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 +1,123 @@
+//
+//  MatmulExecution.cpp
+//  MNN
+//
+//  Created by MNN on 2019/02/28.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+
+#include "backend/opencl/execution/image/MatmulExecution.hpp"
+
+namespace MNN {
+namespace OpenCL {
+
+MatMulExecution::MatMulExecution(const std::vector<Tensor *> &inputs, const MNN::Op *op, Backend *backend,
+                                 bool transposeA, bool transposeB) : CommonExecution(backend, op)
+                                 , mTransposeA(transposeA), mTransposeB(transposeB){
+    mOpenCLBackend = static_cast<OpenCLBackend *>(backend);
+    mAreadySetArg  = false;
+}
+ErrorCode MatMulExecution::onEncode(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
+    mUnits.resize(1);
+    auto &unit = mUnits[0];
+    auto runtime = mOpenCLBackend->getOpenCLRuntime();
+    
+    Tensor *input0 = inputs[0];
+    Tensor *input1 = inputs[1];
+    Tensor *output = outputs[0];
+    
+    std::vector<int> input0Shape = tensorShapeFormat(input0);
+    std::vector<int> input1Shape = tensorShapeFormat(input1);
+    std::vector<int> outputShape = tensorShapeFormat(output);
+    
+    std::vector<uint32_t> mGlobalWorkSize{1, 1};
+    std::vector<uint32_t> mLocalWorkSize{1, 1, 1, 1};
+    
+    
+    std::string kernelName;
+    std::set<std::string> buildOptions;
+    if(mTransposeA) {
+        kernelName = mTransposeB ? "matmul_transA_transB":"matmul_transA";
+    } else {
+        kernelName = mTransposeB ? "matmul_transB":"matmul";
+    }
+    
+    if(inputs.size() > 2) {
+        buildOptions.emplace("-DBIAS");
+    }
+    unit.kernel           = runtime->buildKernel("matmul", kernelName, buildOptions, mOpenCLBackend->getPrecision());
+    mMaxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(unit.kernel));
+
+    //处理二维矩阵相乘，N C相当于H W
+    //二维矩阵相乘
+    if(mTransposeA) {
+        const int height        = input0Shape.at(3);
+        const int outputChannel = input0Shape.at(0);
+        const int width         = mTransposeB ? input1Shape.at(0): input1Shape.at(3);
+        const int outputChannelBlocks = UP_DIV(outputChannel, 4);
+        const int widthblocks         = UP_DIV(width, 4);
+        const int heightblocks        = UP_DIV(height, 4);
+        
+        mGlobalWorkSize = {static_cast<uint32_t>(widthblocks), static_cast<uint32_t>(heightblocks)};
+        cl_int ret = CL_SUCCESS;
+        int idx            = 0;
+        ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[0]);
+        ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[1]);
+        ret |= unit.kernel->get().setArg(idx++, openCLImage(input0));
+        ret |= unit.kernel->get().setArg(idx++, openCLImage(input1));
+        if(inputs.size() > 2) {
+            ret |= unit.kernel->get().setArg(idx++, openCLImage(inputs[2]));
+        }
+        ret |= unit.kernel->get().setArg(idx++, openCLImage(output));
+        ret |= unit.kernel->get().setArg(idx++, static_cast<int>(outputChannel));
+        ret |= unit.kernel->get().setArg(idx++, static_cast<int>(outputChannelBlocks));
+        ret |= unit.kernel->get().setArg(idx++, static_cast<int>(height));
+        MNN_CHECK_CL_SUCCESS(ret, "setArg MatMulExecution transposeA");
+
+        mLocalWorkSize = {mMaxWorkGroupSize / 64, 64, 0};
+    }
+    else {
+        const int height        = input0Shape.at(0);
+        const int outputChannel = input0Shape.at(3);
+        const int width         = mTransposeB ? input1Shape.at(0): input1Shape.at(3);
+        const int outputChannelBlocks = UP_DIV(outputChannel, 4);
+        const int widthblocks         = UP_DIV(width, 4);
+        
+        mGlobalWorkSize = {static_cast<uint32_t>(widthblocks), static_cast<uint32_t>(height)};
+        int 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++, openCLImage(input0));
+        ret |= unit.kernel->get().setArg(idx++, openCLImage(input1));
+        if(inputs.size() > 2) {
+            ret |= unit.kernel->get().setArg(idx++, openCLImage(inputs[2]));
+        }
+        ret |= unit.kernel->get().setArg(idx++, openCLImage(output));
+        ret |= unit.kernel->get().setArg(idx++, static_cast<int>(outputChannel));
+        ret |= unit.kernel->get().setArg(idx++, static_cast<int>(outputChannelBlocks));
+        MNN_CHECK_CL_SUCCESS(ret, "setArg MatMulExecution transposeA");
+
+        mLocalWorkSize = {mMaxWorkGroupSize / 64, 64, 0};
+    }
+
+    mOpenCLBackend->recordKernel2d(unit.kernel, mGlobalWorkSize, mLocalWorkSize);
+    unit.globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1]};
+    unit.localWorkSize = {mLocalWorkSize[0], mLocalWorkSize[1]};
+    return NO_ERROR;
+}
+
+class MatMulCreator : 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 {
+        auto param = op->main_as_MatMul();
+        OPENCL_CREATOR_CHECK(new MatMulExecution(inputs, op, backend, param->transposeA(), param->transposeB()));
+    }
+};
+
+REGISTER_OPENCL_OP_CREATOR(MatMulCreator, OpType_MatMul, IMAGE);
+
+} // namespace OpenCL
+} // namespace MNN