mirror of
https://github.com/ggml-org/llama.cpp.git
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745aa5319b
* llama : deprecate llama_kv_self_ API ggml-ci * llama : allow llama_memory_(nullptr) ggml-ci * memory : add flag for optional data clear in llama_memory_clear ggml-ci
275 lines
8.1 KiB
C++
275 lines
8.1 KiB
C++
#include "arg.h"
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#include "common.h"
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#include "log.h"
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#include "llama.h"
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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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#include <algorithm>
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static void print_usage(int, char ** argv) {
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LOG("\nexample usage:\n");
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LOG("\n %s -m model.gguf --junk 250 --pos 90 --keep 32 --grp-attn-n 2 [--seed 1234]\n", argv[0]);
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LOG("\n");
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}
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int main(int argc, char ** argv) {
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common_params params;
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params.n_junk = 250;
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params.n_keep = 32;
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params.i_pos = -1;
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if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_PASSKEY, print_usage)) {
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return 1;
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}
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common_init();
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int n_junk = params.n_junk;
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int n_keep = params.n_keep;
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int n_grp = params.grp_attn_n;
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int i_pos = params.i_pos;
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if (i_pos == -1) {
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i_pos = rand() % n_junk;
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}
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const std::string prompt_prefix = "There is an important info hidden inside a lot of irrelevant text. Find it and memorize them. I will quiz you about the important information there.";
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const std::string prompt_suffix = " What is the pass key? The pass key is";
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// generate junk text
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params.prompt = prompt_prefix;
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const int passkey = rand() % 50000 + 1;
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for (int i = 0; i < n_junk; i++) {
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if (i % n_junk == i_pos) {
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params.prompt += " The pass key is " + std::to_string(passkey) + ". Remember it. " + std::to_string(passkey) + " is the pass key.";
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}
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params.prompt += " The grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.";
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}
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params.prompt += prompt_suffix;
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// init LLM
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llama_backend_init();
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llama_numa_init(params.numa);
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// initialize the model
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llama_model_params model_params = common_model_params_to_llama(params);
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llama_model * model = llama_model_load_from_file(params.model.path.c_str(), model_params);
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if (model == NULL) {
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LOG_ERR("%s: unable to load model\n" , __func__);
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return 1;
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}
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const llama_vocab * vocab = llama_model_get_vocab(model);
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// initialize the context
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llama_context_params ctx_params = common_context_params_to_llama(params);
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ctx_params.n_ctx = llama_model_n_ctx_train(model)*n_grp + n_keep;
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GGML_ASSERT(ctx_params.n_batch % n_grp == 0 && "n_batch must be divisible by n_grp");
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llama_context * ctx = llama_init_from_model(model, ctx_params);
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if (ctx == NULL) {
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LOG_ERR("%s: failed to create the llama_context\n" , __func__);
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return 1;
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}
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auto sparams = llama_sampler_chain_default_params();
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llama_sampler * smpl = llama_sampler_chain_init(sparams);
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llama_sampler_chain_add(smpl, llama_sampler_init_greedy());
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// tokenize the prompt
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std::vector<llama_token> tokens_list;
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tokens_list = common_tokenize(ctx, params.prompt, true);
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// tokenize the prefix and use it as a sink
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const int n_tokens_prefix = common_tokenize(ctx, prompt_prefix, true).size();
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const int n_tokens_all = tokens_list.size();
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// we leave a margin of 16 tokens for the generated text - it should contain just the passkey
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const int n_predict = 16;
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// total length of the sequences including the prompt
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const int n_len = n_tokens_all + n_predict;
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const int n_ctx = llama_n_ctx(ctx) - n_keep;
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const int n_kv_req = llama_n_ctx(ctx);
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const int n_batch = ctx_params.n_batch;
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const int n_batch_grp = ctx_params.n_batch/n_grp;
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LOG_INF("\n%s: n_len = %d, n_ctx = %d, n_kv_req = %d, n_grp = %d, n_batch = %d, n_junk = %d, i_pos = %d\n", __func__, n_len, n_ctx, n_kv_req, n_grp, n_batch, n_junk, i_pos);
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// print the prompt token-by-token
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LOG_INF("\n");
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LOG_INF("prefix tokens: %d\n", n_tokens_prefix);
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LOG_INF("prompt tokens: %d\n", n_tokens_all);
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//LOG_INF("prompt: %s\n", params.prompt.c_str());
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llama_batch batch = llama_batch_init(params.n_batch, 0, 1);
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int n_past = 0;
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auto * mem = llama_get_memory(ctx);
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// fill the KV cache
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for (int i = 0; i < n_ctx; i += n_batch) {
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if (i > 0 && n_grp > 1) {
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// if SelfExtend is enabled, we compress the position from the last batch by a factor of n_grp
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const int ib = i/n_batch - 1;
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const int bd = n_batch_grp*(n_grp - 1);
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llama_memory_seq_add(mem, 0, n_past - n_batch, n_past, ib*bd);
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llama_memory_seq_div(mem, 0, n_past - n_batch + ib*bd, n_past + ib*bd, n_grp);
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n_past = llama_memory_seq_pos_max(mem, 0) + 1;
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}
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common_batch_clear(batch);
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for (int j = 0; j < n_batch && i + j < n_tokens_all; j++) {
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common_batch_add(batch, tokens_list[i + j], n_past++, { 0 }, false);
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}
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if (i + n_batch >= n_tokens_all) {
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batch.logits[batch.n_tokens - 1] = true;
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}
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if (llama_decode(ctx, batch) != 0) {
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LOG_INF("%s: llama_decode() failed\n", __func__);
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return 1;
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}
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LOG_INF("%s: processed: [%6d, %6d)\n", __func__, i, std::min(i + n_batch, n_tokens_all));
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if (i + n_batch >= n_tokens_all) {
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break;
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}
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}
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for (int i = n_ctx; i < n_tokens_all; i += n_batch) {
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const int n_discard = n_batch;
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LOG_INF("%s: shifting KV cache with %d\n", __func__, n_discard);
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llama_memory_seq_rm (mem, 0, n_keep , n_keep + n_discard);
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llama_memory_seq_add(mem, 0, n_keep + n_discard, n_ctx, -n_discard);
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n_past = llama_memory_seq_pos_max(mem, 0) + 1;
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common_batch_clear(batch);
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for (int j = 0; j < n_batch && i + j < n_tokens_all; j++) {
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common_batch_add(batch, tokens_list[i + j], n_past++, { 0 }, false);
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}
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if (i + n_batch >= n_tokens_all) {
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batch.logits[batch.n_tokens - 1] = true;
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}
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if (llama_decode(ctx, batch) != 0) {
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LOG_ERR("%s: llama_decode() failed\n", __func__);
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return 1;
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}
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LOG_INF("%s: processed: [%6d, %6d)\n", __func__, i, std::min(i + n_batch, n_tokens_all));
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}
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{
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const int n_discard = n_past - n_ctx + n_predict;
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if (n_discard > 0) {
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LOG_INF("%s: shifting KV cache with %d to free space for the answer\n", __func__, n_discard);
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llama_memory_seq_rm (mem, 0, n_keep , n_keep + n_discard);
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llama_memory_seq_add(mem, 0, n_keep + n_discard, n_ctx, -n_discard);
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n_past = llama_memory_seq_pos_max(mem, 0) + 1;
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}
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}
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LOG_INF("\n");
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LOG_INF("%s: passkey = %d, inserted at position %d / %d (token pos: ~%d)\n", __func__, passkey, i_pos, n_junk, (i_pos * n_tokens_all) / n_junk);
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LOG_INF("\n");
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// main loop
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int n_cur = n_tokens_all;
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int n_decode = 0;
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LOG_INF("%s", prompt_suffix.c_str());
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const auto t_main_start = ggml_time_us();
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while (n_cur <= n_len) {
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// sample the next token
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{
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const llama_token new_token_id = llama_sampler_sample(smpl, ctx, batch.n_tokens - 1);
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// is it an end of generation?
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if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_len) {
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LOG("\n");
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break;
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}
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LOG("%s", common_token_to_piece(ctx, new_token_id).c_str());
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n_decode += 1;
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// prepare the next batch
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common_batch_clear(batch);
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// push this new token for next evaluation
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common_batch_add(batch, new_token_id, n_past++, { 0 }, true);
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}
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n_cur += 1;
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// evaluate the current batch with the transformer model
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if (llama_decode(ctx, batch)) {
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LOG_ERR("%s : failed to eval, return code %d\n", __func__, 1);
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return 1;
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}
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}
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LOG("\n");
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const auto t_main_end = ggml_time_us();
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LOG_INF("%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n",
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__func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f));
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LOG("\n");
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llama_perf_context_print(ctx);
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LOG("\n");
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llama_sampler_free(smpl);
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llama_batch_free(batch);
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llama_free(ctx);
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llama_model_free(model);
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llama_backend_free();
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return 0;
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}
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