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"""Convert Dita checkpoints into LeRobot policies or DROID fine-tuning initializations."""
from __future__ import annotations
import argparse
import gc
import hashlib
import json
import shutil
import subprocess
import sys
from dataclasses import dataclass, replace
from datetime import datetime, timezone
from pathlib import Path
from typing import Any
REPO_ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(REPO_ROOT / "src"))
import torch
from huggingface_hub import HfApi
from huggingface_hub.errors import HfHubHTTPError
from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
from lerobot.utils.constants import ACTION
from lerobot_policy_dita import DitaConfig, DitaPolicy, make_dita_pre_post_processors
from lerobot_policy_dita.policy.processor_dita import validate_dita_action_stats
DEFAULT_STATS_ROOT = Path(
"/home/shared/lyanam/huggingface/hub/"
"datasets--openvla--modified_libero_rlds/snapshots/"
"6ce6aaaaabdbe590b1eef5cd29c0d33f14a08551"
)
@dataclass(frozen=True)
class ConversionSpec:
checkpoint: str
output_name: str
repo_id: str
suite: str
stats_glob: str | None
SPECS: dict[str, ConversionSpec] = {
"spatial": ConversionSpec(
"model_ckpt/libero_spatial.pth",
"dita_spatial",
"albus2024/dita_spatial",
"libero_spatial_no_noops",
"libero_spatial_no_noops/1.0.0/dataset_statistics_*.json",
),
"object": ConversionSpec(
"model_ckpt/libero_object.pth",
"dita_object",
"albus2024/dita_object",
"libero_object_no_noops",
"libero_object_no_noops/1.0.0/dataset_statistics_*.json",
),
"goal": ConversionSpec(
"model_ckpt/libero_goal.pth",
"dita_goal",
"albus2024/dita_goal",
"libero_goal_no_noops",
"libero_goal_no_noops/1.0.0/dataset_statistics_*.json",
),
"long": ConversionSpec(
"model_ckpt/libero_long.pth",
"dita_long",
"albus2024/dita_long",
"libero_10_no_noops",
"libero_10_no_noops/1.0.0/dataset_statistics_*.json",
),
"droid_pretrain": ConversionSpec(
"model_ckpt/dita_droid_pretrain.pth",
"dita_droid_pretrain",
"albus2024/dita_droid_pretrain",
"droid_pretrain",
None,
),
}
def sha256_file(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as stream:
for chunk in iter(lambda: stream.read(16 * 1024 * 1024), b""):
digest.update(chunk)
return digest.hexdigest()
def git_revision() -> str:
return subprocess.check_output(
["git", "rev-parse", "HEAD"], cwd=REPO_ROOT, text=True
).strip()
def find_stats_file(stats_root: Path, spec: ConversionSpec) -> Path:
if spec.stats_glob is None:
raise RuntimeError(
"DROID has no bundled action statistics. Convert it as a fine-tuning "
"initialization and bind target-dataset statistics during training."
)
matches = sorted(stats_root.glob(spec.stats_glob))
if len(matches) != 1:
raise FileNotFoundError(
f"Expected exactly one statistics file matching {stats_root / spec.stats_glob}, "
f"found {len(matches)}."
)
return matches[0]
def load_dataset_stats(path: Path) -> tuple[dict[str, dict[str, torch.Tensor]], dict[str, Any]]:
with path.open() as stream:
raw = json.load(stream)
action_stats = raw.get(ACTION)
if not isinstance(action_stats, dict):
raise ValueError(f"Statistics file {path} has no {ACTION!r} mapping.")
for required in ("q01", "q99"):
values = action_stats.get(required)
if not isinstance(values, list) or len(values) != 7:
raise ValueError(f"{path}: action.{required} must contain exactly seven values.")
validate_dita_action_stats({ACTION: action_stats})
tensor_stats = {
ACTION: {
name: torch.as_tensor(values, dtype=torch.float32)
for name, values in action_stats.items()
if isinstance(values, list) and len(values) == 7
}
}
return tensor_stats, raw
def make_dita_config(spec: ConversionSpec, device: str) -> DitaConfig:
return DitaConfig(
dataset_suite=None if spec.suite == "droid_pretrain" else spec.suite,
input_features={
"observation.images.image": PolicyFeature(
type=FeatureType.VISUAL,
shape=(3, 256, 256),
)
},
output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(7,))},
device=device,
num_layers=12,
traj_length=11,
dropout_rate=0.0,
freeze_backbone=False,
optimizer_lr=5e-4 if spec.suite == "libero_10_no_noops" else 1e-4,
use_qformer=True,
prediction_type="epsilon",
attn_implementation="eager",
num_inference_steps=10,
dtype="float16",
tokenizer_max_length=77,
image_resolution=(224, 224),
chunk_size=10,
n_action_steps=1,
history_frames=2,
gripper_threshold=0.5,
gripper_mode="continuous" if spec.suite == "droid_pretrain" else "binary",
normalization_mapping={
"VISUAL": NormalizationMode.IDENTITY,
"ACTION": NormalizationMode.QUANTILES,
"LANGUAGE": NormalizationMode.IDENTITY,
},
push_to_hub=False,
)
def extract_parameter_state(checkpoint_path: Path) -> dict[str, torch.Tensor]:
checkpoint = torch.load(checkpoint_path, map_location="cpu", weights_only=False)
if not isinstance(checkpoint, dict):
raise TypeError(f"{checkpoint_path}: expected a checkpoint dictionary.")
state = checkpoint.get("parameter", checkpoint)
if not isinstance(state, dict) or not state or not all(
isinstance(k, str) and isinstance(v, torch.Tensor) for k, v in state.items()
):
raise TypeError(
f"{checkpoint_path}: expected a tensor state dict or checkpoint['parameter']."
)
return state
def align_and_validate_state(
state: dict[str, torch.Tensor], expected: dict[str, torch.Tensor]
) -> tuple[dict[str, torch.Tensor], str | None]:
prefix: str | None = None
if set(state) == set(expected):
aligned = state
elif state and all(key.startswith("module.") for key in state):
stripped = {key.removeprefix("module."): value for key, value in state.items()}
aligned = stripped
if set(stripped) == set(expected):
prefix = "module."
else:
aligned = state
missing = sorted(set(expected) - set(aligned))
unexpected = sorted(set(aligned) - set(expected))
shape_mismatches = sorted(
(key, tuple(aligned[key].shape), tuple(expected[key].shape))
for key in set(aligned) & set(expected)
if aligned[key].shape != expected[key].shape
)
if missing or unexpected or shape_mismatches:
raise RuntimeError(
"Checkpoint incompatibility:\n"
f"missing keys ({len(missing)}): {missing}\n"
f"unexpected keys ({len(unexpected)}): {unexpected}\n"
f"shape mismatches ({len(shape_mismatches)}): {shape_mismatches}"
)
return aligned, prefix
def model_card(
spec: ConversionSpec,
checkpoint_path: Path,
checkpoint_sha: str,
stats_path: Path,
stats_sha: str,
stats_raw: dict[str, Any],
code_sha: str,
runtime_wheel_name: str | None = None,
) -> str:
q01 = stats_raw[ACTION]["q01"]
q99 = stats_raw[ACTION]["q99"]
install = (
f'pip install "lerobot_policy_dita[libero] @ https://huggingface.co/{spec.repo_id}/resolve/main/{runtime_wheel_name}"'
if runtime_wheel_name
else f'pip install "git+https://github.com/A-Dumbledore/lerobot_policy_dita.git@{code_sha}"'
)
return f"""---
library_name: lerobot
pipeline_tag: robotics
tags:
- lerobot
- dita
- libero
---
# Dita — {spec.suite}
This repository is a strict LeRobot conversion of `{checkpoint_path.name}` from the original
[Dita](https://github.com/RoboDita/Dita) implementation. It is configured for the
`{spec.suite}` action distribution and is not interchangeable with another LIBERO suite.
## Provenance and compatibility contract
- Source checkpoint: `{checkpoint_path.as_posix()}`
- Source checkpoint SHA256: `{checkpoint_sha}`
- Conversion code commit: `{code_sha}`
- Dataset statistics: `{stats_path.as_posix()}`
- Dataset statistics SHA256: `{stats_sha}`
- Image processing: one RGB primary/agentview camera, resized to 224×224, followed once by
ImageNet mean `(0.485, 0.456, 0.406)` and std `(0.229, 0.224, 0.225)`.
- Action processing: dimensions 0–5 use this suite's `q01`/`q99`; dimension 6 is not normalized.
- Action mask: `[true, true, true, true, true, true, false]`.
- q01: `{json.dumps(q01)}`
- q99: `{json.dumps(q99)}`
- Gripper: normalized output is thresholded with `> 0.5`, then the Cartesian dimensions are
unnormalized, then the original Dita gripper sign flip is applied.
- Rollout: `traj_length=11`, two-frame history, `chunk_size=10`, `n_action_steps=1`,
`num_inference_steps=10`, DDIM `prediction_type=epsilon`, and CUDA AMP by default.
- `policy.dtype` controls compute precision (`float32`, `float16`, `bfloat16`);
parameters remain FP32. It is the sole precision setting; `use_amp` is derived internally.
- LIBERO's LeRobot environment processor performs the 180° image rotation. The policy does not
rotate the image a second time.
## Install
```bash
{install}
```
## Evaluate
```bash
lerobot-eval \\
--policy.path={spec.repo_id} \\
--policy.gripper_mode=binary \\
--policy.device=cuda \\
--env.type=libero \\
--env.task={spec.suite.removesuffix('_no_noops')} \\
--env.max_parallel_tasks=1 \\
--eval.batch_size=1 \\
--eval.n_episodes=10 \\
--output_dir=./outputs/{spec.output_name}
```
The processor state in this repository contains the required statistics; no source `.pth` file or
local dataset metadata is needed at evaluation time.
## Validation
The converter checks all parameter names and shapes, loads with `strict=True`, and checks the
saved artifact files. Run the project's parity and converted-checkpoint verification scripts
separately before using the policy; conversion alone does not establish rollout parity.
"""
def pretraining_model_card(
spec: ConversionSpec, checkpoint_sha: str, code_sha: str, runtime_wheel_name: str | None
) -> str:
install = (
f'pip install "lerobot_policy_dita[training] @ https://huggingface.co/{spec.repo_id}/resolve/main/{runtime_wheel_name}"'
if runtime_wheel_name
else 'pip install -e ".[training]" # From the updated lerobot_policy_dita checkout'
)
return f"""---
library_name: lerobot
pipeline_tag: robotics
tags:
- lerobot
- dita
- droid
- pretraining
---
# Dita DROID pretraining initialization
This repository converts the original Dita DROID weights for subsequent fine-tuning.
It contains model weights, a LeRobot Dita configuration, and processor templates without
dataset action statistics. It is not a ready-to-deploy DROID or LIBERO policy.
## Provenance
- Source: `{spec.checkpoint}` from the original [Dita](https://github.com/RoboDita/Dita) release.
- Source checkpoint SHA256: `{checkpoint_sha}`.
- Conversion base Git commit: `{code_sha}`. See `conversion_metadata.json` for the
working-tree status, conversion-script hash, and packaged runtime hash when present.
- All model parameter keys and shapes are checked, followed by `strict=True` loading.
- Optimizer/scheduler state is not exported. No source DROID action statistics are inferred.
## Install
Use Python 3.12. The updated Dita runtime translates LeRobot's standard fine-tuning
normalizer overrides to Dita's custom processors.
```bash
{install}
```
## Fine-tune
Use a LeRobot dataset with seven-dimensional actions, a compatible primary RGB camera,
and task language. Its action metadata must contain seven-element `q01` and `q99` arrays
computed in the target training action convention. The default `gripper_mode=continuous`
uses quantiles for the first six dimensions and dataset `action.min/max` for the gripper.
Continuous outputs are restored to the recorded position units without thresholding or
sign inversion. Use `--policy.gripper_mode=binary` for LIBERO signed training targets
(-1=open, +1=closed), encoded internally as 1=open, 0=closed.
ImageNet image normalization and CLIP text encoding remain fixed.
```bash
lerobot-train \\
--policy.path={spec.repo_id} \\
--policy.gripper_mode=continuous \\
--policy.device=cuda \\
--policy.dtype=bfloat16 \\
--policy.input_features=null \\
--policy.push_to_hub=false \\
--dataset.repo_id=YOUR_USERNAME/YOUR_DATASET \\
--output_dir=outputs/train/dita_droid_finetune
```
`policy.dtype` accepts `float32`, `float16` and `bfloat16` and controls autocast;
weights and optimizer state remain FP32. Use BF16 on a supported GPU, or select
FP32/FP16 explicitly. The default CUDA precision remains FP16 for compatibility.
At the start of a new fine-tuning run, LeRobot binds the target dataset's statistics to
both processor templates. They remain fixed during that run and are saved with the
fine-tuned checkpoint. LeRobot 0.6.1 restores saved statistics on resume. The same
pretraining repository can initialize multiple target datasets; no per-dataset pretraining
upload is needed. Missing quantiles cause an explicit error when processing actions.
The processor templates require cached or downloadable DINOv2/CLIP assets. Load the
fine-tuned checkpoint together with its saved processors for evaluation and deployment.
Any `validation_report.json` documents the checks actually run for this upload; no
task success rate or DROID deployment equivalence is claimed.
"""
def previous_revision(api: HfApi, repo_id: str) -> str | None:
try:
return api.model_info(repo_id).sha
except HfHubHTTPError as error:
if error.response is not None and error.response.status_code == 404:
return None
raise
def convert_one(
spec: ConversionSpec,
*,
output_root: Path,
stats_root: Path,
device: str,
push_to_hub: bool,
repo_id_override: str | None,
runtime_wheel: Path | None = None,
) -> dict[str, Any]:
if repo_id_override:
spec = replace(spec, repo_id=repo_id_override)
pretraining_only = spec.suite == "droid_pretrain"
if runtime_wheel is not None and (not runtime_wheel.is_file() or runtime_wheel.suffix != ".whl"):
raise ValueError(f"Expected an existing runtime wheel: {runtime_wheel}")
checkpoint_path = REPO_ROOT / spec.checkpoint
if not checkpoint_path.is_file():
raise FileNotFoundError(checkpoint_path)
stats_path = None if pretraining_only else find_stats_file(stats_root, spec)
output_dir = output_root / spec.output_name
if output_dir.exists() and any(output_dir.iterdir()):
raise FileExistsError(
f"Refusing to mix artifacts in non-empty {output_dir}; use a fresh output root."
)
output_dir.mkdir(parents=True, exist_ok=True)
checkpoint_sha = sha256_file(checkpoint_path)
stats_sha = sha256_file(stats_path) if stats_path else None
dataset_stats, stats_raw = load_dataset_stats(stats_path) if stats_path else (None, None)
code_sha = git_revision()
config = make_dita_config(spec, device)
policy = DitaPolicy(config)
expected = policy.model.state_dict()
state = extract_parameter_state(checkpoint_path)
aligned, stripped_prefix = align_and_validate_state(state, expected)
load_result = policy.model.load_state_dict(aligned, strict=True)
if load_result.missing_keys or load_result.unexpected_keys:
raise RuntimeError(f"Strict load unexpectedly returned {load_result}.")
policy.save_pretrained(output_dir)
preprocessor, postprocessor = make_dita_pre_post_processors(config, dataset_stats)
preprocessor.save_pretrained(output_dir)
postprocessor.save_pretrained(output_dir)
metadata: dict[str, Any] = {
"converted_at_utc": datetime.now(timezone.utc).isoformat(),
"source_model": "Dita/scripts/llama_dp.py",
"source_checkpoint": spec.checkpoint,
"source_checkpoint_sha256": checkpoint_sha,
"checkpoint_parameter_tensors": len(aligned),
"stripped_prefix": stripped_prefix,
"strict_load": {
"missing_keys": [],
"unexpected_keys": [],
"shape_mismatches": [],
},
"dataset_suite": spec.suite,
"artifact_purpose": "finetuning_initialization" if pretraining_only else "deployment_policy",
"statistics_binding": "target_dataset_at_finetuning" if pretraining_only else "bundled",
"dataset_statistics_file": str(stats_path) if stats_path else None,
"dataset_statistics_sha256": stats_sha,
"action_q01": stats_raw[ACTION]["q01"] if stats_raw else None,
"action_q99": stats_raw[ACTION]["q99"] if stats_raw else None,
"gripper_mode": config.gripper_mode,
"action_normalization_mask": list(config.action_normalization_mask),
"conversion_code_git_sha": code_sha,
"conversion_code_git_dirty": bool(subprocess.check_output(
["git", "status", "--porcelain"], cwd=REPO_ROOT, text=True
).strip()),
"conversion_script_sha256": sha256_file(Path(__file__)),
"target_repo_id": repo_id_override or spec.repo_id,
}
(output_dir / "conversion_metadata.json").write_text(
json.dumps(metadata, indent=2) + "\n"
)
(output_dir / "README.md").write_text(
pretraining_model_card(spec, checkpoint_sha, code_sha, runtime_wheel.name if runtime_wheel else None)
if pretraining_only else model_card(
spec,
checkpoint_path,
checkpoint_sha,
stats_path,
stats_sha,
stats_raw,
code_sha,
runtime_wheel.name if runtime_wheel else None,
)
)
required = {
"config.json",
"model.safetensors",
"policy_preprocessor.json",
"policy_postprocessor.json",
"README.md",
"conversion_metadata.json",
}
missing_artifacts = sorted(required - {path.name for path in output_dir.iterdir()})
if missing_artifacts:
raise RuntimeError(f"Conversion did not produce required files: {missing_artifacts}")
state_files = sorted(path.name for path in output_dir.glob("*.safetensors") if path.name != "model.safetensors")
if not state_files and not pretraining_only:
raise RuntimeError("Processor state safetensors were not saved.")
metadata["processor_state_files"] = state_files
if runtime_wheel is not None:
shutil.copy2(runtime_wheel, output_dir / runtime_wheel.name)
metadata["runtime_wheel"] = runtime_wheel.name
metadata["runtime_wheel_sha256"] = sha256_file(runtime_wheel)
(output_dir / "conversion_metadata.json").write_text(
json.dumps(metadata, indent=2) + "\n"
)
if push_to_hub:
api = HfApi()
account = api.whoami()["name"]
target_repo = repo_id_override or spec.repo_id
if target_repo.split("/", 1)[0] != account:
raise PermissionError(f"Authenticated as {account}, cannot publish {target_repo}.")
old_sha = previous_revision(api, target_repo)
metadata["previous_hub_revision"] = old_sha
(output_dir / "conversion_metadata.json").write_text(
json.dumps(metadata, indent=2) + "\n"
)
api.create_repo(target_repo, repo_type="model", exist_ok=True)
commit = api.upload_folder(
repo_id=target_repo,
repo_type="model",
folder_path=output_dir,
commit_message=f"Synchronize Dita {spec.suite} LeRobot checkpoint",
)
metadata["hub_commit_sha"] = commit.oid
print(f"Uploaded {target_repo} at {commit.oid} (previous: {old_sha})")
print(
f"Converted {checkpoint_path.name}: {len(aligned)} tensors, strict=True, "
f"SHA256={checkpoint_sha}, output={output_dir}"
)
del preprocessor, postprocessor, policy, aligned, state, expected
gc.collect()
if torch.cuda.is_available():
torch.cuda.empty_cache()
return metadata
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
selection = parser.add_mutually_exclusive_group(required=True)
selection.add_argument(
"--checkpoint",
help="spatial/object/goal/long/droid_pretrain, or a known checkpoint filename/path.",
)
selection.add_argument("--all", action="store_true", help="Convert all four deployable LIBERO checkpoints.")
parser.add_argument("--output-dir", type=Path, default=REPO_ROOT / "converted_checkpoints")
parser.add_argument("--stats-root", type=Path, default=DEFAULT_STATS_ROOT)
parser.add_argument("--device", default="cuda")
parser.add_argument("--push-to-hub", action="store_true")
parser.add_argument("--repo-id", help="Override the target repo for a single checkpoint only.")
parser.add_argument("--runtime-wheel", type=Path, help="Bundle an updated Dita runtime wheel with the model.")
return parser.parse_args()
def main() -> None:
args = parse_args()
if args.repo_id and args.all:
raise ValueError("--repo-id can only be used with --checkpoint.")
selected = [SPECS[name] for name in ("spatial", "object", "goal", "long")]
if not args.all:
if args.checkpoint in SPECS:
selected = [SPECS[args.checkpoint]]
else:
checkpoint_path = Path(args.checkpoint).expanduser().resolve()
matches = [
spec
for spec in SPECS.values()
if Path(spec.checkpoint).name == checkpoint_path.name
or (spec.suite == "droid_pretrain" and checkpoint_path.name == "droid_pretrain.pth")
]
if len(matches) != 1:
raise ValueError(
f"Unknown checkpoint {args.checkpoint!r}; expected one of "
f"{sorted(SPECS)} or their checkpoint paths."
)
selected = [replace(matches[0], checkpoint=str(checkpoint_path))]
for spec in selected:
convert_one(
spec,
output_root=args.output_dir,
stats_root=args.stats_root,
device=args.device,
push_to_hub=args.push_to_hub,
repo_id_override=args.repo_id,
runtime_wheel=args.runtime_wheel,
)
if __name__ == "__main__":
main()
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