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This commit is contained in:
Josh Yan 2024-08-20 13:38:33 -07:00
parent edeea1d6f0
commit ce78e400c2
3 changed files with 206 additions and 100 deletions
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25
llm/ext_server/server.cpp vendored
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@ -1285,13 +1285,20 @@ struct llama_server_context
data[i] = data[i] / sqrt(2048);
}
set_image_embeds(ctx, data);
if (ctx)
{
set_image_embeds(ctx, data);
print_image_embeds(ctx);
}
else
{
printf("ctx is null");
}
// generate user_prompt -> this should contain image tokens prepended and a new line appended:
// batch.n_tokens += (int)slot.images.size() * llama_n_embd(model);
std::vector<llama_token> tokens;
std::string prompt = "What is in this image";
std::string prompt = "What is this image";
std::vector<llama_token> text = ::llama_tokenize(ctx, prompt, false, true);
for (int i = 0; i < (int)slot.images.size() * 256; i++)
@ -1301,8 +1308,6 @@ struct llama_server_context
tokens.push_back(2);
printf("btach.n_tokens %d\n", batch.n_tokens);
for (int i = 0; i < text.size(); i++)
{
// printf("token [%d]: %d\n", text[i]);
@ -1312,6 +1317,7 @@ struct llama_server_context
tokens.push_back(108);
batch.n_tokens = (int)slot.images.size() * 256 + 2 + text.size();
printf("btach.n_tokens %d\n", batch.n_tokens);
for (int i = 0; i < batch.n_tokens; i++)
{
@ -1327,12 +1333,14 @@ struct llama_server_context
n_eval = n_batch;
}
printf("n_eval: %d, n_past: %d", n_eval, n_past);
llama_set_causal_attn(ctx, false);
if (llama_decode(ctx, llama_batch_get_one(&tokens[i], n_eval, 0, 0)))
{
printf("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, batch.n_tokens, n_batch, n_past);
return false;
}
n_past += n_eval;
llama_set_causal_attn(ctx, true);
slot.n_past += n_eval;
}
return true;
}
@ -1878,6 +1886,11 @@ struct llama_server_context
// process the prefix of first image
std::vector<llama_token> prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, add_bos_token) : prompt_tokens;
printf("\nprinting prefix tokens");
for (int i = 0; i < prefix_tokens.size(); i++)
{
printf("prefix token[%d]: %d", i, prefix_tokens[i]);
}
int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past;

94
llm/patches/12-paligemma.diff
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@ -1,94 +0,0 @@
diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp
index 7cda5f10..50fbcf08 100644
--- a/examples/llava/clip.cpp
+++ b/examples/llava/clip.cpp
@@ -709,9 +709,12 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
embeddings = ggml_add(ctx0, embeddings, model.mm_0_b);
- embeddings = ggml_gelu(ctx0, embeddings);
- embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings);
- embeddings = ggml_add(ctx0, embeddings, model.mm_2_b);
+ // paligemma missing second linear layer
+ if (model.mm_2_w) {
+ embeddings = ggml_gelu(ctx0, embeddings);
+ 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) {
embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
@@ -2076,7 +2079,10 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
return ctx->vision_model.mm_model_peg_0_b->ne[0];
}
if (ctx->proj_type == PROJECTOR_TYPE_MLP) {
- return ctx->vision_model.mm_2_b->ne[0];
+ // paligemma missing second linear layer
+ if (ctx->vision_model.mm_2_b == nullptr) {
+ return ctx->vision_model.mm_0_b->ne[0];
+ }
}
if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) {
return ctx->vision_model.mm_3_b->ne[0];
diff --git a/include/llama.h b/include/llama.h
index f23355a6..7c6301bf 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -444,6 +444,9 @@ extern "C" {
// Frees all allocated memory
LLAMA_API void llama_free(struct llama_context * ctx);
+ // save image embeddings
+ LLAMA_API void set_image_embeds(struct llama_context *ctx, float *data);
+
LLAMA_API int64_t llama_time_us(void);
LLAMA_API size_t llama_max_devices(void);
diff --git a/src/llama.cpp b/src/llama.cpp
index a7b1c9eb..b0a6bc27 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -2668,6 +2668,7 @@ struct llama_context {
const struct llama_model & model;
+ float *image_embeds;
struct llama_cparams cparams;
struct llama_sampling sampling;
struct llama_kv_cache kv_self;
@@ -2751,6 +2752,10 @@ struct llama_context {
struct ggml_tensor * inp_KQ_mask_cross; // F32 [n_outputs_enc, n_batch]
};
+void set_image_embeds(llama_context *ctx, float *data) {
+ ctx->image_embeds = data;
+}
+
struct llama_lora_weight {
struct ggml_tensor * a = nullptr;
struct ggml_tensor * b = nullptr;
@@ -11599,6 +11604,15 @@ struct llm_build_context {
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+ // set the image embeddings in the input tensor
+ if (lctx.image_embeds) {
+ struct ggml_tensor *image_embeds = ggml_dup_tensor(ctx0, inpL);
+ image_embeds->data = lctx.image_embeds;
+ image_embeds->ne[1] = 256;
+ inpL = ggml_set_2d_inplace(ctx0, inpL, image_embeds, inpL->nb[1], 0);
+ lctx.image_embeds = NULL;
+ }
+
inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
cb(inpL, "inp_scaled", -1);
@@ -14589,7 +14603,7 @@ static int llama_decode_internal(
}
// non-causal masks do not use the KV cache
- if (hparams.causal_attn) {
+ if (hparams.causal_attn || lctx.image_embeds) {
llama_kv_cache_update(&lctx);
// if we have enough unused cells before the current head ->

187
llm/patches/13-paligemma2.diff Normal file
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@ -0,0 +1,187 @@
diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp
index 54aa822c..45d03982 100644
--- a/examples/llava/clip.cpp
+++ b/examples/llava/clip.cpp
@@ -765,9 +765,12 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
embeddings = ggml_add(ctx0, embeddings, model.mm_0_b);
- embeddings = ggml_gelu(ctx0, embeddings);
- embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings);
- embeddings = ggml_add(ctx0, embeddings, model.mm_2_b);
+ // paligemma missing second linear layer
+ if (model.mm_2_w) {
+ embeddings = ggml_gelu(ctx0, embeddings);
+ 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) {
embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
@@ -2542,7 +2545,10 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
return ctx->vision_model.mm_model_peg_0_b->ne[0];
}
if (ctx->proj_type == PROJECTOR_TYPE_MLP) {
- return ctx->vision_model.mm_2_b->ne[0];
+ // paligemma missing second linear layer
+ if (ctx->vision_model.mm_2_b == nullptr) {
+ return ctx->vision_model.mm_0_b->ne[0];
+ }
}
if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) {
return ctx->vision_model.mm_3_b->ne[0];
diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp
index 8c7dd2ae..aeff49ad 100644
--- a/examples/llava/llava-cli.cpp
+++ b/examples/llava/llava-cli.cpp
@@ -36,6 +36,7 @@ static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) {
static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){
std::string str2 = str;
std::vector<llama_token> embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos, true);
+ embd_inp.push_back(108);
eval_tokens(ctx_llama, embd_inp, n_batch, n_past);
return true;
}
@@ -183,9 +184,17 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
}
}
- eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, true);
- llava_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past);
- eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, false);
+ // build user prompt with 256 image tokens
+ user_prompt = "What is this image";
+ std::string image_token_prefix = "";
+ for (int i = 0; i < 256; i++) {
+ image_token_prefix += "<image>";
+ }
+ std::string user_prompt_with_images = image_token_prefix + "<bos>" + user_prompt;
+
+ llama_set_causal_attn(ctx_llava->ctx_llama, false);
+ eval_string(ctx_llava->ctx_llama, user_prompt_with_images.c_str(), params->n_batch, &n_past, false);
+ llama_set_causal_attn(ctx_llava->ctx_llama, true);
// generate the response
@@ -324,6 +333,19 @@ int main(int argc, char ** argv) {
return 1;
}
+ if (!image_embed || !image_embed->embed) {
+ std::cerr << "Error: image_embed or image_embed->embed is null." << std::endl;
+ return 1;
+ }
+
+ // image feature scaling
+ float *data = image_embed->embed;
+ for (int i = 0; i < 2048 * 256; i++) {
+ data[i] = data[i] / sqrt(2048);
+ }
+
+ set_image_embeds(ctx_llava->ctx_llama, image_embed->embed);
+
// process the prompt
process_prompt(ctx_llava, image_embed, &params, params.prompt);
diff --git a/include/llama.h b/include/llama.h
index ce07f4fa..6a376d7b 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -444,6 +444,11 @@ extern "C" {
// Frees all allocated memory
LLAMA_API void llama_free(struct llama_context * ctx);
+ // save image embeddings
+ LLAMA_API void set_image_embeds(struct llama_context *ctx, float *data);
+
+ LLAMA_API void print_image_embeds(struct llama_context *ctx);
+
LLAMA_API int64_t llama_time_us(void);
LLAMA_API size_t llama_max_devices(void);
diff --git a/src/llama.cpp b/src/llama.cpp
index 7f2f0003..f894611a 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -2677,6 +2677,7 @@ struct llama_context {
const struct llama_model & model;
+ float *image_embeds = nullptr;
struct llama_cparams cparams;
struct llama_sampling sampling;
struct llama_kv_cache kv_self;
@@ -2760,6 +2761,22 @@ struct llama_context {
struct ggml_tensor * inp_KQ_mask_cross; // F32 [n_outputs_enc, n_batch]
};
+void set_image_embeds(llama_context *ctx, float *data) {
+ ctx->image_embeds = data;
+ LLAMA_LOG_INFO("image_embeds set");
+}
+
+void print_image_embeds(llama_context *ctx)
+{
+ if (ctx->image_embeds)
+ {
+ for (int i = 0; i < 256; i++)
+ {
+ LLAMA_LOG_INFO("%f ", ctx->image_embeds[i]);
+ }
+ }
+}
+
struct llama_lora_weight {
struct ggml_tensor * a = nullptr;
struct ggml_tensor * b = nullptr;
@@ -11651,15 +11668,32 @@ struct llm_build_context {
}
struct ggml_cgraph * build_gemma() {
+ LLAMA_LOG_INFO("ENTERED BUILD_GEMMA\n");
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
const int64_t n_embd_head_k = hparams.n_embd_head_k;
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
+ LLAMA_LOG_INFO("%s: %s\n", __func__, "checking that embeds exist before building inpL, this should work for paligemma");
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+ // set the image embeddings in the input tensor
+ if (lctx.image_embeds)
+ {
+ LLAMA_LOG_INFO("%s: %s\n", __func__, "checking that embeds exist, this should work for paligemma");
+ struct ggml_tensor *image_embeds = ggml_dup_tensor(ctx0, inpL);
+ image_embeds->data = lctx.image_embeds;
+ image_embeds->ne[1] = 256;
+ inpL = ggml_set_2d_inplace(ctx0, inpL, image_embeds, inpL->nb[1], 0);
+ lctx.image_embeds = NULL;
+ for (int i = 0; i < 20; i++)
+ {
+ LLAMA_LOG_INFO("%s: t->data %f\n", __func__, ((float *)image_embeds->data)[i]);
+ }
+ }
+
inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
cb(inpL, "inp_scaled", -1);
@@ -13842,7 +13876,7 @@ static struct ggml_cgraph * llama_build_graph(
struct ggml_cgraph * result = NULL;
struct llm_build_context llm(lctx, batch, cb, worst_case);
-
+ LLAMA_LOG_INFO("%s: running llm arch = %d", __func__, model.arch);
llm.init();
switch (model.arch) {
@@ -14678,7 +14712,7 @@ static int llama_decode_internal(
}
// non-causal masks do not use the KV cache
- if (hparams.causal_attn) {
+ if (hparams.causal_attn || lctx.image_embeds) {
llama_kv_cache_update(&lctx);
// if we have enough unused cells before the current head ->