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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 IBertConfig, 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 | |
| from ...test_pipeline_mixin import PipelineTesterMixin | |
| if is_torch_available(): | |
| import torch | |
| from torch import nn | |
| from transformers import ( | |
| IBERT_PRETRAINED_MODEL_ARCHIVE_LIST, | |
| 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, | |
| batch_size=13, | |
| seq_length=7, | |
| is_training=True, | |
| use_input_mask=True, | |
| use_token_type_ids=True, | |
| use_labels=True, | |
| vocab_size=99, | |
| hidden_size=32, | |
| num_hidden_layers=5, | |
| num_attention_heads=4, | |
| intermediate_size=37, | |
| hidden_act="gelu", | |
| hidden_dropout_prob=0.1, | |
| attention_probs_dropout_prob=0.1, | |
| max_position_embeddings=512, | |
| type_vocab_size=16, | |
| type_sequence_label_size=2, | |
| initializer_range=0.02, | |
| num_labels=3, | |
| num_choices=4, | |
| scope=None, | |
| ): | |
| self.parent = parent | |
| self.batch_size = batch_size | |
| self.seq_length = seq_length | |
| self.is_training = is_training | |
| self.use_input_mask = use_input_mask | |
| self.use_token_type_ids = use_token_type_ids | |
| self.use_labels = use_labels | |
| self.vocab_size = vocab_size | |
| self.hidden_size = hidden_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.intermediate_size = intermediate_size | |
| self.hidden_act = hidden_act | |
| self.hidden_dropout_prob = hidden_dropout_prob | |
| self.attention_probs_dropout_prob = attention_probs_dropout_prob | |
| self.max_position_embeddings = max_position_embeddings | |
| self.type_vocab_size = type_vocab_size | |
| self.type_sequence_label_size = type_sequence_label_size | |
| self.initializer_range = initializer_range | |
| self.num_labels = num_labels | |
| self.num_choices = num_choices | |
| self.scope = scope | |
| 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 = self.get_config() | |
| return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels | |
| def get_config(self): | |
| return 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, | |
| ) | |
| def get_pipeline_config(self): | |
| config = self.get_config() | |
| config.vocab_size = 300 | |
| return config | |
| 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 | |
| class IBertModelTest(ModelTesterMixin, PipelineTesterMixin, 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 () | |
| ) | |
| pipeline_model_mapping = ( | |
| { | |
| "feature-extraction": IBertModel, | |
| "fill-mask": IBertForMaskedLM, | |
| "question-answering": IBertForQuestionAnswering, | |
| "text-classification": IBertForSequenceClassification, | |
| "token-classification": IBertForTokenClassification, | |
| "zero-shot": IBertForSequenceClassification, | |
| } | |
| 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) | |
| 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.empty(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(nn.Embedding(10, 10)) | |
| x = model.get_output_embeddings() | |
| self.assertTrue(x is None or isinstance(x, 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] | |
| 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 = 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 = nn.Parameter(linear_weight) | |
| linear_dq.weight = 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 = 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 = 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 = nn.LayerNorm(x.shape[1:], 1e-5) | |
| ln_q.weight = nn.Parameter(torch.ones(x.shape[1:])) | |
| ln_q.bias = nn.Parameter(torch.ones(x.shape[1:])) | |
| ln_dq.weight = nn.Parameter(torch.ones(x.shape[1:])) | |
| ln_dq.bias = 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 = nn.Parameter(torch.ones(x.shape[1:])) | |
| ln_dq.bias = 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 = nn.Parameter(torch.ones(x.shape[1:])) | |
| ln_fdq.bias = 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) | |
| 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)) | |
| 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)) | |