69 lines
2.4 KiB
ReStructuredText
69 lines
2.4 KiB
ReStructuredText
|
:orphan:
|
||
|
|
|
||
|
|
TPU training (Advanced)
|
||
|
|
=======================
|
||
|
|
**Audience:** Users looking to apply advanced performance techniques to TPU training.
|
||
|
|
|
||
|
|
----
|
||
|
|
|
||
|
|
Weight Sharing/Tying
|
||
|
|
--------------------
|
||
|
|
Weight Tying/Sharing is a technique where in the module weights are shared among two or more layers.
|
||
|
|
This is a common method to reduce memory consumption and is utilized in many State of the Art
|
||
|
|
architectures today.
|
||
|
|
|
||
|
|
PyTorch XLA requires these weights to be tied/shared after moving the model
|
||
|
|
to the TPU device. To support this requirement Lightning provides a model hook which is
|
||
|
|
called after the model is moved to the device. Any weights that require to be tied should
|
||
|
|
be done in the `on_post_move_to_device` model hook. This will ensure that the weights
|
||
|
|
among the modules are shared and not copied.
|
||
|
|
|
||
|
|
PyTorch Lightning has an inbuilt check which verifies that the model parameter lengths
|
||
|
|
match once the model is moved to the device. If the lengths do not match Lightning
|
||
|
|
throws a warning message.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
|
||
|
|
.. code-block:: python
|
||
|
|
|
||
|
|
from pytorch_lightning.core.lightning import LightningModule
|
||
|
|
from torch import nn
|
||
|
|
from pytorch_lightning.trainer.trainer import Trainer
|
||
|
|
|
||
|
|
|
||
|
|
class WeightSharingModule(LightningModule):
|
||
|
|
def __init__(self):
|
||
|
|
super().__init__()
|
||
|
|
self.layer_1 = nn.Linear(32, 10, bias=False)
|
||
|
|
self.layer_2 = nn.Linear(10, 32, bias=False)
|
||
|
|
self.layer_3 = nn.Linear(32, 10, bias=False)
|
||
|
|
# TPU shared weights are copied independently
|
||
|
|
# on the XLA device and this line won't have any effect.
|
||
|
|
# However, it works fine for CPU and GPU.
|
||
|
|
self.layer_3.weight = self.layer_1.weight
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
x = self.layer_1(x)
|
||
|
|
x = self.layer_2(x)
|
||
|
|
x = self.layer_3(x)
|
||
|
|
return x
|
||
|
|
|
||
|
|
def on_post_move_to_device(self):
|
||
|
|
# Weights shared after the model has been moved to TPU Device
|
||
|
|
self.layer_3.weight = self.layer_1.weight
|
||
|
|
|
||
|
|
|
||
|
|
model = WeightSharingModule()
|
||
|
|
trainer = Trainer(max_epochs=1, accelerator="tpu", devices=8)
|
||
|
|
|
||
|
|
See `XLA Documentation <https://github.com/pytorch/xla/blob/master/TROUBLESHOOTING.md#xla-tensor-quirks>`_
|
||
|
|
|
||
|
|
----
|
||
|
|
|
||
|
|
XLA
|
||
|
|
---
|
||
|
|
XLA is the library that interfaces PyTorch with the TPUs.
|
||
|
|
For more information check out `XLA <https://github.com/pytorch/xla>`_.
|
||
|
|
|
||
|
|
Guide for `troubleshooting XLA <https://github.com/pytorch/xla/blob/master/TROUBLESHOOTING.md>`_
|