CoLT5-attention
Conditionally Routed Attention
Decision gist · record as of 2026-08-14
Yes, with conditions. The package is actively maintained, permissively licensed, and solves a real efficiency problem for transformer-based models. Install if you need efficient attention for long sequences or large feature maps. Be aware that routing behavior is still being refined (author notes improvisation in key-value normalization), and gradient stability at high iteration counts warrants testing on your specific workload before production use.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires PyTorch and CUDA for GPU acceleration; some features (autoregressive attention, cross-attention) benefit from Triton kernel support.
- Low friction installation as a pure Python wheel with four runtime dependencies (torch, einops, local-attention, packaging).
- Active maintenance with a recent release and 231 repository stars.
License · maintenance · safety
permissive license (permissive) — MIT License permits unrestricted use, modification, and distribution with only attribution and inclusion of the license notice required.
last release 2026-08-14 (0 days) · last repo commit 2026-08-14 · 231 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 82,549 downloads/mo, #14,155 on PyPI
Alternatives
Verify before relying
pip install colt5-attention
import torch
from colt5_attention import ConditionalRoutedAttention
attn = ConditionalRoutedAttention(
dim=512,
light_heads=8,
heavy_heads=8,
num_heavy_tokens_q=1024
)
tokens = torch.randn(2, 32768, 512)
mask = torch.ones(2, 32768).bool()
out = attn(tokens, mask=mask)- Stability and gradient behavior at high iteration counts (author notes occasional 1e-1 gradient differences beyond 20 iterations).
- Performance benchmarks against standard attention on typical workloads.
- Full API surface and all available attention variants beyond the core examples.
What it is and what it does
CoLT5-attention is a PyTorch implementation of conditionally routed efficient attention from the CoLT5 architecture. It splits token processing into light and heavy branches, routing only a subset of tokens through computationally expensive operations while keeping the rest in a lightweight path. This reduces overall computation while maintaining model capacity for important tokens.
The package provides multiple attention variants: standard conditional routed attention for sequence processing, cross-attention for long-context scenarios, autoregressive attention for generation, and image attention for vision feature maps. All variants use coordinate descent routing optimized with Triton kernels. The implementation includes feedforward routing and complete transformer blocks, making it suitable as a drop-in component for building efficient transformer encoders.
Use it for
- Reduce attention computation in long-sequence transformers by routing only critical tokens through expensive attention heads.
- Build efficient vision transformers that can process large feature maps (e.g., 256×256) that would be prohibitive with standard attention.
- Implement cross-attention over very large context windows (millions of tokens) by selectively routing key-value pairs.
- Speed up autoregressive generation by applying conditional routing within windowed attention patterns.
- Replace standard attention blocks in existing transformer architectures to lower memory and compute requirements.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
The package is actively maintained, permissively licensed, and solves a real efficiency problem for transformer-based models. Install if you need efficient attention for long sequences or large feature maps. Be aware that routing behavior is still being refined (author notes improvisation in key-value normalization), and gradient stability at high iteration counts warrants testing on your specific workload before production use.
Install
colt5-attention on PyPI
Before you install
Low friction installation as a pure Python wheel with four runtime dependencies (torch, einops, local-attention, packaging). Active maintenance with a recent release and 231 repository stars.
Requires PyTorch and CUDA for GPU acceleration; some features (autoregressive attention, cross-attention) benefit from Triton kernel support.
License in practice
MIT License permits unrestricted use, modification, and distribution with only attribution and inclusion of the license notice required.
Quickstart
pip install colt5-attention
import torch
from colt5_attention import ConditionalRoutedAttention
attn = ConditionalRoutedAttention(
dim=512,
light_heads=8,
heavy_heads=8,
num_heavy_tokens_q=1024
)
tokens = torch.randn(2, 32768, 512)
mask = torch.ones(2, 32768).bool()
out = attn(tokens, mask=mask)Verify before relying
- Stability and gradient behavior at high iteration counts (author notes occasional 1e-1 gradient differences beyond 20 iterations).
- Performance benchmarks against standard attention on typical workloads.
- Full API surface and all available attention variants beyond the core examples.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.6 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packageseinopslocal-attentionpackagingtorch |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 82,549 / month, #14,155 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3.6Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: colt5_attention-0.11.2-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “efficient attention pytorch”
- CoLT5-attentionImplements conditionally routed efficient attention mechanisms for…
- local-attentionImplements local windowed attention for transformer models, allowing…
- flash-attnProvides optimized GPU implementations of scaled dot-product…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also local-attention · mamba-ssm · rotary-embedding-torch · x-transformers · vit-pytorch · axial-positional-embedding · hyper-connections · conformer · flash-attn · fa3-fwd