sergeykolychev commented on a change in pull request #9988: [Perl] Sparse feature. URL: https://github.com/apache/incubator-mxnet/pull/9988#discussion_r173258232
########## File path: perl-package/AI-MXNet/lib/AI/MXNet/NDArray/Sparse.pm ########## @@ -0,0 +1,1342 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +package AI::MXNet::NDArray::Sparse; +use strict; +use warnings; +use AI::MXNet::Base; +use AI::MXNet::Function::Parameters; +use Mouse; +extends 'AI::MXNet::NDArray'; + +=head1 NAME + + AI::MXNet::NDArray::Sparse - Sparse NDArray API of MXNet +=cut + +=head1 DESCRIPTION + + The base class of an NDArray stored in a sparse storage format. + See AI::MXNet::NDArray::CSR and AI::MXNet::NDArray::RowSparse for more details. +=cut + +method _new_alloc_handle( + Stype $stype, + Shape $shape, + AI::MXNet::Context $ctx, + Bool $delay_alloc, + Dtype $dtype, + AuxTypes $aux_types, + Maybe[ArrayRef[Shape]] $aux_shapes= +) +{ + confess("only int64 is supported for aux types") + if (grep { $_ ne 'int64' } @$aux_types); + my $aux_type_ids = [map { DTYPE_STR_TO_MX->{$_} } @$aux_types]; + $aux_shapes //= [map { [0] } @$aux_types]; + my $aux_shape_lens = [map { scalar(@$_) } @$aux_shapes]; + @$aux_shapes = map { @$_ } @$aux_shapes; + my $num_aux = @{ $aux_types }; + my $handle = check_call( + AI::MXNetCAPI::NDArrayCreateSparseEx( + STORAGE_TYPE_STR_TO_ID->{$stype}, + $shape, + scalar(@$shape), + $ctx->device_type_id, + $ctx->device_id, + $delay_alloc, + DTYPE_STR_TO_MX->{$dtype}, + scalar(@$aux_types), + $aux_type_ids, + $aux_shape_lens, + $aux_shapes + ) + ); +} + +method _class_name() +{ + my $class = ref $self || $self; + $class; +} + +sub not_implemented { confess "Not implemented" } +use overload '""' => sub { + my $self = shift; + my $shape_info = join('x', @{ $self->shape }); + sprintf("\n<%s, %s @%s>", $self->_class_name, $shape_info, $self->context); + }, + '+=' => \¬_implemented, + '-=' => \¬_implemented, + '*=' => \¬_implemented, + '/=' => \¬_implemented; +{ + no warnings 'redefine'; + *_sync_copyfrom = *_at = *_slice = *reshape = *size = \¬_implemented; +} + +method _aux_type(Int $i) +{ + return DTYPE_MX_TO_STR->{ + check_call( + AI::MXNetCAPI::NDArrayGetAuxType( + $self->handle, $i + ) + ) + } +} + +method _num_aux() +{ + return scalar(@{ STORAGE_AUX_TYPES->{ $self->stype } }); +} + +method _aux_types() +{ + [map { $self->_aux_type($_) } 0..$self->_num_aux-1]; +} + +=head2 aspdl + + Return a dense PDL object with value copied from this array +=cut + +method aspdl() +{ + return $self->tostype('default')->aspdl; +} + +=head2 astype + + Returns a copy of the array after casting to a specified type. + Parameters + ---------- + dtype : Dtype + The type of the returned array. + Examples + -------- + >>> $x = mx->nd->sparse->zeros('row_sparse', [2,3], dtype=>'float32') + >>> $y = $x->astype('int32') + >>> $y->dtype + <type 'int32'> +=cut + +method astype(Dtype $dtype) +{ + my $res = $self->zeros( + $self->stype, $self->shape, ctx => $self->context, + dtype => $dtype + ); + $self->copyto($res); + return $res; +} + +=head2 copyto + + Copies the value of this array to another array. + + Parameters + ---------- + other : NDArray or NDArray::CSR or NDArray::RowSparse or Context + The destination array or context. + + Returns + ------- + NDArray or CSRNDArray::CSR or NDArray::RowSparse + The copied array. +=cut + +method copyto(AI::MXNet::NDArray|AI::MXNet::Context $other) +{ + if($other->isa('AI::MXNet::NDArray')) + { + if($self->handle eq $other->handle) + { + Carp::cluck('You are attempting to copy an array to itself'); + return; + } + else + { + return __PACKAGE__->_copyto($self, out => $other); + } + } + elsif($other->isa('AI::MXNet::Context')) + { + my $hret = __PACKAGE__->_ndarray_cls( + __PACKAGE__->_new_alloc_handle( + $self->stype, $self->shape, $other, 1, $self->dtype, $self->_aux_types + ) + ); + return __PACKAGE__->_copyto($self, out=>$hret) + } +} + +=head2 check_format + + Check whether the NDArray format is valid. + + Parameters + ---------- + full_check : bool, optional + If `True`, rigorous check, O(N) operations. Otherwise + basic check, O(1) operations (default True). +=cut + +method check_format(Bool $full_check=1) +{ + scalar(check_call(AI::MXNetCAPI::NDArraySyncCheckFormat($self->handle, $full_check))); +} + +=head2 _data + + A deep copy NDArray of the data array associated with the BaseSparseNDArray. + + This function blocks. Do not use it in performance critical code. +=cut + +method _data() +{ + $self->wait_to_read; + my $handle = check_call(AI::MXNetCAPI::NDArrayGetDataNDArray($self->handle)); + return AI::MXNet::NDArray->new(handle => $handle); +} + +=head2 _aux_data + + Get a deep copy NDArray of the i-th aux data array associated with the + AI::MXNet::NDArray::Sparse + + This function blocks. Do not use it in performance critical code. +=cut + +method _aux_data(Int $i) +{ + $self->wait_to_read; + my $handle = check_call(AI::MXNetCAPI::NDArrayGetAuxNDArray($self->handle, $i)); + return AI::MXNet::NDArray->new(handle => $handle); +} + +package AI::MXNet::NDArray::CSR; +use AI::MXNet::Base; +use Mouse; +extends 'AI::MXNet::NDArray::Sparse'; + +=head1 NAME + + AI::MXNet::NDArray::CSR - A sparse representation of 2D NDArray in the Compressed Sparse Row format. +=cut + +=head1 DESCRIPTION + + A AI::MXNet::NDArray::CSR represents an AI::MXNet::NDArray as three separate arrays: `data`, + `indptr` and `indices`. It uses the CSR representation where the column indices for + row i are stored in ``indices[indptr[i]:indptr[i+1]]`` and their corresponding values are stored + in ``data[indptr[i]:indptr[i+1]]``. + + The column indices for a given row are expected to be sorted in ascending order. + Duplicate column entries for the same row are not allowed. + + Example + ------- + >>> $a = mx->nd->array([[0, 1, 0], [2, 0, 0], [0, 0, 0], [0, 0, 3]]); + >>> $a = $a->tostype('csr'); + >>> $a->data->aspdl; + [ 1 2 3] + >>> $a->indices->aspdl + [1 0 2] + >>> $a->indptr->aspdl + [0 1 2 2 3] + + See Also + -------- + csr_matrix: Several ways to construct a CSRNDArray +=cut + +# def __reduce__(self): +# return CSRNDArray, (None,), super(CSRNDArray, self).__getstate__() + +use overload '+=' => sub { ($_[0] + $_[1])->copyto($_[0]) }, + '-=' => sub { ($_[0] - $_[1])->copyto($_[0]) }, + '*=' => sub { ($_[0] * $_[1])->copyto($_[0]) }, + '/=' => sub { ($_[0] / $_[1])->copyto($_[0]) }; + +=head2 slice + + Returns a sliced view of this array. 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