Changes by apply order:
1. Replace all `".."` and `os.pardir` usage with `os.path.dirname(...)`.
2. Replace nested `os.path.dirname(os.path.dirname(...))` call with `str(Path(...).parent.parent)`.
3. Reorder `.absolute()` ~/ `.resolve()`~ and `.parent`: always resolve the path first.
`.parent{...}.absolute()` -> `.absolute().parent{...}`
4. Replace chained `.parent x N` with `.parents[${N - 1}]`: the code is easier to read (see 5.)
`.parent.parent.parent.parent` -> `.parents[3]`
5. ~Replace `.parents[${N - 1}]` with `.parents[${N} - 1]`: the code is easier to read and does not introduce any runtime overhead.~
~`.parents[3]` -> `.parents[4 - 1]`~
6. ~Replace `.parents[2 - 1]` with `.parent.parent`: because the code is shorter and easier to read.~
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129374
Approved by: https://github.com/justinchuby, https://github.com/malfet
Changes by apply order:
1. Replace all `".."` and `os.pardir` usage with `os.path.dirname(...)`.
2. Replace nested `os.path.dirname(os.path.dirname(...))` call with `str(Path(...).parent.parent)`.
3. Reorder `.absolute()` ~/ `.resolve()`~ and `.parent`: always resolve the path first.
`.parent{...}.absolute()` -> `.absolute().parent{...}`
4. Replace chained `.parent x N` with `.parents[${N - 1}]`: the code is easier to read (see 5.)
`.parent.parent.parent.parent` -> `.parents[3]`
5. ~Replace `.parents[${N - 1}]` with `.parents[${N} - 1]`: the code is easier to read and does not introduce any runtime overhead.~
~`.parents[3]` -> `.parents[4 - 1]`~
6. ~Replace `.parents[2 - 1]` with `.parent.parent`: because the code is shorter and easier to read.~
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129374
Approved by: https://github.com/justinchuby, https://github.com/malfet
Remove most references to rockset:
* replace comments and docs with a generic "backend database"
* Delete `upload_to_rockset`, so we no longer need to install the package.
* Do not upload perf stats to rockset as well (we should be completely on DynamoDB now right @huydhn?)
According to VSCode, it went from 41 -> 7 instances of "rockset" in the repo
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139922
Approved by: https://github.com/huydhn, https://github.com/ZainRizvi
* Upload_metrics function to upload to ossci-raw-job-status bucket instead of dynamo
* Moves all added metrics to a field called "info" so ingesting into database table with a strict schema is easier
* Removes the dynamo_key field since it is no longer needed
* Removes the concept of reserved metrics, since they cannot be overwritten by user added metrics anymore
* Moves s3 resource initialization behind a function so import is faster
---
Tested by emitting a metric during run_test and seeing that documents got added to s3
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136799
Approved by: https://github.com/ZainRizvi
`json.dumps(float("inf"))` returns `Infinity`, which is technically invalid json
This is fine if you json.load, but ClickHouse cannot handle it
Solution here: cast inf and nan to string (which ClickHouse is able to cast back to float)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136877
Approved by: https://github.com/huydhn
Changes by apply order:
1. Replace all `".."` and `os.pardir` usage with `os.path.dirname(...)`.
2. Replace nested `os.path.dirname(os.path.dirname(...))` call with `str(Path(...).parent.parent)`.
3. Reorder `.absolute()` ~/ `.resolve()`~ and `.parent`: always resolve the path first.
`.parent{...}.absolute()` -> `.absolute().parent{...}`
4. Replace chained `.parent x N` with `.parents[${N - 1}]`: the code is easier to read (see 5.)
`.parent.parent.parent.parent` -> `.parents[3]`
5. ~Replace `.parents[${N - 1}]` with `.parents[${N} - 1]`: the code is easier to read and does not introduce any runtime overhead.~
~`.parents[3]` -> `.parents[4 - 1]`~
6. ~Replace `.parents[2 - 1]` with `.parent.parent`: because the code is shorter and easier to read.~
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129374
Approved by: https://github.com/justinchuby, https://github.com/malfet
Changes by apply order:
1. Replace all `".."` and `os.pardir` usage with `os.path.dirname(...)`.
2. Replace nested `os.path.dirname(os.path.dirname(...))` call with `str(Path(...).parent.parent)`.
3. Reorder `.absolute()` ~/ `.resolve()`~ and `.parent`: always resolve the path first.
`.parent{...}.absolute()` -> `.absolute().parent{...}`
4. Replace chained `.parent x N` with `.parents[${N - 1}]`: the code is easier to read (see 5.)
`.parent.parent.parent.parent` -> `.parents[3]`
5. ~Replace `.parents[${N - 1}]` with `.parents[${N} - 1]`: the code is easier to read and does not introduce any runtime overhead.~
~`.parents[3]` -> `.parents[4 - 1]`~
6. ~Replace `.parents[2 - 1]` with `.parent.parent`: because the code is shorter and easier to read.~
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129374
Approved by: https://github.com/justinchuby, https://github.com/malfet
Give TD it's own job so that each shard can get the results from this one job artifact and they will always be in sync with each other/no longer need to worry about consistently issues
* Move test discovery to its own file that is not dependent on torch so it can be run without building torch
* Cannot do cpp test discovery before building pytorch
* Move TD calculation to own file that will create a json file with the final results
* TD is now job/build env agnostic
* TD will rank all tests, including those that test jobs may not want to run (ex it will rank distributed tests along with default tests, even though these tests are never run on the same machine together)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118250
Approved by: https://github.com/huydhn
Extend metric library to allow setting global metrics on a process level which will always be emitted.
Current use case for them is to include shard information every time a metric is emitted by run_test.py
<!--
copilot:poem
-->
### <samp>🤖 Generated by Copilot at 0cae92c</samp>
> _`run_test` refactored_
> _Sharding metrics in Rockset_
> _Autumn of testing_
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110035
Approved by: https://github.com/clee2000
Make it so that scripts can import and run the `emit_metrics` function even if they don't have boto3 installed, in which case it will still validate the inputs but skip the actual metric emission part.
It's purely a refactor without any real logic changes
Motivation: So that run_test.py and the target determination code can use this library easily without worrying about if it was imported or if it's dependencies are installed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107070
Approved by: https://github.com/huydhn
TIL, uploading to Rockset has an upper limit of 5000 records per request. So uploading PT2 perf benchmark could fail if that limit was reached, for example https://github.com/pytorch/pytorch/actions/runs/5828810421/job/15849232756
```
HTTP response body: {"message":"The number of documents specified in this request exceeds the maximum allowed limit of 5,000 documents.","message_key":"RECEIVER_REQUEST_MAX_DOCUMENT_LIMIT","type":"INVALIDINPUT","line":null,"column":null,"trace_id":"73fc2eb5-cfd1-4baa-8141-47c7cde87812","error_id":null,"query_id":null,"internal_errors":null}
```
The fix is to upload the results in multiple smaller batches of at most 5000 records.
### Testing
5743 records from https://github.com/pytorch/pytorch/actions/runs/5828810421/job/15849232756 were written in 2 batches (5000 + 743)
```
python3 -m tools.stats.upload_dynamo_perf_stats --workflow-run-id 5821183777 --workflow-run-attempt 1 --repo pytorch/pytorch --head-branch gh/ezyang/2294/head
...
Writing 5000 documents to Rockset
Done!
Writing 743 documents to Rockset
Done!
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107095
Approved by: https://github.com/atalman, https://github.com/seemethere, https://github.com/ZainRizvi
Added a feature to upload test statistics to DynamoDB and Rockset using a new function `emit_metric` in `tools/stats/upload_stats_lib.py`.
Added metrics to measure test reordering effectiveness in `tools/testing/test_selections.py`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/102691
Approved by: https://github.com/malfet
