Georgi Gerganov
GitHub
parent
053b1539c0
commit
0fc16b42e8
11 changed files with 3714 additions and 3674 deletions
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@ -21,6 +21,9 @@ add_library(llama
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llama-impl.cpp
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llama-io.cpp
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llama-kv-cache.cpp
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llama-kv-cache-unified.cpp
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llama-kv-cache-unified-iswa.cpp
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llama-kv-cache-recurrent.cpp
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llama-memory.cpp
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llama-mmap.cpp
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llama-model-loader.cpp
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@ -3,7 +3,10 @@
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#include "llama-impl.h"
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#include "llama-batch.h"
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#include "llama-cparams.h"
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#include "llama-kv-cache.h"
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#include "llama-kv-cache-unified.h"
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#include "llama-kv-cache-unified-iswa.h"
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#include "llama-kv-cache-recurrent.h"
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#include <cassert>
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#include <cmath>
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1132
src/llama-kv-cache-recurrent.cpp
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1132
src/llama-kv-cache-recurrent.cpp
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File diff suppressed because it is too large
Load diff
191
src/llama-kv-cache-recurrent.h
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191
src/llama-kv-cache-recurrent.h
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@ -0,0 +1,191 @@
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#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 <set>
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#include <vector>
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//
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// llama_kv_cache_recurrent
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//
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// TODO: extract the KV cache state used for graph computation into llama_kv_cache_recurrent_state_i
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// see the implementation of llama_kv_cache_unified_state_i for an example how to do it
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class llama_kv_cache_recurrent : public llama_kv_cache {
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public:
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llama_kv_cache_recurrent(
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const llama_model & model,
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ggml_type type_k,
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ggml_type type_v,
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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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~llama_kv_cache_recurrent() = 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 prepare(const std::vector<llama_ubatch> & ubatches);
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// find a contiguous slot of kv cells and emplace the ubatch there
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bool find_slot(const llama_ubatch & ubatch);
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bool get_can_shift() const override;
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// TODO: temporary methods - they are not really const as they do const_cast<>, fix this
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int32_t s_copy(int i) const;
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float s_mask(int i) const;
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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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uint32_t head = 0; // the location where the batch will be placed in the cache (see find_slot())
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uint32_t size = 0; // total number of cells, shared across all sequences
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uint32_t used = 0; // used cells (i.e. at least one seq_id)
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// computed before each graph build
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uint32_t n = 0;
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// TODO: optimize for recurrent state needs
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struct kv_cell {
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llama_pos pos = -1;
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int32_t src = -1; // used to copy states
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int32_t tail = -1;
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std::set<llama_seq_id> seq_id;
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bool has_seq_id(const llama_seq_id & id) const {
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return seq_id.find(id) != seq_id.end();
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}
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bool is_empty() const {
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return seq_id.empty();
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}
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bool is_same_seq(const kv_cell & other) const {
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return seq_id == other.seq_id;
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}
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};
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std::vector<kv_cell> cells;
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std::vector<ggml_tensor *> k_l; // per layer
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std::vector<ggml_tensor *> v_l;
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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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const uint32_t n_seq_max = 1;
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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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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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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_recurrent_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_recurrent_state(llama_memory_status status);
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// used to create a full-cache state
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llama_kv_cache_recurrent_state(
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llama_memory_status status,
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llama_kv_cache_recurrent * kv);
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// used to create a state from a batch
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llama_kv_cache_recurrent_state(
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llama_memory_status status,
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llama_kv_cache_recurrent * kv,
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llama_sbatch sbatch,
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std::vector<llama_ubatch> ubatches);
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virtual ~llama_kv_cache_recurrent_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_recurrent_state specific API
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//
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uint32_t get_n_kv() const;
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uint32_t get_head() const;
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uint32_t get_size() const;
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ggml_tensor * get_k_l(int32_t il) const;
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ggml_tensor * get_v_l(int32_t il) const;
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int32_t s_copy(int i) const;
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float s_mask(int i) const;
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private:
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const llama_memory_status status;
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llama_kv_cache_recurrent * kv;
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llama_sbatch sbatch;
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size_t i_next = 0;
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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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// TODO: extract all the state like `head` and `n` here
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//
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const bool is_full = false;
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};
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249
src/llama-kv-cache-unified-iswa.cpp
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249
src/llama-kv-cache-unified-iswa.cpp
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@ -0,0 +1,249 @@
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#include "llama-kv-cache-unified-iswa.h"
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#include "llama-impl.h"
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#include "llama-batch.h"
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#include "llama-model.h"
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#include <algorithm>
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#include <cassert>
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//
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// llama_kv_cache_unified_iswa
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//
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llama_kv_cache_unified_iswa::llama_kv_cache_unified_iswa(
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const llama_model & model,
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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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bool swa_full,
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uint32_t kv_size,
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uint32_t n_seq_max,
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uint32_t n_ubatch,
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uint32_t n_pad) : hparams(model.hparams) {
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llama_kv_cache_unified::layer_filter_cb filter_base = [&](int32_t il) { return !model.hparams.is_swa(il); };
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llama_kv_cache_unified::layer_filter_cb filter_swa = [&](int32_t il) { return model.hparams.is_swa(il); };
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const uint32_t size_base = kv_size;
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uint32_t size_swa = std::min(size_base, GGML_PAD(hparams.n_swa*n_seq_max + n_ubatch, n_pad));
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// when using full-size SWA cache, we set the SWA cache size to be equal to the base cache size
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if (swa_full) {
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LLAMA_LOG_WARN("%s: using full-size SWA cache (ref: %s)\n",
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__func__, "https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055");
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size_swa = size_base;
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}
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LLAMA_LOG_INFO("%s: creating non-SWA KV cache, size = %u cells\n", __func__, size_base);
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kv_base = std::make_unique<llama_kv_cache_unified>(
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model, std::move(filter_base), type_k, type_v,
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v_trans, offload, size_base, n_seq_max, n_pad,
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0, LLAMA_SWA_TYPE_NONE);
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LLAMA_LOG_INFO("%s: creating SWA KV cache, size = %u cells\n", __func__, size_swa);
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kv_swa = std::make_unique<llama_kv_cache_unified>(
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model, std::move(filter_swa), type_k, type_v,
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v_trans, offload, size_swa, n_seq_max, n_pad,
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hparams.n_swa, hparams.swa_type);
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}
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void llama_kv_cache_unified_iswa::clear() {
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kv_base->clear();
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kv_swa ->clear();
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}
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bool llama_kv_cache_unified_iswa::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
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bool res = true;
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res = res & kv_base->seq_rm(seq_id, p0, p1);
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res = res & kv_swa ->seq_rm(seq_id, p0, p1);
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return res;
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}
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void llama_kv_cache_unified_iswa::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
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kv_base->seq_cp(seq_id_src, seq_id_dst, p0, p1);
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kv_swa ->seq_cp(seq_id_src, seq_id_dst, p0, p1);
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}
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void llama_kv_cache_unified_iswa::seq_keep(llama_seq_id seq_id) {
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kv_base->seq_keep(seq_id);
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kv_swa ->seq_keep(seq_id);
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}
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void llama_kv_cache_unified_iswa::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) {
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kv_base->seq_add(seq_id, p0, p1, shift);
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kv_swa ->seq_add(seq_id, p0, p1, shift);
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}
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void llama_kv_cache_unified_iswa::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
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kv_base->seq_div(seq_id, p0, p1, d);
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kv_swa ->seq_div(seq_id, p0, p1, d);
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}
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llama_pos llama_kv_cache_unified_iswa::seq_pos_min(llama_seq_id seq_id) const {
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// the base cache is a superset of the SWA cache, so we can just check the SWA cache
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return kv_swa->seq_pos_min(seq_id);
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}
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llama_pos llama_kv_cache_unified_iswa::seq_pos_max(llama_seq_id seq_id) const {
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return kv_swa->seq_pos_max(seq_id);
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}
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llama_memory_state_ptr llama_kv_cache_unified_iswa::init_batch(const llama_batch & batch, uint32_t n_ubatch, bool embd_pooled, bool logits_all) {
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GGML_UNUSED(embd_pooled);
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// TODO: if we fail with split_simple, we should attempt different splitting strategies
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// but to do that properly, we first have to refactor the batches to be more flexible
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auto sbatch = llama_sbatch(batch, hparams.n_embd, true, logits_all);
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std::vector<llama_ubatch> ubatches;
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while (sbatch.n_tokens > 0) {
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auto ubatch = sbatch.split_simple(n_ubatch);
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ubatches.push_back(ubatch);
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}
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auto heads_base = kv_base->prepare(ubatches);
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if (heads_base.empty()) {
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return std::make_unique<llama_kv_cache_unified_iswa_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
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}
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auto heads_swa = kv_swa->prepare(ubatches);
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if (heads_swa.empty()) {
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return std::make_unique<llama_kv_cache_unified_iswa_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
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}
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assert(heads_base.size() == heads_swa.size());
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return std::make_unique<llama_kv_cache_unified_iswa_state>(LLAMA_MEMORY_STATUS_SUCCESS,
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this, std::move(sbatch), std::move(heads_base), std::move(heads_swa), std::move(ubatches));
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}
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llama_memory_state_ptr llama_kv_cache_unified_iswa::init_full() {
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return std::make_unique<llama_kv_cache_unified_iswa_state>(LLAMA_MEMORY_STATUS_SUCCESS, this);
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}
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bool llama_kv_cache_unified_iswa::update(llama_context & lctx) {
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bool res = false;
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res = res | kv_base->update(lctx);
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res = res | kv_swa ->update(lctx);
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return res;
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}
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void llama_kv_cache_unified_iswa::defrag_sched(float thold) {
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kv_base->defrag_sched(thold);
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kv_swa ->defrag_sched(thold);
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}
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bool llama_kv_cache_unified_iswa::get_can_shift() const {
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return kv_base->get_size() == kv_swa->get_size();
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}
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void llama_kv_cache_unified_iswa::state_write(llama_io_write_i & io, llama_seq_id seq_id) const {
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kv_base->state_write(io, seq_id);
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kv_swa ->state_write(io, seq_id);
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}
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void llama_kv_cache_unified_iswa::state_read(llama_io_read_i & io, llama_seq_id seq_id) {
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kv_base->state_read(io, seq_id);
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kv_swa ->state_read(io, seq_id);
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}
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llama_kv_cache_unified * llama_kv_cache_unified_iswa::get_base() const {
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return kv_base.get();
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}
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llama_kv_cache_unified * llama_kv_cache_unified_iswa::get_swa() const {
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return kv_swa.get();
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}
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//
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// llama_kv_cache_unified_iswa_state
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//
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llama_kv_cache_unified_iswa_state::llama_kv_cache_unified_iswa_state(llama_memory_status status) : status(status) {}
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llama_kv_cache_unified_iswa_state::llama_kv_cache_unified_iswa_state(
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llama_memory_status status,
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llama_kv_cache_unified_iswa * kv) : status(status) {
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state_base.reset(new llama_kv_cache_unified_state(status, kv->get_base()));
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state_swa .reset(new llama_kv_cache_unified_state(status, kv->get_swa ()));
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}
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llama_kv_cache_unified_iswa_state::llama_kv_cache_unified_iswa_state(
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llama_memory_status status,
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llama_kv_cache_unified_iswa * kv,
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llama_sbatch sbatch,
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std::vector<uint32_t> heads_base,
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std::vector<uint32_t> heads_swa,
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std::vector<llama_ubatch> ubatches)
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: status(status),
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sbatch(std::move(sbatch)),
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ubatches(std::move(ubatches)) {
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// note: here we copy the ubatches. not sure if this is ideal
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state_base.reset(new llama_kv_cache_unified_state(status, kv->get_base(), {}, std::move(heads_base), this->ubatches));
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state_swa .reset(new llama_kv_cache_unified_state(status, kv->get_swa (), {}, std::move(heads_swa), this->ubatches));
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}
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llama_kv_cache_unified_iswa_state:: ~llama_kv_cache_unified_iswa_state() = default;
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bool llama_kv_cache_unified_iswa_state::next() {
|
||||
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
|
||||
|
||||
state_base->next();
|
||||
state_swa ->next();
|
||||
|
||||
if (++i_next >= ubatches.size()) {
|
||||
return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool llama_kv_cache_unified_iswa_state::apply() {
|
||||
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
|
||||
|
||||
bool res = true;
|
||||
|
||||
res = res & state_base->apply();
|
||||
res = res & state_swa ->apply();
|
||||
|
||||
return res;
|
||||
}
|
||||
|
||||
std::vector<int64_t> & llama_kv_cache_unified_iswa_state::out_ids() {
|
||||
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
|
||||
|
||||
return sbatch.out_ids;
|
||||
}
|
||||
|
||||
llama_memory_status llama_kv_cache_unified_iswa_state::get_status() const {
|
||||
return status;
|
||||
}
|
||||
|
||||
const llama_ubatch & llama_kv_cache_unified_iswa_state::get_ubatch() const {
|
||||
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
|
||||
return ubatches[i_next];
|
||||
}
|
||||
|
||||
const llama_kv_cache_unified_state * llama_kv_cache_unified_iswa_state::get_base() const {
|
||||
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
|
||||
|
||||
return state_base.get();
|
||||
}
|
||||
|
||||
const llama_kv_cache_unified_state * llama_kv_cache_unified_iswa_state::get_swa() const {
|
||||
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
|
||||
|
||||
return state_swa.get();
|
||||
}
|
||||
136
src/llama-kv-cache-unified-iswa.h
Normal file
136
src/llama-kv-cache-unified-iswa.h
Normal file
|
|
@ -0,0 +1,136 @@
|
|||
#pragma once
|
||||
|
||||
#include "llama-kv-cache-unified.h"
|
||||
|
||||
#include <vector>
|
||||
|
||||
//
|
||||
// llama_kv_cache_unified_iswa
|
||||
//
|
||||
|
||||
// utilizes two instances of llama_kv_cache_unified
|
||||
// the first instance is for the non-SWA layers of the model and the second instance is for the SWA layers
|
||||
|
||||
class llama_kv_cache_unified_iswa : public llama_kv_cache {
|
||||
public:
|
||||
llama_kv_cache_unified_iswa(
|
||||
const llama_model & model,
|
||||
ggml_type type_k,
|
||||
ggml_type type_v,
|
||||
bool v_trans,
|
||||
bool offload,
|
||||
bool swa_full,
|
||||
uint32_t kv_size,
|
||||
uint32_t n_seq_max,
|
||||
uint32_t n_ubatch,
|
||||
uint32_t n_pad);
|
||||
|
||||
~llama_kv_cache_unified_iswa() = default;
|
||||
|
||||
//
|
||||
// llama_memory_i
|
||||
//
|
||||
|
||||
void clear() override;
|
||||
|
||||
bool seq_rm (llama_seq_id seq_id, llama_pos p0, llama_pos p1) override;
|
||||
void seq_cp (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override;
|
||||
void seq_keep(llama_seq_id seq_id) override;
|
||||
void seq_add (llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) override;
|
||||
void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) override;
|
||||
|
||||
llama_pos seq_pos_min(llama_seq_id seq_id) const override;
|
||||
llama_pos seq_pos_max(llama_seq_id seq_id) const override;
|
||||
|
||||
//
|
||||
// llama_kv_cache
|
||||
//
|
||||
|
||||
llama_memory_state_ptr init_batch(
|
||||
const llama_batch & batch,
|
||||
uint32_t n_ubatch,
|
||||
bool embd_pooled,
|
||||
bool logits_all) override;
|
||||
|
||||
llama_memory_state_ptr init_full() override;
|
||||
|
||||
bool update(llama_context & lctx) override;
|
||||
|
||||
void defrag_sched(float thold) override;
|
||||
|
||||
bool get_can_shift() const override;
|
||||
|
||||
// state write/load
|
||||
|
||||
void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const override;
|
||||
void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1) override;
|
||||
|
||||
//
|
||||
// llama_kv_cache_unified_iswa specific API
|
||||
//
|
||||
|
||||
llama_kv_cache_unified * get_base() const;
|
||||
llama_kv_cache_unified * get_swa () const;
|
||||
|
||||
private:
|
||||
const llama_hparams & hparams;
|
||||
|
||||
std::unique_ptr<llama_kv_cache_unified> kv_base;
|
||||
std::unique_ptr<llama_kv_cache_unified> kv_swa;
|
||||
};
|
||||
|
||||
class llama_kv_cache_unified_iswa_state : public llama_memory_state_i {
|
||||
public:
|
||||
// used for errors
|
||||
llama_kv_cache_unified_iswa_state(llama_memory_status status);
|
||||
|
||||
// used to create a full-cache state
|
||||
llama_kv_cache_unified_iswa_state(
|
||||
llama_memory_status status,
|
||||
llama_kv_cache_unified_iswa * kv);
|
||||
|
||||
// used to create a state from a batch
|
||||
llama_kv_cache_unified_iswa_state(
|
||||
llama_memory_status status,
|
||||
llama_kv_cache_unified_iswa * kv,
|
||||
llama_sbatch sbatch,
|
||||
std::vector<uint32_t> heads_base,
|
||||
std::vector<uint32_t> heads_swa,
|
||||
std::vector<llama_ubatch> ubatches);
|
||||
|
||||
virtual ~llama_kv_cache_unified_iswa_state();
|
||||
|
||||
//
|
||||
// llama_memory_state_i
|
||||
//
|
||||
|
||||
bool next() override;
|
||||
bool apply() override;
|
||||
|
||||
std::vector<int64_t> & out_ids() override;
|
||||
|
||||
llama_memory_status get_status() const override;
|
||||
const llama_ubatch & get_ubatch() const override;
|
||||
|
||||
//
|
||||
// llama_kv_cache_unified_iswa_state specific API
|
||||
//
|
||||
|
||||
const llama_kv_cache_unified_state * get_base() const;
|
||||
const llama_kv_cache_unified_state * get_swa() const;
|
||||
|
||||
private:
|
||||
const llama_memory_status status;
|
||||
|
||||
//llama_kv_cache_unified_iswa * kv;
|
||||
|
||||
llama_sbatch sbatch;
|
||||
|
||||
// the index of the next ubatch to process
|
||||
size_t i_next = 0;
|
||||
|
||||
std::vector<llama_ubatch> ubatches;
|
||||
|
||||
std::unique_ptr<llama_kv_cache_unified_state> state_base;
|
||||
std::unique_ptr<llama_kv_cache_unified_state> state_swa;
|
||||
};
|
||||
1717
src/llama-kv-cache-unified.cpp
Normal file
1717
src/llama-kv-cache-unified.cpp
Normal file
File diff suppressed because it is too large
Load diff
278
src/llama-kv-cache-unified.h
Normal file
278
src/llama-kv-cache-unified.h
Normal file
|
|
@ -0,0 +1,278 @@
|
|||
#pragma once
|
||||
|
||||
#include "llama-batch.h"
|
||||
#include "llama-graph.h"
|
||||
#include "llama-kv-cache.h"
|
||||
#include "llama-kv-cells.h"
|
||||
|
||||
#include <unordered_map>
|
||||
#include <vector>
|
||||
|
||||
struct llama_cparams;
|
||||
struct llama_hparams;
|
||||
struct llama_model;
|
||||
struct llama_context;
|
||||
|
||||
//
|
||||
// llama_kv_cache_unified
|
||||
//
|
||||
|
||||
class llama_kv_cache_unified : public llama_kv_cache {
|
||||
public:
|
||||
static uint32_t get_padding(const llama_cparams & cparams);
|
||||
|
||||
// this callback is used to filter out layers that should not be included in the cache
|
||||
using layer_filter_cb = std::function<bool(int32_t il)>;
|
||||
|
||||
llama_kv_cache_unified(
|
||||
const llama_model & model,
|
||||
layer_filter_cb && filter,
|
||||
ggml_type type_k,
|
||||
ggml_type type_v,
|
||||
bool v_trans,
|
||||
bool offload,
|
||||
uint32_t kv_size,
|
||||
uint32_t n_seq_max,
|
||||
uint32_t n_pad,
|
||||
uint32_t n_swa,
|
||||
llama_swa_type swa_type);
|
||||
|
||||
~llama_kv_cache_unified() = default;
|
||||
|
||||
//
|
||||
// llama_memory_i
|
||||
//
|
||||
|
||||
void clear() override;
|
||||
|
||||
bool seq_rm (llama_seq_id seq_id, llama_pos p0, llama_pos p1) override;
|
||||
void seq_cp (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override;
|
||||
void seq_keep(llama_seq_id seq_id) override;
|
||||
void seq_add (llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) override;
|
||||
void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) override;
|
||||
|
||||
llama_pos seq_pos_min(llama_seq_id seq_id) const override;
|
||||
llama_pos seq_pos_max(llama_seq_id seq_id) const override;
|
||||
|
||||
//
|
||||
// llama_kv_cache
|
||||
//
|
||||
|
||||
llama_memory_state_ptr init_batch(
|
||||
const llama_batch & batch,
|
||||
uint32_t n_ubatch,
|
||||
bool embd_pooled,
|
||||
bool logits_all) override;
|
||||
|
||||
llama_memory_state_ptr init_full() override;
|
||||
|
||||
bool update(llama_context & lctx) override;
|
||||
|
||||
void defrag_sched(float thold) override;
|
||||
|
||||
bool get_can_shift() const override;
|
||||
|
||||
// state write/load
|
||||
|
||||
void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const override;
|
||||
void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1) override;
|
||||
|
||||
//
|
||||
// llama_kv_cache_unified specific API
|
||||
//
|
||||
|
||||
uint32_t get_size() const;
|
||||
|
||||
//
|
||||
// graph_build API
|
||||
//
|
||||
|
||||
uint32_t get_n_kv() const;
|
||||
|
||||
// get views of the current state of the cache
|
||||
ggml_tensor * get_k(ggml_context * ctx, int32_t il, uint32_t n_kv) const;
|
||||
ggml_tensor * get_v(ggml_context * ctx, int32_t il, uint32_t n_kv) const;
|
||||
|
||||
// store k_cur and v_cur in the cache based on the provided head location
|
||||
ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il, uint32_t head_cur) const;
|
||||
ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il, uint32_t head_cur) const;
|
||||
|
||||
//
|
||||
// preparation API
|
||||
//
|
||||
|
||||
// find places for the provided ubatches in the cache, returns the head locations
|
||||
// return empty vector on failure
|
||||
std::vector<uint32_t> prepare(const std::vector<llama_ubatch> & ubatches);
|
||||
|
||||
// return the cell position where we can insert the ubatch
|
||||
// return -1 on failure to find a contiguous slot of kv cells
|
||||
int32_t find_slot(const llama_ubatch & ubatch) const;
|
||||
|
||||
// emplace the ubatch context into slot: [head_cur, head_cur + ubatch.n_tokens)
|
||||
void apply_ubatch(uint32_t head_cur, const llama_ubatch & ubatch);
|
||||
|
||||
//
|
||||
// set_input API
|
||||
//
|
||||
|
||||
void set_input_kq_mask (ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const;
|
||||
void set_input_k_shift (ggml_tensor * dst) const;
|
||||
void set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const;
|
||||
|
||||
private:
|
||||
const llama_model & model;
|
||||
const llama_hparams & hparams;
|
||||
|
||||
struct kv_layer {
|
||||
// layer index in the model
|
||||
// note: can be different from the layer index in the KV cache
|
||||
uint32_t il;
|
||||
|
||||
ggml_tensor * k;
|
||||
ggml_tensor * v;
|
||||
};
|
||||
|
||||
bool do_defrag = false;
|
||||
bool v_trans = true; // the value tensor is transposed
|
||||
|
||||
// the current index from where we start searching for a free slot in the ring buffer of KV cells (see find_slot())
|
||||
// note: this is not part of the KV state and it's only used to speed-up the find_slot() method
|
||||
uint32_t head = 0;
|
||||
|
||||
const uint32_t n_seq_max = 1;
|
||||
|
||||
// required padding
|
||||
const uint32_t n_pad = 1;
|
||||
|
||||
// SWA
|
||||
const uint32_t n_swa = 0;
|
||||
|
||||
const llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
|
||||
|
||||
std::vector<ggml_context_ptr> ctxs;
|
||||
std::vector<ggml_backend_buffer_ptr> bufs;
|
||||
|
||||
llama_kv_cells_unified cells;
|
||||
|
||||
std::vector<kv_layer> layers;
|
||||
|
||||
// model layer id -> KV cache layer id
|
||||
std::unordered_map<int32_t, int32_t> map_layer_ids;
|
||||
|
||||
// defrag
|
||||
struct {
|
||||
std::vector<uint32_t> ids;
|
||||
} defrag_info;
|
||||
|
||||
// return true if cells have been moved
|
||||
bool defrag_prepare(int32_t n_max_nodes);
|
||||
|
||||
size_t total_size() const;
|
||||
|
||||
size_t size_k_bytes() const;
|
||||
size_t size_v_bytes() const;
|
||||
|
||||
bool is_masked_swa(llama_pos p0, llama_pos p1) const;
|
||||
|
||||
ggml_tensor * build_rope_shift(
|
||||
const llama_cparams & cparams,
|
||||
ggml_context * ctx,
|
||||
ggml_tensor * cur,
|
||||
ggml_tensor * shift,
|
||||
ggml_tensor * factors,
|
||||
float freq_base,
|
||||
float freq_scale) const;
|
||||
|
||||
llm_graph_result_ptr build_graph_shift(
|
||||
const llama_cparams & cparams,
|
||||
ggml_context * ctx,
|
||||
ggml_cgraph * gf) const;
|
||||
|
||||
llm_graph_result_ptr build_graph_defrag(
|
||||
const llama_cparams & cparams,
|
||||
ggml_context * ctx,
|
||||
ggml_cgraph * gf) const;
|
||||
|
||||
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;
|
||||
void state_write_data(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges) const;
|
||||
|
||||
bool state_read_meta(llama_io_read_i & io, uint32_t cell_count, llama_seq_id dest_seq_id = -1);
|
||||
bool state_read_data(llama_io_read_i & io, uint32_t cell_count);
|
||||
};
|
||||
|
||||
class llama_kv_cache_unified_state : public llama_memory_state_i {
|
||||
public:
|
||||
// used for errors
|
||||
llama_kv_cache_unified_state(llama_memory_status status);
|
||||
|
||||
// used to create a full-cache state
|
||||
llama_kv_cache_unified_state(
|
||||
llama_memory_status status,
|
||||
llama_kv_cache_unified * kv);
|
||||
|
||||
// used to create a state from a batch
|
||||
llama_kv_cache_unified_state(
|
||||
llama_memory_status status,
|
||||
llama_kv_cache_unified * kv,
|
||||
llama_sbatch sbatch,
|
||||
std::vector<uint32_t> heads,
|
||||
std::vector<llama_ubatch> ubatches);
|
||||
|
||||
virtual ~llama_kv_cache_unified_state();
|
||||
|
||||
//
|
||||
// llama_memory_state_i
|
||||
//
|
||||
|
||||
bool next() override;
|
||||
bool apply() override;
|
||||
|
||||
std::vector<int64_t> & out_ids() override;
|
||||
|
||||
llama_memory_status get_status() const override;
|
||||
const llama_ubatch & get_ubatch() const override;
|
||||
|
||||
//
|
||||
// llama_kv_cache_unified_state specific API
|
||||
//
|
||||
|
||||
uint32_t get_n_kv() const;
|
||||
|
||||
// get views of the current state of the cache
|
||||
ggml_tensor * get_k(ggml_context * ctx, int32_t il) const;
|
||||
ggml_tensor * get_v(ggml_context * ctx, int32_t il) const;
|
||||
|
||||
// store k_cur and v_cur in the cache based on the provided head location
|
||||
ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il) const;
|
||||
ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il) const;
|
||||
|
||||
void set_input_k_shift(ggml_tensor * dst) const;
|
||||
|
||||
void set_input_kq_mask (ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const;
|
||||
void set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const;
|
||||
|
||||
private:
|
||||
const llama_memory_status status;
|
||||
|
||||
llama_kv_cache_unified * kv;
|
||||
|
||||
llama_sbatch sbatch;
|
||||
|
||||
// the index of the next ubatch to process
|
||||
size_t i_next = 0;
|
||||
|
||||
std::vector<uint32_t> heads;
|
||||
std::vector<llama_ubatch> ubatches;
|
||||
|
||||
//
|
||||
// data needed for building the compute graph for the current ubatch:
|
||||
//
|
||||
|
||||
// a heuristic, to avoid attending the full cache if it is not yet utilized
|
||||
// as the cache gets filled, the benefit from this heuristic disappears
|
||||
int32_t n_kv;
|
||||
|
||||
// the beginning of the current slot in which the ubatch will be inserted
|
||||
int32_t head;
|
||||
};
|
||||
File diff suppressed because it is too large
Load diff
|
|
@ -2,21 +2,7 @@
|
|||
|
||||
#include "llama.h"
|
||||
#include "llama-io.h"
|
||||
#include "llama-batch.h"
|
||||
#include "llama-graph.h"
|
||||
#include "llama-memory.h"
|
||||
#include "llama-kv-cells.h"
|
||||
|
||||
#include "ggml-cpp.h"
|
||||
|
||||
#include <set>
|
||||
#include <unordered_map>
|
||||
#include <vector>
|
||||
|
||||
struct llama_cparams;
|
||||
struct llama_hparams;
|
||||
struct llama_model;
|
||||
struct llama_context;
|
||||
|
||||
struct llama_kv_cache : public llama_memory_i {
|
||||
virtual ~llama_kv_cache() = default;
|
||||
|
|
@ -56,581 +42,3 @@ struct llama_kv_cache : public llama_memory_i {
|
|||
virtual void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const = 0;
|
||||
virtual void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1) = 0;
|
||||
};
|
||||
|
||||
//
|
||||
// llama_kv_cache_unified
|
||||
//
|
||||
|
||||
class llama_kv_cache_unified : public llama_kv_cache {
|
||||
public:
|
||||
static uint32_t get_padding(const llama_cparams & cparams);
|
||||
|
||||
// this callback is used to filter out layers that should not be included in the cache
|
||||
using layer_filter_cb = std::function<bool(int32_t il)>;
|
||||
|
||||
llama_kv_cache_unified(
|
||||
const llama_model & model,
|
||||
layer_filter_cb && filter,
|
||||
ggml_type type_k,
|
||||
ggml_type type_v,
|
||||
bool v_trans,
|
||||
bool offload,
|
||||
uint32_t kv_size,
|
||||
uint32_t n_seq_max,
|
||||
uint32_t n_pad,
|
||||
uint32_t n_swa,
|
||||
llama_swa_type swa_type);
|
||||
|
||||
~llama_kv_cache_unified() = default;
|
||||
|
||||
//
|
||||
// llama_memory_i
|
||||
//
|
||||
|
||||
void clear() override;
|
||||
|
||||
bool seq_rm (llama_seq_id seq_id, llama_pos p0, llama_pos p1) override;
|
||||
void seq_cp (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override;
|
||||
void seq_keep(llama_seq_id seq_id) override;
|
||||
void seq_add (llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) override;
|
||||
void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) override;
|
||||
|
||||
llama_pos seq_pos_min(llama_seq_id seq_id) const override;
|
||||
llama_pos seq_pos_max(llama_seq_id seq_id) const override;
|
||||
|
||||
//
|
||||
// llama_kv_cache
|
||||
//
|
||||
|
||||
llama_memory_state_ptr init_batch(
|
||||
const llama_batch & batch,
|
||||
uint32_t n_ubatch,
|
||||
bool embd_pooled,
|
||||
bool logits_all) override;
|
||||
|
||||
llama_memory_state_ptr init_full() override;
|
||||
|
||||
bool update(llama_context & lctx) override;
|
||||
|
||||
void defrag_sched(float thold) override;
|
||||
|
||||
bool get_can_shift() const override;
|
||||
|
||||
// state write/load
|
||||
|
||||
void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const override;
|
||||
void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1) override;
|
||||
|
||||
//
|
||||
// llama_kv_cache_unified specific API
|
||||
//
|
||||
|
||||
uint32_t get_size() const;
|
||||
|
||||
//
|
||||
// graph_build API
|
||||
//
|
||||
|
||||
uint32_t get_n_kv() const;
|
||||
|
||||
// get views of the current state of the cache
|
||||
ggml_tensor * get_k(ggml_context * ctx, int32_t il, uint32_t n_kv) const;
|
||||
ggml_tensor * get_v(ggml_context * ctx, int32_t il, uint32_t n_kv) const;
|
||||
|
||||
// store k_cur and v_cur in the cache based on the provided head location
|
||||
ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il, uint32_t head_cur) const;
|
||||
ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il, uint32_t head_cur) const;
|
||||
|
||||
//
|
||||
// preparation API
|
||||
//
|
||||
|
||||
// find places for the provided ubatches in the cache, returns the head locations
|
||||
// return empty vector on failure
|
||||
std::vector<uint32_t> prepare(const std::vector<llama_ubatch> & ubatches);
|
||||
|
||||
// return the cell position where we can insert the ubatch
|
||||
// return -1 on failure to find a contiguous slot of kv cells
|
||||
int32_t find_slot(const llama_ubatch & ubatch) const;
|
||||
|
||||
// emplace the ubatch context into slot: [head_cur, head_cur + ubatch.n_tokens)
|
||||
void apply_ubatch(uint32_t head_cur, const llama_ubatch & ubatch);
|
||||
|
||||
//
|
||||
// set_input API
|
||||
//
|
||||
|
||||
void set_input_kq_mask (ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const;
|
||||
void set_input_k_shift (ggml_tensor * dst) const;
|
||||
void set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const;
|
||||
|
||||
private:
|
||||
const llama_model & model;
|
||||
const llama_hparams & hparams;
|
||||
|
||||
struct kv_layer {
|
||||
// layer index in the model
|
||||
// note: can be different from the layer index in the KV cache
|
||||
uint32_t il;
|
||||
|
||||
ggml_tensor * k;
|
||||
ggml_tensor * v;
|
||||
};
|
||||
|
||||
bool do_defrag = false;
|
||||
bool v_trans = true; // the value tensor is transposed
|
||||
|
||||
// the current index from where we start searching for a free slot in the ring buffer of KV cells (see find_slot())
|
||||
// note: this is not part of the KV state and it's only used to speed-up the find_slot() method
|
||||
uint32_t head = 0;
|
||||
|
||||
const uint32_t n_seq_max = 1;
|
||||
|
||||
// required padding
|
||||
const uint32_t n_pad = 1;
|
||||
|
||||
// SWA
|
||||
const uint32_t n_swa = 0;
|
||||
|
||||
const llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
|
||||
|
||||
std::vector<ggml_context_ptr> ctxs;
|
||||
std::vector<ggml_backend_buffer_ptr> bufs;
|
||||
|
||||
llama_kv_cells_unified cells;
|
||||
|
||||
std::vector<kv_layer> layers;
|
||||
|
||||
// model layer id -> KV cache layer id
|
||||
std::unordered_map<int32_t, int32_t> map_layer_ids;
|
||||
|
||||
// defrag
|
||||
struct {
|
||||
std::vector<uint32_t> ids;
|
||||
} defrag_info;
|
||||
|
||||
// return true if cells have been moved
|
||||
bool defrag_prepare(int32_t n_max_nodes);
|
||||
|
||||
size_t total_size() const;
|
||||
|
||||
size_t size_k_bytes() const;
|
||||
size_t size_v_bytes() const;
|
||||
|
||||
bool is_masked_swa(llama_pos p0, llama_pos p1) const;
|
||||
|
||||
ggml_tensor * build_rope_shift(
|
||||
const llama_cparams & cparams,
|
||||
ggml_context * ctx,
|
||||
ggml_tensor * cur,
|
||||
ggml_tensor * shift,
|
||||
ggml_tensor * factors,
|
||||
float freq_base,
|
||||
float freq_scale) const;
|
||||
|
||||
llm_graph_result_ptr build_graph_shift(
|
||||
const llama_cparams & cparams,
|
||||
ggml_context * ctx,
|
||||
ggml_cgraph * gf) const;
|
||||
|
||||
llm_graph_result_ptr build_graph_defrag(
|
||||
const llama_cparams & cparams,
|
||||
ggml_context * ctx,
|
||||
ggml_cgraph * gf) const;
|
||||
|
||||
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;
|
||||
void state_write_data(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges) const;
|
||||
|
||||
bool state_read_meta(llama_io_read_i & io, uint32_t cell_count, llama_seq_id dest_seq_id = -1);
|
||||
bool state_read_data(llama_io_read_i & io, uint32_t cell_count);
|
||||
};
|
||||
|
||||
class llama_kv_cache_unified_state : public llama_memory_state_i {
|
||||
public:
|
||||
// used for errors
|
||||
llama_kv_cache_unified_state(llama_memory_status status);
|
||||
|
||||
// used to create a full-cache state
|
||||
llama_kv_cache_unified_state(
|
||||
llama_memory_status status,
|
||||
llama_kv_cache_unified * kv);
|
||||
|
||||
// used to create a state from a batch
|
||||
llama_kv_cache_unified_state(
|
||||
llama_memory_status status,
|
||||
llama_kv_cache_unified * kv,
|
||||
llama_sbatch sbatch,
|
||||
std::vector<uint32_t> heads,
|
||||
std::vector<llama_ubatch> ubatches);
|
||||
|
||||
virtual ~llama_kv_cache_unified_state();
|
||||
|
||||
//
|
||||
// llama_memory_state_i
|
||||
//
|
||||
|
||||
bool next() override;
|
||||
bool apply() override;
|
||||
|
||||
std::vector<int64_t> & out_ids() override;
|
||||
|
||||
llama_memory_status get_status() const override;
|
||||
const llama_ubatch & get_ubatch() const override;
|
||||
|
||||
//
|
||||
// llama_kv_cache_unified_state specific API
|
||||
//
|
||||
|
||||
uint32_t get_n_kv() const;
|
||||
|
||||
// get views of the current state of the cache
|
||||
ggml_tensor * get_k(ggml_context * ctx, int32_t il) const;
|
||||
ggml_tensor * get_v(ggml_context * ctx, int32_t il) const;
|
||||
|
||||
// store k_cur and v_cur in the cache based on the provided head location
|
||||
ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il) const;
|
||||
ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il) const;
|
||||
|
||||
void set_input_k_shift(ggml_tensor * dst) const;
|
||||
|
||||
void set_input_kq_mask (ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const;
|
||||
void set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const;
|
||||
|
||||
private:
|
||||
const llama_memory_status status;
|
||||
|
||||
llama_kv_cache_unified * kv;
|
||||
|
||||
llama_sbatch sbatch;
|
||||
|
||||
// the index of the next ubatch to process
|
||||
size_t i_next = 0;
|
||||
|
||||
std::vector<uint32_t> heads;
|
||||
std::vector<llama_ubatch> ubatches;
|
||||
|
||||
//
|
||||
// data needed for building the compute graph for the current ubatch:
|
||||
//
|
||||
|
||||
// a heuristic, to avoid attending the full cache if it is not yet utilized
|
||||
// as the cache gets filled, the benefit from this heuristic disappears
|
||||
int32_t n_kv;
|
||||
|
||||
// the beginning of the current slot in which the ubatch will be inserted
|
||||
int32_t head;
|
||||
};
|
||||
|
||||
//
|
||||
// llama_kv_cache_unified_iswa
|
||||
//
|
||||
|
||||
// utilizes two instances of llama_kv_cache_unified
|
||||
// the first instance is for the non-SWA layers of the model and the second instance is for the SWA layers
|
||||
|
||||
class llama_kv_cache_unified_iswa : public llama_kv_cache {
|
||||
public:
|
||||
llama_kv_cache_unified_iswa(
|
||||
const llama_model & model,
|
||||
ggml_type type_k,
|
||||
ggml_type type_v,
|
||||
bool v_trans,
|
||||
bool offload,
|
||||
bool swa_full,
|
||||
uint32_t kv_size,
|
||||
uint32_t n_seq_max,
|
||||
uint32_t n_ubatch,
|
||||
uint32_t n_pad);
|
||||
|
||||
~llama_kv_cache_unified_iswa() = default;
|
||||
|
||||
//
|
||||
// llama_memory_i
|
||||
//
|
||||
|
||||
void clear() override;
|
||||
|
||||
bool seq_rm (llama_seq_id seq_id, llama_pos p0, llama_pos p1) override;
|
||||
void seq_cp (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override;
|
||||
void seq_keep(llama_seq_id seq_id) override;
|
||||
void seq_add (llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) override;
|
||||
void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) override;
|
||||
|
||||
llama_pos seq_pos_min(llama_seq_id seq_id) const override;
|
||||
llama_pos seq_pos_max(llama_seq_id seq_id) const override;
|
||||
|
||||
//
|
||||
// llama_kv_cache
|
||||
//
|
||||
|
||||
llama_memory_state_ptr init_batch(
|
||||
const llama_batch & batch,
|
||||
uint32_t n_ubatch,
|
||||
bool embd_pooled,
|
||||
bool logits_all) override;
|
||||
|
||||
llama_memory_state_ptr init_full() override;
|
||||
|
||||
bool update(llama_context & lctx) override;
|
||||
|
||||
void defrag_sched(float thold) override;
|
||||
|
||||
bool get_can_shift() const override;
|
||||
|
||||
// state write/load
|
||||
|
||||
void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const override;
|
||||
void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1) override;
|
||||
|
||||
//
|
||||
// llama_kv_cache_unified_iswa specific API
|
||||
//
|
||||
|
||||
llama_kv_cache_unified * get_base() const;
|
||||
llama_kv_cache_unified * get_swa () const;
|
||||
|
||||
private:
|
||||
const llama_hparams & hparams;
|
||||
|
||||
std::unique_ptr<llama_kv_cache_unified> kv_base;
|
||||
std::unique_ptr<llama_kv_cache_unified> kv_swa;
|
||||
};
|
||||
|
||||
class llama_kv_cache_unified_iswa_state : public llama_memory_state_i {
|
||||
public:
|
||||
// used for errors
|
||||
llama_kv_cache_unified_iswa_state(llama_memory_status status);
|
||||
|
||||
// used to create a full-cache state
|
||||
llama_kv_cache_unified_iswa_state(
|
||||
llama_memory_status status,
|
||||
llama_kv_cache_unified_iswa * kv);
|
||||
|
||||
// used to create a state from a batch
|
||||
llama_kv_cache_unified_iswa_state(
|
||||
llama_memory_status status,
|
||||
llama_kv_cache_unified_iswa * kv,
|
||||
llama_sbatch sbatch,
|
||||
std::vector<uint32_t> heads_base,
|
||||
std::vector<uint32_t> heads_swa,
|
||||
std::vector<llama_ubatch> ubatches);
|
||||
|
||||
virtual ~llama_kv_cache_unified_iswa_state();
|
||||
|
||||
//
|
||||
// llama_memory_state_i
|
||||
//
|
||||
|
||||
bool next() override;
|
||||
bool apply() override;
|
||||
|
||||
std::vector<int64_t> & out_ids() override;
|
||||
|
||||
llama_memory_status get_status() const override;
|
||||
const llama_ubatch & get_ubatch() const override;
|
||||
|
||||
//
|
||||
// llama_kv_cache_unified_iswa_state specific API
|
||||
//
|
||||
|
||||
const llama_kv_cache_unified_state * get_base() const;
|
||||
const llama_kv_cache_unified_state * get_swa() const;
|
||||
|
||||
private:
|
||||
const llama_memory_status status;
|
||||
|
||||
//llama_kv_cache_unified_iswa * kv;
|
||||
|
||||
llama_sbatch sbatch;
|
||||
|
||||
// the index of the next ubatch to process
|
||||
size_t i_next = 0;
|
||||
|
||||
std::vector<llama_ubatch> ubatches;
|
||||
|
||||
std::unique_ptr<llama_kv_cache_unified_state> state_base;
|
||||
std::unique_ptr<llama_kv_cache_unified_state> state_swa;
|
||||
};
|
||||
|
||||
//
|
||||
// llama_kv_cache_recurrent
|
||||
//
|
||||
|
||||
// TODO: extract the KV cache state used for graph computation into llama_kv_cache_recurrent_state_i
|
||||
// see the implementation of llama_kv_cache_unified_state_i for an example how to do it
|
||||
class llama_kv_cache_recurrent : public llama_kv_cache {
|
||||
public:
|
||||
llama_kv_cache_recurrent(
|
||||
const llama_model & model,
|
||||
ggml_type type_k,
|
||||
ggml_type type_v,
|
||||
bool offload,
|
||||
uint32_t kv_size,
|
||||
uint32_t n_seq_max);
|
||||
|
||||
~llama_kv_cache_recurrent() = default;
|
||||
|
||||
//
|
||||
// llama_memory_i
|
||||
//
|
||||
|
||||
void clear() override;
|
||||
|
||||
bool seq_rm (llama_seq_id seq_id, llama_pos p0, llama_pos p1) override;
|
||||
void seq_cp (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override;
|
||||
void seq_keep(llama_seq_id seq_id) override;
|
||||
void seq_add (llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) override;
|
||||
void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) override;
|
||||
|
||||
llama_pos seq_pos_min(llama_seq_id seq_id) const override;
|
||||
llama_pos seq_pos_max(llama_seq_id seq_id) const override;
|
||||
|
||||
//
|
||||
// llama_kv_cache
|
||||
//
|
||||
|
||||
llama_memory_state_ptr init_batch(
|
||||
const llama_batch & batch,
|
||||
uint32_t n_ubatch,
|
||||
bool embd_pooled,
|
||||
bool logits_all) override;
|
||||
|
||||
llama_memory_state_ptr init_full() override;
|
||||
|
||||
bool update(llama_context & lctx) override;
|
||||
|
||||
void defrag_sched(float thold) override;
|
||||
|
||||
bool prepare(const std::vector<llama_ubatch> & ubatches);
|
||||
|
||||
// find a contiguous slot of kv cells and emplace the ubatch there
|
||||
bool find_slot(const llama_ubatch & ubatch);
|
||||
|
||||
bool get_can_shift() const override;
|
||||
|
||||
// TODO: temporary methods - they are not really const as they do const_cast<>, fix this
|
||||
int32_t s_copy(int i) const;
|
||||
float s_mask(int i) const;
|
||||
|
||||
// state write/load
|
||||
|
||||
void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const override;
|
||||
void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1) override;
|
||||
|
||||
uint32_t head = 0; // the location where the batch will be placed in the cache (see find_slot())
|
||||
uint32_t size = 0; // total number of cells, shared across all sequences
|
||||
uint32_t used = 0; // used cells (i.e. at least one seq_id)
|
||||
|
||||
// computed before each graph build
|
||||
uint32_t n = 0;
|
||||
|
||||
// TODO: optimize for recurrent state needs
|
||||
struct kv_cell {
|
||||
llama_pos pos = -1;
|
||||
int32_t src = -1; // used to copy states
|
||||
int32_t tail = -1;
|
||||
|
||||
std::set<llama_seq_id> seq_id;
|
||||
|
||||
bool has_seq_id(const llama_seq_id & id) const {
|
||||
return seq_id.find(id) != seq_id.end();
|
||||
}
|
||||
|
||||
bool is_empty() const {
|
||||
return seq_id.empty();
|
||||
}
|
||||
|
||||
bool is_same_seq(const kv_cell & other) const {
|
||||
return seq_id == other.seq_id;
|
||||
}
|
||||
};
|
||||
|
||||
std::vector<kv_cell> cells;
|
||||
|
||||
std::vector<ggml_tensor *> k_l; // per layer
|
||||
std::vector<ggml_tensor *> v_l;
|
||||
|
||||
private:
|
||||
//const llama_model & model;
|
||||
const llama_hparams & hparams;
|
||||
|
||||
const uint32_t n_seq_max = 1;
|
||||
|
||||
std::vector<ggml_context_ptr> ctxs;
|
||||
std::vector<ggml_backend_buffer_ptr> bufs;
|
||||
|
||||
size_t total_size() const;
|
||||
|
||||
size_t size_k_bytes() const;
|
||||
size_t size_v_bytes() const;
|
||||
|
||||
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;
|
||||
void state_write_data(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges) const;
|
||||
|
||||
bool state_read_meta(llama_io_read_i & io, uint32_t cell_count, llama_seq_id dest_seq_id = -1);
|
||||
bool state_read_data(llama_io_read_i & io, uint32_t cell_count);
|
||||
};
|
||||
|
||||
class llama_kv_cache_recurrent_state : public llama_memory_state_i {
|
||||
public:
|
||||
// used for errors
|
||||
llama_kv_cache_recurrent_state(llama_memory_status status);
|
||||
|
||||
// used to create a full-cache state
|
||||
llama_kv_cache_recurrent_state(
|
||||
llama_memory_status status,
|
||||
llama_kv_cache_recurrent * kv);
|
||||
|
||||
// used to create a state from a batch
|
||||
llama_kv_cache_recurrent_state(
|
||||
llama_memory_status status,
|
||||
llama_kv_cache_recurrent * kv,
|
||||
llama_sbatch sbatch,
|
||||
std::vector<llama_ubatch> ubatches);
|
||||
|
||||
virtual ~llama_kv_cache_recurrent_state();
|
||||
|
||||
//
|
||||
// llama_memory_state_i
|
||||
//
|
||||
|
||||
bool next() override;
|
||||
bool apply() override;
|
||||
|
||||
std::vector<int64_t> & out_ids() override;
|
||||
|
||||
llama_memory_status get_status() const override;
|
||||
const llama_ubatch & get_ubatch() const override;
|
||||
|
||||
//
|
||||
// llama_kv_cache_recurrent_state specific API
|
||||
//
|
||||
|
||||
uint32_t get_n_kv() const;
|
||||
uint32_t get_head() const;
|
||||
uint32_t get_size() const;
|
||||
|
||||
ggml_tensor * get_k_l(int32_t il) const;
|
||||
ggml_tensor * get_v_l(int32_t il) const;
|
||||
|
||||
int32_t s_copy(int i) const;
|
||||
float s_mask(int i) const;
|
||||
|
||||
private:
|
||||
const llama_memory_status status;
|
||||
|
||||
llama_kv_cache_recurrent * kv;
|
||||
|
||||
llama_sbatch sbatch;
|
||||
|
||||
size_t i_next = 0;
|
||||
|
||||
std::vector<llama_ubatch> ubatches;
|
||||
|
||||
//
|
||||
// data needed for building the compute graph for the current ubatch:
|
||||
// TODO: extract all the state like `head` and `n` here
|
||||
//
|
||||
|
||||
const bool is_full = false;
|
||||
};
|
||||
|
|
|
|||
|
|
@ -5,7 +5,10 @@
|
|||
#include "llama-batch.h"
|
||||
#include "llama-cparams.h"
|
||||
#include "llama-model-loader.h"
|
||||
#include "llama-kv-cache.h"
|
||||
|
||||
#include "llama-kv-cache-unified.h"
|
||||
#include "llama-kv-cache-unified-iswa.h"
|
||||
#include "llama-kv-cache-recurrent.h"
|
||||
|
||||
#include "ggml-cpp.h"
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue