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@@ -793,18 +793,25 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
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optimizer.zero_grad()
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def tbptt_split_batch(self, batch, split_size):
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"""
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Return list of batch splits. Each split will be passed to forward_step to enable truncated
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back propagation through time. The default implementation splits root level Tensors and
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Sequences at dim=1 (i.e. time dim). It assumes that each time dim is the same length.
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r"""
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:param batch:
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:param split_size:
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:return:
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When using truncated backpropagation through time, each batch must be split along the time dimension.
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Lightning handles this by default, but for custom behavior override this function.
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Called in the training loop after on_batch_start if `truncated_bptt_steps > 0`.
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Each returned batch split is passed separately to training_step(...).
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Args:
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batch (torch.nn.Tensor): Current batch
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split_size (int): How big the split is
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.. note:: Called in the training loop after on_batch_start if `truncated_bptt_steps > 0`.
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Each returned batch split is passed separately to training_step(...).
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Return:
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list of batch splits. Each split will be passed to forward_step to enable truncated
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back propagation through time. The default implementation splits root level Tensors and
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Sequences at dim=1 (i.e. time dim). It assumes that each time dim is the same length.
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Example
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-------
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.. code-block:: python
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def tbptt_split_batch(self, batch, split_size):
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