Skip to content

remove check for better performance #3

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
xiaolil1 merged 1 commit into from
Jun 17, 2025
Merged

remove check for better performance #3

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
28 changes: 6 additions & 22 deletions bitsandbytes/backends/xpu/ops.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -57,12 +57,12 @@ def _dequantize_4bit_impl(
def _dequantize_blockwise_impl(
A: torch.Tensor, absmax: torch.Tensor, code: torch.Tensor, blocksize: int, dtype: torch.dtype, out: torch.Tensor
) -> None:
torch._check(blocksize in [4096, 2048, 1024, 512, 256, 128, 64])
torch._check(A.dtype == torch.uint8, lambda: f"A must be uint8, got {A.dtype}")
torch._check(
dtype in [torch.float16, torch.bfloat16, torch.float32],
lambda: f"Blockwise dequantization only supports 16bit/32bit floating types, got {dtype}",
)
# torch._check(blocksize in [4096, 2048, 1024, 512, 256, 128, 64])
# torch._check(A.dtype == torch.uint8, lambda: f"A must be uint8, got {A.dtype}")
# torch._check(
# dtype in [torch.float16, torch.bfloat16, torch.float32],
# lambda: f"Blockwise dequantization only supports 16bit/32bit floating types, got {dtype}",
# )

args = (
get_ptr(code),
Expand Down Expand Up @@ -90,22 +90,6 @@ def _gemv_4bit_impl(
blocksize: int,
out: torch.Tensor,
) -> None:
torch._check_is_size(blocksize)
torch._check(
A.numel() == A.size(-1),
lambda: f"A must be a vector with leading dimensions of 1, got {A.shape}",
)
torch._check(
A.dtype in [torch.float16, torch.bfloat16, torch.float32],
lambda: f"A must be float16, bfloat16, or float32, got {A.dtype}",
)
torch._check(
B.dtype in [torch.uint8, torch.bfloat16, torch.float16, torch.float32],
lambda: f"B must be backed by storage of type uint8, bfloat16, float16, or float32, got {B.dtype}",
)
torch._check(absmax.dtype == torch.float32, lambda: f"absmax must be float32, got {absmax.dtype}")
torch._check(code.dtype == torch.float32, lambda: f"code must be float32, got {code.dtype}")

m = ct.c_int32(shapeB[0])
n = ct.c_int32(1)
k = ct.c_int32(shapeB[1])
Expand Down