Change the configuration for training¶

Show usage¶

There are two ways to show the command line options: --help and --print_config

# Show the command line option
python -m espnet2.bin.asr_train --help
# Show the all configuration in yaml format
python -m espnet2.bin.asr_train --print_config

In this section, we use espnet2.bin.asr_train for an example, but the other training tools based on Task class have the same interface, so you can replace it to another command.

Note that ESPnet2 always selects_ instead of - for the separation for the option name to avoid confusion.

# Bad
--batch-size
# Good
--batch_size

A notable feature of --print_config is that it shows the configuration parsing with the given arguments dynamically: You can look up the parameters for a changeable class.

% # Show parameters of Adam optimizer
% python -m espnet2.bin.asr_train --optim adam --print_config
...
optim: adam
optim_conf:
    lr: 0.001
    betas:
    - 0.9
    - 0.999
    eps: 1.0e-08
    weight_decay: 0
    amsgrad: false
...
% # Show parameters of ReduceLROnPlateau scheduler
% python -m espnet2.bin.asr_train --scheduler ReduceLROnPlateau --print_config
...
scheduler: reducelronplateau
scheduler_conf:
    mode: min
    factor: 0.1
    patience: 10
    verbose: false
    threshold: 0.0001
    threshold_mode: rel
    cooldown: 0
    min_lr: 0
    eps: 1.0e-08
...

Configuration file¶

You can find the configuration files for DNN training in conf/train_*.yaml.

ls conf/

We adopt ConfigArgParse for this configuration system. The configuration in YAML format has an equivalent effect to the command line argument. e.g. The following two are equivalent:

# config.yaml
foo: 3
bar: 4

python -m espnet2.bin.asr_train --config conf/config.yaml
python -m espnet2.bin.asr_train --foo 3 --bar 4

Change the configuration for dict type value¶

Some parameters are named as *_conf, e.g. optim_conf, decoder_conf and they has the dict type value. We also provide a way to configure the nested value in such a dict object.

# e.g. Change parameters one by one
python -m espnet2.bin.asr_train --optim_conf lr=0.1 --optim_conf rho=0.8
# e.g. Give the parameters in yaml format
python -m espnet2.bin.asr_train --optim_conf "{lr: 0.1, rho: 0.8}"

Resume training process¶

python -m espnet2.bin.asr_train --resume true

The state of the training process is saved at the end of every epoch as checkpoint.pth and the training process can be resumed from the start of the next epoch. Checkpoint includes the following states.

Model state
Optimizer states
Scheduler states
Reporter state
torch.cuda.amp state (from torch=1.6)

Transfer learning / Fine tuning using pretrained model¶

Use --init_param <file_path>:<src_key>:<dst_key>:<exclude_keys>

# Load all parameters
python -m espnet2.bin.asr_train --init_param model.pth
# Load only the parameters starting with "decoder"
python -m espnet2.bin.asr_train --init_param model.pth:decoder
# Load only the parameters starting with "decoder" and set it to model.decoder
python -m espnet2.bin.asr_train --init_param model.pth:decoder:decoder
# Set parameters to model.decoder
python -m espnet2.bin.asr_train --init_param decoder.pth::decoder
# Load all parameters excluding "decoder.embed"
python -m espnet2.bin.asr_train --init_param model.pth:::decoder.embed
# Load all parameters excluding "encoder" and "decoder.embed"
python -m espnet2.bin.asr_train --init_param model.pth:::encoder,decoder.embed

Freeze parameters¶

python -m espnet2.bin.asr_train --freeze_param encoder.enc encoder.decoder

Change logging interval¶

The result in the middle state of the training will be shown by the specified number:

python -m espnet2.bin.asr_train --log_interval 100

Change the number of iterations in each epoch¶

By default, an epoch indicates using up whole data in the training corpus and the following steps will also run after training for every epoch:

Validation
Saving model and checkpoint
Show result in the epoch

Sometimes the validation after training with a whole corpus is too coarse if using large corpus. For that case, --num_iters_per_epoch can restrict the number of iteration of each epoch.

python -m espnet2.bin.asr_train --num_iters_per_epoch 1000

Note that the training process can’t be resumed at the middle of an epoch because data iterators are stateless, but don’t worry it! Our iterator is built at the start of each epoch and the random seed is fixed by the epoch number, just like:

epoch_iter_factory = Task.build_epoch_iter_factory()
for epoch in range(max_epoch):
  iterator = epoch_iter_factory.build_iter(epoch)

Therefore, the training can be resumed at the start of the epoch.

Weights & Biases integration¶

About Weights & Biases: https://docs.wandb.com/

Installation and setup

See: https://docs.wandb.com/quickstart
```
wandb login
```

Enable wandb

python -m espnet2.bin.asr_train --use_wandb true

and go to the shown URL.

[Option] To use HTTPS PROXY

export HTTPS_PROXY=...your proxy
export CURL_CA_BUNDLE=your.pem
export CURL_CA_BUNDLE=   # Disable SSL certificate verification

Multi GPUs¶

python -m espnet2.bin.asr_train --ngpu 2

Just using CUDA_VISIBLE_DEVICES to specify the device number:

CUDA_VISIBLE_DEVICES=2,3 python -m espnet2.bin.asr_train --ngpu 2

About distributed training, see Distributed training.

The relation between mini-batch size and number of GPUs¶

The batch-size can be changed as follows:

# Change both of the batch_size for training and validation
python -m espnet2.bin.asr_train --batch_size 20
# Change the batch_size for validation
python -m espnet2.bin.asr_train --valid_batch_size 200

The behavior for batch-size during multi-GPU training is different from that of ESPNet1.

ESPNet1: The batch-size will be multiplied by the number of GPUs.

python -m espnet.bin.asr_train --batch_size 10 --ngpu 2 # Actual batch_size is 20 and each GPU devices are assigned to 10

ESPnet2: The batch-size is not changed regardless of the number of GPUs.
- Therefore, you should set a more number of batch-size than that of GPUs.
```
python -m espnet.bin.asr_train --batch_size 10 --ngpu 2 # Actual batch_size is 10 and each GPU devices are assigned to 5
```

Change mini-batch type¶

We adopt variable mini-batch size with considering the dimension of the input features to make the best use of the GPU memory.

There are 6 types:

batch_type	Option to change batch-size	Variable batch-size	Requirement
unsorted	--batch_size	No	-
sorted	--batch_size	No	Length information of features
folded	--batch_size	Yes	Length information of features
length	--batch_bins	Yes	Length information of features
numel	--batch_bins	Yes	Shape information of features
catbel	--batch_size	No	-

Note that –batch_size is ignored if –batch_type=length or –batch_type=numel.

`--batch_type unsorted`¶

This mode has nothing special feature and just creates constant-size mini-batches without any sorting by the length order. If you intend to use ESPnet as not Seq2Seq task, this type may be suitable.

Unlike the other mode, this mode doesn’t require the information of the feature dimension. In other words, it’s not mandatory to prepare shape_file:

python -m espnet.bin.asr_train \
  --batch_size 10 --batch_type unsorted \
  --train_data_path_and_name_and_type "train.scp,feats,npy" \
  --valid_data_path_and_name_and_type "valid.scp,feats,npy" \
  --train_shape_file "train.scp" \
  --valid_shape_file "valid.scp"

This system might seem strange and you might also feel --*_shape_file is verbose because the training corpus can be described totally only using --*_data_path_and_name_and_type.

From the viewpoint of the implementation, we separate the data source for the Dataset and BatchSampler in the term of PyTorch and --*_data_path_and_name_and_type and --*_shape_file correspond to them respectively. From the viewpoint of the training strategy, because variable batch-size is supported according to the length/dimension of each feature, thus we need to prepare the shape information before training.

`--batch_type sorted`¶

This mode creates constant-size mini-batches with sorting by the length order. This mode requires the information of the length.

python -m espnet.bin.asr_train \
  --batch_size 10 --batch_type sorted \
  --train_data_path_and_name_and_type "train.scp,feats,npy" \
  --train_data_path_and_name_and_type "train2.scp,feats2,npy" \
  --valid_data_path_and_name_and_type "valid.scp,feats,npy" \
  --valid_data_path_and_name_and_type "valid2.scp,feats2,npy" \
  --train_shape_file "train_length.txt" \
  --valid_shape_file "valid_length.txt"

e.g. length.txt

sample_id1 1230
sample_id2 156
sample_id3 890
...

Where the fist column indicates the sample id and the second is the length of the corresponding feature. You can see that shape file is input instead in our recipes.

e.g. shape.txt

sample_id1 1230,80
sample_id2 156,80
sample_id3 890,80
...

This file describes the full information of the feature shape; The first number is the length of the sequence and the second or later are the dimension of feature: Length,Dim1,Dim2,....

Only the first number is referred for --batch_type sorted, --batch_type folded and --batch_type length, and the shape information is required only when --batch_type numel.

`--batch_type folded`¶

In ESPnet1, this mode is referred as seq.

This mode creates mini-batch which has the size of base_batch_size // max_i(1 + L_i // f_i). Where L_i is the maximum length in the mini-batch for ith feature and f_i is the --fold length corresponding to the feature. This mode requires the information of length.

python -m espnet.bin.asr_train \
  --batch_size 20 --batch_type folded \
  --train_data_path_and_name_and_type "train.scp,feats,npy" \
  --train_data_path_and_name_and_type "train2.scp,feats2,npy" \
  --valid_data_path_and_name_and_type "valid.scp,feats,npy" \
  --valid_data_path_and_name_and_type "valid2.scp,feats2,npy" \
  --train_shape_file "train_length.scp" \
  --train_shape_file "train_length2.scp" \
  --valid_shape_file "valid_length.scp" \
  --valid_shape_file "valid_length2.scp" \
  --fold_length 5000 \
  --fold_length 300

Note that the repeat number of *_shape_file must equal to the number of --fold_length, but you don’t need to input same number of shape files as the number of data file. i.e. You can give it as follows:

python -m espnet.bin.asr_train \
  --batch_size 20 --batch_type folded \
  --train_data_path_and_name_and_type "train.scp,feats,npy" \
  --train_data_path_and_name_and_type "train2.scp,feats2,npy" \
  --valid_data_path_and_name_and_type "valid.scp,feats,npy" \
  --valid_data_path_and_name_and_type "valid2.scp,feats2,npy" \
  --train_shape_file "train_length.txt" \
  --valid_shape_file "valid_length.txt" \
  --fold_length 5000

In this example, the length of the first feature is considered while the second can be ignored. This technique can be also applied for --batch_type length and --batch_type numel.

`--batch_type length`¶

In ESPnet1, this mode is referred as frame.

You need to specify --batch_bins to determine the mini-batch size instead of --batch_size. Each mini-batch has equal number of bins as possible counting by the total length in the mini-batch; i.e. bins = sum(len(feat) for feats in batch for feat in feats). This mode requires the information of length.

python -m espnet.bin.asr_train \
  --batch_bins 10000 --batch_type length \
  --train_data_path_and_name_and_type "train.scp,feats,npy" \
  --train_data_path_and_name_and_type "train2.scp,feats2,npy" \
  --valid_data_path_and_name_and_type "valid.scp,feats,npy" \
  --valid_data_path_and_name_and_type "valid2.scp,feats2,npy" \
  --train_shape_file "train_length.txt" \
  --train_shape_file "train_length2.txt" \
  --valid_shape_file "valid_length.txt" \
  --valid_shape_file "valid_length2.txt" \

`--batch_type numel`¶

In ESPnet1, this mode is referred as bins.

You need to specify --batch_bins to determine the mini-batch size instead of --batch_size. Each mini-batches has equal number of bins as possible counting by the total number of elements; i.e. bins = sum(numel(feat) for feats in batch for feat in feats), where numel returns the infinite product of the shape of each feature; shape[0] * shape[1] * ...

python -m espnet.bin.asr_train \
  --batch_bins 200000 --batch_type numel \
  --train_data_path_and_name_and_type "train.scp,feats,npy" \
  --train_data_path_and_name_and_type "train2.scp,feats2,npy" \
  --valid_data_path_and_name_and_type  "valid.scp,feats,npy" \
  --valid_data_path_and_name_and_type  "valid2.scp,feats2,npy" \
  --train_shape_file "train_shape.txt" \
  --train_shape_file "train_shape2.txt" \
  --valid_shape_file "valid_shape.txt" \
  --valid_shape_file "valid_shape2.txt"

`--batch_type catbel`¶

This type of batch_type focuses on the case of classification tasks. It guarantees that within each mini-batch, all samples belong to different classes. --batch_size is used to determine the mini-batch size. This batch type does not go along with the default sequence iterator_type. It is instead designed to be used with category iterator_type. Therefore, instead of explicitely giving --batch_type catbel, it is more recommended to give --iterator_type category which will automatically set batch_type to catbel. It is also important to use a preprocessor that adjusts the sample duration to enable mini-batch construction. One example would be espnet2/train/preprocessor/SpkPreprocessor.

python -m espnet.bin.spk_train \
  --batch_bins 256 --iterator_type category \
  --train_data_path_and_name_and_type "train.scp,feats,npy" \
  --valid_data_path_and_name_and_type  "valid.scp,feats,npy" \
  --train_shape_file "train_shape.txt" \
  --valid_shape_file "valid_shape.txt" \

Gradient accumulating¶

There are several ways to deal with larger model architectures than the capacity of your GPU device memory during training.

Using a larger number of GPUs
Using a half decision tensor
Using torch.utils.checkpoint
Gradient accumulating

Gradient accumulating is a technique to handle larger mini-batch than available size.

Split a mini-batch into several numbers and forward and backward for each piece and accumulate the gradients ony by one, while optimizer’s updating is invoked every the number of forwarding just like following:

# accum_grad is the number of pieces
for i, batch in enumerate(iterator):
    loss = net(batch)
    (loss / accum_grad).backward() # Gradients are accumulated
    if i % accum_grad:
        optim.update()
        optim.zero_grads()

Give --accum_grad <int> to use this option.

python -m espnet.bin.asr_train --accum_grad 2

The effective batch_size becomes almost same as accum_grad * batch_size except for:

The random state
Some statistical layers based on mini-batch e.g. BatchNormalization
The case that the batch_size is not unified for each iteration.

Automatic Mixed Precision training¶

python -m espnet.bin.asr_train --use_amp true

Reproducibility and determinization¶

There are some possibilities to make training non-reproducible.

Initialization of parameters that come from PyTorch/ESPnet version difference.
Reducing order for float values during multi GPUs training.
- I don’t know whether NCCL is deterministic or not.
Random seed difference
- We fixed the random seed for each epoch.
CuDNN or some non-deterministic operations for CUDA: See https://pytorch.org/docs/stable/notes/randomness.html

By default, CuDNN performs deterministic mode in our training and it can be turned off by:

python -m espnet.bin.asr_train --cudnn_deterministic false