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fix(modeling): norm weight should be fp32 #4

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fix(modeling): norm weight should be fp32 #4

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5 changes: 4 additions & 1 deletion internlm/model/modeling_internlm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,7 @@
from internlm.core.context import IS_SEQUENCE_PARALLEL, IS_TENSOR_PARALLEL, ParallelMode
from internlm.core.context.parallel_context import global_context as gpc
from internlm.core.context.random import _SEED_MANAGER
from internlm.core.naive_amp import set_output_attr_to_module
from internlm.core.naive_amp import set_fp32_attr_to_module, set_output_attr_to_module
from internlm.initialize.initialize_tensor import normal_, scaled_init_method_normal
from internlm.initialize.launch import GLOBAL_SEED
from internlm.model.embedding import Embedding1D
Expand Down Expand Up @@ -113,6 +113,8 @@ def __init__(
else:
self.norm1 = nn.LayerNorm(hidden_size, eps=layer_norm_epsilon)
self.norm2 = nn.LayerNorm(hidden_size, eps=layer_norm_epsilon)
set_fp32_attr_to_module(self.norm1)
set_fp32_attr_to_module(self.norm2)

if use_swiglu:
self.mlp = FeedForward(
Expand Down Expand Up @@ -360,6 +362,7 @@ def __init__(
self.norm = RMSNorm(hidden_size, eps=layer_norm_epsilon)
else:
self.norm = nn.LayerNorm(hidden_size, eps=layer_norm_epsilon)
set_fp32_attr_to_module(self.norm)
self.head = head_cls(
in_features=hidden_size,
out_features=gpc.get_world_size(ParallelMode.TENSOR) if is_reward else vocab_size,
Expand Down
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