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Seq2seq trainer (#9241)

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* Add label smoothing in Trainer

* Add options for scheduler and Adafactor in Trainer

* Put Seq2SeqTrainer in the main lib

* Apply suggestions from code review

Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Address review comments and adapt scripts

* Documentation

* Move test not using script to tests folder

Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
This commit is contained in:
Sylvain Gugger
2020-12-22 11:33:44 -05:00
committed by GitHub
parent 1fc7119181
commit 490b39e614
20 changed files with 655 additions and 166 deletions
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33
tests/test_trainer_utils.py
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@@ -22,7 +22,10 @@ from transformers.testing_utils import require_torch
if is_torch_available():
from transformers.trainer_pt_utils import DistributedTensorGatherer
import torch
from transformers.modeling_outputs import SequenceClassifierOutput
from transformers.trainer_pt_utils import DistributedTensorGatherer, LabelSmoother
@require_torch
@@ -56,3 +59,31 @@ class TrainerUtilsTest(unittest.TestCase):
self.assertTrue(np.array_equal(result[0], predictions))
self.assertTrue(np.array_equal(result[1][0], predictions))
self.assertTrue(np.array_equal(result[1][1], predictions))
def test_label_smoothing(self):
epsilon = 0.1
num_labels = 12
random_logits = torch.randn(4, 5, num_labels)
random_labels = torch.randint(0, num_labels, (4, 5))
loss = torch.nn.functional.cross_entropy(random_logits.view(-1, num_labels), random_labels.view(-1))
model_output = SequenceClassifierOutput(loss=loss, logits=random_logits)
label_smoothed_loss = LabelSmoother(0.1)(model_output, random_labels)
log_probs = -torch.nn.functional.log_softmax(random_logits, dim=-1)
expected_loss = (1 - epsilon) * loss + epsilon * log_probs.mean()
self.assertTrue(torch.allclose(label_smoothed_loss, expected_loss))
# With a few -100 labels
random_labels[0, 1] = -100
random_labels[2, 1] = -100
random_labels[2, 3] = -100
loss = torch.nn.functional.cross_entropy(random_logits.view(-1, num_labels), random_labels.view(-1))
model_output = SequenceClassifierOutput(loss=loss, logits=random_logits)
label_smoothed_loss = LabelSmoother(0.1)(model_output, random_labels)
log_probs = -torch.nn.functional.log_softmax(random_logits, dim=-1)
# Mask the log probs with the -100 labels
log_probs[0, 1] = 0.0
log_probs[2, 1] = 0.0
log_probs[2, 3] = 0.0
expected_loss = (1 - epsilon) * loss + epsilon * log_probs.sum() / (num_labels * 17)
self.assertTrue(torch.allclose(label_smoothed_loss, expected_loss))