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prepare for "__floordiv__ is deprecated and its behavior will change in a future version of pytorch" (#20211)

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* rounding_mode = "floor"  instead of // to prevent behavioral change

* add other TODO

* use `torch_int_div` from pytrch_utils

* same for tests

* fix copies

* style

* use relative imports when needed

* Co-authored-by: sgugger <sylvain.gugger@gmail.com>
This commit is contained in:
Arthur
2023-03-01 10:49:21 +01:00
committed by GitHub
parent b29e2dcaff
commit 44e3e3fb49
15 changed files with 53 additions and 51 deletions
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13
tests/generation/test_beam_search.py
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@@ -32,6 +32,7 @@ if is_torch_available():
DisjunctiveConstraint,
PhrasalConstraint,
)
from transformers.pytorch_utils import torch_int_div
class BeamSearchTester:
@@ -160,10 +161,8 @@ class BeamSearchTester:
expected_output_scores = cut_expected_tensor(next_scores)
# add num_beams * batch_idx
expected_output_indices = (
cut_expected_tensor(next_indices)
+ (torch.arange(self.num_beams * self.batch_size, device=torch_device) // self.num_beams) * self.num_beams
)
offset = torch_int_div(torch.arange(self.num_beams * self.batch_size, device=torch_device), self.num_beams)
expected_output_indices = cut_expected_tensor(next_indices) + offset * self.num_beams
self.parent.assertListEqual(expected_output_tokens.tolist(), output_tokens.tolist())
self.parent.assertListEqual(expected_output_indices.tolist(), output_indices.tolist())
@@ -399,10 +398,8 @@ class ConstrainedBeamSearchTester:
expected_output_scores = cut_expected_tensor(next_scores)
# add num_beams * batch_idx
expected_output_indices = (
cut_expected_tensor(next_indices)
+ (torch.arange(self.num_beams * self.batch_size, device=torch_device) // self.num_beams) * self.num_beams
)
offset = torch_int_div(torch.arange(self.num_beams * self.batch_size, device=torch_device), self.num_beams)
expected_output_indices = cut_expected_tensor(next_indices) + offset * self.num_beams
self.parent.assertListEqual(expected_output_tokens.tolist(), output_tokens.tolist())
self.parent.assertListEqual(expected_output_indices.tolist(), output_indices.tolist())

13
tests/models/wav2vec2/test_modeling_wav2vec2.py
View File

@@ -71,7 +71,7 @@ if is_torch_available():
_compute_mask_indices,
_sample_negative_indices,
)
from transformers.pytorch_utils import is_torch_less_than_1_9
from transformers.pytorch_utils import is_torch_less_than_1_9, torch_int_div
else:
is_torch_less_than_1_9 = True
@@ -1217,10 +1217,8 @@ class Wav2Vec2UtilsTest(unittest.TestCase):
sequence_length = 10
hidden_size = 4
num_negatives = 3
features = (torch.arange(sequence_length * hidden_size, device=torch_device) // hidden_size).view(
sequence_length, hidden_size
) # each value in vector consits of same value
sequence = torch_int_div(torch.arange(sequence_length * hidden_size, device=torch_device), hidden_size)
features = sequence.view(sequence_length, hidden_size) # each value in vector consits of same value
features = features[None, :].expand(batch_size, sequence_length, hidden_size).contiguous()
# sample negative indices
@@ -1247,9 +1245,8 @@ class Wav2Vec2UtilsTest(unittest.TestCase):
mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device)
mask[-1, sequence_length // 2 :] = 0
features = (torch.arange(sequence_length * hidden_size, device=torch_device) // hidden_size).view(
sequence_length, hidden_size
) # each value in vector consits of same value
sequence = torch_int_div(torch.arange(sequence_length * hidden_size, device=torch_device), hidden_size)
features = sequence.view(sequence_length, hidden_size) # each value in vector consits of same value
features = features[None, :].expand(batch_size, sequence_length, hidden_size).contiguous()
# replace masked feature vectors with -100 to test that those are not sampled