728c4c8a06
Co-authored-by: Jiang Li <jiang1.li@intel.com> Co-authored-by: Abhilash Majumder <abhilash.majumder@intel.com> Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com>
98 lines
2.6 KiB
Python
98 lines
2.6 KiB
Python
"""Custom normalization layers."""
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from typing import Optional, Tuple, Union
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import torch
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import torch.nn as nn
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from vllm.model_executor.custom_op import CustomOp
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class RMSNorm(CustomOp):
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"""Root mean square normalization.
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Computes x -> w * x / sqrt(E[x^2] + eps) where w is the learned weight.
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Refer to https://arxiv.org/abs/1910.07467
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"""
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def __init__(
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self,
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hidden_size: int,
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eps: float = 1e-6,
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) -> None:
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.variance_epsilon = eps
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def forward_native(
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self,
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x: torch.Tensor,
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residual: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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"""PyTorch-native implementation equivalent to forward()."""
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orig_dtype = x.dtype
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x = x.to(torch.float32)
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if residual is not None:
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x = x + residual.to(torch.float32)
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residual = x.to(orig_dtype)
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variance = x.pow(2).mean(dim=-1, keepdim=True)
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x = x * torch.rsqrt(variance + self.variance_epsilon)
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x = x.to(orig_dtype) * self.weight
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if residual is None:
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return x
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else:
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return x, residual
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def forward_cuda(
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self,
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x: torch.Tensor,
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residual: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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from vllm import _custom_ops as ops
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if residual is not None:
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ops.fused_add_rms_norm(
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x,
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residual,
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self.weight.data,
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self.variance_epsilon,
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)
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return x, residual
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out = torch.empty_like(x)
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ops.rms_norm(
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out,
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x,
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self.weight.data,
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self.variance_epsilon,
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)
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return out
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def forward_xpu(
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self,
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x: torch.Tensor,
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residual: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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from vllm._ipex_ops import ipex_ops as ops
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if residual is not None:
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ops.fused_add_rms_norm(
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x,
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residual,
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self.weight.data,
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self.variance_epsilon,
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)
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return x, residual
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out = torch.empty_like(x)
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ops.rms_norm(
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out,
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x,
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self.weight.data,
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self.variance_epsilon,
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)
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return out
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def extra_repr(self) -> str:
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s = f"hidden_size={self.weight.data.size(0)}"
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s += f", eps={self.variance_epsilon}"
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return s
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