test_modeling_blenderbot.py
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test_modeling_blenderbot.py
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	# coding=utf-8
	# Copyright 2021, The HuggingFace Inc. 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.
	""" Testing suite for the PyTorch Blenderbot model. """

	import tempfile
	import unittest

	from transformers import is_torch_available
	from transformers.file_utils import cached_property
	from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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


	if is_torch_available():
	import torch

	from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration, BlenderbotModel, BlenderbotTokenizer
	from transformers.models.blenderbot.modeling_blenderbot import (
	BlenderbotDecoder,
	BlenderbotEncoder,
	BlenderbotForCausalLM,
	)


	def prepare_blenderbot_inputs_dict(
	config,
	input_ids,
	decoder_input_ids,
	attention_mask=None,
	decoder_attention_mask=None,
	head_mask=None,
	decoder_head_mask=None,
	):
	if attention_mask is None:
	attention_mask = input_ids.ne(config.pad_token_id)
	if decoder_attention_mask is None:
	decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id)
	if head_mask is None:
	head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device)
	if decoder_head_mask is None:
	decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
	return {
	"input_ids": input_ids,
	"decoder_input_ids": decoder_input_ids,
	"attention_mask": attention_mask,
	"decoder_attention_mask": attention_mask,
	"head_mask": head_mask,
	"decoder_head_mask": decoder_head_mask,
	}


	@require_torch
	class BlenderbotModelTester:
	def __init__(
	self,
	parent,
	batch_size=13,
	seq_length=7,
	is_training=True,
	use_labels=False,
	vocab_size=99,
	hidden_size=16,
	num_hidden_layers=2,
	num_attention_heads=4,
	intermediate_size=4,
	hidden_act="gelu",
	hidden_dropout_prob=0.1,
	attention_probs_dropout_prob=0.1,
	max_position_embeddings=20,
	eos_token_id=2,
	pad_token_id=1,
	bos_token_id=0,
	):
	self.parent = parent
	self.batch_size = batch_size
	self.seq_length = seq_length
	self.is_training = is_training
	self.use_labels = use_labels
	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.eos_token_id = eos_token_id
	self.pad_token_id = pad_token_id
	self.bos_token_id = bos_token_id

	def prepare_config_and_inputs(self):
	input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
	input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(
	3,
	)
	input_ids[:, -1] = self.eos_token_id # Eos Token

	decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

	config = BlenderbotConfig(
	vocab_size=self.vocab_size,
	d_model=self.hidden_size,
	encoder_layers=self.num_hidden_layers,
	decoder_layers=self.num_hidden_layers,
	encoder_attention_heads=self.num_attention_heads,
	decoder_attention_heads=self.num_attention_heads,
	encoder_ffn_dim=self.intermediate_size,
	decoder_ffn_dim=self.intermediate_size,
	dropout=self.hidden_dropout_prob,
	attention_dropout=self.attention_probs_dropout_prob,
	max_position_embeddings=self.max_position_embeddings,
	eos_token_id=self.eos_token_id,
	bos_token_id=self.bos_token_id,
	pad_token_id=self.pad_token_id,
	)
	inputs_dict = prepare_blenderbot_inputs_dict(config, input_ids, decoder_input_ids)
	return config, inputs_dict

	def prepare_config_and_inputs_for_common(self):
	config, inputs_dict = self.prepare_config_and_inputs()
	return config, inputs_dict

	def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
	model = BlenderbotModel(config=config).get_decoder().to(torch_device).eval()
	input_ids = inputs_dict["input_ids"]
	attention_mask = inputs_dict["attention_mask"]
	head_mask = inputs_dict["head_mask"]

	# first forward pass
	outputs = model(input_ids, attention_mask=attention_mask, head_mask=head_mask, use_cache=True)

	output, past_key_values = outputs.to_tuple()

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

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

	output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
	output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
	"last_hidden_state"
	]

	# 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 check_encoder_decoder_model_standalone(self, config, inputs_dict):
	model = BlenderbotModel(config=config).to(torch_device).eval()
	outputs = model(**inputs_dict)

	encoder_last_hidden_state = outputs.encoder_last_hidden_state
	last_hidden_state = outputs.last_hidden_state

	with tempfile.TemporaryDirectory() as tmpdirname:
	encoder = model.get_encoder()
	encoder.save_pretrained(tmpdirname)
	encoder = BlenderbotEncoder.from_pretrained(tmpdirname).to(torch_device)

	encoder_last_hidden_state_2 = encoder(inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"])[
	0
	]

	self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3)

	with tempfile.TemporaryDirectory() as tmpdirname:
	decoder = model.get_decoder()
	decoder.save_pretrained(tmpdirname)
	decoder = BlenderbotDecoder.from_pretrained(tmpdirname).to(torch_device)

	last_hidden_state_2 = decoder(
	input_ids=inputs_dict["decoder_input_ids"],
	attention_mask=inputs_dict["decoder_attention_mask"],
	encoder_hidden_states=encoder_last_hidden_state,
	encoder_attention_mask=inputs_dict["attention_mask"],
	)[0]

	self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3)


	@require_torch
	class BlenderbotModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
	all_model_classes = (BlenderbotModel, BlenderbotForConditionalGeneration) if is_torch_available() else ()
	all_generative_model_classes = (BlenderbotForConditionalGeneration,) if is_torch_available() else ()
	is_encoder_decoder = True
	test_pruning = False
	test_missing_keys = False

	def setUp(self):
	self.model_tester = BlenderbotModelTester(self)
	self.config_tester = ConfigTester(self, config_class=BlenderbotConfig)

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

	def test_save_load_strict(self):
	config, inputs_dict = self.model_tester.prepare_config_and_inputs()
	for model_class in self.all_model_classes:
	model = model_class(config)

	with tempfile.TemporaryDirectory() as tmpdirname:
	model.save_pretrained(tmpdirname)
	model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
	self.assertEqual(info["missing_keys"], [])

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

	def test_encoder_decoder_model_standalone(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
	self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)

	def test_generate_fp16(self):
	config, input_dict = self.model_tester.prepare_config_and_inputs()
	input_ids = input_dict["input_ids"]
	attention_mask = input_ids.ne(1).to(torch_device)
	model = BlenderbotForConditionalGeneration(config).eval().to(torch_device)
	if torch_device == "cuda":
	model.half()
	model.generate(input_ids, attention_mask=attention_mask)
	model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)


	def assert_tensors_close(a, b, atol=1e-12, prefix=""):
	"""If tensors have different shapes, different values or a and b are not both tensors, raise a nice Assertion error."""
	if a is None and b is None:
	return True
	try:
	if torch.allclose(a, b, atol=atol):
	return True
	raise
	except Exception:
	pct_different = (torch.gt((a - b).abs(), atol)).float().mean().item()
	if a.numel() > 100:
	msg = f"tensor values are {pct_different:.1%} percent different."
	else:
	msg = f"{a} != {b}"
	if prefix:
	msg = prefix + ": " + msg
	raise AssertionError(msg)


	@unittest.skipUnless(torch_device != "cpu", "3B test too slow on CPU.")
	@require_torch
	@require_sentencepiece
	@require_tokenizers
	class Blenderbot3BIntegrationTests(unittest.TestCase):
	ckpt = "facebook/blenderbot-3B"

	@cached_property
	def tokenizer(self):
	return BlenderbotTokenizer.from_pretrained(self.ckpt)

	@slow
	def test_generation_from_short_input_same_as_parlai_3B(self):
	FASTER_GEN_KWARGS = dict(num_beams=1, early_stopping=True, min_length=15, max_length=25)
	TOK_DECODE_KW = dict(skip_special_tokens=True, clean_up_tokenization_spaces=True)

	torch.cuda.empty_cache()
	model = BlenderbotForConditionalGeneration.from_pretrained(self.ckpt).half().to(torch_device)

	src_text = ["Sam"]
	model_inputs = self.tokenizer(src_text, return_tensors="pt").to(torch_device)

	generated_utterances = model.generate(model_inputs, FASTER_GEN_KWARGS)
	tgt_text = 'Sam is a great name. It means "sun" in Gaelic.'

	generated_txt = self.tokenizer.batch_decode(generated_utterances, **TOK_DECODE_KW)
	assert generated_txt[0].strip() == tgt_text

	src_text = "Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like i'm going to throw up.\nand why is that?"

	model_inputs = self.tokenizer([src_text], return_tensors="pt").to(torch_device)

	generated_ids = model.generate(model_inputs, FASTER_GEN_KWARGS)[0]
	reply = self.tokenizer.decode(generated_ids, **TOK_DECODE_KW)

	assert "I think it's because we are so worried about what people think of us." == reply.strip()
	del model


	class BlenderbotStandaloneDecoderModelTester:
	def __init__(
	self,
	parent,
	vocab_size=99,
	batch_size=13,
	d_model=16,
	decoder_seq_length=7,
	is_training=True,
	is_decoder=True,
	use_attention_mask=True,
	use_cache=False,
	use_labels=True,
	decoder_start_token_id=2,
	decoder_ffn_dim=32,
	decoder_layers=4,
	encoder_attention_heads=4,
	decoder_attention_heads=4,
	max_position_embeddings=30,
	is_encoder_decoder=False,
	encoder_no_repeat_ngram_size=0,
	pad_token_id=0,
	bos_token_id=1,
	eos_token_id=2,
	scope=None,
	):
	self.parent = parent
	self.batch_size = batch_size
	self.decoder_seq_length = decoder_seq_length
	# For common tests
	self.seq_length = self.decoder_seq_length
	self.is_training = is_training
	self.use_attention_mask = use_attention_mask
	self.use_labels = use_labels

	self.vocab_size = vocab_size
	self.d_model = d_model
	self.hidden_size = d_model
	self.num_hidden_layers = decoder_layers
	self.decoder_layers = decoder_layers
	self.decoder_ffn_dim = decoder_ffn_dim
	self.encoder_attention_heads = encoder_attention_heads
	self.decoder_attention_heads = decoder_attention_heads
	self.num_attention_heads = decoder_attention_heads
	self.eos_token_id = eos_token_id
	self.bos_token_id = bos_token_id
	self.pad_token_id = pad_token_id
	self.decoder_start_token_id = decoder_start_token_id
	self.use_cache = use_cache
	self.max_position_embeddings = max_position_embeddings
	self.is_encoder_decoder = is_encoder_decoder
	self.encoder_no_repeat_ngram_size = encoder_no_repeat_ngram_size

	self.scope = None
	self.decoder_key_length = decoder_seq_length
	self.base_model_out_len = 2
	self.decoder_attention_idx = 1

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

	attention_mask = None
	if self.use_attention_mask:
	attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)

	lm_labels = None
	if self.use_labels:
	lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)

	config = BlenderbotConfig(
	vocab_size=self.vocab_size,
	d_model=self.d_model,
	decoder_layers=self.decoder_layers,
	decoder_ffn_dim=self.decoder_ffn_dim,
	encoder_attention_heads=self.encoder_attention_heads,
	decoder_attention_heads=self.decoder_attention_heads,
	eos_token_id=self.eos_token_id,
	bos_token_id=self.bos_token_id,
	use_cache=self.use_cache,
	pad_token_id=self.pad_token_id,
	decoder_start_token_id=self.decoder_start_token_id,
	max_position_embeddings=self.max_position_embeddings,
	is_encoder_decoder=self.is_encoder_decoder,
	encoder_no_repeat_ngram_size=self.encoder_no_repeat_ngram_size,
	)

	return (
	config,
	input_ids,
	attention_mask,
	lm_labels,
	)

	def create_and_check_decoder_model_past(
	self,
	config,
	input_ids,
	attention_mask,
	lm_labels,
	):
	config.use_cache = True
	model = BlenderbotDecoder(config=config).to(torch_device).eval()
	# first forward pass
	outputs = model(input_ids, use_cache=True)
	outputs_use_cache_conf = model(input_ids)
	outputs_no_past = model(input_ids, use_cache=False)

	self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
	self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)

	past_key_values = outputs["past_key_values"]

	# create hypothetical next token and extent to next_input_ids
	next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)

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

	output_from_no_past = model(next_input_ids)["last_hidden_state"]
	output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]

	# select random slice
	random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
	output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
	output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()

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

	def create_and_check_decoder_model_attention_mask_past(
	self,
	config,
	input_ids,
	attention_mask,
	lm_labels,
	):
	model = BlenderbotDecoder(config=config).to(torch_device).eval()

	# create attention mask
	attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)

	half_seq_length = input_ids.shape[-1] // 2
	attn_mask[:, half_seq_length:] = 0

	# first forward pass
	past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True)["past_key_values"]
	# past_key_values = model(input_ids, use_cache=True)["past_key_values"]

	# create hypothetical next token and extent to next_input_ids
	next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)

	# change a random masked slice from input_ids
	random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
	random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
	input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens

	# append to next input_ids and attn_mask
	next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
	attn_mask = torch.cat(
	[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
	dim=1,
	)

	# get two different outputs
	output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
	output_from_past = model(next_tokens, past_key_values=past_key_values, attention_mask=attn_mask)[
	"last_hidden_state"
	]

	# select random slice
	random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
	output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
	output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()

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

	def prepare_config_and_inputs_for_common(self):
	config_and_inputs = self.prepare_config_and_inputs()
	(
	config,
	input_ids,
	attention_mask,
	lm_labels,
	) = config_and_inputs

	inputs_dict = {
	"input_ids": input_ids,
	"attention_mask": attention_mask,
	}
	return config, inputs_dict


	@require_torch
	class BlenderbotStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
	all_model_classes = (BlenderbotDecoder, BlenderbotForCausalLM) if is_torch_available() else ()
	all_generative_model_classes = (BlenderbotForCausalLM,) if is_torch_available() else ()
	test_pruning = False
	is_encoder_decoder = False

	def setUp(
	self,
	):
	self.model_tester = BlenderbotStandaloneDecoderModelTester(self, is_training=False)
	self.config_tester = ConfigTester(self, config_class=BlenderbotConfig)

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

	def test_decoder_model_past(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_decoder_model_past(*config_and_inputs)

	def test_decoder_model_attn_mask_past(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs)

	def test_retain_grad_hidden_states_attentions(self):
	# decoder cannot keep gradients
	return

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