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0d80452 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 | #!/usr/bin/env python
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""End-to-end smoke: pipeline output → canonical recipe rendering."""
from __future__ import annotations
from pathlib import Path
import pytest
# ``pyarrow`` and the ``lerobot.datasets`` chain (-> the HF ``datasets``
# library) only ship under the ``dataset`` extra. Skip this module in
# tiers without it instead of erroring at import.
pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
pytest.importorskip("pandas", reason="pandas is required (install lerobot[dataset])")
import pyarrow.parquet as pq # noqa: E402
from lerobot.annotations.steerable_pipeline.config import ( # noqa: E402
AnnotationPipelineConfig,
InterjectionsConfig,
PlanConfig,
VqaConfig,
)
from lerobot.annotations.steerable_pipeline.executor import Executor # noqa: E402
from lerobot.annotations.steerable_pipeline.modules import ( # noqa: E402
GeneralVqaModule,
InterjectionsAndSpeechModule,
PlanSubtasksMemoryModule,
)
from lerobot.annotations.steerable_pipeline.validator import StagingValidator # noqa: E402
from lerobot.annotations.steerable_pipeline.writer import LanguageColumnsWriter # noqa: E402
from lerobot.configs.recipe import MessageTurn, TrainingRecipe # noqa: E402
from lerobot.datasets.language_render import render_sample # noqa: E402
from ._helpers import make_canned_responder # noqa: E402
def _build_style_blend_recipe() -> TrainingRecipe:
"""Inline blend recipe that consumes every style this pipeline produces.
The language schema/DSL work used to ship
``src/lerobot/configs/recipes/pi05_hirobot.yaml`` as a canonical
example, but that file was dropped during review. The contract this
test guards is "the recipe DSL can render non-empty messages from
pipeline output", which doesn't require a specific YAML — so we build
the equivalent blend in code.
"""
return TrainingRecipe(
blend={
"low_level_execution": TrainingRecipe(
weight=0.35,
messages=[
MessageTurn(
role="user",
content="${task}\nPlan: ${plan}\nMemory: ${memory}",
stream="high_level",
),
MessageTurn(role="assistant", content="${subtask}", stream="low_level", target=True),
],
),
"user_interjection_response": TrainingRecipe(
weight=0.16,
bindings={
"speech": "emitted_at(t, role=assistant, tool_name=say)",
"interjection": "emitted_at(t, style=interjection)",
},
messages=[
MessageTurn(role="user", content="${task}", stream="high_level"),
MessageTurn(
role="user",
content="${interjection}",
stream="high_level",
if_present="interjection",
),
MessageTurn(
role="assistant",
content="${plan}",
stream="high_level",
target=True,
if_present="plan",
tool_calls_from="speech",
),
],
),
}
)
def _build_executor() -> Executor:
vlm = make_canned_responder(
{
"atomic subtasks": {
"subtasks": [
{"text": "grasp the bottle", "start": 0.0, "end": 0.5},
{"text": "pour into the cup", "start": 0.5, "end": 1.0},
{"text": "place the bottle down", "start": 1.0, "end": 1.5},
]
},
"compressed semantic memory": {"memory": "poured once"},
"acknowledgement the robot": {"text": "Sure."},
"compact interjection": {
"interjection": "use less water",
"speech": "Using less water.",
},
"frame-grounded visual question": {
"question": "How many cups?",
"answer": {"label": "cup", "count": 1},
},
},
)
config = AnnotationPipelineConfig(
plan=PlanConfig(),
interjections=InterjectionsConfig(max_interjections_per_episode=1, interjection_min_t=0.5),
vqa=VqaConfig(vqa_emission_hz=1.0, K=2),
)
return Executor(
config=config,
plan=PlanSubtasksMemoryModule(vlm=vlm, config=config.plan),
interjections=InterjectionsAndSpeechModule(vlm=vlm, config=config.interjections, seed=config.seed),
vqa=GeneralVqaModule(vlm=vlm, config=config.vqa, seed=config.seed),
writer=LanguageColumnsWriter(),
validator=StagingValidator(),
)
def test_canonical_recipe_renders_nonempty_from_pipeline_output(
single_episode_root: Path,
) -> None:
executor = _build_executor()
summary = executor.run(single_episode_root)
# validator may emit warnings but no errors for the synthetic fixture
assert summary.validation_report.ok, summary.validation_report.summary()
table = pq.read_table(single_episode_root / "data" / "chunk-000" / "file-000.parquet")
persistent_lists = table.column("language_persistent").to_pylist()
events_lists = table.column("language_events").to_pylist()
timestamps = table.column("timestamp").to_pylist()
recipe = _build_style_blend_recipe()
rendered_any = False
for ts, persistent, events in zip(timestamps, persistent_lists, events_lists, strict=True):
result = render_sample(
recipe=recipe,
persistent=persistent,
events=events,
t=float(ts),
sample_idx=0,
dataset_ctx={"task": "Pour water from the bottle into the cup."},
)
if result is None:
continue
if result["messages"]:
rendered_any = True
assert result["target_message_indices"]
break
assert rendered_any, "recipe rendered no messages from pipeline output"
# Sanity: speech atom appears in events column intact
flat_events = [r for ev in events_lists for r in ev]
speech_rows = [r for r in flat_events if r.get("style") is None and r.get("role") == "assistant"]
assert speech_rows
say = speech_rows[0]["tool_calls"][0]
assert say["function"]["name"] == "say"
assert isinstance(say["function"]["arguments"]["text"], str)
# The pipeline does not write a ``tools`` column — the say schema lives
# as a constant (``SAY_TOOL_SCHEMA``) so the language row struct is the
# single source of truth for the v3.1 schema.
assert "tools" not in table.column_names
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