(self, x)
| 363 | self.qzeros = torch.from_numpy(qzeros) |
| 364 | |
| 365 | def forward(self, x): |
| 366 | out_shape = x.shape[:-1] + (self.outfeatures,) |
| 367 | out = QuantLinearFunction.apply(x.reshape(-1, x.shape[-1]), self.qweight, self.scales, |
| 368 | self.qzeros, self.g_idx, self.bits, self.maxq) |
| 369 | out = out + self.bias if self.bias is not None else out |
| 370 | return out.reshape(out_shape) |
| 371 | |
| 372 | def make_quant(module, names, bits, groupsize, name=''): |
| 373 | if isinstance(module, QuantLinear): |
nothing calls this directly
no outgoing calls
no test coverage detected