[zero] chunk manager allows filtering ex-large params (#1393)

colossalai/gemini/chunk_mgr.py

 import torch
+import numpy as np
 from typing import Optional, Dict, Deque, Set, List, Tuple, Iterable
 from collections import deque
 
@@ -61,9 +62,6 @@ class ChunkManager:
         if isinstance(tensor, ColoTensor):
             assert tensor.get_process_group().dp_process_group() == self.process_group.dp_process_group(
             ), f"Chunk Manager can only manage ColoTensor with the same DP process group"
-        if self.chunk_size is not None and tensor.numel() > self.chunk_size:
-            raise ValueError(
-                f'Cannot create chunk, got tensor numel ({tensor.numel()}) > chunk size ({self.chunk_size})')
         try:
             # append the tensor to the last chunk
             self.chunk_groups[group_name][-1].append(tensor)
@@ -71,6 +69,9 @@ class ChunkManager:
             # the except statement will be triggered when there is no chunk or
             # the last chunk in the chunk group is full
             # this will create a new chunk and allocate this chunk to its corresponding process
+            if self.chunk_size is not None and tensor.numel() > self.chunk_size:
+                chunk_size = tensor.numel()
+            else:
                 chunk_size = self.chunk_size or tensor.numel()
             src_rank = self._get_next_src_rank(group_name)
             chunk = Chunk(chunk_size,
@@ -263,7 +264,8 @@ class ChunkManager:
     def search_chunk_size(module: torch.nn.Module,
                           search_range: int,
                           n_grids: int,
-                          min_chunk_size: Optional[int] = None) -> int:
+                          min_chunk_size: Optional[int] = None,
+                          filter_exlarge_params: bool = True) -> int:
         """
         Search for the chunk size for optimal chunk utilization.
 
@@ -278,6 +280,8 @@ class ChunkManager:
         assert search_range % n_grids == 0
         # TODO(ver217): sort params and filter unused ones
         params_numel = [p.numel() for p in module.parameters()]
+        if filter_exlarge_params:
+            params_numel = _filter_exlarge_params(params_numel)
         max_param_numel = max(params_numel)
         if min_chunk_size is not None:
             assert min_chunk_size >= max_param_numel
@@ -330,3 +334,11 @@ class ChunkManager:
         """
         assert tensor not in self.tensor_chunk_map
         self.total_mem[tensor.device.type] += tensor.numel() * tensor.element_size()
+
+
+def _filter_exlarge_params(params_numel: List[int]) -> List[int]:
+    params_numel_arr = np.array(params_numel)
+    std = np.std(params_numel_arr)
+    mean = np.mean(params_numel_arr)
+    upper_limit = mean + 3 * std
+    return list(filter(lambda x: x <= upper_limit, params_numel))