Mixed precision training

Catalyst support a variety of backends for mixed precision training. For the PyTorch versions below 1.6 it’s better to use Nvidia Apex helper. After PyTorch 1.6 release, it’s possible to use AMP natively inside torch package.

Suppose you have the following pipeline with Linear Regression:

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst.dl import SupervisedRunner

# data
num_samples, num_features = int(1e4), int(1e1)
X, y = torch.rand(num_samples, num_features), torch.rand(num_samples)
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, 1)
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [3, 6])

# model training
runner = SupervisedRunner()
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    logdir="./logdir",
    num_epochs=8,
    verbose=True,
)

Nvidia Apex

To use Nvidia Apex fp16 support you firstly need to install it with,

!git clone https://github.com/NVIDIA/apex
!pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./apex

After that you could easily extend our current pipeline with just one line of code:

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst.dl import SupervisedRunner

# data
num_samples, num_features = int(1e4), int(1e1)
X, y = torch.rand(num_samples, num_features), torch.rand(num_samples)
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, 1)
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [3, 6])

# model training
runner = SupervisedRunner()
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    logdir="./logdir",
    num_epochs=8,
    verbose=True,
    apex=True
)

You could also check out the example above in this Google Colab notebook

Torch AMP

If you would like to use native AMP support, you could do the following:

import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst.dl import SupervisedRunner

# data
num_samples, num_features = int(1e4), int(1e1)
X, y = torch.rand(num_samples, num_features), torch.rand(num_samples)
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}

# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, 1)
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [3, 6])

# model training
runner = SupervisedRunner()
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    logdir="./logdir",
    num_epochs=8,
    verbose=True,
    amp=True
)

You could also check out the example above in this Google Colab notebook

Nvidia Apex (Config API)

Firstly, prepare the config. For example:

engine:
    _target_: APEXEngine
    opt_level: "O1"
    ...

After that just run:

catalyst-dl run -C=/path/to/configs --apex

Torch AMP (Config API)

For native AMP support you only need to pass required flag to the run command:

catalyst-dl run -C=/path/to/configs --amp

If you haven’t found the answer for your question, feel free to join our slack for the discussion.