feat(pi05): implement Classifier-Free Guidance (CFG) inference

Add dual-path denoising with configurable cfg_beta scale for language-
conditioned action generation. When cfg_beta > 1.0, VLM prefills both
conditioned and unconditional prompts, and action expert velocities are
interpolated via v = v_uncond + β*(v_cond - v_uncond).

tests/policies/pi0_pi05/test_pi05_cfg.py

@@ -0,0 +1,224 @@
+#!/usr/bin/env python
+
+"""Tests for PI05 Classifier-Free Guidance (CFG) inference."""
+
+import pytest
+
+pytest.importorskip("transformers", reason="transformers is required for PI05")
+
+import torch  # noqa: E402
+
+from lerobot.configs.types import FeatureType, PolicyFeature  # noqa: E402
+from lerobot.policies.pi05 import PI05Config, make_pi05_pre_post_processors  # noqa: E402
+from lerobot.processor.converters import create_transition  # noqa: E402
+from lerobot.processor.rendered_messages_to_task import RenderedMessagesToTaskStep  # noqa: E402
+from lerobot.types import TransitionKey  # noqa: E402
+from lerobot.utils.constants import (  # noqa: E402
+    OBS_LANGUAGE_ATTENTION_MASK,
+    OBS_LANGUAGE_TOKENS,
+    OBS_LANGUAGE_UNCOND_ATTENTION_MASK,
+    OBS_LANGUAGE_UNCOND_TOKENS,
+)
+
+
+class TestRenderedMessagesToTaskBaseTaskPreservation:
+    """Tests that RenderedMessagesToTaskStep preserves base_task for CFG."""
+
+    def test_preserves_string_base_task(self):
+        transition = create_transition(
+            complementary_data={
+                "task": "pick up the cup",
+                "messages": [
+                    {"role": "user", "content": "pick up the cup, Advantage: positive"},
+                ],
+            }
+        )
+        step = RenderedMessagesToTaskStep()
+        out = step(transition)
+        data = out[TransitionKey.COMPLEMENTARY_DATA]
+
+        assert data["base_task"] == "pick up the cup"
+        assert data["task"] == "pick up the cup, Advantage: positive"
+
+    def test_preserves_list_base_task(self):
+        transition = create_transition(
+            complementary_data={
+                "task": ["task1", "task2"],
+                "messages": [
+                    {"role": "user", "content": "rendered with advantage"},
+                ],
+            }
+        )
+        step = RenderedMessagesToTaskStep()
+        out = step(transition)
+        data = out[TransitionKey.COMPLEMENTARY_DATA]
+
+        assert data["base_task"] == ["task1", "task2"]
+
+    def test_no_base_task_when_messages_absent(self):
+        transition = create_transition(complementary_data={"task": "pick up the cup"})
+        step = RenderedMessagesToTaskStep()
+        out = step(transition)
+        data = out[TransitionKey.COMPLEMENTARY_DATA]
+
+        assert "base_task" not in data
+
+
+class TestPi05PrepareStateTokenizerCfg:
+    """Tests for Pi05PrepareStateTokenizerProcessorStep with cfg_enabled."""
+
+    def _make_transition(self, task, base_task=None):
+        complementary_data = {"task": task}
+        if base_task is not None:
+            complementary_data["base_task"] = base_task
+        return create_transition(
+            observation={"observation.state": torch.zeros(1, 14)},
+            complementary_data=complementary_data,
+        )
+
+    def test_cfg_disabled_no_uncond_task(self):
+        from lerobot.policies.pi05.processor_pi05 import Pi05PrepareStateTokenizerProcessorStep
+
+        step = Pi05PrepareStateTokenizerProcessorStep(max_state_dim=14, cfg_enabled=False)
+        transition = self._make_transition(task=["pick up the cup, Advantage: positive"])
+        out = step(transition)
+        data = out[TransitionKey.COMPLEMENTARY_DATA]
+
+        assert "uncond_task" not in data
+
+    def test_cfg_enabled_produces_uncond_task_from_base(self):
+        from lerobot.policies.pi05.processor_pi05 import Pi05PrepareStateTokenizerProcessorStep
+
+        step = Pi05PrepareStateTokenizerProcessorStep(max_state_dim=14, cfg_enabled=True)
+        transition = self._make_transition(
+            task=["pick up the cup, Advantage: positive"],
+            base_task=["pick up the cup"],
+        )
+        out = step(transition)
+        data = out[TransitionKey.COMPLEMENTARY_DATA]
+
+        assert "uncond_task" in data
+        assert len(data["uncond_task"]) == 1
+        # Unconditional prompt uses base_task (no advantage)
+        assert "Advantage" not in data["uncond_task"][0]
+        assert "pick up the cup" in data["uncond_task"][0]
+        assert "State:" in data["uncond_task"][0]
+
+    def test_cfg_enabled_falls_back_to_task_when_no_base(self):
+        from lerobot.policies.pi05.processor_pi05 import Pi05PrepareStateTokenizerProcessorStep
+
+        step = Pi05PrepareStateTokenizerProcessorStep(max_state_dim=14, cfg_enabled=True)
+        transition = self._make_transition(task=["pick up the cup"])
+        out = step(transition)
+        data = out[TransitionKey.COMPLEMENTARY_DATA]
+
+        # Falls back to using task itself as unconditional
+        assert "uncond_task" in data
+        assert "pick up the cup" in data["uncond_task"][0]
+
+
+class TestCfgPipelineConstruction:
+    """Tests that the processor pipeline is constructed correctly for CFG."""
+
+    def _make_config(self, cfg_beta=1.0, recipe_path=None):
+        config = PI05Config(
+            max_action_dim=7,
+            max_state_dim=14,
+            cfg_beta=cfg_beta,
+            recipe_path=recipe_path,
+            device="cpu",
+        )
+        config.input_features = {
+            "observation.state": PolicyFeature(type=FeatureType.STATE, shape=(14,)),
+            "observation.images.base_0_rgb": PolicyFeature(type=FeatureType.VISUAL, shape=(3, 224, 224)),
+        }
+        config.output_features = {
+            "action": PolicyFeature(type=FeatureType.ACTION, shape=(7,)),
+        }
+        return config
+
+    def _make_dataset_stats(self):
+        return {
+            "observation.state": {
+                "mean": torch.zeros(14),
+                "std": torch.ones(14),
+                "min": torch.zeros(14),
+                "max": torch.ones(14),
+                "q01": torch.zeros(14),
+                "q99": torch.ones(14),
+            },
+            "action": {
+                "mean": torch.zeros(7),
+                "std": torch.ones(7),
+                "min": torch.zeros(7),
+                "max": torch.ones(7),
+                "q01": torch.zeros(7),
+                "q99": torch.ones(7),
+            },
+            "observation.images.base_0_rgb": {
+                "mean": torch.zeros(3, 224, 224),
+                "std": torch.ones(3, 224, 224),
+                "q01": torch.zeros(3, 224, 224),
+                "q99": torch.ones(3, 224, 224),
+            },
+        }
+
+    def test_no_uncond_tokenizer_when_cfg_disabled(self):
+        from lerobot.processor import TokenizerProcessorStep
+
+        config = self._make_config(cfg_beta=1.0)
+        preprocessor, _ = make_pi05_pre_post_processors(config, self._make_dataset_stats())
+
+        tokenizer_steps = [s for s in preprocessor.steps if isinstance(s, TokenizerProcessorStep)]
+        assert len(tokenizer_steps) == 1
+
+    def test_uncond_tokenizer_added_when_cfg_enabled(self):
+        from lerobot.processor import TokenizerProcessorStep
+
+        config = self._make_config(cfg_beta=2.0)
+        preprocessor, _ = make_pi05_pre_post_processors(config, self._make_dataset_stats())
+
+        tokenizer_steps = [s for s in preprocessor.steps if isinstance(s, TokenizerProcessorStep)]
+        assert len(tokenizer_steps) == 2
+
+        uncond_tokenizer = tokenizer_steps[1]
+        assert uncond_tokenizer.task_key == "uncond_task"
+        assert uncond_tokenizer.output_tokens_key == OBS_LANGUAGE_UNCOND_TOKENS
+        assert uncond_tokenizer.output_mask_key == OBS_LANGUAGE_UNCOND_ATTENTION_MASK
+
+    def test_cfg_pipeline_produces_both_token_sets(self):
+        config = self._make_config(cfg_beta=2.0)
+        preprocessor, _ = make_pi05_pre_post_processors(config, self._make_dataset_stats())
+
+        batch = {
+            "observation.state": torch.randn(14),
+            "observation.images.base_0_rgb": torch.rand(3, 224, 224),
+            "task": "pick up the cup",
+        }
+        processed = preprocessor(batch)
+
+        assert OBS_LANGUAGE_TOKENS in processed
+        assert OBS_LANGUAGE_ATTENTION_MASK in processed
+        assert OBS_LANGUAGE_UNCOND_TOKENS in processed
+        assert OBS_LANGUAGE_UNCOND_ATTENTION_MASK in processed
+
+        # Both should be tensors with the same shape
+        assert processed[OBS_LANGUAGE_TOKENS].shape == processed[OBS_LANGUAGE_UNCOND_TOKENS].shape
+        assert (
+            processed[OBS_LANGUAGE_ATTENTION_MASK].shape
+            == processed[OBS_LANGUAGE_UNCOND_ATTENTION_MASK].shape
+        )
+
+    def test_cfg_beta_1_no_uncond_tokens_in_output(self):
+        config = self._make_config(cfg_beta=1.0)
+        preprocessor, _ = make_pi05_pre_post_processors(config, self._make_dataset_stats())
+
+        batch = {
+            "observation.state": torch.randn(14),
+            "observation.images.base_0_rgb": torch.rand(3, 224, 224),
+            "task": "pick up the cup",
+        }
+        processed = preprocessor(batch)
+
+        assert OBS_LANGUAGE_TOKENS in processed
+        assert OBS_LANGUAGE_UNCOND_TOKENS not in processed