Fixes for constant_prop_pass

Summary:
The constant_prop_pass didn't properly propagate constants when there were
simple primitives in the argument set. Extend it to see floats, ints, strings, etc.
as constant functions.

This allows this pass to fold additional things like quantize functions on weights.
Sometimes users don't want that, so allow them to use a lambda to skip
some nodes.

Differential Revision: D55942686

Author: dulinriley

exir/passes/constant_prop_pass.py

@@ -4,18 +4,36 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+from typing import Callable, List, Optional
+
 import torch
 from torch._export.utils import get_buffer, get_param, is_buffer, is_param
 from torch._guards import detect_fake_mode
 from torch.export import ExportedProgram
 from torch.export.exported_program import InputKind, InputSpec, TensorArgument
 
 
-def is_const(arg, exported_program, const_data_list) -> bool:
+_PRIMITIVE_TYPES = (
+    float,
+    int,
+    bool,
+    str,
+    torch.Tensor,
+    torch.device,
+    torch.dtype,
+    torch.layout,
+)
+
+
+def is_const(
+    arg: object, exported_program: ExportedProgram, const_data_list: List[str]
+) -> bool:
     if isinstance(arg, (tuple, list)):
         return all(is_const(x, exported_program, const_data_list) for x in arg)
     elif isinstance(arg, dict):
         return all(is_const(x, exported_program, const_data_list) for x in arg.values())
+    elif isinstance(arg, _PRIMITIVE_TYPES):
+        return True
     elif not isinstance(arg, torch.fx.Node) or arg.op != "placeholder":
         return False
     elif (
@@ -27,17 +45,22 @@ def is_const(arg, exported_program, const_data_list) -> bool:
     return False
 
 
-def get_data(exported_program, arg):
+def get_data(exported_program: ExportedProgram, arg):
     if isinstance(arg, (tuple, list)):
         return [get_data(exported_program, x) for x in arg]
+    elif isinstance(arg, _PRIMITIVE_TYPES):
+        return arg
     elif is_param(exported_program, arg):
         return get_param(exported_program, arg)
     elif is_buffer(exported_program, arg):
         return get_buffer(exported_program, arg)
     return None
 
 
-def constant_prop_pass(exported_program: ExportedProgram) -> ExportedProgram:
+def constant_prop_pass(
+    exported_program: ExportedProgram,
+    skip_folding_node_fn: Optional[Callable[[torch.fx.Node], bool]] = None,
+) -> ExportedProgram:
     """
     This pass is for constant propagation for Exported Program with lifted parameters,
     as the parameters will not be shown up as `get_attr` but as `placeholder` to the graph.
@@ -56,12 +79,14 @@ def constant_prop_pass(exported_program: ExportedProgram) -> ExportedProgram:
     if len(has_cond) > 0:
         raise RuntimeError("constant_prop_pass for control flow is not supported yet.")
 
+    first_user_input_idx = -1
     first_user_input = None
-    for node in exported_program.graph.nodes:
+    for i, node in enumerate(exported_program.graph.nodes):
         if (
             node.op == "placeholder"
             and node.name in exported_program.graph_signature.user_inputs
         ):
+            first_user_input_idx = i
             first_user_input = node
             break
 
@@ -79,6 +104,9 @@ def constant_prop_pass(exported_program: ExportedProgram) -> ExportedProgram:
     assert fake_mode is not None
 
     for node in exported_program.graph.nodes:
+        if skip_folding_node_fn is not None and skip_folding_node_fn(node):
+            # Do not process this node if we were told to skip it.
+            continue
         if node.op == "call_function":
             constant_data_name_list = [
                 input_spec.target for input_spec in prop_constant_data
@@ -115,9 +143,11 @@ def constant_prop_pass(exported_program: ExportedProgram) -> ExportedProgram:
                     exported_program.state_dict[prop_constant_tensor_fqn] = (
                         prop_constant_tensor
                     )
-                    exported_program.graph_signature.input_specs.append(
-                        prop_constant_node_input_spec
+                    # Insert new buffers before the first user input.
+                    exported_program.graph_signature.input_specs.insert(
+                        first_user_input_idx, prop_constant_node_input_spec
                     )
+                    first_user_input_idx += 1
 
     # Remove the propogated buffer from the state dict
     for node in exported_program.graph.nodes:
@@ -128,6 +158,16 @@ def constant_prop_pass(exported_program: ExportedProgram) -> ExportedProgram:
         ):
             exported_program.state_dict.pop(node.name, None)
             exported_program.graph.erase_node(node)
+            # Delete the input spec for this deleted buffer.
+            to_erase_idx = []
+            for i, spec in enumerate(exported_program.graph_signature.input_specs):
+                if spec.arg.name == node.name:
+                    to_erase_idx.append(i)
+            assert (
+                len(to_erase_idx) == 1
+            ), f"Should only delete one spec per node, but deleting multiple: {to_erase_idx} {exported_program.graph_signature.input_specs}"
+            for i in reversed(to_erase_idx):
+                exported_program.graph_signature.input_specs.pop(i)
 
     exported_program.graph_module.recompile()
     return exported_program