Cleanup comments and redundant code.

core/shark_turbine/aot/builtins/jittable.py

@@ -214,11 +214,6 @@ def flat_wrapped_f(*args):
         if "functorch_functionalize" in self._passes:
             transformed_f = functorch_functionalize(transformed_f, *flat_pytorch_args)
 
-        for node in transformed_f.graph.nodes:  # type: ignore
-            if node.op == "call_function":
-                if node.target == torch._ops.ops.aten.lift_fresh_copy.default:
-                    print(f"replaced lift_fresh_copy")
-                    node.target = torch._ops.ops.aten.clone.default
         transformed_f.recompile()  # type: ignore
 
         # Ask dynamo to give us an aten graph.
@@ -233,7 +228,7 @@ def flat_wrapped_f(*args):
         )
         logger.debug("Invoking dynamo trace")
         gm, guards = exported_f(*flat_pytorch_args)
-        logger.debug("Dyanmo trace complete")
+        logger.debug("Dynamo trace complete")
 
         # TODO: Add debug logging for the exported graph module.
         # gm.print_readable()

models/turbine_models/custom_models/sd_inference/unet.py

@@ -101,15 +101,17 @@ def export_unet_model(
     target_triple=None,
     max_alloc=None,
     upload_ir=False,
+    decomp_attn=True,
 ):
     mapper = {}
     decomp_list = DEFAULT_DECOMPOSITIONS
-    decomp_list.extend(
-        [
-            torch.ops.aten._scaled_dot_product_flash_attention_for_cpu,
-            torch.ops.aten._scaled_dot_product_flash_attention.default,
-        ]
-    )
+    if decomp_attn:
+        decomp_list.extend(
+            [
+                torch.ops.aten._scaled_dot_product_flash_attention_for_cpu,
+                torch.ops.aten._scaled_dot_product_flash_attention.default,
+            ]
+        )
     dtype = torch.float16 if precision == "fp16" else torch.float32
     unet_model = unet_model.to(dtype)
     utils.save_external_weights(

models/turbine_models/custom_models/sd_inference/utils.py

@@ -8,12 +8,10 @@
     EulerDiscreteScheduler,
 )
 
-winograd_params = "keys=unet.down_blocks.2.resnets.0.conv2.weight keys=unet.down_blocks.2.resnets.1.conv1.weight keys=unet.down_blocks.2.resnets.1.conv2.weight keys=unet.mid_block.resnets.0.conv1.weight keys=unet.mid_block.resnets.0.conv2.weight keys=unet.mid_block.resnets.1.conv1.weight keys=unet.mid_block.resnets.1.conv2.weight keys=unet.up_blocks.0.resnets.0.conv2.weight keys=unet.up_blocks.0.resnets.1.conv2.weight keys=unet.up_blocks.0.resnets.2.conv2.weight keys=unet.up_blocks.0.resnets.0.conv1.weight keys=unet.up_blocks.0.resnets.1.conv1.weight keys=unet.up_blocks.0.resnets.2.conv1.weight keys=unet.up_blocks.0.upsamplers.0.conv.weight"
 # If flags are verified to work on a specific model and improve performance without regressing numerics, add them to this dictionary. If you are working with bleeding edge flags, please add them manually with the --ireec_flags argument.
 gfx94X_flags = {
     "all": [
         "--iree-global-opt-propagate-transposes=true",
-        "--iree-opt-const-eval=false",
         "--iree-opt-outer-dim-concat=true",
         "--iree-rocm-bc-dir=/opt/rocm/amdgcn/bitcode",
         "--iree-vm-target-truncate-unsupported-floats",
@@ -95,6 +93,7 @@ def compile_to_vmfb(
                 "--iree-hal-target-backends=rocm",
                 "--iree-rocm-target-chip=" + target_triple,
                 "--verify=false",
+                "--iree-opt-const-eval=false",
             ]
         )
     elif device == "cuda":

models/turbine_models/custom_models/sd_inference/vae.py

@@ -116,15 +116,17 @@ def export_vae_model(
     max_alloc=None,
     variant="decode",
     upload_ir=False,
+    decomp_attn=True,
 ):
     mapper = {}
     decomp_list = DEFAULT_DECOMPOSITIONS
-    decomp_list.extend(
-        [
-            torch.ops.aten._scaled_dot_product_flash_attention_for_cpu,
-            torch.ops.aten._scaled_dot_product_flash_attention.default,
-        ]
-    )
+    if decomp_attn:
+        decomp_list.extend(
+            [
+                torch.ops.aten._scaled_dot_product_flash_attention_for_cpu,
+                torch.ops.aten._scaled_dot_product_flash_attention.default,
+            ]
+        )
     dtype = torch.float16 if precision == "fp16" else torch.float32
     vae_model = vae_model.to(dtype)
     utils.save_external_weights(