diff options
Diffstat (limited to 'src/runtime/CL/functions/CLReductionOperation.cpp')
-rw-r--r-- | src/runtime/CL/functions/CLReductionOperation.cpp | 54 |
1 files changed, 14 insertions, 40 deletions
diff --git a/src/runtime/CL/functions/CLReductionOperation.cpp b/src/runtime/CL/functions/CLReductionOperation.cpp index 3aa5a813b6..2f9a38601d 100644 --- a/src/runtime/CL/functions/CLReductionOperation.cpp +++ b/src/runtime/CL/functions/CLReductionOperation.cpp @@ -33,30 +33,11 @@ #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/runtime/CL/CLScheduler.h" #include "arm_compute/runtime/Tensor.h" +#include "arm_compute/runtime/Utils.h" #include "support/ToolchainSupport.h" namespace arm_compute { -namespace -{ -unsigned int calculate_number_of_stages(const ITensorInfo *input, unsigned int axis) -{ - // We need only 1 stage for all axis except x-axis and x-axis for QASYMM8. - if(axis != 0 || (axis == 0 && is_data_type_quantized(input->data_type()))) - { - return 1; - } - // Calculate number of WGs. 16 elements per thread, 8 threads per WG - const unsigned int num_of_wg = ceil(input->dimension(0) / 128.f); - - // Calculate number of stages. First stage performs op and the rest reduction sum - // depending on the size of the input. Last stage should have only 1 WG. - const unsigned int num_of_stages = num_of_wg / 128 + 2; - - return num_of_stages; -} -} // namespace - CLReductionOperation::CLReductionOperation(std::shared_ptr<IMemoryManager> memory_manager) : _memory_group(std::move(memory_manager)), _results_vector(), _reduction_kernels_vector(), _border_handlers_vector(), _reshape_kernel(), _op(), _num_of_stages(), _reduction_axis(), _is_serial(), _is_reshape_required(false) @@ -65,15 +46,15 @@ CLReductionOperation::CLReductionOperation(std::shared_ptr<IMemoryManager> memor Status CLReductionOperation::validate(const ITensorInfo *input, const ITensorInfo *output, unsigned int axis, ReductionOperation op, bool keep_dims) { + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis >= TensorShape::num_max_dimensions, "Reduction axis greater than max number of dimensions"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis > 3, "Unsupported reduction axis"); - const unsigned int num_of_stages = calculate_number_of_stages(input, axis); + const unsigned int num_of_stages = calculate_number_of_stages_only_x_axis(input->dimension(0), axis); const bool is_serial = needs_serialized_reduction(op, input->data_type(), axis); - const bool is_arg_min_max = (op == ReductionOperation::ARG_IDX_MAX) || (op == ReductionOperation::ARG_IDX_MIN); - const bool is_reshape_required = !keep_dims || is_arg_min_max; + const bool is_reshape_required = !keep_dims; - if(is_reshape_required) + if(is_reshape_required && output->total_size() != 0) { const TensorInfo expected_output_shape = output->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_reduced_shape(input->tensor_shape(), axis, keep_dims)); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&expected_output_shape, output); @@ -86,7 +67,7 @@ Status CLReductionOperation::validate(const ITensorInfo *input, const ITensorInf const auto input_data_type = input->data_type(); const auto input_num_channles = input->num_channels(); const auto input_qinfo = input->quantization_info(); - const auto output_data_type = is_arg_min_max ? DataType::S32 : output->data_type(); + const auto output_data_type = output->data_type(); auto initialize_tensorinfo = [](TensorInfo & ti, TensorShape shape, DataType data_type, int num_channels, QuantizationInfo qinfo) { @@ -184,8 +165,7 @@ ICLTensor *CLReductionOperation::configure_intermediate_result_vector(ICLTensor return output; } - auto intermediate_result_vector_size = _is_serial ? 1 : _num_of_stages; - const auto is_arg_min_max = (_op == ReductionOperation::ARG_IDX_MAX || _op == ReductionOperation::ARG_IDX_MIN); + auto intermediate_result_vector_size = _is_serial ? 1 : _num_of_stages; if(!_is_reshape_required) { @@ -206,30 +186,24 @@ ICLTensor *CLReductionOperation::configure_intermediate_result_vector(ICLTensor v.allocator()->init(input->info()->clone()->set_tensor_shape(shape)); } - if(is_arg_min_max) - { - _results_vector.back().info()->set_data_type(DataType::S32).set_is_resizable(true).reset_padding(); - } - return _is_reshape_required ? &_results_vector.back() : output; } void CLReductionOperation::configure(ICLTensor *input, ICLTensor *output, unsigned int axis, ReductionOperation op, bool keep_dims) { - _op = op; - _num_of_stages = calculate_number_of_stages(input->info(), axis); - _reduction_axis = axis; - _is_serial = needs_serialized_reduction(op, input->info()->data_type(), axis); - const bool is_arg_min_max = (op == ReductionOperation::ARG_IDX_MAX) || (op == ReductionOperation::ARG_IDX_MIN); - _is_reshape_required = !keep_dims || is_arg_min_max; + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + _op = op; + _num_of_stages = calculate_number_of_stages_only_x_axis(input->info()->dimension(0), axis); + _reduction_axis = axis; + _is_serial = needs_serialized_reduction(op, input->info()->data_type(), axis); + _is_reshape_required = !keep_dims; auto *output_internal = configure_intermediate_result_vector(input, output); - // ArgMinMax might not give initialized output tensor, so initialize here. if(_is_reshape_required) { const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_reduced_shape(input->info()->tensor_shape(), axis, false); - const auto output_data_type = is_arg_min_max ? DataType::S32 : input->info()->data_type(); + const auto output_data_type = input->info()->data_type(); auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape).set_data_type(output_data_type).reset_padding().set_is_resizable(true)); } |