Add Data2Vec (#15507)
* Add data2vec model cloned from roberta * Add checkpoint conversion script * Fix copies * Update docs * Add checkpoint conversion script * Remove fairseq data2vec_text script and fix format * Add comment on where to get data2vec_text.py * Remove mock implementation cheat.py and fix style * Fix copies * Remove TF and Flax classes from init * Add back copy from fairseq data2vec_text.py and fix style * Update model name in docs/source/index.mdx to be CamelCase * Revert model name in table to lower-case to get check_table test to pass * Update src/transformers/models/data2vec/__init__.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/models/data2vec/convert_data2vec_original_pytorch_checkpoint_to_pytorch.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/models/data2vec/modeling_data2vec.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/models/data2vec/modeling_data2vec.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/models/data2vec/modeling_data2vec.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/models/data2vec/modeling_data2vec.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update docs/source/model_doc/data2vec.mdx Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update docs/source/model_doc/data2vec.mdx Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/auto/configuration_auto.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/data2vec/configuration_data2vec.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/data2vec/modeling_data2vec.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/data2vec/modeling_data2vec.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/data2vec/modeling_data2vec.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update tests/test_modeling_data2vec.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/data2vec/configuration_data2vec.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/data2vec/modeling_data2vec.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update documentation * Copy-paste Data2VecConfig from BertConfig * Update config checkpoint to point to edugp/data2vec-nlp-base. Fix style and repo-consistency * Update config special tokens to match RoBERTa * Split multiple assertions and add individual error messages * Rename Data2VecModel to Data2VecForTextModel * Add Data2Vec to _toctree.yml * Rename Data2VecEmbeddings to Data2VecForTextEmbeddings * Add initial Data2VecForAudio model (unfinished). Only matching fairseq's implementation up to the feature encoder (before positional encoding). * finish audio model * finish audio file * Update names and fix style, quality and repo consistency * Remove Data2VecAudioForPretraining. Add tests for Data2VecAudio, mimicking the Wav2Vec2 test suite. Fix bias initilization in positional conv layers. Move back configurations for audio and text to separate files. * add inputs to logits to data2vec' * correct autio models * correct config auto * correct tok auto * Update utils/tests_fetcher.py * delete unnecessary files * delete unnecessary files * further renaming * make all tests pass * finish * remove useless test file * Update tests/test_modeling_common.py * Update utils/check_repo.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/models/data2vec/modeling_data2vec_text.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Fix copies * Update docs * Remove fairseq data2vec_text script and fix format * Add comment on where to get data2vec_text.py * Remove mock implementation cheat.py and fix style * Fix copies * Remove TF and Flax classes from init * Add back copy from fairseq data2vec_text.py and fix style * Update model name in docs/source/index.mdx to be CamelCase * Revert model name in table to lower-case to get check_table test to pass * Update documentation * Update src/transformers/models/data2vec/__init__.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/models/data2vec/convert_data2vec_original_pytorch_checkpoint_to_pytorch.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/models/data2vec/modeling_data2vec.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/models/data2vec/modeling_data2vec.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/models/data2vec/modeling_data2vec.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/models/data2vec/modeling_data2vec.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/models/auto/configuration_auto.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/data2vec/configuration_data2vec.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/data2vec/modeling_data2vec.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/data2vec/modeling_data2vec.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/data2vec/modeling_data2vec.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update tests/test_modeling_data2vec.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/data2vec/configuration_data2vec.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/data2vec/modeling_data2vec.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Copy-paste Data2VecConfig from BertConfig * Update config checkpoint to point to edugp/data2vec-nlp-base. Fix style and repo-consistency * Update config special tokens to match RoBERTa * Split multiple assertions and add individual error messages * Rename Data2VecModel to Data2VecForTextModel * Add Data2Vec to _toctree.yml * Rename Data2VecEmbeddings to Data2VecForTextEmbeddings * Add initial Data2VecForAudio model (unfinished). Only matching fairseq's implementation up to the feature encoder (before positional encoding). * finish audio model * finish audio file * add inputs to logits to data2vec' * Update names and fix style, quality and repo consistency * Remove Data2VecAudioForPretraining. Add tests for Data2VecAudio, mimicking the Wav2Vec2 test suite. Fix bias initilization in positional conv layers. Move back configurations for audio and text to separate files. * correct autio models * correct config auto * correct tok auto * delete unnecessary files * delete unnecessary files * Update utils/tests_fetcher.py * further renaming * make all tests pass * finish * remove useless test file * Update tests/test_modeling_common.py * Update utils/check_repo.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/models/data2vec/modeling_data2vec_text.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Move data2vec tests to new structure * Fix test imports for text tests * Remove fairseq files * Change paper link to arxiv * Modify Data2Vec documentation to reflect that the encoder is not shared across the audio and text models in the current implementation. * Update text model checkpoint to be facebook/data2vec-text-base * Add 'Copy from' statements and update paper links and docs * fix copy from statements * improve copied from * correct more copied from statements * finish copied from stuff * make style * add model to README * add to master Co-authored-by: Eduardo Gonzalez Ponferrada <eduardo@ferrumhealth.com> Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
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tests/data2vec/__init__.py
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tests/data2vec/__init__.py
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tests/data2vec/test_modeling_data2vec_audio.py
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tests/data2vec/test_modeling_data2vec_audio.py
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# coding=utf-8
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# Copyright 2022 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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""" Testing suite for the PyTorch Data2VecAudio model. """
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import math
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import unittest
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import numpy as np
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from datasets import load_dataset
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from tests.test_modeling_common import floats_tensor, ids_tensor, random_attention_mask
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from transformers import Data2VecAudioConfig, is_torch_available
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from transformers.testing_utils import is_pt_flax_cross_test, require_soundfile, 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, _config_zero_init
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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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Data2VecAudioForAudioFrameClassification,
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Data2VecAudioForCTC,
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Data2VecAudioForSequenceClassification,
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Data2VecAudioForXVector,
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Data2VecAudioModel,
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Wav2Vec2Processor,
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)
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from transformers.models.data2vec.modeling_data2vec_audio import _compute_mask_indices
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class Data2VecAudioModelTester:
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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=1024, # speech is longer
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is_training=False,
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hidden_size=16,
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feat_extract_dropout=0.0,
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feat_extract_activation="gelu",
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conv_dim=(32, 32, 32),
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conv_stride=(4, 4, 4),
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conv_kernel=(8, 8, 8),
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conv_bias=False,
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num_conv_pos_embeddings=16,
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num_conv_pos_embedding_groups=2,
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num_hidden_layers=4,
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num_attention_heads=2,
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hidden_dropout_prob=0.1,
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intermediate_size=20,
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layer_norm_eps=1e-5,
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hidden_act="gelu",
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initializer_range=0.02,
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mask_time_prob=0.5,
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mask_time_length=2,
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vocab_size=32,
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num_adapter_layers=1,
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adapter_stride=2,
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tdnn_dim=(32, 32),
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tdnn_kernel=(5, 3),
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tdnn_dilation=(1, 2),
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xvector_output_dim=32,
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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.hidden_size = hidden_size
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self.feat_extract_dropout = feat_extract_dropout
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self.feat_extract_activation = feat_extract_activation
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self.conv_dim = conv_dim
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self.conv_stride = conv_stride
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self.conv_kernel = conv_kernel
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self.conv_bias = conv_bias
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self.num_conv_pos_embeddings = num_conv_pos_embeddings
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self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
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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.intermediate_size = intermediate_size
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self.layer_norm_eps = layer_norm_eps
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.vocab_size = vocab_size
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self.num_adapter_layers = num_adapter_layers
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self.adapter_stride = adapter_stride
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self.mask_time_prob = mask_time_prob
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self.mask_time_length = mask_time_length
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self.scope = scope
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self.tdnn_dim = tdnn_dim
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self.tdnn_kernel = tdnn_kernel
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self.tdnn_dilation = tdnn_dilation
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self.xvector_output_dim = xvector_output_dim
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output_seq_length = self.seq_length
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for kernel, stride in zip(self.conv_kernel, self.conv_stride):
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output_seq_length = (output_seq_length - (kernel - 1)) / stride
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self.output_seq_length = int(math.ceil(output_seq_length))
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self.encoder_seq_length = self.output_seq_length
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self.adapter_output_seq_length = (self.output_seq_length - 1) // adapter_stride + 1
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def prepare_config_and_inputs(self):
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input_values = floats_tensor([self.batch_size, self.seq_length], self.vocab_size)
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attention_mask = random_attention_mask([self.batch_size, self.seq_length])
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config = self.get_config()
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return config, input_values, attention_mask
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def get_config(self):
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return Data2VecAudioConfig(
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hidden_size=self.hidden_size,
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feat_extract_dropout=self.feat_extract_dropout,
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feat_extract_activation=self.feat_extract_activation,
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conv_dim=self.conv_dim,
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conv_stride=self.conv_stride,
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conv_kernel=self.conv_kernel,
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conv_bias=self.conv_bias,
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mask_time_prob=self.mask_time_prob,
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mask_time_length=self.mask_time_length,
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num_conv_pos_embeddings=self.num_conv_pos_embeddings,
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num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups,
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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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hidden_dropout_prob=self.hidden_dropout_prob,
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intermediate_size=self.intermediate_size,
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layer_norm_eps=self.layer_norm_eps,
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hidden_act=self.hidden_act,
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initializer_range=self.initializer_range,
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vocab_size=self.vocab_size,
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num_adapter_layers=self.num_adapter_layers,
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adapter_stride=self.adapter_stride,
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tdnn_dim=self.tdnn_dim,
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tdnn_kernel=self.tdnn_kernel,
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tdnn_dilation=self.tdnn_dilation,
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xvector_output_dim=self.xvector_output_dim,
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)
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def create_and_check_model(self, config, input_values, attention_mask):
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model = Data2VecAudioModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_values, attention_mask=attention_mask)
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self.parent.assertEqual(
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result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size)
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)
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def create_and_check_model_with_adapter(self, config, input_values, attention_mask):
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config.add_adapter = True
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model = Data2VecAudioModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_values, attention_mask=attention_mask)
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self.parent.assertEqual(
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result.last_hidden_state.shape, (self.batch_size, self.adapter_output_seq_length, self.hidden_size)
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)
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def create_and_check_model_with_adapter_proj_dim(self, config, input_values, attention_mask):
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config.add_adapter = True
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config.output_hidden_size = 8
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model = Data2VecAudioModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_values, attention_mask=attention_mask)
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self.parent.assertEqual(
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result.last_hidden_state.shape,
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(self.batch_size, self.adapter_output_seq_length, config.output_hidden_size),
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)
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def create_and_check_batch_inference(self, config, input_values, *args):
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# test does not pass for models making use of `group_norm`
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# check: https://github.com/pytorch/fairseq/issues/3227
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model = Data2VecAudioModel(config=config)
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model.to(torch_device)
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model.eval()
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input_values = input_values[:3]
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attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.bool)
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input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
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# pad input
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for i in range(len(input_lengths)):
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input_values[i, input_lengths[i] :] = 0.0
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attention_mask[i, input_lengths[i] :] = 0.0
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batch_outputs = model(input_values, attention_mask=attention_mask).last_hidden_state
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for i in range(input_values.shape[0]):
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input_slice = input_values[i : i + 1, : input_lengths[i]]
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output = model(input_slice).last_hidden_state
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batch_output = batch_outputs[i : i + 1, : output.shape[1]]
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self.parent.assertTrue(torch.allclose(output, batch_output, atol=1e-3))
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def check_ctc_loss(self, config, input_values, *args):
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model = Data2VecAudioForCTC(config=config)
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model.to(torch_device)
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# make sure that dropout is disabled
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model.eval()
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input_values = input_values[:3]
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attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long)
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input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
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max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
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labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size)
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# pad input
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for i in range(len(input_lengths)):
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input_values[i, input_lengths[i] :] = 0.0
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attention_mask[i, input_lengths[i] :] = 0
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model.config.ctc_loss_reduction = "sum"
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sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
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model.config.ctc_loss_reduction = "mean"
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mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
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self.parent.assertTrue(isinstance(sum_loss, float))
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self.parent.assertTrue(isinstance(mean_loss, float))
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def check_seq_classifier_loss(self, config, input_values, *args):
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model = Data2VecAudioForSequenceClassification(config=config)
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model.to(torch_device)
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# make sure that dropout is disabled
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model.eval()
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input_values = input_values[:3]
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attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long)
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input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
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labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))
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# pad input
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for i in range(len(input_lengths)):
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input_values[i, input_lengths[i] :] = 0.0
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attention_mask[i, input_lengths[i] :] = 0
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masked_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
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unmasked_loss = model(input_values, labels=labels).loss.item()
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self.parent.assertTrue(isinstance(masked_loss, float))
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self.parent.assertTrue(isinstance(unmasked_loss, float))
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self.parent.assertTrue(masked_loss != unmasked_loss)
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def check_ctc_training(self, config, input_values, *args):
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config.ctc_zero_infinity = True
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model = Data2VecAudioForCTC(config=config)
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model.to(torch_device)
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model.train()
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# freeze feature encoder
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model.freeze_feature_encoder()
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input_values = input_values[:3]
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input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
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max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
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labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size)
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# pad input
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for i in range(len(input_lengths)):
|
||||
input_values[i, input_lengths[i] :] = 0.0
|
||||
|
||||
if max_length_labels[i] < labels.shape[-1]:
|
||||
# it's important that we make sure that target lenghts are at least
|
||||
# one shorter than logit lenghts to prevent -inf
|
||||
labels[i, max_length_labels[i] - 1 :] = -100
|
||||
|
||||
loss = model(input_values, labels=labels).loss
|
||||
self.parent.assertFalse(torch.isinf(loss).item())
|
||||
|
||||
loss.backward()
|
||||
|
||||
def check_seq_classifier_training(self, config, input_values, *args):
|
||||
config.ctc_zero_infinity = True
|
||||
model = Data2VecAudioForSequenceClassification(config=config)
|
||||
model.to(torch_device)
|
||||
model.train()
|
||||
|
||||
# freeze everything but the classification head
|
||||
model.freeze_base_model()
|
||||
|
||||
input_values = input_values[:3]
|
||||
|
||||
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
|
||||
labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))
|
||||
|
||||
# pad input
|
||||
for i in range(len(input_lengths)):
|
||||
input_values[i, input_lengths[i] :] = 0.0
|
||||
|
||||
loss = model(input_values, labels=labels).loss
|
||||
self.parent.assertFalse(torch.isinf(loss).item())
|
||||
|
||||
loss.backward()
|
||||
|
||||
def check_xvector_training(self, config, input_values, *args):
|
||||
config.ctc_zero_infinity = True
|
||||
model = Data2VecAudioForXVector(config=config)
|
||||
model.to(torch_device)
|
||||
model.train()
|
||||
|
||||
# freeze everything but the classification head
|
||||
model.freeze_base_model()
|
||||
|
||||
input_values = input_values[:3]
|
||||
|
||||
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
|
||||
labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))
|
||||
|
||||
# pad input
|
||||
for i in range(len(input_lengths)):
|
||||
input_values[i, input_lengths[i] :] = 0.0
|
||||
|
||||
loss = model(input_values, labels=labels).loss
|
||||
self.parent.assertFalse(torch.isinf(loss).item())
|
||||
|
||||
loss.backward()
|
||||
|
||||
def check_labels_out_of_vocab(self, config, input_values, *args):
|
||||
model = Data2VecAudioForCTC(config)
|
||||
model.to(torch_device)
|
||||
model.train()
|
||||
|
||||
input_values = input_values[:3]
|
||||
|
||||
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
|
||||
max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
|
||||
labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size + 100)
|
||||
|
||||
with self.parent.assertRaises(ValueError):
|
||||
model(input_values, labels=labels)
|
||||
|
||||
def prepare_config_and_inputs_for_common(self):
|
||||
config, input_values, attention_mask = self.prepare_config_and_inputs()
|
||||
inputs_dict = {"input_values": input_values, "attention_mask": attention_mask}
|
||||
return config, inputs_dict
|
||||
|
||||
|
||||
@require_torch
|
||||
class Data2VecAudioModelTest(ModelTesterMixin, unittest.TestCase):
|
||||
all_model_classes = (
|
||||
(
|
||||
Data2VecAudioForCTC,
|
||||
Data2VecAudioModel,
|
||||
Data2VecAudioForSequenceClassification,
|
||||
Data2VecAudioForAudioFrameClassification,
|
||||
Data2VecAudioForXVector,
|
||||
)
|
||||
if is_torch_available()
|
||||
else ()
|
||||
)
|
||||
test_pruning = False
|
||||
test_headmasking = False
|
||||
test_torchscript = False
|
||||
|
||||
def setUp(self):
|
||||
self.model_tester = Data2VecAudioModelTester(self)
|
||||
self.config_tester = ConfigTester(self, config_class=Data2VecAudioConfig, hidden_size=37)
|
||||
|
||||
def test_config(self):
|
||||
self.config_tester.run_common_tests()
|
||||
|
||||
def test_model(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_model(*config_and_inputs)
|
||||
|
||||
def test_model_with_adapter(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_model_with_adapter(*config_and_inputs)
|
||||
|
||||
def test_model_with_adapter_proj_dim(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_model_with_adapter_proj_dim(*config_and_inputs)
|
||||
|
||||
def test_ctc_loss_inference(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.check_ctc_loss(*config_and_inputs)
|
||||
|
||||
def test_seq_classifier_loss_inference(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.check_seq_classifier_loss(*config_and_inputs)
|
||||
|
||||
def test_ctc_train(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.check_ctc_training(*config_and_inputs)
|
||||
|
||||
def test_seq_classifier_train(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.check_seq_classifier_training(*config_and_inputs)
|
||||
|
||||
def test_xvector_train(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.check_xvector_training(*config_and_inputs)
|
||||
|
||||
def test_labels_out_of_vocab(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.check_labels_out_of_vocab(*config_and_inputs)
|
||||
|
||||
# Data2VecAudio has no inputs_embeds
|
||||
def test_inputs_embeds(self):
|
||||
pass
|
||||
|
||||
# `input_ids` is renamed to `input_values`
|
||||
def test_forward_signature(self):
|
||||
pass
|
||||
|
||||
# Data2VecAudio cannot resize token embeddings
|
||||
# since it has no tokens embeddings
|
||||
def test_resize_tokens_embeddings(self):
|
||||
pass
|
||||
|
||||
# Data2VecAudio has no inputs_embeds
|
||||
# and thus the `get_input_embeddings` fn
|
||||
# is not implemented
|
||||
def test_model_common_attributes(self):
|
||||
pass
|
||||
|
||||
@is_pt_flax_cross_test
|
||||
# non-robust architecture does not exist in Flax
|
||||
def test_equivalence_flax_to_pt(self):
|
||||
pass
|
||||
|
||||
@is_pt_flax_cross_test
|
||||
# non-robust architecture does not exist in Flax
|
||||
def test_equivalence_pt_to_flax(self):
|
||||
pass
|
||||
|
||||
def test_retain_grad_hidden_states_attentions(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
config.output_hidden_states = True
|
||||
config.output_attentions = True
|
||||
|
||||
# no need to test all models as different heads yield the same functionality
|
||||
model_class = self.all_model_classes[0]
|
||||
model = model_class(config)
|
||||
model.to(torch_device)
|
||||
|
||||
# set layer drop to 0
|
||||
model.config.layerdrop = 0.0
|
||||
|
||||
input_values = inputs_dict["input_values"]
|
||||
|
||||
input_lengths = torch.tensor(
|
||||
[input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device
|
||||
)
|
||||
output_lengths = model._get_feat_extract_output_lengths(input_lengths)
|
||||
|
||||
labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size)
|
||||
inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"])
|
||||
inputs_dict["labels"] = labels
|
||||
|
||||
outputs = model(**inputs_dict)
|
||||
|
||||
output = outputs[0]
|
||||
|
||||
# Encoder-/Decoder-only models
|
||||
hidden_states = outputs.hidden_states[0]
|
||||
attentions = outputs.attentions[0]
|
||||
|
||||
hidden_states.retain_grad()
|
||||
attentions.retain_grad()
|
||||
|
||||
output.flatten()[0].backward(retain_graph=True)
|
||||
|
||||
self.assertIsNotNone(hidden_states.grad)
|
||||
self.assertIsNotNone(attentions.grad)
|
||||
|
||||
def test_initialization(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
configs_no_init = _config_zero_init(config)
|
||||
for model_class in self.all_model_classes:
|
||||
model = model_class(config=configs_no_init)
|
||||
for name, param in model.named_parameters():
|
||||
uniform_init_parms = [
|
||||
"conv.weight",
|
||||
"masked_spec_embed",
|
||||
"codevectors",
|
||||
"quantizer.weight_proj.weight",
|
||||
"project_hid.weight",
|
||||
"project_hid.bias",
|
||||
"project_q.weight",
|
||||
"project_q.bias",
|
||||
"feature_projection.projection.weight",
|
||||
"feature_projection.projection.bias",
|
||||
"objective.weight",
|
||||
]
|
||||
if param.requires_grad:
|
||||
if any([x in name for x in uniform_init_parms]):
|
||||
self.assertTrue(
|
||||
-1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
|
||||
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
||||
)
|
||||
else:
|
||||
self.assertIn(
|
||||
((param.data.mean() * 1e9).round() / 1e9).item(),
|
||||
[0.0, 1.0],
|
||||
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
||||
)
|
||||
|
||||
# overwrite from test_modeling_common
|
||||
def _mock_init_weights(self, module):
|
||||
if hasattr(module, "weight") and module.weight is not None:
|
||||
module.weight.data.fill_(3)
|
||||
if hasattr(module, "weight_g") and module.weight_g is not None:
|
||||
module.weight_g.data.fill_(3)
|
||||
if hasattr(module, "weight_v") and module.weight_v is not None:
|
||||
module.weight_v.data.fill_(3)
|
||||
if hasattr(module, "bias") and module.bias is not None:
|
||||
module.bias.data.fill_(3)
|
||||
if hasattr(module, "codevectors") and module.codevectors is not None:
|
||||
module.codevectors.data.fill_(3)
|
||||
if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None:
|
||||
module.masked_spec_embed.data.fill_(3)
|
||||
|
||||
def test_mask_feature_prob_ctc(self):
|
||||
model = Data2VecAudioForCTC.from_pretrained(
|
||||
"facebook/data2vec-audio-base-960h", mask_feature_prob=0.2, mask_feature_length=2
|
||||
)
|
||||
model.to(torch_device).train()
|
||||
processor = Wav2Vec2Processor.from_pretrained(
|
||||
"hf-internal-testing/tiny-random-wav2vec2", return_attention_mask=True
|
||||
)
|
||||
|
||||
batch_duration_in_seconds = [1, 3, 2, 6]
|
||||
input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds]
|
||||
|
||||
batch = processor(
|
||||
input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt"
|
||||
)
|
||||
|
||||
logits = model(
|
||||
input_values=batch["input_values"].to(torch_device),
|
||||
attention_mask=batch["attention_mask"].to(torch_device),
|
||||
).logits
|
||||
|
||||
self.assertEqual(logits.shape, (4, 299, 32))
|
||||
|
||||
def test_mask_time_prob_ctc(self):
|
||||
model = Data2VecAudioForCTC.from_pretrained(
|
||||
"facebook/data2vec-audio-base-960h", mask_time_prob=0.2, mask_time_length=2
|
||||
)
|
||||
model.to(torch_device).train()
|
||||
processor = Wav2Vec2Processor.from_pretrained(
|
||||
"hf-internal-testing/tiny-random-wav2vec2", return_attention_mask=True
|
||||
)
|
||||
|
||||
batch_duration_in_seconds = [1, 3, 2, 6]
|
||||
input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds]
|
||||
|
||||
batch = processor(
|
||||
input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt"
|
||||
)
|
||||
|
||||
logits = model(
|
||||
input_values=batch["input_values"].to(torch_device),
|
||||
attention_mask=batch["attention_mask"].to(torch_device),
|
||||
).logits
|
||||
|
||||
self.assertEqual(logits.shape, (4, 299, 32))
|
||||
|
||||
@unittest.skip(reason="Feed forward chunking is not implemented")
|
||||
def test_feed_forward_chunking(self):
|
||||
pass
|
||||
|
||||
@slow
|
||||
def test_model_from_pretrained(self):
|
||||
model = Data2VecAudioModel.from_pretrained("facebook/data2vec-audio-base")
|
||||
self.assertIsNotNone(model)
|
||||
|
||||
|
||||
@require_torch
|
||||
class Data2VecAudioUtilsTest(unittest.TestCase):
|
||||
def test_compute_mask_indices(self):
|
||||
batch_size = 4
|
||||
sequence_length = 60
|
||||
mask_prob = 0.5
|
||||
mask_length = 1
|
||||
|
||||
mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
|
||||
mask = torch.from_numpy(mask).to(torch_device)
|
||||
|
||||
self.assertListEqual(mask.sum(axis=-1).tolist(), [mask_prob * sequence_length for _ in range(batch_size)])
|
||||
|
||||
def test_compute_mask_indices_low_prob(self):
|
||||
# with these settings num_masked_spans=0.5, which means probabilistic rounding
|
||||
# ensures that in 5 out of 10 method calls, num_masked_spans=0, and in
|
||||
# the other 5 out of 10, cases num_masked_spans=1
|
||||
n_trials = 100
|
||||
batch_size = 4
|
||||
sequence_length = 100
|
||||
mask_prob = 0.05
|
||||
mask_length = 10
|
||||
|
||||
count_dimensions_masked = 0
|
||||
count_dimensions_not_masked = 0
|
||||
|
||||
for _ in range(n_trials):
|
||||
mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
|
||||
mask = torch.from_numpy(mask).to(torch_device)
|
||||
|
||||
num_masks = torch.sum(mask).item()
|
||||
|
||||
if num_masks > 0:
|
||||
count_dimensions_masked += 1
|
||||
else:
|
||||
count_dimensions_not_masked += 1
|
||||
|
||||
# as we test for at least 10 masked dimension and at least
|
||||
# 10 non-masked dimension, this test could fail with probability:
|
||||
# P(100 coin flips, at most 9 heads) = 1.66e-18
|
||||
self.assertGreater(count_dimensions_masked, int(n_trials * 0.1))
|
||||
self.assertGreater(count_dimensions_not_masked, int(n_trials * 0.1))
|
||||
|
||||
def test_compute_mask_indices_overlap(self):
|
||||
batch_size = 4
|
||||
sequence_length = 80
|
||||
mask_prob = 0.5
|
||||
mask_length = 4
|
||||
|
||||
mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
|
||||
mask = torch.from_numpy(mask).to(torch_device)
|
||||
|
||||
# because of overlap mask don't have to add up exactly to `mask_prob * sequence_length`, but have to be smaller or equal
|
||||
for batch_sum in mask.sum(axis=-1):
|
||||
self.assertTrue(int(batch_sum) <= mask_prob * sequence_length)
|
||||
|
||||
def test_compute_mask_indices_attn_mask_overlap(self):
|
||||
batch_size = 4
|
||||
sequence_length = 80
|
||||
mask_prob = 0.5
|
||||
mask_length = 4
|
||||
|
||||
attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device)
|
||||
attention_mask[:2, sequence_length // 2 :] = 0
|
||||
|
||||
mask = _compute_mask_indices(
|
||||
(batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask
|
||||
)
|
||||
mask = torch.from_numpy(mask).to(torch_device)
|
||||
|
||||
for batch_sum in mask.sum(axis=-1):
|
||||
self.assertTrue(int(batch_sum) <= mask_prob * sequence_length)
|
||||
|
||||
self.assertTrue(mask[:2, sequence_length // 2 :].sum() == 0)
|
||||
|
||||
def test_compute_mask_indices_short_audio(self):
|
||||
batch_size = 4
|
||||
sequence_length = 100
|
||||
mask_prob = 0.05
|
||||
mask_length = 10
|
||||
|
||||
attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device)
|
||||
# force one example to be heavily padded
|
||||
attention_mask[0, 5:] = 0
|
||||
|
||||
mask = _compute_mask_indices(
|
||||
(batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask, min_masks=2
|
||||
)
|
||||
|
||||
# make sure that non-padded examples cannot be padded
|
||||
self.assertFalse(mask[0][attention_mask[0].to(torch.bool).cpu()].any())
|
||||
|
||||
|
||||
@require_torch
|
||||
@require_soundfile
|
||||
@slow
|
||||
class Data2VecAudioModelIntegrationTest(unittest.TestCase):
|
||||
def _load_datasamples(self, num_samples):
|
||||
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
|
||||
# automatic decoding with librispeech
|
||||
speech_samples = ds.sort("id").filter(
|
||||
lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
|
||||
)[:num_samples]["audio"]
|
||||
|
||||
return [x["array"] for x in speech_samples]
|
||||
|
||||
def _load_superb(self, task, num_samples):
|
||||
ds = load_dataset("anton-l/superb_dummy", task, split="test")
|
||||
|
||||
return ds[:num_samples]
|
||||
|
||||
def test_inference_ctc_normal(self):
|
||||
model = Data2VecAudioForCTC.from_pretrained("facebook/data2vec-audio-base-960h")
|
||||
model.to(torch_device)
|
||||
processor = Wav2Vec2Processor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2", do_lower_case=True)
|
||||
input_speech = self._load_datasamples(1)
|
||||
|
||||
input_values = processor(input_speech, return_tensors="pt").input_values.to(torch_device)
|
||||
|
||||
with torch.no_grad():
|
||||
logits = model(input_values).logits
|
||||
|
||||
predicted_ids = torch.argmax(logits, dim=-1)
|
||||
predicted_trans = processor.batch_decode(predicted_ids)
|
||||
|
||||
EXPECTED_TRANSCRIPTIONS = ["a man said to the universe sir i exist"]
|
||||
self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
|
||||
|
||||
def test_inference_ctc_batched(self):
|
||||
model = Data2VecAudioForCTC.from_pretrained("facebook/data2vec-audio-base-960h").to(torch_device)
|
||||
processor = Wav2Vec2Processor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2", do_lower_case=True)
|
||||
|
||||
input_speech = self._load_datasamples(4)
|
||||
|
||||
inputs = processor(input_speech, return_tensors="pt", padding=True)
|
||||
|
||||
input_values = inputs.input_values.to(torch_device)
|
||||
|
||||
with torch.no_grad():
|
||||
logits = model(input_values).logits
|
||||
|
||||
predicted_ids = torch.argmax(logits, dim=-1)
|
||||
predicted_trans = processor.batch_decode(predicted_ids)
|
||||
|
||||
EXPECTED_TRANSCRIPTIONS = [
|
||||
"a man said to the universe sir i exist",
|
||||
"sweat covered brion's body trickling into the tight loin cloth that was the only garment he wore",
|
||||
"the cut on his chest still dripping blood the ache of his overstrained eyes even the soaring arena around him with thousands of spectators were trivialities not worth thinking about",
|
||||
"his instant of panic was followed by a small sharp blow high on his chest",
|
||||
]
|
||||
self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
|
||||
506
tests/data2vec/test_modeling_data2vec_text.py
Normal file
506
tests/data2vec/test_modeling_data2vec_text.py
Normal file
@@ -0,0 +1,506 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2022 The HuggingFace Team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
""" Testing suite for the PyTorch Data2VecAudio model. """
|
||||
|
||||
import unittest
|
||||
|
||||
from tests.test_modeling_common import floats_tensor, ids_tensor, random_attention_mask
|
||||
from transformers import Data2VecTextConfig, is_torch_available
|
||||
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
|
||||
|
||||
from ..generation.test_generation_utils import GenerationTesterMixin
|
||||
from ..test_configuration_common import ConfigTester
|
||||
from ..test_modeling_common import ModelTesterMixin
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
|
||||
from transformers import (
|
||||
Data2VecTextForCausalLM,
|
||||
Data2VecTextForMaskedLM,
|
||||
Data2VecTextForMultipleChoice,
|
||||
Data2VecTextForQuestionAnswering,
|
||||
Data2VecTextForSequenceClassification,
|
||||
Data2VecTextForTokenClassification,
|
||||
Data2VecTextModel,
|
||||
)
|
||||
from transformers.models.data2vec.modeling_data2vec_text import (
|
||||
DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
|
||||
Data2VecTextForTextEmbeddings,
|
||||
create_position_ids_from_input_ids,
|
||||
)
|
||||
|
||||
|
||||
class Data2VecTextModelTester:
|
||||
def __init__(
|
||||
self,
|
||||
parent,
|
||||
):
|
||||
self.parent = parent
|
||||
self.batch_size = 13
|
||||
self.seq_length = 7
|
||||
self.is_training = True
|
||||
self.use_input_mask = True
|
||||
self.use_token_type_ids = True
|
||||
self.use_labels = True
|
||||
self.vocab_size = 99
|
||||
self.hidden_size = 32
|
||||
self.num_hidden_layers = 5
|
||||
self.num_attention_heads = 4
|
||||
self.intermediate_size = 37
|
||||
self.hidden_act = "gelu"
|
||||
self.hidden_dropout_prob = 0.1
|
||||
self.attention_probs_dropout_prob = 0.1
|
||||
self.max_position_embeddings = 512
|
||||
self.type_vocab_size = 16
|
||||
self.type_sequence_label_size = 2
|
||||
self.initializer_range = 0.02
|
||||
self.num_labels = 3
|
||||
self.num_choices = 4
|
||||
self.scope = None
|
||||
|
||||
def prepare_config_and_inputs(self):
|
||||
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
|
||||
|
||||
input_mask = None
|
||||
if self.use_input_mask:
|
||||
input_mask = random_attention_mask([self.batch_size, self.seq_length])
|
||||
|
||||
token_type_ids = None
|
||||
if self.use_token_type_ids:
|
||||
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
|
||||
|
||||
sequence_labels = None
|
||||
token_labels = None
|
||||
choice_labels = None
|
||||
if self.use_labels:
|
||||
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
|
||||
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
|
||||
choice_labels = ids_tensor([self.batch_size], self.num_choices)
|
||||
|
||||
config = self.get_config()
|
||||
|
||||
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
|
||||
def get_config(self):
|
||||
return Data2VecTextConfig(
|
||||
vocab_size=self.vocab_size,
|
||||
hidden_size=self.hidden_size,
|
||||
num_hidden_layers=self.num_hidden_layers,
|
||||
num_attention_heads=self.num_attention_heads,
|
||||
intermediate_size=self.intermediate_size,
|
||||
hidden_act=self.hidden_act,
|
||||
hidden_dropout_prob=self.hidden_dropout_prob,
|
||||
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
|
||||
max_position_embeddings=self.max_position_embeddings,
|
||||
type_vocab_size=self.type_vocab_size,
|
||||
initializer_range=self.initializer_range,
|
||||
)
|
||||
|
||||
def prepare_config_and_inputs_for_decoder(self):
|
||||
(
|
||||
config,
|
||||
input_ids,
|
||||
token_type_ids,
|
||||
input_mask,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
) = self.prepare_config_and_inputs()
|
||||
|
||||
config.is_decoder = True
|
||||
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
|
||||
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
|
||||
|
||||
return (
|
||||
config,
|
||||
input_ids,
|
||||
token_type_ids,
|
||||
input_mask,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
encoder_hidden_states,
|
||||
encoder_attention_mask,
|
||||
)
|
||||
|
||||
def create_and_check_model(
|
||||
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
):
|
||||
model = Data2VecTextModel(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
|
||||
result = model(input_ids, token_type_ids=token_type_ids)
|
||||
result = model(input_ids)
|
||||
|
||||
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
|
||||
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
|
||||
|
||||
def create_and_check_model_as_decoder(
|
||||
self,
|
||||
config,
|
||||
input_ids,
|
||||
token_type_ids,
|
||||
input_mask,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
encoder_hidden_states,
|
||||
encoder_attention_mask,
|
||||
):
|
||||
config.add_cross_attention = True
|
||||
model = Data2VecTextModel(config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
result = model(
|
||||
input_ids,
|
||||
attention_mask=input_mask,
|
||||
token_type_ids=token_type_ids,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
encoder_attention_mask=encoder_attention_mask,
|
||||
)
|
||||
result = model(
|
||||
input_ids,
|
||||
attention_mask=input_mask,
|
||||
token_type_ids=token_type_ids,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
)
|
||||
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
|
||||
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
|
||||
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
|
||||
|
||||
def create_and_check_for_causal_lm(
|
||||
self,
|
||||
config,
|
||||
input_ids,
|
||||
token_type_ids,
|
||||
input_mask,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
encoder_hidden_states,
|
||||
encoder_attention_mask,
|
||||
):
|
||||
model = Data2VecTextForCausalLM(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
|
||||
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
|
||||
|
||||
def create_and_check_decoder_model_past_large_inputs(
|
||||
self,
|
||||
config,
|
||||
input_ids,
|
||||
token_type_ids,
|
||||
input_mask,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
encoder_hidden_states,
|
||||
encoder_attention_mask,
|
||||
):
|
||||
config.is_decoder = True
|
||||
config.add_cross_attention = True
|
||||
model = Data2VecTextForCausalLM(config=config).to(torch_device).eval()
|
||||
|
||||
# make sure that ids don't start with pad token
|
||||
mask = input_ids.ne(config.pad_token_id).long()
|
||||
input_ids = input_ids * mask
|
||||
|
||||
# first forward pass
|
||||
outputs = model(
|
||||
input_ids,
|
||||
attention_mask=input_mask,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
encoder_attention_mask=encoder_attention_mask,
|
||||
use_cache=True,
|
||||
)
|
||||
past_key_values = outputs.past_key_values
|
||||
|
||||
# create hypothetical multiple next token and extent to next_input_ids
|
||||
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
|
||||
|
||||
# make sure that ids don't start with pad token
|
||||
mask = next_tokens.ne(config.pad_token_id).long()
|
||||
next_tokens = next_tokens * mask
|
||||
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
|
||||
|
||||
# append to next input_ids and
|
||||
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
|
||||
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
|
||||
|
||||
output_from_no_past = model(
|
||||
next_input_ids,
|
||||
attention_mask=next_attention_mask,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
encoder_attention_mask=encoder_attention_mask,
|
||||
output_hidden_states=True,
|
||||
)["hidden_states"][0]
|
||||
output_from_past = model(
|
||||
next_tokens,
|
||||
attention_mask=next_attention_mask,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
encoder_attention_mask=encoder_attention_mask,
|
||||
past_key_values=past_key_values,
|
||||
output_hidden_states=True,
|
||||
)["hidden_states"][0]
|
||||
|
||||
# select random slice
|
||||
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
|
||||
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
|
||||
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
|
||||
|
||||
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
|
||||
|
||||
# test that outputs are equal for slice
|
||||
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
|
||||
|
||||
def create_and_check_for_masked_lm(
|
||||
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
):
|
||||
model = Data2VecTextForMaskedLM(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
|
||||
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
|
||||
|
||||
def create_and_check_for_token_classification(
|
||||
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
):
|
||||
config.num_labels = self.num_labels
|
||||
model = Data2VecTextForTokenClassification(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
|
||||
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
|
||||
|
||||
def create_and_check_for_multiple_choice(
|
||||
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
):
|
||||
config.num_choices = self.num_choices
|
||||
model = Data2VecTextForMultipleChoice(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
|
||||
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
|
||||
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
|
||||
result = model(
|
||||
multiple_choice_inputs_ids,
|
||||
attention_mask=multiple_choice_input_mask,
|
||||
token_type_ids=multiple_choice_token_type_ids,
|
||||
labels=choice_labels,
|
||||
)
|
||||
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
|
||||
|
||||
def create_and_check_for_question_answering(
|
||||
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
):
|
||||
model = Data2VecTextForQuestionAnswering(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
result = model(
|
||||
input_ids,
|
||||
attention_mask=input_mask,
|
||||
token_type_ids=token_type_ids,
|
||||
start_positions=sequence_labels,
|
||||
end_positions=sequence_labels,
|
||||
)
|
||||
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
|
||||
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
|
||||
|
||||
def prepare_config_and_inputs_for_common(self):
|
||||
config_and_inputs = self.prepare_config_and_inputs()
|
||||
(
|
||||
config,
|
||||
input_ids,
|
||||
token_type_ids,
|
||||
input_mask,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
) = config_and_inputs
|
||||
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
|
||||
return config, inputs_dict
|
||||
|
||||
|
||||
@require_torch
|
||||
class Data2VecTextModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
|
||||
all_model_classes = (
|
||||
(
|
||||
Data2VecTextForCausalLM,
|
||||
Data2VecTextForMaskedLM,
|
||||
Data2VecTextModel,
|
||||
Data2VecTextForSequenceClassification,
|
||||
Data2VecTextForTokenClassification,
|
||||
Data2VecTextForMultipleChoice,
|
||||
Data2VecTextForQuestionAnswering,
|
||||
)
|
||||
if is_torch_available()
|
||||
else ()
|
||||
)
|
||||
all_generative_model_classes = (Data2VecTextForCausalLM,) if is_torch_available() else ()
|
||||
|
||||
def setUp(self):
|
||||
self.model_tester = Data2VecTextModelTester(self)
|
||||
self.config_tester = ConfigTester(self, config_class=Data2VecTextConfig, hidden_size=37)
|
||||
|
||||
def test_config(self):
|
||||
self.config_tester.run_common_tests()
|
||||
|
||||
def test_model(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_model(*config_and_inputs)
|
||||
|
||||
def test_model_various_embeddings(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
for type in ["absolute", "relative_key", "relative_key_query"]:
|
||||
config_and_inputs[0].position_embedding_type = type
|
||||
self.model_tester.create_and_check_model(*config_and_inputs)
|
||||
|
||||
def test_model_as_decoder(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
|
||||
self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
|
||||
|
||||
def test_model_as_decoder_with_default_input_mask(self):
|
||||
# This regression test was failing with PyTorch < 1.3
|
||||
(
|
||||
config,
|
||||
input_ids,
|
||||
token_type_ids,
|
||||
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_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_masked_lm(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
|
||||
|
||||
def test_for_token_classification(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
|
||||
|
||||
def test_for_multiple_choice(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
|
||||
|
||||
def test_for_question_answering(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
|
||||
|
||||
@slow
|
||||
def test_model_from_pretrained(self):
|
||||
for model_name in DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
||||
model = Data2VecTextModel.from_pretrained(model_name)
|
||||
self.assertIsNotNone(model)
|
||||
|
||||
def test_create_position_ids_respects_padding_index(self):
|
||||
"""Ensure that the default position ids only assign a sequential . This is a regression
|
||||
test for https://github.com/huggingface/transformers/issues/1761
|
||||
|
||||
The position ids should be masked with the embedding object's padding index. Therefore, the
|
||||
first available non-padding position index is Data2VecTextForTextEmbeddings.padding_idx + 1
|
||||
"""
|
||||
config = self.model_tester.prepare_config_and_inputs()[0]
|
||||
model = Data2VecTextForTextEmbeddings(config=config)
|
||||
|
||||
input_ids = torch.as_tensor([[12, 31, 13, model.padding_idx]])
|
||||
expected_positions = torch.as_tensor(
|
||||
[[0 + model.padding_idx + 1, 1 + model.padding_idx + 1, 2 + model.padding_idx + 1, model.padding_idx]]
|
||||
)
|
||||
|
||||
position_ids = create_position_ids_from_input_ids(input_ids, model.padding_idx)
|
||||
self.assertEqual(position_ids.shape, expected_positions.shape)
|
||||
self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))
|
||||
|
||||
def test_create_position_ids_from_inputs_embeds(self):
|
||||
"""Ensure that the default position ids only assign a sequential . This is a regression
|
||||
test for https://github.com/huggingface/transformers/issues/1761
|
||||
|
||||
The position ids should be masked with the embedding object's padding index. Therefore, the
|
||||
first available non-padding position index is Data2VecTextForTextEmbeddings.padding_idx + 1
|
||||
"""
|
||||
config = self.model_tester.prepare_config_and_inputs()[0]
|
||||
embeddings = Data2VecTextForTextEmbeddings(config=config)
|
||||
|
||||
inputs_embeds = torch.empty(2, 4, 30)
|
||||
expected_single_positions = [
|
||||
0 + embeddings.padding_idx + 1,
|
||||
1 + embeddings.padding_idx + 1,
|
||||
2 + embeddings.padding_idx + 1,
|
||||
3 + embeddings.padding_idx + 1,
|
||||
]
|
||||
expected_positions = torch.as_tensor([expected_single_positions, expected_single_positions])
|
||||
position_ids = embeddings.create_position_ids_from_inputs_embeds(inputs_embeds)
|
||||
self.assertEqual(position_ids.shape, expected_positions.shape)
|
||||
self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))
|
||||
|
||||
|
||||
@require_torch
|
||||
class Data2VecTextModelIntegrationTest(TestCasePlus):
|
||||
@slow
|
||||
def test_inference_masked_lm(self):
|
||||
model = Data2VecTextForMaskedLM.from_pretrained("facebook/data2vec-text-base")
|
||||
|
||||
input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
|
||||
with torch.no_grad():
|
||||
output = model(input_ids)[0]
|
||||
expected_shape = torch.Size((1, 11, 50265))
|
||||
self.assertEqual(output.shape, expected_shape)
|
||||
# compare the actual values for a slice.
|
||||
expected_slice = torch.tensor([[[0.2328, 0.0000, 1.1710], [2.2525, 0.0000, 1.9937], [2.1280, 0.0000, 1.8691]]])
|
||||
|
||||
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
|
||||
|
||||
@slow
|
||||
def test_inference_no_head(self):
|
||||
model = Data2VecTextModel.from_pretrained("facebook/data2vec-text-base")
|
||||
|
||||
input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
|
||||
with torch.no_grad():
|
||||
output = model(input_ids)[0]
|
||||
# compare the actual values for a slice.
|
||||
expected_slice = torch.tensor(
|
||||
[[[0.1998, -0.0379, 0.0024], [-0.0971, -0.2214, -0.1798], [-0.0789, -0.2400, -0.1898]]]
|
||||
)
|
||||
|
||||
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
|
||||
Reference in New Issue
Block a user