Fix

paconvert/api_mapping.json

@@ -6638,6 +6638,24 @@
       "out"
     ]
   },
+  "torch.histogramdd": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.histogramdd",
+    "min_input_args": 2,
+    "args_list": [
+      "input",
+      "bins",
+      "*",
+      "range",
+      "weight",
+      "density"
+    ],
+    "kwargs_change": {
+      "input": "x",
+      "range": "ranges",
+      "weight": "weights"
+    }
+  },
   "torch.hstack": {
     "Matcher": "HStackMatcher",
     "args_list": [
@@ -12223,6 +12241,7 @@
   "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts": {
     "Matcher": "Optim2LrSchedulerMatcher",
     "paddle_api": "paddle.optimizer.lr.CosineAnnealingWarmRestarts",
+    "min_input_args": 2,
     "args_list": [
       "optimizer",
       "T_0",

paconvert/api_matcher.py

@@ -2128,6 +2128,8 @@ class TensorToMatcher(BaseMatcher):
     def get_paddle_nodes(self, args, kwargs):
         new_args = self.parse_args(args)
         new_kwargs = self.parse_kwargs(kwargs)
+        if new_kwargs is None:
+            new_kwargs = {}
         if "copy" in new_kwargs:
             new_kwargs.pop("copy")
         if "memory_format" in new_kwargs:

tests/test_histogramdd.py

@@ -0,0 +1,84 @@
+# 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.histogramdd")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[0., 1.], [1., 0.], [2.,0.], [2., 2.]])
+        bins = [3,3]
+        weights = torch.tensor([1., 2., 4., 8.])
+        result = torch.histogramdd(x, bins=bins, weight=weights)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[0., 1.], [1., 0.], [2.,0.], [2., 2.]])
+        bins = [3,3]
+        weights = torch.tensor([1., 2., 4., 8.])
+        result = torch.histogramdd(input=x, bins=bins, weight=weights)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[0., 1.], [1., 0.], [2.,0.], [2., 2.]])
+        bins = [3,3]
+        weights = torch.tensor([1., 2., 4., 8.])
+        result = torch.histogramdd(input=x, weight=weights, bins=bins)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[0., 1.], [1., 0.], [2.,0.], [2., 2.]])
+        bins = [3,3]
+        weights = torch.tensor([1., 2., 4., 8.])
+        result = torch.histogramdd(input=x, bins=bins, range=None, weight=weights, density=True)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_5():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[0., 1.], [1., 0.], [2.,0.], [2., 2.]])
+        bins = [3,3]
+        weights = torch.tensor([1., 2., 4., 8.])
+        result = torch.histogramdd(x, bins, range=None, weight=weights, density=True)
+        """
+    )
+    obj.run(pytorch_code, ["result"])