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(doc/fluxion/embedding) add/convert docstrings to mkdocstrings format
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@ -1,11 +1,34 @@
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from jaxtyping import Float, Int
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from torch import Tensor, device as Device, dtype as DType
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from torch import device as Device, dtype as DType
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from torch.nn import Embedding as _Embedding
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from refiners.fluxion.layers.module import WeightedModule
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class Embedding(_Embedding, WeightedModule): # type: ignore
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class Embedding(_Embedding, WeightedModule):
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"""Embedding layer.
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This layer wraps [`torch.nn.Embedding`][torch.nn.Embedding].
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Receives:
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(Int[Tensor, "batch length"]):
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Returns:
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(Float[Tensor, "batch length embedding_dim"]):
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Example:
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```py
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embedding = fl.Embedding(
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num_embeddings=10,
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embedding_dim=128
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)
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tensor = torch.randint(0, 10, (2, 10))
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output = embedding(tensor)
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assert output.shape == (2, 10, 128)
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```
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"""
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def __init__(
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self,
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num_embeddings: int,
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@ -13,9 +36,18 @@ class Embedding(_Embedding, WeightedModule): # type: ignore
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device: Device | str | None = None,
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dtype: DType | None = None,
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):
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_Embedding.__init__( # type: ignore
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self, num_embeddings=num_embeddings, embedding_dim=embedding_dim, device=device, dtype=dtype
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)
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"""Initializes the Embedding layer.
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def forward(self, x: Int[Tensor, "batch length"]) -> Float[Tensor, "batch length embedding_dim"]: # type: ignore
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return super().forward(x)
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Args:
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num_embeddings: The number of embeddings.
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embedding_dim: The dimension of the embeddings.
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device: The device to use for the embedding layer.
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dtype: The dtype to use for the embedding layer.
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"""
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_Embedding.__init__( # type: ignore
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self,
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num_embeddings=num_embeddings,
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embedding_dim=embedding_dim,
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device=device,
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dtype=dtype,
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)
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