Mirrors/llama.cpp
1
0
Fork 0
mirror of https://github.com/ggerganov/llama.cpp.git synced 2024-12-23 21:17:54 +01:00
Code Issues Packages Projects Releases Wiki Activity

llama-bench : use random tokens to improve accuracy with mixtral (#6069)

Browse Source
This commit is contained in:
slaren 2024-03-15 09:22:24 +01:00 committed by GitHub
parent 4755afd1cb
commit b0bc9f4a9d
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 14 additions and 5 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

19
examples/llama-bench/llama-bench.cpp
View File

@ -8,6 +8,7 @@
#include <cstdio>
#include <cstring>
#include <ctime>
#include <cstdlib>
#include <iterator>
#include <map>
#include <numeric>
@ -1123,15 +1124,19 @@ struct sql_printer : public printer {
static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_batch, int n_threads) {
llama_set_n_threads(ctx, n_threads, n_threads);
//std::vector<llama_token> tokens(n_prompt, llama_token_bos(llama_get_model(ctx)));
//llama_decode(ctx, llama_batch_get_one(tokens.data(), n_prompt, n_past, 0));
//GGML_UNUSED(n_batch);
const llama_model * model = llama_get_model(ctx);
const int32_t n_vocab = llama_n_vocab(model);
std::vector<llama_token> tokens(n_batch);
std::vector<llama_token> tokens(n_batch, llama_token_bos(llama_get_model(ctx)));
int n_processed = 0;
while (n_processed < n_prompt) {
int n_tokens = std::min(n_prompt - n_processed, n_batch);
tokens[0] = n_processed == 0 && llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab;
for (int i = 1; i < n_tokens; i++) {
tokens[i] = std::rand() % n_vocab;
}
llama_decode(ctx, llama_batch_get_one(tokens.data(), n_tokens, n_past + n_processed, 0));
n_processed += n_tokens;
}
@ -1142,11 +1147,15 @@ static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_bat
static void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads) {
llama_set_n_threads(ctx, n_threads, n_threads);
llama_token token = llama_token_bos(llama_get_model(ctx));
const llama_model * model = llama_get_model(ctx);
const int32_t n_vocab = llama_n_vocab(model);
llama_token token = llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab;
for (int i = 0; i < n_gen; i++) {
llama_decode(ctx, llama_batch_get_one(&token, 1, n_past + i, 0));
llama_synchronize(ctx);
token = std::rand() % n_vocab;
}
}