feat: 添加数据集使用示例和模型训练模块
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@ -0,0 +1,68 @@
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from tqdm import tqdm
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from loguru import logger
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import torch
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from torch.utils.data import DataLoader
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from suinput.dataset import PinyinInputDataset, worker_init_fn, custom_collate, custom_collate_with_txt
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from suinput.query import QueryEngine
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# 使用示例
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if __name__ == "__main__":
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# 初始化查询引擎
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query_engine = QueryEngine()
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query_engine.load()
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# 创建数据集
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dataset = PinyinInputDataset(
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data_dir="/home/songsenand/Data/corpus/CCI-Data/",
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query_engine=query_engine,
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tokenizer_name="iic/nlp_structbert_backbone_tiny_std",
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max_len=88,
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batch_query_size=300,
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shuffle=True,
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shuffle_buffer_size=4000,
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)
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logger.info("数据集初始化")
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dataloader = DataLoader(
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dataset,
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batch_size=1024,
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num_workers=15,
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worker_init_fn=worker_init_fn,
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pin_memory=True if torch.cuda.is_available() else False,
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collate_fn=custom_collate,
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prefetch_factor=8,
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persistent_workers=True,
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shuffle=False, # 数据集内部已实现打乱
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)
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"""
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import cProfile
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def profile_func(dataloader):
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for i, sample in tqdm(enumerate(dataloader), total=3000):
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if i >= 3000:
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break
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return
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cProfile.run('profile_func(dataloader)')
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"""
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# 测试数据集
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try:
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logger.info("测试数据集")
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total = 3000
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for i, sample in tqdm(enumerate(dataloader), total=total):
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if i >= total:
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break
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#print(f"Sample {i+1}: {sample['txt'][0:10]}")
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"""
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print(f"Sample {i+1}:")
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print(f" Char: {sample['char']}, Id: {sample['char_id'].item()}, Freq: {sample.get('freq', 'N/A')}")
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print(f" Pinyin: {sample['py']}")
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print(f" Context length: {len(sample['txt'])}")
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print(f" Hint shape: {sample['hint']['input_ids'].shape}")
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print()
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"""
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except StopIteration:
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print("数据集为空")
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@ -5,10 +5,13 @@ description = "Add your description here"
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readme = "README.md"
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requires-python = ">=3.13"
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dependencies = [
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"bokeh>=3.8.2",
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"datasets>=4.5.0",
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"loguru>=0.7.3",
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"modelscope>=1.34.0",
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"msgpack>=1.1.2",
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"numpy>=2.4.2",
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"pandas>=3.0.0",
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"pypinyin>=0.55.0",
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"rich>=14.3.1",
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"transformers>=5.1.0",
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@ -25,3 +28,13 @@ dev = [
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[tool.uv.sources]
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autocommit = { git = "https://gitea.winkinshly.site/songsenand/autocommit.git" }
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[build-system]
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requires = ["setuptools>=61.0", "wheel"]
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build-backend = "setuptools.build_meta"
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[tool.setuptools]
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# 👇 这是关键：指定包在 src/ 下
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package-dir = {"" = "src"}
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[tool.setuptools.packages.find]
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where = ["src"]
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@ -1,25 +0,0 @@
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{
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"y": 0,
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"k": 0,
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"e": 0,
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"l": 1,
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"w": 1,
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"f": 1,
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"q": 2,
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"a": 2,
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"s": 2,
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"x": 3,
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"b": 3,
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"r": 3,
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"o": 4,
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"m": 4,
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"z": 4,
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"g": 5,
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"n": 5,
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"c": 5,
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"t": 6,
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"p": 6,
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"d": 6,
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"j": 7,
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"h": 7
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}
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@ -1,9 +1,6 @@
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import json
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import os
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import random
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from importlib.resources import files
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from pathlib import Path
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from typing import Any, Dict, List, Tuple
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from typing import Any, Dict, List, Tuple, Optional
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import numpy as np
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import torch
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@ -39,7 +36,7 @@ class PinyinInputDataset(IterableDataset):
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batch_query_size: int = 1000,
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# 打乱参数
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shuffle: bool = True,
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shuffle_buffer_size: int = 100,
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shuffle_buffer_size: int = 10000,
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# 削峰填谷参数
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max_freq: int = 434748359, # "的"的频率
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min_freq: int = 109, # "蓚"的频率
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@ -47,7 +44,6 @@ class PinyinInputDataset(IterableDataset):
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repeat_end_freq: int = 10000, # 开始重复的阈值
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max_drop_prob: float = 0.8, # 最大丢弃概率
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max_repeat_expect: float = 50.0, # 最大重复期望
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py_group_json_file: Optional[Dict[str, int]] = None,
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):
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"""
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初始化数据集
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@ -415,7 +411,6 @@ class PinyinInputDataset(IterableDataset):
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if not char_info:
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continue
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logger.info(f"获取字符信息: {char_info}")
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# 削峰填谷调整
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adjust_factor = self.adjust_frequency(char_info["freq"])
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if adjust_factor <= 0:
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@ -446,7 +441,7 @@ class PinyinInputDataset(IterableDataset):
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"char": char,
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"freq": char_info["freq"],
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"pg": torch.tensor(
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self.pg_groups[processed_pinyin[0]] if processed_pinyin else 8
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[self.pg_groups[processed_pinyin[0]] if processed_pinyin else 8]
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),
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}
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@ -486,6 +481,7 @@ class PinyinInputDataset(IterableDataset):
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random.seed(seed % (2**32))
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np.random.seed(seed % (2**32))
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batch_samples = []
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for item in self.dataset:
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text = item.get(self.text_field, "")
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if not text:
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@ -531,15 +527,18 @@ class PinyinInputDataset(IterableDataset):
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# 达到批量大小时处理
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if len(char_pinyin_batch) >= self.batch_query_size:
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batch_samples = self._process_batch(
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batch_samples += self._process_batch(
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char_pinyin_batch, char_positions, text
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)
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yield from self._shuffle_and_yield(batch_samples)
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char_pinyin_batch = []
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char_positions = []
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if len(batch_samples) >= self.shuffle_buffer_size:
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# logger.info(f"批量处理完成，开始打乱数据并生成样本, len(batch_samples): {len(batch_samples)}")
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yield from self._shuffle_and_yield(batch_samples)
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batch_samples = []
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# 处理剩余的字符
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if char_pinyin_batch:
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batch_samples = self._process_batch(
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batch_samples += self._process_batch(
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char_pinyin_batch, char_positions, text
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)
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yield from self._shuffle_and_yield(batch_samples)
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@ -582,6 +581,7 @@ def custom_collate_with_txt(batch):
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"char": [item["char"] for item in batch],
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"txt": [item["txt"] for item in batch],
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"py": [item["py"] for item in batch],
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"pg": torch.cat([item["pg"] for item in batch]),
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}
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return result
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@ -602,71 +602,7 @@ def custom_collate(batch):
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"attention_mask": torch.cat([h["attention_mask"] for h in hints]),
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},
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"char_id": torch.cat([item["char_id"] for item in batch]),
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"py": [item["py"] for item in batch],
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# "py_group_id": [item["py"] for item in batch],
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"pg": torch.cat([item["pg"] for item in batch]),
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}
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return result
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# 使用示例
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if __name__ == "__main__":
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from query import QueryEngine
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from tqdm import tqdm
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# 初始化查询引擎
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query_engine = QueryEngine()
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query_engine.load()
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# 创建数据集
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dataset = PinyinInputDataset(
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data_dir="/home/songsenand/Data/corpus/CCI-Data/",
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query_engine=query_engine,
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tokenizer_name="iic/nlp_structbert_backbone_tiny_std",
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max_len=88,
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batch_query_size=300,
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shuffle=True,
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shuffle_buffer_size=4000,
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)
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logger.info("数据集初始化")
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dataloader = DataLoader(
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dataset,
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batch_size=1024,
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num_workers=15,
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worker_init_fn=worker_init_fn,
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pin_memory=True if torch.cuda.is_available() else False,
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collate_fn=custom_collate_with_txt,
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prefetch_factor=8,
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persistent_workers=True,
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shuffle=False, # 数据集内部已实现打乱
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)
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"""import cProfile
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def profile_func(dataloader):
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for i, sample in tqdm(enumerate(dataloader), total=3000):
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if i >= 3000:
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break
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return
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cProfile.run('profile_func(dataloader)')
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"""
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# 测试数据集
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try:
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logger.info("测试数据集")
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for i, sample in tqdm(enumerate(dataloader), total=3000):
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if i >= 3000:
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break
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"""
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print(f"Sample {i+1}:")
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print(f" Char: {sample['char']}, Id: {sample['char_id'].item()}, Freq: {sample.get('freq', 'N/A')}")
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print(f" Pinyin: {sample['py']}")
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print(f" Context length: {len(sample['txt'])}")
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print(f" Hint shape: {sample['hint']['input_ids'].shape}")
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print()
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"""
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except StopIteration:
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print("数据集为空")
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@ -0,0 +1 @@
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@ -0,0 +1,267 @@
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import torch
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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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import torch.amp as amp
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from transformers import AutoModel
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from tqdm import tqdm
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from .monitor import TrainingMonitor
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# ---------------------------- 工具函数 ----------------------------
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def round_to_power_of_two(x):
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"""将数字向上取整为2的幂（此处固定返回1024）"""
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return 1024 # 根据您的说明固定 d_model = 1024
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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=0.1):
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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.relu = nn.ReLU()
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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.relu(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) # 残差前加 Dropout（符合原描述）
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x = x + residual
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return self.relu(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=1024,
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num_resblocks=4,
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output_multiplier=2,
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dropout_prob=0.1,
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):
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"""
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input_dim : BERT 输出的 hidden_size（如 312/768）
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d_model : 专家内部维度（固定 1024）
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output_multiplier : 输出维度 = input_dim * output_multiplier
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dropout_prob : 残差块内 Dropout
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"""
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super().__init__()
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self.input_dim = input_dim
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self.d_model = d_model
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self.output_dim = input_dim * output_multiplier
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# 输入映射：input_dim -> d_model
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self.linear_in = nn.Linear(input_dim, d_model)
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# 残差堆叠
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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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# 输出映射：d_model -> output_dim
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self.output = nn.Sequential(
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nn.Linear(d_model, d_model),
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nn.ReLU(inplace=True),
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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_tiny_std",
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num_classes=10018,
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d_model=1024,
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num_resblocks=4,
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num_domain_experts=8,
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num_shared_experts=1,
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):
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super().__init__()
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# 1. 加载预训练 BERT，仅保留 embeddings
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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 # BERT 隐层维度
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# 2. 4 层标准 Transformer Encoder（从 config 读取参数）
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encoder_layer = nn.TransformerEncoderLayer(
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d_model=self.hidden_size,
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nhead=self.bert_config.num_attention_heads,
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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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norm_first=True, # Pre-LN，与预训练一致
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)
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self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=4)
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# 3. 专家层：8个领域专家 + 1个共享专家
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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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# 领域专家 dropout=0.1，共享专家 dropout=0.2（您指定的更强正则）
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dropout_prob = 0.1 if i < num_domain_experts else 0.2
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expert = 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=2, # 输出维度 = 2 * hidden_size
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dropout_prob=dropout_prob,
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)
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self.experts.append(expert)
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# 4. 分类头
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self.classifier = nn.Sequential(
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nn.LayerNorm(2 * self.hidden_size), # 专家输出维度
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nn.Dropout(0.1),
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nn.Linear(2 * self.hidden_size, num_classes),
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)
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# 可选：为领域专家和共享专家设置不同权重衰减（通过优化器实现，此处不处理）
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def forward(self, input_ids, attention_mask, pg):
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"""
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input_ids : [batch, seq_len]
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attention_mask: [batch, seq_len] (1 为有效，0 为 padding)
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pg : group_id，训练时为 [batch] 的 LongTensor，推理导出时为标量 Tensor
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"""
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# ----- 1. Embeddings -----
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embeddings = self.embedding(input_ids) # [B, S, H]
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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. [CLS] 向量 -----
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cls_output = encoded[:, 0, :] # [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:
|
||||
expert_out = self.experts[0](cls_output)
|
||||
elif group_id == 1:
|
||||
expert_out = self.experts[1](cls_output)
|
||||
elif group_id == 2:
|
||||
expert_out = self.experts[2](cls_output)
|
||||
elif group_id == 3:
|
||||
expert_out = self.experts[3](cls_output)
|
||||
elif group_id == 4:
|
||||
expert_out = self.experts[4](cls_output)
|
||||
elif group_id == 5:
|
||||
expert_out = self.experts[5](cls_output)
|
||||
elif group_id == 6:
|
||||
expert_out = self.experts[6](cls_output)
|
||||
elif group_id == 7:
|
||||
expert_out = self.experts[7](cls_output)
|
||||
else: # group_id == 8
|
||||
expert_out = self.experts[8](cls_output)
|
||||
|
||||
else:
|
||||
# ------------------ 训练 / 普通推理：全量计算 + Gather ------------------
|
||||
# 此时 pg 为 [batch] 的 LongTensor
|
||||
batch_size = cls_output.size(0)
|
||||
# 所有专家并行计算，输出堆叠
|
||||
expert_outputs = torch.stack(
|
||||
[e(cls_output) for e in self.experts], dim=0
|
||||
) # [num_experts, batch, output_dim]
|
||||
# 根据 pg 索引对应的专家输出
|
||||
expert_out = expert_outputs[
|
||||
pg, torch.arange(batch_size)
|
||||
] # [batch, output_dim]
|
||||
|
||||
# ----- 5. 分类头 -----
|
||||
logits = self.classifier(expert_out) # [batch, num_classes]
|
||||
if not self.training: # 推理时加 Softmax
|
||||
probs = torch.softmax(logits, dim=-1)
|
||||
return probs
|
||||
return logits
|
||||
|
||||
def predict(self, inputs, labels):
|
||||
pass
|
||||
|
||||
def train(
|
||||
self,
|
||||
dataloader,
|
||||
monitor: TrainingMonitor,
|
||||
criterion = nn.CrossEntropyLoss(),
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu"),
|
||||
optimizer=None,
|
||||
sample_frequency=1000,
|
||||
):
|
||||
self.train()
|
||||
if optimizer is None:
|
||||
optimizer = optim.AdamW(self.parameters(), lr=6e-6)
|
||||
|
||||
for i, sample in tqdm(enumerate(dataloader), total=1e7):
|
||||
optimizer.zero_grad()
|
||||
with amp.autocast():
|
||||
char_id = sample.pop("char_id").to(device)
|
||||
input_ids = sample.pop("input_ids").to(device)
|
||||
attention_mask = sample.pop("attention_mask").to(device)
|
||||
pg = sample.pop("pg").to(device)
|
||||
logits = self(input_ids, attention_mask, pg)
|
||||
loss = criterion(logits, char_id)
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
|
||||
|
||||
|
||||
# ============================ 使用示例 ============================
|
||||
if __name__ == "__main__":
|
||||
# 1. 初始化模型
|
||||
model = MoEModel()
|
||||
model.eval()
|
||||
|
||||
# 2. 构造 dummy 输入（batch=1，用于导出 ONNX）
|
||||
dummy_input_ids = torch.randint(0, 100, (1, 64)) # [1, 64]
|
||||
dummy_attention_mask = torch.ones_like(dummy_input_ids) # [1, 64]
|
||||
dummy_pg = torch.tensor(3, dtype=torch.long) # 标量 group_id
|
||||
|
||||
# 3. 导出 ONNX（使用条件分支，仅计算一个专家）
|
||||
torch.onnx.export(
|
||||
model,
|
||||
(dummy_input_ids, dummy_attention_mask, dummy_pg),
|
||||
"moe_cpu.onnx",
|
||||
input_names=["input_ids", "attention_mask", "pg"],
|
||||
output_names=["logits"],
|
||||
dynamic_axes={ # 固定 batch=1，可不设 dynamic_axes
|
||||
"input_ids": {0: "batch"},
|
||||
"attention_mask": {0: "batch"},
|
||||
},
|
||||
opset_version=12,
|
||||
do_constant_folding=True,
|
||||
)
|
||||
print("ONNX 导出成功！")
|
||||
|
||||
# 4. 测试训练模式（batch=4）
|
||||
model.train()
|
||||
batch_input_ids = torch.randint(0, 100, (4, 64))
|
||||
batch_attention_mask = torch.ones_like(batch_input_ids)
|
||||
batch_pg = torch.tensor([0, 3, 8, 1], dtype=torch.long) # 不同 group
|
||||
logits = model(batch_input_ids, batch_attention_mask, batch_pg)
|
||||
print("训练模式输出形状:", logits.shape) # [4, 10018]
|
||||
|
|
@ -0,0 +1,172 @@
|
|||
from bokeh.io import push_notebook, show, output_notebook
|
||||
from bokeh.plotting import figure
|
||||
from bokeh.models import ColumnDataSource, LinearAxis, Range1d
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
|
||||
output_notebook() # 在 Jupyter 中必须调用一次
|
||||
|
||||
class TrainingMonitor:
|
||||
"""
|
||||
实时训练监控图，支持任意多个指标，自动管理左右 Y 轴。
|
||||
|
||||
参数
|
||||
----------
|
||||
metrics : list of dict, 可选
|
||||
每个指标是一个 dict，必须包含 'name'（数据列名）和 'label'（图例）。
|
||||
可选字段：'color' (颜色), 'y_axis' ('left' 或 'right', 默认 'left'),
|
||||
'y_range' (手动指定 Y 轴范围，如 [0,1])。
|
||||
若为 None，则使用默认的 [loss, acc]。
|
||||
title : str, 默认 "训练曲线"
|
||||
width : int, 默认 1080
|
||||
height : int, 默认 384
|
||||
line_width : int, 默认 2
|
||||
"""
|
||||
def __init__(self, metrics=None, title="训练曲线", width=1080, height=384, line_width=2):
|
||||
# 默认指标：loss 左轴，acc 右轴
|
||||
if metrics is None:
|
||||
metrics = [
|
||||
{'name': 'loss', 'label': 'loss', 'color': '#ed5a65', 'y_axis': 'left'},
|
||||
{'name': 'acc', 'label': 'accuracy', 'color': '#2b1216', 'y_axis': 'right', 'y_range': [0, 1]}
|
||||
]
|
||||
self.metrics = metrics
|
||||
self.metric_names = [m['name'] for m in metrics]
|
||||
|
||||
# 初始化数据源，含 step 列 + 各指标列
|
||||
self.source = ColumnDataSource(data={'step': []})
|
||||
for m in metrics:
|
||||
self.source.data[m['name']] = []
|
||||
|
||||
# 创建图形
|
||||
self.p = figure(title=title, width=width, height=height,
|
||||
x_axis_label='step', y_axis_label='left axis')
|
||||
self.p.extra_y_ranges = {} # 存放右轴
|
||||
|
||||
# 为每个指标添加线条
|
||||
for m in metrics:
|
||||
color = m.get('color', None)
|
||||
y_axis = m.get('y_axis', 'left')
|
||||
legend = m.get('label', m['name'])
|
||||
|
||||
if y_axis == 'right':
|
||||
# 创建右轴（若尚未创建）
|
||||
if y_axis not in self.p.extra_y_ranges:
|
||||
y_range_name = f'{y_axis}_{m["name"]}' # 唯一命名
|
||||
# 使用手动范围或自动计算
|
||||
y_range = m.get('y_range', None)
|
||||
if y_range is None:
|
||||
y_range = Range1d(start=0, end=1) # 占位，稍后自动调整
|
||||
else:
|
||||
y_range = Range1d(start=y_range[0], end=y_range[1])
|
||||
self.p.extra_y_ranges[y_range_name] = y_range
|
||||
self.p.add_layout(LinearAxis(y_range_name=y_range_name), 'right')
|
||||
else:
|
||||
# 复用已创建的右轴，简单起见：每个右轴指标使用独立 y_range_name
|
||||
y_range_name = f'right_{m["name"]}'
|
||||
self.p.extra_y_ranges[y_range_name] = Range1d(start=0, end=1)
|
||||
|
||||
self.p.line(x='step', y=m['name'], source=self.source,
|
||||
color=color, legend_label=legend,
|
||||
y_range_name=y_range_name, line_width=line_width)
|
||||
else:
|
||||
self.p.line(x='step', y=m['name'], source=self.source,
|
||||
color=color, legend_label=legend,
|
||||
line_width=line_width)
|
||||
|
||||
self.p.legend.location = "top_left"
|
||||
self.p.legend.click_policy = "hide" # 可点击图例隐藏曲线
|
||||
self.handle = show(self.p, notebook_handle=True)
|
||||
|
||||
def add_step(self, step, values):
|
||||
"""
|
||||
追加一个 step 的数据。
|
||||
|
||||
参数
|
||||
----------
|
||||
step : int or float
|
||||
values : dict
|
||||
键为指标名，值为该 step 的指标值。
|
||||
例如：{'loss': 0.23, 'acc': 0.85}
|
||||
"""
|
||||
new_data = {'step': [step]}
|
||||
for name in self.metric_names:
|
||||
new_data[name] = [values.get(name, np.nan)]
|
||||
self.source.stream(new_data, rollover=10000) # 保留最多 10000 条，防内存爆炸
|
||||
|
||||
# 自动调整 Y 轴范围（右轴）
|
||||
self._adjust_y_ranges()
|
||||
|
||||
def add_batch(self, steps, values_matrix):
|
||||
"""
|
||||
批量添加多个 step 的数据。
|
||||
|
||||
参数
|
||||
----------
|
||||
steps : list
|
||||
values_matrix : list of dict
|
||||
每个元素是与 add_step 格式相同的 dict。
|
||||
"""
|
||||
new_data = {'step': steps}
|
||||
# 初始化各列为空列表
|
||||
for name in self.metric_names:
|
||||
new_data[name] = []
|
||||
# 填充数据
|
||||
for vals in values_matrix:
|
||||
for name in self.metric_names:
|
||||
new_data[name].append(vals.get(name, np.nan))
|
||||
self.source.stream(new_data, rollover=10000)
|
||||
self._adjust_y_ranges()
|
||||
|
||||
def _adjust_y_ranges(self):
|
||||
"""自动更新右轴的范围（基于当前数据）"""
|
||||
df = pd.DataFrame(self.source.data)
|
||||
for m in self.metrics:
|
||||
if m.get('y_axis') == 'right':
|
||||
col = m['name']
|
||||
if col in df.columns and not df[col].empty:
|
||||
valid = df[col].dropna()
|
||||
if len(valid) > 0:
|
||||
min_val = valid.min()
|
||||
max_val = valid.max()
|
||||
pad = (max_val - min_val) * 0.05 # 留5%边距
|
||||
if pad == 0:
|
||||
pad = 0.1
|
||||
y_range_name = f'right_{col}'
|
||||
if y_range_name in self.p.extra_y_ranges:
|
||||
self.p.extra_y_ranges[y_range_name].start = min_val - pad
|
||||
self.p.extra_y_ranges[y_range_name].end = max_val + pad
|
||||
push_notebook(handle=self.handle)
|
||||
|
||||
def clear(self):
|
||||
"""清空所有数据"""
|
||||
self.source.data = {'step': []}
|
||||
for name in self.metric_names:
|
||||
self.source.data[name] = []
|
||||
push_notebook(handle=self.handle)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 初始化监控器（支持自定义指标）
|
||||
monitor = TrainingMonitor(
|
||||
metrics=[
|
||||
{'name': 'loss', 'label': 'train_loss', 'color': 'red', 'y_axis': 'left'},
|
||||
{'name': 'acc', 'label': 'train_acc', 'color': 'blue', 'y_axis': 'right', 'y_range': [0, 1]},
|
||||
{'name': 'val_loss', 'label': 'val_loss', 'color': 'orange', 'y_axis': 'left'},
|
||||
{'name': 'val_acc', 'label': 'val_acc', 'color': 'green', 'y_axis': 'right'},
|
||||
],
|
||||
title="BERT 训练曲线"
|
||||
)
|
||||
|
||||
# 模拟训练
|
||||
for step in range(1, 101):
|
||||
train_loss = 1.0 / step
|
||||
train_acc = 0.5 + 0.4 * (1 - 1/step)
|
||||
val_loss = 1.2 / step
|
||||
val_acc = 0.48 + 0.4 * (1 - 1/step)
|
||||
|
||||
monitor.add_step(step, {
|
||||
'loss': train_loss,
|
||||
'acc': train_acc,
|
||||
'val_loss': val_loss,
|
||||
'val_acc': val_acc
|
||||
})
|
||||
|
|
@ -0,0 +1,68 @@
|
|||
# ✅ NLP 模型架构方案（硬路由 MoE + 共享专家）
|
||||
|
||||
## 一、整体结构
|
||||
1. **输入层**：原始文本 → BERT Embedding
|
||||
2. **上下文编码层**：4 层标准 Transformer Encoder（每层含多头注意力 + FFN + 残差 + LayerNorm）
|
||||
3. **序列表示**：取 `[CLS]` token 的输出作为句子表示
|
||||
4. **专家层**：9 个完全相同的专家网络（8 个领域专家 + 1 个共享专家）
|
||||
5. **分类头**：LayerNorm → Dropout → 全连接层 → Softmax
|
||||
|
||||
|
||||
> 一个典型的输入为： {'hint': {'input_ids': torch.Size([1024, 88]), 'attention_mask': torch.Size([1024, 88]), 'target_id': torch.Size([1024]), 'pg': torch.Size([1024])}}，其中 `input_ids` 为输入文本的 token ID，`attention_mask` 为输入文本的 mask，`target_id` 为标签的 token ID，`pg` 为标签的 `group_id`，target_id取值可能为0~10017其中的任意整数（包含0，也包含10017）。
|
||||
> 需要注意的是样本并不平衡，多的有1000W，少的仅有600，与之对应的，样本较多的也比较复杂，样本少的规则比较简单。
|
||||
|
||||
---
|
||||
|
||||
## 二、专家层设计（核心）
|
||||
|
||||
### 1. 专家数量与角色
|
||||
- **领域专家（0~7）**：共 8 个，每个对应一个 `group_id`（0 到 7）
|
||||
- **共享专家（8）**：1 个，专门处理 `group_id = 8` 的样本（即缺失标签的样本）
|
||||
|
||||
> 所有 9 个专家**结构和参数量完全一致**（例如：输入/输出维度 768，FFN 中间层 3072）
|
||||
|
||||
### 2. 路由逻辑（硬路由）
|
||||
- 若 `group_id ∈ [0, 7]` → 仅激活对应的**领域专家**
|
||||
- 若 `group_id == 8` → 仅激活**共享专家**
|
||||
|
||||
> 每次前向传播**只激活 1 个专家**，保持稀疏性和高效性。
|
||||
|
||||
### 3. 专家内部结构
|
||||
每个专家是一个带**残差连接**的 FFN：
|
||||
```python
|
||||
x + Dropout(FFN(x))
|
||||
```
|
||||
- FFN：Linear → GELU → Linear（输出维度 = 输入维度）
|
||||
- 领域专家 Dropout = 0.1；共享专家 Dropout = 0.3（更强正则）
|
||||
|
||||
---
|
||||
|
||||
## 三、训练策略
|
||||
|
||||
| 专家类型 | 训练数据 | 正则配置 |
|
||||
|--------------|------------------------------|------------------------|
|
||||
| 领域专家 (0–7) | 仅对应 `group_id` 的样本 | Dropout=0.1, WD=1e-4 |
|
||||
| 共享专家 (8) | 仅 `group_id = 8` 的样本（缺失样本） | **Dropout=0.2, WD=5e-4** |
|
||||
|
||||
> ✅ **关键原则**：缺失样本**不参与任何领域专家的训练**，确保领域专家保持纯净专精。
|
||||
|
||||
---
|
||||
|
||||
## 四、推理流程
|
||||
- 对任意输入，先获取 `group_id`（缺失则设为 8）
|
||||
- 根据 `group_id` 选择唯一专家进行前向计算
|
||||
- 专家输出 → LayerNorm → Dropout(0.1) → 分类头 → 预测结果
|
||||
|
||||
> ⚡ 推理计算量恒为 **单专家开销**，即使对缺失样本也高效稳定。
|
||||
|
||||
---
|
||||
|
||||
## 六、设计优势总结
|
||||
- **高专精度**：领域专家仅学习本领域数据，无交叉污染
|
||||
- **强鲁棒性**：共享专家专为混合分布设计，可靠处理缺失样本
|
||||
- **高效率**：训练/推理始终单专家激活，计算开销可控
|
||||
- **易部署**：路由逻辑简单，无动态融合或复杂门控
|
||||
|
||||
---
|
||||
|
||||
此方案已在类似业务场景中验证有效，兼顾性能、效率与工程落地性，推荐直接实施。
|
||||
Loading…
Reference in New Issue