tensor_optim

A no-std compatible, zero-unsafe public API Rust library providing foundational tensor abstractions optimized for embedded and performance-critical machine learning workloads.

Features

• No unsafe code in the entire library — safety guaranteed by design.
• Supports no_std by default; dynamic allocation disabled by default.
• Optional alloc feature enables dynamically sized, heap-allocated tensors (DynTensor).
• Implements elementwise arithmetic traits and indexing for ergonomic tensor operations.
• Provides matrix multiplication and transpose primitives optimized for minimal overhead.
• Extensive test coverage with strict clippy lints and no missing docs.

Overview

tensor_optim offers three primary tensor types representing different ownership and allocation models:

[*] RefTensor is a special tensor type that doesn't own anything and lacks capability like matrix multiplication and transposition.

Safety and Performance

• The public API is 100% safe Rust; no unsafe code is exposed or required by users.
• The design delivers near-zero-cost abstractions without compromising soundness or correctness.
• All unsafe code is contained and used only for optimization purposes.

Explanation

• Use RefTensor to create zero-copy views into existing data without allocation or ownership transfer.
• Use ArrTensor for fixed-size tensors with deterministic, stack-allocated memory layout.
• Enable the alloc feature and use DynTensor for dynamically sized tensors requiring heap allocation and ownership.

All tensor types support (with the exception of RefTensor):

• Elementwise arithmetic via standard Rust operator traits (Add, Sub, Mul, Div and assignment variants).
• Indexing with Index and IndexMut for direct element access.
• Matrix multiplication and transpose operations optimized for minimal overhead.

... and every tensor is row-major.

Feature Flags

• alloc (disabled by default): Enables DynTensor and dynamic heap allocation. Requires allocator support.

Installation

Add this to your Cargo.toml:

[dependencies]

tensor_optim = "0.3.1"

# or enable dynamic tensors:

# tensor_optim = { version = "0.3.1", features = ["alloc"] }

or just run this command: cargo add tensor_optim.

#![no_std] Support

• tensor_optim compiles without the Rust standard library, always.
• To use dynamic tensors (DynTensor), enable the alloc feature.

Testing and Quality

• Extensively tested for correctness, edge cases, and safety.
• Zero unsafe in public API, internal unsafe is forbidden.
• Strict clippy lint enforcement and fulll documentation coverage.

Intended Use Cases

• Embeded ML inference on microcontrollers and real-time systems.
• Kernel or OS-level tensor operations requiring zero runtime overhead.
• Resource-constrained environments where deterministic memory usage is important.
• Foundation for building higher-level Rust ML frameworks or inference engines.

Licensing & Contribution

Licensed under an MIT license.

tensor_optim is a minimal, safe, and high-performance Rust tensor library that empowers embedded and performance-critical ML applications without sacrificing safety or compatibility. Its carefully designed ownership models and optimized core kernels provide the solid foundation needed for next-generation Rust ML frameworks.