Fast-Axolotl
High-performance Rust extensions for Axolotl - drop-in acceleration for existing installations.
Highlights
- Zero-config acceleration - Just
import fast_axolotlbefore axolotl - 77x faster streaming - Rust-based data loading vs HuggingFace datasets
- Parallel hashing - Multi-threaded SHA256 for deduplication
- Cross-platform - Linux, macOS, Windows with Python 3.10-3.13
Quick Start
uv add fast-axolotl
or
pip install fast-axolotl
import fast_axolotl # Auto-installs acceleration shim
# Now use axolotl normally - accelerations are active
import axolotl
Benchmark Results
Tested on Linux x86_64, Python 3.11, 16 CPU cores:
| Operation | Data Size | Rust | Python | Speedup |
|---|---|---|---|---|
| Streaming Data Loading | 50,000 rows | 0.009s | 0.724s | 77x |
| Parallel Hashing (SHA256) | 100,000 rows | 0.027s | 0.052s | 1.9x |
| Token Packing | 10,000 sequences | 0.079s | 0.033s | 0.4x* |
| Batch Padding | 10,000 sequences | 0.200s | 0.105s | 0.5x* |
*Token packing and batch padding show overhead for small datasets due to FFI costs. Performance gains are realized with larger datasets typical in LLM training.
See BENCHMARK.md for detailed results.
Compatibility
All features tested and working:
| Feature | Status |
|---|---|
| Rust Extension Loading | Tested |
| Module Shimming | Tested |
| Streaming (Parquet, JSON, CSV, Arrow) | Tested |
| Token Packing | Tested |
| Parallel Hashing | Tested |
| Batch Padding | Tested |
| Axolotl Integration | Tested |
See COMPATIBILITY.md for full test results.
Features
1. Streaming Data Loading
Memory-efficient streaming for large datasets:
from fast_axolotl import streaming_dataset_reader
for batch in streaming_dataset_reader(
"/path/to/large_dataset.parquet",
dataset_type="parquet",
batch_size=1000,
num_threads=4
):
process(batch)
Supports: Parquet, Arrow, JSON, JSONL, CSV, Text (with ZSTD/Gzip compression)
2. Token Packing
Replace inefficient torch.cat() loops:
from fast_axolotl import pack_sequences
result = pack_sequences(
sequences=[[1, 2, 3], [4, 5], [6, 7, 8, 9]],
max_length=2048,
pad_token_id=0,
eos_token_id=2
)
# Returns: {'input_ids': [...], 'labels': [...], 'attention_mask': [...]}
3. Parallel Hashing
Multi-threaded SHA256 for deduplication:
from fast_axolotl import parallel_hash_rows, deduplicate_indices
hashes = parallel_hash_rows(rows, num_threads=0) # 0 = auto
# Or get unique indices directly
unique_indices, new_hashes = deduplicate_indices(rows)
4. Batch Padding
Efficient sequence padding:
from fast_axolotl import pad_sequences
padded = pad_sequences(
[[1, 2, 3], [4, 5]],
target_length=8,
pad_value=0,
padding_side="right"
)
Installation
From PyPI
uv pip install fast-axolotl
From Source
git clone https://github.com/neul-labs/fast-axolotl
cd fast-axolotl
# Using uv (recommended)
uv pip install -e .
# Or with pip + maturin
pip install maturin
maturin develop --release
Documentation
Configuration
Enable features in your Axolotl config:
# Enable Rust streaming for large datasets
dataset_use_rust_streaming: true
sequence_len: 32768
# Deduplication uses parallel hashing automatically
dedupe: true
Development
git clone https://github.com/neul-labs/fast-axolotl
cd fast-axolotl
uv venv && source .venv/bin/activate
uv pip install -e ".[dev]"
maturin develop
# Run tests
pytest -v
# Run benchmarks
python scripts/benchmark.py
# Run compatibility tests
python scripts/compatibility_test.py
Support
Questions or bugs? Reach out via:
- GitHub Issues: https://github.com/neul-labs/fast-axolotl/issues
- GitHub Discussions: https://github.com/neul-labs/fast-axolotl/discussions
Maintainers
Fast-Axolotl is authored by Dipankar Sarkar (me@dipankar.name) and maintained by the team at Neul Labs.
License
MIT
Metadata
Release files for fast-axolotl 0.2.0
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| fast_axolotl-0.2.0.tar.gz | 208.1 kB | Details |
Built distributions (wheels)
Total release size: 71.5 MB
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