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9a281451ed
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14164 See title Reviewed By: csummersea Differential Revision: D13115470 fbshipit-source-id: d754f558cd06e5f4c1cd00315e912cdb7b50731a
111 lines
3.1 KiB
C++
111 lines
3.1 KiB
C++
#pragma once
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#include "caffe2/core/operator.h"
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#include "caffe2/quantization/server/dnnlowp.h"
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#include "caffe2/utils/eigen_utils.h"
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namespace dnnlowp {
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/**
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* Let consumers of op know that qparams the quantization parameter used
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* for output_index'th output of op.
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*/
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void PropagateOutputTensorQuantizationParams(
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caffe2::OperatorBase* op,
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int output_index,
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const TensorQuantizationParams& qparams);
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/**
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* If input_index'th input is already quantized, return quantization parameter
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* used for the input tensor (should've been set by
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* PropagateOutputTensorQuantizationParams when the producer was invoked).
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* If the input tensor is not quantized, return the quantization parameter
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* chosen by qfactory based on the distribution of the input tensor
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*/
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TensorQuantizationParams GetInputTensorQuantizationParamsOf(
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caffe2::OperatorBase* op,
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int input_index,
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const QuantizationFactory* qfactory,
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bool is_weight = false);
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void SetStaticQuantizationParams(
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caffe2::OperatorBase* op,
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int output_index,
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const TensorQuantizationParams& qparams);
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/**
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* @return true if op's outputs should use static quantization (i.e. op has
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* Y_scale and optionally Y_zero_offset argument).
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*/
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bool HasStaticQuantization(
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const caffe2::OperatorBase* op,
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int output_index = 0);
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/**
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* Get output_index'th quantization parameter.
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* Should be used only when UseStaticQuantization is true
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*/
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TensorQuantizationParams GetStaticQuantizationParamsOf(
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const caffe2::OperatorBase* op,
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int output_index);
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/**
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* Quantize input_index'th input if it's not already quantized.
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* a vector temp should be passed to store quantized results.
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*
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* @return array of quantized values
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*/
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template <typename T>
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const T* QuantizeInputIfNeeded(
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caffe2::OperatorBase* op,
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int input_index,
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const TensorQuantizationParams& qparams,
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std::vector<T>& temp);
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template <typename T>
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const T* RowWiseQuantizeInputIfNeeded(
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caffe2::OperatorBase* op,
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int input_index,
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const std::vector<TensorQuantizationParams>& qparams,
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std::vector<T>& temp);
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struct QuantizationErrorStats {
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float sum_sq{0}, sum_err_sq{0};
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float max_abs_err{0};
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// actual and reference values that resulted in max_abs_err
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float max_err_actual{0}, max_err_ref{0};
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int measure_cnt{0};
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};
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void MeasureQuantizationError(
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const float* actual,
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const float* ref,
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size_t len,
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QuantizationErrorStats* stat);
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void ReportQuantizationError(
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const caffe2::OperatorBase* op,
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const QuantizationErrorStats& stat);
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/**
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* Get QuantizationFactory based on the arguments of op
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*/
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std::unique_ptr<QuantizationFactory> GetQuantizationFactoryOf(
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const caffe2::OperatorBase* op);
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void AdjustOutputTensorQuantizationParamsWithFollowedBy(
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caffe2::OperatorBase* op,
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const std::string& followed_by);
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void ParseDNNLowPOperatorArguments(
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caffe2::OperatorBase* op,
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bool* dequantize_output = nullptr,
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bool* measure_quantization_error = nullptr,
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std::string* followed_by = nullptr);
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caffe2::NetDef AddScaleZeroOffsetArgumentsWithHistogram(
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caffe2::NetDef net_def,
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const std::string& histogram_file_name);
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} // namespace dnnlowp
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