diff options
author | Bach Le <bach@bullno1.com> | 2023-07-12 00:18:43 +0800 |
---|---|---|
committer | GitHub <noreply@github.com> | 2023-07-11 19:18:43 +0300 |
commit | c9c74b4e3f9dcfab8b0032749ff8a579ab4e4d8d (patch) | |
tree | 651d6915218efa83cad8745310f7d1114ca21e2a /llama.cpp | |
parent | 3ec7e596b2ba3f43c22f441254ca2bcfa91102ba (diff) |
llama : add classifier-free guidance (#2135)
* Initial implementation
* Remove debug print
* Restore signature of llama_init_from_gpt_params
* Free guidance context
* Make freeing of guidance_ctx conditional
* Make Classifier-Free Guidance a sampling function
* Correct typo. CFG already means context-free grammar.
* Record sampling time in llama_sample_classifier_free_guidance
* Shift all values by the max value before applying logsoftmax
* Fix styling based on review
Diffstat (limited to 'llama.cpp')
-rw-r--r-- | llama.cpp | 56 |
1 files changed, 56 insertions, 0 deletions
@@ -2167,6 +2167,62 @@ void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, l } } +static void llama_log_softmax(float * array, size_t size) { + float max_l = *std::max_element(array, array + size); + float sum = 0.f; + for (size_t i = 0; i < size; ++i) { + float p = expf(array[i] - max_l); + sum += p; + array[i] = p; + } + + for (size_t i = 0; i < size; ++i) { + array[i] = logf(array[i] / sum); + } +} + +void llama_sample_classifier_free_guidance( + struct llama_context * ctx, + llama_token_data_array * candidates, + struct llama_context * guidance_ctx, + float scale, + float smooth_factor) { + int64_t t_start_sample_us = t_start_sample_us = ggml_time_us(); + + assert(ctx); + auto n_vocab = llama_n_vocab(ctx); + assert(n_vocab == (int)candidates->size); + 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]; + logits_guidance[i] = scale * (logit_base - logit_guidance) + logit_guidance; + } + + llama_log_softmax(logits_guidance, n_vocab); + + for (int i = 0; i < n_vocab; ++i) { + float logit_base = logits_base[i]; + float logit_guidance = logits_guidance[i]; + + candidates->data[i].logit = smooth_factor * logit_guidance + (1.f - smooth_factor) * logit_base; + } + + if (ctx) { + 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, int m, float * mu) { assert(ctx); |