117 lines
4.2 KiB
Python
117 lines
4.2 KiB
Python
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from typing import Any, Callable, Optional, Tuple, Type
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from ..config import registry
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from ..model import Model
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from ..shims import MXNetShim
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from ..types import ArgsKwargs
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from ..util import convert_recursive, is_mxnet_array, is_xp_array, mxnet2xp, xp2mxnet
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@registry.layers("MXNetWrapper.v1")
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def MXNetWrapper(
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mxnet_model,
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convert_inputs: Optional[Callable] = None,
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convert_outputs: Optional[Callable] = None,
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model_class: Type[Model] = Model,
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model_name: str = "mxnet",
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) -> Model[Any, Any]:
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"""Wrap a MXNet model, so that it has the same API as Thinc models.
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To optimize the model, you'll need to create a MXNet optimizer and call
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optimizer.step() after each batch.
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Your MXNet model's forward method can take arbitrary args and kwargs,
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but must return either a single tensor as output or a tuple. You may find the
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MXNet register_forward_hook helpful if you need to adapt the output.
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The convert functions are used to map inputs and outputs to and from your
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MXNet model. Each function should return the converted output, and a callback
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to use during the backward pass. So:
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Xmxnet, get_dX = convert_inputs(X)
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Ymxnet, mxnet_backprop = model.shims[0](Xmxnet, is_train)
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Y, get_dYmxnet = convert_outputs(Ymxnet)
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To allow maximum flexibility, the MXNetShim expects ArgsKwargs objects
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on the way into the forward and backward passes. The ArgsKwargs objects
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will be passed straight into the model in the forward pass, and straight
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into `mxnet.autograd.backward` during the backward pass.
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"""
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if convert_inputs is None:
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convert_inputs = convert_mxnet_default_inputs
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if convert_outputs is None:
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convert_outputs = convert_mxnet_default_outputs
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return model_class(
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model_name,
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forward,
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attrs={"convert_inputs": convert_inputs, "convert_outputs": convert_outputs},
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shims=[MXNetShim(mxnet_model)],
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)
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def forward(model: Model, X: Any, is_train: bool) -> Tuple[Any, Callable]:
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"""Return the output of the wrapped MXNet model for the given input,
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along with a callback to handle the backward pass.
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"""
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convert_inputs = model.attrs["convert_inputs"]
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convert_outputs = model.attrs["convert_outputs"]
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Xmxnet, get_dX = convert_inputs(model, X, is_train)
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Ymxnet, mxnet_backprop = model.shims[0](Xmxnet, is_train)
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Y, get_dYmxnet = convert_outputs(model, (X, Ymxnet), is_train)
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def backprop(dY: Any) -> Any:
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dYmxnet = get_dYmxnet(dY)
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dXmxnet = mxnet_backprop(dYmxnet)
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dX = get_dX(dXmxnet)
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return dX
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return Y, backprop
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# Default conversion functions
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def convert_mxnet_default_inputs(
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model: Model, X: Any, is_train: bool
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) -> Tuple[ArgsKwargs, Callable[[ArgsKwargs], Any]]:
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xp2mxnet_ = lambda x: xp2mxnet(x, requires_grad=is_train)
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converted = convert_recursive(is_xp_array, xp2mxnet_, X)
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if isinstance(converted, ArgsKwargs):
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def reverse_conversion(dXmxnet):
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return convert_recursive(is_mxnet_array, mxnet2xp, dXmxnet)
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return converted, reverse_conversion
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elif isinstance(converted, dict):
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def reverse_conversion(dXmxnet):
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dX = convert_recursive(is_mxnet_array, mxnet2xp, dXmxnet)
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return dX.kwargs
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return ArgsKwargs(args=tuple(), kwargs=converted), reverse_conversion
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elif isinstance(converted, (tuple, list)):
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def reverse_conversion(dXmxnet):
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dX = convert_recursive(is_mxnet_array, mxnet2xp, dXmxnet)
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return dX.args
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return ArgsKwargs(args=tuple(converted), kwargs={}), reverse_conversion
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else:
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def reverse_conversion(dXmxnet):
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dX = convert_recursive(is_mxnet_array, mxnet2xp, dXmxnet)
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return dX.args[0]
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return ArgsKwargs(args=(converted,), kwargs={}), reverse_conversion
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def convert_mxnet_default_outputs(model: Model, X_Ymxnet: Any, is_train: bool):
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X, Ymxnet = X_Ymxnet
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Y = convert_recursive(is_mxnet_array, mxnet2xp, Ymxnet)
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def reverse_conversion(dY: Any) -> ArgsKwargs:
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dYmxnet = convert_recursive(is_xp_array, xp2mxnet, dY)
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return ArgsKwargs(args=((Ymxnet,),), kwargs={"head_grads": dYmxnet})
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return Y, reverse_conversion
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