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update patch

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jmorganca 2024-09-25 22:09:38 -07:00
parent 055cb6b0e2
commit 22d861dfe2
1 changed files with 45 additions and 88 deletions
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133
llm/patches/0009-mllama.patch
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@ -1,4 +1,4 @@
From 9935fbbf26ad4d9ca7735ec6ba4c0a206c0c8329 Mon Sep 17 00:00:00 2001
From 52f526a86b6fdd50784678c02d8212edc2412a5b 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
@ -12,28 +12,27 @@ kv cache once per run
remaining is to implement the cross attention mask
---
include/llama.h | 5 +
src/llama.cpp | 470 ++++++++++++++++++++++++++++++++++++++++++++++--
2 files changed, 461 insertions(+), 14 deletions(-)
include/llama.h | 4 +
src/llama.cpp | 456 ++++++++++++++++++++++++++++++++++++++++++++++--
2 files changed, 447 insertions(+), 13 deletions(-)
diff --git a/include/llama.h b/include/llama.h
index bfc37e88..94ce82a4 100644
index bfc37e88..792520cc 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -449,6 +449,11 @@ extern "C" {
@@ -449,6 +449,10 @@ extern "C" {
struct llama_model * model,
struct llama_context_params params);
+ // TODO (jmorganca): this should most likely be passed in as part of a batch
+ // and not set on the context for all batches.
+ LLAMA_API void llama_set_cross_attn_state(struct llama_context * ctx, float * cross_attn_state);
+ LLAMA_API void llama_reset_cross_attn_state(struct llama_context * ctx);
+
// Frees all allocated memory
LLAMA_API void llama_free(struct llama_context * ctx);
diff --git a/src/llama.cpp b/src/llama.cpp
index b7771f53..72a57a38 100644
index b7771f53..cf70ea90 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -170,6 +170,7 @@ static std::string format(const char * fmt, ...) {
@ -124,16 +123,7 @@ index b7771f53..72a57a38 100644
{
LLM_ARCH_BAICHUAN,
{
@@ -1449,6 +1495,8 @@ static llm_arch llm_arch_from_string(const std::string & name) {
return LLM_ARCH_UNKNOWN;
}
+
+
// helper to handle gguf constants
// usage:
//
@@ -2267,6 +2315,7 @@ enum e_model {
@@ -2267,6 +2313,7 @@ enum e_model {
MODEL_40B,
MODEL_65B,
MODEL_70B,
@ -141,7 +131,7 @@ index b7771f53..72a57a38 100644
MODEL_236B,
MODEL_314B,
MODEL_SMALL,
@@ -2309,6 +2358,7 @@ struct llama_hparams {
@@ -2309,6 +2356,7 @@ struct llama_hparams {
std::array<uint32_t, LLAMA_MAX_LAYERS> n_ff_arr;
std::array<std::array<uint32_t, LLAMA_MAX_LAYERS>, 4> n_bskcn_arr;
@ -149,7 +139,7 @@ index b7771f53..72a57a38 100644
uint32_t n_layer_dense_lead = 0;
uint32_t n_lora_q = 0;
@@ -2372,10 +2422,11 @@ struct llama_hparams {
@@ -2372,10 +2420,11 @@ struct llama_hparams {
if (this->n_expert != other.n_expert) return true;
if (this->n_expert_used != other.n_expert_used) return true;
@ -165,7 +155,7 @@ index b7771f53..72a57a38 100644
if (this->n_rel_attn_bkts != other.n_rel_attn_bkts) return true;
if (this->n_layer_dense_lead != other.n_layer_dense_lead) return true;
@@ -2490,6 +2541,10 @@ struct llama_hparams {
@@ -2490,6 +2539,10 @@ struct llama_hparams {
GGML_ABORT("fatal error");
}
@ -176,7 +166,7 @@ index b7771f53..72a57a38 100644
};
static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");
@@ -2672,6 +2727,16 @@ struct llama_layer {
@@ -2672,6 +2725,16 @@ struct llama_layer {
struct ggml_tensor * ffn_down_scale;
struct ggml_tensor * bskcn_tv;
@ -193,30 +183,20 @@ index b7771f53..72a57a38 100644
};
// very similar to llama_batch,
@@ -3268,6 +3333,10 @@ struct llama_context {
// host buffer for the model output (logits and embeddings)
ggml_backend_buffer_t buf_output = nullptr;
+ // TODO (jmorganca): this should most likely be passed in as part of a batch
+ // and not set on the context for all batches.
+ float * cross_attn_state = nullptr;
+
// decode output (2-dimensional array: [n_outputs][n_vocab])
size_t logits_size = 0; // capacity (of floats) for logits
float * logits = nullptr;
@@ -3317,6 +3386,11 @@ struct llama_context {
@@ -3317,6 +3380,12 @@ struct llama_context {
struct ggml_tensor * inp_pos_bucket; // I32 [n_batch|n_kv, n_batch]
struct ggml_tensor * inp_embd_enc; // F32 [n_embd, n_outputs_enc]
struct ggml_tensor * inp_KQ_mask_cross; // F32 [n_outputs_enc, n_batch]
+
+ // TODO (jmorganca): this should most likely be passed in via
+ // the input. Ideally we remove this state from llama_context
+ // TODO (jmorganca): this should most likely be passed in as part of a batch
+ // and not set on the context for all batches.
+ float * cross_attn_state = nullptr;
+ bool cross_attn_state_first_pass = true;
+ struct ggml_tensor * inp_cross_attn_state; // F32 [4, n_embd, 1061]
};
struct llama_lora_weight {
@@ -3543,6 +3617,18 @@ static bool llama_kv_cache_init(
@@ -3543,6 +3612,18 @@ static bool llama_kv_cache_init(
cache.v_l.reserve(n_layer);
for (int i = 0; i < (int) n_layer; i++) {
@ -235,7 +215,7 @@ index b7771f53..72a57a38 100644
const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i) + hparams.n_embd_k_s();
const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i) + hparams.n_embd_v_s();
@@ -5312,12 +5398,14 @@ static void llm_load_hparams(
@@ -5312,12 +5393,14 @@ static void llm_load_hparams(
}
// zero-out the per-layer hparams
@ -255,7 +235,7 @@ index b7771f53..72a57a38 100644
// n_head_kv is optional, default to n_head
hparams.n_head_kv_arr = hparams.n_head_arr;
@@ -5366,7 +5454,7 @@ static void llm_load_hparams(
@@ -5366,7 +5449,7 @@ static void llm_load_hparams(
ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false);
@ -264,7 +244,7 @@ index b7771f53..72a57a38 100644
if (hparams.n_rot != hparams.n_embd_head_k) {
throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd_head_k));
}
@@ -5404,6 +5492,16 @@ static void llm_load_hparams(
@@ -5404,6 +5487,16 @@ static void llm_load_hparams(
}
}
} break;
@ -281,7 +261,7 @@ index b7771f53..72a57a38 100644
case LLM_ARCH_MINICPM:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@@ -6918,6 +7016,55 @@ static bool llm_load_tensors(
@@ -6918,6 +7011,55 @@ static bool llm_load_tensors(
}
}
} break;
@ -337,7 +317,7 @@ index b7771f53..72a57a38 100644
case LLM_ARCH_GROK:
{
if (n_expert == 0) {
@@ -8678,7 +8825,7 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam
@@ -8678,7 +8820,7 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam
if (model.vocab.type != LLAMA_VOCAB_TYPE_NONE &&
model.hparams.n_vocab != model.vocab.id_to_token.size()) {
@ -346,15 +326,16 @@ index b7771f53..72a57a38 100644
}
if (params.vocab_only) {
@@ -8754,7 +8901,6 @@ static struct ggml_tensor * llm_build_inp_embd(
if (batch.token) {
lctx.inp_tokens = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, batch.n_tokens);
- cb(lctx.inp_tokens, "inp_tokens", -1);
ggml_set_input(lctx.inp_tokens);
@@ -8759,7 +8901,7 @@ static struct ggml_tensor * llm_build_inp_embd(
inpL = ggml_get_rows(ctx, tok_embd, lctx.inp_tokens);
@@ -8769,6 +8915,22 @@ static struct ggml_tensor * llm_build_inp_embd(
} else {
- lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens);
+ lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens);
inpL = lctx.inp_embd;
ggml_set_input(lctx.inp_embd);
}
@@ -8769,6 +8911,22 @@ static struct ggml_tensor * llm_build_inp_embd(
return inpL;
}
@ -377,15 +358,7 @@ index b7771f53..72a57a38 100644
static void llm_build_kv_store(
struct ggml_context * ctx,
const llama_hparams & hparams,
@@ -8790,6 +8952,7 @@ static void llm_build_kv_store(
struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, kv.k_l[il], n_tokens*n_embd_k_gqa, ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa)*kv_head);
cb(k_cache_view, "k_cache_view", il);
+ cb(k_cur, "k_cur", il);
// note: storing RoPE-ed version of K in the KV cache
ggml_build_forward_expand(graph, ggml_cpy(ctx, k_cur, k_cache_view));
@@ -9743,6 +9906,7 @@ struct llm_build_context {
@@ -9743,6 +9901,7 @@ struct llm_build_context {
lctx.inp_pos_bucket = nullptr;
lctx.inp_embd_enc = nullptr;
lctx.inp_KQ_mask_cross = nullptr;
@ -393,7 +366,7 @@ index b7771f53..72a57a38 100644
}
void free() {
@@ -10158,6 +10322,253 @@ struct llm_build_context {
@@ -10158,6 +10317,253 @@ struct llm_build_context {
LLM_NORM_RMS, cb, -1);
cb(cur, "result_norm", -1);
@ -647,7 +620,7 @@ index b7771f53..72a57a38 100644
// lm_head
cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
cb(cur, "result_output", -1);
@@ -15493,6 +15904,10 @@ static struct ggml_cgraph * llama_build_graph(
@@ -15493,6 +15899,10 @@ static struct ggml_cgraph * llama_build_graph(
{
result = llm.build_llama();
} break;
@ -658,31 +631,22 @@ index b7771f53..72a57a38 100644
case LLM_ARCH_BAICHUAN:
{
result = llm.build_baichuan();
@@ -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;
-
ggml_backend_tensor_set(lctx.inp_tokens, batch.token, 0, n_tokens*ggml_element_size(lctx.inp_tokens));
@@ -15753,6 +16163,14 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
ggml_backend_tensor_set(lctx.inp_pos, batch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos));
}
@@ -16123,6 +16537,15 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
}
}
}
+
+ // 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 &&
+ if (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 +16853,10 @@ static int llama_decode_internal(
+
if (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE) {
GGML_ASSERT(lctx.inp_out_ids && "every model that can must skip unused outputs");
const int64_t n_tokens = batch.n_tokens;
@@ -16430,6 +16848,10 @@ static int llama_decode_internal(
llama_set_inputs(lctx, ubatch);
@ -693,7 +657,7 @@ index b7771f53..72a57a38 100644
llama_graph_compute(lctx, gf, n_threads, threadpool);
// update the kv ring buffer
@@ -17586,7 +18013,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
@@ -17586,7 +18008,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
if (llama_model_has_encoder(&model)) {
n_attn_layer *= 3;
}
@ -704,26 +668,19 @@ index b7771f53..72a57a38 100644
}
size_t total_size_org = 0;
@@ -18681,6 +19110,18 @@ struct llama_context * llama_new_context_with_model(
@@ -18681,6 +19105,11 @@ struct llama_context * llama_new_context_with_model(
return ctx;
}
+void llama_set_cross_attn_state(struct llama_context * ctx, float * cross_attn_state) {
+ ctx->cross_attn_state = cross_attn_state;
+}
+
+void llama_reset_cross_attn_state(struct llama_context * ctx) {
+ ctx->cross_attn_state_first_pass = true;
+ if (ctx->cross_attn_state) {
+ free(ctx->cross_attn_state);
+ ctx->cross_attn_state = nullptr;
+ }
+ ctx->cross_attn_state = cross_attn_state;
+}
+
void llama_free(struct llama_context * ctx) {
delete ctx;
}
@@ -18731,6 +19172,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
@@ -18731,6 +19160,7 @@ 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: