pytorch - 💡(How to fix) Fix Heap corruption when compiling models with `nn.LSTM` or `nn.GRU` using `torch.compile(backend='inductor')` [1 participants]

pytorch2026-04-28 01:51:11

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pytorch/pytorch#181690•Fetched 2026-04-28 06:23:42

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jasminetrail

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Error Message

Running torch.compile(model, backend='inductor') on a model that includes nn.LSTM or nn.GRU (with or without preceding convolutional layers) causes the process to abort with a glibc heap error such as free(): invalid pointer or double free or corruption.

Error logs

Fix Action

Fix / Workaround

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

Code Example

import torch
import torch.nn as nn

conv      = nn.Conv2d(10, 2, 3)
inst_norm = nn.InstanceNorm2d(2)
lstm      = nn.LSTM(12, 4, batch_first=True)

def model():
    x = torch.ones([16, 10, 5, 14])
    t = conv(x)
    t = inst_norm(t)
    t = torch.repeat_interleave(t, 2, dim=2)
    t = torch.mean(t, dim=0)         # [2, 12]  — sequence for LSTM
    t, _ = lstm(t)
    return t

# Eager forward works fine
out = model()
assert torch.isfinite(out).all()

# Compiled forward crashes the process
compiled = torch.compile(model, backend='inductor')
compiled()   # ← free(): invalid pointer

---

import torch
import torch.nn as nn

conv_t  = nn.ConvTranspose2d(2, 3, 1)
bn      = nn.BatchNorm2d(3).eval()
gru     = nn.GRU(6, 6, batch_first=True)

def model():
    x = torch.randn([8, 2, 4, 6])
    t = conv_t(x)
    t = bn(t)
    t = torch.amax(t, dim=2)         # [8, 3, 6] — sequence for GRU
    t, _ = gru(t)
    return t

compiled = torch.compile(model, backend='inductor')
compiled()   # ← crashes

---

free(): invalid pointer
Aborted (core dumped)

---

double free or corruption (out)
Aborted (core dumped)

---

corrupted double-linked list
Aborted (core dumped)

---

malloc(): invalid size (unsorted)
Aborted (core dumped)

RAW_BUFFERClick to expand / collapse

🐛 Describe the bug

Minimal Reproducer

import torch
import torch.nn as nn

conv      = nn.Conv2d(10, 2, 3)
inst_norm = nn.InstanceNorm2d(2)
lstm      = nn.LSTM(12, 4, batch_first=True)

def model():
    x = torch.ones([16, 10, 5, 14])
    t = conv(x)
    t = inst_norm(t)
    t = torch.repeat_interleave(t, 2, dim=2)
    t = torch.mean(t, dim=0)         # [2, 12]  — sequence for LSTM
    t, _ = lstm(t)
    return t

# Eager forward works fine
out = model()
assert torch.isfinite(out).all()

# Compiled forward crashes the process
compiled = torch.compile(model, backend='inductor')
compiled()   # ← free(): invalid pointer

GRU variant:

import torch
import torch.nn as nn

conv_t  = nn.ConvTranspose2d(2, 3, 1)
bn      = nn.BatchNorm2d(3).eval()
gru     = nn.GRU(6, 6, batch_first=True)

def model():
    x = torch.randn([8, 2, 4, 6])
    t = conv_t(x)
    t = bn(t)
    t = torch.amax(t, dim=2)         # [8, 3, 6] — sequence for GRU
    t, _ = gru(t)
    return t

compiled = torch.compile(model, backend='inductor')
compiled()   # ← crashes

Error logs

free(): invalid pointer
Aborted (core dumped)

Other observed variants:

double free or corruption (out)
Aborted (core dumped)

corrupted double-linked list
Aborted (core dumped)

malloc(): invalid size (unsorted)
Aborted (core dumped)

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

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

extent analysis

TL;DR

The issue can be mitigated by avoiding the use of torch.compile with the 'inductor' backend for models containing nn.LSTM or nn.GRU layers.

Guidance

The error logs indicate a memory management issue, suggesting that the problem lies in the interaction between PyTorch's compilation process and the recurrent neural network (RNN) layers.
To verify the issue, try running the model without compilation using the eager execution mode, as shown in the minimal reproducer.
As a temporary workaround, consider avoiding the use of torch.compile with the 'inductor' backend for models containing RNN layers.
If possible, try updating PyTorch to a newer version, as this issue may have been addressed in later releases.

Example

No code example is provided, as the issue is likely related to the interaction between PyTorch's compilation process and the RNN layers, rather than a specific code snippet.

Notes

The root cause of the issue is uncertain, but it appears to be related to the memory management of the 'inductor' backend when dealing with RNN layers. Further investigation is needed to determine the exact cause and develop a permanent fix.

Recommendation

Apply workaround: avoid using torch.compile with the 'inductor' backend for models containing nn.LSTM or nn.GRU layers, as this appears to mitigate the issue.

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pytorch - 💡(How to fix) Fix Heap corruption when compiling models with `nn.LSTM` or `nn.GRU` using `torch.compile(backend='inductor')` [1 participants]

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