Synchronize validation and test logging

Outline & Motivation

In the VALIDATION STEP after performing the distributed training process, two ways are given here, one is to continue with the distributed validation process and the other is to do it separately on rank0. But in the previous tutorial, it was hinted that the validation process should be carried out using rank0 in ddp, so I am wondering which one should be used? thanks!!!

Synchronize validation and test logging

When running in distributed mode, we have to ensure that the validation and test step logging calls are synchronized across processes. This is done by adding sync_dist=True to all self.log calls in the validation and test step. This ensures that each GPU worker has the same behaviour when tracking model checkpoints, which is important for later downstream tasks such as testing the best checkpoint across all workers. The sync_dist option can also be used in logging calls during the step methods, but be aware that this can lead to significant communication overhead and slow down your training.

Note if you use any built in metrics or custom metrics that use TorchMetrics, these do not need to be updated and are automatically handled for you.

def validation_step(self, batch, batch_idx):
    x, y = batch
    logits = self(x)
    loss = self.loss(logits, y)
    # Add sync_dist=True to sync logging across all GPU workers (may have performance impact)
    self.log("validation_loss", loss, on_step=True, on_epoch=True, sync_dist=True)


def test_step(self, batch, batch_idx):
    x, y = batch
    logits = self(x)
    loss = self.loss(logits, y)
    # Add sync_dist=True to sync logging across all GPU workers (may have performance impact)
    self.log("test_loss", loss, on_step=True, on_epoch=True, sync_dist=True)

It is possible to perform some computation manually and log the reduced result on rank 0 as follows:

def __init__(self):
    super().__init__()
    self.outputs = []


def test_step(self, batch, batch_idx):
    x, y = batch
    tensors = self(x)
    self.outputs.append(tensors)
    return tensors


def on_test_epoch_end(self):
    mean = torch.mean(self.all_gather(self.outputs))
    self.outputs.clear()  # free memory

    # When you call `self.log` only on rank 0, don't forget to add
    # `rank_zero_only=True` to avoid deadlocks on synchronization.
    # Caveat: monitoring this is unimplemented, see https://github.com/Lightning-AI/lightning/issues/15852
    if self.trainer.is_global_zero:
        self.log("my_reduced_metric", mean, rank_zero_only=True)

Note

It is recommended to validate on single device to ensure each sample/batch gets evaluated exactly once. This is helpful to make sure benchmarking for research papers is done the right way. Otherwise, in a multi-device setting, samples could occur duplicated when DistributedSampler is used, for eg. with strategy="ddp". It replicates some samples on some devices to make sure all devices have same batch size in case of uneven inputs.

Pitch

告知在进行普通任务时的默认方式

Additional context

No response

cc @lantiga @justusschock @awaelchli

Start with the linked “Synchronize validation and test logging” and “Validation loop” documentation, then compare their guidance with the distributed sampler note. Clarify the default validation approach for ordinary tasks and when distributed validation or rank-zero logging is appropriate. Done means the two pages give consistent, unambiguous guidance.