Merge branch 'master' of https://github.com/apache/incubator-mxnet in…

…to fix-inner-product

ci/docker/runtime_functions.sh

@@ -1033,8 +1033,6 @@ integrationtest_ubuntu_cpu_asan() {
     set -ex
     export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libasan.so.5
 
-    # We do not want to fail the build on ASAN errors until memory leaks have been addressed.
-    export ASAN_OPTIONS=exitcode=0
     cd /work/mxnet/build/cpp-package/example/
     /work/mxnet/cpp-package/example/get_data.sh
     ./mlp_cpu

cpp-package/example/alexnet.cpp

@@ -319,6 +319,7 @@ int main(int argc, char const *argv[]) {
   }
   /*don't foget to release the executor*/
   delete exec;
+  delete opt;
   MXNotifyShutdown();
   return 0;
 }

cpp-package/example/charRNN.cpp

@@ -500,6 +500,9 @@ void train(const std::string file, int batch_size, int max_epoch, int start_epoc
     std::string filepath = prefix + "-" + std::to_string(epoch) + ".params";
     SaveCheckpoint(filepath, RNN, exe);
   }
+
+  delete exe;
+  delete opt;
 }
 
 /*The original example, rnn_cell_demo.py, uses default Xavier as initalizer, which relies on
@@ -577,6 +580,9 @@ void trainWithBuiltInRNNOp(const std::string file, int batch_size, int max_epoch
     std::string filepath = prefix + "-" + std::to_string(epoch) + ".params";
     SaveCheckpoint(filepath, RNN, exe);
   }
+
+  delete exe;
+  delete opt;
 }
 
 void predict(std::wstring* ptext, int sequence_length, const std::string param_file,
@@ -639,6 +645,8 @@ void predict(std::wstring* ptext, int sequence_length, const std::string param_f
     next = charIndices[n];
     ptext->push_back(next);
   }
+
+  delete exe;
 }
 
 void predictWithBuiltInRNNOp(std::wstring* ptext, int sequence_length, const std::string param_file,
@@ -693,6 +701,8 @@ void predictWithBuiltInRNNOp(std::wstring* ptext, int sequence_length, const std
     next = charIndices[n];
     ptext->push_back(next);
   }
+
+  delete exe;
 }
 
 int main(int argc, char** argv) {

cpp-package/example/googlenet.cpp

@@ -190,6 +190,7 @@ int main(int argc, char const *argv[]) {
   }
 
   delete exec;
+  delete opt;
   MXNotifyShutdown();
   return 0;
 }

cpp-package/example/inception_bn.cpp

@@ -232,6 +232,7 @@ int main(int argc, char const *argv[]) {
     LG << "Validation Accuracy: " << val_acc.Get();
   }
   delete exec;
+  delete opt;
   MXNotifyShutdown();
   return 0;
 }

cpp-package/example/lenet.cpp

@@ -173,6 +173,7 @@ class Lenet {
          << ", accuracy: " << ValAccuracy(batch_size * 10, lenet);
     }
     delete exe;
+    delete opt;
   }
 
  private:

cpp-package/example/lenet_with_mxdataiter.cpp

@@ -177,6 +177,7 @@ int main(int argc, char const *argv[]) {
   }
 
   delete exec;
+  delete opt;
   MXNotifyShutdown();
   return 0;
 }

cpp-package/example/mlp_cpu.cpp

@@ -139,6 +139,7 @@ int main(int argc, char** argv) {
   }
 
   delete exec;
+  delete opt;
   MXNotifyShutdown();
   return 0;
 }

cpp-package/example/mlp_csv.cpp

@@ -267,6 +267,7 @@ int main(int argc, char** argv) {
     }
 
     delete exec;
+    delete opt;
     MXNotifyShutdown();
     return 0;
 }

cpp-package/example/mlp_gpu.cpp

@@ -155,6 +155,7 @@ int main(int argc, char** argv) {
   }
 
   delete exec;
+  delete opt;
   MXNotifyShutdown();
   return 0;
 }

cpp-package/example/resnet.cpp

@@ -242,6 +242,7 @@ int main(int argc, char const *argv[]) {
     LG << "Validation Accuracy: " << val_acc.Get();
   }
   delete exec;
+  delete opt;
   MXNotifyShutdown();
   return 0;
 }

cpp-package/example/test_optimizer.cpp

@@ -30,6 +30,7 @@ int main(int argc, char** argv) {
   opt = OptimizerRegistry::Find("adam");
   int ret = (opt == 0) ? 1 : 0;
 
+  delete opt;
   MXNotifyShutdown();
   return ret;
 }

cpp-package/example/test_score.cpp

@@ -156,6 +156,7 @@ int main(int argc, char** argv) {
   }
 
   delete exec;
+  delete opt;
   MXNotifyShutdown();
   return score >= MIN_SCORE ? 0 : 1;
 }

example/image-classification/predict-cpp/image-classification-predict.cc

@@ -76,7 +76,9 @@ class BufferFile {
     ifs.seekg(0, std::ios::beg);
     std::cout << file_path.c_str() << " ... " << length_ << " bytes\n";
 
-    buffer_.reset(new char[length_]);
+    // Buffer as null terminated to be converted to string
+    buffer_.reset(new char[length_ + 1]);
+    buffer_[length_] = 0;
     ifs.read(buffer_.get(), length_);
     ifs.close();
   }

include/mkldnn

@@ -0,0 +1 @@
+../3rdparty/mkldnn/include

include/mxnet/ndarray.h

@@ -848,13 +848,17 @@ class NDArray {
     // The shape of aux data. The default value for the shape depends on the type of storage.
     // If aux_shapes[i].Size() is zero, aux data i is empty.
     mxnet::ShapeVector aux_shapes;
+    /*! \brief Reference to the storage to ensure proper destruct order */
+    std::shared_ptr<Storage> storage_ref_;
 
     /*! \brief default cosntructor */
-    Chunk() : static_data(true), delay_alloc(false) {}
+    Chunk() : static_data(true), delay_alloc(false),
+              storage_ref_(Storage::_GetSharedRef()) {}
 
     /*! \brief construct a new chunk */
     Chunk(mxnet::TShape shape, Context ctx_, bool delay_alloc_, int dtype)
-        : static_data(false), delay_alloc(true), ctx(ctx_) {
+        : static_data(false), delay_alloc(true), ctx(ctx_),
+          storage_ref_(Storage::_GetSharedRef()) {
       auto size = shape.Size();
       storage_shape = shape;
       var = Engine::Get()->NewVariable();
@@ -864,7 +868,8 @@ class NDArray {
     }
 
     Chunk(const TBlob &data, int dev_id)
-        : static_data(true), delay_alloc(false) {
+        : static_data(true), delay_alloc(false),
+          storage_ref_(Storage::_GetSharedRef()) {
       CHECK(storage_type == kDefaultStorage);
       var = Engine::Get()->NewVariable();
       if (data.dev_mask() == cpu::kDevMask) {
@@ -881,7 +886,8 @@ class NDArray {
     }
 
     Chunk(int shared_pid, int shared_id, const mxnet::TShape& shape, int dtype)
-        : static_data(false), delay_alloc(false) {
+        : static_data(false), delay_alloc(false),
+          storage_ref_(Storage::_GetSharedRef()) {
       var = Engine::Get()->NewVariable();
       ctx = Context::CPUShared(0);
       shandle.size = shape.Size() * mshadow::mshadow_sizeof(dtype);
@@ -897,7 +903,7 @@ class NDArray {
           const mxnet::ShapeVector &aux_shapes_)
         : static_data(false), delay_alloc(delay_alloc_), storage_type(storage_type_),
           aux_types(aux_types_), ctx(ctx_), storage_shape(storage_shape_),
-          aux_shapes(aux_shapes_) {
+          aux_shapes(aux_shapes_), storage_ref_(Storage::_GetSharedRef()) {
       shandle.ctx = ctx;
       var = Engine::Get()->NewVariable();
       // aux_handles always reflect the correct number of aux data
@@ -914,7 +920,8 @@ class NDArray {
 
     Chunk(const NDArrayStorageType storage_type_, const TBlob &data,
           const std::vector<TBlob> &aux_data, int dev_id)
-        : static_data(true), delay_alloc(false), storage_type(storage_type_) {
+        : static_data(true), delay_alloc(false), storage_type(storage_type_),
+          storage_ref_(Storage::_GetSharedRef()) {
       using namespace mshadow;
       CHECK_NE(storage_type, kDefaultStorage);
       // init var

python/mxnet/contrib/onnx/onnx2mx/_op_translations.py

@@ -65,19 +65,47 @@ def sample_multinomial(attrs, inputs, proto_obj):
 # Arithmetic Operations
 def add(attrs, inputs, proto_obj):
     """Adding two tensors"""
-    return translation_utils.broadcast_arithmetic_helper(attrs, inputs, proto_obj, 'broadcast_add')
+    new_attr = {}
+
+    if 'broadcast' in attrs and attrs['broadcast'] == 1:
+        broadcast_axis = attrs['axis']
+        op_value = translation_utils._fix_broadcast('broadcast_add', inputs,
+                                                    broadcast_axis, proto_obj)
+        return op_value, new_attr, inputs
+    return 'broadcast_add', new_attr, inputs
 
 def subtract(attrs, inputs, proto_obj):
     """Subtracting two tensors"""
-    return translation_utils.broadcast_arithmetic_helper(attrs, inputs, proto_obj, 'broadcast_sub')
+    new_attr = {}
+
+    if 'broadcast' in attrs and attrs['broadcast'] == 1:
+        broadcast_axis = attrs['axis']
+        op_value = translation_utils._fix_broadcast('broadcast_sub', inputs,
+                                                    broadcast_axis, proto_obj)
+        return op_value, new_attr, inputs
+    return 'broadcast_sub', new_attr, inputs
 
 def multiply(attrs, inputs, proto_obj):
     """Multiply two tensors"""
-    return translation_utils.broadcast_arithmetic_helper(attrs, inputs, proto_obj, 'broadcast_mul')
+    new_attr = {}
+
+    if 'broadcast' in attrs and attrs['broadcast'] == 1:
+        broadcast_axis = attrs['axis']
+        op_value = translation_utils._fix_broadcast('broadcast_mul', inputs,
+                                                    broadcast_axis, proto_obj)
+        return op_value, new_attr, inputs
+    return 'broadcast_mul', new_attr, inputs
 
 def divide(attrs, inputs, proto_obj):
     """Divide two tensors"""
-    return translation_utils.broadcast_arithmetic_helper(attrs, inputs, proto_obj, 'broadcast_div')
+    new_attr = {}
+
+    if 'broadcast' in attrs and attrs['broadcast'] == 1:
+        broadcast_axis = attrs['axis']
+        op_value = translation_utils._fix_broadcast('broadcast_div', inputs,
+                                                    broadcast_axis, proto_obj)
+        return op_value, new_attr, inputs
+    return 'broadcast_div', new_attr, inputs
 
 def mean(attrs, inputs, proto_obj):
     """Mean of all the input tensors."""

python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py

@@ -245,17 +245,3 @@ def get_input_shape(sym, proto_obj):
     result = mod.get_outputs()[0].asnumpy()
 
     return result.shape
-
-def broadcast_arithmetic_helper(attrs, inputs, proto_obj, current_op_name):
-    """Helper function for broadcast arithmetic ops."""
-    new_attr = {}
-    op_names = ['batchnorm, convolution, deconvolution']
-    if 'broadcast' in attrs and attrs['broadcast'] == 1:
-        broadcast_axis = attrs['axis']
-        for op_name in op_names:
-            # if input is bias which comes after conv, deconv, batchnorm operators
-            # then only reshape bias term
-            if inputs[0].name.startswith(op_name):
-                op_value = _fix_broadcast(current_op_name, inputs, broadcast_axis, proto_obj)
-                return op_value, new_attr, inputs
-    return current_op_name, new_attr, inputs

src/common/object_pool.h

@@ -132,10 +132,9 @@ struct ObjectPoolAllocatable {
 
 template <typename T>
 ObjectPool<T>::~ObjectPool() {
-  // TODO(hotpxl): mind destruction order
-  // for (auto i : allocated_) {
-  //   free(i);
-  // }
+  for (auto i : allocated_) {
+    free(i);
+  }
 }
 
 template <typename T>

src/engine/threaded_engine.cc

@@ -281,7 +281,7 @@ void ThreadedEngine::DeleteOperator(OprHandle op) {
   this->PushAsync([threaded_opr](RunContext, CallbackOnComplete on_complete) {
       ThreadedOpr::Delete(threaded_opr);
       on_complete();
-    }, Context::CPU(), {}, deps, FnProperty::kAsync, 0,
+    }, Context::CPU(), {}, deps, FnProperty::kDeleteVar, 0,
     "DeleteOperator");
 }
 

src/engine/threaded_engine.h

@@ -352,7 +352,8 @@ class ThreadedEngine : public Engine {
       LOG(INFO) << "ExecuteOprBlock " << opr_block
                 << "shutdown_phase=" << shutdown_phase_;
     }
-    if (!shutdown_phase_) {
+    // still run cleanup in shutdown_phase
+    if (!shutdown_phase_ || threaded_opr->prop == FnProperty::kDeleteVar) {
       try {
         OnStart(threaded_opr);
         if (debug_info) {