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lib/AI/MXNet/Module/Base.pm  view on Meta::CPAN

=head2 get_states

    The states from all devices

    Parameters
    ----------
    $merge_multi_context=1 : Bool
        Default is true (1). In the case when data-parallelism is used, the states
        will be collected from multiple devices. A true value indicate that we
        should merge the collected results so that they look like from a single
        executor.

    Returns
    -------
    If $merge_multi_context is 1, it is like [$out1, $out2]. Otherwise, it
    is like [[$out1_dev1, $out1_dev2], [$out2_dev1, $out2_dev2]]. All the output
    elements are AI::MXNet::NDArray.
=cut

method get_states(Bool $merge_multi_context=1)
{
    assert($self->binded and $self->params_initialized);
    assert(not $merge_multi_context);
    return [];
}

=head2 set_states

    Set value for states. You can specify either $states or $value, not both.

    Parameters
    ----------
    $states= : Maybe[ArrayRef[ArrayRef[AI::MXNet::NDArray]]]
        source states arrays formatted like [[$state1_dev1, $state1_dev2],
            [$state2_dev1, $state2_dev2]].
    $value= : Maybe[Num]
        a single scalar value for all state arrays.
=cut

method set_states(Maybe[ArrayRef[ArrayRef[AI::MXNet::NDArray]]] $states=, Maybe[Num] $value=)
{
    assert($self->binded and $self->params_initialized);
    assert(not $states and not $value);
}


=head2 install_monitor

    Install monitor on all executors

    Parameters
    ----------
    $mon : AI::MXNet::Monitor
=cut

method install_monitor(AI::MXNet::Monitor $mon) { confess("NotImplemented") }

=head2 prepare

    Prepare the module for processing a data batch.

    Usually involves switching a bucket and reshaping.

    Parameters
    ----------
    $data_batch : AI::MXNet::DataBatch
=cut

method prepare(AI::MXNet::DataBatch $data_batch){}

################################################################################
# Computations
################################################################################

=head2 forward

    Forward computation. It supports data batches with different shapes, such as
    different batch sizes or different image sizes.
    If reshaping of data batch relates to modification of symbol or module, such as
    changing image layout ordering or switching from training to predicting, module
    rebinding is required.

    Parameters
    ----------
    $data_batch : DataBatch
        Could be anything with similar API implemented.
    :$is_train= : Bool
        Default is undef, which means is_train takes the value of $self->for_training.
=cut

method forward(AI::MXNet::DataBatch $data_batch, Bool :$is_train=) { confess("NotImplemented") }

=head2 backward

    Backward computation.

    Parameters
    ----------
    $out_grads : Maybe[AI::MXNet::NDArray|ArrayRef[AI::MXNet::NDArray]], optional
        Gradient on the outputs to be propagated back.
        This parameter is only needed when bind is called
        on outputs that are not a loss function.
=cut

method backward(Maybe[AI::MXNet::NDArray|ArrayRef[AI::MXNet::NDArray]] $out_grads=)
{
    confess("NotImplemented")
}

=head2 get_outputs

    The outputs of the previous forward computation.

    Parameters
    ----------
    $merge_multi_context=1 : Bool
=cut

method get_outputs(Bool $merge_multi_context=1) { confess("NotImplemented") }