[ROCm] slow torch.sum optimization by increasing max_values_per_thread in reduce config #135397

hongxiayang · 2024-09-06T22:38:56Z

This change is to optimize torch.sum() performance by increasing max_values_per_thread in setReduceConfig() for ROCm platform.
By increasing this parameter, it uses fewer threadblocks and improved the performance for large tensors.

Test:
Tested on MI300x and H100, and now the MI300x perf improved to 3205GByte/s from ~1690GByte/s for the test case and is slightly better than H100 (3136GByte/s).

import torch
from triton.testing import do_bench
x = torch.randn(2**30, device='cuda')
ms = do_bench(lambda: x.sum(dim=-1))
bandwidth_gbyte = x.numel() * x.dtype.itemsize / (10**9)
time_s = ms / 1000
bw_per_second = bandwidth_gbyte / time_s
print(bw_per_second)

Co-author: @carlobertolli

cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo

…onfig

pytorch-bot · 2024-09-06T22:38:59Z

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✅ You can merge normally! (2 Unrelated Failures)

As of commit 6cd7c04 with merge base de74aaf ():

FLAKY - The following jobs failed but were likely due to flakiness present on trunk:

rocm / linux-focal-rocm6.1-py3.8 / test (default, 1, 6, linux.rocm.gpu.2) (gh) (similar failure)
functorch/test_control_flow.py::TestControlFlow::test_pointwise_associative_scan_binary_operator_reverse_False_combine_mode_pointwise_cuda
rocm / linux-focal-rocm6.1-py3.8 / test (default, 4, 6, linux.rocm.gpu.2) (gh) (similar failure)
inductor/test_loop_ordering.py::LoopOrderingTest::test_fp8_cast_and_t

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hongxiayang · 2024-09-09T21:39:57Z

HI, @malfet : Can you help to merge this PR? The two test failures are not related. Thank you!

malfet · 2024-09-10T21:01:06Z

@pytorchbot merge -f "Lint is green"

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jithunnair-amd · 2024-09-10T21:43:31Z

@pytorchmergebot cherry-pick --help

pytorch-bot · 2024-09-10T21:43:33Z

❌ 🤖 pytorchbot command failed:

@pytorchbot cherry-pick: error: the following arguments are required: --onto, -c/--classification
usage: @pytorchbot cherry-pick --onto ONTO [--fixes FIXES] -c
                               {regression,critical,fixnewfeature,docs,release}

Try @pytorchbot --help for more info.

jithunnair-amd · 2024-09-10T21:50:45Z

@pytorchmergebot cherry-pick --onto release/2.5 -c critical

@carlobertolli

…d in reduce config (#135397) Fixes #132964 This change is to optimize torch.sum() performance by increasing max_values_per_thread in setReduceConfig() for ROCm platform. By increasing this parameter, it uses fewer threadblocks and improved the performance. Test: Tested on MI300x and H100, and now the MI300x perf improved to 3205GByte/s from ~1690GByte/s for the test case and is slightly better than H100 (3136GByte/s). Also tested with other different sizes of tensors and also see perf improvement. ```python import torch from triton.testing import do_bench x = torch.randn(2**30, device='cuda') ms = do_bench(lambda: x.sum(dim=-1)) bandwidth_gbyte = x.numel() * x.dtype.itemsize / (10**9) time_s = ms / 1000 bw_per_second = bandwidth_gbyte / time_s print(bw_per_second) ``` Co-author: @carlobertolli Pull Request resolved: #135397 Approved by: https://github.com/eqy, https://github.com/malfet (cherry picked from commit eb38ee2)

pytorchbot · 2024-09-10T21:54:39Z

Cherry picking #135397

The cherry pick PR is at #135624 and it is recommended to link a critical cherry pick PR with an issue.

Details for Dev Infra team

Raised by workflow job

@carlobertolli

#1588) …d in reduce config (pytorch#135397) Fixes pytorch#132964 This change is to optimize torch.sum() performance by increasing max_values_per_thread in setReduceConfig() for ROCm platform. By increasing this parameter, it uses fewer threadblocks and improved the performance. Test: Tested on MI300x and H100, and now the MI300x perf improved to 3205GByte/s from ~1690GByte/s for the test case and is slightly better than H100 (3136GByte/s). Also tested with other different sizes of tensors and also see perf improvement. ```python import torch from triton.testing import do_bench x = torch.randn(2**30, device='cuda') ms = do_bench(lambda: x.sum(dim=-1)) bandwidth_gbyte = x.numel() * x.dtype.itemsize / (10**9) time_s = ms / 1000 bw_per_second = bandwidth_gbyte / time_s print(bw_per_second) ``` Co-author: @carlobertolli Pull Request resolved: pytorch#135397 Approved by: https://github.com/eqy, https://github.com/malfet Fixes #ISSUE_NUMBER Co-authored-by: hongxyan <hongxyan@amd.com>

@carlobertolli

) Follow-up to pytorch#135397. AMD gpus perform better with fewer thread blocks. So increase the min_values_per_thread as well. This helped improved [CvT](https://github.com/facebookresearch/FAMBench/tree/main/benchmarks/cvt) benchmark performance on MI300X Co-author: @carlobertolli

@carlobertolli

…d in reduce config (pytorch#135397) Fixes pytorch#132964 This change is to optimize torch.sum() performance by increasing max_values_per_thread in setReduceConfig() for ROCm platform. By increasing this parameter, it uses fewer threadblocks and improved the performance. Test: Tested on MI300x and H100, and now the MI300x perf improved to 3205GByte/s from ~1690GByte/s for the test case and is slightly better than H100 (3136GByte/s). Also tested with other different sizes of tensors and also see perf improvement. ```python import torch from triton.testing import do_bench x = torch.randn(2**30, device='cuda') ms = do_bench(lambda: x.sum(dim=-1)) bandwidth_gbyte = x.numel() * x.dtype.itemsize / (10**9) time_s = ms / 1000 bw_per_second = bandwidth_gbyte / time_s print(bw_per_second) ``` Co-author: @carlobertolli Pull Request resolved: pytorch#135397 Approved by: https://github.com/eqy, https://github.com/malfet

functionstackx · 2024-09-22T19:41:43Z

thanks @hongxiayang ! i can confirm that this fixes it

@carlobertolli

#1588) …d in reduce config (pytorch#135397) Fixes pytorch#132964 This change is to optimize torch.sum() performance by increasing max_values_per_thread in setReduceConfig() for ROCm platform. By increasing this parameter, it uses fewer threadblocks and improved the performance. Test: Tested on MI300x and H100, and now the MI300x perf improved to 3205GByte/s from ~1690GByte/s for the test case and is slightly better than H100 (3136GByte/s). Also tested with other different sizes of tensors and also see perf improvement. ```python import torch from triton.testing import do_bench x = torch.randn(2**30, device='cuda') ms = do_bench(lambda: x.sum(dim=-1)) bandwidth_gbyte = x.numel() * x.dtype.itemsize / (10**9) time_s = ms / 1000 bw_per_second = bandwidth_gbyte / time_s print(bw_per_second) ``` Co-author: @carlobertolli Pull Request resolved: pytorch#135397 Approved by: https://github.com/eqy, https://github.com/malfet Fixes #ISSUE_NUMBER Co-authored-by: hongxyan <hongxyan@amd.com> (cherry picked from commit 4360582)

@carlobertolli

) Follow-up to pytorch#135397. AMD gpus perform better with fewer thread blocks. So increase the min_values_per_thread as well. This helped improved [CvT](https://github.com/facebookresearch/FAMBench/tree/main/benchmarks/cvt) benchmark performance on MI300X Co-author: @carlobertolli (cherry picked from commit c1b6f60)

@carlobertolli

#1588) …d in reduce config (pytorch#135397) Fixes pytorch#132964 This change is to optimize torch.sum() performance by increasing max_values_per_thread in setReduceConfig() for ROCm platform. By increasing this parameter, it uses fewer threadblocks and improved the performance. Test: Tested on MI300x and H100, and now the MI300x perf improved to 3205GByte/s from ~1690GByte/s for the test case and is slightly better than H100 (3136GByte/s). Also tested with other different sizes of tensors and also see perf improvement. ```python import torch from triton.testing import do_bench x = torch.randn(2**30, device='cuda') ms = do_bench(lambda: x.sum(dim=-1)) bandwidth_gbyte = x.numel() * x.dtype.itemsize / (10**9) time_s = ms / 1000 bw_per_second = bandwidth_gbyte / time_s print(bw_per_second) ``` Co-author: @carlobertolli Pull Request resolved: pytorch#135397 Approved by: https://github.com/eqy, https://github.com/malfet Fixes #ISSUE_NUMBER Co-authored-by: hongxyan <hongxyan@amd.com>

@carlobertolli

) Follow-up to pytorch#135397. AMD gpus perform better with fewer thread blocks. So increase the min_values_per_thread as well. This helped improved [CvT](https://github.com/facebookresearch/FAMBench/tree/main/benchmarks/cvt) benchmark performance on MI300X Co-author: @carlobertolli

slow sum optimization by increasing max_values_per_thread in reduce c…

6cd7c04

…onfig

pytorch-bot bot added ciflow/rocm Trigger "default" config CI on ROCm module: rocm AMD GPU support for Pytorch release notes: cuda release notes category labels Sep 6, 2024

hongxiayang requested a review from jeffdaily September 6, 2024 22:39

pytorchbot added the open source label Sep 6, 2024

hongxiayang changed the title ~~[ROCm] slow tensor sum optimization by increasing max_values_per_thread in reduce config~~ [ROCm] slow torch.sum optimization by increasing max_values_per_thread in reduce config Sep 6, 2024

hongxiayang marked this pull request as ready for review September 6, 2024 22:52

hongxiayang requested review from eqy and syed-ahmed as code owners September 6, 2024 22:52

hongxiayang mentioned this pull request Sep 6, 2024

ROCm MI300X sum() way slower than H100 #132964

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hongxiayang requested a review from malfet September 9, 2024 21:39

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jerrymannil mentioned this pull request Sep 12, 2024

[ROCm] slow torch.sum optimization by increasing max_values_per_threa… ROCm/pytorch#1588

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jerrymannil mentioned this pull request Sep 13, 2024

[ROCm] torch.sum optimization by increasing min_values_per_thread ROCm/pytorch#1591

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✅ You can merge normally! (2 Unrelated Failures)

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