Get Segmentation fault when I compile the following FusionDefinition #2582
Description
🐛 Describe the bug
This FD is got when I run the DGL/GAT model. Notice that I just use the master branch of this repo.
inputs = [
torch.randn(5, 5, device='cuda').unsqueeze(1).expand((5, 3, 5)),
torch.randn(3, 5, device='cuda').unsqueeze(0).expand((5, 3, 5)),
torch.randn(5, 5, device='cuda').unsqueeze(1).expand((5, 3, 5)),
torch.randn(5, 5, device='cuda').unsqueeze(1).expand((5, 3, 5)),
torch.randn(3, 5, device='cuda').unsqueeze(0).expand((5, 3, 5)),
torch.randn(5, 15, device='cuda')
]
def fusion_func(fd : FusionDefinition) -> None :
T0 = fd.define_tensor(symbolic_sizes=[-1, -1, -1], contiguous=[True, None, True], dtype=DataType.Float, is_cpu=False)
T1 = fd.define_tensor(symbolic_sizes=[-1, -1, -1], contiguous=[None, True, True], dtype=DataType.Float, is_cpu=False)
T2 = fd.define_tensor(symbolic_sizes=[-1, -1, -1], contiguous=[True, True, True], dtype=DataType.Float, is_cpu=False)
T3 = fd.define_tensor(symbolic_sizes=[-1, -1, -1], contiguous=[True, None, True], dtype=DataType.Float, is_cpu=False)
T4 = fd.define_tensor(symbolic_sizes=[-1, -1, -1], contiguous=[None, True, True], dtype=DataType.Float, is_cpu=False)
T5 = fd.define_tensor(symbolic_sizes=[-1, -1], contiguous=[True, True], dtype=DataType.Float, is_cpu=False)
T6 = fd.ops.mul(T0, T1)
T8 = fd.ops.mul(T3, T4)
T9 = fd.ops.mul(T3, T2)
T11 = fd.ops.add(T6, T8)
T12 = fd.ops.sum(T9, axes=[0], keepdim=False, dtype=DataType.Null)
T13 = fd.ops.reshape(T11, original_shape=[5, 3, 5], new_shape=[5, 15])
T14 = fd.ops.add(T5, T13)
fd.add_output(T12)
fd.add_output(T14)Versions
Collecting environment information...
PyTorch version: 1.12.0a0+2c916ef
Is debug build: False
CUDA used to build PyTorch: 11.6
ROCM used to build PyTorch: N/A
OS: Ubuntu 20.04.4 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04) 9.4.0
Clang version: Could not collect
CMake version: version 3.22.3
Libc version: glibc-2.31
Python version: 3.8.12 | packaged by conda-forge | (default, Jan 30 2022, 23:42:07) [GCC 9.4.0] (64-bit runtime)
Python platform: Linux-4.15.0-201-generic-x86_64-with-glibc2.10
Is CUDA available: True
CUDA runtime version: 11.6.112
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3080 Ti
Nvidia driver version: 525.85.05
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.3.3
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.3.3
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.3.3
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.3.3
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.3.3
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.3.3
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.3.3
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
Address sizes: 43 bits physical, 48 bits virtual
CPU(s): 48
On-line CPU(s) list: 0-47
Thread(s) per core: 2
Core(s) per socket: 24
Socket(s): 1
NUMA node(s): 1
Vendor ID: AuthenticAMD
CPU family: 23
Model: 49
Model name: AMD Ryzen Threadripper 3960X 24-Core Processor
Stepping: 0
Frequency boost: enabled
CPU MHz: 2192.588
CPU max MHz: 3800.0000
CPU min MHz: 2200.0000
BogoMIPS: 7585.58
Virtualization: AMD-V
L1d cache: 768 KiB
L1i cache: 768 KiB
L2 cache: 12 MiB
L3 cache: 128 MiB
NUMA node0 CPU(s): 0-47
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, STIBP conditional, RSB filling
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
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 xtopology nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 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 hw_pstate sme ssbd ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 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 arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif umip rdpid overflow_recov succor smca
Versions of relevant libraries:
[pip3] numpy==1.22.3
[pip3] pytorch-quantization==2.1.2
[pip3] torch==1.12.0a0+2c916ef
[pip3] torch-tensorrt==1.1.0a0
[pip3] torchtext==0.12.0a0
[pip3] torchvision==0.13.0a0
[conda] magma-cuda110 2.5.2 5 local
[conda] mkl 2019.5 281 conda-forge
[conda] mkl-include 2019.5 281 conda-forge
[conda] numpy 1.22.3 py38h05e7239_0 conda-forge
[conda] pytorch-quantization 2.1.2 pypi_0 pypi
[conda] torch 1.12.0a0+2c916ef pypi_0 pypi
[conda] torch-tensorrt 1.1.0a0 pypi_0 pypi
[conda] torchtext 0.12.0a0 pypi_0 pypi
[conda] torchvision 0.13.0a0 pypi_0 pypi