arm_compute v18.11
diff --git a/src/runtime/CL/functions/CLSoftmaxLayer.cpp b/src/runtime/CL/functions/CLSoftmaxLayer.cpp
index 7a20d9f..d671846 100644
--- a/src/runtime/CL/functions/CLSoftmaxLayer.cpp
+++ b/src/runtime/CL/functions/CLSoftmaxLayer.cpp
@@ -29,29 +29,80 @@
#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;
-
+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()
+ : _memory_group(std::move(memory_manager)), _max_shift_exp_sum_kernel(), _norm_kernel(), _flatten_kernel_ptr(), _reshape_kernel(), _max(), _sum(), _tmp(), _input_flattened(), _output_flattened(),
+ _needs_flattening(false)
{
}
-void CLSoftmaxLayer::configure(const ICLTensor *input, ICLTensor *output, float beta)
+void CLSoftmaxLayer::configure_reshape_input_kernel(const ICLTensor *input, const ICLTensor *output, size_t axis)
+{
+ // Flatten the input
+ const TensorShape shape_flatten = misc::shape_calculator::compute_softmax_shape(input->info(), axis);
+
+ // Initialize the flat input
+ _input_flattened.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_flatten));
+
+ // If we need to flatten the input, we can use CLFlattenKernel or CLReshapeKernel
+ // If flattening on the third axes, we use CLFlattenKernel.
+ // In all other cases we have to use CLReshapeKernel
+ if(axis != 3)
+ {
+ auto reshape_kernel_ptr = support::cpp14::make_unique<CLReshapeLayerKernel>();
+ reshape_kernel_ptr->configure(input, &_input_flattened);
+ _flatten_kernel_ptr = std::move(reshape_kernel_ptr);
+ }
+ else
+ {
+ auto flatten_kernel_ptr = support::cpp14::make_unique<CLFlattenLayerKernel>();
+ flatten_kernel_ptr->configure(input, &_input_flattened);
+ _flatten_kernel_ptr = std::move(flatten_kernel_ptr);
+ }
+
+ // 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, size_t axis)
{
// Perform validation step
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
- ARM_COMPUTE_ERROR_THROW_ON(CLSoftmaxLayer::validate(input->info(), output->info()));
+ ARM_COMPUTE_ERROR_THROW_ON(CLSoftmaxLayer::validate(input->info(), output->info(), beta, axis));
+
+ // We don't need flattening only in the case the input is 2D and axis is 1
+ _needs_flattening = axis != 1;
+
+ // 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_flattened
+ _memory_group.manage(&_input_flattened);
+
+ // Cofigure _flatten_kernel and _input_flattened
+ configure_reshape_input_kernel(input, output, axis);
+ }
+
+ // 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_flattened : 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 +116,28 @@
_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_flattened
+ _memory_group.manage(&_output_flattened);
+
+ // The normalization kernel stores the result in a flat output tensor
+ _norm_kernel.configure(&_tmp, &_sum, &_output_flattened, beta);
+
+ // Reshape the flat output into a the requested (4D) output
+ _reshape_kernel.configure(&_output_flattened, output);
+
+ // Allocate the intermediate flat tensors
+ _input_flattened.allocator()->allocate();
+ _output_flattened.allocator()->allocate();
+ }
+ else
+ {
+ // Softmax 2D case
+ _norm_kernel.configure(&_tmp, &_sum, output, beta);
+ }
// Allocate intermediate buffers
_tmp.allocator()->allocate();
@@ -74,10 +145,11 @@
_sum.allocator()->allocate();
}
-Status CLSoftmaxLayer::validate(const ITensorInfo *input, const ITensorInfo *output)
+Status CLSoftmaxLayer::validate(const ITensorInfo *input, const ITensorInfo *output, float beta, size_t axis)
{
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");
+ ARM_COMPUTE_UNUSED(beta);
// Create intermediate tensor info
DataType tmp_data_type = is_data_type_quantized_asymmetric(input->data_type()) ? DataType::S32 : input->data_type();
@@ -88,9 +160,32 @@
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 bool needs_flattening = (axis != 1);
+
+ if(needs_flattening)
+ {
+ const TensorShape shape_flatten = misc::shape_calculator::compute_softmax_shape(input, axis);
+ TensorInfo tensor_info_flat(input->clone()->set_tensor_shape(shape_flatten).set_is_resizable(true));
+
+ if(axis != 3)
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(CLReshapeLayerKernel::validate(input, &tensor_info_flat));
+ }
+ else
+ {
+ 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));
+ if(needs_flattening)
+ {
+ const TensorShape shape_flatten = misc::shape_calculator::compute_softmax_shape(input);
+ TensorInfo tensor_info_flat(input->clone()->set_tensor_shape(shape_flatten).set_is_resizable(true));
+ }
+
return Status{};
}
@@ -98,8 +193,21 @@
{
_memory_group.acquire();
- CLScheduler::get().enqueue(_max_shift_exp_sum_kernel, false);
- CLScheduler::get().enqueue(_norm_kernel);
+ if(_needs_flattening)
+ {
+ CLScheduler::get().enqueue(*_flatten_kernel_ptr, false);
+ }
+ CLScheduler::get().enqueue(_max_shift_exp_sum_kernel, false);
+ 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