vbatts/llama.cpp
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llama : apply classifier-free guidance to logits directly (#4951)

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David Friehs 2024-01-15 14:06:52 +01:00 • committed by GitHub
parent d9aa4ffa6e
commit 4483396751
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3 changed files with 57 additions and 29 deletions
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llama.cpp
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@ -7898,39 +7898,59 @@ static void llama_log_softmax(float * array, size_t size) {
}
}
void llama_sample_apply_guidance(
struct llama_context * ctx,
float * logits,
float * logits_guidance,
float scale) {
GGML_ASSERT(ctx);
const auto t_start_sample_us = ggml_time_us();
const auto n_vocab = llama_n_vocab(llama_get_model(ctx));
llama_log_softmax(logits, n_vocab);
llama_log_softmax(logits_guidance, n_vocab);
for (int i = 0; i < n_vocab; ++i) {
auto & l = logits[i];
const auto & g = logits_guidance[i];
l = scale * (l - g) + g;
}
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
void llama_sample_classifier_free_guidance(
struct llama_context * ctx,
llama_token_data_array * candidates,
struct llama_context * guidance_ctx,
float scale) {
int64_t t_start_sample_us = ggml_time_us();
GGML_ASSERT(ctx);
int64_t t_start_sample_us;
auto n_vocab = llama_n_vocab(llama_get_model(ctx));
t_start_sample_us = ggml_time_us();
const size_t n_vocab = llama_n_vocab(llama_get_model(ctx));
GGML_ASSERT(n_vocab == (int)candidates->size);
GGML_ASSERT(n_vocab == candidates->size);
GGML_ASSERT(!candidates->sorted);
std::vector<float> logits_base;
logits_base.reserve(candidates->size);
for (size_t i = 0; i < candidates->size; ++i) {
logits_base.push_back(candidates->data[i].logit);
}
llama_log_softmax(logits_base.data(), candidates->size);
float* logits_guidance = llama_get_logits(guidance_ctx);
llama_log_softmax(logits_guidance, n_vocab);
for (int i = 0; i < n_vocab; ++i) {
float logit_guidance = logits_guidance[i];
float logit_base = logits_base[i];
candidates->data[i].logit = scale * (logit_base - logit_guidance) + logit_guidance;
std::vector<float> logits_base(n_vocab);
for (size_t i = 0; i < n_vocab; ++i) {
logits_base[i] = candidates->data[i].logit;
}
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
float * logits_guidance = llama_get_logits(guidance_ctx);
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
llama_sample_apply_guidance(ctx, logits_base.data(), logits_guidance, scale);
t_start_sample_us = ggml_time_us();
for (size_t i = 0; i < n_vocab; ++i) {
candidates->data[i].logit = logits_base[i];
}
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int32_t m, float * mu) {