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whisper : add integer quantization support (#540)
* whisper : add integer quantization support * examples : add common-ggml + prepare to add "quantize" tool * whisper : quantization tool ready * whisper : fix F32 support * whisper : try to fix shared lib linkage * wasm : update quantized models to Q5 * bench.wasm : remove "medium" button * bench.wasm : fix custom model button * ggml : add Q5_0 and Q5_1 WASM SIMD * wasm : add quantized models to all WASM examples * wasm : bump DB version number to 2 * talk-llama : update example to latest llama.cpp * node : increase test timeout to 10s * readme : add information for model quantization * wasm : add links to other examples
This commit is contained in:
Georgi Gerganov
GitHub
@ -39,12 +39,16 @@ extern "C" {
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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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float p; // probability of the token
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float plog; // log probability of the token
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} llama_token_data;
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typedef struct llama_token_data_array {
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llama_token_data * data;
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size_t size;
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bool sorted;
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} llama_token_data_array;
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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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@ -65,6 +69,20 @@ extern "C" {
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void * progress_callback_user_data;
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};
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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, // except 1d tensors
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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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};
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LLAMA_API struct llama_context_params llama_context_default_params();
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LLAMA_API bool llama_mmap_supported();
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@ -82,27 +100,46 @@ extern "C" {
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// TODO: not great API - very likely to change
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// Returns 0 on success
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// nthread - how many threads to use. If <=0, will use std::thread::hardware_concurrency(), else the number given
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LLAMA_API int llama_model_quantize(
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const char * fname_inp,
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const char * fname_out,
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int itype);
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enum llama_ftype ftype,
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int nthread);
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// Returns the KV cache that will contain the context for the
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// ongoing prediction with the model.
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LLAMA_API const uint8_t * llama_get_kv_cache(struct llama_context * ctx);
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// Returns the size of the KV cache
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LLAMA_API size_t llama_get_kv_cache_size(struct llama_context * ctx);
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// Apply a LoRA adapter to a loaded model
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// path_base_model is the path to a higher quality model to use as a base for
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// the layers modified by the adapter. Can be NULL to use the current loaded model.
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// The model needs to be reloaded before applying a new adapter, otherwise the adapter
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// will be applied on top of the previous one
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// Returns 0 on success
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LLAMA_API 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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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(struct llama_context * ctx);
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// Sets the KV cache containing the current context for the model
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LLAMA_API void llama_set_kv_cache(
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struct llama_context * ctx,
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const uint8_t * kv_cache,
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size_t n_size,
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int n_token_count);
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// Sets the current rng seed.
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LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, int seed);
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// Returns the size in bytes of the state (rng, logits, embedding and kv_cache)
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LLAMA_API size_t llama_get_state_size(struct llama_context * ctx);
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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 * dest);
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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, const uint8_t * src);
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// Save/load session file
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LLAMA_API size_t 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 size_t 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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// Run the llama inference to obtain the logits and probabilities for the next token.
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// tokens + n_tokens is the provided batch of new tokens to process
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@ -148,16 +185,52 @@ extern "C" {
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// Special tokens
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LLAMA_API llama_token llama_token_bos();
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LLAMA_API llama_token llama_token_eos();
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LLAMA_API llama_token llama_token_nl();
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// TODO: improve the last_n_tokens interface ?
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LLAMA_API llama_token llama_sample_top_p_top_k(
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struct llama_context * ctx,
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const llama_token * last_n_tokens_data,
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int last_n_tokens_size,
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int top_k,
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float top_p,
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float temp,
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float repeat_penalty);
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// Sampling functions
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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, llama_token * last_tokens, size_t last_tokens_size, 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, llama_token * last_tokens, size_t last_tokens_size, float alpha_frequency, float alpha_presence);
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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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/// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
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LLAMA_API void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int k, size_t min_keep = 1);
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/// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
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LLAMA_API void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep = 1);
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/// @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
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LLAMA_API void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep = 1);
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/// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
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LLAMA_API void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep = 1);
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LLAMA_API void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates, float temp);
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/// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
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/// @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.
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/// @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.
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/// @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.
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/// @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.
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/// @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.
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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);
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/// @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
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/// @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.
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/// @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.
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/// @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.
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/// @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.
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LLAMA_API llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu);
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/// @details Selects the token with the highest probability.
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LLAMA_API llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates);
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/// @details Randomly selects a token from the candidates based on their probabilities.
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LLAMA_API llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates);
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// Performance information
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LLAMA_API void llama_print_timings(struct llama_context * ctx);
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@ -170,4 +243,15 @@ extern "C" {
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}
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#endif
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// Internal API to be implemented by llama.cpp and used by tests/benchmarks only
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#ifdef LLAMA_API_INTERNAL
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#include <vector>
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#include <string>
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struct ggml_tensor;
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std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx);
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#endif
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#endif // LLAMA_H
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