llama : custom attention mask + parallel decoding + no context swaps (#3228)
* tests : verify that RoPE is "additive" * llama : replace ggml_diag_mask_inf with ggml_add (custom -inf mask) * ggml : ggml_rope now takes a vector with positions instead of n_past * metal : add rope_f16 kernel + optimize cpy kernels * llama : unified KV cache + batch inference API * llama : add new llama_decode() API that works with llama_batch * llama : add cell_max heuristic for more efficient kv_cache * llama : extend llama_kv_cache API * llama : more robust cell_max heuristic + wip shift * metal : disable concurrency optimization * llama : add llama_kv_cache_shift_seq + no more context swaps * llama : apply K-cache roping for Falcon and Baichuan * speculative : fix KV cache management * parallel : example for serving multiple users in parallel * parallel : disable hot-plug to avoid cache fragmentation * fixes : speculative KV cache + llama worst-case graph * llama : extend batch API to select which logits to output * llama : fix worst case graph build * ggml-cuda : update rope implementation for parallel decoding (#3254) * ggml-cuda : update rope implementation for parallel decoding * better solution for p0 computation * fix rope * simpler rope implementation --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * make : add parallel to build + fix static functions in llama.cpp * simple : fix token counting * parallel : various improvements * llama : fix cell_max logic + rename functions * parallel : try smaller batches when the KV cache is fragmented * parallel : fix sequence termination criteria * llama : silence errors KV cache errors * parallel : remove new line from prompt * parallel : process system prompt once + configurable paramters + llama API * parallel : remove question with short answers * parallel : count cache misses * parallel : print misses on each request * parallel : minor * llama : fix n_kv to never become 0 * parallel : rename hot-plug to continuous-batching * llama : improve llama_batch API + simplify parallel example * simple : add parallel decoding support * simple : improve comments + free batch * ggml-cuda : add rope f16, restore performance with parallel decoding (#3272) * ggml-cuda : add rope f16, restore performance * offload KQ_mask with all models * fix rope shift --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * llama : disable MPI for now ggml-ci * train : make KQ_pos memory buffer permanent via dummy scale op * ggml : revert change to ggml_cpy, add ggml_cont_Nd instead (#3275) ggml-ci * parallel : fix bug (extra BOS) + smaller token_prev array * parallel : fix cases where the input prompts can overflow the batch * parallel : add disabled experimental batch chunking in powers of two * llama : llama.h formatting + comments * simple : add README.md * llama : fix kv cache heuristic when context is less than 32 * parallel : fix crash when `-n -1` * llama : simplify returns if/else branches * metal : use mm kernels for batch size > 2 * examples : utilize new llama_get_logits_ith() * examples : add example for batched decoding * examples : do not eval prompt 2 times (close #3348) * server : clear the KV cache beyond n_past before llama_decode * server : avoid context swaps by shifting the KV cache --------- Co-authored-by: slaren <slarengh@gmail.com>
This commit is contained in:
Georgi Gerganov
GitHub
parent
45855b3f1c
commit
ec893798b7
35 changed files with 2700 additions and 673 deletions
327
llama.h
327
llama.h
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@ -37,6 +37,8 @@
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#define LLAMA_DEFAULT_SEED 0xFFFFFFFF
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#define LLAMA_MAX_RNG_STATE (64*1024)
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#define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
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#define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
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@ -60,7 +62,9 @@ extern "C" {
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struct llama_model;
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struct llama_context;
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typedef int llama_token;
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typedef int32_t llama_pos;
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typedef int32_t llama_token;
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typedef int32_t llama_seq_id;
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enum llama_vocab_type {
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LLAMA_VOCAB_TYPE_SPM = 0, // SentencePiece
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@ -80,24 +84,24 @@ extern "C" {
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// model file types
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enum llama_ftype {
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LLAMA_FTYPE_ALL_F32 = 0,
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LLAMA_FTYPE_MOSTLY_F16 = 1, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
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// LLAMA_FTYPE_MOSTLY_Q4_2 = 5, // support has been removed
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// LLAMA_FTYPE_MOSTLY_Q4_3 = 6, // support has been removed
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LLAMA_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q2_K = 10,// except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q3_K_S = 11,// except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q3_K_M = 12,// except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q3_K_L = 13,// except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q4_K_S = 14,// except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q4_K_M = 15,// except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q5_K_S = 16,// except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q5_K_M = 17,// except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q6_K = 18,// except 1d tensors
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LLAMA_FTYPE_MOSTLY_F16 = 1, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
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// LLAMA_FTYPE_MOSTLY_Q4_2 = 5, // support has been removed
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// LLAMA_FTYPE_MOSTLY_Q4_3 = 6, // support has been removed
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LLAMA_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q2_K = 10, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q3_K_S = 11, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q3_K_M = 12, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q3_K_L = 13, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q4_K_S = 14, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q4_K_M = 15, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q5_K_S = 16, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q5_K_M = 17, // except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q6_K = 18, // except 1d tensors
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LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
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};
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@ -116,6 +120,35 @@ extern "C" {
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typedef void (*llama_progress_callback)(float progress, void *ctx);
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// Input data for llama_decode
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// A llama_batch object can contain input about one or many sequences
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// The provided arrays (i.e. token, embd, pos, etc.) must have size of n_tokens
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//
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// - token : the token ids of the input (used when embd is NULL)
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// - embd : token embeddings (i.e. float vector of size n_embd) (used when token is NULL)
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// - pos : the positions of the respective token in the sequence
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// - seq_id : the sequence to which the respective token belongs
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// - logits : if zero, the logits for the respective token will not be output
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//
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typedef struct llama_batch {
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int32_t n_tokens;
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llama_token * token;
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float * embd;
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llama_pos * pos;
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llama_seq_id * seq_id;
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int8_t * logits;
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// NOTE: helpers for smooth API transition - can be deprecated in the future
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// for future-proof code, use the above fields instead and ignore everything below
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//
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// pos[i] = all_pos_0 + i*all_pos_1
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//
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llama_pos all_pos_0; // used if pos == NULL
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llama_pos all_pos_1; // used if pos == NULL
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llama_seq_id all_seq_id; // used if seq_id == NULL
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} llama_batch;
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struct llama_context_params {
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uint32_t seed; // RNG seed, -1 for random
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int32_t n_ctx; // text context
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@ -202,6 +235,7 @@ extern "C" {
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int32_t n_eval;
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};
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// Helpers for getting default parameters
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LLAMA_API struct llama_context_params llama_context_default_params(void);
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LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params(void);
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@ -246,8 +280,10 @@ extern "C" {
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// Get a string describing the model type
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LLAMA_API int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size);
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// Returns the total size of all the tensors in the model in bytes
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LLAMA_API uint64_t llama_model_size(const struct llama_model * model);
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// Returns the total number of parameters in the model
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LLAMA_API uint64_t llama_model_n_params(const struct llama_model * model);
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@ -268,7 +304,7 @@ extern "C" {
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const char * path_lora,
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const char * path_base_model,
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int n_threads),
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"please use llama_model_apply_lora_from_file instead");
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"use llama_model_apply_lora_from_file instead");
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LLAMA_API int llama_model_apply_lora_from_file(
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const struct llama_model * model,
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@ -276,11 +312,53 @@ extern "C" {
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const char * path_base_model,
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int n_threads);
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// Returns the number of tokens in the KV cache
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LLAMA_API int llama_get_kv_cache_token_count(const struct llama_context * ctx);
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//
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// KV cache
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//
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// Sets the current rng seed.
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LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed);
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// Returns the number of tokens in the KV cache
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LLAMA_API DEPRECATED(int llama_get_kv_cache_token_count(const struct llama_context * ctx),
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"avoid using this, it will be removed in the future, instead - count the tokens in user code");
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// Remove all tokens data of cells in [c0, c1)
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LLAMA_API void llama_kv_cache_tokens_rm(
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struct llama_context * ctx,
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int32_t c0,
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int32_t c1);
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// Removes all tokens that belong to the specified sequence and have positions in [p0, p1)
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LLAMA_API void llama_kv_cache_seq_rm(
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struct llama_context * ctx,
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llama_seq_id seq_id,
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llama_pos p0,
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llama_pos p1);
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// Copy all tokens that belong to the specified sequence to another sequence
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// Note that this does not allocate extra KV cache memory - it simply assigns the tokens to the new sequence
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LLAMA_API void llama_kv_cache_seq_cp(
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struct llama_context * ctx,
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llama_seq_id seq_id_src,
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llama_seq_id seq_id_dst,
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llama_pos p0,
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llama_pos p1);
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// Removes all tokens that do not belong to the specified sequence
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LLAMA_API void llama_kv_cache_seq_keep(
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struct llama_context * ctx,
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llama_seq_id seq_id);
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// Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1)
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// If the KV cache is RoPEd, the KV data is updated accordingly
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LLAMA_API void llama_kv_cache_seq_shift(
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struct llama_context * ctx,
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llama_seq_id seq_id,
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llama_pos p0,
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llama_pos p1,
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llama_pos delta);
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//
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// State / sessions
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//
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// Returns the maximum size in bytes of the state (rng, logits, embedding
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// and kv_cache) - will often be smaller after compacting tokens
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// Copies the state to the specified destination address.
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// Destination needs to have allocated enough memory.
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// Returns the number of bytes copied
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LLAMA_API size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst);
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LLAMA_API size_t llama_copy_state_data(
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struct llama_context * ctx,
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uint8_t * dst);
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// Set the state reading from the specified address
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// Returns the number of bytes read
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LLAMA_API size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src);
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LLAMA_API size_t llama_set_state_data(
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struct llama_context * ctx,
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uint8_t * src);
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// Save/load session file
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LLAMA_API bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out);
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LLAMA_API bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count);
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LLAMA_API bool llama_load_session_file(
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struct llama_context * ctx,
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const char * path_session,
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llama_token * tokens_out,
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size_t n_token_capacity,
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size_t * n_token_count_out);
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// Run the llama inference to obtain the logits and probabilities for the next token.
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LLAMA_API bool llama_save_session_file(
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struct llama_context * ctx,
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const char * path_session,
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const llama_token * tokens,
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size_t n_token_count);
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//
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// Decoding
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//
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// Run the llama inference to obtain the logits and probabilities for the next token(s).
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// tokens + n_tokens is the provided batch of new tokens to process
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// n_past is the number of tokens to use from previous eval calls
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// Returns 0 on success
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LLAMA_API int llama_eval(
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// DEPRECATED: use llama_decode() instead
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LLAMA_API DEPRECATED(int llama_eval(
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struct llama_context * ctx,
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const llama_token * tokens,
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int n_tokens,
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llama_token * tokens,
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int32_t n_tokens,
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int n_past,
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int n_threads);
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int n_threads),
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"use llama_decode() instead");
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// Same as llama_eval, but use float matrix input directly.
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LLAMA_API int llama_eval_embd(
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// DEPRECATED: use llama_decode() instead
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LLAMA_API DEPRECATED(int llama_eval_embd(
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struct llama_context * ctx,
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const float * embd,
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int n_tokens,
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float * embd,
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int32_t n_tokens,
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int n_past,
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int n_threads);
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int n_threads),
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"use llama_decode() instead");
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// Export a static computation graph for context of 511 and batch size of 1
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// NOTE: since this functionality is mostly for debugging and demonstration purposes, we hardcode these
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// parameters here to keep things simple
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// IMPORTANT: do not use for anything else other than debugging and testing!
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LLAMA_API int llama_eval_export(struct llama_context * ctx, const char * fname);
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// Return batch for single sequence of tokens starting at pos_0
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//
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// NOTE: this is a helper function to facilitate transition to the new batch API - avoid using it
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//
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LLAMA_API struct llama_batch llama_batch_get_one(
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llama_token * tokens,
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int32_t n_tokens,
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llama_pos pos_0,
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llama_seq_id seq_id);
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// Allocates a batch of tokens on the heap
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// The batch has to be freed with llama_batch_free()
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// If embd != 0, llama_batch.embd will be allocated with size of n_tokens * embd * sizeof(float)
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// Otherwise, llama_batch.token will be allocated to store n_tokens llama_token
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// The rest of the llama_batch members are allocated with size n_tokens
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// All members are left uninitialized
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LLAMA_API struct llama_batch llama_batch_init(
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int32_t n_tokens,
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int32_t embd);
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// Frees a batch of tokens allocated with llama_batch_init()
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LLAMA_API void llama_batch_free(struct llama_batch batch);
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// Positive return values does not mean a fatal error, but rather a warning.
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// 0 - success
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// 1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)
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// < 0 - error
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LLAMA_API int llama_decode(
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struct llama_context * ctx,
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struct llama_batch batch,
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int n_threads);
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// Token logits obtained from the last call to llama_eval()
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// The logits for the last token are stored in the last row
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// Can be mutated in order to change the probabilities of the next token
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// Rows: n_tokens
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// Logits for which llama_batch.logits[i] == 0 are undefined
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// Rows: n_tokens provided with llama_batch
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// Cols: n_vocab
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LLAMA_API float * llama_get_logits(struct llama_context * ctx);
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// Logits for the ith token. Equivalent to:
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// llama_get_logits(ctx) + i*n_vocab
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LLAMA_API float * llama_get_logits_ith(struct llama_context * ctx, int32_t i);
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// Get the embeddings for the input
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// shape: [n_embd] (1-dimensional)
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LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
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// Sampling functions
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//
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// Sets the current rng seed.
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LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed);
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/// @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
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LLAMA_API void llama_sample_repetition_penalty(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float penalty);
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LLAMA_API void llama_sample_repetition_penalty(
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struct llama_context * ctx,
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llama_token_data_array * candidates,
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const llama_token * last_tokens,
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size_t last_tokens_size,
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float penalty);
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/// @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
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LLAMA_API void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float alpha_frequency, float alpha_presence);
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LLAMA_API void llama_sample_frequency_and_presence_penalties(
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struct llama_context * ctx,
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llama_token_data_array * candidates,
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const llama_token * last_tokens,
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size_t last_tokens_size,
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float alpha_frequency,
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float alpha_presence);
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/// @details Apply classifier-free guidance to the logits as described in academic paper "Stay on topic with Classifier-Free Guidance" https://arxiv.org/abs/2306.17806
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/// @param candidates A vector of `llama_token_data` containing the candidate tokens, the logits must be directly extracted from the original generation context without being sorted.
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@ -424,23 +568,54 @@ extern "C" {
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float scale);
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/// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
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LLAMA_API void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates);
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LLAMA_API void llama_sample_softmax(
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struct llama_context * ctx,
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llama_token_data_array * candidates);
|
||||
|
||||
/// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
|
||||
LLAMA_API void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int k, size_t min_keep);
|
||||
LLAMA_API void llama_sample_top_k(
|
||||
struct llama_context * ctx,
|
||||
llama_token_data_array * candidates,
|
||||
int k,
|
||||
size_t min_keep);
|
||||
|
||||
/// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
|
||||
LLAMA_API void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep);
|
||||
LLAMA_API void llama_sample_top_p(
|
||||
struct llama_context * ctx,
|
||||
llama_token_data_array * candidates,
|
||||
float p,
|
||||
size_t min_keep);
|
||||
|
||||
/// @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
|
||||
LLAMA_API void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep);
|
||||
LLAMA_API void llama_sample_tail_free(
|
||||
struct llama_context * ctx,
|
||||
llama_token_data_array * candidates,
|
||||
float z,
|
||||
size_t min_keep);
|
||||
|
||||
/// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
|
||||
LLAMA_API void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep);
|
||||
LLAMA_API void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates, float temp);
|
||||
LLAMA_API void llama_sample_typical(
|
||||
struct llama_context * ctx,
|
||||
llama_token_data_array * candidates,
|
||||
float p,
|
||||
size_t min_keep);
|
||||
|
||||
LLAMA_API void llama_sample_temp(
|
||||
struct llama_context * ctx,
|
||||
llama_token_data_array * candidates,
|
||||
float temp);
|
||||
|
||||
LLAMA_API DEPRECATED(void llama_sample_temperature(
|
||||
struct llama_context * ctx,
|
||||
llama_token_data_array * candidates,
|
||||
float temp),
|
||||
"use llama_sample_temp instead");
|
||||
|
||||
/// @details Apply constraints from grammar
|
||||
LLAMA_API void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * candidates, const struct llama_grammar * grammar);
|
||||
LLAMA_API void llama_sample_grammar(
|
||||
struct llama_context * ctx,
|
||||
llama_token_data_array * candidates,
|
||||
const struct llama_grammar * grammar);
|
||||
|
||||
/// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
|
||||
/// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
|
||||
|
|
@ -448,23 +623,41 @@ extern "C" {
|
|||
/// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
|
||||
/// @param m The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 100`, but you can experiment with different values to see how it affects the performance of the algorithm.
|
||||
/// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
|
||||
LLAMA_API llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int m, float * mu);
|
||||
LLAMA_API llama_token llama_sample_token_mirostat(
|
||||
struct llama_context * ctx,
|
||||
llama_token_data_array * candidates,
|
||||
float tau,
|
||||
float eta,
|
||||
int m,
|
||||
float * mu);
|
||||
|
||||
/// @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
|
||||
/// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
|
||||
/// @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
|
||||
/// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
|
||||
/// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
|
||||
LLAMA_API llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu);
|
||||
LLAMA_API llama_token llama_sample_token_mirostat_v2(
|
||||
struct llama_context * ctx,
|
||||
llama_token_data_array * candidates,
|
||||
float tau,
|
||||
float eta,
|
||||
float * mu);
|
||||
|
||||
/// @details Selects the token with the highest probability.
|
||||
LLAMA_API llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates);
|
||||
LLAMA_API llama_token llama_sample_token_greedy(
|
||||
struct llama_context * ctx,
|
||||
llama_token_data_array * candidates);
|
||||
|
||||
/// @details Randomly selects a token from the candidates based on their probabilities.
|
||||
LLAMA_API llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates);
|
||||
LLAMA_API llama_token llama_sample_token(
|
||||
struct llama_context * ctx,
|
||||
llama_token_data_array * candidates);
|
||||
|
||||
/// @details Accepts the sampled token into the grammar
|
||||
LLAMA_API void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar * grammar, llama_token token);
|
||||
LLAMA_API void llama_grammar_accept_token(
|
||||
struct llama_context * ctx,
|
||||
struct llama_grammar * grammar,
|
||||
llama_token token);
|
||||
|
||||
//
|
||||
// Beam search
|
||||
|
|
@ -472,9 +665,10 @@ extern "C" {
|
|||
|
||||
struct llama_beam_view {
|
||||
const llama_token * tokens;
|
||||
|
||||
size_t n_tokens;
|
||||
float p; // Cumulative beam probability (renormalized relative to all beams)
|
||||
bool eob; // Callback should set this to true when a beam is at end-of-beam.
|
||||
float p; // Cumulative beam probability (renormalized relative to all beams)
|
||||
bool eob; // Callback should set this to true when a beam is at end-of-beam.
|
||||
};
|
||||
|
||||
// Passed to beam_search_callback function.
|
||||
|
|
@ -483,9 +677,10 @@ extern "C" {
|
|||
// These pointers are valid only during the synchronous callback, so should not be saved.
|
||||
struct llama_beams_state {
|
||||
struct llama_beam_view * beam_views;
|
||||
|
||||
size_t n_beams; // Number of elements in beam_views[].
|
||||
size_t common_prefix_length; // Current max length of prefix tokens shared by all beams.
|
||||
bool last_call; // True iff this is the last callback invocation.
|
||||
bool last_call; // True iff this is the last callback invocation.
|
||||
};
|
||||
|
||||
// Type of pointer to the beam_search_callback function.
|
||||
|
|
@ -501,10 +696,18 @@ extern "C" {
|
|||
/// @param n_past Number of tokens already evaluated.
|
||||
/// @param n_predict Maximum number of tokens to predict. EOS may occur earlier.
|
||||
/// @param n_threads Number of threads as passed to llama_eval().
|
||||
LLAMA_API void llama_beam_search(struct llama_context * ctx, llama_beam_search_callback_fn_t callback, void * callback_data, size_t n_beams, int n_past, int n_predict, int n_threads);
|
||||
LLAMA_API void llama_beam_search(
|
||||
struct llama_context * ctx,
|
||||
llama_beam_search_callback_fn_t callback,
|
||||
void * callback_data,
|
||||
size_t n_beams,
|
||||
int n_past,
|
||||
int n_predict,
|
||||
int n_threads);
|
||||
|
||||
// Performance information
|
||||
LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx);
|
||||
|
||||
LLAMA_API void llama_print_timings(struct llama_context * ctx);
|
||||
LLAMA_API void llama_reset_timings(struct llama_context * ctx);
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue