fix(nyz): fix dt dataset compatibility bug

README.md

@@ -74,7 +74,7 @@ It provides **python-first** and **asynchronous-native** task and middleware abs
   - [ACE](https://github.com/opendilab/ACE): [AAAI 2023] ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency
   - [GoBigger](https://github.com/opendilab/GoBigger): [ICLR 2023] Multi-Agent Decision Intelligence Environment
   - [DOS](https://github.com/opendilab/DOS): [CVPR 2023] ReasonNet: End-to-End Driving with Temporal and Global Reasoning
-  - [LightZero](https://github.com/opendilab/LightZero): LightZero: A lightweight and efficient MCTS/AlphaZero/MuZero algorithm toolkit
+  - [LightZero](https://github.com/opendilab/LightZero): A lightweight and efficient MCTS/AlphaZero/MuZero algorithm toolkit
 - Docs and Tutorials
   - [DI-engine-docs](https://github.com/opendilab/DI-engine-docs): Tutorials, best practice and the API reference.
   - [awesome-model-based-RL](https://github.com/opendilab/awesome-model-based-RL): A curated list of awesome Model-Based RL resources

ding/example/dt.py

@@ -1,6 +1,6 @@
 import gym
 from ditk import logging
-from ding.model.template.decision_transformer import DecisionTransformer
+from ding.model.template.dt import DecisionTransformer
 from ding.policy import DTPolicy
 from ding.envs import DingEnvWrapper, BaseEnvManager, BaseEnvManagerV2
 from ding.envs.env_wrappers.env_wrappers import AllinObsWrapper

ding/utils/data/dataset.py

@@ -150,16 +150,18 @@ def __len__(self) -> int:
         return len(self._data['obs']) - self.context_len
 
     def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]:
-        # return {k: self._data[k][idx] for k in self._data.keys()}
-        block_size = self.context_len
-        done_idx = idx + block_size
-        idx = done_idx - block_size
-        states = torch.as_tensor(np.array(self._data['obs'][idx:done_idx]), dtype=torch.float32).view(block_size, -1)
-        actions = torch.as_tensor(self._data['action'][idx:done_idx], dtype=torch.long)
-        rtgs = torch.as_tensor(self._data['reward'][idx:done_idx, 0], dtype=torch.float32)
-        timesteps = torch.as_tensor(range(idx, done_idx), dtype=torch.int64)
-        traj_mask = torch.ones(self.context_len, dtype=torch.long)
-        return timesteps, states, actions, rtgs, traj_mask
+        if self.context_len == 0:  # for other offline RL algorithms
+            return {k: self._data[k][idx] for k in self._data.keys()}
+        else:  # for decision transformer
+            block_size = self.context_len
+            done_idx = idx + block_size
+            idx = done_idx - block_size
+            states = torch.as_tensor(np.array(self._data['obs'][idx:done_idx]), dtype=torch.float32).view(block_size, -1)
+            actions = torch.as_tensor(self._data['action'][idx:done_idx], dtype=torch.long)
+            rtgs = torch.as_tensor(self._data['reward'][idx:done_idx, 0], dtype=torch.float32)
+            timesteps = torch.as_tensor(range(idx, done_idx), dtype=torch.int64)
+            traj_mask = torch.ones(self.context_len, dtype=torch.long)
+            return timesteps, states, actions, rtgs, traj_mask
 
     def _load_data(self, dataset: Dict[str, np.ndarray]) -> None:
         self._data = {}

ding/utils/pytorch_ddp_dist_helper.py

@@ -181,6 +181,12 @@ def simple_group_split(world_size: int, rank: int, num_groups: int) -> List:
 
 def to_ddp_config(cfg: EasyDict) -> EasyDict:
     w = get_world_size()
-    cfg.policy.batch_size = int(np.ceil(cfg.policy.batch_size / w))
-    # cfg.policy.collect.n_sample = int(np.ceil(cfg.policy.collect.n_sample) / w)
+    if 'batch_size' in cfg.policy:
+        cfg.policy.batch_size = int(np.ceil(cfg.policy.batch_size / w))
+    if 'batch_size' in cfg.policy.learn:
+        cfg.policy.learn.batch_size = int(np.ceil(cfg.policy.learn.batch_size / w))
+    if 'n_sample' in cfg.policy.collect:
+        cfg.policy.collect.n_sample = int(np.ceil(cfg.policy.collect.n_sample / w))
+    if 'n_episode' in cfg.policy.collect:
+        cfg.policy.collect.n_episode = int(np.ceil(cfg.policy.collect.n_episode / w))
     return cfg

dizoo/atari/config/serial/pong/pong_dt_config.py

@@ -66,8 +66,3 @@
 )
 Pong_dt_create_config = EasyDict(Pong_dt_create_config)
 create_config = Pong_dt_create_config
-
-if __name__ == "__main__":
-    from ding.entry import serial_pipeline_dt
-    config = deepcopy([main_config, create_config])
-    serial_pipeline_dt(config, seed=0, max_train_iter=1000)

dizoo/box2d/lunarlander/config/lunarlander_dt_config.py

@@ -39,7 +39,7 @@
         learn=dict(
             dataset_path='DI-engine/dizoo/box2d/lunarlander/offline_data/dt_data/dqn_data_1000eps.pkl',  # TODO
             learning_rate=3e-4,
-            batch_size=64,  # training batch size   
+            batch_size=64,  # training batch size
             target_update_freq=100,
         ),
         collect=dict(
@@ -62,8 +62,3 @@
 )
 lunarlander_dt_create_config = EasyDict(lunarlander_dt_create_config)
 create_config = lunarlander_dt_create_config
-
-if __name__ == "__main__":
-    from ding.entry import serial_pipeline_dt, collect_demo_data, eval, serial_pipeline
-    config = deepcopy([main_config, create_config])
-    serial_pipeline_dt(config, seed=0, max_train_iter=1000)