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Merge branch 'prepare-PR-of-minicpm-v2.6' into master
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@tc-mb
tc-mb authored Aug 10, 2024
2 parents 911b437 + ea0c828 commit fc1c860
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Showing 8 changed files with 1,967 additions and 23 deletions.
152 changes: 135 additions & 17 deletions examples/llava/clip.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
// I'll gradually clean and extend it
// Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch
#include "clip.h"
#include "common.h"
#include "log.h"
#include "ggml.h"
#include "ggml-alloc.h"
Expand Down Expand Up @@ -81,6 +82,7 @@ static std::string format(const char * fmt, ...) {
#define KEY_HAS_VIS_ENC "clip.has_vision_encoder"
#define KEY_HAS_LLAVA_PROJ "clip.has_llava_projector"
#define KEY_HAS_MINICPMV_PROJ "clip.has_minicpmv_projector"
#define KEY_MINICPMV_VERSION "clip.minicpmv_version"
#define KEY_USE_GELU "clip.use_gelu"
#define KEY_N_EMBD "clip.%s.embedding_length"
#define KEY_N_FF "clip.%s.feed_forward_length"
Expand Down Expand Up @@ -526,6 +528,7 @@ struct clip_ctx {
bool has_vision_encoder = false;
bool has_llava_projector = false;
bool has_minicpmv_projector = false;
int minicpmv_version = 2;

struct clip_vision_model vision_model;
projector_type proj_type = PROJECTOR_TYPE_MLP;
Expand Down Expand Up @@ -641,7 +644,12 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
if (ctx->has_minicpmv_projector) {
int pos_w = image_size_width/patch_size;
int pos_h = image_size_height/patch_size;
pos_embed = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 4096, pos_w * pos_h, 1);
if (ctx->minicpmv_version == 2) {
pos_embed = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 4096, pos_w * pos_h, 1);
}
else if (ctx->minicpmv_version == 3) {
pos_embed = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 3584, pos_w * pos_h, 1);
}
ggml_set_name(pos_embed, "pos_embed");
ggml_set_input(pos_embed);
}
Expand Down Expand Up @@ -769,7 +777,8 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings);
embeddings = ggml_add(ctx0, embeddings, model.mm_2_b);

} else if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) {
}
else if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) {
embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
embeddings = ggml_add(ctx0, embeddings, model.mm_0_b);
// ggml_tensor_printf(embeddings, "mm_0_w",0,true,false);
Expand Down Expand Up @@ -987,6 +996,72 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
GGML_ASSERT(false);
}
}
// minicpmv projector
else if (ctx->has_minicpmv_projector) {
if (ctx->proj_type == PROJECTOR_TYPE_RESAMPLER) {
struct ggml_tensor * q = model.mm_model_query;
{ // layernorm
q = ggml_norm(ctx0, q, eps);
q = ggml_add(ctx0, ggml_mul(ctx0, q, model.mm_model_ln_q_w), model.mm_model_ln_q_b);
}
struct ggml_tensor *k, *v = ggml_mul_mat(ctx0, model.mm_model_kv_proj, embeddings);
{ // layernorm
v = ggml_norm(ctx0, v, eps);
v = ggml_add(ctx0, ggml_mul(ctx0, v, model.mm_model_ln_kv_w), model.mm_model_ln_kv_b);
}
{ // position
// q = ggml_add(ctx0, q, model.mm_model_pos_embed);
k = ggml_add(ctx0, v, pos_embed);
}

{ // attention
int hidden_size = 4096;
const int d_head = 128;
int n_head = hidden_size/d_head;
int num_query = 96;
if (ctx->minicpmv_version == 2) {
hidden_size = 4096;
n_head = hidden_size/d_head;
num_query = 96;
}
else if (ctx->minicpmv_version == 3) {
hidden_size = 3584;
n_head = hidden_size/d_head;
num_query = 64;
}
struct ggml_tensor * Q = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_q_w, q), model.mm_model_attn_q_b);
Q = ggml_scale_inplace(ctx0, Q, 1.0f / sqrt((float)d_head));
struct ggml_tensor * K = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_k_w, k), model.mm_model_attn_k_b);
struct ggml_tensor * V = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_v_w, v), model.mm_model_attn_v_b);
// permute
Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, num_query, batch_size);
Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3));
Q = ggml_reshape_3d(ctx0, Q, d_head, num_query, n_head * batch_size);
K = ggml_reshape_4d(ctx0, K, d_head, n_head, num_positions, batch_size);
K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3));
K = ggml_reshape_3d(ctx0, K, d_head, num_positions, n_head * batch_size);
V = ggml_reshape_4d(ctx0, V, d_head, n_head, num_positions, batch_size);
V = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3));
V = ggml_reshape_3d(ctx0, V, num_positions, d_head, n_head * batch_size);
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
KQ = ggml_soft_max_inplace(ctx0, KQ);
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ);
KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_query, n_head, batch_size);
KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
KQV = ggml_cont_3d(ctx0, KQV, hidden_size, num_query, batch_size);

embeddings = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_o_w, KQV), model.mm_model_attn_o_b);
}
{ // layernorm
embeddings = ggml_norm(ctx0, embeddings, eps);
embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_model_ln_post_w), model.mm_model_ln_post_b);
}
embeddings = ggml_mul_mat(ctx0, model.mm_model_proj, embeddings);
}
else {
GGML_ASSERT(false);
}
}

// build the graph
ggml_build_forward_expand(gf, embeddings);
Expand Down Expand Up @@ -1149,6 +1224,11 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
new_clip->has_minicpmv_projector = gguf_get_val_bool(ctx, idx);
}

idx = gguf_find_key(ctx, KEY_MINICPMV_VERSION);
if (idx != -1) {
new_clip->minicpmv_version = gguf_get_val_i32(ctx, idx);
}

// GGML_ASSERT(new_clip->has_llava_projector); // see monatis/clip.cpp for image and/or text encoding for semantic search

GGML_ASSERT(new_clip->has_vision_encoder);
Expand Down Expand Up @@ -1910,10 +1990,18 @@ int clip_uhd_num_image_embeds_col(struct clip_ctx * ctx_clip) {
// returns the normalized float tensor for llava-1.5, for spatial_unpad with anyres processing for llava-1.6 it returns the normalized image patch tensors as a vector
// res_imgs memory is being allocated here, previous allocations will be freed if found
bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch * res_imgs) {
if (clip_is_minicpmv(ctx)) {
std::vector<std::vector<clip_image_u8 *>> imgs = uhd_slice_image(img);

if(clip_is_minicpmv(ctx)){
int max_slice_nums = 9;
if (ctx->minicpmv_version == 2) {
max_slice_nums = 9;
}
else if (ctx->minicpmv_version == 3) {
max_slice_nums = 9;
}
std::vector<std::vector<clip_image_u8 *>> imgs = uhd_slice_image(img, max_slice_nums);
res_imgs->size = 0;
for (size_t i = 0; i < imgs.size(); ++i) {
for (size_t i = 0; i < imgs.size(); ++i){
res_imgs->size += imgs[i].size();
}
res_imgs->data = new clip_image_f32[res_imgs->size];
Expand Down Expand Up @@ -2146,7 +2234,12 @@ int clip_n_patches(const struct clip_ctx * ctx) {
if (ctx->proj_type == PROJECTOR_TYPE_LDP || ctx->proj_type == PROJECTOR_TYPE_LDPV2) {
n_patches /= 4;
} else if (ctx->proj_type == PROJECTOR_TYPE_RESAMPLER) {
n_patches = 96;
if (ctx->minicpmv_version == 2) {
n_patches = 96;
}
else if (ctx->minicpmv_version == 3) {
n_patches = 64;
}
}

return n_patches;
Expand Down Expand Up @@ -2282,6 +2375,11 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
const int patch_size = hparams.patch_size;
const int num_patches = ((image_size_width / patch_size) * (image_size_height / patch_size));
const int num_positions = num_patches + (ctx->has_class_embedding ? 1 : 0);
if(ctx->load_image_size==nullptr){
ctx->load_image_size= clip_image_size_init();
}
const int pos_w = ctx->load_image_size->width/patch_size;
const int pos_h = ctx->load_image_size->height/patch_size;

{
struct ggml_tensor * inp_raw = ggml_graph_get_tensor(gf, "inp_raw");
Expand Down Expand Up @@ -2316,8 +2414,18 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
// -> https://huggingface.co/HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit/blob/d66538faeba44480d0bfaa42145eef26f9423199/modeling_siglip.py#L316
struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions");
int* positions_data = (int*)malloc(ggml_nbytes(positions));
for (int i = 0; i < num_positions; i++) {
positions_data[i] = std::floor(70.0*i/num_positions);
int bucket_coords_h[70];
int bucket_coords_w[70];
for (int i = 0; i < pos_h; i++){
bucket_coords_h[i] = std::floor(70.0*i/pos_h);
}
for (int i = 0; i < pos_w; i++){
bucket_coords_w[i] = std::floor(70.0*i/pos_w);
}
for (int i = 0, id = 0; i < pos_h; i++){
for (int j = 0; j < pos_w; j++){
positions_data[id++] = bucket_coords_h[i]*70 + bucket_coords_w[j];
}
}
ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions));
free(positions_data);
Expand All @@ -2328,12 +2436,13 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
// -> https://huggingface.co/Qwen/Qwen-VL/tree/main
// -> https://huggingface.co/Qwen/Qwen-VL/blob/0547ed36a86561e2e42fecec8fd0c4f6953e33c4/visual.py#L23
struct ggml_tensor * pos_embed = ggml_graph_get_tensor(gf, "pos_embed");
if(ctx->load_image_size==nullptr){
ctx->load_image_size= clip_image_size_init();
}
int pos_w = ctx->load_image_size->width/patch_size;
int pos_h = ctx->load_image_size->height/patch_size;
int embed_dim = 4096;
if (ctx->minicpmv_version == 2) {
embed_dim = 4096;
}
else if (ctx->minicpmv_version == 3) {
embed_dim = 3584;
}
auto pos_embed_t = get_2d_sincos_pos_embed(embed_dim, std::make_pair(pos_w, pos_h));

float * pos_embed_data = (float *)malloc(ggml_nbytes(pos_embed));
Expand All @@ -2346,7 +2455,8 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
ggml_backend_tensor_set(pos_embed, pos_embed_data, 0, ggml_nbytes(pos_embed));
free(pos_embed_data);
}
} else {
}
else{
{
if (ctx->has_class_embedding) {
struct ggml_tensor * embeddings = ggml_graph_get_tensor(gf, "embeddings");
Expand Down Expand Up @@ -2548,13 +2658,21 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
return ctx->vision_model.mm_3_b->ne[0];
}
if (ctx->proj_type == PROJECTOR_TYPE_RESAMPLER) {
return 4096;
if (ctx->minicpmv_version == 2) {
return 4096;
}
else if (ctx->minicpmv_version == 3) {
return 3584;
}
}

std::string proj_type = PROJECTOR_TYPE_NAMES[ctx->proj_type];
throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str()));
}

bool clip_is_minicpmv(const struct clip_ctx * ctx) {
return ctx->has_minicpmv_projector;
int clip_is_minicpmv(const struct clip_ctx * ctx) {
if (ctx->has_minicpmv_projector) {
return ctx->minicpmv_version;
}
return 0;
}
2 changes: 1 addition & 1 deletion examples/llava/clip.h
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Original file line number Diff line number Diff line change
Expand Up @@ -85,7 +85,7 @@ CLIP_API bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, cons

CLIP_API bool clip_model_quantize(const char * fname_inp, const char * fname_out, int itype);

CLIP_API bool clip_is_minicpmv(const struct clip_ctx * ctx);
CLIP_API int clip_is_minicpmv(const struct clip_ctx * ctx);

#ifdef __cplusplus
}
Expand Down
11 changes: 9 additions & 2 deletions examples/llava/llava.cpp
View file
Original file line number Diff line number Diff line change
Expand Up @@ -254,9 +254,16 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
image_embd_v[i] = (float *)malloc(clip_embd_nbytes(ctx_clip));
int patch_size=14;
load_image_size->width = img_res_v.data[i].nx;
load_image_size->height = img_res_v.data[i].ny;
load_image_size->height = img_res_v.data[i].ny;
clip_add_load_image_size(ctx_clip, load_image_size);
const bool encoded = clip_image_encode(ctx_clip, n_threads, only_v2_5_reshape_by_patch(&img_res_v.data[i], patch_size), image_embd_v[i]);
bool encoded = false;
int has_minicpmv_projector = clip_is_minicpmv(ctx_clip);
if (has_minicpmv_projector == 2) {
encoded = clip_image_encode(ctx_clip, n_threads, only_v2_5_reshape_by_patch(&img_res_v.data[i], patch_size), image_embd_v[i]);
}
else if (has_minicpmv_projector == 3) {
encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]);
}
if (!encoded) {
LOG_TEE("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size);
return false;
Expand Down
26 changes: 23 additions & 3 deletions examples/llava/minicpmv-cli.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -134,7 +134,13 @@ static void process_image(struct llava_context * ctx_llava, struct llava_image_e
std::string system_prompt;
int idx = 0;
int num_image_embeds = embeds->n_image_pos / clip_n_patches(ctx_llava->ctx_clip);
system_prompt = "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n";
int has_minicpmv_projector = clip_is_minicpmv(ctx_llava->ctx_clip);
if (has_minicpmv_projector == 2) {
system_prompt = "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n";
}
else if (has_minicpmv_projector == 3) {
system_prompt = "<|im_start|>user\n";
}
LOG_TEE("%s: image token past: %d\n", __func__, n_past);
eval_string(ctx_llava->ctx_llama, (system_prompt+"<image>").c_str(), params->n_batch, &n_past, false);
process_eval_image_embed(ctx_llava, embeds, params->n_batch, &n_past, idx++);
Expand Down Expand Up @@ -210,10 +216,24 @@ static struct llava_context * minicpmv_init(gpt_params * params, const std::stri

static struct llama_sampling_context * llama_init(struct llava_context * ctx_llava, gpt_params * params, std::string prompt, int &n_past, bool is_first = false){
std::string user_prompt = prompt;
if (!is_first) user_prompt = "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n" + prompt;
int has_minicpmv_projector = clip_is_minicpmv(ctx_llava->ctx_clip);
if (!is_first) {
if (has_minicpmv_projector == 2) {
user_prompt = "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n" + prompt;
}
else if (has_minicpmv_projector == 3) {
user_prompt = "<|im_start|>user\n" + prompt;
}
}

eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, false);
eval_string(ctx_llava->ctx_llama, "<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", params->n_batch, &n_past, false);
if (has_minicpmv_projector == 2) {
eval_string(ctx_llava->ctx_llama, "<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", params->n_batch, &n_past, false);
}
else if (has_minicpmv_projector == 3) {
eval_string(ctx_llava->ctx_llama, "<|im_end|><|im_start|>assistant\n", params->n_batch, &n_past, false);
}

// generate the response

LOG_TEE("\n");
Expand Down
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