dlpark

A pure Rust implementation of dmlc/dlpack.

This crate focuses on transferring tensors between Rust and Python, and between Rust tensor/array libraries, without copying. It targets DLPack 1.3 and Rust edition 2024.

Installation

dlpark ships no default features — enable the interop backends you need:

cargo add dlpark --features "ndarray half"          # Rust-only
cargo add dlpark --features "pyo3 image"             # Python extension
cargo add dlpark --features "cudarc"                 # CUDA (needs a CUDA toolchain)

Feature groups for testing:

cpu-all — every CPU-testable backend (candle, half, image, ndarray, pyo3) in one go. Used by the cargo test and cargo clippy CI jobs.
miri — candle, half, image, ndarray (no pyo3, whose tests call the Python C API). Used by the Miri job.

Mental model

A producer wraps its data into an allocation::Initialized value, sets the scalar tensor fields, and finishes it into a Managed<M> — an RAII handle over a raw DLPack managed tensor pointer that calls the DLPack deleter on drop. M selects the ABI:

legacy::Dlpack = Managed<DLManagedTensor> — the pre-v0.8 "dltensor" capsule.
versioned::Dlpack = Managed<DLManagedTensorVersioned> — the current "dltensor_versioned" capsule, carrying version and flags.

The high-level producer path converts a boxed container directly:

use dlpark::{allocation::dynamic, ffi::DLManagedTensorVersioned, versioned};
use ndarray::arr2;

let initialized: dynamic::Initialized<DLManagedTensorVersioned> =
    Box::new(arr2(&[[1_i32, 2, 3], [4, 5, 6]])).try_into()?;
let tensor: versioned::Dlpack = unsafe { initialized.finish() };

A consumer receives a Managed and calls validate() to check its descriptor metadata. The resulting TensorRef exposes shape, strides, dtype, device, and size through safe accessors. Dereferencing the data pointer remains unsafe because DLPack does not report allocation bounds. Backend conversions go through the TryFromDlpack trait.

What is DLPack?

DLPack is a common in-memory tensor structure that enables sharing tensor data between different deep learning frameworks. It provides a standardized way to exchange tensor data without copying, making it efficient for framework interoperability.

Key features of DLPack:

Zero-copy tensor sharing between frameworks
Support for various data types and devices (CPU, GPU, etc.)
Memory management through deleter functions
Versioned ABI for compatibility

Versioning

The library implements both legacy and versioned DLPack structures:

legacy::Dlpack: legacy managed tensor capsule support.
versioned::Dlpack: versioned managed tensor capsule support with:
- Major version 1, minor version 3 (the version provided by the bundled headers).
- Additional flags for tensor properties (read-only, copied, sub-byte type padding).

DLPackVersion exposes semantic comparisons: is_compatible_with checks ABI (major-version) compatibility, and supports checks whether a version includes a requested feature level. An incompatible major version is rejected on ingestion and the tensor is released.

Safe abstractions

The library provides a Rust ownership wrapper over the C-style DLPack structures, Managed<M>:

RAII wrapper around a raw managed DLPack tensor pointer.
Automatic cleanup through the DLPack deleter function on drop. If the managed tensor carries a NULL deleter (per the DLPack spec: the producer retains ownership and the consumer must not free it), Drop is a no-op and the allocation plus manager_ctx are not released — the caller that built such a tensor reclaims them through their original owner.
A single ownership model for locally produced and externally received tensors, with legacy::Dlpack and versioned::Dlpack aliases selecting the ABI.
TensorRef as the validated, borrowed descriptor view; TensorMut as the exclusively borrowed, writable-descriptor view.

The ManagedTensorBase trait abstracts the common operations of the legacy and versioned ABIs so allocation and ownership APIs can operate generically over M while preserving the concrete C layout selected by the caller.

Other key features:

Memory safety through Rust's ownership system.
Support for image buffers, ndarray, and candle tensors, plus raw DLPack tensor layouts.
Python interoperability through PyO3.
Optional DLPack 1.3 C Exchange API fast path when a producer type exposes __dlpack_c_exchange_api__.

Producing a tensor

A producer pipeline has three stages: metadata → initialization → finish.

1. Metadata: shape and strides

metadata::Fixed and metadata::Dynamic compose shape and strides with the managed tensor allocation. Each axis value is stored as i64. The storage wrappers select how the values get there:

metadata::Copied<T> — values are copied into the managed tensor allocation. Safe prepare.
metadata::Borrowed<T> — values are borrowed from caller-owned i64 storage. Zero-copy metadata, but requires unsafe prepare_unchecked because the arrays must outlive the managed tensor.

Rank	Copied (safe)	Borrowed (unsafe)
Fixed, known at compile time	`Fixed::new(Copied(shape), Copied(strides))` → `prepare`	`Fixed::new(Borrowed(&shape), Borrowed(&strides))` → `prepare_unchecked`
Dynamic, runtime	`Dynamic::new(Copied(shape_vec), Copied(strides_vec))` → `prepare`	`Dynamic::new(Borrowed(shape_slice), Borrowed(strides_slice))` → `prepare_unchecked`

Copied accepts any integer element type that implements TryInto<i64> (for example u32, i16, usize, isize), not just i64. When the source is already i64, a TypeId fast path uses ptr::copy_nonoverlapping directly; otherwise each value is converted in place. Either way no temporary Vec<i64> is allocated.

2. Initialization

prepare::<M>() returns a PreparedFixed / PreparedDynamic. Calling .initialize(ctx) installs the owning context and deleter and returns an allocation::Initialized<M, _>. The context owns the backing data and any allocation metadata; it must implement OpaqueContext, which is provided for Box<T: Send> and Arc<T: Send + Sync> (the deleter may fire on a different thread).

The boxed container itself is the canonical context: interop producers pass Box<Tensor> / Box<CudaSlice<T>> / Box<ImageBuffer<...>> directly, so the data owner stays alive for the DLPack tensor's lifetime.

3. Scalar fields and finish

Initialized exposes chainable setters before the final finish:

use dlpark::{
    allocation::fixed,
    ffi::{DLDevice, DLDataType, DLManagedTensorVersioned},
    metadata::{Copied, Fixed},
    versioned,
};

let shape = [2_u32, 3];
let strides = [3_i16, 1];
let mut data = vec![0_f32; 6];
let data_ptr = data.as_mut_ptr().cast();

let prepared = Fixed::new(Copied(shape), Copied(strides))
    .prepare::<DLManagedTensorVersioned>()?;
let mut initialized = prepared.initialize(Box::new(data));
initialized
    .set_data(data_ptr)
    .set_dtype(DLDataType::F32)
    .set_device(DLDevice::CPU);
let dlpack: versioned::Dlpack = unsafe { initialized.finish() };

finish is unsafe because the caller asserts the descriptor now satisfies the DLPack contract: data and metadata pointers remain valid until drop, and flags accurately describe aliasing and mutability. set_flags rejects newly asserting IS_COPIED; use set_flags_unchecked after producing a copy. Versioned tensors also expose set_version (rejects an incompatible major version).

Direct conversion

For the common case where a boxed container already determines shape, strides, dtype, and device, skip the manual metadata stage and convert directly:

use dlpark::{allocation::fixed, ffi::DLManagedTensorVersioned, versioned};

let initialized: fixed::Initialized<DLManagedTensorVersioned, 3> =
    Box::new(rgb_image).try_into()?; // image: fixed rank 3, HWC
let tensor: versioned::Dlpack = unsafe { initialized.finish() };

The element type's DLPack descriptor is provided by the DlpackElement trait, implemented for the Rust integer and float primitives (and half::f16 / half::bf16 under the half feature).

Performance of metadata copy

In the included length-64 microbenchmark, the i64 fast path takes approximately 11.7 ns, while allocating temporary Vec<i64> storage and then calling copy_nonoverlapping takes approximately 54.9 ns on the development machine. Reproduce it with:

cargo bench --bench builder -- generic_metadata_copy

The ndarray exporter uses Copied for its usize shape and isize strides, so exporting an owned array does not allocate temporary Vec<i64> metadata.

Reading tensor data

Once you hold a Managed, validate its descriptor into a TensorRef before reading metadata:

let tensor = dlpack.validate()?;
let shape = tensor.shape();                      // &[i64]
let strides = tensor.strides();                  // Option<&[i64]> (None = compact)
let n = tensor.num_elements();
let bytes = tensor.num_bytes();                   // sub-byte-packing aware
let data = unsafe { tensor.cpu_slice::<f32>()? }; // compact CPU data, dtype-checked

TensorRef::cpu_slice validates device, dtype, alignment, and compact layout, but remains unsafe because a descriptor cannot prove the data allocation's bounds. cpu_bytes is the dtype-agnostic variant and also supports packed sub-byte dtypes. Low-level consumers may use TensorRef::offset_data_ptr / offset_bytes_ptr to obtain a device-agnostic pointer with byte_offset applied.

Mutable access. Call validate_mut() to validate metadata and reject READ_ONLY, then use the unsafe mutable data accessor:

let mut tensor = dlpack.validate_mut()?;
let data = unsafe { tensor.cpu_slice_mut::<f32>()? };

Mutable data access is always unsafe because Rust cannot prove the bounds, aliasing, or concurrent use of an external allocation. IS_COPIED does not affect validation; it only reports that a producer made a copy for an exchange operation. Rust zero-copy adapters leave it unset.

Managed::flags() / version() read the versioned fields; flags_mut is unsafe because setting IS_COPIED or clearing READ_ONLY asserts the corresponding ownership/mutability guarantee.

Python exchange paths

The pyo3 feature supports the standard Python DLPack capsule protocol:

legacy::Dlpack consumes or produces legacy "dltensor" capsules.
versioned::Dlpack consumes or produces "dltensor_versioned" capsules.
python::dlpack_device(obj) calls and validates obj.__dlpack_device__(), returning a Rust DLDevice.
When extracting a versioned tensor from a Python object, dlpark first checks the object's type for a __dlpack_c_exchange_api__ PyCapsule named "dlpack_exchange_api". If present, it walks the prev_api chain for a compatible major version and uses the DLPack C Exchange API no-sync function table (managed_tensor_from_py_object_no_sync). Otherwise it calls obj.__dlpack__(max_version=(1, 3)) and consumes the returned capsule. Producers that only implement the legacy no-argument protocol must be extracted as legacy::Dlpack, because they return the incompatible "dltensor" capsule ABI.
Capsule consumption is single-use: extracting renames the capsule to "..._used"; a second extraction raises PyValueError("DLPack capsule has already been consumed").
Consumers can call versioned::Dlpack::extract_with_options(obj, stream, copy) to pass an optional stream and tri-state copy request to __dlpack__; extract_with_stream(obj, stream, copy) is the typed convenience path for GPU consumers. The cudarc feature implements python::DlpackStream for cudarc::driver::CudaStream (and Arc<CudaStream>); other backends can implement the unsafe DlpackStream trait.

The C Exchange API is intended for extension/library use where the consumer borrows tensors and coordinates work on the producer's current stream. It is not a replacement for the normal __dlpack__ ingestion path.

Interop backends

Feature	Producer	Consumer	Data movement
`image`	boxed `ImageBuffer`	borrowed or owning `ImageBuffer`	zero-copy
`ndarray`	boxed owned array	`ArrayViewD` / `ArrayViewMutD`	zero-copy
`candle`	boxed CPU `Tensor`	owned CPU `Tensor`	export is zero-copy; import copies
`cudarc`	boxed `CudaSlice`	owning CUDA slice view	zero-copy

Producer conversions require a Box because the container itself becomes the stable, type-erased DLPack manager_ctx; the library does not implicitly allocate that box. The half feature adds DlpackElement impls for the half crate's 16-bit floating-point types, independent of these adapters.

image

Zero-copy both ways. Producing wraps an ImageBuffer<P, Vec<P::Subpixel>> as a rank-3 HWC tensor ([height, width, channels]). Consuming validates an HWC compact layout and exposes the data either as a borrowed ImageBuffer<P, &[P::Subpixel]> or as an owning ImageBuffer<P, DlpackContainer<M, P::Subpixel>> that keeps the managed tensor alive.

ndarray

Zero-copy both ways for CPU arrays. Producing converts a boxed owned array into a dynamic::Initialized (runtime rank). Consuming yields ArrayViewD<T> (read) or ArrayViewMutD<T> (mut, which rejects READ_ONLY and requires the caller to prove exclusivity — DLPack flags alone cannot establish Rust aliasing).

candle

Zero-copy from candle::Tensor to DLPack (the boxed tensor's Arc-refcounted storage stays alive as the context); the reverse direction always copies, since candle has no borrowed CPU tensor type. Compact-stride sources take a bulk-copy path; arbitrary strides are gathered. CPU only — candle's CUDA backend needs separate integration work. Candle storage sits behind an RwLock, so the exported pointer aliases memory candle itself can still mutate; set DlpackFlags::READ_ONLY on the returned Initialized before finishing if you need to signal read-only intent.

cudarc

Zero-copy in both directions between a cudarc CudaSlice<T> and a DLPack tensor. The 1-D TryFrom producer returns a contiguous default layout (shape = [len], strides = [1]) and leaves IS_COPIED unset; use interop::cudarc::from_cuda_slice for higher-rank tensors. The reverse direction consumes the managed tensor through TryFrom<Managed<M>> for BorrowedCudaSlice<M, T>, keeping the tensor alive for as long as the CUDA view exists — the view's destructor calls CudaSlice::leak before the managed tensor drops, so cudaFree is not called on a DLPack-owned allocation.

Features

No features are enabled by default — enable the backends you need (see Installation).

Feature	Description	Status
`pyo3`	Python interop via pyo3 (capsule protocol + DLPack C Exchange API fast path)	✅
`image`	Zero-copy conversion with image buffers	✅
`ndarray`	Zero-copy conversion with ndarray arrays/views	✅
`half`	`f16`/`bf16` element type support (via half)	✅
`candle`	Conversion with candle `Tensor` — CPU only; candle's CUDA backend needs separate integration work	✅
`cudarc`	Zero-copy conversion with cudarc `CudaSlice<T>` — no automated tests here, needs a CUDA-capable device to exercise	✅

Quick start

Two runnable examples:

examples/dlparkimg — a Python extension module (via pyo3) transferring image::RgbImage to/from Python (e.g. torch.Tensor). Run with uv run main.py.
examples/ndarray-candle — a plain binary round-tripping data through DLPack: ndarray::Array2 → versioned::Dlpack → candle::Tensor → versioned::Dlpack → ndarray view, run with cargo run -p ndarray-candle.

examples/profile_builder.rs profiles the metadata::Fixed / metadata::Dynamic allocation paths (cargo run --release --example profile_builder); benches/builder.rs benchmarks them (cargo bench --bench builder).

Usage examples

Converting between Rust and Python

use dlpark::{allocation::fixed, ffi::DLManagedTensorVersioned, versioned, TryFromDlpack};
use image::ImageBuffer;
use pyo3::prelude::*;

#[pyfunction]
fn read_image(filename: &str) -> PyResult<versioned::Dlpack> {
    let img = image::open(filename)?.to_rgb8();
    let initialized: fixed::Initialized<DLManagedTensorVersioned, 3> =
        Box::new(img).try_into()?;
    Ok(unsafe { initialized.finish() })
}

#[pyfunction]
fn write_image(filename: &str, tensor: versioned::Dlpack) -> PyResult<()> {
    // SAFETY: this extension accepts tensors through the Python DLPack
    // protocol and relies on the producer to provide a valid descriptor.
    let img: ImageBuffer<image::Rgb<u8>, _> =
        unsafe { ImageBuffer::try_from_dlpack(&tensor) }?;
    img.save(filename)?;
    Ok(())
}

Image processing

use dlpark::{allocation::fixed, ffi::DLManagedTensorVersioned, versioned, TryFromDlpack};
use image::{ImageBuffer, Rgb};

let img = ImageBuffer::<Rgb<u8>, _>::from_vec(100, 100, vec![0; 100 * 100 * 3])?;
let initialized: fixed::Initialized<DLManagedTensorVersioned, 3> =
    Box::new(img).try_into()?;
let tensor: versioned::Dlpack = unsafe { initialized.finish() };
let img2 = unsafe { ImageBuffer::<Rgb<u8>, _>::try_from_dlpack(&tensor)? };

ndarray

use dlpark::{allocation::dynamic, ffi::DLManagedTensorVersioned, versioned, TryFromDlpack};
use ndarray::{ArrayD, ArrayViewD, arr2};

let array = arr2(&[[1_i32, 2, 3], [4, 5, 6]]);
let initialized: dynamic::Initialized<DLManagedTensorVersioned> =
    Box::new(array).try_into()?;
let tensor: versioned::Dlpack = unsafe { initialized.finish() };
let view = unsafe { ArrayViewD::<i32>::try_from_dlpack(&tensor)? };

assert_eq!(view[[1, 2]], 6);

let dynamic: ArrayD<i32> = arr2(&[[1, 2], [3, 4]]).into_dyn();
let initialized: dynamic::Initialized<DLManagedTensorVersioned> =
    Box::new(dynamic).try_into()?;
let dynamic_tensor: versioned::Dlpack = unsafe { initialized.finish() };

candle

Zero-copy from candle::Tensor to DLPack; the reverse direction (DLPack to candle::Tensor) always copies, since candle has no borrowed CPU tensor type.

use candle_core::Tensor;
use dlpark::{
    DlpackFlags, TryFromDlpack,
    allocation::dynamic, ffi::DLManagedTensorVersioned, versioned,
};

let tensor = Tensor::new(&[1_f32, 2., 3., 4.], &candle_core::Device::Cpu)?;
let initialized: dynamic::Initialized<DLManagedTensorVersioned> =
    Box::new(tensor).try_into()?;
let dlpack: versioned::Dlpack = unsafe { initialized.finish() };

let tensor2 = unsafe { Tensor::try_from_dlpack(&dlpack)? };
assert_eq!(tensor2.to_vec1::<f32>()?, vec![1., 2., 3., 4.]);

// Signal read-only intent before finishing (candle storage is shared via RwLock):
let tensor = Tensor::new(&[1_f32, 2., 3., 4.], &candle_core::Device::Cpu)?;
let mut initialized: dynamic::Initialized<DLManagedTensorVersioned> =
    Box::new(tensor).try_into()?;
initialized.set_flags(DlpackFlags::READ_ONLY)?;
let dlpack: versioned::Dlpack = unsafe { initialized.finish() };

cudarc

Zero-copy in both directions between a cudarc CudaSlice<T> and a DLPack tensor.

use dlpark::{
    TryFromDlpack,
    allocation::{dynamic, fixed},
    ffi::DLManagedTensorVersioned,
    interop::cudarc::{BorrowedCudaSlice, from_cuda_slice},
    versioned,
};

// 1-D default layout (shape = [len], strides = [1]):
let initialized: fixed::Initialized<DLManagedTensorVersioned, 1> =
    Box::new(cuda_slice).try_into()?;
let dlpack: versioned::Dlpack = unsafe { initialized.finish() };

// Higher-rank:
let initialized: dynamic::Initialized<DLManagedTensorVersioned> =
    from_cuda_slice::<_, DLManagedTensorVersioned>(Box::new(cuda_slice), &[2, 3], &[3, 1])?;
let dlpack: versioned::Dlpack = unsafe { initialized.finish() };

// Reverse direction keeps the managed tensor alive for the CUDA view's lifetime:
let borrowed =
    unsafe { BorrowedCudaSlice::<DLManagedTensorVersioned, f32>::try_from_dlpack(dlpack)? };

Regenerating FFI bindings

The C bindings in src/ffi.rs are generated from the dlpack C header (a git submodule at dlpack/) by the workspace maintenance tool:

mise run bindgen    # = cargo xtask bindgen

It is a manual regeneration step, not a build dependency of the crate.

Name		Name	Last commit message	Last commit date
.cargo		.cargo
.github		.github
.zed		.zed
benches		benches
dlpack @ 84d107b		dlpack @ 84d107b
examples		examples
src		src
xtask		xtask
.gitignore		.gitignore
.gitmodules		.gitmodules
CHANGELOG.md		CHANGELOG.md
Cargo.toml		Cargo.toml
LICENSE		LICENSE
README.md		README.md
cliff.toml		cliff.toml
mise.toml		mise.toml

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dlpark

Installation

Mental model

What is DLPack?

Versioning

Safe abstractions

Producing a tensor

1. Metadata: shape and strides

2. Initialization

3. Scalar fields and finish

Direct conversion

Performance of metadata copy

Reading tensor data

Python exchange paths

Interop backends

image

ndarray

candle

cudarc

Features

Quick start

Usage examples

Converting between Rust and Python

Image processing

ndarray

candle

cudarc

Regenerating FFI bindings

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dlpark

Installation

Mental model

What is DLPack?

Versioning

Safe abstractions

Producing a tensor

1. Metadata: shape and strides

2. Initialization

3. Scalar fields and finish

Direct conversion

Performance of metadata copy

Reading tensor data

Python exchange paths

Interop backends

image

ndarray

candle

cudarc

Features

Quick start

Usage examples

Converting between Rust and Python

Image processing

ndarray

candle

cudarc

Regenerating FFI bindings

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