Channels Last training

Train models faster using channels last format (beta)

With MixedPrecision, image models trained in channels last format on Tensor Cores can increase training throughput over contiguous format. PyTorch observed a 22% improvment in ResNet50 training speed using channels last and 8-35% improvement across a selection of models tested on a V100.

Channels last format is compatible with modern GPUs (Volta, Turing, or newer) and modern CPUs (Ice Lake or newer).

Channels last memory format currently is implemented for NCHW Tensors. Not all PyTorch operators have been converted to support channels last. See (Beta) Channels Last Memory Format in PyTorch tutorial for more details.

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ChannelsLast

 ChannelsLast (after_create=None, before_fit=None, before_epoch=None,
               before_train=None, before_batch=None, after_pred=None,
               after_loss=None, before_backward=None,
               after_cancel_backward=None, after_backward=None,
               before_step=None, after_cancel_step=None, after_step=None,
               after_cancel_batch=None, after_batch=None,
               after_cancel_train=None, after_train=None,
               before_validate=None, after_cancel_validate=None,
               after_validate=None, after_cancel_epoch=None,
               after_epoch=None, after_cancel_fit=None, after_fit=None)

Channels last training using PyTorch’s Channels Last Memory Format (beta)

When a PyTorch model is set to channels last format, PyTorch will automatically convert any compatible NCHW input tensors to NHWC format. ChannelsLast sets the model to channels last format, so no changes to dataloaders or inputs are required.

Note

ChannelsLast should work with most convolutional timm models.

However, it is advised to test each model, as supported operations differ across PyTorch versions.

Using ChannelsLast with unsupported PyTorch operations can lead to “channel thrashing”, where channels last input is converted to contiguous format in an unsupported PyTorch operation, then back to channels last for execution on the tensor core, back to contiguous when returned to the operation, and finally to channels last for the next layer. Too many unsupported operations in a model can lead to reduced performance.

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Learner.to_channelslast

 Learner.to_channelslast (use_amp:bool=True,
                          amp_mode:str|AMPMode=<AMPMode.FP16: 'fp16'>,
                          init_scale:float=65536.0,
                          growth_factor:float=2.0,
                          backoff_factor:float=0.5,
                          growth_interval:int=2000, enabled:bool=True)

Set Learner and inputs to channels_last format and float16 Mixed Precision by default

	Type	Default	Details
use_amp	bool	True	Add `MixedPrecision` with `amp_mode`. Recommended for full channels last performance
amp_mode	str \| AMPMode	AMPMode.FP16	Mixed Precision training mode. Supports fp16 and bf16.
init_scale	float	65536.0
growth_factor	float	2.0
backoff_factor	float	0.5
growth_interval	int	2000
enabled	bool	True

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Learner.to_contiguous

 Learner.to_contiguous (to_fp32:bool=False)

Set Learner and inputs to contiguous_format (default format), optionally to single precision