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https://github.com/ggml-org/llama.cpp.git
synced 2025-11-05 09:36:52 +00:00
Merge branch 'master' into compilade/refactor-kv-cache
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@@ -1,36 +1,13 @@
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#include "arg.h"
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#include "common.h"
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#include "llama.h"
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#include <algorithm>
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#include <cmath>
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#include <cstdio>
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#include <string>
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#include <vector>
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// mutates the input string
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static std::vector<int> parse_list(char * p) {
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std::vector<int> ret;
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char * q = p;
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while (*p) {
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if (*p == ',') {
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*p = '\0';
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ret.push_back(std::atoi(q));
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q = p + 1;
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}
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++p;
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}
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ret.push_back(std::atoi(q));
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return ret;
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}
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static void print_usage(int argc, char ** argv, const gpt_params & params) {
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gpt_params_print_usage(argc, argv, params);
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static void print_usage(int, char ** argv) {
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LOG_TEE("\nexample usage:\n");
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LOG_TEE("\n %s -m model.gguf -c 2048 -b 2048 -ub 512 -npp 128,256,512 -ntg 128,256 -npl 1,2,4,8,16,32 [-pps]\n", argv[0]);
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LOG_TEE("\n");
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@@ -39,8 +16,7 @@ static void print_usage(int argc, char ** argv, const gpt_params & params) {
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int main(int argc, char ** argv) {
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gpt_params params;
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if (!gpt_params_parse(argc, argv, params)) {
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print_usage(argc, argv, params);
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if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_BENCH, print_usage)) {
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return 1;
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}
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@@ -122,12 +98,13 @@ int main(int argc, char ** argv) {
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}
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}
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LOG_TEE("\n");
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LOG_TEE("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, flash_attn = %d, is_pp_shared = %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.is_pp_shared, params.n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
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LOG_TEE("\n");
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LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "B", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s", "T s", "S t/s");
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LOG_TEE("|%6s-|-%6s-|-%4s-|-%6s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|\n", "------", "------", "----", "------", "--------", "--------", "--------", "--------", "--------", "--------");
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if (!params.batched_bench_output_jsonl) {
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LOG_TEE("\n");
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LOG_TEE("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, flash_attn = %d, is_pp_shared = %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.is_pp_shared, params.n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
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LOG_TEE("\n");
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LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "B", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s", "T s", "S t/s");
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LOG_TEE("|%6s-|-%6s-|-%4s-|-%6s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|\n", "------", "------", "----", "------", "--------", "--------", "--------", "--------", "--------", "--------");
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}
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for ( int i_pp = 0; i_pp < (int) n_pp.size(); ++i_pp) {
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for ( int i_tg = 0; i_tg < (int) n_tg.size(); ++i_tg) {
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@@ -195,12 +172,22 @@ int main(int argc, char ** argv) {
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const float speed_tg = pl*tg / t_tg;
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const float speed = n_kv / t;
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LOG_TEE("|%6d | %6d | %4d | %6d | %8.3f | %8.2f | %8.3f | %8.2f | %8.3f | %8.2f |\n", pp, tg, pl, n_kv, t_pp, speed_pp, t_tg, speed_tg, t, speed);
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if(params.batched_bench_output_jsonl) {
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LOG_TEE(
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"{\"n_kv_max\": %d, \"n_batch\": %d, \"n_ubatch\": %d, \"flash_attn\": %d, \"is_pp_shared\": %d, \"n_gpu_layers\": %d, \"n_threads\": %u, \"n_threads_batch\": %u, "
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"\"pp\": %d, \"tg\": %d, \"pl\": %d, \"n_kv\": %d, \"t_pp\": %f, \"speed_pp\": %f, \"t_tg\": %f, \"speed_tg\": %f, \"t\": %f, \"speed\": %f}\n",
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n_kv_max, params.n_batch, params.n_ubatch, params.flash_attn, params.is_pp_shared, params.n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch,
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pp, tg, pl, n_kv, t_pp, speed_pp, t_tg, speed_tg, t, speed
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);
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} else {
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LOG_TEE("|%6d | %6d | %4d | %6d | %8.3f | %8.2f | %8.3f | %8.2f | %8.3f | %8.2f |\n", pp, tg, pl, n_kv, t_pp, speed_pp, t_tg, speed_tg, t, speed);
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}
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}
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}
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}
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llama_print_timings(ctx);
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LOG_TEE("\n");
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llama_perf_context_print(ctx);
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llama_batch_free(batch);
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