Repository: pytorch
Issue: Unable to compile model (Graph break)
Labels: triaged,oncall: pt2,module: dynamo

### 🐛 Describe the bug

When trying to compile a model I received the error `torch._dynamo.exc.Unsupported: Graph break due to unsupported Python builtin _abc._abc_instancecheck. Please file an issue on GitHub so the PyTorch team can add support for it.`

The model is a PyTorch Geometric GNN with [GATv2Conv](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.conv.GATv2Conv.html) layers. More info can be found in [this issue](https://github.com/pyg-team/pytorch_geometric/issues/9603#issuecomment-2297931613).

### Error logs

```
E0819 22:05:31.170000 138952784099136 torch/fx/experimental/recording.py:281] [0/0] failed while running defer_runtime_assert(*(Eq(s1 + u0, s1 + u3), '/home/usevitch/mambaforge/envs/neurosub_debug/lib/pyt
hon3.11/site-packages/torch/__init__.py:1318'), **{'fx_node': None})                                                                                                                                        
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0] failed to eagerly compile backwards for dynamic, suppressing in case backwards not needed   
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0] Traceback (most recent call last):                                                          
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_fu
nctorch/_aot_autograd/jit_compile_runtime_wrappers.py", line 547, in aot_dispatch_autograd                                                                                                                  
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     compiled_bw_func = aot_config.bw_compiler(                                              
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]                        ^^^^^^^^^^^^^^^^^^^^^^^                                              
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_dy
namo/backends/common.py", line 47, in _wrapped_bw_compiler                                                                                                                                                  
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     return disable(disable(bw_compiler)(*args, **kwargs))                                   
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^                                    
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_dy
namo/eval_frame.py", line 600, in _fn                                                                                                                                                                       
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     return fn(*args, **kwargs)                                                              
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]            ^^^^^^^^^^^^^^^^^^^                                                              
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_ut
ils_internal.py", line 84, in wrapper_function                                                                                                                                                              
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     return StrobelightCompileTimeProfiler.profile_compile_time(                             
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^                             
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_st
robelight/compile_time_profiler.py", line 129, in profile_compile_time                                                                                                                                      
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     return func(*args, **kwargs)                                                            
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]            ^^^^^^^^^^^^^^^^^^^^^                                                            
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_dy
namo/utils.py", line 231, in time_wrapper                                                                                                                                                                   
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     r = func(*args, **kwargs)                                                               
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]         ^^^^^^^^^^^^^^^^^^^^^                                                               
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_in
ductor/compile_fx.py", line 1454, in bw_compiler                                                                                                                                                            
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     return inner_compile(                                                                   
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]            ^^^^^^^^^^^^^^                                                                   
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_dy
namo/repro/after_aot.py", line 84, in debug_wrapper                                                                                                                                                         
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     inner_compiled_fn = compiler_fn(gm, example_inputs)                                     
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^                                     
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_in
ductor/debug.py", line 304, in inner                                                                                                                                                                        
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     return fn(*args, **kwargs)                                                              
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]            ^^^^^^^^^^^^^^^^^^^                                                              
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/contextlib.py", line 81
, in inner                                                                                                                                                                                                  
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     return func(*args, **kwds)                                                              
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]            ^^^^^^^^^^^^^^^^^^^                                                              
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/contextlib.py", line 81
, in inner
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     return func(*args, **kwds)
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]            ^^^^^^^^^^^^^^^^^^^
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_in
ductor/compile_fx.py", line 527, in compile_fx_inner
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     compiled_graph = fx_codegen_and_compile(
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]                      ^^^^^^^^^^^^^^^^^^^^^^^
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/contextlib.py", line 81
, in inner
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     return func(*args, **kwds)
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]            ^^^^^^^^^^^^^^^^^^^
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_in
ductor/compile_fx.py", line 738, in fx_codegen_and_compile
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     fake_mode = fake_tensor_prop(gm, example_inputs)
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_in
ductor/compile_fx.py", line 379, in fake_tensor_prop
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     FakeTensorProp(gm, mode=fake_mode).propagate_dont_convert_inputs(
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/fx/
passes/fake_tensor_prop.py", line 69, in propagate_dont_convert_inputs
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     return super().run(*args)
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]            ^^^^^^^^^^^^^^^^^^
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/fx/
interpreter.py", line 146, in run
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     self.env[node] = self.run_node(node)
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]                      ^^^^^^^^^^^^^^^^^^^
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/fx/
passes/fake_tensor_prop.py", line 37, in run_node
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     result = super().run_node(n)
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]              ^^^^^^^^^^^^^^^^^^^
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/fx/
interpreter.py", line 203, in run_node
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     return getattr(self, n.op)(n.target, args, kwargs)
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/fx/
interpreter.py", line 275, in call_function
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     return target(*args, **kwargs)
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]            ^^^^^^^^^^^^^^^^^^^^^^^
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_op
s.py", line 667, in __call__
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     return self_._op(*args, **kwargs)
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]            ^^^^^^^^^^^^^^^^^^^^^^^^^^
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/uti
ls/_stats.py", line 21, in wrapper
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     return fn(*args, **kwargs)
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]            ^^^^^^^^^^^^^^^^^^^
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_su
bclasses/fake_tensor.py", line 1061, in __torch_dispatch__
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     return self.dispatch(func, types, args, kwargs)
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_su
bclasses/fake_tensor.py", line 1450, in dispatch
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     return self._cached_dispatch_impl(func, types, args, kwargs)
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_su
bclasses/fake_tensor.py", line 1153, in _cached_dispatch_impl
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     output = self._dispatch_impl(func, types, args, kwargs)
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_su
bclasses/fake_tensor.py", line 1671, in _dispatch_impl
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     return maybe_propagate_real_tensors(fast_impl(self, *args, **kwargs))
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_su
bclasses/fake_impls.py", line 1062, in fast_binary_impl
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     final_shape = infer_size(final_shape, shape)
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_su
bclasses/fake_impls.py", line 1016, in infer_size
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     torch._check(
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/__i
nit__.py", line 1353, in _check
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     _check_with(RuntimeError, cond, message)
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/__i
nit__.py", line 1318, in _check_with
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     if expect_true(cond):
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]        ^^^^^^^^^^^^^^^^^
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/fx/
experimental/symbolic_shapes.py", line 946, in expect_true
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     return a.node.expect_true(frame.f_code.co_filename, frame.f_lineno)
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/fx/
experimental/sym_node.py", line 435, in expect_true 
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     return self.shape_env.defer_runtime_assert(
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/fx/
experimental/recording.py", line 245, in wrapper
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     return fn(*args, **kwargs)
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]            ^^^^^^^^^^^^^^^^^^^
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/fx/
experimental/symbolic_shapes.py", line 5338, in defer_runtime_assert
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     assert not self.runtime_asserts_frozen, expr
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0] AssertionError: Eq(s1 + u0, s1 + u3)
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0] 
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0] While executing %mul_21 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](ar
gs = (%gather, %index_19), kwargs = {})
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0] Original traceback:
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/code/python/neurosub/compile_error_mwe.py", line 24, in forward
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     x = self.layers[-1](x, edge_index)
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/nn/
modules/module.py", line 1562, in _call_impl
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     return forward_call(*args, **kwargs)
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch_geo
metric/nn/conv/gatv2_conv.py", line 329, in forward 
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     out = self.propagate(edge_index, x=(x_l, x_r), alpha=alpha)
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/tmp/torch_geometric.nn.conv.gatv2_conv_GATv2Conv_propagate_ch0xd74l.py", line 176, 
in propagate
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     out = self.message(
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]   File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch_geo
metric/nn/conv/gatv2_conv.py", line 375, in message 
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0]     return x_j * alpha.unsqueeze(-1)
W0819 22:05:31.171000 138952784099136 torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py:551] [0/0] 
Traceback (most recent call last):
  File "/home/usevitch/code/python/neurosub/compile_error_mwe.py", line 41, in <module>
    out = model(data.x, data.edge_index)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py", line 433, in _fn
    return fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/usevitch/code/python/neurosub/compile_error_mwe.py", line 19, in forward
    def forward(self, x, edge_index):
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/nn/modules/container.py", line 295, in __getitem__
    return self.__class__(list(self._modules.values())[idx])
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/nn/modules/container.py", line 281, in __init__
    self += modules
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/nn/modules/container.py", line 322, in __iadd__
    return self.extend(modules)
           ^^^^^^^^^^^^^^^^^^^^
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/nn/modules/container.py", line 399, in extend
    if not isinstance(modules, container_abcs.Iterable):
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py", line 1116, in __call__
    return self._torchdynamo_orig_callable(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py", line 472, in __call__
    return _compile(
           ^^^^^^^^^
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_utils_internal.py", line 84, in wrapper_function                                                                  
    return StrobelightCompileTimeProfiler.profile_compile_time(                                                                                                                                             
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_strobelight/compile_time_profiler.py", line 129, in profile_compile_time
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/contextlib.py", line 81, in inner 
    return func(*args, **kwds)
           ^^^^^^^^^^^^^^^^^^^
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py", line 817, in _compile
    guarded_code = compile_inner(code, one_graph, hooks, transform)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_dynamo/utils.py", line 231, in time_wrapper
    r = func(*args, **kwargs)
        ^^^^^^^^^^^^^^^^^^^^^
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py", line 636, in compile_inner
    out_code = transform_code_object(code, transform)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_dynamo/bytecode_transformation.py", line 1185, in transform_code_object
    transformations(instructions, code_options)
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py", line 178, in _fn
    return fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py", line 582, in transform
    tracer.run()
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_dynamo/symbolic_convert.py", line 2451, in run
    super().run()
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_dynamo/symbolic_convert.py", line 893, in run
    while self.step():
          ^^^^^^^^^^^
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_dynamo/symbolic_convert.py", line 805, in step
    self.dispatch_table[inst.opcode](self, inst)
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_dynamo/symbolic_convert.py", line 499, in wrapper
    return inner_fn(self, inst)
           ^^^^^^^^^^^^^^^^^^^^
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_dynamo/symbolic_convert.py", line 2059, in CALL
    self.call_function(fn, args, kwargs)
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_dynamo/symbolic_convert.py", line 743, in call_function
    self.push(fn.call_function(self, args, kwargs)) 
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_dynamo/variables/functions.py", line 665, in call_function
    unimplemented(msg)
  File "/home/usevitch/mambaforge/envs/neurosub_debug/lib/python3.11/site-packages/torch/_dynamo/exc.py", line 221, in unimplemented
    raise Unsupported(msg)
torch._dynamo.exc.Unsupported: Graph break due to unsupported Python builtin _abc._abc_instancecheck. Please file an issue on GitHub so the PyTorch team can add support for it. 

from user code:
   File "<frozen abc>", line 119, in __instancecheck__
```

### Minified repro

```python
import torch
from torch import tensor, device
import torch.fx as fx
from torch._dynamo.testing import rand_strided
from math import inf
import torch._inductor.inductor_prims

import torch._dynamo.config
import torch._inductor.config
import torch._functorch.config
import torch.fx.experimental._config
torch._dynamo.config.assume_static_by_default = False
torch._dynamo.config.capture_dynamic_output_shape_ops = True

torch._functorch.config.unlift_effect_tokens = True



isolate_fails_code_str = None



# torch version: 2.4.0+cu121
# torch cuda version: 12.1
# torch git version: e4ee3be4063b7c430974252fdf7db42273388d86


# CUDA Info: 
# nvcc: NVIDIA (R) Cuda compiler driver 
# Copyright (c) 2005-2023 NVIDIA Corporation 
# Built on Tue_Aug_15_22:02:13_PDT_2023 
# Cuda compilation tools, release 12.2, V12.2.140 
# Build cuda_12.2.r12.2/compiler.33191640_0 

# GPU Hardware Info: 
# NVIDIA GeForce RTX 4090 : 2 


from torch.nn import *
class Repro(torch.nn.Module):
    def __init__(self):
        super().__init__()

    
    
    def forward(self, primals_26, sym_size_int_1, primals_5, primals_6, primals_11, primals_12, primals_17, primals_18, primals_23, primals_27, addmm, addmm_1, select_2, select_3, full, exp, index_4, div, full_2, scatter_add_1, mul_5, index_7, add_6, exp_1, index_10, div_1, scatter_add_3, mul_11, index_13, add_11, exp_2, index_16, div_2, scatter_add_5, mul_17, index_19, add_16, exp_3, index_22, div_3, full_11, permute_8, permute_12, permute_16, permute_20, permute_24, permute_28, tangents_1):
        sum_5 = torch.ops.aten.sum.dim_IntList(tangents_1, [0], True)
        view_32 = torch.ops.aten.view.default(sum_5, [2]);  sum_5 = None
        view_33 = torch.ops.aten.view.default(tangents_1, [primals_26, 1, 2]);  tangents_1 = None
        view_5 = torch.ops.aten.view.default(select_2, [-1, 1, 1])
        expand_11 = torch.ops.aten.expand.default(view_5, [sym_size_int_1, 1, 2])
        gather = torch.ops.aten.gather.default(view_33, 0, expand_11);  view_33 = expand_11 = None
        mul_21 = torch.ops.aten.mul.Tensor(gather, index_19);  index_19 = None
        unsqueeze_3 = torch.ops.aten.unsqueeze.default(div_3, -1)
        mul_22 = torch.ops.aten.mul.Tensor(gather, unsqueeze_3);  gather = unsqueeze_3 = None
        sum_6 = torch.ops.aten.sum.dim_IntList(mul_21, [2], True);  mul_21 = None
        squeeze = torch.ops.aten.squeeze.dim(sum_6, -1);  sum_6 = None
        index_put = torch.ops.aten.index_put.default(full_11, [select_3], mul_22, True);  mul_22 = None
        div_5 = torch.ops.aten.div.Tensor(div_3, index_22);  div_3 = None
        neg = torch.ops.aten.neg.default(squeeze)
        mul_23 = torch.ops.aten.mul.Tensor(neg, div_5);  neg = div_5 = None
        div_6 = torch.ops.aten.div.Tensor(squeeze, index_22);  squeeze = index_22 = None
        index_put_1 = torch.ops.aten.index_put.default(full, [select_2], mul_23, True);  mul_23 = None
        view_3 = torch.ops.aten.view.default(select_2, [-1, 1])
        expand = torch.ops.aten.expand.default(view_3, [sym_size_int_1, 1]);  view_3 = None
        gather_1 = torch.ops.aten.gather.default(index_put_1, 0, expand);  index_put_1 = None
        add_19 = torch.ops.aten.add.Tensor(div_6, gather_1);  div_6 = gather_1 = None
        mul_24 = torch.ops.aten.mul.Tensor(add_19, exp_3);  add_19 = exp_3 = None
        unsqueeze_4 = torch.ops.aten.unsqueeze.default(mul_24, -1);  mul_24 = None
        expand_12 = torch.ops.aten.expand.default(unsqueeze_4, [sym_size_int_1, 1, 2]);  unsqueeze_4 = None
        gt_3 = torch.ops.aten.gt.Scalar(add_16, 0)
        mul_18 = torch.ops.aten.mul.Tensor(add_16, 0.2)
        where_3 = torch.ops.aten.where.self(gt_3, add_16, mul_18);  add_16 = mul_18 = None
        mul_25 = torch.ops.aten.mul.Tensor(expand_12, where_3);  where_3 = None
        mul_26 = torch.ops.aten.mul.Tensor(expand_12, primals_23);  expand_12 = primals_23 = None
        sum_7 = torch.ops.aten.sum.dim_IntList(mul_25, [0], True);  mul_25 = None
        mul_27 = torch.ops.aten.mul.Tensor(mul_26, 0.2)
        where_4 = torch.ops.aten.where.self(gt_3, mul_26, mul_27);  gt_3 = mul_26 = mul_27 = None
        index_put_2 = torch.ops.aten.index_put.default(full_11, [select_2], where_4, True)
        index_put_3 = torch.ops.aten.index_put.default(full_11, [select_3], where_4, True);  full_11 = where_4 = None
        add_20 = torch.ops.aten.add.Tensor(index_put, index_put_3);  index_put = index_put_3 = None
        view_34 = torch.ops.aten.view.default(index_put_2, [primals_26, 2]);  index_put_2 = None
        mm = torch.ops.aten.mm.default(view_34, permute_8);  permute_8 = None
        permute_9 = torch.ops.aten.permute.default(view_34, [1, 0])
        mm_1 = torch.ops.aten.mm.default(permute_9, mul_17);  permute_9 = None
        permute_10 = torch.ops.aten.permute.default(mm_1, [1, 0]);  mm_1 = None
        sum_8 = torch.ops.aten.sum.dim_IntList(view_34, [0], True);  view_34 = None
        view_35 = torch.ops.aten.view.default(sum_8, [2]);  sum_8 = None
        permute_11 = torch.ops.aten.permute.default(permute_10, [1, 0]);  permute_10 = None
        view_36 = torch.ops.aten.view.default(add_20, [primals_26, 2]);  add_20 = None
        mm_2 = torch.ops.aten.mm.default(view_36, permute_12);  permute_12 = None
        permute_13 = torch.ops.aten.permute.default(view_36, [1, 0])
        mm_3 = torch.ops.aten.mm.default(permute_13, mul_17);  permute_13 = mul_17 = None
        permute_14 = torch.ops.aten.permute.default(mm_3, [1, 0]);  mm_3 = None
        sum_9 = torch.ops.aten.sum.dim_IntList(view_36, [0], True);  view_36 = None
        view_37 = torch.ops.aten.view.default(sum_9, [2]);  sum_9 = None
        add_21 = torch.ops.aten.add.Tensor(mm, mm_2);  mm = mm_2 = None
        permute_15 = torch.ops.aten.permute.default(permute_14, [1, 0]);  permute_14 = None
        view_23 = torch.ops.aten.view.default(scatter_add_5, [-1, 4]);  scatter_add_5 = None
        add_13 = torch.ops.aten.add.Tensor(view_23, primals_18);  view_23 = primals_18 = None
        mul_16 = torch.ops.aten.mul.Tensor(add_13, 0.7071067811865476)
        erf_2 = torch.ops.aten.erf.default(mul_16);  mul_16 = None
        add_14 = torch.ops.aten.add.Tensor(erf_2, 1);  erf_2 = None
        mul_29 = torch.ops.aten.mul.Tensor(add_14, 0.5);  add_14 = None
        mul_30 = torch.ops.aten.mul.Tensor(add_13, add_13)
        mul_31 = torch.ops.aten.mul.Tensor(mul_30, -0.5);  mul_30 = None
        exp_4 = torch.ops.aten.exp.default(mul_31);  mul_31 = None
        mul_32 = torch.ops.aten.mul.Tensor(exp_4, 0.3989422804014327);  exp_4 = None
        mul_33 = torch.ops.aten.mul.Tensor(add_13, mul_32);  add_13 = mul_32 = None
        add_23 = torch.ops.aten.add.Tensor(mul_29, mul_33);  mul_29 = mul_33 = None
        mul_34 = torch.ops.aten.mul.Tensor(add_21, add_23);  add_21 = add_23 = None
        sum_10 = torch.ops.aten.sum.dim_IntList(mul_34, [0], True)
        view_38 = torch.ops.aten.view.default(sum_10, [4]);  sum_10 = None
        view_39 = torch.ops.aten.view.default(mul_34, [primals_26, 1, 4]);  mul_34 = None
        expand_2 = torch.ops.aten.expand.default(view_5, [sym_size_int_1, 1, 4]);  view_5 = None
        gather_2 = torch.ops.aten.gather.default(view_39, 0, expand_2);  view_39 = None
        mul_35 = torch.ops.aten.mul.Tensor(gather_2, index_13);  index_13 = None
        unsqueeze_2 = torch.ops.aten.unsqueeze.default(div_2, -1)
        mul_36 = torch.ops.aten.mul.Tensor(gather_2, unsqueeze_2);  gather_2 = unsqueeze_2 = None
        sum_11 = torch.ops.aten.sum.dim_IntList(mul_35, [2], True);  mul_35 = None
        squeeze_1 = torch.ops.aten.squeeze.dim(sum_11, -1);  sum_11 = None
        index_put_4 = torch.ops.aten.index_put.default(full_2, [select_3], mul_36, True);  mul_36 = None
        div_8 = torch.ops.aten.div.Tensor(div_2, index_16);  div_2 = None
        neg_1 = torch.ops.aten.neg.default(squeeze_1)
        mul_37 = torch.ops.aten.mul.Tensor(neg_1, div_8);  neg_1 = div_8 = None
        div_9 = torch.ops.aten.div.Tensor(squeeze_1, index_16);  squeeze_1 = index_16 = None
        index_put_5 = torch.ops.aten.index_put.default(full, [select_2], mul_37, True);  mul_37 = None
        gather_3 = torch.ops.aten.gather.default(index_put_5, 0, expand);  index_put_5 = None
        add_24 = torch.ops.aten.add.Tensor(div_9, gather_3);  div_9 = gather_3 = None
        mul_38 = torch.ops.aten.mul.Tensor(add_24, exp_2);  add_24 = exp_2 = None
        unsqueeze_5 = torch.ops.aten.unsqueeze.default(mul_38, -1);  mul_38 = None
        expand_13 = torch.ops.aten.expand.default(unsqueeze_5, [sym_size_int_1, 1, 4]);  unsqueeze_5 = None
        gt_2 = torch.ops.aten.gt.Scalar(add_11, 0)
        mul_12 = torch.ops.aten.mul.Tensor(add_11, 0.2)
        where_2 = torch.ops.aten.where.self(gt_2, add_11, mul_12);  add_11 = mul_12 = None
        mul_39 = torch.ops.aten.mul.Tensor(expand_13, where_2);  where_2 = None
        mul_40 = torch.ops.aten.mul.Tensor(expand_13, primals_17);  expand_13 = primals_17 = None
        sum_12 = torch.ops.aten.sum.dim_IntList(mul_39, [0], True);  mul_39 = None
        mul_41 = torch.ops.aten.mul.Tensor(mul_40, 0.2)
        where_5 = torch.ops.aten.where.self(gt_2, mul_40, mul_41);  gt_2 = mul_40 = mul_41 = None
        index_put_6 = torch.ops.aten.index_put.default(full_2, [select_2], where_5, True)
        index_put_7 = torch.ops.aten.index_put.default(full_2, [select_3], where_5, True);  where_5 = None
        add_25 = torch.ops.aten.add.Tensor(index_put_4, index_put_7);  index_put_4 = index_put_7 = None
        view_40 = torch.ops.aten.view.default(index_put_6, [primals_26, 4]);  index_put_6 = None
        mm_4 = torch.ops.aten.mm.default(view_40, permute_16);  permute_16 = None
        permute_17 = torch.ops.aten.permute.default(view_40, [1, 0])
        mm_5 = torch.ops.aten.mm.default(permute_17, mul_11);  permute_17 = None
        permute_18 = torch.ops.aten.permute.default(mm_5, [1, 0]);  mm_5 = None
        sum_13 = torch.ops.aten.sum.dim_IntList(view_40, [0], True);  view_40 = None
        view_41 = torch.ops.aten.view.default(sum_13, [4]);  sum_13 = None
        permute_19 = torch.ops.aten.permute.default(permute_18, [1, 0]);  permute_18 = None
        view_42 = torch.ops.aten.view.default(add_25, [primals_26, 4]);  add_25 = None
        mm_6 = torch.ops.aten.mm.default(view_42, permute_20);  permute_20 = None
        permute_21 = torch.ops.aten.permute.default(view_42, [1, 0])
        mm_7 = torch.ops.aten.mm.default(permute_21, mul_11);  permute_21 = mul_11 = None
        permute_22 = torch.ops.aten.permute.default(mm_7, [1, 0]);  mm_7 = None
        sum_14 = torch.ops.aten.sum.dim_IntList(view_42, [0], True);  view_42 = None
        view_43 = torch.ops.aten.view.default(sum_14, [4]);  sum_14 = None
        add_26 = torch.ops.aten.add.Tensor(mm_4, mm_6);  mm_4 = mm_6 = None
        permute_23 = torch.ops.aten.permute.default(permute_22, [1, 0]);  permute_22 = None
        view_15 = torch.ops.aten.view.default(scatter_add_3, [-1, 4]);  scatter_add_3 = None
        add_8 = torch.ops.aten.add.Tensor(view_15, primals_12);  view_15 = primals_12 = None
        mul_10 = torch.ops.aten.mul.Tensor(add_8, 0.7071067811865476)
        erf_1 = torch.ops.aten.erf.default(mul_10);  mul_10 = None
        add_9 = torch.ops.aten.add.Tensor(erf_1, 1);  erf_1 = None
        mul_43 = torch.ops.aten.mul.Tensor(add_9, 0.5);  add_9 = None
        mul_44 = torch.ops.aten.mul.Tensor(add_8, add_8)
        mul_45 = torch.ops.aten.mul.Tensor(mul_44, -0.5);  mul_44 = None
        exp_5 = torch.ops.aten.exp.default(mul_45);  mul_45 = None
        mul_46 = torch.ops.aten.mul.Tensor(exp_5, 0.3989422804014327);  exp_5 = None
        mul_47 = torch.ops.aten.mul.Tensor(add_8, mul_46);  add_8 = mul_46 = None
        add_28 = torch.ops.aten.add.Tensor(mul_43, mul_47);  mul_43 = mul_47 = None
        mul_48 = torch.ops.aten.mul.Tensor(add_26, add_28);  add_26 = add_28 = None
        sum_15 = torch.ops.aten.sum.dim_IntList(mul_48, [0], True)
        view_44 = torch.ops.aten.view.default(sum_15, [4]);  sum_15 = None
        view_45 = torch.ops.aten.view.default(mul_48, [primals_26, 1, 4]);  mul_48 = None
        gather_4 = torch.ops.aten.gather.default(view_45, 0, expand_2);  view_45 = None
        mul_49 = torch.ops.aten.mul.Tensor(gather_4, index_7);  index_7 = None
        unsqueeze_1 = torch.ops.aten.unsqueeze.default(div_1, -1)
        mul_50 = torch.ops.aten.mul.Tensor(gather_4, unsqueeze_1);  gather_4 = unsqueeze_1 = None
        sum_16 = torch.ops.aten.sum.dim_IntList(mul_49, [2], True);  mul_49 = None
        squeeze_2 = torch.ops.aten.squeeze.dim(sum_16, -1);  sum_16 = None
        index_put_8 = torch.ops.aten.index_put.default(full_2, [select_3], mul_50, True);  mul_50 = None
        div_11 = torch.ops.aten.div.Tensor(div_1, index_10);  div_1 = None
        neg_2 = torch.ops.aten.neg.default(squeeze_2)
        mul_51 = torch.ops.aten.mul.Tensor(neg_2, div_11);  neg_2 = div_11 = None
        div_12 = torch.ops.aten.div.Tensor(squeeze_2, index_10);  squeeze_2 = index_10 = None
        index_put_9 = torch.ops.aten.index_put.default(full, [select_2], mul_51, True);  mul_51 = None
        gather_5 = torch.ops.aten.gather.default(index_put_9, 0, expand);  index_put_9 = None
        add_29 = torch.ops.aten.add.Tensor(div_12, gather_5);  div_12 = gather_5 = None
        mul_52 = torch.ops.aten.mul.Tensor(add_29, exp_1);  add_29 = exp_1 = None
        unsqueeze_6 = torch.ops.aten.unsqueeze.default(mul_52, -1);  mul_52 = None
        expand_14 = torch.ops.aten.expand.default(unsqueeze_6, [sym_size_int_1, 1, 4]);  unsqueeze_6 = None
        gt_1 = torch.ops.aten.gt.Scalar(add_6, 0)
        mul_6 = torch.ops.aten.mul.Tensor(add_6, 0.2)
        where_1 = torch.ops.aten.where.self(gt_1, add_6, mul_6);  add_6 = mul_6 = None
        mul_53 = torch.ops.aten.mul.Tensor(expand_14, where_1);  where_1 = None
        mul_54 = torch.ops.aten.mul.Tensor(expand_14, primals_11);  expand_14 = primals_11 = None
        sum_17 = torch.ops.aten.sum.dim_IntList(mul_53, [0], True);  mul_53 = None
        mul_55 = torch.ops.aten.mul.Tensor(mul_54, 0.2)
        where_6 = torch.ops.aten.where.self(gt_1, mul_54, mul_55);  gt_1 = mul_54 = mul_55 = None
        index_put_10 = torch.ops.aten.index_put.default(full_2, [select_2], where_6, True)
        index_put_11 = torch.ops.aten.index_put.default(full_2, [select_3], where_6, True);  where_6 = None
        add_30 = torch.ops.aten.add.Tensor(index_put_8, index_put_11);  index_put_8 = index_put_11 = None
        view_46 = torch.ops.aten.view.default(index_put_10, [primals_26, 4]);  index_put_10 = None
        mm_8 = torch.ops.aten.mm.default(view_46, permute_24);  permute_24 = None
        permute_25 = torch.ops.aten.permute.default(view_46, [1, 0])
        mm_9 = torch.ops.aten.mm.default(permute_25, mul_5);  permute_25 = None
        permute_26 = torch.ops.aten.permute.default(mm_9, [1, 0]);  mm_9 = None
        sum_18 = torch.ops.aten.sum.dim_IntList(view_46, [0], True);  view_46 = None
        view_47 = torch.ops.aten.view.default(sum_18, [4]);  sum_18 = None
        permute_27 = torch.ops.aten.permute.default(permute_26, [1, 0]);  permute_26 = None
        view_48 = torch.ops.aten.view.default(add_30, [primals_26, 4]);  add_30 = None
        mm_10 = torch.ops.aten.mm.default(view_48, permute_28);  permute_28 = None
        permute_29 = torch.ops.aten.permute.default(view_48, [1, 0])
        mm_11 = torch.ops.aten.mm.default(permute_29, mul_5);  permute_29 = mul_5 = None
        permute_30 = torch.ops.aten.permute.default(mm_11, [1, 0]);  mm_11 = None
        sum_19 = torch.ops.aten.sum.dim_IntList(view_48, [0], True);  view_48 = None
        view_49 = torch.ops.aten.view.default(sum_19, [4]);  sum_19 = None
        add_31 = torch.ops.aten.add.Tensor(mm_8, mm_10);  mm_8 = mm_10 = None
        permute_31 = torch.ops.aten.permute.default(permute_30, [1, 0]);  permute_30 = None
        view_7 = torch.ops.aten.view.default(scatter_add_1, [-1, 4]);  scatter_add_1 = None
        add_3 = torch.ops.aten.add.Tensor(view_7, primals_6);  view_7 = primals_6 = None
        mul_4 = torch.ops.aten.mul.Tensor(add_3, 0.7071067811865476)
        erf = torch.ops.aten.erf.default(mul_4);  mul_4 = None
        add_4 = torch.ops.aten.add.Tensor(erf, 1);  erf = None
        mul_57 = torch.ops.aten.mul.Tensor(add_4, 0.5);  add_4 = None
        mul_58 = torch.ops.aten.mul.Tensor(add_3, add_3)
        mul_59 = torch.ops.aten.mul.Tensor(mul_58, -0.5);  mul_58 = None
        exp_6 = torch.ops.aten.exp.default(mul_59);  mul_59 = None
        mul_60 = torch.ops.aten.mul.Tensor(exp_6, 0.3989422804014327);  exp_6 = None
        mul_61 = torch.ops.aten.mul.Tensor(add_3, mul_60);  add_3 = mul_60 = None
        add_33 = torch.ops.aten.add.Tensor(mul_57, mul_61);  mul_57 = mul_61 = None
        mul_62 = torch.ops.aten.mul.Tensor(add_31, add_33);  add_31 = add_33 = None
        sum_20 = torch.ops.aten.sum.dim_IntList(mul_62, [0], True)
        view_50 = torch.ops.aten.view.default(sum_20, [4]);  sum_20 = None
        view_51 = torch.ops.aten.view.default(mul_62, [primals_26, 1, 4]);  mul_62 = None
        gather_6 = torch.ops.aten.gather.default(view_51, 0, expand_2);  view_51 = expand_2 = None
        view = torch.ops.aten.view.default(addmm, [-1, 1, 4]);  addmm = None
        index_1 = torch.ops.aten.index.Tensor(view, [select_3]);  view = None
        mul_63 = torch.ops.aten.mul.Tensor(gather_6, index_1)
        unsqueeze = torch.ops.aten.unsqueeze.default(div, -1)
        mul_64 = torch.ops.aten.mul.Tensor(gather_6, unsqueeze);  gather_6 = unsqueeze = None
        sum_21 = torch.ops.aten.sum.dim_IntList(mul_63, [2], True);  mul_63 = None
        squeeze_3 = torch.ops.aten.squeeze.dim(sum_21, -1);  sum_21 = None
        index_put_12 = torch.ops.aten.index_put.default(full_2, [select_3], mul_64, True);  mul_64 = None
        div_14 = torch.ops.aten.div.Tensor(div, index_4);  div = None
        neg_3 = torch.ops.aten.neg.default(squeeze_3)
        mul_65 = torch.ops.aten.mul.Tensor(neg_3, div_14);  neg_3 = div_14 = None
        div_15 = torch.ops.aten.div.Tensor(squeeze_3, index_4);  squeeze_3 = index_4 = None
        index_put_13 = torch.ops.aten.index_put.default(full, [select_2], mul_65, True);  full = mul_65 = None
        gather_7 = torch.ops.aten.gather.default(index_put_13, 0, expand);  index_put_13 = expand = None
        add_34 = torch.ops.aten.add.Tensor(div_15, gather_7);  div_15 = gather_7 = None
        mul_66 = torch.ops.aten.mul.Tensor(add_34, exp);  add_34 = exp = None
        unsqueeze_7 = torch.ops.aten.unsqueeze.default(mul_66, -1);  mul_66 = None
        expand_15 = torch.ops.aten.expand.default(unsqueeze_7, [sym_size_int_1, 1, 4]);  unsqueeze_7 = sym_size_int_1 = None
        view_1 = torch.ops.aten.view.default(addmm_1, [-1, 1, 4]);  addmm_1 = None
        index_2 = torch.ops.aten.index.Tensor(view_1, [select_2]);  view_1 = None
        add_1 = torch.ops.aten.add.Tensor(index_2, index_1);  index_2 = index_1 = None
        gt = torch.ops.aten.gt.Scalar(add_1, 0)
        mul = torch.ops.aten.mul.Tensor(add_1, 0.2)
        where = torch.ops.aten.where.self(gt, add_1, mul);  add_1 = mul = None
        mul_67 = torch.ops.aten.mul.Tensor(expand_15, where);  where = None
        mul_68 = torch.ops.aten.mul.Tensor(expand_15, primals_5);  expand_15 = primals_5 = None
        sum_22 = torch.ops.aten.sum.dim_IntList(mul_67, [0], True);  mul_67 = None
        mul_69 = torch.ops.aten.mul.Tensor(mul_68, 0.2)
        where_7 = torch.ops.aten.where.self(gt, mul_68, mul_69);  gt = mul_68 = mul_69 = None
        index_put_14 = torch.ops.aten.index_put.default(full_2, [select_2], where_7, True);  select_2 = None
        index_put_15 = torch.ops.aten.index_put.default(full_2, [select_3], where_7, True);  full_2 = select_3 = where_7 = None
        add_35 = torch.ops.aten.add.Tensor(index_put_12, index_put_15);  index_put_12 = index_put_15 = None
        view_52 = torch.ops.aten.view.default(index_put_14, [primals_26, 4]);  index_put_14 = None
        permute_32 = torch.ops.aten.permute.default(view_52, [1, 0])
        mm_12 = torch.ops.aten.mm.default(permute_32, primals_27);  permute_32 = None
        permute_33 = torch.ops.aten.permute.default(mm_12, [1, 0]);  mm_12 = None
        sum_23 = torch.ops.aten.sum.dim_IntList(view_52, [0], True);  view_52 = None
        view_53 = torch.ops.aten.view.default(sum_23, [4]);  sum_23 = None
        permute_34 = torch.ops.aten.permute.default(permute_33, [1, 0]);  permute_33 = None
        view_54 = torch.ops.aten.view.default(add_35, [primals_26, 4]);  add_35 = primals_26 = None
        permute_35 = torch.ops.aten.permute.default(view_54, [1, 0])
        mm_13 = torch.ops.aten.mm.default(permute_35, primals_27);  permute_35 = primals_27 = None
        permute_36 = torch.ops.aten.permute.default(mm_13, [1, 0]);  mm_13 = None
        sum_24 = torch.ops.aten.sum.dim_IntList(view_54, [0], True);  view_54 = None
        view_55 = torch.ops.aten.view.default(sum_24, [4]);  sum_24 = None
        permute_37 = torch.ops.aten.permute.default(permute_36, [1, 0]);  permute_36 = None
        return [permute_37, view_55, permute_34, view_53, sum_22, view_50, permute_31, view_49, permute_27, view_47, sum_17, view_44, permute_23, view_43, permute_19, view_41, sum_12, view_38, permute_15, view_37, permute_11, view_35, sum_7, view_32, None, None, None, None, None, None, None, None]
        
def load_args(reader):
    reader.symint(900)  # primals_26
    reader.symint(None)  # sym_size_int_1
    buf0 = reader.storage(None, 16)
    reader.tensor(buf0, (1, 1, 4), is_leaf=True)  # primals_5
    buf1 = reader.storage(None, 16)
    reader.tensor(buf1, (4,), is_leaf=True)  # primals_6
    buf2 = reader.storage(None, 16)
    reader.tensor(buf2, (1, 1, 4), is_leaf=True)  # primals_11
    buf3 = reader.storage(None, 16)
    reader.tensor(buf3, (4,), is_leaf=True)  # primals_12
    buf4 = reader.storage(None, 16)
    reader.tensor(buf4, (1, 1, 4), is_leaf=True)  # primals_17
    buf5 = reader.storage(None, 16)
    reader.tensor(buf5, (4,), is_leaf=True)  # primals_18
    buf6 = reader.storage(None, 8)
    reader.tensor(buf6, (1, 1, 2), is_leaf=True)  # primals_23
    buf7 = reader.storage(None, 8*s1)
    reader.tensor(buf7, (s1, 2), is_leaf=True)  # primals_27
    buf8 = reader.storage(None, 16*s1)
    reader.tensor(buf8, (s1, 4), is_leaf=True)  # addmm
    buf9 = reader.storage(None, 16*s1)
    reader.tensor(buf9, (s1, 4), is_leaf=True)  # addmm_1
    buf10 = reader.storage(None, 16*s1 + 16*u0, dtype_hint=torch.int64)
    reader.tensor(buf10, (s1 + u0,), dtype=torch.int64, storage_offset=Max(1, s1 + u0), is_leaf=True)  # select_2
    reader.tensor(buf10, (s1 + u0,), dtype=torch.int64, is_leaf=True)  # select_3
    buf11 = reader.storage(None, 4*s1)
    reader.tensor(buf11, (s1, 1), is_leaf=True)  # full
    buf12 = reader.storage(None, 4*s1 + 4*u0)
    reader.tensor(buf12, (s1 + u0, 1), is_leaf=True)  # exp
    buf13 = reader.storage(None, 4*s1 + 4*u0)
    reader.tensor(buf13, (s1 + u0, 1), is_leaf=True)  # index_4
    buf14 = reader.storage(None, 4*s1 + 4*u0)
    reader.tensor(buf14, (s1 + u0, 1), is_leaf=True)  # div
    buf15 = reader.storage(None, 16*s1)
    reader.tensor(buf15, (s1, 1, 4), is_leaf=True)  # full_2
    buf16 = reader.storage(None, 16*s1)
    reader.tensor(buf16, (s1, 1, 4), is_leaf=True)  # scatter_add_1
    buf17 = reader.storage(None, 16*s1)
    reader.tensor(buf17, (s1, 4), is_leaf=True)  # mul_5
    buf18 = reader.storage(None, 16*s1 + 16*u1)
    reader.tensor(buf18, (s1 + u1, 1, 4), is_leaf=True)  # index_7
    buf19 = reader.storage(None, 16*s1 + 16*u1)
    reader.tensor(buf19, (s1 + u1, 1, 4), is_leaf=True)  # add_6
    buf20 = reader.storage(None, 4*s1 + 4*u1)
    reader.tensor(buf20, (s1 + u1, 1), is_leaf=True)  # exp_1
    buf21 = reader.storage(None, 4*s1 + 4*u1)
    reader.tensor(buf21, (s1 + u1, 1), is_leaf=True)  # index_10
    buf22 = reader.storage(None, 4*s1 + 4*u1)
    reader.tensor(buf22, (s1 + u1, 1), is_leaf=True)  # div_1
    buf23 = reader.storage(None, 16*s1)
    reader.tensor(buf23, (s1, 1, 4), is_leaf=True)  # scatter_add_3
    buf24 = reader.storage(None, 16*s1)
    reader.tensor(buf24, (s1, 4), is_leaf=True)  # mul_11
    buf25 = reader.storage(None, 16*s1 + 16*u2)
    reader.tensor(buf25, (s1 + u2, 1, 4), is_leaf=True)  # index_13
    buf26 = reader.storage(None, 16*s1 + 16*u2)
    reader.tensor(buf26, (s1 + u2, 1, 4), is_leaf=True)  # add_11
    buf27 = reader.storage(None, 4*s1 + 4*u2)
    reader.tensor(buf27, (s1 + u2, 1), is_leaf=True)  # exp_2
    buf28 = reader.storage(None, 4*s1 + 4*u2)
    reader.tensor(buf28, (s1 + u2, 1), is_leaf=True)  # index_16
    buf29 = reader.storage(None, 4*s1 + 4*u2)
    reader.tensor(buf29, (s1 + u2, 1), is_leaf=True)  # div_2
    buf30 = reader.storage(None, 16*s1)
    reader.tensor(buf30, (s1, 1, 4), is_leaf=True)  # scatter_add_5
    buf31 = reader.storage(None, 16*s1)
    reader.tensor(buf31, (s1, 4), is_leaf=True)  # mul_17
    buf32 = reader.storage(None, 8*s1 + 8*u3)
    reader.tensor(buf32, (s1 + u3, 1, 2), is_leaf=True)  # index_19
    buf33 = reader.storage(None, 8*s1 + 8*u3)
    reader.tensor(buf33, (s1 + u3, 1, 2), is_leaf=True)  # add_16
    buf34 = reader.storage(None, 4*s1 + 4*u3)
    reader.tensor(buf34, (s1 + u3, 1), is_leaf=True)  # exp_3
    buf35 = reader.storage(None, 4*s1 + 4*u3)
    reader.tensor(buf35, (s1 + u3, 1), is_leaf=True)  # index_22
    buf36 = reader.storage(None, 4*s1 + 4*u3)
    reader.tensor(buf36, (s1 + u3, 1), is_leaf=True)  # div_3
    buf37 = reader.storage(None, 8*s1)
    reader.tensor(buf37, (s1, 1, 2), is_leaf=True)  # full_11
    buf38 = reader.storage(None, 32)
    reader.tensor(buf38, (2, 4), is_leaf=True)  # permute_8
    buf39 = reader.storage(None, 32)
    reader.tensor(buf39, (2, 4), is_leaf=True)  # permute_12
    buf40 = reader.storage(None, 64)
    reader.tensor(buf40, (4, 4), is_leaf=True)  # permute_16
    buf41 = reader.storage(None, 64)
    reader.tensor(buf41, (4, 4), is_leaf=True)  # permute_20
    buf42 = reader.storage(None, 64)
    reader.tensor(buf42, (4, 4), is_leaf=True)  # permute_24
    buf43 = reader.storage(None, 64)
    reader.tensor(buf43, (4, 4), is_leaf=True)  # permute_28
    buf44 = reader.storage(None, 8*s1)
    reader.tensor(buf44, (s1, 2), is_leaf=True)  # tangents_1
load_args._version = 0
mod = Repro()
if __name__ == '__main__':
    from torch._dynamo.repro.after_aot import run_repro
    with torch.no_grad():
        run_repro(mod, load_args, accuracy=False, command='minify', save_dir='/home/usevitch/code/python/neurosub/torch_compile_debug/run_2024_08_19_22_23_52_780264-pid_1226344/minifier/checkpoints', tracing_mode='symbolic', check_str=None)
        # To run it separately, do 
        # mod, args = run_repro(mod, load_args, accuracy=False, command='get_args', save_dir='/home/usevitch/code/python/neurosub/torch_compile_debug/run_2024_08_19_22_23_52_780264-pid_1226344/minifier/checkpoints', tracing_mode='symbolic', check_str=None)
        # mod(*args)
```

### Versions

```
Collecting environment information...
PyTorch version: 2.4.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.11.9 | packaged by conda-forge | (main, Apr 19 2024, 18:36:13) [GCC 12.3.0] (64-bit runtime)
Python platform: Linux-6.5.0-44-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 4090
GPU 1: NVIDIA GeForce RTX 4090

Nvidia driver version: 550.90.07
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      48 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             64
On-line CPU(s) list:                0-63
Vendor ID:                          AuthenticAMD
Model name:                         AMD Ryzen Threadripper PRO 5975WX 32-Cores
CPU family:                         25
Model:                              8
Thread(s) per core:                 2
Core(s) per socket:                 32
Socket(s):                          1
Stepping:                           2
Frequency boost:                    enabled
CPU max MHz:                        7006.6401
CPU min MHz:                        1800.0000
BogoMIPS:                           7186.86
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin brs arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm
Virtualization:                     AMD-V
L1d cache:                          1 MiB (32 instances)
L1i cache:                          1 MiB (32 instances)
L2 cache:                           16 MiB (32 instances)
L3 cache:                           128 MiB (4 instances)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-63
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Vulnerable: Safe RET, no microcode
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] numpy==2.0.1
[pip3] torch==2.4.0
[pip3] torch_cluster==1.6.3+pt24cu121
[pip3] torch-geometric==2.6.0
[pip3] torch_scatter==2.1.2+pt24cu121
[pip3] torch_sparse==0.6.18+pt24cu121
[pip3] torch_spline_conv==1.2.2+pt24cu121
[pip3] torchaudio==2.4.0
[pip3] torchvision==0.19.0
[pip3] triton==3.0.0
[conda] numpy                     2.0.1                    pypi_0    pypi
[conda] torch                     2.4.0                    pypi_0    pypi
[conda] torch-cluster             1.6.3+pt24cu121          pypi_0    pypi
[conda] torch-geometric           2.6.0                    pypi_0    pypi
[conda] torch-scatter             2.1.2+pt24cu121          pypi_0    pypi
[conda] torch-sparse              0.6.18+pt24cu121          pypi_0    pypi
[conda] torch-spline-conv         1.2.2+pt24cu121          pypi_0    pypi
[conda] torchaudio                2.4.0                    pypi_0    pypi
[conda] torchvision               0.19.0                   pypi_0    pypi
[conda] triton                    3.0.0                    pypi_0    pypi

```

cc @ezyang @chauhang @penguinwu @rec