update

Signed-off-by: Jade Choghari <chogharijade@gmail.com>

benchmarks/video/smolvla_bench.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python
 """
-Minimal SmolVLA inference + benchmarking.
+Minimal Policy inference + benchmarking.
 
 Features:
 - End-to-end pipeline: dataset -> pre/post-processors -> policy.select_action
@@ -26,8 +26,8 @@ import psutil
 
 from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
 from lerobot.policies.factory import make_policy, make_policy_config
-from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy
+from lerobot.policies.pretrained import PreTrainedPolicy
-from lerobot.policies.smolvla.processor_smolvla import make_smolvla_pre_post_processors
+from lerobot.policies.factory import make_pre_post_processors
 
 
 def bytes_to_human(n: int) -> str:
@@ -64,64 +64,65 @@ def main():
     parser.add_argument("--forwards_per_trial", type=int, default=1, help="Number of forwards per trial")
     parser.add_argument("--warmup", type=int, default=20, help="Warmup forwards (not timed)")
     parser.add_argument("--print_each_trial", action="store_true", help="Print each trial's aggregate time")
+    parser.add_argument("--policy_type", type=str, default="smolvla", help="Type of policy to benchmark")
     args = parser.parse_args()
 
-    # seed & deterministic-ish setup
+    # Seed & deterministic-ish setup
     torch.manual_seed(args.seed)
     torch.cuda.manual_seed_all(args.seed)
     torch.backends.cudnn.benchmark = False
     torch.backends.cudnn.deterministic = False  # leave False to avoid perf cliffs
 
-    # device
+    # Device
     use_cuda = args.device == "cuda" and torch.cuda.is_available()
     device = "cuda" if use_cuda else "cpu"
     if args.device == "cuda" and not use_cuda:
         print("[!] CUDA requested but unavailable. Falling back to CPU.")
 
-    # load dataset metadata
+    # Load dataset metadata
     ds_meta = LeRobotDatasetMetadata(args.repo_id)
 
-    # policy config & creation
+    # Policy config & creation
     cfg = make_policy_config(
-        "smolvla",
+        args.policy_type,
         n_obs_steps=args.n_obs_steps,
-        chunk_size=args.chunk_size,
+        chunk_size=args.chunk_size, # comment this if policy_type = "diffusion"
         n_action_steps=args.n_action_steps,
         device=device,
     )
 
-    policy: SmolVLAPolicy = make_policy(cfg, ds_meta=ds_meta)
+    policy: PreTrainedPolicy = make_policy(cfg, ds_meta=ds_meta)
     policy.eval()
     policy.to(device)
 
     # Pre/post processors
-    preprocessor, postprocessor = make_smolvla_pre_post_processors(cfg, dataset_stats=ds_meta.stats)
+    preprocessor, postprocessor = make_pre_post_processors(cfg, dataset_stats=ds_meta.stats)
 
-    # dataset sample
+    # Dataset sample
     dataset = LeRobotDataset(args.repo_id, episodes=[args.episode])
     sample = dataset[args.sample_index]
 
-    # preprocess once; we will reuse the same batch for all forwards (typical for latency bench)
+    # Preprocess once; we will reuse the same batch for all forwards (typical for latency bench)
     preprocessed_batch = preprocessor(sample)
 
-    # helper to sync for fair timings
+    # Helper to sync for fair timings
     def _sync():
         if use_cuda:
             torch.cuda.synchronize()
 
-    # warmup (to stabilize kernels/caches)
+    # Warmup (to stabilize kernels/caches)
     with torch.no_grad():
         for _ in range(args.warmup):
             _ = policy.select_action(preprocessed_batch)
         _sync()
 
-    # memory footprint before timing
+    # Memory footprint before timing
     process = psutil.Process(os.getpid())
     rss_before = process.memory_info().rss
     if use_cuda:
         torch.cuda.reset_peak_memory_stats()
 
-    # timing
+    # Timing
     trial_times_sec: List[float] = []
 
     with torch.no_grad():
@@ -138,17 +139,17 @@ def main():
                 print(f"[trial {t+1:03d}] total {trial_dur*1000:.3f} ms "
                       f"({(trial_dur/args.forwards_per_trial)*1000:.3f} ms/forward)")
 
-    # memory footprint after timing
+    # Memory footprint after timing
     rss_after = process.memory_info().rss
     rss_delta = rss_after - rss_before
     cuda_peak = torch.cuda.max_memory_allocated() if use_cuda else 0
 
-    # do a single real inference and postprocess to verify everything still works
+    # Do a single real inference and postprocess to verify everything still works
     with torch.no_grad():
         action = policy.select_action(preprocessed_batch)
     postprocessed_action = postprocessor(action)
 
-    # summaries
+    # Summaries
     # Per-forward latencies in ms
     per_forward_ms = [(d / args.forwards_per_trial) * 1000.0 for d in trial_times_sec]
     per_forward_ms_sorted = sorted(per_forward_ms)
@@ -160,10 +161,10 @@ def main():
     p50_ms = percentile(per_forward_ms_sorted, 50)
     p95_ms = percentile(per_forward_ms_sorted, 95)
 
-    # model size
+    # Model size
     num_params = sum(p.numel() for p in policy.parameters())
 
-    print("\n=== SmolVLA Inference Benchmark ===")
+    print("\n=== Inference Benchmark for ===", args.policy_type)
     print(f"Device: {device}")
     print(f"Trials: {args.num_trials} | Forwards/Trial: {args.forwards_per_trial} | Warmup: {args.warmup}")
     print(f"Model params: {num_params:,}")