|
- /**
- * 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:
- *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].
- */
- 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].
- */
- 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. 4D. Must be one of the following types:\n float16, int8. Has format [FRACTAL_NZ].
- *@li b: A matrix Tensor. 4D. Must be one of the following types:\n float16, int8. When type is int8, has format [FRACTAL_Z], \n otherwise has format [FRACTAL_NZ].
- *@li c: A matrix Tensor. 2D or higher. Must be one of the following types: \n float16, int32, float32. When type is int32, has format [ND], \n otherwise has format [FRACTAL_NZ].
- *@li alpha: A 1D Tensor. The shape of alpha is [1].\n Must be one of the following types: float16, int32, float32. Has format [ND].
- *@li beta: A 1D Tensor. The shape of beta is [1].\n Must be one of the following types: float16, int32, float32. Has format [ND].
-
- *@par Attributes:
- *Two attributes, including:
- *@li transpose_a: Optional. A bool.\n If True, changes the shape of "a" from [M, K] to [K, M].\n Reserved parameters, not used for now.
- *@li transpose_b: Optional. A bool.\n If True, changes the shape of "b" from [M, K] to [K, M].\n Reserved parameters, not used for now.
-
- *@par Outputs:
- *@out: The result matrix Tensor. 4D. Must be one of the following types:\n float16, float32, int32. Has format [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(out, 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".
- */
-
- 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)
- .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".
- */
- 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, int32, int8, uint8.
-
- *@par Outputs:
- *y: A Tensor. Has the same type as "x".
-
- */
- 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".
-
- */
- 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, int32, int8, uint8.
-
- *@par Outputs:
- *y: A Tensor. Has the same type as "x".
-
- */
- 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".
-
- */
- 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, int32, int8, uint8.
- *@li diagonal: A Tensor of the same type as "x".
-
- *@par Outputs:
- *y: A Tensor. Has the same type as "x".
-
- */
- 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".
-
- */
- 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. \n
-
- *Must be one of the following types: float16, float32, int8, uint8, bool
- *@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, int8, uint8, bool
-
- *@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".
-
- */
- 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".
-
- */
- 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. \n
-
- *Must be one of the following types: float16, float32, int32, int8, uint8
- *@li indices: An ND Tensor of type int32.
-
-
- *@li updates: An ND Tensor. \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.
-
- *@par Outputs:
- *var: A Tensor. Has the same type and format as input "var".
-
- */
- 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 NCHW, NHWC, or ND Tensor. \n
-
- *Must be one of the following types: float16, float32, int32, int8, uint8
- *@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, float32, int32, int8, uint8
-
- *@par Attributes:
- *@li use_locking: An optional bool. Defaults to "False". If "True", the operation will be protected by a lock.
- *@li isRef: An optional bool. Defaults to "True"
-
- *@par Outputs:
- *var: A Tensor. Has the same type and format as input "var".
-
- */
- 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. \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 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".
-
- */
- 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".
-
- */
- 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. \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 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".
-
- */
- 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".
-
- */
- 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. \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 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".
-
- */
- 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".
-
- */
- 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:\n
- *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".
-
- */
- 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: Reserved.
- *@li offset_x: Reserved.
-
- *@par Outputs:
- *y: The result tensor of type float16, int8, float32.
-
- *@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.
- *@li predictions: A Tensor. Must be one of the following types: float16, float32, int32, int8.
- *@li weights: A Tensor. Must be one of the following types: float16, float32, int32, int8.
-
- *@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()
-
- */
- 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 NCHW, NHWC, or ND Tensor. \n
-
- *Must be one of the following types: float16, float32, int32, int8, uint8
- *@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, 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".
-
- */
- 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 NCHW, NHWC, or ND Tensor. \n
-
- *Must be one of the following types: float16, float32, 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, float32, 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".
-
- */
- 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 NCHW, NHWC, or ND Tensor. \n
-
- *Must be one of the following types: float16, float32, 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, float32, 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".
-
- */
- 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 NCHW, NHWC, or ND Tensor. \n
-
- *Must be one of the following types: float16, float32, int32, int8, uint8
- *@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, 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".
-
- */
- 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).
-
- */
- 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`.
-
- */
- 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.
-
- */
- 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
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