Revert "[dynamo] save/restore system random state more carefully (#145750)"

This reverts commit e3d3f2b22e.

Reverted https://github.com/pytorch/pytorch/pull/145750 on behalf of https://github.com/eellison due to bisected perf regression ([comment](https://github.com/pytorch/pytorch/pull/145750#issuecomment-2620028414))

test/dynamo/test_unspec.py

@@ -1,5 +1,4 @@
 # Owner(s): ["module: dynamo"]
-import contextlib
 import math
 import random
 import unittest
@@ -256,74 +255,6 @@ class UnspecTests(torch._dynamo.test_case.TestCase):
         y2 = opt_fn(inp, *get_rng())
         self.assertEqual(y1, y2)
 
-    def test_random_in_dynamo(self):
-        # test that system random calls still work even
-        # if Dynamo calls random methods.
-
-        exit_stack = contextlib.ExitStack()
-
-        def patch_fn_with_rng_burn(name):
-            orig_fn = eval(name)
-
-            def bad(*args, **kwargs):
-                # burn random call within dynamo
-                random.random()
-                return orig_fn(*args, **kwargs)
-
-            exit_stack.enter_context(unittest.mock.patch(name, bad))
-
-        x = torch.tensor([[1.0, 2.0], [3.0, 4.0]])
-
-        patch_fn_with_rng_burn("torch._dynamo.eval_frame._maybe_set_eval_frame")
-        patch_fn_with_rng_burn("torch._dynamo.convert_frame._compile")
-        patch_fn_with_rng_burn(
-            "torch._dynamo.symbolic_convert.InstructionTranslator.run"
-        )
-
-        def f1(x):
-            # simple test
-            r1 = random.randint(1, 9)
-            y = x + random.uniform(10, 20)
-            r2 = random.randrange(0, 10)
-            return y + r1, r2
-
-        random.seed(1)
-        ref1 = f1(x)
-        opt_f1 = torch.compile(f1, backend="eager", fullgraph=True)
-        random.seed(1)
-        res1 = opt_f1(x)
-        self.assertEqual(ref1, res1)
-
-        def f2(x):
-            # test with graph breaks
-            r1 = random.randint(1, 9)
-            x = x + r1
-            torch._dynamo.graph_break()
-            r2 = random.randint(10, 19)
-            x = x + r2
-            return x, r1, r2
-
-        random.seed(2)
-        ref2 = f2(x)
-        opt_f2 = torch.compile(f2, backend="eager")
-        random.seed(2)
-        res2 = opt_f2(x)
-        self.assertEqual(ref2, res2)
-
-        def f3(x):
-            # test consecutive calls
-            return x + random.randint(1, 10)
-
-        random.seed(3)
-        ref3 = f3(x)
-        ref3_ = f3(x)
-        opt_f3 = torch.compile(f3, backend="eager", fullgraph=True)
-        random.seed(3)
-        res3 = opt_f3(x)
-        res3_ = opt_f3(x)
-        self.assertEqual(ref3, res3)
-        self.assertEqual(ref3_, res3_)
-
     def test_builtin_getitem(self):
         # builtin getitem args[0] is python list and args[1] is unspec
         def fn(x, idx):

torch/_dynamo/convert_frame.py

@@ -12,6 +12,7 @@ import json
 import logging
 import os
 import pstats
+import random
 import subprocess
 import sys
 import threading
@@ -192,14 +193,13 @@ def fx_forward_from_src_skip_result(
     return result
 
 
-# TODO it is possible to move more global state preservation to eval_frame.py/c.
-# See how we preserve Python random state.
 def preserve_global_state(fn: Callable[_P, _T]) -> Callable[_P, _T]:
     """
     Context manager to:
         1) Save/restore torch.is_grad_enabled() state
-        2) Save/restore torch random state
-        3) Monkey patch torch.fx.graph_module._forward_from_src
+        2) Save/restore python random state
+        3) Save/restore torch random state
+        4) Monkey patch torch.fx.graph_module._forward_from_src
     """
 
     @functools.wraps(fn)
@@ -217,6 +217,7 @@ def preserve_global_state(fn: Callable[_P, _T]) -> Callable[_P, _T]:
             prior_mobile_allocator_state = (
                 torch._C._is_default_mobile_cpu_allocator_set()
             )
+            py_rng_state = random.getstate()
             prior_dtype = torch.get_default_dtype()
             torch_rng_state = torch.random.get_rng_state()
             cuda_rng_state = None
@@ -244,6 +245,7 @@ def preserve_global_state(fn: Callable[_P, _T]) -> Callable[_P, _T]:
                 torch.use_deterministic_algorithms(
                     prior_deterministic, warn_only=prior_warn_only
                 )
+                random.setstate(py_rng_state)
                 torch.random.set_rng_state(torch_rng_state)
                 torch.set_default_dtype(prior_dtype)
                 curr_mobile_allocator_state = (

torch/_dynamo/eval_frame.py

@@ -16,7 +16,6 @@ import functools
 import inspect
 import logging
 import os
-import random
 import sys
 import sysconfig
 import textwrap
@@ -535,7 +534,6 @@ class _TorchDynamoContext:
         @functools.wraps(fn)
         def _fn(*args, **kwargs):
             prior = set_eval_frame(None)
-            prev_rng_state = random.getstate()
             try:
                 if is_fx_tracing():
                     if config.error_on_nested_fx_trace:
@@ -569,8 +567,6 @@ class _TorchDynamoContext:
                 _maybe_set_eval_frame(_callback_from_stance(callback))
 
                 try:
-                    # ignore random state updates since beginning of _fn
-                    random.setstate(prev_rng_state)
                     return fn(*args, **kwargs)
                 except ShortenTraceback as e:
                     # Failures in the backend likely don't have useful
@@ -579,8 +575,6 @@ class _TorchDynamoContext:
                 finally:
                     # Restore the dynamic layer stack depth if necessary.
                     set_eval_frame(None)
-                    # NB: assumes no random calls made between fn() and here
-                    prev_rng_state = random.getstate()
                     torch._C._functorch.pop_dynamic_layer_stack_and_undo_to_depth(
                         saved_dynamic_layer_stack_depth
                     )
@@ -590,10 +584,6 @@ class _TorchDynamoContext:
                         cleanup()
             finally:
                 _maybe_set_eval_frame(prior)
-                # ignore random state updates:
-                # - since beginning of _fn if fn was not called
-                # - since end of fn if fn was called (even if exn occurs)
-                random.setstate(prev_rng_state)
 
         # hooks to properly handle inlining
         _fn._torchdynamo_inline = fn  # type: ignore[attr-defined]
@@ -750,22 +740,18 @@ class DisableContext(_TorchDynamoContext):
         @functools.wraps(fn)
         def _fn(*args, **kwargs):
             prior = set_eval_frame(None)
-            prev_rng_state = random.getstate()
             try:
                 prior_skip_guard_eval_unsafe = set_skip_guard_eval_unsafe(
                     _is_skip_guard_eval_unsafe_stance()
                 )
                 _maybe_set_eval_frame(_callback_from_stance(self.callback))
                 try:
-                    random.setstate(prev_rng_state)
                     return fn(*args, **kwargs)
                 finally:
                     set_eval_frame(None)
                     set_skip_guard_eval_unsafe(prior_skip_guard_eval_unsafe)
-                    prev_rng_state = random.getstate()
             finally:
                 _maybe_set_eval_frame(prior)
-                random.setstate(prev_rng_state)
 
         _fn._torchdynamo_disable = True  # type: ignore[attr-defined]
 

torch/_dynamo/output_graph.py

@@ -628,11 +628,9 @@ class OutputGraph:
         """
         global_state = cast(
             dict[str, tuple[Callable[..., Any], bool]],
-            (
-                out
-                if out is not None
-                else self.tracing_context.global_context.global_state
-            ),
+            out
+            if out is not None
+            else self.tracing_context.global_context.global_state,
         )
 
         # TODO - Consider having a torch level API for torch_function_state. As

torch/_dynamo/variables/misc.py

@@ -1713,7 +1713,6 @@ class RandomVariable(VariableTracker):
             tx.output.side_effects.mutation(self)
             state = self.random.getstate()
 
-            # Generate new random object with the same state and call the method
             def call_random_meth(*args, **kwargs):
                 r = random.Random()
                 r.setstate(state)

torch/_dynamo/variables/user_defined.py

@@ -678,8 +678,6 @@ def call_random_fn(tx, fn, args, kwargs):
     args = [x.as_python_constant() for x in args]
     kwargs = {k: v.as_python_constant() for k, v in kwargs.items()}
     random_call_index = len(tx.output.random_calls)
-    # NB: it is probably not important for the example_value to be exactly correct,
-    # we just need the right type
     example_value = fn(*args, **kwargs)
     source = RandomValueSource(random_call_index)
     tx.output.random_calls.append((fn, args, kwargs))
@@ -876,12 +874,7 @@ class UserDefinedObjectVariable(UserDefinedVariable):
             and all(k.is_python_constant() for k in args)
             and all(v.is_python_constant() for v in kwargs.values())
         ):
-            return call_random_fn(
-                tx,
-                self.value,
-                args,
-                kwargs,
-            )
+            return call_random_fn(tx, self.value, args, kwargs)
         elif istype(self.value, types.MethodType):
             func = self.value.__func__
             obj = self.value.__self__

torch/csrc/dynamo/eval_frame.c

@@ -9,14 +9,13 @@
 #include <torch/csrc/dynamo/debug_macros.h>
 #include <torch/csrc/dynamo/extra_state.h>
 #include <torch/csrc/dynamo/framelocals_mapping.h>
-#include <torch/csrc/dynamo/utils.h>
 #include <torch/csrc/utils/python_compat.h>
 
 PyObject* guard_error_hook = NULL;
 const char* cache_lookup_profiler_str = "TorchDynamo Cache Lookup";
 
 typedef struct {
-  int active_dynamo_threads;
+    int active_dynamo_threads;
 } ModuleState;
 
 // static int active_dynamo_threads = 0;
@@ -296,7 +295,6 @@ static PyObject* dynamo_call_callback(
   PyObject* cache_entry_pyobj = CacheEntry_to_obj(cache_entry);
   PyObject* res = PyObject_CallFunction(
       callable, "OOO", frame, cache_entry_pyobj, frame_state);
-
   Py_DECREF(frame);
   Py_DECREF(cache_entry_pyobj);
   return res;
@@ -539,17 +537,6 @@ static PyObject* dynamo_eval_custom_code(
   return result;
 }
 
-static PyObject* random_state = NULL;
-
-static void save_random_state() {
-  Py_XSETREF(random_state, random_getstate(random_module()));
-}
-
-static void restore_random_state() {
-  DEBUG_NULL_CHECK(random_state);
-  random_setstate(random_module(), random_state);
-}
-
 static PyObject* dynamo__custom_eval_frame_shim(
     PyThreadState* tstate,
     THP_EVAL_API_FRAME_OBJECT* frame,
@@ -658,11 +645,6 @@ static PyObject* dynamo__custom_eval_frame(
   // in the shim.
   eval_frame_callback_set(Py_None);
 
-  // Preserve random state - restore before calling
-  // dynamo_eval_[frame_default]/[custom_code]!
-  // lookup or call_callback may burn RNG.
-  save_random_state();
-
   // A callback of Py_False indicates "run only" mode, the cache is checked, but
   // we never compile.
   // Also, if extra is marked as "cache_limit_hit", run in "run only" mode
@@ -703,7 +685,6 @@ static PyObject* dynamo__custom_eval_frame(
         DEBUG_TRACE("skip recursive %s", get_frame_name(frame));
         eval_frame_callback_set(Py_None);
       }
-      restore_random_state();
       PyObject* ret = dynamo_eval_frame_default(tstate, frame, throw_flag);
       if (extra_state_cache_limit_hit(extra)) {
         eval_frame_callback_set(callback);
@@ -716,7 +697,6 @@ static PyObject* dynamo__custom_eval_frame(
     // Re-enable custom behavior
     eval_frame_callback_set(callback);
     *should_clear_frame = 1;
-    restore_random_state();
     return dynamo_eval_custom_code(
         tstate, frame, cached_code, trace_annotation, throw_flag);
   }
@@ -749,7 +729,6 @@ static PyObject* dynamo__custom_eval_frame(
     eval_frame_callback_set(callback);
     *should_clear_frame = 1;
     framelocals_mapping_free(locals);
-    restore_random_state();
     return dynamo_eval_custom_code(
         tstate, frame, cached_code, trace_annotation, throw_flag);
   }
@@ -783,7 +762,6 @@ static PyObject* dynamo__custom_eval_frame(
     // code.
     DEBUG_TRACE("create skip recursive %s", get_frame_name(frame));
     set_extra_state(F_CODE(frame), SKIP_CODE_RECURSIVE);
-    restore_random_state();
     PyObject* r = dynamo_eval_frame_default(tstate, frame, throw_flag);
     // Re-enable custom behavior
     eval_frame_callback_set(callback);
@@ -792,7 +770,6 @@ static PyObject* dynamo__custom_eval_frame(
     // Dynamo returned cache_limit_hit_flag, so we should recursively skip code.
     DEBUG_TRACE("create cache limit hit %s", get_frame_name(frame));
     set_extra_state_cache_limit_hit(extra, true);
-    restore_random_state();
     PyObject* r = dynamo_eval_frame_default(tstate, frame, throw_flag);
     // Re-enable custom behavior
     eval_frame_callback_set(callback);
@@ -814,7 +791,6 @@ static PyObject* dynamo__custom_eval_frame(
     // Re-enable custom behavior
     eval_frame_callback_set(callback);
     *should_clear_frame = 1;
-    restore_random_state();
     return dynamo_eval_custom_code(
         tstate,
         frame,
@@ -827,7 +803,6 @@ static PyObject* dynamo__custom_eval_frame(
     set_extra_state(F_CODE(frame), SKIP_CODE);
     // Re-enable custom behavior
     eval_frame_callback_set(callback);
-    restore_random_state();
     return dynamo_eval_frame_default(tstate, frame, throw_flag);
   }
 }
@@ -856,9 +831,7 @@ static PyTypeObject THPPyInterpreterFrameType = {
 
 #endif // !(IS_PYTHON_3_14_PLUS)
 
-static PyObject* increment_working_threads(
-    PyThreadState* tstate,
-    PyObject* module) {
+static PyObject* increment_working_threads(PyThreadState* tstate, PyObject* module) {
   ModuleState* state = PyModule_GetState(module);
 
   if (state != NULL) {
@@ -871,13 +844,11 @@ static PyObject* increment_working_threads(
   Py_RETURN_NONE;
 }
 
-static PyObject* decrement_working_threads(
-    PyThreadState* tstate,
-    PyObject* module) {
+static PyObject* decrement_working_threads(PyThreadState* tstate, PyObject* module) {
   ModuleState* state = PyModule_GetState(module);
 
   if (state != NULL) {
-    if (state->active_dynamo_threads > 0) {
+      if (state->active_dynamo_threads > 0) {
       state->active_dynamo_threads = state->active_dynamo_threads - 1;
       if (state->active_dynamo_threads == 0) {
         enable_eval_frame_default(tstate);
@@ -888,10 +859,7 @@ static PyObject* decrement_working_threads(
   Py_RETURN_NONE;
 }
 
-static PyObject* set_eval_frame(
-    PyObject* new_callback,
-    PyThreadState* tstate,
-    PyObject* module) {
+static PyObject* set_eval_frame(PyObject* new_callback, PyThreadState* tstate, PyObject* module) {
   // Change the eval frame callback and return the old one
   //  - None: disables TorchDynamo
   //  - False: run-only mode (reuse existing compiles)
@@ -1014,13 +982,13 @@ static PyObject* raise_sigtrap(PyObject* dummy, PyObject* obj) {
   Py_RETURN_NONE;
 }
 
-static int clear_state(PyObject* module) {
-  ModuleState* state = PyModule_GetState(module);
-  if (state) {
-    state->active_dynamo_threads = 0;
-    return 0;
-  }
-  return -1;
+static int clear_state(PyObject *module) {
+    ModuleState* state = PyModule_GetState(module);
+    if (state) {
+        state->active_dynamo_threads = 0;
+        return 0;
+    }
+    return -1;
 }
 
 static PyMethodDef _methods[] = {

torch/csrc/dynamo/utils.cpp

@@ -2,29 +2,6 @@
 
 namespace torch::dynamo {
 
-// random utilities for C dynamo
-
-// random module reference
-py::object _random{py::none()};
-
-PyObject* random_module() {
-  if (_random.is_none()) {
-    _random = py::module_::import("random");
-  }
-  return _random.ptr();
-}
-
-PyObject* random_getstate(PyObject* rng) {
-  py::handle rng_h(rng);
-  py::object state = rng_h.attr("getstate")();
-  return state.release().ptr();
-}
-
-void random_setstate(PyObject* rng, PyObject* state) {
-  py::handle rng_h(rng), state_h(state);
-  rng_h.attr("setstate")(state_h);
-}
-
 static std::array<PyMethodDef, 1> _methods = {{
     {nullptr,
      nullptr,

torch/csrc/dynamo/utils.h

@@ -1,18 +1,10 @@
 #pragma once
-
-#ifdef __cplusplus
-
 #include <torch/csrc/python_headers.h>
 // C2039 MSVC
 #include <pybind11/complex.h>
 #include <torch/csrc/utils/pybind.h>
 
-#endif // __cplusplus
-
 #include <Python.h>
-
-#ifdef __cplusplus
-
 // The visibility attribute is to avoid a warning about storing a field in the
 // struct that has a different visibility (from pybind) than the struct.
 #ifdef _WIN32
@@ -22,32 +14,5 @@
 #endif
 
 namespace torch::dynamo {
-
-extern "C" {
-
-#endif // __cplusplus
-
-// reference to random module
-// returns borrowed reference
-PyObject* random_module();
-
-// rng.getstate()
-// rng can be random module or random.Random object
-// rng: borrowed reference
-// returns new reference
-PyObject* random_getstate(PyObject* rng);
-
-// rng.setstate(state)
-// rng can be random module or random.Random object
-// rng, state: borrowed references
-// no return value
-void random_setstate(PyObject* rng, PyObject* state);
-
-#ifdef __cplusplus
-
-} // extern "C"
-
 PyObject* torch_c_dynamo_utils_init();
 } // namespace torch::dynamo
-
-#endif // __cplusplus