Hi,
I have an issue using torch.compile(). I’m currently using nightly, with the env being:
Singularity> python -m torch.utils.collect_env
Collecting environment information...
PyTorch version: 2.1.0.dev20230904+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A
OS: Debian GNU/Linux 11 (bullseye) (x86_64)
GCC version: (Debian 10.2.1-6) 10.2.1 20210110
Clang version: Could not collect
CMake version: version 3.27.2
Libc version: glibc-2.31
Python version: 3.11.4 (main, Aug 16 2023, 05:31:52) [GCC 10.2.1 20210110] (64-bit runtime)
Python platform: Linux-5.14.0-284.25.1.el9_2.x86_64-x86_64-with-glibc2.31
Is CUDA available: False
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No devices found.
Nvidia driver version: Could not collect
cuDNN version: Could not collect
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): 128
On-line CPU(s) list: 0-127
Thread(s) per core: 1
Core(s) per socket: 64
Socket(s): 2
NUMA node(s): 4
Vendor ID: AuthenticAMD
CPU family: 23
Model: 49
Model name: AMD EPYC 7742 64-Core Processor
Stepping: 0
Frequency boost: enabled
CPU MHz: 3271.206
CPU max MHz: 2250.0000
CPU min MHz: 1500.0000
BogoMIPS: 4499.93
Virtualization: AMD-V
L1d cache: 4 MiB
L1i cache: 4 MiB
L2 cache: 64 MiB
L3 cache: 512 MiB
NUMA node0 CPU(s): 0-31
NUMA node1 CPU(s): 32-63
NUMA node2 CPU(s): 64-95
NUMA node3 CPU(s): 96-127
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Retbleed: Mitigation; untrained return thunk; SMT disabled
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; Retpolines, IBPB conditional, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected
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 nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 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 ibrs 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 rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sme sev sev_es
Versions of relevant libraries:
[pip3] mypy==1.5.1
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.24.1
[pip3] pytorch-lightning==2.0.8
[pip3] pytorch-metric-learning==2.3.0
[pip3] pytorch-ranger==0.1.1
[pip3] pytorch-triton==2.1.0+e6216047b8
[pip3] torch==2.1.0.dev20230904+cu121
[pip3] torch-optimizer==0.3.0
[pip3] torchaudio==2.2.0.dev20230904+cu121
[pip3] torchmetrics==0.11.4
[pip3] torchvision==0.16.0.dev20230904+cu121
[pip3] triton==2.0.0
[conda] Could not collect
And the issue:
Traceback (most recent call last):
File "/usr/local/lib/python3.11/site-packages/torch/fx/graph_module.py", line 274, in __call__
return super(self.cls, obj).__call__(*args, **kwargs) # type: ignore[misc]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<eval_with_key>.179", line 23, in forward
matmul = torch.matmul(permute_2, transpose); permute_2 = transpose = None
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: Cannot call sizes() on tensor with symbolic sizes/strides
Exception raised from sizes_default at ../c10/core/TensorImpl.h:617 (most recent call first):
Call using an FX-traced Module, line 23 of the traced Module's generated forward function:
transpose = permute.transpose(-1, -2); permute = None
matmul = torch.matmul(permute_2, transpose); permute_2 = transpose = None
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
truediv = matmul / 8.0; matmul = None
add = truediv + mul; truediv = mul = None
While executing %submod_1 : [num_users=2] = call_module[target=compiled_submod_1](args = (%getitem, %getitem_1, %getitem_2), kwargs = {})
Original traceback:
None
You can suppress this exception and fall back to eager by setting:
import torch._dynamo
torch._dynamo.config.suppress_errors = True
I tried to add dynamic=True to torch.compile(), but that did not help. The only difference between two runs is the added compilation. The non-compiled run is successful, whereas the compiled one fails on the first step of sanity checking dataloader (with no gradients saved, validation dataloader).