Dedup delegate blobs in emitter

exir/emit/_emit_program.py

@@ -164,7 +164,6 @@ def emit_program(
             operators=[],
             delegates=[],
             operator_cache={},
-            delegate_cache={},
             emit_stacktrace=emit_stacktrace,
             emit_mutable_buffer_names=emit_mutable_buffer_names,
         )

exir/emit/_emitter.py

@@ -146,8 +146,6 @@ class _EmitterState:
     operators: List[Operator]
     delegates: List[BackendDelegate]
     operator_cache: Dict[Tuple[str, str], int]
-    # delegate_cache: the key is hash(delegated_payload) and the value is the index in delegates
-    delegate_cache: Dict[str, int]
     emit_stacktrace: bool
     emit_mutable_buffer_names: bool
 
@@ -1091,7 +1089,7 @@ def _emit_delegate(
         delegate's blob."""
         processed_bytes = lowered_module.processed_bytes
         hashed = hashlib.sha256(processed_bytes).hexdigest()
-        delegate_index = self.emitter_state.delegate_cache.get(hashed)
+        delegate_index = self.program_state.backend_delegate_data_cache.get(hashed)
         delegate_ret = None
 
         if isinstance(self.node.meta["spec"], list):
@@ -1129,28 +1127,20 @@ def _emit_delegate(
         if delegate_index is None:
             # Allocate an entry for the data. TODO(T150113674): Reuse any duplicate entries if
             # present.
-            hashed = hashlib.sha256(processed_bytes).hexdigest()
-            data_index: Optional[int] = (
-                self.program_state.backend_delegate_data_cache.get(hashed)
+            delegate_index = len(self.program_state.backend_delegate_data_cache)
+            self.program_state.backend_delegate_data_cache[hashed] = delegate_index
+            self.program_state.backend_delegate_data.append(
+                BackendDelegateInlineData(data=processed_bytes)
             )
-            if data_index is None:
-                data_index = len(self.program_state.backend_delegate_data)
-                self.program_state.backend_delegate_data_cache[hashed] = data_index
-                self.program_state.backend_delegate_data.append(
-                    BackendDelegateInlineData(data=processed_bytes)
-                )
-
-            backend_delegate = BackendDelegate(
-                id=lowered_module.backend_id,
-                processed=BackendDelegateDataReference(
-                    location=DataLocation.INLINE, index=data_index
-                ),
-                compile_specs=lowered_module.compile_specs,
-            )
-            delegate_index = len(self.emitter_state.delegate_cache)
-            self.emitter_state.delegates.append(backend_delegate)
-            self.emitter_state.delegate_cache[hashed] = delegate_index
 
+        backend_delegate = BackendDelegate(
+            id=lowered_module.backend_id,
+            processed=BackendDelegateDataReference(
+                location=DataLocation.INLINE, index=delegate_index
+            ),
+            compile_specs=lowered_module.compile_specs,
+        )
+        self.emitter_state.delegates.append(backend_delegate)
         # TODO(angelayi) Will need to emit the kwargs too, in the correct order according to the
         # function's spec and with default arguments. This requires us to store the function's spec
         # in to_backend()
@@ -1163,7 +1153,12 @@ def _emit_delegate(
             delegate_args.append(elem.id)
 
         self.chain.instructions.append(
-            Instruction(DelegateCall(delegate_index=delegate_index, args=delegate_args))
+            Instruction(
+                DelegateCall(
+                    delegate_index=len(self.emitter_state.delegates) - 1,
+                    args=delegate_args,
+                )
+            )
         )
 
         return delegate_ret

exir/emit/test/test_emit.py

@@ -1770,6 +1770,60 @@ def forward(self, x):
             len(edge_program_manager.executorch_program.backend_delegate_data), 1
         )
 
+    def test_delegate_deduplicate_with_different_compile_specs(self) -> None:
+        class LowerableSubModel(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, x):
+                return torch.sin(x)
+
+        lowered = LowerableSubModel()
+        example_input = (torch.ones(1),)
+
+        lowered_edge = to_edge(export(lowered, example_input))
+
+        from executorch.exir.backend.compile_spec_schema import CompileSpec
+
+        compile_specs1 = [CompileSpec("config", b"fast")]
+        compile_specs2 = [CompileSpec("config", b"small")]
+        lowered_module1 = to_backend(
+            "BackendWithCompilerDemo", lowered_edge.exported_program(), compile_specs1
+        )
+        lowered_module2 = to_backend(
+            "BackendWithCompilerDemo", lowered_edge.exported_program(), compile_specs2
+        )
+
+        class CompositeModel(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.lowerable1 = lowered_module1
+                self.lowerable2 = lowered_module2
+
+            def forward(self, x):
+                a = self.lowerable1(x)
+                b = self.lowerable2(a)
+                return a, b
+
+        composite_model = CompositeModel()
+        model_inputs = (torch.ones(1),)
+        edge_prog = to_edge(export(composite_model, model_inputs)).to_executorch()
+
+        exported_program = edge_prog.exported_program()
+        program = emit_program({"method1": exported_program}, False).program
+        self.assertEqual(len(program.execution_plan), 1)
+
+        plan = program.execution_plan[0]
+        # Two delegates that point to the same blob.
+        self.assertEqual(len(plan.delegates), 2)
+        self.assertEqual(plan.delegates[0].processed.index, 0)
+        self.assertEqual(plan.delegates[1].processed.index, 0)
+        # Compile specs are different.
+        self.assertEqual(plan.delegates[0].compile_specs, compile_specs1)
+        self.assertEqual(plan.delegates[1].compile_specs, compile_specs2)
+        # Only one delegate blob in the backend_delegate_data.
+        self.assertEqual(len(program.backend_delegate_data), 1)
+
     def test_constant_tagged_mutable_tensors(self) -> None:
         class Net(nn.Module):
             def __init__(self):