🐛 Describe the bug

Description:
There seems to be an issue when reshaping/viewing an evenly sharded DTensor.

Code to Reproduce:
torchrun --nproc_per_node=4 test.py
In this test case has 4 elements on ranks 0-3. The runtime error occurs during the view operation.
Error Type : RuntimeError: ('Attempted to flatten sharded dimension 1, ', 'but only the leftmost dim of a Flatten can be sharded.')

import torch
from torch.distributed._tensor import Replicate, Shard, distribute_tensor, DTensor
from torch.distributed.device_mesh import init_device_mesh
from datetime import timedelta
import torch.nn as nn

new_mesh = init_device_mesh("cuda", [4], mesh_dim_names=["tp"])
x=torch.randn((4,4),dtype=torch.float32,device="cuda")

def a(x):
    d_x = DTensor.from_local(x, device_mesh=new_mesh, placements=[Replicate()])
    d_x = d_x.redistribute(device_mesh=new_mesh, placements=[Shard(dim=1)]) 
    d_x = d_x.view(-1)
    return d_x

out=a(x)

Issue is induce from #149764 patch.

Versions

Collecting environment information...
PyTorch version: 2.8.0a0+34c6371d24.nv25.08
Is debug build: False
CUDA used to build PyTorch: 13.0
ROCM used to build PyTorch: N/A

OS: Ubuntu 24.04.2 LTS (x86_64)
GCC version: (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
Clang version: Could not collect
CMake version: version 3.31.6
Libc version: glibc-2.39

Python version: 3.12.3 (main, Jun 18 2025, 17:59:45) [GCC 13.3.0] (64-bit runtime)
Python platform: Linux-5.15.0-131-generic-x86_64-with-glibc2.39
Is CUDA available: True
CUDA runtime version: 13.0.48
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA A100-SXM4-80GB
GPU 1: NVIDIA A100-SXM4-80GB
GPU 2: NVIDIA A100-SXM4-80GB
GPU 3: NVIDIA A100-SXM4-80GB

Nvidia driver version: 565.57.01
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.12.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.12.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.12.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.12.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.12.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.12.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.12.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.12.0
Is XPU available: False
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: 46 bits physical, 57 bits virtual
Byte Order: Little Endian
CPU(s): 160
On-line CPU(s) list: 0-159
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Platinum 8380 CPU @ 2.30GHz
CPU family: 6
Model: 106
Thread(s) per core: 2
Core(s) per socket: 40
Socket(s): 2
Stepping: 6
CPU(s) scaling MHz: 70%
CPU max MHz: 3400.0000
CPU min MHz: 800.0000
BogoMIPS: 4600.00
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 invpcid_single ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 3.8 MiB (80 instances)
L1i cache: 2.5 MiB (80 instances)
L2 cache: 100 MiB (80 instances)
L3 cache: 120 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-39,80-119
NUMA node1 CPU(s): 40-79,120-159
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable
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 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 SW loop, KVM SW loop
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected

Versions of relevant libraries:
[pip3] intel-openmp==2021.4.0
[pip3] mkl==2021.1.1
[pip3] mkl-devel==2021.1.1
[pip3] mkl-include==2021.1.1
[pip3] mypy_extensions==1.1.0
[pip3] numpy==1.26.4
[pip3] nvidia-cudnn-frontend==1.13.0
[pip3] nvtx==0.2.11
[pip3] onnx==1.18.0
[pip3] optree==0.17.0
[pip3] pynvjitlink==0.7.0
[pip3] pytorch-triton==3.3.1+gitc8757738
[pip3] tbb==2021.13.1
[pip3] torch==2.8.0a0+34c6371d24.nv25.8
[pip3] torch_tensorrt==2.8.0a0
[pip3] torchao==0.12.0+git
[pip3] torchprofile==0.0.4
[pip3] torchvision==0.23.0a0+428a54c9

cc @awgu @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @pragupta @msaroufim @dcci @aditvenk @weifengpy @tianyu-l @XilunWu @SherlockNoMad @ppwwyyxx @H-Huang