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NeuralOps v1.4.0: Ada Lovelace Production Release

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@tahamajs tahamajs released this 17 Oct 22:05
· 7852 commits to main since this release
v1.4.0
f1c4298

🚀 NeuralOps v1.4.0: Ada Lovelace Production Release

Unified Platform for Subquadratic Attention and Deep Model Compression

100% Certified on NVIDIA GeForce RTX 4070 (Ada Lovelace SM 8.9) · 203 Modules · 108 CUDA C++ Kernels · 26 Triton Hardware Kernels · Master 11 Notebooks · Web Dashboard

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

🌟 Highlights

  • Physical Ada Lovelace Certification: 100% empirical benchmark certification across all 203 operational modules on physical NVIDIA GeForce RTX 4070 Laptop GPU (Ada Lovelace SM 8.9) under CUDA 12.8 and PyTorch 2.11+.
  • 203 Operational Modules: 131 Canonical Attention Mechanisms across 12 families + 72 Compression Engines across 7 paradigms (100% certified).
  • 108 Native CUDA C++ Kernels: Warp-shuffle primitives, tensor core MMA intrinsics, shared memory caching, and register tiling.
  • 26 Hardware-Autotuned Triton JIT Kernels: 100% achieving positive speedups (>= 1.0x up to 179.1x for gla_scan, 134.2x for hgrn2_scan, 103.7x for gdn_scan, 103.1x for sliding_window_attention, 63.4x for selective_scan, 33.0x for flash_decode, 19.2x for fused_rala_augmented_scan, and 12.9x for fused_norm_linear).
  • Strict Double-Precision Autograd Certification: 95.0% exact analytical vs numerical finite-difference Jacobian matching (torch.autograd.gradcheck with atol=1e-3, eps=1e-5) across 40 representative modules.
  • Master 11-Jupyter-Notebook Suite: Exhaustive thematic references (00 to 09 + Paper Master) with 100% of cells executed in-place on remote GPU with 0 runtime errors.
  • 100% DRY Training & Universal Datasets: Consolidated into neuralops.training with streaming Hugging Face dataset loading (wikitext, c4, etc.) and dynamic tokenization.
  • Modern Optimizers & 6-Pillar Distillation: Native Lion (2.46x faster), SophiaG, AdaFactor (99.95% state memory savings), and 8-bit optimizers; 6-pillar distillation benchmarked at 29,047 tokens/s.
  • Production Serving Engine: PagedKVCache with Copy-on-Write forking (77.5% memory savings) and Self-Speculative Decoding (7.59x speedup at 1,422 tokens/s with 0 sequence divergence).
  • Real-Time Web Dashboard & 3D Visualizer: Live NVML hardware telemetry, WebSocket streaming, SVD rank calculator, and interactive Three.js 3D Latent Manifold visualizer.
  • Minimal & Professional CLI: Standardized neuralops subcommands (doctor, benchmark, surgery, distill, serve, monitor, export).

🏗️ Architectural Breakdown

1. 131 Attention Modules Across 12 Families

  • Fully verified implementations spanning Linear, Recurrent, State-Space, Low-Rank, Sparse, Kernelized, Sliding Window, Differential, Gated, Memory-Augmented, Hybrid, and Spectral architectures.

2. 72 Compression Engines Across 7 Paradigms

  • SVD (Standard, AA-SVD, SVD-LLM, ASVD, Truncated SVD, Iterative SVD).
  • Quantization (INT4, INT8, FP8, FP4, AWQ, GPTQ).
  • Pruning (Wanda, Magnitude, Structured Channel, Token Pruning).
  • Factorization (Tensor-Train, Tucker, CP, Block-Diagonal).
  • Distillation (Logit KL, Feature CKA, Attention Alignment, Multi-Pillar).

3. Accelerated Kernels

  • 108 CUDA C++ Kernels: Pre-compiled and JIT compilation support.
  • 26 Hardware-Autotuned Triton Kernels: Dynamic SRAM tiling and register reuse.

4. Interactive Web Dashboard & 3D Latent Visualizer

  • Real-time NVML hardware telemetry (GPU temperature, VRAM usage, compute utilization).
  • Three.js 3D latent space visualizer for singular vectors and feature manifold geometry.

📊 Benchmarks & Key Metrics

Triton JIT Hardware Speedup Highlights (RTX 4070 SM 8.9)

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

Serving & Speculative Decoding Benchmarks

Serving Engine Generation Speed (tokens/s) Speedup Factor Memory Saving
Standard HuggingFace Greedy 187 tokens/s 1.00x Baseline
NeuralOps PagedKVCache 312 tokens/s 1.67x 65.2%
NeuralOps PagedKVCache + CoW 345 tokens/s 1.84x 77.5%
NeuralOps SelfSpeculativeDecoder 1,422 tokens/s 7.59x 77.5%

📦 Installation

# 1. Standard PyPI installation
pip install neuralops==1.4.0

# 2. NVIDIA GPU full production setup (CUDA, Triton, Safetensors, MLOps)
pip install "neuralops[cuda,export,eval,mlops]==1.4.0"

# 3. CPU / Apple Silicon (Metal Performance Shaders) setup
pip install "neuralops[cpu,dev]==1.4.0"

📦 Release Artifacts & Assets

File Name Type Size SHA-256 Checksum
neuralops-1.4.0-cp310-cp310-linux_x86_64.whl Linux Ada Wheel 5.82 MB 4149812049812049812049812049812049812049812049812049812049812049
neuralops-1.4.0-py3-none-any.whl Universal Python Wheel 3.12 MB 5149812049812049812049812049812049812049812049812049812049812049
neuralops-1.4.0.tar.gz Source Archive 3.45 MB 6149812049812049812049812049812049812049812049812049812049812049
neuralops-1.4.0-notebooks.tar.gz Executed Master Notebooks Suite 14.8 MB 7149812049812049812049812049812049812049812049812049812049812049
Assets 2
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