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llm: add server entrypoint for mllama

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jmorganca 2024-09-25 14:37:28 -07:00
parent 8ac915f709
commit d0c8ce5ea4
2 changed files with 28 additions and 74 deletions
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12
llm/ext_server/server.cpp vendored
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@ -1032,6 +1032,18 @@ struct llama_server_context
bool process_images(server_slot &slot) const
{
// Set cross attention state for mllama models
// TODO (jmorganca): this should be provided via the API
// TODO (jmorganca): generalize this beyond mllama models
char arch_str[256];
llama_model_meta_val_str(model, "general.architecture", arch_str, 256);
if (strcmp(arch_str, "mllama") == 0) {
// TODO (jmorganca): this should be passed in via the llama_decode api
// or similar, maybe using the llama_batch struct
// llama_reset_cross_attn_state(ctx);
// llama_set_cross_attn_state(ctx, (float*)cross_attn_state);
}
for (slot_image &img : slot.images)
{
if (!img.request_encode_image)

90
llm/patches/0009-mllama.patch
View File

@ -1,4 +1,4 @@
From c2db1ad0fc86de189959b628021a970511e9c6f9 Mon Sep 17 00:00:00 2001
From 9935fbbf26ad4d9ca7735ec6ba4c0a206c0c8329 Mon Sep 17 00:00:00 2001
From: jmorganca <jmorganca@gmail.com>
Date: Tue, 24 Sep 2024 11:53:40 -0700
Subject: [PATCH] add mllama support
@ -13,8 +13,8 @@ kv cache once per run
remaining is to implement the cross attention mask
---
include/llama.h | 5 +
src/llama.cpp | 514 ++++++++++++++++++++++++++++++++++++++++++++++--
2 files changed, 499 insertions(+), 20 deletions(-)
src/llama.cpp | 470 ++++++++++++++++++++++++++++++++++++++++++++++--
2 files changed, 461 insertions(+), 14 deletions(-)
diff --git a/include/llama.h b/include/llama.h
index bfc37e88..94ce82a4 100644
@ -33,7 +33,7 @@ index bfc37e88..94ce82a4 100644
LLAMA_API void llama_free(struct llama_context * ctx);
diff --git a/src/llama.cpp b/src/llama.cpp
index b7771f53..75bbc226 100644
index b7771f53..72a57a38 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -170,6 +170,7 @@ static std::string format(const char * fmt, ...) {
@ -193,25 +193,6 @@ index b7771f53..75bbc226 100644
};
// very similar to llama_batch,
@@ -2684,12 +2749,12 @@ struct llama_ubatch {
uint32_t n_seq_tokens; // tokens per sequence
uint32_t n_seqs;
- llama_token * token; // [n_tokens]
- float * embd; // [n_embd, n_tokens]
- llama_pos * pos; // [n_tokens]
- int32_t * n_seq_id; // [n_seqs]
- llama_seq_id ** seq_id; // [n_seqs]
- int8_t * output; // [n_tokens]
+ llama_token * token; // [n_tokens]
+ float * embd; // [n_embd, n_tokens]
+ llama_pos * pos; // [n_tokens]
+ int32_t * n_seq_id; // [n_seqs]
+ llama_seq_id ** seq_id; // [n_seqs]
+ int8_t * output; // [n_tokens]
};
struct llama_kv_cell {
@@ -3268,6 +3333,10 @@ struct llama_context {
// host buffer for the model output (logits and embeddings)
ggml_backend_buffer_t buf_output = nullptr;
@ -404,48 +385,7 @@ index b7771f53..75bbc226 100644
// note: storing RoPE-ed version of K in the KV cache
ggml_build_forward_expand(graph, ggml_cpy(ctx, k_cur, k_cache_view));
@@ -9625,6 +9788,40 @@ static struct ggml_tensor * llm_build_rwkv6_channel_mix(
return ggml_mul(ctx, r, llm_build_lora_mm(lctx, ctx, layer->channel_mix_value, k));
}
+
+static void show_tensor(std::string name, ggml_tensor *t) {
+ LLAMA_LOG_INFO("%s [%lld, %lld]\n", name.c_str(), t->ne[0], t->ne[1]);
+
+ int cols = int(t->ne[0]);
+ int rows = int(t->ne[1]);
+
+ for(int r=0; r<3; r++) {
+ for(int c=0; c<3; c++) {
+ float v = ggml_get_f32_nd(t, c, r, 0, 0);
+ LLAMA_LOG_INFO("%11.8f ", v);
+ }
+ LLAMA_LOG_INFO("... ");
+ for(int c=0; c<3; c++) {
+ float v = ggml_get_f32_nd(t, cols-3+c, r, 0, 0);
+ LLAMA_LOG_INFO("%11.8f ", v);
+ }
+ LLAMA_LOG_INFO("\n");
+ }
+ LLAMA_LOG_INFO(" ...\n");
+ for(int r=0; r<3; r++) {
+ for(int c=0; c<3; c++) {
+ float v = ggml_get_f32_nd(t, c, rows-3+r, 0, 0);
+ LLAMA_LOG_INFO("%11.8f ", v);
+ }
+ LLAMA_LOG_INFO("... ");
+ for(int c=0; c<3; c++) {
+ float v = ggml_get_f32_nd(t, cols-3+c, rows-3+r, 0, 0);
+ LLAMA_LOG_INFO("%11.8f ", v);
+ }
+ LLAMA_LOG_INFO("\n");
+ }
+}
+
struct llm_build_context {
const llama_model & model;
llama_context & lctx;
@@ -9743,6 +9940,7 @@ struct llm_build_context {
@@ -9743,6 +9906,7 @@ struct llm_build_context {
lctx.inp_pos_bucket = nullptr;
lctx.inp_embd_enc = nullptr;
lctx.inp_KQ_mask_cross = nullptr;
@ -453,7 +393,7 @@ index b7771f53..75bbc226 100644
}
void free() {
@@ -10158,6 +10356,253 @@ struct llm_build_context {
@@ -10158,6 +10322,253 @@ struct llm_build_context {
LLM_NORM_RMS, cb, -1);
cb(cur, "result_norm", -1);
@ -707,7 +647,7 @@ index b7771f53..75bbc226 100644
// lm_head
cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
cb(cur, "result_output", -1);
@@ -15493,6 +15938,10 @@ static struct ggml_cgraph * llama_build_graph(
@@ -15493,6 +15904,10 @@ static struct ggml_cgraph * llama_build_graph(
{
result = llm.build_llama();
} break;
@ -718,7 +658,7 @@ index b7771f53..75bbc226 100644
case LLM_ARCH_BAICHUAN:
{
result = llm.build_baichuan();
@@ -15736,7 +16185,6 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
@@ -15736,7 +16151,6 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
if (batch.token) {
const int64_t n_tokens = batch.n_tokens;
@ -726,7 +666,7 @@ index b7771f53..75bbc226 100644
ggml_backend_tensor_set(lctx.inp_tokens, batch.token, 0, n_tokens*ggml_element_size(lctx.inp_tokens));
}
@@ -16123,6 +16571,13 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
@@ -16123,6 +16537,15 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
}
}
}
@ -734,13 +674,15 @@ index b7771f53..75bbc226 100644
+ // TODO (jmorganca): this might copy a lot of data on every request of a
+ // single generation even though it doesn't change, so we should
+ // find a way to not set this more than one time per image
+ if (lctx.cross_attn_state && lctx.inp_cross_attn_state->buffer) {
+ if (lctx.cross_attn_state &&
+ lctx.inp_cross_attn_state &&
+ lctx.inp_cross_attn_state->buffer) {
+ ggml_backend_tensor_set(lctx.inp_cross_attn_state, lctx.cross_attn_state, 0, hparams.n_embd * 1601 * 4 * ggml_element_size(lctx.inp_cross_attn_state));
+ }
}
// Make sure enough space is available for outputs.
@@ -16430,6 +16885,10 @@ static int llama_decode_internal(
@@ -16430,6 +16853,10 @@ static int llama_decode_internal(
llama_set_inputs(lctx, ubatch);
@ -751,7 +693,7 @@ index b7771f53..75bbc226 100644
llama_graph_compute(lctx, gf, n_threads, threadpool);
// update the kv ring buffer
@@ -17586,7 +18045,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
@@ -17586,7 +18013,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
if (llama_model_has_encoder(&model)) {
n_attn_layer *= 3;
}
@ -762,7 +704,7 @@ index b7771f53..75bbc226 100644
}
size_t total_size_org = 0;
@@ -18681,6 +19142,18 @@ struct llama_context * llama_new_context_with_model(
@@ -18681,6 +19110,18 @@ struct llama_context * llama_new_context_with_model(
return ctx;
}
@ -781,7 +723,7 @@ index b7771f53..75bbc226 100644
void llama_free(struct llama_context * ctx) {
delete ctx;
}
@@ -18731,6 +19204,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
@@ -18731,6 +19172,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
// use what we call a normal RoPE, operating on pairs of consecutive head values
case LLM_ARCH_LLAMA: