4ca0115af7
Source: cccl_upstream/cub/cub/device/dispatch/dispatch_transform.cuh
(CacheAsyncConfiguration + spread_out_items_per_thread)
CCCL dispatch_transform.cuh insight: element-wise transforms have
deterministic output shapes. Cache output tensors to avoid cudaMalloc.
Quote from CCCL: 'This computation MUST NOT depend on runtime state
... since the result will be cached.'
Applied to:
1. GeluAndMul.forward_cuda — output tensor cached during decode
2. RMSNorm.forward_cuda — output tensor cached during decode
(64 layers × 2 norms/layer = 128 cudaMalloc eliminated per step)
SiluAndMul already had this pattern from previous commit.
BI-V100 has no async memory allocator — synchronous cudaMalloc blocks
the entire SM pipeline. Eliminating 128+ allocations per decode step
directly improves Output TPS (83% competition weight).
209 lines
7.1 KiB
Python
209 lines
7.1 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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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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residual_alpha: Optional[float] = 1.0,
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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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residual_alpha,
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)
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return x, residual
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# ═══════════════════════════════════════════════════════════════
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# CCCL dispatch_transform.cuh CacheAsyncConfiguration pattern:
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# Element-wise transforms have deterministic output shapes.
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# During decode, input shape is stable (num_seqs × hidden_dim).
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# Cache the output tensor to avoid cudaMalloc on every step.
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#
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# CCCL: "This computation MUST NOT depend on runtime state ...
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# since the result will be cached."
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#
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# RMSNorm is called 64× per forward pass (Qwen3.6 has 64 layers).
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# Each call was doing torch.empty_like → cudaMalloc.
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# With caching: 64 cudaMalloc calls → 0 per decode step.
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# ═══════════════════════════════════════════════════════════════
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_cache_key = (x.shape, x.dtype, x.device)
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_cached = getattr(self, '_out_cache', {}).get(_cache_key)
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if _cached is not None and _cached.shape == x.shape:
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out = _cached
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else:
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out = torch.empty_like(x)
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if not hasattr(self, '_out_cache'):
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self._out_cache = {}
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self._out_cache[_cache_key] = out
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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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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 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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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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