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Unverified Commit 7c59e32d authored by Matt's avatar Matt Committed by GitHub
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Merge pull request #2 from huggingface/master 

Updating my fork to the latest version
parents b8e2a9c5 c304593d
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Showing with 206 additions and 9 deletions
tests/modeling_test.py
View file @ 7c59e32d
......@@ -28,7 +28,7 @@ import torch
from pytorch_pretrained_bert import (BertConfig, BertModel, BertForMaskedLM,
BertForNextSentencePrediction, BertForPreTraining,
BertForQuestionAnswering, BertForSequenceClassification,
BertForTokenClassification)
BertForTokenClassification, BertForMultipleChoice)
from pytorch_pretrained_bert.modeling import PRETRAINED_MODEL_ARCHIVE_MAP
......@@ -56,6 +56,7 @@ class BertModelTest(unittest.TestCase):
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None):
self.parent = parent
self.batch_size = batch_size
......@@ -77,6 +78,7 @@ class BertModelTest(unittest.TestCase):
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
......@@ -92,9 +94,11 @@ class BertModelTest(unittest.TestCase):
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = BertModelTest.ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = BertModelTest.ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = BertModelTest.ids_tensor([self.batch_size], self.num_choices)
config = BertConfig(
vocab_size_or_config_json_file=self.vocab_size,
......@@ -109,14 +113,14 @@ class BertModelTest(unittest.TestCase):
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def check_loss_output(self, result):
self.parent.assertListEqual(
list(result["loss"].size()),
[])
def create_bert_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels):
def create_bert_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = BertModel(config=config)
model.eval()
all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
......@@ -137,7 +141,7 @@ class BertModelTest(unittest.TestCase):
self.parent.assertListEqual(list(result["pooled_output"].size()), [self.batch_size, self.hidden_size])
def create_bert_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels):
def create_bert_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = BertForMaskedLM(config=config)
model.eval()
loss = model(input_ids, token_type_ids, input_mask, token_labels)
......@@ -153,7 +157,7 @@ class BertModelTest(unittest.TestCase):
list(result["prediction_scores"].size()),
[self.batch_size, self.seq_length, self.vocab_size])
def create_bert_for_next_sequence_prediction(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels):
def create_bert_for_next_sequence_prediction(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = BertForNextSentencePrediction(config=config)
model.eval()
loss = model(input_ids, token_type_ids, input_mask, sequence_labels)
......@@ -170,7 +174,7 @@ class BertModelTest(unittest.TestCase):
[self.batch_size, 2])
def create_bert_for_pretraining(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels):
def create_bert_for_pretraining(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = BertForPreTraining(config=config)
model.eval()
loss = model(input_ids, token_type_ids, input_mask, token_labels, sequence_labels)
......@@ -191,7 +195,7 @@ class BertModelTest(unittest.TestCase):
[self.batch_size, 2])
def create_bert_for_question_answering(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels):
def create_bert_for_question_answering(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = BertForQuestionAnswering(config=config)
model.eval()
loss = model(input_ids, token_type_ids, input_mask, sequence_labels, sequence_labels)
......@@ -212,7 +216,7 @@ class BertModelTest(unittest.TestCase):
[self.batch_size, self.seq_length])
def create_bert_for_sequence_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels):
def create_bert_for_sequence_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = BertForSequenceClassification(config=config, num_labels=self.num_labels)
model.eval()
loss = model(input_ids, token_type_ids, input_mask, sequence_labels)
......@@ -229,7 +233,7 @@ class BertModelTest(unittest.TestCase):
[self.batch_size, self.num_labels])
def create_bert_for_token_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels):
def create_bert_for_token_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = BertForTokenClassification(config=config, num_labels=self.num_labels)
model.eval()
loss = model(input_ids, token_type_ids, input_mask, token_labels)
......@@ -246,6 +250,150 @@ class BertModelTest(unittest.TestCase):
[self.batch_size, self.seq_length, self.num_labels])
def create_bert_for_multiple_choice(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = BertForMultipleChoice(config=config, num_choices=self.num_choices)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
loss = model(multiple_choice_inputs_ids,
multiple_choice_token_type_ids,
multiple_choice_input_mask,
choice_labels)
logits = model(multiple_choice_inputs_ids,
multiple_choice_token_type_ids,
multiple_choice_input_mask)
outputs = {
"loss": loss,
"logits": logits,
}
return outputs
def check_bert_for_multiple_choice(self, result):
self.parent.assertListEqual(
list(result["logits"].size()),
[self.batch_size, self.num_choices])
def create_and_check_bert_for_attentions(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
for model_class in (BertModel, BertForMaskedLM, BertForNextSentencePrediction,
BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification,
BertForTokenClassification):
if model_class in [BertForSequenceClassification,
BertForTokenClassification]:
model = model_class(config=config, num_labels=self.num_labels, output_attentions=True)
else:
model = model_class(config=config, output_attentions=True)
model.eval()
output = model(input_ids, token_type_ids, input_mask)
attentions = output[0]
self.parent.assertEqual(len(attentions), self.num_hidden_layers)
self.parent.assertListEqual(
list(attentions[0].size()),
[self.batch_size, self.num_attention_heads, self.seq_length, self.seq_length])
def create_and_check_bert_for_headmasking(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
for model_class in (BertModel, BertForMaskedLM, BertForNextSentencePrediction,
BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification,
BertForTokenClassification):
if model_class in [BertForSequenceClassification,
BertForTokenClassification]:
model = model_class(config=config,
num_labels=self.num_labels,
keep_multihead_output=True)
else:
model = model_class(config=config, keep_multihead_output=True)
model.eval()
head_mask = torch.ones(self.num_hidden_layers, self.num_attention_heads).to(input_ids.device)
head_mask[0, 1:-1] = 0.0 # Mask all but the first and last heads on the first layer
head_mask[-1, 1:] = 0.0 # Mask all but the first head on the last layer
output = model(input_ids, token_type_ids, input_mask, head_mask=head_mask)
if isinstance(model, BertModel):
output = sum(t.sum() for t in output[0])
elif isinstance(output, (list, tuple)):
output = sum(t.sum() for t in output)
output = output.sum()
output.backward()
multihead_outputs = (model if isinstance(model, BertModel) else model.bert).get_multihead_outputs()
self.parent.assertEqual(len(multihead_outputs), self.num_hidden_layers)
self.parent.assertListEqual(
list(multihead_outputs[0].size()),
[self.batch_size, self.num_attention_heads,
self.seq_length, self.hidden_size // self.num_attention_heads])
self.parent.assertEqual(
len(multihead_outputs[0][:, 1:(self.num_attention_heads-1), :, :].nonzero()),
0)
self.parent.assertEqual(
len(multihead_outputs[0][:, 0, :, :].nonzero()),
self.batch_size * self.seq_length * self.hidden_size // self.num_attention_heads)
self.parent.assertEqual(
len(multihead_outputs[0][:, self.num_attention_heads-1, :, :].nonzero()),
self.batch_size * self.seq_length * self.hidden_size // self.num_attention_heads)
self.parent.assertListEqual(
list(multihead_outputs[1].size()),
[self.batch_size, self.num_attention_heads,
self.seq_length, self.hidden_size // self.num_attention_heads])
self.parent.assertEqual(
len(multihead_outputs[1].nonzero()),
multihead_outputs[1].numel())
self.parent.assertListEqual(
list(multihead_outputs[-1].size()),
[self.batch_size, self.num_attention_heads,
self.seq_length, self.hidden_size // self.num_attention_heads])
self.parent.assertEqual(
len(multihead_outputs[-1][:, 1:, :, :].nonzero()),
0)
self.parent.assertEqual(
len(multihead_outputs[-1][:, 0, :, :].nonzero()),
self.batch_size * self.seq_length * self.hidden_size // self.num_attention_heads)
def create_and_check_bert_for_head_pruning(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
for model_class in (BertModel, BertForMaskedLM, BertForNextSentencePrediction,
BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification,
BertForTokenClassification):
if model_class in [BertForSequenceClassification,
BertForTokenClassification]:
model = model_class(config=config,
num_labels=self.num_labels,
keep_multihead_output=True)
else:
model = model_class(config=config, keep_multihead_output=True)
model.eval()
bert_model = model if isinstance(model, BertModel) else model.bert
heads_to_prune = {0: list(range(1, self.num_attention_heads)),
-1: [0]}
bert_model.prune_heads(heads_to_prune)
output = model(input_ids, token_type_ids, input_mask)
if isinstance(model, BertModel):
output = sum(t.sum() for t in output[0])
elif isinstance(output, (list, tuple)):
output = sum(t.sum() for t in output)
output = output.sum()
output.backward()
multihead_outputs = bert_model.get_multihead_outputs()
self.parent.assertEqual(len(multihead_outputs), self.num_hidden_layers)
self.parent.assertListEqual(
list(multihead_outputs[0].size()),
[self.batch_size, 1,
self.seq_length, self.hidden_size // self.num_attention_heads])
self.parent.assertListEqual(
list(multihead_outputs[1].size()),
[self.batch_size, self.num_attention_heads,
self.seq_length, self.hidden_size // self.num_attention_heads])
self.parent.assertListEqual(
list(multihead_outputs[-1].size()),
[self.batch_size, self.num_attention_heads-1,
self.seq_length, self.hidden_size // self.num_attention_heads])
def test_default(self):
self.run_tester(BertModelTest.BertModelTester(self))
......@@ -300,6 +448,14 @@ class BertModelTest(unittest.TestCase):
tester.check_bert_for_token_classification_output(output_result)
tester.check_loss_output(output_result)
output_result = tester.create_bert_for_multiple_choice(*config_and_inputs)
tester.check_bert_for_multiple_choice(output_result)
tester.check_loss_output(output_result)
tester.create_and_check_bert_for_attentions(*config_and_inputs)
tester.create_and_check_bert_for_headmasking(*config_and_inputs)
tester.create_and_check_bert_for_head_pruning(*config_and_inputs)
@classmethod
def ids_tensor(cls, shape, vocab_size, rng=None, name=None):
"""Creates a random int32 tensor of the shape within the vocab size."""
......
tests/optimization_test.py
View file @ 7c59e32d
......@@ -21,6 +21,11 @@ import unittest
import torch
from pytorch_pretrained_bert import BertAdam
from pytorch_pretrained_bert import OpenAIAdam
from pytorch_pretrained_bert.optimization import ConstantLR, WarmupLinearSchedule, WarmupConstantSchedule, \
WarmupCosineWithWarmupRestartsSchedule, WarmupCosineWithHardRestartsSchedule, WarmupCosineSchedule
import numpy as np
class OptimizationTest(unittest.TestCase):
......@@ -46,5 +51,41 @@ class OptimizationTest(unittest.TestCase):
self.assertListAlmostEqual(w.tolist(), [0.4, 0.2, -0.5], tol=1e-2)
class ScheduleInitTest(unittest.TestCase):
def test_bert_sched_init(self):
m = torch.nn.Linear(50, 50)
optim = BertAdam(m.parameters(), lr=0.001, warmup=.1, t_total=1000, schedule=None)
self.assertTrue(isinstance(optim.param_groups[0]["schedule"], ConstantLR))
optim = BertAdam(m.parameters(), lr=0.001, warmup=.1, t_total=1000, schedule="none")
self.assertTrue(isinstance(optim.param_groups[0]["schedule"], ConstantLR))
optim = BertAdam(m.parameters(), lr=0.001, warmup=.01, t_total=1000)
self.assertTrue(isinstance(optim.param_groups[0]["schedule"], WarmupLinearSchedule))
# shouldn't fail
def test_openai_sched_init(self):
m = torch.nn.Linear(50, 50)
optim = OpenAIAdam(m.parameters(), lr=0.001, warmup=.1, t_total=1000, schedule=None)
self.assertTrue(isinstance(optim.param_groups[0]["schedule"], ConstantLR))
optim = OpenAIAdam(m.parameters(), lr=0.001, warmup=.1, t_total=1000, schedule="none")
self.assertTrue(isinstance(optim.param_groups[0]["schedule"], ConstantLR))
optim = OpenAIAdam(m.parameters(), lr=0.001, warmup=.01, t_total=1000)
self.assertTrue(isinstance(optim.param_groups[0]["schedule"], WarmupLinearSchedule))
# shouldn't fail
class WarmupCosineWithRestartsTest(unittest.TestCase):
def test_it(self):
m = WarmupCosineWithWarmupRestartsSchedule(warmup=0.05, t_total=1000., cycles=5)
x = np.arange(0, 1000)
y = [m.get_lr(xe) for xe in x]
y = np.asarray(y)
expected_zeros = y[[0, 200, 400, 600, 800]]
print(expected_zeros)
expected_ones = y[[50, 250, 450, 650, 850]]
print(expected_ones)
self.assertTrue(np.allclose(expected_ones, 1))
self.assertTrue(np.allclose(expected_zeros, 0))
if __name__ == "__main__":
unittest.main()
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