#include #include #include #include namespace torch { namespace jit { auto scalar_operators_source = CodeTemplate( R"SCRIPT( def mul(a : ${Scalar}, b : Tensor) -> Tensor: return b * a def add(a : ${Scalar}, b : Tensor) -> Tensor: return b + a def ne(a : ${Scalar}, b : Tensor) -> Tensor: return b != a def eq(a : ${Scalar}, b : Tensor) -> Tensor: return b == a def lt(a : ${Scalar}, b : Tensor) -> Tensor: return b > a def le(a : ${Scalar}, b : Tensor) -> Tensor: return b >= a def gt(a : ${Scalar}, b : Tensor) -> Tensor: return b < a def ge(a : ${Scalar}, b : Tensor) -> Tensor: return b <= a def sub(a : ${Scalar}, b : Tensor) -> Tensor: return torch.neg(b) + a def div(a : ${Scalar}, b : Tensor) -> Tensor: return torch.reciprocal(b) * a )SCRIPT"); auto _ntuple_ops = CodeTemplate( R"SCRIPT( def _${name}(x: BroadcastingList${Length}[${Scalar}]) -> List[${Scalar}]: return x )SCRIPT"); auto floordiv = CodeTemplate( R"SCRIPT( def floordiv(self : Tensor, other : ${Rhs_Type}) -> Tensor: return torch.floor_divide(self, other) )SCRIPT"); auto tensor_properties = R"SCRIPT( def ndim(a : Tensor) -> int: return a.dim() def T(a : Tensor) -> Tensor: return a.numpy_T() )SCRIPT"; struct BuiltinFunctionRegistry { const std::vector& getAllBuiltinFunctionsFor( Symbol name) { const static std::vector empty; // when initializing the builtin function library, we will re-enter // getAllBuiltinFunctionsFor since it is called in the compiler to // lookup builtins and initializing the builtin functions calls the // compiler. To avoid deadlocking, we use a recursive mutex (same thread can // re-lock, the mutex without waiting), and report no loaded builtins during // init. std::lock_guard guard(mutex); if (state == INTIIALIZING) { return empty; } else if (state == UNINITIALIZED) { state = INTIIALIZING; loadBuiltinFunctions(); state = INITIALIZED; } AT_ASSERT(state == INITIALIZED); auto it = builtins_by_name_.find(name); if (it == builtins_by_name_.end()) return empty; return it->second; } private: void loadSource(const std::string& source, const std::string& the_namespace) { std::shared_ptr cu = std::make_shared(); modules.emplace_back(cu); cu->define( c10::nullopt, source, nativeResolver(), /*self=*/nullptr); for (auto& method : cu->get_functions()) { builtins_by_name_[Symbol::fromQualString( the_namespace + "::" + method->name())] .push_back(method); } } void loadBuiltinFunctions() { for (auto scalar : {"float", "int"}) { TemplateEnv env; env.s("Scalar", scalar); loadSource(scalar_operators_source.format(env), "aten"); } using str_pair = std::pair; const std::vector name_len = { str_pair("single", "1"), str_pair("pair", "2"), str_pair("triple", "3"), str_pair("quadruple", "4"), }; for (const auto scalar : {"float", "int"}) { for (const auto& pair : name_len) { TemplateEnv env; env.s("Scalar", scalar); env.s("name", pair.first); env.s("Length", pair.second); loadSource(_ntuple_ops.format(env), "aten"); } } for (auto rhs : {"number", "Tensor"}) { TemplateEnv env; env.s("Rhs_Type", rhs); loadSource(floordiv.format(env), "aten"); } // These are under `prim` instead of `aten` since they exist to bind certain // tensor property getters to correpsonding methods loadSource(tensor_properties, "prim"); } enum { UNINITIALIZED, INTIIALIZING, INITIALIZED } state = UNINITIALIZED; std::recursive_mutex mutex; std::vector> modules; std::unordered_map> builtins_by_name_; }; const std::vector& getAllBuiltinFunctionsFor( Symbol name) { static BuiltinFunctionRegistry registry; return registry.getAllBuiltinFunctionsFor(name); } } // namespace jit } // namespace torch