vbatts/llama.cpp
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llama : add llama_vocab, functions -> methods, naming (#11110)

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* llama : functions -> methods (#11110)

* llama : add struct llama_vocab to the API (#11156)

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* hparams : move vocab params to llama_vocab (#11159)

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* vocab : more pimpl (#11165)

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* vocab : minor tokenization optimizations (#11160)

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Co-authored-by: Diego Devesa <slarengh@gmail.com>

* lora : update API names (#11167)

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* llama : update API names to use correct prefix (#11174)

* llama : update API names to use correct prefix

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* cont

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* cont

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* minor [no ci]

* vocab : llama_vocab_add_[be]os -> llama_vocab_get_add_[be]os (#11174)

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* vocab : llama_vocab_n_vocab -> llama_vocab_n_tokens (#11174)

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---------

Co-authored-by: Diego Devesa <slarengh@gmail.com>
This commit is contained in:
Georgi Gerganov 2025-01-12 11:32:42 +02:00 • committed by GitHub
parent c05e8c9934
commit afa8a9ec9b
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68 changed files with 5855 additions and 5400 deletions
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10
examples/batched/batched.cpp
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@ -48,10 +48,12 @@ int main(int argc, char ** argv) {
return 1;
}
const llama_vocab * vocab = llama_model_get_vocab(model);
// tokenize the prompt
std::vector<llama_token> tokens_list;
tokens_list = common_tokenize(model, params.prompt, true);
tokens_list = common_tokenize(vocab, params.prompt, true);
const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size())*n_parallel;
@ -62,7 +64,7 @@ int main(int argc, char ** argv) {
ctx_params.n_ctx = n_kv_req;
ctx_params.n_batch = std::max(n_predict, n_parallel);
llama_context * ctx = llama_new_context_with_model(model, ctx_params);
llama_context * ctx = llama_init_from_model(model, ctx_params);
auto sparams = llama_sampler_chain_default_params();
sparams.no_perf = false;
@ -121,7 +123,7 @@ int main(int argc, char ** argv) {
llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
if (decoder_start_token_id == LLAMA_TOKEN_NULL) {
decoder_start_token_id = llama_token_bos(model);
decoder_start_token_id = llama_vocab_bos(vocab);
}
common_batch_clear(batch);
@ -174,7 +176,7 @@ int main(int argc, char ** argv) {
const llama_token new_token_id = llama_sampler_sample(smpl, ctx, i_batch[i]);
// is it an end of generation? -> mark the stream as finished
if (llama_token_is_eog(model, new_token_id) || n_cur == n_predict) {
if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_predict) {
i_batch[i] = -1;
LOG("\n");
if (n_parallel > 1) {