fix multiprocess remove_columns

swift/llm/utils/dataset.py

@@ -46,15 +46,23 @@ def _update_fingerprint_mac(*args, **kwargs):
 datasets.arrow_dataset.update_fingerprint = _update_fingerprint_mac
 
 
-def partialed_map(self, *args, **kwargs):
-    if 'num_proc' not in kwargs:
-        num_proc = os.environ.get('DATASET_MAP_NPROC')
-        kwargs['num_proc'] = int(num_proc) if num_proc else num_proc
-    return self._origin_map(*args, **kwargs)
+def patch_num_proc(func_name: str):
+    _origin_func_name = f'_origin_{func_name}'
+    _old_func = getattr(HfDataset, func_name)
 
+    def new_func(self, *args, **kwargs):
+        if 'num_proc' not in kwargs:
+            num_proc = os.environ.get('DATASET_MAP_NPROC')
+            if num_proc:
+                kwargs['num_proc'] = int(num_proc)
+        return _old_func(self, *args, **kwargs)
 
-datasets.Dataset._origin_map = datasets.Dataset.map
-datasets.Dataset.map = partialed_map
+    setattr(HfDataset, _origin_func_name, _old_func)
+    setattr(HfDataset, func_name, new_func)
+
+
+for func_name in ['map', 'filter']:
+    patch_num_proc(func_name)
 
 standard_keys = {
     'query', 'query_role', 'response', 'rejected_response', 'system', 'history', 'history_roles', 'images', 'objects',

swift/llm/utils/preprocess.py

@@ -1,8 +1,10 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 import ast
 import os
+from multiprocessing import shared_memory
 from typing import Any, Callable, Dict, List, Literal, Optional, Union
 
+import numpy as np
 from datasets import Dataset as HfDataset
 from datasets import IterableDataset as HfIterableDataset
 from tqdm import tqdm
@@ -30,29 +32,50 @@ def _reduce_columns(cls: type) -> type:
     cls._patching = True
 
     def new_call_func(self, dataset: DATASET_TYPE) -> DATASET_TYPE:
-        self.column_state = set(['images', 'videos', 'audios'])
+        self.key_mapping = {k: i for i, k in enumerate(self.empty_row.keys())}
+        num_proc = int(os.environ.get('DATASET_MAP_NPROC', '1'))
+        self.shared_shm_name = None
+        shm, buffer = None, None
+        if num_proc > 1:  # multiprocess
+            shm = shared_memory.SharedMemory(create=True, size=len(self.key_mapping))
+            self.shared_shm_name = shm.name
+            buffer = shm.buf
+        self.column_state = np.ndarray((len(self.key_mapping), ), dtype=np.bool_, buffer=buffer)
         dataset = call_func(self, dataset)
         if isinstance(dataset, HfIterableDataset) and dataset.features is None:
             features = next(iter(dataset)).keys()
         else:
             features = dataset.features.keys()
         for k in features:
-            if k not in self.column_state:
+            if k in ['images', 'videos', 'audios']:
+                continue
+            k_i = self.key_mapping.get(k, -1)
+            if k_i == -1 or not self.column_state[k_i]:
                 dataset = dataset.remove_columns([k])
+        if shm:
+            shm.close()
+            shm.unlink()
         return dataset
 
     def new_preprocess(self, row: Dict[str, Any]) -> Dict[str, Any]:
+        if self.shared_shm_name is not None:  # multiprocess
+            shm = shared_memory.SharedMemory(name=self.shared_shm_name)
+            column_state = np.ndarray((len(self.key_mapping), ), dtype=np.bool_, buffer=shm.buf)
+        else:
+            column_state = self.column_state
         row = preprocess(self, row)
         for k, v in row.items():
+            k_i = self.key_mapping[k]
+            if column_state[k_i]:
+                continue
             if k == 'query_role':
-                if k not in self.column_state and v and v != 'user':
-                    self.column_state.add(k)
+                if v and v != 'user':
+                    column_state[k_i] = True
             elif k == 'history_roles':
-                if k not in self.column_state and v and any(_v[0] != 'user' or _v[1] != 'assistant' for _v in v):
-                    self.column_state.add(k)
-            else:
-                if v:
-                    self.column_state.add(k)
+                if v and any(_v[0] != 'user' or _v[1] != 'assistant' for _v in v):
+                    column_state[k_i] = True
+            elif v:
+                column_state[k_i] = True
         return row
 
     cls.__call__ = new_call_func
@@ -142,6 +165,7 @@ def __call__(self, dataset: DATASET_TYPE) -> DATASET_TYPE:
         return dataset
 
 
+@_reduce_columns
 class AlpacaPreprocessor(MediaMixin, RowPreprocessMixin):
 
     def __init__(self, concat_inst_inp: Optional[Callable[[str, str], str]] = None, **kwargs):
@@ -194,6 +218,7 @@ def _default_repair_conversations(s: Union[str, Any]) -> Any:
     return s
 
 
+@_reduce_columns
 class ConversationsPreprocessor(MediaMixin, RowPreprocessMixin):
 
     def __init__(self,

swift/llm/utils/template.py

@@ -638,6 +638,7 @@ def _concat_context_list(
                 new_str_list = [system, query, round0, round1]
                 for (old_str, new_str) in zip(old_str_list, new_str_list):
                     if new_str is not None and old_str in context:
+                        assert isinstance(new_str, str), f'new_str: {new_str}'
                         context = context.replace(old_str, new_str)
             if len(context) == 0:
                 continue

swift/llm/utils/utils.py

@@ -431,7 +431,7 @@ def _find_module_list(vision_tower) -> Optional[nn.ModuleList]:
             return
         if isinstance(m, nn.ModuleList) and len(m) >= 10:
             module_lists.append(m)
-    if module_lists is not None:
+    if module_lists:
         return max(module_lists, key=lambda x: len(x))