pytorch - 💡(How to fix) Fix `torch.compile` fails at FakeTensor validation for ConvTranspose2d

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Error Message

eager: torch.Size([2, 1, 0, 0]) Traceback (most recent call last): File "/home/jason/Documents/DLCTestingv2/tests/test_re.py", line 23, in <module> compile_res = torch.compile(m)(x) ^^^^^^^^^^^^^^^^^^^ File "/home/jason/miniconda3/envs/dl/lib/python3.12/site-packages/torch/_dynamo/eval_frame.py", line 473, in call return super().call(args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ... ... File "/home/jason/miniconda3/envs/dl/lib/python3.12/site-packages/torch/_dynamo/utils.py", line 3740, in _wrap_graph_break_with_torch_runtime_err raise exc.with_traceback(e.traceback) from None File "/home/jason/miniconda3/envs/dl/lib/python3.12/site-packages/torch/_dynamo/utils.py", line 3737, in _wrap_graph_break_with_torch_runtime_err gb_fn() File "/home/jason/miniconda3/envs/dl/lib/python3.12/site-packages/torch/_dynamo/utils.py", line 3943, in <lambda> lambda: unimplemented( ^^^^^^^^^^^^^^ File "/home/jason/miniconda3/envs/dl/lib/python3.12/site-packages/torch/_dynamo/exc.py", line 653, in unimplemented raise Unsupported( torch._dynamo.exc.TorchRuntimeError: RuntimeError when making fake tensor call Explanation: Dynamo failed to run FX node with fake tensors: call_function <built-in method conv_transpose2d of type object at 0x72e6ac942d60>((FakeTensor(..., size=(2, 3, 2, 2)), Parameter(FakeTensor(..., size=(3, 1, 5, 5), requires_grad=True)), None, (1, 1), (3, 3), (0, 0), 1, (1, 1)), **{}): got RuntimeError('Given input size per channel: [2, 2]. Calculated output size per channel: [0, 0]. Output size is too small') Hint: Your code may result in an error when running in eager. Please double check that your code doesn't contain a similar error when actually running eager/uncompiled. You can do this by removing the torch.compile call, or by using torch.compiler.set_stance("force_eager").

Developer debug context:

For more details about this graph break, please visit: https://meta-pytorch.github.io/compile-graph-break-site/gb/gb4315.html

from user code: File "/home/jason/Documents/DLCTestingv2/tests/test_re.py", line 14, in forward return self.ct(x) File "/home/jason/miniconda3/envs/dl/lib/python3.12/site-packages/torch/nn/modules/conv.py", line 1198, in forward return F.conv_transpose2d(

Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"

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

Code Example

import torch

class Model(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.ct = torch.nn.ConvTranspose2d(
            in_channels=3, out_channels=1,
            kernel_size=5, stride=1, padding=3,
            bias=False,
        )

    def forward(self, x):
        return self.ct(x)

m = Model()
x = torch.randn(2, 3, 2, 2)

eager_res = m(x)
print("eager:", eager_res.shape)

compile_res = torch.compile(m)(x)
print("compile:", compile_res.shape)

---

eager: torch.Size([2, 1, 0, 0])
Traceback (most recent call last):
  File "/home/jason/Documents/DLCTestingv2/tests/test_re.py", line 23, in <module>
    compile_res = torch.compile(m)(x)
                  ^^^^^^^^^^^^^^^^^^^
  File "/home/jason/miniconda3/envs/dl/lib/python3.12/site-packages/torch/_dynamo/eval_frame.py", line 473, in __call__
    return super().__call__(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
...
...
  File "/home/jason/miniconda3/envs/dl/lib/python3.12/site-packages/torch/_dynamo/utils.py", line 3740, in _wrap_graph_break_with_torch_runtime_err
    raise exc.with_traceback(e.__traceback__) from None
  File "/home/jason/miniconda3/envs/dl/lib/python3.12/site-packages/torch/_dynamo/utils.py", line 3737, in _wrap_graph_break_with_torch_runtime_err
    gb_fn()
  File "/home/jason/miniconda3/envs/dl/lib/python3.12/site-packages/torch/_dynamo/utils.py", line 3943, in <lambda>
    lambda: unimplemented(
            ^^^^^^^^^^^^^^
  File "/home/jason/miniconda3/envs/dl/lib/python3.12/site-packages/torch/_dynamo/exc.py", line 653, in unimplemented
    raise Unsupported(
torch._dynamo.exc.TorchRuntimeError: RuntimeError when making fake tensor call
  Explanation: Dynamo failed to run FX node with fake tensors: call_function <built-in method conv_transpose2d of type object at 0x72e6ac942d60>(*(FakeTensor(..., size=(2, 3, 2, 2)), Parameter(FakeTensor(..., size=(3, 1, 5, 5), requires_grad=True)), None, (1, 1), (3, 3), (0, 0), 1, (1, 1)), **{}): got RuntimeError('Given input size per channel: [2, 2]. Calculated output size per channel: [0, 0]. Output size is too small')
  Hint: Your code may result in an error when running in eager. Please double check that your code doesn't contain a similar error when actually running eager/uncompiled. You can do this by removing the `torch.compile` call, or by using `torch.compiler.set_stance("force_eager")`. 

  Developer debug context: 

 For more details about this graph break, please visit: https://meta-pytorch.github.io/compile-graph-break-site/gb/gb4315.html

from user code:
   File "/home/jason/Documents/DLCTestingv2/tests/test_re.py", line 14, in forward
    return self.ct(x)
  File "/home/jason/miniconda3/envs/dl/lib/python3.12/site-packages/torch/nn/modules/conv.py", line 1198, in forward
    return F.conv_transpose2d(

Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"

---

PyTorch version: 2.12.0+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==9.20.0.48
[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==0.8.1
[pip3] nvidia-nccl-cu12==2.27.5
[pip3] nvidia-nccl-cu13==2.29.7
[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==2.12.0
[pip3] torchaudio==2.11.0
[pip3] torchvision==0.26.0
[pip3] triton==3.7.0
[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         9.20.0.48                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    0.8.1                    pypi_0    pypi
[conda] nvidia-nccl-cu12          2.27.5                   pypi_0    pypi
[conda] nvidia-nccl-cu13          2.29.7                   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                     2.12.0                   pypi_0    pypi
[conda] torchaudio                2.11.0                   pypi_0    pypi
[conda] torchvision               0.26.0                   pypi_0    pypi
[conda] triton                    3.7.0                    pypi_0    pypi
RAW_BUFFERClick to expand / collapse

🐛 Describe the bug

torch.compile fails at FakeTensor validation when a ConvTranspose2d layer is configured such that its output has zero spatial dimensions. Eager mode works fine.

Reproduction

import torch

class Model(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.ct = torch.nn.ConvTranspose2d(
            in_channels=3, out_channels=1,
            kernel_size=5, stride=1, padding=3,
            bias=False,
        )

    def forward(self, x):
        return self.ct(x)

m = Model()
x = torch.randn(2, 3, 2, 2)

eager_res = m(x)
print("eager:", eager_res.shape)

compile_res = torch.compile(m)(x)
print("compile:", compile_res.shape)

Output

eager: torch.Size([2, 1, 0, 0])
Traceback (most recent call last):
  File "/home/jason/Documents/DLCTestingv2/tests/test_re.py", line 23, in <module>
    compile_res = torch.compile(m)(x)
                  ^^^^^^^^^^^^^^^^^^^
  File "/home/jason/miniconda3/envs/dl/lib/python3.12/site-packages/torch/_dynamo/eval_frame.py", line 473, in __call__
    return super().__call__(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
...
...
  File "/home/jason/miniconda3/envs/dl/lib/python3.12/site-packages/torch/_dynamo/utils.py", line 3740, in _wrap_graph_break_with_torch_runtime_err
    raise exc.with_traceback(e.__traceback__) from None
  File "/home/jason/miniconda3/envs/dl/lib/python3.12/site-packages/torch/_dynamo/utils.py", line 3737, in _wrap_graph_break_with_torch_runtime_err
    gb_fn()
  File "/home/jason/miniconda3/envs/dl/lib/python3.12/site-packages/torch/_dynamo/utils.py", line 3943, in <lambda>
    lambda: unimplemented(
            ^^^^^^^^^^^^^^
  File "/home/jason/miniconda3/envs/dl/lib/python3.12/site-packages/torch/_dynamo/exc.py", line 653, in unimplemented
    raise Unsupported(
torch._dynamo.exc.TorchRuntimeError: RuntimeError when making fake tensor call
  Explanation: Dynamo failed to run FX node with fake tensors: call_function <built-in method conv_transpose2d of type object at 0x72e6ac942d60>(*(FakeTensor(..., size=(2, 3, 2, 2)), Parameter(FakeTensor(..., size=(3, 1, 5, 5), requires_grad=True)), None, (1, 1), (3, 3), (0, 0), 1, (1, 1)), **{}): got RuntimeError('Given input size per channel: [2, 2]. Calculated output size per channel: [0, 0]. Output size is too small')
  Hint: Your code may result in an error when running in eager. Please double check that your code doesn't contain a similar error when actually running eager/uncompiled. You can do this by removing the `torch.compile` call, or by using `torch.compiler.set_stance("force_eager")`. 

  Developer debug context: 

 For more details about this graph break, please visit: https://meta-pytorch.github.io/compile-graph-break-site/gb/gb4315.html

from user code:
   File "/home/jason/Documents/DLCTestingv2/tests/test_re.py", line 14, in forward
    return self.ct(x)
  File "/home/jason/miniconda3/envs/dl/lib/python3.12/site-packages/torch/nn/modules/conv.py", line 1198, in forward
    return F.conv_transpose2d(

Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"

Versions

PyTorch version: 2.12.0+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==9.20.0.48
[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==0.8.1
[pip3] nvidia-nccl-cu12==2.27.5
[pip3] nvidia-nccl-cu13==2.29.7
[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==2.12.0
[pip3] torchaudio==2.11.0
[pip3] torchvision==0.26.0
[pip3] triton==3.7.0
[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         9.20.0.48                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    0.8.1                    pypi_0    pypi
[conda] nvidia-nccl-cu12          2.27.5                   pypi_0    pypi
[conda] nvidia-nccl-cu13          2.29.7                   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                     2.12.0                   pypi_0    pypi
[conda] torchaudio                2.11.0                   pypi_0    pypi
[conda] torchvision               0.26.0                   pypi_0    pypi
[conda] triton                    3.7.0                    pypi_0    pypi

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