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[ Bugfix ] Enabling Loading Models With Fused QKV/MLP on Disk with FP8 #5921

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merged 4 commits into from
Jun 28, 2024

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@robertgshaw2-neuralmagic robertgshaw2-neuralmagic commented Jun 27, 2024

SUMMARY:

  • some models have fused q,k,v and fused mlp on disk (e.g. Phi3)
  • our fp8 implementation assumes there is a separate key for each q,k,v during weight loading. Because our kernels require a single scale (torch.scaled_mm), we load 3 scales for QKVColumnLinear. 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 scale
  • this PR fixes the logic by (a) initializing all the scales to min value, (b) skipping the dequant-->quant logic if we detect the model has shared qkv on disk

NOTE:

  • I will admit, this is somewhat of a hacky solution where we are special casing a lot. To handle this cleanly, I think we will need to refactor fp8. @comaniac WDYT

FIX #5915

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@robertgshaw2-neuralmagic robertgshaw2-neuralmagic requested review from mgoin and comaniac and removed request for mgoin June 27, 2024 17:54
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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!

@robertgshaw2-neuralmagic robertgshaw2-neuralmagic changed the title [ Bugfix] Enabling Loading Models With Fused QKV/MLP on Disk with FP8 [ Bugfix ] Enabling Loading Models With Fused QKV/MLP on Disk with FP8 Jun 27, 2024
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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.

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

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Thanks for the quick review!

@DarkLight1337
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To speed up the CI queue for #5905, I've cancelled the distributed tests for the latest CI run in this PR since they won't pass anyway until #5905 has been merged. Please merge main into your branch after that happens so that the CI can pass once again.

auto-merge was automatically disabled June 28, 2024 17:37

Pull Request is not mergeable

@robertgshaw2-neuralmagic robertgshaw2-neuralmagic merged commit 2cd402e into vllm-project:main Jun 28, 2024
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prashantgupta24 pushed a commit to prashantgupta24/vllm-project that referenced this pull request Jun 28, 2024
llmpros pushed a commit to llmpros/vllm that referenced this pull request Jun 28, 2024
llmpros pushed a commit to llmpros/vllm that referenced this pull request Jun 28, 2024
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[Bug]: FP8 checkpoints with fused linear modules fail to load scales correctly
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