pytorch/torch/csrc/jit/pybind_utils.h

#pragma once

#include <ATen/core/ivalue.h>
#include <ATen/core/jit_type.h>
#include <ATen/core/stack.h>
#include <torch/csrc/Device.h>
#include <torch/csrc/jit/operator.h>
#include <torch/csrc/jit/script/module.h>
#include <torch/csrc/jit/tracer.h>
#include <torch/csrc/utils/auto_gil.h>
#include <torch/csrc/utils/pybind.h>
#include <torch/csrc/utils/six.h>

#include <ATen/core/function_schema.h>
#include <c10/util/Exception.h>

#include <algorithm>
#include <cstddef>
#include <string>
#include <utility>
#include <vector>

// The visibility attribute is to avoid a warning about storing a field in the
// struct that has a different visibility (from pybind) than the struct.
#ifdef _WIN32
#define VISIBILITY_HIDDEN
#else
#define VISIBILITY_HIDDEN __attribute__((visibility("hidden")))
#endif

namespace torch {
namespace jit {

// error reporting: when reporting user-caused errors, these functions should
// not use AT_ERROR macros, since these macros add stack trace information
// that is confusing to display to the end user since it always reports
// locations in libtorch code rather than user code.

using tracer::TypedStack;
struct TypedIValue : public std::pair<IValue, TypePtr> {
  using pair::pair;

  IValue& ivalue() {
    return this->first;
  }
  TypePtr& type() {
    return this->second;
  }
};

inline TypedIValue toDictKeyIValue(py::handle key) {
  if (py::isinstance<py::str>(key)) {
    return TypedIValue(
        ConstantString::create(py::cast<std::string>(key)),
        StringType::create());
  } else if (py::isinstance<py::int_>(key)) {
    return TypedIValue(py::cast<int64_t>(key), IntType::create());
  } else if (py::isinstance<py::float_>(key)) {
    return TypedIValue(py::cast<double>(key), FloatType::create());
  } else {
    AT_ERROR("Dictionary inputs may only have string, int, or float keys");
  }
}

inline TypedIValue trySpecializeTensorList(
    std::vector<IValue>& elems,
    TypePtr type) {
  // Since we only call this function for trace inputs, the only options are
  // generic list, and list of tensors. We do not need to check for primitive
  // types.
  if (!type->isSubtypeOf(TensorType::get())) {
    return TypedIValue(elems, ListType::create(type));
  }
  std::vector<at::Tensor> tensors;
  tensors.reserve(elems.size());
  for (auto elem : elems) {
    tensors.push_back(elem.toTensor());
  }
  return TypedIValue(tensors, ListType::ofTensors());
}

inline c10::optional<TypePtr> unifyOrInitializeType(
    TypePtr accum,
    TypePtr unify) {
  if (!accum) {
    return unify;
  }
  return unifyTypes(accum, unify);
}

inline TypedIValue toTypedIValue(py::handle input) {
  if (THPVariable_Check(input.ptr())) {
    auto ten = py::cast<at::Tensor>(input);
    if (ten.is_sparse()) {
      AT_ERROR("sparse tensors not supported");
    }
    return TypedIValue(ten, CompleteTensorType::create(ten));
  } else if (six::isTuple(input)) {
    py::tuple input_tuple = py::cast<py::tuple>(input);
    Stack s;
    std::vector<TypePtr> t;
    s.reserve(input_tuple.size());
    t.reserve(input_tuple.size());
    for (py::handle elem : input_tuple) {
      auto info = toTypedIValue(elem);
      s.push_back(info.first);
      t.push_back(info.second);
    }
    return TypedIValue(Tuple::create(s), TupleType::create(t));
  } else if (PyDict_Check(input.ptr())) {
    // Check to make sure we can generate useful input/output types
    auto dict = py::cast<py::dict>(input);
    c10::impl::GenericDict elems;

    size_t len = py::len(dict);
    if (!len) {
      AT_ERROR("Dictionary inputs must have entries.");
    }
    elems.reserve(len);

    TypePtr keyType = nullptr;
    TypePtr valueType = nullptr;
    for (auto entry : dict) {
      auto keyInfo = toDictKeyIValue(entry.first);
      auto valInfo = toTypedIValue(entry.second);
      auto unifiedKey = unifyOrInitializeType(keyType, keyInfo.second);
      auto unifiedValue = unifyOrInitializeType(valueType, valInfo.second);
      if (!unifiedKey || !unifiedValue) {
        AT_ERROR(
            "Dictionary inputs to traced functions must have consistent type");
      }
      keyType = *unifiedKey;
      valueType = *unifiedValue;
      elems.insert(keyInfo.first, valInfo.first);
    }
    return TypedIValue(
        at::ivalue::GenericDict::create(std::move(elems)),
        DictType::create(keyType, valueType));
  } else if (PyList_Check(input.ptr())) {
    auto list = py::cast<py::list>(input);
    std::vector<IValue> elems;
    size_t len = py::len(list);
    if (!len) {
      AT_ERROR("List trace inputs must have elements");
    }
    elems.reserve(len);

    TypePtr listType = nullptr;
    for (auto elem : list) {
      TypedIValue typedVal = toTypedIValue(elem);
      elems.push_back(typedVal.ivalue());
      auto unify = unifyOrInitializeType(listType, typedVal.type());
      if (!unify) {
        AT_ERROR(
            "List inputs to traced functions must have consistent element type");
      }
      listType = *unify;
    }
    return trySpecializeTensorList(elems, listType);
  } else {
    throw std::runtime_error(c10::str(
        "Only tensors and (possibly nested) tuples of tensors or dicts are supported ",
        "as inputs or outputs of traced functions",
        ", but instead got value of type ",
        py::str(input.get_type().attr("__name__")),
        ".",
        "\nValue: ",
        py::repr(input)));
  }
}

inline IValue toIValue(py::handle input) {
  return toTypedIValue(input).ivalue();
}

inline Stack toStack(const py::tuple& inputs) {
  return toIValue(inputs).toTuple()->elements();
}

inline TypedStack toTypedStack(const py::tuple& inputs) {
  auto info = toTypedIValue(inputs);
  return TypedStack(
      info.ivalue().toTuple()->elements(), info.type()->expect<TupleType>());
}

inline IValue toIValue(
    py::handle obj,
    const TypePtr& type,
    c10::optional<int32_t> N = c10::nullopt);

inline IValue createGenericList(py::handle obj, const TypePtr& elem_type) {
  std::vector<IValue> elems;
  for (auto elem : obj) {
    elems.push_back(toIValue(elem, elem_type));
  }
  return List<IValue>::create(std::move(elems));
}

inline IValue createGenericDict(
    py::handle obj,
    const TypePtr& key_type,
    const TypePtr& value_type) {
  c10::impl::GenericDict elems;
  elems.reserve(py::len(obj));
  for (auto key : obj) {
    elems.insert(
        toIValue(key, key_type), toIValue(obj[key], value_type));
  }
  return at::ivalue::GenericDict::create(std::move(elems));
}

inline IValue toIValue(
    py::handle obj,
    const TypePtr& type,
    c10::optional<int32_t> N) {
  switch (type->kind()) {
    case TypeKind::TensorType:
    case TypeKind::AutogradZeroTensorType:
    case TypeKind::DimensionedTensorType:
    case TypeKind::ProfiledTensorType:
    case TypeKind::CompleteTensorType: {
      auto var = py::cast<autograd::Variable>(obj);
      if (var.is_sparse()) {
        AT_ERROR("sparse tensors not supported");
      }
      return var;
    }
    case TypeKind::FloatType:
      return py::cast<double>(obj);
    case TypeKind::IntType:
      return py::cast<int64_t>(obj);
    case TypeKind::NoneType:
      if (obj != Py_None)
        throw py::cast_error();

      return {};
    case TypeKind::BoolType:
      return py::cast<bool>(obj);
    case TypeKind::TupleType: {
      if (!PyTuple_Check(obj.ptr()))
        throw py::cast_error(); // note: the py::cast does not throw cast_error
                                // because it attempts to iterate a non-tuple
      py::tuple tuple = py::cast<py::tuple>(obj);
      size_t tuple_size = tuple.size();
      const auto& elem_types = type->cast<TupleType>()->elements();
      if (elem_types.size() != tuple_size) {
        throw py::cast_error();
      }
      std::vector<IValue> values;
      values.reserve(tuple_size);
      for (size_t i = 0; i < tuple_size; ++i) {
        values.push_back(toIValue(tuple[i], elem_types[i]));
      }
      return Tuple::create(std::move(values));
    }
    case TypeKind::StringType:
      return ConstantString::create(py::cast<std::string>(obj));
    case TypeKind::DeviceObjType: {
      auto device = reinterpret_cast<THPDevice*>(obj.ptr());
      return device->device;
    }
    case TypeKind::ListType: {
      const auto& elem_type = type->expect<ListType>()->getElementType();
      switch (elem_type->kind()) {
        // allows single int/float to be broadcasted to a fixed size list
        case TypeKind::IntType:
          if (!N || !py::isinstance<py::int_>(obj)) {
            return py::cast<std::vector<int64_t>>(obj);
          } else {
            double value = py::cast<int64_t>(obj);
            std::vector<double> repeated(*N, value);
            return repeated;
          }
        case TypeKind::FloatType:
          if (!N || !py::isinstance<py::float_>(obj)) {
            return py::cast<std::vector<double>>(obj);
          } else {
            double value = py::cast<double>(obj);
            std::vector<double> repeated(*N, value);
            return repeated;
          }
        case TypeKind::DimensionedTensorType:
        case TypeKind::TensorType:
          return py::cast<std::vector<at::Tensor>>(obj);
        default:
          return createGenericList(obj, elem_type);
      }
    }
    case TypeKind::DictType: {
      const auto& dict_type = type->expect<DictType>();
      return createGenericDict(
          obj, dict_type->getKeyType(), dict_type->getValueType());
    }
    case TypeKind::OptionalType: {
      // check if it's a none obj since optional accepts NoneType
      if (obj == Py_None) {
        // check if it's a none obj since optional accepts NoneType
        // return an IValue() to denote a NoneType
        return {};
      }
      return toIValue(obj, type->expect<OptionalType>()->getElementType());
    }
    case TypeKind::ClassType: {
      auto classType = type->expect<ClassType>();
      // 1. create a bare ivalue
      const size_t numAttrs = classType->numAttributes();
      auto userObj = c10::ivalue::Object::create(classType, numAttrs);

      // 2. copy all the contained types
      for (size_t slot = 0; slot < numAttrs; slot++) {
        const auto& attrType = classType->getAttribute(slot);
        const auto& attrName = classType->getAttributeName(slot);

        const auto& contained = py::getattr(obj, attrName.c_str());
        userObj->setSlot(slot, toIValue(contained, attrType));
      }
      return userObj;
    }
    case TypeKind::NumberType:
    case TypeKind::GeneratorType:
    case TypeKind::VarType:
    case TypeKind::FutureType:
      break;
  }
  AT_ERROR(
      "Missing cases in toIValue for type: ",
      type->str(),
      "! File a bug report.");
}

inline IValue argumentToIValue(
    const FunctionSchema& schema,
    size_t argumentPosition,
    py::handle object) {
  const auto& argument = schema.arguments().at(argumentPosition);
  try {
    return toIValue(object, argument.type(), argument.N());
  } catch (const py::cast_error& error) {
    throw std::runtime_error(c10::str(
        schema.name(),
        "() expected value of type ",
        argument.type()->str(),
        " for argument '",
        argument.name(),
        "' in position ",
        argumentPosition,
        ", but instead got value of type ",
        py::str(object.get_type().attr("__name__")),
        ".",
        "\nValue: ",
        py::repr(object),
        "\nDeclaration: ",
        schema));
  }
}

inline IValue returnToIValue(const TypePtr& type, py::handle object) {
  try {
    return toIValue(object, type);
  } catch (const py::cast_error& error) {
    throw std::runtime_error(c10::str(
        " expected value of type ",
        type->str(),
        " for return value but instead got value of type ",
        py::str(object.get_type().attr("__name__")),
        ".",
        "\nValue: ",
        py::repr(object)));
  }
}

inline py::object toPyObject(IValue&& ivalue) {
  if (ivalue.isNone()) {
    return py::none();
  } else if (ivalue.isTensor()) {
    auto tensor = std::move(ivalue).toTensor();
    if (tensor.is_sparse()) {
      AT_ERROR("sparse tensors not supported");
    }
    return py::cast(autograd::Variable(std::move(tensor)));
  } else if (ivalue.isDouble()) {
    return py::cast(ivalue.toDouble());
  } else if (ivalue.isInt()) {
    return py::cast(ivalue.toInt());
  } else if (ivalue.isBool()) {
    return py::cast(ivalue.toBool());
  } else if (ivalue.isString()) {
    return py::cast(ivalue.toStringRef());
  } else if (ivalue.isIntList()) {
    return py::cast(ivalue.toIntListRef());
  } else if (ivalue.isDoubleList()) {
    return py::cast(ivalue.toDoubleListRef());
  } else if (ivalue.isBoolList()) {
    return py::cast(ivalue.toBoolListRef());
  } else if (ivalue.isTensorList()) {
    return py::cast(ivalue.toTensorListRef());
  } else if (ivalue.isGenericList()) {
    auto list = ivalue.toGenericList();
    const auto& elements = list->elements();
    py::list t{elements.size()};
    for (size_t i = 0; i < elements.size(); ++i) {
      t[i] = toPyObject(IValue{elements[i]});
    }
    return std::move(t);
  } else if (ivalue.isTuple()) {
    auto tuple = ivalue.toTuple();
    const auto& elements = tuple->elements();
    py::tuple t{elements.size()};
    for (size_t i = 0; i < elements.size(); ++i) {
      t[i] = toPyObject(IValue{elements[i]});
    }
    return std::move(t);
  } else if (ivalue.isDevice()) {
    return py::cast<py::object>(THPDevice_New(ivalue.toDevice()));
  } else if (ivalue.isGenericDict()) {
    auto dict = ivalue.toGenericDict();
    const auto& elements = dict->elements();
    py::dict py_dict;
    for (auto pair : elements) {
      py_dict[toPyObject(IValue{pair.key()})] = toPyObject(IValue{pair.value()});
    }
    return std::move(py_dict);
  } else if (ivalue.isObject()) {
    const auto obj = ivalue.toObject();
    auto& pyCu = script::CompilationUnit::_get_python_cu();
    const auto classType = pyCu.get_class(c10::QualifiedName(obj->name()));
    AT_ASSERT(classType);
    auto pyClass =
        py::module::import("torch.jit").attr("_get_script_class")(obj->name());
    auto pyObj = pyClass.attr("__new__")(pyClass);

    const auto numAttrs = classType->numAttributes();

    for (size_t slot = 0; slot < numAttrs; slot++) {
      const auto& attrName = classType->getAttributeName(slot);
      IValue v = obj->getSlot(slot);
      py::setattr(pyObj, attrName.c_str(), toPyObject(std::move(v)));
    }
    return pyObj;
  } else {
    AT_ERROR("Missing cases in 'toPyObject'! File a bug report.");
  }
}

struct VISIBILITY_HIDDEN tuple_slice {
  /*implicit*/ tuple_slice(py::tuple tup_)
      : tup(std::move(tup_)), b(0), e(tup.size()) {}
  tuple_slice(py::tuple tup_, int64_t b_)
      : tup(std::move(tup_)), b(b_), e(tup.size()) {}
  tuple_slice(py::tuple tup_, int64_t b_, int64_t e_)
      : tup(std::move(tup_)), b(b_), e(e_) {}
  py::detail::tuple_iterator begin() const {
    return {tup, static_cast<pybind11::ssize_t>(b)};
  }
  py::detail::tuple_iterator end() const {
    return {tup, static_cast<pybind11::ssize_t>(e)};
  }
  size_t size() const {
    return e - b;
  }
  py::detail::tuple_accessor operator[](size_t index) const {
    return {tup, static_cast<size_t>(b + index)};
  }

 private:
  py::tuple tup;
  int64_t b;
  int64_t e;
};

inline Stack createStackForSchema(
    const FunctionSchema& schema,
    const tuple_slice& args,
    const py::kwargs& kwargs = py::kwargs()) {
  if (args.size() + kwargs.size() > schema.arguments().size()) {
    throw std::runtime_error(c10::str(
        schema.name(),
        "() expected at most ",
        schema.arguments().size(),
        " argument(s) but received ",
        args.size() + kwargs.size(),
        " argument(s). Declaration: ",
        schema));
  }
  Stack stack;
  stack.reserve(schema.arguments().size());

  // First push all positional args.
  for (size_t i = 0; i < args.size(); ++i) {
    // Use the type information from the schema to convert the PyObject.
    push(stack, argumentToIValue(schema, i, args[i]));
  }

  // Now for every remaining non-positional argument in the schema, look for it
  // in the kwargs dict and push it if found, or use its default value if it
  // has one.
  size_t consumed_kwargs = 0;
  for (size_t i = args.size(); i < schema.arguments().size(); ++i) {
    const auto& arg = schema.arguments()[i];
    if (kwargs.contains(arg.name().c_str())) {
      push(stack, argumentToIValue(schema, i, kwargs[arg.name().c_str()]));
      consumed_kwargs += 1;
    } else if (arg.default_value()) {
      push(stack, *arg.default_value());
    } else {
      throw std::runtime_error(c10::str(
          schema.name(),
          "() is missing value for argument '",
          arg.name(),
          "'. Declaration: ",
          schema));
    }
  }

  if (consumed_kwargs != kwargs.size()) {
    std::vector<std::string> names;
    for (const auto& kwarg : kwargs) {
      names.emplace_back(py::cast<std::string>(kwarg.first));
    }
    schema.findErrorInKwargs(names);
  }

  return stack;
}

inline py::object createPyObjectForStack(Stack&& stack) {
  if (stack.empty()) {
    return py::none();
  }

  // Return a simple value and not a single-element tuple if there is only one
  // return value.
  if (stack.size() == 1) {
    return toPyObject(std::move(stack[0]));
  }

  // If there is more than one return value, pop them into a py::tuple.
  py::tuple return_values(stack.size());
  for (size_t ret = 0; ret < return_values.size(); ++ret) {
    return_values[ret] = toPyObject(std::move(stack[ret]));
  }

  return std::move(return_values);
}

// TODO: Remove once we clean up the GraphExecutor usage.
inline Stack evilDeprecatedBadCreateStackDoNotUse(
    const py::tuple& tuple,
    at::ArrayRef<Value*> inputs,
    size_t reserve_extra_space = 0) {
  if (tuple.size() != inputs.size()) {
    AT_ERROR(
        "expected " + std::to_string(inputs.size()) + " inputs, but got " +
        std::to_string(tuple.size()));
  }
  Stack result;
  result.reserve(tuple.size() + reserve_extra_space);
  for (size_t i = 0; i < inputs.size(); ++i) {
    result.push_back(toIValue(std::move(tuple[i]), inputs[i]->type()));
  }
  return result;
}

template <typename MethodOrFunction>
inline py::object invokeScriptMethodFromPython(
    MethodOrFunction& callee,
    tuple_slice args,
    py::kwargs kwargs) {
  auto stack = createStackForSchema(
      callee.getSchema(), std::move(args), std::move(kwargs));
  {
    AutoNoGIL no_gil_guard;
    callee.run(stack);
  }
  return toPyObject(std::move(stack.back()));
}

inline py::object invokeOperatorFromPython(
    const Operator& op,
    py::args args,
    py::kwargs kwargs) {
  // Create a stack full of the arguments and keyword arguments.
  auto stack =
      createStackForSchema(op.schema(), std::move(args), std::move(kwargs));

  // Invoke the operation, which puts the return values onto the stack.
  op.getOperation()(stack);

  return createPyObjectForStack(std::move(stack));
}

} // namespace jit
} // namespace torch