updated description for code #2417
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jysohn23
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Aug 12, 2020
API_GUIDE.md
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| - `xm.optimizer_step(optimizer)` no longer needs a barrier. ParallelLoader | ||
| automatically creates an XLA barrier that evaluates the graph. | ||
| - `MpDeviceLoader` loads the training data onto each device. | ||
| - `xm.optimizer_step(optimizer)` creates an XLA barrier that evaluates the graph and update the model's parameters. |
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The description for xm.optimizer_step() should stay as is as it's still the pl.MpDeviceLoader that ends up marking the step (https://github.com/pytorch/xla/blob/master/torch_xla/distributed/parallel_loader.py#L37), unless xm.optimizer_step is called with barrier kwarg.
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@jysohn23 Since the previous example for running on a single xla device no longer uses xm.optimizer_step(optimizer, barrier=True) it wouldn't make sense to have xm.optimizer_step(optimizer) no longer needs a barrier. in the doc. Would something like xm.optimizer_step(optimizer) consolidates the gradients between cores and issues the XLA device step computation work?
jysohn23
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Aug 13, 2020
cjolivier01
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Mar 14, 2021
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Missed updating the description in the previous PR. This PR updates the description for
xm.mark_step()andMpDeviceLoader