Make Softmax kernels on OpenCL stateless
* ClSoftmaxKernel and ClSoftmax are created.
* ClSoftmaxKernel is now state-less and ClSoftmax handles
the internal tensors required for computation.
* add_const_tensor() is added to TensorPack not only
to have symmetric interface but also to benefit from
implicit conversion.
Implements: COMPMID-3998
Change-Id: I4f823121777be24260fd12b2cd71a6ff718c4eed
Signed-off-by: Sang-Hoon Park <sang-hoon.park@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5087
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/runtime/CL/functions/CLSoftmaxLayer.cpp b/src/runtime/CL/functions/CLSoftmaxLayer.cpp
index 93e63dd..938a10a 100644
--- a/src/runtime/CL/functions/CLSoftmaxLayer.cpp
+++ b/src/runtime/CL/functions/CLSoftmaxLayer.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2020 Arm Limited.
+ * Copyright (c) 2017-2021 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -22,34 +22,35 @@
* SOFTWARE.
*/
#include "arm_compute/runtime/CL/functions/CLSoftmaxLayer.h"
-
#include "arm_compute/core/CL/CLHelpers.h"
+#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/KernelDescriptors.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/CLScheduler.h"
-#include "src/core/CL/ICLKernel.h"
-#include "src/core/CL/kernels/CLFillBorderKernel.h"
-#include "src/core/CL/kernels/CLSoftmaxLayerKernel.h"
-#include "src/core/helpers/SoftmaxHelpers.h"
+#include "src/core/gpu/cl/kernels/ClSoftmaxKernel.h"
+#include "src/runtime/gpu/cl/operators/ClPermute.h"
+#include "src/runtime/gpu/cl/operators/ClSoftmax.h"
namespace arm_compute
{
+using OperatorType = opencl::ClSoftmax;
+
+template <bool IS_LOG>
+struct CLSoftmaxLayerGeneric<IS_LOG>::Impl
+{
+ const ICLTensor *src{ nullptr };
+ ICLTensor *dst{ nullptr };
+ std::unique_ptr<OperatorType> op{ nullptr };
+ MemoryGroup memory_group{};
+ std::vector<std::pair<TensorType, std::unique_ptr<CLTensor>>> workspace_tensors{};
+};
+
template <bool IS_LOG>
CLSoftmaxLayerGeneric<IS_LOG>::CLSoftmaxLayerGeneric(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(std::move(memory_manager)),
- _permute_input(),
- _permute_output(),
- _max_shift_exp_sum_kernel(std::make_unique<CLLogits1DMaxShiftExpSumKernel>()),
- _norm_kernel(std::make_unique<CLLogits1DNormKernel>()),
- _max(),
- _sum(),
- _tmp(),
- _input_permuted(),
- _output_permuted(),
- _needs_permute()
+ : _impl(std::make_unique<Impl>())
{
+ _impl->memory_group = MemoryGroup(std::move(memory_manager));
}
template <bool IS_LOG>
@@ -64,118 +65,59 @@
template <bool IS_LOG>
void CLSoftmaxLayerGeneric<IS_LOG>::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, float beta, int32_t axis)
{
- // Perform validation step
- ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
- ARM_COMPUTE_ERROR_THROW_ON(CLSoftmaxLayerGeneric<IS_LOG>::validate(input->info(), output->info(), beta, axis));
+ _impl->src = input;
+ _impl->dst = output;
+ _impl->op = std::make_unique<OperatorType>();
- const size_t actual_axis = static_cast<size_t>(wrap_around(axis, static_cast<int32_t>(input->info()->num_dimensions())));
-
- _needs_permute = actual_axis != 0;
- ICLTensor *tmp_output = output;
- const ICLTensor *tmp_input = _needs_permute ? &_input_permuted : input;
- if(_needs_permute)
- {
- _memory_group.manage(&_input_permuted);
- _memory_group.manage(&_output_permuted);
- _permute_input.configure(compile_context, input, &_input_permuted, softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis));
- tmp_output = &_output_permuted;
- }
-
- // Create intermediate tensors
- DataType tmp_data_type = is_data_type_quantized_asymmetric(tmp_input->info()->data_type()) ? DataType::S32 : tmp_input->info()->data_type();
- TensorInfo tensor_info_tmp(tmp_input->info()->clone()->set_data_type(tmp_data_type));
- _tmp.allocator()->init(tensor_info_tmp);
- TensorShape max_sum_shape = tmp_input->info()->tensor_shape();
- max_sum_shape.set(0, 1);
- _max.allocator()->init(tmp_input->info()->clone()->set_tensor_shape(max_sum_shape));
- _sum.allocator()->init(tmp_input->info()->clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type));
-
- // Set GPU target to kernels
- _max_shift_exp_sum_kernel->set_target(CLScheduler::get().target());
-
- // Manage intermediate buffers
- _memory_group.manage(&_tmp);
- _memory_group.manage(&_max);
- _memory_group.manage(&_sum);
-
- SoftmaxKernelInfo softmax_info;
- softmax_info.beta = beta;
- softmax_info.is_log = IS_LOG;
- softmax_info.input_data_type = tmp_input->info()->data_type();
-
- // Configure kernels
- _max_shift_exp_sum_kernel->configure(compile_context, tmp_input, &_max, &_tmp, &_sum, softmax_info);
- _norm_kernel->configure(compile_context, &_tmp, &_sum, tmp_output, softmax_info);
-
- // Allocate intermediate buffers
- _tmp.allocator()->allocate();
- _max.allocator()->allocate();
- _sum.allocator()->allocate();
- if(_needs_permute)
- {
- _permute_output.configure(compile_context, &_output_permuted, output, softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis));
- _input_permuted.allocator()->allocate();
- _output_permuted.allocator()->allocate();
- }
+ SoftmaxKernelInfo softmax_info{ beta, IS_LOG, input->info()->data_type(), axis };
+ _impl->op->configure(compile_context, *input->info(), *output->info(), softmax_info);
}
template <bool IS_LOG>
Status CLSoftmaxLayerGeneric<IS_LOG>::validate(const ITensorInfo *input, const ITensorInfo *output, float beta, int32_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);
- ARM_COMPUTE_RETURN_ERROR_ON(axis < static_cast<int32_t>(-input->num_dimensions()) || static_cast<int32_t>(input->num_dimensions()) <= axis);
+ SoftmaxKernelInfo softmax_info{ beta, IS_LOG, input->data_type(), axis };
+ return OperatorType::validate(*input, *output, softmax_info);
+}
- const size_t actual_axis = static_cast<size_t>(wrap_around(axis, static_cast<int32_t>(input->num_dimensions())));
- const bool needs_permute = actual_axis != 0;
- if(needs_permute)
+template <bool IS_LOG>
+void CLSoftmaxLayerGeneric<IS_LOG>::allocate_workspace()
+{
+ const auto memory_requirements = _impl->op->workspace();
+ std::for_each(memory_requirements.begin(), memory_requirements.end(), [this](const experimental::MemoryInfo & memory_info)
{
- const PermutationVector permutation_vector = softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis);
- const TensorShape permuted_shape = misc::shape_calculator::compute_permutation_output_shape(*input, permutation_vector);
- TensorInfo input_permuted(input->clone()->set_tensor_shape(permuted_shape));
- ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(input, &input_permuted, permutation_vector));
- TensorInfo output_permuted(output->clone()->set_tensor_shape(permuted_shape));
- ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(&output_permuted, output, permutation_vector));
- }
-
- // Create intermediate tensor info
- DataType tmp_data_type = is_data_type_quantized_asymmetric(input->data_type()) ? DataType::S32 : input->data_type();
- TensorInfo tensor_info_tmp(input->clone()->set_data_type(tmp_data_type).set_is_resizable(true));
-
- TensorShape max_sum_shape = input->tensor_shape();
- max_sum_shape.set(0, 1);
- 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));
-
- SoftmaxKernelInfo softmax_info;
- softmax_info.beta = beta;
- softmax_info.is_log = IS_LOG;
- softmax_info.input_data_type = input->data_type();
-
- 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, softmax_info));
-
- return Status{};
+ auto tensor_info = TensorInfo{ TensorShape(memory_info.size), 1, DataType::U8 };
+ _impl->workspace_tensors.emplace_back(memory_info.type, std::make_unique<CLTensor>());
+ auto tensor = _impl->workspace_tensors.back().second.get();
+ ARM_COMPUTE_ERROR_ON_NULLPTR(tensor);
+ _impl->memory_group.manage(tensor);
+ tensor->allocator()->init(tensor_info);
+ tensor->allocator()->allocate();
+ });
}
template <bool IS_LOG>
void CLSoftmaxLayerGeneric<IS_LOG>::run()
{
- MemoryGroupResourceScope scope_mg(_memory_group);
+ allocate_workspace();
- if(_needs_permute)
+ // Acquire all the temporaries
+ MemoryGroupResourceScope scope_mg(_impl->memory_group);
+
+ ARM_COMPUTE_ERROR_ON_NULLPTR(_impl->src, _impl->dst);
+
+ ITensorPack pack;
+ pack.add_tensor(TensorType::ACL_SRC, _impl->src);
+ pack.add_tensor(TensorType::ACL_DST, _impl->dst);
+
+ std::for_each(_impl->workspace_tensors.begin(), _impl->workspace_tensors.end(), [&pack](std::pair<TensorType, std::unique_ptr<CLTensor>> &wt)
{
- _permute_input.run();
- }
+ auto tensor = wt.second.get();
+ ARM_COMPUTE_ERROR_ON_NULLPTR(tensor);
+ pack.add_tensor(wt.first, tensor);
+ });
- CLScheduler::get().enqueue(*_max_shift_exp_sum_kernel, false);
- CLScheduler::get().enqueue(*_norm_kernel, !_needs_permute);
-
- if(_needs_permute)
- {
- _permute_output.run();
- }
+ _impl->op->run(pack);
}
template class CLSoftmaxLayerGeneric<false>;