# 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. import paddle from paddle import _C_ops from paddle.fluid.framework import core, dygraph_only from paddle.fluid.framework import _current_expected_place, _get_paddle_place from paddle.tensor import to_tensor, max from paddle.fluid.data_feeder import convert_dtype from paddle import in_dynamic_mode from paddle.fluid.layer_helper import LayerHelper import numpy as np __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, values): assert len(indices.shape) == 2 lens = max(indices, axis=1) lens = lens + 1 lens = lens.numpy() if len(values.shape) > 1: lens = np.append(lens, values.shape[1:]) return list(lens) def _get_place(place): place = _get_paddle_place(place) if place is None: place = _current_expected_place() elif not isinstance( place, (core.Place, core.CPUPlace, core.CUDAPinnedPlace, core.CUDAPlace) ): raise ValueError( "'place' must be any of paddle.Place, paddle.CPUPlace, paddle.CUDAPinnedPlace, paddle.CUDAPlace" ) return place def _check_indices_dtype(dtype): if dtype not in [paddle.int8, paddle.int16, paddle.int32, paddle.int64]: raise TypeError( "the dtype of indices must be 'int8' or 'int16' or 'int32' or 'int64'" ) 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`` . Examples: .. code-block:: python import paddle indices = [[0, 1, 2], [1, 2, 0]] values = [1.0, 2.0, 3.0] dense_shape = [3, 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 in_dynamic_mode(): place = _get_place(place) 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.") nnz = indices.shape[1] sparse_dim = indices.shape[0] _check_indices_dtype(indices.dtype) if nnz != values.shape[0]: raise ValueError( "the indices and values must have same number of non-zero, but get {} and {}".format( nnz, values.shape[0] ) ) dense_dim = len(values.shape) - 1 if not indices.place._equals(place): indices = indices._copy_to(place, False) if not values.place._equals(place): values = values._copy_to(place, False) values = _handle_dtype(values, dtype) values.stop_gradient = stop_gradient min_shape = _infer_dense_shape(indices, values) if shape is None: shape = min_shape else: if shape < min_shape: raise ValueError( "the minimun shape required is {}, but get {}".format( min_shape, shape ) ) if len(shape) != sparse_dim + dense_dim: raise ValueError( "the number of dimensions(len(shape) must be sparse_dim({}) + dense_dim({}), but get {}".format( sparse_dim, dense_dim, len(shape) ) ) return _C_ops.sparse_sparse_coo_tensor(values, indices, shape) else: op_type = 'sparse_sparse_coo_tensor' inputs = {'values': values, 'indices': indices} if shape[0] is None: shape[0] = -1 attrs = {'shape': shape} helper = LayerHelper(op_type) out = helper.create_sparse_variable_for_type_inference(dtype) helper.append_op( type=op_type, inputs=inputs, outputs={'out': out}, attrs=attrs ) return out # 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``. Currently, the crows and cols of each batch must be incrementd. 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`` . Examples: .. code-block:: python import paddle 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]) """ place = _get_place(place) 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) _check_indices_dtype(crows.dtype) _check_indices_dtype(cols.dtype) if len(shape) != 2 and len(shape) != 3: raise ValueError( "SparseCsrTensor only support 2-D or 3-D matrix. but get shape {}".format( shape ) ) rows = shape[len(shape) - 2] if not crows.place._equals(place): crows = crows._copy_to(place, False) if not cols.place._equals(place): cols = cols._copy_to(place, False) if not values.place._equals(place): values = values._copy_to(place, False) values = _handle_dtype(values, dtype) values.stop_gradient = stop_gradient if len(crows.shape) != 1 or len(cols.shape) != 1 or len(values.shape) != 1: raise ValueError("The 'crows', 'cols' and 'values' must be 1-D.") if len(cols) != len(values): raise ValueError("the length of cols must be same as length of values") if len(shape) == 2: if crows.shape[0] != rows + 1: raise ValueError( "The length({}) of crows must be equal to the rows({})+1 of matrix.".format( crows.shape[0], rows ) ) if crows[0] != 0: raise ValueError("the 0th value of crows must be 0") if crows[-1] != values.shape[0]: raise ValueError( "the last value of crows must be equal the number of non-zero" ) else: if crows.shape[0] % (rows + 1) != 0: raise ValueError( "The length({}) of crows must be divisible the rows({})+1 of matrix.".format( crows.shape[0], rows ) ) # TODO(zkh2016): check whether the value in crows and cols is legal return core.eager.sparse_csr_tensor( crows, cols, values, shape, stop_gradient )