mirror of
https://github.com/huggingface/transformers.git
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fc38d4c86f
* improving generation * finalized special token behaviour for no_beam_search generation * solved modeling_utils merge conflict * solve merge conflicts in modeling_utils.py * add run_generation improvements from PR #2749 * adapted language generation to not use hardcoded -1 if no padding token is available * remove the -1 removal as hard coded -1`s are not necessary anymore * add lightweight language generation testing for randomely initialized models - just checking whether no errors are thrown * add slow language generation tests for pretrained models using hardcoded output with pytorch seed * delete ipdb * check that all generated tokens are valid * renaming * renaming Generation -> Generate * make style * updated so that generate_beam_search has same token behavior than generate_no_beam_search * consistent return format for run_generation.py * deleted pretrain lm generate tests -> will be added in another PR * cleaning of unused if statements and renaming * run_generate will always return an iterable * make style * consistent renaming * improve naming, make sure generate function always returns the same tensor, add docstring * add slow tests for all lmhead models * make style and improve example comments modeling_utils * better naming and refactoring in modeling_utils * improving generation * finalized special token behaviour for no_beam_search generation * solved modeling_utils merge conflict * solve merge conflicts in modeling_utils.py * add run_generation improvements from PR #2749 * adapted language generation to not use hardcoded -1 if no padding token is available * remove the -1 removal as hard coded -1`s are not necessary anymore * add lightweight language generation testing for randomely initialized models - just checking whether no errors are thrown * add slow language generation tests for pretrained models using hardcoded output with pytorch seed * delete ipdb * check that all generated tokens are valid * renaming * renaming Generation -> Generate * make style * updated so that generate_beam_search has same token behavior than generate_no_beam_search * consistent return format for run_generation.py * deleted pretrain lm generate tests -> will be added in another PR * cleaning of unused if statements and renaming * run_generate will always return an iterable * make style * consistent renaming * improve naming, make sure generate function always returns the same tensor, add docstring * add slow tests for all lmhead models * make style and improve example comments modeling_utils * better naming and refactoring in modeling_utils * changed fast random lm generation testing design to more general one * delete in old testing design in gpt2 * correct old variable name * temporary fix for encoder_decoder lm generation tests - has to be updated when t5 is fixed * adapted all fast random generate tests to new design * better warning description in modeling_utils * better comment * better comment and error message Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>
399 lines
14 KiB
Python
399 lines
14 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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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 is_torch_available
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from .test_configuration_common import ConfigTester
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from .test_modeling_common import ModelTesterMixin, ids_tensor
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from .utils import CACHE_DIR, require_torch, slow, torch_device
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if is_torch_available():
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from transformers import (
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XLMConfig,
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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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)
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from transformers.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_MAP
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@require_torch
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class XLMModelTest(ModelTesterMixin, 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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)
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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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class XLMModelTester(object):
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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_lengths=True,
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use_token_type_ids=True,
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use_labels=True,
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gelu_activation=True,
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sinusoidal_embeddings=False,
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causal=False,
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asm=False,
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n_langs=2,
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vocab_size=99,
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n_special=0,
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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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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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summary_type="last",
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use_proj=True,
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scope=None,
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bos_token_id=0,
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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_lengths = use_input_lengths
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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.gelu_activation = gelu_activation
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self.sinusoidal_embeddings = sinusoidal_embeddings
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self.asm = asm
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self.n_langs = n_langs
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self.vocab_size = vocab_size
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self.n_special = n_special
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self.summary_type = summary_type
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self.causal = causal
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self.use_proj = use_proj
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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.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.n_langs = n_langs
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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.summary_type = summary_type
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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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self.bos_token_id = bos_token_id
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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 = ids_tensor([self.batch_size, self.seq_length], 2).float()
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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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config = 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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bos_token_id=self.bos_token_id,
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)
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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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input_mask,
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)
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def check_loss_output(self, result):
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self.parent.assertListEqual(list(result["loss"].size()), [])
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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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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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outputs = model(input_ids, lengths=input_lengths, langs=token_type_ids)
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outputs = model(input_ids, langs=token_type_ids)
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outputs = model(input_ids)
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sequence_output = outputs[0]
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result = {
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"sequence_output": sequence_output,
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}
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self.parent.assertListEqual(
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list(result["sequence_output"].size()), [self.batch_size, self.seq_length, self.hidden_size]
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)
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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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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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loss, logits = model(input_ids, token_type_ids=token_type_ids, labels=token_labels)
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result = {
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"loss": loss,
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"logits": logits,
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}
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self.parent.assertListEqual(list(result["loss"].size()), [])
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self.parent.assertListEqual(
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list(result["logits"].size()), [self.batch_size, self.seq_length, self.vocab_size]
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)
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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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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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loss, start_logits, end_logits = outputs
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result = {
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"loss": loss,
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"start_logits": start_logits,
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"end_logits": end_logits,
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}
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self.parent.assertListEqual(list(result["start_logits"].size()), [self.batch_size, self.seq_length])
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self.parent.assertListEqual(list(result["end_logits"].size()), [self.batch_size, self.seq_length])
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self.check_loss_output(result)
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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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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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outputs = model(input_ids)
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start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits = outputs
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outputs = 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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outputs = 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,) = outputs
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outputs = model(input_ids, start_positions=sequence_labels, end_positions=sequence_labels)
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(total_loss,) = outputs
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result = {
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"loss": total_loss,
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"start_top_log_probs": start_top_log_probs,
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"start_top_index": start_top_index,
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"end_top_log_probs": end_top_log_probs,
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"end_top_index": end_top_index,
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"cls_logits": cls_logits,
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}
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self.parent.assertListEqual(list(result["loss"].size()), [])
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self.parent.assertListEqual(
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list(result["start_top_log_probs"].size()), [self.batch_size, model.config.start_n_top]
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)
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self.parent.assertListEqual(
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list(result["start_top_index"].size()), [self.batch_size, model.config.start_n_top]
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)
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self.parent.assertListEqual(
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list(result["end_top_log_probs"].size()),
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[self.batch_size, model.config.start_n_top * model.config.end_n_top],
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)
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self.parent.assertListEqual(
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list(result["end_top_index"].size()),
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[self.batch_size, model.config.start_n_top * model.config.end_n_top],
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)
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self.parent.assertListEqual(list(result["cls_logits"].size()), [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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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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(logits,) = model(input_ids)
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loss, logits = model(input_ids, labels=sequence_labels)
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result = {
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"loss": loss,
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"logits": logits,
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}
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self.parent.assertListEqual(list(result["loss"].size()), [])
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self.parent.assertListEqual(
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list(result["logits"].size()), [self.batch_size, self.type_sequence_label_size]
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)
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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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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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def setUp(self):
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self.model_tester = XLMModelTest.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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@slow
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def test_model_from_pretrained(self):
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for model_name in list(XLM_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
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model = XLMModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
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self.assertIsNotNone(model)
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