697 lines
30 KiB
Python
Executable File
697 lines
30 KiB
Python
Executable File
# coding=utf-8
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# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import copy
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import unittest
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from transformers import is_torch_available
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from transformers.testing_utils import require_torch, slow, torch_device
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from .test_configuration_common import ConfigTester
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from .test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
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if is_torch_available():
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import torch
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import torch.nn as nn
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from transformers import (
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IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
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IBertConfig,
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IBertForMaskedLM,
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IBertForMultipleChoice,
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IBertForQuestionAnswering,
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IBertForSequenceClassification,
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IBertForTokenClassification,
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IBertModel,
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)
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from transformers.models.ibert.modeling_ibert import (
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IBertEmbeddings,
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IntGELU,
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IntLayerNorm,
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IntSoftmax,
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QuantAct,
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QuantEmbedding,
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QuantLinear,
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create_position_ids_from_input_ids,
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)
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class IBertModelTester:
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def __init__(
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self,
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parent,
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):
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self.parent = parent
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self.batch_size = 13
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self.seq_length = 7
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self.is_training = True
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self.use_input_mask = True
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self.use_token_type_ids = True
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self.use_labels = True
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self.vocab_size = 99
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self.hidden_size = 32
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self.num_hidden_layers = 5
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self.num_attention_heads = 4
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self.intermediate_size = 37
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self.hidden_act = "gelu"
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self.hidden_dropout_prob = 0.1
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self.attention_probs_dropout_prob = 0.1
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self.max_position_embeddings = 512
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self.type_vocab_size = 16
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self.type_sequence_label_size = 2
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self.initializer_range = 0.02
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self.num_labels = 3
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self.num_choices = 4
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self.scope = None
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.seq_length])
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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sequence_labels = None
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token_labels = None
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choice_labels = None
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if self.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = IBertConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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initializer_range=self.initializer_range,
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quant_mode=True,
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)
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return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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def create_and_check_model(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = IBertModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
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result = model(input_ids, token_type_ids=token_type_ids)
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result = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
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def create_and_check_for_masked_lm(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = IBertForMaskedLM(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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def create_and_check_for_token_classification(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.num_labels = self.num_labels
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model = IBertForTokenClassification(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
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def create_and_check_for_multiple_choice(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.num_choices = self.num_choices
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model = IBertForMultipleChoice(config=config)
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model.to(torch_device)
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model.eval()
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multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
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multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
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multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
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result = model(
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multiple_choice_inputs_ids,
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attention_mask=multiple_choice_input_mask,
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token_type_ids=multiple_choice_token_type_ids,
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labels=choice_labels,
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)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
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def create_and_check_for_question_answering(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = IBertForQuestionAnswering(config=config)
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model.to(torch_device)
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model.eval()
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result = model(
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input_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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start_positions=sequence_labels,
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end_positions=sequence_labels,
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)
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self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
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self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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) = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
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return config, inputs_dict
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@require_torch
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class IBertModelTest(ModelTesterMixin, unittest.TestCase):
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test_pruning = False
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test_torchscript = False
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test_head_masking = False
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test_resize_embeddings = False
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all_model_classes = (
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(
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IBertForMaskedLM,
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IBertModel,
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IBertForSequenceClassification,
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IBertForTokenClassification,
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IBertForMultipleChoice,
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IBertForQuestionAnswering,
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)
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if is_torch_available()
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else ()
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)
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def setUp(self):
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self.model_tester = IBertModelTester(self)
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self.config_tester = ConfigTester(self, config_class=IBertConfig, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_model_various_embeddings(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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# I-BERT only supports absolute embedding
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for type in ["absolute"]:
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config_and_inputs[0].position_embedding_type = type
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_for_masked_lm(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
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def test_for_token_classification(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
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def test_for_multiple_choice(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
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def test_for_question_answering(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
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@slow
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def test_model_from_pretrained(self):
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for model_name in IBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = IBertModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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def test_create_position_ids_respects_padding_index(self):
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"""Ensure that the default position ids only assign a sequential . This is a regression
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test for https://github.com/huggingface/transformers/issues/1761
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The position ids should be masked with the embedding object's padding index. Therefore, the
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first available non-padding position index is IBertEmbeddings.padding_idx + 1
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"""
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config = self.model_tester.prepare_config_and_inputs()[0]
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model = IBertEmbeddings(config=config)
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input_ids = torch.as_tensor([[12, 31, 13, model.padding_idx]])
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expected_positions = torch.as_tensor(
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[[0 + model.padding_idx + 1, 1 + model.padding_idx + 1, 2 + model.padding_idx + 1, model.padding_idx]]
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)
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position_ids = create_position_ids_from_input_ids(input_ids, model.padding_idx)
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self.assertEqual(position_ids.shape, expected_positions.shape)
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self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))
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def test_create_position_ids_from_inputs_embeds(self):
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"""Ensure that the default position ids only assign a sequential . This is a regression
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test for https://github.com/huggingface/transformers/issues/1761
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The position ids should be masked with the embedding object's padding index. Therefore, the
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first available non-padding position index is IBertEmbeddings.padding_idx + 1
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"""
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config = self.model_tester.prepare_config_and_inputs()[0]
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embeddings = IBertEmbeddings(config=config)
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inputs_embeds = torch.Tensor(2, 4, 30)
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expected_single_positions = [
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0 + embeddings.padding_idx + 1,
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1 + embeddings.padding_idx + 1,
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2 + embeddings.padding_idx + 1,
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3 + embeddings.padding_idx + 1,
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]
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expected_positions = torch.as_tensor([expected_single_positions, expected_single_positions])
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position_ids = embeddings.create_position_ids_from_inputs_embeds(inputs_embeds)
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self.assertEqual(position_ids.shape, expected_positions.shape)
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self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))
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# Override
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def test_model_common_attributes(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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self.assertIsInstance(model.get_input_embeddings(), QuantEmbedding)
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model.set_input_embeddings(torch.nn.Embedding(10, 10))
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x = model.get_output_embeddings()
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self.assertTrue(x is None or isinstance(x, torch.nn.Linear))
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# Override
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def test_feed_forward_chunking(self):
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pass # I-BERT does not support chunking
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# Override
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def test_inputs_embeds(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
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if not self.is_encoder_decoder:
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input_ids = inputs["input_ids"]
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del inputs["input_ids"]
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else:
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encoder_input_ids = inputs["input_ids"]
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decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids)
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del inputs["input_ids"]
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inputs.pop("decoder_input_ids", None)
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wte = model.get_input_embeddings()
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if not self.is_encoder_decoder:
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embed, embed_scaling_factor = wte(input_ids)
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inputs["inputs_embeds"] = embed
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else:
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inputs["inputs_embeds"] = wte(encoder_input_ids)
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inputs["decoder_inputs_embeds"] = wte(decoder_input_ids)
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with torch.no_grad():
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model(**inputs)[0]
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@require_torch
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class IBertModelIntegrationTest(unittest.TestCase):
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def test_quant_embedding(self):
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weight_bit = 8
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embedding = QuantEmbedding(2, 4, quant_mode=True, weight_bit=weight_bit)
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embedding_weight = torch.tensor([[-1.0, -2.0, -3.0, -4.0], [5.0, 6.0, 7.0, 8.0]])
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embedding.weight = torch.nn.Parameter(embedding_weight)
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expected_scaling_factor = embedding_weight.abs().max() / (2 ** (weight_bit - 1) - 1)
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x, x_scaling_factor = embedding(torch.tensor(0))
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y, y_scaling_factor = embedding(torch.tensor(1))
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# scaling factor should follow the symmetric quantization rule
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self.assertTrue(torch.allclose(x_scaling_factor, expected_scaling_factor, atol=1e-4))
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self.assertTrue(torch.allclose(x_scaling_factor, expected_scaling_factor, atol=1e-4))
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self.assertTrue(torch.allclose(y_scaling_factor, expected_scaling_factor, atol=1e-4))
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# quantization error should not exceed the scaling factor
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self.assertTrue(torch.allclose(x, embedding_weight[0], atol=expected_scaling_factor))
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self.assertTrue(torch.allclose(y, embedding_weight[1], atol=expected_scaling_factor))
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def test_quant_act(self):
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def _test_range():
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act = QuantAct(activation_bit, act_range_momentum, quant_mode=True)
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# First pass
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x = torch.tensor([[-1.0, -2.0, -3.0, -4.0], [5.0, 6.0, 7.0, 8.0]])
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x_scaling_factor = torch.tensor(1.0)
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y, y_scaling_factor = act(x, x_scaling_factor)
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y_int = y / y_scaling_factor
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# After the first pass, x_min and x_max should be initialized with x.min() and x.max()
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expected_x_min, expected_x_max = x.min(), x.max()
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self.assertTrue(torch.allclose(act.x_min, expected_x_min, atol=1e-4))
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self.assertTrue(torch.allclose(act.x_max, expected_x_max, atol=1e-4))
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# scaling factor should follow the symmetric quantization rule
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expected_range = torch.max(expected_x_min.abs(), expected_x_max.abs())
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expected_scaling_factor = expected_range / (2 ** (activation_bit - 1) - 1)
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self.assertTrue(torch.allclose(y_scaling_factor, expected_scaling_factor, atol=1e-4))
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# quantization error should not exceed the scaling factor
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self.assertTrue(torch.allclose(x, y, atol=expected_scaling_factor))
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# output should be integer
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self.assertTrue(torch.allclose(y_int, y_int.round(), atol=1e-4))
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# Second Pass
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x = torch.tensor([[-1.0, -2.0, -3.0, -4.0], [5.0, 6.0, 7.0, 8.0]]) * 2
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x_scaling_factor = torch.tensor(1.0)
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y, y_scaling_factor = act(x, x_scaling_factor)
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y_int = y / y_scaling_factor
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# From the second pass, x_min and x_max should be updated with moving average
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expected_x_min = expected_x_min * act_range_momentum + x.min() * (1 - act_range_momentum)
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expected_x_max = expected_x_max * act_range_momentum + x.max() * (1 - act_range_momentum)
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self.assertTrue(torch.allclose(act.x_min, expected_x_min, atol=1e-4))
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self.assertTrue(torch.allclose(act.x_max, expected_x_max, atol=1e-4))
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# scaling factor should follow the symmetric quantization rule
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expected_range = torch.max(expected_x_min.abs(), expected_x_max.abs())
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expected_scaling_factor = expected_range / (2 ** (activation_bit - 1) - 1)
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self.assertTrue(torch.allclose(y_scaling_factor, expected_scaling_factor, atol=1e-4))
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# quantization error should not exceed the scaling factor
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x = x.clamp(min=-expected_range, max=expected_range)
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self.assertTrue(torch.allclose(x, y, atol=expected_scaling_factor))
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# output should be integer
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self.assertTrue(torch.allclose(y_int, y_int.round(), atol=1e-4))
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# Third pass, with eval()
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act.eval()
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x = torch.tensor([[-1.0, -2.0, -3.0, -4.0], [5.0, 6.0, 7.0, 8.0]]) * 3
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# In eval mode, min/max and scaling factor must be fixed
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self.assertTrue(torch.allclose(act.x_min, expected_x_min, atol=1e-4))
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self.assertTrue(torch.allclose(act.x_max, expected_x_max, atol=1e-4))
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self.assertTrue(torch.allclose(y_scaling_factor, expected_scaling_factor, atol=1e-4))
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def _test_identity():
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# test if identity and identity_scaling_factor are given
|
|
# should add the input values
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|
act = QuantAct(activation_bit, act_range_momentum, quant_mode=True)
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x = torch.tensor([[-1.0, -2.0, -3.0, -4.0], [5.0, 6.0, 7.0, 8.0]])
|
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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)
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|
y_scaling_factor = torch.tensor(0.5)
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|
z, z_scaling_factor = act(x, x_scaling_factor, y, y_scaling_factor)
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|
z_int = z / z_scaling_factor
|
|
self.assertTrue(torch.allclose(x + y, z, atol=0.1))
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|
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):
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|
linear_q = QuantLinear(2, 4, quant_mode=True, per_channel=per_channel, weight_bit=weight_bit)
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|
linear_dq = QuantLinear(2, 4, quant_mode=False, per_channel=per_channel, weight_bit=weight_bit)
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|
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)
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|
linear_dq.weight = torch.nn.Parameter(linear_weight)
|
|
|
|
q, q_scaling_factor = linear_q(x, x_scaling_factor)
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|
q_int = q / q_scaling_factor
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|
dq, dq_scaling_factor = linear_dq(x, x_scaling_factor)
|
|
|
|
if per_channel:
|
|
q_max = linear_weight.abs().max(dim=1).values
|
|
else:
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|
q_max = linear_weight.abs().max()
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|
expected_scaling_factor = q_max / (2 ** (weight_bit - 1) - 1)
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|
|
|
# scaling factor should follow the symmetric quantization rule
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|
self.assertTrue(torch.allclose(linear_q.fc_scaling_factor, expected_scaling_factor, atol=1e-4))
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|
|
|
# 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))
|