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-rw-r--r--examples/embd-input/embd-input-lib.cpp19
-rw-r--r--examples/infill/infill.cpp18
-rw-r--r--examples/main/main.cpp18
-rw-r--r--examples/parallel/parallel.cpp6
-rw-r--r--examples/save-load-state/save-load-state.cpp5
-rw-r--r--examples/server/server.cpp100
-rw-r--r--examples/speculative/speculative.cpp12
7 files changed, 101 insertions, 77 deletions
diff --git a/examples/embd-input/embd-input-lib.cpp b/examples/embd-input/embd-input-lib.cpp
index 99e6bdad..87a5a1c2 100644
--- a/examples/embd-input/embd-input-lib.cpp
+++ b/examples/embd-input/embd-input-lib.cpp
@@ -128,21 +128,22 @@ bool eval_string(struct MyModel * mymodel,const char* str){
llama_token sampling_id(struct MyModel* mymodel) {
llama_context* ctx = mymodel->ctx;
gpt_params params = mymodel->params;
+ llama_sampling_params & sparams = params.sampling_params;
// int n_ctx = llama_n_ctx(ctx);
// out of user input, sample next token
- const float temp = params.temp;
- const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(llama_get_model(ctx)) : params.top_k;
- const float top_p = params.top_p;
- const float tfs_z = params.tfs_z;
- const float typical_p = params.typical_p;
+ const float temp = sparams.temp;
+ const int32_t top_k = sparams.top_k <= 0 ? llama_n_vocab(llama_get_model(ctx)) : sparams.top_k;
+ const float top_p = sparams.top_p;
+ const float tfs_z = sparams.tfs_z;
+ const float typical_p = sparams.typical_p;
// const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
// const float repeat_penalty = params.repeat_penalty;
// const float alpha_presence = params.presence_penalty;
// const float alpha_frequency = params.frequency_penalty;
- const int mirostat = params.mirostat;
- const float mirostat_tau = params.mirostat_tau;
- const float mirostat_eta = params.mirostat_eta;
+ const int mirostat = sparams.mirostat;
+ const float mirostat_tau = sparams.mirostat_tau;
+ const float mirostat_eta = sparams.mirostat_eta;
// const bool penalize_nl = params.penalize_nl;
llama_token id = 0;
@@ -151,7 +152,7 @@ llama_token sampling_id(struct MyModel* mymodel) {
auto n_vocab = llama_n_vocab(llama_get_model(ctx));
// Apply params.logit_bias map
- for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
+ for (auto it = sparams.logit_bias.begin(); it != sparams.logit_bias.end(); it++) {
logits[it->first] += it->second;
}
diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp
index d994de5e..187623f5 100644
--- a/examples/infill/infill.cpp
+++ b/examples/infill/infill.cpp
@@ -104,6 +104,7 @@ static void sigint_handler(int signo) {
int main(int argc, char ** argv) {
gpt_params params;
+ llama_sampling_params & sparams = params.sampling_params;
g_params = &params;
if (!gpt_params_parse(argc, argv, params)) {
@@ -206,7 +207,7 @@ int main(int argc, char ** argv) {
// load the model and apply lora adapter, if any
LOG("%s: load the model and apply lora adapter, if any\n", __func__);
std::tie(model, ctx) = llama_init_from_gpt_params(params);
- if (params.cfg_scale > 1.f) {
+ if (sparams.cfg_scale > 1.f) {
struct llama_context_params lparams = llama_context_params_from_gpt_params(params);
ctx_guidance = llama_new_context_with_model(model, lparams);
}
@@ -269,9 +270,9 @@ int main(int argc, char ** argv) {
int guidance_offset = 0;
int original_prompt_len = 0;
if (ctx_guidance) {
- LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(params.cfg_negative_prompt));
+ LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(sparams.cfg_negative_prompt));
- guidance_inp = ::llama_tokenize(ctx_guidance, params.cfg_negative_prompt, add_bos);
+ guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, add_bos);
LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp));
std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, add_bos);
@@ -312,7 +313,7 @@ int main(int argc, char ** argv) {
if (ctx_guidance) {
LOG_TEE("\n");
- LOG_TEE("%s: negative prompt: '%s'\n", __func__, params.cfg_negative_prompt.c_str());
+ LOG_TEE("%s: negative prompt: '%s'\n", __func__, sparams.cfg_negative_prompt.c_str());
LOG_TEE("%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size());
for (int i = 0; i < (int) guidance_inp.size(); i++) {
LOG_TEE("%6d -> '%s'\n", guidance_inp[i], llama_token_to_piece(ctx, guidance_inp[i]).c_str());
@@ -358,7 +359,7 @@ int main(int argc, char ** argv) {
}
}
LOG_TEE("sampling: repeat_last_n = %d, repeat_penalty = %f, presence_penalty = %f, frequency_penalty = %f, top_k = %d, tfs_z = %f, top_p = %f, typical_p = %f, temp = %f, mirostat = %d, mirostat_lr = %f, mirostat_ent = %f\n",
- params.repeat_last_n, params.repeat_penalty, params.presence_penalty, params.frequency_penalty, params.top_k, params.tfs_z, params.top_p, params.typical_p, params.temp, params.mirostat, params.mirostat_eta, params.mirostat_tau);
+ sparams.repeat_last_n, sparams.repeat_penalty, sparams.presence_penalty, sparams.frequency_penalty, sparams.top_k, sparams.tfs_z, sparams.top_p, sparams.typical_p, sparams.temp, sparams.mirostat, sparams.mirostat_eta, sparams.mirostat_tau);
LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
LOG_TEE("\n\n");
@@ -376,8 +377,8 @@ int main(int argc, char ** argv) {
LOG_TEE("\n");
{
- auto it = params.logit_bias.find(llama_token_eos(ctx));
- if (it != params.logit_bias.end() && it->second == -INFINITY) {
+ auto it = sparams.logit_bias.find(llama_token_eos(ctx));
+ if (it != sparams.logit_bias.end() && it->second == -INFINITY) {
LOG_TEE("%s: warning: EOS token is disabled, which will cause most grammars to fail\n", __func__);
}
}
@@ -434,6 +435,7 @@ int main(int argc, char ** argv) {
const int n_vocab = llama_n_vocab(model);
+ llama_sampling_context ctx_sampling = llama_sampling_context_init(params, grammar);
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
@@ -552,7 +554,7 @@ int main(int argc, char ** argv) {
if ((int) embd_inp.size() <= n_consumed && !is_interacting) {
- const llama_token id = llama_sample_token(ctx, ctx_guidance, grammar, params, last_tokens, candidates);
+ const llama_token id = llama_sampling_sample(ctx, ctx_guidance, ctx_sampling, last_tokens, candidates);
last_tokens.erase(last_tokens.begin());
last_tokens.push_back(id);
diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index 775a5a20..b39a67d9 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -109,6 +109,7 @@ int main(int argc, char ** argv) {
if (!gpt_params_parse(argc, argv, params)) {
return 1;
}
+ llama_sampling_params & sparams = params.sampling_params;
#ifndef LOG_DISABLE_LOGS
log_set_target(log_filename_generator("main", "log"));
@@ -179,7 +180,7 @@ int main(int argc, char ** argv) {
// load the model and apply lora adapter, if any
LOG("%s: load the model and apply lora adapter, if any\n", __func__);
std::tie(model, ctx) = llama_init_from_gpt_params(params);
- if (params.cfg_scale > 1.f) {
+ if (sparams.cfg_scale > 1.f) {
struct llama_context_params lparams = llama_context_params_from_gpt_params(params);
ctx_guidance = llama_new_context_with_model(model, lparams);
}
@@ -257,9 +258,9 @@ int main(int argc, char ** argv) {
int guidance_offset = 0;
int original_prompt_len = 0;
if (ctx_guidance) {
- LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(params.cfg_negative_prompt));
+ LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(sparams.cfg_negative_prompt));
- guidance_inp = ::llama_tokenize(ctx_guidance, params.cfg_negative_prompt, add_bos);
+ guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, add_bos);
LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp));
std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, add_bos);
@@ -343,7 +344,7 @@ int main(int argc, char ** argv) {
if (ctx_guidance) {
LOG_TEE("\n");
- LOG_TEE("%s: negative prompt: '%s'\n", __func__, params.cfg_negative_prompt.c_str());
+ LOG_TEE("%s: negative prompt: '%s'\n", __func__, sparams.cfg_negative_prompt.c_str());
LOG_TEE("%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size());
for (int i = 0; i < (int) guidance_inp.size(); i++) {
LOG_TEE("%6d -> '%s'\n", guidance_inp[i], llama_token_to_piece(ctx, guidance_inp[i]).c_str());
@@ -395,7 +396,7 @@ int main(int argc, char ** argv) {
}
}
LOG_TEE("sampling: repeat_last_n = %d, repeat_penalty = %f, presence_penalty = %f, frequency_penalty = %f, top_k = %d, tfs_z = %f, top_p = %f, typical_p = %f, temp = %f, mirostat = %d, mirostat_lr = %f, mirostat_ent = %f\n",
- params.repeat_last_n, params.repeat_penalty, params.presence_penalty, params.frequency_penalty, params.top_k, params.tfs_z, params.top_p, params.typical_p, params.temp, params.mirostat, params.mirostat_eta, params.mirostat_tau);
+ sparams.repeat_last_n, sparams.repeat_penalty, sparams.presence_penalty, sparams.frequency_penalty, sparams.top_k, sparams.tfs_z, sparams.top_p, sparams.typical_p, sparams.temp, sparams.mirostat, sparams.mirostat_eta, sparams.mirostat_tau);
LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
LOG_TEE("\n\n");
@@ -413,8 +414,8 @@ int main(int argc, char ** argv) {
LOG_TEE("\n");
{
- auto it = params.logit_bias.find(llama_token_eos(ctx));
- if (it != params.logit_bias.end() && it->second == -INFINITY) {
+ auto it = sparams.logit_bias.find(llama_token_eos(ctx));
+ if (it != sparams.logit_bias.end() && it->second == -INFINITY) {
LOG_TEE("%s: warning: EOS token is disabled, which will cause most grammars to fail\n", __func__);
}
}
@@ -469,6 +470,7 @@ int main(int argc, char ** argv) {
const int n_vocab = llama_n_vocab(model);
+ llama_sampling_context ctx_sampling = llama_sampling_context_init(params, grammar);
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
@@ -625,7 +627,7 @@ int main(int argc, char ** argv) {
LOG("saved session to %s\n", path_session.c_str());
}
- const llama_token id = llama_sample_token(ctx, ctx_guidance, grammar, params, last_tokens, candidates);
+ const llama_token id = llama_sampling_sample(ctx, ctx_guidance, ctx_sampling, last_tokens, candidates);
last_tokens.erase(last_tokens.begin());
last_tokens.push_back(id);
diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp
index 04f1e45b..63ddcd8e 100644
--- a/examples/parallel/parallel.cpp
+++ b/examples/parallel/parallel.cpp
@@ -125,6 +125,8 @@ int main(int argc, char ** argv) {
params.logits_all = true;
std::tie(model, ctx) = llama_init_from_gpt_params(params);
+ llama_sampling_context ctx_sampling = llama_sampling_context_init(params, NULL);
+
// load the prompts from an external file if there are any
if (params.prompt.empty()) {
printf("\n\033[32mNo new questions so proceed with build-in defaults.\033[0m\n");
@@ -339,7 +341,7 @@ int main(int argc, char ** argv) {
//printf("client %d, seq %d, token %d, pos %d, batch %d\n",
// client.id, client.seq_id, client.sampled, client.n_decoded, client.i_batch);
- const llama_token id = llama_sample_token(ctx, NULL, NULL, params, client.tokens_prev, candidates, client.i_batch - i);
+ const llama_token id = llama_sampling_sample(ctx, NULL, ctx_sampling, client.tokens_prev, candidates, client.i_batch - i, client.seq_id);
if (client.n_decoded == 1) {
// start measuring generation time after the first token to make sure all concurrent clients
@@ -384,7 +386,7 @@ int main(int argc, char ** argv) {
n_total_prompt += client.n_prompt;
n_total_gen += client.n_decoded;
-
+ llama_sampling_context_reset(ctx_sampling, client.seq_id);
client.seq_id = -1;
}
diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp
index acc6dbdf..f9e3c98a 100644
--- a/examples/save-load-state/save-load-state.cpp
+++ b/examples/save-load-state/save-load-state.cpp
@@ -8,9 +8,10 @@
int main(int argc, char ** argv) {
gpt_params params;
+ llama_sampling_params & sparams = params.sampling_params;
params.seed = 42;
params.n_threads = 4;
- params.repeat_last_n = 64;
+ sparams.repeat_last_n = 64;
params.prompt = "The quick brown fox";
if (!gpt_params_parse(argc, argv, params)) {
@@ -24,7 +25,7 @@ int main(int argc, char ** argv) {
}
auto n_past = 0;
- auto last_n_tokens_data = std::vector<llama_token>(params.repeat_last_n, 0);
+ auto last_n_tokens_data = std::vector<llama_token>(sparams.repeat_last_n, 0);
// init
llama_model * model;
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 8c5318c6..58af78de 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -200,6 +200,7 @@ struct llama_server_context
llama_model *model = nullptr;
llama_context *ctx = nullptr;
gpt_params params;
+ llama_sampling_context ctx_sampling;
int n_ctx;
grammar_parser::parse_state parsed_grammar;
@@ -254,6 +255,7 @@ struct llama_server_context
if (grammar != nullptr) {
llama_grammar_free(grammar);
grammar = nullptr;
+ ctx_sampling = llama_sampling_context_init(params, NULL);
}
}
@@ -329,8 +331,8 @@ struct llama_server_context
grammar_parser::print_grammar(stderr, parsed_grammar);
{
- auto it = params.logit_bias.find(llama_token_eos(ctx));
- if (it != params.logit_bias.end() && it->second == -INFINITY) {
+ auto it = params.sampling_params.logit_bias.find(llama_token_eos(ctx));
+ if (it != params.sampling_params.logit_bias.end() && it->second == -INFINITY) {
LOG_WARNING("EOS token is disabled, which will cause most grammars to fail", {});
}
}
@@ -339,6 +341,7 @@ struct llama_server_context
grammar = llama_grammar_init(
grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
}
+ ctx_sampling = llama_sampling_context_init(params, grammar);
return true;
}
@@ -550,12 +553,12 @@ struct llama_server_context
std::vector<llama_token_data> candidates;
candidates.reserve(llama_n_vocab(model));
- result.tok = llama_sample_token(ctx, NULL, grammar, params, last_n_tokens, candidates);
+ result.tok = llama_sampling_sample(ctx, NULL, ctx_sampling, last_n_tokens, candidates);
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
- const int32_t n_probs = params.n_probs;
- if (params.temp <= 0 && n_probs > 0)
+ const int32_t n_probs = params.sampling_params.n_probs;
+ if (params.sampling_params.temp <= 0 && n_probs > 0)
{
// For llama_sample_token_greedy we need to sort candidates
llama_sample_softmax(ctx, &candidates_p);
@@ -630,7 +633,7 @@ struct llama_server_context
const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(ctx, token_with_probs.tok);
generated_text += token_text;
- if (params.n_probs > 0)
+ if (params.sampling_params.n_probs > 0)
{
generated_token_probs.push_back(token_with_probs);
}
@@ -1018,34 +1021,35 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
static json format_generation_settings(llama_server_context &llama)
{
- const auto eos_bias = llama.params.logit_bias.find(llama_token_eos(llama.ctx));
- const bool ignore_eos = eos_bias != llama.params.logit_bias.end() &&
+ const auto & sparams = llama.params.sampling_params;
+ const auto eos_bias = sparams.logit_bias.find(llama_token_eos(llama.ctx));
+ const bool ignore_eos = eos_bias != sparams.logit_bias.end() &&
eos_bias->second < 0.0f && std::isinf(eos_bias->second);
return json{
{"n_ctx", llama.n_ctx},
{"model", llama.params.model_alias},
{"seed", llama.params.seed},
- {"temp", llama.params.temp},
- {"top_k", llama.params.top_k},
- {"top_p", llama.params.top_p},
- {"tfs_z", llama.params.tfs_z},
- {"typical_p", llama.params.typical_p},
- {"repeat_last_n", llama.params.repeat_last_n},
- {"repeat_penalty", llama.params.repeat_penalty},
- {"presence_penalty", llama.params.presence_penalty},
- {"frequency_penalty", llama.params.frequency_penalty},
- {"mirostat", llama.params.mirostat},
- {"mirostat_tau", llama.params.mirostat_tau},
- {"mirostat_eta", llama.params.mirostat_eta},
- {"penalize_nl", llama.params.penalize_nl},
+ {"temp", sparams.temp},
+ {"top_k", sparams.top_k},
+ {"top_p", sparams.top_p},
+ {"tfs_z", sparams.tfs_z},
+ {"typical_p", sparams.typical_p},
+ {"repeat_last_n", sparams.repeat_last_n},
+ {"repeat_penalty", sparams.repeat_penalty},
+ {"presence_penalty", sparams.presence_penalty},
+ {"frequency_penalty", sparams.frequency_penalty},
+ {"mirostat", sparams.mirostat},
+ {"mirostat_tau", sparams.mirostat_tau},
+ {"mirostat_eta", sparams.mirostat_eta},
+ {"penalize_nl", sparams.penalize_nl},
{"stop", llama.params.antiprompt},
{"n_predict", llama.params.n_predict},
{"n_keep", llama.params.n_keep},
{"ignore_eos", ignore_eos},
{"stream", llama.stream},
- {"logit_bias", llama.params.logit_bias},
- {"n_probs", llama.params.n_probs},
+ {"logit_bias", sparams.logit_bias},
+ {"n_probs", sparams.n_probs},
{"grammar", llama.params.grammar},
};
}
@@ -1094,7 +1098,7 @@ static json format_final_response(llama_server_context &llama, const std::string
{"timings", format_timings(llama)},
};
- if (llama.params.n_probs > 0)
+ if (llama.params.sampling_params.n_probs > 0)
{
res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs);
}
@@ -1110,7 +1114,7 @@ static json format_partial_response(
{"stop", false},
};
- if (llama.params.n_probs > 0)
+ if (llama.params.sampling_params.n_probs > 0)
{
res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs);
}
@@ -1142,26 +1146,28 @@ static T json_value(const json &body, const std::string &key, const T &default_v
static void parse_options_completion(const json &body, llama_server_context &llama)
{
gpt_params default_params;
+ const auto & default_sparams = default_params.sampling_params;
+ auto & sparams = llama.params.sampling_params;
llama.stream = json_value(body, "stream", false);
llama.params.n_predict = json_value(body, "n_predict", default_params.n_predict);
- llama.params.top_k = json_value(body, "top_k", default_params.top_k);
- llama.params.top_p = json_value(body, "top_p", default_params.top_p);
- llama.params.tfs_z = json_value(body, "tfs_z", default_params.tfs_z);
- llama.params.typical_p = json_value(body, "typical_p", default_params.typical_p);
- llama.params.repeat_last_n = json_value(body, "repeat_last_n", default_params.repeat_last_n);
- llama.params.temp = json_value(body, "temperature", default_params.temp);
- llama.params.repeat_penalty = json_value(body, "repeat_penalty", default_params.repeat_penalty);
- llama.params.presence_penalty = json_value(body, "presence_penalty", default_params.presence_penalty);
- llama.params.frequency_penalty = json_value(body, "frequency_penalty", default_params.frequency_penalty);
- llama.params.mirostat = json_value(body, "mirostat", default_params.mirostat);
- llama.params.mirostat_tau = json_value(body, "mirostat_tau", default_params.mirostat_tau);
- llama.params.mirostat_eta = json_value(body, "mirostat_eta", default_params.mirostat_eta);
- llama.params.penalize_nl = json_value(body, "penalize_nl", default_params.penalize_nl);
+ sparams.top_k = json_value(body, "top_k", default_sparams.top_k);
+ sparams.top_p = json_value(body, "top_p", default_sparams.top_p);
+ sparams.tfs_z = json_value(body, "tfs_z", default_sparams.tfs_z);
+ sparams.typical_p = json_value(body, "typical_p", default_sparams.typical_p);
+ sparams.repeat_last_n = json_value(body, "repeat_last_n", default_sparams.repeat_last_n);
+ sparams.temp = json_value(body, "temperature", default_sparams.temp);
+ sparams.repeat_penalty = json_value(body, "repeat_penalty", default_sparams.repeat_penalty);
+ sparams.presence_penalty = json_value(body, "presence_penalty", default_sparams.presence_penalty);
+ sparams.frequency_penalty = json_value(body, "frequency_penalty", default_sparams.frequency_penalty);
+ sparams.mirostat = json_value(body, "mirostat", default_sparams.mirostat);
+ sparams.mirostat_tau = json_value(body, "mirostat_tau", default_sparams.mirostat_tau);
+ sparams.mirostat_eta = json_value(body, "mirostat_eta", default_sparams.mirostat_eta);
+ sparams.penalize_nl = json_value(body, "penalize_nl", default_sparams.penalize_nl);
llama.params.n_keep = json_value(body, "n_keep", default_params.n_keep);
llama.params.seed = json_value(body, "seed", default_params.seed);
llama.params.grammar = json_value(body, "grammar", default_params.grammar);
- llama.params.n_probs = json_value(body, "n_probs", default_params.n_probs);
+ sparams.n_probs = json_value(body, "n_probs", default_sparams.n_probs);
if (body.count("prompt") != 0)
{
@@ -1172,10 +1178,10 @@ static void parse_options_completion(const json &body, llama_server_context &lla
llama.prompt = "";
}
- llama.params.logit_bias.clear();
+ sparams.logit_bias.clear();
if (json_value(body, "ignore_eos", false))
{
- llama.params.logit_bias[llama_token_eos(llama.ctx)] = -INFINITY;
+ sparams.logit_bias[llama_token_eos(llama.ctx)] = -INFINITY;
}
const auto &logit_bias = body.find("logit_bias");
@@ -1191,11 +1197,11 @@ static void parse_options_completion(const json &body, llama_server_context &lla
{
if (el[1].is_number())
{
- llama.params.logit_bias[tok] = el[1].get<float>();
+ sparams.logit_bias[tok] = el[1].get<float>();
}
else if (el[1].is_boolean() && !el[1].get<bool>())
{
- llama.params.logit_bias[tok] = -INFINITY;
+ sparams.logit_bias[tok] = -INFINITY;
}
}
}
@@ -1215,6 +1221,8 @@ static void parse_options_completion(const json &body, llama_server_context &lla
}
}
+ llama.ctx_sampling = llama_sampling_context_init(llama.params, llama.grammar);
+
LOG_VERBOSE("completion parameters parsed", format_generation_settings(llama));
}
@@ -1423,7 +1431,7 @@ int main(int argc, char **argv)
}
auto probs = llama.generated_token_probs;
- if (llama.params.n_probs > 0 && llama.stopped_word) {
+ if (llama.params.sampling_params.n_probs > 0 && llama.stopped_word) {
const std::vector<llama_token> stop_word_toks = llama_tokenize(llama.ctx, llama.stopping_word, false);
probs = std::vector<completion_token_output>(llama.generated_token_probs.begin(), llama.generated_token_probs.end() - stop_word_toks.size());
}
@@ -1475,7 +1483,7 @@ int main(int argc, char **argv)
std::vector<completion_token_output> probs_output = {};
- if (llama.params.n_probs > 0) {
+ if (llama.params.sampling_params.n_probs > 0) {
const std::vector<llama_token> to_send_toks = llama_tokenize(llama.ctx, to_send, false);
size_t probs_pos = std::min(sent_token_probs_index, llama.generated_token_probs.size());
size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), llama.generated_token_probs.size());
@@ -1596,7 +1604,7 @@ int main(int argc, char **argv)
std::vector<completion_token_output> probs_output = {};
- if (llama.params.n_probs > 0) {
+ if (llama.params.sampling_params.n_probs > 0) {
const std::vector<llama_token> to_send_toks = llama_tokenize(llama.ctx, to_send, false);
size_t probs_pos = std::min(sent_token_probs_index, llama.generated_token_probs.size());
size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), llama.generated_token_probs.size());
diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp
index 75a2e5e2..018dbf9a 100644
--- a/examples/speculative/speculative.cpp
+++ b/examples/speculative/speculative.cpp
@@ -125,6 +125,8 @@ int main(int argc, char ** argv) {
grammar_tgt = llama_grammar_init(grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
}
+ llama_sampling_context ctx_sampling = llama_sampling_context_init(params, grammar_tgt);
+
const auto t_dec_start = ggml_time_us();
while (true) {
@@ -134,7 +136,7 @@ int main(int argc, char ** argv) {
while (true) {
// sample from the target model
- llama_token id = llama_sample_token(ctx_tgt, NULL, grammar_tgt, params, last_tokens, candidates, i_dft);
+ llama_token id = llama_sampling_sample(ctx_tgt, NULL, ctx_sampling, last_tokens, candidates, i_dft);
// remember which tokens were sampled - used for repetition penalties during sampling
last_tokens.erase(last_tokens.begin());
@@ -211,7 +213,13 @@ int main(int argc, char ** argv) {
if (grammar_dft) {
llama_grammar_free(grammar_dft);
}
- grammar_dft = llama_grammar_copy(grammar_tgt);
+ // Note: Hardcoded to sequence id 0, if this ever supports parallel generation
+ // that will need to change.
+ auto it = ctx_sampling.sequence_contexts.find(0);
+ GGML_ASSERT(it != ctx_sampling.sequence_contexts.end());
+ // This is necessary because each sequence id in sequence_contexts
+ // uses a copy of the original grammar.
+ grammar_dft = llama_grammar_copy(it->second.grammar);
LOG("copied target grammar to draft grammar\n");
}
generated by cgit v1.2.3 (git 2.25.1) at 2025-08-17 00:53:51 +0000