Add tests

paconvert/api_mapping.json

@@ -2983,6 +2983,21 @@
       "input": "x"
     }
   },
+  "torch.autocast": {
+    "Matcher": "AutocastMatcher",
+    "paddle_api": "paddle.amp.auto_cast",
+    "args_list": [
+      "device_type",
+      "enabled",
+      "dtype",
+      "cache_enabled"
+    ],
+    "kwargs_change": {
+      "device_type": "",
+      "enabled": "enable",
+      "cache_enabled": ""
+    }
+  },
   "torch.autograd.Function": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.autograd.PyLayer"
@@ -3579,6 +3594,19 @@
       "correction": "ddof"
     }
   },
+  "torch.cpu.amp.autocast": {
+    "Matcher": "AutocastMatcher",
+    "paddle_api": "paddle.amp.auto_cast",
+    "args_list": [
+      "enabled",
+      "dtype",
+      "cache_enabled"
+    ],
+    "kwargs_change": {
+      "enabled": "enable",
+      "cache_enabled": ""
+    }
+  },
   "torch.cross": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.cross",

paconvert/api_matcher.py

@@ -83,7 +83,7 @@ def generate_code(self, kwargs):
         res = "{}({})".format(self.get_paddle_api(), self.kwargs_to_str(new_kwargs))
 
         if dtype_v:
-            res += ".astype({})".format(dtype_v)
+            res += ".cast({})".format(dtype_v)
 
         if pin_memory_v:
             res += ".pin_memory()"
@@ -169,6 +169,33 @@ def generate_code(self, kwargs):
         return code
 
 
+class AutocastMatcher(BaseMatcher):
+    def generate_code(self, kwargs):
+        kwargs_change = {}
+        if "kwargs_change" in self.api_mapping:
+            kwargs_change = self.api_mapping["kwargs_change"]
+        new_kwargs = {}
+        for k in list(kwargs.keys()):
+            if k in kwargs_change:
+                if kwargs_change[k]:
+                    # rename/copy in new_kwargs
+                    if isinstance(kwargs_change[k], list):
+                        for v in kwargs_change[k]:
+                            new_kwargs[v] = kwargs[k]
+                    else:
+                        new_kwargs[kwargs_change[k]] = kwargs[k]
+                else:
+                    # remove in new_kwargs
+                    kwargs.pop(k)
+            else:
+                # copy to new_kwargs
+                new_kwargs[k] = kwargs.pop(k)
+
+        new_kwargs = self.set_paddle_default_kwargs(new_kwargs)
+
+        return "{}({})".format(self.get_paddle_api(), self.kwargs_to_str(new_kwargs))
+
+
 class TorchAddMatcher(BaseMatcher):
     def generate_code(self, kwargs):
         if "alpha" in kwargs:

tests/test_autocast.py

@@ -0,0 +1,95 @@
+# 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.autocast")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        x = torch.tensor([[[-1.3020, -0.1005,  0.5766,  0.6351, -0.8893,  0.0253, -0.1756, 1.2913],
+                            [-0.8833, -0.1369, -0.0168, -0.5409, -0.1511, -0.1240, -1.1870, -1.8816]]])
+        with torch.autocast(device_type='cpu', enabled=False):
+            result = x*x
+
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        x = torch.tensor([[[-1.3020, -0.1005,  0.5766,  0.6351, -0.8893,  0.0253, -0.1756, 1.2913],
+                            [-0.8833, -0.1369, -0.0168, -0.5409, -0.1511, -0.1240, -1.1870, -1.8816]]])
+        with torch.autocast(device_type='cpu'):
+            result = x*x
+
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        x = torch.tensor([[[-1.3020, -0.1005,  0.5766,  0.6351, -0.8893,  0.0253, -0.1756, 1.2913],
+                            [-0.8833, -0.1369, -0.0168, -0.5409, -0.1511, -0.1240, -1.1870, -1.8816]]])
+        with torch.autocast(device_type='cpu', enabled=True, dtype=torch.bfloat16):
+            result = x*x
+
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        x = torch.tensor([[[-1.3020, -0.1005,  0.5766,  0.6351, -0.8893,  0.0253, -0.1756, 1.2913],
+                            [-0.8833, -0.1369, -0.0168, -0.5409, -0.1511, -0.1240, -1.1870, -1.8816]]])
+        # with torch.autocast('cpu', dtype=torch.bfloat16, enabled=True, cache_enabled=None):
+        with torch.autocast('cpu', dtype=torch.bfloat16, enabled=True, cache_enabled=None):
+            result = x*x
+
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_5():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        x = torch.tensor([[[-1.3020, -0.1005,  0.5766,  0.6351, -0.8893,  0.0253, -0.1756, 1.2913],
+                            [-0.8833, -0.1369, -0.0168, -0.5409, -0.1511, -0.1240, -1.1870, -1.8816]]])
+        with torch.autocast(device_type='cpu', dtype=torch.bfloat16, enabled=True, cache_enabled=None):
+            result = x*x
+
+        """
+    )
+    obj.run(pytorch_code, ["result"])

tests/test_cpu_amp_autocast.py

@@ -0,0 +1,94 @@
+# 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.cpu.amp.autocast")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        x = torch.tensor([[[-1.3020, -0.1005,  0.5766,  0.6351, -0.8893,  0.0253, -0.1756, 1.2913],
+                            [-0.8833, -0.1369, -0.0168, -0.5409, -0.1511, -0.1240, -1.1870, -1.8816]]])
+        with torch.cpu.amp.autocast(enabled=False):
+            result = x*x
+
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        x = torch.tensor([[[-1.3020, -0.1005,  0.5766,  0.6351, -0.8893,  0.0253, -0.1756, 1.2913],
+                            [-0.8833, -0.1369, -0.0168, -0.5409, -0.1511, -0.1240, -1.1870, -1.8816]]])
+        with torch.cpu.amp.autocast():
+            result = x*x
+
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        x = torch.tensor([[[-1.3020, -0.1005,  0.5766,  0.6351, -0.8893,  0.0253, -0.1756, 1.2913],
+                            [-0.8833, -0.1369, -0.0168, -0.5409, -0.1511, -0.1240, -1.1870, -1.8816]]])
+        with torch.cpu.amp.autocast(dtype=torch.bfloat16, enabled=True):
+            result = x*x
+
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        x = torch.tensor([[[-1.3020, -0.1005,  0.5766,  0.6351, -0.8893,  0.0253, -0.1756, 1.2913],
+                            [-0.8833, -0.1369, -0.0168, -0.5409, -0.1511, -0.1240, -1.1870, -1.8816]]])
+        with torch.cpu.amp.autocast(True, torch.bfloat16, True):
+            result = x*x
+
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_5():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        x = torch.tensor([[[-1.3020, -0.1005,  0.5766,  0.6351, -0.8893,  0.0253, -0.1756, 1.2913],
+                            [-0.8833, -0.1369, -0.0168, -0.5409, -0.1511, -0.1240, -1.1870, -1.8816]]])
+        with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloat16, cache_enabled=True):
+            result = x*x
+
+        """
+    )
+    obj.run(pytorch_code, ["result"])