[API Compatibility] log_softmax -part

paconvert/api_mapping.json

@@ -6438,7 +6438,9 @@
   "torch.log2": {
     "Matcher": "ChangePrefixMatcher"
   },
-  "torch.log_softmax": {},
+  "torch.log_softmax": {
+    "Matcher": "ChangePrefixMatcher"
+  },
   "torch.logaddexp": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.logaddexp",
@@ -8873,21 +8875,8 @@
     }
   },
   "torch.nn.functional.log_softmax": {
-    "Matcher": "FSoftmaxMatcher",
-    "paddle_api": "paddle.nn.functional.log_softmax",
-    "args_list": [
-      "input",
-      "dim",
-      "_stacklevel",
-      "dtype"
-    ],
-    "kwargs_change": {
-      "input": "x",
-      "dim": "axis",
-      "_stacklevel": "",
-      "dtype": "dtype"
-    },
-    "min_input_args": 1
+    "Matcher": "ChangeAPIMatcher",
+    "paddle_api": "paddle.compat.nn.functional.log_softmax"
   },
   "torch.nn.functional.logsigmoid": {
     "Matcher": "ChangePrefixMatcher"
@@ -11270,20 +11259,7 @@
   },
   "torch.special.log_ndtr": {},
   "torch.special.log_softmax": {
-    "Matcher": "GenericMatcher",
-    "paddle_api": "paddle.nn.functional.log_softmax",
-    "min_input_args": 2,
-    "args_list": [
-      "input",
-      "dim",
-      "*",
-      "dtype"
-    ],
-    "kwargs_change": {
-      "input": "x",
-      "dim": "axis",
-      "dtype": "dtype"
-    }
+    "Matcher": "ChangePrefixMatcher"
   },
   "torch.special.logit": {
     "Matcher": "ChangePrefixMatcher"

tests/test_log_softmax.py

@@ -0,0 +1,215 @@
+# 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.log_softmax")
+
+
+def test_case_1():
+    # positional input and dim
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
+        result = torch.log_softmax(x, 1)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    # keyword dim
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
+        result = torch.log_softmax(x, dim=1)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    # all keyword args
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
+        result = torch.log_softmax(input=x, dim=1)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_4():
+    # dim=0
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
+        result = torch.log_softmax(x, dim=0)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_5():
+    # negative dim
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
+        result = torch.log_softmax(x, dim=-1)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_6():
+    # with dtype
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
+        result = torch.log_softmax(x, 1, dtype=torch.float64)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_7():
+    # all kwargs with dtype, out-of-order
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
+        result = torch.log_softmax(dtype=torch.float64, input=x, dim=0)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_8():
+    # 3D input
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[[1.0, 2.0], [3.0, 4.0]], [[5.0, 6.0], [7.0, 8.0]]])
+        result = torch.log_softmax(x, dim=2)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_9():
+    # 3D input, negative dim
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[[1.0, 2.0], [3.0, 4.0]], [[5.0, 6.0], [7.0, 8.0]]])
+        result = torch.log_softmax(x, dim=-2)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_10():
+    # 1D input
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([1.0, 2.0, 3.0, 4.0])
+        result = torch.log_softmax(x, 0)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_11():
+    # float64 input
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], dtype=torch.float64)
+        result = torch.log_softmax(x, dim=1)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_12():
+    # gradient computation
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], requires_grad=True)
+        result = torch.log_softmax(x, dim=1)
+        result.sum().backward()
+        x_grad = x.grad
+        """
+    )
+    obj.run(pytorch_code, ["result", "x_grad"], check_stop_gradient=False)
+
+
+def test_case_13():
+    # variable args
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
+        args = (x, 1)
+        result = torch.log_softmax(*args)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_14():
+    # all positional parameters including dtype
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
+        result = torch.log_softmax(x, 1, torch.float64)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_15():
+    # all keyword parameters including dtype
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
+        result = torch.log_softmax(input=x, dim=1, dtype=torch.float64)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_16():
+    # kwargs dict unpacking
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
+        kwargs = {'input': x, 'dim': 1, 'dtype': torch.float64}
+        result = torch.log_softmax(**kwargs)
+        """
+    )
+    obj.run(pytorch_code, ["result"])