Save external constant tensors to custom filename

exir/capture/_config.py

@@ -6,7 +6,7 @@
 
 # pyre-unsafe
 from dataclasses import dataclass, field
-from typing import Dict, List, Optional, Union
+from typing import Callable, Dict, List, Optional, Union
 
 import torch
 
@@ -94,9 +94,14 @@ class ExecutorchBackendConfig:
     # Moreover, static views will be elided from the ExecuTorch graph
     remove_view_copy: bool = True
 
-    # If set to true, all constant tensors will be stored in a separate file,
-    # external to the PTE file.
-    external_constants: bool = False
+    # Bool: if True, all constant tensors will be stored in a separate file. If False,
+    #       all constant tensors will be stored in the PTE file.
+    # Callable: a function from torch.fx.Node to Optional[str]. This will be called for each
+    #       placeholder (constant tensor) node, and if it returns a string, that node will be
+    #       tagged with the string. If None, the constant tensor is stored in the PTE file.
+    #       Otherwise, it is stored in a file named by the string. E.g., a function
+    #       lambda x: "model_weights" will save all constants into a file "model_weights.ptd".
+    external_constants: Union[bool, Callable[[torch.fx.Node], Optional[str]]] = False
 
     # If set to true, all trainable weights will be stored in a separate file,
     # external to the PTE file.

exir/emit/test/test_emit.py

@@ -1717,9 +1717,38 @@ def forward(self, x):
         external_map = emitter_output.external_constant_map[
             "_default_external_constant"
         ]
+        self.assertEqual(len(external_map), 2)
         self.assertEqual(external_map["linear.weight"], 0)
         self.assertEqual(external_map["linear.bias"], 1)
 
+    def test_constant_tagged_tensors_custom(self) -> None:
+        class LinearModule(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.linear = torch.nn.Linear(5, 5)
+
+            def forward(self, x):
+                return self.linear(x)
+
+        model = to_edge(
+            export(LinearModule(), (torch.ones(5, 5),), strict=True)
+        ).to_executorch(
+            config=ExecutorchBackendConfig(
+                external_constants=lambda x: (
+                    "linear_weight" if "weight" in x.name else None
+                ),
+            )
+        )
+        emitter_output = model._emitter_output
+        # constant_buffer contains placeholder and linear bias.
+        self.assertEqual(len(emitter_output.program.constant_buffer), 2)
+        # external constant buffer contains linear weight.
+        self.assertEqual(len(emitter_output.external_constant_buffer), 1)
+        # The lambda saves all constants to the key 'linear_weight'.
+        external_map = emitter_output.external_constant_map["linear_weight"]
+        self.assertEqual(len(external_map), 1)
+        self.assertEqual(external_map["linear.weight"], 0)
+
     def test_constant_tagged_tensor_dedup(self) -> None:
         class ConstantModule(nn.Module):
             def __init__(self):

exir/program/_program.py

@@ -1737,11 +1737,19 @@ def to_executorch(  # noqa (FLAKE8) C901
                     # TODO(who?)
                     p.update_placeholder_tensor_specs(program, new_gm)
 
-            # Extract constants if the config says too.
-            if config.external_constants:
+            # Tag constant weights.
+            if (
+                isinstance(config.external_constants, bool)
+                and config.external_constants
+            ):
                 new_gm_res = external_constants_pass(new_gm)
                 new_gm = new_gm_res.graph_module
-            elif config.external_mutable_weights:
+            elif callable(config.external_constants):
+                new_gm_res = external_constants_pass(new_gm, config.external_constants)
+                new_gm = new_gm_res.graph_module
+
+            # Tag mutable weights.
+            if config.external_mutable_weights:
                 new_gm_res = external_mutable_weights_pass(new_gm, program)
                 new_gm = new_gm_res.graph_module