pytorch - ✅(Solved) Fix # `torch.compile` inductor crash: `NotImplementedError: View` when flatten() is used as conv2d bias [1 pull requests, 1 participants]

pytorch2026-04-24 08:20:15

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pytorch/pytorch#181351•Fetched 2026-04-25 06:02:59

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raindrops-0199

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Error Message

InductorError: LoweringException: NotImplementedError: View
  target: aten.convolution.default
  args[2]: TensorBox(
    View(
      StorageBox(
        ComputedBuffer(..., data=Pointwise(
          def inner_fn(index):
              _, _, _, _ = index
              tmp0 = ops.load(buf0, 0)
              tmp1 = ops.to_dtype(tmp0, torch.float32, src_dtype=torch.bool)
              return tmp1
        )),
      ),
      size=[1],
      reindex=lambda i0: [0, 0, 0, 0],
    )
  )

Fix Action

Fix / Workaround

CPU: 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): 8 On-line CPU(s) list: 0-7 Vendor ID: GenuineIntel Model name: 12th Gen Intel(R) Core(TM) i3-12100F CPU family: 6 Model: 151 Thread(s) per core: 2 Core(s) per socket: 4 Socket(s): 1 Stepping: 5 CPU max MHz: 4300.0000 CPU min MHz: 800.0000 BogoMIPS: 6604.80 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 ibpb_exit_to_user Virtualization: VT-x L1d cache: 192 KiB (4 instances) L1i cache: 128 KiB (4 instances) L2 cache: 5 MiB (4 instances) L3 cache: 12 MiB (1 instance) NUMA node(s): 1 NUMA node0 CPU(s): 0-7 Vulnerability Gather data sampling: 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: Not affected 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

PR fix notes

PR #181363: [inductor] Realize conv bias input before freezing layout

Repository: pytorch/pytorch
Author: pandego
State: open | merged: False
Link: https://github.com/pytorch/pytorch/pull/181363

Description (problem / solution / changelog)

Summary

realize convolution bias inputs through ExternKernel.realize_input() before freezing layout in the inductor conv lowering path
add a regression test covering a flatten()-produced bias view passed into F.conv2d under torch.compile

Repro

import torch
import torch.nn.functional as F

def fn(x):
    bias = x.to(torch.bool).float().flatten()
    return F.conv2d(x, x, bias, stride=1, padding=0)

x = torch.ones(1, 1, 1, 1)
torch.compile(fn, backend="inductor", fullgraph=True)(x)

Validation

OMP_PREFIX=/opt/homebrew/opt/libomp uv run --no-sync python3 -m pytest test/inductor/test_cpu_repro.py -k conv_bias_flattened_view -q

Closes #181351

cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @kadeng @muchulee8 @amjames @chauhang @aakhundov @coconutruben @jataylo

Changed files

test/inductor/test_cpu_repro.py (modified, +12/-0)
torch/_inductor/kernel/conv.py (modified, +1/-1)

Code Example

import torch
import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def forward(self, x):
        b = x.to(torch.bool).float()
        bias = b.flatten()
        return F.conv2d(b, b, bias, stride=1, padding=0)

model = Model()
x = torch.ones(1, 1, 1, 1)

ret_eager = model(x.clone())
print(f"Eager OK: {ret_eager.shape}")   # OK: [1, 1, 1, 1]

compiled = torch.compile(model)
ret_compiled = compiled(x.clone())      # crash
print(f"Compile OK: {ret_compiled.shape}")

---

InductorError: LoweringException: NotImplementedError: View
  target: aten.convolution.default
  args[2]: TensorBox(
    View(
      StorageBox(
        ComputedBuffer(..., data=Pointwise(
          def inner_fn(index):
              _, _, _, _ = index
              tmp0 = ops.load(buf0, 0)
              tmp1 = ops.to_dtype(tmp0, torch.float32, src_dtype=torch.bool)
              return tmp1
        )),
      ),
      size=[1],
      reindex=lambda i0: [0, 0, 0, 0],
    )
  )

---

PyTorch version: 2.11.0+cu130
Is debug build: False
CUDA used to build PyTorch: 13.0
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04.3) 11.4.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.35

Python version: 3.12.11 | packaged by Anaconda, Inc. | (main, Jun  5 2025, 13:09:17) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.8.0-101-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
Is XPU available: False
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
Caching allocator config: N/A

CPU:
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):                                  8
On-line CPU(s) list:                     0-7
Vendor ID:                               GenuineIntel
Model name:                              12th Gen Intel(R) Core(TM) i3-12100F
CPU family:                              6
Model:                                   151
Thread(s) per core:                      2
Core(s) per socket:                      4
Socket(s):                               1
Stepping:                                5
CPU max MHz:                             4300.0000
CPU min MHz:                             800.0000
BogoMIPS:                                6604.80
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 ibpb_exit_to_user
Virtualization:                          VT-x
L1d cache:                               192 KiB (4 instances)
L1i cache:                               128 KiB (4 instances)
L2 cache:                                5 MiB (4 instances)
L3 cache:                                12 MiB (1 instance)
NUMA node(s):                            1
NUMA node0 CPU(s):                       0-7
Vulnerability Gather data sampling:      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:    Not affected
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==13.1.0.3
[pip3] nvidia-cublas-cu12==12.8.4.1
[pip3] nvidia-cuda-cupti==13.0.85
[pip3] nvidia-cuda-cupti-cu12==12.8.90
[pip3] nvidia-cuda-nvrtc==13.0.88
[pip3] nvidia-cuda-nvrtc-cu12==12.8.93
[pip3] nvidia-cuda-runtime==13.0.96
[pip3] nvidia-cuda-runtime-cu12==12.8.90
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cudnn-cu13==9.19.0.56
[pip3] nvidia-cufft==12.0.0.61
[pip3] nvidia-cufft-cu12==11.3.3.83
[pip3] nvidia-curand==10.4.0.35
[pip3] nvidia-curand-cu12==10.3.9.90
[pip3] nvidia-cusolver==12.0.4.66
[pip3] nvidia-cusolver-cu12==11.7.3.90
[pip3] nvidia-cusparse==12.6.3.3
[pip3] nvidia-cusparse-cu12==12.5.8.93
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-cusparselt-cu13==0.8.0
[pip3] nvidia-nccl-cu12==2.27.5
[pip3] nvidia-nccl-cu13==2.28.9
[pip3] nvidia-nvjitlink==13.0.88
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvtx==13.0.85
[pip3] nvidia-nvtx-cu12==12.8.90
[pip3] torch==2.11.0
[pip3] torchaudio==2.11.0
[pip3] torchvision==0.26.0
[pip3] triton==3.6.0
[conda] numpy                     2.3.3                    pypi_0    pypi
[conda] nvidia-cublas             13.1.0.3                 pypi_0    pypi
[conda] nvidia-cublas-cu12        12.8.4.1                 pypi_0    pypi
[conda] nvidia-cuda-cupti         13.0.85                  pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.8.90                  pypi_0    pypi
[conda] nvidia-cuda-nvrtc         13.0.88                  pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.8.93                  pypi_0    pypi
[conda] nvidia-cuda-runtime       13.0.96                  pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.8.90                  pypi_0    pypi
[conda] nvidia-cudnn-cu12         9.10.2.21                pypi_0    pypi
[conda] nvidia-cudnn-cu13         9.19.0.56                pypi_0    pypi
[conda] nvidia-cufft              12.0.0.61                pypi_0    pypi
[conda] nvidia-cufft-cu12         11.3.3.83                pypi_0    pypi
[conda] nvidia-curand             10.4.0.35                pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.9.90                pypi_0    pypi
[conda] nvidia-cusolver           12.0.4.66                pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.7.3.90                pypi_0    pypi
[conda] nvidia-cusparse           12.6.3.3                 pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.5.8.93                pypi_0    pypi
[conda] nvidia-cusparselt-cu12    0.7.1                    pypi_0    pypi
[conda] nvidia-cusparselt-cu13    0.8.0                    pypi_0    pypi
[conda] nvidia-nccl-cu12          2.27.5                   pypi_0    pypi
[conda] nvidia-nccl-cu13          2.28.9                   pypi_0    pypi
[conda] nvidia-nvjitlink          13.0.88                  pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.8.93                  pypi_0    pypi
[conda] nvidia-nvtx               13.0.85                  pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.8.90                  pypi_0    pypi
[conda] torch                     2.11.0                   pypi_0    pypi
[conda] torchaudio                2.11.0                   pypi_0    pypi
[conda] torchvision               0.26.0                   pypi_0    pypi
[conda] triton                    3.6.0                    pypi_0    pypi

RAW_BUFFERClick to expand / collapse

🐛 Describe the bug

torch.compile fails during lowering of aten.convolution when the bias argument is a View node produced by flatten() on a tensor derived from to(bool).float().

Eager mode works correctly. The crash happens during inductor lowering.

Reproduction

import torch
import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def forward(self, x):
        b = x.to(torch.bool).float()
        bias = b.flatten()
        return F.conv2d(b, b, bias, stride=1, padding=0)

model = Model()
x = torch.ones(1, 1, 1, 1)

ret_eager = model(x.clone())
print(f"Eager OK: {ret_eager.shape}")   # OK: [1, 1, 1, 1]

compiled = torch.compile(model)
ret_compiled = compiled(x.clone())      # crash
print(f"Compile OK: {ret_compiled.shape}")

Error

InductorError: LoweringException: NotImplementedError: View
  target: aten.convolution.default
  args[2]: TensorBox(
    View(
      StorageBox(
        ComputedBuffer(..., data=Pointwise(
          def inner_fn(index):
              _, _, _, _ = index
              tmp0 = ops.load(buf0, 0)
              tmp1 = ops.to_dtype(tmp0, torch.float32, src_dtype=torch.bool)
              return tmp1
        )),
      ),
      size=[1],
      reindex=lambda i0: [0, 0, 0, 0],
    )
  )

Versions

PyTorch version: 2.11.0+cu130
Is debug build: False
CUDA used to build PyTorch: 13.0
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04.3) 11.4.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.35

Python version: 3.12.11 | packaged by Anaconda, Inc. | (main, Jun  5 2025, 13:09:17) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.8.0-101-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
Is XPU available: False
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
Caching allocator config: N/A

CPU:
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):                                  8
On-line CPU(s) list:                     0-7
Vendor ID:                               GenuineIntel
Model name:                              12th Gen Intel(R) Core(TM) i3-12100F
CPU family:                              6
Model:                                   151
Thread(s) per core:                      2
Core(s) per socket:                      4
Socket(s):                               1
Stepping:                                5
CPU max MHz:                             4300.0000
CPU min MHz:                             800.0000
BogoMIPS:                                6604.80
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 ibpb_exit_to_user
Virtualization:                          VT-x
L1d cache:                               192 KiB (4 instances)
L1i cache:                               128 KiB (4 instances)
L2 cache:                                5 MiB (4 instances)
L3 cache:                                12 MiB (1 instance)
NUMA node(s):                            1
NUMA node0 CPU(s):                       0-7
Vulnerability Gather data sampling:      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:    Not affected
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==13.1.0.3
[pip3] nvidia-cublas-cu12==12.8.4.1
[pip3] nvidia-cuda-cupti==13.0.85
[pip3] nvidia-cuda-cupti-cu12==12.8.90
[pip3] nvidia-cuda-nvrtc==13.0.88
[pip3] nvidia-cuda-nvrtc-cu12==12.8.93
[pip3] nvidia-cuda-runtime==13.0.96
[pip3] nvidia-cuda-runtime-cu12==12.8.90
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cudnn-cu13==9.19.0.56
[pip3] nvidia-cufft==12.0.0.61
[pip3] nvidia-cufft-cu12==11.3.3.83
[pip3] nvidia-curand==10.4.0.35
[pip3] nvidia-curand-cu12==10.3.9.90
[pip3] nvidia-cusolver==12.0.4.66
[pip3] nvidia-cusolver-cu12==11.7.3.90
[pip3] nvidia-cusparse==12.6.3.3
[pip3] nvidia-cusparse-cu12==12.5.8.93
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-cusparselt-cu13==0.8.0
[pip3] nvidia-nccl-cu12==2.27.5
[pip3] nvidia-nccl-cu13==2.28.9
[pip3] nvidia-nvjitlink==13.0.88
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvtx==13.0.85
[pip3] nvidia-nvtx-cu12==12.8.90
[pip3] torch==2.11.0
[pip3] torchaudio==2.11.0
[pip3] torchvision==0.26.0
[pip3] triton==3.6.0
[conda] numpy                     2.3.3                    pypi_0    pypi
[conda] nvidia-cublas             13.1.0.3                 pypi_0    pypi
[conda] nvidia-cublas-cu12        12.8.4.1                 pypi_0    pypi
[conda] nvidia-cuda-cupti         13.0.85                  pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.8.90                  pypi_0    pypi
[conda] nvidia-cuda-nvrtc         13.0.88                  pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.8.93                  pypi_0    pypi
[conda] nvidia-cuda-runtime       13.0.96                  pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.8.90                  pypi_0    pypi
[conda] nvidia-cudnn-cu12         9.10.2.21                pypi_0    pypi
[conda] nvidia-cudnn-cu13         9.19.0.56                pypi_0    pypi
[conda] nvidia-cufft              12.0.0.61                pypi_0    pypi
[conda] nvidia-cufft-cu12         11.3.3.83                pypi_0    pypi
[conda] nvidia-curand             10.4.0.35                pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.9.90                pypi_0    pypi
[conda] nvidia-cusolver           12.0.4.66                pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.7.3.90                pypi_0    pypi
[conda] nvidia-cusparse           12.6.3.3                 pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.5.8.93                pypi_0    pypi
[conda] nvidia-cusparselt-cu12    0.7.1                    pypi_0    pypi
[conda] nvidia-cusparselt-cu13    0.8.0                    pypi_0    pypi
[conda] nvidia-nccl-cu12          2.27.5                   pypi_0    pypi
[conda] nvidia-nccl-cu13          2.28.9                   pypi_0    pypi
[conda] nvidia-nvjitlink          13.0.88                  pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.8.93                  pypi_0    pypi
[conda] nvidia-nvtx               13.0.85                  pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.8.90                  pypi_0    pypi
[conda] torch                     2.11.0                   pypi_0    pypi
[conda] torchaudio                2.11.0                   pypi_0    pypi
[conda] torchvision               0.26.0                   pypi_0    pypi
[conda] triton                    3.6.0                    pypi_0    pypi

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 resolved by avoiding the use of a View node produced by flatten() on a tensor derived from to(bool).float() as the bias argument in aten.convolution when using torch.compile.

Guidance

The error occurs because torch.compile fails to handle the View node produced by flatten() on a tensor derived from to(bool).float() as the bias argument in aten.convolution.
To verify the issue, run the provided reproduction code and check if the error occurs.
As a potential workaround, try to avoid using flatten() on a tensor derived from to(bool).float() as the bias argument in aten.convolution when using torch.compile.
Consider using a different method to create the bias tensor, such as using a tensor with a fixed shape instead of a View node.

Example

import torch
import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def forward(self, x):
        b = x.to(torch.bool).float()
        bias = torch.randn(1)  # Create a tensor with a fixed shape
        return F.conv2d(x, x, bias, stride=1, padding=0)

model = Model()
x = torch.ones(1, 1, 1, 1)

compiled = torch.compile(model)
ret_compiled = compiled(x.clone())
print(f"Compile OK: {ret_compiled.shape}")

Notes

The provided reproduction code and error message suggest that the issue is related to the use of torch.compile with a specific type of tensor as the bias argument in aten.convolution. However, without further information or debugging, it is difficult to provide a more specific solution.

Recommendation

Apply workaround: Avoid using flatten() on a tensor derived from to(bool).float() as the bias argument in aten.convolution when

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pytorch - ✅(Solved) Fix # `torch.compile` inductor crash: `NotImplementedError: View` when flatten() is used as conv2d bias [1 pull requests, 1 participants]

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PR #181363: [inductor] Realize conv bias input before freezing layout

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pytorch - ✅(Solved) Fix # `torch.compile` inductor crash: `NotImplementedError: View` when flatten() is used as conv2d bias [1 pull requests, 1 participants]

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PR fix notes

PR #181363: [inductor] Realize conv bias input before freezing layout

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Changed files

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