8.6 KiB
PyTorch Training
Overview
BigDL-Nano can be used to accelerate PyTorch or PyTorch-Lightning applications on training workloads. These optimizations are either enabled by default or can be easily turned on by setting a parameter or calling a method.
The optimizations in BigDL-Nano are delivered through
-
An extended version of PyTorch-Lightning
Trainerfor LightingModule and easy nn.Module. -
An abstract
TorchNanoto accelerate raw or complex nn.Module.
We will briefly describe here the major features in BigDL-Nano for PyTorch training. You can find complete how to guides for acceleration of pytorch-lightning and pytorch.
Best Known Environment Variables
When you successfully installed bigdl-nano (please refer to installation guide) in a conda environment. You are highly recommeneded to run following command once.
source bigdl-nano-init
BigDL-Nano will export a few environment variables, such as OMP_NUM_THREADS and KMP_AFFINITY, according to your current hardware. Empirically, these environment variables work best for most PyTorch applications. After setting these environment variables, you can just run your applications as usual (e.g., python app.py or jupyter notebook).
Accelerate nn.Module's training
nn.Module is the abstraction used in PyTorch for AI Model. It's common that users' model is easy enough to be handled by a regular training loop. In other cases, users may have highly customized training loop. Nano could support the acceleration for both cases.
nn.Module with regular training loop
Most of the AI model defined in nn.Module could be trained in a similar regular training loop. Any nn.Module that
- Have only one output
- Need only 1 loss function and 1 optimizer (e.g., GAN might not applied)
- Have no special customized checkpoint/evaluation logic
could use Trainer.compile that takes in a PyTorch module, a loss, an optimizer, and other PyTorch objects and "compiles" them into a LightningModule. And then a Trainer instance could be used to train this compiled model.
For example,
from bigdl.nano.pytorch import Trainer
lightning_module = Trainer.compile(pytorch_module, loss, optimizer)
trainer = Trainer(max_epochs=10)
trainer.fit(lightning_module, train_loader)
trainer.fit will apply all the acceleration methods that could generally be applied to any models. While there are some optional acceleration method for which you could easily enable.
nn.Module with customized training loop
The TorchNano (bigdl.nano.pytorch.TorchNano) class is what we use to accelerate raw pytorch code. By using it, we only need to make very few changes to accelerate custom training loop. For example,
from bigdl.nano.pytorch import TorchNano
class MyNano(TorchNano) :
def train(self, ...):
# copy your train loop here and make a few changes
MyNano().train(...)
Accelerate LightningModule's training
The PyTorch Trainer (bigdl.nano.pytorch.Trainer) extends PyTorch Lightning's Trainer and has a few more parameters and methods specific to BigDL-Nano. The Trainer can be directly used to train a LightningModule.
For example,
from pytorch_lightning import LightningModule
from bigdl.nano.pytorch import Trainer
class MyModule(LightningModule):
# LightningModule definition
lightning_module = MyModule()
trainer = Trainer(max_epochs=10)
trainer.fit(lightning_module, train_loader)
Optional Acceleration Methods
Intel® Extension for PyTorch
Intel Extension for PyTorch (a.k.a. IPEX) extends PyTorch with optimizations for an extra performance boost on Intel hardware.
BigDL-Nano integrates IPEX in Trainer and TorchNano. Users can turn on IPEX by setting use_ipex=True.
.. tabs::
.. tab:: Trainer
.. code-block:: python
from bigdl.nano.pytorch import Trainer
trainer = Trainer(max_epochs=10, use_ipex=True)
trainer.fit(...)
.. tab:: TorchNano
.. code-block:: python
from bigdl.nano.pytorch import TorchNano
class MyNano(TorchNano) :
def train(self, ...):
# copy your train loop here and make a few changes
MyNano(use_ipex=True).train(...)
Multi-instance Training
When training on a server with dozens of CPU cores, it is often beneficial to use multiple training instances in a data-parallel fashion to make full use of the CPU cores. However, using PyTorch's DDP API is a little cumbersome and error-prone, and if not configured correctly, it will make the training even slow.
You can just set the num_processes parameter in the Trainer or TorchNano constructor and BigDL-Nano will launch the specific number of processes to perform data-parallel training. Each process will be automatically pinned to a different subset of CPU cores to avoid conflict and maximize training throughput.
.. tabs::
.. tab:: Trainer
.. code-block:: python
from bigdl.nano.pytorch import Trainer
trainer = Trainer(max_epochs=10, num_processes=4)
trainer.fit(...)
.. tab:: TorchNano
.. code-block:: python
from bigdl.nano.pytorch import TorchNano
class MyNano(TorchNano) :
def train(self, ...):
# copy your train loop here and make a few changes
MyNano(num_processes=4).train(...)
Note that the effective batch size in multi-instance training is the batch_size in your dataloader times num_processes so the number of iterations of each epoch will be reduced num_processes fold. A common practice to compensate for that is to gradually increase the learning rate to num_processes times. You can find more details of this trick in this paper published by Facebook.
BFloat16 Mixed Precision
BFloat16 Mixed Precison combines BFloat16 and FP32 during training, which could lead to increased performance and reduced memory usage. Compared to FP16 mixed precison, BFloat16 mixed precision has better numerical stability.
You could instantiate a BigDL-Nano Trainer or TorchNano with precision='bf16' to use BFloat16 mixed precision for training.
.. tabs::
.. tab:: Trainer
.. code-block:: python
from bigdl.nano.pytorch import Trainer
trainer = Trainer(max_epochs=5, precision='bf16')
trainer.fit(...)
.. tab:: TorchNano
.. code-block:: python
from bigdl.nano.pytorch import TorchNano
class MyNano(TorchNano) :
def train(self, ...):
# copy your train loop here and make a few changes
MyNano(precision='bf16').train(...)
Channels Last Memory Format
You could instantiate a BigDL-Nano Trainer or TorchNano with channels_last=True to use the channels last memory format, i.e. NHWC (batch size, height, width, channels), as an alternative way to store tensors in classic/contiguous NCHW order.
.. tabs::
.. tab:: Trainer
.. code-block:: python
from bigdl.nano.pytorch import Trainer
trainer = Trainer(max_epochs=5, channels_last=True)
trainer.fit(...)
.. tab:: TorchNano
.. code-block:: python
from bigdl.nano.pytorch import TorchNano
class MyNano(TorchNano) :
def train(self, ...):
# copy your train loop here and make a few changes
MyNano(channels_last=True).train(...)
Accelerate torchvision data processing
Computer Vision task often needs a data processing pipeline that sometimes constitutes a non-trivial part of the whole training pipeline.
Leveraging OpenCV and libjpeg-turbo, BigDL-Nano can accelerate computer vision data pipelines by providing a drop-in replacement of torchvision's components such as datasets and transforms. Nano provides a patch API patch_torch to accelerate these functions.
from bigdl.nano.pytorch import patch_torch
patch_torch()
from torchvision.datasets import ImageFolder
from torchvision import transforms
data_transform = transforms.Compose([
transforms.Resize(256),
transforms.ColorJitter(),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.Resize(128),
transforms.ToTensor()
])
train_set = ImageFolder(train_path, data_transform)
train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)