mirror of
https://github.com/ggerganov/whisper.cpp.git
synced 2025-06-18 14:58:09 +00:00
sync : ggml (backend v2, k-quants, CUDA opts, Metal opts, etc.) (#1422)
* sync : ggml (backend v2, k-quants, CUDA opts, Metal opts, etc.) * metal : allow env metal variable to override resource path (#1415) * Allow env variable to override resource path * Update ggml-metal.m --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * sync : restore common / main from `master` * sync : restore whisper from `master` * talk-llama : update to latest llama.cpp * ruby : fix build * ggml : fix 32-bit ARM build * ggml : fix MIN / MAX macro collisions + update ios bindings * ggml : fix ifdefs and MIN / MAX again * exampels : fix Obj-C and Swift examples * ggml : fix 32-bit ARM compatibility * ggml : one more attempt to fix 32-bit ARM compat * whisper : fix support for larger graphs --------- Co-authored-by: Chris Raethke <codesoda@users.noreply.github.com>
This commit is contained in:
Georgi Gerganov
GitHub
@ -37,10 +37,12 @@
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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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#define LLAMA_SESSION_VERSION 1
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#define LLAMA_SESSION_VERSION 2
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#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL)
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// Defined when llama.cpp is compiled with support for offloading model layers to GPU.
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@ -60,13 +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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enum llama_log_level {
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LLAMA_LOG_LEVEL_ERROR = 2,
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LLAMA_LOG_LEVEL_WARN = 3,
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LLAMA_LOG_LEVEL_INFO = 4
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};
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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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@ -86,28 +84,36 @@ 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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enum llama_rope_scaling_type {
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LLAMA_ROPE_SCALING_UNSPECIFIED = -1,
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LLAMA_ROPE_SCALING_NONE = 0,
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LLAMA_ROPE_SCALING_LINEAR = 1,
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LLAMA_ROPE_SCALING_YARN = 2,
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LLAMA_ROPE_SCALING_MAX_VALUE = LLAMA_ROPE_SCALING_YARN,
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};
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typedef struct llama_token_data {
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llama_token id; // token id
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float logit; // log-odds of the token
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@ -122,41 +128,75 @@ extern "C" {
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typedef void (*llama_progress_callback)(float progress, void *ctx);
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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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int32_t n_batch; // prompt processing batch size
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int32_t n_gpu_layers; // number of layers to store in VRAM
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int32_t main_gpu; // the GPU that is used for scratch and small tensors
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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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int32_t * n_seq_id;
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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_model_params {
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int32_t n_gpu_layers; // number of layers to store in VRAM
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int32_t main_gpu; // the GPU that is used for scratch and small tensors
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const float * tensor_split; // how to split layers across multiple GPUs (size: LLAMA_MAX_DEVICES)
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// ref: https://github.com/ggerganov/llama.cpp/pull/2054
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float rope_freq_base; // RoPE base frequency
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float rope_freq_scale; // RoPE frequency scaling factor
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// called with a progress value between 0 and 1, pass NULL to disable
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llama_progress_callback progress_callback;
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// context pointer passed to the progress callback
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void * progress_callback_user_data;
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// Keep the booleans together to avoid misalignment during copy-by-value.
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bool low_vram; // if true, reduce VRAM usage at the cost of performance
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bool mul_mat_q; // if true, use experimental mul_mat_q kernels
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bool f16_kv; // use fp16 for KV cache
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bool logits_all; // the llama_eval() call computes all logits, not just the last one
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bool vocab_only; // only load the vocabulary, no weights
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bool use_mmap; // use mmap if possible
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bool use_mlock; // force system to keep model in RAM
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bool embedding; // embedding mode only
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};
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// Signature for logging events
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// Note that text includes the new line character at the end for most events.
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// If your logging mechanism cannot handle that, check if the last character is '\n' and strip it
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// if it exists.
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// It might not exist for progress report where '.' is output repeatedly.
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typedef void (*llama_log_callback)(enum llama_log_level level, const char * text, void * user_data);
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struct llama_context_params {
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uint32_t seed; // RNG seed, -1 for random
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uint32_t n_ctx; // text context, 0 = from model
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uint32_t n_batch; // prompt processing maximum batch size
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uint32_t n_threads; // number of threads to use for generation
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uint32_t n_threads_batch; // number of threads to use for batch processing
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int8_t rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
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// ref: https://github.com/ggerganov/llama.cpp/pull/2054
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float rope_freq_base; // RoPE base frequency, 0 = from model
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float rope_freq_scale; // RoPE frequency scaling factor, 0 = from model
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float yarn_ext_factor; // YaRN extrapolation mix factor, NaN = from model
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float yarn_attn_factor; // YaRN magnitude scaling factor
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float yarn_beta_fast; // YaRN low correction dim
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float yarn_beta_slow; // YaRN high correction dim
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uint32_t yarn_orig_ctx; // YaRN original context size
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// Keep the booleans together to avoid misalignment during copy-by-value.
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bool mul_mat_q; // if true, use experimental mul_mat_q kernels (DEPRECATED - always true)
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bool f16_kv; // use fp16 for KV cache, fp32 otherwise
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bool logits_all; // the llama_eval() call computes all logits, not just the last one
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bool embedding; // embedding mode only
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};
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// model quantization parameters
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typedef struct llama_model_quantize_params {
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@ -165,6 +205,7 @@ extern "C" {
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bool allow_requantize; // allow quantizing non-f32/f16 tensors
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bool quantize_output_tensor; // quantize output.weight
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bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
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bool pure; // disable k-quant mixtures and quantize all tensors to the same type
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} llama_model_quantize_params;
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// grammar types
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@ -215,6 +256,8 @@ 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_model_params llama_model_default_params(void);
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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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@ -228,7 +271,7 @@ extern "C" {
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LLAMA_API struct llama_model * llama_load_model_from_file(
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const char * path_model,
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struct llama_context_params params);
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struct llama_model_params params);
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LLAMA_API void llama_free_model(struct llama_model * model);
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@ -245,25 +288,31 @@ extern "C" {
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LLAMA_API bool llama_mmap_supported (void);
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LLAMA_API bool llama_mlock_supported(void);
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LLAMA_API int llama_n_vocab (const struct llama_context * ctx);
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LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx);
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LLAMA_API int llama_n_ctx (const struct llama_context * ctx);
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LLAMA_API int llama_n_ctx_train(const struct llama_context * ctx);
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LLAMA_API int llama_n_embd (const struct llama_context * ctx);
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LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_context * ctx);
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LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model);
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LLAMA_API int llama_model_n_vocab (const struct llama_model * model);
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LLAMA_API int llama_model_n_ctx (const struct llama_model * model);
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LLAMA_API int llama_model_n_ctx_train(const struct llama_model * model);
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LLAMA_API int llama_model_n_embd (const struct llama_model * model);
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LLAMA_API int llama_n_vocab (const struct llama_model * model);
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LLAMA_API int llama_n_ctx_train(const struct llama_model * model);
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LLAMA_API int llama_n_embd (const struct llama_model * model);
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// Get the model's RoPE frequency scaling factor
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LLAMA_API float llama_rope_freq_scale_train(const struct llama_model * model);
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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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// Get a llama model tensor
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LLAMA_API struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name);
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// Returns 0 on success
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LLAMA_API int llama_model_quantize(
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const char * fname_inp,
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@ -279,21 +328,70 @@ extern "C" {
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LLAMA_API DEPRECATED(int llama_apply_lora_from_file(
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struct llama_context * ctx,
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const char * path_lora,
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float scale,
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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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const char * path_lora,
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const char * path_base_model,
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int n_threads);
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const char * path_lora,
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float scale,
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const char * path_base_model,
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int n_threads);
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//
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// KV cache
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//
|
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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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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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// 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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// Clear the KV cache
|
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LLAMA_API void llama_kv_cache_clear(
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struct llama_context * ctx);
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// Removes all tokens that belong to the specified sequence and have positions in [p0, p1)
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// seq_id < 0 : match any sequence
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// p0 < 0 : [0, p1]
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// p1 < 0 : [p0, inf)
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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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// p0 < 0 : [0, p1]
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// p1 < 0 : [p0, inf)
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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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|
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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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// p0 < 0 : [0, p1]
|
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// p1 < 0 : [p0, inf)
|
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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);
|
||||
|
||||
//
|
||||
// State / sessions
|
||||
//
|
||||
|
||||
// Returns the maximum size in bytes of the state (rng, logits, embedding
|
||||
// and kv_cache) - will often be smaller after compacting tokens
|
||||
@ -302,48 +400,104 @@ extern "C" {
|
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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);
|
||||
|
||||
// Set the state reading from the specified address
|
||||
// Returns the number of bytes read
|
||||
LLAMA_API size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src);
|
||||
LLAMA_API size_t llama_set_state_data(
|
||||
struct llama_context * ctx,
|
||||
uint8_t * src);
|
||||
|
||||
// Save/load session file
|
||||
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);
|
||||
LLAMA_API bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count);
|
||||
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);
|
||||
|
||||
// Run the llama inference to obtain the logits and probabilities for the next token.
|
||||
LLAMA_API bool llama_save_session_file(
|
||||
struct llama_context * ctx,
|
||||
const char * path_session,
|
||||
const llama_token * tokens,
|
||||
size_t n_token_count);
|
||||
|
||||
//
|
||||
// Decoding
|
||||
//
|
||||
|
||||
// Run the llama inference to obtain the logits and probabilities for the next token(s).
|
||||
// tokens + n_tokens is the provided batch of new tokens to process
|
||||
// n_past is the number of tokens to use from previous eval calls
|
||||
// Returns 0 on success
|
||||
LLAMA_API int llama_eval(
|
||||
// DEPRECATED: use llama_decode() instead
|
||||
LLAMA_API DEPRECATED(int llama_eval(
|
||||
struct llama_context * ctx,
|
||||
const llama_token * tokens,
|
||||
int n_tokens,
|
||||
int n_past,
|
||||
int n_threads);
|
||||
llama_token * tokens,
|
||||
int32_t n_tokens,
|
||||
int n_past),
|
||||
"use llama_decode() instead");
|
||||
|
||||
// Same as llama_eval, but use float matrix input directly.
|
||||
LLAMA_API int llama_eval_embd(
|
||||
// DEPRECATED: use llama_decode() instead
|
||||
LLAMA_API DEPRECATED(int llama_eval_embd(
|
||||
struct llama_context * ctx,
|
||||
const float * embd,
|
||||
int n_tokens,
|
||||
int n_past,
|
||||
int n_threads);
|
||||
float * embd,
|
||||
int32_t n_tokens,
|
||||
int n_past),
|
||||
"use llama_decode() instead");
|
||||
|
||||
// Export a static computation graph for context of 511 and batch size of 1
|
||||
// NOTE: since this functionality is mostly for debugging and demonstration purposes, we hardcode these
|
||||
// parameters here to keep things simple
|
||||
// IMPORTANT: do not use for anything else other than debugging and testing!
|
||||
LLAMA_API int llama_eval_export(struct llama_context * ctx, const char * fname);
|
||||
// Return batch for single sequence of tokens starting at pos_0
|
||||
//
|
||||
// NOTE: this is a helper function to facilitate transition to the new batch API - avoid using it
|
||||
//
|
||||
LLAMA_API struct llama_batch llama_batch_get_one(
|
||||
llama_token * tokens,
|
||||
int32_t n_tokens,
|
||||
llama_pos pos_0,
|
||||
llama_seq_id seq_id);
|
||||
|
||||
// Allocates a batch of tokens on the heap that can hold a maximum of n_tokens
|
||||
// Each token can be assigned up to n_seq_max sequence ids
|
||||
// The batch has to be freed with llama_batch_free()
|
||||
// If embd != 0, llama_batch.embd will be allocated with size of n_tokens * embd * sizeof(float)
|
||||
// Otherwise, llama_batch.token will be allocated to store n_tokens llama_token
|
||||
// The rest of the llama_batch members are allocated with size n_tokens
|
||||
// All members are left uninitialized
|
||||
LLAMA_API struct llama_batch llama_batch_init(
|
||||
int32_t n_tokens,
|
||||
int32_t embd,
|
||||
int32_t n_seq_max);
|
||||
|
||||
// Frees a batch of tokens allocated with llama_batch_init()
|
||||
LLAMA_API void llama_batch_free(struct llama_batch batch);
|
||||
|
||||
// Positive return values does not mean a fatal error, but rather a warning.
|
||||
// 0 - success
|
||||
// 1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)
|
||||
// < 0 - error
|
||||
LLAMA_API int llama_decode(
|
||||
struct llama_context * ctx,
|
||||
struct llama_batch batch);
|
||||
|
||||
// Set the number of threads used for decoding
|
||||
// n_threads is the number of threads used for generation (single token)
|
||||
// n_threads_batch is the number of threads used for prompt and batch processing (multiple tokens)
|
||||
LLAMA_API void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch);
|
||||
|
||||
// Token logits obtained from the last call to llama_eval()
|
||||
// The logits for the last token are stored in the last row
|
||||
// Can be mutated in order to change the probabilities of the next token
|
||||
// Rows: n_tokens
|
||||
// Logits for which llama_batch.logits[i] == 0 are undefined
|
||||
// Rows: n_tokens provided with llama_batch
|
||||
// Cols: n_vocab
|
||||
LLAMA_API float * llama_get_logits(struct llama_context * ctx);
|
||||
|
||||
// Logits for the ith token. Equivalent to:
|
||||
// llama_get_logits(ctx) + i*n_vocab
|
||||
LLAMA_API float * llama_get_logits_ith(struct llama_context * ctx, int32_t i);
|
||||
|
||||
// Get the embeddings for the input
|
||||
// shape: [n_embd] (1-dimensional)
|
||||
LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
|
||||
@ -352,50 +506,47 @@ extern "C" {
|
||||
// Vocab
|
||||
//
|
||||
|
||||
LLAMA_API const char * llama_token_get_text(const struct llama_context * ctx, llama_token token);
|
||||
LLAMA_API const char * llama_token_get_text(const struct llama_model * model, llama_token token);
|
||||
|
||||
LLAMA_API float llama_token_get_score(const struct llama_context * ctx, llama_token token);
|
||||
LLAMA_API float llama_token_get_score(const struct llama_model * model, llama_token token);
|
||||
|
||||
LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_context * ctx, llama_token token);
|
||||
LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token);
|
||||
|
||||
// Special tokens
|
||||
LLAMA_API llama_token llama_token_bos(const struct llama_context * ctx); // beginning-of-sentence
|
||||
LLAMA_API llama_token llama_token_eos(const struct llama_context * ctx); // end-of-sentence
|
||||
LLAMA_API llama_token llama_token_nl (const struct llama_context * ctx); // next-line
|
||||
LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence
|
||||
LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence
|
||||
LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line
|
||||
|
||||
// codellama infill tokens
|
||||
LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix
|
||||
LLAMA_API llama_token llama_token_middle(const struct llama_model * model); // Beginning of infill middle
|
||||
LLAMA_API llama_token llama_token_suffix(const struct llama_model * model); // Beginning of infill suffix
|
||||
LLAMA_API llama_token llama_token_eot (const struct llama_model * model); // End of infill middle
|
||||
|
||||
//
|
||||
// Tokenization
|
||||
//
|
||||
|
||||
// Convert the provided text into tokens.
|
||||
// The tokens pointer must be large enough to hold the resulting tokens.
|
||||
// Returns the number of tokens on success, no more than n_max_tokens
|
||||
// Returns a negative number on failure - the number of tokens that would have been returned
|
||||
/// @details Convert the provided text into tokens.
|
||||
/// @param tokens The tokens pointer must be large enough to hold the resulting tokens.
|
||||
/// @return Returns the number of tokens on success, no more than n_max_tokens
|
||||
/// @return Returns a negative number on failure - the number of tokens that would have been returned
|
||||
/// @param special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated as plaintext.
|
||||
/// Does not insert a leading space.
|
||||
LLAMA_API int llama_tokenize(
|
||||
struct llama_context * ctx,
|
||||
const char * text,
|
||||
llama_token * tokens,
|
||||
int n_max_tokens,
|
||||
bool add_bos);
|
||||
|
||||
LLAMA_API int llama_tokenize_with_model(
|
||||
const struct llama_model * model,
|
||||
const char * text,
|
||||
int text_len,
|
||||
llama_token * tokens,
|
||||
int n_max_tokens,
|
||||
bool add_bos);
|
||||
bool add_bos,
|
||||
bool special);
|
||||
|
||||
// Token Id -> Piece.
|
||||
// Uses the vocabulary in the provided context.
|
||||
// Does not write null terminator to the buffer.
|
||||
// User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens.
|
||||
LLAMA_API int llama_token_to_piece(
|
||||
const struct llama_context * ctx,
|
||||
llama_token token,
|
||||
char * buf,
|
||||
int length);
|
||||
|
||||
LLAMA_API int llama_token_to_piece_with_model(
|
||||
const struct llama_model * model,
|
||||
llama_token token,
|
||||
char * buf,
|
||||
@ -418,11 +569,19 @@ extern "C" {
|
||||
// Sampling functions
|
||||
//
|
||||
|
||||
/// @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
|
||||
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);
|
||||
// Sets the current rng seed.
|
||||
LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed);
|
||||
|
||||
/// @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
|
||||
/// @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
|
||||
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);
|
||||
LLAMA_API void llama_sample_repetition_penalties(
|
||||
struct llama_context * ctx,
|
||||
llama_token_data_array * candidates,
|
||||
const llama_token * last_tokens,
|
||||
size_t penalty_last_n,
|
||||
float penalty_repeat,
|
||||
float penalty_freq,
|
||||
float penalty_present);
|
||||
|
||||
/// @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
|
||||
/// @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.
|
||||
@ -435,23 +594,61 @@ extern "C" {
|
||||
float scale);
|
||||
|
||||
/// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
|
||||
LLAMA_API void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates);
|
||||
LLAMA_API void llama_sample_softmax(
|
||||
struct llama_context * ctx,
|
||||
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 Minimum P sampling as described in https://github.com/ggerganov/llama.cpp/pull/3841
|
||||
LLAMA_API void llama_sample_min_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.
|
||||
@ -459,23 +656,42 @@ 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);
|
||||
/// Does not compute the token probabilities. Use llama_sample_softmax() instead.
|
||||
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
|
||||
@ -483,9 +699,10 @@ 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.
|
||||
@ -494,9 +711,10 @@ 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.
|
||||
@ -511,11 +729,17 @@ extern "C" {
|
||||
/// @param n_beams Number of beams to use.
|
||||
/// @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);
|
||||
|
||||
// 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);
|
||||
|
||||
@ -524,7 +748,7 @@ extern "C" {
|
||||
|
||||
// Set callback for all future logging events.
|
||||
// If this is not called, or NULL is supplied, everything is output on stderr.
|
||||
LLAMA_API void llama_log_set(llama_log_callback log_callback, void * user_data);
|
||||
LLAMA_API void llama_log_set(ggml_log_callback log_callback, void * user_data);
|
||||
|
||||
LLAMA_API void llama_dump_timing_info_yaml(FILE * stream, const struct llama_context * ctx);
|
||||
|
||||
@ -540,7 +764,9 @@ extern "C" {
|
||||
|
||||
struct ggml_tensor;
|
||||
|
||||
const std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx);
|
||||
const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map(
|
||||
struct llama_context * ctx
|
||||
);
|
||||
|
||||
#endif // LLAMA_API_INTERNAL
|
||||
|
||||
|
||||
Reference in New Issue
Block a user