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Add cuda compatibility check for using grouped_mm #45001

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Add cuda compatibility check for using grouped_mm #45001

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ba83069
Add cuda compatibility check for grouped_mm
Sai-Suraj-27 Mar 25, 2026
b5e7e79
Explicitly check for torch 2.9 too
Sai-Suraj-27 Mar 26, 2026
0f9f91f
make style
Sai-Suraj-27 Mar 26, 2026
b35fcb1
Merge branch 'main' of github.com:huggingface/transformers into add_c…
Sai-Suraj-27 Mar 26, 2026
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Sai-Suraj-27 Mar 26, 2026
f69622a
Merge branch 'main' into add_cc_check
Sai-Suraj-27 Apr 7, 2026
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21 changes: 20 additions & 1 deletion src/transformers/integrations/moe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,12 @@

from ..utils import logging
from ..utils.generic import GeneralInterface
from ..utils.import_utils import is_torch_available, is_torch_less_or_equal, is_torchdynamo_compiling
from ..utils.import_utils import (
is_torch_available,
is_torch_greater_or_equal,
is_torch_less_or_equal,
is_torchdynamo_compiling,
)


if is_torch_available():
Expand Down Expand Up @@ -275,6 +280,20 @@ def _can_use_grouped_mm(input: torch.Tensor, weight: torch.Tensor, offs: torch.T
# issue: https://github.com/pytorch/pytorch/issues/172440
return False

# On CUDA, `grouped_mm` availability also depends on GPU compute capability:
# `torch.nn.functional.grouped_mm` in torch>=2.10 and `torch._grouped_mm` in torch>=2.9 support SM80+
# but older `torch._grouped_mm` requires SM90+.
if weight.device.type == "cuda":
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@vasqu vasqu Mar 26, 2026

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I think we should add a small comment here for clarification

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@Sai-Suraj-27 Sai-Suraj-27 Mar 26, 2026

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Done!

if hasattr(torch.nn.functional, "grouped_mm"):
return torch.cuda.get_device_capability(weight.device) >= (8, 0)
if hasattr(torch, "_grouped_mm"):
if is_torch_greater_or_equal("2.9", accept_dev=True):
return torch.cuda.get_device_capability(weight.device) >= (8, 0)
else:
return torch.cuda.get_device_capability(weight.device) >= (9, 0)

return False

return hasattr(torch.nn.functional, "grouped_mm") or hasattr(torch, "_grouped_mm")


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