For the user-defined `Mapping` type, it may contain some metadata (e.g., pytorch/tensordict#679, https://github.com/pytorch/pytorch/pull/120195#issue-2141716712). Simply use `type(mapping)({k: v for k, v in mapping.items()})` do not take this metadata into account. This PR uses `copy.copy(mapping)` to create a clone of the original collection and iteratively updates the elements in the cloned collection. This preserves the metadata in the original collection via `copy.copy(...)` rather than relying on the `__init__` method in the user-defined classes.
Reference:
- pytorch/tensordict#679
- #120195Closes#120195
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120553
Approved by: https://github.com/vmoens
As per request from Vision team, adding `collate` function with an extra argument of `collate_fn_map` to dispatch custom collate functions for non-collection objects and specific objects.
If the type of batch element is not present in`collate_fn_map`, it will go through all keys in the insertion order to check if the type is a subclass of the key. If so, it will invoke the corresponding collate functions.
And, `default_collate` will utilize the `collate` function with a few by default collate function for `int`, `float`, `str` and `numpy object`.
Benefit:
- Domain teams can register their own `collate` function to handle their specific type of objects
- Easier for users to extend from the `collate` function.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85748
Approved by: https://github.com/NivekT, https://github.com/pmeier
This is a new version of #15648 based on the latest master branch.
Unlike the previous PR where I fixed a lot of the doctests in addition to integrating xdoctest, I'm going to reduce the scope here. I'm simply going to integrate xdoctest, and then I'm going to mark all of the failing tests as "SKIP". This will let xdoctest run on the dashboards, provide some value, and still let the dashboards pass. I'll leave fixing the doctests themselves to another PR.
In my initial commit, I do the bare minimum to get something running with failing dashboards. The few tests that I marked as skip are causing segfaults. Running xdoctest results in 293 failed, 201 passed tests. The next commits will be to disable those tests. (unfortunately I don't have a tool that will insert the `#xdoctest: +SKIP` directive over every failing test, so I'm going to do this mostly manually.)
Fixes https://github.com/pytorch/pytorch/issues/71105
@ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82797
Approved by: https://github.com/ezyang
Summary:
`default_collate`, `default_convert`, and `pin_memory` convert sequences into lists. I believe they should keep the original type when possible (e.g., I have a class that inherits from `list`, which comes from a 3rd party library that I can't change, and provides extra functionality).
Note it's easy to do when the type supports an iterable in its creation but it's not always the case (e.g., `range`).
Even though this can be accomplished if using a custom `default_collate`/`default_convert`, 1) this is behavior they should support out-of-the-box IMHO, and 2) `pin_memory` still does it.
cc VitalyFedyunin ejguan NivekT
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68779
Reviewed By: wenleix
Differential Revision: D32651129
Pulled By: ejguan
fbshipit-source-id: 17c390934bacc0e4ead060469cf15dde815550b4
Summary:
It can be a non-little save if there are many workers and a large batch size.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61424
Reviewed By: soulitzer
Differential Revision: D29635477
Pulled By: ejguan
fbshipit-source-id: 1fc48b5964e873bd8833ad81bed9d51b0b6d137e
Summary:
Allow np.memmap objects to be processed by default_collate
np.memmap objects has the same behavior as numpy arrays, and the only difference is that they are stored in a binary file on the disk. However, the default_collate function used by PyTorch DataLoader only accepts np.array, and rejects np.memmap by type checking. This commit allows np.memmap objects to be processed by default_collate. In this way, users can use in-disk large arrays with PyTorch DataLoader.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39847
Reviewed By: ezyang
Differential Revision: D22284650
Pulled By: zou3519
fbshipit-source-id: 003e3208a2afd1afc2e4640df14b3446201e00b4
Summary:
Fix issue https://github.com/pytorch/pytorch/issues/23141#
In the below example ```default_collate``` collates each element of the list. Since the second element isn't present in all samples, it is discarded:
```
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import numpy as np
class CustomDataset(Dataset):
def __len__(self):
return 2
def __getitem__(self, idx):
tmp = {
"foo": np.array([1, 2, 3]),
"bar": ["X"] * (idx+1),
}
return tmp
training = CustomDataset()
for batch in DataLoader(training, batch_size=2):
print(batch)
```
Yields
```
{
'foo': tensor(
[
[1, 2, 3],
[1, 2, 3]
]
),
'bar': [
('X', 'X'),
]
}
```
Based on discussion in the issue, it seems the best course of action is to error out in this case. This seems consistent with what is done for tensor elements, as seen in [TensorShape.cpp line 1066](https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/TensorShape.cpp#L1060) which is called when ```torch.stack``` is called. In this PR, I introduce a similar message to error out for lists.
SsnL
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38492
Differential Revision: D21620396
Pulled By: ezyang
fbshipit-source-id: 17f59fbb1ed1f0d9b2185c95b9ebe55ece701b0c
Summary:
Back in April, malmaud added type annotations for `dataloader.py`. However, at about the same time, SsnL in https://github.com/pytorch/pytorch/issues/19228 replaced `_DataLoaderIter` with `_BaseDataLoaderIter` and two subclasses, `_SingleProcessDataLoaderIter`, and `_MultiProcessingDataLoaderIter`. However - probably because these changes happened in parallel at roughly the same time, the type stubs and several other references in the codebase were never updated to match this refactoring.
I've gone ahead and done the updates to reflect the refactoring in https://github.com/pytorch/pytorch/issues/19228, which fixes the specific type stub/impelementation mismatch pointed out in https://github.com/pytorch/pytorch/issues/26673, although not the broader problem that pytorch doesn't have a test to make sure that the `.pyi` type stub files match the real API defined in `.py` files.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27105
Differential Revision: D17813641
Pulled By: ezyang
fbshipit-source-id: ed7ac025c8d6ad3f298dd073347ec83bb4b6600c
Summary:
This is a modified version of https://github.com/pytorch/pytorch/pull/14705 since commit structure for that PR is quite messy.
1. Add `IterableDataset`.
3. So we have 2 data loader mods: `Iterable` and `Map`.
1. `Iterable` if the `dataset` is an instance of `IterableDataset`
2. `Map` o.w.
3. Add better support for non-batch loading (i.e., `batch_size=None` and `batch_sampler=None`). This is useful in doing things like bulk loading.
3. Refactor `DataLoaderIter` into two classes, `_SingleProcessDataLoaderIter` and `_MultiProcessingDataLoaderIter`. Rename some methods to be more generic, e.g., `get_batch` -> `get_data`.
4. Add `torch.utils.data.get_worker_info` which returns worker information in a worker proc (e.g., worker id, dataset obj copy, etc.) and can be used in `IterableDataset.__iter__` and `worker_init_fn` to do per-worker configuration.
5. Add `ChainDataset`, which is the analog of `ConcatDataset` for `IterableDataset`.
7. Import torch.utils.data in `torch/__init__.py`
9. data loader examples and documentations
10. Use `get_worker_info` to detect whether we are in a worker process in `default_collate`
Closes https://github.com/pytorch/pytorch/issues/17909, https://github.com/pytorch/pytorch/issues/18096, https://github.com/pytorch/pytorch/issues/19946, and some of https://github.com/pytorch/pytorch/issues/13023
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19228
Reviewed By: bddppq
Differential Revision: D15058152
fbshipit-source-id: 9e081a901a071d7e4502b88054a34b450ab5ddde
Summary:
Since #1323 tensors are shared with shared memory, but this feature is not active for numpy.
This PR fix this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14534
Differential Revision: D13561649
Pulled By: soumith
fbshipit-source-id: b6bc9e99fb91e8b675c2ef131fba9fa11c1647c0
Summary:
Same as #14668, and was approved there.
ailzhang , please apply this patch to Horizon's `data_streamer.py`: https://gist.github.com/SsnL/020fdb3d6b7016d81b6ba1d04cc41459 Thank you!
Below is the original description at #14668:
As I am working on tasks in https://github.com/pytorch/pytorch/issues/13023, I realized how unreadable the code is because all functions to be run in multiprocessing must be at top global level. Adding more functionalities to `dataloader.py` will only make things worse.
So in this PR, I refactor `dataloader.py` and move much of it into `data._utils`. E.g., the `_worker_loop` and related methods are now in `data._utils.worker`, signal handling code in `data._utils.signal_handling`, collating code in `data._utils.collate`, etc. This split, IMHO, makes code much clearer. I will base my future changes to DataLoader on top of this.
No functionality is changed, except that I added `torch._six.queue`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15331
Reviewed By: yf225
Differential Revision: D13503120
Pulled By: ailzhang
fbshipit-source-id: 94df16b4d80ad1102c437cde0d5a2e62cffe1f8e
Summary:
As I am working on tasks in https://github.com/pytorch/pytorch/issues/13023, I realized how unreadable the code is because all functions to be run in multiprocessing must be at top global level. Adding more functionalities to `dataloader.py` will only make things worse.
So in this PR, I refactor `dataloader.py` and move much of it into `data._utils`. E.g., the `_worker_loop` and related methods are now in `data._utils.worker`, signal handling code in `data._utils.signal_handling`, collating code in `data._utils.collate`, etc. This split, IMHO, makes code much clearer. I will base my future changes to DataLoader on top of this.
No functionality is changed, except that I added `torch._six.queue`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14668
Reviewed By: soumith
Differential Revision: D13289919
Pulled By: ailzhang
fbshipit-source-id: d701bc7bb48f5dd7b163b5be941a9d27eb277a4c
