Replace node.meta source_fn with source_fn_stack (#108595)

Summary:
X-link: pytorch/executorch#210

A resubmit of #108447. Copy over the descriptions:

This is a follow-up of the discussion in #108356, where we want to repalce source_fn with source_fn_stack

Before this PR, for the following example:
```python
backend = EagerAndRecordGraphs()

torch.compile(backend=backend, fullgraph=True)
def cond_f(pred, pred2, x, y):
    def true_fn(pred2, x, y):
        return x + y

    def false_fn(pred2, x, y):
        def true_fn2(x, y):
            return x.sin() - y.cos()

        def false_fn2(x, y):
            return x.cos() - y.sin()

        return control_flow.cond(pred2, true_fn2, false_fn2, (x, y))

    return control_flow.cond(pred, true_fn, false_fn, (pred2, x, y))
```
The graph captured is shown below:
```python
class GraphModule(torch.nn.Module):
    def forward(self, L_pred_ : torch.Tensor, L_pred2_ : torch.Tensor, L_x_ : torch.Tensor, L_y_ : torch.Tensor):
        l_pred_ = L_pred_
        l_pred2_ = L_pred2_
        l_x_ = L_x_
        l_y_ = L_y_

        cond_true_1 = self.cond_true_1
        cond_false_1 = self.cond_false_1
        cond = torch.ops.higher_order.cond(l_pred_, cond_true_1, cond_false_1, [l_pred2_, l_x_, l_y_]);  l_pred_ = cond_true_1 = cond_false_1 = l_pred2_ = l_x_ = l_y_ = None
        return (cond,)

    class GraphModule(torch.nn.Module):
        def forward(self, l_pred2_, l_x_, l_y_):
            add = l_x_ + l_y_;  l_x_ = l_y_ = None
            return add

    class GraphModule(torch.nn.Module):
        def forward(self, l_pred2_, l_x_, l_y_):
            cond_true_0 = self.cond_true_0
            cond_false_0 = self.cond_false_0
            cond = torch.ops.higher_order.cond(l_pred2_, cond_true_0, cond_false_0, [l_x_, l_y_]);  l_pred2_ = cond_true_0 = cond_false_0 = l_x_ = l_y_ = None
            return cond

        class GraphModule(torch.nn.Module):
            def forward(self, l_x_, l_y_):
                sin = l_x_.sin();  l_x_ = None
                cos = l_y_.cos();  l_y_ = None
                sub = sin - cos;  sin = cos = None
                return sub

        class GraphModule(torch.nn.Module):
            def forward(self, l_x_, l_y_):
                cos = l_x_.cos();  l_x_ = None
                sin = l_y_.sin();  l_y_ = None
                sub = cos - sin;  cos = sin = None
                return sub
```
the source_fn for inner cond, sin, cos will be a (name, target) tuple:
```
('cond', <torch._ops.HigherOrderOperator object at xxx>)
('sin', 'sin')
('cos', 'cos')
('sub'. <built-in function sub>)
```

After this pr, the source_fn_stack will be a list of (name, target) tuple. The bottom of stack is the end of the list.
```
[('cond', <torch._ops.HigherOrderOperator object at xxx>), ('cond', <torch._ops.HigherOrderOperator object at xxx>)],
[('cond', <torch._ops.HigherOrderOperator object at xxx>), ('cond', <torch._ops.HigherOrderOperator object at xxx>), ('sin', 'sin')],
[('cond', <torch._ops.HigherOrderOperator object at xxx>), ('cond', <torch._ops.HigherOrderOperator object at xxx>), ('cos', 'cos')]
[('cond', <torch._ops.HigherOrderOperator object at xxx>), ('cond', <torch._ops.HigherOrderOperator object at xxx>), ('sub', <built-in function sub>)]
```


Test Plan:
See added tests in test_higher_order_ops.py and modify existing test.
Also updated bin by running:
"buck2 run @//mode/dev-nosan fbcode//aibench/api:gen_test_files --config client.id=nuclide"

cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx peterbell10 ipiszy ngimel yf225 chenyang78 kadeng muchulee8 aakhundov

Reviewed By: angelayi

Differential Revision: D48984986

Pulled By: ydwu4

test/dynamo/test_aot_autograd.py

@@ -798,10 +798,11 @@ def _prepare_model_args():
                     continue
                 if min_seq_nr < 0:
                     min_seq_nr = seq_nr
-                mod_name = node.meta.get("source_fn", "")
+                source_fn_stack = node.meta.get("source_fn_stack", [])
                 orig_aten = node.meta.get("original_aten", "")
-                if isinstance(mod_name, tuple):
-                    mod_name = mod_name[0]
+                mod_name = ""
+                if len(source_fn_stack) > 0:
+                    mod_name = source_fn_stack[-1][0]
                 # Make all seq_nr relative so it starts at 0
                 seq_nr = seq_nr - min_seq_nr
                 seq_table = seq_table + f"{seq_nr}|{orig_aten}|{mod_name}\n"

test/dynamo/test_export.py

@@ -964,7 +964,7 @@ def forward(self, x):
             if node.op not in {"placeholder", "output"}:
                 self.assertTrue(node.stack_trace is not None)
                 self.assertTrue(node.meta["nn_module_stack"] is not None)
-                self.assertTrue(node.meta["source_fn"] is not None)
+                self.assertTrue(node.meta["source_fn_stack"] is not None)
 
         torch._dynamo.reset()
 
@@ -974,7 +974,7 @@ def forward(self, x):
             if node.op == "call_function":
                 self.assertTrue(node.stack_trace is not None)
                 self.assertTrue(node.meta["nn_module_stack"] is not None)
-                self.assertTrue(node.meta["source_fn"] is not None)
+                self.assertTrue(node.meta["source_fn_stack"] is not None)
                 self.assertTrue(node.meta["val"] is not None)
                 self.assertTrue(node.meta["original_aten"] is not None)
 
@@ -4013,7 +4013,6 @@ def fn(x):
                 self.assertEqual(
                     nd1.meta["nn_module_stack"], nd2.meta["nn_module_stack"]
                 )
-            self.assertEqual(nd1.meta["source_fn"], nd2.meta["source_fn"])
             self.assertEqual(nd1.meta["stack_trace"], nd2.meta["stack_trace"])
 
     def test_preserve_fx_node_metadata_recompile(self):

test/dynamo/test_higher_order_ops.py

@@ -1,5 +1,6 @@
 # Owner(s): ["module: dynamo"]
 import functools
+import pprint
 import re
 import unittest
 
@@ -1692,6 +1693,154 @@ def fn(x):
 
         self.assertTrue(activations.keys() == forward_handles.keys())
 
+    def _get_source_fn_stack(self, gm, node_names):
+        ret = {}
+        for mod in gm.modules():
+            for node in mod.graph.nodes:
+                if node.name in node_names:
+                    actual_stack = [
+                        name for name, _ in node.meta.get("source_fn_stack", [])
+                    ]
+                    ret[node.name] = actual_stack
+        return ret
+
+    def test_wrap_source_fn_stack(self):
+        class MockModule(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.linear = torch.nn.Linear(4, 4)
+
+            def forward(self, x):
+                return self.linear(x)
+
+        mod = MockModule()
+
+        def gn(x):
+            return torch.cos(x) + wrap(mod, x)
+
+        def fn(x):
+            return wrap(gn, x)
+
+        backend = EagerAndRecordGraphs()
+        inp = torch.randn((4, 4))
+        torch.compile(fn, backend=backend, fullgraph=True)(inp)
+
+        gm = backend.graphs[0]
+        actual_stack = self._get_source_fn_stack(gm, {"cos", "add", "linear"})
+        self.assertExpectedInline(
+            pprint.pformat(actual_stack),
+            """\
+{'add': ['wrap', 'add'],
+ 'cos': ['wrap', 'cos'],
+ 'linear': ['wrap', 'wrap', 'linear']}""",
+        )
+
+    def test_cond_source_fn_stack(self):
+        backend = EagerAndRecordGraphs()
+
+        @torch.compile(backend=backend, fullgraph=True)
+        def cond_f(pred, pred2, x, y):
+            def true_fn(pred2, x, y):
+                return x + y
+
+            def false_fn(pred2, x, y):
+                def true_fn2(x, y):
+                    return x.sin() - y.cos()
+
+                def false_fn2(x, y):
+                    return x.cos() - y.sin()
+
+                return control_flow.cond(pred2, true_fn2, false_fn2, [x, y])
+
+            return control_flow.cond(pred, true_fn, false_fn, [pred2, x, y])
+
+        pred = torch.tensor(True)
+        pred2 = torch.tensor(False)
+        xs = torch.randn(2, 3, 3)
+        y = torch.randn(3, 3)
+        cond_f(pred, pred2, xs, y)
+
+        gm = backend.graphs[0]
+        actual_stack = self._get_source_fn_stack(gm, {"cos", "add", "sin", "sub"})
+        self.assertExpectedInline(
+            pprint.pformat(actual_stack),
+            """\
+{'add': ['cond', 'add'],
+ 'cos': ['cond', 'cond', 'cos'],
+ 'sin': ['cond', 'cond', 'sin'],
+ 'sub': ['cond', 'cond', 'sub']}""",
+        )
+
+    def test_map_source_fn_stack(self):
+        backend = EagerAndRecordGraphs()
+
+        xs = torch.randn(2, 3, 3)
+        y = torch.randn(3)
+
+        @torch.compile(backend=backend, fullgraph=True)
+        def map_f(xs, y):
+            def inner(x, y):
+                def inner2(x, y):
+                    return x + y
+
+                return control_flow.map(inner2, x, y) * y.cos()
+
+            return control_flow.map(inner, xs, y).sin()
+
+        result = map_f(xs, y)
+
+        gm = backend.graphs[0]
+        actual_stack = self._get_source_fn_stack(gm, {"cos", "add", "sin"})
+        self.assertExpectedInline(
+            pprint.pformat(actual_stack),
+            """{'add': ['map', 'map', 'add'], 'cos': ['map', 'cos'], 'sin': ['sin']}""",
+        )
+
+    def test_grad_source_fn_stack(self):
+        backend = EagerAndRecordGraphs()
+
+        def fn(x):
+            return x.sin().sum()
+
+        @torch.compile(backend=backend, fullgraph=False)
+        def wrapper_fn(x):
+            return torch.func.grad(torch.func.grad(fn))(x)
+
+        x = torch.randn(())
+
+        wrapper_fn(x)
+        gm = backend.graphs[0]
+        actual_stack = self._get_source_fn_stack(gm, {"sum_1", "sin"})
+        self.assertExpectedInline(
+            pprint.pformat(actual_stack),
+            """\
+{'sin': ['grad_impl', 'grad_impl', 'sin'],
+ 'sum_1': ['grad_impl', 'grad_impl', 'sum_1']}""",
+        )
+
+    def test_vmap_source_fn_stack(self):
+        backend = EagerAndRecordGraphs()
+
+        def inner_fn(x):
+            return torch.func.vmap(lambda x: x.sum(0) + x.sum(1))(x)
+
+        @torch.compile(backend=backend, fullgraph=True)
+        def fn(x):
+            return torch.func.vmap(lambda x: inner_fn(x.cos()))(x)
+
+        x = torch.randn(3, 3, 3, 3)
+        fn(x)
+        gm = backend.graphs[0]
+        actual_stack = self._get_source_fn_stack(gm, {"sum_1", "sum_2", "cos", "add"})
+        self.assertExpectedInline(
+            pprint.pformat(actual_stack),
+            """\
+{'add': ['vmap_impl', 'vmap_impl', 'add'],
+ 'cos': ['vmap_impl', 'cos'],
+ 'sum_1': ['vmap_impl', 'vmap_impl', 'sum_1'],
+ 'sum_2': ['vmap_impl', 'vmap_impl', 'sum_2']}""",
+        )
+
 
 class FuncTorchHigherOrderOpTests(torch._dynamo.test_case.TestCase):
     def run(self, result=None):

test/export/test_export.py

@@ -346,7 +346,7 @@ def forward(self, x):
                 node.name in ep.graph_signature.inputs_to_buffers or
                 node.name in ep.graph_signature.inputs_to_parameters
             ):
-                self.assertTrue("source_fn" in node.meta)
+                self.assertTrue("source_fn_stack" in node.meta)
                 self.assertTrue("nn_module_stack" in node.meta)
 
 
@@ -1071,8 +1071,13 @@ def forward(self, x):
         for mod in gm.modules():
             for node in mod.graph.nodes:
                 if node.name in {"sin", "cos"}:
-                    actual_source_fns.append(node.meta.get("source_fn", None))
-        exp_source_fns = [("cos", "cos"), ("sin", "sin")]
+                    source_fn_st = node.meta.get("source_fn_stack", None)
+                    if source_fn_st is not None:
+                        source_names = []
+                        for source_fn in source_fn_st:
+                            source_names.append(source_fn[0])
+                        actual_source_fns.append(source_names)
+        exp_source_fns = [["cond", "cos"], ["cond", "sin"]]
         self.assertEqual(actual_source_fns, exp_source_fns)
 
     def test_lift_constants(self) -> None:

test/export/test_serialize.py

@@ -273,10 +273,10 @@ def _check_graph_nodes(gm1, gm2, _check_meta=True):
                     #     node1.meta.get("nn_module_stack", None),
                     #     node2.meta.get("nn_module_stack", None),
                     # )
-                    # Check "source_fn" metadata
+                    # Check "source_fn_stack" metadata
                     self.assertEqual(
-                        node1.meta.get("source_fn", None),
-                        node2.meta.get("source_fn", None),
+                        node1.meta.get("source_fn_stack", None),
+                        node2.meta.get("source_fn_stack", None),
                     )
 
         _check_graph_nodes(ep.graph_module, deserialized_ep.graph_module, _check_meta)

test/functorch/test_control_flow.py

@@ -1465,7 +1465,7 @@ def false_fn(x):
             return x * x.sin()
 
         def foo(x):
-            return cond(x.shape[0] == 4, true_fn, false_fn, [x])
+            return cond(x.shape[0] == 4, true_fn, false_fn, (x,))
         inp = torch.randn([4, 3])
         gm, _ = torch._dynamo.export(foo)(inp)
 
@@ -1476,7 +1476,7 @@ def run_with_interpreter(*args):
 
 
         checked_ops = {"add", "mul", "sin", "cos"}
-        checked_meta = ["source_fn", "stack_trace"]
+        checked_meta = ["source_fn_stack", "stack_trace"]
         all_source_fns = collect_meta_for_filtered_nodes(gm, checked_ops, checked_meta)
         new_source_fns = collect_meta_for_filtered_nodes(new_gm, checked_ops, checked_meta)
         self.assertEqual(all_source_fns, new_source_fns)

test/test_fx.py

@@ -1777,13 +1777,13 @@ def forward(self, x):
             if node.op == 'get_attr':
                 node.meta["nn_module_stack"] = "self"
                 node.meta["stack_trace"] = "stack_trace"
-                node.meta["source_fn"] = "source_fn"
+                node.meta["source_fn_stack"] = "source_fn_stack"
         new_gm = Transformer(gm).transform()
         for node in new_gm.graph.nodes:
             if node.op == 'get_attr':
                 self.assertEqual(node.meta["nn_module_stack"], "self")
                 self.assertEqual(node.meta["stack_trace"], "stack_trace")
-                self.assertEqual(node.meta["source_fn"], "source_fn")
+                self.assertEqual(node.meta["source_fn_stack"], "source_fn_stack")
 
 
     def test_interpreter(self):

torch/_dynamo/output_graph.py

@@ -423,11 +423,13 @@ def remove_node(self, *args, **kwargs):
         return self.current_tracer.remove_node(*args, **kwargs)
 
     @contextlib.contextmanager
-    def new_subtracer(self):
+    def new_subtracer(self, source_target):
         new_scope_ctx = enter_new_scope()
         try:
             new_scope_ctx.__enter__()
-            tracer = SubgraphTracer(self, parent=self.current_tracer)
+            tracer = SubgraphTracer(
+                self, parent=self.current_tracer, source_target=source_target
+            )
             self.tracers.append(tracer)
             yield tracer
         finally:
@@ -1181,7 +1183,9 @@ class SubgraphTracer(fx.Tracer):
     compiling and executing the graph.
     """
 
-    def __init__(self, output_graph, parent=None, export_root=False):
+    def __init__(
+        self, output_graph, parent=None, export_root=False, source_target=None
+    ):
         super().__init__()
         self.output_graph = weakref.proxy(output_graph)
         self.graph = torch.fx.Graph()
@@ -1220,6 +1224,16 @@ def __init__(self, output_graph, parent=None, export_root=False):
         self._orig_gm_meta = None
         self._orig_gm_lineno_map = None
         self._orig_gm_firstlineno = None
+        # Each SubgraphTracer is associated with a source target, which indicates
+        # which operator this subgraph is attached to. We compute a source_fn_stack
+        # based on the source tareget. For the root tracer, it's set to [].
+        # This is useful for debugging and transforming the exported graph.
+        if self.parent is None:
+            self.source_fn_stack = []
+        else:
+            self.source_fn_stack = self.parent.source_fn_stack + [
+                (self.graph._target_to_str(source_target), source_target)
+            ]
 
     def create_proxy(
         self,
@@ -1315,6 +1329,24 @@ def get_trace_call_log_str():
                 self._orig_gm_meta = None
                 self._orig_gm_lineno_map = None
                 self._orig_gm_firstlineno = None
+        nn_module_stack = tx.nn_module_stack
+        if nn_module_stack:
+            rv.node.meta["nn_module_stack"] = nn_module_stack.copy()
+
+        if kind in {"call_function", "call_method"}:
+            rv.node.meta["source_fn_stack"] = self.source_fn_stack + [
+                (rv.node.name, target)
+            ]
+        elif kind == "call_module":
+            if self.parent is not None:
+                unimplemented("Invoking an nn.Module inside HigherOrderOperator")
+            # For modules we store the class
+            rv.node.meta["source_fn_stack"] = self.source_fn_stack + [
+                (
+                    rv.node.name,
+                    rv.node.meta["nn_module_stack"][target][1],
+                )
+            ]
 
         # preserve original meta if it is available
         if (
@@ -1332,26 +1364,30 @@ def get_trace_call_log_str():
                 meta = self._orig_gm_meta[node_idx]
                 if "stack_trace" in meta:
                     rv.node.meta["stack_trace"] = meta["stack_trace"]
-                if "nn_module_stack" in meta and "source_fn" in meta:
+                if "nn_module_stack" in meta and "source_fn_stack" in meta:
                     rv.node.meta["nn_module_stack"] = meta["nn_module_stack"]
-                    rv.node.meta["source_fn"] = meta["source_fn"]
+                    rv.node.meta["source_fn_stack"] = meta["source_fn_stack"]
 
         if "nn_module_stack" not in rv.node.meta:
             nn_module_stack = tx.nn_module_stack
             if nn_module_stack:
                 rv.node.meta["nn_module_stack"] = nn_module_stack.copy()
 
-        if "source_fn" not in rv.node.meta:
+        if "source_fn_stack" not in rv.node.meta:
             if kind in {"call_function", "call_method"}:
-                rv.node.meta["source_fn"] = (rv.node.name, target)
+                rv.node.meta["source_fn_stack"] = self.source_fn_stack + [
+                    (rv.node.name, target)
+                ]
             elif kind == "call_module":
                 if self.parent is not None:
                     unimplemented("Invoking an nn.Module inside HigherOrderOperator")
                 # For modules we store the class
-                rv.node.meta["source_fn"] = (
-                    rv.node.name,
-                    rv.node.meta["nn_module_stack"][target][1],
-                )
+                rv.node.meta["source_fn_stack"] = self.source_fn_stack + [
+                    (
+                        rv.node.name,
+                        rv.node.meta["nn_module_stack"][target][1],
+                    )
+                ]
 
         if "stack_trace" not in rv.node.meta:
             frame_summaries: List[traceback.FrameSummary] = []