Revisit how `__truediv__` works for `QuantTensor` about brevitas

[preusser]

d = a / b = a * c = a * (1 / b)
d_bitwidth = a_bitwidth + c_bitwidth = a_bitwidth + b_bitwidth
d_scale = a_scale * c_scale = a_scale / (b_scale * 2^(b_bitwidth - int(signed))

In most of the cases, division is a lossy operation. So, I assume the goal would be to preserve the accuracy that can reasonably be expected in the operands?
These proposed bitwidths and scales are reasonable choices. I'd argue that they are even when representing values by a (fixed) floating-point scale (_scale) and a variable integer value (_val):

d = a / b
  = a_scale/b_scale * a_val/b_val
  = (a_scale/b_scale / 2^b_ws) * (a_val * 2^b_ws / b_val)    // b_ws = b_bitwidth - int(signed)
  = d_scale                    * d_val

The scaling performed for the value computation ensures that even the biggest b can fit into the smallest a preserving whatever accuracy a brought into the operation.
However, I would definitely go for this direct implementation rather than taking a detour through 1/b, which would introduce an avoidable accuracy bottleneck.

[nickfraser]

@preusser, I would appreciate feedback on my proposal here.

[nickfraser]

Thanks @preusser, I will adopt your suggestions into the proposal.