Repository: pytorch
Issue: CheckpointError with `torch.distributed.algorithms._checkpoint.checkpoint_wrapper` and `torch.compile`
Labels: module: checkpoint,triaged,activation-checkpointing

### 🐛 Describe the bug

```python
import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import apply_activation_checkpointing, checkpoint_wrapper, CheckpointImpl


@torch.compile(mode='reduce-overhead')
class SelfAttention(nn.Module):

    def __init__(self, num_heads: int, head_dim: int, norm_eps: float, causal: bool):
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = head_dim
        self.causal = causal
        total_dim = num_heads * head_dim
        
        self.to_qkv = nn.Linear(total_dim, total_dim * 3, bias=False)
        self.to_out = nn.Linear(total_dim, total_dim, bias=False)
        
        self.q_norm = nn.RMSNorm(head_dim, eps=norm_eps)
        self.k_norm = nn.RMSNorm(head_dim, eps=norm_eps)

    def forward(self, x_btc: torch.Tensor):
        states = x_btc
        batch_size, sequence_length, _ = states.shape
        proj: torch.Tensor = self.to_qkv(states)
        proj = proj.view(batch_size, sequence_length, self.num_heads, 3, self.head_dim).transpose(1, 2)
        
        query, key, value = proj.unbind(-2)
        
        query: torch.Tensor = self.q_norm(query)
        key: torch.Tensor = self.k_norm(key)
        
        hidden_states = F.scaled_dot_product_attention(
            query, key, value, is_causal=self.causal
        )
        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, sequence_length, self.num_heads * self.head_dim)
        hidden_states = hidden_states.to(query.dtype)
        
        return self.to_out(hidden_states)


class Block(nn.Module):
    def __init__(self):
        super().__init__()
        self.attn = SelfAttention(1, 64, 1e-5, False)

    def forward(self, x):
        return x + self.attn(x)


class Transformer(nn.Module):
    def __init__(self):
        super().__init__()
        self.blocks = nn.ModuleList([Block() for _ in range(4)])

    def forward(self, x):
        for block in self.blocks:
            x = block(x)
        return x


if __name__ == '__main__':
    mod = Transformer().cuda()
    non_reentrant_wrapper = functools.partial(
        checkpoint_wrapper,
        checkpoint_impl=CheckpointImpl.NO_REENTRANT,
    )
    apply_activation_checkpointing(
        mod, checkpoint_wrapper_fn=non_reentrant_wrapper,
        check_fn=lambda mod: isinstance(mod, Block)
    )
    mod(torch.randn(3, 77, 64).cuda()).sum().backward()
```

Output:
```
/opt/conda/lib/python3.11/site-packages/torch/_inductor/compile_fx.py:167: UserWarning: TensorFloat32 tensor cores for float32 matrix multiplication available but not enabled. Consider setting `torch.set_float32_matmul_precision('high')` for better performance.
  warnings.warn(
Traceback (most recent call last):
  File "/root/bug/repro.py", line 74, in <module>
    mod(torch.randn(3, 77, 64).cuda()).sum().backward()
  File "/opt/conda/lib/python3.11/site-packages/torch/_tensor.py", line 581, in backward
    torch.autograd.backward(
  File "/opt/conda/lib/python3.11/site-packages/torch/autograd/__init__.py", line 347, in backward
    _engine_run_backward(
  File "/opt/conda/lib/python3.11/site-packages/torch/autograd/graph.py", line 825, in _engine_run_backward
    return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/autograd/function.py", line 307, in apply
    return user_fn(self, *args)
           ^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py", line 1740, in backward
    ctx_saved_tensors = ctx.saved_tensors
                        ^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/utils/checkpoint.py", line 1129, in unpack_hook
    frame.check_recomputed_tensors_match(gid)
  File "/opt/conda/lib/python3.11/site-packages/torch/utils/checkpoint.py", line 903, in check_recomputed_tensors_match
    raise CheckpointError(
torch.utils.checkpoint.CheckpointError: torch.utils.checkpoint: Recomputed values for the following tensors have different metadata than during the forward pass.
tensor at position 12:
saved metadata: {'shape': torch.Size([]), 'dtype': torch.int64, 'device': device(type='cpu')}
recomputed metadata: {'shape': torch.Size([]), 'dtype': torch.int64, 'device': device(type='cuda', index=0)}
tensor at position 13:
saved metadata: {'shape': torch.Size([]), 'dtype': torch.int64, 'device': device(type='cpu')}
recomputed metadata: {'shape': torch.Size([]), 'dtype': torch.int64, 'device': device(type='cuda', index=0)}
```

### Versions

```
Collecting environment information...
PyTorch version: 2.5.1+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: Could not collect
CMake version: version 3.30.5
Libc version: glibc-2.35

Python version: 3.11.10 | packaged by conda-forge | (main, Oct 16 2024, 01:27:36) [GCC 13.3.0] (64-bit runtime)
Python platform: Linux-4.18.0-477.21.1.el8_8.x86_64-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.1.105
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA H100 80GB HBM3
GPU 1: NVIDIA H100 80GB HBM3

Nvidia driver version: 535.154.05
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.7
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):                          224
On-line CPU(s) list:             0-223
Vendor ID:                       GenuineIntel
Model name:                      Intel(R) Xeon(R) Platinum 8480+
CPU family:                      6
Model:                           143
Thread(s) per core:              2
Core(s) per socket:              56
Socket(s):                       2
Stepping:                        8
CPU max MHz:                     3800.0000
CPU min MHz:                     800.0000
BogoMIPS:                        4000.00
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 tsc_known_freq 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 cat_l2 cdp_l3 invpcid_single intel_ppin cdp_l2 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 avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
Virtualization:                  VT-x
L1d cache:                       5.3 MiB (112 instances)
L1i cache:                       3.5 MiB (112 instances)
L2 cache:                        224 MiB (112 instances)
L3 cache:                        210 MiB (2 instances)
NUMA node(s):                    2
NUMA node0 CPU(s):               0-55,112-167
NUMA node1 CPU(s):               56-111,168-223
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 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 IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu11==11.11.3.6
[pip3] nvidia-cublas-cu12==12.1.3.1
[pip3] nvidia-cuda-cupti-cu11==11.8.87
[pip3] nvidia-cuda-cupti-cu12==12.1.105
[pip3] nvidia-cuda-nvrtc-cu11==11.8.89
[pip3] nvidia-cuda-nvrtc-cu12==12.1.105
[pip3] nvidia-cuda-runtime-cu11==11.8.89
[pip3] nvidia-cuda-runtime-cu12==12.1.105
[pip3] nvidia-cudnn-cu11==9.1.0.70
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu11==10.9.0.58
[pip3] nvidia-cufft-cu12==11.0.2.54
[pip3] nvidia-curand-cu11==10.3.0.86
[pip3] nvidia-curand-cu12==10.3.2.106
[pip3] nvidia-cusolver-cu11==11.4.1.48
[pip3] nvidia-cusolver-cu12==11.4.5.107
[pip3] nvidia-cusparse-cu11==11.7.5.86
[pip3] nvidia-cusparse-cu12==12.1.0.106
[pip3] nvidia-nccl-cu11==2.21.5
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.1.105
[pip3] nvidia-nvtx-cu11==11.8.86
[pip3] nvidia-nvtx-cu12==12.1.105
[pip3] onnx==1.17.0
[pip3] onnxruntime-gpu==1.20.1
[pip3] onnxscript==0.1.0.dev20250108
[pip3] optree==0.13.0
[pip3] pytorch-triton==3.2.0+git0d4682f0
[pip3] torch==2.5.1+cu121
[pip3] torch.redstone==0.0.6
[pip3] torchaudio==2.5.1+cu121
[pip3] torchdiffeq==0.2.5
[pip3] torchelastic==0.2.2
[pip3] torchprofile==0.0.4
[pip3] torchvision==0.20.1+cu121
[pip3] triton==3.1.0
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] nvidia-cublas-cu11        11.11.3.6                pypi_0    pypi
[conda] nvidia-cublas-cu12        12.1.3.1                 pypi_0    pypi
[conda] nvidia-cuda-cupti-cu11    11.8.87                  pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.1.105                 pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu11    11.8.89                  pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.1.105                 pypi_0    pypi
[conda] nvidia-cuda-runtime-cu11  11.8.89                  pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.1.105                 pypi_0    pypi
[conda] nvidia-cudnn-cu11         9.1.0.70                 pypi_0    pypi
[conda] nvidia-cudnn-cu12         9.1.0.70                 pypi_0    pypi
[conda] nvidia-cufft-cu11         10.9.0.58                pypi_0    pypi
[conda] nvidia-cufft-cu12         11.0.2.54                pypi_0    pypi
[conda] nvidia-curand-cu11        10.3.0.86                pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.2.106               pypi_0    pypi
[conda] nvidia-cusolver-cu11      11.4.1.48                pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.4.5.107               pypi_0    pypi
[conda] nvidia-cusparse-cu11      11.7.5.86                pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.1.0.106               pypi_0    pypi
[conda] nvidia-nccl-cu11          2.21.5                   pypi_0    pypi
[conda] nvidia-nccl-cu12          2.21.5                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.1.105                 pypi_0    pypi
[conda] nvidia-nvtx-cu11          11.8.86                  pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.1.105                 pypi_0    pypi
[conda] optree                    0.13.0                   pypi_0    pypi
[conda] pytorch-triton            3.2.0+git0d4682f0          pypi_0    pypi
[conda] torch                     2.5.1+cu121              pypi_0    pypi
[conda] torch-redstone            0.0.6                    pypi_0    pypi
[conda] torchaudio                2.5.1+cu121              pypi_0    pypi
[conda] torchdiffeq               0.2.5                    pypi_0    pypi
[conda] torchelastic              0.2.2                    pypi_0    pypi
[conda] torchprofile              0.0.4                    pypi_0    pypi
[conda] torchvision               0.20.1+cu121             pypi_0    pypi
[conda] triton                    3.1.0                    pypi_0    pypi
```

cc @soulitzer @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @LucasLLC @pradeepfn