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Optimize the fp-dequantizer to get high memory-BW utilization #5373

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merged 3 commits into from Apr 10, 2024

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RezaYazdaniAminabadi
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This PR removes the for loop inside the dequantizer kernel and use as many threads and blocks as needed to dequantize the quantized matrix. The previous implementation was processing each group per thread block which can reduce the efficiency when have having smaller group-size and also processes more data per-thread which is unnecessary and we can use more parallelism to improve the dequantization performance.

Based on my testing results, for a 4K by 4K matrix, dequantizing from fp8 to bf16 gives 2.5x speedup (improving the BW efficiency from 1 TB/s to 2.5 TB/s on Nvidia H100 GPU).

@jeffra
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jeffra commented Apr 7, 2024

Tagging @JamesTheZ to help review

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JamesTheZ commented Apr 10, 2024

It looks good to me! The format problem needs to be fixed before the approval @RezaYazdaniAminabadi . Thanks.

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It looks good to me! The format problem needs to be fixed before the approval @RezaYazdaniAminabadi . Thanks.

pushed a fix for this, thanks @JamesTheZ

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@loadams loadams enabled auto-merge April 10, 2024 17:43
@loadams loadams added this pull request to the merge queue Apr 10, 2024
Merged via the queue into microsoft:master with commit a8b8215 Apr 10, 2024
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rraminen pushed a commit to ROCm/DeepSpeed that referenced this pull request May 9, 2024
…oft#5373)

This PR removes the for loop inside the dequantizer kernel and use as
many threads and blocks as needed to dequantize the quantized matrix.
The previous implementation was processing each group per thread block
which can reduce the efficiency when have having smaller group-size and
also processes more data per-thread which is unnecessary and we can use
more parallelism to improve the dequantization performance.

Based on my testing results, for a 4K by 4K matrix, dequantizing from
fp8 to bf16 gives 2.5x speedup (improving the BW efficiency from 1 TB/s
to 2.5 TB/s on Nvidia H100 GPU).

---------

Co-authored-by: Reza Yazdani <reza.yazdani@snowflake.com>
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