torch._dynamo.exc.Unsupported: comparison AutogradFunctionVariable() <built-in function is_not> ConstantVariable(NoneType)

[YangQun1]

🐛 Describe the bug

The following code will have a graph break caused by "torch._dynamo.exc.Unsupported: comparison AutogradFunctionVariable() ConstantVariable(NoneType)"

import torch

class MyExp(torch.autograd.Function):
    @staticmethod
    def forward(ctx, i):
        result = i.exp()
        ctx.save_for_backward(result)
        return result

    @staticmethod
    def backward(ctx, grad_output):
        result, = ctx.saved_tensors
        return grad_output * result

def not_inlinable_func(x):
    x = torch.relu(x)
    assert MyExp is not None, "failed to import MyExp"
    loss = x.pow(2.0).sum()
    return loss

class MyModule(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = torch.nn.Conv2d(64, 64, 1,  bias=False)

    def forward(self, x):
        y = self.conv(x)
        loss = not_inlinable_func(y)
        return loss

model = MyModule()
compiled_model = torch.compile(model, backend="inductor")

input = torch.randn([1, 64, 32, 32])
loss = compiled_model(input)

But if I change the MyExp class to a normal class like "assert torch.nn.Module is not None, "fail"", there won't be any graph breaks.

Can we add support for torch.autograd.Function comparison?

Error logs

FAILED INLINING <code object not_inlinable_func at 0x7fcf5de49210, file "/home/quyang/my_test/experiments/test_graph_break
break_graph_if_unsupported triggered compile
Traceback (most recent call last):
File "/home/quyang/venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 470, in wrapper
return inner_fn(self, inst)
File "/home/quyang/venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1213, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/home/quyang/venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 652, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/home/quyang/venv/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 248, in call_function
return super().call_function(tx, args, kwargs)
File "/home/quyang/venv/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 81, in call_function
return tx.inline_user_function_return(
File "/home/quyang/venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 688, in inline_user_functi
return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/home/quyang/venv/lib/python3.10/site-packages/torch/dynamo/symbolic_convert.py", line 2261, in inline_call
return cls.inline_call(parent, func, args, kwargs)
File "/home/quyang/venv/lib/python3.10/site-packages/torch/dynamo/symbolic_convert.py", line 2376, in inline_call
tracer.run()
File "/home/quyang/venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 818, in run
and self.step()
File "/home/quyang/venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 781, in step
getattr(self, inst.opname)(inst)
File "/home/quyang/venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1636, in IS_OP
self.COMPARE_OP(new_inst)
File "/home/quyang/venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1201, in COMPARE_OP
BuiltinVariable(supported_any[op]).call_function(
File "/home/quyang/venv/lib/python3.10/site-packages/torch/_dynamo/variables/builtin.py", line 651, in call_function
result = handler(tx, *args, **kwargs)
File "/home/quyang/venv/lib/python3.10/site-packages/torch/_dynamo/variables/builtin.py", line 1563, in _comparison
_unimplemented()
File "/home/quyang/venv/lib/python3.10/site-packages/torch/_dynamo/variables/builtin.py", line 1453, in _unimplemented
unimplemented(f"comparison {typestr(left)} {op} {typestr(right)}")
File "/home/quyang/venv/lib/python3.10/site-packages/torch/_dynamo/exc.py", line 193, in unimplemented
raise Unsupported(msg)
torch._dynamo.exc.Unsupported: comparison AutogradFunctionVariable() ConstantVariable(NoneType)

Minified repro

No response

Versions

PyTorch version: 2.2.0a0+gitc023606
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 14.0.5 (ssh://gerrit.habana-labs.com:29418/tpc_llvm10 ab097983d16bb8cedaa85e13d6123696de35047a)
CMake version: version 3.28.1
Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.15.0-102-generic-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
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: 43 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 12
On-line CPU(s) list: 0-11
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Gold 6132 CPU @ 2.60GHz
CPU family: 6
Model: 85
Thread(s) per core: 1
Core(s) per socket: 6
Socket(s): 2
Stepping: 0
BogoMIPS: 5187.81
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon nopl xtopology tsc_reliable nonstop_tsc cpuid tsc_known_freq pni pclmulqdq vmx ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow vnmi ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 invpcid avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xsaves arat pku ospke md_clear flush_l1d arch_capabilities
Virtualization: VT-x
Hypervisor vendor: VMware
Virtualization type: full
L1d cache: 384 KiB (12 instances)
L1i cache: 384 KiB (12 instances)
L2 cache: 12 MiB (12 instances)
L3 cache: 38.5 MiB (2 instances)
NUMA node(s): 1
NUMA node0 CPU(s): 0-11
Vulnerability Gather data sampling: Unknown: Dependent on hypervisor status
Vulnerability Itlb multihit: KVM: Mitigation: VMX disabled
Vulnerability L1tf: Mitigation; PTE Inversion; VMX flush not necessary, SMT disabled
Vulnerability Mds: Mitigation; Clear CPU buffers; SMT Host state unknown
Vulnerability Meltdown: Mitigation; PTI
Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT Host state unknown
Vulnerability Retbleed: Mitigation; IBRS
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; IBRS, IBPB conditional, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected

Versions of relevant libraries:
[pip3] habana-torch-dataloader==1.16.0.363
[pip3] habana-torch-plugin==1.16.0.363
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.4
[pip3] torch==2.2.0a0+gitc023606
[pip3] torch-debug==2.2.0a0+gitb658878
[pip3] torch_tb_profiler==0.4.0
[pip3] torchaudio==2.2.0+08901ad
[pip3] torchdata==0.7.1+5e6f7b7
[pip3] torchtext==0.17.0+400da5c
[pip3] torchvision==0.17.0+b2383d4

cc @ezyang @albanD @gqchen @pearu @nikitaved @soulitzer @lezcano @Varal7 @msaroufim @bdhirsh @anijain2305 @chauhang @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng

[YangQun1]

I will close this issue since torch.autograd.Function comparison has been correctly handled by the "handle_is" handler in latest pytorch nightly build, thanks