Summary:
This Diff removes the requirement for `traverse` function that `DataPipe` needs to be hash-able. `traverse` function now is using `id` of `DataPipe` instance rather than `DataPipe` itself as the key for both `cache` and graph.
But, it requires the changes of type of `DataPipeGraph` from `Dict[DataPipe, "DataPipeGraph"]` to `Dict[int, Tuple[DataPipe, "DataPipeGraph"]]`.
Differential Revision: D37354153
Ref PR in TorchData: https://github.com/pytorch/data/pull/559
Pull Request resolved: https://github.com/pytorch/pytorch/pull/80509
Approved by: https://github.com/VitalyFedyunin
This PR adds an attribute and logic to count the number of successful yields from `IterDataPipe`. This information can be useful to fast-forward a DataPipe (or the entire graph) back to a certain state.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79657
Approved by: https://github.com/VitalyFedyunin
Summary:
X-link: https://github.com/pytorch/data/pull/547
Fixes https://github.com/pytorch/data/issues/538
- Improve the validation function to raise warning about unpickable function when either lambda or local function is provided to DataPipe.
- The inner function from functools.partial object is extracted as well for validation
- Mimic the behavior of pickle module for local lambda function: It would only raise Error for the local function rather than lambda function. So, we will raise warning about local function not lambda function.
```py
>>> import pickle
>>> def fn():
... lf = lambda x: x
... pickle.dumps(lf)
>>> pickle.dumps(fn)
AttributeError: Can't pickle local object 'fn.<locals>.<lambda>'
```
This Diff also fixes the Error introduced by https://github.com/pytorch/pytorch/pull/79344
Test Plan:
CI on PyTorch and TorchData
Manually validated the tests from TorchVision
Differential Revision: D37417556
Pull Request resolved: https://github.com/pytorch/pytorch/pull/80232
Approved by: https://github.com/NivekT
Fixes https://github.com/pytorch/data/issues/538
- Improve the validation function to raise warning about unpickable function when either lambda or local function is provided to `DataPipe`.
- The inner function from `functools.partial` object is extracted as well for validation
- Mimic the behavior of `pickle` module for local lambda function: It would only raise Error for the local function rather than `lambda` function. So, we will raise warning about local function not lambda function.
```py
>>> import pickle
>>> def fn():
... lf = lambda x: x
... pickle.dumps(lf)
>>> pickle.dumps(fn)
AttributeError: Can't pickle local object 'fn.<locals>.<lambda>'
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/80140
Approved by: https://github.com/VitalyFedyunin, https://github.com/NivekT
Fixes https://github.com/pytorch/data/issues/426
This PR introduces two main changes:
- It ensures the `ShufflerDataPipe` would share the same seed across distributed processes.
- Users can reset `shuffle` for persistent workers per epoch.
Detail:
- `shared_seed` is shared across distributed and worker processes. It will seed a `shared_rng` to provide seeds to each `ShufflerDataPipe` in the pipeline
- `worker_loop` now accepts a new argument of `shared_seed` to accept this shared seed.
- The `shared_seed` is attached to `_ResumeIteration` for resetting seed per epoch for `persistent worker`
- I choose not to touch `base_seed` simply for BC issue
I used this [script](https://gist.github.com/ejguan/d88f75fa822cb696ab1bc5bc25844f47) to test the result with `world_size=4`. Please check the result in: https://gist.github.com/ejguan/6ee2d2de12ca57f9eb4b97ef5a0e300b
You can see there isn't any duplicated/missing element for each epoch. And, with the same seed, the order of data remains the same across epochs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78765
Approved by: https://github.com/VitalyFedyunin
Fixes https://github.com/pytorch/data/issues/426
This PR introduces two main changes:
- It ensures the `ShufflerDataPipe` would share the same seed across distributed processes.
- Users can reset `shuffle` for persistent workers per epoch.
Detail:
- `shared_seed` is shared across distributed and worker processes. It will seed a `shared_rng` to provide seeds to each `ShufflerDataPipe` in the pipeline
- `worker_loop` now accepts a new argument of `shared_seed` to accept this shared seed.
- The `shared_seed` is attached to `_ResumeIteration` for resetting seed per epoch for `persistent worker`
- I choose not to touch `base_seed` simply for BC issue
I used this [script](https://gist.github.com/ejguan/d88f75fa822cb696ab1bc5bc25844f47) to test the result with `world_size=4`. Please check the result in: https://gist.github.com/ejguan/6ee2d2de12ca57f9eb4b97ef5a0e300b
You can see there isn't any duplicated/missing element for each epoch. And, with the same seed, the order of data remains the same across epochs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78765
Approved by: https://github.com/VitalyFedyunin
This is the first PR to make DataPipe deterministic.
Users should be able to use `torch.manual_seed(seed)` to control the shuffle order for the following cases:
- Directly over `DataPipe`
- For single-process DataLoader
- Multiprocessing DataLoader
Unfortunately, for distributed training, users have to run `apply_shuffle_seed` manually to make sure all distributed processes having the same order of shuffle.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77741
Approved by: https://github.com/VitalyFedyunin, https://github.com/NivekT
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76384
OSS issue discussion: https://github.com/pytorch/data/issues/346
This diff updates `mux` and `mux_longest` data pipe.
`mux`: Yields one element at a time from each of the input Iterable DataPipes (functional name: ``mux``). As in, one element from the 1st input DataPipe, then one element from the 2nd DataPipe in the next iteration, and so on. It ends when the shortest input DataPipe is exhausted.
`mux` example:
```
>>> from torchdata.datapipes.iter import IterableWrapper
>>> dp1, dp2, dp3 = IterableWrapper(range(3)), IterableWrapper(range(10, 15)), IterableWrapper(range(20, 25))
>>> list(dp1.mux(dp2, dp3))
[0, 10, 20, 1, 11, 21, 2, 12, 22]
```
Test Plan:
buck test mode/opt //caffe2/test:datapipe
https://www.internalfb.com/intern/testinfra/testrun/4785074706282345
Differential Revision: D36017945
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77145
Approved by: https://github.com/NivekT, https://github.com/ejguan
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76384
OSS issue discussion: https://github.com/pytorch/data/issues/346
This diff updates `mux` and `mux_longest` data pipe.
`mux`: Yields one element at a time from each of the input Iterable DataPipes (functional name: ``mux``). As in, one element from the 1st input DataPipe, then one element from the 2nd DataPipe in the next iteration, and so on. It ends when the shortest input DataPipe is exhausted.
`mux` example:
```
>>> from torchdata.datapipes.iter import IterableWrapper
>>> dp1, dp2, dp3 = IterableWrapper(range(3)), IterableWrapper(range(10, 15)), IterableWrapper(range(20, 25))
>>> list(dp1.mux(dp2, dp3))
[0, 10, 20, 1, 11, 21, 2, 12, 22]
```
Test Plan:
buck test mode/dev //pytorch/data/test:tests -- --exact 'pytorch/data/test:tests - test_mux_longest_iterdatapipe (test_datapipe.TestDataPipe)'
https://www.internalfb.com/intern/testinfra/testrun/3096224791148107
Reviewed By: ejguan
Differential Revision: D35799965
fbshipit-source-id: 320e71a342ec27e6e9200624aad42f4b99f97c3a
(cherry picked from commit 741ed595275df6c05026ed6f0e78d7052328fb7d)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73396
Separating DataPipes from Dataset into different files. This makes the code more maintainable and simplifies some of the code generation.
I have also tried to move `datapipe.py` into `torch.utils.data.datapipes`, but that will lead to circular import and rewriting many import statements. Should I put more time and go down that path some more?
Fixes https://github.com/pytorch/data/issues/213
Test Plan: Imported from OSS
Reviewed By: ejguan
Differential Revision: D34481962
Pulled By: NivekT
fbshipit-source-id: 42fb26fe7fc334636852cfd8719fc807bdaa7912
(cherry picked from commit 81e76a64e297cb5c58caa951c554e49526173936)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73119
Test if a DataPipe is serializable after its contents are partially read and completely read. This is especially important for DataPipes with buffers.
Test Plan: Imported from OSS
Reviewed By: ejguan
Differential Revision: D34354496
Pulled By: NivekT
fbshipit-source-id: 36971d68b9ca1de81fb254e9a459b8f54fe0f9ff
(cherry picked from commit e8f39a7aa364bd2b19145788f7e67c06f948f81b)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72896
Fixing the issue described here: https://github.com/pytorch/data/issues/214
There will be a follow-up PR in TorchData as well
Test Plan: Imported from OSS
Reviewed By: gchanan
Differential Revision: D34258669
Pulled By: NivekT
fbshipit-source-id: 6dd88250ed14ebe779915dc46139be7e012e9d1b
(cherry picked from commit 025b8ed98019e576bfef04c33a3f33ed1a426a66)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72123
There is a bug to fix the typing system in DataPipe, which would take more than 1 week to fix. I will follow up on it later this month. As branch cut is today, add this PR to disable typing to make sure release works.
Test Plan: Imported from OSS
Reviewed By: VitalyFedyunin
Differential Revision: D33920610
Pulled By: ejguan
fbshipit-source-id: febff849ab2272fd3b1c5127a20f27eb82992d9c
(cherry picked from commit ee103e62e7)
Summary:
Fixes https://github.com/pytorch/pytorch/issues/70103
I used an argument so it can be disabled. I called it `deterministic_order` because `sort` can be confusing, as it's actually sorted but by dir levels.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70435
Reviewed By: albanD
Differential Revision: D33899755
Pulled By: ejguan
fbshipit-source-id: e8a08f03a49120333b2d27f332cd21a3240a02a9
(cherry picked from commit 4616e43ec3)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70215
A few renaming, formatting, and additional tests to make the unit tests better.
cc VitalyFedyunin ejguan NivekT
Test Plan: Imported from OSS
Reviewed By: ejguan
Differential Revision: D33344610
Pulled By: NivekT
fbshipit-source-id: bb36f7452bdc44964c9ce0650c7ae308ba2c5aa5
(cherry picked from commit 0aae20cb27)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71161
Users should import these DataPipes from [TorchData](https://github.com/pytorch/data) if they would like to use them. We will be checking for any downstream library usage before landing this PR.
Test Plan: Imported from OSS
Reviewed By: mruberry
Differential Revision: D33532272
Pulled By: NivekT
fbshipit-source-id: 9dbfb21baf2d1183e0aa379049ad8304753e08a1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70367
This PR renames the `FileLoaderIterDataPipe` to `FileOpenerIterDataPipe`. For the sake of not breaking many CI tests immediately, it still preserves `FileLoader` as an alias. This will allow downstream libraries/users to migrate their use cases before we fully remove all references to `FileLoader` from PyTorch.
Fixes https://github.com/pytorch/data/issues/103. More detailed discussion about this decision is also in the linked issue.
cc VitalyFedyunin ejguan NivekT pmeier Nayef211
Test Plan: Imported from OSS
Reviewed By: ejguan
Differential Revision: D33301648
Pulled By: NivekT
fbshipit-source-id: 59278dcd44e372df0ba2001a4eecbf9792580d0b
