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add server.cpp and patches

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jmorganca 2024-09-26 23:04:06 -07:00 committed by Patrick Devine
parent 5486c57364
commit 7d5e0ff80e
2 changed files with 721 additions and 0 deletions
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28
llm/ext_server/server.cpp vendored
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@ -173,6 +173,8 @@ struct server_slot {
double t_prompt_processing; // ms
double t_token_generation; // ms
float *cross_attn_state = nullptr;
// multitasks
int multitask_id = -1;
@ -200,6 +202,11 @@ struct server_slot {
img.prefix_prompt = "";
}
if (cross_attn_state) {
free(cross_attn_state);
cross_attn_state = nullptr;
}
images.clear();
}
@ -731,6 +738,27 @@ struct llama_server_context
{
const std::vector<uint8_t> image_buffer = base64_decode(img["data"].get<std::string>());
// Check for mllama architecture, which processes images differently than llava
char arch_str[256];
llama_model_meta_val_str(model, "general.architecture", arch_str, 256);
bool is_mllama = strcmp(arch_str, "mllama") == 0;
if (is_mllama) {
LOG_INFO("MLLAMA architecture detected, processing first image", {{"slot_id", slot->id}});
struct clip_image_f32 *img = clip_image_f32_init();
clip_image_load_from_data(image_buffer.data(), image_buffer.size(), 560, 560, 3, 4, img);
const int n = clip_embd_nbytes(clp_ctx);
printf("%s: nbytes %d\n", __func__, n);
slot->cross_attn_state = (float *)malloc(n);
printf("%s: nbytes %d image_embd: %p\n", __func__, n, slot->cross_attn_state);
clip_image_encode(clp_ctx, 1, img, slot->cross_attn_state);
llama_set_cross_attn_state(ctx, slot->cross_attn_state);
break;
}
slot_image img_sl;
img_sl.id = img.count("id") != 0 ? img["id"].get<int>() : slot->images.size();
img_sl.img_data = clip_image_u8_init();

693
llm/patches/0009-mllama.patch Normal file
View File

@ -0,0 +1,693 @@
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
mllama adds cross-attention layers to the standard llama architecture
it also requires a way to input a new tensor: cross_attention_state
once per generation
cross-attention layers don't change and so they are cached in the
kv cache once per run
remaining is to implement the cross attention mask
---
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..792520cc 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -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);
+
// 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..cf70ea90 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -170,6 +170,7 @@ static std::string format(const char * fmt, ...) {
enum llm_arch {
LLM_ARCH_LLAMA,
+ LLM_ARCH_MLLAMA,
LLM_ARCH_FALCON,
LLM_ARCH_BAICHUAN,
LLM_ARCH_GROK,
@@ -219,6 +220,7 @@ enum llm_arch {
static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_LLAMA, "llama" },
+ { LLM_ARCH_MLLAMA, "mllama" },
{ LLM_ARCH_FALCON, "falcon" },
{ LLM_ARCH_GROK, "grok" },
{ LLM_ARCH_GPT2, "gpt2" },
@@ -317,6 +319,7 @@ enum llm_kv {
LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,
LLM_KV_ATTENTION_SLIDING_WINDOW,
LLM_KV_ATTENTION_BLOCK_SKIP_CONNECTION,
+ LLM_KV_ATTENTION_CROSS_ATTENTION_LAYERS,
LLM_KV_ROPE_DIMENSION_COUNT,
LLM_KV_ROPE_FREQ_BASE,
@@ -422,6 +425,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, "%s.attention.relative_buckets_count" },
{ LLM_KV_ATTENTION_SLIDING_WINDOW, "%s.attention.sliding_window" },
{ LLM_KV_ATTENTION_BLOCK_SKIP_CONNECTION, "%s.attention.block_skip_connection.%d" },
+ { LLM_KV_ATTENTION_CROSS_ATTENTION_LAYERS, "%s.attention.cross_attention_layers" },
{ LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" },
{ LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" },
@@ -594,6 +598,14 @@ enum llm_tensor {
LLM_TENSOR_ENC_FFN_UP,
LLM_TENSOR_ENC_OUTPUT_NORM,
LLM_TENSOR_BSKCN_TV,
+ LLM_TENSOR_CROSS_ATTN_K_NORM,
+ LLM_TENSOR_CROSS_ATTN_K_PROJ,
+ LLM_TENSOR_CROSS_ATTN_O_PROJ,
+ LLM_TENSOR_CROSS_ATTN_Q_NORM,
+ LLM_TENSOR_CROSS_ATTN_Q_PROJ,
+ LLM_TENSOR_CROSS_ATTN_V_PROJ,
+ LLM_TENSOR_CROSS_ATTN_ATTN_GATE,
+ LLM_TENSOR_CROSS_ATTN_MLP_GATE,
};
static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
@@ -623,6 +635,40 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
},
},
+ {
+ LLM_ARCH_MLLAMA,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
+ { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
+ { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
+ { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
+ { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
+ { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
+ { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ { LLM_TENSOR_FFN_GATE_EXP, "blk.%d.ffn_gate.%d" },
+ { LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" },
+ { LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" },
+ { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
+ { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
+ { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
+ { LLM_TENSOR_CROSS_ATTN_K_NORM, "blk.%d.cross_attn_k_norm" },
+ { LLM_TENSOR_CROSS_ATTN_K_PROJ, "blk.%d.cross_attn_k_proj" },
+ { LLM_TENSOR_CROSS_ATTN_O_PROJ, "blk.%d.cross_attn_o_proj" },
+ { LLM_TENSOR_CROSS_ATTN_Q_NORM, "blk.%d.cross_attn_q_norm" },
+ { LLM_TENSOR_CROSS_ATTN_Q_PROJ, "blk.%d.cross_attn_q_proj" },
+ { LLM_TENSOR_CROSS_ATTN_V_PROJ, "blk.%d.cross_attn_v_proj" },
+ { LLM_TENSOR_CROSS_ATTN_ATTN_GATE, "blk.%d.cross_attn_attn_gate" },
+ { LLM_TENSOR_CROSS_ATTN_MLP_GATE, "blk.%d.cross_attn_mlp_gate" },
+ },
+ },
{
LLM_ARCH_BAICHUAN,
{
@@ -2267,6 +2313,7 @@ enum e_model {
MODEL_40B,
MODEL_65B,
MODEL_70B,
+ MODEL_90B,
MODEL_236B,
MODEL_314B,
MODEL_SMALL,
@@ -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;
+ std::array<uint32_t, LLAMA_MAX_LAYERS> cross_attn_layers;
uint32_t n_layer_dense_lead = 0;
uint32_t n_lora_q = 0;
@@ -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;
- if (this->n_head_arr != other.n_head_arr) return true;
- if (this->n_head_kv_arr != other.n_head_kv_arr) return true;
- if (this->n_ff_arr != other.n_ff_arr) return true;
- if (this->n_bskcn_arr != other.n_bskcn_arr) return true;
+ if (this->n_head_arr != other.n_head_arr) return true;
+ if (this->n_head_kv_arr != other.n_head_kv_arr) return true;
+ if (this->n_ff_arr != other.n_ff_arr) return true;
+ if (this->n_bskcn_arr != other.n_bskcn_arr) return true;
+ if (this->cross_attn_layers != other.cross_attn_layers) return true;
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 +2539,10 @@ struct llama_hparams {
GGML_ABORT("fatal error");
}
+
+ bool cross_attention_layer(uint32_t il) const {
+ return std::find(cross_attn_layers.begin(), cross_attn_layers.end(), il) != cross_attn_layers.end();
+ }
};
static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");
@@ -2672,6 +2725,16 @@ struct llama_layer {
struct ggml_tensor * ffn_down_scale;
struct ggml_tensor * bskcn_tv;
+
+ // cross attention
+ struct ggml_tensor * cross_attn_k_norm;
+ struct ggml_tensor * cross_attn_k_proj;
+ struct ggml_tensor * cross_attn_o_proj;
+ struct ggml_tensor * cross_attn_q_norm;
+ struct ggml_tensor * cross_attn_q_proj;
+ struct ggml_tensor * cross_attn_v_proj;
+ struct ggml_tensor * cross_attn_attn_gate;
+ struct ggml_tensor * cross_attn_mlp_gate;
};
// very similar to llama_batch,
@@ -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 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 +3612,18 @@ static bool llama_kv_cache_init(
cache.v_l.reserve(n_layer);
for (int i = 0; i < (int) n_layer; i++) {
+ // for cross attention layers
+ if (model.arch == LLM_ARCH_MLLAMA && hparams.cross_attention_layer(i)) {
+ struct ggml_context * ctx = offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front();
+ ggml_tensor * k = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hparams.n_embd_head_k, 6404, hparams.n_head_kv(i));
+ ggml_tensor * v = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hparams.n_embd_head_v, 6404, hparams.n_head_kv(i));
+ ggml_format_name(k, "cache_k_l%d", i);
+ ggml_format_name(v, "cache_v_l%d", i);
+ cache.k_l.push_back(k);
+ cache.v_l.push_back(v);
+ continue;
+ }
+
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 +5393,14 @@ static void llm_load_hparams(
}
// zero-out the per-layer hparams
- std::fill(hparams.n_head_arr.begin(), hparams.n_head_arr.end(), 0);
- std::fill(hparams.n_head_kv_arr.begin(), hparams.n_head_kv_arr.end(), 0);
- std::fill(hparams.n_ff_arr.begin(), hparams.n_ff_arr.end(), 0);
+ std::fill(hparams.n_head_arr.begin(), hparams.n_head_arr.end(), 0);
+ std::fill(hparams.n_head_kv_arr.begin(), hparams.n_head_kv_arr.end(), 0);
+ std::fill(hparams.n_ff_arr.begin(), hparams.n_ff_arr.end(), 0);
+ std::fill(hparams.cross_attn_layers.begin(), hparams.cross_attn_layers.end(), -1);
- ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff_arr, hparams.n_layer);
- ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer);
+ ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff_arr, hparams.n_layer);
+ ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer);
+ ml.get_arr(LLM_KV_ATTENTION_CROSS_ATTENTION_LAYERS, hparams.cross_attn_layers, false);
// n_head_kv is optional, default to n_head
hparams.n_head_kv_arr = hparams.n_head_arr;
@@ -5366,7 +5449,7 @@ static void llm_load_hparams(
ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false);
- if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON) {
+ if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_MLLAMA || model.arch == LLM_ARCH_FALCON) {
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 +5487,16 @@ static void llm_load_hparams(
}
}
} break;
+ case LLM_ARCH_MLLAMA:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+ switch (hparams.n_layer) {
+ case 40: model.type = e_model::MODEL_11B; break;
+ case 100: model.type = e_model::MODEL_90B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
case LLM_ARCH_MINICPM:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@@ -6918,6 +7011,55 @@ static bool llm_load_tensors(
}
}
} break;
+ case LLM_ARCH_MLLAMA:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab+8});
+
+ // output
+ {
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+ model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ // if output is NULL, init from the input tok embed
+ if (model.output == NULL) {
+ model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
+ }
+ }
+
+ for (int i = 0; i < n_layer; ++i) {
+ ggml_context * ctx_layer = ctx_for_layer(i);
+ ggml_context * ctx_split = ctx_for_layer_split(i);
+
+ auto & layer = model.layers[i];
+
+ if (hparams.cross_attention_layer(i)) {
+ layer.cross_attn_k_norm = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_K_NORM, "weight", i), {128});
+ layer.cross_attn_k_proj = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_K_PROJ, "weight", i), {n_embd, 1024});
+ layer.cross_attn_o_proj = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_O_PROJ, "weight", i), {n_embd, n_embd});
+ layer.cross_attn_q_norm = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_Q_NORM, "weight", i), {128});
+ layer.cross_attn_q_proj = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_Q_PROJ, "weight", i), {n_embd, n_embd});
+ layer.cross_attn_v_proj = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_V_PROJ, "weight", i), {n_embd, 1024});
+ layer.cross_attn_attn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_ATTN_GATE, i), {1});
+ layer.cross_attn_mlp_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_CROSS_ATTN_MLP_GATE, i), {1});
+ layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+ layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+ layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
+ layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
+ layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+ } else {
+ layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+ layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head});
+ layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
+ layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
+ layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+ layer.rope_freqs = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FREQS, "weight"), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
+ layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
+ layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
+ layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
+ }
+ }
+ } break;
case LLM_ARCH_GROK:
{
if (n_expert == 0) {
@@ -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()) {
- throw std::runtime_error("vocab size mismatch");
+ LLAMA_LOG_WARN("%s: vocab mismatch %u !- %zu ...\n", __func__, model.hparams.n_vocab, model.vocab.id_to_token.size());
}
if (params.vocab_only) {
@@ -8759,7 +8901,7 @@ static struct ggml_tensor * llm_build_inp_embd(
inpL = ggml_get_rows(ctx, tok_embd, lctx.inp_tokens);
} 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;
}
+static struct ggml_tensor * llm_build_inp_cross_attn_state(
+ struct ggml_context * ctx,
+ struct llama_context & lctx,
+ const llama_hparams & hparams,
+ const llm_build_cb & cb) {
+ const int64_t n_embd = hparams.n_embd;
+
+ struct ggml_tensor * inpCAS;
+ lctx.inp_cross_attn_state = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd, 1601, 4);
+ cb(lctx.inp_cross_attn_state, "inp_cross_attn_state", -1);
+ ggml_set_input(lctx.inp_cross_attn_state);
+ inpCAS = lctx.inp_cross_attn_state;
+
+ return inpCAS;
+}
+
static void llm_build_kv_store(
struct ggml_context * ctx,
const llama_hparams & hparams,
@@ -9743,6 +9901,7 @@ struct llm_build_context {
lctx.inp_pos_bucket = nullptr;
lctx.inp_embd_enc = nullptr;
lctx.inp_KQ_mask_cross = nullptr;
+ lctx.inp_cross_attn_state = nullptr;
}
void free() {
@@ -10158,6 +10317,253 @@ struct llm_build_context {
LLM_NORM_RMS, cb, -1);
cb(cur, "result_norm", -1);
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_mllama() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+
+ // mutable variable, needed during the last layer of the computation to skip unused tokens
+ int32_t n_tokens = this->n_tokens;
+
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+ GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * inpL;
+ struct ggml_tensor * inpCAS;
+
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+ inpCAS = llm_build_inp_cross_attn_state(ctx0, lctx, hparams, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ for (int il = 0; il < n_layer; ++il) {
+ struct ggml_tensor * inpSA = inpL;
+
+ // norm
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.layers[il].attn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_norm", il);
+
+ if (hparams.cross_attention_layer(il)) {
+ if (!lctx.cross_attn_state) {
+ continue;
+ }
+
+ // cross attention layer
+ struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].cross_attn_q_proj, cur);
+ cb(Qcur, "Qcur", il);
+
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+ cb(Qcur, "Qcur", il);
+
+ Qcur = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
+ cb(Qcur, "Qcur", il);
+
+ // TODO: is this required?
+ Qcur = ggml_cont(ctx0, Qcur);
+ cb(Qcur, "Qcur", il);
+
+ Qcur = llm_build_norm(ctx0, Qcur, hparams, model.layers[il].cross_attn_q_norm, NULL, LLM_NORM_RMS, cb, il);
+ cb(Qcur, "Qcur", il);
+
+ struct ggml_tensor * Kcur;
+ if (lctx.cross_attn_state_first_pass) {
+ Kcur = ggml_mul_mat(ctx0, model.layers[il].cross_attn_k_proj, inpCAS);
+ cb(Kcur, "Kcur", il);
+
+ Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, 6404);
+ cb(Kcur, "Kcur", il);
+
+ Kcur = ggml_permute(ctx0, Kcur, 0, 2, 1, 3);
+ cb(Kcur, "Kcur", il);
+
+ // TODO: is this required?
+ Kcur = ggml_cont(ctx0, Kcur);
+ cb(Kcur, "Kcur", il);
+
+ Kcur = llm_build_norm(ctx0, Kcur, hparams, model.layers[il].cross_attn_k_norm, NULL, LLM_NORM_RMS, cb, il);
+ cb(Kcur, "Kcur", il);
+
+ ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, kv_self.k_l[il]));
+ } else {
+ Kcur = ggml_view_tensor(ctx0, kv_self.k_l[il]);
+ cb(Kcur, "Kcur (view)", il);
+ }
+
+ struct ggml_tensor * Vcur;
+ if (lctx.cross_attn_state_first_pass) {
+ Vcur = ggml_mul_mat(ctx0, model.layers[il].cross_attn_v_proj, inpCAS);
+ cb(Vcur, "Vcur", il);
+
+ Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, 6404);
+ cb(Vcur, "Vcur", il);
+
+ Vcur = ggml_permute(ctx0, Vcur, 0, 2, 1, 3);
+ cb(Vcur, "Vcur", il);
+
+ ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, kv_self.v_l[il]));
+ } else {
+ Vcur = ggml_view_tensor(ctx0, kv_self.v_l[il]);
+ cb(Vcur, "Vcur (view)", il);
+ }
+
+ struct ggml_tensor * kq = ggml_mul_mat(ctx0, Kcur, Qcur);
+ cb(kq, "kq", il);
+
+ kq = ggml_scale_inplace(ctx0, kq, 1.0f/sqrtf(float(n_embd_head)));
+ cb(kq, "kq_scaled", il);
+
+ // TODO: apply causal masks
+ struct ggml_tensor * kq_soft_max = ggml_soft_max_inplace(ctx0, kq);
+ cb(kq_soft_max, "kq_soft_max", il);
+
+ Vcur = ggml_cont(ctx0, ggml_transpose(ctx0, Vcur));
+ cb(Vcur, "Vcur", il);
+
+ struct ggml_tensor * kqv = ggml_mul_mat(ctx0, Vcur, kq_soft_max);
+ cb(kqv, "kqv", il);
+
+ struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
+ cb(kqv_merged, "kqv_merged", il);
+
+ cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_head_v*n_head, n_tokens);
+ cb(cur, "kqv_merged_cont", il);
+
+ cur = ggml_mul_mat(ctx0, model.layers[il].cross_attn_o_proj, cur);
+ cb(cur, "cur", il);
+
+ // TODO: do this in place once?
+ cur = ggml_mul(ctx0, cur, ggml_tanh(ctx0, model.layers[il].cross_attn_attn_gate));
+
+ struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // feed-forward network
+ cur = llm_build_norm(ctx0, ffn_inp, hparams,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
+ model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
+ model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ cb(cur, "ffn_out", il);
+
+ // TODO: do this inplace once?
+ cur = ggml_add_inplace(ctx0, ggml_mul_inplace(ctx0, cur, ggml_tanh(ctx0, model.layers[il].cross_attn_mlp_gate)), ffn_inp);
+ cb(cur, "ffn_out", il);
+
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ } else {
+ // self attention layer
+
+ // rope freq factors for llama3; may return nullptr for llama2 and other models
+ struct ggml_tensor * rope_factors = build_rope_factors(il);
+
+ // compute Q and K and RoPE them
+ struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
+ cb(Qcur, "Qcur", il);
+ if (model.layers[il].bq) {
+ Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+ cb(Qcur, "Qcur", il);
+ }
+
+ struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+ if (model.layers[il].bk) {
+ Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+ cb(Kcur, "Kcur", il);
+ }
+
+ struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
+ cb(Vcur, "Vcur", il);
+ if (model.layers[il].bv) {
+ Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+ cb(Vcur, "Vcur", il);
+ }
+
+ Qcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, rope_factors,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, rope_factors,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, model.layers[il].bo,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ n_tokens = n_outputs;
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // feed-forward network
+ cur = llm_build_norm(ctx0, ffn_inp, hparams,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
+ model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
+ model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ cb(cur, "ffn_out", il);
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cb(cur, "ffn_out", il);
+
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+ }
+
+ cur = inpL;
+
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, cb, -1);
+ cb(cur, "result_norm", -1);
+
// lm_head
cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
cb(cur, "result_output", -1);
@@ -15493,6 +15899,10 @@ static struct ggml_cgraph * llama_build_graph(
{
result = llm.build_llama();
} break;
+ case LLM_ARCH_MLLAMA:
+ {
+ result = llm.build_mllama();
+ } break;
case LLM_ARCH_BAICHUAN:
{
result = llm.build_baichuan();
@@ -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));
}
+ // 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.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));
+ }
+
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);
+ // TODO: replace with something better to find out if its
+ // our first actual pass
+ lctx.cross_attn_state_first_pass = false;
+
llama_graph_compute(lctx, gf, n_threads, threadpool);
// update the kv ring buffer
@@ -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;
}
- GGML_ASSERT((qs.n_attention_wv == n_attn_layer) && "n_attention_wv is unexpected");
+ if (qs.n_attention_wv != n_attn_layer) {
+ LLAMA_LOG_WARN("%s: n_attention_wv is unexpected, expected: %d, found: %d\n", __func__, n_attn_layer, qs.n_attention_wv);
+ }
}
size_t total_size_org = 0;
@@ -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_first_pass = true;
+ ctx->cross_attn_state = cross_attn_state;
+}
+
void llama_free(struct llama_context * ctx) {
delete ctx;
}
@@ -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:
+ case LLM_ARCH_MLLAMA:
case LLM_ARCH_BAICHUAN:
case LLM_ARCH_STARCODER:
case LLM_ARCH_PLAMO:
--
2.39.3 (Apple Git-146)