nn.Module.to(memory_format= channels_last format) failed if containing 5D parameters

[flishwang]

🐛 Describe the bug

The following code may failed:

import torch
from torch import nn

class A(nn.Module):
    def __init__(self):
        super().__init__()
        self.p = nn.Parameter(torch.zeros((1, 8, 1, 1, 256)))

a=A().to(memory_format=torch.channels_last)

, which is due to the following code in nn.module:

        def convert(t):
            if convert_to_format is not None and t.dim() in (4, 5):
                return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None,
                            non_blocking, memory_format=convert_to_format)
            return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)

Should it do nothing, or just throws a warning if any parameters cannot be converted to channels_last or channels_last_3d? Or shaw we convert the tensors in different ways, like this?

        def convert(t):
            if ((convert_to_format==torch.channels_last and t.dim() ==4) 
                or (convert_to_format==torch.channels_last_3d and t.dim() ==5)):
                return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None,
                            non_blocking, memory_format=convert_to_format)
            return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)

Versions

/usr/local/lib/python3.10/runpy.py:126: RuntimeWarning: 'torch.utils.collect_env' found in sys.modules after import of package 'torch.utils', but prior to execution of 'torch.utils.collect_env'; this may result in unpredictable behaviour warn(RuntimeWarning(msg)) Collecting environment information... PyTorch version: 2.4.0+cu118 Is debug build: False CUDA used to build PyTorch: 11.8 ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64) GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 Clang version: Could not collect CMake version: version 3.16.3 Libc version: glibc-2.31

Python version: 3.10.13 (main, Apr 26 2024, 04:45:52) [GCC 9.4.0] (64-bit runtime) Python platform: Linux-4.15.0-189-generic-x86_64-with-glibc2.31 Is CUDA available: True CUDA runtime version: 11.8.89 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA RTX A6000 GPU 1: NVIDIA RTX A6000

Nvidia driver version: 525.78.01 cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.6 /usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.6 /usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.6 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.6 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.6 /usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.6 /usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.6 HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True

CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian Address sizes: 46 bits physical, 48 bits virtual CPU(s): 64 On-line CPU(s) list: 0-63 Thread(s) per core: 2 Core(s) per socket: 16 Socket(s): 2 NUMA node(s): 1 Vendor ID: GenuineIntel CPU family: 6 Model: 85 Model name: Intel(R) Xeon(R) Gold 5218 CPU @ 2.30GHz Stepping: 7 CPU MHz: 1504.649 BogoMIPS: 4600.00 Virtualization: VT-x L1d cache: 1 MiB L1i cache: 1 MiB L2 cache: 32 MiB L3 cache: 44 MiB NUMA node0 CPU(s): 0-63 Vulnerability Itlb multihit: KVM: Mitigation: Split huge pages Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Mitigation; TSX disabled 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 pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm arat pln pts pku ospke avx512_vnni md_clear flush_l1d arch_capabilities

Versions of relevant libraries: [pip3] numpy==1.26.4 [pip3] open-clip-torch==2.24.0 [pip3] torch==2.4.0+cu118 [pip3] torchlaunch==1.0 [pip3] torchvision==0.19.0+cu118 [pip3] triton==3.0.0 [conda] Could not collect

[mikaylagawarecki]

Is what you're looking for a=A().to(memory_format=torch.channels_last_3d)

[flishwang]

Is what you're looking for a=A().to(memory_format=torch.channels_last_3d)

Nope. The model is for 2d pictures and the 5d param is something like tokens/masks/attn biases/position embeds.