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https://github.com/ggml-org/llama.cpp.git
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138 lines
4.2 KiB
C++
138 lines
4.2 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-memory.h"
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#include "llama-memory-recurrent.h"
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#include <memory>
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#include <vector>
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//
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// llama_memory_hybrid
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//
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// utilizes instances of llama_memory_recurrent and llama_kv_cache to
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// support models where each layer may be either attention-based or recurrent
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class llama_memory_hybrid : public llama_memory_i {
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public:
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llama_memory_hybrid(
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const llama_model & model,
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/* attn */
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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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uint32_t kv_size,
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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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/* recurrent */
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ggml_type type_r,
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ggml_type type_s,
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uint32_t rs_size,
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/* common */
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uint32_t n_seq_max,
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bool offload,
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bool unified,
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/* layer filters */
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const layer_filter_cb & filter_attn = nullptr,
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const layer_filter_cb & filter_recr = nullptr);
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~llama_memory_hybrid() = default;
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//
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// llama_memory_i
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//
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llama_memory_context_ptr init_batch(
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llama_batch_allocr & balloc,
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uint32_t n_ubatch,
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bool embd_all) override;
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llama_memory_context_ptr init_full() override;
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llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override;
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bool get_can_shift() const override;
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void clear(bool data) 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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// state write/load
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void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) const override;
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void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) override;
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//
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// llama_memory_hybrid specific API
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//
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llama_kv_cache * get_mem_attn() const;
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llama_memory_recurrent * get_mem_recr() const;
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private:
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const llama_hparams & hparams;
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const std::unique_ptr<llama_kv_cache> mem_attn;
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const std::unique_ptr<llama_memory_recurrent> mem_recr;
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};
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class llama_memory_hybrid_context : public llama_memory_context_i {
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public:
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using slot_info_vec_t = llama_kv_cache::slot_info_vec_t;
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// init failure
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explicit llama_memory_hybrid_context(llama_memory_status status);
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// init full
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explicit llama_memory_hybrid_context(llama_memory_hybrid * mem);
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// init update
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explicit llama_memory_hybrid_context(
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llama_memory_hybrid * mem,
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llama_context * lctx,
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bool optimize);
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// init success
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llama_memory_hybrid_context(
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llama_memory_hybrid * mem,
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slot_info_vec_t sinfos_attn,
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std::vector<llama_ubatch> ubatches);
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~llama_memory_hybrid_context() = default;
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bool next() override;
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bool apply() 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_memory_hybrid_context
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//
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const llama_kv_cache_context * get_attn() const;
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const llama_memory_recurrent_context * get_recr() const;
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private:
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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<llama_ubatch> ubatches;
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const llama_memory_context_ptr ctx_attn;
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const llama_memory_context_ptr ctx_recr;
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const llama_memory_status status;
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};
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