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lib/AI/MXNet/Contrib/AutoGrad.pm  view on Meta::CPAN

    my @variable_handles = map { $_->handle } @{ $variables };
    my @gradient_handles = map { $_->handle } @{ $gradients };
    my @grad_reqs;
    if(not ref $grad_reqs)
    {
        @grad_reqs = (GRAD_REQ_MAP->{ $grad_reqs }) x scalar(@variable_handles);
    }
    else
    {
        @grad_reqs = map { GRAD_REQ_MAP->{ $_ } } @{ $grad_reqs };
    }
    check_call(
        AI::MXNetCAPI::AutogradMarkVariables(
            scalar(@variable_handles),
            \@variable_handles,
            \@grad_reqs,
            \@gradient_handles
        )
    );
}

=head2 backward

     Compute the gradients of outputs w.r.t variables.

     Parameters
     ----------
     outputs: array ref of NDArray
     out_grads: array ref of NDArray or undef
     retain_graph: bool, defaults to false
=cut


method backward(
    ArrayRef[AI::MXNet::NDArray] $outputs,
    Maybe[ArrayRef[AI::MXNet::NDArray|Undef]] $out_grads=,
    Bool $retain_graph=0
)
{
    my @output_handles = map { $_->handle } @{ $outputs };
    if(not defined $out_grads)
    {
        check_call(
            AI::MXNetCAPI::AutogradBackward(
                scalar(@output_handles),
                \@output_handles,
                [],
                $retain_graph
            )
        );
        return;
    }

    my @ograd_handles;
    for my $arr (@$out_grads)
    {
        push @ograd_handles, (defined $arr ? $arr->handle : undef);
    }
    assert(
        (@ograd_handles == @output_handles),
        "outputs and out_grads must have the same length"
    );

    check_call(
        AI::MXNetCAPI::AutogradBackward(
            scalar(@output_handles),
            \@output_handles,
            \@ograd_handles,
            $retain_graph
        )
    );
}

=head2 compute_gradient

    Compute the gradients of outputs w.r.t variables.

    Parameters
    ----------
    outputs: array ref of NDArray

    Returns
    -------
    gradients: array ref of NDArray
=cut


method compute_gradient(ArrayRef[AI::MXNet::NDArray] $outputs)
{
    __PACKAGE__->backward($outputs);
}

=head2 grad_and_loss

    Return function that computes both gradient of arguments and loss value.

    Parameters
    ----------
    func: a perl sub
        The forward (loss) function.
    argnum: an int or a array ref of int
        The index of argument to calculate gradient for.

    Returns
    -------
    grad_and_loss_func: a perl sub
        A function that would compute both the gradient of arguments and loss value.
=cut

method grad_and_loss(CodeRef $func, Maybe[Int|ArrayRef[Int]] $argnum=)
{
    return sub {
        my @args = @_;
        my @variables = @_;
        if(defined $argnum)
        {
            my @argnum = ref $argnum ? @$argnum : ($argnum);
            @variables = map { $_[$_] } @argnum;
        }
        map {
            assert(