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Flash + language support (ref #2)

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- Achieved big performance improvement + memory usage reduction
- Can now translate / transcribe different languages
This commit is contained in:
Georgi Gerganov 2022-09-28 20:46:05 +03:00
parent 154fa796dd
commit f888c2373d
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GPG Key ID: 449E073F9DC10735
6 changed files with 1278 additions and 139 deletions
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9
Makefile
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@ -30,11 +30,16 @@ samples:
# runs it on all samples in the folder "./samples":
.PHONY: tiny.en
.PHONY: tiny
.PHONY: base.en
.PHONY: medium.en
.PHONY: base
.PHONY: small.en
.PHONY: small
.PHONY: medium.en
.PHONY: medium
.PHONY: large
tiny.en base.en medium.en small.en: main
tiny.en tiny base.en base small.en small medium.en medium large: main
bash ./download-ggml-model.sh $@
@echo ""
@echo "==============================================="

72
README.md
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@ -4,7 +4,8 @@ C/C++ port of [OpenAI's Whisper](https://github.com/openai/whisper) speech-to-te
- Plain C/C++ implementation without dependencies
- ARM_NEON and AVX intrinsics support
- F16 support
- Mixed F16 / F32 support
- Low memory usage (Flash Attention + Flash Forward)
## Usage
@ -27,9 +28,33 @@ For a quick demo, simply run `make base.en`:
```bash
$ make base.en
Downloading base.en (142 MB just once)
mkdir -p models
models/ggml-base.en.bin 100%[=================================>] 141.11M 7.50MB/s in 19s
gcc -pthread -O3 -mavx -mavx2 -mfma -mf16c -c ggml.c
g++ -pthread -O3 -std=c++11 -c main.cpp
g++ -o main ggml.o main.o
./main -h
usage: ./main [options]
options:
-h, --help show this help message and exit
-s SEED, --seed SEED RNG seed (default: -1)
-t N, --threads N number of threads to use during computation (default: 4)
-T N, --tokens N maximum number of tokens to generate per iteration (default: 64)
-v, --verbose verbose output
--translate translate from source language to english
-ps, --print_special print special tokens
-l LANG, --language LANG spoken language (default: en)
-m FNAME, --model FNAME model path (default: models/ggml-base.en.bin)
-f FNAME, --file FNAME input WAV file path (default: samples/jfk.wav)
bash ./download-ggml-model.sh base.en
Downloading ggml model base.en ...
models/ggml-base.en.bin 100%[=====================================>] 141.11M 7.84MB/s in 18s
Done! Model 'base.en' saved in 'models/ggml-base.en.bin'
You can now use it like this:
$ ./main -m models/ggml-base.en.bin -f samples/jfk.wav
===============================================
Running base.en on all samples in ./samples ...
@ -52,23 +77,24 @@ whisper_model_load: n_text_layer = 6
whisper_model_load: n_mels = 80
whisper_model_load: f16 = 1
whisper_model_load: type = 2
whisper_model_load: mem_required = 782.00 MB
whisper_model_load: mem_required = 611.00 MB
whisper_model_load: adding 1607 extra tokens
whisper_model_load: ggml ctx size = 186.26 MB
whisper_model_load: memory size = 45.66 MB
whisper_model_load: ggml ctx size = 163.43 MB
whisper_model_load: memory size = 22.83 MB
whisper_model_load: model size = 140.54 MB
log_mel_spectrogram: n_sample = 176000, n_len = 1100
log_mel_spectrogram: recording length: 11.000000 s
main: processing 176000 samples (11.0 sec), 4 threads, lang = english, task = transcribe ...
And so my fellow Americans ask not what your country can do for you. Ask what you can do for your country.
main: load time = 60.62 ms
main: mel time = 38.69 ms
main: sample time = 2.36 ms
main: encode time = 875.63 ms / 145.94 ms per layer
main: decode time = 103.17 ms
main: total time = 1081.13 ms
main: load time = 71.89 ms
main: mel time = 36.95 ms
main: sample time = 2.10 ms
main: encode time = 700.94 ms / 116.82 ms per layer
main: decode time = 86.14 ms
main: total time = 898.72 ms
```
The command downloads the `base.en` model converted to custom `ggml` format and runs the inference on all `.wav` samples in the folder `samples`.
@ -81,13 +107,18 @@ make samples
This will download a few more audio files from Wikipedia and convert them to 16-bit WAV format via `ffmpeg`.
You can download and run the other `.en` models as follows:
You can download and run the other models as follows:
```
make tiny.en
make tiny
make base.en
make base
make small.en
make small
make medium.en
make medium
make large
```
For detailed usage instructions, run: `./main -h`
@ -101,10 +132,8 @@ ffmpeg -i input.mp3 -ar 16000 -ac 1 -c:a pcm_s16le output.wav
## Limitations
- Only `.en` models are supported
- Very basic greedy sampling scheme - always pick up the top token
- No timestamps
- English only
- Inference only
- Runs on the CPU
- Only mono-channel 16-bit WAV is supported
@ -113,10 +142,11 @@ ffmpeg -i input.mp3 -ar 16000 -ac 1 -c:a pcm_s16le output.wav
| Model | Disk | Mem |
| --- | --- | --- |
| tiny.en | 75 MB | ~600 MB |
| base.en | 142 MB | ~800 MB |
| small.en | 466 MB | ~1.6 GB |
| medium.en | 1.5 GB | ~3.5 GB |
| tiny | 75 MB | ~460 MB |
| base | 142 MB | ~620 MB |
| small | 466 MB | ~1.3 GB |
| medium | 1.5 GB | ~2.8 GB |
| large | 2.9 GB | ~4.9 GB |
## ggml format

2
download-ggml-model.sh
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@ -6,7 +6,7 @@
ggml_path=$(dirname $(realpath $0))
# Whisper models
models=( "tiny.en" "base.en" "small.en" "medium.en" )
models=( "tiny.en" "tiny" "base.en" "base" "small.en" "small" "medium.en" "medium" "large" )
# list available models
function list_models {

975
ggml.c
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File diff suppressed because it is too large Load Diff

25
ggml.h
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@ -12,6 +12,7 @@ extern "C" {
#define GGML_MAX_NODES 4096
#define GGML_MAX_PARAMS 16
#define GGML_MAX_CONTEXTS 16
#define GGML_MAX_OPT 4
#ifdef __ARM_NEON
// we use the built-in 16-bit float type
@ -71,6 +72,9 @@ enum ggml_op {
GGML_OP_CONV_1D_1S,
GGML_OP_CONV_1D_2S,
GGML_OP_FLASH_ATTN,
GGML_OP_FLASH_FF,
GGML_OP_COUNT,
};
@ -93,6 +97,7 @@ struct ggml_tensor {
struct ggml_tensor * grad;
struct ggml_tensor * src0;
struct ggml_tensor * src1;
struct ggml_tensor * opt[GGML_MAX_OPT];
// thread scheduling
int n_tasks;
@ -182,14 +187,19 @@ struct ggml_tensor * ggml_new_tensor_4d(
int ne2,
int ne3);
struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value);
struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value);
struct ggml_tensor * ggml_dup_tensor (struct ggml_context * ctx, const struct ggml_tensor * src);
struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, const struct ggml_tensor * src);
struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor);
struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value);
struct ggml_tensor * ggml_set_f32 (struct ggml_tensor * tensor, float value);
int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i);
void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value);
float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i);
void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value);
@ -399,6 +409,21 @@ struct ggml_tensor * ggml_conv_1d_2s(
struct ggml_tensor * a,
struct ggml_tensor * b);
struct ggml_tensor * ggml_flash_attn(
struct ggml_context * ctx,
struct ggml_tensor * q,
struct ggml_tensor * k,
struct ggml_tensor * v,
bool masked);
struct ggml_tensor * ggml_flash_ff(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b0,
struct ggml_tensor * b1,
struct ggml_tensor * c0,
struct ggml_tensor * c1);
//
// automatic differentiation
//

320
main.cpp
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@ -1,5 +1,8 @@
#include "ggml.h"
#define USE_FLASH_ATTN
#define USE_FLASH_FF
// third-party utilities
// use your favorite implementations
#define DR_WAV_IMPLEMENTATION
@ -16,6 +19,7 @@
#include <thread>
#include <vector>
// available whisper models
enum e_model {
MODEL_UNKNOWN,
MODEL_TINY,
@ -25,14 +29,116 @@ enum e_model {
MODEL_LARGE,
};
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", } },
};
const size_t MB = 1024*1024;
const std::map<e_model, size_t> MEM_REQ_MODEL = {
{ MODEL_TINY, 100ull*MB },
{ MODEL_BASE, 190ull*MB },
{ MODEL_SMALL, 610ull*MB },
{ MODEL_MEDIUM, 1900ull*MB },
{ MODEL_LARGE, 3600ull*MB },
{ MODEL_TINY, 86ull*MB },
{ MODEL_BASE, 165ull*MB },
{ MODEL_SMALL, 540ull*MB },
{ MODEL_MEDIUM, 1650ull*MB },
{ MODEL_LARGE, 3260ull*MB },
};
const std::map<e_model, size_t> MEM_REQ_ENCODE = {
@ -44,11 +150,11 @@ const std::map<e_model, size_t> MEM_REQ_ENCODE = {
};
const std::map<e_model, size_t> MEM_REQ_ENCODE_LAYER = {
{ MODEL_TINY, 170ull*MB },
{ MODEL_BASE, 230ull*MB },
{ MODEL_SMALL, 350ull*MB },
{ MODEL_MEDIUM, 450ull*MB },
{ MODEL_LARGE, 570ull*MB },
{ MODEL_TINY, 64ull*MB },
{ MODEL_BASE, 84ull*MB },
{ MODEL_SMALL, 128ull*MB },
{ MODEL_MEDIUM, 172ull*MB },
{ MODEL_LARGE, 216ull*MB },
};
const std::map<e_model, size_t> MEM_REQ_DECODE = {
@ -102,6 +208,10 @@ struct whisper_vocab {
id token_solm = 50361; // ??
id token_beg = 50363;
// available tasks
const id token_translate = 50358;
const id token_transcribe = 50359;
bool is_multilingual() const {
return n_vocab == 51865;
}
@ -109,16 +219,18 @@ struct whisper_vocab {
// command-line parameters
struct whisper_params {
int32_t seed = -1; // RNG seed
int32_t seed = -1; // RNG seed, not used currently
int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
// sampling parameter - used for the greedy strategy
int32_t max_tokens_per_iter = 64;
bool verbose = false;
bool translate = false;
bool print_special_tokens = false;
std::string model = "models/ggml-base.en.bin"; // model path
std::string language = "en";
std::string model = "models/ggml-base.en.bin";
std::string fname_inp = "samples/jfk.wav";
};
@ -136,6 +248,15 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
params.max_tokens_per_iter = std::stoi(argv[++i]);
} else if (arg == "-v" || arg == "--verbose") {
params.verbose = true;
} else if (arg == "--translate") {
params.translate = true;
} else if (arg == "-l" || arg == "--language") {
params.language = argv[++i];
if (g_lang.find(params.language) == g_lang.end()) {
fprintf(stderr, "error: unknown language '%s'\n", params.language.c_str());
whisper_print_usage(argc, argv, params);
exit(0);
}
} else if (arg == "-ps" || arg == "--print_special") {
params.print_special_tokens = true;
} else if (arg == "-m" || arg == "--model") {
@ -165,11 +286,11 @@ void whisper_print_usage(int argc, char ** argv, const whisper_params & params)
fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
fprintf(stderr, " -T N, --tokens N maximum number of tokens to generate per iteration (default: %d)\n", params.max_tokens_per_iter);
fprintf(stderr, " -v, --verbose verbose output\n");
fprintf(stderr, " --translate translate from source language to english\n");
fprintf(stderr, " -ps, --print_special print special tokens\n");
fprintf(stderr, " -m FNAME, --model FNAME\n");
fprintf(stderr, " model path (default: %s)\n", params.model.c_str());
fprintf(stderr, " -f FNAME, --file FNAME\n");
fprintf(stderr, " input WAV file path (default: %s)\n", params.fname_inp.c_str());
fprintf(stderr, " -l LANG, --language LANG spoken language (default: %s)\n", params.language.c_str());
fprintf(stderr, " -m FNAME, --model FNAME model path (default: %s)\n", params.model.c_str());
fprintf(stderr, " -f FNAME, --file FNAME input WAV file path (default: %s)\n", params.fname_inp.c_str());
fprintf(stderr, "\n");
}
@ -417,6 +538,7 @@ bool whisper_model_load(const std::string & fname, whisper_model & model, whispe
printf("%s: f16 = %d\n", __func__, hparams.f16);
printf("%s: type = %d\n", __func__, model.type);
// this is the total memory required to run the inference
const size_t mem_required =
MEM_REQ_MODEL.at(model.type) +
MEM_REQ_ENCODE.at(model.type) +
@ -609,11 +731,11 @@ bool whisper_model_load(const std::string & fname, whisper_model & model, whispe
ctx_size += n_text_layer*( n_text_state*ggml_type_size(GGML_TYPE_F32)); // cross_attn_ln_1_b
}
ctx_size += n_text_layer*n_text_ctx*n_text_state*ggml_type_size(GGML_TYPE_F32); // memory_k
ctx_size += n_text_layer*n_text_ctx*n_text_state*ggml_type_size(GGML_TYPE_F32); // memory_v
ctx_size += n_text_layer*n_text_ctx*n_text_state*ggml_type_size(GGML_TYPE_F16); // memory_k
ctx_size += n_text_layer*n_text_ctx*n_text_state*ggml_type_size(GGML_TYPE_F16); // memory_v
ctx_size += n_text_layer*n_audio_ctx*n_text_state*ggml_type_size(GGML_TYPE_F32); // memory_cross_k
ctx_size += n_text_layer*n_audio_ctx*n_text_state*ggml_type_size(GGML_TYPE_F32); // memory_cross_v
ctx_size += n_text_layer*n_audio_ctx*n_text_state*ggml_type_size(GGML_TYPE_F16); // memory_cross_k
ctx_size += n_text_layer*n_audio_ctx*n_text_state*ggml_type_size(GGML_TYPE_F16); // memory_cross_v
ctx_size += (15 + 15*n_audio_layer + 24*n_text_layer)*256; // object overhead
@ -836,22 +958,24 @@ bool whisper_model_load(const std::string & fname, whisper_model & model, whispe
const int n_text_layer = hparams.n_text_layer;
const int n_text_ctx = hparams.n_text_ctx;
// key/value memory for the self-attention layer
{
const int n_mem = n_text_layer*n_text_ctx;
const int n_elements = n_text_state*n_mem;
model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);
model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);
}
// key/value memory for the cross-attention layer
{
const int n_audio_ctx = hparams.n_audio_ctx;
const int n_mem = n_text_layer*n_audio_ctx;
const int n_elements = n_text_state*n_mem;
model.memory_cross_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
model.memory_cross_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
model.memory_cross_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);
model.memory_cross_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);
}
const size_t memory_size =
@ -1057,14 +1181,14 @@ bool whisper_encode(
Qcur),
Qcur);
Qcur = ggml_scale(ctxL, Qcur, ggml_new_f32(ctxL, pow(float(n_state)/n_head, -0.25)));
//Qcur = ggml_scale(ctxL, Qcur, ggml_new_f32(ctxL, pow(float(n_state)/n_head, -0.25)));
// no bias for Key
// note: no bias for Key
struct ggml_tensor * Kcur = ggml_mul_mat(ctxL,
layer.attn_k_w,
cur);
Kcur = ggml_scale(ctxL, Kcur, ggml_new_f32(ctxL, pow(float(n_state)/n_head, -0.25)));
//Kcur = ggml_scale(ctxL, Kcur, ggml_new_f32(ctxL, pow(float(n_state)/n_head, -0.25)));
struct ggml_tensor * Vcur = ggml_mul_mat(ctxL,
layer.attn_v_w,
@ -1078,6 +1202,33 @@ bool whisper_encode(
// ------
#ifdef USE_FLASH_ATTN
struct ggml_tensor * Q =
ggml_permute(ctxL,
ggml_cpy(ctxL,
Qcur,
ggml_new_tensor_3d(ctxL, GGML_TYPE_F16, n_state/n_head, n_head, N)),
0, 2, 1, 3);
struct ggml_tensor * K =
ggml_permute(ctxL,
ggml_cpy(ctxL,
Kcur,
ggml_new_tensor_3d(ctxL, GGML_TYPE_F16, n_state/n_head, n_head, N)),
0, 2, 1, 3);
struct ggml_tensor * V =
ggml_cpy(ctxL,
ggml_permute(ctxL,
ggml_reshape_3d(ctxL,
Vcur,
n_state/n_head, n_head, N),
1, 2, 0, 3),
ggml_new_tensor_3d(ctxL, GGML_TYPE_F16, N, n_state/n_head, n_head)
);
struct ggml_tensor * KQV = ggml_flash_attn(ctxL, Q, K, V, false);
#else
struct ggml_tensor * Q =
ggml_permute(ctxL,
ggml_cpy(ctxL,
@ -1089,38 +1240,19 @@ bool whisper_encode(
ggml_permute(ctxL,
ggml_cpy(ctxL,
Kcur,
ggml_new_tensor_3d(ctxL, GGML_TYPE_F16, n_state/n_head, n_head, N)), // F16 !
ggml_new_tensor_3d(ctxL, GGML_TYPE_F16, n_state/n_head, n_head, N)),
0, 2, 1, 3);
//// BLAS attempt
//struct ggml_tensor * KQ =
// ggml_mul_mat(ctxL,
// ggml_cpy(ctxL, K, ggml_new_tensor_3d(ctxL, GGML_TYPE_F32, n_state/n_head, N, n_head)),
// ggml_cpy(ctxL, Q, ggml_new_tensor_3d(ctxL, GGML_TYPE_F32, n_state/n_head, N, n_head)));
// K * Q
struct ggml_tensor * KQ = ggml_mul_mat(ctxL, K, Q);
//struct ggml_tensor * K =
// ggml_cpy(ctxL,
// ggml_permute(ctxL,
// ggml_reshape_3d(ctxL,
// Kcur,
// n_state/n_head, n_head, N),
// 1, 2, 0, 3),
// ggml_new_tensor_3d(ctxL, GGML_TYPE_F16, N, n_state/n_head, n_head)
// );
struct ggml_tensor * KQ_scaled =
ggml_scale(ctxL,
KQ,
ggml_new_f32(ctxL, 1.0f/sqrt(float(n_state)/n_head))
);
//// K * Q
//struct ggml_tensor * KQ = ggml_mul_mat(ctxL, ggml_transpose(ctxL, K), Q);
//struct ggml_tensor * KQ_scaled =
// ggml_scale(ctxL,
// KQ,
// ggml_new_f32(ctxL, 1.0f/sqrt(float(n_state)/n_head))
// );
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctxL, KQ);
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctxL, KQ_scaled);
//struct ggml_tensor * V_trans =
// ggml_permute(ctxL,
@ -1138,10 +1270,11 @@ bool whisper_encode(
Vcur,
n_state/n_head, n_head, N),
0, 2, 1, 3),
ggml_new_tensor_3d(ctxL, GGML_TYPE_F16, n_state/n_head, N, n_head) // F16 !
ggml_new_tensor_3d(ctxL, GGML_TYPE_F16, n_state/n_head, N, n_head)
);
struct ggml_tensor * KQV = ggml_mul_mat(ctxL, ggml_transpose(ctxL, V), KQ_soft_max);
#endif
struct ggml_tensor * KQV_merged = ggml_permute(ctxL, KQV, 0, 2, 1, 3);
@ -1180,6 +1313,11 @@ bool whisper_encode(
ggml_repeat(ctxL, layer.mlp_ln_b, cur));
}
#ifdef USE_FLASH_FF
cur = ggml_flash_ff(ctxL,
ggml_cpy(ctxL, cur, ggml_new_tensor_2d(ctxL, GGML_TYPE_F16, n_state, N)),
layer.mlp_0_w, layer.mlp_0_b, layer.mlp_1_w, layer.mlp_1_b);
#else
// fully connected
cur = ggml_mul_mat(ctxL,
layer.mlp_0_w,
@ -1200,6 +1338,7 @@ bool whisper_encode(
cur = ggml_add(ctxL,
ggml_repeat(ctxL, layer.mlp_1_b, cur),
cur);
#endif
}
// output from this layer
@ -1368,7 +1507,7 @@ bool whisper_decode(
((int32_t *) position->data)[i] = n_past + i;
}
// wte + wpe
// token encoding + position encoding
struct ggml_tensor * cur =
ggml_add(ctx0,
ggml_get_rows(ctx0, model.d_te, embd),
@ -1420,7 +1559,7 @@ bool whisper_decode(
Qcur = ggml_scale(ctxL, Qcur, ggml_new_f32(ctxL, pow(float(n_state)/n_head, -0.25)));
// no bias for Key
// note: no bias for Key
struct ggml_tensor * Kcur = ggml_mul_mat(ctxL,
layer.attn_k_w,
cur);
@ -1506,7 +1645,7 @@ bool whisper_decode(
// norm
{
cur = ggml_norm(ctxL, inpCA); // Note we use inpCA here
cur = ggml_norm(ctxL, inpCA); // note: we use inpCA here
// cur = ln_0_w*cur + ln_0_b
cur = ggml_add(ctxL,
@ -1589,7 +1728,6 @@ bool whisper_decode(
cur);
}
// add the input
cur = ggml_add(ctxL, cur, inpCA);
@ -1601,8 +1739,7 @@ bool whisper_decode(
{
cur = ggml_norm(ctxL, inpFF);
// cur = ln_2_g*cur + ln_2_b
// [ 768, N]
// cur = mlp_ln_w*cur + mlp_ln_b
cur = ggml_add(ctxL,
ggml_mul(ctxL,
ggml_repeat(ctxL, layer.mlp_ln_w, cur),
@ -1689,11 +1826,11 @@ bool whisper_decode(
probs_out.resize(N*n_vocab);
memcpy(probs_out.data(), ggml_get_data(cur), sizeof(float)*N*n_vocab);
//if (N > 1) {
// const float mem_per_token = ggml_used_mem(ctx0)/1024.0/1024.0/N;
// printf("%s: used_mem = %f MB / %f per token\n", __func__, ggml_used_mem(ctx0)/1024.0/1024.0, mem_per_token);
// printf("%s: max mem = %f MB\n", __func__, mem_per_token*model.hparams.n_text_ctx);
//}
if (N > 1) {
//const float mem_per_token = ggml_used_mem(ctx0)/1024.0/1024.0/N;
//printf("%s: used_mem = %f MB / %f per token\n", __func__, ggml_used_mem(ctx0)/1024.0/1024.0, mem_per_token);
//printf("%s: max mem = %f MB\n", __func__, mem_per_token*model.hparams.n_text_ctx);
}
ggml_free(ctx0);
@ -1981,8 +2118,36 @@ int main(int argc, char ** argv) {
t_mel_us = ggml_time_us() - t_start_us;
}
// print some info about the processing
{
printf("\n");
if (!vocab.is_multilingual()) {
if (params.language != "en" || params.translate) {
params.language = "en";
params.translate = false;
printf("%s: WARNING: model is not multilingual, ignoring language and translation options\n", __func__);
}
}
printf("%s: processing %d samples (%.1f sec), %d threads, lang = %s, task = %s ...\n",
__func__, int(pcmf32.size()), float(pcmf32.size())/SAMPLE_RATE, params.n_threads,
g_lang.at(params.language).second.c_str(),
params.translate ? "translate" : "transcribe");
}
// the accumulated text context so far
std::vector<whisper_vocab::id> prompt_past = { };
// these tokens determine the task that will be performed
std::vector<whisper_vocab::id> prompt_init = { vocab.token_sot };
if (vocab.is_multilingual()) {
prompt_init.push_back(vocab.token_sot + 1 + g_lang.at(params.language).first);
if (params.translate) {
prompt_init.push_back(vocab.token_translate);
} else {
prompt_init.push_back(vocab.token_transcribe);
}
}
// main loop
int seek = 0;
while (true) {
@ -2006,24 +2171,23 @@ int main(int argc, char ** argv) {
std::vector<float> probs;
std::vector<float> logits;
// SOT
// ref: https://github.com/openai/whisper/blob/15ab54826343c27cfaf44ce31e9c8fb63d0aa775/whisper/decoding.py#L506-L526
// TODO: use different initial tokens for different tasks
std::vector<whisper_vocab::id> prompt = { vocab.token_sot };
std::vector<whisper_vocab::id> prompt;
int n_past = 0;
// if we have already generated some text, use it as a prompt to condition the next generation
if (prompt_past.size() > 0) {
int n_take = std::min(model.hparams.n_text_ctx/2, int(prompt_past.size()));
prompt = { vocab.token_prev };
prompt.insert(prompt.end(), prompt_past.end() - n_take, prompt_past.end());
prompt.push_back(vocab.token_sot);
prompt.insert(prompt.begin() + 1, prompt_past.end() - n_take, prompt_past.end());
prompt_past.clear();
prompt_past.insert(prompt_past.end(), prompt.begin() + 1, prompt.end() - 1);
prompt_past.insert(prompt_past.end(), prompt.begin() + 1, prompt.end());
}
prompt.insert(prompt.end(), prompt_init.begin(), prompt_init.end());
bool done = false;
int seek_delta = 100*CHUNK_SIZE;
whisper_vocab::id last_id = 0;
@ -2049,6 +2213,16 @@ int main(int argc, char ** argv) {
n_past += prompt.size();
prompt.clear();
// very basic greedy sampling strategy:
//
// - always take the most probable token
// - if we have accumulated more than 'params.max_tokens_per_iter' -> pick most probable timestamp token
// and advance the sliding window by that amount
// - in the meantime, if we encounter 2 consecutive timestamp tokens, we advance the sliding window too
//
// more sophisticated sampling strategies could be implemented here, but we keep it simple
// feel free to experiment!
//
{
// sample next token
const float temp = 1.0; // TODO