Repo sync (secretflow#366)

# Pull Request

## What problem does this PR solve?

Issue Number: Fixed #

## Possible side effects?

- Performance:

- Backward compatibility:

Author: anakinxc

benchmark/binary_op_bench.py

@@ -14,10 +14,10 @@
 
 import argparse
 import json
+import time
 
 import jax.numpy as jnp
 import numpy as np
-import time
 
 import spu.utils.distributed as ppd
 

benchmark/unary_op_bench.py

@@ -14,10 +14,10 @@
 
 import argparse
 import json
+import time
 
 import jax.numpy as jnp
 import numpy as np
-import time
 
 import spu.utils.distributed as ppd
 

docs/reference/gen_benchmark_report.py

@@ -15,12 +15,13 @@
 # limitations under the License.
 
 
+import argparse
 import json
-import pandas as pd
-import numpy as np
 import os
 from enum import Enum
-import argparse
+
+import numpy as np
+import pandas as pd
 
 g_time_list = [
     ('ns', 1000),

docs/reference/gen_complexity_md.py

@@ -16,9 +16,10 @@
 
 
 import argparse
-from pytablewriter import MarkdownTableWriter
 import json
 
+from pytablewriter import MarkdownTableWriter
+
 
 def main():
     parser = argparse.ArgumentParser(

docs/reference/gen_np_op_status_doc.py

@@ -16,9 +16,10 @@
 
 
 import argparse
-from pytablewriter import MarkdownTableWriter
 import json
+
 from mdutils.mdutils import MdUtils
+from pytablewriter import MarkdownTableWriter
 
 
 def main():

docs/tutorials/cpp_lr_example.rst

@@ -20,11 +20,11 @@ In the first terminal.
 
 .. code-block:: bash
 
-  bazel run //examples/cpp:simple_lr -- -rank 0 -dataset examples/cpp/data/perfect_logit_a.csv -has_label=true
+  bazel run //examples/cpp:simple_lr -- -rank 0 -dataset examples/cpp/perfect_logit_a.csv -has_label=true
 
 In the second terminal.
 
 .. code-block:: bash
 
-  bazel run //examples/cpp:simple_lr -- -rank 1 -dataset examples/cpp/data/perfect_logit_b.csv
+  bazel run //examples/cpp:simple_lr -- -rank 1 -dataset examples/cpp/perfect_logit_b.csv
 

examples/cpp/BUILD.bazel

@@ -25,7 +25,12 @@ spu_cc_binary(
     deps = [
         ":utils",
         "//libspu/device:io",
-        "//libspu/kernel/hal",
+        "//libspu/kernel/hal:public_helper",
+        "//libspu/kernel/hlo:basic_binary",
+        "//libspu/kernel/hlo:basic_unary",
+        "//libspu/kernel/hlo:casting",
+        "//libspu/kernel/hlo:const",
+        "//libspu/kernel/hlo:geometrical",
         "@com_google_absl//absl/strings",
         "@llvm-project//llvm:Support",
         "@yacl//yacl/link:factory",

examples/cpp/simple_lr.cc

@@ -24,45 +24,51 @@
 
 #include "examples/cpp/utils.h"
 #include "spdlog/spdlog.h"
+#include "xtensor/xarray.hpp"
 #include "xtensor/xcsv.hpp"
+#include "xtensor/xview.hpp"
 
 #include "libspu/device/io.h"
-#include "libspu/kernel/hal/hal.h"
-#include "libspu/kernel/hal/type_cast.h"
+#include "libspu/kernel/hal/public_helper.h"
+#include "libspu/kernel/hlo/basic_binary.h"
+#include "libspu/kernel/hlo/basic_unary.h"
+#include "libspu/kernel/hlo/casting.h"
+#include "libspu/kernel/hlo/const.h"
+#include "libspu/kernel/hlo/geometrical.h"
 #include "libspu/mpc/factory.h"
 
 using namespace spu::kernel;
 
 spu::Value train_step(spu::SPUContext* ctx, const spu::Value& x,
                       const spu::Value& y, const spu::Value& w) {
   // Padding x
-  auto padding = hal::constant(ctx, 1.0F, spu::DT_F32, {x.shape()[0], 1});
-  auto padded_x = hal::concatenate(ctx, {x, hal::seal(ctx, padding)}, 1);
-  auto pred = hal::logistic(ctx, hal::matmul(ctx, padded_x, w));
+  auto padding = hlo::Constant(ctx, 1.0F, {x.shape()[0], 1});
+  auto padded_x = hlo::Concatenate(
+      ctx, {x, hlo::Cast(ctx, padding, spu::VIS_SECRET, padding.dtype())}, 1);
+  auto pred = hlo::Logistic(ctx, hlo::Dot(ctx, padded_x, w));
 
   SPDLOG_DEBUG("[SSLR] Err = Pred - Y");
-  auto err = hal::sub(ctx, pred, y);
+  auto err = hlo::Sub(ctx, pred, y);
 
   SPDLOG_DEBUG("[SSLR] Grad = X.t * Err");
-  auto grad = hal::matmul(ctx, hal::transpose(ctx, padded_x), err);
+  auto grad = hlo::Dot(ctx, hlo::Transpose(ctx, padded_x, {}), err);
 
   SPDLOG_DEBUG("[SSLR] Step = LR / B * Grad");
-  auto lr = hal::constant(ctx, 0.0001F, spu::DT_F32);
-  auto msize =
-      hal::constant(ctx, static_cast<float>(y.shape()[0]), spu::DT_F32);
-  auto p1 = hal::mul(ctx, lr, hal::reciprocal(ctx, msize));
-  auto step = hal::mul(ctx, hal::broadcast_to(ctx, p1, grad.shape()), grad);
+  auto lr = hlo::Constant(ctx, 0.0001F, {});
+  auto msize = hlo::Constant(ctx, static_cast<float>(y.shape()[0]), {});
+  auto p1 = hlo::Mul(ctx, lr, hlo::Reciprocal(ctx, msize));
+  auto step = hlo::Mul(ctx, hlo::Broadcast(ctx, p1, grad.shape(), {}), grad);
 
   SPDLOG_DEBUG("[SSLR] W = W - Step");
-  auto new_w = hal::sub(ctx, w, step);
+  auto new_w = hlo::Sub(ctx, w, step);
 
   return new_w;
 }
 
 spu::Value train(spu::SPUContext* ctx, const spu::Value& x, const spu::Value& y,
                  size_t num_epoch, size_t bsize) {
   const size_t num_iter = x.shape()[0] / bsize;
-  auto w = hal::constant(ctx, 0.0F, spu::DT_F32, {x.shape()[1] + 1, 1});
+  auto w = hlo::Constant(ctx, 0.0F, {x.shape()[1] + 1, 1});
 
   // Run train loop
   for (size_t epoch = 0; epoch < num_epoch; ++epoch) {
@@ -73,10 +79,10 @@ spu::Value train(spu::SPUContext* ctx, const spu::Value& x, const spu::Value& y,
       const int64_t rows_end = rows_beg + bsize;
 
       const auto x_slice =
-          hal::slice(ctx, x, {rows_beg, 0}, {rows_end, x.shape()[1]}, {});
+          hlo::Slice(ctx, x, {rows_beg, 0}, {rows_end, x.shape()[1]}, {});
 
       const auto y_slice =
-          hal::slice(ctx, y, {rows_beg, 0}, {rows_end, y.shape()[1]}, {});
+          hlo::Slice(ctx, y, {rows_beg, 0}, {rows_end, y.shape()[1]}, {});
 
       w = train_step(ctx, x_slice, y_slice, w);
     }
@@ -87,9 +93,10 @@ spu::Value train(spu::SPUContext* ctx, const spu::Value& x, const spu::Value& y,
 
 spu::Value inference(spu::SPUContext* ctx, const spu::Value& x,
                      const spu::Value& weight) {
-  auto padding = hal::constant(ctx, 1.0F, spu::DT_F32, {x.shape()[0], 1});
-  auto padded_x = hal::concatenate(ctx, {x, hal::seal(ctx, padding)}, 1);
-  return hal::matmul(ctx, padded_x, weight);
+  auto padding = hlo::Constant(ctx, 1.0F, {x.shape()[0], 1});
+  auto padded_x = hlo::Concatenate(
+      ctx, {x, hlo::Cast(ctx, padding, spu::VIS_SECRET, padding.dtype())}, 1);
+  return hlo::Dot(ctx, padded_x, weight);
 }
 
 float SSE(const xt::xarray<float>& y_true, const xt::xarray<float>& y_pred) {
@@ -143,7 +150,7 @@ std::pair<spu::Value, spu::Value> infeed(spu::SPUContext* sctx,
   auto x = cio.deviceGetVar("x-0");
   // Concatenate all slices
   for (size_t idx = 1; idx < cio.getWorldSize(); ++idx) {
-    x = hal::concatenate(sctx, {x, cio.deviceGetVar(fmt::format("x-{}", idx))},
+    x = hlo::Concatenate(sctx, {x, cio.deviceGetVar(fmt::format("x-{}", idx))},
                          1);
   }
   auto y = cio.deviceGetVar("label");
@@ -175,10 +182,11 @@ int main(int argc, char** argv) {
 
   const auto scores = inference(sctx.get(), x, w);
 
-  xt::xarray<float> revealed_labels =
-      hal::dump_public_as<float>(sctx.get(), hal::reveal(sctx.get(), y));
-  xt::xarray<float> revealed_scores =
-      hal::dump_public_as<float>(sctx.get(), hal::reveal(sctx.get(), scores));
+  xt::xarray<float> revealed_labels = hal::dump_public_as<float>(
+      sctx.get(), hlo::Cast(sctx.get(), y, spu::VIS_PUBLIC, y.dtype()));
+  xt::xarray<float> revealed_scores = hal::dump_public_as<float>(
+      sctx.get(),
+      hlo::Cast(sctx.get(), scores, spu::VIS_PUBLIC, scores.dtype()));
 
   auto mse = MSE(revealed_labels, revealed_scores);
   std::cout << "MSE = " << mse << "\n";

examples/cpp/simple_pphlo.cc

@@ -19,7 +19,6 @@
 // clang-format on
 
 #include "examples/cpp/utils.h"
-#include "spdlog/spdlog.h"
 
 #include "libspu/device/api.h"
 #include "libspu/device/io.h"

examples/cpp/utils.cc

@@ -14,8 +14,8 @@
 
 #include "examples/cpp/utils.h"
 
-#include "absl/strings/match.h"
 #include "absl/strings/str_split.h"
+#include "yacl/link/factory.h"
 
 #include "libspu/core/config.h"
 
@@ -41,7 +41,7 @@ std::shared_ptr<yacl::link::Context> MakeLink(const std::string& parties,
   std::vector<std::string> hosts = absl::StrSplit(parties, ',');
   for (size_t rank = 0; rank < hosts.size(); rank++) {
     const auto id = fmt::format("party{}", rank);
-    lctx_desc.parties.push_back({id, hosts[rank]});
+    lctx_desc.parties.emplace_back(id, hosts[rank]);
   }
   auto lctx = yacl::link::FactoryBrpc().CreateContext(lctx_desc, rank);
   lctx->ConnectToMesh();

examples/python/ir_dump/ir_dump.py

@@ -28,8 +28,8 @@
 import jax.numpy as jnp
 import numpy as np
 
-import spu.utils.distributed as ppd
 import spu.spu_pb2 as spu_pb2
+import spu.utils.distributed as ppd
 
 logging.basicConfig(level=logging.INFO)
 

examples/python/ml/flax_llama7b/flax_llama7b.py

@@ -19,18 +19,20 @@
 
 import argparse
 import json
+from contextlib import contextmanager
+from typing import Any, Optional, Tuple, Union
+
+import flax.linen as nn
 import jax
-import jax.numpy as jnp
 import jax.nn as jnn
-import flax.linen as nn
+import jax.numpy as jnp
+from EasyLM.models.llama.llama_model import FlaxLLaMAForCausalLM, LLaMAConfig
 from flax.linen.linear import Array
-from typing import Any, Optional, Tuple, Union
 from transformers import LlamaTokenizer
-from EasyLM.models.llama.llama_model import LLaMAConfig, FlaxLLaMAForCausalLM
-import spu.utils.distributed as ppd
-from contextlib import contextmanager
+
 import spu.intrinsic as intrinsic
 import spu.spu_pb2 as spu_pb2
+import spu.utils.distributed as ppd
 
 parser = argparse.ArgumentParser(description='distributed driver.')
 parser.add_argument("-c", "--config", default="examples/python/ml/flax_llama/3pc.json")

examples/python/ml/flax_mlp/flax_mlp.py

@@ -106,9 +106,10 @@ def run_on_cpu():
 
 ppd.init(conf["nodes"], conf["devices"])
 
-import cloudpickle as pickle
 import tempfile
 
+import cloudpickle as pickle
+
 
 def compute_score(param, type):
     x_test, y_test = dsutil.breast_cancer(slice(None, None, None), False)

examples/python/ml/flax_resnet/flax_resnet.py

@@ -17,19 +17,17 @@
 # See issue #620.
 # pytype: disable=wrong-arg-count
 
-from typing import Any
 import argparse
 import time
+from typing import Any
 
+import jax
+import jax.numpy as jnp
+import optax
 import tensorflow as tf
 import tensorflow_datasets as tfds
-
-import optax
 from flax.training import train_state
-import jax.numpy as jnp
-import jax
 from jax import random
-
 from models import ResNet18
 
 NUM_CLASSES = 10

examples/python/ml/flax_resnet/models.py

@@ -20,8 +20,8 @@
 from functools import partial
 from typing import Any, Callable, Sequence, Tuple
 
-from flax import linen as nn
 import jax.numpy as jnp
+from flax import linen as nn
 
 ModuleDef = Any
 

examples/python/ml/flax_vae/flax_vae.py

@@ -21,11 +21,11 @@
 import optax
 import tensorflow as tf
 import tensorflow_datasets as tfds
+from flax import linen as nn
+from flax.training import train_state
 from jax import random
 
 import examples.python.ml.flax_vae.utils as vae_utils
-from flax import linen as nn
-from flax.training import train_state
 
 # Replace absl.flags used by original authors with argparse for unittest
 parser = argparse.ArgumentParser(description='distributed driver.')

examples/python/ml/jraph_gnn/jraph_gnn.py

@@ -24,12 +24,12 @@
 
 import logging
 
-from absl import app
 import haiku as hk
 import jax
 import jax.numpy as jnp
 import jraph
 import optax
+from absl import app
 
 
 def get_zacharys_karate_club() -> jraph.GraphsTuple:
@@ -252,9 +252,10 @@ def predict(params):
 
 
 import argparse
-import spu.utils.distributed as ppd
 import json
 
+import spu.utils.distributed as ppd
+
 parser = argparse.ArgumentParser(description="distributed driver.")
 parser.add_argument("-c", "--config", default="examples/python/conf/3pc.json")
 args = parser.parse_args()

examples/python/ml/ml_test.py

@@ -13,21 +13,20 @@
 # limitations under the License.
 
 
+import inspect
 import json
 import logging
+import os
 import sys
 import unittest
 from time import perf_counter
-import os
 
 import multiprocess
 import numpy.testing as npt
 import pandas as pd
-import inspect
 
 import spu.utils.distributed as ppd
 
-
 with open("examples/python/conf/3pc.json", 'r') as file:
     conf = json.load(file)