thunderFX : pass to remove empty autocast regions

thunder/dynamo/compiler.py

@@ -7,7 +7,7 @@
 import torch
 
 from thunder.core.baseutils import run_once
-from thunder.dynamo.utils import recompile_graph
+from thunder.dynamo.utils import recompile_graph, remove_empty_autocast
 from thunder.dynamo.splitter import _splitter
 
 if TYPE_CHECKING:
@@ -72,6 +72,8 @@ def __init__(self, **thunder_options):
         self._torch_compile = partial(torch.compile, **torch_inductor_options)
 
     def __call__(self, gm: torch.fx.GraphModule, sample_args: list[torch.SymInt, torch.Tensor]):
+        gm = remove_empty_autocast(gm)
+
         # Dynamo uses lazy generation of the underlying Python code, so we need to
         # force recompilation of the GraphModule before passing it to Thunder.
         recompile_graph(gm)

thunder/dynamo/utils.py

@@ -512,3 +512,44 @@ def checkpoint_converter(gm: torch.fx.GraphModule, sub_gm: torch.fx.GraphModule)
                 else:
                     function_module = getattr(gm, n.args[0].name)
                 _checkpoint_function_converter(function_module)
+
+
+def remove_empty_autocast(graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
+    """
+    Function to remove empty autocast regions from GraphModule.
+
+    Dynamo can provide empty autocast regions in which case, it is more performant to remove them
+    from the graph than to compile them and pay the cost of calling a wrapped optimized function
+    which does nothing.
+
+    Args:
+        graph_module: Graph module to which this pass is applied.
+
+    """
+
+    empty_autocast_removed_graph_module = copy.deepcopy(graph_module)
+
+    # Dummy init node.
+    prev_node = torch.fx.node.Node(graph_module.graph, "start_node", "call_function", lambda: None, None, None)
+    nodes_to_erase = []
+    for node in empty_autocast_removed_graph_module.graph.nodes:
+        # As _enter_autocast and _exit_autocast functions map the regions created by context manager,
+        # previous `_enter_autocast` will always correspond with current `_exit_autocast`.
+        if (
+            prev_node.target == torch.amp.autocast_mode._enter_autocast
+            and node.target == torch.amp.autocast_mode._exit_autocast
+        ):
+            # NOTE: Order of node being appended matters.
+            # The node to be erased has to have zero users.
+            # So, we remove `_exit_autocast` first (which consumes output from `_enter_autocast`)
+            # and then we can remove the corresponding `_enter_autocast`.
+            nodes_to_erase.append(node)
+            nodes_to_erase.append(prev_node)
+
+        prev_node = node
+
+    # Erase the marked nodes.
+    for node in nodes_to_erase:
+        empty_autocast_removed_graph_module.graph.erase_node(node)
+
+    return empty_autocast_removed_graph_module

thunder/tests/test_dynamo.py

@@ -1,5 +1,6 @@
 import pytest
 import warnings
+import itertools
 import torch
 import torch.fx
 import torch.nn as nn
@@ -515,6 +516,60 @@ def func(x):
     torch.testing.assert_close(actual_grad, expected_grad)
 
 
+def test_empty_autocast():
+    autocast_ops = (torch.amp.autocast_mode._enter_autocast, torch.amp.autocast_mode._exit_autocast)
+
+    def _call_thunder_backend(fn, args):
+        backend = ThunderCompiler()
+        jf = torch.compile(backend=backend)(f)
+        jf(*args)
+        return backend
+
+    # autocast region is removed
+    def f():
+        with torch.autocast(dtype=torch.bfloat16, device_type="cpu"):
+            pass
+        return
+
+    backend = _call_thunder_backend(f, ())
+    assert all(node.target not in autocast_ops for node in backend.subgraph_infos[0].split_graph_module.graph.nodes)
+
+    # Both autocast regions are removed
+    def f(x):
+        with torch.autocast(dtype=torch.bfloat16, device_type="cpu"):
+            pass
+        y = x @ x
+        with torch.autocast(dtype=torch.bfloat16, device_type="cpu"):
+            pass
+        return y
+
+    x = torch.randn(3, 3)
+    backend = _call_thunder_backend(f, (x,))
+
+    all_nodes = itertools.chain(
+        backend.subgraph_infos[0].split_graph_module.graph.nodes,
+        backend.subgraph_infos[0].split_graph_module.thunder_1.graph.nodes,
+    )
+    assert all(node.target not in autocast_ops for node in all_nodes)
+
+    # First autocast region is removed and second isn't
+    def f(x):
+        with torch.autocast(dtype=torch.bfloat16, device_type="cpu"):
+            pass
+        y = x @ x
+        with torch.autocast(dtype=torch.bfloat16, device_type="cpu"):
+            y = y @ y
+        return y
+
+    x = torch.randn(3, 3)
+    backend = _call_thunder_backend(f, (x,))
+    all_nodes = itertools.chain(
+        backend.subgraph_infos[0].split_graph_module.graph.nodes,
+        backend.subgraph_infos[0].split_graph_module.thunder_1.graph.nodes,
+    )
+    assert sum(node.target in autocast_ops for node in all_nodes) == 2
+
+
 # Sample command to run the benchmark using ThunderCompilerGraphBenchmarking
 # pytest thunder/tests/test_dynamo.py -k test_ThunderCompilerGraphBenchmarking_groupby --benchmark-group-by='graph-by-graph:param:GraphID,param:SplitModuleName'
 # For more details, see :class:`thunder.dynamo.compiler_graph_benchmark.ThunderCompilerGraphBenchmarking`