【Hackathon 5th No.47】API转换 103-124 (#346)

paconvert/api_matcher.py

@@ -169,6 +169,20 @@ def get_paddle_class_nodes(self, func, args, kwargs):
         return "delete"
 
 
+class AtleastMatcher(BaseMatcher):
+    def get_paddle_nodes(self, args, kwargs):
+        new_args = self.parse_args(args)
+        if new_args[0][0] == "(" and new_args[0][-1] == ")":
+            new_args[0] = new_args[0][1:-1]
+        if new_args[0][0] == "[" and new_args[0][-1] == "]":
+            new_args[0] = new_args[0][1:-1]
+        new_kwargs = self.parse_kwargs(kwargs)
+        code = "{}({})".format(
+            self.get_paddle_api(), self.args_and_kwargs_to_str(new_args, new_kwargs)
+        )
+        return ast.parse(code).body
+
+
 class UnchangeMatcher(BaseMatcher):
     def get_paddle_class_attribute_nodes(self, node):
         return "unchange"
@@ -1549,7 +1563,11 @@ def generate_code(self, kwargs):
         if len(kwargs) == 0:
             code = f"str({self.paddleClass}.dtype)"
         else:
-            code = f"{self.paddleClass}.astype({kwargs['dtype']})"
+            # For torch.nn.Module.type, torch.nn.Module.type use torch.Tensor.type
+            if "dst_type" in kwargs:
+                code = f"{self.paddleClass}.astype({kwargs['dst_type']})"
+            else:
+                code = f"{self.paddleClass}.astype({kwargs['dtype']})"
         return code
 
 
@@ -2111,61 +2129,21 @@ def get_paddle_nodes(self, args, kwargs):
 
 
 class TensorToMatcher(BaseMatcher):
-    def generate_aux_code(self):
-        CODE_TEMPLATE = textwrap.dedent(
-            """
-            def to(self, *args, **kwargs):
-                args_list = ["x", "y", "non_blocking", "copy", "memory_format"]
-                new_kwargs = {}
-                for i, node in enumerate(args):
-                    k = args_list[i]
-                    new_kwargs[k] = node
-                for node in kwargs:
-                    v = kwargs[node]
-                    new_kwargs[node] = v
-                kwargs = new_kwargs
-                if not kwargs:
-                    return self
-                elif "tensor" in kwargs:
-                    return paddle.cast(self, "{}.dtype".format(kwargs["tensor"]))
-                elif "dtype" in kwargs:
-                    return paddle.cast(self, "{}".format(kwargs["dtype"]))
-                elif "device" in kwargs and "dtype" not in kwargs:
-                    return self
-                elif kwargs:
-                    if "y" not in kwargs and "x" in kwargs:
-                        if isinstance(kwargs["x"], paddle.dtype):
-                            dtype = kwargs["x"]
-                        elif isinstance(kwargs["x"], str) and kwargs["x"] not in ['cpu', 'cuda', 'ipu', 'xpu']:
-                            dtype = kwargs["x"]
-                        elif isinstance(kwargs["x"], paddle.Tensor):
-                            dtype = kwargs["x"].dtype
-                        else:
-                            dtype = self.dtype
-                        return paddle.cast(self, dtype)
-
-                    elif "y" in kwargs and "x" in kwargs:
-                        if isinstance(kwargs["x"], paddle.dtype):
-                            dtype = kwargs["x"]
-                        elif isinstance(kwargs["x"], str):
-                            if x not in ['cpu', 'cuda', 'ipu', 'xpu']:
-                                dtype = kwargs["x"]
-                            else:
-                                dtype = kwargs["y"] if isinstance(kwargs["y"], str) else self.dtype
-                        else:
-                            dtype = kwargs["x"]
-                        return paddle.cast(self, dtype)
-                    else:
-                        return self
-
-            setattr(paddle.Tensor, 'to', to)
-            """
+    def get_paddle_nodes(self, args, kwargs):
+        new_args = self.parse_args(args)
+        new_kwargs = self.parse_kwargs(kwargs)
+        if new_kwargs is None:
+            return None
+        if "copy" in new_kwargs:
+            new_kwargs.pop("copy")
+        if "memory_format" in new_kwargs:
+            new_kwargs.pop("memory_format")
+        if "non_blocking" in new_kwargs:
+            new_kwargs["blocking"] = "not " + new_kwargs.pop("non_blocking").strip("()")
+        code = "{}.to({})".format(
+            self.paddleClass, self.args_and_kwargs_to_str(new_args, new_kwargs)
         )
-        return CODE_TEMPLATE
-
-    def get_paddle_class_nodes(self, func, args, kwargs):
-        self.write_aux_code()
-        return "unchange"
+        return ast.parse(code).body
 
 
 class TensorRequiresGradMatcher(BaseMatcher):
@@ -2964,24 +2942,6 @@ def get_paddle_nodes(self, args, kwargs):
         return ast.parse(code).body
 
 
-class HypotMatcher(BaseMatcher):
-    def generate_code(self, kwargs):
-        if "input" not in kwargs:
-            kwargs["input"] = self.paddleClass
-
-        API_TEMPLATE = textwrap.dedent(
-            """
-            paddle.pow({}**2 + {}**2, 1/2)
-            """
-        )
-        code = API_TEMPLATE.format(kwargs["input"], kwargs["other"])
-
-        if "out" in kwargs and kwargs["out"] != "None":
-            code = "paddle.assign({}, output={})".format(code, kwargs["out"])
-
-        return code
-
-
 class TensorViewMatcher(BaseMatcher):
     def generate_aux_code(self):
         CODE_TEMPLATE = textwrap.dedent(

tests/test_Tensor_diagonal_scatter.py

@@ -23,48 +23,57 @@ def test_case_1():
     pytorch_code = textwrap.dedent(
         """
         import torch
-        input = torch.zeros(3, 3)
-        src = torch.ones(3)
+        input = torch.arange(6.0).reshape((2, 3))
+        src = torch.ones((2,))
         result = input.diagonal_scatter(src)
         """
     )
-    obj.run(
-        pytorch_code,
-        ["result"],
-        unsupport=True,
-        reason="paddle does not support this function temporarily",
-    )
+    obj.run(pytorch_code, ["result"])
 
 
 def test_case_2():
     pytorch_code = textwrap.dedent(
         """
         import torch
-        input = torch.zeros(3, 3)
-        src = torch.ones(3)
+        input = torch.arange(6.0).reshape((2, 3))
+        src = torch.ones((2,))
         result = input.diagonal_scatter(src=src)
         """
     )
-    obj.run(
-        pytorch_code,
-        ["result"],
-        unsupport=True,
-        reason="paddle does not support this function temporarily",
-    )
+    obj.run(pytorch_code, ["result"])
 
 
 def test_case_3():
     pytorch_code = textwrap.dedent(
         """
         import torch
-        input = torch.zeros(3, 3)
-        src = torch.ones(3)
-        result = input.diagonal_scatter(src=src, offset=0, dim1=-2)
+        input = torch.arange(6.0).reshape((2, 3))
+        src = torch.ones((2,))
+        result = input.diagonal_scatter(offset=0, src=src, dim2=1, dim1=-2)
         """
     )
-    obj.run(
-        pytorch_code,
-        ["result"],
-        unsupport=True,
-        reason="paddle does not support this function temporarily",
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        input = torch.arange(6.0).reshape((2, 3))
+        src = torch.ones((2,))
+        result = input.diagonal_scatter(src=src, offset=0, dim1=-2, dim2=1)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_5():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        input = torch.arange(6.0).reshape((2, 3))
+        src = torch.ones((2,))
+        result = input.diagonal_scatter(src, 0, -2, 1)
+        """
     )
+    obj.run(pytorch_code, ["result"])

tests/test_Tensor_hypot.py

@@ -64,3 +64,15 @@ def test_case_4():
         """
     )
     obj.run(pytorch_code, ["result"])
+
+
+def test_case_5():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = torch.tensor([1., 2, 3])
+        b = torch.tensor([4., 5, 6])
+        result = a.hypot(b+1)
+        """
+    )
+    obj.run(pytorch_code, ["result"])

tests/test_Tensor_hypot_.py

@@ -0,0 +1,78 @@
+# 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.hypot_")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = torch.tensor([1., 2, 3])
+        b = torch.tensor([4., 5, 6])
+        result = a.hypot_(b)
+        """
+    )
+    obj.run(pytorch_code, ["result", "a"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = torch.tensor([1., 2, 3])
+        b = torch.tensor([4., 5, 6])
+        result = a.hypot_(other=b)
+        """
+    )
+    obj.run(pytorch_code, ["result", "a"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = torch.tensor([-1., 2, 3])
+        b = torch.tensor([4., 5, 6])
+        result = a.hypot_(other=b)
+        """
+    )
+    obj.run(pytorch_code, ["result", "a"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = torch.tensor([1., 2, 3])
+        b = torch.tensor([4., 5, 6])
+        result = a.hypot_(other=b+1)
+        """
+    )
+    obj.run(pytorch_code, ["result", "a"])
+
+
+def test_case_5():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = torch.tensor([1., 2, 3])
+        b = torch.tensor([4., 5, 6])
+        result = a.hypot_(b+1)
+        """
+    )
+    obj.run(pytorch_code, ["result", "a"])

tests/test_Tensor_index_fill.py

@@ -0,0 +1,82 @@
+# 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.index_fill")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.eye(2, 4)
+        indices = torch.tensor([0, 1])
+        value = -1
+        result = x.index_fill(0, indices, value)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        indices = torch.tensor([0, 1])
+        value = -1
+        result = torch.eye(3, 4).index_fill(1, indices, value)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        indices = torch.tensor([0, 1])
+        dim = 0
+        value = -1
+        result = torch.eye(3, 4).index_fill(index=indices, dim=dim, value=value)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        indices = torch.tensor([0, 3])
+        dim = 0
+        value = -1
+        result = torch.eye(6, 4).index_fill(dim=dim, index=indices, value=value)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_5():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        indices = torch.tensor([0, 3])
+        value = -1
+        result = torch.eye(3, 4).index_fill(1, indices, value)
+        """
+    )
+    obj.run(pytorch_code, ["result"])