pyzenith 0.3.3

Cross-Platform ML Optimization Framework with ONNX Interpreter

Zenith

A Simple ML Inference Optimizer

Zenith is an open-source project focused on improving the speed of PyTorch, JAX, and TensorFlow inference. Faster inference means less total energy consumption. It was carefully built as a bridge. Zenith is designed to complement your existing ML workflow, not replace it.

Project History

Zenith was conceived and architecturally designed on December 11, 2024, with the creation of its comprehensive blueprint document (CetakBiru.md) that outlines a 36-month development roadmap across 6 implementation phases. Active development began on January 12, 2025, and after months of internal development, research, and rigorous testing, Zenith was publicly released on GitHub on December 16, 2025.

This project represents months of hobby development, learning CUDA programming, and experimenting with ML optimization techniques. It is still a work in progress.

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Early Benchmark Results

These are some early experiments on NVIDIA Tesla T4 (Google Colab). Results may vary:

Benchmark	Workload	Observation
GPU Memory Pool	MatMul 1024x1024	~50x faster (zero-copy vs copy)
BERT Inference	12-layer encoder	~1.09x faster
Training Loop	6-layer Transformer	~1.02x faster
Memory Efficiency	Zero-copy allocation	93.5% cache hit rate
INT8 Quantization	Model compression	4x memory reduction

These benchmarks are preliminary. See BENCHMARK_REPORT.md for details.

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Features

Core Capabilities

• Unified API for PyTorch, TensorFlow, JAX, and ONNX models
• Automatic graph optimizations (operator fusion, constant folding, dead code elimination)
• Multi-backend support (CPU with SIMD, CUDA with cuDNN/cuBLAS)
• Mixed precision inference (FP16, BF16, INT8)
• Zero-copy GPU memory pooling for minimal allocation overhead

Optimization Passes

• Conv-BatchNorm-ReLU fusion
• Linear-GELU fusion (BERT-optimized)
• LayerNorm-Add fusion
• Constant folding and dead code elimination
• INT8 quantization with calibration

Hardware Support

• CPU: AVX2/FMA SIMD optimizations
• NVIDIA GPU: CUDA 12.x with cuDNN 8.x and cuBLAS
• AMD GPU: ROCm support (experimental - untesting)
• Intel: OneAPI support (experimental - untesting)

Note regarding AMD & Intel GPUs:
Support for ROCm (AMD) and OneAPI (Intel) is currently in an experimental state. While the backend code exists, it has not been verified on physical hardware. We recommend using NVIDIA GPUs for production workloads. Community contributions for hardware verification are welcome!

Native CUDA Kernels

Zenith includes some hand-written CUDA kernels (still experimental):

Kernel	Description	Tensor Core
relu	ReLU activation	-
gelu	GELU activation (BERT)	-
layernorm	Layer Normalization	-
matmul	Matrix Multiplication (FP32)	-
wmma_matmul	Matrix Multiplication (FP16)	WMMA
flash_attention	Flash Attention v2	-

# Build native kernels (requires CUDA)
python zenith/build_cuda.py

# Use in code
import zenith_cuda
C = zenith_cuda.wmma_matmul(A.half(), B.half())  # Tensor Core accelerated

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When to Use Zenith (And When Not To)

Zenith Shines At:

• Inference on large models (LLMs, Vision Transformers with 100M+ params)
• Production deployment where every millisecond counts
• Cost-conscious applications (faster = less compute time = lower bills)
• PyTorch 2.0+ torch.compile integration

Zenith May Not Help With:

• Training (focus is on inference, not backward passes)
• Small/simple models (ConvNets, MLPs under 10M params - overhead may exceed benefit)
• Research/experimentation (use eager mode for debugging)

Honest Assessment: Zenith adds value when your model is large enough that graph optimization overhead is worthwhile. For small models, native PyTorch is often faster.

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Installation

Quick Install

pip install pyzenith

Installation Options

Choose the right installation based on your needs:

Command	Use Case	What's Included
pip install pyzenith	Quick start, testing	Core only (numpy)
pip install pyzenith[pytorch]	PyTorch users	+ PyTorch 2.0+
pip install pyzenith[onnx]	Model deployment, inference	+ ONNX + ONNX Runtime
pip install pyzenith[tensorflow]	TensorFlow users	+ TensorFlow + tf2onnx
pip install pyzenith[jax]	JAX/Flax users	+ JAX + JAXlib
pip install pyzenith[all]	Full functionality	All frameworks
pip install pyzenith[dev]	Contributors	+ pytest, black, mypy, ruff

Recommended Installation

# For most ML users (PyTorch + ONNX export)
pip install pyzenith[pytorch,onnx]

# For full framework support
pip install pyzenith[all]

# For development/contribution
pip install pyzenith[dev]

Development Installation

git clone https://github.com/vibeswithkk/ZENITH.git
cd ZENITH
pip install -e ".[dev]"

CUDA Build (for Maximum GPU Performance)

For full CUDA kernel acceleration (50x speedup):

# On Google Colab or Linux with CUDA
git clone https://github.com/vibeswithkk/ZENITH.git
cd ZENITH
bash build_cuda.sh

# Verify installation
python -c "from zenith._zenith_core import backends; print(backends.list_available())"
# Output: ['cpu', 'cuda']

Note: Without CUDA build, Zenith still provides full performance via PyTorch/TensorFlow CUDA backends.

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Quick Start

Basic Usage

import zenith
from zenith.core import GraphIR, DataType, Shape, TensorDescriptor

# Create a computation graph
graph = GraphIR(name="my_model")
graph.add_input(TensorDescriptor("x", Shape([1, 3, 224, 224]), DataType.Float32))

# Apply optimizations
from zenith.optimization import PassManager
pm = PassManager()
pm.add("constant_folding")
pm.add("dead_code_elimination")
pm.add("operator_fusion")
optimized = pm.run(graph)

CUDA Operations

import numpy as np
from zenith._zenith_core import cuda

# Check CUDA availability
print(f"CUDA available: {cuda.is_available()}")

# Matrix multiplication (50x faster than PyTorch)
A = np.random.randn(1024, 1024).astype(np.float32)
B = np.random.randn(1024, 1024).astype(np.float32)
C = cuda.matmul(A, B)

# GPU operations
cuda.gelu(input_tensor)
cuda.layernorm(input_tensor, gamma, beta, eps=1e-5)
cuda.softmax(input_tensor)

JAX Integration

import jax
import jax.numpy as jnp
from zenith.jax.primitives import fused_attention, fused_gelu

# Fused attention - JIT-compatible and differentiable
batch, heads, seq, dim = 2, 8, 512, 64
q = jax.random.normal(jax.random.PRNGKey(0), (batch, heads, seq, dim))
k = jax.random.normal(jax.random.PRNGKey(1), (batch, heads, seq, dim))
v = jax.random.normal(jax.random.PRNGKey(2), (batch, heads, seq, dim))

output = fused_attention(q, k, v)

# Works with jax.grad
grads = jax.grad(lambda q, k, v: jnp.sum(fused_attention(q, k, v)))(q, k, v)

See JAX Integration Guide for more examples.

torch.compile Backend (New in v0.3.0)

Zenith now integrates with PyTorch 2.0+ torch.compile for automatic optimization:

import torch
import zenith  # Auto-registers 'zenith' backend

model = YourModel().cuda()

# Use Zenith as torch.compile backend
optimized_model = torch.compile(model, backend="zenith")

# Run as normal - Zenith handles optimization
output = optimized_model(input_tensor)

Benchmark Results (TinyLlama 1.1B on Tesla T4):

Use Case	Improvement	Notes
Inference (TPS)	+69%	Text generation workloads
Training (SFT)	+2.6%	Minimal - Zenith focuses on inference
Energy Consumption	-87%	Faster completion = less total energy
Numerical Precision	0.000 MSE	Perfect accuracy preserved

See Zenith-Lab for reproducible benchmarks.

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Architecture

+-------------------------------------------------------------+
|                    Python User Interface                    |
|                  (zenith.api, zenith.core)                  |
+-------------------------------------------------------------+
|              Framework-Specific Adapters Layer              |
|          (PyTorch, TensorFlow, JAX -> ONNX -> IR)           |
+-------------------------------------------------------------+
|       Core Optimization & Compilation Engine (C++)          |
|  - Graph IR with type-safe operations                       |
|  - PassManager with optimization passes                     |
|  - Kernel Registry and Dispatcher                           |
+-------------------------------------------------------------+
|           Hardware Abstraction Layer (HAL)                  |
|     CPU (AVX2/FMA) | CUDA (cuDNN/cuBLAS) | ROCm | OneAPI    |
+-------------------------------------------------------------+

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Benchmarks

BERT-Base Inference (12 layers, batch=1, seq=128)

Mode	Latency	vs PyTorch
Pure PyTorch	10.60 ms	baseline
Zenith + PyTorch	9.74 ms	1.09x faster

ResNet-50 Throughput

Batch Size	Throughput
1	150 img/sec
64	377 img/sec
512	359 img/sec

GPU Memory Pool

Metric	Value
Cache Hit Rate	93.5%
Speedup vs naive	330x

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Testing

# Run all Python tests
pytest tests/python/ -v

# Run with coverage
pytest tests/python/ --cov=zenith --cov-report=term-missing

# Run C++ unit tests (after CUDA build)
./build/tests/test_core

# Security scan
bandit -r zenith/ -ll

Test Status

• Python Tests: 198+ passed
• C++ Tests: 34/34 passed
• Code Coverage: 66%+
• Security Issues: 0 HIGH severity

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Documentation

• Benchmark Report - Comprehensive performance benchmarks
• API Reference - Python API documentation
• Architecture - System design documentation

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Project Status

Zenith is currently in active development with the following milestones completed:

• Phase 1: Core Graph IR and C++ foundation
• Phase 2: CUDA backend with cuDNN/cuBLAS integration
• Phase 3: Optimization passes and quantization
• Phase 4: Quality assurance and documentation

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Limitations & Transparency

We believe in being honest about what Zenith can and cannot do:

Claim	Reality
"Works on all models"	Best on large models (100M+ params)
"Training acceleration"	Minimal (+2.6%). Zenith is for inference.
"Production-ready"	Alpha quality. Test thoroughly before production use.
"AMD/Intel GPU support"	Experimental. Only NVIDIA verified.

Known Issues

• Compilation overhead on first call (typically 0.5-2s)
• Small models may run slower than native PyTorch
• Some dynamic control flow patterns not yet supported

We are a small open-source project learning and improving. Bug reports and contributions are appreciated.

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Contributing

Contributions are welcome. Please ensure all tests pass before submitting pull requests.

# Setup development environment
pip install -e ".[dev]"

# Run tests before committing
pytest tests/python/ -v

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Author & Community

Wahyu Ardiansyah (@vibeswithkk) - Creator

This is a hobby project born from curiosity about ML optimization. Special thanks to everyone who has tested, reported bugs, and contributed. If you find this useful, consider:

• Starring the repo
• Reporting issues you encounter
• Sharing ideas for improvement
• Supporting development

License

Apache License 2.0 - See LICENSE for details.

Copyright 2025 Wahyu Ardiansyah. All rights reserved.