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add some links to API reference
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@ -8,7 +8,7 @@ Adapters are the final and most high-level abstraction in Refiners. They are the
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An Adapter is [generally](#higher-level-adapters) a Chain that replaces a Module (the target) in another Chain (the parent). Typically the target will become a child of the adapter.
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In code terms, `Adapter` is a generic mixin. Adapters subclass `type(parent)` and `Adapter[type(target)]`. For instance, if you adapt a Conv2d in a Sum, the definition of the Adapter could look like:
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In code terms, [`Adapter`][refiners.fluxion.adapters.Adapter] is a generic mixin. Adapters subclass `type(parent)` and `Adapter[type(target)]`. For instance, if you adapt a `Conv2d` in a `Sum`, the definition of the Adapter could look like:
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```py
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class MyAdapter(fl.Sum, fl.Adapter[fl.Conv2d]):
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@ -68,7 +68,7 @@ Starting from the same model as earlier, let us assume we want to:
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- invert the order of the Linear and Chain B in Chain A ;
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- replace the first child block of chain B with the original Chain A.
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This Adapter that will perform a `structural_copy` of part of its target, which means it will duplicate all Chain nodes but keep pointers to the same `WeightedModule`s, and hence not use extra GPU memory.
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This Adapter that will perform a [`structural_copy`][refiners.fluxion.layers.Chain.structural_copy] of part of its target, which means it will duplicate all Chain nodes but keep pointers to the same [`WeightedModule`][refiners.fluxion.layers.WeightedModule]s, and hence not use extra GPU memory.
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```py
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class MyAdapter(fl.Chain, fl.Adapter[fl.Chain]):
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@ -98,7 +98,7 @@ Note that the Linear is in the Chain twice now, but that does not matter as long
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As before, we can call eject the adapter to go back to the original model.
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## A real-world example: LoraAdapter
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## A real-world example: [LoraAdapter][refiners.fluxion.adapters.LoraAdapter]
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A popular example of adaptation is [LoRA](https://arxiv.org/abs/2106.09685). You can check out [how we implement it in Refiners](https://github.com/finegrain-ai/refiners/blob/main/src/refiners/fluxion/adapters/lora.py).
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@ -5,7 +5,7 @@ icon: material/family-tree
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# Chain
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When we say models are implemented in a declarative way in Refiners, what this means in practice is they are implemented as Chains. `Chain` is a Python class to implement trees of modules. It is a subclass of Refiners' `Module`, which is in turn a subclass of PyTorch's `Module`. All inner nodes of a Chain are subclasses of `Chain`, and leaf nodes are subclasses of Refiners' `Module`.
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When we say models are implemented in a declarative way in Refiners, what this means in practice is they are implemented as Chains. [`Chain`][refiners.fluxion.layers.Chain] is a Python class to implement trees of modules. It is a subclass of Refiners' [`Module`][refiners.fluxion.layers.Module], which is in turn a subclass of PyTorch's `Module`. All inner nodes of a Chain are subclasses of `Chain`, and leaf nodes are subclasses of Refiners' `Module`.
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## A first example
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@ -41,9 +41,10 @@ class BasicModel(fl.Chain):
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)
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```
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> **Note** - We often use the namespace `fl` which means `fluxion`, which is the name of the part of Refiners that implements basic layers.
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!!! note
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We often use the namespace `fl` which means `fluxion`, which is the name of the part of Refiners that implements basic layers.
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As of writing, Refiners does not include a `Softmax` layer by default, but as you can see you can easily call arbitrary code using `fl.Lambda`. Alternatively, if you just wanted to write `Softmax()`, you could implement it like this:
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As of writing, Refiners does not include a `Softmax` layer by default, but as you can see you can easily call arbitrary code using [`fl.Lambda`][refiners.fluxion.layers.Lambda]. Alternatively, if you just wanted to write `Softmax()`, you could implement it like this:
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```py
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class Softmax(fl.Module):
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@ -51,7 +52,8 @@ class Softmax(fl.Module):
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return torch.nn.functional.softmax(x)
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```
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> Note that we use type hints here. All of Refiners' codebase is typed, which makes it a pleasure to use if your downstream code is typed too.
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!!! note
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Notice the type hints here. All of Refiners' codebase is typed, which makes it a pleasure to use if your downstream code is typed too.
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## Inspecting and manipulating
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@ -44,7 +44,7 @@ m.set_context("my context", {"my key": 4})
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m() # prints 6
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```
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As you can see, to use the context, you define it by subclassing any `Chain` and defining `init_context`. You can set the context with the `set_context` method or the `SetContext` layer, and you can access it anywhere down the provider's tree with `UseContext`.
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As you can see, to use the context, you define it by subclassing any `Chain` and defining `init_context`. You can set the context with the [`set_context`][refiners.fluxion.layers.Chain.set_context] method or the [`SetContext`][refiners.fluxion.layers.SetContext] layer, and you can access it anywhere down the provider's tree with [`UseContext`][refiners.fluxion.layers.UseContext].
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## Simplifying complex models with Context
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