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#include "ggml.h"
#include "llama.h"
#include "common.h"
#include "llama-vocab.h"
#ifdef _WIN32
#define WIN32_LEAN_AND_MEAN
#ifndef NOMINMAX
# define NOMINMAX
#endif
#include <windows.h>
#endif
#include <algorithm>
#include <cstdlib>
#include <cstdio>
#include <string>
#include <vector>
static void print_usage(int, char ** argv) {
LOG_TEE("\nexample usage:\n");
LOG_TEE("\n %s -m model.gguf -c 8192 -b 2048 -ub 512\n", argv[0]);
LOG_TEE("\n");
}
int main(int argc, char ** argv) {
gpt_params params;
if (!gpt_params_parse(argc, argv, params)) {
print_usage(argc, argv);
return 1;
}
// init LLM
llama_backend_init();
llama_numa_init(params.numa);
// initialize the model
llama_model_params model_params = llama_model_params_from_gpt_params(params);
llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
if (model == NULL) {
fprintf(stderr , "%s: error: unable to load model\n" , __func__);
return 1;
}
llama_context_params ctx_params = llama_context_params_from_gpt_params(params);
llama_context * ctx = llama_new_context_with_model(model, ctx_params);
if (ctx == NULL) {
fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
return 1;
}
const unsigned int n_kv_max = llama_n_ctx(ctx);
const llama_vocab * vocab = llama_get_vocab(ctx);
llama_token bos = llama_token_bos_impl(*vocab);
//llama_token eos = llama_token_eos_impl(*vocab);
const unsigned int n_vocab = llama_n_vocab(model);
// decode in batches of ctx_params.n_batch tokens
auto decode_helper = [](llama_context * ctx, llama_batch & batch, int32_t n_batch) {
for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) {
const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
llama_batch batch_view = {
n_tokens,
batch.token + i,
nullptr,
batch.pos + i,
batch.n_seq_id + i,
batch.seq_id + i,
batch.logits + i,
};
const int ret = llama_decode(ctx, batch_view);
if (ret != 0) {
LOG_TEE("failed to decode the batch, n_batch = %d, ret = %d\n", n_batch, ret);
return false;
}
llama_synchronize(ctx);
}
return true;
};
const unsigned int pp = params.n_ubatch;
const unsigned int tg = params.n_ubatch / 4;
if (!params.sweep_bench_output_jsonl) {
LOG_TEE("\n");
LOG_TEE("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, flash_attn = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, params.n_batch, params.n_ubatch, params.flash_attn, params.n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
LOG_TEE("\n");
LOG_TEE("|%6s | %6s | %6s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s");
LOG_TEE("|%6s-|-%6s-|-%6s-|-%8s-|-%8s-|-%8s-|-%8s-|\n", "------", "------", "------", "--------", "--------", "--------", "--------");
}
llama_batch batch = llama_batch_init(n_kv_max, 0, 1);
// warm up
if (params.warmup) {
llama_batch_add(batch, bos, 0, { 0 }, false);
if (!decode_helper(ctx, batch, ctx_params.n_batch)) {
LOG_TEE("%s: llama_decode() failed\n", __func__);
return 1;
}
}
if (params.batch_warmup) {
// clean up KV cache after generation
llama_kv_cache_seq_rm(ctx, 0, params.n_ubatch, -1);
// prepare batch of pp size for prompt processing performance measurement
llama_batch_clear(batch);
for (unsigned int i = 0; i < params.n_ubatch; ++i) {
llama_batch_add(batch, std::rand() % n_vocab, i, { 0 }, false);
}
if (!decode_helper(ctx, batch, ctx_params.n_ubatch)) {
LOG_TEE("%s: llama_decode() failed\n", __func__);
return 1;
}
}
llama_batch_clear(batch);
llama_kv_cache_clear(ctx);
for (unsigned int n_kv = 0; n_kv < n_kv_max; n_kv += params.n_ubatch) {
// clean up KV cache before generation
llama_kv_cache_seq_rm(ctx, 0, n_kv, -1);
// first measure token generation performance at this context size
const auto t_tg_start = ggml_time_us();
for (unsigned int i = 0; i < tg; ++i) {
llama_batch_clear(batch);
llama_batch_add(batch, std::rand() % n_vocab, n_kv + i, { 0 }, true);
if (!decode_helper(ctx, batch, ctx_params.n_batch)) {
LOG_TEE("%s: llama_decode() failed\n", __func__);
return 1;
}
}
const auto t_tg_end = ggml_time_us();
// clean up KV cache after generation
llama_kv_cache_seq_rm(ctx, 0, n_kv, -1);
// prepare batch of pp size for prompt processing performance measurement
llama_batch_clear(batch);
for (unsigned int i = 0; i < pp; ++i) {
llama_batch_add(batch, std::rand() % n_vocab, n_kv + i, { 0 }, false);
}
batch.logits[batch.n_tokens - 1] = true;
// measure prompt processing performance
const auto t_pp_start = ggml_time_us();
if (!decode_helper(ctx, batch, ctx_params.n_batch)) {
LOG_TEE("%s: llama_decode() failed\n", __func__);
return 1;
}
const auto t_pp_end = ggml_time_us();
// calculate and print metrics
const float t_pp = (t_pp_end - t_pp_start) / 1000000.0f;
const float t_tg = (t_tg_end - t_tg_start) / 1000000.0f;
const float speed_pp = pp / t_pp;
const float speed_tg = tg / t_tg;
if(params.sweep_bench_output_jsonl) {
LOG_TEE(
"{\"n_kv_max\": %d, \"n_batch\": %d, \"n_ubatch\": %d, \"flash_attn\": %d, \"n_gpu_layers\": %d, \"n_threads\": %u, \"n_threads_batch\": %u, "
"\"pp\": %d, \"tg\": %d, \"n_kv\": %d, \"t_pp\": %f, \"speed_pp\": %f, \"t_tg\": %f, \"speed_tg\": %f }\n",
n_kv_max, params.n_batch, params.n_ubatch, params.flash_attn, params.n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch,
pp, tg, n_kv, t_pp, speed_pp, t_tg, speed_tg
);
} else {
LOG_TEE("|%6d | %6d | %6d | %8.3f | %8.2f | %8.3f | %8.2f |\n", pp, tg, n_kv, t_pp, speed_pp, t_tg, speed_tg);
}
}
llama_batch_free(batch);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
return 0;
}
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