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 +1,103 @@
+//
+//  ConvolutionGroup.cpp
+//  MNN
+//
+//  Created by MNN on 2018/08/06.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+
+#include "backend/cpu/compute/ConvolutionGroup.hpp"
+#include "backend/cpu/compute/CommonOptFunction.h"
+#include "core/Macro.h"
+#include "core/TensorUtils.hpp"
+
+namespace MNN {
+ConvolutionGroup::ConvolutionGroup(Backend *b, const std::vector<std::shared_ptr<Execution>> &subConvolution)
+    : MNN::Execution(b) {
+    mSubConvolution = subConvolution;
+    MNN_ASSERT(subConvolution.size() > 1);
+
+    mInputRaw.reset(new Tensor(4));
+    mInputUnit.reset(new Tensor(4, Tensor::CAFFE_C4));
+    mOutputRaw.reset(new Tensor(4));
+    mOutputUnit.reset(new Tensor(4, Tensor::CAFFE_C4));
+
+    mInputUnitWrap.push_back(mInputUnit.get());
+    mOutputUnitWrap.push_back(mOutputUnit.get());
+}
+
+ErrorCode ConvolutionGroup::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
+    auto ib = inputs[0]->buffer();
+    auto ob = outputs[0]->buffer();
+    ::memcpy(mInputRaw->buffer().dim, ib.dim, ib.dimensions * sizeof(halide_dimension_t));
+    mInputRaw->buffer().dimensions    = ib.dimensions;
+
+    ::memcpy(mInputUnit->buffer().dim, ib.dim, ib.dimensions * sizeof(halide_dimension_t));
+    mInputUnit->buffer().dimensions    = ib.dimensions;
+    mInputUnit->buffer().dim[1].extent = ib.dim[1].extent / mSubConvolution.size();
+    TensorUtils::getDescribe(mInputUnit.get())->dimensionFormat = MNN_DATA_FORMAT_NC4HW4;
+    TensorUtils::setLinearLayout(mInputUnit.get());
+
+    ::memcpy(mOutputRaw->buffer().dim, ob.dim, ob.dimensions * sizeof(halide_dimension_t));
+    mOutputRaw->buffer().dimensions    = ob.dimensions;
+
+    ::memcpy(mOutputUnit->buffer().dim, ob.dim, ob.dimensions * sizeof(halide_dimension_t));
+    mOutputUnit->buffer().dimensions    = ob.dimensions;
+    mOutputUnit->buffer().dim[1].extent = ob.dim[1].extent / mSubConvolution.size();
+    TensorUtils::getDescribe(mOutputUnit.get())->dimensionFormat = MNN_DATA_FORMAT_NC4HW4;
+    TensorUtils::setLinearLayout(mOutputUnit.get());
+
+    bool res = backend()->onAcquireBuffer(mOutputUnit.get(), Backend::DYNAMIC);
+    res = res && backend()->onAcquireBuffer(mInputUnit.get(), Backend::DYNAMIC);
+    res = res && backend()->onAcquireBuffer(mInputRaw.get(), Backend::DYNAMIC);
+    res = res && backend()->onAcquireBuffer(mOutputRaw.get(), Backend::DYNAMIC);
+    if (!res) {
+        return OUT_OF_MEMORY;
+    }
+
+    for (auto &iter : mSubConvolution) {
+        iter->onResize(mInputUnitWrap, mOutputUnitWrap);
+    }
+
+    backend()->onReleaseBuffer(mOutputUnit.get(), Backend::DYNAMIC);
+    backend()->onReleaseBuffer(mInputUnit.get(), Backend::DYNAMIC);
+    backend()->onReleaseBuffer(mInputRaw.get(), Backend::DYNAMIC);
+    backend()->onReleaseBuffer(mOutputRaw.get(), Backend::DYNAMIC);
+
+    return NO_ERROR;
+}
+
+ErrorCode ConvolutionGroup::onExecute(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
+    auto input           = inputs[0];
+    auto output          = outputs[0];
+    int batch            = input->buffer().dim[0].extent;
+    auto core = static_cast<CPUBackend*>(backend())->functions();
+    auto inputBatchSize  = input->width() * input->height() * UP_DIV(input->channel(), core->pack) * core->pack;
+    auto outputBatchSize = output->width() * output->height() * UP_DIV(output->channel(), core->pack) * core->pack;
+
+    auto srcOrigin = input->host<uint8_t>();
+    auto dstOrigin = output->host<uint8_t>();
+    int inputArea = input->width() * input->height() * input->batch();
+    int outputArea = output->width() * output->height() * output->batch();
+    int inputOffset[] = {
+        inputArea,
+        inputArea
+    };
+    int outputOffset[] = {
+        outputArea,
+        outputArea
+    };
+    core->MNNUnpackCUnit(mInputRaw->host<float>(), (float*)srcOrigin, inputArea, input->channel(), inputOffset);
+    int inputGroupSize   = inputArea * input->channel() / mSubConvolution.size();
+    int outputGroupSize  = outputArea * output->channel() / mSubConvolution.size();
+    int subInputChannel  = input->channel() / mSubConvolution.size();
+    int subOutputChannel = output->channel() / mSubConvolution.size();
+    for (int group = 0; group < mSubConvolution.size(); ++group) {
+        core->MNNPackCUnit(mInputUnit->host<float>(), (const float*)(mInputRaw->host<uint8_t>() + group * inputGroupSize * core->bytes), inputArea, subInputChannel, inputOffset);
+        mSubConvolution[group]->onExecute(mInputUnitWrap, mOutputUnitWrap);
+        core->MNNUnpackCUnit((float*)(mOutputRaw->host<uint8_t>() + group * outputGroupSize * core->bytes), mOutputUnit->host<float>(), outputArea, subOutputChannel, outputOffset);
+    }
+    core->MNNPackCUnit((float*)dstOrigin, mOutputRaw->host<float>(), outputArea, output->channel(), outputOffset);
+    return NO_ERROR;
+}
+} // namespace MNN