fix(data): 修复数据加载模块中的拼写错误并优化内存映射加载逻辑

khaosz/data/__init__.py

@@ -4,7 +4,7 @@ from khaosz.data.dataset import (
     DpoDataset, 
     SftDataset, 
     PpoDataset, 
-    MutiSegmentFetcher,
+    MultiSegmentFetcher,
     DatasetLoader,
     load_pkl_files, 
 )
@@ -18,7 +18,7 @@ __all__ = [
     "DpoDataset",
     "SftDataset",
     "PpoDataset",
-    "MutiSegmentFetcher",
+    "MultiSegmentFetcher",
     "DatasetLoader",
     "load_pkl_files",
     "BpeTokenizer",

khaosz/data/dataset.py

@@ -1,34 +1,80 @@
+import os
+import json
 import torch
 import bisect
-import pickle as pkl
 
 from abc import ABC, abstractmethod
 from torch import Tensor
 from torch.utils.data import Dataset
-from typing import Callable, List, Dict, Literal, Optional, Union
+from typing import Callable, List, Dict, Literal, Optional, Tuple, Union
-  
-MutiSeg = Dict[str, List[Tensor]]
-Seg = Dict[str, Tensor]
 
-def load_pkl_files(paths: List[str]):
+Seg = List[Tensor]  
-    segments: MutiSeg = {}
+MultiSeg = Dict[str, Seg]
-    total_samples = 0
 
-    for path in paths:
+
-        with open(path, "rb") as f:
+def load_mmap_files(root_path: str, shared: bool=True) -> Tuple[MultiSeg, int]:
-            pkl_file: Seg = pkl.load(f)
+    """Load memory-mapped binary files as torch tensors.
-        for key, value in pkl_file.items():
-            if key not in segments:
-                segments[key] = []
-            segments[key].append(value)
-        first_key = list(pkl_file.keys())[0]
-        total_samples += pkl_file[first_key].numel()
     
-    return segments, total_samples
+    Loads configuration from file_mapper.json in the specified directory, then loads
+    corresponding binary files as memory-mapped tensors. Returns tensors grouped by key
+    and total number of elements.
+    
+    Args:
+        root_path: Root directory path containing file_mapper.json and binary files
+        shared: Whether to load tensors in shared mode. If True, tensors can be 
+                shared between processes
+        
+    Raises:
+        FileNotFoundError: If file_mapper.json or any binary file in config is missing
+        KeyError: If dtype in config is not in supported DTYPE_MAP
+        json.JSONDecodeError: If config file is not valid JSON
+        
+    Returns:
+        Tuple containing:
+        - MultiSeg: Dictionary of tensors grouped by key, structure: {key: [tensor1, tensor2, ...]}
+        - int: Total number of elements across all tensors
+    """
+    
+    DTYPE_MAP = {
+        "float32": torch.float32,
+        "float64": torch.float64,
+        "int32": torch.int32,
+        "int64": torch.int64,
+        "bool": torch.bool,
+    }
+    
+    metadata_list = []
+    mmap_shared_group: MultiSeg = {}
+    
+    file_mapper_path = os.path.join(root_path, "file_mapper.json")
+    if not os.path.exists(file_mapper_path):
+        raise FileNotFoundError(f"File mapper not found: {file_mapper_path}")
+    
+    with open(file_mapper_path, "r") as f:
+        metadata_list = json.load(f)
+    
+    num_samples = sum(metadata["size"] for metadata in metadata_list)
+    
+    for metadata in metadata_list:
+        file_path = os.path.join(root_path, metadata["file_name"])
+        if not os.path.exists(file_path):
+            raise FileNotFoundError(f"Binary data file not found: {file_path}")
+        
+        size = metadata["size"]
+        dtype = DTYPE_MAP[metadata["dtype"]]
+        segment_key = metadata["key"]
+        mmap_tensor = torch.from_file(file_path, shared=shared, size=size, dtype=dtype)
+        
+        if segment_key not in mmap_shared_group:
+            mmap_shared_group[segment_key] = []
+            
+        mmap_shared_group[segment_key].append(mmap_tensor)
+    
+    return mmap_shared_group, num_samples
 
 
 class BaseSegmentFetcher:
-    def __init__(self, segments: List[Tensor]):
+    def __init__(self, segments: Seg):
         self.segments = segments
         self.cum_lengths = []
         total = 0
@@ -58,8 +104,8 @@ class BaseSegmentFetcher:
         return torch.cat(result_segments, dim=0)
     
 
-class MutiSegmentFetcher:
+class MultiSegmentFetcher:
-    def __init__(self, muti_segments: MutiSeg):
+    def __init__(self, muti_segments: MultiSeg):
         self.muti_keys = list(muti_segments.keys())
         self.muti_fetchers = {
             key: BaseSegmentFetcher(segments)
@@ -82,29 +128,17 @@ class MutiSegmentFetcher:
 
 
 class BaseDataset(Dataset, ABC):
-    def __init__(self, window_size: int, stride: int):
+    def __init__(self, window_size: int, stride: int, share_memory: bool=False):
         super().__init__()
-        self.segments: MutiSeg = {}
+        self.segments: MultiSeg = {}
         self.window_size = window_size
         self.stride = stride
         self.total_samples = None
 
-    def save(self, save_path: str):
-        keys = list(self.segments.keys())
-        if not keys:
-            return
-        
-        first_item = self.segments[keys[0]]
-        segment_size = len(first_item)
-        
-        for i in range(segment_size):
-            formated_segment = {key: self.segments[key][i] for key in keys}
-            pkl.dump(formated_segment, open(f"{save_path}_{i}.pkl", "wb"))
-    
     def load(self, load_path: Union[str, List[str]]):
         paths = [load_path] if isinstance(load_path, str) else load_path
-        self.segments, self.total_samples = load_pkl_files(paths)
+        self.segments, self.total_samples = load_mmap_files(paths)
-        self.fetcher = MutiSegmentFetcher(self.segments)
+        self.fetcher = MultiSegmentFetcher(self.segments)
         
     def get_index(self, index: int) -> int:
         begin_idx = min(index * self.stride, self.total_samples - self.window_size - 1)
@@ -126,7 +160,7 @@ class BaseDataset(Dataset, ABC):
 class SeqDataset(BaseDataset):
     def __init__(self, window_size: int, stride: int):
         super().__init__(window_size, stride)
-        self.fetcher = MutiSegmentFetcher(self.segments)
+        self.fetcher = MultiSegmentFetcher(self.segments)
 
     def _fetch_data(self, begin_idx: int, end_idx: int) -> Tensor:
         return self.fetcher.key_fetch(begin_idx, end_idx, "sequence")
@@ -144,7 +178,7 @@ class SeqDataset(BaseDataset):
 class SftDataset(BaseDataset):
     def __init__(self, window_size: int, stride: int):
         super().__init__(window_size, stride)
-        self.fetcher = MutiSegmentFetcher(self.segments)
+        self.fetcher = MultiSegmentFetcher(self.segments)
 
     def _fetch_data(self, begin_idx: int, end_idx: int, key: str) -> Tensor:
         return self.fetcher.key_fetch(begin_idx, end_idx, key)
@@ -162,7 +196,7 @@ class SftDataset(BaseDataset):
 class DpoDataset(BaseDataset):
     def __init__(self, window_size: int, stride: int):
         super().__init__(window_size, stride)
-        self.fetcher = MutiSegmentFetcher(self.segments)
+        self.fetcher = MultiSegmentFetcher(self.segments)
 
     def _fetch_data(self, begin_idx: int, end_idx: int, key: str) -> Tensor:
         return self.fetcher.key_fetch(begin_idx, end_idx, key)
@@ -181,7 +215,7 @@ class DpoDataset(BaseDataset):
 class PpoDataset(BaseDataset):
     def __init__(self, window_size: int, stride: int):
         super().__init__(window_size, stride)
-        self.fetcher = MutiSegmentFetcher(self.segments)
+        self.fetcher = MultiSegmentFetcher(self.segments)
 
     def _fetch_data(self, begin_idx: int, end_idx: int, key: str) -> Tensor:
         return self.fetcher.key_fetch(begin_idx, end_idx, key)