[Misc] Use torch.compile for GemmaRMSNorm (#7642)

This commit is contained in:
Woosuk Kwon
2024-08-22 01:14:13 -07:00
committed by GitHub
parent 8c6f694a79
commit b3856bef7d

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@ -114,10 +114,12 @@ class GemmaRMSNorm(CustomOp):
self.weight = nn.Parameter(torch.zeros(hidden_size))
self.variance_epsilon = eps
def forward_native(
self,
@staticmethod
def forward_static(
weight: torch.Tensor,
variance_epsilon: float,
x: torch.Tensor,
residual: Optional[torch.Tensor] = None,
residual: Optional[torch.Tensor],
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
"""PyTorch-native implementation equivalent to forward()."""
orig_dtype = x.dtype
@ -127,17 +129,32 @@ class GemmaRMSNorm(CustomOp):
x = x.float()
variance = x.pow(2).mean(dim=-1, keepdim=True)
x = x * torch.rsqrt(variance + self.variance_epsilon)
x = x * torch.rsqrt(variance + variance_epsilon)
# Llama does x.to(float16) * w whilst Gemma is (x * w).to(float16)
# See https://github.com/huggingface/transformers/pull/29402
x = x * (1.0 + self.weight.float())
x = x * (1.0 + weight.float())
x = x.to(orig_dtype)
return x if residual is None else (x, residual)
def forward_native(
self,
x: torch.Tensor,
residual: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
"""PyTorch-native implementation equivalent to forward()."""
return self.forward_static(self.weight.data, self.variance_epsilon, x,
residual)
def forward_cuda(
self,
x: torch.Tensor,
residual: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
# TODO(woosuk): Implement an optimized kernel for GemmaRMSNorm.
if torch.compiler.is_compiling():
return self.forward_native(x, residual)
if not getattr(self, "_is_compiled", False):
self.forward_static = torch.compile( # type: ignore
self.forward_static)
self._is_compiled = True
return self.forward_native(x, residual)