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add footnote for PyTorch paper and link to walk

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Pierre Chapuis 2024-02-02 14:28:15 +01:00 committed by Cédric Deltheil
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docs/concepts/chain.md
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@ -9,7 +9,7 @@ When we say models are implemented in a declarative way in Refiners, what this m
## A first example ## A first example
To give you an idea of how it looks, let us take an example similar to the one from the PyTorch paper: To give you an idea of how it looks, let us take an example similar to the one from the PyTorch paper[^1]:
```py ```py
class BasicModel(nn.Module): class BasicModel(nn.Module):
@ -106,7 +106,7 @@ Did it work? Let's see:
## Accessing and iterating ## Accessing and iterating
There are also many ways to access or iterate nodes even if they are deep in the tree. Most of them are implemented using a powerful iterator named `walk`. However, most of the time, you can use simpler helpers. For instance, to iterate all the modules in the tree that hold weights (the `Conv2d` and the `Linear`s), we can just do: There are also many ways to access or iterate nodes even if they are deep in the tree. Most of them are implemented using a powerful iterator named [`walk`][refiners.fluxion.layers.Chain.walk]. However, most of the time, you can use simpler helpers. For instance, to iterate all the modules in the tree that hold weights (the `Conv2d` and the `Linear`s), we can just do:
```py ```py
for x in m.layers(fl.WeightedModule): for x in m.layers(fl.WeightedModule):
@ -120,3 +120,5 @@ Conv2d(in_channels=1, out_channels=128, kernel_size=(3, 3), device=cpu, dtype=fl
Linear(in_features=128, out_features=40, device=cpu, dtype=float32) Linear(in_features=128, out_features=40, device=cpu, dtype=float32)
Linear(in_features=40, out_features=10, device=cpu, dtype=float32 Linear(in_features=40, out_features=10, device=cpu, dtype=float32
``` ```
[^1]: Paszke et al., 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library.