MisconfigurationException

Bug description

lightning_fabric.utilities.exceptionslightning_fabric.utilities.exceptions..MisconfigurationExceptionMisconfigurationException: : You are trying to self.log() but the loop's result collection is not registered yet. This is most likely because you are trying to log in a predict hook, but it doesn't support loggingYou are trying to self.log() but the loop's result collection is not registered yet. This is most likely because you are trying to log in a predict hook, but it doesn't support logging

What version are you seeing the problem on?

v2.2

How to reproduce the bug

No response

Error messages and logs

# Error messages and logs here please

Environment

Collecting environment information...
PyTorch version: 2.0.1
Is debug build: False
CUDA used to build PyTorch: Could not collect
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.10.12 (main, Jul 5 2023, 18:54:27) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.5.0-18-generic-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration:
GPU 0: NVIDIA GeForce RTX 4090
GPU 1: NVIDIA GeForce RTX 4090

Nvidia driver version: 535.154.05
cuDNN version: Could not collect
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
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: 13th Gen Intel(R) Core(TM) i9-13900F
CPU family: 6
Model: 183
Thread(s) per core: 2
Core(s) per socket: 24
Socket(s): 1
Stepping: 1
CPU max MHz: 5600.0000
CPU min MHz: 800.0000
BogoMIPS: 3993.60
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 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 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 Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: 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, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected

Versions of relevant libraries:
[pip3] easy-torch==1.3.2
[pip3] mlm-pytorch==0.1.0
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.22.3
[pip3] pytorch-lightning==2.0.8
[pip3] torch==2.0.1
[pip3] torch-geometric==2.3.1
[pip3] torch-scatter==2.1.2
[pip3] torch-sparse==0.6.18
[pip3] torch-summary==1.4.5
[pip3] torchaudio==2.0.2
[pip3] torchinfo==1.8.0
[pip3] torchmetrics==1.1.2
[pip3] torchvision==0.15.2
[pip3] triton==2.1.0
[conda] blas 1.0 mkl
[conda] easy-torch 1.3.2 pypi_0 pypi
[conda] mkl 2023.1.0 h213fc3f_46343
[conda] mkl-service 2.4.0 py310h5eee18b_1
[conda] mkl_fft 1.3.6 py310h1128e8f_1
[conda] mkl_random 1.2.2 py310h1128e8f_1
[conda] mlm-pytorch 0.1.0 pypi_0 pypi
[conda] numpy 1.22.3 pypi_0 pypi
[conda] numpy-base 1.25.2 py310hb5e798b_0
[conda] pyg 2.4.0 py310_torch_2.0.0_cu118 pyg
[conda] pytorch 2.0.1 cpu_py310hdc00b08_0
[conda] pytorch-cuda 11.8 h7e8668a_5 pytorch
[conda] pytorch-lightning 2.0.8 pypi_0 pypi
[conda] pytorch-mutex 1.0 cuda pytorch
[conda] pytorch-scatter 2.1.2 py310_torch_2.0.0_cu118 pyg
[conda] pytorch-sparse 0.6.18 py310_torch_2.0.0_cu118 pyg
[conda] torch 2.1.0 pypi_0 pypi
[conda] torch-geometric 2.3.1 pypi_0 pypi
[conda] torch-summary 1.4.5 pypi_0 pypi
[conda] torchaudio 2.0.2 py310_cu118 pytorch
[conda] torchinfo 1.8.0 pypi_0 pypi
[conda] torchmetrics 1.1.2 pypi_0 pypi
[conda] torchvision 0.15.2 py310_cu118 pytorch
[conda] triton 2.1.0 pypi_0 pypi

More info

No response

Start by creating a minimal reproduction of the self.log() call in a predict hook using the reported v2.2 environment, then trace the result collection registration that raises MisconfigurationException. Done means the behavior is reproducible and the expected logging behavior in predict hooks is clearly established.