diff --git a/torchbenchmark/util/backends/torchdynamo.py b/torchbenchmark/util/backends/torchdynamo.py
index a37e65684f..b26a204f4a 100644
--- a/torchbenchmark/util/backends/torchdynamo.py
+++ b/torchbenchmark/util/backends/torchdynamo.py
@@ -184,18 +184,16 @@ def apply_torchdynamo_args(
         if args.quantization:
             import torchao
             from torchao.quantization import (
-                change_linear_weights_to_int4_woqtensors,
-                change_linear_weights_to_int8_dqtensors,
-                change_linear_weights_to_int8_woqtensors,
+                quantize, int8_weight_only, int4_weight_only, int8_dynamic_activation_int8_weight
             )
-            torch._dynamo.epilogue_fusion = False
+            from torchao.utils import unwrap_tensor_subclass
+
             torch._dynamo.config.automatic_dynamic_shapes = False
-            torch._dynamo.config.force_parameter_static_shapes = False
+            torch._inductor.config.force_fuse_int_mm_with_mul = True
+            torch._inductor.config.use_mixed_mm = True
             torch._dynamo.config.cache_size_limit = 10000
             assert "cuda" in model.device
             module, example_inputs = model.get_module()
-            torch._inductor.config.force_fuse_int_mm_with_mul = True
-            torch._inductor.config.use_mixed_mm = True
             if isinstance(example_inputs, tuple([tuple, list])):
                 example_inputs = tuple([
                     x.to(torch.bfloat16)
@@ -209,22 +207,22 @@ def apply_torchdynamo_args(
                     module(**example_inputs)
                 else:
                     module(*example_inputs)
-
             if args.quantization == "int8dynamic":
-                change_linear_weights_to_int8_dqtensors(module)
+                quantize(module, int8_dynamic_activation_int8_weight(), set_inductor_config=False)
             elif args.quantization == "int8weightonly":
-                change_linear_weights_to_int8_woqtensors(module)
+                quantize(module, int8_weight_only(), set_inductor_config=False)
             elif args.quantization == "int4weightonly":
-                change_linear_weights_to_int4_woqtensors(module)
-            elif args.quantization == "autoquant":
-                torchao.autoquant(module, error_on_unseen=False)
+                quantize(module, int4_weight_only(), set_inductor_config=False)
+            if args.quantization == "autoquant":
+                torchao.autoquant(module, error_on_unseen=False, mode=["interpolate", .85], set_inductor_config=False)
                 if isinstance(example_inputs, dict):
                     module(**example_inputs)
                 else:
                     module(*example_inputs)
                 from torchao.quantization.autoquant import AUTOQUANT_CACHE
                 assert len(AUTOQUANT_CACHE)>0, f"Err: found no autoquantizable layers in model {type(module)}, stopping autoquantization"
-
+            else:
+                unwrap_tensor_subclass(module)
 
         if args.freeze_prepack_weights:
             torch._inductor.config.freezing = True