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https://github.com/ggerganov/llama.cpp.git
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simple : fixes
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8845160058
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@ -1,6 +1,7 @@
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#include "common.h"
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#include "common.h"
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#include "llama.h"
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#include "llama.h"
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#include <algorithm>
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#include <cmath>
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#include <cmath>
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#include <cstdio>
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#include <cstdio>
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#include <string>
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#include <string>
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@ -42,7 +43,9 @@ int main(int argc, char ** argv) {
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llama_context_params ctx_params = llama_context_default_params();
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llama_context_params ctx_params = llama_context_default_params();
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ctx_params.seed = 1234;
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ctx_params.seed = 1234;
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ctx_params.n_ctx = 2048;
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ctx_params.n_ctx = n_len*n_parallel; // FIXME: use n_kv_req instead (tokenize with model after #3301)
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ctx_params.n_batch = std::max(n_len, n_parallel);
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// ctx_params.n_gpu_layers = 99; // offload all layers to the GPU
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llama_model * model = llama_load_model_from_file(params.model.c_str(), ctx_params);
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llama_model * model = llama_load_model_from_file(params.model.c_str(), ctx_params);
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@ -66,11 +69,11 @@ int main(int argc, char ** argv) {
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const int n_ctx = llama_n_ctx(ctx);
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const int n_ctx = llama_n_ctx(ctx);
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const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size())*n_parallel;
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const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size())*n_parallel;
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LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_parallel = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, n_parallel, n_kv_req);
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LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_batch = %d, n_parallel = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, ctx_params.n_batch, n_parallel, n_kv_req);
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// make sure the KV cache is big enough to hold all the prompt and generated tokens
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// make sure the KV cache is big enough to hold all the prompt and generated tokens
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if (n_kv_req > n_ctx) {
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if (n_kv_req > n_ctx) {
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LOG_TEE("%s: error: n_kv_req > n_ctx, the required KV cache size is not big enough\n", __func__);
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LOG_TEE("%s: error: n_kv_req (%d) > n_ctx, the required KV cache size is not big enough\n", __func__, n_kv_req);
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LOG_TEE("%s: either reduce n_parallel or increase n_ctx\n", __func__);
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LOG_TEE("%s: either reduce n_parallel or increase n_ctx\n", __func__);
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return 1;
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return 1;
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}
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}
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@ -88,7 +91,7 @@ int main(int argc, char ** argv) {
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// create a llama_batch with size 512
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// create a llama_batch with size 512
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// we use this object to submit token data for decoding
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// we use this object to submit token data for decoding
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llama_batch batch = llama_batch_init(512, 0);
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llama_batch batch = llama_batch_init(std::max(tokens_list.size(), (size_t)n_parallel), 0);
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// evaluate the initial prompt
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// evaluate the initial prompt
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batch.n_tokens = tokens_list.size();
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batch.n_tokens = tokens_list.size();
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@ -133,12 +136,6 @@ int main(int argc, char ** argv) {
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const auto t_main_start = ggml_time_us();
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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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while (n_cur <= n_len) {
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// evaluate the current batch with the transformer model
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if (llama_decode(ctx, batch, params.n_threads)) {
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fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
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return 1;
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}
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// prepare the next batch
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// prepare the next batch
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batch.n_tokens = 0;
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batch.n_tokens = 0;
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@ -149,8 +146,8 @@ int main(int argc, char ** argv) {
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continue;
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continue;
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}
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}
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auto n_vocab = llama_n_vocab(ctx);
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auto n_vocab = llama_n_vocab(ctx);
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auto logits = llama_get_logits(ctx) + i_batch[i] * n_vocab;
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auto * logits = llama_get_logits(ctx) + i_batch[i] * n_vocab;
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std::vector<llama_token_data> candidates;
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std::vector<llama_token_data> candidates;
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candidates.reserve(n_vocab);
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candidates.reserve(n_vocab);
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@ -178,7 +175,7 @@ int main(int argc, char ** argv) {
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i_batch[i] = -1;
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i_batch[i] = -1;
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LOG_TEE("\n");
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LOG_TEE("\n");
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if (n_parallel > 1) {
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if (n_parallel > 1) {
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LOG_TEE("%s: stream %d finished", __func__, i);
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LOG_TEE("%s: stream %d finished at n_cur = %d", __func__, i, n_cur);
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}
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}
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continue;
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continue;
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@ -211,6 +208,12 @@ int main(int argc, char ** argv) {
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}
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}
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n_cur += 1;
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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, params.n_threads)) {
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fprintf(stderr, "%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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}
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LOG_TEE("\n");
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LOG_TEE("\n");
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