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[ Bugfix ] Enabling Loading Models With Fused QKV/MLP on Disk with FP8 #5921
[ Bugfix ] Enabling Loading Models With Fused QKV/MLP on Disk with FP8 #5921
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Overall LGTM, but yeah these logics become messy and we should refactor along with linear layers. Hopefully the weight loading refactoring could help with this.
One side note: I also have a requirement that loads a sharded state dicts directly and bypass the normal weight loading process. In this case the weight shapes are mismatching because we initialized the weights in the original shape and transpose after weight loading. We should avoid this and make sure the initialized weights are already in the final shape.
@@ -111,6 +112,7 @@ def _create_scale_param( | |||
scale = Parameter(torch.empty(len(output_partition_sizes), | |||
dtype=torch.float32), | |||
requires_grad=False) | |||
scale[:] = torch.finfo(torch.float8_e4m3fn).min |
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What's the purpose of this?
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- For QKVColumnLinear, we create 3 scales (one for each logical partition)
- There is only one scale for qkv on disk for phi
- In this PR, we load this one scale into one of the 3 spots
- In
process_weights_after_loading
, we select the max of the 3 scales to use - By initializing the scales to
min
, we are guaranteed to select the "real" scale from disk
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I validated this works with the Phi 3 mini FP8 models we have, thanks!
yeah, dipika's work should help with this. Though Im not quite sure how we can avoid the 3 vs 1 scales initialized issue since we don't know that the model is serialized fused until after we peek into the safetensors |
Thanks for the quick review! |
Pull Request is not mergeable
vllm-project#5921) Co-authored-by: Robert Shaw <rshaw@neuralmagic>
vllm-project#5921) Co-authored-by: Robert Shaw <rshaw@neuralmagic>
vllm-project#5921) Co-authored-by: Robert Shaw <rshaw@neuralmagic>
SUMMARY:
torch.scaled_mm
), we load 3 scales forQKVColumnLinear
. Then, after the weights have been loaded, we select the biggest scale of the group, dequantize each logical weight individually and re-quantize with the biggest scaleNOTE:
FIX #5915
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