feat(model/overlap_handler.py): fix lint error

internlm/model/moe.py

@@ -53,7 +53,6 @@ def __init__(
         device=None,
         dtype=None,
     ):
-
         super().__init__()
 
         assert (

internlm/model/overlap_handler.py

@@ -10,7 +10,10 @@
 from internlm.core.naive_amp import NaiveAMPModel
 from internlm.model.embedding import Embedding1D
 from internlm.model.linear import FSTPLinear, ScaleColumnParallelLinear
-from internlm.model.utils import all_gather_raw_memory_pool, all_gather_raw_bias_memory_pool
+from internlm.model.utils import (
+    all_gather_raw_bias_memory_pool,
+    all_gather_raw_memory_pool,
+)
 from internlm.utils.common import get_current_device
 
 
@@ -25,7 +28,7 @@ def __init__(self, model: Union[nn.Module, nn.ModuleList], process_group) -> Non
         self.fstp_modules = []
         self.module_name = ["Wqkv", "out_proj", "w1", "w2", "w3"]
         self.fstp_global_handle = dict()  # key: fstp module; value: module global all-gather op handle
-        self.bias_global_handle = dict() # key: fstp module; value: module bias global all-gather op handle
+        self.bias_global_handle = dict()  # key: fstp module; value: module bias global all-gather op handle
         self.module_to_index = dict()  # key: fstp module; value: transformer block index
         self.index_to_fstp_modules = dict()  # key: transformer block index; value: fsdp modules
         self.head = []
@@ -77,13 +80,13 @@ def get_zero_by_shape(self, size: tuple, dtype, device) -> torch.Tensor:
             self.zero_const_pool[size] = torch.zeros(*size, dtype=dtype, device=device).contiguous()
 
         return self.zero_const_pool[size]
-    
+
     def _initialize_module_shape(self):
         hidden_size = gpc.config.HIDDEN_SIZE
         mlp_ratio = gpc.config.MLP_RATIO
         mlp_hidden_size = int(hidden_size * mlp_ratio)
         mlp_hidden_size = 256 * ((mlp_hidden_size + 256 - 1) // 256)
-        
+
         self.module_shape["Wqkv"] = (3 * hidden_size, hidden_size)
         self.module_shape["out_proj"] = (hidden_size, hidden_size)
         self.module_shape["w1"] = (mlp_hidden_size, hidden_size)
@@ -96,7 +99,7 @@ def _initialize_memory_pool(self) -> None:
         self.all_gather_bias_memory_pool = []
         self.reduce_scatter_memory_pool = {}
         self.module_shape = {}
-        
+
         self._initialize_module_shape()
         dtype = gpc.config.model.get("dtype", torch.half)
         device = get_current_device()
@@ -107,10 +110,14 @@ def _initialize_memory_pool(self) -> None:
                 weight[name] = torch.zeros(self.module_shape[name], dtype=dtype, device=device).contiguous()
             self.all_gather_memory_pool.append(weight)  # containing two groups of block weight
 
+    def clear_memory_pool(self) -> None:
+        self.zero_const_pool = {}
+        self.reduce_scatter_memory_pool = {}
+
     def get_all_gather_memory(self, module):
         block_index = self.module_to_index[module]
         return self.all_gather_memory_pool[block_index % 2][module._fstp_name]
-    
+
     def get_bias_memory(self, module: nn.Module):
         block_index = self.module_to_index[module]
         # if the bias memory pool is empty or module has been not allocated memory
@@ -119,19 +126,20 @@ def get_bias_memory(self, module: nn.Module):
             for _ in range(2):
                 weight = {}
                 weight[module._fstp_name] = torch.zeros(
-                                                    self.module_shape[module._fstp_name][0], 
-                                                    dtype=gpc.config.model.get("dtype", torch.half),
-                                                    device=get_current_device()).contiguous()
+                    self.module_shape[module._fstp_name][0],
+                    dtype=gpc.config.model.get("dtype", torch.half),
+                    device=get_current_device(),
+                ).contiguous()
                 self.all_gather_bias_memory_pool.append(weight)
         elif module._fstp_name not in self.all_gather_bias_memory_pool[0]:
             for i in range(2):
                 self.all_gather_bias_memory_pool[i][module._fstp_name] = torch.zeros(
-                                                    self.module_shape[module._fstp_name][0], 
-                                                    dtype=gpc.config.model.get("dtype", torch.half),
-                                                    device=get_current_device()).contiguous()
-
+                    self.module_shape[module._fstp_name][0],
+                    dtype=gpc.config.model.get("dtype", torch.half),
+                    device=get_current_device(),
+                ).contiguous()
+
         return self.all_gather_bias_memory_pool[block_index % 2][module._fstp_name]
-
 
     def get_reduce_scatter_memory(self, key):
         return_idx = 0
@@ -170,7 +178,7 @@ def get_reduce_scatter_memory(self, key):
 
     def release_reduce_scatter_memory(self, key, index):
         self.reduce_scatter_memory_pool[key][index].idle = True
-   
+
     def _all_gather_block_weight_memory_pool(self, block_index: int):
         fstp_modules = self.index_to_fstp_modules[block_index]
         for module in fstp_modules:
@@ -182,7 +190,7 @@ def _all_gather_block_weight_memory_pool(self, block_index: int):
                     module=module,
                 )
                 self.bias_global_handle[module] = bias_handle
-                
+
             weight_handle = all_gather_raw_memory_pool(
                 module.weight,
                 self.process_group,

internlm/model/utils.py

@@ -140,6 +140,7 @@ def all_gather_raw_memory_pool(
     )
     return handle
 
+
 def all_gather_raw_bias_memory_pool(
     input_: Tensor,
     process_group: ProcessGroup,

train.py

@@ -298,8 +298,7 @@ def main(args):
             prof.step()
 
             if gpc.fstp_handler is not None:
-                gpc.fstp_handler.zero_const_pool = {}
-                gpc.fstp_handler.reduce_scatter_memory_pool = {}
+                gpc.fstp_handler.clear_memory_pool()
             # torch.cuda.memory._dump_snapshot(f"my_snapshot_{gpc.get_global_rank()}.pickle")
             torch.cuda.reset_peak_memory_stats()