|
- /**
- * 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.
- */
-
- /*!
- * \file matrix_calculation_ops.h
- * \brief
- */
- #ifndef OPS_BUILT_IN_OP_PROTO_INC_MATRIX_CALCULATION_OPS_H_
- #define OPS_BUILT_IN_OP_PROTO_INC_MATRIX_CALCULATION_OPS_H_
-
- #include "graph/operator_reg.h"
-
- namespace ge {
-
- /**
- *@brief Multiplies matrix "a" by matrix "b", producing "a * b" . \n
-
- *@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] . \n
-
- *@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] . \n
-
- *@par Outputs:
- *y: The result matrix Tensor. 2D. Must be one of the following types: float16,
- * float32, int32. Has format [ND, NHWC, FRACTAL_NZ] . \n
-
- *@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" . \n
-
- *@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] . \n
-
- *@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] . \n
-
- *@par Outputs:
- *y: The result matrix Tensor. 2D. Must be one of the following types: float16,
- * float32, int32. Has format [ND, NHWC, FRACTAL_NZ] . \n
-
- *@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 . \n
-
- *@attention Constraints:
- * For better performance, The k-axis must be aligned to 16 (input type
- * is float16) or 32 (input type is int8). \n
-
- *@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 . \n
-
- *@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] . \n
-
- *@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" . \n
-
- *@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] . \n
-
- *@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] . \n
-
- *@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" . \n
-
- *@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)
-
- /**
- *@brief Computes half the L2 norm of a tensor without the sqrt . \n
-
- *@par Inputs:
-
- * x: A Tensor.
- * TensorType::FloatingDataType() . \n
-
- *@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 . \n
-
- *@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 . \n
-
- *@par Outputs:
- *y: A Tensor. Has the same type as "x" . \n
-
- *@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 . \n
-
- *@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" . \n
-
- *@par Outputs:
- *y: A Tensor. Has the same type as "x" . \n
-
- *@par Third-party framework compatibility
- * Compatible with the TensorFlow operator MatrixDiag.
- *
- * @par Restrictions:
- * Warning: THIS FUNCTION IS DEPRECATED. Please use MatrixDiag instead.
- */
- 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 . \n
-
- *@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 . \n
-
- *@par Outputs:
- *y: A Tensor. Has the same type as "x" . \n
-
- *@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 . \n
-
- *@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" . \n
-
- *@par Outputs:
- *y: A Tensor. Has the same type as "x" . \n
-
- *@par Third-party framework compatibility
- * Compatible with the TensorFlow operator MatrixDiagPart.
- *
- * @par Restrictions:
- * Warning: THIS FUNCTION IS DEPRECATED. Please use MatrixDiagPart instead.
- */
- 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 . \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: 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 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 . \n
-
- *@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" . \n
-
- *@par Outputs:
- *y: A Tensor. Has the same type as "x" . \n
-
- *@par Third-party framework compatibility
- * Compatible with the TensorFlow operator MatrixSetDiag.
- *
- * @par Restrictions:
- * Warning: THIS FUNCTION IS DEPRECATED. Please use MatrixSetDiag instead.
- */
- 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 . \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)
-
- } // namespace ge
-
- #endif // OPS_BUILT_IN_OP_PROTO_INC_MATRIX_CALCULATION_OPS_H_
|