[API Compatibility] add torch.optim.*/ utils.data.*

paconvert/api_mapping.json

@@ -8497,39 +8497,7 @@
     }
   },
   "torch.optim.Adagrad": {
-    "Matcher": "GenericMatcher",
-    "paddle_api": "paddle.optimizer.Adagrad",
-    "min_input_args": 1,
-    "args_list": [
-      "params",
-      "lr",
-      "lr_decay",
-      "weight_decay",
-      "initial_accumulator_value",
-      "eps",
-      "foreach",
-      "*",
-      "maximize",
-      "differentiable",
-      "fused"
-    ],
-    "unsupport_args": [
-      "lr_decay",
-      "foreach",
-      "maximize",
-      "differentiable",
-      "fused"
-    ],
-    "kwargs_change": {
-      "params": "parameters",
-      "lr": "learning_rate",
-      "eps": "epsilon"
-    },
-    "paddle_default_kwargs": {
-      "weight_decay": 0.0,
-      "epsilon": 1e-10,
-      "learning_rate": 0.01
-    }
+    "Matcher": "ChangePrefixMatcher"
   },
   "torch.optim.Adam": {
     "Matcher": "OptimAdamMatcher",
@@ -8569,39 +8537,7 @@
     }
   },
   "torch.optim.AdamW": {
-    "Matcher": "OptimAdamMatcher",
-    "paddle_api": "paddle.optimizer.AdamW",
-    "min_input_args": 1,
-    "args_list": [
-      "params",
-      "lr",
-      "betas",
-      "eps",
-      "weight_decay",
-      "amsgrad",
-      "*",
-      "maximize",
-      "foreach",
-      "capturable",
-      "differentiable",
-      "fused"
-    ],
-    "unsupport_args": [
-      "amsgrad",
-      "maximize",
-      "foreach",
-      "capturable",
-      "differentiable",
-      "fused"
-    ],
-    "kwargs_change": {
-      "params": "parameters",
-      "lr": "learning_rate",
-      "eps": "epsilon"
-    },
-    "paddle_default_kwargs": {
-      "weight_decay": 0.0
-    }
+    "Matcher": "ChangePrefixMatcher"
   },
   "torch.optim.Adamax": {
     "Matcher": "OptimAdamMatcher",
@@ -8683,13 +8619,7 @@
     }
   },
   "torch.optim.Optimizer": {
-    "Matcher": "OptimOptimizerMatcher",
-    "paddle_api": "paddle.optimizer.Optimizer",
-    "min_input_args": 2,
-    "args_list": [
-      "params",
-      "defaults"
-    ]
+    "Matcher": "ChangePrefixMatcher"
   },
   "torch.optim.Optimizer.add_param_group": {
     "Matcher": "ChangeAPIMatcher",
@@ -8812,38 +8742,7 @@
     ]
   },
   "torch.optim.SGD": {
-    "Matcher": "GenericMatcher",
-    "paddle_api": "paddle.optimizer.SGD",
-    "min_input_args": 1,
-    "args_list": [
-      "params",
-      "lr",
-      "momentum",
-      "dampening",
-      "weight_decay",
-      "nesterov",
-      "*",
-      "maximize",
-      "foreach",
-      "differentiable",
-      "fused"
-    ],
-    "unsupport_args": [
-      "momentum",
-      "dampening",
-      "nesterov",
-      "maximize",
-      "foreach",
-      "differentiable",
-      "fused"
-    ],
-    "kwargs_change": {
-      "params": "parameters",
-      "lr": "learning_rate"
-    },
-    "paddle_default_kwargs": {
-      "weight_decay": 0.0
-    }
+    "Matcher": "ChangePrefixMatcher"
   },
   "torch.optim.lr_scheduler.ConstantLR": {
     "Matcher": "ConstantLRMatcher",
@@ -10240,39 +10139,7 @@
     "Matcher": "ChangePrefixMatcher"
   },
   "torch.utils.data.DataLoader": {
-    "Matcher": "GenericMatcher",
-    "paddle_api": "paddle.io.DataLoader",
-    "min_input_args": 1,
-    "args_list": [
-      "dataset",
-      "batch_size",
-      "shuffle",
-      "sampler",
-      "batch_sampler",
-      "num_workers",
-      "collate_fn",
-      "pin_memory",
-      "drop_last",
-      "timeout",
-      "worker_init_fn",
-      "multiprocessing_context",
-      "generator",
-      "*",
-      "prefetch_factor",
-      "persistent_workers",
-      "pin_memory_device"
-    ],
-    "kwargs_change": {
-      "pin_memory": "",
-      "multiprocessing_context": "",
-      "generator": "",
-      "persistent_workers": "",
-      "pin_memory_device": ""
-    },
-    "unsupport_args": [
-      "sampler",
-      "prefetch_factor"
-    ]
+    "Matcher": "ChangePrefixMatcher"
   },
   "torch.utils.data.Dataset": {
     "Matcher": "ChangePrefixMatcher"
@@ -10321,6 +10188,9 @@
   "torch.utils.data._utils.collate.default_collate": {
     "Matcher": "ChangePrefixMatcher"
   },
+  "torch.utils.data.dataloader.DataLoader": {
+    "Matcher": "ChangePrefixMatcher"
+  },
   "torch.utils.data.dataloader.default_collate": {
     "Matcher": "ChangePrefixMatcher"
   },

tests/code_library/code_case/paddle_code/class_method_static_call.py

@@ -1,7 +1,7 @@
 import paddle
 
 
-class PT_Optimizer(paddle.optimizer.Optimizer):
+class PT_Optimizer(paddle.optim.Optimizer):
     pass
 
 

tests/distributed/utils_data_distributed_DistributedSampler.py

@@ -0,0 +1,56 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import os
+
+import numpy as np
+import torch
+import torch.distributed as dist
+from torch.utils.data import Dataset
+from torch.utils.data.distributed import DistributedSampler
+
+dist.init_process_group(backend="nccl")
+rank = dist.get_rank()
+torch.cuda.set_device(rank)
+
+
+class RandomDataset(Dataset):
+    def __init__(self, num_samples):
+        self.num_samples = num_samples
+
+    def __getitem__(self, idx):
+        image = np.random.random([16]).astype("float32")
+        label = np.random.randint(0, 9, (1,)).astype("int64")
+        return image, label
+
+    def __len__(self):
+        return self.num_samples
+
+
+dataset = RandomDataset(16)
+sampler = DistributedSampler(
+    dataset=dataset,
+    num_replicas=None,
+    rank=None,
+    shuffle=False,
+    seed=0,
+    drop_last=False,
+)
+
+data = [i for i in sampler]
+data = torch.tensor(data).squeeze()
+if rank == 0:
+    print(data)
+    torch.save(data, os.environ["DUMP_FILE"])

tests/test_nn_modules_module__IncompatibleKeys.py

@@ -0,0 +1,71 @@
+# Copyright (c) 2026 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import textwrap
+
+from apibase import APIBase
+
+obj = APIBase("torch.nn.modules.module._IncompatibleKeys")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        model = torch.nn.Linear(1, 2)
+        incompatible_keys = model.load_state_dict({"a": 2.0}, strict=False)
+        """
+    )
+    obj.run(pytorch_code, ["incompatible_keys"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        model = torch.nn.Linear(2, 3)
+        missing, unexpected = model.load_state_dict({"b": -2.0}, strict=False)
+        """
+    )
+    obj.run(pytorch_code, ["missing", "unexpected"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        model = torch.nn.Linear(6, 2)
+        result = model.load_state_dict({"c": -6.0}, strict=False)
+        result_0 = result[0]
+        result_1 = result[1]
+        result_missing = result.missing_keys
+        result_unexpected = result.unexpected_keys
+        """
+    )
+    obj.run(
+        pytorch_code, ["result_0", "result_1", "result_missing", "result_unexpected"]
+    )
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        from collections import namedtuple
+        model = torch.nn.Linear(6, 2)
+        result = model.load_state_dict({"c": -6.0}, strict=False)
+        is_tuple = isinstance(result, tuple)
+        """
+    )
+    obj.run(pytorch_code, ["is_tuple"])

tests/test_optim_Adagrad.py

@@ -82,37 +82,25 @@ def test_case_7():
 def test_case_8():
     pytorch_code = textwrap.dedent(
         generate_optimizer_test_code(
-            "torch.optim.Adagrad(params=conv.parameters(), lr=0.01, lr_decay=0, weight_decay=0, initial_accumulator_value=0, eps=1e-10, foreach=None,  maximize=False, differentiable=False)"
+            "torch.optim.Adagrad(params=conv.parameters(), lr=0.01, lr_decay=0.1, weight_decay=0, initial_accumulator_value=0, eps=1e-10, foreach=None,  maximize=False, differentiable=False)"
         )
     )
-    obj.run(
-        pytorch_code,
-        unsupport=True,
-        reason="`lr_decay`, `foreach`, 'maximize` and `differentiable` is not supported.",
-    )
+    obj.run(pytorch_code, ["result"])
 
 
 def test_case_9():
     pytorch_code = textwrap.dedent(
         generate_optimizer_test_code(
-            "torch.optim.Adagrad(conv.parameters(), 0.01, 0, 0, 0, 1e-10, None,  maximize=False, differentiable=False)"
+            "torch.optim.Adagrad(conv.parameters(), 0.01, 0.1, 0.01, 0, 1e-10, None,  maximize=True, differentiable=False)"
         )
     )
-    obj.run(
-        pytorch_code,
-        unsupport=True,
-        reason="`lr_decay`, `foreach`, 'maximize` and `differentiable` is not supported.",
-    )
+    obj.run(pytorch_code, ["result"])
 
 
 def test_case_10():
     pytorch_code = textwrap.dedent(
         generate_optimizer_test_code(
-            "torch.optim.Adagrad(params=conv.parameters(), lr_decay=0, lr=0.01, initial_accumulator_value=0, weight_decay=0, eps=1e-10, foreach=None,  maximize=False, differentiable=False)"
+            "torch.optim.Adagrad(params=conv.parameters(), lr_decay=0, lr=0.01, initial_accumulator_value=0, weight_decay=0, eps=1e-10, foreach=None,  maximize=True, differentiable=False)"
         )
     )
-    obj.run(
-        pytorch_code,
-        unsupport=True,
-        reason="`lr_decay`, `foreach`, 'maximize` and `differentiable` is not supported.",
-    )
+    obj.run(pytorch_code, ["result"])