vbatts/llama.cpp
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This commit is contained in:
Yutong Dai 2024-08-22 17:33:53 +00:00
parent ba0861e384
commit e353c037d8
3 changed files with 231 additions and 119 deletions
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1
.gitignore vendored
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@ -132,3 +132,4 @@ poetry.toml
# Test models for lora adapters
/lora-tests
examples/xgenmm/imgs/*.csv

188
examples/xgenmm/playground.ipynb
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161
examples/xgenmm/test_anyres_img.cpp
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@ -1,14 +1,13 @@
#include "ggml.h"
#include "common.h"
#include "clip.h"
#include "xgenmm.h"
#include "llama.h"
#include <cstdio>
#include <cstdlib>
#include <fstream>
#include <vector>
#include "clip.h"
#include "common.h"
#include "ggml.h"
#include "llama.h"
#include "xgenmm.h"
struct clip_image_u8
{
@ -351,6 +350,69 @@ void print_img(clip_image_u8* img)
}
}
void img_to_csv(clip_image_u8* img, const char* filename)
{
std::ofstream outFile(filename);
if (!outFile.is_open())
{
std::cerr << "Error opening file!" << std::endl;
}
const int nx = img->nx;
const int ny = img->ny;
for (int k = 0; k < 3; k++)
{
for (int y = 0; y < ny; y++)
{
for (int x = 0; x < nx; x++)
{
outFile << int(img->buf[3 * (y * nx + x) + k]);
if (x < nx - 1)
{
outFile << ",";
}
}
outFile << std::endl;
}
outFile << std::endl;
}
outFile.close();
printf("file saved to %s\n", filename);
}
void tensor_to_csv(clip_image_f32* img, const char* filename)
{
std::ofstream outFile(filename);
if (!outFile.is_open())
{
std::cerr << "Error opening file!" << std::endl;
}
const int nx = img->nx;
const int ny = img->ny;
for (int k = 0; k < 3; k++)
{
for (int y = 0; y < ny; y++)
{
for (int x = 0; x < nx; x++)
{
outFile << float(img->buf[3 * (y * nx + x) + k]);
if (x < nx - 1)
{
outFile << ",";
}
}
outFile << std::endl;
}
outFile << std::endl;
}
outFile.close();
printf("file saved to %s\n", filename);
}
int main(){
/*
Pytorch Image Processing Pipeline
@ -447,55 +509,58 @@ int main(){
clip_image_u8* image_original_resize = clip_image_u8_init();
bicubic_resize(*img, *image_original_resize, 384, 384);
printf("**********************************\n");
print_img(image_original_resize);
img_to_csv(image_original_resize, "/export/home/llama.cpp/examples/xgenmm/imgs/image_original_resize.csv");
printf("num pixels: %d\n", image_original_resize->buf.size());
printf("raw img: nx:%d | ny:%d\n", image_original_resize->nx, image_original_resize->ny);
// printf("num pixels: %d\n", image_original_resize->buf.size());
// printf("raw img: nx:%d | ny:%d\n", image_original_resize->nx, image_original_resize->ny);
// /*
// part of:
// encode_image_with_clip
// */
// clip_image_f32_batch img_res_v;
// img_res_v.size = 0;
// img_res_v.data = nullptr;
/*
part of:
encode_image_with_clip
*/
clip_image_f32_batch img_res_v;
img_res_v.size = 0;
img_res_v.data = nullptr;
// if (!clip_image_preprocess(ctx, img, &img_res_v))
// {
// LOG_TEE("%s: unable to preprocess image\n", __func__);
// delete[] img_res_v.data;
// return false;
// }
// printf("img->nx:%ld | img->ny:%ld\n", img->nx, img->ny);
// // printf("img_res_v.size:%ld\n", img_res_v.size);
// printf("img_res_v->nx:%ld | img_res_v->ny:%ld\n", img_res_v.data->nx, img_res_v.data->ny);
// // std::cout << img_res_v.data->nx << " | " << img_res_v.data->ny << std::endl;
// // std::cout << img_res_v.data->buf.size() << std::endl;
if (!clip_image_preprocess(ctx, img, &img_res_v))
{
LOG_TEE("%s: unable to preprocess image\n", __func__);
delete[] img_res_v.data;
return false;
}
printf("img->nx:%ld | img->ny:%ld\n", img->nx, img->ny);
// printf("img_res_v.size:%ld\n", img_res_v.size);
printf("img_res_v->nx:%ld | img_res_v->ny:%ld\n", img_res_v.data->nx, img_res_v.data->ny);
// std::cout << img_res_v.data->nx << " | " << img_res_v.data->ny << std::endl;
// std::cout << img_res_v.data->buf.size() << std::endl;
// const char* mm_patch_merge_type = clip_patch_merge_type(ctx);
// printf("mm_patch_merge_type:%s\n", mm_patch_merge_type);
const char* mm_patch_merge_type = clip_patch_merge_type(ctx);
printf("mm_patch_merge_type:%s\n", mm_patch_merge_type);
std::string basename = "/export/home/llama.cpp/examples/xgenmm/imgs/image_res";
for (size_t i = 0; i < img_res_v.size; i++) {
const int nx = img_res_v.data[i].nx;
const int ny = img_res_v.data[i].ny;
printf("i:%d | nx:%d | ny:%d\n", i, nx, ny);
// for (size_t i = 0; i < img_res_v.size; i++) {
// const int nx = img_res_v.data[i].nx;
// const int ny = img_res_v.data[i].ny;
// printf("i:%d | nx:%d | ny:%d\n", i, nx, ny);
// const int n = nx * ny;
const int n = nx * ny;
// for (int k = 0; k < 1; k++) {
// for (int y = 0; y < 5; y++) {
// for (int x = 0; x < 10; x++) {
// // data[(i * 3 * n) + k * n + y * nx + x] = imgs->data[i].buf[3 * (y * nx + x) + k];
// printf("%.4f ", img_res_v.data[i].buf[3 * (y * nx + x) + k]);
// }
// printf("\n");
// }
// printf("\n");
// }
// }
for (int k = 0; k < 1; k++) {
for (int y = 0; y < 5; y++) {
for (int x = 0; x < 10; x++) {
// data[(i * 3 * n) + k * n + y * nx + x] = imgs->data[i].buf[3 * (y * nx + x) + k];
printf("%.4f ", img_res_v.data[i].buf[3 * (y * nx + x) + k]);
}
printf("\n");
}
printf("\n");
}
std::string fileName = basename + "_" + std::to_string(i) + ".csv";
tensor_to_csv(&img_res_v.data[i], fileName.c_str());
}
// /*