[Feature] DQN compatibility with compile

sota-implementations/dqn/config_atari.yaml

@@ -39,3 +39,8 @@ loss:
   gamma: 0.99
   hard_update_freq: 10_000
   num_updates: 1
+
+compile:
+  compile: False
+  compile_mode:
+  cudagraphs: False

sota-implementations/dqn/config_cartpole.yaml

@@ -38,3 +38,8 @@ loss:
   gamma: 0.99
   hard_update_freq: 50
   num_updates: 1
+
+compile:
+  compile: False
+  compile_mode:
+  cudagraphs: False

sota-implementations/dqn/dqn_atari.py

@@ -8,14 +8,14 @@
 Deep Q-Learning Algorithm on Atari Environments.
 """
 import tempfile
-import time
+import warnings
 
 import hydra
 import torch.nn
 import torch.optim
 import tqdm
-from tensordict.nn import TensorDictSequential
-from torchrl._utils import logger as torchrl_logger
+from tensordict.nn import CudaGraphModule, TensorDictSequential
+from torchrl._utils import timeit
 
 from torchrl.collectors import SyncDataCollector
 from torchrl.data import LazyMemmapStorage, TensorDictReplayBuffer
@@ -58,18 +58,6 @@ def main(cfg: "DictConfig"):  # noqa: F821
         greedy_module,
     ).to(device)
 
-    # Create the collector
-    collector = SyncDataCollector(
-        create_env_fn=make_env(cfg.env.env_name, frame_skip, device),
-        policy=model_explore,
-        frames_per_batch=frames_per_batch,
-        total_frames=total_frames,
-        device=device,
-        storing_device=device,
-        max_frames_per_traj=-1,
-        init_random_frames=init_random_frames,
-    )
-
     # Create the replay buffer
     if cfg.buffer.scratch_dir is None:
         tempdir = tempfile.TemporaryDirectory()
@@ -127,25 +115,68 @@ def main(cfg: "DictConfig"):  # noqa: F821
         )
     test_env.eval()
 
+    def update(sampled_tensordict):
+        loss_td = loss_module(sampled_tensordict)
+        q_loss = loss_td["loss"]
+        optimizer.zero_grad()
+        q_loss.backward()
+        torch.nn.utils.clip_grad_norm_(
+            list(loss_module.parameters()), max_norm=max_grad
+        )
+        optimizer.step()
+        target_net_updater.step()
+        return q_loss.detach()
+
+    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"
+        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)
+
+    # Create the collector
+    collector = SyncDataCollector(
+        create_env_fn=make_env(cfg.env.env_name, frame_skip, device),
+        policy=model_explore,
+        frames_per_batch=frames_per_batch,
+        total_frames=total_frames,
+        device=device,
+        storing_device=device,
+        max_frames_per_traj=-1,
+        init_random_frames=init_random_frames,
+        compile_policy={"mode": compile_mode} if compile_mode is not None else False,
+        cudagraph_policy=cfg.compile.cudagraphs,
+    )
+
     # Main loop
     collected_frames = 0
-    start_time = time.time()
-    sampling_start = time.time()
     num_updates = cfg.loss.num_updates
     max_grad = cfg.optim.max_grad_norm
     num_test_episodes = cfg.logger.num_test_episodes
     q_losses = torch.zeros(num_updates, device=device)
     pbar = tqdm.tqdm(total=total_frames)
-    for i, data in enumerate(collector):
 
+    c_iter = iter(collector)
+    for i in range(len(collector)):
+        with timeit("collecting"):
+            data = next(c_iter)
         log_info = {}
-        sampling_time = time.time() - sampling_start
         pbar.update(data.numel())
         data = data.reshape(-1)
         current_frames = data.numel() * frame_skip
         collected_frames += current_frames
         greedy_module.step(current_frames)
-        replay_buffer.extend(data)
+        with timeit("rb - extend"):
+            replay_buffer.extend(data)
 
         # Get and log training rewards and episode lengths
         episode_rewards = data["next", "episode_reward"][data["next", "done"]]
@@ -167,24 +198,13 @@ def main(cfg: "DictConfig"):  # noqa: F821
             continue
 
         # optimization steps
-        training_start = time.time()
         for j in range(num_updates):
-
-            sampled_tensordict = replay_buffer.sample()
-            sampled_tensordict = sampled_tensordict.to(device)
-
-            loss_td = loss_module(sampled_tensordict)
-            q_loss = loss_td["loss"]
-            optimizer.zero_grad()
-            q_loss.backward()
-            torch.nn.utils.clip_grad_norm_(
-                list(loss_module.parameters()), max_norm=max_grad
-            )
-            optimizer.step()
-            target_net_updater.step()
-            q_losses[j].copy_(q_loss.detach())
-
-        training_time = time.time() - training_start
+            with timeit("rb - sample"):
+                sampled_tensordict = replay_buffer.sample()
+                sampled_tensordict = sampled_tensordict.to(device)
+            with timeit("update"):
+                q_loss = update(sampled_tensordict)
+            q_losses[j].copy_(q_loss)
 
         # Get and log q-values, loss, epsilon, sampling time and training time
         log_info.update(
@@ -193,48 +213,45 @@ def main(cfg: "DictConfig"):  # noqa: F821
                 / frames_per_batch,
                 "train/q_loss": q_losses.mean().item(),
                 "train/epsilon": greedy_module.eps,
-                "train/sampling_time": sampling_time,
-                "train/training_time": training_time,
             }
         )
 
         # Get and log evaluation rewards and eval time
-        with torch.no_grad(), set_exploration_type(ExplorationType.DETERMINISTIC):
+        with torch.no_grad(), set_exploration_type(
+            ExplorationType.DETERMINISTIC
+        ), timeit("eval"):
             prev_test_frame = ((i - 1) * frames_per_batch) // test_interval
             cur_test_frame = (i * frames_per_batch) // test_interval
             final = current_frames >= collector.total_frames
             if (i >= 1 and (prev_test_frame < cur_test_frame)) or final:
                 model.eval()
-                eval_start = time.time()
                 test_rewards = eval_model(
                     model, test_env, num_episodes=num_test_episodes
                 )
-                eval_time = time.time() - eval_start
                 log_info.update(
                     {
                         "eval/reward": test_rewards,
-                        "eval/eval_time": eval_time,
                     }
                 )
                 model.train()
 
+        if i % 200 == 0:
+            timeit.print()
+            log_info.update(timeit.todict(prefix="time"))
+            timeit.erase()
+
         # Log all the information
         if logger:
             for key, value in log_info.items():
                 logger.log_scalar(key, value, step=collected_frames)
 
         # update weights of the inference policy
         collector.update_policy_weights_()
-        sampling_start = time.time()
 
     collector.shutdown()
     if not test_env.is_closed:
         test_env.close()
 
-    end_time = time.time()
-    execution_time = end_time - start_time
-    torchrl_logger.info(f"Training took {execution_time:.2f} seconds to finish")
-
 
 if __name__ == "__main__":
     main()