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)

ggml-ci

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

ggml-ci

* vocab : more pimpl (#11165)

ggml-ci

* vocab : minor tokenization optimizations (#11160)

ggml-ci

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

* lora : update API names (#11167)

ggml-ci

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

* llama : update API names to use correct prefix

ggml-ci

* cont

ggml-ci

* cont

ggml-ci

* minor [no ci]

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

ggml-ci

* vocab : llama_vocab_n_vocab -> llama_vocab_n_tokens (#11174)

ggml-ci

---------

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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19
examples/gritlm/gritlm.cpp
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@ -11,6 +11,7 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve
std::vector<std::vector<float>> result;
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
llama_batch batch = llama_batch_init(llama_n_batch(ctx), 0, 1);
@ -19,16 +20,16 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve
const std::string input_string = instruction + sentences[i];
std::vector<llama_token> inputs = common_tokenize(model, input_string, true, false);
std::vector<llama_token> inputs = common_tokenize(vocab, input_string, true, false);
const int32_t n_toks = inputs.size();
// GritLM seems to have EOS = ""
// https://github.com/ContextualAI/gritlm/blob/92025b16534712b31b3c4aaaf069350e222bd5f8/gritlm/gritlm.py#L18
// inputs.push_back(llama_token_eos(model));
// inputs.push_back(llama_vocab_eos(vocab));
// we want to ignore instruction tokens for mean pooling
const int32_t n_inst = common_tokenize(model, instruction, true, false).size();
const int32_t n_inst = common_tokenize(vocab, instruction, true, false).size();
#ifdef GRIT_DEBUG
// debug tokens - should be matching as referenced in the GritLM sample
@ -52,7 +53,7 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve
llama_decode(ctx, batch);
// get embedding dimensions
uint64_t n_embd = llama_n_embd(model);
uint64_t n_embd = llama_model_n_embd(model);
// allocate embedding output
std::vector<float> emb_unorm(n_embd, 0.0f);
@ -97,7 +98,9 @@ static std::string generate(llama_context * ctx, llama_sampler * smpl, const std
std::string result;
const llama_model * model = llama_get_model(ctx);
llama_token eos_token = llama_token_eos(model);
const llama_vocab * vocab = llama_model_get_vocab(model);
llama_token eos_token = llama_vocab_eos(vocab);
llama_kv_cache_clear(ctx);
llama_set_embeddings(ctx, false);
@ -105,7 +108,7 @@ static std::string generate(llama_context * ctx, llama_sampler * smpl, const std
llama_batch bat = llama_batch_init(llama_n_batch(ctx), 0, 1);
std::vector<llama_token> inputs = common_tokenize(model, prompt, false, true);
std::vector<llama_token> inputs = common_tokenize(vocab, prompt, false, true);
int32_t i_current_token = 0;
while (true) {
@ -168,7 +171,7 @@ int main(int argc, char * argv[]) {
llama_model * model = llama_model_load_from_file(params.model.c_str(), mparams);
// create generation context
llama_context * ctx = llama_new_context_with_model(model, cparams);
llama_context * ctx = llama_init_from_model(model, cparams);
auto sparams = llama_sampler_chain_default_params();
@ -197,7 +200,7 @@ int main(int argc, char * argv[]) {
const std::vector<std::vector<float>> d_rep = encode(ctx, documents, gritlm_instruction(""));
const std::vector<std::vector<float>> q_rep = encode(ctx, queries, gritlm_instruction(instruction));
const int n_embd = llama_n_embd(model);
const int n_embd = llama_model_n_embd(model);
const float cosine_sim_q0_d0 = common_embd_similarity_cos(q_rep[0].data(), d_rep[0].data(), n_embd);
const float cosine_sim_q0_d1 = common_embd_similarity_cos(q_rep[0].data(), d_rep[1].data(), n_embd);