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(docstrings) apply @deltheil suggestions
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@ -43,7 +43,8 @@ conversion = [
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"tqdm>=4.62.3",
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]
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doc = [
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"black>=24.1.1", # required by mkdocs to format the signatures
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# required by mkdocs to format the signatures
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"black>=24.1.1",
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"mkdocs-material>=9.5.6",
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"mkdocstrings[python]>=0.24.0",
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"mkdocs-literate-nav>=0.6.1",
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@ -9,7 +9,7 @@ from refiners.fluxion.adapters.adapter import Adapter
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class Lora(fl.Chain, ABC):
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"""Low-rank approximation (LoRA) layer.
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"""Low-Rank Adaptation (LoRA) layer.
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This layer is composed of two [`WeightedModule`][refiners.fluxion.layers.WeightedModule]:
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@ -156,7 +156,7 @@ class Lora(fl.Chain, ABC):
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return LoraAdapter(layer, self), parent
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def load_weights(self, down_weight: Tensor, up_weight: Tensor) -> None:
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"""Load the weights of the LoRA.
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"""Load the (pre-trained) weights of the LoRA.
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Args:
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down_weight: The down weight.
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@ -169,7 +169,7 @@ class Lora(fl.Chain, ABC):
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class LinearLora(Lora):
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"""Low-rank approximation (LoRA) layer for linear layers.
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"""Low-Rank Adaptation (LoRA) layer for linear layers.
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This layer uses two [`Linear`][refiners.fluxion.layers.Linear] layers as its down and up layers.
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"""
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@ -255,7 +255,7 @@ class LinearLora(Lora):
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class Conv2dLora(Lora):
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"""Low-rank approximation (LoRA) layer for 2D convolutional layers.
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"""Low-Rank Adaptation (LoRA) layer for 2D convolutional layers.
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This layer uses two [`Conv2d`][refiners.fluxion.layers.Conv2d] layers as its down and up layers.
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"""
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@ -391,12 +391,12 @@ class LoraAdapter(fl.Sum, Adapter[fl.WeightedModule]):
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@property
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def loras(self) -> dict[str, Lora]:
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"""The LoRA layers."""
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"""The LoRA layers indexed by name."""
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return {lora.name: lora for lora in self.layers(Lora)}
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@property
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def scales(self) -> dict[str, float]:
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"""The scales of the LoRA layers."""
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"""The scales of the LoRA layers indexed by names."""
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return {lora.name: lora.scale for lora in self.layers(Lora)}
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@scales.setter
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@ -407,6 +407,9 @@ class LoraAdapter(fl.Sum, Adapter[fl.WeightedModule]):
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def add_lora(self, lora: Lora, /) -> None:
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"""Add a LoRA layer to the adapter.
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Raises:
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AssertionError: If the adapter already contains a LoRA layer with the same name.
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Args:
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lora: The LoRA layer to add.
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"""
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@ -416,6 +419,9 @@ class LoraAdapter(fl.Sum, Adapter[fl.WeightedModule]):
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def remove_lora(self, name: str, /) -> Lora | None:
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"""Remove a LoRA layer from the adapter.
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Note:
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If the adapter doesn't contain a LoRA layer with the given name, nothing happens and `None` is returned.
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Args:
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name: The name of the LoRA layer to remove.
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"""
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@ -23,7 +23,7 @@ class ContextProvider:
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self.contexts[key] = value
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def get_context(self, key: str) -> Any:
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"""Retreive a value from the context.
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"""Retrieve a value from the context.
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Args:
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key: The key of the context.
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@ -34,7 +34,7 @@ class ContextProvider:
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return self.contexts.get(key)
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def update_contexts(self, new_contexts: Contexts) -> None:
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"""Update the contexts with new contexts.
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"""Update or set the contexts with new contexts.
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Args:
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new_contexts: The new contexts.
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@ -55,7 +55,7 @@ class ReLU(Activation):
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output = relu(tensor)
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expected_output = torch.tensor([[0.0, 0.0, 1.0]])
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assert torch.allclose(output, expected_output)
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assert torch.equal(output, expected_output)
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```
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"""
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@ -20,7 +20,9 @@ def scaled_dot_product_attention(
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) -> Float[Tensor, "batch source_sequence_length dim"]:
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"""Scaled Dot Product Attention.
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Optimization depends on which pytorch backend is used.
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Note:
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Optimization depends on which PyTorch backend is used.
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See [[arXiv:1706.03762] Attention Is All You Need (Equation 1)](https://arxiv.org/abs/1706.03762) for more details.
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See also [torch.nn.functional.scaled_dot_product_attention](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html).
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"""
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@ -213,7 +215,7 @@ class Attention(Chain):
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which transforms the 3 inputs into Query, Key and Value
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- a [`ScaledDotProductAttention`][refiners.fluxion.layers.attentions.ScaledDotProductAttention] layer
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- a [`Linear`][refiners.fluxion.layers.linear.Linear] layer,
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which further transforms the output of the
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which projects the output of the
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[`ScaledDotProductAttention`][refiners.fluxion.layers.attentions.ScaledDotProductAttention] layer
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Receives:
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@ -461,7 +463,7 @@ class SelfAttention2d(SelfAttention):
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) -> Float[Tensor, "batch height*width channels"]:
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"""Transform a 2D Tensor into a sequence.
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The height and width of the input Tensor are stored in the context,
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The height and width of the input Tensor are stored in a `"reshape"` context,
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so that the output Tensor can be transformed back into a 2D Tensor in the `sequence_to_tensor_2d` method.
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"""
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height, width = x.shape[-2:]
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@ -480,7 +482,7 @@ class SelfAttention2d(SelfAttention):
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) -> Float[Tensor, "batch channels height width"]:
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"""Transform a sequence into a 2D Tensor.
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The height and width of the output Tensor are retrieved from the context,
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The height and width of the output Tensor are retrieved from the `"reshape"` context,
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which was set in the `tensor_2d_to_sequence` method.
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"""
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height, width = self.use_context("reshape").values()
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@ -17,7 +17,7 @@ class Identity(Module):
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tensor = torch.randn(10, 10)
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output = identity(tensor)
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assert torch.allclose(tensor, output)
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assert torch.equal(tensor, output)
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```
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"""
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@ -51,9 +51,9 @@ class GetArg(Module):
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torch.randn(20, 20),
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torch.randn(30, 30),
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)
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output = get_arg(inputs)
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output = get_arg(*inputs)
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assert torch.allclose(tensor[1], output)
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assert id(inputs[1]) == id(output)
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```
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"""
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