Repository: pytorch
Issue: Iterating dataloader fails on sliced dataset 
Labels: module: dataloader,triaged

Hi,

When creating a map-style dataset and a dataloader on it, I found that the dataloader can't handle a sliced version of the dataset. I believe it has something to do with how sliced tensors are stored as opposed to full, unsliced one. 

Perhaps what I'm attempting to do s a bad practice, but in any case, I think it's a bit of unexpected behaviour. More importantly, the traceback doesn't provide enough insight on what went wrong, and perhaps even a bit misleading, I believe.


```python
import torch


class MyDataset(torch.utils.data.Dataset):
  def __init__(self, inputs, targets):
    self.inputs = inputs
    self.targets = targets

  def __len__(self):
      return len(self.targets)

  def __getitem__(self, index):
      return self.inputs[index], self.targets[index]


dataset = MyDataset(torch.rand((50, 2, 100)), torch.rand((50, 1, 100)))
dataset_sliced = dataset[:25]

dataloader1 = torch.utils.data.DataLoader(dataset, 5)
dataloader2 = torch.utils.data.DataLoader(dataset_sliced, 5)

for x, y in dataloader1:
  print(x, y) # everything works

for x, y in dataloader2: # Error here!
  print(x, y) 
```

```
RuntimeError                              Traceback (most recent call last)
[<ipython-input-20-00eb2dbecce6>](https://localhost:8080/#) in <cell line: 22>()
     20   print(x, y) # everything works
     21 
---> 22 for x, y in dataloader2:
     23   print(x, y) # Error!
     24 

5 frames
[/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py](https://localhost:8080/#) in __next__(self)
    629                 # TODO(https://github.com/pytorch/pytorch/issues/76750)
    630                 self._reset()  # type: ignore[call-arg]
--> 631             data = self._next_data()
    632             self._num_yielded += 1
    633             if self._dataset_kind == _DatasetKind.Iterable and \

[/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py](https://localhost:8080/#) in _next_data(self)
    673     def _next_data(self):
    674         index = self._next_index()  # may raise StopIteration
--> 675         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
    676         if self._pin_memory:
    677             data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)

[/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py](https://localhost:8080/#) in fetch(self, possibly_batched_index)
     52         else:
     53             data = self.dataset[possibly_batched_index]
---> 54         return self.collate_fn(data)

[/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/collate.py](https://localhost:8080/#) in default_collate(batch)
    314         >>> default_collate(batch)  # Handle `CustomType` automatically
    315     """
--> 316     return collate(batch, collate_fn_map=default_collate_fn_map)

[/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/collate.py](https://localhost:8080/#) in collate(batch, collate_fn_map)
    139     if collate_fn_map is not None:
    140         if elem_type in collate_fn_map:
--> 141             return collate_fn_map[elem_type](batch, collate_fn_map=collate_fn_map)
    142 
    143         for collate_type in collate_fn_map:

[/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/collate.py](https://localhost:8080/#) in collate_tensor_fn(batch, collate_fn_map)
    211         storage = elem._typed_storage()._new_shared(numel, device=elem.device)
    212         out = elem.new(storage).resize_(len(batch), *list(elem.size()))
--> 213     return torch.stack(batch, 0, out=out)
    214 
    215 

RuntimeError: stack expects each tensor to be equal size, but got [25, 2, 100] at entry 0 and [25, 1, 100] at entry 1
```



### Versions

From colab:

Collecting environment information...
PyTorch version: 2.3.1+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
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: 14.0.0-1ubuntu1.1
CMake version: version 3.27.9
Libc version: glibc-2.35

Python version: 3.10.12 (main, Mar 22 2024, 16:50:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.1.85+-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: Could not collect
Nvidia driver version: Could not collect
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
Address sizes:                        46 bits physical, 48 bits virtual
Byte Order:                           Little Endian
CPU(s):                               2
On-line CPU(s) list:                  0,1
Vendor ID:                            GenuineIntel
Model name:                           Intel(R) Xeon(R) CPU @ 2.20GHz
CPU family:                           6
Model:                                79
Thread(s) per core:                   2
Core(s) per socket:                   1
Socket(s):                            1
Stepping:                             0
BogoMIPS:                             4399.99
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 tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities
Hypervisor vendor:                    KVM
Virtualization type:                  full
L1d cache:                            32 KiB (1 instance)
L1i cache:                            32 KiB (1 instance)
L2 cache:                             256 KiB (1 instance)
L3 cache:                             55 MiB (1 instance)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0,1
Vulnerability Gather data sampling:   Not affected
Vulnerability Itlb multihit:          Not affected
Vulnerability L1tf:                   Mitigation; PTE Inversion
Vulnerability Mds:                    Vulnerable; SMT Host state unknown
Vulnerability Meltdown:               Vulnerable
Vulnerability Mmio stale data:        Vulnerable
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed:               Vulnerable
Vulnerability Spec rstack overflow:   Not affected
Vulnerability Spec store bypass:      Vulnerable
Vulnerability Spectre v1:             Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers
Vulnerability Spectre v2:             Vulnerable; IBPB: disabled; STIBP: disabled; PBRSB-eIBRS: Not affected; BHI: Vulnerable (Syscall hardening enabled)
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Vulnerable

Versions of relevant libraries:
[pip3] numpy==1.25.2
[pip3] torch==2.3.1+cu121
[pip3] torchaudio==2.3.1+cu121
[pip3] torchsummary==1.5.1
[pip3] torchtext==0.18.0
[pip3] torchvision==0.18.1+cu121
[pip3] triton==2.3.1
[conda] Could not collect


cc @andrewkho @gokulavasan @SsnL @VitalyFedyunin @dzhulgakov