wehub-resource-sync
422 lines
20 KiB
C++
422 lines
20 KiB
C++
//
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// RasterBufExecution.cpp
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// MNN
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//
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// Created by MNN on 2020/05/12.
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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/RasterBufExecution.hpp"
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#include "core/Macro.h"
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#include "core/TensorUtils.hpp"
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#include "core/OpCommonUtils.hpp"
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#include "backend/opencl/core/OpenCLBackend.hpp"
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namespace MNN {
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namespace OpenCL {
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RasterBufExecution::RasterBufExecution(const std::vector<Tensor *> &inputs, const MNN::Op *op, Backend *backend)
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: CommonExecution(backend, op) {
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mOpenCLBackend = (OpenCLBackend *)backend;
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//nothing to do
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}
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ErrorCode RasterBufExecution::onEncode(const std::vector<Tensor *> &____inputs, const std::vector<Tensor *> &outputs) {
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#ifdef LOG_VERBOSE
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MNN_PRINT("start RasterBufExecution onResize !\n");
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#endif
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mTempInput.clear();
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mCombineInfo.clear();
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mTempOutput = nullptr;
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MNN_ASSERT(outputs.size() == 1);
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auto output = outputs[0];
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if (!____inputs.empty()) {
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OpCommonUtils::rasterInputReset(____inputs, outputs[0]);
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}
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auto des = TensorUtils::getDescribe(output);
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auto outputDes = TensorUtils::getDescribe(output);
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auto regionNum = des->regions.size();
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auto mOpenCLBackend = static_cast<OpenCLBackend*>(backend());
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auto runtime = mOpenCLBackend->getOpenCLRuntime();
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int kernel_idx = 0;
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auto outputShape = tensorShapeFormat(output);
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mFast = false;
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if (outputDes->dimensionFormat == MNN_DATA_FORMAT_NC4HW4) {
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mFast = true;
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for (int i=0; i< des->regions.size(); ++i) {
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auto& slice = des->regions[i];
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if (TensorUtils::getDescribe(slice.origin)->dimensionFormat != MNN_DATA_FORMAT_NC4HW4) {
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mFast = false;
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break;
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}
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if (!OpCommonUtils::canBlitFast(slice, output, 4, true)) {
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mFast = false;
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break;
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}
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}
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}
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mNeedZero = !TensorUtils::regionIsFull(output);
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mNeedZero = mNeedZero || ((outputShape[3] % 4) != 0 && MNN_DATA_FORMAT_NC4HW4 == outputDes->dimensionFormat && !mFast);
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if(mFast == false){
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CanCombine(outputs);
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regionNum = mCombineInfo.size();
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}
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mUnits.resize(regionNum);
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if(mNeedZero)
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{
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mUnits.resize(regionNum + 1);
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int region[] = {outputShape[0], outputShape[3], outputShape[1], outputShape[2]};//nchw
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if(MNN_DATA_FORMAT_NC4HW4 == outputDes->dimensionFormat){
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region[1] = ROUND_UP(outputShape[3], 4);
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}
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Unit &unit = mUnits[kernel_idx++];
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unit.kernel = runtime->buildKernel("raster_buf", "buffer_set_zero", {}, mOpenCLBackend->getPrecision(), output, output);
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unit.localWorkSize = {8, 8};
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unit.globalWorkSize = {(uint32_t)UP_DIV((region[2] * region[3]), 8)*8,
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(uint32_t)UP_DIV((region[0] * region[1]), 8)*8};
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int global_dim0 = region[2] * region[3];
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int global_dim1 = region[0] * region[1];
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uint32_t idx = 0;
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cl_int ret = CL_SUCCESS;
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ret |= unit.kernel->get().setArg(idx++, global_dim0);
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ret |= unit.kernel->get().setArg(idx++, global_dim1);
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ret |= unit.kernel->get().setArg(idx++, openCLBuffer(output));
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if(ret != CL_SUCCESS)
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{
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MNN_PRINT("setArg err %d\n", (int)ret);
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}
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mOpenCLBackend->recordKernel2d(unit.kernel, {(uint32_t)UP_DIV((region[2] * region[3]), 8)*8,
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(uint32_t)UP_DIV((region[0] * region[1]), 8)*8}, {8, 8});
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}
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if(mFast)
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{
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// nc4hw4 buffer raster
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for (auto& slice : des->regions)
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{
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auto origin = slice.origin;
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auto inputShape = tensorShapeFormat(origin);
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Tensor::InsideDescribe::Region C4Region;
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OpCommonUtils::turnToPackRegion(slice, C4Region, output, 4, true);
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Unit &unit = mUnits[kernel_idx++];
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unit.kernel = runtime->buildKernel("raster_buf", "raster_nc4hw4_buffer", {}, mOpenCLBackend->getPrecision(), origin, output);
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const std::vector<uint32_t> gws = {(uint32_t)C4Region.size[2],
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(uint32_t)C4Region.size[1],
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(uint32_t)C4Region.size[0]};
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uint32_t mMaxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(unit.kernel));
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auto outputShape = tensorShapeFormat(output);
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auto sliceShape = tensorShapeFormat(slice.origin);
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uint32_t idx = 0;
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cl_int ret = CL_SUCCESS;
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ret |= unit.kernel->get().setArg(idx++, gws[0]);
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ret |= unit.kernel->get().setArg(idx++, gws[1]);
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ret |= unit.kernel->get().setArg(idx++, gws[2]);
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ret |= unit.kernel->get().setArg(idx++, openCLBuffer(slice.origin));
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ret |= unit.kernel->get().setArg(idx++, C4Region.src.offset);
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ret |= unit.kernel->get().setArg(idx++, C4Region.src.stride[0]);
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ret |= unit.kernel->get().setArg(idx++, C4Region.src.stride[1]);
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ret |= unit.kernel->get().setArg(idx++, C4Region.src.stride[2]);
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ret |= unit.kernel->get().setArg(idx++, sliceShape[1]);
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ret |= unit.kernel->get().setArg(idx++, sliceShape[2]);
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ret |= unit.kernel->get().setArg(idx++, sliceShape[3]);
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ret |= unit.kernel->get().setArg(idx++, openCLBuffer(output));
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ret |= unit.kernel->get().setArg(idx++, C4Region.dst.offset);
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ret |= unit.kernel->get().setArg(idx++, C4Region.dst.stride[0]);
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ret |= unit.kernel->get().setArg(idx++, C4Region.dst.stride[1]);
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ret |= unit.kernel->get().setArg(idx++, C4Region.dst.stride[2]);
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ret |= unit.kernel->get().setArg(idx++, outputShape[1]);
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ret |= unit.kernel->get().setArg(idx++, outputShape[2]);
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ret |= unit.kernel->get().setArg(idx++, outputShape[3]);
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if(ret != CL_SUCCESS)
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{
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MNN_PRINT("setArg err %d\n", (int)ret);
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}
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std::string name = "raster_nc4hw4_buffer";
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const std::vector<uint32_t> lws = localWS3DDefault(gws, mMaxWorkGroupSize, mOpenCLBackend->getOpenCLRuntime(), name, unit.kernel, mOpenCLBackend->getCLTuneLevel(), "raster_buf").first;
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unit.localWorkSize = {lws[0], lws[1], lws[2]};
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unit.globalWorkSize = {ROUND_UP(gws[0], std::max((uint32_t)1, lws[0])),
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ROUND_UP(gws[1], std::max((uint32_t)1, lws[1])),
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ROUND_UP(gws[2], std::max((uint32_t)1, lws[2]))};
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mOpenCLBackend->recordKernel3d(unit.kernel, gws, lws);
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}
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return NO_ERROR;
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}
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for(auto& info : mCombineInfo){
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auto slice = info.mRegion;
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int nums = info.mCanCombineNum;
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int src_offset = info.mSrc_offset;
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int dst_offset = info.mDst_offset;
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std::set<std::string> buildOptions;
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auto origin = slice.origin;
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auto inputShape = tensorShapeFormat(origin);
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buildOptions.emplace("-DINPUT_FORMAT=" + std::to_string(TensorUtils::getDescribe(origin)->dimensionFormat));
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buildOptions.emplace("-DOUTPUT_FORMAT=" + std::to_string(outputDes->dimensionFormat));
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// Detect L2 cache-set thrashing in NC4HW4 tensors:
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// When NC4HW4 tensor has N (batch) as power-of-2 and H*W=1,
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// channel groups are spaced N*4 elements apart. Consecutive work-items
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// access consecutive channels → different channel groups → same cache set.
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// Fix: reshape 1D traversal into 2D (batch × channel) so consecutive
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// work-items walk the batch dimension (contiguous in NC4HW4 memory).
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bool inputIsNC4HW4 = TensorUtils::getDescribe(origin)->dimensionFormat == MNN_DATA_FORMAT_NC4HW4;
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bool outputIsNC4HW4 = outputDes->dimensionFormat == MNN_DATA_FORMAT_NC4HW4;
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auto isPow2 = [](int v) { return v > 0 && (v & (v - 1)) == 0; };
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// Check if we have a 1D raster with NC4HW4 tensor whose batch dim is power-of-2
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int nc4_N = 0, nc4_C = 0;
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bool needTranspose = false;
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if (slice.size[0] == 1 && slice.size[1] == 1 && slice.src.stride[2] == 1 && slice.dst.stride[2] == 1) {
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if (inputIsNC4HW4 && inputShape[1] * inputShape[2] == 1) {
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// Input is NC4HW4 with H*W=1, N=inputShape[0], C=inputShape[3]
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nc4_N = inputShape[0];
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nc4_C = inputShape[3];
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} else if (outputIsNC4HW4 && outputShape[1] * outputShape[2] == 1) {
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// Output is NC4HW4 with H*W=1, N=outputShape[0], C=outputShape[3]
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nc4_N = outputShape[0];
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nc4_C = outputShape[3];
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}
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if (nc4_N >= 256 && isPow2(nc4_N) && nc4_C > 4 && nc4_N * nc4_C == slice.size[2]) {
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needTranspose = true;
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}
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}
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Unit &unit = mUnits[kernel_idx++];
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unit.kernel = runtime->buildKernel("raster_buf", "raster_direct_buffer", buildOptions, mOpenCLBackend->getPrecision(), origin, output);
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if (needTranspose) {
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// 2D traversal: x=batch(N), y=channel(C)
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// inputIndex = x * C + y (instead of original x where in_c = x%C, in_b = x/C)
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// This makes consecutive work-items access same channel group, different batches
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const std::vector<uint32_t> gws = {(uint32_t)nc4_N * nums, (uint32_t)nc4_C, 1u};
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uint32_t mMaxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(unit.kernel));
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// Transposed strides: x walks batch (stride=C), y walks channel (stride=1)
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int srcStride0_t = slice.src.stride[0];
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int srcStride1_t = 1;
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int srcStride2_t = nc4_C;
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int dstStride0_t = slice.dst.stride[0];
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int dstStride1_t = 1;
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int dstStride2_t = nc4_C;
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uint32_t idx = 0;
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cl_int ret = CL_SUCCESS;
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ret |= unit.kernel->get().setArg(idx++, gws[0]);
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ret |= unit.kernel->get().setArg(idx++, gws[1]);
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ret |= unit.kernel->get().setArg(idx++, gws[2]);
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ret |= unit.kernel->get().setArg(idx++, (int)nc4_N); // size_x = N (batch per combine group)
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ret |= unit.kernel->get().setArg(idx++, openCLBuffer(origin));
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ret |= unit.kernel->get().setArg(idx++, slice.src.offset);
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ret |= unit.kernel->get().setArg(idx++, src_offset);
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ret |= unit.kernel->get().setArg(idx++, srcStride0_t);
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ret |= unit.kernel->get().setArg(idx++, srcStride1_t);
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ret |= unit.kernel->get().setArg(idx++, srcStride2_t);
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ret |= unit.kernel->get().setArg(idx++, inputShape[2]);
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ret |= unit.kernel->get().setArg(idx++, inputShape[1]);
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ret |= unit.kernel->get().setArg(idx++, inputShape[3]);
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ret |= unit.kernel->get().setArg(idx++, inputShape[0]);
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ret |= unit.kernel->get().setArg(idx++, openCLBuffer(output));
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ret |= unit.kernel->get().setArg(idx++, slice.dst.offset);
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ret |= unit.kernel->get().setArg(idx++, dst_offset);
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ret |= unit.kernel->get().setArg(idx++, dstStride0_t);
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ret |= unit.kernel->get().setArg(idx++, dstStride1_t);
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ret |= unit.kernel->get().setArg(idx++, dstStride2_t);
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ret |= unit.kernel->get().setArg(idx++, outputShape[2]);
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ret |= unit.kernel->get().setArg(idx++, outputShape[1]);
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ret |= unit.kernel->get().setArg(idx++, outputShape[3]);
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ret |= unit.kernel->get().setArg(idx++, outputShape[0]);
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if (ret != CL_SUCCESS) {
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MNN_PRINT("setArg err %d\n", (int)ret);
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}
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std::string name = "raster_buffer_transpose";
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const std::vector<uint32_t> lws =
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localWS3DDefault(gws, mMaxWorkGroupSize, mOpenCLBackend->getOpenCLRuntime(), name, unit.kernel,
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mOpenCLBackend->getCLTuneLevel(), "raster_buf")
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.first;
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unit.localWorkSize = {lws[0], lws[1], lws[2]};
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unit.globalWorkSize = {ROUND_UP(gws[0], std::max((uint32_t)1, lws[0])),
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ROUND_UP(gws[1], std::max((uint32_t)1, lws[1])),
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ROUND_UP(gws[2], std::max((uint32_t)1, lws[2]))};
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mOpenCLBackend->recordKernel3d(unit.kernel, gws, lws);
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} else {
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// Original path
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const std::vector<uint32_t> gws = {(uint32_t)slice.size[2] * nums, (uint32_t)slice.size[1],
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(uint32_t)slice.size[0]};
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uint32_t mMaxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(unit.kernel));
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uint32_t idx = 0;
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cl_int ret = CL_SUCCESS;
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ret |= unit.kernel->get().setArg(idx++, gws[0]);
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ret |= unit.kernel->get().setArg(idx++, gws[1]);
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ret |= unit.kernel->get().setArg(idx++, gws[2]);
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ret |= unit.kernel->get().setArg(idx++, slice.size[2]);
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ret |= unit.kernel->get().setArg(idx++, openCLBuffer(origin));
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ret |= unit.kernel->get().setArg(idx++, slice.src.offset);
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ret |= unit.kernel->get().setArg(idx++, src_offset);
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ret |= unit.kernel->get().setArg(idx++, slice.src.stride[0]);
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ret |= unit.kernel->get().setArg(idx++, slice.src.stride[1]);
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ret |= unit.kernel->get().setArg(idx++, slice.src.stride[2]);
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ret |= unit.kernel->get().setArg(idx++, inputShape[2]);
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ret |= unit.kernel->get().setArg(idx++, inputShape[1]);
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ret |= unit.kernel->get().setArg(idx++, inputShape[3]);
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ret |= unit.kernel->get().setArg(idx++, inputShape[0]);
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ret |= unit.kernel->get().setArg(idx++, openCLBuffer(output));
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ret |= unit.kernel->get().setArg(idx++, slice.dst.offset);
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ret |= unit.kernel->get().setArg(idx++, dst_offset);
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ret |= unit.kernel->get().setArg(idx++, slice.dst.stride[0]);
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ret |= unit.kernel->get().setArg(idx++, slice.dst.stride[1]);
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ret |= unit.kernel->get().setArg(idx++, slice.dst.stride[2]);
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ret |= unit.kernel->get().setArg(idx++, outputShape[2]);
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ret |= unit.kernel->get().setArg(idx++, outputShape[1]);
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ret |= unit.kernel->get().setArg(idx++, outputShape[3]);
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ret |= unit.kernel->get().setArg(idx++, outputShape[0]);
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if (ret != CL_SUCCESS) {
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MNN_PRINT("setArg err %d\n", (int)ret);
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}
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std::string name = "raster_buffer";
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const std::vector<uint32_t> lws =
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localWS3DDefault(gws, mMaxWorkGroupSize, mOpenCLBackend->getOpenCLRuntime(), name, unit.kernel,
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mOpenCLBackend->getCLTuneLevel(), "raster_buf")
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.first;
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unit.localWorkSize = {lws[0], lws[1], lws[2]};
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unit.globalWorkSize = {gws[0], gws[1], gws[2]};
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mOpenCLBackend->recordKernel3d(unit.kernel, gws, lws);
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}
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}
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#ifdef LOG_VERBOSE
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MNN_PRINT("end RasterBufExecution onResize !\n");
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#endif
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return NO_ERROR;
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}
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class RasterBufCreator : public OpenCLBackend::Creator {
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public:
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virtual ~RasterBufCreator() = default;
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virtual Execution *onCreate(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs, const MNN::Op *op,
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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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OPENCL_CREATOR_CHECK(new RasterBufExecution(inputs, op, backend));
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}
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};
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void RasterBufExecution::CanCombine(const std::vector<Tensor *> &outputs){
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auto des = TensorUtils::getDescribe(outputs[0]);
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auto regions = des->regions;
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Tensor* origin;
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int size0, size1, size2, src_offset, dst_offset, last_src_offset, last_dst_offset, src_sride0, src_sride1, src_sride2, dst_sride0, dst_sride1, dst_sride2;
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int canCombineNum = 0;
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for(auto& slice : des->regions){
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bool res = true;
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if(canCombineNum == 0){
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origin = slice.origin;
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size0 = slice.size[0];
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size1 = slice.size[1];
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size2 = slice.size[2];
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src_sride0 = slice.src.stride[0];
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src_sride1 = slice.src.stride[1];
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src_sride2 = slice.src.stride[2];
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dst_sride0 = slice.dst.stride[0];
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dst_sride1 = slice.dst.stride[1];
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dst_sride2 = slice.dst.stride[2];
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canCombineNum++;
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// push back
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mCombineInfo.push_back(CanCombineInfo(slice, 0, 0, 1));
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} else if(canCombineNum == 1){
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res &= slice.origin == origin;
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res &= slice.size[0] == size0;
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res &= slice.size[1] == size1;
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res &= slice.size[2] == size2;
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res &= slice.src.stride[0] == src_sride0;
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res &= slice.src.stride[1] == src_sride1;
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res &= slice.src.stride[2] == src_sride2;
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res &= slice.dst.stride[0] == dst_sride0;
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res &= slice.dst.stride[1] == dst_sride1;
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res &= slice.dst.stride[2] == dst_sride2;
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if(res){
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src_offset = slice.src.offset - last_src_offset;
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dst_offset = slice.dst.offset - last_dst_offset;
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canCombineNum++;
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// change canCombineNum
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mCombineInfo.back().mSrc_offset = src_offset;
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mCombineInfo.back().mDst_offset = dst_offset;
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mCombineInfo.back().mCanCombineNum = canCombineNum;
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} else{
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origin = slice.origin;
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size0 = slice.size[0];
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size1 = slice.size[1];
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size2 = slice.size[2];
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src_sride0 = slice.src.stride[0];
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src_sride1 = slice.src.stride[1];
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src_sride2 = slice.src.stride[2];
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dst_sride0 = slice.dst.stride[0];
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dst_sride1 = slice.dst.stride[1];
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dst_sride2 = slice.dst.stride[2];
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// recover
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canCombineNum = 1;
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// push back
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mCombineInfo.push_back(CanCombineInfo(slice, 0, 0, 1));
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}
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} else{
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res &= slice.origin == origin;
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res &= slice.size[0] == size0;
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res &= slice.size[1] == size1;
|
||
res &= slice.size[2] == size2;
|
||
res &= slice.src.stride[0] == src_sride0;
|
||
res &= slice.src.stride[1] == src_sride1;
|
||
res &= slice.src.stride[2] == src_sride2;
|
||
res &= slice.dst.stride[0] == dst_sride0;
|
||
res &= slice.dst.stride[1] == dst_sride1;
|
||
res &= slice.dst.stride[2] == dst_sride2;
|
||
res &= slice.src.offset - last_src_offset == src_offset;
|
||
res &= slice.dst.offset - last_dst_offset == dst_offset;
|
||
if(res){
|
||
canCombineNum++;
|
||
// change canCombineNum
|
||
mCombineInfo.back().mSrc_offset = src_offset;
|
||
mCombineInfo.back().mDst_offset = dst_offset;
|
||
mCombineInfo.back().mCanCombineNum = canCombineNum;
|
||
} else{
|
||
origin = slice.origin;
|
||
size0 = slice.size[0];
|
||
size1 = slice.size[1];
|
||
size2 = slice.size[2];
|
||
src_sride0 = slice.src.stride[0];
|
||
src_sride1 = slice.src.stride[1];
|
||
src_sride2 = slice.src.stride[2];
|
||
dst_sride0 = slice.dst.stride[0];
|
||
dst_sride1 = slice.dst.stride[1];
|
||
dst_sride2 = slice.dst.stride[2];
|
||
// recover
|
||
canCombineNum = 1;
|
||
// push back
|
||
mCombineInfo.push_back(CanCombineInfo(slice, 0, 0, 1));
|
||
|
||
}
|
||
}
|
||
last_src_offset = slice.src.offset;
|
||
last_dst_offset = slice.dst.offset;
|
||
}
|
||
}
|
||
|
||
REGISTER_OPENCL_OP_CREATOR(RasterBufCreator, OpType_Raster, BUFFER);
|
||
|
||
} // namespace OpenCL
|
||
} // namespace MNN
|
||
#endif /* MNN_OPENCL_BUFFER_CLOSED */ |