xref: /aosp_15_r20/external/pytorch/torch/csrc/jit/mobile/model_tracer/MobileModelRunner.cpp (revision da0073e96a02ea20f0ac840b70461e3646d07c45)

#include <torch/csrc/jit/mobile/model_tracer/MobileModelRunner.h>
#include <torch/csrc/jit/mobile/model_tracer/TensorUtils.h>

namespace torch::jit::mobile {

std::vector<std::vector<at::IValue>> MobileModelRunner::
    ivalue_to_bundled_inputs(const c10::IValue& bundled_inputs) {
  CAFFE_ENFORCE(
      bundled_inputs.isList(),
      "Expected get_all_bundled_inputs to ",
      "return a list but got a ",
      bundled_inputs.tagKind(),
      " instead");

  c10::List<at::IValue> all_inputs = bundled_inputs.toList();
  CAFFE_ENFORCE(
      !all_inputs.empty(),
      "Expected at least 1 bundled input, ",
      "but found none. Please use ",
      "torch.utils.bundled_inputs.augment_model_with_bundled_inputs to add.");

  std::vector<std::vector<at::IValue>> ret;
  for (at::IValue input : all_inputs) {
    CAFFE_ENFORCE(
        input.isTuple(),
        "Expected list element to be a tuple ",
        "but got a ",
        input.tagKind(),
        " instead");
    ret.push_back(input.toTupleRef().elements());
  }

  return ret;
}

std::unordered_map<std::string, std::string> MobileModelRunner::
    ivalue_to_bundled_inputs_map(const c10::IValue& bundled_inputs) {
  CAFFE_ENFORCE(
      bundled_inputs.isGenericDict(),
      "Expected get_bundled_inputs_functions_and_info to ",
      "return a dict but got a ",
      bundled_inputs.tagKind(),
      " instead");

  c10::Dict<at::IValue, at::IValue> all_inputs = bundled_inputs.toGenericDict();
  CAFFE_ENFORCE(
      !all_inputs.empty(),
      "Expected at least 1 function with bundled inputs, ",
      "but found none. Please use ",
      "torch.utils.bundled_inputs.augment_model_with_bundled_inputs to add.");

  std::unordered_map<std::string, std::string> ret;
  for (auto& input : all_inputs) {
    const at::IValue& function_name = input.key();
    const at::IValue& nested_dict = input.value();
    CAFFE_ENFORCE(
        function_name.isString(),
        "Expected function with inputs to be a string ",
        "but got a ",
        function_name.tagKind(),
        " instead");
    CAFFE_ENFORCE(
        nested_dict.isGenericDict(),
        "Expected function name to map to dictionary ",
        "but got a ",
        nested_dict.tagKind(),
        " instead");

    // Got the nested dict now need to convert that into std types
    c10::Dict<at::IValue, at::IValue> function_and_info_ival_dict =
        nested_dict.toGenericDict();
    std::unordered_map<std::string, std::vector<std::string>>
        function_and_info_dict;
    for (auto& entry : function_and_info_ival_dict) {
      const at::IValue& key = entry.key();
      const at::IValue& value = entry.value();
      CAFFE_ENFORCE(
          key.isString(),
          "Expected extra information key to be a string ",
          "but got a ",
          value.tagKind(),
          " instead");
      CAFFE_ENFORCE(
          value.isList(),
          "Expected extra information values to be a list ",
          "but got a ",
          value.tagKind(),
          " instead");

      // Got the value of the nested dict entry now need to convert it to std
      // types
      std::vector<std::string> data_list;
      c10::List<at::IValue> ival_data = value.toList();
      for (at::IValue data : ival_data) {
        CAFFE_ENFORCE(
            data.isString(),
            "Expected list element of nested dict entries to be a string ",
            "but got a ",
            data.tagKind(),
            " instead");
        data_list.push_back(data.toStringRef());
      }

      // Add entry into std type mapping
      function_and_info_dict[key.toStringRef()] = data_list;
    }

    // Could store the full mapping of std types, but the 'info' section isnt
    // needed here
    std::string input_function =
        function_and_info_dict["get_inputs_function_name"][0];
    ret[function_name.toStringRef()] = input_function;
  }

  return ret;
}

std::vector<std::vector<at::IValue>> MobileModelRunner::
    get_all_bundled_inputs() {
  auto has_bundled_input = module_->find_method("get_all_bundled_inputs");
  CAFFE_ENFORCE(
      has_bundled_input,
      "Model does not have bundled inputs. ",
      "Use torch.utils.bundled_inputs.augment_model_with_bundled_inputs to add.");

  c10::IValue bundled_inputs = module_->run_method("get_all_bundled_inputs");
  return ivalue_to_bundled_inputs(bundled_inputs);
}

std::unordered_map<std::string, std::vector<std::vector<at::IValue>>>
MobileModelRunner::get_many_functions_bundled_inputs() {
  auto has_bundled_input =
      module_->find_method("get_bundled_inputs_functions_and_info");
  CAFFE_ENFORCE(
      has_bundled_input,
      "Model does not have bundled inputs. ",
      "Use torch.utils.bundled_inputs.augment_many_model_functions_with_bundled_inputs to add.");

  auto ival_bundled_inputs_mapping =
      module_->run_method("get_bundled_inputs_functions_and_info");
  auto bundled_inputs_mapping =
      ivalue_to_bundled_inputs_map(ival_bundled_inputs_mapping);

  std::unordered_map<std::string, std::vector<std::vector<at::IValue>>> ret;

  for (auto& entry : bundled_inputs_mapping) {
    std::string function_name = entry.first;
    std::string function_to_call = entry.second;

    auto has_func_to_call = module_->find_method(function_to_call);
    CAFFE_ENFORCE(
        has_func_to_call,
        "Model does not have ",
        function_to_call,
        "Use torch.utils.bundled_inputs.augment_many_model_functions_with_bundled_inputs to add.");

    c10::IValue bundled_inputs = module_->run_method(function_to_call);
    ret[function_name] = ivalue_to_bundled_inputs(bundled_inputs);
  }
  return ret;
}

std::vector<at::IValue> MobileModelRunner::run_with_inputs(
    std::vector<std::vector<at::IValue>> const& bundled_inputs) {
  std::vector<at::IValue> ret;
  ret.reserve(bundled_inputs.size());
  for (std::vector<at::IValue> const& input : bundled_inputs) {
    ret.emplace_back(module_->forward(input));
  }
  return ret;
}

std::vector<at::IValue> MobileModelRunner::run_with_inputs(
    const std::string& function_name,
    std::vector<std::vector<at::IValue>> const& bundled_inputs) const {
  std::vector<at::IValue> ret;
  ret.reserve(bundled_inputs.size());
  auto has_bundled_input = module_->find_method(function_name);
  CAFFE_ENFORCE(
      has_bundled_input,
      "Model does not have the method named ",
      function_name,
      "Please ensure that it was exported correctly");
  for (std::vector<at::IValue> const& input : bundled_inputs) {
    auto func = module_->get_method(function_name);
    ret.emplace_back(func(input));
  }
  return ret;
}

void MobileModelRunner::run_argless_functions(
    const std::vector<std::string>& functions) {
  for (auto& function_name : functions) {
    if (module_->find_method(function_name)) {
      module_->run_method(function_name);
    }
  }
}

bool MobileModelRunner::set_has_metal_gpu_operators(
    std::set<std::string> const& op_list) {
  for (std::string const& op : op_list) {
    if (op.find("metal::") == 0 || op.find("metal_prepack::") == 0 ||
        op.find("metal_prepack_unet::") == 0) {
      return true;
    }
  }
  return false;
}

void MobileModelRunner::for_each_tensor_in_bundled_inputs(
    std::function<void(const ::at::Tensor&)> const& func) {
  if (has_new_style_bundled_inputs()) {
    // Get the bundled inputs and access the arg level ivalues stored within
    auto bundled_inputs_mapping = this->get_many_functions_bundled_inputs();

    // Loop over functions
    for (auto& entry : bundled_inputs_mapping) {
      std::vector<std::vector<at::IValue>> bundled_inputs = entry.second;
      // Loop through inputs
      for (const std::vector<at::IValue>& input : bundled_inputs) {
        // Loop through values in an input
        for (const at::IValue& iv : input) {
          for_each_tensor_in_ivalue(iv, func);
        }
      }
    }
  } else {
    c10::IValue iv = module_->run_method("get_all_bundled_inputs");
    for_each_tensor_in_ivalue(iv, func);
  }
}
} // namespace torch::jit::mobile