[Feature] GAIL compatibility with compile

sota-implementations/gail/config.yaml

@@ -41,6 +41,11 @@ gail:
   gp_lambda: 10.0
   device: null
 
+compile:
+  compile: False
+  compile_mode:
+  cudagraphs: False
+
 replay_buffer:
   dataset: halfcheetah-expert-v2
   batch_size: 256

sota-implementations/gail/gail.py

@@ -18,11 +18,12 @@
 from ppo_utils import eval_model, make_env, make_ppo_models
 from torchrl.collectors import SyncDataCollector
 from torchrl.data import LazyMemmapStorage, TensorDictReplayBuffer
+from tensordict.nn import CudaGraphModule
 from torchrl.data.replay_buffers.samplers import SamplerWithoutReplacement
 
 from torchrl.envs import set_gym_backend
 from torchrl.envs.utils import ExplorationType, set_exploration_type
-from torchrl.objectives import ClipPPOLoss, GAILLoss
+from torchrl.objectives import ClipPPOLoss, GAILLoss, group_optimizers
 from torchrl.objectives.value.advantages import GAE
 from torchrl.record import VideoRecorder
 from torchrl.record.loggers import generate_exp_name, get_logger
@@ -69,20 +70,9 @@ def main(cfg: "DictConfig"):  # noqa: F821
     np.random.seed(cfg.env.seed)
 
     # Create models (check utils_mujoco.py)
-    actor, critic = make_ppo_models(cfg.env.env_name)
+    actor, critic = make_ppo_models(cfg.env.env_name, compile=cfg.compile.compile)
     actor, critic = actor.to(device), critic.to(device)
 
-    # Create collector
-    collector = SyncDataCollector(
-        create_env_fn=make_env(cfg.env.env_name, device),
-        policy=actor,
-        frames_per_batch=cfg.ppo.collector.frames_per_batch,
-        total_frames=cfg.ppo.collector.total_frames,
-        device=device,
-        storing_device=device,
-        max_frames_per_traj=-1,
-    )
-
     # Create data buffer
     data_buffer = TensorDictReplayBuffer(
         storage=LazyMemmapStorage(cfg.ppo.collector.frames_per_batch),
@@ -111,6 +101,30 @@ def main(cfg: "DictConfig"):  # noqa: F821
     # Create optimizers
     actor_optim = torch.optim.Adam(actor.parameters(), lr=cfg.ppo.optim.lr, eps=1e-5)
     critic_optim = torch.optim.Adam(critic.parameters(), lr=cfg.ppo.optim.lr, eps=1e-5)
+    optim = group_optimizers(actor_optim, critic_optim)
+    del actor_optim, critic_optim
+
+    compile_mode = None
+    if cfg.compile.compile:
+        compile_mode = cfg.compile.compile_mode
+        if compile_mode in ("", None):
+            if cfg.compile.cudagraphs:
+                compile_mode = "default"
+            else:
+                compile_mode = "reduce-overhead"
+
+    # Create collector
+    collector = SyncDataCollector(
+        create_env_fn=make_env(cfg.env.env_name, device),
+        policy=actor,
+        frames_per_batch=cfg.ppo.collector.frames_per_batch,
+        total_frames=cfg.ppo.collector.total_frames,
+        device=device,
+        storing_device=device,
+        max_frames_per_traj=-1,
+        compile_policy={"mode": compile_mode} if compile_mode is not None else False,
+        cudagraph_policy=cfg.compile.cudagraphs,
+    )
 
     # Create replay buffer
     replay_buffer = make_offline_replay_buffer(cfg.replay_buffer)
@@ -138,32 +152,9 @@ def main(cfg: "DictConfig"):  # noqa: F821
             VideoRecorder(logger, tag="rendering/test", in_keys=["pixels"])
         )
     test_env.eval()
+    num_network_updates = torch.zeros((), dtype=torch.int64, device=device)
 
-    # Training loop
-    collected_frames = 0
-    num_network_updates = 0
-    pbar = tqdm.tqdm(total=cfg.ppo.collector.total_frames)
-
-    # extract cfg variables
-    cfg_loss_ppo_epochs = cfg.ppo.loss.ppo_epochs
-    cfg_optim_anneal_lr = cfg.ppo.optim.anneal_lr
-    cfg_optim_lr = cfg.ppo.optim.lr
-    cfg_loss_anneal_clip_eps = cfg.ppo.loss.anneal_clip_epsilon
-    cfg_loss_clip_epsilon = cfg.ppo.loss.clip_epsilon
-    cfg_logger_test_interval = cfg.logger.test_interval
-    cfg_logger_num_test_episodes = cfg.logger.num_test_episodes
-
-    for i, data in enumerate(collector):
-
-        log_info = {}
-        frames_in_batch = data.numel()
-        collected_frames += frames_in_batch
-        pbar.update(data.numel())
-
-        # Update discriminator
-        # Get expert data
-        expert_data = replay_buffer.sample()
-        expert_data = expert_data.to(device)
+    def update(data, expert_data, num_network_updates=num_network_updates):
         # Add collector data to expert data
         expert_data.set(
             discriminator_loss.tensor_keys.collector_action,
@@ -176,9 +167,9 @@ def main(cfg: "DictConfig"):  # noqa: F821
         d_loss = discriminator_loss(expert_data)
 
         # Backward pass
-        discriminator_optim.zero_grad()
         d_loss.get("loss").backward()
         discriminator_optim.step()
+        discriminator_optim.zero_grad(set_to_none=True)
 
         # Compute discriminator reward
         with torch.no_grad():
@@ -188,32 +179,19 @@ def main(cfg: "DictConfig"):  # noqa: F821
         # Set discriminator rewards to tensordict
         data.set(("next", "reward"), d_rewards)
 
-        # Get training rewards and episode lengths
-        episode_rewards = data["next", "episode_reward"][data["next", "done"]]
-        if len(episode_rewards) > 0:
-            episode_length = data["next", "step_count"][data["next", "done"]]
-            log_info.update(
-                {
-                    "train/reward": episode_rewards.mean().item(),
-                    "train/episode_length": episode_length.sum().item()
-                    / len(episode_length),
-                }
-            )
         # Update PPO
         for _ in range(cfg_loss_ppo_epochs):
-
             # Compute GAE
             with torch.no_grad():
                 data = adv_module(data)
             data_reshape = data.reshape(-1)
 
             # Update the data buffer
+            data_buffer.empty()
             data_buffer.extend(data_reshape)
 
-            for _, batch in enumerate(data_buffer):
-
-                # Get a data batch
-                batch = batch.to(device)
+            for batch in data_buffer:
+                optim.zero_grad(set_to_none=True)
 
                 # Linearly decrease the learning rate and clip epsilon
                 alpha = 1.0
@@ -233,20 +211,66 @@ def main(cfg: "DictConfig"):  # noqa: F821
                 actor_loss = loss["loss_objective"] + loss["loss_entropy"]
 
                 # Backward pass
-                actor_loss.backward()
-                critic_loss.backward()
+                (actor_loss + critic_loss).backward()
 
                 # Update the networks
-                actor_optim.step()
-                critic_optim.step()
-                actor_optim.zero_grad()
-                critic_optim.zero_grad()
+                optim.step()
+        return d_loss.detach()
+
+    if cfg.compile.compile:
+        update = torch.compile(update, mode=compile_mode)
+    if cfg.compile.cudagraphs:
+        warnings.warn(
+            "CudaGraphModule is experimental and may lead to silently wrong results. Use with caution.",
+            category=UserWarning,
+        )
+        update = CudaGraphModule(update, warmup=50)
+
+    # Training loop
+    collected_frames = 0
+    pbar = tqdm.tqdm(total=cfg.ppo.collector.total_frames)
+
+    # extract cfg variables
+    cfg_loss_ppo_epochs = cfg.ppo.loss.ppo_epochs
+    cfg_optim_anneal_lr = cfg.ppo.optim.anneal_lr
+    cfg_optim_lr = cfg.ppo.optim.lr
+    cfg_loss_anneal_clip_eps = cfg.ppo.loss.anneal_clip_epsilon
+    cfg_loss_clip_epsilon = cfg.ppo.loss.clip_epsilon
+    cfg_logger_test_interval = cfg.logger.test_interval
+    cfg_logger_num_test_episodes = cfg.logger.num_test_episodes
+
+    for i, data in enumerate(collector):
+
+        log_info = {}
+        frames_in_batch = data.numel()
+        collected_frames += frames_in_batch
+        pbar.update(data.numel())
+
+        # Update discriminator
+        # Get expert data
+        expert_data = replay_buffer.sample()
+        expert_data = expert_data.to(device)
+
+        d_loss = update(data, expert_data)
+
+        # Get training rewards and episode lengths
+        episode_rewards = data["next", "episode_reward"][data["next", "done"]]
+        if len(episode_rewards) > 0:
+            episode_length = data["next", "step_count"][data["next", "done"]]
+
+            log_info.update(
+                {
+                    "train/reward": episode_rewards.mean().item(),
+                    "train/episode_length": episode_length.sum().item()
+                                            / len(episode_length),
+                }
+            )
 
         log_info.update(
             {
-                "train/actor_loss": actor_loss.item(),
-                "train/critic_loss": critic_loss.item(),
-                "train/discriminator_loss": d_loss["loss"].item(),
+                # "train/actor_loss": actor_loss.item(),
+                # "train/critic_loss": critic_loss.item(),
+                "train/discriminator_loss": d_loss["loss"],
                 "train/lr": alpha * cfg_optim_lr,
                 "train/clip_epsilon": (
                     alpha * cfg_loss_clip_epsilon

sota-implementations/gail/ppo_utils.py

@@ -42,7 +42,7 @@ def make_env(env_name="HalfCheetah-v4", device="cpu", from_pixels: bool = False)
 # --------------------------------------------------------------------
 
 
-def make_ppo_models_state(proof_environment):
+def make_ppo_models_state(proof_environment, compile):
 
     # Define input shape
     input_shape = proof_environment.observation_spec["observation"].shape
@@ -54,6 +54,7 @@ def make_ppo_models_state(proof_environment):
         "low": proof_environment.action_spec_unbatched.space.low,
         "high": proof_environment.action_spec_unbatched.space.high,
         "tanh_loc": False,
+        "safe_tanh": not compile,
     }
 
     # Define policy architecture
@@ -116,9 +117,9 @@ def make_ppo_models_state(proof_environment):
     return policy_module, value_module
 
 
-def make_ppo_models(env_name):
+def make_ppo_models(env_name, compile):
     proof_environment = make_env(env_name, device="cpu")
-    actor, critic = make_ppo_models_state(proof_environment)
+    actor, critic = make_ppo_models_state(proof_environment, compile=compile)
     return actor, critic