Merge 1c154e9ea5 into 1f0bccb279
This commit is contained in:
Eve
GitHub
commit
95f2c5d475
14 changed files with 66 additions and 32 deletions
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@ -119,6 +119,15 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
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if (params.n_threads <= 0) {
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params.n_threads = std::thread::hardware_concurrency();
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}
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} else if (arg == "-ppt" || arg == "--pp-threads") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.pp_threads = std::stoi(argv[i]);
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if (params.pp_threads <= 0) {
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params.pp_threads = std::thread::hardware_concurrency();
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}
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} else if (arg == "-p" || arg == "--prompt") {
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if (++i >= argc) {
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invalid_param = true;
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@ -545,6 +554,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
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fprintf(stdout, " --color colorise output to distinguish prompt and user input from generations\n");
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fprintf(stdout, " -s SEED, --seed SEED RNG seed (default: -1, use random seed for < 0)\n");
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fprintf(stdout, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
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fprintf(stdout, " -ppt N, --pp-threads N\n");
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fprintf(stdout, " number of threads to use during prompt processing (default: %d)\n", params.pp_threads);
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fprintf(stdout, " -p PROMPT, --prompt PROMPT\n");
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fprintf(stdout, " prompt to start generation with (default: empty)\n");
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fprintf(stdout, " -e process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n");
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@ -19,6 +19,7 @@ int32_t get_num_physical_cores();
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struct gpt_params {
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uint32_t seed = -1; // RNG seed
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int32_t n_threads = get_num_physical_cores();
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int32_t pp_threads = get_num_physical_cores();
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int32_t n_predict = -1; // new tokens to predict
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int32_t n_ctx = 512; // context size
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int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)
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@ -83,7 +83,7 @@ bool eval_float(void * model, float * input, int N){
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if (n_eval > n_batch) {
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n_eval = n_batch;
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}
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if (llama_eval_embd(ctx, (input+i*n_emb), n_eval, n_past, params.n_threads)) {
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if (llama_eval_embd(ctx, (input+i*n_emb), n_eval, n_past, params.n_threads, params.n_threads)) {
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fprintf(stderr, "%s : failed to eval\n", __func__);
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return false;
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}
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@ -104,7 +104,7 @@ bool eval_tokens(void * model, std::vector<llama_token> tokens) {
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if (n_eval > params.n_batch) {
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n_eval = params.n_batch;
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}
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if (llama_eval(ctx, &tokens[i], n_eval, n_past, params.n_threads)) {
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if (llama_eval(ctx, &tokens[i], n_eval, n_past, params.n_threads, params.n_threads)) {
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fprintf(stderr, "%s : failed to eval\n", __func__);
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return false;
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}
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@ -50,8 +50,8 @@ int main(int argc, char ** argv) {
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// print system information
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{
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fprintf(stderr, "\n");
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fprintf(stderr, "system_info: n_threads = %d / %d | %s\n",
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params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
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fprintf(stderr, "system_info: n_threads = %d / %d | pp_threads = %d / %d | %s\n",
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params.n_threads, std::thread::hardware_concurrency(), params.pp_threads, std::thread::hardware_concurrency(), llama_print_system_info());
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}
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int n_past = 0;
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@ -74,7 +74,7 @@ int main(int argc, char ** argv) {
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if (params.embedding){
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if (embd_inp.size() > 0) {
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if (llama_eval(ctx, embd_inp.data(), embd_inp.size(), n_past, params.n_threads)) {
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if (llama_eval(ctx, embd_inp.data(), embd_inp.size(), n_past, params.n_threads, params.pp_threads)) {
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fprintf(stderr, "%s : failed to eval\n", __func__);
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return 1;
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}
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@ -853,7 +853,7 @@ static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_bat
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int n_processed = 0;
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while (n_processed < n_prompt) {
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int n_tokens = std::min(n_prompt - n_processed, n_batch);
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llama_eval(ctx, tokens.data(), n_tokens, n_past + n_processed, n_threads);
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llama_eval(ctx, tokens.data(), n_tokens, n_past + n_processed, n_threads, n_threads);
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n_processed += n_tokens;
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}
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}
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@ -861,7 +861,7 @@ static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_bat
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static void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads) {
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llama_token token = llama_token_bos();
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for (int i = 0; i < n_gen; i++) {
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llama_eval(ctx, &token, 1, n_past + i, n_threads);
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llama_eval(ctx, &token, 1, n_past + i, n_threads, n_threads);
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}
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}
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@ -263,6 +263,7 @@ These options help improve the performance and memory usage of the LLaMA models.
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### Number of Threads
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- `-t N, --threads N`: Set the number of threads to use during computation. For optimal performance, it is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores). Using the correct number of threads can greatly improve performance.
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- `-ppt N, --pp-threads N`: Set the number of threads to use during prompt processing only.
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### Mlock
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@ -133,8 +133,8 @@ int main(int argc, char ** argv) {
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// print system information
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{
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fprintf(stderr, "\n");
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fprintf(stderr, "system_info: n_threads = %d / %d | %s\n",
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params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
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fprintf(stderr, "system_info: n_threads = %d / %d | pp_threads = %d / %d | %s\n",
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params.n_threads, std::thread::hardware_concurrency(), params.pp_threads, std::thread::hardware_concurrency(), llama_print_system_info());
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}
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// determine the maximum memory usage needed to do inference for the given n_batch and n_ctx parameters
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@ -144,7 +144,7 @@ int main(int argc, char ** argv) {
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fprintf(stderr, "%s: testing memory usage for n_batch = %d, n_ctx = %d\n", __func__, params.n_batch, params.n_ctx);
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const std::vector<llama_token> tmp(params.n_batch, llama_token_bos());
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llama_eval(ctx, tmp.data(), tmp.size(), params.n_ctx, params.n_threads);
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llama_eval(ctx, tmp.data(), tmp.size(), params.n_ctx, params.n_threads, params.pp_threads);
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}
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llama_print_timings(ctx);
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@ -406,7 +406,7 @@ int main(int argc, char ** argv) {
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// do one empty run to warm up the model
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{
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const std::vector<llama_token> tmp = { llama_token_bos(), };
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llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
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llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads, params.pp_threads);
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llama_reset_timings(ctx);
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}
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@ -513,7 +513,7 @@ int main(int argc, char ** argv) {
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for (int i = 0; i < input_size; i += params.n_batch) {
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int n_eval = std::min(input_size - i, params.n_batch);
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if (llama_eval(ctx_guidance, input_buf + i, n_eval, n_past_guidance, params.n_threads)) {
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if (llama_eval(ctx_guidance, input_buf + i, n_eval, n_past_guidance, params.n_threads, params.pp_threads)) {
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fprintf(stderr, "%s : failed to eval\n", __func__);
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return 1;
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}
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@ -527,7 +527,7 @@ int main(int argc, char ** argv) {
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if (n_eval > params.n_batch) {
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n_eval = params.n_batch;
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}
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if (llama_eval(ctx, &embd[i], n_eval, n_past, params.n_threads)) {
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if (llama_eval(ctx, &embd[i], n_eval, n_past, params.n_threads, params.pp_threads)) {
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fprintf(stderr, "%s : failed to eval\n", __func__);
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return 1;
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}
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@ -66,7 +66,7 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
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tokens[batch_start] = llama_token_bos();
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}
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if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * n_batch, params.n_threads)) {
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if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * n_batch, params.n_threads, params.pp_threads)) {
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fprintf(stderr, "%s : failed to eval\n", __func__);
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return;
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}
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@ -233,7 +233,7 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
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}
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// Evaluate the query
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if (llama_eval(ctx, query_embd.data(), query_embd.size(), 0, params.n_threads)) {
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if (llama_eval(ctx, query_embd.data(), query_embd.size(), 0, params.n_threads, params.n_threads)) {
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fprintf(stderr, "%s : failed to eval\n", __func__);
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return;
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}
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@ -337,8 +337,8 @@ int main(int argc, char ** argv) {
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// print system information
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{
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fprintf(stderr, "\n");
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fprintf(stderr, "system_info: n_threads = %d / %d | %s\n",
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params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
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fprintf(stderr, "system_info: pp_threads = %d / %d | %s\n",
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params.pp_threads, std::thread::hardware_concurrency(), llama_print_system_info());
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}
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if (params.hellaswag) {
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@ -10,6 +10,7 @@ int main(int argc, char ** argv) {
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gpt_params params;
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params.seed = 42;
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params.n_threads = 4;
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params.pp_threads = 4;
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params.repeat_last_n = 64;
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params.prompt = "The quick brown fox";
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@ -56,7 +57,7 @@ int main(int argc, char ** argv) {
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}
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// evaluate prompt
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llama_eval(ctx, tokens.data(), n_prompt_tokens, n_past, params.n_threads);
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llama_eval(ctx, tokens.data(), n_prompt_tokens, n_past, params.n_threads, params.pp_threads);
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last_n_tokens_data.insert(last_n_tokens_data.end(), tokens.data(), tokens.data() + n_prompt_tokens);
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n_past += n_prompt_tokens;
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@ -93,7 +94,7 @@ int main(int argc, char ** argv) {
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last_n_tokens_data.push_back(next_token);
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printf("%s", next_token_str);
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if (llama_eval(ctx, &next_token, 1, n_past, params.n_threads)) {
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if (llama_eval(ctx, &next_token, 1, n_past, params.n_threads, params.pp_threads)) {
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fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
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llama_free(ctx);
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llama_free_model(model);
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@ -153,7 +154,7 @@ int main(int argc, char ** argv) {
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last_n_tokens_data.push_back(next_token);
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printf("%s", next_token_str);
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if (llama_eval(ctx2, &next_token, 1, n_past, params.n_threads)) {
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if (llama_eval(ctx2, &next_token, 1, n_past, params.n_threads, params.pp_threads)) {
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fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
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llama_free(ctx2);
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llama_free_model(model);
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@ -5,6 +5,7 @@ This example demonstrates a simple HTTP API server and a simple web front end to
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Command line options:
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- `--threads N`, `-t N`: Set the number of threads to use during computation.
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- `-ppt N`, `--pp-threads N`: Set the number of threads to use during prompt processing only.
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- `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`).
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- `-m ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses.
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- `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096.
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@ -385,7 +385,7 @@ struct llama_server_context
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{
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n_eval = params.n_batch;
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}
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if (llama_eval(ctx, &embd[n_past], n_eval, n_past, params.n_threads))
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if (llama_eval(ctx, &embd[n_past], n_eval, n_past, params.n_threads, params.pp_threads))
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{
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LOG_ERROR("failed to eval", {
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{"n_eval", n_eval},
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@ -651,6 +651,8 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms,
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fprintf(stdout, " -h, --help show this help message and exit\n");
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fprintf(stdout, " -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled");
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fprintf(stdout, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
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fprintf(stdout, " -ppt N, --pp-threads N\n");
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fprintf(stdout, " number of threads to use during prompt processing (default: %d)\n", params.pp_threads);
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fprintf(stdout, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
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fprintf(stdout, " -gqa N, --gqa N grouped-query attention factor (TEMP!!! use 8 for LLaMAv2 70B) (default: %d)\n", params.n_gqa);
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fprintf(stdout, " -eps N, --rms-norm-eps N rms norm eps (TEMP!!! use 1e-5 for LLaMAv2) (default: %.1e)\n", params.rms_norm_eps);
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@ -822,6 +824,15 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
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}
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params.n_threads = std::stoi(argv[i]);
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}
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else if (arg == "-ppt" || arg == "--pp-threads")
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{
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if (++i >= argc)
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{
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invalid_param = true;
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break;
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}
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params.pp_threads = std::stoi(argv[i]);
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}
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else if (arg == "-b" || arg == "--batch-size")
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{
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if (++i >= argc)
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@ -1185,6 +1196,7 @@ int main(int argc, char **argv)
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{"commit", BUILD_COMMIT}});
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LOG_INFO("system info", {
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{"n_threads", params.n_threads},
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{"pp_threads", params.pp_threads},
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{"total_threads", std::thread::hardware_concurrency()},
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{"system_info", llama_print_system_info()},
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});
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@ -123,7 +123,7 @@ int main(int argc, char ** argv)
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// Evaluate the tokens :
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//---------------------------------
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if ( llama_eval( ctx , tokens_list.data() , int(tokens_list.size()) , llama_get_kv_cache_token_count( ctx ) , params.n_threads ) )
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if ( llama_eval( ctx , tokens_list.data() , int(tokens_list.size()) , llama_get_kv_cache_token_count( ctx ) , params.n_threads , params.n_threads ) )
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{
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fprintf( stderr, "%s : failed to eval\n" , __func__ );
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return 1;
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21
llama.cpp
21
llama.cpp
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@ -1786,7 +1786,8 @@ static struct ggml_cgraph * llama_build_graph(
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// - embd embeddings input
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// - n_tokens number of tokens
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// - n_past: the context size so far
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// - n_threads: number of threads to use
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// - n_threads: number of threads to use for inference
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// - pp_threads: number of threads to use for prompt processing
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//
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static bool llama_eval_internal(
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llama_context & lctx,
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@ -1795,6 +1796,7 @@ static bool llama_eval_internal(
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int n_tokens,
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int n_past,
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int n_threads,
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int pp_threads,
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const char * cgraph_fname) {
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LLAMA_ASSERT((!tokens && embd) || (tokens && !embd));
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@ -1838,7 +1840,8 @@ static bool llama_eval_internal(
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// for big prompts, if BLAS is enabled, it is better to use only one thread
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// otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
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n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : n_threads;
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pp_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : pp_threads;
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n_threads = N > 1 ? pp_threads : n_threads;
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struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1];
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struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 2];
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@ -3484,7 +3487,7 @@ struct llama_context * llama_new_context_with_model(
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if (ggml_mpi_rank(ctx->ctx_mpi) > 0) {
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// Enter a blocking eval loop with dummy input, letting rank=0 drive the process
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const std::vector<llama_token> tmp(ctx->model.hparams.n_ctx, llama_token_bos());
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while (!llama_eval(ctx, tmp.data(), tmp.size(), 0, 0)) {};
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while (!llama_eval(ctx, tmp.data(), tmp.size(), 0, 0, 0)) {};
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llama_backend_free();
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exit(1);
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}
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@ -4176,8 +4179,9 @@ int llama_eval(
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const llama_token * tokens,
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int n_tokens,
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int n_past,
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int n_threads) {
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if (!llama_eval_internal(*ctx, tokens, nullptr, n_tokens, n_past, n_threads, nullptr)) {
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int n_threads,
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int pp_threads) {
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if (!llama_eval_internal(*ctx, tokens, nullptr, n_tokens, n_past, n_threads, pp_threads, nullptr)) {
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LLAMA_LOG_ERROR("%s: failed to eval\n", __func__);
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return 1;
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}
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@ -4198,8 +4202,9 @@ int llama_eval_embd(
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const float * embd,
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int n_tokens,
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||||
int n_past,
|
||||
int n_threads) {
|
||||
if (!llama_eval_internal(*ctx, nullptr, embd, n_tokens, n_past, n_threads, nullptr)) {
|
||||
int n_threads,
|
||||
int pp_threads) {
|
||||
if (!llama_eval_internal(*ctx, nullptr, embd, n_tokens, n_past, n_threads, pp_threads, nullptr)) {
|
||||
LLAMA_LOG_ERROR("%s: failed to eval\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
|
|
@ -4220,7 +4225,7 @@ int llama_eval_export(struct llama_context * ctx, const char * fname) {
|
|||
|
||||
const std::vector<llama_token> tmp(n_batch, llama_token_bos());
|
||||
|
||||
if (!llama_eval_internal(*ctx, tmp.data(), nullptr, tmp.size(), n_ctx, 1, fname)) {
|
||||
if (!llama_eval_internal(*ctx, tmp.data(), nullptr, tmp.size(), n_ctx, 1, 1, fname)) {
|
||||
LLAMA_LOG_ERROR("%s: failed to eval\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
|
|
|
|||
6
llama.h
6
llama.h
|
|
@ -308,7 +308,8 @@ extern "C" {
|
|||
const llama_token * tokens,
|
||||
int n_tokens,
|
||||
int n_past,
|
||||
int n_threads);
|
||||
int n_threads,
|
||||
int pp_threads);
|
||||
|
||||
// Same as llama_eval, but use float matrix input directly.
|
||||
LLAMA_API int llama_eval_embd(
|
||||
|
|
@ -316,7 +317,8 @@ extern "C" {
|
|||
const float * embd,
|
||||
int n_tokens,
|
||||
int n_past,
|
||||
int n_threads);
|
||||
int n_threads,
|
||||
int pp_threads);
|
||||
|
||||
// Export a static computation graph for context of 511 and batch size of 1
|
||||
// NOTE: since this functionality is mostly for debugging and demonstration purposes, we hardcode these
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue