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support training lm_head with fp32 #1161

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support training lm_head with fp32 #1161

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1 change: 1 addition & 0 deletions xtuner/v1/config/fsdp.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,7 @@ class FSDPConfig(BaseModel):
# TODO: (caoweihan) Convert `torch.dtype` to `Annotated` for compatibility with cyclopts
param_dtype: Annotated[torch.dtype, Parameter(help="Data type for model parameters")] = torch.bfloat16
reduce_dtype: Annotated[torch.dtype, Parameter(help="Data type for reduction operations")] = torch.bfloat16
lm_head_fp32: Annotated[bool, Parameter(help="Use float32 for language model head")] = False
torch_compile: Annotated[bool, Parameter(help="Enable model compilation for faster inference")] = False
compile_targets: Annotated[
Optional[Tuple[str, ...]],
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6 changes: 5 additions & 1 deletion xtuner/v1/model/dense/dense.py
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Expand Up @@ -186,6 +186,10 @@ def fully_shard(
mp_policy = MixedPrecisionPolicy(
param_dtype=self.fsdp_config.param_dtype, reduce_dtype=fsdp_config.reduce_dtype
)
if self.fsdp_config.lm_head_fp32:
lm_head_mp_policy = MixedPrecisionPolicy(param_dtype=torch.float32, reduce_dtype=torch.float32)
else:
lm_head_mp_policy = mp_policy
num_recompute_layers = int(self.config.num_hidden_layers * self.fsdp_config.recompute_ratio)

generator = torch.Generator()
Expand Down Expand Up @@ -236,7 +240,7 @@ def fully_shard(
fully_shard(
self.lm_head,
mesh=self.fsdp_mesh if self.hsdp_mesh is None else self.hsdp_mesh,
mp_policy=mp_policy,
mp_policy=lm_head_mp_policy,
reshard_after_forward=self.fsdp_config.reshard_after_forward,
offload_policy=CPUOffloadPolicy() if self.fsdp_config.cpu_offload else None,
)
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6 changes: 5 additions & 1 deletion xtuner/v1/model/moe/moe.py
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Expand Up @@ -653,6 +653,10 @@ def fully_shard(
mp_policy = MixedPrecisionPolicy(
param_dtype=self.fsdp_config.param_dtype, reduce_dtype=fsdp_config.reduce_dtype
)
if self.fsdp_config.lm_head_fp32:
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Declaring it when using it

lm_head_mp_policy = MixedPrecisionPolicy(param_dtype=torch.float32, reduce_dtype=torch.float32)
else:
lm_head_mp_policy = mp_policy
num_recompute_layers = int(self.config.num_hidden_layers * self.fsdp_config.recompute_ratio)

for layer_idx, layer in tqdm(self.layers.items(), desc="[FSDP Sharding]"):
Expand Down Expand Up @@ -698,7 +702,7 @@ def fully_shard(
fully_shard(
self.lm_head,
mesh=self.fsdp_mesh if self.hsdp_mesh is None else self.hsdp_mesh,
mp_policy=mp_policy,
mp_policy=lm_head_mp_policy,
reshard_after_forward=self.fsdp_config.reshard_after_forward,
offload_policy=CPUOffloadPolicy() if self.fsdp_config.cpu_offload else None,
)
Expand Down
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