mirror of
https://github.com/ggml-org/llama.cpp.git
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kv-cache : refactor + add llama_memory_state_i (#13746)
* kv-cache : simplify the "struct llama_kv_cache" interface ggml-ci * kv-cache : revert the (n_swa + n_ubatch) change (for next PR) ggml-ci * kv-cache : some comments ggml-ci * context : fix graph reserve for multiple sequences ggml-ci * kv-cache : fix typo [no ci] * kv-cache : fix find_slot() logic for free slots ggml-ci * llama : add TODO for deprecating the defrag API in the future * kv-cache : improve find_slot() using min/max seq pos info ggml-ci * llama : handle aborts and compute errors ggml-ci * memory : extract state into llama_memory_state ggml-ci * kv-cache : add comments ggml-ci * server : update batching logic to reset n_batch on successful decode * server : upon full re-processing, remove the sequence from the cache * kv-cache : add TODO for doing split_equal when split_simple fails ggml-ci
This commit is contained in:
Georgi Gerganov
@@ -2,6 +2,7 @@
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#include "llama.h"
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#include "llama-io.h"
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#include "llama-batch.h"
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#include "llama-graph.h"
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#include "llama-memory.h"
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#include "llama-kv-cells.h"
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@@ -14,48 +15,35 @@
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struct llama_cparams;
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struct llama_hparams;
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struct llama_ubatch;
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struct llama_sbatch;
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struct llama_model;
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struct llama_context;
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struct llama_kv_cache : public llama_memory_i {
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virtual ~llama_kv_cache() = default;
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// call if batch processing fails - restores the cache state
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virtual void restore() = 0;
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// split the input batch into a set of ubatches and verify that they can fit into the cache
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// return a state object containing the ubatches and KV cache state required to process them
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// check the llama_memory_state_i::get_status() for the result
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virtual 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) = 0;
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// call after successful batch processing - clears any pending state
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virtual void commit() = 0;
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// simulate full cache, used for allocating worst-case compute buffers
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virtual llama_memory_state_ptr init_full() = 0;
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// process any pending defrag/shift/etc. operations
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// optionally call once before processing a new batch
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// return true if any operations were performed
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virtual bool update(llama_context & lctx) = 0;
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// schedule a defrag if the fragmentation threshold is exceeded. otherwise, do nothing
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// TODO: change to
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// llama_memory_state_ptr init_defrag(float thold) = 0;
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//
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virtual void defrag_sched(float thold) = 0;
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// simulate full cache, used for allocating worst-case compute buffers
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// TODO: remove
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virtual void set_full() = 0;
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//
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// batch processing
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//
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// =============================================================================================================
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// TODO: refactor and simplify this [TAG: KV_API]
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virtual llama_sbatch sbatch_init(const llama_batch & batch, bool logits_all) = 0;
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// different KV caches require different batch splitting strategies
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virtual llama_ubatch ubatch_next(llama_sbatch & sbatch, uint32_t n_ubatch, bool embd_pooled) const = 0;
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// find an empty slot of size "n_tokens" in the cache
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virtual bool find_slot(const llama_ubatch & batch) = 0;
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// =============================================================================================================
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// getters
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virtual bool get_can_shift() const = 0;
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@@ -69,25 +57,6 @@ struct llama_kv_cache : public llama_memory_i {
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virtual void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1) = 0;
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};
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//
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// llama_kv_cache_guard
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//
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struct llama_kv_cache_guard {
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llama_kv_cache_guard(llama_kv_cache * kv) : kv(kv) {}
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~llama_kv_cache_guard() {
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kv->restore();
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}
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void commit() {
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kv->commit();
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}
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private:
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llama_kv_cache * kv;
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};
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//
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// llama_kv_cache_unified
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//
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@@ -133,23 +102,18 @@ public:
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// llama_kv_cache
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//
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void restore() override;
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void commit() override;
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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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bool update(llama_context & ctx) 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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void set_full() override;
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llama_sbatch sbatch_init(const llama_batch & batch, bool logits_all) override;
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llama_ubatch ubatch_next(llama_sbatch & sbatch, uint32_t n_ubatch, bool embd_pooled) const override;
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// updates the cache head
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// Note: On success, it's important that cache.head points
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// to the first cell of the slot.
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bool find_slot(const llama_ubatch & batch) override;
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bool get_can_shift() const override;
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// state write/load
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@@ -161,18 +125,40 @@ public:
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// llama_kv_cache_unified specific API
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//
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uint32_t get_n() const;
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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) const;
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ggml_tensor * get_v(ggml_context * ctx, int32_t il) const;
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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 current 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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// 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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void prune_swa(llama_seq_id seq_id, llama_pos pmin, llama_pos pmax);
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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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@@ -194,11 +180,9 @@ private:
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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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uint32_t head = 0; // the location where the batch will be placed in the cache (see find_slot())
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// computed before each graph build
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// TODO: cells should start to maintain this value dynamically based on the edits
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uint32_t n = 0;
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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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@@ -220,24 +204,6 @@ private:
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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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// recovery information used to restore the KV cells to their original state in case of a failure
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// TODO: do not store as a state in the llama_kv_cache object, instead return upon batch preparation
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// to achieve that, first need to refactor the llama_kv_cache interface [TAG: KV_API]
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struct {
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void clear() {
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states.clear();
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}
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struct state {
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uint32_t i;
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llama_kv_cells_unified cells;
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};
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// stack with the partial states before each ubatch
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std::vector<state> states;
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} recovery;
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// defrag
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struct {
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std::vector<uint32_t> ids;
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@@ -279,13 +245,88 @@ private:
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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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//
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// llama_kv_cache_unified_iswa
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//
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// utilizes two instances of llama_kv_cache_unified
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// the first instance is for the non-SWA layers of the model and the second instance is for the SWA layers
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// upon successful commit, the SWA cache removes old tokens outside the n_swa window
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class llama_kv_cache_unified_iswa : public llama_kv_cache {
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public:
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@@ -322,20 +363,18 @@ public:
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// llama_kv_cache
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//
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void restore() override;
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void commit() override;
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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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bool update(llama_context & ctx) 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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void set_full() override;
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llama_sbatch sbatch_init(const llama_batch & batch, bool logits_all) override;
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llama_ubatch ubatch_next(llama_sbatch & sbatch, uint32_t n_ubatch, bool embd_pooled) const override;
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bool find_slot(const llama_ubatch & batch) override;
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bool get_can_shift() const override;
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// state write/load
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@@ -347,58 +386,80 @@ public:
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// llama_kv_cache_unified_iswa specific API
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//
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llama_kv_cache_unified * get_kv_base() const;
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llama_kv_cache_unified * get_kv_swa () const;
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llama_kv_cache_unified * get_base() const;
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llama_kv_cache_unified * get_swa () const;
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private:
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const llama_hparams & hparams;
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bool do_prune = true;
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struct {
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struct entry {
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llama_pos pmin;
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llama_pos pmax;
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};
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void clear() {
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pos.clear();
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}
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// used to perform SWA pruning of old tokens
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std::unordered_map<llama_seq_id, entry> pos;
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} pending;
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std::unique_ptr<llama_kv_cache_unified> kv_base;
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std::unique_ptr<llama_kv_cache_unified> kv_swa;
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};
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class llama_kv_cache_unified_iswa_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_iswa_state(llama_memory_status status);
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// used to create a full-cache state
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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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// used to create a state from a batch
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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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virtual ~llama_kv_cache_unified_iswa_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_iswa_state specific API
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//
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const llama_kv_cache_unified_state * get_base() const;
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const llama_kv_cache_unified_state * get_swa() const;
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private:
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const 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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// 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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std::unique_ptr<llama_kv_cache_unified_state> state_base;
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std::unique_ptr<llama_kv_cache_unified_state> state_swa;
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};
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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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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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llama_kv_cache_recurrent(
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const llama_model & model,
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ggml_type type_k,
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@@ -428,19 +489,22 @@ public:
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// llama_kv_cache
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//
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void restore() override;
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void commit() override;
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llama_memory_state_ptr init_batch(
|
||||
const llama_batch & batch,
|
||||
uint32_t n_ubatch,
|
||||
bool embd_pooled,
|
||||
bool logits_all) override;
|
||||
|
||||
bool update(llama_context & ctx) override;
|
||||
llama_memory_state_ptr init_full() override;
|
||||
|
||||
bool update(llama_context & lctx) override;
|
||||
|
||||
void defrag_sched(float thold) override;
|
||||
|
||||
void set_full() override;
|
||||
bool prepare(const std::vector<llama_ubatch> & ubatches);
|
||||
|
||||
llama_sbatch sbatch_init(const llama_batch & batch, bool logits_all) override;
|
||||
llama_ubatch ubatch_next(llama_sbatch & sbatch, uint32_t n_ubatch, bool embd_pooled) const override;
|
||||
|
||||
bool find_slot(const llama_ubatch & batch) override;
|
||||
// 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;
|
||||
|
||||
@@ -460,6 +524,27 @@ public:
|
||||
// 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
|
||||
@@ -469,26 +554,11 @@ private:
|
||||
//const llama_model & model;
|
||||
const llama_hparams & hparams;
|
||||
|
||||
// commit/restore cache
|
||||
// TODO: rework for recurrent cache
|
||||
struct slot_range {
|
||||
uint32_t c0 = 0; // note: these are cell indices, not sequence positions
|
||||
uint32_t c1 = 0;
|
||||
};
|
||||
|
||||
// pending cell updates that are not yet committed
|
||||
struct {
|
||||
std::vector<slot_range> ranges;
|
||||
} pending;
|
||||
|
||||
const uint32_t n_seq_max = 1;
|
||||
|
||||
std::vector<ggml_context_ptr> ctxs;
|
||||
std::vector<ggml_backend_buffer_ptr> bufs;
|
||||
|
||||
// find how many cells are currently in use
|
||||
uint32_t cell_max() const;
|
||||
|
||||
size_t total_size() const;
|
||||
|
||||
size_t size_k_bytes() const;
|
||||
@@ -500,3 +570,67 @@ private:
|
||||
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;
|
||||
};
|
||||
|
||||
Reference in New Issue
Block a user