COMPMID-3391: Implement Async interfaces
Change-Id: I8168cea5056ff48a0253ebb8c88ea549a3ea69a2
Signed-off-by: Michalis Spyrou <michalis.spyrou@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/3335
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
diff --git a/src/runtime/NEON/INEOperator.cpp b/src/runtime/NEON/INEOperator.cpp
new file mode 100644
index 0000000..c24d5c4
--- /dev/null
+++ b/src/runtime/NEON/INEOperator.cpp
@@ -0,0 +1,53 @@
+/*
+ * Copyright (c) 2020 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/runtime/NEON/INEOperator.h"
+#include "arm_compute/runtime/NEON/NEScheduler.h"
+
+namespace arm_compute
+{
+namespace experimental
+{
+INEOperator::INEOperator(IRuntimeContext *ctx)
+ : _kernel(), _ctx(ctx), _workspace()
+{
+}
+
+void INEOperator::run(std::vector<InputOperatorTensors *> &inputs, std::vector<OutputOperatorTensors *> &outputs, std::vector<OperatorTensors *> &workspace)
+{
+ ARM_COMPUTE_UNUSED(workspace);
+
+ if(inputs.empty() || outputs.empty())
+ {
+ ARM_COMPUTE_ERROR("No inputs provided");
+ }
+
+ NEScheduler::get().schedule_op(_kernel.get(), Window::DimY, inputs, outputs);
+}
+
+void INEOperator::prepare(std::vector<OperatorTensors *> constants)
+{
+ ARM_COMPUTE_UNUSED(constants);
+}
+} // namespace experimental
+} // namespace arm_compute
diff --git a/src/runtime/NEON/functions/NEGenerateProposalsLayer.cpp b/src/runtime/NEON/functions/NEGenerateProposalsLayer.cpp
index 82880ba..dabbeba 100644
--- a/src/runtime/NEON/functions/NEGenerateProposalsLayer.cpp
+++ b/src/runtime/NEON/functions/NEGenerateProposalsLayer.cpp
@@ -31,9 +31,9 @@
NEGenerateProposalsLayer::NEGenerateProposalsLayer(std::shared_ptr<IMemoryManager> memory_manager)
: _memory_group(memory_manager),
_permute_deltas_kernel(),
- _flatten_deltas_kernel(),
+ _flatten_deltas(),
_permute_scores_kernel(),
- _flatten_scores_kernel(),
+ _flatten_scores(),
_compute_anchors_kernel(),
_bounding_box_kernel(),
_pad_kernel(),
@@ -95,12 +95,12 @@
{
_memory_group.manage(&_deltas_permuted);
_permute_deltas_kernel.configure(deltas, &_deltas_permuted, PermutationVector{ 2, 0, 1 });
- _flatten_deltas_kernel.configure(&_deltas_permuted, &_deltas_flattened);
+ _flatten_deltas.configure(&_deltas_permuted, &_deltas_flattened);
_deltas_permuted.allocator()->allocate();
}
else
{
- _flatten_deltas_kernel.configure(deltas, &_deltas_flattened);
+ _flatten_deltas.configure(deltas, &_deltas_flattened);
}
const TensorShape flatten_shape_scores(1, total_num_anchors);
@@ -112,12 +112,12 @@
{
_memory_group.manage(&_scores_permuted);
_permute_scores_kernel.configure(scores, &_scores_permuted, PermutationVector{ 2, 0, 1 });
- _flatten_scores_kernel.configure(&_scores_permuted, &_scores_flattened);
+ _flatten_scores.configure(&_scores_permuted, &_scores_flattened);
_scores_permuted.allocator()->allocate();
}
else
{
- _flatten_scores_kernel.configure(scores, &_scores_flattened);
+ _flatten_scores.configure(scores, &_scores_flattened);
}
Tensor *anchors_to_use = &_all_anchors;
@@ -244,12 +244,12 @@
}
TensorInfo deltas_flattened_info(deltas->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true));
- ARM_COMPUTE_RETURN_ON_ERROR(NEReshapeLayerKernel::validate(&deltas_permuted_info, &deltas_flattened_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEReshapeLayer::validate(&deltas_permuted_info, &deltas_flattened_info));
TensorInfo scores_flattened_info(scores->clone()->set_tensor_shape(TensorShape(1, total_num_anchors)).set_is_resizable(true));
TensorInfo proposals_4_roi_values(deltas->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true));
- ARM_COMPUTE_RETURN_ON_ERROR(NEReshapeLayerKernel::validate(&scores_permuted_info, &scores_flattened_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEReshapeLayer::validate(&scores_permuted_info, &scores_flattened_info));
TensorInfo *proposals_4_roi_values_to_use = &proposals_4_roi_values;
TensorInfo proposals_4_roi_values_quantized(deltas->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true));
@@ -327,8 +327,8 @@
NEScheduler::get().schedule(&_permute_scores_kernel, Window::DimY);
}
- NEScheduler::get().schedule(&_flatten_deltas_kernel, Window::DimY);
- NEScheduler::get().schedule(&_flatten_scores_kernel, Window::DimY);
+ _flatten_deltas.run();
+ _flatten_scores.run();
if(_is_qasymm8)
{
diff --git a/src/runtime/NEON/functions/NEReductionOperation.cpp b/src/runtime/NEON/functions/NEReductionOperation.cpp
index 80ebe67..a895147 100644
--- a/src/runtime/NEON/functions/NEReductionOperation.cpp
+++ b/src/runtime/NEON/functions/NEReductionOperation.cpp
@@ -54,7 +54,7 @@
} // namespace
NEReductionOperation::NEReductionOperation(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(memory_manager), _reduction_kernel(), _fill_border_kernel(), _reshape_kernel(), _output_internal(), _window_split(0), _reduction_axis(), _is_reshape_required(false)
+ : _memory_group(memory_manager), _reduction_kernel(), _fill_border_kernel(), _reshape(), _output_internal(), _window_split(0), _reduction_axis(), _is_reshape_required(false)
{
}
@@ -91,7 +91,7 @@
if(is_reshape_required)
{
- ARM_COMPUTE_RETURN_ON_ERROR(NEReshapeLayerKernel::validate(output_internal, output));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEReshapeLayer::validate(output_internal, output));
}
return Status{};
@@ -171,7 +171,7 @@
if(_is_reshape_required)
{
- _reshape_kernel.configure(output_internal, output);
+ _reshape.configure(output_internal, output);
_output_internal.allocator()->allocate();
}
}
@@ -185,7 +185,7 @@
NEScheduler::get().schedule(&_reduction_kernel, _window_split);
if(_is_reshape_required)
{
- NEScheduler::get().schedule(&_reshape_kernel, Window::DimY);
+ _reshape.run();
}
}
} // namespace arm_compute
diff --git a/src/runtime/NEON/functions/NEReshapeLayer.cpp b/src/runtime/NEON/functions/NEReshapeLayer.cpp
index 0a9f42d..680abef 100644
--- a/src/runtime/NEON/functions/NEReshapeLayer.cpp
+++ b/src/runtime/NEON/functions/NEReshapeLayer.cpp
@@ -25,13 +25,17 @@
#include "arm_compute/core/NEON/kernels/NEReshapeLayerKernel.h"
#include "arm_compute/core/Validate.h"
+#include "arm_compute/runtime/NEON/NEScheduler.h"
+#include "arm_compute/runtime/Types.h"
#include "support/MemorySupport.h"
#include <utility>
namespace arm_compute
{
-void NEReshapeLayer::configure(const ITensor *input, ITensor *output)
+namespace experimental
+{
+void NEReshapeLayer::configure(const ITensorInfo *input, ITensorInfo *output)
{
auto k = arm_compute::support::cpp14::make_unique<NEReshapeLayerKernel>();
k->configure(input, output);
@@ -40,9 +44,41 @@
Status NEReshapeLayer::validate(const ITensorInfo *input, const ITensorInfo *output)
{
+ return arm_compute::NEReshapeLayer::validate(input, output);
+}
+
+MemoryRequirements NEReshapeLayer::workspace() const
+{
+ return MemoryRequirements{};
+}
+} // namespace experimental
+
+void NEReshapeLayer::configure(const ITensor *input, ITensor *output)
+{
+ _input = input;
+ _output = output;
+
+ auto k = arm_compute::support::cpp14::make_unique<NEReshapeLayerKernel>();
+ k->configure(input->info(), output->info());
+ _kernel = std::move(k);
+}
+
+Status NEReshapeLayer::validate(const ITensorInfo *input, const ITensorInfo *output)
+{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_RETURN_ON_ERROR(NEReshapeLayerKernel::validate(input, output));
return Status{};
}
+
+void NEReshapeLayer::run()
+{
+ InputOperatorTensors src_0 = std::make_pair(TensorType::ACL_SRC, _input);
+ OutputOperatorTensors dst_0 = std::make_pair(TensorType::ACL_DST, _output);
+
+ std::vector<InputOperatorTensors *> inputs = { &src_0 };
+ std::vector<OutputOperatorTensors *> outputs = { &dst_0 };
+
+ NEScheduler::get().schedule_op(_kernel.get(), Window::DimY, inputs, outputs);
+}
} // namespace arm_compute
diff --git a/src/runtime/NEON/functions/NESoftmaxLayer.cpp b/src/runtime/NEON/functions/NESoftmaxLayer.cpp
index 5509ede..5cd6a55 100644
--- a/src/runtime/NEON/functions/NESoftmaxLayer.cpp
+++ b/src/runtime/NEON/functions/NESoftmaxLayer.cpp
@@ -32,8 +32,8 @@
{
template <bool IS_LOG>
NESoftmaxLayerGeneric<IS_LOG>::NESoftmaxLayerGeneric(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(std::move(memory_manager)), _max_kernel(), _softmax_kernel(), _flat_or_reshape_kernel_ptr(nullptr), _fill_border_kernel(), _reshape_kernel(), _max(), _tmp(), _input_flattened(),
- _output_flattened(), _needs_flattening(false)
+ : _memory_group(std::move(memory_manager)), _max_kernel(), _softmax_kernel(), _flat_or_reshape_ptr(nullptr), _fill_border_kernel(), _reshape(), _max(), _tmp(), _input_flattened(), _output_flattened(),
+ _needs_flattening(false)
{
}
@@ -46,23 +46,20 @@
// 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 NEFlattenKernel or NEReshapeKernel
- // If the number of reduced axes is 3 (max dimension), which means collapsing all axes except the batch axis, we use NEFlattenKernel.
- // In all other cases we have to use NEReshapeKernel
// Note that the "other cases" include both:
// 1. first_n_reduce_axes < 3: Reduce the first 1 (no need to reduce) or 2 dimensions (inclusive)
// 2. first_n_reduce_axes == 4: Reduce all 4 dimensions. This can only be handled by NEReshapeKernel instead of NEFlattenKernel.
if(first_n_reduce_axes == 3)
{
- auto flatten_kernel_ptr = support::cpp14::make_unique<NEFlattenLayerKernel>();
+ auto flatten_kernel_ptr = support::cpp14::make_unique<NEFlattenLayer>();
flatten_kernel_ptr->configure(input, &_input_flattened);
- _flat_or_reshape_kernel_ptr = std::move(flatten_kernel_ptr);
+ _flat_or_reshape_ptr = std::move(flatten_kernel_ptr);
}
else
{
- auto reshape_kernel_ptr = support::cpp14::make_unique<NEReshapeLayerKernel>();
+ auto reshape_kernel_ptr = support::cpp14::make_unique<NEReshapeLayer>();
reshape_kernel_ptr->configure(input, &_input_flattened);
- _flat_or_reshape_kernel_ptr = std::move(reshape_kernel_ptr);
+ _flat_or_reshape_ptr = std::move(reshape_kernel_ptr);
}
// We need to init the output tensor here. Indeed, the reshape kernel expects
@@ -127,7 +124,7 @@
_input_flattened.allocator()->allocate();
// Reshape the flat output into the requested (4D) output
- _reshape_kernel.configure(&_output_flattened, output);
+ _reshape.configure(&_output_flattened, output);
// Allocate the intermediate flat tensors
_output_flattened.allocator()->allocate();
@@ -174,11 +171,11 @@
if(first_n_reduce_axes == 3)
{
- ARM_COMPUTE_RETURN_ON_ERROR(NEFlattenLayerKernel::validate(input, &tensor_info_flat));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEFlattenLayer::validate(input, &tensor_info_flat));
}
else
{
- ARM_COMPUTE_RETURN_ON_ERROR(NEReshapeLayerKernel::validate(input, &tensor_info_flat));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEReshapeLayer::validate(input, &tensor_info_flat));
}
}
@@ -195,7 +192,7 @@
if(_needs_flattening)
{
- NEScheduler::get().schedule(_flat_or_reshape_kernel_ptr.get(), Window::DimY);
+ _flat_or_reshape_ptr->run();
}
NEScheduler::get().schedule(&_fill_border_kernel, Window::DimY);
@@ -204,11 +201,11 @@
if(_needs_flattening)
{
- NEScheduler::get().schedule(&_reshape_kernel, Window::DimY);
+ _reshape.run();
}
}
template class NESoftmaxLayerGeneric<false>;
template class NESoftmaxLayerGeneric<true>;
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute