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92 lines
3.2 KiB
92 lines
3.2 KiB
/**
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* Copyright 2019-2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef GE_OP_QUANTIZE_OPS_H
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#define GE_OP_QUANTIZE_OPS_H
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#include "../graph/operator_reg.h"
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namespace ge {
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REG_OP(QuantizedInnerProduct)
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.INPUT(x, TensorType({DT_UINT8}))
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.INPUT(w, TensorType({DT_INT8}))
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.OPTIONAL_INPUT(b, TensorType({DT_INT32}))
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.OPTIONAL_INPUT(scale_q, TensorType({DT_FLOAT16}))
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.OPTIONAL_INPUT(offset_q, TensorType({DT_FLOAT16}))
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.OPTIONAL_INPUT(scale_deq_req, TensorType({DT_FLOAT16}))
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.OPTIONAL_INPUT(offset_req, TensorType({DT_FLOAT16}))
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.OUTPUT(y, TensorType({DT_FLOAT16}))
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.REQUIRED_ATTR(quant_algo, ListInt)
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.REQUIRED_ATTR(scale_sqrt, ListInt)
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.REQUIRED_ATTR(num_output, Int)
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.ATTR(transpose, Bool, false)
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.ATTR(bias_term, Bool, false)
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.ATTR(axis, Int, 1)
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.OP_END_FACTORY_REG(QuantizedInnerProduct)
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/**
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* @brief Dequantizes the input tensor into a float tensor.\n
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* [input_min_range, input_max_range] are scalar floats that specify the range
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* for "output_data".
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* The "mode" attribute controls exactly which calculations are used to convert\n
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* the float values to their quantized equivalents.
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* @par Inputs:
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* @li input_data: A Tensor. Must be one of the following types: int8, uint8,
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* int32.
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* @li input_min_range: A Tensor of type float32.
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* Specifies the minimum scalar value possibly produced for the input.
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* @li input_max_range: A Tensor of type float32.
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* Specifies the maximum scalar value possibly produced for the input.
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* @par Attributes:
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* mode: An optional string from: "MIN_COMBINED", "MIN_FIRST", and "SCALED".
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* Defaults to "MIN_COMBINED".
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* @par Outputs:
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* output_data: A dictionary of type float32.
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* @attention Constraints:
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* @li "input_min_range" and "input_max_range" have the same shapes.
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* @li "input_data" and "output_data" have the same shapes.
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*/
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REG_OP(Dequantize)
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.INPUT(x, TensorType(DT_QINT8, DT_QUINT8, DT_QINT32, DT_QINT16, DT_QUINT16))
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.INPUT(min_range, TensorType{DT_FLOAT})
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.INPUT(max_range, TensorType{DT_FLOAT})
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.OUTPUT(y, TensorType({DT_FLOAT}))
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.ATTR(mode, String, "MIN_COMBINED")
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.OP_END_FACTORY_REG(Dequantize)
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REG_OP(AscendQuant)
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.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT32}))
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.OUTPUT(y, TensorType({DT_INT8}))
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.REQUIRED_ATTR(scale, Float)
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.REQUIRED_ATTR(sqrt_mode, Bool)
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.REQUIRED_ATTR(offset, Float)
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.ATTR(round_mode, String, "Round")
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.OP_END_FACTORY_REG(AscendQuant)
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REG_OP(AscendDequant)
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.INPUT(x, TensorType({DT_INT32}))
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.INPUT(deq_scale, TensorType({DT_FLOAT16, DT_UINT64}))
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.OUTPUT(y, TensorType({DT_FLOAT16}))
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.REQUIRED_ATTR(sqrt_mode, Bool)
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.REQUIRED_ATTR(relu_flag, Bool)
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.OP_END_FACTORY_REG(AscendDequant)
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} // namespace ge
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#endif // GE_OP_QUANTIZE_OPS_H
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