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clip : Add Qwen2.5VL support (#12402)

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* implment vision model architecture, gguf convertor

* handle window attention inputs

* add debug utils

* fix few incorrect tensor memory layout

* move position id remap out of ggml to avoid int32 cuda operations

* cleaning up

* ignore transformers Qwen2_5_xxx type check

* remove not so often use `qwen2vl-cli` debug functions

* remove commented-out code blocks

* fix attn weight scaling after rebase

* add `PROJECTOR_TYPE_QWEN2_5_VL`

* remove `KEY_USE_GLU_MLP`, `KEY_USE_RMS_NORM`

* replace `KEY_FULLATTN_BLK_IDX` with `KEY_WIN_ATTN_PATTERN`

* remove `attn_window_size` from gguf

* fix model conversion

* clean up

* fix merging problem

* add test

---------

Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
This commit is contained in:
HimariO
2025-04-27 16:10:34 +08:00
committed by GitHub
parent 2d451c8059
commit ca2bb89eac
6 changed files with 594 additions and 102 deletions
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96
examples/llava/qwen2vl-cli.cpp
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@@ -23,6 +23,9 @@
#include <algorithm>
#include <iostream>
#include <fstream>
#include <limits>
#include <cassert>
#include <cmath>
static bool qwen2vl_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed,
@@ -367,14 +370,14 @@ static void debug_test_mrope_2d() {
// 1. Initialize backend
ggml_backend_t backend = NULL;
std::string backend_name = "";
#ifdef GGML_USE_CUDA
fprintf(stderr, "%s: using CUDA backend\n", __func__);
backend = ggml_backend_cuda_init(0); // init device 0
backend_name = "cuda";
if (!backend) {
fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
}
#endif
// #ifdef GGML_USE_CUDA
// fprintf(stderr, "%s: using CUDA backend\n", __func__);
// backend = ggml_backend_cuda_init(0); // init device 0
// backend_name = "cuda";
// if (!backend) {
// fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
// }
// #endif
// if there aren't GPU Backends fallback to CPU backend
if (!backend) {
backend = ggml_backend_cpu_init();
@@ -483,28 +486,82 @@ static void debug_test_mrope_2d() {
ggml_backend_free(backend);
}
static void debug_dump_img_embed(struct llava_context * ctx_llava) {
int n_embd = llama_model_n_embd(llama_get_model(ctx_llava->ctx_llama));
int ne = n_embd * 4;
float vals[56 * 56 * 3];
enum model_output_type {
conv3d,
patch_embed,
patch_win_attn_scatter,
first_attn_layer,
last_attn_layer,
attn_softmax,
final_layer,
};
static void debug_dump_img_embed(struct llava_context * ctx_llava, model_output_type output_type) {
constexpr int ih = 140;
constexpr int iw = 196;
// constexpr int ih = 56;
// constexpr int iw = 56;
// int n_embd = llama_model_n_embd(llama_get_model(ctx_llava->ctx_llama));
int n_embd = 1280;
int merge = 1;
if (output_type == model_output_type::final_layer) {
n_embd = 2048;
merge = 2;
}
else if (output_type == model_output_type::attn_softmax) {
merge = 1;
n_embd = (ih/14/merge) * (iw/14/merge) * 16;
}
int ne = (ih/14/merge) * (iw/14/merge) * n_embd;
float vals[iw * ih * 3];
// float embd[ne];
std::vector<float> embd;
embd.resize(ne);
for (int i = 0; i < 56*56; i++)
for (int i = 0; i < iw*ih; i++)
{
for (int c = 0; c < 3; c++)
vals[i * 3 + c] = (float)(i % (56 * 56)) / (56*56);
vals[i * 3 + c] = (float)i / (iw*ih);
}
clip_encode_float_image(ctx_llava->ctx_clip, 16, vals, 56, 56, embd.data());
clip_encode_float_image(ctx_llava->ctx_clip, 8, vals, ih, iw, embd.data());
std::ofstream outFile("img_embed.bin", std::ios::binary);
std::string file_postfix = "";
switch (output_type)
{
case model_output_type::conv3d:
file_postfix = "conv3d";
break;
case model_output_type::patch_embed:
file_postfix = "patch_embed";
break;
case model_output_type::patch_win_attn_scatter:
file_postfix = "scatter";
break;
case model_output_type::first_attn_layer:
file_postfix = "first_attn";
break;
case model_output_type::last_attn_layer:
file_postfix = "last_attn";
break;
case model_output_type::attn_softmax:
file_postfix = "attn_softmax";
break;
case model_output_type::final_layer:
file_postfix = "final";
break;
default:
break;
}
auto output_path = "img_embed_" + file_postfix + ".bin";
std::ofstream outFile(output_path, std::ios::binary);
if (outFile.is_open()) {
outFile.write(reinterpret_cast<const char*>(embd.data()), ne * sizeof(float));
outFile.close();
std::cout << "Data successfully written to mrope.bin" << std::endl;
std::cout << "Data successfully written to ::[ " << output_path << std::endl;
} else {
std::cerr << "Error opening file!" << std::endl;
}
@@ -551,8 +608,9 @@ int main(int argc, char ** argv) {
} else if (params.image[0].empty()) {
auto ctx_llava = llava_init_context(&params, model);
debug_test_mrope_2d();
debug_dump_img_embed(ctx_llava);
// debug_test_mrope_2d();
debug_dump_img_embed(ctx_llava, model_output_type::final_layer);
// debug_dump_img_embed(ctx_llava, model_output_type::last_attn_layer);
llama_perf_context_print(ctx_llava->ctx_llama);
ctx_llava->model = NULL;