hasattr tracing for torch tensor is unsupported

🐛 Describe the bug

Using hasattr on tensor in torch.compile causes graph breaks, while getattr is not.

Error logs

Traceback (most recent call last): File "/home/tbohutyn/workspace/testy/cpu_var_tracker.py", line 19, in main() File "/home/tbohutyn/workspace/testy/cpu_var_tracker.py", line 15, in main fn(t1) File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py", line 451, in _fn return fn(*args, kwargs) File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 921, in catch_errors return callback(frame, cache_entry, hooks, frame_state, skip=1) File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 400, in _convert_frame_assert return _compile( File "/usr/lib/python3.10/contextlib.py", line 79, in inner return func(*args, *kwds) File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 676, in _compile guarded_code = compile_inner(code, one_graph, hooks, transform) File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py", line 262, in time_wrapper r = func(args, kwargs) File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 535, in compile_inner out_code = transform_code_object(code, transform) File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/bytecode_transformation.py", line 1036, in transform_code_object transformations(instructions, code_options) File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 165, in _fn return fn(*args, *kwargs) File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 500, in transform tracer.run() File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2099, in run super().run() File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 815, in run and self.step() File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 778, in step getattr(self, inst.opname)(inst) File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 494, in wrapper return inner_fn(self, inst) File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 1169, in CALL_FUNCTION self.call_function(fn, args, {}) File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 679, in call_function self.push(fn.call_function(self, args, kwargs)) File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/builtin.py", line 935, in call_function return handler(tx, args, kwargs) File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/builtin.py", line 700, in return lambda tx, args, kwargs: obj.call_function( File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/builtin.py", line 935, in call_function return handler(tx, args, kwargs) File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/builtin.py", line 810, in builtin_dipatch rv = handler(tx, args, kwargs) File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/builtin.py", line 739, in call_self_handler result = self_handler(tx, args, **kwargs) File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/builtin.py", line 1394, in call_hasattr return obj.call_hasattr(tx, name) File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/base.py", line 308, in call_hasattr unimplemented(f"hasattr {self.class.name} {name}") File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/exc.py", line 190, in unimplemented raise Unsupported(msg) torch._dynamo.exc.Unsupported: hasattr TensorVariable attr

from user code: File "/home/tbohutyn/workspace/testy/cpu_var_tracker.py", line 6, in fn if hasattr(x, "attr"):

Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information

You can suppress this exception and fall back to eager by setting: import torch._dynamo torch._dynamo.config.suppress_errors = True

Minified repro

import torch

# using fullgraph to raise an exception in place of graph breaks
@torch.compile(backend='inductor', fullgraph=True) 
def fn(x):
    if hasattr(x, "attr"):
        return x + 1
    else:
        return x - 1

def main():
    t1 = torch.tensor([6.])
    t1.attr = False
    fn(t1)

if __name__ == "__main__":
    main()

Versions

Collecting environment information... PyTorch version: 2.3.1a0+gitbede712 Is debug build: False CUDA used to build PyTorch: None 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 3.29.6 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-5.15.0-107-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; BHI Syscall hardening, KVM SW loop Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected

Versions of relevant libraries: [pip3] numpy==1.23.5 [pip3] pytorch-lightning==2.3.1 [pip3] torch==2.3.1a0+gitbede712 [pip3] torch_tb_profiler==0.4.0 [pip3] torchaudio==2.3.0+952ea74 [pip3] torchdata==0.7.1+5e6f7b7 [pip3] torchmetrics==1.4.0.post0 [pip3] torchtext==0.18.0a0+9bed85d [pip3] torchvision==0.18.1a0+fe70bc8 [conda] Could not collect

cc @ezyang @anijain2305 @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng

pytorch / pytorch