Hello,

I have encountered this problem while trying to perform per-channel quantization on weights with ch_axis=1 quantization parameter. It causes the “index out of bounds error” when dimention of axis 1 of the weight tensor is smaller than dimention of axis 0(in the following example 100 is smaller than 110(note that 100 will be axis 1 in weight matrix)). If the axis 0 dimention is smaller(when changing 110 to 90) the error doesn’t occure. It is not reproducible with ch_axis=0 quantization parameter. With ch_axis=0 it doesn’t matter if one axis dimention is bigger or smaller then the other.

Here is a minimal example that fails:

```
import torch
import torch.nn as nn
from torch.quantization.observer import PerChannelMinMaxObserver, MinMaxObserver
class QTestNet1(nn.Module):
def __init__(self):
super().__init__()
self.linear = nn.Linear(100,110, bias=False)
def set_qconfig(self):
self.linear.qconfig = torch.quantization.QConfig(
weight=PerChannelMinMaxObserver.with_args(dtype=torch.qint8, ch_axis=1),
activation=MinMaxObserver.with_args(qscheme=torch.per_tensor_affine)
)
def forward(self, x):
x = self.linear(x)
return x
model = QTestNet1()
model.set_qconfig()
model_prepared = torch.quantization.prepare_qat(model, inplace=False)
input_x = torch.randn(1,100)
model_prepared(input_x).shape # just checking that forward doesn't fail
model_int8 = torch.quantization.convert(model_prepared.eval()) # error
```

This is a full error:

```
/usr/local/lib/python3.7/dist-packages/torch/ao/quantization/quantize.py in convert(module, mapping, inplace, remove_qconfig, is_reference, convert_custom_config_dict)
519 _convert(
520 module, mapping, inplace=True, is_reference=is_reference,
--> 521 convert_custom_config_dict=convert_custom_config_dict)
522 if remove_qconfig:
523 _remove_qconfig(module)
/usr/local/lib/python3.7/dist-packages/torch/ao/quantization/quantize.py in _convert(module, mapping, inplace, is_reference, convert_custom_config_dict)
557 _convert(mod, mapping, True, # inplace
558 is_reference, convert_custom_config_dict)
--> 559 reassign[name] = swap_module(mod, mapping, custom_module_class_mapping)
560
561 for key, value in reassign.items():
/usr/local/lib/python3.7/dist-packages/torch/ao/quantization/quantize.py in swap_module(mod, mapping, custom_module_class_mapping)
590 new_mod = qmod.from_float(mod, weight_qparams)
591 else:
--> 592 new_mod = qmod.from_float(mod)
593 swapped = True
594
/usr/local/lib/python3.7/dist-packages/torch/nn/quantized/modules/linear.py in from_float(cls, mod)
271 mod.out_features,
272 dtype=dtype)
--> 273 qlinear.set_weight_bias(qweight, mod.bias)
274 qlinear.scale = float(act_scale)
275 qlinear.zero_point = int(act_zp)
/usr/local/lib/python3.7/dist-packages/torch/nn/quantized/modules/linear.py in set_weight_bias(self, w, b)
232
233 def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None:
--> 234 self._packed_params.set_weight_bias(w, b)
235
236 @classmethod
/usr/local/lib/python3.7/dist-packages/torch/nn/quantized/modules/linear.py in set_weight_bias(self, weight, bias)
25 def set_weight_bias(self, weight: torch.Tensor, bias: Optional[torch.Tensor]) -> None:
26 if self.dtype == torch.qint8:
---> 27 self._packed_params = torch.ops.quantized.linear_prepack(weight, bias)
28 elif self.dtype == torch.float16:
29 self._packed_params = torch.ops.quantized.linear_prepack_fp16(weight, bias)
/usr/local/lib/python3.7/dist-packages/torch/_ops.py in __call__(self, *args, **kwargs)
141 # We save the function ptr as the `op` attribute on
142 # OpOverloadPacket to access it here.
--> 143 return self._op(*args, **kwargs or {})
144
145 # TODO: use this to make a __dir__
IndexError: select(): index 100 out of range for tensor of size [100] at dimension 0
```

I am using torch==1.12.1+cu113 from google colab.