COMPMID-485: Memory Manager

Change-Id: Ib421b7622838f050038cd81e7426bb1413a7d6e6
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/87376
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

3 files changed, 47 insertions, 18 deletions

diff --git a/src/runtime/NEON/functions/NEConvolutionLayer.cpp b/src/runtime/NEON/functions/NEConvolutionLayer.cpp
index 1c87f60a29..0466a4a501 100644
--- a/src/runtime/NEON/functions/NEConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEConvolutionLayer.cpp
@@ -34,8 +34,8 @@
 
 using namespace arm_compute;
 
-NEConvolutionLayerReshapeWeights::NEConvolutionLayerReshapeWeights()
-    : _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false)
+NEConvolutionLayerReshapeWeights::NEConvolutionLayerReshapeWeights(std::shared_ptr<IMemoryManager> memory_manager)
+    : _memory_group(std::move(memory_manager)), _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false)
 {
 }
 
@@ -68,6 +68,7 @@ void NEConvolutionLayerReshapeWeights::configure(const ITensor *weights, const I
         TensorInfo         info_wr(shape_wr, 1, weights->info()->data_type(), weights->info()->fixed_point_position());
 
         _weights_reshaped.allocator()->init(info_wr);
+        _memory_group.manage(&_weights_reshaped);
         _weights_reshape_kernel.configure(weights, biases, &_weights_reshaped);
         _weights_transposed_kernel.configure(&_weights_reshaped, output);
         _weights_reshaped.allocator()->allocate();
@@ -80,16 +81,20 @@ void NEConvolutionLayerReshapeWeights::configure(const ITensor *weights, const I
 
 void NEConvolutionLayerReshapeWeights::run()
 {
+    _memory_group.acquire();
+
     NEScheduler::get().schedule(&_weights_reshape_kernel, 3);
     if(_transpose1xW)
     {
         NEScheduler::get().schedule(&_weights_transposed_kernel, Window::DimY);
     }
+
+    _memory_group.release();
 }
 
-NEConvolutionLayer::NEConvolutionLayer()
-    : _input_im2col_kernel(), _input_interleave_kernel(), _reshape_weights(), _mm_kernel(), _output_col2im_kernel(), _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(),
-      _gemm_output(), _has_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false)
+NEConvolutionLayer::NEConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
+    : _memory_group(std::move(memory_manager)), _input_im2col_kernel(), _input_interleave_kernel(), _reshape_weights(), _mm_kernel(), _output_col2im_kernel(), _input_im2col_reshaped(),
+      _input_interleaved_reshaped(), _weights_reshaped(), _gemm_output(), _has_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false)
 {
 }
 
@@ -175,6 +180,7 @@ void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights,
     shape_im2col.set(1, mat_input_rows);
     shape_im2col.set(2, 1);
     _input_im2col_reshaped.allocator()->init(TensorInfo(shape_im2col, 1, dt, fixed_point_position));
+    _memory_group.manage(&_input_im2col_reshaped);
 
     // Create tensor (interleave) to prepare input tensor for GEMM
     if(!_is_fully_connected_convolution)
@@ -183,6 +189,7 @@ void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights,
         shape_interleaved.set(0, shape_interleaved.x() * 4);
         shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
         _input_interleaved_reshaped.allocator()->init(TensorInfo(shape_interleaved, 1, dt, fixed_point_position));
+        _memory_group.manage(&_input_interleaved_reshaped);
     }
 
     // Create GEMM output tensor
@@ -190,6 +197,7 @@ void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights,
     shape_gemm.set(0, mat_weights_cols);
     shape_gemm.set(1, mat_input_rows);
     _gemm_output.allocator()->init(TensorInfo(shape_gemm, 1, dt, fixed_point_position));
+    _memory_group.manage(&_gemm_output);
 
     // Configure kernels
     _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _has_bias);
@@ -201,8 +209,11 @@ void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights,
     {
         _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
         _mm_kernel.configure(&_input_interleaved_reshaped, weights, &_gemm_output, 1.0f);
+        _input_interleaved_reshaped.allocator()->allocate();
     }
+    _input_im2col_reshaped.allocator()->allocate();
     _output_col2im_kernel.configure(&_gemm_output, output, std::make_pair(conv_w, conv_h));
+    _gemm_output.allocator()->allocate();
 
     ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) || (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one");
 
@@ -211,12 +222,6 @@ void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights,
     {
         _weights_reshaped.allocator()->allocate();
     }
-    _input_im2col_reshaped.allocator()->allocate();
-    if(!_is_fully_connected_convolution)
-    {
-        _input_interleaved_reshaped.allocator()->allocate();
-    }
-    _gemm_output.allocator()->allocate();
 }
 
 void NEConvolutionLayer::run()
@@ -228,6 +233,8 @@ void NEConvolutionLayer::run()
         _reshape_weights.run();
     }
 
+    _memory_group.acquire();
+
     // Run input reshaping
     NEScheduler::get().schedule(&_input_im2col_kernel, Window::DimY);
     if(!_is_fully_connected_convolution)
@@ -241,4 +248,6 @@ void NEConvolutionLayer::run()
 
     // Reshape output matrix
     NEScheduler::get().schedule(&_output_col2im_kernel, Window::DimY);
+
+    _memory_group.release();
 }
diff --git a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp
index 39983bf643..2e8d10598d 100644
--- a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp
+++ b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp
@@ -32,8 +32,8 @@
 
 namespace arm_compute
 {
-NEFullyConnectedLayerReshapeWeights::NEFullyConnectedLayerReshapeWeights()
-    : _transpose_kernel(), _transpose1xW_kernel(), _transpose_output(), _transpose_weights(false), _is_batched_fc_layer(false)
+NEFullyConnectedLayerReshapeWeights::NEFullyConnectedLayerReshapeWeights(std::shared_ptr<IMemoryManager> memory_manager)
+    : _memory_group(std::move(memory_manager)), _transpose_kernel(), _transpose1xW_kernel(), _transpose_output(), _transpose_weights(false), _is_batched_fc_layer(false)
 {
 }
 
@@ -58,6 +58,7 @@ void NEFullyConnectedLayerReshapeWeights::configure(const ITensor *input, ITenso
             // Initialize the output tensor for transpose
             TensorShape shape_transposed(input->info()->dimension(1), input->info()->dimension(0));
             _transpose_output.allocator()->init(TensorInfo(shape_transposed, 1, data_type, fixed_point_position));
+            _memory_group.manage(&_transpose_output);
             _transpose_kernel.configure(input, &_transpose_output);
 
             // Configure transpose 1xW kernel
@@ -87,6 +88,8 @@ void NEFullyConnectedLayerReshapeWeights::configure(const ITensor *input, ITenso
 
 void NEFullyConnectedLayerReshapeWeights::run()
 {
+    _memory_group.acquire();
+
     if(_transpose_weights)
     {
         NEScheduler::get().schedule(&_transpose_kernel, Window::DimY);
@@ -96,11 +99,13 @@ void NEFullyConnectedLayerReshapeWeights::run()
     {
         NEScheduler::get().schedule(&_transpose1xW_kernel, Window::DimY);
     }
+
+    _memory_group.release();
 }
 
-NEFullyConnectedLayer::NEFullyConnectedLayer()
-    : _im2col_kernel(), _reshape_weights_kernel(), _interleave4x4_kernel(), _mm_kernel(), _accumulate_biases_kernel(), _im2col_output(), _interleave4x4_output(), _reshape_weights_output(),
-      _are_weights_reshaped(false), _is_batched_fc_layer(false), _linearize_input(false), _accumulate_biases(false)
+NEFullyConnectedLayer::NEFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager)
+    : _memory_group(std::move(memory_manager)), _im2col_kernel(), _reshape_weights_kernel(), _interleave4x4_kernel(), _mm_kernel(), _accumulate_biases_kernel(), _im2col_output(), _interleave4x4_output(),
+      _reshape_weights_output(), _are_weights_reshaped(false), _is_batched_fc_layer(false), _linearize_input(false), _accumulate_biases(false)
 {
 }
 
@@ -191,6 +196,7 @@ void NEFullyConnectedLayer::configure(const ITensor *input, const ITensor *weigh
         _im2col_output.allocator()->init(TensorInfo(shape_im2col, 1, data_type, fixed_point_position));
 
         // Configure im2col kernel
+        _memory_group.manage(&_im2col_output);
         _im2col_kernel.configure(input, &_im2col_output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false);
 
         multiply_input = &_im2col_output;
@@ -204,6 +210,7 @@ void NEFullyConnectedLayer::configure(const ITensor *input, const ITensor *weigh
         _interleave4x4_output.allocator()->init(TensorInfo(shape_interleaved, 1, data_type, fixed_point_position));
 
         // Configure interleave4x4 kernel
+        _memory_group.manage(&_interleave4x4_output);
         _interleave4x4_kernel.configure(multiply_input, &_interleave4x4_output);
 
         multiply_input = &_interleave4x4_output;
@@ -248,6 +255,8 @@ void NEFullyConnectedLayer::run()
         _reshape_weights_kernel.run();
     }
 
+    _memory_group.acquire();
+
     // Linearize input if it comes from a convolutional layer
     if(_linearize_input)
     {
@@ -268,5 +277,7 @@ void NEFullyConnectedLayer::run()
     {
         NEScheduler::get().schedule(&_accumulate_biases_kernel, Window::DimY);
     }
+
+    _memory_group.release();
 }
 } // namespace arm_compute
diff --git a/src/runtime/NEON/functions/NESoftmaxLayer.cpp b/src/runtime/NEON/functions/NESoftmaxLayer.cpp
index 13dfa4a51e..cc5d4e91c3 100644
--- a/src/runtime/NEON/functions/NESoftmaxLayer.cpp
+++ b/src/runtime/NEON/functions/NESoftmaxLayer.cpp
@@ -31,8 +31,8 @@
 
 using namespace arm_compute;
 
-NESoftmaxLayer::NESoftmaxLayer()
-    : _max_kernel(), _shift_exp_sum_kernel(), _norm_kernel(), _fill_border_kernel(), _max(), _sum(), _tmp()
+NESoftmaxLayer::NESoftmaxLayer(std::shared_ptr<IMemoryManager> memory_manager)
+    : _memory_group(std::move(memory_manager)), _max_kernel(), _shift_exp_sum_kernel(), _norm_kernel(), _fill_border_kernel(), _max(), _sum(), _tmp()
 {
 }
 
@@ -50,6 +50,11 @@ void NESoftmaxLayer::configure(ITensor *input, ITensor *output)
     _max.allocator()->init(tensor_info_max_sum);
     _sum.allocator()->init(tensor_info_max_sum);
 
+    // Manage intermediate buffers
+    _memory_group.manage(&_tmp);
+    _memory_group.manage(&_max);
+    _memory_group.manage(&_sum);
+
     // Configure Kernels
     _max_kernel.configure(input, &_max);
     _shift_exp_sum_kernel.configure(input, &_max, &_tmp, &_sum);
@@ -64,8 +69,12 @@ void NESoftmaxLayer::configure(ITensor *input, ITensor *output)
 
 void NESoftmaxLayer::run()
 {
+    _memory_group.acquire();
+
     NEScheduler::get().schedule(&_fill_border_kernel, Window::DimY);
     NEScheduler::get().schedule(&_max_kernel, Window::DimY);
     NEScheduler::get().schedule(&_shift_exp_sum_kernel, Window::DimY);
     NEScheduler::get().schedule(&_norm_kernel, Window::DimY);
+
+    _memory_group.release();
 }