Self-Compression QAT and Linear #1342
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Hi @lliangthomas, thanks for your contribution. Have you had a chance to do any full training runs with evaluation results? I'm inclined to move this under |
| Forward pass with weight quantization | ||
| """ | ||
| quant_max = torch.maximum(2. ** -self.float_exponents * self.weight, -2. ** (self.bit_depth.relu() - 1)) | ||
| quant_weight = torch.minimum(quant_max, 2. ** (self.bit_depth.relu() - 1) - 1) |
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can we rewrite this as follows so it's easier to read?
qmax = 2. ** (self.bit_depth.relu() - 1) - 1
qmin = -2. ** (self.bit_depth.relu() - 1)
qweight = torch.clamp(2. ** -self.float_exponents * self.weight, qmin, qmax)
| quant_max = torch.maximum(2. ** -self.float_exponents * self.weight, -2. ** (self.bit_depth.relu() - 1)) | ||
| quant_weight = torch.minimum(quant_max, 2. ** (self.bit_depth.relu() - 1) - 1) | ||
| rounded_weight = (quant_weight.round() - quant_weight).detach() + quant_weight | ||
| return F.linear(x, 2. ** self.float_exponents * rounded_weight) |
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What does the backward pass look like? I think we need to wrap all of this in an autograd.Function and define a backward pass since this has non-differentiable ops like round()
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| _get_qmin_qmax, | |||
| ) | |||
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| class SelfCompressionQATQuantizer(torch.nn.Module): | |||
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This feels like a pretty experimental feature. I think this belongs better to a separate folder under torchao/prototype
Implemented self-compression QAT and linear layer from this paper as a solution to #658
If there are more features or more integration to be done with the current QAT schemes in TorchAO (self-compression seems orthogonal right now), let me know.
@msaroufim @HDCharles