【Hackathon 5th No.45】convert 75-81 (#341)

* convert IndependentTransform

* fix bug

* fix bug

* fix bug

* fix bug

* fix bug

* transfer ReshapeTransform

* Update test_distributions_transforms_ReshapeTransform.py

* transfer ExpTransform and PowerTransform

* fix bug

* fix bug

* transfer TanhTransform, AbsTransform, AffineTransform

paconvert/api_mapping.json

@@ -5529,6 +5529,30 @@
       "tag"
     ]
   },
+  "torch.distributions.AbsTransform": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.distribution.AbsTransform",
+    "args_list": [
+      "cache_size"
+    ],
+    "kwargs_change": {
+      "cache_size": ""
+    }
+  },
+  "torch.distributions.AffineTransform": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.distribution.AffineTransform",
+    "args_list": [
+      "loc",
+      "scale",
+      "event_dim",
+      "cache_size"
+    ],
+    "kwargs_change": {
+      "event_dim": "",
+      "cache_size": ""
+    }
+  },
   "torch.distributions.Bernoulli": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.distribution.Bernoulli",
@@ -5663,6 +5687,11 @@
   },
   "torch.distributions.Distribution.sample_n": {},
   "torch.distributions.Distribution.set_default_validate_args": {},
+  "torch.distributions.ExpTransform": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.distribution.ExpTransform",
+    "args_list": []
+  },
   "torch.distributions.ExponentialFamily": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.distribution.ExponentialFamily",
@@ -5720,6 +5749,18 @@
       "validate_args": ""
     }
   },
+  "torch.distributions.IndependentTransform": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.distribution.IndependentTransform",
+    "args_list": [
+      "base_transform",
+      "reinterpreted_batch_ndims"
+    ],
+    "kwargs_change": {
+      "base_transform": "base",
+      "reinterpreted_batch_ndims": "reinterpreted_batch_rank"
+    }
+  },
   "torch.distributions.Laplace": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.distribution.Laplace",
@@ -5776,6 +5817,30 @@
       "validate_args": ""
     }
   },
+  "torch.distributions.PowerTransform": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.distribution.PowerTransform",
+    "args_list": [
+      "exponent",
+      "cache_size"
+    ],
+    "kwargs_change": {
+      "exponent": "power",
+      "cache_size": ""
+    }
+  },
+  "torch.distributions.ReshapeTransform": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.distribution.ReshapeTransform",
+    "args_list": [
+      "in_shape",
+      "out_shape"
+    ],
+    "kwargs_change": {
+      "in_shape": "in_event_shape",
+      "out_shape": "out_event_shape"
+    }
+  },
   "torch.distributions.SigmoidTransform": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.distribution.SigmoidTransform",
@@ -5824,6 +5889,16 @@
       "cache_size": ""
     }
   },
+  "torch.distributions.TanhTransform": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.distribution.TanhTransform",
+    "args_list": [
+      "cache_size"
+    ],
+    "kwargs_change": {
+      "cache_size": ""
+    }
+  },
   "torch.distributions.Transform": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.distribution.Transform",

tests/test_distributions_AbsTransform.py

@@ -0,0 +1,43 @@
+# 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.distributions.AbsTransform")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.ones((2,3))
+        t = torch.distributions.AbsTransform()
+        result = t(x)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.ones((2,3))
+        t = torch.distributions.AbsTransform(cache_size=0)
+        result = t(x)
+        """
+    )
+    obj.run(pytorch_code, ["result"])

tests/test_distributions_AffineTransform.py

@@ -0,0 +1,47 @@
+# 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.distributions.AffineTransform")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+
+        x = torch.tensor(1.)
+        y = torch.tensor(2.)
+        affine = torch.distributions.AffineTransform(x, y)
+        result = affine.forward_shape([1, 2])
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+
+        x = torch.tensor(1.)
+        y = torch.tensor(2.)
+        affine = torch.distributions.AffineTransform(x, y, 1)
+        result = affine.forward_shape([1, 2])
+        """
+    )
+    obj.run(pytorch_code, ["result"])

tests/test_distributions_ExpTransform.py

@@ -0,0 +1,31 @@
+# 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.distributions.ExpTransform")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+
+        exp = torch.distributions.ExpTransform()
+        result = exp.forward_shape([1, 2])
+        """
+    )
+    obj.run(pytorch_code, ["result"])

tests/test_distributions_IndependentTransform.py

@@ -0,0 +1,52 @@
+# 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.distributions.IndependentTransform")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        multi_exp = torch.distributions.IndependentTransform(torch.distributions.Transform(), 1)
+        result = multi_exp.forward_shape([1, 2])
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        multi_exp = torch.distributions.IndependentTransform(torch.distributions.SoftmaxTransform(), 2)
+        result = multi_exp.forward_shape([1, 2])
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        multi_exp = torch.distributions.IndependentTransform(torch.distributions.SoftmaxTransform(), 2)
+        result = multi_exp.forward_shape([1, 2, 3])
+        """
+    )
+    obj.run(pytorch_code, ["result"])

tests/test_distributions_PowerTransform.py

@@ -0,0 +1,31 @@
+# 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.distributions.PowerTransform")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+
+        power = torch.distributions.PowerTransform(torch.tensor(2.))
+        result = power.forward_shape([1, 2])
+        """
+    )
+    obj.run(pytorch_code, ["result"])

tests/test_distributions_ReshapeTransform.py

@@ -0,0 +1,31 @@
+# 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.distributions.ReshapeTransform")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+
+        reshape_transform = torch.distributions.ReshapeTransform((2, 3), (3, 2))
+        result = reshape_transform.forward_shape((1,2,3))
+        """
+    )
+    obj.run(pytorch_code, ["result"])

tests/test_distributions_TanhTransform.py

@@ -0,0 +1,43 @@
+# 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.distributions.TanhTransform")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.ones((2,3))
+        t = torch.distributions.TanhTransform()
+        result = t(x)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.ones((2,3))
+        t = torch.distributions.TanhTransform(cache_size=0)
+        result = t(x)
+        """
+    )
+    obj.run(pytorch_code, ["result"])