test_modeling_common.py
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test_modeling_common.py
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	# coding=utf-8
	# Copyright 2019 HuggingFace Inc.
	#
	# 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 copy
	import gc
	import inspect
	import os.path
	import random
	import tempfile
	import unittest
	from typing import List, Tuple

	from transformers import is_torch_available
	from transformers.file_utils import WEIGHTS_NAME
	from transformers.testing_utils import require_torch, require_torch_multi_gpu, slow, torch_device


	if is_torch_available():
	import numpy as np
	import torch

	from transformers import (
	BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
	MODEL_FOR_CAUSAL_LM_MAPPING,
	MODEL_FOR_MASKED_LM_MAPPING,
	MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
	MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
	MODEL_FOR_QUESTION_ANSWERING_MAPPING,
	MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
	MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
	MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
	MODEL_MAPPING,
	AdaptiveEmbedding,
	BertConfig,
	BertModel,
	PretrainedConfig,
	PreTrainedModel,
	)


	def _config_zero_init(config):
	configs_no_init = copy.deepcopy(config)
	for key in configs_no_init.__dict__.keys():
	if "_range" in key or "_std" in key or "initializer_factor" in key:
	setattr(configs_no_init, key, 1e-10)
	return configs_no_init


	@require_torch
	class ModelTesterMixin:

	model_tester = None
	all_model_classes = ()
	all_generative_model_classes = ()
	test_torchscript = True
	test_pruning = True
	test_resize_embeddings = True
	test_head_masking = True
	test_missing_keys = True
	test_model_parallel = False
	is_encoder_decoder = False

	def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
	inputs_dict = copy.deepcopy(inputs_dict)
	if model_class in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.values():
	inputs_dict = {
	k: v.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous()
	if isinstance(v, torch.Tensor) and v.ndim > 1
	else v
	for k, v in inputs_dict.items()
	}

	if return_labels:
	if model_class in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.values():
	inputs_dict["labels"] = torch.ones(self.model_tester.batch_size, dtype=torch.long, device=torch_device)
	elif model_class in MODEL_FOR_QUESTION_ANSWERING_MAPPING.values():
	inputs_dict["start_positions"] = torch.zeros(
	self.model_tester.batch_size, dtype=torch.long, device=torch_device
	)
	inputs_dict["end_positions"] = torch.zeros(
	self.model_tester.batch_size, dtype=torch.long, device=torch_device
	)
	elif model_class in [
	*MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.values(),
	*MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING.values(),
	]:
	inputs_dict["labels"] = torch.zeros(
	self.model_tester.batch_size, dtype=torch.long, device=torch_device
	)
	elif model_class in [
	*MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.values(),
	*MODEL_FOR_CAUSAL_LM_MAPPING.values(),
	*MODEL_FOR_MASKED_LM_MAPPING.values(),
	*MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.values(),
	]:
	inputs_dict["labels"] = torch.zeros(
	(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
	)
	return inputs_dict

	def test_save_load(self):
	config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

	for model_class in self.all_model_classes:
	model = model_class(config)
	model.to(torch_device)
	model.eval()
	with torch.no_grad():
	outputs = model(**self._prepare_for_class(inputs_dict, model_class))

	out_2 = outputs[0].cpu().numpy()
	out_2[np.isnan(out_2)] = 0

	with tempfile.TemporaryDirectory() as tmpdirname:
	model.save_pretrained(tmpdirname)
	model = model_class.from_pretrained(tmpdirname)
	model.to(torch_device)
	with torch.no_grad():
	after_outputs = model(**self._prepare_for_class(inputs_dict, model_class))

	# Make sure we don't have nans
	out_1 = after_outputs[0].cpu().numpy()
	out_1[np.isnan(out_1)] = 0
	max_diff = np.amax(np.abs(out_1 - out_2))
	self.assertLessEqual(max_diff, 1e-5)

	def test_save_load__keys_to_ignore_on_save(self):
	config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

	for model_class in self.all_model_classes:
	model = model_class(config)
	_keys_to_ignore_on_save = getattr(model, "_keys_to_ignore_on_save", None)
	if _keys_to_ignore_on_save is None:
	continue

	# check the keys are in the original state_dict
	for k in _keys_to_ignore_on_save:
	self.assertIn(k, model.state_dict())

	# check that certain keys didn't get saved with the model
	with tempfile.TemporaryDirectory() as tmpdirname:
	model.save_pretrained(tmpdirname)
	output_model_file = os.path.join(tmpdirname, WEIGHTS_NAME)
	state_dict_saved = torch.load(output_model_file)
	for k in _keys_to_ignore_on_save:
	self.assertNotIn(k, state_dict_saved)

	def test_initialization(self):
	config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

	configs_no_init = _config_zero_init(config)
	for model_class in self.all_model_classes:
	model = model_class(config=configs_no_init)
	for name, param in model.named_parameters():
	if param.requires_grad:
	self.assertIn(
	((param.data.mean() * 1e9).round() / 1e9).item(),
	[0.0, 1.0],
	msg="Parameter {} of model {} seems not properly initialized".format(name, model_class),
	)

	def test_determinism(self):
	config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

	for model_class in self.all_model_classes:
	model = model_class(config)
	model.to(torch_device)
	model.eval()
	with torch.no_grad():
	first = model(**self._prepare_for_class(inputs_dict, model_class))[0]
	second = model(**self._prepare_for_class(inputs_dict, model_class))[0]

	out_1 = first.cpu().numpy()
	out_2 = second.cpu().numpy()
	out_1 = out_1[~np.isnan(out_1)]
	out_2 = out_2[~np.isnan(out_2)]
	max_diff = np.amax(np.abs(out_1 - out_2))
	self.assertLessEqual(max_diff, 1e-5)

	def test_forward_signature(self):
	config, _ = self.model_tester.prepare_config_and_inputs_for_common()

	for model_class in self.all_model_classes:
	model = model_class(config)
	signature = inspect.signature(model.forward)
	# signature.parameters is an OrderedDict => so arg_names order is deterministic
	arg_names = [*signature.parameters.keys()]

	if model.config.is_encoder_decoder:
	expected_arg_names = [
	"input_ids",
	"attention_mask",
	"decoder_input_ids",
	"decoder_attention_mask",
	]
	expected_arg_names.extend(
	["head_mask", "decoder_head_mask", "encoder_outputs"]
	if "head_mask" and "decoder_head_mask" in arg_names
	else ["encoder_outputs"]
	)
	self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
	else:
	expected_arg_names = ["input_ids"]
	self.assertListEqual(arg_names[:1], expected_arg_names)

	def test_training(self):
	if not self.model_tester.is_training:
	return

	config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
	config.return_dict = True

	for model_class in self.all_model_classes:
	if model_class in MODEL_MAPPING.values():
	continue
	model = model_class(config)
	model.to(torch_device)
	model.train()
	inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
	loss = model(**inputs).loss
	loss.backward()

	def test_training_gradient_checkpointing(self):
	config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
	if not self.model_tester.is_training or not hasattr(config, "gradient_checkpointing"):
	return

	config.gradient_checkpointing = True
	config.use_cache = False
	config.return_dict = True

	for model_class in self.all_model_classes:
	if model_class in MODEL_MAPPING.values():
	continue
	model = model_class(config)
	model.to(torch_device)
	model.train()
	inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
	loss = model(**inputs).loss
	loss.backward()

	def test_attention_outputs(self):
	config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
	config.return_dict = True

	seq_len = getattr(self.model_tester, "seq_length", None)
	decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
	encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
	decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
	encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
	chunk_length = getattr(self.model_tester, "chunk_length", None)
	if chunk_length is not None and hasattr(self.model_tester, "num_hashes"):
	encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes

	for model_class in self.all_model_classes:
	inputs_dict["output_attentions"] = True
	inputs_dict["output_hidden_states"] = False
	config.return_dict = True
	model = model_class(config)
	model.to(torch_device)
	model.eval()
	with torch.no_grad():
	outputs = model(**self._prepare_for_class(inputs_dict, model_class))
	attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
	self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)

	# check that output_attentions also work using config
	del inputs_dict["output_attentions"]
	config.output_attentions = True
	model = model_class(config)
	model.to(torch_device)
	model.eval()
	with torch.no_grad():
	outputs = model(**self._prepare_for_class(inputs_dict, model_class))
	attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
	self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)

	if chunk_length is not None:
	self.assertListEqual(
	list(attentions[0].shape[-4:]),
	[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
	)
	else:
	self.assertListEqual(
	list(attentions[0].shape[-3:]),
	[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
	)
	out_len = len(outputs)

	if self.is_encoder_decoder:
	correct_outlen = 5

	# loss is at first position
	if "labels" in inputs_dict:
	correct_outlen += 1 # loss is added to beginning
	# Question Answering model returns start_logits and end_logits
	if model_class in MODEL_FOR_QUESTION_ANSWERING_MAPPING.values():
	correct_outlen += 1 # start_logits and end_logits instead of only 1 output
	if "past_key_values" in outputs:
	correct_outlen += 1 # past_key_values have been returned

	self.assertEqual(out_len, correct_outlen)

	# decoder attentions
	decoder_attentions = outputs.decoder_attentions
	self.assertIsInstance(decoder_attentions, (list, tuple))
	self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
	self.assertListEqual(
	list(decoder_attentions[0].shape[-3:]),
	[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
	)

	# cross attentions
	cross_attentions = outputs.cross_attentions
	self.assertIsInstance(cross_attentions, (list, tuple))
	self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
	self.assertListEqual(
	list(cross_attentions[0].shape[-3:]),
	[
	self.model_tester.num_attention_heads,
	decoder_seq_length,
	encoder_key_length,
	],
	)

	# Check attention is always last and order is fine
	inputs_dict["output_attentions"] = True
	inputs_dict["output_hidden_states"] = True
	model = model_class(config)
	model.to(torch_device)
	model.eval()
	with torch.no_grad():
	outputs = model(**self._prepare_for_class(inputs_dict, model_class))

	if hasattr(self.model_tester, "num_hidden_states_types"):
	added_hidden_states = self.model_tester.num_hidden_states_types
	elif self.is_encoder_decoder:
	added_hidden_states = 2
	else:
	added_hidden_states = 1
	self.assertEqual(out_len + added_hidden_states, len(outputs))

	self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

	self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
	if chunk_length is not None:
	self.assertListEqual(
	list(self_attentions[0].shape[-4:]),
	[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
	)
	else:
	self.assertListEqual(
	list(self_attentions[0].shape[-3:]),
	[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
	)

	def test_torchscript(self):
	config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
	self._create_and_check_torchscript(config, inputs_dict)

	def test_torchscript_output_attentions(self):
	config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
	config.output_attentions = True
	self._create_and_check_torchscript(config, inputs_dict)

	def test_torchscript_output_hidden_state(self):
	config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
	config.output_hidden_states = True
	self._create_and_check_torchscript(config, inputs_dict)

	def _create_and_check_torchscript(self, config, inputs_dict):
	if not self.test_torchscript:
	return

	configs_no_init = _config_zero_init(config) # To be sure we have no Nan
	configs_no_init.torchscript = True
	for model_class in self.all_model_classes:
	model = model_class(config=configs_no_init)
	model.to(torch_device)
	model.eval()
	inputs = self._prepare_for_class(inputs_dict, model_class)

	try:
	if model.config.is_encoder_decoder:
	model.config.use_cache = False # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward
	input_ids = inputs["input_ids"]
	attention_mask = inputs["attention_mask"]
	decoder_input_ids = inputs["decoder_input_ids"]
	decoder_attention_mask = inputs["decoder_attention_mask"]
	traced_model = torch.jit.trace(
	model, (input_ids, attention_mask, decoder_input_ids, decoder_attention_mask)
	)
	else:
	input_ids = inputs["input_ids"]
	traced_model = torch.jit.trace(model, input_ids)
	except RuntimeError:
	self.fail("Couldn't trace module.")

	with tempfile.TemporaryDirectory() as tmp_dir_name:
	pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")

	try:
	torch.jit.save(traced_model, pt_file_name)
	except Exception:
	self.fail("Couldn't save module.")

	try:
	loaded_model = torch.jit.load(pt_file_name)
	except Exception:
	self.fail("Couldn't load module.")

	model.to(torch_device)
	model.eval()

	loaded_model.to(torch_device)
	loaded_model.eval()

	model_state_dict = model.state_dict()
	loaded_model_state_dict = loaded_model.state_dict()

	self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))

	models_equal = True
	for layer_name, p1 in model_state_dict.items():
	p2 = loaded_model_state_dict[layer_name]
	if p1.data.ne(p2.data).sum() > 0:
	models_equal = False

	self.assertTrue(models_equal)

	def test_headmasking(self):
	if not self.test_head_masking:
	return

	global_rng.seed(42)
	config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
	global_rng.seed()

	inputs_dict["output_attentions"] = True
	config.output_hidden_states = True
	configs_no_init = _config_zero_init(config) # To be sure we have no Nan
	for model_class in self.all_model_classes:
	model = model_class(config=configs_no_init)
	model.to(torch_device)
	model.eval()

	# Prepare head_mask
	# Set require_grad after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior)
	head_mask = torch.ones(
	self.model_tester.num_hidden_layers,
	self.model_tester.num_attention_heads,
	device=torch_device,
	)
	head_mask[0, 0] = 0
	head_mask[-1, :-1] = 0
	head_mask.requires_grad_(requires_grad=True)
	inputs = self._prepare_for_class(inputs_dict, model_class).copy()
	inputs["head_mask"] = head_mask
	if model.config.is_encoder_decoder:
	signature = inspect.signature(model.forward)
	arg_names = [*signature.parameters.keys()]
	if "decoder_head_mask" in arg_names: # necessary diferentiation because of T5 model
	inputs["decoder_head_mask"] = head_mask
	outputs = model(**inputs, return_dict=True)

	# Test that we can get a gradient back for importance score computation
	output = sum(t.sum() for t in outputs[0])
	output = output.sum()
	output.backward()
	multihead_outputs = head_mask.grad

	self.assertIsNotNone(multihead_outputs)
	self.assertEqual(len(multihead_outputs), self.model_tester.num_hidden_layers)

	def check_attentions_validity(attentions):
	# Remove Nan
	for t in attentions:
	self.assertLess(
	torch.sum(torch.isnan(t)), t.numel() / 4
	) # Check we don't have more than 25% nans (arbitrary)
	attentions = [
	t.masked_fill(torch.isnan(t), 0.0) for t in attentions
	] # remove them (the test is less complete)

	self.assertAlmostEqual(attentions[0][..., 0, :, :].flatten().sum().item(), 0.0)
	self.assertNotEqual(attentions[0][..., -1, :, :].flatten().sum().item(), 0.0)
	if len(attentions) > 2: # encoder-decoder models have only 2 layers in each module
	self.assertNotEqual(attentions[1][..., 0, :, :].flatten().sum().item(), 0.0)
	self.assertAlmostEqual(attentions[-1][..., -2, :, :].flatten().sum().item(), 0.0)
	self.assertNotEqual(attentions[-1][..., -1, :, :].flatten().sum().item(), 0.0)

	if model.config.is_encoder_decoder:
	check_attentions_validity(outputs.encoder_attentions)
	check_attentions_validity(outputs.decoder_attentions)
	else:
	check_attentions_validity(outputs.attentions)

	def test_head_pruning(self):
	if not self.test_pruning:
	return

	for model_class in self.all_model_classes:
	(
	config,
	inputs_dict,
	) = self.model_tester.prepare_config_and_inputs_for_common()

	if "head_mask" in inputs_dict:
	del inputs_dict["head_mask"]

	inputs_dict["output_attentions"] = True
	config.output_hidden_states = False
	model = model_class(config=config)
	model.to(torch_device)
	model.eval()
	heads_to_prune = {
	0: list(range(1, self.model_tester.num_attention_heads)),
	-1: [0],
	}
	model.prune_heads(heads_to_prune)
	with torch.no_grad():
	outputs = model(**self._prepare_for_class(inputs_dict, model_class))

	attentions = outputs[-1]

	self.assertEqual(attentions[0].shape[-3], 1)
	self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
	self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)

	def test_head_pruning_save_load_from_pretrained(self):
	if not self.test_pruning:
	return

	for model_class in self.all_model_classes:
	(
	config,
	inputs_dict,
	) = self.model_tester.prepare_config_and_inputs_for_common()

	if "head_mask" in inputs_dict:
	del inputs_dict["head_mask"]

	inputs_dict["output_attentions"] = True
	config.output_hidden_states = False
	model = model_class(config=config)
	model.to(torch_device)
	model.eval()
	heads_to_prune = {
	0: list(range(1, self.model_tester.num_attention_heads)),
	-1: [0],
	}
	model.prune_heads(heads_to_prune)

	with tempfile.TemporaryDirectory() as temp_dir_name:
	model.save_pretrained(temp_dir_name)
	model = model_class.from_pretrained(temp_dir_name)
	model.to(torch_device)

	with torch.no_grad():
	outputs = model(**self._prepare_for_class(inputs_dict, model_class))
	attentions = outputs[-1]
	self.assertEqual(attentions[0].shape[-3], 1)
	self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
	self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)

	def test_head_pruning_save_load_from_config_init(self):
	if not self.test_pruning:
	return

	for model_class in self.all_model_classes:
	(
	config,
	inputs_dict,
	) = self.model_tester.prepare_config_and_inputs_for_common()

	if "head_mask" in inputs_dict:
	del inputs_dict["head_mask"]

	inputs_dict["output_attentions"] = True
	config.output_hidden_states = False

	heads_to_prune = {
	0: list(range(1, self.model_tester.num_attention_heads)),
	-1: [0],
	}
	config.pruned_heads = heads_to_prune

	model = model_class(config=config)
	model.to(torch_device)
	model.eval()

	with torch.no_grad():
	outputs = model(**self._prepare_for_class(inputs_dict, model_class))
	attentions = outputs[-1]

	self.assertEqual(attentions[0].shape[-3], 1)
	self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads)
	self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)

	def test_head_pruning_integration(self):
	if not self.test_pruning:
	return

	for model_class in self.all_model_classes:
	(
	config,
	inputs_dict,
	) = self.model_tester.prepare_config_and_inputs_for_common()

	if "head_mask" in inputs_dict:
	del inputs_dict["head_mask"]

	inputs_dict["output_attentions"] = True
	config.output_hidden_states = False

	heads_to_prune = {0: [0], 1: [1, 2]}
	config.pruned_heads = heads_to_prune

	model = model_class(config=config)
	model.to(torch_device)
	model.eval()

	with torch.no_grad():
	outputs = model(**self._prepare_for_class(inputs_dict, model_class))
	attentions = outputs[-1]

	self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1)
	self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
	self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads)
	self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)

	with tempfile.TemporaryDirectory() as temp_dir_name:
	model.save_pretrained(temp_dir_name)
	model = model_class.from_pretrained(temp_dir_name)
	model.to(torch_device)

	with torch.no_grad():
	outputs = model(**self._prepare_for_class(inputs_dict, model_class))
	attentions = outputs[-1]

	self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1)
	self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
	self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads)
	self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)

	heads_to_prune = {0: [0], 2: [1, 2]}
	model.prune_heads(heads_to_prune)

	with torch.no_grad():
	outputs = model(**self._prepare_for_class(inputs_dict, model_class))
	attentions = outputs[-1]

	self.assertEqual(attentions[0].shape[-3], self.model_tester.num_attention_heads - 1)
	self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads - 2)
	self.assertEqual(attentions[2].shape[-3], self.model_tester.num_attention_heads - 2)
	self.assertEqual(attentions[3].shape[-3], self.model_tester.num_attention_heads)

	self.assertDictEqual(model.config.pruned_heads, {0: [0], 1: [1, 2], 2: [1, 2]})

	def test_hidden_states_output(self):
	def check_hidden_states_output(inputs_dict, config, model_class):
	model = model_class(config)
	model.to(torch_device)
	model.eval()

	with torch.no_grad():
	outputs = model(**self._prepare_for_class(inputs_dict, model_class))

	hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

	expected_num_layers = getattr(
	self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
	)
	self.assertEqual(len(hidden_states), expected_num_layers)

	if hasattr(self.model_tester, "encoder_seq_length"):
	seq_length = self.model_tester.encoder_seq_length
	if hasattr(self.model_tester, "chunk_length") and self.model_tester.chunk_length > 1:
	seq_length = seq_length * self.model_tester.chunk_length
	else:
	seq_length = self.model_tester.seq_length

	self.assertListEqual(
	list(hidden_states[0].shape[-2:]),
	[seq_length, self.model_tester.hidden_size],
	)

	if config.is_encoder_decoder:
	hidden_states = outputs.decoder_hidden_states

	self.assertIsInstance(hidden_states, (list, tuple))
	self.assertEqual(len(hidden_states), expected_num_layers)
	seq_len = getattr(self.model_tester, "seq_length", None)
	decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)

	self.assertListEqual(
	list(hidden_states[0].shape[-2:]),
	[decoder_seq_length, self.model_tester.hidden_size],
	)

	config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

	for model_class in self.all_model_classes:
	inputs_dict["output_hidden_states"] = True
	check_hidden_states_output(inputs_dict, config, model_class)

	# check that output_hidden_states also work using config
	del inputs_dict["output_hidden_states"]
	config.output_hidden_states = True

	check_hidden_states_output(inputs_dict, config, model_class)

	def test_retain_grad_hidden_states_attentions(self):
	config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
	config.output_hidden_states = True
	config.output_attentions = True

	# no need to test all models as different heads yield the same functionality
	model_class = self.all_model_classes[0]
	model = model_class(config)
	model.to(torch_device)

	inputs = self._prepare_for_class(inputs_dict, model_class)

	outputs = model(**inputs)

	print(outputs)
	output = outputs[0]

	if config.is_encoder_decoder:
	# Seq2Seq models
	encoder_hidden_states = outputs.encoder_hidden_states[0]
	encoder_attentions = outputs.encoder_attentions[0]
	encoder_hidden_states.retain_grad()
	encoder_attentions.retain_grad()

	decoder_hidden_states = outputs.decoder_hidden_states[0]
	decoder_attentions = outputs.decoder_attentions[0]
	decoder_hidden_states.retain_grad()
	decoder_attentions.retain_grad()

	cross_attentions = outputs.cross_attentions[0]
	cross_attentions.retain_grad()

	output.flatten()[0].backward(retain_graph=True)

	self.assertIsNotNone(encoder_hidden_states.grad)
	self.assertIsNotNone(encoder_attentions.grad)
	self.assertIsNotNone(decoder_hidden_states.grad)
	self.assertIsNotNone(decoder_attentions.grad)
	self.assertIsNotNone(cross_attentions.grad)
	else:
	# Encoder-/Decoder-only models
	hidden_states = outputs.hidden_states[0]
	attentions = outputs.attentions[0]

	hidden_states.retain_grad()
	attentions.retain_grad()

	output.flatten()[0].backward(retain_graph=True)

	self.assertIsNotNone(hidden_states.grad)
	self.assertIsNotNone(attentions.grad)

	def test_feed_forward_chunking(self):
	(
	original_config,
	inputs_dict,
	) = self.model_tester.prepare_config_and_inputs_for_common()
	for model_class in self.all_model_classes:
	torch.manual_seed(0)
	config = copy.deepcopy(original_config)
	model = model_class(config)
	model.to(torch_device)
	model.eval()

	hidden_states_no_chunk = model(**self._prepare_for_class(inputs_dict, model_class))[0]

	torch.manual_seed(0)
	config.chunk_size_feed_forward = 1
	model = model_class(config)
	model.to(torch_device)
	model.eval()

	hidden_states_with_chunk = model(**self._prepare_for_class(inputs_dict, model_class))[0]
	self.assertTrue(torch.allclose(hidden_states_no_chunk, hidden_states_with_chunk, atol=1e-3))

	def test_resize_tokens_embeddings(self):
	(
	original_config,
	inputs_dict,
	) = self.model_tester.prepare_config_and_inputs_for_common()
	if not self.test_resize_embeddings:
	return

	for model_class in self.all_model_classes:
	config = copy.deepcopy(original_config)
	model = model_class(config)
	model.to(torch_device)

	if self.model_tester.is_training is False:
	model.eval()

	model_vocab_size = config.vocab_size
	# Retrieve the embeddings and clone theme
	model_embed = model.resize_token_embeddings(model_vocab_size)
	cloned_embeddings = model_embed.weight.clone()

	# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
	model_embed = model.resize_token_embeddings(model_vocab_size + 10)
	self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
	# Check that it actually resizes the embeddings matrix
	self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
	# Check that the model can still do a forward pass successfully (every parameter should be resized)
	model(**self._prepare_for_class(inputs_dict, model_class))

	# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
	model_embed = model.resize_token_embeddings(model_vocab_size - 15)
	self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
	# Check that it actually resizes the embeddings matrix
	self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)

	# Check that the model can still do a forward pass successfully (every parameter should be resized)
	# Input ids should be clamped to the maximum size of the vocabulary
	inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 1)

	# make sure that decoder_input_ids are resized as well
	if "decoder_input_ids" in inputs_dict:
	inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
	model(**self._prepare_for_class(inputs_dict, model_class))

	# Check that adding and removing tokens has not modified the first part of the embedding matrix.
	models_equal = True
	for p1, p2 in zip(cloned_embeddings, model_embed.weight):
	if p1.data.ne(p2.data).sum() > 0:
	models_equal = False

	self.assertTrue(models_equal)

	def test_resize_embeddings_untied(self):
	(
	original_config,
	inputs_dict,
	) = self.model_tester.prepare_config_and_inputs_for_common()
	if not self.test_resize_embeddings:
	return

	original_config.tie_word_embeddings = False

	# if model cannot untied embeddings -> leave test
	if original_config.tie_word_embeddings:
	return

	for model_class in self.all_model_classes:
	config = copy.deepcopy(original_config)
	model = model_class(config).to(torch_device)

	# if no output embeddings -> leave test
	if model.get_output_embeddings() is None:
	continue

	# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
	model_vocab_size = config.vocab_size
	model.resize_token_embeddings(model_vocab_size + 10)
	self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
	output_embeds = model.get_output_embeddings()
	self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10)
	# Check bias if present
	if output_embeds.bias is not None:
	self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10)
	# Check that the model can still do a forward pass successfully (every parameter should be resized)
	model(**self._prepare_for_class(inputs_dict, model_class))

	# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
	model.resize_token_embeddings(model_vocab_size - 15)
	self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
	# Check that it actually resizes the embeddings matrix
	output_embeds = model.get_output_embeddings()
	self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15)
	# Check bias if present
	if output_embeds.bias is not None:
	self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15)
	# Check that the model can still do a forward pass successfully (every parameter should be resized)
	# Input ids should be clamped to the maximum size of the vocabulary
	inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 1)
	if "decoder_input_ids" in inputs_dict:
	inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
	# Check that the model can still do a forward pass successfully (every parameter should be resized)
	model(**self._prepare_for_class(inputs_dict, model_class))

	def test_model_common_attributes(self):
	config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

	for model_class in self.all_model_classes:
	model = model_class(config)
	self.assertIsInstance(model.get_input_embeddings(), (torch.nn.Embedding, AdaptiveEmbedding))
	model.set_input_embeddings(torch.nn.Embedding(10, 10))
	x = model.get_output_embeddings()
	self.assertTrue(x is None or isinstance(x, torch.nn.Linear))

	def test_correct_missing_keys(self):
	if not self.test_missing_keys:
	return
	config, _ = self.model_tester.prepare_config_and_inputs_for_common()

	for model_class in self.all_model_classes:
	model = model_class(config)
	base_model_prefix = model.base_model_prefix

	if hasattr(model, base_model_prefix):
	with tempfile.TemporaryDirectory() as temp_dir_name:
	model.base_model.save_pretrained(temp_dir_name)
	model, loading_info = model_class.from_pretrained(temp_dir_name, output_loading_info=True)

	with self.subTest(msg="Missing keys for {}".format(model.__class__.__name__)):
	self.assertGreater(len(loading_info["missing_keys"]), 0)

	def test_tie_model_weights(self):
	if not self.test_torchscript:
	return

	config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

	def check_same_values(layer_1, layer_2):
	equal = True
	for p1, p2 in zip(layer_1.weight, layer_2.weight):
	if p1.data.ne(p2.data).sum() > 0:
	equal = False
	return equal

	for model_class in self.all_model_classes:
	config.torchscript = True
	model_not_tied = model_class(config)
	if model_not_tied.get_output_embeddings() is None:
	continue

	config_tied = copy.deepcopy(config)
	config_tied.torchscript = False
	model_tied = model_class(config_tied)
	params_tied = list(model_tied.parameters())
	# Check that the embedding layer and decoding layer are the same in size and in value
	# self.assertTrue(check_same_values(embeddings, decoding))

	# # Check that after modification, they remain the same.
	# embeddings.weight.data.div_(2)
	# # Check that the embedding layer and decoding layer are the same in size and in value
	# self.assertTrue(embeddings.weight.shape, decoding.weight.shape)
	# self.assertTrue(check_same_values(embeddings, decoding))

	# # Check that after modification, they remain the same.
	# decoding.weight.data.div_(4)
	# # Check that the embedding layer and decoding layer are the same in size and in value
	# self.assertTrue(embeddings.weight.shape, decoding.weight.shape)
	# self.assertTrue(check_same_values(embeddings, decoding))

	# Check that after resize they remain tied.
	model_tied.resize_token_embeddings(config.vocab_size + 10)
	params_tied_2 = list(model_tied.parameters())
	self.assertEqual(len(params_tied_2), len(params_tied))

	# decoding.weight.data.mul_(20)
	# # Check that the embedding layer and decoding layer are the same in size and in value
	# self.assertTrue(model.transformer.wte.weight.shape, model.lm_head.weight.shape)
	# self.assertTrue(check_same_values(model.transformer.wte, model.lm_head))

	def test_model_outputs_equivalence(self):

	config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

	def set_nan_tensor_to_zero(t):
	t[t != t] = 0
	return t

	def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
	with torch.no_grad():
	tuple_output = model(tuple_inputs, return_dict=False, additional_kwargs)
	dict_output = model(dict_inputs, return_dict=True, additional_kwargs).to_tuple()

	def recursive_check(tuple_object, dict_object):
	if isinstance(tuple_object, (List, Tuple)):
	for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
	recursive_check(tuple_iterable_value, dict_iterable_value)
	elif tuple_object is None:
	return
	else:
	self.assertTrue(
	torch.allclose(
	set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
	),
	msg=f"Tuple and dict output are not equal. Difference: {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`: {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}.",
	)

	recursive_check(tuple_output, dict_output)

	for model_class in self.all_model_classes:
	model = model_class(config)
	model.to(torch_device)
	model.eval()

	tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
	dict_inputs = self._prepare_for_class(inputs_dict, model_class)
	check_equivalence(model, tuple_inputs, dict_inputs)

	tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
	dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
	check_equivalence(model, tuple_inputs, dict_inputs)

	tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
	dict_inputs = self._prepare_for_class(inputs_dict, model_class)
	check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})

	tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
	dict_inputs = self._prepare_for_class(inputs_dict, model_class)
	check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})

	tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
	dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
	check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})

	tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
	dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
	check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})

	tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
	dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
	check_equivalence(
	model, tuple_inputs, dict_inputs, {"output_hidden_states": True, "output_attentions": True}
	)

	def test_inputs_embeds(self):
	config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

	for model_class in self.all_model_classes:
	model = model_class(config)
	model.to(torch_device)
	model.eval()

	inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))

	if not self.is_encoder_decoder:
	input_ids = inputs["input_ids"]
	del inputs["input_ids"]
	else:
	encoder_input_ids = inputs["input_ids"]
	decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids)
	del inputs["input_ids"]
	inputs.pop("decoder_input_ids", None)

	wte = model.get_input_embeddings()
	if not self.is_encoder_decoder:
	inputs["inputs_embeds"] = wte(input_ids)
	else:
	inputs["inputs_embeds"] = wte(encoder_input_ids)
	inputs["decoder_inputs_embeds"] = wte(decoder_input_ids)

	with torch.no_grad():
	model(**inputs)[0]

	@require_torch_multi_gpu
	def test_multi_gpu_data_parallel_forward(self):
	config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

	# some params shouldn't be scattered by nn.DataParallel
	# so just remove them if they are present.
	blacklist_non_batched_params = ["head_mask", "decoder_head_mask"]
	for k in blacklist_non_batched_params:
	inputs_dict.pop(k, None)

	# move input tensors to cuda:O
	for k, v in inputs_dict.items():
	if torch.is_tensor(v):
	inputs_dict[k] = v.to(0)

	for model_class in self.all_model_classes:
	model = model_class(config=config)
	model.to(0)
	model.eval()

	# Wrap model in nn.DataParallel
	model = torch.nn.DataParallel(model)
	with torch.no_grad():
	_ = model(**self._prepare_for_class(inputs_dict, model_class))

	@require_torch_multi_gpu
	def test_model_parallelization(self):
	if not self.test_model_parallel:
	return

	# a candidate for testing_utils
	def get_current_gpu_memory_use():
	""" returns a list of cuda memory allocations per GPU in MBs"""

	per_device_memory = []
	for id in range(torch.cuda.device_count()):
	with torch.cuda.device(id):
	per_device_memory.append(torch.cuda.memory_allocated() >> 20)

	return per_device_memory

	# Needs a large model to see the difference.
	config = self.model_tester.get_large_model_config()

	for model_class in self.all_parallelizable_model_classes:
	torch.cuda.empty_cache()

	# 1. single gpu memory load + unload + memory measurements
	# Retrieve initial memory usage (can easily be ~0.6-1.5GB if cuda-kernels have been preloaded by previous tests)
	memory_at_start = get_current_gpu_memory_use()

	# Put model on device 0 and take a memory snapshot
	model = model_class(config)
	model.to("cuda:0")
	memory_after_model_load = get_current_gpu_memory_use()

	# The memory use on device 0 should be higher than it was initially.
	self.assertGreater(memory_after_model_load[0], memory_at_start[0])

	del model
	gc.collect()
	torch.cuda.empty_cache()

	# 2. MP test
	# it's essential to re-calibrate the usage before the next stage
	memory_at_start = get_current_gpu_memory_use()

	# Spread model layers over multiple devices
	model = model_class(config)
	model.parallelize()
	memory_after_parallelization = get_current_gpu_memory_use()

	# Assert that the memory use on all devices is higher than it was when loaded only on CPU
	for n in range(torch.cuda.device_count()):
	self.assertGreater(memory_after_parallelization[n], memory_at_start[n])

	# Assert that the memory use of device 0 is lower than it was when the entire model was loaded on it
	self.assertLess(memory_after_parallelization[0], memory_after_model_load[0])

	# Assert that the memory use of device 1 is higher than it was when the entire model was loaded
	# on device 0 and device 1 wasn't used at all
	self.assertGreater(memory_after_parallelization[1], memory_after_model_load[1])

	del model
	gc.collect()
	torch.cuda.empty_cache()

	@require_torch_multi_gpu
	def test_model_parallel_equal_results(self):
	if not self.test_model_parallel:
	return

	config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

	for model_class in self.all_parallelizable_model_classes:
	inputs_dict = self._prepare_for_class(inputs_dict, model_class)

	def cast_to_device(dictionary, device):
	output = {}
	for k, v in dictionary.items():
	if isinstance(v, torch.Tensor):
	output[k] = v.to(device)
	else:
	output[k] = v

	return output

	model = model_class(config)
	output = model(**cast_to_device(inputs_dict, "cpu"))

	model.parallelize()

	parallel_output = model(**cast_to_device(inputs_dict, "cuda:0"))

	for value, parallel_value in zip(output, parallel_output):
	if isinstance(value, torch.Tensor):
	self.assertTrue(torch.allclose(value, parallel_value.to("cpu"), atol=1e-7))
	elif isinstance(value, (Tuple, List)):
	for value_, parallel_value_ in zip(value, parallel_value):
	self.assertTrue(torch.allclose(value_, parallel_value_.to("cpu"), atol=1e-7))

	@require_torch_multi_gpu
	def test_model_parallel_beam_search(self):
	if not self.test_model_parallel:
	return

	all_generative_and_parallelizable_model_classes = tuple(
	set(self.all_generative_model_classes).intersection(self.all_parallelizable_model_classes)
	)

	config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

	for model_class in all_generative_and_parallelizable_model_classes:
	inputs_dict = self._prepare_for_class(inputs_dict, model_class)
	model = model_class(config)

	def cast_to_device(dictionary, device):
	output = {}
	for k, v in dictionary.items():
	if isinstance(v, torch.Tensor):
	output[k] = v.to(device)
	else:
	output[k] = v

	return output

	model.parallelize()
	model.generate(**cast_to_device(inputs_dict, "cuda:0"), num_beams=2)


	global_rng = random.Random()


	def ids_tensor(shape, vocab_size, rng=None, name=None):
	# Creates a random int32 tensor of the shape within the vocab size
	if rng is None:
	rng = global_rng

	total_dims = 1
	for dim in shape:
	total_dims *= dim

	values = []
	for _ in range(total_dims):
	values.append(rng.randint(0, vocab_size - 1))

	return torch.tensor(data=values, dtype=torch.long, device=torch_device).view(shape).contiguous()


	def random_attention_mask(shape, rng=None, name=None):
	attn_mask = ids_tensor(shape, vocab_size=2, rng=None, name=None)
	# make sure that at least one token is attended to for each batch
	attn_mask[:, -1] = 1
	return attn_mask


	def floats_tensor(shape, scale=1.0, rng=None, name=None):
	"""Creates a random float32 tensor"""
	if rng is None:
	rng = global_rng

	total_dims = 1
	for dim in shape:
	total_dims *= dim

	values = []
	for _ in range(total_dims):
	values.append(rng.random() * scale)

	return torch.tensor(data=values, dtype=torch.float, device=torch_device).view(shape).contiguous()


	@require_torch
	class ModelUtilsTest(unittest.TestCase):
	@slow
	def test_model_from_pretrained(self):
	for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
	config = BertConfig.from_pretrained(model_name)
	self.assertIsNotNone(config)
	self.assertIsInstance(config, PretrainedConfig)

	model = BertModel.from_pretrained(model_name)
	model, loading_info = BertModel.from_pretrained(model_name, output_loading_info=True)
	self.assertIsNotNone(model)
	self.assertIsInstance(model, PreTrainedModel)
	for value in loading_info.values():
	self.assertEqual(len(value), 0)

	config = BertConfig.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)

	# Not sure this is the intended behavior. TODO fix Lysandre & Thom
	config.name_or_path = model_name

	model = BertModel.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
	self.assertEqual(model.config.output_hidden_states, True)
	self.assertEqual(model.config, config)

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