[SR] Make sigrid_transforms fusion work on graph outputs

Pull Request resolved: #73091

This is a re-work of D33669034; the change was backed out due to a data race causing crashes.

The `output_types` vector was the culprit. It was previously lazily initialized on the first iteration. This was problematic because of static runtime's hidden assumption that ops are thread-safe.

The re-work now only does the list unpack fusion if the output dtypes can be statically determined, e.g. if the sigrid transforms instance and `use_offsets` are both constant. Note that this is true for all the models we care about. Also, we were already partially making this assumption by dereferencing the `std::optional` sigrid transforms instance in most of the ops. Another advantage of this is that it makes the code simpler compared to D33669034.

Once the output types are determined, they can be moved into the op lambda and shared as read-only data.
ghstack-source-id: 149756644

Differential Revision: [D34290401](https://our.internmc.facebook.com/intern/diff/D34290401/)

**NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D34290401/)!

Author: mikeiovine

torch/csrc/jit/runtime/static/passes.cpp

@@ -718,8 +718,40 @@ void EliminateTrivialEquallySplit(std::shared_ptr<torch::jit::Graph>& graph) {
   }
 }
 
-// NB: The alias type of the fused op needs to be changed to
-// c10::AliasAnalysisKind::PURE_FUNCTION to make alias analysis work.
+namespace {
+
+bool shouldNotFuseListUnpackSpecialCase(Node* node) {
+  const static std::array<c10::Symbol, 3> sigrid_transforms_symbols{
+      c10::Symbol::fromQualString("fb::variadic_sigrid_transforms_torch_bind"),
+      c10::Symbol::fromQualString("fb::sigrid_transforms_torch_bind"),
+      c10::Symbol::fromQualString("fb::sigrid_transforms")};
+
+  if (std::find(
+          sigrid_transforms_symbols.begin(),
+          sigrid_transforms_symbols.end(),
+          node->kind()) == sigrid_transforms_symbols.end()) {
+    return false;
+  }
+
+  // To fuse with sigrid transforms, we must be able to statically determine
+  // `instance` and `use_offsets` - these two together let us statically
+  // determine the types of the outputs. Rationale: it is a huge pain to write
+  // fused sigrid transforms without static type information, and these two
+  // arguments are indeed statically known in every model we've seen.
+  // The reason why trying to fuse the outputs is annoying without static type
+  // information is that, if one of the outputs is not managed, you need to
+  // reset to an empty tensor of the correct type each iteration. So, if we
+  // can't collect types ahead of time, we would have to do it lazily on the
+  // first iteration, which would could be wasteful in terms of time/memory
+  // - either each thread would have its own set of output types, or we would
+  // need a lock to prevent data races.
+  const auto num_inputs = node->inputs().size();
+  return !toIValue(node->input(0)).has_value() ||
+      !toIValue(node->input(num_inputs - 1)).has_value();
+}
+
+} // namespace
+
 void FuseListUnpack(std::shared_ptr<torch::jit::Graph>& graph) {
   const FastMap<c10::Symbol, c10::Symbol> unfused_to_fused = {
       OP_PAIR("fb::equally_split", "static_runtime::fused_equally_split"),
@@ -744,12 +776,7 @@ void FuseListUnpack(std::shared_ptr<torch::jit::Graph>& graph) {
           "fb::gather_ranges_to_dense_v2",
           "static_runtime::fused_gather_ranges_to_dense_v2")};
 
-  AliasDb alias_db(
-      graph,
-      /*isFrozen=*/false);
   // replacement contains (old_node, new_node, list_unpack_node)
-  const std::vector<Value*> graph_outputs(
-      graph->outputs().begin(), graph->outputs().end());
   std::vector<std::tuple<Node*, Node*, Node*>> replacement;
   DepthFirstGraphNodeIterator graph_it(graph);
   for (auto node = graph_it.next(); node != nullptr; node = graph_it.next()) {
@@ -773,20 +800,8 @@ void FuseListUnpack(std::shared_ptr<torch::jit::Graph>& graph) {
       continue;
     }
 
-    const bool checks_all_outputs =
-        node->kind() == fromQualString("fb::equally_split") ||
-        node->kind() == fromQualString("fb::gather_ranges_to_dense") ||
-        node->kind() == fromQualString("fb::gather_ranges_to_dense_v2");
-
-    if (!checks_all_outputs) {
-      // If any output of the ListUnpack node is unmanaged, disable fusion
-      // since the fused op assumes all outputs are either managed or not.
-      // Ops excluded here check all outputs.
-      const std::vector<Value*> list_unpack_outputs_vec(
-          list_unpack_outputs.begin(), list_unpack_outputs.end());
-      if (alias_db.mayContainAlias(list_unpack_outputs_vec, graph_outputs)) {
-        continue;
-      }
+    if (shouldNotFuseListUnpackSpecialCase(node)) {
+      continue;
     }
 
     const auto& new_sym = unfused_to_fused_it->second;
@@ -813,12 +828,6 @@ void FuseListUnpack(std::shared_ptr<torch::jit::Graph>& graph) {
     list_unpack_node->destroy();
     old_node->destroy();
   }
-
-#ifndef NDEBUG
-  graph->lint();
-  AliasDb db2(graph);
-  torch::jit::Lint(&db2);
-#endif
 } // namespace jit
 
 void EnableStaticRuntimeLayerNorm(std::shared_ptr<torch::jit::Graph>& graph) {