[COMPMID-1353] Add support for 4D Softmax layer on OpenCL
Change-Id: I4342d4240fe5b1aab234c015684a1216c3990a5f
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/145631
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
diff --git a/src/runtime/CL/functions/CLSoftmaxLayer.cpp b/src/runtime/CL/functions/CLSoftmaxLayer.cpp
index 7a20d9f..3a7d6c7 100644
--- a/src/runtime/CL/functions/CLSoftmaxLayer.cpp
+++ b/src/runtime/CL/functions/CLSoftmaxLayer.cpp
@@ -29,14 +29,32 @@
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/runtime/CL/CLMemoryGroup.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
-using namespace arm_compute;
-
-CLSoftmaxLayer::CLSoftmaxLayer(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(std::move(memory_manager)), _max_shift_exp_sum_kernel(), _norm_kernel(), _max(), _sum(), _tmp()
+namespace arm_compute
{
+CLSoftmaxLayer::CLSoftmaxLayer(std::shared_ptr<IMemoryManager> memory_manager)
+ : _memory_group(std::move(memory_manager)), _max_shift_exp_sum_kernel(), _norm_kernel(), _flatten_kernel(), _reshape_kernel(), _max(), _sum(), _tmp(), _input_flat(), _output_flat(),
+ _needs_flattening(false)
+{
+}
+
+void CLSoftmaxLayer::configure_flatten_kernel(const ICLTensor *input, const ICLTensor *output)
+{
+ // Flatten the input
+ const TensorShape shape_flatten = misc::shape_calculator::compute_flatten_shape(input->info());
+
+ // Initialize the flat input
+ _input_flat.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_flatten));
+
+ // Configure the flatten_kernel
+ _flatten_kernel.configure(input, &_input_flat);
+
+ // We need to init the output tensor here. Indeed, the reshape kernel expects
+ // both tensors to be already initialized
+ auto_init_if_empty(*output->info(), *input->info()->clone());
}
void CLSoftmaxLayer::configure(const ICLTensor *input, ICLTensor *output, float beta)
@@ -45,13 +63,32 @@
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_ERROR_THROW_ON(CLSoftmaxLayer::validate(input->info(), output->info()));
+ _needs_flattening = input->info()->num_dimensions() > 2;
+
+ // If we are dealing with a 4D tensor, we will:
+ // - Flatten the input, so that we end up with a [width*height*depth] * batches 2D tensor
+ // - Execute all the pipeline (reduction + normalization) on the flattened tensor
+ // - Reshape the flattened output into the real output
+ if(_needs_flattening)
+ {
+ // Add to the memory manager _input_flat
+ _memory_group.manage(&_input_flat);
+
+ // Cofigure _flatten_kernel and _input_flat
+ configure_flatten_kernel(input, output);
+ }
+
+ // We want to deal with a 2D input. Either it is the flattened version of the original input (4D case)
+ // or it is the original input case (2D case)
+ const ICLTensor *input_2D = (_needs_flattening ? &_input_flat : input);
+
// Create intermediate tensors shapes
- const TensorInfo input_info = input->info()->clone()->reset_padding().set_is_resizable(true);
- DataType tmp_data_type = is_data_type_quantized_asymmetric(input->info()->data_type()) ? DataType::S32 : input->info()->data_type();
- TensorInfo tensor_info_tmp(input_info.clone()->set_data_type(tmp_data_type));
+ TensorInfo input_info = input_2D->info()->clone()->reset_padding().set_is_resizable(true);
+ DataType tmp_data_type = is_data_type_quantized_asymmetric(input_2D->info()->data_type()) ? DataType::S32 : input_2D->info()->data_type();
+ TensorInfo tensor_info_tmp(input_info.clone()->set_data_type(tmp_data_type));
_tmp.allocator()->init(tensor_info_tmp);
- TensorShape max_sum_shape = input->info()->tensor_shape();
+ TensorShape max_sum_shape = input_2D->info()->tensor_shape();
max_sum_shape.set(0, 1);
_max.allocator()->init(input_info.clone()->set_tensor_shape(max_sum_shape));
_sum.allocator()->init(input_info.clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type));
@@ -65,8 +102,28 @@
_memory_group.manage(&_sum);
// Configure kernels
- _max_shift_exp_sum_kernel.configure(input, &_max, &_tmp, &_sum, beta);
- _norm_kernel.configure(&_tmp, &_sum, output, beta);
+ _max_shift_exp_sum_kernel.configure(input_2D, &_max, &_tmp, &_sum, beta);
+
+ if(_needs_flattening)
+ {
+ // Add to the memory manager _output_flat
+ _memory_group.manage(&_output_flat);
+
+ // The normalization kernel stores the result in a flat output tensor
+ _norm_kernel.configure(&_tmp, &_sum, &_output_flat, beta);
+
+ // Reshape the flat output into a the requested (4D) output
+ _reshape_kernel.configure(&_output_flat, output);
+
+ // Allocate the intermediate flat tensors
+ _input_flat.allocator()->allocate();
+ _output_flat.allocator()->allocate();
+ }
+ else
+ {
+ // Softmax 2D case
+ _norm_kernel.configure(&_tmp, &_sum, output, beta);
+ }
// Allocate intermediate buffers
_tmp.allocator()->allocate();
@@ -77,7 +134,7 @@
Status CLSoftmaxLayer::validate(const ITensorInfo *input, const ITensorInfo *output)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->num_dimensions() > 2, "Only 2D inputs are supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->num_dimensions() > 4, "Only up to 4 dimensions are supported");
// Create intermediate tensor info
DataType tmp_data_type = is_data_type_quantized_asymmetric(input->data_type()) ? DataType::S32 : input->data_type();
@@ -88,6 +145,14 @@
TensorInfo tensor_info_max(input->clone()->set_tensor_shape(max_sum_shape).set_is_resizable(true));
TensorInfo tensor_info_sum(input->clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type).set_quantization_info(QuantizationInfo()).set_is_resizable(true));
+ const TensorShape shape_flatten = misc::shape_calculator::compute_flatten_shape(input);
+ TensorInfo tensor_info_flat(input->clone()->set_tensor_shape(shape_flatten).set_is_resizable(true));
+
+ if(input->num_dimensions() > 2) // needs flattening
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(CLFlattenLayerKernel::validate(input, &tensor_info_flat));
+ }
+
ARM_COMPUTE_RETURN_ON_ERROR(CLLogits1DMaxShiftExpSumKernel::validate(input, &tensor_info_max, &tensor_info_tmp, &tensor_info_sum));
ARM_COMPUTE_RETURN_ON_ERROR(CLLogits1DNormKernel::validate(&tensor_info_tmp, &tensor_info_sum, output));
@@ -97,9 +162,21 @@
void CLSoftmaxLayer::run()
{
_memory_group.acquire();
+ if(_needs_flattening)
+ {
+ CLScheduler::get().enqueue(_flatten_kernel, false);
+ }
CLScheduler::get().enqueue(_max_shift_exp_sum_kernel, false);
- CLScheduler::get().enqueue(_norm_kernel);
+ CLScheduler::get().enqueue(_norm_kernel, !_needs_flattening);
+ if(_needs_flattening)
+ {
+ CLScheduler::get().enqueue(_reshape_kernel, true);
+ }
+
+ // Relase intermediate buffers
_memory_group.release();
}
+
+} // namespace arm_compute