Repository: pytorch
Issue: RuntimeError: Expected grad_output.numel() <= std::numeric_limits<int32_t>::max() to be true, but got false.
Labels: needs reproduction,module: autograd,module: nn,module: error checking,triaged,module: 64-bit

### 🐛 Describe the bug

I am attempting to train a convolutional autoencoder model with pytorch.  I am using: 

torch==2.3.1 + cuda 12.1 and 4 GPUs.  

I have attempted this training with both pytorch lightning (pl==2.3.3) and torch's distributed data parallel functions.  Both result in the same error.

The model code is as follows:
```
class LightningAutoencoder(pl.LightningModule):
    def __init__(self):
        super(LightningAutoencoder, self).__init__()
        self.encoder = torch.nn.Sequential(
            torch.nn.Conv2d(3, 64, 3, padding=1),
            torch.nn.ReLU(True),
            torch.nn.Conv2d(64, 64, 3, padding=1),
            torch.nn.ReLU(True),
            torch.nn.Conv2d(64, 64, 3, padding=1),
            torch.nn.ReLU(True),
            torch.nn.MaxPool2d(2, stride=2),
            torch.nn.Conv2d(64, 3, 3, padding=1),
            torch.nn.ReLU(True),
            torch.nn.MaxPool2d(2, stride=2)
        )

        self.decoder = torch.nn.Sequential(
            torch.nn.Upsample(scale_factor=2, mode='nearest'),
            torch.nn.Conv2d(3, 64, 3, padding=1),
            torch.nn.ReLU(True),
            torch.nn.Conv2d(64, 128, 3, padding=1),
            torch.nn.ReLU(True),
            torch.nn.Conv2d(128, 128, 3, padding=1),
            torch.nn.ReLU(True),
            torch.nn.Upsample(scale_factor=2, mode='nearest'),
            torch.nn.Conv2d(128, 64, 3, padding=1),
            torch.nn.ReLU(True),
            torch.nn.Conv2d(64, 3, 3, padding=1),
            torch.nn.Sigmoid()
        )

    def forward(self, input):
        compressed = self.encoder(input)
        reconstructed = self.decoder(compressed)
        return reconstructed

    def on_before_backward(self, loss):
        params = self.state_dict()
        for k, v in params.items():
            grads = v
            print(f"Param {k} Step {self.trainer.global_step} numel: {grads.numel()} size: {grads.size()}")

    def configure_optimizers(self):
        optimizer = torch.optim.Adam(self.parameters(), lr=1e-5)
        return optimizer

    def mse_loss(self, true, pred):
        return torch.nn.MSELoss()(true, pred)

    def training_step(self, train_batch, batch_idx):
        model_input, ground_truth = train_batch
        prediction = self.forward(model_input)
        loss = self.calc_loss(ground_truth, prediction)
        torch.cuda.empty_cache()
        return loss

    def reorient(self, image_tensor : torch.tensor) -> torch.tensor:
        '''
        Going from 3 channels R G B to a tensor whose final dimension is 3
        representing the RGB values at each pixel.
        Params:
            image_tensor (tensor): A set of images or a single image with size [batch, 3, width, height]
        Returns:
            reorient (tensor): A tensor whose size is now [batch, width, height, 3]
        '''
        reorient = image_tensor.transpose(1, -1)
        return reorient

    def bce_term(self, true : torch.tensor, pred : torch.tensor) -> torch.tensor:
        '''
        The BCE term of the loss function that penalizes the model
        for overpredicting white space. At the current weighting (1:1
        perceputal loss : BCE), it also turns images black and white.
        Params:
            true (tensor): The true values of the image.
            pred (tensor): The output of the autoencoder.
        Returns:
            bce (float): The BCE error for each pixel being either white (1) or
                        non-white (0).
        '''
        true = self.reorient(true)
        true = torch.sum(true, dim=-1) / 3
        true = torch.floor(true)

        pred = self.reorient(pred)
        pred = torch.sum(pred, dim=-1) / 3
        bce = torch.nn.BCELoss()(pred, true)
        return bce

    def calc_loss(self, true, pred):
        mse_loss = self.mse_loss(true, pred)
        bce_loss = self.bce_term(true, pred)
        loss = mse_loss + bce_loss
        return loss
```

I have all the images I'm using for training in an image directory that is loaded into a basic pytorch dataset.  I have tried both the standard pytorch map dataset and iterable datasets. I've just provided the code for the most basic implementation of my error

When I train with a batch size = 10, with 10 images in my image directory, everything works fine. I print out the number of elements and sizes of each layer in my model that has gradients and they are as follows:

```
Param encoder.0.weight Step 0 numel: 1728 size: torch.Size([64, 3, 3, 3])
Param encoder.0.bias Step 0 numel: 64 size: torch.Size([64])
Param encoder.2.weight Step 0 numel: 36864 size: torch.Size([64, 64, 3, 3])
Param encoder.2.bias Step 0 numel: 64 size: torch.Size([64])
Param encoder.4.weight Step 0 numel: 36864 size: torch.Size([64, 64, 3, 3])
Param encoder.4.bias Step 0 numel: 64 size: torch.Size([64])
Param encoder.7.weight Step 0 numel: 1728 size: torch.Size([3, 64, 3, 3])
Param encoder.7.bias Step 0 numel: 3 size: torch.Size([3])
Param decoder.1.weight Step 0 numel: 1728 size: torch.Size([64, 3, 3, 3])
Param decoder.1.bias Step 0 numel: 64 size: torch.Size([64])
Param decoder.3.weight Step 0 numel: 73728 size: torch.Size([128, 64, 3, 3])
Param decoder.3.bias Step 0 numel: 128 size: torch.Size([128])
Param decoder.5.weight Step 0 numel: 147456 size: torch.Size([128, 128, 3, 3])
Param decoder.5.bias Step 0 numel: 128 size: torch.Size([128])
Param decoder.8.weight Step 0 numel: 73728 size: torch.Size([64, 128, 3, 3])
Param decoder.8.bias Step 0 numel: 64 size: torch.Size([64])
Param decoder.10.weight Step 0 numel: 1728 size: torch.Size([3, 64, 3, 3])
Param decoder.10.bias Step 0 numel: 3 size: torch.Size([3])
```
If I increase the number of images I'm training on by copying each image once in my image directory so that I now of 20 images (image n + its duplicate), I get an interesting error. Note that my batch size is still 10, so I don't believe I should be running into any problems.

```
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
HPU available: False, using: 0 HPUs
/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:75: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `pytorch_lightning` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default
Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/4
Initializing distributed: GLOBAL_RANK: 2, MEMBER: 3/4
Initializing distributed: GLOBAL_RANK: 3, MEMBER: 4/4
Initializing distributed: GLOBAL_RANK: 1, MEMBER: 2/4
----------------------------------------------------------------------------------------------------
distributed_backend=nccl
All distributed processes registered. Starting with 4 processes
----------------------------------------------------------------------------------------------------

LOCAL_RANK: 2 - CUDA_VISIBLE_DEVICES: [0,1,2,3]
LOCAL_RANK: 3 - CUDA_VISIBLE_DEVICES: [0,1,2,3]
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]
LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1,2,3]

  | Name    | Type       | Params | Mode 
-----------------------------------------------
0 | encoder | Sequential | 77.4 K | train
1 | decoder | Sequential | 298 K  | train
-----------------------------------------------
376 K     Trainable params
0         Non-trainable params
376 K     Total params
1.505     Total estimated model params size (MB)
/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=27` in the `DataLoader` to improve performance.
/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py:298: The number of training batches (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.
Epoch 0:   0%|                                                                                                             | 0/1 [00:00<?, ?it/s]Param encoder.0.weight Step 0 numel: 1728 size: torch.Size([64, 3, 3, 3])
Param encoder.0.bias Step 0 numel: 64 size: torch.Size([64])
Param encoder.2.weight Step 0 numel: 36864 size: torch.Size([64, 64, 3, 3])
Param encoder.2.bias Step 0 numel: 64 size: torch.Size([64])
Param encoder.4.weight Step 0 numel: 36864 size: torch.Size([64, 64, 3, 3])
Param encoder.4.bias Step 0 numel: 64 size: torch.Size([64])
Param encoder.7.weight Step 0 numel: 1728 size: torch.Size([3, 64, 3, 3])
Param encoder.7.bias Step 0 numel: 3 size: torch.Size([3])
Param decoder.1.weight Step 0 numel: 1728 size: torch.Size([64, 3, 3, 3])
Param decoder.1.bias Step 0 numel: 64 size: torch.Size([64])
Param decoder.3.weight Step 0 numel: 73728 size: torch.Size([128, 64, 3, 3])
Param decoder.3.bias Step 0 numel: 128 size: torch.Size([128])
Param decoder.5.weight Step 0 numel: 147456 size: torch.Size([128, 128, 3, 3])
Param decoder.5.bias Step 0 numel: 128 size: torch.Size([128])
Param decoder.8.weight Step 0 numel: 73728 size: torch.Size([64, 128, 3, 3])
Param decoder.8.bias Step 0 numel: 64 size: torch.Size([64])
Param decoder.10.weight Step 0 numel: 1728 size: torch.Size([3, 64, 3, 3])
Param decoder.10.bias Step 0 numel: 3 size: torch.Size([3])
Param encoder.0.weight Step 0 numel: 1728 size: torch.Size([64, 3, 3, 3])
Param encoder.0.bias Step 0 numel: 64 size: torch.Size([64])
Param encoder.2.weight Step 0 numel: 36864 size: torch.Size([64, 64, 3, 3])
Param encoder.2.bias Step 0 numel: 64 size: torch.Size([64])
Param encoder.4.weight Step 0 numel: 36864 size: torch.Size([64, 64, 3, 3])
Param encoder.4.bias Step 0 numel: 64 size: torch.Size([64])
Param encoder.7.weight Step 0 numel: 1728 size: torch.Size([3, 64, 3, 3])
Param encoder.7.bias Step 0 numel: 3 size: torch.Size([3])
Param decoder.1.weight Step 0 numel: 1728 size: torch.Size([64, 3, 3, 3])
Param decoder.1.bias Step 0 numel: 64 size: torch.Size([64])
Param decoder.3.weight Step 0 numel: 73728 size: torch.Size([128, 64, 3, 3])
Param decoder.3.bias Step 0 numel: 128 size: torch.Size([128])
Param decoder.5.weight Step 0 numel: 147456 size: torch.Size([128, 128, 3, 3])
Param decoder.5.bias Step 0 numel: 128 size: torch.Size([128])
Param decoder.8.weight Step 0 numel: 73728 size: torch.Size([64, 128, 3, 3])
Param decoder.8.bias Step 0 numel: 64 size: torch.Size([64])
Param decoder.10.weight Step 0 numel: 1728 size: torch.Size([3, 64, 3, 3])
Param decoder.10.bias Step 0 numel: 3 size: torch.Size([3])
Param encoder.0.weight Step 0 numel: 1728 size: torch.Size([64, 3, 3, 3])
Param encoder.0.bias Step 0 numel: 64 size: torch.Size([64])
Param encoder.2.weight Step 0 numel: 36864 size: torch.Size([64, 64, 3, 3])
Param encoder.2.bias Step 0 numel: 64 size: torch.Size([64])
Param encoder.4.weight Step 0 numel: 36864 size: torch.Size([64, 64, 3, 3])
Param encoder.4.bias Step 0 numel: 64 size: torch.Size([64])
Param encoder.7.weight Step 0 numel: 1728 size: torch.Size([3, 64, 3, 3])
Param encoder.7.bias Step 0 numel: 3 size: torch.Size([3])
Param decoder.1.weight Step 0 numel: 1728 size: torch.Size([64, 3, 3, 3])
Param decoder.1.bias Step 0 numel: 64 size: torch.Size([64])
Param decoder.3.weight Step 0 numel: 73728 size: torch.Size([128, 64, 3, 3])
Param decoder.3.bias Step 0 numel: 128 size: torch.Size([128])
Param decoder.5.weight Step 0 numel: 147456 size: torch.Size([128, 128, 3, 3])
Param decoder.5.bias Step 0 numel: 128 size: torch.Size([128])
Param decoder.8.weight Step 0 numel: 73728 size: torch.Size([64, 128, 3, 3])
Param decoder.8.bias Step 0 numel: 64 size: torch.Size([64])
Param decoder.10.weight Step 0 numel: 1728 size: torch.Size([3, 64, 3, 3])
Param decoder.10.bias Step 0 numel: 3 size: torch.Size([3])
Param encoder.0.weight Step 0 numel: 1728 size: torch.Size([64, 3, 3, 3])
Param encoder.0.bias Step 0 numel: 64 size: torch.Size([64])
Param encoder.2.weight Step 0 numel: 36864 size: torch.Size([64, 64, 3, 3])
Param encoder.2.bias Step 0 numel: 64 size: torch.Size([64])
Param encoder.4.weight Step 0 numel: 36864 size: torch.Size([64, 64, 3, 3])
Param encoder.4.bias Step 0 numel: 64 size: torch.Size([64])
Param encoder.7.weight Step 0 numel: 1728 size: torch.Size([3, 64, 3, 3])
Param encoder.7.bias Step 0 numel: 3 size: torch.Size([3])
Param decoder.1.weight Step 0 numel: 1728 size: torch.Size([64, 3, 3, 3])
Param decoder.1.bias Step 0 numel: 64 size: torch.Size([64])
Param decoder.3.weight Step 0 numel: 73728 size: torch.Size([128, 64, 3, 3])
Param decoder.3.bias Step 0 numel: 128 size: torch.Size([128])
Param decoder.5.weight Step 0 numel: 147456 size: torch.Size([128, 128, 3, 3])
Param decoder.5.bias Step 0 numel: 128 size: torch.Size([128])
Param decoder.8.weight Step 0 numel: 73728 size: torch.Size([64, 128, 3, 3])
Param decoder.8.bias Step 0 numel: 64 size: torch.Size([64])
Param decoder.10.weight Step 0 numel: 1728 size: torch.Size([3, 64, 3, 3])
Param decoder.10.bias Step 0 numel: 3 size: torch.Size([3])
[rank3]: Traceback (most recent call last):
[rank3]:   File "/mnt/ceph_rbd/pytorch_github/run_autoencoder.py", line 135, in <module>
[rank3]:     main()
[rank3]:   File "/mnt/ceph_rbd/pytorch_github/run_autoencoder.py", line 132, in main
[rank3]:     trainer.fit(model, train_dataloader)
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 543, in fit
[rank3]:     call._call_and_handle_interrupt(
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 43, in _call_and_handle_interrupt
[rank3]:     return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 105, in launch
[rank3]:     return function(*args, **kwargs)
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 579, in _fit_impl
[rank3]:     self._run(model, ckpt_path=ckpt_path)
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 986, in _run
[rank3]:     results = self._run_stage()
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1030, in _run_stage
[rank3]:     self.fit_loop.run()
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 205, in run
[rank3]:     self.advance()
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 363, in advance
[rank3]:     self.epoch_loop.run(self._data_fetcher)
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 140, in run
[rank3]:     self.advance(data_fetcher)
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 250, in advance
[rank3]:     batch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs)
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 190, in run
[rank3]:     self._optimizer_step(batch_idx, closure)
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 268, in _optimizer_step
[rank3]:     call._call_lightning_module_hook(
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 159, in _call_lightning_module_hook
[rank3]:     output = fn(*args, **kwargs)
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/core/module.py", line 1308, in optimizer_step
[rank3]:     optimizer.step(closure=optimizer_closure)
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/core/optimizer.py", line 153, in step
[rank3]:     step_output = self._strategy.optimizer_step(self._optimizer, closure, **kwargs)
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/strategies/ddp.py", line 270, in optimizer_step
[rank3]:     optimizer_output = super().optimizer_step(optimizer, closure, model, **kwargs)
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 238, in optimizer_step
[rank3]:     return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, **kwargs)
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision.py", line 122, in optimizer_step
[rank3]:     return optimizer.step(closure=closure, **kwargs)
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/torch/optim/optimizer.py", line 391, in wrapper
[rank3]:     out = func(*args, **kwargs)
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/torch/optim/optimizer.py", line 76, in _use_grad
[rank3]:     ret = func(self, *args, **kwargs)
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/torch/optim/adam.py", line 148, in step
[rank3]:     loss = closure()
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision.py", line 108, in _wrap_closure
[rank3]:     closure_result = closure()
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 144, in __call__
[rank3]:     self._result = self.closure(*args, **kwargs)
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
[rank3]:     return func(*args, **kwargs)
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 138, in closure
[rank3]:     self._backward_fn(step_output.closure_loss)
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 239, in backward_fn
[rank3]:     call._call_strategy_hook(self.trainer, "backward", loss, optimizer)
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 311, in _call_strategy_hook
[rank3]:     output = fn(*args, **kwargs)
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 212, in backward
[rank3]:     self.precision_plugin.backward(closure_loss, self.lightning_module, optimizer, *args, **kwargs)
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision.py", line 72, in backward
[rank3]:     model.backward(tensor, *args, **kwargs)
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/core/module.py", line 1103, in backward
[rank3]:     loss.backward(*args, **kwargs)
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/torch/_tensor.py", line 525, in backward
[rank3]:     torch.autograd.backward(
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/torch/autograd/__init__.py", line 267, in backward
[rank3]:     _engine_run_backward(
[rank3]:   File "/opt/conda/lib/python3.10/site-packages/torch/autograd/graph.py", line 744, in _engine_run_backward
[rank3]:     return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[rank3]: RuntimeError: Expected grad_output.numel() <= std::numeric_limits<int32_t>::max() to be true, but got false.  (Could this error message be improved?  If so, please report an enhancement request to PyTorch.)
[rank2]: Traceback (most recent call last):
[rank2]:   File "/mnt/ceph_rbd/pytorch_github/run_autoencoder.py", line 135, in <module>
[rank2]:     main()
[rank2]:   File "/mnt/ceph_rbd/pytorch_github/run_autoencoder.py", line 132, in main
[rank2]:     trainer.fit(model, train_dataloader)
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 543, in fit
[rank2]:     call._call_and_handle_interrupt(
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 43, in _call_and_handle_interrupt
[rank2]:     return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 105, in launch
[rank2]:     return function(*args, **kwargs)
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 579, in _fit_impl
[rank2]:     self._run(model, ckpt_path=ckpt_path)
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 986, in _run
[rank2]:     results = self._run_stage()
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1030, in _run_stage
[rank2]:     self.fit_loop.run()
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 205, in run
[rank2]:     self.advance()
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 363, in advance
[rank2]:     self.epoch_loop.run(self._data_fetcher)
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 140, in run
[rank2]:     self.advance(data_fetcher)
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 250, in advance
[rank2]:     batch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs)
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 190, in run
[rank2]:     self._optimizer_step(batch_idx, closure)
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 268, in _optimizer_step
[rank2]:     call._call_lightning_module_hook(
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 159, in _call_lightning_module_hook
[rank2]:     output = fn(*args, **kwargs)
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/core/module.py", line 1308, in optimizer_step
[rank2]:     optimizer.step(closure=optimizer_closure)
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/core/optimizer.py", line 153, in step
[rank2]:     step_output = self._strategy.optimizer_step(self._optimizer, closure, **kwargs)
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/strategies/ddp.py", line 270, in optimizer_step
[rank2]:     optimizer_output = super().optimizer_step(optimizer, closure, model, **kwargs)
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 238, in optimizer_step
[rank2]:     return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, **kwargs)
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision.py", line 122, in optimizer_step
[rank2]:     return optimizer.step(closure=closure, **kwargs)
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/torch/optim/optimizer.py", line 391, in wrapper
[rank2]:     out = func(*args, **kwargs)
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/torch/optim/optimizer.py", line 76, in _use_grad
[rank2]:     ret = func(self, *args, **kwargs)
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/torch/optim/adam.py", line 148, in step
[rank2]:     loss = closure()
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision.py", line 108, in _wrap_closure
[rank2]:     closure_result = closure()
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 144, in __call__
[rank2]:     self._result = self.closure(*args, **kwargs)
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
[rank2]:     return func(*args, **kwargs)
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 138, in closure
[rank2]:     self._backward_fn(step_output.closure_loss)
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 239, in backward_fn
[rank2]:     call._call_strategy_hook(self.trainer, "backward", loss, optimizer)
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 311, in _call_strategy_hook
[rank2]:     output = fn(*args, **kwargs)
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 212, in backward
[rank2]:     self.precision_plugin.backward(closure_loss, self.lightning_module, optimizer, *args, **kwargs)
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision.py", line 72, in backward
[rank2]:     model.backward(tensor, *args, **kwargs)
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/core/module.py", line 1103, in backward
[rank2]:     loss.backward(*args, **kwargs)
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/torch/_tensor.py", line 525, in backward
[rank2]:     torch.autograd.backward(
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/torch/autograd/__init__.py", line 267, in backward
[rank2]:     _engine_run_backward(
[rank2]:   File "/opt/conda/lib/python3.10/site-packages/torch/autograd/graph.py", line 744, in _engine_run_backward
[rank2]:     return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[rank2]: RuntimeError: Expected grad_output.numel() <= std::numeric_limits<int32_t>::max() to be true, but got false.  (Could this error message be improved?  If so, please report an enhancement request to PyTorch.)
[rank1]: Traceback (most recent call last):
[rank1]:   File "/mnt/ceph_rbd/pytorch_github/run_autoencoder.py", line 135, in <module>
[rank1]:     main()
[rank1]:   File "/mnt/ceph_rbd/pytorch_github/run_autoencoder.py", line 132, in main
[rank1]:     trainer.fit(model, train_dataloader)
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 543, in fit
[rank1]:     call._call_and_handle_interrupt(
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 43, in _call_and_handle_interrupt
[rank1]:     return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 105, in launch
[rank1]:     return function(*args, **kwargs)
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 579, in _fit_impl
[rank1]:     self._run(model, ckpt_path=ckpt_path)
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 986, in _run
[rank1]:     results = self._run_stage()
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1030, in _run_stage
[rank1]:     self.fit_loop.run()
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 205, in run
[rank1]:     self.advance()
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 363, in advance
[rank1]:     self.epoch_loop.run(self._data_fetcher)
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 140, in run
[rank1]:     self.advance(data_fetcher)
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 250, in advance
[rank1]:     batch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs)
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 190, in run
[rank1]:     self._optimizer_step(batch_idx, closure)
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 268, in _optimizer_step
[rank1]:     call._call_lightning_module_hook(
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 159, in _call_lightning_module_hook
[rank1]:     output = fn(*args, **kwargs)
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/core/module.py", line 1308, in optimizer_step
[rank1]:     optimizer.step(closure=optimizer_closure)
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/core/optimizer.py", line 153, in step
[rank1]:     step_output = self._strategy.optimizer_step(self._optimizer, closure, **kwargs)
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/strategies/ddp.py", line 270, in optimizer_step
[rank1]:     optimizer_output = super().optimizer_step(optimizer, closure, model, **kwargs)
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 238, in optimizer_step
[rank1]:     return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, **kwargs)
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision.py", line 122, in optimizer_step
[rank1]:     return optimizer.step(closure=closure, **kwargs)
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/torch/optim/optimizer.py", line 391, in wrapper
[rank1]:     out = func(*args, **kwargs)
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/torch/optim/optimizer.py", line 76, in _use_grad
[rank1]:     ret = func(self, *args, **kwargs)
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/torch/optim/adam.py", line 148, in step
[rank1]:     loss = closure()
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision.py", line 108, in _wrap_closure
[rank1]:     closure_result = closure()
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 144, in __call__
[rank1]:     self._result = self.closure(*args, **kwargs)
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
[rank1]:     return func(*args, **kwargs)
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 138, in closure
[rank1]:     self._backward_fn(step_output.closure_loss)
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 239, in backward_fn
[rank1]:     call._call_strategy_hook(self.trainer, "backward", loss, optimizer)
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 311, in _call_strategy_hook
[rank1]:     output = fn(*args, **kwargs)
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 212, in backward
[rank1]:     self.precision_plugin.backward(closure_loss, self.lightning_module, optimizer, *args, **kwargs)
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision.py", line 72, in backward
[rank1]:     model.backward(tensor, *args, **kwargs)
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/core/module.py", line 1103, in backward
[rank1]:     loss.backward(*args, **kwargs)
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/torch/_tensor.py", line 525, in backward
[rank1]:     torch.autograd.backward(
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/torch/autograd/__init__.py", line 267, in backward
[rank1]:     _engine_run_backward(
[rank1]:   File "/opt/conda/lib/python3.10/site-packages/torch/autograd/graph.py", line 744, in _engine_run_backward
[rank1]:     return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[rank1]: RuntimeError: Expected grad_output.numel() <= std::numeric_limits<int32_t>::max() to be true, but got false.  (Could this error message be improved?  If so, please report an enhancement request to PyTorch.)
[rank0]: Traceback (most recent call last):
[rank0]:   File "/mnt/ceph_rbd/pytorch_github/run_autoencoder.py", line 135, in <module>
[rank0]:     main()
[rank0]:   File "/mnt/ceph_rbd/pytorch_github/run_autoencoder.py", line 132, in main
[rank0]:     trainer.fit(model, train_dataloader)
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 543, in fit
[rank0]:     call._call_and_handle_interrupt(
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 43, in _call_and_handle_interrupt
[rank0]:     return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 105, in launch
[rank0]:     return function(*args, **kwargs)
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 579, in _fit_impl
[rank0]:     self._run(model, ckpt_path=ckpt_path)
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 986, in _run
[rank0]:     results = self._run_stage()
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1030, in _run_stage
[rank0]:     self.fit_loop.run()
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 205, in run
[rank0]:     self.advance()
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 363, in advance
[rank0]:     self.epoch_loop.run(self._data_fetcher)
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 140, in run
[rank0]:     self.advance(data_fetcher)
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 250, in advance
[rank0]:     batch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs)
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 190, in run
[rank0]:     self._optimizer_step(batch_idx, closure)
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 268, in _optimizer_step
[rank0]:     call._call_lightning_module_hook(
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 159, in _call_lightning_module_hook
[rank0]:     output = fn(*args, **kwargs)
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/core/module.py", line 1308, in optimizer_step
[rank0]:     optimizer.step(closure=optimizer_closure)
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/core/optimizer.py", line 153, in step
[rank0]:     step_output = self._strategy.optimizer_step(self._optimizer, closure, **kwargs)
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/strategies/ddp.py", line 270, in optimizer_step
[rank0]:     optimizer_output = super().optimizer_step(optimizer, closure, model, **kwargs)
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 238, in optimizer_step
[rank0]:     return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, **kwargs)
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision.py", line 122, in optimizer_step
[rank0]:     return optimizer.step(closure=closure, **kwargs)
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/torch/optim/optimizer.py", line 391, in wrapper
[rank0]:     out = func(*args, **kwargs)
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/torch/optim/optimizer.py", line 76, in _use_grad
[rank0]:     ret = func(self, *args, **kwargs)
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/torch/optim/adam.py", line 148, in step
[rank0]:     loss = closure()
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision.py", line 108, in _wrap_closure
[rank0]:     closure_result = closure()
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 144, in __call__
[rank0]:     self._result = self.closure(*args, **kwargs)
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
[rank0]:     return func(*args, **kwargs)
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 138, in closure
[rank0]:     self._backward_fn(step_output.closure_loss)
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 239, in backward_fn
[rank0]:     call._call_strategy_hook(self.trainer, "backward", loss, optimizer)
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 311, in _call_strategy_hook
[rank0]:     output = fn(*args, **kwargs)
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 212, in backward
[rank0]:     self.precision_plugin.backward(closure_loss, self.lightning_module, optimizer, *args, **kwargs)
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision.py", line 72, in backward
[rank0]:     model.backward(tensor, *args, **kwargs)
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/pytorch_lightning/core/module.py", line 1103, in backward
[rank0]:     loss.backward(*args, **kwargs)
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/torch/_tensor.py", line 525, in backward
[rank0]:     torch.autograd.backward(
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/torch/autograd/__init__.py", line 267, in backward
[rank0]:     _engine_run_backward(
[rank0]:   File "/opt/conda/lib/python3.10/site-packages/torch/autograd/graph.py", line 744, in _engine_run_backward
[rank0]:     return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[rank0]: RuntimeError: Expected grad_output.numel() <= std::numeric_limits<int32_t>::max() to be true, but got false.  (Could this error message be improved?  If so, please report an enhancement request to PyTorch.)
Epoch 0:   0%|          | 0/1 [00:04<?, ?it/s]    
```
Notice that I'm using 4 GPUs so I have 4 print outs of each gradient's size and numel.

I do not understand why changing the total number of training images (but NOT the batch size) would result in a gradient that is larger than 2^32. This also does not appear to be an OOM error because monitoring with nvidia-smi during the training process shows no GPU at full capacity.  The gradient sizes are not different from their sizes during the training run that did work.

Does anyone have an explanation for this error?

### Versions

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] pytorch-lightning==2.3.3
[pip3] torch==2.3.1
[pip3] torchmetrics==1.4.0.post0
[pip3] torchvision==0.18.1
[pip3] triton==2.3.1
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] pytorch-lightning         2.3.3                    pypi_0    pypi
[conda] torch                     2.3.1                    pypi_0    pypi
[conda] torchmetrics              1.4.0.post0              pypi_0    pypi
[conda] torchvision               0.18.1                   pypi_0    pypi
[conda] triton                    2.3.1                    pypi_0    pypi

cc @ezyang @albanD @gqchen @pearu @nikitaved @soulitzer @Varal7 @mruberry @jbschlosser @walterddr @mikaylagawarecki @malfet @XilunWu @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o