pytorch - 💡(How to fix) Fix Eval-mode `BatchNorm`/`GroupNorm` produces wrong output under `torch.compile(backend='inductor')` [1 participants]

🐛 Describe the bug

When a model contains nn.BatchNorm1d/BatchNorm2d or nn.GroupNorm in eval mode (.eval()), torch.compile(backend='inductor') produces output that is numerically wrong compared to eager execution, with max_diff >= 1.0.

In eval mode, BatchNorm normalises using frozen running_mean and running_var statistics rather than the current batch statistics. Inductor performs constant folding on eval-mode normalisation layers, pre-computing the effective scale and bias from these frozen statistics. When this fold is incorrect — for example when it uses the wrong statistics, applies the fold in the wrong order, or interacts incorrectly with a subsequent GroupNorm — the compiled output diverges significantly from eager.

Minimal Reproducer

import torch
import torch.nn as nn

bn  = nn.BatchNorm1d(10).eval()   # running_mean=0, running_var=1 (default init)
elu = nn.ELU()
gn  = nn.GroupNorm(10, 10).eval()

def model():
    x = torch.ones([6, 10, 12])
    t = bn(x)          # eval: normalise with running_mean/var, apply weight/bias
    t = elu(t)
    t = gn(t)          # ← mismatch appears after this second norm layer
    t = torch.log(torch.clamp(t, min=1e-6))
    return t

eager_out    = model()
compiled_out = torch.compile(model, backend='inductor')()

print("max diff:", (eager_out - compiled_out).abs().max().item())
# Expected: ~0.0
# Actual:    1.288699

Variant with F.scaled_dot_product_attention:

import torch
import torch.nn as nn
import torch.nn.functional as F

conv = nn.Conv2d(10, 3, 3)
ln   = nn.LayerNorm([7])

def model():
    x = torch.ones([11, 10, 3, 9])
    t = F.scaled_dot_product_attention(x, x, x)
    t = conv(t)
    t = ln(t)
    t = torch.log(torch.clamp(t, min=1e-6))
    return t

eager_out    = model()
compiled_out = torch.compile(model, backend='inductor')()

assert torch.allclose(eager_out, compiled_out, atol=1e-4), \
    f"max_diff={(eager_out - compiled_out).abs().max().item():.6f}"

Error logs

Traceback (most recent call last):
  File "program.py", line 34, in <module>
    assert torch.allclose(_eager_out, _compiled_out, atol=1e-4, rtol=1e-4), (
AssertionError: eager/compiled mismatch: max_diff=1.288699

Other observed max_diff values across the 15 programs:

max_diff=1.288699
max_diff=1.821913
max_diff=1.070833
max_diff=1.000000   (multiple cases)

Versions

Collecting environment information... PyTorch version: 2.11.0+cu130 Is debug build: False CUDA used to build PyTorch: 13.0 ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.5 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04.3) 11.4.0 Clang version: 15.0.0 ([email protected]:llvm/llvm-project.git 4ba6a9c9f65bbc8bd06e3652cb20fd4dfc846137) CMake version: version 3.22.1 Libc version: glibc-2.35

Python version: 3.10.12 (main, Mar 3 2026, 11:56:32) [GCC 11.4.0] (64-bit runtime) Python platform: Linux-6.8.0-94-generic-x86_64-with-glibc2.35 Is CUDA available: False CUDA runtime version: No CUDA CUDA_MODULE_LOADING set to: N/A GPU models and configuration: No CUDA Nvidia driver version: No CUDA cuDNN version: No CUDA Is XPU available: False HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True Caching allocator config: N/A

CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 52 bits physical, 57 bits virtual Byte Order: Little Endian CPU(s): 384 On-line CPU(s) list: 0-383 Vendor ID: AuthenticAMD Model name: AMD EPYC 9684X 96-Core Processor CPU family: 25 Model: 17 Thread(s) per core: 2 Core(s) per socket: 96 Socket(s): 2 Stepping: 2 BogoMIPS: 5099.98 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 amd_lbr_v2 nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic 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 hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local user_shstk avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc amd_ibpb_ret arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif x2avic v_spec_ctrl vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid overflow_recov succor smca fsrm flush_l1d debug_swap ibpb_exit_to_user Virtualization: AMD-V L1d cache: 6 MiB (192 instances) L1i cache: 6 MiB (192 instances) L2 cache: 192 MiB (192 instances) L3 cache: 2.3 GiB (24 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-95,192-287 NUMA node1 CPU(s): 96-191,288-383 Vulnerability Gather data sampling: Not affected Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Reg file data sampling: Not affected Vulnerability Retbleed: Not affected Vulnerability Spec rstack overflow: Mitigation; Safe RET Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Vulnerability Vmscape: Mitigation; IBPB before exit to userspace

Versions of relevant libraries: [pip3] numpy==2.2.6 [pip3] nvidia-cublas==13.1.0.3 [pip3] nvidia-cuda-cupti==13.0.85 [pip3] nvidia-cuda-nvrtc==13.0.88 [pip3] nvidia-cuda-runtime==13.0.96 [pip3] nvidia-cudnn-cu13==9.19.0.56 [pip3] nvidia-cufft==12.0.0.61 [pip3] nvidia-curand==10.4.0.35 [pip3] nvidia-cusolver==12.0.4.66 [pip3] nvidia-cusparse==12.6.3.3 [pip3] nvidia-cusparselt-cu13==0.8.0 [pip3] nvidia-nccl-cu13==2.28.9 [pip3] nvidia-nvjitlink==13.0.88 [pip3] nvidia-nvtx==13.0.85 [pip3] torch==2.11.0 [pip3] triton==3.6.0 [conda] Could not collect

cc @chauhang @penguinwu