pat-vla 0.1.1

Progressive Action Tokenizer framework with FSQ quantization

PAT: Progressive Action Tokenizer

pat is a PyTorch training framework for a progressive action tokenizer inspired by OAT (vendor/oat/oat/tokenizer/oat/), with:

• register-based encoder
• FSQ action quantization
• progressive decoding via nested token dropout
• tokenizer training loop with validation, reconstruction eval, EMA, and checkpoints

Install

pip install -e .

Architecture compare

1️⃣ 模型结构对比

UAT ActionCodec

1 输入 (b, seq_len, action_dim)
2     ↓
3 Perceiver Encoder
4 ├── EmbodimentEmbedding (机器人类型嵌入)
5 ├── Input Projection (max_action_dim → encoder_dim)
6 ├── Positional Embedding (Fourier/Sinusoidal)
7 ├── CLS Tokens (可学习的查询向量)
8 └── Transformer Blocks (Cross-Attention + Self-Attention)
9     ↓
10 Latent (b, n_tokens_per_quantizer, z_dim)
11     ↓
12 Vector Quantizer (VQ/RVQ)
13     ↓
14 Perceiver Decoder
15 ├── EmbodimentEmbedding
16 ├── Input Projection (z_dim → decoder_dim)
17 ├── Positional Embedding
18 └── Transformer Blocks
19     ↓
20 输出 (b, seq_len, max_action_dim)

关键特点：

• CLS Token 机制：使用可学习的 CLS token 作为查询，通过 Cross-Attention 从输入中提取信息
• Embodiment-Aware：每个机器人类型有独立的 embedding，支持动态扩展
• Flexible Attention：支持因果掩码、自注意力可选配置

VQVAE

 2     ↓
 3 CNN Encoder
 4 ├── Conv1d (in_channels → ch)
 5 ├── DownSample Blocks (3 层，每层 2x 下采样)
 6 │   ├── ConvBlock + Downsample
 7 │   └── ConvBlock
 8 └── Conv1d (→ z_channels)
 9     ↓
 10 Latent (b, z_channels, seq_len/8)
 11     ↓
 12 Residual Vector Quantizer (RVQ)
 13     ↓
 14 CNN Decoder
 15 ├── Conv1d (z_channels → ch*8)
 16 ├── UpSample Blocks (3 层，每层 2x 上采样)
 17 │   ├── ConvBlock + Upsample
 18 │   └── ConvBlock
 19 └── Conv1d (→ in_channels)
 20     ↓
 21 输出 (b, seq_len, action_dim)

关键特点：

• 纯 CNN 架构：使用卷积进行时空特征提取
• 固定压缩率：时间维度压缩 8 倍 (3 次 2x 下采样)
• MultiVQVAE 变体：将动作分解为 pos/rot/grip 三个独立 VQVAE

---

2️⃣ 构建方法对比

UAT 配置方式

  1 config = UATConfig(
  2     embodiment_config={
  3         "robot_A": {"action_dim": 7, "freq": 20, "duration": 1.0},
  4         "robot_B": {"action_dim": 10, "freq": 15, "duration": 1.0},
  5     },
  6     n_tokens=16,          # 总 token 数
  7     n_quantizers=1,       # VQ=1, RVQ≥2
  8     z_dim=512,            # 潜在空间维度
  9     vq_type="vq",         # "vq" 或 "rvq"
  10     vq_codebook_size=2048,
  11     encoder_dim=256,
  12     encoder_n_layers=6,
  13     encoder_n_heads=8,
  14     decoder_dim=256,
  15     decoder_n_layers=6,
  16 )
  17 
  18 model = ActionCodec(config)

动态扩展能力：

 2     "robot_C": {"action_dim": 12, "freq": 25, "duration": 1.5}
 3 })

VQVAE 配置方式

 2 model = VQVAE(
 3     input_dim=7,              # 动作维度
 4     embedding_dim=256,        # VQ 嵌入维度
 5     cnn_config={
 6         "hidden_size": 64,
 7         "output_size": 512,
 8         "dropout": 0.0,
 9     },
 10     num_embeddings=2048,      # codebook 大小
 11     action_horizon=24,        # 时间长度
 12     n_codebooks=4,            # RVQ 量化器数量
 13     commitment_cost=0.25,
 14 )
 15 
 16 # MultiVQVAE (动作分解)
 17 model = MultiVQVAE(
 18     input_dim={
 19         "pos": 6,   # 位置
 20         "rot": 12,  # 旋转 (6D)
 21         "grip": 2,  # 夹爪
 22     },
 23     embedding_dim=256,
 24     cnn_config={...},
 25     n_codebooks={"pos": 6, "rot": 3, "grip": 1},
 26 )