support graph-by-graph benchmarking for PyTorch native checkpointing

thunder/dynamo/compiler_graph_benchmark.py

@@ -2,6 +2,7 @@
 from itertools import chain
 from pytest_benchmark.fixture import BenchmarkFixture
 from typing import TYPE_CHECKING
+from looseversion import LooseVersion
 
 import torch
 from thunder.dynamo import ThunderCompiler
@@ -124,6 +125,23 @@ def run_bench(self, gm: torch.fx.GraphModule, name: str, *sample_args):
 
     def __call__(self, gm: torch.fx.GraphModule, sample_args: list[torch.SymInt, torch.Tensor]):
         split_module = super().__call__(gm, sample_args)
+
+        def has_checkpoint_node(g):
+            if g.find_nodes(op="call_function", target=torch.ops.higher_order.tag_activation_checkpoint):
+                return True
+            for n in g.nodes:
+                if n.op == "call_module" and has_checkpoint_node(getattr(g.owning_module, n.target).graph):
+                    return True
+            return False
+
+        if LooseVersion(torch.__version__) < LooseVersion("2.6.0"):
+            # NOTE: PyTorch 2.6 changes the structure of GraphModule when using activation checkpointing.
+            # It's hard to retrieve the example input tensor for the GraphModule contains checkpoint operator before PyTorch 2.6
+            if has_checkpoint_node(split_module.graph):
+                raise RuntimeError(
+                    "The benchmarking of the Torch activation checkpointing is only supported with PyTorch version 2.6 or later."
+                )
+
         compiled_functions_to_submodule = {
             v.compiled_fn: k for k, v in self.subgraph_infos[self.graph_idx].submodule_to_compiled_functions.items()
         }

thunder/dynamo/splitter.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 from typing import TYPE_CHECKING
+import copy
 
 import torch
 from torch.fx.passes.split_module import split_module
@@ -131,9 +132,10 @@ def callback(node) -> int:
         return partition_cnt
 
     # `split_module` iterates over nodes and determines the partition to place them based on the callback.
-    split_gm: torch.fx.GraphModule = split_module(
+    original_split_gm: torch.fx.GraphModule = split_module(
         gm, root_m=None, split_callback=callback, keep_original_order=True, keep_original_node_name=True
     )
+    split_gm = copy.deepcopy(original_split_gm)
 
     def is_thunder_supported_partition(node: torch.fx.Node) -> bool:
         return node.name.startswith("submod") and int(node.name.replace("submod_", "")) in supported_partitions
@@ -142,6 +144,7 @@ def is_thunder_supported_partition(node: torch.fx.Node) -> bool:
     thunder_compiled_fns = []
     submodule_to_compiled_fns = {}
     for node in split_gm.graph.nodes:
+        node_name = node.name
         if is_thunder_supported_partition(node):
             graph_module = getattr(split_gm, node.name)
             # Replace PyTorch operators within the checkpointed function with the corresponding Thunder operators
@@ -150,13 +153,17 @@ def is_thunder_supported_partition(node: torch.fx.Node) -> bool:
             # Update the node name from "submod_*" to "thunder_*" for more user-friendly names
             update_node_and_submodule(split_gm, node, node.name.replace("submod", "thunder"), jit_fn)
             thunder_compiled_fns.append(jit_fn)
-            submodule_to_compiled_fns[graph_module] = CompiledFunction(jit_fn, CompilerType.THUNDER)
+            submodule_to_compiled_fns[getattr(original_split_gm, node_name)] = CompiledFunction(
+                jit_fn, CompilerType.THUNDER
+            )
         elif node.name.startswith("submod"):  # For inductor
             graph_module = getattr(split_gm, node.name)
             jit_fn = torch_inductor(graph_module)
             # Update the node name from "submod_*" to "inductor_*" for more user-friendly names
             update_node_and_submodule(split_gm, node, node.name.replace("submod", "inductor"), jit_fn)
-            submodule_to_compiled_fns[graph_module] = CompiledFunction(jit_fn, CompilerType.TORCH_INDUCTOR)
+            submodule_to_compiled_fns[getattr(original_split_gm, node_name)] = CompiledFunction(
+                jit_fn, CompilerType.TORCH_INDUCTOR
+            )
         else:
             # Everything else is a glue code to call and pass outputs between the other partitions.
             pass
@@ -166,6 +173,7 @@ def is_thunder_supported_partition(node: torch.fx.Node) -> bool:
 
     return split_gm, SubgraphInfo(
         gm,
+        original_split_gm,
         split_gm,
         thunder_compiled_fns,
         submodule_to_compiled_fns,

thunder/dynamo/utils.py

@@ -80,17 +80,21 @@ class SubgraphInfo:
 
     Attributes:
         original_graph_module: The original graph module.
-        split_graph_module: The graph module for the split subgraph.
+        original_split_graph_module: The original split graph module before any transformations are applied.
+            Specifically, before the :func:`checkpoint_converter` replaces the Torch operators with Thunder symbols,
+            and before any submodules are compiled by Thunder.
+        split_graph_module: The graph module for the split subgraph. It contains the compiled thunder/inductor modules.
         thunder_compiled_fns: List of thunder optimized callables.
             This could be :obj:`None` if there the graph module was not supported by thunder.
             Look at the :attr:`split_reasons` for further information.
-        submodule_to_compiled_functions: Dict from subgraph to compiled function.
+        submodule_to_compiled_functions: Dict from subgraph in :attr:`original_split_graph_module` to compiled function.
             This will be a dict with one pair in case the graph was not split.
         split_reasons: List of reasons explaining why the subgraph was split.
             Present only if there are was a split.
     """
 
     original_graph_module: torch.fx.GraphModule
+    original_split_graph_module: torch.fx.GraphModule | None
     split_graph_module: torch.fx.GraphModule | None
     thunder_compiled_fns: list[Callable] | None
     submodule_to_compiled_functions: dict[torch.fx.GraphModule, CompiledFunction]
@@ -466,8 +470,7 @@ def _checkpoint_function_converter(gm: torch.fx.GraphModule):
     Args:
         gm (torch.fx.GraphModule): The GraphModule of the checkpointed function, which is modified inplace.
     """
-    new_graph = copy.deepcopy(gm.graph)
-    for n in new_graph.nodes:
+    for n in gm.graph.nodes:
         # replace the torch operator in "call_function" node
         if n.op == "call_function":
             assert isinstance(n.target, Callable)
@@ -476,19 +479,18 @@ def _checkpoint_function_converter(gm: torch.fx.GraphModule):
             check(
                 n.target in _torch_to_thunder_function_map, lambda: f"Unexpected {n.target}, not registered in Thunder"
             )
-            with new_graph.inserting_before(n):
-                thunder_node = new_graph.call_function(
+            with gm.graph.inserting_before(n):
+                thunder_node = gm.graph.call_function(
                     _torch_to_thunder_function_map[n.target], args=n.args, kwargs=n.kwargs
                 )
             n.replace_all_uses_with(thunder_node)
-            new_graph.erase_node(n)
+            gm.graph.erase_node(n)
         else:
             if n.op == "call_module":
                 raise RuntimeError(
                     "Unexpected call_module detected inside a checkpoint. This should have been inlined in dynamo graphs"
                 )
-    new_graph.lint()
-    gm.graph = new_graph
+    gm.graph.lint()
     recompile_graph(gm)
 
 

thunder/tests/test_dynamo.py

@@ -5,6 +5,7 @@
 import torch.fx
 import torch.nn as nn
 import torch.nn.functional as F
+from looseversion import LooseVersion
 
 from thunder import dtypes
 from thunder.dynamo import ThunderCompiler
@@ -445,6 +446,10 @@ def func(x):
             IS_WINDOWS,
             reason="torch.compile Windows support is still WIP - https://github.com/pytorch/pytorch/issues/122094",
         ),
+        pytest.mark.skipif(
+            LooseVersion(torch.__version__) < LooseVersion("2.6.0"),
+            reason="Skip until the Torch bug is fixed - https://github.com/pytorch/pytorch/pull/139275",
+        ),
     ),
 )
 @requiresCUDA
@@ -639,6 +644,35 @@ def f(x):
     compiled(x)
 
 
+@pytest.mark.skipif(
+    LooseVersion(torch.__version__) < LooseVersion("2.6.0"),
+    reason="The checkpoint function becomes a submodule of the module containing `tag_activation_checkpoint` in PyTorch 2.6.0.",
+)
+@requiresCUDA
+def test_ThunderCompilerGraphBenchmarking_checkpoint(benchmark):
+    class SimpleModel(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.layer1 = nn.Linear(10, 20)
+
+        def forward(self, x):
+            x = torch.utils.checkpoint.checkpoint(self.layer1, x)
+            x = F.relu(x)
+            return x
+
+    x = torch.randn(5, 10).cuda().requires_grad_()
+    model = SimpleModel().cuda().train()
+
+    exe_backend = ThunderCompiler()
+    backend = ThunderCompilerGraphBenchmarking(
+        benchmark, executors={"inductor": torch.compile, "thunderfx": torch.compile(backend=exe_backend)}
+    )
+    # Using torch.compile here fails with "TypeError: cannot pickle '_io.TextIOWrapper' object" in
+    # https://github.com/Lightning-AI/pytorch-lightning/blob/828fd998961f6a60f92c35254bb94d6e049ad069/src/lightning/fabric/wrappers.py#L421
+    jf = torch._dynamo.optimize(backend=backend)(model)
+    out = jf(x)
+
+
 @requiresCUDA
 @pytest.mark.filterwarnings(r"ignore:`torch\.cpu\.amp\.autocast\((.*?)\)` is deprecated.*:FutureWarning")
 def test_checkpoint_converter():