AI-MXNet
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lib/AI/MXNet/Executor.pm view on Meta::CPAN
package AI::MXNet::Executor;
use strict;
use warnings;
use AI::MXNet::Base;
use AI::MXNet::Context;
use Mouse;
use AI::MXNet::Types;
use AI::MXNet::Function::Parameters;
has 'handle' => (is => 'ro', isa => 'ExecutorHandle', required => 1);
has 'arg_arrays' => (is => 'rw', isa => 'Maybe[ArrayRef[AI::MXNet::NDArray]]');
has 'grad_arrays' => (is => 'rw', isa => 'Maybe[ArrayRef[Undef|AI::MXNet::NDArray]]');
has 'aux_arrays' => (is => 'rw', isa => 'Maybe[ArrayRef[AI::MXNet::NDArray]]');
has '_symbol' => (is => 'rw', init_arg => 'symbol', isa => 'AI::MXNet::Symbol');
has '_ctx' => (is => 'rw', init_arg => 'ctx', isa => 'AI::MXNet::Context' );
has '_grad_req' => (is => 'rw', init_arg => 'grad_req', isa => 'Maybe[Str|ArrayRef[Str]|HashRef[Str]]');
has '_group2ctx' => (is => 'rw', init_arg => 'group2ctx', isa => 'Maybe[HashRef[AI::MXNet::Context]]');
has '_monitor_callback' => (is => 'rw', isa => 'CodeRef');
has [qw/_arg_dict
_grad_dict
_aux_dict
_output_dict
outputs
_output_dirty/] => (is => 'rw', init_arg => undef);
=head1 NAME
AI::MXNet::Executor - The actual executing object of MXNet.
=head2 new
Constructor, used by AI::MXNet::Symbol->bind and by AI::MXNet::Symbol->simple_bind.
Parameters
----------
handle: ExecutorHandle
ExecutorHandle is generated by calling bind.
See Also
--------
AI::MXNet::Symbol->bind : how to create the AI::MXNet::Executor.
=cut
sub BUILD
{
my $self = shift;
my ($symbol, $ctx, $grad_req, $group2ctx)
=
($self->_symbol, $self->_ctx, $self->_grad_req, $self->_group2ctx);
$symbol = $symbol->deepcopy;
$ctx = $ctx->deepcopy;
if(ref $grad_req)
{
if(ref $grad_req eq 'ARRAY')
{
$grad_req = [ @{ $grad_req }];
}
elsif(ref $grad_req eq 'HASH')
{
$grad_req = { %{ $grad_req } };
}
}
if(ref $group2ctx)
{
$group2ctx = { %{ $group2ctx } };
}
$self->_symbol($symbol);
$self->_ctx($ctx);
$self->_grad_req($grad_req);
$self->_group2ctx($group2ctx);
$self->outputs($self->_get_outputs);
}
sub DEMOLISH
{
check_call(AI::MXNetCAPI::ExecutorFree(shift->handle));
}
# Get the dictionary given name and ndarray pairs.
func _get_dict(
ArrayRef[Str] $names,
ArrayRef[Maybe[AI::MXNet::NDArray]] $ndarrays
)
{
lib/AI/MXNet/Executor.pm view on Meta::CPAN
}
if(join(',', @{ $arg_dict->{$name}->shape }) ne join(',', @{ $array->shape }))
{
my $expected = $arg_dict->{$name}->shape;
my $got = $array->shape;
confess("Shape not match! Argument $name, need: @$expected, received: @$got'");
}
$arg_dict->{ $name } .= $array;
}
}
check_call(AI::MXNetCAPI::ExecutorForward(
$self->handle,
$is_train
)
);
if($self->_output_dirty)
{
AI::MXNet::Logging->warning(
"Calling forward the second time after forward(is_train=1) "
."without calling backward first. Is this intended?"
);
}
$self->_output_dirty($is_train);
return $self->outputs;
}
=head2 backward
Do a backward pass to get the gradient of the arguments.
Parameters
----------
out_grads : NDArray or an array ref of NDArrays or hash ref of NDArrays, optional.
The 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]|HashRef[AI::MXNet::NDArray]] $out_grads=)
{
$out_grads //= [];
if(blessed $out_grads)
{
$out_grads = [$out_grads];
}
elsif(ref $out_grads eq 'HASH')
{
$out_grads = [ @{ $out_grads }{ @{ $self->symbol->list_outputs() } } ];
}
check_call(
AI::MXNetCAPI::ExecutorBackward(
$self->handle,
scalar(@{ $out_grads }),
[map { $_->handle } @{ $out_grads }]
)
);
if(not $self->_output_dirty)
{
AI::MXNet::Logging->warning(
"Calling backward without calling forward(is_train=True) "
."first. Behavior is undefined."
);
}
$self->_output_dirty(0);
}
=head2 set_monitor_callback
Install callback.
Parameters
----------
callback : subref
Takes a string and an NDArrayHandle.
=cut
method set_monitor_callback(CodeRef $callback)
{
$self->_monitor_callback($callback);
check_call(
AI::MXNetCAPI::ExecutorSetMonitorCallback(
$self->handle,
$self->_monitor_callback
)
);
}
=head2 arg_dict
Get a hash ref representation of the argument arrays.
Returns
-------
arg_dict : HashRef[AI::MXNet::NDArray]
The map that maps a name of the arguments to the NDArrays.
=cut
method arg_dict()
{
if(not defined $self->_arg_dict)
{
$self->_arg_dict(_get_dict(
$self->_symbol->list_arguments(),
$self->arg_arrays
)
);
}
return $self->_arg_dict;
}
=head2 grad_dict
Get a hash ref representation of the gradient arrays.
Returns
-------
grad_dict : HashRef[AI::MXNet::NDArray]
The map that maps a name of the arguments to the gradient NDArrays.
=cut
method grad_dict()
lib/AI/MXNet/Executor.pm view on Meta::CPAN
Maybe[Bool] $allow_extra_params=
)
{
my %arg_dict = %{ $self->arg_dict };
while (my ($name, $array) = each %{ $arg_params })
{
if(exists $arg_dict{ $name })
{
my $dst = $arg_dict{ $name };
$array->astype($dst->dtype)->copyto($dst);
}
elsif(not $allow_extra_params)
{
confess("Found name \"$name\" that is not in the arguments");
}
}
if(defined $aux_params)
{
my %aux_dict = %{ $self->aux_dict };
while (my ($name, $array) = each %{ $aux_params })
{
if(exists $aux_dict{ $name })
{
my $dst = $aux_dict{ $name };
$array->astype($dst->dtype)->copyto($dst);
}
elsif(not $allow_extra_params)
{
confess("Found name \"$name\" that is not in the arguments");
}
}
}
}
=head2 reshape
Returns new executor with the same symbol and shared memory,
but different input/output shapes.
For runtime reshaping, variable length sequences, etc.
The returned executor shares state with the current one,
and cannot be used in parallel with it.
Parameters
----------
$kwargs : HashRef[Shape]
new shape for arguments.
:$partial_shaping : bool
Whether to allow changing the shape of unspecified arguments.
:$allow_up_sizing : bool
Whether to allow allocating new ndarrays that's larger than the original.
Returns
-------
$exec : AI::MXNet::Executor
A new executor that shares memory with self.
=cut
method reshape(HashRef[Shape] $kwargs, Int :$partial_shaping=0, Int :$allow_up_sizing=0)
{
my ($arg_shapes, undef, $aux_shapes) = $self->_symbol->infer_shape(%{ $kwargs });
confess("Insufficient argument shapes provided.")
unless defined $arg_shapes;
my %new_arg_dict;
my %new_grad_dict;
my $i = 0;
for my $name (@{ $self->_symbol->list_arguments() })
{
my $new_shape = $arg_shapes->[$i];
my $arr = $self->arg_arrays->[$i];
my $darr;
if(@{ $self->grad_arrays })
{
$darr = $self->grad_arrays->[$i];
}
if(
$partial_shaping
or
exists $kwargs->{ $name }
or
join(',', @{ $new_shape }) eq join(',', @{ $arr->shape })
)
{
if(AI::MXNet::NDArray->size($new_shape) > $arr->size)
{
confess(
"New shape of arg:$name larger than original. "
."First making a big executor and then down sizing it "
."is more efficient than the reverse."
."If you really want to up size, set \$allow_up_sizing=1 "
."to enable allocation of new arrays."
) unless $allow_up_sizing;
$new_arg_dict{ $name } = AI::MXNet::NDArray->empty(
$new_shape,
ctx => $arr->context,
dtype => $arr->dtype
);
if(defined $darr)
{
$new_grad_dict{ $name } = AI::MXNet::NDArray->empty(
$new_shape,
ctx => $darr->context,
dtype => $arr->dtype
);
}
}
else
{
$new_arg_dict{ $name } = $arr->reshape($new_shape);
if(defined $darr)
{
$new_grad_dict{ $name } = $darr->reshape($new_shape);
}
}
}
else
{
confess(
"Shape of unspecified array arg:$name changed. "
."This can cause the new executor to not share parameters "
."with the old one. Please check for error in network."
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