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whisper.cpp
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#define WHISPER_BUILD
#include "whisper.h"
#include "ggml.h"
#include <algorithm>
#include <cassert>
#define _USE_MATH_DEFINES
#include <cmath>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <map>
#include <string>
#include <thread>
#include <vector>
#include <regex>
#include <random>
#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(tensor); \
} 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)) { \
fprintf(stderr, "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
// 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", } },
{ "iw", { 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 = 1024*1024;
static const std::map<e_model, size_t> MEM_REQ_MODEL = {
{ MODEL_TINY, 74ull*MB },
{ MODEL_BASE, 142ull*MB },
{ MODEL_SMALL, 466ull*MB },
{ MODEL_MEDIUM, 1464ull*MB },
{ MODEL_LARGE, 2952ull*MB },
};
static const std::map<e_model, size_t> MEM_REQ_KV_SELF = {
{ MODEL_TINY, 3ull*MB },
{ MODEL_BASE, 6ull*MB },
{ MODEL_SMALL, 16ull*MB },
{ MODEL_MEDIUM, 43ull*MB },
{ MODEL_LARGE, 71ull*MB },
};
static const std::map<e_model, size_t> MEM_REQ_KV_CROSS = {
{ MODEL_TINY, 9ull*MB },
{ MODEL_BASE, 18ull*MB },
{ MODEL_SMALL, 53ull*MB },
{ MODEL_MEDIUM, 141ull*MB },
{ MODEL_LARGE, 235ull*MB },
};
static const std::map<e_model, size_t> MEM_REQ_ENCODE = {
{ MODEL_TINY, 80ull*MB },
{ MODEL_BASE, 128ull*MB },
{ MODEL_SMALL, 300ull*MB },
{ MODEL_MEDIUM, 680ull*MB },
{ MODEL_LARGE, 1100ull*MB },
};
static const std::map<e_model, size_t> MEM_REQ_ENCODE_LAYER = {
{ MODEL_TINY, 104ull*MB },
{ MODEL_BASE, 138ull*MB },
{ MODEL_SMALL, 208ull*MB },
{ MODEL_MEDIUM, 280ull*MB },
{ MODEL_LARGE, 354ull*MB },
};
static const std::map<e_model, size_t> MEM_REQ_DECODE = {
{ MODEL_TINY, 200ull*MB },
{ MODEL_BASE, 202ull*MB },
{ MODEL_SMALL, 204ull*MB },
{ MODEL_MEDIUM, 206ull*MB },
{ MODEL_LARGE, 208ull*MB },
};
static const std::map<e_model, size_t> MEM_REQ_DECODE_LAYER = {
{ MODEL_TINY, 32ull*MB },
{ MODEL_BASE, 44ull*MB },
{ MODEL_SMALL, 64ull*MB },
{ MODEL_MEDIUM, 84ull*MB },
{ MODEL_LARGE, 110ull*MB },
};
struct whisper_mel {
int n_len;
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;
id token_eot = 50256;
id token_sot = 50257;
id token_prev = 50360;
id token_solm = 50361; // ??
id token_not = 50362; // no timestamps
id token_beg = 50363;
// available tasks
static const id token_translate = 50358;
static const id token_transcribe = 50359;
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;
};
// 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 f16 = 1;
};
// 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;
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 interation (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 decoders 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
};
struct whisper_context {
int64_t t_load_us = 0;
int64_t t_mel_us = 0;
int64_t t_sample_us = 0;
int64_t t_encode_us = 0;
int64_t t_decode_us = 0;
int64_t t_start_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
int32_t n_fail_p = 0; // number of logprob threshold failures
int32_t n_fail_h = 0; // number of entropy threshold failures
ggml_type wtype; // weight type (FP32 or FP16)
whisper_mel mel;
whisper_model model;
whisper_vocab vocab;
// cross-attention KV cache for the decoders
// shared between all decoders
whisper_kv_cache kv_cross;
whisper_decoder decoders[WHISPER_MAX_DECODERS] = {};
// memory buffers used by encode / decode contexts
std::vector<uint8_t> buf_compute;
std::vector<uint8_t> buf_compute_layer;
// 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<std::pair<double, whisper_vocab::id>> logits_id;
mutable std::mt19937 rng; // used for sampling at t > 0.0
// [EXPERIMENTAL] token-level timestamps data
int64_t t_beg;
int64_t t_last;
whisper_token tid_last;
std::vector<float> energy; // PCM signal energy
// [EXPERIMENTAL] speed-up techniques
int32_t exp_n_audio_ctx; // 0 - use default
};
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,
const size_t mem_bytes,
struct whisper_kv_cache & cache,
ggml_type wtype,
int n_ctx) {
cache.buf.resize(mem_bytes);
struct ggml_init_params params;
params.mem_size = cache.buf.size();
params.mem_buffer = cache.buf.data();
cache.ctx = ggml_init(params);
if (!cache.ctx) {
fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__);
return false;
}
const int n_text_state = hparams.n_text_state;
const int n_text_layer = hparams.n_text_layer;
const int n_mem = n_text_layer*n_ctx;
const int n_elements = n_text_state*n_mem;
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_size(wtype));
struct ggml_init_params params;
params.mem_size = cache.buf.size();
params.mem_buffer = cache.buf.data();
cache.ctx = ggml_init(params);
if (!cache.ctx) {
fprintf(stderr, "%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) {
fprintf(stderr, "%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 != 0x67676d6c) {
fprintf(stderr, "%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.f16);
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;
}
// for the big tensors, we have the option to store the data in 16-bit floats
// in order to save memory and also to speed up the computation
wctx.wtype = model.hparams.f16 ? GGML_TYPE_F16 : GGML_TYPE_F32;
const size_t scale = model.hparams.f16 ? 1 : 2;
fprintf(stderr, "%s: n_vocab = %d\n", __func__, hparams.n_vocab);
fprintf(stderr, "%s: n_audio_ctx = %d\n", __func__, hparams.n_audio_ctx);
fprintf(stderr, "%s: n_audio_state = %d\n", __func__, hparams.n_audio_state);
fprintf(stderr, "%s: n_audio_head = %d\n", __func__, hparams.n_audio_head);
fprintf(stderr, "%s: n_audio_layer = %d\n", __func__, hparams.n_audio_layer);
fprintf(stderr, "%s: n_text_ctx = %d\n", __func__, hparams.n_text_ctx);
fprintf(stderr, "%s: n_text_state = %d\n", __func__, hparams.n_text_state);
fprintf(stderr, "%s: n_text_head = %d\n", __func__, hparams.n_text_head);
fprintf(stderr, "%s: n_text_layer = %d\n", __func__, hparams.n_text_layer);
fprintf(stderr, "%s: n_mels = %d\n", __func__, hparams.n_mels);
fprintf(stderr, "%s: f16 = %d\n", __func__, hparams.f16);
fprintf(stderr, "%s: type = %d\n", __func__, model.type);
// print memory requirements
{
// this is the total memory required to run the inference
const size_t mem_required =
scale*MEM_REQ_MODEL.at (model.type) +
scale*MEM_REQ_KV_CROSS.at (model.type) +
scale*std::max(MEM_REQ_ENCODE.at(model.type), MEM_REQ_DECODE.at(model.type)) +
scale*std::max(MEM_REQ_ENCODE_LAYER.at(model.type), MEM_REQ_DECODE_LAYER.at(model.type));
// this is the memory required by one decoder
const size_t mem_required_decoder =
scale*MEM_REQ_KV_SELF.at(model.type);
fprintf(stderr, "%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(model.type));
if (!kv_cache_init(model.hparams, scale*MEM_REQ_KV_SELF.at(model.type), wctx.decoders[0].kv_self, wctx.wtype, model.hparams.n_text_ctx)) {
fprintf(stderr, "%s: kv_cache_init() failed for self-attention cache\n", __func__);
return false;
}
{
const size_t memory_size = ggml_nbytes(wctx.decoders[0].kv_self.k) + ggml_nbytes(wctx.decoders[0].kv_self.v);
fprintf(stderr, "%s: kv self size = %7.2f MB\n", __func__, memory_size/1024.0/1024.0);
}
if (!kv_cache_init(model.hparams, scale*MEM_REQ_KV_CROSS.at(model.type), wctx.kv_cross, wctx.wtype, model.hparams.n_audio_ctx)) {
fprintf(stderr, "%s: kv_cache_init() failed for cross-attention cache\n", __func__);
return false;
}
{
const size_t memory_size = ggml_nbytes(wctx.kv_cross.k) + ggml_nbytes(wctx.kv_cross.v);
fprintf(stderr, "%s: kv cross size = %7.2f MB\n", __func__, memory_size/1024.0/1024.0);
}
wctx.buf_compute.resize (scale*std::max(MEM_REQ_ENCODE.at(model.type), MEM_REQ_DECODE.at(model.type)));
wctx.buf_compute_layer.resize(scale*std::max(MEM_REQ_ENCODE_LAYER.at(model.type), MEM_REQ_DECODE_LAYER.at(model.type)));
}
// 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
{
int32_t n_vocab = 0;
read_safe(loader, n_vocab);
//if (n_vocab != model.hparams.n_vocab) {
// fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
// __func__, fname.c_str(), n_vocab, model.hparams.n_vocab);
// return false;
//}
std::string word;
std::vector<char> tmp;
tmp.reserve(128);
for (int i = 0; i < n_vocab; i++) {
uint32_t len;
read_safe(loader, len);
if (len > 0) {
tmp.resize(len);
loader->read(loader->context, &tmp[0], tmp.size()); // read to buffer
word.assign(&tmp[0], tmp.size());
} else {
// seems like we have an empty-string token in multi-language models (i = 50256)
//fprintf(stderr, "%s: warning: empty-string token in vocab, i = %d\n", __func__, i);
word = "";
}
vocab.token_to_id[word] = i;
vocab.id_to_token[i] = word;
//printf("%s: vocab[%d] = '%s'\n", __func__, i, word.c_str());
}
vocab.n_vocab = model.hparams.n_vocab;
if (vocab.is_multilingual()) {
vocab.token_eot++;
vocab.token_sot++;
vocab.token_prev++;
vocab.token_solm++;
vocab.token_not++;
vocab.token_beg++;
}
if (n_vocab < model.hparams.n_vocab) {
fprintf(stderr, "%s: adding %d extra tokens\n", __func__, model.hparams.n_vocab - n_vocab);
for (int i = n_vocab; i < model.hparams.n_vocab; i++) {
if (i > vocab.token_beg) {
word = "[_TT_" + std::to_string(i - vocab.token_beg) + "]";
} else if (i == vocab.token_eot) {
word = "[_EOT_]";
} else if (i == vocab.token_sot) {
word = "[_SOT_]";
} else if (i == vocab.token_prev) {
word = "[_PREV_]";
} else if (i == vocab.token_not) {
word = "[_NOT_]";
} else if (i == vocab.token_beg) {
word = "[_BEG_]";
} else {
word = "[_extra_token_" + std::to_string(i) + "]";
}
vocab.token_to_id[word] = i;
vocab.id_to_token[i] = word;
}
}
wctx.logits.reserve(vocab.n_vocab*model.hparams.n_text_ctx);
wctx.logits_id.reserve(n_vocab);
// TAGS: WHISPER_DECODER_INIT
wctx.decoders[0].sequence.tokens.reserve(model.hparams.n_text_ctx);
wctx.decoders[0].probs.reserve (vocab.n_vocab);
wctx.decoders[0].logits.reserve (vocab.n_vocab);
wctx.decoders[0].logprobs.reserve(vocab.n_vocab);
}
size_t ctx_size = 0;
const ggml_type wtype = wctx.wtype;
{
const auto & hparams = model.hparams;
const int n_vocab = hparams.n_vocab;
const int n_audio_ctx = hparams.n_audio_ctx;
const int n_audio_state = hparams.n_audio_state;
const int n_audio_layer = hparams.n_audio_layer;
const int n_text_ctx = hparams.n_text_ctx;
const int n_text_state = hparams.n_text_state;
const int n_text_layer = hparams.n_text_layer;
const int n_mels = hparams.n_mels;
// encoder
{
ctx_size += n_audio_ctx*n_audio_state*ggml_type_size(GGML_TYPE_F32); // e_pe;
ctx_size += 3*n_mels*n_audio_state*ggml_type_size(wtype); // e_conv_1_w
ctx_size += n_audio_state*ggml_type_size(GGML_TYPE_F32); // e_conv_1_b
ctx_size += 3*n_audio_state*n_audio_state*ggml_type_size(wtype); // e_conv_2_w
ctx_size += n_audio_state*ggml_type_size(GGML_TYPE_F32); // e_conv_2_b
ctx_size += n_audio_state*ggml_type_size(GGML_TYPE_F32); // e_ln_w;
ctx_size += n_audio_state*ggml_type_size(GGML_TYPE_F32); // e_ln_b;
}
// decoder
{
ctx_size += n_text_ctx*n_text_state*ggml_type_size(GGML_TYPE_F32); // d_pe;
ctx_size += n_vocab*n_text_state*ggml_type_size(wtype); // d_te;
ctx_size += n_text_state*ggml_type_size(GGML_TYPE_F32); // d_ln_w;
ctx_size += n_text_state*ggml_type_size(GGML_TYPE_F32); // d_ln_b;
}
// encoder layers
{
ctx_size += n_audio_layer*(n_audio_state*ggml_type_size(GGML_TYPE_F32)); // mlp_ln_w
ctx_size += n_audio_layer*(n_audio_state*ggml_type_size(GGML_TYPE_F32)); // mlp_ln_b
ctx_size += n_audio_layer*(4*n_audio_state*n_audio_state*ggml_type_size(wtype)); // mlp_0_w
ctx_size += n_audio_layer*( 4*n_audio_state*ggml_type_size(GGML_TYPE_F32)); // mlp_0_b
ctx_size += n_audio_layer*(4*n_audio_state*n_audio_state*ggml_type_size(wtype)); // mlp_1_w
ctx_size += n_audio_layer*( n_audio_state*ggml_type_size(GGML_TYPE_F32)); // mlp_1_b
ctx_size += n_audio_layer*(n_audio_state*ggml_type_size(GGML_TYPE_F32)); // attn_ln_0_w
ctx_size += n_audio_layer*(n_audio_state*ggml_type_size(GGML_TYPE_F32)); // attn_ln_0_b
ctx_size += n_audio_layer*(n_audio_state*n_audio_state*ggml_type_size(wtype)); // attn_q_w
ctx_size += n_audio_layer*( n_audio_state*ggml_type_size(GGML_TYPE_F32)); // attn_q_b
ctx_size += n_audio_layer*(n_audio_state*n_audio_state*ggml_type_size(wtype)); // attn_k_w
ctx_size += n_audio_layer*(n_audio_state*n_audio_state*ggml_type_size(wtype)); // attn_v_w
ctx_size += n_audio_layer*( n_audio_state*ggml_type_size(GGML_TYPE_F32)); // attn_v_b
ctx_size += n_audio_layer*(n_audio_state*n_audio_state*ggml_type_size(wtype)); // attn_ln_1_w
ctx_size += n_audio_layer*( n_audio_state*ggml_type_size(GGML_TYPE_F32)); // attn_ln_1_b
}
// decoder layers
{
ctx_size += n_text_layer*(n_text_state*ggml_type_size(GGML_TYPE_F32)); // mlp_ln_w
ctx_size += n_text_layer*(n_text_state*ggml_type_size(GGML_TYPE_F32)); // mlp_ln_b
ctx_size += n_text_layer*(4*n_text_state*n_text_state*ggml_type_size(wtype)); // mlp_0_w
ctx_size += n_text_layer*( 4*n_text_state*ggml_type_size(GGML_TYPE_F32)); // mlp_0_b
ctx_size += n_text_layer*(4*n_text_state*n_text_state*ggml_type_size(wtype)); // mlp_1_w
ctx_size += n_text_layer*( n_text_state*ggml_type_size(GGML_TYPE_F32)); // mlp_1_b
ctx_size += n_text_layer*(n_text_state*ggml_type_size(GGML_TYPE_F32)); // attn_ln_0_w
ctx_size += n_text_layer*(n_text_state*ggml_type_size(GGML_TYPE_F32)); // attn_ln_0_b
ctx_size += n_text_layer*(n_text_state*n_text_state*ggml_type_size(wtype)); // attn_q_w
ctx_size += n_text_layer*( n_text_state*ggml_type_size(GGML_TYPE_F32)); // attn_q_b
ctx_size += n_text_layer*(n_text_state*n_text_state*ggml_type_size(wtype)); // attn_k_w
ctx_size += n_text_layer*(n_text_state*n_text_state*ggml_type_size(wtype)); // attn_v_w
ctx_size += n_text_layer*( n_text_state*ggml_type_size(GGML_TYPE_F32)); // attn_v_b
ctx_size += n_text_layer*(n_text_state*n_text_state*ggml_type_size(wtype)); // attn_ln_1_w
ctx_size += n_text_layer*( n_text_state*ggml_type_size(GGML_TYPE_F32)); // attn_ln_1_b
//
ctx_size += n_text_layer*(n_text_state*ggml_type_size(GGML_TYPE_F32)); // cross_attn_ln_0_w
ctx_size += n_text_layer*(n_text_state*ggml_type_size(GGML_TYPE_F32)); // cross_attn_ln_0_b
ctx_size += n_text_layer*(n_text_state*n_text_state*ggml_type_size(wtype)); // cross_attn_q_w
ctx_size += n_text_layer*( n_text_state*ggml_type_size(GGML_TYPE_F32)); // cross_attn_q_b
ctx_size += n_text_layer*(n_text_state*n_text_state*ggml_type_size(wtype)); // cross_attn_k_w
ctx_size += n_text_layer*(n_text_state*n_text_state*ggml_type_size(wtype)); // cross_attn_v_w
ctx_size += n_text_layer*( n_text_state*ggml_type_size(GGML_TYPE_F32)); // cross_attn_v_b
ctx_size += n_text_layer*(n_text_state*n_text_state*ggml_type_size(wtype)); // cross_attn_ln_1_w
ctx_size += n_text_layer*( n_text_state*ggml_type_size(GGML_TYPE_F32)); // cross_attn_ln_1_b
}
ctx_size += (15 + 15*n_audio_layer + 24*n_text_layer)*256; // object overhead
fprintf(stderr, "%s: model ctx = %7.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
}
// create the ggml context
{
struct ggml_init_params params;
params.mem_size = wctx.model.buf->size();