Merge branch 'main' into feat/robotwin-benchmark

tests/policies/multi_task_dit/test_multi_task_dit.py

@@ -147,6 +147,7 @@ def test_multi_task_dit_policy_forward(batch_size: int, state_dim: int, action_d
     )
 
     policy = MultiTaskDiTPolicy(config=config)
+    policy.to(config.device)
     policy.train()
 
     # Use preprocessor to handle tokenization
@@ -336,6 +337,7 @@ def test_multi_task_dit_policy_select_action(batch_size: int, state_dim: int, ac
     )
 
     policy = MultiTaskDiTPolicy(config=config)
+    policy.to(config.device)
     policy.eval()
     policy.reset()  # Reset queues before inference
 
@@ -390,6 +392,7 @@ def test_multi_task_dit_policy_diffusion_objective():
     config.validate_features()
 
     policy = MultiTaskDiTPolicy(config=config)
+    policy.to(config.device)
     policy.train()
 
     # Use preprocessor to handle tokenization
@@ -468,6 +471,7 @@ def test_multi_task_dit_policy_flow_matching_objective():
     config.validate_features()
 
     policy = MultiTaskDiTPolicy(config=config)
+    policy.to(config.device)
     policy.train()
 
     # Use preprocessor to handle tokenization
@@ -533,16 +537,12 @@ def test_multi_task_dit_policy_save_and_load(tmp_path):
     )
 
     policy = MultiTaskDiTPolicy(config=config)
+    policy.to(config.device)
     policy.eval()
 
-    # Get device before saving
-    device = next(policy.parameters()).device
-
     policy.save_pretrained(root)
     loaded_policy = MultiTaskDiTPolicy.from_pretrained(root, config=config)
-
-    # Explicitly move loaded_policy to the same device
-    loaded_policy.to(device)
+    loaded_policy.to(config.device)
     loaded_policy.eval()
 
     batch = create_train_batch(
@@ -565,10 +565,6 @@ def test_multi_task_dit_policy_save_and_load(tmp_path):
         with seeded_context(12):
             # Process batch through preprocessor
             processed_batch = preprocessor(batch)
-            # Move batch to the same device as the policy
-            for key in processed_batch:
-                if isinstance(processed_batch[key], torch.Tensor):
-                    processed_batch[key] = processed_batch[key].to(device)
             # Collect policy values before saving
             loss, _ = policy.forward(processed_batch)
 
@@ -608,6 +604,7 @@ def test_multi_task_dit_policy_get_optim_params():
     )
 
     policy = MultiTaskDiTPolicy(config=config)
+    policy.to(config.device)
     param_groups = policy.get_optim_params()
 
     # Should have 2 parameter groups: non-vision and vision encoder

tests/teleoperators/test_reachy2_teleoperator.py

@@ -18,6 +18,11 @@ from unittest.mock import MagicMock, patch
 
 import pytest
 
+from lerobot.utils.import_utils import is_package_available
+
+if not is_package_available("reachy2_sdk"):
+    pytest.skip("reachy2_sdk not available", allow_module_level=True)
+
 from lerobot.teleoperators.reachy2_teleoperator import (
     REACHY2_ANTENNAS_JOINTS,
     REACHY2_L_ARM_JOINTS,