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[XPU] [RMSNorm] revert weightless change on xpu (#46987)

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Signed-off-by: Zhu, Zufang <zufang.zhu@intel.com>
Co-authored-by: Kunshang Ji <kunshang.ji@intel.com>
This commit is contained in:
zofia
2026-06-29 19:03:06 +00:00
committed by GitHub
co-authored by Kunshang Ji
parent 8ad4a01825
commit 72f639927f
1 changed files with 4 additions and 11 deletions
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vllm/kernels/xpu_ops.py
+4 -11
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@@ -31,10 +31,8 @@ def rms_norm(
) -> Tensor:
assert variance_size is None
if weight is None:
# Weightless _C ops are CUDA-only; native skips the multiply on XPU.
return ir.ops.rms_norm.impls["native"].impl_fn(
x, weight, epsilon, variance_size
)
# Kernel requires weight tensor, pass ones
weight = torch.ones(x.shape[-1], device=x.device, dtype=x.dtype)
output = torch.empty(x.shape, device=x.device, dtype=x.dtype)
torch.ops._C.rms_norm(output, x, weight, epsilon)
return output
@@ -61,12 +59,7 @@ def fused_add_rms_norm(
) -> tuple[Tensor, Tensor]:
assert variance_size is None
if weight is None:
# Weightless _C ops are CUDA-only; native skips the multiply on XPU.
output, residual = ir.ops.fused_add_rms_norm.impls["native"].impl_fn(
x, x_residual, weight, epsilon, variance_size
)
x.copy_(output)
x_residual.copy_(residual)
return x, x_residual
# Kernel requires weight tensor, pass ones
weight = torch.ones(x.shape[-1], device=x.device, dtype=x.dtype)
torch.ops._C.fused_add_rms_norm(x, x_residual, weight, epsilon)
return x, x_residual