tests / test_modeling_bert_generation.py

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	# coding=utf-8
	# Copyright 2020 The HuggingFace Team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.


	import unittest

	from transformers import is_torch_available
	from transformers.testing_utils import require_torch, slow, torch_device

	from .test_configuration_common import ConfigTester
	from .test_generation_utils import GenerationTesterMixin
	from .test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


	if is_torch_available():
	import torch

	from transformers import BertGenerationConfig, BertGenerationDecoder, BertGenerationEncoder


	class BertGenerationEncoderTester:
	def __init__(
	self,
	parent,
	batch_size=13,
	seq_length=7,
	is_training=True,
	use_input_mask=True,
	vocab_size=99,
	hidden_size=32,
	num_hidden_layers=5,
	num_attention_heads=4,
	intermediate_size=37,
	hidden_act="gelu",
	hidden_dropout_prob=0.1,
	attention_probs_dropout_prob=0.1,
	max_position_embeddings=50,
	initializer_range=0.02,
	use_labels=True,
	scope=None,
	):
	self.parent = parent
	self.batch_size = batch_size
	self.seq_length = seq_length
	self.is_training = is_training
	self.use_input_mask = use_input_mask
	self.vocab_size = vocab_size
	self.hidden_size = hidden_size
	self.num_hidden_layers = num_hidden_layers
	self.num_attention_heads = num_attention_heads
	self.intermediate_size = intermediate_size
	self.hidden_act = hidden_act
	self.hidden_dropout_prob = hidden_dropout_prob
	self.attention_probs_dropout_prob = attention_probs_dropout_prob
	self.max_position_embeddings = max_position_embeddings
	self.initializer_range = initializer_range
	self.use_labels = use_labels
	self.scope = scope

	def prepare_config_and_inputs(self):
	input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

	input_mask = None
	if self.use_input_mask:
	input_mask = random_attention_mask([self.batch_size, self.seq_length])

	if self.use_labels:
	token_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

	config = BertGenerationConfig(
	vocab_size=self.vocab_size,
	hidden_size=self.hidden_size,
	num_hidden_layers=self.num_hidden_layers,
	num_attention_heads=self.num_attention_heads,
	intermediate_size=self.intermediate_size,
	hidden_act=self.hidden_act,
	hidden_dropout_prob=self.hidden_dropout_prob,
	attention_probs_dropout_prob=self.attention_probs_dropout_prob,
	max_position_embeddings=self.max_position_embeddings,
	is_decoder=False,
	initializer_range=self.initializer_range,
	)

	return config, input_ids, input_mask, token_labels

	def prepare_config_and_inputs_for_decoder(self):
	(
	config,
	input_ids,
	input_mask,
	token_labels,
	) = self.prepare_config_and_inputs()

	config.is_decoder = True
	encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
	encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)

	return (
	config,
	input_ids,
	input_mask,
	token_labels,
	encoder_hidden_states,
	encoder_attention_mask,
	)

	def create_and_check_model(
	self,
	config,
	input_ids,
	input_mask,
	token_labels,
	**kwargs,
	):
	model = BertGenerationEncoder(config=config)
	model.to(torch_device)
	model.eval()
	result = model(input_ids, attention_mask=input_mask)
	result = model(input_ids)
	self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))

	def create_and_check_model_as_decoder(
	self,
	config,
	input_ids,
	input_mask,
	token_labels,
	encoder_hidden_states,
	encoder_attention_mask,
	**kwargs,
	):
	config.add_cross_attention = True
	model = BertGenerationEncoder(config=config)
	model.to(torch_device)
	model.eval()
	result = model(
	input_ids,
	attention_mask=input_mask,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	)
	result = model(
	input_ids,
	attention_mask=input_mask,
	encoder_hidden_states=encoder_hidden_states,
	)
	self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))

	def create_and_check_decoder_model_past_large_inputs(
	self,
	config,
	input_ids,
	input_mask,
	token_labels,
	encoder_hidden_states,
	encoder_attention_mask,
	**kwargs,
	):
	config.is_decoder = True
	config.add_cross_attention = True
	model = BertGenerationDecoder(config=config).to(torch_device).eval()

	# first forward pass
	outputs = model(
	input_ids,
	attention_mask=input_mask,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	use_cache=True,
	)
	past_key_values = outputs.past_key_values

	# create hypothetical multiple next token and extent to next_input_ids
	next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
	next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)

	# append to next input_ids and
	next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
	next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)

	output_from_no_past = model(
	next_input_ids,
	attention_mask=next_attention_mask,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	output_hidden_states=True,
	)["hidden_states"][0]
	output_from_past = model(
	next_tokens,
	attention_mask=next_attention_mask,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	past_key_values=past_key_values,
	output_hidden_states=True,
	)["hidden_states"][0]

	# select random slice
	random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
	output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
	output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()

	self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

	# test that outputs are equal for slice
	self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))

	def create_and_check_for_causal_lm(
	self,
	config,
	input_ids,
	input_mask,
	token_labels,
	*args,
	):
	model = BertGenerationDecoder(config)
	model.to(torch_device)
	model.eval()
	result = model(input_ids, attention_mask=input_mask, labels=token_labels)
	self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))

	def prepare_config_and_inputs_for_common(self):
	config, input_ids, input_mask, token_labels = self.prepare_config_and_inputs()
	inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
	return config, inputs_dict


	@require_torch
	class BertGenerationEncoderTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):

	all_model_classes = (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
	all_generative_model_classes = (BertGenerationDecoder,) if is_torch_available() else ()

	def setUp(self):
	self.model_tester = BertGenerationEncoderTester(self)
	self.config_tester = ConfigTester(self, config_class=BertGenerationConfig, hidden_size=37)

	def test_config(self):
	self.config_tester.run_common_tests()

	def test_model(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_model(*config_and_inputs)

	def test_model_as_bert(self):
	config, input_ids, input_mask, token_labels = self.model_tester.prepare_config_and_inputs()
	config.model_type = "bert"
	self.model_tester.create_and_check_model(config, input_ids, input_mask, token_labels)

	def test_model_as_decoder(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
	self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)

	def test_decoder_model_past_with_large_inputs(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
	self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)

	def test_model_as_decoder_with_default_input_mask(self):
	# This regression test was failing with PyTorch < 1.3
	(
	config,
	input_ids,
	input_mask,
	token_labels,
	encoder_hidden_states,
	encoder_attention_mask,
	) = self.model_tester.prepare_config_and_inputs_for_decoder()

	input_mask = None

	self.model_tester.create_and_check_model_as_decoder(
	config,
	input_ids,
	input_mask,
	token_labels,
	encoder_hidden_states,
	encoder_attention_mask,
	)

	def test_for_causal_lm(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
	self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)

	@slow
	def test_model_from_pretrained(self):
	model = BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")
	self.assertIsNotNone(model)


	@require_torch
	class BertGenerationEncoderIntegrationTest(unittest.TestCase):
	@slow
	def test_inference_no_head_absolute_embedding(self):
	model = BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")
	input_ids = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]])
	output = model(input_ids)[0]
	expected_shape = torch.Size([1, 8, 1024])
	self.assertEqual(output.shape, expected_shape)
	expected_slice = torch.tensor(
	[[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]]
	)
	self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))


	@require_torch
	class BertGenerationDecoderIntegrationTest(unittest.TestCase):
	@slow
	def test_inference_no_head_absolute_embedding(self):
	model = BertGenerationDecoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")
	input_ids = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]])
	output = model(input_ids)[0]
	expected_shape = torch.Size([1, 8, 50358])
	self.assertEqual(output.shape, expected_shape)
	expected_slice = torch.tensor(
	[[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]]
	)
	self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))