mirror of
https://github.com/ggml-org/llama.cpp.git
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279 lines
8.6 KiB
C++
279 lines
8.6 KiB
C++
#pragma once
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#include "llama-batch.h"
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#include "llama-graph.h"
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#include "llama-kv-cache.h"
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#include "llama-kv-cells.h"
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#include <unordered_map>
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#include <vector>
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struct llama_cparams;
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struct llama_hparams;
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struct llama_model;
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struct llama_context;
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//
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// llama_kv_cache_unified
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//
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class llama_kv_cache_unified : public llama_kv_cache {
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public:
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static uint32_t get_padding(const llama_cparams & cparams);
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// this callback is used to filter out layers that should not be included in the cache
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using layer_filter_cb = std::function<bool(int32_t il)>;
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llama_kv_cache_unified(
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const llama_model & model,
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layer_filter_cb && filter,
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ggml_type type_k,
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ggml_type type_v,
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bool v_trans,
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bool offload,
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uint32_t kv_size,
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uint32_t n_seq_max,
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uint32_t n_pad,
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uint32_t n_swa,
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llama_swa_type swa_type);
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~llama_kv_cache_unified() = default;
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//
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// llama_memory_i
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//
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void clear() override;
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bool seq_rm (llama_seq_id seq_id, llama_pos p0, llama_pos p1) override;
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void seq_cp (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override;
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void seq_keep(llama_seq_id seq_id) override;
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void seq_add (llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) override;
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void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) override;
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llama_pos seq_pos_min(llama_seq_id seq_id) const override;
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llama_pos seq_pos_max(llama_seq_id seq_id) const override;
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//
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// llama_kv_cache
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//
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llama_memory_state_ptr init_batch(
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const llama_batch & batch,
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uint32_t n_ubatch,
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bool embd_pooled,
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bool logits_all) override;
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llama_memory_state_ptr init_full() override;
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bool update(llama_context & lctx) override;
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void defrag_sched(float thold) override;
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bool get_can_shift() const override;
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// state write/load
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void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const override;
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void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1) override;
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//
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// llama_kv_cache_unified specific API
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//
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uint32_t get_size() const;
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//
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// graph_build API
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//
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uint32_t get_n_kv() const;
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// get views of the current state of the cache
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ggml_tensor * get_k(ggml_context * ctx, int32_t il, uint32_t n_kv) const;
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ggml_tensor * get_v(ggml_context * ctx, int32_t il, uint32_t n_kv) const;
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// store k_cur and v_cur in the cache based on the provided head location
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ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il, uint32_t head_cur) const;
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ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il, uint32_t head_cur) const;
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//
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// preparation API
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//
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// find places for the provided ubatches in the cache, returns the head locations
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// return empty vector on failure
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std::vector<uint32_t> prepare(const std::vector<llama_ubatch> & ubatches);
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// return the cell position where we can insert the ubatch
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// return -1 on failure to find a contiguous slot of kv cells
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int32_t find_slot(const llama_ubatch & ubatch) const;
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// emplace the ubatch context into slot: [head_cur, head_cur + ubatch.n_tokens)
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void apply_ubatch(uint32_t head_cur, const llama_ubatch & ubatch);
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//
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// set_input API
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//
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void set_input_kq_mask (ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const;
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void set_input_k_shift (ggml_tensor * dst) const;
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void set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const;
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private:
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const llama_model & model;
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const llama_hparams & hparams;
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struct kv_layer {
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// layer index in the model
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// note: can be different from the layer index in the KV cache
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uint32_t il;
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ggml_tensor * k;
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ggml_tensor * v;
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};
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bool do_defrag = false;
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bool v_trans = true; // the value tensor is transposed
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// the current index from where we start searching for a free slot in the ring buffer of KV cells (see find_slot())
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// note: this is not part of the KV state and it's only used to speed-up the find_slot() method
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uint32_t head = 0;
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const uint32_t n_seq_max = 1;
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// required padding
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const uint32_t n_pad = 1;
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// SWA
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const uint32_t n_swa = 0;
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const llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
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std::vector<ggml_context_ptr> ctxs;
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std::vector<ggml_backend_buffer_ptr> bufs;
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llama_kv_cells_unified cells;
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std::vector<kv_layer> layers;
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// model layer id -> KV cache layer id
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std::unordered_map<int32_t, int32_t> map_layer_ids;
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// defrag
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struct {
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std::vector<uint32_t> ids;
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} defrag_info;
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// return true if cells have been moved
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bool defrag_prepare(int32_t n_max_nodes);
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size_t total_size() const;
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size_t size_k_bytes() const;
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size_t size_v_bytes() const;
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bool is_masked_swa(llama_pos p0, llama_pos p1) const;
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ggml_tensor * build_rope_shift(
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const llama_cparams & cparams,
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ggml_context * ctx,
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ggml_tensor * cur,
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ggml_tensor * shift,
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ggml_tensor * factors,
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float freq_base,
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float freq_scale) const;
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llm_graph_result_ptr build_graph_shift(
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const llama_cparams & cparams,
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ggml_context * ctx,
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ggml_cgraph * gf) const;
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llm_graph_result_ptr build_graph_defrag(
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const llama_cparams & cparams,
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ggml_context * ctx,
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ggml_cgraph * gf) const;
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void state_write_meta(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges, llama_seq_id seq_id = -1) const;
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void state_write_data(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges) const;
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bool state_read_meta(llama_io_read_i & io, uint32_t cell_count, llama_seq_id dest_seq_id = -1);
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bool state_read_data(llama_io_read_i & io, uint32_t cell_count);
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};
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class llama_kv_cache_unified_state : public llama_memory_state_i {
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public:
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// used for errors
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llama_kv_cache_unified_state(llama_memory_status status);
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// used to create a full-cache state
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llama_kv_cache_unified_state(
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llama_memory_status status,
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llama_kv_cache_unified * kv);
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// used to create a state from a batch
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llama_kv_cache_unified_state(
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llama_memory_status status,
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llama_kv_cache_unified * kv,
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llama_sbatch sbatch,
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std::vector<uint32_t> heads,
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std::vector<llama_ubatch> ubatches);
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virtual ~llama_kv_cache_unified_state();
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//
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// llama_memory_state_i
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//
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bool next() override;
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bool apply() override;
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std::vector<int64_t> & out_ids() override;
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llama_memory_status get_status() const override;
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const llama_ubatch & get_ubatch() const override;
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//
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// llama_kv_cache_unified_state specific API
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//
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uint32_t get_n_kv() const;
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// get views of the current state of the cache
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ggml_tensor * get_k(ggml_context * ctx, int32_t il) const;
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ggml_tensor * get_v(ggml_context * ctx, int32_t il) const;
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// store k_cur and v_cur in the cache based on the provided head location
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ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il) const;
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ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il) const;
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void set_input_k_shift(ggml_tensor * dst) const;
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void set_input_kq_mask (ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const;
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void set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const;
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private:
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const llama_memory_status status;
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llama_kv_cache_unified * kv;
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llama_sbatch sbatch;
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// the index of the next ubatch to process
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size_t i_next = 0;
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std::vector<uint32_t> heads;
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std::vector<llama_ubatch> ubatches;
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//
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// data needed for building the compute graph for the current ubatch:
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//
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// a heuristic, to avoid attending the full cache if it is not yet utilized
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// as the cache gets filled, the benefit from this heuristic disappears
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int32_t n_kv;
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// the beginning of the current slot in which the ubatch will be inserted
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int32_t head;
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};
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