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7467 lines (5960 loc) · 261 KB
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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 GGML_USE_CUDA
#include "ggml-cuda.h"
#include "whisper-mel-cuda.hpp"
#endif
#ifdef GGML_USE_SYCL
#include "ggml-sycl.h"
#endif
#ifdef GGML_USE_VULKAN
#include "ggml-vulkan.h"
#endif
#ifdef GGML_USE_BLAS
#include "ggml-blas.h"
#endif
#ifdef WHISPER_USE_OPENVINO
#include "openvino/whisper-openvino-encoder.h"
#endif
#include "ggml.h"
#include "ggml-alloc.h"
#include "ggml-backend.h"
#include "whisper-mel.hpp"
#include <atomic>
#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>
#include <codecvt>
#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
#ifdef __GNUC__
#ifdef __MINGW32__
#define WHISPER_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
#else
#define WHISPER_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
#endif
#else
#define WHISPER_ATTRIBUTE_FORMAT(...)
#endif
//
// logging
//
WHISPER_ATTRIBUTE_FORMAT(2, 3)
static void whisper_log_internal (ggml_log_level level, const char * format, ...);
static void whisper_log_callback_default(ggml_log_level level, const char * text, void * user_data);
#define WHISPER_LOG_ERROR(...) whisper_log_internal(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
#define WHISPER_LOG_WARN(...) whisper_log_internal(GGML_LOG_LEVEL_WARN , __VA_ARGS__)
#define WHISPER_LOG_INFO(...) whisper_log_internal(GGML_LOG_LEVEL_INFO , __VA_ARGS__)
// define this to enable verbose trace logging - useful for debugging purposes
//#define WHISPER_DEBUG
#if defined(WHISPER_DEBUG)
#define WHISPER_LOG_DEBUG(...) whisper_log_internal(GGML_LOG_LEVEL_DEBUG, __VA_ARGS__)
#else
#define WHISPER_LOG_DEBUG(...)
#endif
#define WHISPER_ASSERT(x) \
do { \
if (!(x)) { \
WHISPER_LOG_ERROR("WHISPER_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
abort(); \
} \
} while (0)
//#define WHISPER_USE_FLASH_FF
#define WHISPER_MAX_DECODERS 8
#define WHISPER_MAX_NODES 4096
//
// ggml helpers
//
static bool ggml_graph_compute_helper(
struct ggml_cgraph * graph,
std::vector<uint8_t> & buf,
int n_threads,
ggml_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();
}
return ggml_graph_compute(graph, &plan);
}
static bool ggml_graph_compute_helper(
ggml_backend_sched_t sched,
struct ggml_cgraph * graph,
int n_threads) {
for (int i = 0; i < ggml_backend_sched_get_n_backends(sched); ++i) {
ggml_backend_t backend = ggml_backend_sched_get_backend(sched, i);
if (ggml_backend_is_cpu(backend)) {
ggml_backend_cpu_set_n_threads(backend, n_threads);
}
#ifdef GGML_USE_BLAS
if (ggml_backend_is_blas(backend)) {
ggml_backend_blas_set_n_threads(backend, n_threads);
}
#endif
#ifdef GGML_USE_METAL
if (ggml_backend_is_metal(backend)) {
ggml_backend_metal_set_n_cb(backend, n_threads);
}
#endif
}
bool t = ggml_backend_sched_graph_compute(sched, graph) == GGML_STATUS_SUCCESS;
ggml_backend_sched_reset(sched);
return t;
}
// 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
// TODO: CUDA is currently broken - seems ggml_mul_mat does not handle views correctly
#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<e_model, std::string> g_model_name = {
{ MODEL_UNKNOWN, "unknown" },
{ MODEL_TINY, "tiny" },
{ MODEL_BASE, "base" },
{ MODEL_SMALL, "small" },
{ MODEL_MEDIUM, "medium" },
{ MODEL_LARGE, "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", } },
{ "yue", { 99, "cantonese", } },
};
// [EXPERIMENTAL] Token-level timestamps with DTW
static const whisper_ahead g_aheads_tiny_en[] = { {1, 0}, {2, 0}, {2, 5}, {3, 0}, {3, 1}, {3, 2}, {3, 3}, {3, 4} };
static const whisper_ahead g_aheads_tiny[] = { {2, 2}, {3, 0}, {3, 2}, {3, 3}, {3, 4}, {3, 5} };
static const whisper_ahead g_aheads_base_en[] = { {3, 3}, {4, 7}, {5, 1}, {5, 5}, {5, 7} };
static const whisper_ahead g_aheads_base[] = { {3, 1}, {4, 2}, {4, 3}, {4, 7}, {5, 1}, {5, 2}, {5, 4}, {5, 6} };
static const whisper_ahead g_aheads_small_en[] = { {6, 6}, {7, 0}, {7, 3}, {7, 8}, {8, 2}, {8, 5}, {8, 7}, {9, 0}, {9, 4}, {9, 8}, {9, 10}, {10, 0}, {10, 1}, {10, 2}, {10, 3}, {10, 6}, {10, 11}, {11, 2}, {11, 4} };
static const whisper_ahead g_aheads_small[] = { {5, 3}, {5, 9}, {8, 0}, {8, 4}, {8, 7}, {8, 8}, {9, 0}, {9, 7}, {9, 9}, {10, 5} };
static const whisper_ahead g_aheads_medium_en[] = { {11, 4}, {14, 1}, {14, 12}, {14, 14}, {15, 4}, {16, 0}, {16, 4}, {16, 9}, {17, 12}, {17, 14}, {18, 7}, {18, 10}, {18, 15}, {20, 0}, {20, 3}, {20, 9}, {20, 14}, {21, 12} };
static const whisper_ahead g_aheads_medium[] = { {13, 15}, {15, 4}, {15, 15}, {16, 1}, {20, 0}, {23, 4} };
static const whisper_ahead g_aheads_large_v1[] = { {9, 19}, {11, 2}, {11, 4}, {11, 17}, {22, 7}, {22, 11}, {22, 17}, {23, 2}, {23, 15} };
static const whisper_ahead g_aheads_large_v2[] = { {10, 12}, {13, 17}, {16, 11}, {16, 12}, {16, 13}, {17, 15}, {17, 16}, {18, 4}, {18, 11}, {18, 19}, {19, 11}, {21, 2}, {21, 3}, {22, 3}, {22, 9}, {22, 12}, {23, 5}, {23, 7}, {23, 13}, {25, 5}, {26, 1}, {26, 12}, {27, 15} };
static const whisper_ahead g_aheads_large_v3[] = { {7, 0}, {10, 17}, {12, 18}, {13, 12}, {16, 1}, {17, 14}, {19, 11}, {21, 4}, {24, 1}, {25, 6} };
static const std::map<whisper_alignment_heads_preset, whisper_aheads> g_aheads {
{ WHISPER_AHEADS_TINY_EN, { 8, g_aheads_tiny_en } },
{ WHISPER_AHEADS_TINY, { 6, g_aheads_tiny } },
{ WHISPER_AHEADS_BASE_EN, { 5, g_aheads_base_en } },
{ WHISPER_AHEADS_BASE, { 8, g_aheads_base } },
{ WHISPER_AHEADS_SMALL_EN, { 19, g_aheads_small_en } },
{ WHISPER_AHEADS_SMALL, { 10, g_aheads_small } },
{ WHISPER_AHEADS_MEDIUM_EN, { 18, g_aheads_medium_en } },
{ WHISPER_AHEADS_MEDIUM, { 6, g_aheads_medium } },
{ WHISPER_AHEADS_LARGE_V1, { 9, g_aheads_large_v1 } },
{ WHISPER_AHEADS_LARGE_V2, { 23, g_aheads_large_v2 } },
{ WHISPER_AHEADS_LARGE_V3, { 10, g_aheads_large_v3 } },
};
static std::vector<uint32_t> get_alignment_heads_by_layer(const whisper_context_params & cparams, int il, int32_t n_text_layer, int32_t n_head);
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;
}
int num_languages() const {
return n_vocab - 51765 - (is_multilingual() ? 1 : 0);
}
};
struct whisper_segment {
int64_t t0;
int64_t t1;
std::string text;
std::vector<whisper_token_data> tokens;
bool speaker_turn_next;
};
struct whisper_batch {
int32_t n_tokens;
whisper_token * token;
whisper_pos * pos;
int32_t * n_seq_id; // always 1, here for consistency with llama.cpp
whisper_seq_id ** seq_id; // null terminated
int8_t * logits;
};
static struct whisper_batch whisper_batch_init(int32_t n_tokens, int32_t n_seq_max) {
whisper_batch batch = { 0, nullptr, nullptr, nullptr, nullptr, nullptr, };
batch.token = (whisper_token * ) malloc(sizeof(whisper_token) * (n_tokens));
batch.pos = (whisper_pos *) malloc(sizeof(whisper_pos) * (n_tokens));
batch.n_seq_id = (int32_t *) malloc(sizeof(int32_t) * (n_tokens));
batch.seq_id = (whisper_seq_id **) malloc(sizeof(whisper_seq_id *) * (n_tokens + 1));
for (int i = 0; i < n_tokens; ++i) {
batch.seq_id[i] = (whisper_seq_id *) malloc(sizeof(whisper_seq_id) * n_seq_max);
}
batch.seq_id[n_tokens] = nullptr;
batch.logits = (int8_t *) malloc(sizeof(int8_t) * n_tokens);
return batch;
}
static void whisper_batch_free(struct whisper_batch batch) {
if (batch.token) free(batch.token);
if (batch.pos) free(batch.pos);
if (batch.n_seq_id) free(batch.n_seq_id);
if (batch.seq_id) {
for (int i = 0; batch.seq_id[i]; ++i) {
free(batch.seq_id[i]);
}
free(batch.seq_id);
}
if (batch.logits) free(batch.logits);
}
static void whisper_batch_prep_legacy(whisper_batch & batch, const whisper_token * tokens, int n_tokens, int n_past, int seq_id) {
batch.n_tokens = n_tokens;
for (int i = 0; i < n_tokens; ++i) {
if (tokens) {
batch.token[i] = tokens[i];
}
batch.pos [i] = n_past + i;
batch.n_seq_id[i] = 1;
batch.seq_id [i][0] = seq_id;
batch.logits [i] = 0;
}
batch.logits[n_tokens - 1] = 1;
}
// 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()) {}
};
// ggml_backend_sched wrapper for whisper usage
struct whisper_sched {
ggml_backend_sched_t sched = nullptr;
std::vector<uint8_t> meta;
};
static size_t whisper_sched_size(struct whisper_sched & allocr) {
size_t size = allocr.meta.size();
for (int i = 0; i < ggml_backend_sched_get_n_backends(allocr.sched); ++i) {
ggml_backend_t backend = ggml_backend_sched_get_backend(allocr.sched, i);
size += ggml_backend_sched_get_buffer_size(allocr.sched, backend);
}
return size;
}
// measure the memory usage of a graph and prepare the allocr's internal data buffer
static bool whisper_sched_graph_init(struct whisper_sched & allocr, std::vector<ggml_backend_t> backends, std::function<struct ggml_cgraph *()> && get_graph) {
auto & sched = allocr.sched;
auto & meta = allocr.meta;
sched = ggml_backend_sched_new(backends.data(), nullptr, backends.size(), WHISPER_MAX_NODES, false);
meta.resize(ggml_tensor_overhead()*WHISPER_MAX_NODES + ggml_graph_overhead());
// since there are dependencies between the different graphs,
// we need to allocate them instead of only reserving to get the correct compute buffer size
if (!ggml_backend_sched_alloc_graph(sched, get_graph())) {
// failed to allocate the compute buffer
WHISPER_LOG_ERROR("%s: failed to allocate the compute buffer\n", __func__);
return false;
}
ggml_backend_sched_reset(sched);
return true;
}
// 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_cell {
whisper_pos pos = -1;
std::set<whisper_seq_id> seq_id;
bool has_seq_id(const whisper_seq_id & id) const {
return seq_id.find(id) != seq_id.end();
}
};
struct whisper_kv_cache {
uint32_t head = 0;
uint32_t size = 0;
// computed before each graph build
uint32_t n = 0;
std::vector<whisper_kv_cell> cells;
struct ggml_tensor * k;
struct ggml_tensor * v;
struct ggml_context * ctx = nullptr;
ggml_backend_buffer_t buffer = nullptr;
};
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;
// ggml context that contains all the meta information about the model tensors
struct ggml_context * ctx = nullptr;
// the model backend data is read-only and can be shared between processors
ggml_backend_buffer_t buffer = nullptr;
// tensors
int n_loaded;
std::map<std::string, struct ggml_tensor *> tensors;
};
struct whisper_partial_utf8 {
uint32_t value; // bit value so far (unshifted)
int n_remain; // num bytes remaining; -1 indicates invalid sequence
};
struct whisper_grammar {
/*const*/ std::vector<std::vector<whisper_grammar_element>> rules;
std::vector<std::vector<const whisper_grammar_element *>> stacks;
// buffer for partially generated UTF-8 sequence from accepted tokens
whisper_partial_utf8 partial_utf8;
};
struct whisper_grammar_candidate {
whisper_token id;
const uint32_t * code_points;
whisper_partial_utf8 partial_utf8;
};
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 {
// the currently generated sequence of tokens
whisper_sequence sequence;
// grammar parse state of generated sequence of tokens
whisper_grammar grammar;
int i_batch; // the index of the token in the current batch
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;
// 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
};
// [EXPERIMENTAL] Token-level timestamps with DTW
struct whisper_aheads_masks {
std::vector<struct ggml_tensor *> m; // One mask per text layer.
struct ggml_context * ctx = nullptr;
ggml_backend_buffer_t buffer = 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_batchd_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_batchd = 0; // number of decoder calls with n_tokens < 16 (batch decoding)
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
// unified self-attention KV cache for all decoders
whisper_kv_cache kv_self;
// cross-attention KV cache for the decoders
// shared between all decoders
whisper_kv_cache kv_cross;
// padded buffer for flash-attention
whisper_kv_cache kv_pad;
whisper_mel mel;
whisper_mel_calc * mel_calc = nullptr;
whisper_mel_calc * mel_calc_fallback = nullptr;
whisper_batch batch;
whisper_decoder decoders[WHISPER_MAX_DECODERS];
std::vector<ggml_backend_t> backends;
// - stores meta info about the intermediate tensors into the `meta` buffers
whisper_sched sched_conv;
whisper_sched sched_encode;
whisper_sched sched_cross;
whisper_sched sched_decode;
// result of the encoder
struct ggml_tensor * embd_conv = nullptr;
struct ggml_tensor * embd_enc = nullptr;
// helpers for GPU offloading
std::vector<float> inp_mask;
// 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;
int lang_id = 0; // english by default
std::string path_model; // populated by whisper_init_from_file_with_params()
#ifdef WHISPER_USE_COREML
whisper_coreml_context * ctx_coreml = 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] Token-level timestamps with DTW
whisper_aheads_masks aheads_masks;
ggml_tensor * aheads_cross_QKs = nullptr;
std::vector<float> aheads_cross_QKs_data;
// [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_context_params params;
whisper_model model;
whisper_vocab vocab;
whisper_state * state = nullptr;
std::string path_model; // populated by whisper_init_from_file_with_params()
};
struct whisper_global {
// We save the log callback globally
ggml_log_callback log_callback = whisper_log_callback_default;
void * log_callback_user_data = nullptr;
};
static whisper_global g_state;
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 whisper_kv_cache_init(
struct whisper_kv_cache & cache,
ggml_backend_t backend,
ggml_type wtype,
int64_t n_text_state,
int64_t n_text_layer,
int n_ctx) {
const int64_t n_mem = n_text_layer*n_ctx;
const int64_t n_elements = n_text_state*n_mem;
struct ggml_init_params params = {
/*.mem_size =*/ 2*ggml_tensor_overhead(),
/*.mem_buffer =*/ nullptr,
/*.no_alloc =*/ true,
};
cache.head = 0;
cache.size = n_ctx;
cache.cells.clear();
cache.cells.resize(n_ctx);
cache.ctx = ggml_init(params);
if (!cache.ctx) {
WHISPER_LOG_ERROR("%s: failed to allocate memory for the kv cache context\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);
cache.buffer = ggml_backend_alloc_ctx_tensors(cache.ctx, backend);
if (!cache.buffer) {
WHISPER_LOG_ERROR("%s: failed to allocate memory for the kv cache\n", __func__);
return false;
}
ggml_backend_buffer_clear(cache.buffer, 0);
return true;
}
static void whisper_kv_cache_free(struct whisper_kv_cache & cache) {
ggml_free(cache.ctx);
ggml_backend_buffer_free(cache.buffer);
cache.ctx = nullptr;
}
static bool whisper_kv_cache_find_slot(
struct whisper_kv_cache & cache,
const struct whisper_batch & batch) {
const uint32_t n_ctx = cache.size;
const uint32_t n_tokens = batch.n_tokens;
if (n_tokens > n_ctx) {
WHISPER_LOG_ERROR("%s: n_tokens=%d > n_ctx=%d\n", __func__, n_tokens, n_ctx);
return false;
}
uint32_t n_tested = 0;
while (true) {
if (cache.head + n_tokens > n_ctx) {
n_tested += n_ctx - cache.head;
cache.head = 0;
continue;
}
bool found = true;
for (uint32_t i = 0; i < n_tokens; i++) {
if (cache.cells[cache.head + i].pos >= 0) {
found = false;
cache.head += i + 1;
n_tested += i + 1;
break;
}
}
if (found) {
break;