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- tokenizer_config.json +10 -0
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- unis_vision_tokens.txt +0 -0
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| 1 |
+
<div align="center">
|
| 2 |
+
<h1>Xiaomi-Robotics-U0: Unified Embodied Synthesis with World Foundation Models</h1>
|
| 3 |
+
<p>
|
| 4 |
+
Xiaomi Robotics
|
| 5 |
+
</p>
|
| 6 |
+
<p>
|
| 7 |
+
<strong>Project Page</strong>: <a href="https://robotics.xiaomi.com/xiaomi-robotics-u0.html">Website</a> |
|
| 8 |
+
<strong>HF Models</strong>: <a href="https://huggingface.co/collections/XiaomiRobotics/xiaomi-robotics-u0">Collection</a> |
|
| 9 |
+
<strong>Paper</strong>: TBD
|
| 10 |
+
</p>
|
| 11 |
+
</div>
|
| 12 |
+
|
| 13 |
+
<div align="center">
|
| 14 |
+
<img src="assets/architecture.png" alt="Xiaomi-Robotics-U0 model architecture with autoregressive generation and FlashAR acceleration." width="100%" />
|
| 15 |
+
</div>
|
| 16 |
+
|
| 17 |
+
| | **Highlight** | **Summary** |
|
| 18 |
+
| :-: | :-- | :-- |
|
| 19 |
+
| 🧠 | **World Foundation Model** | A 38B autoregressive model for text, images, and embodied observations, initialized from EMU3.5. |
|
| 20 |
+
| 🧩 | **Unified Token Space** | Uses a shared discrete visual tokenizer and a single next-token objective across multimodal sequences. |
|
| 21 |
+
| 🤖 | **Embodied Synthesis** | Bridges foundation image generation with robot-centric scene, transfer, and video generation. |
|
| 22 |
+
| ⚡ | **Xiaomi-Robotics-U0-FlashAR Acceleration** | Decodes visual tokens in anti-diagonal groups and supports vLLM batching for high-resolution inference. |
|
| 23 |
+
| 📦 | **Open Inference Repo** | Provides inference code, composable configs, Gradio entry points, and AR / FlashAR vLLM patch sets. |
|
| 24 |
+
| 📈 | **1024x1024 T2I Speed** | On one H20, FlashAR vLLM reaches 5.44 s/img, 82.86x faster than AR eager and 3.04x faster than FlashAR eager. |
|
| 25 |
+
|
| 26 |
+
<div align="center">
|
| 27 |
+
<img src="assets/illustrate.png" alt="Xiaomi-Robotics-U0 task examples across image generation, embodied scene generation, transfer, and video generation." width="100%" />
|
| 28 |
+
</div>
|
| 29 |
+
|
| 30 |
+
Xiaomi-Robotics-U0 exposes five public task types through one autoregressive framework:
|
| 31 |
+
|
| 32 |
+
| | **Task** | **Input → Output** |
|
| 33 |
+
| :-: | :-- | :-- |
|
| 34 |
+
| 🎨 | **T2I** | Text prompt → image. |
|
| 35 |
+
| 🖼️ | **X2I** | Reference image plus instruction → generated or edited image. |
|
| 36 |
+
| 🧭 | **Scene Gen** | Scene and task description → multi-view embodied observations. |
|
| 37 |
+
| 🔁 | **Transfer** | Conditioned embodied observation → target RGB multi-view scene. |
|
| 38 |
+
| 🎬 | **Video Gen** | Initial observation and task context → embodied video rollout. |
|
| 39 |
+
|
| 40 |
+
## News
|
| 41 |
+
|
| 42 |
+
- [July 2026] 🎉 Released the Technical Report.
|
| 43 |
+
- [July 2026] 🔥 Released Xiaomi-Robotics-U0 and Xiaomi-Robotics-U0-FlashAR weights.
|
| 44 |
+
- [July 2026] 💻 Inference code and scripts are now live!
|
| 45 |
+
|
| 46 |
+
## Table of Contents
|
| 47 |
+
|
| 48 |
+
1. [Model & Weights](#1-model--weights)
|
| 49 |
+
2. [Quick Start](#2-quick-start)
|
| 50 |
+
3. [Gradio Demo](#3-gradio-demo)
|
| 51 |
+
4. [Citation](#4-citation)
|
| 52 |
+
|
| 53 |
+
## 1. Model & Weights
|
| 54 |
+
|
| 55 |
+
The currently released model weights support `Scene Gen`, `Transfer`, `T2I`, and `X2I`.
|
| 56 |
+
|
| 57 |
+
| Model name | HF Weight |
|
| 58 |
+
| ---------- | --------- |
|
| 59 |
+
| Xiaomi-Robotics-U0 | [Hugging Face](https://huggingface.co/XiaomiRobotics/Xiaomi-Robotics-U0) |
|
| 60 |
+
| Xiaomi-Robotics-U0-FlashAR | [Hugging Face](https://huggingface.co/XiaomiRobotics/Xiaomi-Robotics-U0-FlashAR) |
|
| 61 |
+
| VisionTokenizer | [Hugging Face](https://huggingface.co/BAAI/Emu3.5-VisionTokenizer/) |
|
| 62 |
+
|
| 63 |
+
The `Xiaomi-Robotics-U0-Video` checkpoint is coming soon.
|
| 64 |
+
|
| 65 |
+
## 2. Quick Start
|
| 66 |
+
|
| 67 |
+
### Environment Setup
|
| 68 |
+
|
| 69 |
+
Create one conda environment for the backend you plan to run:
|
| 70 |
+
|
| 71 |
+
| Use case | Conda environment | Notes |
|
| 72 |
+
| -------- | ----------------- | ----- |
|
| 73 |
+
| Eager inference | `xr-u0-eager` | Works for both `--engine ar` and `--engine flashar`. |
|
| 74 |
+
| AR vLLM inference | `xr-u0-ar-vllm` | Applies the AR vLLM patch set. |
|
| 75 |
+
| Xiaomi-Robotics-U0-FlashAR vLLM inference | `xr-u0-flashar-vllm` | Applies the FlashAR vLLM patch set for speed-up. |
|
| 76 |
+
|
| 77 |
+
```bash
|
| 78 |
+
git clone https://github.com/XiaomiRobotics/Xiaomi-Robotics-U0.git
|
| 79 |
+
cd Xiaomi-Robotics-U0
|
| 80 |
+
```
|
| 81 |
+
|
| 82 |
+
For eager inference:
|
| 83 |
+
|
| 84 |
+
```bash
|
| 85 |
+
conda create -n xr-u0-eager python=3.10 -y
|
| 86 |
+
conda activate xr-u0-eager
|
| 87 |
+
pip install -U pip
|
| 88 |
+
pip install -r requirements-ar.txt
|
| 89 |
+
pip install -e .
|
| 90 |
+
```
|
| 91 |
+
|
| 92 |
+
For AR vLLM inference:
|
| 93 |
+
|
| 94 |
+
```bash
|
| 95 |
+
conda create -n xr-u0-ar-vllm python=3.12 -y
|
| 96 |
+
conda activate xr-u0-ar-vllm
|
| 97 |
+
pip install -U pip
|
| 98 |
+
pip install -r requirements-vllm.txt
|
| 99 |
+
pip install -e .
|
| 100 |
+
python -m xr_u0_ar.apply_vllm_patches
|
| 101 |
+
```
|
| 102 |
+
|
| 103 |
+
For Xiaomi-Robotics-U0-FlashAR vLLM inference:
|
| 104 |
+
|
| 105 |
+
```bash
|
| 106 |
+
conda create -n xr-u0-flashar-vllm python=3.12 -y
|
| 107 |
+
conda activate xr-u0-flashar-vllm
|
| 108 |
+
pip install -U pip
|
| 109 |
+
pip install -r requirements-vllm.txt
|
| 110 |
+
pip install -e .
|
| 111 |
+
python -m xr_u0_flashar.apply_vllm_patches
|
| 112 |
+
```
|
| 113 |
+
|
| 114 |
+
Keep the AR and FlashAR vLLM patch sets in separate conda environments. The
|
| 115 |
+
patch scripts check that the installed vLLM version is exactly `0.11.0`.
|
| 116 |
+
|
| 117 |
+
### RGB Transfer Depth Setup (Optional)
|
| 118 |
+
|
| 119 |
+
RGB Transfer uses Depth Anything 3 (DA3) to convert RGB reference images into
|
| 120 |
+
inverse-depth maps before Xiaomi-Robotics-U0 inference. Install the optional DA3
|
| 121 |
+
dependencies in the same conda environment that will run Xiaomi-Robotics-U0:
|
| 122 |
+
|
| 123 |
+
```bash
|
| 124 |
+
pip install -e ".[depth]"
|
| 125 |
+
python -m pip install --no-deps "depth-anything-3 @ git+https://github.com/ByteDance-Seed/Depth-Anything-3.git"
|
| 126 |
+
```
|
| 127 |
+
|
| 128 |
+
The default DA3 model is `depth-anything/DA3-LARGE-1.1`. You can pass the Hub ID
|
| 129 |
+
directly, or override it with a local `DA3-LARGE-1.1` directory:
|
| 130 |
+
|
| 131 |
+
```bash
|
| 132 |
+
python scripts/inference.py \
|
| 133 |
+
--engine flashar --backend vllm --task transfer \
|
| 134 |
+
--input-image-type rgb \
|
| 135 |
+
--da3-model-path depth-anything/DA3-LARGE-1.1
|
| 136 |
+
```
|
| 137 |
+
|
| 138 |
+
You can also download it first and pass the local directory:
|
| 139 |
+
|
| 140 |
+
```bash
|
| 141 |
+
huggingface-cli download depth-anything/DA3-LARGE-1.1 \
|
| 142 |
+
--local-dir <local-da3-model-dir>
|
| 143 |
+
```
|
| 144 |
+
|
| 145 |
+
### Configuration
|
| 146 |
+
|
| 147 |
+
Xiaomi-Robotics-U0 uses composable Python config files. Values such as `model_path`,
|
| 148 |
+
`tokenizer_path`, and `vq_path` can be local directories or HuggingFace Hub IDs
|
| 149 |
+
for automatic download.
|
| 150 |
+
|
| 151 |
+
| File | What to edit |
|
| 152 |
+
| ---- | ------------ |
|
| 153 |
+
| `configs/base.py` | Model, tokenizer, and `VisionTokenizer` paths: `model_path`, `tokenizer_path`, `vq_path`. |
|
| 154 |
+
| `configs/tasks/*.py` | Per-task examples, prompts, CFG, shapes, and input images. |
|
| 155 |
+
| `configs/tasks/common.py` | Shared task helpers and common sampling parameters. |
|
| 156 |
+
| `configs/runtimes.py` | Eager/vLLM runtime parameters such as `max_num_seqs`, `max_num_batched_tokens`, and `gpu_memory_utilization`. |
|
| 157 |
+
| `configs/profiles.py` | Resource profiles. `multi-gpu` sets eager device mapping or vLLM tensor parallelism. |
|
| 158 |
+
|
| 159 |
+
CLI arguments override the config files, which is useful for quick tests:
|
| 160 |
+
|
| 161 |
+
```bash
|
| 162 |
+
python scripts/inference.py \
|
| 163 |
+
--engine ar --backend eager --task t2i \
|
| 164 |
+
--model-path <Xiaomi-Robotics-U0-HF-ID-or-local-path> \
|
| 165 |
+
--tokenizer-path <Xiaomi-Robotics-U0-HF-ID-or-local-path> \
|
| 166 |
+
--vq-path <VisionTokenizer-HF-ID-or-local-path> \
|
| 167 |
+
--dry-run
|
| 168 |
+
```
|
| 169 |
+
|
| 170 |
+
Use `--dry-run` whenever you want to inspect the final composed config without
|
| 171 |
+
loading a model.
|
| 172 |
+
|
| 173 |
+
### Inference
|
| 174 |
+
|
| 175 |
+
All tasks use the same entry point:
|
| 176 |
+
|
| 177 |
+
```bash
|
| 178 |
+
python scripts/inference.py \
|
| 179 |
+
--engine <ar|flashar> \
|
| 180 |
+
--backend <eager|vllm> \
|
| 181 |
+
--task <t2i|x2i|scene-gen|transfer>
|
| 182 |
+
```
|
| 183 |
+
|
| 184 |
+
Use `--engine ar` with `Xiaomi-Robotics-U0` for `T2I`, `X2I`, `Scene Gen`, and `Transfer`.
|
| 185 |
+
Use `--engine flashar` with `Xiaomi-Robotics-U0-FlashAR` to speed up those tasks.
|
| 186 |
+
The `Video Gen` code remains available through `--engine ar --task video-gen`, while
|
| 187 |
+
the required `Xiaomi-Robotics-U0-Video` checkpoint is coming soon.
|
| 188 |
+
|
| 189 |
+
Task examples come from `configs/tasks/*.py`. Override them from the CLI with
|
| 190 |
+
`--prompt` and `--reference-image` when needed. Transfer uses depth-map
|
| 191 |
+
references by default; RGB Transfer needs `--input-image-type rgb`; see
|
| 192 |
+
[RGB Transfer Depth Setup](#rgb-transfer-depth-setup).
|
| 193 |
+
|
| 194 |
+
Xiaomi-Robotics-U0-FlashAR vLLM keeps `enable_prefix_caching = False` by default.
|
| 195 |
+
|
| 196 |
+
### Distributed Inference
|
| 197 |
+
|
| 198 |
+
Set visible GPUs and select the multi-GPU profile:
|
| 199 |
+
|
| 200 |
+
```bash
|
| 201 |
+
CUDA_VISIBLE_DEVICES=0,1 \
|
| 202 |
+
python scripts/inference.py --engine flashar --backend vllm --task t2i --profile multi-gpu
|
| 203 |
+
```
|
| 204 |
+
|
| 205 |
+
## 3. Gradio Demo
|
| 206 |
+
|
| 207 |
+
The demo runs Xiaomi-Robotics-U0-FlashAR through a FastAPI model server and a Gradio UI. It
|
| 208 |
+
covers `T2I`, `X2I`, `Scene Gen`, and `Transfer`.
|
| 209 |
+
|
| 210 |
+
Use the `xr-u0-flashar-vllm` environment and install the UI dependencies:
|
| 211 |
+
|
| 212 |
+
```bash
|
| 213 |
+
conda activate xr-u0-flashar-vllm
|
| 214 |
+
pip install -e ".[demo]"
|
| 215 |
+
```
|
| 216 |
+
|
| 217 |
+
Set paths with environment variables or the matching server CLI arguments:
|
| 218 |
+
|
| 219 |
+
```bash
|
| 220 |
+
export XR_U0_FLASHAR_MODEL_DIR=<flashar-model-or-local-path>
|
| 221 |
+
export XR_U0_FLASHAR_TOKENIZER_DIR=<flashar-tokenizer-or-local-path>
|
| 222 |
+
export XR_U0_VISION_TOKENIZER_DIR=<VisionTokenizer-HF-ID-or-local-path>
|
| 223 |
+
# Optional; only needed when using RGB images for Transfer.
|
| 224 |
+
export XR_U0_DA3_MODEL_PATH=depth-anything/DA3-LARGE-1.1
|
| 225 |
+
```
|
| 226 |
+
|
| 227 |
+
`XR_U0_DA3_MODEL_PATH` is optional. Depth-map Transfer examples do not use it.
|
| 228 |
+
For RGB Transfer, first install the optional DA3 dependencies as described in
|
| 229 |
+
[RGB Transfer Depth Setup](#rgb-transfer-depth-setup).
|
| 230 |
+
|
| 231 |
+
Start the API server and UI in two terminals:
|
| 232 |
+
|
| 233 |
+
```bash
|
| 234 |
+
CUDA_VISIBLE_DEVICES=0 python demo/flashar_api_server.py --load-on-startup
|
| 235 |
+
# CUDA_VISIBLE_DEVICES=0,1 \
|
| 236 |
+
# python demo/flashar_api_server.py --load-on-startup --tensor-parallel-size 2
|
| 237 |
+
# export XR_U0_FLASHAR_TP=2
|
| 238 |
+
```
|
| 239 |
+
|
| 240 |
+
```bash
|
| 241 |
+
python demo/flashar_gradio_app.py --api-url http://127.0.0.1:8000
|
| 242 |
+
```
|
| 243 |
+
|
| 244 |
+
Open `http://127.0.0.1:7860`. Outputs are saved under
|
| 245 |
+
`outputs/gradio_flashar/` with neighboring audit JSON files.
|
| 246 |
+
|
| 247 |
+
## 4. Citation
|
| 248 |
+
|
| 249 |
+
Citation information is TBD.
|
assets/architecture.png
ADDED
|
Git LFS Details
|
assets/illustrate.png
ADDED
|
Git LFS Details
|
config.json
ADDED
|
@@ -0,0 +1,44 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"UNISForCausalLM"
|
| 4 |
+
],
|
| 5 |
+
"auto_map": {
|
| 6 |
+
"AutoConfig": "configuration_unis.UNISConfig"
|
| 7 |
+
},
|
| 8 |
+
"attention_dropout": 0.1,
|
| 9 |
+
"boi_token_id": 151852,
|
| 10 |
+
"bos_token_id": 151849,
|
| 11 |
+
"eof_token_id": 151847,
|
| 12 |
+
"eoi_token_id": 151853,
|
| 13 |
+
"eol_token_id": 151846,
|
| 14 |
+
"eos_token_id": 151850,
|
| 15 |
+
"head_dim": 128,
|
| 16 |
+
"hidden_act": "silu",
|
| 17 |
+
"hidden_size": 5120,
|
| 18 |
+
"image_area": 518400,
|
| 19 |
+
"img_token_id": 151851,
|
| 20 |
+
"initializer_range": 0.02,
|
| 21 |
+
"intermediate_size": 25600,
|
| 22 |
+
"max_position_embeddings": 16384,
|
| 23 |
+
"model_type": "UNIS",
|
| 24 |
+
"num_attention_heads": 64,
|
| 25 |
+
"num_hidden_layers": 64,
|
| 26 |
+
"num_key_value_heads": 8,
|
| 27 |
+
"pad_token_id": 151643,
|
| 28 |
+
"pretraining_tp": 1,
|
| 29 |
+
"rms_norm_eps": 1e-06,
|
| 30 |
+
"rope_scaling": null,
|
| 31 |
+
"rope_theta": 1000000,
|
| 32 |
+
"tie_word_embeddings": false,
|
| 33 |
+
"torch_dtype": "bfloat16",
|
| 34 |
+
"transformers_version": "4.51.3",
|
| 35 |
+
"use_cache": true,
|
| 36 |
+
"vocab_size": 282926,
|
| 37 |
+
"qkv_bias": true,
|
| 38 |
+
"image_size": [
|
| 39 |
+
262144
|
| 40 |
+
],
|
| 41 |
+
"patch_size": 16,
|
| 42 |
+
"ibq_cache_dir": null,
|
| 43 |
+
"vq_type": "ibq"
|
| 44 |
+
}
|
configuration_unis.py
ADDED
|
@@ -0,0 +1,217 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# coding=utf-8
|
| 2 |
+
# Copyright 2024 The Emu team, BAAI and The HuggingFace Inc. team. All rights reserved.
|
| 3 |
+
#
|
| 4 |
+
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
| 5 |
+
# and OPT implementations in this library. It has been modified from its
|
| 6 |
+
# original forms to accommodate minor architectural differences compared
|
| 7 |
+
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
| 8 |
+
#
|
| 9 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 10 |
+
# you may not use this file except in compliance with the License.
|
| 11 |
+
# You may obtain a copy of the License at
|
| 12 |
+
#
|
| 13 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
| 14 |
+
#
|
| 15 |
+
# Unless required by applicable law or agreed to in writing, software
|
| 16 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 17 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 18 |
+
# See the License for the specific language governing permissions and
|
| 19 |
+
# limitations under the License.
|
| 20 |
+
""" Xiaomi-Robotics-U0 model configuration"""
|
| 21 |
+
|
| 22 |
+
from typing import Optional
|
| 23 |
+
|
| 24 |
+
from transformers.configuration_utils import PretrainedConfig
|
| 25 |
+
from transformers.utils import logging
|
| 26 |
+
|
| 27 |
+
|
| 28 |
+
logger = logging.get_logger(__name__)
|
| 29 |
+
|
| 30 |
+
UNIS_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
|
| 31 |
+
|
| 32 |
+
|
| 33 |
+
class UNISConfig(PretrainedConfig):
|
| 34 |
+
r"""
|
| 35 |
+
This is the configuration class to store the configuration of a [`UNISModel`]. It is used to instantiate an Xiaomi-Robotics-U0
|
| 36 |
+
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
|
| 37 |
+
defaults will yield a similar configuration to that of the Xiaomi-Robotics-U0-8B.
|
| 38 |
+
|
| 39 |
+
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
| 40 |
+
documentation from [`PretrainedConfig`] for more information.
|
| 41 |
+
|
| 42 |
+
|
| 43 |
+
Args:
|
| 44 |
+
vocab_size (`int`, *optional*, defaults to 184622):
|
| 45 |
+
Vocabulary size of the Xiaomi-Robotics-U0 model. Defines the number of different tokens that can be represented by the
|
| 46 |
+
`inputs_ids` passed when calling [`UNISModel`]
|
| 47 |
+
hidden_size (`int`, *optional*, defaults to 4096):
|
| 48 |
+
Dimension of the hidden representations.
|
| 49 |
+
intermediate_size (`int`, *optional*, defaults to 14336):
|
| 50 |
+
Dimension of the MLP representations.
|
| 51 |
+
num_hidden_layers (`int`, *optional*, defaults to 32):
|
| 52 |
+
Number of hidden layers in the Transformer decoder.
|
| 53 |
+
num_attention_heads (`int`, *optional*, defaults to 32):
|
| 54 |
+
Number of attention heads for each attention layer in the Transformer decoder.
|
| 55 |
+
num_key_value_heads (`int`, *optional*, defaults to 8):
|
| 56 |
+
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
| 57 |
+
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
| 58 |
+
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
| 59 |
+
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
| 60 |
+
by meanpooling all the original heads within that group. For more details checkout [this
|
| 61 |
+
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
|
| 62 |
+
`num_attention_heads`.
|
| 63 |
+
head_dim (`int`, *optional*, defaults to 128):
|
| 64 |
+
The attention head dimension.
|
| 65 |
+
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
| 66 |
+
The non-linear activation function (function or string) in the decoder.
|
| 67 |
+
max_position_embeddings (`int`, *optional*, defaults to 9216):
|
| 68 |
+
The maximum sequence length that this model might ever be used with. Emu supports up to 9216 tokens,
|
| 69 |
+
initializer_range (`float`, *optional*, defaults to 0.02):
|
| 70 |
+
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
| 71 |
+
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
|
| 72 |
+
The epsilon used by the rms normalization layers.
|
| 73 |
+
use_cache (`bool`, *optional*, defaults to `True`):
|
| 74 |
+
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
| 75 |
+
relevant if `config.is_decoder=True`.
|
| 76 |
+
pad_token_id (`int`, *optional*, 151643):
|
| 77 |
+
Padding token id.
|
| 78 |
+
bos_token_id (`int`, *optional*, defaults to 151849):
|
| 79 |
+
Beginning of stream token id.
|
| 80 |
+
eos_token_id (`int`, *optional*, defaults to 151850):
|
| 81 |
+
End of stream token id.
|
| 82 |
+
img_token_id (`int`, *optional*, defaults to 151851):
|
| 83 |
+
image token id.
|
| 84 |
+
boi_token_id (`int`, *optional*, defaults to 151852):
|
| 85 |
+
Beginning of image token id.
|
| 86 |
+
eoi_token_id (`int`, *optional*, defaults to 151853):
|
| 87 |
+
End of image token id.
|
| 88 |
+
eol_token_id (`int`, *optional*, defaults to 151846):
|
| 89 |
+
End of line token id.
|
| 90 |
+
eof_token_id (`int`, *optional*, defaults to 151847):
|
| 91 |
+
End of line token id.
|
| 92 |
+
image_area (`int`, *optional*, defaults to 1024 * 1024)
|
| 93 |
+
generated image area (image area used in training)
|
| 94 |
+
pretraining_tp (`int`, *optional*, defaults to 1):
|
| 95 |
+
Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
|
| 96 |
+
document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
|
| 97 |
+
necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
|
| 98 |
+
issue](https://github.com/pytorch/pytorch/issues/76232).
|
| 99 |
+
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
| 100 |
+
Whether to tie weight embeddings
|
| 101 |
+
rope_theta (`float`, *optional*, defaults to 1_000_000.0):
|
| 102 |
+
The base period of the RoPE embeddings.
|
| 103 |
+
rope_scaling (`Dict`, *optional*):
|
| 104 |
+
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
|
| 105 |
+
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
|
| 106 |
+
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
|
| 107 |
+
`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
|
| 108 |
+
these scaling strategies behave:
|
| 109 |
+
https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
|
| 110 |
+
experimental feature, subject to breaking API changes in future versions.
|
| 111 |
+
attention_dropout (`float`, *optional*, defaults to 0.1):
|
| 112 |
+
The dropout ratio for the attention probabilities.
|
| 113 |
+
|
| 114 |
+
```python
|
| 115 |
+
>>> from transformers import UNISModel, UNISConfig
|
| 116 |
+
|
| 117 |
+
>>> # Initializing a Xiaomi-Robotics-U0-8b style configuration
|
| 118 |
+
>>> configuration = UNISConfig()
|
| 119 |
+
|
| 120 |
+
>>> # Initializing a model from the Xiaomi-Robotics-U0-8b style configuration
|
| 121 |
+
>>> model = UNISModel(configuration)
|
| 122 |
+
|
| 123 |
+
>>> # Accessing the model configuration
|
| 124 |
+
>>> configuration = model.config
|
| 125 |
+
```"""
|
| 126 |
+
|
| 127 |
+
model_type = "UNIS"
|
| 128 |
+
keys_to_ignore_at_inference = ["past_key_values"]
|
| 129 |
+
|
| 130 |
+
def __init__(
|
| 131 |
+
self,
|
| 132 |
+
vocab_size: int = 184622,
|
| 133 |
+
hidden_size: int = 4096,
|
| 134 |
+
intermediate_size: int = 14336,
|
| 135 |
+
num_hidden_layers: int = 32,
|
| 136 |
+
num_attention_heads: int = 32,
|
| 137 |
+
num_key_value_heads: Optional[int] = 8,
|
| 138 |
+
head_dim=128,
|
| 139 |
+
hidden_act: str = "silu",
|
| 140 |
+
max_position_embeddings: int = 9216,
|
| 141 |
+
initializer_range: float = 0.02,
|
| 142 |
+
rms_norm_eps: float = 1e-5,
|
| 143 |
+
use_cache: bool = True,
|
| 144 |
+
pad_token_id: int = 151643,
|
| 145 |
+
bos_token_id: int = 151849,
|
| 146 |
+
eos_token_id: int = 151850,
|
| 147 |
+
img_token_id: int = 151851,
|
| 148 |
+
boi_token_id: int = 151852,
|
| 149 |
+
eoi_token_id: int = 151853,
|
| 150 |
+
eol_token_id: int = 151846,
|
| 151 |
+
eof_token_id: int = 151847,
|
| 152 |
+
image_area: int = 1024 * 1024,
|
| 153 |
+
pretraining_tp: int = 1,
|
| 154 |
+
tie_word_embeddings: bool = False,
|
| 155 |
+
rope_theta: float = 1000000.0,
|
| 156 |
+
rope_scaling: Optional = None,
|
| 157 |
+
attention_dropout: float = 0.1,
|
| 158 |
+
**kwargs,
|
| 159 |
+
):
|
| 160 |
+
self.vocab_size = vocab_size
|
| 161 |
+
self.max_position_embeddings = max_position_embeddings
|
| 162 |
+
self.hidden_size = hidden_size
|
| 163 |
+
self.intermediate_size = intermediate_size
|
| 164 |
+
self.num_hidden_layers = num_hidden_layers
|
| 165 |
+
self.num_attention_heads = num_attention_heads
|
| 166 |
+
|
| 167 |
+
# for backward compatibility
|
| 168 |
+
if num_key_value_heads is None:
|
| 169 |
+
num_key_value_heads = num_attention_heads
|
| 170 |
+
|
| 171 |
+
self.num_key_value_heads = num_key_value_heads
|
| 172 |
+
self.head_dim = head_dim
|
| 173 |
+
self.hidden_act = hidden_act
|
| 174 |
+
self.initializer_range = initializer_range
|
| 175 |
+
self.rms_norm_eps = rms_norm_eps
|
| 176 |
+
self.pretraining_tp = pretraining_tp
|
| 177 |
+
self.use_cache = use_cache
|
| 178 |
+
self.rope_theta = rope_theta
|
| 179 |
+
self.rope_scaling = rope_scaling
|
| 180 |
+
self._rope_scaling_validation()
|
| 181 |
+
self.attention_dropout = attention_dropout
|
| 182 |
+
|
| 183 |
+
self.img_token_id = img_token_id
|
| 184 |
+
self.boi_token_id = boi_token_id
|
| 185 |
+
self.eoi_token_id = eoi_token_id
|
| 186 |
+
self.eol_token_id = eol_token_id
|
| 187 |
+
self.eof_token_id = eof_token_id
|
| 188 |
+
self.image_area = image_area
|
| 189 |
+
|
| 190 |
+
super().__init__(
|
| 191 |
+
pad_token_id=pad_token_id,
|
| 192 |
+
bos_token_id=bos_token_id,
|
| 193 |
+
eos_token_id=eos_token_id,
|
| 194 |
+
tie_word_embeddings=tie_word_embeddings,
|
| 195 |
+
**kwargs,
|
| 196 |
+
)
|
| 197 |
+
|
| 198 |
+
def _rope_scaling_validation(self):
|
| 199 |
+
"""
|
| 200 |
+
Validate the `rope_scaling` configuration.
|
| 201 |
+
"""
|
| 202 |
+
if self.rope_scaling is None:
|
| 203 |
+
return
|
| 204 |
+
|
| 205 |
+
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
|
| 206 |
+
raise ValueError(
|
| 207 |
+
"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
|
| 208 |
+
f"got {self.rope_scaling}"
|
| 209 |
+
)
|
| 210 |
+
rope_scaling_type = self.rope_scaling.get("type", None)
|
| 211 |
+
rope_scaling_factor = self.rope_scaling.get("factor", None)
|
| 212 |
+
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
|
| 213 |
+
raise ValueError(
|
| 214 |
+
f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
|
| 215 |
+
)
|
| 216 |
+
if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
|
| 217 |
+
raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
|
generation_config.json
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_from_model_config": true,
|
| 3 |
+
"bos_token_id": 151849,
|
| 4 |
+
"eos_token_id": 151850,
|
| 5 |
+
"pad_token_id": 151643,
|
| 6 |
+
"transformers_version": "4.51.3"
|
| 7 |
+
}
|
model-00001-of-00014.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
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|
| 3 |
+
size 4952416200
|
model-00002-of-00014.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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|
| 3 |
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size 4959876616
|
model-00003-of-00014.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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|
| 3 |
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size 4875989712
|
model-00004-of-00014.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
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|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
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|
| 3 |
+
size 4875989752
|
model-00005-of-00014.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
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|
| 3 |
+
size 4875989752
|
model-00006-of-00014.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
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|
| 3 |
+
size 4875989752
|
model-00007-of-00014.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
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|
| 3 |
+
size 4875989752
|
model-00008-of-00014.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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|
| 3 |
+
size 4875989752
|
model-00009-of-00014.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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|
| 3 |
+
size 4875989752
|
model-00010-of-00014.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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|
| 3 |
+
size 4875989752
|
model-00011-of-00014.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:9ae5286d40ed4bf4311fff1c1fa8406ab06325cb05f2452c77f2ae5de3276999
|
| 3 |
+
size 4875989752
|
model-00012-of-00014.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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|
| 3 |
+
size 4875989752
|
model-00013-of-00014.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:3f14ffc312e5945f85e8094c021f97878e4130d5a0a56bd278736dd916085aac
|
| 3 |
+
size 4875989752
|
model-00014-of-00014.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3d0eef0a62edcfc9fea51775619e1901b2a43dfbd556fc68fd1918e358999b7c
|
| 3 |
+
size 4658823704
|
model.safetensors.index.json
ADDED
|
@@ -0,0 +1,714 @@
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|
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|
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|
|
|
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|
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| 643 |
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|
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|
| 645 |
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|
| 646 |
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|
| 647 |
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
| 666 |
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|
| 667 |
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|
| 668 |
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|
| 669 |
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|
| 670 |
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|
| 671 |
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|
| 672 |
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|
| 673 |
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|
| 674 |
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|
| 675 |
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|
| 676 |
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|
| 677 |
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|
| 678 |
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|
| 679 |
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|
| 680 |
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|
| 681 |
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|
| 682 |
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| 683 |
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|
| 684 |
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|
| 685 |
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|
| 686 |
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|
| 687 |
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|
| 688 |
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|
| 689 |
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"model.layers.7.self_attn.v_proj.weight": "model-00002-of-00014.safetensors",
|
| 690 |
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|
| 691 |
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|
| 692 |
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"model.layers.8.mlp.gate_proj.weight": "model-00003-of-00014.safetensors",
|
| 693 |
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|
| 694 |
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|
| 695 |
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"model.layers.8.self_attn.k_norm.weight": "model-00003-of-00014.safetensors",
|
| 696 |
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|
| 697 |
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|
| 698 |
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|
| 699 |
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|
| 700 |
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|
| 701 |
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"model.layers.9.input_layernorm.weight": "model-00003-of-00014.safetensors",
|
| 702 |
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"model.layers.9.mlp.down_proj.weight": "model-00003-of-00014.safetensors",
|
| 703 |
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"model.layers.9.mlp.gate_proj.weight": "model-00003-of-00014.safetensors",
|
| 704 |
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"model.layers.9.mlp.up_proj.weight": "model-00003-of-00014.safetensors",
|
| 705 |
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"model.layers.9.post_attention_layernorm.weight": "model-00003-of-00014.safetensors",
|
| 706 |
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"model.layers.9.self_attn.k_norm.weight": "model-00003-of-00014.safetensors",
|
| 707 |
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"model.layers.9.self_attn.k_proj.weight": "model-00003-of-00014.safetensors",
|
| 708 |
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"model.layers.9.self_attn.o_proj.weight": "model-00003-of-00014.safetensors",
|
| 709 |
+
"model.layers.9.self_attn.q_norm.weight": "model-00003-of-00014.safetensors",
|
| 710 |
+
"model.layers.9.self_attn.q_proj.weight": "model-00003-of-00014.safetensors",
|
| 711 |
+
"model.layers.9.self_attn.v_proj.weight": "model-00003-of-00014.safetensors",
|
| 712 |
+
"model.norm.weight": "model-00014-of-00014.safetensors"
|
| 713 |
+
}
|
| 714 |
+
}
|
tokenization_unis.py
ADDED
|
@@ -0,0 +1,279 @@
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|
| 1 |
+
# Copyright 2025 BAAI. and/or its affiliates.
|
| 2 |
+
# SPDX-License-Identifier: Apache-2.0
|
| 3 |
+
"""Tokenization classes for Xiaomi-Robotics-U0."""
|
| 4 |
+
|
| 5 |
+
import base64
|
| 6 |
+
import logging
|
| 7 |
+
import os
|
| 8 |
+
import unicodedata
|
| 9 |
+
from typing import Collection, Dict, List, Set, Tuple, Union
|
| 10 |
+
|
| 11 |
+
import tiktoken
|
| 12 |
+
from transformers import PreTrainedTokenizer, AddedToken
|
| 13 |
+
|
| 14 |
+
logger = logging.getLogger(__name__)
|
| 15 |
+
|
| 16 |
+
|
| 17 |
+
VOCAB_FILES_NAMES = {"vocab_file": "unis.tiktoken"}
|
| 18 |
+
|
| 19 |
+
PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
|
| 20 |
+
ENDOFTEXT = "<|endoftext|>"
|
| 21 |
+
IMSTART = "<|im_start|>"
|
| 22 |
+
IMEND = "<|im_end|>"
|
| 23 |
+
# as the default behavior is changed to allow special tokens in
|
| 24 |
+
# regular texts, the surface forms of special tokens need to be
|
| 25 |
+
# as different as possible to minimize the impact
|
| 26 |
+
EXTRAS = tuple((f"<|extra_{i}|>" for i in range(205)))
|
| 27 |
+
# changed to use actual index to avoid misconfiguration with vocabulary expansion
|
| 28 |
+
SPECIAL_START_ID = 151643
|
| 29 |
+
|
| 30 |
+
|
| 31 |
+
def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
|
| 32 |
+
with open(tiktoken_bpe_file, "rb") as f:
|
| 33 |
+
contents = f.read()
|
| 34 |
+
return {
|
| 35 |
+
base64.b64decode(token): int(rank)
|
| 36 |
+
for token, rank in (line.split() for line in contents.splitlines() if line)
|
| 37 |
+
}
|
| 38 |
+
|
| 39 |
+
|
| 40 |
+
class UNISTokenizer(PreTrainedTokenizer):
|
| 41 |
+
"""Xiaomi-Robotics-U0 tokenizer."""
|
| 42 |
+
|
| 43 |
+
vocab_files_names = VOCAB_FILES_NAMES
|
| 44 |
+
|
| 45 |
+
def __init__(
|
| 46 |
+
self,
|
| 47 |
+
vocab_file,
|
| 48 |
+
errors="replace",
|
| 49 |
+
extra_vocab_file=None,
|
| 50 |
+
special_tokens_file=None,
|
| 51 |
+
**kwargs,
|
| 52 |
+
):
|
| 53 |
+
super().__init__(**kwargs)
|
| 54 |
+
|
| 55 |
+
# how to handle errors in decoding UTF-8 byte sequences
|
| 56 |
+
# use ignore if you are in streaming inference
|
| 57 |
+
self.errors = errors
|
| 58 |
+
|
| 59 |
+
self.mergeable_ranks = _load_tiktoken_bpe(vocab_file) # type: Dict[bytes, int]
|
| 60 |
+
|
| 61 |
+
vision_tokens = []
|
| 62 |
+
if special_tokens_file is not None:
|
| 63 |
+
with open(special_tokens_file, 'r') as f:
|
| 64 |
+
vision_tokens = [
|
| 65 |
+
token.strip()
|
| 66 |
+
for token in f.readlines()
|
| 67 |
+
if len(token.strip()) > 0
|
| 68 |
+
]
|
| 69 |
+
SPECIAL_TOKENS = tuple(
|
| 70 |
+
enumerate(
|
| 71 |
+
(
|
| 72 |
+
(
|
| 73 |
+
ENDOFTEXT,
|
| 74 |
+
IMSTART,
|
| 75 |
+
IMEND,
|
| 76 |
+
)
|
| 77 |
+
+ EXTRAS
|
| 78 |
+
+ tuple(vision_tokens)
|
| 79 |
+
),
|
| 80 |
+
start=SPECIAL_START_ID,
|
| 81 |
+
)
|
| 82 |
+
)
|
| 83 |
+
self.special_tokens = {token: index for index, token in SPECIAL_TOKENS}
|
| 84 |
+
self.special_tokens_set = set(t for _, t in SPECIAL_TOKENS)
|
| 85 |
+
|
| 86 |
+
# try load extra vocab from file
|
| 87 |
+
if extra_vocab_file is not None:
|
| 88 |
+
used_ids = set(self.mergeable_ranks.values()) | set(self.special_tokens.values())
|
| 89 |
+
extra_mergeable_ranks = _load_tiktoken_bpe(extra_vocab_file)
|
| 90 |
+
for token, index in extra_mergeable_ranks.items():
|
| 91 |
+
if token in self.mergeable_ranks:
|
| 92 |
+
logger.info(f"extra token {token} exists, skipping")
|
| 93 |
+
continue
|
| 94 |
+
if index in used_ids:
|
| 95 |
+
logger.info(f'the index {index} for extra token {token} exists, skipping')
|
| 96 |
+
continue
|
| 97 |
+
self.mergeable_ranks[token] = index
|
| 98 |
+
# the index may be sparse after this, but don't worry tiktoken.Encoding will handle this
|
| 99 |
+
|
| 100 |
+
enc = tiktoken.Encoding(
|
| 101 |
+
"Xiaomi-Robotics-U0",
|
| 102 |
+
pat_str=PAT_STR,
|
| 103 |
+
mergeable_ranks=self.mergeable_ranks,
|
| 104 |
+
special_tokens=self.special_tokens,
|
| 105 |
+
)
|
| 106 |
+
assert (
|
| 107 |
+
len(self.mergeable_ranks) + len(self.special_tokens) == enc.n_vocab
|
| 108 |
+
), f"{len(self.mergeable_ranks) + len(self.special_tokens)} != {enc.n_vocab} in encoding"
|
| 109 |
+
|
| 110 |
+
self.decoder = {
|
| 111 |
+
v: k for k, v in self.mergeable_ranks.items()
|
| 112 |
+
} # type: dict[int, bytes|str]
|
| 113 |
+
self.decoder.update({v: k for k, v in self.special_tokens.items()})
|
| 114 |
+
|
| 115 |
+
self.tokenizer = enc # type: tiktoken.Encoding
|
| 116 |
+
|
| 117 |
+
self.eod_id = self.tokenizer.eot_token
|
| 118 |
+
|
| 119 |
+
def __getstate__(self):
|
| 120 |
+
# for pickle lovers
|
| 121 |
+
state = self.__dict__.copy()
|
| 122 |
+
del state["tokenizer"]
|
| 123 |
+
return state
|
| 124 |
+
|
| 125 |
+
def __setstate__(self, state):
|
| 126 |
+
# tokenizer is not python native; don't pass it; rebuild it
|
| 127 |
+
self.__dict__.update(state)
|
| 128 |
+
enc = tiktoken.Encoding(
|
| 129 |
+
"Xiaomi-Robotics-U0",
|
| 130 |
+
pat_str=PAT_STR,
|
| 131 |
+
mergeable_ranks=self.mergeable_ranks,
|
| 132 |
+
special_tokens=self.special_tokens,
|
| 133 |
+
)
|
| 134 |
+
self.tokenizer = enc
|
| 135 |
+
|
| 136 |
+
def __len__(self) -> int:
|
| 137 |
+
return self.tokenizer.n_vocab
|
| 138 |
+
|
| 139 |
+
def get_vocab(self) -> Dict[bytes, int]:
|
| 140 |
+
return self.mergeable_ranks
|
| 141 |
+
|
| 142 |
+
def convert_tokens_to_ids(
|
| 143 |
+
self, tokens: Union[bytes, str, List[Union[bytes, str]]]
|
| 144 |
+
) -> List[int]:
|
| 145 |
+
ids = []
|
| 146 |
+
if isinstance(tokens, (str, bytes)):
|
| 147 |
+
if tokens in self.special_tokens:
|
| 148 |
+
return self.special_tokens[tokens]
|
| 149 |
+
else:
|
| 150 |
+
return self.mergeable_ranks.get(tokens)
|
| 151 |
+
for token in tokens:
|
| 152 |
+
if token in self.special_tokens:
|
| 153 |
+
ids.append(self.special_tokens[token])
|
| 154 |
+
else:
|
| 155 |
+
ids.append(self.mergeable_ranks.get(token))
|
| 156 |
+
return ids
|
| 157 |
+
|
| 158 |
+
def _add_tokens(
|
| 159 |
+
self,
|
| 160 |
+
new_tokens: Union[List[str], List[AddedToken]],
|
| 161 |
+
special_tokens: bool = False,
|
| 162 |
+
) -> int:
|
| 163 |
+
if not special_tokens and new_tokens:
|
| 164 |
+
raise ValueError("Adding regular tokens is not supported")
|
| 165 |
+
for token in new_tokens:
|
| 166 |
+
surface_form = token.content if isinstance(token, AddedToken) else token
|
| 167 |
+
if surface_form not in self.special_tokens_set:
|
| 168 |
+
raise ValueError("Adding unknown special tokens is not supported")
|
| 169 |
+
return 0
|
| 170 |
+
|
| 171 |
+
def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
|
| 172 |
+
"""
|
| 173 |
+
Save only the vocabulary of the tokenizer (vocabulary).
|
| 174 |
+
|
| 175 |
+
Returns:
|
| 176 |
+
`Tuple(str)`: Paths to the files saved.
|
| 177 |
+
"""
|
| 178 |
+
file_path = os.path.join(save_directory, "unis.tiktoken")
|
| 179 |
+
with open(file_path, "w", encoding="utf8") as w:
|
| 180 |
+
for k, v in self.mergeable_ranks.items():
|
| 181 |
+
line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
|
| 182 |
+
w.write(line)
|
| 183 |
+
return (file_path,)
|
| 184 |
+
|
| 185 |
+
def tokenize(
|
| 186 |
+
self,
|
| 187 |
+
text: str,
|
| 188 |
+
allowed_special: Union[Set, str] = "all",
|
| 189 |
+
disallowed_special: Union[Collection, str] = (),
|
| 190 |
+
**kwargs,
|
| 191 |
+
) -> List[Union[bytes, str]]:
|
| 192 |
+
"""
|
| 193 |
+
Converts a string in a sequence of tokens.
|
| 194 |
+
|
| 195 |
+
Args:
|
| 196 |
+
text (`str`):
|
| 197 |
+
The sequence to be encoded.
|
| 198 |
+
allowed_special (`Literal["all"]` or `set`):
|
| 199 |
+
The surface forms of the tokens to be encoded as special tokens in regular texts.
|
| 200 |
+
Default to "all".
|
| 201 |
+
disallowed_special (`Literal["all"]` or `Collection`):
|
| 202 |
+
The surface forms of the tokens that should not be in regular texts and trigger errors.
|
| 203 |
+
Default to an empty tuple.
|
| 204 |
+
|
| 205 |
+
kwargs (additional keyword arguments, *optional*):
|
| 206 |
+
Will be passed to the underlying model specific encode method.
|
| 207 |
+
|
| 208 |
+
Returns:
|
| 209 |
+
`List[bytes|str]`: The list of tokens.
|
| 210 |
+
"""
|
| 211 |
+
tokens = []
|
| 212 |
+
text = unicodedata.normalize("NFC", text)
|
| 213 |
+
|
| 214 |
+
# this implementation takes a detour: text -> token id -> token surface forms
|
| 215 |
+
for t in self.tokenizer.encode(
|
| 216 |
+
text, allowed_special=allowed_special, disallowed_special=disallowed_special
|
| 217 |
+
):
|
| 218 |
+
tokens.append(self.decoder[t])
|
| 219 |
+
return tokens
|
| 220 |
+
|
| 221 |
+
def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
|
| 222 |
+
"""
|
| 223 |
+
Converts a sequence of tokens in a single string.
|
| 224 |
+
"""
|
| 225 |
+
text = ""
|
| 226 |
+
temp = b""
|
| 227 |
+
for t in tokens:
|
| 228 |
+
if isinstance(t, str):
|
| 229 |
+
if temp:
|
| 230 |
+
text += temp.decode("utf-8", errors=self.errors)
|
| 231 |
+
temp = b""
|
| 232 |
+
text += t
|
| 233 |
+
elif isinstance(t, bytes):
|
| 234 |
+
temp += t
|
| 235 |
+
else:
|
| 236 |
+
raise TypeError("token should only be of type types or str")
|
| 237 |
+
if temp:
|
| 238 |
+
text += temp.decode("utf-8", errors=self.errors)
|
| 239 |
+
return text
|
| 240 |
+
|
| 241 |
+
@property
|
| 242 |
+
def vocab_size(self):
|
| 243 |
+
return self.tokenizer.n_vocab
|
| 244 |
+
|
| 245 |
+
def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
|
| 246 |
+
"""Converts an id to a token, special tokens included"""
|
| 247 |
+
if index in self.decoder:
|
| 248 |
+
return self.decoder[index]
|
| 249 |
+
raise ValueError("unknown ids")
|
| 250 |
+
|
| 251 |
+
def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
|
| 252 |
+
"""Converts a token to an id using the vocab, special tokens included"""
|
| 253 |
+
if token in self.special_tokens:
|
| 254 |
+
return self.special_tokens[token]
|
| 255 |
+
if token in self.mergeable_ranks:
|
| 256 |
+
return self.mergeable_ranks[token]
|
| 257 |
+
raise ValueError("unknown token")
|
| 258 |
+
|
| 259 |
+
def _tokenize(self, text: str, **kwargs):
|
| 260 |
+
"""
|
| 261 |
+
Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
|
| 262 |
+
vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
|
| 263 |
+
|
| 264 |
+
Do NOT take care of added tokens.
|
| 265 |
+
"""
|
| 266 |
+
raise NotImplementedError
|
| 267 |
+
|
| 268 |
+
def _decode(
|
| 269 |
+
self,
|
| 270 |
+
token_ids: Union[int, List[int]],
|
| 271 |
+
skip_special_tokens: bool = False,
|
| 272 |
+
errors: str = None,
|
| 273 |
+
**kwargs,
|
| 274 |
+
) -> str:
|
| 275 |
+
if isinstance(token_ids, int):
|
| 276 |
+
token_ids = [token_ids]
|
| 277 |
+
if skip_special_tokens:
|
| 278 |
+
token_ids = [i for i in token_ids if i < self.eod_id]
|
| 279 |
+
return self.tokenizer.decode(token_ids, errors=errors or self.errors)
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"model_max_length": 1000000000000000000,
|
| 3 |
+
"tokenizer_class": "UNISTokenizer",
|
| 4 |
+
"auto_map": {
|
| 5 |
+
"AutoTokenizer": [
|
| 6 |
+
"tokenization_unis.UNISTokenizer",
|
| 7 |
+
null
|
| 8 |
+
]
|
| 9 |
+
}
|
| 10 |
+
}
|
unis.tiktoken
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
unis_vision_tokens.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|