Moved int8_mm_dequant from CPU to default backend

bitsandbytes/backends/cpu/ops.py

@@ -1,6 +1,5 @@
 from collections.abc import Sequence
 import ctypes as ct
-from typing import Optional
 
 import torch
 
@@ -24,29 +23,6 @@ def _(A: torch.Tensor, B: torch.Tensor):
         ).reshape(*A.shape[:-1], B.shape[0])
 
 
-@register_kernel("bitsandbytes::int8_mm_dequant", "cpu")
-def _(
-    A: torch.Tensor,
-    row_stats: torch.Tensor,
-    col_stats: torch.Tensor,
-    dtype: Optional[torch.dtype] = None,
-    bias: Optional[torch.Tensor] = None,
-) -> torch.Tensor:
-    torch._check(A.dtype == torch.int32, lambda: f"A must be int32, got {A.dtype}")
-    torch._check(row_stats.dtype == torch.float32, lambda: f"row_stats must be float32, got {row_stats.dtype}")
-    torch._check(col_stats.dtype == torch.float32, lambda: f"col_stats must be float32, got {col_stats.dtype}")
-
-    A_calc = A.view(-1, A.shape[-1])
-    row_stats = row_stats.reshape(-1).unsqueeze(-1)
-    col_stats = col_stats.reshape(-1).unsqueeze(0)
-
-    out = A_calc * (row_stats * col_stats) * 6.200124e-05
-    if bias is not None:
-        out += bias
-
-    return out.to(dtype or torch.float16)
-
-
 @register_kernel("bitsandbytes::quantize_blockwise", "cpu")
 def _(A: torch.Tensor, code: torch.Tensor, blocksize: int) -> tuple[torch.Tensor, torch.Tensor]:
     torch._check_is_size(blocksize)

bitsandbytes/backends/default/ops.py

@@ -6,6 +6,29 @@
 from ..._ops import register_kernel
 
 
+@register_kernel("bitsandbytes::int8_mm_dequant", "default")
+def _(
+    A: torch.Tensor,
+    row_stats: torch.Tensor,
+    col_stats: torch.Tensor,
+    dtype: Optional[torch.dtype] = None,
+    bias: Optional[torch.Tensor] = None,
+) -> torch.Tensor:
+    torch._check(A.dtype == torch.int32, lambda: f"A must be int32, got {A.dtype}")
+    torch._check(row_stats.dtype == torch.float32, lambda: f"row_stats must be float32, got {row_stats.dtype}")
+    torch._check(col_stats.dtype == torch.float32, lambda: f"col_stats must be float32, got {col_stats.dtype}")
+
+    A_calc = A.view(-1, A.shape[-1])
+    row_stats = row_stats.reshape(-1).unsqueeze(-1)
+    col_stats = col_stats.reshape(-1).unsqueeze(0)
+
+    out = A_calc * (row_stats * col_stats) * 6.200124e-05
+    if bias is not None:
+        out += bias
+
+    return out.to(dtype or torch.float16)
+
+
 @register_kernel("bitsandbytes::int8_mixed_scaled_mm", "default")
 def _(
     A: torch.Tensor,