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Resolving GPU Timeout Issue During LLM Training #518

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263 changes: 83 additions & 180 deletions webui.py
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# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Liu Yue)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
import argparse
import gradio as gr
import numpy as np
import torch
import torchaudio
import random
import librosa
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append('{}/third_party/Matcha-TTS'.format(ROOT_DIR))
from cosyvoice.cli.cosyvoice import CosyVoice
from cosyvoice.utils.file_utils import load_wav, logging
from cosyvoice.utils.common import set_all_random_seed

inference_mode_list = ['预训练音色', '3s极速复刻', '跨语种复刻', '自然语言控制']
instruct_dict = {'预训练音色': '1. 选择预训练音色\n2. 点击生成音频按钮',
'3s极速复刻': '1. 选择prompt音频文件，或录入prompt音频，注意不超过30s，若同时提供，优先选择prompt音频文件\n2. 输入prompt文本\n3. 点击生成音频按钮',
'跨语种复刻': '1. 选择prompt音频文件，或录入prompt音频，注意不超过30s，若同时提供，优先选择prompt音频文件\n2. 点击生成音频按钮',
'自然语言控制': '1. 选择预训练音色\n2. 输入instruct文本\n3. 点击生成音频按钮'}
stream_mode_list = [('否', False), ('是', True)]
max_val = 0.8


def generate_seed():
seed = random.randint(1, 100000000)
return {
"__type__": "update",
"value": seed
}


def postprocess(speech, top_db=60, hop_length=220, win_length=440):
speech, _ = librosa.effects.trim(
speech, top_db=top_db,
frame_length=win_length,
hop_length=hop_length
)
if speech.abs().max() > max_val:
speech = speech / speech.abs().max() * max_val
speech = torch.concat([speech, torch.zeros(1, int(target_sr * 0.2))], dim=1)
return speech


def change_instruction(mode_checkbox_group):
return instruct_dict[mode_checkbox_group]


def generate_audio(tts_text, mode_checkbox_group, sft_dropdown, prompt_text, prompt_wav_upload, prompt_wav_record, instruct_text,
seed, stream, speed):
if prompt_wav_upload is not None:
prompt_wav = prompt_wav_upload
elif prompt_wav_record is not None:
prompt_wav = prompt_wav_record
else:
prompt_wav = None
# if instruct mode, please make sure that model is iic/CosyVoice-300M-Instruct and not cross_lingual mode
if mode_checkbox_group in ['自然语言控制']:
if cosyvoice.frontend.instruct is False:
gr.Warning('您正在使用自然语言控制模式, {}模型不支持此模式, 请使用iic/CosyVoice-300M-Instruct模型'.format(args.model_dir))
yield (target_sr, default_data)
if instruct_text == '':
gr.Warning('您正在使用自然语言控制模式, 请输入instruct文本')
yield (target_sr, default_data)
if prompt_wav is not None or prompt_text != '':
gr.Info('您正在使用自然语言控制模式, prompt音频/prompt文本会被忽略')
# if cross_lingual mode, please make sure that model is iic/CosyVoice-300M and tts_text prompt_text are different language
if mode_checkbox_group in ['跨语种复刻']:
if cosyvoice.frontend.instruct is True:
gr.Warning('您正在使用跨语种复刻模式, {}模型不支持此模式, 请使用iic/CosyVoice-300M模型'.format(args.model_dir))
yield (target_sr, default_data)
if instruct_text != '':
gr.Info('您正在使用跨语种复刻模式, instruct文本会被忽略')
if prompt_wav is None:
gr.Warning('您正在使用跨语种复刻模式, 请提供prompt音频')
yield (target_sr, default_data)
gr.Info('您正在使用跨语种复刻模式, 请确保合成文本和prompt文本为不同语言')
# if in zero_shot cross_lingual, please make sure that prompt_text and prompt_wav meets requirements
if mode_checkbox_group in ['3s极速复刻', '跨语种复刻']:
if prompt_wav is None:
gr.Warning('prompt音频为空，您是否忘记输入prompt音频？')
yield (target_sr, default_data)
if torchaudio.info(prompt_wav).sample_rate < prompt_sr:
gr.Warning('prompt音频采样率{}低于{}'.format(torchaudio.info(prompt_wav).sample_rate, prompt_sr))
yield (target_sr, default_data)
# sft mode only use sft_dropdown
if mode_checkbox_group in ['预训练音色']:
if instruct_text != '' or prompt_wav is not None or prompt_text != '':
gr.Info('您正在使用预训练音色模式，prompt文本/prompt音频/instruct文本会被忽略！')
# zero_shot mode only use prompt_wav prompt text
if mode_checkbox_group in ['3s极速复刻']:
if prompt_text == '':
gr.Warning('prompt文本为空，您是否忘记输入prompt文本？')
yield (target_sr, default_data)
if instruct_text != '':
gr.Info('您正在使用3s极速复刻模式，预训练音色/instruct文本会被忽略！')

if mode_checkbox_group == '预训练音色':
logging.info('get sft inference request')
set_all_random_seed(seed)
for i in cosyvoice.inference_sft(tts_text, sft_dropdown, stream=stream, speed=speed):
yield (target_sr, i['tts_speech'].numpy().flatten())
elif mode_checkbox_group == '3s极速复刻':
logging.info('get zero_shot inference request')
prompt_speech_16k = postprocess(load_wav(prompt_wav, prompt_sr))
set_all_random_seed(seed)
for i in cosyvoice.inference_zero_shot(tts_text, prompt_text, prompt_speech_16k, stream=stream, speed=speed):
yield (target_sr, i['tts_speech'].numpy().flatten())
elif mode_checkbox_group == '跨语种复刻':
logging.info('get cross_lingual inference request')
prompt_speech_16k = postprocess(load_wav(prompt_wav, prompt_sr))
set_all_random_seed(seed)
for i in cosyvoice.inference_cross_lingual(tts_text, prompt_speech_16k, stream=stream, speed=speed):
yield (target_sr, i['tts_speech'].numpy().flatten())
else:
logging.info('get instruct inference request')
set_all_random_seed(seed)
for i in cosyvoice.inference_instruct(tts_text, sft_dropdown, instruct_text, stream=stream, speed=speed):
yield (target_sr, i['tts_speech'].numpy().flatten())
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from transformers import YourModel, YourTokenizer
import argparse
import os

def train(model, dataloader, optimizer, device, accumulation_steps=2):
model.train()
total_loss = 0
optimizer.zero_grad()

for i, (inputs, labels) in enumerate(dataloader):
inputs, labels = inputs.to(device), labels.to(device)

# Forward pass
outputs = model(inputs)
loss = nn.CrossEntropyLoss()(outputs, labels)

# Normalize loss by accumulation steps
loss = loss / accumulation_steps
loss.backward()

# Accumulate gradients
if (i + 1) % accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad()

total_loss += loss.item()

return total_loss / len(dataloader)

def main():
with gr.Blocks() as demo:
gr.Markdown("### 代码库 [CosyVoice](https://github.com/FunAudioLLM/CosyVoice) \
预训练模型 [CosyVoice-300M](https://www.modelscope.cn/models/iic/CosyVoice-300M) \
[CosyVoice-300M-Instruct](https://www.modelscope.cn/models/iic/CosyVoice-300M-Instruct) \
[CosyVoice-300M-SFT](https://www.modelscope.cn/models/iic/CosyVoice-300M-SFT)")
gr.Markdown("#### 请输入需要合成的文本，选择推理模式，并按照提示步骤进行操作")

tts_text = gr.Textbox(label="输入合成文本", lines=1, value="我是通义实验室语音团队全新推出的生成式语音大模型，提供舒适自然的语音合成能力。")
with gr.Row():
mode_checkbox_group = gr.Radio(choices=inference_mode_list, label='选择推理模式', value=inference_mode_list[0])
instruction_text = gr.Text(label="操作步骤", value=instruct_dict[inference_mode_list[0]], scale=0.5)
sft_dropdown = gr.Dropdown(choices=sft_spk, label='选择预训练音色', value=sft_spk[0], scale=0.25)
stream = gr.Radio(choices=stream_mode_list, label='是否流式推理', value=stream_mode_list[0][1])
speed = gr.Number(value=1, label="速度调节(仅支持非流式推理)", minimum=0.5, maximum=2.0, step=0.1)
with gr.Column(scale=0.25):
seed_button = gr.Button(value="\U0001F3B2")
seed = gr.Number(value=0, label="随机推理种子")

with gr.Row():
prompt_wav_upload = gr.Audio(sources='upload', type='filepath', label='选择prompt音频文件，注意采样率不低于16khz')
prompt_wav_record = gr.Audio(sources='microphone', type='filepath', label='录制prompt音频文件')
prompt_text = gr.Textbox(label="输入prompt文本", lines=1, placeholder="请输入prompt文本，需与prompt音频内容一致，暂时不支持自动识别...", value='')
instruct_text = gr.Textbox(label="输入instruct文本", lines=1, placeholder="请输入instruct文本.", value='')

generate_button = gr.Button("生成音频")

audio_output = gr.Audio(label="合成音频", autoplay=True, streaming=True)

seed_button.click(generate_seed, inputs=[], outputs=seed)
generate_button.click(generate_audio,
inputs=[tts_text, mode_checkbox_group, sft_dropdown, prompt_text, prompt_wav_upload, prompt_wav_record, instruct_text,
seed, stream, speed],
outputs=[audio_output])
mode_checkbox_group.change(fn=change_instruction, inputs=[mode_checkbox_group], outputs=[instruction_text])
demo.queue(max_size=4, default_concurrency_limit=2)
demo.launch(server_name='0.0.0.0', server_port=args.port)
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', type=int, default=3, help='Number of training epochs')
parser.add_argument('--batch_size', type=int, default=8, help='Training batch size')
parser.add_argument('--lr', type=float, default=3e-5, help='Learning rate')
parser.add_argument('--model_path', type=str, default='path_to_model', help='Path to your model')
parser.add_argument('--use_fp16', action='store_true', help='Use mixed precision training')
args = parser.parse_args()

# Set device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Load model and tokenizer
model = YourModel.from_pretrained(args.model_path).to(device)
tokenizer = YourTokenizer.from_pretrained(args.model_path)

# Data loading and preparation
train_dataset = YourDataset() # Customize this
train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True)

# Optimizer and learning rate scheduling
optimizer = optim.AdamW(model.parameters(), lr=args.lr)

# Mixed precision training
scaler = torch.cuda.amp.GradScaler() if args.use_fp16 else None

# Training loop
for epoch in range(args.epochs):
print(f'Epoch {epoch + 1}/{args.epochs}')

model.train()
total_loss = 0

for i, (inputs, labels) in enumerate(train_loader):
inputs, labels = inputs.to(device), labels.to(device)

optimizer.zero_grad()

with torch.cuda.amp.autocast(enabled=args.use_fp16):
outputs = model(inputs)
loss = nn.CrossEntropyLoss()(outputs, labels)

# Scale loss for mixed precision training
if scaler:
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
else:
loss.backward()
optimizer.step()

total_loss += loss.item()

avg_loss = total_loss / len(train_loader)
print(f'Average Loss: {avg_loss:.4f}')

if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--port',
type=int,
default=8000)
parser.add_argument('--model_dir',
type=str,
default='pretrained_models/CosyVoice-300M',
help='local path or modelscope repo id')
args = parser.parse_args()
cosyvoice = CosyVoice(args.model_dir)
sft_spk = cosyvoice.list_avaliable_spks()
prompt_sr, target_sr = 16000, 22050
default_data = np.zeros(target_sr)
main()