mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-07-16 15:47:35 +00:00
579 lines
19 KiB
C++
579 lines
19 KiB
C++
#pragma once
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#include "llama.h"
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#include "llama-batch.h"
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#include "llama-cparams.h"
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#include "llama-model.h"
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#include "llama-kv-cache.h"
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#include "llama-adapter.h"
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#include "ggml-cpp.h"
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#include <map>
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#include <unordered_map>
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#include <vector>
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#include <set>
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using llama_loras = std::unordered_map<struct llama_adapter_lora *, float>;
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struct llama_context {
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llama_context(const llama_model & model);
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virtual ~llama_context();
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virtual void synchronize();
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virtual uint32_t n_ctx() const = 0;
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virtual uint32_t n_batch() const = 0;
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virtual uint32_t n_ubatch() const = 0;
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virtual uint32_t n_seq_max() const = 0;
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virtual llama_kv_cache * get_kv_self() = 0;
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virtual const llama_kv_cache * get_kv_self() const = 0;
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virtual void kv_self_update() = 0;
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virtual enum llama_pooling_type pooling_type() const = 0;
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virtual float * get_logits() = 0;
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virtual float * get_logits_ith(int32_t i) = 0;
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virtual float * get_embeddings() = 0;
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virtual float * get_embeddings_ith(int32_t i) = 0;
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virtual float * get_embeddings_seq(llama_seq_id seq_id) = 0;
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int64_t n_pos_per_token() const; // vision
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virtual ggml_context_ptr init();
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virtual int decode(llama_batch & inp_batch) = 0;
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virtual int encode(llama_batch & inp_batch) = 0;
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// graph build API (generic)
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// do mat_mul, while optionally apply lora
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virtual ggml_tensor * build_lora_mm(
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ggml_context * ctx0,
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ggml_tensor * w,
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ggml_tensor * cur);
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// do mat_mul_id, while optionally apply lora
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virtual ggml_tensor * build_lora_mm_id(
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ggml_context * ctx0,
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ggml_tensor * w, // struct ggml_tensor * as
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ggml_tensor * cur, // struct ggml_tensor * b
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ggml_tensor * ids);
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// graph build API (context-specific)
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virtual ggml_tensor * build_inp_embd(
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ggml_context * ctx0,
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ggml_tensor * tok_embd,
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const llama_ubatch & ubatch) = 0;
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virtual ggml_tensor * build_inp_pos(
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ggml_context * ctx0,
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int32_t n_tokens) = 0;
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virtual ggml_tensor * build_inp_out_ids(
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ggml_context * ctx0,
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int32_t n_tokens,
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bool worst_case) = 0;
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virtual ggml_tensor * build_inp_mean(
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ggml_context * ctx0,
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int32_t n_tokens) = 0;
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virtual ggml_tensor * build_inp_cls(
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ggml_context * ctx0,
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int32_t n_tokens) = 0;
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virtual void build_attn_inp(
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ggml_context * ctx0,
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int32_t n_tokens,
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bool causal,
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bool swa,
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bool worst_case) = 0;
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virtual void build_attn_kv_store(
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ggml_context * ctx0,
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ggml_cgraph * graph,
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ggml_tensor * k_cur,
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ggml_tensor * v_cur,
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int32_t n_tokens,
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int64_t il,
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bool worst_case) = 0;
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virtual ggml_tensor * build_attn_qkv(
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ggml_context * ctx0,
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ggml_cgraph * graph,
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ggml_tensor * wo,
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ggml_tensor * wo_b,
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ggml_tensor * q_cur,
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int32_t n_tokens,
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float kq_scale,
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int il,
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bool worst_case) = 0;
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virtual ggml_tensor * build_soft_max_ext(
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ggml_context * ctx0,
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ggml_tensor * kq,
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float kq_scale) = 0;
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virtual ggml_tensor * get_rope_factors(int il) = 0;
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virtual void build_k_shift(
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ggml_context * ctx0,
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ggml_cgraph * graph) = 0;
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// find holes from the beginning of the KV cache and fill them by moving data from the end of the cache
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virtual void build_defrag(
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ggml_context * ctx0,
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ggml_cgraph * graph) = 0;
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virtual ggml_tensor * build_inp_embd_enc(
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ggml_context * ctx0,
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int32_t n_tokens,
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bool worst_case) = 0;
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virtual ggml_tensor * build_inp_KQ_mask_cross(
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ggml_context * ctx0,
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int32_t n_tokens,
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bool worst_case) = 0;
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virtual ggml_tensor * build_inp_s_copy(
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ggml_context * ctx0,
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bool worst_case) = 0;
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virtual ggml_tensor * build_inp_s_mask(
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ggml_context * ctx0,
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bool worst_case) = 0;
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virtual ggml_tensor * build_copy_mask_state(
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ggml_context * ctx0,
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ggml_cgraph * graph,
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ggml_tensor * s,
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ggml_tensor * state_copy,
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ggml_tensor * state_mask,
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int32_t n_tokens,
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int32_t n_state,
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int32_t n_seqs,
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bool worst_case) = 0;
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virtual ggml_tensor * build_mamba_layer(
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ggml_context * ctx0,
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ggml_cgraph * graph,
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ggml_tensor * cur,
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ggml_tensor * state_copy,
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ggml_tensor * state_mask,
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const llama_ubatch & ubatch,
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int il,
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bool worst_case) = 0;
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virtual ggml_tensor * build_rwkv_token_shift_load(
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ggml_context * ctx0,
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ggml_cgraph * graph,
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ggml_tensor * state_copy,
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ggml_tensor * state_mask,
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const llama_ubatch & ubatch,
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int il,
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bool worst_case) = 0;
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virtual ggml_tensor * build_rwkv_token_shift_store(
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ggml_context * ctx0,
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ggml_tensor * token_shift,
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const llama_ubatch & ubatch,
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int il,
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bool worst_case) = 0;
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virtual ggml_tensor * build_rwkv6_time_mix(
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ggml_context * ctx0,
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ggml_cgraph * graph,
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ggml_tensor * cur,
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ggml_tensor * x_prev,
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ggml_tensor * state_copy,
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ggml_tensor * state_mask,
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const llama_ubatch & ubatch,
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int il,
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bool worst_case) = 0;
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// state save/load
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virtual size_t state_get_size() = 0;
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virtual size_t state_get_data( uint8_t * dst, size_t size) = 0;
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virtual size_t state_set_data(const uint8_t * src, size_t size) = 0;
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virtual size_t state_seq_get_size(llama_seq_id seq_id) = 0;
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virtual size_t state_seq_get_data(llama_seq_id seq_id, uint8_t * dst, size_t size) = 0;
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virtual size_t state_seq_set_data(llama_seq_id seq_id, const uint8_t * src, size_t size) = 0;
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virtual bool state_load_file(
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const char * filepath,
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llama_token * tokens_out,
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size_t n_token_capacity,
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size_t * n_token_count_out) = 0;
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virtual bool state_save_file(
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const char * filepath,
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const llama_token * tokens,
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size_t n_token_count) = 0;
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virtual size_t state_seq_load_file(
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llama_seq_id seq_id,
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const char * filepath,
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llama_token * tokens_out,
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size_t n_token_capacity,
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size_t * n_token_count_out) = 0;
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virtual size_t state_seq_save_file(
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llama_seq_id seq_id,
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const char * filepath,
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const llama_token * tokens,
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size_t n_token_count) = 0;
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// members
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const llama_model & model;
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llama_cparams cparams;
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llama_adapter_cvec cvec;
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llama_loras loras;
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ggml_threadpool_t threadpool = nullptr;
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ggml_threadpool_t threadpool_batch = nullptr;
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ggml_abort_callback abort_callback = nullptr;
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void * abort_callback_data = nullptr;
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std::vector<ggml_backend_ptr> backends;
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std::vector<std::pair<ggml_backend_t, ggml_backend_set_n_threads_t>> set_n_threads_fns;
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ggml_backend_t backend_cpu = nullptr;
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ggml_backend_sched_ptr sched;
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// memory buffers used to evaluate the model
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std::vector<uint8_t> buf_compute_meta;
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// perf
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bool has_evaluated_once = false;
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mutable int64_t t_start_us;
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mutable int64_t t_load_us;
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mutable int64_t t_p_eval_us = 0;
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mutable int64_t t_eval_us = 0;
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mutable int64_t t_compute_start_us = 0;
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mutable int64_t n_queued_tokens = 0;
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mutable int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1)
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mutable int32_t n_eval = 0; // number of eval calls
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};
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// TODO: make implementation details private
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struct llama_context_unified : public llama_context {
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struct batch_manager;
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// TODO: tmp until llama-model starts implementing the graph build function
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typedef std::function<ggml_cgraph *(llama_context &, const llama_ubatch &, bool worst_case)> build_graph_callback;
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llama_context_unified(
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const llama_model & model,
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const llama_context_params & params,
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build_graph_callback && cb_build_graph);
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virtual ~llama_context_unified();
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virtual uint32_t n_ctx() const override;
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virtual uint32_t n_batch() const override;
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virtual uint32_t n_ubatch() const override;
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virtual uint32_t n_seq_max() const override;
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virtual llama_kv_cache * get_kv_self() override;
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virtual const llama_kv_cache * get_kv_self() const override;
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virtual void kv_self_update() override;
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virtual enum llama_pooling_type pooling_type() const override;
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virtual float * get_logits() override;
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virtual float * get_logits_ith(int32_t i) override;
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virtual float * get_embeddings() override;
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virtual float * get_embeddings_ith(int32_t i) override;
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virtual float * get_embeddings_seq(llama_seq_id seq_id) override;
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virtual ggml_context_ptr init() override;
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virtual int decode(llama_batch & inp_batch) override;
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virtual int encode(llama_batch & inp_batch) override;
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llama_sbatch sbatch;
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build_graph_callback cb_build_graph;
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// host buffer for the model output (logits and embeddings)
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ggml_backend_buffer_ptr buf_output;
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// decode output (2-dimensional array: [n_outputs][n_vocab])
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size_t logits_size = 0; // capacity (of floats) for logits
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float * logits = nullptr;
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std::vector<int32_t> output_ids; // map batch token positions to ids of the logits and embd buffers
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size_t output_size = 0; // capacity (of tokens positions) for the output buffers
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int32_t n_outputs = 0; // number of actually-used outputs in the current ubatch or last logical batch
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bool logits_all = false;
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bool need_reserve = false;
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// embeddings output (2-dimensional array: [n_outputs][n_embd])
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// populated only when pooling_type == LLAMA_POOLING_TYPE_NONE
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size_t embd_size = 0; // capacity (of floats) for embeddings
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float * embd = nullptr;
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// sequence embeddings output (map of [n_embd] vectors)
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// populated only when pooling_type != LLAMA_POOLING_TYPE_NONE
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std::map<llama_seq_id, std::vector<float>> embd_seq;
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virtual std::unique_ptr<batch_manager> prepare_batch(const llama_batch & batch);
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// returns the result of ggml_backend_sched_graph_compute_async execution
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enum ggml_status compute_graph(
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ggml_cgraph * graph,
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bool batched);
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// max token position across all sequences in the current context
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llama_pos pos_max() const;
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// certain implementations could require a padding for the context size
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uint32_t get_ctx_padding(const llama_cparams & cparams) const;
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void prepare_k_shift();
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void prepare_defrag();
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void set_inputs(const llama_ubatch & ubatch);
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// make the outputs have the same order they had in the user-provided batch
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// TODO: maybe remove this
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void reorder_outputs();
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// Make sure enough space is available for outputs.
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// Returns max number of outputs for which space was reserved.
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size_t reserve_outputs(size_t n_outputs);
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// input tensors
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struct ggml_tensor * inp_tokens; // I32 [n_batch]
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struct ggml_tensor * inp_embd; // F32 [n_embd, n_batch]
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struct ggml_tensor * inp_pos; // I32 [n_batch]
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struct ggml_tensor * inp_out_ids; // I32 [n_outputs]
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struct ggml_tensor * inp_mean; // F32 [n_batch, n_batch]
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struct ggml_tensor * inp_cls; // I32 [n_batch]
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// === unified KV cache ===
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llama_kv_cache kv_self;
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struct ggml_tensor * inp_KQ_mask; // F32 [kv_size, n_batch]
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struct ggml_tensor * inp_KQ_mask_cnv; // [kv_size, n_batch]
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struct ggml_tensor * inp_KQ_mask_swa; // F32 [kv_size, n_batch]
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struct ggml_tensor * inp_KQ_mask_swa_cnv; // [kv_size, n_batch]
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struct ggml_tensor * inp_K_shift; // I32 [kv_size]
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virtual ggml_tensor * build_inp_embd(
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ggml_context * ctx0,
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ggml_tensor * tok_embd,
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const llama_ubatch & ubatch) override;
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virtual ggml_tensor * build_inp_pos(
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ggml_context * ctx0,
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int32_t n_tokens) override;
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virtual ggml_tensor * build_inp_out_ids(
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ggml_context * ctx0,
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int32_t n_tokens,
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bool worst_case) override;
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virtual ggml_tensor * build_inp_mean(
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ggml_context * ctx0,
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int32_t n_tokens) override;
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virtual ggml_tensor * build_inp_cls(
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ggml_context * ctx0,
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int32_t n_tokens) override;
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virtual void build_attn_inp(
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ggml_context * ctx0,
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int32_t n_tokens,
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bool causal,
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bool swa,
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bool worst_case) override;
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virtual void build_attn_kv_store(
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ggml_context * ctx0,
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ggml_cgraph * graph,
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ggml_tensor * k_cur,
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ggml_tensor * v_cur,
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int32_t n_tokens,
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int64_t il,
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bool worst_case) override;
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virtual ggml_tensor * build_attn_qkv(
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ggml_context * ctx0,
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ggml_cgraph * graph,
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ggml_tensor * wo,
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ggml_tensor * wo_b,
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ggml_tensor * q_cur,
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int32_t n_tokens,
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float kq_scale,
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int il,
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bool worst_case) override;
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virtual ggml_tensor * build_soft_max_ext(
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ggml_context * ctx0,
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ggml_tensor * kq,
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float kq_scale) override;
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virtual ggml_tensor * get_rope_factors(int il) override;
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virtual void build_k_shift(
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ggml_context * ctx0,
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ggml_cgraph * graph) override;
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// find holes from the beginning of the KV cache and fill them by moving data from the end of the cache
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virtual void build_defrag(
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ggml_context * ctx0,
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ggml_cgraph * graph) override;
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// === encoder-decoder ===
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// whether we are computing encoder output or decoder output
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bool is_encoding = false;
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// output of the encoder part of the encoder-decoder models
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std::vector<float> embd_enc;
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std::vector<std::set<llama_seq_id>> seq_ids_enc;
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struct ggml_tensor * inp_embd_enc; // F32 [n_embd, n_outputs_enc]
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struct ggml_tensor * inp_pos_bucket; // I32 [n_batch|n_kv, n_batch]
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struct ggml_tensor * inp_KQ_mask_cross; // F32 [n_outputs_enc, n_batch]
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virtual ggml_tensor * build_inp_embd_enc(
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ggml_context * ctx0,
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int32_t n_tokens,
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bool worst_case) override;
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virtual ggml_tensor * build_inp_KQ_mask_cross(
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ggml_context * ctx0,
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int32_t n_tokens,
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bool worst_case) override;
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// === recurrent ===
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struct ggml_tensor * inp_s_copy; // I32 [kv_size]
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struct ggml_tensor * inp_s_mask; // F32 [1, n_kv]
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// TODO: add recurrent cache
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// TODO: add mamba-specific llama_context
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// TODO: change these to build_mamba_inp and hide `state_copy` and `state_mask` inside the llama_context impl
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virtual ggml_tensor * build_inp_s_copy(
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ggml_context * ctx0,
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bool worst_case) override;
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virtual ggml_tensor * build_inp_s_mask(
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ggml_context * ctx0,
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bool worst_case) override;
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virtual ggml_tensor * build_copy_mask_state(
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ggml_context * ctx0,
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ggml_cgraph * graph,
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ggml_tensor * s,
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ggml_tensor * state_copy,
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ggml_tensor * state_mask,
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int32_t n_tokens,
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int32_t n_state,
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int32_t n_seqs,
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bool worst_case) override;
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virtual ggml_tensor * build_mamba_layer(
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ggml_context * ctx0,
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ggml_cgraph * graph,
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ggml_tensor * cur,
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ggml_tensor * state_copy,
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ggml_tensor * state_mask,
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const llama_ubatch & ubatch,
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int il,
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bool worst_case) override;
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virtual ggml_tensor * build_rwkv_token_shift_load(
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ggml_context * ctx0,
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ggml_cgraph * graph,
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ggml_tensor * state_copy,
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ggml_tensor * state_mask,
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const llama_ubatch & ubatch,
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int il,
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bool worst_case) override;
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virtual ggml_tensor * build_rwkv_token_shift_store(
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ggml_context * ctx0,
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ggml_tensor * token_shift,
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const llama_ubatch & ubatch,
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int il,
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bool worst_case) override;
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virtual ggml_tensor * build_rwkv6_time_mix(
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ggml_context * ctx0,
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ggml_cgraph * graph,
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ggml_tensor * cur,
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ggml_tensor * x_prev,
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ggml_tensor * state_copy,
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ggml_tensor * state_mask,
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const llama_ubatch & ubatch,
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int il,
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bool worst_case) override;
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// state save/load
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virtual size_t state_get_size() override;
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virtual size_t state_get_data( uint8_t * dst, size_t size) override;
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virtual size_t state_set_data(const uint8_t * src, size_t size) override;
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virtual size_t state_seq_get_size(llama_seq_id seq_id) override;
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virtual size_t state_seq_get_data(llama_seq_id seq_id, uint8_t * dst, size_t size) override;
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virtual size_t state_seq_set_data(llama_seq_id seq_id, const uint8_t * src, size_t size) override;
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virtual bool state_load_file(
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const char * filepath,
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llama_token * tokens_out,
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size_t n_token_capacity,
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size_t * n_token_count_out) override;
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virtual bool state_save_file(
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const char * filepath,
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const llama_token * tokens,
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size_t n_token_count) override;
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virtual size_t state_seq_load_file(
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llama_seq_id seq_id,
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const char * filepath,
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llama_token * tokens_out,
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size_t n_token_capacity,
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size_t * n_token_count_out) override;
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virtual size_t state_seq_save_file(
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llama_seq_id seq_id,
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const char * filepath,
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const llama_token * tokens,
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size_t n_token_count) override;
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private:
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size_t state_get_data(struct llama_data_write & data_ctx);
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size_t state_set_data(struct llama_data_read & data_ctx);
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size_t state_seq_get_data(struct llama_data_write & data_ctx, llama_seq_id seq_id);
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size_t state_seq_set_data(struct llama_data_read & data_ctx, llama_seq_id seq_id);
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};
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// For internal test use
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// TODO: remove
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const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map(struct llama_context * ctx);
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