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

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Mar 15, 2024
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llama-bench : use random tokens to improve accuracy with mixtral #6069

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19 changes: 14 additions & 5 deletions examples/llama-bench/llama-bench.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@
#include <cstdio>
#include <cstring>
#include <ctime>
#include <cstdlib>
#include <iterator>
#include <map>
#include <numeric>
Expand Down Expand Up @@ -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;
}
Expand All @@ -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;
}
}

Expand Down
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