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Pepijn
dad2cf1178
The executor previously claimed it would "optionally hand off" to
datatrove's LocalPipelineExecutor or SlurmPipelineExecutor — but it
already runs phases inline in every code path, and HF Jobs (see
``examples/annotation/run_hf_job.py``) is the actual distribution
strategy. Stop pretending we have an executor selector.
* `executor.py`: drop `select_executor_class`, the "kind" log line, and
the references to LocalPipelineExecutor / SlurmPipelineExecutor.
Module docstring now says distribution is delegated to HF Jobs.
* `config.py`: drop `auto_threshold`, `force_local`, `slurm_partition`,
`slurm_gpus`, `slurm_time`, `workers`. `ExecutorConfig` keeps only
`episode_parallelism`. While here, prune the longer "why" docstrings
on every field down to the load-bearing bits — full story moves to
`docs/source/annotation_pipeline.mdx`.
* `pyproject.toml`: drop `datatrove>=0.4.0,<2.0.0` from the
`[annotations]` extra; the dep was only there for the (never used)
cluster executors. Comment block notes the new HF-Jobs delegation.
* `reader.py`, `lerobot_annotate.py`: drop their own datatrove /
flavor-namespace mentions.
* `docs/source/annotation_pipeline.mdx`:
- remove the flavor-namespace / sidecar paragraph (out of scope —
"multiple revisions = multiple copies" is dataset-level policy);
- remove the "writer drops the legacy `subtask_index` column" note
(already covered by PR 1's intentional-break call-out);
- remove the chat-template + `apply_chat_template(messages, tools=...)`
line (covered by Tools doc);
- replace the "executor picks Local vs Slurm" paragraph with
`--executor.episode_parallelism` and a pointer to HF Jobs;
- rewrite the style→recipe section to talk about "recipes" generically
instead of pinning a specific YAML;
- add a "Running on Hugging Face Jobs" section pointing at
`examples/annotation/run_hf_job.py`;
- add a "Running locally" example matching the CLI's docstring
(`uv run lerobot-annotate --root=... --vlm.model_id=...`);
- extend the paper-inspirations list with Pi0.7 and Steerable VLA
Policies (Zhao 2025) for Module 3.
Tests: same 3 pre-existing failures as before this commit (2 module
assertions still in flight; 1 carryover from PR 1). 41/44 pass.
Pre-commit clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
177 lines
8.2 KiB
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177 lines
8.2 KiB
Plaintext
# Annotation Pipeline
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`lerobot-annotate` populates the two language columns introduced by the
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[Language Columns and Recipes](./language_and_recipes) page —
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`language_persistent` and `language_events` — directly into
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`data/chunk-*/file-*.parquet`.
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## What the pipeline produces
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Three modules write into a per-episode staging tree, then a single writer
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rewrites the data shards in place:
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| Style / atom | Column | Module |
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| ------------------------------------------- | --------------------- | -------- |
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| `subtask` (Pi0.7-style "how, not what") | `language_persistent` | Module 1 |
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| `plan` (initial + refresh on interjection) | `language_persistent` | Module 1 |
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| `memory` (MEM-style compression) | `language_persistent` | Module 1 |
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| `task_aug` (rephrasings of canonical task) | `language_persistent` | Module 1 |
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| `interjection` | `language_events` | Module 2 |
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| speech tool-call atom (`style=null`, `say`) | `language_events` | Module 2 |
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| `vqa` (user / assistant pair) | `language_events` | Module 3 |
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The writer does **not** add a `tools` column to the parquet — the tool
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catalog lives at `meta/info.json["tools"]` instead (see
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[Tools](./tools)). After every annotation run the pipeline ensures the
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canonical `say` schema is present in that list, preserving any tools the
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user pre-declared.
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If you want to declare additional tools for a dataset before annotation
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runs, edit `meta/info.json["tools"]` directly — the pipeline preserves
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anything already there. Implementations of those tools live under
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`src/lerobot/tools/`; one file per tool, registered via
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`TOOL_REGISTRY`. See the [Tools](./tools) doc for the authoring guide.
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## Running locally
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Install the extra and invoke the console script. Episode-level
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concurrency comes from `--executor.episode_parallelism` (default 16);
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that is the only knob the in-process executor exposes.
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```bash
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uv sync --extra annotations
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uv run lerobot-annotate \
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--root=/path/to/dataset \
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--vlm.model_id=Qwen/Qwen2.5-VL-7B-Instruct
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```
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The pipeline attaches actual camera footage to every Module 1/2/3 prompt
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by default, decoded from the dataset's first `observation.images.*`
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stream. Override with `--vlm.camera_key=observation.images.<name>` to
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pin a specific viewpoint. Datasets with no video tracks fall back to
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text-only prompts automatically.
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**Module 1 sees the whole episode as one video block.** Subtask
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decomposition gets a `{"type":"video", "video":[<frames>]}` block
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covering the entire demonstration; Qwen-VL pools temporally on its own
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and decides where to cut. There is no keyframe stride or count knob —
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`--module_1.max_video_frames` (default 128) only caps the frames packed
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into the video block as a model-capacity bound. Module 2 attaches a
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short window of frames around the interjection timestamp; Module 3
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attaches the exact emission frame to each VQA pair.
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## Running on Hugging Face Jobs
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Distributed annotation is delegated to
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[Hugging Face Jobs](https://huggingface.co/docs/hub/en/jobs). The repo
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ships a launcher script you copy and edit for your dataset:
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```bash
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HF_TOKEN=hf_... uv run python examples/annotation/run_hf_job.py
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```
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[`examples/annotation/run_hf_job.py`](https://github.com/huggingface/lerobot/blob/main/examples/annotation/run_hf_job.py)
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spawns one `h200x2` job that:
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1. installs the branch under test plus the annotation extras,
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2. boots two vllm servers (one per GPU) for the chosen model,
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3. runs Modules 1 / 2 / 3 across the dataset via `lerobot-annotate`,
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4. uploads the annotated dataset to `--push_to_hub`.
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To target a different dataset, model, or hub repo, edit the `CMD` block
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inside the script — every flag in there maps directly onto a CLI flag of
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`lerobot-annotate` (see `lerobot-annotate --help` for the full list).
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## Style-to-recipe consumer mapping
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The pipeline's outputs are designed to be consumed by recipes (see
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[Language Columns and Recipes](./language_and_recipes)) — typically:
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- low-level / high-level / memory-update branches consume
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`subtask`/`plan`/`memory` from `language_persistent`.
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- An interjection-response branch consumes `interjection` events plus
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the paired speech atom (merged into one assistant target turn via
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`tool_calls_from`) and the same-timestamp `plan` refresh.
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- A VQA branch consumes the `(vqa, user)` and `(vqa, assistant)` pairs
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from `language_events`.
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## Why the design splits state from events
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Two things drive the scope:
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1. **Persistent state vs exact-event split.** Persistent rows
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(`subtask`, `plan`, `memory`) broadcast per episode and answer "what
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state is in force at this frame?". Event rows (`interjection`, `vqa`,
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speech) only appear on the exact frame whose timestamp matches the
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emission. The pipeline writes timestamps taken straight from the
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source parquet — no floating-point recomputation.
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2. **One Qwen-VL pass.** All three modules share a single VLM client
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(vLLM if available, transformers fallback) so the cost is one model
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load per dataset, not three.
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## Module independence and staged reruns
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Each module writes its raw output to
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`<root>/.annotate_staging/episode_{N:06d}/<module>.jsonl`. That makes
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prompt iteration cheap — re-running one module overwrites only its own
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JSONL file before the writer composes the final parquet. Modules can be
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disabled via `--module_1.enabled=false` (and similarly for 2 and 3) to
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test them in isolation.
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## Validation/report checks before final write
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Before the writer runs, `StagingValidator` checks:
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- exact frame-timestamp alignment for every event row;
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- no orphan speech / interjection pairs;
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- `plan` is refreshed at every interjection timestamp;
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- `memory` rows fall on subtask boundaries (warning, not error);
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- VQA assistant `content` parses as JSON in one of the
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bbox / keypoint / count / attribute / spatial shapes;
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- every row routes to the column dictated by `column_for_style(style)`.
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Errors abort the writer (`--skip_validation=true` overrides for debugging).
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## Paper inspirations per module
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- **Module 1 — subtasks.** Hi Robot ([Shi 2025](https://arxiv.org/abs/2502.19417))
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atom granularity ("pick up one piece of lettuce", "place bowl to box");
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Pi0.7 ([Physical Intelligence 2025](https://pi.website/pi07)) "how, not
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what" detail.
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- **Module 1 — memory.** MEM ([Torne 2026](https://arxiv.org/abs/2603.03596))
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compression directive: keep only minimal relevant information; functional
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outcomes preserved, specific attributes dropped.
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- **Module 2 — interjections.** Hi Robot scenario taxonomy: negative task,
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situated correction, specific constraint, preference. Speech is a
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tool-call-only atom (`tool_calls=[{type:function, function:{name:"say",
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arguments:{text:...}}}]`).
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- **Module 3 — VQA.** ECoT ([Zawalski 2024](https://arxiv.org/abs/2407.08693))
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grounded features (bounding boxes in pixel `[x_min, y_min, x_max, y_max]`,
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keypoints) and Steerable VLA Policies ([Zhao 2025](https://arxiv.org/abs/2509.07626))
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multi-abstraction grounding. Pi0.7 also grounds answers across
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multiple abstraction levels.
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Future maintainers should adjust the prompt templates in
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`src/lerobot/annotations/steerable_pipeline/prompts/` against these
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references rather than rewriting from scratch.
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## Compute and list-size estimates
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Per episode, the pipeline issues O(`max_steps`) Module 1 calls,
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O(`max_interjections_per_episode`) Module 2 calls, and
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O(`vqa_emission_hz × episode_seconds`) Module 3 calls. With defaults
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(8 subtasks, 1 interjection, 1 Hz × 3 pairs) and 30-second episodes, that
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is ~50 VLM calls per episode. `language_persistent` per episode is ~10s of
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KB at most (parquet dictionary-encodes one entry per episode);
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`language_events` is empty on most frames and is bounded by the number of
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emissions, not `num_frames × num_emissions`.
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## Reproducibility via seed and prompt hashes
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`--seed` (default 1729) feeds the per-episode RNGs that select interjection
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timestamps and VQA question types. Combined with the deterministic prompt
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templates checked into `prompts/`, two runs at the same seed against the
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same dataset and the same model checkpoint produce byte-identical staging
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artifacts. Prompt edits are recorded by file hash; future tooling can pin
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expected `(seed, prompt_hash)` pairs into the dataset card.
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