Training Hangs During HuBERT Pretraining with DDP When Loss Becomes Invalid

🐛 Describe the bug

During pretraining HuBERT with custom data, I encountered a problem where the training would hang after progressing to a certain extent. It took considerable time to identify the issue, as no logs were generated until I received an NCCL 30-minute timeout error.

The problem arises in the following section: while training with LibriSpeech data didn't result in loss becoming NaN, it did happen with my custom data, leading to this issue. In the code below return None, it calls all_gather to aggregate num_frame. However, if NaN occurs in a specific GPU process, it never reaches this all_gather, causing the training to stall at this point.

audio/examples/hubert/lightning_modules.py

Lines 252 to 257 in b7b7b5d

    
           if torch.isinf(loss) or torch.isnan(loss): 
        
               opt.zero_grad() 
        
               return None 
        
           # normalize the loss based on the sum of num_frame across all GPUs 
        
           num_frames = self.all_gather(num_frame)

To reproduce the problem, by returning on any rank while using DDP (multiple GPUs).
Enabling TORCH_DISTRIBUTED_DEBUG=DETAIL provides a stack trace, aiding in pinpointing the issue.

--- a/lightning_modules.py
+++ b/lightning_modules.py
@@ -251,7 +251,8 @@ class HuBERTPreTrainModule(LightningModule):
         opt.zero_grad()
         with torch.cuda.amp.autocast(enabled=True):
             loss, num_frame = self._step(batch, batch_idx, "train")
-        if torch.isinf(loss) or torch.isnan(loss):
+        if torch.isinf(loss) or torch.isnan(loss) or self.local_rank==0:
+            print("is none")
             opt.zero_grad()

To resolve this issue, we need to modify the code to ensure that all_gather is reached by all replicas. Otherwise, when encountering an invalid loss, sharing this information among processes via all_reduce and skipping the update for that step could be a viable solution.

Versions

python collect_env.py
PyTorch version: 2.1.2+cu118
Is debug build: False

CUDA used to build PyTorch: 11.8 [0/168]
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: Could not collect
CMake version: version 3.16.3
Libc version: glibc-2.31

Python version: 3.10.8 (main, Jun 8 2023, 10:18:35) [GCC 9.4.0] (64-bit runtime)
Python platform: Linux-5.15.0-94-generic-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA RTX A6000
GPU 1: NVIDIA RTX A6000
GPU 2: NVIDIA RTX A6000
GPU 3: NVIDIA RTX A6000

Nvidia driver version: 520.61.05
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: 46 bits physical, 48 bits virtual
CPU(s): 32
On-line CPU(s) list: 0-31
Thread(s) per core: 2
Core(s) per socket: 8
Socket(s): 2
NUMA node(s): 2
Vendor ID: GenuineIntel
CPU family: 6
Model: 85
Model name: Intel(R) Xeon(R) Gold 6234 CPU @ 3.30GHz
Stepping: 7
CPU MHz: 1200.000
CPU max MHz: 4000.0000
CPU min MHz: 1200.0000
BogoMIPS: 6600.00
Virtualization: VT-x
L1d cache: 512 KiB
L1i cache: 512 KiB
L2 cache: 16 MiB
L3 cache: 49.5 MiB
NUMA node0 CPU(s): 0-7,16-23
NUMA node1 CPU(s): 8-15,24-31
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Itlb multihit: KVM: Mitigation: VMX disabled
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed: Mitigation; Enhanced IBRS
Vulnerability Spec rstack overflow: 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; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Mitigation; TSX disabled
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 pclmulq
dq 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 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shad
ow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pl
n pts pku ospke avx512_vnni md_clear flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] pytorch-lightning==2.2.0
[pip3] torch==2.1.2+cu118
[pip3] torchaudio==2.1.2+cu118
[pip3] torchmetrics==1.3.1
[pip3] torchvision==0.16.0+cu118
[pip3] triton==2.1.0
[conda] Could not collect

Hi @jojonki, thanks for reporting the bug. It is a known issue in pytorch_lightning (Lightning-AI/pytorch-lightning#5243)

Would you like to open a PR for the fix? Thanks.

Hi @nateanl
Oh, I didn't know that. I will follow the issue. Thanks!

	if torch.isinf(loss) or torch.isnan(loss):
	opt.zero_grad()
	return None

	# normalize the loss based on the sum of num_frame across all GPUs
	num_frames = self.all_gather(num_frame)