…97863)

Revisit `torch._six.string_classes` (which is `(str, bytes)`) removal: `isinstance(obj, string_classes) -> isinstance(obj, str)`.

Both `str` and `bytes` are `Sequence` classes.

```python
In [1]: from typing import Sequence

In [2]: issubclass(bytes, Sequence)
Out[2]: True

In [3]: issubclass(str, Sequence)
Out[3]: True
```

Re-add `bytes` to type guards like:

```python
def is_seq(obj):
    return isinstance(obj, Sequence) and not isinstance(obj, (str, bytes))
```

Ref:

- pytorch#94709 (comment)
- pytorch#97737
- pytorch#97789
Pull Request resolved: pytorch#97863
Approved by: https://github.com/Skylion007, https://github.com/albanD

…97789, #97863) (#98055)

* [DataLoader] Short circuit pin_memory recursion when operating on bytes (#97737)

Slack thread: https://pytorch.slack.com/archives/GEEQ2K4MD/p1679962409906099

I was seeing some massive (~2x) slowdowns on a job after running it on PyTorch 2.0. From some profiling in `py-spy` it looked like the pin_memory thread was doing a lot more work than before. Looking at a trace in `nsys` I saw the thread doing the forward pass having a bunch of `pthread_cond_timedwait` with GIL reacquire calls in it’s call stack, and it seemed like the thread doing the forward pass was getting blocked (waiting for the GIL) by the pin memory thread (which was holding the GIL).

After some debugging I found out the issue. If a `bytes` was passed into `pin_memory`, previously in 1.13 (before #94709) it would short-circuit and return here
https://github.com/pytorch/pytorch/blob/d922c29a22e4bf0fba49526f7536395eb8cd66f4/torch/utils/data/_utils/pin_memory.py#L54-L55
since `bytes` was in `torch._six.string_classes`:
```
>>> from torch._six import string_classes
>>> string_classes
(<class 'str'>, <class 'bytes'>)
>>>
```

However after #94709, if a `bytes` was passed into `pin_memory` it would fall into here instead
https://github.com/pytorch/pytorch/blob/c263bd43e8e8502d4726643bc6fd046f0130ac0e/torch/utils/data/_utils/pin_memory.py#L68-L73
because the previous check is now doing `isinstance(data, str)` instead of `isinstance(data, (str, bytes))`!
https://github.com/pytorch/pytorch/blob/c263bd43e8e8502d4726643bc6fd046f0130ac0e/torch/utils/data/_utils/pin_memory.py#L56-L57

As a result, `pin_memory` gets called recursively for each element in the `bytes` leading to a ton of wasted recursion. This also explains the slowdown / GIL contention I was seeing.

This PR simply changes `isinstance(data, str)` to `isinstance(data, (str, bytes))` to match the behavior before #94709

Pull Request resolved: #97737
Approved by: https://github.com/albanD, https://github.com/NivekT

* [DataLoader] Fix  collation logic (#97789)

Similar to #97737, a previous auto-refactor changed how `bytes` are handled during collation, which can potentially lead to performance regression. This PR undoes that.
Pull Request resolved: #97789
Approved by: https://github.com/albanD

* Revisit `torch._six.string_classes` removal (#94709) (#97863)

Revisit `torch._six.string_classes` (which is `(str, bytes)`) removal: `isinstance(obj, string_classes) -> isinstance(obj, str)`.

Both `str` and `bytes` are `Sequence` classes.

```python
In [1]: from typing import Sequence

In [2]: issubclass(bytes, Sequence)
Out[2]: True

In [3]: issubclass(str, Sequence)
Out[3]: True
```

Re-add `bytes` to type guards like:

```python
def is_seq(obj):
    return isinstance(obj, Sequence) and not isinstance(obj, (str, bytes))
```

Ref:

- #94709 (comment)
- #97737
- #97789
Pull Request resolved: #97863
Approved by: https://github.com/Skylion007, https://github.com/albanD

---------

Co-authored-by: Eric Zhang <ezhang887@gmail.com>
Co-authored-by: Kevin Tse <ktse@fb.com>

According to pytorch/pytorch#97863 torch._six has been removed. I propose the following modification to avoid the error "module 'torch' has no attribute '_six'".
This solution is also suggested in other projects.