mirror of
https://github.com/ggerganov/whisper.cpp.git
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talk-llama : sync llama.cpp
ggml-ci
This commit is contained in:
Georgi Gerganov
@@ -2,60 +2,34 @@
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#include "llama.h"
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#include "llama-io.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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#include "ggml-cpp.h"
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#include <set>
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#include <unordered_map>
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#include <vector>
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struct llama_cparams;
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struct llama_hparams;
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struct llama_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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@@ -68,435 +42,3 @@ struct llama_kv_cache : public llama_memory_i {
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virtual void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const = 0;
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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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class llama_kv_cache_unified : public llama_kv_cache {
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public:
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static uint32_t get_padding(const llama_cparams & cparams);
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// this callback is used to filter out layers that should not be included in the cache
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using layer_filter_cb = std::function<bool(int32_t il)>;
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llama_kv_cache_unified(
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const llama_model & model,
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layer_filter_cb && filter,
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ggml_type type_k,
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ggml_type type_v,
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bool v_trans,
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bool offload,
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uint32_t kv_size,
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uint32_t n_seq_max,
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uint32_t n_pad,
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uint32_t n_swa,
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llama_swa_type swa_type);
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~llama_kv_cache_unified() = default;
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//
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// llama_memory_i
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//
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void clear() override;
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bool seq_rm (llama_seq_id seq_id, llama_pos p0, llama_pos p1) override;
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void seq_cp (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override;
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void seq_keep(llama_seq_id seq_id) override;
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void seq_add (llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) override;
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void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) override;
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llama_pos seq_pos_min(llama_seq_id seq_id) const override;
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llama_pos seq_pos_max(llama_seq_id seq_id) const override;
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//
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// llama_kv_cache
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//
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void restore() override;
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void commit() override;
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bool update(llama_context & ctx) 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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void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const override;
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void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1) override;
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//
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// llama_kv_cache_unified specific API
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//
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uint32_t get_n() const;
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uint32_t get_size() 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 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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void prune_swa(llama_seq_id seq_id, llama_pos pmin, llama_pos pmax);
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void set_input_kq_mask (ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const;
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void set_input_k_shift (ggml_tensor * dst) const;
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void set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const;
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private:
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const llama_model & model;
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const llama_hparams & hparams;
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struct kv_layer {
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// layer index in the model
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// note: can be different from the layer index in the KV cache
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uint32_t il;
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ggml_tensor * k;
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ggml_tensor * v;
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};
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bool do_defrag = false;
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bool v_trans = true; // the value tensor is transposed
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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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const uint32_t n_seq_max = 1;
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// required padding
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const uint32_t n_pad = 1;
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// SWA
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const uint32_t n_swa = 0;
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const llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
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std::vector<ggml_context_ptr> ctxs;
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std::vector<ggml_backend_buffer_ptr> bufs;
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llama_kv_cells_unified cells;
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std::vector<kv_layer> layers;
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// model layer id -> KV cache layer id
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std::unordered_map<int32_t, int32_t> map_layer_ids;
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// 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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} defrag_info;
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// return true if cells have been moved
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bool defrag_prepare(int32_t n_max_nodes);
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size_t total_size() const;
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size_t size_k_bytes() const;
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size_t size_v_bytes() const;
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bool is_masked_swa(llama_pos p0, llama_pos p1) const;
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ggml_tensor * build_rope_shift(
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const llama_cparams & cparams,
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ggml_context * ctx,
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ggml_tensor * cur,
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ggml_tensor * shift,
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ggml_tensor * factors,
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float freq_base,
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float freq_scale) const;
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llm_graph_result_ptr build_graph_shift(
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const llama_cparams & cparams,
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ggml_context * ctx,
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ggml_cgraph * gf) const;
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llm_graph_result_ptr build_graph_defrag(
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const llama_cparams & cparams,
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ggml_context * ctx,
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ggml_cgraph * gf) const;
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void state_write_meta(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges, llama_seq_id seq_id = -1) const;
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void state_write_data(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges) const;
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bool state_read_meta(llama_io_read_i & io, uint32_t cell_count, llama_seq_id dest_seq_id = -1);
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bool state_read_data(llama_io_read_i & io, uint32_t cell_count);
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};
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//
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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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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_batch,
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uint32_t n_pad);
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~llama_kv_cache_unified_iswa() = 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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void restore() override;
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void commit() override;
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bool update(llama_context & ctx) 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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void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const override;
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void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1) override;
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//
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// llama_kv_cache_unified_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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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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//
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// llama_kv_cache_recurrent
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//
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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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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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void restore() override;
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void commit() override;
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bool update(llama_context & ctx) 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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// 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)
|
||||
|
||||
// computed before each graph build
|
||||
uint32_t n = 0;
|
||||
|
||||
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;
|
||||
|
||||
// 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;
|
||||
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);
|
||||
};
|
||||
|
||||
Reference in New Issue
Block a user