116 lines
3.2 KiB
Python
116 lines
3.2 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 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 ArrayInfo, get_width, partial
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InT = Floats2d
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OutT = Floats2d
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@registry.layers("Softmax.v1")
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def Softmax(
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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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) -> Model[InT, OutT]:
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if init_W is None:
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init_W = zero_init
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if init_b is None:
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init_b = zero_init
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return Model(
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"softmax",
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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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attrs={"softmax_normalize": True, "softmax_temperature": 1.0},
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)
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@registry.layers("Softmax.v2")
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def Softmax_v2(
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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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normalize_outputs: bool = True,
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temperature: float = 1.0,
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) -> Model[InT, OutT]:
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if init_W is None:
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init_W = zero_init
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if init_b is None:
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init_b = zero_init
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validate_temperature(temperature)
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return Model(
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"softmax",
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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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attrs={
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"softmax_normalize": normalize_outputs,
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"softmax_temperature": temperature,
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},
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)
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def forward(model: Model[InT, OutT], X: InT, is_train: bool) -> Tuple[OutT, Callable]:
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normalize = model.attrs["softmax_normalize"] or is_train
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temperature = model.attrs["softmax_temperature"]
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validate_temperature(temperature)
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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 = model.ops.affine(X, W, b)
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if normalize:
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Y = model.ops.softmax(Y, temperature=temperature)
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array_info = ArrayInfo.from_array(Y)
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def backprop(dY: InT) -> OutT:
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array_info.check_consistency(dY)
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if temperature != 1.0:
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dY = dY / temperature
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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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def backprop_unnormalized(dY: InT):
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msg = "backprop is not supported for an unnormalized Softmax layer"
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raise ValueError(msg)
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if normalize:
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return Y, backprop
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else:
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return Y, backprop_unnormalized
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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 and model.has_dim("nI") is None:
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model.set_dim("nI", get_width(X))
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if Y is not None and model.has_dim("nO") is 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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def validate_temperature(temperature):
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if temperature <= 0.0:
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msg = "softmax temperature must not be zero or negative"
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raise ValueError(msg)
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