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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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/*!
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* \file matrix_calculation_ops.h
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* \brief
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*/
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#ifndef OPS_BUILT_IN_OP_PROTO_INC_MATRIX_CALCULATION_OPS_H_
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#define OPS_BUILT_IN_OP_PROTO_INC_MATRIX_CALCULATION_OPS_H_
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#include "graph/operator_reg.h"
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namespace ge {
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/**
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*@brief Multiplies matrix "a" by matrix "b", producing "a * b" . \n
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*@par Inputs:
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*Three inputs, including:
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* @li x1: A matrix Tensor. 2D. Must be one of the following types: float16,
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* float32, int32. Has format [ND, NHWC, FRACTAL_NZ].
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* @li x2: A matrix Tensor. 2D. Must be one of the following types: float16,
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* float32, int32. Has format [ND, NHWC, FRACTAL_NZ].
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* @li bias: A optional 1D Tensor. Must be one of the following types: float16,
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* float32, int32. Has format [ND, NHWC] . \n
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*@par Attributes:
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*@li transpose_x1: A bool. If True, changes the shape of "x1" from [M, K] to [K, M].
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*@li transpose_x2: A bool. If True, changes the shape of "x2" from [M, K] to [K, M] . \n
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*@par Outputs:
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*y: The result matrix Tensor. 2D. Must be one of the following types: float16,
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* float32, int32. Has format [ND, NHWC, FRACTAL_NZ] . \n
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*@par Third-party framework compatibility
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* Compatible with the TensorFlow operator BatchMatmul.
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*/
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REG_OP(MatMul)
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.INPUT(x1, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
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.INPUT(x2, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
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.OPTIONAL_INPUT(bias, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
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.OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
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.ATTR(transpose_x1, Bool, false)
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.ATTR(transpose_x2, Bool, false)
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.OP_END_FACTORY_REG(MatMul)
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/**
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*@brief Multiplies matrix "a" by matrix "b", producing "a * b" . \n
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*@par Inputs:
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*Two inputs, including:
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* @li x1: A matrix Tensor. 2D. Must be one of the following types: float16,
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* float32, int32. Has format [ND, NHWC, FRACTAL_NZ].
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* @li x2: A matrix Tensor. 2D. Must be one of the following types: float16,
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* float32, int32. Has format [ND, NHWC, FRACTAL_NZ].
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* @li bias: A 1D Tensor. Must be one of the following types: float16,
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* float32, int32. Has format [ND, NHWC] . \n
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*@par Attributes:
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*@li transpose_x1: A bool. If True, changes the shape of "x1" from [M, K] to [K, M].
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*@li transpose_x2: A bool. If True, changes the shape of "x2" from [M, K] to [K, M] . \n
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*@par Outputs:
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*y: The result matrix Tensor. 2D. Must be one of the following types: float16,
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* float32, int32. Has format [ND, NHWC, FRACTAL_NZ] . \n
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*@par Third-party framework compatibility
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* Compatible with the TensorFlow operator BatchMatmul.
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*/
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REG_OP(MatMulV2)
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.INPUT(x1, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32, DT_INT8}))
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.INPUT(x2, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32, DT_INT8}))
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.OPTIONAL_INPUT(bias, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
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.OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
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.OPTIONAL_INPUT(offset_w, TensorType({DT_INT8}))
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.ATTR(transpose_x1, Bool, false)
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.ATTR(transpose_x2, Bool, false)
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.ATTR(offset_x, Int, 0)
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.OP_END_FACTORY_REG(MatMulV2)
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/**
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*@brief Performs Matrix-to-matrix Multiply, producing c=alpha[0]*a*b+beta[0]*c . \n
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*@attention Constraints:
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* For better performance, The k-axis must be aligned to 16 (input type
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* is float16) or 32 (input type is int8). \n
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*@par Inputs:
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*Five inputs, including:
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*@li a: A matrix Tensor. Must be one of the following types: float16, int8.
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* Has format [ND, FRACTAL_NZ]. 2D(ND) or 4D(FRACTAL_NZ).
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*@li b: A matrix Tensor. Must be one of the following types: float16, int8.
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* Has format [ND, FRACTAL_NZ, FRACTAL_Z]. 2D(ND) or 4D(FRACTAL_NZ, FRACTAL_Z).
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*@li c: A matrix Tensor. Must be one of the following types: float16, int32,
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* float32. has format [ND, FRACTAL_NZ]. 2D(ND) or 4D(FRACTAL_NZ).
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*@li alpha: A 1D Tensor. The shape of alpha is [1].Must be one of the following
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* types: float16, int32, float32. Has format [ND].
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*@li beta: A 1D Tensor. The shape of beta is [1]. Must be one of the following
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* types: float16, int32, float32. Has format [ND].
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* The format of a, b, c has restriction:\n
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* When type of a is int8 and type of c is int32, the format of a, b, c should
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* all be ND, or a is FRACTAL_NZ and b is FRACTAL_Z and c is ND.\n
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* When type of a is int8 and type of c is float32, the format of a, b, c should
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* all be ND or a is FRACTAL_NZ and b is FRACTAL_Z and c is FRACTAL_NZ.\n
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* When type of a is float16 and type of c is float16, the format of a, b, c
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* should all be ND or FRACTAL_NZ.\n
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* When type of a is float16 and type of c is float32, the format of a, b, c
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* should all be ND or FRACTAL_NZ . \n
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*@par Attributes:
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*Two attributes, including:
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*@li transpose_a: Optional. A bool. If True, changes the shape of "a" from
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* [M, K] to [K, M].
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*@li transpose_b: Optional. A bool. If True, changes the shape of "b" from
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* [K, N] to [N, K] . \n
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*@par Outputs:
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*y: The result matrix Tensor. Must be one of the following types: float16,
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* float32, int32. Has format [ND, FRACTAL_NZ], the format should be equal to a.
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* 2D(ND) or 4D(FRACTAL_NZ).
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*/
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REG_OP(GEMM)
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.INPUT(a, TensorType({DT_FLOAT16, DT_INT8}))
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.INPUT(b, TensorType({DT_FLOAT16, DT_INT8}))
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.INPUT(c, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
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.INPUT(alpha, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
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.INPUT(beta, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
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.OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
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.ATTR(transpose_a, Bool, false)
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.ATTR(transpose_b, Bool, false)
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.OP_END_FACTORY_REG(GEMM)
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/**
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*@brief Multiplies matrix "a" by matrix "b", producing "a * b" . \n
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*@par Inputs:
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*Three inputs, including:
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* @li x1: A matrix Tensor. Must be one of the following types: float16,
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* float32, int32. 2D or higher. Has format [ND, NHWC, FRACTAL_NZ].
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* @li x2: A matrix Tensor. Must be one of the following types: float16,
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* float32, int32. 2D or higher. Has format [ND, NHWC, FRACTAL_NZ] . \n
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*@par Attributes:
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*@li adj_x1: A bool. If True, changes the shape of "x1" from [B, M, K] to [B, K, M].
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*@li adj_x2: A bool. If True, changes the shape of "x2" from [B, M, K] to [B, K, M] . \n
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*@par Outputs:
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*y: The result matrix Tensor. 2D or higher. Must be one of the following types: float16,
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* float32, int32. 2D or higher. Has format [ND, NHWC, FRACTAL_NZ]. Has the same shape length as "x1" and "x2" . \n
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*@par Third-party framework compatibility
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* Compatible with the TensorFlow operator BatchMatmul.
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*/
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REG_OP(BatchMatMul)
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.INPUT(x1, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
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.INPUT(x2, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
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.OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
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.ATTR(adj_x1, Bool, false)
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.ATTR(adj_x2, Bool, false)
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.OP_END_FACTORY_REG(BatchMatMul)
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/**
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* @brief Multiplies matrix "a" by matrix "b", producing "a * b" . \n
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* @par Inputs:
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* Three inputs, including:
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* @li x1: A matrix Tensor. Must be one of the following types: float16,
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* float32, int32. 2D or higher. Has format [ND, NHWC, FRACTAL_NZ].
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* @li x2: A matrix Tensor. Must be one of the following types: float16,
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* float32, int32. 2D or higher. Has format [ND, NHWC, FRACTAL_NZ] . \n
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* @li bias: A matrix Tensor. Must be one of the following types: float16,
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* float32, int32. 2D or higher. Has format [ND, NHWC, FRACTAL_NZ] . \n
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* @par Attributes:
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* @li adj_x: A bool. If True, changes the shape of "x1" from [B, M, K] to [B, K, M].
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* @li adj_y: A bool. If True, changes the shape of "x2" from [B, M, K] to [B, K, M] . \n
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* @par Outputs:
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* y: The result matrix Tensor. 2D or higher. Must be one of the following types: float16,
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* float32, int32. 2D or higher. Has format [ND, NHWC, FRACTAL_NZ]. Has the same shape length as "x1" and "x2" . \n
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* @par Third-party framework compatibility
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* Compatible with the TensorFlow operator BatchMatmul.
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*/
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REG_OP(BatchMatMulV2)
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.INPUT(x1, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
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.INPUT(x2, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
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.OPTIONAL_INPUT(bias, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
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.OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
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.ATTR(adj_x1, Bool, false)
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.ATTR(adj_x2, Bool, false)
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.OP_END_FACTORY_REG(BatchMatMulV2)
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/**
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*@brief Computes half the L2 norm of a tensor without the sqrt . \n
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*@par Inputs:
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* x: A Tensor.
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* TensorType::FloatingDataType() . \n
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*@par Outputs:
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*y: A Tensor. Has the same type as "x".
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*@par Third-party framework compatibility
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*Compatible with the TensorFlow operator L2Loss.
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*/
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REG_OP(L2Loss)
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.INPUT(x, TensorType::FloatingDataType())
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.OUTPUT(y, TensorType::FloatingDataType())
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.OP_END_FACTORY_REG(L2Loss)
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/**
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*@brief: Returns a batched diagonal tensor with a given batched diagonal values . \n
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*@par Inputs:
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*x: A Tensor. Must be one of the following types:
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* float16, float32, double, int32, uint8, int16, int8, complex64, int64,
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* qint8, quint8, qint32, uint16, complex128, uint32, uint64 . \n
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*@par Outputs:
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*y: A Tensor. Has the same type as "x" . \n
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*@par Third-party framework compatibility
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* Compatible with the TensorFlow operator MatrixDiag.
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*/
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REG_OP(MatrixDiag)
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.INPUT(x, TensorType::BasicType())
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.OUTPUT(y, TensorType::BasicType())
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.OP_END_FACTORY_REG(MatrixDiag)
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/**
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*@brief: Returns a batched diagonal tensor with a given batched diagonal values . \n
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*@par Inputs:
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* Two inputs, including:
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*@li x: A Tensor. Must be one of the following types: float16, float32, int32, int8, uint8.
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*@li assist: A Tensor of the same type as "x" . \n
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*@par Outputs:
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*y: A Tensor. Has the same type as "x" . \n
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*@par Third-party framework compatibility
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* Compatible with the TensorFlow operator MatrixDiag.
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*
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* @par Restrictions:
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* Warning: THIS FUNCTION IS DEPRECATED. Please use MatrixDiag instead.
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*/
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REG_OP(MatrixDiagD)
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.INPUT(x, TensorType::BasicType())
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.INPUT(assist, TensorType::BasicType())
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.OUTPUT(y, TensorType::BasicType())
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.OP_END_FACTORY_REG(MatrixDiagD)
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/**
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*@brief: Returns the batched diagonal part of a batched tensor . \n
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*@par Inputs:
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*x: A Tensor. Must be one of the following types:
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* float16, float32, double, int32, uint8, int16, int8, complex64, int64,
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* qint8, quint8, qint32, uint16, complex128, uint32, uint64 . \n
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*@par Outputs:
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*y: A Tensor. Has the same type as "x" . \n
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*@par Third-party framework compatibility
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* Compatible with the TensorFlow operator MatrixDiagPart.
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*/
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REG_OP(MatrixDiagPart)
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.INPUT(x, TensorType::BasicType())
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.OUTPUT(y, TensorType::BasicType())
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.OP_END_FACTORY_REG(MatrixDiagPart)
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/**
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*@brief: Returns the batched diagonal part of a batched tensor . \n
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*@par Inputs:
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* Two inputs, including:
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*@li x: A Tensor. Must be one of the following types: float16, float32, int32, int8, uint8.
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*@li assist: A Tensor of the same type as "x" . \n
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*@par Outputs:
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*y: A Tensor. Has the same type as "x" . \n
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*@par Third-party framework compatibility
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* Compatible with the TensorFlow operator MatrixDiagPart.
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*
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* @par Restrictions:
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* Warning: THIS FUNCTION IS DEPRECATED. Please use MatrixDiagPart instead.
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*/
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REG_OP(MatrixDiagPartD)
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.INPUT(x, TensorType::BasicType())
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.INPUT(assist, TensorType::BasicType())
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.OUTPUT(y, TensorType::BasicType())
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.OP_END_FACTORY_REG(MatrixDiagPartD)
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/**
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*@brief: Returns a batched matrix tensor with new batched diagonal values . \n
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*@par Inputs:
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* Two inputs, including:
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*@li x: A Tensor. Must be one of the following types:
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* float16, float32, double, int32, uint8, int16, int8, complex64, int64,
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* qint8, quint8, qint32, uint16, complex128, uint32, uint64.
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*@li diagonal: A Tensor of the same type as "x" . \n
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*@par Outputs:
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*y: A Tensor. Has the same type as "x" . \n
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*@par Third-party framework compatibility
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* Compatible with the TensorFlow operator MatrixSetDiag.
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*/
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REG_OP(MatrixSetDiag)
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.INPUT(x, TensorType::BasicType())
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.INPUT(diagonal, TensorType::BasicType())
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.OUTPUT(y, TensorType::BasicType())
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.OP_END_FACTORY_REG(MatrixSetDiag)
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/**
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*@brief: Returns a batched matrix tensor with new batched diagonal values . \n
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*@par Inputs:
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* Three inputs, including:
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*@li x: A Tensor. Must be one of the following types: float16, float32, int32, int8, uint8.
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*@li diagonal: A Tensor of the same type as "x".
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*@li assist: A Tensor of the same type as "x" . \n
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*@par Outputs:
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*y: A Tensor. Has the same type as "x" . \n
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*@par Third-party framework compatibility
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* Compatible with the TensorFlow operator MatrixSetDiag.
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*
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* @par Restrictions:
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* Warning: THIS FUNCTION IS DEPRECATED. Please use MatrixSetDiag instead.
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*/
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REG_OP(MatrixSetDiagD)
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.INPUT(x, TensorType::BasicType())
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.INPUT(diagonal, TensorType::BasicType())
|
|
|
.INPUT(assist, TensorType::BasicType())
|
|
|
.OUTPUT(y, TensorType::BasicType())
|
|
|
.OP_END_FACTORY_REG(MatrixSetDiagD)
|
|
|
|
|
|
/**
|
|
|
*@brief Applies sparse "updates" to individual values or slices in a Variable . \n
|
|
|
|
|
|
*@par Inputs:
|
|
|
* Three inputs, including:
|
|
|
*@li var: An ND Tensor.
|
|
|
*Must be one of the following types: float16, float32, int8, uint8, double,
|
|
|
* int64, complex64, qint8, quint8, qint32, uint16, complex128, half, uint32,
|
|
|
* uint64
|
|
|
*@li indices: An ND Tensor.
|
|
|
*Must be one of the following types: int32, int64
|
|
|
*@li updates: An ND Tensor.
|
|
|
*Must be one of the following types: float16, float32, int8, uint8, double,
|
|
|
* int64, complex64, qint8, quint8, qint32, uint16, complex128, half, uint32,
|
|
|
* uint64
|
|
|
|
|
|
*@par Attributes:
|
|
|
*use_locking: An optional bool. Defaults to "False". If "True",
|
|
|
* the operation will be protected by a lock . \n
|
|
|
|
|
|
*@par Outputs:
|
|
|
*var: A Tensor. Has the same type and format as input "var" . \n
|
|
|
|
|
|
*@par Third-party framework compatibility
|
|
|
* Compatible with the TensorFlow operator ScatterNdUpdate.
|
|
|
*/
|
|
|
REG_OP(ScatterNdUpdate)
|
|
|
.INPUT(var, TensorType::BasicType())
|
|
|
.INPUT(indices, TensorType::IndexNumberType())
|
|
|
.INPUT(updates, TensorType::BasicType())
|
|
|
.OUTPUT(var, TensorType::BasicType())
|
|
|
.ATTR(use_locking, Bool, false)
|
|
|
.OP_END_FACTORY_REG(ScatterNdUpdate)
|
|
|
|
|
|
/**
|
|
|
*@brief Applies sparse addition to individual values or slices in a Variable . \n
|
|
|
|
|
|
*@par Inputs:
|
|
|
* Three inputs, including:
|
|
|
*@li x: An ND Tensor. \n
|
|
|
|
|
|
*Must be one of the following types: float16, float32, bool, int8, uint8
|
|
|
*@li indices: An ND Tensor. \n
|
|
|
|
|
|
*Must be one of the following types: int32
|
|
|
*@li updates: An ND Tensor. \n
|
|
|
|
|
|
*Must be one of the following types: float16, float32, bool, int8, uint8
|
|
|
|
|
|
*@par Outputs:
|
|
|
*y: A Tensor. Has the same type and format as input "x" . \n
|
|
|
|
|
|
*@par Third-party framework compatibility
|
|
|
* Compatible with the TensorFlow operator TensorScatterUpdate.
|
|
|
*/
|
|
|
REG_OP(TensorScatterUpdate)
|
|
|
.INPUT(x, TensorType::BasicType())
|
|
|
.INPUT(indices, TensorType::IndexNumberType())
|
|
|
.INPUT(updates, TensorType::BasicType())
|
|
|
.OUTPUT(y, TensorType::BasicType())
|
|
|
.OP_END_FACTORY_REG(TensorScatterUpdate)
|
|
|
|
|
|
/**
|
|
|
*@brief Adds sparse "updates" to a variable reference . \n
|
|
|
|
|
|
*@par Inputs:
|
|
|
* Three inputs, including:
|
|
|
*@li var: An ND Tensor . \n
|
|
|
|
|
|
*Must be one of the following types: float16, float32, int32, int8, uint8
|
|
|
*@li indices: An ND Tensor of type int32 or int64.
|
|
|
|
|
|
|
|
|
*@li updates: An Tensor. format:NCHW, NHWC . \n
|
|
|
|
|
|
*Must be one of the following types: float16, float32, int32, int8, uint8
|
|
|
|
|
|
*@par Attributes:
|
|
|
* use_locking: An optional bool. Defaults to "False". If "True", the operation
|
|
|
* will be protected by a lock . \n
|
|
|
|
|
|
*@par Outputs:
|
|
|
*var: A Tensor. Has the same type and format as input "var" . \n
|
|
|
|
|
|
*@par Third-party framework compatibility
|
|
|
* Compatible with the TensorFlow operator ScatterAdd.
|
|
|
*/
|
|
|
REG_OP(ScatterAdd)
|
|
|
.INPUT(var, TensorType({DT_FLOAT16, DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8}))
|
|
|
.INPUT(indices, TensorType::IndexNumberType())
|
|
|
.INPUT(updates, TensorType({DT_FLOAT16, DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8}))
|
|
|
.OUTPUT(var, TensorType({DT_FLOAT16, DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8}))
|
|
|
.ATTR(use_locking, Bool, false)
|
|
|
.OP_END_FACTORY_REG(ScatterAdd)
|
|
|
|
|
|
/**
|
|
|
*@brief Divides a variable reference by sparse updates . \n
|
|
|
|
|
|
*@par Inputs:
|
|
|
* Three inputs, including:
|
|
|
*@li var: An ND Tensor.
|
|
|
*Must be one of the following types: float16, float, int32, int8, uint8
|
|
|
|
|
|
*@li indices: An ND Tensor.
|
|
|
*Must be one of the following types: int32
|
|
|
*@li updates: An ND Tensor.
|
|
|
*Must be one of the following types: float16, float, int32, int8, uint8
|
|
|
|
|
|
*@par Attributes:
|
|
|
*@li use_locking: An optional bool. Defaults to "False". If "True",
|
|
|
* the operation will be protected by a lock . \n
|
|
|
|
|
|
*@par Outputs:
|
|
|
*var: A Tensor. Has the same type and format as input "var" . \n
|
|
|
|
|
|
*@par Third-party framework compatibility
|
|
|
* Compatible with the TensorFlow operator ScatterDiv.
|
|
|
*/
|
|
|
REG_OP(ScatterDiv)
|
|
|
.INPUT(var, TensorType({DT_FLOAT16, DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8}))
|
|
|
.INPUT(indices, TensorType({DT_INT32}))
|
|
|
.INPUT(updates, TensorType({DT_FLOAT16, DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8}))
|
|
|
.OUTPUT(var, TensorType({DT_FLOAT16, DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8}))
|
|
|
.ATTR(use_locking, Bool, false)
|
|
|
.OP_END_FACTORY_REG(ScatterDiv)
|
|
|
|
|
|
/**
|
|
|
*@brief Applies sparse addition to individual values or slices in a Variable . \n
|
|
|
|
|
|
*@par Inputs:
|
|
|
* Three inputs, including:
|
|
|
*@li var: An ND Tensor.
|
|
|
*Must be one of the following types: float16, float, int32, int8, uint8
|
|
|
*@li indices: An ND Tensor.
|
|
|
*Must be one of the following types: int32
|
|
|
*@li updates: An ND Tensor.
|
|
|
*Must be one of the following types: float16, float, int32, int8, uint8
|
|
|
*@par Attributes:
|
|
|
*use_locking: An optional bool. Defaults to "False". If "True",
|
|
|
* the operation will be protected by a lock . \n
|
|
|
|
|
|
*@par Outputs:
|
|
|
*var: A Tensor. Has the same type and format as input "var" . \n
|
|
|
|
|
|
*@par Third-party framework compatibility
|
|
|
* Compatible with the TensorFlow operator ScatterNdAdd.
|
|
|
*/
|
|
|
REG_OP(ScatterNdAdd)
|
|
|
.INPUT(var, TensorType({DT_FLOAT16, DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8}))
|
|
|
.INPUT(indices, TensorType::IndexNumberType())
|
|
|
.INPUT(updates, TensorType({DT_FLOAT16, DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8}))
|
|
|
.OUTPUT(var, TensorType({DT_FLOAT16, DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8}))
|
|
|
.ATTR(use_locking, Bool, false)
|
|
|
.OP_END_FACTORY_REG(ScatterNdAdd)
|
|
|
|
|
|
/**
|
|
|
*@brief Applies sparse addition to individual values or slices in a Variable . \n
|
|
|
|
|
|
*@par Inputs:
|
|
|
* Three inputs, including:
|
|
|
*@li x: An ND Tensor. \n
|
|
|
|
|
|
*Must be one of the following types: float16, float32, int32, int8, uint8
|
|
|
*@li indices: An ND Tensor. \n
|
|
|
|
|
|
*Must be one of the following types: int32
|
|
|
*@li updates: An ND Tensor. \n
|
|
|
|
|
|
* Must be one of the following types: float16, float32, int32, int8, uint8
|
|
|
|
|
|
*@par Outputs:
|
|
|
*y: A Tensor. Has the same type and format as input "x" . \n
|
|
|
|
|
|
*@par Third-party framework compatibility
|
|
|
* Compatible with the TensorFlow operator TensorScatterAdd.
|
|
|
*/
|
|
|
REG_OP(TensorScatterAdd)
|
|
|
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8}))
|
|
|
.INPUT(indices, TensorType::IndexNumberType())
|
|
|
.INPUT(updates, TensorType({DT_FLOAT16, DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8}))
|
|
|
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8}))
|
|
|
.OP_END_FACTORY_REG(TensorScatterAdd)
|
|
|
|
|
|
/**
|
|
|
*@brief Applies sparse subtraction to individual values or slices in a Variable . \n
|
|
|
|
|
|
*@par Inputs:
|
|
|
* Three inputs, including:
|
|
|
*@li var: An ND Tensor.
|
|
|
*Must be one of the following types: float16, float, int32, int8, uint8
|
|
|
*@li indices: An ND Tensor.
|
|
|
*Must be one of the following types: int32, int64
|
|
|
*@li updates: An ND Tensor.
|
|
|
*Must be one of the following types: float16, float, int32, int8, uint8
|
|
|
|
|
|
*@par Attributes:
|
|
|
*use_locking: An optional bool. Defaults to "False". If "True",
|
|
|
* the operation will be protected by a lock . \n
|
|
|
|
|
|
*@par Outputs:
|
|
|
* var: A Tensor. Has the same type and format as input "var" . \n
|
|
|
|
|
|
*@par Third-party framework compatibility
|
|
|
* Compatible with the TensorFlow operator ScatterNdSub.
|
|
|
*/
|
|
|
REG_OP(ScatterNdSub)
|
|
|
.INPUT(var, TensorType({DT_FLOAT16, DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8}))
|
|
|
.INPUT(indices, TensorType::IndexNumberType())
|
|
|
.INPUT(updates, TensorType({DT_FLOAT16, DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8}))
|
|
|
.OUTPUT(var, TensorType({DT_FLOAT16, DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8}))
|
|
|
.ATTR(use_locking, Bool, false)
|
|
|
.OP_END_FACTORY_REG(ScatterNdSub)
|
|
|
|
|
|
/**
|
|
|
*@brief Applies sparse addition to individual values or slices in a Variable . \n
|
|
|
|
|
|
*@par Inputs:
|
|
|
* Three inputs, including:
|
|
|
*@li x: An ND Tensor. \n
|
|
|
|
|
|
*Must be one of the following types: float16, float32, int32, int8, uint8
|
|
|
*@li indices: An ND Tensor. \n
|
|
|
|
|
|
*Must be one of the following types: int32
|
|
|
*@li updates: An ND Tensor. \n
|
|
|
|
|
|
*Must be one of the following types: float16, float32, int32, int8, uint8
|
|
|
|
|
|
*@par Outputs:
|
|
|
* y: A Tensor. Has the same type and format as input "x" . \n
|
|
|
|
|
|
*@par Third-party framework compatibility
|
|
|
* Compatible with the TensorFlow operator TensorScatterSub.
|
|
|
*/
|
|
|
REG_OP(TensorScatterSub)
|
|
|
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8}))
|
|
|
.INPUT(indices, TensorType::IndexNumberType())
|
|
|
.INPUT(updates, TensorType({DT_FLOAT16, DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8}))
|
|
|
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8}))
|
|
|
.OP_END_FACTORY_REG(TensorScatterSub)
|
|
|
|
|
|
/**
|
|
|
*@brief Subtracts sparse updates to a variable reference . \n
|
|
|
|
|
|
*@par Inputs:
|
|
|
* Three inputs, including:
|
|
|
*@li var: An ND Tensor.
|
|
|
*Must be one of the following types: float16, float, int32, int8, uint8
|
|
|
*@li indices: An ND Tensor.
|
|
|
*Must be one of the following types: int32, int64
|
|
|
*@li updates: An ND Tensor.
|
|
|
*Must be one of the following types: float16, float, int32, int8, uint8
|
|
|
*@par Attributes:
|
|
|
*use_locking: An optional bool. Defaults to "False". If "True",
|
|
|
* the operation will be protected by a lock . \n
|
|
|
|
|
|
*@par Outputs:
|
|
|
* var: A Tensor. Has the same type and format as input "var" . \n
|
|
|
|
|
|
*@par Third-party framework compatibility
|
|
|
* Compatible with the TensorFlow operator ScatterSub.
|
|
|
*/
|
|
|
REG_OP(ScatterSub)
|
|
|
.INPUT(var, TensorType({DT_FLOAT16, DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8}))
|
|
|
.INPUT(indices, TensorType::IndexNumberType())
|
|
|
.INPUT(updates, TensorType({DT_FLOAT16, DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8}))
|
|
|
.OUTPUT(var, TensorType({DT_FLOAT16, DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8}))
|
|
|
.ATTR(use_locking, Bool, false)
|
|
|
.OP_END_FACTORY_REG(ScatterSub)
|
|
|
|
|
|
/**
|
|
|
*@brief: Returns the batched diagonal part of a batched tensor with "assist" . \n
|
|
|
|
|
|
*@par Inputs:
|
|
|
* Two inputs, including:
|
|
|
* @li x: A Tensor of type float16, float32, or int32.
|
|
|
* @li assist: A Tensor of the same type as "x" . \n
|
|
|
|
|
|
*@par Outputs:
|
|
|
*y: A Tensor. Has the same type as "x" . \n
|
|
|
|
|
|
*@par Third-party framework compatibility
|
|
|
* Compatible with the TensorFlow operator DiagPart.
|
|
|
*
|
|
|
* @par Restrictions:
|
|
|
* Warning: THIS FUNCTION IS DEPRECATED. Please use DiagPart instead.
|
|
|
*/
|
|
|
REG_OP(DiagPartD)
|
|
|
.INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
|
|
|
.INPUT(assist, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
|
|
|
.OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
|
|
|
.OP_END_FACTORY_REG(DiagPartD)
|
|
|
|
|
|
/**
|
|
|
*@brief: Returns the batched diagonal part of a batched tensor . \n
|
|
|
|
|
|
*@par Inputs:
|
|
|
*x: A Tensor. Must be one of the following types:
|
|
|
* float16, float32, int32, int64, double, complex64, complex128 . \n
|
|
|
|
|
|
*@par Outputs:
|
|
|
*y: A Tensor. Has the same type as "x" . \n
|
|
|
|
|
|
*@par Third-party framework compatibility
|
|
|
* Compatible with the TensorFlow operator DiagPart.
|
|
|
*/
|
|
|
REG_OP(DiagPart)
|
|
|
.INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32, DT_INT64, DT_DOUBLE,
|
|
|
DT_COMPLEX64, DT_COMPLEX128}))
|
|
|
.OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32, DT_INT64, DT_DOUBLE,
|
|
|
DT_COMPLEX64, DT_COMPLEX128}))
|
|
|
.OP_END_FACTORY_REG(DiagPart)
|
|
|
|
|
|
/**
|
|
|
*@brief Also known as a "fully-connected" layer, computes an inner product with a set of learned weights, and (optionally) adds biases . \n
|
|
|
|
|
|
*@par Inputs:
|
|
|
* Four inputs, including:
|
|
|
*@li x: A Tensor of type float16, int8.
|
|
|
*@li w: A weight matrix of type float16, int8.
|
|
|
*@li b: A Tensor of type float16, int32, float32.
|
|
|
*@li offset_w: A Tensor of type int8 . \n
|
|
|
|
|
|
*@par Attributes:
|
|
|
*@li num_output: Reserved.
|
|
|
*@li transpose: A bool, specifying weight whether to transpose, either "true" or "false". Defaults to "false".
|
|
|
*@li axis: Optional. A int, 1 or 2, specifying which dimension the input "K" starts from. Defaults to 1.
|
|
|
* The product of the subsequent dimensions starting form first dimension or the second dimension is "K".
|
|
|
*@li offset_x: Reserved . \n
|
|
|
|
|
|
*@par Outputs:
|
|
|
*y: The result tensor of type float16, int32, float32 . \n
|
|
|
|
|
|
*@par Third-party framework compatibility
|
|
|
* Compatible with the Caffe operator InnerProduct . \n
|
|
|
|
|
|
*@par Quantization supported or not
|
|
|
* Yes
|
|
|
*/
|
|
|
REG_OP(FullyConnection)
|
|
|
.INPUT(x, TensorType({DT_FLOAT16, DT_INT8}))
|
|
|
.INPUT(w, TensorType({DT_FLOAT16, DT_INT8}))
|
|
|
.OPTIONAL_INPUT(b, TensorType({DT_FLOAT16, DT_INT32,DT_FLOAT32}))
|
|
|
.OPTIONAL_INPUT(offset_w, TensorType({DT_INT8}))
|
|
|
.OUTPUT(y, TensorType({DT_FLOAT16, DT_INT32,DT_FLOAT32}))
|
|
|
.REQUIRED_ATTR(num_output, Int)
|
|
|
.ATTR(transpose, Bool, false)
|
|
|
.ATTR(axis, Int, 1)
|
|
|
.ATTR(offset_x, Int, 0)
|
|
|
.OP_END_FACTORY_REG(FullyConnection)
|
|
|
|
|
|
/**
|
|
|
*@brief Also known as a "fully-connected-compress" layer, computes an inner product with a set of learned weights, and (optionally) adds biases . \n
|
|
|
|
|
|
*@par Inputs:
|
|
|
* Four inputs, including:
|
|
|
*@li x: A Tensor of type uint8, int8.
|
|
|
*@li w: A weight matrix of type int8, int8.
|
|
|
*@li w: A compress index matrix of type int8, int8.
|
|
|
*@li b: A Tensor of type float16, int32, int32.
|
|
|
*@li offset_w: A Tensor of type int8.i
|
|
|
|
|
|
*@par Attributes:
|
|
|
*@li num_output: Reserved.
|
|
|
*@li transpose: A bool, specifying whether to transpose, either "true" or "false". Defaults to "false".
|
|
|
*@li axis: Reserved.
|
|
|
*@li offset_x: Reserved . \n
|
|
|
|
|
|
*@par Outputs:
|
|
|
*y: The result tensor of type int32 . \n
|
|
|
|
|
|
*@par Third-party framework compatibility
|
|
|
* Compatible with the Caffe operator InnerProduct . \n
|
|
|
|
|
|
*@par Quantization supported or not
|
|
|
* Yes
|
|
|
*/
|
|
|
REG_OP(FullyConnectionCompress)
|
|
|
.INPUT(x, TensorType({DT_UINT8, DT_INT8}))
|
|
|
.INPUT(w, TensorType({DT_INT8}))
|
|
|
.INPUT(comress_index, TensorType({DT_INT8}))
|
|
|
.OPTIONAL_INPUT(b, TensorType({DT_INT32}))
|
|
|
.OPTIONAL_INPUT(offset_w, TensorType({DT_INT8}))
|
|
|
.OUTPUT(y, TensorType({DT_INT32}))
|
|
|
.REQUIRED_ATTR(num_output, Int)
|
|
|
.ATTR(transpose, Bool, false)
|
|
|
.ATTR(axis, Int, 1)
|
|
|
.ATTR(offset_x, Int, 0)
|
|
|
.OP_END_FACTORY_REG(FullyConnectionCompress)
|
|
|
|
|
|
/**
|
|
|
*@brief Computes the confusion matrix from predictions and labels . \n
|
|
|
|
|
|
*@par Inputs:
|
|
|
* Three inputs, including:
|
|
|
*@li labels: A Tensor. Must be one of the following types: float16, float32,
|
|
|
* int32, int8, uint8.
|
|
|
*@li predictions: A Tensor. Must be one of the following types: float16,
|
|
|
* float32, int32, int8, uint8.
|
|
|
*@li weights: A Tensor. Must be one of the following types: float16, float32,
|
|
|
* int32, int8, uint8 . \n
|
|
|
|
|
|
*@par Attributes:
|
|
|
*@li num_classes: An integer for the shape of the output matrix.
|
|
|
* No default value.
|
|
|
*@li dtype: Data type of the confusion matrix. No default value . \n
|
|
|
|
|
|
*@par Outputs:
|
|
|
*y: A Tensor. Has the same type and format as input "labels"
|
|
|
|
|
|
*@attention Constraints:
|
|
|
*@li "weights", "labels", and "predictions" are 1D tensors.
|
|
|
*@li The output is with shape (num_classes, num_classes),
|
|
|
* where, 1 <= num_classes <= 4096 . \n
|
|
|
|
|
|
*@see Region()
|
|
|
|
|
|
*@par Third-party framework compatibility
|
|
|
* Compatible with the TensorFlow operator ConfusionMatrix.
|
|
|
*/
|
|
|
REG_OP(ConfusionMatrix)
|
|
|
.INPUT(labels, TensorType({DT_FLOAT, DT_INT32, DT_FLOAT16, DT_INT8, DT_UINT8}))
|
|
|
.INPUT(predictions, TensorType({DT_FLOAT, DT_INT32, DT_FLOAT16, DT_INT8, DT_UINT8}))
|
|
|
.OPTIONAL_INPUT(weights, TensorType({DT_FLOAT, DT_INT32, DT_FLOAT16, DT_INT8, DT_UINT8}))
|
|
|
.OUTPUT(y, TensorType({DT_FLOAT, DT_INT32, DT_FLOAT16, DT_INT8, DT_UINT8}))
|
|
|
.REQUIRED_ATTR(num_classes, Int)
|
|
|
.REQUIRED_ATTR(dtype, String)
|
|
|
.OP_END_FACTORY_REG(ConfusionMatrix)
|
|
|
|
|
|
/**
|
|
|
*@brief Multiplies sparse updates into a variable reference . \n
|
|
|
|
|
|
*@par Inputs:
|
|
|
* Three inputs, including:
|
|
|
*@li var: An ND Tensor.
|
|
|
*Must be one of the following types: float16, float, int32, int8, uint8
|
|
|
*@li indices: An ND Tensor.
|
|
|
*Must be one of the following types: int32
|
|
|
*@li updates: An ND Tensor . \n
|
|
|
|
|
|
*Must be one of the following types: float16, float, int32, int8, uint8
|
|
|
|
|
|
*@par Attributes:
|
|
|
*use_locking: An optional bool. Defaults to "False". If "True", the operation
|
|
|
* will be protected by a lock . \n
|
|
|
|
|
|
*@par Outputs:
|
|
|
*var: A Tensor. Has the same type and format as input "var" . \n
|
|
|
|
|
|
*@par Third-party framework compatibility
|
|
|
* Compatible with the TensorFlow operator ScatterMul.
|
|
|
*/
|
|
|
REG_OP(ScatterMul)
|
|
|
.INPUT(var, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8}))
|
|
|
.INPUT(indices, TensorType({DT_INT32}))
|
|
|
.INPUT(updates, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8}))
|
|
|
.OUTPUT(var, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT32,DT_INT8,DT_UINT8}))
|
|
|
.ATTR(use_locking, Bool, false)
|
|
|
.OP_END_FACTORY_REG(ScatterMul)
|
|
|
|
|
|
/**
|
|
|
*@brief Reduces sparse updates into a variable reference using
|
|
|
* the "min" operation . \n
|
|
|
|
|
|
*@par Inputs:
|
|
|
* Three inputs, including:
|
|
|
*@li var: An ND Tensor.
|
|
|
*Must be one of the following types: float16, float, int32
|
|
|
|
|
|
*@li indices: An ND Tensor.
|
|
|
*Must be one of the following types: int32
|
|
|
|
|
|
*@li updates: An ND Tensor.
|
|
|
*Must be one of the following types: float16, float, int32
|
|
|
|
|
|
*@par Attributes:
|
|
|
*use_locking: An optional bool. Defaults to "False". If "True", the operation
|
|
|
* will be protected by a lock . \n
|
|
|
|
|
|
*@par Outputs:
|
|
|
*var: A Tensor. Has the same type and format as input "var" . \n
|
|
|
|
|
|
*@par Third-party framework compatibility
|
|
|
* Compatible with the TensorFlow operator ScatterMin.
|
|
|
*/
|
|
|
REG_OP(ScatterMin)
|
|
|
.INPUT(var, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT32}))
|
|
|
.INPUT(indices, TensorType({DT_INT32}))
|
|
|
.INPUT(updates, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT32}))
|
|
|
.OUTPUT(var, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT32}))
|
|
|
.ATTR(use_locking, Bool, false)
|
|
|
.OP_END_FACTORY_REG(ScatterMin)
|
|
|
|
|
|
/**
|
|
|
*@brief Reduces sparse updates into a variable reference using the "max" operation . \n
|
|
|
|
|
|
*@par Inputs:
|
|
|
* Three inputs, including:
|
|
|
*@li var: An ND Tensor . \n
|
|
|
|
|
|
*Must be one of the following types: float16, float, int32
|
|
|
*@li indices: An NCHW, NHWC, or ND Tensor . \n
|
|
|
|
|
|
*Must be one of the following types: int32
|
|
|
*@li updates: An NCHW, NHWC, or ND Tensor . \n
|
|
|
|
|
|
*Must be one of the following types: float16, float, int32
|
|
|
|
|
|
*@par Attributes:
|
|
|
*use_locking: An optional bool. Defaults to "False".
|
|
|
* If "True", the operation will be protected by a lock . \n
|
|
|
|
|
|
*@par Outputs:
|
|
|
*var: A Tensor. Has the same type and format as input "var" . \n
|
|
|
|
|
|
*@par Third-party framework compatibility
|
|
|
* Compatible with the TensorFlow operator ScatterMax.
|
|
|
*/
|
|
|
REG_OP(ScatterMax)
|
|
|
.INPUT(var, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT32}))
|
|
|
.INPUT(indices, TensorType({DT_INT32}))
|
|
|
.INPUT(updates, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT32}))
|
|
|
.OUTPUT(var, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT32}))
|
|
|
.ATTR(use_locking, Bool, false)
|
|
|
.OP_END_FACTORY_REG(ScatterMax)
|
|
|
|
|
|
/**
|
|
|
*@brief Applies sparse updates to a variable reference . \n
|
|
|
|
|
|
*@par Inputs:
|
|
|
* Three inputs, including:
|
|
|
*@li var: An ND Tensor . \n
|
|
|
|
|
|
*Must be one of the following types: float16, float, int32, int8, uint8
|
|
|
*@li indices: An ND Tensor . \n
|
|
|
|
|
|
*Must be one of the following types: int32
|
|
|
*@li updates: An ND Tensor . \n
|
|
|
|
|
|
*Must be one of the following types: float16, float, int32, int8, uint8
|
|
|
|
|
|
*@par Attributes:
|
|
|
*use_locking: An optional bool. Defaults to "False". If "True",
|
|
|
* the operation will be protected by a lock . \n
|
|
|
|
|
|
*@par Outputs:
|
|
|
*var: A Tensor. Has the same type and format as input "var" . \n
|
|
|
|
|
|
*@par Third-party framework compatibility
|
|
|
* Compatible with the TensorFlow operator ScatterUpdate.
|
|
|
*/
|
|
|
REG_OP(ScatterUpdate)
|
|
|
.INPUT(var, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT8,DT_UINT8}))
|
|
|
.INPUT(indices, TensorType({DT_INT32}))
|
|
|
.INPUT(updates, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT8,DT_UINT8}))
|
|
|
.OUTPUT(var, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT8,DT_UINT8}))
|
|
|
.ATTR(use_locking, Bool, false)
|
|
|
.OP_END_FACTORY_REG(ScatterUpdate)
|
|
|
|
|
|
/**
|
|
|
*@brief Returns a tensor with the `k[0]`-th to `k[1]`-th diagonals of the batched `input` . \n
|
|
|
|
|
|
*@par Inputs:
|
|
|
* Three inputs, including:
|
|
|
*@li input: Rank `r` tensor where `r >= 2`. \n
|
|
|
|
|
|
*@li k: \n
|
|
|
*Diagonal offset(s). Positive value means superdiagonal, 0 refers to the main \n
|
|
|
*diagonal, and negative value means subdiagonals. `k` can be a single integer \n
|
|
|
*(for a single diagonal) or a pair of integers specifying the low and high ends \n
|
|
|
*of a matrix band. `k[0]` must not be larger than `k[1]`. \n
|
|
|
|
|
|
*@li padding_value: The value to fill the area outside the specified diagonal band with. \n
|
|
|
|
|
|
*@par Outputs:
|
|
|
*diagonal: The extracted diagonal(s) . \n
|
|
|
|
|
|
*@par Third-party framework compatibility
|
|
|
* Compatible with the TensorFlow operator ScatterUpdate.
|
|
|
*/
|
|
|
REG_OP(MatrixDiagPartV2)
|
|
|
.INPUT(input, TensorType::BasicType())
|
|
|
.INPUT(k, TensorType({DT_INT32}))
|
|
|
.INPUT(padding_value, TensorType::BasicType())
|
|
|
.OUTPUT(diagonal, TensorType::BasicType())
|
|
|
.OP_END_FACTORY_REG(MatrixDiagPartV2)
|
|
|
|
|
|
/**
|
|
|
*@brief Returns a batched matrix tensor with new batched diagonal values . \n
|
|
|
|
|
|
*@par Inputs:
|
|
|
* Three inputs, including:
|
|
|
*@li input: "Rank `r+1`, where `r >= 1`. \n
|
|
|
|
|
|
*@li diagonal: Rank `r` when `k` is an integer or `k[0] == k[1]`. Otherwise, it has rank `r+1`. \n
|
|
|
|
|
|
*@li k:
|
|
|
*Diagonal offset(s). Positive value means superdiagonal, 0 refers to the main \n
|
|
|
*diagonal, and negative value means subdiagonals. `k` can be a single integer \n
|
|
|
*(for a single diagonal) or a pair of integers specifying the low and high ends \n
|
|
|
*of a matrix band. `k[0]` must not be larger than `k[1]`. \n
|
|
|
|
|
|
*@par Outputs:
|
|
|
*output: Rank `r+1`, with `output.shape = input.shape` . \n
|
|
|
|
|
|
*@par Third-party framework compatibility
|
|
|
* Compatible with the TensorFlow operator ScatterUpdate.
|
|
|
*/
|
|
|
REG_OP(MatrixSetDiagV2)
|
|
|
.INPUT(input, TensorType::BasicType())
|
|
|
.INPUT(diagonal, TensorType::BasicType())
|
|
|
.INPUT(k, TensorType({DT_INT32}))
|
|
|
.OUTPUT(output, TensorType::BasicType())
|
|
|
.OP_END_FACTORY_REG(MatrixSetDiagV2)
|
|
|
|
|
|
/**
|
|
|
*@brief Returns a batched diagonal tensor with given batched diagonal values . \n
|
|
|
|
|
|
*@par Inputs:
|
|
|
* Five inputs, including:
|
|
|
*@li diagonal: Rank `r`, where `r >= 1` \n
|
|
|
|
|
|
*@li k:
|
|
|
*Diagonal offset(s). Positive value means superdiagonal, 0 refers to the main \n
|
|
|
*diagonal, and negative value means subdiagonals. `k` can be a single integer \n
|
|
|
*(for a single diagonal) or a pair of integers specifying the low and high ends \n
|
|
|
*of a matrix band. `k[0]` must not be larger than `k[1]`. \n
|
|
|
|
|
|
*@li num_rows:
|
|
|
*The number of rows of the output matrix. If it is not provided, the op assumes \n
|
|
|
*the output matrix is a square matrix and infers the matrix size from k and the \n
|
|
|
*innermost dimension of `diagonal`. \n
|
|
|
|
|
|
*@li num_cols: An NCHW, NHWC, or ND Tensor.
|
|
|
*The number of columns of the output matrix. If it is not provided, the op \n
|
|
|
*assumes the output matrix is a square matrix and infers the matrix size from \n
|
|
|
*k and the innermost dimension of `diagonal`. \n
|
|
|
|
|
|
*@li padding_value: The number to fill the area outside the specified diagonal band with. \n
|
|
|
|
|
|
*@par Outputs:
|
|
|
*output: Has rank `r+1` when `k` is an integer or `k[0] == k[1]`, rank `r` otherwise . \n
|
|
|
|
|
|
*@par Third-party framework compatibility
|
|
|
* Compatible with the TensorFlow operator ScatterUpdate.
|
|
|
*/
|
|
|
REG_OP(MatrixDiagV2)
|
|
|
.INPUT(diagonal, TensorType::BasicType())
|
|
|
.INPUT(k, TensorType({DT_INT32}))
|
|
|
.INPUT(num_rows, TensorType({DT_INT32}))
|
|
|
.INPUT(num_cols, TensorType({DT_INT32}))
|
|
|
.INPUT(padding_value, TensorType::BasicType())
|
|
|
.OUTPUT(output, TensorType::BasicType())
|
|
|
.OP_END_FACTORY_REG(MatrixDiagV2)
|
|
|
|
|
|
REG_OP(IndexAdd)
|
|
|
.INPUT(var, TensorType({DT_INT32, DT_INT8, DT_UINT8, DT_FLOAT32, DT_FLOAT16}))
|
|
|
.INPUT(indices, TensorType({DT_INT32}))
|
|
|
.INPUT(updates, TensorType({DT_INT32, DT_INT8, DT_UINT8, DT_FLOAT32, DT_FLOAT16}))
|
|
|
.OUTPUT(var_out, TensorType({DT_INT32, DT_INT8, DT_UINT8, DT_FLOAT32, DT_FLOAT16}))
|
|
|
.ATTR(axis, Int, 0)
|
|
|
.OP_END_FACTORY_REG(IndexAdd)
|
|
|
|
|
|
/**
|
|
|
*@brief: Returns the upper triangular part of a matrix (2-D tensor) or batch of matrices input \n
|
|
|
|
|
|
*@par Inputs:
|
|
|
* Two inputs, including:
|
|
|
*@li x: A Tensor. Must be one of the following types:
|
|
|
* float16, float32, double, int32, uint8, int16, int8, complex64, int64,
|
|
|
* qint8, quint8, qint32, uint16, complex128, uint32, uint64.
|
|
|
*@li diagonal:(int, optional) – the diagonal to consider。\n
|
|
|
|
|
|
*@par Outputs:
|
|
|
*y: A Tensor. Has the same type as "x" . \n
|
|
|
|
|
|
*@par Third-party framework compatibility
|
|
|
* Compatible with the Pytorch operator Triu.
|
|
|
*/
|
|
|
REG_OP(Triu)
|
|
|
.INPUT(x, TensorType::BasicType())
|
|
|
.ATTR(diagonal, Int, 0)
|
|
|
.OUTPUT(y, TensorType::BasicType())
|
|
|
.OP_END_FACTORY_REG(Triu)
|
|
|
|
|
|
/**
|
|
|
*@brief: Returns the upper triangular part of a matrix (2-D tensor) or batch of matrices input \n
|
|
|
|
|
|
*@par Inputs:
|
|
|
* Two inputs, including:
|
|
|
*@li x: A Tensor. Must be one of the following types:
|
|
|
* float16, float32, double, int32, uint8, int16, int8, complex64, int64,
|
|
|
* qint8, quint8, qint32, uint16, complex128, uint32, uint64.
|
|
|
*@li diagonal:(int, optional) – the diagonal to consider。\n
|
|
|
|
|
|
*@par Outputs:
|
|
|
*y: A Tensor. Has the same type as "x" . \n
|
|
|
|
|
|
*@par Third-party framework compatibility
|
|
|
* Compatible with the Pytorch operator Tril.
|
|
|
*/
|
|
|
REG_OP(Tril)
|
|
|
.INPUT(x, TensorType::BasicType())
|
|
|
.ATTR(diagonal, Int, 0)
|
|
|
.OUTPUT(y, TensorType::BasicType())
|
|
|
.OP_END_FACTORY_REG(Tril)
|
|
|
|
|
|
} // namespace ge
|
|
|
|
|
|
#endif // OPS_BUILT_IN_OP_PROTO_INC_MATRIX_CALCULATION_OPS_H_
|
