vbatts/llama.cpp
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llama: apply the mllama support patch

Browse source 
Signed-off-by: YiYing He <yiying@secondstate.io>
This commit is contained in:
YiYing He 2025-01-15 17:07:09 +08:00
parent cde3833239
commit 45a89e0cec
16 changed files with 440 additions and 11 deletions
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5
examples/llava/llava.cpp
View file

@ -443,7 +443,7 @@ struct llava_embd_batch {
std::vector<llama_seq_id *> seq_ids;
std::vector<int8_t> logits;
llama_batch batch;
llava_embd_batch(float * embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) {
llava_embd_batch(float * embd, int32_t n_embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) {
pos .resize(n_tokens);
n_seq_id.resize(n_tokens);
seq_ids .resize(n_tokens + 1);
@ -455,6 +455,7 @@ struct llava_embd_batch {
/*n_tokens =*/ n_tokens,
/*tokens =*/ nullptr,
/*embd =*/ embd,
/*n_embd =*/ n_embd,
/*pos =*/ pos.data(),
/*n_seq_id =*/ n_seq_id.data(),
/*seq_id =*/ seq_ids.data(),
@ -478,7 +479,7 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_
n_eval = n_batch;
}
float * embd = image_embed->embed+i*n_embd;
llava_embd_batch llava_batch = llava_embd_batch(embd, n_eval, *n_past, 0);
llava_embd_batch llava_batch = llava_embd_batch(embd, n_embd, n_eval, *n_past, 0);
if (llama_decode(ctx_llama, llava_batch.batch)) {
LOG_ERR("%s : failed to eval\n", __func__);
return false;

5
include/llama.h
View file

@ -249,6 +249,7 @@ extern "C" {
llama_token * token;
float * embd;
int32_t n_embd;
llama_pos * pos;
int32_t * n_seq_id;
llama_seq_id ** seq_id;
@ -343,6 +344,7 @@ extern "C" {
bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
bool flash_attn; // whether to use flash attention [EXPERIMENTAL]
bool no_perf; // whether to measure performance timings
bool cross_attn; // whether to use cross attention
// Abort callback
// if it returns true, execution of llama_decode() will be aborted
@ -443,6 +445,9 @@ extern "C" {
struct llama_context_params params),
"use llama_init_from_model instead");
// TODO: 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_attention(struct llama_context * ctx, bool cross_attn_state);
// Frees all allocated memory
LLAMA_API void llama_free(struct llama_context * ctx);

44
src/llama-arch.cpp
View file

@ -6,6 +6,7 @@
static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_LLAMA, "llama" },
{ LLM_ARCH_MLLAMA, "mllama" },
{ LLM_ARCH_DECI, "deci" },
{ LLM_ARCH_FALCON, "falcon" },
{ LLM_ARCH_GROK, "grok" },
@ -125,6 +126,7 @@ 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_SCALE, "%s.attention.scale" },
{ LLM_KV_ATTENTION_CROSS_ATTENTION_LAYERS, "%s.attention.cross_attention_layers" },
{ LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" },
{ LLM_KV_ROPE_DIMENSION_SECTIONS, "%s.rope.dimension_sections" },
@ -223,6 +225,40 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ 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_DECI,
{
@ -1445,6 +1481,14 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
{LLM_TENSOR_CONVNEXT_PW1, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_CONVNEXT_PW2, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_CONVNEXT_GAMMA, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
{LLM_TENSOR_CROSS_ATTN_K_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
{LLM_TENSOR_CROSS_ATTN_K_PROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_CROSS_ATTN_O_PROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_CROSS_ATTN_Q_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
{LLM_TENSOR_CROSS_ATTN_Q_PROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_CROSS_ATTN_V_PROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_CROSS_ATTN_ATTN_GATE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
{LLM_TENSOR_CROSS_ATTN_MLP_GATE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
};
LLM_KV::LLM_KV(llm_arch arch, const char * suffix) : arch(arch), suffix(suffix) {}

10
src/llama-arch.h
View file

@ -10,6 +10,7 @@
enum llm_arch {
LLM_ARCH_LLAMA,
LLM_ARCH_MLLAMA,
LLM_ARCH_DECI,
LLM_ARCH_FALCON,
LLM_ARCH_BAICHUAN,
@ -129,6 +130,7 @@ enum llm_kv {
LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,
LLM_KV_ATTENTION_SLIDING_WINDOW,
LLM_KV_ATTENTION_SCALE,
LLM_KV_ATTENTION_CROSS_ATTENTION_LAYERS,
LLM_KV_ROPE_DIMENSION_COUNT,
LLM_KV_ROPE_DIMENSION_SECTIONS,
@ -327,6 +329,14 @@ enum llm_tensor {
LLM_TENSOR_POS_NET_ATTN_K,
LLM_TENSOR_POS_NET_ATTN_V,
LLM_TENSOR_POS_NET_ATTN_OUT,
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,
};
enum llm_tensor_layer {

3
src/llama-batch.cpp
View file

@ -316,6 +316,7 @@ struct llama_batch llama_batch_get_one(
/*n_tokens =*/ n_tokens,
/*tokens =*/ tokens,
/*embd =*/ nullptr,
/*n_embd =*/ 0,
/*pos =*/ nullptr,
/*n_seq_id =*/ nullptr,
/*seq_id =*/ nullptr,
@ -328,6 +329,7 @@ struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_
/*n_tokens =*/ 0,
/*tokens =*/ nullptr,
/*embd =*/ nullptr,
/*n_embd =*/ 0,
/*pos =*/ nullptr,
/*n_seq_id =*/ nullptr,
/*seq_id =*/ nullptr,
@ -336,6 +338,7 @@ struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_
if (embd) {
batch.embd = (float *) malloc(sizeof(float) * n_tokens_alloc * embd);
batch.n_embd = embd;
} else {
batch.token = (llama_token *) malloc(sizeof(llama_token) * n_tokens_alloc);
}

19
src/llama-context.cpp
View file

@ -74,10 +74,19 @@ void llama_set_inputs(llama_context & lctx, const llama_ubatch & ubatch) {
}
if (ubatch.embd) {
const int64_t n_embd = hparams.n_embd;
const int64_t n_tokens = ubatch.n_tokens;
if (lctx.inp_cross_attn_state && lctx.inp_cross_attn_state->buffer) {
ggml_backend_tensor_set(lctx.inp_cross_attn_state, ubatch.embd, 0, ggml_nbytes(lctx.inp_cross_attn_state));
// zero out inp_embd since it's not used
float * inp_embd_data = (float *)lctx.inp_embd->data;
for (int i = 0; i < ggml_nelements(lctx.inp_embd); ++i) {
inp_embd_data[i] = 0.0f;
}
} else {
const int64_t n_embd = hparams.n_embd;
const int64_t n_tokens = ubatch.n_tokens;
ggml_backend_tensor_set(lctx.inp_embd, ubatch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd));
ggml_backend_tensor_set(lctx.inp_embd, ubatch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd));
}
}
if (ubatch.pos && lctx.inp_pos) {
@ -657,6 +666,10 @@ void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn) {
ctx->cparams.causal_attn = causal_attn;
}
void llama_set_cross_attention(struct llama_context * ctx, bool cross_attention) {
ctx->cparams.cross_attn = cross_attention;
}
void llama_synchronize(struct llama_context * ctx) {
ggml_backend_sched_synchronize(ctx->sched.get());

1
src/llama-context.h
View file

@ -107,6 +107,7 @@ 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]
struct ggml_tensor * inp_cross_attn_state; // F32 [4, n_embd, 1061]
};
// TODO: make these methods of llama_context

1
src/llama-cparams.h
View file

@ -29,6 +29,7 @@ struct llama_cparams {
bool offload_kqv;
bool flash_attn;
bool no_perf;
bool cross_attn;
enum llama_pooling_type pooling_type;

6
src/llama-hparams.cpp
View file

@ -2,6 +2,8 @@
#include "ggml.h"
#include <algorithm>
uint32_t llama_hparams::n_head(uint32_t il) const {
if (il < n_layer) {
return n_head_arr[il];
@ -69,3 +71,7 @@ uint32_t llama_hparams::n_embd_v_s() const {
// corresponds to Mamba's ssm_states size
return ssm_d_state * ssm_d_inner;
}
bool llama_hparams::cross_attention_layers(uint32_t il) const {
return std::find(cross_attn_layers.begin(), cross_attn_layers.end(), il) != cross_attn_layers.end();
}

4
src/llama-hparams.h
View file

@ -49,6 +49,7 @@ struct llama_hparams {
std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_arr;
std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_kv_arr;
std::array<uint32_t, LLAMA_MAX_LAYERS> n_ff_arr;
std::array<uint32_t, LLAMA_MAX_LAYERS> cross_attn_layers;
uint32_t n_layer_dense_lead = 0;
uint32_t n_lora_q = 0;
@ -133,6 +134,9 @@ struct llama_hparams {
// dimension of the recurrent state embeddings
uint32_t n_embd_v_s() const;
// cross attention layers
bool cross_attention_layers(uint32_t il) const;
};
static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");

11
src/llama-kv-cache.cpp
View file

@ -91,6 +91,17 @@ bool llama_kv_cache_init(
return false;
}
// for cross attention layers
if (model.arch == LLM_ARCH_MLLAMA && hparams.cross_attention_layers(i)) {
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;
}
ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size);
ggml_tensor * v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*kv_size);
ggml_format_name(k, "cache_k_l%d", i);

2
src/llama-model-loader.cpp
View file

@ -315,6 +315,8 @@ namespace GGUFMeta {
return true;
}
template bool llama_model_loader::get_arr<std::array<unsigned int, 512>>(enum llm_kv kid, std::array<unsigned int, 512>& result, bool required);
template<typename T, size_t N_MAX>
bool llama_model_loader::get_arr(const std::string & key, std::array<T, N_MAX> & result, bool required) {
const int kid = gguf_find_key(meta.get(), key.c_str());

65
src/llama-model.cpp
View file

@ -435,9 +435,11 @@ void llama_model::load_hparams(llama_model_loader & ml) {
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, false);
ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer, false);
ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff_arr, hparams.n_layer, false);
ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer, false);
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;
@ -486,7 +488,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false);
if (arch == LLM_ARCH_LLAMA || arch == LLM_ARCH_DECI || arch == LLM_ARCH_FALCON) {
if (arch == LLM_ARCH_LLAMA || arch == LLM_ARCH_MLLAMA || arch == LLM_ARCH_DECI || 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));
}
@ -530,6 +532,16 @@ void llama_model::load_hparams(llama_model_loader & ml) {
}
}
} 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: type = LLM_TYPE_11B; break;
case 100: type = LLM_TYPE_90B; break;
default: type = LLM_TYPE_UNKNOWN;
}
} break;
case LLM_ARCH_DECI:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@ -1556,6 +1568,52 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
}
}
} break;
case LLM_ARCH_MLLAMA:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab+8}, 0);
// output
{
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
output = create_tensor(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 (output == NULL) {
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
}
}
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
if (hparams.cross_attention_layers(i)) {
layer.cross_attn_k_norm = create_tensor(tn(LLM_TENSOR_CROSS_ATTN_K_NORM, "weight", i), {128}, 0);
layer.cross_attn_k_proj = create_tensor(tn(LLM_TENSOR_CROSS_ATTN_K_PROJ, "weight", i), {n_embd, 1024}, 0);
layer.cross_attn_o_proj = create_tensor(tn(LLM_TENSOR_CROSS_ATTN_O_PROJ, "weight", i), {n_embd, n_embd}, 0);
layer.cross_attn_q_norm = create_tensor(tn(LLM_TENSOR_CROSS_ATTN_Q_NORM, "weight", i), {128}, 0);
layer.cross_attn_q_proj = create_tensor(tn(LLM_TENSOR_CROSS_ATTN_Q_PROJ, "weight", i), {n_embd, n_embd}, 0);
layer.cross_attn_v_proj = create_tensor(tn(LLM_TENSOR_CROSS_ATTN_V_PROJ, "weight", i), {n_embd, 1024}, 0);
layer.cross_attn_attn_gate = create_tensor(tn(LLM_TENSOR_CROSS_ATTN_ATTN_GATE, i), {1}, 0);
layer.cross_attn_mlp_gate = create_tensor(tn(LLM_TENSOR_CROSS_ATTN_MLP_GATE, i), {1}, 0);
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
} else {
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
}
}
} break;
case LLM_ARCH_DECI:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@ -3868,6 +3926,7 @@ enum llama_rope_type llama_model_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_DECI:
case LLM_ARCH_BAICHUAN:
case LLM_ARCH_STARCODER:

12
src/llama-model.h
View file

@ -9,6 +9,7 @@
#include <string>
#include <unordered_map>
#include <vector>
#include <stdexcept>
struct llama_model_loader;
@ -62,6 +63,7 @@ enum llm_type {
LLM_TYPE_40B,
LLM_TYPE_65B,
LLM_TYPE_70B,
LLM_TYPE_90B,
LLM_TYPE_236B,
LLM_TYPE_314B,
LLM_TYPE_671B,
@ -281,6 +283,16 @@ struct llama_layer {
struct ggml_tensor * ffn_up_scale = nullptr;
struct ggml_tensor * ffn_down_scale = nullptr;
// cross attention
struct ggml_tensor * cross_attn_k_norm = nullptr;
struct ggml_tensor * cross_attn_k_proj = nullptr;
struct ggml_tensor * cross_attn_o_proj = nullptr;
struct ggml_tensor * cross_attn_q_norm = nullptr;
struct ggml_tensor * cross_attn_q_proj = nullptr;
struct ggml_tensor * cross_attn_v_proj = nullptr;
struct ggml_tensor * cross_attn_attn_gate = nullptr;
struct ggml_tensor * cross_attn_mlp_gate = nullptr;
struct llama_layer_posnet posnet;
struct llama_layer_convnext convnext;

4
src/llama-quant.cpp
View file

@ -632,7 +632,9 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
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;

259
src/llama.cpp
View file

@ -154,6 +154,21 @@ 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 = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd, 1601, 4);
cb(inpCAS, "inp_cross_attn_state", -1);
ggml_set_input(inpCAS);
lctx.inp_cross_attn_state = inpCAS;
return inpCAS;
}
static void llm_build_kv_store(
struct ggml_context * ctx,
const llama_hparams & hparams,
@ -1157,6 +1172,7 @@ 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() {
@ -1639,6 +1655,240 @@ struct llm_build_context {
return gf;
}
struct ggml_cgraph * build_mllama() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), 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, ubatch, 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_layers(il)) {
if (!ubatch.embd && !cparams.cross_attn) {
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_cont(ctx0, ggml_permute(ctx0, Qcur, 0, 2, 1, 3));
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, * Vcur;
if (ubatch.embd) {
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_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));
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]));
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 {
Kcur = ggml_view_tensor(ctx0, kv_self.k_l[il]);
cb(Kcur, "Kcur (view)", il);
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);
// TODO: apply causal masks
struct ggml_tensor * kq_soft_max = ggml_soft_max_ext(ctx0, kq, nullptr, 1.f/sqrtf(float(n_embd_head)), hparams.f_max_alibi_bias);
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);
ggml_build_forward_expand(gf, cur);
return gf;
}
struct ggml_cgraph * build_deci() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
@ -8196,6 +8446,10 @@ static struct ggml_cgraph * llama_build_graph(
{
result = llm.build_llama();
} break;
case LLM_ARCH_MLLAMA:
{
result = llm.build_mllama();
} break;
case LLM_ARCH_DECI:
{
result = llm.build_deci();
@ -8482,7 +8736,7 @@ static int llama_prepare_sbatch(
n_outputs = 1;
}
lctx.sbatch.from_batch(batch, n_embd,
lctx.sbatch.from_batch(batch, batch.n_embd,
/* simple_split */ !lctx.kv_self.recurrent,
/* logits_all */ n_outputs == n_tokens_all);
@ -8869,7 +9123,7 @@ static int llama_encode_impl(
const int64_t n_embd = hparams.n_embd;
lctx.sbatch.from_batch(batch, n_embd, /* simple_split */ true, /* logits_all */ true);
lctx.sbatch.from_batch(batch, batch.n_embd, /* simple_split */ true, /* logits_all */ true);
const llama_ubatch ubatch = lctx.sbatch.split_simple(n_tokens);
@ -9355,6 +9609,7 @@ struct llama_context_params llama_context_default_params() {
/*.offload_kqv =*/ true,
/*.flash_attn =*/ false,
/*.no_perf =*/ true,
/*.cross_attn =*/ false,
/*.abort_callback =*/ nullptr,
/*.abort_callback_data =*/ nullptr,
};