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Reduce memory usage and allocate enough memory for largest context (#473)

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* Reduce memory usage and allocate enough memory for large contexts

* Simpler scratch buffer usage

* Reenable BLAS for quantized mul_mat

* Fix number of layers in 30B and 65B

* Fix KV cache size for F32
This commit is contained in:
Georgi Gerganov 2023-03-24 23:17:37 +02:00 committed by GitHub
parent 31572d9665
commit 7a9b6c3a8b
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GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 307 additions and 80 deletions
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12
ggml.c
View File

@ -5846,7 +5846,8 @@ static bool ggml_compute_forward_mul_mat_use_blas(
const struct ggml_tensor * src0, const struct ggml_tensor * src0,
const struct ggml_tensor * src1, const struct ggml_tensor * src1,
struct ggml_tensor * dst) { struct ggml_tensor * dst) {
UNUSED(src0); const int ne00 = src0->ne[0];
const int ne01 = src0->ne[1];
const int ne10 = src1->ne[0]; const int ne10 = src1->ne[0];
@ -5856,7 +5857,14 @@ static bool ggml_compute_forward_mul_mat_use_blas(
// TODO: find the optimal values for these // TODO: find the optimal values for these
if (ggml_is_contiguous(src0) && if (ggml_is_contiguous(src0) &&
ggml_is_contiguous(src1) && ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32))) { ggml_is_contiguous(src1) && ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32))) {
//printf("BLAS: %d %d %d\n", ne0, ne1, ne10);
//// disable BLAS for Q4_0 and Q4_1
//// looks like there is no benefit and we only waste a lot of memory
//if (src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1) {
// return false;
//}
//printf("BLAS: %d %d %d %d %d\n", ne0, ne1, ne10, ne00, ne01);
return true; return true;
} }

326
llama.cpp
View File

@ -5,12 +5,25 @@
#include <cinttypes> #include <cinttypes>
#include <fstream> #include <fstream>
#include <random> #include <random>
#include <map>
#include <unordered_map> #include <unordered_map>
#include <queue> #include <queue>
#include <regex> #include <regex>
#include <cassert> #include <cassert>
#include <cstring> #include <cstring>
#define LLAMA_USE_SCRATCH
#define LLAMA_MAX_SCRATCH_BUFFERS 16
#define LLAMA_ASSERT(x) \
do { \
if (!(x)) { \
fprintf(stderr, "LLAMA_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
abort(); \
} \
} while (0)
// determine number of model parts based on the dimension // determine number of model parts based on the dimension
static const std::unordered_map<int, int> LLAMA_N_PARTS = { static const std::unordered_map<int, int> LLAMA_N_PARTS = {
{ 4096, 1 }, { 4096, 1 },
@ -19,6 +32,52 @@ static const std::unordered_map<int, int> LLAMA_N_PARTS = {
{ 8192, 8 }, { 8192, 8 },
}; };
// available llama models
enum e_model {
MODEL_UNKNOWN,
MODEL_7B,
MODEL_13B,
MODEL_30B,
MODEL_65B,
};
static const size_t MB = 1024*1024;
// computed for n_ctx == 2048
// TODO: dynamically determine these sizes
// needs modifications in ggml
static const std::map<e_model, size_t> MEM_REQ_SCRATCH0 = {
{ MODEL_7B, 512ull*MB },
{ MODEL_13B, 512ull*MB },
{ MODEL_30B, 512ull*MB },
{ MODEL_65B, 512ull*MB },
};
static const std::map<e_model, size_t> MEM_REQ_SCRATCH1 = {
{ MODEL_7B, 512ull*MB },
{ MODEL_13B, 512ull*MB },
{ MODEL_30B, 512ull*MB },
{ MODEL_65B, 512ull*MB },
};
// 2*n_embd*n_ctx*n_layer*sizeof(float16)
static const std::map<e_model, size_t> MEM_REQ_KV_SELF = {
{ MODEL_7B, 1026ull*MB },
{ MODEL_13B, 1608ull*MB },
{ MODEL_30B, 3124ull*MB },
{ MODEL_65B, 5120ull*MB },
};
// this is mostly needed for temporary mul_mat buffers to dequantize the data
// not actually needed if BLAS is disabled
static const std::map<e_model, size_t> MEM_REQ_EVAL = {
{ MODEL_7B, 768ull*MB },
{ MODEL_13B, 1024ull*MB },
{ MODEL_30B, 1280ull*MB },
{ MODEL_65B, 1536ull*MB },
};
// default hparams (LLaMA 7B) // default hparams (LLaMA 7B)
struct llama_hparams { struct llama_hparams {
int32_t n_vocab = 32000; int32_t n_vocab = 32000;
@ -50,7 +109,20 @@ struct llama_layer {
struct ggml_tensor * w3; struct ggml_tensor * w3;
}; };
struct llama_kv_cache {
struct ggml_tensor * k;
struct ggml_tensor * v;
struct ggml_context * ctx;
std::vector<uint8_t> buf;
int n; // number of tokens currently in the cache
};
struct llama_model { struct llama_model {
e_model type = MODEL_UNKNOWN;
llama_hparams hparams; llama_hparams hparams;
struct ggml_tensor * tok_embeddings; struct ggml_tensor * tok_embeddings;
@ -60,12 +132,18 @@ struct llama_model {
std::vector<llama_layer> layers; std::vector<llama_layer> layers;
// key + value memory // context
struct ggml_tensor * memory_k;
struct ggml_tensor * memory_v;
//
struct ggml_context * ctx; struct ggml_context * ctx;
// key + value cache for the self attention
// TODO: move to llama_state
struct llama_kv_cache kv_self;
// the model memory buffer
std::vector<uint8_t> buf;
// tensors
int n_loaded;
std::unordered_map<std::string, struct ggml_tensor *> tensors; std::unordered_map<std::string, struct ggml_tensor *> tensors;
}; };
@ -105,8 +183,88 @@ struct llama_context {
// input embedding (1-dimensional array: [n_embd]) // input embedding (1-dimensional array: [n_embd])
std::vector<float> embedding; std::vector<float> embedding;
// memory buffers used to evaluate the model
// TODO: move in llama_state
std::vector<uint8_t> buf_compute;
std::vector<uint8_t> buf_scratch[LLAMA_MAX_SCRATCH_BUFFERS];
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;
if (i == -1) {
last_size = ggml_set_scratch(ctx, { 0, 0, nullptr, });
} else {
auto & buf = buf_scratch[i];
last_size = ggml_set_scratch(ctx, { 0, buf.size(), buf.data(), });
}
if (buf_last >= 0) {
buf_max_size[buf_last] = std::max(buf_max_size[buf_last], last_size);
}
buf_last = i;
#else
(void) i;
(void) ctx;
#endif
}
size_t get_buf_max_mem(int i) const {
#if defined(LLAMA_USE_SCRATCH)
return buf_max_size[i];
#else
(void) i;
return 0;
#endif
}
}; };
//
// kv cache
//
static bool kv_cache_init(
const struct llama_hparams & hparams,
struct llama_kv_cache & cache,
ggml_type wtype,
int n_ctx) {
const int n_embd = hparams.n_embd;
const int n_layer = hparams.n_layer;
const int n_mem = n_layer*n_ctx;
const int n_elements = n_embd*n_mem;
cache.buf.resize(2*n_elements*ggml_type_size(wtype) + 2u*MB);
struct ggml_init_params params;
params.mem_size = cache.buf.size();
params.mem_buffer = cache.buf.data();
cache.ctx = ggml_init(params);
if (!cache.ctx) {
fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__);
return false;
}
cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
return true;
}
static void kv_cache_free(struct llama_kv_cache & cache) {
if (cache.ctx) {
ggml_free(cache.ctx);
cache.ctx = nullptr;
}
}
struct llama_context_params llama_context_default_params() { struct llama_context_params llama_context_default_params() {
struct llama_context_params result = { struct llama_context_params result = {
/*.n_ctx =*/ 512, /*.n_ctx =*/ 512,
@ -204,6 +362,22 @@ static bool llama_model_load(
fprintf(stderr, "%s: use '--n_parts 1' if necessary\n", __func__); fprintf(stderr, "%s: use '--n_parts 1' if necessary\n", __func__);
} }
if (hparams.n_layer == 32) {
model.type = e_model::MODEL_7B;
}
if (hparams.n_layer == 40) {
model.type = e_model::MODEL_13B;
}
if (hparams.n_layer == 60) {
model.type = e_model::MODEL_30B;
}
if (hparams.n_layer == 80) {
model.type = e_model::MODEL_65B;
}
fprintf(stderr, "%s: n_vocab = %d\n", __func__, hparams.n_vocab); fprintf(stderr, "%s: n_vocab = %d\n", __func__, hparams.n_vocab);
fprintf(stderr, "%s: n_ctx = %d\n", __func__, hparams.n_ctx); fprintf(stderr, "%s: n_ctx = %d\n", __func__, hparams.n_ctx);
fprintf(stderr, "%s: n_embd = %d\n", __func__, hparams.n_embd); fprintf(stderr, "%s: n_embd = %d\n", __func__, hparams.n_embd);
@ -214,6 +388,7 @@ static bool llama_model_load(
fprintf(stderr, "%s: f16 = %d\n", __func__, hparams.f16); fprintf(stderr, "%s: f16 = %d\n", __func__, hparams.f16);
fprintf(stderr, "%s: n_ff = %d\n", __func__, n_ff); fprintf(stderr, "%s: n_ff = %d\n", __func__, n_ff);
fprintf(stderr, "%s: n_parts = %d\n", __func__, n_parts); fprintf(stderr, "%s: n_parts = %d\n", __func__, n_parts);
fprintf(stderr, "%s: type = %d\n", __func__, model.type);
} }
// load vocab // load vocab
@ -307,11 +482,32 @@ static bool llama_model_load(
fprintf(stderr, "%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0)); fprintf(stderr, "%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
} }
// print memory requirements
{
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 =
ctx_size +
MEM_REQ_SCRATCH0.at(model.type) +
MEM_REQ_SCRATCH1.at(model.type) +
MEM_REQ_EVAL.at (model.type);
// this is the memory required by one llama_state
const size_t mem_required_state =
scale*MEM_REQ_KV_SELF.at(model.type);
fprintf(stderr, "%s: mem required = %7.2f MB (+ %7.2f MB per state)\n", __func__,
mem_required / 1024.0 / 1024.0, mem_required_state / 1024.0 / 1024.0);
}
// create the ggml context // create the ggml context
{ {
lctx.model.buf.resize(ctx_size);
struct ggml_init_params params = { struct ggml_init_params params = {
/*.mem_size =*/ ctx_size, /*.mem_size =*/ lctx.model.buf.size(),
/*.mem_buffer =*/ NULL, /*.mem_buffer =*/ lctx.model.buf.data(),
}; };
model.ctx = ggml_init(params); model.ctx = ggml_init(params);
@ -374,25 +570,6 @@ static bool llama_model_load(
} }
} }
// key + value memory
{
const auto & hparams = model.hparams;
const int n_embd = hparams.n_embd;
const int n_layer = hparams.n_layer;
const int n_ctx = hparams.n_ctx;
const int n_mem = n_layer*n_ctx;
const int n_elements = n_embd*n_mem;
model.memory_k = ggml_new_tensor_1d(ctx, memory_type, n_elements);
model.memory_v = ggml_new_tensor_1d(ctx, memory_type, n_elements);
const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
fprintf(stderr, "%s: memory_size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
}
const size_t file_offset = fin.tellg(); const size_t file_offset = fin.tellg();
fin.close(); fin.close();
@ -416,9 +593,10 @@ static bool llama_model_load(
// load weights // load weights
{ {
int n_tensors = 0;
size_t total_size = 0; size_t total_size = 0;
model.n_loaded = 0;
fprintf(stderr, "%s: ", __func__); fprintf(stderr, "%s: ", __func__);
while (true) { while (true) {
@ -583,7 +761,10 @@ static bool llama_model_load(
} }
//fprintf(stderr, "%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0); //fprintf(stderr, "%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
if (++n_tensors % 8 == 0) { model.n_loaded++;
// progress
if (model.n_loaded % 8 == 0) {
fprintf(stderr, "."); fprintf(stderr, ".");
fflush(stderr); fflush(stderr);
} }
@ -591,7 +772,13 @@ static bool llama_model_load(
fprintf(stderr, " done\n"); fprintf(stderr, " done\n");
fprintf(stderr, "%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors); fprintf(stderr, "%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, model.n_loaded);
if (model.n_loaded == 0) {
fprintf(stderr, "%s: WARN no tensors loaded from model file - assuming empty model for testing\n", __func__);
} else if (model.n_loaded != (int) model.tensors.size()) {
fprintf(stderr, "%s: ERROR not all tensors loaded from model file - expected %zu, got %d\n", __func__, model.tensors.size(), model.n_loaded);
return false;
}
} }
fin.close(); fin.close();
@ -622,6 +809,10 @@ static bool llama_eval_internal(
const auto & model = lctx.model; const auto & model = lctx.model;
const auto & hparams = model.hparams; const auto & hparams = model.hparams;
auto & kv_self = model.kv_self;
LLAMA_ASSERT(!!kv_self.ctx);
const int n_embd = hparams.n_embd; const int n_embd = hparams.n_embd;
const int n_layer = hparams.n_layer; const int n_layer = hparams.n_layer;
const int n_ctx = hparams.n_ctx; const int n_ctx = hparams.n_ctx;
@ -630,27 +821,11 @@ static bool llama_eval_internal(
const int n_rot = hparams.n_embd/hparams.n_head; const int n_rot = hparams.n_embd/hparams.n_head;
auto & mem_per_token = lctx.mem_per_token; auto & mem_per_token = lctx.mem_per_token;
auto & buf_compute = lctx.buf_compute;
// TODO: fix this hardcoded size
static size_t buf_size = 2048u*1024*1024; // TMP !!!
static void * buf = malloc(buf_size);
if (mem_per_token > 0 && mem_per_token*N > buf_size) {
const size_t buf_size_new = 1.3*(mem_per_token*N); // add 30% to account for ggml object overhead
//fprintf(stderr, "\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
// reallocate
buf_size = buf_size_new;
buf = realloc(buf, buf_size);
if (buf == nullptr) {
fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
return false;
}
}
struct ggml_init_params params = { struct ggml_init_params params = {
/*.mem_size =*/ buf_size, /*.mem_size =*/ buf_compute.size(),
/*.mem_buffer =*/ buf, /*.mem_buffer =*/ buf_compute.data(),
}; };
struct ggml_context * ctx0 = ggml_init(params); struct ggml_context * ctx0 = ggml_init(params);
@ -667,6 +842,8 @@ static bool llama_eval_internal(
struct ggml_tensor * cur; struct ggml_tensor * cur;
lctx.use_buf(ctx0, 0);
// norm // norm
{ {
cur = ggml_rms_norm(ctx0, inpL); cur = ggml_rms_norm(ctx0, inpL);
@ -685,8 +862,8 @@ static bool llama_eval_internal(
// store key and value to memory // store key and value to memory
if (N >= 1) { if (N >= 1) {
struct ggml_tensor * k = ggml_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past)); struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));
struct ggml_tensor * v = ggml_view_1d(ctx0, model.memory_v, N*n_embd, (ggml_element_size(model.memory_v)*n_embd)*(il*n_ctx + n_past)); struct ggml_tensor * v = ggml_view_1d(ctx0, kv_self.v, N*n_embd, (ggml_element_size(kv_self.v)*n_embd)*(il*n_ctx + n_past));
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k)); ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v)); ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
@ -707,7 +884,7 @@ static bool llama_eval_internal(
ggml_permute(ctx0, ggml_permute(ctx0,
ggml_rope(ctx0, ggml_rope(ctx0,
ggml_reshape_3d(ctx0, ggml_reshape_3d(ctx0,
ggml_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_k)*n_embd), ggml_view_1d(ctx0, kv_self.k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kv_self.k)*n_embd),
n_embd/n_head, n_head, n_past + N), n_embd/n_head, n_head, n_past + N),
n_past, n_rot, 1), n_past, n_rot, 1),
0, 2, 1, 3); 0, 2, 1, 3);
@ -733,7 +910,7 @@ static bool llama_eval_internal(
ggml_cpy(ctx0, ggml_cpy(ctx0,
ggml_permute(ctx0, ggml_permute(ctx0,
ggml_reshape_3d(ctx0, ggml_reshape_3d(ctx0,
ggml_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_v)*n_embd), ggml_view_1d(ctx0, kv_self.v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kv_self.v)*n_embd),
n_embd/n_head, n_head, n_past + N), n_embd/n_head, n_head, n_past + N),
1, 2, 0, 3), 1, 2, 0, 3),
ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_past + N, n_embd/n_head, n_head)); ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_past + N, n_embd/n_head, n_head));
@ -755,6 +932,8 @@ static bool llama_eval_internal(
cur); cur);
} }
lctx.use_buf(ctx0, 1);
struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA); struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
// feed-forward network // feed-forward network
@ -773,7 +952,6 @@ static bool llama_eval_internal(
model.layers[il].w3, model.layers[il].w3,
cur); cur);
cur = ggml_mul_mat(ctx0, cur = ggml_mul_mat(ctx0,
model.layers[il].w1, model.layers[il].w1,
cur); cur);
@ -788,17 +966,20 @@ static bool llama_eval_internal(
cur); cur);
} }
cur = ggml_add(ctx0, cur, inpFF); cur = ggml_add(ctx0, cur, inpFF);
// input for next layer // input for next layer
inpL = cur; inpL = cur;
} }
lctx.use_buf(ctx0, 0);
// used at the end to optionally extract the embeddings // used at the end to optionally extract the embeddings
struct ggml_tensor * embeddings = NULL; struct ggml_tensor * embeddings = NULL;
// norm // norm
{ {
inpL = ggml_rms_norm(ctx0, inpL); inpL = ggml_rms_norm(ctx0, inpL);
// inpL = norm*inpL // inpL = norm*inpL
@ -810,9 +991,9 @@ static bool llama_eval_internal(
} }
// lm_head // lm_head
{ inpL = ggml_mul_mat(ctx0, model.output, inpL);
inpL = ggml_mul_mat(ctx0, model.output, inpL);
} lctx.use_buf(ctx0, -1);
// logits -> probs // logits -> probs
//inpL = ggml_soft_max(ctx0, inpL); //inpL = ggml_soft_max(ctx0, inpL);
@ -854,7 +1035,13 @@ static bool llama_eval_internal(
if (mem_per_token == 0) { if (mem_per_token == 0) {
mem_per_token = ggml_used_mem(ctx0)/N; mem_per_token = ggml_used_mem(ctx0)/N;
} }
//fprintf(stderr, "used_mem = %zu\n", ggml_used_mem(ctx0));
#if 0
printf("\n%s: used_mem = %.3f MB, scratch -- %.3f MB %.3f MB\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);
#endif
ggml_free(ctx0); ggml_free(ctx0);
@ -1427,9 +1614,9 @@ struct llama_context * llama_init_from_file(
ctx->rng = std::mt19937(params.seed); ctx->rng = std::mt19937(params.seed);
ctx->logits_all = params.logits_all; ctx->logits_all = params.logits_all;
ggml_type type_memory = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32; ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
if (!llama_model_load(path_model, *ctx, params.n_ctx, params.n_parts, type_memory, if (!llama_model_load(path_model, *ctx, params.n_ctx, params.n_parts, memory_type,
params.vocab_only)) { params.vocab_only)) {
fprintf(stderr, "%s: failed to load model\n", __func__); fprintf(stderr, "%s: failed to load model\n", __func__);
llama_free(ctx); llama_free(ctx);
@ -1448,6 +1635,17 @@ struct llama_context * llama_init_from_file(
// reserve memory for context buffers // reserve memory for context buffers
{ {
if (!kv_cache_init(ctx->model.hparams, ctx->model.kv_self, memory_type, ctx->model.hparams.n_ctx)) {
fprintf(stderr, "%s: kv_cache_init() failed for self-attention cache\n", __func__);
llama_free(ctx);
return nullptr;
}
{
const size_t memory_size = ggml_nbytes(ctx->model.kv_self.k) + ggml_nbytes(ctx->model.kv_self.v);
fprintf(stderr, "%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
}
const auto & hparams = ctx->model.hparams; const auto & hparams = ctx->model.hparams;
if (params.logits_all) { if (params.logits_all) {
ctx->logits.reserve(hparams.n_ctx*hparams.n_vocab); ctx->logits.reserve(hparams.n_ctx*hparams.n_vocab);
@ -1458,12 +1656,19 @@ struct llama_context * llama_init_from_file(
if (params.embedding){ if (params.embedding){
ctx->embedding.reserve(hparams.n_embd); ctx->embedding.reserve(hparams.n_embd);
} }
ctx->buf_compute.resize(MEM_REQ_EVAL.at(ctx->model.type));
ctx->buf_scratch[0].resize(MEM_REQ_SCRATCH0.at(ctx->model.type));
ctx->buf_scratch[1].resize(MEM_REQ_SCRATCH1.at(ctx->model.type));
} }
return ctx; return ctx;
} }
void llama_free(struct llama_context * ctx) { void llama_free(struct llama_context * ctx) {
kv_cache_free(ctx->model.kv_self);
if (ctx->model.ctx) { if (ctx->model.ctx) {
ggml_free(ctx->model.ctx); ggml_free(ctx->model.ctx);
} }
@ -1619,4 +1824,3 @@ const char * llama_print_system_info(void) {
return s.c_str(); return s.c_str();
} }

23
main.cpp
View File

@ -217,11 +217,23 @@ int main(int argc, char ** argv) {
params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info()); params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
} }
// determine the required inference memory per token: // determine the maximum memory usage needed to do inference for the given n_batch and n_predict parameters
// TODO: better way to do that // uncomment the "used_mem" line in llama.cpp to see the results
{ if (params.mem_test) {
const std::vector<llama_token> tmp = { 0, 1, 2, 3 }; {
llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads); const std::vector<llama_token> tmp(params.n_batch, 0);
llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
}
{
const std::vector<llama_token> tmp = { 0, };
llama_eval(ctx, tmp.data(), tmp.size(), params.n_predict - 1, params.n_threads);
}
llama_print_timings(ctx);
llama_free(ctx);
return 0;
} }
if (params.perplexity) { if (params.perplexity) {
@ -508,7 +520,6 @@ int main(int argc, char ** argv) {
#endif #endif
llama_print_timings(ctx); llama_print_timings(ctx);
llama_free(ctx); llama_free(ctx);
set_console_state(CONSOLE_STATE_DEFAULT); set_console_state(CONSOLE_STATE_DEFAULT);

10
utils.cpp
View File

@ -79,8 +79,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
break; break;
} }
params.n_ctx = std::stoi(argv[i]); params.n_ctx = std::stoi(argv[i]);
} else if (arg == "--memory_f16") { } else if (arg == "--memory_f32") {
params.memory_f16 = true; params.memory_f16 = false;
} else if (arg == "--top_p") { } else if (arg == "--top_p") {
if (++i >= argc) { if (++i >= argc) {
invalid_param = true; invalid_param = true;
@ -111,6 +111,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
break; break;
} }
params.n_batch = std::stoi(argv[i]); params.n_batch = std::stoi(argv[i]);
params.n_batch = std::min(512, params.n_batch);
} else if (arg == "-m" || arg == "--model") { } else if (arg == "-m" || arg == "--model") {
if (++i >= argc) { if (++i >= argc) {
invalid_param = true; invalid_param = true;
@ -131,6 +132,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
params.use_color = true; params.use_color = true;
} else if (arg == "--mlock") { } else if (arg == "--mlock") {
params.use_mlock = true; params.use_mlock = true;
} else if (arg == "--mtest") {
params.mem_test = true;
} else if (arg == "-r" || arg == "--reverse-prompt") { } else if (arg == "-r" || arg == "--reverse-prompt") {
if (++i >= argc) { if (++i >= argc) {
invalid_param = true; invalid_param = true;
@ -193,7 +196,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
fprintf(stderr, " --repeat_penalty N penalize repeat sequence of tokens (default: %.1f)\n", params.repeat_penalty); fprintf(stderr, " --repeat_penalty N penalize repeat sequence of tokens (default: %.1f)\n", params.repeat_penalty);
fprintf(stderr, " -c N, --ctx_size N size of the prompt context (default: %d)\n", params.n_ctx); fprintf(stderr, " -c N, --ctx_size N size of the prompt context (default: %d)\n", params.n_ctx);
fprintf(stderr, " --ignore-eos ignore end of stream token and continue generating\n"); fprintf(stderr, " --ignore-eos ignore end of stream token and continue generating\n");
fprintf(stderr, " --memory_f16 use f16 instead of f32 for memory key+value\n"); fprintf(stderr, " --memory_f32 use f32 instead of f16 for memory key+value\n");
fprintf(stderr, " --temp N temperature (default: %.1f)\n", params.temp); fprintf(stderr, " --temp N temperature (default: %.1f)\n", params.temp);
fprintf(stderr, " --n_parts N number of model parts (default: -1 = determine from dimensions)\n"); fprintf(stderr, " --n_parts N number of model parts (default: -1 = determine from dimensions)\n");
fprintf(stderr, " -b N, --batch_size N batch size for prompt processing (default: %d)\n", params.n_batch); fprintf(stderr, " -b N, --batch_size N batch size for prompt processing (default: %d)\n", params.n_batch);
@ -201,6 +204,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
if (ggml_mlock_supported()) { if (ggml_mlock_supported()) {
fprintf(stderr, " --mlock force system to keep model in RAM rather than swapping or compressing\n"); fprintf(stderr, " --mlock force system to keep model in RAM rather than swapping or compressing\n");
} }
fprintf(stderr, " --mtest compute maximum memory usage\n");
fprintf(stderr, " -m FNAME, --model FNAME\n"); fprintf(stderr, " -m FNAME, --model FNAME\n");
fprintf(stderr, " model path (default: %s)\n", params.model.c_str()); fprintf(stderr, " model path (default: %s)\n", params.model.c_str());
fprintf(stderr, "\n"); fprintf(stderr, "\n");

16
utils.h
View File

@ -14,12 +14,13 @@
// //
struct gpt_params { struct gpt_params {
int32_t seed = -1; // RNG seed int32_t seed = -1; // RNG seed
int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency()); int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
int32_t n_predict = 128; // new tokens to predict int32_t n_predict = 128; // new tokens to predict
int32_t repeat_last_n = 64; // last n tokens to penalize int32_t repeat_last_n = 64; // last n tokens to penalize
int32_t n_parts = -1; // amount of model parts (-1 = determine from model dimensions) int32_t n_parts = -1; // amount of model parts (-1 = determine from model dimensions)
int32_t n_ctx = 512; //context size int32_t n_ctx = 512; // context size
int32_t n_batch = 8; // batch size for prompt processing
// sampling parameters // sampling parameters
int32_t top_k = 40; int32_t top_k = 40;
@ -27,15 +28,13 @@ struct gpt_params {
float temp = 0.80f; float temp = 0.80f;
float repeat_penalty = 1.10f; float repeat_penalty = 1.10f;
int32_t n_batch = 8; // batch size for prompt processing
std::string model = "models/lamma-7B/ggml-model.bin"; // model path std::string model = "models/lamma-7B/ggml-model.bin"; // model path
std::string prompt = ""; std::string prompt = "";
std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
bool memory_f16 = false; // use f16 instead of f32 for memory kv bool memory_f16 = true; // use f16 instead of f32 for memory kv
bool random_prompt = false; // do not randomize prompt if none provided bool random_prompt = false; // do not randomize prompt if none provided
bool use_color = false; // use color to distinguish generations and inputs bool use_color = false; // use color to distinguish generations and inputs
bool interactive = false; // interactive mode bool interactive = false; // interactive mode
@ -47,6 +46,7 @@ struct gpt_params {
bool ignore_eos = false; // do not stop generating after eos bool ignore_eos = false; // do not stop generating after eos
bool perplexity = false; // compute perplexity over the prompt bool perplexity = false; // compute perplexity over the prompt
bool use_mlock = false; // use mlock to keep model in memory bool use_mlock = false; // use mlock to keep model in memory
bool mem_test = false; // compute maximum memory usage
}; };
bool gpt_params_parse(int argc, char ** argv, gpt_params & params); bool gpt_params_parse(int argc, char ** argv, gpt_params & params);