Add Pi0 (#681)

Co-authored-by: Simon Alibert <simon.alibert@huggingface.co>
Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com>
Co-authored-by: Pablo <pablo.montalvo.leroux@gmail.com>

lerobot/common/policies/pi0/conversion_scripts/benchmark.py

@@ -0,0 +1,68 @@
+import torch
+
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.common.policies.factory import make_policy
+from lerobot.configs.policies import PreTrainedConfig
+
+torch.backends.cudnn.benchmark = True
+
+
+def main():
+    device = "cuda"
+    dataset_repo_id = "danaaubakirova/koch_test"
+    # model_name = "pi0_base"
+    # ckpt_torch_dir = Path.home() / f".cache/openpi/openpi-assets/checkpoints/{model_name}_pytorch"
+    ckpt_torch_dir = "lerobot/pi0"
+
+    dataset = LeRobotDataset(dataset_repo_id, episodes=[0])
+
+    dataloader = torch.utils.data.DataLoader(
+        dataset,
+        num_workers=0,
+        batch_size=1,
+    )
+
+    batch = next(iter(dataloader))
+
+    # To device
+    for k in batch:
+        if isinstance(batch[k], torch.Tensor):
+            batch[k] = batch[k].to(device=device, dtype=torch.float32)
+
+    cfg = PreTrainedConfig.from_pretrained(ckpt_torch_dir)
+    cfg.pretrained_path = ckpt_torch_dir
+    policy = make_policy(cfg, device, ds_meta=dataset.meta)
+
+    # policy = torch.compile(policy, mode="reduce-overhead")
+
+    warmup_iters = 10
+    benchmark_iters = 30
+
+    # Warmup
+    for _ in range(warmup_iters):
+        torch.cuda.synchronize()
+        policy.select_action(batch)
+        policy.reset()
+        torch.cuda.synchronize()
+
+    # Benchmark
+    start_event = torch.cuda.Event(enable_timing=True)
+    end_event = torch.cuda.Event(enable_timing=True)
+
+    start_event.record()
+    for _ in range(benchmark_iters):
+        policy.select_action(batch)
+        policy.reset()
+    end_event.record()
+
+    # Synchronize and measure time
+    torch.cuda.synchronize()
+    elapsed_time_ms = start_event.elapsed_time(end_event)
+
+    avg_time_per_iter = elapsed_time_ms / benchmark_iters
+    print(f"Average execution time per iteration: {avg_time_per_iter:.3f} ms")
+
+
+if __name__ == "__main__":
+    with torch.inference_mode():
+        main()