = pd_op.relu_ ( 788 ) +208: ( 790 ) = pd_op.conv2d ( 93 ) ( 789 ) +209: ( 89 ) ( 90 ) ( 796 ) ( 795 ) ( 794 ) ( 793 ) ( 792 ) ( 791 ) = pd_op.batch_norm_ ( 91 ) ( 92 ) ( 89 ) ( 90 ) ( 790 ) +210: ( 791 ) ( 797 ) = pd_op.relu_ ( 791 ) +211: ( 798 ) = pd_op.conv2d ( 88 ) ( 797 ) +212: ( 85 ) ( 804 ) ( 803 ) ( 802 ) ( 801 ) ( 800 ) ( 84 ) ( 799 ) = pd_op.batch_norm_ ( 86 ) ( 87 ) ( 84 ) ( 85 ) ( 798 ) +213: ( 799 ) ( 805 ) = pd_op.relu_ ( 799 ) +214: ( 806 ) = pd_op.conv2d ( 83 ) ( 805 ) +215: ( 79 ) ( 812 ) ( 811 ) ( 80 ) ( 810 ) ( 809 ) ( 808 ) ( 807 ) = pd_op.batch_norm_ ( 81 ) ( 82 ) ( 79 ) ( 80 ) ( 806 ) +216: ( 807 ) ( 813 ) = pd_op.add_ ( 789 ) ( 807 ) +217: ( 813 ) ( 814 ) = pd_op.relu_ ( 813 ) +218: ( 815 ) = pd_op.conv2d ( 78 ) ( 814 ) +219: ( 74 ) ( 821 ) ( 820 ) ( 819 ) ( 75 ) ( 818 ) ( 817 ) ( 816 ) = pd_op.batch_norm_ ( 74 ) ( 76 ) ( 77 ) ( 75 ) ( 815 ) +220: ( 816 ) ( 822 ) = pd_op.relu_ ( 816 ) +221: ( 823 ) = pd_op.conv2d ( 73 ) ( 822 ) +222: ( 829 ) ( 69 ) ( 70 ) ( 828 ) ( 827 ) ( 826 ) ( 825 ) ( 824 ) = pd_op.batch_norm_ ( 71 ) ( 72 ) ( 69 ) ( 70 ) ( 823 ) +223: ( 824 ) ( 830 ) = pd_op.relu_ ( 824 ) +224: ( 831 ) = pd_op.conv2d ( 68 ) ( 830 ) +225: ( 64 ) ( 65 ) ( 837 ) ( 836 ) ( 835 ) ( 834 ) ( 833 ) ( 832 ) = pd_op.batch_norm_ ( 66 ) ( 67 ) ( 64 ) ( 65 ) ( 831 ) +226: ( 832 ) ( 838 ) = pd_op.add_ ( 814 ) ( 832 ) +227: ( 838 ) ( 839 ) = pd_op.relu_ ( 838 ) +228: ( 840 ) = pd_op.pool2d ( 39 ) ( 839 ) +229: ( 840 ) ( 841 ) = pd_op.squeeze_ ( 38 ) ( 840 ) +230: ( 842 ) = pd_op.matmul ( 63 ) ( 841 ) +231: ( 842 ) ( 843 ) = pd_op.add_ ( 62 ) ( 842 ) +232: ( 844 ) = pd_op.matmul ( 61 ) ( 841 ) +233: ( 844 ) ( 845 ) = pd_op.add_ ( 60 ) ( 844 ) +234: ( 843 ) ( 846 ) = pd_op.softmax_ ( 843 ) +235: ( 847 ) = pd_op.slice ( 43 ) ( 44 ) ( 720 ) +236: ( 848 ) = pd_op.arange ( 41 ) ( 847 ) ( 42 ) +237: ( 849 ) = pd_op.shape64 ( 848 ) +238: ( 850 ) = pd_op.slice ( 43 ) ( 44 ) ( 849 ) +239: ( 851 ) = pd_op.create_array +240: ( 852 ) = pd_op.full +241: ( 853 ) = pd_op.memcpy_h2d ( 850 ) +242: ( 854 ) = pd_op.less_than ( 853 ) ( 852 ) +243: ( 855 ) = pd_op.full +244: ( 856 ) = pd_op.full +245: ( 857 ) = pd_op.full +246: ( 851 ) ( 861 ) ( 860 ) ( 859 ) ( 858 ) = while_instruction ( 851 ) ( 18 ) ( 17 ) ( 332 ) ( 749 ) ( 848 ) ( 857 ) ( 856 ) ( 855 ) ( 850 ) ( 852 ) ( 854 ) +247: ( 863 ) ( 862 ) = pd_op.array_to_tensor ( 851 ) +248: ( 865 ) ( 864 ) = pd_op.array_to_tensor ( 726 ) +249: ( 866 ) = pd_op.slice ( 51 ) ( 52 ) ( 864 ) +250: ( 867 ) = pd_op.slice ( 43 ) ( 44 ) ( 864 ) +251: ( 868 ) = pd_op.subtract ( 867 ) ( 866 ) +252: ( 869 ) = pd_op.slice ( 50 ) ( 51 ) ( 864 ) +253: ( 870 ) = pd_op.slice ( 52 ) ( 43 ) ( 864 ) +254: ( 871 ) = pd_op.subtract ( 870 ) ( 869 ) +255: ( 872 ) = pd_op.slice ( 43 ) ( 44 ) ( 864 ) +256: ( 873 ) = pd_op.scale ( 37 ) ( 868 ) +257: ( 874 ) = pd_op.add ( 873 ) ( 872 ) +258: ( 875 ) = pd_op.slice ( 52 ) ( 43 ) ( 864 ) +259: ( 876 ) = pd_op.scale ( 37 ) ( 871 ) +260: ( 877 ) = pd_op.add ( 876 ) ( 875 ) +261: ( 878 ) = pd_op.strided_slice ( 50 ) ( 36 ) ( 44 ) ( 845 ) +262: ( 878 ) ( 879 ) = pd_op.scale_ ( 35 ) ( 878 ) +263: ( 880 ) = pd_op.strided_slice ( 50 ) ( 36 ) ( 43 ) ( 845 ) +264: ( 880 ) ( 881 ) = pd_op.scale_ ( 35 ) ( 880 ) +265: ( 882 ) = pd_op.strided_slice ( 36 ) ( 50 ) ( 52 ) ( 845 ) +266: ( 882 ) ( 883 ) = pd_op.scale_ ( 34 ) ( 882 ) +267: ( 884 ) = pd_op.strided_slice ( 36 ) ( 50 ) ( 51 ) ( 845 ) +268: ( 884 ) ( 885 ) = pd_op.scale_ ( 34 ) ( 884 ) +269: ( 883 ) ( 886 ) = pd_op.clip_ ( 32 ) ( 33 ) ( 883 ) +270: ( 885 ) ( 887 ) = pd_op.clip_ ( 32 ) ( 33 ) ( 885 ) +271: ( 888 ) = pd_op.unsqueeze ( 43 ) ( 868 ) +272: ( 889 ) = pd_op.memcpy_h2d ( 888 ) +273: ( 879 ) ( 890 ) = pd_op.multiply_ ( 889 ) ( 879 ) +274: ( 874 ) ( 891 ) = pd_op.unsqueeze_ ( 43 ) ( 874 ) +275: ( 892 ) = pd_op.memcpy_h2d ( 891 ) +276: ( 890 ) ( 893 ) = pd_op.add_ ( 892 ) ( 890 ) +277: ( 894 ) = pd_op.unsqueeze ( 43 ) ( 871 ) +278: ( 895 ) = pd_op.memcpy_h2d ( 894 ) +279: ( 881 ) ( 896 ) = pd_op.multiply_ ( 895 ) ( 881 ) +280: ( 877 ) ( 897 ) = pd_op.unsqueeze_ ( 43 ) ( 877 ) +281: ( 898 ) = pd_op.memcpy_h2d ( 897 ) +282: ( 896 ) ( 899 ) = pd_op.add_ ( 898 ) ( 896 ) +283: ( 886 ) ( 900 ) = pd_op.exp_ ( 886 ) +284: ( 868 ) ( 901 ) = pd_op.unsqueeze_ ( 43 ) ( 868 ) +285: ( 902 ) = pd_op.memcpy_h2d ( 901 ) +286: ( 900 ) ( 903 ) = pd_op.multiply_ ( 902 ) ( 900 ) +287: ( 887 ) ( 904 ) = pd_op.exp_ ( 887 ) +288: ( 871 ) ( 905 ) = pd_op.unsqueeze_ ( 43 ) ( 871 ) +289: ( 906 ) = pd_op.memcpy_h2d ( 905 ) +290: ( 904 ) ( 907 ) = pd_op.multiply_ ( 906 ) ( 904 ) +291: ( 908 ) = pd_op.scale ( 37 ) ( 903 ) +292: ( 909 ) = pd_op.subtract ( 908 ) ( 893 ) +293: ( 910 ) = pd_op.scale ( 37 ) ( 907 ) +294: ( 911 ) = pd_op.subtract ( 910 ) ( 899 ) +295: ( 903 ) ( 912 ) = pd_op.scale_ ( 37 ) ( 903 ) +296: ( 893 ) ( 913 ) = pd_op.add_ ( 912 ) ( 893 ) +297: ( 907 ) ( 914 ) = pd_op.scale_ ( 37 ) ( 907 ) +298: ( 899 ) ( 915 ) = pd_op.add_ ( 914 ) ( 899 ) +299: ( 916 909 911 913 915 ) = builtin_combine_instruction ( 915 ) ( 913 ) ( 911 ) ( 909 ) +300: ( 917 ) = pd_op.stack ( 916 909 911 913 915 ) +301: ( 918 ) = pd_op.slice ( 48 ) ( 44 ) ( 846 ) +302: ( 919 ) = pd_op.shape64 ( 917 ) +303: ( 920 ) = pd_op.slice ( 43 ) ( 44 ) ( 919 ) +304: ( 921 920 31 30 ) = builtin_combine_instruction ( 30 ) ( 31 ) ( 920 ) +305: ( 922 ) = pd_op.stack ( 921 920 31 30 ) +306: ( 923 ) = pd_op.expand ( 922 ) ( 917 ) +307: ( 924 ) = pd_op.slice ( 43 ) ( 44 ) ( 862 ) +308: ( 924 ) ( 925 ) = pd_op.unsqueeze_ ( 43 ) ( 924 ) +309: ( 926 ) = pd_op.slice ( 52 ) ( 43 ) ( 862 ) +310: ( 926 ) ( 927 ) = pd_op.unsqueeze_ ( 43 ) ( 926 ) +311: ( 928 ) = pd_op.full_like ( 703 ) ( 925 ) +312: ( 929 ) = pd_op.slice ( 43 ) ( 44 ) ( 923 ) +313: ( 930 ) = pd_op.memcpy_h2d ( 927 ) +314: ( 931 ) = pd_op.minimum ( 930 ) ( 929 ) +315: ( 932 ) = pd_op.memcpy_h2d ( 928 ) +316: ( 933 ) = pd_op.maximum ( 932 ) ( 931 ) +317: ( 934 ) = pd_op.slice ( 52 ) ( 43 ) ( 923 ) +318: ( 935 ) = pd_op.memcpy_h2d ( 925 ) +319: ( 936 ) = pd_op.minimum ( 935 ) ( 934 ) +320: ( 937 ) = pd_op.memcpy_h2d ( 928 ) +321: ( 938 ) = pd_op.maximum ( 937 ) ( 936 ) +322: ( 939 ) = pd_op.slice ( 51 ) ( 52 ) ( 923 ) +323: ( 940 ) = pd_op.memcpy_h2d ( 927 ) +324: ( 941 ) = pd_op.minimum ( 940 ) ( 939 ) +325: ( 942 ) = pd_op.memcpy_h2d ( 928 ) +326: ( 943 ) = pd_op.maximum ( 942 ) ( 941 ) +327: ( 944 ) = pd_op.slice ( 50 ) ( 51 ) ( 923 ) +328: ( 945 ) = pd_op.memcpy_h2d ( 925 ) +329: ( 946 ) = pd_op.minimum ( 945 ) ( 944 ) +330: ( 947 ) = pd_op.memcpy_h2d ( 928 ) +331: ( 948 ) = pd_op.maximum ( 947 ) ( 946 ) +332: ( 949 933 938 943 948 ) = builtin_combine_instruction ( 948 ) ( 943 ) ( 938 ) ( 933 ) +333: ( 950 ) = pd_op.stack ( 949 933 938 943 948 ) +334: ( 951 ) = pd_op.memcpy_d2h ( 950 ) +335: ( 952 ) = pd_op.memcpy_d2h ( 918 ) +336: ( 955 ) ( 954 ) ( 953 ) = pd_op.multiclass_nms3 ( 749 ) ( 952 ) ( 951 ) +337: ( 956 ) = pd_op.shape64 ( 953 ) +338: ( 957 ) = pd_op.slice ( 43 ) ( 44 ) ( 956 ) +339: ( 958 ) = pd_op.memcpy_h2d ( 957 ) +340: ( 959 ) = pd_op.equal ( 29 ) ( 958 ) +341: ( 960 ) = if_instruction ( 953 ) ( 84 ) ( 99 ) ( 92 ) ( 96 ) ( 97 ) ( 94 ) ( 100 ) ( 69 ) ( 36 ) ( 108 ) ( 66 ) ( 78 ) ( 75 ) ( 77 ) ( 104 ) ( 73 ) ( 70 ) ( 105 ) ( 68 ) ( 44 ) ( 88 ) ( 959 ) ( 110 ) ( 59 ) ( 13 ) ( 14 ) ( 76 ) ( 80 ) ( 42 ) ( 58 ) ( 107 ) ( 67 ) ( 955 ) ( 102 ) ( 83 ) ( 79 ) ( 82 ) ( 81 ) ( 43 ) ( 64 ) ( 15 ) ( 65 ) ( 71 ) ( 89 ) ( 72 ) ( 103 ) ( 113 ) ( 86 ) ( 56 ) ( 106 ) ( 74 ) ( 111 ) ( 112 ) ( 109 ) ( 93 ) ( 52 ) ( 95 ) ( 16 ) ( 85 ) ( 41 ) ( 87 ) ( 691 ) ( 90 ) ( 101 ) ( 91 ) ( 98 ) +342: ( 961 ) = pd_op.shape64 ( 955 ) +343: ( 962 ) = pd_op.slice ( 43 ) ( 44 ) ( 961 ) +344: ( 963 ) = pd_op.arange ( 962 ) ( 41 ) ( 42 ) +345: ( 964 ) = pd_op.shape64 ( 963 ) +346: ( 965 ) = pd_op.slice ( 43 ) ( 44 ) ( 964 ) +347: ( 966 ) = pd_op.create_array +348: ( 967 ) = pd_op.create_array +349: ( 968 ) = pd_op.full +350: ( 969 ) = pd_op.full +351: ( 970 ) = pd_op.memcpy_h2d ( 965 ) +352: ( 971 ) = pd_op.less_than ( 970 ) ( 968 ) +353: ( 972 ) = pd_op.full +354: ( 973 ) = pd_op.full +355: ( 974 ) = pd_op.full +356: ( 967 ) ( 966 ) ( 979 ) ( 978 ) ( 977 ) ( 976 ) ( 975 ) = while_instruction ( 953 ) ( 955 ) ( 971 ) ( 27 ) ( 968 ) ( 972 ) ( 974 ) ( 11 ) ( 973 ) ( 12 ) ( 969 ) ( 965 ) ( 28 ) ( 967 ) ( 966 ) ( 963 ) +357: ( 981 ) ( 980 ) = pd_op.array_to_tensor ( 966 ) +358: ( 983 ) ( 982 ) = pd_op.array_to_tensor ( 967 ) +359: ( 984 ) = pd_op.divide ( 334 ) ( 332 ) +360: ( 984 ) ( 985 ) = pd_op.scale_ ( 26 ) ( 984 ) +361: ( 985 ) ( 986 ) = pd_op.floor_ ( 985 ) +362: ( 987 ) = pd_op.shape64 ( 982 ) +363: ( 988 ) = pd_op.slice ( 43 ) ( 44 ) ( 987 ) +364: ( 989 ) = pd_op.arange ( 988 ) ( 41 ) ( 42 ) +365: ( 990 ) = pd_op.shape64 ( 989 ) +366: ( 991 ) = pd_op.slice ( 43 ) ( 44 ) ( 990 ) +367: ( 992 ) = pd_op.create_array +368: ( 993 ) = pd_op.create_array +369: ( 994 ) = pd_op.full +370: ( 995 ) = pd_op.memcpy_h2d ( 991 ) +371: ( 996 ) = pd_op.less_than ( 995 ) ( 994 ) +372: ( 997 ) = pd_op.full +373: ( 998 ) = pd_op.full +374: ( 999 ) = pd_op.full +375: ( 1000 ) = pd_op.full +376: ( 1001 ) = pd_op.full +377: ( 993 ) ( 1007 ) ( 1006 ) ( 1005 ) ( 992 ) ( 1008 ) ( 1004 ) ( 1003 ) ( 1002 ) = while_instruction ( 26 ) ( 5 ) ( 8 ) ( 9 ) ( 6 ) ( 44 ) ( 986 ) ( 10 ) ( 996 ) ( 994 ) ( 991 ) ( 979 ) ( 989 ) ( 997 ) ( 7 ) ( 1001 ) ( 999 ) ( 998 ) ( 1000 ) ( 993 ) ( 982 ) ( 334 ) ( 992 ) ( 30 ) +378: ( 1010 ) ( 1009 ) = pd_op.array_to_tensor ( 992 ) +379: ( 1012 ) ( 1011 ) = pd_op.array_to_tensor ( 993 ) +380: ( 1013 ) = pd_op.slice ( 43 ) ( 44 ) ( 980 ) +381: ( 1014 ) = pd_op.slice ( 52 ) ( 43 ) ( 980 ) +382: ( 1015 ) = pd_op.slice ( 36 ) ( 52 ) ( 980 ) +383: ( 1015 ) ( 1016 ) = pd_op.divide_ ( 1011 ) ( 1015 ) +384: ( 1017 ) = pd_op.slice ( 43 ) ( 44 ) ( 1009 ) +385: ( 1018 ) = pd_op.slice ( 52 ) ( 43 ) ( 1009 ) +386: ( 1019 ) = pd_op.full_like ( 703 ) ( 1017 ) +387: ( 1020 ) = pd_op.slice ( 43 ) ( 44 ) ( 1016 ) +388: ( 1021 ) = pd_op.minimum ( 1018 ) ( 1020 ) +389: ( 1022 ) = pd_op.maximum ( 1019 ) ( 1021 ) +390: ( 1023 ) = pd_op.slice ( 52 ) ( 43 ) ( 1016 ) +391: ( 1024 ) = pd_op.minimum ( 1017 ) ( 1023 ) +392: ( 1025 ) = pd_op.maximum ( 1019 ) ( 1024 ) +393: ( 1026 ) = pd_op.slice ( 51 ) ( 52 ) ( 1016 ) +394: ( 1027 ) = pd_op.minimum ( 1018 ) ( 1026 ) +395: ( 1028 ) = pd_op.maximum ( 1019 ) ( 1027 ) +396: ( 1029 ) = pd_op.slice ( 50 ) ( 51 ) ( 1016 ) +397: ( 1030 ) = pd_op.minimum ( 1017 ) ( 1029 ) +398: ( 1031 ) = pd_op.maximum ( 1019 ) ( 1030 ) +399: ( 1032 1022 1025 1028 1031 ) = builtin_combine_instruction ( 1028 ) ( 1025 ) ( 1031 ) ( 1022 ) +400: ( 1033 ) = pd_op.stack ( 1032 1022 1025 1028 1031 ) +401: ( 1034 ) = pd_op.slice ( 51 ) ( 52 ) ( 1033 ) +402: ( 1035 ) = pd_op.slice ( 43 ) ( 44 ) ( 1033 ) +403: ( 1036 ) = pd_op.subtract ( 1035 ) ( 1034 ) +404: ( 1037 ) = pd_op.slice ( 50 ) ( 51 ) ( 1033 ) +405: ( 1038 ) = pd_op.slice ( 52 ) ( 43 ) ( 1033 ) +406: ( 1039 ) = pd_op.subtract ( 1038 ) ( 1037 ) +407: ( 1040 ) = pd_op.memcpy_h2d ( 1039 ) +408: ( 1041 ) = pd_op.greater_than ( 25 ) ( 1040 ) +409: ( 1042 ) = pd_op.memcpy_h2d ( 1036 ) +410: ( 1043 ) = pd_op.greater_than ( 25 ) ( 1042 ) +411: ( 1044 ) = pd_op.logical_and ( 1043 ) ( 1041 ) +412: ( 1044 ) ( 1045 ) = pd_op.unsqueeze_ ( 43 ) ( 1044 ) +413: ( 1046 ) = pd_op.full_like ( 26 ) ( 1013 ) +414: ( 1046 ) ( 1047 ) = pd_op.scale_ ( 24 ) ( 1046 ) +415: ( 1048 ) = pd_op.memcpy_h2d ( 1013 ) +416: ( 1049 ) = pd_op.memcpy_h2d ( 1047 ) +417: ( 1048 ) ( 1050 ) = pd_op.where_ ( 1049 ) ( 1048 ) ( 1045 ) +418: ( 1051 1050 1014 1033 ) = builtin_combine_instruction ( 1033 ) ( 1014 ) ( 1050 ) +419: ( 1052 ) = pd_op.memcpy_h2d ( 1014 ) +420: ( 1053 ) = pd_op.memcpy_h2d ( 1033 ) +421: ( 1054 1050 1052 1053 ) = builtin_combine_instruction ( 1053 ) ( 1052 ) ( 1050 ) +422: ( 1055 ) = pd_op.concat ( 23 ) ( 1054 1050 1052 1053 ) +423: ( 1056 ) = pd_op.shape64 ( 960 ) +424: ( 1057 ) = pd_op.slice ( 43 ) ( 44 ) ( 1056 ) +425: ( 1058 ) = pd_op.cast ( 1009 ) +426: ( 1059 ) = pd_op.slice ( 43 ) ( 44 ) ( 1058 ) +427: ( 1060 ) = pd_op.max ( 22 ) ( 1059 ) +428: ( 1061 ) = pd_op.slice ( 52 ) ( 43 ) ( 1058 ) +429: ( 1062 ) = pd_op.max ( 22 ) ( 1061 ) +430: ( 1063 ) = pd_op.cast ( 1060 ) +431: ( 1064 ) = pd_op.cast ( 1062 ) +432: ( 1065 1057 1063 1064 ) = builtin_combine_instruction ( 1064 ) ( 1063 ) ( 1057 ) +433: ( 1066 ) = pd_op.stack ( 1065 1057 1063 1064 ) +434: ( 1067 ) = pd_op.full_with_tensor ( 1066 ) ( 703 ) +435: ( 1068 ) = pd_op.memcpy_d2h ( 1067 ) +436: ( 1069 ) = pd_op.scale ( 26 ) ( 1068 ) +437: ( 1070 ) = pd_op.shape64 ( 982 ) +438: ( 1071 ) = pd_op.slice ( 43 ) ( 44 ) ( 1070 ) +439: ( 1072 ) = pd_op.arange ( 1071 ) ( 41 ) ( 42 ) +440: ( 1073 ) = pd_op.shape64 ( 1072 ) +441: ( 1074 ) = pd_op.slice ( 43 ) ( 44 ) ( 1073 ) +442: ( 1075 ) = pd_op.full +443: ( 1076 ) = pd_op.full +444: ( 1077 ) = pd_op.memcpy_h2d ( 1074 ) +445: ( 1078 ) = pd_op.less_than ( 1077 ) ( 1075 ) +446: ( 1079 ) = pd_op.full +447: ( 1080 ) = pd_op.full +448: ( 1081 ) = pd_op.full +449: ( 1082 ) = pd_op.full +450: ( 1083 ) = pd_op.full +451: ( 1084 ) = pd_op.full +452: ( 1093 ) ( 1092 ) ( 1090 ) ( 1089 ) ( 1088 ) ( 1091 ) ( 1087 ) ( 1086 ) ( 1085 ) = while_instruction ( 2 ) ( 1 ) ( 0 ) ( 1074 ) ( 1084 ) ( 1083 ) ( 982 ) ( 1082 ) ( 1081 ) ( 1076 ) ( 42 ) ( 1080 ) ( 1079 ) ( 36 ) ( 1069 ) ( 1075 ) ( 20 ) ( 3 ) ( 1072 ) ( 1078 ) ( 1055 ) ( 960 ) ( 26 ) ( 21 ) ( 43 ) ( 41 ) ( 1058 ) ( 52 ) ( 4 ) ( 23 ) ( 44 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> constant_folding@_174513116887061917064 -> 0xd062b30 +1 -> constant_folding@_174513116885637196163 -> 0xd04b8a0 +2 -> constant_folding@_174513116884162097162 -> 0xcf7caa0 +3 -> constant_folding@_174513116882724144161 -> 0xcdb8360 +4 -> constant_folding@_174513116880873974160 -> 0xee71f60 +5 -> constant_folding@_174513116876880248258 -> 0xee71fe0 +6 -> constant_folding@_174513116875198908257 -> 0xc386660 +7 -> constant_folding@_174513116872098526255 -> 0xd061370 +8 -> constant_folding@_174513116870501220354 -> 0xcea7bc0 +9 -> constant_folding@_174513116869046783353 -> 0xcf2b9b0 +10 -> constant_folding@_174513116867667180352 -> 0xee46cc0 +11 -> constant_folding@_174513116865440940351 -> 0xcf3edb0 +12 -> constant_folding@_174513116864033493350 -> 0xcf3a750 +13 -> constant_folding@_174513116861634597449 -> 0xcf09130 +14 -> constant_folding@_174513116859911589448 -> 0xcf41cc0 +15 -> constant_folding@_174513116857122500446 -> 0xd0606f0 +16 -> constant_folding@_174513116855594796545 -> 0xcf11a20 +17 -> constant_folding@_174513116853662943544 -> 0xcdb8c50 +18 -> constant_folding@_174513116852268476543 -> 0xcfbe9a0 +19 -> constant_folding@_174513116850011146642 -> 0xcf2d600 +20 -> constant_folding@_174513116844224746641 -> 0xcdb72f0 +21 -> constant_folding@_174513116842705804740 -> 0xcf92430 +22 -> constant_folding@_174513116841278885739 -> 0xcdb8970 +23 -> constant_folding@_174513116839857308738 -> 0xcdb8950 +24 -> constant_folding@_174513116838085921737 -> 0xcf83ea0 +25 -> constant_folding@_174513116836671179736 -> 0xcf806d0 +26 -> constant_folding@_174513116835231166835 -> 0xcf0c190 +27 -> constant_folding@_174513116833656139834 -> 0xcf419a0 +28 -> constant_folding@_174513116830651898832 -> 0xed1a750 +29 -> constant_folding@_174513116827593559930 -> 0xcf437e0 +30 -> constant_folding@_174513116825929047929 -> 0xcf7c3f0 +31 -> constant_folding@_174513116824495087928 -> 0xcf3ae90 +32 -> constant_folding@_174513116823069183927 -> 0xcf64350 +33 -> constant_folding@_174513116821619066926 -> 0xcff2a20 +34 -> constant_folding@_174513116820193818025 -> 0xce87a10 +35 -> constant_folding@_174513116818740431024 -> 0xce98460 +36 -> constant_folding@_174513116817322536023 -> 0xce89990 +37 -> constant_folding@_174513116815886735022 -> 0xcf7aeb0 +38 -> constant_folding@_174513116814471704121 -> 0xd05fe30 +39 -> constant_folding@_174513116813053746120 -> 0xcf61980 +40 -> constant_folding@_ +I0420 14:39:33.668047 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 +1 -> 2 +2 -> 3 +3 -> 4 +4 -> 5 +5 -> 6 14 +6 -> 7 +7 -> 8 +8 -> 9 +9 -> 10 +10 -> 11 +11 -> 12 +12 -> 13 +13 -> 16 +14 -> 15 +15 -> 16 +16 -> 17 +17 -> 18 +18 -> 19 +19 -> 20 +20 -> 21 +21 -> 22 +22 -> 23 +23 -> 24 +24 -> 25 +25 -> 26 +26 -> 27 +27 -> 28 +28 -> 29 +29 -> 30 +30 -> 31 +31 -> 32 +32 -> 33 +33 -> 34 +34 -> 35 +35 -> 36 +36 -> 37 +37 -> 38 46 +38 -> 39 +39 -> 40 +40 -> 41 +41 -> 42 +42 -> 43 +43 -> 44 +44 -> 45 +45 -> 48 +46 -> 47 +47 -> 48 +48 -> 49 +49 -> 50 +50 -> 51 +51 -> 52 +52 -> 53 +53 -> 54 +54 -> 55 +55 -> 56 +56 -> 57 +57 -> 58 +58 -> 59 +59 -> 60 +60 -> 61 +61 -> 62 +62 -> 63 +63 -> 64 +64 -> 65 +65 -> 66 +66 -> 67 +67 -> 68 +68 -> 69 +69 -> 70 +70 -> 71 +71 -> 72 +72 -> 73 +73 -> 74 +74 -> 75 +75 -> 76 +76 -> 77 +77 -> 78 +78 -> 79 +79 -> 80 88 +80 -> 81 +81 -> 82 +82 -> 83 +83 -> 84 +84 -> 85 +85 -> 86 +86 -> 87 +87 -> 90 +88 -> 89 +89 -> 90 +90 -> 91 +91 -> 92 +92 -> 93 +93 -> 94 +94 -> 95 +95 -> 96 +96 -> 97 +97 -> 98 +98 -> 99 +99 -> 100 +100 -> 101 +101 -> 102 +102 -> 103 +103 -> 104 +104 -> 105 +105 -> 106 +106 -> 107 +107 -> 108 +108 -> 109 +109 -> 110 +110 -> 111 +111 -> 112 +112 -> 113 +113 -> 114 +114 -> 115 +115 -> 116 +116 -> 117 +117 -> 118 +118 -> 119 +119 -> 120 +120 -> 121 +121 -> 122 +122 -> 123 +123 -> 124 +124 -> 125 +125 -> 126 +126 -> 127 +127 -> 128 +128 -> 129 +129 -> 130 +130 -> 131 +131 -> 132 +132 -> 133 +133 -> 134 +134 -> 135 +135 -> 136 +136 -> 137 +137 -> 138 +138 -> 139 +139 -> 140 +140 -> 141 +141 -> 142 +142 -> 143 +143 -> 144 +144 -> 145 147 149 +145 -> 146 +146 -> 187 +147 -> 148 +148 -> 187 +149 -> 150 151 +150 -> 156 +151 -> 152 +152 -> 154 +153 -> 155 158 +154 -> 155 +155 -> 159 +156 -> 157 +157 -> 158 +158 -> 159 +159 -> 160 +160 -> 161 162 +161 -> 163 +162 -> 163 +163 -> 164 +164 -> 165 +165 -> 166 +166 -> 167 +167 -> 187 +168 -> 169 235 +169 -> 170 +170 -> 171 +171 -> 172 +172 -> 176 +173 -> 187 +174 -> 187 +175 -> 177 +176 -> 177 +177 -> 187 +178 -> 187 +179 -> 187 +180 -> 187 +181 -> 187 +182 -> 187 +183 -> 187 +184 -> 187 +185 -> 187 +186 -> 187 +187 -> 188 189 248 +188 -> 193 246 +189 -> 190 +190 -> 191 +191 -> 192 +192 -> 195 +193 -> 194 +194 -> 195 +195 -> 196 204 +196 -> 197 +197 -> 198 +198 -> 199 +199 -> 200 +200 -> 201 +201 -> 202 +202 -> 203 +203 -> 206 +204 -> 205 +205 -> 206 +206 -> 207 +207 -> 208 +208 -> 209 +209 -> 210 +210 -> 211 +211 -> 212 +212 -> 213 +213 -> 214 +214 -> 215 +215 -> 216 +216 -> 217 +217 -> 218 +218 -> 219 +219 -> 220 +220 -> 221 +221 -> 222 +222 -> 223 +223 -> 224 +224 -> 225 +225 -> 226 +226 -> 227 +227 -> 228 +228 -> 229 +229 -> 230 232 +230 -> 231 +231 -> 234 +232 -> 233 +233 -> 261 263 265 267 +234 -> 301 +235 -> 236 +236 -> 237 +237 -> 238 +238 -> 241 +239 -> 246 +240 -> 242 +241 -> 242 +242 -> 246 +243 -> 246 +244 -> 246 +245 -> 246 +246 -> 247 +247 -> 307 309 +248 -> 249 250 252 253 255 258 +249 -> 251 +250 -> 251 +251 -> 256 271 +252 -> 254 +253 -> 254 +254 -> 259 277 +255 -> 257 +256 -> 257 284 +257 -> 274 +258 -> 260 +259 -> 260 288 +260 -> 280 +261 -> 262 +262 -> 273 +263 -> 264 +264 -> 279 +265 -> 266 +266 -> 269 +267 -> 268 +268 -> 270 +269 -> 283 +270 -> 287 +271 -> 272 284 +272 -> 273 +273 -> 276 +274 -> 275 +275 -> 276 +276 -> 292 +277 -> 278 288 +278 -> 279 +279 -> 282 +280 -> 281 +281 -> 282 +282 -> 294 +283 -> 286 +284 -> 285 +285 -> 286 +286 -> 291 +287 -> 290 +288 -> 289 +289 -> 290 +290 -> 293 +291 -> 292 295 +292 -> 296 +293 -> 294 297 +294 -> 298 +295 -> 296 +296 -> 299 +297 -> 298 +298 -> 299 +299 -> 300 +300 -> 302 +301 -> 335 +302 -> 303 +303 -> 304 +304 -> 305 +305 -> 306 +306 -> 312 317 322 327 +307 -> 308 +308 -> 311 318 328 +309 -> 310 +310 -> 313 323 +311 -> 315 320 325 330 +312 -> 314 +313 -> 314 +314 -> 316 +315 -> 316 +316 -> 332 +317 -> 319 +318 -> 319 +319 -> 321 +320 -> 321 +321 -> 332 +322 -> 324 +323 -> 324 +324 -> 326 +325 -> 326 +326 -> 332 +327 -> 329 +328 -> 329 +329 -> 331 +330 -> 331 +331 -> 332 +332 -> 333 +333 -> 334 +334 -> 336 +335 -> 336 +336 -> 337 342 +337 -> 338 +338 -> 339 +339 -> 340 +340 -> 341 +341 -> 423 +342 -> 343 +343 -> 344 +344 -> 345 +345 -> 346 +346 -> 351 +347 -> 356 +348 -> 356 +349 -> 352 +350 -> 356 +351 -> 352 +352 -> 356 +353 -> 356 +354 -> 356 +355 -> 356 +356 -> 357 358 +357 -> 380 381 382 +358 -> 362 437 +359 -> 360 +360 -> 361 +361 -> 377 +362 -> 363 +363 -> 364 +364 -> 365 +365 -> 366 +366 -> 370 +367 -> 377 +368 -> 377 +369 -> 371 +370 -> 371 +371 -> 377 +372 -> 377 +373 -> 377 +374 -> 377 +375 -> 377 +376 -> 377 +377 -> 378 379 +378 -> 384 385 425 +379 -> 383 +380 -> 413 415 +381 -> 418 +382 -> 383 +383 -> 387 390 393 396 +384 -> 386 391 397 +385 -> 388 394 +386 -> 389 392 395 398 +387 -> 388 +388 -> 389 +389 -> 399 +390 -> 391 +391 -> 392 +392 -> 399 +393 -> 394 +394 -> 395 +395 -> 399 +396 -> 397 +397 -> 398 +398 -> 399 +399 -> 400 +400 -> 401 402 404 405 +401 -> 403 +402 -> 403 +403 -> 409 +404 -> 406 +405 -> 406 +406 -> 407 +407 -> 408 +408 -> 411 +409 -> 410 +410 -> 411 +411 -> 412 +412 -> 417 +413 -> 414 +414 -> 416 +415 -> 417 +416 -> 417 +417 -> 418 +418 -> 419 420 +419 -> 421 +420 -> 421 +421 -> 422 +422 -> 452 +423 -> 424 +424 -> 432 +425 -> 426 428 +426 -> 427 +427 -> 430 +428 -> 429 +429 -> 431 +430 -> 432 +431 -> 432 +432 -> 433 +433 -> 434 +434 -> 435 +435 -> 436 +436 -> 452 +437 -> 438 +438 -> 439 +439 -> 440 +440 -> 441 +441 -> 444 +442 -> 445 +443 -> 452 +444 -> 445 +445 -> 452 +446 -> 452 +447 -> 452 +448 -> 452 +449 -> 452 +450 -> 452 +451 -> 452 + +I0420 14:39:33.668385 115867 pir_interpreter.cc:1591] pir interpreter is running by trace mode ... +[2025-4-20 14:39:33] [CNNL] [Warning][cnnlGetConvolutionForwardAlgorithm] is deprecated and will be removed in the future release. See cnnlFindConvolutionForwardAlgorithm() API for replacement. +[2025-4-20 14:39:33] [CNNL] [Warning]When calculating multiplication of complex_float data, it is required to use [cnnlGetOpTensorWorkspaceSize_v2] to apply for workspace. +I0420 14:39:33.737112 115867 pir_interpreter.cc:1569] value info of interpretercore 0xce45330 +value -> var_name -> id -> variable* +0xd0506e0 -> 0xce453301745131172820051325_inner_var_1154 -> 1154 -> 0xce5dce0 +0xd050200 -> 0xce453301745131172820051325_inner_var_1152 -> 1152 -> 0xce5d540 +0xcf9a1b0 -> 0xce453301745131172820051325_inner_var_1150 -> 1150 -> 0xce5c720 +0xcf9a0e0 -> 0xce453301745131172820051325_inner_var_1149 -> 1149 -> 0xce5c640 +0xd04f800 -> 0xce453301745131172820051325_inner_var_1147 -> 1147 -> 0xce5c6a0 +0xcf75cc0 -> 0xcf59f401745131171435049990_inner_var_725 -> 725 -> 0xcf962a0 +0xcf99f40 -> 0xce453301745131172820051325_inner_var_1145 -> 1145 -> 0xce5b0f0 +0xee46040 -> 0xce453301745131172820051325_inner_var_1144 -> 1144 -> 0xce5b920 +0xd0774f0 -> 0xcf59f401745131171435049990_inner_var_726 -> 726 -> 0xca82ce0 +0xcf99290 -> 0xce453301745131172820051325_inner_var_1142 -> 1142 -> 0xce5a890 +0xcf99070 -> 0xce453301745131172820051325_inner_var_1141 -> 1141 -> 0xce5a8b0 +0xcdb7168 -> 0xce453301745131172820051325_inner_var_1139 -> 1139 -> 0xce5a050 +0xcdb7180 -> 0xce453301745131172820051325_inner_var_1138 -> 1138 -> 0xce59830 +0xca547c0 -> 0xce453301745131172820051325_inner_var_1134 -> 1134 -> 0xc871a30 +0xca54890 -> 0xce453301745131172820051325_inner_var_1133 -> 1133 -> 0xc8939e0 +0xee455c0 -> 0xce453301745131172820051325_inner_var_1132 -> 1132 -> 0xc871660 +0xca54e60 -> 0xce453301745131172820051325_inner_var_1131 -> 1131 -> 0xc871290 +0xca54400 -> 0xce453301745131172820051325_inner_var_1130 -> 1130 -> 0xc870f00 +0xcf981c0 -> 0xce453301745131172820051325_inner_var_1129 -> 1129 -> 0xc893a60 +0xcf980f0 -> 0xce453301745131172820051325_inner_var_1128 -> 1128 -> 0xc893b50 +0xcf98020 -> 0xce453301745131172820051325_inner_var_1127 -> 1127 -> 0xc893a20 +0xcf97f50 -> 0xce453301745131172820051325_inner_var_1125 -> 1125 -> 0xc891ec0 +0xcf984d0 -> 0xce453301745131172820051325_inner_var_1124 -> 1124 -> 0xc893240 +0xcf97b90 -> 0xce453301745131172820051325_inner_var_1123 -> 1123 -> 0xc892eb0 +0xcf96a40 -> 0xce453301745131172820051325_inner_var_1122 -> 1122 -> 0xc891f40 +0xcf96970 -> 0xce453301745131172820051325_inner_var_1121 -> 1121 -> 0xc892030 +0xcf968a0 -> 0xce453301745131172820051325_inner_var_1120 -> 1120 -> 0xc891f00 +0xcf967d0 -> 0xce453301745131172820051325_inner_var_1118 -> 1118 -> 0xd0075f0 +0xcf96d50 -> 0xce453301745131172820051325_inner_var_1117 -> 1117 -> 0xd008b50 +0xcf96410 -> 0xce453301745131172820051325_inner_var_1116 -> 1116 -> 0xd0087c0 +0xca9d760 -> 0xce453301745131172820051325_inner_var_1113 -> 1113 -> 0xd007630 +0xca9d2d0 -> 0xce453301745131172820051325_inner_var_1109 -> 1109 -> 0xce47140 +0xd0365f8 -> fetch_name_1 -> 982 -> 0xcbe4d60 +0xcaf6bd0 -> batch_norm2d_20.w_1_deepcopy_105 -> 228 -> 0xcf82fa0 +0xccad180 -> 0xcf59f401745131171435049990_inner_var_530 -> 530 -> 0xccad0a0 +0xccad198 -> 0xcf59f401745131171435049990_inner_var_529 -> 529 -> 0xce93f90 +0xccad1b0 -> 0xcf59f401745131171435049990_inner_var_528 -> 528 -> 0xcaeafb0 +0xccad1c8 -> 0xcf59f401745131171435049990_inner_var_527 -> 527 -> 0xcad93f0 +0xc80c4d0 -> conv2d_36.w_0_deepcopy_182 -> 151 -> 0xd05fe10 +0xee86328 -> 0xcf59f401745131171435049990_inner_var_763 -> 763 -> 0xca55c90 +0xd04e680 -> batch_norm2d_46.b_0_deepcopy_241 -> 96 -> 0xcf60140 +0xee3ff40 -> batch_norm2d_51.w_2_deepcopy_268 -> 69 -> 0xcf2c930 +0xce903b0 -> 0xcf59f401745131171435049990_inner_var_917 -> 917 -> 0xceaa600 +0xcccde50 -> 0xcf59f401745131171435049990_inner_var_525 -> 525 -> 0xcad8d00 +0xcccc970 -> 0xcf59f401745131171435049990_inner_var_507 -> 507 -> 0xcec9370 +0xee8aa90 -> 0xcf59f401745131171435049990_inner_var_794 -> 794 -> 0xce96090 +0xcccc988 -> 0xcf59f401745131171435049990_inner_var_506 -> 506 -> 0xcf207c0 +0xee8aad8 -> 0xcf59f401745131171435049990_inner_var_791 -> 791 -> 0xca84830 +0xcccc9d0 -> 0xcf59f401745131171435049990_inner_var_503 -> 503 -> 0xcad4220 +0xee75060 -> 0xcf59f401745131171435049990_inner_var_386 -> 386 -> 0xcf17420 +0xcccc9e8 -> 0xcf59f401745131171435049990_inner_var_502 -> 502 -> 0xcc9ec80 +0xce91510 -> 0xcf59f401745131171435049990_inner_var_911 -> 911 -> 0xcf8f250 +0xccdcdf0 -> 0xcf59f401745131171435049990_inner_var_946 -> 946 -> 0xcf04e80 +0xcccb928 -> 0xcf59f401745131171435049990_inner_var_494 -> 494 -> 0xccc8990 +0xd041790 -> constant_folding@_174513116825929047929 -> 30 -> 0xcf7c3f0 +0xcccf820 -> 0xcf59f401745131171435049990_inner_var_492 -> 492 -> 0xcabc760 +0xcb0cfa0 -> batch_norm2d_6.w_1_deepcopy_35 -> 298 -> 0xcf2f870 +0xccd0740 -> 0xcf59f401745131171435049990_inner_var_491 -> 491 -> 0xcf19870 +0xce8a520 -> 0xcf59f401745131171435049990_inner_var_879 -> 879 -> 0xcee45d0 +0xccd0770 -> 0xcf59f401745131171435049990_inner_var_489 -> 489 -> 0xcf95c60 +0xccb8a80 -> 0xcf59f401745131171435049990_inner_var_440 -> 440 -> 0xcedd7e0 +0xca9f7c0 -> 0xcf59f401745131171435049990_inner_var_569 -> 569 -> 0xcf3e600 +0xee880e0 -> 0xcf59f401745131171435049990_inner_var_788 -> 788 -> 0xd054cd0 +0xcccfd80 -> 0xcf59f401745131171435049990_inner_var_485 -> 485 -> 0xcadbd40 +0xcabb010 -> 0xcf59f401745131171435049990_inner_var_887 -> 887 -> 0xcf06390 +0xcccb2e0 -> 0xcf59f401745131171435049990_inner_var_508 -> 508 -> 0xcec4d60 +0xcaf7370 -> batch_norm2d_20.w_0_deepcopy_103 -> 230 -> 0xcf82460 +0xcdbdab0 -> 0xcf59f401745131171435049990_inner_var_682 -> 682 -> 0xd046ed0 +0xccc6be0 -> 0xcf59f401745131171435049990_inner_var_1054 -> 1054 -> 0xcaab8c0 +0xcdbd710 -> 0xcf59f401745131171435049990_inner_var_671 -> 671 -> 0xccd9c80 +0xccbdda0 -> 0xcf59f401745131171435049990_inner_var_1014 -> 1014 -> 0xcbed7a0 +0xcdbd728 -> 0xcf59f401745131171435049990_inner_var_670 -> 670 -> 0xcf716d0 +0xccb6400 -> 0xcf59f401745131171435049990_inner_var_475 -> 475 -> 0xcca5d30 +0xca64c30 -> batch_norm2d_2.w_1_deepcopy_15 -> 318 -> 0xcf7feb0 +0xccce208 -> 0xcf59f401745131171435049990_inner_var_473 -> 473 -> 0xce8df60 +0xccce238 -> 0xcf59f401745131171435049990_inner_var_471 -> 471 -> 0xd034260 +0xccce250 -> 0xcf59f401745131171435049990_inner_var_470 -> 470 -> 0xcec1100 +0xca85520 -> batch_norm2d_8.w_2_deepcopy_46 -> 287 -> 0xceff0c0 +0xcc9e7e0 -> constant_folding@_174513116872098526255 -> 7 -> 0xd061370 +0xccb6060 -> 0xcf59f401745131171435049990_inner_var_464 -> 464 -> 0xcab8eb0 +0xcdbc1c8 -> 0xcf59f401745131171435049990_inner_var_659 -> 659 -> 0xcab68b0 +0xccb6078 -> 0xcf59f401745131171435049990_inner_var_463 -> 463 -> 0xcec4600 +0xccb54f0 -> 0xcf59f401745131171435049990_inner_var_460 -> 460 -> 0xced79a0 +0xccb95d0 -> 0xcf59f401745131171435049990_inner_var_458 -> 458 -> 0xcef8830 +0xccb4b68 -> 0xcf59f401745131171435049990_inner_var_454 -> 454 -> 0xd0385d0 +0xccb28c0 -> 0xcf59f401745131171435049990_inner_var_757 -> 757 -> 0xcf08950 +0xccb4b98 -> 0xcf59f401745131171435049990_inner_var_452 -> 452 -> 0xcca3870 +0xccb9bd0 -> 0xcf59f401745131171435049990_inner_var_448 -> 448 -> 0xcf17050 +0xccb9c00 -> 0xcf59f401745131171435049990_inner_var_446 -> 446 -> 0xcbf35d0 +0xccb9c18 -> 0xcf59f401745131171435049990_inner_var_445 -> 445 -> 0xcf21760 +0xd052500 -> 0xcf59f401745131171435049990_inner_var_640 -> 640 -> 0xca95610 +0xccb4b80 -> 0xcf59f401745131171435049990_inner_var_453 -> 453 -> 0xee47f90 +0xccb8f20 -> 0xcf59f401745131171435049990_inner_var_444 -> 444 -> 0xce89bf0 +0xccca460 -> 0xcf59f401745131171435049990_inner_var_1070 -> 1070 -> 0xcfbdaa0 +0xccd0788 -> 0xcf59f401745131171435049990_inner_var_488 -> 488 -> 0xcf75a50 +0xccac460 -> 0xcf59f401745131171435049990_inner_var_534 -> 534 -> 0xcca5770 +0xca9f790 -> 0xcf59f401745131171435049990_inner_var_571 -> 571 -> 0xcea9710 +0xee86340 -> 0xcf59f401745131171435049990_inner_var_762 -> 762 -> 0xcfcedc0 +0xccb7990 -> 0xcf59f401745131171435049990_inner_var_434 -> 434 -> 0xcebccc0 +0xcadfa10 -> 0xcf59f401745131171435049990_inner_var_537 -> 537 -> 0xca889b0 +0xccabfb0 -> 0xcf59f401745131171435049990_inner_var_520 -> 520 -> 0xccc8370 +0xee86358 -> 0xcf59f401745131171435049990_inner_var_761 -> 761 -> 0xca85c80 +0xccb79a8 -> 0xcf59f401745131171435049990_inner_var_433 -> 433 -> 0xce94af0 +0xce894c0 -> 0xcf59f401745131171435049990_inner_var_353 -> 353 -> 0xcf596e0 +0xcf77920 -> 0xcf59f401745131171435049990_inner_var_730 -> 730 -> 0xce9c550 +0xccbc420 -> 0xcf59f401745131171435049990_inner_var_421 -> 421 -> 0xccc3bd0 +0xcaf5650 -> batch_norm2d_16.w_2_deepcopy_86 -> 247 -> 0xcfa79c0 +0xcf75b70 -> 0xce453301745131172820051325_inner_var_1107 -> 1107 -> 0xce460b0 +0xccbc438 -> 0xcf59f401745131171435049990_inner_var_420 -> 420 -> 0xcfcbe20 +0xccc2c30 -> 0xcf59f401745131171435049990_inner_var_1034 -> 1034 -> 0xceb6ad0 +0xccbbad0 -> 0xcf59f401745131171435049990_inner_var_419 -> 419 -> 0xca984f0 +0xd058908 -> 0xcf59f401745131171435049990_inner_var_367 -> 367 -> 0xce91c20 +0xccbb100 -> 0xcf59f401745131171435049990_inner_var_418 -> 418 -> 0xcaed490 +0xccbb240 -> 0xcf59f401745131171435049990_inner_var_417 -> 417 -> 0xcaecc00 +0xccbf790 -> 0xcf59f401745131171435049990_inner_var_1020 -> 1020 -> 0xcbef330 +0xccbb288 -> 0xcf59f401745131171435049990_inner_var_414 -> 414 -> 0xccc05c0 +0xccbb2a0 -> 0xcf59f401745131171435049990_inner_var_413 -> 413 -> 0xcec7620 +0xccba560 -> 0xcf59f401745131171435049990_inner_var_411 -> 411 -> 0xcad7ec0 +0xccba1c0 -> 0xcf59f401745131171435049990_inner_var_407 -> 407 -> 0xd0727d0 +0xccba1d8 -> 0xcf59f401745131171435049990_inner_var_406 -> 406 -> 0xcf78850 +0xcbf0ad0 -> batch_norm2d_44.w_0_deepcopy_230 -> 107 -> 0xcf1cb30 +0xcdc4890 -> 0xcf59f401745131171435049990_inner_var_722 -> 722 -> 0xceb3f10 +0xccba208 -> 0xcf59f401745131171435049990_inner_var_404 -> 404 -> 0xd04f4b0 +0xca56390 -> 0xcf59f401745131171435049990_inner_var_715 -> 715 -> 0xcad9880 +0xcae9830 -> 0xcf59f401745131171435049990_inner_var_842 -> 842 -> 0xd06a7d0 +0xcc97280 -> 0xcf59f401745131171435049990_inner_var_401 -> 401 -> 0xcee7350 +0xcc98950 -> 0xcf59f401745131171435049990_inner_var_400 -> 400 -> 0xca602d0 +0xd037a30 -> 0xcf59f401745131171435049990_inner_var_989 -> 989 -> 0xcbe7410 +0xcc98980 -> 0xcf59f401745131171435049990_inner_var_398 -> 398 -> 0xca947c0 +0xee8aa60 -> 0xcf59f401745131171435049990_inner_var_796 -> 796 -> 0xcf16f50 +0xee74c60 -> 0xcf59f401745131171435049990_inner_var_393 -> 393 -> 0xccb9310 +0xd048c30 -> 0xcf59f401745131171435049990_inner_var_706 -> 706 -> 0xd03e890 +0xca99330 -> 0xcf59f401745131171435049990_inner_var_732 -> 732 -> 0xca801c0 +0xcc97860 -> 0xcf59f401745131171435049990_inner_var_392 -> 392 -> 0xd0639d0 +0xcc97878 -> 0xcf59f401745131171435049990_inner_var_391 -> 391 -> 0xcf38990 +0xce8be10 -> 0xcf59f401745131171435049990_inner_var_885 -> 885 -> 0xcf19ef0 +0xccb73c0 -> 0xcf59f401745131171435049990_inner_var_459 -> 459 -> 0xceca990 +0xccde390 -> 0xcf59f401745131171435049990_inner_var_957 -> 957 -> 0xca5e670 +0xcc97890 -> 0xcf59f401745131171435049990_inner_var_390 -> 390 -> 0xca9b230 +0xcdb5730 -> batch_norm2d_23.w_0_deepcopy_118 -> 215 -> 0xd060ed0 +0xccd07a0 -> 0xcf59f401745131171435049990_inner_var_487 -> 487 -> 0xceac560 +0xcc993c0 -> 0xcf59f401745131171435049990_inner_var_403 -> 403 -> 0xcad0c10 +0xcae8810 -> 0xcf59f401745131171435049990_inner_var_968 -> 968 -> 0xcbe1be0 +0xcc978c0 -> 0xcf59f401745131171435049990_inner_var_388 -> 388 -> 0xcca2020 +0xce8e100 -> 0xcf59f401745131171435049990_inner_var_897 -> 897 -> 0xcf21030 +0xee74890 -> 0xcf59f401745131171435049990_inner_var_384 -> 384 -> 0xccdad60 +0xee748a8 -> 0xcf59f401745131171435049990_inner_var_383 -> 383 -> 0xcad2150 +0xca55d20 -> conv2d_14.w_0_deepcopy_72 -> 261 -> 0xcf52990 +0xee748c0 -> 0xcf59f401745131171435049990_inner_var_382 -> 382 -> 0xcef7030 +0xee748f0 -> 0xcf59f401745131171435049990_inner_var_380 -> 380 -> 0xced4880 +0xcc9c7f0 -> batch_norm2d_28.w_1_deepcopy_145 -> 188 -> 0xd073510 +0xcae2030 -> 0xcf59f401745131171435049990_inner_var_551 -> 551 -> 0xcee4790 +0xee73c40 -> 0xcf59f401745131171435049990_inner_var_378 -> 378 -> 0xd0732d0 +0xcc962b0 -> 0xcf59f401745131171435049990_inner_var_377 -> 377 -> 0xcaa4d20 +0xccb2538 -> 0xcf59f401745131171435049990_inner_var_978 -> 978 -> 0xcbe4500 +0xccc5d20 -> 0xcf59f401745131171435049990_inner_var_1047 -> 1047 -> 0xcada0c0 +0xd058320 -> 0xcf59f401745131171435049990_inner_var_376 -> 376 -> 0xcf20080 +0xcc9ad10 -> batch_norm2d_26.w_2_deepcopy_136 -> 197 -> 0xd06d600 +0xee732e0 -> 0xcf59f401745131171435049990_inner_var_375 -> 375 -> 0xccdfc40 +0xcccb8b0 -> 0xcf59f401745131171435049990_inner_var_499 -> 499 -> 0xcca7260 +0xcdbf878 -> 0xcf59f401745131171435049990_inner_var_688 -> 688 -> 0xcaa66e0 +0xee732f8 -> 0xcf59f401745131171435049990_inner_var_374 -> 374 -> 0xcec14b0 +0xee73328 -> 0xcf59f401745131171435049990_inner_var_372 -> 372 -> 0xca7bf50 +0xd058920 -> 0xcf59f401745131171435049990_inner_var_366 -> 366 -> 0xcf1d290 +0xd03a3f0 -> 0xcf59f401745131171435049990_inner_var_998 -> 998 -> 0xcbe95a0 +0xd058938 -> 0xcf59f401745131171435049990_inner_var_365 -> 365 -> 0xcebea10 +0xce93e10 -> constant_folding@_174513116823069183927 -> 32 -> 0xcf64350 +0xce960d0 -> batch_norm2d_29.w_1_deepcopy_150 -> 183 -> 0xd076240 +0xccab050 -> 0xcf59f401745131171435049990_inner_var_516 -> 516 -> 0xcedc210 +0xcc989b0 -> 0xcf59f401745131171435049990_inner_var_396 -> 396 -> 0xc8757d0 +0xd056230 -> 0xcf59f401745131171435049990_inner_var_656 -> 656 -> 0xca90720 +0xd057790 -> 0xcf59f401745131171435049990_inner_var_360 -> 360 -> 0xd04cf50 +0xcdb8020 -> 0xcf59f401745131171435049990_inner_var_721 -> 721 -> 0xcf76450 +0xcabd920 -> 0xcf59f401745131171435049990_inner_var_875 -> 875 -> 0xd061a70 +0xcf740f0 -> 0xcf59f401745131171435049990_inner_var_718 -> 718 -> 0xcaa8730 +0xcf22190 -> 0xcf59f401745131171435049990_inner_var_354 -> 354 -> 0xca62f10 +0xcfacb90 -> batch_norm2d_42.w_0_deepcopy_213 -> 120 -> 0xcf3c660 +0xcdba8a0 -> 0xcf59f401745131171435049990_inner_var_725 -> 725 -> 0xcf962a0 +0xcc96880 -> 0xcf59f401745131171435049990_inner_var_352 -> 352 -> 0xca7dcc0 +0xcc96898 -> 0xcf59f401745131171435049990_inner_var_351 -> 351 -> 0xd0580a0 +0xcc968b0 -> 0xcf59f401745131171435049990_inner_var_350 -> 350 -> 0xcec2420 +0xcc968f8 -> 0xcf59f401745131171435049990_inner_var_347 -> 347 -> 0xca9b750 +0xcf73cd0 -> 0xcf59f401745131171435049990_inner_var_720 -> 720 -> 0xced4fb0 +0xcf4ea30 -> 0xcf59f401745131171435049990_inner_var_343 -> 343 -> 0xcca3d60 +0xccb7d60 -> 0xcf59f401745131171435049990_inner_var_443 -> 443 -> 0xd069a30 +0xcbf0900 -> batch_norm2d_44.b_0_deepcopy_231 -> 106 -> 0xcfa37a0 +0xcdb8e30 -> 0xcf59f401745131171435049990_inner_var_341 -> 341 -> 0xcf97690 +0xccb4f70 -> 0xcf59f401745131171435049990_inner_var_467 -> 467 -> 0xcab88e0 +0xcaee1a0 -> batch_norm2d_10.w_1_deepcopy_55 -> 278 -> 0xcf526a0 +0xcdb8e90 -> 0xcf59f401745131171435049990_inner_var_337 -> 337 -> 0xce9b570 +0xca83ff0 -> 0xcf59f401745131171435049990_inner_var_1033 -> 1033 -> 0xcf3eed0 +0xcf05e90 -> 0xcf59f401745131171435049990_inner_var_335 -> 335 -> 0xcf1b790 +0xcfaa870 -> conv2d_13.w_0_deepcopy_67 -> 266 -> 0xcf52460 +0xca53c58 -> 0xcf59f401745131171435049990_inner_var_620 -> 620 -> 0xcf18440 +0xced6440 -> scale_factor -> 334 -> 0xcdbf3e0 +0xcdb8e60 -> 0xcf59f401745131171435049990_inner_var_339 -> 339 -> 0xcf009c0 +0xcaf7b10 -> batch_norm2d_19.w_2_deepcopy_101 -> 232 -> 0xcf7d9f0 +0xcdb92b0 -> batch_norm2d_0.w_1_deepcopy_5 -> 328 -> 0xcf547a0 +0x118c9970 -> conv2d_1.w_0_deepcopy_7 -> 326 -> 0xcf500a0 +0xca9b9b0 -> 0xee8d0201745131172819836665body_block_arg_9 -> 1103 -> 0xc874230 +0x118c9550 -> batch_norm2d_1.w_0_deepcopy_8 -> 325 -> 0xcf37900 +0x118c9180 -> batch_norm2d_1.b_0_deepcopy_9 -> 324 -> 0xcf540f0 +0xccbc408 -> 0xcf59f401745131171435049990_inner_var_422 -> 422 -> 0xd047d80 +0xcb0d370 -> batch_norm2d_6.b_0_deepcopy_34 -> 299 -> 0xcf2d1b0 +0x118c8db0 -> batch_norm2d_1.w_1_deepcopy_10 -> 323 -> 0xcf3f550 +0xced5d20 -> image -> 333 -> 0xccc5060 +0xd0461a0 -> 0xcf59f401745131171435049990_inner_var_695 -> 695 -> 0xcca9b90 +0xcdb7ba0 -> 0xcf59f401745131171435049990_inner_var_705 -> 705 -> 0xcf07ef0 +0xcdb8e78 -> 0xcf59f401745131171435049990_inner_var_338 -> 338 -> 0xcebb210 +0x118c7ea0 -> batch_norm2d_2.b_0_deepcopy_14 -> 319 -> 0xcf7e0d0 +0xca63920 -> batch_norm2d_3.w_1_deepcopy_20 -> 313 -> 0xcf576f0 +0xcaba360 -> 0xcf59f401745131171435049990_inner_var_892 -> 892 -> 0xcf5b3c0 +0xca63550 -> batch_norm2d_3.w_2_deepcopy_21 -> 312 -> 0xcf21ae0 +0xca63180 -> conv2d_4.w_0_deepcopy_22 -> 311 -> 0xc80efb0 +0xca74d70 -> batch_norm2d_4.w_0_deepcopy_23 -> 310 -> 0xcf906f0 +0xcccf2f8 -> 0xcf59f401745131171435049990_inner_var_479 -> 479 -> 0xccdd6a0 +0xca74200 -> batch_norm2d_4.w_2_deepcopy_26 -> 307 -> 0xcf40460 +0xcf6dbe0 -> conv2d_56.w_0_deepcopy_280 -> 58 -> 0xcf344c0 +0xd050950 -> 0xce453301745131172820051325_inner_var_1155 -> 1155 -> 0xce5e0b0 +0xcae6ec8 -> 0xcf59f401745131171435049990_inner_var_826 -> 826 -> 0xced62a0 +0xccbb270 -> 0xcf59f401745131171435049990_inner_var_415 -> 415 -> 0xca942d0 +0xccc3d10 -> 0xcf59f401745131171435049990_inner_var_1038 -> 1038 -> 0xcfb34a0 +0xca73e30 -> conv2d_5.w_0_deepcopy_27 -> 306 -> 0xcfc00b0 +0xca55900 -> batch_norm2d_14.w_0_deepcopy_73 -> 260 -> 0xcfc6c40 +0xce94e70 -> constant_folding@_174513115176548053017 -> 42 -> 0xcf8c2c0 +0xcb0db10 -> conv2d_6.w_0_deepcopy_32 -> 301 -> 0xcc9ffb0 +0xee73340 -> 0xcf59f401745131171435049990_inner_var_371 -> 371 -> 0xcf563c0 +0xcb0d740 -> batch_norm2d_6.w_0_deepcopy_33 -> 300 -> 0xcf42180 +0xcbf30d0 -> batch_norm2d_38.b_0_deepcopy_194 -> 139 -> 0xcf14990 +0xccb79f0 -> 0xcf59f401745131171435049990_inner_var_430 -> 430 -> 0xcead070 +0xcb0cbd0 -> batch_norm2d_6.w_2_deepcopy_36 -> 297 -> 0xcce2400 +0xce91cd0 -> 0xcf59f401745131171435049990_inner_var_913 -> 913 -> 0xce9aac0 +0xccdc440 -> 0xcf59f401745131171435049990_inner_var_951 -> 951 -> 0xcf4a820 +0xca86880 -> batch_norm2d_7.w_2_deepcopy_41 -> 292 -> 0xd05a990 +0xee3f360 -> linear_0.b_0_deepcopy_275 -> 62 -> 0xd041df0 +0xd0365e0 -> 0xcf59f401745131171435049990_inner_var_983 -> 983 -> 0xcbe59c0 +0xca85cc0 -> batch_norm2d_8.b_0_deepcopy_44 -> 289 -> 0xceee210 +0xcf97460 -> 0xce453301745131172820051325_inner_var_1119 -> 1119 -> 0xc891f60 +0xca85150 -> conv2d_9.w_0_deepcopy_47 -> 286 -> 0xcf32540 +0xcc968c8 -> 0xcf59f401745131171435049990_inner_var_349 -> 349 -> 0xcad41e0 +0xcaeed60 -> conv2d_10.w_0_deepcopy_52 -> 281 -> 0xcf508b0 +0xca669c0 -> conv2d_11.w_0_deepcopy_57 -> 276 -> 0xcf50ed0 +0xcdb8ea8 -> 0xcf59f401745131171435049990_inner_var_336 -> 336 -> 0xca79260 +0xca77ae0 -> batch_norm2d_33.w_1_deepcopy_170 -> 163 -> 0xcf21710 +0xca73a60 -> batch_norm2d_5.w_0_deepcopy_28 -> 305 -> 0xcf8df90 +0xccbc790 -> 0xcf59f401745131171435049990_inner_var_435 -> 435 -> 0xd048180 +0xcfab3e0 -> batch_norm2d_12.b_0_deepcopy_64 -> 269 -> 0xcf1dca0 +0xccadc20 -> 0xcf59f401745131171435049990_inner_var_535 -> 535 -> 0xcecc7c0 +0xca76430 -> batch_norm2d_49.w_0_deepcopy_255 -> 82 -> 0xcfa0ad0 +0xca66d90 -> batch_norm2d_10.w_2_deepcopy_56 -> 277 -> 0xcf52d40 +0xccb4190 -> 0xcf59f401745131171435049990_inner_var_451 -> 451 -> 0xcf7c250 +0xd0577c0 -> 0xcf59f401745131171435049990_inner_var_358 -> 358 -> 0xccb4a70 +0xee73310 -> 0xcf59f401745131171435049990_inner_var_373 -> 373 -> 0xccaa1d0 +0xca732c0 -> batch_norm2d_5.w_1_deepcopy_30 -> 303 -> 0xcf63a70 +0xca864b0 -> conv2d_8.w_0_deepcopy_42 -> 291 -> 0xcf3f300 +0xee46150 -> 0xcf59f401745131171435049990_inner_var_848 -> 848 -> 0xce87810 +0xccb8160 -> 0xcf59f401745131171435049990_inner_var_436 -> 436 -> 0xcea5740 +0xcdb9a50 -> batch_norm2d_0.w_0_deepcopy_3 -> 330 -> 0xcf557c0 +0xcaf1780 -> batch_norm2d_13.w_1_deepcopy_70 -> 263 -> 0xcfc0710 +0xca55160 -> batch_norm2d_14.w_1_deepcopy_75 -> 258 -> 0xcfc9610 +0xcccc9b8 -> 0xcf59f401745131171435049990_inner_var_504 -> 504 -> 0xca705e0 +0xd04e4b0 -> batch_norm2d_46.w_1_deepcopy_242 -> 95 -> 0xcf90660 +0xee8aaa8 -> 0xcf59f401745131171435049990_inner_var_793 -> 793 -> 0xcf22070 +0xca73690 -> batch_norm2d_5.b_0_deepcopy_29 -> 304 -> 0xcf35350 +0xccbc3c0 -> 0xcf59f401745131171435049990_inner_var_425 -> 425 -> 0xee8cc70 +0xccc0910 -> 0xcf59f401745131171435049990_inner_var_1025 -> 1025 -> 0xcaac650 +0xcaf3d30 -> batch_norm2d_14.w_2_deepcopy_76 -> 257 -> 0xcfca3d0 +0xca515f8 -> 0xcf59f401745131171435049990_inner_var_601 -> 601 -> 0xc80e2c0 +0xcc9d360 -> conv2d_28.w_0_deepcopy_142 -> 191 -> 0xcfa8980 +0xee8aef0 -> 0xcf59f401745131171435049990_inner_var_798 -> 798 -> 0xcdbe600 +0xcfacf80 -> batch_norm2d_41.w_2_deepcopy_211 -> 122 -> 0xcfc7d60 +0xcaef180 -> batch_norm2d_9.w_2_deepcopy_51 -> 282 -> 0xcf48440 +0xca9e6c0 -> 0xcf59f401745131171435049990_inner_var_565 -> 565 -> 0xceb58e0 +0xccbc3f0 -> 0xcf59f401745131171435049990_inner_var_423 -> 423 -> 0xcea8fb0 +0xccd1610 -> batch_norm2d_39.w_1_deepcopy_200 -> 133 -> 0xcf3a220 +0xca64490 -> conv2d_3.w_0_deepcopy_17 -> 316 -> 0xcf15a30 +0xcccd280 -> 0xcf59f401745131171435049990_inner_var_510 -> 510 -> 0xcf12c40 +0xcae7f80 -> 0xcf59f401745131171435049990_inner_var_837 -> 837 -> 0xcf540d0 +0xccc0290 -> 0xcf59f401745131171435049990_inner_var_1023 -> 1023 -> 0xcbefea0 +0xccc5230 -> 0xcf59f401745131171435049990_inner_var_1045 -> 1045 -> 0xcfb5340 +0xcbf1600 -> conv2d_46.w_0_deepcopy_224 -> 113 -> 0xcf788c0 +0xee748d8 -> 0xcf59f401745131171435049990_inner_var_381 -> 381 -> 0xced3620 +0xce95010 -> constant_folding@_174513115110199844916 -> 43 -> 0xcf2d1d0 +0xca9c950 -> 0xce453301745131172820051325_inner_var_1105 -> 1105 -> 0xce45f90 +0xcbf13e0 -> batch_norm2d_43.w_0_deepcopy_225 -> 112 -> 0xcf61c60 +0xca515e0 -> 0xcf59f401745131171435049990_inner_var_602 -> 602 -> 0xceef0c0 +0xcbf0e70 -> batch_norm2d_43.w_2_deepcopy_228 -> 109 -> 0xcf9baf0 +0xce94b30 -> constant_folding@_174513116811615103119 -> 40 -> 0xcf90680 +0xca67250 -> batch_norm2d_37.w_1_deepcopy_190 -> 143 -> 0xcf9e170 +0xcf9a010 -> 0xce453301745131172820051325_inner_var_1146 -> 1146 -> 0xce5c0c0 +0xccd0758 -> 0xcf59f401745131171435049990_inner_var_490 -> 490 -> 0xcedd640 +0xca9f760 -> 0xcf59f401745131171435049990_inner_var_573 -> 573 -> 0xced1430 +0xcbf01c0 -> batch_norm2d_45.w_0_deepcopy_235 -> 102 -> 0xcf346f0 +0xed55b30 -> 0xcf59f401745131171435049990_inner_var_346 -> 346 -> 0xcea9360 +0xcabc890 -> 0xcf59f401745131171435049990_inner_var_871 -> 871 -> 0xcf028c0 +0xcfac450 -> conv2d_43.w_0_deepcopy_218 -> 116 -> 0xcf23170 +0xcbf3390 -> batch_norm2d_36.b_0_deepcopy_184 -> 149 -> 0xcf1aa60 +0xccad150 -> 0xcf59f401745131171435049990_inner_var_532 -> 532 -> 0xcf806b0 +0xd04efe0 -> batch_norm2d_45.b_0_deepcopy_236 -> 101 -> 0xcfa4800 +0xd04ee10 -> batch_norm2d_45.w_1_deepcopy_237 -> 100 -> 0xcf2bf50 +0xccbb6d0 -> 0xcf59f401745131171435049990_inner_var_427 -> 427 -> 0xced0d30 +0xca9bc18 -> 0xcf59f401745131171435049990_inner_var_743 -> 743 -> 0xcf42f50 +0xee867e0 -> 0xcf59f401745131171435049990_inner_var_766 -> 766 -> 0xccd2480 +0xccc4170 -> 0xcf59f401745131171435049990_inner_var_1039 -> 1039 -> 0xcfb38c0 +0xccdf440 -> 0xcf59f401745131171435049990_inner_var_1032 -> 1032 -> 0xcbecb40 +0xcdbe7e8 -> 0xcf59f401745131171435049990_inner_var_678 -> 678 -> 0xca65250 +0xcdc0c00 -> batch_norm2d_25.b_0_deepcopy_129 -> 204 -> 0xd066e30 +0xcdb5b50 -> conv2d_23.w_0_deepcopy_117 -> 216 -> 0xcf1a470 +0xcaf3960 -> conv2d_15.w_0_deepcopy_77 -> 256 -> 0xcf52c60 +0xccce220 -> 0xcf59f401745131171435049990_inner_var_472 -> 472 -> 0xcf6cb40 +0xd04dd70 -> batch_norm2d_47.b_0_deepcopy_246 -> 91 -> 0xcf9e810 +0xcaf4eb0 -> batch_norm2d_17.w_0_deepcopy_88 -> 245 -> 0xcf6f0e0 +0xcfac9c0 -> batch_norm2d_42.b_0_deepcopy_214 -> 119 -> 0xcf84050 +0xd0754b0 -> constant_folding@_174513116887061917064 -> 0 -> 0xd062b30 +0xce96870 -> batch_norm2d_29.w_0_deepcopy_148 -> 185 -> 0xcfa2740 +0xce94170 -> constant_folding@_174513116820193818025 -> 34 -> 0xce87a10 +0xca9a100 -> 0xcf59f401745131171435049990_inner_var_735 -> 735 -> 0xcc9ee20 +0xcaef920 -> batch_norm2d_9.b_0_deepcopy_49 -> 284 -> 0xcf0a7c0 +0xcae6e80 -> 0xcf59f401745131171435049990_inner_var_829 -> 829 -> 0xee88030 +0xcae6eb0 -> 0xcf59f401745131171435049990_inner_var_827 -> 827 -> 0xcef88f0 +0xccb8a50 -> 0xcf59f401745131171435049990_inner_var_442 -> 442 -> 0xd06eb50 +0xca749a0 -> batch_norm2d_4.b_0_deepcopy_24 -> 309 -> 0xcf02720 +0xcc9f1a0 -> constant_folding@_174513116859911589448 -> 14 -> 0xcf41cc0 +0xcc98968 -> 0xcf59f401745131171435049990_inner_var_399 -> 399 -> 0xcabae00 +0xca65660 -> conv2d_12.w_0_deepcopy_62 -> 271 -> 0xcf51cc0 +0xd04d310 -> batch_norm2d_36.w_2_deepcopy_186 -> 147 -> 0xcf37410 +0xccbb2b8 -> 0xcf59f401745131171435049990_inner_var_412 -> 412 -> 0xcf7cbb0 +0xcaf5df0 -> batch_norm2d_16.b_0_deepcopy_84 -> 249 -> 0xcfce890 +0xca9f778 -> 0xcf59f401745131171435049990_inner_var_572 -> 572 -> 0xee3f300 +0xca76090 -> batch_norm2d_49.w_1_deepcopy_257 -> 80 -> 0xcf420e0 +0xcbf1210 -> batch_norm2d_43.b_0_deepcopy_226 -> 111 -> 0xcf9ef90 +0xcaef550 -> batch_norm2d_9.w_1_deepcopy_50 -> 283 -> 0xcf44e90 +0xca8bb20 -> 0xcf59f401745131171435049990_inner_var_1075 -> 1075 -> 0xee76210 +0xca75ec0 -> batch_norm2d_49.w_2_deepcopy_258 -> 79 -> 0xcf24f50 +0xee400e0 -> batch_norm2d_51.w_1_deepcopy_267 -> 70 -> 0xcfa59c0 +0xca75ad0 -> batch_norm2d_50.w_0_deepcopy_260 -> 77 -> 0xcf20930 +0xccb6800 -> 0xcf59f401745131171435049990_inner_var_468 -> 468 -> 0xccdaac0 +0xcaa18e0 -> 0xcf59f401745131171435049990_inner_var_589 -> 589 -> 0xcec3af0 +0xcccc080 -> 0xcf59f401745131171435049990_inner_var_501 -> 501 -> 0xcad9eb0 +0xca75930 -> batch_norm2d_50.b_0_deepcopy_261 -> 76 -> 0xcfa3840 +0xca767d0 -> batch_norm2d_48.w_2_deepcopy_253 -> 84 -> 0xcf3e380 +0xcdbd6e0 -> 0xcf59f401745131171435049990_inner_var_673 -> 673 -> 0xceb72b0 +0xcccad60 -> 0xcf59f401745131171435049990_inner_var_1077 -> 1077 -> 0xcfbd260 +0xce96c40 -> conv2d_29.w_0_deepcopy_147 -> 186 -> 0xcf95890 +0xee40780 -> batch_norm2d_50.w_2_deepcopy_263 -> 74 -> 0xcf2bc00 +0xcbf34d0 -> batch_norm2d_38.w_0_deepcopy_193 -> 140 -> 0xcfaa490 +0xcdbc198 -> 0xcf59f401745131171435049990_inner_var_661 -> 661 -> 0xcea3d70 +0xccbee80 -> 0xcf59f401745131171435049990_inner_var_1018 -> 1018 -> 0xcbeeb90 +0xee405e0 -> conv2d_54.w_0_deepcopy_264 -> 73 -> 0xd059850 +0xd057808 -> 0xcf59f401745131171435049990_inner_var_355 -> 355 -> 0xcca80b0 +0xc80d510 -> batch_norm2d_39.w_2_deepcopy_201 -> 132 -> 0xcf15bf0 +0xcbf0ca0 -> conv2d_47.w_0_deepcopy_229 -> 108 -> 0xcf6e400 +0xd045ca0 -> 0xcf59f401745131171435049990_inner_var_699 -> 699 -> 0xca7ed30 +0xcdbf8c0 -> 0xcf59f401745131171435049990_inner_var_685 -> 685 -> 0xca54780 +0xcccb8f8 -> 0xcf59f401745131171435049990_inner_var_496 -> 496 -> 0xcf597c0 +0xcabab30 -> 0xcf59f401745131171435049990_inner_var_865 -> 865 -> 0xca88040 +0xcfad8e0 -> batch_norm2d_40.w_2_deepcopy_206 -> 127 -> 0xcf35500 +0xee3f880 -> batch_norm2d_52.w_1_deepcopy_272 -> 65 -> 0xcf9ab00 +0xee3f6e0 -> batch_norm2d_52.w_2_deepcopy_273 -> 64 -> 0xcf3ce10 +0xca88400 -> 0xcf59f401745131171435049990_inner_var_361 -> 361 -> 0xcca6850 +0xcfacdb0 -> conv2d_42.w_0_deepcopy_212 -> 121 -> 0xcf9c9b0 +0xcf6dda0 -> conv2d_transpose_0.w_0_deepcopy_278 -> 59 -> 0xcf28750 +0x118c8610 -> conv2d_2.w_0_deepcopy_12 -> 321 -> 0xcfcc130 +0xca870f0 -> im_shape -> 332 -> 0xcec9720 +0xee874c0 -> 0xcf59f401745131171435049990_inner_var_768 -> 768 -> 0xcdb56f0 +0xca76f10 -> conv2d_51.w_0_deepcopy_249 -> 88 -> 0xccd6fb0 +0xcaf3590 -> batch_norm2d_15.w_0_deepcopy_78 -> 255 -> 0xcfcbe00 +0xca64860 -> batch_norm2d_2.w_2_deepcopy_16 -> 317 -> 0xcf808f0 +0xd041450 -> constant_folding@_174513116830651898832 -> 28 -> 0xed1a750 +0xee87460 -> 0xcf59f401745131171435049990_inner_var_772 -> 772 -> 0xce96460 +0xee40420 -> batch_norm2d_51.w_0_deepcopy_265 -> 72 -> 0xcf2f740 +0xcae88c0 -> 0xcf59f401745131171435049990_inner_var_846 -> 846 -> 0xca57220 +0xccdf100 -> 0xcf59f401745131171435049990_inner_var_958 -> 958 -> 0xca5d5f0 +0xca665a0 -> batch_norm2d_11.w_0_deepcopy_58 -> 275 -> 0xcf53290 +0xccbccf0 -> 0xcf59f401745131171435049990_inner_var_428 -> 428 -> 0xcab8450 +0xee73730 -> 0xcf59f401745131171435049990_inner_var_385 -> 385 -> 0xcf14230 +0xcbf0390 -> conv2d_48.w_0_deepcopy_234 -> 103 -> 0xcf6e750 +0xd041110 -> constant_folding@_174513116835231166835 -> 26 -> 0xcf0c190 +0xd040f70 -> constant_folding@_174513116836671179736 -> 25 -> 0xcf806d0 +0xd052fa0 -> 0xcf59f401745131171435049990_inner_var_641 -> 641 -> 0xced0120 +0xccd5e50 -> batch_norm2d_31.w_1_deepcopy_160 -> 173 -> 0xcfa09b0 +0xcae5e08 -> 0xcf59f401745131171435049990_inner_var_818 -> 818 -> 0xd076ae0 +0xccccd40 -> 0xcf59f401745131171435049990_inner_var_517 -> 517 -> 0xca726f0 +0xccba190 -> 0xcf59f401745131171435049990_inner_var_409 -> 409 -> 0xceb3080 +0xce8fa80 -> 0xcf59f401745131171435049990_inner_var_906 -> 906 -> 0xcea5b20 +0xccabf98 -> 0xcf59f401745131171435049990_inner_var_521 -> 521 -> 0xcab5850 +0xcadf9f8 -> 0xcf59f401745131171435049990_inner_var_538 -> 538 -> 0xcccac60 +0xee3fbc0 -> batch_norm2d_52.w_0_deepcopy_270 -> 67 -> 0xcf2c170 +0xcc9ffd0 -> 0xcf59f401745131171435049990_inner_var_971 -> 971 -> 0xcbe2840 +0x118c9d40 -> batch_norm2d_0.w_2_deepcopy_6 -> 327 -> 0xcf55d60 +0xee3f1a0 -> linear_1.w_0_deepcopy_276 -> 61 -> 0xcf61960 +0xccc2030 -> 0xcf59f401745131171435049990_inner_var_1031 -> 1031 -> 0xcaaddd0 +0xcfac7f0 -> batch_norm2d_42.w_1_deepcopy_215 -> 118 -> 0xcf1c020 +0xca8be40 -> 0xcf59f401745131171435049990_inner_var_1076 -> 1076 -> 0xee76630 +0xd0577a8 -> 0xcf59f401745131171435049990_inner_var_359 -> 359 -> 0xca758f0 +0xccb79d8 -> 0xcf59f401745131171435049990_inner_var_431 -> 431 -> 0xcade0a0 +0xcca0990 -> 0xcf59f401745131171435049990_inner_var_973 -> 973 -> 0xcbe3080 +0xce944b0 -> constant_folding@_174513116817322536023 -> 36 -> 0xce89990 +0xc80c210 -> conv2d_38.w_0_deepcopy_192 -> 141 -> 0xd068b90 +0xccc06f0 -> 0xcf59f401745131171435049990_inner_var_1024 -> 1024 -> 0xcfae170 +0xccb4b38 -> 0xcf59f401745131171435049990_inner_var_456 -> 456 -> 0xcabc9a0 +0xd0405b0 -> constant_folding@_174513116850011146642 -> 19 -> 0xcf2d600 +0xd04ea70 -> conv2d_49.w_0_deepcopy_239 -> 98 -> 0xcfbfd60 +0xccd0b10 -> 0xcf59f401745131171435049990_inner_var_500 -> 500 -> 0xca91ea0 +0xcabbfc0 -> 0xcf59f401745131171435049990_inner_var_869 -> 869 -> 0xcf1c9b0 +0xca56b50 -> batch_norm2d_35.w_1_deepcopy_180 -> 153 -> 0xcf3e960 +0xcaefcf0 -> batch_norm2d_9.w_0_deepcopy_48 -> 285 -> 0xcf0fa10 +0xca60da0 -> 0xcf59f401745131171435049990_inner_var_964 -> 964 -> 0xcbe0d60 +0xd040c30 -> constant_folding@_174513116839857308738 -> 23 -> 0xcdb8950 +0xd058968 -> 0xcf59f401745131171435049990_inner_var_363 -> 363 -> 0xcadee10 +0xca50380 -> 0xcf59f401745131171435049990_inner_var_598 -> 598 -> 0xcf24180 +0xccb7028 -> 0xcf59f401745131171435049990_inner_var_954 -> 954 -> 0xca5da10 +0xcc95050 -> 0xcf59f401745131171435049990_inner_var_855 -> 855 -> 0xcca8320 +0xee3f520 -> linear_0.w_0_deepcopy_274 -> +I0420 14:39:33.833269 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:33.855278 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:33.864012 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "memcpy_d2h(phi_kernel)" (%arg_0 {stop_gradient:true}) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2543} : (custom_device_tensor) -> cpu_tensor + (%1) = "scale_(phi_kernel)" (%0, %2) {bias:1,bias_after_scale:true,is_inplace:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale_",origin_id:2544,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%3) = "builtin.combine" [id:2545] (%arg_0) {origin_id:505,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%4) = "stack(phi_kernel)" (%3) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2546,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%5) = "builtin.combine" [id:2547] (%1) {origin_id:507,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%6) = "stack(phi_kernel)" (%5) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2548,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%7) = "slice(phi_kernel)" (%8, %4, %6) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2549,stop_gradient:[true]} : (custom_device_tensor<-1xi64>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor + (%9) = "memcpy_d2h(phi_kernel)" (%7) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2550} : (custom_device_tensor) -> cpu_tensor + (%10) = "scale_(phi_kernel)" (%9, %2) {bias:1,bias_after_scale:true,is_inplace:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale_",origin_id:2551,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%11) = "builtin.combine" [id:2552] (%7) {origin_id:512,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%12) = "stack(phi_kernel)" (%11) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2553,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%13) = "builtin.combine" [id:2554] (%10) {origin_id:514,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%14) = "stack(phi_kernel)" (%13) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2555,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%15) = "slice(phi_kernel)" (%16, %12, %14) {axes:[0],decrease_axis:[],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2556,stop_gradient:[false]} : (custom_device_tensor<-1x15x-1x-1xf32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x15x-1x-1xf32> + (%17) = "memcpy_d2h(phi_kernel)" (%7) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2557} : (custom_device_tensor) -> cpu_tensor + (%18) = "scale_(phi_kernel)" (%17, %2) {bias:1,bias_after_scale:true,is_inplace:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale_",origin_id:2558,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%19) = "builtin.combine" [id:2559] (%7) {origin_id:519,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%20) = "stack(phi_kernel)" (%19) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2560,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%21) = "builtin.combine" [id:2561] (%18) {origin_id:521,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%22) = "stack(phi_kernel)" (%21) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2562,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%23) = "slice(phi_kernel)" (%24, %20, %22) {axes:[0],decrease_axis:[],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2563,stop_gradient:[false]} : (custom_device_tensor<-1x60x-1x-1xf32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x60x-1x-1xf32> + (%25) = "memcpy_d2h(phi_kernel)" (%7) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2564} : (custom_device_tensor) -> cpu_tensor + (%26) = "scale_(phi_kernel)" (%25, %2) {bias:1,bias_after_scale:true,is_inplace:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale_",origin_id:2565,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%27) = "builtin.combine" [id:2566] (%7) {origin_id:526,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%28) = "stack(phi_kernel)" (%27) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2567,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%29) = "builtin.combine" [id:2568] (%26) {origin_id:528,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%30) = "stack(phi_kernel)" (%29) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2569,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%31) = "slice(phi_kernel)" (%32, %28, %30) {axes:[0],decrease_axis:[],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2570,stop_gradient:[false]} : (custom_device_tensor<-1x2xf32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<-1x2xf32> + (%33) = "full_like(phi_kernel)" (%34, %2) {dtype:float32,kernel_key:,kernel_name:"full_like",op_name:"pd_op.full_like",origin_id:2571,place:Place(undefined:0),stop_gradient:[true]} : (custom_device_tensor<-1x4xf32>, cpu_tensor<1xf32>) -> custom_device_tensor<-1x4xf32> + (%35) = "memcpy_d2h(phi_kernel)" (%15) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2572} : (custom_device_tensor<-1x15x-1x-1xf32>) -> cpu_tensor<-1x15x-1x-1xf32> + (%36) = "memcpy_d2h(phi_kernel)" (%23) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2573} : (custom_device_tensor<-1x60x-1x-1xf32>) -> cpu_tensor<-1x60x-1x-1xf32> + (%37) = "memcpy_d2h(phi_kernel)" (%31) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2574} : (custom_device_tensor<-1x2xf32>) -> cpu_tensor<-1x2xf32> + (%38) = "memcpy_d2h(phi_kernel)" (%34) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2575} : (custom_device_tensor<-1x4xf32>) -> cpu_tensor<-1x4xf32> + (%39) = "memcpy_d2h(phi_kernel)" (%33) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2576} : (custom_device_tensor<-1x4xf32>) -> cpu_tensor<-1x4xf32> + (%40, %41, %42) = "generate_proposals(phi_kernel)" (%35, %36, %37, %38, %39) {eta:1,kernel_key:,kernel_name:"generate_proposals",min_size:0,nms_thresh:0.7,op_name:"pd_op.generate_proposals",origin_id:2577,pixel_offset:false,post_nms_top_n:1000,pre_nms_top_n:6000,stop_gradient:[true,true,true]} : (cpu_tensor<-1x15x-1x-1xf32>, cpu_tensor<-1x60x-1x-1xf32>, cpu_tensor<-1x2xf32>, cpu_tensor<-1x4xf32>, cpu_tensor<-1x4xf32>) -> cpu_tensor<-1x4xf32>, cpu_tensor<-1x1xf32>, cpu_tensor<-1xf32> + (%43) = "flatten(phi_kernel)" (%41) {kernel_key:,kernel_name:"flatten",op_name:"pd_op.flatten",origin_id:2578,start_axis:0,stop_axis:1,stop_gradient:[true]} : (cpu_tensor<-1x1xf32>) -> cpu_tensor<-1xf32> + (%44) = "array_length(phi_kernel)" (%45) {kernel_key:,kernel_name:"array_length",op_name:"pd_op.array_length",origin_id:2579} : (cpu_tensor_array) -> cpu_tensor<1xi64> + (%46) = "array_write_(phi_kernel)" (%45, %40, %44) {is_inplace:true,kernel_key:,kernel_name:"array_write",op_name:"pd_op.array_write_",origin_id:2580} : (cpu_tensor_array, cpu_tensor<-1x4xf32>, cpu_tensor<1xi64>) -> cpu_tensor_array + (%47) = "shape64(phi_kernel)" (%40) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2581,stop_gradient:[true]} : (cpu_tensor<-1x4xf32>) -> cpu_tensor<2xi64> + (%48) = "slice(phi_kernel)" (%47, %49, %50) {axes:[0],decrease_axis:[],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2582,stop_gradient:[true]} : (cpu_tensor<2xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<1xi64> + (%51) = "array_length(phi_kernel)" (%52) {kernel_key:,kernel_name:"array_length",op_name:"pd_op.array_length",origin_id:2583} : (cpu_tensor_array) -> cpu_tensor<1xi64> + (%53) = "array_write_(phi_kernel)" (%52, %48, %51) {is_inplace:true,kernel_key:,kernel_name:"array_write",op_name:"pd_op.array_write_",origin_id:2584} : (cpu_tensor_array, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor_array + (%54) = "memcpy_d2h(phi_kernel)" (%arg_0) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2585} : (custom_device_tensor) -> cpu_tensor + (%55) = "scale_(phi_kernel)" (%54, %2) {bias:1,bias_after_scale:true,is_inplace:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale_",origin_id:2586,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%56) = "less_than(phi_kernel)" (%55, %57) {kernel_key:,kernel_name:"less_than",op_name:"pd_op.less_than",origin_id:2587,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> cpu_tensor + (%58) = "memcpy_h2d(phi_kernel)" (%56) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2588} : (cpu_tensor) -> custom_device_tensor + (%59) = "memcpy_h2d(phi_kernel)" (%55) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2589} : (cpu_tensor) -> custom_device_tensor + (%60) = "memcpy_h2d(phi_kernel)" (%40) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2590} : (cpu_tensor<-1x4xf32>) -> custom_device_tensor<-1x4xf32> + (%61) = "memcpy_h2d(phi_kernel)" (%42) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2591} : (cpu_tensor<-1xf32>) -> custom_device_tensor<-1xf32> + (%62) = "memcpy_h2d(phi_kernel)" (%41) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2592} : (cpu_tensor<-1x1xf32>) -> custom_device_tensor<-1x1xf32> + (%63) = "memcpy_h2d(phi_kernel)" (%43) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2593} : (cpu_tensor<-1xf32>) -> custom_device_tensor<-1xf32> + (%64) = "memcpy_h2d(phi_kernel)" (%40) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2594} : (cpu_tensor<-1x4xf32>) -> custom_device_tensor<-1x4xf32> + () = "cf.yield" [id:2595] (%58, %59, %34, %7, %24, %60, %61, %62, %16, %63, %64) {origin_id:546} : (custom_device_tensor, custom_device_tensor, custom_device_tensor<-1x4xf32>, custom_device_tensor, custom_device_tensor<-1x60x-1x-1xf32>, custom_device_tensor<-1x4xf32>, custom_device_tensor<-1xf32>, custom_device_tensor<-1x1xf32>, custom_device_tensor<-1x15x-1x-1xf32>, custom_device_tensor<-1xf32>, custom_device_tensor<-1x4xf32>) -> +} +I0420 14:39:33.864136 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 1104 ) = pd_op.memcpy_d2h ( 1094 ) +1: ( 1104 ) ( 1105 ) = pd_op.scale_ ( 19 ) ( 1104 ) +2: ( 1106 1094 ) = builtin_combine_instruction ( 1094 ) +3: ( 1107 ) = pd_op.stack ( 1106 1094 ) +4: ( 1108 1105 ) = builtin_combine_instruction ( 1105 ) +5: ( 1109 ) = pd_op.stack ( 1108 1105 ) +6: ( 1110 ) = pd_op.slice ( 1109 ) ( 1107 ) ( 722 ) +7: ( 1111 ) = pd_op.memcpy_d2h ( 1110 ) +8: ( 1111 ) ( 1112 ) = pd_op.scale_ ( 19 ) ( 1111 ) +9: ( 1113 1110 ) = builtin_combine_instruction ( 1110 ) +10: ( 1114 ) = pd_op.stack ( 1113 1110 ) +11: ( 1115 1112 ) = builtin_combine_instruction ( 1112 ) +12: ( 1116 ) = pd_op.stack ( 1115 1112 ) +13: ( 1117 ) = pd_op.slice ( 1116 ) ( 1114 ) ( 696 ) +14: ( 1118 ) = pd_op.memcpy_d2h ( 1110 ) +15: ( 1118 ) ( 1119 ) = pd_op.scale_ ( 19 ) ( 1118 ) +16: ( 1120 1110 ) = builtin_combine_instruction ( 1110 ) +17: ( 1121 ) = pd_op.stack ( 1120 1110 ) +18: ( 1122 1119 ) = builtin_combine_instruction ( 1119 ) +19: ( 1123 ) = pd_op.stack ( 1122 1119 ) +20: ( 1124 ) = pd_op.slice ( 1121 ) ( 1123 ) ( 698 ) +21: ( 1125 ) = pd_op.memcpy_d2h ( 1110 ) +22: ( 1125 ) ( 1126 ) = pd_op.scale_ ( 19 ) ( 1125 ) +23: ( 1127 1110 ) = builtin_combine_instruction ( 1110 ) +24: ( 1128 ) = pd_op.stack ( 1127 1110 ) +25: ( 1129 1126 ) = builtin_combine_instruction ( 1126 ) +26: ( 1130 ) = pd_op.stack ( 1129 1126 ) +27: ( 1131 ) = pd_op.slice ( 1130 ) ( 1128 ) ( 332 ) +28: ( 1132 ) = pd_op.full_like ( 19 ) ( 719 ) +29: ( 1133 ) = pd_op.memcpy_d2h ( 1117 ) +30: ( 1134 ) = pd_op.memcpy_d2h ( 1124 ) +31: ( 1135 ) = pd_op.memcpy_d2h ( 1131 ) +32: ( 1136 ) = pd_op.memcpy_d2h ( 719 ) +33: ( 1137 ) = pd_op.memcpy_d2h ( 1132 ) +34: ( 1140 ) ( 1139 ) ( 1138 ) = pd_op.generate_proposals ( 1137 ) ( 1136 ) ( 1135 ) ( 1134 ) ( 1133 ) +35: ( 1141 ) = pd_op.flatten ( 1139 ) +36: ( 1142 ) = pd_op.array_length ( 726 ) +37: ( 726 ) = pd_op.array_write_ ( 1142 ) ( 1138 ) ( 726 ) +38: ( 1143 ) = pd_op.shape64 ( 1138 ) +39: ( 1144 ) = pd_op.slice ( 43 ) ( 44 ) ( 1143 ) +40: ( 1145 ) = pd_op.array_length ( 725 ) +41: ( 725 ) = pd_op.array_write_ ( 1145 ) ( 1144 ) ( 725 ) +42: ( 1146 ) = pd_op.memcpy_d2h ( 1094 ) +43: ( 1146 ) ( 1147 ) = pd_op.scale_ ( 19 ) ( 1146 ) +44: ( 1148 ) = pd_op.less_than ( 724 ) ( 1147 ) +45: ( 1149 ) = pd_op.memcpy_h2d ( 1148 ) +46: ( 1150 ) = pd_op.memcpy_h2d ( 1147 ) +47: ( 1151 ) = pd_op.memcpy_h2d ( 1138 ) +48: ( 1152 ) = pd_op.memcpy_h2d ( 1140 ) +49: ( 1153 ) = pd_op.memcpy_h2d ( 1139 ) +50: ( 1154 ) = pd_op.memcpy_h2d ( 1141 ) +51: ( 1155 ) = pd_op.memcpy_h2d ( 1138 ) +52: = yield_instruction ( 698 ) ( 1155 ) ( 1154 ) ( 1153 ) ( 1152 ) ( 1151 ) ( 1110 ) ( 719 ) ( 696 ) ( 1150 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> constant_folding@_174513116887061917064 -> 0xd062b30 +1 -> constant_folding@_174513116885637196163 -> 0xd04b8a0 +2 -> constant_folding@_174513116884162097162 -> 0xcf7caa0 +3 -> constant_folding@_174513116882724144161 -> 0xcdb8360 +4 -> constant_folding@_174513116880873974160 -> 0xee71f60 +5 -> constant_folding@_174513116876880248258 -> 0xee71fe0 +6 -> constant_folding@_174513116875198908257 -> 0xc386660 +7 -> constant_folding@_174513116872098526255 -> 0xd061370 +8 -> constant_folding@_174513116870501220354 -> 0xcea7bc0 +9 -> constant_folding@_174513116869046783353 -> 0xcf2b9b0 +10 -> constant_folding@_174513116867667180352 -> 0xee46cc0 +11 -> constant_folding@_174513116865440940351 -> 0xcf3edb0 +12 -> constant_folding@_174513116864033493350 -> 0xcf3a750 +13 -> constant_folding@_174513116861634597449 -> 0xcf09130 +14 -> constant_folding@_174513116859911589448 -> 0xcf41cc0 +15 -> constant_folding@_174513116857122500446 -> 0xd0606f0 +16 -> constant_folding@_174513116855594796545 -> 0xcf11a20 +17 -> constant_folding@_174513116853662943544 -> 0xcdb8c50 +18 -> constant_folding@_174513116852268476543 -> 0xcfbe9a0 +19 -> constant_folding@_174513116850011146642 -> 0xcf2d600 +20 -> constant_folding@_174513116844224746641 -> 0xcdb72f0 +21 -> constant_folding@_174513116842705804740 -> 0xcf92430 +22 -> constant_folding@_174513116841278885739 -> 0xcdb8970 +23 -> constant_folding@_174513116839857308738 -> 0xcdb8950 +24 -> constant_folding@_174513116838085921737 -> 0xcf83ea0 +25 -> constant_folding@_174513116836671179736 -> 0xcf806d0 +26 -> constant_folding@_174513116835231166835 -> 0xcf0c190 +27 -> constant_folding@_174513116833656139834 -> 0xcf419a0 +28 -> constant_folding@_174513116830651898832 -> 0xed1a750 +29 -> constant_folding@_174513116827593559930 -> 0xcf437e0 +30 -> constant_folding@_174513116825929047929 -> 0xcf7c3f0 +31 -> constant_folding@_174513116824495087928 -> 0xcf3ae90 +32 -> constant_folding@_174513116823069183927 -> 0xcf64350 +33 -> constant_folding@_174513116821619066926 -> 0xcff2a20 +34 -> constant_folding@_174513116820193818025 -> 0xce87a10 +35 -> constant_folding@_174513116818740431024 -> 0xce98460 +36 -> constant_folding@_174513116817322536023 -> 0xce89990 +37 -> constant_folding@_174513116815886735022 -> 0xcf7aeb0 +38 -> constant_folding@_174513116814471704121 -> 0xd05fe30 +39 -> constant_folding@_174513116813053746120 -> 0xcf61980 +40 -> constant_folding@_174513116811615103119 -> 0xcf90680 +41 -> constant_folding@_174513115258237754718 -> 0xcfce240 +42 -> constant_folding@_174513115176548053017 -> 0xcf8c2c0 +43 -> constant_folding@_174513115110199844916 -> 0xcf2d1d0 +44 -> constant_folding@_174513115013851471515 -> 0xcf1a280 +45 -> constant_folding@_174513114941937302314 -> 0xcf04b20 +46 -> constant_folding@_174513114857650263913 -> 0xcf7d3f0 +47 -> constant_folding@_174513114736420657211 -> 0xd059870 +48 -> constant_folding@_174513114667774590410 -> 0xcf7f8b0 +49 -> constant_folding@_17451311460158745859 -> 0xcf7a460 +50 -> constant_folding@_17451311452954254518 -> 0xd05b470 +51 -> constant_folding@_17451311445836590377 -> 0xceddd30 +52 -> constant_folding@_17451311436834671136 -> 0xcf820a0 +53 -> constant_folding@_17451311430626338225 -> 0xcf624a0 +54 -> constant_folding@_17451311422710820934 -> 0xcede1e0 +55 -> constant_folding@_17451311415972965563 -> 0xd05ecd0 +56 -> constant_folding@_17451311399980650772 -> 0xcf619e0 +57 -> constant_folding@_17451311284029886470 -> 0xd069f60 +58 -> conv2d_56.w_0_deepcopy_280 -> 0xcf344c0 +59 -> conv2d_transpose_0.w_0_deepcopy_278 -> 0xcf28750 +60 -> linear_1.b_0_deepcopy_277 -> 0xcf3e700 +61 -> linear_1.w_0_deepcopy_276 -> 0xcf61960 +62 -> linear_0.b_0_deepcopy_275 -> 0xd041df0 +63 -> linear_0.w_0_deepcopy_274 -> 0xcfa9fb0 +64 -> batch_norm2d_52.w_2_deepcopy_273 -> 0xcf3ce10 +65 -> batch_norm2d_52.w_1_deepcopy_272 -> 0xcf9ab00 +66 -> batch_norm2d_52.b_0_deepcopy_271 -> 0xcf193d0 +67 -> batch_norm2d_52.w_0_deepcopy_270 -> 0xcf2c170 +68 -> conv2d_55.w_0_deepcopy_269 -> 0xcf3b8c0 +69 -> batch_norm2d_51.w_2_deepcopy_268 -> 0xcf2c930 +70 -> batch_norm2d_51.w_1_deepcopy_267 -> 0xcfa59c0 +71 -> batch_norm2d_51.b_0_deepcopy_266 -> 0xcf1b990 +72 -> batch_norm2d_51.w_0_deepcopy_265 -> 0xcf2f740 +73 -> conv2d_54.w_0_deepcopy_264 -> 0xd059850 +74 -> batch_norm2d_50.w_2_deepcopy_263 -> 0xcf2bc00 +75 -> batch_norm2d_50.w_1_deepcopy_262 -> 0xcf33710 +76 -> batch_norm2d_50.b_0_deepcopy_261 -> 0xcfa3840 +77 -> batch_norm2d_50.w_0_deepcopy_260 -> 0xcf20930 +78 -> conv2d_53.w_0_deepcopy_259 -> 0xcfa3490 +79 -> batch_norm2d_49.w_2_deepcopy_258 -> 0xcf24f50 +80 -> batch_norm2d_49.w_1_deepcopy_257 -> 0xcf420e0 +81 -> batch_norm2d_49.b_0_deepcopy_256 -> 0xcf39210 +82 -> batch_norm2d_49.w_0_deepcopy_255 -> 0xcfa0ad0 +83 -> conv2d_52.w_0_deepcopy_254 -> 0xcfc1c10 +84 -> batch_norm2d_48.w_2_deepcopy_253 -> 0xcf3e380 +85 -> batch_norm2d_48.w_1_deepcopy_252 -> 0xcf28490 +86 -> batch_norm2d_48.b_0_deepcopy_251 -> 0xcf165b0 +87 -> batch_norm2d_48.w_0_deepcopy_250 -> 0xcf3e660 +88 -> conv2d_51.w_0_deepcopy_249 -> 0xccd6fb0 +89 -> batch_norm2d_47.w_2_deepcopy_248 -> 0xcf33b60 +90 -> batch_norm2d_47.w_1_deepcopy_247 -> 0xcf230d0 +91 -> batch_norm2d_47.b_0_deepcopy_246 -> 0xcf9e810 +92 -> batch_norm2d_47.w_0_deepcopy_245 -> 0xcf3a620 +93 -> conv2d_50.w_0_deepcopy_244 -> 0xcf64fa0 +94 -> batch_norm2d_46.w_2_deepcopy_243 -> 0xcfa1c80 +95 -> batch_norm2d_46.w_1_deepcopy_242 -> 0xcf90660 +96 -> batch_norm2d_46.b_0_deepcopy_241 -> 0xcf60140 +97 -> batch_norm2d_46.w_0_deepcopy_240 -> 0xcf93910 +98 -> conv2d_49.w_0_deepcopy_239 -> 0xcfbfd60 +99 -> batch_norm2d_45.w_2_deepcopy_238 -> 0xcf2ccb0 +100 -> batch_norm2d_45.w_1_deepcopy_237 -> 0xcf2bf50 +101 -> batch_norm2d_45.b_0_deepcopy_236 -> 0xcfa4800 +102 -> batch_norm2d_45.w_0_deepcopy_235 -> 0xcf346f0 +103 -> conv2d_48.w_0_deepcopy_234 -> 0xcf6e750 +104 -> batch_norm2d_44.w_2_deepcopy_233 -> 0xcf9c920 +105 -> batch_norm2d_44.w_1_deepcopy_232 -> 0xce972f0 +106 -> batch_norm2d_44.b_0_deepcopy_231 -> 0xcfa37a0 +107 -> batch_norm2d_44.w_0_deepcopy_230 -> 0xcf1cb30 +108 -> conv2d_47.w_0_deepcopy_229 -> 0xcf6e400 +109 -> batch_norm2d_43.w_2_deepcopy_228 -> 0xcf9baf0 +110 -> batch_norm2d_43.w_1_deepcopy_227 -> 0xcf2a900 +111 -> batch_norm2d_43.b_0_deepcopy_226 -> 0xcf9ef90 +112 -> batch_norm2d_43.w_0_deepcopy_225 -> 0xcf61c60 +113 -> conv2d_46.w_0_deepcopy_224 -> 0xcf788c0 +114 -> conv2d_45.w_0_deepcopy_222 -> 0xcf30990 +115 -> conv2d_44.w_0_deepcopy_220 -> 0xcf36b60 +116 -> conv2d_43.w_0_deepcopy_218 -> 0xcf23170 +117 -> batch_norm2d_42.w_2_deepcopy_216 -> 0xcfa3f80 +118 -> batch_norm2d_42.w_1_deepcopy_215 -> 0xcf1c020 +119 -> batch_norm2d_42.b_0_deepcopy_214 -> 0xcf84050 +120 -> batch_norm2d_42.w_0_deepcopy_213 -> 0xcf3c660 +121 -> conv2d_42.w_0_deepcopy_212 -> 0xcf9c9b0 +122 -> batch_norm2d_41.w_2_deepcopy_211 -> 0xcfc7d60 +123 -> batch_norm2d_41.w_1_deepcopy_210 -> 0xcf2da20 +124 -> batch_norm2d_41.b_0_deepcopy_209 -> 0xcf8ca40 +125 -> batch_norm2d_41.w_0_deepcopy_208 -> 0xcfc5960 +126 -> conv2d_41.w_0_deepcopy_207 -> 0xcf91450 +127 -> batch_norm2d_40.w_2_deepcopy_206 -> 0xcf35500 +128 -> batch_norm2d_40.w_1_deepcopy_205 -> 0xcfc3720 +129 -> batch_norm2d_40.b_0_deepcopy_204 -> 0xcf41f80 +130 -> batch_norm2d_40.w_0_deepcopy_203 -> 0xcf63a50 +131 -> conv2d_40.w_0_deepcopy_202 -> 0xc80ecf0 +132 -> batch_norm2d_39.w_2_deepcopy_201 -> 0xcf15bf0 +133 -> batch_norm2d_39.w_1_deepcopy_200 -> 0xcf3a220 +134 -> batch_norm2d_39.b_0_deepcopy_199 -> 0xcf35220 +135 -> batch_norm2d_39.w_0_deepcopy_198 -> 0xcf34d40 +136 -> conv2d_39.w_0_deepcopy_197 -> 0xd0753d0 +137 -> batch_norm2d_38.w_2_deepcopy_196 -> 0xcfa49c0 +138 -> batch_norm2d_38.w_1_deepcopy_195 -> 0xcefd090 +139 -> batch_norm2d_38.b_0_deepcopy_194 -> 0xcf14990 +140 -> batch_norm2d_38.w_0_deepcopy_193 -> 0xcfaa490 +141 -> conv2d_38.w_0_deepcopy_192 -> 0xd068b90 +142 -> batch_norm2d_37.w_2_deepcopy_191 -> 0xcf286a0 +143 -> batch_norm2d_37.w_1_deepcopy_190 -> 0xcf9e170 +144 -> batch_norm2d_37.b_0_deepcopy_189 -> 0xcf8d310 +145 -> batch_norm2d_37.w_0_deepcopy_188 -> 0xcf9cbb0 +146 -> conv2d_37.w_0_deepcopy_187 -> 0xd063c10 +147 -> batch_norm2d_36.w_2_deepcopy_186 -> 0xcf37410 +148 -> batch_norm2d_36.w_1_deepcopy_185 -> 0xcf24090 +149 -> batch_norm2d_36.b_0_deepcopy_184 -> 0xcf1aa60 +150 -> batch_norm2d_36.w_0_deepcopy_183 -> 0xd065350 +151 -> conv2d_36.w_0_deepcopy_182 -> 0xd05fe10 +152 -> batch_norm2d_35.w_2_deepcopy_181 -> 0xcf14710 +153 -> batch_norm2d_35.w_1_deepcopy_180 -> 0xcf3e960 +154 -> batch_norm2d_35.b_0_deepcopy_179 -> 0xcf40ec0 +155 -> batch_norm2d_35.w_0_deepcopy_178 -> 0xcf396f0 +156 -> conv2d_35.w_0_deepcopy_177 -> 0xd05bfc0 +157 -> batch_norm2d_34.w_2_deepcopy_176 -> 0xcf3b470 +158 -> batch_norm2d_34.w_1_deepcopy_175 -> 0xcf38a90 +159 -> batch_norm2d_34.b_0_deepcopy_174 -> 0xcf3a9e0 +160 -> batch_norm2d_34.w_0_deepcopy_173 -> 0xcfbfa40 +161 -> conv2d_34.w_0_deepcopy_172 -> 0xcf831a0 +162 -> batch_norm2d_33.w_2_deepcopy_171 -> 0xcf3a3e0 +163 -> batch_norm2d_33.w_1_deepcopy_170 -> 0xcf21710 +164 -> batch_norm2d_33.b_0_deepcopy_169 -> 0xcfa22f0 +165 -> batch_norm2d_33.w_0_deepcopy_168 -> 0xcfa2c80 +166 -> conv2d_33.w_0_deepcopy_167 -> 0xcf80d10 +167 -> batch_norm2d_32.w_2_deepcopy_166 -> 0xcf91ac0 +168 -> batch_norm2d_32.w_1_deepcopy_165 -> 0xcf3d360 +169 -> batch_norm2d_32.b_0_deepcopy_164 -> 0xcf3e100 +170 -> batch_norm2d_32.w_0_deepcopy_163 -> 0xcf218b0 +171 -> conv2d_32.w_0_deepcopy_162 -> 0xcf8c2a0 +172 -> batch_norm2d_31.w_2_deepcopy_161 -> 0xcf37b70 +173 -> batch_norm2d_31.w_1_deepcopy_160 -> 0xcfa09b0 +174 -> batch_norm2d_31.b_0_deepcopy_159 -> 0xcfa3680 +175 -> batch_norm2d_31.w_0_deepcopy_158 -> 0xcf910a0 +176 -> conv2d_31.w_0_deepcopy_157 -> 0xcf7c3d0 +177 -> batch_norm2d_30.w_2_deepcopy_156 -> 0xc809ae0 +178 -> batch_norm2d_30.w_1_deepcopy_155 -> 0xcf63ec0 +179 -> batch_norm2d_30.b_0_deepcopy_154 -> 0xcf28de0 +180 -> batch_norm2d_30.w_0_deepcopy_153 -> 0xcf3c2d0 +181 -> conv2d_30.w_0_deepcopy_152 -> 0xcf7ae90 +182 -> batch_norm2d_29.w_2_deepcopy_151 -> 0xcf93530 +183 -> batch_norm2d_29.w_1_deepcopy_150 -> 0xd076240 +184 -> batch_norm2d_29.b_0_deepcopy_149 -> 0xd074f90 +185 -> batch_norm2d_29.w_0_deepcopy_148 -> 0xcfa2740 +186 -> conv2d_29.w_0_deepcopy_147 -> 0xcf95890 +187 -> batch_norm2d_28.w_2_deepcopy_146 -> 0xd074250 +188 -> batch_norm2d_28.w_1_deepcopy_145 -> 0xd073510 +189 -> batch_norm2d_28.b_0_deepcopy_144 -> 0xd071b20 +190 -> batch_norm2d_28.w_0_deepcopy_143 -> 0xd0727b0 +191 -> conv2d_28.w_0_deepcopy_142 -> 0xcfa8980 +192 -> batch_norm2d_27.w_2_deepcopy_141 -> 0xd06fa30 +193 -> batch_norm2d_27.w_1_deepcopy_140 -> 0xd06ff30 +194 -> batch_norm2d_27.b_0_deepcopy_139 -> 0xd06e400 +195 -> batch_norm2d_27.w_0_deepcopy_138 -> 0xd06f070 +196 -> conv2d_27.w_0_deepcopy_137 -> 0xcf9e030 +197 -> batch_norm2d_26.w_2_deepcopy_136 -> 0xd06d600 +198 -> batch_norm2d_26.w_1_deepcopy_135 -> 0xd06c980 +199 -> batch_norm2d_26.b_0_deepcopy_134 -> 0xd06ad10 +200 -> batch_norm2d_26.w_0_deepcopy_133 -> 0xd06b960 +201 -> conv2d_26.w_0_deepcopy_132 -> 0xcf71280 +202 -> batch_norm2d_25.w_2_deepcopy_131 -> 0xd069fd0 +203 -> batch_norm2d_25.w_1_deepcopy_130 -> 0xd069110 +204 -> batch_norm2d_25.b_0_deepcopy_129 -> 0xd066e30 +205 -> batch_norm2d_25.w_0_deepcopy_128 -> 0xd067f70 +206 -> conv2d_25.w_0_deepcopy_127 -> 0xcf91990 +207 -> batch_norm2d_24.w_2_deepcopy_126 -> 0xd066330 +208 -> batch_norm2d_24.w_1_deepcopy_125 -> 0xd065590 +209 -> batch_norm2d_24.b_0_deepcopy_124 -> 0xd062fb0 +210 -> batch_norm2d_24.w_0_deepcopy_123 -> 0xd0641f0 +211 -> conv2d_24.w_0_deepcopy_122 -> 0xcf91780 +212 -> batch_norm2d_23.w_2_deepcopy_121 -> 0xd062570 +213 -> batch_norm2d_23.w_1_deepcopy_120 -> 0xd061ab0 +214 -> batch_norm2d_23.b_0_deepcopy_119 -> 0xd060170 +215 -> batch_norm2d_23.w_0_deepcopy_118 -> 0xd060ed0 +216 -> conv2d_23.w_0_deepcopy_117 -> 0xcf1a470 +217 -> batch_norm2d_22.w_2_deepcopy_116 -> 0xd05f3f0 +218 -> batch_norm2d_22.w_1_deepcopy_115 -> 0xd05e730 +219 -> batch_norm2d_22.b_0_deepcopy_114 -> 0xcf3fa10 +220 -> batch_norm2d_22.w_0_deepcopy_113 -> 0xd05d850 +221 -> conv2d_22.w_0_deepcopy_112 -> 0xcfa5f90 +222 -> batch_norm2d_21.w_2_deepcopy_111 -> 0xd05c7d0 +223 -> batch_norm2d_21.w_1_deepcopy_110 -> 0xcf9c4b0 +224 -> batch_norm2d_21.b_0_deepcopy_109 -> 0xcf23400 +225 -> batch_norm2d_21.w_0_deepcopy_108 -> 0xd05afe0 +226 -> conv2d_21.w_0_deepcopy_107 -> 0xcf41b60 +227 -> batch_norm2d_20.w_2_deepcopy_106 -> 0xcf83e60 +228 -> batch_norm2d_20.w_1_deepcopy_105 -> 0xcf82fa0 +229 -> batch_norm2d_20.b_0_deepcopy_104 -> 0xcf814b0 +230 -> batch_norm2d_20.w_0_deepcopy_103 -> 0xcf82460 +231 -> conv2d_20.w_0_deepcopy_102 -> 0xcf95570 +232 -> batch_norm2d_19.w_2_deepcopy_101 -> 0xcf7d9f0 +233 -> batch_norm2d_19.w_1_deepcopy_100 -> 0xcf7c850 +234 -> batch_norm2d_19.b_0_deepcopy_99 -> 0xcf7ac10 +235 -> batch_norm2d_19.w_0_deepcopy_98 -> 0xcf7bc50 +236 -> conv2d_19.w_0_deepcopy_97 -> 0xcf31440 +237 -> batch_norm2d_18.w_2_deepcopy_96 -> 0xcf79bd0 +238 -> batch_norm2d_18.w_1_deepcopy_95 -> 0xcf84390 +239 -> batch_norm2d_18.b_0_deepcopy_94 -> 0xcf71dd0 +240 -> batch_norm2d_18.w_0_deepcopy_93 -> 0xcfa68e0 +241 -> conv2d_18.w_0_deepcopy_92 -> 0xcf22d20 +242 -> batch_norm2d_17.w_2_deepcopy_91 -> 0xcf70e80 +243 -> batch_norm2d_17.w_1_deepcopy_90 -> 0xcf6ff00 +244 -> batch_norm2d_17.b_0_deepcopy_89 -> 0xcfa8c60 +245 -> batch_norm2d_17.w_0_deepcopy_88 -> 0xcf6f0e0 +246 -> conv2d_17.w_0_deepcopy_87 -> 0xcf17680 +247 -> batch_norm2d_16.w_2_deepcopy_86 -> 0xcfa79c0 +248 -> batch_norm2d_16.w_1_deepcopy_85 -> 0xcfa6d50 +249 -> batch_norm2d_16.b_0_deepcopy_84 -> 0xcfce890 +250 -> batch_norm2d_16.w_0_deepcopy_83 -> 0xcf30ac0 +251 -> conv2d_16.w_0_deepcopy_82 -> 0xcf536b0 +252 -> batch_norm2d_15.w_2_deepcopy_81 -> 0xcfcda90 +253 -> batch_norm2d_15.w_1_deepcopy_80 -> 0xcfcce30 +254 -> batch_norm2d_15.b_0_deepcopy_79 -> 0xcfcb200 +255 -> batch_norm2d_15.w_0_deepcopy_78 -> 0xcfcbe00 +256 -> conv2d_15.w_0_deepcopy_77 -> 0xcf52c60 +257 -> batch_norm2d_14.w_2_deepcopy_76 -> 0xcfca3d0 +258 -> batch_norm2d_14.w_1_deepcopy_75 -> 0xcfc9610 +259 -> batch_norm2d_14.b_0_deepcopy_74 -> 0xcfc2eb0 +260 -> batch_norm2d_14.w_0_deepcopy_73 -> 0xcfc6c40 +261 -> conv2d_14.w_0_deepcopy_72 -> 0xcf52990 +262 -> batch_norm2d_13.w_2_deepcopy_71 -> 0xcfc1870 +263 -> batch_norm2d_13.w_1_deepcopy_70 -> 0xcfc0710 +264 -> batch_norm2d_13.b_0_deepcopy_69 -> 0xcf31c30 +265 -> batch_norm2d_13.w_0_deepcopy_68 -> 0xcf38dc0 +266 -> conv2d_13.w_0_deepcopy_67 -> 0xcf52460 +267 -> batch_norm2d_12.w_2_deepcopy_66 -> 0xcf289b0 +268 -> batch_norm2d_12.w_1_deepcopy_65 -> 0xcf259b0 +269 -> batch_norm2d_12.b_0_deepcopy_64 -> 0xcf1dca0 +270 -> batch_norm2d_12.w_0_deepcopy_63 -> 0xcf20e70 +271 -> conv2d_12.w_0_deepcopy_62 -> 0xcf51cc0 +272 -> batch_norm2d_11.w_2_deepcopy_61 -> 0xcf17bc0 +273 -> batch_norm2d_11.w_1_deepcopy_60 -> 0xcf14fe0 +274 -> batch_norm2d_11.b_0_deepcopy_59 -> 0xcf53430 +275 -> batch_norm2d_11.w_0_deepcopy_58 -> 0xcf53290 +276 -> conv2d_11.w_0_deepcopy_57 -> 0xcf50ed0 +277 -> batch_norm2d_10.w_2_deepcopy_56 -> 0xcf52d40 +278 -> batch_norm2d_10.w_1_deepcopy_55 -> 0xcf526a0 +279 -> batch_norm2d_10.b_0_deepcopy_54 -> 0xcf51350 +280 -> batch_norm2d_10.w_0_deepcopy_53 -> 0xcf50fb0 +281 -> conv2d_10.w_0_deepcopy_52 -> 0xcf508b0 +282 -> batch_norm2d_9.w_2_deepcopy_51 -> 0xcf48440 +283 -> batch_norm2d_9.w_1_deepcopy_50 -> 0xcf44e90 +284 -> batch_norm2d_9.b_0_deepcopy_49 -> 0xcf0a7c0 +285 -> batch_norm2d_9.w_0_deepcopy_48 -> 0xcf0fa10 +286 -> conv2d_9.w_0_deepcopy_47 -> 0xcf32540 +287 -> batch_norm2d_8.w_2_deepcopy_46 -> 0xceff0c0 +288 -> batch_norm2d_8.w_1_deepcopy_45 -> 0xcef9a90 +289 -> batch_norm2d_8.b_0_deepcopy_44 -> 0xceee210 +290 -> batch_norm2d_8.w_0_deepcopy_43 -> 0xcef0d80 +291 -> conv2d_8.w_0_deepcopy_42 -> 0xcf3f300 +292 -> batch_norm2d_7.w_2_deepcopy_41 -> 0xd05a990 +293 -> batch_norm2d_7.w_1_deepcopy_40 -> 0xd05ae90 +294 -> batch_norm2d_7.b_0_deepcopy_39 -> 0xcfa23b0 +295 -> batch_norm2d_7.w_0_deepcopy_38 -> 0xcf261b0 +296 -> conv2d_7.w_0_deepcopy_37 -> 0xcf3b640 +297 -> batch_norm2d_6.w_2_deepcopy_36 -> 0xcce2400 +298 -> batch_norm2d_6.w_1_deepcopy_35 -> 0xcf2f870 +299 -> batch_norm2d_6.b_0_deepcopy_34 -> 0xcf2d1b0 +300 -> batch_norm2d_6.w_0_deepcopy_33 -> 0xcf42180 +301 -> conv2d_6.w_0_deepcopy_32 -> 0xcc9ffb0 +302 -> batch_norm2d_5.w_2_deepcopy_31 -> 0xcf306a0 +303 -> batch_norm2d_5.w_1_deepcopy_30 -> 0xcf63a70 +304 -> batch_norm2d_5.b_0_deepcopy_29 -> 0xcf35350 +305 -> batch_norm2d_5.w_0_deepcopy_28 -> 0xcf8df90 +306 -> conv2d_5.w_0_deepcopy_27 -> 0xcfc00b0 +307 -> batch_norm2d_4.w_2_deepcopy_26 -> 0xcf40460 +308 -> batch_norm2d_4.w_1_deepcopy_25 -> 0xcf8df70 +309 -> batch_norm2d_4.b_0_deepcopy_24 -> 0xcf02720 +310 -> batch_norm2d_4.w_0_deepcopy_23 -> 0xcf906f0 +311 -> conv2d_4.w_0_deepcopy_22 -> 0xc80efb0 +312 -> batch_norm2d_3.w_2_deepcopy_21 -> 0xcf21ae0 +313 -> batch_norm2d_3.w_1_deepcopy_20 -> 0xcf576f0 +314 -> batch_norm2d_3.b_0_deepcopy_19 -> 0xc80e280 +315 -> batch_norm2d_3.w_0_deepcopy_18 -> 0xcf34c10 +316 -> conv2d_3.w_0_deepcopy_17 -> 0xcf15a30 +317 -> batch_norm2d_2.w_2_deepcopy_16 -> 0xcf808f0 +318 -> batch_norm2d_2.w_1_deepcopy_15 -> 0xcf7feb0 +319 -> batch_norm2d_2.b_0_deepcopy_14 -> 0xcf7e0d0 +320 -> batch_norm2d_2.w_0_deepcopy_13 -> 0xcf7f110 +321 -> conv2d_2.w_0_deepcopy_12 -> 0xcfcc130 +322 -> batch_norm2d_1.w_2_deepcopy_11 -> 0xcf506f0 +323 -> batch_norm2d_1.w_1_deepcopy_10 -> 0xcf3f550 +324 -> batch_norm2d_1.b_0_deepcopy_9 -> 0xcf540f0 +325 -> batch_norm2d_1.w_0_deepcopy_8 -> 0xcf37900 +326 -> conv2d_1.w_0_deepcopy_7 -> 0xcf500a0 +327 -> batch_norm2d_0.w_2_deepcopy_6 -> 0xcf55d60 +328 -> batch_norm2d_0.w_1_deepcopy_5 -> 0xcf547a0 +329 -> batch_norm2d_0.b_0_deepcopy_4 -> 0xcf34070 +330 -> batch_norm2d_0.w_0_deepcopy_3 -> 0xcf557c0 +331 -> conv2d_0.w_0_deepcopy_2 -> 0xcf54fc0 +332 -> im_shape -> 0xcec9720 +333 -> image -> 0xccc5060 +334 -> scale_factor -> 0xcdbf3e0 +335 -> 0xcf59f401745131171435049990_inner_var_335 -> 0xcf1b790 +336 -> 0xcf59f401745131171435049990_inner_var_336 -> 0xca79260 +337 -> 0xcf59f401745131171435049990_inner_var_337 -> 0xce9b570 +338 -> 0xcf59f401745131171435049990_inner_var_338 -> 0xcebb210 +339 -> 0xcf59f401745131171435049990_inner_var_339 -> 0xcf009c0 +340 -> 0xcf59f401745131171435049990_inner_var_340 -> 0xd0606b0 +341 -> 0xcf59f401745131171435049990_inner_var_341 -> 0xcf97690 +342 -> 0xcf59f401745131171435049990_inner_var_342 -> 0xca791e0 +343 -> 0xcf59f401745131171435049990_inner_var_343 -> 0xcca3d60 +344 -> 0xcf59f401745131171435049990_inner_var_344 -> 0xcdbf7b0 +345 -> 0xcf59f401745131171435049990_inner_var_345 -> 0xceb2ca0 +346 -> 0xcf59f401745131171435049990_inner_var_346 -> 0xcea9360 +347 -> 0xcf59f401745131171435049990_inner_var_347 -> 0xca9b750 +348 -> 0xcf59f401745131171435049990_inner_var_348 -> 0xcef9b60 +349 -> 0xcf59f401745131171435049990_inner_var_349 -> 0xcad41e0 +350 -> 0xcf59f401745131171435049990_inner_var_350 -> 0xcec2420 +351 -> 0xcf59f401745131171435049990_inner_var_351 -> 0xd0580a0 +352 -> 0xcf59f401745131171435049990_inner_var_352 -> 0xca7dcc0 +353 -> 0xcf59f401745131171435049990_inner_var_353 -> 0xcf596e0 +354 -> 0xcf59f401745131171435049990_inner_var_354 -> 0xca62f10 +355 -> 0xcf59f401745131171435049990_inner_var_355 -> 0xcca80b0 +356 -> 0xcf59f401745131171435049990_inner_var_356 -> 0xccd5df0 +357 -> 0xcf59f401745131171435049990_inner_var_357 -> 0xca883e0 +358 -> 0xcf59f401745131171435049990_inner_var_358 -> 0xccb4a70 +359 -> 0xcf59f401745131171435049990_inner_var_359 -> 0xca758f0 +360 -> 0xcf59f401745131171435049990_inner_var_360 -> 0xd04cf50 +361 -> 0xcf59f401745131171435049990_inner_var_361 -> 0xcca6850 +362 -> 0xcf59f401745131171435049990_inner_var_362 -> 0xcf6d1c0 +363 -> 0xcf59f401745131171435049990_inner_var_363 -> 0xcadee10 +364 -> 0xcf59f401745131171435049990_inner_var_364 -> 0xcccf200 +365 -> 0xcf59f401745131171435049990_inner_var_365 -> 0xcebea10 +366 -> 0xcf59f401745131171435049990_inner_var_366 -> 0xcf1d290 +367 -> 0xcf59f401745131171435049990_inner_var_367 -> 0xce91c20 +368 -> 0xcf59f401745131171435049990_inner_var_368 -> 0xccd76c0 +369 -> 0xcf59f401745131171435049990_inner_var_369 -> 0xca58e30 +370 -> 0xcf59f401745131171435049990_inner_var_370 -> 0xce97c00 +371 -> 0xcf59f401745131171435049990_inner_var_371 -> 0xcf563c0 +372 -> 0xcf59f401745131171435049990_inner_var_372 -> 0xca7bf50 +373 -> 0xcf59f401745131171435049990_inner_var_373 -> 0xccaa1d0 +374 -> 0xcf59f401745131171435049990_inner_var_374 -> 0xcec14b0 +375 -> 0xcf59f401745131171435049990_inner_var_375 -> 0xccdfc40 +376 -> 0xcf59f401745131171435049990_inner_var_376 -> 0xcf20080 +377 -> 0xcf59f401745131171435049990_inner_var_377 -> 0xcaa4d20 +378 -> 0xcf59f401745131171435049990_inner_var_378 -> 0xd0732d0 +379 -> 0xcf59f401745131171435049990_inner_var_379 -> 0xcec7d50 +380 -> 0xcf59f401745131171435049990_inner_var_380 -> 0xced4880 +381 -> 0xcf59f401745131171435049990_inner_var_381 -> 0xced3620 +382 -> 0xcf59f401745131171435049990_inner_var_382 -> 0xcef7030 +383 -> 0xcf59f401745131171435049990_inner_var_383 -> 0xcad2150 +384 -> 0xcf59f401745131171435049990_inner_var_384 -> 0xccdad60 +385 -> 0xcf59f401745131171435049990_inner_var_385 -> 0xcf14230 +386 -> 0xcf59f401745131171435049990_inner_var_386 -> 0xcf17420 +387 -> 0xcf59f401745131171435049990_inner_var_387 -> 0xccb2480 +388 -> 0xcf59f401745131171435049990_inner_var_388 -> 0xcca2020 +389 -> 0xcf59f401745131171435049990_inner_var_389 -> 0xd040710 +390 -> 0xcf59f401745131171435049990_inner_var_390 -> 0xca9b230 +391 -> 0xcf59f401745131171435049990_inner_var_391 -> 0xcf38990 +392 -> 0xcf59f401745131171435049990_inner_var_392 -> 0xd0639d0 +393 -> 0xcf59f401745131171435049990_inner_var_393 -> 0xccb9310 +394 -> 0xcf59f401745131171435049990_inner_var_394 -> 0xccc15b0 +395 -> 0xcf59f401745131171435049990_inner_var_395 -> 0xcef9860 +396 -> 0xcf59f401745131171435049990_inner_var_396 -> 0xc8757d0 +397 -> 0xcf59f401745131171435049990_inner_var_397 -> 0xceee010 +398 -> 0xcf59f401745131171435049990_inner_var_398 -> 0xca947c0 +399 -> 0xcf59f401745131171435049990_inner_var_399 -> 0xcabae00 +400 -> 0xcf59f401745131171435049990_inner_var_400 -> 0xca602d0 +401 -> 0xcf59f401745131171435049990_inner_var_401 -> 0xcee7350 +402 -> 0xcf59f401745131171435049990_inner_var_402 -> 0xcf99c00 +403 -> 0xcf59f401745131171435049990_inner_var_403 -> 0xcad0c10 +404 -> 0xcf59f401745131171435049990_inner_var_404 -> 0xd04f4b0 +405 -> 0xcf59f401745131171435049990_inner_var_405 -> 0xccc21e0 +406 -> 0xcf59f401745131171435049990_inner_var_406 -> 0xcf78850 +407 -> 0xcf59f401745131171435049990_inner_var_407 -> 0xd0727d0 +408 -> 0xcf59f401745131171435049990_inner_var_408 -> 0xceb0f20 +409 -> 0xcf59f401745131171435049990_inner_var_409 -> 0xceb3080 +410 -> 0xcf59f401745131171435049990_inner_var_410 -> 0xcf0a000 +411 -> 0xcf59f401745131171435049990_inner_var_411 -> 0xcad7ec0 +412 -> 0xcf59f401745131171435049990_inner_var_412 -> 0xcf7cbb0 +413 -> 0xcf59f401745131171435049990_inner_var_413 -> 0xcec7620 +414 -> 0xcf59f401745131171435049990_inner_var_414 -> 0xccc05c0 +415 -> 0xcf59f401745131171435049990_inner_var_415 -> 0xca942d0 +416 -> 0xcf59f401745131171435049990_inner_var_416 -> 0xceccf20 +417 -> 0xcf59f401745131171435049990_inner_var_417 -> 0xcaecc00 +418 -> 0xcf59f401745131171435049990_inner_var_418 -> 0xcaed490 +419 -> 0xcf59f401745131171435049990_inner_var_419 -> 0xca984f0 +420 -> 0xcf59f401745131171435049990_inner_var_420 -> 0xcfcbe20 +421 -> 0xcf59f401745131171435049990_inner_var_421 -> 0xccc3bd0 +422 -> 0xcf59f401745131171435049990_inner_var_422 -> 0xd047d80 +423 -> 0xcf59f401745131171435049990_inner_var_423 -> 0xcea8fb0 +424 -> 0xcf59f401745131171435049990_inner_var_424 -> 0xca62570 +425 -> 0xcf59f401745131171435049990_inner_var_425 -> 0xee8cc70 +426 -> 0xcf59f401745131171435049990_inner_var_426 -> 0xceb5560 +427 -> 0xcf59f401745131171435049990_inner_var_427 -> 0xced0d30 +428 -> 0xcf59f401745131171435049990_inner_var_428 -> 0xcab8450 +429 -> 0xcf59f401745131171435049990_inner_var_429 -> 0xcab60d0 +430 -> 0xcf59f401745131171435049990_inner_var_430 -> 0xcead070 +431 -> 0xcf59f401745131171435049990_inner_var_431 -> 0xcade0a0 +432 -> 0xcf59f401745131171435049990_inner_var_432 -> 0xcabcfc0 +433 -> 0xcf59f401745131171435049990_inner_var_433 -> 0xce94af0 +434 -> 0xcf59f401745131171435049990_inner_var_434 -> 0xcebccc0 +435 -> 0xcf59f401745131171435049990_inner_var_435 -> 0xd048180 +436 -> 0xcf59f401745131171435049990_inner_var_436 -> 0xcea5740 +437 -> 0xcf59f401745131171435049990_inner_var_437 -> 0xcab9830 +438 -> 0xcf59f401745131171435049990_inner_var_438 -> 0xcf97aa0 +439 -> 0xcf59f401745131171435049990_inner_var_439 -> 0xd06ce40 +440 -> 0xcf59f401745131171435049990_inner_var_440 -> 0xcedd7e0 +441 -> 0xcf59f401745131171435049990_inner_var_441 -> 0xcea0cd0 +442 -> 0xcf59f401745131171435049990_inner_var_442 -> 0xd06eb50 +443 -> 0xcf59f401745131171435049990_inner_var_443 -> 0xd069a30 +444 -> 0xcf59f401745131171435049990_inner_var_444 -> 0xce89bf0 +445 -> 0xcf59f401745131171435049990_inner_var_445 -> 0xcf21760 +446 -> 0xcf59f401745131171435049990_inner_var_446 -> 0xcbf35d0 +447 -> 0xcf59f401745131171435049990_inner_var_447 -> 0xcab49d0 +448 -> 0xcf59f401745131171435049990_inner_var_448 -> 0xcf17050 +449 -> 0xcf59f401745131171435049990_inner_var_449 -> 0xee40240 +450 -> 0xcf59f401745131171435049990_inner_var_450 -> 0xd040f30 +451 -> 0xcf59f401745131171435049990_inner_var_451 -> 0xcf7c250 +452 -> 0xcf59f401745131171435049990_inner_var_452 -> 0xcca3870 +453 -> 0xcf59f401745131171435049990_inner_var_453 -> 0xee47f90 +454 -> 0xcf59f401745131171435049990_inner_var_454 -> 0xd0385d0 +455 -> 0xcf59f401745131171435049990_inner_var_455 -> 0xcfa62c0 +456 -> 0xcf59f401745131171435049990_inner_var_456 -> 0xcabc9a0 +457 -> 0xcf59f401745131171435049990_inner_var_457 -> 0xcefb360 +458 -> 0xcf59f401745131171435049990_inner_var_458 -> 0xcef8830 +459 -> 0xcf59f401745131171435049990_inner_var_459 -> 0xceca990 +460 -> 0xcf59f401745131171435049990_inner_var_460 -> 0xced79a0 +461 -> 0xcf59f401745131171435049990_inner_var_461 -> 0xce94610 +462 -> 0xcf59f401745131171435049990_inner_var_462 -> 0xcab5be0 +463 -> 0xcf59f401745131171435049990_inner_var_463 -> 0xcec4600 +464 -> 0xcf59f401745131171435049990_inner_var_464 -> 0xcab8eb0 +465 -> 0xcf59f401745131171435049990_inner_var_465 -> 0xee400a0 +466 -> 0xcf59f401745131171435049990_inner_var_466 -> 0xcab7250 +467 -> 0xcf59f401745131171435049990_inner_var_467 -> 0xcab88e0 +468 -> 0xcf59f401745131171435049990_inner_var_468 -> 0xccdaac0 +469 -> 0xcf59f401745131171435049990_inner_var_469 -> 0xcad09b0 +470 -> 0xcf59f401745131171435049990_inner_var_470 -> 0xcec1100 +471 -> 0xcf59f401745131171435049990_inner_var_471 -> 0xd034260 +472 -> 0xcf59f401745131171435049990_inner_var_472 -> 0xcf6cb40 +473 -> 0xcf59f401745131171435049990_inner_var_473 -> 0xce8df60 +474 -> 0xcf59f401745131171435049990_inner_var_474 -> 0xcf36750 +475 -> 0xcf59f401745131171435049990_inner_var_475 -> 0xcca5d30 +476 -> 0xcf59f401745131171435049990_inner_var_476 -> 0xcf24750 +477 -> 0xcf59f401745131171435049990_inner_var_477 -> 0xcf1ba00 +478 -> 0xcf59f401745131171435049990_inner_var_478 -> 0xd075c80 +479 -> 0xcf59f401745131171435049990_inner_var_479 -> 0xccdd6a0 +480 -> 0xcf59f401745131171435049990_inner_var_480 -> 0xca8acd0 +481 -> 0xcf59f401745131171435049990_inner_var_481 -> 0xd071b60 +482 -> 0xcf59f401745131171435049990_inner_var_482 -> 0xcea01c0 +483 -> 0xcf59f401745131171435049990_inner_var_483 -> 0xceb1680 +484 -> 0xcf59f401745131171435049990_inner_var_484 -> 0xccdbe30 +485 -> 0xcf59f401745131171435049990_inner_var_485 -> 0xcadbd40 +486 -> 0xcf59f40174513117143504999 +I0420 14:39:33.864784 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 2 +1 -> 4 +2 -> 3 42 +3 -> 6 +4 -> 5 +5 -> 6 +6 -> 7 +7 -> 8 9 +8 -> 11 +9 -> 10 14 +10 -> 13 16 +11 -> 12 +12 -> 13 +13 -> 29 +14 -> 15 16 +15 -> 18 +16 -> 17 21 +17 -> 20 23 +18 -> 19 +19 -> 20 +20 -> 30 +21 -> 22 23 +22 -> 25 +23 -> 24 +24 -> 27 +25 -> 26 +26 -> 27 +27 -> 31 +28 -> 33 +29 -> 34 +30 -> 34 +31 -> 34 +32 -> 34 +33 -> 34 +34 -> 35 37 38 47 48 49 51 +35 -> 50 +36 -> 37 +38 -> 39 +39 -> 41 +40 -> 41 +42 -> 43 +43 -> 44 46 +44 -> 45 +46 -> 52 +47 -> 52 +48 -> 52 +49 -> 52 +50 -> 52 +51 -> 52 + +[2025-4-20 14:39:33] [CNNL] [Warning][cnnlStridedSlice] is deprecated and will be removed in the future release, please use [cnnlStridedSlice_v2] instead. +I0420 14:39:34.791170 115867 pir_interpreter.cc:1569] value info of interpretercore 0xcdfe7a0 +value -> var_name -> id -> variable* +0xccb1980 -> 0xcdfe7a01745131172833993105_inner_var_1094 -> 1094 -> 0xcdff1e0 +0xc4bce10 -> 0xcf59f401745131171435049990_inner_var_1093 -> 1093 -> 0xee7ac30 +0xc4bce30 -> 0xcf59f401745131171435049990_inner_var_1092 -> 1092 -> 0xee7a810 +0xc4bce70 -> 0xcf59f401745131171435049990_inner_var_1090 -> 1090 -> 0xee79fd0 +0xc4bce88 -> 0xcf59f401745131171435049990_inner_var_1089 -> 1089 -> 0xee79bb0 +0xc4bcea0 -> 0xcf59f401745131171435049990_inner_var_1088 -> 1088 -> 0xee79790 +0xc4bceb8 -> fetch_name_2 -> 1087 -> 0xee79370 +0xca8d4b0 -> 0xcf59f401745131171435049990_inner_var_1082 -> 1082 -> 0xee77ef0 +0xca8d060 -> 0xcf59f401745131171435049990_inner_var_1081 -> 1081 -> 0xee77ad0 +0xca8cc10 -> 0xcf59f401745131171435049990_inner_var_1080 -> 1080 -> 0xee776b0 +0xca8b670 -> 0xcf59f401745131171435049990_inner_var_1074 -> 1074 -> 0xee75df0 +0xccca970 -> 0xcf59f401745131171435049990_inner_var_1069 -> 1069 -> 0xcfbd680 +0xccca1d0 -> 0xcf59f401745131171435049990_inner_var_1067 -> 1067 -> 0xcfbca10 +0xccc7780 -> 0xcf59f401745131171435049990_inner_var_1066 -> 1066 -> 0xcfbc9f0 +0xccc76b0 -> 0xcf59f401745131171435049990_inner_var_1064 -> 1064 -> 0xcfbc2e0 +0xccc75e0 -> 0xcf59f401745131171435049990_inner_var_1063 -> 1063 -> 0xcfbbec0 +0xccc9550 -> 0xcf59f401745131171435049990_inner_var_1062 -> 1062 -> 0xcfbbaa0 +0xccc8d10 -> 0xcf59f401745131171435049990_inner_var_1060 -> 1060 -> 0xcfbb260 +0xccc8660 -> 0xcf59f401745131171435049990_inner_var_1059 -> 1059 -> 0xcfbae40 +0xccc7510 -> 0xcf59f401745131171435049990_inner_var_1058 -> 1058 -> 0xcfbaa20 +0xccc7e10 -> 0xcf59f401745131171435049990_inner_var_1057 -> 1057 -> 0xcfba600 +0xccc6ed0 -> fetch_name_0 -> 1055 -> 0xcaabf30 +0xccc2730 -> 0xcf59f401745131171435049990_inner_var_1049 -> 1049 -> 0xcfbe6a0 +0xccc5820 -> 0xcf59f401745131171435049990_inner_var_1046 -> 1046 -> 0xcfb51a0 +0xccc4e10 -> 0xcf59f401745131171435049990_inner_var_1044 -> 1044 -> 0xcfb4940 +0xccc4890 -> 0xcf59f401745131171435049990_inner_var_1043 -> 1043 -> 0xcfb4520 +0xccc4410 -> 0xcf59f401745131171435049990_inner_var_1041 -> 1041 -> 0xcfb3ce0 +0xca8c7c0 -> 0xcf59f401745131171435049990_inner_var_1079 -> 1079 -> 0xee77290 +0xca83d20 -> 0xcf59f401745131171435049990_inner_var_1040 -> 1040 -> 0xcf78df0 +0xccc38a0 -> 0xcf59f401745131171435049990_inner_var_1037 -> 1037 -> 0xcfb3080 +0xccc19b0 -> 0xcf59f401745131171435049990_inner_var_1029 -> 1029 -> 0xcaad590 +0xccbf440 -> 0xcf59f401745131171435049990_inner_var_1019 -> 1019 -> 0xcbeef60 +0xccbea10 -> 0xcf59f401745131171435049990_inner_var_1017 -> 1017 -> 0xcbedfe0 +0xccbe210 -> 0xcf59f401745131171435049990_inner_var_1015 -> 1015 -> 0xcbedbc0 +0xd03b830 -> 0xcf59f401745131171435049990_inner_var_1008 -> 1008 -> 0xcbebec0 +0xd03b8b0 -> 0xcf59f401745131171435049990_inner_var_1003 -> 1003 -> 0xcbeaa20 +0xd03b8c8 -> 0xcf59f401745131171435049990_inner_var_1002 -> 1002 -> 0xcbe8940 +0xd03b180 -> 0xcf59f401745131171435049990_inner_var_1001 -> 1001 -> 0xcbea200 +0xd03ad00 -> 0xcf59f401745131171435049990_inner_var_1000 -> 1000 -> 0xcbe9de0 +0xccddec0 -> 0xcf59f401745131171435049990_inner_var_993 -> 993 -> 0xcbe7f80 +0xd037ee0 -> 0xcf59f401745131171435049990_inner_var_988 -> 988 -> 0xcbe55a0 +0xd035b10 -> 0xcf59f401745131171435049990_inner_var_987 -> 987 -> 0xcbe6260 +0xd036f20 -> 0xcf59f401745131171435049990_inner_var_985 -> 985 -> 0xcbe62a0 +0xca8c270 -> 0xcf59f401745131171435049990_inner_var_1078 -> 1078 -> 0xee76e70 +0xccb2520 -> 0xcf59f401745131171435049990_inner_var_979 -> 979 -> 0xcbe4920 +0xccb2550 -> 0xcf59f401745131171435049990_inner_var_977 -> 977 -> 0xcbe40e0 +0xccb2568 -> 0xcf59f401745131171435049990_inner_var_976 -> 976 -> 0xcbe3cc0 +0xcca0e10 -> 0xcf59f401745131171435049990_inner_var_974 -> 974 -> 0xcbe34a0 +0xca5f9b0 -> 0xcf59f401745131171435049990_inner_var_970 -> 970 -> 0xcbe1870 +0xca61eb0 -> 0xcf59f401745131171435049990_inner_var_965 -> 965 -> 0xcbe1180 +0xca68510 -> 0xcf59f401745131171435049990_inner_var_962 -> 962 -> 0xcbe0520 +0xca5f8e0 -> 0xcf59f401745131171435049990_inner_var_961 -> 961 -> 0xca5f6f0 +0xce93150 -> 0xcf59f401745131171435049990_inner_var_960 -> 960 -> 0xca5ea90 +0xccdfb30 -> 0xcf59f401745131171435049990_inner_var_959 -> 959 -> 0xca5eeb0 +0xccdefc0 -> 0xcf59f401745131171435049990_inner_var_956 -> 956 -> 0xca5e250 +0xccb7010 -> 0xcf59f401745131171435049990_inner_var_955 -> 955 -> 0xca5de30 +0xccb7040 -> 0xcf59f401745131171435049990_inner_var_953 -> 953 -> 0xca5d1d0 +0xccde4c0 -> 0xcf59f401745131171435049990_inner_var_952 -> 952 -> 0xceea310 +0xcccae30 -> 0xcf59f401745131171435049990_inner_var_1073 -> 1073 -> 0xee759d0 +0xccdc370 -> 0xcf59f401745131171435049990_inner_var_950 -> 950 -> 0xcfc2b30 +0xce92b70 -> 0xcf59f401745131171435049990_inner_var_949 -> 949 -> 0xcf017f0 +0xccdb370 -> 0xcf59f401745131171435049990_inner_var_947 -> 947 -> 0xcee32f0 +0xccdc060 -> 0xcf59f401745131171435049990_inner_var_943 -> 943 -> 0xcef8160 +0xccd7c10 -> 0xcf59f401745131171435049990_inner_var_937 -> 937 -> 0xced4800 +0xccd6fd0 -> 0xcf59f401745131171435049990_inner_var_935 -> 935 -> 0xcecbfe0 +0xccda790 -> 0xcf59f401745131171435049990_inner_var_934 -> 934 -> 0xced8b80 +0xccc3500 -> 0xcf59f401745131171435049990_inner_var_1036 -> 1036 -> 0xcfb2c60 +0xce92d70 -> 0xcf59f401745131171435049990_inner_var_932 -> 932 -> 0xcecf430 +0xce92e40 -> 0xcf59f401745131171435049990_inner_var_930 -> 930 -> 0xceac320 +0xd037740 -> 0xcf59f401745131171435049990_inner_var_990 -> 990 -> 0xcbe77e0 +0xccd9610 -> 0xcf59f401745131171435049990_inner_var_929 -> 929 -> 0xcec7570 +0xccd8e00 -> 0xcf59f401745131171435049990_inner_var_927 -> 927 -> 0xcebe990 +0xccd8970 -> 0xcf59f401745131171435049990_inner_var_926 -> 926 -> 0xcebc860 +0xccd8600 -> 0xcf59f401745131171435049990_inner_var_925 -> 925 -> 0xcec3310 +0xce92ca0 -> 0xcf59f401745131171435049990_inner_var_922 -> 922 -> 0xceb1250 +0xce93040 -> 0xcf59f401745131171435049990_inner_var_921 -> 921 -> 0xceb7a40 +0xccd72b0 -> 0xcf59f401745131171435049990_inner_var_920 -> 920 -> 0xceb6370 +0xccdac50 -> 0xcf59f401745131171435049990_inner_var_936 -> 936 -> 0xcf105d0 +0xce904e0 -> 0xcf59f401745131171435049990_inner_var_919 -> 919 -> 0xcea2d80 +0xce934a0 -> 0xcf59f401745131171435049990_inner_var_918 -> 918 -> 0xcea6ce0 +0xce8b970 -> 0xcf59f401745131171435049990_inner_var_916 -> 916 -> 0xcea2620 +0xd03b898 -> 0xcf59f401745131171435049990_inner_var_1004 -> 1004 -> 0xcbeae40 +0xd0395f0 -> 0xcf59f401745131171435049990_inner_var_994 -> 994 -> 0xcbe8520 +0xce90d50 -> 0xcf59f401745131171435049990_inner_var_909 -> 909 -> 0xce9bd00 +0xce909b0 -> 0xcf59f401745131171435049990_inner_var_908 -> 908 -> 0xcf11580 +0xce8f6b0 -> 0xcf59f401745131171435049990_inner_var_904 -> 904 -> 0xcf7ad50 +0xce8f3d0 -> 0xcf59f401745131171435049990_inner_var_903 -> 903 -> 0xcf48420 +0xce8ed50 -> 0xcf59f401745131171435049990_inner_var_902 -> 902 -> 0xcf17fd0 +0xce8e8e0 -> 0xcf59f401745131171435049990_inner_var_900 -> 900 -> 0xcaa2690 +0xce8e600 -> 0xcf59f401745131171435049990_inner_var_899 -> 899 -> 0xcfc3f20 +0xc4bced0 -> 0xcf59f401745131171435049990_inner_var_1086 -> 1086 -> 0xee78f50 +0xce8d810 -> 0xcf59f401745131171435049990_inner_var_894 -> 894 -> 0xcf07c60 +0xce8cf50 -> 0xcf59f401745131171435049990_inner_var_891 -> 891 -> 0xceb85d0 +0xce8cbf0 -> 0xcf59f401745131171435049990_inner_var_890 -> 890 -> 0xcf15cd0 +0xccc1e10 -> 0xcf59f401745131171435049990_inner_var_1030 -> 1030 -> 0xcaad9b0 +0xcaba090 -> 0xcf59f401745131171435049990_inner_var_889 -> 889 -> 0xcf35d30 +0xccb2d90 -> 0xcf59f401745131171435049990_inner_var_886 -> 886 -> 0xcf83140 +0xccbfe10 -> 0xcf59f401745131171435049990_inner_var_1022 -> 1022 -> 0xcbefad0 +0xce8b130 -> 0xcf59f401745131171435049990_inner_var_884 -> 884 -> 0xcf82600 +0xce8b5d0 -> 0xcf59f401745131171435049990_inner_var_883 -> 883 -> 0xcaf0b00 +0xce8a8c0 -> 0xcf59f401745131171435049990_inner_var_882 -> 882 -> 0xcee18b0 +0xcf74e30 -> 0xcf59f401745131171435049990_inner_var_880 -> 880 -> 0xceed1c0 +0xca56690 -> 0xcf59f401745131171435049990_inner_var_878 -> 878 -> 0xcf7a830 +0xce89ca0 -> 0xcf59f401745131171435049990_inner_var_877 -> 877 -> 0xca98d00 +0xcabd4c0 -> 0xcf59f401745131171435049990_inner_var_874 -> 874 -> 0xcee6180 +0xcabc430 -> 0xcf59f401745131171435049990_inner_var_870 -> 870 -> 0xca841f0 +0xcabbc20 -> 0xcf59f401745131171435049990_inner_var_868 -> 868 -> 0xccacef0 +0xccc30a0 -> 0xcf59f401745131171435049990_inner_var_1035 -> 1035 -> 0xceda8f0 +0xcabb320 -> 0xcf59f401745131171435049990_inner_var_866 -> 866 -> 0xee8cac0 +0xcabab48 -> 0xcf59f401745131171435049990_inner_var_864 -> 864 -> 0xcc99a90 +0xc4bce50 -> 0xcf59f401745131171435049990_inner_var_1091 -> 1091 -> 0xee7a3f0 +0xcaba6f0 -> 0xcf59f401745131171435049990_inner_var_863 -> 863 -> 0xcef8a10 +0xcaba708 -> 0xcf59f401745131171435049990_inner_var_862 -> 862 -> 0xcfbe720 +0xccbd010 -> 0xcf59f401745131171435049990_inner_var_861 -> 861 -> 0xcf78ad0 +0xccbd028 -> 0xcf59f401745131171435049990_inner_var_860 -> 860 -> 0xcf53e00 +0xc4bcee8 -> 0xcf59f401745131171435049990_inner_var_1085 -> 1085 -> 0xee76a50 +0xcaec4a0 -> 0xcf59f401745131171435049990_inner_var_856 -> 856 -> 0xcf1baf0 +0xcc951b0 -> 0xcf59f401745131171435049990_inner_var_852 -> 852 -> 0xcf28a70 +0xccb2ea0 -> 0xcf59f401745131171435049990_inner_var_850 -> 850 -> 0xcf0e480 +0xccb27b0 -> 0xcf59f401745131171435049990_inner_var_847 -> 847 -> 0xcf378c0 +0xccc6970 -> 0xcf59f401745131171435049990_inner_var_1053 -> 1053 -> 0xcaab510 +0xcaea110 -> 0xcf59f401745131171435049990_inner_var_845 -> 845 -> 0xcf156e0 +0xccdbd20 -> 0xcf59f401745131171435049990_inner_var_941 -> 941 -> 0xcee3b70 +0xcae9a30 -> 0xcf59f401745131171435049990_inner_var_843 -> 843 -> 0xee86de0 +0xcae8f00 -> 0xcf59f401745131171435049990_inner_var_841 -> 841 -> 0xcdbbda0 +0xcae9030 -> 0xcf59f401745131171435049990_inner_var_840 -> 840 -> 0xccb5c80 +0xcae57f0 -> 0xcf59f401745131171435049990_inner_var_839 -> 839 -> 0xcf6fa60 +0xcae7f98 -> 0xcf59f401745131171435049990_inner_var_836 -> 836 -> 0xcca6740 +0xcae6e98 -> 0xcf59f401745131171435049990_inner_var_828 -> 828 -> 0xce8a480 +0xd03b850 -> 0xcf59f401745131171435049990_inner_var_1007 -> 1007 -> 0xcbebaa0 +0xcae6ee0 -> 0xcf59f401745131171435049990_inner_var_825 -> 825 -> 0xccde190 +0xd03b868 -> 0xcf59f401745131171435049990_inner_var_1006 -> 1006 -> 0xcbeb680 +0xcae6ef8 -> 0xcf59f401745131171435049990_inner_var_824 -> 824 -> 0xd034610 +0xd0369b0 -> 0xcf59f401745131171435049990_inner_var_984 -> 984 -> 0xcbe5de0 +0xcae6590 -> 0xcf59f401745131171435049990_inner_var_823 -> 823 -> 0xcf1af20 +0xcae5df0 -> 0xcf59f401745131171435049990_inner_var_819 -> 819 -> 0xca54b70 +0xcae5e20 -> 0xcf59f401745131171435049990_inner_var_817 -> 817 -> 0xcf16e80 +0xcae5e38 -> 0xcf59f401745131171435049990_inner_var_816 -> 816 -> 0xca57ab0 +0xcae9610 -> 0xcf59f401745131171435049990_inner_var_853 -> 853 -> 0xcefff10 +0xcae50c0 -> 0xcf59f401745131171435049990_inner_var_815 -> 815 -> 0xcf19b60 +0xee8c030 -> 0xcf59f401745131171435049990_inner_var_814 -> 814 -> 0xcf16450 +0xcae4960 -> 0xcf59f401745131171435049990_inner_var_810 -> 810 -> 0xcf131f0 +0xcae4978 -> 0xcf59f401745131171435049990_inner_var_809 -> 809 -> 0xcf1bdf0 +0xcae4990 -> 0xcf59f401745131171435049990_inner_var_808 -> 808 -> 0xcf28470 +0xee8c430 -> 0xcf59f401745131171435049990_inner_var_806 -> 806 -> 0xcfc6ca0 +0xd038b80 -> 0xcf59f401745131171435049990_inner_var_991 -> 991 -> 0xcbe7bb0 +0xee8bb80 -> 0xcf59f401745131171435049990_inner_var_800 -> 800 -> 0xcefcab0 +0xee8bb98 -> 0xcf59f401745131171435049990_inner_var_799 -> 799 -> 0xee73140 +0xee89e90 -> 0xcf59f401745131171435049990_inner_var_790 -> 790 -> 0xca78980 +0xee85d40 -> 0xcf59f401745131171435049990_inner_var_789 -> 789 -> 0xcae6ce0 +0xee89500 -> 0xcf59f401745131171435049990_inner_var_787 -> 787 -> 0xcaa0680 +0xcaec8f0 -> 0xcf59f401745131171435049990_inner_var_857 -> 857 -> 0xcf17520 +0xee89518 -> 0xcf59f401745131171435049990_inner_var_786 -> 786 -> 0xcf35750 +0xee89530 -> 0xcf59f401745131171435049990_inner_var_785 -> 785 -> 0xcdbbe60 +0xee89548 -> 0xcf59f401745131171435049990_inner_var_784 -> 784 -> 0xee883c0 +0xee89560 -> 0xcf59f401745131171435049990_inner_var_783 -> 783 -> 0xca52390 +0xee89578 -> 0xcf59f401745131171435049990_inner_var_782 -> 782 -> 0xcfbe560 +0xee86e90 -> 0xcf59f401745131171435049990_inner_var_781 -> 781 -> 0xccb97f0 +0xee88710 -> 0xcf59f401745131171435049990_inner_var_776 -> 776 -> 0xcf22a30 +0xee88728 -> 0xcf59f401745131171435049990_inner_var_775 -> 775 -> 0xcabd0a0 +0xee87a30 -> 0xcf59f401745131171435049990_inner_var_774 -> 774 -> 0xccd2c20 +0xee87478 -> 0xcf59f401745131171435049990_inner_var_771 -> 771 -> 0xced6a50 +0xee874a8 -> 0xcf59f401745131171435049990_inner_var_769 -> 769 -> 0xca6eea0 +0xccbe670 -> 0xcf59f401745131171435049990_inner_var_1016 -> 1016 -> 0xcbee150 +0xcabae40 -> 0xcf59f401745131171435049990_inner_var_895 -> 895 -> 0xd067330 +0xee874d8 -> 0xcf59f401745131171435049990_inner_var_767 -> 767 -> 0xd04d780 +0xee86388 -> 0xcf59f401745131171435049990_inner_var_759 -> 759 -> 0xc988150 +0xccb3d30 -> 0xcf59f401745131171435049990_inner_var_758 -> 758 -> 0xee48330 +0xccb2fb0 -> 0xcf59f401745131171435049990_inner_var_756 -> 756 -> 0xca65620 +0xccb1e80 -> 0xcf59f401745131171435049990_inner_var_755 -> 755 -> 0xccbf1b0 +0xccb0a60 -> 0xcf59f401745131171435049990_inner_var_753 -> 753 -> 0xca81b10 +0xcabb720 -> 0xcf59f401745131171435049990_inner_var_867 -> 867 -> 0x3be6bd0 +0xd051040 -> 0xcf59f401745131171435049990_inner_var_751 -> 751 -> 0xcfce090 +0xccdb860 -> 0xcf59f401745131171435049990_inner_var_939 -> 939 -> 0xceef440 +0xccb0340 -> 0xcf59f401745131171435049990_inner_var_750 -> 750 -> 0xcf6e0b0 +0xccb0358 -> 0xcf59f401745131171435049990_inner_var_749 -> 749 -> 0xced2ea0 +0xca9bb80 -> 0xcf59f401745131171435049990_inner_var_748 -> 748 -> 0xceca5e0 +0xd039fa0 -> 0xcf59f401745131171435049990_inner_var_997 -> 997 -> 0xcbe9180 +0xca9bbe0 -> 0xcf59f401745131171435049990_inner_var_745 -> 745 -> 0xcf98e10 +0xca9bc78 -> 0xcf59f401745131171435049990_inner_var_739 -> 739 -> 0xca95ba0 +0xcae4930 -> 0xcf59f401745131171435049990_inner_var_812 -> 812 -> 0xca6ad40 +0xca9aee0 -> 0xcf59f401745131171435049990_inner_var_738 -> 738 -> 0xccc6500 +0xca9aa50 -> 0xcf59f401745131171435049990_inner_var_737 -> 737 -> 0xcf6d020 +0xca9a5c0 -> 0xcf59f401745131171435049990_inner_var_736 -> 736 -> 0xced39a0 +0xca99c50 -> 0xcf59f401745131171435049990_inner_var_734 -> 734 -> 0xd039050 +0xce8d450 -> 0xcf59f401745131171435049990_inner_var_893 -> 893 -> 0xcf04fb0 +0xcf77330 -> 0xcf59f401745131171435049990_inner_var_729 -> 729 -> 0xced2af0 +0xcf74af0 -> 0xcf59f401745131171435049990_inner_var_728 -> 728 -> 0xca846e0 +0xcf76ce0 -> 0xcf59f401745131171435049990_inner_var_727 -> 727 -> 0xd0549d0 +0xcf760e0 -> 0xcf59f401745131171435049990_inner_var_724 -> 724 -> 0xcf27be0 +0xcf74bc0 -> 0xcf59f401745131171435049990_inner_var_723 -> 723 -> 0xca57c30 +0xcf74520 -> 0xcf59f401745131171435049990_inner_var_719 -> 719 -> 0xca81ea0 +0xd047360 -> 0xcf59f401745131171435049990_inner_var_716 -> 716 -> 0xcecb900 +0xcae5dc0 -> 0xcf59f401745131171435049990_inner_var_821 -> 821 -> 0xcf29870 +0xcf732d0 -> 0xcf59f401745131171435049990_inner_var_714 -> 714 -> 0xca90a80 +0xd049690 -> 0xcf59f401745131171435049990_inner_var_709 -> 709 -> 0xcf01cf0 +0xcb0e8c0 -> 0xcf59f401745131171435049990_inner_var_708 -> 708 -> 0xd0375b0 +0xce91930 -> 0xcf59f401745131171435049990_inner_var_912 -> 912 -> 0xce9fd90 +0xd0471c0 -> 0xcf59f401745131171435049990_inner_var_707 -> 707 -> 0xcef8d90 +0xca8a9a0 -> 0xcf59f401745131171435049990_inner_var_1071 -> 1071 -> 0xcfbdec0 +0xd047290 -> 0xcf59f401745131171435049990_inner_var_704 -> 704 -> 0xcee99f0 +0xcdba950 -> 0xcf59f401745131171435049990_inner_var_703 -> 703 -> 0xce95310 +0xcdbb530 -> 0xcf59f401745131171435049990_inner_var_700 -> 700 -> 0xd033b90 +0xd046aa0 -> 0xcf59f401745131171435049990_inner_var_697 -> 697 -> 0xcf30f60 +0xd046680 -> 0xcf59f401745131171435049990_inner_var_696 -> 696 -> 0xcf039f0 +0xd045980 -> 0xcf59f401745131171435049990_inner_var_693 -> 693 -> 0xed1b7a0 +0xcae7fb0 -> 0xcf59f401745131171435049990_inner_var_835 -> 835 -> 0xc3a1720 +0xcdbfc30 -> 0xcf59f401745131171435049990_inner_var_690 -> 690 -> 0xca95690 +0xcdbf860 -> 0xcf59f401745131171435049990_inner_var_689 -> 689 -> 0xcabba50 +0xcdbe7a0 -> 0xcf59f401745131171435049990_inner_var_681 -> 681 -> 0xcea5ea0 +0xcdbe7b8 -> 0xcf59f401745131171435049990_inner_var_680 -> 680 -> 0xca9c030 +0xcdbe7d0 -> 0xcf59f401745131171435049990_inner_var_679 -> 679 -> 0xcea3640 +0xccc7930 -> 0xcf59f401745131171435049990_inner_var_1065 -> 1065 -> 0xcaabf50 +0xcdbe800 -> 0xcf59f401745131171435049990_inner_var_677 -> 677 -> 0xcec8c10 +0xcdbc7c0 -> 0xcf59f401745131171435049990_inner_var_674 -> 674 -> 0xcc9c7b0 +0xce91170 -> 0xcf59f401745131171435049990_inner_var_910 -> 910 -> 0xcf715a0 +0xcdbcd20 -> 0xcf59f401745131171435049990_inner_var_667 -> 667 -> 0xcf1dc10 +0xcdbc150 -> 0xcf59f401745131171435049990_inner_var_664 -> 664 -> 0xcf027a0 +0xcdbc168 -> 0xcf59f401745131171435049990_inner_var_663 -> 663 -> 0xcecf4b0 +0xcdbc180 -> 0xcf59f401745131171435049990_inner_var_662 -> 662 -> 0xcae2480 +0xcae4948 -> 0xcf59f401745131171435049990_inner_var_811 -> 811 -> 0xcf34430 +0xd055540 -> 0xcf59f401745131171435049990_inner_var_657 -> 657 -> 0xcf2c6b0 +0xca9bc00 -> 0xcf59f401745131171435049990_inner_var_744 -> 744 -> 0xcf27c80 +0xd056248 -> 0xcf59f401745131171435049990_inner_var_655 -> 655 -> 0xca8f450 +0xca9bc30 -> 0xcf59f401745131171435049990_inner_var_742 -> 742 -> 0xd03ab70 +0xd056278 -> 0xcf59f401745131171435049990_inner_var_653 -> 653 -> 0xd03da00 +0xca9bc48 -> 0xcf59f401745131171435049990_inner_var_741 -> 741 -> 0xca910c0 +0xd056290 -> 0xcf59f401745131171435049990_inner_var_652 -> 652 -> 0xcf38de0 +0xca9bc60 -> 0xcf59f401745131171435049990_inner_var_740 -> 740 -> 0xcfcad00 +0xd0562a8 -> 0xcf59f401745131171435049990_inner_var_651 -> 651 -> 0xcea71c0 +0xccbd058 -> 0xcf59f401745131171435049990_inner_var_858 -> 858 -> 0xcaa5610 +0xd055940 -> 0xcf59f401745131171435049990_inner_var_650 -> 650 -> 0xca621e0 +0xccd7a00 -> 0xcf59f401745131171435049990_inner_var_923 -> 923 -> 0xcebdad0 +0xd054060 -> 0xcf59f401745131171435049990_inner_var_649 -> 649 -> 0xd041750 +0xd0551a0 -> 0xcf59f401745131171435049990_inner_var_646 -> 646 -> 0xcf39480 +0xccc0e20 -> 0xcf59f401745131171435049990_inner_var_1026 -> 1026 -> 0xcaaca20 +0xd0551b8 -> 0xcf59f401745131171435049990_inner_var_645 -> 645 -> 0xcf73560 +0xd0551d0 -> 0xcf59f401745131171435049990_inner_var_644 -> 644 -> 0xcee4230 +0xd0551e8 -> 0xcf59f401745131171435049990_inner_var_643 -> 643 -> 0xcefff70 +0xced6910 -> 0xcf59f401745131171435049990_inner_var_726 -> 726 -> 0xca82ce0 +0xd053c90 -> 0xcf59f401745131171435049990_inner_var_639 -> 639 -> 0xccc0a20 +0xccc83b0 -> 0xcf59f401745131171435049990_inner_var_1068 -> 1068 -> 0xcfbce40 +0xd053ca8 -> 0xcf59f401745131171435049990_inner_var_638 -> 638 -> 0xcef7de0 +0xd053cc0 -> 0xcf59f401745131171435049990_inner_var_637 -> 637 -> 0xced4c00 +0xd053cf0 -> 0xcf59f401745131171435049990_inner_var_635 -> 635 -> 0xcf41960 +0xccbd860 -> 0xcf59f401745131171435049990_inner_var_1013 -> 1013 -> 0xcbed380 +0xca520b0 -> 0xcf59f401745131171435049990_inner_var_632 -> 632 -> 0xcec0d50 +0xd052ad0 -> 0xcf59f401745131171435049990_inner_var_631 -> 631 -> 0xceb7710 +0xccc71f0 -> 0xcf59f401745131171435049990_inner_var_1056 -> 1056 -> 0xcaac2e0 +0xd052ae8 -> 0xcf59f401745131171435049990_inner_var_630 -> 630 -> 0xce8d6a0 +0xee8bb20 -> 0xcf59f401745131171435049990_inner_var_804 -> 804 -> 0xccd05a0 +0xd052b00 -> 0xcf59f401745131171435049990_inner_var_629 -> 629 -> 0xccbed40 +0xee8bb38 -> 0xcf59f401745131171435049990_inner_var_803 -> 803 -> 0xcf00390 +0xd052b18 -> 0xcf59f401745131171435049990_inner_var_628 -> 628 -> 0xcec27d0 +0xee8bb68 -> 0xcf59f401745131171435049990_inner_var_801 -> 801 -> 0xcefbce0 +0xd052b48 -> 0xcf59f401745131171435049990_inner_var_626 -> 626 -> 0xd076d00 +0xd051ea0 -> 0xcf59f401745131171435049990_inner_var_625 -> 625 -> 0xcad50a0 +0xce8dd10 -> 0xcf59f401745131171435049990_inner_var_896 -> 896 -> 0xcf0cdf0 +0xcae6190 -> 0xcf59f401745131171435049990_inner_var_830 -> 830 -> 0xccc0b50 +0xca52cf0 -> 0xcf59f401745131171435049990_inner_var_624 -> 624 -> 0xceaedf0 +0xca53c10 -> 0xcf59f401745131171435049990_inner_var_623 -> 623 -> 0xcee6000 +0xca53c28 -> 0xcf59f401745131171435049990_inner_var_622 -> 622 -> 0xcf1c190 +0xca53c40 -> 0xcf59f401745131171435049990_inner_var_621 -> 621 -> 0xcf6ce80 +0xca53c88 -> 0xcf59f401745131171435049990_inner_var_618 -> 618 -> 0xceb8980 +0xd048050 -> 0xcf59f401745131171435049990_inner_var_702 -> 702 -> 0xd0344d0 +0xca53250 -> 0xcf59f401745131171435049990_inner_var_617 -> 617 -> 0xccd1710 +0xca4fe20 -> 0xcf59f401745131171435049990_inner_var_616 -> 616 -> 0xca8f7e0 +0xca52680 -> 0xcf59f401745131171435049990_inner_var_614 -> 614 -> 0xcdbd150 +0xca52698 -> 0xcf59f401745131171435049990_inner_var_613 -> 613 -> 0xcaddfc0 +0xca526c8 -> 0xcf59f401745131171435049990_inner_var_611 -> 611 -> 0xcf5b640 +0xca84410 -> 0xcf59f401745131171435049990_inner_var_1010 -> 1010 -> 0xcbec720 +0xca526f8 -> 0xcf59f401745131171435049990_inner_var_609 -> 609 -> 0xca98d50 +0xca84850 -> 0xcf59f401745131171435049990_inner_var_1012 -> 1012 -> 0xcbecf60 +0xca50890 -> 0xcf59f401745131171435049990_inner_var_607 -> 607 -> 0xcf15d30 +0xccc1280 -> 0xcf59f401745131171435049990_inner_var_1027 -> 1027 -> 0xcaacdf0 +0xca51580 -> 0xcf59f401745131171435049990_inner_var_606 -> 606 -> 0xee71db0 +0xca51598 -> 0xcf59f401745131171435049990_inner_var_605 -> 605 -> 0xcad27d0 +0xd0533a0 -> 0xcf59f401745131171435049990_inner_var_633 -> 633 -> 0xcee5560 +0xca515b0 -> 0xcf59f401745131171435049990_inner_var_604 -> 604 -> 0xccb23e0 +0xca515c8 -> 0xcf59f401745131171435049990_inner_var_603 -> 603 -> 0xcefd140 +0xd049fe0 -> 0xcf59f401745131171435049990_inner_var_713 -> 713 -> 0xce9bd80 +0xca50c90 -> 0xcf59f401745131171435049990_inner_var_600 -> 600 -> 0xceb8250 +0xca50398 -> 0xcf59f401745131171435049990_inner_var_597 -> 597 -> 0xccb5920 +0xee45cc0 -> 0xcf59f401745131171435049990_inner_var_773 -> 773 -> 0xca6a0e0 +0xca503b0 -> 0xcf59f401745131171435049990_inner_var_596 -> 596 -> 0xcf1fa00 +0xca503f8 -> 0xcf59f401745131171435049990_inner_var_593 -> 593 -> 0xcea7e20 +0xcdba7f0 -> 0xcf59f401745131171435049990_inner_var_992 -> 992 -> 0xcbe6240 +0xca4f6d0 -> 0xcf59f401745131171435049990_inner_var_592 -> 592 -> 0xced6750 +0xccdc930 -> 0xcf59f401745131171435049990_inner_var_944 -> 944 -> 0xceff540 +0xcaa0bf0 -> 0xcf59f401745131171435049990_inner_var_591 -> 591 -> 0xd060cb0 +0xcaa18f8 -> 0xcf59f401745131171435049990_inner_var_588 -> 588 -> 0xed1b2d0 +0xcaa1910 -> 0xcf59f401745131171435049990_inner_var_587 -> 587 -> 0xcf22ec0 +0xcaa1928 -> 0xcf59f401745131171435049990_inner_var_586 -> 586 -> 0xcab6ec0 +0xcaa1940 -> 0xcf59f401745131171435049990_inner_var_585 -> 585 -> 0xca8d7c0 +0xcaa1958 -> 0xcf59f401745131171435049990_inner_var_584 -> 584 -> 0xced5ae0 +0xca9fb30 -> 0xcf59f401745131171435049990_inner_var_582 -> 582 -> 0xccbdb90 +0xcae7fc8 -> 0xcf59f401745131171435049990_inner_var_834 -> 834 -> 0xca616f0 +0xcaa0820 -> 0xcf59f401745131171435049990_inner_var_581 -> 581 -> 0xcca6620 +0xcae7fe0 -> 0xcf59f401745131171435049990_inner_var_833 -> 833 -> 0xca93340 +0xcaa0838 -> 0xcf59f401745131171435049990_inner_var_580 -> 580 -> 0xccc5af0 +0xcae7ff8 -> 0xcf59f401745131171435049990_inner_var_832 -> 832 -> 0xccd79a0 +0xcaa0850 -> 0xcf59f401745131171435049990_inner_var_579 -> 579 -> 0xca6bd30 +0xee45f30 -> 0xcf59f401745131171435049990_inner_var_754 -> 754 -> 0xd05a7c0 +0xcaa0868 -> 0xcf59f401745131171435049990_inner_var_578 -> 578 -> 0xd0415b0 +0xd0361a0 -> 0xcf59f401745131171435049990_inner_var_981 -> 981 -> 0xcbe5180 +0xcaa0880 -> 0xcf59f401745131171435049990_inner_var_577 -> 577 -> 0xca8c400 +0xd0361b8 -> 0xcf59f401745131171435049990_inner_var_980 -> 980 -> 0xcbe4d40 +0xccda330 -> 0xcf59f401745131171435049990_inner_var_942 -> 942 -> 0xceebb60 +0xcaa0898 -> 0xcf59f401745131171435049990_inner_var_576 -> 576 -> 0xcefc390 +0xca9ff30 -> 0xcf59f401745131171435049990_inner_var_575 -> 575 -> 0xcedf140 +0xca9eda0 -> 0xcf59f401745131171435049990_inner_var_567 -> 567 -> 0xd048710 +0xcadf3d0 -> 0xcf59f401745131171435049990_inner_var_566 -> 566 -> 0xcebf980 +0xca9e1b0 -> 0xcf59f401745131171435049990_inner_var_564 -> 564 -> 0xcf7ffc0 +0xca9e1e0 -> 0xcf59f401745131171435049990_inner_var_562 -> 562 -> 0xcf9a7b0 +0xca9e1f8 -> 0xcf59f401745131171435049990_inner_var_561 -> 561 -> 0xee3fb80 +0xccd9f50 -> 0xcf59f401745131171435049990_inner_var_933 -> 933 -> 0xceb9ec0 +0xca9e210 -> 0xcf59f401745131171435049990_inner_var_560 -> 560 -> 0xccc9660 +0xca9e228 -> 0xcf59f401745131171435049990_inner_var_559 -> 559 -> 0xcef0070 +0xd053d08 -> 0xcf59f401745131171435049990_inner_var_634 -> 634 -> 0xcfa0f80 +0xcae3110 -> 0xcf59f401745131171435049990_inner_var_558 -> 558 -> 0xcabcf60 +0xcae2c00 -> 0xcf59f401745131171435049990_inner_var_557 -> 557 -> 0xca60560 +0xcae2c30 -> 0xcf59f401745131171435049990_inner_var_555 -> 555 -> 0xcce0c70 +0xcae2c78 -> 0xcf59f401745131171435049990_inner_var_552 -> 552 -> 0xcca1c90 +0xcaf0090 -> 0xcf59f401745131171435049990_inner_var_550 -> 550 -> 0xcebdb50 +0xd049dc0 -> 0xcf59f401745131171435049990_inner_var_712 -> 712 -> 0xee40740 +0xcaf0f00 -> 0xcf59f401745131171435049990_inner_var_549 -> 549 -> 0xca9d030 +0xcaf0f30 -> 0xcf59f401745131171435049990_inner_var_547 -> 547 -> 0xce9cd30 +0xcaf0f60 -> 0xcf59f401745131171435049990_inner_var_545 -> 545 -> 0xee747c0 +0xcaea780 -> 0xcf59f401745131171435049990_inner_var_849 -> 849 -> 0xed1b940 +0xcdbdeb0 -> 0xcf59f401745131171435049990_inner_var_675 -> 675 -> 0xcebaab0 +0xcaf0f78 -> 0xcf59f401745131171435049990_inner_var_544 -> 544 -> 0xcc9f700 +0xca68ee0 -> 0xcf59f401745131171435049990_inner_var_963 -> 963 -> 0xcbe0940 +0xccdd130 -> 0xcf59f401745131171435049990_inner_var_948 -> 948 -> 0xcfc20c0 +0xcf73e00 -> 0xcf59f401745131171435049990_inner_var_717 -> 717 -> 0xcead7d0 +0xccad6c0 -> 0xcf59f401745131171435049990_inner_var_542 -> 542 -> 0xcef52c0 +0xee87490 -> 0xcf59f401745131171435049990_inner_var_770 -> 770 -> 0xcf097c0 +0xcaf9b00 -> batch_norm2d_21.b_0_deepcopy_109 -> 224 -> 0xcf23400 +0xcaf9730 -> batch_norm2d_21.w_1_deepcopy_110 -> 223 -> 0xcf9c4b0 +0xcaf9360 -> batch_norm2d_21.w_2_deepcopy_111 -> 222 -> 0xd05c7d0 +0xccce5c0 -> 0xcf59f401745131171435049990_inner_var_484 -> 484 -> 0xccdbe30 +0xcaf5a20 -> batch_norm2d_16.w_1_deepcopy_85 -> 248 -> 0xcfa6d50 +0xcaf2df0 -> batch_norm2d_15.w_1_deepcopy_80 -> 253 -> 0xcfcce30 +0xccd3ba0 -> batch_norm2d_18.w_0_deepcopy_93 -> 240 -> 0xcfa68e0 +0xccd9180 -> 0xcf59f401745131171435049990_inner_var_928 -> 928 -> 0xcec9a50 +0xcaf83d0 -> batch_norm2d_22.w_1_deepcopy_115 -> 218 -> 0xd05e730 +0xcaf2650 -> conv2d_16.w_0_deepcopy_82 -> 251 -> 0xcf536b0 +0xccd3400 -> batch_norm2d_18.w_1_deepcopy_95 -> 238 -> 0xcf84390 +0xca51a50 -> 0xcf59f401745131171435049990_inner_var_608 -> 608 -> 0xcf1d860 +0xcae2c48 -> 0xcf59f401745131171435049990_inner_var_554 -> 554 -> 0xcad4be0 +0xcaf6430 -> conv2d_21.w_0_deepcopy_107 -> 226 -> 0xcf41b60 +0xcaf8f90 -> conv2d_22.w_0_deepcopy_112 -> 221 -> 0xcfa5f90 +0xcdbc620 -> 0xcf59f401745131171435049990_inner_var_665 -> 665 -> 0xca78840 +0xccce1f0 -> 0xcf59f401745131171435049990_inner_var_474 -> 474 -> 0xcf36750 +0xccb2580 -> 0xcf59f401745131171435049990_inner_var_975 -> 975 -> 0xcbe2420 +0xcaf87a0 -> batch_norm2d_22.b_0_deepcopy_114 -> 219 -> 0xcf3fa10 +0xd049dd8 -> 0xcf59f401745131171435049990_inner_var_711 -> 711 -> 0xced67d0 +0xcaf0f18 -> 0xcf59f401745131171435049990_inner_var_548 -> 548 -> 0xca83730 +0xcaf31c0 -> batch_norm2d_15.b_0_deepcopy_79 -> 254 -> 0xcfcb200 +0xccd3f70 -> conv2d_18.w_0_deepcopy_92 -> 241 -> 0xcf22d20 +0xcdb5f70 -> batch_norm2d_22.w_2_deepcopy_116 -> 217 -> 0xd05f3f0 +0xee72b90 -> constant_folding@_174513116882724144161 -> 3 -> 0xcdb8360 +0xcdb4bc0 -> batch_norm2d_23.w_2_deepcopy_121 -> 212 -> 0xd062570 +0xcdc1f10 -> batch_norm2d_24.b_0_deepcopy_124 -> 209 -> 0xd062fb0 +0xca76600 -> conv2d_52.w_0_deepcopy_254 -> 83 -> 0xcfc1c10 +0xccc6720 -> 0xcf59f401745131171435049990_inner_var_1052 -> 1052 -> 0xcaab530 +0xca9e1c8 -> 0xcf59f401745131171435049990_inner_var_563 -> 563 -> 0xd03c730 +0xcdc1b40 -> batch_norm2d_24.w_1_deepcopy_125 -> 208 -> 0xd065590 +0xcdc13a0 -> conv2d_25.w_0_deepcopy_127 -> 206 -> 0xcf91990 +0xcdc0fd0 -> batch_norm2d_25.w_0_deepcopy_128 -> 205 -> 0xd067f70 +0xd0588f0 -> 0xcf59f401745131171435049990_inner_var_368 -> 368 -> 0xccd76c0 +0xcdc0830 -> batch_norm2d_25.w_1_deepcopy_130 -> 203 -> 0xd069110 +0xcdc04a0 -> batch_norm2d_25.w_2_deepcopy_131 -> 202 -> 0xd069fd0 +0xcdb8e48 -> 0xcf59f401745131171435049990_inner_var_340 -> 340 -> 0xd0606b0 +0xce92490 -> 0xcf59f401745131171435049990_inner_var_915 -> 915 -> 0xceac080 +0xcc9bc50 -> conv2d_26.w_0_deepcopy_132 -> 201 -> 0xcf71280 +0xd04e850 -> batch_norm2d_46.w_0_deepcopy_240 -> 97 -> 0xcf93910 +0xc80d3d0 -> batch_norm2d_36.w_0_deepcopy_183 -> 150 -> 0xd065350 +0xee8cd20 -> 0xcf59f401745131171435049990_inner_var_813 -> 813 -> 0xcee8540 +0xee8aa78 -> 0xcf59f401745131171435049990_inner_var_795 -> 795 -> 0xcf124f0 +0xcc98998 -> 0xcf59f401745131171435049990_inner_var_397 -> 397 -> 0xceee010 +0xcc9b0e0 -> batch_norm2d_26.w_1_deepcopy_135 -> 198 -> 0xd06c980 +0xcfad320 -> batch_norm2d_41.b_0_deepcopy_209 -> 124 -> 0xcf8ca40 +0xee74908 -> 0xcf59f401745131171435049990_inner_var_379 -> 379 -> 0xcec7d50 +0xd0412b0 -> constant_folding@_174513116833656139834 -> 27 -> 0xcf419a0 +0xd03a860 -> 0xcf59f401745131171435049990_inner_var_999 -> 999 -> 0xcbe99c0 +0xccb7a08 -> 0xcf59f401745131171435049990_inner_var_429 -> 429 -> 0xcab60d0 +0xcc9cbc0 -> batch_norm2d_28.b_0_deepcopy_144 -> 189 -> 0xd071b20 +0xcf6d200 -> constant_folding@_17451311436834671136 -> 52 -> 0xcf820a0 +0xca9e840 -> 0xcf59f401745131171435049990_inner_var_574 -> 574 -> 0xca8f060 +0xcaf5280 -> conv2d_17.w_0_deepcopy_87 -> 246 -> 0xcf17680 +0xee73358 -> 0xcf59f401745131171435049990_inner_var_370 -> 370 -> 0xce97c00 +0xca84428 -> 0xcf59f401745131171435049990_inner_var_1009 -> 1009 -> 0xcbec2e0 +0xcfab5d0 -> batch_norm2d_30.b_0_deepcopy_154 -> 179 -> 0xcf28de0 +0xee45a10 -> 0xcf59f401745131171435049990_inner_var_967 -> 967 -> 0xcbe15a0 +0xcaf4ae0 -> batch_norm2d_17.b_0_deepcopy_89 -> 244 -> 0xcfa8c60 +0xcc9d730 -> batch_norm2d_27.w_2_deepcopy_141 -> 192 -> 0xd06fa30 +0xccb0df0 -> 0xcdfe7a01745131172833993105_inner_var_1095 -> 1095 -> 0xcdff200 +0xd047430 -> 0xcf59f4017451311 +I0420 14:39:34.803431 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:34.809697 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:34.811352 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "slice_array_dense(phi_kernel)" (%1, %2) {kernel_key:,kernel_name:"slice_array_dense",op_name:"pd_op.slice_array_dense",origin_id:2604,stop_gradient:[true]} : (cpu_tensor_array, cpu_tensor<1xi64>) -> cpu_tensor<-1x4xf32> + (%3) = "memcpy_h2d(phi_kernel)" (%0) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2605} : (cpu_tensor<-1x4xf32>) -> custom_device_tensor<-1x4xf32> + () = "cf.yield" [id:2606] (%3) {origin_id:556} : (custom_device_tensor<-1x4xf32>) -> +} +I0420 14:39:34.811374 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 1094 ) = pd_op.slice_array_dense ( 44 ) ( 726 ) +1: ( 1095 ) = pd_op.memcpy_h2d ( 1094 ) +2: = yield_instruction ( 1095 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> constant_folding@_174513116887061917064 -> 0xd062b30 +1 -> constant_folding@_174513116885637196163 -> 0xd04b8a0 +2 -> constant_folding@_174513116884162097162 -> 0xcf7caa0 +3 -> constant_folding@_174513116882724144161 -> 0xcdb8360 +4 -> constant_folding@_174513116880873974160 -> 0xee71f60 +5 -> constant_folding@_174513116876880248258 -> 0xee71fe0 +6 -> constant_folding@_174513116875198908257 -> 0xc386660 +7 -> constant_folding@_174513116872098526255 -> 0xd061370 +8 -> constant_folding@_174513116870501220354 -> 0xcea7bc0 +9 -> constant_folding@_174513116869046783353 -> 0xcf2b9b0 +10 -> constant_folding@_174513116867667180352 -> 0xee46cc0 +11 -> constant_folding@_174513116865440940351 -> 0xcf3edb0 +12 -> constant_folding@_174513116864033493350 -> 0xcf3a750 +13 -> constant_folding@_174513116861634597449 -> 0xcf09130 +14 -> constant_folding@_174513116859911589448 -> 0xcf41cc0 +15 -> constant_folding@_174513116857122500446 -> 0xd0606f0 +16 -> constant_folding@_174513116855594796545 -> 0xcf11a20 +17 -> constant_folding@_174513116853662943544 -> 0xcdb8c50 +18 -> constant_folding@_174513116852268476543 -> 0xcfbe9a0 +19 -> constant_folding@_174513116850011146642 -> 0xcf2d600 +20 -> constant_folding@_174513116844224746641 -> 0xcdb72f0 +21 -> constant_folding@_174513116842705804740 -> 0xcf92430 +22 -> constant_folding@_174513116841278885739 -> 0xcdb8970 +23 -> constant_folding@_174513116839857308738 -> 0xcdb8950 +24 -> constant_folding@_174513116838085921737 -> 0xcf83ea0 +25 -> constant_folding@_174513116836671179736 -> 0xcf806d0 +26 -> constant_folding@_174513116835231166835 -> 0xcf0c190 +27 -> constant_folding@_174513116833656139834 -> 0xcf419a0 +28 -> constant_folding@_174513116830651898832 -> 0xed1a750 +29 -> constant_folding@_174513116827593559930 -> 0xcf437e0 +30 -> constant_folding@_174513116825929047929 -> 0xcf7c3f0 +31 -> constant_folding@_174513116824495087928 -> 0xcf3ae90 +32 -> constant_folding@_174513116823069183927 -> 0xcf64350 +33 -> constant_folding@_174513116821619066926 -> 0xcff2a20 +34 -> constant_folding@_174513116820193818025 -> 0xce87a10 +35 -> constant_folding@_174513116818740431024 -> 0xce98460 +36 -> constant_folding@_174513116817322536023 -> 0xce89990 +37 -> constant_folding@_174513116815886735022 -> 0xcf7aeb0 +38 -> constant_folding@_174513116814471704121 -> 0xd05fe30 +39 -> constant_folding@_174513116813053746120 -> 0xcf61980 +40 -> constant_folding@_174513116811615103119 -> 0xcf90680 +41 -> constant_folding@_174513115258237754718 -> 0xcfce240 +42 -> constant_folding@_174513115176548053017 -> 0xcf8c2c0 +43 -> constant_folding@_174513115110199844916 -> 0xcf2d1d0 +44 -> constant_folding@_174513115013851471515 -> 0xcf1a280 +45 -> constant_folding@_174513114941937302314 -> 0xcf04b20 +46 -> constant_folding@_174513114857650263913 -> 0xcf7d3f0 +47 -> constant_folding@_174513114736420657211 -> 0xd059870 +48 -> constant_folding@_174513114667774590410 -> 0xcf7f8b0 +49 -> constant_folding@_17451311460158745859 -> 0xcf7a460 +50 -> constant_folding@_17451311452954254518 -> 0xd05b470 +51 -> constant_folding@_17451311445836590377 -> 0xceddd30 +52 -> constant_folding@_17451311436834671136 -> 0xcf820a0 +53 -> constant_folding@_17451311430626338225 -> 0xcf624a0 +54 -> constant_folding@_17451311422710820934 -> 0xcede1e0 +55 -> constant_folding@_17451311415972965563 -> 0xd05ecd0 +56 -> constant_folding@_17451311399980650772 -> 0xcf619e0 +57 -> constant_folding@_17451311284029886470 -> 0xd069f60 +58 -> conv2d_56.w_0_deepcopy_280 -> 0xcf344c0 +59 -> conv2d_transpose_0.w_0_deepcopy_278 -> 0xcf28750 +60 -> linear_1.b_0_deepcopy_277 -> 0xcf3e700 +61 -> linear_1.w_0_deepcopy_276 -> 0xcf61960 +62 -> linear_0.b_0_deepcopy_275 -> 0xd041df0 +63 -> linear_0.w_0_deepcopy_274 -> 0xcfa9fb0 +64 -> batch_norm2d_52.w_2_deepcopy_273 -> 0xcf3ce10 +65 -> batch_norm2d_52.w_1_deepcopy_272 -> 0xcf9ab00 +66 -> batch_norm2d_52.b_0_deepcopy_271 -> 0xcf193d0 +67 -> batch_norm2d_52.w_0_deepcopy_270 -> 0xcf2c170 +68 -> conv2d_55.w_0_deepcopy_269 -> 0xcf3b8c0 +69 -> batch_norm2d_51.w_2_deepcopy_268 -> 0xcf2c930 +70 -> batch_norm2d_51.w_1_deepcopy_267 -> 0xcfa59c0 +71 -> batch_norm2d_51.b_0_deepcopy_266 -> 0xcf1b990 +72 -> batch_norm2d_51.w_0_deepcopy_265 -> 0xcf2f740 +73 -> conv2d_54.w_0_deepcopy_264 -> 0xd059850 +74 -> batch_norm2d_50.w_2_deepcopy_263 -> 0xcf2bc00 +75 -> batch_norm2d_50.w_1_deepcopy_262 -> 0xcf33710 +76 -> batch_norm2d_50.b_0_deepcopy_261 -> 0xcfa3840 +77 -> batch_norm2d_50.w_0_deepcopy_260 -> 0xcf20930 +78 -> conv2d_53.w_0_deepcopy_259 -> 0xcfa3490 +79 -> batch_norm2d_49.w_2_deepcopy_258 -> 0xcf24f50 +80 -> batch_norm2d_49.w_1_deepcopy_257 -> 0xcf420e0 +81 -> batch_norm2d_49.b_0_deepcopy_256 -> 0xcf39210 +82 -> batch_norm2d_49.w_0_deepcopy_255 -> 0xcfa0ad0 +83 -> conv2d_52.w_0_deepcopy_254 -> 0xcfc1c10 +84 -> batch_norm2d_48.w_2_deepcopy_253 -> 0xcf3e380 +85 -> batch_norm2d_48.w_1_deepcopy_252 -> 0xcf28490 +86 -> batch_norm2d_48.b_0_deepcopy_251 -> 0xcf165b0 +87 -> batch_norm2d_48.w_0_deepcopy_250 -> 0xcf3e660 +88 -> conv2d_51.w_0_deepcopy_249 -> 0xccd6fb0 +89 -> batch_norm2d_47.w_2_deepcopy_248 -> 0xcf33b60 +90 -> batch_norm2d_47.w_1_deepcopy_247 -> 0xcf230d0 +91 -> batch_norm2d_47.b_0_deepcopy_246 -> 0xcf9e810 +92 -> batch_norm2d_47.w_0_deepcopy_245 -> 0xcf3a620 +93 -> conv2d_50.w_0_deepcopy_244 -> 0xcf64fa0 +94 -> batch_norm2d_46.w_2_deepcopy_243 -> 0xcfa1c80 +95 -> batch_norm2d_46.w_1_deepcopy_242 -> 0xcf90660 +96 -> batch_norm2d_46.b_0_deepcopy_241 -> 0xcf60140 +97 -> batch_norm2d_46.w_0_deepcopy_240 -> 0xcf93910 +98 -> conv2d_49.w_0_deepcopy_239 -> 0xcfbfd60 +99 -> batch_norm2d_45.w_2_deepcopy_238 -> 0xcf2ccb0 +100 -> batch_norm2d_45.w_1_deepcopy_237 -> 0xcf2bf50 +101 -> batch_norm2d_45.b_0_deepcopy_236 -> 0xcfa4800 +102 -> batch_norm2d_45.w_0_deepcopy_235 -> 0xcf346f0 +103 -> conv2d_48.w_0_deepcopy_234 -> 0xcf6e750 +104 -> batch_norm2d_44.w_2_deepcopy_233 -> 0xcf9c920 +105 -> batch_norm2d_44.w_1_deepcopy_232 -> 0xce972f0 +106 -> batch_norm2d_44.b_0_deepcopy_231 -> 0xcfa37a0 +107 -> batch_norm2d_44.w_0_deepcopy_230 -> 0xcf1cb30 +108 -> conv2d_47.w_0_deepcopy_229 -> 0xcf6e400 +109 -> batch_norm2d_43.w_2_deepcopy_228 -> 0xcf9baf0 +110 -> batch_norm2d_43.w_1_deepcopy_227 -> 0xcf2a900 +111 -> batch_norm2d_43.b_0_deepcopy_226 -> 0xcf9ef90 +112 -> batch_norm2d_43.w_0_deepcopy_225 -> 0xcf61c60 +113 -> conv2d_46.w_0_deepcopy_224 -> 0xcf788c0 +114 -> conv2d_45.w_0_deepcopy_222 -> 0xcf30990 +115 -> conv2d_44.w_0_deepcopy_220 -> 0xcf36b60 +116 -> conv2d_43.w_0_deepcopy_218 -> 0xcf23170 +117 -> batch_norm2d_42.w_2_deepcopy_216 -> 0xcfa3f80 +118 -> batch_norm2d_42.w_1_deepcopy_215 -> 0xcf1c020 +119 -> batch_norm2d_42.b_0_deepcopy_214 -> 0xcf84050 +120 -> batch_norm2d_42.w_0_deepcopy_213 -> 0xcf3c660 +121 -> conv2d_42.w_0_deepcopy_212 -> 0xcf9c9b0 +122 -> batch_norm2d_41.w_2_deepcopy_211 -> 0xcfc7d60 +123 -> batch_norm2d_41.w_1_deepcopy_210 -> 0xcf2da20 +124 -> batch_norm2d_41.b_0_deepcopy_209 -> 0xcf8ca40 +125 -> batch_norm2d_41.w_0_deepcopy_208 -> 0xcfc5960 +126 -> conv2d_41.w_0_deepcopy_207 -> 0xcf91450 +127 -> batch_norm2d_40.w_2_deepcopy_206 -> 0xcf35500 +128 -> batch_norm2d_40.w_1_deepcopy_205 -> 0xcfc3720 +129 -> batch_norm2d_40.b_0_deepcopy_204 -> 0xcf41f80 +130 -> batch_norm2d_40.w_0_deepcopy_203 -> 0xcf63a50 +131 -> conv2d_40.w_0_deepcopy_202 -> 0xc80ecf0 +132 -> batch_norm2d_39.w_2_deepcopy_201 -> 0xcf15bf0 +133 -> batch_norm2d_39.w_1_deepcopy_200 -> 0xcf3a220 +134 -> batch_norm2d_39.b_0_deepcopy_199 -> 0xcf35220 +135 -> batch_norm2d_39.w_0_deepcopy_198 -> 0xcf34d40 +136 -> conv2d_39.w_0_deepcopy_197 -> 0xd0753d0 +137 -> batch_norm2d_38.w_2_deepcopy_196 -> 0xcfa49c0 +138 -> batch_norm2d_38.w_1_deepcopy_195 -> 0xcefd090 +139 -> batch_norm2d_38.b_0_deepcopy_194 -> 0xcf14990 +140 -> batch_norm2d_38.w_0_deepcopy_193 -> 0xcfaa490 +141 -> conv2d_38.w_0_deepcopy_192 -> 0xd068b90 +142 -> batch_norm2d_37.w_2_deepcopy_191 -> 0xcf286a0 +143 -> batch_norm2d_37.w_1_deepcopy_190 -> 0xcf9e170 +144 -> batch_norm2d_37.b_0_deepcopy_189 -> 0xcf8d310 +145 -> batch_norm2d_37.w_0_deepcopy_188 -> 0xcf9cbb0 +146 -> conv2d_37.w_0_deepcopy_187 -> 0xd063c10 +147 -> batch_norm2d_36.w_2_deepcopy_186 -> 0xcf37410 +148 -> batch_norm2d_36.w_1_deepcopy_185 -> 0xcf24090 +149 -> batch_norm2d_36.b_0_deepcopy_184 -> 0xcf1aa60 +150 -> batch_norm2d_36.w_0_deepcopy_183 -> 0xd065350 +151 -> conv2d_36.w_0_deepcopy_182 -> 0xd05fe10 +152 -> batch_norm2d_35.w_2_deepcopy_181 -> 0xcf14710 +153 -> batch_norm2d_35.w_1_deepcopy_180 -> 0xcf3e960 +154 -> batch_norm2d_35.b_0_deepcopy_179 -> 0xcf40ec0 +155 -> batch_norm2d_35.w_0_deepcopy_178 -> 0xcf396f0 +156 -> conv2d_35.w_0_deepcopy_177 -> 0xd05bfc0 +157 -> batch_norm2d_34.w_2_deepcopy_176 -> 0xcf3b470 +158 -> batch_norm2d_34.w_1_deepcopy_175 -> 0xcf38a90 +159 -> batch_norm2d_34.b_0_deepcopy_174 -> 0xcf3a9e0 +160 -> batch_norm2d_34.w_0_deepcopy_173 -> 0xcfbfa40 +161 -> conv2d_34.w_0_deepcopy_172 -> 0xcf831a0 +162 -> batch_norm2d_33.w_2_deepcopy_171 -> 0xcf3a3e0 +163 -> batch_norm2d_33.w_1_deepcopy_170 -> 0xcf21710 +164 -> batch_norm2d_33.b_0_deepcopy_169 -> 0xcfa22f0 +165 -> batch_norm2d_33.w_0_deepcopy_168 -> 0xcfa2c80 +166 -> conv2d_33.w_0_deepcopy_167 -> 0xcf80d10 +167 -> batch_norm2d_32.w_2_deepcopy_166 -> 0xcf91ac0 +168 -> batch_norm2d_32.w_1_deepcopy_165 -> 0xcf3d360 +169 -> batch_norm2d_32.b_0_deepcopy_164 -> 0xcf3e100 +170 -> batch_norm2d_32.w_0_deepcopy_163 -> 0xcf218b0 +171 -> conv2d_32.w_0_deepcopy_162 -> 0xcf8c2a0 +172 -> batch_norm2d_31.w_2_deepcopy_161 -> 0xcf37b70 +173 -> batch_norm2d_31.w_1_deepcopy_160 -> 0xcfa09b0 +174 -> batch_norm2d_31.b_0_deepcopy_159 -> 0xcfa3680 +175 -> batch_norm2d_31.w_0_deepcopy_158 -> 0xcf910a0 +176 -> conv2d_31.w_0_deepcopy_157 -> 0xcf7c3d0 +177 -> batch_norm2d_30.w_2_deepcopy_156 -> 0xc809ae0 +178 -> batch_norm2d_30.w_1_deepcopy_155 -> 0xcf63ec0 +179 -> batch_norm2d_30.b_0_deepcopy_154 -> 0xcf28de0 +180 -> batch_norm2d_30.w_0_deepcopy_153 -> 0xcf3c2d0 +181 -> conv2d_30.w_0_deepcopy_152 -> 0xcf7ae90 +182 -> batch_norm2d_29.w_2_deepcopy_151 -> 0xcf93530 +183 -> batch_norm2d_29.w_1_deepcopy_150 -> 0xd076240 +184 -> batch_norm2d_29.b_0_deepcopy_149 -> 0xd074f90 +185 -> batch_norm2d_29.w_0_deepcopy_148 -> 0xcfa2740 +186 -> conv2d_29.w_0_deepcopy_147 -> 0xcf95890 +187 -> batch_norm2d_28.w_2_deepcopy_146 -> 0xd074250 +188 -> batch_norm2d_28.w_1_deepcopy_145 -> 0xd073510 +189 -> batch_norm2d_28.b_0_deepcopy_144 -> 0xd071b20 +190 -> batch_norm2d_28.w_0_deepcopy_143 -> 0xd0727b0 +191 -> conv2d_28.w_0_deepcopy_142 -> 0xcfa8980 +192 -> batch_norm2d_27.w_2_deepcopy_141 -> 0xd06fa30 +193 -> batch_norm2d_27.w_1_deepcopy_140 -> 0xd06ff30 +194 -> batch_norm2d_27.b_0_deepcopy_139 -> 0xd06e400 +195 -> batch_norm2d_27.w_0_deepcopy_138 -> 0xd06f070 +196 -> conv2d_27.w_0_deepcopy_137 -> 0xcf9e030 +197 -> batch_norm2d_26.w_2_deepcopy_136 -> 0xd06d600 +198 -> batch_norm2d_26.w_1_deepcopy_135 -> 0xd06c980 +199 -> batch_norm2d_26.b_0_deepcopy_134 -> 0xd06ad10 +200 -> batch_norm2d_26.w_0_deepcopy_133 -> 0xd06b960 +201 -> conv2d_26.w_0_deepcopy_132 -> 0xcf71280 +202 -> batch_norm2d_25.w_2_deepcopy_131 -> 0xd069fd0 +203 -> batch_norm2d_25.w_1_deepcopy_130 -> 0xd069110 +204 -> batch_norm2d_25.b_0_deepcopy_129 -> 0xd066e30 +205 -> batch_norm2d_25.w_0_deepcopy_128 -> 0xd067f70 +206 -> conv2d_25.w_0_deepcopy_127 -> 0xcf91990 +207 -> batch_norm2d_24.w_2_deepcopy_126 -> 0xd066330 +208 -> batch_norm2d_24.w_1_deepcopy_125 -> 0xd065590 +209 -> batch_norm2d_24.b_0_deepcopy_124 -> 0xd062fb0 +210 -> batch_norm2d_24.w_0_deepcopy_123 -> 0xd0641f0 +211 -> conv2d_24.w_0_deepcopy_122 -> 0xcf91780 +212 -> batch_norm2d_23.w_2_deepcopy_121 -> 0xd062570 +213 -> batch_norm2d_23.w_1_deepcopy_120 -> 0xd061ab0 +214 -> batch_norm2d_23.b_0_deepcopy_119 -> 0xd060170 +215 -> batch_norm2d_23.w_0_deepcopy_118 -> 0xd060ed0 +216 -> conv2d_23.w_0_deepcopy_117 -> 0xcf1a470 +217 -> batch_norm2d_22.w_2_deepcopy_116 -> 0xd05f3f0 +218 -> batch_norm2d_22.w_1_deepcopy_115 -> 0xd05e730 +219 -> batch_norm2d_22.b_0_deepcopy_114 -> 0xcf3fa10 +220 -> batch_norm2d_22.w_0_deepcopy_113 -> 0xd05d850 +221 -> conv2d_22.w_0_deepcopy_112 -> 0xcfa5f90 +222 -> batch_norm2d_21.w_2_deepcopy_111 -> 0xd05c7d0 +223 -> batch_norm2d_21.w_1_deepcopy_110 -> 0xcf9c4b0 +224 -> batch_norm2d_21.b_0_deepcopy_109 -> 0xcf23400 +225 -> batch_norm2d_21.w_0_deepcopy_108 -> 0xd05afe0 +226 -> conv2d_21.w_0_deepcopy_107 -> 0xcf41b60 +227 -> batch_norm2d_20.w_2_deepcopy_106 -> 0xcf83e60 +228 -> batch_norm2d_20.w_1_deepcopy_105 -> 0xcf82fa0 +229 -> batch_norm2d_20.b_0_deepcopy_104 -> 0xcf814b0 +230 -> batch_norm2d_20.w_0_deepcopy_103 -> 0xcf82460 +231 -> conv2d_20.w_0_deepcopy_102 -> 0xcf95570 +232 -> batch_norm2d_19.w_2_deepcopy_101 -> 0xcf7d9f0 +233 -> batch_norm2d_19.w_1_deepcopy_100 -> 0xcf7c850 +234 -> batch_norm2d_19.b_0_deepcopy_99 -> 0xcf7ac10 +235 -> batch_norm2d_19.w_0_deepcopy_98 -> 0xcf7bc50 +236 -> conv2d_19.w_0_deepcopy_97 -> 0xcf31440 +237 -> batch_norm2d_18.w_2_deepcopy_96 -> 0xcf79bd0 +238 -> batch_norm2d_18.w_1_deepcopy_95 -> 0xcf84390 +239 -> batch_norm2d_18.b_0_deepcopy_94 -> 0xcf71dd0 +240 -> batch_norm2d_18.w_0_deepcopy_93 -> 0xcfa68e0 +241 -> conv2d_18.w_0_deepcopy_92 -> 0xcf22d20 +242 -> batch_norm2d_17.w_2_deepcopy_91 -> 0xcf70e80 +243 -> batch_norm2d_17.w_1_deepcopy_90 -> 0xcf6ff00 +244 -> batch_norm2d_17.b_0_deepcopy_89 -> 0xcfa8c60 +245 -> batch_norm2d_17.w_0_deepcopy_88 -> 0xcf6f0e0 +246 -> conv2d_17.w_0_deepcopy_87 -> 0xcf17680 +247 -> batch_norm2d_16.w_2_deepcopy_86 -> 0xcfa79c0 +248 -> batch_norm2d_16.w_1_deepcopy_85 -> 0xcfa6d50 +249 -> batch_norm2d_16.b_0_deepcopy_84 -> 0xcfce890 +250 -> batch_norm2d_16.w_0_deepcopy_83 -> 0xcf30ac0 +251 -> conv2d_16.w_0_deepcopy_82 -> 0xcf536b0 +252 -> batch_norm2d_15.w_2_deepcopy_81 -> 0xcfcda90 +253 -> batch_norm2d_15.w_1_deepcopy_80 -> 0xcfcce30 +254 -> batch_norm2d_15.b_0_deepcopy_79 -> 0xcfcb200 +255 -> batch_norm2d_15.w_0_deepcopy_78 -> 0xcfcbe00 +256 -> conv2d_15.w_0_deepcopy_77 -> 0xcf52c60 +257 -> batch_norm2d_14.w_2_deepcopy_76 -> 0xcfca3d0 +258 -> batch_norm2d_14.w_1_deepcopy_75 -> 0xcfc9610 +259 -> batch_norm2d_14.b_0_deepcopy_74 -> 0xcfc2eb0 +260 -> batch_norm2d_14.w_0_deepcopy_73 -> 0xcfc6c40 +261 -> conv2d_14.w_0_deepcopy_72 -> 0xcf52990 +262 -> batch_norm2d_13.w_2_deepcopy_71 -> 0xcfc1870 +263 -> batch_norm2d_13.w_1_deepcopy_70 -> 0xcfc0710 +264 -> batch_norm2d_13.b_0_deepcopy_69 -> 0xcf31c30 +265 -> batch_norm2d_13.w_0_deepcopy_68 -> 0xcf38dc0 +266 -> conv2d_13.w_0_deepcopy_67 -> 0xcf52460 +267 -> batch_norm2d_12.w_2_deepcopy_66 -> 0xcf289b0 +268 -> batch_norm2d_12.w_1_deepcopy_65 -> 0xcf259b0 +269 -> batch_norm2d_12.b_0_deepcopy_64 -> 0xcf1dca0 +270 -> batch_norm2d_12.w_0_deepcopy_63 -> 0xcf20e70 +271 -> conv2d_12.w_0_deepcopy_62 -> 0xcf51cc0 +272 -> batch_norm2d_11.w_2_deepcopy_61 -> 0xcf17bc0 +273 -> batch_norm2d_11.w_1_deepcopy_60 -> 0xcf14fe0 +274 -> batch_norm2d_11.b_0_deepcopy_59 -> 0xcf53430 +275 -> batch_norm2d_11.w_0_deepcopy_58 -> 0xcf53290 +276 -> conv2d_11.w_0_deepcopy_57 -> 0xcf50ed0 +277 -> batch_norm2d_10.w_2_deepcopy_56 -> 0xcf52d40 +278 -> batch_norm2d_10.w_1_deepcopy_55 -> 0xcf526a0 +279 -> batch_norm2d_10.b_0_deepcopy_54 -> 0xcf51350 +280 -> batch_norm2d_10.w_0_deepcopy_53 -> 0xcf50fb0 +281 -> conv2d_10.w_0_deepcopy_52 -> 0xcf508b0 +282 -> batch_norm2d_9.w_2_deepcopy_51 -> 0xcf48440 +283 -> batch_norm2d_9.w_1_deepcopy_50 -> 0xcf44e90 +284 -> batch_norm2d_9.b_0_deepcopy_49 -> 0xcf0a7c0 +285 -> batch_norm2d_9.w_0_deepcopy_48 -> 0xcf0fa10 +286 -> conv2d_9.w_0_deepcopy_47 -> 0xcf32540 +287 -> batch_norm2d_8.w_2_deepcopy_46 -> 0xceff0c0 +288 -> batch_norm2d_8.w_1_deepcopy_45 -> 0xcef9a90 +289 -> batch_norm2d_8.b_0_deepcopy_44 -> 0xceee210 +290 -> batch_norm2d_8.w_0_deepcopy_43 -> 0xcef0d80 +291 -> conv2d_8.w_0_deepcopy_42 -> 0xcf3f300 +292 -> batch_norm2d_7.w_2_deepcopy_41 -> 0xd05a990 +293 -> batch_norm2d_7.w_1_deepcopy_40 -> 0xd05ae90 +294 -> batch_norm2d_7.b_0_deepcopy_39 -> 0xcfa23b0 +295 -> batch_norm2d_7.w_0_deepcopy_38 -> 0xcf261b0 +296 -> conv2d_7.w_0_deepcopy_37 -> 0xcf3b640 +297 -> batch_norm2d_6.w_2_deepcopy_36 -> 0xcce2400 +298 -> batch_norm2d_6.w_1_deepcopy_35 -> 0xcf2f870 +299 -> batch_norm2d_6.b_0_deepcopy_34 -> 0xcf2d1b0 +300 -> batch_norm2d_6.w_0_deepcopy_33 -> 0xcf42180 +301 -> conv2d_6.w_0_deepcopy_32 -> 0xcc9ffb0 +302 -> batch_norm2d_5.w_2_deepcopy_31 -> 0xcf306a0 +303 -> batch_norm2d_5.w_1_deepcopy_30 -> 0xcf63a70 +304 -> batch_norm2d_5.b_0_deepcopy_29 -> 0xcf35350 +305 -> batch_norm2d_5.w_0_deepcopy_28 -> 0xcf8df90 +306 -> conv2d_5.w_0_deepcopy_27 -> 0xcfc00b0 +307 -> batch_norm2d_4.w_2_deepcopy_26 -> 0xcf40460 +308 -> batch_norm2d_4.w_1_deepcopy_25 -> 0xcf8df70 +309 -> batch_norm2d_4.b_0_deepcopy_24 -> 0xcf02720 +310 -> batch_norm2d_4.w_0_deepcopy_23 -> 0xcf906f0 +311 -> conv2d_4.w_0_deepcopy_22 -> 0xc80efb0 +312 -> batch_norm2d_3.w_2_deepcopy_21 -> 0xcf21ae0 +313 -> batch_norm2d_3.w_1_deepcopy_20 -> 0xcf576f0 +314 -> batch_norm2d_3.b_0_deepcopy_19 -> 0xc80e280 +315 -> batch_norm2d_3.w_0_deepcopy_18 -> 0xcf34c10 +316 -> conv2d_3.w_0_deepcopy_17 -> 0xcf15a30 +317 -> batch_norm2d_2.w_2_deepcopy_16 -> 0xcf808f0 +318 -> batch_norm2d_2.w_1_deepcopy_15 -> 0xcf7feb0 +319 -> batch_norm2d_2.b_0_deepcopy_14 -> 0xcf7e0d0 +320 -> batch_norm2d_2.w_0_deepcopy_13 -> 0xcf7f110 +321 -> conv2d_2.w_0_deepcopy_12 -> 0xcfcc130 +322 -> batch_norm2d_1.w_2_deepcopy_11 -> 0xcf506f0 +323 -> batch_norm2d_1.w_1_deepcopy_10 -> 0xcf3f550 +324 -> batch_norm2d_1.b_0_deepcopy_9 -> 0xcf540f0 +325 -> batch_norm2d_1.w_0_deepcopy_8 -> 0xcf37900 +326 -> conv2d_1.w_0_deepcopy_7 -> 0xcf500a0 +327 -> batch_norm2d_0.w_2_deepcopy_6 -> 0xcf55d60 +328 -> batch_norm2d_0.w_1_deepcopy_5 -> 0xcf547a0 +329 -> batch_norm2d_0.b_0_deepcopy_4 -> 0xcf34070 +330 -> batch_norm2d_0.w_0_deepcopy_3 -> 0xcf557c0 +331 -> conv2d_0.w_0_deepcopy_2 -> 0xcf54fc0 +332 -> im_shape -> 0xcec9720 +333 -> image -> 0xccc5060 +334 -> scale_factor -> 0xcdbf3e0 +335 -> 0xcf59f401745131171435049990_inner_var_335 -> 0xcf1b790 +336 -> 0xcf59f401745131171435049990_inner_var_336 -> 0xca79260 +337 -> 0xcf59f401745131171435049990_inner_var_337 -> 0xce9b570 +338 -> 0xcf59f401745131171435049990_inner_var_338 -> 0xcebb210 +339 -> 0xcf59f401745131171435049990_inner_var_339 -> 0xcf009c0 +340 -> 0xcf59f401745131171435049990_inner_var_340 -> 0xd0606b0 +341 -> 0xcf59f401745131171435049990_inner_var_341 -> 0xcf97690 +342 -> 0xcf59f401745131171435049990_inner_var_342 -> 0xca791e0 +343 -> 0xcf59f401745131171435049990_inner_var_343 -> 0xcca3d60 +344 -> 0xcf59f401745131171435049990_inner_var_344 -> 0xcdbf7b0 +345 -> 0xcf59f401745131171435049990_inner_var_345 -> 0xceb2ca0 +346 -> 0xcf59f401745131171435049990_inner_var_346 -> 0xcea9360 +347 -> 0xcf59f401745131171435049990_inner_var_347 -> 0xca9b750 +348 -> 0xcf59f401745131171435049990_inner_var_348 -> 0xcef9b60 +349 -> 0xcf59f401745131171435049990_inner_var_349 -> 0xcad41e0 +350 -> 0xcf59f401745131171435049990_inner_var_350 -> 0xcec2420 +351 -> 0xcf59f401745131171435049990_inner_var_351 -> 0xd0580a0 +352 -> 0xcf59f401745131171435049990_inner_var_352 -> 0xca7dcc0 +353 -> 0xcf59f401745131171435049990_inner_var_353 -> 0xcf596e0 +354 -> 0xcf59f401745131171435049990_inner_var_354 -> 0xca62f10 +355 -> 0xcf59f401745131171435049990_inner_var_355 -> 0xcca80b0 +356 -> 0xcf59f401745131171435049990_inner_var_356 -> 0xccd5df0 +357 -> 0xcf59f401745131171435049990_inner_var_357 -> 0xca883e0 +358 -> 0xcf59f401745131171435049990_inner_var_358 -> 0xccb4a70 +359 -> 0xcf59f401745131171435049990_inner_var_359 -> 0xca758f0 +360 -> 0xcf59f401745131171435049990_inner_var_360 -> 0xd04cf50 +361 -> 0xcf59f401745131171435049990_inner_var_361 -> 0xcca6850 +362 -> 0xcf59f401745131171435049990_inner_var_362 -> 0xcf6d1c0 +363 -> 0xcf59f401745131171435049990_inner_var_363 -> 0xcadee10 +364 -> 0xcf59f401745131171435049990_inner_var_364 -> 0xcccf200 +365 -> 0xcf59f401745131171435049990_inner_var_365 -> 0xcebea10 +366 -> 0xcf59f401745131171435049990_inner_var_366 -> 0xcf1d290 +367 -> 0xcf59f401745131171435049990_inner_var_367 -> 0xce91c20 +368 -> 0xcf59f401745131171435049990_inner_var_368 -> 0xccd76c0 +369 -> 0xcf59f401745131171435049990_inner_var_369 -> 0xca58e30 +370 -> 0xcf59f401745131171435049990_inner_var_370 -> 0xce97c00 +371 -> 0xcf59f401745131171435049990_inner_var_371 -> 0xcf563c0 +372 -> 0xcf59f401745131171435049990_inner_var_372 -> 0xca7bf50 +373 -> 0xcf59f401745131171435049990_inner_var_373 -> 0xccaa1d0 +374 -> 0xcf59f401745131171435049990_inner_var_374 -> 0xcec14b0 +375 -> 0xcf59f401745131171435049990_inner_var_375 -> 0xccdfc40 +376 -> 0xcf59f401745131171435049990_inner_var_376 -> 0xcf20080 +377 -> 0xcf59f401745131171435049990_inner_var_377 -> 0xcaa4d20 +378 -> 0xcf59f401745131171435049990_inner_var_378 -> 0xd0732d0 +379 -> 0xcf59f401745131171435049990_inner_var_379 -> 0xcec7d50 +380 -> 0xcf59f401745131171435049990_inner_var_380 -> 0xced4880 +381 -> 0xcf59f401745131171435049990_inner_var_381 -> 0xced3620 +382 -> 0xcf59f401745131171435049990_inner_var_382 -> 0xcef7030 +383 -> 0xcf59f401745131171435049990_inner_var_383 -> 0xcad2150 +384 -> 0xcf59f401745131171435049990_inner_var_384 -> 0xccdad60 +385 -> 0xcf59f401745131171435049990_inner_var_385 -> 0xcf14230 +386 -> 0xcf59f401745131171435049990_inner_var_386 -> 0xcf17420 +387 -> 0xcf59f401745131171435049990_inner_var_387 -> 0xccb2480 +388 -> 0xcf59f401745131171435049990_inner_var_388 -> 0xcca2020 +389 -> 0xcf59f401745131171435049990_inner_var_389 -> 0xd040710 +390 -> 0xcf59f401745131171435049990_inner_var_390 -> 0xca9b230 +391 -> 0xcf59f401745131171435049990_inner_var_391 -> 0xcf38990 +392 -> 0xcf59f401745131171435049990_inner_var_392 -> 0xd0639d0 +393 -> 0xcf59f401745131171435049990_inner_var_393 -> 0xccb9310 +394 -> 0xcf59f401745131171435049990_inner_var_394 -> 0xccc15b0 +395 -> 0xcf59f401745131171435049990_inner_var_395 -> 0xcef9860 +396 -> 0xcf59f401745131171435049990_inner_var_396 -> 0xc8757d0 +397 -> 0xcf59f401745131171435049990_inner_var_397 -> 0xceee010 +398 -> 0xcf59f401745131171435049990_inner_var_398 -> 0xca947c0 +399 -> 0xcf59f401745131171435049990_inner_var_399 -> 0xcabae00 +400 -> 0xcf59f401745131171435049990_inner_var_400 -> 0xca602d0 +401 -> 0xcf59f401745131171435049990_inner_var_401 -> 0xcee7350 +402 -> 0xcf59f401745131171435049990_inner_var_402 -> 0xcf99c00 +403 -> 0xcf59f401745131171435049990_inner_var_403 -> 0xcad0c10 +404 -> 0xcf59f401745131171435049990_inner_var_404 -> 0xd04f4b0 +405 -> 0xcf59f401745131171435049990_inner_var_405 -> 0xccc21e0 +406 -> 0xcf59f401745131171435049990_inner_var_406 -> 0xcf78850 +407 -> 0xcf59f401745131171435049990_inner_var_407 -> 0xd0727d0 +408 -> 0xcf59f401745131171435049990_inner_var_408 -> 0xceb0f20 +409 -> 0xcf59f401745131171435049990_inner_var_409 -> 0xceb3080 +410 -> 0xcf59f401745131171435049990_inner_var_410 -> 0xcf0a000 +411 -> 0xcf59f401745131171435049990_inner_var_411 -> 0xcad7ec0 +412 -> 0xcf59f401745131171435049990_inner_var_412 -> 0xcf7cbb0 +413 -> 0xcf59f401745131171435049990_inner_var_413 -> 0xcec7620 +414 -> 0xcf59f401745131171435049990_inner_var_414 -> 0xccc05c0 +415 -> 0xcf59f401745131171435049990_inner_var_415 -> 0xca942d0 +416 -> 0xcf59f401745131171435049990_inner_var_416 -> 0xceccf20 +417 -> 0xcf59f401745131171435049990_inner_var_417 -> 0xcaecc00 +418 -> 0xcf59f401745131171435049990_inner_var_418 -> 0xcaed490 +419 -> 0xcf59f401745131171435049990_inner_var_419 -> 0xca984f0 +420 -> 0xcf59f401745131171435049990_inner_var_420 -> 0xcfcbe20 +421 -> 0xcf59f401745131171435049990_inner_var_421 -> 0xccc3bd0 +422 -> 0xcf59f401745131171435049990_inner_var_422 -> 0xd047d80 +423 -> 0xcf59f401745131171435049990_inner_var_423 -> 0xcea8fb0 +424 -> 0xcf59f401745131171435049990_inner_var_424 -> 0xca62570 +425 -> 0xcf59f401745131171435049990_inner_var_425 -> 0xee8cc70 +426 -> 0xcf59f401745131171435049990_inner_var_426 -> 0xceb5560 +427 -> 0xcf59f401745131171435049990_inner_var_427 -> 0xced0d30 +428 -> 0xcf59f401745131171435049990_inner_var_428 -> 0xcab8450 +429 -> 0xcf59f401745131171435049990_inner_var_429 -> 0xcab60d0 +430 -> 0xcf59f401745131171435049990_inner_var_430 -> 0xcead070 +431 -> 0xcf59f401745131171435049990_inner_var_431 -> 0xcade0a0 +432 -> 0xcf59f401745131171435049990_inner_var_432 -> 0xcabcfc0 +433 -> 0xcf59f401745131171435049990_inner_var_433 -> 0xce94af0 +434 -> 0xcf59f401745131171435049990_inner_var_434 -> 0xcebccc0 +435 -> 0xcf59f401745131171435049990_inner_var_435 -> 0xd048180 +436 -> 0xcf59f401745131171435049990_inner_var_436 -> 0xcea5740 +437 -> 0xcf59f401745131171435049990_inner_var_437 -> 0xcab9830 +438 -> 0xcf59f401745131171435049990_inner_var_438 -> 0xcf97aa0 +439 -> 0xcf59f401745131171435049990_inner_var_439 -> 0xd06ce40 +440 -> 0xcf59f401745131171435049990_inner_var_440 -> 0xcedd7e0 +441 -> 0xcf59f401745131171435049990_inner_var_441 -> 0xcea0cd0 +442 -> 0xcf59f401745131171435049990_inner_var_442 -> 0xd06eb50 +443 -> 0xcf59f401745131171435049990_inner_var_443 -> 0xd069a30 +444 -> 0xcf59f401745131171435049990_inner_var_444 -> 0xce89bf0 +445 -> 0xcf59f401745131171435049990_inner_var_445 -> 0xcf21760 +446 -> 0xcf59f401745131171435049990_inner_var_446 -> 0xcbf35d0 +447 -> 0xcf59f401745131171435049990_inner_var_447 -> 0xcab49d0 +448 -> 0xcf59f401745131171435049990_inner_var_448 -> 0xcf17050 +449 -> 0xcf59f401745131171435049990_inner_var_449 -> 0xee40240 +450 -> 0xcf59f401745131171435049990_inner_var_450 -> 0xd040f30 +451 -> 0xcf59f401745131171435049990_inner_var_451 -> 0xcf7c250 +452 -> 0xcf59f401745131171435049990_inner_var_452 -> 0xcca3870 +453 -> 0xcf59f401745131171435049990_inner_var_453 -> 0xee47f90 +454 -> 0xcf59f401745131171435049990_inner_var_454 -> 0xd0385d0 +455 -> 0xcf59f401745131171435049990_inner_var_455 -> 0xcfa62c0 +456 -> 0xcf59f401745131171435049990_inner_var_456 -> 0xcabc9a0 +457 -> 0xcf59f401745131171435049990_inner_var_457 -> 0xcefb360 +458 -> 0xcf59f401745131171435049990_inner_var_458 -> 0xcef8830 +459 -> 0xcf59f401745131171435049990_inner_var_459 -> 0xceca990 +460 -> 0xcf59f401745131171435049990_inner_var_460 -> 0xced79a0 +461 -> 0xcf59f401745131171435049990_inner_var_461 -> 0xce94610 +462 -> 0xcf59f401745131171435049990_inner_var_462 -> 0xcab5be0 +463 -> 0xcf59f401745131171435049990_inner_var_463 -> 0xcec4600 +464 -> 0xcf59f401745131171435049990_inner_var_464 -> 0xcab8eb0 +465 -> 0xcf59f401745131171435049990_inner_var_465 -> 0xee400a0 +466 -> 0xcf59f401745131171435049990_inner_var_466 -> 0xcab7250 +467 -> 0xcf59f401745131171435049990_inner_var_467 -> 0xcab88e0 +468 -> 0xcf59f401745131171435049990_inner_var_468 -> 0xccdaac0 +469 -> 0xcf59f401745131171435049990_inner_var_469 -> 0xcad09b0 +470 -> 0xcf59f401745131171435049990_inner_var_470 -> 0xcec1100 +471 -> 0xcf59f401745131171435049990_inner_var_471 -> 0xd034260 +472 -> 0xcf59f401745131171435049990_inner_var_472 -> 0xcf6cb40 +473 -> 0xcf59f401745131171435049990_inner_var_473 -> 0xce8df60 +474 -> 0xcf59f401745131171435049990_inner_var_474 -> 0xcf36750 +475 -> 0xcf59f401745131171435049990_inner_var_475 -> 0xcca5d30 +476 -> 0xcf59f401745131171435049990_inner_var_476 -> 0xcf24750 +477 -> 0xcf59f401745131171435049990_inner_var_477 -> 0xcf1ba00 +478 -> 0xcf59f401745131171435049990_inner_var_478 -> 0xd075c80 +479 -> 0xcf59f401745131171435049990_inner_var_479 -> 0xccdd6a0 +480 -> 0xcf59f401745131171435049990_inner_var_480 -> 0xca8acd0 +481 -> 0xcf59f401745131171435049990_inner_var_481 -> 0xd071b60 +482 -> 0xcf59f401745131171435049990_inner_var_482 -> 0xcea01c0 +483 -> 0xcf59f401745131171435049990_inner_var_483 -> 0xceb1680 +484 -> 0xcf59f401745131171435049990_inner_var_484 -> 0xccdbe30 +485 -> 0xcf59f401745131171435049990_inner_var_485 -> 0xcadbd40 +486 -> 0xcf59f401745131171435049990_inner_var_486 -> 0xcf57730 +487 -> 0xcf59f401745131171435049990_inner_var_487 -> 0xceac560 +488 -> 0xcf59f401745131171435049990_inner_var_488 -> 0xcf75a50 +489 -> 0xcf59f401745131171435049990_inner_var_489 -> 0xcf95c60 +490 -> 0xcf59f401745131171435049990_inner_var_490 -> 0xcedd640 +491 -> 0xcf59f401745131171435049990_inner_var_491 -> 0xcf19870 +492 -> 0xcf59f401745131171435049990_inner_var_492 -> 0xcabc760 +493 -> 0xcf59f401745131171435049990_inner_var_493 -> 0xcefd440 +494 -> 0xcf59f401745131171435049990_inner_var_494 -> 0xccc8990 +495 -> 0xcf59f401745131171435049990_inner_var_495 -> 0xd059d20 +496 -> 0xcf59f401745131171435049990_inner_var_496 -> 0xcf597c0 +497 -> 0xcf59f401745131171435049990_inner_var_497 -> 0xcaa2350 +498 -> 0xcf59f401745131171435049990_inner_var_498 -> 0xcae1300 +499 -> 0xcf59f401745131171435049990_inner_var_499 -> 0xcca7260 +500 -> 0xcf59f401745131171435049990_inner_var_500 -> 0xca91ea0 +501 -> 0xcf59f401745131171435049990_inner_var_501 -> 0xcad9eb0 +502 -> 0xcf59f401745131171435049990_inner_var_502 -> 0xcc9ec80 +503 -> 0xcf59f401745131171435049990_inner_var_503 -> 0xcad4220 +504 -> 0xcf59f401745131171435049990_inner_var_504 -> 0xca705e0 +505 -> 0xcf59f401745131171435049990_inner_var_505 -> 0xd03c340 +506 -> 0xcf59f401745131171435049990_inner_var_506 -> 0xcf207c0 +507 -> 0xcf59f401745131171435049990_inner_var_507 -> 0xcec9370 +508 -> 0xcf59f401745131171435049990_inner_var_508 -> 0xcec4d60 +509 -> 0xcf59f401745131171435049990_inner_var_509 -> 0xcec6ae0 +510 -> 0xcf59f401745131171435049990_inner_var_510 -> 0xcf12c40 +511 -> 0xcf59f401745131171435049990_inner_var_511 -> 0xce9f300 +512 -> 0xcf59f401745131171435049990_inner_var_512 -> 0xd065010 +513 -> 0xcf59f401745131171435049990_inner_var_513 -> 0xca91140 +514 -> 0xcf59f401745131171435049990_inner_var_514 -> 0xce8b8c0 +515 -> 0xcf59f401745131171435049990_inner_var_515 -> 0xcef5240 +516 -> 0xcf59f401745131171435049990_inner_var_516 -> 0xcedc210 +517 -> 0xcf59f401745131171435049990_inner_var_517 -> 0xca726f0 +518 -> 0xcf59f401745131171435049990_inner_var_518 -> 0xccda1a0 +519 -> 0xcf59f401745131171435049990_inner_var_519 -> 0xcf6d840 +520 -> 0xcf59f401745131171435049990_inner_var_520 -> 0xccc8370 +521 -> 0xcf59f401745131171435049990_inner_var_521 -> 0xcab5850 +522 -> 0xcf59f401745131171435049990_inner_var_522 -> 0xced8c00 +523 -> 0xcf59f401745131171435049990_inner_var_523 -> 0xca9d1e0 +524 -> 0xcf59f401745131171435049990_inner_var_524 -> 0xca987a0 +525 -> 0xcf59f401745131171435049990_inner_var_525 -> 0xcad8d00 +526 -> 0xcf59f401745131171435049990_inner_var_526 -> 0xceb4dd0 +527 -> 0xcf59f401745131171435049990_inner_ +I0420 14:39:34.812067 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 +1 -> 2 + +[2025-4-20 14:39:34] [CNNL] [Warning][cnnlAdaptivePoolingForward] is deprecated and will be removed in the future release, please use [cnnlAdaptivePoolingForward_v2] instead. +I0420 14:39:34.942133 115867 pir_interpreter.cc:1569] value info of interpretercore 0xc8cd010 +value -> var_name -> id -> variable* +0xcab9d00 -> 0xc8cd0101745131172921903234_inner_var_1129 -> 1129 -> 0xc9fd470 +0xcab9ab0 -> 0xc8cd0101745131172921903234_inner_var_1128 -> 1128 -> 0xc9fdf60 +0xcab8f90 -> 0xc8cd0101745131172921903234_inner_var_1127 -> 1127 -> 0xc9fde80 +0xcab98e0 -> 0xc8cd0101745131172921903234_inner_var_1126 -> 1126 -> 0xc9fdea0 +0xcab9540 -> 0xc8cd0101745131172921903234_inner_var_1125 -> 1125 -> 0xc9fdee0 +0xee46260 -> 0xcf59f401745131171435049990_inner_var_851 -> 851 -> 0xca7d0c0 +0xcab8630 -> 0xc8cd0101745131172921903234_inner_var_1121 -> 1121 -> 0xc9fc7f0 +0xcab8110 -> 0xc8cd0101745131172921903234_inner_var_1119 -> 1119 -> 0xc9b4cc0 +0xcab73c0 -> 0xc8cd0101745131172921903234_inner_var_1117 -> 1117 -> 0xc9b5e90 +0xcab6380 -> 0xc8cd0101745131172921903234_inner_var_1116 -> 1116 -> 0xc9b4d40 +0xcab62b0 -> 0xc8cd0101745131172921903234_inner_var_1115 -> 1115 -> 0xc9b4e30 +0xcab61e0 -> 0xc8cd0101745131172921903234_inner_var_1114 -> 1114 -> 0xc9b4d00 +0xcab6c90 -> 0xc8cd0101745131172921903234_inner_var_1113 -> 1113 -> 0xc9b4d60 +0xcab6110 -> 0xc8cd0101745131172921903234_inner_var_1112 -> 1112 -> 0xc8cdfb0 +0xccb38a0 -> 0xc8cd0101745131172921903234_inner_var_1111 -> 1111 -> 0xc9b4480 +0xcab5d50 -> 0xc8cd0101745131172921903234_inner_var_1110 -> 1110 -> 0xc8ce1e0 +0xd0365f8 -> fetch_name_1 -> 982 -> 0xcbe4d60 +0xcaf6bd0 -> batch_norm2d_20.w_1_deepcopy_105 -> 228 -> 0xcf82fa0 +0xccad180 -> 0xcf59f401745131171435049990_inner_var_530 -> 530 -> 0xccad0a0 +0xccad198 -> 0xcf59f401745131171435049990_inner_var_529 -> 529 -> 0xce93f90 +0xccad1b0 -> 0xcf59f401745131171435049990_inner_var_528 -> 528 -> 0xcaeafb0 +0xccad1c8 -> 0xcf59f401745131171435049990_inner_var_527 -> 527 -> 0xcad93f0 +0xc80c4d0 -> conv2d_36.w_0_deepcopy_182 -> 151 -> 0xd05fe10 +0xee86328 -> 0xcf59f401745131171435049990_inner_var_763 -> 763 -> 0xca55c90 +0xd04e680 -> batch_norm2d_46.b_0_deepcopy_241 -> 96 -> 0xcf60140 +0xee3ff40 -> batch_norm2d_51.w_2_deepcopy_268 -> 69 -> 0xcf2c930 +0xce903b0 -> 0xcf59f401745131171435049990_inner_var_917 -> 917 -> 0xceaa600 +0xcccde50 -> 0xcf59f401745131171435049990_inner_var_525 -> 525 -> 0xcad8d00 +0xcccc970 -> 0xcf59f401745131171435049990_inner_var_507 -> 507 -> 0xcec9370 +0xee8aa90 -> 0xcf59f401745131171435049990_inner_var_794 -> 794 -> 0xce96090 +0xcccc988 -> 0xcf59f401745131171435049990_inner_var_506 -> 506 -> 0xcf207c0 +0xee8aad8 -> 0xcf59f401745131171435049990_inner_var_791 -> 791 -> 0xca84830 +0xcccc9d0 -> 0xcf59f401745131171435049990_inner_var_503 -> 503 -> 0xcad4220 +0xee75060 -> 0xcf59f401745131171435049990_inner_var_386 -> 386 -> 0xcf17420 +0xcccc9e8 -> 0xcf59f401745131171435049990_inner_var_502 -> 502 -> 0xcc9ec80 +0xce91510 -> 0xcf59f401745131171435049990_inner_var_911 -> 911 -> 0xcf8f250 +0xccdcdf0 -> 0xcf59f401745131171435049990_inner_var_946 -> 946 -> 0xcf04e80 +0xcccb928 -> 0xcf59f401745131171435049990_inner_var_494 -> 494 -> 0xccc8990 +0xd041790 -> constant_folding@_174513116825929047929 -> 30 -> 0xcf7c3f0 +0xcccf820 -> 0xcf59f401745131171435049990_inner_var_492 -> 492 -> 0xcabc760 +0xcb0cfa0 -> batch_norm2d_6.w_1_deepcopy_35 -> 298 -> 0xcf2f870 +0xccd0740 -> 0xcf59f401745131171435049990_inner_var_491 -> 491 -> 0xcf19870 +0xce8a520 -> 0xcf59f401745131171435049990_inner_var_879 -> 879 -> 0xcee45d0 +0xccd0770 -> 0xcf59f401745131171435049990_inner_var_489 -> 489 -> 0xcf95c60 +0xccb8a80 -> 0xcf59f401745131171435049990_inner_var_440 -> 440 -> 0xcedd7e0 +0xca9f7c0 -> 0xcf59f401745131171435049990_inner_var_569 -> 569 -> 0xcf3e600 +0xee880e0 -> 0xcf59f401745131171435049990_inner_var_788 -> 788 -> 0xd054cd0 +0xcccfd80 -> 0xcf59f401745131171435049990_inner_var_485 -> 485 -> 0xcadbd40 +0xcabb010 -> 0xcf59f401745131171435049990_inner_var_887 -> 887 -> 0xcf06390 +0xcccb2e0 -> 0xcf59f401745131171435049990_inner_var_508 -> 508 -> 0xcec4d60 +0xcaf7370 -> batch_norm2d_20.w_0_deepcopy_103 -> 230 -> 0xcf82460 +0xcdbdab0 -> 0xcf59f401745131171435049990_inner_var_682 -> 682 -> 0xd046ed0 +0xccc6be0 -> 0xcf59f401745131171435049990_inner_var_1054 -> 1054 -> 0xcaab8c0 +0xcdbd710 -> 0xcf59f401745131171435049990_inner_var_671 -> 671 -> 0xccd9c80 +0xccbdda0 -> 0xcf59f401745131171435049990_inner_var_1014 -> 1014 -> 0xcbed7a0 +0xcdbd728 -> 0xcf59f401745131171435049990_inner_var_670 -> 670 -> 0xcf716d0 +0xccb6400 -> 0xcf59f401745131171435049990_inner_var_475 -> 475 -> 0xcca5d30 +0xca64c30 -> batch_norm2d_2.w_1_deepcopy_15 -> 318 -> 0xcf7feb0 +0xccce208 -> 0xcf59f401745131171435049990_inner_var_473 -> 473 -> 0xce8df60 +0xccce238 -> 0xcf59f401745131171435049990_inner_var_471 -> 471 -> 0xd034260 +0xccce250 -> 0xcf59f401745131171435049990_inner_var_470 -> 470 -> 0xcec1100 +0xca85520 -> batch_norm2d_8.w_2_deepcopy_46 -> 287 -> 0xceff0c0 +0xcc9e7e0 -> constant_folding@_174513116872098526255 -> 7 -> 0xd061370 +0xccb6060 -> 0xcf59f401745131171435049990_inner_var_464 -> 464 -> 0xcab8eb0 +0xcdbc1c8 -> 0xcf59f401745131171435049990_inner_var_659 -> 659 -> 0xcab68b0 +0xccb6078 -> 0xcf59f401745131171435049990_inner_var_463 -> 463 -> 0xcec4600 +0xccb54f0 -> 0xcf59f401745131171435049990_inner_var_460 -> 460 -> 0xced79a0 +0xccb95d0 -> 0xcf59f401745131171435049990_inner_var_458 -> 458 -> 0xcef8830 +0xccb4b68 -> 0xcf59f401745131171435049990_inner_var_454 -> 454 -> 0xd0385d0 +0xccb28c0 -> 0xcf59f401745131171435049990_inner_var_757 -> 757 -> 0xcf08950 +0xccb4b98 -> 0xcf59f401745131171435049990_inner_var_452 -> 452 -> 0xcca3870 +0xccb9bd0 -> 0xcf59f401745131171435049990_inner_var_448 -> 448 -> 0xcf17050 +0xccb9c00 -> 0xcf59f401745131171435049990_inner_var_446 -> 446 -> 0xcbf35d0 +0xccb9c18 -> 0xcf59f401745131171435049990_inner_var_445 -> 445 -> 0xcf21760 +0xd052500 -> 0xcf59f401745131171435049990_inner_var_640 -> 640 -> 0xca95610 +0xccb4b80 -> 0xcf59f401745131171435049990_inner_var_453 -> 453 -> 0xee47f90 +0xccb8f20 -> 0xcf59f401745131171435049990_inner_var_444 -> 444 -> 0xce89bf0 +0xccca460 -> 0xcf59f401745131171435049990_inner_var_1070 -> 1070 -> 0xcfbdaa0 +0xccd0788 -> 0xcf59f401745131171435049990_inner_var_488 -> 488 -> 0xcf75a50 +0xccac460 -> 0xcf59f401745131171435049990_inner_var_534 -> 534 -> 0xcca5770 +0xca9f790 -> 0xcf59f401745131171435049990_inner_var_571 -> 571 -> 0xcea9710 +0xee86340 -> 0xcf59f401745131171435049990_inner_var_762 -> 762 -> 0xcfcedc0 +0xccb7990 -> 0xcf59f401745131171435049990_inner_var_434 -> 434 -> 0xcebccc0 +0xcadfa10 -> 0xcf59f401745131171435049990_inner_var_537 -> 537 -> 0xca889b0 +0xccabfb0 -> 0xcf59f401745131171435049990_inner_var_520 -> 520 -> 0xccc8370 +0xee86358 -> 0xcf59f401745131171435049990_inner_var_761 -> 761 -> 0xca85c80 +0xccb79a8 -> 0xcf59f401745131171435049990_inner_var_433 -> 433 -> 0xce94af0 +0xce894c0 -> 0xcf59f401745131171435049990_inner_var_353 -> 353 -> 0xcf596e0 +0xcf77920 -> 0xcf59f401745131171435049990_inner_var_730 -> 730 -> 0xce9c550 +0xccbc420 -> 0xcf59f401745131171435049990_inner_var_421 -> 421 -> 0xccc3bd0 +0xcaf5650 -> batch_norm2d_16.w_2_deepcopy_86 -> 247 -> 0xcfa79c0 +0xccbc438 -> 0xcf59f401745131171435049990_inner_var_420 -> 420 -> 0xcfcbe20 +0xccc2c30 -> 0xcf59f401745131171435049990_inner_var_1034 -> 1034 -> 0xceb6ad0 +0xccbbad0 -> 0xcf59f401745131171435049990_inner_var_419 -> 419 -> 0xca984f0 +0xd058908 -> 0xcf59f401745131171435049990_inner_var_367 -> 367 -> 0xce91c20 +0xcab7780 -> 0xc8cd0101745131172921903234_inner_var_1120 -> 1120 -> 0xc9fc410 +0xccbb100 -> 0xcf59f401745131171435049990_inner_var_418 -> 418 -> 0xcaed490 +0xccbb240 -> 0xcf59f401745131171435049990_inner_var_417 -> 417 -> 0xcaecc00 +0xccbf790 -> 0xcf59f401745131171435049990_inner_var_1020 -> 1020 -> 0xcbef330 +0xccbb288 -> 0xcf59f401745131171435049990_inner_var_414 -> 414 -> 0xccc05c0 +0xcab7920 -> 0xc8cd0101745131172921903234_inner_var_1123 -> 1123 -> 0xc9fcc10 +0xccbb2a0 -> 0xcf59f401745131171435049990_inner_var_413 -> 413 -> 0xcec7620 +0xccba560 -> 0xcf59f401745131171435049990_inner_var_411 -> 411 -> 0xcad7ec0 +0xccba1c0 -> 0xcf59f401745131171435049990_inner_var_407 -> 407 -> 0xd0727d0 +0xccba1d8 -> 0xcf59f401745131171435049990_inner_var_406 -> 406 -> 0xcf78850 +0xcbf0ad0 -> batch_norm2d_44.w_0_deepcopy_230 -> 107 -> 0xcf1cb30 +0xcdc4890 -> 0xcf59f401745131171435049990_inner_var_722 -> 722 -> 0xceb3f10 +0xccba208 -> 0xcf59f401745131171435049990_inner_var_404 -> 404 -> 0xd04f4b0 +0xca56390 -> 0xcf59f401745131171435049990_inner_var_715 -> 715 -> 0xcad9880 +0xcae9830 -> 0xcf59f401745131171435049990_inner_var_842 -> 842 -> 0xd06a7d0 +0xcc97280 -> 0xcf59f401745131171435049990_inner_var_401 -> 401 -> 0xcee7350 +0xcc98950 -> 0xcf59f401745131171435049990_inner_var_400 -> 400 -> 0xca602d0 +0xd037a30 -> 0xcf59f401745131171435049990_inner_var_989 -> 989 -> 0xcbe7410 +0xcc98980 -> 0xcf59f401745131171435049990_inner_var_398 -> 398 -> 0xca947c0 +0xee8aa60 -> 0xcf59f401745131171435049990_inner_var_796 -> 796 -> 0xcf16f50 +0xee74c60 -> 0xcf59f401745131171435049990_inner_var_393 -> 393 -> 0xccb9310 +0xd048c30 -> 0xcf59f401745131171435049990_inner_var_706 -> 706 -> 0xd03e890 +0xca99330 -> 0xcf59f401745131171435049990_inner_var_732 -> 732 -> 0xca801c0 +0xcc97860 -> 0xcf59f401745131171435049990_inner_var_392 -> 392 -> 0xd0639d0 +0xcc97878 -> 0xcf59f401745131171435049990_inner_var_391 -> 391 -> 0xcf38990 +0xce8be10 -> 0xcf59f401745131171435049990_inner_var_885 -> 885 -> 0xcf19ef0 +0xccb73c0 -> 0xcf59f401745131171435049990_inner_var_459 -> 459 -> 0xceca990 +0xccde390 -> 0xcf59f401745131171435049990_inner_var_957 -> 957 -> 0xca5e670 +0xcc97890 -> 0xcf59f401745131171435049990_inner_var_390 -> 390 -> 0xca9b230 +0xcdb5730 -> batch_norm2d_23.w_0_deepcopy_118 -> 215 -> 0xd060ed0 +0xccd07a0 -> 0xcf59f401745131171435049990_inner_var_487 -> 487 -> 0xceac560 +0xcc993c0 -> 0xcf59f401745131171435049990_inner_var_403 -> 403 -> 0xcad0c10 +0xcae8810 -> 0xcf59f401745131171435049990_inner_var_968 -> 968 -> 0xcbe1be0 +0xcc978c0 -> 0xcf59f401745131171435049990_inner_var_388 -> 388 -> 0xcca2020 +0xce8e100 -> 0xcf59f401745131171435049990_inner_var_897 -> 897 -> 0xcf21030 +0xee74890 -> 0xcf59f401745131171435049990_inner_var_384 -> 384 -> 0xccdad60 +0xee748a8 -> 0xcf59f401745131171435049990_inner_var_383 -> 383 -> 0xcad2150 +0xca55d20 -> conv2d_14.w_0_deepcopy_72 -> 261 -> 0xcf52990 +0xee748c0 -> 0xcf59f401745131171435049990_inner_var_382 -> 382 -> 0xcef7030 +0xee748f0 -> 0xcf59f401745131171435049990_inner_var_380 -> 380 -> 0xced4880 +0xcc9c7f0 -> batch_norm2d_28.w_1_deepcopy_145 -> 188 -> 0xd073510 +0xcae2030 -> 0xcf59f401745131171435049990_inner_var_551 -> 551 -> 0xcee4790 +0xee73c40 -> 0xcf59f401745131171435049990_inner_var_378 -> 378 -> 0xd0732d0 +0xcc962b0 -> 0xcf59f401745131171435049990_inner_var_377 -> 377 -> 0xcaa4d20 +0xccb2538 -> 0xcf59f401745131171435049990_inner_var_978 -> 978 -> 0xcbe4500 +0xccc5d20 -> 0xcf59f401745131171435049990_inner_var_1047 -> 1047 -> 0xcada0c0 +0xd058320 -> 0xcf59f401745131171435049990_inner_var_376 -> 376 -> 0xcf20080 +0xcc9ad10 -> batch_norm2d_26.w_2_deepcopy_136 -> 197 -> 0xd06d600 +0xee732e0 -> 0xcf59f401745131171435049990_inner_var_375 -> 375 -> 0xccdfc40 +0xcccb8b0 -> 0xcf59f401745131171435049990_inner_var_499 -> 499 -> 0xcca7260 +0xcdbf878 -> 0xcf59f401745131171435049990_inner_var_688 -> 688 -> 0xcaa66e0 +0xee732f8 -> 0xcf59f401745131171435049990_inner_var_374 -> 374 -> 0xcec14b0 +0xee73328 -> 0xcf59f401745131171435049990_inner_var_372 -> 372 -> 0xca7bf50 +0xd058920 -> 0xcf59f401745131171435049990_inner_var_366 -> 366 -> 0xcf1d290 +0xd058938 -> 0xcf59f401745131171435049990_inner_var_365 -> 365 -> 0xcebea10 +0xce93e10 -> constant_folding@_174513116823069183927 -> 32 -> 0xcf64350 +0xce960d0 -> batch_norm2d_29.w_1_deepcopy_150 -> 183 -> 0xd076240 +0xc8b6720 -> 0xcf59f401745131171435049990_inner_var_362 -> 362 -> 0xcf6d1c0 +0xd057790 -> 0xcf59f401745131171435049990_inner_var_360 -> 360 -> 0xd04cf50 +0xcdb8020 -> 0xcf59f401745131171435049990_inner_var_721 -> 721 -> 0xcf76450 +0xcabd920 -> 0xcf59f401745131171435049990_inner_var_875 -> 875 -> 0xd061a70 +0xcf22190 -> 0xcf59f401745131171435049990_inner_var_354 -> 354 -> 0xca62f10 +0xcfacb90 -> batch_norm2d_42.w_0_deepcopy_213 -> 120 -> 0xcf3c660 +0xcdba8a0 -> 0xcf59f401745131171435049990_inner_var_725 -> 725 -> 0xcf962a0 +0xcc96880 -> 0xcf59f401745131171435049990_inner_var_352 -> 352 -> 0xca7dcc0 +0xcc96898 -> 0xcf59f401745131171435049990_inner_var_351 -> 351 -> 0xd0580a0 +0xcc968b0 -> 0xcf59f401745131171435049990_inner_var_350 -> 350 -> 0xcec2420 +0xcc968f8 -> 0xcf59f401745131171435049990_inner_var_347 -> 347 -> 0xca9b750 +0xcf73cd0 -> 0xcf59f401745131171435049990_inner_var_720 -> 720 -> 0xced4fb0 +0xcf4ea30 -> 0xcf59f401745131171435049990_inner_var_343 -> 343 -> 0xcca3d60 +0xccb7d60 -> 0xcf59f401745131171435049990_inner_var_443 -> 443 -> 0xd069a30 +0xcbf0900 -> batch_norm2d_44.b_0_deepcopy_231 -> 106 -> 0xcfa37a0 +0xcdb8e30 -> 0xcf59f401745131171435049990_inner_var_341 -> 341 -> 0xcf97690 +0xccb4f70 -> 0xcf59f401745131171435049990_inner_var_467 -> 467 -> 0xcab88e0 +0xcaee1a0 -> batch_norm2d_10.w_1_deepcopy_55 -> 278 -> 0xcf526a0 +0xcdb8e90 -> 0xcf59f401745131171435049990_inner_var_337 -> 337 -> 0xce9b570 +0xca83ff0 -> 0xcf59f401745131171435049990_inner_var_1033 -> 1033 -> 0xcf3eed0 +0xcf05e90 -> 0xcf59f401745131171435049990_inner_var_335 -> 335 -> 0xcf1b790 +0xcfaa870 -> conv2d_13.w_0_deepcopy_67 -> 266 -> 0xcf52460 +0xca53c58 -> 0xcf59f401745131171435049990_inner_var_620 -> 620 -> 0xcf18440 +0xced6440 -> scale_factor -> 334 -> 0xcdbf3e0 +0xcdb8e60 -> 0xcf59f401745131171435049990_inner_var_339 -> 339 -> 0xcf009c0 +0xcaf7b10 -> batch_norm2d_19.w_2_deepcopy_101 -> 232 -> 0xcf7d9f0 +0xcdb92b0 -> batch_norm2d_0.w_1_deepcopy_5 -> 328 -> 0xcf547a0 +0x118c9970 -> conv2d_1.w_0_deepcopy_7 -> 326 -> 0xcf500a0 +0x118c9550 -> batch_norm2d_1.w_0_deepcopy_8 -> 325 -> 0xcf37900 +0xccbc408 -> 0xcf59f401745131171435049990_inner_var_422 -> 422 -> 0xd047d80 +0xcb0d370 -> batch_norm2d_6.b_0_deepcopy_34 -> 299 -> 0xcf2d1b0 +0x118c8db0 -> batch_norm2d_1.w_1_deepcopy_10 -> 323 -> 0xcf3f550 +0xced5d20 -> image -> 333 -> 0xccc5060 +0xd0461a0 -> 0xcf59f401745131171435049990_inner_var_695 -> 695 -> 0xcca9b90 +0xcdb7ba0 -> 0xcf59f401745131171435049990_inner_var_705 -> 705 -> 0xcf07ef0 +0x118c7ea0 -> batch_norm2d_2.b_0_deepcopy_14 -> 319 -> 0xcf7e0d0 +0xca63920 -> batch_norm2d_3.w_1_deepcopy_20 -> 313 -> 0xcf576f0 +0xcaba360 -> 0xcf59f401745131171435049990_inner_var_892 -> 892 -> 0xcf5b3c0 +0xca63550 -> batch_norm2d_3.w_2_deepcopy_21 -> 312 -> 0xcf21ae0 +0xca63180 -> conv2d_4.w_0_deepcopy_22 -> 311 -> 0xc80efb0 +0xca74d70 -> batch_norm2d_4.w_0_deepcopy_23 -> 310 -> 0xcf906f0 +0xcccf2f8 -> 0xcf59f401745131171435049990_inner_var_479 -> 479 -> 0xccdd6a0 +0xca74200 -> batch_norm2d_4.w_2_deepcopy_26 -> 307 -> 0xcf40460 +0xcab79f0 -> 0xc8cd0101745131172921903234_inner_var_1124 -> 1124 -> 0xc9fd450 +0xcf6dbe0 -> conv2d_56.w_0_deepcopy_280 -> 58 -> 0xcf344c0 +0xcae6ec8 -> 0xcf59f401745131171435049990_inner_var_826 -> 826 -> 0xced62a0 +0xccbb270 -> 0xcf59f401745131171435049990_inner_var_415 -> 415 -> 0xca942d0 +0xccc3d10 -> 0xcf59f401745131171435049990_inner_var_1038 -> 1038 -> 0xcfb34a0 +0xca73e30 -> conv2d_5.w_0_deepcopy_27 -> 306 -> 0xcfc00b0 +0xca55900 -> batch_norm2d_14.w_0_deepcopy_73 -> 260 -> 0xcfc6c40 +0xce94e70 -> constant_folding@_174513115176548053017 -> 42 -> 0xcf8c2c0 +0xcb0db10 -> conv2d_6.w_0_deepcopy_32 -> 301 -> 0xcc9ffb0 +0xee73340 -> 0xcf59f401745131171435049990_inner_var_371 -> 371 -> 0xcf563c0 +0xcb0d740 -> batch_norm2d_6.w_0_deepcopy_33 -> 300 -> 0xcf42180 +0xcbf30d0 -> batch_norm2d_38.b_0_deepcopy_194 -> 139 -> 0xcf14990 +0xccb79f0 -> 0xcf59f401745131171435049990_inner_var_430 -> 430 -> 0xcead070 +0xcb0cbd0 -> batch_norm2d_6.w_2_deepcopy_36 -> 297 -> 0xcce2400 +0xce91cd0 -> 0xcf59f401745131171435049990_inner_var_913 -> 913 -> 0xce9aac0 +0xccdc440 -> 0xcf59f401745131171435049990_inner_var_951 -> 951 -> 0xcf4a820 +0xca86880 -> batch_norm2d_7.w_2_deepcopy_41 -> 292 -> 0xd05a990 +0xee3f360 -> linear_0.b_0_deepcopy_275 -> 62 -> 0xd041df0 +0xd0365e0 -> 0xcf59f401745131171435049990_inner_var_983 -> 983 -> 0xcbe59c0 +0xca85cc0 -> batch_norm2d_8.b_0_deepcopy_44 -> 289 -> 0xceee210 +0xca85150 -> conv2d_9.w_0_deepcopy_47 -> 286 -> 0xcf32540 +0xcc968c8 -> 0xcf59f401745131171435049990_inner_var_349 -> 349 -> 0xcad41e0 +0xcaeed60 -> conv2d_10.w_0_deepcopy_52 -> 281 -> 0xcf508b0 +0xca669c0 -> conv2d_11.w_0_deepcopy_57 -> 276 -> 0xcf50ed0 +0xcdb8ea8 -> 0xcf59f401745131171435049990_inner_var_336 -> 336 -> 0xca79260 +0xca77ae0 -> batch_norm2d_33.w_1_deepcopy_170 -> 163 -> 0xcf21710 +0xca73a60 -> batch_norm2d_5.w_0_deepcopy_28 -> 305 -> 0xcf8df90 +0xccbc790 -> 0xcf59f401745131171435049990_inner_var_435 -> 435 -> 0xd048180 +0x118c9180 -> batch_norm2d_1.b_0_deepcopy_9 -> 324 -> 0xcf540f0 +0xcab4a10 -> 0xc8cd0101745131172921903234_inner_var_1105 -> 1105 -> 0xc8cdaa0 +0xcfab3e0 -> batch_norm2d_12.b_0_deepcopy_64 -> 269 -> 0xcf1dca0 +0xccadc20 -> 0xcf59f401745131171435049990_inner_var_535 -> 535 -> 0xcecc7c0 +0xca76430 -> batch_norm2d_49.w_0_deepcopy_255 -> 82 -> 0xcfa0ad0 +0xca66d90 -> batch_norm2d_10.w_2_deepcopy_56 -> 277 -> 0xcf52d40 +0xccb4190 -> 0xcf59f401745131171435049990_inner_var_451 -> 451 -> 0xcf7c250 +0xd0577c0 -> 0xcf59f401745131171435049990_inner_var_358 -> 358 -> 0xccb4a70 +0xee73310 -> 0xcf59f401745131171435049990_inner_var_373 -> 373 -> 0xccaa1d0 +0xca732c0 -> batch_norm2d_5.w_1_deepcopy_30 -> 303 -> 0xcf63a70 +0xca864b0 -> conv2d_8.w_0_deepcopy_42 -> 291 -> 0xcf3f300 +0xee46150 -> 0xcf59f401745131171435049990_inner_var_848 -> 848 -> 0xce87810 +0xccb8160 -> 0xcf59f401745131171435049990_inner_var_436 -> 436 -> 0xcea5740 +0xcdb9a50 -> batch_norm2d_0.w_0_deepcopy_3 -> 330 -> 0xcf557c0 +0xcaf1780 -> batch_norm2d_13.w_1_deepcopy_70 -> 263 -> 0xcfc0710 +0xca55160 -> batch_norm2d_14.w_1_deepcopy_75 -> 258 -> 0xcfc9610 +0xcccc9b8 -> 0xcf59f401745131171435049990_inner_var_504 -> 504 -> 0xca705e0 +0xd04e4b0 -> batch_norm2d_46.w_1_deepcopy_242 -> 95 -> 0xcf90660 +0xee8aaa8 -> 0xcf59f401745131171435049990_inner_var_793 -> 793 -> 0xcf22070 +0xca73690 -> batch_norm2d_5.b_0_deepcopy_29 -> 304 -> 0xcf35350 +0xccbc3c0 -> 0xcf59f401745131171435049990_inner_var_425 -> 425 -> 0xee8cc70 +0xccc0910 -> 0xcf59f401745131171435049990_inner_var_1025 -> 1025 -> 0xcaac650 +0xcaf3d30 -> batch_norm2d_14.w_2_deepcopy_76 -> 257 -> 0xcfca3d0 +0xca515f8 -> 0xcf59f401745131171435049990_inner_var_601 -> 601 -> 0xc80e2c0 +0xcc9d360 -> conv2d_28.w_0_deepcopy_142 -> 191 -> 0xcfa8980 +0xee8aef0 -> 0xcf59f401745131171435049990_inner_var_798 -> 798 -> 0xcdbe600 +0xcfacf80 -> batch_norm2d_41.w_2_deepcopy_211 -> 122 -> 0xcfc7d60 +0xcaef180 -> batch_norm2d_9.w_2_deepcopy_51 -> 282 -> 0xcf48440 +0xca9e6c0 -> 0xcf59f401745131171435049990_inner_var_565 -> 565 -> 0xceb58e0 +0xccbc3f0 -> 0xcf59f401745131171435049990_inner_var_423 -> 423 -> 0xcea8fb0 +0xccd1610 -> batch_norm2d_39.w_1_deepcopy_200 -> 133 -> 0xcf3a220 +0xca64490 -> conv2d_3.w_0_deepcopy_17 -> 316 -> 0xcf15a30 +0xcccd280 -> 0xcf59f401745131171435049990_inner_var_510 -> 510 -> 0xcf12c40 +0xcae7f80 -> 0xcf59f401745131171435049990_inner_var_837 -> 837 -> 0xcf540d0 +0xccc0290 -> 0xcf59f401745131171435049990_inner_var_1023 -> 1023 -> 0xcbefea0 +0xccc5230 -> 0xcf59f401745131171435049990_inner_var_1045 -> 1045 -> 0xcfb5340 +0xcbf1600 -> conv2d_46.w_0_deepcopy_224 -> 113 -> 0xcf788c0 +0xee748d8 -> 0xcf59f401745131171435049990_inner_var_381 -> 381 -> 0xced3620 +0xce95010 -> constant_folding@_174513115110199844916 -> 43 -> 0xcf2d1d0 +0xcbf13e0 -> batch_norm2d_43.w_0_deepcopy_225 -> 112 -> 0xcf61c60 +0xca515e0 -> 0xcf59f401745131171435049990_inner_var_602 -> 602 -> 0xceef0c0 +0xcbf0e70 -> batch_norm2d_43.w_2_deepcopy_228 -> 109 -> 0xcf9baf0 +0xce94b30 -> constant_folding@_174513116811615103119 -> 40 -> 0xcf90680 +0xca67250 -> batch_norm2d_37.w_1_deepcopy_190 -> 143 -> 0xcf9e170 +0xccd0758 -> 0xcf59f401745131171435049990_inner_var_490 -> 490 -> 0xcedd640 +0xca9f760 -> 0xcf59f401745131171435049990_inner_var_573 -> 573 -> 0xced1430 +0xcbf01c0 -> batch_norm2d_45.w_0_deepcopy_235 -> 102 -> 0xcf346f0 +0xcaea920 -> 0xc8cd0101745131172921903234_inner_var_1100 -> 1100 -> 0xc8cdbd0 +0xed55b30 -> 0xcf59f401745131171435049990_inner_var_346 -> 346 -> 0xcea9360 +0xcabc890 -> 0xcf59f401745131171435049990_inner_var_871 -> 871 -> 0xcf028c0 +0xcfac450 -> conv2d_43.w_0_deepcopy_218 -> 116 -> 0xcf23170 +0xcbf3390 -> batch_norm2d_36.b_0_deepcopy_184 -> 149 -> 0xcf1aa60 +0xccad150 -> 0xcf59f401745131171435049990_inner_var_532 -> 532 -> 0xcf806b0 +0xd04efe0 -> batch_norm2d_45.b_0_deepcopy_236 -> 101 -> 0xcfa4800 +0xd04ee10 -> batch_norm2d_45.w_1_deepcopy_237 -> 100 -> 0xcf2bf50 +0xccbb6d0 -> 0xcf59f401745131171435049990_inner_var_427 -> 427 -> 0xced0d30 +0xca9bc18 -> 0xcf59f401745131171435049990_inner_var_743 -> 743 -> 0xcf42f50 +0xee867e0 -> 0xcf59f401745131171435049990_inner_var_766 -> 766 -> 0xccd2480 +0xccc4170 -> 0xcf59f401745131171435049990_inner_var_1039 -> 1039 -> 0xcfb38c0 +0xccdf440 -> 0xcf59f401745131171435049990_inner_var_1032 -> 1032 -> 0xcbecb40 +0xcdbe7e8 -> 0xcf59f401745131171435049990_inner_var_678 -> 678 -> 0xca65250 +0xcdc0c00 -> batch_norm2d_25.b_0_deepcopy_129 -> 204 -> 0xd066e30 +0xcdb5b50 -> conv2d_23.w_0_deepcopy_117 -> 216 -> 0xcf1a470 +0xcaf3960 -> conv2d_15.w_0_deepcopy_77 -> 256 -> 0xcf52c60 +0xccce220 -> 0xcf59f401745131171435049990_inner_var_472 -> 472 -> 0xcf6cb40 +0xd04dd70 -> batch_norm2d_47.b_0_deepcopy_246 -> 91 -> 0xcf9e810 +0xcaf4eb0 -> batch_norm2d_17.w_0_deepcopy_88 -> 245 -> 0xcf6f0e0 +0xcfac9c0 -> batch_norm2d_42.b_0_deepcopy_214 -> 119 -> 0xcf84050 +0xd0754b0 -> constant_folding@_174513116887061917064 -> 0 -> 0xd062b30 +0xce96870 -> batch_norm2d_29.w_0_deepcopy_148 -> 185 -> 0xcfa2740 +0xce94170 -> constant_folding@_174513116820193818025 -> 34 -> 0xce87a10 +0xca9a100 -> 0xcf59f401745131171435049990_inner_var_735 -> 735 -> 0xcc9ee20 +0xcaef920 -> batch_norm2d_9.b_0_deepcopy_49 -> 284 -> 0xcf0a7c0 +0xcae6e80 -> 0xcf59f401745131171435049990_inner_var_829 -> 829 -> 0xee88030 +0xcae6eb0 -> 0xcf59f401745131171435049990_inner_var_827 -> 827 -> 0xcef88f0 +0xccb8a50 -> 0xcf59f401745131171435049990_inner_var_442 -> 442 -> 0xd06eb50 +0xca749a0 -> batch_norm2d_4.b_0_deepcopy_24 -> 309 -> 0xcf02720 +0xcc9f1a0 -> constant_folding@_174513116859911589448 -> 14 -> 0xcf41cc0 +0xcc98968 -> 0xcf59f401745131171435049990_inner_var_399 -> 399 -> 0xcabae00 +0xca65660 -> conv2d_12.w_0_deepcopy_62 -> 271 -> 0xcf51cc0 +0xd04d310 -> batch_norm2d_36.w_2_deepcopy_186 -> 147 -> 0xcf37410 +0xccbb2b8 -> 0xcf59f401745131171435049990_inner_var_412 -> 412 -> 0xcf7cbb0 +0xcaf5df0 -> batch_norm2d_16.b_0_deepcopy_84 -> 249 -> 0xcfce890 +0xca9f778 -> 0xcf59f401745131171435049990_inner_var_572 -> 572 -> 0xee3f300 +0xca76090 -> batch_norm2d_49.w_1_deepcopy_257 -> 80 -> 0xcf420e0 +0xcbf1210 -> batch_norm2d_43.b_0_deepcopy_226 -> 111 -> 0xcf9ef90 +0xcaef550 -> batch_norm2d_9.w_1_deepcopy_50 -> 283 -> 0xcf44e90 +0xca8bb20 -> 0xcf59f401745131171435049990_inner_var_1075 -> 1075 -> 0xee76210 +0xca75ec0 -> batch_norm2d_49.w_2_deepcopy_258 -> 79 -> 0xcf24f50 +0xee400e0 -> batch_norm2d_51.w_1_deepcopy_267 -> 70 -> 0xcfa59c0 +0xca75ad0 -> batch_norm2d_50.w_0_deepcopy_260 -> 77 -> 0xcf20930 +0xccb6800 -> 0xcf59f401745131171435049990_inner_var_468 -> 468 -> 0xccdaac0 +0xcaa18e0 -> 0xcf59f401745131171435049990_inner_var_589 -> 589 -> 0xcec3af0 +0xcccc080 -> 0xcf59f401745131171435049990_inner_var_501 -> 501 -> 0xcad9eb0 +0xca75930 -> batch_norm2d_50.b_0_deepcopy_261 -> 76 -> 0xcfa3840 +0xca767d0 -> batch_norm2d_48.w_2_deepcopy_253 -> 84 -> 0xcf3e380 +0xcdbd6e0 -> 0xcf59f401745131171435049990_inner_var_673 -> 673 -> 0xceb72b0 +0xcccad60 -> 0xcf59f401745131171435049990_inner_var_1077 -> 1077 -> 0xcfbd260 +0xce96c40 -> conv2d_29.w_0_deepcopy_147 -> 186 -> 0xcf95890 +0xee40780 -> batch_norm2d_50.w_2_deepcopy_263 -> 74 -> 0xcf2bc00 +0xcbf34d0 -> batch_norm2d_38.w_0_deepcopy_193 -> 140 -> 0xcfaa490 +0xcdbc198 -> 0xcf59f401745131171435049990_inner_var_661 -> 661 -> 0xcea3d70 +0xccbee80 -> 0xcf59f401745131171435049990_inner_var_1018 -> 1018 -> 0xcbeeb90 +0xcab7d20 -> 0xc8cd0101745131172921903234_inner_var_1118 -> 1118 -> 0xc9b6220 +0xee405e0 -> conv2d_54.w_0_deepcopy_264 -> 73 -> 0xd059850 +0xd057808 -> 0xcf59f401745131171435049990_inner_var_355 -> 355 -> 0xcca80b0 +0xc80d510 -> batch_norm2d_39.w_2_deepcopy_201 -> 132 -> 0xcf15bf0 +0xcbf0ca0 -> conv2d_47.w_0_deepcopy_229 -> 108 -> 0xcf6e400 +0xd045ca0 -> 0xcf59f401745131171435049990_inner_var_699 -> 699 -> 0xca7ed30 +0xcdbf8c0 -> 0xcf59f401745131171435049990_inner_var_685 -> 685 -> 0xca54780 +0xcccb8f8 -> 0xcf59f401745131171435049990_inner_var_496 -> 496 -> 0xcf597c0 +0xee3fa20 -> batch_norm2d_52.b_0_deepcopy_271 -> 66 -> 0xcf193d0 +0xcabab30 -> 0xcf59f401745131171435049990_inner_var_865 -> 865 -> 0xca88040 +0xcfad8e0 -> batch_norm2d_40.w_2_deepcopy_206 -> 127 -> 0xcf35500 +0xee3f880 -> batch_norm2d_52.w_1_deepcopy_272 -> 65 -> 0xcf9ab00 +0xee3f6e0 -> batch_norm2d_52.w_2_deepcopy_273 -> 64 -> 0xcf3ce10 +0xca88400 -> 0xcf59f401745131171435049990_inner_var_361 -> 361 -> 0xcca6850 +0xcfacdb0 -> conv2d_42.w_0_deepcopy_212 -> 121 -> 0xcf9c9b0 +0x118c8610 -> conv2d_2.w_0_deepcopy_12 -> 321 -> 0xcfcc130 +0xca870f0 -> im_shape -> 332 -> 0xcec9720 +0xee874c0 -> 0xcf59f401745131171435049990_inner_var_768 -> 768 -> 0xcdb56f0 +0xca76f10 -> conv2d_51.w_0_deepcopy_249 -> 88 -> 0xccd6fb0 +0xcaf3590 -> batch_norm2d_15.w_0_deepcopy_78 -> 255 -> 0xcfcbe00 +0xca64860 -> batch_norm2d_2.w_2_deepcopy_16 -> 317 -> 0xcf808f0 +0xd041450 -> constant_folding@_174513116830651898832 -> 28 -> 0xed1a750 +0xee87460 -> 0xcf59f401745131171435049990_inner_var_772 -> 772 -> 0xce96460 +0xee40420 -> batch_norm2d_51.w_0_deepcopy_265 -> 72 -> 0xcf2f740 +0xcae88c0 -> 0xcf59f401745131171435049990_inner_var_846 -> 846 -> 0xca57220 +0xccdf100 -> 0xcf59f401745131171435049990_inner_var_958 -> 958 -> 0xca5d5f0 +0xca665a0 -> batch_norm2d_11.w_0_deepcopy_58 -> 275 -> 0xcf53290 +0xccbccf0 -> 0xcf59f401745131171435049990_inner_var_428 -> 428 -> 0xcab8450 +0xee73730 -> 0xcf59f401745131171435049990_inner_var_385 -> 385 -> 0xcf14230 +0xcbf0390 -> conv2d_48.w_0_deepcopy_234 -> 103 -> 0xcf6e750 +0xd041110 -> constant_folding@_174513116835231166835 -> 26 -> 0xcf0c190 +0xd040f70 -> constant_folding@_174513116836671179736 -> 25 -> 0xcf806d0 +0xd052fa0 -> 0xcf59f401745131171435049990_inner_var_641 -> 641 -> 0xced0120 +0xccd5e50 -> batch_norm2d_31.w_1_deepcopy_160 -> 173 -> 0xcfa09b0 +0xcae5e08 -> 0xcf59f401745131171435049990_inner_var_818 -> 818 -> 0xd076ae0 +0xccccd40 -> 0xcf59f401745131171435049990_inner_var_517 -> 517 -> 0xca726f0 +0xccba190 -> 0xcf59f401745131171435049990_inner_var_409 -> 409 -> 0xceb3080 +0xce8fa80 -> 0xcf59f401745131171435049990_inner_var_906 -> 906 -> 0xcea5b20 +0xccabf98 -> 0xcf59f401745131171435049990_inner_var_521 -> 521 -> 0xcab5850 +0xcadf9f8 -> 0xcf59f401745131171435049990_inner_var_538 -> 538 -> 0xcccac60 +0xee3fbc0 -> batch_norm2d_52.w_0_deepcopy_270 -> 67 -> 0xcf2c170 +0xcc9ffd0 -> 0xcf59f401745131171435049990_inner_var_971 -> 971 -> 0xcbe2840 +0x118c9d40 -> batch_norm2d_0.w_2_deepcopy_6 -> 327 -> 0xcf55d60 +0xee3f1a0 -> linear_1.w_0_deepcopy_276 -> 61 -> 0xcf61960 +0xccc2030 -> 0xcf59f401745131171435049990_inner_var_1031 -> 1031 -> 0xcaaddd0 +0xcfac7f0 -> batch_norm2d_42.w_1_deepcopy_215 -> 118 -> 0xcf1c020 +0xca8be40 -> 0xcf59f401745131171435049990_inner_var_1076 -> 1076 -> 0xee76630 +0xd0577a8 -> 0xcf59f401745131171435049990_inner_var_359 -> 359 -> 0xca758f0 +0xccb79d8 -> 0xcf59f401745131171435049990_inner_var_431 -> 431 -> 0xcade0a0 +0xcca0990 -> 0xcf59f401745131171435049990_inner_var_973 -> 973 -> 0xcbe3080 +0xce944b0 -> constant_folding@_174513116817322536023 -> 36 -> 0xce89990 +0xc80c210 -> conv2d_38.w_0_deepcopy_192 -> 141 -> 0xd068b90 +0xccc06f0 -> 0xcf59f401745131171435049990_inner_var_1024 -> 1024 -> 0xcfae170 +0xccb4b38 -> 0xcf59f401745131171435049990_inner_var_456 -> 456 -> 0xcabc9a0 +0xd0405b0 -> constant_folding@_174513116850011146642 -> 19 -> 0xcf2d600 +0xd04ea70 -> conv2d_49.w_0_deepcopy_239 -> 98 -> 0xcfbfd60 +0xccd0b10 -> 0xcf59f401745131171435049990_inner_var_500 -> 500 -> 0xca91ea0 +0xcabbfc0 -> 0xcf59f401745131171435049990_inner_var_869 -> 869 -> 0xcf1c9b0 +0xca56b50 -> batch_norm2d_35.w_1_deepcopy_180 -> 153 -> 0xcf3e960 +0xcaefcf0 -> batch_norm2d_9.w_0_deepcopy_48 -> 285 -> 0xcf0fa10 +0xca60da0 -> 0xcf59f401745131171435049990_inner_var_964 -> 964 -> 0xcbe0d60 +0xd040c30 -> constant_folding@_174513116839857308738 -> 23 -> 0xcdb8950 +0xd058968 -> 0xcf59f401745131171435049990_inner_var_363 -> 363 -> 0xcadee10 +0xca50380 -> 0xcf59f401745131171435049990_inner_var_598 -> 598 -> 0xcf24180 +0xccb7028 -> 0xcf59f401745131171435049990_inner_var_954 -> 954 -> 0xca5da10 +0xcc95050 -> 0xcf59f401745131171435049990_inner_var_855 -> 855 -> 0xcca8320 +0xee3f520 -> linear_0.w_0_deepcopy_274 -> 63 -> 0xcfa9fb0 +0xca526b0 -> 0xcf59f401745131171435049990_inner_var_612 -> 612 -> 0xcf1f290 +0xca9f7a8 -> 0xcf59f401745131171435049990_inner_var_570 -> 570 -> 0xceac1e0 +0xcf6c9e0 -> constant_folding@_174513114736420657211 -> 47 -> 0xd059870 +0xccd0f10 -> 0xcf59f401745131171435049990_inner_var_493 -> 493 -> 0xcefd440 +0xccda260 -> 0xcf59f401745131171435049990_inner_var_940 -> 940 -> 0xcee0510 +0xcccf680 -> 0xcf59f401745131171435049990_inner_var_483 -> 483 -> 0xceb1680 +0xcb0c430 -> batch_norm2d_7.w_0_deepcopy_38 -> 295 -> 0xcf261b0 +0xd04c7d0 -> conv2d_35.w_0_deepcopy_177 -> 156 -> 0xd05bfc0 +0xca75790 -> batch_norm2d_50.w_1_deepcopy_262 -> 75 -> 0xcf33710 +0xcaf2280 -> batch_norm2d_16.w_0_deepcopy_83 -> 250 -> 0xcf30ac0 +0xee886b0 -> 0xcf59f401745131171435049990_inner_var_780 -> 780 -> 0xcef3d90 +0xcccc9a0 -> 0xcf59f401745131171435049990_inner_var_505 -> 505 -> 0xd03c340 +0xca65a30 -> batch_norm2d_11.w_2_deepcopy_61 -> 272 -> 0xcf17bc0 +0xcaf0f48 -> 0xcf59f401745131171435049990_inner_var_546 -> 546 -> 0xca69690 +0xccbb258 -> 0xcf59f401745131171435049990_inner_var_416 -> 416 -> 0xceccf20 +0xd040750 -> constant_folding@_174513116844224746641 -> 20 -> 0xcdb72f0 +0xce88b90 -> constant_folding@_174513116876880248258 -> 5 -> 0xee71fe0 +0xd04d9d0 -> batch_norm2d_47.w_2_deepcopy_248 -> 89 -> 0xcf33b60 +0xccd58c0 -> batch_norm2d_32.w_0_deepcopy_163 -> 170 -> 0xcf218b0 +0xcfad710 -> conv2d_41.w_0_deepcopy_207 -> 126 -> 0xcf91450 +0xd04dba0 -> batch_norm2d_47.w_1_deepcopy_247 -> 90 -> 0xcf230d0 +0xd052b30 -> 0xcf59f401745131171435049990_inner_var_627 -> 627 -> 0xcad39c0 +0xee8bb50 -> 0xcf59f401745131171435049990_inner_var_802 -> 802 - +I0420 14:39:35.004853 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:39:35.019733 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:39:35.025707 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "memcpy_d2h(phi_kernel)" (%arg_0 {stop_gradient:true}) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2661} : (custom_device_tensor) -> cpu_tensor + (%1) = "scale_(phi_kernel)" (%0, %2) {bias:1,bias_after_scale:true,is_inplace:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale_",origin_id:2662,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%3) = "builtin.combine" [id:2663] (%arg_0) {origin_id:620,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%4) = "stack(phi_kernel)" (%3) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2664,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%5) = "builtin.combine" [id:2665] (%1) {origin_id:622,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%6) = "stack(phi_kernel)" (%5) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2666,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%7) = "slice(phi_kernel)" (%8, %4, %6) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2667,stop_gradient:[true]} : (custom_device_tensor<-1xi64>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor + (%9) = "memcpy_d2h(phi_kernel)" (%7) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2668} : (custom_device_tensor) -> cpu_tensor + (%10) = "scale_(phi_kernel)" (%9, %2) {bias:1,bias_after_scale:true,is_inplace:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale_",origin_id:2669,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%11) = "builtin.combine" [id:2670] (%7) {origin_id:627,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%12) = "stack(phi_kernel)" (%11) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2671,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%13) = "builtin.combine" [id:2672] (%10) {origin_id:629,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%14) = "stack(phi_kernel)" (%13) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2673,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%15) = "slice(phi_kernel)" (%16, %12, %14) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2674,stop_gradient:[true]} : (cpu_tensor<-1xi64>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%17) = "memcpy_d2h(phi_kernel)" (%7) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2675} : (custom_device_tensor) -> cpu_tensor + (%18) = "scale_(phi_kernel)" (%17, %2) {bias:1,bias_after_scale:true,is_inplace:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale_",origin_id:2676,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%19) = "builtin.combine" [id:2677] (%7) {origin_id:634,stop_gradient:[true]} : (custom_device_tensor) -> vec[custom_device_tensor] + (%20) = "stack(phi_kernel)" (%19) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2678,stop_gradient:[true]} : (vec[custom_device_tensor]) -> custom_device_tensor<1xi64> + (%21) = "builtin.combine" [id:2679] (%18) {origin_id:636,stop_gradient:[true]} : (cpu_tensor) -> vec[cpu_tensor] + (%22) = "stack(phi_kernel)" (%21) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2680,stop_gradient:[true]} : (vec[cpu_tensor]) -> cpu_tensor<1xi64> + (%23) = "slice(phi_kernel)" (%24, %20, %22) {axes:[0],decrease_axis:[0],infer_flags:[-1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2681,stop_gradient:[false]} : (custom_device_tensor<-1x2xf32>, custom_device_tensor<1xi64>, cpu_tensor<1xi64>) -> custom_device_tensor<2xf32> + (%25) = "builtin.combine" [id:2682] (%15, %26) {origin_id:640,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor] + (%27) = "stack(phi_kernel)" (%25) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2683,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor]) -> cpu_tensor<2xi64> + (%28) = "expand(phi_kernel)" (%23, %27) {kernel_key:,kernel_name:"expand",op_name:"pd_op.expand",origin_id:2684,stop_gradient:[false]} : (custom_device_tensor<2xf32>, cpu_tensor<2xi64>) -> custom_device_tensor<-1x2xf32> + (%29) = "array_length(phi_kernel)" (%30) {kernel_key:,kernel_name:"array_length",op_name:"pd_op.array_length",origin_id:2685} : (cpu_tensor_array) -> cpu_tensor<1xi64> + (%31) = "memcpy_d2h(phi_kernel)" (%28) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2686} : (custom_device_tensor<-1x2xf32>) -> cpu_tensor<-1x2xf32> + (%32) = "array_write_(phi_kernel)" (%30, %31, %29) {is_inplace:true,kernel_key:,kernel_name:"array_write",op_name:"pd_op.array_write_",origin_id:2687} : (cpu_tensor_array, cpu_tensor<-1x2xf32>, cpu_tensor<1xi64>) -> cpu_tensor_array + (%33) = "memcpy_d2h(phi_kernel)" (%arg_0) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2688} : (custom_device_tensor) -> cpu_tensor + (%34) = "scale_(phi_kernel)" (%33, %2) {bias:1,bias_after_scale:true,is_inplace:true,kernel_key:,kernel_name:"scale",op_name:"pd_op.scale_",origin_id:2689,stop_gradient:[true]} : (cpu_tensor, cpu_tensor<1xf32>) -> cpu_tensor + (%35) = "less_than(phi_kernel)" (%34, %36) {kernel_key:,kernel_name:"less_than",op_name:"pd_op.less_than",origin_id:2690,stop_gradient:[true]} : (cpu_tensor, cpu_tensor) -> cpu_tensor + (%37) = "memcpy_h2d(phi_kernel)" (%35) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2691} : (cpu_tensor) -> custom_device_tensor + (%38) = "memcpy_h2d(phi_kernel)" (%34) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2692} : (cpu_tensor) -> custom_device_tensor + (%39) = "memcpy_h2d(phi_kernel)" (%15) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2693} : (cpu_tensor) -> custom_device_tensor + () = "cf.yield" [id:2694] (%37, %38, %28, %7, %39) {origin_id:648} : (custom_device_tensor, custom_device_tensor, custom_device_tensor<-1x2xf32>, custom_device_tensor, custom_device_tensor) -> +} +I0420 14:39:35.025817 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 1098 ) = pd_op.memcpy_d2h ( 1094 ) +1: ( 1098 ) ( 1099 ) = pd_op.scale_ ( 18 ) ( 1098 ) +2: ( 1100 1094 ) = builtin_combine_instruction ( 1094 ) +3: ( 1101 ) = pd_op.stack ( 1100 1094 ) +4: ( 1102 1099 ) = builtin_combine_instruction ( 1099 ) +5: ( 1103 ) = pd_op.stack ( 1102 1099 ) +6: ( 1104 ) = pd_op.slice ( 1103 ) ( 1101 ) ( 848 ) +7: ( 1105 ) = pd_op.memcpy_d2h ( 1104 ) +8: ( 1105 ) ( 1106 ) = pd_op.scale_ ( 18 ) ( 1105 ) +9: ( 1107 1104 ) = builtin_combine_instruction ( 1104 ) +10: ( 1108 ) = pd_op.stack ( 1107 1104 ) +11: ( 1109 1106 ) = builtin_combine_instruction ( 1106 ) +12: ( 1110 ) = pd_op.stack ( 1109 1106 ) +13: ( 1111 ) = pd_op.slice ( 1110 ) ( 1108 ) ( 749 ) +14: ( 1112 ) = pd_op.memcpy_d2h ( 1104 ) +15: ( 1112 ) ( 1113 ) = pd_op.scale_ ( 18 ) ( 1112 ) +16: ( 1114 1104 ) = builtin_combine_instruction ( 1104 ) +17: ( 1115 ) = pd_op.stack ( 1114 1104 ) +18: ( 1116 1113 ) = builtin_combine_instruction ( 1113 ) +19: ( 1117 ) = pd_op.stack ( 1116 1113 ) +20: ( 1118 ) = pd_op.slice ( 1117 ) ( 1115 ) ( 332 ) +21: ( 1119 1111 17 ) = builtin_combine_instruction ( 17 ) ( 1111 ) +22: ( 1120 ) = pd_op.stack ( 1119 1111 17 ) +23: ( 1121 ) = pd_op.expand ( 1120 ) ( 1118 ) +24: ( 1122 ) = pd_op.array_length ( 851 ) +25: ( 1123 ) = pd_op.memcpy_d2h ( 1121 ) +26: ( 851 ) = pd_op.array_write_ ( 1122 ) ( 1123 ) ( 851 ) +27: ( 1124 ) = pd_op.memcpy_d2h ( 1094 ) +28: ( 1124 ) ( 1125 ) = pd_op.scale_ ( 18 ) ( 1124 ) +29: ( 1126 ) = pd_op.less_than ( 850 ) ( 1125 ) +30: ( 1127 ) = pd_op.memcpy_h2d ( 1126 ) +31: ( 1128 ) = pd_op.memcpy_h2d ( 1125 ) +32: ( 1129 ) = pd_op.memcpy_h2d ( 1111 ) +33: = yield_instruction ( 1129 ) ( 1104 ) ( 1121 ) ( 1128 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> constant_folding@_174513116887061917064 -> 0xd062b30 +1 -> constant_folding@_174513116885637196163 -> 0xd04b8a0 +2 -> constant_folding@_174513116884162097162 -> 0xcf7caa0 +3 -> constant_folding@_174513116882724144161 -> 0xcdb8360 +4 -> constant_folding@_174513116880873974160 -> 0xee71f60 +5 -> constant_folding@_174513116876880248258 -> 0xee71fe0 +6 -> constant_folding@_174513116875198908257 -> 0xc386660 +7 -> constant_folding@_174513116872098526255 -> 0xd061370 +8 -> constant_folding@_174513116870501220354 -> 0xcea7bc0 +9 -> constant_folding@_174513116869046783353 -> 0xcf2b9b0 +10 -> constant_folding@_174513116867667180352 -> 0xee46cc0 +11 -> constant_folding@_174513116865440940351 -> 0xcf3edb0 +12 -> constant_folding@_174513116864033493350 -> 0xcf3a750 +13 -> constant_folding@_174513116861634597449 -> 0xcf09130 +14 -> constant_folding@_174513116859911589448 -> 0xcf41cc0 +15 -> constant_folding@_174513116857122500446 -> 0xd0606f0 +16 -> constant_folding@_174513116855594796545 -> 0xcf11a20 +17 -> constant_folding@_174513116853662943544 -> 0xcdb8c50 +18 -> constant_folding@_174513116852268476543 -> 0xcfbe9a0 +19 -> constant_folding@_174513116850011146642 -> 0xcf2d600 +20 -> constant_folding@_174513116844224746641 -> 0xcdb72f0 +21 -> constant_folding@_174513116842705804740 -> 0xcf92430 +22 -> constant_folding@_174513116841278885739 -> 0xcdb8970 +23 -> constant_folding@_174513116839857308738 -> 0xcdb8950 +24 -> constant_folding@_174513116838085921737 -> 0xcf83ea0 +25 -> constant_folding@_174513116836671179736 -> 0xcf806d0 +26 -> constant_folding@_174513116835231166835 -> 0xcf0c190 +27 -> constant_folding@_174513116833656139834 -> 0xcf419a0 +28 -> constant_folding@_174513116830651898832 -> 0xed1a750 +29 -> constant_folding@_174513116827593559930 -> 0xcf437e0 +30 -> constant_folding@_174513116825929047929 -> 0xcf7c3f0 +31 -> constant_folding@_174513116824495087928 -> 0xcf3ae90 +32 -> constant_folding@_174513116823069183927 -> 0xcf64350 +33 -> constant_folding@_174513116821619066926 -> 0xcff2a20 +34 -> constant_folding@_174513116820193818025 -> 0xce87a10 +35 -> constant_folding@_174513116818740431024 -> 0xce98460 +36 -> constant_folding@_174513116817322536023 -> 0xce89990 +37 -> constant_folding@_174513116815886735022 -> 0xcf7aeb0 +38 -> constant_folding@_174513116814471704121 -> 0xd05fe30 +39 -> constant_folding@_174513116813053746120 -> 0xcf61980 +40 -> constant_folding@_174513116811615103119 -> 0xcf90680 +41 -> constant_folding@_174513115258237754718 -> 0xcfce240 +42 -> constant_folding@_174513115176548053017 -> 0xcf8c2c0 +43 -> constant_folding@_174513115110199844916 -> 0xcf2d1d0 +44 -> constant_folding@_174513115013851471515 -> 0xcf1a280 +45 -> constant_folding@_174513114941937302314 -> 0xcf04b20 +46 -> constant_folding@_174513114857650263913 -> 0xcf7d3f0 +47 -> constant_folding@_174513114736420657211 -> 0xd059870 +48 -> constant_folding@_174513114667774590410 -> 0xcf7f8b0 +49 -> constant_folding@_17451311460158745859 -> 0xcf7a460 +50 -> constant_folding@_17451311452954254518 -> 0xd05b470 +51 -> constant_folding@_17451311445836590377 -> 0xceddd30 +52 -> constant_folding@_17451311436834671136 -> 0xcf820a0 +53 -> constant_folding@_17451311430626338225 -> 0xcf624a0 +54 -> constant_folding@_17451311422710820934 -> 0xcede1e0 +55 -> constant_folding@_17451311415972965563 -> 0xd05ecd0 +56 -> constant_folding@_17451311399980650772 -> 0xcf619e0 +57 -> constant_folding@_17451311284029886470 -> 0xd069f60 +58 -> conv2d_56.w_0_deepcopy_280 -> 0xcf344c0 +59 -> conv2d_transpose_0.w_0_deepcopy_278 -> 0xcf28750 +60 -> linear_1.b_0_deepcopy_277 -> 0xcf3e700 +61 -> linear_1.w_0_deepcopy_276 -> 0xcf61960 +62 -> linear_0.b_0_deepcopy_275 -> 0xd041df0 +63 -> linear_0.w_0_deepcopy_274 -> 0xcfa9fb0 +64 -> batch_norm2d_52.w_2_deepcopy_273 -> 0xcf3ce10 +65 -> batch_norm2d_52.w_1_deepcopy_272 -> 0xcf9ab00 +66 -> batch_norm2d_52.b_0_deepcopy_271 -> 0xcf193d0 +67 -> batch_norm2d_52.w_0_deepcopy_270 -> 0xcf2c170 +68 -> conv2d_55.w_0_deepcopy_269 -> 0xcf3b8c0 +69 -> batch_norm2d_51.w_2_deepcopy_268 -> 0xcf2c930 +70 -> batch_norm2d_51.w_1_deepcopy_267 -> 0xcfa59c0 +71 -> batch_norm2d_51.b_0_deepcopy_266 -> 0xcf1b990 +72 -> batch_norm2d_51.w_0_deepcopy_265 -> 0xcf2f740 +73 -> conv2d_54.w_0_deepcopy_264 -> 0xd059850 +74 -> batch_norm2d_50.w_2_deepcopy_263 -> 0xcf2bc00 +75 -> batch_norm2d_50.w_1_deepcopy_262 -> 0xcf33710 +76 -> batch_norm2d_50.b_0_deepcopy_261 -> 0xcfa3840 +77 -> batch_norm2d_50.w_0_deepcopy_260 -> 0xcf20930 +78 -> conv2d_53.w_0_deepcopy_259 -> 0xcfa3490 +79 -> batch_norm2d_49.w_2_deepcopy_258 -> 0xcf24f50 +80 -> batch_norm2d_49.w_1_deepcopy_257 -> 0xcf420e0 +81 -> batch_norm2d_49.b_0_deepcopy_256 -> 0xcf39210 +82 -> batch_norm2d_49.w_0_deepcopy_255 -> 0xcfa0ad0 +83 -> conv2d_52.w_0_deepcopy_254 -> 0xcfc1c10 +84 -> batch_norm2d_48.w_2_deepcopy_253 -> 0xcf3e380 +85 -> batch_norm2d_48.w_1_deepcopy_252 -> 0xcf28490 +86 -> batch_norm2d_48.b_0_deepcopy_251 -> 0xcf165b0 +87 -> batch_norm2d_48.w_0_deepcopy_250 -> 0xcf3e660 +88 -> conv2d_51.w_0_deepcopy_249 -> 0xccd6fb0 +89 -> batch_norm2d_47.w_2_deepcopy_248 -> 0xcf33b60 +90 -> batch_norm2d_47.w_1_deepcopy_247 -> 0xcf230d0 +91 -> batch_norm2d_47.b_0_deepcopy_246 -> 0xcf9e810 +92 -> batch_norm2d_47.w_0_deepcopy_245 -> 0xcf3a620 +93 -> conv2d_50.w_0_deepcopy_244 -> 0xcf64fa0 +94 -> batch_norm2d_46.w_2_deepcopy_243 -> 0xcfa1c80 +95 -> batch_norm2d_46.w_1_deepcopy_242 -> 0xcf90660 +96 -> batch_norm2d_46.b_0_deepcopy_241 -> 0xcf60140 +97 -> batch_norm2d_46.w_0_deepcopy_240 -> 0xcf93910 +98 -> conv2d_49.w_0_deepcopy_239 -> 0xcfbfd60 +99 -> batch_norm2d_45.w_2_deepcopy_238 -> 0xcf2ccb0 +100 -> batch_norm2d_45.w_1_deepcopy_237 -> 0xcf2bf50 +101 -> batch_norm2d_45.b_0_deepcopy_236 -> 0xcfa4800 +102 -> batch_norm2d_45.w_0_deepcopy_235 -> 0xcf346f0 +103 -> conv2d_48.w_0_deepcopy_234 -> 0xcf6e750 +104 -> batch_norm2d_44.w_2_deepcopy_233 -> 0xcf9c920 +105 -> batch_norm2d_44.w_1_deepcopy_232 -> 0xce972f0 +106 -> batch_norm2d_44.b_0_deepcopy_231 -> 0xcfa37a0 +107 -> batch_norm2d_44.w_0_deepcopy_230 -> 0xcf1cb30 +108 -> conv2d_47.w_0_deepcopy_229 -> 0xcf6e400 +109 -> batch_norm2d_43.w_2_deepcopy_228 -> 0xcf9baf0 +110 -> batch_norm2d_43.w_1_deepcopy_227 -> 0xcf2a900 +111 -> batch_norm2d_43.b_0_deepcopy_226 -> 0xcf9ef90 +112 -> batch_norm2d_43.w_0_deepcopy_225 -> 0xcf61c60 +113 -> conv2d_46.w_0_deepcopy_224 -> 0xcf788c0 +114 -> conv2d_45.w_0_deepcopy_222 -> 0xcf30990 +115 -> conv2d_44.w_0_deepcopy_220 -> 0xcf36b60 +116 -> conv2d_43.w_0_deepcopy_218 -> 0xcf23170 +117 -> batch_norm2d_42.w_2_deepcopy_216 -> 0xcfa3f80 +118 -> batch_norm2d_42.w_1_deepcopy_215 -> 0xcf1c020 +119 -> batch_norm2d_42.b_0_deepcopy_214 -> 0xcf84050 +120 -> batch_norm2d_42.w_0_deepcopy_213 -> 0xcf3c660 +121 -> conv2d_42.w_0_deepcopy_212 -> 0xcf9c9b0 +122 -> batch_norm2d_41.w_2_deepcopy_211 -> 0xcfc7d60 +123 -> batch_norm2d_41.w_1_deepcopy_210 -> 0xcf2da20 +124 -> batch_norm2d_41.b_0_deepcopy_209 -> 0xcf8ca40 +125 -> batch_norm2d_41.w_0_deepcopy_208 -> 0xcfc5960 +126 -> conv2d_41.w_0_deepcopy_207 -> 0xcf91450 +127 -> batch_norm2d_40.w_2_deepcopy_206 -> 0xcf35500 +128 -> batch_norm2d_40.w_1_deepcopy_205 -> 0xcfc3720 +129 -> batch_norm2d_40.b_0_deepcopy_204 -> 0xcf41f80 +130 -> batch_norm2d_40.w_0_deepcopy_203 -> 0xcf63a50 +131 -> conv2d_40.w_0_deepcopy_202 -> 0xc80ecf0 +132 -> batch_norm2d_39.w_2_deepcopy_201 -> 0xcf15bf0 +133 -> batch_norm2d_39.w_1_deepcopy_200 -> 0xcf3a220 +134 -> batch_norm2d_39.b_0_deepcopy_199 -> 0xcf35220 +135 -> batch_norm2d_39.w_0_deepcopy_198 -> 0xcf34d40 +136 -> conv2d_39.w_0_deepcopy_197 -> 0xd0753d0 +137 -> batch_norm2d_38.w_2_deepcopy_196 -> 0xcfa49c0 +138 -> batch_norm2d_38.w_1_deepcopy_195 -> 0xcefd090 +139 -> batch_norm2d_38.b_0_deepcopy_194 -> 0xcf14990 +140 -> batch_norm2d_38.w_0_deepcopy_193 -> 0xcfaa490 +141 -> conv2d_38.w_0_deepcopy_192 -> 0xd068b90 +142 -> batch_norm2d_37.w_2_deepcopy_191 -> 0xcf286a0 +143 -> batch_norm2d_37.w_1_deepcopy_190 -> 0xcf9e170 +144 -> batch_norm2d_37.b_0_deepcopy_189 -> 0xcf8d310 +145 -> batch_norm2d_37.w_0_deepcopy_188 -> 0xcf9cbb0 +146 -> conv2d_37.w_0_deepcopy_187 -> 0xd063c10 +147 -> batch_norm2d_36.w_2_deepcopy_186 -> 0xcf37410 +148 -> batch_norm2d_36.w_1_deepcopy_185 -> 0xcf24090 +149 -> batch_norm2d_36.b_0_deepcopy_184 -> 0xcf1aa60 +150 -> batch_norm2d_36.w_0_deepcopy_183 -> 0xd065350 +151 -> conv2d_36.w_0_deepcopy_182 -> 0xd05fe10 +152 -> batch_norm2d_35.w_2_deepcopy_181 -> 0xcf14710 +153 -> batch_norm2d_35.w_1_deepcopy_180 -> 0xcf3e960 +154 -> batch_norm2d_35.b_0_deepcopy_179 -> 0xcf40ec0 +155 -> batch_norm2d_35.w_0_deepcopy_178 -> 0xcf396f0 +156 -> conv2d_35.w_0_deepcopy_177 -> 0xd05bfc0 +157 -> batch_norm2d_34.w_2_deepcopy_176 -> 0xcf3b470 +158 -> batch_norm2d_34.w_1_deepcopy_175 -> 0xcf38a90 +159 -> batch_norm2d_34.b_0_deepcopy_174 -> 0xcf3a9e0 +160 -> batch_norm2d_34.w_0_deepcopy_173 -> 0xcfbfa40 +161 -> conv2d_34.w_0_deepcopy_172 -> 0xcf831a0 +162 -> batch_norm2d_33.w_2_deepcopy_171 -> 0xcf3a3e0 +163 -> batch_norm2d_33.w_1_deepcopy_170 -> 0xcf21710 +164 -> batch_norm2d_33.b_0_deepcopy_169 -> 0xcfa22f0 +165 -> batch_norm2d_33.w_0_deepcopy_168 -> 0xcfa2c80 +166 -> conv2d_33.w_0_deepcopy_167 -> 0xcf80d10 +167 -> batch_norm2d_32.w_2_deepcopy_166 -> 0xcf91ac0 +168 -> batch_norm2d_32.w_1_deepcopy_165 -> 0xcf3d360 +169 -> batch_norm2d_32.b_0_deepcopy_164 -> 0xcf3e100 +170 -> batch_norm2d_32.w_0_deepcopy_163 -> 0xcf218b0 +171 -> conv2d_32.w_0_deepcopy_162 -> 0xcf8c2a0 +172 -> batch_norm2d_31.w_2_deepcopy_161 -> 0xcf37b70 +173 -> batch_norm2d_31.w_1_deepcopy_160 -> 0xcfa09b0 +174 -> batch_norm2d_31.b_0_deepcopy_159 -> 0xcfa3680 +175 -> batch_norm2d_31.w_0_deepcopy_158 -> 0xcf910a0 +176 -> conv2d_31.w_0_deepcopy_157 -> 0xcf7c3d0 +177 -> batch_norm2d_30.w_2_deepcopy_156 -> 0xc809ae0 +178 -> batch_norm2d_30.w_1_deepcopy_155 -> 0xcf63ec0 +179 -> batch_norm2d_30.b_0_deepcopy_154 -> 0xcf28de0 +180 -> batch_norm2d_30.w_0_deepcopy_153 -> 0xcf3c2d0 +181 -> conv2d_30.w_0_deepcopy_152 -> 0xcf7ae90 +182 -> batch_norm2d_29.w_2_deepcopy_151 -> 0xcf93530 +183 -> batch_norm2d_29.w_1_deepcopy_150 -> 0xd076240 +184 -> batch_norm2d_29.b_0_deepcopy_149 -> 0xd074f90 +185 -> batch_norm2d_29.w_0_deepcopy_148 -> 0xcfa2740 +186 -> conv2d_29.w_0_deepcopy_147 -> 0xcf95890 +187 -> batch_norm2d_28.w_2_deepcopy_146 -> 0xd074250 +188 -> batch_norm2d_28.w_1_deepcopy_145 -> 0xd073510 +189 -> batch_norm2d_28.b_0_deepcopy_144 -> 0xd071b20 +190 -> batch_norm2d_28.w_0_deepcopy_143 -> 0xd0727b0 +191 -> conv2d_28.w_0_deepcopy_142 -> 0xcfa8980 +192 -> batch_norm2d_27.w_2_deepcopy_141 -> 0xd06fa30 +193 -> batch_norm2d_27.w_1_deepcopy_140 -> 0xd06ff30 +194 -> batch_norm2d_27.b_0_deepcopy_139 -> 0xd06e400 +195 -> batch_norm2d_27.w_0_deepcopy_138 -> 0xd06f070 +196 -> conv2d_27.w_0_deepcopy_137 -> 0xcf9e030 +197 -> batch_norm2d_26.w_2_deepcopy_136 -> 0xd06d600 +198 -> batch_norm2d_26.w_1_deepcopy_135 -> 0xd06c980 +199 -> batch_norm2d_26.b_0_deepcopy_134 -> 0xd06ad10 +200 -> batch_norm2d_26.w_0_deepcopy_133 -> 0xd06b960 +201 -> conv2d_26.w_0_deepcopy_132 -> 0xcf71280 +202 -> batch_norm2d_25.w_2_deepcopy_131 -> 0xd069fd0 +203 -> batch_norm2d_25.w_1_deepcopy_130 -> 0xd069110 +204 -> batch_norm2d_25.b_0_deepcopy_129 -> 0xd066e30 +205 -> batch_norm2d_25.w_0_deepcopy_128 -> 0xd067f70 +206 -> conv2d_25.w_0_deepcopy_127 -> 0xcf91990 +207 -> batch_norm2d_24.w_2_deepcopy_126 -> 0xd066330 +208 -> batch_norm2d_24.w_1_deepcopy_125 -> 0xd065590 +209 -> batch_norm2d_24.b_0_deepcopy_124 -> 0xd062fb0 +210 -> batch_norm2d_24.w_0_deepcopy_123 -> 0xd0641f0 +211 -> conv2d_24.w_0_deepcopy_122 -> 0xcf91780 +212 -> batch_norm2d_23.w_2_deepcopy_121 -> 0xd062570 +213 -> batch_norm2d_23.w_1_deepcopy_120 -> 0xd061ab0 +214 -> batch_norm2d_23.b_0_deepcopy_119 -> 0xd060170 +215 -> batch_norm2d_23.w_0_deepcopy_118 -> 0xd060ed0 +216 -> conv2d_23.w_0_deepcopy_117 -> 0xcf1a470 +217 -> batch_norm2d_22.w_2_deepcopy_116 -> 0xd05f3f0 +218 -> batch_norm2d_22.w_1_deepcopy_115 -> 0xd05e730 +219 -> batch_norm2d_22.b_0_deepcopy_114 -> 0xcf3fa10 +220 -> batch_norm2d_22.w_0_deepcopy_113 -> 0xd05d850 +221 -> conv2d_22.w_0_deepcopy_112 -> 0xcfa5f90 +222 -> batch_norm2d_21.w_2_deepcopy_111 -> 0xd05c7d0 +223 -> batch_norm2d_21.w_1_deepcopy_110 -> 0xcf9c4b0 +224 -> batch_norm2d_21.b_0_deepcopy_109 -> 0xcf23400 +225 -> batch_norm2d_21.w_0_deepcopy_108 -> 0xd05afe0 +226 -> conv2d_21.w_0_deepcopy_107 -> 0xcf41b60 +227 -> batch_norm2d_20.w_2_deepcopy_106 -> 0xcf83e60 +228 -> batch_norm2d_20.w_1_deepcopy_105 -> 0xcf82fa0 +229 -> batch_norm2d_20.b_0_deepcopy_104 -> 0xcf814b0 +230 -> batch_norm2d_20.w_0_deepcopy_103 -> 0xcf82460 +231 -> conv2d_20.w_0_deepcopy_102 -> 0xcf95570 +232 -> batch_norm2d_19.w_2_deepcopy_101 -> 0xcf7d9f0 +233 -> batch_norm2d_19.w_1_deepcopy_100 -> 0xcf7c850 +234 -> batch_norm2d_19.b_0_deepcopy_99 -> 0xcf7ac10 +235 -> batch_norm2d_19.w_0_deepcopy_98 -> 0xcf7bc50 +236 -> conv2d_19.w_0_deepcopy_97 -> 0xcf31440 +237 -> batch_norm2d_18.w_2_deepcopy_96 -> 0xcf79bd0 +238 -> batch_norm2d_18.w_1_deepcopy_95 -> 0xcf84390 +239 -> batch_norm2d_18.b_0_deepcopy_94 -> 0xcf71dd0 +240 -> batch_norm2d_18.w_0_deepcopy_93 -> 0xcfa68e0 +241 -> conv2d_18.w_0_deepcopy_92 -> 0xcf22d20 +242 -> batch_norm2d_17.w_2_deepcopy_91 -> 0xcf70e80 +243 -> batch_norm2d_17.w_1_deepcopy_90 -> 0xcf6ff00 +244 -> batch_norm2d_17.b_0_deepcopy_89 -> 0xcfa8c60 +245 -> batch_norm2d_17.w_0_deepcopy_88 -> 0xcf6f0e0 +246 -> conv2d_17.w_0_deepcopy_87 -> 0xcf17680 +247 -> batch_norm2d_16.w_2_deepcopy_86 -> 0xcfa79c0 +248 -> batch_norm2d_16.w_1_deepcopy_85 -> 0xcfa6d50 +249 -> batch_norm2d_16.b_0_deepcopy_84 -> 0xcfce890 +250 -> batch_norm2d_16.w_0_deepcopy_83 -> 0xcf30ac0 +251 -> conv2d_16.w_0_deepcopy_82 -> 0xcf536b0 +252 -> batch_norm2d_15.w_2_deepcopy_81 -> 0xcfcda90 +253 -> batch_norm2d_15.w_1_deepcopy_80 -> 0xcfcce30 +254 -> batch_norm2d_15.b_0_deepcopy_79 -> 0xcfcb200 +255 -> batch_norm2d_15.w_0_deepcopy_78 -> 0xcfcbe00 +256 -> conv2d_15.w_0_deepcopy_77 -> 0xcf52c60 +257 -> batch_norm2d_14.w_2_deepcopy_76 -> 0xcfca3d0 +258 -> batch_norm2d_14.w_1_deepcopy_75 -> 0xcfc9610 +259 -> batch_norm2d_14.b_0_deepcopy_74 -> 0xcfc2eb0 +260 -> batch_norm2d_14.w_0_deepcopy_73 -> 0xcfc6c40 +261 -> conv2d_14.w_0_deepcopy_72 -> 0xcf52990 +262 -> batch_norm2d_13.w_2_deepcopy_71 -> 0xcfc1870 +263 -> batch_norm2d_13.w_1_deepcopy_70 -> 0xcfc0710 +264 -> batch_norm2d_13.b_0_deepcopy_69 -> 0xcf31c30 +265 -> batch_norm2d_13.w_0_deepcopy_68 -> 0xcf38dc0 +266 -> conv2d_13.w_0_deepcopy_67 -> 0xcf52460 +267 -> batch_norm2d_12.w_2_deepcopy_66 -> 0xcf289b0 +268 -> batch_norm2d_12.w_1_deepcopy_65 -> 0xcf259b0 +269 -> batch_norm2d_12.b_0_deepcopy_64 -> 0xcf1dca0 +270 -> batch_norm2d_12.w_0_deepcopy_63 -> 0xcf20e70 +271 -> conv2d_12.w_0_deepcopy_62 -> 0xcf51cc0 +272 -> batch_norm2d_11.w_2_deepcopy_61 -> 0xcf17bc0 +273 -> batch_norm2d_11.w_1_deepcopy_60 -> 0xcf14fe0 +274 -> batch_norm2d_11.b_0_deepcopy_59 -> 0xcf53430 +275 -> batch_norm2d_11.w_0_deepcopy_58 -> 0xcf53290 +276 -> conv2d_11.w_0_deepcopy_57 -> 0xcf50ed0 +277 -> batch_norm2d_10.w_2_deepcopy_56 -> 0xcf52d40 +278 -> batch_norm2d_10.w_1_deepcopy_55 -> 0xcf526a0 +279 -> batch_norm2d_10.b_0_deepcopy_54 -> 0xcf51350 +280 -> batch_norm2d_10.w_0_deepcopy_53 -> 0xcf50fb0 +281 -> conv2d_10.w_0_deepcopy_52 -> 0xcf508b0 +282 -> batch_norm2d_9.w_2_deepcopy_51 -> 0xcf48440 +283 -> batch_norm2d_9.w_1_deepcopy_50 -> 0xcf44e90 +284 -> batch_norm2d_9.b_0_deepcopy_49 -> 0xcf0a7c0 +285 -> batch_norm2d_9.w_0_deepcopy_48 -> 0xcf0fa10 +286 -> conv2d_9.w_0_deepcopy_47 -> 0xcf32540 +287 -> batch_norm2d_8.w_2_deepcopy_46 -> 0xceff0c0 +288 -> batch_norm2d_8.w_1_deepcopy_45 -> 0xcef9a90 +289 -> batch_norm2d_8.b_0_deepcopy_44 -> 0xceee210 +290 -> batch_norm2d_8.w_0_deepcopy_43 -> 0xcef0d80 +291 -> conv2d_8.w_0_deepcopy_42 -> 0xcf3f300 +292 -> batch_norm2d_7.w_2_deepcopy_41 -> 0xd05a990 +293 -> batch_norm2d_7.w_1_deepcopy_40 -> 0xd05ae90 +294 -> batch_norm2d_7.b_0_deepcopy_39 -> 0xcfa23b0 +295 -> batch_norm2d_7.w_0_deepcopy_38 -> 0xcf261b0 +296 -> conv2d_7.w_0_deepcopy_37 -> 0xcf3b640 +297 -> batch_norm2d_6.w_2_deepcopy_36 -> 0xcce2400 +298 -> batch_norm2d_6.w_1_deepcopy_35 -> 0xcf2f870 +299 -> batch_norm2d_6.b_0_deepcopy_34 -> 0xcf2d1b0 +300 -> batch_norm2d_6.w_0_deepcopy_33 -> 0xcf42180 +301 -> conv2d_6.w_0_deepcopy_32 -> 0xcc9ffb0 +302 -> batch_norm2d_5.w_2_deepcopy_31 -> 0xcf306a0 +303 -> batch_norm2d_5.w_1_deepcopy_30 -> 0xcf63a70 +304 -> batch_norm2d_5.b_0_deepcopy_29 -> 0xcf35350 +305 -> batch_norm2d_5.w_0_deepcopy_28 -> 0xcf8df90 +306 -> conv2d_5.w_0_deepcopy_27 -> 0xcfc00b0 +307 -> batch_norm2d_4.w_2_deepcopy_26 -> 0xcf40460 +308 -> batch_norm2d_4.w_1_deepcopy_25 -> 0xcf8df70 +309 -> batch_norm2d_4.b_0_deepcopy_24 -> 0xcf02720 +310 -> batch_norm2d_4.w_0_deepcopy_23 -> 0xcf906f0 +311 -> conv2d_4.w_0_deepcopy_22 -> 0xc80efb0 +312 -> batch_norm2d_3.w_2_deepcopy_21 -> 0xcf21ae0 +313 -> batch_norm2d_3.w_1_deepcopy_20 -> 0xcf576f0 +314 -> batch_norm2d_3.b_0_deepcopy_19 -> 0xc80e280 +315 -> batch_norm2d_3.w_0_deepcopy_18 -> 0xcf34c10 +316 -> conv2d_3.w_0_deepcopy_17 -> 0xcf15a30 +317 -> batch_norm2d_2.w_2_deepcopy_16 -> 0xcf808f0 +318 -> batch_norm2d_2.w_1_deepcopy_15 -> 0xcf7feb0 +319 -> batch_norm2d_2.b_0_deepcopy_14 -> 0xcf7e0d0 +320 -> batch_norm2d_2.w_0_deepcopy_13 -> 0xcf7f110 +321 -> conv2d_2.w_0_deepcopy_12 -> 0xcfcc130 +322 -> batch_norm2d_1.w_2_deepcopy_11 -> 0xcf506f0 +323 -> batch_norm2d_1.w_1_deepcopy_10 -> 0xcf3f550 +324 -> batch_norm2d_1.b_0_deepcopy_9 -> 0xcf540f0 +325 -> batch_norm2d_1.w_0_deepcopy_8 -> 0xcf37900 +326 -> conv2d_1.w_0_deepcopy_7 -> 0xcf500a0 +327 -> batch_norm2d_0.w_2_deepcopy_6 -> 0xcf55d60 +328 -> batch_norm2d_0.w_1_deepcopy_5 -> 0xcf547a0 +329 -> batch_norm2d_0.b_0_deepcopy_4 -> 0xcf34070 +330 -> batch_norm2d_0.w_0_deepcopy_3 -> 0xcf557c0 +331 -> conv2d_0.w_0_deepcopy_2 -> 0xcf54fc0 +332 -> im_shape -> 0xcec9720 +333 -> image -> 0xccc5060 +334 -> scale_factor -> 0xcdbf3e0 +335 -> 0xcf59f401745131171435049990_inner_var_335 -> 0xcf1b790 +336 -> 0xcf59f401745131171435049990_inner_var_336 -> 0xca79260 +337 -> 0xcf59f401745131171435049990_inner_var_337 -> 0xce9b570 +338 -> 0xcf59f401745131171435049990_inner_var_338 -> 0xcebb210 +339 -> 0xcf59f401745131171435049990_inner_var_339 -> 0xcf009c0 +340 -> 0xcf59f401745131171435049990_inner_var_340 -> 0xd0606b0 +341 -> 0xcf59f401745131171435049990_inner_var_341 -> 0xcf97690 +342 -> 0xcf59f401745131171435049990_inner_var_342 -> 0xca791e0 +343 -> 0xcf59f401745131171435049990_inner_var_343 -> 0xcca3d60 +344 -> 0xcf59f401745131171435049990_inner_var_344 -> 0xcdbf7b0 +345 -> 0xcf59f401745131171435049990_inner_var_345 -> 0xceb2ca0 +346 -> 0xcf59f401745131171435049990_inner_var_346 -> 0xcea9360 +347 -> 0xcf59f401745131171435049990_inner_var_347 -> 0xca9b750 +348 -> 0xcf59f401745131171435049990_inner_var_348 -> 0xcef9b60 +349 -> 0xcf59f401745131171435049990_inner_var_349 -> 0xcad41e0 +350 -> 0xcf59f401745131171435049990_inner_var_350 -> 0xcec2420 +351 -> 0xcf59f401745131171435049990_inner_var_351 -> 0xd0580a0 +352 -> 0xcf59f401745131171435049990_inner_var_352 -> 0xca7dcc0 +353 -> 0xcf59f401745131171435049990_inner_var_353 -> 0xcf596e0 +354 -> 0xcf59f401745131171435049990_inner_var_354 -> 0xca62f10 +355 -> 0xcf59f401745131171435049990_inner_var_355 -> 0xcca80b0 +356 -> 0xcf59f401745131171435049990_inner_var_356 -> 0xccd5df0 +357 -> 0xcf59f401745131171435049990_inner_var_357 -> 0xca883e0 +358 -> 0xcf59f401745131171435049990_inner_var_358 -> 0xccb4a70 +359 -> 0xcf59f401745131171435049990_inner_var_359 -> 0xca758f0 +360 -> 0xcf59f401745131171435049990_inner_var_360 -> 0xd04cf50 +361 -> 0xcf59f401745131171435049990_inner_var_361 -> 0xcca6850 +362 -> 0xcf59f401745131171435049990_inner_var_362 -> 0xcf6d1c0 +363 -> 0xcf59f401745131171435049990_inner_var_363 -> 0xcadee10 +364 -> 0xcf59f401745131171435049990_inner_var_364 -> 0xcccf200 +365 -> 0xcf59f401745131171435049990_inner_var_365 -> 0xcebea10 +366 -> 0xcf59f401745131171435049990_inner_var_366 -> 0xcf1d290 +367 -> 0xcf59f401745131171435049990_inner_var_367 -> 0xce91c20 +368 -> 0xcf59f401745131171435049990_inner_var_368 -> 0xccd76c0 +369 -> 0xcf59f401745131171435049990_inner_var_369 -> 0xca58e30 +370 -> 0xcf59f401745131171435049990_inner_var_370 -> 0xce97c00 +371 -> 0xcf59f401745131171435049990_inner_var_371 -> 0xcf563c0 +372 -> 0xcf59f401745131171435049990_inner_var_372 -> 0xca7bf50 +373 -> 0xcf59f401745131171435049990_inner_var_373 -> 0xccaa1d0 +374 -> 0xcf59f401745131171435049990_inner_var_374 -> 0xcec14b0 +375 -> 0xcf59f401745131171435049990_inner_var_375 -> 0xccdfc40 +376 -> 0xcf59f401745131171435049990_inner_var_376 -> 0xcf20080 +377 -> 0xcf59f401745131171435049990_inner_var_377 -> 0xcaa4d20 +378 -> 0xcf59f401745131171435049990_inner_var_378 -> 0xd0732d0 +379 -> 0xcf59f401745131171435049990_inner_var_379 -> 0xcec7d50 +380 -> 0xcf59f401745131171435049990_inner_var_380 -> 0xced4880 +381 -> 0xcf59f401745131171435049990_inner_var_381 -> 0xced3620 +382 -> 0xcf59f401745131171435049990_inner_var_382 -> 0xcef7030 +383 -> 0xcf59f401745131171435049990_inner_var_383 -> 0xcad2150 +384 -> 0xcf59f401745131171435049990_inner_var_384 -> 0xccdad60 +385 -> 0xcf59f401745131171435049990_inner_var_385 -> 0xcf14230 +386 -> 0xcf59f401745131171435049990_inner_var_386 -> 0xcf17420 +387 -> 0xcf59f401745131171435049990_inner_var_387 -> 0xccb2480 +388 -> 0xcf59f401745131171435049990_inner_var_388 -> 0xcca2020 +389 -> 0xcf59f401745131171435049990_inner_var_389 -> 0xd040710 +390 -> 0xcf59f401745131171435049990_inner_var_390 -> 0xca9b230 +391 -> 0xcf59f401745131171435049990_inner_var_391 -> 0xcf38990 +392 -> 0xcf59f401745131171435049990_inner_var_392 -> 0xd0639d0 +393 -> 0xcf59f401745131171435049990_inner_var_393 -> 0xccb9310 +394 -> 0xcf59f401745131171435049990_inner_var_394 -> 0xccc15b0 +395 -> 0xcf59f401745131171435049990_inner_var_395 -> 0xcef9860 +396 -> 0xcf59f401745131171435049990_inner_var_396 -> 0xc8757d0 +397 -> 0xcf59f401745131171435049990_inner_var_397 -> 0xceee010 +398 -> 0xcf59f401745131171435049990_inner_var_398 -> 0xca947c0 +399 -> 0xcf59f401745131171435049990_inner_var_399 -> 0xcabae00 +400 -> 0xcf59f401745131171435049990_inner_var_400 -> 0xca602d0 +401 -> 0xcf59f401745131171435049990_inner_var_401 -> 0xcee7350 +402 -> 0xcf59f401745131171435049990_inner_var_402 -> 0xcf99c00 +403 -> 0xcf59f401745131171435049990_inner_var_403 -> 0xcad0c10 +404 -> 0xcf59f401745131171435049990_inner_var_404 -> 0xd04f4b0 +405 -> 0xcf59f401745131171435049990_inner_var_405 -> 0xccc21e0 +406 -> 0xcf59f401745131171435049990_inner_var_406 -> 0xcf78850 +407 -> 0xcf59f401745131171435049990_inner_var_407 -> 0xd0727d0 +408 -> 0xcf59f401745131171435049990_inner_var_408 -> 0xceb0f20 +409 -> 0xcf59f401745131171435049990_inner_var_409 -> 0xceb3080 +410 -> 0xcf59f401745131171435049990_inner_var_410 -> 0xcf0a000 +411 -> 0xcf59f401745131171435049990_inner_var_411 -> 0xcad7ec0 +412 -> 0xcf59f401745131171435049990_inner_var_412 -> 0xcf7cbb0 +413 -> 0xcf59f401745131171435049990_inner_var_413 -> 0xcec7620 +414 -> 0xcf59f401745131171435049990_inner_var_414 -> 0xccc05c0 +415 -> 0xcf59f401745131171435049990_inner_var_415 -> 0xca942d0 +416 -> 0xcf59f401745131171435049990_inner_var_416 -> 0xceccf20 +417 -> 0xcf59f401745131171435049990_inner_var_417 -> 0xcaecc00 +418 -> 0xcf59f401745131171435049990_inner_var_418 -> 0xcaed490 +419 -> 0xcf59f401745131171435049990_inner_var_419 -> 0xca984f0 +420 -> 0xcf59f401745131171435049990_inner_var_420 -> 0xcfcbe20 +421 -> 0xcf59f401745131171435049990_inner_var_421 -> 0xccc3bd0 +422 -> 0xcf59f401745131171435049990_inner_var_422 -> 0xd047d80 +423 -> 0xcf59f401745131171435049990_inner_var_423 -> 0xcea8fb0 +424 -> 0xcf59f401745131171435049990_inner_var_424 -> 0xca62570 +425 -> 0xcf59f401745131171435049990_inner_var_425 -> 0xee8cc70 +426 -> 0xcf59f401745131171435049990_inner_var_426 -> 0xceb5560 +427 -> 0xcf59f401745131171435049990_inner_var_427 -> 0xced0d30 +428 -> 0xcf59f401745131171435049990_inner_var_428 -> 0xcab8450 +429 -> 0xcf59f401745131171435049990_inner_var_429 -> 0xcab60d0 +430 -> 0xcf59f401745131171435049990_inner_var_430 -> 0xcead070 +431 -> 0xcf59f401745131171435049990_inner_var_431 -> 0xcade0a0 +432 -> 0xcf59f401745131171435049990_inner_var_432 -> 0xcabcfc0 +433 -> 0xcf59f401745131171435049990_inner_var_433 -> 0xce94af0 +434 -> 0xcf59f401745131171435049990_inner_var_434 -> 0xcebccc0 +435 -> 0xcf59f401745131171435049990_inner_var_435 -> 0xd048180 +436 -> 0xcf59f401745131171435049990_inner_var_436 -> 0xcea5740 +437 -> 0xcf59f401745131171435049990_inner_var_437 -> 0xcab9830 +438 -> 0xcf59f401745131171435049990_inner_var_438 -> 0xcf97aa0 +439 -> 0xcf59f401745131171435049990_inner_var_439 -> 0xd06ce40 +440 -> 0xcf59f401745131171435049990_inner_var_440 -> 0xcedd7e0 +441 -> 0xcf59f401745131171435049990_inner_var_441 -> 0xcea0cd0 +442 -> 0xcf59f401745131171435049990_inner_var_442 -> 0xd06eb50 +443 -> 0xcf59f401745131171435049990_inner_var_443 -> 0xd069a30 +444 -> 0xcf59f401745131171435049990_inner_var_444 -> 0xce89bf0 +445 -> 0xcf59f401745131171435049990_inner_var_445 -> 0xcf21760 +446 -> 0xcf59f401745131171435049990_inner_var_446 -> 0xcbf35d0 +447 -> 0xcf59f401745131171435049990_inner_var_447 -> 0xcab49d0 +448 -> 0xcf59f401745131171435049990_inner_var_448 -> 0xcf17050 +449 -> 0xcf59f401745131171435049990_inner_var_449 -> 0xee40240 +450 -> 0xcf59f401745131171435049990_inner_var_450 -> 0xd040f30 +451 -> 0xcf59f401745131171435049990_inner_var_451 -> 0xcf7c250 +452 -> 0xcf59f401745131171435049990_inner_var_452 -> 0xcca3870 +453 -> 0xcf59f401745131171435049990_inner_var_453 -> 0xee47f90 +454 -> 0xcf59f401745131171435049990_inner_var_454 -> 0xd0385d0 +455 -> 0xcf59f401745131171435049990_inner_var_455 -> 0xcfa62c0 +456 -> 0xcf59f401745131171435049990_inner_var_456 -> 0xcabc9a0 +457 -> 0xcf59f401745131171435049990_inner_var_457 -> 0xcefb360 +458 -> 0xcf59f401745131171435049990_inner_var_458 -> 0xcef8830 +459 -> 0xcf59f401745131171435049990_inner_var_459 -> 0xceca990 +460 -> 0xcf59f401745131171435049990_inner_var_460 -> 0xced79a0 +461 -> 0xcf59f401745131171435049990_inner_var_461 -> 0xce94610 +462 -> 0xcf59f401745131171435049990_inner_var_462 -> 0xcab5be0 +463 -> 0xcf59f401745131171435049990_inner_var_463 -> 0xcec4600 +464 -> 0xcf59f401745131171435049990_inner_var_464 -> 0xcab8eb0 +465 -> 0xcf59f401745131171435049990_inner_var_465 -> 0xee400a0 +466 -> 0xcf59f401745131171435049990_inner_var_466 -> 0xcab7250 +467 -> 0xcf59f401745131171435049990_inner_var_467 -> 0xcab88e0 +468 -> 0xcf59f401745131171435049990_inner_var_468 -> 0xccdaac0 +469 -> 0xcf59f401745131171435049990_inner_var_469 -> 0xcad09b0 +470 -> 0xcf59f401745131171435049990_inner_var_470 -> 0xcec1100 +471 -> 0xcf59f401745131171435049990_inner_var_471 -> 0xd034260 +472 -> 0xcf59f401745131171435049990_inner_var_472 -> 0xcf6cb40 +473 -> 0xcf59f401745131171435049990_inner_var_473 -> 0xce8df60 +474 -> 0xcf59f401745131171435049990_inner_var_474 -> 0xcf36750 +475 -> 0xcf59f401745131171435049990_inner_var_475 -> 0xcca5d30 +476 -> 0xcf59f401745131171435049990_inner_var_476 -> 0xcf24750 +477 -> 0xcf59f401745131171435049990_inner_var_477 -> 0xcf1ba00 +478 -> 0xcf59f401745131171435049990_inner_var_478 -> 0xd075c80 +479 -> 0xcf59f401745131171435049990_inner_var_479 -> 0xccdd6a0 +480 -> 0xcf59f401745131171435049990_inner_var_480 -> 0xca8acd0 +481 -> 0xcf59f401745131171435049990_inner_var_481 -> 0xd071b60 +482 -> 0xcf59f401745131171435049990_inner_var_482 -> 0xcea01c0 +483 -> 0xcf59f401745131171435049990_inner_var_483 -> 0xceb1680 +484 -> 0xcf59f401745131171435049990_inner_var_484 -> 0xccdbe30 +485 -> 0xcf59f401745131171435049990_inner_var_485 -> 0xcadbd40 +486 -> 0xcf59f401745131171435049990_inner_var_486 -> 0xcf57730 +487 -> 0xcf59f401745131171435049990_inner_var_487 -> 0xceac560 +488 -> 0xcf59f401745131171435049990_inner_var_488 -> 0xcf75a50 +489 -> 0xcf59f401745131171435049990_inner_var_489 -> 0xcf95c60 +490 -> 0xcf59f401745131171435049990_inner_var_490 -> 0xcedd640 +491 -> 0xcf59f401745131171435049990_inner_var_491 -> 0xcf19870 +492 -> 0xcf59f401745131171435049990_inner_var_492 -> 0xcabc760 +493 -> 0xcf59f401745131171435049990_inner_var_493 -> 0xcefd440 +494 -> 0xcf59f401745131171435049990_inner_var_494 -> 0xccc8990 +495 -> 0xcf59f401745131171435049990_inner_var_495 -> 0xd059d20 +496 -> 0xcf59f401745131171435049990_inner_var_496 -> 0xcf597c0 +497 -> 0xcf59f401745131171435049990_inner_var_497 -> 0xcaa2350 +498 -> 0xcf59f401745131171435049990_inner_var_498 -> 0xcae1300 +499 -> 0xcf59f401745131171435049990_inner_var_499 -> 0xcca7260 +500 -> 0xcf59f401745131171435049990_inner_var_500 -> 0xca91ea0 +501 -> 0xcf59f401745131171435049990_inner_var_501 -> 0xcad9eb0 +502 -> 0xcf59f401745 +I0420 14:39:35.026636 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 1 2 +1 -> 4 +2 -> 3 27 +3 -> 6 +4 -> 5 +5 -> 6 +6 -> 7 +7 -> 8 9 +8 -> 11 +9 -> 10 14 +10 -> 13 16 +11 -> 12 +12 -> 13 +13 -> 21 +14 -> 15 16 +15 -> 18 +16 -> 17 +17 -> 20 +18 -> 19 +19 -> 20 +20 -> 23 +21 -> 22 32 +22 -> 23 +23 -> 25 33 +24 -> 26 +25 -> 26 +27 -> 28 +28 -> 29 31 +29 -> 30 +31 -> 33 +32 -> 33 + +Id() +(size_t) $0 = 341 +I0420 14:40:57.125478 115867 pir_interpreter.cc:1569] value info of interpretercore 0x11e67160 +value -> var_name -> id -> variable* +0xca5f810 -> 0x11e671601745131173052620480_inner_var_1206 -> 1206 -> 0xe7119f0 +0xca5f740 -> 0x11e671601745131173052620480_inner_var_1204 -> 1204 -> 0xe710d40 +0xccdfeb0 -> 0x11e671601745131173052620480_inner_var_1203 -> 1203 -> 0xe70f9d0 +0xca5a360 -> 0x11e671601745131173052620480_inner_var_1202 -> 1202 -> 0xe710630 +0xcae1e60 -> 0x11e671601745131173052620480_inner_var_1201 -> 1201 -> 0xe710210 +0xcae1b30 -> 0x11e671601745131173052620480_inner_var_1200 -> 1200 -> 0xe70fdf0 +0xcadff20 -> 0x11e671601745131173052620480_inner_var_1198 -> 1198 -> 0xe70f5f0 +0xcae1080 -> 0x11e671601745131173052620480_inner_var_1196 -> 1196 -> 0xe70edd0 +0xcae0b60 -> 0x11e671601745131173052620480_inner_var_1193 -> 1193 -> 0xe70e1b0 +0xca5a5f0 -> 0x11e671601745131173052620480_inner_var_1192 -> 1192 -> 0xe70e170 +0xcae06c0 -> 0x11e671601745131173052620480_inner_var_1191 -> 1191 -> 0x11e82ba0 +0xcadfd80 -> 0x11e671601745131173052620480_inner_var_1190 -> 1190 -> 0xe70db00 +0xccd7d90 -> 0x11e671601745131173052620480_inner_var_1189 -> 1189 -> 0xe70d730 +0xcce0e50 -> 0x11e671601745131173052620480_inner_var_1188 -> 1188 -> 0xe70d360 +0xca5a470 -> 0x11e671601745131173052620480_inner_var_1187 -> 1187 -> 0x11e84bb0 +0xca5a770 -> 0x11e671601745131173052620480_inner_var_1185 -> 1185 -> 0x11e82aa0 +0xca5a060 -> 0x11e671601745131173052620480_inner_var_1183 -> 1183 -> 0x11e82b80 +0xca68680 -> 0x11e671601745131173052620480_inner_var_1182 -> 1182 -> 0xe709a70 +0xca58f38 -> 0x11e671601745131173052620480_inner_var_1178 -> 1178 -> 0xe709ad0 +0xca58f50 -> 0x11e671601745131173052620480_inner_var_1177 -> 1177 -> 0x11e80420 +0xca58f80 -> 0x11e671601745131173052620480_inner_var_1175 -> 1175 -> 0xe709f10 +0xca58f98 -> 0x11e671601745131173052620480_inner_var_1174 -> 1174 -> 0xe709a10 +0xca584c0 -> 0x11e671601745131173052620480_inner_var_1173 -> 1173 -> 0x11e84ab0 +0xca71e00 -> 0x11e671601745131173052620480_inner_var_1172 -> 1172 -> 0x11e85120 +0xca72ab8 -> 0x11e671601745131173052620480_inner_var_1168 -> 1168 -> 0x11e84f50 +0xca72ad0 -> 0x11e671601745131173052620480_inner_var_1167 -> 1167 -> 0x11e84f30 +0xca721c0 -> 0x11e671601745131173052620480_inner_var_1165 -> 1165 -> 0x11e82a80 +0xca70a20 -> 0x11e671601745131173052620480_inner_var_1164 -> 1164 -> 0x11e830a0 +0xca71a70 -> 0x11e671601745131173052620480_inner_var_1163 -> 1163 -> 0x11e843c0 +0xca71ab8 -> 0x11e671601745131173052620480_inner_var_1160 -> 1160 -> 0x11e82ed0 +0xca71ad0 -> 0x11e671601745131173052620480_inner_var_1159 -> 1159 -> 0x11e82eb0 +0xca71ae8 -> 0x11e671601745131173052620480_inner_var_1158 -> 1158 -> 0x11e82a00 +0xca70ee0 -> 0x11e671601745131173052620480_inner_var_1157 -> 1157 -> 0x11e7e3c0 +0xca6fa20 -> 0x11e671601745131173052620480_inner_var_1156 -> 1156 -> 0x11e80a90 +0xca70690 -> 0x11e671601745131173052620480_inner_var_1154 -> 1154 -> 0x11e81d90 +0xca706a8 -> 0x11e671601745131173052620480_inner_var_1153 -> 1153 -> 0x11e80440 +0xca706d8 -> 0x11e671601745131173052620480_inner_var_1151 -> 1151 -> 0x11e808a0 +0xca706f0 -> 0x11e671601745131173052620480_inner_var_1150 -> 1150 -> 0x11e80880 +0xca70708 -> 0x11e671601745131173052620480_inner_var_1149 -> 1149 -> 0x11e80380 +0xca6f550 -> 0x11e671601745131173052620480_inner_var_1146 -> 1146 -> 0x11e7fc50 +0xca6f598 -> 0x11e671601745131173052620480_inner_var_1143 -> 1143 -> 0x11e7e760 +0xca6f5b0 -> 0x11e671601745131173052620480_inner_var_1142 -> 1142 -> 0x11e7e740 +0xca6f5c8 -> 0x11e671601745131173052620480_inner_var_1141 -> 1141 -> 0x11e7e240 +0xca6d500 -> 0x11e671601745131173052620480_inner_var_1139 -> 1139 -> 0x11dd8ee0 +0xca6e550 -> 0x11e671601745131173052620480_inner_var_1138 -> 1138 -> 0x11e7db10 +0xca6d9c0 -> 0x11e671601745131173052620480_inner_var_1132 -> 1132 -> 0x11dfb9b0 +0xca6bd50 -> 0x11e671601745131173052620480_inner_var_1130 -> 1130 -> 0x11e43a00 +0xca6d138 -> 0x11e671601745131173052620480_inner_var_1126 -> 1126 -> 0x11e43830 +0xca6d168 -> 0x11e671601745131173052620480_inner_var_1124 -> 1124 -> 0x11e65a90 +0xca6ac10 -> 0x11e671601745131173052620480_inner_var_1123 -> 1123 -> 0x11dfb9f0 +0xca6c338 -> 0x11e671601745131173052620480_inner_var_1121 -> 1121 -> 0x11dfba30 +0xca6c350 -> 0x11e671601745131173052620480_inner_var_1120 -> 1120 -> 0x11e66b10 +0xca6c398 -> 0x11e671601745131173052620480_inner_var_1117 -> 1117 -> 0x11dfb970 +0xccde1e0 -> 0x11e671601745131173052620480_inner_var_1115 -> 1115 -> 0x11e6eff0 +0xca6b1e0 -> 0x11e671601745131173052620480_inner_var_1114 -> 1114 -> 0x11dfb0d0 +0xca6b1f8 -> 0x11e671601745131173052620480_inner_var_1113 -> 1113 -> 0x11dfacb0 +0xca6b210 -> 0x11e671601745131173052620480_inner_var_1112 -> 1112 -> 0x11e66af0 +0xca6b228 -> 0x11e671601745131173052620480_inner_var_1111 -> 1111 -> 0x11e6ee20 +0xca6b240 -> 0x11e671601745131173052620480_inner_var_1110 -> 1110 -> 0x11e6ee00 +0xca6b258 -> 0x11e671601745131173052620480_inner_var_1109 -> 1109 -> 0x11e6e950 +0xd0365f8 -> fetch_name_1 -> 982 -> 0xcbe4d60 +0xcaf6bd0 -> batch_norm2d_20.w_1_deepcopy_105 -> 228 -> 0xcf82fa0 +0xccad180 -> 0xcf59f401745131171435049990_inner_var_530 -> 530 -> 0xccad0a0 +0xccad198 -> 0xcf59f401745131171435049990_inner_var_529 -> 529 -> 0xce93f90 +0xccad1b0 -> 0xcf59f401745131171435049990_inner_var_528 -> 528 -> 0xcaeafb0 +0xccad1c8 -> 0xcf59f401745131171435049990_inner_var_527 -> 527 -> 0xcad93f0 +0xc80c4d0 -> conv2d_36.w_0_deepcopy_182 -> 151 -> 0xd05fe10 +0xee86328 -> 0xcf59f401745131171435049990_inner_var_763 -> 763 -> 0xca55c90 +0xd04e680 -> batch_norm2d_46.b_0_deepcopy_241 -> 96 -> 0xcf60140 +0xee3ff40 -> batch_norm2d_51.w_2_deepcopy_268 -> 69 -> 0xcf2c930 +0xce903b0 -> 0xcf59f401745131171435049990_inner_var_917 -> 917 -> 0xceaa600 +0xcccde50 -> 0xcf59f401745131171435049990_inner_var_525 -> 525 -> 0xcad8d00 +0xcccc970 -> 0xcf59f401745131171435049990_inner_var_507 -> 507 -> 0xcec9370 +0xee8aa90 -> 0xcf59f401745131171435049990_inner_var_794 -> 794 -> 0xce96090 +0xcccc988 -> 0xcf59f401745131171435049990_inner_var_506 -> 506 -> 0xcf207c0 +0xee8aad8 -> 0xcf59f401745131171435049990_inner_var_791 -> 791 -> 0xca84830 +0xcccc9d0 -> 0xcf59f401745131171435049990_inner_var_503 -> 503 -> 0xcad4220 +0xee75060 -> 0xcf59f401745131171435049990_inner_var_386 -> 386 -> 0xcf17420 +0xcccc9e8 -> 0xcf59f401745131171435049990_inner_var_502 -> 502 -> 0xcc9ec80 +0xce91510 -> 0xcf59f401745131171435049990_inner_var_911 -> 911 -> 0xcf8f250 +0xccdcdf0 -> 0xcf59f401745131171435049990_inner_var_946 -> 946 -> 0xcf04e80 +0xcccb928 -> 0xcf59f401745131171435049990_inner_var_494 -> 494 -> 0xccc8990 +0xd041790 -> constant_folding@_174513116825929047929 -> 30 -> 0xcf7c3f0 +0xcccf820 -> 0xcf59f401745131171435049990_inner_var_492 -> 492 -> 0xcabc760 +0xcb0cfa0 -> batch_norm2d_6.w_1_deepcopy_35 -> 298 -> 0xcf2f870 +0xccd0740 -> 0xcf59f401745131171435049990_inner_var_491 -> 491 -> 0xcf19870 +0xce8a520 -> 0xcf59f401745131171435049990_inner_var_879 -> 879 -> 0xcee45d0 +0xccd0770 -> 0xcf59f401745131171435049990_inner_var_489 -> 489 -> 0xcf95c60 +0xccb8a80 -> 0xcf59f401745131171435049990_inner_var_440 -> 440 -> 0xcedd7e0 +0xca9f7c0 -> 0xcf59f401745131171435049990_inner_var_569 -> 569 -> 0xcf3e600 +0xca6c320 -> 0x11e671601745131173052620480_inner_var_1122 -> 1122 -> 0x11e43080 +0xee880e0 -> 0xcf59f401745131171435049990_inner_var_788 -> 788 -> 0xd054cd0 +0xcccfd80 -> 0xcf59f401745131171435049990_inner_var_485 -> 485 -> 0xcadbd40 +0xcabb010 -> 0xcf59f401745131171435049990_inner_var_887 -> 887 -> 0xcf06390 +0xcccb2e0 -> 0xcf59f401745131171435049990_inner_var_508 -> 508 -> 0xcec4d60 +0xcaf7370 -> batch_norm2d_20.w_0_deepcopy_103 -> 230 -> 0xcf82460 +0xcdbdab0 -> 0xcf59f401745131171435049990_inner_var_682 -> 682 -> 0xd046ed0 +0xccc6be0 -> 0xcf59f401745131171435049990_inner_var_1054 -> 1054 -> 0xcaab8c0 +0xcdbd710 -> 0xcf59f401745131171435049990_inner_var_671 -> 671 -> 0xccd9c80 +0xccbdda0 -> 0xcf59f401745131171435049990_inner_var_1014 -> 1014 -> 0xcbed7a0 +0xcdbd728 -> 0xcf59f401745131171435049990_inner_var_670 -> 670 -> 0xcf716d0 +0xccb6400 -> 0xcf59f401745131171435049990_inner_var_475 -> 475 -> 0xcca5d30 +0xca64c30 -> batch_norm2d_2.w_1_deepcopy_15 -> 318 -> 0xcf7feb0 +0xccce208 -> 0xcf59f401745131171435049990_inner_var_473 -> 473 -> 0xce8df60 +0xccce238 -> 0xcf59f401745131171435049990_inner_var_471 -> 471 -> 0xd034260 +0xccce250 -> 0xcf59f401745131171435049990_inner_var_470 -> 470 -> 0xcec1100 +0xca85520 -> batch_norm2d_8.w_2_deepcopy_46 -> 287 -> 0xceff0c0 +0xcc9e7e0 -> constant_folding@_174513116872098526255 -> 7 -> 0xd061370 +0xccb6060 -> 0xcf59f401745131171435049990_inner_var_464 -> 464 -> 0xcab8eb0 +0xca6d108 -> 0x11e671601745131173052620480_inner_var_1128 -> 1128 -> 0x11e6ed30 +0xcdbc1c8 -> 0xcf59f401745131171435049990_inner_var_659 -> 659 -> 0xcab68b0 +0xccb6078 -> 0xcf59f401745131171435049990_inner_var_463 -> 463 -> 0xcec4600 +0xccb54f0 -> 0xcf59f401745131171435049990_inner_var_460 -> 460 -> 0xced79a0 +0xccb95d0 -> 0xcf59f401745131171435049990_inner_var_458 -> 458 -> 0xcef8830 +0xccb4b68 -> 0xcf59f401745131171435049990_inner_var_454 -> 454 -> 0xd0385d0 +0xccb28c0 -> 0xcf59f401745131171435049990_inner_var_757 -> 757 -> 0xcf08950 +0xccb4b98 -> 0xcf59f401745131171435049990_inner_var_452 -> 452 -> 0xcca3870 +0xccb9bd0 -> 0xcf59f401745131171435049990_inner_var_448 -> 448 -> 0xcf17050 +0xccb9c00 -> 0xcf59f401745131171435049990_inner_var_446 -> 446 -> 0xcbf35d0 +0xccb9c18 -> 0xcf59f401745131171435049990_inner_var_445 -> 445 -> 0xcf21760 +0xd052500 -> 0xcf59f401745131171435049990_inner_var_640 -> 640 -> 0xca95610 +0xccb4b80 -> 0xcf59f401745131171435049990_inner_var_453 -> 453 -> 0xee47f90 +0xccb8f20 -> 0xcf59f401745131171435049990_inner_var_444 -> 444 -> 0xce89bf0 +0xccca460 -> 0xcf59f401745131171435049990_inner_var_1070 -> 1070 -> 0xcfbdaa0 +0xccd0788 -> 0xcf59f401745131171435049990_inner_var_488 -> 488 -> 0xcf75a50 +0xccac460 -> 0xcf59f401745131171435049990_inner_var_534 -> 534 -> 0xcca5770 +0xca9f790 -> 0xcf59f401745131171435049990_inner_var_571 -> 571 -> 0xcea9710 +0xee86340 -> 0xcf59f401745131171435049990_inner_var_762 -> 762 -> 0xcfcedc0 +0xccb7990 -> 0xcf59f401745131171435049990_inner_var_434 -> 434 -> 0xcebccc0 +0xcadfa10 -> 0xcf59f401745131171435049990_inner_var_537 -> 537 -> 0xca889b0 +0xccabfb0 -> 0xcf59f401745131171435049990_inner_var_520 -> 520 -> 0xccc8370 +0xee86358 -> 0xcf59f401745131171435049990_inner_var_761 -> 761 -> 0xca85c80 +0xccb79a8 -> 0xcf59f401745131171435049990_inner_var_433 -> 433 -> 0xce94af0 +0xce894c0 -> 0xcf59f401745131171435049990_inner_var_353 -> 353 -> 0xcf596e0 +0xca6e960 -> 0x11e671601745131173052620480_inner_var_1140 -> 1140 -> 0x11dd88c0 +0xcf77920 -> 0xcf59f401745131171435049990_inner_var_730 -> 730 -> 0xce9c550 +0xccbc420 -> 0xcf59f401745131171435049990_inner_var_421 -> 421 -> 0xccc3bd0 +0xcaf5650 -> batch_norm2d_16.w_2_deepcopy_86 -> 247 -> 0xcfa79c0 +0xccbc438 -> 0xcf59f401745131171435049990_inner_var_420 -> 420 -> 0xcfcbe20 +0xccc2c30 -> 0xcf59f401745131171435049990_inner_var_1034 -> 1034 -> 0xceb6ad0 +0xccbbad0 -> 0xcf59f401745131171435049990_inner_var_419 -> 419 -> 0xca984f0 +0xd058908 -> 0xcf59f401745131171435049990_inner_var_367 -> 367 -> 0xce91c20 +0xccbb100 -> 0xcf59f401745131171435049990_inner_var_418 -> 418 -> 0xcaed490 +0xccbb240 -> 0xcf59f401745131171435049990_inner_var_417 -> 417 -> 0xcaecc00 +0xccbf790 -> 0xcf59f401745131171435049990_inner_var_1020 -> 1020 -> 0xcbef330 +0xccbb288 -> 0xcf59f401745131171435049990_inner_var_414 -> 414 -> 0xccc05c0 +0xccbb2a0 -> 0xcf59f401745131171435049990_inner_var_413 -> 413 -> 0xcec7620 +0xccba1c0 -> 0xcf59f401745131171435049990_inner_var_407 -> 407 -> 0xd0727d0 +0xccba1d8 -> 0xcf59f401745131171435049990_inner_var_406 -> 406 -> 0xcf78850 +0xcbf0ad0 -> batch_norm2d_44.w_0_deepcopy_230 -> 107 -> 0xcf1cb30 +0xcdc4890 -> 0xcf59f401745131171435049990_inner_var_722 -> 722 -> 0xceb3f10 +0xccba208 -> 0xcf59f401745131171435049990_inner_var_404 -> 404 -> 0xd04f4b0 +0xca56390 -> 0xcf59f401745131171435049990_inner_var_715 -> 715 -> 0xcad9880 +0xcae9830 -> 0xcf59f401745131171435049990_inner_var_842 -> 842 -> 0xd06a7d0 +0xcc97280 -> 0xcf59f401745131171435049990_inner_var_401 -> 401 -> 0xcee7350 +0xcc98950 -> 0xcf59f401745131171435049990_inner_var_400 -> 400 -> 0xca602d0 +0xd037a30 -> 0xcf59f401745131171435049990_inner_var_989 -> 989 -> 0xcbe7410 +0xcc98980 -> 0xcf59f401745131171435049990_inner_var_398 -> 398 -> 0xca947c0 +0xee8aa60 -> 0xcf59f401745131171435049990_inner_var_796 -> 796 -> 0xcf16f50 +0xee74c60 -> 0xcf59f401745131171435049990_inner_var_393 -> 393 -> 0xccb9310 +0xd048c30 -> 0xcf59f401745131171435049990_inner_var_706 -> 706 -> 0xd03e890 +0xca99330 -> 0xcf59f401745131171435049990_inner_var_732 -> 732 -> 0xca801c0 +0xcc97860 -> 0xcf59f401745131171435049990_inner_var_392 -> 392 -> 0xd0639d0 +0xcc97878 -> 0xcf59f401745131171435049990_inner_var_391 -> 391 -> 0xcf38990 +0xce8be10 -> 0xcf59f401745131171435049990_inner_var_885 -> 885 -> 0xcf19ef0 +0xccb73c0 -> 0xcf59f401745131171435049990_inner_var_459 -> 459 -> 0xceca990 +0xccde390 -> 0xcf59f401745131171435049990_inner_var_957 -> 957 -> 0xca5e670 +0xcc97890 -> 0xcf59f401745131171435049990_inner_var_390 -> 390 -> 0xca9b230 +0xcdb5730 -> batch_norm2d_23.w_0_deepcopy_118 -> 215 -> 0xd060ed0 +0xccd07a0 -> 0xcf59f401745131171435049990_inner_var_487 -> 487 -> 0xceac560 +0xcc993c0 -> 0xcf59f401745131171435049990_inner_var_403 -> 403 -> 0xcad0c10 +0xcae8810 -> 0xcf59f401745131171435049990_inner_var_968 -> 968 -> 0xcbe1be0 +0xcc978c0 -> 0xcf59f401745131171435049990_inner_var_388 -> 388 -> 0xcca2020 +0xce8e100 -> 0xcf59f401745131171435049990_inner_var_897 -> 897 -> 0xcf21030 +0xee74890 -> 0xcf59f401745131171435049990_inner_var_384 -> 384 -> 0xccdad60 +0xee748a8 -> 0xcf59f401745131171435049990_inner_var_383 -> 383 -> 0xcad2150 +0xca55d20 -> conv2d_14.w_0_deepcopy_72 -> 261 -> 0xcf52990 +0xee748c0 -> 0xcf59f401745131171435049990_inner_var_382 -> 382 -> 0xcef7030 +0xee748f0 -> 0xcf59f401745131171435049990_inner_var_380 -> 380 -> 0xced4880 +0xcc9c7f0 -> batch_norm2d_28.w_1_deepcopy_145 -> 188 -> 0xd073510 +0xcae2030 -> 0xcf59f401745131171435049990_inner_var_551 -> 551 -> 0xcee4790 +0xee73c40 -> 0xcf59f401745131171435049990_inner_var_378 -> 378 -> 0xd0732d0 +0xcc962b0 -> 0xcf59f401745131171435049990_inner_var_377 -> 377 -> 0xcaa4d20 +0xccb2538 -> 0xcf59f401745131171435049990_inner_var_978 -> 978 -> 0xcbe4500 +0xccc5d20 -> 0xcf59f401745131171435049990_inner_var_1047 -> 1047 -> 0xcada0c0 +0xd058320 -> 0xcf59f401745131171435049990_inner_var_376 -> 376 -> 0xcf20080 +0xcc9ad10 -> batch_norm2d_26.w_2_deepcopy_136 -> 197 -> 0xd06d600 +0xca6e568 -> 0x11e671601745131173052620480_inner_var_1137 -> 1137 -> 0x11dd8900 +0xee732e0 -> 0xcf59f401745131171435049990_inner_var_375 -> 375 -> 0xccdfc40 +0xcccb8b0 -> 0xcf59f401745131171435049990_inner_var_499 -> 499 -> 0xcca7260 +0xcdbf878 -> 0xcf59f401745131171435049990_inner_var_688 -> 688 -> 0xcaa66e0 +0xca6e580 -> 0x11e671601745131173052620480_inner_var_1136 -> 1136 -> 0x11e43500 +0xee732f8 -> 0xcf59f401745131171435049990_inner_var_374 -> 374 -> 0xcec14b0 +0xca6e5b0 -> 0x11e671601745131173052620480_inner_var_1134 -> 1134 -> 0x11dd8cf0 +0xee73328 -> 0xcf59f401745131171435049990_inner_var_372 -> 372 -> 0xca7bf50 +0xd058920 -> 0xcf59f401745131171435049990_inner_var_366 -> 366 -> 0xcf1d290 +0xd03a3f0 -> 0xcf59f401745131171435049990_inner_var_998 -> 998 -> 0xcbe95a0 +0xd058938 -> 0xcf59f401745131171435049990_inner_var_365 -> 365 -> 0xcebea10 +0xce93e10 -> constant_folding@_174513116823069183927 -> 32 -> 0xcf64350 +0xce960d0 -> batch_norm2d_29.w_1_deepcopy_150 -> 183 -> 0xd076240 +0xc8b6720 -> 0xcf59f401745131171435049990_inner_var_362 -> 362 -> 0xcf6d1c0 +0xccab050 -> 0xcf59f401745131171435049990_inner_var_516 -> 516 -> 0xcedc210 +0xcc989b0 -> 0xcf59f401745131171435049990_inner_var_396 -> 396 -> 0xc8757d0 +0xd056230 -> 0xcf59f401745131171435049990_inner_var_656 -> 656 -> 0xca90720 +0xd057790 -> 0xcf59f401745131171435049990_inner_var_360 -> 360 -> 0xd04cf50 +0xcdb8020 -> 0xcf59f401745131171435049990_inner_var_721 -> 721 -> 0xcf76450 +0xcabd920 -> 0xcf59f401745131171435049990_inner_var_875 -> 875 -> 0xd061a70 +0xcf740f0 -> 0xcf59f401745131171435049990_inner_var_718 -> 718 -> 0xcaa8730 +0xcf22190 -> 0xcf59f401745131171435049990_inner_var_354 -> 354 -> 0xca62f10 +0xcfacb90 -> batch_norm2d_42.w_0_deepcopy_213 -> 120 -> 0xcf3c660 +0xcdba8a0 -> 0xcf59f401745131171435049990_inner_var_725 -> 725 -> 0xcf962a0 +0xcc96898 -> 0xcf59f401745131171435049990_inner_var_351 -> 351 -> 0xd0580a0 +0xcc968b0 -> 0xcf59f401745131171435049990_inner_var_350 -> 350 -> 0xcec2420 +0xcc96880 -> 0xcf59f401745131171435049990_inner_var_352 -> 352 -> 0xca7dcc0 +0xcce0300 -> 0x11e671601745131173052620480_inner_var_1094 -> 1094 -> 0x11e67a90 +0xcc968f8 -> 0xcf59f401745131171435049990_inner_var_347 -> 347 -> 0xca9b750 +0xcf73cd0 -> 0xcf59f401745131171435049990_inner_var_720 -> 720 -> 0xced4fb0 +0xcf4ea30 -> 0xcf59f401745131171435049990_inner_var_343 -> 343 -> 0xcca3d60 +0xccb7d60 -> 0xcf59f401745131171435049990_inner_var_443 -> 443 -> 0xd069a30 +0xcbf0900 -> batch_norm2d_44.b_0_deepcopy_231 -> 106 -> 0xcfa37a0 +0xcae1880 -> 0x11e671601745131173052620480_inner_var_1199 -> 1199 -> 0xe70f560 +0xcdb8e30 -> 0xcf59f401745131171435049990_inner_var_341 -> 341 -> 0xcf97690 +0xccb4f70 -> 0xcf59f401745131171435049990_inner_var_467 -> 467 -> 0xcab88e0 +0xcaee1a0 -> batch_norm2d_10.w_1_deepcopy_55 -> 278 -> 0xcf526a0 +0xcdb8e90 -> 0xcf59f401745131171435049990_inner_var_337 -> 337 -> 0xce9b570 +0xca83ff0 -> 0xcf59f401745131171435049990_inner_var_1033 -> 1033 -> 0xcf3eed0 +0xcf05e90 -> 0xcf59f401745131171435049990_inner_var_335 -> 335 -> 0xcf1b790 +0xcfaa870 -> conv2d_13.w_0_deepcopy_67 -> 266 -> 0xcf52460 +0xca53c58 -> 0xcf59f401745131171435049990_inner_var_620 -> 620 -> 0xcf18440 +0xced6440 -> scale_factor -> 334 -> 0xcdbf3e0 +0xcdb8e60 -> 0xcf59f401745131171435049990_inner_var_339 -> 339 -> 0xcf009c0 +0xcaf7b10 -> batch_norm2d_19.w_2_deepcopy_101 -> 232 -> 0xcf7d9f0 +0x118c9970 -> conv2d_1.w_0_deepcopy_7 -> 326 -> 0xcf500a0 +0x118c9550 -> batch_norm2d_1.w_0_deepcopy_8 -> 325 -> 0xcf37900 +0x118c9180 -> batch_norm2d_1.b_0_deepcopy_9 -> 324 -> 0xcf540f0 +0xccbc408 -> 0xcf59f401745131171435049990_inner_var_422 -> 422 -> 0xd047d80 +0xcb0d370 -> batch_norm2d_6.b_0_deepcopy_34 -> 299 -> 0xcf2d1b0 +0xca6f568 -> 0x11e671601745131173052620480_inner_var_1145 -> 1145 -> 0x11e7e300 +0x118c8db0 -> batch_norm2d_1.w_1_deepcopy_10 -> 323 -> 0xcf3f550 +0xced5d20 -> image -> 333 -> 0xccc5060 +0xd0461a0 -> 0xcf59f401745131171435049990_inner_var_695 -> 695 -> 0xcca9b90 +0xcdb7ba0 -> 0xcf59f401745131171435049990_inner_var_705 -> 705 -> 0xcf07ef0 +0xcdb8e78 -> 0xcf59f401745131171435049990_inner_var_338 -> 338 -> 0xcebb210 +0x118c7ea0 -> batch_norm2d_2.b_0_deepcopy_14 -> 319 -> 0xcf7e0d0 +0xca63920 -> batch_norm2d_3.w_1_deepcopy_20 -> 313 -> 0xcf576f0 +0xcaba360 -> 0xcf59f401745131171435049990_inner_var_892 -> 892 -> 0xcf5b3c0 +0xca63550 -> batch_norm2d_3.w_2_deepcopy_21 -> 312 -> 0xcf21ae0 +0xca63180 -> conv2d_4.w_0_deepcopy_22 -> 311 -> 0xc80efb0 +0xca74d70 -> batch_norm2d_4.w_0_deepcopy_23 -> 310 -> 0xcf906f0 +0xcccf2f8 -> 0xcf59f401745131171435049990_inner_var_479 -> 479 -> 0xccdd6a0 +0xca74200 -> batch_norm2d_4.w_2_deepcopy_26 -> 307 -> 0xcf40460 +0xcf6dbe0 -> conv2d_56.w_0_deepcopy_280 -> 58 -> 0xcf344c0 +0xcae6ec8 -> 0xcf59f401745131171435049990_inner_var_826 -> 826 -> 0xced62a0 +0xccbb270 -> 0xcf59f401745131171435049990_inner_var_415 -> 415 -> 0xca942d0 +0xccc3d10 -> 0xcf59f401745131171435049990_inner_var_1038 -> 1038 -> 0xcfb34a0 +0xca73e30 -> conv2d_5.w_0_deepcopy_27 -> 306 -> 0xcfc00b0 +0xca55900 -> batch_norm2d_14.w_0_deepcopy_73 -> 260 -> 0xcfc6c40 +0xce94e70 -> constant_folding@_174513115176548053017 -> 42 -> 0xcf8c2c0 +0xcb0db10 -> conv2d_6.w_0_deepcopy_32 -> 301 -> 0xcc9ffb0 +0xca6e5c8 -> 0x11e671601745131173052620480_inner_var_1133 -> 1133 -> 0x11dd8840 +0xca69c20 -> 0x11e671601745131173052620480_inner_var_1131 -> 1131 -> 0x11e43a20 +0xee73340 -> 0xcf59f401745131171435049990_inner_var_371 -> 371 -> 0xcf563c0 +0xcce0740 -> 0x11e671601745131173052620480_inner_var_1095 -> 1095 -> 0x11e64f60 +0xcb0d740 -> batch_norm2d_6.w_0_deepcopy_33 -> 300 -> 0xcf42180 +0xcbf30d0 -> batch_norm2d_38.b_0_deepcopy_194 -> 139 -> 0xcf14990 +0xccb79f0 -> 0xcf59f401745131171435049990_inner_var_430 -> 430 -> 0xcead070 +0xcb0cbd0 -> batch_norm2d_6.w_2_deepcopy_36 -> 297 -> 0xcce2400 +0xce91cd0 -> 0xcf59f401745131171435049990_inner_var_913 -> 913 -> 0xce9aac0 +0xccdc440 -> 0xcf59f401745131171435049990_inner_var_951 -> 951 -> 0xcf4a820 +0xca86880 -> batch_norm2d_7.w_2_deepcopy_41 -> 292 -> 0xd05a990 +0xee3f360 -> linear_0.b_0_deepcopy_275 -> 62 -> 0xd041df0 +0xd0365e0 -> 0xcf59f401745131171435049990_inner_var_983 -> 983 -> 0xcbe59c0 +0xca85cc0 -> batch_norm2d_8.b_0_deepcopy_44 -> 289 -> 0xceee210 +0xca60410 -> 0x11e671601745131173052620480_inner_var_1205 -> 1205 -> 0xe711350 +0xca85150 -> conv2d_9.w_0_deepcopy_47 -> 286 -> 0xcf32540 +0xcc968c8 -> 0xcf59f401745131171435049990_inner_var_349 -> 349 -> 0xcad41e0 +0xcaeed60 -> conv2d_10.w_0_deepcopy_52 -> 281 -> 0xcf508b0 +0xca669c0 -> conv2d_11.w_0_deepcopy_57 -> 276 -> 0xcf50ed0 +0xcdb8ea8 -> 0xcf59f401745131171435049990_inner_var_336 -> 336 -> 0xca79260 +0xca77ae0 -> batch_norm2d_33.w_1_deepcopy_170 -> 163 -> 0xcf21710 +0xca73a60 -> batch_norm2d_5.w_0_deepcopy_28 -> 305 -> 0xcf8df90 +0xccbc790 -> 0xcf59f401745131171435049990_inner_var_435 -> 435 -> 0xd048180 +0xcfab3e0 -> batch_norm2d_12.b_0_deepcopy_64 -> 269 -> 0xcf1dca0 +0xccadc20 -> 0xcf59f401745131171435049990_inner_var_535 -> 535 -> 0xcecc7c0 +0xca71a88 -> 0x11e671601745131173052620480_inner_var_1162 -> 1162 -> 0x11e82ac0 +0xca76430 -> batch_norm2d_49.w_0_deepcopy_255 -> 82 -> 0xcfa0ad0 +0xca66d90 -> batch_norm2d_10.w_2_deepcopy_56 -> 277 -> 0xcf52d40 +0xccb4190 -> 0xcf59f401745131171435049990_inner_var_451 -> 451 -> 0xcf7c250 +0xd0577c0 -> 0xcf59f401745131171435049990_inner_var_358 -> 358 -> 0xccb4a70 +0xca6e598 -> 0x11e671601745131173052620480_inner_var_1135 -> 1135 -> 0x11dd8d10 +0xee73310 -> 0xcf59f401745131171435049990_inner_var_373 -> 373 -> 0xccaa1d0 +0xca732c0 -> batch_norm2d_5.w_1_deepcopy_30 -> 303 -> 0xcf63a70 +0xca864b0 -> conv2d_8.w_0_deepcopy_42 -> 291 -> 0xcf3f300 +0xee46150 -> 0xcf59f401745131171435049990_inner_var_848 -> 848 -> 0xce87810 +0xccb8160 -> 0xcf59f401745131171435049990_inner_var_436 -> 436 -> 0xcea5740 +0xcdb9a50 -> batch_norm2d_0.w_0_deepcopy_3 -> 330 -> 0xcf557c0 +0xcaf1780 -> batch_norm2d_13.w_1_deepcopy_70 -> 263 -> 0xcfc0710 +0xca55160 -> batch_norm2d_14.w_1_deepcopy_75 -> 258 -> 0xcfc9610 +0xcccc9b8 -> 0xcf59f401745131171435049990_inner_var_504 -> 504 -> 0xca705e0 +0xd04e4b0 -> batch_norm2d_46.w_1_deepcopy_242 -> 95 -> 0xcf90660 +0xee8aaa8 -> 0xcf59f401745131171435049990_inner_var_793 -> 793 -> 0xcf22070 +0xca73690 -> batch_norm2d_5.b_0_deepcopy_29 -> 304 -> 0xcf35350 +0xccbc3c0 -> 0xcf59f401745131171435049990_inner_var_425 -> 425 -> 0xee8cc70 +0xccc0910 -> 0xcf59f401745131171435049990_inner_var_1025 -> 1025 -> 0xcaac650 +0xcaf3d30 -> batch_norm2d_14.w_2_deepcopy_76 -> 257 -> 0xcfca3d0 +0xca515f8 -> 0xcf59f401745131171435049990_inner_var_601 -> 601 -> 0xc80e2c0 +0xcc9d360 -> conv2d_28.w_0_deepcopy_142 -> 191 -> 0xcfa8980 +0xee8aef0 -> 0xcf59f401745131171435049990_inner_var_798 -> 798 -> 0xcdbe600 +0xcfacf80 -> batch_norm2d_41.w_2_deepcopy_211 -> 122 -> 0xcfc7d60 +0xcaef180 -> batch_norm2d_9.w_2_deepcopy_51 -> 282 -> 0xcf48440 +0xca9e6c0 -> 0xcf59f401745131171435049990_inner_var_565 -> 565 -> 0xceb58e0 +0xccbc3f0 -> 0xcf59f401745131171435049990_inner_var_423 -> 423 -> 0xcea8fb0 +0xccd1610 -> batch_norm2d_39.w_1_deepcopy_200 -> 133 -> 0xcf3a220 +0xca64490 -> conv2d_3.w_0_deepcopy_17 -> 316 -> 0xcf15a30 +0xcccd280 -> 0xcf59f401745131171435049990_inner_var_510 -> 510 -> 0xcf12c40 +0xcae7f80 -> 0xcf59f401745131171435049990_inner_var_837 -> 837 -> 0xcf540d0 +0xccc0290 -> 0xcf59f401745131171435049990_inner_var_1023 -> 1023 -> 0xcbefea0 +0xccc5230 -> 0xcf59f401745131171435049990_inner_var_1045 -> 1045 -> 0xcfb5340 +0xcbf1600 -> conv2d_46.w_0_deepcopy_224 -> 113 -> 0xcf788c0 +0xee748d8 -> 0xcf59f401745131171435049990_inner_var_381 -> 381 -> 0xced3620 +0xce95010 -> constant_folding@_174513115110199844916 -> 43 -> 0xcf2d1d0 +0xcbf13e0 -> batch_norm2d_43.w_0_deepcopy_225 -> 112 -> 0xcf61c60 +0xca515e0 -> 0xcf59f401745131171435049990_inner_var_602 -> 602 -> 0xceef0c0 +0xcbf0e70 -> batch_norm2d_43.w_2_deepcopy_228 -> 109 -> 0xcf9baf0 +0xce94b30 -> constant_folding@_174513116811615103119 -> 40 -> 0xcf90680 +0xccba560 -> 0xcf59f401745131171435049990_inner_var_411 -> 411 -> 0xcad7ec0 +0xccdf2a0 -> 0x11e671601745131173052620480_inner_var_1097 -> 1097 -> 0xcc2b620 +0xca67250 -> batch_norm2d_37.w_1_deepcopy_190 -> 143 -> 0xcf9e170 +0xccd0758 -> 0xcf59f401745131171435049990_inner_var_490 -> 490 -> 0xcedd640 +0xca9f760 -> 0xcf59f401745131171435049990_inner_var_573 -> 573 -> 0xced1430 +0xcbf01c0 -> batch_norm2d_45.w_0_deepcopy_235 -> 102 -> 0xcf346f0 +0xed55b30 -> 0xcf59f401745131171435049990_inner_var_346 -> 346 -> 0xcea9360 +0xcabc890 -> 0xcf59f401745131171435049990_inner_var_871 -> 871 -> 0xcf028c0 +0xcfac450 -> conv2d_43.w_0_deepcopy_218 -> 116 -> 0xcf23170 +0xcbf3390 -> batch_norm2d_36.b_0_deepcopy_184 -> 149 -> 0xcf1aa60 +0xccad150 -> 0xcf59f401745131171435049990_inner_var_532 -> 532 -> 0xcf806b0 +0xd04efe0 -> batch_norm2d_45.b_0_deepcopy_236 -> 101 -> 0xcfa4800 +0xd04ee10 -> batch_norm2d_45.w_1_deepcopy_237 -> 100 -> 0xcf2bf50 +0xccbb6d0 -> 0xcf59f401745131171435049990_inner_var_427 -> 427 -> 0xced0d30 +0xca9bc18 -> 0xcf59f401745131171435049990_inner_var_743 -> 743 -> 0xcf42f50 +0xee867e0 -> 0xcf59f401745131171435049990_inner_var_766 -> 766 -> 0xccd2480 +0xccc4170 -> 0xcf59f401745131171435049990_inner_var_1039 -> 1039 -> 0xcfb38c0 +0xccdf440 -> 0xcf59f401745131171435049990_inner_var_1032 -> 1032 -> 0xcbecb40 +0xcdbe7e8 -> 0xcf59f401745131171435049990_inner_var_678 -> 678 -> 0xca65250 +0xcdc0c00 -> batch_norm2d_25.b_0_deepcopy_129 -> 204 -> 0xd066e30 +0xca6fde0 -> 0x11e671601745131173052620480_inner_var_1148 -> 1148 -> 0x11e7e2c0 +0xcdb5b50 -> conv2d_23.w_0_deepcopy_117 -> 216 -> 0xcf1a470 +0xcaf3960 -> conv2d_15.w_0_deepcopy_77 -> 256 -> 0xcf52c60 +0xccce220 -> 0xcf59f401745131171435049990_inner_var_472 -> 472 -> 0xcf6cb40 +0xd04dd70 -> batch_norm2d_47.b_0_deepcopy_246 -> 91 -> 0xcf9e810 +0xcaf4eb0 -> batch_norm2d_17.w_0_deepcopy_88 -> 245 -> 0xcf6f0e0 +0xcfac9c0 -> batch_norm2d_42.b_0_deepcopy_214 -> 119 -> 0xcf84050 +0xd0754b0 -> constant_folding@_174513116887061917064 -> 0 -> 0xd062b30 +0xce96870 -> batch_norm2d_29.w_0_deepcopy_148 -> 185 -> 0xcfa2740 +0xce94170 -> constant_folding@_174513116820193818025 -> 34 -> 0xce87a10 +0xca9a100 -> 0xcf59f401745131171435049990_inner_var_735 -> 735 -> 0xcc9ee20 +0xcaef920 -> batch_norm2d_9.b_0_deepcopy_49 -> 284 -> 0xcf0a7c0 +0xcae6e80 -> 0xcf59f401745131171435049990_inner_var_829 -> 829 -> 0xee88030 +0xcae6eb0 -> 0xcf59f401745131171435049990_inner_var_827 -> 827 -> 0xcef88f0 +0xccb8a50 -> 0xcf59f401745131171435049990_inner_var_442 -> 442 -> 0xd06eb50 +0xca749a0 -> batch_norm2d_4.b_0_deepcopy_24 -> 309 -> 0xcf02720 +0xcc9f1a0 -> constant_folding@_174513116859911589448 -> 14 -> 0xcf41cc0 +0xcc98968 -> 0xcf59f401745131171435049990_inner_var_399 -> 399 -> 0xcabae00 +0xca65660 -> conv2d_12.w_0_deepcopy_62 -> 271 -> 0xcf51cc0 +0xd04d310 -> batch_norm2d_36.w_2_deepcopy_186 -> 147 -> 0xcf37410 +0xccbb2b8 -> 0xcf59f401745131171435049990_inner_var_412 -> 412 -> 0xcf7cbb0 +0xcaf5df0 -> batch_norm2d_16.b_0_deepcopy_84 -> 249 -> 0xcfce890 +0xca9f778 -> 0xcf59f401745131171435049990_inner_var_572 -> 572 -> 0xee3f300 +0xca76090 -> batch_norm2d_49.w_1_deepcopy_257 -> 80 -> 0xcf420e0 +0xcbf1210 -> batch_norm2d_43.b_0_deepcopy_226 -> 111 -> 0xcf9ef90 +0xcaef550 -> batch_norm2d_9.w_1_deepcopy_50 -> 283 -> 0xcf44e90 +0xca8bb20 -> 0xcf59f401745131171435049990_inner_var_1075 -> 1075 -> 0xee76210 +0xca75ec0 -> batch_norm2d_49.w_2_deepcopy_258 -> 79 -> 0xcf24f50 +0xee400e0 -> batch_norm2d_51.w_1_deepcopy_267 -> 70 -> 0xcfa59c0 +0xca75ad0 -> batch_norm2d_50.w_0_deepcopy_260 -> 77 -> 0xcf20930 +0xccb6800 -> 0xcf59f401745131171435049990_inner_var_468 -> 468 -> 0xccdaac0 +0xcaa18e0 -> 0xcf59f401745131171435049990_inner_var_589 -> 589 -> 0xcec3af0 +0xcccc080 -> 0xcf59f401745131171435049990_inner_var_501 -> 501 -> 0xcad9eb0 +0xca75930 -> batch_norm2d_50.b_0_deepcopy_261 -> 76 -> 0xcfa3840 +0xca767d0 -> batch_norm2d_48.w_2_deepcopy_253 -> 84 -> 0xcf3e380 +0xcdbd6e0 -> 0xcf59f401745131171435049990_inner_var_673 -> 673 -> 0xceb72b0 +0xcccad60 -> 0xcf59f401745131171435049990_inner_var_1077 -> 1077 -> 0xcfbd260 +0xce96c40 -> conv2d_29.w_0_deepcopy_147 -> 186 -> 0xcf95890 +0xee40780 -> batch_norm2d_50.w_2_deepcopy_263 -> 74 -> 0xcf2bc00 +0xcbf34d0 -> batch_norm2d_38.w_0_deepcopy_193 -> 140 -> 0xcfaa490 +0xcdbc198 -> 0xcf59f401745131171435049990_inner_var_661 -> 661 -> 0xcea3d70 +0xccbee80 -> 0xcf59f401745131171435049990_inner_var_1018 -> 1018 -> 0xcbeeb90 +0xee405e0 -> conv2d_54.w_0_deepcopy_264 -> 73 -> 0xd059850 +0xd057808 -> 0xcf59f401745131171435049990_inner_var_355 -> 355 -> 0xcca80b0 +0xc80d510 -> batch_norm2d_39.w_2_deepcopy_201 -> 132 -> 0xcf15bf0 +0xcbf0ca0 -> conv2d_47.w_0_deepcopy_229 -> 108 -> 0xcf6e400 +0xd045ca0 -> 0xcf59f401745131171435049990_inner_var_699 -> 699 -> 0xca7ed30 +0xcdbf8c0 -> 0xcf59f401745131171435049990_inner_var_685 -> 685 -> 0xca54780 +0xcccb8f8 -> 0xcf59f401745131171435049990_inner_var_496 -> 496 -> 0xcf597c0 +0xee3fa20 -> batch_norm2d_52.b_0_deepcopy_271 -> 66 -> 0xcf193d0 +0xcabab30 -> 0xcf59f401745131171435049990_inner_var_865 -> 865 -> 0xca88040 +0xcfad8e0 -> batch_norm2d_40.w_2_deepcopy_206 -> 127 -> 0xcf35500 +0xee3f880 -> batch_norm2d_52.w_1_deepcopy_272 -> 65 -> 0xcf9ab00 +0xee3f6e0 -> batch_norm2d_52.w_2_deepcopy_273 -> 64 -> 0xcf3ce10 +0xca88400 -> 0xcf59f401745131171435049990_inner_var_361 -> 361 -> 0xcca6850 +0xcfacdb0 -> conv2d_42.w_0_deepcopy_212 -> 121 -> 0xcf9c9b0 +0xcf6dda0 -> conv2d_transpose_0.w_0_deepcopy_278 -> 59 -> 0xcf28750 +0x118c8610 -> conv2d_2.w_0_deepcopy_12 -> 321 -> 0xcfcc130 +0xca870f0 -> im_shape -> 332 -> 0xcec9720 +0xee874c0 -> 0xcf59f401745131171435049990_inner_var_768 -> 768 -> 0xcdb56f0 +0xca76f10 -> conv2d_51.w_0_deepcopy_249 -> 88 -> 0xccd6fb0 +0xcaf3590 -> batch_norm2d_15.w_0_deepcopy_78 -> 255 -> 0xcfcbe00 +0xca64860 -> batch_norm2d_2.w_2_deepcopy_16 -> 317 -> 0xcf808f0 +0xd041450 -> constant_folding@_1 +I0420 14:40:57.273088 115867 pir_interpreter.cc:1582] Done BuildInstruction +I0420 14:40:57.297755 115867 pir_interpreter.cc:1585] Done PreAnalysis +I0420 14:40:57.310034 115867 pir_interpreter.cc:1587] ======================== The network executed by pir interpreter ======================== +{ + (%0) = "slice(phi_kernel)" (%1, %2, %3) {axes:[1],decrease_axis:[],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2791,stop_gradient:[false]} : (cpu_tensor<-1x6xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1x4xf32> + (%4) = "slice(phi_kernel)" (%1, %5, %6) {axes:[1],decrease_axis:[1],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2792,stop_gradient:[false]} : (cpu_tensor<-1x6xf32>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor<-1xf32> + (%7) = "cast(phi_kernel)" (%4) {dtype:int32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:2793,stop_gradient:[false]} : (cpu_tensor<-1xf32>) -> cpu_tensor<-1xi32> + (%8) = "memcpy_h2d(phi_kernel)" (%0) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2794} : (cpu_tensor<-1x4xf32>) -> custom_device_tensor<-1x4xf32> + (%9) = "memcpy_h2d(phi_kernel)" (%10) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2795} : (cpu_tensor<-1xi32>) -> custom_device_tensor<-1xi32> + (%11) = "roi_align(phi_kernel)" (%12, %8, %9) {aligned:true,kernel_key:,kernel_name:"roi_align",op_name:"pd_op.roi_align",origin_id:2796,pooled_height:14,pooled_width:14,sampling_ratio:-1,spatial_scale:0.0625,stop_gradient:[false]} : (custom_device_tensor<-1x1024x-1x-1xf32>, custom_device_tensor<-1x4xf32>, custom_device_tensor<-1xi32>) -> custom_device_tensor<-1x1024x14x14xf32> + (%13) = "conv2d(phi_kernel)" (%11, %14) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2797,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (custom_device_tensor<-1x1024x14x14xf32>, custom_device_tensor<512x1024x1x1xf32>) -> custom_device_tensor<-1x512x14x14xf32> + (%15, %16, %17, %18, %19, %20) = "batch_norm_(phi_kernel)" (%13, %21, %22, %23, %24) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2798,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x14x14xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x14x14xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%25) = "relu_(phi_kernel)" (%15) {is_inplace:true,kernel_key:,kernel_name:"relu",op_name:"pd_op.relu_",origin_id:2799,stop_gradient:[false]} : (custom_device_tensor<-1x512x14x14xf32>) -> custom_device_tensor<-1x512x14x14xf32> + (%26) = "conv2d(phi_kernel)" (%25, %27) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2800,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[2,2]} : (custom_device_tensor<-1x512x14x14xf32>, custom_device_tensor<512x512x3x3xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%28, %29, %30, %31, %32, %33) = "batch_norm_(phi_kernel)" (%26, %34, %35, %36, %37) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2801,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%38) = "relu_(phi_kernel)" (%28) {is_inplace:true,kernel_key:,kernel_name:"relu",op_name:"pd_op.relu_",origin_id:2802,stop_gradient:[false]} : (custom_device_tensor<-1x512x7x7xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%39) = "conv2d(phi_kernel)" (%38, %40) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2803,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<2048x512x1x1xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%41, %42, %43, %44, %45, %46) = "batch_norm_(phi_kernel)" (%39, %47, %48, %49, %50) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2804,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>) -> custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<-1xu8> + (%51) = "conv2d(phi_kernel)" (%11, %52) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2805,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[2,2]} : (custom_device_tensor<-1x1024x14x14xf32>, custom_device_tensor<2048x1024x1x1xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%53, %54, %55, %56, %57, %58) = "batch_norm_(phi_kernel)" (%51, %59, %60, %61, %62) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2806,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>) -> custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<-1xu8> + (%63) = "add_(phi_kernel)" (%41, %53) {is_inplace:true,kernel_key:,kernel_name:"add",op_name:"pd_op.add_",origin_id:2807,stop_gradient:[false]} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%64) = "relu_(phi_kernel)" (%63) {is_inplace:true,kernel_key:,kernel_name:"relu",op_name:"pd_op.relu_",origin_id:2808,stop_gradient:[false]} : (custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%65) = "conv2d(phi_kernel)" (%64, %66) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2809,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<512x2048x1x1xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%67, %68, %69, %70, %71, %72) = "batch_norm_(phi_kernel)" (%65, %73, %74, %75, %76) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2810,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%77) = "relu_(phi_kernel)" (%67) {is_inplace:true,kernel_key:,kernel_name:"relu",op_name:"pd_op.relu_",origin_id:2811,stop_gradient:[false]} : (custom_device_tensor<-1x512x7x7xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%78) = "conv2d(phi_kernel)" (%77, %79) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2812,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1]} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512x512x3x3xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%80, %81, %82, %83, %84, %85) = "batch_norm_(phi_kernel)" (%78, %86, %87, %88, %89) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2813,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%90) = "relu_(phi_kernel)" (%80) {is_inplace:true,kernel_key:,kernel_name:"relu",op_name:"pd_op.relu_",origin_id:2814,stop_gradient:[false]} : (custom_device_tensor<-1x512x7x7xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%91) = "conv2d(phi_kernel)" (%90, %92) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2815,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<2048x512x1x1xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%93, %94, %95, %96, %97, %98) = "batch_norm_(phi_kernel)" (%91, %99, %100, %101, %102) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2816,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>) -> custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<-1xu8> + (%103) = "add_(phi_kernel)" (%93, %64) {is_inplace:true,kernel_key:,kernel_name:"add",op_name:"pd_op.add_",origin_id:2817,stop_gradient:[false]} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%104) = "relu_(phi_kernel)" (%103) {is_inplace:true,kernel_key:,kernel_name:"relu",op_name:"pd_op.relu_",origin_id:2818,stop_gradient:[false]} : (custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%105) = "conv2d(phi_kernel)" (%104, %106) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2819,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<512x2048x1x1xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%107, %108, %109, %110, %111, %112) = "batch_norm_(phi_kernel)" (%105, %113, %114, %115, %116) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2820,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%117) = "relu_(phi_kernel)" (%107) {is_inplace:true,kernel_key:,kernel_name:"relu",op_name:"pd_op.relu_",origin_id:2821,stop_gradient:[false]} : (custom_device_tensor<-1x512x7x7xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%118) = "conv2d(phi_kernel)" (%117, %119) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2822,padding_algorithm:"EXPLICIT",paddings:[1,1],stop_gradient:[false],strides:[1,1]} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512x512x3x3xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%120, %121, %122, %123, %124, %125) = "batch_norm_(phi_kernel)" (%118, %126, %127, %128, %129) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2823,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>) -> custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<512xf32>, custom_device_tensor<-1xu8> + (%130) = "relu_(phi_kernel)" (%120) {is_inplace:true,kernel_key:,kernel_name:"relu",op_name:"pd_op.relu_",origin_id:2824,stop_gradient:[false]} : (custom_device_tensor<-1x512x7x7xf32>) -> custom_device_tensor<-1x512x7x7xf32> + (%131) = "conv2d(phi_kernel)" (%130, %132) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2825,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (custom_device_tensor<-1x512x7x7xf32>, custom_device_tensor<2048x512x1x1xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%133, %134, %135, %136, %137, %138) = "batch_norm_(phi_kernel)" (%131, %139, %140, %141, %142) {data_format:"NCHW",epsilon:1e-05,is_inplace:true,is_test:true,kernel_key:,kernel_name:"batch_norm",momentum:0.9,op_name:"pd_op.batch_norm_",origin_id:2826,stop_gradient:[false,false,false,false,false,false],trainable_statistics:false,use_global_stats:true} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>) -> custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<2048xf32>, custom_device_tensor<-1xu8> + (%143) = "add_(phi_kernel)" (%133, %104) {is_inplace:true,kernel_key:,kernel_name:"add",op_name:"pd_op.add_",origin_id:2827,stop_gradient:[false]} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%144) = "relu_(phi_kernel)" (%143) {is_inplace:true,kernel_key:,kernel_name:"relu",op_name:"pd_op.relu_",origin_id:2828,stop_gradient:[false]} : (custom_device_tensor<-1x2048x7x7xf32>) -> custom_device_tensor<-1x2048x7x7xf32> + (%145) = "conv2d_transpose(phi_kernel)" (%144, %146, %147) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d_transpose",op_name:"pd_op.conv2d_transpose",origin_id:2829,output_padding:[],padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[2,2]} : (custom_device_tensor<-1x2048x7x7xf32>, custom_device_tensor<2048x256x2x2xf32>, cpu_tensor<0xi64>) -> custom_device_tensor<-1x256x14x14xf32> + (%148) = "add_(phi_kernel)" (%145, %149) {is_inplace:true,kernel_key:,kernel_name:"add",op_name:"pd_op.add_",origin_id:2830,stop_gradient:[false]} : (custom_device_tensor<-1x256x14x14xf32>, custom_device_tensor<1x256x1x1xf32>) -> custom_device_tensor<-1x256x14x14xf32> + (%150) = "relu_(phi_kernel)" (%148) {is_inplace:true,kernel_key:,kernel_name:"relu",op_name:"pd_op.relu_",origin_id:2831,stop_gradient:[false]} : (custom_device_tensor<-1x256x14x14xf32>) -> custom_device_tensor<-1x256x14x14xf32> + (%151) = "conv2d(phi_kernel)" (%150, %152) {data_format:"NCHW",dilations:[1,1],groups:1,kernel_key:,kernel_name:"conv2d",op_name:"pd_op.conv2d",origin_id:2832,padding_algorithm:"EXPLICIT",paddings:[0,0],stop_gradient:[false],strides:[1,1]} : (custom_device_tensor<-1x256x14x14xf32>, custom_device_tensor<80x256x1x1xf32>) -> custom_device_tensor<-1x80x14x14xf32> + (%153) = "add_(phi_kernel)" (%151, %154) {is_inplace:true,kernel_key:,kernel_name:"add",op_name:"pd_op.add_",origin_id:2833,stop_gradient:[false]} : (custom_device_tensor<-1x80x14x14xf32>, custom_device_tensor<1x80x1x1xf32>) -> custom_device_tensor<-1x80x14x14xf32> + (%155) = "shape64(phi_kernel)" (%153) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2834,stop_gradient:[true]} : (custom_device_tensor<-1x80x14x14xf32>) -> cpu_tensor<4xi64> + (%156) = "slice(phi_kernel)" (%155, %5, %6) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2835,stop_gradient:[true]} : (cpu_tensor<4xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%157) = "arange(phi_kernel)" (%158, %156, %159) {dtype:int64,kernel_key:,kernel_name:"arange",op_name:"pd_op.arange",origin_id:2836,place:Place(undefined:0),stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor, cpu_tensor<1xi64>) -> custom_device_tensor<-1xi64> + (%160) = "cast(phi_kernel)" (%157) {dtype:int32,kernel_key:,kernel_name:"cast",op_name:"pd_op.cast",origin_id:2837,stop_gradient:[true]} : (custom_device_tensor<-1xi64>) -> custom_device_tensor<-1xi32> + (%161) = "builtin.combine" [id:2838] (%160, %7) {origin_id:855,stop_gradient:[false]} : (custom_device_tensor<-1xi32>, cpu_tensor<-1xi32>) -> vec[custom_device_tensor<-1xi32>,cpu_tensor<-1xi32>] + (%162) = "memcpy_d2h(phi_kernel)" (%160) {dst_place_type:0,kernel_key:,kernel_name:"memcpy_d2h",op_name:"pd_op.memcpy_d2h",origin_id:2839} : (custom_device_tensor<-1xi32>) -> cpu_tensor<-1xi32> + (%163) = "builtin.combine" [id:2840] (%162, %7) {origin_id:2840} : (cpu_tensor<-1xi32>, cpu_tensor<-1xi32>) -> vec[cpu_tensor<-1xi32>,cpu_tensor<-1xi32>] + (%164) = "broadcast_tensors(phi_kernel)" (%163) {kernel_key:,kernel_name:"broadcast_tensors",op_name:"pd_op.broadcast_tensors",origin_id:2841,stop_gradient:[false]} : (vec[cpu_tensor<-1xi32>,cpu_tensor<-1xi32>]) -> vec[cpu_tensor<-1xi32>,cpu_tensor<-1xi32>] + (%165, %166) = "builtin.split" [id:2842] (%164) {origin_id:857,stop_gradient:[false,false]} : (vec[cpu_tensor<-1xi32>,cpu_tensor<-1xi32>]) -> cpu_tensor<-1xi32>, cpu_tensor<-1xi32> + (%167) = "builtin.combine" [id:2843] (%165, %166) {origin_id:858,stop_gradient:[false]} : (cpu_tensor<-1xi32>, cpu_tensor<-1xi32>) -> vec[cpu_tensor<-1xi32>,cpu_tensor<-1xi32>] + (%168) = "stack(phi_kernel)" (%167) {axis:-1,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2844,stop_gradient:[false]} : (vec[cpu_tensor<-1xi32>,cpu_tensor<-1xi32>]) -> cpu_tensor<-1x2xi32> + (%169) = "memcpy_h2d(phi_kernel)" (%168) {dst_place_type:1,kernel_key:,kernel_name:"memcpy_h2d",op_name:"pd_op.memcpy_h2d",origin_id:2845} : (cpu_tensor<-1x2xi32>) -> custom_device_tensor<-1x2xi32> + (%170) = "gather_nd(phi_kernel)" (%153, %169) {kernel_key:,kernel_name:"gather_nd",op_name:"pd_op.gather_nd",origin_id:2846,stop_gradient:[false]} : (custom_device_tensor<-1x80x14x14xf32>, custom_device_tensor<-1x2xi32>) -> custom_device_tensor<-1x14x14xf32> + (%171) = "shape64(phi_kernel)" (%160) {kernel_key:,kernel_name:"shape64",op_name:"pd_op.shape64",origin_id:2847,stop_gradient:[true]} : (custom_device_tensor<-1xi32>) -> cpu_tensor<1xi64> + (%172) = "slice(phi_kernel)" (%171, %5, %6) {axes:[0],decrease_axis:[0],infer_flags:[1],kernel_key:,kernel_name:"slice",op_name:"pd_op.slice",origin_id:2848,stop_gradient:[true]} : (cpu_tensor<1xi64>, cpu_tensor<1xi64>, cpu_tensor<1xi64>) -> cpu_tensor + (%173) = "builtin.combine" [id:2849] (%172, %174, %174) {origin_id:871,stop_gradient:[true]} : (cpu_tensor, cpu_tensor, cpu_tensor) -> vec[cpu_tensor,cpu_tensor,cpu_tensor] + (%175) = "stack(phi_kernel)" (%173) {axis:0,kernel_key:,kernel_name:"stack",op_name:"pd_op.stack",origin_id:2850,stop_gradient:[true]} : (vec[cpu_tensor,cpu_tensor,cpu_tensor]) -> cpu_tensor<3xi64> + (%176) = "reshape_(phi_kernel)" (%170, %175) {is_inplace:true,kernel_key:,kernel_name:"reshape",op_name:"pd_op.reshape_",origin_id:2851,stop_gradient:[false]} : (custom_device_tensor<-1x14x14xf32>, cpu_tensor<3xi64>) -> custom_device_tensor<-1x14x14xf32> + (%177) = "sigmoid_(phi_kernel)" (%176) {is_inplace:true,kernel_key:,kernel_name:"sigmoid",op_name:"pd_op.sigmoid_",origin_id:2852,stop_gradient:[false]} : (custom_device_tensor<-1x14x14xf32>) -> custom_device_tensor<-1x14x14xf32> + () = "cf.yield" [id:2853] (%177) {origin_id:875} : (custom_device_tensor<-1x14x14xf32>) -> +} +I0420 14:40:57.310196 115867 pir_interpreter.cc:1587] ======================== The instruction executed by pir interpreter ======================== +{outputs} = instruction_name[idx] ({inputs}) +0: ( 1094 ) = pd_op.slice ( 36 ) ( 52 ) ( 953 ) +1: ( 1095 ) = pd_op.slice ( 43 ) ( 44 ) ( 953 ) +2: ( 1096 ) = pd_op.cast ( 1095 ) +3: ( 1097 ) = pd_op.memcpy_h2d ( 1094 ) +4: ( 1098 ) = pd_op.memcpy_h2d ( 955 ) +5: ( 1099 ) = pd_op.roi_align ( 1098 ) ( 1097 ) ( 691 ) +6: ( 1100 ) = pd_op.conv2d ( 113 ) ( 1099 ) +7: ( 109 ) ( 1106 ) ( 110 ) ( 1105 ) ( 1104 ) ( 1103 ) ( 1102 ) ( 1101 ) = pd_op.batch_norm_ ( 111 ) ( 112 ) ( 109 ) ( 110 ) ( 1100 ) +8: ( 1101 ) ( 1107 ) = pd_op.relu_ ( 1101 ) +9: ( 1108 ) = pd_op.conv2d ( 108 ) ( 1107 ) +10: ( 1114 ) ( 104 ) ( 1113 ) ( 1112 ) ( 105 ) ( 1111 ) ( 1110 ) ( 1109 ) = pd_op.batch_norm_ ( 106 ) ( 107 ) ( 104 ) ( 105 ) ( 1108 ) +11: ( 1109 ) ( 1115 ) = pd_op.relu_ ( 1109 ) +12: ( 1116 ) = pd_op.conv2d ( 103 ) ( 1115 ) +13: ( 1122 ) ( 1121 ) ( 1120 ) ( 99 ) ( 1119 ) ( 1118 ) ( 100 ) ( 1117 ) = pd_op.batch_norm_ ( 102 ) ( 99 ) ( 100 ) ( 101 ) ( 1116 ) +14: ( 1123 ) = pd_op.conv2d ( 98 ) ( 1099 ) +15: ( 94 ) ( 95 ) ( 1129 ) ( 1128 ) ( 1127 ) ( 1126 ) ( 1125 ) ( 1124 ) = pd_op.batch_norm_ ( 96 ) ( 97 ) ( 94 ) ( 95 ) ( 1123 ) +16: ( 1117 ) ( 1130 ) = pd_op.add_ ( 1124 ) ( 1117 ) +17: ( 1130 ) ( 1131 ) = pd_op.relu_ ( 1130 ) +18: ( 1132 ) = pd_op.conv2d ( 93 ) ( 1131 ) +19: ( 90 ) ( 1138 ) ( 1137 ) ( 1136 ) ( 1135 ) ( 1134 ) ( 89 ) ( 1133 ) = pd_op.batch_norm_ ( 91 ) ( 89 ) ( 90 ) ( 92 ) ( 1132 ) +20: ( 1133 ) ( 1139 ) = pd_op.relu_ ( 1133 ) +21: ( 1140 ) = pd_op.conv2d ( 88 ) ( 1139 ) +22: ( 84 ) ( 85 ) ( 1146 ) ( 1145 ) ( 1144 ) ( 1143 ) ( 1142 ) ( 1141 ) = pd_op.batch_norm_ ( 86 ) ( 87 ) ( 84 ) ( 85 ) ( 1140 ) +23: ( 1141 ) ( 1147 ) = pd_op.relu_ ( 1141 ) +24: ( 1148 ) = pd_op.conv2d ( 83 ) ( 1147 ) +25: ( 79 ) ( 80 ) ( 1154 ) ( 1153 ) ( 1152 ) ( 1151 ) ( 1150 ) ( 1149 ) = pd_op.batch_norm_ ( 81 ) ( 82 ) ( 79 ) ( 80 ) ( 1148 ) +26: ( 1149 ) ( 1155 ) = pd_op.add_ ( 1131 ) ( 1149 ) +27: ( 1155 ) ( 1156 ) = pd_op.relu_ ( 1155 ) +28: ( 1157 ) = pd_op.conv2d ( 78 ) ( 1156 ) +29: ( 74 ) ( 1163 ) ( 1162 ) ( 75 ) ( 1161 ) ( 1160 ) ( 1159 ) ( 1158 ) = pd_op.batch_norm_ ( 76 ) ( 77 ) ( 75 ) ( 74 ) ( 1157 ) +30: ( 1158 ) ( 1164 ) = pd_op.relu_ ( 1158 ) +31: ( 1165 ) = pd_op.conv2d ( 73 ) ( 1164 ) +32: ( 69 ) ( 70 ) ( 1171 ) ( 1170 ) ( 1169 ) ( 1168 ) ( 1167 ) ( 1166 ) = pd_op.batch_norm_ ( 71 ) ( 72 ) ( 69 ) ( 70 ) ( 1165 ) +33: ( 1166 ) ( 1172 ) = pd_op.relu_ ( 1166 ) +34: ( 1173 ) = pd_op.conv2d ( 68 ) ( 1172 ) +35: ( 1179 ) ( 1178 ) ( 1177 ) ( 64 ) ( 65 ) ( 1176 ) ( 1175 ) ( 1174 ) = pd_op.batch_norm_ ( 66 ) ( 67 ) ( 64 ) ( 65 ) ( 1173 ) +36: ( 1174 ) ( 1180 ) = pd_op.add_ ( 1156 ) ( 1174 ) +37: ( 1180 ) ( 1181 ) = pd_op.relu_ ( 1180 ) +38: ( 1182 ) = pd_op.conv2d_transpose ( 15 ) ( 59 ) ( 1181 ) +39: ( 1182 ) ( 1183 ) = pd_op.add_ ( 14 ) ( 1182 ) +40: ( 1183 ) ( 1184 ) = pd_op.relu_ ( 1183 ) +41: ( 1185 ) = pd_op.conv2d ( 58 ) ( 1184 ) +42: ( 1185 ) ( 1186 ) = pd_op.add_ ( 56 ) ( 1185 ) +43: ( 1187 ) = pd_op.shape64 ( 1186 ) +44: ( 1188 ) = pd_op.slice ( 43 ) ( 44 ) ( 1187 ) +45: ( 1189 ) = pd_op.arange ( 41 ) ( 1188 ) ( 42 ) +46: ( 1190 ) = pd_op.cast ( 1189 ) +47: ( 1191 1190 1096 ) = builtin_combine_instruction ( 1096 ) ( 1190 ) +48: ( 1192 ) = pd_op.memcpy_d2h ( 1190 ) +49: ( 1193 1192 1096 ) = builtin_combine_instruction ( 1096 ) ( 1192 ) +50: ( 1194 1195 1196 ) = pd_op.broadcast_tensors ( 1193 1192 1096 ) +51: ( 1197 1195 1196 ) = builtin_combine_instruction ( 1196 ) ( 1195 ) +52: ( 1198 ) = pd_op.stack ( 1197 1195 1196 ) +53: ( 1199 ) = pd_op.memcpy_h2d ( 1198 ) +54: ( 1200 ) = pd_op.gather_nd ( 1199 ) ( 1186 ) +55: ( 1201 ) = pd_op.shape64 ( 1190 ) +56: ( 1202 ) = pd_op.slice ( 43 ) ( 44 ) ( 1201 ) +57: ( 1203 1202 13 13 ) = builtin_combine_instruction ( 13 ) ( 1202 ) +58: ( 1204 ) = pd_op.stack ( 1203 1202 13 13 ) +59: ( 1200 ) ( 1205 ) = pd_op.reshape_ ( 1204 ) ( 1200 ) +60: ( 1205 ) ( 1206 ) = pd_op.sigmoid_ ( 1205 ) +61: = yield_instruction ( 1206 ) +---------------------------var_id -> var_name -> variable*--------------------------- +0 -> constant_folding@_174513116887061917064 -> 0xd062b30 +1 -> constant_folding@_174513116885637196163 -> 0xd04b8a0 +2 -> constant_folding@_174513116884162097162 -> 0xcf7caa0 +3 -> constant_folding@_174513116882724144161 -> 0xcdb8360 +4 -> constant_folding@_174513116880873974160 -> 0xee71f60 +5 -> constant_folding@_174513116876880248258 -> 0xee71fe0 +6 -> constant_folding@_174513116875198908257 -> 0xc386660 +7 -> constant_folding@_174513116872098526255 -> 0xd061370 +8 -> constant_folding@_174513116870501220354 -> 0xcea7bc0 +9 -> constant_folding@_174513116869046783353 -> 0xcf2b9b0 +10 -> constant_folding@_174513116867667180352 -> 0xee46cc0 +11 -> constant_folding@_174513116865440940351 -> 0xcf3edb0 +12 -> constant_folding@_174513116864033493350 -> 0xcf3a750 +13 -> constant_folding@_174513116861634597449 -> 0xcf09130 +14 -> constant_folding@_174513116859911589448 -> 0xcf41cc0 +15 -> constant_folding@_174513116857122500446 -> 0xd0606f0 +16 -> constant_folding@_174513116855594796545 -> 0xcf11a20 +17 -> constant_folding@_174513116853662943544 -> 0xcdb8c50 +18 -> constant_folding@_174513116852268476543 -> 0xcfbe9a0 +19 -> constant_folding@_174513116850011146642 -> 0xcf2d600 +20 -> constant_folding@_174513116844224746641 -> 0xcdb72f0 +21 -> constant_folding@_174513116842705804740 -> 0xcf92430 +22 -> constant_folding@_174513116841278885739 -> 0xcdb8970 +23 -> constant_folding@_174513116839857308738 -> 0xcdb8950 +24 -> constant_folding@_174513116838085921737 -> 0xcf83ea0 +25 -> constant_folding@_174513116836671179736 -> 0xcf806d0 +26 -> constant_folding@_174513116835231166835 -> 0xcf0c190 +27 -> constant_folding@_174513116833656139834 -> 0xcf419a0 +28 -> constant_folding@_174513116830651898832 -> 0xed1a750 +29 -> constant_folding@_174513116827593559930 -> 0xcf437e0 +30 -> constant_folding@_174513116825929047929 -> 0xcf7c3f0 +31 -> constant_folding@_174513116824495087928 -> 0xcf3ae90 +32 -> constant_folding@_174513116823069183927 -> 0xcf64350 +33 -> constant_folding@_174513116821619066926 -> 0xcff2a20 +34 -> constant_folding@_174513116820193818025 -> 0xce87a10 +35 -> constant_folding@_174513116818740431024 -> 0xce98460 +36 -> constant_folding@_174513116817322536023 -> 0xce89990 +37 -> constant_folding@_174513116815886735022 -> 0xcf7aeb0 +38 -> constant_folding@_174513116814471704121 -> 0xd05fe30 +39 -> constant_folding@_174513116813053746120 -> 0xcf61980 +40 -> constant_folding@_174513116811615103119 -> 0xcf90680 +41 -> constant_folding@_174513115258237754718 -> 0xcfce240 +42 -> constant_folding@_174513115176548053017 -> 0xcf8c2c0 +43 -> constant_folding@_174513115110199844916 -> 0xcf2d1d0 +44 -> constant_folding@_174513115013851471515 -> 0xcf1a280 +45 -> constant_folding@_174513114941937302314 -> 0xcf04b20 +46 -> constant_folding@_174513114857650263913 -> 0xcf7d3f0 +47 -> constant_folding@_174513114736420657211 -> 0xd059870 +48 -> constant_folding@_174513114667774590410 -> 0xcf7f8b0 +49 -> constant_folding@_17451311460158745859 -> 0xcf7a460 +50 -> constant_folding@_17451311452954254518 -> 0xd05b470 +51 -> constant_folding@_17451311445836590377 -> 0xceddd30 +52 -> constant_folding@_17451311436834671136 -> 0xcf820a0 +53 -> constant_folding@_17451311430626338225 -> 0xcf624a0 +54 -> constant_folding@_17451311422710820934 -> 0xcede1e0 +55 -> constant_folding@_17451311415972965563 -> 0xd05ecd0 +56 -> constant_folding@_17451311399980650772 -> 0xcf619e0 +57 -> constant_folding@_17451311284029886470 -> 0xd069f60 +58 -> conv2d_56.w_0_deepcopy_280 -> 0xcf344c0 +59 -> conv2d_transpose_0.w_0_deepcopy_278 -> 0xcf28750 +60 -> linear_1.b_0_deepcopy_277 -> 0xcf3e700 +61 -> linear_1.w_0_deepcopy_276 -> 0xcf61960 +62 -> linear_0.b_0_deepcopy_275 -> 0xd041df0 +63 -> linear_0.w_0_deepcopy_274 -> 0xcfa9fb0 +64 -> batch_norm2d_52.w_2_deepcopy_273 -> 0xcf3ce10 +65 -> batch_norm2d_52.w_1_deepcopy_272 -> 0xcf9ab00 +66 -> batch_norm2d_52.b_0_deepcopy_271 -> 0xcf193d0 +67 -> batch_norm2d_52.w_0_deepcopy_270 -> 0xcf2c170 +68 -> conv2d_55.w_0_deepcopy_269 -> 0xcf3b8c0 +69 -> batch_norm2d_51.w_2_deepcopy_268 -> 0xcf2c930 +70 -> batch_norm2d_51.w_1_deepcopy_267 -> 0xcfa59c0 +71 -> batch_norm2d_51.b_0_deepcopy_266 -> 0xcf1b990 +72 -> batch_norm2d_51.w_0_deepcopy_265 -> 0xcf2f740 +73 -> conv2d_54.w_0_deepcopy_264 -> 0xd059850 +74 -> batch_norm2d_50.w_2_deepcopy_263 -> 0xcf2bc00 +75 -> batch_norm2d_50.w_1_deepcopy_262 -> 0xcf33710 +76 -> batch_norm2d_50.b_0_deepcopy_261 -> 0xcfa3840 +77 -> batch_norm2d_50.w_0_deepcopy_260 -> 0xcf20930 +78 -> conv2d_53.w_0_deepcopy_259 -> 0xcfa3490 +79 -> batch_norm2d_49.w_2_deepcopy_258 -> 0xcf24f50 +80 -> batch_norm2d_49.w_1_deepcopy_257 -> 0xcf420e0 +81 -> batch_norm2d_49.b_0_deepcopy_256 -> 0xcf39210 +82 -> batch_norm2d_49.w_0_deepcopy_255 -> 0xcfa0ad0 +83 -> conv2d_52.w_0_deepcopy_254 -> 0xcfc1c10 +84 -> batch_norm2d_48.w_2_deepcopy_253 -> 0xcf3e380 +85 -> batch_norm2d_48.w_1_deepcopy_252 -> 0xcf28490 +86 -> batch_norm2d_48.b_0_deepcopy_251 -> 0xcf165b0 +87 -> batch_norm2d_48.w_0_deepcopy_250 -> 0xcf3e660 +88 -> conv2d_51.w_0_deepcopy_249 -> 0xccd6fb0 +89 -> batch_norm2d_47.w_2_deepcopy_248 -> 0xcf33b60 +90 -> batch_norm2d_47.w_1_deepcopy_247 -> 0xcf230d0 +91 -> batch_norm2d_47.b_0_deepcopy_246 -> 0xcf9e810 +92 -> batch_norm2d_47.w_0_deepcopy_245 -> 0xcf3a620 +93 -> conv2d_50.w_0_deepcopy_244 -> 0xcf64fa0 +94 -> batch_norm2d_46.w_2_deepcopy_243 -> 0xcfa1c80 +95 -> batch_norm2d_46.w_1_deepcopy_242 -> 0xcf90660 +96 -> batch_norm2d_46.b_0_deepcopy_241 -> 0xcf60140 +97 -> batch_norm2d_46.w_0_deepcopy_240 -> 0xcf93910 +98 -> conv2d_49.w_0_deepcopy_239 -> 0xcfbfd60 +99 -> batch_norm2d_45.w_2_deepcopy_238 -> 0xcf2ccb0 +100 -> batch_norm2d_45.w_1_deepcopy_237 -> 0xcf2bf50 +101 -> batch_norm2d_45.b_0_deepcopy_236 -> 0xcfa4800 +102 -> batch_norm2d_45.w_0_deepcopy_235 -> 0xcf346f0 +103 -> conv2d_48.w_0_deepcopy_234 -> 0xcf6e750 +104 -> batch_norm2d_44.w_2_deepcopy_233 -> 0xcf9c920 +105 -> batch_norm2d_44.w_1_deepcopy_232 -> 0xce972f0 +106 -> batch_norm2d_44.b_0_deepcopy_231 -> 0xcfa37a0 +107 -> batch_norm2d_44.w_0_deepcopy_230 -> 0xcf1cb30 +108 -> conv2d_47.w_0_deepcopy_229 -> 0xcf6e400 +109 -> batch_norm2d_43.w_2_deepcopy_228 -> 0xcf9baf0 +110 -> batch_norm2d_43.w_1_deepcopy_227 -> 0xcf2a900 +111 -> batch_norm2d_43.b_0_deepcopy_226 -> 0xcf9ef90 +112 -> batch_norm2d_43.w_0_deepcopy_225 -> 0xcf61c60 +113 -> conv2d_46.w_0_deepcopy_224 -> 0xcf788c0 +114 -> conv2d_45.w_0_deepcopy_222 -> 0xcf30990 +115 -> conv2d_44.w_0_deepcopy_220 -> 0xcf36b60 +116 -> conv2d_43.w_0_deepcopy_218 -> 0xcf23170 +117 -> batch_norm2d_42.w_2_deepcopy_216 -> 0xcfa3f80 +118 -> batch_norm2d_42.w_1_deepcopy_215 -> 0xcf1c020 +119 -> batch_norm2d_42.b_0_deepcopy_214 -> 0xcf84050 +120 -> batch_norm2d_42.w_0_deepcopy_213 -> 0xcf3c660 +121 -> conv2d_42.w_0_deepcopy_212 -> 0xcf9c9b0 +122 -> batch_norm2d_41.w_2_deepcopy_211 -> 0xcfc7d60 +123 -> batch_norm2d_41.w_1_deepcopy_210 -> 0xcf2da20 +124 -> batch_norm2d_41.b_0_deepcopy_209 -> 0xcf8ca40 +125 -> batch_norm2d_41.w_0_deepcopy_208 -> 0xcfc5960 +126 -> conv2d_41.w_0_deepcopy_207 -> 0xcf91450 +127 -> batch_norm2d_40.w_2_deepcopy_206 -> 0xcf35500 +128 -> batch_norm2d_40.w_1_deepcopy_205 -> 0xcfc3720 +129 -> batch_norm2d_40.b_0_deepcopy_204 -> 0xcf41f80 +130 -> batch_norm2d_40.w_0_deepcopy_203 -> 0xcf63a50 +131 -> conv2d_40.w_0_deepcopy_202 -> 0xc80ecf0 +132 -> batch_norm2d_39.w_2_deepcopy_201 -> 0xcf15bf0 +133 -> batch_norm2d_39.w_1_deepcopy_200 -> 0xcf3a220 +134 -> batch_norm2d_39.b_0_deepcopy_199 -> 0xcf35220 +135 -> batch_norm2d_39.w_0_deepcopy_198 -> 0xcf34d40 +136 -> conv2d_39.w_0_deepcopy_197 -> 0xd0753d0 +137 -> batch_norm2d_38.w_2_deepcopy_196 -> 0xcfa49c0 +138 -> batch_norm2d_38.w_1_deepcopy_195 -> 0xcefd090 +139 -> batch_norm2d_38.b_0_deepcopy_194 -> 0xcf14990 +140 -> batch_norm2d_38.w_0_deepcopy_193 -> 0xcfaa490 +141 -> conv2d_38.w_0_deepcopy_192 -> 0xd068b90 +142 -> batch_norm2d_37.w_2_deepcopy_191 -> 0xcf286a0 +143 -> batch_norm2d_37.w_1_deepcopy_190 -> 0xcf9e170 +144 -> batch_norm2d_37.b_0_deepcopy_189 -> 0xcf8d310 +145 -> batch_norm2d_37.w_0_deepcopy_188 -> 0xcf9cbb0 +146 -> conv2d_37.w_0_deepcopy_187 -> 0xd063c10 +147 -> batch_norm2d_36.w_2_deepcopy_186 -> 0xcf37410 +148 -> batch_norm2d_36.w_1_deepcopy_185 -> 0xcf24090 +149 -> batch_norm2d_36.b_0_deepcopy_184 -> 0xcf1aa60 +150 -> batch_norm2d_36.w_0_deepcopy_183 -> 0xd065350 +151 -> conv2d_36.w_0_deepcopy_182 -> 0xd05fe10 +152 -> batch_norm2d_35.w_2_deepcopy_181 -> 0xcf14710 +153 -> batch_norm2d_35.w_1_deepcopy_180 -> 0xcf3e960 +154 -> batch_norm2d_35.b_0_deepcopy_179 -> 0xcf40ec0 +155 -> batch_norm2d_35.w_0_deepcopy_178 -> 0xcf396f0 +156 -> conv2d_35.w_0_deepcopy_177 -> 0xd05bfc0 +157 -> batch_norm2d_34.w_2_deepcopy_176 -> 0xcf3b470 +158 -> batch_norm2d_34.w_1_deepcopy_175 -> 0xcf38a90 +159 -> batch_norm2d_34.b_0_deepcopy_174 -> 0xcf3a9e0 +160 -> batch_norm2d_34.w_0_deepcopy_173 -> 0xcfbfa40 +161 -> conv2d_34.w_0_deepcopy_172 -> 0xcf831a0 +162 -> batch_norm2d_33.w_2_deepcopy_171 -> 0xcf3a3e0 +163 -> batch_norm2d_33.w_1_deepcopy_170 -> 0xcf21710 +164 -> batch_norm2d_33.b_0_deepcopy_169 -> 0xcfa22f0 +165 -> batch_norm2d_33.w_0_deepcopy_168 -> 0xcfa2c80 +166 -> conv2d_33.w_0_deepcopy_167 -> 0xcf80d10 +167 -> batch_norm2d_32.w_2_deepcopy_166 -> 0xcf91ac0 +168 -> batch_norm2d_32.w_1_deepcopy_165 -> 0xcf3d360 +169 -> batch_norm2d_32.b_0_deepcopy_164 -> 0xcf3e100 +170 -> batch_norm2d_32.w_0_deepcopy_163 -> 0xcf218b0 +171 -> conv2d_32.w_0_deepcopy_162 -> 0xcf8c2a0 +172 -> batch_norm2d_31.w_2_deepcopy_161 -> 0xcf37b70 +173 -> batch_norm2d_31.w_1_deepcopy_160 -> 0xcfa09b0 +174 -> batch_norm2d_31.b_0_deepcopy_159 -> 0xcfa3680 +175 -> batch_norm2d_31.w_0_deepcopy_158 -> 0xcf910a0 +176 -> conv2d_31.w_0_deepcopy_157 -> 0xcf7c3d0 +177 -> batch_norm2d_30.w_2_deepcopy_156 -> 0xc809ae0 +178 -> batch_norm2d_30.w_1_deepcopy_155 -> 0xcf63ec0 +179 -> batch_norm2d_30.b_0_deepcopy_154 -> 0xcf28de0 +180 -> batch_norm2d_30.w_0_deepcopy_153 -> 0xcf3c2d0 +181 -> conv2d_30.w_0_deepcopy_152 -> 0xcf7ae90 +182 -> batch_norm2d_29.w_2_deepcopy_151 -> 0xcf93530 +183 -> batch_norm2d_29.w_1_deepcopy_150 -> 0xd076240 +184 -> batch_norm2d_29.b_0_deepcopy_149 -> 0xd074f90 +185 -> batch_norm2d_29.w_0_deepcopy_148 -> 0xcfa2740 +186 -> conv2d_29.w_0_deepcopy_147 -> 0xcf95890 +187 -> batch_norm2d_28.w_2_deepcopy_146 -> 0xd074250 +188 -> batch_norm2d_28.w_1_deepcopy_145 -> 0xd073510 +189 -> batch_norm2d_28.b_0_deepcopy_144 -> 0xd071b20 +190 -> batch_norm2d_28.w_0_deepcopy_143 -> 0xd0727b0 +191 -> conv2d_28.w_0_deepcopy_142 -> 0xcfa8980 +192 -> batch_norm2d_27.w_2_deepcopy_141 -> 0xd06fa30 +193 -> batch_norm2d_27.w_1_deepcopy_140 -> 0xd06ff30 +194 -> batch_norm2d_27.b_0_deepcopy_139 -> 0xd06e400 +195 -> batch_norm2d_27.w_0_deepcopy_138 -> 0xd06f070 +196 -> conv2d_27.w_0_deepcopy_137 -> 0xcf9e030 +197 -> batch_norm2d_26.w_2_deepcopy_136 -> 0xd06d600 +198 -> batch_norm2d_26.w_1_deepcopy_135 -> 0xd06c980 +199 -> batch_norm2d_26.b_0_deepcopy_134 -> 0xd06ad10 +200 -> batch_norm2d_26.w_0_deepcopy_133 -> 0xd06b960 +201 -> conv2d_26.w_0_deepcopy_132 -> 0xcf71280 +202 -> batch_norm2d_25.w_2_deepcopy_131 -> 0xd069fd0 +203 -> batch_norm2d_25.w_1_deepcopy_130 -> 0xd069110 +204 -> batch_norm2d_25.b_0_deepcopy_129 -> 0xd066e30 +205 -> batch_norm2d_25.w_0_deepcopy_128 -> 0xd067f70 +206 -> conv2d_25.w_0_deepcopy_127 -> 0xcf91990 +207 -> batch_norm2d_24.w_2_deepcopy_126 -> 0xd066330 +208 -> batch_norm2d_24.w_1_deepcopy_125 -> 0xd065590 +209 -> batch_norm2d_24.b_0_deepcopy_124 -> 0xd062fb0 +210 -> batch_norm2d_24.w_0_deepcopy_123 -> 0xd0641f0 +211 -> conv2d_24.w_0_deepcopy_122 -> 0xcf91780 +212 -> batch_norm2d_23.w_2_deepcopy_121 -> 0xd062570 +213 -> batch_norm2d_23.w_1_deepcopy_120 -> 0xd061ab0 +214 -> batch_norm2d_23.b_0_deepcopy_119 -> 0xd060170 +215 -> batch_norm2d_23.w_0_deepcopy_118 -> 0xd060ed0 +216 -> conv2d_23.w_0_deepcopy_117 -> 0xcf1a470 +217 -> batch_norm2d_22.w_2_deepcopy_116 -> 0xd05f3f0 +218 -> batch_norm2d_22.w_1_deepcopy_115 -> 0xd05e730 +219 -> batch_norm2d_22.b_0_deepcopy_114 -> 0xcf3fa10 +220 -> batch_norm2d_22.w_0_deepcopy_113 -> 0xd05d850 +221 -> conv2d_22.w_0_deepcopy_112 -> 0xcfa5f90 +222 -> batch_norm2d_21.w_2_deepcopy_111 -> 0xd05c7d0 +223 -> batch_norm2d_21.w_1_deepcopy_110 -> 0xcf9c4b0 +224 -> batch_norm2d_21.b_0_deepcopy_109 -> 0xcf23400 +225 -> batch_norm2d_21.w_0_deepcopy_108 -> 0xd05afe0 +226 -> conv2d_21.w_0_deepcopy_107 -> 0xcf41b60 +227 -> batch_norm2d_20.w_2_deepcopy_106 -> 0xcf83e60 +228 -> batch_norm2d_20.w_1_deepcopy_105 -> 0xcf82fa0 +229 -> batch_norm2d_20.b_0_deepcopy_104 -> 0xcf814b0 +230 -> batch_norm2d_20.w_0_deepcopy_103 -> 0xcf82460 +231 -> conv2d_20.w_0_deepcopy_102 -> 0xcf95570 +232 -> batch_norm2d_19.w_2_deepcopy_101 -> 0xcf7d9f0 +233 -> batch_norm2d_19.w_1_deepcopy_100 -> 0xcf7c850 +234 -> batch_norm2d_19.b_0_deepcopy_99 -> 0xcf7ac10 +235 -> batch_norm2d_19.w_0_deepcopy_98 -> 0xcf7bc50 +236 -> conv2d_19.w_0_deepcopy_97 -> 0xcf31440 +237 -> batch_norm2d_18.w_2_deepcopy_96 -> 0xcf79bd0 +238 -> batch_norm2d_18.w_1_deepcopy_95 -> 0xcf84390 +239 -> batch_norm2d_18.b_0_deepcopy_94 -> 0xcf71dd0 +240 -> batch_norm2d_18.w_0_deepcopy_93 -> 0xcfa68e0 +241 -> conv2d_18.w_0_deepcopy_92 -> 0xcf22d20 +242 -> batch_norm2d_17.w_2_deepcopy_91 -> 0xcf70e80 +243 -> batch_norm2d_17.w_1_deepcopy_90 -> 0xcf6ff00 +244 -> batch_norm2d_17.b_0_deepcopy_89 -> 0xcfa8c60 +245 -> batch_norm2d_17.w_0_deepcopy_88 -> 0xcf6f0e0 +246 -> conv2d_17.w_0_deepcopy_87 -> 0xcf17680 +247 -> batch_norm2d_16.w_2_deepcopy_86 -> 0xcfa79c0 +248 -> batch_norm2d_16.w_1_deepcopy_85 -> 0xcfa6d50 +249 -> batch_norm2d_16.b_0_deepcopy_84 -> 0xcfce890 +250 -> batch_norm2d_16.w_0_deepcopy_83 -> 0xcf30ac0 +251 -> conv2d_16.w_0_deepcopy_82 -> 0xcf536b0 +252 -> batch_norm2d_15.w_2_deepcopy_81 -> 0xcfcda90 +253 -> batch_norm2d_15.w_1_deepcopy_80 -> 0xcfcce30 +254 -> batch_norm2d_15.b_0_deepcopy_79 -> 0xcfcb200 +255 -> batch_norm2d_15.w_0_deepcopy_78 -> 0xcfcbe00 +256 -> conv2d_15.w_0_deepcopy_77 -> 0xcf52c60 +257 -> batch_norm2d_14.w_2_deepcopy_76 -> 0xcfca3d0 +258 -> batch_norm2d_14.w_1_deepcopy_75 -> 0xcfc9610 +259 -> batch_norm2d_14.b_0_deepcopy_74 -> 0xcfc2eb0 +260 -> batch_norm2d_14.w_0_deepcopy_73 -> 0xcfc6c40 +261 -> conv2d_14.w_0_deepcopy_72 -> 0xcf52990 +262 -> batch_norm2d_13.w_2_deepcopy_71 -> 0xcfc1870 +263 -> batch_norm2d_13.w_1_deepcopy_70 -> 0xcfc0710 +264 -> batch_norm2d_13.b_0_deepcopy_69 -> 0xcf31c30 +265 -> batch_norm2d_13.w_0_deepcopy_68 -> 0xcf38dc0 +266 -> conv2d_13.w_0_deepcopy_67 -> 0xcf52460 +267 -> batch_norm2d_12.w_2_deepcopy_66 -> 0xcf289b0 +268 -> batch_norm2d_12.w_1_deepcopy_65 -> 0xcf259b0 +269 -> batch_norm2d_12.b_0_deepcopy_64 -> 0xcf1dca0 +270 -> batch_norm2d_12.w_0_deepcopy_63 -> 0xcf20e70 +271 -> conv2d_12.w_0_deepcopy_62 -> 0xcf51cc0 +272 -> batch_norm2d_11.w_2_deepcopy_61 -> 0xcf17bc0 +273 -> batch_norm2d_11.w_1_deepcopy_60 -> 0xcf14fe0 +274 -> batch_norm2d_11.b_0_deepcopy_59 -> 0xcf53430 +275 -> batch_norm2d_11.w_0_deepcopy_58 -> 0xcf53290 +276 -> conv2d_11.w_0_deepcopy_57 -> 0xcf50ed0 +277 -> batch_norm2d_10.w_2_deepcopy_56 -> 0xcf52d40 +278 -> batch_norm2d_10.w_1_deepcopy_55 -> 0xcf526a0 +279 -> batch_norm2d_10.b_0_deepcopy_54 -> 0xcf51350 +280 -> batch_norm2d_10.w_0_deepcopy_53 -> 0xcf50fb0 +281 -> conv2d_10.w_0_deepcopy_52 -> 0xcf508b0 +282 -> batch_norm2d_9.w_2_deepcopy_51 -> 0xcf48440 +283 -> batch_norm2d_9.w_1_deepcopy_50 -> 0xcf44e90 +284 -> batch_norm2d_9.b_0_deepcopy_49 -> 0xcf0a7c0 +285 -> batch_norm2d_9.w_0_deepcopy_48 -> 0xcf0fa10 +286 -> conv2d_9.w_0_deepcopy_47 -> 0xcf32540 +287 -> batch_norm2d_8.w_2_deepcopy_46 -> 0xceff0c0 +288 -> batch_norm2d_8.w_1_deepcopy_45 -> 0xcef9a90 +289 -> batch_norm2d_8.b_0_deepcopy_44 -> 0xceee210 +290 -> batch_norm2d_8.w_0_deepcopy_43 -> 0xcef0d80 +291 -> conv2d_8.w_0_deepcopy_42 -> 0xcf3f300 +292 -> batch_norm2d_7.w_2_deepcopy_41 -> 0xd05a990 +293 -> batch_norm2d_7.w_1_deepcopy_40 -> 0xd05ae90 +294 -> batch_norm2d_7.b_0_deepcopy_39 -> 0xcfa23b0 +295 -> batch_norm2d_7.w_0_deepcopy_38 -> 0xcf261b0 +296 -> conv2d_7.w_0_deepcopy_37 -> 0xcf3b640 +297 -> batch_norm2d_6.w_2_deepcopy_36 -> 0xcce2400 +298 -> batch_norm2d_6.w_1_deepcopy_35 -> 0xcf2f870 +299 -> batch_norm2d_6.b_0_deepcopy_34 -> 0xcf2d1b0 +300 -> batch_norm2d_6.w_0_deepcopy_33 -> 0xcf42180 +301 -> conv2d_6.w_0_deepcopy_32 -> 0xcc9ffb0 +302 -> batch_norm2d_5.w_2_deepcopy_31 -> 0xcf306a0 +303 -> batch_norm2d_5.w_1_deepcopy_30 -> 0xcf63a70 +304 -> batch_norm2d_5.b_0_deepcopy_29 -> 0xcf35350 +305 -> batch_norm2d_5.w_0_deepcopy_28 -> 0xcf8df90 +306 -> conv2d_5.w_0_deepcopy_27 -> 0xcfc00b0 +307 -> batch_norm2d_4.w_2_deepcopy_26 -> 0xcf40460 +308 -> batch_norm2d_4.w_1_deepcopy_25 -> 0xcf8df70 +309 -> batch_norm2d_4.b_0_deepcopy_24 -> 0xcf02720 +310 -> batch_norm2d_4.w_0_deepcopy_23 -> 0xcf906f0 +311 -> conv2d_4.w_0_deepcopy_22 -> 0xc80efb0 +312 -> batch_norm2d_3.w_2_deepcopy_21 -> 0xcf21ae0 +313 -> batch_norm2d_3.w_1_deepcopy_20 -> 0xcf576f0 +314 -> batch_norm2d_3.b_0_deepcopy_19 -> 0xc80e280 +315 -> batch_norm2d_3.w_0_deepcopy_18 -> 0xcf34c10 +316 -> conv2d_3.w_0_deepcopy_17 -> 0xcf15a30 +317 -> batch_norm2d_2.w_2_deepcopy_16 -> 0xcf808f0 +318 -> batch_norm2d_2.w_1_deepcopy_15 -> 0xcf7feb0 +319 -> batch_norm2d_2.b_0_deepcopy_14 -> 0xcf7e0d0 +320 -> batch_norm2d_2.w_0_deepcopy_13 -> 0xcf7f110 +321 -> conv2d_2.w_0_deepcopy_12 -> 0xcfcc130 +322 -> batch_norm2d_1.w_2_deepcopy_11 -> 0xcf506f0 +323 -> batch_norm2d_1.w_1_deepcopy_10 -> 0xcf3f550 +324 -> batch_norm2d_1.b_0_deepcopy_9 -> 0xcf540f0 +325 -> batch_norm2d_1.w_0_deepcopy_8 -> 0xcf37900 +326 -> conv2d_1.w_0_deepcopy_7 -> 0xcf500a0 +327 -> batch_norm2d_0.w_2_deepcopy_6 -> 0xcf55d60 +328 -> batch_norm2d_0.w_1_deepcopy_5 -> 0xcf547a0 +329 -> batch_norm2d_0.b_0_deepcopy_4 -> 0xcf34070 +330 -> batch_norm2d_0.w_0_deepcopy_3 -> 0xcf557c0 +331 -> conv2d_0.w_0_deepcopy_2 -> 0xcf54fc0 +332 -> im_shape -> 0xcec9720 +333 -> image -> 0xccc5060 +334 -> scale_factor -> 0xcdbf3e0 +335 -> 0xcf59f401745131171435049990_inner_var_335 -> 0xcf1b790 +336 -> 0xcf59f401745131171435049990_inner_var_336 -> 0xca79260 +337 -> 0xcf59f401745131171435049990_inner_var_337 -> 0xce9b570 +338 -> 0xcf59f401745131171435049990_inner_var_338 -> 0xcebb210 +339 -> 0xcf59f401745131171435049990_inner_var_339 -> 0xcf009c0 +340 -> 0xcf59f401745131171435049990_inner_var_340 -> 0xd0606b0 +341 -> 0xcf59f401745131171435049990_inner_var_341 -> 0xcf97690 +342 -> 0xcf59f401745131171435049990_inner_var_342 -> 0xca791e0 +343 -> 0xcf59f401745131171435049990_inner_var_343 -> 0xcca3d60 +344 -> 0xcf59f401745131171435049990_inner_var_344 -> 0xcdbf7b0 +345 -> 0xcf59f401745131171435049990_inner_var_345 -> 0xceb2ca0 +346 -> 0xcf59f401745131171435049990_inner_var_346 -> 0xcea9360 +347 -> 0xcf59f401745131171435049990_inner_var_347 -> 0xca9b750 +348 -> 0xcf59f401745131171435049990_inner_var_348 -> 0xcef9b60 +349 -> 0xcf59f401745131171435049990_inner_var_349 -> 0xcad41e0 +350 -> 0xcf59f401745131171435049990_inner_var_350 -> 0xcec2420 +351 -> 0xcf59f401745131171435049990_inner_var_351 -> 0xd0580a0 +352 -> 0xcf59f401745131171435049990_inner_var_352 -> 0xca7dcc0 +353 -> 0xcf59f401745131171435049990_inner_var_353 -> 0xcf596e0 +354 -> 0xcf59f401745131171435049990_inner_var_354 -> 0xca62f10 +355 -> 0xcf59f401745131171435049990_inner_var_355 -> 0xcca80b0 +356 -> 0xcf59f401745131171435049990_inner_var_356 -> 0xccd5df0 +357 -> 0xcf59f401745131171435049990_inner_var_357 -> 0xca883e0 +358 -> 0xcf59f401745131171435049990_inner_var_358 -> 0xccb4a70 +359 -> 0xcf59f401745131171435049990_inner_var_359 -> 0xca758f0 +360 -> 0xcf59f401745131171435049990_inner_var_360 -> 0xd04cf50 +361 -> 0xcf59f401745131171435049990_inner_var_361 -> 0xcca6850 +362 -> 0xcf59f401745131171435049990_inner_var_362 -> 0xcf6d1c0 +363 -> 0xcf59f401745131171435049990_inner_var_363 -> 0xcadee10 +364 -> 0xcf59f401745131171435049990_inner_var_364 -> 0xcccf200 +365 -> 0xcf59f401745131171435049990_inner_var_365 -> 0xcebea10 +366 -> 0xcf59f401745131171435049990_inner_var_366 -> 0xcf1d290 +367 -> 0xcf59f401745131171435049990_inner_var_367 -> 0xce91c20 +368 -> 0xcf59f401745131171435049990_inner_var_368 -> 0xccd76c0 +369 -> 0xcf59f401745131171435049990_inner_var_369 -> 0xca58e30 +370 -> 0xcf59f401745131171435049990_inner_var_370 -> 0xce97c00 +371 -> 0xcf59f401745131171435049990_inner_var_371 -> 0xcf563c0 +372 -> 0xcf59f401745131171435049990_inner_var_372 -> 0xca7bf50 +373 -> 0xcf59f401745131171435049990_inner_var_373 -> 0xccaa1d0 +374 -> 0xcf59f401745131171435049990_inner_var_374 -> 0xcec14b0 +375 -> 0xcf59f401745131171435049990_inner_var_375 -> 0xccdfc40 +376 -> 0xcf59f401745131171435049990_inner_var_376 -> 0xcf20080 +377 -> 0xcf59f401745131171435049990_inner_var_377 -> 0xcaa4d20 +378 -> 0xcf59f401745131171435049990_inner_var_378 -> 0xd0732d0 +379 -> 0xcf59f401745131171435049990_inner_var_379 -> 0xcec7d50 +380 -> 0xcf59f401745131171435049990_inner_var_380 -> 0xced4880 +381 -> 0xcf59f401745131171435049990_inner_var_381 -> 0xced3620 +382 -> 0xcf59f401745131171435049990_inner_var_382 -> 0xcef7030 +383 -> 0xcf59f401745131171435049990_inner_var_383 -> 0xcad2150 +384 -> 0xcf59f401745131171435049990_inner_var_384 -> 0xccdad60 +385 -> 0xcf59f401745131171435049990_inner_var_385 -> 0xcf14230 +386 -> 0xcf59f401745131171435049990_inner_var_386 -> 0xcf17420 +387 -> 0xcf59f401745131171435049990_inner_var_387 -> 0xccb2480 +388 -> 0xcf59f401745131171435049990_inner_var_388 -> 0xcca2020 +389 -> 0xcf59f401745131171435049990_inner_var_389 -> 0xd040710 +390 -> 0xcf59f401745131171435049990_inner_var_390 -> 0xca9b230 +391 -> 0xcf59f401745131171435049990_inner_var_391 -> 0xcf38990 +392 -> 0xcf59f401745131171435049990_inner_var_392 -> 0xd0639d0 +393 -> 0xcf59f401745131171435049990_inner_var_393 -> 0xccb9310 +394 -> 0xcf59f401745131171435049990_inner_var_394 -> 0xccc15b0 +395 -> 0xcf59f401745131171435049990_inner_var_395 -> 0xcef9860 +396 -> 0xcf59f401745131171435049990_inner_var_396 -> 0xc8757d0 +397 -> 0xcf59f401745131171435049990_inner_var_397 -> 0xceee010 +398 -> 0xcf59f401745131171435049990_inner_var_398 -> 0xca947c0 +399 -> 0xcf59f401745131171435049990_inner_var_399 -> 0xcabae00 +400 -> 0xcf59f401745131171435049990_inner_var_400 -> 0xca602d0 +401 -> 0xcf59f401745131171435049990_inner_var_401 -> 0xcee7350 +402 -> 0xcf59f401745131171435049990_inner_var_402 -> 0xcf99c00 +403 -> 0xcf59f401745131171435049990_inner_var_403 -> 0xcad0c10 +404 -> 0xcf59f401745131171435049990_inner_var_404 -> 0xd04f4b0 +405 -> 0xcf59f401745131171435049990_inner_var_405 -> 0xccc21e0 +406 -> 0xcf59f401745131171435049990_inner_var_406 -> 0xcf78850 +407 -> 0xcf59f401745131171435049990_inner_var_407 -> 0xd0727d0 +408 -> 0xcf59f401745131171435049990_inner_var_408 -> 0xceb0f20 +409 -> 0xcf59f401745131171435049990_inner_var_409 -> 0xceb3080 +410 -> 0xcf59f401745131171435049990_inner_var_410 -> 0xcf0a000 +411 -> 0xcf59f401745131171435049990_inner_var_411 -> 0xcad7ec0 +412 -> 0xcf59f401745131171435049990_inner_var_412 -> 0xcf7cbb0 +413 -> 0xcf59f401745131171435049990_inner_var_413 -> 0xcec7620 +414 -> 0xcf59f401745131171435049990_inner_var_414 -> 0xccc05c0 +415 -> 0xcf59f401745131171435049990_inner_var_415 -> 0xca942d0 +416 -> 0xcf59f401745131171435049990_inner_var_416 -> 0xceccf20 +417 -> 0xcf59f401745131171435049990_inner_var_417 -> 0xcaecc00 +418 -> 0xcf59f401745131171435049990_inner_var_418 -> 0xcaed490 +419 -> 0xcf59f401745131171435049990_inner_var_419 -> 0xca984f0 +420 -> 0xcf59f401745131171435049990_inner_var_420 -> 0xcfcbe20 +421 -> 0xcf59f401745131171435049990_inner_var_421 -> 0xccc3bd0 +422 -> 0xcf59f401745131171435049990_inner_var_422 -> 0xd047d80 +423 -> 0xcf59f401745131171435049990_inner_var_423 -> 0xcea8fb0 +424 -> 0xcf59f401745131171435049990_inner_var_424 -> 0xca62570 +425 -> 0xcf59f401745131171435049990_inner_var_425 -> 0xee8cc70 +426 -> 0xcf59f401745131171435049990_inner_var_426 -> 0xceb5560 +427 -> 0xcf59f401745131171435049990_inner_var_427 -> 0xced0d30 +428 -> 0xcf59f401745131171435049990_inner_var_428 -> 0xcab8450 +429 -> 0xcf59f401745131171435049990_inner_var_429 -> 0xcab60d0 +430 -> 0xcf59f401745131171435049990_inner_var_430 -> 0xcead070 +431 -> 0xcf59f401745131171435049990_inner_var_431 -> 0xcade0a0 +432 -> 0xcf59f401745131171435049990_inner_var_432 -> 0xcabcfc0 +433 -> 0xcf59f401745131171435049990_inner_var_433 -> 0xce94af0 +434 -> 0xcf59f401745131171435049990_inner_var_434 -> 0xcebccc0 +435 -> 0xcf59f401745131171435049990_inner_var_435 -> 0xd048180 +436 -> 0xcf59f401745131171435049990_inner_var_436 -> 0xcea5740 +437 -> 0xcf59f401745131171435049990_inner_var_437 -> 0xcab9830 +438 -> 0xcf59f401745131171435049990_inner_var_438 -> 0xcf97aa0 +439 -> 0xcf59f401745131171435049990_inner_var_439 -> 0xd06ce40 +440 -> 0xcf59f401745131171435049990_inner_var_440 -> 0xcedd7e0 +441 -> 0xcf59f401745131171435049990_inner_var_441 -> 0xcea0cd0 +442 -> 0xcf59f401745131171435049990_inner_var_442 -> 0xd06eb50 +443 -> 0xcf59f401745131171435049990_inner_var_443 -> 0xd069a30 +444 -> 0xcf59f401745131171435049990_inner_var_444 -> 0xce89bf0 +445 -> 0xcf59f401745131171435049990_inner_var_445 -> 0xcf21760 +446 -> 0xcf59f401745131171435049990_inner_var_446 -> 0xcbf35d0 +447 -> 0xcf59f401745131171435049990_inner_var_447 -> 0xcab49d0 +448 -> 0xcf59f401745131171435049990_inner_var_448 -> 0xcf17050 +449 -> 0xcf59f401745131171435049990_inner_var_449 -> 0xee40240 +450 -> 0xcf59f401745131171435049990_inner_var_450 -> 0xd040f30 +451 -> 0xcf59f401745131171435049990_inner_var_451 -> 0xcf7c250 +452 -> 0xcf59f401745131171435049990_inner_var_452 -> 0xcca3870 +453 -> 0xcf59f401745131171435049990_inner_var_453 -> 0xee47f90 +454 -> 0xcf59f401745131171435049990_inner_var_454 -> 0xd0385d0 +455 -> 0xcf59f401745131171435049990_inner_var_455 -> 0xcfa62c0 +456 -> 0xcf59f401745131171435049990_inner_var_456 -> 0xcabc9a0 +457 -> 0xcf59f401745131171435049990_inner_var_457 -> 0xcefb360 +458 -> 0xcf59f401745131171435049990_inner_var_458 -> 0xcef8830 +459 -> 0xcf59f401745131171435049990_inner_var_459 -> 0xceca990 +460 -> 0xcf59f401745131171435049990_inner_var_460 -> 0xced79a0 +461 -> 0xcf59f401745131171435049990_inner_var_461 -> 0xce94610 +462 -> 0xcf59f401745131171435049990_inner_var_462 -> 0xcab5be0 +463 -> 0xcf59f401745131171435049990_inner_var_463 -> 0xcec4600 +464 -> 0xcf59f401745131171435049990_inner_var_464 -> 0xcab8eb0 +465 -> 0xcf59f401745131171435049990_inner_var_465 -> 0xee400a0 +466 -> 0xcf59f40 +I0420 14:40:57.310817 115867 pir_interpreter.cc:1587] ======================= The dependency of all instruction ======================== +id -> down_stream_id +0 -> 3 +1 -> 2 +2 -> 47 +3 -> 5 +4 -> 5 +5 -> 6 14 +6 -> 7 +7 -> 8 +8 -> 9 +9 -> 10 +10 -> 11 +11 -> 12 +12 -> 13 +13 -> 16 +14 -> 15 +15 -> 16 +16 -> 17 +17 -> 18 +18 -> 19 +19 -> 20 +20 -> 21 +21 -> 22 +22 -> 23 +23 -> 24 +24 -> 25 +25 -> 26 +26 -> 27 +27 -> 28 +28 -> 29 +29 -> 30 +30 -> 31 +31 -> 32 +32 -> 33 +33 -> 34 +34 -> 35 +35 -> 36 +36 -> 37 +37 -> 38 +38 -> 39 +39 -> 40 +40 -> 41 +41 -> 42 +42 -> 43 +43 -> 44 +44 -> 45 +45 -> 46 +46 -> 47 +47 -> 48 55 +48 -> 49 +49 -> 50 +50 -> 51 +51 -> 52 +52 -> 53 +53 -> 54 +54 -> 59 +55 -> 56 +56 -> 57 +57 -> 58 +58 -> 59 +59 -> 60 +60 -> 61 + +2025-04-20 14:41:04.833591: [cnrtError] [115867] [Card: 0] Error occurred during calling 'cnQueueSync' in CNDrv interface. +2025-04-20 14:41:04.833645: [cnrtError] [115867] [Card: 0] Return value is 100124, CN_INVOKE_ERROR_ADDRESS_SPACE. +2025-04-20 14:41:04.833654: [cnrtError] [115867] [Card: 0] cnrtQueueSync: MLU queue sync failed. +I0420 14:41:10.464324 115867 op_call_stack.cc:127] MLU CNRT error(632015), cnrtErrorCndrvFuncCall: failed to call the driver-api function. (at /paddle/backends/mlu/runtime/runtime.cc:229) +W0420 14:41:10.464382 115867 pir_interpreter.cc:1989] Instruction OP id: 341, Ir OP id is null, if_instruction raises an EnforceNotMet exception common::enforce::EnforceNotMet +I0420 14:41:10.464473 115867 pir_interpreter.cc:1712] Exception caught EnforceNotMet +Traceback (most recent call last): + File "/work/PaddleX/paddlex/utils/result_saver.py", line 28, in wrap + result = func(self, *args, **kwargs) + File "/work/PaddleX/paddlex/engine.py", line 49, in run + for res in self._model.predict(): + File "/work/PaddleX/paddlex/model.py", line 131, in predict + yield from predictor(**predict_kwargs) + File "/work/PaddleX/paddlex/inference/models/base/predictor/base_predictor.py", line 213, in __call__ + yield from self.apply(input, **kwargs) + File "/work/PaddleX/paddlex/inference/models/base/predictor/base_predictor.py", line 269, in apply + prediction = self.process(batch_data, **kwargs) + File "/work/PaddleX/paddlex/inference/models/instance_segmentation/predictor.py", line 130, in process + batch_preds = self.infer(batch_inputs) + File "/work/PaddleX/paddlex/inference/models/common/static_infer.py", line 334, in __call__ + pred = self.infer(x) + File "/work/PaddleX/paddlex/inference/models/common/static_infer.py", line 291, in __call__ + self.predictor.run() +paddle.base.libpaddle.EnforceNotMet: In user code: + + MLU CNRT error(632015), cnrtErrorCndrvFuncCall: failed to call the driver-api function. (at /paddle/backends/mlu/runtime/runtime.cc:229) + [operator < pd_op.if > error] +Process 115867 exited with status = 1 (0x00000001) diff --git a/mycheck.py b/mycheck.py new file mode 100644 index 000000000..017f3bcdf --- /dev/null +++ b/mycheck.py @@ -0,0 +1,35 @@ +import paddle +import paddle.base as base +import numpy as np +def sample_output_one_dimension(out, dim): + # count numbers of different categories + sample_prob = np.zeros(dim).astype("float32") + sample_index_prob = np.unique(out, return_counts=True) + sample_prob[sample_index_prob[0]] = sample_index_prob[1] + sample_prob /= sample_prob.sum() + return sample_prob + +paddle.enable_static() +paddle.seed(100) +for _ in range(100): + print(f"start epoch {_}") + paddle.set_device("mlu:0") + startup_program = base.Program() + train_program = base.Program() + with base.program_guard(train_program, startup_program): + x = paddle.static.data("x", shape=[4], dtype="float32") + outs = [paddle.multinomial(x, num_samples=200000, replacement=True) for _ in range(100)] + + out = paddle.concat(outs, axis=0) + # out = paddle.multinomial(x, num_samples=250000, replacement=True) + place = base.CustomPlace("mlu", 0) + exe = base.Executor(place) + + exe.run(startup_program) + x_np = np.random.rand(4).astype("float32") + out = exe.run(train_program, feed={"x": x_np}, fetch_list=[out]) + + sample_prob = sample_output_one_dimension(out, 4) + prob = x_np / x_np.sum(axis=-1, keepdims=True) + print(f"target prob: {prob}") + np.testing.assert_allclose(sample_prob, prob, rtol=0, atol=0.01) \ No newline at end of file