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[jit] move script-related implementation out of torch/jit/__init__.py (#40902)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/40902 See the bottom of this stack for context. Test Plan: Imported from OSS Reviewed By: eellison Differential Revision: D22360210 Pulled By: suo fbshipit-source-id: 4275127173a36982ce9ad357aa344435b98e1faf
This commit is contained in:
Michael Suo
committed by
Facebook GitHub Bot
Facebook GitHub Bot
parent
6c9b869930
commit
c93e96fbd9
@ -6858,12 +6858,12 @@ a")
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if GRAPH_EXECUTOR == ProfilingMode.LEGACY:
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FileCheck().check("Double").check_same("aten::tensor").run(torch.jit.last_executed_optimized_graph())
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with set_default_dtype(torch.float):
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del torch.jit._jit_caching_layer[foo]
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del torch.jit._state._jit_caching_layer[foo]
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self.assertEqual(torch.jit.script(foo)(1.), foo(1.), exact_dtype=True)
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if GRAPH_EXECUTOR == ProfilingMode.LEGACY:
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FileCheck().check("Float").check_same("aten::tensor").run(torch.jit.last_executed_optimized_graph())
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with set_default_dtype(torch.half):
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del torch.jit._jit_caching_layer[foo]
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del torch.jit._state._jit_caching_layer[foo]
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self.assertEqual(torch.jit.script(foo)(1.), foo(1.), exact_dtype=True)
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if GRAPH_EXECUTOR == ProfilingMode.LEGACY:
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FileCheck().check("Half").check_same("aten::tensor").run(torch.jit.last_executed_optimized_graph())
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@ -13381,8 +13381,8 @@ a")
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self.checkScript(invoke_function, ())
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# testing that the functions are cached
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compiled_fns_1 = torch.jit._get_overloads(test_simple)
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compiled_fns_2 = torch.jit._get_overloads(test_simple)
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compiled_fns_1 = torch.jit._script._get_overloads(test_simple)
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compiled_fns_2 = torch.jit._script._get_overloads(test_simple)
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for a, b in zip(compiled_fns_1, compiled_fns_2):
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self.assertIs(a.graph, b.graph)
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@ -61,7 +61,7 @@ TypePtr tryInferTypeWithTypeHint(
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"The RRef being created contains a ScriptModule, "
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"must provide its ModuleInterface type hint. ");
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c10::QualifiedName type_qualified_name = c10::QualifiedName(
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py::cast<std::string>(py::module::import("torch.jit")
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py::cast<std::string>(py::module::import("torch._jit_internal")
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.attr("_qualified_name")(type_hint)));
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TypePtr type_hint_ptr =
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jit::get_python_cu()->get_interface(type_qualified_name);
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@ -177,7 +177,7 @@ struct VISIBILITY_HIDDEN PythonFutureWrapper
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// locations in libtorch code rather than user code.
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inline std::shared_ptr<CompilationUnit> get_python_cu() {
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return py::module::import("torch.jit")
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return py::module::import("torch.jit._state")
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.attr("_python_cu")
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.cast<std::shared_ptr<CompilationUnit>>();
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}
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@ -285,9 +285,9 @@ inline InferredType tryToInferType(py::handle input) {
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py::bool_ isClass =
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py::module::import("inspect").attr("isclass")(input.get_type());
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if (py::cast<bool>(isClass)) {
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py::str qualifiedName = py::module::import("torch.jit")
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py::str qualifiedName = py::module::import("torch._jit_internal")
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.attr("_qualified_name")(input.get_type());
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auto pyClass = py::module::import("torch.jit")
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auto pyClass = py::module::import("torch.jit._state")
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.attr("_get_script_class")(qualifiedName);
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if (!pyClass.is_none()) {
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auto cu = get_python_cu();
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@ -648,7 +648,7 @@ inline IValue toIValue(
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} else {
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// We inspect the value to found the compiled TorchScript class
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// and then create a ivalue::Object from that class type.
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py::str qualified_name = py::module::import("torch.jit")
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py::str qualified_name = py::module::import("torch._jit_internal")
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.attr("_qualified_name")(obj.get_type());
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auto pyCu = get_python_cu();
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classType = pyCu->get_class(c10::QualifiedName(qualified_name));
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@ -875,8 +875,8 @@ inline py::object toPyObject(IValue ivalue) {
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}
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const auto classType = pyCu->get_class(c10::QualifiedName(obj->name()));
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AT_ASSERT(classType);
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auto pyClass =
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py::module::import("torch.jit").attr("_get_script_class")(obj->name());
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auto pyClass = py::module::import("torch.jit._state")
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.attr("_get_script_class")(obj->name());
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if (pyClass.is_none()) {
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std::stringstream err;
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err << "Unknown reference to ScriptClass ";
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@ -664,7 +664,7 @@ bool isNamedTupleClass(const py::object& obj) {
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TypePtr registerNamedTuple(const py::object& obj, const SourceRange& loc) {
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TORCH_INTERNAL_ASSERT(isNamedTupleClass(obj));
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auto qualifiedName = c10::QualifiedName(py::cast<std::string>(
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py::module::import("torch.jit").attr("_qualified_name")(obj)));
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py::module::import("torch._jit_internal").attr("_qualified_name")(obj)));
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// Currently don't support default values
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if (py::hasattr(obj, "_field_defaults")) {
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auto default_dict = py::cast<std::map<std::string, py::object>>(
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@ -821,7 +821,7 @@ std::shared_ptr<SugaredValue> toSugaredValue(
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py::bool_ isClass = py::module::import("inspect").attr("isclass")(obj);
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if (py::cast<bool>(isClass)) {
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py::str qualifiedName =
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py::module::import("torch.jit").attr("_qualified_name")(obj);
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py::module::import("torch._jit_internal").attr("_qualified_name")(obj);
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auto pyCu = get_python_cu();
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auto qualname = c10::QualifiedName(qualifiedName);
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if (auto classType = pyCu->get_class(qualname)) {
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@ -837,7 +837,7 @@ std::shared_ptr<SugaredValue> toSugaredValue(
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// Register class
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auto rcb = py::module::import("torch._jit_internal")
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.attr("createResolutionCallbackForClassMethods")(obj);
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py::module::import("torch.jit")
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py::module::import("torch.jit._script")
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.attr("_recursive_compile_class")(obj, loc);
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// Return class
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@ -855,7 +855,7 @@ std::shared_ptr<SugaredValue> toSugaredValue(
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py::bool_ isFunction = py::module::import("inspect").attr("isfunction")(obj);
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if (py::cast<bool>(isFunction)) {
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auto overloads =
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py::module::import("torch.jit").attr("_get_overloads")(obj);
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py::module::import("torch.jit._script").attr("_get_overloads")(obj);
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if (!overloads.is_none()) {
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auto compiled_fns = py::cast<std::vector<StrongFunctionPtr>>(overloads);
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return std::make_shared<FunctionValue>(std::move(compiled_fns));
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@ -115,8 +115,9 @@ struct PythonResolver : public Resolver {
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return registerNamedTuple(obj, loc);
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}
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auto qualifiedName = c10::QualifiedName(py::cast<std::string>(
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py::module::import("torch.jit").attr("_qualified_name")(obj)));
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auto qualifiedName = c10::QualifiedName(
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py::cast<std::string>(py::module::import("torch._jit_internal")
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.attr("_qualified_name")(obj)));
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return get_python_cu()->get_type(qualifiedName);
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}
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@ -25,8 +25,8 @@ def get_arg_return_types_from_interface(module_interface):
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assert getattr(
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module_interface, "__torch_script_interface__", False
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), "Expect a TorchScript class interface decorated by @torch.jit.interface."
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qualified_name = torch.jit._qualified_name(module_interface)
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cu = torch.jit._python_cu
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qualified_name = torch._jit_internal._qualified_name(module_interface)
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cu = torch.jit._state._python_cu
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module_interface_c = cu.get_interface(qualified_name)
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assert (
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"forward" in module_interface_c.getMethodNames()
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@ -101,7 +101,7 @@ def instantiate_scriptable_remote_module_template(module_interface_cls):
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)
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# Generate the template instance name.
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module_interface_cls_name = torch.jit._qualified_name(module_interface_cls).replace(
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module_interface_cls_name = torch._jit_internal._qualified_name(module_interface_cls).replace(
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".", "_"
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)
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generated_module_name = f"{_FILE_PREFIX}{module_interface_cls_name}"
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@ -532,7 +532,7 @@ def remote(to, func, args=None, kwargs=None, timeout=UNSET_RPC_TIMEOUT):
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# (builtin, script, python)
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if qualified_name is None:
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func_name = (
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torch.jit._qualified_name(func)
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torch._jit_internal._qualified_name(func)
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if isinstance(func, torch.jit.ScriptFunction)
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else func.__qualname__
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)
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@ -607,7 +607,7 @@ def _invoke_rpc(to, func, rpc_type, args=None, kwargs=None, rpc_timeout=UNSET_RP
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# (builtin, script, python)
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if qualified_name is None:
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func_name = (
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torch.jit._qualified_name(func)
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torch._jit_internal._qualified_name(func)
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if isinstance(func, torch.jit.ScriptFunction)
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else func.__qualname__
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)
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@ -645,7 +645,7 @@ def _invoke_rpc(to, func, rpc_type, args=None, kwargs=None, rpc_timeout=UNSET_RP
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elif isinstance(func, torch.jit.ScriptFunction):
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fut = _invoke_rpc_torchscript(
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dst_worker_info.name,
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torch.jit._qualified_name(func),
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torch._jit_internal._qualified_name(func),
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args,
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kwargs,
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rpc_timeout,
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@ -1,44 +1,29 @@
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import torch._C
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import torch._jit_internal as _jit_internal
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import torch.jit.annotations
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import torch.testing
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import torch.jit._recursive
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from torch.jit._recursive import ScriptMethodStub, wrap_cpp_module
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from torch.jit._builtins import _find_builtin, _get_builtin_table, _register_builtin # noqa
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from torch._jit_internal import Future, _qualified_name
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from torch.autograd import Variable, function
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from torch.jit.frontend import get_jit_class_def, get_jit_def, get_default_args
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from torch._jit_internal import Future
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from torch.nn import Module
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from torch.serialization import validate_cuda_device
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from torch._six import PY37, with_metaclass, string_classes, get_function_from_type
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from torch._six import string_classes
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from torch.utils import set_module
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from torch.autograd.grad_mode import _DecoratorContextManager
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from typing import Optional, List
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import collections
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import contextlib
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import copy
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import functools
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import inspect
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import os
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import pathlib
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import pickle
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import re
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import sys
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import textwrap
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import warnings
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import weakref
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# These are imported so users can access them from the `torch.jit` module
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from torch._jit_internal import Final, _overload, _overload_method
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from torch._jit_internal import ignore, export, unused
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from torch.jit._script import Attribute, ScriptModule
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from torch.jit._script import script, Attribute, ScriptModule, is_scripting, script_method, \
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RecursiveScriptModule, ScriptWarning, interface
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from torch.jit._trace import trace, trace_module, TracedModule, TracerWarning, TracingCheckError, \
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is_tracing, ONNXTracedModule, _unique_state_dict, _flatten, TopLevelTracedModule
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from torch.jit._state import _python_cu, _enabled
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_jit_script_class_compile = torch._C._jit_script_class_compile
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set_module(Future, "torch.jit")
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_fork = torch._C.fork
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@ -250,7 +235,7 @@ def load(f, map_location=None, _extra_files=DEFAULT_EXTRA_FILES_MAP):
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cpp_module = torch._C.import_ir_module_from_buffer(cu, f.read(), map_location, _extra_files)
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# TODO: Pretty sure this approach loses ConstSequential status and such
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return torch.jit._recursive.wrap_cpp_module(cpp_module)
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return wrap_cpp_module(cpp_module)
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def validate_map_location(map_location=None):
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if isinstance(map_location, str):
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@ -312,13 +297,6 @@ def _get_trace_graph(f, args=(), kwargs=None, strict=True, _force_outplace=False
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return outs
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class ConstMap:
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def __init__(self, const_mapping):
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self.const_mapping = const_mapping
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def __getattr__(self, attr):
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return self.const_mapping[attr]
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def fork(func, *args, **kwargs):
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"""
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Creates an asynchronous task executing `func` and a reference to the value
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@ -509,9 +487,6 @@ def _try_get_overloaded_fn(mod, field):
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return mod._overloads.get(field, None) if isinstance(mod, ScriptModule) else None
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class ScriptWarning(Warning):
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pass
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@contextlib.contextmanager
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def _disable_emit_hooks():
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hooks = torch._C._jit_get_emit_hooks()
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@ -529,258 +504,6 @@ def _disable_emit_hooks_decorator(_DecoratorContextManager): # noqa: F811
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torch._C._jit_set_emit_hooks(self.hooks[0], self.hooks[1])
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# ScriptClasses must be new-style classes because we construct them using their
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# __new__ method.
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def _is_new_style_class(cls):
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if hasattr(cls, '__class__'):
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return ('__dict__' in dir(cls) or hasattr(cls, '__slots__'))
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def whichmodule(obj):
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"""Find the module an object belong to."""
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module_name = getattr(obj, '__module__', None)
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# Protect the iteration by using a list copy of sys.modules against dynamic
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# modules that trigger imports of other modules upon calls to getattr.
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for name, module in list(sys.modules.items()):
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if name == '__main__' or module is None:
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continue
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try:
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if _getattribute(module, name)[0] is obj:
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return module_name
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except AttributeError:
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pass
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return '__main__'
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def _recursive_compile_class(obj, loc):
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_qual_name = _qualified_name(obj)
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# We're starting a new compilation, so update the error call stack in
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# case it fails
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error_stack = torch._C.CallStack(_qual_name, loc)
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rcb = _jit_internal.createResolutionCallbackForClassMethods(obj)
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_compile_and_register_class(obj, rcb, _qual_name)
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def _compile_and_register_class(obj, rcb, qualified_name):
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ast = get_jit_class_def(obj, obj.__name__)
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_jit_script_class_compile(qualified_name, ast, rcb)
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_add_script_class(obj, qualified_name)
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def script(obj, optimize=None, _frames_up=0, _rcb=None):
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r"""
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Scripting a function or ``nn.Module`` will inspect the source code, compile
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it as TorchScript code using the TorchScript compiler, and return a :class:`ScriptModule` or
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:class:`ScriptFunction`. TorchScript itself is a subset of the Python language, so not all
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features in Python work, but we provide enough functionality to compute on
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tensors and do control-dependent operations. For a complete guide, see the
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:ref:`language-reference`.
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``torch.jit.script`` can be used as a function for modules and functions, and as a decorator
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``@torch.jit.script`` for :ref:`torchscript-classes` and functions.
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Arguments:
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obj (callable, class, or ``nn.Module``): The ``nn.Module``, function, or class type to
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compile.
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Returns:
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If ``obj`` is ``nn.Module``, ``script`` returns
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a :class:`ScriptModule` object. The returned :class:`ScriptModule` will
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have the same set of sub-modules and parameters as the
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original ``nn.Module``. If ``obj`` is a standalone function,
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a :class:`ScriptFunction` will be returned.
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**Scripting a function**
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The ``@torch.jit.script`` decorator will construct a :class:`ScriptFunction`
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by compiling the body of the function.
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Example (scripting a function):
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.. testcode::
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import torch
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@torch.jit.script
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def foo(x, y):
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if x.max() > y.max():
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r = x
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else:
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r = y
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return r
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print(type(foo)) # torch.jit.ScriptFuncion
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# See the compiled graph as Python code
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print(foo.code)
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# Call the function using the TorchScript interpreter
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foo(torch.ones(2, 2), torch.ones(2, 2))
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.. testoutput::
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:hide:
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...
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**Scripting an nn.Module**
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Scripting an ``nn.Module`` by default will compile the ``forward`` method and recursively
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compile any methods, submodules, and functions called by ``forward``. If a ``nn.Module`` only uses
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features supported in TorchScript, no changes to the original module code should be necessary. ``script``
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will construct :class:`ScriptModule` that has copies of the attributes, parameters, and methods of
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the original module.
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Example (scripting a simple module with a Parameter):
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.. testcode::
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import torch
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class MyModule(torch.nn.Module):
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def __init__(self, N, M):
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super(MyModule, self).__init__()
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# This parameter will be copied to the new ScriptModule
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self.weight = torch.nn.Parameter(torch.rand(N, M))
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# When this submodule is used, it will be compiled
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self.linear = torch.nn.Linear(N, M)
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def forward(self, input):
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output = self.weight.mv(input)
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# This calls the `forward` method of the `nn.Linear` module, which will
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# cause the `self.linear` submodule to be compiled to a `ScriptModule` here
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output = self.linear(output)
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return output
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scripted_module = torch.jit.script(MyModule(2, 3))
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Example (scripting a module with traced submodules):
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.. testcode::
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class MyModule(nn.Module):
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def __init__(self):
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super(MyModule, self).__init__()
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# torch.jit.trace produces a ScriptModule's conv1 and conv2
|
||||
self.conv1 = torch.jit.trace(nn.Conv2d(1, 20, 5), torch.rand(1, 1, 16, 16))
|
||||
self.conv2 = torch.jit.trace(nn.Conv2d(20, 20, 5), torch.rand(1, 20, 16, 16))
|
||||
|
||||
def forward(self, input):
|
||||
input = F.relu(self.conv1(input))
|
||||
input = F.relu(self.conv2(input))
|
||||
return input
|
||||
|
||||
scripted_module = torch.jit.script(MyModule())
|
||||
|
||||
To compile a method other than ``forward`` (and recursively compile anything it calls), add
|
||||
the :func:`@torch.jit.export <torch.jit.export>` decorator to the method. To opt out of compilation
|
||||
use :func:`@torch.jit.ignore <torch.jit.ignore>` or :func:`@torch.jit.unused <torch.jit.unused>`.
|
||||
|
||||
Example (an exported and ignored method in a module)::
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
class MyModule(nn.Module):
|
||||
def __init__(self):
|
||||
super(MyModule, self).__init__()
|
||||
|
||||
@torch.jit.export
|
||||
def some_entry_point(self, input):
|
||||
return input + 10
|
||||
|
||||
@torch.jit.ignore
|
||||
def python_only_fn(self, input):
|
||||
# This function won't be compiled, so any
|
||||
# Python APIs can be used
|
||||
import pdb
|
||||
pdb.set_trace()
|
||||
|
||||
def forward(self, input):
|
||||
if self.training:
|
||||
self.python_only_fn(input)
|
||||
return input * 99
|
||||
|
||||
scripted_module = torch.jit.script(MyModule())
|
||||
print(scripted_module.some_entry_point(torch.randn(2, 2)))
|
||||
print(scripted_module(torch.randn(2, 2)))
|
||||
"""
|
||||
if not _enabled:
|
||||
return obj
|
||||
|
||||
if optimize is not None:
|
||||
warnings.warn("`optimize` is deprecated and has no effect. Use `with torch.jit.optimized_execution() instead")
|
||||
if isinstance(obj, ScriptModule):
|
||||
return obj
|
||||
|
||||
if isinstance(obj, torch.nn.Module):
|
||||
return torch.jit._recursive.create_script_module(obj, torch.jit._recursive.infer_methods_to_compile)
|
||||
|
||||
qualified_name = _qualified_name(obj)
|
||||
if inspect.isclass(obj):
|
||||
# If this type is a `nn.Module` subclass, they probably meant to pass
|
||||
# an instance instead of a Module
|
||||
if issubclass(obj, torch.nn.Module):
|
||||
raise RuntimeError("Type '{}' cannot be compiled since it inherits"
|
||||
" from nn.Module,"
|
||||
" pass an instance instead".format(obj))
|
||||
|
||||
if not _is_new_style_class(obj):
|
||||
raise RuntimeError("TorchScript classes must be new-style classes. "
|
||||
"Please inherit from 'object'.")
|
||||
if len(obj.mro()) > 2:
|
||||
raise RuntimeError("TorchScript classes does not support inheritance yet. "
|
||||
"Please directly inherit from 'object'.")
|
||||
if _rcb is None:
|
||||
_rcb = _jit_internal.createResolutionCallbackFromFrame(_frames_up + 1)
|
||||
_compile_and_register_class(obj, _rcb, qualified_name)
|
||||
return obj
|
||||
else:
|
||||
# this is a decorated fn, and we need to the underlying fn and its rcb
|
||||
if hasattr(obj, "__script_if_tracing_wrapper"):
|
||||
obj = obj.__original_fn
|
||||
_rcb = _jit_internal.createResolutionCallbackFromClosure(obj)
|
||||
|
||||
_check_directly_compile_overloaded(obj)
|
||||
maybe_already_compiled_fn = _try_get_jit_cached_function(obj)
|
||||
if maybe_already_compiled_fn:
|
||||
return maybe_already_compiled_fn
|
||||
ast = get_jit_def(obj, obj.__name__)
|
||||
if _rcb is None:
|
||||
_rcb = _jit_internal.createResolutionCallbackFromClosure(obj)
|
||||
fn = torch._C._jit_script_compile(qualified_name, ast, _rcb, get_default_args(obj))
|
||||
# Forward docstrings
|
||||
fn.__doc__ = obj.__doc__
|
||||
_set_jit_function_cache(obj, fn)
|
||||
return fn
|
||||
|
||||
def interface(obj):
|
||||
if not inspect.isclass(obj):
|
||||
raise RuntimeError("interface must be applied to a class")
|
||||
if not _is_new_style_class(obj):
|
||||
raise RuntimeError("TorchScript interfaces must inherit from 'object'")
|
||||
|
||||
# Expected MRO is:
|
||||
# User module
|
||||
# torch.nn.modules.module.Module
|
||||
# object
|
||||
is_module_interface = issubclass(obj, torch.nn.Module) and len(obj.mro()) == 3
|
||||
|
||||
if not is_module_interface and len(obj.mro()) > 2:
|
||||
raise RuntimeError("TorchScript interface does not support inheritance yet. "
|
||||
"Please directly inherit from 'object' or 'nn.Module'.")
|
||||
|
||||
qualified_name = _qualified_name(obj)
|
||||
rcb = _jit_internal.createResolutionCallbackFromFrame(1)
|
||||
# if this type is a `nn.Module` subclass, generate an module interface type
|
||||
# instead of a class interface type, an module interface type only compile
|
||||
# the user provided methods as part of the interface
|
||||
ast = get_jit_class_def(obj, obj.__name__)
|
||||
torch._C._jit_script_interface_compile(qualified_name, ast, rcb, is_module_interface)
|
||||
obj.__torch_script_interface__ = True
|
||||
return obj
|
||||
|
||||
|
||||
def _script_if_tracing(fn):
|
||||
"""
|
||||
Compiles ``fn`` when it is first called during tracing. ``torch.jit.script``
|
||||
@ -805,473 +528,6 @@ def _script_if_tracing(fn):
|
||||
|
||||
return wrapper
|
||||
|
||||
|
||||
def script_method(fn):
|
||||
if not _enabled:
|
||||
return fn
|
||||
# NOTE: we need to traverse two frames here because the meta-class frame
|
||||
# for ScriptModule will be present, as opposed to invoking @script on a
|
||||
# a function or invoking define() on a CompilationUnit.
|
||||
# The stack will look like:
|
||||
#
|
||||
# 0. createResolutionCallback()
|
||||
# 1. script_method()
|
||||
# 2. ScriptModule metaclass frame
|
||||
# 3. Surrounding scope
|
||||
#
|
||||
# createResolutionCallback internally adds 1 to get us to the scope of this
|
||||
# function (the calling function). Adding 2 gets us to the proper surrounding scope.
|
||||
_rcb = _jit_internal.createResolutionCallbackFromFrame(frames_up=2)
|
||||
ast = get_jit_def(fn, fn.__name__, self_name="ScriptModule")
|
||||
return ScriptMethodStub(_rcb, ast, fn)
|
||||
|
||||
|
||||
|
||||
# These OrderedDictWrapper classes replace the actual OrderedDicts in
|
||||
# module with versions that get/set properties inside of Module.
|
||||
# This allows us to reuse most of nn.Module while still storing the
|
||||
# data in C++.
|
||||
# Each OrderedDict needs to support:
|
||||
# x not in view
|
||||
# x in view
|
||||
# view[name] = ...
|
||||
# view.values()
|
||||
# del view[name]
|
||||
# view.items()
|
||||
# view.keys()
|
||||
# len(view)
|
||||
|
||||
class OrderedDictWrapper(object):
|
||||
def __init__(self, _c):
|
||||
self._c = _c
|
||||
|
||||
def keys(self):
|
||||
return [k for k, v in self.items()]
|
||||
|
||||
def values(self):
|
||||
return [v for k, v in self.items()]
|
||||
|
||||
def __len__(self):
|
||||
return len(self.values())
|
||||
|
||||
def __delitem__(self, k):
|
||||
raise RuntimeError("cannot delete methods or parameters of a script module")
|
||||
|
||||
def items(self):
|
||||
return self._c.items()
|
||||
|
||||
def __setitem__(self, k, v):
|
||||
if k not in self:
|
||||
raise RuntimeError("Can't add a new parameter after ScriptModule construction."
|
||||
" Tried to add '{}".format(k))
|
||||
self._c.setattr(k, v)
|
||||
|
||||
def __contains__(self, k):
|
||||
return self._c.contains(k)
|
||||
|
||||
def __getitem__(self, k):
|
||||
if k not in self:
|
||||
raise KeyError(k)
|
||||
return self._c.getattr(k)
|
||||
|
||||
|
||||
class OrderedModuleDict(OrderedDictWrapper):
|
||||
def __init__(self, module, python_dict):
|
||||
super(OrderedModuleDict, self).__init__(torch._C.ModuleDict(module))
|
||||
# contains _both_ script modules and non-script python-only modules
|
||||
|
||||
# because script modules are subclassed in python and the
|
||||
# C++ Module class will not hold references to them,
|
||||
# to ensure that you always get the same python value here
|
||||
# we store it in the python dict as well
|
||||
self._python_modules = python_dict
|
||||
|
||||
def items(self):
|
||||
r = self._python_modules.items()
|
||||
return r
|
||||
|
||||
def __contains__(self, k):
|
||||
return k in self._python_modules
|
||||
|
||||
def __setitem__(self, k, v):
|
||||
# Cases where sub-module can be re-assigned after ScriptModule construction
|
||||
# 1. If the attr is an module interface type, it's guaranteed that the module is
|
||||
# not inlined in the graph, so it's safe to swap a new ScriptModule in.
|
||||
# 2. if the new value if a ScriptModule with the same JIT type, IR won't change
|
||||
# and it's legit to swap a new module in.
|
||||
# In these two cases we allow swapping a new scripted module and update the
|
||||
# corresponding python module dict to keep sync.
|
||||
# Note: the value to be swapped in has to be ScriptModule instead of nn.Module,
|
||||
# otherwise it's illegal and we throw error.
|
||||
if isinstance(v, ScriptModule):
|
||||
self._c.setattr(k, v)
|
||||
self._python_modules[k] = v
|
||||
else:
|
||||
raise RuntimeError("Cannot re-assign modules in a ScriptModule with non-scripted "
|
||||
"module, tried to replace existing module '{}': {}".format(k, v))
|
||||
|
||||
|
||||
def __getitem__(self, k):
|
||||
return self._python_modules[k]
|
||||
|
||||
|
||||
if _enabled:
|
||||
class RecursiveScriptModule(ScriptModule):
|
||||
# XXX: RecursiveScriptModule inherits from ScriptModule for the sole
|
||||
# reason that it retains the existing isinstance(ScriptModule)
|
||||
# behavior.
|
||||
r"""
|
||||
The core data structure in TorchScript is the ``ScriptModule``. It is an
|
||||
analogue of torch's ``nn.Module`` and represents an entire model as a tree of
|
||||
submodules. Like normal modules, each individual module in a ``ScriptModule`` can
|
||||
have submodules, parameters, and methods. In ``nn.Module``\s methods are implemented
|
||||
as Python functions, but in ``ScriptModule``\s methods are implemented as
|
||||
TorchScript functions, a statically-typed subset of Python that contains all
|
||||
of PyTorch's built-in Tensor operations. This difference allows your
|
||||
``ScriptModule``\s code to run without the need for a Python interpreter.
|
||||
|
||||
``ScriptModule``\s should not be created manually, instead use
|
||||
either :func:`tracing <torch.jit.trace>` or :func:`scripting <torch.jit.script>`.
|
||||
Tracing and scripting can be applied incrementally and :ref:`composed as necessary <Types>`.
|
||||
|
||||
* Tracing records the tensor operations as executed with a set of example inputs and uses these
|
||||
operations to construct a computation graph. You can use the full dynamic behavior of Python with tracing,
|
||||
but values other than Tensors and control flow aren't captured in the graph.
|
||||
|
||||
* Scripting inspects the Python code of the model
|
||||
and compiles it to TorchScript. Scripting allows the use of many `types`_ of values and supports dynamic control flow.
|
||||
Many, but not all features of Python are supported by the compiler, so changes to the source code may be necessary.
|
||||
"""
|
||||
_disable_script_meta = True
|
||||
|
||||
def __init__(self, cpp_module):
|
||||
self.__dict__['_initializing'] = True
|
||||
self._c = cpp_module
|
||||
super(RecursiveScriptModule, self).__init__()
|
||||
# Delete the 'training' attribute set up by `Module.__init__`. It
|
||||
# will get set on the underlying cpp module, so we delete it here
|
||||
# to avoid this version shadowing the cpp module version.
|
||||
delattr(self, 'training')
|
||||
|
||||
@staticmethod
|
||||
def _construct(cpp_module, init_fn):
|
||||
"""
|
||||
Construct a RecursiveScriptModule that's ready for use. PyTorch
|
||||
code should use this to construct a RecursiveScriptModule instead
|
||||
of instead of calling `__init__` directly, as it makes sure the
|
||||
object is properly finalized (and in the future we may take
|
||||
control of how the RecursiveScriptModule instance is created).
|
||||
|
||||
Arguments:
|
||||
cpp_module: The C++ Module that will hold the actual state of
|
||||
this RecursiveScriptModule instance.
|
||||
init_fn: Lambda that initializes the RecursiveScriptModule passed to it.
|
||||
"""
|
||||
script_module = RecursiveScriptModule(cpp_module)
|
||||
init_fn(script_module)
|
||||
|
||||
# Finalize the ScriptModule: replace the nn.Module state with our
|
||||
# custom implementations and flip the _initializing bit.
|
||||
RecursiveScriptModule._finalize_scriptmodule(script_module)
|
||||
return script_module
|
||||
|
||||
@staticmethod
|
||||
def _finalize_scriptmodule(script_module):
|
||||
script_module._parameters = OrderedDictWrapper(torch._C.ParameterDict(script_module._c))
|
||||
script_module._buffers = OrderedDictWrapper(torch._C.BufferDict(script_module._c))
|
||||
script_module._modules = OrderedModuleDict(script_module._c, script_module._modules)
|
||||
script_module._initializing = False
|
||||
|
||||
def _reconstruct(self, cpp_module):
|
||||
"""
|
||||
Re-construct an instance of RecursiveScriptModule using an instance of a C++ module.
|
||||
|
||||
Arguments:
|
||||
cpp_module: The C++ module that this RecursiveScriptModule will be rebuilt around.
|
||||
"""
|
||||
self.__init__(cpp_module)
|
||||
|
||||
# Copy the concrete type from the C++ module to this ScriptModule.
|
||||
self._concrete_type = torch._C.ConcreteModuleType.from_jit_type(self._c._type())
|
||||
|
||||
# Copy submodules from the C++ module to this ScriptModule.
|
||||
modules = {}
|
||||
for name, cpp_module in torch._C.ModuleDict(self._c).items():
|
||||
modules[name] = wrap_cpp_module(cpp_module)
|
||||
self._modules = OrderedModuleDict(self._c, modules)
|
||||
|
||||
# Copy parameters and buffers.
|
||||
self._parameters = OrderedDictWrapper(torch._C.ParameterDict(self._c))
|
||||
self._buffers = OrderedDictWrapper(torch._C.BufferDict(self._c))
|
||||
|
||||
# Get rid of the functions from the old C++ module.
|
||||
self.__dict__ = {k: v for k, v in self.__dict__.items() if not isinstance(v, torch._C.ScriptMethod)}
|
||||
self.__dict__['_initializing'] = False
|
||||
|
||||
@property
|
||||
def graph(self):
|
||||
r"""
|
||||
Returns a string representation of the internal graph for the
|
||||
``forward`` method. See `interpreting-graphs` for details.
|
||||
"""
|
||||
return self.forward.graph
|
||||
|
||||
@property
|
||||
def inlined_graph(self):
|
||||
r"""
|
||||
Returns a string representation of the internal graph for the
|
||||
``forward`` method. This graph will be preprocessed to inline all function and method calls.
|
||||
See `interpreting-graphs` for details.
|
||||
"""
|
||||
return self.forward.inlined_graph
|
||||
|
||||
@property
|
||||
def code(self):
|
||||
r"""
|
||||
Returns a pretty-printed representation (as valid Python syntax) of
|
||||
the internal graph for the ``forward`` method. See `inspecting-code`
|
||||
for details.
|
||||
"""
|
||||
return self.forward.code
|
||||
|
||||
@property
|
||||
def code_with_constants(self):
|
||||
r"""
|
||||
Returns a tuple of:
|
||||
|
||||
[0] a pretty-printed representation (as valid Python syntax) of
|
||||
the internal graph for the ``forward`` method. See `code`.
|
||||
[1] a ConstMap following the CONSTANT.cN format of the output in [0].
|
||||
The indices in the [0] output are keys to the underlying constant's values.
|
||||
|
||||
See `inspecting-code` for details.
|
||||
"""
|
||||
r = self.forward.code_with_constants
|
||||
return (r[0], ConstMap(r[1]))
|
||||
|
||||
def save(self, *args, **kwargs):
|
||||
r"""
|
||||
save(f, _extra_files=ExtraFilesMap{})
|
||||
|
||||
See :func:`torch.jit.save <torch.jit.save>` for details.
|
||||
"""
|
||||
return self._c.save(*args, **kwargs)
|
||||
|
||||
def _save_for_lite_interpreter(self, *args, **kwargs):
|
||||
r"""
|
||||
_save_for_lite_interpreter(f)
|
||||
|
||||
Add (or update) the bytecode session to the script model. The updated model is used
|
||||
in lite interpreter for mobile applications.
|
||||
|
||||
Arguments:
|
||||
f: a string containing a file name.
|
||||
_extra_files: Map from filename to contents which will be stored as part of 'f'.
|
||||
|
||||
"""
|
||||
return self._c._save_for_mobile(*args, **kwargs)
|
||||
|
||||
def _save_to_buffer_for_lite_interpreter(self, *args, **kwargs):
|
||||
return self._c._save_to_buffer_for_mobile(*args, **kwargs)
|
||||
|
||||
def save_to_buffer(self, *args, **kwargs):
|
||||
return self._c.save_to_buffer(*args, **kwargs)
|
||||
|
||||
def get_debug_state(self, *args, **kwargs):
|
||||
return self._c.get_debug_state()
|
||||
|
||||
def extra_repr(self):
|
||||
return 'original_name={}'.format(self.original_name)
|
||||
|
||||
def graph_for(self, *args, **kwargs):
|
||||
return self.forward.graph_for(*args, **kwargs)
|
||||
|
||||
@property
|
||||
def original_name(self):
|
||||
if type(self) == str(self._c._type().name()):
|
||||
return ''
|
||||
return str(self._c._type().name())
|
||||
|
||||
def define(self, src):
|
||||
# We use frames_up=1 to get to the proper surrounding scope. The stack
|
||||
# will look like:
|
||||
# 0. createResolutionCallback
|
||||
# 1. define()
|
||||
# 2. surrounding scope.
|
||||
#
|
||||
# createResolutionCallback internally adds 1 to get us to our frame, then
|
||||
# we add 1 to get to the proper surrounding scope.
|
||||
rcb = _jit_internal.createResolutionCallbackFromFrame(frames_up=1)
|
||||
self._c._define(self._concrete_type, src, rcb)
|
||||
|
||||
def __getattr__(self, attr):
|
||||
if '_initializing' not in self.__dict__:
|
||||
raise RuntimeError("ScriptModule has not been initialized, did you forget to call super's init?")
|
||||
|
||||
if self._initializing:
|
||||
return super(RecursiveScriptModule, self).__getattr__(attr)
|
||||
|
||||
# _modules check is before hasattr since modules are included as attributes in _c,
|
||||
# but we want to get the python wrapper from _modules instead of the raw _c object.
|
||||
if attr in self._modules:
|
||||
return self._modules[attr]
|
||||
elif self._c.hasattr(attr):
|
||||
return self._c.getattr(attr)
|
||||
elif self._c._has_method(attr):
|
||||
script_method = self._c._get_method(attr)
|
||||
# cache method so future calls do not go through __getattr__
|
||||
# to improve invocation performance
|
||||
self.__dict__[attr] = script_method
|
||||
return script_method
|
||||
|
||||
return super(RecursiveScriptModule, self).__getattr__(attr)
|
||||
|
||||
def __setattr__(self, attr, value):
|
||||
if self._initializing:
|
||||
return super(RecursiveScriptModule, self).__setattr__(attr, value)
|
||||
|
||||
if attr in self._modules:
|
||||
self._modules[attr] = value
|
||||
elif self._c.hasattr(attr):
|
||||
self._c.setattr(attr, value)
|
||||
elif hasattr(self, "_concrete_type") and attr in self._concrete_type.get_constants().keys():
|
||||
# TODO: we don't have _concrete_type set after load(), and in general we lose constant information.
|
||||
# We should encode constants as class type attributes (or something) so it persists across save/load.
|
||||
raise AttributeError("Cannot mutate TorchScript constant value: '{}'. Value: '{}'".format(attr, value))
|
||||
else:
|
||||
# We allow setting Python attributes on the ScriptModule, for
|
||||
# when people want to stash some convenience info on it.
|
||||
# TODO: it's possible that the following is confusing:
|
||||
# s = torch.jit.script(...)
|
||||
# s.python_attr = ...
|
||||
# s.save() <--- this doesn't have `python_attr`
|
||||
# It's fairly trivial to save enough info to warn in this case.
|
||||
return super(RecursiveScriptModule, self).__setattr__(attr, value)
|
||||
|
||||
def __getstate__(self):
|
||||
raise pickle.PickleError(
|
||||
"ScriptModules cannot be deepcopied using copy.deepcopy or saved using torch.save. " +
|
||||
"Mixed serialization of script and non-script modules is not supported. " +
|
||||
"For purely script modules use my_script_module.save(<filename>) instead.")
|
||||
|
||||
def __copy__(self):
|
||||
return torch.jit._recursive.wrap_cpp_module(copy.copy(self._c))
|
||||
|
||||
def __deepcopy__(self, memo):
|
||||
return torch.jit._recursive.wrap_cpp_module(copy.deepcopy(self._c, memo))
|
||||
|
||||
# Python magic methods do method lookups on an object's class type, instead of looking up
|
||||
# the method defines on the class instance. In order to continue to expose the magic methods
|
||||
# of builtin-containers (ModuleList, Sequential, ModuleDict) to python we
|
||||
# define magic methods here as a shim to the correct attribute.
|
||||
def forward_magic_method(self, method_name, *args, **kwargs):
|
||||
self_method = getattr(self, method_name)
|
||||
if getattr(self_method, "__func__", None) == getattr(RecursiveScriptModule, method_name):
|
||||
raise NotImplementedError()
|
||||
return self_method(*args, **kwargs)
|
||||
|
||||
def __iter__(self):
|
||||
return self.forward_magic_method("__iter__")
|
||||
|
||||
def __getitem__(self, idx):
|
||||
return self.forward_magic_method("__getitem__", idx)
|
||||
|
||||
def __len__(self):
|
||||
return self.forward_magic_method("__len__")
|
||||
|
||||
def __contains__(self, key):
|
||||
return self.forward_magic_method("__contains__", key)
|
||||
|
||||
# dir is defined by the base nn.Module, so instead of throwing if
|
||||
# it is not overriden, we call into the nn.Module __dir__ method
|
||||
def __dir__(self):
|
||||
self_method = self.__dir__
|
||||
if self_method.__func__ == get_function_from_type(RecursiveScriptModule, "__dir__"):
|
||||
return super(RecursiveScriptModule, self).__dir__()
|
||||
return self_method()
|
||||
|
||||
# to resolve bool(value), python looks if __bool__ is defined then __iter__
|
||||
# is defined then returns true for classes. because __iter__() on this
|
||||
# class throws if it isn't overriden, we define __bool__ to preserve default behavior
|
||||
def __bool__(self):
|
||||
self_method = self.__bool__
|
||||
if self_method.__func__ == get_function_from_type(RecursiveScriptModule, "__bool__"):
|
||||
return True
|
||||
return self_method()
|
||||
|
||||
def _replicate_for_data_parallel(self):
|
||||
# we have to initialize ScriptModule properly so that
|
||||
# it works with pybind11
|
||||
def init_fn(script_module):
|
||||
# Don't do anything here, we'll initialize the ScriptModule below
|
||||
return
|
||||
return RecursiveScriptModule._construct(self._c._replicate_for_data_parallel(), init_fn)
|
||||
|
||||
# Need to copy all RecursiveScriptModule methods to ScriptModule.
|
||||
#
|
||||
# This is because `super(MyScriptModule, self).foo()` does not use
|
||||
# `__getattr__` to look up `foo`. So we need to make each method available on
|
||||
# the ScriptModule manually.
|
||||
for name, item in RecursiveScriptModule.__dict__.items():
|
||||
if not callable(item) and not isinstance(item, property):
|
||||
continue
|
||||
if name.startswith('__') or hasattr(ScriptModule, name):
|
||||
continue
|
||||
# We can copy over the implementation wholesale because besides the
|
||||
# `super()` thing above, ScriptModule behaves exactly like
|
||||
# RecursiveScriptModule
|
||||
setattr(ScriptModule, name, item)
|
||||
|
||||
def _get_methods(cls):
|
||||
import inspect
|
||||
# In Python 3 unbound methods are functions, but in Python 2 they are methods
|
||||
return inspect.getmembers(cls, predicate=lambda x: inspect.isfunction(x) or inspect.ismethod(x))
|
||||
|
||||
|
||||
_compiled_methods_whitelist = {
|
||||
'forward', 'register_buffer', 'register_parameter', 'add_module',
|
||||
'_apply', 'apply', 'cuda', 'cpu', 'to', 'type', 'float', 'double', 'half',
|
||||
'state_dict', '_save_to_state_dict', 'load_state_dict',
|
||||
'_load_from_state_dict', '_named_members', 'parameters', 'named_parameters',
|
||||
'buffers', 'named_buffers', 'children', 'named_children', 'modules',
|
||||
'named_modules', 'zero_grad', 'share_memory', '_get_name', 'extra_repr',
|
||||
'_slow_forward', '_tracing_name', 'eval', 'train',
|
||||
}
|
||||
|
||||
|
||||
def _make_fail(name):
|
||||
def fail(self, *args, **kwargs):
|
||||
raise RuntimeError(name + " is not supported on ScriptModules")
|
||||
return fail
|
||||
|
||||
for name, method in _get_methods(torch.nn.Module):
|
||||
if name.startswith('__'):
|
||||
continue
|
||||
if name not in RecursiveScriptModule.__dict__ and name not in _compiled_methods_whitelist:
|
||||
setattr(RecursiveScriptModule, method.__name__, _make_fail(name))
|
||||
|
||||
def is_scripting():
|
||||
r"""
|
||||
Function that returns True when in compilation and False otherwise. This
|
||||
is useful especially with the @unused decorator to leave code in your
|
||||
model that is not yet TorchScript compatible.
|
||||
.. testcode::
|
||||
|
||||
import torch
|
||||
|
||||
@torch.jit.unused
|
||||
def unsupported_linear_op(x):
|
||||
return x
|
||||
|
||||
def linear(x):
|
||||
if not torch.jit.is_scripting():
|
||||
return torch.linear(x)
|
||||
else:
|
||||
return unsupported_linear_op(x)
|
||||
"""
|
||||
return False
|
||||
|
||||
|
||||
def _unwrap_optional(x):
|
||||
assert x is not None, "Unwrapping null optional"
|
||||
return x
|
||||
@ -1282,108 +538,6 @@ _register_builtin(wait, 'aten::wait')
|
||||
_register_builtin(is_scripting, 'aten::is_scripting')
|
||||
|
||||
|
||||
# Caching: we currently cache compilation of free functions and overloaded functions.
|
||||
# To cache free functions we hold a weak ref to the function object and
|
||||
# map to the compiled fn's qualified name.
|
||||
# To cache overloaded functions we hold a weak ref to the function obj and
|
||||
# map to all of its overloaded compiled fns.
|
||||
# In the future we could consider caching more types of objects so that
|
||||
# aliasing is preserved across separate compilations of the same object.
|
||||
|
||||
_jit_caching_layer = weakref.WeakKeyDictionary()
|
||||
_jit_function_overload_caching = weakref.WeakKeyDictionary()
|
||||
|
||||
def _try_get_jit_cached_overloads(key):
|
||||
qual_names = _jit_function_overload_caching.get(key, None)
|
||||
if qual_names:
|
||||
return [_python_cu.find_function(qual_name) for qual_name in qual_names]
|
||||
else:
|
||||
return None
|
||||
|
||||
def _set_jit_overload_cache(key, compiled_fns):
|
||||
_jit_function_overload_caching[key] = [fn.qualified_name for fn in compiled_fns]
|
||||
|
||||
def _try_get_jit_cached_function(key):
|
||||
if getattr(key, "__disable_jit_function_caching__", False) is True:
|
||||
return None
|
||||
qual_name = _jit_caching_layer.get(key, None)
|
||||
if qual_name:
|
||||
return _python_cu.find_function(qual_name)
|
||||
else:
|
||||
return None
|
||||
|
||||
def _set_jit_function_cache(key, value):
|
||||
# only free functions currently supported
|
||||
assert isinstance(value, torch.jit.ScriptFunction)
|
||||
_jit_caching_layer[key] = value.qualified_name
|
||||
|
||||
|
||||
# qualified_name => ScriptClass mapping
|
||||
_script_classes = {}
|
||||
|
||||
|
||||
def _add_script_class(cls, name):
|
||||
cls.__torch_script_class__ = True
|
||||
global _script_classes
|
||||
_script_classes[name] = cls
|
||||
|
||||
|
||||
def _get_script_class(name):
|
||||
global _script_classes
|
||||
if name not in _script_classes:
|
||||
return None
|
||||
return _script_classes[name]
|
||||
|
||||
# overloads are registered in _jit_internal and compiled here so that _overload
|
||||
# can be used in nn/functional.py without an import cycle
|
||||
|
||||
def _check_overload_defaults(impl_defaults, overload_defaults, loc):
|
||||
for name, overload_value in overload_defaults.items():
|
||||
if name not in impl_defaults or impl_defaults[name] != overload_value:
|
||||
raise torch.jit.frontend.FrontendError(
|
||||
loc, "Default parameters on overloads do not affect the runtime so they "
|
||||
"must equal to the default parameter on the implementation function. Found on "
|
||||
"parameter {name}".format(name=name))
|
||||
|
||||
def _compile_function_with_overload(overload_fn, qual_name, impl_fn):
|
||||
overload_decl = torch.jit.get_jit_def(overload_fn, overload_fn.__name__).decl()
|
||||
overload_signature = torch.jit.annotations.get_signature(overload_fn, None, None, inspect.ismethod(overload_fn))
|
||||
impl_ast = torch.jit.get_jit_def(impl_fn, impl_fn.__name__)
|
||||
overload_defaults = get_default_args(overload_fn)
|
||||
implementation_defaults = get_default_args(impl_fn)
|
||||
_rcb = _jit_internal.createResolutionCallbackFromClosure(impl_fn)
|
||||
_check_overload_defaults(implementation_defaults, overload_defaults, overload_decl.range())
|
||||
fn = torch._C._jit_script_compile_overload(qual_name, overload_decl, impl_ast, _rcb,
|
||||
implementation_defaults, overload_signature)
|
||||
return fn
|
||||
|
||||
def _get_overloads(obj):
|
||||
# check for cached compiled fns
|
||||
existing_compiled_fns = _try_get_jit_cached_overloads(obj)
|
||||
qual_name = _qualified_name(obj)
|
||||
uncompiled_overloads = _jit_internal._get_fn_overloads(qual_name)
|
||||
if uncompiled_overloads is None:
|
||||
return existing_compiled_fns
|
||||
|
||||
compiled_fns = []
|
||||
for overload_fn in uncompiled_overloads:
|
||||
compiled_fns.append(_compile_function_with_overload(overload_fn, qual_name, obj))
|
||||
|
||||
if existing_compiled_fns:
|
||||
compiled_fns = existing_compiled_fns + compiled_fns
|
||||
|
||||
# cache compilation, remove information stored to do compilation
|
||||
_set_jit_overload_cache(obj, compiled_fns)
|
||||
_jit_internal._clear_fn_overloads(qual_name)
|
||||
return compiled_fns
|
||||
|
||||
def _check_directly_compile_overloaded(obj):
|
||||
qual_name = _qualified_name(obj)
|
||||
if _jit_internal._get_fn_overloads(qual_name) or _try_get_jit_cached_overloads(obj):
|
||||
raise RuntimeError("Function {} cannot be directly compiled because it"
|
||||
" is overloaded. It must be used in a context of a function"
|
||||
" where its inputs can determine which overload to call.".format(qual_name))
|
||||
|
||||
# torch.jit.Error
|
||||
Error = torch._C.JITException
|
||||
set_module(Error, "torch.jit")
|
||||
|
||||
@ -6,7 +6,7 @@ import functools
|
||||
import warnings
|
||||
|
||||
import torch._jit_internal as _jit_internal
|
||||
from torch.jit.frontend import get_default_args
|
||||
from torch.jit.frontend import get_default_args, get_jit_def
|
||||
from torch.jit._builtins import _find_builtin
|
||||
from torch.nn import Module
|
||||
from torch._six import get_function_from_type, bind_method
|
||||
@ -31,7 +31,7 @@ blacklist = [
|
||||
|
||||
def make_stub(func, name):
|
||||
rcb = _jit_internal.createResolutionCallbackFromClosure(func)
|
||||
ast = torch.jit.get_jit_def(func, name, self_name="RecursiveScriptModule")
|
||||
ast = get_jit_def(func, name, self_name="RecursiveScriptModule")
|
||||
return ScriptMethodStub(rcb, ast, func)
|
||||
|
||||
def make_stub_from_method(nn_module, method_name):
|
||||
@ -477,16 +477,16 @@ def get_overload_name_mapping(overload_info):
|
||||
def _check_no_signature(func):
|
||||
signature = torch.jit.annotations.get_signature(func, None, _jit_internal.fake_range(), inspect.ismethod(func))
|
||||
if signature is None:
|
||||
qual_name = torch.jit._qualified_name(func)
|
||||
qual_name = _jit_internal._qualified_name(func)
|
||||
raise RuntimeError("Must explicitly add type annotations to overloaded functions: {}".format(qual_name))
|
||||
|
||||
def make_stubs_for_overloads(overload_info):
|
||||
overload_stubs = []
|
||||
for orig_fn, overloads in overload_info.items():
|
||||
orig_ast = torch.jit.get_jit_def(orig_fn, orig_fn.__name__, self_name="RecursiveScriptModule")
|
||||
orig_ast = get_jit_def(orig_fn, orig_fn.__name__, self_name="RecursiveScriptModule")
|
||||
for overload_name, overload_fn in overloads:
|
||||
_check_no_signature(overload_fn)
|
||||
over_ast = torch.jit.get_jit_def(overload_fn, overload_fn.__name__, self_name="RecursiveScriptModule")
|
||||
over_ast = get_jit_def(overload_fn, overload_fn.__name__, self_name="RecursiveScriptModule")
|
||||
new_ast = torch._C._replace_overloaded_method_decl(over_ast.decl(), orig_ast, overload_name)
|
||||
_rcb = _jit_internal.createResolutionCallbackFromClosure(orig_fn)
|
||||
overload_stubs.append(ScriptMethodStub(_rcb, new_ast, overload_fn))
|
||||
|
||||
@ -6,15 +6,27 @@ This module contains functionality to support the JIT's scripting frontend, nota
|
||||
This is not intended to be imported directly; please use the exposed
|
||||
functionalities in `torch.jit`.
|
||||
"""
|
||||
import torch
|
||||
|
||||
import functools
|
||||
import collections
|
||||
import inspect
|
||||
import copy
|
||||
import pickle
|
||||
import warnings
|
||||
|
||||
from torch.jit._recursive import ScriptMethodStub
|
||||
import torch
|
||||
import torch._jit_internal as _jit_internal
|
||||
from torch.jit._recursive import ScriptMethodStub, wrap_cpp_module
|
||||
from torch.nn import Module
|
||||
from torch.jit._state import _enabled
|
||||
from torch._six import with_metaclass
|
||||
from torch._six import with_metaclass, get_function_from_type
|
||||
from torch.jit.frontend import get_jit_def, get_default_args, get_jit_class_def
|
||||
from torch._jit_internal import _qualified_name
|
||||
from torch.jit._state import (
|
||||
_try_get_jit_cached_function,
|
||||
_try_get_jit_cached_overloads,
|
||||
_set_jit_function_cache,
|
||||
_set_jit_overload_cache,
|
||||
)
|
||||
|
||||
if _enabled:
|
||||
Attribute = collections.namedtuple("Attribute", ["value", "type"])
|
||||
@ -24,6 +36,111 @@ else:
|
||||
return value
|
||||
|
||||
|
||||
# ScriptClasses must be new-style classes because we construct them using their
|
||||
# __new__ method.
|
||||
def _is_new_style_class(cls):
|
||||
if hasattr(cls, "__class__"):
|
||||
return "__dict__" in dir(cls) or hasattr(cls, "__slots__")
|
||||
|
||||
|
||||
def _compile_and_register_class(obj, rcb, qualified_name):
|
||||
ast = get_jit_class_def(obj, obj.__name__)
|
||||
torch._C._jit_script_class_compile(qualified_name, ast, rcb)
|
||||
torch.jit._state._add_script_class(obj, qualified_name)
|
||||
|
||||
|
||||
# These OrderedDictWrapper classes replace the actual OrderedDicts in
|
||||
# module with versions that get/set properties inside of Module.
|
||||
# This allows us to reuse most of nn.Module while still storing the
|
||||
# data in C++.
|
||||
# Each OrderedDict needs to support:
|
||||
# x not in view
|
||||
# x in view
|
||||
# view[name] = ...
|
||||
# view.values()
|
||||
# del view[name]
|
||||
# view.items()
|
||||
# view.keys()
|
||||
# len(view)
|
||||
|
||||
|
||||
class OrderedDictWrapper(object):
|
||||
def __init__(self, _c):
|
||||
self._c = _c
|
||||
|
||||
def keys(self):
|
||||
return [k for k, v in self.items()]
|
||||
|
||||
def values(self):
|
||||
return [v for k, v in self.items()]
|
||||
|
||||
def __len__(self):
|
||||
return len(self.values())
|
||||
|
||||
def __delitem__(self, k):
|
||||
raise RuntimeError("cannot delete methods or parameters of a script module")
|
||||
|
||||
def items(self):
|
||||
return self._c.items()
|
||||
|
||||
def __setitem__(self, k, v):
|
||||
if k not in self:
|
||||
raise RuntimeError(
|
||||
"Can't add a new parameter after ScriptModule construction."
|
||||
" Tried to add '{}".format(k)
|
||||
)
|
||||
self._c.setattr(k, v)
|
||||
|
||||
def __contains__(self, k):
|
||||
return self._c.contains(k)
|
||||
|
||||
def __getitem__(self, k):
|
||||
if k not in self:
|
||||
raise KeyError(k)
|
||||
return self._c.getattr(k)
|
||||
|
||||
|
||||
class OrderedModuleDict(OrderedDictWrapper):
|
||||
def __init__(self, module, python_dict):
|
||||
super(OrderedModuleDict, self).__init__(torch._C.ModuleDict(module))
|
||||
# contains _both_ script modules and non-script python-only modules
|
||||
|
||||
# because script modules are subclassed in python and the
|
||||
# C++ Module class will not hold references to them,
|
||||
# to ensure that you always get the same python value here
|
||||
# we store it in the python dict as well
|
||||
self._python_modules = python_dict
|
||||
|
||||
def items(self):
|
||||
r = self._python_modules.items()
|
||||
return r
|
||||
|
||||
def __contains__(self, k):
|
||||
return k in self._python_modules
|
||||
|
||||
def __setitem__(self, k, v):
|
||||
# Cases where sub-module can be re-assigned after ScriptModule construction
|
||||
# 1. If the attr is an module interface type, it's guaranteed that the module is
|
||||
# not inlined in the graph, so it's safe to swap a new ScriptModule in.
|
||||
# 2. if the new value if a ScriptModule with the same JIT type, IR won't change
|
||||
# and it's legit to swap a new module in.
|
||||
# In these two cases we allow swapping a new scripted module and update the
|
||||
# corresponding python module dict to keep sync.
|
||||
# Note: the value to be swapped in has to be ScriptModule instead of nn.Module,
|
||||
# otherwise it's illegal and we throw error.
|
||||
if isinstance(v, ScriptModule):
|
||||
self._c.setattr(k, v)
|
||||
self._python_modules[k] = v
|
||||
else:
|
||||
raise RuntimeError(
|
||||
"Cannot re-assign modules in a ScriptModule with non-scripted "
|
||||
"module, tried to replace existing module '{}': {}".format(k, v)
|
||||
)
|
||||
|
||||
def __getitem__(self, k):
|
||||
return self._python_modules[k]
|
||||
|
||||
|
||||
# For each user-defined class that subclasses ScriptModule, this meta-class:
|
||||
# (1) finds all the methods annotated with @script_method in a ScriptModule and
|
||||
# removes them from the class attributes
|
||||
@ -90,6 +207,38 @@ class _CachedForward(object):
|
||||
return self.__getattr__("forward")
|
||||
|
||||
|
||||
class ScriptWarning(Warning):
|
||||
pass
|
||||
|
||||
|
||||
def script_method(fn):
|
||||
if not _enabled:
|
||||
return fn
|
||||
# NOTE: we need to traverse two frames here because the meta-class frame
|
||||
# for ScriptModule will be present, as opposed to invoking @script on a
|
||||
# a function or invoking define() on a CompilationUnit.
|
||||
# The stack will look like:
|
||||
#
|
||||
# 0. createResolutionCallback()
|
||||
# 1. script_method()
|
||||
# 2. ScriptModule metaclass frame
|
||||
# 3. Surrounding scope
|
||||
#
|
||||
# createResolutionCallback internally adds 1 to get us to the scope of this
|
||||
# function (the calling function). Adding 2 gets us to the proper surrounding scope.
|
||||
_rcb = _jit_internal.createResolutionCallbackFromFrame(frames_up=2)
|
||||
ast = get_jit_def(fn, fn.__name__, self_name="ScriptModule")
|
||||
return ScriptMethodStub(_rcb, ast, fn)
|
||||
|
||||
|
||||
class ConstMap:
|
||||
def __init__(self, const_mapping):
|
||||
self.const_mapping = const_mapping
|
||||
|
||||
def __getattr__(self, attr):
|
||||
return self.const_mapping[attr]
|
||||
|
||||
|
||||
if _enabled:
|
||||
# this is a Python 'non-data descriptor' that causes the first access
|
||||
# to ScriptModule's forward to lookup the forward method and stash
|
||||
@ -154,16 +303,753 @@ if _enabled:
|
||||
#
|
||||
# createResolutionCallback internally adds 1 to get us to our frame, then
|
||||
# we add 1 to get to the proper surrounding scope.
|
||||
rcb = torch._jit_internal.createResolutionCallbackFromFrame(frames_up=1)
|
||||
rcb = _jit_internal.createResolutionCallbackFromFrame(frames_up=1)
|
||||
ast = torch._C._parse_source_def(src)
|
||||
self._methods[ast.name().name] = ScriptMethodStub(rcb, ast, None)
|
||||
|
||||
def _replicate_for_data_parallel(self):
|
||||
return self._actual_script_module._replicate_for_data_parallel()
|
||||
|
||||
class RecursiveScriptModule(ScriptModule):
|
||||
# XXX: RecursiveScriptModule inherits from ScriptModule for the sole
|
||||
# reason that it retains the existing isinstance(ScriptModule)
|
||||
# behavior.
|
||||
r"""
|
||||
The core data structure in TorchScript is the ``ScriptModule``. It is an
|
||||
analogue of torch's ``nn.Module`` and represents an entire model as a tree of
|
||||
submodules. Like normal modules, each individual module in a ``ScriptModule`` can
|
||||
have submodules, parameters, and methods. In ``nn.Module``\s methods are implemented
|
||||
as Python functions, but in ``ScriptModule``\s methods are implemented as
|
||||
TorchScript functions, a statically-typed subset of Python that contains all
|
||||
of PyTorch's built-in Tensor operations. This difference allows your
|
||||
``ScriptModule``\s code to run without the need for a Python interpreter.
|
||||
|
||||
``ScriptModule``\s should not be created manually, instead use
|
||||
either :func:`tracing <torch.jit.trace>` or :func:`scripting <torch.jit.script>`.
|
||||
Tracing and scripting can be applied incrementally and :ref:`composed as necessary <Types>`.
|
||||
|
||||
* Tracing records the tensor operations as executed with a set of example inputs and uses these
|
||||
operations to construct a computation graph. You can use the full dynamic behavior of Python with tracing,
|
||||
but values other than Tensors and control flow aren't captured in the graph.
|
||||
|
||||
* Scripting inspects the Python code of the model
|
||||
and compiles it to TorchScript. Scripting allows the use of many `types`_ of values and supports dynamic control flow.
|
||||
Many, but not all features of Python are supported by the compiler, so changes to the source code may be necessary.
|
||||
"""
|
||||
_disable_script_meta = True
|
||||
|
||||
def __init__(self, cpp_module):
|
||||
self.__dict__["_initializing"] = True
|
||||
self._c = cpp_module
|
||||
super(RecursiveScriptModule, self).__init__()
|
||||
# Delete the 'training' attribute set up by `Module.__init__`. It
|
||||
# will get set on the underlying cpp module, so we delete it here
|
||||
# to avoid this version shadowing the cpp module version.
|
||||
delattr(self, "training")
|
||||
|
||||
@staticmethod
|
||||
def _construct(cpp_module, init_fn):
|
||||
"""
|
||||
Construct a RecursiveScriptModule that's ready for use. PyTorch
|
||||
code should use this to construct a RecursiveScriptModule instead
|
||||
of instead of calling `__init__` directly, as it makes sure the
|
||||
object is properly finalized (and in the future we may take
|
||||
control of how the RecursiveScriptModule instance is created).
|
||||
|
||||
Arguments:
|
||||
cpp_module: The C++ Module that will hold the actual state of
|
||||
this RecursiveScriptModule instance.
|
||||
init_fn: Lambda that initializes the RecursiveScriptModule passed to it.
|
||||
"""
|
||||
script_module = RecursiveScriptModule(cpp_module)
|
||||
init_fn(script_module)
|
||||
|
||||
# Finalize the ScriptModule: replace the nn.Module state with our
|
||||
# custom implementations and flip the _initializing bit.
|
||||
RecursiveScriptModule._finalize_scriptmodule(script_module)
|
||||
return script_module
|
||||
|
||||
@staticmethod
|
||||
def _finalize_scriptmodule(script_module):
|
||||
script_module._parameters = OrderedDictWrapper(
|
||||
torch._C.ParameterDict(script_module._c)
|
||||
)
|
||||
script_module._buffers = OrderedDictWrapper(
|
||||
torch._C.BufferDict(script_module._c)
|
||||
)
|
||||
script_module._modules = OrderedModuleDict(
|
||||
script_module._c, script_module._modules
|
||||
)
|
||||
script_module._initializing = False
|
||||
|
||||
def _reconstruct(self, cpp_module):
|
||||
"""
|
||||
Re-construct an instance of RecursiveScriptModule using an instance of a C++ module.
|
||||
|
||||
Arguments:
|
||||
cpp_module: The C++ module that this RecursiveScriptModule will be rebuilt around.
|
||||
"""
|
||||
self.__init__(cpp_module)
|
||||
|
||||
# Copy the concrete type from the C++ module to this ScriptModule.
|
||||
self._concrete_type = torch._C.ConcreteModuleType.from_jit_type(
|
||||
self._c._type()
|
||||
)
|
||||
|
||||
# Copy submodules from the C++ module to this ScriptModule.
|
||||
modules = {}
|
||||
for name, cpp_module in torch._C.ModuleDict(self._c).items():
|
||||
modules[name] = wrap_cpp_module(cpp_module)
|
||||
self._modules = OrderedModuleDict(self._c, modules)
|
||||
|
||||
# Copy parameters and buffers.
|
||||
self._parameters = OrderedDictWrapper(torch._C.ParameterDict(self._c))
|
||||
self._buffers = OrderedDictWrapper(torch._C.BufferDict(self._c))
|
||||
|
||||
# Get rid of the functions from the old C++ module.
|
||||
self.__dict__ = {
|
||||
k: v
|
||||
for k, v in self.__dict__.items()
|
||||
if not isinstance(v, torch._C.ScriptMethod)
|
||||
}
|
||||
self.__dict__["_initializing"] = False
|
||||
|
||||
@property
|
||||
def graph(self):
|
||||
r"""
|
||||
Returns a string representation of the internal graph for the
|
||||
``forward`` method. See `interpreting-graphs`_ for details.
|
||||
"""
|
||||
return self.forward.graph
|
||||
|
||||
@property
|
||||
def inlined_graph(self):
|
||||
r"""
|
||||
Returns a string representation of the internal graph for the
|
||||
``forward`` method. This graph will be preprocessed to inline all function and method calls.
|
||||
See `interpreting-graphs`_ for details.
|
||||
"""
|
||||
return self.forward.inlined_graph
|
||||
|
||||
@property
|
||||
def code(self):
|
||||
r"""
|
||||
Returns a pretty-printed representation (as valid Python syntax) of
|
||||
the internal graph for the ``forward`` method. See `inspecting-code`_
|
||||
for details.
|
||||
"""
|
||||
return self.forward.code
|
||||
|
||||
@property
|
||||
def code_with_constants(self):
|
||||
r"""
|
||||
Returns a tuple of:
|
||||
|
||||
[0] a pretty-printed representation (as valid Python syntax) of
|
||||
the internal graph for the ``forward`` method. See `code`.
|
||||
[1] a ConstMap following the CONSTANT.cN format of the output in [0].
|
||||
The indices in the [0] output are keys to the underlying constant's values.
|
||||
|
||||
See `inspecting-code`_ for details.
|
||||
"""
|
||||
r = self.forward.code_with_constants
|
||||
return (r[0], ConstMap(r[1]))
|
||||
|
||||
def save(self, *args, **kwargs):
|
||||
r"""
|
||||
save(f, _extra_files=ExtraFilesMap{})
|
||||
|
||||
See :func:`torch.jit.save <torch.jit.save>` for details.
|
||||
"""
|
||||
return self._c.save(*args, **kwargs)
|
||||
|
||||
def _save_for_lite_interpreter(self, *args, **kwargs):
|
||||
r"""
|
||||
_save_for_lite_interpreter(f)
|
||||
|
||||
Add (or update) the bytecode session to the script model. The updated model is used
|
||||
in lite interpreter for mobile applications.
|
||||
|
||||
Arguments:
|
||||
f: a string containing a file name.
|
||||
_extra_files: Map from filename to contents which will be stored as part of 'f'.
|
||||
|
||||
"""
|
||||
return self._c._save_for_mobile(*args, **kwargs)
|
||||
|
||||
def _save_to_buffer_for_lite_interpreter(self, *args, **kwargs):
|
||||
return self._c._save_to_buffer_for_mobile(*args, **kwargs)
|
||||
|
||||
def save_to_buffer(self, *args, **kwargs):
|
||||
return self._c.save_to_buffer(*args, **kwargs)
|
||||
|
||||
def get_debug_state(self, *args, **kwargs):
|
||||
return self._c.get_debug_state()
|
||||
|
||||
def extra_repr(self):
|
||||
return "original_name={}".format(self.original_name)
|
||||
|
||||
def graph_for(self, *args, **kwargs):
|
||||
return self.forward.graph_for(*args, **kwargs)
|
||||
|
||||
@property
|
||||
def original_name(self):
|
||||
if type(self) == str(self._c._type().name()):
|
||||
return ""
|
||||
return str(self._c._type().name())
|
||||
|
||||
def define(self, src):
|
||||
# We use frames_up=1 to get to the proper surrounding scope. The stack
|
||||
# will look like:
|
||||
# 0. createResolutionCallback
|
||||
# 1. define()
|
||||
# 2. surrounding scope.
|
||||
#
|
||||
# createResolutionCallback internally adds 1 to get us to our frame, then
|
||||
# we add 1 to get to the proper surrounding scope.
|
||||
rcb = _jit_internal.createResolutionCallbackFromFrame(frames_up=1)
|
||||
self._c._define(self._concrete_type, src, rcb)
|
||||
|
||||
def __getattr__(self, attr):
|
||||
if "_initializing" not in self.__dict__:
|
||||
raise RuntimeError(
|
||||
"ScriptModule has not been initialized, did you forget to call super's init?"
|
||||
)
|
||||
|
||||
if self._initializing:
|
||||
return super(RecursiveScriptModule, self).__getattr__(attr)
|
||||
|
||||
# _modules check is before hasattr since modules are included as attributes in _c,
|
||||
# but we want to get the python wrapper from _modules instead of the raw _c object.
|
||||
if attr in self._modules:
|
||||
return self._modules[attr]
|
||||
elif self._c.hasattr(attr):
|
||||
return self._c.getattr(attr)
|
||||
elif self._c._has_method(attr):
|
||||
script_method = self._c._get_method(attr)
|
||||
# cache method so future calls do not go through __getattr__
|
||||
# to improve invocation performance
|
||||
self.__dict__[attr] = script_method
|
||||
return script_method
|
||||
|
||||
return super(RecursiveScriptModule, self).__getattr__(attr)
|
||||
|
||||
def __setattr__(self, attr, value):
|
||||
if self._initializing:
|
||||
return super(RecursiveScriptModule, self).__setattr__(attr, value)
|
||||
|
||||
if attr in self._modules:
|
||||
self._modules[attr] = value
|
||||
elif self._c.hasattr(attr):
|
||||
self._c.setattr(attr, value)
|
||||
elif (
|
||||
hasattr(self, "_concrete_type")
|
||||
and attr in self._concrete_type.get_constants().keys()
|
||||
):
|
||||
# TODO: we don't have _concrete_type set after load(), and in general we lose constant information.
|
||||
# We should encode constants as class type attributes (or something) so it persists across save/load.
|
||||
raise AttributeError(
|
||||
"Cannot mutate TorchScript constant value: '{}'. Value: '{}'".format(
|
||||
attr, value
|
||||
)
|
||||
)
|
||||
else:
|
||||
# We allow setting Python attributes on the ScriptModule, for
|
||||
# when people want to stash some convenience info on it.
|
||||
# TODO: it's possible that the following is confusing:
|
||||
# s = torch.jit.script(...)
|
||||
# s.python_attr = ...
|
||||
# s.save() <--- this doesn't have `python_attr`
|
||||
# It's fairly trivial to save enough info to warn in this case.
|
||||
return super(RecursiveScriptModule, self).__setattr__(attr, value)
|
||||
|
||||
def __getstate__(self):
|
||||
raise pickle.PickleError(
|
||||
"ScriptModules cannot be deepcopied using copy.deepcopy or saved using torch.save. "
|
||||
+ "Mixed serialization of script and non-script modules is not supported. "
|
||||
+ "For purely script modules use my_script_module.save(<filename>) instead."
|
||||
)
|
||||
|
||||
def __copy__(self):
|
||||
return torch.jit._recursive.wrap_cpp_module(copy.copy(self._c))
|
||||
|
||||
def __deepcopy__(self, memo):
|
||||
return torch.jit._recursive.wrap_cpp_module(copy.deepcopy(self._c, memo))
|
||||
|
||||
# Python magic methods do method lookups on an object's class type, instead of looking up
|
||||
# the method defines on the class instance. In order to continue to expose the magic methods
|
||||
# of builtin-containers (ModuleList, Sequential, ModuleDict) to python we
|
||||
# define magic methods here as a shim to the correct attribute.
|
||||
def forward_magic_method(self, method_name, *args, **kwargs):
|
||||
self_method = getattr(self, method_name)
|
||||
if getattr(self_method, "__func__", None) == getattr(
|
||||
RecursiveScriptModule, method_name
|
||||
):
|
||||
raise NotImplementedError()
|
||||
return self_method(*args, **kwargs)
|
||||
|
||||
def __iter__(self):
|
||||
return self.forward_magic_method("__iter__")
|
||||
|
||||
def __getitem__(self, idx):
|
||||
return self.forward_magic_method("__getitem__", idx)
|
||||
|
||||
def __len__(self):
|
||||
return self.forward_magic_method("__len__")
|
||||
|
||||
def __contains__(self, key):
|
||||
return self.forward_magic_method("__contains__", key)
|
||||
|
||||
# dir is defined by the base nn.Module, so instead of throwing if
|
||||
# it is not overriden, we call into the nn.Module __dir__ method
|
||||
def __dir__(self):
|
||||
self_method = self.__dir__
|
||||
if self_method.__func__ == get_function_from_type(
|
||||
RecursiveScriptModule, "__dir__"
|
||||
):
|
||||
return super(RecursiveScriptModule, self).__dir__()
|
||||
return self_method()
|
||||
|
||||
# to resolve bool(value), python looks if __bool__ is defined then __iter__
|
||||
# is defined then returns true for classes. because __iter__() on this
|
||||
# class throws if it isn't overriden, we define __bool__ to preserve default behavior
|
||||
def __bool__(self):
|
||||
self_method = self.__bool__
|
||||
if self_method.__func__ == get_function_from_type(
|
||||
RecursiveScriptModule, "__bool__"
|
||||
):
|
||||
return True
|
||||
return self_method()
|
||||
|
||||
def _replicate_for_data_parallel(self):
|
||||
# we have to initialize ScriptModule properly so that
|
||||
# it works with pybind11
|
||||
def init_fn(script_module):
|
||||
# Don't do anything here, we'll initialize the ScriptModule below
|
||||
return
|
||||
|
||||
return RecursiveScriptModule._construct(
|
||||
self._c._replicate_for_data_parallel(), init_fn
|
||||
)
|
||||
|
||||
# Need to copy all RecursiveScriptModule methods to ScriptModule.
|
||||
#
|
||||
# This is because `super(MyScriptModule, self).foo()` does not use
|
||||
# `__getattr__` to look up `foo`. So we need to make each method available on
|
||||
# the ScriptModule manually.
|
||||
for name, item in RecursiveScriptModule.__dict__.items():
|
||||
if not callable(item) and not isinstance(item, property):
|
||||
continue
|
||||
if name.startswith("__") or hasattr(ScriptModule, name):
|
||||
continue
|
||||
# We can copy over the implementation wholesale because besides the
|
||||
# `super()` thing above, ScriptModule behaves exactly like
|
||||
# RecursiveScriptModule
|
||||
setattr(ScriptModule, name, item)
|
||||
|
||||
def _get_methods(cls):
|
||||
import inspect
|
||||
|
||||
# In Python 3 unbound methods are functions, but in Python 2 they are methods
|
||||
return inspect.getmembers(
|
||||
cls, predicate=lambda x: inspect.isfunction(x) or inspect.ismethod(x)
|
||||
)
|
||||
|
||||
_compiled_methods_whitelist = {
|
||||
"forward",
|
||||
"register_buffer",
|
||||
"register_parameter",
|
||||
"add_module",
|
||||
"_apply",
|
||||
"apply",
|
||||
"cuda",
|
||||
"cpu",
|
||||
"to",
|
||||
"type",
|
||||
"float",
|
||||
"double",
|
||||
"half",
|
||||
"state_dict",
|
||||
"_save_to_state_dict",
|
||||
"load_state_dict",
|
||||
"_load_from_state_dict",
|
||||
"_named_members",
|
||||
"parameters",
|
||||
"named_parameters",
|
||||
"buffers",
|
||||
"named_buffers",
|
||||
"children",
|
||||
"named_children",
|
||||
"modules",
|
||||
"named_modules",
|
||||
"zero_grad",
|
||||
"share_memory",
|
||||
"_get_name",
|
||||
"extra_repr",
|
||||
"_slow_forward",
|
||||
"_tracing_name",
|
||||
"eval",
|
||||
"train",
|
||||
}
|
||||
|
||||
def _make_fail(name):
|
||||
def fail(self, *args, **kwargs):
|
||||
raise RuntimeError(name + " is not supported on ScriptModules")
|
||||
|
||||
return fail
|
||||
|
||||
for name, method in _get_methods(torch.nn.Module):
|
||||
if name.startswith("__"):
|
||||
continue
|
||||
if (
|
||||
name not in RecursiveScriptModule.__dict__
|
||||
and name not in _compiled_methods_whitelist
|
||||
):
|
||||
setattr(RecursiveScriptModule, method.__name__, _make_fail(name))
|
||||
|
||||
|
||||
else:
|
||||
# TODO MAKE SURE THAT DISABLING WORKS
|
||||
class ScriptModule(torch.nn.Module):
|
||||
def __init__(self):
|
||||
super(ScriptModule, self).__init__()
|
||||
|
||||
class RecursiveScriptModule(ScriptModule):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
|
||||
def script(obj, optimize=None, _frames_up=0, _rcb=None):
|
||||
r"""
|
||||
Scripting a function or ``nn.Module`` will inspect the source code, compile
|
||||
it as TorchScript code using the TorchScript compiler, and return a :class:`ScriptModule` or
|
||||
:class:`ScriptFunction`. TorchScript itself is a subset of the Python language, so not all
|
||||
features in Python work, but we provide enough functionality to compute on
|
||||
tensors and do control-dependent operations. For a complete guide, see the
|
||||
:ref:`language-reference`.
|
||||
|
||||
``torch.jit.script`` can be used as a function for modules and functions, and as a decorator
|
||||
``@torch.jit.script`` for :ref:`torchscript-classes` and functions.
|
||||
|
||||
Arguments:
|
||||
obj (callable, class, or ``nn.Module``): The ``nn.Module``, function, or class type to
|
||||
compile.
|
||||
|
||||
Returns:
|
||||
If ``obj`` is ``nn.Module``, ``script`` returns
|
||||
a :class:`ScriptModule` object. The returned :class:`ScriptModule` will
|
||||
have the same set of sub-modules and parameters as the
|
||||
original ``nn.Module``. If ``obj`` is a standalone function,
|
||||
a :class:`ScriptFunction` will be returned.
|
||||
|
||||
**Scripting a function**
|
||||
The ``@torch.jit.script`` decorator will construct a :class:`ScriptFunction`
|
||||
by compiling the body of the function.
|
||||
|
||||
Example (scripting a function):
|
||||
|
||||
.. testcode::
|
||||
|
||||
import torch
|
||||
|
||||
@torch.jit.script
|
||||
def foo(x, y):
|
||||
if x.max() > y.max():
|
||||
r = x
|
||||
else:
|
||||
r = y
|
||||
return r
|
||||
|
||||
print(type(foo)) # torch.jit.ScriptFuncion
|
||||
|
||||
# See the compiled graph as Python code
|
||||
print(foo.code)
|
||||
|
||||
# Call the function using the TorchScript interpreter
|
||||
foo(torch.ones(2, 2), torch.ones(2, 2))
|
||||
|
||||
.. testoutput::
|
||||
:hide:
|
||||
|
||||
...
|
||||
|
||||
**Scripting an nn.Module**
|
||||
Scripting an ``nn.Module`` by default will compile the ``forward`` method and recursively
|
||||
compile any methods, submodules, and functions called by ``forward``. If a ``nn.Module`` only uses
|
||||
features supported in TorchScript, no changes to the original module code should be necessary. ``script``
|
||||
will construct :class:`ScriptModule` that has copies of the attributes, parameters, and methods of
|
||||
the original module.
|
||||
|
||||
Example (scripting a simple module with a Parameter):
|
||||
|
||||
.. testcode::
|
||||
|
||||
import torch
|
||||
|
||||
class MyModule(torch.nn.Module):
|
||||
def __init__(self, N, M):
|
||||
super(MyModule, self).__init__()
|
||||
# This parameter will be copied to the new ScriptModule
|
||||
self.weight = torch.nn.Parameter(torch.rand(N, M))
|
||||
|
||||
# When this submodule is used, it will be compiled
|
||||
self.linear = torch.nn.Linear(N, M)
|
||||
|
||||
def forward(self, input):
|
||||
output = self.weight.mv(input)
|
||||
|
||||
# This calls the `forward` method of the `nn.Linear` module, which will
|
||||
# cause the `self.linear` submodule to be compiled to a `ScriptModule` here
|
||||
output = self.linear(output)
|
||||
return output
|
||||
|
||||
scripted_module = torch.jit.script(MyModule(2, 3))
|
||||
|
||||
Example (scripting a module with traced submodules):
|
||||
|
||||
.. testcode::
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
class MyModule(nn.Module):
|
||||
def __init__(self):
|
||||
super(MyModule, self).__init__()
|
||||
# torch.jit.trace produces a ScriptModule's conv1 and conv2
|
||||
self.conv1 = torch.jit.trace(nn.Conv2d(1, 20, 5), torch.rand(1, 1, 16, 16))
|
||||
self.conv2 = torch.jit.trace(nn.Conv2d(20, 20, 5), torch.rand(1, 20, 16, 16))
|
||||
|
||||
def forward(self, input):
|
||||
input = F.relu(self.conv1(input))
|
||||
input = F.relu(self.conv2(input))
|
||||
return input
|
||||
|
||||
scripted_module = torch.jit.script(MyModule())
|
||||
|
||||
To compile a method other than ``forward`` (and recursively compile anything it calls), add
|
||||
the :func:`@torch.jit.export <torch.jit.export>` decorator to the method. To opt out of compilation
|
||||
use :func:`@torch.jit.ignore <torch.jit.ignore>` or :func:`@torch.jit.unused <torch.jit.unused>`.
|
||||
|
||||
Example (an exported and ignored method in a module)::
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
class MyModule(nn.Module):
|
||||
def __init__(self):
|
||||
super(MyModule, self).__init__()
|
||||
|
||||
@torch.jit.export
|
||||
def some_entry_point(self, input):
|
||||
return input + 10
|
||||
|
||||
@torch.jit.ignore
|
||||
def python_only_fn(self, input):
|
||||
# This function won't be compiled, so any
|
||||
# Python APIs can be used
|
||||
import pdb
|
||||
pdb.set_trace()
|
||||
|
||||
def forward(self, input):
|
||||
if self.training:
|
||||
self.python_only_fn(input)
|
||||
return input * 99
|
||||
|
||||
scripted_module = torch.jit.script(MyModule())
|
||||
print(scripted_module.some_entry_point(torch.randn(2, 2)))
|
||||
print(scripted_module(torch.randn(2, 2)))
|
||||
"""
|
||||
if not _enabled:
|
||||
return obj
|
||||
|
||||
if optimize is not None:
|
||||
warnings.warn(
|
||||
"`optimize` is deprecated and has no effect. Use `with torch.jit.optimized_execution() instead"
|
||||
)
|
||||
if isinstance(obj, ScriptModule):
|
||||
return obj
|
||||
|
||||
if isinstance(obj, torch.nn.Module):
|
||||
return torch.jit._recursive.create_script_module(
|
||||
obj, torch.jit._recursive.infer_methods_to_compile
|
||||
)
|
||||
|
||||
qualified_name = _qualified_name(obj)
|
||||
if inspect.isclass(obj):
|
||||
# If this type is a `nn.Module` subclass, they probably meant to pass
|
||||
# an instance instead of a Module
|
||||
if issubclass(obj, torch.nn.Module):
|
||||
raise RuntimeError(
|
||||
"Type '{}' cannot be compiled since it inherits"
|
||||
" from nn.Module,"
|
||||
" pass an instance instead".format(obj)
|
||||
)
|
||||
|
||||
if not _is_new_style_class(obj):
|
||||
raise RuntimeError(
|
||||
"TorchScript classes must be new-style classes. "
|
||||
"Please inherit from 'object'."
|
||||
)
|
||||
if len(obj.mro()) > 2:
|
||||
raise RuntimeError(
|
||||
"TorchScript classes does not support inheritance yet. "
|
||||
"Please directly inherit from 'object'."
|
||||
)
|
||||
if _rcb is None:
|
||||
_rcb = _jit_internal.createResolutionCallbackFromFrame(_frames_up + 1)
|
||||
_compile_and_register_class(obj, _rcb, qualified_name)
|
||||
return obj
|
||||
else:
|
||||
# this is a decorated fn, and we need to the underlying fn and its rcb
|
||||
if hasattr(obj, "__script_if_tracing_wrapper"):
|
||||
obj = obj.__original_fn
|
||||
_rcb = _jit_internal.createResolutionCallbackFromClosure(obj)
|
||||
|
||||
_check_directly_compile_overloaded(obj)
|
||||
maybe_already_compiled_fn = _try_get_jit_cached_function(obj)
|
||||
if maybe_already_compiled_fn:
|
||||
return maybe_already_compiled_fn
|
||||
ast = get_jit_def(obj, obj.__name__)
|
||||
if _rcb is None:
|
||||
_rcb = _jit_internal.createResolutionCallbackFromClosure(obj)
|
||||
fn = torch._C._jit_script_compile(
|
||||
qualified_name, ast, _rcb, get_default_args(obj)
|
||||
)
|
||||
# Forward docstrings
|
||||
fn.__doc__ = obj.__doc__
|
||||
_set_jit_function_cache(obj, fn)
|
||||
return fn
|
||||
|
||||
|
||||
def is_scripting():
|
||||
r"""
|
||||
Function that returns True when in compilation and False otherwise. This
|
||||
is useful especially with the @unused decorator to leave code in your
|
||||
model that is not yet TorchScript compatible.
|
||||
.. testcode::
|
||||
|
||||
import torch
|
||||
|
||||
@torch.jit.unused
|
||||
def unsupported_linear_op(x):
|
||||
return x
|
||||
|
||||
def linear(x):
|
||||
if not torch.jit.is_scripting():
|
||||
return torch.linear(x)
|
||||
else:
|
||||
return unsupported_linear_op(x)
|
||||
"""
|
||||
return False
|
||||
|
||||
|
||||
# overloads are registered in _jit_internal and compiled here so that _overload
|
||||
# can be used in nn/functional.py without an import cycle
|
||||
|
||||
|
||||
def _check_overload_defaults(impl_defaults, overload_defaults, loc):
|
||||
for name, overload_value in overload_defaults.items():
|
||||
if name not in impl_defaults or impl_defaults[name] != overload_value:
|
||||
raise torch.jit.frontend.FrontendError(
|
||||
loc,
|
||||
"Default parameters on overloads do not affect the runtime so they "
|
||||
"must equal to the default parameter on the implementation function. Found on "
|
||||
"parameter {name}".format(name=name),
|
||||
)
|
||||
|
||||
|
||||
def _compile_function_with_overload(overload_fn, qual_name, impl_fn):
|
||||
overload_decl = get_jit_def(overload_fn, overload_fn.__name__).decl()
|
||||
overload_signature = torch.jit.annotations.get_signature(
|
||||
overload_fn, None, None, inspect.ismethod(overload_fn)
|
||||
)
|
||||
impl_ast = get_jit_def(impl_fn, impl_fn.__name__)
|
||||
overload_defaults = get_default_args(overload_fn)
|
||||
implementation_defaults = get_default_args(impl_fn)
|
||||
_rcb = _jit_internal.createResolutionCallbackFromClosure(impl_fn)
|
||||
_check_overload_defaults(
|
||||
implementation_defaults, overload_defaults, overload_decl.range()
|
||||
)
|
||||
fn = torch._C._jit_script_compile_overload(
|
||||
qual_name,
|
||||
overload_decl,
|
||||
impl_ast,
|
||||
_rcb,
|
||||
implementation_defaults,
|
||||
overload_signature,
|
||||
)
|
||||
return fn
|
||||
|
||||
|
||||
def _get_overloads(obj):
|
||||
# check for cached compiled fns
|
||||
existing_compiled_fns = _try_get_jit_cached_overloads(obj)
|
||||
qual_name = _qualified_name(obj)
|
||||
uncompiled_overloads = _jit_internal._get_fn_overloads(qual_name)
|
||||
if uncompiled_overloads is None:
|
||||
return existing_compiled_fns
|
||||
|
||||
compiled_fns = []
|
||||
for overload_fn in uncompiled_overloads:
|
||||
compiled_fns.append(
|
||||
_compile_function_with_overload(overload_fn, qual_name, obj)
|
||||
)
|
||||
|
||||
if existing_compiled_fns:
|
||||
compiled_fns = existing_compiled_fns + compiled_fns
|
||||
|
||||
# cache compilation, remove information stored to do compilation
|
||||
_set_jit_overload_cache(obj, compiled_fns)
|
||||
_jit_internal._clear_fn_overloads(qual_name)
|
||||
return compiled_fns
|
||||
|
||||
|
||||
def _check_directly_compile_overloaded(obj):
|
||||
qual_name = _qualified_name(obj)
|
||||
if _jit_internal._get_fn_overloads(qual_name) or _try_get_jit_cached_overloads(obj):
|
||||
raise RuntimeError(
|
||||
"Function {} cannot be directly compiled because it"
|
||||
" is overloaded. It must be used in a context of a function"
|
||||
" where its inputs can determine which overload to call.".format(qual_name)
|
||||
)
|
||||
|
||||
|
||||
def interface(obj):
|
||||
if not inspect.isclass(obj):
|
||||
raise RuntimeError("interface must be applied to a class")
|
||||
if not _is_new_style_class(obj):
|
||||
raise RuntimeError("TorchScript interfaces must inherit from 'object'")
|
||||
|
||||
# Expected MRO is:
|
||||
# User module
|
||||
# torch.nn.modules.module.Module
|
||||
# object
|
||||
is_module_interface = issubclass(obj, torch.nn.Module) and len(obj.mro()) == 3
|
||||
|
||||
if not is_module_interface and len(obj.mro()) > 2:
|
||||
raise RuntimeError(
|
||||
"TorchScript interface does not support inheritance yet. "
|
||||
"Please directly inherit from 'object' or 'nn.Module'."
|
||||
)
|
||||
|
||||
qualified_name = _qualified_name(obj)
|
||||
rcb = _jit_internal.createResolutionCallbackFromFrame(1)
|
||||
# if this type is a `nn.Module` subclass, generate an module interface type
|
||||
# instead of a class interface type, an module interface type only compile
|
||||
# the user provided methods as part of the interface
|
||||
ast = get_jit_class_def(obj, obj.__name__)
|
||||
torch._C._jit_script_interface_compile(
|
||||
qualified_name, ast, rcb, is_module_interface
|
||||
)
|
||||
obj.__torch_script_interface__ = True
|
||||
return obj
|
||||
|
||||
|
||||
def _recursive_compile_class(obj, loc):
|
||||
_qual_name = _qualified_name(obj)
|
||||
# We're starting a new compilation, so update the error call stack in
|
||||
# case it fails
|
||||
error_stack = torch._C.CallStack(_qual_name, loc)
|
||||
rcb = _jit_internal.createResolutionCallbackForClassMethods(obj)
|
||||
_compile_and_register_class(obj, rcb, _qual_name)
|
||||
|
||||
@ -7,6 +7,7 @@ functionalities in `torch.jit`.
|
||||
"""
|
||||
import torch
|
||||
import os
|
||||
import weakref
|
||||
|
||||
|
||||
class EnabledProxy:
|
||||
@ -55,3 +56,55 @@ def enable():
|
||||
# live in here. It's defined in Python because doing in cpp creates static
|
||||
# destruction order issues.
|
||||
_python_cu = torch._C.CompilationUnit()
|
||||
|
||||
|
||||
# qualified_name => ScriptClass mapping
|
||||
_script_classes = {}
|
||||
|
||||
def _add_script_class(cls, name):
|
||||
cls.__torch_script_class__ = True
|
||||
global _script_classes
|
||||
_script_classes[name] = cls
|
||||
|
||||
|
||||
def _get_script_class(name):
|
||||
global _script_classes
|
||||
if name not in _script_classes:
|
||||
return None
|
||||
return _script_classes[name]
|
||||
|
||||
|
||||
# Caching: we currently cache compilation of free functions and overloaded functions.
|
||||
# To cache free functions we hold a weak ref to the function object and
|
||||
# map to the compiled fn's qualified name.
|
||||
# To cache overloaded functions we hold a weak ref to the function obj and
|
||||
# map to all of its overloaded compiled fns.
|
||||
# In the future we could consider caching more types of objects so that
|
||||
# aliasing is preserved across separate compilations of the same object.
|
||||
|
||||
_jit_caching_layer = weakref.WeakKeyDictionary()
|
||||
_jit_function_overload_caching = weakref.WeakKeyDictionary()
|
||||
|
||||
def _try_get_jit_cached_overloads(key):
|
||||
qual_names = _jit_function_overload_caching.get(key, None)
|
||||
if qual_names:
|
||||
return [_python_cu.find_function(qual_name) for qual_name in qual_names]
|
||||
else:
|
||||
return None
|
||||
|
||||
def _set_jit_overload_cache(key, compiled_fns):
|
||||
_jit_function_overload_caching[key] = [fn.qualified_name for fn in compiled_fns]
|
||||
|
||||
def _try_get_jit_cached_function(key):
|
||||
if getattr(key, "__disable_jit_function_caching__", False) is True:
|
||||
return None
|
||||
qual_name = _jit_caching_layer.get(key, None)
|
||||
if qual_name:
|
||||
return _python_cu.find_function(qual_name)
|
||||
else:
|
||||
return None
|
||||
|
||||
def _set_jit_function_cache(key, value):
|
||||
# only free functions currently supported
|
||||
assert isinstance(value, torch.jit.ScriptFunction)
|
||||
_jit_caching_layer[key] = value.qualified_name
|
||||
|
||||
@ -287,7 +287,7 @@ def try_ann_to_type(ann, loc):
|
||||
return ClassType(_qualified_name(ann))
|
||||
ignored_builtin_classes = (torch.nn.Module, tuple, list)
|
||||
if torch._jit_internal.can_compile_class(ann) and not issubclass(ann, ignored_builtin_classes):
|
||||
torch.jit._recursive_compile_class(ann, loc)
|
||||
torch.jit._script._recursive_compile_class(ann, loc)
|
||||
return ClassType(_qualified_name(ann))
|
||||
|
||||
# Maybe resolve a NamedTuple to a Tuple Type
|
||||
|
||||
@ -17,6 +17,7 @@ from torch._C._jit_tree_views import (
|
||||
from torch._utils_internal import get_source_lines_and_file
|
||||
|
||||
from torch._jit_internal import SourceContext, should_drop
|
||||
import torch.jit.annotations
|
||||
|
||||
# Borrowed from cPython implementation
|
||||
# https://github.com/python/cpython/blob/561612d8456cfab5672c9b445521113b847bd6b3/Lib/textwrap.py#L411#
|
||||
|
||||
@ -21,7 +21,7 @@ def _init_script_module():
|
||||
|
||||
def _is_jit_enabled():
|
||||
import torch.jit
|
||||
return torch.jit._enabled
|
||||
return torch.jit._state._enabled
|
||||
|
||||
|
||||
# Check if we can safely replicate the module.
|
||||
@ -111,8 +111,8 @@ def replicate(network, devices, detach=False):
|
||||
module_indices[module] = i
|
||||
for j in range(num_replicas):
|
||||
replica = module._replicate_for_data_parallel()
|
||||
# This is a temporary fix for DDP. DDP needs to access the
|
||||
# replicated model parameters. It used to do so through
|
||||
# This is a temporary fix for DDP. DDP needs to access the
|
||||
# replicated model parameters. It used to do so through
|
||||
# `mode.parameters()`. The fix added in #33907 for DP stops the
|
||||
# `parameters()` API from exposing the replicated parameters.
|
||||
# Hence, we add a `_former_parameters` dict here to support DDP.
|
||||
|
||||
@ -1033,7 +1033,7 @@ class JitRpcTest(
|
||||
with torch.autograd.profiler.profile() as prof:
|
||||
prof_key = _build_rpc_profiling_key(
|
||||
RPCExecMode.ASYNC,
|
||||
torch.jit._qualified_name(one_arg),
|
||||
torch._jit_internal._qualified_name(one_arg),
|
||||
"worker0",
|
||||
"worker1",
|
||||
)
|
||||
@ -1045,7 +1045,7 @@ class JitRpcTest(
|
||||
# After that, this test should be modified to validate the function time.
|
||||
events = prof.function_events
|
||||
function_event = get_function_event(events, prof_key)
|
||||
self.assertTrue(torch.jit._qualified_name(one_arg) in function_event.name)
|
||||
self.assertTrue(torch._jit_internal._qualified_name(one_arg) in function_event.name)
|
||||
|
||||
@dist_init
|
||||
def test_rpc_async_jit_profiled(self):
|
||||
@ -1063,7 +1063,7 @@ class JitRpcTest(
|
||||
|
||||
# Ensure rpc_async call is profiled
|
||||
function_events = prof.function_events
|
||||
qual_name = torch.jit._qualified_name(two_args_two_kwargs)
|
||||
qual_name = torch._jit_internal._qualified_name(two_args_two_kwargs)
|
||||
rpc_async_jit_event = [
|
||||
event
|
||||
for event in function_events
|
||||
|
||||
@ -995,7 +995,7 @@ class RpcTest(RpcAgentTestFixture):
|
||||
self.assertTrue(self_worker_name in rpc_event.name)
|
||||
self.assertTrue(dst_worker_name in rpc_event.name)
|
||||
if isinstance(func, torch.jit.ScriptFunction):
|
||||
self.assertTrue(torch.jit._qualified_name(func) in rpc_event.name)
|
||||
self.assertTrue(torch._jit_internal._qualified_name(func) in rpc_event.name)
|
||||
else:
|
||||
self.assertTrue(func.__name__ in rpc_event.name)
|
||||
self.assertTrue(rpc_exec_mode.value in rpc_event.name)
|
||||
@ -1485,7 +1485,7 @@ class RpcTest(RpcAgentTestFixture):
|
||||
with torch.autograd.profiler.profile() as pf:
|
||||
key = _build_rpc_profiling_key(
|
||||
RPCExecMode.ASYNC,
|
||||
torch.jit._qualified_name(my_script_func),
|
||||
torch._jit_internal._qualified_name(my_script_func),
|
||||
"worker1",
|
||||
"worker0",
|
||||
)
|
||||
@ -1502,9 +1502,9 @@ class RpcTest(RpcAgentTestFixture):
|
||||
self.assertEqual(result, expected)
|
||||
events = pf.function_events
|
||||
rpc_event = get_function_event(
|
||||
events, torch.jit._qualified_name(my_script_func)
|
||||
events, torch._jit_internal._qualified_name(my_script_func)
|
||||
)
|
||||
self.assertTrue(torch.jit._qualified_name(my_script_func) in rpc_event.name)
|
||||
self.assertTrue(torch._jit_internal._qualified_name(my_script_func) in rpc_event.name)
|
||||
|
||||
@dist_init
|
||||
def test_py_class_constructor(self):
|
||||
|
||||
Reference in New Issue
Block a user