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[New Model] DeepSeek-V3.2 (Rebased to Main) (#25896)

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Signed-off-by: Chen Zhang <zhangch99@outlook.com>
Signed-off-by: youkaichao <youkaichao@gmail.com>
Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com>
Signed-off-by: mgoin <mgoin64@gmail.com>
Signed-off-by: NickLucche <nlucches@redhat.com>
Signed-off-by: Yongye Zhu <zyy1102000@gmail.com>
Signed-off-by: Barry Kang <43644113+Barry-Delaney@users.noreply.github.com>
Signed-off-by: Lucia Fang <fanglu@meta.com>
Co-authored-by: Chen Zhang <zhangch99@outlook.com>
Co-authored-by: youkaichao <youkaichao@gmail.com>
Co-authored-by: Lucas Wilkinson <lwilkins@redhat.com>
Co-authored-by: Robert Shaw <114415538+robertgshaw2-redhat@users.noreply.github.com>
Co-authored-by: Lucas Wilkinson <LucasWilkinson@users.noreply.github.com>
Co-authored-by: yewentao256 <zhyanwentao@126.com>
Co-authored-by: Wentao Ye <44945378+yewentao256@users.noreply.github.com>
Co-authored-by: mgoin <mgoin64@gmail.com>
Co-authored-by: Lucia Fang <116399278+luccafong@users.noreply.github.com>
Co-authored-by: Lucia Fang <fanglu@meta.com>
Co-authored-by: NickLucche <nlucches@redhat.com>
Co-authored-by: Siyuan Fu <siyuanf@nvidia.com>
Co-authored-by: Matthew Bonanni <mbonanni@redhat.com>
Co-authored-by: Xiaozhu Meng <mxz297@gmail.com>
Co-authored-by: Barry Kang <43644113+Barry-Delaney@users.noreply.github.com>
This commit is contained in:
Yongye Zhu
2025-09-30 05:14:41 -04:00
committed by GitHub
parent e23cacda35
commit fa7e254a7f
71 changed files with 3915 additions and 218 deletions
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18
vllm/model_executor/layers/layernorm.py
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@@ -5,6 +5,7 @@ from typing import Optional, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
import vllm.envs as envs
from vllm.model_executor.custom_op import CustomOp
@@ -375,3 +376,20 @@ class PolyNorm(CustomOp):
x: torch.Tensor,
) -> torch.Tensor:
return poly_norm(x, self.weight, self.bias, self.variance_epsilon)
class LayerNorm(nn.Module):
"""
Layer Normalization.
"""
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
self.dim = dim
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim, dtype=torch.float32))
self.bias = nn.Parameter(torch.zeros(dim, dtype=torch.float32))
def forward(self, x: torch.Tensor):
return F.layer_norm(x.float(), (self.dim, ), self.weight, self.bias,
self.eps).type_as(x)