vbatts/llama.cpp
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

llama : llama_perf + option to disable timings during decode (#9355)

Browse source 
* llama : llama_perf + option to disable timings during decode

ggml-ci

* common : add llama_arg

* Update src/llama.cpp

Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>

* perf : separate functions in the API

ggml-ci

* perf : safer pointer handling + naming update

ggml-ci

* minor : better local var name

* perf : abort on invalid sampler pointer

ggml-ci

---------

Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
This commit is contained in:
Georgi Gerganov 2024-09-13 09:53:38 +03:00 • committed by GitHub
parent bd35cb0ae3
commit 0abc6a2c25
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
23 changed files with 135 additions and 91 deletions
Download patch file Download diff file Expand all files Collapse all files

34
src/llama-sampling.cpp
View file

@ -1669,3 +1669,37 @@ uint32_t llama_sampler_get_seed(const struct llama_sampler * smpl) {
return LLAMA_DEFAULT_SEED;
}
// perf
struct llama_perf_sampler_data llama_perf_sampler(const struct llama_sampler * chain) {
struct llama_perf_sampler_data data = {};
if (chain == nullptr || chain->iface != &llama_sampler_chain_i) {
GGML_ABORT("%s: invalid sampler passed - requires a sampler created with llama_sampler_chain_init()\n", __func__);
}
const auto * ctx = (const struct llama_sampler_chain *) chain->ctx;
data.t_sample_ms = 1e-3 * ctx->t_sample_us;
data.n_sample = std::max(0, ctx->n_sample);
return data;
}
void llama_perf_sampler_print(const struct llama_sampler * chain) {
const auto data = llama_perf_sampler(chain);
LLAMA_LOG_INFO("%s: sampling time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n",
__func__, data.t_sample_ms, data.n_sample, data.t_sample_ms / data.n_sample, 1e3 / data.t_sample_ms * data.n_sample);
}
void llama_perf_sampler_reset(struct llama_sampler * chain) {
if (chain == nullptr || chain->iface != &llama_sampler_chain_i) {
GGML_ABORT("%s: invalid sampler passed - requires a sampler created with llama_sampler_chain_init()\n", __func__);
}
auto * ctx = (struct llama_sampler_chain *) chain->ctx;
ctx->t_sample_us = ctx->n_sample = 0;
}

103
src/llama.cpp
View file

@ -2486,6 +2486,7 @@ struct llama_cparams {
bool causal_attn;
bool offload_kqv;
bool flash_attn;
bool no_perf;
enum llama_pooling_type pooling_type;
@ -6661,8 +6662,6 @@ static bool llm_load_tensors(
bool use_mlock,
llama_progress_callback progress_callback,
void * progress_callback_user_data) {
model.t_start_us = ggml_time_us();
auto & hparams = model.hparams;
model.split_mode = split_mode;
@ -8593,14 +8592,13 @@ static bool llm_load_tensors(
}
}
// loading time will be recalculate after the first eval, so
// we take page faults deferred by mmap() into consideration
model.t_load_us = ggml_time_us() - model.t_start_us;
return true;
}
// Returns 0 on success, -1 on error, and -2 on cancellation via llama_progress_callback
static int llama_model_load(const std::string & fname, llama_model & model, llama_model_params & params) {
model.t_start_us = ggml_time_us();
try {
llama_model_loader ml(fname, params.use_mmap, params.check_tensors, params.kv_overrides);
@ -8662,6 +8660,10 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam
return -1;
}
// loading time will be recalculate after the first eval, so
// we take page faults deferred by mmap() into consideration
model.t_load_us = ggml_time_us() - model.t_start_us;
return 0;
}
@ -17949,6 +17951,7 @@ struct llama_context_params llama_context_default_params() {
/*.embeddings =*/ false,
/*.offload_kqv =*/ true,
/*.flash_attn =*/ false,
/*.no_perf =*/ true,
/*.abort_callback =*/ nullptr,
/*.abort_callback_data =*/ nullptr,
};
@ -18159,6 +18162,7 @@ struct llama_context * llama_new_context_with_model(
cparams.embeddings = params.embeddings;
cparams.offload_kqv = params.offload_kqv;
cparams.flash_attn = params.flash_attn;
cparams.no_perf = params.no_perf;
cparams.pooling_type = params.pooling_type;
cparams.n_ctx = params.n_ctx == 0 ? hparams.n_ctx_train : params.n_ctx;
@ -20077,10 +20081,14 @@ void llama_synchronize(struct llama_context * ctx) {
// add the evaluation to the stats
if (ctx->n_queued_tokens == 1) {
ctx->t_eval_us += ggml_time_us() - ctx->t_compute_start_us;
if (!ctx->cparams.no_perf) {
ctx->t_eval_us += ggml_time_us() - ctx->t_compute_start_us;
}
ctx->n_eval++;
} else if (ctx->n_queued_tokens > 1) {
ctx->t_p_eval_us += ggml_time_us() - ctx->t_compute_start_us;
if (!ctx->cparams.no_perf) {
ctx->t_p_eval_us += ggml_time_us() - ctx->t_compute_start_us;
}
ctx->n_p_eval += ctx->n_queued_tokens;
}
@ -20688,65 +20696,40 @@ const char * llama_print_system_info(void) {
return s.c_str();
}
void llama_perf_print(const void * ctx, enum llama_perf_type type) {
switch (type) {
case LLAMA_PERF_TYPE_CONTEXT:
{
const auto * p = (const struct llama_context *) ctx;
struct llama_perf_context_data llama_perf_context(const struct llama_context * ctx) {
struct llama_perf_context_data data = {};
const double t_start_ms = 1e-3 * p->t_start_us;
const double t_end_ms = 1.00 * ggml_time_ms();
const double t_load_ms = 1e-3 * p->t_load_us;
const double t_p_eval_ms = 1e-3 * p->t_p_eval_us;
const double t_eval_ms = 1e-3 * p->t_eval_us;
const int32_t n_p_eval = std::max(0, p->n_p_eval);
const int32_t n_eval = std::max(1, p->n_eval);
LLAMA_LOG_INFO("%s: load time = %10.2f ms\n", __func__, t_load_ms);
LLAMA_LOG_INFO("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
__func__, t_p_eval_ms, n_p_eval, t_p_eval_ms / n_p_eval, 1e3 / t_p_eval_ms * n_p_eval);
LLAMA_LOG_INFO("%s: eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n",
__func__, t_eval_ms, n_eval, t_eval_ms / n_eval, 1e3 / t_eval_ms * n_eval);
LLAMA_LOG_INFO("%s: total time = %10.2f ms / %5d tokens\n", __func__, (t_end_ms - t_start_ms), (n_p_eval + n_eval));
} break;
case LLAMA_PERF_TYPE_SAMPLER_CHAIN:
{
const auto * smpl = (const struct llama_sampler *) ctx;
const auto * p = (const struct llama_sampler_chain *) smpl->ctx;
const double t_sampler_ms = 1e-3 * p->t_sample_us;
const int32_t n_sampler = std::max(0, p->n_sample);
LLAMA_LOG_INFO("%s: sampling time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n",
__func__, t_sampler_ms, n_sampler, t_sampler_ms / n_sampler, 1e3 / t_sampler_ms * n_sampler);
} break;
default:
GGML_ABORT("invalid perf type");
if (ctx == nullptr) {
return data;
}
data.t_start_ms = 1e-3 * ctx->t_start_us;
data.t_load_ms = 1e-3 * ctx->t_load_us;
data.t_p_eval_ms = 1e-3 * ctx->t_p_eval_us;
data.t_eval_ms = 1e-3 * ctx->t_eval_us;
data.n_p_eval = std::max(1, ctx->n_p_eval);
data.n_eval = std::max(1, ctx->n_eval);
return data;
}
void llama_perf_reset(void * ctx, enum llama_perf_type type) {
switch (type) {
case LLAMA_PERF_TYPE_CONTEXT:
{
auto * p = (struct llama_context *) ctx;
void llama_perf_context_print(const struct llama_context * ctx) {
const auto data = llama_perf_context(ctx);
p->t_start_us = ggml_time_us();
p->t_eval_us = p->n_eval = 0;
p->t_p_eval_us = p->n_p_eval = 0;
} break;
case LLAMA_PERF_TYPE_SAMPLER_CHAIN:
{
auto * smpl = (struct llama_sampler *) ctx;
auto * p = (struct llama_sampler_chain *) smpl->ctx;
const double t_end_ms = 1e-3 * ggml_time_us();
p->t_sample_us = p->n_sample = 0;
} break;
default:
GGML_ABORT("invalid perf type");
}
LLAMA_LOG_INFO("%s: load time = %10.2f ms\n", __func__, data.t_load_ms);
LLAMA_LOG_INFO("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
__func__, data.t_p_eval_ms, data.n_p_eval, data.t_p_eval_ms / data.n_p_eval, 1e3 / data.t_p_eval_ms * data.n_p_eval);
LLAMA_LOG_INFO("%s: eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n",
__func__, data.t_eval_ms, data.n_eval, data.t_eval_ms / data.n_eval, 1e3 / data.t_eval_ms * data.n_eval);
LLAMA_LOG_INFO("%s: total time = %10.2f ms / %5d tokens\n", __func__, (t_end_ms - data.t_start_ms), (data.n_p_eval + data.n_eval));
}
void llama_perf_context_reset(struct llama_context * ctx) {
ctx->t_start_us = ggml_time_us();
ctx->t_eval_us = ctx->n_eval = 0;
ctx->t_p_eval_us = ctx->n_p_eval = 0;
}
void llama_perf_dump_yaml(FILE * stream, const llama_context * ctx) {