mirror of
https://github.com/ikawrakow/ik_llama.cpp.git
synced 2026-06-28 04:30:15 -05:00
2233 lines
77 KiB
C++
2233 lines
77 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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void llama_set_mtp_target_context(struct llama_context * ctx, struct llama_context * target_ctx);
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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_stage_params stage;
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common_speculative_type type;
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common_params_speculative params;
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common_speculative_config(
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const common_speculative_stage_params & s,
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const common_params_speculative & p = common_params_speculative{})
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: stage(s), type(s.type), 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 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_pos draft_base_pos,
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llama_seq_id draft_seq_id,
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llama_tokens & result) {
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GGML_UNUSED(draft_base_pos);
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GGML_UNUSED(draft_seq_id);
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draft(params, prompt_tgt, id_last, result);
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}
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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;
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static common_speculative_state_mtp * common_speculative_get_mtp_state(common_speculative * spec);
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static const common_speculative_state_mtp * common_speculative_get_mtp_state(const common_speculative * spec);
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static void mtp_invalidate_cached_drafts(common_speculative_state_mtp & state);
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static std::vector<llama_token> mtp_speculative_gen_draft(
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common_speculative_state_mtp & state,
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struct common_sampler * smpl,
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struct llama_context * ctx,
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int n_draft,
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float p_min,
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llama_token id_last,
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llama_pos n_past,
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llama_seq_id seq_id,
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bool constant_draft_positions = false);
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static int32_t mtp_update_kv_cache(struct llama_context * ctx, const llama_batch & batch, bool is_prompt_warmup);
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struct mtp_last_embd {
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std::vector<float> embd;
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float prob = 0.0f;
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int last_id = -1;
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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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// For Gemma 4 external MTP assistant: draft positions are held constant
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bool constant_draft_positions = false;
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int n_embd = 0;
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std::unordered_map<llama_seq_id, std::vector<float>> target_hidden_by_seq;
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std::unordered_map<llama_seq_id, mtp_last_embd> draft_cache_by_seq;
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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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llama_context * ctx_mtp,
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bool constant_draft_positions = false)
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: common_speculative_state(type)
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, ctx_tgt(ctx_tgt)
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, ctx_mtp(ctx_mtp)
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, constant_draft_positions(constant_draft_positions)
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{
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struct common_params_sampling sparams;
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sparams.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_mtp), sparams);
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llama_set_mtp_target_context(ctx_mtp, ctx_tgt);
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n_embd = llama_mtp_state_n_embd(ctx_mtp);
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LOG_INF("%s: MTP context ready (n_ctx=%d, constant_draft_positions=%s)\n", __func__,
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llama_n_ctx(ctx_mtp), constant_draft_positions ? "true" : "false");
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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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target_hidden_by_seq.clear();
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draft_cache_by_seq.clear();
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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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draft(params, prompt_tgt, id_last, -1, 0, result);
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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_pos draft_base_pos,
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llama_seq_id seq_id,
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llama_tokens & result) override {
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const llama_pos mtp_pos_max = llama_kv_cache_seq_pos_max(ctx_mtp, seq_id);
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const bool has_draft_base_pos = draft_base_pos >= 0;
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// Prefer the target slot position when the caller has it. Gemma4 external MTP reads
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// the target KV cache directly, so ctx_mtp's own KV position is not authoritative.
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const llama_pos n_past = has_draft_base_pos
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? draft_base_pos
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: (mtp_pos_max >= 0 ? mtp_pos_max + 1 : (llama_pos) prompt_tgt.size());
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if (!has_draft_base_pos && !prompt_tgt.empty() && mtp_pos_max < (llama_pos)prompt_tgt.size() - 1) {
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LOG_WRN("%s: MTP context not fully warmed up: pos_max = %d, expected = %d\n",
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__func__, (int)mtp_pos_max, (int)prompt_tgt.size() - 1);
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}
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if (has_draft_base_pos && !constant_draft_positions && mtp_pos_max < n_past - 1) {
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LOG_WRN("%s: MTP context not fully warmed up: pos_max = %d, expected >= %d\n",
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__func__, (int)mtp_pos_max, (int)n_past - 1);
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}
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llama_context * ctx = ctx_mtp;
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const auto hidden_it = target_hidden_by_seq.find(seq_id);
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if (hidden_it == target_hidden_by_seq.end() || (int) hidden_it->second.size() != n_embd) {
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LOG_WRN("%s: missing target hidden state for seq_id %d\n", __func__, (int) seq_id);
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result.clear();
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return;
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}
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if (!llama_set_draft_input_hidden_state_copy(ctx, hidden_it->second.data(), hidden_it->second.size())) {
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result.clear();
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return;
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}
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result = mtp_speculative_gen_draft(
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*this,
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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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constant_draft_positions
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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());
|
||
|
||
result.clear();
|
||
result.reserve(params.n_max);
|
||
|
||
if (reuse_n == 0) {
|
||
llama_kv_cache_clear(ctx_dft);
|
||
prompt_dft.clear();
|
||
} else {
|
||
// this happens when a previous draft has been discarded (for example, due to being too small), but the
|
||
// target model agreed with it. in this case, we simply pass back the previous results to save compute
|
||
if (reuse_i + reuse_n < (int) prompt_dft.size() && prompt_dft[reuse_i + reuse_n] == id_last) {
|
||
for (int i = reuse_i + reuse_n + 1; i < (int) prompt_dft.size(); ++i) {
|
||
result.push_back(prompt_dft[i]);
|
||
|
||
if (params.n_max <= (int) result.size()) {
|
||
break;
|
||
}
|
||
}
|
||
|
||
return;
|
||
}
|
||
|
||
if (reuse_i > 0) {
|
||
llama_kv_cache_seq_rm (ctx_dft, 0, 0, reuse_i);
|
||
llama_kv_cache_seq_add(ctx_dft, 0, reuse_i, -1, -reuse_i);
|
||
|
||
prompt_dft.erase(prompt_dft.begin(), prompt_dft.begin() + reuse_i);
|
||
}
|
||
|
||
if (reuse_n < (int) prompt_dft.size()) {
|
||
llama_kv_cache_seq_rm (ctx_dft, 0, reuse_n, -1);
|
||
prompt_dft.erase(prompt_dft.begin() + reuse_n, prompt_dft.end());
|
||
}
|
||
}
|
||
|
||
// prepare a batch to evaluate any new tokens in the prompt
|
||
common_batch_clear(batch);
|
||
|
||
for (size_t i = i_start + reuse_n; i < prompt_cur.size(); ++i) {
|
||
//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]);
|
||
common_batch_add(batch, prompt_cur[i], i - i_start, { 0 }, false);
|
||
|
||
prompt_dft.push_back(prompt_cur[i]);
|
||
}
|
||
|
||
// we should rarely end-up here during normal decoding
|
||
if (batch.n_tokens > 0) {
|
||
//LOG_DBG("%s: draft prompt batch: %s\n", __func__, string_from(ctx, batch).c_str());
|
||
|
||
llama_decode(ctx_dft, batch);
|
||
}
|
||
|
||
const llama_pos n_past = prompt_dft.size();
|
||
|
||
LOG_DBG("%s: n_past = %d\n", __func__, n_past);
|
||
|
||
common_batch_clear(batch);
|
||
common_batch_add (batch, id_last, n_past, { 0 }, true);
|
||
|
||
prompt_dft.push_back(id_last);
|
||
|
||
//LOG_DBG("%s: draft prompt: %s\n", __func__, string_from(ctx_dft, prompt_dft).c_str());
|
||
|
||
llama_decode(ctx_dft, batch);
|
||
|
||
common_sampler_reset(smpl);
|
||
|
||
// sample n_draft tokens from the draft model
|
||
for (int i = 0; i < params.n_max; ++i) {
|
||
common_batch_clear(batch);
|
||
|
||
common_sampler_sample(smpl, ctx_dft, 0, true);
|
||
|
||
const auto * cur_p = common_sampler_get_candidates(smpl, true);
|
||
|
||
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(ctx_dft, cur_p->data[k].id).c_str());
|
||
}
|
||
|
||
// add drafted token for each sequence
|
||
const llama_token id = cur_p->data[0].id;
|
||
|
||
common_sampler_accept(smpl, nullptr, id, true);
|
||
|
||
// only collect very high-confidence draft tokens
|
||
if (cur_p->data[0].p < params.p_min) {
|
||
if (i == 0) {
|
||
result.push_back(id);
|
||
}
|
||
break;
|
||
}
|
||
|
||
result.push_back(id);
|
||
|
||
if (params.n_max <= (int) result.size()) {
|
||
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<common_speculative_config> configs; // resolved stage config for each implementation
|
||
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 bool common_speculative_stage_chain_matches(
|
||
const std::vector<common_speculative_stage_params> & stages,
|
||
const std::vector<common_speculative_config> & configs) {
|
||
if (stages.size() != configs.size()) {
|
||
return false;
|
||
}
|
||
|
||
for (size_t i = 0; i < stages.size(); ++i) {
|
||
if (stages[i].type != configs[i].type) {
|
||
return false;
|
||
}
|
||
}
|
||
|
||
return true;
|
||
}
|
||
|
||
static common_params_speculative common_speculative_get_runtime_params(
|
||
const common_speculative_config & config,
|
||
const common_params_speculative & params,
|
||
const common_speculative_stage_params & stage) {
|
||
common_params_speculative result = config.params;
|
||
|
||
result.type = config.type;
|
||
result.n_max = stage.has_n_max_override() ? stage.n_max : params.n_max;
|
||
result.n_min = stage.has_n_min_override() ? stage.n_min : params.n_min;
|
||
result.p_min = stage.has_p_min_override() ? stage.p_min : params.p_min;
|
||
|
||
if (config.type == COMMON_SPECULATIVE_TYPE_SUFFIX) {
|
||
result.suffix_min_match_len = stage.has_suffix_min_match_len_override()
|
||
? stage.suffix_min_match_len
|
||
: params.suffix_min_match_len;
|
||
}
|
||
|
||
result.n_max = std::max(result.n_max, 0);
|
||
result.n_min = std::max(0, std::min(result.n_min, result.n_max));
|
||
result.stages.clear();
|
||
|
||
return result;
|
||
}
|
||
|
||
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) {
|
||
std::string normalized = name;
|
||
std::replace(normalized.begin(), normalized.end(), '-', '_');
|
||
|
||
const auto it = common_speculative_type_from_name_map.find(normalized);
|
||
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) {
|
||
std::string chain_error;
|
||
if (!common_speculative_validate_chain(params, &chain_error)) {
|
||
LOG_ERR("%s: invalid speculative stage chain: %s\n", __func__, chain_error.c_str());
|
||
return nullptr;
|
||
}
|
||
|
||
const auto stages = params.get_resolved_stages();
|
||
if (params.model_dft && llama_model_is_gemma4_mtp_assistant(params.model_dft)) {
|
||
const bool has_draft_stage = std::any_of(stages.begin(), stages.end(), [](const common_speculative_stage_params & stage) {
|
||
return stage.type == COMMON_SPECULATIVE_TYPE_DRAFT;
|
||
});
|
||
|
||
if (has_draft_stage) {
|
||
LOG_ERR("%s: Gemma4 assistant models only support MTP stages; omit -md for self-spec-only runs or use -mtp/--spec-stage mtp for assistant-backed MTP\n", __func__);
|
||
return nullptr;
|
||
}
|
||
}
|
||
|
||
const bool needs_draft_ctx = std::any_of(stages.begin(), stages.end(), [¶ms](const common_speculative_stage_params & stage) {
|
||
return stage.type == COMMON_SPECULATIVE_TYPE_DRAFT ||
|
||
(stage.type == COMMON_SPECULATIVE_TYPE_MTP && params.model_dft != nullptr);
|
||
});
|
||
|
||
llama_context * ctx_dft = nullptr;
|
||
if (needs_draft_ctx) {
|
||
if (!params.model_dft) {
|
||
LOG_ERR("%s: draft speculative stage requires a loaded draft model\n", __func__);
|
||
return nullptr;
|
||
}
|
||
|
||
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 resolved stage chain.
|
||
std::vector<common_speculative_config> configs = {};
|
||
configs.reserve(stages.size());
|
||
|
||
for (const auto & stage : stages) {
|
||
common_params_speculative stage_params = params.with_stage_overrides(stage);
|
||
|
||
if (stage.type == COMMON_SPECULATIVE_TYPE_NGRAM_MOD && !stage_params.ngram_mod) {
|
||
stage_params.ngram_mod = std::make_shared<common_ngram_mod>(stage_params.ngram_size_n, 4*1024*1024);
|
||
|
||
LOG_INF("%s: initialized ngram_mod with n=%d, size=%zu (%.3f MB)\n", __func__,
|
||
stage_params.ngram_size_n, stage_params.ngram_mod->size(),
|
||
(float)(stage_params.ngram_mod->size_bytes())/1024/1024);
|
||
|
||
if (stage_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__, stage_params.ngram_size_n);
|
||
}
|
||
}
|
||
|
||
configs.push_back(common_speculative_config(stage, stage_params));
|
||
}
|
||
|
||
if (!configs.empty() && llama_model_has_recurrent(llama_get_model(ctx_tgt))) {
|
||
const int ckpt_tokens = std::max(1, params.get_max_stage_n_max() + 1);
|
||
const int actual_mode = llama_spec_ckpt_init(ctx_tgt, params.recurrent_ckpt_mode, ckpt_tokens);
|
||
if (actual_mode == LLAMA_SPEC_CKPT_NONE) {
|
||
LOG_ERR("%s: failed to prepare recurrent checkpoint mode '%s' during speculative init (max_tokens=%d)\n",
|
||
__func__,
|
||
params.recurrent_ckpt_mode == LLAMA_SPEC_CKPT_PER_STEP ? "per-step" :
|
||
params.recurrent_ckpt_mode == LLAMA_SPEC_CKPT_GPU_FALLBACK ? "gpu-fallback" :
|
||
params.recurrent_ckpt_mode == LLAMA_SPEC_CKPT_CPU ? "cpu" : "auto",
|
||
ckpt_tokens);
|
||
if (ctx_dft != nullptr) {
|
||
llama_free(ctx_dft);
|
||
}
|
||
return nullptr;
|
||
}
|
||
llama_spec_ckpt_discard(ctx_tgt);
|
||
params.recurrent_ckpt_mode = actual_mode;
|
||
}
|
||
|
||
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 = */ config.params.replacements
|
||
));
|
||
break;
|
||
}
|
||
case COMMON_SPECULATIVE_TYPE_MTP: {
|
||
llama_context * ctx_mtp = ctx_dft;
|
||
if (!ctx_mtp) {
|
||
const llama_model * model = llama_get_model(ctx_tgt);
|
||
ctx_mtp = llama_init_from_model(const_cast<llama_model *>(model), config.params.cparams_dft);
|
||
if (!ctx_mtp) {
|
||
LOG_ERR("%s: failed to create MTP context\n", __func__);
|
||
return nullptr;
|
||
}
|
||
}
|
||
ctx_dft = nullptr;
|
||
|
||
const bool use_constant_draft_positions = llama_model_is_gemma4_mtp_assistant(llama_get_model(ctx_mtp));
|
||
impls.push_back(std::make_unique<common_speculative_state_mtp>(
|
||
config.type, ctx_tgt, ctx_mtp, use_constant_draft_positions));
|
||
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(
|
||
config.params.lookup_cache_static, config.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 {
|
||
/* .configs = */ std::move(configs),
|
||
/* .impls = */ std::move(impls)
|
||
};
|
||
|
||
// initialize autotune if requested
|
||
if (params.autotune && params.has_composite_stage_chain()) {
|
||
LOG_WRN("Autotune disabled — explicit speculative stage chains are not supported yet\n");
|
||
} else 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, llama_get_model(ctx_tgt));
|
||
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++;
|
||
}
|
||
}
|
||
|
||
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_pos draft_base_pos,
|
||
llama_seq_id draft_seq_id) {
|
||
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);
|
||
}
|
||
|
||
const auto runtime_stages = params.get_resolved_stages();
|
||
const bool use_runtime_stage_overrides = common_speculative_stage_chain_matches(runtime_stages, spec->configs);
|
||
|
||
spec->curr_impl = nullptr; // reset current implementation
|
||
|
||
for (size_t i = 0; i < spec->impls.size(); ++i) {
|
||
auto & impl = spec->impls[i];
|
||
const auto & runtime_stage = use_runtime_stage_overrides ? runtime_stages[i] : spec->configs[i].stage;
|
||
common_params_speculative impl_params = common_speculative_get_runtime_params(spec->configs[i], params, runtime_stage);
|
||
result.clear();
|
||
|
||
{
|
||
common_time_meas tm(impl->t_draft_us, !impl->gen_perf);
|
||
impl->draft(impl_params, prompt_tgt, id_last, draft_base_pos, draft_seq_id, result);
|
||
impl->n_call_draft++;
|
||
}
|
||
|
||
if (result.empty()) {
|
||
continue;
|
||
}
|
||
|
||
if (common_speculative_type_is_self_spec(impl->type) && impl_params.n_min > 0 && (int)result.size() < impl_params.n_min) {
|
||
LOG_DBG("%s: impl %s drafted %zu tokens, below fallback threshold %d - trying next implementation\n",
|
||
__func__, common_speculative_type_to_str(impl->type).c_str(), result.size(), impl_params.n_min);
|
||
result.clear();
|
||
continue;
|
||
}
|
||
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();
|
||
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++;
|
||
}
|
||
|
||
if (impl->type != COMMON_SPECULATIVE_TYPE_MTP) {
|
||
if (auto * mtp_state = common_speculative_get_mtp_state(spec); mtp_state != nullptr) {
|
||
mtp_invalidate_cached_drafts(*mtp_state);
|
||
}
|
||
}
|
||
}
|
||
|
||
static bool common_speculative_has_type(const common_speculative * spec, common_speculative_type type) {
|
||
if (spec == nullptr) {
|
||
return false;
|
||
}
|
||
|
||
return std::any_of(spec->configs.begin(), spec->configs.end(), [type](const common_speculative_config & config) {
|
||
return config.type == type;
|
||
});
|
||
}
|
||
|
||
static int common_speculative_ctx_mtp_n_embd(llama_context * ctx) {
|
||
return ctx ? (int) llama_mtp_state_n_embd(ctx) : 0;
|
||
}
|
||
|
||
static bool common_speculative_batch_token_has_seq_id(
|
||
const llama_batch & batch,
|
||
int token_index,
|
||
llama_seq_id seq_id) {
|
||
if (batch.n_seq_id == nullptr || batch.seq_id == nullptr || batch.n_seq_id[token_index] <= 0 || batch.seq_id[token_index] == nullptr) {
|
||
return false;
|
||
}
|
||
|
||
for (int i = 0; i < batch.n_seq_id[token_index]; ++i) {
|
||
if (batch.seq_id[token_index][i] == seq_id) {
|
||
return true;
|
||
}
|
||
}
|
||
|
||
return false;
|
||
}
|
||
|
||
static bool common_speculative_batch_is_exact_single_seq(
|
||
const llama_batch & batch,
|
||
llama_seq_id seq_id) {
|
||
if (batch.n_tokens <= 0 || batch.n_seq_id == nullptr || batch.seq_id == nullptr) {
|
||
return false;
|
||
}
|
||
|
||
for (int i = 0; i < batch.n_tokens; ++i) {
|
||
if (batch.n_seq_id[i] != 1 || batch.seq_id[i] == nullptr || batch.seq_id[i][0] != seq_id) {
|
||
return false;
|
||
}
|
||
}
|
||
|
||
return true;
|
||
}
|
||
|
||
static int common_speculative_copy_seq_batch(
|
||
const llama_batch & batch,
|
||
llama_seq_id seq_id,
|
||
llama_batch & seq_batch) {
|
||
if (batch.token == nullptr || batch.pos == nullptr) {
|
||
return -1;
|
||
}
|
||
|
||
if (batch.n_tokens < 1) {
|
||
return 0;
|
||
}
|
||
|
||
std::vector<int> token_indices;
|
||
token_indices.reserve(batch.n_tokens);
|
||
for (int i = 0; i < batch.n_tokens; ++i) {
|
||
if (common_speculative_batch_token_has_seq_id(batch, i, seq_id)) {
|
||
token_indices.push_back(i);
|
||
}
|
||
}
|
||
|
||
if (token_indices.empty()) {
|
||
return 0;
|
||
}
|
||
|
||
seq_batch = llama_batch_init((int) token_indices.size(), 0, 1);
|
||
for (const int i : token_indices) {
|
||
common_batch_add(seq_batch, batch.token[i], batch.pos[i], { seq_id }, batch.logits != nullptr && batch.logits[i]);
|
||
}
|
||
|
||
return (int) token_indices.size();
|
||
}
|
||
|
||
static bool common_speculative_feature_view_copy_batch_rows(
|
||
const common_speculative_feature_view & view,
|
||
const llama_batch & batch,
|
||
llama_seq_id seq_id,
|
||
std::vector<float> * hidden_rows) {
|
||
if (hidden_rows == nullptr || view.kind != COMMON_SPECULATIVE_FEATURE_HIDDEN_STATE || view.width <= 0 || batch.n_tokens <= 0 || batch.pos == nullptr) {
|
||
return false;
|
||
}
|
||
|
||
std::unordered_map<llama_pos, const float *> rows_by_pos;
|
||
rows_by_pos.reserve(view.rows.size());
|
||
for (const auto & row : view.rows) {
|
||
if (row.seq_id == seq_id && row.data != nullptr) {
|
||
rows_by_pos[row.pos] = row.data;
|
||
}
|
||
}
|
||
|
||
hidden_rows->clear();
|
||
hidden_rows->reserve((size_t) batch.n_tokens * view.width);
|
||
for (int i = 0; i < batch.n_tokens; ++i) {
|
||
auto it = rows_by_pos.find(batch.pos[i]);
|
||
if (it == rows_by_pos.end()) {
|
||
hidden_rows->clear();
|
||
return false;
|
||
}
|
||
|
||
hidden_rows->insert(hidden_rows->end(), it->second, it->second + view.width);
|
||
}
|
||
|
||
return hidden_rows->size() == (size_t) batch.n_tokens * view.width;
|
||
}
|
||
|
||
static bool common_speculative_capture_target_features(
|
||
common_speculative * spec,
|
||
const common_speculative_feature_view & features);
|
||
|
||
static bool common_speculative_feature_view_from_hidden_rows(
|
||
const std::vector<float> & hidden_rows,
|
||
int32_t width,
|
||
llama_seq_id seq_id,
|
||
llama_pos pos_base,
|
||
common_speculative_feature_view & view) {
|
||
view = {};
|
||
view.kind = COMMON_SPECULATIVE_FEATURE_HIDDEN_STATE;
|
||
view.width = width;
|
||
|
||
if (width <= 0 || hidden_rows.empty() || hidden_rows.size() % (size_t) width != 0) {
|
||
return false;
|
||
}
|
||
|
||
const size_t n_rows = hidden_rows.size() / (size_t) width;
|
||
view.rows.reserve(n_rows);
|
||
for (size_t i = 0; i < n_rows; ++i) {
|
||
view.rows.push_back({
|
||
/* .seq_id = */ seq_id,
|
||
/* .pos = */ pos_base + (llama_pos) i,
|
||
/* .data = */ hidden_rows.data() + i * (size_t) width,
|
||
});
|
||
}
|
||
|
||
return true;
|
||
}
|
||
|
||
static bool common_speculative_collect_target_batch_features(
|
||
const common_speculative * spec,
|
||
llama_context * ctx,
|
||
const llama_batch & batch,
|
||
common_speculative_feature_view & features) {
|
||
features = {};
|
||
if (!common_speculative_has_type(spec, COMMON_SPECULATIVE_TYPE_MTP)) {
|
||
return true;
|
||
}
|
||
|
||
if (!llama_spec_get_hidden_feature_view(ctx, batch, features)) {
|
||
return false;
|
||
}
|
||
|
||
return true;
|
||
}
|
||
|
||
static bool common_speculative_collect_target_seq_batch_features(
|
||
const common_speculative * spec,
|
||
llama_context * ctx,
|
||
const llama_batch & batch,
|
||
llama_seq_id seq_id,
|
||
common_speculative_feature_view & features) {
|
||
features = {};
|
||
if (!common_speculative_has_type(spec, COMMON_SPECULATIVE_TYPE_MTP)) {
|
||
return true;
|
||
}
|
||
|
||
if (!llama_spec_get_hidden_feature_view_for_seq(ctx, batch, seq_id, features)) {
|
||
return false;
|
||
}
|
||
|
||
return true;
|
||
}
|
||
|
||
bool common_speculative_capture_output_hidden(
|
||
common_speculative * spec,
|
||
llama_context * ctx,
|
||
int32_t output_index,
|
||
llama_seq_id seq_id,
|
||
llama_pos pos) {
|
||
if (!common_speculative_has_type(spec, COMMON_SPECULATIVE_TYPE_MTP)) {
|
||
return true;
|
||
}
|
||
|
||
common_speculative_feature_view features;
|
||
if (!llama_spec_get_hidden_feature_view_from_output_index(ctx, output_index, seq_id, pos, features)) {
|
||
return false;
|
||
}
|
||
|
||
return common_speculative_capture_target_features(spec, features);
|
||
}
|
||
|
||
bool common_speculative_ensure_sequence_hidden(
|
||
common_speculative * spec,
|
||
llama_context * ctx,
|
||
llama_seq_id seq_id,
|
||
llama_pos pos) {
|
||
if (!common_speculative_has_type(spec, COMMON_SPECULATIVE_TYPE_MTP) || common_speculative_has_sequence_hidden(spec, seq_id)) {
|
||
return true;
|
||
}
|
||
|
||
return common_speculative_capture_output_hidden(spec, ctx, -1, seq_id, pos);
|
||
}
|
||
|
||
int32_t common_speculative_on_target_seq_batch(
|
||
common_speculative * spec,
|
||
llama_context * ctx_tgt,
|
||
const llama_batch & batch,
|
||
llama_seq_id seq_id,
|
||
bool is_prompt_warmup) {
|
||
llama_context * ctx_mtp = common_speculative_get_companion_ctx(spec);
|
||
ctx_mtp = ctx_mtp ? ctx_mtp : ctx_tgt;
|
||
if (ctx_tgt == nullptr || ctx_mtp == nullptr || batch.n_tokens <= 0) {
|
||
return 0;
|
||
}
|
||
|
||
const int n_embd_src = common_speculative_ctx_mtp_n_embd(ctx_tgt);
|
||
const int n_embd_dst = common_speculative_ctx_mtp_n_embd(ctx_mtp);
|
||
if (n_embd_src <= 0 || n_embd_dst <= 0) {
|
||
return -1;
|
||
}
|
||
|
||
if (n_embd_src != n_embd_dst) {
|
||
LOG_ERR("MTP warmup hidden state width mismatch: n_embd_src = %d, n_embd_dst = %d\n", n_embd_src, n_embd_dst);
|
||
return -1;
|
||
}
|
||
|
||
common_speculative_feature_view feature_view;
|
||
const llama_batch * batch_for_spec = &batch;
|
||
llama_batch seq_batch = {};
|
||
const bool needs_seq_split = is_prompt_warmup && !common_speculative_batch_is_exact_single_seq(batch, seq_id);
|
||
|
||
if (needs_seq_split) {
|
||
const int n_seq_tokens = common_speculative_copy_seq_batch(batch, seq_id, seq_batch);
|
||
if (n_seq_tokens <= 0) {
|
||
return n_seq_tokens < 0 ? -1 : 0;
|
||
}
|
||
|
||
if (!common_speculative_collect_target_seq_batch_features(spec, ctx_tgt, batch, seq_id, feature_view)) {
|
||
llama_batch_free(seq_batch);
|
||
return -1;
|
||
}
|
||
|
||
batch_for_spec = &seq_batch;
|
||
} else {
|
||
if (!common_speculative_collect_target_batch_features(spec, ctx_tgt, batch, feature_view)) {
|
||
return -1;
|
||
}
|
||
}
|
||
|
||
const int32_t ret = common_speculative_on_target_batch(spec, *batch_for_spec, feature_view, is_prompt_warmup);
|
||
if (needs_seq_split) {
|
||
llama_batch_free(seq_batch);
|
||
}
|
||
|
||
return ret;
|
||
}
|
||
|
||
bool common_speculative_copy_output_hidden_rows(
|
||
const common_speculative * spec,
|
||
llama_context * ctx,
|
||
const std::vector<int32_t> & output_indices,
|
||
std::vector<float> & hidden_rows) {
|
||
hidden_rows.clear();
|
||
if (!common_speculative_has_type(spec, COMMON_SPECULATIVE_TYPE_MTP)) {
|
||
return true;
|
||
}
|
||
|
||
return llama_spec_copy_hidden_rows_from_output_indices(ctx, output_indices, hidden_rows);
|
||
}
|
||
|
||
static bool common_speculative_build_commit_tokens(
|
||
common_speculative_type spec_type_used,
|
||
llama_token sampled_before,
|
||
const std::vector<llama_token> & ids,
|
||
std::vector<llama_token> & commit_tokens) {
|
||
commit_tokens.clear();
|
||
if (ids.empty()) {
|
||
return true;
|
||
}
|
||
|
||
if (spec_type_used == COMMON_SPECULATIVE_TYPE_MTP) {
|
||
commit_tokens = ids;
|
||
return true;
|
||
}
|
||
|
||
commit_tokens.reserve(ids.size());
|
||
commit_tokens.push_back(sampled_before);
|
||
if (ids.size() > 1) {
|
||
commit_tokens.insert(commit_tokens.end(), ids.begin(), ids.end() - 1);
|
||
}
|
||
|
||
return commit_tokens.size() == ids.size();
|
||
}
|
||
|
||
static bool common_speculative_apply_hidden_rows(
|
||
common_speculative * spec,
|
||
llama_seq_id seq_id,
|
||
llama_pos pos_base,
|
||
const std::vector<llama_token> & ids,
|
||
const std::vector<float> & hidden_rows) {
|
||
auto * mtp_state = common_speculative_get_mtp_state(spec);
|
||
if (mtp_state == nullptr || ids.empty()) {
|
||
return true;
|
||
}
|
||
|
||
const size_t expected_floats = ids.size() * (size_t) mtp_state->n_embd;
|
||
if (mtp_state->n_embd <= 0 || hidden_rows.size() != expected_floats) {
|
||
return false;
|
||
}
|
||
|
||
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], pos_base + (llama_pos) i, { seq_id }, true);
|
||
}
|
||
|
||
common_speculative_feature_view feature_view;
|
||
const bool have_feature_view = common_speculative_feature_view_from_hidden_rows(
|
||
hidden_rows, mtp_state->n_embd, seq_id, pos_base, feature_view);
|
||
const int32_t ret = have_feature_view
|
||
? common_speculative_on_target_batch(spec, accepted_batch, feature_view, false)
|
||
: -1;
|
||
|
||
llama_batch_free(accepted_batch);
|
||
return ret == 0;
|
||
}
|
||
|
||
bool common_speculative_commit_accepted_hidden_rows(
|
||
common_speculative * spec,
|
||
common_speculative_type spec_type_used,
|
||
llama_seq_id seq_id,
|
||
llama_pos pos_base,
|
||
llama_token sampled_before,
|
||
const std::vector<llama_token> & ids,
|
||
const std::vector<float> & hidden_rows) {
|
||
if (!common_speculative_has_type(spec, COMMON_SPECULATIVE_TYPE_MTP) || ids.empty()) {
|
||
return true;
|
||
}
|
||
|
||
std::vector<llama_token> commit_tokens;
|
||
if (!common_speculative_build_commit_tokens(spec_type_used, sampled_before, ids, commit_tokens)) {
|
||
return false;
|
||
}
|
||
|
||
return common_speculative_apply_hidden_rows(spec, seq_id, pos_base, commit_tokens, hidden_rows);
|
||
}
|
||
|
||
bool common_speculative_commit_accepted_output(
|
||
common_speculative * spec,
|
||
llama_context * ctx,
|
||
common_speculative_type spec_type_used,
|
||
llama_seq_id seq_id,
|
||
llama_pos pos_base,
|
||
llama_token sampled_before,
|
||
const std::vector<llama_token> & ids,
|
||
const std::vector<int32_t> & output_indices) {
|
||
if (!common_speculative_has_type(spec, COMMON_SPECULATIVE_TYPE_MTP) || ids.empty()) {
|
||
return true;
|
||
}
|
||
|
||
std::vector<float> hidden_rows;
|
||
if (!common_speculative_copy_output_hidden_rows(spec, ctx, output_indices, hidden_rows)) {
|
||
return false;
|
||
}
|
||
|
||
return common_speculative_commit_accepted_hidden_rows(
|
||
spec,
|
||
spec_type_used,
|
||
seq_id,
|
||
pos_base,
|
||
sampled_before,
|
||
ids,
|
||
hidden_rows);
|
||
}
|
||
|
||
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
|
||
// ----------------------------------------------------------------------------
|
||
|
||
static common_speculative_state_mtp * common_speculative_get_mtp_state(common_speculative * spec) {
|
||
if (!spec) {
|
||
return nullptr;
|
||
}
|
||
|
||
for (auto & impl : spec->impls) {
|
||
if (impl->type != COMMON_SPECULATIVE_TYPE_MTP) {
|
||
continue;
|
||
}
|
||
|
||
if (auto * mtp_state = dynamic_cast<common_speculative_state_mtp *>(impl.get())) {
|
||
return mtp_state;
|
||
}
|
||
}
|
||
|
||
return nullptr;
|
||
}
|
||
|
||
static const common_speculative_state_mtp * common_speculative_get_mtp_state(const common_speculative * spec) {
|
||
return common_speculative_get_mtp_state(const_cast<common_speculative *>(spec));
|
||
}
|
||
|
||
static mtp_last_embd & mtp_get_last_embd(common_speculative_state_mtp & state, llama_seq_id seq_id) {
|
||
auto & last = state.draft_cache_by_seq[seq_id];
|
||
if ((int) last.embd.size() != state.n_embd) {
|
||
last.embd.resize(state.n_embd);
|
||
}
|
||
return last;
|
||
}
|
||
|
||
static void mtp_invalidate_cached_draft(common_speculative_state_mtp & state, llama_seq_id seq_id) {
|
||
auto it = state.draft_cache_by_seq.find(seq_id);
|
||
if (it == state.draft_cache_by_seq.end()) {
|
||
return;
|
||
}
|
||
|
||
it->second.last_id = -1;
|
||
it->second.prob = 0.0f;
|
||
}
|
||
|
||
static void mtp_invalidate_cached_drafts(common_speculative_state_mtp & state) {
|
||
for (auto & entry : state.draft_cache_by_seq) {
|
||
entry.second.last_id = -1;
|
||
entry.second.prob = 0.0f;
|
||
}
|
||
}
|
||
|
||
static void mtp_store_target_hidden(
|
||
common_speculative_state_mtp & state,
|
||
llama_seq_id seq_id,
|
||
const float * hidden,
|
||
int32_t width) {
|
||
if (hidden == nullptr || width <= 0) {
|
||
return;
|
||
}
|
||
|
||
auto & stored = state.target_hidden_by_seq[seq_id];
|
||
stored.assign(hidden, hidden + width);
|
||
}
|
||
|
||
static void mtp_clear_target_hidden(common_speculative_state_mtp & state, llama_seq_id seq_id) {
|
||
state.target_hidden_by_seq.erase(seq_id);
|
||
state.draft_cache_by_seq.erase(seq_id);
|
||
}
|
||
|
||
static bool common_speculative_capture_target_features(common_speculative * spec, const common_speculative_feature_view & features) {
|
||
auto * mtp_state = common_speculative_get_mtp_state(spec);
|
||
if (mtp_state == nullptr || features.kind != COMMON_SPECULATIVE_FEATURE_HIDDEN_STATE || features.width <= 0) {
|
||
return false;
|
||
}
|
||
|
||
bool captured = false;
|
||
for (const auto & row : features.rows) {
|
||
if (row.data == nullptr) {
|
||
continue;
|
||
}
|
||
|
||
mtp_store_target_hidden(*mtp_state, row.seq_id, row.data, features.width);
|
||
mtp_invalidate_cached_draft(*mtp_state, row.seq_id);
|
||
captured = true;
|
||
}
|
||
|
||
return captured;
|
||
}
|
||
|
||
bool common_speculative_has_sequence_hidden(const common_speculative * spec, llama_seq_id seq_id) {
|
||
const auto * mtp_state = common_speculative_get_mtp_state(spec);
|
||
if (mtp_state == nullptr) {
|
||
return false;
|
||
}
|
||
|
||
auto it = mtp_state->target_hidden_by_seq.find(seq_id);
|
||
return it != mtp_state->target_hidden_by_seq.end() && !it->second.empty();
|
||
}
|
||
|
||
void common_speculative_clear_sequence_hidden(common_speculative * spec, llama_seq_id seq_id) {
|
||
auto * mtp_state = common_speculative_get_mtp_state(spec);
|
||
if (mtp_state == nullptr) {
|
||
return;
|
||
}
|
||
|
||
mtp_clear_target_hidden(*mtp_state, seq_id);
|
||
}
|
||
|
||
llama_context * common_speculative_get_companion_ctx(common_speculative * spec) {
|
||
if (auto * mtp_state = common_speculative_get_mtp_state(spec); mtp_state != nullptr) {
|
||
return mtp_state->ctx_mtp;
|
||
}
|
||
|
||
return nullptr;
|
||
}
|
||
|
||
static int32_t mtp_accept_batch(
|
||
common_speculative_state_mtp & state,
|
||
const llama_batch & accepted_batch,
|
||
llama_seq_id seq_id,
|
||
const float * hidden_rows) {
|
||
if (accepted_batch.n_tokens == 0 || hidden_rows == nullptr) {
|
||
return 0;
|
||
}
|
||
|
||
const size_t hidden_rows_floats = (size_t) accepted_batch.n_tokens * state.n_embd;
|
||
if (!llama_set_draft_input_hidden_state_copy(state.ctx_mtp, hidden_rows, hidden_rows_floats)) {
|
||
return -1;
|
||
}
|
||
if (mtp_update_kv_cache(state.ctx_mtp, accepted_batch, false) != 0) {
|
||
return -1;
|
||
}
|
||
|
||
auto & last = mtp_get_last_embd(state, seq_id);
|
||
const float * embd = llama_get_embeddings_ith(state.ctx_mtp, accepted_batch.n_tokens - 1);
|
||
if (embd != nullptr) {
|
||
std::memcpy(last.embd.data(), embd, last.embd.size() * sizeof(float));
|
||
if (!llama_set_draft_input_hidden_state_copy(state.ctx_mtp, last.embd.data(), last.embd.size())) {
|
||
return -1;
|
||
}
|
||
last.last_id = common_sampler_sample_speculative(nullptr, state.ctx_mtp, accepted_batch.n_tokens - 1, &last.prob);
|
||
}
|
||
|
||
return 0;
|
||
}
|
||
|
||
int32_t common_speculative_on_target_batch(
|
||
common_speculative * spec,
|
||
const llama_batch & batch,
|
||
const common_speculative_feature_view & features,
|
||
bool is_prompt_warmup) {
|
||
auto * mtp_state = common_speculative_get_mtp_state(spec);
|
||
if (mtp_state == nullptr) {
|
||
return 0;
|
||
}
|
||
|
||
if (features.kind != COMMON_SPECULATIVE_FEATURE_HIDDEN_STATE || features.width <= 0 || batch.n_tokens <= 0) {
|
||
return 0;
|
||
}
|
||
|
||
if (batch.n_seq_id == nullptr || batch.seq_id == nullptr || batch.n_seq_id[0] <= 0 || batch.seq_id[0] == nullptr) {
|
||
return -1;
|
||
}
|
||
|
||
const llama_seq_id seq_id = batch.seq_id[0][0];
|
||
for (int i = 0; i < batch.n_tokens; ++i) {
|
||
if (batch.n_seq_id[i] != 1 || batch.seq_id[i] == nullptr || batch.seq_id[i][0] != seq_id) {
|
||
return -1;
|
||
}
|
||
}
|
||
|
||
std::vector<float> hidden_rows_storage;
|
||
if (!common_speculative_feature_view_copy_batch_rows(features, batch, seq_id, &hidden_rows_storage)) {
|
||
return -1;
|
||
}
|
||
|
||
const float * first_hidden = hidden_rows_storage.data();
|
||
const float * last_hidden = hidden_rows_storage.data() + (size_t) (batch.n_tokens - 1) * features.width;
|
||
mtp_store_target_hidden(*mtp_state, seq_id, last_hidden, features.width);
|
||
|
||
if (mtp_state->constant_draft_positions) {
|
||
mtp_invalidate_cached_draft(*mtp_state, seq_id);
|
||
return 0;
|
||
}
|
||
|
||
if (is_prompt_warmup) {
|
||
if (!llama_set_draft_input_hidden_state_copy(mtp_state->ctx_mtp, hidden_rows_storage.data(), hidden_rows_storage.size())) {
|
||
return -1;
|
||
}
|
||
const int32_t ret = mtp_update_kv_cache(mtp_state->ctx_mtp, batch, true);
|
||
mtp_invalidate_cached_draft(*mtp_state, seq_id);
|
||
return ret;
|
||
}
|
||
|
||
return mtp_accept_batch(*mtp_state, batch, seq_id, first_hidden);
|
||
}
|
||
|
||
common_speculative_type common_speculative_current_type(const common_speculative * spec) {
|
||
if (spec == nullptr || spec->curr_impl == nullptr) {
|
||
return COMMON_SPECULATIVE_TYPE_NONE;
|
||
}
|
||
|
||
return spec->curr_impl->type;
|
||
}
|
||
|
||
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_companion_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(
|
||
common_speculative_state_mtp & state,
|
||
struct common_sampler * smpl,
|
||
struct llama_context * ctx,
|
||
int n_draft,
|
||
float p_min,
|
||
llama_token id_last,
|
||
llama_pos n_past,
|
||
llama_seq_id seq_id,
|
||
bool constant_draft_positions) {
|
||
|
||
llama_tokens drafts;
|
||
drafts.reserve(n_draft);
|
||
|
||
if (!smpl) return drafts;
|
||
|
||
if (n_draft <= 0) {
|
||
mtp_invalidate_cached_draft(state, seq_id);
|
||
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;
|
||
llama_pos current_n_past = n_past;
|
||
const int n_embd = llama_mtp_state_n_embd(ctx);
|
||
|
||
auto & last = mtp_get_last_embd(state, seq_id);
|
||
int i0 = 0;
|
||
if (last.last_id >= 0) {
|
||
if (last.prob < p_min) {
|
||
n_draft = 1;
|
||
}
|
||
current_input_id = last.last_id;
|
||
last.last_id = -1;
|
||
drafts.push_back(current_input_id);
|
||
current_n_past++;
|
||
if (!llama_set_draft_input_hidden_state_copy(ctx, last.embd.data(), last.embd.size())) {
|
||
llama_batch_free(mtp_batch);
|
||
llama_set_mtp_op_type(ctx, MTP_OP_NONE);
|
||
return drafts;
|
||
}
|
||
i0 = 1;
|
||
}
|
||
|
||
int n_decode = 0;
|
||
for (int i = i0; i < n_draft; ++i) {
|
||
mtp_batch.n_tokens = 0;
|
||
const llama_pos draft_pos = constant_draft_positions ? n_past : current_n_past;
|
||
common_batch_add(mtp_batch, current_input_id, draft_pos, {seq_id}, true);
|
||
|
||
++n_decode;
|
||
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) {
|
||
break;
|
||
}
|
||
|
||
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));
|
||
if (!llama_set_draft_input_hidden_state_copy(ctx, last.embd.data(), last.embd.size())) {
|
||
break;
|
||
}
|
||
|
||
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 the state contained in `last` had a valid token id and probability, it means that we
|
||
// have previously run an "accept" batch, where the token sampled from the main model was included.
|
||
// In that case, we need to discard all tokens that we ran here to get the KV cache to the correct state.
|
||
// => for i0 = 1 we discard from n_past
|
||
// But if we did not have a valid last token_id, it means the first token we run was sampled from the
|
||
// main model. Hence we want to keep this token in the KV cache and discard all other tokens.
|
||
// => for i0 = 0 we discard from n_past + 1
|
||
if (n_decode > 0) {
|
||
llama_kv_cache_seq_rm(ctx, seq_id, n_past + 1 - i0, n_past + n_decode + 2);
|
||
}
|
||
|
||
return drafts;
|
||
}
|
||
|
||
|
||
int32_t mtp_update_kv_cache(struct llama_context * ctx, const llama_batch& batch, bool is_prompt_warmup) {
|
||
if (batch.n_tokens == 0) {
|
||
return 0;
|
||
}
|
||
|
||
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);
|
||
|
||
// We never need all logits. We only need the logits of the last token so we can sample
|
||
// the next draft token. In the MTP_OP_WARMUP case we do not need logits at all, but just
|
||
// in case we also get the logits of the last token.
|
||
llama_batch mtp_batch = batch;
|
||
for (int i = 0; i < mtp_batch.n_tokens; ++i) {
|
||
mtp_batch.logits[i] = false;
|
||
}
|
||
mtp_batch.logits[mtp_batch.n_tokens-1] = true;
|
||
if (is_prompt_warmup) {
|
||
llama_set_mtp_op_type(ctx, MTP_OP_WARMUP);
|
||
} else {
|
||
llama_set_mtp_op_type(ctx, MTP_OP_UPDATE_ACCEPTED);
|
||
}
|
||
|
||
const int32_t ret = llama_decode(ctx, mtp_batch);
|
||
llama_set_mtp_op_type(ctx, MTP_OP_NONE);
|
||
return ret;
|
||
}
|