When running a minimal model example of the WaveRNN, my google colab session using a T4 (16 GB RAM) GPU is unable complete a forward pass due to a cuDNN error: CUDNN_STATUS_NOT_SUPPORTED. I suspect this is due to the large size of the waveform and specgram. If I set the n_time variable below to anything > 331, the same error occurs. However, if I set n_time to anything < 330, then the error does not occur and the forward pass completes successfully.
The code snippet is provided in this Google colab: Google Colab
Steps to recreate:
1) Create model
import torch
from torch import nn
import torchaudio
from torchaudio.models.wavernn import WaveRNN
device = "cuda" if torch.cuda.is_available() else "cpu"
n_time = 2401 # choosing n_time < 331 then no error, >= 331 then error
kernel_size = 5
hop_length = 200
bits = 8
n_freq = 80
model = WaveRNN(
upsample_scales=[5, 5, 8],
n_classes=2**bits,
hop_length=hop_length,
n_freq=80
)
model = model.to(device)
model.train()
waveform = torch.zeros((1, 1, (n_time - kernel_size + 1)*hop_length))
specgram = torch.zeros((1, 1, n_freq, n_time))
waveform = waveform.to(device)
specgram = specgram.to(device)
print(waveform.device, specgram.device, waveform.shape, specgram.shape)
print(waveform.is_contiguous(), specgram.is_contiguous())
print(f"{torch.cuda.memory_allocated()/1024**2:.3f}")
print(f"{torch.cuda.memory_reserved()/1024**2:.3f}")
Output
cuda:0 cuda:0 torch.Size([1, 1, 479400]) torch.Size([1, 1, 80, 2401])
True True
19.187
44.000
2) Run forward pass
torch.cuda.memory._record_memory_history()
output = model(waveform, specgram)
Output
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-3-86c87abd6ab4> in <cell line: 0>()
1 torch.cuda.memory._record_memory_history()
2
----> 3 output = model(waveform, specgram)
5 frames
/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py in _wrapped_call_impl(self, *args, **kwargs)
1737 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1738 else:
-> 1739 return self._call_impl(*args, **kwargs)
1740
1741 # torchrec tests the code consistency with the following code
/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py in _call_impl(self, *args, **kwargs)
1748 or _global_backward_pre_hooks or _global_backward_hooks
1749 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1750 return forward_call(*args, **kwargs)
1751
1752 result = None
/usr/local/lib/python3.11/dist-packages/torchaudio/models/wavernn.py in forward(self, waveform, specgram)
309 x = self.fc(x)
310 res = x
--> 311 x, _ = self.rnn1(x, h1)
312
313 x = x + res
/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py in _wrapped_call_impl(self, *args, **kwargs)
1737 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1738 else:
-> 1739 return self._call_impl(*args, **kwargs)
1740
1741 # torchrec tests the code consistency with the following code
/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py in _call_impl(self, *args, **kwargs)
1748 or _global_backward_pre_hooks or _global_backward_hooks
1749 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1750 return forward_call(*args, **kwargs)
1751
1752 result = None
/usr/local/lib/python3.11/dist-packages/torch/nn/modules/rnn.py in forward(self, input, hx)
1391 self.check_forward_args(input, hx, batch_sizes)
1392 if batch_sizes is None:
-> 1393 result = _VF.gru(
1394 input,
1395 hx,
RuntimeError: cuDNN error: CUDNN_STATUS_NOT_SUPPORTED. This error may appear if you passed in a non-contiguous input.
3) print allocated memory
torch.cuda.memory._dump_snapshot("cuda_memory.pickle")
print(f"{torch.cuda.memory_allocated()/1024**2:.3f}")
print(f"{torch.cuda.memory_reserved()/1024**2:.3f}")
Output
1771.498
2762.000
With the google collab, you can visualize the memory allocation with the cuda_memory.pickle snapshot via https://pytorch.org/memory_viz
The machine environment information:
!wget https://raw.githubusercontent.com/pytorch/pytorch/main/torch/utils/collect_env.py
!python collect_env.py
Output
Collecting environment information...
PyTorch version: 2.6.0+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: version 3.31.6
Libc version: glibc-2.35
Python version: 3.11.12 (main, Apr 9 2025, 08:55:54) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.1.123+-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.5.82
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: Tesla T4
Nvidia driver version: 550.54.15
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.2.1
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
Address sizes: 46 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 2
On-line CPU(s) list: 0,1
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) CPU @ 2.00GHz
CPU family: 6
Model: 85
Thread(s) per core: 2
Core(s) per socket: 1
Socket(s): 1
Stepping: 3
BogoMIPS: 4000.28
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat md_clear arch_capabilities
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 32 KiB (1 instance)
L1i cache: 32 KiB (1 instance)
L2 cache: 1 MiB (1 instance)
L3 cache: 38.5 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0,1
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Mitigation; PTE Inversion
Vulnerability Mds: Vulnerable; SMT Host state unknown
Vulnerability Meltdown: Vulnerable
Vulnerability Mmio stale data: Vulnerable
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Vulnerable
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1: Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers
Vulnerability Spectre v2: Vulnerable; IBPB: disabled; STIBP: disabled; PBRSB-eIBRS: Not affected; BHI: Vulnerable
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Vulnerable
Versions of relevant libraries:
[pip3] numpy==2.0.2
[pip3] nvidia-cublas-cu12==12.5.3.2
[pip3] nvidia-cuda-cupti-cu12==12.5.82
[pip3] nvidia-cuda-nvrtc-cu12==12.5.82
[pip3] nvidia-cuda-runtime-cu12==12.5.82
[pip3] nvidia-cudnn-cu12==9.3.0.75
[pip3] nvidia-cufft-cu12==11.2.3.61
[pip3] nvidia-curand-cu12==10.3.6.82
[pip3] nvidia-cusolver-cu12==11.6.3.83
[pip3] nvidia-cusparse-cu12==12.5.1.3
[pip3] nvidia-cusparselt-cu12==0.6.2
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.5.82
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] nvtx==0.2.11
[pip3] optree==0.15.0
[pip3] pynvjitlink-cu12==0.5.2
[pip3] torch==2.6.0+cu124
[pip3] torchaudio==2.6.0+cu124
[pip3] torchsummary==1.5.1
[pip3] torchvision==0.21.0+cu124
[pip3] triton==3.2.0
[conda] Could not collect