pytorch - 💡(How to fix) Fix [Dynamo] torch.* op returned non-Tensor when compiling nn.Transformer with nested tensor internal function

pytorch2026-04-20 12:00:14

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Error Message

Original exception: torch.* op returned non-Tensor Explanation: torch.* ops that return a non-Tensor cannot be traced into the Dynamo FX graph output

Developer debug context: example_value type: bool; op: call_function; target: <built-in method _nested_tensor_from_mask_left_aligned of type object at 0x...>

from user code: File "test.py", line 32, in forward x = self.transformer(x, src_key_padding_mask=mask) File "/path/to/torch/nn/modules/transformer.py", line 467, in forward ) and not torch._nested_tensor_from_mask_left_aligned(

Root Cause

When compiling a model that uses nn.TransformerEncoder with torch.compile(fullgraph=True), Dynamo fails because an internal PyTorch function (torch._nested_tensor_from_mask_left_aligned) returns a bool instead of a Tensor. The eager mode executes successfully. code:

import torch
import torch.nn as nn

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 DynamicShapeTransformer(nn.Module):
    def __init__(self, d_model=512, nhead=8, num_layers=6):
        super().__init__()
        self.embedding = nn.Embedding(1000, d_model)
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=d_model, nhead=nhead, dim_feedforward=2048, 
            dropout=0.1, activation='gelu', batch_first=True
        )
        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)

    def forward(self, x, mask=None):
        x = self.embedding(x)
        return self.transformer(x, src_key_padding_mask=mask)

model = DynamicShapeTransformer().eval()
batch_size, seq_len = 4, 200
x = torch.randint(0, 1000, (batch_size, seq_len))
mask = torch.randint(0, 2, (batch_size, seq_len)).bool()

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

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

---

Original exception:
 torch.* op returned non-Tensor
  Explanation: torch.* ops that return a non-Tensor cannot be traced into the Dynamo FX graph output

  Developer debug context: example_value type: bool; op: call_function; 
  target: <built-in method _nested_tensor_from_mask_left_aligned of type object at 0x...>

from user code:
   File "test.py", line 32, in forward
    x = self.transformer(x, src_key_padding_mask=mask)
  File "/path/to/torch/nn/modules/transformer.py", line 467, in forward
    ) and not torch._nested_tensor_from_mask_left_aligned(

RAW_BUFFERClick to expand / collapse

🐛 Describe the bug

import torch
import torch.nn as nn

class DynamicShapeTransformer(nn.Module):
    def __init__(self, d_model=512, nhead=8, num_layers=6):
        super().__init__()
        self.embedding = nn.Embedding(1000, d_model)
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=d_model, nhead=nhead, dim_feedforward=2048, 
            dropout=0.1, activation='gelu', batch_first=True
        )
        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)

    def forward(self, x, mask=None):
        x = self.embedding(x)
        return self.transformer(x, src_key_padding_mask=mask)

model = DynamicShapeTransformer().eval()
batch_size, seq_len = 4, 200
x = torch.randint(0, 1000, (batch_size, seq_len))
mask = torch.randint(0, 2, (batch_size, seq_len)).bool()

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

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

output:

Original exception:
 torch.* op returned non-Tensor
  Explanation: torch.* ops that return a non-Tensor cannot be traced into the Dynamo FX graph output

  Developer debug context: example_value type: bool; op: call_function; 
  target: <built-in method _nested_tensor_from_mask_left_aligned of type object at 0x...>

from user code:
   File "test.py", line 32, in forward
    x = self.transformer(x, src_key_padding_mask=mask)
  File "/path/to/torch/nn/modules/transformer.py", line 467, in forward
    ) and not torch._nested_tensor_from_mask_left_aligned(

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 @ipiszy @kadeng @muchulee8 @amjames @aakhundov @coconutruben @jataylo

extent analysis

TL;DR

The issue can be worked around by disabling fullgraph compilation or using an older version of PyTorch where the torch._nested_tensor_from_mask_left_aligned function returns a Tensor.

Guidance

The error occurs because torch._nested_tensor_from_mask_left_aligned returns a bool instead of a Tensor when using torch.compile(fullgraph=True).
To verify the issue, try running the model without compilation or with fullgraph=False.
The problem seems to be related to the interaction between PyTorch's compilation and the nn.TransformerEncoder module.
As a temporary workaround, consider disabling fullgraph compilation by setting fullgraph=False when calling torch.compile.

Example

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

Notes

This issue appears to be specific to the development version of PyTorch (2.10.0.dev20251124+cpu) and may be resolved in future releases.
The provided workaround may have performance implications, as fullgraph compilation can provide significant speedups.

Recommendation

Apply workaround: use fullgraph=False when compiling the model, as this allows the model to run without errors, although it may not provide the same performance benefits as fullgraph compilation.

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pytorch - 💡(How to fix) Fix [Dynamo] torch.* op returned non-Tensor when compiling nn.Transformer with nested tensor internal function

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pytorch - 💡(How to fix) Fix [Dynamo] torch.* op returned non-Tensor when compiling nn.Transformer with nested tensor internal function

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