Parameter ... has been marked as ready twice
Labels: oncall: distributed

### 🐛 Describe the bug

I have the following code in file named `debug.py`

```python
import os
import torch
import argparse


class CustomModel(torch.nn.Module):
    def __init__(self):
        super(CustomModel, self).__init__()
        self.w1 = torch.nn.Parameter(torch.tensor(1.5), requires_grad=True)
        self.w2 = torch.nn.Parameter(torch.tensor(1.5), requires_grad=True)
        self.w3 = torch.nn.Parameter(torch.tensor(1.5), requires_grad=True)
    
    def forward(self, x, order):
        if order == 0:
            return self.w1 * x
        elif order == 1:
            return self.w2 * x
        else:
            return self.w3 * x


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--local_rank", type=int, default=-1)
    args = parser.parse_args()

    local_rank = args.local_rank
    torch.cuda.set_device(local_rank)
    device = torch.device("cuda", local_rank)
    torch.distributed.init_process_group(backend="nccl")
    
    model = CustomModel()
    model.to(device)

    # setup distributed
    model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[local_rank],
                                                          output_device=local_rank,
                                                          find_unused_parameters=True)

    x = torch.tensor(1.)
    y1 = model(x, 0)
    y2 = model(x, 1)
    y3 = model(x, 2)
    y1.backward()


if __name__ == "__main__":
    main()
```

I run this code with the following command 
```shell
TORCH_DISTRIBUTED_DEBUG=DETAIL python -m torch.distributed.launch --nproc_per_node=3 debug.py
```
Then, I got the error
<pre>
Expected to mark a variable ready only once. This error is caused by one of the following reasons: 1) Use of a module parameter outside the `forward` function. Please make sure model parameters are not shared across multiple concurrent forward-backward passes. or try to use _set_static_graph() as a workaround if this module graph does not change during training loop.2) Reused parameters in multiple reentrant backward passes. For example, if you use multiple `checkpoint` functions to wrap the same part of your model, it would result in the same set of parameters been used by different reentrant backward passes multiple times, and hence marking a variable ready multiple times. DDP does not support such use cases in default. You can try to use _set_static_graph() as a workaround if your module graph does not change over iterations.
Parameter at index 0 with name .w1 has been marked as ready twice. This means that multiple autograd engine  hooks have fired for this particular parameter during this iteration.
</pre>

If I change the line `y1.backward()` to `y2.backward()`, the parameter that `has been marked as ready twice` change to `.w2`

As the error suggested, there might have shared parameters across multiple forward-backward pass or a same set of parameters used by multiple backward passes. However, I find none of these two suggestions match the above provided code.

The error disappeared if I set `find_unused_parameters=False`. However, in my actual code, this setting caused another error, which was `Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by passing the keyword argument find_unused_parameters=True to torch.nn.parallel.DistributedDataParallel...`.

I am able to change my code to fix the problem. But the focus of my question is why such a simple code above produced the error? Why was there a parameter that `has been marked as ready twice`?

### Versions

Collecting environment information...
PyTorch version: 1.12.1+cu116
Is debug build: False
CUDA used to build PyTorch: 11.6
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: 3.8.10 (default, May 26 2023, 14:05:08)  [GCC 9.4.0] (64-bit runtime)
Python platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.29
Is CUDA available: True
CUDA runtime version: 10.1.243
CUDA_MODULE_LOADING set to: 
GPU models and configuration: 
GPU 0: Tesla P100-PCIE-12GB
GPU 1: Tesla P100-PCIE-12GB
GPU 2: Tesla P100-PCIE-12GB

Nvidia driver version: 470.199.02
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):                          32
On-line CPU(s) list:             0-31
Thread(s) per core:              2
Core(s) per socket:              8
Socket(s):                       2
NUMA node(s):                    2
Vendor ID:                       GenuineIntel
CPU family:                      6
Model:                           85
Model name:                      Intel(R) Xeon(R) Silver 4110 CPU @ 2.10GHz
Stepping:                        4
CPU MHz:                         2100.000
CPU max MHz:                     3000,0000
CPU min MHz:                     800,0000
BogoMIPS:                        4200.00
Virtualization:                  VT-x
L1d cache:                       512 KiB
L1i cache:                       512 KiB
L2 cache:                        16 MiB
L3 cache:                        22 MiB
NUMA node0 CPU(s):               0-7,16-23
NUMA node1 CPU(s):               8-15,24-31
Vulnerability Itlb multihit:     KVM: Mitigation: VMX disabled
Vulnerability L1tf:              Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
Vulnerability Mds:               Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Meltdown:          Mitigation; PTI
Vulnerability Mmio stale data:   Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:          Mitigation; IBRS
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; IBRS, IBPB conditional, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Mitigation; Clear CPU buffers; SMT vulnerable
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 cdp_l3 invpcid_single pti intel_ppin ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke md_clear flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.19.5
[pip3] torch==1.12.1+cu116
[pip3] torchaudio==0.12.1+cu116
[pip3] torchvision==0.13.1+cu116
[conda] Could not collect

cc @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @osalpekar @jiayisuse @H-Huang @kwen2501 @awgu