转换规则 No.299/305/316/317/322

paconvert/api_mapping.json

@@ -540,7 +540,15 @@
   },
   "torch.Tensor.copysign": {},
   "torch.Tensor.copysign_": {},
-  "torch.Tensor.corrcoef": {},
+  "torch.Tensor.corrcoef": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.Tensor.corrcoef",
+    "args_list": [],
+    "kwargs_change": {},
+    "paddle_default_kwargs": {
+      "rowvar": true
+    }
+  },
   "torch.Tensor.cos": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.Tensor.cos"
@@ -3503,6 +3511,19 @@
       "out"
     ]
   },
+  "torch.cumulative_trapezoid": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.cumulative_trapezoid",
+    "args_list": [
+      "y",
+      "x",
+      "dx",
+      "dim"
+    ],
+    "kwargs_change": {
+      "dim": "axis"
+    }
+  },
   "torch.deg2rad": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.deg2rad",
@@ -9003,6 +9024,28 @@
       "input": "x"
     }
   },
+  "torch.special.i1": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.i1",
+    "args_list": [
+      "input",
+      "out"
+    ],
+    "kwargs_change": {
+      "input": "x"
+    }
+  },
+  "torch.special.i1e": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.i1e",
+    "args_list": [
+      "input",
+      "out"
+    ],
+    "kwargs_change": {
+      "input": "x"
+    }
+  },
   "torch.special.log1p": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.log1p",

tests/test_Tensor_corrcoef.py

@@ -0,0 +1,51 @@
+# 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.corrcoef")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[ 0.7308,  1.0060,  0.5270,  1.4516],
+                        [-0.1383,  1.5706,  0.4724,  0.4141],
+                        [ 0.1193,  0.2829,  0.9037,  0.3957],
+                        [-0.8202, -0.6474, -0.1631, -0.6543]])
+        result = x.corrcoef()
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[-0.1533,  2.3020, -0.1771,  0.5928],
+                          [ 0.4338, -0.6537,  0.2296,  0.5946],
+                          [-0.4932,  1.8386, -0.1039,  1.0440],
+                          [ 0.1735, -0.8303, -0.3821, -0.4384],
+                          [-0.1533,  2.3020, -0.1771,  0.5928],
+                          [ 0.4338, -0.6537,  0.2296,  0.5946],
+                          [-0.4932,  1.8386, -0.1039,  1.0440],
+                          [ 0.1735, -0.8303, -0.3821, -0.4384]])
+        result = x.corrcoef()
+        """
+    )
+    obj.run(pytorch_code, ["result"])

tests/test_cumulative_trapezoid.py

@@ -0,0 +1,74 @@
+# 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.cumulative_trapezoid")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        result = torch.cumulative_trapezoid(torch.tensor([1.0, 1, 1, 0, 1]))
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        y = torch.tensor([1, 1, 1, 0, 1]).type(torch.float32)
+        x = torch.tensor([1, 2, 3, 0, 1]).type(torch.float32)
+        result = torch.cumulative_trapezoid(y, x)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        y = torch.tensor([[0.6, 0.0, 0.0, 0.0]])
+        result = torch.cumulative_trapezoid(y, dx=2)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        y = torch.arange(9).reshape(3, 3).type(torch.float32)
+        result = torch.cumulative_trapezoid(y, dim=0)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_5():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        y = torch.arange(9).reshape(3, 3).type(torch.float32)
+        result = torch.cumulative_trapezoid(y, dim=1)
+        """
+    )
+    obj.run(pytorch_code, ["result"])

tests/test_special_i0.py

@@ -0,0 +1,53 @@
+# 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.special.i0")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        result = torch.special.i0(torch.tensor([1.0, 2.0, 3.0]))
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([1.0, 2.0, 3.0])
+        result = torch.special.i0(x)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = [1.0, 2.0, 3.0]
+        out = torch.tensor([])
+        result = torch.special.i0(torch.tensor(x), out=out)
+        """
+    )
+    obj.run(pytorch_code, ["result", "out"])

tests/test_special_i1.py

@@ -0,0 +1,53 @@
+# 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.special.i1")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        result = torch.special.i1(torch.tensor([1.0, 2.0, 3.0]))
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([1.0, 2.0, 3.0])
+        result = torch.special.i1(x)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = [1.0, 2.0, 3.0]
+        out = torch.tensor([])
+        result = torch.special.i1(torch.tensor(x), out=out)
+        """
+    )
+    obj.run(pytorch_code, ["result", "out"])

tests/test_special_i1e.py

@@ -0,0 +1,53 @@
+# 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.special.i1e")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        result = torch.special.i1e(torch.tensor([1.0, 2.0, 3.0]))
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([1.0, 2.0, 3.0])
+        result = torch.special.i1e(x)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = [1.0, 2.0, 3.0]
+        out = torch.tensor([])
+        result = torch.special.i1e(torch.tensor(x), out=out)
+        """
+    )
+    obj.run(pytorch_code, ["result", "out"])