EngineX-Hygon/enginex-hygon-llama.cpp
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xiezhongtao
2026-01-16 18:12:13 +08:00
parent 9d7890f8c6
commit 7e0d40b535
380 changed files with 18454 additions and 38808 deletions
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143
common/sampling.cpp
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@@ -120,34 +120,17 @@ struct common_sampler {
}
void set_logits(struct llama_context * ctx, int idx) {
const float * sampled_probs = llama_get_sampled_probs_ith (ctx, idx);
const float * sampled_logits = llama_get_sampled_logits_ith (ctx, idx);
const llama_token * sampled_ids = llama_get_sampled_candidates_ith(ctx, idx);
const auto * logits = llama_get_logits_ith(ctx, idx);
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
const int n_vocab = llama_vocab_n_tokens(vocab);
if (sampled_probs) {
const uint32_t sampled_probs_count = llama_get_sampled_probs_count_ith(ctx, idx);
cur.resize(sampled_probs_count);
for (uint32_t i = 0; i < sampled_probs_count; ++i) {
cur[i] = llama_token_data{sampled_ids[i], sampled_logits[i], sampled_probs[i]};
}
} else if (sampled_logits) {
const uint32_t sampled_logits_count = llama_get_sampled_logits_count_ith(ctx, idx);
cur.resize(sampled_logits_count);
for (uint32_t i = 0; i < sampled_logits_count; i++) {
cur[i] = llama_token_data{sampled_ids[i], sampled_logits[i], 0.0f};
}
} else {
const auto * logits = llama_get_logits_ith(ctx, idx);
GGML_ASSERT(logits != nullptr);
cur.resize(n_vocab);
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f};
}
cur.resize(n_vocab);
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f};
}
cur_p = { cur.data(), cur.size(), -1, false };
@@ -167,16 +150,16 @@ std::string common_params_sampling::print() const {
"\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n"
"\tdry_multiplier = %.3f, dry_base = %.3f, dry_allowed_length = %d, dry_penalty_last_n = %d\n"
"\ttop_k = %d, top_p = %.3f, min_p = %.3f, xtc_probability = %.3f, xtc_threshold = %.3f, typical_p = %.3f, top_n_sigma = %.3f, temp = %.3f\n"
"\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f, adaptive_target = %.3f, adaptive_decay = %.3f",
"\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f",
penalty_last_n, penalty_repeat, penalty_freq, penalty_present,
dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n,
top_k, top_p, min_p, xtc_probability, xtc_threshold, typ_p, top_n_sigma, temp,
mirostat, mirostat_eta, mirostat_tau, adaptive_target, adaptive_decay);
mirostat, mirostat_eta, mirostat_tau);
return std::string(result);
}
struct common_sampler * common_sampler_init(const struct llama_model * model, struct common_params_sampling & params) {
struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params) {
const llama_vocab * vocab = llama_model_get_vocab(model);
llama_sampler_chain_params lparams = llama_sampler_chain_default_params();
@@ -196,30 +179,24 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
#endif // LLAMA_USE_LLGUIDANCE
} else {
std::vector<std::string> trigger_patterns;
std::vector<std::string> patterns_anywhere;
std::vector<llama_token> trigger_tokens;
for (const auto & trigger : params.grammar_triggers) {
switch (trigger.type) {
case COMMON_GRAMMAR_TRIGGER_TYPE_WORD:
{
const auto & word = trigger.value;
trigger_patterns.push_back(regex_escape(word));
patterns_anywhere.push_back(regex_escape(word));
break;
}
case COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN:
{
trigger_patterns.push_back(trigger.value);
patterns_anywhere.push_back(trigger.value);
break;
}
case COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL:
{
const auto & pattern = trigger.value;
std::string anchored = "^$";
if (!pattern.empty()) {
anchored = (pattern.front() != '^' ? "^" : "")
+ pattern
+ (pattern.back() != '$' ? "$" : "");
}
trigger_patterns.push_back(anchored);
trigger_patterns.push_back(trigger.value);
break;
}
case COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN:
@@ -233,6 +210,10 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
}
}
if (!patterns_anywhere.empty()) {
trigger_patterns.push_back("^[\\s\\S]*?(" + string_join(patterns_anywhere, "|") + ")[\\s\\S]*");
}
std::vector<const char *> trigger_patterns_c;
trigger_patterns_c.reserve(trigger_patterns.size());
for (const auto & regex : trigger_patterns) {
@@ -255,9 +236,6 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
}
if (params.mirostat == 0) {
bool use_adaptive_p = false; // see below
for (const auto & cnstr : params.samplers) {
switch (cnstr) {
case COMMON_SAMPLER_TYPE_DRY:
@@ -267,54 +245,43 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
for (const auto & str : params.dry_sequence_breakers) {
c_breakers.push_back(str.c_str());
}
samplers.push_back(llama_sampler_init_dry(vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
samplers.push_back(llama_sampler_init_dry (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
}
break;
case COMMON_SAMPLER_TYPE_TOP_K:
samplers.push_back(llama_sampler_init_top_k(params.top_k));
samplers.push_back(llama_sampler_init_top_k (params.top_k));
break;
case COMMON_SAMPLER_TYPE_TOP_P:
samplers.push_back(llama_sampler_init_top_p(params.top_p, params.min_keep));
samplers.push_back(llama_sampler_init_top_p (params.top_p, params.min_keep));
break;
case COMMON_SAMPLER_TYPE_TOP_N_SIGMA:
samplers.push_back(llama_sampler_init_top_n_sigma(params.top_n_sigma));
break;
case COMMON_SAMPLER_TYPE_MIN_P:
samplers.push_back(llama_sampler_init_min_p(params.min_p, params.min_keep));
samplers.push_back(llama_sampler_init_min_p (params.min_p, params.min_keep));
break;
case COMMON_SAMPLER_TYPE_XTC:
samplers.push_back(llama_sampler_init_xtc(params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
samplers.push_back(llama_sampler_init_xtc (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
break;
case COMMON_SAMPLER_TYPE_TYPICAL_P:
samplers.push_back(llama_sampler_init_typical(params.typ_p, params.min_keep));
samplers.push_back(llama_sampler_init_typical (params.typ_p, params.min_keep));
break;
case COMMON_SAMPLER_TYPE_TEMPERATURE:
samplers.push_back(llama_sampler_init_temp_ext(params.temp, params.dynatemp_range, params.dynatemp_exponent));
samplers.push_back(llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent));
break;
case COMMON_SAMPLER_TYPE_INFILL:
samplers.push_back(llama_sampler_init_infill(vocab));
samplers.push_back(llama_sampler_init_infill (vocab));
break;
case COMMON_SAMPLER_TYPE_PENALTIES:
samplers.push_back(llama_sampler_init_penalties(params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
break;
case COMMON_SAMPLER_TYPE_ADAPTIVE_P:
// the `adaptive-p` sampler is like `dist` and `mirostat` in that it selects
// a single token, so we will add `dist` at the end of the chain by default,
// unless the user specifically included `adaptive-p`. we set this flag here
// so we know to add the sampler at the very end.
use_adaptive_p = true;
samplers.push_back(llama_sampler_init_penalties (params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
break;
default:
GGML_ASSERT(false && "unknown sampler type");
}
}
if (use_adaptive_p) {
// only if user explicitly included adaptive-p sampler
samplers.push_back(llama_sampler_init_adaptive_p(params.adaptive_target, params.adaptive_decay, params.seed));
} else {
// default: sample from distribution
samplers.push_back(llama_sampler_init_dist(params.seed));
}
samplers.push_back(llama_sampler_init_dist(params.seed));
} else if (params.mirostat == 1) {
samplers.push_back(llama_sampler_init_temp(params.temp));
samplers.push_back(llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100));
@@ -329,12 +296,6 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
llama_sampler_chain_add(chain, smpl);
}
if (grmr && params.backend_sampling) {
LOG_WRN("%s: backend sampling is not compatible with grammar, disabling\n", __func__);
params.backend_sampling = false;
}
auto * result = new common_sampler {
/* .params = */ params,
/* .grmr = */ grmr,
@@ -348,21 +309,15 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
}
void common_sampler_free(struct common_sampler * gsmpl) {
if (!gsmpl) {
return;
if (gsmpl) {
llama_sampler_free(gsmpl->grmr);
llama_sampler_free(gsmpl->chain);
delete gsmpl;
}
llama_sampler_free(gsmpl->grmr);
llama_sampler_free(gsmpl->chain);
delete gsmpl;
}
void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar) {
if (!gsmpl) {
return;
}
const auto tm = gsmpl->tm();
if (gsmpl->grmr && accept_grammar) {
@@ -375,10 +330,6 @@ void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, boo
}
void common_sampler_reset(struct common_sampler * gsmpl) {
if (!gsmpl) {
return;
}
gsmpl->reset();
}
@@ -439,10 +390,6 @@ void common_perf_print(const struct llama_context * ctx, const struct common_sam
}
struct llama_sampler * common_sampler_get(const struct common_sampler * gsmpl) {
if (!gsmpl) {
return nullptr;
}
return gsmpl->chain;
}
@@ -458,25 +405,6 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co
auto & chain = gsmpl->chain;
auto & cur_p = gsmpl->cur_p; // initialized by set_logits
// Check if a backend sampler has already sampled a token in which case we
// return that token id directly.
{
id = llama_get_sampled_token_ith(ctx, idx);
if (id != LLAMA_TOKEN_NULL) {
LOG_DBG("%s: Backend sampler selected token: '%d'. Will not run any CPU samplers\n", __func__, id);
GGML_ASSERT(!gsmpl->grmr && "using grammar in combination with backend sampling is not supported");
// TODO: simplify
gsmpl->cur.resize(1);
gsmpl->cur[0] = { id, 0.0f, 1.0f };
cur_p = { gsmpl->cur.data(), gsmpl->cur.size(), 0, true };
return id;
}
}
gsmpl->set_logits(ctx, idx);
if (grammar_first) {
@@ -639,7 +567,6 @@ char common_sampler_type_to_chr(enum common_sampler_type cnstr) {
case COMMON_SAMPLER_TYPE_XTC: return 'x';
case COMMON_SAMPLER_TYPE_INFILL: return 'i';
case COMMON_SAMPLER_TYPE_PENALTIES: return 'e';
case COMMON_SAMPLER_TYPE_ADAPTIVE_P: return 'a';
default : return '?';
}
}
@@ -656,7 +583,6 @@ std::string common_sampler_type_to_str(enum common_sampler_type cnstr) {
case COMMON_SAMPLER_TYPE_XTC: return "xtc";
case COMMON_SAMPLER_TYPE_INFILL: return "infill";
case COMMON_SAMPLER_TYPE_PENALTIES: return "penalties";
case COMMON_SAMPLER_TYPE_ADAPTIVE_P: return "adaptive_p";
default : return "";
}
}
@@ -673,7 +599,6 @@ std::vector<common_sampler_type> common_sampler_types_from_names(const std::vect
{ "xtc", COMMON_SAMPLER_TYPE_XTC },
{ "infill", COMMON_SAMPLER_TYPE_INFILL },
{ "penalties", COMMON_SAMPLER_TYPE_PENALTIES },
{ "adaptive_p", COMMON_SAMPLER_TYPE_ADAPTIVE_P },
};
// since samplers names are written multiple ways
@@ -689,7 +614,6 @@ std::vector<common_sampler_type> common_sampler_types_from_names(const std::vect
{ "typ", COMMON_SAMPLER_TYPE_TYPICAL_P },
{ "min-p", COMMON_SAMPLER_TYPE_MIN_P },
{ "temp", COMMON_SAMPLER_TYPE_TEMPERATURE },
{ "adaptive-p", COMMON_SAMPLER_TYPE_ADAPTIVE_P },
};
std::vector<common_sampler_type> samplers;
@@ -726,7 +650,6 @@ std::vector<common_sampler_type> common_sampler_types_from_chars(const std::stri
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_XTC), COMMON_SAMPLER_TYPE_XTC },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_INFILL), COMMON_SAMPLER_TYPE_INFILL },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_PENALTIES), COMMON_SAMPLER_TYPE_PENALTIES },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_ADAPTIVE_P), COMMON_SAMPLER_TYPE_ADAPTIVE_P },
};
std::vector<common_sampler_type> samplers;