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[BUG] V2.2 only detect one XLA device with CUDA12.1 #6771

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Mon-ius opened this issue Mar 19, 2024 · 10 comments
Open

[BUG] V2.2 only detect one XLA device with CUDA12.1 #6771

Mon-ius opened this issue Mar 19, 2024 · 10 comments

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@Mon-ius
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Mon-ius commented Mar 19, 2024

Environment setup

conda create -n xla python=3.10 cuda -c nvidia -y
conda activate xla
pip install torch torchvision torchaudio
pip install https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.2.0-cp310-cp310-manylinux_2_28_x86_64.whl

conda env config vars set LD_LIBRARY_PATH="$CONDA_PREFIX/lib"
conda env config vars set HF_HOME="/dev/shm"
conda env config vars set PJRT_DEVICE=CUDA
conda deactivate && sleep 5
conda activate xla

python -c "import torch; print(torch.__version__, torch.version.cuda);"

echo 'import torch
import torch_xla.core.xla_model as xm
import torch_xla.runtime as xr
num_devices = xr.global_runtime_device_count()
print(num_devices)

dev = xm.xla_device()
t1 = torch.randn(3,3,device=dev)
t2 = torch.randn(3,3,device=dev)
print(t1 + t2)' | tee /tmp/run.py

PJRT_DEVICE=CUDA GPU_NUM_DEVICES=4 python /tmp/run.py

Torch

❯ python -c "import torch; print(torch.__version__, torch.version.cuda);"                                                                 (xla)
2.2.1+cu121 12.1

XLA

❯ python -c "import torch_xla; print(torch_xla.__version__);"                                                                             (xla)
2.2.0

CUDA XLA

❯ PJRT_DEVICE=CUDA GPU_NUM_DEVICES=4 python /tmp/run.py                                                                                   (xla)
1
tensor([[-0.2121,  1.5588, -0.6952],
        [-0.7885, -0.2022,  0.9242],
        [ 0.8555, -1.8699,  1.4333]], device='xla:0')
@Mon-ius Mon-ius changed the title [BUG] V2.2 only detect one XLA device [BUG] V2.2 only detect one XLA device with CUDA12.1 Mar 19, 2024
@JackCaoG
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@vanbasten23 can you take a look since it is GPU?

@vanbasten23
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This is an issue we have fixed after v2.2. You can use our nightly wheel to see the update.

@Mon-ius
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Mon-ius commented Mar 20, 2024

Hi @vanbasten23

I only find the CUDA version, and use:

pip install https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.2.0-cp310-cp310-manylinux_2_28_x86_64.whl

Can u indicate where is the nightly wheel?

Also, I am looking for the Py11 version just like TPU has. So I can align the same dev env 🤗

@Mon-ius
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Mon-ius commented Mar 20, 2024

Seems no cp11 build for CUDA so far, can u add this build arg to the CI?

wheels/cuda/11.2/torch-1.12-cp37-cp37m-linux_x86_64.whl
wheels/cuda/11.2/torch-1.13-cp37-cp37m-linux_x86_64.whl
wheels/cuda/11.2/torch-1.13-cp38-cp38-linux_x86_64.whl
wheels/cuda/11.2/torch_xla-1.12-cp37-cp37m-linux_x86_64.whl
wheels/cuda/11.2/torch_xla-1.13-cp37-cp37m-linux_x86_64.whl
wheels/cuda/11.2/torch_xla-1.13-cp38-cp38-linux_x86_64.whl
wheels/cuda/11.7/torch-2.0-cp38-cp38-linux_x86_64.whl
wheels/cuda/11.7/torch-nightly+20230404-cp38-cp38-linux_x86_64.whl
wheels/cuda/11.7/torch-nightly+20230406-cp38-cp38-linux_x86_64.whl
wheels/cuda/11.7/torch-nightly+20230407-cp38-cp38-linux_x86_64.whl
wheels/cuda/11.7/torch-nightly+20230408-cp38-cp38-linux_x86_64.whl
wheels/cuda/11.7/torch-nightly+20230409-cp38-cp38-linux_x86_64.whl
wheels/cuda/11.7/torch-nightly+20230410-cp38-cp38-linux_x86_64.whl
wheels/cuda/11.7/torch-nightly+20230411-cp38-cp38-linux_x86_64.whl
wheels/cuda/11.7/torch-nightly+20230412-cp38-cp38-linux_x86_64.whl
wheels/cuda/11.7/torch-nightly+20230413-cp38-cp38-linux_x86_64.whl
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wheels/cuda/12.1/torch_xla-nightly+20231014-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231015-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231016-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231017-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231018-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231019-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231020-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231021-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231022-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231023-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231024-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231025-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231026-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231027-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231028-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231029-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231030-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231031-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231101-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231102-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231103-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231104-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231105-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231106-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231107-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231108-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231109-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231110-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231111-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231112-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231113-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231114-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231115-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231116-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231117-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231118-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231119-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231120-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231128-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231129-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231130-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231201-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231202-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231203-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231204-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231205-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231206-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231207-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231208-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231209-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231210-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231211-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231212-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231213-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231214-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231215-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231216-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231217-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231218-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231219-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231220-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231221-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231222-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231223-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231224-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231225-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231226-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231227-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231228-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231229-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231230-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20231231-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240101-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240102-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240103-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240104-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240105-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240106-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240107-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240108-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240109-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240110-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240111-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240112-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240113-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240114-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240115-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240116-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240117-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240118-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240119-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240120-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240121-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240122-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240123-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240124-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240125-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240126-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240127-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240128-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240129-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240130-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240131-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240201-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240202-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240203-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240204-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240205-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240206-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240207-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240208-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240209-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240210-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240211-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240212-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240213-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240214-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240215-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240216-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240217-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240218-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240219-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240220-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240221-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240222-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240223-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240224-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240225-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240226-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240227-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240228-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240229-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240301-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240302-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240303-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240304-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240305-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240306-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240307-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240308-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240309-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240310-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240311-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240312-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240313-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240314-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240315-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240316-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240317-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240318-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240319-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torch_xla_cuda_plugin-0.0.1-py3-none-any.whl
wheels/cuda/12.1/torch_xla_cuda_plugin-2.3.0-py3-none-any.whl
wheels/cuda/12.1/torchvision-0.18.0a0+2bababf-cp38-cp38-linux_x86_64.whl
wheels/cuda/12.1/torchvision-0.18.0a0+325175-cp38-cp38-linux_x86_64.whl

@vanbasten23 vanbasten23 mentioned this issue Mar 21, 2024
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@vanbasten23
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sure, let me create a cp311 cuda 12.1 nightly wheel for you

@Mon-ius
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Mon-ius commented Mar 22, 2024

@vanbasten23 thx. But there seems no such build yet when I search the official release bucket.

from google.cloud import storage

client = storage.Client()
bucket_name = 'pytorch-xla-releases'
directory_name = 'wheels/cuda'
blobs = client.list_blobs(bucket_name, prefix=directory_name)
for blob in blobs:
    print(blob.name)

Would u plz indicate the location of the wheel?

@vanbasten23
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vanbasten23 commented Mar 22, 2024 •
edited
Loading

I created a PR #6793 to create the wheel. The new wheel has not been generated yet.

@Mon-ius
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Mon-ius commented Mar 24, 2024

@vanbasten23 Nice, I just saw these available,

wheels/cuda/12.1/torch-nightly+20240322-cp311-cp311-linux_x86_64.whl
wheels/cuda/12.1/torch-nightly+20240323-cp311-cp311-linux_x86_64.whl
wheels/cuda/12.1/torch-nightly+20240324-cp311-cp311-linux_x86_64.whl
wheels/cuda/12.1/torch-nightly-cp311-cp311-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240322-cp311-cp311-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240323-cp311-cp311-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly+20240324-cp311-cp311-linux_x86_64.whl
wheels/cuda/12.1/torch_xla-nightly-cp311-cp311-linux_x86_64.whl
wheels/cuda/12.1/torchvision-0.19.0a0+480eec2-cp311-cp311-linux_x86_64.whl
wheels/cuda/12.1/torchvision-0.19.0a0+a0c79b3-cp311-cp311-linux_x86_64.whl

@Mon-ius
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Mon-ius commented Mar 24, 2024 •
edited
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@vanbasten23 Just tested with my early script that works with v2.2 in python3.10 on the new release.

conda create -n x3 python=3.11 cuda -c nvidia -y
conda activate x3

conda env config vars set LD_LIBRARY_PATH="$CONDA_PREFIX/lib"
conda env config vars set HF_HOME=/dev/shm
conda env config vars set PJRT_DEVICE=CUDA
conda env config vars set GPU_NUM_DEVICES=4

conda deactivate
sleep 5
conda activate x3

pip install https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch-nightly-cp311-cp311-linux_x86_64.whl
pip install https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torchvision-0.19.0a0+480eec2-cp311-cp311-linux_x86_64.whl
pip install https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-nightly-cp311-cp311-linux_x86_64.whl

python -c "import torch; print(torch.__version__, torch.version.cuda);"
python -c "import torch_xla; print(torch_xla.__version__);"
python -c "import torch; import torch_xla.core.xla_model as xm;"

echo 'import torch
import torch_xla.core.xla_model as xm

devices = xm.get_xla_supported_devices()
for device in devices:
    print(f"- {device}")

dev = devices[0]
t1 = torch.randn(3,3,device=dev)
t2 = torch.randn(3,3,device=dev)
print(t1 + t2)' | tee /tmp/run.py

PJRT_DEVICE=CUDA GPU_NUM_DEVICES=4 python /tmp/run.py

It returns many errors, that

2.3.0 None
2.3.0+git84e7feb
import torch
import torch_xla.core.xla_model as xm

devices = xm.get_xla_supported_devices()
for device in devices:
    print(f"- {device}")

dev = devices[0]
t1 = torch.randn(3,3,device=dev)
t2 = torch.randn(3,3,device=dev)
print(t1 + t2)
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1711301801.333061  980545 service.cc:145] XLA service 0x74c9240 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1711301801.333138  980545 service.cc:153]   StreamExecutor device (0): NVIDIA GeForce RTX 3090, Compute Capability 8.6
I0000 00:00:1711301801.333150  980545 service.cc:153]   StreamExecutor device (1): NVIDIA GeForce RTX 3090, Compute Capability 8.6
I0000 00:00:1711301801.333159  980545 service.cc:153]   StreamExecutor device (2): NVIDIA GeForce RTX 3090, Compute Capability 8.6
I0000 00:00:1711301801.333166  980545 service.cc:153]   StreamExecutor device (3): NVIDIA GeForce RTX 3090, Compute Capability 8.6
I0000 00:00:1711301801.335741  980545 se_gpu_pjrt_client.cc:853] Using BFC allocator.
I0000 00:00:1711301801.335846  980545 gpu_helpers.cc:107] XLA backend allocating 19078594560 bytes on device 0 for BFCAllocator.
I0000 00:00:1711301801.335925  980545 gpu_helpers.cc:107] XLA backend allocating 19076825088 bytes on device 1 for BFCAllocator.
I0000 00:00:1711301801.335982  980545 gpu_helpers.cc:107] XLA backend allocating 19078594560 bytes on device 2 for BFCAllocator.
I0000 00:00:1711301801.336060  980545 gpu_helpers.cc:107] XLA backend allocating 19078594560 bytes on device 3 for BFCAllocator.
I0000 00:00:1711301801.336104  980545 gpu_helpers.cc:147] XLA backend will use up to 6359531520 bytes on device 0 for CollectiveBFCAllocator.
I0000 00:00:1711301801.336167  980545 gpu_helpers.cc:147] XLA backend will use up to 6358941696 bytes on device 1 for CollectiveBFCAllocator.
I0000 00:00:1711301801.336209  980545 gpu_helpers.cc:147] XLA backend will use up to 6359531520 bytes on device 2 for CollectiveBFCAllocator.
I0000 00:00:1711301801.336258  980545 gpu_helpers.cc:147] XLA backend will use up to 6359531520 bytes on device 3 for CollectiveBFCAllocator.
- xla:0
- xla:1
- xla:2
- xla:3
2024-03-25 01:36:41.732772: E external/xla/xla/stream_executor/cuda/cuda_dnn.cc:536] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
2024-03-25 01:36:41.732843: E external/xla/xla/stream_executor/cuda/cuda_dnn.cc:540] Memory usage: 6073221120 bytes free, 25438126080 bytes total.
2024-03-25 01:36:41.742476: E external/xla/xla/stream_executor/cuda/cuda_dnn.cc:536] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
2024-03-25 01:36:41.742528: E external/xla/xla/stream_executor/cuda/cuda_dnn.cc:540] Memory usage: 6073221120 bytes free, 25438126080 bytes total.
Traceback (most recent call last):
  File "/tmp/run.py", line 11, in <module>
    print(t1 + t2)
  File "/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torch/_tensor.py", line 464, in __repr__
    return torch._tensor_str._str(self, tensor_contents=tensor_contents)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torch/_tensor_str.py", line 697, in _str
    return _str_intern(self, tensor_contents=tensor_contents)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torch/_tensor_str.py", line 432, in _str_intern
    self = self.to("cpu")
           ^^^^^^^^^^^^^^
RuntimeError: Bad StatusOr access: FAILED_PRECONDITION: DNN library initialization failed. Look at the errors above for more details.
Cloning into '/tmp/xla'...
remote: Enumerating objects: 836, done.
remote: Counting objects: 100% (836/836), done.
remote: Compressing objects: 100% (660/660), done.
remote: Total 836 (delta 192), reused 337 (delta 149), pack-reused 0
Receiving objects: 100% (836/836), 6.18 MiB | 1.07 MiB/s, done.
Resolving deltas: 100% (192/192), done.
/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: 'libjpeg.so.62: cannot open shared object file: No such file or directory'If you don't plan on using image functionality from `torchvision.io`, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have `libjpeg` or `libpng` installed before building `torchvision` from source?
  warn(
/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: 'libjpeg.so.62: cannot open shared object file: No such file or directory'If you don't plan on using image functionality from `torchvision.io`, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have `libjpeg` or `libpng` installed before building `torchvision` from source?
  warn(
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1711301817.009561  981149 coordination_service.cc:365] Initializing CoordinationService
I0000 00:00:1711301817.012505  981992 coordination_service.cc:579] /job:jax_worker/replica:0/task:0 has connected to coordination service. Incarnation: 59903888089181655
I0000 00:00:1711301817.012560  981992 coordination_service.cc:541] Waiting for 3/4 tasks to connect.
I0000 00:00:1711301817.012581  981992 coordination_service.cc:544] Example stragglers:
/job:jax_worker/replica:0/task:1
/job:jax_worker/replica:0/task:2
/job:jax_worker/replica:0/task:3
I0000 00:00:1711301817.014741  981149 coordination_service_agent.cc:303] Coordination agent has successfully connected.
/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: 'libjpeg.so.62: cannot open shared object file: No such file or directory'If you don't plan on using image functionality from `torchvision.io`, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have `libjpeg` or `libpng` installed before building `torchvision` from source?
  warn(
/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: 'libjpeg.so.62: cannot open shared object file: No such file or directory'If you don't plan on using image functionality from `torchvision.io`, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have `libjpeg` or `libpng` installed before building `torchvision` from source?
  warn(
/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: 'libjpeg.so.62: cannot open shared object file: No such file or directory'If you don't plan on using image functionality from `torchvision.io`, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have `libjpeg` or `libpng` installed before building `torchvision` from source?
  warn(
I0000 00:00:1711301817.105569  981992 coordination_service.cc:579] /job:jax_worker/replica:0/task:1 has connected to coordination service. Incarnation: 4703672692789624982
I0000 00:00:1711301817.105641  981992 coordination_service.cc:541] Waiting for 2/4 tasks to connect.
I0000 00:00:1711301817.105654  981992 coordination_service.cc:544] Example stragglers:
/job:jax_worker/replica:0/task:2
/job:jax_worker/replica:0/task:3
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1711301817.107504  981151 coordination_service_agent.cc:303] Coordination agent has successfully connected.
I0000 00:00:1711301817.148883  981992 coordination_service.cc:579] /job:jax_worker/replica:0/task:2 has connected to coordination service. Incarnation: 12612764239641476566
I0000 00:00:1711301817.148925  981992 coordination_service.cc:541] Waiting for 1/4 tasks to connect.
I0000 00:00:1711301817.148937  981992 coordination_service.cc:544] Example stragglers:
/job:jax_worker/replica:0/task:3
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1711301817.150797  981150 coordination_service_agent.cc:303] Coordination agent has successfully connected.
I0000 00:00:1711301817.182356  981992 coordination_service.cc:579] /job:jax_worker/replica:0/task:3 has connected to coordination service. Incarnation: 11699435496465749719
I0000 00:00:1711301817.182396  981992 coordination_service.cc:541] Waiting for 0/4 tasks to connect.
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1711301817.184309  981146 coordination_service_agent.cc:303] Coordination agent has successfully connected.
I0000 00:00:1711301817.509779  981146 service.cc:145] XLA service 0x7eb7350 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1711301817.509841  981146 service.cc:153]   StreamExecutor device (0): NVIDIA GeForce RTX 3090, Compute Capability 8.6
I0000 00:00:1711301817.510264  981150 service.cc:145] XLA service 0x71b63a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1711301817.510321  981150 service.cc:153]   StreamExecutor device (0): NVIDIA GeForce RTX 3090, Compute Capability 8.6
I0000 00:00:1711301817.510504  981146 se_gpu_pjrt_client.cc:853] Using BFC allocator.
I0000 00:00:1711301817.510616  981146 gpu_helpers.cc:107] XLA backend allocating 19078594560 bytes on device 3 for BFCAllocator.
I0000 00:00:1711301817.510698  981146 gpu_helpers.cc:147] XLA backend will use up to 6359531520 bytes on device 3 for CollectiveBFCAllocator.
I0000 00:00:1711301817.510978  981150 se_gpu_pjrt_client.cc:853] Using BFC allocator.
I0000 00:00:1711301817.511093  981150 gpu_helpers.cc:107] XLA backend allocating 19078594560 bytes on device 2 for BFCAllocator.
I0000 00:00:1711301817.515231  981150 gpu_helpers.cc:147] XLA backend will use up to 6359531520 bytes on device 2 for CollectiveBFCAllocator.
I0000 00:00:1711301817.516087  981151 service.cc:145] XLA service 0x732c3a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1711301817.516151  981151 service.cc:153]   StreamExecutor device (0): NVIDIA GeForce RTX 3090, Compute Capability 8.6
I0000 00:00:1711301817.516948  981151 se_gpu_pjrt_client.cc:853] Using BFC allocator.
I0000 00:00:1711301817.517044  981151 gpu_helpers.cc:107] XLA backend allocating 19076825088 bytes on device 1 for BFCAllocator.
I0000 00:00:1711301817.517113  981151 gpu_helpers.cc:147] XLA backend will use up to 6358941696 bytes on device 1 for CollectiveBFCAllocator.
I0000 00:00:1711301817.546057  981149 service.cc:145] XLA service 0x7ab31b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1711301817.546114  981149 service.cc:153]   StreamExecutor device (0): NVIDIA GeForce RTX 3090, Compute Capability 8.6
I0000 00:00:1711301817.546841  981149 se_gpu_pjrt_client.cc:853] Using BFC allocator.
I0000 00:00:1711301817.546921  981149 gpu_helpers.cc:107] XLA backend allocating 19078594560 bytes on device 0 for BFCAllocator.
I0000 00:00:1711301817.546982  981149 gpu_helpers.cc:147] XLA backend will use up to 6359531520 bytes on device 0 for CollectiveBFCAllocator.
/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torch_xla/core/xla_model.py:105: UserWarning: `devkind` argument is deprecated and will be removed in a future release.
  warnings.warn("`devkind` argument is deprecated and will be removed in a "
/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torch/__init__.py:747: UserWarning: torch.set_default_tensor_type() is deprecated as of PyTorch 2.1, please use torch.set_default_dtype() and torch.set_default_device() as alternatives. (Triggered internally at /src/pytorch/torch/csrc/tensor/python_tensor.cpp:431.)
  _C._set_default_tensor_type(t)
==> Preparing data..
/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torch_xla/core/xla_model.py:105: UserWarning: `devkind` argument is deprecated and will be removed in a future release.
  warnings.warn("`devkind` argument is deprecated and will be removed in a "
/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torch/__init__.py:747: UserWarning: torch.set_default_tensor_type() is deprecated as of PyTorch 2.1, please use torch.set_default_dtype() and torch.set_default_device() as alternatives. (Triggered internally at /src/pytorch/torch/csrc/tensor/python_tensor.cpp:431.)
  _C._set_default_tensor_type(t)
==> Preparing data..
/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torch_xla/core/xla_model.py:105: UserWarning: `devkind` argument is deprecated and will be removed in a future release.
  warnings.warn("`devkind` argument is deprecated and will be removed in a "
/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torch_xla/core/xla_model.py:105: UserWarning: `devkind` argument is deprecated and will be removed in a future release.
  warnings.warn("`devkind` argument is deprecated and will be removed in a "
/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torch/__init__.py:747: UserWarning: torch.set_default_tensor_type() is deprecated as of PyTorch 2.1, please use torch.set_default_dtype() and torch.set_default_device() as alternatives. (Triggered internally at /src/pytorch/torch/csrc/tensor/python_tensor.cpp:431.)
  _C._set_default_tensor_type(t)
/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torch/__init__.py:747: UserWarning: torch.set_default_tensor_type() is deprecated as of PyTorch 2.1, please use torch.set_default_dtype() and torch.set_default_device() as alternatives. (Triggered internally at /src/pytorch/torch/csrc/tensor/python_tensor.cpp:431.)
  _C._set_default_tensor_type(t)
==> Preparing data..
==> Preparing data..
2024-03-25 01:37:01.474342: E external/xla/xla/stream_executor/cuda/cuda_dnn.cc:536] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
2024-03-25 01:37:01.474569: E external/xla/xla/stream_executor/cuda/cuda_dnn.cc:540] Memory usage: 6073221120 bytes free, 25438126080 bytes total.
2024-03-25 01:37:01.509990: E external/xla/xla/stream_executor/cuda/cuda_dnn.cc:536] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
2024-03-25 01:37:01.510185: E external/xla/xla/stream_executor/cuda/cuda_dnn.cc:540] Memory usage: 6073221120 bytes free, 25438126080 bytes total.
2024-03-25 01:37:01.699300: E external/xla/xla/stream_executor/cuda/cuda_dnn.cc:536] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
2024-03-25 01:37:01.699710: E external/xla/xla/stream_executor/cuda/cuda_dnn.cc:540] Memory usage: 6015025152 bytes free, 25435766784 bytes total.
2024-03-25 01:37:01.722541: E external/xla/xla/stream_executor/cuda/cuda_dnn.cc:536] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
2024-03-25 01:37:01.722824: E external/xla/xla/stream_executor/cuda/cuda_dnn.cc:540] Memory usage: 6015025152 bytes free, 25435766784 bytes total.
2024-03-25 01:37:01.829707: E external/xla/xla/stream_executor/cuda/cuda_dnn.cc:536] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
2024-03-25 01:37:01.829798: E external/xla/xla/stream_executor/cuda/cuda_dnn.cc:540] Memory usage: 6073221120 bytes free, 25438126080 bytes total.
2024-03-25 01:37:01.847263: E external/xla/xla/stream_executor/cuda/cuda_dnn.cc:536] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
2024-03-25 01:37:01.847327: E external/xla/xla/stream_executor/cuda/cuda_dnn.cc:540] Memory usage: 6073221120 bytes free, 25438126080 bytes total.
2024-03-25 01:37:01.877594: E external/xla/xla/stream_executor/cuda/cuda_dnn.cc:536] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
2024-03-25 01:37:01.877671: E external/xla/xla/stream_executor/cuda/cuda_dnn.cc:540] Memory usage: 6073221120 bytes free, 25438126080 bytes total.
2024-03-25 01:37:01.894749: E external/xla/xla/stream_executor/cuda/cuda_dnn.cc:536] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
2024-03-25 01:37:01.894809: E external/xla/xla/stream_executor/cuda/cuda_dnn.cc:540] Memory usage: 6073221120 bytes free, 25438126080 bytes total.
I0000 00:00:1711301822.462938  981150 coordination_service_agent.cc:472] Coordination agent has initiated Shutdown().
I0000 00:00:1711301822.477856  981146 coordination_service_agent.cc:472] Coordination agent has initiated Shutdown().
I0000 00:00:1711301822.485911  981151 coordination_service_agent.cc:472] Coordination agent has initiated Shutdown().
I0000 00:00:1711301822.556361  981149 coordination_service_agent.cc:472] Coordination agent has initiated Shutdown().
I0000 00:00:1711301822.557900  981991 coordination_service.cc:1195] Shutdown barrier in coordination service has passed.
I0000 00:00:1711301822.558010  981991 coordination_service.cc:686] /job:jax_worker/replica:0/task:3 has disconnected from coordination service.
I0000 00:00:1711301822.558024  981991 coordination_service.cc:686] /job:jax_worker/replica:0/task:0 has disconnected from coordination service.
I0000 00:00:1711301822.558037  981991 coordination_service.cc:686] /job:jax_worker/replica:0/task:2 has disconnected from coordination service.
I0000 00:00:1711301822.558047  981991 coordination_service.cc:686] /job:jax_worker/replica:0/task:1 has disconnected from coordination service.
I0000 00:00:1711301822.558357  981150 coordination_service_agent.cc:491] Coordination agent has successfully shut down.
I0000 00:00:1711301822.558396  981149 coordination_service_agent.cc:491] Coordination agent has successfully shut down.
I0000 00:00:1711301822.558452  981151 coordination_service_agent.cc:491] Coordination agent has successfully shut down.
I0000 00:00:1711301822.558472  981146 coordination_service_agent.cc:491] Coordination agent has successfully shut down.
concurrent.futures.process._RemoteTraceback:
"""
Traceback (most recent call last):
  File "/data/.dev/conda/envs/x3/lib/python3.11/concurrent/futures/process.py", line 261, in _process_worker
    r = call_item.fn(*call_item.args, **call_item.kwargs)
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/data/.dev/conda/envs/x3/lib/python3.11/concurrent/futures/process.py", line 210, in _process_chunk
    return [fn(*args) for args in chunk]
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/data/.dev/conda/envs/x3/lib/python3.11/concurrent/futures/process.py", line 210, in <listcomp>
    return [fn(*args) for args in chunk]
            ^^^^^^^^^
  File "/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torch_xla/runtime.py", line 95, in wrapper
    return fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torch_xla/_internal/pjrt.py", line 78, in _run_thread_per_device
    replica_results = list(
                      ^^^^^
  File "/data/.dev/conda/envs/x3/lib/python3.11/concurrent/futures/_base.py", line 619, in result_iterator
    yield _result_or_cancel(fs.pop())
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/data/.dev/conda/envs/x3/lib/python3.11/concurrent/futures/_base.py", line 317, in _result_or_cancel
    return fut.result(timeout)
           ^^^^^^^^^^^^^^^^^^^
  File "/data/.dev/conda/envs/x3/lib/python3.11/concurrent/futures/_base.py", line 456, in result
    return self.__get_result()
           ^^^^^^^^^^^^^^^^^^^
  File "/data/.dev/conda/envs/x3/lib/python3.11/concurrent/futures/_base.py", line 401, in __get_result
    raise self._exception
  File "/data/.dev/conda/envs/x3/lib/python3.11/concurrent/futures/thread.py", line 58, in run
    result = self.fn(*self.args, **self.kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torch_xla/_internal/pjrt.py", line 71, in _thread_fn
    return fn()
           ^^^^
  File "/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torch_xla/_internal/pjrt.py", line 190, in __call__
    self.fn(runtime.global_ordinal(), *self.args, **self.kwargs)
  File "/tmp/xla/test/test_train_mp_imagenet.py", line 369, in _mp_fn
    accuracy = train_imagenet()
               ^^^^^^^^^^^^^^^^
  File "/tmp/xla/test/test_train_mp_imagenet.py", line 265, in train_imagenet
    xm.broadcast_master_param(model)
  File "/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torch_xla/core/xla_model.py", line 1468, in broadcast_master_param
    mark_step()
  File "/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torch_xla/core/xla_model.py", line 1056, in mark_step
    torch_xla._XLAC._xla_step_marker(
RuntimeError: Bad StatusOr access: FAILED_PRECONDITION: DNN library initialization failed. Look at the errors above for more details.
"""

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/tmp/xla/test/test_train_mp_imagenet.py", line 377, in <module>
    xmp.spawn(_mp_fn, args=(FLAGS,), nprocs=FLAGS.num_cores)
  File "/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torch_xla/runtime.py", line 95, in wrapper
    return fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 38, in spawn
    return pjrt.spawn(fn, nprocs, start_method, args)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torch_xla/_internal/pjrt.py", line 214, in spawn
    run_multiprocess(spawn_fn, start_method=start_method)
  File "/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torch_xla/runtime.py", line 95, in wrapper
    return fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torch_xla/_internal/pjrt.py", line 174, in run_multiprocess
    replica_results = list(
                      ^^^^^
  File "/data/.dev/conda/envs/x3/lib/python3.11/site-packages/torch_xla/_internal/pjrt.py", line 175, in <genexpr>
    itertools.chain.from_iterable(
                                 ^
  File "/data/.dev/conda/envs/x3/lib/python3.11/concurrent/futures/process.py", line 620, in _chain_from_iterable_of_lists
    for element in iterable:
  File "/data/.dev/conda/envs/x3/lib/python3.11/concurrent/futures/_base.py", line 619, in result_iterator
    yield _result_or_cancel(fs.pop())
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/data/.dev/conda/envs/x3/lib/python3.11/concurrent/futures/_base.py", line 317, in _result_or_cancel
    return fut.result(timeout)
           ^^^^^^^^^^^^^^^^^^^
  File "/data/.dev/conda/envs/x3/lib/python3.11/concurrent/futures/_base.py", line 456, in result
    return self.__get_result()
           ^^^^^^^^^^^^^^^^^^^
  File "/data/.dev/conda/envs/x3/lib/python3.11/concurrent/futures/_base.py", line 401, in __get_result
    raise self._exception
RuntimeError: Bad StatusOr access: FAILED_PRECONDITION: DNN library initialization failed. Look at the errors above for more details.

As far as I known, there are still many works to do with v2.3, so could u plz build the stable version v2.2.0 with py11 as well for further test on cuda device?

@vanbasten23
Copy link
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Hi @Mon-ius , we are releasing pytorch/xla v2.3 in a month so it's unlikely the team will have the bandwidth to build a v2.2 with py11.

I tried your script on the nightly and I don't see the error. Could you use the nightly wheel or wait for v2.3 which will be out in a month?

Also, the GPU_NUM_DEVICES flag is only used for multi-processing script. For single-processing, it'll be moot.

Moreover, when installing torch and torchvision, I suggest to use the wheel from official pytorch website such as pip install torch torchvision https://pytorch.org/get-started/locally/, as also recommended in torch_xla index page.

@Mon-ius Mon-ius mentioned this issue May 23, 2024
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@vanbasten23 @Mon-ius @JackCaoG