Method PrepareData

paddle/fluid/framework/operator.cc:2525–2841

}

Scope* OperatorWithKernel::PrepareData(
    const Scope& scope,
    const phi::KernelKey& expected_kernel_key,
    std::vector<std::string>* transferred_inplace_vars,
    RuntimeContext* ctx,
    const phi::Place& place) const {
  Scope* new_scope = nullptr;

  const std::unordered_set<std::string>* no_buffer_ins = nullptr;
  if (info_) {
    auto& no_buffer_inferer = info_->NoNeedBufferVarsInferer();
    // Some op may not register NoNeedBufferVarsInferer
    if (no_buffer_inferer) {
      no_buffer_ins = &(no_buffer_inferer(Inputs(), Outputs(), Attrs()));
      if (no_buffer_ins->empty()) no_buffer_ins = nullptr;
    }
  }

  auto has_infer_varkernel_fn =
      (run_phi_kernel_ && phi_kernel_->get_kerneltype_forvar_fn_ != nullptr);
  phi::AttributeMap infer_attrs{};
  auto fluid_attrs = Attrs();
  phi::GetKernelTypeForVarContext infer_varkernel_context =
      BuildGetKernelTypeForVarContext(expected_kernel_key,
                                      fluid_attrs,
                                      &infer_attrs,
                                      has_infer_varkernel_fn);

  const auto& name_map = Inputs();
  auto prepare_input_data = [&](const std::string& in_name,
                                std::vector<Variable*>* in_vars,
                                const phi::TensorArgDef* in_def,
                                bool should_skip_input) -> void {
    auto& name_vec = name_map.at(in_name);
    for (size_t i = 0; i < in_vars->size(); ++i) {
      const auto& var_name = name_vec[i];
      auto* var = in_vars->at(i);

      // Only tensor can be transfer to another device.
      if (var == nullptr || !VarIsTensor(*var)) {
        continue;
      }

      auto* tensor_in = GetDenseTensorOrSelectedRowsValueFromVar(*var);

      // When no_buffer_ins then checking of phi::DenseTensor::holder_ is
      // not a thread safe. And for infershape scenario checks
      // to be omitted are not really needed
      if (should_skip_input == true) {
#ifdef PADDLE_WITH_DNNL
        // Var without buffer may be needed
        // for some situation like InferShape().
        // In this situation We cannot skip Var analysis, as
        // ONEDNN shape of Var may differ from NHWC Var
        // In such situation corresponding resized Var
        // has to be created and registered
        if ((tensor_in->layout() == DataLayout::ONEDNN) &&
            (var->IsType<DenseTensor>() == true) &&