mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-20 21:14:14 +08:00
df458be4e5
```torch/fx/passes/annotate_getitem_nodes.py``` was changed to support the new type hinting annotations. Pull Request resolved: https://github.com/pytorch/pytorch/pull/143257 Approved by: https://github.com/justinchuby, https://github.com/albanD
498 lines
20 KiB
Python
498 lines
20 KiB
Python
# Owner(s): ["module: nn"]
|
|
|
|
import itertools
|
|
import random
|
|
|
|
import torch
|
|
import torch.nn.utils.rnn as rnn_utils
|
|
from torch.testing._internal.common_utils import run_tests, TestCase
|
|
|
|
|
|
class PackedSequenceTest(TestCase):
|
|
_type_by_name = {
|
|
"torch.DoubleTensor": (torch.DoubleTensor, "double"),
|
|
"torch.FloatTensor": (torch.FloatTensor, "float"),
|
|
# We leave out `'torch.HalfTensor': (torch.HalfTensor, 'half'),`
|
|
# because of an error in `pad_packed_sequence`
|
|
# > AttributeError: 'torch.HalfTensor' object has no attribute 'fill_'
|
|
"torch.LongTensor": (torch.LongTensor, "long"),
|
|
"torch.IntTensor": (torch.IntTensor, "int"),
|
|
"torch.ShortTensor": (torch.ShortTensor, "short"),
|
|
"torch.CharTensor": (torch.CharTensor, "char"),
|
|
"torch.ByteTensor": (torch.ByteTensor, "byte"),
|
|
}
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
self.batch_size = 5
|
|
self.max_length = 6
|
|
|
|
def _ordered_sequence(self, tensor_type):
|
|
"""Create ordered list of random sequences"""
|
|
seqs = [
|
|
tensor_type(random.randint(1, self.max_length))
|
|
for _ in range(self.batch_size)
|
|
]
|
|
if tensor_type == torch.ByteTensor:
|
|
seqs = [s.random_(0, 256) for s in seqs]
|
|
else:
|
|
seqs = [s.random_(-128, 128) for s in seqs]
|
|
ordered = sorted(seqs, key=len, reverse=True)
|
|
return ordered
|
|
|
|
def _padded_sequence(self, tensor_type):
|
|
"""Create Tensor of random padded sequences"""
|
|
ordered = self._ordered_sequence(tensor_type)
|
|
lengths = [len(i) for i in ordered]
|
|
padded_tensor = rnn_utils.pad_sequence(ordered)
|
|
return padded_tensor, lengths
|
|
|
|
def test_type_casts(self):
|
|
"""Test type casting of `PackedSequence` against type casting of tensor"""
|
|
for input_type, _ in self._type_by_name.values():
|
|
for expected_type_str, (_, cast_str) in self._type_by_name.items():
|
|
for enforce_sorted in [True, False]:
|
|
padded, lengths = self._padded_sequence(input_type)
|
|
packed = rnn_utils.pack_padded_sequence(
|
|
padded, lengths, enforce_sorted=enforce_sorted
|
|
)
|
|
# Apply cast to `PackedSequence` instance and unpack
|
|
masked = getattr(packed, cast_str)()
|
|
unpacked, _ = rnn_utils.pad_packed_sequence(masked)
|
|
self.assertEqual(unpacked.type(), expected_type_str)
|
|
|
|
def test_wrong_order(self):
|
|
a = torch.ones(25, 300)
|
|
b = torch.ones(22, 300)
|
|
b_a = rnn_utils.pad_sequence([b, a])
|
|
self.assertRaises(
|
|
RuntimeError,
|
|
lambda: rnn_utils.pack_padded_sequence(b_a, [22, 25], enforce_sorted=True),
|
|
)
|
|
|
|
def test_pad_sequence_with_tensor_sequences(self):
|
|
seq_tuple_input = torch.nn.utils.rnn.pad_sequence(
|
|
(torch.tensor([[7, 6]]), torch.tensor([[-7, -1]]))
|
|
)
|
|
seq_tensor_input = torch.nn.utils.rnn.pad_sequence(
|
|
torch.tensor([[[7, 6]], [[-7, -1]]])
|
|
)
|
|
self.assertEqual(seq_tuple_input, seq_tensor_input)
|
|
self.assertEqual(seq_tuple_input.shape, torch.Size([1, 2, 2]))
|
|
|
|
def test_pad_sequence_with_non_iterable_sequences(self):
|
|
msg = r"Expected iterable for input sequences, but got arg of type"
|
|
with self.assertRaisesRegex(RuntimeError, msg):
|
|
torch.nn.utils.rnn.pad_sequence(5)
|
|
|
|
def test_total_length(self):
|
|
padded, lengths = self._padded_sequence(torch.FloatTensor)
|
|
max_length = max(lengths)
|
|
packed = rnn_utils.pack_padded_sequence(padded, lengths)
|
|
# test ValueError if total_length < max_length
|
|
for total_length in (-1, 0, max_length - 1):
|
|
for batch_first in (True, False):
|
|
|
|
def err_fn():
|
|
rnn_utils.pad_packed_sequence(
|
|
packed, batch_first=batch_first, total_length=total_length
|
|
)
|
|
|
|
self.assertRaisesRegex(
|
|
ValueError,
|
|
r"Expected total_length to be at least the "
|
|
r"length of the longest sequence in input",
|
|
err_fn,
|
|
)
|
|
# test that pad_packed_sequence returns results of correct length
|
|
for batch_first in (True, False):
|
|
no_extra_pad, _ = rnn_utils.pad_packed_sequence(
|
|
packed, batch_first=batch_first
|
|
)
|
|
for total_length_delta in (0, 1, 8):
|
|
total_length = max_length + total_length_delta
|
|
unpacked, lengths_out = rnn_utils.pad_packed_sequence(
|
|
packed, batch_first=batch_first, total_length=total_length
|
|
)
|
|
self.assertEqual(lengths, lengths_out)
|
|
self.assertEqual(unpacked.size(1 if batch_first else 0), total_length)
|
|
if total_length_delta == 0:
|
|
ref_output = no_extra_pad
|
|
elif batch_first:
|
|
extra_pad = no_extra_pad.new_zeros(
|
|
self.batch_size, total_length_delta
|
|
)
|
|
ref_output = torch.cat([no_extra_pad, extra_pad], 1)
|
|
else:
|
|
extra_pad = no_extra_pad.new_zeros(
|
|
total_length_delta, self.batch_size
|
|
)
|
|
ref_output = torch.cat([no_extra_pad, extra_pad], 0)
|
|
self.assertEqual(unpacked, ref_output)
|
|
|
|
def test_to(self):
|
|
for enforce_sorted in (True, False):
|
|
padded, lengths = self._padded_sequence(torch.IntTensor)
|
|
a = rnn_utils.pack_padded_sequence(
|
|
padded, lengths, enforce_sorted=enforce_sorted
|
|
).cpu()
|
|
|
|
self.assertIs(a, a.to("cpu"))
|
|
self.assertIs(a, a.cpu())
|
|
self.assertIs(a, a.to("cpu", dtype=torch.int32))
|
|
self.assertEqual(a.long(), a.to(torch.int64))
|
|
|
|
if torch.cuda.is_available():
|
|
for cuda in [
|
|
"cuda",
|
|
"cuda:0" if torch.cuda.device_count() == 1 else "cuda:1",
|
|
]:
|
|
b = a.cuda(device=cuda)
|
|
self.assertIs(b, b.to(cuda))
|
|
self.assertIs(b, b.cuda())
|
|
self.assertEqual(a, b.to("cpu"))
|
|
self.assertEqual(b, a.to(cuda))
|
|
self.assertEqual(a, b.to("cpu", dtype=torch.int32))
|
|
self.assertIs(b, b.to(dtype=torch.int32))
|
|
self.assertEqual(b.long(), b.to(dtype=torch.int64))
|
|
|
|
def test_to_memory_format(self):
|
|
m = torch.nn.Conv2d(in_channels=16, out_channels=32, kernel_size=2, bias=True)
|
|
m = m.to(memory_format=torch.channels_last)
|
|
for param in m.parameters():
|
|
if param.dim() == 4:
|
|
self.assertTrue(param.is_contiguous(memory_format=torch.channels_last))
|
|
|
|
def test_pad_sequence(self):
|
|
def pad(tensor, length):
|
|
return torch.cat(
|
|
[
|
|
tensor.data,
|
|
tensor.data.new(
|
|
length - tensor.size(0), *tensor.size()[1:]
|
|
).zero_(),
|
|
]
|
|
)
|
|
|
|
# single dimensional
|
|
a = torch.tensor([1, 2, 3])
|
|
b = torch.tensor([4, 5])
|
|
c = torch.tensor([6])
|
|
|
|
# batch_first = true
|
|
expected = torch.tensor([[4, 5, 0], [1, 2, 3], [6, 0, 0]])
|
|
padded = rnn_utils.pad_sequence([b, a, c], True)
|
|
self.assertEqual(padded, expected)
|
|
|
|
# batch_first = false
|
|
padded = rnn_utils.pad_sequence([b, a, c])
|
|
self.assertEqual(padded, expected.transpose(0, 1))
|
|
|
|
# padding_side = "left", batch_first=True
|
|
expected = torch.tensor([[0, 4, 5], [1, 2, 3], [0, 0, 6]])
|
|
padded = rnn_utils.pad_sequence(
|
|
[b, a, c],
|
|
batch_first=True,
|
|
padding_side="left",
|
|
)
|
|
self.assertEqual(padded, expected)
|
|
|
|
# padding_side = "left", batch_first=False
|
|
padded = rnn_utils.pad_sequence(
|
|
[b, a, c],
|
|
batch_first=False,
|
|
padding_side="left",
|
|
)
|
|
self.assertEqual(padded, expected.transpose(0, 1))
|
|
|
|
# pad with non-zero value
|
|
expected = torch.tensor([[4, 5, 1], [1, 2, 3], [6, 1, 1]])
|
|
padded = rnn_utils.pad_sequence([b, a, c], True, 1)
|
|
self.assertEqual(padded, expected)
|
|
|
|
# Test pad sorted sequence
|
|
expected = torch.tensor([[1, 2, 3], [4, 5, 0], [6, 0, 0]])
|
|
padded = rnn_utils.pad_sequence([a, b, c], True)
|
|
self.assertEqual(padded, expected)
|
|
|
|
# more dimensions
|
|
maxlen = 9
|
|
for num_dim in (0, 1, 2, 3):
|
|
sequences: list[torch.Tensor] = []
|
|
trailing_dims = [4] * num_dim
|
|
for i in range(1, maxlen + 1):
|
|
seq_len = i * i
|
|
sequences.append(torch.rand(seq_len, 5, *trailing_dims))
|
|
random.shuffle(sequences)
|
|
# batch first = true
|
|
expected = torch.stack([pad(seq, maxlen * maxlen) for seq in sequences])
|
|
padded = rnn_utils.pad_sequence(sequences, True)
|
|
self.assertEqual(padded, expected)
|
|
|
|
# batch first = false
|
|
padded = rnn_utils.pad_sequence(sequences)
|
|
self.assertEqual(padded, expected.transpose(0, 1))
|
|
|
|
# padding_side = "left", batch_first=True
|
|
expected = torch.stack(
|
|
[pad(seq.flip(0), maxlen * maxlen).flip(0) for seq in sequences]
|
|
)
|
|
padded = rnn_utils.pad_sequence(
|
|
sequences,
|
|
batch_first=True,
|
|
padding_side="left",
|
|
)
|
|
self.assertEqual(padded, expected)
|
|
|
|
# padding_side = "left", batch_first=False
|
|
padded = rnn_utils.pad_sequence(
|
|
sequences,
|
|
batch_first=False,
|
|
padding_side="left",
|
|
)
|
|
self.assertEqual(padded, expected.transpose(0, 1))
|
|
|
|
def test_unpad_sequence(self):
|
|
# single dimensional
|
|
a = torch.tensor([1, 2, 3])
|
|
b = torch.tensor([4, 5])
|
|
c = torch.tensor([6])
|
|
sequences = [a, b, c]
|
|
|
|
lengths = torch.as_tensor([v.size(0) for v in sequences])
|
|
for batch_first in [True, False]:
|
|
padded_sequences = rnn_utils.pad_sequence(
|
|
sequences, batch_first=batch_first
|
|
)
|
|
unpadded_sequences = rnn_utils.unpad_sequence(
|
|
padded_sequences, lengths, batch_first=batch_first
|
|
)
|
|
self.assertEqual(sequences, unpadded_sequences)
|
|
|
|
# more dimensions
|
|
maxlen = 9
|
|
for num_dim in (0, 1, 2, 3):
|
|
sequences = []
|
|
trailing_dims = [4] * num_dim
|
|
for i in range(1, maxlen + 1):
|
|
seq_len = i * i
|
|
sequences.append(torch.rand(seq_len, 5, *trailing_dims))
|
|
random.shuffle(sequences)
|
|
|
|
lengths = torch.as_tensor([v.size(0) for v in sequences])
|
|
padded_sequences = rnn_utils.pad_sequence(
|
|
sequences, batch_first=batch_first
|
|
)
|
|
unpadded_sequences = rnn_utils.unpad_sequence(
|
|
padded_sequences, lengths, batch_first=batch_first
|
|
)
|
|
self.assertEqual(sequences, unpadded_sequences)
|
|
|
|
def test_pack_sequence(self):
|
|
def _compatibility_test(sequences, lengths, batch_first, enforce_sorted=False):
|
|
padded = rnn_utils.pad_sequence(sequences, batch_first)
|
|
packed = rnn_utils.pack_sequence(sequences, enforce_sorted)
|
|
unpacked = rnn_utils.pad_packed_sequence(packed, batch_first)
|
|
self.assertEqual(padded, unpacked[0])
|
|
pack_padded = rnn_utils.pack_padded_sequence(
|
|
padded, lengths, batch_first, enforce_sorted
|
|
)
|
|
self.assertEqual(packed, pack_padded)
|
|
|
|
# single dimensional
|
|
a = torch.tensor([1, 2, 3])
|
|
b = torch.tensor([4, 5])
|
|
c = torch.tensor([6])
|
|
packed = rnn_utils.pack_sequence([a, b, c], enforce_sorted=False)
|
|
expected = torch.tensor([1, 4, 6, 2, 5, 3])
|
|
self.assertEqual(packed.batch_sizes, [3, 2, 1])
|
|
self.assertEqual(packed.data.data, expected)
|
|
self.assertEqual(packed.sorted_indices, [0, 1, 2])
|
|
self.assertEqual(packed.unsorted_indices, [0, 1, 2])
|
|
|
|
packed_unsorted = rnn_utils.pack_sequence([b, c, a], enforce_sorted=False)
|
|
self.assertEqual(packed_unsorted.batch_sizes, [3, 2, 1])
|
|
self.assertEqual(packed_unsorted.data.data, expected)
|
|
self.assertEqual(packed_unsorted.sorted_indices, [2, 0, 1])
|
|
self.assertEqual(packed_unsorted.unsorted_indices, [1, 2, 0])
|
|
|
|
# single dimensional, enforce_sorted = True
|
|
packed_enforce_sorted = rnn_utils.pack_sequence([a, b, c], enforce_sorted=True)
|
|
self.assertEqual(packed_enforce_sorted.batch_sizes, [3, 2, 1])
|
|
self.assertEqual(packed_enforce_sorted.data.data, expected)
|
|
self.assertTrue(packed_enforce_sorted.sorted_indices is None)
|
|
self.assertTrue(packed_enforce_sorted.unsorted_indices is None)
|
|
|
|
with self.assertRaisesRegex(RuntimeError, "must be sorted in decreasing order"):
|
|
rnn_utils.pack_sequence([b, c, a], enforce_sorted=True)
|
|
|
|
with self.assertRaisesRegex(
|
|
RuntimeError, "You can pass `enforce_sorted=False`"
|
|
):
|
|
rnn_utils.pack_sequence([b, c, a], enforce_sorted=True)
|
|
|
|
# more dimensions
|
|
maxlen = 9
|
|
for num_dim in (0, 1, 2, 3):
|
|
sequences = []
|
|
lengths = []
|
|
trailing_dims = [4] * num_dim
|
|
for i in range(maxlen, 0, -1):
|
|
seq_len = i * i
|
|
lengths.append(seq_len)
|
|
sequences.append(torch.rand(seq_len, 5, *trailing_dims))
|
|
unsorted_sequences = [s.clone() for s in sequences]
|
|
random.shuffle(unsorted_sequences)
|
|
unsorted_sequences_lengths = [t.size(0) for t in unsorted_sequences]
|
|
|
|
# compatibility with other utilities
|
|
for batch_first in (True, False):
|
|
for enforce_sorted in (True, False):
|
|
_compatibility_test(sequences, lengths, batch_first, enforce_sorted)
|
|
_compatibility_test(
|
|
unsorted_sequences, unsorted_sequences_lengths, batch_first
|
|
)
|
|
|
|
def test_unpack_sequence(self):
|
|
# single dimensional
|
|
a = torch.tensor([1, 2, 3])
|
|
b = torch.tensor([4, 5])
|
|
c = torch.tensor([6])
|
|
sequences = [a, b, c]
|
|
|
|
packed_sequences = rnn_utils.pack_sequence(sequences, enforce_sorted=False)
|
|
unpacked_sequences = rnn_utils.unpack_sequence(packed_sequences)
|
|
self.assertEqual(sequences, unpacked_sequences)
|
|
|
|
# more dimensions
|
|
maxlen = 9
|
|
for num_dim in (0, 1, 2, 3):
|
|
sequences = []
|
|
trailing_dims = [4] * num_dim
|
|
for i in range(1, maxlen + 1):
|
|
seq_len = i * i
|
|
sequences.append(torch.rand(seq_len, 5, *trailing_dims))
|
|
random.shuffle(sequences)
|
|
|
|
packed_sequences = rnn_utils.pack_sequence(sequences, enforce_sorted=False)
|
|
unpacked_sequences = rnn_utils.unpack_sequence(packed_sequences)
|
|
self.assertEqual(sequences, unpacked_sequences)
|
|
|
|
def test_pack_padded_sequence(self):
|
|
def generate_test_case(sorted_lengths, should_shuffle):
|
|
def pad(tensor, length):
|
|
return torch.cat(
|
|
[
|
|
tensor,
|
|
tensor.new(length - tensor.size(0), *tensor.size()[1:]).zero_(),
|
|
]
|
|
)
|
|
|
|
max_length = sorted_lengths[0]
|
|
batch_sizes = [
|
|
sum(map(bool, filter(lambda x: x >= i, sorted_lengths)))
|
|
for i in range(1, max_length + 1)
|
|
]
|
|
padded = torch.cat(
|
|
[
|
|
pad(
|
|
i * 100 + torch.arange(1.0, 5 * l + 1).view(l, 1, 5), max_length
|
|
)
|
|
for i, l in enumerate(sorted_lengths, 1)
|
|
],
|
|
1,
|
|
)
|
|
expected_data = [
|
|
[
|
|
torch.arange(1.0, 6) + (i + 1) * 100 + 5 * n
|
|
for i in range(batch_size)
|
|
]
|
|
for n, batch_size in enumerate(batch_sizes)
|
|
]
|
|
expected_data = list(itertools.chain.from_iterable(expected_data))
|
|
expected_data = torch.stack(expected_data, dim=0)
|
|
|
|
if should_shuffle:
|
|
# Shuffle the padded sequence to create an unsorted sequence
|
|
permutation = list(range(len(sorted_lengths)))
|
|
random.shuffle(permutation)
|
|
|
|
unsorted_indices = torch.tensor(permutation)
|
|
padded = padded.index_select(1, unsorted_indices)
|
|
lengths = torch.tensor(sorted_lengths).index_select(0, unsorted_indices)
|
|
else:
|
|
unsorted_indices = None
|
|
lengths = sorted_lengths
|
|
|
|
return (
|
|
padded.requires_grad_(),
|
|
lengths,
|
|
expected_data,
|
|
batch_sizes,
|
|
unsorted_indices,
|
|
)
|
|
|
|
test_cases = [
|
|
# sorted_lengths, should_shuffle
|
|
[[10, 8, 4, 2, 2, 2, 1], False],
|
|
[[11, 10, 8, 6, 4, 3, 1], False],
|
|
[[11, 10, 8, 6, 4, 3, 1], True],
|
|
]
|
|
|
|
for test_case, batch_first in itertools.product(test_cases, (True, False)):
|
|
sorted_lengths, should_shuffle = test_case
|
|
(
|
|
padded,
|
|
lengths,
|
|
expected_data,
|
|
batch_sizes,
|
|
unsorted_indices,
|
|
) = generate_test_case(sorted_lengths, should_shuffle)
|
|
|
|
src = padded
|
|
if batch_first:
|
|
src = src.transpose(0, 1)
|
|
|
|
# check output
|
|
packed = rnn_utils.pack_padded_sequence(
|
|
src, lengths, batch_first=batch_first, enforce_sorted=not should_shuffle
|
|
)
|
|
self.assertEqual(packed.data.data, expected_data)
|
|
self.assertEqual(packed.batch_sizes, batch_sizes)
|
|
self.assertEqual(packed.unsorted_indices, unsorted_indices)
|
|
|
|
# test inverse
|
|
unpacked, unpacked_len = rnn_utils.pad_packed_sequence(
|
|
packed, batch_first=batch_first
|
|
)
|
|
self.assertEqual(unpacked, src)
|
|
self.assertEqual(unpacked_len, lengths)
|
|
|
|
# check grad
|
|
if padded.grad is not None:
|
|
padded.grad.data.zero_()
|
|
grad_output = unpacked.data.clone().normal_()
|
|
unpacked.backward(grad_output)
|
|
if batch_first:
|
|
grad_output.transpose_(0, 1)
|
|
for i, l in enumerate(lengths):
|
|
self.assertEqual(padded.grad.data[:l, i], grad_output[:l, i])
|
|
if l < 10:
|
|
self.assertEqual(padded.grad.data[l:, i].abs().sum(), 0)
|
|
|
|
# test error messages
|
|
with self.assertRaisesRegex(
|
|
RuntimeError, "You can pass `enforce_sorted=False`"
|
|
):
|
|
packed = rnn_utils.pack_padded_sequence(torch.randn(3, 3), [1, 3, 2])
|
|
with self.assertRaisesRegex(RuntimeError, "empty tensor"):
|
|
packed = rnn_utils.pack_padded_sequence(torch.randn(0, 0), [])
|
|
with self.assertRaisesRegex(RuntimeError, "empty tensor"):
|
|
packed = rnn_utils.pack_padded_sequence(
|
|
torch.randn([0, 1, 10]), torch.randn([11, 14, 14, 2]), True
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
run_tests()
|