COMPMID-1096 - Add fast_math flag to CLConvolutionLayer

COMPMID-1103 - CLWinogradConvolutionLayer mismatches

Change-Id: Iceaa9482a1790ec39d2720c220261aaea8043978
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/129398
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>

1 files changed, 17 insertions, 20 deletions

diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp
index 97ef895434..83281e1747 100644
--- a/src/runtime/CL/functions/CLConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLConvolutionLayer.cpp
@@ -43,32 +43,33 @@ CLConvolutionLayer::CLConvolutionLayer(std::shared_ptr<IMemoryManager> memory_ma
 }
 
 void CLConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
-                                   const Size2D &dilation, const ActivationLayerInfo &act_info)
+                                   const Size2D &dilation, const ActivationLayerInfo &act_info, bool enable_fast_math)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
-    ARM_COMPUTE_ERROR_THROW_ON(CLConvolutionLayer::validate(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, weights_info, dilation, act_info));
+    ARM_COMPUTE_ERROR_THROW_ON(CLConvolutionLayer::validate(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, weights_info, dilation, act_info,
+                                                            enable_fast_math));
 
     switch(CLConvolutionLayer::get_convolution_method(input->info(), weights->info(), output->info(), conv_info,
-                                                      weights_info, act_info, CLScheduler::get().target(), dilation))
+                                                      weights_info, act_info, CLScheduler::get().target(), dilation, enable_fast_math))
     {
         case ConvolutionMethod::WINOGRAD:
         {
             auto f = arm_compute::support::cpp14::make_unique<CLWinogradConvolutionLayer>(_memory_manager);
-            f->configure(input, weights, biases, output, conv_info);
+            f->configure(input, weights, biases, output, conv_info, act_info, enable_fast_math);
             _function = std::move(f);
             break;
         }
         case ConvolutionMethod::DIRECT:
         {
             auto f = arm_compute::support::cpp14::make_unique<CLDirectConvolutionLayer>();
-            f->configure(input, weights, biases, output, conv_info);
+            f->configure(input, weights, biases, output, conv_info, act_info);
             _function = std::move(f);
             break;
         }
         case ConvolutionMethod::GEMM:
         {
             auto f = arm_compute::support::cpp14::make_unique<CLGEMMConvolutionLayer>(_memory_manager);
-            f->configure(input, weights, biases, output, conv_info, weights_info, dilation);
+            f->configure(input, weights, biases, output, conv_info, weights_info, dilation, act_info);
             _function = std::move(f);
             break;
         }
@@ -79,19 +80,18 @@ void CLConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, c
 }
 
 Status CLConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
-                                    const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info)
+                                    const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info, bool enable_fast_math)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
 
-    //Configure if the parameters match the direct convolution or the gemm-based
     const GPUTarget gpu_target = CLScheduler::get().target();
 
-    switch(CLConvolutionLayer::get_convolution_method(input, weights, output, conv_info, weights_info, act_info, gpu_target, dilation))
+    switch(CLConvolutionLayer::get_convolution_method(input, weights, output, conv_info, weights_info, act_info, gpu_target, dilation, enable_fast_math))
     {
         case ConvolutionMethod::WINOGRAD:
         {
             //Validate Winograd
-            CLWinogradConvolutionLayer::validate(input, weights, biases, output, conv_info);
+            CLWinogradConvolutionLayer::validate(input, weights, biases, output, conv_info, act_info, enable_fast_math);
             break;
         }
         case ConvolutionMethod::DIRECT:
@@ -115,25 +115,22 @@ Status CLConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo
 }
 
 ConvolutionMethod CLConvolutionLayer::get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info,
-                                                             const WeightsInfo &weights_info, const ActivationLayerInfo &act_info, const GPUTarget gpu_target, const Size2D &dilation)
+                                                             const WeightsInfo &weights_info, const ActivationLayerInfo &act_info, const GPUTarget gpu_target, const Size2D &dilation, bool enable_fast_math)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input);
     ARM_COMPUTE_ERROR_ON_NULLPTR(output);
     ARM_COMPUTE_ERROR_ON_NULLPTR(weights);
-    ARM_COMPUTE_UNUSED(output);
     ARM_COMPUTE_UNUSED(weights_info);
     ARM_COMPUTE_UNUSED(gpu_target);
 
-    const size_t idx_w = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
-    const size_t idx_h = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
-    const size_t idx_c = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL);
-
-    if((input->data_type() == DataType::F32) && (input->data_layout() == DataLayout::NCHW) && (input->dimension(idx_c) > 3) && (weights->dimension(idx_w) == 3) && (weights->dimension(idx_h) == 3)
-       && (weights->num_dimensions() <= 4) && (conv_info.stride().first == 1) && (conv_info.stride().second == 1) && (dilation == Size2D(1U, 1U)) && (!act_info.enabled()))
+    if(dilation != Size2D(1U, 1U))
+    {
+        return ConvolutionMethod::GEMM;
+    }
+    else
     {
-        return ConvolutionMethod::WINOGRAD;
+        return bool(CLWinogradConvolutionLayer::validate(input, weights, nullptr, output, conv_info, act_info, enable_fast_math)) ? ConvolutionMethod::WINOGRAD : ConvolutionMethod::GEMM;
     }
-    return ConvolutionMethod::GEMM;
 }
 
 void CLConvolutionLayer::run()