563 lines
22 KiB
Python
563 lines
22 KiB
Python
# coding=utf-8
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# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
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# Copyright 2021 NVIDIA Corporation. 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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""" Testing suite for the PyTorch QDQBERT model. """
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import unittest
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from transformers import QDQBertConfig, is_torch_available
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from transformers.testing_utils import require_pytorch_quantization, 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, floats_tensor, ids_tensor, random_attention_mask
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if is_torch_available():
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import torch
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from transformers import (
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QDQBertForMaskedLM,
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QDQBertForMultipleChoice,
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QDQBertForNextSentencePrediction,
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QDQBertForQuestionAnswering,
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QDQBertForSequenceClassification,
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QDQBertForTokenClassification,
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QDQBertLMHeadModel,
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QDQBertModel,
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)
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from transformers.models.qdqbert.modeling_qdqbert import QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST
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class QDQBertModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_token_type_ids=True,
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use_labels=True,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=5,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=16,
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type_sequence_label_size=2,
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initializer_range=0.02,
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num_labels=3,
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num_choices=4,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_token_type_ids = use_token_type_ids
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.num_labels = num_labels
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self.num_choices = num_choices
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self.scope = scope
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def prepare_config_and_inputs(self):
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# Set default quantizers before creating the model.
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import pytorch_quantization.nn as quant_nn
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from pytorch_quantization.tensor_quant import QuantDescriptor
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# The default tensor quantizer is set to use Max calibration method
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input_desc = QuantDescriptor(num_bits=8, calib_method="max")
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# The default tensor quantizer is set to be per-channel quantization for weights
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weight_desc = QuantDescriptor(num_bits=8, axis=((0,)))
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quant_nn.QuantLinear.set_default_quant_desc_input(input_desc)
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quant_nn.QuantLinear.set_default_quant_desc_weight(weight_desc)
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# For the test cases, since QDQBert model is tested in one run without calibration, the quantized tensors are set as fake quantized tensors which give float type tensors in the end.
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quant_nn.TensorQuantizer.use_fb_fake_quant = True
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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 = self.get_config()
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return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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def get_config(self):
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return QDQBertConfig(
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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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is_decoder=False,
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initializer_range=self.initializer_range,
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)
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def prepare_config_and_inputs_for_decoder(self):
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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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) = self.prepare_config_and_inputs()
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config.is_decoder = True
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encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
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encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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return (
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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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encoder_hidden_states,
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encoder_attention_mask,
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)
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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 = QDQBertModel(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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def create_and_check_model_as_decoder(
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self,
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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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encoder_hidden_states,
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encoder_attention_mask,
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):
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config.add_cross_attention = True
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model = QDQBertModel(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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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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)
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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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encoder_hidden_states=encoder_hidden_states,
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)
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_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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def create_and_check_for_causal_lm(
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self,
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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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encoder_hidden_states,
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encoder_attention_mask,
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):
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model = QDQBertLMHeadModel(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_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 = QDQBertForMaskedLM(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_model_for_causal_lm_as_decoder(
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self,
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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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encoder_hidden_states,
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encoder_attention_mask,
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):
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config.add_cross_attention = True
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model = QDQBertLMHeadModel(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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labels=token_labels,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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)
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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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labels=token_labels,
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encoder_hidden_states=encoder_hidden_states,
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)
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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_decoder_model_past_large_inputs(
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self,
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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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encoder_hidden_states,
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encoder_attention_mask,
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):
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config.is_decoder = True
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config.add_cross_attention = True
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model = QDQBertLMHeadModel(config=config)
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model.to(torch_device)
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model.eval()
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# first forward pass
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outputs = model(
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input_ids,
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attention_mask=input_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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use_cache=True,
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)
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past_key_values = outputs.past_key_values
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# create hypothetical multiple next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
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next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
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# append to next input_ids and
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next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
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next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
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output_from_no_past = model(
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next_input_ids,
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attention_mask=next_attention_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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output_hidden_states=True,
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)["hidden_states"][0]
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output_from_past = model(
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next_tokens,
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attention_mask=next_attention_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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past_key_values=past_key_values,
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output_hidden_states=True,
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)["hidden_states"][0]
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# select random slice
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random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
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output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
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output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
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self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
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# test that outputs are equal for slice
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self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
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def create_and_check_for_next_sequence_prediction(
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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 = QDQBertForNextSentencePrediction(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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labels=sequence_labels,
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)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, 2))
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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 = QDQBertForQuestionAnswering(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 create_and_check_for_sequence_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 = QDQBertForSequenceClassification(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=sequence_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
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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 = QDQBertForTokenClassification(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 = QDQBertForMultipleChoice(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 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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@require_pytorch_quantization
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class QDQBertModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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QDQBertModel,
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QDQBertForMaskedLM,
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QDQBertForMultipleChoice,
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QDQBertForNextSentencePrediction,
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QDQBertForQuestionAnswering,
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QDQBertForSequenceClassification,
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QDQBertForTokenClassification,
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QDQBertLMHeadModel,
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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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all_generative_model_classes = (QDQBertLMHeadModel,) if is_torch_available() else ()
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def setUp(self):
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self.model_tester = QDQBertModelTester(self)
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self.config_tester = ConfigTester(self, config_class=QDQBertConfig, 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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for type in ["absolute", "relative_key", "relative_key_query"]:
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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_model_as_decoder(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
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self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
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def test_model_as_decoder_with_default_input_mask(self):
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# This regression test was failing with PyTorch < 1.3
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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,
|
|
sequence_labels,
|
|
token_labels,
|
|
choice_labels,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
) = self.model_tester.prepare_config_and_inputs_for_decoder()
|
|
|
|
input_mask = None
|
|
|
|
self.model_tester.create_and_check_model_as_decoder(
|
|
config,
|
|
input_ids,
|
|
token_type_ids,
|
|
input_mask,
|
|
sequence_labels,
|
|
token_labels,
|
|
choice_labels,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
)
|
|
|
|
def test_for_causal_lm(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
|
|
self.model_tester.create_and_check_for_causal_lm(*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_causal_lm_decoder(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
|
|
self.model_tester.create_and_check_model_for_causal_lm_as_decoder(*config_and_inputs)
|
|
|
|
def test_decoder_model_past_with_large_inputs(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
|
|
self.model_tester.create_and_check_decoder_model_past_large_inputs(*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_next_sequence_prediction(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_for_next_sequence_prediction(*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_for_sequence_classification(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_for_sequence_classification(*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)
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
for model_name in QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
|
model = QDQBertModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
# Override
|
|
def test_feed_forward_chunking(self):
|
|
# feed forward chunking is not supported in QDQBert
|
|
pass
|
|
|
|
|
|
@require_torch
|
|
@require_pytorch_quantization
|
|
class QDQBertModelIntegrationTest(unittest.TestCase):
|
|
@slow
|
|
def test_inference_no_head_absolute_embedding(self):
|
|
# Set default quantizers before creating the model.
|
|
import pytorch_quantization.nn as quant_nn
|
|
from pytorch_quantization.tensor_quant import QuantDescriptor
|
|
|
|
# The default tensor quantizer is set to use Max calibration method
|
|
input_desc = QuantDescriptor(num_bits=8, calib_method="max")
|
|
# The default tensor quantizer is set to be per-channel quantization for weights
|
|
weight_desc = QuantDescriptor(num_bits=8, axis=((0,)))
|
|
quant_nn.QuantLinear.set_default_quant_desc_input(input_desc)
|
|
quant_nn.QuantLinear.set_default_quant_desc_weight(weight_desc)
|
|
|
|
model = QDQBertModel.from_pretrained("bert-base-uncased")
|
|
input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
|
|
attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
|
|
output = model(input_ids, attention_mask=attention_mask)[0]
|
|
expected_shape = torch.Size((1, 11, 768))
|
|
self.assertEqual(output.shape, expected_shape)
|
|
expected_slice = torch.tensor(
|
|
[[[0.4571, -0.0735, 0.8594], [0.2774, -0.0278, 0.8794], [0.3548, -0.0473, 0.7593]]]
|
|
)
|
|
self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))
|