mirror of https://github.com/alibaba/MNN.git
231 lines
10 KiB
C++
231 lines
10 KiB
C++
//
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// SoftmaxBufExecution.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/SoftmaxBufExecution.hpp"
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namespace MNN {
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namespace OpenCL {
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SoftmaxBufExecution::SoftmaxBufExecution(const std::vector<Tensor *> &inputs, int axis, const MNN::Op* Op, Backend *backend)
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: CommonExecution(backend, Op) {
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mAxis = axis;
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mOpenCLBackend = static_cast<OpenCLBackend *>(backend);
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auto kernel = mOpenCLBackend->getOpenCLRuntime()->buildKernel("softmax_buf", "softmax_buf", {"-DSOFTMAX_LOCAL_SIZE=512"}, mOpenCLBackend->getPrecision());
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mMaxWorkGroupSize = static_cast<uint32_t>(mOpenCLBackend->getOpenCLRuntime()->getMaxWorkGroupSize(kernel));
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}
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int SoftmaxBufExecution::getLocalSize(int size, int maxGroupSize){
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int local_size = 1;
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while(local_size * 2 <= maxGroupSize && local_size * 2 <= size){
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local_size *= 2;
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}
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return local_size;
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}
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ErrorCode SoftmaxBufExecution::onEncode(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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mUnits.clear();
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Tensor *input = inputs[0];
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Tensor *output = outputs[0];
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const auto dims = input->buffer().dimensions;
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auto runtime = mOpenCLBackend->getOpenCLRuntime();
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auto MaxLocalSize = std::min(std::min(runtime->getMaxWorkItemSizes()[0], mMaxWorkGroupSize), static_cast<uint32_t>(256));
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const auto layout = TensorUtils::getDescribe(input)->dimensionFormat;
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mNeedUnpackC4 = layout == MNN_DATA_FORMAT_NC4HW4;
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if (mNeedUnpackC4) {
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int totalSize = 1;
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for (int i = 1; i < dims; ++i) {
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totalSize *= input->length(i);
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}
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mTempTensor.reset(Tensor::createDevice<float>({totalSize}));
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mOpenCLBackend->onAcquireBuffer(mTempTensor.get(), Backend::DYNAMIC);
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mOpenCLBackend->onReleaseBuffer(mTempTensor.get(), Backend::DYNAMIC);
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}
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int inside = 1;
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int outside = 1;
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int channel = 1;
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for (int i = 0; i < mAxis; ++i) {
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outside *= input->length(i);
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}
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channel = input->length(mAxis);
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for (int i = mAxis + 1; i < dims; ++i) {
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inside *= input->length(i);
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}
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// NC4HW4 -> NCHW
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if(mNeedUnpackC4){
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Unit unit;
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std::vector<int> outputShape = tensorShapeFormat(input);
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int shape[4] = {outputShape[0], outputShape[3], outputShape[1], outputShape[2]};//N C H W
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std::set<std::string> buildOptions;
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buildOptions.emplace("-DINPUT_FORMAT=MNN_DATA_FORMAT_NC4HW4");
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buildOptions.emplace("-DOUTPUT_FORMAT=MNN_DATA_FORMAT_NCHW");
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unit.kernel = runtime->buildKernel("buffer_convert_buf", "buffer_convert_to_buffer", buildOptions, mOpenCLBackend->getPrecision(), input, output);
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mGlobalWorkSize = {static_cast<uint32_t>(shape[2] * shape[3]), static_cast<uint32_t>(shape[1]), static_cast<uint32_t>(shape[0])};
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cl_int ret = CL_SUCCESS;
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uint32_t idx = 0;
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ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[0]);
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ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[1]);
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ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[2]);
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ret |= unit.kernel->get().setArg(idx++, openCLBuffer(input));
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ret |= unit.kernel->get().setArg(idx++, sizeof(shape), shape);
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ret |= unit.kernel->get().setArg(idx++, openCLBuffer(output));
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MNN_CHECK_CL_SUCCESS(ret, "setArg buffer_convert_to_buffer");
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const uint32_t maxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(unit.kernel));
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mLocalWorkSize = {16, std::max((uint32_t)1, maxWorkGroupSize / 16), 1};
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mOpenCLBackend->recordKernel3d(unit.kernel, mGlobalWorkSize, mLocalWorkSize);
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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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mUnits.emplace_back(unit);
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}
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// softmax
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{
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Unit unit;
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int localSize = getLocalSize(channel, MaxLocalSize);
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if(localSize < 4){
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localSize = 1;
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}
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std::set<std::string> buildOptions = mBuildOptions;
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buildOptions.emplace("-DARGMAX_LOCAL_SIZE=" + std::to_string(localSize));
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std::string kernelName;
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if(inside == 1){
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buildOptions.emplace("-DSOFTMAX_LOCAL_SIZE=" + std::to_string(localSize));
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unit.kernel = runtime->buildKernel("self_attention_buf", "softmax_inside", buildOptions, mOpenCLBackend->getPrecision(), inputs[0], outputs[0]);
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mGlobalWorkSize = {static_cast<uint32_t>(localSize), static_cast<uint32_t>(outside), static_cast<uint32_t>(1)};
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}
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else if(inside % 4 == 0){
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unit.kernel = runtime->buildKernel("softmax_buf", "softmax_v4_buf", buildOptions, mOpenCLBackend->getPrecision());
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mGlobalWorkSize = {static_cast<uint32_t>(localSize), static_cast<uint32_t>(UP_DIV(inside, 4)), static_cast<uint32_t>(outside)};
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}else {
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unit.kernel = runtime->buildKernel("softmax_buf", "softmax_buf", buildOptions, mOpenCLBackend->getPrecision());
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mGlobalWorkSize = {static_cast<uint32_t>(localSize), static_cast<uint32_t>(inside), static_cast<uint32_t>(outside)};
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}
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mMaxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(unit.kernel));
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mLocalWorkSize = {(uint32_t)(localSize), 1, 1};
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cl_int ret = CL_SUCCESS;
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uint32_t idx = 0;
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ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[0]);
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ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[1]);
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ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[2]);
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if(mNeedUnpackC4){
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ret |= unit.kernel->get().setArg(idx++, openCLImage(output));
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ret |= unit.kernel->get().setArg(idx++, openCLImage(mTempTensor.get()));
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}else{
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ret |= unit.kernel->get().setArg(idx++, openCLImage(input));
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ret |= unit.kernel->get().setArg(idx++, openCLImage(output));
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}
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if(inside == 1){
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ret |= unit.kernel->get().setArg(idx++, channel);
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int shape[4] = {1, outside, channel, 1};
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ret |= unit.kernel->get().setArg(idx++, shape);
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} else {
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ret |= unit.kernel->get().setArg(idx++, inside);
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ret |= unit.kernel->get().setArg(idx++, outside);
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ret |= unit.kernel->get().setArg(idx++, channel);
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}
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MNN_CHECK_CL_SUCCESS(ret, "setArg SoftmaxBufExecution");
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if(localSize == 1){
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std::string programName = "softmax_buf";
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if(inside == 1){
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programName = "self_attention_buf";
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}
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mLocalWorkSize = localWS3DDefault(mGlobalWorkSize, mMaxWorkGroupSize, mOpenCLBackend->getOpenCLRuntime(), "softmax_buf", unit.kernel, mOpenCLBackend->getCLTuneLevel(), programName).first;
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}
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mOpenCLBackend->recordKernel3d(unit.kernel, mGlobalWorkSize, mLocalWorkSize);
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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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mUnits.emplace_back(unit);
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}
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// NCHW -> NC4HW4
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if(mNeedUnpackC4){
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Unit unit;
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std::vector<int> outputShape = tensorShapeFormat(output);
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int shape[4] = {outputShape[0], outputShape[3], outputShape[1], outputShape[2]};//N C H W
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std::set<std::string> buildOptions;
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buildOptions.emplace("-DINPUT_FORMAT=MNN_DATA_FORMAT_NCHW");
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buildOptions.emplace("-DOUTPUT_FORMAT=MNN_DATA_FORMAT_NC4HW4");
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unit.kernel = runtime->buildKernel("buffer_convert_buf", "buffer_convert_to_buffer", buildOptions, mOpenCLBackend->getPrecision(), input, output);
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mGlobalWorkSize = {static_cast<uint32_t>(shape[2] * shape[3]), static_cast<uint32_t>(shape[1]), static_cast<uint32_t>(shape[0])};
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cl_int ret = CL_SUCCESS;
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uint32_t idx = 0;
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ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[0]);
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ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[1]);
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ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[2]);
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ret |= unit.kernel->get().setArg(idx++, openCLBuffer(mTempTensor.get()));
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ret |= unit.kernel->get().setArg(idx++, sizeof(shape), shape);
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ret |= unit.kernel->get().setArg(idx++, openCLBuffer(output));
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MNN_CHECK_CL_SUCCESS(ret, "setArg buffer_convert_to_buffer");
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const uint32_t maxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(unit.kernel));
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mLocalWorkSize = {16, std::max((uint32_t)1, maxWorkGroupSize / 16), 1};
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mOpenCLBackend->recordKernel3d(unit.kernel, mGlobalWorkSize, mLocalWorkSize);
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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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mUnits.emplace_back(unit);
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}
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return NO_ERROR;
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}
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class SoftmaxBufCreator : 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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TensorUtils::setTensorSupportPack(inputs[i], false);
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}
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for (int i = 0; i < outputs.size(); ++i) {
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TensorUtils::setTensorSupportPack(outputs[i], false);
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}
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auto dimType = inputs[0]->getDimensionType();
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if (dimType == Tensor::TENSORFLOW && inputs[0]->dimensions() == 4) {
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int index[4] = {0, 2, 3, 1};
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auto axis = op->main_as_Axis()->axis();
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if (axis < 0) {
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axis = inputs[0]->dimensions() + axis;
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}
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axis = index[axis];
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//1 : channel //2 : height
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if (1 == axis || 2 == axis || 3 == axis) {
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return new SoftmaxBufExecution(inputs, axis, op, backend);
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}
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return nullptr;
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} else {
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auto axis = op->main_as_Axis()->axis();
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if (axis < 0) {
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axis = inputs[0]->dimensions() + axis;
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}
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if (1 == axis || 2 == axis || 3 == axis) {
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return new SoftmaxBufExecution(inputs, axis, op, backend);
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}
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return nullptr;
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}
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}
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};
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REGISTER_OPENCL_OP_CREATOR(SoftmaxBufCreator, OpType_Softmax, 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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