628 lines
24 KiB
Python
628 lines
24 KiB
Python
import math
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import pickle
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from importlib.resources import files
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from pathlib import Path
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from typing import Optional, Union
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import torch
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import torch.amp as amp
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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from loguru import logger
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from modelscope import AutoModel, AutoTokenizer
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from tqdm.autonotebook import tqdm
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from suinput.dataset import PG
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from .monitor import TrainingMonitor, send_serverchan_message
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def eval_dataloader(path: Union[str, Path] = (files(__package__) / "eval_dataset")):
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return [pickle.load(file.open("rb")) for file in Path(path).glob("*.pkl")]
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# ---------------------------- 残差块 ----------------------------
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class ResidualBlock(nn.Module):
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def __init__(self, dim, dropout_prob=0.3):
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super().__init__()
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self.linear1 = nn.Linear(dim, dim)
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self.ln1 = nn.LayerNorm(dim)
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self.linear2 = nn.Linear(dim, dim)
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self.ln2 = nn.LayerNorm(dim)
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self.gelu = nn.GELU()
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self.dropout = nn.Dropout(dropout_prob)
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def forward(self, x):
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residual = x
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x = self.gelu(self.linear1(x))
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x = self.ln1(x)
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x = self.linear2(x)
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x = self.ln2(x)
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x = self.dropout(x)
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x = x + residual
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return self.gelu(x)
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# ---------------------------- 专家网络 ----------------------------
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class Expert(nn.Module):
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def __init__(
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self,
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input_dim,
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d_model=768,
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num_resblocks=3,
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output_multiplier=1,
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dropout_prob=0.3,
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):
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super().__init__()
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self.output_dim = input_dim * output_multiplier
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self.linear_in = nn.Linear(input_dim, d_model)
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self.res_blocks = nn.ModuleList(
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[ResidualBlock(d_model, dropout_prob) for _ in range(num_resblocks)]
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)
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self.output = nn.Sequential(
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nn.Linear(d_model, d_model),
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nn.GELU(inplace=True),
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nn.Dropout(dropout_prob),
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nn.Linear(d_model, self.output_dim),
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)
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def forward(self, x):
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x = self.linear_in(x)
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for block in self.res_blocks:
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x = block(x)
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return self.output(x)
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# ---------------------------- 主模型（MoE + 硬路由）------------------------
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class MoEModel(nn.Module):
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def __init__(
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self,
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pretrained_model_name="iic/nlp_structbert_backbone_lite_std",
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num_classes=10018,
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output_multiplier=2,
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d_model=768,
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num_resblocks=4,
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num_domain_experts=12,
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num_shared_experts=1,
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):
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super().__init__()
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self.output_multiplier = output_multiplier
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# 1. 加载预训练 Embedding (Lite: hidden_size=512)
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logger.info(f"Loading backbone: {pretrained_model_name}")
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bert = AutoModel.from_pretrained(pretrained_model_name)
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self.embedding = bert.embeddings
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self.bert_config = bert.config
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self.hidden_size = self.bert_config.hidden_size # 512
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self.device = None
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# 2. Transformer Encoder
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encoder_layer = nn.TransformerEncoderLayer(
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d_model=self.hidden_size,
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nhead=8,
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dim_feedforward=self.bert_config.intermediate_size,
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dropout=self.bert_config.hidden_dropout_prob,
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activation="gelu",
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batch_first=True,
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)
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self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=4)
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# 3. 专家系统
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total_experts = num_domain_experts + num_shared_experts
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self.experts = nn.ModuleList()
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for i in range(total_experts):
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dropout = 0.3 if i < num_domain_experts else 0.4
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self.experts.append(
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Expert(
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input_dim=self.hidden_size,
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d_model=d_model,
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num_resblocks=num_resblocks,
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output_multiplier=self.output_multiplier,
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dropout_prob=dropout,
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)
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)
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self.expert_bias = nn.Embedding(
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total_experts, self.hidden_size * self.output_multiplier
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)
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# 4. 分类头
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self.classifier = nn.Sequential(
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nn.LayerNorm(self.hidden_size * self.output_multiplier),
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nn.Dropout(0.4),
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nn.Linear(self.hidden_size * self.output_multiplier, num_classes),
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)
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def to(self, device):
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"""重写 to 方法，记录设备"""
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self.device = device
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return super().to(device)
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def forward(self, input_ids, attention_mask, pg, p_start):
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"""
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ONNX 兼容的 Forward 函数
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Args:
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input_ids: [B, L]
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attention_mask: [B, L]
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pg: [B] 拼音组 ID
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p_start: [B] 拼音起始索引位置 (整数 Tensor)
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"""
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# ----- 1. Embeddings -----
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embeddings = self.embedding(input_ids)
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# ----- 2. Transformer Encoder -----
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# padding mask: True 表示忽略该位置
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padding_mask = attention_mask == 0
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encoded = self.encoder(
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embeddings, src_key_padding_mask=padding_mask
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) # [B, S, H]
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# ----- 3. ONNX 兼容的 Span Pooling (向量化实现) -----
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"""
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思路：
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我们不能用循环去切片。我们要构造一个 Mask 矩阵。
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目标：对于每个样本 i，生成一个长度为 L 的向量，其中 p_start[i] < index < p_end[i] 的位置为 1，其余为 0。
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步骤：
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1. 生成位置索引轴：indices = [0, 1, 2, ..., L-1] (Shape: [L])
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2. 扩展维度以匹配 Batch：
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indices: [1, L]
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p_start: [B, 1]
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p_end: [B, 1]
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3. 逻辑比较 (Broadcasting)：
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mask = (indices > p_start) & (indices < p_end)
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结果 Shape: [B, L] (Boolean)
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4. 应用 Mask：
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masked_encoded = encoded * mask.unsqueeze(-1)
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5. 求和并归一化：
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sum_vec = masked_encoded.sum(dim=1)
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count = mask.sum(dim=1).clamp(min=1) # 防止除零
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pooled = sum_vec / count
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"""
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B, L, H = encoded.shape
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device = encoded.device
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# 生成位置轴 [0, 1, ..., L-1]
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positions = torch.arange(L, device=device).unsqueeze(0) # [1, L]
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# 调整 p_start 形状为 [B, 1] 以便广播
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p_start_exp = p_start.unsqueeze(1) # [B, 1]
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span_mask = positions >= p_start_exp
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# 转换为 Float 用于乘法
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span_mask_float = span_mask.float() # [B, L]
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# 应用 Mask
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# encoded: [B, L, H] -> mask: [B, L, 1]
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masked_encoded = encoded * span_mask_float.unsqueeze(-1)
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# 求和
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span_sum = masked_encoded.sum(dim=1) # [B, H]
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# 计算有效长度 (防止除以 0)
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span_count = span_mask_float.sum(dim=1, keepdim=True).clamp(min=1.0) # [B, 1]
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# 平均池化
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pooled = span_sum / span_count # [B, H]
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# ----- 4. 专家路由（硬路由）-----
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if torch.jit.is_tracing():
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# ------------------ ONNX 导出模式：条件分支（batch=1）------------------
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# 此时 pg 为标量 Tensor，转换为 Python int
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group_id = pg.item() if torch.is_tensor(pg) else pg
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if group_id == 0:
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expert_out = self.experts[0](pooled) + self.expert_bias(
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torch.tensor(0, device=pooled.device)
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)
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elif group_id == 1:
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expert_out = self.experts[1](pooled) + self.expert_bias(
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torch.tensor(1, device=pooled.device)
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)
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elif group_id == 2:
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expert_out = self.experts[2](pooled) + self.expert_bias(
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torch.tensor(2, device=pooled.device)
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)
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elif group_id == 3:
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expert_out = self.experts[3](pooled) + self.expert_bias(
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torch.tensor(3, device=pooled.device)
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)
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elif group_id == 4:
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expert_out = self.experts[4](pooled) + self.expert_bias(
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torch.tensor(4, device=pooled.device)
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)
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elif group_id == 5:
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expert_out = self.experts[5](pooled) + self.expert_bias(
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torch.tensor(5, device=pooled.device)
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)
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elif group_id == 6:
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expert_out = self.experts[6](pooled) + self.expert_bias(
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torch.tensor(6, device=pooled.device)
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)
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elif group_id == 7:
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expert_out = self.experts[7](pooled) + self.expert_bias(
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torch.tensor(7, device=pooled.device)
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)
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elif group_id == 8:
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expert_out = self.experts[8](pooled) + self.expert_bias(
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torch.tensor(8, device=pooled.device)
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)
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elif group_id == 9:
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expert_out = self.experts[9](pooled) + self.expert_bias(
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torch.tensor(9, device=pooled.device)
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)
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elif group_id == 10:
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expert_out = self.experts[10](pooled) + self.expert_bias(
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torch.tensor(10, device=pooled.device)
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)
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elif group_id == 11:
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expert_out = self.experts[11](pooled) + self.expert_bias(
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torch.tensor(11, device=pooled.device)
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)
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else: # group_id == 12
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expert_out = self.experts[12](pooled) + self.expert_bias(
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torch.tensor(12, device=pooled.device)
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)
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else:
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batch_size = pooled.size(0)
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# 并行计算所有专家输出
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expert_outputs = torch.stack(
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[e(pooled) for e in self.experts], dim=0
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) # [E, B, D]
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# 根据 pg 索引专家输出
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expert_out = expert_outputs[pg, torch.arange(batch_size)] # [B, D]
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# 添加专家偏置
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bias = self.expert_bias(pg) # [B, D]
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expert_out = expert_out + bias
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# ----- 5. 分类头 -----
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logits = self.classifier(expert_out) # [batch, num_classes]
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if not self.training: # 推理时加 Softmax
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probs = torch.softmax(logits, dim=-1)
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return probs
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return logits
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def model_eval(self, eval_dataloader, criterion):
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"""
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在验证集上评估模型，返回准确率和平均损失。
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参数：
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eval_dataloader: DataLoader，提供 'input_ids', 'attention_mask', 'pg', 'char_id'
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criterion: 损失函数，默认为 CrossEntropyLoss()
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返回：
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accuracy: float, 准确率
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avg_loss: float, 平均损失
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"""
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self.eval()
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total_loss = 0.0
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correct = 0
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total = 0
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with torch.no_grad():
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for batch in eval_dataloader:
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# 移动数据到模型设备
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input_ids = batch["hint"]["input_ids"].to(self.device)
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attention_mask = batch["hint"]["attention_mask"].to(self.device)
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pg = batch["pg"].to(self.device)
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p_start = batch["p_start"].to(self.device)
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labels = batch["char_id"].to(self.device)
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# 前向传播
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probs = self(input_ids, attention_mask, pg)
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loss = criterion(log_probs, 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 = probs.argmax(dim=-1)
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correct += (preds == labels).sum().item()
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total += labels.size(0)
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avg_loss = total_loss / total if total > 0 else 0.0
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accuracy = correct / total if total > 0 else 0.0
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return accuracy, avg_loss
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def gen_predict_sample(self, text, py, tokenizer=None):
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"""
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生成用于预测的样本数据。
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参数:
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text (str): 输入的文本内容。
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py (list): 与文本对应的拼音列表。
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tokenizer (PreTrainedTokenizer, optional): 用于文本编码的分词器。如果未提供且实例中没有默认分词器，
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则会自动加载预训练的分词器。
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返回:
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dict: 包含以下键值的字典：
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- "hint": 包含编码后的输入特征，包括 "input_ids" 和 "attention_mask"。
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- "pg": 一个张量，表示拼音的第一个字符在 PG 映射中的索引。
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功能说明:
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1. 如果未提供分词器且实例中不存在默认分词器，则从预训练模型加载分词器。
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2. 使用分词器对输入文本和拼音进行编码，设置最大长度为 88，并进行填充和截断。
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3. 构造样本字典，包含编码后的输入特征和拼音映射张量。
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"""
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# 如果未提供分词器且实例中没有默认分词器，则加载预训练分词器
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if tokenizer is None and not hasattr(self, "tokenizer"):
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self.tokenizer = AutoTokenizer.from_pretrained(
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"iic/nlp_structbert_backbone_tiny_std"
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)
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else:
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# 使用传入的分词器或实例中的默认分词器
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self.tokenizer = tokenizer or self.tokenizer
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# 对输入文本和拼音进行编码，生成模型所需的输入格式
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hint = self.tokenizer(
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text,
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py,
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max_length=88,
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padding="max_length",
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truncation=True,
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return_tensors="pt",
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)
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# 构造样本字典
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sample = {}
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sample["hint"] = {
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"input_ids": hint["input_ids"],
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"attention_mask": hint["attention_mask"],
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}
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# 将拼音的第一个字符映射为 PG 中的索引并转换为张量
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sample["pg"] = torch.tensor([PG[py[0]]])
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sample["p_start"] = torch.tensor([len(text)])
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return sample
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def predict(self, text, py, tokenizer=None):
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"""
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基于输入的文本和拼音，生成 sample 字典进行预测，支持批量/单样本，可选调试打印错误样本信息。
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参数：
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text : str
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输入的文本。
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py : str
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输入的拼音。
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tokenizer : Tokenizer, optional
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用于分词的分词器，默认为 None。
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debug : bool
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是否打印预测错误的样本信息。
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返回：
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preds : torch.Tensor
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[batch] 预测类别标签（若输入为单样本且无 batch 维度，则返回标量）
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"""
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self.eval() # 将模型设置为评估模式，关闭dropout等训练时需要的层
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# ------------------ 1. 提取并规范化输入 ------------------
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# 判断是否为单样本（input_ids 无 batch 维度）
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sample = self.gen_predict_sample(text, py, tokenizer) # 生成预测所需的样本数据
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input_ids = sample["hint"]["input_ids"] # 获取输入ID
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attention_mask = sample["hint"]["attention_mask"] # 获取注意力掩码
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pg = sample["pg"] # 获取拼音引导
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has_batch_dim = input_ids.dim() > 1 # 判断输入是否有batch维度
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# 如果没有batch维度，则添加batch维度
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if not has_batch_dim:
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input_ids = input_ids.unsqueeze(0) # 在第0维添加batch维度
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attention_mask = attention_mask.unsqueeze(0) # 在第0维添加batch维度
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# 如果拼音引导是标量，则扩展为与输入ID相同的batch大小
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if pg.dim() == 0:
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pg = pg.unsqueeze(0).expand(input_ids.size(0)) # 扩展拼音引导的batch维度
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# ------------------ 2. 移动设备 ------------------
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# 将输入数据移动到模型所在设备（GPU/CPU）
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input_ids = input_ids.to(self.device)
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attention_mask = attention_mask.to(self.device)
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pg = pg.to(self.device)
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# ------------------ 3. 推理 ------------------
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# 使用torch.no_grad()上下文管理器，不计算梯度，节省内存
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with torch.no_grad():
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logits = self(input_ids, attention_mask, pg) # 前向传播获取logits
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preds = torch.softmax(logits, dim=-1).argmax(dim=-1) # [batch]
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# ------------------ 4. 返回结果（保持与输入维度一致） ------------------
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if not has_batch_dim:
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return preds.squeeze(0) # 返回标量
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return preds
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def fit(
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self,
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train_dataloader, # 训练数据加载器
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eval_dataloader=None, # 评估数据加载器，可选
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monitor: Optional[TrainingMonitor] = None, # 训练监控器，用于记录训练过程
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criterion=None, # 损失函数
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optimizer=None, # 优化器
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||
num_epochs=1, # 训练轮数
|
||
stop_batch=1e6, # 最大训练批次数
|
||
eval_frequency=500,
|
||
grad_accum_steps=1, # 梯度累积步数
|
||
clip_grad_norm=1.0, # 梯度裁剪的范数
|
||
loss_weight=None,
|
||
mixed_precision=True,
|
||
weight_decay=0.1,
|
||
warmup_ratio=0.1,
|
||
label_smoothing=0.15,
|
||
lr=1e-4,
|
||
):
|
||
"""
|
||
训练模型，支持混合精度、梯度累积、学习率调度、实时监控。
|
||
|
||
参数：
|
||
# TODO: 添加参数注释
|
||
"""
|
||
# 确保模型在正确的设备上（GPU或CPU）
|
||
if self.device is None:
|
||
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||
self.to(self.device)
|
||
|
||
# 切换到训练模式
|
||
self.train()
|
||
|
||
# 默认优化器设置
|
||
if optimizer is None:
|
||
optimizer = optim.AdamW(self.parameters(), lr=lr, weight_decay=weight_decay)
|
||
|
||
# 损失函数设置
|
||
if criterion is None:
|
||
if loss_weight is not None:
|
||
criterion = nn.CrossEntropyLoss(
|
||
weight=loss_weight, label_smoothing=label_smoothing
|
||
)
|
||
else:
|
||
criterion = nn.CrossEntropyLoss(label_smoothing=label_smoothing)
|
||
|
||
# 混合精度缩放器
|
||
scaler = amp.GradScaler(enabled=mixed_precision)
|
||
|
||
total_steps = stop_batch
|
||
warmup_steps = int(total_steps * warmup_ratio)
|
||
logger.info(f"Training Start: Steps={total_steps}, Warmup={warmup_steps}")
|
||
processed_batches = 0 # 新增：实际处理的 batch 数量计数器
|
||
batch_loss_sum = 0.0
|
||
optimizer.zero_grad()
|
||
|
||
for epoch in range(num_epochs):
|
||
for batch_idx, batch in enumerate(
|
||
tqdm(train_dataloader, total=int(stop_batch))
|
||
):
|
||
processed_batches += 1
|
||
|
||
# LR Schedule
|
||
if processed_batches < warmup_steps:
|
||
current_lr = lr * (processed_batches / warmup_steps)
|
||
else:
|
||
progress = (processed_batches - warmup_steps) / (
|
||
total_steps - warmup_steps
|
||
)
|
||
current_lr = lr * (0.5 * (1.0 + math.cos(math.pi * progress)))
|
||
for param_group in optimizer.param_groups:
|
||
param_group["lr"] = current_lr
|
||
|
||
# ---------- 移动数据 ----------
|
||
input_ids = batch["hint"]["input_ids"].to(self.device)
|
||
attention_mask = batch["hint"]["attention_mask"].to(self.device)
|
||
pg = batch["pg"].to(self.device)
|
||
p_start = batch["p_start"].to(self.device) # [B]
|
||
labels = batch["char_id"].to(self.device)
|
||
|
||
# 混合精度前向
|
||
# Forward
|
||
with torch.amp.autocast(
|
||
device_type=self.device.type, enabled=mixed_precision
|
||
):
|
||
logits = self(input_ids, attention_mask, pg, p_start)
|
||
loss = criterion(logits, labels)
|
||
loss = loss / grad_accum_steps
|
||
|
||
# 反向传播
|
||
scaler.scale(loss).backward()
|
||
|
||
# 梯度累积
|
||
if (processed_batches) % grad_accum_steps == 0:
|
||
scaler.unscale_(optimizer)
|
||
torch.nn.utils.clip_grad_norm_(self.parameters(), clip_grad_norm)
|
||
|
||
has_nan = False
|
||
for p in self.parameters():
|
||
if p.grad is not None and torch.isnan(p.grad).any():
|
||
has_nan = True
|
||
break
|
||
|
||
if not has_nan:
|
||
scaler.step(optimizer)
|
||
scaler.update()
|
||
else:
|
||
logger.warning("NaN detected, skipping step.")
|
||
optimizer.zero_grad()
|
||
batch_loss_sum += loss.item() * grad_accum_steps
|
||
|
||
# 周期性评估
|
||
if eval_dataloader and global_step % eval_frequency == 0:
|
||
self.eval()
|
||
acc, eval_loss = self.model_eval(eval_dataloader, criterion)
|
||
if global_step == 0:
|
||
avg_loss = eval_loss
|
||
self.train()
|
||
if monitor:
|
||
monitor.add_step(
|
||
global_step, {"loss": avg_loss, "acc": acc}
|
||
)
|
||
logger.info(
|
||
f"step: {global_step}, loss: {avg_loss:.4f}, acc: {acc:.4f}, eval_loss: {eval_loss:.4f}"
|
||
)
|
||
batch_loss_sum = 0.0
|
||
if processed_batches - 1 >= stop_batch:
|
||
break
|
||
global_step += 1
|
||
|
||
try:
|
||
res_acc, res_loss = self.model_eval(eval_dataloader, criterion)
|
||
to_wechat_response = send_serverchan_message(
|
||
title="训练完成",
|
||
content=f"训练完成，acc: {res_acc:.4f}, loss: {res_loss:.4f}",
|
||
)
|
||
logger.info(f"训练完成，acc: {res_acc:.4f}, loss: {res_loss:.4f}")
|
||
logger.info(f"发送消息: {to_wechat_response}")
|
||
except Exception as e:
|
||
logger.error(f"发送消息失败: {e}")
|
||
|
||
def load_from_state_dict(self, state_dict_path: Union[str, Path]):
|
||
state_dict = torch.load(
|
||
state_dict_path, weights_only=True, map_location=self.device
|
||
)
|
||
self.load_state_dict(state_dict)
|
||
|
||
def load_from_pretrained_base_model(
|
||
self,
|
||
BaseModel,
|
||
snapshot_path: Union[str, Path],
|
||
device=torch.device("cuda" if torch.cuda.is_available() else "cpu"),
|
||
*args,
|
||
**kwargs,
|
||
):
|
||
base_model = BaseModel(*args, **kwargs)
|
||
base_model.load_state_dict(torch.load(snapshot_path, map_location=device))
|
||
self_static_dict = self.state_dict()
|
||
pretrained_dict = base_model.state_dict()
|
||
|
||
freeze_layers = []
|
||
|
||
for key in self_static_dict.keys():
|
||
if key in pretrained_dict.keys():
|
||
if self_static_dict[key].shape == pretrained_dict[key].shape:
|
||
self_static_dict[key] = pretrained_dict[key].to(self.device)
|
||
freeze_layers.append(key)
|
||
self.load_state_dict(self_static_dict)
|
||
for name, param in self.named_parameters():
|
||
if name in freeze_layers:
|
||
param.requires_grad = False
|
||
|
||
# --- ONNX 导出辅助函数 ---
|
||
def export_onnx(self, output_path, dummy_input):
|
||
"""
|
||
dummy_input 应该是一个字典或元组，包含:
|
||
(input_ids, attention_mask, pg, p_start)
|
||
"""
|
||
self.eval()
|
||
input_names = ["input_ids", "attention_mask", "pg", "p_start"]
|
||
output_names = ["logits"]
|
||
|
||
torch.onnx.export(
|
||
self,
|
||
dummy_input,
|
||
output_path,
|
||
input_names=input_names,
|
||
output_names=output_names,
|
||
dynamic_axes={
|
||
"input_ids": {0: "batch_size", 1: "seq_len"},
|
||
"attention_mask": {0: "batch_size", 1: "seq_len"},
|
||
"pg": {0: "batch_size"},
|
||
"p_start": {0: "batch_size"},
|
||
"logits": {0: "batch_size"},
|
||
},
|
||
opset_version=14, # 推荐使用 14+ 以支持更好的算子
|
||
do_constant_folding=True,
|
||
)
|
||
logger.info(f"ONNX model exported to {output_path}")
|