134 lines
4.8 KiB
C++
134 lines
4.8 KiB
C++
/*******************************************************************************
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* Copyright (c) 2019 Konduit K.K.
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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#ifndef DEV_TESTSARMCOMPUTEUTILS_H
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#define DEV_TESTSARMCOMPUTEUTILS_H
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#include <legacy/NativeOps.h>
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#include <array/NDArray.h>
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#include <graph/Context.h>
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#include <ops/declarable/PlatformHelper.h>
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#include <system/platform_boilerplate.h>
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#include <arm_compute/runtime/NEON/NEFunctions.h>
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#include <arm_compute/core/Types.h>
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#include <arm_compute/core/TensorInfo.h>
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#include <arm_compute/core/TensorShape.h>
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#include <arm_compute/core/Strides.h>
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#include <arm_compute/core/Helpers.h>
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#include <arm_compute/core/ITensor.h>
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#include <arm_compute/core/Types.h>
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#include <arm_compute/core/Validate.h>
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#include <arm_compute/core/Window.h>
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#include <arm_compute/runtime/Tensor.h>
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#include <arm_compute/runtime/TensorAllocator.h>
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#include <iostream>
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using namespace samediff;
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namespace sd {
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namespace ops {
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namespace platforms {
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using Arm_DataType = arm_compute::DataType;
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using Arm_Tensor = arm_compute::Tensor;
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using Arm_ITensor = arm_compute::ITensor;
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using Arm_TensorInfo = arm_compute::TensorInfo;
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using Arm_TensorShape = arm_compute::TensorShape;
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using Arm_Strides = arm_compute::Strides;
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/**
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* Here we actually declare our platform helpers
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*/
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DECLARE_PLATFORM(maxpool2d, ENGINE_CPU);
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DECLARE_PLATFORM(avgpool2d, ENGINE_CPU);
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//utils
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Arm_DataType getArmType(const sd::DataType& dType);
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Arm_TensorInfo getArmTensorInfo(int rank, Nd4jLong* bases, sd::DataType ndArrayType, arm_compute::DataLayout layout = arm_compute::DataLayout::UNKNOWN);
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Arm_TensorInfo getArmTensorInfo(const NDArray& arr, arm_compute::DataLayout layout = arm_compute::DataLayout::UNKNOWN);
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Arm_Tensor getArmTensor(const NDArray& arr, arm_compute::DataLayout layout = arm_compute::DataLayout::UNKNOWN);
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void copyFromTensor(const Arm_Tensor& inTensor, NDArray& output);
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void copyToTensor(const NDArray& input, Arm_Tensor& outTensor);
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void print_tensor(Arm_ITensor& tensor, const char* msg);
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bool isArmcomputeFriendly(const NDArray& arr);
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template<typename F>
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class ArmFunction {
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public:
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template<typename ...Args>
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void configure(NDArray *input , NDArray *output, arm_compute::DataLayout layout, Args&& ...args) {
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auto inInfo = getArmTensorInfo(*input, layout);
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auto outInfo = getArmTensorInfo(*output, layout);
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in.allocator()->init(inInfo);
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out.allocator()->init(outInfo);
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armFunction.configure(&in,&out,std::forward<Args>(args) ...);
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if (in.info()->has_padding()) {
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//allocate and copy
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in.allocator()->allocate();
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//copy
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copyToTensor(*input, in);
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}
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else {
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//import buffer
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void* buff = input->buffer();
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in.allocator()->import_memory(buff);
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}
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if (out.info()->has_padding()) {
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//store pointer to our array to copy after run
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out.allocator()->allocate();
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outNd = output;
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}
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else {
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//import
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void* buff = output->buffer();
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out.allocator()->import_memory(buff);
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}
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}
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void run() {
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armFunction.run();
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if (outNd) {
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copyFromTensor(out, *outNd);
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}
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}
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private:
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Arm_Tensor in;
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Arm_Tensor out;
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NDArray *outNd=nullptr;
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F armFunction{};
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};
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}
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}
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}
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#endif //DEV_TESTSARMCOMPUTEUTILS_H
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