You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
969 lines
33 KiB
969 lines
33 KiB
/**
|
|
* Copyright 2019-2020 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.
|
|
*/
|
|
|
|
#ifndef GE_OP_MATRIX_CALCULATION_OPS_H
|
|
#define GE_OP_MATRIX_CALCULATION_OPS_H
|
|
|
|
#include "graph/operator_reg.h"
|
|
|
|
namespace ge {
|
|
|
|
/**
|
|
*@brief Multiplies matrix "a" by matrix "b", producing "a * b".
|
|
|
|
*@par Inputs:
|
|
*Three inputs, including:
|
|
* @li x1: A matrix Tensor. 2D. Must be one of the following types: float16,
|
|
* float32, int32. Has format [ND, NHWC, FRACTAL_NZ].
|
|
* @li x2: A matrix Tensor. 2D. Must be one of the following types: float16,
|
|
* float32, int32. Has format [ND, NHWC, FRACTAL_NZ].
|
|
* @li bias: A optional 1D Tensor. Must be one of the following types: float16,
|
|
* float32, int32. Has format [ND, NHWC].
|
|
|
|
*@par Attributes:
|
|
*@li transpose_a: A bool. If True, changes the shape of "x1" from [M, K] to [K, M].
|
|
*@li transpose_b: A bool. If True, changes the shape of "x2" from [M, K] to [K, M].
|
|
|
|
*@par Outputs:
|
|
*y: The result matrix Tensor. 2D. Must be one of the following types: float16,
|
|
* float32, int32. Has format [ND, NHWC, FRACTAL_NZ].
|
|
|
|
*@par Third-party framework compatibility
|
|
* Compatible with the TensorFlow operator BatchMatmul.
|
|
*/
|
|
REG_OP(MatMul)
|
|
.INPUT(x1, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
|
|
.INPUT(x2, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
|
|
.OPTIONAL_INPUT(bias, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
|
|
.OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
|
|
.ATTR(transpose_x1, Bool, false)
|
|
.ATTR(transpose_x2, Bool, false)
|
|
.OP_END_FACTORY_REG(MatMul)
|
|
|
|
/**
|
|
*@brief Multiplies matrix "a" by matrix "b", producing "a * b".
|
|
|
|
*@par Inputs:
|
|
*Two inputs, including:
|
|
* @li x1: A matrix Tensor. 2D. Must be one of the following types: float16,
|
|
* float32, int32. Has format [ND, NHWC, FRACTAL_NZ].
|
|
* @li x2: A matrix Tensor. 2D. Must be one of the following types: float16,
|
|
* float32, int32. Has format [ND, NHWC, FRACTAL_NZ].
|
|
* @li bias: A 1D Tensor. Must be one of the following types: float16,
|
|
* float32, int32. Has format [ND, NHWC].
|
|
|
|
*@par Attributes:
|
|
*@li transpose_a: A bool. If True, changes the shape of "x1" from [M, K] to [K, M].
|
|
*@li transpose_b: A bool. If True, changes the shape of "x2" from [M, K] to [K, M].
|
|
|
|
*@par Outputs:
|
|
*y: The result matrix Tensor. 2D. Must be one of the following types: float16,
|
|
* float32, int32. Has format [ND, NHWC, FRACTAL_NZ].
|
|
|
|
*@par Third-party framework compatibility
|
|
* Compatible with the TensorFlow operator BatchMatmul.
|
|
*/
|
|
REG_OP(MatMulV2)
|
|
.INPUT(x1, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32, DT_INT8}))
|
|
.INPUT(x2, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32, DT_INT8}))
|
|
.OPTIONAL_INPUT(bias, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
|
|
.OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
|
|
.OPTIONAL_INPUT(offset_w, TensorType({DT_INT8}))
|
|
.ATTR(transpose_x1, Bool, false)
|
|
.ATTR(transpose_x2, Bool, false)
|
|
.ATTR(offset_x, Int, 0)
|
|
.OP_END_FACTORY_REG(MatMulV2)
|
|
|
|
|
|
/**
|
|
*@brief Performs Matrix-to-matrix Multiply, producing c=alpha[0]*a*b+beta[0]*c.
|
|
|
|
*@par Inputs:
|
|
*Five inputs, including:
|
|
*@li a: A matrix Tensor. Must be one of the following types: float16, int8.
|
|
* Has format [ND, FRACTAL_NZ]. 2D(ND) or 4D(FRACTAL_NZ).
|
|
*@li b: A matrix Tensor. Must be one of the following types: float16, int8.
|
|
* Has format [ND, FRACTAL_NZ, FRACTAL_Z]. 2D(ND) or 4D(FRACTAL_NZ, FRACTAL_Z).
|
|
*@li c: A matrix Tensor. Must be one of the following types: float16, int32,
|
|
* float32. has format [ND, FRACTAL_NZ]. 2D(ND) or 4D(FRACTAL_NZ).
|
|
*@li alpha: A 1D Tensor. The shape of alpha is [1].Must be one of the following
|
|
* types: float16, int32, float32. Has format [ND].
|
|
*@li beta: A 1D Tensor. The shape of beta is [1]. Must be one of the following
|
|
* types: float16, int32, float32. Has format [ND].
|
|
* The format of a, b, c has restriction:\n
|
|
* When type of a is int8 and type of c is int32, the format of a, b, c should
|
|
* all be ND, or a is FRACTAL_NZ and b is FRACTAL_Z and c is ND.\n
|
|
* When type of a is int8 and type of c is float32, the format of a, b, c should
|
|
* all be ND or a is FRACTAL_NZ and b is FRACTAL_Z and c is FRACTAL_NZ.\n
|
|
* When type of a is float16 and type of c is float16, the format of a, b, c
|
|
* should all be ND or FRACTAL_NZ.\n
|
|
* When type of a is float16 and type of c is float32, the format of a, b, c
|
|
* should all be ND or FRACTAL_NZ.
|
|
|
|
*@par Attributes:
|
|
*Two attributes, including:
|
|
*@li transpose_a: Optional. A bool. If True, changes the shape of "a" from
|
|
* [M, K] to [K, M].
|
|
*@li transpose_b: Optional. A bool. If True, changes the shape of "b" from
|
|
* [K, N] to [N, K].
|
|
|
|
*@par Outputs:
|
|
*y: The result matrix Tensor. Must be one of the following types: float16,
|
|
* float32, int32. Has format [ND, FRACTAL_NZ], the format should be equal to a.
|
|
* 2D(ND) or 4D(FRACTAL_NZ).
|
|
*/
|
|
|
|
REG_OP(GEMM)
|
|
.INPUT(a, TensorType({DT_FLOAT16, DT_INT8}))
|
|
.INPUT(b, TensorType({DT_FLOAT16, DT_INT8}))
|
|
.INPUT(c, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
|
|
.INPUT(alpha, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
|
|
.INPUT(beta, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
|
|
.OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
|
|
.ATTR(transpose_a, Bool, false)
|
|
.ATTR(transpose_b, Bool, false)
|
|
.OP_END_FACTORY_REG(GEMM)
|
|
|
|
/**
|
|
*@brief Multiplies matrix "a" by matrix "b", producing "a * b".
|
|
|
|
*@par Inputs:
|
|
*Three inputs, including:
|
|
* @li x1: A matrix Tensor. Must be one of the following types: float16,
|
|
* float32, int32. 2D or higher. Has format [ND, NHWC, FRACTAL_NZ].
|
|
* @li x2: A matrix Tensor. Must be one of the following types: float16,
|
|
* float32, int32. 2D or higher. Has format [ND, NHWC, FRACTAL_NZ].
|
|
|
|
*@par Attributes:
|
|
*@li adj_x: A bool. If True, changes the shape of "x1" from [B, M, K] to [B, K, M].
|
|
*@li adj_y: A bool. If True, changes the shape of "x2" from [B, M, K] to [B, K, M].
|
|
|
|
*@par Outputs:
|
|
*y: The result matrix Tensor. 2D or higher. Must be one of the following types: float16,
|
|
* float32, int32. 2D or higher. Has format [ND, NHWC, FRACTAL_NZ]. Has the same shape length as "x1" and "x2".
|
|
|
|
*@par Third-party framework compatibility
|
|
* Compatible with the TensorFlow operator BatchMatmul.
|
|
*/
|
|
|
|
REG_OP(BatchMatMul)
|
|
.INPUT(x1, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
|
|
.INPUT(x2, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
|
|
.OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
|
|
.ATTR(adj_x1, Bool, false)
|
|
.ATTR(adj_x2, Bool, false)
|
|
.OP_END_FACTORY_REG(BatchMatMul)
|
|
|
|
REG_OP(MeanCCE)
|
|
.INPUT(x, TensorType::ALL())
|
|
.INPUT(indices, TensorType::ALL())
|
|
.OUTPUT(y, TensorType::ALL())
|
|
.ATTR(keep_dims, Bool, false)
|
|
.ATTR(value1, ListInt, {})
|
|
.ATTR(mode, Int, 3) // 0:max pooling or 1:avg pooling
|
|
.ATTR(pad_mode, Int, 0)
|
|
.ATTR(global_pooling, Bool, true) // tensorflow have no attr, set default value
|
|
.ATTR(window, ListInt, {1,1}) // kernel size
|
|
.ATTR(pad, ListInt, {0,0,0,0}) // pad size
|
|
.ATTR(stride, ListInt, {1,1}) // stride size
|
|
.ATTR(ceil_mode, Int, 0)
|
|
.ATTR(data_mode, Int, 1)
|
|
.ATTR(nan_opt, Int, 0)
|
|
.ATTR(fomart, Int, 0)
|
|
.OP_END_FACTORY_REG(MeanCCE)
|
|
|
|
REG_OP(MeanGrad)
|
|
.INPUT(x, TensorType::ALL())
|
|
.OUTPUT(y, TensorType::ALL())
|
|
.ATTR(mode, Int, 1) // 0:max pooling or 1:avg pooling
|
|
.ATTR(pad_mode, Int, 0)
|
|
.ATTR(global_pooling, Bool, false)
|
|
.ATTR(window, ListInt, {1,1}) // kernel size
|
|
.ATTR(pad, ListInt, {0,0,0,0}) // pad size
|
|
.ATTR(stride, ListInt, {1,1}) // stride size
|
|
.ATTR(ceil_mode, Int, 0)
|
|
.ATTR(data_mode, Int, 1)
|
|
.ATTR(nan_opt, Int, 0)
|
|
.ATTR(mean_grad_output_shape_value, ListInt, {1,1,1,1})
|
|
.ATTR(mean_grad_output_shape_format, Int, 1) //must be NHWC
|
|
.OP_END_FACTORY_REG(MeanGrad)
|
|
|
|
REG_OP(MatMulCCE)
|
|
.INPUT(x1, TensorType({DT_FLOAT}))
|
|
.INPUT(x2, TensorType({DT_FLOAT}))
|
|
.OPTIONAL_INPUT(x3, TensorType({DT_FLOAT}))
|
|
.OUTPUT(y, TensorType({DT_FLOAT}))
|
|
.ATTR(transpose_a, Bool, false)
|
|
.ATTR(transpose_b, Bool, false)
|
|
.ATTR(has_bias, Bool, false)
|
|
.OP_END_FACTORY_REG(MatMulCCE)
|
|
|
|
/**
|
|
*@brief Computes half the L2 norm of a tensor without the sqrt.
|
|
|
|
*@par Inputs:
|
|
|
|
* x: A Tensor.
|
|
* TensorType::FloatingDataType().
|
|
|
|
*@par Outputs:
|
|
*y: A Tensor. Has the same type as "x".
|
|
*@par Third-party framework compatibility
|
|
*Compatible with the TensorFlow operator L2Loss.
|
|
*/
|
|
REG_OP(L2Loss)
|
|
.INPUT(x, TensorType::FloatingDataType())
|
|
.OUTPUT(y, TensorType::FloatingDataType())
|
|
.OP_END_FACTORY_REG(L2Loss)
|
|
|
|
/**
|
|
*@brief: Returns a batched diagonal tensor with a given batched diagonal values.
|
|
|
|
*@par Inputs:
|
|
*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.
|
|
|
|
*@par Outputs:
|
|
*y: A Tensor. Has the same type as "x".
|
|
|
|
*@par Third-party framework compatibility
|
|
* Compatible with the TensorFlow operator MatrixDiag.
|
|
*/
|
|
REG_OP(MatrixDiag)
|
|
.INPUT(x, TensorType::BasicType())
|
|
.OUTPUT(y, TensorType::BasicType())
|
|
.OP_END_FACTORY_REG(MatrixDiag)
|
|
|
|
/**
|
|
*@brief: Returns a batched diagonal tensor with a given batched diagonal values.
|
|
|
|
*@par Inputs:
|
|
* Two inputs, including:
|
|
*@li x: A Tensor. Must be one of the following types: float16, float32, int32, int8, uint8.
|
|
*@li assist: A Tensor of the same type as "x".
|
|
|
|
*@par Outputs:
|
|
*y: A Tensor. Has the same type as "x".
|
|
|
|
*@par Third-party framework compatibility
|
|
* Compatible with the TensorFlow operator MatrixDiag.
|
|
*/
|
|
REG_OP(MatrixDiagD)
|
|
.INPUT(x, TensorType::BasicType())
|
|
.INPUT(assist, TensorType::BasicType())
|
|
.OUTPUT(y, TensorType::BasicType())
|
|
.OP_END_FACTORY_REG(MatrixDiagD)
|
|
|
|
/**
|
|
*@brief: Returns the batched diagonal part of a batched tensor.
|
|
|
|
*@par Inputs:
|
|
*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.
|
|
|
|
*@par Outputs:
|
|
*y: A Tensor. Has the same type as "x".
|
|
|
|
*@par Third-party framework compatibility
|
|
* Compatible with the TensorFlow operator MatrixDiagPart.
|
|
*/
|
|
REG_OP(MatrixDiagPart)
|
|
.INPUT(x, TensorType::BasicType())
|
|
.OUTPUT(y, TensorType::BasicType())
|
|
.OP_END_FACTORY_REG(MatrixDiagPart)
|
|
|
|
/**
|
|
*@brief: Returns the batched diagonal part of a batched tensor.
|
|
|
|
*@par Inputs:
|
|
* Two inputs, including:
|
|
*@li x: A Tensor. Must be one of the following types: float16, float32, int32, int8, uint8.
|
|
*@li assist: A Tensor of the same type as "x".
|
|
|
|
*@par Outputs:
|
|
*y: A Tensor. Has the same type as "x".
|
|
|
|
*@par Third-party framework compatibility
|
|
* Compatible with the TensorFlow operator MatrixDiagPart.
|
|
*/
|
|
REG_OP(MatrixDiagPartD)
|
|
.INPUT(x, TensorType::BasicType())
|
|
.INPUT(assist, TensorType::BasicType())
|
|
.OUTPUT(y, TensorType::BasicType())
|
|
.OP_END_FACTORY_REG(MatrixDiagPartD)
|
|
|
|
/**
|
|
*@brief: Returns a batched matrix tensor with new batched diagonal values.
|
|
|
|
*@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: A Tensor of the same type as "x".
|
|
|
|
*@par Outputs:
|
|
*y: A Tensor. Has the same type as "x".
|
|
|
|
*@par Third-party framework compatibility
|
|
* Compatible with the TensorFlow operator MatrixSetDiag.
|
|
*/
|
|
REG_OP(MatrixSetDiag)
|
|
.INPUT(x, TensorType::BasicType())
|
|
.INPUT(diagonal, TensorType::BasicType())
|
|
.OUTPUT(y, TensorType::BasicType())
|
|
.OP_END_FACTORY_REG(MatrixSetDiag)
|
|
|
|
/**
|
|
*@brief: Returns a batched matrix tensor with new batched diagonal values.
|
|
|
|
*@par Inputs:
|
|
* Three inputs, including:
|
|
*@li x: A Tensor. Must be one of the following types: float16, float32, int32, int8, uint8.
|
|
*@li diagonal: A Tensor of the same type as "x".
|
|
*@li assist: A Tensor of the same type as "x".
|
|
|
|
*@par Outputs:
|
|
*y: A Tensor. Has the same type as "x".
|
|
|
|
*@par Third-party framework compatibility
|
|
* Compatible with the TensorFlow operator MatrixSetDiag.
|
|
*/
|
|
REG_OP(MatrixSetDiagD)
|
|
.INPUT(x, TensorType::BasicType())
|
|
.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.
|
|
|
|
*@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.
|
|
|
|
*@par Outputs:
|
|
*var: A Tensor. Has the same type and format as input "var".
|
|
|
|
*@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.
|
|
|
|
*@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".
|
|
|
|
*@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.
|
|
|
|
*@par Inputs:
|
|
* Three inputs, including:
|
|
*@li var: An ND Tensor.
|
|
|
|
*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.
|
|
|
|
*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.
|
|
|
|
*@par Outputs:
|
|
*var: A Tensor. Has the same type and format as input "var".
|
|
|
|
*@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.
|
|
|
|
*@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.
|
|
|
|
*@par Outputs:
|
|
*var: A Tensor. Has the same type and format as input "var".
|
|
|
|
*@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.
|
|
|
|
*@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.
|
|
|
|
*@par Outputs:
|
|
*var: A Tensor. Has the same type and format as input "var".
|
|
|
|
*@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.
|
|
|
|
*@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".
|
|
|
|
*@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.
|
|
|
|
*@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.
|
|
|
|
*@par Outputs:
|
|
*var: A Tensor. Has the same type and format as input "var".
|
|
|
|
*@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.
|
|
|
|
*@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".
|
|
|
|
*@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.
|
|
|
|
*@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.
|
|
|
|
*@par Outputs:
|
|
*var: A Tensor. Has the same type and format as input "var".
|
|
|
|
*@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".
|
|
|
|
*@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".
|
|
|
|
*@par Outputs:
|
|
*y: A Tensor. Has the same type as "x".
|
|
|
|
*@par Third-party framework compatibility
|
|
* Compatible with the TensorFlow operator DiagPart.
|
|
*/
|
|
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.
|
|
|
|
*@par Inputs:
|
|
*x: A Tensor. Must be one of the following types:
|
|
* float16, float32, int32, int64, double, complex64, complex128.
|
|
|
|
*@par Outputs:
|
|
*y: A Tensor. Has the same type as "x".
|
|
|
|
*@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.
|
|
|
|
*@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.
|
|
|
|
*@par Attributes:
|
|
*@li num_output: Reserved.
|
|
*@li transpose: A bool, specifying whether to transpose, either "true" or "false". Defaults to "false".
|
|
*@li axis: Optional. A int. 1 or 2.
|
|
*@li offset_x: Reserved.
|
|
|
|
*@par Outputs:
|
|
*y: The result tensor of type float16, int32, float32.
|
|
|
|
*@par Third-party framework compatibility
|
|
* Compatible with the Caffe operator InnerProduct.
|
|
|
|
*@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 Computes the confusion matrix from predictions and labels.
|
|
|
|
*@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.
|
|
|
|
*@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.
|
|
|
|
*@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.
|
|
|
|
*@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.
|
|
|
|
*@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.
|
|
|
|
*@par Outputs:
|
|
*var: A Tensor. Has the same type and format as input "var".
|
|
|
|
*@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.
|
|
|
|
*@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.
|
|
|
|
*@par Outputs:
|
|
*var: A Tensor. Has the same type and format as input "var".
|
|
|
|
*@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.
|
|
|
|
*@par Inputs:
|
|
* Three inputs, including:
|
|
*@li var: An ND Tensor.
|
|
|
|
*Must be one of the following types: float16, float, int32
|
|
*@li indices: An NCHW, NHWC, or ND Tensor.
|
|
|
|
*Must be one of the following types: int32
|
|
*@li updates: An NCHW, NHWC, or 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.
|
|
|
|
*@par Outputs:
|
|
*var: A Tensor. Has the same type and format as input "var".
|
|
|
|
*@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.
|
|
|
|
*@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.
|
|
|
|
*@par Outputs:
|
|
*var: A Tensor. Has the same type and format as input "var".
|
|
|
|
*@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`.
|
|
|
|
*@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).
|
|
|
|
*@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.
|
|
|
|
*@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`.
|
|
|
|
*@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.
|
|
|
|
*@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.
|
|
|
|
*@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)
|
|
|
|
} // namespace ge
|
|
|
|
#endif // GE_OP_MATRIX_CALCULATION_OPS_H
|