单测case增强4

paconvert/api_mapping.json

@@ -11054,11 +11054,13 @@
       "layer_norm_eps",
       "batch_first",
       "norm_first",
+      "bias",
       "device",
       "dtype"
     ],
     "kwargs_change": {
       "norm_first": "normalize_before",
+      "bias": "bias_attr",
       "device": "",
       "dtype": ""
     },
@@ -11090,11 +11092,13 @@
       "layer_norm_eps",
       "batch_first",
       "norm_first",
+      "bias",
       "device",
       "dtype"
     ],
     "kwargs_change": {
       "norm_first": "normalize_before",
+      "bias": "bias_attr",
       "device": "",
       "dtype": ""
     },
@@ -11134,11 +11138,13 @@
       "layer_norm_eps",
       "batch_first",
       "norm_first",
+      "bias",
       "device",
       "dtype"
     ],
     "kwargs_change": {
       "norm_first": "normalize_before",
+      "bias": "bias_attr",
       "device": "",
       "dtype": ""
     },
@@ -12092,7 +12098,7 @@
     }
   },
   "torch.nn.functional.log_softmax": {
-    "Matcher": "RequireDimMatcher",
+    "Matcher": "SoftmaxMatcher",
     "paddle_api": "paddle.nn.functional.log_softmax",
     "args_list": [
       "input",
@@ -12215,9 +12221,6 @@
     "kwargs_change": {
       "input": "x"
     },
-    "unsupport_args": [
-      "output_size"
-    ],
     "min_input_args": 3
   },
   "torch.nn.functional.max_unpool2d": {
@@ -12605,7 +12608,7 @@
     "min_input_args": 2
   },
   "torch.nn.functional.softmax": {
-    "Matcher": "RequireDimMatcher",
+    "Matcher": "SoftmaxMatcher",
     "paddle_api": "paddle.nn.functional.softmax",
     "args_list": [
       "input",

paconvert/api_matcher.py

@@ -3829,8 +3829,8 @@ def generate_code(self, kwargs):
 class SoftmaxMatcher(BaseMatcher):
     def generate_code(self, kwargs):
         if "dim" not in kwargs or "None" in kwargs["dim"]:
-            return None
-
+            kwargs.pop("dim", "None")
+            kwargs["axis"] = 0
         return GenericMatcher.generate_code(self, kwargs)
 
 

tests/test_nn_LogSoftmax.py

@@ -70,9 +70,22 @@ def test_case_3():
         result = model(x)
         """
     )
-    obj.run(
-        pytorch_code,
-        ["result"],
-        unsupport=True,
-        reason="When dim is None, paddle and pytorch generate different results",
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        x = torch.tensor([[[2.0, 3.0, 4.0, 5.0],
+                        [3.0, 4.0, 5.0, 6.0],
+                        [7.0, 8.0, 8.0, 9.0]],
+                        [[1.0, 2.0, 3.0, 4.0],
+                        [5.0, 6.0, 7.0, 8.0],
+                        [6.0, 7.0, 8.0, 9.0]]])
+        model = nn.LogSoftmax(dim=None)
+        result = model(x)
+        """
     )
+    obj.run(pytorch_code, ["result"])

tests/test_nn_Softmax.py

@@ -70,9 +70,22 @@ def test_case_3():
         result = model(x)
         """
     )
-    obj.run(
-        pytorch_code,
-        ["result"],
-        unsupport=True,
-        reason="When dim is None, paddle and pytorch generate different results",
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        x = torch.tensor([[[2.0, 3.0, 4.0, 5.0],
+                        [3.0, 4.0, 5.0, 6.0],
+                        [7.0, 8.0, 8.0, 10.0]],
+                        [[1.0, 2.0, 3.0, 4.0],
+                        [5.0, 6.0, 7.0, 8.0],
+                        [6.0, 7.0, 8.0, 9.0]]])
+        model = nn.Softmax(dim=None)
+        result = model(x)
+        """
     )
+    obj.run(pytorch_code, ["result"])

tests/test_nn_Transformer.py

@@ -148,3 +148,72 @@ def test_case_7():
         unsupport=True,
         reason="paddle unsupport batch_first args",
     )
+
+
+def test_case_8():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        transformer_model = torch.nn.Transformer(d_model=512,
+                nhead=8, num_encoder_layers=6,
+                num_decoder_layers=6, dim_feedforward=2048,
+                dropout=0.1, activation='relu',
+                custom_encoder=None, custom_decoder=None,
+                layer_norm_eps=1e-05, batch_first=False,
+                norm_first=False, bias=False,
+                device=None, dtype=None)
+        src = torch.rand((10, 32, 512))
+        tgt = torch.rand((10, 32, 512))
+        result = transformer_model(src, tgt)
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        check_value=False,
+        unsupport=True,
+        reason="paddle unsupport layer_norm_eps args",
+    )
+
+
+def test_case_9():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        transformer_model = torch.nn.Transformer(512,
+                8, 6, 6, 2048,
+                0.1, 'relu',
+                None, None,
+                1e-05, False,
+                False, False,
+                None, None)
+        src = torch.rand((10, 32, 512))
+        tgt = torch.rand((10, 32, 512))
+        result = transformer_model(src, tgt)
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        check_value=False,
+        unsupport=True,
+        reason="paddle unsupport layer_norm_eps args",
+    )
+
+
+def test_case_10():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        transformer_model = torch.nn.Transformer(d_model=512,
+                nhead=8, num_encoder_layers=6,
+                num_decoder_layers=6, dim_feedforward=2048,
+                dropout=0.1, activation='relu',
+                custom_encoder=None, custom_decoder=None,
+                norm_first=False, bias=True, device=None, dtype=None)
+        src = torch.rand((10, 32, 512))
+        tgt = torch.rand((10, 32, 512))
+        result = transformer_model(src, tgt)
+        """
+    )
+    obj.run(pytorch_code, ["result"], check_value=False)

tests/test_nn_TransformerDecoderLayer.py

@@ -78,3 +78,60 @@ def test_case_4():
         unsupport=True,
         reason="paddle unsupport batch_first args",
     )
+
+
+def test_case_5():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        x = torch.ones(10, 32,512)
+        tgt = torch.ones(10, 32, 512)
+        model = nn.TransformerDecoderLayer(d_model=512, nhead=8,dim_feedforward=2048, dropout=0.1,
+                                            activation="relu", layer_norm_eps=1e-06, batch_first=False,
+                                            norm_first=False, bias=True, device=None, dtype=None)
+        result = model(tgt,x)
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        unsupport=True,
+        reason="paddle unsupport batch_first args",
+    )
+
+
+def test_case_6():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        x = torch.ones(10, 32,512)
+        tgt = torch.ones(10, 32, 512)
+        model = nn.TransformerDecoderLayer(512, 8,2048, 0.1, "relu", 1e-06, False,
+                                            False, True, None, None)
+        result = model(tgt,x)
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        unsupport=True,
+        reason="paddle unsupport batch_first args",
+    )
+
+
+def test_case_7():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        x = torch.ones(10, 32,512)
+        tgt = torch.ones(10, 32, 512)
+        model = nn.TransformerDecoderLayer(d_model=512, nhead=8,dim_feedforward=2048, dropout=0.1,
+                                            activation="relu", layer_norm_eps=1e-06,
+                                            norm_first=False, bias=True, device=None, dtype=None)
+        result = model(tgt,x)
+        """
+    )
+    obj.run(pytorch_code, ["result"], check_value=False)

tests/test_nn_TransformerEncoderLayer.py

@@ -123,3 +123,64 @@ def test_case_7():
         """
     )
     obj.run(pytorch_code, ["result"], check_value=False)
+
+
+def test_case_8():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        tgt = torch.ones(10, 32, 512)
+        model = nn.TransformerEncoderLayer(d_model=512, nhead=8, dim_feedforward=2048, dropout=0.1,
+                                            activation="relu", layer_norm_eps=1e-05, batch_first=False,
+                                            norm_first=False, bias=True, device=None, dtype=None)
+        result = model(tgt)
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        check_value=False,
+        unsupport=True,
+        reason="paddle unsupport batch_first args",
+    )
+
+
+def test_case_9():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        tgt = torch.ones(10, 32, 512)
+        model = nn.TransformerEncoderLayer(512, 8, 2048, 0.1,
+                                           "relu", 1e-05, False,
+                                            False, True, "cpu", None)
+        result = model(tgt)
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        check_value=False,
+        unsupport=True,
+        reason="paddle unsupport batch_first args",
+    )
+
+
+def test_case_10():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        tgt = torch.ones(10, 32, 512)
+        model = nn.TransformerEncoderLayer(512, 8,
+                2048,
+                0.1, 'relu',
+                norm_first=False,
+                device=None,
+                bias=True,
+                dtype=torch.float32)
+        result = model(tgt)
+        """
+    )
+    obj.run(pytorch_code, ["result"], check_value=False)

tests/test_nn_Upsample.py

@@ -127,3 +127,21 @@ def test_case_6():
     obj.run(
         pytorch_code, unsupport=True, reason="paddle unsupport recompute_scale_factor "
     )
+
+
+def test_case_7():
+    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, align_corners=True, mode='bilinear')
+        result = m(input)
+        """
+    )
+    obj.run(pytorch_code, ["result"])