fix triton kernel on the correct device (#1691)

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

bitsandbytes/backends/triton/ops.py

@@ -9,6 +9,8 @@ from . import triton_kernels
 # 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]:
@@ -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()
@@ -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
 
@@ -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(
@@ -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:
@@ -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
 
 
@@ -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(
@@ -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,