转换规则 No.240-242. (#119)

* Add 240-242.

* refine matcher of torch.utils.data.random_split.

* refine matcher of torch.utils.data.random_split.

* refine matcher of torch.utils.data.random_split.

paconvert/api_mapping.json

@@ -8610,6 +8610,35 @@
       "data_source"
     ]
   },
+  "torch.utils.data.random_split": {
+    "Matcher": "RandomSplitMatcher",
+    "paddle_api": "paddle.io.random_split",
+    "args_list": [
+      "dataset",
+      "lengths",
+      "generator"
+    ]
+  },
+  "torch.utils.dlpack.from_dlpack": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.utils.dlpack.from_dlpack",
+    "args_list": [
+      "ext_tensor"
+    ],
+    "kwargs_change": {
+      "ext_tensor": "dlpack"
+    }
+  },
+  "torch.utils.dlpack.to_dlpack": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.utils.dlpack.to_dlpack",
+    "args_list": [
+      "tensor"
+    ],
+    "kwargs_change": {
+      "tensor": "x"
+    }
+  },
   "torch.nn.functional.l1_loss": {
     "Matcher": "SizeAverageMatcher",
     "paddle_api": "paddle.nn.functional.l1_loss",

paconvert/api_matcher.py

@@ -3645,3 +3645,24 @@ class SizeAverageMatcher(BaseMatcher):
     def generate_code(self, kwargs):
         process_reduce_and_size_average(kwargs)
         return GenericMatcher.generate_code(self, kwargs)
+
+
+class RandomSplitMatcher(BaseMatcher):
+    def generate_code(self, kwargs):
+        API_TEMPLATE = textwrap.dedent(
+            """
+            dataset_lengths = {}
+            if sum(dataset_lengths) <= 1:
+                dataset_lengths = [int(length * {}.__len__()) for length in dataset_lengths]
+            {}({})
+            """
+        )
+        lenghts_v = kwargs["lengths"].strip("\n")
+        kwargs["lengths"] = "dataset_lengths"
+        code = API_TEMPLATE.format(
+            lenghts_v,
+            kwargs["dataset"],
+            self.get_paddle_api(),
+            self.kwargs_to_str(kwargs),
+        )
+        return code.strip("\n")

tests/test_utils_data_random_split.py

@@ -0,0 +1,181 @@
+# 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 textwrap
+
+from apibase import APIBase
+
+obj = APIBase("torch.utils.data.random_split")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        from torch.utils.data import Dataset
+
+        class Data(Dataset):
+            def __init__(self):
+                self.x = [0,1,2,3,4,5,6,7,8,9]
+
+            def __getitem__(self, idx):
+                return self.x[idx]
+
+            def __len__(self):
+                return len(self.x)
+
+
+        data = Data()
+
+        datasets = torch.utils.data.random_split(data, [3, 7])
+
+        results = []
+        for d in datasets:
+            results.append(d.__len__())
+        """
+    )
+    obj.run(pytorch_code, ["results"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        from torch.utils.data import Dataset
+
+        class Data(Dataset):
+            def __init__(self):
+                self.x = [0,1,2,3,4,5,6,7,8,9]
+
+            def __getitem__(self, idx):
+                return self.x[idx]
+
+            def __len__(self):
+                return len(self.x)
+
+
+        data = Data()
+        datasets = torch.utils.data.random_split(data, [3, 3, 4])
+
+        results = []
+        for d in datasets:
+            results.append(d.__len__())
+        """
+    )
+    obj.run(pytorch_code, ["results"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        from torch.utils.data import Dataset
+
+        class Data(Dataset):
+            def __init__(self):
+                self.x = [0,1,2,3,4,5,6,7,8,9]
+
+            def __getitem__(self, idx):
+                return self.x[idx]
+
+            def __len__(self):
+                return len(self.x)
+
+
+        data = Data()
+        lengths = [3, 3, 4]
+        datasets = torch.utils.data.random_split(data, lengths)
+
+        results = []
+        for d in datasets:
+            results.append(d.__len__())
+        """
+    )
+    obj.run(pytorch_code, ["results"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        from torch.utils.data import Dataset
+
+        class Data(Dataset):
+            def __init__(self):
+                self.x = [0,1,2,3,4,5,6,7,8,9]
+
+            def __getitem__(self, idx):
+                return self.x[idx]
+
+            def __len__(self):
+                return len(self.x)
+
+
+        data = Data()
+        lengths = [0.4, 0.4, 0.2]
+        datasets = torch.utils.data.random_split(data, lengths)
+
+        results = []
+        for d in datasets:
+            results.append(d.__len__())
+        """
+    )
+    obj.run(pytorch_code, ["results"])
+
+
+def test_case_5():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+
+        datasets = torch.utils.data.random_split(range(30), [0.4, 0.4, 0.2])
+
+        results = []
+        for d in datasets:
+            results.append(d.__len__())
+        """
+    )
+    obj.run(pytorch_code, ["results"])
+
+
+def test_case_6():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        lengths = [0.4, 0.4, 0.2]
+        data = range(30)
+        datasets = torch.utils.data.random_split(data, lengths)
+
+        results = []
+        for d in datasets:
+            results.append(d.__len__())
+        """
+    )
+    obj.run(pytorch_code, ["results"])
+
+
+def test_case_7():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        lengths = [0.4, 0.4, 0.2]
+        data = range(30)
+        datasets = torch.utils.data.random_split(data, lengths,generator=torch.Generator().manual_seed(42))
+
+        results = []
+        for d in datasets:
+            results.append(d.__len__())
+        """
+    )
+    obj.run(pytorch_code, ["results"])

tests/test_utils_dlpack_from_dlpack.py

@@ -0,0 +1,115 @@
+# 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 textwrap
+
+from apibase import APIBase
+
+obj = APIBase("torch.utils.dlpack.from_dlpack")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        t = torch.arange(4).int()
+        capsule = torch.utils.dlpack.to_dlpack(t)
+        result = torch.utils.dlpack.from_dlpack(capsule)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        t = torch.arange(4).long()
+        capsule = torch.utils.dlpack.to_dlpack(t)
+        result = torch.utils.dlpack.from_dlpack(capsule)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        t = torch.arange(4).half()
+        capsule = torch.utils.dlpack.to_dlpack(t)
+        result = torch.utils.dlpack.from_dlpack(capsule)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        t = torch.arange(4).double()
+        capsule = torch.utils.dlpack.to_dlpack(t)
+        result = torch.utils.dlpack.from_dlpack(capsule)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_5():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        t = torch.arange(4).float()
+        capsule = torch.utils.dlpack.to_dlpack(t)
+        result = torch.utils.dlpack.from_dlpack(capsule)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_6():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        t = torch.arange(4).short()
+        capsule = torch.utils.dlpack.to_dlpack(t)
+        result = torch.utils.dlpack.from_dlpack(capsule)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_7():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        t = torch.arange(4).byte()
+        capsule = torch.utils.dlpack.to_dlpack(t)
+        result = torch.utils.dlpack.from_dlpack(capsule)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_8():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        t = torch.arange(4).char()
+        capsule = torch.utils.dlpack.to_dlpack(t)
+        result = torch.utils.dlpack.from_dlpack(capsule)
+        """
+    )
+    obj.run(pytorch_code, ["result"])