Fix perf regression caused by #122074 (#126996)

The original change was about 9.5% slower than then before #122074 .
This improves it to be only about 1.4% slower.

Also touched up some unrelated nits that the linter complained about.

Fixes #126293

Ran torchbench 3 times on each change. Perf values before (stable), after (fix),
and with #122074 backed out (backout):
```
../inductor-tools/scripts/modelbench/inductor_single_run.sh single inference performance torchbench pyhpc_isoneutral_mixing amp first dynamic cpp
stable:
43.948x
45.754x
44.906x

fix:
47.505x
49.987x
47.493x

backout:
48.243x
48.199x
48.192x

../inductor-tools/scripts/modelbench/inductor_single_run.sh single inference performance torchbench pyhpc_equation_of_state amp first static default
stable:
15.224x
13.286x
15.354x

fix:
16.402x
16.370x
16.183x

backout:
16.554x
16.675x
16.787x

../inductor-tools/scripts/modelbench/inductor_single_run.sh single inference performance torchbench lennard_jones float32 first static default
stable:
1.712x
1.651x
1.640x

fix:
1.804x
1.798x
1.792x

backout:
1.864x
1.824x
1.836x
```

Pull Request resolved: #126996
Approved by: https://github.com/jansel

torch/_C/_functorch.pyi

@@ -33,11 +33,6 @@ def _grad_decrement_nesting() -> int: ...
 def _jvp_increment_nesting() -> int: ...
 def _jvp_decrement_nesting() -> int: ...
 
-class _PreserveDynamicLayerStack:
-    def __init__(self): ...
-    def __enter__(self): ...
-    def __exit__(self, exc_type, exc_value, traceback): ...
-
 # Defined in aten/src/ATen/functorch/Interpreter.h
 class TransformType(Enum):
     Torch: TransformType = ...
@@ -80,7 +75,9 @@ class CVmapInterpreterPtr:
 
 class DynamicLayer: ...
 
+def get_dynamic_layer_stack_depth() -> int: ...
 def get_interpreter_stack() -> list[CInterpreter]: ...
 def peek_interpreter_stack() -> CInterpreter: ...
 def pop_dynamic_layer_stack() -> DynamicLayer: ...
+def pop_dynamic_layer_stack_and_undo_to_depth(int) -> None: ...
 def push_dynamic_layer_stack(dl: DynamicLayer) -> int: ...

torch/_dynamo/eval_frame.py

@@ -406,13 +406,25 @@ def _fn(*args, **kwargs):
 
             cleanups = [enter() for enter in self.enter_exit_hooks]
             prior = set_eval_frame(callback)
+
+            # Ensure that if an assertion occurs after graph pushes
+            # something onto the DynamicLayerStack then we pop it off (the
+            # constructed graph code isn't guarded with try/finally).
+            #
+            # This used to be a context but putting a `with` here is a noticible
+            # perf regression (#126293)
+            saved_dynamic_layer_stack_depth = (
+                torch._C._functorch.get_dynamic_layer_stack_depth()
+            )
+
             try:
-                # Ensure that if an assertion occurs after graph pushes
-                # something onto the DynamicLayerStack then we pop it off (the
-                # constructed graph code isn't guarded with try/finally).
-                with torch._C._functorch._PreserveDynamicLayerStack():
-                    return fn(*args, **kwargs)
+                return fn(*args, **kwargs)
             finally:
+                # Restore the dynamic layer stack depth if necessary.
+                torch._C._functorch.pop_dynamic_layer_stack_and_undo_to_depth(
+                    saved_dynamic_layer_stack_depth
+                )
+
                 set_eval_frame(prior)
                 for cleanup in cleanups:
                     cleanup()

torch/csrc/functorch/init.cpp

@@ -23,9 +23,7 @@
 
 // This file contains functorch's Python bindings.
 
-namespace torch {
-namespace functorch {
-namespace impl {
+namespace torch::functorch::impl {
 
 using namespace at::functorch;
 
@@ -378,7 +376,7 @@ static int64_t currentLevel() {
 static std::optional<int64_t> maybe_current_level() {
   auto maybe_layer = maybeCurrentDynamicLayer();
   if (maybe_layer.has_value()) {
-    int current_level = maybe_layer->layerId();
+    int64_t current_level = maybe_layer->layerId();
     return current_level;
   }
   return nullopt;
@@ -405,36 +403,29 @@ static void dump_local_tls() {
 
 namespace {
 
-// An RAII to save and restore the DynamicLayer stack.
-struct PreserveDynamicLayerStack {
-  size_t m_oldDepth;
-
-  ~PreserveDynamicLayerStack() {
-    while (at::functorch::getDynamicLayerStack().size() > m_oldDepth) {
-      const auto& top = at::functorch::getDynamicLayerStack().back();
-      switch (top.key()) {
-        case at::functorch::TransformType::Vmap:
-          _vmap_decrement_nesting();
-          break;
-        case at::functorch::TransformType::Grad:
-          _grad_decrement_nesting();
-          break;
-        case at::functorch::TransformType::Jvp:
-          _jvp_decrement_nesting();
-          break;
-        case at::functorch::TransformType::Functionalize:
-          _func_decrement_nesting();
-          break;
-        case at::functorch::TransformType::Torch:
-          popDynamicLayerAndDeleteMetadata();
-          break;
-      }
+// Pop the DynamicLayer stack until it's at the given depth.
+void popDynamicLayerStackToDepth(size_t depth) {
+  while (at::functorch::getDynamicLayerStack().size() > depth) {
+    const auto top = popDynamicLayer();
+    switch (top.key()) {
+      case at::functorch::TransformType::Vmap:
+        _vmap_decrement_nesting();
+        break;
+      case at::functorch::TransformType::Grad:
+        _grad_decrement_nesting();
+        break;
+      case at::functorch::TransformType::Jvp:
+        _jvp_decrement_nesting();
+        break;
+      case at::functorch::TransformType::Functionalize:
+        _func_decrement_nesting();
+        break;
+      case at::functorch::TransformType::Torch:
+        popDynamicLayerAndDeleteMetadata();
+        break;
     }
   }
-
-  PreserveDynamicLayerStack()
-      : m_oldDepth(at::functorch::getDynamicLayerStack().size()) {}
-};
+}
 
 } // anonymous namespace
 
@@ -540,6 +531,7 @@ void initFuncTorchBindings(PyObject* module) {
           return c10::nullopt;
         }
         std::vector<Interpreter> result;
+        result.reserve(stack.size());
         for (auto i : stack) {
           result.push_back(i.interpreter());
         }
@@ -553,6 +545,12 @@ void initFuncTorchBindings(PyObject* module) {
     auto result = stack.back().interpreter();
     return result;
   });
+  m.def("get_dynamic_layer_stack_depth", []() -> size_t {
+    return getDynamicLayerStack().size();
+  });
+  m.def(
+      "pop_dynamic_layer_stack_and_undo_to_depth",
+      &popDynamicLayerStackToDepth);
   m.def("pop_dynamic_layer_stack", &popDynamicLayer);
   m.def("push_dynamic_layer_stack", [](DynamicLayer layer) -> int64_t {
     return pushDynamicLayer(std::move(layer));
@@ -598,11 +596,6 @@ void initFuncTorchBindings(PyObject* module) {
       .def(
           "functionalizeAddBackViews",
           &FunctionalizeInterpreterPtr::functionalizeAddBackViews);
-
-  torch::impl::py_context_manager<PreserveDynamicLayerStack>(
-      m, "_PreserveDynamicLayerStack");
 }
 
-} // namespace impl
-} // namespace functorch
-} // namespace torch
+} // namespace torch::functorch::impl