Incorporate latest changes in the shark_dynamo backend.

shark/examples/shark_dynamo/basic_examples.py

@@ -1,6 +1,6 @@
-import torchdynamo
 import torch
 import torch_mlir
+import torch._dynamo as torchdynamo
 from shark.sharkdynamo.utils import make_shark_compiler
 
 

shark/sharkdynamo/utils.py

@@ -3,7 +3,7 @@ import time
 from typing import List, Optional
 import torch
 from torch.fx.experimental.proxy_tensor import make_fx
-from functorch._src.compile_utils import strip_overloads
+from torch._functorch.compile_utils import strip_overloads
 from shark.shark_inference import SharkInference
 from torch._decomp import get_decompositions
 
@@ -119,14 +119,19 @@ def make_shark_compiler(use_tracing: bool, device: str, verbose=False):
             example_inputs,
             output_type=torch_mlir.OutputType.LINALG_ON_TENSORS,
         )
+        import io
+
+        bytecode_stream = io.BytesIO()
+        linalg_module.operation.write_bytecode(bytecode_stream)
+        mlir_module = bytecode_stream.getvalue()
 
         shark_module = SharkInference(
-            linalg_module, "forward", mlir_dialect="linalg", device=device
+            mlir_module, mlir_dialect="linalg", device=device
         )
         shark_module.compile()
 
         def forward(*inputs):
-            result = shark_module.forward(inputs)
+            result = shark_module("forward", inputs)
             result = tuple() if result is None else result
             return (result,) if was_unwrapped else result