diff --git a/test/profiler/test_profiler.py b/test/profiler/test_profiler.py
index cf5e32bfed578..729c380afdea3 100644
--- a/test/profiler/test_profiler.py
+++ b/test/profiler/test_profiler.py
@@ -1259,7 +1259,6 @@ def test_nested_tensor_with_shapes(self):
                 self.assertTrue(len(e.input_shapes[0]) > 0)
 
 
-
 def find_node_with_name(nodes, name):
     for node in nodes:
         if node.name() == name:
@@ -1268,6 +1267,17 @@ def find_node_with_name(nodes, name):
         if result is not None:
             return result
 
+
+class SimpleNet(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.fc1 = nn.Linear(10, 5)
+        self.fc2 = nn.Linear(5, 2)
+
+    def forward(self, x):
+        return self.fc2(self.fc1(x))
+
+
 class TestTorchTidyProfiler(TestCase):
 
     def test_pointers_and_ids(self):
@@ -1336,6 +1346,7 @@ def get_fields(op_name, index):
         self.assertEqual(c_id, c_id_new)
         self.assertEqual(d_id, c_id_new)
 
+
     def test_extra_fields(self):
         with profile(with_stack=True, profile_memory=True) as p:
             _ = torch.ones((1,))
@@ -1477,18 +1488,10 @@ def flat_out_extrafields(nodes, out=None):
                 flat_out_extrafields(node.children, out)
             return out
 
-        class simpleNet(nn.Module):
-            def __init__(self):
-                super().__init__()
-                self.fc1 = nn.Linear(10, 5)
-                self.fc2 = nn.Linear(5, 2)
-
-            def forward(self, x):
-                return self.fc2(self.fc1(x))
 
         inputs = torch.rand(10)
         with torch.profiler.profile(with_stack=True, profile_memory=True) as p:
-            net = simpleNet()
+            net = SimpleNet()
             out = net(inputs)
 
         modules = flat_out_extrafields(p.profiler.kineto_results.experimental_event_tree())
@@ -1499,6 +1502,32 @@ def forward(self, x):
         expected += [(name, val.storage().data_ptr()) for name, val in net.fc2._parameters.items()]
         self.assertEqual(expected, params, f"{expected} vs. {params}")
 
+    def test_optimizer(self):
+
+        def flat_out_extrafields(nodes, out=None):
+            if out is None:
+                out = []
+            for node in nodes:
+                if isinstance(node.extra_fields, _ExtraFields_PyCall) and node.extra_fields.opt:
+                    out.append(node.extra_fields.opt.self)
+                flat_out_extrafields(node.children, out)
+            return out
+
+        inputs = torch.rand(10)
+        with torch.profiler.profile(with_stack=True, profile_memory=True) as p:
+            net = SimpleNet()
+            opt = torch.optim.SGD(net.parameters(), lr=0.01, momentum=0.9)
+
+            opt.zero_grad()
+            out = net(inputs)
+            loss = torch.nn.functional.cross_entropy(out, torch.rand(2))
+            loss.backward()
+            opt.step()
+
+        opts = flat_out_extrafields(p.profiler.kineto_results.experimental_event_tree())
+        self.assertEqual(len(opts), 1, f"Expected 1 optimizer, got {len(opts)}")
+        self.assertEqual(id(opt), opts[0], f"Optimizer addr ({id(opt)}) vs. Profiled optimizer addr ({opts[0]})")
+
     def test_allocations(self):
         gc.collect()
         with profile(profile_memory=True) as p:
diff --git a/torch/csrc/autograd/profiler_python.cpp b/torch/csrc/autograd/profiler_python.cpp
index debc1f6f83274..fdca317cc88c3 100644
--- a/torch/csrc/autograd/profiler_python.cpp
+++ b/torch/csrc/autograd/profiler_python.cpp
@@ -37,8 +37,8 @@ namespace torch {
 namespace profiler {
 namespace impl {
 namespace {
-enum CallType { PyCall = 0, PyModuleCall, PyCCall };
-static constexpr size_t CallTypeSize = 3;
+enum CallType { PyCall = 0, PyModuleCall, PyCCall, PyOptimizerCall };
+static constexpr size_t CallTypeSize = 4;
 using no_ephemeral_t = std::tuple<>;
 
 // ============================================================================
@@ -63,7 +63,11 @@ struct CodeLocation {
   int line_number_{0};
 };
 
-PyCodeObject* nnModuleCode() {
+template <CallType C>
+PyCodeObject* getCode();
+
+template <>
+PyCodeObject* getCode<CallType::PyModuleCall>() {
   static auto module_call_code = []() {
     pybind11::gil_scoped_acquire gil;
     auto res = py::module::import("torch.nn")
@@ -75,14 +79,21 @@ PyCodeObject* nnModuleCode() {
     return (PyCodeObject*)res;
   }();
   return module_call_code;
-}
-
-template <CallType C>
-PyCodeObject* getCode();
+};
 
 template <>
-PyCodeObject* getCode<CallType::PyModuleCall>() {
-  return nnModuleCode();
+PyCodeObject* getCode<CallType::PyOptimizerCall>() {
+  static auto optimizer_step_code = []() {
+    pybind11::gil_scoped_acquire gil;
+    auto res = py::module::import("torch.optim")
+                   .attr("Optimizer")
+                   .attr("_optimizer_step_code")
+                   .attr("__code__")
+                   .ptr();
+    TORCH_INTERNAL_ASSERT(PyCode_Check(res));
+    return (PyCodeObject*)res;
+  }();
+  return optimizer_step_code;
 };
 
 } // namespace
@@ -215,6 +226,25 @@ struct Config<CallType::PyCCall> {
   static constexpr EventType event_type = EventType::PyCCall;
 };
 
+template <>
+struct Config<CallType::PyOptimizerCall> {
+  using key_t = PyOptimizerSelf;
+  using cls_t = PyOptimizerCls;
+  using ephemeral_t = PyFrameObject*;
+  struct info_t {
+    cls_t cls_;
+    std::vector<void*> params_;
+    std::vector<std::pair<std::string, void*>> states_;
+  };
+  struct cache_t {
+    c10::optional<CodeLocation>
+        location_; // optim.Optimizer._optimizer_step_code;
+    ska::flat_hash_map<key_t, info_t> optimizer_data_;
+    ska::flat_hash_map<cls_t, at::StringView> cls_names_;
+  };
+  static constexpr EventType event_type = EventType::PyCall;
+};
+
 // ============================================================================
 // == Callsite & ValueCache: Storage during profiling =========================
 // ============================================================================
@@ -245,6 +275,7 @@ class Callsite {
 using PyCallKey = Config<CallType::PyCall>::key_t;
 using PyModuleCallKey = Config<CallType::PyModuleCall>::key_t;
 using PyCCallKey = Config<CallType::PyCCall>::key_t;
+using PyOptimizerCallKey = Config<CallType::PyOptimizerCall>::key_t;
 
 class ValueCache {
  public:
@@ -254,11 +285,11 @@ class ValueCache {
   template <CallType C>
   auto load(const Callsite<C>& callsite, size_t python_tid) const {
     auto caller = load<CallType::PyCall>(callsite.caller_);
-    TORCH_INTERNAL_ASSERT(!caller.second.has_value());
+    TORCH_INTERNAL_ASSERT(!caller.module_info_.has_value());
     return ExtraFields<Config<C>::event_type>{
         /*end_time_ns=*/std::numeric_limits<time_t>::min(),
         python_tid,
-        caller.first,
+        caller.frame_state_,
         load<C>(callsite.value_)};
   }
 
@@ -354,6 +385,39 @@ ExtraFields<EventType::PyCall>::args_t ValueCache::load<CallType::PyModuleCall>(
           cache.cls_names_.at(cls),
           cache.modules_and_params_.at(key).second}};
 }
+template <>
+void ValueCache::store<CallType::PyOptimizerCall>(
+    const PyOptimizerCallKey& key,
+    Config<CallType::PyOptimizerCall>::ephemeral_t frame) {
+  auto& cache = std::get<CallType::PyOptimizerCall>(state_);
+  if (C10_UNLIKELY(
+          cache.optimizer_data_.find(key) == cache.optimizer_data_.end())) {
+    auto cls = set_class<CallType::PyOptimizerCall>(this, cache, key, frame);
+    py::list param_groups_handle =
+        py::handle((PyObject*)key).attr("param_groups");
+    std::vector<void*> params_;
+    std::vector<std::pair<std::string, void*>> states_;
+
+    cache.optimizer_data_[key] = {cls, params_, states_};
+  }
+}
+
+template <>
+ExtraFields<EventType::PyCall>::args_t ValueCache::load<
+    CallType::PyOptimizerCall>(const PyOptimizerCallKey& key) const {
+  auto& cache = std::get<CallType::PyOptimizerCall>(state_);
+  auto cls = cache.optimizer_data_.at(key).cls_;
+  auto frame_state = std::get<CallType::PyCall>(state_).at(*cache.location_);
+  return {
+      frame_state,
+      c10::nullopt,
+      OptimizerInfo{
+          key,
+          cls,
+          cache.cls_names_.at(cls),
+          cache.optimizer_data_.at(key).params_,
+          cache.optimizer_data_.at(key).states_}};
+}
 
 template <>
 void ValueCache::store<CallType::PyCCall>(
@@ -565,13 +629,16 @@ class PythonTracer final : public python_tracer::PythonTracerBase {
 
   torch::profiler::impl::RecordQueue* queue_;
   PyCodeObject* module_call_code_;
+  PyCodeObject* optimizer_hook_;
 
   std::deque<ThreadLocalResults> thread_local_results_;
   ValueCache value_cache_;
 };
 
 PythonTracer::PythonTracer(torch::profiler::impl::RecordQueue* queue)
-    : queue_(queue), module_call_code_(nnModuleCode()) {
+    : queue_(queue),
+      module_call_code_(getCode<CallType::PyModuleCall>()),
+      optimizer_hook_(getCode<CallType::PyOptimizerCall>()) {
   TORCH_CHECK(queue_ != nullptr);
 
   bool expected{false};
@@ -682,6 +749,14 @@ void PythonTracer::recordPyCall(ThreadLocalResults& tls, PyFrameObject* frame) {
       TORCH_INTERNAL_ASSERT(back != nullptr);
       return tls.intern<CallType::PyModuleCall, E>(
           frame, self.get(), back.get());
+    } else if (code.get() == optimizer_hook_) {
+      auto locals = THPObjectPtr(PyFrame_GetLocals(frame));
+      auto self = THPObjectPtr(PyDict_GetItemString(locals, "self"));
+      Py_INCREF(self.get());
+      auto back = THPFrameObjectPtr(PyFrame_GetBack(frame));
+      TORCH_INTERNAL_ASSERT(back != nullptr);
+      return tls.intern<CallType::PyOptimizerCall, E>(
+          frame, self.get(), back.get());
     } else {
       auto back = THPFrameObjectPtr(PyFrame_GetBack(frame));
       auto f_back = (back.get() != nullptr) ? back.get() : frame;
diff --git a/torch/csrc/profiler/collection.h b/torch/csrc/profiler/collection.h
index 59ba446b9e8eb..447f4a9a1cd72 100644
--- a/torch/csrc/profiler/collection.h
+++ b/torch/csrc/profiler/collection.h
@@ -219,6 +219,8 @@ using strong_t = strong::
 using PyModuleSelf = strong_t<PyObject*, struct PyModuleSelf_>;
 using PyModuleCls = strong_t<PyObject*, struct PyModuleCls_>;
 using PyMethod = strong_t</*PyMethodDef*/ void*, struct PyMethod_>;
+using PyOptimizerSelf = strong_t<PyObject*, struct PyOptSelf_>;
+using PyOptimizerCls = strong_t<PyObject*, struct PyOptimizer_>;
 
 struct NNModuleInfo {
   PyModuleSelf self_;
@@ -230,6 +232,15 @@ struct NNModuleInfo {
   size_t id_{std::numeric_limits<size_t>::max()};
 };
 
+struct OptimizerInfo {
+  PyOptimizerSelf self_;
+  PyOptimizerCls opt_;
+  at::StringView opt_name_;
+
+  std::vector<void*> params_addr_;
+  std::vector<std::pair<std::string, void*>> opt_state_;
+};
+
 struct PyExtraFieldsBase {
   PyExtraFieldsBase(time_t end_time_ns, size_t python_tid, PyFrameState caller)
       : end_time_ns_{end_time_ns}, python_tid_{python_tid}, caller_{caller} {}
@@ -244,7 +255,11 @@ struct PyExtraFieldsBase {
 
 template <>
 struct ExtraFields<EventType::PyCall> : public PyExtraFieldsBase {
-  using args_t = std::pair<PyFrameState, c10::optional<NNModuleInfo>>;
+  using args_t = struct {
+    PyFrameState frame_state_;
+    c10::optional<NNModuleInfo> module_info_;
+    c10::optional<OptimizerInfo> opt_info_;
+  };
 
   ExtraFields(
       time_t end_time_ns,
@@ -252,11 +267,13 @@ struct ExtraFields<EventType::PyCall> : public PyExtraFieldsBase {
       PyFrameState caller,
       args_t args)
       : PyExtraFieldsBase(end_time_ns, python_tid, caller),
-        callsite_{args.first},
-        module_{args.second} {}
+        callsite_{args.frame_state_},
+        module_{args.module_info_},
+        opt_{args.opt_info_} {}
 
   PyFrameState callsite_;
   c10::optional<NNModuleInfo> module_;
+  c10::optional<OptimizerInfo> opt_;
 };
 
 template <>
diff --git a/torch/csrc/profiler/python/init.cpp b/torch/csrc/profiler/python/init.cpp
index 20630600bd5e9..724098b8ebbdd 100644
--- a/torch/csrc/profiler/python/init.cpp
+++ b/torch/csrc/profiler/python/init.cpp
@@ -174,8 +174,13 @@ void initPythonBindings(PyObject* module) {
       .def_property_readonly(
           "cls_name", [](const NNModuleInfo& s) { return s.cls_name_.str(); });
 
+  py::class_<OptimizerInfo>(m, "_OptInfo")
+      .def_property_readonly("self", [](const OptimizerInfo& a) {
+        return reinterpret_cast<intptr_t>(a.self_.value_of());
+      });
+
   py::class_<ExtraFields<EventType::PyCall>>(m, "_ExtraFields_PyCall")
-      .def_readonly("module", &ExtraFields<EventType::PyCall>::module_)
+      .def_readonly("opt", &ExtraFields<EventType::PyCall>::opt_)
       .def_readonly("callsite", &ExtraFields<EventType::PyCall>::callsite_)
       .def_readonly("caller", &ExtraFields<EventType::PyCall>::caller_)
       .def_readonly("module", &ExtraFields<EventType::PyCall>::module_);
diff --git a/torch/optim/optimizer.py b/torch/optim/optimizer.py
index 7b32603babac6..9422b3b0f94d1 100644
--- a/torch/optim/optimizer.py
+++ b/torch/optim/optimizer.py
@@ -114,6 +114,19 @@ def _cuda_graph_capture_health_check(self):
                       "instance, capturable=True can impair performance, and you should set capturable=False.")
                 self._warned_capturable_if_run_uncaptured = True
 
+    def _optimizer_step_code(self):
+        """Entry point for `torch.profile.profiler`.
+
+        When python tracing is enabled the profiler will hook into this
+        function at the CPython level to inspect the optimizer's parameters and
+        param groups. It is called it after `step()` since many optimizers
+        lazily initialize state.
+
+        This is a workaround due to lack of a proper step hook on the optimizer,
+        and will be removed if it exists.
+        """
+        pass
+
     def _hook_for_profile(self):
         self._zero_grad_profile_name = "Optimizer.zero_grad#{}.zero_grad".format(self.__class__.__name__)
 
@@ -124,7 +137,10 @@ def wrapper(*args, **kwargs):
                 obj, *_ = args
                 profile_name = "Optimizer.step#{}.step".format(obj.__class__.__name__)
                 with torch.autograd.profiler.record_function(profile_name):
-                    return func(*args, **kwargs)
+                    out = func(*args, **kwargs)
+                    obj._optimizer_step_code()
+                    return out
+
             return wrapper
 
         hooked = getattr(self.__class__.step, "hooked", None)