mthrok
commented
1 year ago Hi @zanussbaum
This looks like data-dependent issue. The code path is tested in many places, and I have never seen this. Any chance you can share the data?
Closed zanussbaum closed 1 year ago
mthrok
commented
1 year ago Hi @zanussbaum
This looks like data-dependent issue. The code path is tested in many places, and I have never seen this. Any chance you can share the data?
zanussbaum
commented
1 year ago @mthrok Hm that's weird. I'm able to load the same file in torchaudio when using 1.13 but not on this latest version.
I've tested it by running the example in ImageBind and I am able to get a forward pass to work with 1.13 but not 2.1 for the following code
import data
import torch
from models import imagebind_model
from models.imagebind_model import ModalityType
text_list=["A dog.", "A car", "A bird"]
image_paths=[".assets/dog_image.jpg", ".assets/car_image.jpg", ".assets/bird_image.jpg"]
audio_paths=[".assets/dog_audio.wav", ".assets/car_audio.wav", ".assets/bird_audio.wav"]
device = "cuda:0" if torch.cuda.is_available() else "cpu"
# Instantiate model
model = imagebind_model.imagebind_huge(pretrained=True)
model.eval()
model.to(device)
# Load data
inputs = {
ModalityType.TEXT: data.load_and_transform_text(text_list, device),
ModalityType.VISION: data.load_and_transform_vision_data(image_paths, device),
ModalityType.AUDIO: data.load_and_transform_audio_data(audio_paths, device),
}
with torch.no_grad():
embeddings = model(inputs)
print(
"Vision x Text: ",
torch.softmax(embeddings[ModalityType.VISION] @ embeddings[ModalityType.TEXT].T, dim=-1),
)
print(
"Audio x Text: ",
torch.softmax(embeddings[ModalityType.AUDIO] @ embeddings[ModalityType.TEXT].T, dim=-1),
)
print(
"Vision x Audio: ",
torch.softmax(embeddings[ModalityType.VISION] @ embeddings[ModalityType.AUDIO].T, dim=-1),
)Although I should note that to get this working with torchaudio 1.13, I had to switch machines as the H100s (IIUC) use a different instruction set and require torch 2.0.0 > to use the GPUs effectively. But I was using the same audio file in the repo above
zanussbaum
commented
1 year ago Hm this now seems to work with
Versions of relevant libraries:
[pip3] numpy==1.24.1
[pip3] pytorch-triton==2.1.0+440fd1bf20
[pip3] torch==2.1.0.dev20230709+cu121
[pip3] torchaudio==2.1.0.dev20230709+cu121
[pip3] torchvision==0.16.0.dev20230709+cu121
[conda] Could not collectClosing as this seems to be resolved
philgzl
commented
1 year ago This needs to be reopened. Facing the same issue on multiple systems, even with the versions mentioned above. Here is one my files: 00000_mixture.flac.tar.gz
Both torchaudio.load and torchaudio.info cause the segmentation fault:
import torchaudio
torchaudio.info('00000_mixture.flac') # Segmentation faultI have no problem with earlier nightly versions, e.g. this works fine:
torch==2.1.0.dev20230508+cu121
torchaudio==2.1.0.dev20230508+cu121
torchvision==0.16.0.dev20230508+cu121I did not try and find which nightly version introduced the bug though.
philgzl
commented
1 year ago Mmh nevermind, latest nightly builds works for me. Weird.
🐛 Describe the bug
returns
Segmentation fault (core dumped)Running
gdb --args python -c "import torchaudio; torchaudio.load('.assets/bird_audio.wav')"shows the following stack trackVersions
Here is my
ffmpegversion also as it seems that in #3411 that it needs to be < 5