test_modeling_ibert.py
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test_modeling_ibert.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 copy
	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_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


	if is_torch_available():
	import torch
	import torch.nn as nn

	from transformers import (
	IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
	IBertConfig,
	IBertForMaskedLM,
	IBertForMultipleChoice,
	IBertForQuestionAnswering,
	IBertForSequenceClassification,
	IBertForTokenClassification,
	IBertModel,
	)
	from transformers.models.ibert.modeling_ibert import (
	IBertEmbeddings,
	IntGELU,
	IntLayerNorm,
	IntSoftmax,
	QuantAct,
	QuantEmbedding,
	QuantLinear,
	create_position_ids_from_input_ids,
	)


	class IBertModelTester:
	def __init__(
	self,
	parent,
	):
	self.parent = parent
	self.batch_size = 13
	self.seq_length = 7
	self.is_training = True
	self.use_input_mask = True
	self.use_token_type_ids = True
	self.use_labels = True
	self.vocab_size = 99
	self.hidden_size = 32
	self.num_hidden_layers = 5
	self.num_attention_heads = 4
	self.intermediate_size = 37
	self.hidden_act = "gelu"
	self.hidden_dropout_prob = 0.1
	self.attention_probs_dropout_prob = 0.1
	self.max_position_embeddings = 512
	self.type_vocab_size = 16
	self.type_sequence_label_size = 2
	self.initializer_range = 0.02
	self.num_labels = 3
	self.num_choices = 4
	self.scope = None

	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])

	token_type_ids = None
	if self.use_token_type_ids:
	token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)

	sequence_labels = None
	token_labels = None
	choice_labels = None
	if self.use_labels:
	sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
	token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
	choice_labels = ids_tensor([self.batch_size], self.num_choices)

	config = IBertConfig(
	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,
	type_vocab_size=self.type_vocab_size,
	initializer_range=self.initializer_range,
	quant_mode=True,
	)

	return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

	def create_and_check_model(
	self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
	):
	model = IBertModel(config=config)
	model.to(torch_device)
	model.eval()
	result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
	result = model(input_ids, token_type_ids=token_type_ids)
	result = model(input_ids)

	self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
	self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))

	def create_and_check_for_masked_lm(
	self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
	):
	model = IBertForMaskedLM(config=config)
	model.to(torch_device)
	model.eval()
	result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
	self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))

	def create_and_check_for_token_classification(
	self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
	):
	config.num_labels = self.num_labels
	model = IBertForTokenClassification(config=config)
	model.to(torch_device)
	model.eval()
	result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
	self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))

	def create_and_check_for_multiple_choice(
	self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
	):
	config.num_choices = self.num_choices
	model = IBertForMultipleChoice(config=config)
	model.to(torch_device)
	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()
	result = model(
	multiple_choice_inputs_ids,
	attention_mask=multiple_choice_input_mask,
	token_type_ids=multiple_choice_token_type_ids,
	labels=choice_labels,
	)
	self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))

	def create_and_check_for_question_answering(
	self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
	):
	model = IBertForQuestionAnswering(config=config)
	model.to(torch_device)
	model.eval()
	result = model(
	input_ids,
	attention_mask=input_mask,
	token_type_ids=token_type_ids,
	start_positions=sequence_labels,
	end_positions=sequence_labels,
	)
	self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
	self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))

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


	@require_torch
	class IBertModelTest(ModelTesterMixin, unittest.TestCase):

	test_pruning = False
	test_torchscript = False
	test_head_masking = False
	test_resize_embeddings = False

	all_model_classes = (
	(
	IBertForMaskedLM,
	IBertModel,
	IBertForSequenceClassification,
	IBertForTokenClassification,
	IBertForMultipleChoice,
	IBertForQuestionAnswering,
	)
	if is_torch_available()
	else ()
	)

	def setUp(self):
	self.model_tester = IBertModelTester(self)
	self.config_tester = ConfigTester(self, config_class=IBertConfig, 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_various_embeddings(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	# I-BERT only supports absolute embedding
	for type in ["absolute"]:
	config_and_inputs[0].position_embedding_type = type
	self.model_tester.create_and_check_model(*config_and_inputs)

	def test_for_masked_lm(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)

	def test_for_token_classification(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_for_token_classification(*config_and_inputs)

	def test_for_multiple_choice(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)

	def test_for_question_answering(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_for_question_answering(*config_and_inputs)

	@slow
	def test_model_from_pretrained(self):
	for model_name in IBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
	model = IBertModel.from_pretrained(model_name)
	self.assertIsNotNone(model)

	def test_create_position_ids_respects_padding_index(self):
	"""Ensure that the default position ids only assign a sequential . This is a regression
	test for https://github.com/huggingface/transformers/issues/1761

	The position ids should be masked with the embedding object's padding index. Therefore, the
	first available non-padding position index is IBertEmbeddings.padding_idx + 1
	"""
	config = self.model_tester.prepare_config_and_inputs()[0]
	model = IBertEmbeddings(config=config)

	input_ids = torch.as_tensor([[12, 31, 13, model.padding_idx]])
	expected_positions = torch.as_tensor(
	[[0 + model.padding_idx + 1, 1 + model.padding_idx + 1, 2 + model.padding_idx + 1, model.padding_idx]]
	)

	position_ids = create_position_ids_from_input_ids(input_ids, model.padding_idx)
	self.assertEqual(position_ids.shape, expected_positions.shape)
	self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))

	def test_create_position_ids_from_inputs_embeds(self):
	"""Ensure that the default position ids only assign a sequential . This is a regression
	test for https://github.com/huggingface/transformers/issues/1761

	The position ids should be masked with the embedding object's padding index. Therefore, the
	first available non-padding position index is IBertEmbeddings.padding_idx + 1
	"""
	config = self.model_tester.prepare_config_and_inputs()[0]
	embeddings = IBertEmbeddings(config=config)

	inputs_embeds = torch.Tensor(2, 4, 30)
	expected_single_positions = [
	0 + embeddings.padding_idx + 1,
	1 + embeddings.padding_idx + 1,
	2 + embeddings.padding_idx + 1,
	3 + embeddings.padding_idx + 1,
	]
	expected_positions = torch.as_tensor([expected_single_positions, expected_single_positions])
	position_ids = embeddings.create_position_ids_from_inputs_embeds(inputs_embeds)
	self.assertEqual(position_ids.shape, expected_positions.shape)
	self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))

	# Override
	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(), QuantEmbedding)
	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))

	# Override
	def test_feed_forward_chunking(self):
	pass # I-BERT does not support chunking

	# Override
	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:
	embed, embed_scaling_factor = wte(input_ids)
	inputs["inputs_embeds"] = embed
	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
	class IBertModelIntegrationTest(unittest.TestCase):
	def test_quant_embedding(self):
	weight_bit = 8
	embedding = QuantEmbedding(2, 4, quant_mode=True, weight_bit=weight_bit)
	embedding_weight = torch.tensor([[-1.0, -2.0, -3.0, -4.0], [5.0, 6.0, 7.0, 8.0]])
	embedding.weight = torch.nn.Parameter(embedding_weight)

	expected_scaling_factor = embedding_weight.abs().max() / (2 ** (weight_bit - 1) - 1)
	x, x_scaling_factor = embedding(torch.tensor(0))
	y, y_scaling_factor = embedding(torch.tensor(1))

	# scaling factor should follow the symmetric quantization rule
	self.assertTrue(torch.allclose(x_scaling_factor, expected_scaling_factor, atol=1e-4))
	self.assertTrue(torch.allclose(x_scaling_factor, expected_scaling_factor, atol=1e-4))
	self.assertTrue(torch.allclose(y_scaling_factor, expected_scaling_factor, atol=1e-4))

	# quantization error should not exceed the scaling factor
	self.assertTrue(torch.allclose(x, embedding_weight[0], atol=expected_scaling_factor))
	self.assertTrue(torch.allclose(y, embedding_weight[1], atol=expected_scaling_factor))

	def test_quant_act(self):
	def _test_range():
	act = QuantAct(activation_bit, act_range_momentum, quant_mode=True)

	# First pass
	x = torch.tensor([[-1.0, -2.0, -3.0, -4.0], [5.0, 6.0, 7.0, 8.0]])
	x_scaling_factor = torch.tensor(1.0)
	y, y_scaling_factor = act(x, x_scaling_factor)
	y_int = y / y_scaling_factor

	# After the first pass, x_min and x_max should be initialized with x.min() and x.max()
	expected_x_min, expected_x_max = x.min(), x.max()
	self.assertTrue(torch.allclose(act.x_min, expected_x_min, atol=1e-4))
	self.assertTrue(torch.allclose(act.x_max, expected_x_max, atol=1e-4))

	# scaling factor should follow the symmetric quantization rule
	expected_range = torch.max(expected_x_min.abs(), expected_x_max.abs())
	expected_scaling_factor = expected_range / (2 ** (activation_bit - 1) - 1)
	self.assertTrue(torch.allclose(y_scaling_factor, expected_scaling_factor, atol=1e-4))

	# quantization error should not exceed the scaling factor
	self.assertTrue(torch.allclose(x, y, atol=expected_scaling_factor))

	# output should be integer
	self.assertTrue(torch.allclose(y_int, y_int.round(), atol=1e-4))

	# Second Pass
	x = torch.tensor([[-1.0, -2.0, -3.0, -4.0], [5.0, 6.0, 7.0, 8.0]]) * 2
	x_scaling_factor = torch.tensor(1.0)
	y, y_scaling_factor = act(x, x_scaling_factor)
	y_int = y / y_scaling_factor

	# From the second pass, x_min and x_max should be updated with moving average
	expected_x_min = expected_x_min * act_range_momentum + x.min() * (1 - act_range_momentum)
	expected_x_max = expected_x_max * act_range_momentum + x.max() * (1 - act_range_momentum)
	self.assertTrue(torch.allclose(act.x_min, expected_x_min, atol=1e-4))
	self.assertTrue(torch.allclose(act.x_max, expected_x_max, atol=1e-4))

	# scaling factor should follow the symmetric quantization rule
	expected_range = torch.max(expected_x_min.abs(), expected_x_max.abs())
	expected_scaling_factor = expected_range / (2 ** (activation_bit - 1) - 1)
	self.assertTrue(torch.allclose(y_scaling_factor, expected_scaling_factor, atol=1e-4))

	# quantization error should not exceed the scaling factor
	x = x.clamp(min=-expected_range, max=expected_range)
	self.assertTrue(torch.allclose(x, y, atol=expected_scaling_factor))

	# output should be integer
	self.assertTrue(torch.allclose(y_int, y_int.round(), atol=1e-4))

	# Third pass, with eval()
	act.eval()
	x = torch.tensor([[-1.0, -2.0, -3.0, -4.0], [5.0, 6.0, 7.0, 8.0]]) * 3

	# In eval mode, min/max and scaling factor must be fixed
	self.assertTrue(torch.allclose(act.x_min, expected_x_min, atol=1e-4))
	self.assertTrue(torch.allclose(act.x_max, expected_x_max, atol=1e-4))
	self.assertTrue(torch.allclose(y_scaling_factor, expected_scaling_factor, atol=1e-4))

	def _test_identity():
	# test if identity and identity_scaling_factor are given
	# should add the input values
	act = QuantAct(activation_bit, act_range_momentum, quant_mode=True)
	x = torch.tensor([[-1.0, -2.0, -3.0, -4.0], [5.0, 6.0, 7.0, 8.0]])
	y = torch.tensor([[6.0, -7.0, 1.0, -2.0], [3.0, -4.0, -8.0, 5.0]])
	x_scaling_factor = torch.tensor(1.0)
	y_scaling_factor = torch.tensor(0.5)
	z, z_scaling_factor = act(x, x_scaling_factor, y, y_scaling_factor)
	z_int = z / z_scaling_factor
	self.assertTrue(torch.allclose(x + y, z, atol=0.1))
	self.assertTrue(torch.allclose(z_int, z_int.round(), atol=1e-4))

	activation_bit = 8
	act_range_momentum = 0.95
	_test_range()
	_test_identity()

	def test_quant_linear(self):
	def _test(per_channel):
	linear_q = QuantLinear(2, 4, quant_mode=True, per_channel=per_channel, weight_bit=weight_bit)
	linear_dq = QuantLinear(2, 4, quant_mode=False, per_channel=per_channel, weight_bit=weight_bit)
	linear_weight = torch.tensor([[-1.0, 2.0, 3.0, -4.0], [5.0, -6.0, -7.0, 8.0]]).T
	linear_q.weight = torch.nn.Parameter(linear_weight)
	linear_dq.weight = torch.nn.Parameter(linear_weight)

	q, q_scaling_factor = linear_q(x, x_scaling_factor)
	q_int = q / q_scaling_factor
	dq, dq_scaling_factor = linear_dq(x, x_scaling_factor)

	if per_channel:
	q_max = linear_weight.abs().max(dim=1).values
	else:
	q_max = linear_weight.abs().max()
	expected_scaling_factor = q_max / (2 ** (weight_bit - 1) - 1)

	# scaling factor should follow the symmetric quantization rule
	self.assertTrue(torch.allclose(linear_q.fc_scaling_factor, expected_scaling_factor, atol=1e-4))

	# output of the normal linear layer and the quantized linear layer should be similar
	self.assertTrue(torch.allclose(q, dq, atol=0.5))

	# output of the quantized linear layer should be integer
	self.assertTrue(torch.allclose(q_int, q_int.round(), atol=1e-4))

	weight_bit = 8
	x = torch.tensor([[2.0, -5.0], [-3.0, 4.0]])
	x_scaling_factor = torch.tensor([1.0])
	_test(True)
	_test(False)

	def test_int_gelu(self):
	gelu_q = IntGELU(quant_mode=True)
	gelu_dq = torch.nn.GELU()

	x_int = torch.range(-10000, 10000, 1)
	x_scaling_factor = torch.tensor(0.001)
	x = x_int * x_scaling_factor

	q, q_scaling_factor = gelu_q(x, x_scaling_factor)
	q_int = q / q_scaling_factor
	dq = gelu_dq(x)

	# output of the normal GELU and the quantized GELU should be similar
	self.assertTrue(torch.allclose(q, dq, atol=0.5))

	# output of the quantized GELU layer should be integer
	self.assertTrue(torch.allclose(q_int, q_int.round(), atol=1e-4))

	def test_force_dequant_gelu(self):
	x_int = torch.range(-10000, 10000, 1)
	x_scaling_factor = torch.tensor(0.001)
	x = x_int * x_scaling_factor

	gelu_dq = IntGELU(quant_mode=False)
	gelu_fdqs_dict = {
	True: [
	IntGELU(quant_mode=True, force_dequant="nonlinear"),
	IntGELU(quant_mode=True, force_dequant="gelu"),
	],
	False: [
	IntGELU(quant_mode=True, force_dequant="none"),
	IntGELU(quant_mode=True, force_dequant="softmax"),
	IntGELU(quant_mode=True, force_dequant="layernorm"),
	],
	}

	dq, dq_scaling_factor = gelu_dq(x, x_scaling_factor)
	for label, gelu_fdqs in gelu_fdqs_dict.items():
	for gelu_fdq in gelu_fdqs:
	q, q_scaling_factor = gelu_fdq(x, x_scaling_factor)
	if label:
	self.assertTrue(torch.allclose(q, dq, atol=1e-4))
	else:
	self.assertFalse(torch.allclose(q, dq, atol=1e-4))

	def test_int_softmax(self):
	output_bit = 8
	softmax_q = IntSoftmax(output_bit, quant_mode=True)
	softmax_dq = torch.nn.Softmax()

	# x_int = torch.range(-10000, 10000, 1)
	def _test(array):
	x_int = torch.tensor(array)
	x_scaling_factor = torch.tensor(0.1)
	x = x_int * x_scaling_factor

	q, q_scaling_factor = softmax_q(x, x_scaling_factor)
	q_int = q / q_scaling_factor
	dq = softmax_dq(x)

	# output of the normal Softmax and the quantized Softmax should be similar
	self.assertTrue(torch.allclose(q, dq, atol=0.5))

	# output of the quantized GELU layer should be integer
	self.assertTrue(torch.allclose(q_int, q_int.round(), atol=1e-4))

	# Output of the quantize Softmax should not exceed the output_bit
	self.assertTrue(q.abs().max() < 2 ** output_bit)

	array = [[i + j for j in range(10)] for i in range(-10, 10)]
	_test(array)
	array = [[i + j for j in range(50)] for i in range(-10, 10)]
	_test(array)
	array = [[i + 100 * j for j in range(2)] for i in range(-10, 10)]
	_test(array)

	def test_force_dequant_softmax(self):
	output_bit = 8
	array = [[i + j for j in range(10)] for i in range(-10, 10)]
	x_int = torch.tensor(array)
	x_scaling_factor = torch.tensor(0.1)
	x = x_int * x_scaling_factor

	softmax_dq = IntSoftmax(output_bit, quant_mode=False)
	softmax_fdqs_dict = {
	True: [
	IntSoftmax(output_bit, quant_mode=True, force_dequant="nonlinear"),
	IntSoftmax(output_bit, quant_mode=True, force_dequant="softmax"),
	],
	False: [
	IntSoftmax(output_bit, quant_mode=True, force_dequant="none"),
	IntSoftmax(output_bit, quant_mode=True, force_dequant="gelu"),
	IntSoftmax(output_bit, quant_mode=True, force_dequant="layernorm"),
	],
	}

	dq, dq_scaling_factor = softmax_dq(x, x_scaling_factor)
	for label, softmax_fdqs in softmax_fdqs_dict.items():
	for softmax_fdq in softmax_fdqs:
	q, q_scaling_factor = softmax_fdq(x, x_scaling_factor)
	if label:
	self.assertTrue(torch.allclose(q, dq, atol=1e-4))
	else:
	self.assertFalse(torch.allclose(q, dq, atol=1e-4))

	def test_int_layernorm(self):
	output_bit = 8

	# some random matrix
	array = [[[i * j * j + j for j in range(5, 15)]] for i in range(-10, 10)]
	x_int = torch.tensor(array)
	x_scaling_factor = torch.tensor(0.1)
	x = x_int * x_scaling_factor

	ln_q = IntLayerNorm(x.shape[1:], 1e-5, quant_mode=True, output_bit=output_bit)
	ln_dq = torch.nn.LayerNorm(x.shape[1:], 1e-5)

	ln_q.weight = torch.nn.Parameter(torch.ones(x.shape[1:]))
	ln_q.bias = torch.nn.Parameter(torch.ones(x.shape[1:]))
	ln_dq.weight = torch.nn.Parameter(torch.ones(x.shape[1:]))
	ln_dq.bias = torch.nn.Parameter(torch.ones(x.shape[1:]))

	q, q_scaling_factor = ln_q(x, x_scaling_factor)
	q_int = q / q_scaling_factor
	dq = ln_dq(x)

	# output of the normal LN and the quantized LN should be similar
	self.assertTrue(torch.allclose(q, dq, atol=0.5))

	# output of the quantized GELU layer should be integer
	self.assertTrue(torch.allclose(q_int, q_int.round(), atol=1e-4))

	def test_force_dequant_layernorm(self):
	output_bit = 8
	array = [[[i * j * j + j for j in range(5, 15)]] for i in range(-10, 10)]
	x_int = torch.tensor(array)
	x_scaling_factor = torch.tensor(0.1)
	x = x_int * x_scaling_factor

	ln_dq = IntLayerNorm(x.shape[1:], 1e-5, quant_mode=False, output_bit=output_bit)
	ln_fdqs_dict = {
	True: [
	IntLayerNorm(x.shape[1:], 1e-5, quant_mode=True, output_bit=output_bit, force_dequant="nonlinear"),
	IntLayerNorm(x.shape[1:], 1e-5, quant_mode=True, output_bit=output_bit, force_dequant="layernorm"),
	],
	False: [
	IntLayerNorm(x.shape[1:], 1e-5, quant_mode=True, output_bit=output_bit, force_dequant="none"),
	IntLayerNorm(x.shape[1:], 1e-5, quant_mode=True, output_bit=output_bit, force_dequant="gelu"),
	IntLayerNorm(x.shape[1:], 1e-5, quant_mode=True, output_bit=output_bit, force_dequant="softmax"),
	],
	}

	ln_dq.weight = torch.nn.Parameter(torch.ones(x.shape[1:]))
	ln_dq.bias = torch.nn.Parameter(torch.ones(x.shape[1:]))
	dq, dq_scaling_factor = ln_dq(x, x_scaling_factor)
	for label, ln_fdqs in ln_fdqs_dict.items():
	for ln_fdq in ln_fdqs:
	ln_fdq.weight = torch.nn.Parameter(torch.ones(x.shape[1:]))
	ln_fdq.bias = torch.nn.Parameter(torch.ones(x.shape[1:]))
	q, q_scaling_factor = ln_fdq(x, x_scaling_factor)
	if label:
	self.assertTrue(torch.allclose(q, dq, atol=1e-4))
	else:
	self.assertFalse(torch.allclose(q, dq, atol=1e-4))

	def quantize(self, model):
	# Helper function that quantizes the given model
	# Recursively convert all the `quant_mode` attributes as `True`
	if hasattr(model, "quant_mode"):
	model.quant_mode = True
	elif type(model) == nn.Sequential:
	for n, m in model.named_children():
	self.quantize(m)
	elif type(model) == nn.ModuleList:
	for n in model:
	self.quantize(n)
	else:
	for attr in dir(model):
	mod = getattr(model, attr)
	if isinstance(mod, nn.Module) and mod != model:
	self.quantize(mod)

	@slow
	def test_inference_masked_lm(self):
	# I-BERT should be "equivalent" to RoBERTa if not quantized
	# Test coped from `test_modeling_roberta.py`
	model = IBertForMaskedLM.from_pretrained("kssteven/ibert-roberta-base")
	input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
	output = model(input_ids)[0]
	expected_shape = torch.Size((1, 11, 50265))
	self.assertEqual(output.shape, expected_shape)
	expected_slice = torch.tensor(
	[[[33.8802, -4.3103, 22.7761], [4.6539, -2.8098, 13.6253], [1.8228, -3.6898, 8.8600]]]
	)
	self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))

	# I-BERT should be "similar" to RoBERTa if quantized
	self.quantize(model)
	output = model(input_ids)[0]
	self.assertEqual(output.shape, expected_shape)
	self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=0.1))

	@slow
	def test_inference_classification_head(self):
	# I-BERT should be "equivalent" to RoBERTa if not quantized
	# Test coped from `test_modeling_roberta.py`
	model = IBertForSequenceClassification.from_pretrained("kssteven/ibert-roberta-large-mnli")
	input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
	output = model(input_ids)[0]
	expected_shape = torch.Size((1, 3))
	self.assertEqual(output.shape, expected_shape)
	expected_tensor = torch.tensor([[-0.9469, 0.3913, 0.5118]])
	self.assertTrue(torch.allclose(output, expected_tensor, atol=1e-4))

	# I-BERT should be "similar" to RoBERTa if quantized
	self.quantize(model)
	output = model(input_ids)[0]
	self.assertEqual(output.shape, expected_shape)
	self.assertTrue(torch.allclose(output, expected_tensor, atol=0.1))

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