sunweice
commented
2 weeks ago @staticmethod
def collate_fn(batch: Union[dict, TensorDict]):
"""Collate function compatible with TensorDicts that reassembles a list of dicts."""
return TensorDict(
{key: torch.stack([b[key] for b in batch]) for key in batch[0].keys()},
batch_size=torch.Size([len(batch)]),
_run_checks=False,
)It seems I've found the reason. In (https://github.com/pytorch/tensordict/pull/175#discussion_r1084354980), it's mentioned that TensorDict does not support the _run_checks parameter. and after I delete the _run_checks=False，the code can run
fedebotu
Describe the bug
I'm using RL4CO, and when I followed the method on this website （https://rl4co.readthedocs.io/en/latest/examples/1-quickstart/）for testing, I encountered an error. What should I do?
To Reproduce
IN step “trainer.fit(model)”
the error is :
val_file not set. Generating dataset instead test_file not set. Generating dataset instead
ValueError Traceback (most recent call last) Cell In[5], line 1 ----> 1 trainer.fit(model)
File ~\anaconda3\envs\RL4CO\lib\site-packages\rl4co\utils\trainer.py:146, in RL4COTrainer.fit(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path) 141 log.warning( 142 "Overriding gradient_clip_val to None for 'automatic_optimization=False' models" 143 ) 144 self.gradient_clip_val = None --> 146 super().fit( 147 model=model, 148 train_dataloaders=train_dataloaders, 149 val_dataloaders=val_dataloaders, 150 datamodule=datamodule, 151 ckpt_path=ckpt_path, 152 )
File ~\anaconda3\envs\RL4CO\lib\site-packages\lightning\pytorch\trainer\trainer.py:538, in Trainer.fit(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path) 536 self.state.status = TrainerStatus.RUNNING 537 self.training = True --> 538 call._call_and_handle_interrupt( 539 self, self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path 540 )
File ~\anaconda3\envs\RL4CO\lib\site-packages\lightning\pytorch\trainer\call.py:47, in _call_and_handle_interrupt(trainer, trainer_fn, *args, kwargs) 45 if trainer.strategy.launcher is not None: 46 return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, *kwargs) ---> 47 return trainer_fn(args, kwargs) 49 except _TunerExitException: 50 _call_teardown_hook(trainer)
File ~\anaconda3\envs\RL4CO\lib\site-packages\lightning\pytorch\trainer\trainer.py:574, in Trainer._fit_impl(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path) 567 assert self.state.fn is not None 568 ckpt_path = self._checkpoint_connector._select_ckpt_path( 569 self.state.fn, 570 ckpt_path, 571 model_provided=True, 572 model_connected=self.lightning_module is not None, 573 ) --> 574 self._run(model, ckpt_path=ckpt_path) 576 assert self.state.stopped 577 self.training = False
File ~\anaconda3\envs\RL4CO\lib\site-packages\lightning\pytorch\trainer\trainer.py:943, in Trainer._run(self, model, ckpt_path) 940 log.debug(f"{self.class.name}: preparing data") 941 self._data_connector.prepare_data() --> 943 call._call_setup_hook(self) # allow user to set up LightningModule in accelerator environment 944 log.debug(f"{self.class.name}: configuring model") 945 call._call_configure_model(self)
File ~\anaconda3\envs\RL4CO\lib\site-packages\lightning\pytorch\trainer\call.py:104, in _call_setup_hook(trainer) 102 _call_lightning_datamodule_hook(trainer, "setup", stage=fn) 103 _call_callback_hooks(trainer, "setup", stage=fn) --> 104 _call_lightning_module_hook(trainer, "setup", stage=fn) 106 trainer.strategy.barrier("post_setup")
File ~\anaconda3\envs\RL4CO\lib\site-packages\lightning\pytorch\trainer\call.py:167, in _call_lightning_module_hook(trainer, hook_name, pl_module, *args, *kwargs) 164 pl_module._current_fx_name = hook_name 166 with trainer.profiler.profile(f"[LightningModule]{pl_module.class.name}.{hook_name}"): --> 167 output = fn(args, **kwargs) 169 # restore current_fx when nested context 170 pl_module._current_fx_name = prev_fx_name
File ~\anaconda3\envs\RL4CO\lib\site-packages\rl4co\models\rl\common\base.py:155, in RL4COLitModule.setup(self, stage) 153 self.dataloader_names = None 154 self.setup_loggers() --> 155 self.post_setup_hook()
File ~\anaconda3\envs\RL4CO\lib\site-packages\rl4co\models\rl\reinforce\reinforce.py:110, in REINFORCE.post_setup_hook(self, stage) 108 def post_setup_hook(self, stage="fit"): 109 # Make baseline taking model itself and train_dataloader from model as input --> 110 self.baseline.setup( 111 self.policy, 112 self.env, 113 batch_size=self.val_batch_size, 114 device=get_lightning_device(self), 115 dataset_size=self.data_cfg["val_data_size"], 116 )
File ~\anaconda3\envs\RL4CO\lib\site-packages\rl4co\models\rl\reinforce\baselines.py:117, in WarmupBaseline.setup(self, *args, kw) 116 def setup(self, *args, *kw): --> 117 self.baseline.setup(args, kw)
File ~\anaconda3\envs\RL4CO\lib\site-packages\rl4co\models\rl\reinforce\baselines.py:174, in RolloutBaseline.setup(self, *args, kw) 173 def setup(self, *args, *kw): --> 174 self._update_policy(args, kw)
File ~\anaconda3\envs\RL4CO\lib\site-packages\rl4co\models\rl\reinforce\baselines.py:187, in RolloutBaseline._update_policy(self, policy, env, batch_size, device, dataset_size, dataset) 183 self.dataset = env.dataset(batch_size=[dataset_size]) 185 log.info("Evaluating baseline policy on evaluation dataset") 186 self.bl_vals = ( --> 187 self.rollout(self.policy, env, batch_size, device, self.dataset).cpu().numpy() 188 ) 189 self.mean = self.bl_vals.mean()
File ~\anaconda3\envs\RL4CO\lib\site-packages\rl4co\models\rl\reinforce\baselines.py:242, in RolloutBaseline.rollout(self, policy, env, batch_size, device, dataset) 238 return policy(batch, env, decode_type="greedy")["reward"] 240 dl = DataLoader(dataset, batch_size=batch_size, collate_fn=dataset.collate_fn) --> 242 rewards = torch.cat([eval_policy(batch) for batch in dl], 0) 243 return rewards
File ~\anaconda3\envs\RL4CO\lib\site-packages\rl4co\models\rl\reinforce\baselines.py:242, in(.0)
238 return policy(batch, env, decode_type="greedy")["reward"]
240 dl = DataLoader(dataset, batch_size=batch_size, collate_fn=dataset.collate_fn)
--> 242 rewards = torch.cat([eval_policy(batch) for batch in dl], 0)
243 return rewards
File ~\anaconda3\envs\RL4CO\lib\site-packages\torch\utils\data\dataloader.py:630, in _BaseDataLoaderIter.next(self) 627 if self._sampler_iter is None: 628 # TODO(https://github.com/pytorch/pytorch/issues/76750) 629 self._reset() # type: ignore[call-arg] --> 630 data = self._next_data() 631 self._num_yielded += 1 632 if self._dataset_kind == _DatasetKind.Iterable and \ 633 self._IterableDataset_len_called is not None and \ 634 self._num_yielded > self._IterableDataset_len_called:
File ~\anaconda3\envs\RL4CO\lib\site-packages\torch\utils\data\dataloader.py:673, in _SingleProcessDataLoaderIter._next_data(self) 671 def _next_data(self): 672 index = self._next_index() # may raise StopIteration --> 673 data = self._dataset_fetcher.fetch(index) # may raise StopIteration 674 if self._pin_memory: 675 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)
File ~\anaconda3\envs\RL4CO\lib\site-packages\torch\utils\data_utils\fetch.py:55, in _MapDatasetFetcher.fetch(self, possibly_batched_index) 53 else: 54 data = self.dataset[possibly_batched_index] ---> 55 return self.collate_fn(data)
File ~\anaconda3\envs\RL4CO\lib\site-packages\rl4co\data\dataset.py:37, in TensorDictDataset.collate_fn(batch) 34 @staticmethod 35 def collate_fn(batch: Union[dict, TensorDict]): 36 """Collate function compatible with TensorDicts that reassembles a list of dicts.""" ---> 37 return TensorDict( 38 {key: torch.stack([b[key] for b in batch]) for key in batch[0].keys()}, 39 batch_size=torch.Size([len(batch)]), 40 _run_checks=False, 41 )
File ~\anaconda3\envs\RL4CO\lib\site-packages\tensordict_td.py:240, in TensorDict.init(self, source, batch_size, device, names, non_blocking, lock, kwargs) 229 def init( 230 self, 231 source: T | dict[str, CompatibleType] = None, (...) 237 kwargs, 238 ) -> None: 239 if (source is not None) and kwargs: --> 240 raise ValueError( 241 "Either a dictionary or a sequence of kwargs must be provided, not both." 242 ) 243 source = source if not kwargs else kwargs 244 if names and is_dynamo_compiling():
ValueError: Either a dictionary or a sequence of kwargs must be provided, not both.