[dynamo] improved graph break messages for some common graph break sites [1/N] (#146525)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/146525
Approved by: https://github.com/jansel

.flake8

@@ -38,6 +38,7 @@ per-file-ignores =
     torchgen/api/types/__init__.py: F401,F403
     torchgen/executorch/api/types/__init__.py: F401,F403
     test/dynamo/test_higher_order_ops.py: B950
+    test/dynamo/test_graph_break_messages.py: B950
     torch/testing/_internal/dynamo_test_failures.py: B950
     # TOR901 is only for test, we want to ignore it for everything else.
     # It's not easy to configure this without affecting other per-file-ignores,

test/dynamo/test_activation_checkpointing.py

@@ -1251,7 +1251,7 @@ Non-primal fwd outputs from model w/o backward hook: {mod_no_hook_fwd_outputs_no
         x = torch.randn(4, 4).to(device)
         opt_fn = torch.compile(fn, fullgraph=True)
         with self.assertRaisesRegex(
-            torch._dynamo.exc.Unsupported, "skip function graph_break in file"
+            torch._dynamo.exc.Unsupported, "User-inserted graph break"
         ):
             opt_fn(x)
 

test/dynamo/test_autograd_function.py

@@ -271,7 +271,10 @@ class AutogradFunctionTests(torch._dynamo.test_case.TestCase):
         model = CustomFuncBwdPrintModule()
         opt_model = torch.compile(model, backend="eager", fullgraph=True)
         x = torch.randn(2, 2, dtype=torch.double, requires_grad=True)
-        with self.assertRaisesRegex(torch._dynamo.exc.Unsupported, "builtin: print"):
+        with self.assertRaisesRegex(
+            torch._dynamo.exc.Unsupported,
+            "Dynamo does not know how to trace builtin operator `print`",
+        ):
             opt_model(x)
 
     def test_stride_in_bwd(self):

test/dynamo/test_decorators.py

@@ -330,7 +330,7 @@ class DecoratorTests(torch._dynamo.test_case.TestCase):
             fn3(torch.randn(4, 5))
             self.assertFalse(True)
         except torch._dynamo.exc.Unsupported as e:
-            self.assertIn("call torch._dynamo.disable() wrapped function", str(e))
+            self.assertIn("Skip calling `torch.compiler.disable()`d function", str(e))
 
     def test_disable_optimize(self):
         cnt = torch._dynamo.testing.CompileCounter()

test/dynamo/test_exc.py

@@ -37,7 +37,12 @@ class ExcTests(LoggingTestCase):
                 torch.randn(1)
             ),
             """\
-'skip function graph_break in file _dynamo/decorators.py'
+Call to `torch._dynamo.graph_break()`
+  Explanation: User-inserted graph break. Message: None
+  Hint: Remove the `torch._dynamo.graph_break()` call.
+
+  Developer debug context: Called `torch._dynamo.graph_break()` with args `[]`, kwargs `{}`
+
 
 from user code:
    File "test_exc.py", line N, in fn001
@@ -171,7 +176,12 @@ from user code:
             munge_exc(record.getMessage()),
             """\
 Graph break in user code at test_exc.py:N
-Reason: Unsupported: 'skip function graph_break in file _dynamo/decorators.py'
+Graph Break Reason: Call to `torch._dynamo.graph_break()`
+  Explanation: User-inserted graph break. Message: None
+  Hint: Remove the `torch._dynamo.graph_break()` call.
+
+  Developer debug context: Called `torch._dynamo.graph_break()` with args `[]`, kwargs `{}`
+
 User code traceback:
   File "test_exc.py", line N, in fn001
     return fn002(x)

test/dynamo/test_graph_break_messages.py

@@ -0,0 +1,505 @@
+# Owner(s): ["module: dynamo"]
+
+import re
+import unittest
+import warnings
+
+import torch
+import torch._dynamo
+import torch._dynamo.config
+import torch._dynamo.test_case
+import torch.utils._pytree as python_pytree
+from torch._dynamo.exc import Unsupported
+from torch._dynamo.utils import counters
+from torch.testing._internal.common_utils import IS_FBCODE, scoped_load_inline
+
+
+"""
+NOTE Adding tests to this file:
+
+It is good practice to add a minimal repro for each graph break site (i.e. `unimplemented()` call
+to make sure that there aren't any errors that occur when generating graph break messages.
+
+If a graph break message test fails because the graph break no longer repros,
+it is good practice to find a new minimal repro that causes the graph break.
+If this is too much work, it is likely safe to skip/remove the test, assuming
+it was previously passing and the graph break message is not changed.
+However, if you add a new graph break or modify a graph break message, you should
+make sure that there is a test for it.
+"""
+
+
+class GraphBreakMessagesTest(torch._dynamo.test_case.TestCase):
+    maxDiff = None
+
+    def test_dynamic_shape_operator(self):
+        def fn():
+            return torch.nonzero(torch.rand([10, 10]))
+
+        self.assertExpectedInlineMunged(
+            Unsupported,
+            lambda: torch.compile(fn, backend="eager", fullgraph=True)(),
+            """\
+Dynamic shape operator
+  Explanation: Operator `aten.nonzero.default`'s output shape depends on input Tensor data.
+  Hint: Enable tracing of dynamic shape operators with `torch._dynamo.config.capture_dynamic_output_shape_ops = True`
+
+  Developer debug context: aten.nonzero.default
+
+
+from user code:
+   File "test_graph_break_messages.py", line N, in fn
+    return torch.nonzero(torch.rand([10, 10]))""",
+        )
+
+    def test_dynamic_shape_operator_no_meta_kernel(self):
+        def fn():
+            return torch.linalg.lstsq(torch.rand(10, 10), torch.rand(10, 10))
+
+        with torch._dynamo.config.patch(capture_dynamic_output_shape_ops=True):
+            self.assertExpectedInlineMunged(
+                Unsupported,
+                lambda: torch.compile(fn, backend="eager", fullgraph=True)(),
+                """\
+Dynamic shape operator (no meta kernel)
+  Explanation: Operator `aten.linalg_lstsq.default` does not have a meta kernel that supports dynamic output shapes
+  Hint: Please report an issue to PyTorch
+
+  Developer debug context: aten.linalg_lstsq.default
+
+
+from user code:
+   File "test_graph_break_messages.py", line N, in fn
+    return torch.linalg.lstsq(torch.rand(10, 10), torch.rand(10, 10))""",
+            )
+
+    def test_data_dependent_operator(self):
+        def fn(x):
+            return x.item()
+
+        self.assertExpectedInlineMunged(
+            Unsupported,
+            lambda: torch.compile(fn, backend="eager", fullgraph=True)(
+                torch.Tensor([1])
+            ),
+            """\
+Tensor.item
+
+from user code:
+   File "test_graph_break_messages.py", line N, in fn
+    return x.item()""",
+        )
+
+    def test_data_dependent_operator2(self):
+        def fn(x):
+            return torch.equal(x, x)
+
+        with torch._dynamo.config.patch(capture_scalar_outputs=True):
+            self.assertExpectedInlineMunged(
+                Unsupported,
+                lambda: torch.compile(fn, backend="eager", fullgraph=True)(
+                    torch.ones(3)
+                ),
+                """\
+Data dependent operator
+  Explanation: Operator `aten.equal.default` has a non-Tensor output whose value is dependent on the data of Tensor inputs.
+  Hint: Consider wrapping the operator into a PyTorch-understood custom operator (see https:/pytorch.org/tutorials/advanced/custom_ops_landing_page.html)
+
+  Developer debug context: aten.equal.default
+
+
+from user code:
+   File "test_graph_break_messages.py", line N, in fn
+    return torch.equal(x, x)""",
+            )
+
+    def test_super_call_method(self):
+        def fn(it):
+            return [x + 1 for x in it]
+
+        self.assertExpectedInlineMunged(
+            Unsupported,
+            lambda: torch.compile(fn, backend="eager", fullgraph=True)(
+                zip(range(5), range(10))
+            ),
+            """\
+Unsupported method call
+  Explanation: Dynamo does not know how to trace method `__iter__` of class `zip`
+  Hint: Avoid calling `zip.__iter__` in your code.
+  Hint: Please report an issue to PyTorch.
+  Hint: Dynamo does not fully support tracing builtin iterators (e.g. `map`, `zip`, `enumerate`) passed in from uncompiled to compiled regions (e.g. `torch.compile(fn)(enumerate(...))`). This can happen unintentionally if a previous graph break happens with a builtin iterator in the local scope.
+
+  Developer debug context: call_method UserDefinedObjectVariable(zip) __iter__ () {}
+
+
+from user code:
+   File "test_graph_break_messages.py", line N, in fn
+    return [x + 1 for x in it]""",
+        )
+
+    def test_super_call_function(self):
+        def fn(it):
+            return [x + 1 for x in it()]
+
+        self.assertExpectedInlineMunged(
+            Unsupported,
+            lambda: torch.compile(fn, backend="eager", fullgraph=True)(
+                zip(range(5), range(10))
+            ),
+            """\
+Unsupported function call
+  Explanation: Dynamo does not know how to trace the function `UserDefinedObjectVariable(zip)`
+  Hint: Avoid calling `UserDefinedObjectVariable(zip)` in your code.
+  Hint: Please report an issue to PyTorch.
+
+  Developer debug context: call_function UserDefinedObjectVariable(zip) [] {}
+
+
+from user code:
+   File "test_graph_break_messages.py", line N, in fn
+    return [x + 1 for x in it()]""",
+        )
+
+    def test_unsupported_context(self):
+        def fn(obj):
+            with obj:
+                return 1
+
+        self.assertExpectedInlineMunged(
+            Unsupported,
+            lambda: torch.compile(fn, backend="eager", fullgraph=True)(3),
+            """\
+Unsupported context manager
+  Explanation: Dynamo does not know how to enter a `int` context manager.
+  Hint: Avoid using the unsupported context manager.
+  Hint: File an issue to PyTorch. Simple context managers can potentially be supported, but note that context managers can't be supported in general
+
+  Developer debug context: Attempted SETUP_WITH/BEFORE_WITH on ConstantVariable(int: 3)
+
+
+from user code:
+   File "test_graph_break_messages.py", line N, in fn
+    with obj:""",
+        )
+
+    def test_backend_fake_tensor_exc(self):
+        def bad_backend(gm, ex):
+            raise torch._subclasses.fake_tensor.UnsupportedFakeTensorException("test")
+
+        def fn(x):
+            return x + 1
+
+        self.assertExpectedInlineMunged(
+            Unsupported,
+            lambda: torch.compile(fn, backend=bad_backend, fullgraph=True)(
+                torch.ones(3, 3)
+            ),
+            """\
+Backend compiler exception
+  Explanation: Backend compiler `bad_backend` failed with test. Adding a graph break.
+  Hint: Report an issue to the backend compiler repo.
+
+  Developer debug context: Backend: bad_backend
+Exception:test
+Traceback:
+  File "test_graph_break_messages.py", line N, in fn
+    return x + 1""",
+        )
+
+    def test_unsupported_builtin(self):
+        def fn():
+            print("abc")
+
+        self.assertExpectedInlineMunged(
+            Unsupported,
+            lambda: torch.compile(fn, backend="eager", fullgraph=True)(),
+            """\
+Failed to trace builtin operator
+  Explanation: Dynamo does not know how to trace builtin operator `print` with argument types ['str'] (has_kwargs False)
+  Hint: Avoid calling builtin `print` with argument types ['str']. Consider using an equivalent alternative function/method to `print`.
+  Hint: If you are attempting to call a logging function (e.g. `print`), you can try adding it to `torch._dynamo.config.reorderable_logging_functions`.
+  Hint: Please report an issue to PyTorch.
+
+  Developer debug context: builtin print [<class 'torch._dynamo.variables.constant.ConstantVariable'>] False
+
+
+from user code:
+   File "test_graph_break_messages.py", line N, in fn
+    print("abc")""",
+        )
+
+    def test_skipfile_call(self):
+        def fn():
+            return unittest.skip("test")
+
+        def post_munge(s):
+            return re.sub(r"file `.*case\.py`", "file `case.py`", s)
+
+        self.assertExpectedInlineMunged(
+            Unsupported,
+            lambda: torch.compile(fn, backend="eager", fullgraph=True)(),
+            """\
+Attempted to call function marked as skipped
+  Explanation: Dynamo developers have intentionally marked that the function `skip` in file `case.py` should not be traced.
+  Hint: Avoid calling the function `skip`.
+  Hint: Remove the function `skip` or the file `case.py` from torch/_dynamo/trace_rules.py. More graph breaks may occur as a result of attempting to trace into the function.
+  Hint: Please file an issue to PyTorch.
+
+  Developer debug context: module: unittest.case, qualname: skip, skip reason: <missing reason>
+
+
+from user code:
+   File "test_graph_break_messages.py", line N, in fn
+    return unittest.skip("test")""",
+            post_munge=post_munge,
+        )
+
+    def test_skipfile_dynamo_call(self):
+        def fn():
+            torch._dynamo.disable()
+
+        self.assertExpectedInlineMunged(
+            Unsupported,
+            lambda: torch.compile(fn, backend="eager", fullgraph=True)(),
+            """\
+Attempted to call function marked as skipped
+  Explanation: Dynamo developers have intentionally marked that the function `disable` in file `_dynamo/decorators.py` should not be traced.
+  Hint: Avoid calling the function `disable`.
+
+  Developer debug context: module: torch._dynamo.decorators, qualname: disable, skip reason: <missing reason>
+
+
+from user code:
+   File "test_graph_break_messages.py", line N, in fn
+    torch._dynamo.disable()""",
+        )
+
+    def test_skipfile_inline(self):
+        class Foo:
+            fn = unittest.skip
+
+        def fn():
+            Foo().fn()
+
+        def post_munge(s):
+            return re.sub(r"`.*case\.py`", "`case.py`", s)
+
+        self.assertExpectedInlineMunged(
+            Unsupported,
+            lambda: torch.compile(fn, backend="eager", fullgraph=True)(),
+            """\
+Attempted to inline function marked as skipped
+  Explanation: Dynamo developers have intentionally marked that the function `skip` should not be traced.
+  Hint: Avoid calling the function `skip`.
+  Hint: Remove the function `case.py` from torch/_dynamo/trace_rules.py. More graph breaks may occur as a result of attempting to trace into the function.
+  Hint: Please file an issue to PyTorch.
+
+  Developer debug context: qualname: skip, name: skip, filename: `case.py`, skip reason: skipped according trace_rules.lookup SKIP_DIRS
+
+
+from user code:
+   File "test_graph_break_messages.py", line N, in fn
+    Foo().fn()""",
+            post_munge=post_munge,
+        )
+
+    def test_disable(self):
+        @torch.compiler.disable
+        def inner():
+            return 1
+
+        def fn():
+            return inner()
+
+        def post_munge(s):
+            return re.sub(
+                r"<function GraphBreakMessagesTest\.test_disable\.<locals>\.inner at 0x[0-9A-Fa-f]+>",
+                "<function inner>",
+                s,
+            )
+
+        self.assertExpectedInlineMunged(
+            Unsupported,
+            lambda: torch.compile(fn, backend="eager", fullgraph=True)(),
+            """\
+Skip calling `torch.compiler.disable()`d function
+  Explanation: Skip calling function `<function inner>` since it was wrapped with `torch.compiler.disable`
+  Hint: Remove the `torch.compiler.disable` call
+
+  Developer debug context: <function inner>
+
+
+from user code:
+   File "test_graph_break_messages.py", line N, in fn
+    return inner()""",
+            post_munge=post_munge,
+        )
+
+    def test_dynamo_graph_break_fn(self):
+        def fn():
+            torch._dynamo.graph_break()
+
+        self.assertExpectedInlineMunged(
+            Unsupported,
+            lambda: torch.compile(fn, backend="eager", fullgraph=True)(),
+            """\
+Call to `torch._dynamo.graph_break()`
+  Explanation: User-inserted graph break. Message: None
+  Hint: Remove the `torch._dynamo.graph_break()` call.
+
+  Developer debug context: Called `torch._dynamo.graph_break()` with args `[]`, kwargs `{}`
+
+
+from user code:
+   File "test_graph_break_messages.py", line N, in fn
+    torch._dynamo.graph_break()""",
+        )
+
+    def test_dynamo_graph_break_fn_with_msg(self):
+        def fn():
+            torch._dynamo.graph_break(msg="test graph break")
+
+        self.assertExpectedInlineMunged(
+            Unsupported,
+            lambda: torch.compile(fn, backend="eager", fullgraph=True)(),
+            """\
+Call to `torch._dynamo.graph_break()`
+  Explanation: User-inserted graph break. Message: test graph break
+  Hint: Remove the `torch._dynamo.graph_break()` call.
+
+  Developer debug context: Called `torch._dynamo.graph_break()` with args `[]`, kwargs `{'msg': ConstantVariable(str: 'test graph break')}`
+
+
+from user code:
+   File "test_graph_break_messages.py", line N, in fn
+    torch._dynamo.graph_break(msg="test graph break")""",
+        )
+
+    def test_warnings(self):
+        def fn():
+            warnings.warn("test")
+
+        self.assertExpectedInlineMunged(
+            Unsupported,
+            lambda: torch.compile(fn, backend="eager", fullgraph=True)(),
+            """\
+Attempted to call function marked as skipped
+  Explanation: Dynamo does not know how to trace the Python builtin `_warnings.warn`.
+  Hint: If you are attempting to call a logging function (e.g. `_warnings.warn`), you can try adding it to `torch._dynamo.config.reorderable_logging_functions`.
+  Hint: Please file an issue on GitHub so the PyTorch team can add support for it.
+
+  Developer debug context: module: _warnings, qualname: warn, skip reason: <missing reason>
+
+
+from user code:
+   File "test_graph_break_messages.py", line N, in fn
+    warnings.warn("test")""",
+        )
+
+    @unittest.skipIf(not python_pytree._cxx_pytree_exists, "missing optree package")
+    def test_optree_graph_break_message(self):
+        import optree
+
+        @torch.compile(backend="eager")
+        def fn(x):
+            d = {"a": 1}
+            optree.tree_flatten(d)
+            return torch.sin(x)
+
+        fn(torch.randn(4))
+        self.assertEqual(len(counters["graph_break"]), 1)
+        first_graph_break = next(iter(counters["graph_break"].keys()))
+        self.assertExpectedInline(
+            first_graph_break,
+            """\
+Attempted to call function marked as skipped
+  Explanation: Dynamo cannot trace optree C/C++ function optree._C.PyCapsule.flatten.
+  Hint:  Consider using torch.utils._pytree - https://github.com/pytorch/pytorch/blob/main/torch/utils/_pytree.py
+
+  Developer debug context: module: optree._C, qualname: PyCapsule.flatten, skip reason: <missing reason>
+""",
+        )
+
+    @scoped_load_inline
+    @torch._dynamo.config.patch(inline_inbuilt_nn_modules=False)
+    @unittest.skipIf(IS_FBCODE, "inline cpp_extension doesn't work in fbcode")
+    def test_cpp_extension_recommends_custom_ops(self, load_inline):
+        cpp_source = """
+        #include <torch/extension.h>
+        at::Tensor foobar(const at::Tensor& x) {
+            return x.clone();
+        }
+        """
+        module = load_inline(
+            name="mylib",
+            cpp_sources=cpp_source,
+            functions="foobar",
+            verbose=True,
+        )
+
+        x = torch.ones(2, 2, requires_grad=True)
+        counters.clear()
+
+        @torch.compile(backend="eager")
+        def f(x):
+            return module.foobar(x)
+
+        with self.assertWarnsOnceRegex(
+            UserWarning,
+            "(?s).*https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html.*",
+        ):
+            f(x)
+        self.assertEqual(len(counters["graph_break"]), 1)
+        first_graph_break = next(iter(counters["graph_break"].keys()))
+
+        first_graph_break = re.sub(r"mylib(_v\d+)?", "mylib", first_graph_break)
+
+        self.assertExpectedInline(
+            first_graph_break,
+            """\
+Attempted to call function marked as skipped
+  Explanation: Dynamo does not know how to trace the builtin `mylib.PyCapsule.foobar.` This function is either a Python builtin (e.g. _warnings.warn) or a third-party C/C++ Python extension (perhaps created with pybind).
+  Hint: If it is a Python builtin, please file an issue on GitHub so the PyTorch team can add support for it and see the next case for a workaround.
+  Hint: If it is a third-party C/C++ Python extension, please either wrap it into a PyTorch-understood custom operator (see https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html for more details) or, if it is traceable, use `torch.compiler.allow_in_graph`.
+
+  Developer debug context: module: mylib, qualname: PyCapsule.foobar, skip reason: <missing reason>
+""",
+        )
+
+        cpp_source = """
+        #include <torch/extension.h>
+        at::Tensor baz(const at::Tensor& x) {
+            return x.clone();
+        }
+        """
+        module2 = load_inline(
+            name="mylib2",
+            cpp_sources=cpp_source,
+            functions="baz",
+            verbose=True,
+        )
+
+        torch._dynamo.reset()
+
+        # Test that each warning only happens once
+        @torch.compile(backend="eager")
+        def f(x):
+            module2.baz(x)
+            module.foobar(x)
+            module.foobar(x)
+            module2.baz(x)
+            module.foobar(x)
+            module2.baz(x)
+            return x.clone()
+
+        with warnings.catch_warnings(record=True) as ws:
+            warnings.simplefilter("always")
+            f(x)
+            f(x)
+        self.assertEqual(len(ws), 2)
+
+
+if __name__ == "__main__":
+    from torch._dynamo.test_case import run_tests
+
+    run_tests()

test/dynamo/test_hooks.py

@@ -512,7 +512,9 @@ class HooksTests(torch._dynamo.test_case.TestCase):
         x2 = torch.ones(4, requires_grad=True)
         with compiled_autograd._enable(compiler_fn):
             dynamo_out = torch.compile(mod, backend="inductor", fullgraph=True)(x2, obj)
-            with self.assertRaisesRegex(torch._dynamo.exc.Unsupported, "builtin: str"):
+            with self.assertRaisesRegex(
+                torch._dynamo.exc.Unsupported, "Failed to trace builtin operator"
+            ):
                 dynamo_out[0].backward(torch.ones(4))
 
         self.assertEqual(obj.count, 2)

test/dynamo/test_misc.py

@@ -293,24 +293,6 @@ class MiscTests(torch._inductor.test_case.TestCase):
         with self.assertRaises(TypeError):
             fn(torch.randn(16))
 
-    @unittest.skipIf(not python_pytree._cxx_pytree_exists, "missing optree package")
-    def test_optree_graph_break_message(self):
-        import optree
-
-        @torch.compile(backend="eager")
-        def fn(x):
-            d = {"a": 1}
-            optree.tree_flatten(d)
-            return torch.sin(x)
-
-        fn(torch.randn(4))
-        self.assertEqual(len(counters["graph_break"]), 1)
-        first_graph_break = list(counters["graph_break"].keys())[0]
-        self.assertExpectedInline(
-            first_graph_break,
-            "Graph break for an optree C/C++ function optree._C.PyCapsule.flatten. Consider using torch.utils._pytree - https://github.com/pytorch/pytorch/blob/main/torch/utils/_pytree.py",
-        )
-
     def test_scalar_device_movement(self):
         if not torch._dynamo.config.assume_static_by_default:
             self.skipTest("Doesn't work with symints")
@@ -324,74 +306,6 @@ class MiscTests(torch._inductor.test_case.TestCase):
         res_compiled = add_fn(2, 3, torch.tensor(0.0))
         self.assertEqual(res, res_compiled)
 
-    @scoped_load_inline
-    @skipIfNNModuleInlined("fails internal CI")
-    @unittest.skipIf(IS_FBCODE, "inline cpp_extension doesn't work in fbcode")
-    def test_cpp_extension_recommends_custom_ops(self, load_inline):
-        cpp_source = """
-        #include <torch/extension.h>
-        at::Tensor foobar(const at::Tensor& x) {
-            return x.clone();
-        }
-        """
-        module = load_inline(
-            name="mylib",
-            cpp_sources=cpp_source,
-            functions="foobar",
-            verbose=True,
-        )
-
-        x = torch.ones(2, 2, requires_grad=True)
-        counters.clear()
-
-        @torch.compile(backend="eager")
-        def f(x):
-            return module.foobar(x)
-
-        with self.assertWarnsOnceRegex(
-            UserWarning,
-            ".*https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html.*",
-        ):
-            f(x)
-        self.assertEqual(len(counters["graph_break"]), 1)
-        first_graph_break = list(counters["graph_break"].keys())[0]
-        self.assertExpectedInline(
-            first_graph_break,
-            """Graph break due to unsupported builtin mylib.PyCapsule.foobar. This function is either a Python builtin (e.g. _warnings.warn) or a third-party C/C++ Python extension (perhaps created with pybind). If it is a Python builtin, please file an issue on GitHub so the PyTorch team can add support for it and see the next case for a workaround. If it is a third-party C/C++ Python extension, please either wrap it into a PyTorch-understood custom operator (see https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html for more details) or, if it is traceable, use torch.compiler.allow_in_graph.""",
-        )
-
-        cpp_source = """
-        #include <torch/extension.h>
-        at::Tensor baz(const at::Tensor& x) {
-            return x.clone();
-        }
-        """
-        module2 = load_inline(
-            name="mylib2",
-            cpp_sources=cpp_source,
-            functions="baz",
-            verbose=True,
-        )
-
-        torch._dynamo.reset()
-
-        # Test that each warning only happens once
-        @torch.compile(backend="eager")
-        def f(x):
-            module2.baz(x)
-            module.foobar(x)
-            module.foobar(x)
-            module2.baz(x)
-            module.foobar(x)
-            module2.baz(x)
-            return x.clone()
-
-        with warnings.catch_warnings(record=True) as ws:
-            warnings.simplefilter("always")
-            f(x)
-            f(x)
-        self.assertEqual(len(ws), 2)
-
     def test_callpacked(self):
         def call_packed(args):
             a, b, c = args
@@ -8967,7 +8881,7 @@ def ___make_guard_fn():
         # and so the guard story for the objects passed into input just isn't there atm.
         with self.assertRaisesRegex(
             torch._dynamo.exc.Unsupported,
-            "^call_method UserDefinedObjectVariable\\(set\\).*",
+            "Unsupported method call",
         ):
             foo(inp)
 
@@ -10643,7 +10557,7 @@ ShapeEnv not equal: field values don't match:
         # Should only be one restart per event
         (restart_reason,) = metrics[0].restart_reasons
         self.assertTrue(
-            "skip function graph_break" in restart_reason,
+            "User-inserted graph break" in restart_reason,
             "Should have logged graph break reason",
         )
         self.assertTrue(
@@ -10653,7 +10567,7 @@ ShapeEnv not equal: field values don't match:
 
         (restart_reason,) = metrics[1].restart_reasons
         self.assertTrue(
-            "skip function graph_break" in restart_reason,
+            "User-inserted graph break" in restart_reason,
             "Should have logged graph break reason",
         )
         self.assertTrue(

test/test_custom_ops.py

@@ -1772,8 +1772,12 @@ def forward(self, x_1):
         self.assertExpectedInline(
             next(iter(counters["graph_break"].keys())).replace(";", "\n"),
             """\
-dynamic shape operator: _torch_testing.numpy_nonzero.default
- to enable, set torch._dynamo.config.capture_dynamic_output_shape_ops = True""",
+Dynamic shape operator
+  Explanation: Operator `_torch_testing.numpy_nonzero.default`'s output shape depends on input Tensor data.
+  Hint: Enable tracing of dynamic shape operators with `torch._dynamo.config.capture_dynamic_output_shape_ops = True`
+
+  Developer debug context: _torch_testing.numpy_nonzero.default
+""",
         )
 
     # pre-existing problem: torch.compile(dynamic=True) will, by default,

torch/_dynamo/decorators.py

@@ -206,7 +206,7 @@ def disallow_in_graph(fn):
 
 
 @_disallow_in_graph_helper(throw_if_not_allowed=False)
-def graph_break():
+def graph_break(msg=""):
     """Force a graph break"""
 
 

torch/_dynamo/exc.py

@@ -441,6 +441,69 @@ def unimplemented(
     raise Unsupported(msg, case_name=case_name)
 
 
+def unimplemented_v2_with_warning(
+    e: Exception,
+    code: types.CodeType,
+    gb_type: str,
+    context: str,
+    explanation: str,
+    hints: list[str],
+) -> NoReturn:
+    # This function calls unimplemented internally and eventually graph breaks
+    # or falls to eager. unimplemented itself does not print any user warnings,
+    # i.e., its very silent. This helper function is intended when an error is
+    # encountered in the torch.compile stack which is worth showing as warning
+    # to the user. For example, if AOT Autograd backend fails with a fake tensor
+    # exception, its ok to fallback to eager but not silently. Here, we can use
+    # this function to log the message and the stack trace.
+    graph_break_msg = format_error_msg_verbose(e, code)
+    torch._logging.trace_structured(
+        "artifact",
+        metadata_fn=lambda: {
+            "name": "dynamo_graph_break_reason",
+            "encoding": "string",
+        },
+        payload_fn=lambda: graph_break_msg,
+    )
+    graph_breaks_log.debug("%s", graph_break_msg)
+    unimplemented_v2(gb_type, context, explanation, hints, from_exc=e, log_warning=True)
+
+
+# TODO replace old unimplemented later
+def unimplemented_v2(
+    gb_type: str,
+    context: str,
+    explanation: str,
+    hints: list[str],
+    *,
+    from_exc: Any = _NOTHING,
+    log_warning: bool = False,
+) -> NoReturn:
+    """
+    Called within dynamo to cause a graph break.
+    Args:
+        gb_type: Context-free graph break type. It should be a short string without any
+                 information specific to the tracing context (i.e. no dynamically-generated strings)
+        context: Developer context for the graph break. It can contain tracing context/dynamic strings.
+        explanation: User-facing context-dependent explanation for the graph break. Can be dynamic.
+        hints: List of user-facing hints for the graph break.
+    """
+    hints_str = "\n".join(hints)
+    hints_str = textwrap.indent(hints_str, "  Hint: ")
+    msg = f"""\
+{gb_type}
+  Explanation: {explanation}
+{hints_str}
+
+  Developer debug context: {context}
+"""
+    if log_warning:
+        log.warning(msg)
+    if from_exc is not _NOTHING:
+        raise Unsupported(msg) from from_exc
+    raise Unsupported(msg)
+
+
 def warning(msg: str) -> None:
     counters["warnings"][msg] += 1
     assert msg != os.environ.get("BREAK", False)

torch/_dynamo/output_graph.py

@@ -82,7 +82,7 @@ from .exc import (
     exceptions_allowed_to_be_fallback,
     SkipFrame,
     unimplemented,
-    unimplemented_with_warning,
+    unimplemented_v2_with_warning,
 )
 from .graph_deduplication import apply_graph_deduplication
 from .graph_region_tracker import GraphRegionTracker
@@ -652,9 +652,11 @@ class OutputGraph:
         """
         global_state = cast(
             dict[str, tuple[Callable[..., Any], bool]],
+            (
                 out
                 if out is not None
-            else self.tracing_context.global_context.global_state,
+                else self.tracing_context.global_context.global_state
+            ),
         )
 
         # TODO - Consider having a torch level API for torch_function_state. As
@@ -1475,12 +1477,12 @@ class OutputGraph:
         gm._param_name_to_source = self.param_name_to_source  # type: ignore[assignment]
         gm._source_to_user_stacks = self.source_to_user_stacks  # type: ignore[assignment]
 
-        try:
         name = (
             self.compiler_fn.__name__
             if hasattr(self.compiler_fn, "__name__")
-                else ""
+            else "<unknown compiler_fn>"
         )
+        try:
             _step_logger()(logging.INFO, f"calling compiler function {name}")
             compiler_fn = self.compiler_fn
             if config.verify_correctness:
@@ -1495,12 +1497,16 @@ class OutputGraph:
                 raise BackendCompilerFailed(
                     self.compiler_fn, e, inspect.currentframe()
                 ).with_traceback(e.__traceback__) from None
-            msg = (
-                f"Backend compiler failed with {str(e)} at \n"
-                f"{self.root_tx.format_frame_summary()}"
-                "Adding a graph break."
+            unimplemented_v2_with_warning(
+                e,
+                self.root_tx.f_code,
+                gb_type="Backend compiler exception",
+                context=f"Backend: {name}\nException:{str(e)}\nTraceback:\n{self.root_tx.format_frame_summary()}",
+                explanation=f"Backend compiler `{name}` failed with {str(e)}. Adding a graph break.",
+                hints=[
+                    "Report an issue to the backend compiler repo.",
+                ],
             )
-            unimplemented_with_warning(e, self.root_tx.f_code, msg)
         except SkipFrame as e:
             # The backend compiler has requested that we skip the frame, instead of
             # aborting execution.

torch/_dynamo/symbolic_convert.py

@@ -74,7 +74,13 @@ from .bytecode_transformation import (
 )
 from .code_context import code_context
 from .codegen import PyCodegen
-from .exc import ArgsMismatchError, BackendCompilerFailed, unimplemented, Unsupported
+from .exc import (
+    ArgsMismatchError,
+    BackendCompilerFailed,
+    unimplemented,
+    unimplemented_v2,
+    Unsupported,
+)
 from .funcname_cache import get_funcname
 from .guards import GuardBuilder, install_guard
 from .output_graph import GraphCompileReason, OutputGraph
@@ -488,7 +494,7 @@ def log_graph_break(code_options, reason="", exc_info=False, user_stack=None):
 
     user_stack_formatted = "".join(traceback.format_list(user_stack))
     user_stack_trace = (
-        "Graph break in user code at %s:%s\nReason: %s\nUser code traceback:\n%s"  # noqa: UP031
+        "Graph break in user code at %s:%s\nGraph Break Reason: %s\nUser code traceback:\n%s"  # noqa: UP031
         % (
             frame_loc[0],
             frame_loc[1],
@@ -523,7 +529,7 @@ def log_graph_break(code_options, reason="", exc_info=False, user_stack=None):
         # exercised by
         #   python test/dynamo/test_misc.py -k test_duplicate_graph_break_log
         graph_break_log.debug(
-            "Graph break (details suppressed) in user code at %s:%s\nReason: %s",
+            "Graph break (user stack suppressed due to duplicate graph break) in user code at %s:%s\nGraph Break Reason: %s",
             frame_loc[0],
             frame_loc[1],
             reason,
@@ -762,7 +768,7 @@ def break_graph_if_unsupported(*, push):
                 log_graph_break(
                     self.code_options,
                     exc_info=True,
-                    reason=f"Unsupported: {excp}",
+                    reason=str(excp),
                     user_stack=excp.real_stack,
                 )
 
@@ -2546,7 +2552,16 @@ class InstructionTranslatorBase(
         if not isinstance(
             ctx, (ContextWrappingVariable, GenericContextWrappingVariable)
         ):
-            unimplemented(f"{inst.opname} {ctx}")
+            unimplemented_v2(
+                gb_type="Unsupported context manager",
+                context=f"Attempted SETUP_WITH/BEFORE_WITH on {ctx}",
+                explanation=f"Dynamo does not know how to enter a `{ctx.python_type_name()}` context manager.",
+                hints=[
+                    "Avoid using the unsupported context manager.",
+                    "File an issue to PyTorch. Simple context managers can potentially be supported, "
+                    "but note that context managers can't be supported in general",
+                ],
+            )
 
         if (
             isinstance(ctx, GenericContextWrappingVariable)
@@ -3289,15 +3304,36 @@ class InliningInstructionTranslator(InstructionTranslatorBase):
                     False, "allowlist in dynamo known function"
                 )
             fn_qualname = func.fn.__qualname__ if hasattr(func, "fn") else ""
-            unimplemented(
-                f"'inline in skipfiles: {fn_qualname} | {func.get_name()} {func.get_filename()}, {result.reason}'"
+            hints = [
+                f"Avoid calling the function `{fn_qualname}`.",
+            ]
+            if "_dynamo" not in func.get_filename():
+                hints += [
+                    f"Remove the function `{fn_qualname}` or the file `{func.get_filename()}` "
+                    "from torch/_dynamo/trace_rules.py. More graph breaks may occur as a result of "
+                    "attempting to trace into the function.",
+                    "Please file an issue to PyTorch.",
+                    # TODO suggest mark_force_inline when implemented
+                ]
+            unimplemented_v2(
+                gb_type="Attempted to inline function marked as skipped",
+                context=f"qualname: {fn_qualname}, name: {func.get_name()}, "
+                f"filename: `{func.get_filename()}`, skip reason: {result.reason}",
+                explanation=f"Dynamo developers have intentionally marked that the function `{fn_qualname}` "
+                "should not be traced.",
+                hints=hints,
             )
 
         if isinstance(func, UserFunctionVariable) and inspect.getattr_static(
             func.get_function(), "_torchdynamo_disable", False
         ):
-            unimplemented(
-                f"call torch._dynamo.disable() wrapped function {func.get_function()}"
+            unimplemented_v2(
+                gb_type="Skip inlining `torch.compiler.disable()`d function",
+                context=str(func.get_function()),
+                explanation=f"Skip inlining function {func.get_function()} since it was wrapped with `torch.compiler.disable`",
+                hints=[
+                    "Remove the `torch.compiler.disable` call",
+                ],
             )
         else:
             return result

torch/_dynamo/utils.py

@@ -3004,6 +3004,7 @@ def get_fake_value(node, tx, allow_non_graph_fake=False):
     from .exc import (
         TorchRuntimeError,
         unimplemented,
+        unimplemented_v2,
         Unsupported,
         UserError,
         UserErrorType,
@@ -3063,23 +3064,50 @@ def get_fake_value(node, tx, allow_non_graph_fake=False):
         if isinstance(
             cause, torch._subclasses.fake_tensor.DataDependentOutputException
         ):
-            unimplemented(
-                f"data dependent operator: {cause.func}; "
-                "to enable, set torch._dynamo.config.capture_scalar_outputs = True"
+            # capture_scalar_outputs only works for these ops right now
+            # see torch/_subclasses/fake_impls.py
+            if cause.func in (
+                torch.ops.aten.item.default,
+                torch.ops.aten._local_scalar_dense.default,
+            ):
+                # does this actually get triggered?
+                hints = [
+                    "Enable tracing of data-dependent output operators with "
+                    "`torch._dynamo.config.capture_scalar_outputs = True`",
+                ]
+            else:
+                hints = [
+                    "Consider wrapping the operator into a PyTorch-understood custom operator "
+                    "(see https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html)",
+                ]
+            unimplemented_v2(
+                gb_type="Data dependent operator",
+                context=str(cause.func),
+                explanation=f"Operator `{cause.func}` has a non-Tensor output "
+                "whose value is dependent on the data of Tensor inputs.",
+                hints=hints,
             )
         elif isinstance(
             cause, torch._subclasses.fake_tensor.DynamicOutputShapeException
         ):
             if not torch._dynamo.config.capture_dynamic_output_shape_ops:
-                unimplemented(
-                    f"dynamic shape operator: {cause.func}; "
-                    "to enable, set torch._dynamo.config.capture_dynamic_output_shape_ops = True"
+                unimplemented_v2(
+                    gb_type="Dynamic shape operator",
+                    context=str(cause.func),
+                    explanation=f"Operator `{cause.func}`'s output shape depends on input Tensor data.",
+                    hints=[
+                        "Enable tracing of dynamic shape operators with "
+                        "`torch._dynamo.config.capture_dynamic_output_shape_ops = True`",
+                    ],
                 )
             else:
-                unimplemented(
-                    f"dynamic shape operator: {cause.func}; "
-                    "Operator does not have a meta kernel that supports dynamic output shapes, "
-                    "please report an issue to PyTorch"
+                unimplemented_v2(
+                    gb_type="Dynamic shape operator (no meta kernel)",
+                    context=str(cause.func),
+                    explanation=f"Operator `{cause.func}` does not have a meta kernel that supports dynamic output shapes",
+                    hints=[
+                        "Please report an issue to PyTorch",
+                    ],
                 )
         elif isinstance(
             cause, torch._subclasses.fake_tensor.UnsupportedOperatorException

torch/_dynamo/variables/base.py

@@ -21,7 +21,7 @@ from typing import Any, Callable, Optional, Sequence, TYPE_CHECKING
 
 from .. import variables
 from ..current_scope_id import current_scope_id
-from ..exc import unimplemented
+from ..exc import unimplemented, unimplemented_v2
 from ..guards import GuardBuilder, install_guard
 from ..source import AttrSource, Source
 from ..utils import cmp_name_to_op_mapping, istype
@@ -310,6 +310,12 @@ class VariableTracker(metaclass=VariableTrackerMeta):
         except NotImplementedError:
             raise NotImplementedError(f"{self} has no type") from None
 
+    def python_type_name(self):
+        try:
+            return self.python_type().__name__
+        except NotImplementedError:
+            return "<unknown type>"
+
     def as_python_constant(self):
         """For constants"""
         raise NotImplementedError(f"{self} is not a constant")
@@ -408,7 +414,15 @@ class VariableTracker(metaclass=VariableTrackerMeta):
         args: Sequence["VariableTracker"],
         kwargs: "dict[str, VariableTracker]",
     ) -> "VariableTracker":
-        unimplemented(f"call_function {self} {args} {kwargs}")
+        unimplemented_v2(
+            gb_type="Unsupported function call",
+            context=f"call_function {self} {args} {kwargs}",
+            explanation=f"Dynamo does not know how to trace the function `{self.debug_repr()}`",
+            hints=[
+                f"Avoid calling `{self.debug_repr()}` in your code.",
+                "Please report an issue to PyTorch.",
+            ],
+        )
 
     def call_method(
         self,
@@ -450,7 +464,27 @@ class VariableTracker(metaclass=VariableTrackerMeta):
                     self.as_python_constant(), other.as_python_constant()
                 )
             )
-        unimplemented(f"call_method {self} {name} {args} {kwargs}")
+        hints = [
+            f"Avoid calling `{self.python_type_name()}.{name}` in your code.",
+            "Please report an issue to PyTorch.",
+        ]
+        # additional hint for method calls on improperly constructed iterators
+        if isinstance(self, variables.UserDefinedObjectVariable) and name in (
+            "__iter__",
+            "__next__",
+        ):
+            hints.append(
+                "Dynamo does not fully support tracing builtin iterators (e.g. `map`, `zip`, `enumerate`) "
+                "passed in from uncompiled to compiled regions (e.g. `torch.compile(fn)(enumerate(...))`). "
+                "This can happen unintentionally if a previous graph break happens with a builtin iterator "
+                "in the local scope."
+            )
+        unimplemented_v2(
+            gb_type="Unsupported method call",
+            context=f"call_method {self} {name} {args} {kwargs}",
+            explanation=f"Dynamo does not know how to trace method `{name}` of class `{self.python_type_name()}`",
+            hints=hints,
+        )
 
     def set_name_hint(self, name):
         pass

torch/_dynamo/variables/builtin.py

@@ -24,6 +24,7 @@ from ..exc import (
     ObservedAttributeError,
     raise_observed_exception,
     unimplemented,
+    unimplemented_v2,
     Unsupported,
     UserError,
     UserErrorType,
@@ -902,9 +903,24 @@ class BuiltinVariable(VariableTracker):
 
             handlers.append(constant_fold_handler)
 
-        error_msg = f"builtin: {fn.__name__} {arg_types} {has_kwargs}"
+        def call_unimplemented_v2(args):
+            real_arg_types = [arg.python_type_name() for arg in args]
+            unimplemented_v2(
+                gb_type="Failed to trace builtin operator",
+                context=f"builtin {fn.__name__} {arg_types} {has_kwargs}",
+                explanation=f"Dynamo does not know how to trace builtin operator `{fn.__name__}` "
+                f"with argument types {real_arg_types} (has_kwargs {has_kwargs})",
+                hints=[
+                    f"Avoid calling builtin `{fn.__name__}` with argument types {real_arg_types}. "
+                    f"Consider using an equivalent alternative function/method to `{fn.__name__}`.",
+                    "If you are attempting to call a logging function (e.g. `print`), "
+                    "you can try adding it to `torch._dynamo.config.reorderable_logging_functions`.",
+                    "Please report an issue to PyTorch.",
+                ],
+            )
+
         if len(handlers) == 0:
-            return lambda *args: unimplemented(error_msg)
+            return lambda tx, args, kwargs: call_unimplemented_v2(args)
         elif len(handlers) == 1:
             (handler,) = handlers
 
@@ -912,7 +928,7 @@ class BuiltinVariable(VariableTracker):
                 rv = handler(tx, args, kwargs)
                 if rv:
                     return rv
-                unimplemented(error_msg)
+                call_unimplemented_v2(args)
 
         else:
 
@@ -921,7 +937,7 @@ class BuiltinVariable(VariableTracker):
                     rv = fn(tx, args, kwargs)
                     if rv:
                         return rv
-                unimplemented(error_msg)
+                call_unimplemented_v2(args)
 
         return builtin_dispatch
 

torch/_dynamo/variables/functions.py

@@ -49,6 +49,7 @@ from ..exc import (
     raise_observed_exception,
     SkipFrame,
     unimplemented,
+    unimplemented_v2,
     Unsupported,
 )
 from ..guards import GuardBuilder, install_guard
@@ -1136,7 +1137,26 @@ class SkipFunctionVariable(VariableTracker):
         kwargs: "dict[str, VariableTracker]",
     ) -> "VariableTracker":
         if inspect.getattr_static(self.value, "_torchdynamo_disable", False):
-            unimplemented(f"call torch._dynamo.disable() wrapped function {self.value}")
+            unimplemented_v2(
+                gb_type="Skip calling `torch.compiler.disable()`d function",
+                context=str(self.value),
+                explanation=f"Skip calling function `{self.value}` since it was wrapped with `torch.compiler.disable`",
+                hints=[
+                    "Remove the `torch.compiler.disable` call",
+                ],
+            )
+        elif self.value is torch._dynamo.graph_break:
+            graph_break_msg = kwargs.get("msg", None)
+            if graph_break_msg:
+                graph_break_msg = graph_break_msg.as_python_constant()
+            unimplemented_v2(
+                gb_type="Call to `torch._dynamo.graph_break()`",
+                context=f"Called `torch._dynamo.graph_break()` with args `{args}`, kwargs `{kwargs}`",
+                explanation=f"User-inserted graph break. Message: {graph_break_msg}",
+                hints=[
+                    "Remove the `torch._dynamo.graph_break()` call.",
+                ],
+            )
         elif isinstance(self.value, types.WrapperDescriptorType):
             msg = (
                 f"Graph break due to unsupported wrapper descriptor {self.value}. "
@@ -1148,49 +1168,79 @@ class SkipFunctionVariable(VariableTracker):
         else:
             try:
                 path = inspect.getfile(self.value)
-                msg = f"'skip function {self.value.__qualname__} in file {path}'"
+                explanation = (
+                    f"Dynamo developers have intentionally marked that the function `{self.value.__qualname__}` "
+                    f"in file `{path}` should not be traced."
+                )
+                hints = [
+                    f"Avoid calling the function `{self.value.__qualname__}`.",
+                ]
+                # TODO improve trace_rules reasoning to provide better hints.
+                # How do we tell that a function/file should NOT be removed from skip files?
+                # Do a very basic check for now.
+                if "_dynamo" not in path:
+                    hints += [
+                        f"Remove the function `{self.value.__qualname__}` or the file `{path}` "
+                        "from torch/_dynamo/trace_rules.py. More graph breaks may occur as a result of "
+                        "attempting to trace into the function.",
+                        "Please file an issue to PyTorch.",
+                        # TODO suggest mark_force_inline when implemented
+                    ]
             except TypeError:
                 known_python_builtin_modules = {"_abc", "_warnings"}
                 if self.value.__module__ in known_python_builtin_modules:
-                    msg = (
-                        f"Graph break due to unsupported Python builtin {self.value.__module__}.{self.value.__qualname__}. "
-                        f"Please file an issue on GitHub "
-                        f"so the PyTorch team can add support for it. "
+                    explanation = (
+                        f"Dynamo does not know how to trace the Python builtin "
+                        f"`{self.value.__module__}.{self.value.__qualname__}`."
                     )
+                    hints = [
+                        "If you are attempting to call a logging function (e.g. `_warnings.warn`), "
+                        "you can try adding it to `torch._dynamo.config.reorderable_logging_functions`.",
+                        "Please file an issue on GitHub "
+                        "so the PyTorch team can add support for it. ",
+                    ]
                 elif (
                     self.value.__module__ is not None
                     and self.value.__module__.startswith("optree")
                 ):
-                    msg = (
-                        f"Graph break for an optree C/C++ function {self.value.__module__}.{self.value.__qualname__}."
-                        f" Consider using torch.utils._pytree - "
-                        f"https://github.com/pytorch/pytorch/blob/main/torch/utils/_pytree.py"
-                    )
+                    explanation = f"Dynamo cannot trace optree C/C++ function {self.value.__module__}.{self.value.__qualname__}."
+                    hints = [
+                        " Consider using torch.utils._pytree - "
+                        "https://github.com/pytorch/pytorch/blob/main/torch/utils/_pytree.py"
+                    ]
                     # also warn on it because most users won't see the graph break message
-                    torch._dynamo.utils.warn_once(msg)
+                    torch._dynamo.utils.warn_once(explanation + "\n" + "\n".join(hints))
                 else:
-                    msg = (
-                        f"Graph break due to unsupported builtin {self.value.__module__}.{self.value.__qualname__}. "
+                    explanation = (
+                        f"Dynamo does not know how to trace the builtin `{self.value.__module__}.{self.value.__qualname__}.` "
                         f"This function is either a Python builtin (e.g. _warnings.warn) "
-                        f"or a third-party C/C++ Python extension (perhaps created with pybind). "
-                        f"If it is a Python builtin, please file an issue on GitHub "
-                        f"so the PyTorch team can add support for it and see the next case for a workaround. "
-                        f"If it is a third-party C/C++ Python extension, please "
-                        f"either wrap it into a PyTorch-understood custom operator "
-                        f"(see https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html "
-                        f"for more details) or, if it is traceable, use "
-                        f"torch.compiler.allow_in_graph."
+                        f"or a third-party C/C++ Python extension (perhaps created with pybind)."
                     )
+                    hints = [
+                        "If it is a Python builtin, please file an issue on GitHub "
+                        "so the PyTorch team can add support for it and see the next case for a workaround.",
+                        "If it is a third-party C/C++ Python extension, please "
+                        "either wrap it into a PyTorch-understood custom operator "
+                        "(see https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html "
+                        "for more details) or, if it is traceable, use "
+                        "`torch.compiler.allow_in_graph`.",
+                    ]
                     # also warn on it because most users won't see the graph break message
-                    torch._dynamo.utils.warn_once(msg)
+                    torch._dynamo.utils.warn_once(explanation + "\n" + "\n".join(hints))
             if self.value.__qualname__ == "allow_in_graph":
-                msg = (
+                explanation = (
                     "Found an allow_in_graph decorator to a function which "
                     "is created inside the parent function that is getting "
                     "compiled. This is not supported for now."
                 )
-            msg += f"', {self.reason}'" if self.reason else ""
-            unimplemented(msg)
+                hints = []
+            reason = self.reason if self.reason else "<missing reason>"
+            unimplemented_v2(
+                gb_type="Attempted to call function marked as skipped",
+                context=f"module: {self.value.__module__}, qualname: {self.value.__qualname__}, skip reason: {reason}",
+                explanation=explanation,
+                hints=hints,
+            )
 
     def call_obj_hasattr(self, tx: "InstructionTranslator", name):
         return variables.ConstantVariable.create(hasattr(self.value, name))

torch/testing/_internal/common_utils.py

@@ -3102,13 +3102,16 @@ class TestCase(expecttest.TestCase):
 
     # Munges exceptions that internally contain stack traces, using munge_exc
     def assertExpectedInlineMunged(
-        self, exc_type, callable, expect, *, suppress_suffix=True
+        self, exc_type, callable, expect, *, suppress_suffix=True, post_munge=None,
     ):
         try:
             callable()
         except exc_type as e:
+            munged = munge_exc(e, suppress_suffix=suppress_suffix, skip=1)
+            if post_munge:
+                munged = post_munge(munged)
             self.assertExpectedInline(
-                munge_exc(e, suppress_suffix=suppress_suffix, skip=1), expect, skip=1
+                munged, expect, skip=1
             )
             return
         self.fail(msg="Did not raise when expected to")