AI-MXNet
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lib/AI/MXNet/NDArray.pm view on Meta::CPAN
my $convert_func = $pdl_type->convertfunc;
$source_array = $source_array->$convert_func;
}
$source_array = pdl($pdl_type, [@{ $source_array->unpdl } ? $source_array->unpdl->[0] : 0 ])
unless @{ $source_array->shape->unpdl };
my $pdl_shape = $source_array->shape->unpdl;
my $pdl_shape_str = join(',', ref($source_array) eq 'PDL' ? reverse @{ $pdl_shape } : @{ $pdl_shape });
my $ndary_shape_str = join(',', @{ $self->shape });
if($pdl_shape_str ne $ndary_shape_str)
{
confess("Shape inconsistant: expected $ndary_shape_str vs got $pdl_shape_str")
}
my $perl_pack_type = DTYPE_MX_TO_PERL->{$dtype};
my $buf;
## special handling for float16
if($perl_pack_type eq 'S')
{
$buf = pack("S*", map { AI::MXNetCAPI::_float_to_half($_) } unpack ("f*", ${$source_array->get_dataref}));
}
else
{
$buf = ${$source_array->get_dataref};
}
check_call(AI::MXNetCAPI::NDArraySyncCopyFromCPU($self->handle, $buf, $self->size));
return $self;
}
=head2 aspdl
Returns a copied PDL array of current array.
Returns
-------
array : PDL
A copy of the array content.
=cut
method aspdl()
{
my $dtype = $self->dtype;
my $pdl_type = PDL::Type->new(DTYPE_MX_TO_PDL->{ $dtype });
my $pdl = PDL->new_from_specification($pdl_type, reverse @{ $self->shape });
my $perl_pack_type = DTYPE_MX_TO_PERL->{$dtype};
my $buf = pack("$perl_pack_type*", (0)x$self->size);
check_call(AI::MXNetCAPI::NDArraySyncCopyToCPU($self->handle, $buf, $self->size));
## special handling for float16
if($perl_pack_type eq 'S')
{
$buf = pack("f*", map { AI::MXNetCAPI::_half_to_float($_) } unpack("S*", $buf));
}
${$pdl->get_dataref} = $buf;
$pdl->upd_data;
return $pdl;
}
=head2 asmpdl
Returns copied PDL::Matrix objectt of current array.
Requires caller to "use PDL::Matrix" in user space.
Returns
-------
array : PDL::Matrix
A copy of array content.
=cut
method asmpdl()
{
my $dtype = $self->dtype;
my $pdl_type = PDL::Type->new(DTYPE_MX_TO_PDL->{ $dtype });
my $pdl = PDL::Matrix->new_from_specification($pdl_type, @{ $self->shape });
my $perl_pack_type = DTYPE_MX_TO_PERL->{$dtype};
my $buf = pack("$perl_pack_type*", (0)x$self->size);
check_call(AI::MXNetCAPI::NDArraySyncCopyToCPU($self->handle, $buf, $self->size));
## special handling for float16
if($perl_pack_type eq 'S')
{
$buf = pack("f*", map { AI::MXNetCAPI::_half_to_float($_) } unpack("S*", $buf));
}
${$pdl->get_dataref} = $buf;
$pdl->upd_data;
return $pdl;
}
=head2 _slice
Returns sliced NDArray that shares memory with the current one.
Parameters
----------
start : int
Starting index of slice.
stop : int
Finishing index of slice.
=cut
method _slice (
Index $start,
Index $stop
)
{
confess("start $start > stop $stop") if $start > $stop;
my $handle = check_call(
AI::MXNetCAPI::NDArraySlice(
$self->handle,
$start,
$stop
)
);
return __PACKAGE__->new(handle => $handle, writable => $self->writable);
}
=head2 _at
Returns a sub NDArray that shares memory with current one.
Parameters
----------
idx : int
index of the sub array.
=cut
method _at(Index $idx)
{
my $handle = check_call(
AI::MXNetCAPI::NDArrayAt(
$self->handle, $idx >=0 ? $idx : $self->shape->[0] + $idx
)
);
return __PACKAGE__->new(handle => $handle, writable => $self->writable);
}
=head2 reshape
Returns a reshaped NDArray that shares the memory with current one.
One shape dimension can be -1. In this case, the value is inferred
from the length of the array and remaining dimensions.
Parameters
----------
new_shape : Shape
new shape of NDArray
=cut
method reshape(ArrayRef[Int] $new_shape)
{
my $i = -1;
my @inferred = map { $i++; $_ == -1 ? ($i) : () } @$new_shape;
assert((@inferred <= 1), 'Only one dimension can be inferred.');
if(@inferred)
{
$new_shape->[$inferred[0]] = product(@{ $self->shape })/product(map { abs($_) } @{ $new_shape });
}
my $handle = check_call(
AI::MXNetCAPI::NDArrayReshape(
$self->handle,
scalar(@$new_shape),
$new_shape
)
);
return __PACKAGE__->new(handle => $handle, writable => $self->writable);
}
=head2 ndim
Returns the number of dimensions of this array.
=cut
method ndim()
{
scalar(@{ $self->shape });
}
=head2 moveaxis
Moves the 'source' axis into the 'destination' position
while leaving the other axes in their original order
Parameters
----------
source : int
Original position of the axes to move.
destination : int
Destination position for each of the original axes.
Returns
-------
result :NDArray
Array with moved axes.
Examples
--------
> $X = mx->nd->array([[1, 2, 3],
[4, 5, 6]]);
> print Dumper($X->moveaxis(0, 1)->shape)
> [3, 2]
=cut
method moveaxis(Int $source, Int $dest)
{
my @axes = 0..$self->ndim-1;
$source += @axes if $source < 0;
$dest += @axes if $dest < 0;
assert($source < @axes);
assert($dest < @axes);
my ($to_move) = splice(@axes, $source, 1);
splice(@axes, $dest, 0, $to_move);
return __PACKAGE__->transpose($self, \@axes);
}
=head2 broadcast_to
Broadcasting the current NDArray into the given shape.
Parameters
---------
Shape $shape : the shape to broadcast
=cut
method broadcast_to(Shape $shape)
{
my $cur_shape = $self->shape;
my $err_str = "operands could not be broadcast together with remapped shapes"
."[original->remapped]: [@$cur_shape] and requested shape [@$shape]";
if(@$shape < @$cur_shape)
{
confess($err_str);
}
@$cur_shape = ((1)x(@$shape - @$cur_shape), @$cur_shape);
my $cur_shape_arr = pdl($cur_shape);
my $broadcasting_axes = ($cur_shape_arr != pdl($shape))->which->unpdl;
if (grep { $cur_shape->[$_] != 1 } @$broadcasting_axes)
{
confess($err_str);
}
if(join(',',@$cur_shape) ne join(',',@{ $self->shape }))
{
return __PACKAGE__->SUPER::broadcast_to($self->reshape($cur_shape),{ shape => $shape });
}
else
{
return __PACKAGE__->SUPER::broadcast_to($self, { shape => $shape });
}
}
=head2 wait_to_read
Block until all pending write operations on the NDArray are finished.
This function will return when all the pending writes to the current
NDArray are finished. There can be pending reads going on when the
function returns.
=cut
method wait_to_read()
{
check_call(AI::MXNetCAPI::NDArrayWaitToRead($self->handle));
}
=head2 shape
Get the shape of current NDArray.
Returns
-------
an array ref representing the shape of current ndarray
=cut
method shape()
{
return scalar(check_call(AI::MXNetCAPI::NDArrayGetShape($self->handle)));
}
=head2 size
Number of elements in the array.
=cut
method size(Shape|Undef $shape=)
{
my $size = 1;
map { $size *= $_ } @{ $shape//$self->shape };
return $size;
}
=head2 context
The context of the NDArray.
Returns
-------
$context : AI::MXNet::Context
=cut
method context()
{
my ($dev_type_id, $dev_id) = check_call(
AI::MXNetCAPI::NDArrayGetContext($self->handle)
);
return AI::MXNet::Context->new(
device_type => AI::MXNet::Context::devtype2str->{ $dev_type_id },
device_id => $dev_id
);
}
=head2 dtype
The data type of current NDArray.
Returns
-------
a data type string ('float32', 'float64', 'float16', 'uint8', 'int32')
representing the data type of the ndarray.
'float32' is the default dtype for the ndarray class.
=cut
method dtype()
{
my $dtype = check_call(
AI::MXNetCAPI::NDArrayGetDType(
$self->handle
)
);
return DTYPE_MX_TO_STR->{ $dtype };
}
=head2 copyto
Copy the content of current array to another entity.
When another entity is the NDArray, the content is copied over.
When another entity is AI::MXNet::Context, a new NDArray in the context
will be created.
Parameters
----------
other : NDArray or Context
Target NDArray or context we want to copy data to.
Returns
-------
dst : NDArray
=cut
method copyto(AI::MXNet::Context|AI::MXNet::NDArray $other)
{
if(blessed($other) and $other->isa('AI::MXNet::Context'))
{
my $hret = __PACKAGE__->empty(
$self->shape,
ctx => $other,
dtype => $self->dtype
);
return __PACKAGE__->_copyto($self, { out => $hret });
}
else
{
if ($other->handle eq $self->handle)
{
Carp::cluck('copy an array to itself, is it intended?');
}
return __PACKAGE__->_copyto($self, { out => $other });
}
}
=head2 copy
Makes a copy of the current ndarray in the same context
Returns
------
$copy : NDArray
=cut
method copy()
{
return $self->copyto($self->context);
}
## alias for PDL::NiceSlice
*sever = \©
=head2 T
Get transpose of the NDArray.
Works only on 2-D matrices.
=cut
method T()
{
if (@{$self->shape} > 2)
{
confess('Only 2D matrix is allowed to be transposed');
}
return __PACKAGE__->transpose($self);
}
=head2 astype
Returns copied ndarray of current array with the specified type.
Parameters
----------
$dtype : Dtype
Returns
-------
$array : ndarray
A copy of the array content.
=cut
method astype(Dtype $dtype)
{
my $res = __PACKAGE__->empty($self->shape, ctx => $self->context, dtype => $dtype);
$self->copyto($res);
return $res;
}
=head2 as_in_context
Returns an NDArray in the target context.
If the array is already in that context, self is returned. Otherwise, a copy is
made.
Parameters
----------
context : AI::MXNet::Context
The target context we want the return value to live in.
Returns
-------
A copy or self as an NDArray in the target context.
=cut
method as_in_context(AI::MXNet::Context $context)
{
return $self if $self->context == $context;
return $self->copyto($context);
}
=head2 onehot_encode
One hot encoding indices into matrix out.
Parameters
----------
indices: NDArray
An NDArray containing indices of the categorical features.
out: NDArray
The result of the encoding.
Returns
-------
$out: NDArray
=cut
method onehot_encode(AI::MXNet::NDArray $indices, AI::MXNet::NDArray $out)
{
return __PACKAGE__->_onehot_encode($indices, $out, { out => $out });
}
=head2 _ufunc_helper(lhs, rhs, fn_array, lfn_scalar, rfn_scalar):
Helper function for element-wise operation
The function will perform numpy-like broadcasting if needed and call different functions
Parameters
----------
lhs : NDArray or numeric value
left hand side operand
rhs : NDArray or numeric value
right hand side operand
fn_array : function
function to be called if both lhs and rhs are of NDArray type
lfn_scalar : function
function to be called if lhs is NDArray while rhs is numeric value
rfn_scalar : function
function to be called if lhs is numeric value while rhs is NDArray;
if none is provided, then the function is commutative, so rfn_scalar is equal to lfn_scalar
Returns
-------
out: NDArray
result array
=cut
sub _ufunc_helper
{
my ($lhs, $rhs, $fn_array, $lfn_scalar, $rfn_scalar, $reverse) = @_;
($rhs, $lhs) = ($lhs, $rhs) if $reverse and $rfn_scalar;
if(not ref $lhs)
{
if(not $rfn_scalar)
{
return __PACKAGE__->can($lfn_scalar)->(__PACKAGE__, $rhs, $lhs);
}
else
{
return __PACKAGE__->can($rfn_scalar)->(__PACKAGE__, $rhs, $lhs);
}
}
elsif(not ref $rhs)
{
return __PACKAGE__->can($lfn_scalar)->(__PACKAGE__, $lhs, $rhs);
}
else
{
return __PACKAGE__->can($fn_array)->(__PACKAGE__, $lhs, $rhs);
}
}
method stringify($other=, $reverse=)
{
sprintf("<%s %s @%s>", ref($self), join('x', @{ $self->shape }), $self->context);
}
method iadd(AI::MXNet::NDArray|Num $other, $reverse=)
{
confess('trying to add to a readonly NDArray') unless $self->writable;
return ref $other
? __PACKAGE__->broadcast_add($self, $other, { out => $self })
: __PACKAGE__->_plus_scalar($self, $other, { out => $self })
}
method add(AI::MXNet::NDArray|Num $other, $reverse=)
{
return _ufunc_helper(
$self,
$other,
qw/broadcast_add _plus_scalar/
);
}
method subtract(AI::MXNet::NDArray|Num $other, $reverse=)
{
return _ufunc_helper(
$self,
$other,
qw/broadcast_sub _minus_scalar _rminus_scalar/,
$reverse
);
}
lib/AI/MXNet/NDArray.pm view on Meta::CPAN
The dtype of the NDArray, defaults to 'float32'.
Returns
-------
out: Array
The created NDArray.
=cut
method array(PDL|PDL::Matrix|ArrayRef|AI::MXNet::NDArray $source_array, AI::MXNet::Context :$ctx=AI::MXNet::Context->current_ctx, Dtype :$dtype='float32')
{
if(blessed $source_array and $source_array->isa('AI::MXNet::NDArray'))
{
my $arr = __PACKAGE__->empty($source_array->shape, ctx => $ctx, dtype => $dtype);
$arr .= $source_array;
return $arr;
}
my $pdl_type = PDL::Type->new(DTYPE_MX_TO_PDL->{ $dtype });
if(not blessed($source_array))
{
$source_array = eval {
pdl($pdl_type, $source_array);
};
confess($@) if $@;
}
$source_array = pdl($pdl_type, [@{ $source_array->unpdl } ? $source_array->unpdl->[0] : 0 ]) unless @{ $source_array->shape->unpdl };
my $shape = $source_array->shape->unpdl;
my $arr = __PACKAGE__->empty([ref($source_array) eq 'PDL' ? reverse @{ $shape } : @{ $shape }], ctx => $ctx, dtype => $dtype );
$arr .= $source_array;
return $arr;
}
=head2 concatenate
Concatenates an array ref of NDArrays along the first dimension.
Parameters
----------
$arrays : array ref of NDArrays
Arrays to be concatenate. They must have identical shape except
for the first dimension. They also must have the same data type.
:$axis=0 : int
The axis along which to concatenate.
:$always_copy=1 : bool
Default is 1. When not 1, if the arrays only contain one
NDArray, that element will be returned directly, avoid copying.
Returns
-------
An NDArray in the same context as $arrays->[0]->context.
=cut
method concatenate(ArrayRef[AI::MXNet::NDArray] $arrays, Index :$axis=0, :$always_copy=1)
{
confess("no arrays provided") unless @$arrays > 0;
if(not $always_copy and @$arrays == 1)
{
return $arrays->[0];
}
my $shape_axis = $arrays->[0]->shape->[$axis];
my $shape_rest1 = [@{ $arrays->[0]->shape }[0..($axis-1)]];
my $shape_rest2 = [@{ $arrays->[0]->shape }[($axis+1)..(@{ $arrays->[0]->shape }-1)]];
my $dtype = $arrays->[0]->dtype;
my $i = 1;
for my $arr (@{ $arrays }[1..(@{ $arrays }-1)])
{
$shape_axis += $arr->shape->[$axis];
my $arr_shape_rest1 = [@{ $arr->shape }[0..($axis-1)]];
my $arr_shape_rest2 = [@{ $arr->shape }[($axis+1)..(@{ $arr->shape }-1)]];
confess("first array $arrays->[0] and $i array $arr do not match")
unless join(',',@$arr_shape_rest1) eq join(',',@$shape_rest1);
confess("first array $arrays->[0] and $i array $arr do not match")
unless join(',',@$arr_shape_rest2) eq join(',',@$shape_rest2);
confess("first array $arrays->[0] and $i array $arr dtypes do not match")
unless join(',',@$arr_shape_rest2) eq join(',',@$shape_rest2);
$i++;
}
my $ret_shape = [@$shape_rest1, $shape_axis, @$shape_rest2];
my $ret = __PACKAGE__->empty($ret_shape, ctx => $arrays->[0]->context, dtype => $dtype);
my $idx = 0;
my $begin = [(0)x@$ret_shape];
my $end = [@$ret_shape];
for my $arr (@$arrays)
{
if ($axis == 0)
{
$ret->slice([$idx,($idx+$arr->shape->[0]-1)]) .= $arr;
}
else
{
$begin->[$axis] = $idx;
$end->[$axis] = $idx+$arr->shape->[$axis];
__PACKAGE__->_crop_assign(
$ret, $arr,
{
out => $ret,
begin => $begin,
end => $end
}
);
}
$idx += $arr->shape->[$axis];
}
return $ret
}
=head2 arange
Similar function in the MXNet ndarray as numpy.arange
See Also https://docs.scipy.org/doc/numpy/reference/generated/numpy.arange.html.
Parameters
----------
:$start=0 : number, optional
Start of interval. The interval includes this value. The default start value is 0.
$stop= : number, optional
End of interval. The interval does not include this value.
:$step=1 : number, optional
Spacing between the values
:$repeat=1 : number, optional
The repeating time of all elements.
E.g repeat=3, the element a will be repeated three times --> a, a, a.
:$ctx : Context, optional
The context of the NDArray, defaultw to current default context.
:$dtype : data type, optional
The value type of the NDArray, defaults to float32
Returns
-------
$out : NDArray
The created NDArray
=cut
method arange(Index :$start=0, Index :$stop=, Index :$step=1, Index :$repeat=1,
AI::MXNet::Context :$ctx=AI::MXNet::Context->current_ctx, Dtype :$dtype='float32')
{
return __PACKAGE__->_arange({
start => $start,
(defined $stop ? (stop => $stop) : ()),
step => $step,
repeat => $repeat,
dtype => $dtype,
ctx => "$ctx"
});
}
=head2 load
Loads ndarrays from a binary file.
You can also use Storable to do the job if you only work with Perl.
The advantage of load/save is the file is language agnostic.
This means the file saved using save can be loaded by other language binding of mxnet.
You also get the benefit being able to directly load/save from cloud storage(S3, HDFS)
Parameters
----------
fname : str
The name of the file.Can be S3 or HDFS address (remember built with S3 support).
Example of fname:
- `s3://my-bucket/path/my-s3-ndarray`
- `hdfs://my-bucket/path/my-hdfs-ndarray`
- `/path-to/my-local-ndarray`
Returns
-------
$out : array ref of NDArrays or hash ref with NDArrays
=cut
method load(Str $filename)
{
my ($handles, $names) = check_call(AI::MXNetCAPI::NDArrayLoad($filename));
if (not @$names)
{
return [map { __PACKAGE__->new(handle => $_) } @$handles];
}
else
{
my $n = @$names;
my $h = @$handles;
confess("Handles [$h] and names [$n] count mismatch") unless $h == $n;
my %ret;
@ret{ @$names } = map { __PACKAGE__->new(handle => $_) } @$handles;
return \%ret;
}
}
=head2 save
Save array ref of NDArray or hash of str->NDArray to a binary file.
You can also use Storable to do the job if you only work with Perl.
The advantage of load/save is the file is language agnostic.
This means the file saved using save can be loaded by other language binding of mxnet.
You also get the benefit being able to directly load/save from cloud storage(S3, HDFS)
Parameters
----------
fname : str
The name of the file.Can be S3 or HDFS address (remember built with S3 support).
Example of fname:
- `s3://my-bucket/path/my-s3-ndarray`
- `hdfs://my-bucket/path/my-hdfs-ndarray`
- `/path-to/my-local-ndarray`
$data : array ref of NDArrays or hash ref of NDArrays
The data to be saved.
=cut
method save(Str $filename, ArrayRef[AI::MXNet::NDArray]|HashRef[AI::MXNet::NDArray] $data)
{
my $handles = [];
my $names = [];
if(ref $data eq 'HASH')
{
for my $name (keys %$data)
{
push @$names, $name;
push @$handles, $data->{ $name }->handle;
}
}
else
{
@$handles = map { $_->handle } @$data;
}
check_call(
AI::MXNetCAPI::NDArraySave(
$filename,
scalar(@$handles),
$handles,
$names
)
);
}
=head2 imdecode
Decode an image from string. Requires OpenCV to work.
Parameters
----------
$str_img : str
binary image data
:$clip_rect : iterable of 4 int
clip decoded image to rectangle (x0, y0, x1, y1)
:$out= : Maybe[NDArray]
output buffer. can be 3 dimensional (c, h, w) or 4 dimensional (n, c, h, w)
:$index : int
output decoded image to i-th slice of 4 dimensional buffer
:$channels=3 : int
number of channels to output. Decode to grey scale when channels = 1.
$mean= : Maybe[NDArray]
subtract mean from decode image before outputting.
=cut
method imdecode($str_img, ArrayRef[Int] :$clip_rect=[0, 0, 0, 0],
Maybe[AI::MXNet::NDArray] :$out=, Int :$index=0, Int :$channels=3, Maybe[AI::MXNet::NDArray] :$mean=)
{
return __PACKAGE__->_imdecode(
$mean//__PACKAGE__->_new_empty_handle(),
$index,
@$clip_rect,
$channels,
length($str_img),
{ str_img => $str_img, ($out ? (out => $out) : ()) }
);
}
=head2 _new_empty_handle
Returns a new empty handle.
Empty handle can be used to hold result
Returns
-------
a new empty ndarray handle
=cut
sub _new_empty_handle
{
my $hdl = check_call(AI::MXNetCAPI::NDArrayCreateNone());
return $hdl;
}
=head2 _new_alloc_handle
Returns a new handle with specified shape and context.
Empty handle is only used to hold results
Returns
-------
a new empty ndarray handle
=cut
func _new_alloc_handle($shape, $ctx, $delay_alloc, $dtype)
{
my $hdl = check_call(AI::MXNetCAPI::NDArrayCreateEx(
$shape,
scalar(@$shape),
$ctx->device_type_id,
$ctx->device_id,
$delay_alloc,
$dtype)
);
return $hdl;
}
=head2 waitall
Wait for all async operations to finish in MXNet.
This function is used for benchmarks only.
=cut
method waitall()
{
check_call(AI::MXNetCAPI::NDArrayWaitAll());
}
=head2 _fresh_grad
Parameters:
----------
Maybe[Bool] $state=
Whether this array's corresponding gradient array
(registered via `autograd->mark_variables`) has been
updated by `autograd->backward` since last reset.
`_fresh_grad` need to be manually set to False
after consuming gradient (usually after updating this
array).
=cut
method _fresh_grad(Maybe[Bool] $state=)
{
if(defined $state)
{
check_call(AI::MXNetCAPI::NDArraySetGradState($self->handle, $state));
return $state;
}
else
{
return scalar(check_call(AI::MXNetCAPI::NDArrayGetGradState($self->handle)));
}
}
=head2 detach
Returns a new NDArray, detached from the current graph.
=cut
method detach()
{
my $handle = check_call(AI::MXNetCAPI::NDArrayDetach($self->handle));
return __PACKAGE__->new(handle => $handle);
}
method backward(Maybe[AI::MXNet::NDArray] $out_grad=, Bool $retain_graph=0)
{
check_call(
AI::MXNetCAPI::AutogradBackward(
1,
[$self->handle],
[defined $out_grad ? $out_grad->handle : undef],
$retain_graph
)
)
}
method CachedOp(@args) { AI::MXNet::CachedOp->new(@args) }
my $lvalue_methods = join "\n", map {"use attributes 'AI::MXNet::NDArray', \\&AI::MXNet::NDArray::$_, 'lvalue';"}
qw/at slice aspdl asmpdl reshape copy sever T astype as_in_context copyto empty zero ones full
array/;
eval << "EOV" if ($^V and $^V >= 5.006007);
{
no warnings qw(misc);
$lvalue_methods
}
EOV
__PACKAGE__->meta->make_immutable;
( run in 0.865 second using v1.01-cache-2.11-cpan-39bf76dae61 )