vrr/koboldcpp
2
0
Fork 0
mirror of https://github.com/LostRuins/koboldcpp.git synced 2025-09-11 09:34:37 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

Merge branch 'master' into concedo_experimental

Browse source 
# Conflicts:
#	.devops/nix/package.nix
#	README.md
#	ggml-metal.m
#	llama.cpp
#	scripts/sync-ggml.last
#	tests/test-backend-ops.cpp
This commit is contained in:
Concedo 2024-03-05 15:54:05 +08:00
commit ac43e0115c
28 changed files with 1225 additions and 309 deletions
Download patch file Download diff file Expand all files Collapse all files

79
common/sampling.cpp
View file

@ -295,6 +295,77 @@ static llama_token llama_sampling_sample_impl(
return id;
}
static llama_token_data_array llama_sample_probability_distribution_impl(
struct llama_sampling_context * ctx_sampling,
struct llama_context * ctx_main,
struct llama_context * ctx_cfg,
const int idx) {
const llama_sampling_params & params = ctx_sampling->params;
const int n_vocab = llama_n_vocab(llama_get_model(ctx_main));
const int32_t penalty_last_n = params.penalty_last_n < 0 ? params.n_prev : params.penalty_last_n;
const float penalty_repeat = params.penalty_repeat;
const float penalty_freq = params.penalty_freq;
const float penalty_present = params.penalty_present;
const bool penalize_nl = params.penalize_nl;
auto & prev = ctx_sampling->prev;
auto & cur = ctx_sampling->cur;
// Get a pointer to the logits
float * logits = llama_get_logits_ith(ctx_main, idx);
// Declare original_logits at the beginning of the function scope
std::vector<float> original_logits;
// apply params.logit_bias map
for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
logits[it->first] += it->second;
}
if (ctx_cfg) {
float * logits_guidance = llama_get_logits_ith(ctx_cfg, idx);
llama_sample_apply_guidance(ctx_main, logits, logits_guidance, params.cfg_scale);
}
cur.clear();
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
cur.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
}
llama_token_data_array cur_p = { cur.data(), cur.size(), false };
// apply penalties
const auto& penalty_tokens = params.use_penalty_prompt_tokens ? params.penalty_prompt_tokens : prev;
const int penalty_tokens_used_size = std::min((int)penalty_tokens.size(), penalty_last_n);
if (penalty_tokens_used_size) {
const float nl_logit = logits[llama_token_nl(llama_get_model(ctx_main))];
llama_sample_repetition_penalties(ctx_main, &cur_p,
penalty_tokens.data() + penalty_tokens.size() - penalty_tokens_used_size,
penalty_tokens_used_size, penalty_repeat, penalty_freq, penalty_present);
if (!penalize_nl) {
for (size_t idx = 0; idx < cur_p.size; idx++) {
if (cur_p.data[idx].id == llama_token_nl(llama_get_model(ctx_main))) {
cur_p.data[idx].logit = nl_logit;
break;
}
}
}
}
// apply grammar checks
if (ctx_sampling->grammar != NULL) {
llama_sample_grammar(ctx_main, &cur_p, ctx_sampling->grammar);
}
llama_sample_softmax(ctx_main, &cur_p);
return cur_p;
}
llama_token llama_sampling_sample(
struct llama_sampling_context * ctx_sampling,
struct llama_context * ctx_main,
@ -304,6 +375,14 @@ llama_token llama_sampling_sample(
return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, false);
}
llama_token_data_array llama_sampling_probability_distribution(
struct llama_sampling_context * ctx_sampling,
struct llama_context * ctx_main,
struct llama_context * ctx_cfg,
const int idx) {
return llama_sample_probability_distribution_impl(ctx_sampling,ctx_main, ctx_cfg, idx);
}
void llama_sampling_accept(
struct llama_sampling_context * ctx_sampling,
struct llama_context * ctx_main,