489 lines
17 KiB
Python
489 lines
17 KiB
Python
# 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 unittest
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from transformers import XLMConfig, 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_generation_utils import GenerationTesterMixin
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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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from transformers import (
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XLMForMultipleChoice,
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XLMForQuestionAnswering,
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XLMForQuestionAnsweringSimple,
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XLMForSequenceClassification,
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XLMForTokenClassification,
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XLMModel,
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XLMWithLMHeadModel,
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)
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from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
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class XLMModelTester:
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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_lengths = True
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self.use_token_type_ids = True
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self.use_labels = True
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self.gelu_activation = True
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self.sinusoidal_embeddings = False
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self.causal = False
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self.asm = False
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self.n_langs = 2
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self.vocab_size = 99
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self.n_special = 0
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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.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_sequence_label_size = 2
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self.initializer_range = 0.02
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self.num_labels = 2
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self.num_choices = 4
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self.summary_type = "last"
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self.use_proj = True
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self.scope = None
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self.bos_token_id = 0
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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 = random_attention_mask([self.batch_size, self.seq_length])
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input_lengths = None
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if self.use_input_lengths:
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input_lengths = (
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ids_tensor([self.batch_size], vocab_size=2) + self.seq_length - 2
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) # small variation of 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.n_langs)
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sequence_labels = None
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token_labels = None
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is_impossible_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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is_impossible_labels = ids_tensor([self.batch_size], 2).float()
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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 (
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config,
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input_ids,
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token_type_ids,
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input_lengths,
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sequence_labels,
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token_labels,
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is_impossible_labels,
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choice_labels,
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input_mask,
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)
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def get_config(self):
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return XLMConfig(
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vocab_size=self.vocab_size,
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n_special=self.n_special,
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emb_dim=self.hidden_size,
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n_layers=self.num_hidden_layers,
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n_heads=self.num_attention_heads,
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dropout=self.hidden_dropout_prob,
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attention_dropout=self.attention_probs_dropout_prob,
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gelu_activation=self.gelu_activation,
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sinusoidal_embeddings=self.sinusoidal_embeddings,
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asm=self.asm,
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causal=self.causal,
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n_langs=self.n_langs,
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max_position_embeddings=self.max_position_embeddings,
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initializer_range=self.initializer_range,
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summary_type=self.summary_type,
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use_proj=self.use_proj,
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num_labels=self.num_labels,
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bos_token_id=self.bos_token_id,
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)
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def create_and_check_xlm_model(
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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_lengths,
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sequence_labels,
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token_labels,
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is_impossible_labels,
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choice_labels,
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input_mask,
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):
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model = XLMModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, lengths=input_lengths, langs=token_type_ids)
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result = model(input_ids, langs=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_xlm_lm_head(
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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_lengths,
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sequence_labels,
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token_labels,
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is_impossible_labels,
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choice_labels,
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input_mask,
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):
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model = XLMWithLMHeadModel(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, token_type_ids=token_type_ids, labels=token_labels)
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self.parent.assertEqual(result.loss.shape, ())
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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_xlm_simple_qa(
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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_lengths,
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sequence_labels,
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token_labels,
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is_impossible_labels,
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choice_labels,
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input_mask,
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):
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model = XLMForQuestionAnsweringSimple(config)
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model.to(torch_device)
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model.eval()
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outputs = model(input_ids)
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outputs = model(input_ids, start_positions=sequence_labels, end_positions=sequence_labels)
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result = outputs
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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_xlm_qa(
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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_lengths,
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sequence_labels,
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token_labels,
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is_impossible_labels,
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choice_labels,
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input_mask,
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):
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model = XLMForQuestionAnswering(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids)
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result_with_labels = model(
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input_ids,
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start_positions=sequence_labels,
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end_positions=sequence_labels,
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cls_index=sequence_labels,
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is_impossible=is_impossible_labels,
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p_mask=input_mask,
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)
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result_with_labels = model(
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input_ids,
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start_positions=sequence_labels,
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end_positions=sequence_labels,
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cls_index=sequence_labels,
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is_impossible=is_impossible_labels,
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)
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(total_loss,) = result_with_labels.to_tuple()
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result_with_labels = model(input_ids, start_positions=sequence_labels, end_positions=sequence_labels)
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(total_loss,) = result_with_labels.to_tuple()
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self.parent.assertEqual(result_with_labels.loss.shape, ())
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self.parent.assertEqual(result.start_top_log_probs.shape, (self.batch_size, model.config.start_n_top))
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self.parent.assertEqual(result.start_top_index.shape, (self.batch_size, model.config.start_n_top))
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self.parent.assertEqual(
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result.end_top_log_probs.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
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)
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self.parent.assertEqual(
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result.end_top_index.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
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)
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self.parent.assertEqual(result.cls_logits.shape, (self.batch_size,))
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def create_and_check_xlm_sequence_classif(
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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_lengths,
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sequence_labels,
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token_labels,
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is_impossible_labels,
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choice_labels,
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input_mask,
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):
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model = XLMForSequenceClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids)
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result = model(input_ids, labels=sequence_labels)
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self.parent.assertEqual(result.loss.shape, ())
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
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def create_and_check_xlm_token_classif(
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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_lengths,
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sequence_labels,
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token_labels,
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is_impossible_labels,
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choice_labels,
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input_mask,
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):
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config.num_labels = self.num_labels
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model = XLMForTokenClassification(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, 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_xlm_for_multiple_choice(
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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_lengths,
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sequence_labels,
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token_labels,
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is_impossible_labels,
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choice_labels,
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input_mask,
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):
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config.num_choices = self.num_choices
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model = XLMForMultipleChoice(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_lengths,
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sequence_labels,
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token_labels,
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is_impossible_labels,
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choice_labels,
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input_mask,
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) = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths}
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return config, inputs_dict
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@require_torch
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class XLMModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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XLMModel,
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XLMWithLMHeadModel,
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XLMForQuestionAnswering,
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XLMForSequenceClassification,
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XLMForQuestionAnsweringSimple,
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XLMForTokenClassification,
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XLMForMultipleChoice,
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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 = (
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(XLMWithLMHeadModel,) if is_torch_available() else ()
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) # TODO (PVP): Check other models whether language generation is also applicable
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# XLM has 2 QA models -> need to manually set the correct labels for one of them here
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
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inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
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if return_labels:
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if model_class.__name__ == "XLMForQuestionAnswering":
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inputs_dict["start_positions"] = torch.zeros(
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self.model_tester.batch_size, dtype=torch.long, device=torch_device
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)
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inputs_dict["end_positions"] = torch.zeros(
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self.model_tester.batch_size, dtype=torch.long, device=torch_device
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)
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return inputs_dict
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def setUp(self):
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self.model_tester = XLMModelTester(self)
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self.config_tester = ConfigTester(self, config_class=XLMConfig, emb_dim=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_xlm_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_xlm_model(*config_and_inputs)
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def test_xlm_lm_head(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_xlm_lm_head(*config_and_inputs)
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def test_xlm_simple_qa(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_xlm_simple_qa(*config_and_inputs)
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def test_xlm_qa(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_xlm_qa(*config_and_inputs)
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def test_xlm_sequence_classif(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_xlm_sequence_classif(*config_and_inputs)
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def test_xlm_token_classif(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_xlm_token_classif(*config_and_inputs)
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def test_xlm_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_xlm_for_multiple_choice(*config_and_inputs)
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def _check_attentions_for_generate(
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self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1
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):
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self.assertIsInstance(attentions, tuple)
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self.assertListEqual(
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[isinstance(iter_attentions, tuple) for iter_attentions in attentions], [True] * len(attentions)
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)
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self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups)
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for idx, iter_attentions in enumerate(attentions):
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# adds PAD dummy token
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tgt_len = min_length + idx + 1
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src_len = min_length + idx + 1
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expected_shape = (
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batch_size * num_beam_groups,
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config.num_attention_heads,
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tgt_len,
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src_len,
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)
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# check attn size
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self.assertListEqual(
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[layer_attention.shape for layer_attention in iter_attentions], [expected_shape] * len(iter_attentions)
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)
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def _check_hidden_states_for_generate(
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self, batch_size, hidden_states, min_length, max_length, config, use_cache=False, num_beam_groups=1
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):
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self.assertIsInstance(hidden_states, tuple)
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self.assertListEqual(
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[isinstance(iter_hidden_states, tuple) for iter_hidden_states in hidden_states],
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[True] * len(hidden_states),
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)
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self.assertEqual(len(hidden_states), (max_length - min_length) * num_beam_groups)
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for idx, iter_hidden_states in enumerate(hidden_states):
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# adds PAD dummy token
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seq_len = min_length + idx + 1
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expected_shape = (batch_size * num_beam_groups, seq_len, config.hidden_size)
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# check hidden size
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self.assertListEqual(
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[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states],
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[expected_shape] * len(iter_hidden_states),
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)
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pass
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@slow
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def test_model_from_pretrained(self):
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for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = XLMModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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@require_torch
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class XLMModelLanguageGenerationTest(unittest.TestCase):
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@slow
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def test_lm_generate_xlm_mlm_en_2048(self):
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model = XLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048")
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model.to(torch_device)
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input_ids = torch.tensor([[14, 447]], dtype=torch.long, device=torch_device) # the president
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expected_output_ids = [
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14,
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447,
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14,
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447,
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14,
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447,
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14,
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447,
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14,
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447,
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14,
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447,
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14,
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447,
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14,
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447,
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14,
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447,
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14,
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447,
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] # the president the president the president the president the president the president the president the president the president the president
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# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
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output_ids = model.generate(input_ids, do_sample=False)
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self.assertListEqual(output_ids[0].cpu().numpy().tolist(), expected_output_ids)
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