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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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