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Distributed Training

Warning

The usage of distributed had been moved to Distributed Training.

Mixed Precision Training

Nvidia introduced the Tensor Core unit into the Volta and Turing architectures to support FP32 and FP16 mixed precision computing. They further support BF16 in Ampere architectures. With automatic mixed precision training enabled, some operators operate at FP16/BF16 and the rest operate at FP32, which reduces training time and storage requirements without changing the model or degrading its training precision, thus supporting training with larger batch sizes, larger models, and larger input sizes.

PyTorch officially supports amp from 1.6. If you are interested in the implementation of automatic mixing precision, you can refer to Mixed Precision Training.

MMEngine provides the wrapper AmpOptimWrapper for auto-mixing precision training, just set type='AmpOptimWrapper' in optim_wrapper to enable auto-mixing precision training, no other code changes are needed.

runner = Runner(
    model=ResNet18(),
    work_dir='./work_dir',
    train_dataloader=train_dataloader_cfg,
    optim_wrapper=dict(
        type='AmpOptimWrapper',
        # If you want to use bfloat16, uncomment the following line
        # dtype='bfloat16',  # valid values: ('float16', 'bfloat16', None)
        optimizer=dict(type='SGD', lr=0.001, momentum=0.9)),
    train_cfg=dict(by_epoch=True, max_epochs=3),
)
runner.train()

Warning

Up till PyTorch 1.13, torch.bfloat16 performance on Convolution is bad unless manually set environment variable TORCH_CUDNN_V8_API_ENABLED=1. More context at PyTorch issue

Model Compilation

PyTorch introduced torch.compile in its 2.0 release. It compiles your model to speedup trainning & validation. This feature can be enabled since MMEngine v0.7.0, by passing to Runner an extra cfg dict with compile keyword:

runner = Runner(
    model=ResNet18(),
    ...  # other arguments you want
    cfg=dict(compile=True)
)

For advanced usage, you can also change compile options as illustrated in torch.compile API Documentation. For example:

compile_options = dict(backend='inductor', mode='max-autotune')
runner = Runner(
    model=ResNet18(),
    ...  # other arguments you want
    cfg=dict(compile=compile_options)
)

This feature is only available for PyTorch >= 2.0.0.

Warning

torch.compile is still under development by PyTorch team. Some models may fail compilation. If you encounter errors during compilation, you can refer to PyTorch Dynamo FAQ for quick fix, or TorchDynamo Troubleshooting to post an issue in PyTorch.

Using faster Optimizers

If Ascend devices are used, you can use the Ascend optimizers to shorten the training time of the model. The optimizers supported by Ascend devices are as follows:

  • NpuFusedAdadelta

  • NpuFusedAdam

  • NpuFusedAdamP

  • NpuFusedAdamW

  • NpuFusedBertAdam

  • NpuFusedLamb

  • NpuFusedRMSprop

  • NpuFusedRMSpropTF

  • NpuFusedSGD

The usage is the same as native optimizers, and you can refer to Using Optimizers for more information.

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