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llava : support for Yi-VL and fix for mobileVLM (#5093)

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* Support for Yi-VL, templating fix for mobileVLM

* ws

* Update examples/llava/clip.cpp

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Update llava-cli.cpp

* Update clip.cpp

bugfix for new conversions

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
John 2024-01-27 16:09:18 +01:00 committed by GitHub
parent 753eafed0e
commit 6db2b41a76
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2 changed files with 92 additions and 10 deletions
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70
examples/llava/clip.cpp
View File

@ -98,6 +98,7 @@ static std::string format(const char * fmt, ...) {
enum projector_type { enum projector_type {
PROJECTOR_TYPE_MLP, PROJECTOR_TYPE_MLP,
PROJECTOR_TYPE_MLP_NORM,
PROJECTOR_TYPE_LDP, PROJECTOR_TYPE_LDP,
PROJECTOR_TYPE_UNKNOWN, PROJECTOR_TYPE_UNKNOWN,
}; };
@ -304,10 +305,18 @@ struct clip_vision_model {
struct ggml_tensor * projection; struct ggml_tensor * projection;
// LLaVA projection // LLaVA projection
struct ggml_tensor * mm_0_w; struct ggml_tensor * mm_0_w = NULL;
struct ggml_tensor * mm_0_b; struct ggml_tensor * mm_0_b = NULL;
struct ggml_tensor * mm_2_w; struct ggml_tensor * mm_2_w = NULL;
struct ggml_tensor * mm_2_b; struct ggml_tensor * mm_2_b = NULL;
// Yi type models with mlp+normalization projection
struct ggml_tensor * mm_1_w = NULL; // Yi type models have 0, 1, 3, 4
struct ggml_tensor * mm_1_b = NULL;
struct ggml_tensor * mm_3_w = NULL;
struct ggml_tensor * mm_3_b = NULL;
struct ggml_tensor * mm_4_w = NULL;
struct ggml_tensor * mm_4_b = NULL;
// MobileVLM projection // MobileVLM projection
struct ggml_tensor * mm_model_mlp_1_w; struct ggml_tensor * mm_model_mlp_1_w;
@ -460,6 +469,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
// pre-layernorm // pre-layernorm
{ {
embeddings = ggml_norm(ctx0, embeddings, eps); embeddings = ggml_norm(ctx0, embeddings, eps);
ggml_set_name(embeddings, "pre_ln");
embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.pre_ln_w), model.pre_ln_b); embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.pre_ln_w), model.pre_ln_b);
} }
@ -575,6 +585,27 @@ 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_mul_mat(ctx0, model.mm_2_w, embeddings);
embeddings = ggml_add(ctx0, embeddings, model.mm_2_b); embeddings = ggml_add(ctx0, embeddings, model.mm_2_b);
} 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);
// First LayerNorm
embeddings = ggml_norm(ctx0, embeddings, eps);
embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_1_w),
model.mm_1_b);
// GELU activation
embeddings = ggml_gelu(ctx0, embeddings);
// Second linear layer
embeddings = ggml_mul_mat(ctx0, model.mm_3_w, embeddings);
embeddings = ggml_add(ctx0, embeddings, model.mm_3_b);
// Second LayerNorm
embeddings = ggml_norm(ctx0, embeddings, eps);
embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_4_w),
model.mm_4_b);
} }
else if (ctx->proj_type == PROJECTOR_TYPE_LDP) { else if (ctx->proj_type == PROJECTOR_TYPE_LDP) {
// MobileVLM projector // MobileVLM projector
@ -808,6 +839,11 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
else { else {
new_clip->proj_type = PROJECTOR_TYPE_MLP; new_clip->proj_type = PROJECTOR_TYPE_MLP;
} }
if (new_clip->proj_type == PROJECTOR_TYPE_MLP) {
if (gguf_find_tensor(ctx, format(TN_LLAVA_PROJ, 3, "weight").c_str()) != -1) {
new_clip->proj_type = PROJECTOR_TYPE_MLP_NORM;
}
}
} }
#ifdef GGML_USE_CUBLAS #ifdef GGML_USE_CUBLAS
@ -956,11 +992,29 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias")); vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias"));
// LLaVA projection // LLaVA projection
if (new_clip->proj_type == PROJECTOR_TYPE_MLP) { if (new_clip->proj_type == PROJECTOR_TYPE_MLP || new_clip->proj_type == PROJECTOR_TYPE_MLP_NORM) {
vision_model.mm_0_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "weight")); vision_model.mm_0_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "weight"));
vision_model.mm_0_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "bias")); vision_model.mm_0_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "bias"));
vision_model.mm_2_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "weight")); try {
vision_model.mm_2_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias")); // Yi-type llava
vision_model.mm_1_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 1, "weight"));
vision_model.mm_1_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 1, "bias"));
} catch (std::runtime_error & e) { }
try {
// missing in Yi-type llava
vision_model.mm_2_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "weight"));
vision_model.mm_2_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias"));
} catch (std::runtime_error & e) { }
try {
// Yi-type llava
vision_model.mm_3_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 3, "weight"));
vision_model.mm_3_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 3, "bias"));
} catch (std::runtime_error & e) { }
try {
// Yi-type llava
vision_model.mm_4_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 4, "weight"));
vision_model.mm_4_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 4, "bias"));
} catch (std::runtime_error & e) { }
} }
else if (new_clip->proj_type == PROJECTOR_TYPE_LDP) { else if (new_clip->proj_type == PROJECTOR_TYPE_LDP) {
// MobileVLM projection // MobileVLM projection
@ -1432,6 +1486,8 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
} }
else if (ctx->proj_type == PROJECTOR_TYPE_MLP) { else if (ctx->proj_type == PROJECTOR_TYPE_MLP) {
return ctx->vision_model.mm_2_b->ne[0]; return ctx->vision_model.mm_2_b->ne[0];
} else if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) {
return ctx->vision_model.mm_3_b->ne[0];
} }
else { else {
std::string proj_type = PROJECTOR_TYPE_NAMES[ctx->proj_type]; std::string proj_type = PROJECTOR_TYPE_NAMES[ctx->proj_type];

32
examples/llava/llava-cli.cpp
View File

@ -148,10 +148,35 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict; const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict;
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx_llava->ctx_llama)); const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx_llava->ctx_llama));
// llava chat format is "<system_prompt>\nUSER:<image_embeddings>\n<textual_prompt>\nASSISTANT:" std::string system_prompt, user_prompt;
eval_string(ctx_llava->ctx_llama, "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:", params->n_batch, &n_past, add_bos); size_t image_pos = prompt.find("<image>");
if (image_pos != std::string::npos) {
// new templating mode: Provide the full prompt including system message and use <image> as a placeholder for the image
system_prompt = prompt.substr(0, image_pos);
user_prompt = prompt.substr(image_pos + std::string("<image>").length());
// We replace \n with actual newlines in user_prompt, just in case -e was not used in templating string
size_t pos = 0;
while ((pos = user_prompt.find("\\n", pos)) != std::string::npos) {
user_prompt.replace(pos, 2, "\n");
pos += 1; // Advance past the replaced newline
}
while ((pos = system_prompt.find("\\n", pos)) != std::string::npos) {
system_prompt.replace(pos, 2, "\n");
pos += 1; // Advance past the replaced newline
}
printf("system_prompt: %s\n", system_prompt.c_str());
printf("user_prompt: %s\n", user_prompt.c_str());
} else {
// llava-1.5 native mode
system_prompt = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:";
user_prompt = prompt + "\nASSISTANT:";
}
eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, add_bos);
llava_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past); llava_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past);
eval_string(ctx_llava->ctx_llama, (prompt + "\nASSISTANT:").c_str(), params->n_batch, &n_past, false); eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, false);
// generate the response // generate the response
@ -162,6 +187,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
for (int i = 0; i < max_tgt_len; i++) { for (int i = 0; i < max_tgt_len; i++) {
const char * tmp = sample(ctx_sampling, ctx_llava->ctx_llama, &n_past); const char * tmp = sample(ctx_sampling, ctx_llava->ctx_llama, &n_past);
if (strcmp(tmp, "</s>") == 0) break; if (strcmp(tmp, "</s>") == 0) break;
if (strstr(tmp, "###")) break; // Yi-VL behavior
printf("%s", tmp); printf("%s", tmp);
fflush(stdout); fflush(stdout);