RuntimeError: No such operator fbgemm::jagged_2d_to_dense
Open
Nobody has claimed this yet.
- Dominant language
- C++
- Stars
- 1.6k
- Forks
- 787
- PR merge metrics
- No merged PRs in 30d
Description
Hi, I tried to run torchrec_dlrm on torchbench base on Intel GPU and got this error:
Traceback (most recent call last):
File "/home/gta/miniconda3/envs/yongliang/lib/python3.11/site-packages/torch/_ops.py", line 757, in __getattr__
op, overload_names = torch._C._jit_get_operation(qualified_op_name)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: No such operator fbgemm::jagged_2d_to_dense
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/gta/miniconda3/envs/yongliang/lib/python3.11/site-packages/fbgemm_gpu/__init__.py", line 22, in <module>
from . import _fbgemm_gpu_docs # noqa: F401, E402
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/gta/miniconda3/envs/yongliang/lib/python3.11/site-packages/fbgemm_gpu/_fbgemm_gpu_docs.py", line 19, in <module>
torch.ops.fbgemm.jagged_2d_to_dense,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/gta/miniconda3/envs/yongliang/lib/python3.11/site-packages/torch/_ops.py", line 761, in __getattr__
raise AttributeError(
AttributeError: '_OpNamespace' 'fbgemm' object has no attribute 'jagged_2d_to_dense'
I tried to re-install fbgemm-gpu as the document recommended but it couldn't help.
Is this issue expected on Intel device?
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reproducing the torchrec_dlrm run on the Intel GPU, then inspect fbgemm_gpu/init.py and _fbgemm_gpu_docs.py around torch.ops.fbgemm.jagged_2d_to_dense. Compare the installed operator registration with the failing stack trace and establish whether Intel devices are supported. Done means identifying the compatibility cause and documenting or fixing the supported behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- cpp, python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100