Enable Q/DQ opt with multiple inputs

backends/cadence/aot/compiler_funcs.py

@@ -8,7 +8,8 @@
 
 import logging
 import operator
-from typing import Any, Optional, Union
+from collections.abc import Mapping, Sequence
+from typing import Any, cast, Optional, Union
 
 import torch
 from torch._inductor.decomposition import remove_decompositions
@@ -301,23 +302,27 @@ def __init__(
                 "Warning: Using pre-quantized inputs. This should only be done when calibration has been confirmed."
                 "Incorrect quantization parameters can lead to significant accuracy degradation."
             )
-        if isinstance(input_args, list):
-            self.quant_args = extract_input_quant_params_from_graph(module, input_args)
-        elif isinstance(input_args, dict):
+        if isinstance(input_args, Sequence) and not isinstance(
+            input_args, (str, bytes)
+        ):
+            self.quant_args = extract_input_quant_params_from_graph(
+                module, list(input_args)
+            )
+        elif isinstance(input_args, Mapping):
             # dict[int, QuantArgs] — use directly
             # dict[int, list[str]] — extract quant params from graph, keyed by input index
             first_value = next(iter(input_args.values()), None)
             if (
-                isinstance(first_value, (list, tuple))
+                isinstance(first_value, (list, tuple, Sequence))
+                and not isinstance(first_value, (str, bytes))
                 and first_value
                 and isinstance(first_value[0], str)
             ):
                 # Values are lists of node names: extract quant params and map
                 # to the caller-specified input indices.
                 for input_idx, node_names in input_args.items():
-                    assert isinstance(node_names, list)
                     extracted = extract_input_quant_params_from_graph(
-                        module, node_names
+                        module, list(cast(Sequence[str], node_names))
                     )
                     # Use the first extracted quant params for this input index.
                     if extracted:
@@ -430,6 +435,7 @@ def _get_transparent_ops() -> set[Any]:
         torch.ops.aten.view.default,
         torch.ops.aten.reshape.default,
         torch.ops.aten.split.Tensor,
+        torch.ops.aten.chunk.default,
         torch.ops.aten.slice_copy.Tensor,
         torch.ops.aten.permute_copy.default,
         torch.ops.aten.permute.default,