77 lines
2.2 KiB
Python
77 lines
2.2 KiB
Python
from typing import Callable, Optional, Tuple, cast
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from ..config import registry
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from ..initializers import glorot_uniform_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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InT = Floats2d
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OutT = Floats2d
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@registry.layers("Mish.v1")
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def Mish(
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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[InT, OutT]:
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"""Dense layer with mish activation.
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https://arxiv.org/pdf/1908.08681.pdf
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"""
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if init_W is None:
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init_W = glorot_uniform_init
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if init_b is None:
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init_b = zero_init
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model: Model[InT, OutT] = Model(
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"mish",
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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, cast(Model[InT, OutT], LayerNorm(nI=nO)))
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if dropout is not None:
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model = chain(model, cast(Model[InT, OutT], Dropout(dropout)))
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return model
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def forward(model: Model[InT, OutT], X: InT, is_train: bool) -> Tuple[OutT, 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_pre_mish = model.ops.gemm(X, W, trans2=True)
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Y_pre_mish += b
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Y = model.ops.mish(Y_pre_mish)
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def backprop(dY: OutT) -> InT:
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dY_pre_mish = model.ops.backprop_mish(dY, Y_pre_mish)
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model.inc_grad("W", model.ops.gemm(dY_pre_mish, X, trans1=True))
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model.inc_grad("b", dY_pre_mish.sum(axis=0))
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dX = model.ops.gemm(dY_pre_mish, W)
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return dX
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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[InT, OutT],
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X: Optional[InT] = None,
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Y: Optional[OutT] = 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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