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))

R11484/source_code/transformers/tests/test_modeling_ibert.pya0ebe5d8b65emaster

test_modeling_ibert.pyView Options

R11484/source_code/transformers/tests/test_modeling_ibert.py
a0ebe5d8b65emaster

test_modeling_ibert.py
View Options