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https://github.com/finegrain-ai/refiners.git
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(doc/fluxion/basic) add/convert docstrings to mkdocstrings format
This commit is contained in:
Laurent
Laureηt
parent
a7c048f5fb
commit
77b97b3c8e
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@ -1,11 +1,26 @@
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import torch
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from torch import Size, Tensor, device as Device, dtype as DType, randn
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from torch import Size, Tensor, device as Device, dtype as DType
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from torch.nn import Parameter as TorchParameter
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from refiners.fluxion.layers.module import Module, WeightedModule
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class Identity(Module):
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"""Identity operator layer.
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This layer simply returns the input tensor.
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Example:
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```py
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identity = fl.Identity()
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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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```
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"""
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def __init__(self) -> None:
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super().__init__()
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@ -23,6 +38,25 @@ class View(Module):
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class GetArg(Module):
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"""GetArg operation layer.
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This layer returns the nth tensor of the input arguments.
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Example:
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```py
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get_arg = fl.GetArg(1)
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inputs = (
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torch.randn(10, 10),
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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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assert torch.allclose(tensor[1], output)
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```
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"""
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def __init__(self, index: int) -> None:
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super().__init__()
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self.index = index
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@ -32,28 +66,86 @@ class GetArg(Module):
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class Flatten(Module):
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def __init__(self, start_dim: int = 0, end_dim: int = -1) -> None:
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"""Flatten operation layer.
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This layer flattens the input tensor between the given dimensions.
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See also [`torch.flatten`][torch.flatten].
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Example:
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```py
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flatten = fl.Flatten(start_dim=1)
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tensor = torch.randn(10, 10, 10)
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output = flatten(tensor)
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assert output.shape == (10, 100)
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```
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"""
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def __init__(
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self,
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start_dim: int = 0,
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end_dim: int = -1,
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) -> None:
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super().__init__()
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self.start_dim = start_dim
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self.end_dim = end_dim
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def forward(self, x: Tensor) -> Tensor:
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return x.flatten(self.start_dim, self.end_dim)
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return torch.flatten(
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input=x,
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start_dim=self.start_dim,
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end_dim=self.end_dim,
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)
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class Unflatten(Module):
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"""Unflatten operation layer.
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This layer unflattens the input tensor at the given dimension with the given sizes.
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See also [`torch.unflatten`][torch.unflatten].
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Example:
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```py
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unflatten = fl.Unflatten(dim=1)
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tensor = torch.randn(10, 100)
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output = unflatten(tensor, sizes=(10, 10))
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assert output_unflatten.shape == (10, 10, 10)
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```
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"""
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def __init__(self, dim: int) -> None:
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super().__init__()
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self.dim = dim
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def forward(self, x: Tensor, sizes: Size) -> Tensor:
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return x.unflatten(self.dim, sizes) # type: ignore
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return torch.unflatten(
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input=x,
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dim=self.dim,
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sizes=sizes,
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)
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class Reshape(Module):
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"""
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Reshape the input tensor to the given shape. The shape must be compatible with the input tensor shape. The batch
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dimension is preserved.
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"""Reshape operation layer.
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This layer reshapes the input tensor to a specific shape (which must be compatible with the original shape).
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See also [torch.reshape][torch.reshape].
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Warning:
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The first dimension (batch dimension) is forcefully preserved.
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Example:
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```py
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reshape = fl.Reshape(5, 2)
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tensor = torch.randn(2, 10, 1)
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output = reshape(tensor)
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assert output.shape == (2, 5, 2)
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```
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"""
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def __init__(self, *shape: int) -> None:
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@ -61,30 +153,95 @@ class Reshape(Module):
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self.shape = shape
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def forward(self, x: Tensor) -> Tensor:
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return x.reshape(x.shape[0], *self.shape)
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return torch.reshape(
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input=x,
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shape=(x.shape[0], *self.shape),
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)
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class Transpose(Module):
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"""Transpose operation layer.
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This layer transposes the input tensor between the two given dimensions.
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See also [`torch.transpose`][torch.transpose].
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Example:
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```py
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transpose = fl.Transpose(dim0=1, dim1=2)
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tensor = torch.randn(10, 20, 30)
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output = transpose(tensor)
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assert output.shape == (10, 30, 20)
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```
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"""
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def __init__(self, dim0: int, dim1: int) -> None:
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super().__init__()
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self.dim0 = dim0
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self.dim1 = dim1
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def forward(self, x: Tensor) -> Tensor:
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return x.transpose(self.dim0, self.dim1)
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return torch.transpose(
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input=x,
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dim0=self.dim0,
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dim1=self.dim1,
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)
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class Permute(Module):
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"""Permute operation layer.
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This layer permutes the input tensor according to the given dimensions.
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See also [`torch.permute`][torch.permute].
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Example:
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```py
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permute = fl.Permute(2, 0, 1)
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tensor = torch.randn(10, 20, 30)
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output = permute(tensor)
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assert output.shape == (30, 10, 20)
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```
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"""
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def __init__(self, *dims: int) -> None:
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super().__init__()
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self.dims = dims
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def forward(self, x: Tensor) -> Tensor:
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return x.permute(*self.dims)
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return torch.permute(
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input=x,
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dims=self.dims,
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)
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class Slicing(Module):
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def __init__(self, dim: int = 0, start: int = 0, end: int | None = None, step: int = 1) -> None:
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"""Slicing operation layer.
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This layer slices the input tensor at the given dimension between the given start and end indices.
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See also [`torch.index_select`][torch.index_select].
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Example:
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```py
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slicing = fl.Slicing(dim=1, start=50)
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tensor = torch.randn(10, 100)
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output = slicing(tensor)
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assert output.shape == (10, 50)
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assert torch.allclose(output, tensor[:, 50:])
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```
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"""
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def __init__(
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self,
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dim: int = 0,
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start: int = 0,
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end: int | None = None,
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step: int = 1,
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) -> None:
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super().__init__()
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self.dim = dim
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self.start = start
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@ -93,55 +250,162 @@ class Slicing(Module):
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def forward(self, x: Tensor) -> Tensor:
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dim_size = x.shape[self.dim]
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# compute start index
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start = self.start if self.start >= 0 else dim_size + self.start
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start = max(min(start, dim_size), 0)
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# compute end index
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end = self.end or dim_size
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end = end if end >= 0 else dim_size + end
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start = max(min(start, dim_size), 0)
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end = max(min(end, dim_size), 0)
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if start >= end:
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return self.get_empty_slice(x)
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indices = torch.arange(start=start, end=end, step=self.step, device=x.device)
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return x.index_select(self.dim, indices)
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def get_empty_slice(self, x: Tensor) -> Tensor:
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"""
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Return an empty slice of the same shape as the input tensor to mimic PyTorch's slicing behavior.
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"""
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if start >= end:
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return self._get_empty_slice(x)
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# compute indices
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indices = torch.arange(
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start=start,
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end=end,
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step=self.step,
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device=x.device,
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)
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return torch.index_select(
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input=x,
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dim=self.dim,
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index=indices,
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)
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def _get_empty_slice(self, x: Tensor) -> Tensor:
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"""Get an empty slice of the same shape as the input tensor (to mimic PyTorch's slicing behavior)."""
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shape = list(x.shape)
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shape[self.dim] = 0
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return torch.empty(*shape, device=x.device)
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class Squeeze(Module):
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"""Squeeze operation layer.
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This layer squeezes the input tensor at the given dimension.
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See also [`torch.squeeze`][torch.squeeze].
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Example:
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```py
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squeeze = fl.Squeeze(dim=1)
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tensor = torch.randn(10, 1, 10)
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output = squeeze(tensor)
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assert output.shape == (10, 10)
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```
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"""
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def __init__(self, dim: int) -> None:
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super().__init__()
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self.dim = dim
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def forward(self, x: Tensor) -> Tensor:
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return x.squeeze(self.dim)
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return torch.squeeze(
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input=x,
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dim=self.dim,
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)
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class Unsqueeze(Module):
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"""Unsqueeze operation layer.
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This layer unsqueezes the input tensor at the given dimension.
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See also [`torch.unsqueeze`][torch.unsqueeze].
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Example:
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```py
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unsqueeze = fl.Unsqueeze(dim=1)
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tensor = torch.randn(10, 10)
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output = unsqueeze(tensor)
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assert output.shape == (10, 1, 10)
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```
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"""
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def __init__(self, dim: int) -> None:
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super().__init__()
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self.dim = dim
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def forward(self, x: Tensor) -> Tensor:
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return x.unsqueeze(self.dim)
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return torch.unsqueeze(
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input=x,
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dim=self.dim,
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)
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class Sin(Module):
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"""Sine operator layer.
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This layer applies the sine function to the input tensor.
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See also [`torch.sin`][torch.sin].
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Example:
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```py
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sin = fl.Sin()
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tensor = torch.tensor([0, torch.pi])
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output = sin(tensor)
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expected_output = torch.tensor([0.0, 0.0])
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assert torch.allclose(output, expected_output, atol=1e-6)
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```
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"""
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def forward(self, x: Tensor) -> Tensor:
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return torch.sin(input=x)
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class Cos(Module):
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"""Cosine operator layer.
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This layer applies the cosine function to the input tensor.
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See also [`torch.cos`][torch.cos].
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Example:
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```py
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cos = fl.Cos()
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tensor = torch.tensor([0, torch.pi])
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output = cos(tensor)
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expected_output = torch.tensor([1.0, -1.0])
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assert torch.allclose(output, expected_output, atol=1e-6)
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```
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"""
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def forward(self, x: Tensor) -> Tensor:
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return torch.cos(input=x)
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class Multiply(Module):
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def __init__(self, scale: float = 1.0, bias: float = 0.0) -> None:
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"""Multiply operator layer.
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This layer scales and shifts the input tensor by the given scale and bias.
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Example:
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```py
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multiply = fl.Multiply(scale=2, bias=1)
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tensor = torch.ones(1)
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output = multiply(tensor)
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assert torch.allclose(output, torch.tensor([3.0]))
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```
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"""
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def __init__(
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self,
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scale: float = 1.0,
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bias: float = 0.0,
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) -> None:
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super().__init__()
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self.scale = scale
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self.bias = bias
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@ -151,14 +415,40 @@ class Multiply(Module):
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class Parameter(WeightedModule):
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"""
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A layer that wraps a tensor as a parameter. This is useful to create a parameter that is not a weight or a bias.
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"""Parameter layer.
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This layer simple wraps a PyTorch [`Parameter`][torch.nn.parameter.Parameter].
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When called, it simply returns the [`Parameter`][torch.nn.parameter.Parameter] Tensor.
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Attributes:
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weight (torch.nn.parameter.Parameter): The parameter Tensor.
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"""
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def __init__(self, *dims: int, device: Device | str | None = None, dtype: DType | None = None) -> None:
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def __init__(
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self,
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*dims: int,
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requires_grad: bool = True,
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device: Device | str | None = None,
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dtype: DType | None = None,
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) -> None:
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super().__init__()
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self.dims = dims
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self.weight = TorchParameter(randn(*dims, device=device, dtype=dtype))
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self.weight = TorchParameter(
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requires_grad=requires_grad,
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data=torch.randn(
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*dims,
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device=device,
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dtype=dtype,
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),
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)
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def forward(self, x: Tensor) -> Tensor:
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return self.weight.expand(x.shape[0], *self.dims)
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@property
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def requires_grad(self) -> bool:
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return self.weight.requires_grad
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@requires_grad.setter
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def requires_grad(self, value: bool) -> None:
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self.weight.requires_grad = value
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