forked from EngineX-Cambricon/enginex-mlu370-vllm
add qwen3
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219
vllm-v0.6.2/vllm/model_executor/layers/layernorm.py
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219
vllm-v0.6.2/vllm/model_executor/layers/layernorm.py
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"""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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@CustomOp.register("rms_norm")
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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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var_hidden_size: Optional[int] = None,
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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self.variance_epsilon = eps
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self.variance_size_override = (None if var_hidden_size == hidden_size
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else var_hidden_size)
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self.weight = nn.Parameter(torch.ones(hidden_size))
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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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hidden_size = x.shape[-1]
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if hidden_size != self.hidden_size:
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raise ValueError("Expected hidden_size to be "
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f"{self.hidden_size}, but found: {hidden_size}")
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if self.variance_size_override is None:
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x_var = x
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else:
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if hidden_size < self.variance_size_override:
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raise ValueError(
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"Expected hidden_size to be at least "
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f"{self.variance_size_override}, but found: {hidden_size}")
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x_var = x[:, :, :self.variance_size_override]
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variance = x_var.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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if self.variance_size_override is not None:
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return self.forward_native(x, residual)
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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_hpu(
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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_hpu_extension.ops import HPUFusedRMSNorm
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if HPUFusedRMSNorm is None:
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return self.forward_native(x, residual)
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if residual is not None:
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orig_shape = x.shape
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residual += x.view(residual.shape)
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# Note: HPUFusedRMSNorm requires 3D tensors as inputs
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x = HPUFusedRMSNorm.apply(residual, self.weight,
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self.variance_epsilon)
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return x.view(orig_shape), residual
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x = HPUFusedRMSNorm.apply(x, self.weight, self.variance_epsilon)
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return x
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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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if self.variance_size_override is not None:
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return self.forward_native(x, residual)
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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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return ops.rms_norm(
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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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def forward_mlu(
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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 _mlu_ops as mlu_ops
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x = x.view(-1, self.weight.data.shape[0])
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if residual is not None:
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residual = residual.view(-1, self.weight.data.shape[0])
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return mlu_ops.fused_rms_norm(x, residual, self.weight.data, None, None, self.variance_epsilon, True)
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else:
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return mlu_ops.fused_rms_norm(x, residual, self.weight.data, None, None, self.variance_epsilon, False)
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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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@CustomOp.register("gemma_rms_norm")
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class GemmaRMSNorm(CustomOp):
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"""RMS normalization for Gemma.
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Two differences from the above RMSNorm:
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1. x * (1 + w) instead of x * w.
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2. (x * w).to(orig_dtype) instead of x.to(orig_dtype) * w.
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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.zeros(hidden_size))
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self.variance_epsilon = eps
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@staticmethod
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def forward_static(
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weight: torch.Tensor,
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variance_epsilon: float,
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x: torch.Tensor,
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residual: Optional[torch.Tensor],
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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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if residual is not None:
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x = x + residual
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residual = x
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x = x.float()
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variance = x.pow(2).mean(dim=-1, keepdim=True)
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x = x * torch.rsqrt(variance + variance_epsilon)
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# Llama does x.to(float16) * w whilst Gemma is (x * w).to(float16)
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# See https://github.com/huggingface/transformers/pull/29402
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x = x * (1.0 + weight.float())
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x = x.to(orig_dtype)
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return x if residual is None else (x, residual)
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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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return self.forward_static(self.weight.data, self.variance_epsilon, x,
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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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if torch.compiler.is_compiling():
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return self.forward_native(x, residual)
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if not getattr(self, "_is_compiled", False):
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self.forward_static = torch.compile( # type: ignore
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self.forward_static)
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self._is_compiled = True
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return self.forward_native(x, residual)
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