linear with scaling will cause illegal memory access error

[efsotr]

import bitblas
import torch

# enabling debug output
bitblas.set_log_level("Debug")

model = bitblas.Linear(
 in_features=1024,
 out_features=1024,
 bias=False,
 A_dtype="float16", # activation A dtype
 W_dtype="uint4", # weight W dtype
 accum_dtype="float32", # accumulation dtype
 out_dtype="float16", # output dtype
 # configs for weight only quantization
 group_size=None, # setting for grouped quantization
 with_scaling=True, # setting for scaling factor
 with_zeros=False, # setting for zeros
 zeros_mode=None, # setting for how to calculating zeros
 # Target optimization var for dynamic symbolic.
 # For detailed information please checkout docs/PythonAPI.md
 # By default, the optimization var is [1, 16, 32, 64, 128, 256, 512]
 opt_M=[1, 16, 32, 64, 128],
)

# Create an integer weight tensor
intweight = torch.randint(0, 15, (1024, 1024), dtype=torch.int8).cuda()

# Load and transform weights into the BitBLAS linear module
model.load_and_transform_weight(intweight)
model.scales.uniform_(0.1, 0.2)

# Set the model to evaluation mode
model.eval()

dummpy_input = torch.randn(1, 1024, dtype=torch.float16).cuda()

print(model.qweight, model.scales, dummpy_input)

output = model(dummpy_input)

print(output)

output

2025-03-10 16:58:41 [BitBLAS:INFO]: Loaded 10 operators from database.
BitBLAS Operator found in global_operator_cache.
tensor([[ 38, 112, 41, ..., 112, -22, 56],
 [ 37, 122, -88, ..., 121, 74, 5],
 [ 96, 105, -72, ..., 71, 19, -50],
 ...,
 [ 41, -114, -27, ..., -107, -87, -63],
 [ -68, 26, -61, ..., 17, -34, 62],
 [ -69, 104, 10, ..., -92, 90, 118]], device='cuda:0',
 dtype=torch.int8) tensor([[0.1353],
 [0.1842],
 [0.1820],
 ...,
 [0.1693],
 [0.1836],
 [0.1566]], dtype=torch.float16) tensor([[ 0.2061, -1.6357, 1.1240, ..., -0.5273, 1.7285, 2.1289]],
 device='cuda:0', dtype=torch.float16)

error

File ~/anaconda3/envs/profile/lib/python3.10/site-packages/torch/_tensor_str.py:145, in _Formatter.__init__(self, tensor)
 142 self.max_width = max(self.max_width, len(value_str))
 144 else:
--> 145 nonzero_finite_vals = torch.masked_select(
 146 tensor_view, torch.isfinite(tensor_view) & tensor_view.ne(0)
 147 )
 149 if nonzero_finite_vals.numel() == 0:
 150 # no valid number, do nothing
 151 return

RuntimeError: CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.