Introduce apply_torch_ops_passes to test mul fusion e2e (pytorch#11741)

Summary:

This diff 
1) extends export_to_edge() with apply_torch_ops_passes() call
We need this to grab the actual mul argument value and fusing in later passes. Otherwise, the constant gets lifted by _lower_ep_to_edge() 
2) implements e2e mul fusion test.
We test that both ReplaceMulTensorWithMulAndFullOpsPass() and FuseMulTensorIntoQuantPass() passes get applied correctly and which results into mul.Tensor being removed completely.

Reviewed By: hsharma35

Differential Revision: D76469613

Author: eigen-k

backends/cadence/aot/compiler.py

@@ -8,7 +8,7 @@
 
 import logging
 from pathlib import Path
-from typing import Callable, cast, Optional
+from typing import Optional
 
 import executorch.backends.cadence.aot.ops_registrations  # noqa
 import torch
@@ -32,7 +32,6 @@
     ExecutorchBackendConfig,
     ExecutorchProgramManager,
 )
-from executorch.exir.pass_base import PassResult
 from executorch.exir.passes import ToOutVarPass
 from executorch.exir.passes.sym_shape_eval_pass import HintBasedSymShapeEvalPass
 from executorch.exir.program._program import to_edge_with_preserved_ops
@@ -41,7 +40,7 @@
 from torch.export.exported_program import ExportedProgram
 from torchao.quantization.pt2e.quantize_pt2e import convert_pt2e, prepare_pt2e
 
-from .passes import get_cadence_passes
+from .passes import apply_exir_ops_passes, apply_torch_ops_passes
 
 from .utils import print_ops_info
 
@@ -256,14 +255,18 @@ def export_to_edge(
     inputs: tuple[object, ...],
     dump_graphs: bool = False,
     constant_methods: Optional[dict[str, object]] = None,
+    core_aten_exceptions: Optional[list[torch._ops.OpOverload]] = None,
 ) -> EdgeProgramManager:
     assert isinstance(model, torch.nn.Module), "model should be an nn.Module"
 
     # Export the model into an ExportedProgram.
     expo_program = trace(model, inputs)
 
+    # Apply passes which transform the ExportedProgram before it gets lowered to edge.
+    expo_program = apply_torch_ops_passes(expo_program)
+
     # Lower the model to edge IR.
-    edge_prog_manager = _lower_ep_to_edge(expo_program, dump_graphs, constant_methods)
+    edge_prog_manager = _lower_ep_to_edge(expo_program, dump_graphs, constant_methods, core_aten_exceptions)
 
     return edge_prog_manager
 
@@ -305,14 +308,7 @@ def _lower_ep_to_cadence(
     Lower an existing ExportedProgram to edge IR and apply frontend optimization passes.
     """
     edge_prog_manager = _lower_ep_to_edge(program, dump_graphs=dump_graphs)
-    cadence_passes = get_cadence_passes(opt_level)
-
-    # Run a couple required passes for quant/dequant ops
-    cadence_prog_manager = edge_prog_manager.transform(
-        cast(
-            list[Callable[[torch.fx.GraphModule], Optional[PassResult]]], cadence_passes
-        )
-    )
+    cadence_prog_manager = apply_exir_ops_passes(opt_level, edge_prog_manager)
     return cadence_prog_manager
 
 
@@ -323,14 +319,7 @@ def export_to_cadence(
     opt_level: int = 1,
 ) -> EdgeProgramManager:
     edge_prog_manager = export_to_edge(model, inputs, dump_graphs=dump_graphs)
-    cadence_passes = get_cadence_passes(opt_level)
-
-    # Run a couple required passes for quant/dequant ops
-    cadence_prog_manager = edge_prog_manager.transform(
-        cast(
-            list[Callable[[torch.fx.GraphModule], Optional[PassResult]]], cadence_passes
-        )
-    )
+    cadence_prog_manager = apply_exir_ops_passes(opt_level, edge_prog_manager)
     return cadence_prog_manager
 
 
@@ -367,15 +356,8 @@ def export_to_executorch_gen_etrecord(
     memory_config: Optional[MemoryConfig] = None,
     dump_graphs: bool = False,
 ) -> ExecutorchProgramManager:
-    cadence_passes = get_cadence_passes(opt_level)
     edge_prog_manager = export_to_edge(model, inputs, dump_graphs)
-
-    # Run a couple required passes for quant/dequant ops
-    cadence_prog_manager = edge_prog_manager.transform(
-        cast(
-            list[Callable[[torch.fx.GraphModule], Optional[PassResult]]], cadence_passes
-        )
-    )
+    cadence_prog_manager = apply_exir_ops_passes(opt_level, edge_prog_manager)
 
     # Print some information to terminal
     print_ops_info(

backends/cadence/aot/fuse_ops.py

@@ -1127,6 +1127,7 @@ class CadenceFuseOpsInGraph:
         FuseCascadedTransposeOrPermuteOps,
         FuseCascadedViewOps,
         FuseQuantDequantToRequantizePass,
+        FuseMulTensorIntoQuantPass,
         FuseMulTensorIntoDequantPass,
         FuseMulScalarIntoDequantPass,
         FuseFullThenReshapePass,

backends/cadence/aot/passes.py

@@ -6,7 +6,7 @@
 
 # pyre-strict
 
-from typing import Any, List, Optional
+from typing import Any, Callable, cast, List, Optional
 
 import torch
 import torch.fx
@@ -28,13 +28,18 @@
     RemoveRedundantOps,
 )
 from executorch.backends.cadence.aot.reorder_ops import CadenceReorderOpsInGraph
-from executorch.backends.cadence.aot.replace_ops import CadenceReplaceOpsInGraph
+from executorch.backends.cadence.aot.replace_ops import (
+    CadenceReplaceOpsInGraph,
+    ReplaceMulTensorWithMulAndFullOpsPass,
+)
 from executorch.backends.cadence.aot.simplify_ops import CadenceSimplifyOpsInGraph
+from executorch.exir import EdgeProgramManager
 from executorch.exir.pass_base import ExportPass, PassResult
 from executorch.exir.pass_manager import PassManager, PassType
 from executorch.exir.passes import dead_code_elimination_pass
 from executorch.exir.passes.scalar_to_tensor_pass import ScalarToTensorPass
 from executorch.exir.passes.spec_prop_pass import SpecPropPass
+from torch.export.exported_program import ExportedProgram
 
 
 @register_cadence_pass(CadencePassAttribute(opt_level=0))
@@ -89,14 +94,37 @@ def get_passes_in_default_order() -> List[ExportPass]:
     return pytree.tree_flatten(passes)[0]
 
 
-def get_cadence_passes(
+def apply_exir_ops_passes(
     opt_level: int,
-) -> List[Optional[PassResult]]:
+    edge_prog_manager: EdgeProgramManager,
+) -> EdgeProgramManager:
     passes = get_passes_in_default_order()
     pass_filter = create_cadence_pass_filter(opt_level)
-    filtered_passes = [
-        # pyre-ignore[20]: Expect argument graph_module
-        filtered_pass()
+    cadence_passes = [
+        (
+            lambda graph_module, filtered_pass=filtered_pass: filtered_pass()(
+                graph_module
+            )
+        )
         for filtered_pass in list(filter(pass_filter, passes))
     ]
-    return filtered_passes
+    cadence_prog_manager = edge_prog_manager.transform(
+        cast(
+            list[Callable[[torch.fx.GraphModule], Optional[PassResult]]], cadence_passes
+        )
+    )
+    return cadence_prog_manager
+
+
+def apply_torch_ops_passes(expo_program: ExportedProgram) -> ExportedProgram:
+    """
+    Applies compiler passes on torch.ops IR, including torch.ops.aten, torch.ops.cadence, etc.
+    expo_program is expected to be the output of the torch.export.export().
+    """
+
+    aten_passes: List[Callable[[torch.fx.GraphModule], Optional[PassResult]]] = [
+        ReplaceMulTensorWithMulAndFullOpsPass()
+    ]
+    # TODO(T230417247): Use PassResult which is currently ignored.
+    PassManager(aten_passes)(expo_program.graph_module)
+    return expo_program