From efbf6c8fd54159b26eda43eea7a12fce491ca13a Mon Sep 17 00:00:00 2001 From: giuros01 Date: Mon, 3 Sep 2018 09:53:53 +0100 Subject: [COMPMID-386] Github: Support SoftmaxLayer on different number of dimensions? Change-Id: I7422b977538ff29930a90f078badc2edee78af93 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/146638 Tested-by: Jenkins Reviewed-by: Georgios Pinitas --- src/runtime/CL/functions/CLSoftmaxLayer.cpp | 83 ++++++++++++++++++++--------- 1 file changed, 57 insertions(+), 26 deletions(-) (limited to 'src/runtime/CL/functions/CLSoftmaxLayer.cpp') diff --git a/src/runtime/CL/functions/CLSoftmaxLayer.cpp b/src/runtime/CL/functions/CLSoftmaxLayer.cpp index 3a7d6c770b..d6718467d5 100644 --- a/src/runtime/CL/functions/CLSoftmaxLayer.cpp +++ b/src/runtime/CL/functions/CLSoftmaxLayer.cpp @@ -36,34 +36,48 @@ namespace arm_compute { CLSoftmaxLayer::CLSoftmaxLayer(std::shared_ptr 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(), + : _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_flatten_kernel(const ICLTensor *input, const ICLTensor *output) +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_flatten_shape(input->info()); + const TensorShape shape_flatten = misc::shape_calculator::compute_softmax_shape(input->info(), axis); // Initialize the flat input - _input_flat.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_flatten)); + _input_flattened.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); + // 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(); + reshape_kernel_ptr->configure(input, &_input_flattened); + _flatten_kernel_ptr = std::move(reshape_kernel_ptr); + } + else + { + auto flatten_kernel_ptr = support::cpp14::make_unique(); + 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) +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)); - _needs_flattening = input->info()->num_dimensions() > 2; + // 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 @@ -71,16 +85,16 @@ void CLSoftmaxLayer::configure(const ICLTensor *input, ICLTensor *output, float // - Reshape the flattened output into the real output if(_needs_flattening) { - // Add to the memory manager _input_flat - _memory_group.manage(&_input_flat); + // Add to the memory manager _input_flattened + _memory_group.manage(&_input_flattened); - // Cofigure _flatten_kernel and _input_flat - configure_flatten_kernel(input, output); + // 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_flat : input); + const ICLTensor *input_2D = (_needs_flattening ? &_input_flattened : input); // Create intermediate tensors shapes TensorInfo input_info = input_2D->info()->clone()->reset_padding().set_is_resizable(true); @@ -106,18 +120,18 @@ void CLSoftmaxLayer::configure(const ICLTensor *input, ICLTensor *output, float if(_needs_flattening) { - // Add to the memory manager _output_flat - _memory_group.manage(&_output_flat); + // 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_flat, beta); + _norm_kernel.configure(&_tmp, &_sum, &_output_flattened, beta); // Reshape the flat output into a the requested (4D) output - _reshape_kernel.configure(&_output_flat, output); + _reshape_kernel.configure(&_output_flattened, output); // Allocate the intermediate flat tensors - _input_flat.allocator()->allocate(); - _output_flat.allocator()->allocate(); + _input_flattened.allocator()->allocate(); + _output_flattened.allocator()->allocate(); } else { @@ -131,10 +145,11 @@ void CLSoftmaxLayer::configure(const ICLTensor *input, ICLTensor *output, float _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() > 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(); @@ -145,26 +160,42 @@ Status CLSoftmaxLayer::validate(const ITensorInfo *input, const ITensorInfo *out 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)); + const bool needs_flattening = (axis != 1); - if(input->num_dimensions() > 2) // needs flattening + if(needs_flattening) { - ARM_COMPUTE_RETURN_ON_ERROR(CLFlattenLayerKernel::validate(input, &tensor_info_flat)); + 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{}; } void CLSoftmaxLayer::run() { _memory_group.acquire(); + if(_needs_flattening) { - CLScheduler::get().enqueue(_flatten_kernel, false); + CLScheduler::get().enqueue(*_flatten_kernel_ptr, false); } CLScheduler::get().enqueue(_max_shift_exp_sum_kernel, false); -- cgit v1.2.1