【Hackathon 7th No.39】为 Paddle 代码转换工具新增 API 转换规则（第 6 组）

paconvert/api_mapping.json

@@ -10115,6 +10115,20 @@
       "output_size"
     ]
   },
+  "torch.nn.AdaptiveLogSoftmaxWithLoss": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.nn.AdaptiveLogSoftmaxWithLoss",
+    "min_input_args": 3,
+    "args_list": [
+      "in_features",
+      "n_classes",
+      "cutoffs",
+      "div_value",
+      "head_bias",
+      "device",
+      "dtype"
+    ]
+  },
   "torch.nn.AdaptiveMaxPool1d": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.nn.AdaptiveMaxPool1D",
@@ -10328,6 +10342,17 @@
       "groups"
     ]
   },
+  "torch.nn.CircularPad3d": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.nn.Pad3D",
+    "min_input_args": 1,
+    "args_list": [
+      "padding"
+    ],
+    "paddle_default_kwargs": {
+      "mode": "'circular'"
+    }
+  },
   "torch.nn.ConstantPad1d": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.nn.Pad1D",
@@ -10964,6 +10989,28 @@
     ],
     "min_input_args": 0
   },
+  "torch.nn.LPPool1d": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.nn.LPPool1D",
+    "min_input_args": 2,
+    "args_list": [
+      "norm_type",
+      "kernel_size",
+      "stride",
+      "ceil_mode"
+    ]
+  },
+  "torch.nn.LPPool2d": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.nn.LPPool2D",
+    "min_input_args": 2,
+    "args_list": [
+      "norm_type",
+      "kernel_size",
+      "stride",
+      "ceil_mode"
+    ]
+  },
   "torch.nn.LSTM": {
     "Matcher": "RNNMatcher",
     "paddle_api": "paddle.nn.LSTM",
@@ -11931,7 +11978,7 @@
       "dim": "axis"
     },
     "paddle_default_kwargs": {
-      "axis": 0
+      "axis": null
     }
   },
   "torch.nn.Softmax2d": {
@@ -11942,6 +11989,20 @@
     },
     "min_input_args": 0
   },
+  "torch.nn.Softmin": {
+    "Matcher": "SoftminMatcher",
+    "paddle_api": "paddle.nn.Softmax",
+    "args_list": [
+      "dim"
+    ],
+    "kwargs_change": {
+      "dim": "axis"
+    },
+    "paddle_default_kwargs": {
+      "axis": null
+    },
+    "min_input_args": 0
+  },
   "torch.nn.Softplus": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.nn.Softplus",
@@ -13145,6 +13206,36 @@
       "input": "x"
     }
   },
+  "torch.nn.functional.lp_pool1d": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.nn.functional.lp_pool1d",
+    "min_input_args": 2,
+    "args_list": [
+      "input",
+      "norm_type",
+      "kernel_size",
+      "stride",
+      "ceil_mode"
+    ],
+    "kwargs_change": {
+      "input": "x"
+    }
+  },
+  "torch.nn.functional.lp_pool2d": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.nn.functional.lp_pool2d",
+    "min_input_args": 2,
+    "args_list": [
+      "input",
+      "norm_type",
+      "kernel_size",
+      "stride",
+      "ceil_mode"
+    ],
+    "kwargs_change": {
+      "input": "x"
+    }
+  },
   "torch.nn.functional.margin_ranking_loss": {
     "Matcher": "SizeAverageMatcher",
     "paddle_api": "paddle.nn.functional.margin_ranking_loss",
@@ -13734,6 +13825,19 @@
       "input": "x"
     }
   },
+  "torch.nn.functional.threshold_": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.nn.functional.thresholded_relu_",
+    "min_input_args": 3,
+    "args_list": [
+      "input",
+      "threshold",
+      "value"
+    ],
+    "kwargs_change": {
+      "input": "x"
+    }
+  },
   "torch.nn.functional.triplet_margin_loss": {
     "Matcher": "SizeAverageMatcher",
     "paddle_api": "paddle.nn.functional.triplet_margin_loss",
@@ -14125,6 +14229,19 @@
     },
     "min_input_args": 1
   },
+  "torch.nn.utils.parametrizations.weight_norm": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.nn.utils.weight_norm",
+    "args_list": [
+      "module",
+      "name",
+      "dim"
+    ],
+    "kwargs_change": {
+      "module": "layer"
+    },
+    "min_input_args": 1
+  },
   "torch.nn.utils.remove_weight_norm": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.nn.utils.remove_weight_norm",
@@ -14502,6 +14619,40 @@
       "lr": "learning_rate"
     }
   },
+  "torch.optim.NAdam": {
+    "Matcher": "OptimAdamMatcher",
+    "paddle_api": "paddle.optimizer.NAdam",
+    "min_input_args": 1,
+    "args_list": [
+      "params",
+      "lr",
+      "betas",
+      "eps",
+      "weight_decay",
+      "momentum_decay",
+      "decoupled_weight_decay",
+      "*",
+      "foreach",
+      "maximize",
+      "capturable",
+      "differentiable"
+    ],
+    "unsupport_args": [
+      "decoupled_weight_decay",
+      "foreach",
+      "maximize",
+      "capturable",
+      "differentiable"
+    ],
+    "kwargs_change": {
+      "params": "parameters",
+      "lr": "learning_rate",
+      "eps": "epsilon"
+    },
+    "paddle_default_kwargs": {
+      "weight_decay": 0.0
+    }
+  },
   "torch.optim.Optimizer": {
     "Matcher": "OptimOptimizerMatcher",
     "paddle_api": "paddle.optimizer.Optimizer",
@@ -14542,6 +14693,39 @@
   "torch.optim.Optimizer.zero_grad": {
     "min_input_args": 0
   },
+  "torch.optim.RAdam": {
+    "Matcher": "OptimAdamMatcher",
+    "paddle_api": "paddle.optimizer.RAdam",
+    "min_input_args": 1,
+    "args_list": [
+      "params",
+      "lr",
+      "betas",
+      "eps",
+      "weight_decay",
+      "decoupled_weight_decay",
+      "*",
+      "foreach",
+      "maximize",
+      "capturable",
+      "differentiable"
+    ],
+    "unsupport_args": [
+      "decoupled_weight_decay",
+      "foreach",
+      "maximize",
+      "capturable",
+      "differentiable"
+    ],
+    "kwargs_change": {
+      "params": "parameters",
+      "lr": "learning_rate",
+      "eps": "epsilon"
+    },
+    "paddle_default_kwargs": {
+      "weight_decay": 0.0
+    }
+  },
   "torch.optim.RMSprop": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.optimizer.RMSProp",

paconvert/api_matcher.py

@@ -4281,6 +4281,32 @@ def generate_code(self, kwargs):
         return GenericMatcher.generate_code(self, kwargs)
 
 
+class SoftminMatcher(BaseMatcher):
+    def generate_code(self, kwargs):
+        self.paddle_api = "paddle_aux.Softmin"
+        self.write_aux_code()
+        return GenericMatcher.generate_code(self, kwargs)
+
+    def generate_aux_code(self):
+        CODE_TEMPLATE = textwrap.dedent(
+            """
+            def _get_softmax_dim(axis: int) -> int:
+                if axis == 0 or axis == 1 or axis == 3:
+                    ret = 0
+                else:
+                    ret = 1
+                return ret
+
+            class Softmin(paddle.nn.Softmax):
+                def forward(self, x):
+                    if self._axis is None:
+                        return paddle.nn.functional.softmax(-x, _get_softmax_dim(x.ndim))
+                    return paddle.nn.functional.softmax(-x, self._axis)
+            """
+        )
+        return CODE_TEMPLATE
+
+
 class OptimOptimizerMatcher(BaseMatcher):
     def generate_code(self, kwargs):
         code = "paddle.optimizer.Optimizer(parameters={}, **{})".format(

tests/test_nn_AdaptiveLogSoftmaxWithLoss.py

@@ -0,0 +1,99 @@
+# 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.nn.AdaptiveLogSoftmaxWithLoss")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        input = torch.tensor([[ 0.9368637 , -0.0361056 , -0.98917043,  0.06605113,  1.5254455 ],
+                            [-1.0518035 , -1.0024613 ,  0.18699688, -0.35807893,  0.25628588],
+                            [-0.900478  , -0.41495147,  0.84707606, -1.7883497 ,  1.3243382 ]])
+        target = torch.tensor([1, 1, 1])
+        asfm = torch.nn.AdaptiveLogSoftmaxWithLoss(5, 4, [2])
+        out, loss = asfm(input,target)
+        """
+    )
+    obj.run(pytorch_code, ["out", "loss"], check_value=False)
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        input = torch.tensor([[ 0.9368637 , -0.0361056 , -0.98917043,  0.06605113,  1.5254455 ],
+                            [-1.0518035 , -1.0024613 ,  0.18699688, -0.35807893,  0.25628588],
+                            [-0.900478  , -0.41495147,  0.84707606, -1.7883497 ,  1.3243382 ]])
+        target = torch.tensor([1, 1, 1])
+        asfm = torch.nn.AdaptiveLogSoftmaxWithLoss(5, 4, [3], div_value=2.0)
+        out, loss = asfm(input,target)
+        """
+    )
+    obj.run(pytorch_code, ["out", "loss"], check_value=False)
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        input = torch.tensor([[ 0.9368637 , -0.0361056 , -0.98917043,  0.06605113,  1.5254455 ],
+                            [-1.0518035 , -1.0024613 ,  0.18699688, -0.35807893,  0.25628588],
+                            [-0.900478  , -0.41495147,  0.84707606, -1.7883497 ,  1.3243382 ]])
+        target = torch.tensor([1, 1, 1])
+        asfm = torch.nn.AdaptiveLogSoftmaxWithLoss(5, 4, [1], div_value=3.8, head_bias=True)
+        out, loss = asfm(input,target)
+        """
+    )
+    obj.run(pytorch_code, ["out", "loss"], check_value=False)
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        input = torch.tensor([[ 0.9368637 , -0.0361056 , -0.98917043,  0.06605113,  1.5254455 ],
+                            [-1.0518035 , -1.0024613 ,  0.18699688, -0.35807893,  0.25628588],
+                            [-0.900478  , -0.41495147,  0.84707606, -1.7883497 ,  1.3243382 ]])
+        target = torch.tensor([1, 1, 1])
+        asfm = torch.nn.AdaptiveLogSoftmaxWithLoss(in_features=5, n_classes=8, cutoffs=[5], div_value=3.8, head_bias=True)
+        out, loss = asfm(input,target)
+        """
+    )
+    obj.run(pytorch_code, ["out", "loss"], check_value=False)
+
+
+def test_case_5():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        import torch.nn as nn
+        input = torch.tensor([[ 0.9368637 , -0.0361056 , -0.98917043,  0.06605113,  1.5254455 ],
+                            [-1.0518035 , -1.0024613 ,  0.18699688, -0.35807893,  0.25628588],
+                            [-0.900478  , -0.41495147,  0.84707606, -1.7883497 ,  1.3243382 ]])
+        target = torch.tensor([1, 1, 1])
+        asfm = torch.nn.AdaptiveLogSoftmaxWithLoss(n_classes=8, in_features=5, div_value=3.8, cutoffs=[5], head_bias=True)
+        out, loss = asfm(input,target)
+        """
+    )
+    obj.run(pytorch_code, ["out", "loss"], check_value=False)