Fix incorrect memory handling in ported functions
Details of the functions:
- ClSoftmax
- CpuSoftmax
- CpuPool2d
Change-Id: Icd2c14d5df010c3b2301e2693ce6f414d7c61916
Resolves: COMPMID-4404
Signed-off-by: Manuel Bottini <manuel.bottini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5797
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/NEON/functions/NESoftmaxLayer.cpp b/src/runtime/NEON/functions/NESoftmaxLayer.cpp
index 3f1e43a..af8546d 100644
--- a/src/runtime/NEON/functions/NESoftmaxLayer.cpp
+++ b/src/runtime/NEON/functions/NESoftmaxLayer.cpp
@@ -23,6 +23,7 @@
*/
#include "arm_compute/runtime/NEON/functions/NESoftmaxLayer.h"
#include "arm_compute/core/Validate.h"
+#include "arm_compute/runtime/MemoryGroup.h"
#include "arm_compute/runtime/Tensor.h"
#include "src/core/cpu/kernels/CpuSoftmaxKernel.h"
#include "src/core/helpers/SoftmaxHelpers.h"
@@ -36,16 +37,17 @@
const ITensor *src{ nullptr };
ITensor *dst{ nullptr };
Tensor max{ nullptr };
- Tensor tmp{ nullptr };
- Tensor input_permuted{ nullptr };
- Tensor output_permuted{ nullptr };
std::unique_ptr<cpu::CpuSoftmaxGeneric<IS_LOG>> op{ nullptr };
+ MemoryGroup memory_group{};
+ ITensorPack run_pack{};
+ WorkspaceData<Tensor> workspace_tensors{};
};
template <bool IS_LOG>
NESoftmaxLayerGeneric<IS_LOG>::NESoftmaxLayerGeneric(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(std::move(memory_manager)), _impl(std::make_unique<Impl>())
+ : _impl(std::make_unique<Impl>())
{
+ _impl->memory_group = MemoryGroup(std::move(memory_manager));
}
template <bool IS_LOG>
@@ -65,64 +67,8 @@
_impl->op = std::make_unique<cpu::CpuSoftmaxGeneric<IS_LOG>>();
_impl->op->configure(input->info(), output->info(), beta, axis);
- const unsigned int actual_axis = static_cast<unsigned int>(wrap_around(axis, static_cast<int32_t>(input->info()->num_dimensions())));
- const bool needs_permute = actual_axis > 0;
- if(needs_permute)
- {
- // Add to the memory manager _input_permuted
- auto permute_input = std::make_unique<cpu::CpuPermute>();
- _memory_group.manage(&_impl->input_permuted);
- permute_input->configure(input->info(), _impl->input_permuted.info(), softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis));
- }
-
- // We want to deal with a 2D input. Either it is the permuted version of the original input (4D case)
- // or it is the original input case (2D case)
- ITensor *tmp_input = (needs_permute ? &_impl->input_permuted : input);
-
- // Create intermediate tensors shapes
- const TensorInfo input_info = tmp_input->info()->clone()->reset_padding().set_is_resizable(true);
- DataType tmp_data_type = is_data_type_quantized_asymmetric(tmp_input->info()->data_type()) ? DataType::F32 : tmp_input->info()->data_type();
- TensorInfo tensor_info_tmp(input_info.clone()->set_data_type(tmp_data_type));
-
- // Init intermediate tensors
- TensorShape max_sum_shape = tmp_input->info()->tensor_shape();
- max_sum_shape.set(0, 1);
- _impl->max.allocator()->init(input_info.clone()->set_tensor_shape(max_sum_shape));
- _impl->tmp.allocator()->init(tensor_info_tmp);
-
- // Manage intermediate buffers
- _memory_group.manage(&_impl->max);
- _memory_group.manage(&_impl->tmp);
-
- // Configure kernels
- auto max_kernel = std::make_unique<cpu::kernels::CpuLogits1DMaxKernel>();
- auto softmax_kernel = std::make_unique<cpu::kernels::CpuLogits1DSoftmaxKernel<IS_LOG>>();
- max_kernel->configure(tmp_input->info(), _impl->max.info());
-
- if(needs_permute)
- {
- auto permute_output = std::make_unique<cpu::CpuPermute>();
- // Add to the memory manager _output_permuted
- _memory_group.manage(&_impl->output_permuted);
-
- // The normalization kernel stores the result in a permuted output tensor
- softmax_kernel->configure(tmp_input->info(), _impl->max.info(), _impl->output_permuted.info(), beta, _impl->tmp.info());
- _impl->input_permuted.allocator()->allocate();
-
- // Re-permute the permuted output into the requested (4D) output
- permute_output->configure(_impl->output_permuted.info(), output->info(), softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis));
-
- // Allocate the intermediate permuted tensors
- _impl->output_permuted.allocator()->allocate();
- }
- else
- {
- softmax_kernel->configure(tmp_input->info(), _impl->max.info(), output->info(), beta, _impl->tmp.info());
- }
-
- // Allocate intermediate buffers
- _impl->max.allocator()->allocate();
- _impl->tmp.allocator()->allocate();
+ _impl->run_pack = { { TensorType::ACL_SRC, _impl->src }, { TensorType::ACL_DST, _impl->dst } };
+ _impl->workspace_tensors = manage_workspace<Tensor>(_impl->op->workspace(), _impl->memory_group, _impl->run_pack);
}
template <bool IS_LOG>
@@ -136,15 +82,10 @@
template <bool IS_LOG>
void NESoftmaxLayerGeneric<IS_LOG>::run()
{
- MemoryGroupResourceScope scope_mg(_memory_group);
- ITensorPack pack;
- pack.add_tensor(TensorType::ACL_SRC, _impl->src);
- pack.add_tensor(TensorType::ACL_DST, _impl->dst);
- pack.add_tensor(TensorType::ACL_INT_0, &_impl->tmp);
- pack.add_tensor(TensorType::ACL_INT_1, &_impl->max);
- pack.add_tensor(TensorType::ACL_INT_2, &_impl->input_permuted);
- pack.add_tensor(TensorType::ACL_INT_3, &_impl->output_permuted);
- _impl->op->run(pack);
+ // Acquire all the temporaries
+ MemoryGroupResourceScope scope_mg(_impl->memory_group);
+ ARM_COMPUTE_ERROR_ON_NULLPTR(_impl->src, _impl->dst);
+ _impl->op->run(_impl->run_pack);
}
template class NESoftmaxLayerGeneric<false>;