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update llama.cpp

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This commit is contained in:
Michael Yang
2023-08-01 16:22:03 -07:00
parent da52f5bfdd
commit 7a1c3e62dc
18 changed files with 2603 additions and 493 deletions
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267
llama/llama.cpp
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@@ -1,5 +1,5 @@
/**
* llama.cpp - git d91f3f0c55663719ea03b76311e8c36ed55eb0e2
* llama.cpp - git c574bddb368424b5996cbee2ec45ec050967d404
*
* MIT License
*
@@ -82,8 +82,14 @@
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
#if !defined(GGML_USE_CUBLAS) && !defined(GGML_USE_METAL)
#include "ggml-alloc.h"
#define LLAMA_USE_ALLOCATOR
#else
#define LLAMA_USE_SCRATCH
#define LLAMA_MAX_SCRATCH_BUFFERS 16
#endif
// available llama models
enum e_model {
@@ -353,13 +359,22 @@ struct llama_model {
struct llama_context {
llama_context(const llama_model & model) : model(model), t_load_us(model.t_load_us), t_start_us(model.t_start_us) {}
#ifdef GGML_USE_METAL
~llama_context() {
if (model_owner) {
delete &model;
}
#ifdef GGML_USE_METAL
if (ctx_metal) {
ggml_metal_free(ctx_metal);
}
}
#endif
#ifdef LLAMA_USE_ALLOCATOR
if (alloc) {
ggml_allocr_free(alloc);
}
#endif
}
std::mt19937 rng;
bool has_evaluated_once = false;
@@ -397,7 +412,17 @@ struct llama_context {
// memory buffers used to evaluate the model
// TODO: move in llama_state
llama_ctx_buffer buf_compute;
#ifdef LLAMA_USE_ALLOCATOR
llama_ctx_buffer buf_alloc;
ggml_allocr * alloc = NULL;
#endif
#ifdef LLAMA_USE_SCRATCH
llama_ctx_buffer buf_scratch[LLAMA_MAX_SCRATCH_BUFFERS];
int buf_last = 0;
size_t buf_max_size[LLAMA_MAX_SCRATCH_BUFFERS] = { 0 };
#endif
#ifdef GGML_USE_METAL
ggml_metal_context * ctx_metal = NULL;
@@ -407,9 +432,6 @@ struct llama_context {
ggml_mpi_context * ctx_mpi = NULL;
#endif
int buf_last = 0;
size_t buf_max_size[LLAMA_MAX_SCRATCH_BUFFERS] = { 0 };
void use_buf(struct ggml_context * ctx, int i) {
#if defined(LLAMA_USE_SCRATCH)
size_t last_size = 0;
@@ -905,6 +927,7 @@ struct llama_context_params llama_context_default_params() {
/*.progress_callback =*/ nullptr,
/*.progress_callback_user_data =*/ nullptr,
/*.low_vram =*/ false,
/*.mul_mat_q =*/ false,
/*.f16_kv =*/ true,
/*.logits_all =*/ false,
/*.vocab_only =*/ false,
@@ -1032,6 +1055,7 @@ static void llama_model_load_internal(
int n_gpu_layers,
int main_gpu,
const float * tensor_split,
const bool mul_mat_q,
float rope_freq_base,
float rope_freq_scale,
bool low_vram,
@@ -1160,9 +1184,11 @@ static void llama_model_load_internal(
}
(void) main_gpu;
(void) mul_mat_q;
#if defined(GGML_USE_CUBLAS)
fprintf(stderr, "%s: using CUDA for GPU acceleration\n", __func__);
ggml_cuda_set_main_device(main_gpu);
ggml_cuda_set_mul_mat_q(mul_mat_q);
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_GPU
#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU_SPLIT
#elif defined(GGML_USE_CLBLAST)
@@ -1256,12 +1282,16 @@ static void llama_model_load_internal(
const size_t scale = memory_type == GGML_TYPE_F32 ? 2 : 1;
// this is the total memory required to run the inference
const size_t mem_required =
size_t mem_required =
ctx_size +
mmapped_size - vram_weights + // weights in VRAM not in memory
mmapped_size - vram_weights; // weights in VRAM not in memory
#ifndef LLAMA_USE_ALLOCATOR
mem_required +=
MEM_REQ_SCRATCH0(hparams.n_ctx).at(model.type) +
MEM_REQ_SCRATCH1().at(model.type) +
MEM_REQ_EVAL().at(model.type);
#endif
// this is the memory required by one llama_state
const size_t mem_required_state =
@@ -1367,6 +1397,7 @@ static bool llama_model_load(
int n_gpu_layers,
int main_gpu,
const float * tensor_split,
const bool mul_mat_q,
float rope_freq_base,
float rope_freq_scale,
bool low_vram,
@@ -1377,7 +1408,8 @@ static bool llama_model_load(
llama_progress_callback progress_callback,
void *progress_callback_user_data) {
try {
llama_model_load_internal(fname, model, vocab, n_ctx, n_batch, n_gqa, rms_norm_eps, n_gpu_layers, main_gpu, tensor_split, rope_freq_base, rope_freq_scale, low_vram, memory_type,
llama_model_load_internal(fname, model, vocab, n_ctx, n_batch, n_gqa, rms_norm_eps, n_gpu_layers,
main_gpu, tensor_split, mul_mat_q, rope_freq_base, rope_freq_scale, low_vram, memory_type,
use_mmap, use_mlock, vocab_only, progress_callback, progress_callback_user_data);
return true;
} catch (const std::exception & err) {
@@ -1386,32 +1418,15 @@ static bool llama_model_load(
}
}
// evaluate the transformer
//
// - lctx: llama context
// - tokens: new batch of tokens to process
// - embd embeddings input
// - n_tokens number of tokens
// - n_past: the context size so far
// - n_threads: number of threads to use
//
static bool llama_eval_internal(
static struct ggml_cgraph * llama_build_graph(
llama_context & lctx,
const llama_token * tokens,
const float * embd,
int n_tokens,
int n_past,
int n_threads,
const char * cgraph_fname) {
int n_past) {
LLAMA_ASSERT((!tokens && embd) || (tokens && !embd));
#ifdef GGML_USE_MPI
ggml_mpi_eval_init(lctx.ctx_mpi, &n_tokens, &n_past, &n_threads);
#endif
const int64_t t_start_us = ggml_time_us();
const int N = n_tokens;
const auto & model = lctx.model;
@@ -1427,10 +1442,8 @@ static bool llama_eval_internal(
const int64_t n_head = hparams.n_head;
const int64_t n_head_kv = hparams.n_head_kv;
const int64_t n_embd_head = hparams.n_embd_head();
const int64_t n_vocab = hparams.n_vocab;
const int64_t n_embd_gqa = hparams.n_embd_gqa();
LLAMA_ASSERT(n_embd_head == hparams.n_rot);
const float freq_base = hparams.rope_freq_base;
@@ -1442,26 +1455,35 @@ static bool llama_eval_internal(
auto & mem_per_token = lctx.mem_per_token;
auto & buf_compute = lctx.buf_compute;
struct ggml_init_params params = {
/*.mem_size =*/ buf_compute.size,
/*.mem_buffer =*/ buf_compute.addr,
/*.no_alloc =*/ false,
};
#ifdef LLAMA_USE_ALLOCATOR
params.no_alloc = true;
#endif
struct ggml_context * ctx0 = ggml_init(params);
ggml_cgraph * gf = ggml_new_graph(ctx0);
// for big prompts, if BLAS is enabled, it is better to use only one thread
// otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : n_threads;
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
if (tokens) {
struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
#ifdef LLAMA_USE_ALLOCATOR
ggml_allocr_alloc(lctx.alloc, inp_tokens);
if (!ggml_allocr_is_measure(lctx.alloc)) {
memcpy(inp_tokens->data, tokens, N*ggml_element_size(inp_tokens));
}
#else
memcpy(inp_tokens->data, tokens, N*ggml_element_size(inp_tokens));
#endif
ggml_set_name(inp_tokens, "inp_tokens");
inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
@@ -1471,7 +1493,15 @@ static bool llama_eval_internal(
#endif
inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N);
#ifdef LLAMA_USE_ALLOCATOR
ggml_allocr_alloc(lctx.alloc, inpL);
if (!ggml_allocr_is_measure(lctx.alloc)) {
memcpy(inpL->data, embd, N * n_embd * ggml_element_size(inpL));
}
#else
memcpy(inpL->data, embd, N * n_embd * ggml_element_size(inpL));
#endif
}
const int i_gpu_start = n_layer - n_gpu_layers;
@@ -1498,6 +1528,17 @@ static bool llama_eval_internal(
}
#endif // GGML_USE_CUBLAS
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
#ifdef LLAMA_USE_ALLOCATOR
ggml_allocr_alloc(lctx.alloc, KQ_scale);
if (!ggml_allocr_is_measure(lctx.alloc)) {
ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
}
#else
ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
#endif
ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
for (int il = 0; il < n_layer; ++il) {
ggml_format_name(inpL, "layer_inp_%d", il);
@@ -1593,9 +1634,6 @@ static bool llama_eval_internal(
ggml_set_name(KQ, "KQ");
// KQ_scaled = KQ / sqrt(n_embd_head)
struct ggml_tensor * KQ_scale = ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head));
ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
// KQ_scaled shape [n_past + N, N, n_head, 1]
struct ggml_tensor * KQ_scaled = ggml_scale_inplace(ctx0, KQ, KQ_scale);
offload_func_kq(KQ_scaled);
@@ -1711,9 +1749,6 @@ static bool llama_eval_internal(
lctx.use_buf(ctx0, 0);
// used at the end to optionally extract the embeddings
struct ggml_tensor * embeddings = NULL;
// norm
{
cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps);
@@ -1724,8 +1759,6 @@ static bool llama_eval_internal(
cur = ggml_mul(ctx0, cur, model.norm);
// offload_func_nr(cur); // TODO CPU + GPU mirrored backend
ggml_set_name(cur, "result_norm");
embeddings = cur;
}
// lm_head
@@ -1737,12 +1770,88 @@ static bool llama_eval_internal(
// logits -> probs
//cur = ggml_soft_max_inplace(ctx0, cur);
// run the computation
ggml_build_forward_expand(gf, cur);
// fprintf(stderr, "graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf.n_nodes, gf.n_leafs);
if (mem_per_token == 0) {
mem_per_token = ggml_used_mem(ctx0)/N;
}
#if 0
printf("\n%s: used_mem: eval ctx %.3f MB, scratch %.3f MB %.3f MB, work buf %.3f MB, n_past = %d, N = %d\n", __func__,
ggml_used_mem(ctx0)/1024.0/1024.0,
lctx.get_buf_max_mem(0)/1024.0/1024.0,
lctx.get_buf_max_mem(1)/1024.0/1024.0,
lctx.work_buffer.size()/1024.0/1024.0,
n_past, N);
#endif
ggml_free(ctx0);
return gf;
}
// evaluate the transformer
//
// - lctx: llama context
// - tokens: new batch of tokens to process
// - embd embeddings input
// - n_tokens number of tokens
// - n_past: the context size so far
// - n_threads: number of threads to use
//
static bool llama_eval_internal(
llama_context & lctx,
const llama_token * tokens,
const float * embd,
int n_tokens,
int n_past,
int n_threads,
const char * cgraph_fname) {
LLAMA_ASSERT((!tokens && embd) || (tokens && !embd));
const int64_t t_start_us = ggml_time_us();
#ifdef GGML_USE_MPI
ggml_mpi_eval_init(lctx.ctx_mpi, &n_tokens, &n_past, &n_threads);
#endif
const int N = n_tokens;
const auto & model = lctx.model;
const auto & hparams = model.hparams;
const auto & kv_self = lctx.kv_self;
LLAMA_ASSERT(!!kv_self.ctx);
const int64_t n_embd = hparams.n_embd;
const int64_t n_vocab = hparams.n_vocab;
#ifdef LLAMA_USE_ALLOCATOR
ggml_allocr_reset(lctx.alloc);
#endif
ggml_cgraph * gf = llama_build_graph(lctx, tokens, embd, n_tokens, n_past);
#ifdef LLAMA_USE_ALLOCATOR
ggml_allocr_alloc_graph(lctx.alloc, gf);
#endif
// fprintf(stderr, "graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
// for big prompts, if BLAS is enabled, it is better to use only one thread
// otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : n_threads;
struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1];
struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 2];
LLAMA_ASSERT(strcmp(res->name, "result_output") == 0);
LLAMA_ASSERT(strcmp(embeddings->name, "result_norm") == 0);
#if GGML_USE_MPI
const int64_t n_layer = hparams.n_layer;
ggml_mpi_graph_compute_pre(lctx.ctx_mpi, gf, n_layer);
#endif
@@ -1754,7 +1863,10 @@ static bool llama_eval_internal(
//}
ggml_metal_set_n_cb (lctx.ctx_metal, n_threads);
ggml_metal_graph_compute(lctx.ctx_metal, gf);
ggml_metal_get_tensor (lctx.ctx_metal, cur);
ggml_metal_get_tensor (lctx.ctx_metal, res);
if (!lctx.embedding.empty()) {
ggml_metal_get_tensor(lctx.ctx_metal, embeddings);
}
} else {
// IMPORTANT:
// Since we don't have efficient Matrix x Matrix Metal multiplication yet, we fallback to vanilla
@@ -1785,8 +1897,6 @@ static bool llama_eval_internal(
// update kv token count
lctx.kv_self.n = n_past + N;
struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1];
if (cgraph_fname) {
ggml_graph_export(gf, cgraph_fname);
}
@@ -1824,21 +1934,6 @@ static bool llama_eval_internal(
memcpy(embedding_out.data(), (float *) ggml_get_data(embeddings) + (n_embd*(N - 1)), sizeof(float)*n_embd);
}
if (mem_per_token == 0) {
mem_per_token = ggml_used_mem(ctx0)/N;
}
#if 0
printf("\n%s: used_mem: eval ctx %.3f MB, scratch %.3f MB %.3f MB, work buf %.3f MB, n_past = %d, N = %d\n", __func__,
ggml_used_mem(ctx0)/1024.0/1024.0,
lctx.get_buf_max_mem(0)/1024.0/1024.0,
lctx.get_buf_max_mem(1)/1024.0/1024.0,
lctx.work_buffer.size()/1024.0/1024.0,
n_past, N);
#endif
ggml_free(ctx0);
// measure the performance only for the single-token evals
if (N == 1) {
lctx.t_eval_us += ggml_time_us() - t_start_us;
@@ -1950,7 +2045,9 @@ struct llama_tokenizer {
if (token == vocab_.token_to_id.end()) {
// output any symbols that did not form tokens as bytes.
for (int j = 0; j < (int) symbol.n; ++j) {
llama_vocab::id token_id = static_cast<uint8_t>(symbol.text[j]) + 3;
// NOTE: old version, before #2420 - not sure what are the implications of this
//llama_vocab::id token_id = static_cast<uint8_t>(symbol.text[j]) + 3;
llama_vocab::id token_id = vocab_.token_to_id.at(std::string(1, symbol.text[j]));
output.push_back(token_id);
}
} else {
@@ -3127,7 +3224,7 @@ struct llama_model * llama_load_model_from_file(
ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
if (!llama_model_load(path_model, *model, model->vocab, params.n_ctx, params.n_batch, params.n_gqa, params.rms_norm_eps, params.n_gpu_layers,
params.main_gpu, params.tensor_split, params.rope_freq_base, params.rope_freq_scale,params.low_vram,
params.main_gpu, params.tensor_split, params.mul_mat_q, params.rope_freq_base, params.rope_freq_scale,params.low_vram,
memory_type, params.use_mmap, params.use_mlock, params.vocab_only, params.progress_callback,
params.progress_callback_user_data)) {
delete model;
@@ -3204,10 +3301,47 @@ struct llama_context * llama_new_context_with_model(
ctx->embedding.resize(hparams.n_embd);
}
ctx->buf_compute.resize(MEM_REQ_EVAL().at(ctx->model.type) + ggml_graph_overhead());
#ifdef LLAMA_USE_ALLOCATOR
{
static const size_t tensor_alignment = 32;
// the compute buffer is used to store the tensor and graph structs, while the allocator buffer is used for the tensor data
ctx->buf_compute.resize(ggml_tensor_overhead()*GGML_MAX_NODES + ggml_graph_overhead());
// create measure allocator
ctx->alloc = ggml_allocr_new_measure(tensor_alignment);
// build worst-case graph
int n_tokens = std::min((int)hparams.n_ctx, params.n_batch);
int n_past = hparams.n_ctx - n_tokens;
llama_token token = llama_token_bos(); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
ggml_cgraph * gf = llama_build_graph(*ctx, &token, NULL, n_tokens, n_past);
// measure memory requirements for the graph
size_t alloc_size = ggml_allocr_alloc_graph(ctx->alloc, gf) + tensor_alignment;
fprintf(stderr, "%s: compute buffer total size = %7.2f MB\n", __func__, (ctx->buf_compute.size + alloc_size) / 1024.0 / 1024.0);
// debug - for comparison with scratch buffer
//size_t prev_req =
// MEM_REQ_SCRATCH0(hparams.n_ctx).at(ctx->model.type) +
// MEM_REQ_SCRATCH1().at(ctx->model.type) +
// MEM_REQ_EVAL().at(ctx->model.type);
//fprintf(stderr, "%s: (debug) equivalent with scratch buffer = %7.2f MB\n", __func__, prev_req / 1024.0 / 1024.0);
// recreate allocator with exact memory requirements
ggml_allocr_free(ctx->alloc);
ctx->buf_alloc.resize(alloc_size);
ctx->alloc = ggml_allocr_new(ctx->buf_alloc.addr, ctx->buf_alloc.size, tensor_alignment);
}
#else
ctx->buf_compute.resize(MEM_REQ_EVAL().at(ctx->model.type) + ggml_graph_overhead());
#endif
#ifdef LLAMA_USE_SCRATCH
ctx->buf_scratch[0].resize(MEM_REQ_SCRATCH0(hparams.n_ctx).at(ctx->model.type));
ctx->buf_scratch[1].resize(MEM_REQ_SCRATCH1().at(ctx->model.type));
#endif
}
#ifdef GGML_USE_METAL
@@ -3277,9 +3411,6 @@ struct llama_context * llama_init_from_file(
}
void llama_free(struct llama_context * ctx) {
if (ctx->model_owner) {
delete &ctx->model;
}
delete ctx;
}