[CUDA] MatMulNBits benchmark #24564
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[CUDA] MatMulNBits benchmark #24564
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/azp run Linux QNN CI Pipeline, Win_TRT_Minimal_CUDA_Test_CI, Windows ARM64 QNN CI Pipeline, Windows GPU Doc Gen CI Pipeline, Windows x64 QNN CI Pipeline |
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Azure Pipelines successfully started running 5 pipeline(s). |
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The fourth row in the benchmark table appears to have a higher standard deviation (129.3) than the other rows. Is there a specific reason as to why that happens? |
kunal-vaishnavi
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Apr 26, 2025
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The result was from a shared VM. It might be impacted by other people's jobs at that time. |
jywu-msft
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Apr 30, 2025
### Description Cherry pick the following into [rel-1.22.0](https://github.com/microsoft/onnxruntime/tree/rel-1.22.0) - (#24487) - (#24466) - (#24493) - (#24484) - (#24494) - (#24489) - (#24504) - (#24510) - (#24456) - (#24537) - (#24501) - (#24519) - (#24513) - (#24539) - (#24514) - (#24542) - (#24585) Not added: Planning to cherry pick Cuda Matmulnbits PRs once the fix for failing cuda pipeline is ready - (#24491) - (#24509) - (#24564) --------- Co-authored-by: Adrian Lizarraga <adlizarraga@microsoft.com> Co-authored-by: minfhong-quic <quic_minfhong@quicinc.com> Co-authored-by: minfhong-quic <minfhong-quic@quicinc.com> Co-authored-by: Justin Chu <justinchuby@users.noreply.github.com> Co-authored-by: Prathik Rao <prathik.rao@gmail.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Ankan Banerjee <ankan.ban@gmail.com> Co-authored-by: Maximilian Müller <maximilianm@nvidia.com> Co-authored-by: Gaurav Garg <gaugarg@nvidia.com> Co-authored-by: iraut <iraut@nvidia.com> Co-authored-by: Hrishikesh Manohar <hrishikeshm@nvidia.com> Co-authored-by: Maximilian Müller <44298237+gedoensmax@users.noreply.github.com> Co-authored-by: Scott McKay <skottmckay@gmail.com> Co-authored-by: Jiajia Qin <jiajiaqin@microsoft.com> Co-authored-by: kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com> Co-authored-by: xhcao <xinghua.cao@intel.com>
jatinwadhwa921
pushed a commit
to intel/onnxruntime
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Apr 30, 2025
### Description Cherry pick the following into [rel-1.22.0](https://github.com/microsoft/onnxruntime/tree/rel-1.22.0) - (microsoft#24487) - (microsoft#24466) - (microsoft#24493) - (microsoft#24484) - (microsoft#24494) - (microsoft#24489) - (microsoft#24504) - (microsoft#24510) - (microsoft#24456) - (microsoft#24537) - (microsoft#24501) - (microsoft#24519) - (microsoft#24513) - (microsoft#24539) - (microsoft#24514) - (microsoft#24542) - (microsoft#24585) Not added: Planning to cherry pick Cuda Matmulnbits PRs once the fix for failing cuda pipeline is ready - (microsoft#24491) - (microsoft#24509) - (microsoft#24564) --------- Co-authored-by: vraspar <vrajang@outlook.com> Co-authored-by: Adrian Lizarraga <adlizarraga@microsoft.com> Co-authored-by: minfhong-quic <quic_minfhong@quicinc.com> Co-authored-by: minfhong-quic <minfhong-quic@quicinc.com> Co-authored-by: Justin Chu <justinchuby@users.noreply.github.com> Co-authored-by: Prathik Rao <prathik.rao@gmail.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Ankan Banerjee <ankan.ban@gmail.com> Co-authored-by: Maximilian Müller <maximilianm@nvidia.com> Co-authored-by: Gaurav Garg <gaugarg@nvidia.com> Co-authored-by: iraut <iraut@nvidia.com> Co-authored-by: Hrishikesh Manohar <hrishikeshm@nvidia.com> Co-authored-by: Maximilian Müller <44298237+gedoensmax@users.noreply.github.com> Co-authored-by: Scott McKay <skottmckay@gmail.com> Co-authored-by: Jiajia Qin <jiajiaqin@microsoft.com> Co-authored-by: kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com> Co-authored-by: xhcao <xinghua.cao@intel.com>
vraspar
pushed a commit
that referenced
this pull request
May 1, 2025
### Description 1. Add benchmark script for MatMulNBits. 2. Update kernel based on benchmark results: - Change kernel back to handle m=1 - Use simple loop kernel instead of unrolling - Change partial sum to float type to trade-off precision and performance (less precision loss, no obvious performance drop) Example output of benchmark: ``` ------------------------------------------------------------------------------------------------------------------------ Benchmarking MatMulNBits on NVIDIA A100-SXM4-80GB (Compute Capability: 8.0) ------------------------------------------------------------------------------------------------------------------------ CUDA Graph | M | N | K | Bits | Block Size | Threads | Latency (us) | StdDev (us) | TFLOPS ------------------------------------------------------------------------------------------------------------------------ True | 1 | 3072 | 8192 | 4 | 32 | 0 | 95.7 | 5.7 | 0.526 True | 1 | 3072 | 8192 | 8 | 32 | 0 | 110.7 | 81.1 | 0.454 True | 1 | 3072 | 8192 | 4 | 128 | 0 | 93.7 | 41.2 | 0.537 True | 1 | 3072 | 8192 | 8 | 128 | 0 | 105.0 | 129.3 | 0.479 True | 1 | 5120 | 3072 | 4 | 32 | 0 | 86.7 | 49.9 | 0.363 True | 1 | 5120 | 3072 | 8 | 32 | 0 | 90.1 | 41.1 | 0.349 True | 1 | 5120 | 3072 | 4 | 128 | 0 | 83.9 | 46.7 | 0.375 True | 1 | 5120 | 3072 | 8 | 128 | 0 | 85.2 | 57.1 | 0.369 True | 1 | 8192 | 3072 | 4 | 32 | 0 | 107.3 | 29.2 | 0.469 True | 1 | 8192 | 3072 | 8 | 32 | 0 | 102.3 | 57.1 | 0.492 True | 1 | 8192 | 3072 | 4 | 128 | 0 | 99.2 | 61.2 | 0.507 True | 1 | 8192 | 3072 | 8 | 128 | 0 | 97.5 | 47.4 | 0.516 True | 1 | 200064 | 3072 | 4 | 32 | 0 | 1456.4 | 11.0 | 0.844 True | 1 | 200064 | 3072 | 8 | 32 | 0 | 1336.4 | 10.3 | 0.920 True | 1 | 200064 | 3072 | 4 | 128 | 0 | 1261.6 | 16.6 | 0.974 True | 1 | 200064 | 3072 | 8 | 128 | 0 | 1232.6 | 17.9 | 0.997 True | 256 | 3072 | 8192 | 4 | 32 | 0 | 211.1 | 5.8 | 61.030 True | 256 | 3072 | 8192 | 8 | 32 | 0 | 217.8 | 62.8 | 59.154 True | 256 | 3072 | 8192 | 4 | 128 | 0 | 208.7 | 63.3 | 61.751 True | 256 | 3072 | 8192 | 8 | 128 | 0 | 213.0 | 58.2 | 60.491 True | 256 | 5120 | 3072 | 4 | 32 | 0 | 151.9 | 57.4 | 53.028 True | 256 | 5120 | 3072 | 8 | 32 | 0 | 156.2 | 71.1 | 51.554 True | 256 | 5120 | 3072 | 4 | 128 | 0 | 151.4 | 22.6 | 53.198 True | 256 | 5120 | 3072 | 8 | 128 | 0 | 154.6 | 47.1 | 52.092 True | 256 | 8192 | 3072 | 4 | 32 | 0 | 219.0 | 4.4 | 58.847 True | 256 | 8192 | 3072 | 8 | 32 | 0 | 226.6 | 14.5 | 56.860 True | 256 | 8192 | 3072 | 4 | 128 | 0 | 206.7 | 39.9 | 62.333 True | 256 | 8192 | 3072 | 8 | 128 | 0 | 216.2 | 41.3 | 59.587 True | 256 | 200064 | 3072 | 4 | 32 | 0 | 3110.9 | 11.3 | 101.152 True | 256 | 200064 | 3072 | 8 | 32 | 0 | 3290.9 | 8.3 | 95.619 True | 256 | 200064 | 3072 | 4 | 128 | 0 | 3055.2 | 10.2 | 102.995 True | 256 | 200064 | 3072 | 8 | 128 | 0 | 3220.4 | 9.8 | 97.712 True | 1024 | 3072 | 8192 | 4 | 32 | 0 | 363.6 | 40.2 | 141.754 True | 1024 | 3072 | 8192 | 8 | 32 | 0 | 369.0 | 46.0 | 139.669 True | 1024 | 3072 | 8192 | 4 | 128 | 0 | 362.8 | 55.6 | 142.052 True | 1024 | 3072 | 8192 | 8 | 128 | 0 | 367.5 | 56.5 | 140.256 True | 1024 | 5120 | 3072 | 4 | 32 | 0 | 221.6 | 58.1 | 145.383 True | 1024 | 5120 | 3072 | 8 | 32 | 0 | 225.4 | 56.6 | 142.938 True | 1024 | 5120 | 3072 | 4 | 128 | 0 | 220.2 | 36.9 | 146.306 True | 1024 | 5120 | 3072 | 8 | 128 | 0 | 224.1 | 57.8 | 143.751 True | 1024 | 8192 | 3072 | 4 | 32 | 0 | 346.2 | 41.8 | 148.854 True | 1024 | 8192 | 3072 | 8 | 32 | 0 | 352.8 | 21.6 | 146.097 True | 1024 | 8192 | 3072 | 4 | 128 | 0 | 344.5 | 18.9 | 149.627 True | 1024 | 8192 | 3072 | 8 | 128 | 0 | 350.6 | 10.6 | 147.016 True | 1024 | 200064 | 3072 | 4 | 32 | 0 | 6822.0 | 44.1 | 184.504 True | 1024 | 200064 | 3072 | 8 | 32 | 0 | 7018.5 | 38.4 | 179.339 True | 1024 | 200064 | 3072 | 4 | 128 | 0 | 6757.8 | 51.5 | 186.257 True | 1024 | 200064 | 3072 | 8 | 128 | 0 | 6947.7 | 38.1 | 181.167 ------------------------------------------------------------------------------------------------------------------------ ``` ### Motivation and Context Follow up with #24509
jywu-msft
pushed a commit
that referenced
this pull request
May 1, 2025
### Description Cherry pick the following into [rel-1.22.0](https://github.com/microsoft/onnxruntime/tree/rel-1.22.0) - (#24491) - (#24509) - (#24564) - (#24574) - (#24582) - (#24584) - (#24568) - (#24587) - (#24563) - (#24592) - (#24526) - (#24552) - (#24588) - (#24605) - (#24606) --------- Co-authored-by: Jing Fang <126209182+fajin-corp@users.noreply.github.com> Co-authored-by: Tianlei Wu <tlwu@microsoft.com> Co-authored-by: Baiju Meswani <bmeswani@microsoft.com> Co-authored-by: Scott McKay <skottmckay@gmail.com> Co-authored-by: Mark Schofield <mschofie@microsoft.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Ashwath Shankarnarayan <quic_ashwshan@quicinc.com> Co-authored-by: saurabh <saurabh1.kale@intel.com> Co-authored-by: Adrian Lizarraga <adlizarraga@microsoft.com> Co-authored-by: Hector Li <hecli@microsoft.com>
ankitm3k
pushed a commit
to intel/onnxruntime
that referenced
this pull request
May 12, 2025
### Description 1. Add benchmark script for MatMulNBits. 2. Update kernel based on benchmark results: - Change kernel back to handle m=1 - Use simple loop kernel instead of unrolling - Change partial sum to float type to trade-off precision and performance (less precision loss, no obvious performance drop) Example output of benchmark: ``` ------------------------------------------------------------------------------------------------------------------------ Benchmarking MatMulNBits on NVIDIA A100-SXM4-80GB (Compute Capability: 8.0) ------------------------------------------------------------------------------------------------------------------------ CUDA Graph | M | N | K | Bits | Block Size | Threads | Latency (us) | StdDev (us) | TFLOPS ------------------------------------------------------------------------------------------------------------------------ True | 1 | 3072 | 8192 | 4 | 32 | 0 | 95.7 | 5.7 | 0.526 True | 1 | 3072 | 8192 | 8 | 32 | 0 | 110.7 | 81.1 | 0.454 True | 1 | 3072 | 8192 | 4 | 128 | 0 | 93.7 | 41.2 | 0.537 True | 1 | 3072 | 8192 | 8 | 128 | 0 | 105.0 | 129.3 | 0.479 True | 1 | 5120 | 3072 | 4 | 32 | 0 | 86.7 | 49.9 | 0.363 True | 1 | 5120 | 3072 | 8 | 32 | 0 | 90.1 | 41.1 | 0.349 True | 1 | 5120 | 3072 | 4 | 128 | 0 | 83.9 | 46.7 | 0.375 True | 1 | 5120 | 3072 | 8 | 128 | 0 | 85.2 | 57.1 | 0.369 True | 1 | 8192 | 3072 | 4 | 32 | 0 | 107.3 | 29.2 | 0.469 True | 1 | 8192 | 3072 | 8 | 32 | 0 | 102.3 | 57.1 | 0.492 True | 1 | 8192 | 3072 | 4 | 128 | 0 | 99.2 | 61.2 | 0.507 True | 1 | 8192 | 3072 | 8 | 128 | 0 | 97.5 | 47.4 | 0.516 True | 1 | 200064 | 3072 | 4 | 32 | 0 | 1456.4 | 11.0 | 0.844 True | 1 | 200064 | 3072 | 8 | 32 | 0 | 1336.4 | 10.3 | 0.920 True | 1 | 200064 | 3072 | 4 | 128 | 0 | 1261.6 | 16.6 | 0.974 True | 1 | 200064 | 3072 | 8 | 128 | 0 | 1232.6 | 17.9 | 0.997 True | 256 | 3072 | 8192 | 4 | 32 | 0 | 211.1 | 5.8 | 61.030 True | 256 | 3072 | 8192 | 8 | 32 | 0 | 217.8 | 62.8 | 59.154 True | 256 | 3072 | 8192 | 4 | 128 | 0 | 208.7 | 63.3 | 61.751 True | 256 | 3072 | 8192 | 8 | 128 | 0 | 213.0 | 58.2 | 60.491 True | 256 | 5120 | 3072 | 4 | 32 | 0 | 151.9 | 57.4 | 53.028 True | 256 | 5120 | 3072 | 8 | 32 | 0 | 156.2 | 71.1 | 51.554 True | 256 | 5120 | 3072 | 4 | 128 | 0 | 151.4 | 22.6 | 53.198 True | 256 | 5120 | 3072 | 8 | 128 | 0 | 154.6 | 47.1 | 52.092 True | 256 | 8192 | 3072 | 4 | 32 | 0 | 219.0 | 4.4 | 58.847 True | 256 | 8192 | 3072 | 8 | 32 | 0 | 226.6 | 14.5 | 56.860 True | 256 | 8192 | 3072 | 4 | 128 | 0 | 206.7 | 39.9 | 62.333 True | 256 | 8192 | 3072 | 8 | 128 | 0 | 216.2 | 41.3 | 59.587 True | 256 | 200064 | 3072 | 4 | 32 | 0 | 3110.9 | 11.3 | 101.152 True | 256 | 200064 | 3072 | 8 | 32 | 0 | 3290.9 | 8.3 | 95.619 True | 256 | 200064 | 3072 | 4 | 128 | 0 | 3055.2 | 10.2 | 102.995 True | 256 | 200064 | 3072 | 8 | 128 | 0 | 3220.4 | 9.8 | 97.712 True | 1024 | 3072 | 8192 | 4 | 32 | 0 | 363.6 | 40.2 | 141.754 True | 1024 | 3072 | 8192 | 8 | 32 | 0 | 369.0 | 46.0 | 139.669 True | 1024 | 3072 | 8192 | 4 | 128 | 0 | 362.8 | 55.6 | 142.052 True | 1024 | 3072 | 8192 | 8 | 128 | 0 | 367.5 | 56.5 | 140.256 True | 1024 | 5120 | 3072 | 4 | 32 | 0 | 221.6 | 58.1 | 145.383 True | 1024 | 5120 | 3072 | 8 | 32 | 0 | 225.4 | 56.6 | 142.938 True | 1024 | 5120 | 3072 | 4 | 128 | 0 | 220.2 | 36.9 | 146.306 True | 1024 | 5120 | 3072 | 8 | 128 | 0 | 224.1 | 57.8 | 143.751 True | 1024 | 8192 | 3072 | 4 | 32 | 0 | 346.2 | 41.8 | 148.854 True | 1024 | 8192 | 3072 | 8 | 32 | 0 | 352.8 | 21.6 | 146.097 True | 1024 | 8192 | 3072 | 4 | 128 | 0 | 344.5 | 18.9 | 149.627 True | 1024 | 8192 | 3072 | 8 | 128 | 0 | 350.6 | 10.6 | 147.016 True | 1024 | 200064 | 3072 | 4 | 32 | 0 | 6822.0 | 44.1 | 184.504 True | 1024 | 200064 | 3072 | 8 | 32 | 0 | 7018.5 | 38.4 | 179.339 True | 1024 | 200064 | 3072 | 4 | 128 | 0 | 6757.8 | 51.5 | 186.257 True | 1024 | 200064 | 3072 | 8 | 128 | 0 | 6947.7 | 38.1 | 181.167 ------------------------------------------------------------------------------------------------------------------------ ``` ### Motivation and Context Follow up with microsoft#24509
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Description
Example output of benchmark:
Motivation and Context
Follow up with #24509