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#include <time.h>
#include <stdio.h>
#include <stdlib.h>
#include <stdint.h>
#include <vector>
#include <cmath>
#include "fp16/Float16.h"
namespace rknpu2
{
/**
* @brief generate random buffer
*
*/
template <typename T>
void generate_random_buffer(T *buffer, size_t size, std::vector<float> range)
{
if (buffer == nullptr || size == 0)
{
return;
}
// 设置随机种子
srand((unsigned)time(NULL));
float min = range[0], max = range[1];
for (size_t i = 0; i < size; ++i)
{
buffer[i] = static_cast<T>(min + (max - min) * (static_cast<double>(rand()) / RAND_MAX));
}
}
template void generate_random_buffer(int8_t *buffer, size_t size, std::vector<float> range);
template void generate_random_buffer(float16 *buffer, size_t size, std::vector<float> range);
/**
* @brief convert norm layout to perf layout
* norm layout: [M,K]
* perf layout: [K/subK, M, subK]
*/
template <typename Ti, typename To>
void norm_layout_to_perf_layout(Ti *src, To *dst, int32_t M, int32_t K, int32_t subK, bool isInt4Type)
{
int outter_size = (int)std::ceil(K * 1.0f / subK);
for (int i = 0; i < outter_size; i++)
{
for (int m = 0; m < M; m++)
{
for (int j = 0; j < subK; j++)
{
int ki = i * subK + j;
if (isInt4Type)
{
int input_index = m * K + ki;
int output_index = i * M * subK + m * subK + j;
int8_t int4 = src[input_index];
if (ki >= K)
{
int4 = 0;
}
else
{
int4 = int4 & 0xf;
}
if (output_index % 2 == 0)
{
dst[output_index / 2] = int4;
}
else
{
int8_t temp = dst[output_index / 2];
int8_t result = temp | (int4 << 4);
dst[output_index / 2] = result;
}
}
else
{
if (ki >= K)
{
dst[i * M * subK + m * subK + j] = 0;
}
else
{
dst[i * M * subK + m * subK + j] = src[m * K + ki];
}
}
}
}
}
}
template void norm_layout_to_perf_layout<int8_t, int8_t>(int8_t *src, int8_t *dst, int32_t M, int32_t K, int32_t subK,
bool isInt4Type);
template void norm_layout_to_perf_layout<float16, float16>(float16 *src, float16 *dst, int32_t M, int32_t K,
int32_t subK, bool isInt4Type);
/**
* @brief convert norm layout to native layout
* norm layout: [K,N]
* native layout: [N1, K1, subN, subK]
*
*/
template <typename Ti, typename To>
void norm_layout_to_native_layout(Ti *src, To *dst, int32_t K, int32_t N, int32_t subN, int32_t subK, bool isInt4Type)
{
int N_remain = (int)std::ceil(N * 1.0f / subN);
int K_remain = (int)std::ceil(K * 1.0f / subK);
for (int i = 0; i < N_remain; i++)
{
for (int j = 0; j < K_remain; j++)
{
for (int n = 0; n < subN; n++)
{
int ni = i * subN + n;
for (int k = 0; k < subK; k++)
{
int ki = j * subK + k;
if (isInt4Type)
{
int input_index = ki * N + ni;
int output_index = i * (K_remain * subN * subK) + j * (subN * subK) + n * subK + k;
int8_t int4 = src[input_index];
if (ki < K && ni < N)
{
int4 = int4 & 0xf;
}
else
{
int4 = 0;
}
if (output_index % 2 == 0)
{
dst[output_index / 2] = int4 << 4;
}
else
{
int8_t temp = dst[output_index / 2];
int8_t result = temp | int4;
dst[output_index / 2] = result;
}
}
else
{
if (ki < K && ni < N)
{
dst[((i * K_remain + j) * subN + n) * subK + k] = src[ki * N + ni];
}
else
{
dst[((i * K_remain + j) * subN + n) * subK + k] = 0;
}
}
}
}
}
}
}
template void norm_layout_to_native_layout<int8_t, int8_t>(int8_t *src, int8_t *dst, int32_t K, int32_t N, int32_t subN,
int32_t subK, bool isInt4Type);
template void norm_layout_to_native_layout<float16, float16>(float16 *src, float16 *dst, int32_t K, int32_t N,
int32_t subN, int32_t subK, bool isInt4Type);
/**
* @brief convert perf to norm layout
* perf layout: [K1, M, subK]
* norm layout: [M,K]
*
*/
template <typename Ti, typename To>
void perf_layout_to_norm_layout(Ti *src, To *dst, int32_t M, int32_t K, int32_t K_remain, int32_t subK)
{
for (int i = 0; i < K_remain; i++)
{
for (int j = 0; j < subK; j++)
{
for (int m = 0; m < M; m++)
{
int ki = i * subK + j;
if (ki < K)
{
dst[m * K + ki] = src[i * M * subK + m * subK + j];
}
}
}
}
}
template void perf_layout_to_norm_layout<int8_t, int8_t>(int8_t *src, int8_t *dst, int32_t M, int32_t K,
int32_t K_remain, int32_t subK);
template void perf_layout_to_norm_layout<int16_t, int16_t>(int16_t *src, int16_t *dst, int32_t M, int32_t K,
int32_t K_remain, int32_t subK);
template void perf_layout_to_norm_layout<int32_t, int32_t>(int32_t *src, int32_t *dst, int32_t M, int32_t K,
int32_t K_remain, int32_t subK);
template void perf_layout_to_norm_layout<float, float>(float *src, float *dst, int32_t M, int32_t K, int32_t K_remain,
int32_t subK);
template void perf_layout_to_norm_layout<float16, float16>(float16 *src, float16 *dst, int32_t M, int32_t K, int32_t K_remain,
int32_t subK);
template <typename T>
bool arraysEqual(const std::vector<T> &arr1, const std::vector<T> &arr2, float eps)
{
if (arr1.size() != arr2.size())
{
return false;
}
for (size_t i = 0; i < arr1.size(); ++i)
{
if (std::abs(arr1[i] - arr2[i]) > eps)
{
return false;
}
}
return true;
}
template bool arraysEqual<float>(const std::vector<float> &arr1, const std::vector<float> &arr2, float eps);
template bool arraysEqual<int32_t>(const std::vector<int32_t> &arr1, const std::vector<int32_t> &arr2, float eps);
template bool arraysEqual<int16_t>(const std::vector<int16_t> &arr1, const std::vector<int16_t> &arr2, float eps);
template bool arraysEqual<int8_t>(const std::vector<int8_t> &arr1, const std::vector<int8_t> &arr2, float eps);
template <typename T>
bool arraysCosineSimilarity(const std::vector<T> &arr1, const std::vector<T> &arr2, float eps)
{
if (arr1.size() != arr2.size())
{
return false;
}
// 计算点积
#pragma omp parallel for reduction(+ : dotProduct)
double dotProduct = 0.0;
for (size_t i = 0; i < arr1.size(); ++i)
{
dotProduct += arr1[i] * arr2[i];
}
// 计算向量范数
#pragma omp parallel for reduction(+ : normA, normB)
double normA = 0.0, normB = 0.0;
for (size_t i = 0; i < arr1.size(); ++i)
{
normA += std::pow(arr1[i], 2);
normB += std::pow(arr2[i], 2);
}
// 避免除以零
if (normA == 0.0 || normB == 0.0)
{
return false;
}
if ((dotProduct / (std::sqrt(normA) * std::sqrt(normB))) < eps)
{
return false;
}
return true;
}
template bool arraysCosineSimilarity<float>(const std::vector<float> &arr1, const std::vector<float> &arr2, float eps);
template bool arraysCosineSimilarity<int32_t>(const std::vector<int32_t> &arr1, const std::vector<int32_t> &arr2,
float eps);
template bool arraysCosineSimilarity<int16_t>(const std::vector<int16_t> &arr1, const std::vector<int16_t> &arr2,
float eps);
// 转置模板函数
template <typename T>
void transposeB(const T *input, T *output, int32_t K, int32_t N)
{
for (int32_t k = 0; k < K; ++k)
{
for (int32_t n = 0; n < N; ++n)
{
output[n * K + k] = input[k * N + n];
}
}
}
template void transposeB<int8_t>(const int8_t *input, int8_t *output, int32_t K, int32_t N);
template void transposeB<float16>(const float16 *input, float16 *output, int32_t K, int32_t N);
// 4bit数据类型的特殊处理函数
void transpose4bit(const int8_t *input, int8_t *output, int32_t K, int32_t N)
{
for (int32_t k = 0; k < K; ++k)
{
for (int32_t n = 0; n < N; ++n)
{
int32_t input_idx = (k * N + n) / 2;
int32_t input_offset = (k * N + n) % 2;
int32_t output_idx = (n * K + k) / 2;
int32_t output_offset = (n * K + k) % 2;
uint8_t value = (input[input_idx] >> (4 * input_offset)) & 0xF;
if (output_offset == 0)
{
output[output_idx] = (output[output_idx] & 0xF0) | value;
}
else
{
output[output_idx] = (output[output_idx] & 0x0F) | (value << 4);
}
}
}
}
} // namespace rknn |