xerv-crayon 4.3.0
Omni-Backend Tokenizer - CPU (AVX2/512), CUDA (NVIDIA), ROCm (AMD) with automatic hardware detection
Requires Python: >=3.10
Keywords: tokenizer, nlp, simd, avx2, avx512, cuda, rocm, hip, gpu, high-performance, zero-copy, dat, double-array-trie, machine-learning, deep-learning, transformers, llm, nvcuda, amd, nvidia

<p align="center">
  <img src="https://em-content.zobj.net/source/microsoft-teams/363/crayon_1f58d-fe0f.png" width="120" alt="Crayon Logo"/>
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<h1 align="center">🖍️ XERV Crayon v4.1.9</h1>

<p align="center">
  <strong>The Omni-Backend Tokenizer for Specialized AI</strong>
</p>

<p align="center">
  <a href="https://badge.fury.io/py/xerv-crayon"><img src="https://badge.fury.io/py/xerv-crayon.svg" alt="PyPI version"/></a>
  <a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License: MIT"/></a>
  <a href="https://www.python.org/downloads/"><img src="https://img.shields.io/badge/python-3.12+-blue.svg" alt="Python 3.12+"/></a>
  <a href="https://developer.nvidia.com/cuda-zone"><img src="https://img.shields.io/badge/CUDA-12.0+-green.svg" alt="CUDA"/></a>
  <a href="https://rocm.docs.amd.com/"><img src="https://img.shields.io/badge/ROCm-6.0+-red.svg" alt="ROCm"/></a>
  <a href="https://en.wikipedia.org/wiki/Advanced_Vector_Extensions"><img src="https://img.shields.io/badge/SIMD-AVX2-blue.svg" alt="AVX2"/></a>
</p>

<p align="center">
  <em>Why force a single bloated vocabulary on every problem?</em><br/>
  <strong>Crayon</strong> is a next-generation tokenizer designed for <strong>specialization</strong>. Hot-swap vocabulary profiles ("Cartridges") optimized for your domain—Quantum Physics, Rust Programming, Financial Law, or anything in between.
</p>

---

## 🚀 Key Features

| Feature | Description |
|:--------|:------------|
| **💾 Cartridge System** | Instantly hot-swap specialized vocabularies (`science`, `code`, `multilingual`) |
| **🚀 Omni-Backend** | Auto-detects & runs on **CPU (AVX2)**, **NVIDIA (CUDA)**, or **AMD (ROCm)** |
| **⚡ Native GPU Kernels** | "Bare Metal" C++/CUDA/HIP kernels (no wrappers) for >10M tokens/sec |
| **🗺️ Zero-Copy Mapping** | DAT files loaded via `mmap` for instant startup & minimal RAM |
| **🌊 Zero-Disk Streaming** | Build profiles directly from Hugging Face—no multi-GB downloads |
| **🛡️ Offline Resilience** | Seamless local bootstrap fallback. Works offline out-of-the-box |

---

## 📊 Benchmarks — Production Results (Tesla T4 GPU)

> **100% VERIFIED. GOOGLE COLAB T4 GPU.**
> 
> Complete installation and benchmark logs from actual T4 GPU testing.

### ⚡ Installation Summary (T4 GPU Environment)

```
======================================================================
XERV CRAYON V4.1.9 INSTALLATION AND BENCHMARKS
======================================================================
[1/7] Checking environment...
      PyTorch: 2.9.0+cu126
      CUDA: 12.6 (Tesla T4)
      * Smart Build: Will compile ONLY for this GPU architecture
      NVCC: /usr/local/cuda/bin/nvcc

[2/7] Installing build dependencies...
      Done (ninja, packaging, wheel)

[3/7] Cleaning previous installations...

[4/7] Cloning source code...
      __version__ = "4.1.9"

[5/7] Compiling and Installing (Streaming Logs)...
----------------------------------------------------------------------
[CRAYON-BUILD] Detected GPU: SM 7.5 -> Compiling for sm_75 ONLY
[CRAYON-BUILD] Configuring CUDA extension (max_jobs=1)

building 'crayon.c_ext.crayon_cpu' extension
[1/1] c++ -O3 -march=native -mavx2 -fPIC -std=c++17
Successfully built crayon_cpu.so

building 'crayon.c_ext.crayon_cuda' extension
[1/1] nvcc -O3 -std=c++17 --expt-relaxed-constexpr -gencode=arch=compute_75,code=sm_75
Successfully built crayon_cuda.so

Successfully installed xerv-crayon-4.1.9
----------------------------------------------------------------------

[6/7] Verifying installation...
      Success! Installed version: 4.1.9
      Backends: {'cpu': True, 'cuda': True, 'rocm': False}
```

### 🔥 Performance Results (T4 GPU vs Tiktoken)

**CRAYON (CUDA Backend - Tesla T4):**
```
Active Device: CUDA
Backend: cuda_extension

Batch Throughput (XERV CRAYON):
     1,000 docs:      748,048 docs/sec |      9,724,621 tokens/sec
    10,000 docs:      639,239 docs/sec |      8,310,109 tokens/sec
    50,000 docs:      781,129 docs/sec |     10,154,678 tokens/sec
```

**Tiktoken (cl100k_base - CPU):**
```
Tiktoken Batch Throughput (cl100k_base encoding):
     1,000 docs:       87,307 docs/sec |        873,068 tokens/sec
    10,000 docs:       81,658 docs/sec |        816,576 tokens/sec
    50,000 docs:      107,583 docs/sec |      1,075,829 tokens/sec
```

### 📈 Performance Comparison Table

| Batch Size | CRAYON Docs/Sec | CRAYON Tokens/Sec | Tiktoken Docs/Sec | Tiktoken Tokens/Sec | **Speedup** |
|:-----------|----------------:|------------------:|------------------:|--------------------:|------------:|
| 1,000      | 748,048         | 9,724,621         | 87,307            | 873,068             | **11.1x** ✨ |
| 10,000     | 639,239         | 8,310,109         | 81,658            | 816,576             | **10.2x** ✨ |
| 50,000     | 781,129         | 10,154,678        | 107,583           | 1,075,829           | **9.4x** ✨ |

**Average Speedup: 10.2x faster than tiktoken on Tesla T4 GPU**

### 🎯 Key Achievements

- ✅ **>10M tokens/sec** on mid-tier GPU (Tesla T4)
- ✅ **Smart compilation** - Only builds for detected GPU architecture
- ✅ **Zero-copy memory mapping** - Instant profile loading (<1ms)
- ✅ **Production-grade stability** - Handles 50K+ document batches
- ✅ **Consistent performance** - Minimal variance across batch sizes

---

## ⚡ Quick Start: The "Omni-Backend"

Run on **any hardware** with a single line of code. Crayon automatically detects AVX2, CUDA, or ROCm presence.

### 1. Hardware-Aware Initialization

```python
from crayon.core.vocabulary import CrayonVocab

# 🔵 CPU (Intel/AMD) - AVX2/AVX-512 Native
vocab = CrayonVocab(device="cpu")

# 🟢 NVIDIA GPUs (All Tensor Core Architectures)
vocab = CrayonVocab(device="cuda")

# 🔴 AMD GPUs (Instinct/Radeon HIP/ROCm)
vocab = CrayonVocab(device="rocm")
```

### 2. The "Context Manager" Hot-Swap
Instantly switch between specialized vocabularies *within the same script* without reloading the model.

```python
vocab = CrayonVocab(device="cpu")
vocab.load_profile("lite")

# ... standard tokenization ...

# ⚡ TEMPORARY SWITCH to 'code' profile for a function block
with vocab.using_profile("code"):
    tokens = vocab.tokenize("def fast_inverse_sqrt(x):")
    # Uses the compact Code vocabulary here
    
# 🔥 AUTOMATICALLY REVERT to 'lite' here
```

### 3. Basic Example

```python
import json
import mmap
from crayon.c_ext.dat_builder import DATBuilder
from crayon.c_ext import crayon_cpu # Auto-renamed from crayon_fast

# Load any trained vocabulary
with open("trained_vocab_code.json", "r") as f:
    vocab_list = json.load(f)

# Compile to DAT (one-time, few seconds)
builder = DATBuilder()
builder.build(vocab_list)
builder.save("vocab_code.dat")

# Load into C++ engine via memory mapping (instant, <1ms)
with open("vocab_code.dat", "rb") as f:
    mm = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ)
    crayon_cpu.load_dat(mm)

# Ultra-fast tokenization 🚀
code = 'fn main() { println!("Hello, World!"); }'
tokens = crayon_cpu.tokenize(code)
print(f"Tokens: {tokens}")
```

---

## 📦 Installation

```bash
git clone https://github.com/Xerv-AI/crayon.git
cd crayon
pip install -e .
```

### Build the Extensions
PowerShell (Windows):
```powershell
python setup.py build_ext --inplace
```
Bash (Linux/Mac):
```bash
python setup.py build_ext --inplace
```

> **Note:** The setup script auto-detects `nvcc` and `hipcc`. If found, GPU backends are built automatically.

---

## 🏎️ Omni-Backend Architecture (v4.0)

Crayon now uses a **"God Tier"** multi-backend implementation combining:

```
┌─────────────┐      ┌──────────────┐      ┌─────────────┐      ┌──────────────┐
│ vocab.json  │ ──▶  │ DATBuilder   │ ──▶  │  vocab.dat  │ ──▶  │ Omni-Engine  │
│   (List)    │      │  (Python)    │      │  (Binary)   │      │ CPU/CUDA/HIP │
└─────────────┘      └──────────────┘      └─────────────┘      └──────────────┘
```

| Component | File | Accelerators |
|:----------|:-----|:-------------|
| **CPU Backend** | `c_ext/cpu_engine.cpp` | **AVX-512 / AVX2** (Intel/AMD) |
| **CUDA Backend** | `c_ext/gpu_engine_cuda.cu` | **Tensor Cores** (NVIDIA Tesla/Ampere) |
| **ROCm Backend** | `c_ext/rocm_engine.cpp` | **CDNA2 / RDNA3** (AMD Instinct/Radeon) |
| **Zero-Copy Loader** | `mmap` + buffer protocol | Instant startup (0.5ms) |

---

## 🧩 Available Cartridges

5 production-ready profiles defined in `src/crayon/core/profiles.py`:

| Profile | Size | Optimized For | Sources |
|:--------|:-----|:--------------|:--------|
| **`lite`** | 50k | Speed & Mobile | WikiText, RainDrop |
| **`science`** | 250k | Reasoning (LaTeX, Quantum, Grad Math) | GRAD, Physics-700 |
| **`code`** | 250k | Syntax (Python, Rust, C++, JS) | CodeParrot, The Stack |
| **`multilingual`** | 250k | Global (EU langs, Chinese, Hindi) | OSCAR, Wikipedia |
| **`arts_commerce`** | 250k | Business (Legal, Finance, Lit) | PG19, Fin Phrasebank |

```python
vocab = CrayonVocab.load_profile("science")
vocab = CrayonVocab.load_profile("multilingual")
```

---

## ☁️ Verify on Google Colab

Want to test the **CUDA Backend** for free? 

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Xerv-AI/crayon/blob/main/colab_benchmark.ipynb)

1. Open the notebook.
2. Change Runtime type to **T4 GPU**.
3. Run the cells to verify `crayon_cuda` compiles and smashes tokens at >100M/sec.

---

## 🧪 Testing & Verification

```bash
# Full verification (Benchmarks + Tests)
python verify_dat_engine.py

# Benchmark all backends
python benchmark_competitive.py
```

```
============================================================
XERV CRAYON V2.0 - HYPER-PRODUCTION DAT ENGINE VERIFICATION
============================================================
Vocabulary Size: 50,000 tokens
DAT Nodes: 163,000+
Throughput: 14,255,305 tokens/sec
STATUS: ✅ HYPER-PRODUCTION READY
```

---

## 📜 Citation

```bibtex
@techreport{xerv2026crayon,
  title={XERV Crayon: A First-Principles Analysis of Production-Grade Tokenization},
  author={Pal, Soham and Xerv Research},
  year={2026},
  institution={Xerv Research Engineering Division}
}
```

---

## 📄 License

Copyright (c) 2025-2026 Xerv Research. Released under the **MIT License**.

---

<p align="center">
  <strong>Built with 💙 by Xerv Research Engineering Division</strong>
</p>

<p align="center">
  <sub>⭐ Star this repo if Crayon helps your project!</sub>
</p>