TIM-VX/src/tim/transform/ops/deconv2d_layout_inference.h

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#ifndef TIM_LAYOUT_INFER_DECONV2D_LAYOUT_INFERENCE_H_
#define TIM_LAYOUT_INFER_DECONV2D_LAYOUT_INFERENCE_H_

#include "ops/op_layout_inference.h"
#include "permute_vector.h"
#include "builtin_op_impl.h"
#include "tim/vx/ops/deconv.h"

namespace tim {
namespace transform {
class DeConv2dLayoutInfer : public OpLayoutInfer {
 public:
  DeConv2dLayoutInfer(
      const std::shared_ptr<vx::Operation>& op,
      std::shared_ptr<layout_inference_impl::LayoutInferContext>& context)
      : OpLayoutInfer(op, context) {}

  void OnInputs(
      std::vector<std::shared_ptr<vx::Tensor>>& next_tensors) override {
    vx::DataLayout layout = op_->impl()->layout_;
    auto required_pv = MakeShared(4);
    if (layout == vx::DataLayout::CWHN) {
      required_pv = std::make_shared<PermuteVector<4>>(kCWHN2WHCN);
    }
    auto src_inputs = op_->impl()->InputsTensor();

    for (const auto& in : src_inputs) {
      std::shared_ptr<vx::Tensor> infer_tensor;
      std::shared_ptr<IPermuteVector> trans_pv;
      if (in->IsConstTensor() &&
          !(in->GetSpec().attr_ & vx::TensorAttribute::INPUT)) {
        // For bias
        if (in->GetShape().size() == 1) {
          infer_tensor = context_->infer_graph_->CreateTensor(in->GetSpec(),
                                                              in->GetDataRef());
          trans_pv = MakeShared(1);
        } else {
          // For weight
          if (!required_pv->IsAligned()) {
            auto src_deconv2d =
                std::static_pointer_cast<vx::ops::DeConv2d>(op_);
            // Support TVM Kernel Layout
            if (src_deconv2d->KernelDataLayout() == vx::DataLayout::OcIcWH) {
              trans_pv = std::make_shared<PermuteVector<4>>(kOcIcWH2WHIcOc);
              infer_tensor = PermuteConstTensor(in, trans_pv);
            } else if (src_deconv2d->KernelDataLayout() ==
                       vx::DataLayout::WHIcOc) {
              infer_tensor = context_->infer_graph_->CreateTensor(
                  in->GetSpec(), in->GetDataRef());
              trans_pv = MakeShared(required_pv->Rank());
            } else {
              infer_tensor = PermuteConstTensor(in, required_pv);
              trans_pv = required_pv;
            }
          } else {
            infer_tensor = context_->infer_graph_->CreateTensor(
                in->GetSpec(), in->GetDataRef());
            trans_pv = MakeShared(required_pv->Rank());
          }
        }
      } else {
        // For bias
        if (in->GetShape().size() == 1) {
          infer_tensor = context_->GetMapedTensor(in);
          trans_pv = MakeShared(1);
        } else {
          // For input/weight
          auto pv = context_->GetPermuteVector(in);
          auto final_pv = pv->Reverse()->Add(required_pv);
          if (!final_pv->IsAligned()) {
            infer_tensor =
                InsertPermute(context_->GetMapedTensor(in), final_pv);
            trans_pv = required_pv;
          } else {
            infer_tensor = context_->GetMapedTensor(in);
            trans_pv = pv;
          }
        }
      }
      context_->UpdateTensorMap(in, infer_tensor);
      context_->SetPermuteVector(in, trans_pv);
    }

    auto pad_type =
        TranslatePadType(op_->impl()->node()->nn_param.deconv.pad_type);
    std::array<uint32_t, 2> ksize = {
        op_->impl()->node()->nn_param.deconv.ksize[0],
        op_->impl()->node()->nn_param.deconv.ksize[1]};
    std::array<uint32_t, 2> stride = {
        op_->impl()->node()->nn_param.deconv.stride[0],
        op_->impl()->node()->nn_param.deconv.stride[1]};
    std::array<uint32_t, 2> output_padding = {
        op_->impl()->node()->nn_param.deconv.output_padding[0],
        op_->impl()->node()->nn_param.deconv.output_padding[0]};
    std::array<uint32_t, 4> pad = {op_->impl()->node()->nn_param.deconv.pad[0],
                                   op_->impl()->node()->nn_param.deconv.pad[1],
                                   op_->impl()->node()->nn_param.deconv.pad[2],
                                   op_->impl()->node()->nn_param.deconv.pad[3]};
    int32_t oc_count = op_->impl()->node()->nn_param.deconv.weights;
    const uint32_t group = op_->impl()->node()->nn_param.deconv.group;

    auto deconv = context_->infer_graph_->CreateOperation<vx::ops::DeConv2d>(
        oc_count, pad_type, ksize, stride, output_padding, pad, group);
    auto infer_out = CreateOutputsTensor(required_pv);
    for (const auto& i_src : src_inputs) {
      (*deconv).BindInput(context_->GetMapedTensor(i_src));
    }
    (*deconv).BindOutput(infer_out[0]);

    context_->SetPermuteVector(op_->impl()->OutputsTensor()[0], required_pv);
    next_tensors.push_back(op_->impl()->OutputsTensor()[0]);
  }
};

}  // namespace transform
}  // namespace tim

#endif