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Add benchmarks to CI #479

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Add benchmarks to CI #479

wants to merge 1 commit into from

Commits on Aug 25, 2022

  1. Add benchmarks to CI (pytorch#479) 

    Summary:
## Types of changes

- [ ] Bug fix (non-breaking change which fixes an issue)
- [X] New feature (non-breaking change which adds functionality)
- [ ] Breaking change (fix or feature that would cause existing functionality to change)
- [ ] Docs change / refactoring / dependency upgrade

## Motivation and Context / Related issue
There's a task pytorch#368 for committing benchmark code. In this change I add these benchmarks into CI integration tests. To choose thresholds I ran the benchmarks locally on all  the layers with (batch size: 16, num_runs: 100, num_repeats: 20, forward_only: False), and generated the following report:

|              | memory*  | memory* | memory*     | memory* | memory*      | runtime                | runtime              | runtime            | runtime                | runtime            |
|--------------|---------|--------|------------|--------|-------------|------------------------|----------------------|--------------------|------------------------|--------------------|
| value        | control | dp     | dp/control | gsm    | gsm/control | control                | dp                   | dp/control         | gsm                    | gsm/control        |
| base_layer   |         |        |            |        |             |                        |                      |                    |                        |                    |
| conv         | 0.0     |        |            | 0.0    |             | 2.021756922606001      |                      |                    | 3.2889059911645036     | 1.6267563891534373 |
| embedding    | 0.0     |        |            | 0.0    |             | 0.002484286398502263   |                      |                    | 0.013664713416999803   | 5.5004581698946    |
| groupnorm    | 0.0     |        |            | 0.0    |             | 0.0001871487290072764  |                      |                    | 0.00043170701800136156 | 2.306759016165034  |
| gru          | 0.0     | 0.0    |            | 0.0    |             | 0.045029744959007065   | 0.057370035271503174 | 1.2740475284443677 | 0.2402042072270033     | 5.334345274344187  |
| instancenorm | 0.0     |        |            | 0.0    |             | 0.004493124293996517   |                      |                    | 0.006058429501005777   | 1.3483779002287433 |
| layernorm    | 0.0     |        |            | 0.0    |             | 0.00011227587499979562 |                      |                    | 0.0002241125804985131  | 1.9960884784814286 |
| linear       | 0.0     |        |            | 0.0    |             | 0.001010556231000001   |                      |                    | 0.003052972127999998   | 3.021080900148341  |
| lstm         | 0.0     | 0.0    |            | 0.0    |             | 0.052634652085002925   | 0.06508583683050075  | 1.2365586975931682 | 0.2982182763324963     | 5.665816425477371  |
| mha          | 0.0     | 0.0    |            | 0.0    |             | 0.018872260358001765   | 0.01870937360499738  | 0.9913689854890476 | 0.02688384014700477    | 1.424516175435558  |
| rnn          | 0.0     | 0.0    |            | 0.0    |             | 0.01576623683249454    | 0.02184348723049516  | 1.3854597937711604 | 0.10178373254250346    | 6.455803856296582  |

(*) This report wasn't generated on a machine with CUDA so the memory wasn't measured. Will update later when it runs in CI on a GPU machine.

Using the report and section 3 in the [paper](https://arxiv.org/pdf/2109.12298.pdf), I parameterised the runtime and memory thresholds for different layers.

## How Has This Been Tested (if it applies)
 I ran the jobs locally and generated reports.

## Checklist

- [X] The documentation is up-to-date with the changes I made.
- [X] I have read the **CONTRIBUTING** document and completed the CLA (see **CONTRIBUTING**).
- [ ] All tests passed, and additional code has been covered with new tests.

Pull Request resolved: pytorch#479

Differential Revision: D38999201

Pulled By: moaradwan

fbshipit-source-id: 3d02931970e39ea331674c9f0676db9e22c5edaa
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    Attia Radwan authored and facebook-github-bot committed Aug 25, 2022
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