🐛 Describe the bug

tensor.size() doesn't behave the same as tensor.size(dim=None), though docs that seem to indicate it should. The default value of dim is described as None here:

https://pytorch.org/docs/stable/generated/torch.Tensor.size.html

When passing None explicitly, it seems to be interpreting dim as if it's a name for a named tensor. cc: @zou3519

In [8]: t = torch.randn( (3,4,5) )

In [9]: t.size()
Out[9]: torch.Size([3, 4, 5])

In [10]: t.size(dim=0)
Out[10]: 3

In [11]: t.size(dim=None)
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
Cell In[11], line 1
----> 1 t.size(dim=None)

RuntimeError: Please look up dimensions by name, got: name = None.

In [12]: t.size(None)
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
Cell In[12], line 1
----> 1 t.size(None)

RuntimeError: Please look up dimensions by name, got: name = None.

In [14]: t.size("foo")
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
Cell In[14], line 1
----> 1 t.size("foo")

RuntimeError: Name 'foo' not found in Tensor[None, None, None].

Versions

PyTorch version: 2.2.0.dev20230922
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Clang version: Could not collect
CMake version: version 3.16.3
Libc version: glibc-2.31

Python version: 3.11.5 (main, Sep 11 2023, 13:54:46) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-1037-aws-x86_64-with-glibc2.31
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
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): 32
On-line CPU(s) list: 0-31
Thread(s) per core: 2
Core(s) per socket: 16
Socket(s): 1
NUMA node(s): 1
Vendor ID: GenuineIntel
CPU family: 6
Model: 85
Model name: Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz
Stepping: 4
CPU MHz: 2500.006
BogoMIPS: 5000.01
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 512 KiB
L1i cache: 512 KiB
L2 cache: 16 MiB
L3 cache: 33 MiB
NUMA node0 CPU(s): 0-31
Vulnerability Itlb multihit: KVM: Mitigation: VMX unsupported
Vulnerability L1tf: Mitigation; PTE Inversion
Vulnerability Mds: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Meltdown: Mitigation; PTI
Vulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Retbleed: Vulnerable
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Retpolines, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected
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 mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single pti fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves ida arat pku ospke
Versions of relevant libraries:
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.24.3
[pip3] torch==2.2.0.dev20230922
[pip3] torchaudio==2.2.0.dev20230922
[pip3] torchvision==0.17.0.dev20230922
[pip3] triton==2.1.0
[conda] blas 1.0 mkl
[conda] brotlipy 0.7.0 py311h9bf148f_1002 pytorch-nightly
[conda] cffi 1.15.1 py311h9bf148f_3 pytorch-nightly
[conda] cryptography 38.0.4 py311h46ebde7_0 pytorch-nightly
[conda] filelock 3.9.0 py311_0 pytorch-nightly
[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch-nightly
[conda] mkl 2021.4.0 h06a4308_640
[conda] mkl-service 2.4.0 py311h9bf148f_0 pytorch-nightly
[conda] mkl_fft 1.3.1 py311hc796f24_0 pytorch-nightly
[conda] mkl_random 1.2.2 py311hbba84a0_0 pytorch-nightly
[conda] mpmath 1.2.1 py311_0 pytorch-nightly
[conda] numpy 1.24.3 py311hc206e33_0
[conda] numpy-base 1.24.3 py311hfd5febd_0
[conda] packaging 22.0 py311_0 pytorch-nightly
[conda] pillow 9.3.0 py311h3fd9d12_2 pytorch-nightly
[conda] pluggy 1.0.0 py311_1 pytorch-nightly
[conda] pysocks 1.7.1 py311_0 pytorch-nightly
[conda] pytorch 2.2.0.dev20230922 py3.11_cuda12.1_cudnn8.9.2_0 pytorch-nightly
[conda] pytorch-cuda 12.1 ha16c6d3_5 pytorch-nightly
[conda] pytorch-mutex 1.0 cuda pytorch-nightly
[conda] requests 2.28.1 py311_0 pytorch-nightly
[conda] tomli 2.0.1 py311_0 pytorch-nightly
[conda] torchaudio 2.2.0.dev20230922 py311_cu121 pytorch-nightly
[conda] torchtriton 2.1.0+6e4932cda8 py311 pytorch-nightly
[conda] torchvision 0.17.0.dev20230922 py311_cu121 pytorch-nightly
[conda] urllib3 1.26.14 py311_0 pytorch-nightly

cc @zou3519 @albanD