chore: import upstream snapshot with attribution

2026-07-13 13:33:03 +08:00
commit 5b57521aa1
8226 changed files with 3425766 additions and 0 deletions
@@ -0,0 +1,85 @@
+//
+//  SelectBufExecution.cpp
+//  MNN
+//
+//  Created by MNN on 2023/08/11.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+
+#ifndef MNN_OPENCL_BUFFER_CLOSED
+#include "backend/opencl/execution/buffer/SelectBufExecution.hpp"
+#include "core/Macro.h"
+#include "core/TensorUtils.hpp"
+#include "backend/opencl/core/OpenCLBackend.hpp"
+
+namespace MNN {
+namespace OpenCL {
+
+SelectBufExecution::SelectBufExecution(Backend* backend, const MNN::Op* Op) : CommonExecution(backend, Op) {
+    // Do nothing
+}
+ErrorCode SelectBufExecution::onEncode(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
+    mUnits.resize(1);
+    auto &unit = mUnits[0];
+    auto inSize1 = inputs[1]->elementSize();
+    auto inSize2 = inputs[2]->elementSize();
+    auto openCLBackend = static_cast<OpenCLBackend*>(backend());
+    auto runtime       = openCLBackend->getOpenCLRuntime();
+    std::set<std::string> buildOptions = mBuildOptions;
+    if(inSize1 == 1)
+        buildOptions.emplace("-DINSIZE1_EUQAL_1");
+    if(inSize2 == 1)
+        buildOptions.emplace("-DINSIZE2_EUQAL_1");
+    unit.kernel = runtime->buildKernel("select_buf", "select_buf", buildOptions, openCLBackend->getPrecision());
+    mMaxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(unit.kernel));
+
+    std::vector<int> outputShape = tensorShapeFormat(outputs[0]);
+    int outSize = 0;
+    if(MNN::MNN_DATA_FORMAT_NC4HW4 == TensorUtils::getDescribe(outputs[0])->dimensionFormat){
+        outSize = outputShape[0] * outputShape[1] * outputShape[2] * ROUND_UP(outputShape[3], 4);
+    }else{
+        outSize = outputShape[0] * outputShape[1] * outputShape[2] * outputShape[3];
+    }
+
+    mGlobalWorkSize = {
+        static_cast<uint32_t>(outSize),
+        1
+    };
+
+    uint32_t idx = 0;
+    cl_int ret = CL_SUCCESS;
+    ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[0]);
+    ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[1]);
+    ret |= unit.kernel->get().setArg(idx++, openCLBuffer(inputs[0]));
+    ret |= unit.kernel->get().setArg(idx++, openCLBuffer(inputs[1]));
+    ret |= unit.kernel->get().setArg(idx++, openCLBuffer(inputs[2]));
+    ret |= unit.kernel->get().setArg(idx++, openCLBuffer(outputs[0]));
+    MNN_CHECK_CL_SUCCESS(ret, "setArg SelectBufExecution");
+
+    std::string kernelName = "select_buf";
+    mLocalSize = localWS2DDefault(mGlobalWorkSize, mMaxWorkGroupSize, openCLBackend->getOpenCLRuntime(), kernelName, unit.kernel, openCLBackend->getCLTuneLevel(), "select_buf").first;
+    openCLBackend->recordKernel2d(unit.kernel, mGlobalWorkSize, mLocalSize);
+    unit.globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1]};
+    unit.localWorkSize = {mLocalSize[0], mLocalSize[1]};
+    return NO_ERROR;
+}
+
+class SelectBufCreator : public OpenCLBackend::Creator {
+public:
+    virtual Execution* onCreate(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs,
+                                const MNN::Op* op, Backend* backend) const override {
+        for (int i = 0; i < inputs.size(); ++i) {
+            TensorUtils::setTensorSupportPack(inputs[i], false);
+        }
+        for (int i = 0; i < outputs.size(); ++i) {
+            TensorUtils::setTensorSupportPack(outputs[i], false);
+        }
+        OPENCL_CREATOR_CHECK(new SelectBufExecution(backend, op));
+    }
+};
+
+REGISTER_OPENCL_OP_CREATOR(SelectBufCreator, OpType_Select, BUFFER);
+
+} // namespace OpenCL
+} // namespace MNN
+#endif /* MNN_OPENCL_BUFFER_CLOSED */