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59 lines
1.4 KiB
59 lines
1.4 KiB
from typing import Any, Callable, Tuple
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import numpy
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from thinc.backends import Ops
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from ..config import registry
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from ..model import Model
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@registry.layers("with_cpu.v1")
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def with_cpu(layer: Model, ops: Ops) -> Model:
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layer.to_cpu()
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return Model(
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f"with_cpu({layer.name})",
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forward,
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layers=[layer],
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ops=ops,
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init=init,
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dims={name: layer.maybe_get_dim(name) for name in layer.dim_names},
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)
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def forward(model: Model, X: Any, is_train: bool) -> Tuple[Any, Callable]:
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cpu_outputs, backprop = model.layers[0].begin_update(_to_cpu(X))
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gpu_outputs = _to_device(model.ops, cpu_outputs)
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def with_cpu_backprop(d_outputs):
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cpu_d_outputs = _to_cpu(d_outputs)
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return backprop(cpu_d_outputs)
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return gpu_outputs, with_cpu_backprop
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def init(model: Model, X: Any, Y: Any) -> None:
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model.layers[0].initialize(X, Y)
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def _to_cpu(X):
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if isinstance(X, numpy.ndarray):
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return X
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elif isinstance(X, tuple):
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return tuple([_to_cpu(x) for x in X])
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elif isinstance(X, list):
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return [_to_cpu(x) for x in X]
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elif hasattr(X, "get"):
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return X.get()
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else:
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return X
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def _to_device(ops, X):
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if isinstance(X, tuple):
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return tuple([_to_device(ops, x) for x in X])
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elif isinstance(X, list):
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return [_to_device(ops, x) for x in X]
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else:
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return ops.asarray(X)
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