912 lines
33 KiB
Python
912 lines
33 KiB
Python
import json
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import math
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import os
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import random
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from datetime import datetime
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from pathlib import Path
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from typing import Any, Callable, Dict, List, Optional, Tuple, Union
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.optim as optim
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import typer
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from loguru import logger
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from rich.console import Console
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from rich.panel import Panel
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from rich.progress import (
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BarColumn,
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Progress,
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SpinnerColumn,
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TextColumn,
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TimeElapsedColumn,
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TimeRemainingColumn,
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)
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from rich.table import Table
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from torch import autocast
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from torch.amp.grad_scaler import GradScaler
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from torch.utils.data import DataLoader
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from torch.utils.tensorboard import SummaryWriter
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from .dataset import PinyinInputDataset
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# 导入模型和数据
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from .model import InputMethodEngine
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class Trainer:
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"""
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输入法模型训练器
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实现训练InputMethodEngine模型，支持：
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- 预热+余弦退火学习率调度
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- TensorBoard日志记录
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- AdamW优化器（weight_decay=0.1）
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- 混合精度训练
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- CrossEntropyLoss损失函数（支持weight和label_smoothing）
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- Rich终端美化输出
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"""
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def __init__(
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self,
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model: InputMethodEngine,
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train_dataloader: DataLoader,
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eval_dataloader: DataLoader,
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total_steps: int,
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output_dir: str = "./output",
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num_epochs: int = 10,
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learning_rate: float = 1e-4,
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min_learning_rate: float = 1e-6,
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weight_decay: float = 0.1,
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warmup_ratio: float = 0.1,
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label_smoothing: float = 0.15,
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loss_weight: Optional[torch.Tensor] = None,
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grad_accum_steps: int = 1,
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clip_grad_norm: float = 1.0,
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eval_frequency: int = 500,
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save_frequency: int = 10000,
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mixed_precision: bool = True,
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device: Optional[torch.device] = None,
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use_tensorboard: bool = True,
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):
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"""
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初始化训练器
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Args:
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model: 要训练的InputMethodEngine模型
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train_dataloader: 训练数据加载器
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eval_dataloader: 评估数据加载器（可选）
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output_dir: 输出目录，用于保存模型和日志
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num_epochs: 训练轮数
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total_steps: 总训练步数，如果为None则根据epochs计算
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learning_rate: 最大学习率（预热后）
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min_learning_rate: 最小学习率（余弦退火后的最低值）
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weight_decay: AdamW优化器的权重衰减
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warmup_ratio: 热身步数占总步数的比例
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label_smoothing: CrossEntropyLoss的标签平滑参数
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loss_weight: CrossEntropyLoss的类别权重
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grad_accum_steps: 梯度累积步数
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clip_grad_norm: 梯度裁剪的最大范数
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eval_frequency: 评估频率（步数）
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save_frequency: 保存检查点频率（步数）
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mixed_precision: 是否使用混合精度训练
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device: 训练设备，如果为None则自动选择
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use_tensorboard: 是否使用TensorBoard记录
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"""
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self.model = model
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self.train_dataloader = train_dataloader
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self.eval_dataloader = list([i for i in eval_dataloader])
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self.output_dir = Path(output_dir)
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self.num_epochs = num_epochs
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self.learning_rate = learning_rate
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self.min_learning_rate = min_learning_rate
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self.weight_decay = weight_decay
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self.warmup_ratio = warmup_ratio
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self.label_smoothing = label_smoothing
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self.loss_weight = loss_weight
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self.grad_accum_steps = grad_accum_steps
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self.clip_grad_norm = clip_grad_norm
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self.eval_frequency = eval_frequency
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self.save_frequency = save_frequency
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self.mixed_precision = mixed_precision
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self.use_tensorboard = use_tensorboard
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# 设置设备
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logger.info(f"GPU可用： {torch.cuda.is_available()}")
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if device is None:
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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else:
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self.device = device
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# 移动模型到设备
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self.model.to(self.device)
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# 创建输出目录
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self.output_dir.mkdir(parents=True, exist_ok=True)
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self.checkpoint_dir = self.output_dir / "checkpoints"
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self.checkpoint_dir.mkdir(exist_ok=True)
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# 计算总步数
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self.total_steps = total_steps
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self.warmup_steps = int(self.total_steps * warmup_ratio)
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# 初始化优化器
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self.optimizer = optim.AdamW(
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model.parameters(),
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lr=learning_rate,
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weight_decay=weight_decay,
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betas=(0.9, 0.999),
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eps=1e-8,
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)
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# 初始化损失函数
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if loss_weight is not None:
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self.criterion = nn.CrossEntropyLoss(
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weight=loss_weight.to(self.device), label_smoothing=label_smoothing
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)
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else:
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self.criterion = nn.CrossEntropyLoss(label_smoothing=label_smoothing)
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# 初始化混合精度训练器
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device_type = "cuda" if "cuda" in str(self.device) else "cpu"
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self.scaler = GradScaler(device_type, enabled=mixed_precision)
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# 初始化TensorBoard
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if use_tensorboard:
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self.writer = SummaryWriter(log_dir=self.output_dir / "tensorboard")
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else:
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self.writer = None
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# 初始化Rich控制台
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self.console = Console()
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# 训练状态
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self.current_step = 0
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self.current_epoch = 0
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self.best_eval_loss = float("inf")
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# 学习率调度函数
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self.lr_scheduler = self._create_lr_scheduler()
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logger.info(f"Trainer initialized with device: {self.device}")
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logger.info(
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f"Total steps: {self.total_steps}, Warmup steps: {self.warmup_steps}"
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)
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logger.info(f"Learning rate: {learning_rate}, Weight decay: {weight_decay}")
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def _create_lr_scheduler(self) -> Callable[[int], float]:
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"""创建学习率调度函数（预热 + 余弦退火）"""
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def lr_scheduler(step: int) -> float:
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if step < self.warmup_steps:
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# 线性预热
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return self.learning_rate * (step / self.warmup_steps)
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else:
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# 余弦退火
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progress = (step - self.warmup_steps) / (
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self.total_steps - self.warmup_steps
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)
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cosine_decay = 0.5 * (1 + math.cos(math.pi * progress))
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decayed_lr = (
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self.min_learning_rate
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+ (self.learning_rate - self.min_learning_rate) * cosine_decay
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)
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return decayed_lr
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return lr_scheduler
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def _get_current_lr(self) -> float:
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"""获取当前学习率"""
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return self.lr_scheduler(self.current_step)
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def _update_learning_rate(self):
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"""更新优化器中的学习率"""
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current_lr = self._get_current_lr()
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for param_group in self.optimizer.param_groups:
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param_group["lr"] = current_lr
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return current_lr
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def train_step(
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self, batch: Dict[str, torch.Tensor]
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) -> Tuple[float, Dict[str, Any]]:
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"""
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执行单个训练步骤
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Args:
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batch: 包含输入数据的批次
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Returns:
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loss: 损失值
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metrics: 训练指标字典
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"""
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self.model.train()
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# 移动数据到设备
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input_ids = batch["input_ids"].to(self.device)
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token_type_ids = batch["token_type_ids"].to(self.device)
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attention_mask = batch["attention_mask"].to(self.device)
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history_slot_ids = batch["history_slot_ids"].to(self.device)
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pinyin_ids = batch["pinyin_ids"].to(self.device)
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labels = batch["labels"].to(self.device).squeeze(-1) # [batch_size]
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# 混合精度训练
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with autocast(device_type=self.device.type, enabled=self.mixed_precision):
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# 前向传播
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logits = self.model(
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input_ids=input_ids,
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token_type_ids=token_type_ids,
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attention_mask=attention_mask,
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pinyin_ids=pinyin_ids,
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history_slot_ids=history_slot_ids,
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)
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# 计算损失
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loss = self.criterion(logits, labels)
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loss = loss / self.grad_accum_steps
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# 反向传播
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self.scaler.scale(loss).backward()
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metrics = {
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"loss": loss.item() * self.grad_accum_steps,
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"lr": self._get_current_lr(),
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}
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# 计算准确率
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with torch.no_grad():
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preds = torch.argmax(logits, dim=-1)
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correct = (preds == labels).sum().item()
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total = labels.size(0)
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metrics["accuracy"] = correct / total if total > 0 else 0.0
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return loss.item() * self.grad_accum_steps, metrics
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def evaluate(self) -> Dict[str, float]:
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"""
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在评估集上评估模型
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Returns:
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评估指标字典
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"""
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if self.eval_dataloader is None:
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return {}
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self.model.eval()
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total_loss = 0.0
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total_correct = 0
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total_samples = 0
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with torch.no_grad():
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for batch in self.eval_dataloader:
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# 移动数据到设备
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input_ids = batch["input_ids"].to(self.device)
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token_type_ids = batch["token_type_ids"].to(self.device)
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attention_mask = batch["attention_mask"].to(self.device)
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history_slot_ids = batch["history_slot_ids"].to(self.device)
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pinyin_ids = batch["pinyin_ids"].to(self.device)
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labels = batch["labels"].to(self.device).squeeze(-1)
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# 前向传播
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logits = self.model(
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input_ids=input_ids,
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token_type_ids=token_type_ids,
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attention_mask=attention_mask,
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pinyin_ids=pinyin_ids,
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history_slot_ids=history_slot_ids,
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)
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# 计算损失
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loss = self.criterion(logits, labels)
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total_loss += loss.item() * labels.size(0)
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# 计算准确率
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preds = torch.argmax(logits, dim=-1)
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correct = (preds == labels).sum().item()
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total_correct += correct
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total_samples += labels.size(0)
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avg_loss = total_loss / total_samples if total_samples > 0 else 0.0
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accuracy = total_correct / total_samples if total_samples > 0 else 0.0
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return {"eval_loss": avg_loss, "eval_accuracy": accuracy}
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def save_checkpoint(self, filename: str, is_best: bool = False):
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"""
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保存检查点
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Args:
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filename: 检查点文件名
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is_best: 是否是最佳模型
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"""
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checkpoint_path = self.checkpoint_dir / filename
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checkpoint = {
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"step": self.current_step,
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"epoch": self.current_epoch,
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"model_state_dict": self.model.state_dict(),
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"optimizer_state_dict": self.optimizer.state_dict(),
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"scaler_state_dict": self.scaler.state_dict(),
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"best_eval_loss": self.best_eval_loss,
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"config": {
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"learning_rate": self.learning_rate,
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"weight_decay": self.weight_decay,
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"warmup_ratio": self.warmup_ratio,
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"label_smoothing": self.label_smoothing,
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"total_steps": self.total_steps,
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},
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}
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torch.save(checkpoint, checkpoint_path)
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logger.info(f"Checkpoint saved to {checkpoint_path}")
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if is_best:
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best_path = self.checkpoint_dir / "best_model.pt"
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torch.save(checkpoint, best_path)
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logger.info(f"Best model saved to {best_path}")
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def load_checkpoint(self, checkpoint_path: Union[str, Path]):
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"""
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加载检查点
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Args:
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checkpoint_path: 检查点文件路径
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"""
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checkpoint = torch.load(checkpoint_path, map_location=self.device)
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self.model.load_state_dict(checkpoint["model_state_dict"])
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self.optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
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self.scaler.load_state_dict(checkpoint["scaler_state_dict"])
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self.current_step = checkpoint["step"]
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self.current_epoch = checkpoint["epoch"]
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self.best_eval_loss = checkpoint["best_eval_loss"]
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logger.info(f"Checkpoint loaded from {checkpoint_path}")
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logger.info(
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f"Resuming from step {self.current_step}, epoch {self.current_epoch}"
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)
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def _log_to_tensorboard(self, metrics: Dict[str, float], step: int):
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"""将指标记录到TensorBoard"""
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if self.writer is None:
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return
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for key, value in metrics.items():
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self.writer.add_scalar(key, value, step)
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def _create_progress_bar(self) -> Progress:
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"""创建Rich进度条"""
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return Progress(
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SpinnerColumn(),
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TextColumn("[progress.description]{task.description}"),
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BarColumn(),
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TextColumn("[progress.percentage]{task.percentage:>3.0f}%"),
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TimeElapsedColumn(),
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TimeRemainingColumn(),
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console=self.console,
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expand=True,
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)
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def _print_training_info(self):
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"""打印训练信息"""
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info_table = Table(
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title="Training Configuration",
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show_header=True,
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header_style="bold magenta",
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)
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info_table.add_column("Parameter", style="cyan")
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info_table.add_column("Value", style="green")
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info_table.add_row("Device", str(self.device))
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info_table.add_row("Total Steps", str(self.total_steps))
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info_table.add_row("Warmup Steps", str(self.warmup_steps))
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info_table.add_row("Learning Rate", f"{self.learning_rate:.2e}")
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info_table.add_row("Min Learning Rate", f"{self.min_learning_rate:.2e}")
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info_table.add_row("Weight Decay", str(self.weight_decay))
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info_table.add_row("Label Smoothing", str(self.label_smoothing))
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info_table.add_row("Gradient Accumulation", str(self.grad_accum_steps))
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info_table.add_row("Mixed Precision", str(self.mixed_precision))
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self.console.print(info_table)
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def train(self, resume_from: Optional[str] = None):
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"""
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主训练循环
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Args:
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resume_from: 从哪个检查点恢复训练（可选）
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"""
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# 如果提供了检查点，则恢复训练
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if resume_from is not None:
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self.load_checkpoint(resume_from)
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# 打印训练信息
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self._print_training_info()
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# 初始化训练状态
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global_step = self.current_step
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accumulated_loss = 0.0
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accumulated_accuracy = 0.0
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accumulation_counter = 0
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# 创建进度条
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with self._create_progress_bar() as progress:
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epoch_task = progress.add_task(
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f"[cyan]Epoch {self.current_epoch + 1}/{self.num_epochs}",
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total=self.total_steps,
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)
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# 训练循环
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for epoch in range(self.current_epoch, self.num_epochs):
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self.current_epoch = epoch
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progress.update(
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epoch_task, description=f"[cyan]Epoch {epoch + 1}/{self.num_epochs}"
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)
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for batch_idx, batch in enumerate(self.train_dataloader):
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# 更新学习率
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current_lr = self._update_learning_rate()
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# 训练步骤
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loss, metrics = self.train_step(batch)
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# 累积指标
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accumulated_loss += loss
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accumulated_accuracy += metrics.get("accuracy", 0.0)
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accumulation_counter += 1
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# 梯度累积：每grad_accum_steps步更新一次参数
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if (global_step + 1) % self.grad_accum_steps == 0:
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# 梯度裁剪
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self.scaler.unscale_(self.optimizer)
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torch.nn.utils.clip_grad_norm_(
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self.model.parameters(), self.clip_grad_norm
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)
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# 更新参数
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self.scaler.step(self.optimizer)
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self.scaler.update()
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self.optimizer.zero_grad()
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# 更新进度条
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progress.update(
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epoch_task,
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advance=1,
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description=f"[cyan]Epoch {epoch + 1}/{self.num_epochs} | "
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f"Step {global_step}/{self.total_steps} | "
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f"Loss: {loss:.4f} | "
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f"LR: {current_lr:.2e}",
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)
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|
||
# 定期评估和记录
|
||
if (global_step + 1) % self.eval_frequency == 0:
|
||
# 计算平均指标
|
||
avg_loss = accumulated_loss / accumulation_counter
|
||
avg_accuracy = accumulated_accuracy / accumulation_counter
|
||
|
||
# 评估模型
|
||
eval_metrics = self.evaluate()
|
||
|
||
# 准备日志指标
|
||
log_metrics = {
|
||
"train/loss": avg_loss,
|
||
"train/accuracy": avg_accuracy,
|
||
"train/learning_rate": current_lr,
|
||
}
|
||
|
||
if eval_metrics:
|
||
log_metrics.update(
|
||
{
|
||
"eval/loss": eval_metrics["eval_loss"],
|
||
"eval/accuracy": eval_metrics["eval_accuracy"],
|
||
}
|
||
)
|
||
|
||
# 更新最佳模型
|
||
if eval_metrics["eval_loss"] < self.best_eval_loss:
|
||
self.best_eval_loss = eval_metrics["eval_loss"]
|
||
self.save_checkpoint(
|
||
f"step_{global_step + 1}.pt", is_best=True
|
||
)
|
||
|
||
# 记录到TensorBoard
|
||
self._log_to_tensorboard(log_metrics, global_step)
|
||
|
||
# 打印日志
|
||
log_text = (
|
||
f"[Epoch {epoch + 1}/{self.num_epochs}] "
|
||
f"[Step {global_step}/{self.total_steps}] "
|
||
f"Train Loss: {avg_loss:.4f} | "
|
||
f"Train Acc: {avg_accuracy:.4f} | "
|
||
f"LR: {current_lr:.2e}"
|
||
)
|
||
|
||
if eval_metrics:
|
||
log_text += (
|
||
f" | Eval Loss: {eval_metrics['eval_loss']:.4f} | "
|
||
f"Eval Acc: {eval_metrics['eval_accuracy']:.4f}"
|
||
)
|
||
|
||
progress.console.log(log_text)
|
||
|
||
# 重置累积指标
|
||
accumulated_loss = 0.0
|
||
accumulated_accuracy = 0.0
|
||
accumulation_counter = 0
|
||
|
||
# 定期保存检查点
|
||
if (global_step + 1) % self.save_frequency == 0:
|
||
self.save_checkpoint(f"step_{global_step}.pt")
|
||
|
||
# 更新步数
|
||
global_step += 1
|
||
self.current_step = global_step
|
||
|
||
# 检查是否达到总步数
|
||
if global_step >= self.total_steps:
|
||
progress.update(epoch_task, completed=self.total_steps)
|
||
break
|
||
|
||
# 重置进度条
|
||
progress.reset(epoch_task)
|
||
|
||
# 每个epoch结束后保存检查点
|
||
self.save_checkpoint(f"epoch_{epoch + 1}.pt")
|
||
|
||
# 检查是否达到总步数
|
||
if global_step >= self.total_steps:
|
||
break
|
||
|
||
# 训练完成
|
||
logger.info("Training completed!")
|
||
|
||
# 保存最终模型
|
||
self.save_checkpoint("final_model.pt")
|
||
|
||
# 关闭TensorBoard写入器
|
||
if self.writer is not None:
|
||
self.writer.close()
|
||
|
||
|
||
def worker_init_fn(worker_id: int) -> None:
|
||
"""
|
||
初始化每个DataLoader worker的随机种子，确保可复现性
|
||
|
||
Args:
|
||
worker_id: worker的ID
|
||
"""
|
||
worker_seed = torch.initial_seed() % (2**32)
|
||
np.random.seed(worker_seed)
|
||
random.seed(worker_seed)
|
||
|
||
|
||
def collate_fn(batch: List[Dict[str, Any]]) -> Dict[str, Any]:
|
||
"""
|
||
自定义批处理函数，将多个样本组合成一个batch
|
||
|
||
Args:
|
||
batch: 样本列表，每个样本是一个字典
|
||
|
||
Returns:
|
||
批处理后的字典，tensor字段已stack，字符串字段保持为列表
|
||
"""
|
||
# 处理tensor字段 - 使用squeeze去除多余的batch维度
|
||
input_ids = torch.stack([item["input_ids"].squeeze(0) for item in batch])
|
||
token_type_ids = torch.stack([item["token_type_ids"].squeeze(0) for item in batch])
|
||
attention_mask = torch.stack([item["attention_mask"].squeeze(0) for item in batch])
|
||
labels = torch.stack([item["label"].squeeze(0) for item in batch])
|
||
history_slot_ids = torch.stack([item["history_slot_ids"] for item in batch])
|
||
pinyin_ids = torch.stack([item["pinyin_ids"] for item in batch])
|
||
|
||
# 字符串字段保持为列表
|
||
prefixes = [item["prefix"] for item in batch]
|
||
suffixes = [item["suffix"] for item in batch]
|
||
pinyins = [item["pinyin"] for item in batch]
|
||
|
||
return {
|
||
"input_ids": input_ids,
|
||
"token_type_ids": token_type_ids,
|
||
"attention_mask": attention_mask,
|
||
"labels": labels,
|
||
"history_slot_ids": history_slot_ids,
|
||
"prefix": prefixes,
|
||
"suffix": suffixes,
|
||
"pinyin": pinyins,
|
||
"pinyin_ids": pinyin_ids,
|
||
}
|
||
|
||
|
||
# Typer CLI应用
|
||
app = typer.Typer(help="输入法模型训练命令行工具", add_completion=False)
|
||
|
||
|
||
@app.command()
|
||
def train(
|
||
# 数据参数
|
||
train_data_path: str = typer.Option(
|
||
..., "--train-data-path", "-t", help="训练数据集路径"
|
||
),
|
||
eval_data_path: str = typer.Option(
|
||
..., "--eval-data-path", "-e", help="评估数据集路径"
|
||
),
|
||
output_dir: str = typer.Option("./output", "--output-dir", "-o", help="输出目录"),
|
||
# 模型参数
|
||
vocab_size: int = typer.Option(10019, "--vocab-size", help="词汇表大小"),
|
||
pinyin_vocab_size: int = typer.Option(
|
||
30, "--pinyin-vocab-size", help="拼音词汇表大小"
|
||
),
|
||
max_iter_length: int = typer.Option(
|
||
1024 * 1024 * 128, "--max_iter_length", help="数据集大小"
|
||
),
|
||
dim: int = typer.Option(512, "--dim", help="模型维度"),
|
||
num_slots: int = typer.Option(8, "--num-slots", help="历史槽位数量"),
|
||
n_layers: int = typer.Option(4, "--n-layers", help="Transformer层数"),
|
||
n_heads: int = typer.Option(4, "--n-heads", help="注意力头数"),
|
||
num_experts: int = typer.Option(20, "--num-experts", help="MoE专家数量"),
|
||
max_seq_len: int = typer.Option(128, "--max-seq-len", help="最大序列长度"),
|
||
use_pinyin: bool = typer.Option(False, "--use-pinyin", help="是否使用拼音特征"),
|
||
# 训练参数
|
||
batch_size: int = typer.Option(128, "--batch-size", "-b", help="批次大小"),
|
||
num_epochs: int = typer.Option(10, "--num-epochs", help="训练轮数"),
|
||
learning_rate: float = typer.Option(1e-5, "--learning-rate", "-lr", help="学习率"),
|
||
min_learning_rate: float = typer.Option(
|
||
1e-9, "--min-learning-rate", help="最小学习率"
|
||
),
|
||
weight_decay: float = typer.Option(0.1, "--weight-decay", help="权重衰减"),
|
||
warmup_ratio: float = typer.Option(0.1, "--warmup-ratio", help="热身步数比例"),
|
||
label_smoothing: float = typer.Option(
|
||
0.15, "--label-smoothing", help="标签平滑参数"
|
||
),
|
||
grad_accum_steps: int = typer.Option(1, "--grad-accum-steps", help="梯度累积步数"),
|
||
clip_grad_norm: float = typer.Option(1.0, "--clip-grad-norm", help="梯度裁剪范数"),
|
||
eval_frequency: int = typer.Option(500, "--eval-frequency", help="评估频率"),
|
||
save_frequency: int = typer.Option(10000, "--save-frequency", help="保存频率"),
|
||
# 其他参数
|
||
mixed_precision: bool = typer.Option(
|
||
True, "--mixed-precision/--no-mixed-precision", help="是否使用混合精度训练"
|
||
),
|
||
use_tensorboard: bool = typer.Option(
|
||
True, "--tensorboard/--no-tensorboard", help="是否使用TensorBoard"
|
||
),
|
||
resume_from: Optional[str] = typer.Option(
|
||
None, "--resume-from", help="从检查点恢复训练"
|
||
),
|
||
seed: int = typer.Option(42, "--seed", help="随机种子"),
|
||
):
|
||
"""
|
||
训练输入法模型
|
||
"""
|
||
torch.multiprocessing.set_sharing_strategy("file_system")
|
||
|
||
# 设置随机种子
|
||
torch.manual_seed(seed)
|
||
if torch.cuda.is_available():
|
||
torch.cuda.manual_seed_all(seed)
|
||
|
||
console = Console()
|
||
|
||
# 打印配置信息
|
||
console.print(
|
||
Panel.fit("[bold cyan]输入法模型训练配置[/bold cyan]", border_style="cyan")
|
||
)
|
||
|
||
config_table = Table(show_header=True, header_style="bold magenta")
|
||
config_table.add_column("Category", style="cyan")
|
||
config_table.add_column("Parameter", style="green")
|
||
config_table.add_column("Value", style="yellow")
|
||
|
||
# 添加配置信息
|
||
config_table.add_row("数据", "训练数据路径", train_data_path)
|
||
config_table.add_row("数据", "评估数据路径", eval_data_path)
|
||
config_table.add_row("数据", "输出目录", output_dir)
|
||
config_table.add_row("数据", "批次大小", str(batch_size))
|
||
|
||
config_table.add_row("模型", "词汇表大小", str(vocab_size))
|
||
config_table.add_row("模型", "拼音词汇表", str(pinyin_vocab_size))
|
||
config_table.add_row("模型", "模型维度", str(dim))
|
||
config_table.add_row("模型", "槽位数量", str(num_slots))
|
||
config_table.add_row("模型", "Transformer层数", str(n_layers))
|
||
config_table.add_row("模型", "注意力头数", str(n_heads))
|
||
config_table.add_row("模型", "MoE专家数", str(num_experts))
|
||
config_table.add_row("模型", "使用拼音", str(use_pinyin))
|
||
|
||
config_table.add_row("训练", "训练轮数", str(num_epochs))
|
||
config_table.add_row("训练", "学习率", f"{learning_rate:.2e}")
|
||
config_table.add_row("训练", "最小学习率", f"{min_learning_rate:.2e}")
|
||
config_table.add_row("训练", "权重衰减", str(weight_decay))
|
||
config_table.add_row("训练", "热身比例", str(warmup_ratio))
|
||
config_table.add_row("训练", "标签平滑", str(label_smoothing))
|
||
config_table.add_row("训练", "梯度累积", str(grad_accum_steps))
|
||
config_table.add_row("训练", "梯度裁剪", str(clip_grad_norm))
|
||
config_table.add_row("训练", "混合精度", str(mixed_precision))
|
||
|
||
console.print(config_table)
|
||
|
||
# 创建输出目录
|
||
output_path = Path(output_dir)
|
||
output_path.mkdir(parents=True, exist_ok=True)
|
||
|
||
# 保存配置
|
||
config = {
|
||
"train_data_path": train_data_path,
|
||
"eval_data_path": eval_data_path,
|
||
"output_dir": output_dir,
|
||
"vocab_size": vocab_size,
|
||
"pinyin_vocab_size": pinyin_vocab_size,
|
||
"dim": dim,
|
||
"num_slots": num_slots,
|
||
"n_layers": n_layers,
|
||
"n_heads": n_heads,
|
||
"num_experts": num_experts,
|
||
"max_seq_len": max_seq_len,
|
||
"use_pinyin": use_pinyin,
|
||
"batch_size": batch_size,
|
||
"num_epochs": num_epochs,
|
||
"learning_rate": learning_rate,
|
||
"min_learning_rate": min_learning_rate,
|
||
"weight_decay": weight_decay,
|
||
"warmup_ratio": warmup_ratio,
|
||
"label_smoothing": label_smoothing,
|
||
"grad_accum_steps": grad_accum_steps,
|
||
"clip_grad_norm": clip_grad_norm,
|
||
"eval_frequency": eval_frequency,
|
||
"save_frequency": save_frequency,
|
||
"mixed_precision": mixed_precision,
|
||
"use_tensorboard": use_tensorboard,
|
||
"seed": seed,
|
||
"max_iter_length": max_iter_length,
|
||
}
|
||
|
||
config_file = output_path / "training_config.json"
|
||
with open(config_file, "w", encoding="utf-8") as f:
|
||
json.dump(config, f, indent=2, ensure_ascii=False)
|
||
|
||
logger.info(f"Configuration saved to {config_file}")
|
||
|
||
# 创建数据加载器
|
||
console.print("[bold cyan]正在创建数据加载器...[/bold cyan]")
|
||
|
||
# 训练数据集
|
||
train_dataset = PinyinInputDataset(
|
||
data_path=train_data_path,
|
||
max_workers=-1, # 自动选择worker数量
|
||
max_iter_length=max_iter_length,
|
||
max_seq_length=max_seq_len,
|
||
text_field="text",
|
||
py_style_weight=(9, 2, 1),
|
||
shuffle_buffer_size=5000,
|
||
length_weights={1: 10, 2: 50, 3: 50, 4: 40, 5: 15, 6: 10, 7: 5, 8: 2},
|
||
)
|
||
|
||
# 训练数据加载器
|
||
# 注意：PinyinInputDataset是IterableDataset，所以不能使用shuffle参数
|
||
# 多worker配置：每个worker处理数据集的一个分片，由dataset.__iter__中的shard处理
|
||
train_dataloader = DataLoader(
|
||
train_dataset,
|
||
batch_size=batch_size,
|
||
num_workers=min(max(1, (os.cpu_count() or 1) - 1), 25),
|
||
pin_memory=torch.cuda.is_available(),
|
||
worker_init_fn=worker_init_fn,
|
||
collate_fn=collate_fn,
|
||
prefetch_factor=64, # 每个worker预取64个batch，适合大内存场景
|
||
persistent_workers=True, # 保持worker进程存活，避免重建开销
|
||
)
|
||
|
||
# 评估数据集（使用相同的设置，但可以调整参数）
|
||
eval_dataset = PinyinInputDataset(
|
||
data_path=eval_data_path,
|
||
max_workers=-1,
|
||
max_iter_length=1024, # 评估集较小
|
||
max_seq_length=max_seq_len,
|
||
text_field="text",
|
||
py_style_weight=(9, 2, 1),
|
||
shuffle_buffer_size=1000,
|
||
length_weights={1: 10, 2: 50, 3: 50, 4: 40, 5: 15, 6: 10, 7: 5, 8: 2},
|
||
)
|
||
|
||
eval_dataloader = DataLoader(
|
||
eval_dataset,
|
||
batch_size=batch_size,
|
||
num_workers=1,
|
||
pin_memory=torch.cuda.is_available(),
|
||
worker_init_fn=worker_init_fn,
|
||
collate_fn=collate_fn,
|
||
prefetch_factor=64, # 每个worker预取64个batch
|
||
persistent_workers=True, # 保持worker进程存活
|
||
)
|
||
|
||
console.print("[bold cyan]正在创建模型...[/bold cyan]")
|
||
model = InputMethodEngine(
|
||
vocab_size=vocab_size,
|
||
pinyin_vocab_size=pinyin_vocab_size,
|
||
dim=dim,
|
||
num_slots=num_slots,
|
||
n_layers=n_layers,
|
||
n_heads=n_heads,
|
||
num_experts=num_experts,
|
||
max_seq_len=max_seq_len,
|
||
)
|
||
|
||
console.print(
|
||
f"[green]✓ 模型创建完成，参数量: {sum(p.numel() for p in model.parameters()):,}[/green]"
|
||
)
|
||
|
||
# 创建训练器
|
||
console.print("[bold cyan]正在创建训练器...[/bold cyan]")
|
||
trainer = Trainer(
|
||
model=model,
|
||
train_dataloader=train_dataloader,
|
||
eval_dataloader=eval_dataloader,
|
||
total_steps=int(max_iter_length / batch_size),
|
||
output_dir=output_dir,
|
||
num_epochs=num_epochs,
|
||
learning_rate=learning_rate,
|
||
min_learning_rate=min_learning_rate,
|
||
weight_decay=weight_decay,
|
||
warmup_ratio=warmup_ratio,
|
||
label_smoothing=label_smoothing,
|
||
grad_accum_steps=grad_accum_steps,
|
||
clip_grad_norm=clip_grad_norm,
|
||
eval_frequency=eval_frequency,
|
||
save_frequency=save_frequency,
|
||
mixed_precision=mixed_precision,
|
||
use_tensorboard=use_tensorboard,
|
||
)
|
||
|
||
console.print("[green]✓ 训练器创建完成[/green]")
|
||
|
||
# 开始训练
|
||
console.print("\n[bold cyan]开始训练...[/bold cyan]")
|
||
console.print(f"开始时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
|
||
|
||
trainer.train(resume_from=resume_from)
|
||
|
||
console.print("[bold green]✓ 训练完成![/bold green]")
|
||
console.print(f"结束时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
|
||
console.print(f"模型和日志保存在: {output_dir}")
|
||
|
||
|
||
@app.command()
|
||
def evaluate(
|
||
checkpoint_path: str = typer.Option(..., "--checkpoint", "-c", help="检查点路径"),
|
||
data_path: str = typer.Option(..., "--data-path", "-d", help="数据集路径"),
|
||
batch_size: int = typer.Option(32, "--batch-size", "-b", help="批次大小"),
|
||
):
|
||
"""
|
||
评估训练好的模型
|
||
"""
|
||
console = Console()
|
||
console.print(f"[bold cyan]评估模型: {checkpoint_path}[/bold cyan]")
|
||
|
||
# 这里应该实现评估逻辑
|
||
# 1. 加载检查点
|
||
# 2. 创建数据加载器
|
||
# 3. 评估模型
|
||
|
||
console.print("[yellow]评估功能待实现[/yellow]")
|
||
|
||
|
||
@app.command()
|
||
def export(
|
||
checkpoint_path: str = typer.Option(..., "--checkpoint", "-c", help="检查点路径"),
|
||
output_path: str = typer.Option(
|
||
"./exported_model.onnx", "--output", "-o", help="输出路径"
|
||
),
|
||
):
|
||
"""
|
||
导出模型为ONNX格式
|
||
"""
|
||
console = Console()
|
||
console.print(f"[bold cyan]导出模型到: {output_path}[/bold cyan]")
|
||
|
||
# 这里应该实现导出逻辑
|
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# 1. 加载检查点
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# 2. 导出为ONNX
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console.print("[yellow]导出功能待实现[/yellow]")
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if __name__ == "__main__":
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app()
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