fix(internlm/train/training_internlm.py): update wrap class and fix lint error

internlm/train/training_internlm.py

@@ -8,6 +8,8 @@ from typing import Callable, Iterable, Union
 
 import torch
 import torch.distributed as dist
+from flash_attn.modules.embedding import ParallelGPT2Embeddings
+from flash_attn.modules.mlp import ParallelFusedMLP
 from torch import nn
 from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
 from torch.distributed.fsdp.fully_sharded_data_parallel import (
@@ -17,7 +19,7 @@ from torch.distributed.fsdp.fully_sharded_data_parallel import (
 from torch.distributed.fsdp.wrap import transformer_auto_wrap_policy
 from torch.utils.data import ConcatDataset, DataLoader
 
-from internlm.core.context import IS_TENSOR_PARALLEL, ParallelMode
+from internlm.core.context import ParallelMode
 from internlm.core.context import global_context as gpc
 from internlm.core.context.random import set_mode
 from internlm.core.naive_amp import NaiveAMPModel
@@ -32,9 +34,11 @@ from internlm.data.packed_dataset import (
     get_packed_dataset_without_short_length,
 )
 from internlm.data.utils import DATASET_TYPE_IDS_MAP, unpack_data
-from internlm.model.modeling_internlm import (
-    PackedFlashBaseLayer1D,
-    PackedFlashInternLm1D,
+from internlm.model.embedding import Embedding1D
+from internlm.model.linear import (
+    FeedForward,
+    RewardModelLinear,
+    ScaleColumnParallelLinear,
 )
 from internlm.model.multi_head_attention import MHA
 from internlm.model.utils import try_import_RMSNorm
@@ -52,6 +56,7 @@ from internlm.utils.parallel import sync_model_param, sync_model_param_within_tp
 from internlm.utils.registry import MODEL_INITIALIZER
 from internlm.utils.timeout import llm_timeout
 
+RMSNorm = try_import_RMSNorm()
 logger = get_logger(__file__)
 
 
@@ -106,12 +111,20 @@ def initialize_model():
 
 
 def wrap_FSDP_model(model: Union[nn.Module, nn.ModuleList]):
-    RMSNorm = try_import_RMSNorm()
     if gpc.config.parallel.use_fsdp:
         # set wrap_policy for fsdp wrap
         transformer_wrap_policy = functools.partial(
             transformer_auto_wrap_policy,
-            transformer_layer_cls={PackedFlashBaseLayer1D, PackedFlashInternLm1D, MHA, RMSNorm},
+            transformer_layer_cls={
+                Embedding1D,
+                ParallelGPT2Embeddings,
+                MHA,
+                RMSNorm,
+                FeedForward,
+                ParallelFusedMLP,
+                RewardModelLinear,
+                ScaleColumnParallelLinear,
+            },
         )
 
         # wrap the model