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common : reimplement logging (#9418)

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https://github.com/ggerganov/llama.cpp/pull/9418
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Georgi Gerganov 2024-09-15 20:46:12 +03:00 • committed by GitHub
parent e6deac31f7
commit 6262d13e0b
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54 changed files with 2092 additions and 2419 deletions
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107
examples/speculative/speculative.cpp
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@ -1,13 +1,16 @@
#include "arg.h"
#include "common.h"
#include "sampling.h"
#include "log.h"
#include "llama.h"
#include <algorithm>
#include <cstdio>
#include <cstring>
#include <random>
#include <set>
#include <string>
#include <vector>
#include <set>
#include <random>
#define SPEC_VOCAB_MAX_SIZE_DIFFERENCE 100
#define SPEC_VOCAB_CHECK_START_TOKEN_ID 5
@ -33,8 +36,10 @@ int main(int argc, char ** argv) {
return 1;
}
gpt_init();
if (params.model_draft.empty()) {
fprintf(stderr, "%s: error: --model-draft is required\n", __func__);
LOG_ERR("%s: --model-draft is required\n", __func__);
return 1;
}
@ -47,12 +52,6 @@ int main(int argc, char ** argv) {
std::default_random_engine rng(params.sparams.seed);
std::uniform_real_distribution<> u_dist;
#ifndef LOG_DISABLE_LOGS
log_set_target(log_filename_generator("speculative", "log"));
LOG_TEE("Log start\n");
log_dump_cmdline(argc, argv);
#endif // LOG_DISABLE_LOGS
// init llama.cpp
llama_backend_init();
llama_numa_init(params.numa);
@ -81,14 +80,14 @@ int main(int argc, char ** argv) {
ctx_dft = llama_init_dft.context;
const bool vocab_type_tgt = llama_vocab_type(model_tgt);
LOG("vocab_type tgt: %d\n", vocab_type_tgt);
LOG_DBG("vocab_type tgt: %d\n", vocab_type_tgt);
const bool vocab_type_dft = llama_vocab_type(model_dft);
LOG("vocab_type dft: %d\n", vocab_type_dft);
LOG_DBG("vocab_type dft: %d\n", vocab_type_dft);
if (vocab_type_tgt != vocab_type_dft) {
fprintf(stderr, "%s: error: draft model vocab type must match target model to use speculation but ", __func__);
fprintf(stderr, "vocab_type_dft = %d while vocab_type_tgt = %d\n", vocab_type_dft, vocab_type_tgt);
LOG_ERR("%s: draft model vocab type must match target model to use speculation but ", __func__);
LOG_ERR("vocab_type_dft = %d while vocab_type_tgt = %d\n", vocab_type_dft, vocab_type_tgt);
return 1;
}
@ -98,7 +97,7 @@ int main(int argc, char ** argv) {
llama_token_bos(model_tgt) != llama_token_bos(model_dft) ||
llama_token_eos(model_tgt) != llama_token_eos(model_dft)
) {
fprintf(stderr, "%s: error: draft model special tokens must match target model to use speculation\n", __func__);
LOG_ERR("%s: draft model special tokens must match target model to use speculation\n", __func__);
return 1;
}
@ -110,8 +109,8 @@ int main(int argc, char ** argv) {
: n_vocab_dft - n_vocab_tgt;
if (vocab_diff > SPEC_VOCAB_MAX_SIZE_DIFFERENCE) {
fprintf(stderr, "%s: error: draft model vocab must closely match target model to use speculation but ", __func__);
fprintf(stderr, "target vocab size %d does not match draft vocab size %d - difference %d, max allowed %d\n",
LOG_ERR("%s: draft model vocab must closely match target model to use speculation but ", __func__);
LOG_ERR("target vocab size %d does not match draft vocab size %d - difference %d, max allowed %d\n",
n_vocab_tgt, llama_n_vocab(model_dft), vocab_diff, SPEC_VOCAB_MAX_SIZE_DIFFERENCE);
return 1;
}
@ -120,8 +119,8 @@ int main(int argc, char ** argv) {
const char * token_text_tgt = llama_token_get_text(model_tgt, i);
const char * token_text_dft = llama_token_get_text(model_dft, i);
if (std::strcmp(token_text_tgt, token_text_dft) != 0) {
fprintf(stderr, "%s: error: draft model vocab must match target model to use speculation but ", __func__);
fprintf(stderr, "token %d content differs - target '%s', draft '%s'\n", i,
LOG_ERR("%s: draft model vocab must match target model to use speculation but ", __func__);
LOG_ERR("token %d content differs - target '%s', draft '%s'\n", i,
llama_token_to_piece(ctx_tgt, i).c_str(),
llama_token_to_piece(ctx_dft, i).c_str());
return 1;
@ -138,18 +137,16 @@ int main(int argc, char ** argv) {
const int max_tokens_list_size = max_context_size - 4;
if ((int) inp.size() > max_tokens_list_size) {
fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size);
LOG_ERR("%s: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size);
return 1;
}
fprintf(stderr, "\n\n");
LOG("\n\n");
for (auto id : inp) {
fprintf(stderr, "%s", llama_token_to_piece(ctx_tgt, id).c_str());
LOG("%s", llama_token_to_piece(ctx_tgt, id).c_str());
}
fflush(stderr);
const int n_input = inp.size();
const auto t_enc_start = ggml_time_us();
@ -211,7 +208,7 @@ int main(int argc, char ** argv) {
active_seqs.insert(s);
const auto & tokens = drafts[s].tokens;
LOG("draft %d: %s\n", s, LOG_TOKENS_TOSTR_PRETTY(ctx_dft, tokens).c_str());
LOG_DBG("draft %d: %s\n", s, string_from(ctx_dft, tokens).c_str());
}
int i_dft = 0;
@ -254,7 +251,7 @@ int main(int argc, char ** argv) {
continue;
}
LOG("verifying sequence #%d at pos #%d from %d active sequence(s)\n", s, i_dft, (int) active_seqs.size());
LOG_DBG("verifying sequence #%d at pos #%d from %d active sequence(s)\n", s, i_dft, (int) active_seqs.size());
float r = u_dist(rng);
llama_token_data_array dist_dft = { drafts[s].dists[i_dft].data() , drafts[s].dists[i_dft].size(), LLAMA_TOKEN_NULL, true };
@ -272,7 +269,7 @@ int main(int argc, char ** argv) {
break;
}
}
LOG("r = %f, p_dft = %f, p_tgt = %f\n", r, p_dft, p_tgt);
LOG_DBG("r = %f, p_dft = %f, p_tgt = %f\n", r, p_dft, p_tgt);
if (r <= p_tgt / p_dft) {
s_keep = s;
accept = true;
@ -280,10 +277,10 @@ int main(int argc, char ** argv) {
token_str = llama_token_to_piece(ctx_tgt, token_id);
gpt_sampler_accept(smpl, token_id, true);
LOG("draft token %d of sequence %d (%d, '%s') accepted\n", i_dft, s, token_id, token_str.c_str());
LOG_DBG("draft token %d of sequence %d (%d, '%s') accepted\n", i_dft, s, token_id, token_str.c_str());
break;
} else {
LOG("draft token %d of sequence %d (%d, '%s') rejected\n", i_dft, s, drafts[s].tokens[i_dft], llama_token_to_piece(ctx_tgt, drafts[s].tokens[i_dft]).c_str());
LOG_DBG("draft token %d of sequence %d (%d, '%s') rejected\n", i_dft, s, drafts[s].tokens[i_dft], llama_token_to_piece(ctx_tgt, drafts[s].tokens[i_dft]).c_str());
drafts[s].active = false;
// calculate residual probability
@ -338,7 +335,7 @@ int main(int argc, char ** argv) {
if (!accept) {
// all drafted tokens were rejected
// sample from the target model
LOG("all drafted tokens were rejected, sampling from residual distribution\n");
LOG_DBG("all drafted tokens were rejected, sampling from residual distribution\n");
std::vector<float> probs(dist_tgt.size);
for (size_t i = 0; i < dist_tgt.size; ++i) {
probs[i] = dist_tgt.data[i].p;
@ -356,13 +353,11 @@ int main(int argc, char ** argv) {
// greedy verification
// sample from the target model
LOG("sampling target: s_keep = %3d, i_dft = %3d, i_batch_tgt = %3d\n", s_keep, i_dft, drafts[s_keep].i_batch_tgt[i_dft]);
LOG_DBG("sampling target: s_keep = %3d, i_dft = %3d, i_batch_tgt = %3d\n", s_keep, i_dft, drafts[s_keep].i_batch_tgt[i_dft]);
token_id = gpt_sampler_sample(smpl, ctx_tgt, drafts[s_keep].i_batch_tgt[i_dft]);
gpt_sampler_accept(smpl, token_id, true);
//LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, smpl->prev).c_str());
token_str = llama_token_to_piece(ctx_tgt, token_id);
for (int s = 0; s < n_seq_dft; ++s) {
@ -371,7 +366,7 @@ int main(int argc, char ** argv) {
}
if (i_dft < (int) drafts[s].tokens.size() && token_id == drafts[s].tokens[i_dft]) {
LOG("the sampled target token matches the %dth drafted token of sequence %d (%d, '%s') - accepted\n", i_dft, s, token_id, token_str.c_str());
LOG_DBG("the sampled target token matches the %dth drafted token of sequence %d (%d, '%s') - accepted\n", i_dft, s, token_id, token_str.c_str());
s_keep = s;
accept = true;
@ -393,26 +388,24 @@ int main(int argc, char ** argv) {
++i_dft;
if (params.use_color) {
// Color token according to its origin sequence
printf("\u001b[%dm%s\u001b[37m", (36 - s_keep % 6), token_str.c_str());
LOG("\u001b[%dm%s\u001b[37m", (36 - s_keep % 6), token_str.c_str());
} else {
printf("%s", token_str.c_str());
LOG("%s", token_str.c_str());
}
fflush(stdout);
continue;
} else {
printf("%s", token_str.c_str());
fflush(stdout);
LOG("%s", token_str.c_str());
break;
}
}
}
{
LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", token_id, token_str.c_str());
LOG_DBG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", token_id, token_str.c_str());
// TODO: simplify
{
LOG("keeping sequence %d, n_past_tgt = %d, n_past_dft = %d\n", s_keep, n_past_tgt, n_past_dft);
LOG_DBG("keeping sequence %d, n_past_tgt = %d, n_past_dft = %d\n", s_keep, n_past_tgt, n_past_dft);
llama_kv_cache_seq_keep(ctx_dft, s_keep);
llama_kv_cache_seq_cp (ctx_dft, s_keep, 0, -1, -1);
@ -439,7 +432,7 @@ int main(int argc, char ** argv) {
llama_batch_add (batch_dft, token_id, n_past_dft, { 0 }, true);
llama_kv_cache_seq_rm(ctx_dft, 0, n_past_dft, -1);
// LOG("dft batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_dft, batch_dft).c_str());
// LOG_DBG("dft batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_dft, batch_dft).c_str());
llama_decode(ctx_dft, batch_dft);
++n_past_dft;
@ -486,7 +479,7 @@ int main(int argc, char ** argv) {
const auto * cur_p = gpt_sampler_get_candidates(drafts[s].smpl);
for (int k = 0; k < std::min(n_seq_dft + 3, (int) cur_p->size); ++k) {
LOG(" - draft candidate %3d for seq %3d, pos %3d: %6d (%8.3f) '%s'\n",
LOG_DBG(" - draft candidate %3d for seq %3d, pos %3d: %6d (%8.3f) '%s'\n",
k, s, i, cur_p->data[k].id, cur_p->data[k].p, llama_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
}
@ -495,7 +488,7 @@ int main(int argc, char ** argv) {
// attempt to split the branch if the probability is high enough
for (int f = 1; f < 8; ++f) {
if (n_seq_cur < n_seq_dft && cur_p->data[f].p > p_split) {
LOG("splitting seq %3d into %3d\n", s, n_seq_cur);
LOG_DBG("splitting seq %3d into %3d\n", s, n_seq_cur);
llama_kv_cache_seq_rm(ctx_dft, n_seq_cur, -1, -1);
llama_kv_cache_seq_cp(ctx_dft, s, n_seq_cur, -1, -1);
@ -584,7 +577,7 @@ int main(int argc, char ** argv) {
llama_kv_cache_seq_cp(ctx_tgt, 0, s, -1, -1);
}
// LOG("target batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_tgt, batch_tgt).c_str());
// LOG_DBG("target batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_tgt, batch_tgt).c_str());
llama_decode(ctx_tgt, batch_tgt);
++n_past_tgt;
}
@ -602,23 +595,25 @@ int main(int argc, char ** argv) {
auto t_dec_end = ggml_time_us();
LOG_TEE("\n\n");
LOG("\n\n");
LOG_TEE("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input, (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f));
LOG_TEE("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict / ((t_dec_end - t_dec_start) / 1e6f));
LOG_INF("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input, (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f));
LOG_INF("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict / ((t_dec_end - t_dec_start) / 1e6f));
LOG_TEE("\n");
LOG_TEE("n_draft = %d\n", n_draft);
LOG_TEE("n_predict = %d\n", n_predict);
LOG_TEE("n_drafted = %d\n", n_drafted);
LOG_TEE("n_accept = %d\n", n_accept);
LOG_TEE("accept = %.3f%%\n", 100.0f * n_accept / n_drafted);
LOG_INF("\n");
LOG_INF("n_draft = %d\n", n_draft);
LOG_INF("n_predict = %d\n", n_predict);
LOG_INF("n_drafted = %d\n", n_drafted);
LOG_INF("n_accept = %d\n", n_accept);
LOG_INF("accept = %.3f%%\n", 100.0f * n_accept / n_drafted);
LOG_TEE("\ndraft:\n\n");
LOG_INF("\n");
LOG_INF("draft:\n\n");
// TODO: print sampling/grammar timings for all drafts
llama_perf_context_print(ctx_dft);
LOG_TEE("\ntarget:\n\n");
LOG_INF("\n");
LOG_INF("target:\n\n");
gpt_perf_print(ctx_tgt, smpl);
gpt_sampler_free(smpl);
@ -637,7 +632,7 @@ int main(int argc, char ** argv) {
llama_backend_free();
fprintf(stderr, "\n\n");
LOG("\n\n");
return 0;
}