mirror of
https://github.com/ggerganov/whisper.cpp.git
synced 2024-11-07 08:34:37 +01:00
Initial release
This commit is contained in:
Georgi Gerganov
commit
b0a11594ae
3
.gitignore
vendored
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3
.gitignore
vendored
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|||||||
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sync.sh
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main
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||||||
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*.o
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109
Makefile
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109
Makefile
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|
main: ggml.o main.o
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g++ -o main ggml.o main.o
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ggml.o: ggml.c ggml.h
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gcc -O3 -mavx -mavx2 -mfma -mf16c -c ggml.c
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|
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main.o: main.cpp ggml.h
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g++ -O3 -std=c++11 -c main.cpp
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# clean up the directory
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clean:
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rm -f *.o main
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# run the program
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run: main
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./main
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# download the following audio samples into folder "./samples":
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.PHONY: samples
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samples:
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@echo "Downloading samples..."
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mkdir -p samples
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@wget --quiet --show-progress -O samples/gb0.ogg https://upload.wikimedia.org/wikipedia/commons/2/22/George_W._Bush%27s_weekly_radio_address_%28November_1%2C_2008%29.oga
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@wget --quiet --show-progress -O samples/gb1.ogg https://upload.wikimedia.org/wikipedia/commons/1/1f/George_W_Bush_Columbia_FINAL.ogg
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@wget --quiet --show-progress -O samples/hp0.ogg https://upload.wikimedia.org/wikipedia/en/d/d4/En.henryfphillips.ogg
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@echo "Converting to 16-bit WAV ..."
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@ffmpeg -loglevel -0 -y -i samples/gb0.ogg -ar 16000 -ac 1 -c:a pcm_s16le samples/gb0.wav
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@ffmpeg -loglevel -0 -y -i samples/gb1.ogg -ar 16000 -ac 1 -c:a pcm_s16le samples/gb1.wav
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@ffmpeg -loglevel -0 -y -i samples/hp0.ogg -ar 16000 -ac 1 -c:a pcm_s16le samples/hp0.wav
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.PHONY: tiny.en
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tiny.en: main
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||||||
|
@echo "Downloading tiny.en (75 MB just once)"
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mkdir -p models
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|
@if [ ! -f models/ggml-tiny.en.bin ]; then \
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|
wget --quiet --show-progress -O models/ggml-tiny.en.bin https://ggml.ggerganov.com/ggml-model-whisper-tiny.en.bin ; \
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|
fi
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||||||
|
@echo "==============================================="
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|
@echo "Running tiny.en on all samples in ./samples ..."
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|
@echo "==============================================="
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@echo ""
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|
@for f in samples/*.wav; do \
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||||||
|
echo "----------------------------------------------" ; \
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||||||
|
echo "[+] Running base.en on $$f ... (run 'ffplay $$f' to listen)" ; \
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echo "----------------------------------------------" ; \
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echo "" ; \
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./main -m models/ggml-tiny.en.bin -f $$f ; \
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echo "" ; \
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|
done
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||||||
|
.PHONY: base.en
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|
base.en: main
|
||||||
|
@echo "Downloading base.en (142 MB just once)"
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||||||
|
mkdir -p models
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|
@if [ ! -f models/ggml-base.en.bin ]; then \
|
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|
wget --quiet --show-progress -O models/ggml-base.en.bin https://ggml.ggerganov.com/ggml-model-whisper-base.en.bin ; \
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|
fi
|
||||||
|
@echo "==============================================="
|
||||||
|
@echo "Running base.en on all samples in ./samples ..."
|
||||||
|
@echo "==============================================="
|
||||||
|
@echo ""
|
||||||
|
@for f in samples/*.wav; do \
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||||||
|
echo "----------------------------------------------" ; \
|
||||||
|
echo "[+] Running base.en on $$f ... (run 'ffplay $$f' to listen)" ; \
|
||||||
|
echo "----------------------------------------------" ; \
|
||||||
|
echo "" ; \
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||||||
|
./main -m models/ggml-base.en.bin -f $$f ; \
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|
echo "" ; \
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|
done
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|
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|
.PHONY: small.en
|
||||||
|
small.en: main
|
||||||
|
@echo "Downloading small.en (466 MB just once)"
|
||||||
|
mkdir -p models
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||||||
|
@if [ ! -f models/ggml-small.en.bin ]; then \
|
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|
wget --quiet --show-progress -O models/ggml-small.en.bin https://ggml.ggerganov.com/ggml-model-whisper-small.en.bin ; \
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||||||
|
fi
|
||||||
|
@echo "==============================================="
|
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|
@echo "Running small.en on all samples in ./samples ..."
|
||||||
|
@echo "==============================================="
|
||||||
|
@echo ""
|
||||||
|
@for f in samples/*.wav; do \
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||||||
|
echo "----------------------------------------------" ; \
|
||||||
|
echo "[+] Running base.en on $$f ... (run 'ffplay $$f' to listen)" ; \
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||||||
|
echo "----------------------------------------------" ; \
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|
echo "" ; \
|
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|
./main -m models/ggml-small.en.bin -f $$f ; \
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|
echo "" ; \
|
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|
done
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|
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|
.PHONY: medium.en
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||||||
|
medium.en: main
|
||||||
|
@echo "Downloading medium.en (1.5 GB just once)"
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|
mkdir -p models
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|
@if [ ! -f models/ggml-medium.en.bin ]; then \
|
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|
wget --quiet --show-progress -O models/ggml-medium.en.bin https://ggml.ggerganov.com/ggml-model-whisper-medium.en.bin ; \
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||||||
|
fi
|
||||||
|
@echo "==============================================="
|
||||||
|
@echo "Running medium.en on all samples in ./samples ..."
|
||||||
|
@echo "==============================================="
|
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|
@echo ""
|
||||||
|
@for f in samples/*.wav; do \
|
||||||
|
echo "----------------------------------------------" ; \
|
||||||
|
echo "[+] Running base.en on $$f ... (run 'ffplay $$f' to listen)" ; \
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|
echo "----------------------------------------------" ; \
|
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|
echo "" ; \
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|
./main -m models/ggml-medium.en.bin -f $$f ; \
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|
echo "" ; \
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|
done
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328
convert-pt-to-ggml.py
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328
convert-pt-to-ggml.py
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|
# Convert Whisper transformer model from PyTorch to ggml format
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|
#
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|
# Usage: python convert-pt-to-ggml.py ~/.cache/whisper/medium.pt ~/path/to/repo/whisper/ ./models/whisper-medium
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|
#
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|
# You need to clone the original repo in ~/path/to/repo/whisper/
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|
#
|
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|
# git clone https://github.com/openai/whisper ~/path/to/repo/whisper/
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|
#
|
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|
# It is used to various assets needed by the algorithm:
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|
#
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|
# - tokenizer
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|
# - mel filters
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|
#
|
||||||
|
# Also, you need to have the original models in ~/.cache/whisper/
|
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|
# See the original repo for more details.
|
||||||
|
#
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||||||
|
# This script loads the specified model and whisper assets and saves them in ggml format.
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||||||
|
# The output is a single binary file containing the following information:
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||||||
|
#
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||||||
|
# - hparams
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||||||
|
# - mel filters
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||||||
|
# - tokenizer vocab
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||||||
|
# - model variables
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||||||
|
#
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||||||
|
# For each variable, write the following:
|
||||||
|
#
|
||||||
|
# - Number of dimensions (int)
|
||||||
|
# - Name length (int)
|
||||||
|
# - Dimensions (int[n_dims])
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||||||
|
# - Name (char[name_length])
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||||||
|
# - Data (float[n_dims])
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||||||
|
#
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||||||
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||||||
|
import io
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||||||
|
import os
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||||||
|
import sys
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||||||
|
import struct
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||||||
|
import json
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|
import code
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||||||
|
import torch
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||||||
|
import numpy as np
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||||||
|
|
||||||
|
from transformers import GPTJForCausalLM
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||||||
|
from transformers import GPT2TokenizerFast
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||||||
|
|
||||||
|
# ref: https://github.com/openai/whisper/blob/8cf36f3508c9acd341a45eb2364239a3d81458b9/whisper/tokenizer.py#L10-L110
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||||||
|
LANGUAGES = {
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||||||
|
"en": "english",
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||||||
|
"zh": "chinese",
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||||||
|
"de": "german",
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||||||
|
"es": "spanish",
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||||||
|
"ru": "russian",
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||||||
|
"ko": "korean",
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||||||
|
"fr": "french",
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||||||
|
"ja": "japanese",
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||||||
|
"pt": "portuguese",
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||||||
|
"tr": "turkish",
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||||||
|
"pl": "polish",
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|
"ca": "catalan",
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|
"nl": "dutch",
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||||||
|
"ar": "arabic",
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||||||
|
"sv": "swedish",
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||||||
|
"it": "italian",
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||||||
|
"id": "indonesian",
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||||||
|
"hi": "hindi",
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||||||
|
"fi": "finnish",
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||||||
|
"vi": "vietnamese",
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||||||
|
"iw": "hebrew",
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||||||
|
"uk": "ukrainian",
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||||||
|
"el": "greek",
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||||||
|
"ms": "malay",
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||||||
|
"cs": "czech",
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||||||
|
"ro": "romanian",
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||||||
|
"da": "danish",
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||||||
|
"hu": "hungarian",
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||||||
|
"ta": "tamil",
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|
"no": "norwegian",
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|
"th": "thai",
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|
"ur": "urdu",
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||||||
|
"hr": "croatian",
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||||||
|
"bg": "bulgarian",
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||||||
|
"lt": "lithuanian",
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||||||
|
"la": "latin",
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||||||
|
"mi": "maori",
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||||||
|
"ml": "malayalam",
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||||||
|
"cy": "welsh",
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||||||
|
"sk": "slovak",
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||||||
|
"te": "telugu",
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||||||
|
"fa": "persian",
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||||||
|
"lv": "latvian",
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||||||
|
"bn": "bengali",
|
||||||
|
"sr": "serbian",
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||||||
|
"az": "azerbaijani",
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||||||
|
"sl": "slovenian",
|
||||||
|
"kn": "kannada",
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||||||
|
"et": "estonian",
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||||||
|
"mk": "macedonian",
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||||||
|
"br": "breton",
|
||||||
|
"eu": "basque",
|
||||||
|
"is": "icelandic",
|
||||||
|
"hy": "armenian",
|
||||||
|
"ne": "nepali",
|
||||||
|
"mn": "mongolian",
|
||||||
|
"bs": "bosnian",
|
||||||
|
"kk": "kazakh",
|
||||||
|
"sq": "albanian",
|
||||||
|
"sw": "swahili",
|
||||||
|
"gl": "galician",
|
||||||
|
"mr": "marathi",
|
||||||
|
"pa": "punjabi",
|
||||||
|
"si": "sinhala",
|
||||||
|
"km": "khmer",
|
||||||
|
"sn": "shona",
|
||||||
|
"yo": "yoruba",
|
||||||
|
"so": "somali",
|
||||||
|
"af": "afrikaans",
|
||||||
|
"oc": "occitan",
|
||||||
|
"ka": "georgian",
|
||||||
|
"be": "belarusian",
|
||||||
|
"tg": "tajik",
|
||||||
|
"sd": "sindhi",
|
||||||
|
"gu": "gujarati",
|
||||||
|
"am": "amharic",
|
||||||
|
"yi": "yiddish",
|
||||||
|
"lo": "lao",
|
||||||
|
"uz": "uzbek",
|
||||||
|
"fo": "faroese",
|
||||||
|
"ht": "haitian creole",
|
||||||
|
"ps": "pashto",
|
||||||
|
"tk": "turkmen",
|
||||||
|
"nn": "nynorsk",
|
||||||
|
"mt": "maltese",
|
||||||
|
"sa": "sanskrit",
|
||||||
|
"lb": "luxembourgish",
|
||||||
|
"my": "myanmar",
|
||||||
|
"bo": "tibetan",
|
||||||
|
"tl": "tagalog",
|
||||||
|
"mg": "malagasy",
|
||||||
|
"as": "assamese",
|
||||||
|
"tt": "tatar",
|
||||||
|
"haw": "hawaiian",
|
||||||
|
"ln": "lingala",
|
||||||
|
"ha": "hausa",
|
||||||
|
"ba": "bashkir",
|
||||||
|
"jw": "javanese",
|
||||||
|
"su": "sundanese",
|
||||||
|
}
|
||||||
|
|
||||||
|
# ref: https://github.com/openai/whisper/blob/8cf36f3508c9acd341a45eb2364239a3d81458b9/whisper/tokenizer.py#L273-L292
|
||||||
|
def build_tokenizer(path_to_whisper_repo: str, name: str = "gpt2"):
|
||||||
|
os.environ["TOKENIZERS_PARALLELISM"] = "false"
|
||||||
|
path = os.path.join(path_to_whisper_repo, "whisper/assets", name)
|
||||||
|
tokenizer = GPT2TokenizerFast.from_pretrained(path)
|
||||||
|
|
||||||
|
specials = [
|
||||||
|
"<|startoftranscript|>",
|
||||||
|
*[f"<|{lang}|>" for lang in LANGUAGES.keys()],
|
||||||
|
"<|translate|>",
|
||||||
|
"<|transcribe|>",
|
||||||
|
"<|startoflm|>",
|
||||||
|
"<|startofprev|>",
|
||||||
|
"<|nocaptions|>",
|
||||||
|
"<|notimestamps|>",
|
||||||
|
]
|
||||||
|
|
||||||
|
tokenizer.add_special_tokens(dict(additional_special_tokens=specials))
|
||||||
|
return tokenizer
|
||||||
|
|
||||||
|
# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
|
||||||
|
def bytes_to_unicode():
|
||||||
|
"""
|
||||||
|
Returns list of utf-8 byte and a corresponding list of unicode strings.
|
||||||
|
The reversible bpe codes work on unicode strings.
|
||||||
|
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
|
||||||
|
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
|
||||||
|
This is a signficant percentage of your normal, say, 32K bpe vocab.
|
||||||
|
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
|
||||||
|
And avoids mapping to whitespace/control characters the bpe code barfs on.
|
||||||
|
"""
|
||||||
|
bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
|
||||||
|
cs = bs[:]
|
||||||
|
n = 0
|
||||||
|
for b in range(2**8):
|
||||||
|
if b not in bs:
|
||||||
|
bs.append(b)
|
||||||
|
cs.append(2**8+n)
|
||||||
|
n += 1
|
||||||
|
cs = [chr(n) for n in cs]
|
||||||
|
return dict(zip(bs, cs))
|
||||||
|
|
||||||
|
|
||||||
|
if len(sys.argv) < 4:
|
||||||
|
print("Usage: convert-pt-to-ggml.py model.pt path-to-whisper-repo dir-output [use-f32]\n")
|
||||||
|
sys.exit(1)
|
||||||
|
|
||||||
|
fname_inp = sys.argv[1]
|
||||||
|
dir_whisper = sys.argv[2]
|
||||||
|
dir_out = sys.argv[3]
|
||||||
|
|
||||||
|
# try to load PyTorch binary data
|
||||||
|
try:
|
||||||
|
model_bytes = open(fname_inp, "rb").read()
|
||||||
|
with io.BytesIO(model_bytes) as fp:
|
||||||
|
checkpoint = torch.load(fp, map_location="cpu")
|
||||||
|
except:
|
||||||
|
print("Error: failed to load PyTorch model file: %s" % fname_inp)
|
||||||
|
sys.exit(1)
|
||||||
|
|
||||||
|
hparams = checkpoint["dims"]
|
||||||
|
print("hparams:", hparams)
|
||||||
|
|
||||||
|
list_vars = checkpoint["model_state_dict"]
|
||||||
|
|
||||||
|
#print(list_vars['encoder.positional_embedding'])
|
||||||
|
#print(list_vars['encoder.conv1.weight'])
|
||||||
|
#print(list_vars['encoder.conv1.weight'].shape)
|
||||||
|
|
||||||
|
# load mel filters
|
||||||
|
n_mels = hparams["n_mels"]
|
||||||
|
with np.load(os.path.join(dir_whisper, "whisper/assets", "mel_filters.npz")) as f:
|
||||||
|
filters = torch.from_numpy(f[f"mel_{n_mels}"])
|
||||||
|
#print (filters)
|
||||||
|
|
||||||
|
#code.interact(local=locals())
|
||||||
|
|
||||||
|
multilingual = hparams["n_vocab"] == 51865
|
||||||
|
tokenizer = build_tokenizer(dir_whisper, multilingual and "multilingual" or "gpt2")
|
||||||
|
|
||||||
|
#print(tokenizer)
|
||||||
|
#print(tokenizer.name_or_path)
|
||||||
|
#print(len(tokenizer.additional_special_tokens))
|
||||||
|
dir_tokenizer = tokenizer.name_or_path
|
||||||
|
|
||||||
|
# output in the same directory as the model
|
||||||
|
fname_out = dir_out + "/ggml-model.bin"
|
||||||
|
|
||||||
|
with open(dir_tokenizer + "/vocab.json", "r") as f:
|
||||||
|
tokens = json.load(f)
|
||||||
|
|
||||||
|
# use 16-bit or 32-bit floats
|
||||||
|
use_f16 = True
|
||||||
|
if len(sys.argv) > 4:
|
||||||
|
use_f16 = False
|
||||||
|
fname_out = dir_out + "/ggml-model-f32.bin"
|
||||||
|
|
||||||
|
fout = open(fname_out, "wb")
|
||||||
|
|
||||||
|
fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
|
||||||
|
fout.write(struct.pack("i", hparams["n_vocab"]))
|
||||||
|
fout.write(struct.pack("i", hparams["n_audio_ctx"]))
|
||||||
|
fout.write(struct.pack("i", hparams["n_audio_state"]))
|
||||||
|
fout.write(struct.pack("i", hparams["n_audio_head"]))
|
||||||
|
fout.write(struct.pack("i", hparams["n_audio_layer"]))
|
||||||
|
fout.write(struct.pack("i", hparams["n_text_ctx"]))
|
||||||
|
fout.write(struct.pack("i", hparams["n_text_state"]))
|
||||||
|
fout.write(struct.pack("i", hparams["n_text_head"]))
|
||||||
|
fout.write(struct.pack("i", hparams["n_text_layer"]))
|
||||||
|
fout.write(struct.pack("i", hparams["n_mels"]))
|
||||||
|
fout.write(struct.pack("i", use_f16))
|
||||||
|
|
||||||
|
# write mel filters
|
||||||
|
fout.write(struct.pack("i", filters.shape[0]))
|
||||||
|
fout.write(struct.pack("i", filters.shape[1]))
|
||||||
|
for i in range(filters.shape[0]):
|
||||||
|
for j in range(filters.shape[1]):
|
||||||
|
fout.write(struct.pack("f", filters[i][j]))
|
||||||
|
|
||||||
|
byte_encoder = bytes_to_unicode()
|
||||||
|
byte_decoder = {v:k for k, v in byte_encoder.items()}
|
||||||
|
|
||||||
|
fout.write(struct.pack("i", len(tokens)))
|
||||||
|
|
||||||
|
for key in tokens:
|
||||||
|
text = bytearray([byte_decoder[c] for c in key]).decode('utf-8', errors='replace').encode('utf-8')
|
||||||
|
fout.write(struct.pack("i", len(text)))
|
||||||
|
fout.write(text)
|
||||||
|
|
||||||
|
for name in list_vars.keys():
|
||||||
|
data = list_vars[name].squeeze().numpy()
|
||||||
|
print("Processing variable: " + name + " with shape: ", data.shape)
|
||||||
|
|
||||||
|
# reshape conv bias from [n] to [n, 1]
|
||||||
|
if name == "encoder.conv1.bias" or \
|
||||||
|
name == "encoder.conv2.bias":
|
||||||
|
data = data.reshape(data.shape[0], 1)
|
||||||
|
print(" Reshaped variable: " + name + " to shape: ", data.shape)
|
||||||
|
|
||||||
|
n_dims = len(data.shape);
|
||||||
|
|
||||||
|
# looks like the whisper models are in f16 by default
|
||||||
|
# so we need to convert the small tensors to f32 until we fully support f16 in ggml
|
||||||
|
# ftype == 0 -> float32, ftype == 1 -> float16
|
||||||
|
ftype = 1;
|
||||||
|
if use_f16:
|
||||||
|
if n_dims < 2 or \
|
||||||
|
name == "encoder.conv1.bias" or \
|
||||||
|
name == "encoder.conv2.bias" or \
|
||||||
|
name == "encoder.positional_embedding" or \
|
||||||
|
name == "decoder.positional_embedding":
|
||||||
|
ftype = 0
|
||||||
|
data = data.astype(np.float32)
|
||||||
|
print(" Converting to float32")
|
||||||
|
data = data.astype(np.float32)
|
||||||
|
ftype = 0
|
||||||
|
else:
|
||||||
|
data = data.astype(np.float32)
|
||||||
|
ftype = 0
|
||||||
|
|
||||||
|
#if name.startswith("encoder"):
|
||||||
|
# if name.endswith("mlp.0.weight") or \
|
||||||
|
# name.endswith("mlp.2.weight"):
|
||||||
|
# print(" Transposing")
|
||||||
|
# data = data.transpose()
|
||||||
|
|
||||||
|
# header
|
||||||
|
str = name.encode('utf-8')
|
||||||
|
fout.write(struct.pack("iii", n_dims, len(str), ftype))
|
||||||
|
for i in range(n_dims):
|
||||||
|
fout.write(struct.pack("i", data.shape[n_dims - 1 - i]))
|
||||||
|
fout.write(str);
|
||||||
|
|
||||||
|
# data
|
||||||
|
data.tofile(fout)
|
||||||
|
|
||||||
|
fout.close()
|
||||||
|
|
||||||
|
print("Done. Output file: " + fname_out)
|
||||||
|
print("")
|
||||||
527
ggml.h
Normal file
527
ggml.h
Normal file
@ -0,0 +1,527 @@
|
|||||||
|
#pragma once
|
||||||
|
|
||||||
|
#ifdef __cplusplus
|
||||||
|
extern "C" {
|
||||||
|
#endif
|
||||||
|
|
||||||
|
#include <stdint.h>
|
||||||
|
#include <stddef.h>
|
||||||
|
#include <stdbool.h>
|
||||||
|
|
||||||
|
#define GGML_MAX_DIMS 4
|
||||||
|
#define GGML_MAX_NODES 4096
|
||||||
|
#define GGML_MAX_PARAMS 16
|
||||||
|
#define GGML_MAX_CONTEXTS 16
|
||||||
|
|
||||||
|
#ifdef __ARM_NEON
|
||||||
|
// we use the built-in 16-bit float type
|
||||||
|
typedef __fp16 ggml_fp16_t;
|
||||||
|
#else
|
||||||
|
typedef uint16_t ggml_fp16_t;
|
||||||
|
#endif
|
||||||
|
|
||||||
|
float ggml_fp16_to_fp32(ggml_fp16_t x);
|
||||||
|
ggml_fp16_t ggml_fp32_to_fp16(float x);
|
||||||
|
|
||||||
|
struct ggml_object;
|
||||||
|
struct ggml_context;
|
||||||
|
|
||||||
|
enum ggml_type {
|
||||||
|
GGML_TYPE_I8,
|
||||||
|
GGML_TYPE_I16,
|
||||||
|
GGML_TYPE_I32,
|
||||||
|
GGML_TYPE_F16,
|
||||||
|
GGML_TYPE_F32,
|
||||||
|
GGML_TYPE_COUNT,
|
||||||
|
};
|
||||||
|
|
||||||
|
enum ggml_op {
|
||||||
|
GGML_OP_NONE = 0,
|
||||||
|
|
||||||
|
GGML_OP_DUP,
|
||||||
|
GGML_OP_ADD,
|
||||||
|
GGML_OP_SUB,
|
||||||
|
GGML_OP_MUL,
|
||||||
|
GGML_OP_DIV,
|
||||||
|
GGML_OP_SQR,
|
||||||
|
GGML_OP_SQRT,
|
||||||
|
GGML_OP_SUM,
|
||||||
|
GGML_OP_MEAN,
|
||||||
|
GGML_OP_REPEAT,
|
||||||
|
GGML_OP_ABS,
|
||||||
|
GGML_OP_SGN,
|
||||||
|
GGML_OP_NEG,
|
||||||
|
GGML_OP_STEP,
|
||||||
|
GGML_OP_RELU,
|
||||||
|
GGML_OP_GELU,
|
||||||
|
GGML_OP_NORM, // normalize
|
||||||
|
|
||||||
|
GGML_OP_MUL_MAT,
|
||||||
|
|
||||||
|
GGML_OP_SCALE,
|
||||||
|
GGML_OP_CPY,
|
||||||
|
GGML_OP_RESHAPE,
|
||||||
|
GGML_OP_VIEW,
|
||||||
|
GGML_OP_PERMUTE,
|
||||||
|
GGML_OP_TRANSPOSE,
|
||||||
|
GGML_OP_GET_ROWS,
|
||||||
|
GGML_OP_DIAG_MASK_INF,
|
||||||
|
GGML_OP_SOFT_MAX,
|
||||||
|
GGML_OP_ROPE,
|
||||||
|
GGML_OP_CONV_1D_1S,
|
||||||
|
GGML_OP_CONV_1D_2S,
|
||||||
|
|
||||||
|
GGML_OP_COUNT,
|
||||||
|
};
|
||||||
|
|
||||||
|
// n-dimensional tensor
|
||||||
|
struct ggml_tensor {
|
||||||
|
enum ggml_type type;
|
||||||
|
|
||||||
|
int n_dims;
|
||||||
|
int ne[GGML_MAX_DIMS]; // number of elements
|
||||||
|
size_t nb[GGML_MAX_DIMS]; // stride in bytes:
|
||||||
|
// nb[0] = sizeof(type)
|
||||||
|
// nb[1] = nb[0] * ne[0] + padding
|
||||||
|
// nb[i] = nb[i-1] * ne[i-1]
|
||||||
|
|
||||||
|
// compute data
|
||||||
|
enum ggml_op op;
|
||||||
|
|
||||||
|
bool is_param;
|
||||||
|
|
||||||
|
struct ggml_tensor * grad;
|
||||||
|
struct ggml_tensor * src0;
|
||||||
|
struct ggml_tensor * src1;
|
||||||
|
|
||||||
|
// thread scheduling
|
||||||
|
int n_tasks;
|
||||||
|
|
||||||
|
// performance
|
||||||
|
int perf_runs;
|
||||||
|
int64_t perf_cycles;
|
||||||
|
int64_t perf_time_us;
|
||||||
|
|
||||||
|
void * data;
|
||||||
|
char pad[8];
|
||||||
|
};
|
||||||
|
|
||||||
|
// computation graph
|
||||||
|
struct ggml_cgraph {
|
||||||
|
int n_nodes;
|
||||||
|
int n_leafs;
|
||||||
|
int n_threads;
|
||||||
|
|
||||||
|
size_t work_size;
|
||||||
|
struct ggml_tensor * work;
|
||||||
|
|
||||||
|
struct ggml_tensor * nodes[GGML_MAX_NODES];
|
||||||
|
struct ggml_tensor * grads[GGML_MAX_NODES];
|
||||||
|
struct ggml_tensor * leafs[GGML_MAX_NODES];
|
||||||
|
|
||||||
|
// performance
|
||||||
|
int perf_runs;
|
||||||
|
int64_t perf_cycles;
|
||||||
|
int64_t perf_time_us;
|
||||||
|
};
|
||||||
|
|
||||||
|
struct ggml_init_params {
|
||||||
|
// memory pool
|
||||||
|
size_t mem_size; // bytes
|
||||||
|
void * mem_buffer; // if NULL, memory will be allocated internally
|
||||||
|
};
|
||||||
|
|
||||||
|
int64_t ggml_time_ms(void);
|
||||||
|
int64_t ggml_time_us(void);
|
||||||
|
int64_t ggml_cycles(void);
|
||||||
|
int64_t ggml_cycles_per_ms(void);
|
||||||
|
|
||||||
|
void ggml_print_object (const struct ggml_object * obj);
|
||||||
|
void ggml_print_objects(const struct ggml_context * ctx);
|
||||||
|
|
||||||
|
int ggml_nelements(const struct ggml_tensor * tensor);
|
||||||
|
size_t ggml_nbytes (const struct ggml_tensor * tensor);
|
||||||
|
|
||||||
|
size_t ggml_type_size (enum ggml_type type);
|
||||||
|
size_t ggml_element_size(const struct ggml_tensor * tensor);
|
||||||
|
|
||||||
|
struct ggml_context * ggml_init(struct ggml_init_params params);
|
||||||
|
void ggml_free(struct ggml_context * ctx);
|
||||||
|
|
||||||
|
size_t ggml_used_mem(const struct ggml_context * ctx);
|
||||||
|
|
||||||
|
struct ggml_tensor * ggml_new_tensor(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
enum ggml_type type,
|
||||||
|
int n_dims,
|
||||||
|
const int *ne);
|
||||||
|
|
||||||
|
struct ggml_tensor * ggml_new_tensor_1d(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
enum ggml_type type,
|
||||||
|
int ne0);
|
||||||
|
|
||||||
|
struct ggml_tensor * ggml_new_tensor_2d(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
enum ggml_type type,
|
||||||
|
int ne0,
|
||||||
|
int ne1);
|
||||||
|
|
||||||
|
struct ggml_tensor * ggml_new_tensor_3d(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
enum ggml_type type,
|
||||||
|
int ne0,
|
||||||
|
int ne1,
|
||||||
|
int ne2);
|
||||||
|
|
||||||
|
struct ggml_tensor * ggml_new_tensor_4d(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
enum ggml_type type,
|
||||||
|
int ne0,
|
||||||
|
int ne1,
|
||||||
|
int ne2,
|
||||||
|
int ne3);
|
||||||
|
|
||||||
|
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_f32 (struct ggml_tensor * tensor, float 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);
|
||||||
|
|
||||||
|
void * ggml_get_data (const struct ggml_tensor * tensor);
|
||||||
|
float * ggml_get_data_f32(const struct ggml_tensor * tensor);
|
||||||
|
|
||||||
|
//
|
||||||
|
// operations on tensors with backpropagation
|
||||||
|
//
|
||||||
|
|
||||||
|
struct ggml_tensor * ggml_dup(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a);
|
||||||
|
|
||||||
|
struct ggml_tensor * ggml_add(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a,
|
||||||
|
struct ggml_tensor * b);
|
||||||
|
|
||||||
|
struct ggml_tensor * ggml_sub(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a,
|
||||||
|
struct ggml_tensor * b);
|
||||||
|
|
||||||
|
struct ggml_tensor * ggml_mul(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a,
|
||||||
|
struct ggml_tensor * b);
|
||||||
|
|
||||||
|
struct ggml_tensor * ggml_div(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a,
|
||||||
|
struct ggml_tensor * b);
|
||||||
|
|
||||||
|
struct ggml_tensor * ggml_sqr(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a);
|
||||||
|
|
||||||
|
struct ggml_tensor * ggml_sqrt(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a);
|
||||||
|
|
||||||
|
// return scalar
|
||||||
|
// TODO: compute sum along rows
|
||||||
|
struct ggml_tensor * ggml_sum(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a);
|
||||||
|
|
||||||
|
// mean along rows
|
||||||
|
struct ggml_tensor * ggml_mean(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a);
|
||||||
|
|
||||||
|
// if a is the same shape as b, and a is not parameter, return a
|
||||||
|
// otherwise, return a new tensor: repeat(a) to fit in b
|
||||||
|
struct ggml_tensor * ggml_repeat(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a,
|
||||||
|
struct ggml_tensor * b);
|
||||||
|
|
||||||
|
struct ggml_tensor * ggml_abs(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a);
|
||||||
|
|
||||||
|
struct ggml_tensor * ggml_sgn(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a);
|
||||||
|
|
||||||
|
struct ggml_tensor * ggml_neg(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a);
|
||||||
|
|
||||||
|
struct ggml_tensor * ggml_step(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a);
|
||||||
|
|
||||||
|
struct ggml_tensor * ggml_relu(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a);
|
||||||
|
|
||||||
|
// TODO: double-check this computation is correct
|
||||||
|
struct ggml_tensor * ggml_gelu(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a);
|
||||||
|
|
||||||
|
// normalize along rows
|
||||||
|
// TODO: eps is hardcoded to 1e-5 for now
|
||||||
|
struct ggml_tensor * ggml_norm(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a);
|
||||||
|
|
||||||
|
// A: m rows, n columns
|
||||||
|
// B: p rows, n columns (i.e. we transpose it internally)
|
||||||
|
// result is m columns, p rows
|
||||||
|
struct ggml_tensor * ggml_mul_mat(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a,
|
||||||
|
struct ggml_tensor * b);
|
||||||
|
|
||||||
|
//
|
||||||
|
// operations on tensors without backpropagation
|
||||||
|
//
|
||||||
|
|
||||||
|
// in-place, returns view(a)
|
||||||
|
struct ggml_tensor * ggml_scale(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a,
|
||||||
|
struct ggml_tensor * b);
|
||||||
|
|
||||||
|
// a -> b, return view(b)
|
||||||
|
struct ggml_tensor * ggml_cpy(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a,
|
||||||
|
struct ggml_tensor * b);
|
||||||
|
|
||||||
|
// return view(a), b specifies the new shape
|
||||||
|
// TODO: when we start computing gradient, make a copy instead of view
|
||||||
|
struct ggml_tensor * ggml_reshape(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a,
|
||||||
|
struct ggml_tensor * b);
|
||||||
|
|
||||||
|
// return view(a)
|
||||||
|
// TODO: when we start computing gradient, make a copy instead of view
|
||||||
|
struct ggml_tensor * ggml_reshape_2d(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a,
|
||||||
|
int ne0,
|
||||||
|
int ne1);
|
||||||
|
|
||||||
|
// return view(a)
|
||||||
|
// TODO: when we start computing gradient, make a copy instead of view
|
||||||
|
struct ggml_tensor * ggml_reshape_3d(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a,
|
||||||
|
int ne0,
|
||||||
|
int ne1,
|
||||||
|
int ne2);
|
||||||
|
|
||||||
|
// offset in bytes
|
||||||
|
struct ggml_tensor * ggml_view_1d(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a,
|
||||||
|
int ne0,
|
||||||
|
size_t offset);
|
||||||
|
|
||||||
|
struct ggml_tensor * ggml_view_2d(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a,
|
||||||
|
int ne0,
|
||||||
|
int ne1,
|
||||||
|
size_t nb1, // row stride in bytes
|
||||||
|
size_t offset);
|
||||||
|
|
||||||
|
struct ggml_tensor * ggml_permute(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a,
|
||||||
|
int axis0,
|
||||||
|
int axis1,
|
||||||
|
int axis2,
|
||||||
|
int axis3);
|
||||||
|
|
||||||
|
// alias for ggml_permute(ctx, a, 1, 0, 2, 3)
|
||||||
|
struct ggml_tensor * ggml_transpose(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a);
|
||||||
|
|
||||||
|
struct ggml_tensor * ggml_get_rows(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a,
|
||||||
|
struct ggml_tensor * b);
|
||||||
|
|
||||||
|
// set elements above the diagonal to -INF
|
||||||
|
// in-place, returns view(a)
|
||||||
|
struct ggml_tensor * ggml_diag_mask_inf(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a,
|
||||||
|
int n_past);
|
||||||
|
|
||||||
|
// in-place, returns view(a)
|
||||||
|
struct ggml_tensor * ggml_soft_max(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a);
|
||||||
|
|
||||||
|
// rotary position embedding
|
||||||
|
// in-place, returns view(a)
|
||||||
|
// if mode == 1, skip n_past elements
|
||||||
|
// TODO: avoid creating a new tensor every time
|
||||||
|
struct ggml_tensor * ggml_rope(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a,
|
||||||
|
int n_past,
|
||||||
|
int n_dims,
|
||||||
|
int mode);
|
||||||
|
|
||||||
|
// padding = 1
|
||||||
|
// TODO: we don't support extra parameters for now
|
||||||
|
// that's why we are hard-coding the stride, padding, and dilation
|
||||||
|
// not great ..
|
||||||
|
struct ggml_tensor * ggml_conv_1d_1s(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a,
|
||||||
|
struct ggml_tensor * b);
|
||||||
|
|
||||||
|
struct ggml_tensor * ggml_conv_1d_2s(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * a,
|
||||||
|
struct ggml_tensor * b);
|
||||||
|
|
||||||
|
//
|
||||||
|
// automatic differentiation
|
||||||
|
//
|
||||||
|
|
||||||
|
void ggml_set_param(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_tensor * tensor);
|
||||||
|
|
||||||
|
void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
|
||||||
|
|
||||||
|
struct ggml_cgraph ggml_build_forward (struct ggml_tensor * tensor);
|
||||||
|
struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep);
|
||||||
|
|
||||||
|
void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph);
|
||||||
|
void ggml_graph_reset (struct ggml_cgraph * cgraph);
|
||||||
|
|
||||||
|
// print info and performance information for the graph
|
||||||
|
void ggml_graph_print(const struct ggml_cgraph * cgraph);
|
||||||
|
|
||||||
|
// dump the graph into a file using the dot format
|
||||||
|
void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph * gf, const char * filename);
|
||||||
|
|
||||||
|
//
|
||||||
|
// optimization
|
||||||
|
//
|
||||||
|
|
||||||
|
// optimization methods
|
||||||
|
enum ggml_opt_type {
|
||||||
|
GGML_OPT_ADAM,
|
||||||
|
GGML_OPT_LBFGS,
|
||||||
|
};
|
||||||
|
|
||||||
|
// linesearch methods
|
||||||
|
enum ggml_linesearch {
|
||||||
|
GGML_LINESEARCH_DEFAULT = 1,
|
||||||
|
|
||||||
|
GGML_LINESEARCH_BACKTRACKING_ARMIJO = 0,
|
||||||
|
GGML_LINESEARCH_BACKTRACKING_WOLFE = 1,
|
||||||
|
GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE = 2,
|
||||||
|
};
|
||||||
|
|
||||||
|
// optimization return values
|
||||||
|
enum ggml_opt_result {
|
||||||
|
GGML_OPT_OK = 0,
|
||||||
|
GGML_OPT_DID_NOT_CONVERGE,
|
||||||
|
GGML_OPT_NO_CONTEXT,
|
||||||
|
GGML_OPT_INVALID_WOLFE,
|
||||||
|
GGML_OPT_FAIL,
|
||||||
|
|
||||||
|
GGML_LINESEARCH_FAIL = -128,
|
||||||
|
GGML_LINESEARCH_MINIMUM_STEP,
|
||||||
|
GGML_LINESEARCH_MAXIMUM_STEP,
|
||||||
|
GGML_LINESEARCH_MAXIMUM_ITERATIONS,
|
||||||
|
GGML_LINESEARCH_INVALID_PARAMETERS,
|
||||||
|
};
|
||||||
|
|
||||||
|
// optimization parameters
|
||||||
|
//
|
||||||
|
// see ggml.c (ggml_opt_default_params) for default values
|
||||||
|
//
|
||||||
|
struct ggml_opt_params {
|
||||||
|
enum ggml_opt_type type;
|
||||||
|
|
||||||
|
int n_threads;
|
||||||
|
|
||||||
|
// delta-based convergence test
|
||||||
|
//
|
||||||
|
// if past == 0 - disabled
|
||||||
|
// if past > 0:
|
||||||
|
// stop if |f(x) - f(x_past)| < delta * max(1, |f(x)|)
|
||||||
|
//
|
||||||
|
int past;
|
||||||
|
float delta;
|
||||||
|
|
||||||
|
// maximum number of iterations without improvement
|
||||||
|
//
|
||||||
|
// if 0 - disabled
|
||||||
|
// if > 0:
|
||||||
|
// assume convergence if no cost improvement in this number of iterations
|
||||||
|
//
|
||||||
|
int max_no_improvement;
|
||||||
|
|
||||||
|
bool print_forward_graph;
|
||||||
|
bool print_backward_graph;
|
||||||
|
|
||||||
|
union {
|
||||||
|
// ADAM parameters
|
||||||
|
struct {
|
||||||
|
int n_iter;
|
||||||
|
|
||||||
|
float alpha; // learning rate
|
||||||
|
float beta1;
|
||||||
|
float beta2;
|
||||||
|
float eps; // epsilon for numerical stability
|
||||||
|
float eps_f; // epsilon for convergence test
|
||||||
|
float eps_g; // epsilon for convergence test
|
||||||
|
} adam;
|
||||||
|
|
||||||
|
// LBFGS parameters
|
||||||
|
struct {
|
||||||
|
int m; // number of corrections to approximate the inv. Hessian
|
||||||
|
int n_iter;
|
||||||
|
int max_linesearch;
|
||||||
|
|
||||||
|
float eps; // convergence tolerance
|
||||||
|
float ftol; // line search tolerance
|
||||||
|
float wolfe;
|
||||||
|
float min_step;
|
||||||
|
float max_step;
|
||||||
|
|
||||||
|
enum ggml_linesearch linesearch;
|
||||||
|
} lbfgs;
|
||||||
|
};
|
||||||
|
};
|
||||||
|
|
||||||
|
struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type);
|
||||||
|
|
||||||
|
// optimize the function defined by the tensor f
|
||||||
|
enum ggml_opt_result ggml_opt(
|
||||||
|
struct ggml_context * ctx,
|
||||||
|
struct ggml_opt_params params,
|
||||||
|
struct ggml_tensor * f);
|
||||||
|
|
||||||
|
#ifdef __cplusplus
|
||||||
|
}
|
||||||
|
#endif
|
||||||
1
models/.gitignore
vendored
Normal file
1
models/.gitignore
vendored
Normal file
@ -0,0 +1 @@
|
|||||||
|
*.bin
|
||||||
1
samples/.gitignore
vendored
Normal file
1
samples/.gitignore
vendored
Normal file
@ -0,0 +1 @@
|
|||||||
|
*
|
||||||
BIN
samples/jfk.wav
Normal file
BIN
samples/jfk.wav
Normal file
Binary file not shown.
Loading…
Reference in New Issue
Block a user