950 lines
38 KiB
Python
950 lines
38 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 os
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import pickle
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import tempfile
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import unittest
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import pytest
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from transformers import HubertConfig, is_torch_available
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from transformers.testing_utils import require_soundfile, require_torch, slow, torch_device
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from transformers.utils import is_torch_fx_available
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import (
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ModelTesterMixin,
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_config_zero_init,
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floats_tensor,
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ids_tensor,
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random_attention_mask,
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)
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from ...test_pipeline_mixin import PipelineTesterMixin
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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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HubertForCTC,
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HubertForSequenceClassification,
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HubertModel,
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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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if is_torch_fx_available():
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from transformers.utils.fx import symbolic_trace
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class HubertModelTester:
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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_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=(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=2,
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num_attention_heads=2,
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hidden_dropout_prob=0.1, # this is most likely not correctly set yet
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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.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.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
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def prepare_config_and_inputs(self):
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input_values = floats_tensor([self.batch_size, self.seq_length], scale=1.0)
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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 HubertConfig(
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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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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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do_stable_layer_norm=self.do_stable_layer_norm,
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)
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def create_and_check_model(self, config, input_values, attention_mask):
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model = HubertModel(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 = HubertModel(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 = HubertForCTC(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 = HubertForSequenceClassification(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 = HubertForCTC(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)):
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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 lengths are at least
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# one shorter than logit lengths 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_training(self, config, input_values, *args):
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config.ctc_zero_infinity = True
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model = HubertForSequenceClassification(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 = HubertForCTC(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 HubertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (HubertForCTC, HubertForSequenceClassification, HubertModel) if is_torch_available() else ()
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pipeline_model_mapping = (
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{
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"audio-classification": HubertForSequenceClassification,
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"automatic-speech-recognition": HubertForCTC,
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"feature-extraction": HubertModel,
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}
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if is_torch_available()
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else {}
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)
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fx_compatible = True
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test_pruning = False
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test_headmasking = False
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def setUp(self):
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self.model_tester = HubertModelTester(self)
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self.config_tester = ConfigTester(self, config_class=HubertConfig, 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_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_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_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_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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# Hubert 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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# Hubert 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_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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"conv.parametrizations.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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# Hubert cannot be TorchScripted because of torch.nn.utils.weight_norm
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def _create_and_check_torch_fx_tracing(self, config, inputs_dict, output_loss=False):
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# TODO: fix it
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self.skipTest("torch 2.1 breaks torch fx tests for wav2vec2/hubert.")
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if not is_torch_fx_available() or not self.fx_compatible:
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return
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configs_no_init = _config_zero_init(config) # To be sure we have no Nan
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configs_no_init.return_dict = False
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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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model.to(torch_device)
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model.eval()
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inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=output_loss)
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try:
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if model.config.is_encoder_decoder:
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model.config.use_cache = False # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward
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labels = inputs.get("labels", None)
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input_names = [
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"attention_mask",
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"decoder_attention_mask",
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"decoder_input_ids",
|
|
"input_features",
|
|
"input_ids",
|
|
"input_values",
|
|
]
|
|
if labels is not None:
|
|
input_names.append("labels")
|
|
|
|
filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names}
|
|
input_names = list(filtered_inputs.keys())
|
|
|
|
model_output = model(**filtered_inputs)
|
|
|
|
traced_model = symbolic_trace(model, input_names)
|
|
traced_output = traced_model(**filtered_inputs)
|
|
else:
|
|
input_names = [
|
|
"attention_mask",
|
|
"bbox",
|
|
"input_features",
|
|
"input_ids",
|
|
"input_values",
|
|
"pixel_values",
|
|
"token_type_ids",
|
|
"visual_feats",
|
|
"visual_pos",
|
|
]
|
|
|
|
labels = inputs.get("labels", None)
|
|
start_positions = inputs.get("start_positions", None)
|
|
end_positions = inputs.get("end_positions", None)
|
|
if labels is not None:
|
|
input_names.append("labels")
|
|
if start_positions is not None:
|
|
input_names.append("start_positions")
|
|
if end_positions is not None:
|
|
input_names.append("end_positions")
|
|
|
|
filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names}
|
|
input_names = list(filtered_inputs.keys())
|
|
|
|
model_output = model(**filtered_inputs)
|
|
|
|
traced_model = symbolic_trace(model, input_names)
|
|
traced_output = traced_model(**filtered_inputs)
|
|
|
|
except Exception as e:
|
|
self.fail(f"Couldn't trace module: {e}")
|
|
|
|
def flatten_output(output):
|
|
flatten = []
|
|
for x in output:
|
|
if isinstance(x, (tuple, list)):
|
|
flatten += flatten_output(x)
|
|
elif not isinstance(x, torch.Tensor):
|
|
continue
|
|
else:
|
|
flatten.append(x)
|
|
return flatten
|
|
|
|
model_output = flatten_output(model_output)
|
|
traced_output = flatten_output(traced_output)
|
|
num_outputs = len(model_output)
|
|
|
|
for i in range(num_outputs):
|
|
self.assertTrue(
|
|
torch.allclose(model_output[i], traced_output[i]),
|
|
f"traced {i}th output doesn't match model {i}th output for {model_class}",
|
|
)
|
|
|
|
# Test that the model can be serialized and restored properly
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
pkl_file_name = os.path.join(tmp_dir_name, "model.pkl")
|
|
try:
|
|
with open(pkl_file_name, "wb") as f:
|
|
pickle.dump(traced_model, f)
|
|
with open(pkl_file_name, "rb") as f:
|
|
loaded = pickle.load(f)
|
|
except Exception as e:
|
|
self.fail(f"Couldn't serialize / deserialize the traced model: {e}")
|
|
|
|
loaded_output = loaded(**filtered_inputs)
|
|
loaded_output = flatten_output(loaded_output)
|
|
|
|
for i in range(num_outputs):
|
|
self.assertTrue(
|
|
torch.allclose(model_output[i], loaded_output[i]),
|
|
f"serialized model {i}th output doesn't match model {i}th output for {model_class}",
|
|
)
|
|
|
|
# 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, "masked_spec_embed") and module.masked_spec_embed is not None:
|
|
module.masked_spec_embed.data.fill_(3)
|
|
|
|
@unittest.skip(reason="Feed forward chunking is not implemented")
|
|
def test_feed_forward_chunking(self):
|
|
pass
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model = HubertModel.from_pretrained("facebook/hubert-base-ls960")
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
@require_torch
|
|
class HubertRobustModelTest(ModelTesterMixin, unittest.TestCase):
|
|
all_model_classes = (HubertForCTC, HubertForSequenceClassification, HubertModel) if is_torch_available() else ()
|
|
test_pruning = False
|
|
test_headmasking = False
|
|
|
|
def setUp(self):
|
|
self.model_tester = HubertModelTester(
|
|
self, conv_stride=(3, 3, 3), feat_extract_norm="layer", do_stable_layer_norm=True
|
|
)
|
|
self.config_tester = ConfigTester(self, config_class=HubertConfig, 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_batched_inference(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_batch_inference(*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_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)
|
|
|
|
# Hubert has no inputs_embeds
|
|
def test_inputs_embeds(self):
|
|
pass
|
|
|
|
# `input_ids` is renamed to `input_values`
|
|
def test_forward_signature(self):
|
|
pass
|
|
|
|
# Hubert cannot resize token embeddings
|
|
# since it has no tokens embeddings
|
|
def test_resize_tokens_embeddings(self):
|
|
pass
|
|
|
|
# Hubert has no inputs_embeds
|
|
# and thus the `get_input_embeddings` fn
|
|
# is not implemented
|
|
def test_model_common_attributes(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",
|
|
"conv.parametrizations.weight",
|
|
"masked_spec_embed",
|
|
"quantizer.weight_proj.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, "masked_spec_embed") and module.masked_spec_embed is not None:
|
|
module.masked_spec_embed.data.fill_(3)
|
|
|
|
@unittest.skip(reason="Feed forward chunking is not implemented")
|
|
def test_feed_forward_chunking(self):
|
|
pass
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model = HubertModel.from_pretrained("facebook/hubert-large-ls960-ft")
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
@require_torch
|
|
class HubertUtilsTest(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_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)
|
|
|
|
|
|
@require_torch
|
|
@require_soundfile
|
|
@slow
|
|
class HubertModelIntegrationTest(unittest.TestCase):
|
|
def _load_datasamples(self, num_samples):
|
|
from datasets import load_dataset
|
|
|
|
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):
|
|
from datasets import load_dataset
|
|
|
|
ds = load_dataset("anton-l/superb_dummy", task, split="test")
|
|
|
|
return ds[:num_samples]
|
|
|
|
def test_inference_ctc_batched(self):
|
|
model = HubertForCTC.from_pretrained("facebook/hubert-large-ls960-ft", torch_dtype=torch.float16).to(
|
|
torch_device
|
|
)
|
|
processor = Wav2Vec2Processor.from_pretrained("facebook/hubert-large-ls960-ft", do_lower_case=True)
|
|
|
|
input_speech = self._load_datasamples(2)
|
|
|
|
inputs = processor(input_speech, return_tensors="pt", padding=True)
|
|
|
|
input_values = inputs.input_values.half().to(torch_device)
|
|
attention_mask = inputs.attention_mask.to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
logits = model(input_values, attention_mask=attention_mask).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",
|
|
]
|
|
self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
|
|
|
|
def test_inference_keyword_spotting(self):
|
|
model = HubertForSequenceClassification.from_pretrained(
|
|
"superb/hubert-base-superb-ks", torch_dtype=torch.float16
|
|
).to(torch_device)
|
|
processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-ks")
|
|
input_data = self._load_superb("ks", 4)
|
|
inputs = processor(input_data["speech"], return_tensors="pt", padding=True)
|
|
|
|
input_values = inputs.input_values.half().to(torch_device)
|
|
attention_mask = inputs.attention_mask.to(torch_device)
|
|
with torch.no_grad():
|
|
outputs = model(input_values, attention_mask=attention_mask)
|
|
predicted_logits, predicted_ids = torch.max(outputs.logits, dim=-1)
|
|
|
|
expected_labels = [2, 6, 10, 9]
|
|
# s3prl logits for the same batch
|
|
expected_logits = torch.tensor([7.6692, 17.7795, 11.1562, 11.8232], dtype=torch.float16, device=torch_device)
|
|
|
|
self.assertListEqual(predicted_ids.tolist(), expected_labels)
|
|
self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=3e-2))
|
|
|
|
def test_inference_intent_classification(self):
|
|
model = HubertForSequenceClassification.from_pretrained(
|
|
"superb/hubert-base-superb-ic", torch_dtype=torch.float16
|
|
).to(torch_device)
|
|
processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-ic")
|
|
input_data = self._load_superb("ic", 4)
|
|
inputs = processor(input_data["speech"], return_tensors="pt", padding=True)
|
|
|
|
input_values = inputs.input_values.half().to(torch_device)
|
|
attention_mask = inputs.attention_mask.to(torch_device)
|
|
with torch.no_grad():
|
|
outputs = model(input_values, attention_mask=attention_mask)
|
|
|
|
predicted_logits_action, predicted_ids_action = torch.max(outputs.logits[:, :6], dim=-1)
|
|
predicted_logits_object, predicted_ids_object = torch.max(outputs.logits[:, 6:20], dim=-1)
|
|
predicted_logits_location, predicted_ids_location = torch.max(outputs.logits[:, 20:24], dim=-1)
|
|
|
|
expected_labels_action = [1, 0, 4, 3]
|
|
expected_logits_action = torch.tensor(
|
|
[5.9052, 12.5865, 4.4840, 10.0240], dtype=torch.float16, device=torch_device
|
|
)
|
|
expected_labels_object = [1, 10, 3, 4]
|
|
expected_logits_object = torch.tensor(
|
|
[5.5316, 11.7946, 8.1672, 23.2415], dtype=torch.float16, device=torch_device
|
|
)
|
|
expected_labels_location = [0, 0, 0, 1]
|
|
expected_logits_location = torch.tensor(
|
|
[5.2053, 8.9577, 10.0447, 8.1481], dtype=torch.float16, device=torch_device
|
|
)
|
|
|
|
self.assertListEqual(predicted_ids_action.tolist(), expected_labels_action)
|
|
self.assertListEqual(predicted_ids_object.tolist(), expected_labels_object)
|
|
self.assertListEqual(predicted_ids_location.tolist(), expected_labels_location)
|
|
|
|
# TODO: lower the tolerance after merging the padding fix https://github.com/pytorch/fairseq/pull/3572
|
|
self.assertTrue(torch.allclose(predicted_logits_action, expected_logits_action, atol=3e-1))
|
|
self.assertTrue(torch.allclose(predicted_logits_object, expected_logits_object, atol=3e-1))
|
|
self.assertTrue(torch.allclose(predicted_logits_location, expected_logits_location, atol=3e-1))
|
|
|
|
def test_inference_speaker_identification(self):
|
|
model = HubertForSequenceClassification.from_pretrained(
|
|
"superb/hubert-base-superb-sid", torch_dtype=torch.float16
|
|
).to(torch_device)
|
|
processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-sid")
|
|
input_data = self._load_superb("si", 4)
|
|
|
|
output_logits = []
|
|
with torch.no_grad():
|
|
for example in input_data["speech"]:
|
|
input = processor(example, return_tensors="pt", padding=True)
|
|
output = model(input.input_values.half().to(torch_device), attention_mask=None)
|
|
output_logits.append(output.logits[0])
|
|
output_logits = torch.stack(output_logits)
|
|
predicted_logits, predicted_ids = torch.max(output_logits, dim=-1)
|
|
|
|
expected_labels = [5, 1, 1, 3]
|
|
# s3prl logits for the same batch
|
|
expected_logits = torch.tensor(
|
|
[78231.5547, 123166.6094, 122785.4141, 84851.2969], dtype=torch.float16, device=torch_device
|
|
)
|
|
|
|
self.assertListEqual(predicted_ids.tolist(), expected_labels)
|
|
# TODO: lower the tolerance after merging the padding fix https://github.com/pytorch/fairseq/pull/3572
|
|
self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=10))
|
|
|
|
def test_inference_emotion_recognition(self):
|
|
model = HubertForSequenceClassification.from_pretrained(
|
|
"superb/hubert-base-superb-er", torch_dtype=torch.float16
|
|
).to(torch_device)
|
|
processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-er")
|
|
input_data = self._load_superb("er", 4)
|
|
inputs = processor(input_data["speech"], return_tensors="pt", padding=True)
|
|
|
|
input_values = inputs.input_values.half().to(torch_device)
|
|
attention_mask = inputs.attention_mask.to(torch_device)
|
|
with torch.no_grad():
|
|
outputs = model(input_values, attention_mask=attention_mask)
|
|
predicted_logits, predicted_ids = torch.max(outputs.logits, dim=-1)
|
|
|
|
expected_labels = [1, 1, 2, 2]
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# s3prl logits for the same batch
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expected_logits = torch.tensor([2.8384, 2.3389, 3.8564, 4.5558], dtype=torch.float16, device=torch_device)
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|
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self.assertListEqual(predicted_ids.tolist(), expected_labels)
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# TODO: lower the tolerance after merging the padding fix https://github.com/pytorch/fairseq/pull/3572
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self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=1e-1))
|
|
|
|
def test_inference_distilhubert(self):
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|
model = HubertModel.from_pretrained("ntu-spml/distilhubert").to(torch_device)
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|
processor = Wav2Vec2FeatureExtractor.from_pretrained("ntu-spml/distilhubert")
|
|
|
|
# TODO: can't test on batched inputs due to incompatible padding https://github.com/pytorch/fairseq/pull/3572
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|
input_speech = self._load_datasamples(1)
|
|
|
|
inputs = processor(input_speech, return_tensors="pt", padding=True)
|
|
|
|
input_values = inputs.input_values.to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
outputs = model(input_values).last_hidden_state
|
|
|
|
# expected outputs taken from the original SEW implementation
|
|
expected_outputs_first = torch.tensor(
|
|
[
|
|
[
|
|
[-0.3505, 0.1167, 0.0608, 0.1294],
|
|
[-0.3085, 0.0481, 0.1106, 0.0955],
|
|
[-0.3107, -0.0391, 0.0739, 0.1360],
|
|
[-0.2385, -0.1795, -0.0928, 0.2389],
|
|
]
|
|
],
|
|
device=torch_device,
|
|
)
|
|
expected_outputs_last = torch.tensor(
|
|
[
|
|
[
|
|
[-0.0732, 0.0255, 0.0529, -0.1372],
|
|
[-0.0812, 0.1259, 0.0564, -0.0438],
|
|
[-0.0054, 0.0758, -0.0002, -0.1617],
|
|
[0.0133, -0.0320, -0.0687, 0.0062],
|
|
]
|
|
],
|
|
device=torch_device,
|
|
)
|
|
expected_output_sum = -3776.0730
|
|
|
|
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) < 0.1)
|