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pytorch - ✅(Solved) Fix torch.compile incorrectly accepts torch.celu_ with alpha=0 while eager mode errors [2 pull requests, 3 comments, 3 participants]

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pytorch/pytorch#178480•Fetched 2026-04-08 01:30:19
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torch.celu_ should reject alpha=0, but torch.compile runs it successfully instead of raising the same error as eager mode.

Error Message

import torch

def call_func(x, alpha=1.): return torch.celu_(x, alpha)

x = torch.randn(3, 4) alpha = 0.0

Eager

try: call_func(x.clone(), alpha) except Exception as e: print(type(e).name, e)

Compiled

compiled = torch.compile(call_func, dynamic=True) print(compiled(x.clone(), alpha).shape)

Root Cause

torch.celu_ should reject alpha=0, but torch.compile runs it successfully instead of raising the same error as eager mode.

Fix Action

Fix / Workaround

CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 46 bits physical, 57 bits virtual Byte Order: Little Endian CPU(s): 112 On-line CPU(s) list: 0-111 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) Gold 6330 CPU @ 2.00GHz CPU family: 6 Model: 106 Thread(s) per core: 2 Core(s) per socket: 28 Socket(s): 2 Stepping: 6 CPU(s) scaling MHz: 28% CPU max MHz: 3100.0000 CPU min MHz: 800.0000 BogoMIPS: 4000.00 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities Virtualization: VT-x L1d cache: 2.6 MiB (56 instances) L1i cache: 1.8 MiB (56 instances) L2 cache: 70 MiB (56 instances) L3 cache: 84 MiB (2 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62,64,66,68,70,72,74,76,78,80,82,84,86,88,90,92,94,96,98,100,102,104,106,108,110 NUMA node1 CPU(s): 1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,43,45,47,49,51,53,55,57,59,61,63,65,67,69,71,73,75,77,79,81,83,85,87,89,91,93,95,97,99,101,103,105,107,109,111 Vulnerability Gather data sampling: Mitigation; Microcode Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Retbleed: Not affected Vulnerability Spec rstack overflow: Not affected 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, RSB filling, PBRSB-eIBRS SW sequence Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected

PR fix notes

PR #178591: fixes:division by zero check for inductor

Description (problem / solution / changelog)

fixes: #178480 add check to division by zero for inductor.

add code checking the divisor is zero for the function in floordiv/truncdiv/truediv in class CppOverrides

cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @kadeng @muchulee8 @amjames @chauhang @aakhundov @coconutruben @jataylo

Changed files

  • test/inductor/test_cpu_repro.py (modified, +22/-0)
  • torch/_inductor/codegen/cpp.py (modified, +13/-0)

PR #179375: fix(compile): add celu decomposition with alpha=0 validation

Description (problem / solution / changelog)

Summary

This PR adds a decomposition for aten.celu and aten.celu_ that validates the alpha parameter, ensuring torch.compile correctly rejects alpha=0 just like eager mode.

Problem

Currently, torch.celu_(x, alpha=0) in eager mode raises:

RuntimeError: ZeroDivisionError: alpha cannot be 0 for CELU

But when using torch.compile, the operation incorrectly succeeds without any error.

Solution

Added decompositions in torch/_decomp/decompositions.py that:

  1. Validate alpha != 0 using torch._check()
  2. Implement celu using torch.where + torch.exp (matching the math formula)

Changes

  • torch/_decomp/decompositions.py: Added decompositions for aten.celu and aten.celu_ with alpha validation

Fixes #178480

cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @kadeng @chauhang @amjames @Lucaskabela @jataylo @azahed98

Changed files

  • test/dynamo/test_modules.py (modified, +35/-0)
  • torch/_refs/nn/functional/__init__.py (modified, +4/-0)

Code Example

import torch

def call_func(x, alpha=1.):
    return torch.celu_(x, alpha)

x = torch.randn(3, 4)
alpha = 0.0

# Eager
try:
    call_func(x.clone(), alpha)
except Exception as e:
    print(type(e).__name__, e)

# Compiled
compiled = torch.compile(call_func, dynamic=True)
print(compiled(x.clone(), alpha).shape)
RAW_BUFFERClick to expand / collapse

🐛 Describe the bug

Summary

torch.celu_ should reject alpha=0, but torch.compile runs it successfully instead of raising the same error as eager mode.

Reproduction

import torch

def call_func(x, alpha=1.):
    return torch.celu_(x, alpha)

x = torch.randn(3, 4)
alpha = 0.0

# Eager
try:
    call_func(x.clone(), alpha)
except Exception as e:
    print(type(e).__name__, e)

# Compiled
compiled = torch.compile(call_func, dynamic=True)
print(compiled(x.clone(), alpha).shape)

Expected

Both eager and compiled execution should raise an error for alpha=0.

Actual

  • Eager: RuntimeError: ZeroDivisionError: alpha cannot be 0 for CELU
  • Compiled: returns torch.Size([3, 4])

Versions

PyTorch version: 2.10.0+cu128 Is debug build: False CUDA used to build PyTorch: 12.8 ROCM used to build PyTorch: N/A

OS: Ubuntu 24.04.4 LTS (x86_64) GCC version: (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0 Clang version: Could not collect CMake version: Could not collect Libc version: glibc-2.39

Python version: 3.12.0 | packaged by Anaconda, Inc. | (main, Oct 2 2023, 17:29:18) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-6.5.11-060511-generic-x86_64-with-glibc2.39 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: GPU models and configuration: GPU 0: NVIDIA RTX A6000 GPU 1: NVIDIA RTX A6000

Nvidia driver version: 580.65.06 cuDNN version: Could not collect 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: 46 bits physical, 57 bits virtual Byte Order: Little Endian CPU(s): 112 On-line CPU(s) list: 0-111 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) Gold 6330 CPU @ 2.00GHz CPU family: 6 Model: 106 Thread(s) per core: 2 Core(s) per socket: 28 Socket(s): 2 Stepping: 6 CPU(s) scaling MHz: 28% CPU max MHz: 3100.0000 CPU min MHz: 800.0000 BogoMIPS: 4000.00 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities Virtualization: VT-x L1d cache: 2.6 MiB (56 instances) L1i cache: 1.8 MiB (56 instances) L2 cache: 70 MiB (56 instances) L3 cache: 84 MiB (2 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62,64,66,68,70,72,74,76,78,80,82,84,86,88,90,92,94,96,98,100,102,104,106,108,110 NUMA node1 CPU(s): 1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,43,45,47,49,51,53,55,57,59,61,63,65,67,69,71,73,75,77,79,81,83,85,87,89,91,93,95,97,99,101,103,105,107,109,111 Vulnerability Gather data sampling: Mitigation; Microcode Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Retbleed: Not affected Vulnerability Spec rstack overflow: Not affected 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, RSB filling, PBRSB-eIBRS SW sequence Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected

Versions of relevant libraries: [pip3] numpy==2.4.1 [pip3] nvidia-cublas-cu12==12.8.4.1 [pip3] nvidia-cuda-cupti-cu12==12.8.90 [pip3] nvidia-cuda-nvrtc-cu12==12.8.93 [pip3] nvidia-cuda-runtime-cu12==12.8.90 [pip3] nvidia-cudnn-cu12==9.10.2.21 [pip3] nvidia-cufft-cu12==11.3.3.83 [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-nccl-cu12==2.27.5 [pip3] nvidia-nvjitlink-cu12==12.8.93 [pip3] nvidia-nvtx-cu12==12.8.90 [pip3] optree==0.18.0 [pip3] torch==2.10.0 [pip3] triton==3.6.0 [conda] numpy 2.4.1 pypi_0 pypi [conda] nvidia-cublas-cu12 12.8.4.1 pypi_0 pypi [conda] nvidia-cuda-cupti-cu12 12.8.90 pypi_0 pypi [conda] nvidia-cuda-nvrtc-cu12 12.8.93 pypi_0 pypi [conda] nvidia-cuda-runtime-cu12 12.8.90 pypi_0 pypi [conda] nvidia-cudnn-cu12 9.10.2.21 pypi_0 pypi [conda] nvidia-cufft-cu12 11.3.3.83 pypi_0 pypi [conda] nvidia-curand-cu12 10.3.9.90 pypi_0 pypi [conda] nvidia-cusolver-cu12 11.7.3.90 pypi_0 pypi [conda] nvidia-cusparse-cu12 12.5.8.93 pypi_0 pypi [conda] nvidia-cusparselt-cu12 0.7.1 pypi_0 pypi [conda] nvidia-nccl-cu12 2.27.5 pypi_0 pypi [conda] nvidia-nvjitlink-cu12 12.8.93 pypi_0 pypi [conda] nvidia-nvtx-cu12 12.8.90 pypi_0 pypi [conda] optree 0.18.0 pypi_0 pypi [conda] torch 2.10.0 pypi_0 pypi [conda] triton 3.6.0 pypi_0 pypi

cc @albanD @mruberry @jbschlosser @walterddr @mikaylagawarecki @malfet @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @kadeng @amjames @Lucaskabela @jataylo

extent analysis

Fix Plan

To fix the issue where torch.compile does not raise an error for alpha=0 in torch.celu_, we need to add a check for alpha before calling torch.celu_.

Here are the steps:

  • Check if alpha is zero before calling torch.celu_.
  • If alpha is zero, raise a RuntimeError with the appropriate message.

Code Changes

import torch

def call_func(x, alpha=1.):
    if alpha == 0.0:
        raise RuntimeError("ZeroDivisionError: alpha cannot be 0 for CELU")
    return torch.celu_(x, alpha)

x = torch.randn(3, 4)
alpha = 0.0

# Eager
try:
    call_func(x.clone(), alpha)
except Exception as e:
    print(type(e).__name__, e)

# Compiled
compiled = torch.compile(call_func, dynamic=True)
try:
    print(compiled(x.clone(), alpha).shape)
except Exception as e:
    print(type(e).__name__, e)

Verification

To verify that the fix worked, run the code with alpha=0 and check that it raises a RuntimeError with the correct message in both eager and compiled modes.

Extra Tips

  • Always check the input values for validity before calling functions that may raise errors.
  • Use try-except blocks to catch and handle exceptions in your code.
  • Make sure to test your code thoroughly after making changes to ensure that it works as expected.

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pytorch - ✅(Solved) Fix torch.compile incorrectly accepts torch.celu_ with alpha=0 while eager mode errors [2 pull requests, 3 comments, 3 participants]