#include "speculative.h" #include "log.h" #include "common.h" #include "sampling.h" #include struct seq_draft { }; struct common_speculative { struct common_speculative_params params; llama_batch batch_dft; struct common_sampler * smpl; std::vector i_batch_tgt; std::vector tokens; }; struct common_speculative * common_speculative_init(struct common_speculative_params params) { auto * result = new common_speculative { /* .params = */ params, /* .batch_dft = */ llama_batch_init(llama_n_batch(params.ctx_dft), 0, 1), /* .smpl = */ nullptr, /* .i_batch_tgt = */ {}, /* .tokens = */ {}, }; // TODO: optimize or pass from outside? #if 0 { common_sampler_params sparams; sparams.no_perf = false; sparams.top_k = 40; sparams.top_p = 0.9; sparams.samplers = { COMMON_SAMPLER_TYPE_TOP_K, COMMON_SAMPLER_TYPE_TOP_P, COMMON_SAMPLER_TYPE_INFILL, }; result->smpl = common_sampler_init(params.model_dft, sparams); } #else { common_sampler_params sparams; sparams.no_perf = false; sparams.top_k = 10; sparams.samplers = { COMMON_SAMPLER_TYPE_TOP_K, }; result->smpl = common_sampler_init(params.model_dft, sparams); } #endif result->batch_dft = llama_batch_init(llama_n_batch(params.ctx_dft), 0, 1); return result; } void common_speculative_free(struct common_speculative * spec) { common_sampler_free(spec->smpl); llama_batch_free(spec->batch_dft); delete spec; } void common_speculative_set_prompt(struct common_speculative * spec, llama_token * tokens, int32_t n_tokens) { llama_kv_cache_clear(spec->params.ctx_dft); // TODO: error handling llama_decode(spec->params.ctx_dft, llama_batch_get_one(tokens, n_tokens)); } void common_speculative_add_draft( struct common_speculative * spec, struct llama_batch & batch_tgt, llama_token id_last, int n_past) { spec->tokens.clear(); spec->i_batch_tgt.clear(); spec->i_batch_tgt.push_back(0); common_sampler_reset(spec->smpl); common_batch_clear(spec->batch_dft); common_batch_add (spec->batch_dft, id_last, n_past, { 0 }, true); llama_decode(spec->params.ctx_dft, spec->batch_dft); // sample n_draft tokens from the draft model for (int i = 0; i < spec->params.n_draft; ++i) { common_batch_clear(spec->batch_dft); common_sampler_sample(spec->smpl, spec->params.ctx_dft, 0, true); const auto * cur_p = common_sampler_get_candidates(spec->smpl); for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) { LOG_DBG(" - draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n", k, i, cur_p->data[k].id, cur_p->data[k].p, common_token_to_piece(spec->params.ctx_dft, cur_p->data[k].id).c_str()); } // add drafted token for each sequence const llama_token id = cur_p->data[0].id; // only collect very high-confidence draft tokens if (cur_p->data[0].p < 0.75 && spec->tokens.size() >= 0) { break; } common_sampler_accept(spec->smpl, id, true); spec->tokens.push_back(id); // add unique drafted tokens to the target batch spec->i_batch_tgt.push_back(batch_tgt.n_tokens); common_batch_add(batch_tgt, id, n_past + i + 1, { 0 }, true); if (batch_tgt.n_tokens > spec->params.n_draft) { break; } common_batch_add(spec->batch_dft, id, n_past + i + 1, { 0 }, true); // evaluate the drafted tokens on the draft model llama_decode(spec->params.ctx_dft, spec->batch_dft); } // don't waste time on small batches // TODO: do not evaluate the draft model for tha many rounds if (batch_tgt.n_tokens < spec->params.n_min) { batch_tgt.n_tokens = 1; spec->tokens.resize(0); spec->i_batch_tgt.resize(1); } // print current draft sequences LOG_DBG("draft %s\n", string_from(spec->params.ctx_dft, spec->tokens).c_str()); } std::vector common_speculative_sample( struct common_speculative * spec, struct common_sampler * smpl, struct llama_context * ctx_tgt) { return common_sampler_sample_n(smpl, ctx_tgt, spec->i_batch_tgt, spec->tokens); }