Source code for mindspore.ops.operations.sparse_ops

# coding: utf-8

# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================

"""Operators for sparse operators."""

from ..._checkparam import Validator as validator
from ...common import dtype as mstype
from ..primitive import PrimitiveWithInfer, prim_attr_register


[docs]class SparseToDense(PrimitiveWithInfer): """ Converts a sparse representation into a dense tensor. Inputs: - **indices** (Tensor) - A 2-D Tensor, represents the position of the element in the sparse tensor. Support int32, int64, each element value should be a non-negative int number. The shape is :math:`(n, 2)`. - **values** (Tensor) - A 1-D Tensor, represents the value corresponding to the position in the `indices`. The shape should be :math:`(n,)`. - **sparse_shape** (tuple(int)) - A positive int tuple which specifies the shape of sparse tensor, should have 2 elements, represent sparse tensor shape is :math:`(N, C)`. Returns: Tensor, converted from sparse tensor. The dtype is same as `values`, and the shape is `sparse_shape`. Raises: TypeError: If the dtype of `indices` is neither int32 nor int64. ValueError: If `sparse_shape`, shape of `indices and shape of `values` don't meet the parameter description. Supported Platforms: ``CPU`` Examples: >>> indices = Tensor([[0, 1], [1, 2]]) >>> values = Tensor([1, 2], dtype=ms.float32) >>> sparse_shape = (3, 4) >>> sparse_to_dense = ops.SparseToDense() >>> out = sparse_to_dense(indices, values, sparse_shape) >>> print(out) [[0 1 0 0] [0 0 2 0] [0 0 0 0]] """ @prim_attr_register def __init__(self): """Initialize SparseToDense.""" self.init_prim_io_names(inputs=['indices', 'values', 'dense_shape'], outputs=['output']) def __infer__(self, indices, values, sparse_shape): validator.check_tensor_dtype_valid('indices', indices['dtype'], [mstype.int32, mstype.int64], self.name) validator.check_tensor_dtype_valid('values', values['dtype'], mstype.number_type + (mstype.bool_,), self.name) indices_shape = indices['shape'] if len(indices_shape) != 2: raise ValueError(f"For '{self.name}', the 'indices' must be a 2-D tensor, " f"but got 'indices' shape: {indices_shape}.") values_shape = values['shape'] if len(values_shape) != 1 or values_shape[0] != indices_shape[0]: raise ValueError(f"For '{self.name}', the 'values' must be a 1-D tensor and the first dimension length " f"must be equal to the first dimension length of 'indices', " f"but got 'indices' shape: {indices_shape}, 'values' shape: {values_shape}.") sparse_shape_v = sparse_shape['value'] for i in sparse_shape_v: if isinstance(i, bool) or not isinstance(i, int) or i <= 0: raise ValueError(f"For '{self.name}', all elements in 'sparse_shape' must be " f"positive int number, but got 'sparse_shape': {sparse_shape_v}.") if len(sparse_shape_v) != indices_shape[1]: raise ValueError(f"For '{self.name}', the length of 'sparse_shape' should be equal to the second dimension " f"length of 'indices', but got the second dimension length of 'indices': " f"{indices_shape[1]}, length of 'sparse_shape': {len(sparse_shape_v)}.") out = {'shape': sparse_shape['value'], 'dtype': values['dtype'], 'value': None} return out
[docs]class SparseTensorDenseMatmul(PrimitiveWithInfer): """ Multiplies sparse matrix `A` by dense matrix `B`. The rank of sparse matrix and dense matrix must be equal to `2`. Args: adjoint_st (bool): If true, sparse tensor is transposed before multiplication. Default: False. adjoint_dt (bool): If true, dense tensor is transposed before multiplication. Default: False. Inputs: - **indices** (Tensor) - A 2-D Tensor, represents the position of the element in the sparse tensor. Support int32, int64, each element value should be a non-negative int number. The shape is :math:`(n, 2)`. - **values** (Tensor) - A 1-D Tensor, represents the value corresponding to the position in the `indices`. Support float16, float32, float64, int32, int64. The shape should be :math:`(n,)`. - **sparse_shape** (tuple(int)) - A positive int tuple which specifies the shape of sparse tensor, should have 2 elements, represent sparse tensor shape is :math:`(N, C)`. - **dense** (Tensor) - A 2-D Tensor, the dtype is same as `values`. If `adjoint_st` is False and `adjoint_dt` is False, the shape must be :math:`(C, M)`. If `adjoint_st` is False and `adjoint_dt` is True, the shape must be :math:`(M, C)`. If `adjoint_st` is True and `adjoint_dt` is False, the shape must be :math:`(N, M)`. If `adjoint_st` is True and `adjoint_dt` is True, the shape must be :math:`(M, N)`. Outputs: Tensor, the dtype is the same as `values`. If `adjoint_st` is False, the shape is :math:`(N, M)`. If `adjoint_st` is True, the shape is :math:`(C, M)`. Raises: TypeError: If the type of `adjoint_st` or `adjoint_dt` is not bool, or the dtype of `indices`, dtype of `values` and dtype of `dense` don't meet the parameter description. ValueError: If `sparse_shape`, shape of `indices, shape of `values`, and shape of `dense` don't meet the parameter description. Supported Platforms: ``CPU`` Examples: >>> indices = Tensor([[0, 1], [1, 2]], dtype=ms.int32) >>> values = Tensor([1, 2], dtype=ms.float32) >>> sparse_shape = (3, 4) >>> dense = Tensor([[1,1], [2,2], [3,3 ], [4, 4]], dtype=ms.float32) >>> sparse_dense_matmul = ops.SparseTensorDenseMatmul() >>> out = sparse_dense_matmul(indices, values, sparse_shape, dense) >>> print(out) [[2 2] [6 6] [0 0]] """ @prim_attr_register def __init__(self, adjoint_st=False, adjoint_dt=False): """Initialize SparseTensorDenseMatmul""" self.adjoint_st = adjoint_st self.adjoint_dt = adjoint_dt self.init_prim_io_names(inputs=['indices', 'values', 'sparse_shape', 'dense'], outputs=['output']) self.add_prim_attr('adjoint_st', self.adjoint_st) self.add_prim_attr('adjoint_dt', self.adjoint_dt) validator.check_value_type("adjoint_st", adjoint_st, [bool], self.name) validator.check_value_type("adjoint_dt", adjoint_dt, [bool], self.name) def __infer__(self, indices, values, sparse_shape, dense): validator.check_tensor_dtype_valid('indices', indices['dtype'], [mstype.int32, mstype.int64], self.name) valid_types = (mstype.float16, mstype.float32, mstype.float64, mstype.int32, mstype.int64) args = {'values': values['dtype'], 'dense': dense['dtype']} validator.check_tensors_dtypes_same_and_valid(args, valid_types, self.name) indices_shape = indices['shape'] if len(indices_shape) != 2 or indices_shape[1] != 2: raise ValueError(f"For '{self.name}', the 'indices' must be a 2-D tensor and " f"the second dimension length must be 2, but got 'indices' shape: {indices_shape}.") values_shape = values['shape'] if len(values_shape) != 1 or values_shape[0] != indices_shape[0]: raise ValueError(f"For '{self.name}', the 'values' must be a 1-D tensor and " f"the first dimension length must be equal to the first dimension length of 'indices', " f"but got 'indices' shape: {indices_shape}, 'values' shape: {values_shape}.") a_shape = sparse_shape['value'][::-1] if self.adjoint_st else sparse_shape['value'] b_shape = dense['shape'][::-1] if self.adjoint_dt else dense['shape'] for i in a_shape: if isinstance(i, bool) or not isinstance(i, int) or i <= 0: raise ValueError(f"For '{self.name}', all elements in 'sparse_shape' must be " f"positive int number, but got 'sparse_shape': {a_shape}.") if len(a_shape) != 2 or len(b_shape) != 2: raise ValueError(f"For '{self.name}', both the length of 'sparse_shape' and the tensor " f"rank of 'dense' should be equal to 2, but got the length of " f"'sparse_shape': {len(a_shape)}, " f"the tensor rank of 'dense': {len(b_shape)}.") if a_shape[1] != b_shape[0]: raise ValueError(f"For '{self.name}', the second dimension length of 'sparse_shape' must be equal to the " f"first dimension length of 'dense', but got " f"the tensor shape of 'sparse': {a_shape} and the tensor shape of 'dense': {b_shape}. " f"Don't meet the condition for matmul") out_shape = [a_shape[0], b_shape[1]] out = {'shape': tuple(out_shape), 'dtype': values['dtype'], 'value': None} return out