self.manual_backward() makes all gradients gone

Nobody has claimed this yet.

### Bug description

If you try to train GAN with lightning module using multi-GPU, face some erros like this:

```
[rank0]: RuntimeError: It looks like your LightningModule has parameters that were not used in producing the loss returned by training_step. If this is intentional, you must enable the detection of unused parameters in DDP, either by setting the string value `strategy='ddp_find_unused_parameters_true'` or by setting the flag in the strategy with `strategy=DDPStrategy(find_unused_parameters=True)`.
```

For inspection, I tried to train GAN training codes with this kind of snippets in the `main.py`:

```
import os

os.environ["TORCH_CPP_LOG_LEVEL"] = "INFO"
os.environ["TORCH_DISTRIBUTED_DEBUG"] = "DETAIL"
```

and then you can face this kind of errors:

```
[rank1]:[I reducer.cpp:1949] [Rank 1] Parameter: discriminator.discs.1.conv.weight_orig did not get gradient in backwards pass.
[rank1]:[I reducer.cpp:1949] [Rank 1] Parameter: discriminator.discs.1.conv.bias did not get gradient in backwards pass.
[rank1]:[I reducer.cpp:1949] [Rank 1] Parameter: discriminator.discs.1.down_blocks.3.norm.bias did not get gradient in backwards pass.
[rank1]:[I reducer.cpp:1949] [Rank 1] Parameter: discriminator.discs.1.down_blocks.3.norm.weight did not get gradient in backwards pass.
[rank1]:[I reducer.cpp:1949] [Rank 1] Parameter: discriminator.discs.1.down_blocks.3.conv.weight_orig did not get gradient in backwards pass.
[rank1]:[I reducer.cpp:1949] [Rank 1] Parameter: discriminator.discs.1.down_blocks.3.conv.bias did not get gradient in backwards pass.
[rank1]:[I reducer.cpp:1949] [Rank 1] Parameter: discriminator.discs.1.down_blocks.2.norm.bias did not get gradient in backwards pass.
[rank1]:[I reducer.cpp:1949] [Rank 1] Parameter: discriminator.discs.1.down_blocks.2.norm.weight did not get gradient in backwards pass.
[rank1]:[I reducer.cpp:1949] [Rank 1] Parameter: discriminator.discs.1.down_blocks.2.conv.weight_orig did not get gradient in backwards pass.
[rank1]:[I reducer.cpp:1949] [Rank 1] Parameter: discriminator.discs.1.down_blocks.2.conv.bias did not get gradient in backwards pass.
[rank1]:[I reducer.cpp:1949] [Rank 1] Parameter: discriminator.discs.1.down_blocks.1.norm.bias did not get gradient in backwards pass.
[rank1]:[I reducer.cpp:1949] [Rank 1] Parameter: discriminator.discs.1.down_blocks.1.norm.weight did not get gradient in backwards pass.
[rank1]:[I reducer.cpp:1949] [Rank 1] Parameter: discriminator.discs.1.down_blocks.1.conv.weight_orig did not get gradient in backwards pass.
[rank1]:[I reducer.cpp:1949] [Rank 1] Parameter: discriminator.discs.1.down_blocks.1.conv.bias did not get gradient in backwards pass.
[rank1]:[I reducer.cpp:1949] [Rank 1] Parameter: discriminator.discs.1.down_blocks.0.conv.weight_orig did not get gradient in backwards pass.
[rank1]:[I reducer.cpp:1949] [Rank 1] Parameter: discriminator.discs.1.down_blocks.0.conv.bias did not get gradient in backwards pass.
[rank0]:[I reducer.cpp:1949] [Rank 0] Parameter: discriminator.discs.1.conv.weight_orig did not get gradient in backwards pass.
[rank0]:[I reducer.cpp:1949] [Rank 0] Parameter: discriminator.discs.1.conv.bias did not get gradient in backwards pass.
[rank0]:[I reducer.cpp:1949] [Rank 0] Parameter: discriminator.discs.1.down_blocks.3.norm.bias did not get gradient in backwards pass.
[rank0]:[I reducer.cpp:1949] [Rank 0] Parameter: discriminator.discs.1.down_blocks.3.norm.weight did not get gradient in backwards pass.
[rank0]:[I reducer.cpp:1949] [Rank 0] Parameter: discriminator.discs.1.down_blocks.3.conv.weight_orig did not get gradient in backwards pass.
[rank0]:[I reducer.cpp:1949] [Rank 0] Parameter: discriminator.discs.1.down_blocks.3.conv.bias did not get gradient in backwards pass.
[rank0]:[I reducer.cpp:1949] [Rank 0] Parameter: discriminator.discs.1.down_blocks.2.norm.bias did not get gradient in backwards pass.
[rank0]:[I reducer.cpp:1949] [Rank 0] Parameter: discriminator.discs.1.down_blocks.2.norm.weight did not get gradient in backwards pass.
[rank0]:[I reducer.cpp:1949] [Rank 0] Parameter: discriminator.discs.1.down_blocks.2.conv.weight_orig did not get gradient in backwards pass.
[rank0]:[I reducer.cpp:1949] [Rank 0] Parameter: discriminator.discs.1.down_blocks.2.conv.bias did not get gradient in backwards pass.
[rank0]:[I reducer.cpp:1949] [Rank 0] Parameter: discriminator.discs.1.down_blocks.1.norm.bias did not get gradient in backwards pass.
[rank0]:[I reducer.cpp:1949] [Rank 0] Parameter: discriminator.discs.1.down_blocks.1.norm.weight did not get gradient in backwards pass.
[rank0]:[I reducer.cpp:1949] [Rank 0] Parameter: discriminator.discs.1.down_blocks.1.conv.weight_orig did not get gradient in backwards pass.
[rank0]:[I reducer.cpp:1949] [Rank 0] Parameter: discriminator.discs.1.down_blocks.1.conv.bias did not get gradient in backwards pass.
[rank0]:[I reducer.cpp:1949] [Rank 0] Parameter: discriminator.discs.1.down_blocks.0.conv.weight_orig did not get gradient in backwards pass.
[rank0]:[I reducer.cpp:1949] [Rank 0] Parameter: discriminator.discs.1.down_blocks.0.conv.bias did not get gradient in backwards pass.
```

I think this problem comes from the some wrong codes in the lightning module; if you successfully run one of the `manual_backward()` codes, then your the other `manual_backward()` codes cannot run right way since the first call might remove all the gradients of other module.

In [this](https://github.com/Lightning-AI/pytorch-lightning/issues/20328#issue-2575563178), someone suggests a way to call only one `manual_backward()` codes, but I think it would be a little different from normal training strategy of GAN:

```
self.manual_backward(d_loss + g_loss) --> I think this would be problem, but I cannot find no other way to solve unused parameter issue

self.toggle_optimizer(optimizer_d)
optimizer_d.step()
optimizer_d.zero_grad()
self.untoggle_optimizer(optimizer_d)
self.toggle_optimizer(optimizer_g)
optimizer_g.step()
optimizer_g.zero_grad()
self.untoggle_optimizer(optimizer_g)
```

Is there anyone to solve this problem?

### What version are you seeing the problem on?

v2.5

### How to reproduce the bug

```python
class G_base(pl.LightningModule):
def __init__(self, cfg):
super().__init__()

self.cfg = cfg
self.gen_scales = cfg.generator.scales
self.dis_scales = cfg.discriminator.scales

self.generator = Generator(cfg)
self.discriminator = Discriminator(**cfg.discriminator)

self.automatic_optimization = False

def training_step(self, batch):
src_img, drv_img = batch["src"], batch["drv"]

opt_G, opt_D = self.optimizers()

output = self.generator(src_img, drv_img)

### Gen ###
self.toggle_optimizer(opt_G)
gan_g_loss = 0

pyramid_generated_gen = {"prediction_1": output["gen_img"]}

disc_map_generated_gen = self.discriminator(pyramid_generated_gen)

for scale in self.dis_scales:
key = "prediction_map_%s" % scale
value = (1 - disc_map_generated_gen[key]) ** 2
gan_g_loss += value.mean()

opt_G.zero_grad()
self.manual_backward(gan_g_loss)
opt_G.step()
self.untoggle_optimizer(opt_G)

self.log_dict({"gan_g_loss": gan_g_loss}, prog_bar=True)

### Dis ###
self.toggle_optimizer(opt_D)
gan_d_loss = 0

pyramid_real = {"prediction_1": drv_img}
pyramid_generated = {"prediction_1": output["gen_img"].detach()}

disc_map_real = self.discriminator(pyramid_real)
disc_map_generated = self.discriminator(pyramid_generated)

for scale in self.dis_scales:
key = "prediction_map_%s" % scale
value = (1 - disc_map_real[key]) ** 2 + disc_map_generated[key] ** 2
gan_d_loss += value.mean()

opt_D.zero_grad()
self.manual_backward(gan_d_loss)
opt_D.step()
self.untoggle_optimizer(opt_D)

self.log_dict({"gan_d_loss": gan_d_loss}, prog_bar=True)
```

### Error messages and logs

```
# Error messages and logs here please
```

### Environment

Current environment

```
aiohappyeyeballs 2.6.1
aiohttp 3.11.14
aiosignal 1.3.2
annotated-types 0.7.0
antlr4-python3-runtime 4.9.3
anykeystore 0.2
apex 0.9.10.dev0
attrs 25.3.0
certifi 2025.1.31
charset-normalizer 3.4.1
click 8.1.8
cryptacular 1.6.2
decorator 4.4.2
defusedxml 0.7.1
docker-pycreds 0.4.0
facenet-pytorch 2.6.0
filelock 3.18.0
flow-vis 0.1
frozenlist 1.5.0
fsspec 2025.3.0
gitdb 4.0.12
GitPython 3.1.44
greenlet 3.0.3
h5py 3.11.0
hupper 1.12.1
idna 3.10
Jinja2 3.1.6
lightning-utilities 0.14.1
MarkupSafe 3.0.2
mpmath 1.3.0
multidict 6.2.0
munkres 1.1.4
munkres 1.1.4
natsort 8.4.0
natsort 8.4.0
networkx 3.4.2
numpy 1.26.4
nvidia-cublas-cu12 12.1.3.1
nvidia-cuda-cupti-cu12 12.1.105
nvidia-cuda-nvrtc-cu12 12.1.105
nvidia-cuda-runtime-cu12 12.1.105
nvidia-cudnn-cu12 8.9.2.26
nvidia-cufft-cu12 11.0.2.54
nvidia-curand-cu12 10.3.2.106
nvidia-cusolver-cu12 11.4.5.107
nvidia-cusparse-cu12 12.1.0.106
nvidia-cusparselt-cu12 0.6.2
nvidia-nccl-cu12 2.19.3
nvidia-nvjitlink-cu12 12.4.127
nvidia-nvtx-cu12 12.1.105
oauthlib 3.2.2
omegaconf 2.3.0
opencv-python 4.11.0.86
packaging 24.2
PasteDeploy 3.1.0
pbkdf2 1.3
pillow 10.2.0
pip 25.0
plaster 1.1.2
plaster-pastedeploy 1.0.1
platformdirs 4.3.6
proglog 0.1.10
propcache 0.3.0
protobuf 5.29.3
psutil 7.0.0
pyav 11.4.1
pycocotools 2.0.8
pycocotools 2.0.8
pydantic 2.10.6
pydantic_core 2.27.2
pyramid 2.0.2
pyramid-mailer 0.15.1
python3-openid 3.2.0
pytorch-lightning 2.5.0.post0
PyYAML 6.0.2
repoze.sendmail 4.4.1
requests 2.32.3
requests-oauthlib 2.0.0
sentry-sdk 2.23.1
setproctitle 1.3.5
setuptools 75.8.0
six 1.17.0
slack_sdk 3.35.0
smmap 5.0.2
SQLAlchemy 2.0.30
sympy 1.13.1
tensorboardX 2.6.2.2
torch 2.2.2
torchaudio 2.2.2
torchmetrics 1.6.3
torchvision 0.17.2
tqdm 4.67.1
transaction 4.0
translationstring 1.4
triton 2.2.0
typing_extensions 4.12.2
urllib3 2.3.0
velruse 1.1.1
venusian 3.1.0
wandb 0.19.8
WebOb 1.8.7
wheel 0.45.1
WTForms 3.1.2
wtforms-recaptcha 0.3.2
yarl 1.18.3
zope.deprecation 5.0
zope.interface 6.4.post2
zope.sqlalchemy 3.1
```

### More info

_No response_

cc @justusschock @borda

Start with the `G_base.training_step` reproduction in `main.py`, focusing on `manual_backward()`, `toggle_optimizer()`, and the two optimizer updates under multi-GPU DDP. Reproduce the reported unused-parameter and missing-gradient messages, then trace whether the generator and discriminator updates retain their expected gradients. Done means both GAN updates complete without the reported DDP errors or lost gradients.

Written by the indexing model from the issue text.