71 lines
2.1 KiB
Python
71 lines
2.1 KiB
Python
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from typing import Callable, Optional, Tuple, cast
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from ..config import registry
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from ..initializers import he_normal_init, zero_init
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from ..model import Model
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from ..types import Floats1d, Floats2d
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from ..util import get_width, partial
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from .chain import chain
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from .dropout import Dropout
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from .layernorm import LayerNorm
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@registry.layers("HardSwish.v1")
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def HardSwish(
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nO: Optional[int] = None,
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nI: Optional[int] = None,
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*,
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init_W: Optional[Callable] = None,
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init_b: Optional[Callable] = None,
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dropout: Optional[float] = None,
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normalize: bool = False,
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) -> Model[Floats2d, Floats2d]:
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if init_W is None:
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init_W = he_normal_init
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if init_b is None:
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init_b = zero_init
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model: Model[Floats2d, Floats2d] = Model(
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"hardswish",
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forward,
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init=partial(init, init_W, init_b),
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dims={"nO": nO, "nI": nI},
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params={"W": None, "b": None},
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)
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if normalize:
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model = chain(model, LayerNorm(nI=nO))
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if dropout is not None:
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model = chain(model, cast(Model[Floats2d, Floats2d], Dropout(dropout)))
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return model
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def forward(
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model: Model[Floats2d, Floats2d], X: Floats2d, is_train: bool
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) -> Tuple[Floats2d, Callable]:
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W = cast(Floats2d, model.get_param("W"))
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b = cast(Floats1d, model.get_param("b"))
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Y_preact = model.ops.affine(X, W, b)
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Y = model.ops.hard_swish(Y_preact)
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def backprop(dY: Floats2d) -> Floats2d:
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dY = model.ops.backprop_hard_swish(dY, Y_preact, inplace=False)
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model.inc_grad("b", dY.sum(axis=0))
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model.inc_grad("W", model.ops.gemm(dY, X, trans1=True))
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return model.ops.gemm(dY, W)
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return Y, backprop
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def init(
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init_W: Callable,
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init_b: Callable,
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model: Model[Floats2d, Floats2d],
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X: Optional[Floats2d] = None,
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Y: Optional[Floats2d] = None,
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) -> None:
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if X is not None:
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model.set_dim("nI", get_width(X))
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if Y is not None:
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model.set_dim("nO", get_width(Y))
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model.set_param("W", init_W(model.ops, (model.get_dim("nO"), model.get_dim("nI"))))
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model.set_param("b", init_b(model.ops, (model.get_dim("nO"),)))
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