You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
225 lines
7.9 KiB
225 lines
7.9 KiB
/**
|
|
* Copyright 2019-2020 Huawei Technologies Co., Ltd
|
|
*
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
*/
|
|
|
|
/*!
|
|
* \file quantize_ops.h
|
|
* \brief
|
|
*/
|
|
#ifndef OPS_BUILT_IN_OP_PROTO_INC_QUANTIZE_OPS_H_
|
|
#define OPS_BUILT_IN_OP_PROTO_INC_QUANTIZE_OPS_H_
|
|
#include "graph/operator_reg.h"
|
|
|
|
namespace ge {
|
|
|
|
/**
|
|
* @brief Dequantizes the input tensor into a float tensor.
|
|
* [min_range, max_range] are float32 tensors that specify the range
|
|
* for "y".
|
|
* The "mode" attribute controls exactly which calculations are used to convert
|
|
* the float values to their quantized equivalents.
|
|
* @par Inputs:
|
|
* @li x: A Tensor. Must be one of the following types: int8, uint8,
|
|
* int32.
|
|
* @li min_range: A Tensor of type float32.
|
|
* Specifies the minimum scalar value possibly produced for the input.
|
|
* @li max_range: A Tensor of type float32.
|
|
* Specifies the maximum scalar value possibly produced for the input . \n
|
|
|
|
* @par Attributes:
|
|
* mode: An optional string from: "MIN_COMBINED", "MIN_FIRST", and "SCALED".
|
|
* Defaults to "MIN_COMBINED" . \n
|
|
|
|
* @par Outputs:
|
|
* y: A dictionary of type float32 . \n
|
|
|
|
* @attention Constraints:
|
|
* @li "min_range" and "max_range" have the same shapes.
|
|
* @li "x" and "y" have the same shapes . \n
|
|
|
|
* @par Third-party framework compatibility
|
|
* Compatible with the TensorFlow operator Dequantize.
|
|
*/
|
|
REG_OP(Dequantize)
|
|
.INPUT(x, TensorType(DT_QINT8, DT_QUINT8, DT_QINT32, DT_QINT16, DT_QUINT16))
|
|
.INPUT(min_range, TensorType{DT_FLOAT})
|
|
.INPUT(max_range, TensorType{DT_FLOAT})
|
|
.OUTPUT(y, TensorType({DT_FLOAT}))
|
|
.ATTR(mode, String, "MIN_COMBINED")
|
|
.OP_END_FACTORY_REG(Dequantize)
|
|
|
|
/**
|
|
*@brief Quantizes the input . \n
|
|
|
|
*@par Inputs:
|
|
*x: An NC1HWC0 tensor of type float16 or float32, specifying the input . \n
|
|
|
|
*@par Attributes:
|
|
*@li scale: A required float32, specifying the scaling ratio.
|
|
*@li offset: A required float16, specifying the offset.
|
|
*@li sqrt_mode: A optional bool, specifying whether to perform square root on "scale", either "True" or "False". Defaults to "False".
|
|
*@li round_mode: An optional string, specifying the float16 to int8 cast type.
|
|
* The value range is [Round, Floor, Ceiling, Truncate]. Defaults to "Round" . \n
|
|
|
|
*@par Outputs:
|
|
*y: The quantized output tensor of type int8 and with format NC1HWC0 . \n
|
|
|
|
*@par Third-party framework compatibility
|
|
* It is a custom operator. It has no corresponding operator in Caffe.
|
|
*/
|
|
REG_OP(AscendQuant)
|
|
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT32}))
|
|
.OUTPUT(y, TensorType({DT_INT8}))
|
|
.REQUIRED_ATTR(scale, Float)
|
|
.REQUIRED_ATTR(offset, Float)
|
|
.ATTR(sqrt_mode, Bool, false)
|
|
.ATTR(round_mode, String, "Round")
|
|
.OP_END_FACTORY_REG(AscendQuant)
|
|
|
|
/**
|
|
*@brief Dequantizes the input . \n
|
|
|
|
*@par Inputs:
|
|
*@li x: An NC1HWC0 tensor of type int32, specifying the input.
|
|
*@li deq_scale: An NC1HWC0 tensor of type float16 or uint64, specifying the scaling ratio . \n
|
|
|
|
*@par Attributes:
|
|
*@li sqrt_mode: A optional bool, specifying whether to perform square root on "scale", either "True" or "False". Defaults to "False".
|
|
*@li relu_flag: A optional bool, specifying whether to perform ReLU, either "True" or "False". Defaults to "False".
|
|
*@li dtype: A optional int32, specifying the output data type. Defaults to "DT_FLOAT" . \n
|
|
|
|
*@par Outputs:
|
|
*y: The dequantized output tensor of type float16 or float32 and with format NC1HWC0 . \n
|
|
|
|
*@par Third-party framework compatibility
|
|
* It is a custom operator. It has no corresponding operator in Caffe.
|
|
*/
|
|
REG_OP(AscendDequant)
|
|
.INPUT(x, TensorType({DT_INT32}))
|
|
.INPUT(deq_scale, TensorType({DT_FLOAT16, DT_UINT64}))
|
|
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT}))
|
|
.ATTR(sqrt_mode, Bool, false)
|
|
.ATTR(relu_flag, Bool, false)
|
|
.ATTR(dtype, Int, DT_FLOAT)
|
|
.OP_END_FACTORY_REG(AscendDequant)
|
|
|
|
/**
|
|
*@brief Anti quantizes the input . \n
|
|
|
|
*@par Inputs:
|
|
*x: An NC1HWC0 tensor of type int8, specifying the input . \n
|
|
|
|
*@par Attributes:
|
|
*@li scale: A required float32 scale.
|
|
*@li offset: A required float32 offset.
|
|
*@li dtype: A optional int32, specifying the output data type. Defaults to "DT_FLOAT".
|
|
*@li sqrt_mode: A optional bool, specifying whether to perform square root on "scale", either "True" or "False". Defaults to "False" . \n
|
|
|
|
*@par Outputs:
|
|
*y: The dequantized output tensor of type float16 or float32 and with format NC1HWC0 . \n
|
|
|
|
*@par Third-party framework compatibility
|
|
* It is a custom operator. It has no corresponding operator in Caffe.
|
|
*/
|
|
REG_OP(AscendAntiQuant)
|
|
.INPUT(x, TensorType({DT_INT8}))
|
|
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT}))
|
|
.REQUIRED_ATTR(scale, Float)
|
|
.REQUIRED_ATTR(offset, Float)
|
|
.ATTR(dtype, Int, DT_FLOAT)
|
|
.ATTR(sqrt_mode, Bool, false)
|
|
.OP_END_FACTORY_REG(AscendAntiQuant)
|
|
|
|
/**
|
|
*@brief Dequantizes the input of int16 . \n
|
|
|
|
*@par Inputs:
|
|
*@li x0: An NC1HWC0 tensor of type int32, specifying the input.
|
|
*@li deq_scale: An NC1HWC0 tensor of type uint64, specifying the scaling ratio.
|
|
*@li x1: An NC1HWC0 tensor of type int16, specifying the input . \n
|
|
|
|
*@par Attributes:
|
|
*relu_flag: A optional bool, specifying whether to perform ReLU, either "True" or "False". Defaults to "False" . \n
|
|
|
|
*@par Outputs:
|
|
*y: The dequantized output tensor of type int16 and with format NC1HWC0 . \n
|
|
|
|
*@par Third-party framework compatibility
|
|
* It is a custom operator. It has no corresponding operator in Caffe.
|
|
*/
|
|
REG_OP(AscendDequantS16)
|
|
.INPUT(x0, TensorType({DT_INT32}))
|
|
.INPUT(deq_scale, TensorType({DT_UINT64}))
|
|
.OPTIONAL_INPUT(x1, TensorType({DT_INT16}))
|
|
.OUTPUT(y, TensorType({DT_INT16}))
|
|
.ATTR(relu_flag, Bool, false)
|
|
.OP_END_FACTORY_REG(AscendDequantS16)
|
|
|
|
/**
|
|
*@brief Requantizes the input . \n
|
|
|
|
*@par Inputs:
|
|
*@li x: An NC1HWC0 tensor of type int32, specifying the input.
|
|
*@li req_scale: An NC1HWC0 tensor of type uint64, specifying the scaling ratio . \n
|
|
|
|
*@par Attributes:
|
|
*relu_flag: A optional bool, specifying whether to perform ReLU, either "True" or "False". Defaults to "False" . \n
|
|
|
|
*@par Outputs:
|
|
*y: The dequantized output tensor of type int8 and with format NC1HWC0 . \n
|
|
|
|
*@par Third-party framework compatibility
|
|
* It is a custom operator. It has no corresponding operator in Caffe.
|
|
*/
|
|
REG_OP(AscendRequant)
|
|
.INPUT(x, TensorType({DT_INT32}))
|
|
.INPUT(req_scale, TensorType({DT_UINT64}))
|
|
.OUTPUT(y, TensorType({DT_INT8}))
|
|
.ATTR(relu_flag, Bool, false)
|
|
.OP_END_FACTORY_REG(AscendRequant)
|
|
|
|
/**
|
|
*@brief Requantizes the input of int16 . \n
|
|
|
|
*@par Inputs:
|
|
*@li x: An NC1HWC0 tensor of type int16, specifying the input.
|
|
*@li req_scale: An NC1HWC0 tensor of type uint64, specifying the scaling ratio.
|
|
*@li x1: An NC1HWC0 tensor of type int16 . \n
|
|
|
|
*@par Attributes:
|
|
*@li dual_output: A optional bool, specifying whether to perform dual ouput, either "True" or "False". Defaults to "False".
|
|
*@li relu_flag: A optional bool, specifying whether to perform ReLU, either "True" or "False". Defaults to "False" . \n
|
|
|
|
*@par Outputs:
|
|
*@li y: The dequantized output tensor of type int8 and with format NC1HWC0.
|
|
*@li y1: The dequantized output tensor of type int16 and with format NC1HWC0 . \n
|
|
|
|
*@par Third-party framework compatibility
|
|
* It is a custom operator. It has no corresponding operator in Caffe.
|
|
*/
|
|
REG_OP(AscendRequantS16)
|
|
.INPUT(x, TensorType({DT_INT16}))
|
|
.INPUT(req_scale, TensorType({DT_UINT64}))
|
|
.OPTIONAL_INPUT(x1, TensorType({DT_INT16}))
|
|
.OUTPUT(y, TensorType({DT_INT8}))
|
|
.OUTPUT(y1, TensorType({DT_INT16}))
|
|
.ATTR(dual_output, Bool, false)
|
|
.ATTR(relu_flag, Bool, false)
|
|
.OP_END_FACTORY_REG(AscendRequantS16)
|
|
|
|
} // namespace ge
|
|
|
|
#endif // OPS_BUILT_IN_OP_PROTO_INC_QUANTIZE_OPS_H_
|