mithril-checkpoint-agent

name	mithril-checkpoint-agent
description	Build mithril-checkpoint compression for PyTorch models. Use when implementing byte grouping, compression pipeline, or checkpoint I/O. Use when this capability is needed.
metadata	{"author":"gar-ai"}

Mithril Checkpoint Agent

Build checkpoint compression for PyTorch models with 10-20x lossless compression.

Status

Read crates/mithril-checkpoint/STATUS.md for current progress.

Reference Documentation

• SPEC.md - Full product specification
• checkpoint/SPEC.md - Detailed implementation spec (if exists)
• RESEARCH.md - Papers and prior art (LMC, ZipNN, Check-N-Run)

Module Responsibilities

bytegroup

bfloat16 byte grouping for better compression:

/// Group bf16 bytes: [h0,l0,h1,l1,...] → [h0,h1,...,l0,l1,...]
pub fn byte_group_bf16(data: &[u8]) -> Vec<u8> {
    let n = data.len() / 2;
    let mut grouped = Vec::with_capacity(data.len());
    for i in 0..n { grouped.push(data[i * 2]); }     // high bytes
    for i in 0..n { grouped.push(data[i * 2 + 1]); } // low bytes
    grouped
}

/// Ungroup: [h0,h1,...,l0,l1,...] → [h0,l0,h1,l1,...]
pub fn byte_ungroup_bf16(data: &[u8]) -> Vec<u8>;

Why: High bytes (exponent) compress better together. ~20% better ratio.

pipeline

Compression pipeline combining byte grouping + zstd:

pub struct CheckpointCompressor {
    compressor: ZstdCompressor,
}

impl CheckpointCompressor {
    pub fn compress(&self, data: &[u8], dtype: DType) -> Result<Vec<u8>> {
        let grouped = match dtype {
            DType::BFloat16 | DType::Float16 => byte_group_bf16(data),
            _ => data.to_vec(),
        };
        self.compressor.compress(&grouped)
    }

    pub fn decompress(&self, data: &[u8], dtype: DType, size: usize) -> Result<Vec<u8>>;
}

formats

Read PyTorch checkpoint formats:

• state_dict - PyTorch pickle format
• safetensors - HuggingFace format (preferred)

Target Metrics

Metric	Target
Compression ratio	≥10x (lossless)
Throughput	≥2.5 GiB/s
Memory overhead	≤2x checkpoint size

Key Dependencies

mithril-core = { workspace = true }
zstd = { workspace = true }
rayon = { workspace = true }  # Parallel compression

Test Fixtures

• fixtures/checkpoints/small_model.bin - 10MB bf16 test data

Testing

cargo test -p mithril-checkpoint
cargo bench -p mithril-checkpoint

Implementation Order

1. Implement bytegroup module with tests
2. Implement pipeline module
3. Add format readers (safetensors first)
4. Run benchmarks, optimize for throughput
5. Update STATUS.md

Completion Criteria

• Compress/decompress roundtrip is bit-exact
• ≥10x compression on bf16 data
• ≥2.5 GiB/s throughput
• Unit tests pass
• STATUS.md updated to COMPLETE

---

Converted and distributed by TomeVault — claim your Tome and manage your conversions.