[ET-VK] Add support for binary symint ops

Pull Request resolved: #11257

## Changes

* Add an implementation for binary operators which add symbolic integers.

## Motivation

Support executing llama models with dynamic shapes. This operator shows up when exporting with dynamic shapes.
ghstack-source-id: 287234982
@exported-using-ghexport

Differential Revision: [D75238029](https://our.internmc.facebook.com/intern/diff/D75238029/)

Author: SS-JIA

backends/vulkan/_passes/tag_memory_meta_pass.py

@@ -22,6 +22,7 @@
 from executorch.exir.dialects._ops import ops as exir_ops
 
 from executorch.exir.pass_base import ExportPass, PassResult
+from executorch.exir.tensor import TensorSpec
 
 logger: logging.Logger = logging.getLogger("")
 logger.setLevel(logging.INFO)
@@ -51,8 +52,12 @@ def insert_transition_node(
             exir_ops.edge.aten.clone.default,
             (arg,),
         )
+        print(arg)
+        print(arg.meta["val"])
+        print(arg.meta["spec"])
         clone_node.meta["val"] = arg.meta["val"]
-        clone_node.meta["spec"] = deepcopy(arg.meta["spec"])
+        # clone_node.meta["spec"] = deepcopy(arg.meta["spec"])
+        clone_node.meta["spec"] = TensorSpec.from_tensor(clone_node.meta["val"])
         clone_node.meta["spec"].const = False
         set_memory_metadata(clone_node, storage, layout)
         arg.replace_all_uses_with(clone_node, lambda x, y=node: x == y)

backends/vulkan/op_registry.py

@@ -230,6 +230,7 @@ def update_features_impl(op: OpKey):
         exir_ops.edge.quantized_decomposed.dequantize_per_channel.default,
         # Symbolic integer ops
         torch.ops.aten.sym_size.int,
+        operator.add,
     ]
 )
 def register_ephemeral_op(features: OpFeatures):

backends/vulkan/runtime/graph/ops/impl/SymIntOps.cpp

@@ -11,19 +11,27 @@
 
 namespace vkcompute {
 
+//
+// sym_size
+//
+
+void sym_size_impl(ComputeGraph* graph, const std::vector<ValueRef>& args) {
+  ValueRef in_tensor = args[0];
+  ValueRef dim = args[1];
+  ValueRef out_symint = args[2];
+
+  int64_t dim_val = graph->extract_scalar<int64_t>(dim);
+  int64_t size_at_dim = graph->size_at<int64_t>(dim_val, in_tensor);
+
+  graph->set_symint(out_symint, static_cast<int32_t>(size_at_dim));
+}
+
 void resize_sym_size_node(
     ComputeGraph* graph,
     const std::vector<ArgGroup>& args,
     const std::vector<ValueRef>& extra_args) {
   (void)args; // Unused parameter
-
-  ValueRef out_symint_ref = extra_args[0];
-  ValueRef in_tensor_ref = extra_args[1];
-
-  int64_t dim = graph->extract_scalar<int64_t>(extra_args[2]);
-  int64_t size_at_dim = graph->size_at<int64_t>(dim, in_tensor_ref);
-
-  graph->set_symint(out_symint_ref, static_cast<int32_t>(size_at_dim));
+  sym_size_impl(graph, extra_args);
 }
 
 /*
@@ -32,21 +40,50 @@ void resize_sym_size_node(
  * specified dimension.
  */
 void sym_size_int(ComputeGraph& graph, const std::vector<ValueRef>& args) {
-  ValueRef in_tensor = args[0];
-  ValueRef dim = args[1];
-  ValueRef out_symint = args[2];
+  sym_size_impl(&graph, args);
 
-  int64_t dim_val = graph.extract_scalar<int64_t>(dim);
+  graph.execute_nodes().emplace_back(
+      new ExecuteNode(resize_sym_size_node, args));
+}
+
+//
+// binary operators
+//
+
+void sym_add_impl(ComputeGraph* graph, const std::vector<ValueRef>& args) {
+  ValueRef a = args[0];
+  ValueRef b = args[1];
+  ValueRef out = args[2];
+
+  int32_t a_val = graph->read_symint(a);
+  int32_t b_val = graph->read_symint(b);
+  int32_t result = a_val + b_val;
+
+  graph->set_symint(out, result);
+}
+
+void resize_sym_add_node(
+    ComputeGraph* graph,
+    const std::vector<ArgGroup>& args,
+    const std::vector<ValueRef>& extra_args) {
+  (void)args; // Unused parameter
+  sym_add_impl(graph, extra_args);
+}
 
-  int64_t size_at_dim = graph.size_at<int64_t>(dim_val, in_tensor);
-  graph.set_symint(out_symint, static_cast<int32_t>(size_at_dim));
+/*
+ * This operator takes two symints as inputs and produces a symint as output.
+ * The output symint's value is the sum of the two input symints.
+ */
+void sym_add(ComputeGraph& graph, const std::vector<ValueRef>& args) {
+  sym_add_impl(&graph, args);
 
   graph.execute_nodes().emplace_back(
-      new ExecuteNode(resize_sym_size_node, {out_symint, in_tensor, dim}));
+      new ExecuteNode(resize_sym_add_node, args));
 }
 
 REGISTER_OPERATORS {
   VK_REGISTER_OP(sym_size.int, sym_size_int);
+  VK_REGISTER_OP(add, sym_add);
 }
 
 } // namespace vkcompute

backends/vulkan/serialization/vulkan_graph_builder.py

@@ -340,17 +340,21 @@ def process_call_function_node(self, node) -> None:
 
         self.seen_ops.add(node.target)
 
-        for i, schema_arg in enumerate(node.target._schema.arguments):
-            if not schema_arg.kwarg_only and i < len(node.args):
-                function_arg = node.args[i]
-            elif schema_arg.name in node.kwargs:
-                function_arg = node.kwargs[schema_arg.name]
-            else:
-                function_arg = schema_arg.default_value
-
-            # Create a Value for each function argument. If the argument has been
-            # previously encountered, then use the existing Value id.
-            operator_call_args.append(self.get_or_create_value_for(function_arg))
+        if hasattr(node.target, "_schema"):
+            for i, schema_arg in enumerate(node.target._schema.arguments):
+                if not schema_arg.kwarg_only and i < len(node.args):
+                    function_arg = node.args[i]
+                elif schema_arg.name in node.kwargs:
+                    function_arg = node.kwargs[schema_arg.name]
+                else:
+                    function_arg = schema_arg.default_value
+
+                # Create a Value for each function argument. If the argument has been
+                # previously encountered, then use the existing Value id.
+                operator_call_args.append(self.get_or_create_value_for(function_arg))
+        else:
+            for i, arg_node in enumerate(node.args):
+                operator_call_args.append(self.get_or_create_value_for(arg_node))
 
         # Add output node
         operator_call_args.append(self.create_node_value(node))