ztensor 1.0.2

Python bindings for the zTensor library.

ztensor

Simple tensor serialization format

Key Features

• Simple Spec — Minimalist spec for easy parsing.
• Zero-Copy Access — Instant memory-mapping (mmap) with no extra RAM copying overhead.
• Efficient Writes — Supports streaming and append-only operations without rewriting files.
• Future-Proof — Decouples physical storage from logical representation for long-term compatibility.

Tools

• Rust Core — High-performance, SIMD-aligned implementation.
• Python API — First-class bindings for NumPy and PyTorch.
• Universal Converters — CLI tools to easily convert Pickle, SafeTensors, and GGUF files.

Comparison

Feature	zTensor	SafeTensors	GGUF	Pickle	HDF5
Zero-Copy Read	✅	✅	✅	❌	⚠️
Safe (No Exec)	✅	✅	✅	❌	✅
Streaming / Append	✅	❌	❌	❌	✅
Sparse Tensors	✅	❌	❌	✅	✅
Compression	✅	❌	❌	❌	✅
Quantization	✅	✅	✅	✅	✅
Parser Complexity	🟢 Low	🟢 Low	🟡 Med	🔴 High	🔴 High

Benchmark

See benchmark for more details.

Installation

Python

pip install ztensor

Rust

[dependencies]
ztensor = "0.1"

CLI

cargo install ztensor-cli

Quick Start: Python

Basic Usage with NumPy

import numpy as np
from ztensor import Writer, Reader

# Write tensors
with Writer("model.zt") as w:
    w.add_tensor("weights", np.random.randn(1024, 768).astype(np.float32))
    w.add_tensor("bias", np.zeros(768, dtype=np.float32))

# Read tensors (zero-copy where possible)
with Reader("model.zt") as r:
    # Returns a numpy-like view
    weights = r.read_tensor("weights")
    print(f"Weights shape: {weights.shape}, dtype: {weights.dtype}")

PyTorch Integration

import torch
from ztensor import Writer, Reader

# Write PyTorch tensors directly
t = torch.randn(10, 10)
with Writer("torch_model.zt") as w:
    w.add_tensor("embedding", t)

# Read back as PyTorch tensors
with Reader("torch_model.zt") as r:
    # 'to="torch"' returns a torch.Tensor sharing memory with the file (if mmap)
    embedding = r.read_tensor("embedding", to="torch")
    print(embedding.size())

Sparse Tensors

Supports CSR (Compressed Sparse Row) and COO (Coordinate) formats.

import scipy.sparse
from ztensor import Writer, Reader

csr = scipy.sparse.csr_matrix([[1, 0], [0, 2]], dtype=np.float32)

with Writer("sparse.zt") as w:
    # Add CSR tensor
    w.add_sparse_csr("my_csr", csr.data, csr.indices, csr.indptr, csr.shape)

with Reader("sparse.zt") as r:
    # Read back as scipy.sparse.csr_matrix
    matrix = r.read_tensor("my_csr", to="numpy")

Compression

Use Zstandard (zstd) compression to reduce file size.

with Writer("compressed.zt") as w:
    w.add_tensor("big_data", data, compress=True)

Quick Start: Rust

Basic Usage

use ztensor::{ZTensorWriter, ZTensorReader, DType, Compression, ChecksumAlgorithm};

// Write
let mut writer = ZTensorWriter::create("model.zt")?;
// Zero-copy generic write with generic Compression
writer.add_object("weights", vec![1024, 768], DType::F32, 
                 Compression::Raw, &data_vec, ChecksumAlgorithm::None)?;
writer.finalize()?;

// Read
let mut reader = ZTensorReader::open("model.zt")?;
// Read as specific type (automatically handles endianness)
let weights: Vec<f32> = reader.read_object_as("weights")?;

Sparse Tensors

// Write CSR (using generic method)
writer.add_csr_object(
    "sparse_data",
    vec![100, 100],      // shape
    DType::F32,
    &values,             // &[f32]
    &indices,            // &[u64]
    &indptr,             // &[u64]
    Compression::Raw,
    ChecksumAlgorithm::None
)?;

// Read CSR
let csr = reader.read_csr_object::<f32>("sparse_data")?;
println!("Values: {:?}", csr.values);

Compression

// Write with compression (level 3)
writer.add_object(
    "compressed_data",
    vec![512, 512],
    DType::F32,
    Compression::Zstd(3), // Use zstd encoding with level 3
    &data,                // &[f32]
    ChecksumAlgorithm::Crc32c // Optional checksum
)?;

// Read (auto-decompresses)
let data: Vec<f32> = reader.read_object_as("compressed_data")?;

CLI

The ztensor CLI tool allows you to inspect and manipulate zTensor files.

Inspect Metadata

Print tensor names, shapes, and properties.

ztensor info model.zt

Convert Other Formats

Convert SafeTensors, GGUF, or Pickle files to zTensor.

# Auto-detect format from extension
ztensor convert model.safetensors -o model.zt

# Explicit format with compression (default level 3)
ztensor convert -f gguf -c llama.gguf -o llama.zt

# Specify compression level (1-22)
ztensor convert model.safetensors -o model.zt -l 10

# Delete originals after conversion
ztensor convert --delete-original *.safetensors -o model.zt

Compression Tools

# Compress an existing raw file (default level 3)
ztensor compress raw.zt -o compressed.zt

# Compress with specific level (1-22)
ztensor compress raw.zt -o highly_compressed.zt -l 19

# Decompress a file
ztensor decompress compressed.zt -o raw.zt

File Management

# Merge multiple files into one
ztensor merge part1.zt part2.zt -o merged.zt

# Migrate legacy v0.1.0 files to v1.1.0
ztensor migrate old_model.zt -o new_model.zt

Download from HuggingFace

Download safetensors from HuggingFace and convert to zTensor:

ztensor download-hf microsoft/resnet-18 -o ./models
ztensor download-hf openai-community/gpt2 -o ./models --compress

Supported Data Types

Type	Description
float32, float16, bfloat16, float64	Floating point
int8, int16, int32, int64	Signed integers
uint8, uint16, uint32, uint64	Unsigned integers
bool	Boolean

File Format

See SPEC.md for the complete specification.

License

MIT