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 +1,102 @@
+
+//
+//  SplitGeluBufExecution.cpp
+//  MNN
+//
+//  Created by MNN on 2024/06/26.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+#ifdef MNN_SUPPORT_TRANSFORMER_FUSE
+
+#include "backend/opencl/execution/buffer/SplitGeluBufExecution.hpp"
+
+namespace MNN {
+namespace OpenCL {
+
+SplitGeluBufExecution::SplitGeluBufExecution(const MNN::Op* op, Backend* backend) : CommonExecution(backend, op) {
+    mOpenCLBackend = static_cast<OpenCLBackend *>(backend);
+}
+
+ErrorCode SplitGeluBufExecution::onEncode(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
+    auto runtime = static_cast<OpenCLBackend*>(backend())->getOpenCLRuntime();
+
+    MNN_ASSERT(outputs.size() == 1);
+    auto input = inputs[0];
+    auto output = outputs[0];
+    MNN_ASSERT(input->dimensions() == 3);
+    MNN_ASSERT(output->dimensions() == 3);
+    if(inputs.size() > 1) {
+        MNN_ASSERT(inputs[1]->dimensions() == 1);
+        MNN_ASSERT(inputs[1]->length(0) == inputs[0]->length(2));
+    }
+    
+    mUnits.clear();
+    mUnits.resize(1);
+    std::vector<int> outputShape = tensorShapeFormat(output);
+    int shape[4] = {outputShape[0], outputShape[3], outputShape[1], outputShape[2]};
+    std::set<std::string> buildOptions;
+    if(inputs.size() > 1) {
+        buildOptions.emplace("-DDOUBLE_INPUTS");
+    }
+    int pack_wh = 1;
+    if(shape[2] % 16 == 0) {
+        pack_wh = 16;
+        buildOptions.emplace("-DWH_16");
+    } else if(shape[2] % 4 == 0) {
+        pack_wh = 4;
+        buildOptions.emplace("-DWH_4");
+    }
+    auto &unit = mUnits[0];
+    std::string kernelName = "splitgelu_buf";
+    unit.kernel = runtime->buildKernel("splitgelu_buf", kernelName, buildOptions, mOpenCLBackend->getPrecision());
+    
+    auto maxWorkGroupSize  = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(unit.kernel));
+
+    mGWS = {static_cast<uint32_t>(UP_DIV(shape[2], pack_wh)),
+            static_cast<uint32_t>(shape[0] * shape[1])};
+
+    uint32_t idx = 0;
+    cl_int ret = CL_SUCCESS;
+    ret |= unit.kernel->get().setArg(idx++, mGWS[0]);
+    ret |= unit.kernel->get().setArg(idx++, mGWS[1]);
+    ret |= unit.kernel->get().setArg(idx++, openCLBuffer(input));
+    if(inputs.size() > 1) {
+        ret |= unit.kernel->get().setArg(idx++, openCLBuffer(inputs[1]));
+    }
+    ret |= unit.kernel->get().setArg(idx++, openCLBuffer(output));
+    ret |= unit.kernel->get().setArg(idx++, sizeof(shape), shape);
+
+    MNN_CHECK_CL_SUCCESS(ret, "setArg SplitGeluBufExecution");
+    
+    mLWS = localWS2DDefault(mGWS, maxWorkGroupSize, runtime, "splitgelu_buf", unit.kernel, mOpenCLBackend->getCLTuneLevel(), "splitgelu_buf").first;
+
+    unit.globalWorkSize  = {mGWS[0], mGWS[1]};
+    unit.localWorkSize   = {mLWS[0], mLWS[1]};
+    
+    mOpenCLBackend->recordKernel2d(unit.kernel, mGWS, mLWS);
+    mOpenCLBackend->endRecord(mRecording);
+    return NO_ERROR;
+    
+}
+
+
+class SplitGeluBufCreator : public OpenCLBackend::Creator {
+public:
+    virtual ~SplitGeluBufCreator() = default;
+    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 SplitGeluBufExecution(op, backend));
+    }
+};
+
+REGISTER_OPENCL_OP_CREATOR_TRANSFORMER(SplitGeluBufCreator, OpType_SplitGeLU, BUFFER);
+} // namespace OpenCL
+} // namespace MNN
+#endif/* MNN_SUPPORT_TRANSFORMER_FUSE */