转换规则 No.228/229 (#248)

* Add tests

paconvert/api_mapping.json

@@ -10427,6 +10427,33 @@
       "stable": ""
     }
   },
+  "torch.sparse.addmm": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.sparse.addmm",
+    "args_list": [
+      "input",
+      "mat1",
+      "mat2",
+      "alpha",
+      "beta"
+    ],
+    "kwargs_change": {
+      "mat1": "x",
+      "mat2": "y"
+    }
+  },
+  "torch.sparse.mm": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.sparse.matmul",
+    "args_list": [
+      "sparse",
+      "dense"
+    ],
+    "kwargs_change": {
+      "sparse": "x",
+      "dense": "y"
+    }
+  },
   "torch.sparse.sum": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.sparse.sum",

tests/test_sparse_addmm.py

@@ -0,0 +1,109 @@
+# 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.sparse.addmm")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1., 2, 3], [3, 4, 5], [3, 4, 5]])
+        i = torch.tensor([[0, 1, 2],
+                        [1, 0, 1]])
+        v = torch.tensor([3, 4, 5], dtype=torch.float32)
+        mat1 = torch.sparse_coo_tensor(i, v, [3, 3])
+        mat2 = torch.tensor([[1., 2, 3], [3, 4, 5], [3, 4, 5]])
+        result = None
+        if torch.cuda.is_available():
+            result = torch.sparse.addmm(x, mat1, mat2)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1., 2, 3], [3, 4, 5], [3, 4, 5]])
+        i = torch.tensor([[0, 1, 2],
+                        [1, 0, 1]])
+        v = torch.tensor([3, 4, 5], dtype=torch.float32)
+        mat1 = torch.sparse_coo_tensor(i, v, [3, 3])
+        mat2 = torch.tensor([[1., 2, 3], [3, 4, 5], [3, 4, 5]])
+        result = None
+        if torch.cuda.is_available():
+            result = torch.sparse.addmm(input=x, mat1=mat1, mat2=mat2, beta=0.6, alpha=0.7)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1., 2, 3], [3, 4, 5], [3, 4, 5]])
+        i = torch.tensor([[0, 1, 2],
+                        [1, 0, 1]])
+        v = torch.tensor([3, 4, 5], dtype=torch.float32)
+        mat1 = torch.sparse_coo_tensor(i, v, [3, 3])
+        mat2 = torch.tensor([[1., 2, 3], [3, 4, 5], [3, 4, 5]])
+        result = None
+        if torch.cuda.is_available():
+            result = torch.sparse.addmm(beta=0.6, alpha=0.7, input=x, mat1=mat1, mat2=mat2)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1., 2, 3], [3, 4, 5], [3, 4, 5]])
+        i = torch.tensor([[0, 1, 2],
+                        [1, 0, 1]])
+        v = torch.tensor([3, 4, 5], dtype=torch.float32)
+        mat1 = torch.sparse_coo_tensor(i, v, [3, 3])
+        mat2 = torch.tensor([[1., 2, 3], [3, 4, 5], [3, 4, 5]])
+        result = None
+        if torch.cuda.is_available():
+            result = torch.sparse.addmm(x, mat1, mat2, beta=0.6, alpha=0.7)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_5():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([[1., 2, 3], [3, 4, 5], [3, 4, 5]])
+        i = torch.tensor([[0, 1, 2],
+                        [1, 0, 1]])
+        v = torch.tensor([3, 4, 5], dtype=torch.float32)
+        mat1 = torch.sparse_coo_tensor(i, v, [3, 3])
+        mat2 = torch.tensor([[1., 2, 3], [3, 4, 5], [3, 4, 5]])
+        result = None
+        if torch.cuda.is_available():
+            result = torch.sparse.addmm(input=x, mat1=mat1, mat2=mat2, beta=0.6, alpha=0.7)
+        """
+    )
+    obj.run(pytorch_code, ["result"])

tests/test_sparse_mm.py

@@ -0,0 +1,67 @@
+# 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.sparse.mm")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        indices = [[0, 1, 2], [1, 2, 0]]
+        values = [1., 2., 3.]
+        x = torch.sparse_coo_tensor(indices, values, [3, 3])
+        dense = torch.ones([3, 2])
+        result = None
+        if torch.cuda.is_available():
+            result = torch.sparse.mm(x, dense)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        indices = [[0, 1, 2], [1, 2, 0]]
+        values = [1., 2., 3.]
+        x = torch.sparse_coo_tensor(indices, values, [3, 3])
+        dense = torch.ones([3, 2])
+        result = None
+        if torch.cuda.is_available():
+            result = torch.sparse.mm(sparse=x, dense=dense)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        indices = [[0, 1, 2], [1, 2, 0]]
+        values = [1., 2., 3.]
+        x = torch.sparse_coo_tensor(indices, values, [3, 3])
+        dense = torch.ones([3, 2])
+        result = None
+        if torch.cuda.is_available():
+            result = torch.sparse.mm(dense=dense, sparse=x)
+        """
+    )
+    obj.run(pytorch_code, ["result"])