203 lines
8.5 KiB
Python
203 lines
8.5 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 (
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DistributedLengthGroupedSampler,
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DistributedSamplerWithLoop,
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DistributedTensorGatherer,
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LabelSmoother,
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LengthGroupedSampler,
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SequentialDistributedSampler,
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get_parameter_names,
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)
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class TstLayer(torch.nn.Module):
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def __init__(self, hidden_size):
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super().__init__()
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self.linear1 = torch.nn.Linear(hidden_size, hidden_size)
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self.ln1 = torch.nn.LayerNorm(hidden_size)
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self.linear2 = torch.nn.Linear(hidden_size, hidden_size)
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self.ln2 = torch.nn.LayerNorm(hidden_size)
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self.bias = torch.nn.Parameter(torch.zeros(hidden_size))
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def forward(self, x):
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h = self.ln1(torch.nn.functional.relu(self.linear1(x)))
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h = torch.nn.functional.relu(self.linear2(x))
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return self.ln2(x + h + self.bias)
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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(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(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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def test_group_by_length(self):
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# Get some inputs of random lengths
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lengths = torch.randint(0, 25, (100,)).tolist()
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# Put one bigger than the others to check it ends up in first position
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lengths[32] = 50
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indices = list(LengthGroupedSampler(lengths, 4, lengths=lengths))
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# The biggest element should be first
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self.assertEqual(lengths[indices[0]], 50)
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# The indices should be a permutation of range(100)
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self.assertEqual(list(sorted(indices)), list(range(100)))
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def test_distributed_length_grouped(self):
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# Get some inputs of random lengths
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lengths = torch.randint(0, 25, (100,)).tolist()
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# Put one bigger than the others to check it ends up in first position
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lengths[32] = 50
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indices_process_0 = list(DistributedLengthGroupedSampler(lengths, 4, 2, 0, lengths=lengths))
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indices_process_1 = list(DistributedLengthGroupedSampler(lengths, 4, 2, 1, lengths=lengths))
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# The biggest element should be first
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self.assertEqual(lengths[indices_process_0[0]], 50)
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# The indices should be a permutation of range(100)
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self.assertEqual(list(sorted(indices_process_0 + indices_process_1)), list(range(100)))
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def test_get_parameter_names(self):
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model = torch.nn.Sequential(TstLayer(128), torch.nn.ModuleList([TstLayer(128), TstLayer(128)]))
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# fmt: off
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self.assertEqual(
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get_parameter_names(model, [torch.nn.LayerNorm]),
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['0.linear1.weight', '0.linear1.bias', '0.linear2.weight', '0.linear2.bias', '0.bias', '1.0.linear1.weight', '1.0.linear1.bias', '1.0.linear2.weight', '1.0.linear2.bias', '1.0.bias', '1.1.linear1.weight', '1.1.linear1.bias', '1.1.linear2.weight', '1.1.linear2.bias', '1.1.bias']
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)
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# fmt: on
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def test_distributed_sampler_with_loop(self):
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batch_size = 16
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for length in [23, 64, 123]:
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dataset = list(range(length))
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shard1 = DistributedSamplerWithLoop(dataset, batch_size, num_replicas=2, rank=0)
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shard2 = DistributedSamplerWithLoop(dataset, batch_size, num_replicas=2, rank=1)
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# Set seeds
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shard1.set_epoch(0)
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shard2.set_epoch(0)
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# Sample
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samples1 = list(shard1)
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samples2 = list(shard2)
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self.assertTrue(len(samples1) % batch_size == 0)
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self.assertTrue(len(samples2) % batch_size == 0)
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total = []
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for sample1, sample2 in zip(samples1, samples2):
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total += [sample1, sample2]
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self.assertEqual(set(total[:length]), set(dataset))
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self.assertEqual(set(total[length:]), set(total[: (len(total) - length)]))
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def test_sequential_distributed_sampler(self):
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batch_size = 16
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for length in [23, 64, 123]:
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dataset = list(range(length))
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shard1 = SequentialDistributedSampler(dataset, num_replicas=2, rank=0)
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shard2 = SequentialDistributedSampler(dataset, num_replicas=2, rank=1)
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# Sample
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samples1 = list(shard1)
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samples2 = list(shard2)
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total = samples1 + samples2
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self.assertListEqual(total[:length], dataset)
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self.assertListEqual(total[length:], dataset[: (len(total) - length)])
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# With a batch_size passed
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shard1 = SequentialDistributedSampler(dataset, num_replicas=2, rank=0, batch_size=batch_size)
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shard2 = SequentialDistributedSampler(dataset, num_replicas=2, rank=1, batch_size=batch_size)
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# Sample
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samples1 = list(shard1)
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samples2 = list(shard2)
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self.assertTrue(len(samples1) % batch_size == 0)
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self.assertTrue(len(samples2) % batch_size == 0)
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total = samples1 + samples2
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self.assertListEqual(total[:length], dataset)
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self.assertListEqual(total[length:], dataset[: (len(total) - length)])
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