Based on https://github.com/pytorch/pytorch/pull/126376, this PR tries to update all PT callers (e.g., `Tensor.is_pinned()`, `Tensor.pin_memory()`) to not pass `device` argument.
As for `storage/untyped_storage.is_pinned()/pin_memory()`, we keep the `device` argument but passing `device` is discouraged. And if not given, the default `device` is still 'cuda' for BC.
Additionally, based on device-agnostic pin_memory, `pin_memory_device` argument of `torch.utils.data.DataLoader` is discouraged now. For BC, explictly passing this argument is still effective. If not given, the default `device` will be the current accelerator.
Fixes#124908
Relates https://github.com/pytorch/pytorch/pull/126376
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131858
Approved by: https://github.com/albanD
Co-authored-by: albanD <desmaison.alban@gmail.com>
* Automatically applies ruff rule 401. Turns loops into equivalent list comprehensions which are faster and do not leak the scope of the loop variables.
* list comprehensions not only often have better typing, but are 50+% faster than for loops on overhead. They also preserve length information etc and are better for the interpreter to optimize.
* Manually went back and made mypy happy after the change.
* Also fixed style lints in files covered by flake8 but not by pyfmt
Pull Request resolved: https://github.com/pytorch/pytorch/pull/140980
Approved by: https://github.com/justinchuby, https://github.com/malfet
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
Fixes https://github.com/pytorch/pytorch/issues/118129
Suppressions automatically added with
```
import re
with open("error_file.txt", "r") as f:
errors = f.readlines()
error_lines = {}
for error in errors:
match = re.match(r"(.*):(\d+):\d+: error:.*\[(.*)\]", error)
if match:
file_path, line_number, error_type = match.groups()
if file_path not in error_lines:
error_lines[file_path] = {}
error_lines[file_path][int(line_number)] = error_type
for file_path, lines in error_lines.items():
with open(file_path, "r") as f:
code = f.readlines()
for line_number, error_type in sorted(lines.items(), key=lambda x: x[0], reverse=True):
code[line_number - 1] = code[line_number - 1].rstrip() + f" # type: ignore[{error_type}]\n"
with open(file_path, "w") as f:
f.writelines(code)
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Co-authored-by: Catherine Lee <csl@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118533
Approved by: https://github.com/Skylion007, https://github.com/zou3519
Fixes https://github.com/pytorch/pytorch/issues/118129
Suppressions automatically added with
```
import re
with open("error_file.txt", "r") as f:
errors = f.readlines()
error_lines = {}
for error in errors:
match = re.match(r"(.*):(\d+):\d+: error:.*\[(.*)\]", error)
if match:
file_path, line_number, error_type = match.groups()
if file_path not in error_lines:
error_lines[file_path] = {}
error_lines[file_path][int(line_number)] = error_type
for file_path, lines in error_lines.items():
with open(file_path, "r") as f:
code = f.readlines()
for line_number, error_type in sorted(lines.items(), key=lambda x: x[0], reverse=True):
code[line_number - 1] = code[line_number - 1].rstrip() + f" # type: ignore[{error_type}]\n"
with open(file_path, "w") as f:
f.writelines(code)
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118533
Approved by: https://github.com/Skylion007, https://github.com/zou3519
Alternative to https://github.com/pytorch/pytorch/pull/107034, implements @ezyang 's suggestion from https://github.com/pytorch/pytorch/pull/107034#discussion_r1292857201.
This PR addresses https://fb.workplace.com/groups/pytorch.oss.dev/posts/1699944830430051 and does a bunch of stacked changes:
- Make `Generator` class support GC;this makes all `Generator` instances tracked and accessile through Python's GC.
- Use the GC to retrieve all existing Generator instances in Dataloader's `_worker_loop` and re-seed them: this extends what is already applied to the global/default Generator, which is already re-seeded.
~TODO: a bit of docs and justification, which I'll do if this PR is mergeable.~ -- Done
CC @albanD @ezyang as previously discussed
BC-Breaking Note
-------------------
We now re-seed all `Generator` instances within the `Dataloader` workers' loop to ensure that their RNG is different across workers.
Previously, the RNG of user-defined `Generators` would be the same across workers, which could lead to wrong training procedures. This only affects user-defined `Generators`, not the default `Generator` (which was already re-seeded).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107131
Approved by: https://github.com/ezyang
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
The [fastNLP](https://github.com/fastnlp/fastNLP/blob/v0.6.0/fastNLP/core/batch.py#L51) model uses DataSetGetter to fetch data from the dataset. The following code breaks because of https://github.com/pytorch/pytorch/pull/84301:
```
from fastNLP.io.pipe.qa import CMRC2018BertPipe
input_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), ".data", "cmrc2018-sim")
data_bundle = CMRC2018BertPipe().process_from_file(paths=input_dir)
data_bundle.rename_field('chars', 'words')
data_bundle.get_dataset('dev')
dataset = DataSetGetter(dataset, as_numpy)
dataiter = torch.utils.data.DataLoader(dataset=dataset)
for batch in dataiter:
# data-processing...
```
This is because for the `DataSetGetter` class, the following condition holds:
```
# hasattr(dataset_getter, '__getitems__') == True
# dataset_getter.__getitems__ == None
```
This PR adds an additional check to make sure `__getitems__` is only called when it is not None.
This error was found by the torchbench nightly CI, original error stack trace:
```
ERROR: test_fastNLP_Bert_train_cuda (__main__.TestBenchmark)
----------------------------------------------------------------------
components._impl.workers.subprocess_rpc.ChildTraceException: Traceback (most recent call last):
File "/home/circleci/project/components/_impl/workers/subprocess_rpc.py", line 470, in _run_block
exec( # noqa: P204
File "<subprocess-worker>", line 35, in <module>
File "<subprocess-worker>", line 12, in _run_in_worker_f
File "/home/circleci/project/torchbenchmark/util/model.py", line 16, in __call__
obj = type.__call__(cls, *args, **kwargs)
File "/home/circleci/project/torchbenchmark/models/fastNLP_Bert/__init__.py", line 93, in __init__
self.example_inputs = self._prefetch(example_inputs)
File "/home/circleci/project/torchbenchmark/models/fastNLP_Bert/__init__.py", line 133, in _prefetch
for batch_x, batch_y in example_inputs:
File "/home/circleci/miniconda3/lib/python3.8/site-packages/fastNLP/core/batch.py", line 266, in __iter__
for indices, batch_x, batch_y in self.dataiter:
File "/home/circleci/miniconda3/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 681, in __next__
data = self._next_data()
File "/home/circleci/miniconda3/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 719, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/home/circleci/miniconda3/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 56, in fetch
data = self.dataset.__getitems__(possibly_batched_index)
TypeError: 'NoneType' object is not callable
```
Full error log: https://app.circleci.com/pipelines/github/pytorch/benchmark/5143/workflows/0676f36d-0ab4-42bd-adb4-90e6b0df76d1/jobs/5293
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85099
Approved by: https://github.com/ejguan
This PR requires PR is landed: https://github.com/pytorch/pytorch/pull/83202
## changes
- For `apply_shuffle_setting` and `apply_shuffle_seed`, it makes sure it will apply shuffle setting to each of DataPipe that contains a method called `set_shuffle` or `set_seed`.
- Change the API from `apply_shuffle_seed` to `apply_random_seed`.
- Fix a bug that `apply_shuffle_seed` only accepts DataPipe that is hashable. After the PR, this function uses `id` to prevent seeding the same DataPipe multiple times per epoch.
- Fix another bug from `shuffler` that `reset` with `_enable=False` would also reset `_seed`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83741
Approved by: https://github.com/NivekT
Summary: This diffs add a check in the fetcher, that if the dataset to be fetched has a function "getitems" then use it for fetching a batch of elements, as oppose to one by one. This is benefical for io bounded usage.
Differential Revision: D39145980
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84301
Approved by: https://github.com/VitalyFedyunin
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
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
