fix rollout policy revision loading

Co-authored-by: RaviTeja-Kondeti <rkondet3@asu.edu>

src/lerobot/policies/factory.py

@@ -177,6 +177,7 @@ def make_pre_post_processors(
             return make_groot_pre_post_processors_from_pretrained(
                 config=policy_cfg,
                 pretrained_path=pretrained_path,
+                revision=pretrained_revision,
                 dataset_stats=kwargs.get("dataset_stats"),
                 dataset_meta=kwargs.get("dataset_meta"),
                 preprocessor_overrides=kwargs.get("preprocessor_overrides"),

src/lerobot/policies/groot/processor_groot.py

@@ -475,6 +475,7 @@ def make_groot_pre_post_processors_from_pretrained(
     config: GrootConfig,
     pretrained_path: str,
     *,
+    revision: str | None = None,
     dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
     dataset_meta: Any | None = None,
     preprocessor_overrides: dict[str, Any] | None = None,
@@ -511,6 +512,7 @@ def make_groot_pre_post_processors_from_pretrained(
 
     preprocessor, postprocessor = _load_groot_processor_pipelines(
         pretrained_path,
+        revision=revision,
         preprocessor_overrides=preprocessor_overrides,
         postprocessor_overrides=postprocessor_overrides,
         preprocessor_config_filename=preprocessor_config_filename,
@@ -526,6 +528,7 @@ def make_groot_pre_post_processors_from_pretrained(
 def _load_groot_processor_pipelines(
     pretrained_path: str,
     *,
+    revision: str | None,
     preprocessor_overrides: dict[str, Any],
     postprocessor_overrides: dict[str, Any],
     preprocessor_config_filename: str,
@@ -540,6 +543,7 @@ def _load_groot_processor_pipelines(
     preprocessor = PolicyProcessorPipeline.from_pretrained(
         pretrained_model_name_or_path=pretrained_path,
         config_filename=preprocessor_config_filename,
+        revision=revision,
         overrides=preprocessor_overrides,
         to_transition=batch_to_transition,
         to_output=transition_to_batch,
@@ -547,6 +551,7 @@ def _load_groot_processor_pipelines(
     postprocessor = PolicyProcessorPipeline.from_pretrained(
         pretrained_model_name_or_path=pretrained_path,
         config_filename=postprocessor_config_filename,
+        revision=revision,
         overrides=postprocessor_overrides,
         to_transition=policy_action_to_transition,
         to_output=transition_to_policy_action,

src/lerobot/rollout/configs.py

@@ -326,8 +326,17 @@ class RolloutConfig:
 
         policy_path = parser.get_path_arg("policy")
         if policy_path:
-            cli_overrides = parser.get_cli_overrides("policy")
-            self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides)
+            yaml_overrides = parser.get_yaml_overrides("policy")
+            cli_overrides = parser.get_cli_overrides("policy") or []
+            policy_overrides = yaml_overrides + cli_overrides
+            pretrained_revision = parser.parse_arg("pretrained_revision", cli_overrides)
+            if pretrained_revision is None:
+                pretrained_revision = parser.parse_arg("pretrained_revision", yaml_overrides)
+            self.policy = PreTrainedConfig.from_pretrained(
+                policy_path,
+                revision=pretrained_revision,
+                cli_overrides=policy_overrides,
+            )
             self.policy.pretrained_path = policy_path
         if self.policy is None:
             raise ValueError("--policy.path is required for rollout")

src/lerobot/rollout/context.py

@@ -27,7 +27,7 @@ from threading import Event
 
 import torch
 
-from lerobot.configs import FeatureType
+from lerobot.configs import FeatureType, PreTrainedConfig
 from lerobot.datasets import (
     LeRobotDataset,
     aggregate_pipeline_dataset_features,
@@ -159,6 +159,35 @@ class RolloutContext:
 # ---------------------------------------------------------------------------
 
 
+def _load_pretrained_policy(policy_config: PreTrainedConfig) -> PreTrainedPolicy:
+    """Load policy weights, keeping adapter and base-model revisions independent."""
+    pretrained_revision = policy_config.pretrained_revision
+    policy_class = get_policy_class(policy_config.type)
+
+    if not policy_config.use_peft:
+        return policy_class.from_pretrained(
+            policy_config.pretrained_path,
+            config=policy_config,
+            revision=pretrained_revision,
+        )
+
+    from peft import PeftConfig, PeftModel
+
+    peft_path = policy_config.pretrained_path
+    peft_config = PeftConfig.from_pretrained(peft_path, revision=pretrained_revision)
+    policy = policy_class.from_pretrained(
+        pretrained_name_or_path=peft_config.base_model_name_or_path,
+        config=policy_config,
+        revision=peft_config.revision,
+    )
+    return PeftModel.from_pretrained(
+        policy,
+        peft_path,
+        config=peft_config,
+        revision=pretrained_revision,
+    )
+
+
 def build_rollout_context(
     cfg: RolloutConfig,
     shutdown_event: Event,
@@ -176,7 +205,6 @@ def build_rollout_context(
     # --- 1. Policy (heavy I/O, but no hardware yet) -------------------
     logger.info("Loading policy from '%s'...", cfg.policy.pretrained_path)
     policy_config = cfg.policy
-    policy_class = get_policy_class(policy_config.type)
 
     if hasattr(policy_config, "compile_model"):
         policy_config.compile_model = cfg.use_torch_compile
@@ -187,17 +215,7 @@ def build_rollout_context(
             "Please use `cpu` or `cuda` backend."
         )
 
-    if policy_config.use_peft:
-        from peft import PeftConfig, PeftModel
-
-        peft_path = policy_config.pretrained_path
-        peft_config = PeftConfig.from_pretrained(peft_path)
-        policy = policy_class.from_pretrained(
-            pretrained_name_or_path=peft_config.base_model_name_or_path, config=policy_config
-        )
-        policy = PeftModel.from_pretrained(policy, peft_path, config=peft_config)
-    else:
-        policy = policy_class.from_pretrained(policy_config.pretrained_path, config=policy_config)
+    policy = _load_pretrained_policy(policy_config)
 
     if is_rtc:
         policy.config.rtc_config = cfg.inference.rtc
@@ -392,6 +410,7 @@ def build_rollout_context(
     preprocessor, postprocessor = make_pre_post_processors(
         policy_cfg=policy_config,
         pretrained_path=cfg.policy.pretrained_path,
+        pretrained_revision=policy_config.pretrained_revision,
         dataset_stats=dataset_stats,
         preprocessor_overrides={
             "device_processor": {"device": cfg.device},

tests/test_rollout.py

@@ -17,6 +17,8 @@
 from __future__ import annotations
 
 import dataclasses
+import sys
+from types import SimpleNamespace
 from unittest.mock import MagicMock
 
 import pytest
@@ -106,6 +108,109 @@ def test_sentry_config_defaults():
     assert cfg.target_video_file_size_mb is None
 
 
+def test_rollout_config_passes_policy_pretrained_revision(monkeypatch):
+    from lerobot.configs import PreTrainedConfig, parser
+    from lerobot.rollout import RolloutConfig
+    from tests.mocks.mock_robot import MockRobotConfig
+
+    captured = {}
+
+    def fake_from_pretrained(cls, pretrained_name_or_path, **kwargs):
+        captured["pretrained_name_or_path"] = pretrained_name_or_path
+        captured.update(kwargs)
+        return SimpleNamespace(device="cpu", pretrained_revision=kwargs["revision"])
+
+    monkeypatch.setattr(parser, "get_yaml_overrides", lambda _: ["--pretrained_revision=yaml-sha"])
+    monkeypatch.setattr(
+        sys,
+        "argv",
+        ["lerobot-rollout", "--policy.path=user/policy", "--policy.pretrained_revision=cli-sha"],
+    )
+    monkeypatch.setattr(PreTrainedConfig, "from_pretrained", classmethod(fake_from_pretrained))
+
+    cfg = RolloutConfig(robot=MockRobotConfig())
+
+    assert captured["pretrained_name_or_path"] == "user/policy"
+    assert captured["revision"] == "cli-sha"
+    assert captured["cli_overrides"] == [
+        "--pretrained_revision=yaml-sha",
+        "--pretrained_revision=cli-sha",
+    ]
+    assert cfg.policy.pretrained_path == "user/policy"
+    assert cfg.policy.pretrained_revision == "cli-sha"
+
+
+def test_load_pretrained_policy_passes_revision(monkeypatch):
+    import lerobot.rollout.context as rollout_context
+
+    policy_config = SimpleNamespace(
+        type="mock",
+        use_peft=False,
+        pretrained_path="user/policy",
+        pretrained_revision="policy-sha",
+    )
+    policy_class = MagicMock()
+    loaded_policy = MagicMock()
+    policy_class.from_pretrained.return_value = loaded_policy
+    monkeypatch.setattr(rollout_context, "get_policy_class", lambda _: policy_class)
+
+    policy = rollout_context._load_pretrained_policy(policy_config)
+
+    assert policy is loaded_policy
+    policy_class.from_pretrained.assert_called_once_with(
+        "user/policy",
+        config=policy_config,
+        revision="policy-sha",
+    )
+
+
+def test_load_pretrained_peft_policy_keeps_adapter_and_base_revisions_separate(monkeypatch):
+    import lerobot.rollout.context as rollout_context
+
+    policy_config = SimpleNamespace(
+        type="mock",
+        use_peft=True,
+        pretrained_path="user/adapter",
+        pretrained_revision="adapter-sha",
+    )
+    policy_class = MagicMock()
+    base_policy = MagicMock()
+    policy_class.from_pretrained.return_value = base_policy
+    monkeypatch.setattr(rollout_context, "get_policy_class", lambda _: policy_class)
+
+    peft_config = SimpleNamespace(
+        base_model_name_or_path="user/base-policy",
+        revision="base-sha",
+    )
+    peft_config_from_pretrained = MagicMock(return_value=peft_config)
+    adapted_policy = MagicMock()
+    peft_model_from_pretrained = MagicMock(return_value=adapted_policy)
+    monkeypatch.setitem(
+        sys.modules,
+        "peft",
+        SimpleNamespace(
+            PeftConfig=SimpleNamespace(from_pretrained=peft_config_from_pretrained),
+            PeftModel=SimpleNamespace(from_pretrained=peft_model_from_pretrained),
+        ),
+    )
+
+    policy = rollout_context._load_pretrained_policy(policy_config)
+
+    assert policy is adapted_policy
+    peft_config_from_pretrained.assert_called_once_with("user/adapter", revision="adapter-sha")
+    policy_class.from_pretrained.assert_called_once_with(
+        pretrained_name_or_path="user/base-policy",
+        config=policy_config,
+        revision="base-sha",
+    )
+    peft_model_from_pretrained.assert_called_once_with(
+        base_policy,
+        "user/adapter",
+        config=peft_config,
+        revision="adapter-sha",
+    )
+
+
 # ---------------------------------------------------------------------------
 # RolloutRingBuffer
 # ---------------------------------------------------------------------------