Arm backend: Merge RetraceFoldedDtypesPass into FoldAndAnnotateQParam… (#15377)

The pass RetraceFoldedDtypesPass carries out extra processing after the
output of FoldAndAnnotateQParamsPass, meaning that they are tightly
coupled and always run in sequence.

Merge these two passes together into FoldAndAnnotateQParamsPass.

### Test plan
No behavior is modified. Tests already in place are sufficient.

Signed-off-by: Martin Lindström <Martin.Lindstroem@arm.com>

Author: martinlsm

backends/arm/_passes/__init__.py

@@ -72,7 +72,6 @@
 from .fold_qdq_with_annotated_qparams_pass import (  # noqa
     FoldAndAnnotateQParamsPass,
     QuantizeOperatorArguments,
-    RetraceFoldedDtypesPass,
 )
 from .fuse_batchnorm2d_pass import FuseBatchnorm2DPass  # noqa
 from .fuse_constant_ops_pass import ComputeConstantOpsAOT, FuseConstantArgsPass  # noqa

backends/arm/_passes/arm_pass_manager.py

@@ -88,7 +88,6 @@
     RemoveNoopPass,
     ReplaceInfValues,
     ReplaceScalarWithTensorByProfilePass,
-    RetraceFoldedDtypesPass,
     RewriteConv2dPass,
     RewriteMatmulPass,
     RewriteUpsamplePass,
@@ -176,7 +175,6 @@ def _tosa_INT_pipeline(
         self.add_pass(QuantizeOperatorArguments())
         self.add_pass(ConvertELUParamsPass())
         self.add_pass(FoldAndAnnotateQParamsPass(exported_program))  # type: ignore[call-arg]
-        self.add_pass(RetraceFoldedDtypesPass())
         self.add_pass(UnsqueezeScalarPlaceholdersPass(exported_program))
         self.add_pass(MatchArgRanksPass(exported_program))
         if self.tosa_spec.is_U55_subset:
@@ -271,7 +269,6 @@ def _tosa_FP_pipeline(
         self.add_pass(AnnotateDecomposedMatmulPass())
         self.add_pass(QuantizeOperatorArguments())
         self.add_pass(FoldAndAnnotateQParamsPass(exported_program))  # type: ignore[call-arg]
-        self.add_pass(RetraceFoldedDtypesPass())
         self.add_pass(UnsqueezeScalarPlaceholdersPass(exported_program))
         self.add_pass(MatchArgRanksPass(exported_program))
         self.add_pass(DecomposeAdaptiveAvgPool2dPass())

backends/arm/_passes/fold_qdq_with_annotated_qparams_pass.py

@@ -13,6 +13,7 @@
 from executorch.backends.arm._passes.arm_pass_utils import (
     get_param_tensor,
     is_param_node,
+    set_node_arg,
 )
 from executorch.backends.arm._passes.insert_table_ops import InsertTableOpsPass
 
@@ -22,7 +23,6 @@
 from executorch.exir import ExportedProgram
 
 from executorch.exir.dialects._ops import ops as exir_ops
-from executorch.exir.dialects.edge._ops import EdgeOpOverload
 
 from executorch.exir.pass_base import ExportPass, PassResult
 from torch.fx import GraphModule, Node
@@ -66,38 +66,6 @@ def get_output_qparams(node: Node) -> dict[int, QuantArgs]:
     return output_qparams
 
 
-class RetraceFoldedDtypesPass(ArmPass):
-    """
-    FoldAndAnnotateQParamsPass folds dq and q nodes. When the graph is retraced
-    some operators are retraced to types that cannot be handled by TOSA. One
-    such example is sum.dim_IntList:
-        q (int8) -> dq (fp32) -> sum (fp32) -> q (int8) ...
-    After folding it becomes:
-        q (int8)              -> sum (int64) ->         ...
-    This pass changes types of ops in self.targeted_ops, such as sum, so that
-    the output type of that matches the type of the output_qparams.
-    """
-
-    _passes_required_after: Set[Type[ExportPass]] = set()
-
-    targeted_ops: Set[EdgeOpOverload] = {
-        exir_ops.edge.aten.sum.dim_IntList,
-    }
-
-    def call_operator(self, op, args, kwargs, meta):
-        if op not in self.targeted_ops:
-            return super().call_operator(op, args, kwargs, meta, False)
-
-        node_kwargs = kwargs.copy()
-        output_qparams = meta["output_qparams"]
-        if len(output_qparams) == 0:
-            return super().call_operator(op, args, kwargs, meta, False)
-
-        output_dtype = output_qparams[0].dtype
-        node_kwargs["dtype"] = output_dtype
-        return super().call_operator(op, args, node_kwargs, meta, True)
-
-
 class FoldAndAnnotateQParamsPass(ArmPass):
     """
     A pass that walks the graph and removes any DQ and Q nodes before and after the target
@@ -129,7 +97,6 @@ class FoldAndAnnotateQParamsPass(ArmPass):
     """
 
     _passes_required_after: Set[Type[ExportPass]] = {
-        RetraceFoldedDtypesPass,
         InsertTableOpsPass,
         RemoveNoopPass,
     }
@@ -234,6 +201,16 @@ def call(self, graph_module: GraphModule) -> PassResult:
                 user.replace_all_uses_with(n)
                 graph_module.graph.erase_node(user)
 
+            # Some op(s) contain a "dtype" key in their node kwargs. Set this
+            # to the type of output qparams.
+            output_qparams = n.meta["output_qparams"]
+            if (
+                n.target in {exir_ops.edge.aten.sum.dim_IntList}
+                and len(output_qparams) > 0
+            ):
+                output_dtype = output_qparams[0].dtype
+                set_node_arg(n, "dtype", output_dtype)
+
         # retrace the graph to update the fake tensor types
         graph_module = super().call(graph_module).graph_module