【PaddlePaddle Hackathon 5th No.42】转换规则 第一组

paconvert/api_mapping.json

@@ -2309,7 +2309,25 @@
     ]
   },
   "torch.Tensor.qscheme": {},
-  "torch.Tensor.quantile": {},
+  "torch.Tensor.quantile": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.Tensor.quantile",
+    "min_input_args": 1,
+    "args_list": [
+      "q",
+      "dim",
+      "keepdim",
+      "*",
+      "interpolation"
+    ],
+    "kwargs_change": {
+      "dim": "axis",
+      "input": "x"
+    },
+    "unsupport_args": [
+      "interpolation"
+    ]
+  },
   "torch.Tensor.rad2deg": {
     "Matcher": "UnchangeMatcher"
   },
@@ -2712,7 +2730,22 @@
       "correction": "unbiased"
     }
   },
-  "torch.Tensor.stft": {},
+  "torch.Tensor.stft": {
+    "Matcher": "StftMatcher",
+    "paddle_api": "paddle.signal.stft",
+    "min_input_args": 1,
+    "args_list": [
+      "n_fft",
+      "hop_length",
+      "win_length",
+      "window",
+      "center",
+      "normalized",
+      "onesided",
+      "length",
+      "return_complex"
+    ]
+  },
   "torch.Tensor.storage": {},
   "torch.Tensor.storage_offset": {},
   "torch.Tensor.storage_type": {},
@@ -2820,7 +2853,11 @@
     "paddle_api": "paddle.Tensor.tanh_"
   },
   "torch.Tensor.tensor_split": {},
-  "torch.Tensor.tile": {},
+  "torch.Tensor.tile": {
+    "Matcher": "TensorTileMatcher",
+    "min_input_args": 1,
+    "paddle_api": "paddle.Tensor.tile"
+  },
   "torch.Tensor.to": {
     "Matcher": "TensorToMatcher"
   },
@@ -2829,7 +2866,14 @@
     "paddle_api": "paddle.Tensor.to_dense"
   },
   "torch.Tensor.to_mkldnn": {},
-  "torch.Tensor.to_sparse": {},
+  "torch.Tensor.to_sparse": {
+    "Matcher": "GenericMatcher",
+    "min_input_args": 0,
+    "paddle_api": "paddle.Tensor.to_sparse_coo",
+    "args_list": [
+      "sparse_dim"
+    ]
+  },
   "torch.Tensor.tolist": {
     "Matcher": "UnchangeMatcher"
   },
@@ -7190,6 +7234,27 @@
       "dim"
     ]
   },
+  "torch.nanquantile": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.nanquantile",
+    "min_input_args": 2,
+    "args_list": [
+      "input",
+      "q",
+      "dim",
+      "keepdim",
+      "*",
+      "interpolation",
+      "out"
+    ],
+    "kwargs_change": {
+      "input": "x",
+      "dim": "axis"
+    },
+    "unsupport_args": [
+      "interpolation "
+    ]
+  },
   "torch.nansum": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.nansum",
@@ -10988,6 +11053,7 @@
   "torch.quantile": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.quantile",
+    "min_input_args": 2,
     "args_list": [
       "input",
       "q",
@@ -12011,6 +12077,25 @@
       "correction": "unbiased"
     }
   },
+  "torch.stft": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.signal.stft",
+    "args_list": [
+      "input",
+      "n_fft",
+      "hop_length",
+      "win_length",
+      "window",
+      "center",
+      "normalized",
+      "onesided",
+      "length",
+      "return_complex"
+    ],
+    "kwargs_change": {
+      "input": "x"
+    }
+  },
   "torch.subtract": {
     "Matcher": "Num2TensorBinaryWithAlphaMatcher",
     "paddle_api": "paddle.subtract",

paconvert/api_matcher.py

@@ -473,6 +473,41 @@ def get_paddle_nodes(self, args, kwargs):
         return ast.parse(code).body
 
 
+class TensorTileMatcher(BaseMatcher):
+    def get_paddle_class_nodes(self, func, args, kwargs):
+        self.parse_func(func)
+
+        if len(args) == 1 and isinstance(args[0], (ast.List, ast.Tuple)):
+            repeat_times_list = self.parse_args(args)[0]
+        else:  # len(args) >= 1
+            repeat_times_list = self.parse_args(args)
+
+        kwargs = self.parse_kwargs(kwargs)
+        if kwargs is None:
+            return None
+
+        if "reps" in kwargs:
+            kwargs = {"repeat_times": kwargs.pop("reps"), **kwargs}
+        else:
+            kwargs = {"repeat_times": str(repeat_times_list).replace("'", ""), **kwargs}
+
+        code = "{}.tile({})".format(self.paddleClass, self.kwargs_to_str(kwargs))
+        return ast.parse(code).body
+
+
+class StftMatcher(BaseMatcher):
+    def generate_code(self, kwargs):
+        if "input" not in kwargs:
+            kwargs["x"] = self.paddleClass
+        if ("return_complex" in kwargs) and (kwargs.pop("return_complex") == "(False)"):
+            code = "paddle.as_real(paddle.signal.stft({}))".format(
+                self.kwargs_to_str(kwargs)
+            )
+        else:
+            code = "paddle.signal.stft({})".format(self.kwargs_to_str(kwargs))
+        return code
+
+
 class DeviceMatcher(BaseMatcher):
     def generate_code(self, kwargs):
         if len(kwargs) == 1:

tests/test_Tensor_quantile.py

@@ -0,0 +1,69 @@
+# 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.quantile")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        result = torch.tensor([[ 0.0795, -1.2117,  0.9765], [ 1.1707,  0.6706,  0.4884]],dtype=torch.float64).quantile(0.6)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        result = torch.tensor([[ 0.0795, -1.2117,  0.9765], [ 1.1707,  0.6706,  0.4884]],dtype=torch.float64).quantile(0.6, dim=None)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        result = torch.tensor([[ 0.0795, -1.2117,  0.9765], [ 1.1707,  0.6706,  0.4884]],dtype=torch.float64).quantile(0.6, dim=0, keepdim=False)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        result = torch.tensor([[ 0.0795, -1.2117,  0.9765], [ 1.1707,  0.6706,  0.4884]],dtype=torch.float64).quantile(0.6, dim=1, keepdim=False)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_5():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        result = torch.tensor([[ 0.0795, -1.2117,  0.9765], [ 1.1707,  0.6706,  0.4884]],dtype=torch.float64).quantile(0.6, dim=1, keepdim=True)
+        """
+    )
+    obj.run(pytorch_code, ["result"])

tests/test_Tensor_stft.py

@@ -0,0 +1,69 @@
+# 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.stft")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[ (5.975718021392822+0j)                  ,
+           (5.975718021392822+0j)                  ,
+           (5.341437339782715+0j)                  ,
+           (5.404394626617432+0j)                  ,
+           (5.404394626617432+0j)                  ],
+         [ (0.0629572868347168+0j)                 ,
+           0.0629572868347168j                     ,
+          (-0.0629572868347168-0.6342806816101074j),
+           (0.6342806816101074+0j)                 ,
+           0.6342806816101074j                     ],
+         [(-0.4979677200317383+0j)                 ,
+           (0.4979677200317383+0j)                 ,
+           (0.13631296157836914+0j)                ,
+          (-0.19927024841308594+0j)                ,
+           (0.19927024841308594+0j)                ]])
+        result = x.stft(n_fft=4, onesided=False, return_complex=True)
+        """
+    )
+    obj.run(pytorch_code, ["result"], rtol=1e-1, atol=1e-04)
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[ (5.975718021392822+0j)                  ,
+           (5.975718021392822+0j)                  ,
+           (5.341437339782715+0j)                  ,
+           (5.404394626617432+0j)                  ,
+           (5.404394626617432+0j)                  ],
+         [ (0.0629572868347168+0j)                 ,
+           0.0629572868347168j                     ,
+          (-0.0629572868347168-0.6342806816101074j),
+           (0.6342806816101074+0j)                 ,
+           0.6342806816101074j                     ],
+         [(-0.4979677200317383+0j)                 ,
+           (0.4979677200317383+0j)                 ,
+           (0.13631296157836914+0j)                ,
+          (-0.19927024841308594+0j)                ,
+           (0.19927024841308594+0j)                ]])
+        result = x.stft(n_fft=4, onesided=False, return_complex=False)
+        """
+    )
+    obj.run(pytorch_code, ["result"], rtol=1e-1, atol=1e-04)

tests/test_Tensor_tile.py

@@ -30,6 +30,18 @@ def test_case_1():
     obj.run(
         pytorch_code,
         ["result"],
-        unsupport=True,
-        reason="Paddle not support this api convert now",
+    )
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = torch.Tensor([[1.,2.], [3.,4.]])
+        result = a.tile(1, 2)
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
     )

tests/test_Tensor_to_sparse.py

@@ -0,0 +1,49 @@
+# 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.to_sparse")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = torch.Tensor([[1.,2.], [3.,4.]])
+        b = a.to_sparse(1)
+        result = b.to_dense()
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+    )
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = torch.Tensor([[1.,2.], [3.,4.]])
+        b = a.to_sparse(sparse_dim = 1)
+        result = b.to_dense()
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+    )