555 lines
21 KiB
Python
555 lines
21 KiB
Python
# coding=utf-8
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# Copyright 2021 The HuggingFace Inc. 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 Hubert model. """
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import math
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import unittest
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import pytest
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from tests.test_modeling_common import floats_tensor, ids_tensor, random_attention_mask
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from transformers import SEWConfig, is_torch_available
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from transformers.testing_utils import require_datasets, 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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SEWForCTC,
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SEWForSequenceClassification,
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SEWModel,
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Wav2Vec2FeatureExtractor,
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Wav2Vec2Processor,
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)
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from transformers.models.hubert.modeling_hubert import _compute_mask_indices
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class SEWModelTester:
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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=32,
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feat_extract_norm="group",
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feat_extract_dropout=0.0,
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feat_extract_activation="gelu",
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conv_dim=(64, 32, 32),
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conv_stride=(5, 2, 1),
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conv_kernel=(10, 3, 1),
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conv_bias=False,
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num_conv_pos_embeddings=31,
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num_conv_pos_embedding_groups=2,
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squeeze_factor=2,
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num_hidden_layers=4,
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num_attention_heads=2,
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hidden_dropout=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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vocab_size=32,
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do_stable_layer_norm=False,
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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_norm = feat_extract_norm
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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.squeeze_factor = squeeze_factor
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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 = hidden_dropout
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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.do_stable_layer_norm = do_stable_layer_norm
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self.scope = scope
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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 // self.squeeze_factor
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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 SEWConfig(
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hidden_size=self.hidden_size,
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feat_extract_norm=self.feat_extract_norm,
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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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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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squeeze_factor=self.squeeze_factor,
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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=self.hidden_dropout,
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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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)
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def create_and_check_model(self, config, input_values, attention_mask):
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model = SEWModel(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_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 = SEWModel(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 = SEWForCTC(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_ctc_training(self, config, input_values, *args):
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config.ctc_zero_infinity = True
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model = SEWForCTC(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_extractor()
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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)):
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input_values[i, input_lengths[i] :] = 0.0
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if max_length_labels[i] < labels.shape[-1]:
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# it's important that we make sure that target lenghts are at least
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# one shorter than logit lenghts to prevent -inf
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labels[i, max_length_labels[i] - 1 :] = -100
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loss = model(input_values, labels=labels).loss
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self.parent.assertFalse(torch.isinf(loss).item())
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loss.backward()
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def check_seq_classifier_loss(self, config, input_values, *args):
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model = SEWForSequenceClassification(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_seq_classifier_training(self, config, input_values, *args):
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config.ctc_zero_infinity = True
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model = SEWForSequenceClassification(config=config)
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model.to(torch_device)
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model.train()
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# freeze everything but the classification head
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model.freeze_base_model()
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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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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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loss = model(input_values, labels=labels).loss
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self.parent.assertFalse(torch.isinf(loss).item())
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loss.backward()
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def check_labels_out_of_vocab(self, config, input_values, *args):
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model = SEWForCTC(config)
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model.to(torch_device)
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model.train()
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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 + 100)
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with pytest.raises(ValueError):
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model(input_values, labels=labels)
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def prepare_config_and_inputs_for_common(self):
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config, input_values, attention_mask = self.prepare_config_and_inputs()
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inputs_dict = {"input_values": input_values, "attention_mask": attention_mask}
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return config, inputs_dict
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@require_torch
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class SEWModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (SEWForCTC, SEWModel, SEWForSequenceClassification) if is_torch_available() else ()
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test_pruning = False
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test_headmasking = False
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test_torchscript = False
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def setUp(self):
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self.model_tester = SEWModelTester(self)
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self.config_tester = ConfigTester(self, config_class=SEWConfig, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_ctc_loss_inference(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.check_ctc_loss(*config_and_inputs)
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def test_ctc_train(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.check_ctc_training(*config_and_inputs)
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def test_labels_out_of_vocab(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.check_labels_out_of_vocab(*config_and_inputs)
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# Hubert has no inputs_embeds
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def test_inputs_embeds(self):
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pass
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# `input_ids` is renamed to `input_values`
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def test_forward_signature(self):
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pass
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# SEW cannot resize token embeddings
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# since it has no tokens embeddings
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def test_resize_tokens_embeddings(self):
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pass
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# SEW has no inputs_embeds
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# and thus the `get_input_embeddings` fn
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# is not implemented
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def test_model_common_attributes(self):
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pass
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def test_retain_grad_hidden_states_attentions(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.output_hidden_states = True
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config.output_attentions = True
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# no need to test all models as different heads yield the same functionality
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model_class = self.all_model_classes[0]
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model = model_class(config)
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model.to(torch_device)
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# set layer drop to 0
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model.config.layerdrop = 0.0
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input_values = inputs_dict["input_values"]
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input_lengths = torch.tensor(
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[input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device
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)
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output_lengths = model._get_feat_extract_output_lengths(input_lengths)
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labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size)
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inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"])
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inputs_dict["labels"] = labels
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outputs = model(**inputs_dict)
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output = outputs[0]
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# Encoder-/Decoder-only models
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hidden_states = outputs.hidden_states[0]
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attentions = outputs.attentions[0]
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hidden_states.retain_grad()
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attentions.retain_grad()
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output.flatten()[0].backward(retain_graph=True)
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self.assertIsNotNone(hidden_states.grad)
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self.assertIsNotNone(attentions.grad)
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def test_seq_classifier_loss_inference(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.check_seq_classifier_loss(*config_and_inputs)
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def test_seq_classifier_train(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.check_seq_classifier_training(*config_and_inputs)
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def test_initialization(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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configs_no_init = _config_zero_init(config)
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for model_class in self.all_model_classes:
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model = model_class(config=configs_no_init)
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for name, param in model.named_parameters():
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uniform_init_parms = [
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"conv.weight",
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"masked_spec_embed",
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"quantizer.weight_proj.weight",
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]
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if param.requires_grad:
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if any([x in name for x in uniform_init_parms]):
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self.assertTrue(
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-1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
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msg=f"Parameter {name} of model {model_class} seems not properly initialized",
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)
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else:
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self.assertIn(
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((param.data.mean() * 1e9).round() / 1e9).item(),
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[0.0, 1.0],
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msg=f"Parameter {name} of model {model_class} seems not properly initialized",
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)
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# overwrite from test_modeling_common
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def _mock_init_weights(self, module):
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if hasattr(module, "weight") and module.weight is not None:
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module.weight.data.fill_(3)
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if hasattr(module, "weight_g") and module.weight_g is not None:
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module.weight_g.data.fill_(3)
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if hasattr(module, "weight_v") and module.weight_v is not None:
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module.weight_v.data.fill_(3)
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if hasattr(module, "bias") and module.bias is not None:
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module.bias.data.fill_(3)
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if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None:
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module.masked_spec_embed.data.fill_(3)
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@slow
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def test_model_from_pretrained(self):
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model = SEWModel.from_pretrained("asapp/sew-tiny-100k")
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self.assertIsNotNone(model)
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@require_torch
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class SEWUtilsTest(unittest.TestCase):
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def test_compute_mask_indices(self):
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batch_size = 4
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sequence_length = 60
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mask_prob = 0.5
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mask_length = 1
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mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
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mask = torch.from_numpy(mask).to(torch_device)
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self.assertListEqual(mask.sum(axis=-1).tolist(), [mask_prob * sequence_length for _ in range(batch_size)])
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def test_compute_mask_indices_overlap(self):
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batch_size = 4
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sequence_length = 80
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mask_prob = 0.5
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mask_length = 4
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mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
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mask = torch.from_numpy(mask).to(torch_device)
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|
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# because of overlap mask don't have to add up exactly to `mask_prob * sequence_length`, but have to be smaller or equal
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for batch_sum in mask.sum(axis=-1):
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self.assertTrue(int(batch_sum) <= mask_prob * sequence_length)
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|
|
|
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|
@require_torch
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|
@require_datasets
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@require_soundfile
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@slow
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class SEWModelIntegrationTest(unittest.TestCase):
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def _load_datasamples(self, num_samples):
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from datasets import load_dataset
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|
|
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ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
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# automatic decoding with librispeech
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|
speech_samples = ds.sort("id").filter(
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lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
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|
)[:num_samples]["audio"]
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|
|
|
return [x["array"] for x in speech_samples]
|
|
|
|
def test_inference_pretrained_batched(self):
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model = SEWModel.from_pretrained("asapp/sew-tiny-100k").to(torch_device)
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processor = Wav2Vec2FeatureExtractor.from_pretrained("asapp/sew-tiny-100k")
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|
|
|
input_speech = self._load_datasamples(2)
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|
|
|
inputs = processor(input_speech, return_tensors="pt", padding=True)
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|
|
|
input_values = inputs.input_values.to(torch_device)
|
|
|
|
with torch.no_grad():
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|
outputs = model(input_values).last_hidden_state
|
|
|
|
# expected outputs taken from the original SEW implementation
|
|
expected_outputs_first = torch.tensor(
|
|
[
|
|
[
|
|
[0.1509, 0.5372, 0.3061, -0.1694],
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|
[-0.1700, 0.5764, 0.2753, -0.1299],
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|
[0.1281, 0.7949, 0.2342, -0.1624],
|
|
[-0.1627, 0.6710, 0.2215, -0.1317],
|
|
],
|
|
[
|
|
[0.0408, 1.4355, 0.8605, -0.0968],
|
|
[0.0393, 1.2368, 0.6826, 0.0364],
|
|
[-0.1269, 1.9215, 1.1677, -0.1297],
|
|
[-0.1654, 1.6524, 0.6877, -0.0196],
|
|
],
|
|
],
|
|
device=torch_device,
|
|
)
|
|
expected_outputs_last = torch.tensor(
|
|
[
|
|
[
|
|
[1.3379, -0.1450, -0.1500, -0.0515],
|
|
[0.8364, -0.1680, -0.1248, -0.0689],
|
|
[1.2791, -0.1507, -0.1523, -0.0564],
|
|
[0.8208, -0.1690, -0.1199, -0.0751],
|
|
],
|
|
[
|
|
[0.6959, -0.0861, -0.1235, -0.0861],
|
|
[0.4700, -0.1686, -0.1141, -0.1199],
|
|
[1.0776, -0.1137, -0.0124, -0.0472],
|
|
[0.5774, -0.1675, -0.0376, -0.0823],
|
|
],
|
|
],
|
|
device=torch_device,
|
|
)
|
|
expected_output_sum = 62146.7422
|
|
|
|
self.assertTrue(torch.allclose(outputs[:, :4, :4], expected_outputs_first, atol=5e-3))
|
|
self.assertTrue(torch.allclose(outputs[:, -4:, -4:], expected_outputs_last, atol=5e-3))
|
|
self.assertTrue(abs(outputs.sum() - expected_output_sum) < 5)
|
|
|
|
def test_inference_ctc_batched(self):
|
|
model = SEWForCTC.from_pretrained("asapp/sew-tiny-100k-ft-ls100h").to(torch_device)
|
|
processor = Wav2Vec2Processor.from_pretrained("asapp/sew-tiny-100k-ft-ls100h", do_lower_case=True)
|
|
|
|
input_speech = self._load_datasamples(2)
|
|
|
|
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",
|
|
"swet covered brian's body trickling into the tightloine closs hat was the only garment he wore",
|
|
]
|
|
self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
|