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