[Bugfix][Model] Fix gpt-oss batch invariance

[jzakrzew]

Purpose

GPT-OSS is listed as verified in the batch invariance doc, but rerunning the provided tests on an H100 suggests it does not in fact work in all the claimed supported configurations:

# VLLM_TEST_MODEL="openai/gpt-oss-20b" uv run pytest tests/v1/determinism/test_batch_invariance.py -k "bs1_vs_bsN and not MLA"
...

============================================================================================================== short test summary info ==============================================================================================================
FAILED tests/v1/determinism/test_batch_invariance.py::test_logprobs_bitwise_batch_invariance_bs1_vs_bsN[FLASH_ATTN] - Failed: Batch invariance violated in 32/32 prompts. See output above for details.
FAILED tests/v1/determinism/test_batch_invariance.py::test_logprobs_bitwise_batch_invariance_bs1_vs_bsN[TRITON_ATTN] - Failed: Batch invariance violated in 32/32 prompts. See output above for details.
============================================================================================= 2 failed, 15 deselected, 17 warnings in 67.67s (0:01:07) ==============================================================================================

Details of the setup at the end of the description.

The root cause of this issue seems to be cublas and the fact that vLLM does not replace cublas calls aggressively enough in batch invariant mode. (It also makes some bad assumptions about cublas behavior on Hopper in batch_invariance.py, but that is out of scope of this PR)

This PR fixes it by doing two things:

1. Replacing direct use of nn.Linear for the router layer with ReplicatedLinear
2. Making UnquantizedLinearMethod unconditionally call the batch invariant linear layer when in batch invariance mode on CUDA, instead of doing this in the router layer only. GPT-OSS on H100 needs this for qkv and output projection too.

Test Plan

# VLLM_TEST_MODEL="openai/gpt-oss-20b" uv run pytest tests/v1/determinism/test_batch_invariance.py -k "bs1_vs_bsN and not MLA"

Test Result

...

tests/v1/determinism/test_batch_invariance.py ..                                                                                                                                                                                              [100%]

================================================================================================= 2 passed, 15 deselected, 17 warnings in 100.57s (0:01:40) =============================================================================================

The output of python collect_env.py

Collecting environment information...
uv is set

Python version               : 3.12.3 (main, Jan 22 2026, 20:57:42) [GCC 13.3.0] (64-bit runtime)
Python platform              : Linux-5.15.0-52-generic-x86_64-with-glibc2.39

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 13.1.115
CUDA_MODULE_LOADING set to   :
GPU models and configuration : GPU 0: NVIDIA H100 80GB HBM3
Nvidia driver version        : 580.105.08
cuDNN version                : Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.17.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.17.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.17.1
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.17.1
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.17.1
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.17.1
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.17.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.17.1
HIP runtime version          : N/A
MIOpen runtime version       : N/A
Is XNNPACK available         : True

==============================
          CPU Info
==============================
Architecture:                    x86_64
CPU op-mode(s):                  32-bit, 64-bit
Address sizes:                   48 bits physical, 48 bits virtual
Byte Order:                      Little Endian
CPU(s):                          48
On-line CPU(s) list:             0-47
Vendor ID:                       AuthenticAMD
Model name:                      AMD EPYC 7413 24-Core Processor
CPU family:                      25
Model:                           1
Thread(s) per core:              2
Core(s) per socket:              24
Socket(s):                       1
Stepping:                        1
Frequency boost:                 enabled
CPU(s) scaling MHz:              58%
CPU max MHz:                     3630.8101
CPU min MHz:                     1500.0000
BogoMIPS:                        5300.17
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca
Virtualization:                  AMD-V
L1d cache:                       768 KiB (24 instances)
L1i cache:                       768 KiB (24 instances)
L2 cache:                        12 MiB (24 instances)
L3 cache:                        128 MiB (4 instances)
NUMA node(s):                    1
NUMA node0 CPU(s):               0-47
Vulnerability Itlb multihit:     Not affected
Vulnerabil
[pip3] nvidia-cudnn-frontend==1.18.0
[pip3] nvidia-cufft-cu12==11.3.3.83
[pip3] nvidia-cufile-cu12==1.13.1.3
[pip3] nvidia-curand-cu12==10.3.9.90
[pip3] nvidia-cusolver-cu12==11.7.3.90
[pip3] nvidia-cusparse-cu12==12.5.8.93
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-cutlass-dsl==4.4.0
[pip3] nvidia-cutlass-dsl-libs-base==4.4.0
[pip3] nvidia-ml-py==13.590.48
[pip3] nvidia-nccl-cu12==2.27.5
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvshmem-cu12==3.4.5
[pip3] nvidia-nvtx-cu12==12.8.90                                                                                                                                                                                                             [0/1879]
[pip3] pyzmq==27.1.0
[pip3] torch==2.10.0
[pip3] torchaudio==2.10.0
[pip3] torchvision==0.25.0
[pip3] transformers==4.57.6
[pip3] triton==3.6.0
[conda] Could not collect

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.1.dev14246+g845ee348e (git sha: 845ee348e)
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
        GPU0    NIC0    NIC1    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NODE    NODE    0-47    0               N/A
NIC0    NODE     X      PIX
NIC1    NODE    PIX      X

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks

NIC Legend:

  NIC0: mlx5_0
  NIC1: mlx5_1

==============================
     Environment Variables
==============================
NVIDIA_VISIBLE_DEVICES=void
NVIDIA_REQUIRE_CUDA=cuda>=13.1 brand=unknown,driver>=535,driver<536 brand=grid,driver>=535,driver<536 brand=tesla,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=vapps,driver>=535,driver<536 brand=vpc,driver>=535,driver<536 brand=vcs,driver>=535,driver<536 brand=vws,driver>=535,driver<536 brand=cloudgaming,driver>=535,driver<536 brand=unknown,driver>=550,driver<551 brand=grid,driver>=550,driver<551 brand=tesla,driver>=550,driver<551 brand=nvidia,driver>=550,driver<551 brand=quadro,driver>=550,driver<551 brand=quadrortx,driver>=550,driver<551 brand=nvidiartx,driver>=550,driver<551 brand=vapps,driver>=550,driver<551 brand=vpc,driver>=550,driver<551 brand=vcs,driver>=550,driver<551 brand=vws,driver>=550,driver<551 brand=cloudgaming,driver>=550,driver<551 brand=unknown,driver>=570,driver<571 brand=grid,driver>=570,driver<571 brand=tesla,driver>=570,driver<571 brand=nvidia,driver>=570,driver<571 brand=quadro,driver>=570,driver<571 brand=quadrortx,driver>=570,driver<571 brand=nvidiartx,driver>=570,driver<571 brand=vapps,driver>=570,driver<571 brand=vpc,driver>=570,driver<571 brand=vcs,driver>=570,driver<571 brand=vws,driver>=570,driver<571 brand=cloudgaming,driver>=570,driver<571 brand=unknown,driver>=575,driver<576 brand=grid,driver>=575,driver<576 brand=tesla,driver>=575,driver<576 brand=nvidia,driver>=575,driver<576 brand=quadro,driver>=575,driver<576 brand=quadrortx,driver>=575,driver<576 brand=nvidiartx,driver>=575,driver<576 brand=vapps,driver>=575,driver<576 brand=vpc,driver>=575,driver<576 brand=vcs,driver>=575,driver<576 brand=vws,driver>=575,driver<576 brand=cloudgaming,driver>=575,driver<576 brand=unknown,driver>=580,driver<581 brand=grid,driver>=580,driver<581 brand=tesla,driver>=580,driver<581 brand=nvidia,driver>=580,driver<581 brand=quadro,driver>=580,driver<581 brand=quadrortx,driver>=580,driver<581 brand=nvidiartx,driver>=580,driver<581 brand=vapps,driver>=580,driver<581 brand=vpc,driver>=580,driver<581 brand=vcs,driver>=580,driver<581 brand=vws,driver>=580,driver<581 brand=cloudgaming,driver>=580,driver<581
NVIDIA_DRIVER_CAPABILITIES=compute,utility
NVIDIA_PRODUCT_NAME=CUDA
CUDA_VERSION=13.1.1
LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64:/usr/local/cuda/lib64
NVIDIA_CTK_LIBCUDA_DIR=/usr/lib/x86_64-linux-gnu
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_root

[jzakrzew]

cc @yewentao256 @tijyojwad

[gemini-code-assist]

Code Review

This pull request correctly addresses a batch invariance bug in the gpt-oss model. The changes are twofold: first, the router layer in gpt_oss.py is updated from torch.nn.Linear to vllm.ReplicatedLinear, which is necessary to integrate it with vLLM's linear layer handling. Second, the batch-invariant linear method in linear.py is now applied to all unquantized linear layers under CUDA when batch invariance is enabled, instead of being restricted to MoE router gates. This generalization is crucial for ensuring deterministic behavior across all relevant layers, including QKV and output projections, as required by the model on H100 GPUs. The changes are well-justified, targeted, and appear to be a solid fix for the reported issue.

[yewentao256]

LGTM, thanks for the work!