Architectures / Recommendation

💠 RQ-VAE Semantic ID Tokenizer

The tokenizer that turns an item content embedding into a short tuple of discrete codes, giving items a coarse-to-fine hierarchy a decoder can generate.

From Rajput et al. (2023). Recommender Systems with Generative Retrieval. NeurIPS 2023. This page is the graph, not the PDF: open it, edit it, verify it, export it.

Every number on this page is computed from the graph by the same functions the app runs, not written by hand.

When to pick it

Structure

13 layers. Output shapes are propagated from the input shape, batch dimension excluded.

What the verifier says

The same 43 structural checks that run on every edit in the app, on this graph.

The PyTorch it exports

Generated from the graph above. First 46 lines; the app exports the whole file, plus the training loop, the data contract and a deploy bundle.

# Architecture designed with Neurarch: https://neurarch.com
# PyTorch: compatible with Python 3.8+ and torch>=1.12
# Colab: pip install torch torchvision  (usually pre-installed)

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Tuple

class ResidualVQ(nn.Module):
    """Residual vector quantization: each level quantizes what the level above
    could not represent, which is what gives a semantic ID its coarse-to-fine
    hierarchy."""

    def __init__(self, num_quantizers: int = 8, codebook_size: int = 1024, embed_dim: int = 256):
        super().__init__()
        self.codebooks = nn.ModuleList(
            [nn.Embedding(codebook_size, embed_dim) for _ in range(num_quantizers)])

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        residual, out = x, torch.zeros_like(x)
        for cb in self.codebooks:
            dist = (residual.unsqueeze(-2) - cb.weight).pow(2).sum(-1)
            q = cb(dist.argmin(-1))
            out = out + q
            residual = residual - q
        return x + (out - x).detach()  # straight-through


class RQ_VAESemanticIDTokenizer(nn.Module):
    def __init__(self):
        super().__init__()

        self.linear_1 = nn.Linear(768, 512)
        self.linear_2 = nn.Linear(512, 256)
        self.linear_3 = nn.Linear(256, 128)
        self.residualVQ_1 = ResidualVQ(num_quantizers=4, codebook_size=256, embed_dim=128)
        self.linear_4 = nn.Linear(128, 256)
        self.linear_5 = nn.Linear(256, 512)
        self.linear_6 = nn.Linear(512, 768)

    def forward(self, x):
        # Item content embedding shape: [768]
        linear_e1 = self.linear_1(x)
        relu_a1 = F.relu(linear_e1)
        linear_e2 = self.linear_2(relu_a1)

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