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graphengine/third_party/fwkacllib/inc/ops/matrix_calculation_ops.h

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/**
* 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_a, Bool, false)
.ATTR(transpose_b, Bool, false)
.OP_END_FACTORY_REG(MatMul)
REG_OP(MatMulV2)
.INPUT(x1, TensorType({DT_FLOAT16, DT_FLOAT16, DT_INT8, DT_INT8}))
.INPUT(x2, TensorType({DT_FLOAT16, DT_FLOAT16, DT_INT8, DT_INT8}))
.INPUT(alpha, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32, DT_FLOAT}))
.INPUT(beta, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32, DT_FLOAT}))
.INPUT(bias, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32, DT_FLOAT}))
.OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32, DT_FLOAT}))
.OP_END_FACTORY_REG(MatMulV2)
/**
*@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_x, Bool, false)
.ATTR(adj_y, 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)
REG_OP(MatrixDiag)
.INPUT(x, TensorType::BasicType())
.OUTPUT(y, TensorType::BasicType())
.OP_END_FACTORY_REG(MatrixDiag)
REG_OP(MatrixDiagD)
.INPUT(x, TensorType::BasicType())
.INPUT(assist, TensorType::BasicType())
.OUTPUT(y, TensorType::BasicType())
.OP_END_FACTORY_REG(MatrixDiagD)
REG_OP(MatrixDiagPart)
.INPUT(x, TensorType::BasicType())
.OUTPUT(y, TensorType::BasicType())
.OP_END_FACTORY_REG(MatrixDiagPart)
REG_OP(MatrixDiagPartD)
.INPUT(x, TensorType::BasicType())
.INPUT(assist, TensorType::BasicType())
.OUTPUT(y, TensorType::BasicType())
.OP_END_FACTORY_REG(MatrixDiagPartD)
REG_OP(MatrixSetDiag)
.INPUT(x, TensorType::BasicType())
.INPUT(diagonal, TensorType::BasicType())
.OUTPUT(y, TensorType::BasicType())
.OP_END_FACTORY_REG(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)
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)
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)
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)
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)
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)
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)
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)
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)
REG_OP(InnerProduct)
.INPUT(x, TensorType({DT_FLOAT16, DT_INT8}))
.INPUT(w, TensorType({DT_FLOAT16, DT_INT8}))
.OPTIONAL_INPUT(b, TensorType({DT_FLOAT16, DT_INT32}))
.OPTIONAL_INPUT(offset_w, TensorType({DT_INT8}))
.OUTPUT(y, TensorType({DT_FLOAT16, DT_INT32}))
.REQUIRED_ATTR(num_output, Int)
.ATTR(transpose, Bool, false)
.ATTR(bias_term, Bool, true)
.ATTR(axis, Int, 1)
.ATTR(offset_a, Int, 0)
.OP_END_FACTORY_REG(InnerProduct)
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)
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)
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)
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)
REG_OP(SparseApplyAdagrad)
.INPUT(var, TensorType({DT_FLOAT}))
.INPUT(accum, TensorType({DT_FLOAT}))
.INPUT(lr, TensorType({DT_FLOAT}))
.INPUT(grad, TensorType({DT_FLOAT}))
.INPUT(indices, TensorType({DT_INT32}))
.OUTPUT(var, TensorType({DT_FLOAT}))
.ATTR(use_locking, Bool, false)
.OP_END_FACTORY_REG(SparseApplyAdagrad)
REG_OP(SparseApplyAdagradD)
.INPUT(var, TensorType({DT_FLOAT}))
.INPUT(accum, TensorType({DT_FLOAT}))
.INPUT(grad, TensorType({DT_FLOAT}))
.INPUT(indices, TensorType({DT_INT32}))
.OUTPUT(var, TensorType({DT_FLOAT}))
.REQUIRED_ATTR(lr, Float)
.ATTR(use_locking, Bool, false)
.OP_END_FACTORY_REG(SparseApplyAdagradD)
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 Update relevant entries in '*var' according to the Ftrl-proximal scheme.
*@par Inputs:
* Four inputs, including:
*@li var: An NCHW, NHWC, or ND Tensor of type float32.
*@li accum: An NCHW, NHWC, or ND Tensor of type float32.
*@li grad: An NCHW, NHWC, or ND Tensor of type float32.
*@li indices: An NCHW, NHWC, or ND Tensor of type int32.
*@par Attributes:
*@li lr: Required, used for computation.
*@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".
*/
REG_OP(SparseApplyFtrlV2)
.INPUT(var, TensorType({DT_FLOAT}))
.INPUT(accum, TensorType({DT_FLOAT}))
.INPUT(linear, TensorType({DT_FLOAT}))
.INPUT(grad, TensorType({DT_FLOAT}))
.INPUT(indices, TensorType({DT_INT32}))
.INPUT(lr, TensorType({DT_FLOAT}))
.INPUT(l1, TensorType({DT_FLOAT}))
.INPUT(l2, TensorType({DT_FLOAT}))
.INPUT(l2_shrinkage, TensorType({DT_FLOAT}))
.INPUT(lr_power, TensorType({DT_FLOAT}))
.OUTPUT(var, TensorType({DT_FLOAT}))
.ATTR(use_locking, Bool, false)
.OP_END_FACTORY_REG(SparseApplyFtrlV2)
REG_OP(SparseApplyFtrlV2D)
.INPUT(var, TensorType({DT_FLOAT}))
.INPUT(accum, TensorType({DT_FLOAT}))
.INPUT(linear, TensorType({DT_FLOAT}))
.INPUT(grad, TensorType({DT_FLOAT}))
.INPUT(indices, TensorType({DT_INT32}))
.OUTPUT(var, TensorType({DT_FLOAT}))
.REQUIRED_ATTR(lr, Float)
.REQUIRED_ATTR(l1, Float)
.REQUIRED_ATTR(l2, Float)
.REQUIRED_ATTR(l2_shrinkage, Float)
.REQUIRED_ATTR(lr_power, Float)
.ATTR(use_locking, Bool, false)
.OP_END_FACTORY_REG(SparseApplyFtrlV2D)
} // namespace ge
#endif // GE_OP_MATRIX_CALCULATION_OPS_H
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