modelscope--ms-swift
wehub-resource-sync
332 行
12 KiB
Python
332 行
12 KiB
Python
# Copyright (c) ModelScope Contributors. All rights reserved.
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import gradio as gr
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from functools import partial
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from typing import Dict, Type
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from swift.arguments import get_supported_tuners
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from swift.utils import get_device_count, get_logger
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from ..base import BaseUI
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from ..llm_train import LLMTrain
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from .advanced import RLHFAdvanced
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from .dataset import RLHFDataset
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from .hyper import RLHFHyper
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from .model import RLHFModel
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from .optimizer import RLHFOptimizer
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from .quantization import RLHFQuantization
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from .report_to import RLHFReportTo
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from .rlhf import RLHF
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from .runtime import RLHFRuntime
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from .save import RLHFSave
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from .tuner import RLHFTuner
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logger = get_logger()
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class LLMRLHF(LLMTrain):
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group = 'llm_rlhf'
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sub_ui = [
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RLHFModel,
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RLHFDataset,
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RLHFHyper,
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RLHFRuntime,
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RLHFTuner,
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RLHFOptimizer,
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RLHF,
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RLHFQuantization,
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RLHFSave,
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RLHFReportTo,
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RLHFAdvanced,
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]
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locale_dict: Dict[str, Dict] = {
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'llm_rlhf': {
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'label': {
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'zh': 'LLM人类对齐',
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'en': 'LLM RLHF',
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}
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},
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'train_stage': {
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'label': {
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'zh': '训练Stage',
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'en': 'Train Stage'
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},
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'info': {
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'zh': '请注意选择与此匹配的数据集',
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'en': 'Please choose matched dataset'
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}
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},
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'submit_alert': {
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'value': {
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'zh':
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'任务已开始，请查看tensorboard或日志记录，请勿关闭终端，否则训练过程将被打断',
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'en':
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'Task started, please check the tensorboard or log file, '
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'do not close the terminal, otherwise the training process will be interrupted'
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}
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},
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'dataset_alert': {
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'value': {
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'zh': '请选择或填入一个数据集',
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'en': 'Please input or select a dataset'
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}
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},
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'submit': {
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'value': {
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'zh': '🚀 开始训练',
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'en': '🚀 Begin'
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}
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},
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'dry_run': {
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'label': {
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'zh': '仅生成运行命令',
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'en': 'Dry-run'
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},
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'info': {
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'zh': '仅生成运行命令，开发者自行运行',
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'en': 'Generate run command only, for manually running'
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}
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},
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'gpu_id': {
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'label': {
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'zh': '选择可用GPU',
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'en': 'Choose GPU'
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},
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'info': {
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'zh': '选择训练使用的GPU号，如CUDA不可用只能选择CPU',
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'en': 'Select GPU to train'
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}
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},
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'rlhf_type': {
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'label': {
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'zh': '人类对齐算法类型',
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'en': 'RLHF type'
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},
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},
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'tuner_type': {
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'label': {
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'zh': '训练方式',
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'en': 'Train type'
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},
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'info': {
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'zh': '选择训练的方式',
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'en': 'Select the tuner type'
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}
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},
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'seed': {
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'label': {
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'zh': '随机数种子',
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'en': 'Seed'
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},
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'info': {
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'zh': '选择随机数种子',
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'en': 'Select a random seed'
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}
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},
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'torch_dtype': {
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'label': {
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'zh': '训练精度',
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'en': 'Training Precision'
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},
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'info': {
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'zh': '选择训练精度',
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'en': 'Select the training precision'
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}
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},
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'envs': {
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'label': {
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'zh': '环境变量',
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'en': 'Extra env vars'
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},
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},
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'use_ddp': {
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'label': {
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'zh': '使用DDP',
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'en': 'Use DDP'
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},
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'info': {
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'zh': '是否使用数据并行训练',
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'en': 'Use Distributed Data Parallel to train'
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}
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},
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'ddp_num': {
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'label': {
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'zh': 'DDP分片数量',
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'en': 'Number of DDP sharding'
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},
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'info': {
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'zh': '启用多少进程的数据并行',
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'en': 'The data parallel size of DDP'
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}
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},
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'use_liger_kernel': {
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'label': {
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'zh': '使用Liger kernel',
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'en': 'Use Liger kernel'
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},
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'info': {
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'zh': 'Liger kernel可以有效降低显存使用',
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'en': 'Liger kernel can reduce memory usage'
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}
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},
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'sequence_parallel_size': {
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'label': {
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'zh': '序列并行大小',
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'en': 'Sequence parallel size',
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},
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'info': {
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'zh': '当前支持CPT/SFT/DPO/GRPO',
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'en': 'Currently supports CPT/SFT/DPO/GRPO',
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}
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},
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'deepspeed': {
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'label': {
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'zh': 'DeepSpeed',
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'en': 'DeepSpeed',
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},
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'info': {
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'zh': '可以选择下拉列表，也支持传入路径',
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'en': 'Choose from the dropbox or fill in a valid path',
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}
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},
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'resume_checkpoint_alert': {
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'value': {
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'zh': '检测到"args.json"在{}中，将从此检查点开始断点续训',
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'en': 'Detected that "args.json" is in {}, will start breakpoint resume training from this checkpoint'
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}
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},
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'resume_only_model_alert': {
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'value': {
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'zh':
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'检测到"args.json"在{}中，但未检测到优化器参数，将仅加载模型参数开始断点续训',
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'en':
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'"args.json" is detected in {}, but optimizer parameters are not detected. '
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'Only model parameters will be loaded to start breakpoint continuation training'
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}
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},
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'more_params': {
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'label': {
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'zh': '其他高级参数',
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'en': 'Other params'
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},
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'info': {
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'zh': '以json格式或--xxx xxx命令行格式填入',
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'en': 'Fill in with json format or --xxx xxx cmd format'
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}
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},
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'extra_params': {
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'label': {
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'zh': '其他参数设置',
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'en': 'Extra settings'
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},
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},
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'train_param': {
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'label': {
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'zh': '训练参数设置',
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'en': 'Train settings'
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},
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},
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}
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@classmethod
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def do_build_ui(cls, base_tab: Type['BaseUI']):
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with gr.TabItem(elem_id='llm_rlhf', label=''):
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default_device = 'cpu'
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device_count = get_device_count()
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if device_count > 0:
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default_device = '0'
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with gr.Blocks():
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RLHFModel.build_ui(base_tab)
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RLHFDataset.build_ui(base_tab)
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with gr.Accordion(elem_id='train_param', open=True):
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with gr.Row():
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gr.Dropdown(elem_id='rlhf_type', scale=2)
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gr.Dropdown(elem_id='tuner_type', scale=2, choices=list(get_supported_tuners()))
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gr.Textbox(elem_id='seed', scale=2)
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gr.Dropdown(elem_id='torch_dtype', scale=2)
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gr.Checkbox(elem_id='use_liger_kernel', scale=2)
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with gr.Row():
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gr.Dropdown(
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elem_id='gpu_id',
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multiselect=True,
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choices=[str(i) for i in range(device_count)] + ['cpu'],
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value=default_device,
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scale=4)
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gr.Checkbox(elem_id='use_ddp', value=False, scale=4)
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gr.Textbox(elem_id='ddp_num', value='1', scale=4)
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gr.Dropdown(
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elem_id='deepspeed',
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scale=4,
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allow_custom_value=True,
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value=None,
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choices=['zero0', 'zero1', 'zero2', 'zero3', 'zero2_offload', 'zero3_offload'])
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gr.Textbox(elem_id='sequence_parallel_size', lines=1, scale=4)
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RLHFHyper.build_ui(base_tab)
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RLHFRuntime.build_ui(base_tab)
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with gr.Row(equal_height=True):
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gr.Textbox(elem_id='envs', scale=12)
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gr.Checkbox(elem_id='dry_run', value=False, scale=4)
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submit = gr.Button(elem_id='submit', scale=4, variant='primary')
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RLHFTuner.build_ui(base_tab)
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RLHFOptimizer.build_ui(base_tab)
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RLHF.build_ui(base_tab)
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with gr.Accordion(elem_id='extra_params', open=False):
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with gr.Tabs():
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RLHFAdvanced.build_ui(base_tab)
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RLHFQuantization.build_ui(base_tab)
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RLHFSave.build_ui(base_tab)
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RLHFReportTo.build_ui(base_tab)
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with gr.Row():
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gr.Textbox(elem_id='more_params', lines=4, scale=20)
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base_tab.element('gpu_id').change(
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cls.update_ddp_num,
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[base_tab.element('gpu_id'), base_tab.element('use_ddp')], base_tab.element('ddp_num'))
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base_tab.element('use_ddp').change(
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cls.update_ddp_num,
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[base_tab.element('gpu_id'), base_tab.element('use_ddp')], base_tab.element('ddp_num'))
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cls.element('tuner_type').change(
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RLHFHyper.update_lr,
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inputs=[base_tab.element('tuner_type')],
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outputs=[cls.element('learning_rate')])
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cls.element('rlhf_type').change(
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RLHF.update_beta, inputs=[base_tab.element('rlhf_type')], outputs=[base_tab.element('beta')])
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submit.click(
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cls.train_local,
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list(cls.valid_elements().values()), [
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cls.element('running_cmd'),
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cls.element('logging_dir'),
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cls.element('runtime_tab'),
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cls.element('running_tasks'),
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cls.element('train_record'),
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],
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queue=True)
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base_tab.element('running_tasks').change(
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partial(RLHFRuntime.task_changed, base_tab=base_tab), [base_tab.element('running_tasks')],
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list(base_tab.valid_elements().values()) + [cls.element('log')] + RLHFRuntime.all_plots)
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RLHFRuntime.element('kill_task').click(
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RLHFRuntime.kill_task,
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[RLHFRuntime.element('running_tasks')],
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[RLHFRuntime.element('running_tasks')] + [RLHFRuntime.element('log')] + RLHFRuntime.all_plots,
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).then(RLHFRuntime.reset, [], [RLHFRuntime.element('logging_dir')] + [RLHFHyper.element('output_dir')])
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@classmethod
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def prepare_sub_to_filter(cls):
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tabs_relation_dict = {
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key: val
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for key, val in zip(['tuner_type', 'optimizer'], [RLHFTuner.tabs_to_filter, RLHFOptimizer.tabs_to_filter])
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}
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return tabs_relation_dict
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@classmethod
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def filter_rlhf_args(cls, uncleaned_kwargs):
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cur_rlhf_type = uncleaned_kwargs.get('rlhf_type', 'dpo')
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cur_selected = RLHF.rlhf_args_dict.pop(cur_rlhf_type, None)
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for _, vals in RLHF.rlhf_args_dict.items():
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for rlhf_arg in vals:
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if uncleaned_kwargs.get(rlhf_arg) and (cur_selected is None or rlhf_arg not in cur_selected):
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uncleaned_kwargs.pop(rlhf_arg)
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