【PaddlePaddle Hackathon 3 No.23】为 Paddle 新增 paddle.incubate.sparse.is_same_shape 稀疏 API (#45086)

* add paddle.incubate.sparse.is_same_shape

* add paddle.incubate.sparse.is_same_shape

* add paddle.incubate.sparse.is_same_shape

* add paddle.incubate.sparse.is_same_shape

* fix doc

paddle/fluid/pybind/eager_method.cc

@@ -1571,6 +1571,15 @@ static PyObject* tensor_method_to_sparse_csr(TensorObject* self,
   EAGER_CATCH_AND_THROW_RETURN_NULL
 }
 
+static PyObject* tensor_method_is_same_shape(TensorObject* self,
+                                             PyObject* args,
+                                             PyObject* kwargs) {
+  EAGER_TRY
+  auto other = CastPyArg2Tensor(PyTuple_GET_ITEM(args, 0), 0);
+  return ToPyObject(self->tensor.shape() == other.shape());
+  EAGER_CATCH_AND_THROW_RETURN_NULL
+}
+
 static PyObject* tensor__inplace_version(TensorObject* self,
                                          PyObject* args,
                                          PyObject* kwargs) {
@@ -1966,6 +1975,10 @@ PyMethodDef variable_methods[] = {
      (PyCFunction)(void (*)(void))tensor_method_is_sparse_csr,
      METH_VARARGS | METH_KEYWORDS,
      NULL},
+    {"is_same_shape",
+     (PyCFunction)(void (*)(void))tensor_method_is_same_shape,
+     METH_VARARGS | METH_KEYWORDS,
+     NULL},
     {"to_sparse_csr",
      (PyCFunction)(void (*)(void))tensor_method_to_sparse_csr,
      METH_VARARGS | METH_KEYWORDS,

python/paddle/fluid/tests/unittests/test_sparse_is_same_shape.py

+# Copyright (c) 2022 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.
+
+from __future__ import print_function
+import unittest
+
+import paddle
+from paddle.incubate.sparse.binary import is_same_shape
+
+
+class TestSparseIsSameShapeAPI(unittest.TestCase):
+    """
+    test paddle.incubate.sparse.is_same_shape
+    """
+
+    def setUp(self):
+        self.shapes = [[2, 5, 8], [3, 4]]
+        self.tensors = [
+            paddle.rand(self.shapes[0]),
+            paddle.rand(self.shapes[0]),
+            paddle.rand(self.shapes[1])
+        ]
+        self.sparse_dim = 2
+
+    def test_dense_dense(self):
+        self.assertTrue(is_same_shape(self.tensors[0], self.tensors[1]))
+        self.assertFalse(is_same_shape(self.tensors[0], self.tensors[2]))
+        self.assertFalse(is_same_shape(self.tensors[1], self.tensors[2]))
+
+    def test_dense_csr(self):
+        self.assertTrue(
+            is_same_shape(self.tensors[0], self.tensors[1].to_sparse_csr()))
+        self.assertFalse(
+            is_same_shape(self.tensors[0], self.tensors[2].to_sparse_csr()))
+        self.assertFalse(
+            is_same_shape(self.tensors[1], self.tensors[2].to_sparse_csr()))
+
+    def test_dense_coo(self):
+        self.assertTrue(
+            is_same_shape(self.tensors[0],
+                          self.tensors[1].to_sparse_coo(self.sparse_dim)))
+        self.assertFalse(
+            is_same_shape(self.tensors[0],
+                          self.tensors[2].to_sparse_coo(self.sparse_dim)))
+        self.assertFalse(
+            is_same_shape(self.tensors[1],
+                          self.tensors[2].to_sparse_coo(self.sparse_dim)))
+
+    def test_csr_dense(self):
+        self.assertTrue(
+            is_same_shape(self.tensors[0].to_sparse_csr(), self.tensors[1]))
+        self.assertFalse(
+            is_same_shape(self.tensors[0].to_sparse_csr(), self.tensors[2]))
+        self.assertFalse(
+            is_same_shape(self.tensors[1].to_sparse_csr(), self.tensors[2]))
+
+    def test_csr_csr(self):
+        self.assertTrue(
+            is_same_shape(self.tensors[0].to_sparse_csr(),
+                          self.tensors[1].to_sparse_csr()))
+        self.assertFalse(
+            is_same_shape(self.tensors[0].to_sparse_csr(),
+                          self.tensors[2].to_sparse_csr()))
+        self.assertFalse(
+            is_same_shape(self.tensors[1].to_sparse_csr(),
+                          self.tensors[2].to_sparse_csr()))
+
+    def test_csr_coo(self):
+        self.assertTrue(
+            is_same_shape(self.tensors[0].to_sparse_csr(),
+                          self.tensors[1].to_sparse_coo(self.sparse_dim)))
+        self.assertFalse(
+            is_same_shape(self.tensors[0].to_sparse_csr(),
+                          self.tensors[2].to_sparse_coo(self.sparse_dim)))
+        self.assertFalse(
+            is_same_shape(self.tensors[1].to_sparse_csr(),
+                          self.tensors[2].to_sparse_coo(self.sparse_dim)))
+
+    def test_coo_dense(self):
+        self.assertTrue(
+            is_same_shape(self.tensors[0].to_sparse_coo(self.sparse_dim),
+                          self.tensors[1]))
+        self.assertFalse(
+            is_same_shape(self.tensors[0].to_sparse_coo(self.sparse_dim),
+                          self.tensors[2]))
+        self.assertFalse(
+            is_same_shape(self.tensors[1].to_sparse_coo(self.sparse_dim),
+                          self.tensors[2]))
+
+    def test_coo_csr(self):
+        self.assertTrue(
+            is_same_shape(self.tensors[0].to_sparse_coo(self.sparse_dim),
+                          self.tensors[1].to_sparse_csr()))
+        self.assertFalse(
+            is_same_shape(self.tensors[0].to_sparse_coo(self.sparse_dim),
+                          self.tensors[2].to_sparse_csr()))
+        self.assertFalse(
+            is_same_shape(self.tensors[1].to_sparse_coo(self.sparse_dim),
+                          self.tensors[2].to_sparse_csr()))
+
+    def test_coo_coo(self):
+        self.assertTrue(
+            is_same_shape(self.tensors[0].to_sparse_coo(self.sparse_dim),
+                          self.tensors[1].to_sparse_coo(self.sparse_dim)))
+        self.assertFalse(
+            is_same_shape(self.tensors[0].to_sparse_coo(self.sparse_dim),
+                          self.tensors[2].to_sparse_coo(self.sparse_dim)))
+        self.assertFalse(
+            is_same_shape(self.tensors[1].to_sparse_coo(self.sparse_dim),
+                          self.tensors[2].to_sparse_coo(self.sparse_dim)))
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/incubate/sparse/__init__.py

@@ -42,6 +42,7 @@ from .binary import add
 from .binary import divide
 from .binary import multiply
 from .binary import subtract
+from .binary import is_same_shape
 
 from .multiary import addmm
 
@@ -77,4 +78,5 @@ __all__ = [
     'multiply',
     'divide',
     'coalesce',
+    'is_same_shape',
 ]

python/paddle/incubate/sparse/binary.py

@@ -399,3 +399,36 @@ def divide(x, y, name=None):
         if y.dtype != x.dtype:
             y = _C_ops.sparse_cast(y, None, x.dtype)
         return _C_ops.sparse_divide(x, y)
+
+
+@dygraph_only
+def is_same_shape(x, y):
+    """
+    Return the results of shape comparison between two Tensors, check whether x.shape equal to y.shape.
+    Any two type Tensor among DenseTensor/SparseCooTensor/SparseCsrTensor are supported.
+
+    Args:
+        x (Tensor): The input tensor. It can be DenseTensor/SparseCooTensor/SparseCsrTensor.
+        y (Tensor): The input tensor. It can be DenseTensor/SparseCooTensor/SparseCsrTensor.
+
+    Returns:
+        bool: True for same shape and False for different shape.
+
+    Examples:
+
+        .. code-block:: python
+
+            import paddle
+
+            x = paddle.rand([2, 3, 8])
+            y = paddle.rand([2, 3, 8])
+            y = y.to_sparse_csr()
+            z = paddle.rand([2, 5])
+
+            paddle.incubate.sparse.is_same_shape(x, y)
+            # True
+            paddle.incubate.sparse.is_same_shape(x, z)
+            # False
+
+    """
+    return x.is_same_shape(y)