[DDP] Add `PackedSequence` support when `device_ids` is specified (#86614)
Before this PR, if a user runs DDP with `device_ids` specified and with a `PackedSequence` input, then the execution will error with something like:
```
raise ValueError(
ValueError: batch_sizes should always be on CPU. Instances of PackedSequence should never be created manually. They should be instantiated by
functions like pack_sequence and pack_padded_sequences in nn.utils.rnn. https://pytorch.org/docs/stable/nn.html...
```
This is because the DDP forward calls `_to_kwargs()`, which calls `_recursive_to()`, which moves the inputs to GPU. However, `_is_namedtuple(packed_sequence)` returns `True`, leading to the branch `return [type(obj)(*args) for args in zip(*map(to_map, obj))]`, which tries to construct a `PackedSequence` directly via `type(obj)(*args)`, leading to the error.
Repro for `_is_namedtuple(packed_sequence)` returning `True`:
```
import random
import torch
import torch.nn.utils.rnn as rnn_utils
from torch.nn.parallel.scatter_gather import _is_namedtuple
def _ordered_sequence(tensor_type):
seqs = [tensor_type(random.randint(1, 256))
for _ in range(32)]
seqs = [s.random_(-128, 128) for s in seqs]
ordered = sorted(seqs, key=len, reverse=True)
return ordered
def _padded_sequence(tensor_type):
ordered = _ordered_sequence(tensor_type)
lengths = [len(i) for i in ordered]
padded_tensor = rnn_utils.pad_sequence(ordered)
return padded_tensor, lengths
padded, lengths = _padded_sequence(torch.Tensor)
packed = rnn_utils.pack_padded_sequence(
padded, lengths, enforce_sorted=False)
print(type(packed), packed.data.device)
print(_is_namedtuple(packed))
```
Test Plan:
```
python test/distributed/test_c10d_nccl.py -k test_ddp_packed_sequence
```
Without the fix, the added unit test fails with the expected error.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86614
Approved by: https://github.com/rohan-varma
