90 lines
3.8 KiB
Python
90 lines
3.8 KiB
Python
# coding=utf-8
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# Copyright 2018 the HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from transformers.file_utils import is_torch_available
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from transformers.testing_utils import require_torch
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if is_torch_available():
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import torch
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from transformers.modeling_outputs import SequenceClassifierOutput
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from transformers.trainer_pt_utils import DistributedTensorGatherer, LabelSmoother
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@require_torch
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class TrainerUtilsTest(unittest.TestCase):
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def test_distributed_tensor_gatherer(self):
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# Simulate a result with a dataset of size 21, 4 processes and chunks of lengths 2, 3, 1
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world_size = 4
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num_samples = 21
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input_indices = [
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[0, 1, 6, 7, 12, 13, 18, 19],
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[2, 3, 4, 8, 9, 10, 14, 15, 16, 20, 0, 1],
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[5, 11, 17, 2],
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]
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predictions = np.random.normal(size=(num_samples, 13))
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gatherer = DistributedTensorGatherer(world_size=world_size, num_samples=num_samples)
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for indices in input_indices:
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gatherer.add_arrays(predictions[indices])
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result = gatherer.finalize()
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self.assertTrue(np.array_equal(result, predictions))
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# With nested tensors
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gatherer = DistributedTensorGatherer(world_size=world_size, num_samples=num_samples)
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for indices in input_indices:
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gatherer.add_arrays([predictions[indices], [predictions[indices], predictions[indices]]])
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result = gatherer.finalize()
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self.assertTrue(isinstance(result, list))
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self.assertTrue(len(result), 2)
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self.assertTrue(isinstance(result[1], list))
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self.assertTrue(len(result[1]), 2)
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self.assertTrue(np.array_equal(result[0], predictions))
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self.assertTrue(np.array_equal(result[1][0], predictions))
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self.assertTrue(np.array_equal(result[1][1], predictions))
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def test_label_smoothing(self):
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epsilon = 0.1
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num_labels = 12
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random_logits = torch.randn(4, 5, num_labels)
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random_labels = torch.randint(0, num_labels, (4, 5))
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loss = torch.nn.functional.cross_entropy(random_logits.view(-1, num_labels), random_labels.view(-1))
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model_output = SequenceClassifierOutput(loss=loss, logits=random_logits)
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label_smoothed_loss = LabelSmoother(0.1)(model_output, random_labels)
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log_probs = -torch.nn.functional.log_softmax(random_logits, dim=-1)
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expected_loss = (1 - epsilon) * loss + epsilon * log_probs.mean()
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self.assertTrue(torch.allclose(label_smoothed_loss, expected_loss))
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# With a few -100 labels
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random_labels[0, 1] = -100
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random_labels[2, 1] = -100
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random_labels[2, 3] = -100
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loss = torch.nn.functional.cross_entropy(random_logits.view(-1, num_labels), random_labels.view(-1))
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model_output = SequenceClassifierOutput(loss=loss, logits=random_logits)
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label_smoothed_loss = LabelSmoother(0.1)(model_output, random_labels)
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log_probs = -torch.nn.functional.log_softmax(random_logits, dim=-1)
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# Mask the log probs with the -100 labels
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log_probs[0, 1] = 0.0
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log_probs[2, 1] = 0.0
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log_probs[2, 3] = 0.0
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expected_loss = (1 - epsilon) * loss + epsilon * log_probs.sum() / (num_labels * 17)
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self.assertTrue(torch.allclose(label_smoothed_loss, expected_loss))
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