Reinplace.py

Summary: Pass attempts to reinplace index_put if it is safe to do so.

Differential Revision: D77204122

Author: JacobSzwejbka

exir/passes/TARGETS

@@ -31,6 +31,7 @@ python_library(
         ":sym_shape_eval_pass",
         ":sym_to_tensor_pass",
         ":weights_to_outputs_pass",
+        ":reinplace_pass",
         "//caffe2:torch",
         "//executorch/exir:common",
         "//executorch/exir:control_flow",
@@ -68,6 +69,17 @@ python_library(
     ],
 )
 
+python_library(
+    name = "reinplace_pass",
+    srcs = [
+        "reinplace.py",
+    ],
+    deps = [
+        "//caffe2:torch",
+        "//executorch/exir/dialects:lib",
+    ],
+)
+
 python_library(
     name = "insert_write_back_for_buffers_pass",
     srcs = [

exir/passes/reinplace.py

@@ -0,0 +1,109 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# pyre-strict
+
+from typing import Set
+
+import torch
+from executorch.exir.dialects._ops import ops
+from torch.export import ExportedProgram
+
+
+def _is_view_copy(node: torch.fx.Node) -> bool:
+    """Check if a node is a view_copy operation."""
+    return node.op == "call_function" and node.target in (
+        torch.ops.aten.view_copy.default,
+        ops.edge.aten.view_copy.default,
+    )
+
+
+def _is_index_put(node: torch.fx.Node) -> bool:
+    """Check if a node is an index_put operation."""
+    return node.op == "call_function" and node.target in (
+        torch.ops.aten.index_put.default,
+        ops.edge.aten.index_put.default,
+    )
+
+
+def _is_safe_to_reinplace(
+    node: torch.fx.Node,
+    later_nodes: Set[torch.fx.Node],
+    inputs: Set[torch.fx.Node],
+    mutable_inputs: Set[torch.fx.Node],
+) -> bool:
+    # This node is used later in the graph so we can't reinplace it
+    # There is probably a faster way to do this but this works for now.
+    if node in later_nodes:
+        return False
+    
+    # If its not an input then we can reinplace it
+    if node not in inputs:
+        return True
+    # If its a mutable input then we can reinplace it
+    elif node in mutable_inputs:
+        return True
+    else: # input but not mutable input
+        return False
+
+
+
+def _is_user_input(node: torch.fx.Node, exported_program: ExportedProgram) -> bool:
+    return node.target in exported_program.graph_signature.user_inputs
+
+def _is_buffer(node: torch.fx.Node, exported_program: ExportedProgram) -> bool:
+    return node.target in exported_program.graph_signature.inputs_to_buffers
+
+def _is_mutable_user_input(node: torch.fx.Node, exported_program: ExportedProgram) -> bool:
+    return node.target in exported_program.graph_signature.user_inputs_to_mutate.values()
+
+def _is_mutable_buffer(node: torch.fx.Node, exported_program: ExportedProgram) -> bool:
+    if node.target not in exported_program.graph_signature.inputs_to_buffers:
+        return False
+    buf = exported_program.graph_signature.inputs_to_buffers[node.target]
+    return buf in exported_program.graph_signature.buffers_to_mutate.values()
+
+def reinplace_pass(ep: ExportedProgram) -> ExportedProgram:
+    """
+    Pass that loops over nodes in an exported program and collects the first argument
+    of every call_function node that is a view_copy operation.
+    
+    Args:
+        exported_program: The ExportedProgram to analyze
+        
+    Returns:
+        Set of nodes that are first arguments to view_copy operations
+    """
+    seen_nodes: Set[torch.fx.Node] = set()
+    # Get all placeholders
+    placeholders = set()
+    for node in ep.graph.nodes:
+        if node.op == "placeholder":
+            placeholders.add(node)
+    # Get all inputs that we could potentially mutate
+    inputs = set([node for node in placeholders if _is_user_input(node, ep) or _is_buffer(node, ep)])
+    mutable_nodes = set([node for node in placeholders if _is_mutable_user_input(node, ep) or _is_mutable_buffer(node, ep)])
+
+    results = set()
+    for node in reversed(ep.graph.nodes):
+        if _is_index_put(node):
+            # Check if this index_put node is safe to inplace
+            # The first argument is the base tensor being indexed into
+            first_arg = node.args[0]
+            if _is_safe_to_reinplace(first_arg, seen_nodes, inputs, mutable_nodes):
+                # This index_put is safe to reinplace
+                with ep.graph.inserting_before(node):
+                    new_node = ep.graph.call_function(
+                        ops.edge.aten.index_put_.default, args=node.args
+                    )
+                    new_node.meta["val"] = node.meta["val"]
+                    node.replace_all_uses_with(new_node)
+                    ep.graph.erase_node(node)
+                results.add(first_arg)
+        elif node.op == "call_function":
+            seen_nodes.update(node.all_input_nodes)
+            
+    return ep

exir/tests/TARGETS

@@ -136,6 +136,18 @@ python_unittest(
     ],
 )
 
+python_unittest(
+    name = "reinplace_pass",
+    srcs = [
+        "test_reinplace_pass.py",
+    ],
+    deps = [
+        "//caffe2:torch",
+        "//executorch/exir:lib",
+        "//executorch/exir/passes:lib",
+    ],
+)
+
 cpp_library(
     name = "test_lib",
     srcs = [

exir/tests/test_reinplace_pass.py

@@ -0,0 +1,102 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# pyre-strict
+
+import unittest
+
+import torch
+from executorch.exir import to_edge
+from executorch.exir.passes.reinplace import (
+    reinplace_pass,
+)
+from torch.export import export
+from torch.export.graph_signature import InputKind, OutputKind
+
+
+class TestReinplacePass(unittest.TestCase):
+    def test_index_put_reinplace(self) -> None:
+        """Test that index_put on a mutable buffer can be reinplaced."""
+        
+        class IndexPutModel(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.register_buffer("state", torch.zeros(5))
+            
+            def forward(self, indices: torch.Tensor, values: torch.Tensor) -> torch.Tensor:
+                # index_put on buffer (non-user input) should be safe
+                self.state.index_put_((indices,), values)
+                return self.state
+        
+        model = IndexPutModel()
+        indices = torch.tensor([0])
+        values = torch.tensor([1.0])
+        
+        exported_program = export(model, (indices, values), strict=True)
+        print(exported_program.graph)
+        edge_program = to_edge(exported_program).exported_program()
+        
+        # Find the index_put node
+        index_put_node = None
+        for node in edge_program.graph.nodes:
+            if (node.op == "call_function" and 
+                "index_put" in str(node.target)):
+                index_put_node = node
+                break
+        
+        self.assertIsNotNone(index_put_node, "Should find an index_put node")
+        
+        ep = reinplace_pass(edge_program)
+        # Find the index_put node
+        index_put_node = None
+        for node in ep.graph.nodes:
+            if (node.op == "call_function" and 
+                "index_put_" in str(node.target)):
+                index_put_node = node
+                break
+        
+        self.assertIsNotNone(index_put_node, "Should find an index_put_ node")
+    def test_cant_reinplace(self) -> None:
+        """Test that index_put on a mutable buffer that is viewed later is not safe."""
+        
+        class IndexPutModel(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.register_buffer("state", torch.zeros(5))
+            
+            def forward(self, indices: torch.Tensor, values: torch.Tensor) -> torch.Tensor:
+                # index_put on buffer (non-user input) should be safe
+                x = self.state.index_put((indices,), values)
+                self.state.add_(1)
+                return x
+        
+        model = IndexPutModel()
+        indices = torch.tensor([0])
+        values = torch.tensor([1.0])
+        
+        exported_program = export(model, (indices, values), strict=True)
+        edge_program = to_edge(exported_program).exported_program()
+        
+        # Find the index_put node
+        index_put_node = None
+        for node in edge_program.graph.nodes:
+            if (node.op == "call_function" and 
+                "index_put" in str(node.target)):
+                index_put_node = node
+                break
+        
+        self.assertIsNotNone(index_put_node, "Should find an index_put node")
+        
+        ep = reinplace_pass(edge_program)
+        # Find the index_put node
+        index_put_node = None
+        for node in ep.graph.nodes:
+            if (node.op == "call_function" and 
+                "index_put" in str(node.target) and "index_put_" not in str(node.target)):
+                index_put_node = node
+                break
+        
+        self.assertIsNotNone(index_put_node, "Should still find an index_put node")