phivenv/Lib/site-packages/torch/include/ATen/native/quantized/AffineQuantizerBase.h

#pragma once
#include <c10/macros/Export.h>
#include <c10/core/ScalarType.h>

namespace at::native {

// Quantize a float value into a uint value given scale and zero_point
template <typename T>
TORCH_API T quantize_val(double scale, int64_t zero_point, float value);
// TODO combine this with quantize_val once the numerics for ARM are aligned
// with it
template <typename T>
T quantize_val_arm(
    const float scale,
    const int32_t zero_point,
    const float value);
template <typename T, int precision = 8>
void quantize_vec(
    double scale,
    int64_t zero_point,
    const float* src,
    T* dst,
    size_t count = 8);
template <typename T>
TORCH_API float dequantize_val(double scale, int64_t zero_point, T value);
template <typename T>
TORCH_API float dequantize_vec(
    double scale,
    int64_t zero_point,
    const T* src,
    float* dst,
    size_t count = 8);
template <typename SRC_T, typename DST_T>
TORCH_API DST_T requantize_val(double, int64_t, double, int64_t, SRC_T src);

// Given a multiplier and a zero_point, requantize int32_t computed values back
// to quantized values. See comment above
// make_per_tensor_affine_quantizer function for the usage of int64_t
template <typename DST_T>
TORCH_API DST_T
requantize_from_int(double multiplier, int64_t zero_point, int64_t src);

int quantize_val_float_qparams(float scale, float zero_point, float value, int qmin, int qmax);

} // namespace at