apache · sergeykolychev · Jun 30, 2018 · Apr 19, 2018 · Jun 27, 2018 · Jun 28, 2018
@@ -1,5 +1,12 @@
 Revision history for Perl extension AI::MXNet
 
+1.3     Tue Jun 26 20:57:40 PDT 2018
+        - Major Gluon update towards parity with Python's API.
+        - Miscellaneous bugfixes and improvements.
+        - New Engine API.
+        - Module::reshape moved to C++ backend.
+        - Examples were updated to work on multi-gpu boxes
+
 1.23    Thu Apr 19 15:38:10 PDT 2018
         - Support for image operations on symbols and ndarrays.
 

@@ -25,6 +25,7 @@ lib/AI/MXNet/Contrib.pm
 lib/AI/MXNet/Contrib/NDArray.pm
 lib/AI/MXNet/Contrib/Symbol.pm
 lib/AI/MXNet/CudaModule.pm
+lib/AI/MXNet/Engine.pm
 lib/AI/MXNet/Executor.pm
 lib/AI/MXNet/Executor/Group.pm
 lib/AI/MXNet/Function/Parameters.pm
@@ -38,6 +39,7 @@ lib/AI/MXNet/Gluon/Data/Vision.pm
 lib/AI/MXNet/Gluon/Loss.pm
 lib/AI/MXNet/Gluon/Mouse.pm
 lib/AI/MXNet/Gluon/NN.pm
+lib/AI/MXNet/Gluon/NN/Activation.pm
 lib/AI/MXNet/Gluon/NN/BasicLayers.pm
 lib/AI/MXNet/Gluon/NN/ConvLayers.pm
 lib/AI/MXNet/Gluon/Parameter.pm
@@ -97,10 +99,12 @@ t/test_autograd.t
 t/test_base.t
 t/test_conv.t
 t/test_cuda_module.t
+t/test_engine.t
 t/test_executor.t
 t/test_gluon.t
 t/test_gluon_data.t
 t/test_gluon_rnn.t
+t/test_gluon_trainer.t
 t/test_infer_shape.t
 t/test_init.t
 t/test_io.t

@@ -30,8 +30,8 @@
       },
       "runtime" : {
          "requires" : {
-            "AI::MXNetCAPI" : "1.2",
-            "AI::NNVMCAPI" : "1.2",
+            "AI::MXNetCAPI" : "1.3",
+            "AI::NNVMCAPI" : "1.3",
             "Function::Parameters" : "1.0705",
             "Hash::Ordered" : "0.012",
             "GraphViz" : "2.14",
@@ -45,5 +45,5 @@
       }
    },
    "release_status" : "stable",
-   "version" : "1.23"
+   "version" : "1.3"
 }
@@ -17,12 +17,12 @@ no_index:
     - t
     - inc
 requires:
-  AI::MXNetCAPI: '1.2'
-  AI::NNVMCAPI: '1.2'
+  AI::MXNetCAPI: '1.3'
+  AI::NNVMCAPI: '1.3'
   Function::Parameters: '1.0705'
   Hash::Ordered: '0.012'
   GraphViz: '2.14'
   Mouse: v2.1.0
   PDL: '2.007'
   PDL::CCS: '1.23.4'
-version: '1.23'
+version: '1.3'
@@ -1,5 +1,5 @@
 This archive contains the distribution AI-MXNet,
-version 1.23:
+version 1.3:
 
   Perl interface to MXNet machine learning library
 

@@ -233,22 +233,23 @@ package main;
     initializer         => mx->init->Xavier(factor_type => "in", magnitude => 2.34),
     num_epoch           => $num_epoch,
     batch_end_callback  => mx->callback->Speedometer($batch_size, $disp_batches),
-    ($chkp_epoch ? (epoch_end_callback  => [mx->rnn->do_rnn_checkpoint($stack, $chkp_prefix, $chkp_epoch), \&sample]) : ())
+    ($chkp_epoch ? (epoch_end_callback  => [mx->callback->module_checkpoint($model, $chkp_prefix, $chkp_epoch), \&sample]) : ())
 );
 
+my $chkp = 1;
 sub sample {
     return if not $sample_size;
-    $model->reshape(data_shapes=>[['data',[1, $seq_size]]], label_shapes=>[['softmax_label',[1, $seq_size]]]);
+    my $inference_model = mx->mod->Module->load($chkp_prefix, $chkp++);
+    $inference_model->bind(data_shapes=>[['data',[1, $seq_size]]], label_shapes=>[['softmax_label',[1, $seq_size]]]);
     my $input = mx->nd->array($fdata->slice([0, $seq_size-1]))->reshape([1, $seq_size]);
     $| = 1;
     for (0..$sample_size-1)
     {
-        $model->forward(mx->io->DataBatch(data=>[$input]), is_train => 0);
-        my $prob = $model->get_outputs(0)->[0][0]->at($seq_size-1)->aspdl;
+        $inference_model->forward(mx->io->DataBatch(data=>[$input]), is_train => 0);
+        my $prob = $inference_model->get_outputs(0)->[0][0]->at($seq_size-1)->aspdl;
         my $next_char = Math::Random::Discrete->new($prob->reshape(-1)->unpdl, [0..scalar(keys %vocabulary)-1])->rand;
         print "$reverse_vocab{$next_char}";
         $input->at(0)->slice([0, $seq_size-2]) .= $input->at(0)->slice([1, $seq_size-1])->copy;
         $input->at(0)->at($seq_size-1) .= $next_char;
     }
-    $model->reshape(data_shapes=>[['data',[$batch_size, $seq_size]]], label_shapes=>[['softmax_label',[$batch_size, $seq_size]]]);
 }
@@ -48,7 +48,7 @@
     $net->add(nn->Dense(10));
 });
 $net->hybridize() if $hybridize;
-$net->load_params('mnist.params') if $load_params;
+$net->load_parameters('mnist.params') if $load_params;
 # data
 
 sub transformer
@@ -130,7 +130,7 @@ sub train
         my ($val_name, $val_acc) = test($ctx);
         print "[Epoch $epoch] Validation: $val_name=$val_acc\n"
     }
-    $net->save_params('mnist.params');
+    $net->save_parameters('mnist.params');
 }
 
 train($epochs, $cuda ? mx->gpu(0) : mx->cpu);
@@ -50,7 +50,8 @@ use AI::MXNet::AutoGrad;
 use AI::MXNet::Gluon;
 use AI::MXNet::NDArray::Sparse;
 use AI::MXNet::Symbol::Sparse;
-our $VERSION = '1.22';
+use AI::MXNet::Engine;
+our $VERSION = '1.3';
 
 sub import
 {
@@ -76,6 +77,7 @@ sub import
             sub Context { shift; AI::MXNet::Context->new(\@_) }
             sub context { 'AI::MXNet::Context' }
             sub cpu { AI::MXNet::Context->cpu(\$_[1]//0) }
+            sub cpu_pinned { AI::MXNet::Context->cpu_pinned(\$_[1]//0) }
             sub gpu { AI::MXNet::Context->gpu(\$_[1]//0) }
             sub kv { 'AI::MXNet::KVStore' }
             sub recordio { 'AI::MXNet::RecordIO' }
@@ -92,8 +94,10 @@ sub import
             sub contrib { 'AI::MXNet::Contrib' }
             sub linalg { 'AI::MXNet::LinAlg' }
             sub autograd { 'AI::MXNet::AutoGrad' }
+            sub engine { 'AI::MXNet::Engine' }
             sub name { '$short_name' }
             sub rtc { '$short_name' }
+            sub gluon { 'AI::MXNet::Gluon' }
             sub CudaModule { shift; AI::MXNet::CudaModule->new(\@_) }
             sub AttrScope { shift; AI::MXNet::Symbol::AttrScope->new(\@_) }
             *AI::MXNet::Symbol::AttrScope::current = sub { \$${short_name}::AttrScope; };
@@ -106,6 +110,8 @@ sub import
             *AI::MXNet::Context::current_context = sub { \$${short_name}::Context; };
             *AI::MXNet::Context::set_current = sub { \$${short_name}::Context = \$_[1]; };
             \$${short_name}::Context = AI::MXNet::Context->new(device_type => 'cpu', device_id => 0);
+            package nd;
+            \@nd::ISA = ('AI::MXNet::NDArray');
             1;
 EOP
             eval $short_name_package;

@@ -27,7 +27,7 @@ sub AUTOLOAD
     my $sub = "_${prefix}_$name";
     {
         no strict 'refs';
-        *{"$class::$name"} = sub { shift; $real_class->$sub(@_); };
+        *{"${class}::$name"} = sub { shift; $real_class->$sub(@_); };
     }
     goto $class->can($name);
 }

@@ -21,8 +21,8 @@ use warnings;
 use PDL;
 use PDL::Types ();
 use PDL::CCS::Nd;
-use AI::MXNetCAPI 1.2;
-use AI::NNVMCAPI 1.2;
+use AI::MXNetCAPI 1.3;
+use AI::NNVMCAPI 1.3;
 use AI::MXNet::Types;
 use Time::HiRes;
 use Scalar::Util qw(blessed);
@@ -169,9 +169,16 @@ sub zip
 
 sub enumerate
 {
-    my ($sub, @arrays) = @_;
-    my $len = @{ $arrays[0] };
-    zip($sub, [0..$len-1], @arrays);
+    if('CODE' eq ref $_[0])
+    {
+        # continue supporting the callback style
+        my $code = shift;
+        my $len = @{ $_[0] };
+        $code->(@$_) for AI::MXNetCAPI::py_zip([0..$len-1], map { \@$_ } @_);
+        return;
+    }
+    my $len = @{ $_[0] };
+    return AI::MXNetCAPI::py_zip([0..$len-1], map { \@$_ } @_);
 }
 
 =head2 product

@@ -32,10 +32,17 @@ has 'handle'   => (is => 'ro', isa => 'CachedOpHandle', required => 1);
 around BUILDARGS => sub {
     my $orig  = shift;
     my $class = shift;
-    my ($sym) = @_;
+    my ($sym, $flags) = @_;
+    for my $key (keys %$flags)
+    {
+        $flags->{ $key } = "(" .join(", ", map { defined($_) ? $_ : 'None' } @{ $flags->{ $key } }) .")"
+                if ref $flags->{ $key } eq 'ARRAY';
+    }
     my $handle = check_call(
-        AI::MXNetCAPI::CreateCachedOp(
-            $sym->handle
+        AI::MXNetCAPI::CreateCachedOpEx(
+            $sym->handle,
+            scalar(keys %{ $flags//{} }),
+            $flags//{},
         )
     );
     return $class->$orig(handle => $handle);
@@ -84,8 +91,8 @@ sub call
     {
         $out = [];
     }
-    my $output = check_call(
-        AI::MXNetCAPI::InvokeCachedOp(
+    my ($output, $stypes) = check_call(
+        AI::MXNetCAPI::InvokeCachedOpEx(
             $self->handle,
             scalar(@args),
             [map { $_->handle } @args],
@@ -95,11 +102,12 @@ sub call
     return $original_output if defined $original_output;
     if(@$output == 1)
     {
-        return AI::MXNet::NDArray->_ndarray_cls($output->[0]);
+        return AI::MXNet::NDArray->_ndarray_cls($output->[0], 1, $stypes->[0]);
     }
     else
     {
-        return [map { AI::MXNet::NDArray->_ndarray_cls($_) } @$output];
+        my $i = 0;
+        return [map { AI::MXNet::NDArray->_ndarray_cls($_, 1, $stypes->[$i++]) } @$output];
     }
 }
 

@@ -19,6 +19,7 @@ package AI::MXNet::Context;
 use strict;
 use warnings;
 use Mouse;
+use AI::MXNet::Base;
 use AI::MXNet::Types;
 use AI::MXNet::Function::Parameters;
 use constant devtype2str => { 1 => 'cpu', 2 => 'gpu', 3 => 'cpu_pinned' };
@@ -111,6 +112,28 @@ method cpu(Int $device_id=0)
     return $self->new(device_type => 'cpu', device_id => $device_id);
 }
 
+=head2 cpu_pinned
+
+    Returns a CPU pinned memory context. Copying from CPU pinned memory to GPU
+    is faster than from normal CPU memory.
+
+    Parameters
+    ----------
+    device_id : int, optional
+        The device id of the device. `device_id` is not needed for CPU.
+        This is included to make interface compatible with GPU.
+
+    Returns
+    -------
+    context : Context
+        The corresponding CPU pinned memory context.
+=cut
+
+method cpu_pinned(Int $device_id=0)
+{
+    return $self->new(device_type => 'cpu_pinned', device_id => $device_id);
+}
+
 =head2 gpu
 
     Returns a GPU context.
@@ -139,6 +162,27 @@ method gpu(Int $device_id=0)
     $default_ctx : AI::MXNet::Context
 =cut
 
+
+=head2 num_gpus
+
+    Query CUDA for the number of GPUs present.
+
+    Raises
+    ------
+    Will raise an exception on any CUDA error.
+
+    Returns
+    -------
+    count : int
+        The number of GPUs.
+
+=cut
+
+method num_gpus()
+{
+    return scalar(check_call(AI::MXNetCAPI::GetGPUCount()));
+}
+
 method current_ctx()
 {
     return $AI::MXNet::current_ctx;