update getting orig graph

py/torch_tensorrt/dynamo/lowering/passes/lower_efficient_attention.py

@@ -5,7 +5,6 @@
 import torch
 from torch_tensorrt.dynamo.lowering.passes.pass_utils import (
     clean_up_graph_after_modifications,
-    get_tensor_placeholders,
 )
 
 logger = logging.getLogger(__name__)
@@ -36,34 +35,13 @@ def efficient_attention_replacement() -> (
 ):
     """Constructs the original and replacement functions for efficient attention"""
 
-    # Empty boilerplate function taking in three Tensors and returning one
-    def boilerplate(
-        query: torch.Tensor, key: torch.Tensor, value: torch.Tensor
-    ) -> torch.Tensor:
-        ...
-
-    # Trace boilerplate function and extract placeholder and output nodes
-    orig = torch.fx.symbolic_trace(boilerplate)
-    q, k, v = get_tensor_placeholders(orig)
-    output = [node for node in orig.graph.nodes if node.op == "output"][0]
-
-    # Graph types to replace are those which use the _scaled_dot_product_efficient_attention
-    # function and extract only the first element
-    with orig.graph.inserting_before(output):
-        att = orig.graph.call_function(
-            torch.ops.aten._scaled_dot_product_efficient_attention.default,
-            args=(q, k, v, None, False),
+    # Original graph
+    def orig(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor:
+        outputs = torch.ops.aten._scaled_dot_product_efficient_attention.default(
+            q, k, v, None, False
         )
-        out = orig.graph.call_function(
-            operator.getitem,
-            args=(att, 0),
-        )
-
-    # Assign the output of the graph to be the single getitem output
-    output.args = (out,)
-
-    orig.graph.lint()
-    orig.recompile()
+        out = operator.getitem(outputs, 0)
+        return out
 
     # Replacement graph consists of the functional version of scaled_dot_product_attention
     def replacement(