---
license: apache-2.0
task_categories:
- robotics
tags:
- robotics
- text-embeddings
- t5
- wan2.2
size_categories:
- 1M<n<10M
---

# RoboTwin 2.0 3D — T5 Text Embedding Cache

Precomputed [UMT5-XXL](https://huggingface.co/google/umt5-xxl) text embeddings for the
**1,039,891 unique task prompts** of the RoboTwin 2.0 3D dataset
([`flex-pi/robotwin_3d`](https://huggingface.co/datasets/flex-pi/robotwin_3d)), as consumed by
Wan2.2-TI2V-5B / FastWAM.

Precomputing these takes substantial GPU time; this cache lets you skip it.

## Contents

| Path | Description |
|---|---|
| `shards/shard_NNNNN.safetensors` | 520 shards, 2,000 prompts each (last one 1,891), ~2.10 GB per shard |
| `manifest.txt` | The 1,039,891 prompt hashes, **sorted**, one per line — line `i` is row `i % 2000` of shard `i // 2000` |

Each shard holds two tensors, row-aligned:

| Tensor | Shape | Dtype |
|---|---|---|
| `contexts` | `[N, 128, 4096]` | `bfloat16` |
| `masks` | `[N, 128]` | `bool` |

The shard's `__metadata__["keys"]` is a JSON list of that shard's N hashes, in row order.

The key for a prompt is `sha256(prompt.encode("utf-8")).hexdigest()`. Prompts themselves live in
`meta/tasks.jsonl` of the main dataset.

`masks` marks the valid tokens (mean 40.9 of 128). Values beyond the mask are **not** zero — they are
the raw T5 outputs. Zero them yourself if your model does not apply the mask.

## Usage

Random access without downloading everything — resolve one prompt to its shard, fetch only that shard:

```python
import bisect, hashlib, json
from huggingface_hub import hf_hub_download
from safetensors import safe_open

REPO = "flex-pi/robotwin_3d_text_embeds_cache"
SHARD = 2000

manifest = hf_hub_download(REPO, "manifest.txt", repo_type="dataset")
keys = open(manifest).read().split()          # sorted

def get(prompt):
    h = hashlib.sha256(prompt.encode("utf-8")).hexdigest()
    i = bisect.bisect_left(keys, h)
    if i == len(keys) or keys[i] != h:
        raise KeyError(prompt)
    path = hf_hub_download(REPO, f"shards/shard_{i // SHARD:05d}.safetensors", repo_type="dataset")
    with safe_open(path, framework="pt") as f:
        row = i % SHARD
        return f.get_slice("contexts")[row], f.get_slice("masks")[row]

context, mask = get("Lift the medium-sized green bottle ensuring it remains upright.")
print(context.shape, context.dtype, int(mask.sum()))   # (128, 4096) torch.bfloat16 49
```

`safe_open` + `get_slice` reads only the requested row, so this does not load the whole 2 GB shard
into memory.

### Rebuilding the original per-prompt `.pt` layout

Some code expects `{cache_dir}/{sha256}.t5_len128.wan22ti2v5b.pt` holding `{"context", "mask"}`:

```python
import json, os, torch
from safetensors import safe_open

def unpack(shard_path, out_dir):
    os.makedirs(out_dir, exist_ok=True)
    with safe_open(shard_path, framework="pt") as f:
        ks = json.loads(f.metadata()["keys"])
        C, M = f.get_slice("contexts"), f.get_slice("masks")
        for r, k in enumerate(ks):
            torch.save({"context": C[r], "mask": M[r]},
                       os.path.join(out_dir, f"{k}.t5_len128.wan22ti2v5b.pt"))
```

Note this expands to ~1 TB across 1,039,891 files.

## Provenance

Repacked byte-exactly from the original per-prompt `.pt` cache — tensors are bit-identical, verified
by round-trip comparison against the source files. Encoder: UMT5-XXL, context length 128.