[API Compatibility No.246, 247] align torch.Tensor.retain_grad And torch.Tensor.sparse_mask with ChangePrefix -part

paconvert/api_mapping.json

@@ -2198,9 +2198,7 @@
   "torch.Tensor.resolve_conj": {},
   "torch.Tensor.resolve_neg": {},
   "torch.Tensor.retain_grad": {
-    "Matcher": "ChangeAPIMatcher",
-    "paddle_api": "paddle.Tensor.retain_grads",
-    "min_input_args": 0
+    "Matcher": "ChangePrefixMatcher"
   },
   "torch.Tensor.retains_grad": {},
   "torch.Tensor.roll": {
@@ -2371,8 +2369,7 @@
     "Matcher": "ChangePrefixMatcher"
   },
   "torch.Tensor.sparse_mask": {
-    "Matcher": "ChangeAPIMatcher",
-    "paddle_api": "paddle.sparse.mask_as"
+    "Matcher": "ChangePrefixMatcher"
   },
   "torch.Tensor.sparse_resize_": {},
   "torch.Tensor.sparse_resize_and_clear_": {},

tests/test_Tensor_sparse_mask.py

@@ -0,0 +1,79 @@
+# 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.sparse_mask")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        dense = torch.tensor([[1, 2, 3], [4, 5, 6]])
+        indices = torch.tensor([[0, 1], [1, 2]])
+        values = torch.tensor([0, 0])
+        mask = torch.sparse_coo_tensor(indices, values, dense.size())
+        result_sparse = dense.sparse_mask(mask)
+        result = result_sparse.to_dense()
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        dense = torch.tensor([[5, 0, 3], [0, 8, 2]])
+        indices = torch.tensor([[0, 1], [1, 0]])   # 提取 (0,1)=0 和 (1,0)=0
+        values = torch.tensor([1, 1])
+        mask = torch.sparse_coo_tensor(indices, values, dense.size())
+        result_sparse = dense.sparse_mask(mask)
+        result = result_sparse.to_dense()
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        dense = torch.arange(24).reshape(2, 3, 4)
+        indices = torch.tensor([[0, 1], [1, 2], [3, 0]])  # 提取两个位置
+        values = torch.tensor([-1, -1])
+        mask = torch.sparse_coo_tensor(indices, values, dense.size())
+        result_sparse = dense.sparse_mask(mask)
+        result = result_sparse.to_dense()
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        dense = torch.tensor([[7, 8, 9], [10, 11, 12]])
+        indices = torch.tensor([[0, 1], [0, 2]])
+        values = torch.tensor([1, 1])
+        mask = torch.sparse_coo_tensor(indices, values, dense.size())
+        result_sparse = dense.sparse_mask(mask=mask)
+        result = result_sparse.to_dense()
+        """
+    )
+    obj.run(pytorch_code, ["result"])