Dedup constants in emitter

exir/emit/_emitter.py

@@ -466,6 +466,15 @@ def _tensor_spec_to_evalue(
                 and spec.extra_tensor_info.location == TensorDataLocation.EXTERNAL
             ):
                 buffer_idx = self.program_state.external_constant_hash.get(hashed, -1)
+                if buffer_idx != -1:
+                    # Save the constant tag for the external tensor
+                    if constant_tag not in self.program_state.external_constant_map:
+                        # pyre-ignore Undefined attribute [16]: `Optional` has no attribute `fully_qualified_name`.
+                        self.program_state.external_constant_map[constant_tag] = {}
+                    # pyre-ignore Undefined attribute [16]: `Optional` has no attribute `fully_qualified_name`.
+                    self.program_state.external_constant_map[constant_tag][
+                        spec.extra_tensor_info.fully_qualified_name
+                    ] = buffer_idx
             else:
                 buffer_idx = self.program_state.cached_spec_hash_values.get(hashed, -1)
 

exir/emit/test/test_emit.py

@@ -1719,6 +1719,45 @@ def forward(self, x):
         self.assertEqual(external_map["linear.weight"], 0)
         self.assertEqual(external_map["linear.bias"], 1)
 
+    def test_constant_tagged_tensor_dedup(self) -> None:
+        class ConstantModule(nn.Module):
+            def __init__(self):
+                super().__init__()
+                constant_value = torch.tensor([1.0, 2.0, 3.0])
+
+                # Register the same value with two different names as persistent buffers
+                self.register_buffer(
+                    "constant_a", constant_value.clone(), persistent=True
+                )
+                self.register_buffer(
+                    "constant_b", constant_value.clone(), persistent=True
+                )
+
+            def forward(self, x):
+                return x + self.constant_a + self.constant_b
+
+        model = to_edge(
+            export(ConstantModule(), (torch.ones(1, 3),), strict=True)
+        ).to_executorch(
+            config=ExecutorchBackendConfig(
+                external_constants=True,
+            )
+        )
+        emitter_output = model._emitter_output
+        # Check that constant_buffer is empty besides the non-constant placeholder 0.
+        self.assertEqual(len(emitter_output.program.constant_buffer), 1)
+        # Check that constant weights are in the external constant buffer.
+        self.assertEqual(len(emitter_output.external_constant_buffer), 1)
+        # Setting external_constants=True, saves all constants to the key
+        # '_default_external_constant'.
+        external_map = emitter_output.external_constant_map[
+            "_default_external_constant"
+        ]
+        self.assertEqual(len(external_map), 2)
+        # Confirm that the same tensor is used for both constants.
+        self.assertEqual(external_map["constant_a"], 0)
+        self.assertEqual(external_map["constant_b"], 0)
+
     def test_delegate_deduplicate(self) -> None:
         class SharedModule(torch.nn.Module):
             def __init__(self):