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fix triton kernel with the correct device #1691

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Merged
matthewdouglas merged 2 commits into from
Jul 1, 2025
Merged

fix triton kernel with the correct device #1691

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6eed2b0
fix triton kernel on the correct device
jiqing-feng Jul 1, 2025
b1d5593
Merge branch 'main' into triton
jiqing-feng Jul 1, 2025
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67 changes: 39 additions & 28 deletions bitsandbytes/backends/triton/ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,8 @@
# from bitsandbytes.functional import get_4bit_type
# _FP4_QUANT_TABLE = get_4bit_type("fp4", device="xpu")
# _NF4_QUANT_TABLE = get_4bit_type("nf4", device="xpu")
device_type = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
torch_accelerator_module = getattr(torch, device_type, torch.cuda)


def quantize_blockwise(A: torch.Tensor, code: torch.Tensor, blocksize: int) -> tuple[torch.Tensor, torch.Tensor]:
Expand All @@ -21,7 +23,9 @@ def quantize_blockwise(A: torch.Tensor, code: torch.Tensor, blocksize: int) -> t
absmax = torch.empty((blocks,), device=A.device, dtype=A.dtype)
out = torch.empty_like(A.flatten(), dtype=torch.uint8)

triton_kernels.quantize_blockwise_triton(A, blocksize, code, blocks, absmax, out)
with torch_accelerator_module.device(A.device):
triton_kernels.quantize_blockwise_triton(A, blocksize, code, blocks, absmax, out)

out = out.reshape(A.shape)

return out, absmax.float()
Expand All @@ -35,13 +39,14 @@ def dequantize_blockwise(
# torch._check(dtype == torch.float32, lambda: f"dtype must be float32 on xpu, got {dtype}")

out = torch.empty_like(A, dtype=dtype, device=A.device)
triton_kernels.dequant_int8_blockwise(
A,
code,
absmax,
out,
blocksize,
)
with torch_accelerator_module.device(A.device):
triton_kernels.dequant_int8_blockwise(
A,
code,
absmax,
out,
blocksize,
)

return out

Expand All @@ -55,13 +60,14 @@ def dequantize_blockwise_inplace(
torch._check(out.device == A.device, lambda: f"Expected out.device == {A.device}, got {out.device}")
torch._check(out.dtype == dtype, lambda: f"Expected out.dtype == {dtype}, got {out.dtype}")

triton_kernels.dequant_int8_blockwise(
A,
code,
absmax,
out,
blocksize,
)
with torch_accelerator_module.device(A.device):
triton_kernels.dequant_int8_blockwise(
A,
code,
absmax,
out,
blocksize,
)


def quantize_4bit(
Expand All @@ -84,9 +90,10 @@ def quantize_4bit(
absmax = torch.empty((blocks * 2,), device=A.device, dtype=A.dtype)
out = torch.empty((n // 2, 1), device=A.device, dtype=torch.uint8)

triton_kernels.quantize_4bit_blockwise_triton(
A, blocksize, quant_type, blocks, absmax, num_elements=n, quantized_out=out
)
with torch_accelerator_module.device(A.device):
triton_kernels.quantize_4bit_blockwise_triton(
A, blocksize, quant_type, blocks, absmax, num_elements=n, quantized_out=out
)
packed = out

if quant_storage != torch.uint8:
Expand Down Expand Up @@ -119,7 +126,9 @@ def dequantize_4bit(

out = torch.empty(shape, dtype=dtype, device=A.device)

triton_kernels._dequantize_4bit_impl(A, absmax, blocksize, quant_type, dtype, out=out)
with torch_accelerator_module.device(A.device):
triton_kernels._dequantize_4bit_impl(A, absmax, blocksize, quant_type, dtype, out=out)

return out


Expand All @@ -134,7 +143,8 @@ def dequantize_4bit_inplace(
) -> None:
torch._check(out.shape == shape, lambda: f"Expected out.shape == {shape}, got {out.shape}")
torch._check(out.dtype == dtype, lambda: f"Expected out.dtype == {dtype}, got {out.dtype}")
triton_kernels._dequantize_4bit_impl(A, absmax, blocksize, quant_type, dtype, out=out)
with torch_accelerator_module.device(A.device):
triton_kernels._dequantize_4bit_impl(A, absmax, blocksize, quant_type, dtype, out=out)


def gemv_4bit(
Expand All @@ -150,14 +160,15 @@ def gemv_4bit(

B_dq_triton = torch.empty(shapeB, dtype=A.dtype, device=A.device)

triton_kernels._dequantize_4bit_impl_passing_code(
B,
absmax,
blocksize,
code,
dtype=A.dtype,
out=B_dq_triton,
)
with torch_accelerator_module.device(A.device):
triton_kernels._dequantize_4bit_impl_passing_code(
B,
absmax,
blocksize,
code,
dtype=A.dtype,
out=B_dq_triton,
)

return torch.nn.functional.linear(
A,
Expand Down