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#pragma once
#include <vector>
#include "caffe2/operators/group_norm_op.h"
#include "caffe2/quantization/server/dnnlowp_op.h"
namespace caffe2 {
using GroupNormFP32Op = GroupNormOp<float, CPUContext>;
template <typename T>
class GroupNormDNNLowPOp final : public DNNLowPOp<T, GroupNormFP32Op> {
public:
USE_OPERATOR_FUNCTIONS(CPUContext);
USE_DNNLOWP_OPERATOR_BASE_FUNCTIONS(T, GroupNormFP32Op);
GroupNormDNNLowPOp(const OperatorDef& operator_def, Workspace* ws);
bool RunOnDevice() override;
private:
bool GetQuantizationParameters();
void QuantizeGamma();
void QuantizeGammaImpl();
void QuantizeBeta();
bool RunOnDeviceWithOrderNCHW();
bool RunOnDeviceWithOrderNHWC();
void QuantizedGroupMomentsNCHW(
int N,
int G,
int K,
int HxW,
const T* X,
int32_t* mu,
int32_t* rsig);
void QuantizedGroupMomentsNHWC(
int N,
int G,
int K,
int HxW,
const T* X,
int32_t* mu,
int32_t* rsig);
void DequantizedGroupMomentsNCHW(
int N,
int G,
int K,
int HxW,
const T* X,
float* mu,
float* rsig);
void DequantizedGroupMomentsNHWC(
int N,
int G,
int K,
int HxW,
const T* X,
float* mu,
float* rsig);
void ComputeQuantizedInvStd(
int N,
const float* var,
float* rsig,
int32_t* rsig_quantized);
void ComputeQuantizedFusedParams(
int N,
int G,
int K,
const int32_t* mu,
const int32_t* rsig,
const int32_t* gamma,
const int32_t* beta,
int32_t* scale,
int32_t* bias);
void ComputeDequantizedFusedParams(
int N,
int G,
int K,
const float* mu,
const float* rsig,
const float* gamma,
const float* beta,
float* scale,
float* bias);
void AffineBatchChannelQuantizedNCHW(
int N,
int C,
int HxW,
const T* X,
const int32_t* scale,
const int32_t* bias,
T* Y);
void AffineBatchChannelQuantizedNHWC(
int N,
int C,
int HxW,
const T* X,
const int32_t* scale,
const int32_t* bias,
T* Y);
void AffineBatchChannelDequantizedNCHW(
int N,
int C,
int HxW,
const float* X,
const float* scale,
const float* bias,
float* Y);
void AffineBatchChannelDequantizedNHWC(
int N,
int C,
int HxW,
const float* X,
const float* scale,
const float* bias,
float* Y);
const bool is_test_;
const int group_;
const float epsilon_;
const StorageOrder order_;
const bool is_param_constant_;
std::vector<int32_t> mu_quantized_;
std::vector<int32_t> rsig_quantized_;
std::vector<float> mu_dequantized_;
std::vector<float> rsig_dequantized_;
dnnlowp::TensorQuantizationParams rsig_qparams_;
std::vector<int32_t> gamma_quantized_;
std::vector<int32_t> beta_quantized_;
std::vector<float> gamma_dequantized_;
std::vector<float> beta_dequantized_;
const int32_t* gamma_quantized_data_ = nullptr;
const int32_t* beta_quantized_data_ = nullptr;
const float* gamma_dequantized_data_ = nullptr;
const float* beta_dequantized_data_ = nullptr;
std::vector<int32_t> scale_quantized_;
std::vector<int32_t> bias_quantized_;
std::vector<float> scale_dequantized_;
std::vector<float> bias_dequantized_;
dnnlowp::TensorQuantizationParams internal_qparams_;
std::vector<float> X_dequantized_;
std::vector<int32_t> Y_int32_;
float cached_X_qparams_scale_ = 0.0f;
// Input: X, gamma, beta
// Output: Y, mu, inv_sig
INPUT_TAGS(INPUT, GAMMA, BETA);
OUTPUT_TAGS(OUTPUT, MU, INV_SIGMA);
};
namespace internal {
template <typename T>
void VectorMomentsAVX2(const int N, const T* src, int64_t* sum, int64_t* sumsq);
void ComputeQuantizedFusedParamsAVX2(
const int N,
const int G,
const int K,
const int32_t X_zero_point,
const int32_t* mu,
const int32_t* rsig,
const int32_t* gamma,
int32_t* scale,
int32_t* bias);
template <typename T>
void AffineBatchChannelAndRequantizeNCHWAVX2(
const int N,
const int C,
const int HxW,
const dnnlowp::RequantizationParams& params,
const T* X,
const int32_t* scale,
const int32_t* bias,
T* Y);
template <typename T>
void AffineBatchChannelAndRequantizeNHWCAVX2(
const int N,
const int C,
const int HxW,
const dnnlowp::RequantizationParams& params,
const T* X,
const int32_t* scale,
const int32_t* bias,
T* Y);
} // namespace internal
} // namespace caffe2
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