Parameter error for dataloader

[weir12]

When you set batch normalization (requiring more than one sample per step) and drop_last=false, it will cause an error when the last sample per epoch (the remainder of the total sample divided by the mini batch is 1)

``` Traceback (most recent call last): File "/cluster/home/ouliang/daisy/rna_velocity/allen_scvelo.py", line 31, in vae.train() File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/velovi/_model.py", line 195, in train return runner() File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/scvi/train/_trainrunner.py", line 99, in __call__ self.trainer.fit(self.training_plan, self.data_splitter) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/scvi/train/_trainer.py", line 186, in fit super().fit(*args, **kwargs) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py", line 532, in fit call._call_and_handle_interrupt( File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/lightning/pytorch/trainer/call.py", line 43, in _call_and_handle_interrupt return trainer_fn(*args, **kwargs) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py", line 571, in _fit_impl self._run(model, ckpt_path=ckpt_path) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py", line 980, in _run results = self._run_stage() File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py", line 1023, in _run_stage self.fit_loop.run() File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py", line 202, in run self.advance() File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py", line 355, in advance self.epoch_loop.run(self._data_fetcher) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/lightning/pytorch/loops/training_epoch_loop.py", line 133, in run self.advance(data_fetcher) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/lightning/pytorch/loops/training_epoch_loop.py", line 219, in advance batch_output = self.automatic_optimization.run(trainer.optimizers[0], kwargs) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/lightning/pytorch/loops/optimization/automatic.py", line 188, in run self._optimizer_step(kwargs.get("batch_idx", 0), closure) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/lightning/pytorch/loops/optimization/automatic.py", line 266, in _optimizer_step call._call_lightning_module_hook( File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/lightning/pytorch/trainer/call.py", line 146, in _call_lightning_module_hook output = fn(*args, **kwargs) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/lightning/pytorch/core/module.py", line 1276, in optimizer_step optimizer.step(closure=optimizer_closure) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/lightning/pytorch/core/optimizer.py", line 161, in step step_output = self._strategy.optimizer_step(self._optimizer, closure, **kwargs) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/lightning/pytorch/strategies/strategy.py", line 231, in optimizer_step return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, **kwargs) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/lightning/pytorch/plugins/precision/precision_plugin.py", line 116, in optimizer_step return optimizer.step(closure=closure, **kwargs) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/torch/optim/optimizer.py", line 280, in wrapper out = func(*args, **kwargs) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/torch/optim/optimizer.py", line 33, in _use_grad ret = func(self, *args, **kwargs) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/torch/optim/adamw.py", line 148, in step loss = closure() File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/lightning/pytorch/plugins/precision/precision_plugin.py", line 103, in _wrap_closure closure_result = closure() File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/lightning/pytorch/loops/optimization/automatic.py", line 142, in __call__ self._result = self.closure(*args, **kwargs) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context return func(*args, **kwargs) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/lightning/pytorch/loops/optimization/automatic.py", line 128, in closure step_output = self._step_fn() File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/lightning/pytorch/loops/optimization/automatic.py", line 315, in _training_step training_step_output = call._call_strategy_hook(trainer, "training_step", *kwargs.values()) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/lightning/pytorch/trainer/call.py", line 294, in _call_strategy_hook output = fn(*args, **kwargs) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/lightning/pytorch/strategies/strategy.py", line 380, in training_step return self.model.training_step(*args, **kwargs) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/scvi/train/_trainingplans.py", line 342, in training_step _, _, scvi_loss = self.forward(batch, loss_kwargs=self.loss_kwargs) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/scvi/train/_trainingplans.py", line 278, in forward return self.module(*args, **kwargs) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/scvi/module/base/_decorators.py", line 32, in auto_transfer_args return fn(self, *args, **kwargs) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/scvi/module/base/_base_module.py", line 205, in forward return _generic_forward( File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/scvi/module/base/_base_module.py", line 749, in _generic_forward inference_outputs = module.inference(**inference_inputs, **inference_kwargs) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/scvi/module/base/_decorators.py", line 32, in auto_transfer_args return fn(self, *args, **kwargs) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/velovi/_module.py", line 367, in inference qz_m, qz_v, z = self.z_encoder(encoder_input) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/scvi/nn/_base_components.py", line 286, in forward q = self.encoder(x, *cat_list) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/scvi/nn/_base_components.py", line 175, in forward x = layer(x) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/torch/nn/modules/batchnorm.py", line 171, in forward return F.batch_norm( File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/torch/nn/functional.py", line 2448, in batch_norm _verify_batch_size(input.size()) File "/cluster/home/ouliang/mambaforge/lib/python3.10/site-packages/torch/nn/functional.py", line 2416, in _verify_batch_size raise ValueError("Expected more than 1 value per channel when training, got input size {}".format(size)) ValueError: Expected more than 1 value per channel when training, got input size torch.Size([1, 256]) ```

Please fix this problem in time