commit 52ba29e936b8e711e8acdfe819e36f884d4f3fe1
author Jenkins <bsgcomp@arm.com> Wed Aug 29 15:32:11 2018 +0000
committer Michele Di Giorgio <michele.digiorgio@arm.com> Thu Aug 30 17:15:09 2018 +0100
tree 23a8e15208ad7fcf07a37ed9119979cde01dcdc8
parent e2542c9f35ca427286822cd0c9296f49914f78b0

arm_compute v18.08

src/runtime/CL/functions/CLRNNLayer.cpp

diff --git a/src/runtime/CL/functions/CLRNNLayer.cpp b/src/runtime/CL/functions/CLRNNLayer.cpp
index 4843ba6..1809e6e 100644
--- a/src/runtime/CL/functions/CLRNNLayer.cpp
+++ b/src/runtime/CL/functions/CLRNNLayer.cpp
@@ -36,7 +36,8 @@
 using namespace arm_compute::misc::shape_calculator;
 
 CLRNNLayer::CLRNNLayer(std::shared_ptr<IMemoryManager> memory_manager)
-    : _memory_group(std::move(memory_manager)), _gemm_state_f(), _add_kernel(), _activation_kernel(), _fully_connected_kernel(), _copy_kernel(), _fully_connected_out(), _gemm_output(), _add_output()
+    : _memory_group(std::move(memory_manager)), _gemm_state_f(), _add_kernel(), _activation_kernel(), _fully_connected_kernel(), _copy_kernel(), _fully_connected_out(), _gemm_output(), _add_output(),
+      _is_prepared(false)
 {
 }
 
@@ -57,7 +58,7 @@
 
     auto shape_info = TensorInfo(compute_rnn_shape(recurrent_weights, hidden_state->dimension(idx_height)), 1, input->data_type());
 
-    ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, weights, bias, &shape_info, true, false));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, weights, bias, &shape_info));
     ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(hidden_state, recurrent_weights, nullptr, &shape_info, 1.f, 0.f));
     ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAdditionKernel::validate(&shape_info, &shape_info, &shape_info, ConvertPolicy::SATURATE));
     ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(&shape_info, &shape_info, info));
@@ -74,12 +75,14 @@
     const int   idx_height = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT);
     TensorShape shape      = compute_rnn_shape(recurrent_weights->info(), hidden_state->info()->dimension(idx_height));
 
+    _is_prepared = false;
+
     _fully_connected_out.allocator()->init(TensorInfo(shape, 1, input->info()->data_type()));
     _gemm_output.allocator()->init(TensorInfo(shape, 1, input->info()->data_type()));
 
     // Manage intermediate buffers and configure
     _memory_group.manage(&_fully_connected_out);
-    _fully_connected_kernel.configure(input, weights, bias, &_fully_connected_out, true, false);
+    _fully_connected_kernel.configure(input, weights, bias, &_fully_connected_out);
 
     _memory_group.manage(&_gemm_output);
     _gemm_state_f.configure(hidden_state, recurrent_weights, nullptr, &_gemm_output, 1.f, 0.f);
@@ -100,7 +103,10 @@
 
 void CLRNNLayer::run()
 {
+    prepare();
+
     _memory_group.acquire();
+
     _fully_connected_kernel.run();
     _gemm_state_f.run();
     CLScheduler::get().enqueue(_add_kernel);
@@ -108,5 +114,17 @@
 
     // copy hidden out to output
     CLScheduler::get().enqueue(_copy_kernel);
+
     _memory_group.release();
+}
+
+void CLRNNLayer::prepare()
+{
+    if(!_is_prepared)
+    {
+        _fully_connected_kernel.prepare();
+        _gemm_state_f.prepare();
+
+        _is_prepared = true;
+    }
 }
\ No newline at end of file