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* Fix TypeError: Object of type int64 is not JSON serializable * Convert numpy.float64 and numpy.int64 to float and int for json serialization * Black reformatted examples/pytorch/token-classification/run_ner_no_trainer.py * * make style
652 lines
26 KiB
Python
652 lines
26 KiB
Python
# coding=utf-8
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# Copyright 2022 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 MCTCT model. """
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import inspect
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import math
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import unittest
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from datasets import load_dataset
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from transformers import MCTCTConfig, is_torch_available
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from transformers.testing_utils import 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, floats_tensor, ids_tensor
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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 MCTCTForCTC, MCTCTModel, MCTCTProcessor
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class MCTCTModelTester:
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def __init__(
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self,
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parent,
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batch_size=10,
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seq_length=40, # speech is longer
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is_training=False,
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vocab_size=32,
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hidden_size=128 * 4,
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num_hidden_layers=4,
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intermediate_size=20,
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num_attention_heads=4,
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attention_head_dim=128,
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max_position_embeddings=920,
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layer_norm_eps=1e-5,
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layerdrop=0.3,
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hidden_act="relu",
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initializer_range=0.02,
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hidden_dropout_prob=0.3,
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attention_probs_dropout_prob=0.3,
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conv_glu_dim=1,
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conv_dropout=0.3,
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num_conv_layers=1,
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conv_kernel=(7,),
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conv_stride=(3,),
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input_feat_per_channel=80,
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input_channels=1,
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conv_channels=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 # speech is longer
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self.is_training = is_training
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.intermediate_size = intermediate_size
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self.num_attention_heads = num_attention_heads
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self.attention_head_dim = attention_head_dim
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self.max_position_embeddings = max_position_embeddings
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self.layer_norm_eps = layer_norm_eps
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self.layerdrop = layerdrop
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.conv_glu_dim = conv_glu_dim
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self.conv_dropout = conv_dropout
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self.num_conv_layers = num_conv_layers
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self.conv_kernel = conv_kernel
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self.conv_stride = conv_stride
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self.input_feat_per_channel = input_feat_per_channel
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self.input_channels = input_channels
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self.conv_channels = conv_channels
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output_seq_length = self.seq_length
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dilation = 1
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for _, kernel_sz, stride in zip(range(self.num_conv_layers), self.conv_kernel, self.conv_stride):
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padding = kernel_sz // 2
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output_seq_length = output_seq_length + 2 * padding - dilation * (kernel_sz - 1) - 1
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output_seq_length = torch.div(output_seq_length, stride, rounding_mode="trunc") + 1
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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_features = floats_tensor(
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[self.batch_size, self.seq_length, self.input_feat_per_channel], self.vocab_size
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)
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attention_mask = torch.ones([self.batch_size, self.seq_length], dtype=torch.long, device=torch_device)
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config = self.get_config()
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return config, input_features, attention_mask
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def get_config(self):
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return MCTCTConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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intermediate_size=self.intermediate_size,
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num_attention_heads=self.num_attention_heads,
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attention_head_dim=self.attention_head_dim,
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max_position_embeddings=self.max_position_embeddings,
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layer_norm_eps=self.layer_norm_eps,
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layerdrop=self.layerdrop,
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hidden_act=self.hidden_act,
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initializer_range=self.initializer_range,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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conv_glu_dim=self.conv_glu_dim,
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conv_dropout=self.conv_dropout,
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num_conv_layers=self.num_conv_layers,
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conv_kernel=self.conv_kernel,
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conv_stride=self.conv_stride,
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input_feat_per_channel=self.input_feat_per_channel,
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input_channels=self.input_channels,
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conv_channels=self.conv_channels,
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)
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def create_and_check_model(self, config, input_features, attention_mask):
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model = MCTCTModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_features, attention_mask=attention_mask)
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self.parent.assertEqual(
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result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size)
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)
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def create_and_check_model_for_ctc(self, config, input_features, attention_mask):
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config.add_adapter = True
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config.output_hidden_size = 2 * config.hidden_size
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model = MCTCTForCTC(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_features, attention_mask=attention_mask)
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self.parent.assertEqual(
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result.logits.shape, (self.batch_size, self.adapter_output_seq_length, self.vocab_size)
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)
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def create_and_check_batch_inference(self, config, input_features, *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 = MCTCTModel(config=config)
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model.to(torch_device)
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model.eval()
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input_features = input_features[:3]
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attention_mask = torch.ones(input_features.shape[:-1], device=torch_device, dtype=torch.bool)
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input_lengths = [input_features.shape[-1] // i for i in [2, 2, 1]]
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# pad input
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for i in range(len(input_lengths)):
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input_features[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_features, attention_mask=attention_mask).last_hidden_state
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for i in range(input_features.shape[0]):
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input_slice = input_features[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_features, *args):
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model = MCTCTForCTC(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_features = input_features[:3]
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# input_features is a 2D window for each sequence
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attention_mask = torch.ones(input_features.shape[:-1], device=torch_device, dtype=torch.long)
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# -2 since input_features is a 2D window for each sequence in batch
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input_lengths = [input_features.shape[-2] // i for i in [2, 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_features.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_features[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_features, 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_features, 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_features, *args):
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config.ctc_zero_infinity = True
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model = MCTCTForCTC(config=config)
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model.to(torch_device)
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model.train()
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input_features = input_features[:3]
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input_lengths = [input_features.shape[-2] // i for i in [2, 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_features.shape[0], max(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_features[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_features, 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_features, *args):
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model = MCTCTForCTC(config)
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model.to(torch_device)
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model.train()
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input_features = input_features[:3]
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input_lengths = [input_features.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_features.shape[0], max(max_length_labels) - 2), model.config.vocab_size + 100)
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with self.parent.assertRaises(ValueError):
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model(input_features, labels=labels)
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def prepare_config_and_inputs_for_common(self):
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config, input_features, attention_mask = self.prepare_config_and_inputs()
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inputs_dict = {"input_features": input_features, "attention_mask": attention_mask}
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return config, inputs_dict
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@require_torch
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class MCTCTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (MCTCTForCTC, MCTCTModel) if is_torch_available() else ()
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pipeline_model_mapping = (
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{"automatic-speech-recognition": MCTCTForCTC, "feature-extraction": MCTCTModel} if is_torch_available() else {}
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)
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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 = MCTCTModelTester(self)
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self.config_tester = ConfigTester(self, config_class=MCTCTConfig, 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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# MCTCT 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_features`
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def test_forward_signature(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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signature = inspect.signature(model.forward)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = [
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"input_features",
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"attention_mask",
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"head_mask",
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"output_attentions",
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"output_hidden_states",
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"return_dict",
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]
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self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
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# MCTCT 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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# MCTCT has no inputs_embeds
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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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config.layerdrop = 0.0
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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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input_features = inputs_dict["input_features"]
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input_lengths = torch.tensor(
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[input_features.shape[1] for _ in range(input_features.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_features.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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"masked_spec_embed",
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"codevectors",
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"quantizer.weight_proj.weight",
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"project_hid.weight",
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"project_hid.bias",
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"project_q.weight",
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"project_q.bias",
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"feature_projection.projection.weight",
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"feature_projection.projection.bias",
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"objective.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, "codevectors") and module.codevectors is not None:
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module.codevectors.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 = MCTCTModel.from_pretrained("speechbrain/m-ctc-t-large")
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self.assertIsNotNone(model)
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@require_torch
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class MCTCTRobustModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (MCTCTForCTC, MCTCTModel) 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 = MCTCTModelTester(self)
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self.config_tester = ConfigTester(self, config_class=MCTCTConfig, 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_batched_inference(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_batch_inference(*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)
|
|
|
|
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)
|
|
|
|
# MCTCT has no inputs_embeds
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|
def test_inputs_embeds(self):
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|
pass
|
|
|
|
# `input_ids` is renamed to `input_features`
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|
def test_forward_signature(self):
|
|
pass
|
|
|
|
# MCTCT 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
|
|
|
|
# MCTCT has no inputs_embeds
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|
# 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):
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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
|
|
|
|
# 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)
|
|
|
|
# set layer drop to 0
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|
model.config.layerdrop = 0.0
|
|
|
|
input_features = inputs_dict["input_features"]
|
|
|
|
input_lengths = torch.tensor(
|
|
[input_features.shape[1] for _ in range(input_features.shape[0])], dtype=torch.long, device=torch_device
|
|
)
|
|
output_lengths = model._get_feat_extract_output_lengths(input_lengths)
|
|
|
|
labels = ids_tensor((input_features.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size)
|
|
inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"])
|
|
inputs_dict["labels"] = labels
|
|
|
|
outputs = model(**inputs_dict)
|
|
|
|
output = outputs[0]
|
|
|
|
# Encoder-/Decoder-only models
|
|
hidden_states = outputs.hidden_states[0]
|
|
attentions = outputs.attentions[0]
|
|
|
|
hidden_states.retain_grad()
|
|
attentions.retain_grad()
|
|
|
|
output.flatten()[0].backward(retain_graph=True)
|
|
|
|
self.assertIsNotNone(hidden_states.grad)
|
|
self.assertIsNotNone(attentions.grad)
|
|
|
|
def test_initialization(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
configs_no_init = _config_zero_init(config)
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config=configs_no_init)
|
|
for name, param in model.named_parameters():
|
|
uniform_init_parms = [
|
|
"conv.weight",
|
|
"masked_spec_embed",
|
|
"codevectors",
|
|
"quantizer.weight_proj.weight",
|
|
"project_hid.weight",
|
|
"project_hid.bias",
|
|
"project_q.weight",
|
|
"project_q.bias",
|
|
"feature_projection.projection.weight",
|
|
"feature_projection.projection.bias",
|
|
"objective.weight",
|
|
]
|
|
if param.requires_grad:
|
|
if any(x in name for x in uniform_init_parms):
|
|
self.assertTrue(
|
|
-1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
|
|
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
|
)
|
|
else:
|
|
self.assertIn(
|
|
((param.data.mean() * 1e9).round() / 1e9).item(),
|
|
[0.0, 1.0],
|
|
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
|
)
|
|
|
|
# overwrite from test_modeling_common
|
|
def _mock_init_weights(self, module):
|
|
if hasattr(module, "weight") and module.weight is not None:
|
|
module.weight.data.fill_(3)
|
|
if hasattr(module, "weight_g") and module.weight_g is not None:
|
|
module.weight_g.data.fill_(3)
|
|
if hasattr(module, "weight_v") and module.weight_v is not None:
|
|
module.weight_v.data.fill_(3)
|
|
if hasattr(module, "bias") and module.bias is not None:
|
|
module.bias.data.fill_(3)
|
|
if hasattr(module, "codevectors") and module.codevectors is not None:
|
|
module.codevectors.data.fill_(3)
|
|
if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None:
|
|
module.masked_spec_embed.data.fill_(3)
|
|
|
|
@unittest.skip(reason="Feed forward chunking is not implemented")
|
|
def test_feed_forward_chunking(self):
|
|
pass
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model = MCTCTModel.from_pretrained("speechbrain/m-ctc-t-large")
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
@require_torch
|
|
@require_soundfile
|
|
@slow
|
|
class MCTCTModelIntegrationTest(unittest.TestCase):
|
|
def _load_datasamples(self, num_samples):
|
|
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
|
|
# automatic decoding with librispeech
|
|
speech_samples = ds.sort("id").filter(
|
|
lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
|
|
)[:num_samples]["audio"]
|
|
|
|
return [x["array"] for x in speech_samples]
|
|
|
|
def test_inference_ctc_normal(self):
|
|
model = MCTCTForCTC.from_pretrained("speechbrain/m-ctc-t-large")
|
|
model.to(torch_device)
|
|
processor = MCTCTProcessor.from_pretrained("speechbrain/m-ctc-t-large", do_lower_case=True)
|
|
input_speech = self._load_datasamples(1)
|
|
|
|
input_features = processor(input_speech, return_tensors="pt").input_features.to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
logits = model(input_features).logits
|
|
|
|
predicted_ids = torch.argmax(logits, dim=-1)
|
|
predicted_trans = processor.batch_decode(predicted_ids)
|
|
|
|
EXPECTED_TRANSCRIPTIONS = ["a man said to the universe, sir, i exist."]
|
|
self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
|
|
|
|
def test_inference_ctc_normal_batched(self):
|
|
model = MCTCTForCTC.from_pretrained("speechbrain/m-ctc-t-large")
|
|
model.to(torch_device)
|
|
processor = MCTCTProcessor.from_pretrained("speechbrain/m-ctc-t-large", do_lower_case=True)
|
|
|
|
input_speech = self._load_datasamples(2)
|
|
|
|
inputs = processor(input_speech, return_tensors="pt", padding=True)
|
|
|
|
input_features = inputs.input_features.to(torch_device)
|
|
attention_mask = inputs.attention_mask.to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
logits = model(input_features, 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-lowing clossa was the only germent huor."',
|
|
]
|
|
self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
|
|
|
|
def test_inference_ctc_robust_batched(self):
|
|
model = MCTCTForCTC.from_pretrained("speechbrain/m-ctc-t-large").to(torch_device)
|
|
processor = MCTCTProcessor.from_pretrained("speechbrain/m-ctc-t-large", do_lower_case=True)
|
|
|
|
input_speech = self._load_datasamples(4)
|
|
|
|
inputs = processor(input_speech, return_tensors="pt", padding=True, return_attention_mask=True)
|
|
|
|
input_features = inputs.input_features.to(torch_device)
|
|
attention_mask = inputs.attention_mask.to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
logits = model(input_features, 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-lowing clossa was the only germent huor." "',
|
|
"\"the cadona's chest still-dripping bloodthe acofis overstrained eyes, even the soring arena around him"
|
|
" with thousands of spectators retrivialities not worth-thinking about.",
|
|
"his instant panic was followed by a small sharp blow high on his chestr.",
|
|
]
|
|
self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
|