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pytorch - 💡(How to fix) Fix [Dynamo] Failed to trace builtin complex() function when mixing Python complex with torch tensors

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Error Message

Original exception: Failed to trace builtin operator Explanation: Dynamo does not know how to trace builtin operator complex with argument types ['float', 'float'] (has_kwargs False) Hint: Avoid calling builtin complex with argument types ['float', 'float'].

Developer debug context: builtin complex [ConstantVariable, ConstantVariable] False

from user code: x = x + complex(20.0, 0.0)

Fix Action

Fix / Workaround

Vulnerability Reg file data sampling: Mitigation; Clear Register File

Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl

Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization

Code Example

import torch
import torch.nn as nn

class ComplexModel(nn.Module):
    def forward(self, x):
        # Python builtin complex() causes tracing failure
        return x + complex(20.0, 0.0)

model = ComplexModel().eval()
x = torch.tensor([complex(8.0, 6.0)], dtype=torch.complex64)

compiled_model = torch.compile(model, fullgraph=True)

with torch.no_grad():
    output = compiled_model(x)  # Error here

---

Original exception:
 Failed to trace builtin operator
  Explanation: Dynamo does not know how to trace builtin operator `complex` with argument types ['float', 'float'] (has_kwargs False)
  Hint: Avoid calling builtin `complex` with argument types ['float', 'float'].

  Developer debug context: builtin complex [ConstantVariable, ConstantVariable] False

from user code:
    x = x + complex(20.0, 0.0)
RAW_BUFFERClick to expand / collapse

🐛 Describe the bug

When using Python's builtin complex() function inside a torch.compile region to create complex numbers that are then added to PyTorch complex tensors, Dynamo fails with Failed to trace builtin operator. The eager mode executes successfully (outputs 0.0). code:

import torch
import torch.nn as nn

class ComplexModel(nn.Module):
    def forward(self, x):
        # Python builtin complex() causes tracing failure
        return x + complex(20.0, 0.0)

model = ComplexModel().eval()
x = torch.tensor([complex(8.0, 6.0)], dtype=torch.complex64)

compiled_model = torch.compile(model, fullgraph=True)

with torch.no_grad():
    output = compiled_model(x)  # Error here

output:

Original exception:
 Failed to trace builtin operator
  Explanation: Dynamo does not know how to trace builtin operator `complex` with argument types ['float', 'float'] (has_kwargs False)
  Hint: Avoid calling builtin `complex` with argument types ['float', 'float'].

  Developer debug context: builtin complex [ConstantVariable, ConstantVariable] False

from user code:
    x = x + complex(20.0, 0.0)

Versions

Environment Information PyTorch Build Details:

PyTorch version: 2.10.0.dev20251124+cpu

Is debug build: False

CUDA used to build PyTorch: Could not collect

ROCM used to build PyTorch: N/A

OS and Compilers:

OS: Ubuntu 24.04.1 LTS (x86_64)

GCC version: (Ubuntu 10.5.0-4ubuntu2) 10.5.0

Clang version: 18.1.3 (1)

CMake version: version 3.28.3

Libc version: glibc-2.39

Python Environment:

Python version: 3.12.3 (main, Nov 6 2025, 13:44:16) [GCC 13.3.0] (64-bit runtime)

Python platform: Linux-6.14.0-36-generic-x86_64-with-glibc2.39

Is CUDA available: False

CUDA runtime version: Could not collect

CUDA_MODULE_LOADING set to: N/A

GPU Information:

GPU models and configuration: GPU 0: NVIDIA GeForce RTX 4060 Laptop GPU

Nvidia driver version: 580.95.05

cuDNN version: Could not collect

Is XPU available: False

HIP runtime version: N/A

MIOpen runtime version: N/A

Is XNNPACK available: True

Caching allocator config: N/A

CPU Information:

Architecture: x86_64

CPU op-mode(s): 32-bit, 64-bit

Address sizes: 39 bits physical, 48 bits virtual

Byte Order: Little Endian

CPU(s): 32

On-line CPU(s) list: 0-31

Vendor ID: GenuineIntel

Model name: Intel(R) Core(TM) i9-14900HX

CPU family: 6

Model: 183

Thread(s) per core: 2

Core(s) per socket: 24

Socket(s): 1

Stepping: 1

CPU(s) scaling MHz: 33%

CPU max MHz: 5800.0000

CPU min MHz: 800.0000

BogoMIPS: 4838.40

Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect user_shstk avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi vnmi umip pku ospke waitpkg gfni vaes vpclmulqdq rdpid movdiri movdir64b fsrm md_clear serialize arch_lbr ibt flush_l1d arch_capabilities

Virtualization: VT-x

L1d cache: 896 KiB (24 instances)

L1i cache: 1.3 MiB (24 instances)

L2 cache: 32 MiB (12 instances)

L3 cache: 36 MiB (1 instance)

NUMA node(s): 1

NUMA node0 CPU(s): 0-31

Vulnerability Gather data sampling: Not affected

Vulnerability Ghostwrite: Not affected

Vulnerability Indirect target selection: 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 Reg file data sampling: Mitigation; Clear Register File

Vulnerability Retbleed: Not affected

Vulnerability Spec rstack overflow: Not affected

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; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S

Vulnerability Srbds: Not affected

Vulnerability Tsa: Not affected

Vulnerability Tsx async abort: Not affected

Vulnerability Vmscape: Mitigation; IBPB before exit to userspace

Versions of Relevant Libraries:

[pip3] numpy==2.3.3

[pip3] nvidia-cublas-cu12==12.1.3.1

[pip3] nvidia-cuda-cupti-cu12==12.1.105

[pip3] nvidia-cuda-nvrtc-cu12==12.1.105

[pip3] nvidia-cuda-runtime-cu12==12.1.105

[pip3] nvidia-cudnn-cu12==9.1.0.70

[pip3] nvidia-cufft-cu12==11.0.2.54

[pip3] nvidia-curand-cu12==10.3.2.106

[pip3] nvidia-cusolver-cu12==11.4.5.107

[pip3] nvidia-cusparse-cu12==12.1.0.106

[pip3] nvidia-nccl-cu12==2.21.5

[pip3] nvidia-nvjitlink-cu12==12.9.86

[pip3] nvidia-nvtx-cu12==12.1.105

[pip3] optree==0.18.0

[pip3] pytorch-triton-rocm==3.5.0

[pip3] torch==2.10.0.dev20251124+cpu

[pip3] torchao==0.15.0.dev20251124+cpu

[pip3] torchdata==0.12.0.dev20250909+cpu

[pip3] torchtext==0.17.0.dev20240912+cpu

[pip3] triton==3.1.0

[conda] Could not collect

cc @ezyang @anjali411 @dylanbespalko @mruberry @nikitaved @amjames @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @kadeng @Lucaskabela @jataylo @azahed98

extent analysis

TL;DR

The issue can be resolved by avoiding the use of Python's builtin complex() function inside a torch.compile region.

Guidance

  • The error message indicates that Dynamo does not know how to trace the builtin complex operator, suggesting that the issue is related to the tracing of Python code within the compiled region.
  • To mitigate this, consider using PyTorch's tensor operations to create complex numbers instead of the Python complex() function.
  • Verify that the issue is resolved by checking if the compiled model can execute without errors and produce the expected output.
  • If the issue persists, try to isolate the problem by testing the model with different inputs and checking if the error occurs consistently.

Example

import torch
import torch.nn as nn

class ComplexModel(nn.Module):
    def forward(self, x):
        # Use PyTorch's tensor operations to create complex numbers
        complex_num = torch.tensor(20.0) + 0j
        return x + complex_num

model = ComplexModel().eval()
x = torch.tensor([complex(8.0, 6.0)], dtype=torch.complex64)

compiled_model = torch.compile(model, fullgraph=True)

with torch.no_grad():
    output = compiled_model(x)

Notes

The provided code snippet is a minimal example and may not cover all possible use cases. The issue may be specific to the version of PyTorch being used (2.10.0.dev20251124+cpu).

Recommendation

Apply a workaround by using PyTorch's tensor operations to create complex numbers instead of the Python complex() function, as shown in the example code snippet. This should allow the model to compile and execute without errors.

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