mirror of
https://github.com/ikawrakow/ik_llama.cpp.git
synced 2026-05-11 16:40:16 +00:00
1518 lines
52 KiB
C++
1518 lines
52 KiB
C++
#include "speculative.h"
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#include "common.h"
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#include "ggml.h"
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#include "llama.h"
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#include "log.h"
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#include "ngram-cache.h"
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#include "ngram-map.h"
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#include "ngram-mod.h"
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#include "sampling.h"
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#include "suffix-tree.h"
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#include <algorithm>
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#include <cstring>
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#include <iomanip>
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#include <map>
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#include <unordered_map>
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#define SPEC_VOCAB_MAX_SIZE_DIFFERENCE 128
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#define SPEC_VOCAB_CHECK_START_TOKEN_ID 5
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const std::vector<enum common_speculative_type> common_speculative_types = {
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COMMON_SPECULATIVE_TYPE_NONE,
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COMMON_SPECULATIVE_TYPE_DRAFT,
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COMMON_SPECULATIVE_TYPE_MTP,
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COMMON_SPECULATIVE_TYPE_EAGLE3,
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COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE,
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COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K,
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COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V,
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COMMON_SPECULATIVE_TYPE_NGRAM_MOD,
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COMMON_SPECULATIVE_TYPE_NGRAM_CACHE,
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COMMON_SPECULATIVE_TYPE_SUFFIX
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};
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const std::map<std::string, enum common_speculative_type> common_speculative_type_from_name_map = {
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{"none", COMMON_SPECULATIVE_TYPE_NONE},
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{"draft", COMMON_SPECULATIVE_TYPE_DRAFT},
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{"mtp", COMMON_SPECULATIVE_TYPE_MTP},
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{"eagle3", COMMON_SPECULATIVE_TYPE_EAGLE3},
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{"ngram_simple", COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE},
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{"ngram_map_k", COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K},
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{"ngram_map_k4v", COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V},
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{"ngram_mod", COMMON_SPECULATIVE_TYPE_NGRAM_MOD},
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{"ngram_cache", COMMON_SPECULATIVE_TYPE_NGRAM_CACHE},
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{"suffix", COMMON_SPECULATIVE_TYPE_SUFFIX}
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};
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struct common_speculative_config {
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common_speculative_type type;
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common_params_speculative params;
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common_speculative_config(common_speculative_type t,
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const common_params_speculative & p = common_params_speculative{}) : type(t), params(p) {}
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};
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static bool common_speculative_are_compatible(
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const llama_model * model_tgt,
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const llama_model * model_dft) {
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const llama_vocab * vocab_tgt = llama_model_get_vocab(model_tgt);
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const llama_vocab * vocab_dft = llama_model_get_vocab(model_dft);
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const bool vocab_type_tgt = llama_vocab_type(vocab_tgt);
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LOG_DBG("%s: vocab_type tgt: %d\n", __func__, vocab_type_tgt);
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const bool vocab_type_dft = llama_vocab_type(vocab_dft);
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LOG_DBG("%s: vocab_type dft: %d\n", __func__, vocab_type_dft);
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if (vocab_type_tgt != vocab_type_dft) {
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LOG_DBG("%s: draft model vocab type must match target model to use speculation but ", __func__);
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LOG_DBG("vocab_type_dft = %d while vocab_type_tgt = %d\n", vocab_type_dft, vocab_type_tgt);
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return false;
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}
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if (
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llama_vocab_get_add_bos(vocab_tgt) != llama_vocab_get_add_bos(vocab_dft) ||
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llama_vocab_get_add_eos(vocab_tgt) != llama_vocab_get_add_eos(vocab_dft) ||
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llama_vocab_bos(vocab_tgt) != llama_vocab_bos(vocab_dft) ||
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llama_vocab_eos(vocab_tgt) != llama_vocab_eos(vocab_dft)
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) {
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LOG_DBG("%s: draft model special tokens must match target model to use speculation\n", __func__);
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return false;
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}
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{
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const int n_vocab_tgt = llama_vocab_n_tokens(vocab_tgt);
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const int n_vocab_dft = llama_vocab_n_tokens(vocab_dft);
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const int vocab_diff = n_vocab_tgt > n_vocab_dft
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? n_vocab_tgt - n_vocab_dft
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: n_vocab_dft - n_vocab_tgt;
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if (vocab_diff > SPEC_VOCAB_MAX_SIZE_DIFFERENCE) {
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LOG_DBG("%s: draft model vocab must closely match target model to use speculation but ", __func__);
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LOG_DBG("target vocab size %d does not match draft vocab size %d - difference %d, max allowed %d\n",
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n_vocab_tgt, llama_vocab_n_tokens(vocab_dft), vocab_diff, SPEC_VOCAB_MAX_SIZE_DIFFERENCE);
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return false;
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}
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for (int i = SPEC_VOCAB_CHECK_START_TOKEN_ID; i < std::min(n_vocab_tgt, n_vocab_dft); ++i) {
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const char * token_text_tgt = llama_vocab_get_text(vocab_tgt, i);
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const char * token_text_dft = llama_vocab_get_text(vocab_dft, i);
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if (std::strcmp(token_text_tgt, token_text_dft) != 0) {
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LOG_DBG("%s: draft model vocab must match target model to use speculation but ", __func__);
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LOG_DBG("token %d content differs - target '%s', draft '%s'\n", i,
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common_token_to_piece(vocab_tgt, i).c_str(),
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common_token_to_piece(vocab_dft, i).c_str());
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return false;
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}
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}
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}
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return true;
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}
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// state of an implementation of speculative decoding
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//
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// each implementation has a unique type and a state that is implementation-specific
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// in a subclass of common_speculative_state
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struct common_speculative_state {
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const enum common_speculative_type type;
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size_t n_call_begin = 0; // number of times this implementation was called for refresh.
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size_t n_call_draft = 0; // number of times this implementation was called for generation.
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size_t n_call_accept = 0; // number of times this implementation was called for accumulation.
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size_t n_gen_drafts = 0; // number of times a draft or part was generated by this implementation.
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size_t n_acc_drafts = 0; // number of times a draft or part was accepted by the target model.
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size_t n_gen_tokens = 0; // number of tokens generated by this implementation.
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size_t n_acc_tokens = 0; // number of tokens accepted by the target model.
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// TODO: track performance of most recent calls
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const bool gen_perf = true; // whether to generate performance stats.
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int64_t t_begin_us = 0; // total time spent in refresh of this implementation in microseconds.
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int64_t t_draft_us = 0; // total time spent in generating drafts in this implementation in microseconds.
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int64_t t_accept_us = 0; // total time spent in accumulation of this implementation in microseconds.
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common_speculative_state(enum common_speculative_type type) : type(type) {}
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virtual ~common_speculative_state() = default;
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virtual void begin(const llama_tokens & prompt) = 0;
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virtual void draft(
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const common_params_speculative & params,
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const llama_tokens & prompt_tgt,
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llama_token id_last,
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llama_tokens & result) = 0;
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virtual void accept(uint16_t n_accepted) = 0;
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};
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struct common_speculative_state_mtp : public common_speculative_state {
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llama_context * ctx_tgt;
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llama_context * ctx_mtp = nullptr;
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common_sampler * smpl;
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common_speculative_state_mtp(
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enum common_speculative_type type,
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llama_context * ctx_tgt,
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const llama_context_params & mtp_cparams)
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: common_speculative_state(type)
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, ctx_tgt(ctx_tgt)
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{
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struct common_params_sampling params;
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params.samplers_sequence = {
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llama_sampler_type::DIST,
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};
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smpl = common_sampler_init(llama_get_model(ctx_tgt), params);
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const llama_model * model = llama_get_model(ctx_tgt);
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ctx_mtp = llama_init_from_model(const_cast<llama_model *>(model), mtp_cparams);
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if (ctx_mtp) {
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LOG_INF("%s: created MTP context (n_ctx=%d)\n", __func__, llama_n_ctx(ctx_mtp));
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} else {
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LOG_ERR("%s: failed to create MTP context\n", __func__);
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}
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}
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~common_speculative_state_mtp() override {
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common_sampler_free(smpl);
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if (ctx_mtp) {
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llama_free(ctx_mtp);
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}
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}
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void begin(const llama_tokens & prompt) override {
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GGML_UNUSED(prompt);
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}
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void draft(
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const common_params_speculative & params,
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const llama_tokens & prompt_tgt,
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llama_token id_last,
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llama_tokens & result) override {
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int32_t n_past = (int32_t)prompt_tgt.size();
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llama_seq_id seq_id = 0;
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llama_pos mtp_pos_max = llama_kv_cache_seq_pos_max(ctx_mtp, seq_id);
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if (mtp_pos_max >= n_past) {
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llama_kv_cache_seq_rm(ctx_mtp, seq_id, n_past, -1);
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}
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llama_context * ctx = ctx_mtp;
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result = mtp_speculative_gen_draft(
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smpl,
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ctx,
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params.n_max,
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params.p_min,
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id_last,
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n_past,
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seq_id
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);
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}
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void accept(uint16_t n_accepted) override {
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GGML_UNUSED(n_accepted);
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}
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};
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struct common_speculative_state_draft : public common_speculative_state {
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llama_context * ctx_tgt; // only used for retokenizing from ctx_dft
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llama_context * ctx_dft;
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common_sampler * smpl;
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llama_batch batch;
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llama_tokens prompt_dft;
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bool vocab_cmpt = true; // whether retokenization is needed
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std::unordered_map<std::string, std::string> vocab_map;
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common_speculative_state_draft(
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enum common_speculative_type type,
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llama_context * ctx_tgt,
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llama_context * ctx_dft,
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const std::vector<std::pair<std::string, std::string>> & replacements)
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: common_speculative_state(type)
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, ctx_tgt(ctx_tgt)
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, ctx_dft(ctx_dft)
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{
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batch = llama_batch_init(llama_n_batch(ctx_dft), 0, 1);
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smpl = nullptr;
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{
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struct common_params_sampling params;
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params.top_k = 10;
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params.samplers_sequence = {
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llama_sampler_type::TOP_K,
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llama_sampler_type::DIST, // needed to get probabilities
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};
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smpl = common_sampler_init(llama_get_model(ctx_dft), params);
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}
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vocab_cmpt = common_speculative_are_compatible(llama_get_model(ctx_tgt), llama_get_model(ctx_dft));
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LOG_DBG("vocab_cmpt = %d\n", vocab_cmpt);
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if (!vocab_cmpt) {
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LOG_WRN("the target and draft vocabs are not compatible - tokens will be translated between the two\n");
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for (const auto & pair : replacements) {
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vocab_map[pair.first] = pair.second;
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}
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}
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}
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~common_speculative_state_draft() override {
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llama_free(ctx_dft);
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common_sampler_free(smpl);
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llama_batch_free(batch);
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}
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void begin(const llama_tokens & prompt) override {
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GGML_UNUSED(prompt);
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}
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void draft(
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const common_params_speculative & params,
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const llama_tokens & prompt_tgt,
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llama_token id_last,
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llama_tokens & result) override {
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auto * spec = this;
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auto & batch = spec->batch;
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auto & ctx_tgt = spec->ctx_tgt;
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auto & ctx_dft = spec->ctx_dft;
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auto & smpl = spec->smpl;
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auto & prompt_dft = spec->prompt_dft;
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int reuse_i = 0;
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int reuse_n = 0;
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const int n_ctx = llama_n_ctx(ctx_dft) - params.n_max;
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llama_tokens prompt_cnv;
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if (!spec->vocab_cmpt) {
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// convert id_last to draft vocab. llama_detokenize is called directly to avoid an allocation
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const auto * model_tgt = llama_get_model(ctx_tgt);
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const auto * vocab_tgt = llama_model_get_vocab(model_tgt);
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std::string text;
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text = common_detokenize(ctx_tgt, prompt_tgt, true);
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text = replace_to_dft(text);
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LOG_DBG("%s: main->draft detokenized string: '%s'\n", __func__, text.c_str());
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prompt_cnv = common_tokenize(ctx_dft, text, false, true);
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int32_t n_chars = llama_detokenize(vocab_tgt, &id_last, 1, nullptr, 0, false, false);
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GGML_ASSERT(n_chars < 0 && "failed to detokenize id_last");
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text.resize(-n_chars);
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llama_detokenize(vocab_tgt, &id_last, 1, text.data(), text.size(), false, false);
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text = replace_to_dft(text);
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LOG_DBG("main->draft detokenized id_last(%d): '%s'\n", id_last, text.c_str());
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id_last = common_tokenize(ctx_dft, text, false, true)[0];
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}
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const llama_tokens & prompt_cur = spec->vocab_cmpt ? prompt_tgt : prompt_cnv;
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const int i_start = std::max<int>(0, (int) prompt_cur.size() - n_ctx);
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// reuse as much as possible from the old draft context
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// ideally, the draft context should be as big as the target context and we will always reuse the entire prompt
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for (int i = 0; i < (int) prompt_dft.size(); ++i) {
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int cur = 0;
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while (i_start + cur < (int) prompt_cur.size() &&
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i + cur < (int) prompt_dft.size() &&
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prompt_cur[i_start + cur] == prompt_dft[i + cur]) {
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cur++;
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}
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if ((cur >= 256 || n_ctx >= (int) prompt_cur.size()) && cur > reuse_n) {
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reuse_i = i;
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reuse_n = cur;
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}
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}
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LOG_DBG("%s: reuse_i = %d, reuse_n = %d, prompt = %d\n", __func__, reuse_i, reuse_n, (int) prompt_dft.size());
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result.clear();
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result.reserve(params.n_max);
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if (reuse_n == 0) {
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llama_kv_cache_clear(ctx_dft);
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prompt_dft.clear();
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} else {
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// this happens when a previous draft has been discarded (for example, due to being too small), but the
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// target model agreed with it. in this case, we simply pass back the previous results to save compute
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if (reuse_i + reuse_n < (int) prompt_dft.size() && prompt_dft[reuse_i + reuse_n] == id_last) {
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for (int i = reuse_i + reuse_n + 1; i < (int) prompt_dft.size(); ++i) {
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result.push_back(prompt_dft[i]);
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if (params.n_max <= (int) result.size()) {
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break;
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}
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}
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return;
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}
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if (reuse_i > 0) {
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llama_kv_cache_seq_rm (ctx_dft, 0, 0, reuse_i);
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llama_kv_cache_seq_add(ctx_dft, 0, reuse_i, -1, -reuse_i);
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prompt_dft.erase(prompt_dft.begin(), prompt_dft.begin() + reuse_i);
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}
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if (reuse_n < (int) prompt_dft.size()) {
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llama_kv_cache_seq_rm (ctx_dft, 0, reuse_n, -1);
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prompt_dft.erase(prompt_dft.begin() + reuse_n, prompt_dft.end());
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}
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}
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// prepare a batch to evaluate any new tokens in the prompt
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common_batch_clear(batch);
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for (size_t i = i_start + reuse_n; i < prompt_cur.size(); ++i) {
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//LOG_DBG("i = %d, i_start = %d, reuse_n = %d, i - i_start = %d, id = %6d\n", i, i_start, reuse_n, i - i_start, prompt_cur[i]);
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common_batch_add(batch, prompt_cur[i], i - i_start, { 0 }, false);
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prompt_dft.push_back(prompt_cur[i]);
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}
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// we should rarely end-up here during normal decoding
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if (batch.n_tokens > 0) {
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//LOG_DBG("%s: draft prompt batch: %s\n", __func__, string_from(ctx, batch).c_str());
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llama_decode(ctx_dft, batch);
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}
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const llama_pos n_past = prompt_dft.size();
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LOG_DBG("%s: n_past = %d\n", __func__, n_past);
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common_batch_clear(batch);
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common_batch_add (batch, id_last, n_past, { 0 }, true);
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prompt_dft.push_back(id_last);
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//LOG_DBG("%s: draft prompt: %s\n", __func__, string_from(ctx_dft, prompt_dft).c_str());
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llama_decode(ctx_dft, batch);
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common_sampler_reset(smpl);
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// sample n_draft tokens from the draft model
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for (int i = 0; i < params.n_max; ++i) {
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common_batch_clear(batch);
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common_sampler_sample(smpl, ctx_dft, 0, true);
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const auto * cur_p = common_sampler_get_candidates(smpl, true);
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for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
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LOG_DBG(" - draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
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k, i, cur_p->data[k].id, cur_p->data[k].p, common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
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}
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// add drafted token for each sequence
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const llama_token id = cur_p->data[0].id;
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common_sampler_accept(smpl, nullptr, id, true);
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result.push_back(id);
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|
||
if (params.n_max <= (int) result.size()) {
|
||
break;
|
||
}
|
||
|
||
// only collect very high-confidence draft tokens
|
||
if (cur_p->data[0].p < params.p_min) {
|
||
break;
|
||
}
|
||
|
||
common_batch_add(batch, id, n_past + i + 1, { 0 }, true);
|
||
|
||
// evaluate the drafted tokens on the draft model
|
||
llama_decode(ctx_dft, batch);
|
||
|
||
prompt_dft.push_back(id);
|
||
}
|
||
|
||
if (!spec->vocab_cmpt) {
|
||
std::string detokenized = common_detokenize(ctx_dft, result, true);
|
||
detokenized = replace_to_tgt(detokenized);
|
||
LOG_DBG("draft->main detokenized string: '%s'\n", detokenized.c_str());
|
||
result = common_tokenize(ctx_tgt, detokenized, false, true);
|
||
if (result.size() > (size_t)params.n_max) {
|
||
result.resize(params.n_max);
|
||
}
|
||
}
|
||
}
|
||
|
||
void accept(uint16_t n_accepted) override {
|
||
// noop
|
||
GGML_UNUSED(n_accepted);
|
||
}
|
||
|
||
std::string replace_to_dft(const std::string & input) const {
|
||
std::string result = input;
|
||
|
||
for (const auto & pair : this->vocab_map) {
|
||
size_t pos = result.find(pair.first);
|
||
while (pos != std::string::npos) {
|
||
result.replace(pos, pair.first.length(), pair.second);
|
||
pos = result.find(pair.first, pos + pair.second.length());
|
||
}
|
||
}
|
||
|
||
return result;
|
||
}
|
||
|
||
std::string replace_to_tgt(const std::string & input) const {
|
||
std::string result = input;
|
||
|
||
for (const auto & pair : this->vocab_map) {
|
||
size_t pos = result.find(pair.second);
|
||
while (pos != std::string::npos) {
|
||
result.replace(pos, pair.second.length(), pair.first);
|
||
pos = result.find(pair.second, pos + pair.first.length());
|
||
}
|
||
}
|
||
|
||
return result;
|
||
}
|
||
};
|
||
|
||
struct common_speculative_state_eagle3 : public common_speculative_state {
|
||
common_speculative_state_eagle3(enum common_speculative_type type) : common_speculative_state(type) {}
|
||
|
||
void begin(const llama_tokens & prompt) override {
|
||
GGML_UNUSED(prompt);
|
||
}
|
||
|
||
void draft(
|
||
const common_params_speculative & params,
|
||
const llama_tokens & prompt_tgt,
|
||
llama_token id_last,
|
||
llama_tokens & draft_tokens) override {
|
||
// TODO: implement
|
||
GGML_UNUSED(params);
|
||
GGML_UNUSED(prompt_tgt);
|
||
GGML_UNUSED(id_last);
|
||
GGML_UNUSED(draft_tokens);
|
||
}
|
||
|
||
void accept(uint16_t n_accepted) override {
|
||
// noop
|
||
GGML_UNUSED(n_accepted);
|
||
}
|
||
};
|
||
|
||
// state of self-speculation (simple implementation, not ngram-map)
|
||
struct common_speculative_state_ngram_simple : public common_speculative_state {
|
||
common_ngram_simple_config config;
|
||
|
||
common_speculative_state_ngram_simple(
|
||
enum common_speculative_type type,
|
||
common_ngram_simple_config config)
|
||
: common_speculative_state(type), config(config) {}
|
||
|
||
void begin(const llama_tokens & prompt) override {
|
||
GGML_UNUSED(prompt);
|
||
}
|
||
|
||
void draft(
|
||
const common_params_speculative & params,
|
||
const llama_tokens & prompt_tgt,
|
||
llama_token id_last,
|
||
llama_tokens & result) override {
|
||
|
||
result = common_ngram_simple_draft(config, prompt_tgt, id_last);
|
||
GGML_UNUSED(params);
|
||
}
|
||
|
||
void accept(uint16_t n_accepted) override {
|
||
// noop
|
||
GGML_UNUSED(n_accepted);
|
||
}
|
||
};
|
||
|
||
struct common_speculative_state_ngram_map_k : public common_speculative_state {
|
||
// draft ngram map for speculative decoding without draft model
|
||
common_ngram_map map;
|
||
|
||
common_speculative_state_ngram_map_k(
|
||
enum common_speculative_type type,
|
||
common_ngram_map map)
|
||
: common_speculative_state(type), map(std::move(map)) {}
|
||
|
||
void begin(const llama_tokens & prompt) override {
|
||
common_ngram_map_begin(map, prompt);
|
||
}
|
||
|
||
void draft(
|
||
const common_params_speculative & params,
|
||
const llama_tokens & prompt_tgt,
|
||
llama_token id_last,
|
||
llama_tokens & result) override {
|
||
common_ngram_map_draft(map, prompt_tgt, id_last, result);
|
||
GGML_UNUSED(params);
|
||
}
|
||
|
||
void accept(uint16_t n_accepted) override {
|
||
common_ngram_map_accept(map, n_accepted);
|
||
}
|
||
};
|
||
|
||
struct common_speculative_state_ngram_mod : public common_speculative_state {
|
||
common_ngram_mod & mod;
|
||
|
||
// the last position in the prompt that was added to the ngram container
|
||
size_t i_last = 0;
|
||
|
||
// length of the last drafted n‑gram (number of tokens returned by draft)
|
||
size_t n_draft_last = 0;
|
||
|
||
// consecutive accept rounds with low acceptance fraction (< 0.5)
|
||
int n_low = 0;
|
||
|
||
// enable trace logging if LLAMA_TRACE is set
|
||
const bool verbose;
|
||
|
||
common_speculative_state_ngram_mod(enum common_speculative_type type, common_ngram_mod & mod)
|
||
: common_speculative_state(type), mod(mod), verbose(std::getenv("LLAMA_TRACE") != nullptr) {
|
||
static_assert(sizeof(llama_token) == sizeof(common_ngram_mod::entry_t));
|
||
}
|
||
|
||
void begin(const llama_tokens & prompt) override {
|
||
i_last = 0;
|
||
|
||
n_draft_last = 0;
|
||
n_low = 0;
|
||
|
||
const size_t n = mod.get_n();
|
||
|
||
if (prompt.size() < n) {
|
||
return;
|
||
}
|
||
|
||
for (size_t i = 0; i < prompt.size() - n; ++i) {
|
||
mod.add(prompt.data() + i);
|
||
}
|
||
|
||
i_last = prompt.size() - n;
|
||
|
||
const double f = (double)mod.get_used() / (double)mod.size();
|
||
LOG_INF("%s: ngram_mod occupancy = %zu/%zu (%.2f)\n", __func__, mod.get_used(), mod.size(), f);
|
||
|
||
constexpr double f_thold = 0.25;
|
||
if (f > f_thold) {
|
||
LOG_WRN("%s: ngram_mod occupancy %.2f exceeds threshold (%.2f) - resetting\n", __func__, f, f_thold);
|
||
|
||
mod.reset();
|
||
}
|
||
}
|
||
|
||
void draft(
|
||
const common_params_speculative & params,
|
||
const llama_tokens & prompt_tgt,
|
||
llama_token id_last,
|
||
llama_tokens & result) override {
|
||
GGML_UNUSED(params);
|
||
|
||
n_draft_last = 0;
|
||
|
||
const size_t cur_len = prompt_tgt.size();
|
||
if (cur_len < mod.get_n()) {
|
||
return;
|
||
}
|
||
|
||
const size_t n = mod.get_n();
|
||
|
||
// add new ngrams in chunks
|
||
if (i_last + 32 < cur_len) {
|
||
for (size_t i = i_last; i < cur_len - n; ++i) {
|
||
mod.add(prompt_tgt.data() + i);
|
||
}
|
||
|
||
i_last = cur_len - n;
|
||
}
|
||
|
||
result.resize(n + params.n_max);
|
||
for (size_t i = 0; i < n - 1; ++i) {
|
||
result[i] = prompt_tgt[cur_len - n + 1 + i];
|
||
}
|
||
result[n - 1] = id_last;
|
||
|
||
for (int i = 0; i < params.n_max; ++i) {
|
||
const llama_token token = mod.get(result.data() + i);
|
||
if (token == common_ngram_mod::EMPTY) {
|
||
if (i < params.n_min) {
|
||
result.clear();
|
||
return;
|
||
}
|
||
|
||
result.resize(n + i);
|
||
break;
|
||
}
|
||
result[n + i] = token;
|
||
}
|
||
|
||
// only return the m tokens that were drafted
|
||
for (size_t i = 0; n + i < result.size(); ++i) {
|
||
result[i] = result[n + i];
|
||
}
|
||
result.resize(result.size() - n);
|
||
|
||
// store length of drafted n‑gram for later acceptance analysis
|
||
n_draft_last = result.size();
|
||
}
|
||
|
||
void accept(uint16_t n_accepted) override {
|
||
if (verbose) {
|
||
LOG_INF("%s: accepted %d tokens from %zu drafted tokens\n", __func__, n_accepted, n_draft_last);
|
||
}
|
||
|
||
// compute acceptance fraction if we have a recorded draft length
|
||
if (n_draft_last > 0) {
|
||
const double f_acc = (double)n_accepted / (double)n_draft_last;
|
||
if (f_acc < 0.5) {
|
||
n_low++;
|
||
if (n_low >= 3) {
|
||
LOG_WRN("%s: low acceptance streak (%d) – resetting ngram_mod\n", __func__, n_low);
|
||
|
||
mod.reset();
|
||
n_low = 0;
|
||
i_last = 0;
|
||
}
|
||
} else {
|
||
n_low = 0;
|
||
}
|
||
}
|
||
}
|
||
};
|
||
|
||
struct common_speculative_state_ngram_cache : public common_speculative_state {
|
||
uint16_t n_draft;
|
||
bool save_dynamic;
|
||
bool save_static;
|
||
|
||
common_ngram_cache ngram_cache_context;
|
||
common_ngram_cache ngram_cache_dynamic;
|
||
common_ngram_cache ngram_cache_static;
|
||
|
||
size_t cache_size = 0; // number of tokens in n-gram cache
|
||
|
||
common_speculative_state_ngram_cache(
|
||
const enum common_speculative_type type,
|
||
const std::string & path_static,
|
||
const std::string & path_dynamic,
|
||
uint16_t n_draft,
|
||
bool save_dynamic,
|
||
bool save_static)
|
||
: common_speculative_state(type)
|
||
, n_draft(n_draft)
|
||
, save_dynamic(save_dynamic)
|
||
, save_static(save_static)
|
||
{
|
||
if (!path_static.empty()) {
|
||
try {
|
||
ngram_cache_static = common_ngram_cache_load(path_static);
|
||
} catch (...) {
|
||
LOG_ERR("failed to open static lookup cache: %s", path_static.c_str());
|
||
GGML_ABORT("Couldn't read static lookup cache");
|
||
}
|
||
}
|
||
|
||
if (!path_dynamic.empty()) {
|
||
try {
|
||
ngram_cache_dynamic = common_ngram_cache_load(path_dynamic);
|
||
} catch (...) {
|
||
LOG_ERR("failed to open dynamic lookup cache: %s", path_dynamic.c_str());
|
||
GGML_ABORT("Couldn't read dynamic lookup cache");
|
||
}
|
||
}
|
||
}
|
||
|
||
void begin(const llama_tokens & prompt) override {
|
||
GGML_UNUSED(prompt);
|
||
}
|
||
|
||
void draft(
|
||
const common_params_speculative & params,
|
||
const llama_tokens & prompt_tgt,
|
||
llama_token id_last,
|
||
llama_tokens & result) override {
|
||
GGML_UNUSED(params);
|
||
|
||
if (cache_size < prompt_tgt.size() + 1) {
|
||
llama_tokens tokens_new;
|
||
tokens_new.reserve(prompt_tgt.size() + 1 - cache_size);
|
||
for (size_t j = cache_size; j < prompt_tgt.size(); ++j) {
|
||
tokens_new.push_back(prompt_tgt[j]);
|
||
}
|
||
tokens_new.push_back(id_last); // add the last token
|
||
|
||
// Update context ngram cache with new prompt_tgt:
|
||
common_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX,
|
||
tokens_new, tokens_new.size(), false);
|
||
cache_size = prompt_tgt.size() + 1;
|
||
}
|
||
|
||
llama_tokens inp;
|
||
inp.reserve(prompt_tgt.size() + 1);
|
||
for (size_t j = 0; j < prompt_tgt.size(); ++j) {
|
||
inp.push_back(prompt_tgt[j]);
|
||
}
|
||
inp.push_back(id_last);
|
||
|
||
result.push_back(id_last);
|
||
|
||
common_ngram_cache_draft(inp, result, n_draft, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX,
|
||
ngram_cache_context,
|
||
ngram_cache_dynamic,
|
||
ngram_cache_static);
|
||
|
||
if (result.size() > 0) {
|
||
// delete first token in result (which is the id_last token)
|
||
result.erase(result.begin());
|
||
}
|
||
}
|
||
|
||
void accept(uint16_t n_accepted) override {
|
||
// TODO: noop
|
||
GGML_UNUSED(n_accepted);
|
||
}
|
||
};
|
||
|
||
struct common_speculative_state_suffix : public common_speculative_state {
|
||
common_suffix_tree tree;
|
||
common_suffix_tree corpus_tree;
|
||
bool has_corpus = false;
|
||
size_t cache_size = 0;
|
||
|
||
// Acceptance feedback
|
||
size_t n_draft_last = 0;
|
||
bool had_accept = false;
|
||
int n_low = 0;
|
||
float base_p_min = 0.1f;
|
||
float eff_p_min = 0.1f;
|
||
|
||
common_speculative_state_suffix(
|
||
enum common_speculative_type type,
|
||
int max_depth,
|
||
const std::string & corpus_path,
|
||
const llama_model * model)
|
||
: common_speculative_state(type)
|
||
, tree(max_depth)
|
||
, corpus_tree(max_depth)
|
||
{
|
||
if (!corpus_path.empty()) {
|
||
std::function<std::vector<llama_token>(const std::string &)> tokenize_fn;
|
||
if (model) {
|
||
tokenize_fn = [model](const std::string & text) -> std::vector<llama_token> {
|
||
return common_tokenize(model, text, false, true);
|
||
};
|
||
}
|
||
has_corpus = corpus_tree.load_corpus(corpus_path, tokenize_fn);
|
||
}
|
||
}
|
||
|
||
void begin(const llama_tokens & prompt) override {
|
||
cache_size = 0;
|
||
n_draft_last = 0;
|
||
had_accept = false;
|
||
n_low = 0;
|
||
GGML_UNUSED(prompt);
|
||
}
|
||
|
||
void draft(
|
||
const common_params_speculative & params,
|
||
const llama_tokens & prompt_tgt,
|
||
llama_token id_last,
|
||
llama_tokens & result) override {
|
||
|
||
base_p_min = params.p_min;
|
||
if (n_draft_last > 0 && !had_accept) {
|
||
if (++n_low >= 3) {
|
||
eff_p_min = std::min(eff_p_min + 0.1f, 0.5f);
|
||
n_low = 0;
|
||
}
|
||
}
|
||
had_accept = false;
|
||
|
||
if (cache_size < prompt_tgt.size() + 1) {
|
||
llama_tokens tokens_new;
|
||
tokens_new.reserve(prompt_tgt.size() + 1 - cache_size);
|
||
for (size_t j = cache_size; j < prompt_tgt.size(); ++j) {
|
||
tokens_new.push_back(prompt_tgt[j]);
|
||
}
|
||
tokens_new.push_back(id_last);
|
||
|
||
tree.extend(tokens_new.data(), (int)tokens_new.size());
|
||
cache_size = prompt_tgt.size() + 1;
|
||
}
|
||
|
||
const int ctx_len = std::min((int)(prompt_tgt.size() + 1), tree.max_depth());
|
||
llama_tokens context;
|
||
context.reserve(ctx_len);
|
||
const int ctx_start = (int)prompt_tgt.size() + 1 - ctx_len;
|
||
for (int j = ctx_start; j < (int)prompt_tgt.size(); ++j) {
|
||
context.push_back(prompt_tgt[j]);
|
||
}
|
||
context.push_back(id_last);
|
||
const int min_match_len = std::max(1, params.suffix_min_match_len);
|
||
|
||
result = tree.speculate(
|
||
context.data(), (int)context.size(),
|
||
params.n_max,
|
||
eff_p_min,
|
||
1,
|
||
min_match_len);
|
||
|
||
if (has_corpus) {
|
||
auto corpus_result = corpus_tree.speculate(
|
||
context.data(), (int)context.size(),
|
||
params.n_max,
|
||
eff_p_min,
|
||
1,
|
||
min_match_len);
|
||
if (corpus_result.size() > result.size()) {
|
||
result = std::move(corpus_result);
|
||
}
|
||
}
|
||
|
||
n_draft_last = result.size();
|
||
}
|
||
|
||
void accept(uint16_t n_accepted) override {
|
||
if (n_draft_last == 0) {
|
||
return;
|
||
}
|
||
had_accept = true;
|
||
const double f_acc = (double)n_accepted / (double)n_draft_last;
|
||
if (f_acc < 0.5) {
|
||
if (++n_low >= 3) {
|
||
eff_p_min = std::min(eff_p_min + 0.1f, 0.5f);
|
||
n_low = 0;
|
||
}
|
||
} else {
|
||
n_low = 0;
|
||
if (eff_p_min > base_p_min) {
|
||
eff_p_min = std::max(eff_p_min - 0.05f, base_p_min);
|
||
}
|
||
}
|
||
}
|
||
};
|
||
|
||
struct common_speculative {
|
||
std::vector<std::unique_ptr<common_speculative_state>> impls; // list of implementations to use and their states
|
||
common_speculative_state * curr_impl = nullptr; // current implementation in use (for stats)
|
||
std::unique_ptr<spec_tuner> tuner;
|
||
int last_n_drafted = 0;
|
||
int64_t t_step_start_us = 0;
|
||
};
|
||
|
||
static common_ngram_map get_common_ngram_map(const common_speculative_config & config) {
|
||
uint16_t size_key = config.params.ngram_size_n;
|
||
uint16_t size_value = config.params.ngram_size_m;
|
||
bool key_only = (config.type == COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K);
|
||
uint16_t min_hits = config.params.ngram_min_hits;
|
||
|
||
return common_ngram_map(size_key, size_value, key_only, min_hits);
|
||
}
|
||
|
||
static common_speculative_state_ngram_cache create_state_ngram_cache(
|
||
const std::string & path_static, const std::string & path_dynamic,
|
||
const common_speculative_config & config) {
|
||
uint16_t n_draft = 8; // TODO get from config?
|
||
|
||
// TODO bool param in common/common.h to set save_static/save_dynamic?
|
||
bool save_static = false;
|
||
bool save_dynamic = false;
|
||
|
||
common_speculative_state_ngram_cache state(config.type, path_static, path_dynamic, n_draft, save_static, save_dynamic);
|
||
|
||
return state;
|
||
}
|
||
|
||
std::string common_speculative_type_name_str() {
|
||
std::string result;
|
||
for (size_t i = 0; i < common_speculative_types.size(); i++) {
|
||
if (i > 0) {
|
||
result += ", ";
|
||
}
|
||
result += common_speculative_type_to_str(common_speculative_types[i]);
|
||
}
|
||
return result;
|
||
}
|
||
|
||
std::string common_speculative_type_to_str(enum common_speculative_type type) {
|
||
switch (type) {
|
||
case COMMON_SPECULATIVE_TYPE_NONE: return "none";
|
||
case COMMON_SPECULATIVE_TYPE_DRAFT: return "draft";
|
||
case COMMON_SPECULATIVE_TYPE_MTP: return "mtp";
|
||
case COMMON_SPECULATIVE_TYPE_EAGLE3: return "eagle3";
|
||
case COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE: return "ngram_simple";
|
||
case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K: return "ngram_map_k";
|
||
case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V: return "ngram_map_k4v";
|
||
case COMMON_SPECULATIVE_TYPE_NGRAM_MOD: return "ngram_mod";
|
||
case COMMON_SPECULATIVE_TYPE_NGRAM_CACHE: return "ngram_cache";
|
||
case COMMON_SPECULATIVE_TYPE_SUFFIX: return "suffix";
|
||
default: return "unknown";
|
||
}
|
||
}
|
||
|
||
enum common_speculative_type common_speculative_type_from_name(const std::string & name) {
|
||
const auto it = common_speculative_type_from_name_map.find(name);
|
||
if (it == common_speculative_type_from_name_map.end()) {
|
||
return COMMON_SPECULATIVE_TYPE_COUNT;
|
||
}
|
||
return it->second;
|
||
}
|
||
|
||
bool common_speculative_is_compat(llama_context * ctx_tgt) {
|
||
bool res = true;
|
||
|
||
llama_kv_cache_clear(ctx_tgt);
|
||
|
||
// eval 2 tokens to check if the context is compatible
|
||
std::vector<llama_token> tmp;
|
||
tmp.push_back(0);
|
||
tmp.push_back(0);
|
||
|
||
int ret = llama_decode(ctx_tgt, llama_batch_get_one(tmp.data(), tmp.size(), 0, 0));
|
||
if (ret != 0) {
|
||
LOG_ERR("%s: llama_decode() failed: %d\n", __func__, ret);
|
||
res = false;
|
||
goto done;
|
||
}
|
||
|
||
// try to remove the last tokens
|
||
if (!llama_kv_cache_seq_rm(ctx_tgt, 0, 1, -1)) {
|
||
LOG_WRN("%s: the target context does not support partial sequence removal\n", __func__);
|
||
res = false;
|
||
goto done;
|
||
}
|
||
|
||
done:
|
||
llama_kv_cache_clear(ctx_tgt);
|
||
llama_synchronize(ctx_tgt);
|
||
|
||
return res;
|
||
}
|
||
|
||
// initialization of the speculative decoding system
|
||
//
|
||
common_speculative * common_speculative_init(
|
||
common_params_speculative & params,
|
||
llama_context * ctx_tgt) {
|
||
llama_context * ctx_dft = nullptr;
|
||
if (params.model_dft) {
|
||
ctx_dft = llama_init_from_model(params.model_dft, params.cparams_dft);
|
||
if (ctx_dft == nullptr) {
|
||
LOG_ERR("%s", "failed to create draft context\n");
|
||
return nullptr;
|
||
}
|
||
}
|
||
|
||
// Compute the implementations to use based on the config and their order of preference
|
||
std::vector<common_speculative_config> configs = {}; // list of speculative configs to try
|
||
{
|
||
bool has_draft = !params.mparams_dft.path.empty();
|
||
bool has_draft_eagle3 = false; // TODO PR-18039: if params.speculative.eagle3
|
||
bool has_mtp = (params.type == COMMON_SPECULATIVE_TYPE_MTP);
|
||
|
||
bool has_ngram_cache = (params.type == COMMON_SPECULATIVE_TYPE_NGRAM_CACHE);
|
||
bool has_ngram_simple = (params.type == COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE);
|
||
bool has_ngram_map_k = (params.type == COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K);
|
||
bool has_ngram_map_k4v = (params.type == COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V);
|
||
bool has_ngram_mod = (params.type == COMMON_SPECULATIVE_TYPE_NGRAM_MOD);
|
||
bool has_suffix = (params.type == COMMON_SPECULATIVE_TYPE_SUFFIX);
|
||
|
||
// In a more complex implementation we could use the same implementation but with different parameters.
|
||
// This was initially used in PR-18471 but removed to simplify the code.
|
||
if (has_ngram_simple) {
|
||
// This implementation can guess a lot of tokens without any draft model.
|
||
configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE, params));
|
||
}
|
||
if (has_ngram_map_k) {
|
||
configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K, params));
|
||
}
|
||
if (has_ngram_map_k4v) {
|
||
// This implementation can guess tokens with high acceptance rate but is more expensive.
|
||
configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V, params));
|
||
}
|
||
if (has_ngram_mod) {
|
||
// shared instance for all speculative decoding contexts
|
||
if (!params.ngram_mod) {
|
||
params.ngram_mod = std::make_shared<common_ngram_mod>(params.ngram_size_n, 4*1024*1024);
|
||
|
||
LOG_INF("%s: initialized ngram_mod with n=%d, size=%zu (%.3f MB)\n", __func__,
|
||
params.ngram_size_n, params.ngram_mod->size(),
|
||
(float)(params.ngram_mod->size_bytes())/1024/1024);
|
||
|
||
if (params.ngram_size_n < 16) {
|
||
LOG_WRN("%s: ngram_mod n=%d is too small - poor quality is possible, see: https://github.com/ggml-org/llama.cpp/pull/19164\n", __func__, params.ngram_size_n);
|
||
}
|
||
}
|
||
|
||
configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_MOD, params));
|
||
}
|
||
if (has_ngram_cache) {
|
||
configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_CACHE, params));
|
||
}
|
||
if (has_suffix) {
|
||
configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_SUFFIX, params));
|
||
}
|
||
if (has_mtp) {
|
||
configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_MTP, params));
|
||
}
|
||
if (has_draft) {
|
||
configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT, params));
|
||
}
|
||
if (has_draft_eagle3) {
|
||
configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_EAGLE3, params));
|
||
}
|
||
}
|
||
|
||
std::vector<std::unique_ptr<common_speculative_state>> impls = {};
|
||
|
||
for (const common_speculative_config & config : configs) {
|
||
LOG_DBG("%s: adding implementation %s\n", __func__, common_speculative_type_to_str(config.type).c_str());
|
||
switch (config.type) {
|
||
case COMMON_SPECULATIVE_TYPE_NONE:
|
||
break;
|
||
case COMMON_SPECULATIVE_TYPE_DRAFT: {
|
||
impls.push_back(std::make_unique<common_speculative_state_draft>(config.type,
|
||
/* .ctx_tgt = */ ctx_tgt,
|
||
/* .ctx_dft = */ ctx_dft,
|
||
/* .replacements = */ params.replacements
|
||
));
|
||
break;
|
||
}
|
||
case COMMON_SPECULATIVE_TYPE_MTP: {
|
||
auto mtp_state = std::make_unique<common_speculative_state_mtp>(config.type,
|
||
/* .ctx_tgt = */ ctx_tgt,
|
||
/* .mtp_cparams = */ params.cparams_dft
|
||
);
|
||
if (!mtp_state->ctx_mtp) {
|
||
LOG_ERR("%s: failed to create MTP context\n", __func__);
|
||
return nullptr;
|
||
}
|
||
impls.push_back(std::move(mtp_state));
|
||
break;
|
||
}
|
||
case COMMON_SPECULATIVE_TYPE_EAGLE3: {
|
||
impls.push_back(std::make_unique<common_speculative_state_eagle3>(config.type));
|
||
break;
|
||
}
|
||
case COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE: {
|
||
common_ngram_map ngram_map = get_common_ngram_map(config);
|
||
|
||
uint16_t ngram_size_key = ngram_map.size_key;
|
||
uint16_t mgram_size_value = ngram_map.size_value;
|
||
|
||
auto config_simple = common_ngram_simple_config {
|
||
/* .size_ngram = */ ngram_size_key,
|
||
/* .size_mgram = */ mgram_size_value
|
||
};
|
||
auto state = std::make_unique<common_speculative_state_ngram_simple>(
|
||
/* .type = */ config.type,
|
||
/* .state = */ config_simple
|
||
);
|
||
impls.push_back(std::move(state));
|
||
break;
|
||
}
|
||
case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K:
|
||
case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V: {
|
||
impls.push_back(std::make_unique<common_speculative_state_ngram_map_k>(
|
||
(config.type),
|
||
get_common_ngram_map(config)
|
||
));
|
||
break;
|
||
}
|
||
case COMMON_SPECULATIVE_TYPE_NGRAM_MOD: {
|
||
GGML_ASSERT(config.params.ngram_mod);
|
||
impls.push_back(std::make_unique<common_speculative_state_ngram_mod>(config.type, *config.params.ngram_mod));
|
||
break;
|
||
}
|
||
case COMMON_SPECULATIVE_TYPE_NGRAM_CACHE: {
|
||
auto state = create_state_ngram_cache(
|
||
params.lookup_cache_static, params.lookup_cache_dynamic, config);
|
||
impls.push_back(std::make_unique<common_speculative_state_ngram_cache>(state));
|
||
break;
|
||
}
|
||
case COMMON_SPECULATIVE_TYPE_SUFFIX: {
|
||
int depth = config.params.suffix_max_depth > 0 ? config.params.suffix_max_depth : 64;
|
||
const llama_model * model = llama_get_model(ctx_tgt);
|
||
impls.push_back(std::make_unique<common_speculative_state_suffix>(
|
||
config.type, depth, config.params.suffix_corpus, model));
|
||
break;
|
||
}
|
||
default:
|
||
break;
|
||
}
|
||
}
|
||
|
||
if (impls.empty()) {
|
||
LOG_WRN("%s", "no implementations specified for speculative decoding\n");
|
||
return nullptr;
|
||
}
|
||
|
||
auto * result = new common_speculative {
|
||
/* .impls = */ std::move(impls)
|
||
};
|
||
|
||
// initialize autotune if requested
|
||
if (params.autotune && !result->impls.empty()) {
|
||
auto actual_type = result->impls[0]->type;
|
||
if (actual_type != COMMON_SPECULATIVE_TYPE_NONE &&
|
||
actual_type != COMMON_SPECULATIVE_TYPE_EAGLE3) {
|
||
result->tuner = std::make_unique<spec_tuner>();
|
||
result->tuner->init(actual_type, params);
|
||
LOG_DBG("Autotune initialized for %s, tuning %zu parameters\n",
|
||
common_speculative_type_to_str(actual_type).c_str(),
|
||
result->tuner->coords.size());
|
||
} else {
|
||
LOG_WRN("Autotune disabled — speculative type %s is not supported for autotuning\n",
|
||
common_speculative_type_to_str(actual_type).c_str());
|
||
}
|
||
}
|
||
|
||
return result;
|
||
}
|
||
|
||
void common_speculative_free(common_speculative * spec) {
|
||
if (spec == nullptr) {
|
||
return;
|
||
}
|
||
|
||
delete spec;
|
||
}
|
||
|
||
void common_speculative_begin(common_speculative * spec, const llama_tokens & prompt) {
|
||
if (spec == nullptr) {
|
||
return;
|
||
}
|
||
|
||
for (auto & impl : spec->impls) {
|
||
common_time_meas tm(impl->t_begin_us, !impl->gen_perf);
|
||
impl->begin(prompt);
|
||
impl->n_call_begin++;
|
||
}
|
||
}
|
||
|
||
struct mtp_last_embd {
|
||
std::vector<float> embd;
|
||
float prob;
|
||
int last_id = -1;
|
||
};
|
||
|
||
// Hopefully never called concurrently from multiple threads
|
||
static mtp_last_embd & mtp_get_last_embd(const llama_context * ctx) {
|
||
static std::unordered_map<const llama_context *, mtp_last_embd> map;
|
||
auto & last = map[ctx];
|
||
if (last.embd.empty()) {
|
||
auto n_embd = llama_model_n_embd(llama_get_model(ctx));
|
||
last.embd.resize(n_embd);
|
||
}
|
||
return last;
|
||
}
|
||
|
||
llama_tokens common_speculative_draft(
|
||
common_speculative * spec,
|
||
common_params_speculative & params,
|
||
const llama_tokens & prompt_tgt, // specified in target model vocab
|
||
llama_token id_last) {
|
||
llama_tokens result;
|
||
|
||
spec->t_step_start_us = ggml_time_us();
|
||
|
||
// apply autotune proposal if enabled
|
||
if (spec->tuner && spec->tuner->enabled) {
|
||
spec->tuner->propose(params);
|
||
}
|
||
|
||
spec->curr_impl = nullptr; // reset current implementation
|
||
|
||
for (auto & impl : spec->impls) {
|
||
{
|
||
common_time_meas tm(impl->t_draft_us, !impl->gen_perf);
|
||
impl->draft(params, prompt_tgt, id_last, result);
|
||
impl->n_call_draft++;
|
||
}
|
||
|
||
if (!result.empty()) {
|
||
LOG_DBG("%s: called impl %s, hist size = %zu, call_count = %zu, gen = %zu\n", __func__,
|
||
common_speculative_type_to_str(impl.get()->type).c_str(), prompt_tgt.size(),
|
||
impl.get()->n_call_draft, result.size());
|
||
|
||
spec->curr_impl = impl.get(); // set current implementation for stats
|
||
impl->n_gen_drafts++;
|
||
impl->n_gen_tokens += result.size();
|
||
|
||
break; // We have a draft, so break out of the loop and return it.
|
||
}
|
||
}
|
||
|
||
// store draft count for tuner feedback
|
||
if (spec->tuner && spec->tuner->enabled) {
|
||
spec->last_n_drafted = (int)result.size();
|
||
}
|
||
|
||
return result;
|
||
}
|
||
|
||
void common_speculative_accept(common_speculative * spec, uint16_t n_accepted) {
|
||
if (spec->tuner && spec->tuner->enabled && spec->t_step_start_us > 0) {
|
||
int64_t step_time_us = ggml_time_us() - spec->t_step_start_us;
|
||
double step_tps = (step_time_us > 100)
|
||
? (n_accepted + 1.0) * 1e6 / (double)step_time_us
|
||
: 0.0;
|
||
spec->tuner->accept_feedback(n_accepted, spec->last_n_drafted, step_tps);
|
||
spec->t_step_start_us = 0;
|
||
}
|
||
|
||
common_speculative_state * impl = spec->curr_impl;
|
||
|
||
if (!impl) {
|
||
return;
|
||
}
|
||
|
||
{
|
||
common_time_meas tm(impl->t_accept_us, !impl->gen_perf);
|
||
if (n_accepted > 0) {
|
||
impl->n_acc_drafts++;
|
||
impl->n_acc_tokens += n_accepted;
|
||
}
|
||
|
||
impl->accept(n_accepted);
|
||
impl->n_call_accept++;
|
||
}
|
||
}
|
||
|
||
void common_speculative_print_stats(const common_speculative * spec, double slot_tps, int n_decoded, int n_past, common_params_speculative * active_params) {
|
||
if (spec == nullptr) {
|
||
return;
|
||
}
|
||
|
||
for (const auto & impl : spec->impls) {
|
||
std::string str_perf;
|
||
if (impl->gen_perf) {
|
||
std::ostringstream oss;
|
||
oss << std::fixed << std::setprecision(3) << impl->t_begin_us / 1000.0 << ", ";
|
||
oss << std::fixed << std::setprecision(3) << impl->t_draft_us / 1000.0 << ", ";
|
||
oss << std::fixed << std::setprecision(3) << impl->t_accept_us / 1000.0;
|
||
str_perf = ", dur(b,g,a) = " + oss.str() + " ms";
|
||
} else {
|
||
str_perf = "";
|
||
}
|
||
|
||
LOG_INF("statistics %s: #calls(b,g,a) = %zu %zu %zu, #gen drafts = %zu, #acc drafts = %zu, #gen tokens = %zu, #acc tokens = %zu%s\n",
|
||
common_speculative_type_to_str(impl->type).c_str(),
|
||
impl->n_call_begin, impl->n_call_draft, impl->n_call_accept,
|
||
impl->n_gen_drafts,
|
||
impl->n_acc_drafts,
|
||
impl->n_gen_tokens,
|
||
impl->n_acc_tokens,
|
||
str_perf.c_str());
|
||
}
|
||
|
||
if (spec->tuner && spec->tuner->enabled && slot_tps > 0.0 && n_decoded > 0) {
|
||
auto * mutable_spec = const_cast<common_speculative *>(spec);
|
||
if (active_params) {
|
||
mutable_spec->tuner->end_of_request(slot_tps, n_past, *active_params);
|
||
} else {
|
||
common_params_speculative tmp_params;
|
||
mutable_spec->tuner->end_of_request(slot_tps, n_past, tmp_params);
|
||
}
|
||
}
|
||
}
|
||
|
||
// ----------------------------------------------------------------------------
|
||
// MTP
|
||
// ----------------------------------------------------------------------------
|
||
|
||
llama_context * common_speculative_get_mtp_ctx(common_speculative * spec) {
|
||
if (!spec) return nullptr;
|
||
|
||
for (auto & impl : spec->impls) {
|
||
if (impl->type == COMMON_SPECULATIVE_TYPE_MTP) {
|
||
auto * mtp_state = dynamic_cast<common_speculative_state_mtp *>(impl.get());
|
||
if (mtp_state) {
|
||
return mtp_state->ctx_mtp;
|
||
}
|
||
}
|
||
}
|
||
return nullptr;
|
||
}
|
||
|
||
void common_speculative_context_shift(
|
||
common_speculative * spec,
|
||
llama_seq_id seq_id,
|
||
llama_pos kv_keep,
|
||
llama_pos kv_discard,
|
||
llama_pos kv_past) {
|
||
if (auto * ctx_mtp = common_speculative_get_mtp_ctx(spec); ctx_mtp != nullptr) {
|
||
llama_kv_cache_seq_rm (ctx_mtp, seq_id, kv_keep, kv_keep + kv_discard);
|
||
llama_kv_cache_seq_add(ctx_mtp, seq_id, kv_keep + kv_discard, kv_past, -kv_discard);
|
||
}
|
||
}
|
||
|
||
std::vector<llama_token> mtp_speculative_gen_draft(
|
||
struct common_sampler * smpl,
|
||
struct llama_context * ctx,
|
||
int n_draft,
|
||
float p_min,
|
||
llama_token id_last,
|
||
int32_t n_past,
|
||
llama_seq_id seq_id) {
|
||
|
||
llama_tokens drafts;
|
||
drafts.reserve(n_draft);
|
||
|
||
if (!smpl) return drafts;
|
||
|
||
common_sampler_reset(smpl);
|
||
|
||
llama_batch mtp_batch = llama_batch_init(1, 0, 1);
|
||
llama_set_mtp_op_type(ctx, MTP_OP_DRAFT_GEN);
|
||
|
||
float prob;
|
||
auto prob_ptr = p_min > 0 ? &prob : nullptr;
|
||
|
||
llama_token current_input_id = id_last;
|
||
int32_t current_n_past = n_past;
|
||
const int n_embd = llama_model_n_embd(llama_get_model(ctx));
|
||
|
||
auto & last = mtp_get_last_embd(ctx);
|
||
int i0 = 0;
|
||
if (last.last_id >= 0) {
|
||
if (last.prob < p_min) {
|
||
return drafts;
|
||
}
|
||
current_input_id = last.last_id;
|
||
last.last_id = -1;
|
||
drafts.push_back(current_input_id);
|
||
current_n_past++;
|
||
llama_set_draft_input_hidden_state(ctx, last.embd.data());
|
||
i0 = 1;
|
||
}
|
||
|
||
for (int i = i0; i < n_draft; ++i) {
|
||
mtp_batch.n_tokens = 0;
|
||
common_batch_add(mtp_batch, current_input_id, current_n_past, {seq_id}, true);
|
||
|
||
if (llama_decode(ctx, mtp_batch) != 0) {
|
||
break;
|
||
}
|
||
|
||
llama_token id_next = common_sampler_sample_speculative(smpl, ctx, 0, prob_ptr);
|
||
if (i > 0 && prob_ptr && prob < p_min) {
|
||
return drafts;
|
||
}
|
||
|
||
drafts.push_back(id_next);
|
||
|
||
const float * emb = llama_get_embeddings_ith(ctx, 0);
|
||
if (!emb) {
|
||
break;
|
||
}
|
||
|
||
// Keep a stable copy because later decode steps reuse ctx->embd storage.
|
||
memcpy(last.embd.data(), emb, n_embd * sizeof(float));
|
||
llama_set_draft_input_hidden_state(ctx, last.embd.data());
|
||
|
||
current_input_id = id_next;
|
||
current_n_past++;
|
||
|
||
if (prob_ptr && prob < p_min) {
|
||
break;
|
||
}
|
||
}
|
||
llama_batch_free(mtp_batch);
|
||
llama_set_mtp_op_type(ctx, MTP_OP_NONE);
|
||
|
||
// Purge the metadata for the draft tokens.
|
||
// This prevents cache state corruption where two cells map to the same logical position.
|
||
if (!drafts.empty()) {
|
||
llama_kv_cache_seq_rm(ctx, seq_id, n_past, current_n_past);
|
||
}
|
||
|
||
return drafts;
|
||
}
|
||
|
||
|
||
void mtp_update_kv_cache(struct llama_context * ctx, const llama_batch& batch, bool is_prompt_warmup) {
|
||
if (batch.n_tokens == 0) {
|
||
return;
|
||
}
|
||
|
||
llama_seq_id seq_id = batch.seq_id[0][0];
|
||
llama_pos start_pos = batch.pos[0];
|
||
|
||
if (llama_kv_cache_seq_pos_max(ctx, seq_id) >= start_pos) {
|
||
llama_kv_cache_seq_rm(ctx, seq_id, start_pos, -1);
|
||
}
|
||
|
||
LOG_DBG("[MTP-UPDATE|%s] Updating %d tokens for seq_id %d from pos %d...\n",
|
||
is_prompt_warmup ? "PROMPT_WARMUP" : "GEN_ACCEPTED", batch.n_tokens, seq_id, (int)start_pos);
|
||
|
||
llama_batch mtp_batch = batch;
|
||
if (is_prompt_warmup) {
|
||
llama_set_mtp_op_type(ctx, MTP_OP_WARMUP);
|
||
// We don't need the logits when doing warmup
|
||
for (int i = 0; i < mtp_batch.n_tokens; ++i) {
|
||
mtp_batch.logits[i] = false;
|
||
}
|
||
// This is just in case to not run into empty tensor issues
|
||
mtp_batch.logits[mtp_batch.n_tokens-1] = true;
|
||
} else {
|
||
llama_set_mtp_op_type(ctx, MTP_OP_UPDATE_ACCEPTED);
|
||
for (int i = 0; i < mtp_batch.n_tokens; ++i) {
|
||
mtp_batch.logits[i] = true;
|
||
}
|
||
}
|
||
|
||
llama_decode(ctx, mtp_batch);
|
||
llama_set_mtp_op_type(ctx, MTP_OP_NONE);
|
||
}
|
||
|
||
void mtp_accept_tokens(
|
||
struct llama_context * ctx,
|
||
const std::vector<llama_token> & ids,
|
||
int32_t n_past_base,
|
||
llama_seq_id seq_id) {
|
||
if (ids.empty()) {
|
||
return;
|
||
}
|
||
|
||
llama_batch accepted_batch = llama_batch_init(ids.size(), 0, 1);
|
||
for (size_t i = 0; i < ids.size(); ++i) {
|
||
common_batch_add(accepted_batch, ids[i], n_past_base + i, { seq_id }, true);
|
||
}
|
||
|
||
mtp_update_kv_cache(ctx, accepted_batch, false);
|
||
|
||
auto & last = mtp_get_last_embd(ctx);
|
||
auto embd = llama_get_embeddings_ith(ctx, ids.size() - 1);
|
||
if (embd) {
|
||
std::memcpy(last.embd.data(), embd, last.embd.size()*sizeof(float));
|
||
llama_set_draft_input_hidden_state(ctx, last.embd.data());
|
||
last.last_id = common_sampler_sample_speculative(nullptr, ctx, ids.size() - 1, &last.prob);
|
||
}
|
||
|
||
llama_batch_free(accepted_batch);
|
||
}
|