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whisper.cpp
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#include "whisper.h"
#ifdef WHISPER_USE_COREML
#include "coreml/whisper-encoder.h"
#endif
#ifdef GGML_USE_METAL
# include "ggml-metal.h"
#endif
#ifdef WHISPER_USE_OPENVINO
#include "openvino/whisper-openvino-encoder.h"
#endif
#include "ggml.h"
#include "ggml-alloc.h"
#include <algorithm>
#include <cassert>
#define _USE_MATH_DEFINES
#include <cmath>
#include <cstdio>
#include <cstdarg>
#include <cstring>
#include <fstream>
#include <map>
#include <set>
#include <string>
#include <thread>
#include <vector>
#include <regex>
#include <random>
#include <functional>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
#if defined(GGML_BIG_ENDIAN)
#include <bit>
template<typename T>
static T byteswap(T value) {
return std::byteswap(value);
}
template<>
float byteswap(float value) {
return std::bit_cast<float>(byteswap(std::bit_cast<std::uint32_t>(value)));
}
template<typename T>
static void byteswap_tensor_data(ggml_tensor * tensor) {
T * datum = reinterpret_cast<T *>(tensor->data);
for (int i = 0; i < ggml_nelements(tensor); i++) {
datum[i] = byteswap(datum[i]);
}
}
static void byteswap_tensor(ggml_tensor * tensor) {
switch (tensor->type) {
case GGML_TYPE_I16: {
byteswap_tensor_data<int16_t>(tensor);
break;
}
case GGML_TYPE_F16: {
byteswap_tensor_data<ggml_fp16_t>(tensor);
break;
}
case GGML_TYPE_I32: {
byteswap_tensor_data<int32_t>(tensor);
break;
}
case GGML_TYPE_F32: {
byteswap_tensor_data<float>(tensor);
break;
}
default: { // GML_TYPE_I8
break;
}
}
}
#define BYTESWAP_VALUE(d) d = byteswap(d)
#define BYTESWAP_FILTERS(f) \
do { \
for (auto & datum : f.data) { \
datum = byteswap(datum); \
} \
} while (0)
#define BYTESWAP_TENSOR(t) \
do { \
byteswap_tensor(t); \
} while (0)
#else
#define BYTESWAP_VALUE(d) do {} while (0)
#define BYTESWAP_FILTERS(f) do {} while (0)
#define BYTESWAP_TENSOR(t) do {} while (0)
#endif
#define WHISPER_ASSERT(x) \
do { \
if (!(x)) { \
log("WHISPER_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
abort(); \
} \
} while (0)
// define this to enable verbose trace logging - useful for debugging purposes
//#define WHISPER_DEBUG
#if defined(WHISPER_DEBUG)
#define WHISPER_PRINT_DEBUG(...) \
do { \
fprintf(stderr, __VA_ARGS__); \
} while (0)
#else
#define WHISPER_PRINT_DEBUG(...)
#endif
//#define WHISPER_USE_FLASH_ATTN
//#define WHISPER_USE_FLASH_FF
#define WHISPER_MAX_DECODERS 16
//
// ggml helpers
//
static void ggml_graph_compute_helper(
std::vector<uint8_t> & buf,
ggml_cgraph * graph,
int n_threads,
whisper_abort_callback abort_callback,
void * abort_callback_data) {
struct ggml_cplan plan = ggml_graph_plan(graph, n_threads);
plan.abort_callback = abort_callback;
plan.abort_callback_data = abort_callback_data;
if (plan.work_size > 0) {
buf.resize(plan.work_size);
plan.work_data = buf.data();
}
ggml_graph_compute(graph, &plan);
}
// faster matrix multiplications for tensors that do not have dimension 0 divisible by "pad"
// the idea is to represent the original matrix multiplication:
//
// Z = X @ Y
//
// with the sum of two matrix multiplications:
//
// Z = (X_0 @ Y_0) + (X_1 @ Y_1)
//
// here X_0 and Y_0 are views of X and Y that have dimension 0 divisible by "pad"
// and X_1 and Y_1 are the remaining views. X_1 and Y_1 end up being small matrices that can be processed with more
// general-purpose kernels
//
static struct ggml_tensor * ggml_mul_mat_pad(struct ggml_context * ctx, struct ggml_tensor * x, struct ggml_tensor * y, int pad = 32) {
// use padding only if dimension 0 is at least 8 times larger than the padding
// else we won't get much benefit from the optimization
const int n_pad_req = 8;
if (x->ne[0] % pad == 0 || x->ne[0] / pad < n_pad_req) {
return ggml_mul_mat(ctx, x, y);
}
struct ggml_tensor * x_0 = ggml_view_3d(ctx, x, (x->ne[0]/pad)*pad, x->ne[1], x->ne[2], x->nb[1], x->nb[2], 0);
struct ggml_tensor * x_1 = ggml_view_3d(ctx, x, x->ne[0]%pad, x->ne[1], x->ne[2], x->nb[1], x->nb[2], x_0->ne[0]*x_0->nb[0]);
struct ggml_tensor * y_0 = ggml_view_3d(ctx, y, (y->ne[0]/pad)*pad, y->ne[1], y->ne[2], y->nb[1], y->nb[2], 0);
struct ggml_tensor * y_1 = ggml_view_3d(ctx, y, y->ne[0]%pad, y->ne[1], y->ne[2], y->nb[1], y->nb[2], y_0->ne[0]*y_0->nb[0]);
return ggml_add(ctx,
ggml_mul_mat(ctx, x_0, y_0),
ggml_mul_mat(ctx, x_1, y_1));
}
// TODO: check if other platforms can benefit from this optimization
#if defined(GGML_USE_METAL)
#define ggml_mul_mat ggml_mul_mat_pad
#endif
// available whisper models
enum e_model {
MODEL_UNKNOWN,
MODEL_TINY,
MODEL_BASE,
MODEL_SMALL,
MODEL_MEDIUM,
MODEL_LARGE,
};
static const std::map<std::string, std::pair<int, std::string>> g_lang = {
{ "en", { 0, "english", } },
{ "zh", { 1, "chinese", } },
{ "de", { 2, "german", } },
{ "es", { 3, "spanish", } },
{ "ru", { 4, "russian", } },
{ "ko", { 5, "korean", } },
{ "fr", { 6, "french", } },
{ "ja", { 7, "japanese", } },
{ "pt", { 8, "portuguese", } },
{ "tr", { 9, "turkish", } },
{ "pl", { 10, "polish", } },
{ "ca", { 11, "catalan", } },
{ "nl", { 12, "dutch", } },
{ "ar", { 13, "arabic", } },
{ "sv", { 14, "swedish", } },
{ "it", { 15, "italian", } },
{ "id", { 16, "indonesian", } },
{ "hi", { 17, "hindi", } },
{ "fi", { 18, "finnish", } },
{ "vi", { 19, "vietnamese", } },
{ "he", { 20, "hebrew", } },
{ "uk", { 21, "ukrainian", } },
{ "el", { 22, "greek", } },
{ "ms", { 23, "malay", } },
{ "cs", { 24, "czech", } },
{ "ro", { 25, "romanian", } },
{ "da", { 26, "danish", } },
{ "hu", { 27, "hungarian", } },
{ "ta", { 28, "tamil", } },
{ "no", { 29, "norwegian", } },
{ "th", { 30, "thai", } },
{ "ur", { 31, "urdu", } },
{ "hr", { 32, "croatian", } },
{ "bg", { 33, "bulgarian", } },
{ "lt", { 34, "lithuanian", } },
{ "la", { 35, "latin", } },
{ "mi", { 36, "maori", } },
{ "ml", { 37, "malayalam", } },
{ "cy", { 38, "welsh", } },
{ "sk", { 39, "slovak", } },
{ "te", { 40, "telugu", } },
{ "fa", { 41, "persian", } },
{ "lv", { 42, "latvian", } },
{ "bn", { 43, "bengali", } },
{ "sr", { 44, "serbian", } },
{ "az", { 45, "azerbaijani", } },
{ "sl", { 46, "slovenian", } },
{ "kn", { 47, "kannada", } },
{ "et", { 48, "estonian", } },
{ "mk", { 49, "macedonian", } },
{ "br", { 50, "breton", } },
{ "eu", { 51, "basque", } },
{ "is", { 52, "icelandic", } },
{ "hy", { 53, "armenian", } },
{ "ne", { 54, "nepali", } },
{ "mn", { 55, "mongolian", } },
{ "bs", { 56, "bosnian", } },
{ "kk", { 57, "kazakh", } },
{ "sq", { 58, "albanian", } },
{ "sw", { 59, "swahili", } },
{ "gl", { 60, "galician", } },
{ "mr", { 61, "marathi", } },
{ "pa", { 62, "punjabi", } },
{ "si", { 63, "sinhala", } },
{ "km", { 64, "khmer", } },
{ "sn", { 65, "shona", } },
{ "yo", { 66, "yoruba", } },
{ "so", { 67, "somali", } },
{ "af", { 68, "afrikaans", } },
{ "oc", { 69, "occitan", } },
{ "ka", { 70, "georgian", } },
{ "be", { 71, "belarusian", } },
{ "tg", { 72, "tajik", } },
{ "sd", { 73, "sindhi", } },
{ "gu", { 74, "gujarati", } },
{ "am", { 75, "amharic", } },
{ "yi", { 76, "yiddish", } },
{ "lo", { 77, "lao", } },
{ "uz", { 78, "uzbek", } },
{ "fo", { 79, "faroese", } },
{ "ht", { 80, "haitian creole", } },
{ "ps", { 81, "pashto", } },
{ "tk", { 82, "turkmen", } },
{ "nn", { 83, "nynorsk", } },
{ "mt", { 84, "maltese", } },
{ "sa", { 85, "sanskrit", } },
{ "lb", { 86, "luxembourgish", } },
{ "my", { 87, "myanmar", } },
{ "bo", { 88, "tibetan", } },
{ "tl", { 89, "tagalog", } },
{ "mg", { 90, "malagasy", } },
{ "as", { 91, "assamese", } },
{ "tt", { 92, "tatar", } },
{ "haw", { 93, "hawaiian", } },
{ "ln", { 94, "lingala", } },
{ "ha", { 95, "hausa", } },
{ "ba", { 96, "bashkir", } },
{ "jw", { 97, "javanese", } },
{ "su", { 98, "sundanese", } },
};
static const size_t MB = 1ull*1024*1024;
// TODO: avoid using GGUF
static const std::map<ggml_type, std::map<e_model, size_t>> MEM_REQ_MODEL = {
{ GGML_TYPE_F32,
{
{ MODEL_TINY, 74ull*MB },
{ MODEL_BASE, 142ull*MB },
{ MODEL_SMALL, 466ull*MB },
{ MODEL_MEDIUM, 1464ull*MB },
{ MODEL_LARGE, 2952ull*MB },
},
},
{ GGML_TYPE_F16,
{
{ MODEL_TINY, 74ull*MB },
{ MODEL_BASE, 142ull*MB },
{ MODEL_SMALL, 466ull*MB },
{ MODEL_MEDIUM, 1464ull*MB },
{ MODEL_LARGE, 2952ull*MB },
},
},
{ GGML_TYPE_Q4_0,
{
{ MODEL_TINY, 26ull*MB },
{ MODEL_BASE, 50ull*MB },
{ MODEL_SMALL, 154ull*MB },
{ MODEL_MEDIUM, 470ull*MB },
{ MODEL_LARGE, 940ull*MB },
},
},
{ GGML_TYPE_Q4_1,
{
{ MODEL_TINY, 32ull*MB },
{ MODEL_BASE, 58ull*MB },
{ MODEL_SMALL, 182ull*MB },
{ MODEL_MEDIUM, 562ull*MB },
{ MODEL_LARGE, 1124ull*MB },
},
},
{ GGML_TYPE_Q5_0,
{
{ MODEL_TINY, 30ull*MB },
{ MODEL_BASE, 54ull*MB },
{ MODEL_SMALL, 170ull*MB },
{ MODEL_MEDIUM, 516ull*MB },
{ MODEL_LARGE, 1034ull*MB },
},
},
{ GGML_TYPE_Q5_1,
{
{ MODEL_TINY, 32ull*MB },
{ MODEL_BASE, 58ull*MB },
{ MODEL_SMALL, 182ull*MB },
{ MODEL_MEDIUM, 562ull*MB },
{ MODEL_LARGE, 1124ull*MB },
},
},
{ GGML_TYPE_Q8_0,
{
{ MODEL_TINY, 45ull*MB },
{ MODEL_BASE, 84ull*MB },
{ MODEL_SMALL, 268ull*MB },
{ MODEL_MEDIUM, 834ull*MB },
{ MODEL_LARGE, 1674ull*MB },
},
},
};
struct whisper_mel {
int n_len;
int n_len_org;
int n_mel;
std::vector<float> data;
};
struct whisper_filters {
int32_t n_mel;
int32_t n_fft;
std::vector<float> data;
};
struct whisper_vocab {
using id = int32_t;
using token = std::string;
int n_vocab = 51864;
std::map<token, id> token_to_id;
std::map<id, token> id_to_token;
// reference: https://github.com/openai/whisper/blob/248b6cb124225dd263bb9bd32d060b6517e067f8/whisper/tokenizer.py#L334-L349
id token_eot = 50256;
id token_sot = 50257;
// task tokens (used only for multilingual models)
id token_translate = 50357;
id token_transcribe = 50358;
// other special tokens
id token_solm = 50359; // [TDRZ] used by tinydiarize models to indicate speaker turn
id token_prev = 50360;
id token_nosp = 50361;
id token_not = 50362; // no timestamps
id token_beg = 50363; // begin timestamps
bool is_multilingual() const {
return n_vocab == 51865;
}
};
struct whisper_segment {
int64_t t0;
int64_t t1;
std::string text;
std::vector<whisper_token_data> tokens;
bool speaker_turn_next;
};
// medium
// hparams: {
// 'n_mels': 80,
// 'n_vocab': 51864,
// 'n_audio_ctx': 1500,
// 'n_audio_state': 1024,
// 'n_audio_head': 16,
// 'n_audio_layer': 24,
// 'n_text_ctx': 448,
// 'n_text_state': 1024,
// 'n_text_head': 16,
// 'n_text_layer': 24
// }
//
// default hparams (Whisper tiny)
struct whisper_hparams {
int32_t n_vocab = 51864;
int32_t n_audio_ctx = 1500;
int32_t n_audio_state = 384;
int32_t n_audio_head = 6;
int32_t n_audio_layer = 4;
int32_t n_text_ctx = 448;
int32_t n_text_state = 384;
int32_t n_text_head = 6;
int32_t n_text_layer = 4;
int32_t n_mels = 80;
int32_t ftype = 1;
float eps = 1e-5f;
};
// audio encoding layer
struct whisper_layer_encoder {
// encoder.blocks.*.attn_ln
struct ggml_tensor * attn_ln_0_w;
struct ggml_tensor * attn_ln_0_b;
// encoder.blocks.*.attn.out
struct ggml_tensor * attn_ln_1_w;
struct ggml_tensor * attn_ln_1_b;
// encoder.blocks.*.attn.query
struct ggml_tensor * attn_q_w;
struct ggml_tensor * attn_q_b;
// encoder.blocks.*.attn.key
struct ggml_tensor * attn_k_w;
// encoder.blocks.*.attn.value
struct ggml_tensor * attn_v_w;
struct ggml_tensor * attn_v_b;
// encoder.blocks.*.mlp_ln
struct ggml_tensor * mlp_ln_w;
struct ggml_tensor * mlp_ln_b;
// encoder.blocks.*.mlp.0
struct ggml_tensor * mlp_0_w;
struct ggml_tensor * mlp_0_b;
// encoder.blocks.*.mlp.2
struct ggml_tensor * mlp_1_w;
struct ggml_tensor * mlp_1_b;
};
// token decoding layer
struct whisper_layer_decoder {
// decoder.blocks.*.attn_ln
struct ggml_tensor * attn_ln_0_w;
struct ggml_tensor * attn_ln_0_b;
// decoder.blocks.*.attn.out
struct ggml_tensor * attn_ln_1_w;
struct ggml_tensor * attn_ln_1_b;
// decoder.blocks.*.attn.query
struct ggml_tensor * attn_q_w;
struct ggml_tensor * attn_q_b;
// decoder.blocks.*.attn.key
struct ggml_tensor * attn_k_w;
// decoder.blocks.*.attn.value
struct ggml_tensor * attn_v_w;
struct ggml_tensor * attn_v_b;
// decoder.blocks.*.cross_attn_ln
struct ggml_tensor * cross_attn_ln_0_w;
struct ggml_tensor * cross_attn_ln_0_b;
// decoder.blocks.*.cross_attn.out
struct ggml_tensor * cross_attn_ln_1_w;
struct ggml_tensor * cross_attn_ln_1_b;
// decoder.blocks.*.cross_attn.query
struct ggml_tensor * cross_attn_q_w;
struct ggml_tensor * cross_attn_q_b;
// decoder.blocks.*.cross_attn.key
struct ggml_tensor * cross_attn_k_w;
// decoder.blocks.*.cross_attn.value
struct ggml_tensor * cross_attn_v_w;
struct ggml_tensor * cross_attn_v_b;
// decoder.blocks.*.mlp_ln
struct ggml_tensor * mlp_ln_w;
struct ggml_tensor * mlp_ln_b;
// decoder.blocks.*.mlp.0
struct ggml_tensor * mlp_0_w;
struct ggml_tensor * mlp_0_b;
// decoder.blocks.*.mlp.2
struct ggml_tensor * mlp_1_w;
struct ggml_tensor * mlp_1_b;
};
struct whisper_kv_cache {
struct ggml_tensor * k;
struct ggml_tensor * v;
struct ggml_context * ctx;
// buf points to the memory allocated for both ggml_tensor 'k' and 'v' (see kv_cache_init)
std::vector<uint8_t> buf;
int n; // number of tokens currently in the cache
};
struct whisper_model {
e_model type = MODEL_UNKNOWN;
whisper_hparams hparams;
whisper_filters filters;
// encoder.positional_embedding
struct ggml_tensor * e_pe;
// encoder.conv1
struct ggml_tensor * e_conv_1_w;
struct ggml_tensor * e_conv_1_b;
// encoder.conv2
struct ggml_tensor * e_conv_2_w;
struct ggml_tensor * e_conv_2_b;
// encoder.ln_post
struct ggml_tensor * e_ln_w;
struct ggml_tensor * e_ln_b;
// decoder.positional_embedding
struct ggml_tensor * d_pe;
// decoder.token_embedding
struct ggml_tensor * d_te;
// decoder.ln
struct ggml_tensor * d_ln_w;
struct ggml_tensor * d_ln_b;
std::vector<whisper_layer_encoder> layers_encoder;
std::vector<whisper_layer_decoder> layers_decoder;
// context
struct ggml_context * ctx;
// the model memory buffer is read-only and can be shared between processors
std::vector<uint8_t> * buf;
// tensors
int n_loaded;
std::map<std::string, struct ggml_tensor *> tensors;
};
struct whisper_sequence {
std::vector<whisper_token_data> tokens;
// the accumulated transcription in the current iteration (used to truncate the tokens array)
int result_len;
double sum_logprobs_all; // the sum of the log probabilities of the tokens
double sum_logprobs; // the sum of the log probabilities of the tokens (first result_len tokens)
double avg_logprobs; // the average log probability of the tokens
double entropy; // the entropy of the tokens
double score; // likelihood rank score
};
// TAGS: WHISPER_DECODER_INIT
struct whisper_decoder {
// each decoder keeps its own KV-cache
whisper_kv_cache kv_self;
// the currently generated sequence of tokens
whisper_sequence sequence;
int seek_delta; // the window shift found so far based on the decoded timestamp tokens
bool failed; // has the current segment failed to decode?
bool completed; // has the decoder completed the current segment?
bool has_ts; // have we already sampled a non-beg timestamp token for the current segment?
// new token probs, logits and logprobs after the last whisper_decode (1-dimensional array: [n_vocab])
std::vector<float> probs;
std::vector<float> logits;
std::vector<float> logprobs;
std::vector<whisper_token> tokens_tmp; // used for whisper_decode calls
};
// replace std::pair by using customized pair struct (reason: std::pair is very slow)
template<typename A, typename B>
struct whisper_pair {
A first;
B second;
// Define a constructor that takes two arguments.
whisper_pair(const A& a, const B& b) : first(a), second(b) {}
// Define a constructor that takes no argument.
whisper_pair() : first(A()), second(B()) {}
};
// beam-search helpers
struct kv_buf {
std::vector<uint8_t> k;
std::vector<uint8_t> v;
};
// ggml_allocr wrapper for whisper usage
struct whisper_allocr {
ggml_allocr * alloc = nullptr;
std::vector<uint8_t> meta;
std::vector<uint8_t> data;
};
static size_t whisper_allocr_size(struct whisper_allocr & allocr) {
return allocr.meta.size() + allocr.data.size();
}
// measure the memory usage of a graph and prepare the allocr's internal data buffer
static void whisper_allocr_graph_init(struct whisper_allocr & allocr, std::function<struct ggml_cgraph *()> && get_graph) {
const int tensor_alignment = 32;
auto & alloc = allocr.alloc;
auto & meta = allocr.meta;
auto & data = allocr.data;
meta.resize(ggml_tensor_overhead()*GGML_MAX_NODES + ggml_graph_overhead());
alloc = ggml_allocr_new_measure(tensor_alignment);
const size_t alloc_size = ggml_allocr_alloc_graph(alloc, get_graph()) + tensor_alignment;
ggml_allocr_free(alloc);
data.resize(alloc_size);
alloc = ggml_allocr_new(data.data(), data.size(), tensor_alignment);
}
static void whisper_allocr_free(struct whisper_allocr & allocr) {
if (allocr.alloc) {
ggml_allocr_free(allocr.alloc);
allocr.alloc = nullptr;
}
}
struct whisper_state {
int64_t t_sample_us = 0;
int64_t t_encode_us = 0;
int64_t t_decode_us = 0;
int64_t t_prompt_us = 0;
int64_t t_mel_us = 0;
int32_t n_sample = 0; // number of tokens sampled
int32_t n_encode = 0; // number of encoder calls
int32_t n_decode = 0; // number of decoder calls with n_tokens == 1 (text-generation)
int32_t n_prompt = 0; // number of decoder calls with n_tokens > 1 (prompt encoding)
int32_t n_fail_p = 0; // number of logprob threshold failures
int32_t n_fail_h = 0; // number of entropy threshold failures
// cross-attention KV cache for the decoders
// shared between all decoders
whisper_kv_cache kv_cross;
whisper_mel mel;
whisper_decoder decoders[WHISPER_MAX_DECODERS] = {};
// buffer for swapping KV caches between decoders during beam-search
std::vector<kv_buf> kv_swap_bufs;
// reusable buffer for `struct ggml_graph_plan.work_data`
std::vector<uint8_t> work_buffer;
// ggml-alloc:
// - stores meta info about the intermediate tensors into the `meta` buffers
// - stores the actual tensor data into the `data` buffers
whisper_allocr alloc_conv;
whisper_allocr alloc_encode;
whisper_allocr alloc_cross;
whisper_allocr alloc_decode;
// result of the encoder
struct ggml_tensor * embd_conv = nullptr;
struct ggml_tensor * embd_enc = nullptr;
// decode output (2-dimensional array: [n_tokens][n_vocab])
std::vector<float> logits;
std::vector<whisper_segment> result_all;
std::vector<whisper_token> prompt_past;
// work container used to avoid memory allocations
std::vector<whisper_pair<double, whisper_vocab::id>> logits_id;
mutable std::mt19937 rng; // used for sampling at t > 0.0
int lang_id = 0; // english by default
std::string path_model; // populated by whisper_init_from_file()
#ifdef WHISPER_USE_COREML
whisper_coreml_context * ctx_coreml = nullptr;
#endif
#ifdef GGML_USE_METAL
ggml_metal_context * ctx_metal = nullptr;
#endif
#ifdef WHISPER_USE_OPENVINO
whisper_openvino_context * ctx_openvino = nullptr;
#endif
// [EXPERIMENTAL] token-level timestamps data
int64_t t_beg = 0;
int64_t t_last = 0;
whisper_token tid_last;
std::vector<float> energy; // PCM signal energy
// [EXPERIMENTAL] speed-up techniques
int32_t exp_n_audio_ctx = 0; // 0 - use default
};
struct whisper_context {
int64_t t_load_us = 0;
int64_t t_start_us = 0;
ggml_type wtype = ggml_type::GGML_TYPE_F16; // weight type (FP32 / FP16 / QX)
ggml_type itype = ggml_type::GGML_TYPE_F16; // intermediate type (FP32 or FP16)
whisper_model model;
whisper_vocab vocab;
whisper_state * state = nullptr;
std::string path_model; // populated by whisper_init_from_file()
};
static void whisper_default_log(const char * text) {
fprintf(stderr, "%s", text);
}
static whisper_log_callback whisper_log = whisper_default_log;
#ifdef __GNUC__
#ifdef __MINGW32__
__attribute__((gnu_format(printf, 1, 2)))
#else
__attribute__((format(printf, 1, 2)))
#endif
#endif
static void log(const char * fmt, ...) {
if (!whisper_log) return;
char buf[1024];
va_list args;
va_start(args, fmt);
vsnprintf(buf, sizeof(buf), fmt, args);
whisper_log(buf);
}
template<typename T>
static void read_safe(whisper_model_loader * loader, T & dest) {
loader->read(loader->context, &dest, sizeof(T));
BYTESWAP_VALUE(dest);
}
static bool kv_cache_init(
const struct whisper_hparams & hparams,
struct whisper_kv_cache & cache,
ggml_type wtype,
int n_ctx) {
const int64_t n_text_state = hparams.n_text_state;
const int64_t n_text_layer = hparams.n_text_layer;
const int64_t n_mem = n_text_layer*n_ctx;
const int64_t n_elements = n_text_state*n_mem;
const size_t mem_bytes = 2*(ggml_type_size(wtype)*n_elements + ggml_tensor_overhead());
cache.buf.resize(mem_bytes);
struct ggml_init_params params = {
/*.mem_size =*/ cache.buf.size(),
/*.mem_buffer =*/ cache.buf.data(),
/*.no_alloc =*/ false,
};
cache.ctx = ggml_init(params);
if (!cache.ctx) {
log("%s: failed to allocate memory for kv cache\n", __func__);
return false;
}
cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
return true;
}
static bool kv_cache_reinit(struct whisper_kv_cache & cache) {
WHISPER_ASSERT(cache.ctx);
const int n_elements = ggml_nelements(cache.k);
WHISPER_ASSERT(n_elements == ggml_nelements(cache.v));
const ggml_type wtype = cache.k->type;
WHISPER_ASSERT(wtype == cache.v->type);
WHISPER_ASSERT(cache.buf.size() >= 2*n_elements*ggml_type_sizef(wtype));
struct ggml_init_params params = {
/*.mem_size =*/ cache.buf.size(),
/*.mem_buffer =*/ cache.buf.data(),
/*.no_alloc =*/ false,
};
cache.ctx = ggml_init(params);
if (!cache.ctx) {
log("%s: failed to allocate memory for kv cache\n", __func__);
return false;
}
cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
return true;
}
static void kv_cache_free(struct whisper_kv_cache & cache) {
if (cache.ctx) {
ggml_free(cache.ctx);
cache.ctx = nullptr;
}
}
// load the model from a ggml file
//
// file format:
//
// - hparams
// - pre-computed mel filters
// - vocab
// - weights
//
// see the convert-pt-to-ggml.py script for details
//
static bool whisper_model_load(struct whisper_model_loader * loader, whisper_context & wctx) {
log("%s: loading model\n", __func__);
const int64_t t_start_us = ggml_time_us();
wctx.t_start_us = t_start_us;
auto & model = wctx.model;
auto & vocab = wctx.vocab;
// verify magic
{
uint32_t magic;
read_safe(loader, magic);
if (magic != GGML_FILE_MAGIC) {
log("%s: invalid model data (bad magic)\n", __func__);
return false;
}
}
//load hparams
{
auto & hparams = model.hparams;
read_safe(loader, hparams.n_vocab);
read_safe(loader, hparams.n_audio_ctx);
read_safe(loader, hparams.n_audio_state);
read_safe(loader, hparams.n_audio_head);
read_safe(loader, hparams.n_audio_layer);
read_safe(loader, hparams.n_text_ctx);
read_safe(loader, hparams.n_text_state);
read_safe(loader, hparams.n_text_head);
read_safe(loader, hparams.n_text_layer);
read_safe(loader, hparams.n_mels);
read_safe(loader, hparams.ftype);
assert(hparams.n_text_state == hparams.n_audio_state);
if (hparams.n_audio_layer == 4) {
model.type = e_model::MODEL_TINY;
}
if (hparams.n_audio_layer == 6) {
model.type = e_model::MODEL_BASE;
}
if (hparams.n_audio_layer == 12) {
model.type = e_model::MODEL_SMALL;
}
if (hparams.n_audio_layer == 24) {
model.type = e_model::MODEL_MEDIUM;
}
if (hparams.n_audio_layer == 32) {
model.type = e_model::MODEL_LARGE;
}
const int32_t qntvr = hparams.ftype / GGML_QNT_VERSION_FACTOR;
hparams.ftype %= GGML_QNT_VERSION_FACTOR;
// for the big tensors, we have the option to store the data in 16-bit floats or quantized
// in order to save memory and also to speed up the computation
wctx.wtype = ggml_ftype_to_ggml_type((ggml_ftype) (model.hparams.ftype));
if (wctx.wtype == GGML_TYPE_COUNT) {
log("%s: invalid model (bad ftype value %d)\n", __func__, model.hparams.ftype);
return false;
}
const size_t scale = model.hparams.ftype ? 1 : 2;
log("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
log("%s: n_audio_ctx = %d\n", __func__, hparams.n_audio_ctx);
log("%s: n_audio_state = %d\n", __func__, hparams.n_audio_state);
log("%s: n_audio_head = %d\n", __func__, hparams.n_audio_head);
log("%s: n_audio_layer = %d\n", __func__, hparams.n_audio_layer);
log("%s: n_text_ctx = %d\n", __func__, hparams.n_text_ctx);
log("%s: n_text_state = %d\n", __func__, hparams.n_text_state);
log("%s: n_text_head = %d\n", __func__, hparams.n_text_head);
log("%s: n_text_layer = %d\n", __func__, hparams.n_text_layer);
log("%s: n_mels = %d\n", __func__, hparams.n_mels);
log("%s: ftype = %d\n", __func__, model.hparams.ftype);
log("%s: qntvr = %d\n", __func__, qntvr);
log("%s: type = %d\n", __func__, model.type);
// print memory requirements
{
// TODO
//log("%s: mem required = %7.2f MB (+ %7.2f MB per decoder)\n", __func__,
// mem_required / 1024.0 / 1024.0, mem_required_decoder / 1024.0 / 1024.0);
}
// initialize all memory buffers
// always have at least one decoder
wctx.model.buf = new std::vector<uint8_t>();
wctx.model.buf->resize(scale*MEM_REQ_MODEL.at(wctx.wtype).at(model.type));
// we skip initialization of the state until it is needed
// because it might be that state will always be provided externally.
}
// load mel filters
{
auto & filters = wctx.model.filters;
read_safe(loader, filters.n_mel);
read_safe(loader, filters.n_fft);
filters.data.resize(filters.n_mel * filters.n_fft);
loader->read(loader->context, filters.data.data(), filters.data.size() * sizeof(float));
BYTESWAP_FILTERS(filters);
}
// load vocab