Upsample

paconvert/api_mapping.json

@@ -6682,6 +6682,19 @@
       "unflattened_size": "shape"
     }
   },
+  "torch.nn.Upsample": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.nn.Upsample",
+    "args_list": [
+      "size",
+      "scale_factor",
+      "mode",
+      "align_corners"
+    ],
+    "unsupport_args": [
+      "recompute_scale_factor"
+    ]
+  },
   "torch.nn.UpsamplingBilinear2d": {
     "Matcher": "UpsampleMatcher",
     "paddle_api": "paddle.nn.UpsamplingBilinear2D",
@@ -9061,10 +9074,32 @@
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.utils.cpp_extension.BuildExtension.with_options"
   },
+  "torch.utils.cpp_extension.CUDAExtension": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.utils.cpp_extension.CUDAExtension",
+    "args_list": [
+      "name",
+      "sources"
+    ],
+    "kwargs_change": {
+      "name": ""
+    }
+  },
   "torch.utils.cpp_extension.CUDA_HOME": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.utils.cpp_extension.cpp_extension.CUDA_HOME"
   },
+  "torch.utils.cpp_extension.CppExtension": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.utils.cpp_extension.CppExtension",
+    "args_list": [
+      "name",
+      "sources"
+    ],
+    "kwargs_change": {
+      "name": ""
+    }
+  },
   "torch.utils.data.BatchSampler": {
     "Matcher": "TorchUtilDataBatchSampler",
     "args_list": [
@@ -9117,6 +9152,13 @@
       "data_source"
     ]
   },
+  "torch.utils.data.SequentialSampler": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.io.SequenceSampler",
+    "args_list": [
+      "data_source"
+    ]
+  },
   "torch.utils.data.default_collate": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.io.dataloader.collate.default_collate_fn",

paconvert/api_matcher.py

@@ -3539,6 +3539,13 @@ def generate_code(self, kwargs):
         return GenericMatcher.generate_code(self, kwargs)
 
 
+class TensorIsSpareMatcher(BaseMatcher):
+    def get_paddle_class_attribute_nodes(self, node):
+        self.parse_func(node)
+        code = "{}()".format(self.paddle_api)
+        return ast.parse(code).body[0].value
+
+
 class LuMatcher(BaseMatcher):
     def generate_code(self, kwargs):
         out_v = kwargs.pop("out") if "out" in kwargs else None

paconvert/attribute_mapping.json

@@ -22,7 +22,10 @@
   },
   "torch.Tensor.is_meta": {},
   "torch.Tensor.is_quantized": {},
-  "torch.Tensor.is_sparse": {},
+  "torch.Tensor.is_sparse": {
+    "Matcher": "TensorIsSpareMatcher",
+    "paddle_api": "paddle.Tensor.is_sparse"
+  },
   "torch.Tensor.mH": {},
   "torch.Tensor.mT": {},
   "torch.Tensor.names": {},

tests/test_Tensor_is_sparse.py

@@ -0,0 +1,29 @@
+# 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.Tensor.is_sparse")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = torch.tensor([[ 0.9254, -0.6213]])
+        result = a.is_sparse
+        """
+    )
+    obj.run(pytorch_code, ["result"])

tests/test_nn_Upsample.py

@@ -0,0 +1,127 @@
+# 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.nn.Upsample")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        input = torch.tensor([[[[ 1.1524,  0.4714,  0.2857],
+         [-1.2533, -0.9829, -1.0981],
+         [ 0.1507, -1.1431, -2.0361]],
+
+        [[ 0.1024, -0.4482,  0.4137],
+         [ 0.9385,  0.4565,  0.7702],
+         [ 0.4135, -0.2587,  0.0482]]]])
+        m = torch.nn.Upsample(scale_factor=2, mode='nearest')
+        result = m(input)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        input = torch.tensor([[[[ 1.1524,  0.4714,  0.2857],
+         [-1.2533, -0.9829, -1.0981],
+         [ 0.1507, -1.1431, -2.0361]],
+
+        [[ 0.1024, -0.4482,  0.4137],
+         [ 0.9385,  0.4565,  0.7702],
+         [ 0.4135, -0.2587,  0.0482]]]])
+        m = torch.nn.Upsample(scale_factor=2, mode='bilinear')
+        result = m(input)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        input = torch.tensor([[[[ 1.1524,  0.4714,  0.2857],
+         [-1.2533, -0.9829, -1.0981],
+         [ 0.1507, -1.1431, -2.0361]],
+
+        [[ 0.1024, -0.4482,  0.4137],
+         [ 0.9385,  0.4565,  0.7702],
+         [ 0.4135, -0.2587,  0.0482]]]])
+        m = torch.nn.Upsample(scale_factor=2, mode='bilinear',align_corners=True)
+        result = m(input)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        input = torch.tensor([[[[ 1.1524,  0.4714,  0.2857],
+         [-1.2533, -0.9829, -1.0981],
+         [ 0.1507, -1.1431, -2.0361]],
+
+        [[ 0.1024, -0.4482,  0.4137],
+         [ 0.9385,  0.4565,  0.7702],
+         [ 0.4135, -0.2587,  0.0482]]]])
+        m = torch.nn.Upsample(size=(2,2))
+        result = m(input)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_5():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        input = torch.tensor([[[[ 1.1524,  0.4714,  0.2857],
+         [-1.2533, -0.9829, -1.0981],
+         [ 0.1507, -1.1431, -2.0361]],
+
+        [[ 0.1024, -0.4482,  0.4137],
+         [ 0.9385,  0.4565,  0.7702],
+         [ 0.4135, -0.2587,  0.0482]]]])
+        m = torch.nn.Upsample(scale_factor=2, mode='bilinear',align_corners=False)
+        result = m(input)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_6():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        input = torch.tensor([[[[ 1.1524,  0.4714,  0.2857],
+         [-1.2533, -0.9829, -1.0981],
+         [ 0.1507, -1.1431, -2.0361]],
+
+        [[ 0.1024, -0.4482,  0.4137],
+         [ 0.9385,  0.4565,  0.7702],
+         [ 0.4135, -0.2587,  0.0482]]]])
+        m = torch.nn.Upsample(scale_factor=2, mode='bilinear',recompute_scale_factor=True)
+        result = m(input)
+        """
+    )
+    obj.run(pytorch_code, unsupport=True, reason="paddle unsupport")

tests/test_utils_cpp_extension_CUDAExtension.py

@@ -0,0 +1,36 @@
+# 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.cpp_extension.CUDAExtension")
+
+
+# The cuda compile not supports
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        from torch.utils.cpp_extension import CUDAExtension
+
+        CUDAExtension(
+                name='cuda_extension',
+                sources=['extension.cpp', 'extension_kernel.cu'],
+                extra_compile_args={'cxx': ['-g'],
+                                    'nvcc': ['-O2']})
+        result = True
+        """
+    )
+    obj.run(pytorch_code, ["result"])

tests/test_utils_cpp_extension_CppExtension.py

@@ -0,0 +1,35 @@
+# 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.cpp_extension.CppExtension")
+
+
+# The cpp compile not supports
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        from torch.utils.cpp_extension import CppExtension
+
+        CppExtension(
+                name='cuda_extension',
+                sources=['extension.cpp'],
+                extra_compile_args=['-g'])
+        result = True
+        """
+    )
+    obj.run(pytorch_code, ["result"])