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justheuristic 2023-04-10 21:26:52 +03:00 committed by GitHub
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commit 5e456be50e
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1 changed files with 12 additions and 1 deletions
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bitsandbytes/autograd/_functions.py
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@ -221,6 +221,17 @@ bmm_cublas = MatMul8bit.apply
matmul_cublas = MatMul8bit.apply
def supports_igemmlt(device: torch.device) -> bool:
"""check if this device supports the optimized int8 kernel"""
if torch.cuda.get_device_capability(device=device) < (7, 5):
return False
device_name = torch.cuda.get_device_name(device=device)
nvidia16_models = ('GTX 1630', 'GTX 1650', 'GTX 1660') # https://en.wikipedia.org/wiki/GeForce_16_series
if any(model_name in device_name for model_name in nvidia16_models):
return False # these devices are technically cuda 7.5-capable, but they lack tensor cores
return True
@dataclass
class MatmulLtState:
tile_indices: Optional[torch.Tensor] = None
@ -270,7 +281,7 @@ class MatMul8bitLt(torch.autograd.Function):
@staticmethod
def forward(ctx, A, B, out=None, bias=None, state=MatmulLtState):
using_igemmlt = torch.cuda.get_device_capability(device=A.device) >= (7, 5) and not state.force_no_igemmlt
using_igemmlt = supports_igemmlt(A.device) and not state.force_no_igemmlt
# default of pytorch behavior if inputs are empty
ctx.is_empty = False
if prod(A.shape) == 0: