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Pepijn
80b7708a61
Closes the visual-grounding gap flagged after the initial PR review:
modules now decode actual camera frames at the relevant timestamps and
attach them as `{"type":"image", "image":<PIL>}` content blocks to the
VLM prompts.
- New `frames.py`:
- `FrameProvider` Protocol; `VideoFrameProvider` decodes from the
dataset's first `observation.images.*` stream via
`LeRobotDatasetMetadata.get_video_file_path` and
`decode_video_frames`, with the same `from_timestamp` shift the main
dataset uses.
- Per-process LRU cache so co-timestamped Module 1 plan-update + Module
2 calls share decode work.
- `make_frame_provider` falls back to a null provider when the dataset
has no video tracks → text-only prompts (graceful absence).
- Modules 1/2/3 take an optional `frame_provider` (default null) and
prepend image blocks before the text block.
- Module 1 attaches `keyframes_per_episode` keyframes to the subtask
decomposition prompt.
- Module 2 attaches the frame at the interjection timestamp.
- Module 3 attaches the exact emission frame to each VQA pair.
- VlmConfig: backend now defaults to `vllm`; default model is
`Qwen/Qwen3.6-27B-FP8`. New knobs: `--vlm.tensor_parallel_size`,
`--vlm.camera_key` (override the keyframe stream).
- `_make_vllm_client` honours `tensor_parallel_size` so 27B-FP8 sharded
on 2× GPUs works out of the box.
- `test_module3_attaches_frame_image_block_to_prompt` asserts modules
emit one image block per VQA prompt at the exact emission timestamp.
- Docs: example switched to `imstevenpmwork/super_poulain_draft` +
Qwen3.6-27B-FP8 + tensor_parallel_size=2; documents the keyframe
attachment behaviour and the no-video fallback.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
223 lines
8.7 KiB
Python
223 lines
8.7 KiB
Python
#!/usr/bin/env python
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# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Module 1/2/3 unit tests with stubbed VLMs."""
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from __future__ import annotations
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import json
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Any
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from lerobot.annotations.steerable_pipeline.config import (
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Module1Config,
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Module2Config,
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Module3Config,
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)
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from lerobot.annotations.steerable_pipeline.modules import (
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GeneralVqaModule,
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InterjectionsAndSpeechModule,
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PlanSubtasksMemoryModule,
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)
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from lerobot.annotations.steerable_pipeline.reader import iter_episodes
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from lerobot.annotations.steerable_pipeline.staging import EpisodeStaging
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from lerobot.annotations.steerable_pipeline.vlm_client import StubVlmClient
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from ._helpers import make_canned_responder
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@dataclass
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class _StubFrameProvider:
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"""Returns one sentinel object per requested timestamp."""
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sentinel: Any = field(default_factory=lambda: object())
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calls: list[tuple[int, tuple[float, ...]]] = field(default_factory=list)
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def frames_at(self, record, timestamps):
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self.calls.append((record.episode_index, tuple(timestamps)))
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return [self.sentinel] * len(timestamps)
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def _spy_responder(captured: list[list[dict[str, Any]]], reply: Any):
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def responder(messages):
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captured.append(list(messages))
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return reply
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return StubVlmClient(responder=responder)
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def test_module1_plan_memory_subtask_smoke(fixture_dataset_root: Path, tmp_path: Path) -> None:
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vlm = make_canned_responder(
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{
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"Decompose the demonstration": {
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"subtasks": [
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{"text": "grasp the handle of the sponge", "start": 0.0, "end": 0.4},
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{"text": "wipe the counter from left to right", "start": 0.4, "end": 0.8},
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{"text": "place the sponge into the sink", "start": 0.8, "end": 1.1},
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]
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},
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"write a concise hierarchical PLAN": {"plan": "1. grasp\n2. wipe\n3. place"},
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"Update the memory": {"memory": "wiped the counter once"},
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},
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)
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module = PlanSubtasksMemoryModule(vlm=vlm, config=Module1Config())
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record = next(iter_episodes(fixture_dataset_root))
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staging = EpisodeStaging(tmp_path / "stage", record.episode_index)
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module.run_episode(record, staging)
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rows = staging.read("module_1")
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styles = {r["style"] for r in rows}
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assert {"subtask", "plan", "memory"}.issubset(styles)
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# subtask timestamps must be exact frame timestamps
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frame_set = set(record.frame_timestamps)
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for row in rows:
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assert row["timestamp"] in frame_set
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# exactly one plan row at t0
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plan_rows = [r for r in rows if r["style"] == "plan"]
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assert len(plan_rows) == 1
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assert plan_rows[0]["timestamp"] == record.frame_timestamps[0]
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def test_module2_at_t0_emits_speech_only_no_interjection(fixture_dataset_root: Path, tmp_path: Path) -> None:
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vlm = make_canned_responder(
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{"acknowledgement the robot": {"text": "Sure, on it."}},
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)
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module = InterjectionsAndSpeechModule(
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vlm=vlm,
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config=Module2Config(max_interjections_per_episode=0),
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)
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record = next(iter_episodes(fixture_dataset_root))
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staging = EpisodeStaging(tmp_path / "stage", record.episode_index)
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module.run_episode(record, staging)
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rows = staging.read("module_2")
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assert len(rows) == 1
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only = rows[0]
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assert only["role"] == "assistant"
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assert only["style"] is None
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assert only["content"] is None
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assert only["timestamp"] == record.frame_timestamps[0]
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assert only["tool_calls"][0]["function"]["name"] == "say"
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def test_module2_mid_episode_emits_paired_interjection_and_speech(
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fixture_dataset_root: Path, tmp_path: Path
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) -> None:
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vlm = make_canned_responder(
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{
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"acknowledgement the robot": {"text": "OK."},
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"ONE realistic interruption": {
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"interjection": "actually skip the dishes",
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"speech": "Skipping the dishes.",
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},
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},
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)
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module = InterjectionsAndSpeechModule(
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vlm=vlm,
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config=Module2Config(max_interjections_per_episode=1, interjection_min_t=0.2),
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seed=7,
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)
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record = next(iter_episodes(fixture_dataset_root))
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staging = EpisodeStaging(tmp_path / "stage", record.episode_index)
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module.run_episode(record, staging)
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rows = staging.read("module_2")
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interjections = [r for r in rows if r["style"] == "interjection"]
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speeches = [r for r in rows if r["style"] is None and r["role"] == "assistant"]
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assert len(interjections) == 1
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assert len(speeches) >= 2 # initial t=0 + one paired with the interjection
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inter_t = interjections[0]["timestamp"]
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assert any(abs(s["timestamp"] - inter_t) < 1e-9 for s in speeches)
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def test_module3_vqa_unique_per_frame(single_episode_root: Path, tmp_path: Path) -> None:
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payload = {
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"question": "How many cups?",
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"answer": {"label": "cup", "count": 2, "note": "white & blue"},
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}
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vlm = make_canned_responder({"frame-grounded visual question": payload})
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module = GeneralVqaModule(
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vlm=vlm,
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config=Module3Config(vqa_emission_hz=1.0, K=3),
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seed=1,
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)
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record = next(iter_episodes(single_episode_root))
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staging = EpisodeStaging(tmp_path / "stage", record.episode_index)
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module.run_episode(record, staging)
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rows = staging.read("module_3")
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user_ts = [r["timestamp"] for r in rows if r["role"] == "user" and r["style"] == "vqa"]
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assistant_ts = [r["timestamp"] for r in rows if r["role"] == "assistant" and r["style"] == "vqa"]
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# at most one user (vqa) per frame; same for assistant
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assert len(user_ts) == len(set(user_ts))
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assert len(assistant_ts) == len(set(assistant_ts))
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# every emitted timestamp must be an exact source frame timestamp
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frame_set = set(record.frame_timestamps)
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for ts in user_ts + assistant_ts:
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assert ts in frame_set
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def test_module3_attaches_frame_image_block_to_prompt(single_episode_root: Path, tmp_path: Path) -> None:
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"""Each VQA prompt must carry a single image block at the emission frame."""
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captured: list[list[dict[str, Any]]] = []
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payload = {
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"question": "How many cups?",
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"answer": {"label": "cup", "count": 1},
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}
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provider = _StubFrameProvider()
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module = GeneralVqaModule(
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vlm=_spy_responder(captured, payload),
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config=Module3Config(vqa_emission_hz=1.0, K=1),
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seed=0,
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frame_provider=provider,
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)
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record = next(iter_episodes(single_episode_root))
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staging = EpisodeStaging(tmp_path / "stage", record.episode_index)
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module.run_episode(record, staging)
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assert captured, "no VLM calls made"
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for messages in captured:
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content = messages[0]["content"]
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image_blocks = [b for b in content if isinstance(b, dict) and b.get("type") == "image"]
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text_blocks = [b for b in content if isinstance(b, dict) and b.get("type") == "text"]
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assert len(image_blocks) == 1, f"expected 1 image block per VQA prompt, got {content}"
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assert image_blocks[0]["image"] is provider.sentinel
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assert len(text_blocks) == 1
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# provider was called once per emission with the exact emission timestamp
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for ep_idx, ts_tuple in provider.calls:
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assert ep_idx == record.episode_index
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assert len(ts_tuple) == 1
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assert ts_tuple[0] in record.frame_timestamps
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def test_module3_assistant_content_is_valid_json(single_episode_root: Path, tmp_path: Path) -> None:
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payload = {
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"question": "Where is the cup?",
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"answer": {"detections": [{"label": "cup", "bbox_format": "xyxy", "bbox": [10, 20, 50, 80]}]},
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}
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vlm = make_canned_responder({"frame-grounded visual question": payload})
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module = GeneralVqaModule(
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vlm=vlm,
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config=Module3Config(vqa_emission_hz=1.0, K=2),
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seed=2,
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)
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record = next(iter_episodes(single_episode_root))
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staging = EpisodeStaging(tmp_path / "stage", record.episode_index)
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module.run_episode(record, staging)
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rows = staging.read("module_3")
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for row in rows:
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if row["role"] == "assistant" and row["style"] == "vqa":
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decoded = json.loads(row["content"])
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assert "detections" in decoded
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