mirror of https://github.com/alibaba/MNN.git
338 lines
18 KiB
C++
338 lines
18 KiB
C++
//
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// BinaryBufExecution.cpp
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// MNN
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//
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// Created by MNN on 2019/02/28.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#ifndef MNN_OPENCL_BUFFER_CLOSED
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#include "backend/opencl/execution/buffer/BinaryBufExecution.hpp"
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#include "core/Macro.h"
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#include "core/TensorUtils.hpp"
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namespace MNN {
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namespace OpenCL {
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BinaryBufExecution::BinaryBufExecution(const std::vector<Tensor *> &inputs, const std::string &compute, const MNN::Op *op, Backend *backend)
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: CommonExecution(backend, op), mCompute(compute) {
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mBuildOptions.emplace("-DOPERATOR=" + compute);
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}
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uint32_t BinaryBufExecution::realSize(const Tensor* tensor) {
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uint32_t num = 1;
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for(int i = 0; i < tensor->dimensions(); i++) {
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num *= tensor->length(i);
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}
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return num;
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}
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#ifdef MNN_SUPPORT_INTEL_SUBGROUP
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ErrorCode BinaryBufExecution::SubgroupOnResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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auto openCLBackend = static_cast<OpenCLBackend *>(backend());
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auto output = outputs[0];
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auto inputShape0 = tensorShapeFormat(inputs[0]);
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auto inputShape1 = tensorShapeFormat(inputs[1]);
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auto outputShape = tensorShapeFormat(output);
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auto runTime = ((OpenCLBackend *)backend())->getOpenCLRuntime();
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int shape[4] = {outputShape[0], outputShape[1], outputShape[2], outputShape[3]};
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int fullCount[2] = {1, 1};
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int input0_c_pack = TensorUtils::getTensorChannelPack(inputs[0]);
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int input1_c_pack = TensorUtils::getTensorChannelPack(inputs[1]);
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int output_c_pack = TensorUtils::getTensorChannelPack(output);
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int activationType = 0;
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if(mOp->type() == OpType_BinaryOp) {
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activationType = mOp->main_as_BinaryOp()->activationType();
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}
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auto &unit = mUnits[0];
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std::string kernelName = "binary_buf_c" + std::to_string(input0_c_pack) + "_c" + std::to_string(input1_c_pack) +
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"_c" + std::to_string(output_c_pack);
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unit.kernel = runTime->buildKernel("binary_subgroup_buf", kernelName, mBuildOptions);
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mMaxWorkGroupSize = static_cast<uint32_t>(runTime->getMaxWorkGroupSize(unit.kernel));
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fullCount[0] = realSize(inputs[0]) == 1 ? 0 : 1;
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fullCount[1] = realSize(inputs[1]) == 1 ? 0 : 1;
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auto input0pad = TensorUtils::getDescribe(inputs[0])->mPads;
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auto input1pad = TensorUtils::getDescribe(inputs[1])->mPads;
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auto outputpad = TensorUtils::getDescribe(output)->mPads;
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uint32_t index = 0;
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cl_int ret = CL_SUCCESS;
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if (input0_c_pack == 16 && input1_c_pack == 16) {
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mGlobalWorkSize = {(uint32_t)UP_DIV(outputShape[2], 4) * outputShape[1],
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(uint32_t)ROUND_UP(outputShape[3], 16), (uint32_t)outputShape[0]};
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unit.globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1], mGlobalWorkSize[2]};
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unit.localWorkSize = {1, 16, 1};
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ret |= unit.kernel.setArg(index++, mGlobalWorkSize[0]);
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ret |= unit.kernel.setArg(index++, mGlobalWorkSize[1]);
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ret |= unit.kernel.setArg(index++, mGlobalWorkSize[2]);
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ret |= unit.kernel.setArg(index++, openCLBuffer(inputs[0]));
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ret |= unit.kernel.setArg(index++, openCLBuffer(inputs[1]));
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ret |= unit.kernel.setArg(index++, openCLBuffer(output));
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ret |= unit.kernel.setArg(index++, shape);
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ret |= unit.kernel.setArg(index++, fullCount);
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ret |= unit.kernel.setArg(index++, activationType);
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ret |= unit.kernel.setArg(index++, static_cast<uint32_t>(input0pad.left));
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ret |= unit.kernel.setArg(index++, static_cast<uint32_t>(input0pad.right));
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ret |= unit.kernel.setArg(index++, static_cast<uint32_t>(input1pad.left));
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ret |= ret |= unit.kernel.setArg(index++, static_cast<uint32_t>(input1pad.right));
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ret |= unit.kernel.setArg(index++, static_cast<uint32_t>(outputpad.left));
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ret |= unit.kernel.setArg(index++, static_cast<uint32_t>(outputpad.right));
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MNN_CHECK_CL_SUCCESS(ret, "setArg BinaryBufExecution C16");
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} else {
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mGlobalWorkSize = {(uint32_t)outputShape[2] * outputShape[1], (uint32_t)UP_DIV(outputShape[3], 4),
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(uint32_t)outputShape[0]};
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ret |= unit.kernel.setArg(index++, mGlobalWorkSize[0]);
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ret |= unit.kernel.setArg(index++, mGlobalWorkSize[1]);
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ret |= unit.kernel.setArg(index++, mGlobalWorkSize[2]);
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ret |= unit.kernel.setArg(index++, openCLBuffer(inputs[0]));
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ret |= unit.kernel.setArg(index++, openCLBuffer(inputs[1]));
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ret |= unit.kernel.setArg(index++, openCLBuffer(output));
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ret |= unit.kernel.setArg(index++, shape);
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ret |= unit.kernel.setArg(index++, fullCount);
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ret |= unit.kernel.setArg(index++, activationType);
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ret |= unit.kernel.setArg(index++, static_cast<uint32_t>(input0pad.left));
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ret |= unit.kernel.setArg(index++, static_cast<uint32_t>(input0pad.right));
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ret |= unit.kernel.setArg(index++, static_cast<uint32_t>(input1pad.left));
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ret |= unit.kernel.setArg(index++, static_cast<uint32_t>(input1pad.right));
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ret |= unit.kernel.setArg(index++, static_cast<uint32_t>(outputpad.left));
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ret |= unit.kernel.setArg(index++, static_cast<uint32_t>(outputpad.right));
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MNN_CHECK_CL_SUCCESS(ret, "setArg BinaryBufExecution");
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mLocalWorkSize = localWS3DDefault(mGlobalWorkSize, mMaxWorkGroupSize, openCLBackend->getOpenCLRuntime(), kernelName, unit.kernel).first;
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unit.globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1], mGlobalWorkSize[2]};
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unit.localWorkSize = {mLocalWorkSize[0], mLocalWorkSize[1], mLocalWorkSize[2]};
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}
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for (int i = 2; i < inputs.size(); ++i) {
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fullCount[0] = 1;
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fullCount[1] = realSize(inputs[i]) == 1 ? 0 : 1;
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auto &unit = mUnits[i-1];
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int input0_c_pack_tmp = TensorUtils::getTensorChannelPack(output);
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int input1_c_pack_tmp = TensorUtils::getTensorChannelPack(inputs[i]);
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int output_c_pack_tmp = TensorUtils::getTensorChannelPack(output);
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std::string kernelNameTmp = "binary_buf_c" + std::to_string(input0_c_pack_tmp) + "_c" + std::to_string(input1_c_pack_tmp) +
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"_c" + std::to_string(output_c_pack_tmp);
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unit.kernel = runTime->buildKernel("binary_subgroup_buf", kernelNameTmp, mBuildOptions);
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mMaxWorkGroupSize = static_cast<uint32_t>(runTime->getMaxWorkGroupSize(unit.kernel));
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auto input0padtmp = TensorUtils::getDescribe(output)->mPads;
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auto input1padtmp = TensorUtils::getDescribe(inputs[i])->mPads;
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auto outputpadtmp = TensorUtils::getDescribe(output)->mPads;
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uint32_t index = 0;
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if (input0_c_pack_tmp == 16 && input1_c_pack_tmp == 16) {
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mGlobalWorkSize = {(uint32_t)UP_DIV(outputShape[2], 4) * outputShape[1],
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(uint32_t)ROUND_UP(outputShape[3], 16), (uint32_t)outputShape[0]};
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unit.globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1], mGlobalWorkSize[2]};
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unit.localWorkSize = {1, 16, 1};
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ret |= unit.kernel.setArg(index++, mGlobalWorkSize[0]);
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ret |= unit.kernel.setArg(index++, mGlobalWorkSize[1]);
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ret |= unit.kernel.setArg(index++, mGlobalWorkSize[2]);
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ret |= unit.kernel.setArg(index++, openCLBuffer(output));
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ret |= unit.kernel.setArg(index++, openCLBuffer(inputs[i]));
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ret |= unit.kernel.setArg(index++, openCLBuffer(output));
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ret |= unit.kernel.setArg(index++, shape);
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ret |= unit.kernel.setArg(index++, fullCount);
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ret |= unit.kernel.setArg(index++, activationType);
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ret |= unit.kernel.setArg(index++, static_cast<uint32_t>(input0padtmp.left));
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ret |= unit.kernel.setArg(index++, static_cast<uint32_t>(input0padtmp.right));
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ret |= unit.kernel.setArg(index++, static_cast<uint32_t>(input1padtmp.left));
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ret |= unit.kernel.setArg(index++, static_cast<uint32_t>(input1padtmp.right));
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ret |= unit.kernel.setArg(index++, static_cast<uint32_t>(outputpadtmp.left));
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ret |= unit.kernel.setArg(index++, static_cast<uint32_t>(outputpadtmp.right));
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MNN_CHECK_CL_SUCCESS(ret, "setArg BinaryBufExecution C16 MultiInput");
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} else {
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mGlobalWorkSize = {(uint32_t)outputShape[2] * outputShape[1], (uint32_t)UP_DIV(outputShape[3], 4),
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(uint32_t)outputShape[0]};
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ret |= unit.kernel.setArg(index++, mGlobalWorkSize[0]);
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ret |= unit.kernel.setArg(index++, mGlobalWorkSize[1]);
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ret |= unit.kernel.setArg(index++, mGlobalWorkSize[2]);
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ret |= unit.kernel.setArg(index++, openCLBuffer(output));
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ret |= unit.kernel.setArg(index++, openCLBuffer(inputs[i]));
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ret |= unit.kernel.setArg(index++, openCLBuffer(output));
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ret |= unit.kernel.setArg(index++, shape);
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ret |= unit.kernel.setArg(index++, fullCount);
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ret |= unit.kernel.setArg(index++, activationType);
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ret |= unit.kernel.setArg(index++, static_cast<uint32_t>(input0padtmp.left));
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ret |= unit.kernel.setArg(index++, static_cast<uint32_t>(input0padtmp.right));
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ret |= unit.kernel.setArg(index++, static_cast<uint32_t>(input1padtmp.left));
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ret |= unit.kernel.setArg(index++, static_cast<uint32_t>(input1padtmp.right));
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ret |= unit.kernel.setArg(index++, static_cast<uint32_t>(outputpadtmp.left));
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ret |= unit.kernel.setArg(index++, static_cast<uint32_t>(outputpadtmp.right));
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MNN_CHECK_CL_SUCCESS(ret, "setArg BinaryBufExecution MultiInput");
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mLocalWorkSize = localWS3DDefault(mGlobalWorkSize, mMaxWorkGroupSize, openCLBackend->getOpenCLRuntime(), kernelNameTmp, unit.kernel).first;
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unit.globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1], mGlobalWorkSize[2]};
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unit.localWorkSize = {mLocalWorkSize[0], mLocalWorkSize[1], mLocalWorkSize[2]};
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}
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}
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return NO_ERROR;
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}
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#endif /* MNN_SUPPORT_INTEL_SUBGROUP */
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ErrorCode BinaryBufExecution::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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MNN_ASSERT(inputs.size() >= 2);
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mUnits.resize(inputs.size() - 1);
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auto openCLBackend = static_cast<OpenCLBackend*>(backend());
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auto output = outputs[0];
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auto inputShape0 = tensorShapeFormat(inputs[0]);
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auto inputShape1 = tensorShapeFormat(inputs[1]);
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auto outputShape = tensorShapeFormat(output);
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auto runTime = ((OpenCLBackend *)backend())->getOpenCLRuntime();
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#ifdef MNN_SUPPORT_INTEL_SUBGROUP
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if (runTime->isSupportedIntelSubgroup()) {
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return SubgroupOnResize(inputs, outputs);
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}
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#endif /* MNN_SUPPORT_INTEL_SUBGROUP */
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int shape[4] = {outputShape[0], outputShape[1], outputShape[2], UP_DIV(outputShape[3], 4)};
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int fullCount[2] = {1, 1};
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int activationType = 0;
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if(mOp->type() == OpType_BinaryOp) {
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activationType = mOp->main_as_BinaryOp()->activationType();
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}
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auto &unit = mUnits[0];
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unit.kernel = runTime->buildKernel("binary_buf", "binary_buf", mBuildOptions);
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mMaxWorkGroupSize = static_cast<uint32_t>(runTime->getMaxWorkGroupSize(unit.kernel));
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mGlobalWorkSize = {(uint32_t)UP_DIV(outputShape[3], 4) * outputShape[0],
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(uint32_t)outputShape[1]*outputShape[2]};
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fullCount[0] = realSize(inputs[0]) == 1 ? 0 : 1;
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fullCount[1] = realSize(inputs[1]) == 1 ? 0 : 1;
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uint32_t index = 0;
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cl_int ret = CL_SUCCESS;
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ret |= unit.kernel.setArg(index++, mGlobalWorkSize[0]);
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ret |= unit.kernel.setArg(index++, mGlobalWorkSize[1]);
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ret |= unit.kernel.setArg(index++, openCLBuffer(inputs[0]));
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ret |= unit.kernel.setArg(index++, openCLBuffer(inputs[1]));
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ret |= unit.kernel.setArg(index++, openCLBuffer(output));
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ret |= unit.kernel.setArg(index++, shape);
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ret |= unit.kernel.setArg(index++, fullCount);
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ret |= unit.kernel.setArg(index++, activationType);
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MNN_CHECK_CL_SUCCESS(ret, "setArg BinaryBufExecution");
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std::string name = "binary_buf";
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mLocalWorkSize = localWS2DDefault(mGlobalWorkSize, mMaxWorkGroupSize, openCLBackend->getOpenCLRuntime(), name, unit.kernel).first;
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unit.globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1]};
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unit.localWorkSize = {mLocalWorkSize[0], mLocalWorkSize[1]};
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for (int i = 2; i < inputs.size(); ++i) {
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fullCount[0] = 1;
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fullCount[1] = realSize(inputs[i]) == 1 ? 0 : 1;
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auto &unit = mUnits[i-1];
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unit.kernel = runTime->buildKernel("binary_buf", "binary_buf", mBuildOptions);
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uint32_t index = 0;
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ret |= unit.kernel.setArg(index++, mGlobalWorkSize[0]);
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ret |= unit.kernel.setArg(index++, mGlobalWorkSize[1]);
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ret |= unit.kernel.setArg(index++, openCLBuffer(output));
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ret |= unit.kernel.setArg(index++, openCLBuffer(inputs[i]));
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ret |= unit.kernel.setArg(index++, openCLBuffer(output));
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ret |= unit.kernel.setArg(index++, shape);
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ret |= unit.kernel.setArg(index++, fullCount);
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ret |= unit.kernel.setArg(index++, activationType);
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MNN_CHECK_CL_SUCCESS(ret, "setArg BinaryBufExecution MultiInput");
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unit.globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1]};
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unit.localWorkSize = {mLocalWorkSize[0], mLocalWorkSize[1]};
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}
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return NO_ERROR;
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}
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class BinaryBufCreator : public OpenCLBackend::Creator {
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public:
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virtual Execution *onCreate(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs,
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const MNN::Op *op, Backend *backend) const override {
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for (int i = 0; i < inputs.size(); ++i) {
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int channel = inputs[i]->channel();
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if (channel >= 16) {
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TensorUtils::setTensorChannelPack(inputs[i], 16);
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}
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}
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if (op->type() == OpType_Eltwise) {
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switch (op->main_as_Eltwise()->type()) {
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case EltwiseType_SUM:
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return new BinaryBufExecution(inputs, "in0+in1", op, backend);
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case EltwiseType_PROD:
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return new BinaryBufExecution(inputs, "in0*in1", op, backend);
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case EltwiseType_SUB:
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return new BinaryBufExecution(inputs, "in0-in1", op, backend);
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case EltwiseType_MAXIMUM:
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return new BinaryBufExecution(inputs, "in0>in1?in0:in1", op, backend);
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default:
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break;
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}
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return nullptr;
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}
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if (op->type() == OpType_BinaryOp) {
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MNN_ASSERT(inputs.size() > 1);
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switch (op->main_as_BinaryOp()->opType()) {
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case BinaryOpOperation_MUL:
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return new BinaryBufExecution(inputs, "in0*in1", op, backend);
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case BinaryOpOperation_ADD:
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return new BinaryBufExecution(inputs, "in0+in1", op, backend);
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case BinaryOpOperation_SUB:
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return new BinaryBufExecution(inputs, "in0-in1", op, backend);
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case BinaryOpOperation_REALDIV:
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return new BinaryBufExecution(inputs, "sign(in1)*in0/(fabs(in1)>(FLOAT4)((FLOAT)0.0000001)?fabs(in1):(FLOAT4)((FLOAT)0.0000001))", op, backend);
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case BinaryOpOperation_MINIMUM:
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return new BinaryBufExecution(inputs, "in0>in1?in1:in0", op, backend);
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case BinaryOpOperation_MAXIMUM:
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return new BinaryBufExecution(inputs, "in0>in1?in0:in1", op, backend);
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case BinaryOpOperation_GREATER:
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return new BinaryBufExecution(inputs, "convert_float4(-isgreater(in0,in1))", op, backend);
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case BinaryOpOperation_LESS:
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return new BinaryBufExecution(inputs, "convert_float4(-isless(in0,in1))", op, backend);
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case BinaryOpOperation_LESS_EQUAL:
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return new BinaryBufExecution(inputs, "convert_float4(-islessequal(in0,in1))", op, backend);
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case BinaryOpOperation_GREATER_EQUAL:
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return new BinaryBufExecution(inputs, "convert_float4(-isgreaterequal(in0,in1))", op, backend);
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case BinaryOpOperation_EQUAL:
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return new BinaryBufExecution(inputs, "convert_float4(-isequal(in0,in1))", op, backend);
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case BinaryOpOperation_FLOORDIV:
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return new BinaryBufExecution(inputs, "floor(sign(in1)*in0/(fabs(in1)>(FLOAT4)((FLOAT)0.0000001)?fabs(in1):(FLOAT4)((FLOAT)0.0000001)))", op, backend);
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case BinaryOpOperation_FLOORMOD:
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return new BinaryBufExecution(inputs, "in0-floor(sign(in1)*in0/(fabs(in1)>(FLOAT4)((FLOAT)0.0000001)?fabs(in1):(FLOAT4)((FLOAT)0.0000001)))*in1", op, backend);
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case BinaryOpOperation_POW:
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return new BinaryBufExecution(inputs, "pow(in0,in1)", op, backend);
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case BinaryOpOperation_SquaredDifference:
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return new BinaryBufExecution(inputs, "(in0-in1)*(in0-in1)", op, backend);
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case BinaryOpOperation_ATAN2:
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return new BinaryBufExecution(inputs, "(in1==(FLOAT4)0?(sign(in0)*(FLOAT4)(PI/2)):(atan(in0/in1)+(in1>(FLOAT4)0?(FLOAT4)0:sign(in0)*(FLOAT4)PI)))", op, backend);
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case BinaryOpOperation_NOTEQUAL:
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return new BinaryBufExecution(inputs, "convert_float4(-isnotequal(in0,in1))", op, backend);
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case BinaryOpOperation_MOD:
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return new BinaryBufExecution(inputs, "in0-floor(sign(in1)*in0/(fabs(in1)>(FLOAT4)((FLOAT)0.0000001)?fabs(in1):(FLOAT4)((FLOAT)0.0000001)))*in1", op, backend);
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default:
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break;
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}
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return nullptr;
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}
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return nullptr;
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}
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};
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OpenCLCreatorRegister<BinaryBufCreator> __eltwiseBuf_op(OpType_Eltwise, BUFFER);
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OpenCLCreatorRegister<BinaryBufCreator> __binaryBuf_op(OpType_BinaryOp, BUFFER);
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} // namespace OpenCL
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} // namespace MNN
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#endif /* MNN_OPENCL_BUFFER_CLOSED */
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