54 lines
1.5 KiB
Python
54 lines
1.5 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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#
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# This source code is licensed under the BSD license found in the
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# LICENSE file in the root directory of this source tree.
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from abc import ABCMeta, abstractmethod
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from dataclasses import asdict, dataclass
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from typing import Optional, Type, TypeVar
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import torch.nn as nn
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from xformers.components import Activation
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Self = TypeVar("Self", bound="Feedforward")
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@dataclass
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class FeedforwardConfig:
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name: str
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dim_model: int
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dropout: float
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activation: Activation
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# Define the common interface, every feedforward block needs to derive from it
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class Feedforward(nn.Module, metaclass=ABCMeta):
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@abstractmethod
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def __init__(
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self,
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dim_model: Optional[int] = None,
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dropout: Optional[float] = None,
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activation: Optional[Activation] = None,
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*args,
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**kwargs,
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):
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super().__init__()
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# This feedforward requires a CUDA accelerator
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self.requires_cuda = False
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# This feedforward requires a context length which is squared, often due to 2D pooling
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self.requires_squared_context = False
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@classmethod
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def from_config(cls: Type[Self], config: FeedforwardConfig) -> Self:
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# Generate the class inputs from the config
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fields = asdict(config)
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# Skip all Nones so that default values are used
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fields = {k: v for k, v in fields.items() if v is not None}
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return cls(**fields)
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