meta-pytorch / meta-pytorch/data
Linter for DataPipe/DataLoader2
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Description
🚀 The feature
This issue proposes the addition of a linter for DataPipes and DataLoader2. The linter can analyze the graph of DataPipes and input arguments to DataLoaderV, and inform the users if any errors may occur ahead of time. The incomplete list of issues that the linter may try to analyze and raise is below. Please feel free to edit the list directly to add more or comment below.
Essential:
- Multiple references to the same iterator/DataPipe
- This can cause issue when serialized, suggest users to
fork
- This can cause issue when serialized, suggest users to
- Duplicate usage of shuffle/batch/collate
- Shuffle/batch/collate are missing?
- Warn if shuffling is not done?
- Warn if sharding is not specificed for Distributed/Multiprocessing
- Warn about shuffling before sharding (not mandatory because inputs may be pre-shuffled)
- Multiprocess/distributed behavior related to sharding/shuffling
- Warn if filter appears between on_disk_cache and end_caching sections.
- Find unreachable children within graph and warns (because they might prevent buffers from being empty in
forkand etc) - Warn about passing DataPipes that have already been partially read (invalid state), but are passed into DataLoader (and we might have to force
resetthe DataPipe in DataLoader) - Detect what external packages are not installed within DataPipe graph
Nice-to-have:
- Check DataPipe object size and warn if it is too big (e.g. premature initialization of large structures)
- Check if
forkdatapipe creates two or more copies ofStreamWrapperorIOBase
Motivation, pitch
Having a linter will encourage best practices of DataPipe usages and reduces the number of unexpected bugs/behaviors in the data loading process during runtime.
Alternatives
Only raise exceptions during runtime.
Additional context
This linter is expected to work with DataPipes and DataLoaderV2. We should consider if it should work with the original DataLoader as well (and how).
cc: @VitalyFedyunin @ejguan
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by surveying the DataPipe graph and DataLoaderV2 entry points mentioned in the issue, then determine how a linter would inspect them and which checks belong in an initial scope. Done means an agreed set of diagnostics is implemented for the selected DataPipe/DataLoaderV2 cases, with behavior for warnings and errors documented and verified.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data-engineering, tooling
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100