diff --git a/tests/conftest.py b/tests/conftest.py
index 692fe7e8..f75cda95 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -8,6 +8,7 @@
 
 @pytest.fixture(autouse=True)
 def run_around_tests():
+    torch._dynamo.config.inline_inbuilt_nn_modules = False
     torch.manual_seed(0)
     yield
     torch._dynamo.reset()
diff --git a/tt_torch/dynamo/passes.py b/tt_torch/dynamo/passes.py
index 312513ea..3fc345f2 100644
--- a/tt_torch/dynamo/passes.py
+++ b/tt_torch/dynamo/passes.py
@@ -77,19 +77,35 @@ def apply_decompositions(
     if decompositions is None:
         return gm
 
-    gm = make_fx(
-        functionalize(gm),
-        decomposition_table=decompositions,
-    )(*example_inputs)
+    with torch.no_grad():
+        decompositions = get_decompositions(decompose_ops)
+        fake_tensor_mode = torch._dynamo.utils.detect_fake_mode(example_inputs)
+        fake_tensor_mode.allow_non_fake_inputs = True
+        gm = make_fx(
+            gm,
+            tracing_mode="symbolic",
+            _allow_non_fake_inputs=True,
+            decomposition_table=decompositions,
+        )(*example_inputs)
 
     return gm
 
 
+def bypass_redundant_getitem(gm):
+    for node in gm.graph.nodes:
+        if node.op == "call_function" and "getitem" in node.name:
+            if isinstance(node.args[0], tuple):
+                idx = node.args[1]
+                if isinstance(idx, int):
+                    node.replace_all_uses_with(node.args[0][idx])
+    return gm
+
+
 def constant_fold(gm, example_inputs):
     gm = const_fold.split_const_subgraphs(gm)
+    gm.run_folding()
+    graph_constants = []
 
-    # run the module to generate the consteval constants
-    _ = gm(*example_inputs)
     for node in gm.graph.nodes:
         if node.op == "get_attr" and node.name == "_fx_const_folded_attrs":
             gm.graph.inserting_before(node)
@@ -97,14 +113,20 @@ def constant_fold(gm, example_inputs):
             if isinstance(gm._FX_CONST_FOLDED_ATTRS, torch.Tensor):
                 placeholder = gm.graph.placeholder(f"_fx_const_folded_attrs")
                 node.replace_all_uses_with(placeholder)
+                if len(gm._FX_CONST_FOLDED_ATTRS.data.shape) == 0:
+                    graph_constants = (
+                        gm._FX_CONST_FOLDED_ATTRS.data
+                    ) = gm._FX_CONST_FOLDED_ATTRS.data.unsqueeze(0)
                 graph_constants = [gm._FX_CONST_FOLDED_ATTRS.data]
             else:
-                for idx, (key, value) in enumerate(node.users.items()):
+                for idx, key in enumerate(node.users.keys()):
                     placeholder = gm.graph.placeholder(f"_fx_const_folded_attrs_{idx}")
                     key.replace_all_uses_with(placeholder)
+                for param in gm._FX_CONST_FOLDED_ATTRS:
+                    if len(param.data.shape) == 0:
+                        param.data = param.data.unsqueeze(0)
                 graph_constants = [param.data for param in gm._FX_CONST_FOLDED_ATTRS]
 
-    gm.graph.eliminate_dead_code()
     return gm, graph_constants
 
 
@@ -115,6 +137,7 @@ def pass_pipeline(gm: torch.fx.GraphModule, example_inputs, compiler_config):
         gm, graph_constants = constant_fold(gm, example_inputs)
     else:
         graph_constants = []
+    gm = bypass_redundant_getitem(gm)
     reduce_graph(gm)
     run_shape_prop(gm, example_inputs + graph_constants)
     return gm, graph_constants