Binary Recall not working with float

[mrudorfer]

🐛 Describe the bug

Hi, when using the BinaryRecall, I'm getting an error that RuntimeError: "bitwise_and_cpu" not implemented for 'Float'.
This might be related to a previously closed issue here.
I had installed torcheval 0.0.7 from pip, with python 3.11 / torch 2.5.0 / cuda 12.4.

Code to reproduce:

import torch
from torcheval.metrics import BinaryRecall

metric = BinaryRecall()
predictions = torch.Tensor([0.2, 0.3, 0.5, 0.7])
targets = torch.Tensor([1.0, 0.0, 1.0, 0.0])

metric.update(predictions, targets)

Result:

Traceback (most recent call last):
  File "scripts/test_scripty.py", line 8, in <module>
    metric.update(predictions, targets)
  File "lib/python3.11/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "lib/python3.11/site-packages/torcheval/metrics/classification/recall.py", line 93, in update
    num_tp, num_true_labels = _binary_recall_update(input, target, self.threshold)
                              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "lib/python3.11/site-packages/torcheval/metrics/functional/classification/recall.py", line 59, in _binary_recall_update
    num_tp = (input & target).sum()
              ~~~~~~^~~~~~~~
RuntimeError: "bitwise_and_cpu" not implemented for 'Float'

For the related metrics BinaryPrecision and BinaryAccuracy, it seems to work fine.

Versions

Collecting environment information...
PyTorch version: 2.5.0
Is debug build: False
CUDA used to build PyTorch: 12.4
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: Could not collect
Libc version: glibc-2.31

Python version: 3.11.10 (main, Oct  3 2024, 07:29:13) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-122-generic-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 10.1.243
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3080
Nvidia driver version: 535.183.01
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
Byte Order:                           Little Endian
Address sizes:                        46 bits physical, 48 bits virtual
CPU(s):                               24
On-line CPU(s) list:                  0-23
Thread(s) per core:                   1
Core(s) per socket:                   16
Socket(s):                            1
NUMA node(s):                         1
Vendor ID:                            GenuineIntel
CPU family:                           6
Model:                                183
Model name:                           13th Gen Intel(R) Core(TM) i7-13700
Stepping:                             1
CPU MHz:                              2100.000
CPU max MHz:                          5200.0000
CPU min MHz:                          800.0000
BogoMIPS:                             4224.00
Virtualisation:                       VT-x
L1d cache:                            384 KiB
L1i cache:                            256 KiB
L2 cache:                             16 MiB
NUMA node0 CPU(s):                    0-23
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 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 and seccomp
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; BHI BHI_DIS_S
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected
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 smx 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 invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi 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 umip pku ospke waitpkg gfni vaes vpclmulqdq tme rdpid movdiri movdir64b fsrm md_clear serialize pconfig arch_lbr flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] torch==2.5.0
[pip3] torchaudio==2.5.0
[pip3] torcheval==0.0.7
[pip3] torchvision==0.20.0
[pip3] triton==3.1.0
[conda] blas                      1.0                         mkl  
[conda] cuda-cudart               12.4.127                      0    nvidia
[conda] cuda-cupti                12.4.127                      0    nvidia
[conda] cuda-libraries            12.4.1                        0    nvidia
[conda] cuda-nvrtc                12.4.127                      0    nvidia
[conda] cuda-nvtx                 12.4.127                      0    nvidia
[conda] cuda-opencl               12.6.77                       0    nvidia
[conda] cuda-runtime              12.4.1                        0    nvidia
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] libcublas                 12.4.5.8                      0    nvidia
[conda] libcufft                  11.2.1.3                      0    nvidia
[conda] libcurand                 10.3.7.77                     0    nvidia
[conda] libcusolver               11.6.1.9                      0    nvidia
[conda] libcusparse               12.3.1.170                    0    nvidia
[conda] libjpeg-turbo             2.0.0                h9bf148f_0    pytorch
[conda] libnvjitlink              12.4.127                      0    nvidia
[conda] mkl                       2023.1.0         h213fc3f_46344  
[conda] mkl-service               2.4.0           py311h5eee18b_1  
[conda] mkl_fft                   1.3.10          py311h5eee18b_0  
[conda] mkl_random                1.2.7           py311ha02d727_0  
[conda] numpy                     1.26.4          py311h08b1b3b_0  
[conda] numpy-base                1.26.4          py311hf175353_0  
[conda] pytorch                   2.5.0           py3.11_cuda12.4_cudnn9.1.0_0    pytorch
[conda] pytorch-cuda              12.4                 hc786d27_7    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torchaudio                2.5.0               py311_cu124    pytorch
[conda] torcheval                 0.0.7                    pypi_0    pypi
[conda] torchtriton               3.1.0                     py311    pytorch
[conda] torchvision               0.20.0              py311_cu124    pytorch