调整数据集打乱缓冲区大小并优化样本处理逻辑

src/suinput/dataset.py

@@ -1,9 +1,6 @@
-import json
 import os
 import random
-from importlib.resources import files
+from typing import Any, Dict, List, Tuple, Optional
-from pathlib import Path
-from typing import Any, Dict, List, Tuple
 
 import numpy as np
 import torch
@@ -39,7 +36,7 @@ class PinyinInputDataset(IterableDataset):
         batch_query_size: int = 1000,
         # 打乱参数
         shuffle: bool = True,
-        shuffle_buffer_size: int = 100,
+        shuffle_buffer_size: int = 10000,
         # 削峰填谷参数
         max_freq: int = 434748359,  # "的"的频率
         min_freq: int = 109,  # "蓚"的频率
@@ -47,7 +44,6 @@ class PinyinInputDataset(IterableDataset):
         repeat_end_freq: int = 10000,  # 开始重复的阈值
         max_drop_prob: float = 0.8,  # 最大丢弃概率
         max_repeat_expect: float = 50.0,  # 最大重复期望
-        py_group_json_file: Optional[Dict[str, int]] = None,
     ):
         """
         初始化数据集
@@ -415,7 +411,6 @@ class PinyinInputDataset(IterableDataset):
             if not char_info:
                 continue
 
-            logger.info(f"获取字符信息: {char_info}")
             # 削峰填谷调整
             adjust_factor = self.adjust_frequency(char_info["freq"])
             if adjust_factor <= 0:
@@ -446,7 +441,7 @@ class PinyinInputDataset(IterableDataset):
                 "char": char,
                 "freq": char_info["freq"],
                 "pg": torch.tensor(
-                    self.pg_groups[processed_pinyin[0]] if processed_pinyin else 8
+                    [self.pg_groups[processed_pinyin[0]] if processed_pinyin else 8]
                 ),
             }
 
@@ -486,6 +481,7 @@ class PinyinInputDataset(IterableDataset):
             random.seed(seed % (2**32))
             np.random.seed(seed % (2**32))
         
+        batch_samples = []
         for item in self.dataset:
             text = item.get(self.text_field, "")
             if not text:
@@ -531,15 +527,18 @@ class PinyinInputDataset(IterableDataset):
 
                 # 达到批量大小时处理
                 if len(char_pinyin_batch) >= self.batch_query_size:
-                    batch_samples = self._process_batch(
+                    batch_samples += self._process_batch(
                         char_pinyin_batch, char_positions, text
                     )
-                    yield from self._shuffle_and_yield(batch_samples)
                     char_pinyin_batch = []
                     char_positions = []
+                if len(batch_samples) >= self.shuffle_buffer_size:
+                    # 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)}")
+                    yield from self._shuffle_and_yield(batch_samples)
+                    batch_samples = []
             # 处理剩余的字符
             if char_pinyin_batch:
-                batch_samples = self._process_batch(
+                batch_samples += self._process_batch(
                     char_pinyin_batch, char_positions, text
                 )
         yield from self._shuffle_and_yield(batch_samples)
@@ -582,6 +581,7 @@ def custom_collate_with_txt(batch):
         "char": [item["char"] for item in batch],
         "txt": [item["txt"] for item in batch],
         "py": [item["py"] for item in batch],
+        "pg": torch.cat([item["pg"] for item in batch]),
     }
 
     return result
@@ -602,71 +602,7 @@ def custom_collate(batch):
             "attention_mask": torch.cat([h["attention_mask"] for h in hints]),
         },
         "char_id": torch.cat([item["char_id"] for item in batch]),
-        "py": [item["py"] for item in batch],
+        "pg": torch.cat([item["pg"] for item in batch]),
-        # "py_group_id": [item["py"] for item in batch],
     }
 
     return result
-
-
-# 使用示例
-if __name__ == "__main__":
-    from query import QueryEngine
-    from tqdm import tqdm
-
-    # 初始化查询引擎
-    query_engine = QueryEngine()
-    query_engine.load()
-
-    # 创建数据集
-    dataset = PinyinInputDataset(
-        data_dir="/home/songsenand/Data/corpus/CCI-Data/",
-        query_engine=query_engine,
-        tokenizer_name="iic/nlp_structbert_backbone_tiny_std",
-        max_len=88,
-        batch_query_size=300,
-        shuffle=True,
-        shuffle_buffer_size=4000,
-    )
-
-    logger.info("数据集初始化")
-    dataloader = DataLoader(
-        dataset,
-        batch_size=1024,
-        num_workers=15,
-        worker_init_fn=worker_init_fn,
-        pin_memory=True if torch.cuda.is_available() else False,
-        collate_fn=custom_collate_with_txt,
-        prefetch_factor=8,
-        persistent_workers=True,
-        shuffle=False,  # 数据集内部已实现打乱
-    )
-
-    """import cProfile
-
-    def profile_func(dataloader):
-        for i, sample in tqdm(enumerate(dataloader), total=3000):
-            if i >= 3000:
-                break
-        return
-
-
-    cProfile.run('profile_func(dataloader)')
-
-    """
-    # 测试数据集
-    try:
-        logger.info("测试数据集")
-        for i, sample in tqdm(enumerate(dataloader), total=3000):
-            if i >= 3000:
-                break
-            """
-            print(f"Sample {i+1}:")
-            print(f"  Char: {sample['char']}, Id: {sample['char_id'].item()}, Freq: {sample.get('freq', 'N/A')}")
-            print(f"  Pinyin: {sample['py']}")
-            print(f"  Context length: {len(sample['txt'])}")
-            print(f"  Hint shape: {sample['hint']['input_ids'].shape}")
-            print()
-            """
-    except StopIteration:
-        print("数据集为空")