Function GetDeviceForInput

tensorflow/core/common_runtime/eager/execute.cc:347–375

}

Status GetDeviceForInput(const EagerContext* ctx, TensorHandle* tensor_handle,
                         Device** result) {
  Device* cpu_device = ctx->HostCPU();
  string device_name;
  if (tensor_handle->IsRemote()) {
    Device* device = tensor_handle->device();
    device_name = device != nullptr ? device->name() : cpu_device->name();
    *result = (device == nullptr ? cpu_device : device);
  } else if (tensor_handle->dtype == DT_RESOURCE) {
    // Use the resource's actual device because it is the device that will
    // influence partitioning the multi-device function.
    const Tensor* tensor;
    TF_RETURN_IF_ERROR(tensor_handle->Tensor(&tensor));
    const ResourceHandle& handle = tensor->flat<ResourceHandle>()(0);
    device_name = handle.device();

    Device* input_device;
    TF_RETURN_IF_ERROR(
        ctx->FindDeviceFromName(device_name.c_str(), &input_device));
    *result = input_device;
  } else if (MTypeFromDType(tensor_handle->dtype) == HOST_MEMORY) {
    *result = cpu_device;
  } else {
    Device* device = tensor_handle->device();
    device_name = device != nullptr ? device->name() : cpu_device->name();
    *result = (device == nullptr ? cpu_device : device);
  }
  return Status::OK();
}

// Appends a TensorShape object to Fprint128 hash.
// For best performance, we would like to avoid dynamic memory allocation in