pytorch - 💡(How to fix) Fix [Dynamo] GetAttrVariable(AutogradFunctionVariable(), setup_context) has no type when compiling custom autograd.Function

pytorch2026-04-20 11:09:06

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Error Message

Original exception: GetAttrVariable(AutogradFunctionVariable(), setup_context) has no type

from user code: File "test.py", line 36, in forward x = self.custom_relu(self.fc1(x)) File "/path/to/torch/autograd/function.py", line 569, in apply is_setup_ctx_defined = _is_setup_context_defined(cls.setup_context) File "/path/to/torch/autograd/function.py", line 594, in _is_setup_context_defined return fn != _SingleLevelFunction.setup_context File "/path/to/torch/_dynamo/polyfills/init.py", line 300, in cmp_ne if isinstance(type(a).ne, types.FunctionType):

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 ReLUFunction(torch.autograd.Function):
    @staticmethod
    def forward(ctx, x):
        return x.clamp(min=0)

    @staticmethod
    def backward(ctx, grad):
        return grad

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.relu = ReLUFunction.apply
        self.fc = nn.Linear(784, 128)

    def forward(self, x):
        return self.relu(self.fc(x.view(x.size(0), -1)))

model = Model().eval()
x = torch.randn(4, 1, 28, 28)

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

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

---

Original exception:
 GetAttrVariable(AutogradFunctionVariable(), setup_context) has no type

from user code:
   File "test.py", line 36, in forward
    x = self.custom_relu(self.fc1(x))
  File "/path/to/torch/autograd/function.py", line 569, in apply
    is_setup_ctx_defined = _is_setup_context_defined(cls.setup_context)
  File "/path/to/torch/autograd/function.py", line 594, in _is_setup_context_defined
    return fn != _SingleLevelFunction.setup_context
  File "/path/to/torch/_dynamo/polyfills/__init__.py", line 300, in cmp_ne
    if isinstance(type(a).__ne__, types.FunctionType):

RAW_BUFFERClick to expand / collapse

🐛 Describe the bug

When compiling a model that uses a custom torch.autograd.Function with torch.compile(fullgraph=True), Dynamo fails with a type inference error: GetAttrVariable(AutogradFunctionVariable(), setup_context) has no type. The eager mode executes successfully without any errors. code:

import torch
import torch.nn as nn

class ReLUFunction(torch.autograd.Function):
    @staticmethod
    def forward(ctx, x):
        return x.clamp(min=0)

    @staticmethod
    def backward(ctx, grad):
        return grad

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.relu = ReLUFunction.apply
        self.fc = nn.Linear(784, 128)

    def forward(self, x):
        return self.relu(self.fc(x.view(x.size(0), -1)))

model = Model().eval()
x = torch.randn(4, 1, 28, 28)

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

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

output:

Original exception:
 GetAttrVariable(AutogradFunctionVariable(), setup_context) has no type

from user code:
   File "test.py", line 36, in forward
    x = self.custom_relu(self.fc1(x))
  File "/path/to/torch/autograd/function.py", line 569, in apply
    is_setup_ctx_defined = _is_setup_context_defined(cls.setup_context)
  File "/path/to/torch/autograd/function.py", line 594, in _is_setup_context_defined
    return fn != _SingleLevelFunction.setup_context
  File "/path/to/torch/_dynamo/polyfills/__init__.py", line 300, in cmp_ne
    if isinstance(type(a).__ne__, types.FunctionType):

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 @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @kadeng @amjames @Lucaskabela @jataylo @azahed98

extent analysis

TL;DR

The issue is likely due to a type inference error when using a custom torch.autograd.Function with torch.compile(fullgraph=True), and a potential workaround is to modify the custom function or disable fullgraph compilation.

Guidance

The error message suggests a type inference issue with the custom ReLUFunction, specifically with the setup_context attribute.
To mitigate this, you can try modifying the ReLUFunction to include a setup_context method or attribute, even if it's just a placeholder.
Alternatively, you can try disabling fullgraph compilation by setting fullgraph=False in the torch.compile call.
Verify that the custom function is correctly defined and registered with PyTorch.

Example

class ReLUFunction(torch.autograd.Function):
    @staticmethod
    def setup_context(ctx):
        # Add a setup_context method to the custom function
        pass

    @staticmethod
    def forward(ctx, x):
        return x.clamp(min=0)

    @staticmethod
    def backward(ctx, grad):
        return grad

Notes

The provided code snippet and error message suggest that the issue is related to the custom ReLUFunction and its interaction with torch.compile.
The workaround of modifying the custom function or disabling fullgraph compilation may not be a permanent solution and may require further investigation.

Recommendation

Apply workaround: Modify the custom ReLUFunction to include a setup_context method or attribute, or disable fullgraph compilation by setting fullgraph=False in the torch.compile call. This should allow the code to compile and run without errors, but may not be a permanent solution.

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pytorch - 💡(How to fix) Fix [Dynamo] GetAttrVariable(AutogradFunctionVariable(), setup_context) has no type when compiling custom autograd.Function

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pytorch - 💡(How to fix) Fix [Dynamo] GetAttrVariable(AutogradFunctionVariable(), setup_context) has no type when compiling custom autograd.Function

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