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Adding Float8 Linear variants supporting inference-only with lower overhead #283
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Co-authored-by: Mauricio Serrano <[email protected]>
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Hi folks, thanks for the PR and we definitely want this functionality. We have some changes coming up to set up the inference path, let me give a preview and @drisspg is planning to publish a more detailed RFC related to float8 + inference UX soon.
In the framing above, this PR would be adding additional options to (2). Are you ok to wait a little bit for us to put out the official plan? |
vkuzo
reviewed
Jun 14, 2024
| super(Float8SWLinear, self).__init__(in_features=in_features, out_features=out_features, bias=bias) | ||
| self.w_inv_s = None | ||
| self.dtype = torch.float8_e4m3fn | ||
| self.use_triton = use_triton |
vkuzo
reviewed
Jun 14, 2024
| x_f8 = x.to(self.dtype) | ||
| ishape= list(x_f8.shape) | ||
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| if ishape[0] == 0: # special case handling for mixtral |
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is this not supported by scaled_mm today? cc @drisspg
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no longer needed |
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The changes include two new Float8 Linear implementations that removes some extra wiring in
Float8Linearunnecessary for inference-only use cases to result in lower latency.Float8SWLinearsupports direct fp8 type direct downcast for activation, and Static per-tensor scale for Weight. Our analysis shows that using this results in no loss of accuracy in Llama models.Float8DASWLinearsupports Dynamic per-tensor scale for Activation, and Static per-tensor scale for Weight. This is used when activation tensor requires dynamic scaling. Compared toFloat8SWLinear, this has higher overhead introduced by dynamic activation tensor scale calculation. The overhead can be mitigated when used withtorch.compile.cc: @ani300