转换规则 No.164

paconvert/api_alias_mapping.json

@@ -7,6 +7,7 @@
   "torch.distributions.cauchy.Cauchy": "torch.distributions.Cauchy",
   "torch.distributions.continuous_bernoulli.ContinuousBernoulli": "torch.distributions.ContinuousBernoulli",
   "torch.distributions.dirichlet.Dirichlet": "torch.distributions.Dirichlet",
+  "torch.distributions.distribution.Distribution": "torch.distributions.Distribution",
   "torch.distributions.exp_family.ExponentialFamily": "torch.distributions.ExponentialFamily",
   "torch.distributions.exponential.Exponential": "torch.distributions.Exponential",
   "torch.distributions.geometric.Geometric": "torch.distributions.Geometric",

tests/test_distributions_Distribution.py

@@ -0,0 +1,86 @@
+# 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
+
+import paddle
+from apibase import APIBase
+
+
+class ResultAPIBase(APIBase):
+    def compare(
+        self,
+        name,
+        pytorch_result,
+        paddle_result,
+        check_value=True,
+        check_dtype=True,
+        check_stop_gradient=True,
+        rtol=1.0e-6,
+        atol=0.0,
+    ):
+        assert isinstance(paddle_result, paddle.distribution.Distribution)
+
+
+obj = ResultAPIBase("torch.distributions.Distribution")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        result = torch.distributions.Distribution()
+        """
+    )
+    obj.run(pytorch_code, ["result"], check_value=False)
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        result = torch.distributions.Distribution(batch_shape=torch.Size([]), event_shape=torch.Size([]))
+        """
+    )
+    obj.run(pytorch_code, ["result"], check_value=False)
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        result = torch.distributions.Distribution(event_shape=torch.Size([]), batch_shape=torch.Size([]), validate_args=None)
+        """
+    )
+    obj.run(pytorch_code, ["result"], check_value=False)
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        result = torch.distributions.Distribution(torch.Size([]), torch.Size([]), validate_args=None)
+        """
+    )
+    obj.run(pytorch_code, ["result"], check_value=False)
+
+
+def test_case_5():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        result = torch.distributions.Distribution(batch_shape=torch.Size([]), event_shape=torch.Size([]), validate_args=None)
+        """
+    )
+    obj.run(pytorch_code, ["result"], check_value=False)