Add paddle.sparse and three Sparse API

python/paddle/__init__.py

@@ -72,6 +72,7 @@
 import paddle.reader  # noqa: F401
 import paddle.static  # noqa: F401
 import paddle.vision  # noqa: F401
+import paddle.sparse  # noqa: F401
 
 from .tensor.attribute import is_complex  # noqa: F401
 from .tensor.attribute import is_integer  # noqa: F401

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

@@ -16,24 +16,25 @@
 import unittest
 import numpy as np
 import paddle
-from paddle import _C_ops
 from paddle.fluid.framework import _test_eager_guard
 
 
 class TestSparseActivation(unittest.TestCase):
     def test_sparse_relu(self):
         with _test_eager_guard():
             x = [[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]]
-            dense_x = paddle.to_tensor(x, dtype='float32')
-            dense_shape = [3, 4]
-            stop_gradient = True
+            dense_x = paddle.to_tensor(x, dtype='float32', stop_gradient=False)
             sparse_dim = 2
-            sparse_coo_x = dense_x.to_sparse_coo(sparse_dim)
-            #TODO(zhangkaihuo): change to test the corresponding API: paddle.sparse.relu(sparse_coo_x)
-            sparse_act_out = _C_ops.final_state_sparse_relu(sparse_coo_x)
-            correct_result = [0, 2, 0, 4, 5]
-            actual_result = sparse_act_out.non_zero_elements().numpy()
-            assert np.array_equal(correct_result, actual_result)
+            sparse_x = dense_x.to_sparse_coo(sparse_dim)
+            sparse_relu = paddle.sparse.ReLU()
+            sparse_out = sparse_relu(sparse_x)
+            dense_relu = paddle.nn.ReLU()
+            #TODO: replace non_zero_elements() as values()
+            dense_out = dense_relu(sparse_x.non_zero_elements())
+            actual_result = sparse_out.non_zero_elements().numpy()
+            assert np.array_equal(dense_out.numpy(), actual_result)
+            dense_out.backward(dense_out)
+            sparse_out.backward(sparse_out)
 
 
 if __name__ == "__main__":

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

@@ -16,27 +16,37 @@
 import unittest
 import numpy as np
 import paddle
-from paddle import _C_ops
-from paddle.fluid import core
+import paddle.fluid.core as core
 from paddle.fluid.framework import _test_eager_guard
 
 
-class TestSparseUtils(unittest.TestCase):
-    def test_create_sparse_coo_tensor(self):
+class TestSparseCreate(unittest.TestCase):
+    def test_create_coo_by_tensor(self):
         with _test_eager_guard():
             non_zero_indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
             non_zero_elements = [1, 2, 3, 4, 5]
             dense_shape = [3, 4]
             dense_indices = paddle.to_tensor(non_zero_indices)
             dense_elements = paddle.to_tensor(
                 non_zero_elements, dtype='float32')
-            stop_gradient = False
-            coo = core.eager.sparse_coo_tensor(dense_indices, dense_elements,
-                                               dense_shape, stop_gradient)
+            coo = paddle.sparse.sparse_coo_tensor(
+                dense_indices, dense_elements, dense_shape, stop_gradient=False)
+            assert np.array_equal(non_zero_indices,
+                                  coo.non_zero_indices().numpy())
+            assert np.array_equal(non_zero_elements,
+                                  coo.non_zero_elements().numpy())
 
+    def test_create_coo_by_np(self):
+        with _test_eager_guard():
+            indices = [[0, 1, 2], [1, 2, 0]]
+            values = [1.0, 2.0, 3.0]
+            dense_shape = [2, 3]
+            coo = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape)
             print(coo)
+            assert np.array_equal(indices, coo.non_zero_indices().numpy())
+            assert np.array_equal(values, coo.non_zero_elements().numpy())
 
-    def test_create_sparse_csr_tensor(self):
+    def test_create_csr_by_tensor(self):
         with _test_eager_guard():
             non_zero_crows = [0, 2, 3, 5]
             non_zero_cols = [1, 3, 2, 0, 1]
@@ -47,12 +57,77 @@ def test_create_sparse_csr_tensor(self):
             dense_elements = paddle.to_tensor(
                 non_zero_elements, dtype='float32')
             stop_gradient = False
-            csr = core.eager.sparse_csr_tensor(dense_crows, dense_cols,
-                                               dense_elements, dense_shape,
-                                               stop_gradient)
-
+            csr = paddle.sparse.sparse_csr_tensor(
+                dense_crows,
+                dense_cols,
+                dense_elements,
+                dense_shape,
+                stop_gradient=stop_gradient)
             print(csr)
 
+    def test_create_csr_by_np(self):
+        with _test_eager_guard():
+            crows = [0, 2, 3, 5]
+            cols = [1, 3, 2, 0, 1]
+            values = [1, 2, 3, 4, 5]
+            dense_shape = [3, 4]
+            csr = paddle.sparse.sparse_csr_tensor(crows, cols, values,
+                                                  dense_shape)
+            assert np.array_equal(crows, csr.non_zero_crows().numpy())
+            assert np.array_equal(cols, csr.non_zero_cols().numpy())
+            assert np.array_equal(values, csr.non_zero_elements().numpy())
+
+    def test_place(self):
+        with _test_eager_guard():
+            place = core.CPUPlace()
+            indices = [[0, 1], [0, 1]]
+            values = [1.0, 2.0]
+            dense_shape = [2, 2]
+            coo = paddle.sparse.sparse_coo_tensor(
+                indices, values, dense_shape, place=place)
+            assert coo.place.is_cpu_place()
+            assert coo.non_zero_elements().place.is_cpu_place()
+            assert coo.non_zero_indices().place.is_cpu_place()
+
+            crows = [0, 2, 3, 5]
+            cols = [1, 3, 2, 0, 1]
+            values = [1.0, 2.0, 3.0, 4.0, 5.0]
+            csr = paddle.sparse.sparse_csr_tensor(
+                crows, cols, values, [3, 5], place=place)
+            assert csr.place.is_cpu_place()
+            assert csr.non_zero_crows().place.is_cpu_place()
+            assert csr.non_zero_cols().place.is_cpu_place()
+            assert csr.non_zero_elements().place.is_cpu_place()
+
+    def test_dtype(self):
+        with _test_eager_guard():
+            indices = [[0, 1], [0, 1]]
+            values = [1.0, 2.0]
+            dense_shape = [2, 2]
+            indices = paddle.to_tensor(indices, dtype='int32')
+            values = paddle.to_tensor(values, dtype='float32')
+            coo = paddle.sparse.sparse_coo_tensor(
+                indices, values, dense_shape, dtype='float64')
+            assert coo.dtype == paddle.float64
+
+            crows = [0, 2, 3, 5]
+            cols = [1, 3, 2, 0, 1]
+            values = [1.0, 2.0, 3.0, 4.0, 5.0]
+            csr = paddle.sparse.sparse_csr_tensor(
+                crows, cols, values, [3, 5], dtype='float16')
+            assert csr.dtype == paddle.float16
+
+    def test_create_coo_no_shape(self):
+        with _test_eager_guard():
+            indices = [[0, 1], [0, 1]]
+            values = [1.0, 2.0]
+            indices = paddle.to_tensor(indices, dtype='int32')
+            values = paddle.to_tensor(values, dtype='float32')
+            coo = paddle.sparse.sparse_coo_tensor(indices, values)
+            assert [2, 2] == coo.shape
+
+
+class TestSparseConvert(unittest.TestCase):
     def test_to_sparse_coo(self):
         with _test_eager_guard():
             x = [[0, 1, 0, 2], [0, 0, 3, 0], [4, 5, 0, 0]]

python/paddle/sparse/__init__.py

@@ -0,0 +1,19 @@
+#   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 .creation import sparse_coo_tensor
+from .creation import sparse_csr_tensor
+from .layer.activation import ReLU
+
+__all__ = ['sparse_coo_tensor', 'sparse_csr_tensor', 'ReLU']

python/paddle/sparse/creation.py

@@ -0,0 +1,191 @@
+#   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 paddle import _C_ops
+from ..framework import core, dygraph_only
+from ..tensor import to_tensor
+from ..tensor import max
+from ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype, convert_dtype
+
+__all__ = [
+    'sparse_coo_tensor',
+    'sparse_csr_tensor',
+]
+
+
+def _handle_dtype(data, dtype):
+    if dtype:
+        if convert_dtype(dtype) != convert_dtype(data.dtype):
+            return data.astype(convert_dtype(dtype))
+    return data
+
+
+def _infer_dense_shape(indices):
+    assert len(indices.shape) == 2
+    lens = max(indices, axis=1)
+    lens = lens + 1
+    return list(lens.numpy())
+
+
+@dygraph_only
+def sparse_coo_tensor(indices,
+                      values,
+                      shape=None,
+                      dtype=None,
+                      place=None,
+                      stop_gradient=True):
+    r"""
+    Constructs a sparse ``paddle.Tensor`` in coordinate format according to the indices 
+    and values of the specified non-zero elements.
+
+    Args:
+        indices(list|tuple|ndarray|Tensor): the indices of non-zero elements.
+            Can be a list, tuple, numpy\.ndarray, paddle\.Tensor. The indices must be 2-D.
+        values(list|tuple|ndarray|Tensor): Initial values for the tensor.
+            Can be a scalar, list, tuple, numpy\.ndarray, paddle\.Tensor.
+        shape(list|tuple, optional): The shape of the sparse tensor also represents the shape of
+            original dense tensor. If not provided the smallest shape will be inferred to 
+            hold all elements.
+        dtype(str|np.dtype, optional): The desired data type of returned tensor. Can be 'bool' , 'float16' , 
+            'float32' , 'float64' , 'int8' , 'int16' , 'int32' , 'int64' , 'uint8',
+            'complex64' , 'complex128'. Default: None, infers dtype from ``data`` 
+            except for python float number which gets dtype from ``get_default_type`` .
+        place(CPUPlace|CUDAPinnedPlace|CUDAPlace|str, optional): The place to allocate Tensor. Can be  
+            CPUPlace, CUDAPinnedPlace, CUDAPlace. Default: None, means global place. If ``place`` is 
+            string, It can be ``cpu``, ``gpu:x`` and ``gpu_pinned``, where ``x`` is the index of the GPUs. 
+        stop_gradient(bool, optional): Whether to block the gradient propagation of Autograd. Default: True.
+
+    Returns:
+        Tensor: A Tensor constructed from ``indices`` and ``values`` .
+
+    Raises:
+        TypeError: If the data type of ``values`` is not list, tuple, numpy.ndarray, paddle.Tensor
+        ValueError: If ``values`` is tuple|list, it can't contain nested tuple|list with different lengths , such as: [[1, 2], [3, 4, 5]]. If the ``indices`` is not a 2-D. 
+        TypeError: If ``dtype`` is not bool, float16, float32, float64, int8, int16, int32, int64, uint8, complex64, complex128
+        ValueError: If ``place`` is not paddle.CPUPlace, paddle.CUDAPinnedPlace, paddle.CUDAPlace or specified pattern string. 
+
+    Examples:
+
+    .. code-block:: python
+
+        import paddle
+        from paddle.fluid.framework import _test_eager_guard
+
+        with _test_eager_guard():
+            indices = [[0, 1, 2], [1, 2, 0]]
+            values = [1.0, 2.0, 3.0]
+            dense_shape = [2, 3]
+            coo = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape)
+            # print(coo)
+            # Tensor(shape=[2, 3], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
+            #       indices=[[0, 1, 2],
+            #                [1, 2, 0]],
+            #       values=[1., 2., 3.])
+    """
+
+    if not isinstance(indices, core.eager.Tensor):
+        indices = to_tensor(
+            indices, dtype=None, place=place, stop_gradient=True)
+    if not isinstance(values, core.eager.Tensor):
+        values = to_tensor(values, dtype, place, stop_gradient)
+    if len(indices.shape) != 2:
+        raise ValueError("'indices' must be 2-D.")
+    if place is not None:
+        indices = indices._copy_to(place, False)
+        values = values._copy_to(place, False)
+    values = _handle_dtype(values, dtype)
+    if shape is None:
+        shape = _infer_dense_shape(indices)
+    return core.eager.sparse_coo_tensor(indices, values, shape, stop_gradient)
+
+
+#TODO: need to support shape is None
+@dygraph_only
+def sparse_csr_tensor(crows,
+                      cols,
+                      values,
+                      shape,
+                      dtype=None,
+                      place=None,
+                      stop_gradient=True):
+    r"""
+    Constructs a sparse ``paddle.Tensor`` in CSR(Compressed Sparse Row) format according to the 
+    ``crows``, ``cols`` and ``values``.
+
+    Args:
+        crows(list|tuple|ndarray|Tensor): 1-D array, each element in the rows represents the 
+            starting position of the first non-zero element of each row in values. 
+            Can be a list, tuple, numpy\.ndarray, paddle\.Tensor. 
+        cols(list|tuple|ndarray|Tensor): 1-D array, the column of non-zero elements.
+            Can be a list, tuple, numpy\.ndarray, paddle\.Tensor. 
+        values(list|tuple|ndarray|Tensor): 1-D array, the non-zero elements.
+            Can be a scalar, list, tuple, numpy\.ndarray, paddle\.Tensor.
+        shape(list|tuple, optional): The shape of the sparse tensor also represents the shape of
+            original dense tensor. 
+            hold all elements.
+        dtype(str|np.dtype, optional): The desired data type of returned tensor. Can be 'bool' , 'float16' , 
+            'float32' , 'float64' , 'int8' , 'int16' , 'int32' , 'int64' , 'uint8',
+            'complex64' , 'complex128'. Default: None, infers dtype from ``data`` 
+            except for python float number which gets dtype from ``get_default_type`` .
+        place(CPUPlace|CUDAPinnedPlace|CUDAPlace|str, optional): The place to allocate Tensor. Can be  
+            CPUPlace, CUDAPinnedPlace, CUDAPlace. Default: None, means global place. If ``place`` is 
+            string, It can be ``cpu``, ``gpu:x`` and ``gpu_pinned``, where ``x`` is the index of the GPUs. 
+        stop_gradient(bool, optional): Whether to block the gradient propagation of Autograd. Default: True.
+
+    Returns:
+        Tensor: A Tensor constructed from ``crows``, ``cols`` and ``values`` .
+
+    Raises:
+        TypeError: If the data type of ``values`` is not list, tuple, numpy.ndarray, paddle.Tensor
+        ValueError: If ``values`` is tuple|list, it can't contain nested tuple|list with different lengths , such as: [[1, 2], [3, 4, 5]]. If the ``crow``, ``cols`` and ``values`` is not a 2-D. 
+        TypeError: If ``dtype`` is not bool, float16, float32, float64, int8, int16, int32, int64, uint8, complex64, complex128
+        ValueError: If ``place`` is not paddle.CPUPlace, paddle.CUDAPinnedPlace, paddle.CUDAPlace or specified pattern string. 
+
+    Examples:
+
+    .. code-block:: python
+
+        import paddle
+        from paddle.fluid.framework import _test_eager_guard
+
+        with _test_eager_guard():
+            crows = [0, 2, 3, 5]
+            cols = [1, 3, 2, 0, 1]
+            values = [1, 2, 3, 4, 5]
+            dense_shape = [3, 4]
+            csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape)
+            # print(csr)
+            # Tensor(shape=[3, 4], dtype=paddle.int64, place=Place(gpu:0), stop_gradient=True,
+            #       crows=[0, 2, 3, 5],
+            #       cols=[1, 3, 2, 0, 1],
+            #       values=[1, 2, 3, 4, 5])
+    """
+    if not isinstance(crows, core.eager.Tensor):
+        crows = to_tensor(crows, dtype=None, place=place, stop_gradient=True)
+    if not isinstance(cols, core.eager.Tensor):
+        cols = to_tensor(cols, dtype=None, place=place, stop_gradient=True)
+    if not isinstance(values, core.eager.Tensor):
+        values = to_tensor(values, dtype, place, stop_gradient)
+    if len(crows.shape) != 1 or len(cols.shape) != 1 or len(values.shape) != 1:
+        raise ValueError(
+            "SparseCsrTensor only support 2-D or 3-D matrix. The 'crows', 'cols' and 'values' must be 1-D."
+        )
+
+    if place is not None:
+        crows = crows._copy_to(place, False)
+        cols = cols._copy_to(place, False)
+        values = values._copy_to(place, False)
+    values = _handle_dtype(values, dtype)
+    return core.eager.sparse_csr_tensor(crows, cols, values, shape,
+                                        stop_gradient)