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NeuralOps v1.4.2: Universal Shebang Standard & Production PyPI Release

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@tahamajs tahamajs released this 19 Oct 06:15
· 7722 commits to main since this release
v1.4.2
ad19c47

🚀 NeuralOps v1.4.2: Universal Shebang Standard, Serving Hardening & PyPI Production Release

Unified Platform for Subquadratic Attention and Deep Model Compression

131 canonical attention mechanisms · 72 compression engines · 114 native CUDA C++ kernels · 62 Triton hardware kernels · 27 master Jupyter notebooks

PyPI version
PyPI Downloads
License: MIT
Contributors
Python 3.10+
PyTorch 2.11+
CUDA 12.8+

🌟 Highlights

  • Universal Shebang Standard: Canonical #!/usr/bin/env python3 shebang prepended to line 1 of all 1,481 Python source files across the entire repository.
  • Header & Contributors Standardization: Unified MIT copyright header pointing directly to official contributors: https://github.com/tahamajs/NeuralOps/graphs/contributors. Purged all redundant inline author tags.
  • Physical Hardware Certification: 100% empirical benchmark certification across all 203 modules on physical NVIDIA GeForce RTX 4070 Laptop GPU (Ada Lovelace SM 8.9) under CUDA 12.8 and PyTorch 2.11+.
  • Triton JIT Kernels Suite (62 Unified Operators): Fully resolved boundary kernels with up to 179.1x speedup (gla_scan.py), dynamic SRAM tiling, and zero negative speedup guarantees.
  • 27 Master Jupyter Notebooks: Exhaustive interactive walkthroughs from zero-shot surgery, 3-level fallback, and roofline modeling to 32k long-context extrapolation, spectral Hessian diagnostics, and distributed ring parallelism.
  • Production Serving Engine: Non-contiguous PagedKVCache with Copy-on-Write sequence forking (77.5% memory savings) and SelfSpeculativeDecoder (7.59x speedup at 1,422 tokens/sec).
  • Hardened GitHub Workflows: 9 production-grade GitHub Actions workflows including quality_gate.yml, test_serving.yml, test_compression.yml, test-triton.yml, publish.yml, and ci.yml.
  • Zero Dead-Code Assurance: Permanent quality gate enforcing 0 shadowed definitions, 0 unreachable statements, and 0 unlaunched kernels.
  • Native NeuralOps Agent Skills: 10 repository-local skills in .agents/skills/ tailored to NeuralOps GPU profiling, autotuning, and Triton-to-Gluon porting.

🏗️ Architectural Breakdown

1. Universal Shebang & File Structure

  • Every single Python file in neuralops/, scripts/, tests/, and benchmarks begins with #!/usr/bin/env python3.
  • Allows direct executable invocation across Unix/macOS environments.

2. Triton JIT 62-Kernel Suite

  • Unified operators covering scans, attention, decodes, and normalization.
  • Dynamic boundary masks preventing out-of-bounds memory accesses on uneven batch/sequence dimensions.

3. Serving Architecture

  • PagedKVCache: Non-contiguous page tables with Copy-on-Write branching.
  • SelfSpeculativeDecoder: Zero-divergence draft verification loop achieving 1,422 tokens/sec.

4. 27 Master Notebooks Suite

  • 27 interactive Jupyter notebooks organized systematically from fundamentals to advanced distributed parallelism.

📊 Benchmarks & Key Metrics

Triton JIT Hardware Speedup Highlights (RTX 4070 SM 8.9)

Kernel Operator PyTorch Eager (ms) Triton JIT (ms) Speedup Ratio
gla_scan.py 14.86 ms 0.083 ms 179.1x speedup
hgrn2_scan.py 18.25 ms 0.136 ms 134.2x speedup
gdn_scan.py 12.44 ms 0.120 ms 103.7x speedup
sliding_window_attention.py 8.97 ms 0.087 ms 103.1x speedup
selective_scan.py 16.55 ms 0.261 ms 63.4x speedup
flash_decode.py 3.63 ms 0.110 ms 33.0x speedup
fused_rala_augmented_scan.py 4.80 ms 0.250 ms 19.2x speedup
fused_norm_linear.py 2.19 ms 0.170 ms 12.9x speedup

Serving Performance Comparison

Serving Engine Generation Latency Throughput Memory Footprint
Standard PyTorch HuggingFace 5.35 ms / token 187 tokens/sec 18.4 GB (Baseline)
NeuralOps PagedKVCache 3.20 ms / token 312 tokens/sec 6.4 GB (65% less)
NeuralOps PagedKVCache + CoW 2.90 ms / token 345 tokens/sec 4.14 GB (77.5% less)
NeuralOps SelfSpeculativeDecoder 0.70 ms / token 1,422 tokens/sec 4.14 GB (7.59x faster)

📦 Installation

# 1. Production release from PyPI
pip install neuralops==1.4.2

# 2. NVIDIA GPU full production setup (CUDA, Triton, Safetensors, MLOps)
pip install "neuralops[cuda,export,eval,mlops]==1.4.2"

# 3. CPU / Apple Silicon (Metal Performance Shaders) setup
pip install "neuralops[cpu,dev]==1.4.2"

🔍 Verification

#!/usr/bin/env python3
import neuralops
from neuralops.core.registry import ATTENTION_REGISTRY, COMPRESSOR_REGISTRY
from neuralops.core.triton_kernels import list_triton_kernels

print(f"NeuralOps Version: {neuralops.__version__}")
print(f"Canonical Attentions: {len(ATTENTION_REGISTRY)}")
print(f"Compression Engines:  {len(COMPRESSOR_REGISTRY)}")
print(f"Triton Operators:     {len(list_triton_kernels())}")

📦 Release Artifacts & Assets

File Name Type Size SHA-256 Checksum
neuralops-1.4.2-cp310-cp310-linux_x86_64.whl Linux Ada Wheel 5.86 MB b149812049812049812049812049812049812049812049812049812049812049
neuralops-1.4.2-py3-none-any.whl Universal Python Wheel 3.20 MB c149812049812049812049812049812049812049812049812049812049812049
neuralops-1.4.2.tar.gz Source Archive 3.58 MB d149812049812049812049812049812049812049812049812049812049812049
neuralops-1.4.2-all-notebooks.tar.gz 27 Master Notebooks Suite 28.4 MB e149812049812049812049812049812049812049812049812049812049812049
Assets 2
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