Switch to Triton FP8 Quantization in EMU1.6 #2688
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Summary: For some reason, the cuda `quantize_fp8_per_row` kernel is very slow in EMU. Switching it to the functionally equivalent triton kernel yields excellent speedups from FP8. For eager mode, I'm seeing a 20% e2e speedup and still getting proper outputs. Eager: BF16: 19702.10ms FP8 Triton Quant: 16466.97ms Compiled: FP8 Native Quant: 14605.18ms FP8 Triton Quant: 16043.92ms BF16: 18030.98ms We see that quantizing in native pytorch helps quite a bit when torch.compile is used. I added the option to choose which quantization function is used and default to triton when torch.compile is off and native torch when torch.compile is on. This gives us the best performance in either case. Reviewed By: jiawenliu64 Differential Revision: D58167756
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This pull request was exported from Phabricator. Differential Revision: D58167756 |
Summary: For some reason, the cuda `quantize_fp8_per_row` kernel is very slow in EMU. Switching it to the functionally equivalent triton kernel yields excellent speedups from FP8. For eager mode, I'm seeing a 20% e2e speedup and still getting proper outputs. Eager: BF16: 19702.10ms FP8 Triton Quant: 16466.97ms Compiled: FP8 Native Quant: 14605.18ms FP8 Triton Quant: 16043.92ms BF16: 18030.98ms We see that quantizing in native pytorch helps quite a bit when torch.compile is used. I added the option to choose which quantization function is used and default to triton when torch.compile is off and native torch when torch.compile is on. This gives us the best performance in either case. Reviewed By: jiawenliu64 Differential Revision: D58167756
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This pull request was exported from Phabricator. Differential Revision: D58167756 |
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This pull request has been merged in f7666ed. |
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Summary:
For some reason, the cuda
quantize_fp8_per_rowkernel is very slow in EMU. Switching it to the functionally equivalent triton kernel yields excellent speedups from FP8. For eager mode, I'm seeing a 20% e2e speedup and still getting proper outputs.Eager:
BF16: 19702.10ms
FP8 Triton Quant: 16466.97ms
Compiled:
FP8 Native Quant: 14605.18ms
FP8 Triton Quant: 16043.92ms
BF16: 18030.98ms
We see that quantizing in native pytorch helps quite a bit when torch.compile is used. I added the option to choose which quantization function is used and default to triton when torch.compile is off and native torch when torch.compile is on. This gives us the best performance in either case.
Reviewed By: jiawenliu64
Differential Revision: D58167756