schema_matching.cpp

#include <torch/csrc/jit/frontend/schema_matching.h>

#include <ATen/core/interned_strings.h>
#include <ATen/core/jit_type.h>
#include <c10/util/Exception.h>
#include <c10/util/Optional.h>
#include <c10/util/irange.h>
#include <caffe2/serialize/versions.h>
#include <torch/csrc/jit/frontend/builtin_functions.h>
#include <torch/csrc/jit/frontend/error_report.h>
#include <torch/csrc/jit/frontend/function_schema_parser.h>
#include <torch/csrc/jit/ir/ir.h>
#include <torch/csrc/jit/operator_upgraders/utils.h>
#include <torch/csrc/jit/operator_upgraders/version_map.h>
#include <torch/csrc/jit/runtime/operator.h>

namespace torch::jit {

static inline TypePtr unwrapOptional(TypePtr opt_type) {
  if (auto dyn = opt_type->castRaw<c10::DynamicType>()) {
    return unwrapOptional(dyn->fallback());
  }
  if (auto unwrap_list_type = opt_type->cast<OptionalType>()) {
    return unwrap_list_type->getElementType();
  }
  return opt_type;
}

static inline bool isIntOrFloatUsedAsList(
    const Value* value,
    const Argument& arg) {
  // Look for int[N] or float[N]
  const auto& v_type = value->type();
  if (v_type != FloatType::get() && v_type != IntType::get())
    return false;
  auto arg_type = unwrapOptional(arg.type());
  auto list_type = arg_type->cast<ListType>();
  return list_type && list_type->getElementType() == v_type && arg.N();
}

/// Returns true if `type` is a Tuple in which all the elements have the
/// same type or if it's a subtype of `list_type_`.
bool convertibleToList(const TypePtr& type, const TypePtr& list_type_) {
  auto list_type = list_type_->castRaw<ListType>();
  if (!list_type) {
    return false;
  }
  if (type->isSubtypeOf(*list_type_)) {
    return true;
  }
  if (auto tuple = type->castRaw<TupleType>()) {
    return std::all_of(
        tuple->elements().begin(),
        tuple->elements().end(),
        [&](const TypePtr& t) {
          // TODO: resolve VarType if necessary
          return t->isSubtypeOf(*list_type->getElementType());
        });
  }
  return false;
}

// Applies implicit conversion from value trying to turn it into type
// concrete_type. It succeeds if `return_value->isSubtypeOf(concrete_type)`
Value* tryConvertToType(
    const SourceRange& loc,
    Graph& graph,
    const TypePtr& concrete_type,
    Value* value,
    bool allow_conversions) {
  // treat conversion to Optional[T] as conversions to T
  if (OptionalTypePtr op = concrete_type->cast<OptionalType>()) {
    if (value->type()->kind() != OptionalType::Kind &&
        !value->type()->isSubtypeOf(*NoneType::get())) {
      return tryConvertToType(
          loc, graph, op->getElementType(), value, allow_conversions);
    }
  }

  // allow temporary, unannotated list literals `[]` to match to arbitrary list
  // types
  if (value->node()->kind() == prim::EmptyListLiteral &&
      concrete_type->cast<ListType>()) {
    value = graph
                .insertNode(graph.createList(
                    concrete_type->cast<ListType>()->getElementType(), {}))
                ->output();
  }

  if (auto value_tuple = value->type()->cast<TupleType>()) {
    // Allow homogeneous tuples to be casted implicitly to lists of appropriate
    // types
    if (convertibleToList(value->type(), unwrapOptional(concrete_type))) {
      auto unpacked = createTupleUnpack(value);
      auto elem_type =
          unwrapOptional(concrete_type)->expectRef<ListType>().getElementType();
      value = graph.insertNode(graph.createList(elem_type, unpacked))->output();
    }

    // inductively apply implicit conversions to tuples
    if (auto concrete_tuple = concrete_type->cast<TupleType>()) {
      if (!value_tuple->isSubtypeOf(*concrete_tuple) &&
          concrete_tuple->elements().size() == value_tuple->elements().size()) {
        auto unpacked = createTupleUnpack(value);
        std::vector<Value*> converted;
        for (size_t i = 0; i < concrete_tuple->elements().size(); ++i) {
          converted.emplace_back(tryConvertToType(
              loc,
              graph,
              concrete_tuple->elements().at(i),
              unpacked.at(i),
              allow_conversions));
        }
        value = graph.insertNode(graph.createTuple(converted))->output();
      }
    }
  }

  // implicit conversions
  if (allow_conversions) {
    // Convert tensor or number to concrete int/float types
    bool value_isa_tensor = value->type()->isSubtypeOf(*TensorType::get());
    bool value_equals_number = *value->type() == *NumberType::get();
    bool concrete_float = *concrete_type == *FloatType::get();
    bool concrete_complex = *concrete_type == *ComplexType::get();
    bool concrete_int = *concrete_type == *IntType::get();
    bool concrete_number = *concrete_type == *NumberType::get();
    if (value_isa_tensor) {
      if (concrete_float) {
        value = graph.insert(aten::FloatImplicit, {value}, {}, loc);
      } else if (concrete_complex) {
        value = graph.insert(aten::ComplexImplicit, {value}, {}, loc);
      } else if (concrete_int) {
        value = graph.insert(aten::IntImplicit, {value}, {}, loc);
      } else if (concrete_number) {
        value = graph.insert(aten::ScalarImplicit, {value}, {}, loc);
      }
    } else if (value_equals_number) {
      if (concrete_float) {
        value = graph.insert(aten::Float, {value}, {}, loc);
      } else if (concrete_complex) {
        value = graph.insert(aten::Complex, {value}, {}, loc);
      } else if (concrete_int) {
        value = graph.insert(aten::Int, {value}, {}, loc);
      }
    }

    // Convert strings to device
    if (value->type()->isSubtypeOf(*StringType::get()) &&
        concrete_type->isSubtypeOf(*DeviceObjType::get())) {
      return graph.insert(aten::device, {value}, {}, loc);
    }
  }

  return value;
}

// Checks if `named_value` can be used as a value for `arg`. If `arg` is a
// VarType, it will be added to the type_env through `matchTypeVariables` as
// the corresponding actual type. If `allow_conversions` is true, implicit
// conversions to the `arg` type may be performed through `tryConvertToType`.
static Value* tryMatchArgument(
    const Argument& arg,
    Graph& graph,
    const SourceRange& loc,
    const NamedValue& named_value,
    std::ostream* failure_messages,
    const std::function<std::ostream&()>& err,
    bool allow_conversions,
    TypeEnv& type_env) {
  Value* value = named_value.value(graph);

  // Some functions that take lists of integers or floats for fixed size arrays
  // also allow single ints/floats to be passed in their place. The single
  // int/float is then repeated to the length of the list
  if (isIntOrFloatUsedAsList(value, arg)) {
    std::vector<Value*> repeated(*arg.N(), value);
    value =
        graph.insertNode(graph.createList(value->type(), repeated))->output();
  }

  // Resolve VarType variables
  const MatchTypeReturn matched =
      matchTypeVariables(arg.type(), value->type(), type_env);
  if (!matched.success()) {
    if (failure_messages) {
      err() << "Could not match type " << value->type()->repr_str() << " to "
            << arg.type()->repr_str() << " in argument '" << arg.name()
            << "': " << matched.reason() << ".\n";
    }
    return nullptr;
  }
  const auto concrete_type = tryEvalTypeVariables(arg.type(), type_env);
  if (!concrete_type) {
    if (failure_messages) {
      err() << "Type variables in type " << arg.type()->repr_str()
            << " could not be inferred from actual type "
            << value->type()->repr_str();
    }
    return nullptr;
  }

  // Check if the value can be matched to the arg through any implicit
  // conversions
  value = tryConvertToType(loc, graph, concrete_type, value, allow_conversions);
  std::stringstream ss;
  if (!value->type()->isSubtypeOfExt(
          *concrete_type, /*why_not=*/(failure_messages) ? &ss : nullptr)) {
    if (failure_messages) {
      auto& ostream = err()
          << arg.formatTypeMismatchMsg(value->type()->repr_str());

      if (auto pt = value->type()->cast<TensorType>()) {
        if (pt->isInferredType()) {
          std::string inferred_type_hint;
          inferred_type_hint = c10::str(
              "Inferred the value for argument '",
              arg.name(),
              "' to be of type 'Tensor' ",
              "because it was not annotated with an explicit type.\n");
          ostream << inferred_type_hint;
        }
      }

      if (auto v = value->type()->cast<ListType>()) {
        if (v->getElementType()->isSubtypeOf(*TensorType::get())) {
          ostream << "Empty lists default to List[Tensor]. Add a variable "
                     "annotation to the assignment to create an empty list "
                     "of another type (torch.jit.annotate(List[T, []]) where T "
                     "is the type of elements in the list for Python 2)\n";
        }
      }

      ostream << ss.str();
    }

    return nullptr;
  }
  return value;
}

c10::optional<size_t> findInputWithName(
    const std::string& name,
    at::ArrayRef<NamedValue> kwargs,
    bool is_aten) {
  for (const auto i : c10::irange(kwargs.size())) {
    // TS doesn't understand that the self argument in function
    // scheams is renamed to input for the functional variant
    if (is_aten && name == "self" && kwargs[i].name() == "input") {
      return i;
    }
    if (kwargs[i].name() == name) {
      return i;
    }
  }
  return c10::nullopt;
}

/// Creates a list with the provided values if each value's type can be matched
/// to an argument with type `elem_type`. If a type in `varargs` does not match
/// `elem_type`, nullptr is returned. This is used for creating lists from
/// varargs so that calls like torch.zeros(1, 2, 3) will be matched to
/// aten::zeros(int[]).
static Value* tryCreateList(
    const TypePtr& elem_type,
    Graph& graph,
    const SourceRange& loc,
    at::ArrayRef<NamedValue> varargs,
    std::ostream* failure_messages,
    const std::function<std::ostream&()>& err,
    bool convert_tensor_to_num,
    TypeEnv& type_env) {
  Argument elem_arg("<varargs>", elem_type);
  std::vector<Value*> list_elements;
  for (const auto& named_value : varargs) {
    // Try to convert named_value to elem_type
    Value* matched_value = tryMatchArgument(
        /*arg=*/elem_arg,
        graph,
        loc,
        named_value,
        failure_messages,
        err,
        /*allow_conversions=*/convert_tensor_to_num,
        type_env);
    if (!matched_value) {
      return nullptr;
    }
    list_elements.push_back(matched_value);
  }

  return graph.insertNode(graph.createList(elem_type, list_elements))->output();
}

// Check if it is possible to convert all the remaining non-kwarg arguments
// to a list. This allows zeros(IntArrayRef sizes) to work with zeros(1, 2) or
// zeros(1)
static bool varargsCanBeUsedAsList(
    const FunctionSchema& schema,
    size_t arg_index,
    const Argument& arg) {
  // The arg must be the last one in the arg list that is not a kwarg
  bool is_last_argument = arg_index + 1 == schema.arguments().size() ||
      schema.arguments()[arg_index + 1].kwarg_only();

  auto arg_type = arg.type();
  if (auto dyn = arg_type->castRaw<c10::DynamicType>()) {
    arg_type = dyn->fallback();
  }

  // The formal must be a list
  bool argument_is_list = arg_type->kind() == TypeKind::ListType;

  // matching varargs of typevar list nyi
  bool typevar_list = argument_is_list &&
      arg_type->castRaw<ListType>()->getElementType()->cast<VarType>();

  // it must not be a broadcasting list like int[3],
  // otherwise a single int is a valid input
  bool arg_is_broadcasting_list = bool(arg.N());

  return is_last_argument && argument_is_list && !arg_is_broadcasting_list &&
      !typevar_list;
}

bool isBlockListedSchema(const FunctionSchema& schema) {
  // Note (@zasdfgbnm):
  // This is a workaround for https://github.com/pytorch/pytorch/issues/47964
  // Currently JIT does not distinguish ScalarType vs int, so there is really
  // no way to distinguish x.view(1) vs x.view(torch.int8). So we have to
  // hardcode the aten::view.dtype here to block this overload. This blocklist
  // should be removed when JIT fully suports ScalarType as its own type.
  if (schema.name() == "aten::view" && schema.overload_name() == "dtype") {
    return true;
  }
  // Note (@tugsbayasgalan)
  // TorchScript doesn't suport kwargs so this op collides with aten.max.others
  // since both of them have 2 Tensor inputs. Since we don't expect users to
  // use this op in TS, we just skip it
  if (schema.name() == "aten::max" && schema.overload_name() == "unary_out") {
    return true;
  }
  if (schema.name() == "aten::min" && schema.overload_name() == "unary_out") {
    return true;
  }
  return false;
}

static c10::optional<MatchedSchema> tryMatchSchema(
    const FunctionSchema& schema,
    const SourceRange& loc,
    Graph& graph,
    at::ArrayRef<NamedValue> args,
    at::ArrayRef<NamedValue> kwargs,
    c10::optional<NamedValue> self,
    std::ostream* failure_messages,
    bool allow_conversions) {
  if (isBlockListedSchema(schema)) {
    return c10::nullopt;
  }

  auto err = [&]() -> std::ostream& {
    *failure_messages << "\n" << schema << ":\n";
    return *failure_messages;
  };

  // For VarTypes, maps VarType name to actual type as it's used with these
  // args
  TypeEnv type_env;
  std::vector<Value*> positional_inputs;
  std::vector<bool> used_kwarg(kwargs.size(), false);

  auto schema_namespace = schema.operator_name().getNamespace();
  bool is_aten = false;
  if (schema_namespace.has_value()) {
    if (schema_namespace.value() == "aten") {
      is_aten = true;
    }
  }
  // if we finish the loop will we have consumed all arguments?
  size_t used_args = 0;
  for (const auto schema_i : c10::irange(schema.arguments().size())) {
    const auto& arg = schema.arguments()[schema_i];
    c10::optional<NamedValue> actual_named_value;
    if (arg.name() == "self" && self) {
      actual_named_value = self;
      self = c10::nullopt;
    } else if (!arg.kwarg_only() && used_args < args.size()) {
      // Try to convert all the remaining non-kwarg arguments (used_args) to a
      // list. Allow zeros(IntArrayRef sizes) to work with zeros(1, 2) or
      // zeros(1)
      if (allow_conversions && varargsCanBeUsedAsList(schema, schema_i, arg)) {
        auto value = args[used_args].value(graph);
        const auto& actual_type = value->type();
        // The actual cannot already be a list
        if (actual_type->kind() != TypeKind::ListType &&
            !convertibleToList(actual_type, unwrapOptional(arg.type()))) {
          auto formal_type = unwrapOptional(arg.type())
                                 ->expectRef<ListType>()
                                 .getElementType();

          Value* list = tryCreateList(
              formal_type,
              graph,
              loc,
              at::ArrayRef<NamedValue>(args).slice(used_args),
              failure_messages,
              err,
              allow_conversions,
              type_env);
          if (!list) {
            return c10::nullopt;
          }
          used_args = args.size();
          positional_inputs.push_back(list);
          continue;
        }
      }

      // Set actual_named_value to the argument and mark the arg position as
      // used
      actual_named_value = args[used_args];
      used_args++;
    } else if (
        auto kwarg_idx = findInputWithName(arg.name(), kwargs, is_aten)) {
      const NamedValue& nv = kwargs[*kwarg_idx];
      if (used_kwarg[*kwarg_idx]) {
        if (failure_messages) {
          err() << "Argument " << nv.name()
                << " specified twice in schema, submit a bug report!\n";
        }
        return c10::nullopt;
      }
      used_kwarg[*kwarg_idx] = true;
      actual_named_value = nv;
    } else if (arg.default_value()) {
      // Argument has a default value and no value was provided, so use the
      // default
      actual_named_value = NamedValue(*arg.default_value());
    } else {
      if (failure_messages) {
        err() << "Argument " << schema.arguments()[schema_i].name()
              << " not provided.\n";
      }
      return c10::nullopt;
    }

    // Make sure the actual_named_value found matches the type of arg
    Value* positional = tryMatchArgument(
        arg,
        graph,
        loc,
        *actual_named_value,
        failure_messages,
        err,
        allow_conversions,
        type_env);
    if (!positional) {
      return c10::nullopt;
    }
    positional_inputs.push_back(positional);
  }
  // check for unused self argument
  if (self != c10::nullopt) {
    if (failure_messages) {
      err() << "Provided self argument not used in schema.\n";
    }
    return c10::nullopt;
  }

  if (schema.is_vararg()) {
    for (; used_args < args.size(); ++used_args) {
      positional_inputs.push_back(args[used_args].value(graph));
    }
  }

  // check for unused positional arguments
  if (used_args < args.size()) {
    if (failure_messages) {
      err() << "Expected at most " << used_args << " arguments "
            << "but found " << args.size() << " positional arguments.\n";
    }
    return c10::nullopt;
  }
  // check for unused kwargs
  for (const auto i : c10::irange(kwargs.size())) {
    const auto& nv = kwargs[i];
    if (!used_kwarg[i]) {
      if (failure_messages) {
        if (!schema.argumentIndexWithName(nv.name())) {
          err() << "Keyword argument " << nv.name() << " unknown.\n";
        } else {
          err() << "Keyword argument " << nv.name() << " specified twice.\n";
        }
      }
      return c10::nullopt;
    }
  }

  const auto& returns = schema.returns();
  auto return_types = fmap(returns, [&](const Argument& r) {
    TypePtr result = tryEvalTypeVariables(r.type(), type_env);
    TORCH_INTERNAL_ASSERT(
        result, r.type()->repr_str(), " has unbound type variables.");
    return result;
  });
  // Codegen does not support return of namedtuples with undefined field names.
  // Therefore, either all or none returns has field names.
  bool return_has_field_names =
      std::all_of(returns.begin(), returns.end(), [&](const Argument& r) {
        return r.name().length() > 0;
      });
  c10::OptNameList return_field_names = c10::nullopt;
  if (return_has_field_names) {
    return_field_names =
        fmap(returns, [&](const Argument& r) { return r.name(); });
  }

  // construct the full name of the schema for easier look up
  auto schema_name = getFullSchemaName(schema);

  return MatchedSchema{
      std::move(positional_inputs),
      std::move(return_types),
      std::move(return_field_names),
      schema_name};
}

MatchedSchema matchSchema(
    const ::c10::FunctionSchema& schema,
    const SourceRange& loc,
    Graph& graph,
    at::ArrayRef<NamedValue> args,
    at::ArrayRef<NamedValue> kwargs,
    const c10::optional<NamedValue>& self) {
  std::stringstream failure_messages;
  if (auto result = tryMatchSchema(
          schema,
          loc,
          graph,
          args,
          kwargs,
          self,
          &failure_messages,
          /*allow_conversions=*/true)) {
    return *result;
  }
  throw ErrorReport(loc) << failure_messages.str();
}

static std::string prefixLine(
    const std::string& str,
    const std::string& prefix) {
  std::stringstream ss;
  bool was_newline = true;
  for (auto c : str) {
    if (was_newline)
      ss << prefix;
    ss.put(c);
    was_newline = c == '\n';
  }
  return ss.str();
}

std::pair<size_t, MatchedSchema> matchSchemas(
    const std::vector<const FunctionSchema*>& schemas,
    const SourceRange& loc,
    Graph& graph,
    at::ArrayRef<NamedValue> args,
    at::ArrayRef<NamedValue> kwargs,
    const c10::optional<NamedValue>& self,
    bool render_errors) {
  TORCH_INTERNAL_ASSERT(!schemas.empty());
  // if there is only one schema, we do not need to try without conversions
  // first. this is faster and puts less dead code in the graph.
  if (schemas.size() == 1) {
    return std::make_pair(
        0, matchSchema(*schemas.at(0), loc, graph, args, kwargs, self));
  }
  std::stringstream failure_messages;
  for (bool allow_conversions : {false, true}) {
    // clear previous error messages
    failure_messages.str("");
    for (const auto i : c10::irange(schemas.size())) {
      const auto matched_schema = tryMatchSchema(
          *schemas[i],
          loc,
          graph,
          args,
          kwargs,
          self,
          render_errors ? &failure_messages : nullptr,
          allow_conversions);
      if (matched_schema) {
        return std::make_pair(i, *matched_schema);
      }
    }
  }
  // we optimistically assume this call will not error, and avoid formatting the
  // error strings. If we discover it did error, then we replay it, recording
  // the errors.
  if (!render_errors) {
    return matchSchemas(
        schemas, loc, graph, args, kwargs, self, /*render_errors=*/true);
  }

  throw ErrorReport(loc) << "Arguments for call are not valid.\n"
                         << "The following variants are available:\n"
                         << prefixLine(failure_messages.str(), "  ")
                         << "\nThe original call is";
  throw ErrorReport(loc) << failure_messages.str();
}

// pack outputs of a function following python rules. If there is a single value
// return a SimpleValue, otherwise pack all the values into a Tuple.
static Value* packOutputs(
    Graph& g,
    at::ArrayRef<Value*> values,
    c10::OptNameList field_names) {
  if (values.size() == 1) {
    return values[0];
  }
  std::shared_ptr<FunctionSchema> schema;
  TupleTypePtr named_tuple = nullptr;
  if (field_names) {
    auto types = fmap(values, [](Value* v) { return v->type(); });
    named_tuple =
        TupleType::createNamed(c10::nullopt, field_names.value(), types);
  }
  return g.insertNode(g.createTuple(values, named_tuple))->output();
}

// Given a successful match between operator schema and symbol, emit a node
// with the appropriate inputs and outputs.
static Value* emitBuiltinNode(
    const MatchedSchema& matched_schema,
    const SourceRange& loc,
    Graph& graph,
    Symbol name,
    c10::optional<size_t> version) {
  auto n = graph.insertNode(graph.create(name, matched_schema.inputs, 0))
               ->setSourceRange(loc);

  for (auto& ret : matched_schema.return_types) {
    n->addOutput()->setType(ret);
  }

  // assert that we did indeed create an op that has implementation
  // otherwise schema and dispatch are not in sync ONLY if the op is up
  // to date with the server version
  if (!version.has_value() ||
      isOpSymbolCurrent(matched_schema.schema_name, version.value())) {
    n->getOperation();
  } else {
    n->setHistoricSchemaName(matched_schema.schema_name);
  }

  return packOutputs(graph, n->outputs(), matched_schema.return_field_names);
}

std::string getFullSchemaName(const ::c10::FunctionSchema& schema) {
  if (!schema.overload_name().empty()) {
    return schema.operator_name().name + "." + schema.overload_name();
  }
  return schema.operator_name().name;
}

// Search for operators matching the provided symbol name and input types.
// If one is found, emit a node to the graph for that operator.
Value* emitBuiltinCall(
    const SourceRange& loc,
    Graph& graph,
    Symbol name,
    at::ArrayRef<NamedValue> args,
    at::ArrayRef<NamedValue> kwargs,
    const c10::optional<NamedValue>& self) {
  const auto& variants = getAllOperatorsFor(name);
  const auto& builtin_functions = getAllBuiltinFunctionsFor(name);

  // first let's set the graph's version
  auto graph_version = graph.get_op_version();

  std::stringstream failure_messages;
  std::vector<const FunctionSchema*> schemas;
  // we append them later to schemas because
  // parseSchema returns rvalue which can not
  // be casted to const pointer.
  std::vector<FunctionSchema> upgrader_schemas;
  schemas.reserve(variants.size());
  for (const std::shared_ptr<Operator>& op : variants) {
    bool found_upgrader = false;
    auto op_name = getFullSchemaName(op->schema());
    if (graph_version.has_value()) {
      auto version_entry = get_operator_version_map().find(op_name);
      if (version_entry != get_operator_version_map().end()) {
        auto old_schema_entry =
            findUpgrader(version_entry->second, graph_version.value());
        if (old_schema_entry.has_value()) {
          FunctionSchema old_schema =
              parseSchema(old_schema_entry.value().old_schema);
          upgrader_schemas.push_back(old_schema);
          found_upgrader = true;
        } else {
          if (!isOpCurrentBasedOnUpgraderEntries(
                  version_entry->second, graph_version.value())) {
            TORCH_INTERNAL_ASSERT(false, "Valid upgrader must be present");
          }
        }
      }
    }
    if (!found_upgrader)
      schemas.push_back(&op->schema());
  }

  // we might have seen old historic
  // ops that are deprecated
  if (variants.empty()) {
    auto oldSchemas =
        loadPossibleHistoricOps(name.toQualString(), graph_version);
    upgrader_schemas.reserve(oldSchemas.size());
    for (const auto& old_schema_entry : oldSchemas) {
      FunctionSchema old_schema = parseSchema(old_schema_entry);
      upgrader_schemas.emplace_back(old_schema);
    }
  }

  // TODO (tugsuu): make sure this is optimized later
  for (const auto& schema : upgrader_schemas) {
    schemas.push_back(&schema);
  }

  for (const auto method : builtin_functions) {
    method->ensure_defined();
    schemas.push_back(&method->getSchema());
  }

  // no operators found with the same name, print out similarly named operators
  if (schemas.empty()) {
    const auto close_symbols = findSimilarOperators(name);
    auto error = ErrorReport(loc);
    const auto& user_function_name = name.toQualString();
    error << "Unknown builtin op: " << user_function_name << ".\n";
    if (close_symbols.empty()) {
      error
          << "Could not find any similar ops to " << user_function_name
          << ". This op may not exist or may not be currently supported in TorchScript.\n";
    } else {
      error << "Here are some suggestions: \n";
      for (const auto& sym : close_symbols) {
        error << "\t" << sym.toQualString() << "\n";
      }
      error << "\nThe original call is";
    }
    throw error;
  }

  auto matched = matchSchemas(schemas, loc, graph, args, kwargs, self);

  if (matched.first < variants.size() + upgrader_schemas.size()) {
    return emitBuiltinNode(matched.second, loc, graph, name, graph_version);
  } else {
    auto& fn = *builtin_functions[matched.first - variants.size()];
    // we inline builtin calls because they are normally very small
    // wrappers and are not useful for keeping around to debug
    return insertGraph(
               graph, *toGraphFunction(fn).graph(), matched.second.inputs)
        .at(0);
  }
}

} // namespace torch::jit