Puffer speed up

baselines/ippo/ippo_pufferlib.py

@@ -15,6 +15,8 @@
 import numpy as np
 import wandb
 from box import Box
+import time
+import random
 
 from integrations.rl.puffer import ppo
 from integrations.rl.puffer.puffer_env import env_creator
@@ -32,6 +34,73 @@
 
 app = Typer()
 
+def log_normal(mean, scale, clip):
+    '''Samples normally spaced points on a log 10 scale.
+    mean: Your center sample point
+    scale: standard deviation in base 10 orders of magnitude
+    clip: maximum standard deviations
+
+    Example: mean=0.001, scale=1, clip=2 will produce data from
+    0.1 to 0.00001 with most of it between 0.01 and 0.0001
+    '''
+    return 10**np.clip(
+        np.random.normal(
+            np.log10(mean),
+            scale,
+        ),
+        a_min = np.log10(mean) - clip,
+        a_max = np.log10(mean) + clip,
+    )
+
+def logit_normal(mean, scale, clip):
+    '''log normal but for logit data like gamma and gae_lambda'''
+    return 1 - log_normal(1 - mean, scale, clip)
+
+def uniform_pow2(min, max):
+    '''Uniform distribution over powers of 2 between min and max inclusive'''
+    min_base = np.log2(min)
+    max_base = np.log2(max)
+    return 2**np.random.randint(min_base, max_base+1)
+
+def uniform(min, max):
+    '''Uniform distribution between min and max inclusive'''
+    return np.random.uniform(min, max)
+
+def int_uniform(min, max):
+    '''Uniform distribution between min and max inclusive'''
+    return np.random.randint(min, max+1)
+
+def sample_hyperparameters(sweep_config):
+    samples = {}
+    for name, param in sweep_config.items():
+        if name in ('method', 'name', 'metric'):
+            continue
+
+        assert isinstance(param, dict)
+        if any(isinstance(param[k], dict) for k in param):
+            samples[name] = sample_hyperparameters(param)
+        elif 'values' in param:
+            assert 'distribution' not in param
+            samples[name] = random.choice(param['values'])
+        elif 'distribution' in param:
+            if param['distribution'] == 'uniform':
+                samples[name] = uniform(param['min'], param['max'])
+            elif param['distribution'] == 'int_uniform':
+                samples[name] = int_uniform(param['min'], param['max'])
+            elif param['distribution'] == 'uniform_pow2':
+                samples[name] = uniform_pow2(param['min'], param['max'])
+            elif param['distribution'] == 'log_normal':
+                samples[name] = log_normal(
+                    param['mean'], param['scale'], param['clip'])
+            elif param['distribution'] == 'logit_normal':
+                samples[name] = logit_normal(
+                    param['mean'], param['scale'], param['clip'])
+            else:
+                raise ValueError(f'Invalid distribution: {param["distribution"]}')
+        else:
+            raise ValueError('Must specify either values or distribution')
+
+    return samples
 
 def get_model_parameters(policy):
     """Helper function to count the number of trainable parameters."""
@@ -56,42 +125,20 @@ def make_policy(env, config):
         dropout=config.train.network.dropout,
     ).to(config.train.device)
 
-
-def train(args, make_env):
+def train(args, vecenv):
     """Main training loop for the PPO agent."""
 
-    backend_mapping = {
-        # Note: Only native backend is currently supported with GPUDrive
-        "native": pufferlib.vector.Native,
-        "serial": pufferlib.vector.Serial,
-        "multiprocessing": pufferlib.vector.Multiprocessing,
-        "ray": pufferlib.vector.Ray,
-    }
-
-    backend = backend_mapping.get(args.vec.backend)
-    if not backend:
-        raise ValueError("Invalid --vec.backend.")
-
-    vecenv = pufferlib.vector.make(
-        make_env,
-        num_envs=1,  # GPUDrive is already batched
-        num_workers=args.vec.num_workers,
-        batch_size=args.vec.env_batch_size,
-        zero_copy=args.vec.zero_copy,
-        backend=backend,
-    )
-
     policy = make_policy(env=vecenv.driver_env, config=args).to(
         args.train.device
     )
 
     args.train.network.num_parameters = get_model_parameters(policy)
     args.train.env = args.environment.name
 
-    args.wandb = init_wandb(args, args.train.exp_id, id=args.train.exp_id)
-    args.train.__dict__.update(dict(args.wandb.config.train))
+    wandb_run = init_wandb(args, args.train.exp_id, id=args.train.exp_id)
+    args.train.update(dict(wandb_run.config.train))
 
-    data = ppo.create(args.train, vecenv, policy, wandb=args.wandb)
+    data = ppo.create(args.train, vecenv, policy, wandb=wandb_run)
     while data.global_step < args.train.total_timesteps:
         try:
             ppo.evaluate(data)  # Rollout
@@ -107,8 +154,24 @@ def train(args, make_env):
     ppo.evaluate(data)
     ppo.close(data)
 
+def set_experiment_metadata(config):
+    datetime_ = datetime.now().strftime("%m_%d_%H_%M_%S_%f")[:-3]
+    if config["train"]["resample_scenes"]:
+        if config["train"]["resample_scenes"]:
+            dataset_size = config["train"]["resample_dataset_size"]
+        config["train"][
+            "exp_id"
+        ] = f'PPO_R_{dataset_size}__{datetime_}'
+    else:
+        dataset_size = str(config["environment"]["k_unique_scenes"])
+        config["train"][
+            "exp_id"
+        ] = f'PPO_S_{dataset_size}__{datetime_}'
+
+    config["environment"]["dataset_size"] = dataset_size
+
 
-def init_wandb(args, name, id=None, resume=True):
+def init_wandb(args, name, id=None, resume=True, tag=None):
     wandb.init(
         id=id or wandb.util.generate_id(),
         project=args.wandb.project,
@@ -128,29 +191,6 @@ def init_wandb(args, name, id=None, resume=True):
 
     return wandb
 
-
-def sweep(args, project="PPO", sweep_name="my_sweep"):
-    """Initialize a WandB sweep with hyperparameters."""
-    sweep_id = wandb.sweep(
-        sweep=dict(
-            method="random",
-            name=sweep_name,
-            metric={"goal": "maximize", "name": "environment/episode_return"},
-            parameters={
-                "learning_rate": {
-                    "distribution": "log_uniform_values",
-                    "min": 1e-4,
-                    "max": 1e-1,
-                },
-                "batch_size": {"values": [512, 1024, 2048]},
-                "minibatch_size": {"values": [128, 256, 512]},
-            },
-        ),
-        project=project,
-    )
-    wandb.agent(sweep_id, lambda: train(args), count=100)
-
-
 @app.command()
 def run(
     config_path: Annotated[
@@ -185,6 +225,7 @@ def run(
     project: Annotated[Optional[str], typer.Option(help="WandB project name")] = None,
     entity: Annotated[Optional[str], typer.Option(help="WandB entity name")] = None,
     group: Annotated[Optional[str], typer.Option(help="WandB group name")] = None,
+    max_runs: Annotated[Optional[int], typer.Option(help="Maximum number of sweep runs")] = 100,
     render: Annotated[Optional[int], typer.Option(help="Whether to render the environment; 0 or 1")] = None,
 ):
     """Run PPO training with the given configuration."""
@@ -240,21 +281,6 @@ def run(
         {k: v for k, v in wandb_config.items() if v is not None}
     )
 
-    datetime_ = datetime.now().strftime("%m_%d_%H_%M_%S_%f")[:-3]
-
-    if config["train"]["resample_scenes"]:
-        if config["train"]["resample_scenes"]:
-            dataset_size = config["train"]["resample_dataset_size"]
-        config["train"][
-            "exp_id"
-        ] = f'{config["train"]["exp_id"]}__R_{dataset_size}__{datetime_}'
-    else:
-        dataset_size = str(config["environment"]["k_unique_scenes"])
-        config["train"][
-            "exp_id"
-        ] = f'{config["train"]["exp_id"]}__S_{dataset_size}__{datetime_}'
-
-    config["environment"]["dataset_size"] = dataset_size
     config["train"]["device"] = config["train"].get(
         "device", "cpu"
     )  # Default to 'cpu' if not set
@@ -279,11 +305,29 @@ def run(
         train_config=config.train,
         device=config.train.device,
     )
+    vecenv = pufferlib.vector.make(
+        make_env,
+        num_envs=1,  # GPUDrive is already batched
+        num_workers=config.vec.num_workers,
+        batch_size=config.vec.env_batch_size,
+        zero_copy=config.vec.zero_copy,
+        backend=pufferlib.vector.Native,
+    )
 
     if config.mode == "train":
-        train(config, make_env)
-
+        set_experiment_metadata(config)
+        train(config, vecenv)
+    elif config.mode == "sweep":
+        for i in range(max_runs):
+            np.random.seed(int(time.time()))
+            random.seed(int(time.time()))
+            set_experiment_metadata(config)
+            hypers = sample_hyperparameters(config.sweep)
+            config.train.update(hypers['train'])
+            config.environment.update(hypers['environment'])
+            train(config, vecenv)
 
 if __name__ == "__main__":
-
-    app()
+    import cProfile
+    cProfile.run('app()', 'profiled')
+    #app()

examples/experiments/ippo_ff_p1_self_play.yaml

@@ -1,14 +1,14 @@
-mode: "train"
+mode: "sweep"
 use_rnn: false
 eval_model_path: null
 baseline: false
-data_dir: "data/processed/training"
+data_dir: "data/processed/examples"
 
 environment: # Overrides default environment configs (see pygpudrive/env/config.py)
   name: "gpudrive"
-  num_worlds: 100 # Number of parallel environments
-  k_unique_scenes: 100 # Number of unique scenes to sample from
-  max_controlled_agents: 128 # Maximum number of agents controlled by the model. Make sure this aligns with the variable kMaxAgentCount in src/consts.hpp
+  num_worlds: 300 # Number of parallel environments
+  k_unique_scenes: 3 # Number of unique scenes to sample from
+  max_controlled_agents: 64 # Maximum number of agents controlled by the model. Make sure this aligns with the variable kMaxAgentCount in src/consts.hpp
   ego_state: true
   road_map_obs: true
   partner_obs: true
@@ -26,11 +26,11 @@ environment: # Overrides default environment configs (see pygpudrive/env/config.
   sampling_seed: 42 # If given, the set of scenes to sample from will be deterministic, if None, the set of scenes will be random
   obs_radius: 50.0 # Visibility radius of the agents
 wandb:
-  entity: ""
+  entity: "jsuarez"
   project: "paper_1_self_play"
   group: "verify"
   mode: "online" # Options: online, offline, disabled
-  tags: ["ppo", "ff"]
+  tags: ["ppo", "ff", "basic-sweep"]
 
 ## NOTES
 ## Good batch size: 128 * number of controlled agents (e.g. 2**18)
@@ -46,24 +46,24 @@ train:
   compile_mode: "reduce-overhead"
 
   # # # Data sampling # # #
-  resample_scenes: true
+  resample_scenes: False
   resample_criterion: "global_step"
   resample_dataset_size: 500 # Number of unique scenes to sample from
-  resample_interval: 1_000_000
+  resample_interval: 20_000_000
   resample_limit: 1000 # Resample until the limit is reached; set to a large number to continue resampling indefinitely
   sample_with_replacement: true
   shuffle_dataset: false
 
   # # # PPO # # #
   torch_deterministic: false
-  total_timesteps: 1_000_000_000
+  total_timesteps: 50_000_000
   batch_size: 131_072
-  minibatch_size: 8192
+  minibatch_size: 16384
   learning_rate: 3e-4
   anneal_lr: false
   gamma: 0.99
   gae_lambda: 0.95
-  update_epochs: 5
+  update_epochs: 1
   norm_adv: true
   clip_coef: 0.2
   clip_vloss: false
@@ -76,7 +76,7 @@ train:
   # # # Network # # #
   network:
     input_dim: 64 # Embedding of the input features
-    hidden_dim: 128 # Latent dimension
+    hidden_dim: 192 # Latent dimension
     pred_heads_arch: [64] # Arch of the prediction heads (actor and critic)
     num_transformer_layers: 0 # Number of transformer layers
     dropout: 0.01
@@ -96,6 +96,48 @@ train:
   render_format: "mp4" # Options: gif, mp4
   render_fps: 15 # Frames per second
 
+sweep:
+  train:
+    learning_rate:
+      distribution: "log_normal"
+      mean: 0.005
+      scale: 1.0
+      clip: 2.0
+
+    ent_coef:
+      distribution: "log_normal"
+      mean: 0.005
+      scale: 0.5
+      clip: 1.0
+
+    gamma:
+      distribution: "logit_normal"
+      mean: 0.98
+      scale: 0.5
+      clip: 1.0
+
+    gae_lambda:
+      distribution: "logit_normal"
+      mean: 0.95
+      scale: 0.5
+      clip: 1.0
+
+  environment:
+    collision_weight:
+      distribution: "uniform"
+      min: -1.0
+      max: 0.0
+
+    off_road_weight:
+      distribution: "uniform"
+      min: -1.0
+      max: 0.0
+
+    goal_achieved_weight:
+      distribution: "uniform"
+      min: 0.0
+      max: 1.0
+
 vec:
   backend: "native" # Only native is currently supported
   num_workers: 1

integrations/rl/puffer/ppo.py

@@ -391,7 +391,7 @@ def train(data):
 
 
 def close(data):
-    data.vecenv.close()
+    #data.vecenv.close()
     data.utilization.stop()
     config = data.config
     if data.wandb is not None: