rm useless code comments

Signed-off-by: jiqing-feng <[email protected]>

Author: jiqing-feng

bitsandbytes/backends/cpu/ops.py

@@ -275,40 +275,3 @@ def _(
                 out = out.to(dtype)
 
             return out
-
-# def unpack_weight_packed_for_cpu(packed_qweight: torch.Tensor, block_n: int = 32):
-#     """
-#     Inverse of convert_weight_packed_for_cpu.
-#     packed_qweight: (N, K//2) uint8, each byte = (high<<4)|low, both 4-bit values in 0..15
-#     returns: qweight_final (N, K) uint8 with original 4-bit values (0..15)
-#     """
-#     assert packed_qweight.dtype == torch.uint8
-#     assert packed_qweight.dim() == 2
-#     N, K_half = packed_qweight.shape
-#     assert N % block_n == 0
-#     BIT_COUNT = block_n  # 32
-#     # reshape to rows of 32 packed bytes
-#     qw = packed_qweight.reshape(-1, BIT_COUNT)           # [(N//block_n)*K_half, 32]
-#     low  = (qw & 0x0F)
-#     high = (qw >> 4) & 0x0F
-#     # restore 64 nibbles (low first then high, matching original pack order)
-#     restored = torch.cat([low, high], dim=1)             # [..., 64]
-#     # reshape back (inverse of flatten)
-#     restored = restored.reshape(N // block_n, K_half, block_n, 2)  # [N/block_n, K//2, block_n, 2]
-#     # inverse transpose
-#     restored = restored.transpose(-3, -2)                # [N/block_n, block_n, K//2, 2]
-#     # final shape
-#     qweight_final = restored.reshape(N, K_half * 2).to(torch.uint8)
-#     return qweight_final
-
-
-# _NF4_QUANT_TABLE = torch.tensor([ -1.0, -0.6961928009986877, -0.5250730514526367, -0.39491748809814453, -0.28444138169288635, -0.18477343022823334, -0.09105003625154495, 0.0,
-#                                   0.07958029955625534, 0.16093020141124725, 0.24611230194568634, 0.33791524171829224, 0.44070982933044434, 0.5626170039176941, 0.7229568362236023, 1.0 ], dtype=torch.float32)
-
-# def fused_matmul(x, packed_weight, scales, group_size):
-#     unpacked_weight = unpack_weight_packed_for_cpu(packed_weight)
-#     shape = unpacked_weight.shape
-#     # original_weight = _INT4_0_TO_15_TABLE[unpacked_weight.reshape(-1).int()].reshape(shape) * scales.T.repeat_interleave(group_size, dim=1)
-#     original_weight = _NF4_QUANT_TABLE[unpacked_weight.reshape(-1).int()].reshape(shape) * scales.T.repeat_interleave(group_size, dim=1)
-#     res = torch.matmul(x, original_weight.T.to(x.dtype))
-#     return res