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
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+//
+//  CPUInnerProduct.cpp
+//  MNN
+//
+//  Created by MNN on 2018/08/02.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+
+#include "backend/cpu/CPUInnerProduct.hpp"
+#include "core/AutoStorage.h"
+#include "backend/cpu/CPUConvolution.hpp"
+#include "backend/cpu/compute/CommonOptFunction.h"
+#include "backend/cpu/compute/ConvOpt.h"
+#include "core/Macro.h"
+
+namespace MNN {
+
+class CPUInnerProductExecutor : public Execution {
+public:
+    CPUInnerProductExecutor(Backend *bn, const MNN::Op *op) : Execution(bn) {
+        auto parameter  = op->main_as_InnerProduct();
+        int outputCount = parameter->outputCount();
+        int srcCount    = parameter->weight()->size() / outputCount;
+        mWeight.reset(CPUConvolution::reorderWeightSize(srcCount, outputCount, 1, 4, 4));
+        if (mWeight.get() == nullptr) {
+            mValid = false;
+            return;
+        }
+        mWeight.clear();
+        AutoStorage<float> cache(mWeight.size());
+        CPUConvolution::reorderWeight(mWeight.get(), parameter->weight()->data(), srcCount, outputCount, 1, cache.get());
+        mBias.reset(ALIGN_UP4(outputCount));
+        mBias.clear();
+        ::memcpy(mBias.get(), parameter->bias()->data(), parameter->bias()->size() * sizeof(float));
+        mInputPad.reset(new Tensor(2));
+        mOutputPad.reset(new Tensor(2));
+    }
+    virtual ~CPUInnerProductExecutor() = default;
+    virtual ErrorCode onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) override {
+        auto input                         = inputs[0];
+        auto output                        = outputs[0];
+        mOutputPad->buffer().dim[1].extent = ALIGN_UP4(output->buffer().dim[1].extent);
+        mOutputPad->buffer().dim[0].extent = output->buffer().dim[0].extent;
+        mInputPad->buffer().dim[1].extent  = ALIGN_UP4(input->buffer().dim[1].extent);
+        mInputPad->buffer().dim[0].extent  = input->buffer().dim[0].extent;
+
+        backend()->onAcquireBuffer(mOutputPad.get(), Backend::DYNAMIC);
+        backend()->onAcquireBuffer(mInputPad.get(), Backend::DYNAMIC);
+
+        backend()->onReleaseBuffer(mOutputPad.get(), Backend::DYNAMIC);
+        backend()->onReleaseBuffer(mInputPad.get(), Backend::DYNAMIC);
+        return NO_ERROR;
+    }
+
+    virtual ErrorCode onExecute(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) override {
+        auto input  = inputs[0];
+        auto output = outputs[0];
+
+        auto originSource = input->host<float>();
+        int srcDepthQuad  = mInputPad->buffer().dim[1].extent / 4;
+        int dstDepthQuad  = mOutputPad->buffer().dim[1].extent / 4;
+        auto width        = mInputPad->buffer().dim[0].extent;
+
+        auto source = mInputPad->host<float>();
+        MNNPackC4(source, originSource, width, input->buffer().dim[1].extent);
+        auto dest = mOutputPad->host<float>();
+        MNNGemmFloatCommon_4(dest, source, mWeight.get(), srcDepthQuad, 4 * width, dstDepthQuad, width, 0);
+
+        MNNAddBias(dest, mBias.get(), width, dstDepthQuad);
+        auto originDest = output->host<float>();
+        MNNUnpackC4(originDest, dest, width, output->buffer().dim[1].extent);
+
+        return NO_ERROR;
+    }
+
+private:
+    AutoStorage<float> mWeight;
+    AutoStorage<float> mBias;
+
+    std::unique_ptr<Tensor> mInputPad;
+    std::unique_ptr<Tensor> mOutputPad;
+};
+
+Execution *CPUInnerProductCreator::onCreate(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs,
+                                            const MNN::Op *op, Backend *backend) const {
+    return new CPUInnerProductExecutor(backend, op);
+}
+
+REGISTER_CPU_OP_CREATOR(CPUInnerProductCreator, OpType_InnerProduct);
+
+} // namespace MNN