metaljax — a Metal backend for JAX

metaljax lets unmodified JAX code run on Apple-silicon GPUs:

$ JAX_PLATFORMS=metal python -c \
    "import jax.numpy as jnp; a = jnp.array([1, 2, 3]); print(2 * a, (2*a).device)"
[2 4 6] MetalDevice(id=0)

From JAX's point of view it is a regular PJRT backend: jax.devices() reports a METAL device, and jit, grad, vmap, lax.scan, jax.random (threefry), optax training loops, etc. all work. Under the hood the compiled StableHLO programs are interpreted onto MLX arrays, which execute on the GPU via Metal.

Status: beta. Real training runs work end-to-end (transformer and recurrent language models with optax, including long lax.scan training loops), transformer training steps run within a few percent of PyTorch's MPS backend, and every release is gated by a whole-model correctness sweep against the CPU backend. Coverage gaps remain — unsupported ops fail with a clear UnsupportedOpError. If a Metal backend ever lands upstream in the JAX ecosystem, this package will be deprecated in its favor.

Install

pip install metaljax

Requirements: Apple-silicon Mac, macOS 14+, Python 3.12+, jax 0.11.x (installed automatically). Then select the backend per program:

JAX_PLATFORMS=metal python -c "import jax; print(jax.devices())"

CPU remains the default backend when JAX_PLATFORMS is unset, so installing metaljax does not change existing workflows. Installing from the source distribution (rather than the wheel) additionally requires the Xcode command-line tools, since the PJRT plugin compiles at build time.

How it works

jax.jit(f)(x)
  │  StableHLO (serialized portable artifact)
  ▼
plugin/metal_pjrt.cc          ── PJRT C-API dylib loaded by jaxlib.
  │                              No dependencies; trampolines every call
  ▼                              back into Python (same process, GIL).
src/metaljax/engine.py        ── compile: deserialize + wrap Interpreter
  │                              execute: run on device buffers
  ▼
src/metaljax/interpreter.py   ── walks the StableHLO module op by op
  │  + src/metaljax/ops/*     ── one handler per op family
  ▼
mlx.core                      ── lazy Metal arrays; unified memory

• The dylib implements PJRT API v0.114 (plugin/vendor/pjrt_c_api.h, vendored at jaxlib's exact openxla/xla pin).
• Registration happens through the jax_plugins namespace package (src/jax_plugins/metal/), at priority −1: CPU stays the default backend unless you opt in via JAX_PLATFORMS.

Requirements

• Apple-silicon Mac (developed on an M5 Max, macOS 26.5, Xcode 26.6 — any arm64 Mac with a recent Xcode/CLT should work).
• uv (only for creating the venv).
• Python 3.14 and jax/jaxlib 0.11.x (what the venv setup below installs; the vendored PJRT header matches jaxlib 0.11.0).

Developing from source

git clone https://github.com/eterevsky/metaljax && cd metaljax
uv venv --python 3.14 .venv
uv pip install -p .venv/bin/python jax mlx numpy pytest
uv pip install -p .venv/bin/python -e .
./plugin/build.sh          # builds plugin/build/libmetal_pjrt.dylib (clang)

Verify:

JAX_PLATFORMS=metal .venv/bin/python -c "import jax; print(jax.devices())"

should print [MetalDevice(id=0)].

Running the tests

The pytest suite lowers each construct with jax.jit(...).lower(), runs the StableHLO module through the interpreter on the GPU, and compares against the JAX CPU backend (this exercises the interpreter directly and does not need the plugin dylib):

.venv/bin/python -m pytest tests/ -q

Current suite: 129 tests across elementwise/transcendental ops, shapes and broadcasting, dot_general/einsum, reductions and cumulative ops, control flow (while/cond/scan), gather/scatter, RNG, and bf16/f16/x64 dtype handling.

End-to-end smoke test through the real plugin (device buffers, compile, execute, PJRT events):

JAX_PLATFORMS=metal .venv/bin/python -c "
import jax, jax.numpy as jnp
g = jax.jit(jax.grad(lambda x: jnp.sum(jnp.tanh(x) ** 2)))(jnp.arange(4.0))
print(g, g.device)"

Using metaljax from another project

Add metaljax to your dependencies (it declares jax itself):

[project]
dependencies = ["metaljax"]

and set JAX_PLATFORMS=metal (or jax.config.update("jax_platforms", "metal") before first use).

To develop against a local checkout instead, use a path source:

[tool.uv.sources]
metaljax = { path = "../metaljax", editable = true }

(with an editable install, build the plugin once in the checkout via ./plugin/build.sh). A git source (metaljax = { git = "https://github.com/eterevsky/metaljax" }) works too; like sdist installs it compiles the plugin during the build, which needs the Xcode command-line tools.

Environment variables

Repository layout

CLAUDE.md                  project decisions/status (kept current)
pyproject.toml             python package + jax_plugins entry point
src/metaljax/
  interpreter.py           StableHLO walker (SSA env, blocks, funcs)
  ops/                     op handlers: elementwise, shape, linalg,
                           reduction, control, gather
  engine.py                PJRT-facing compile/execute/buffer layer
  dtypes.py, _ir.py        dtype tables, MLIR context & attr decoding
src/jax_plugins/metal/     backend registration (priority -1)
plugin/
  metal_pjrt.cc            the PJRT C-API dylib (no deps, ~1100 lines)
  vendor/pjrt_c_api.h      vendored PJRT header (API 0.114)
  build.sh                 clang build → plugin/build/libmetal_pjrt.dylib
tests/                     pytest suite (Metal vs CPU)
scripts/                   benchmark & training drivers

Benchmarks

Full training steps (fwd + bwd + AdamW), f32, M5 Max, via scripts/bench_compare.py (16 timed steps after warmup):

¹ torch uses its hand-fused nn.GRU kernel; metaljax generates its kernel from the StableHLO loop body and lands within 10%.

How: pure programs and counted-loop (scan/fori_loop) bodies are traced once through mx.compile and replayed as fused Metal graphs; small statically-counted loops are unrolled into the enclosing trace, so e.g. a whole recurrent-model training step (forward scan + backward + AdamW) becomes a single graph replay. On top of that, recurrent scan bodies that pattern-match as elementwise/matvec cells (rnn/gru/mgru/lrnn/rglru family — forward and the AD-generated backward loop) compile to a single generated persistent Metal kernel: the whole scan is one kernel launch, with state in registers (small cells), register-block lanes (small block matvecs, in-lane reductions, and narrow rectangular readouts — the lrnn family), or one threadgroup per batch element with the feature dim as the thread axis (full-width cells like gru.256, including rectangular fused-gate dots like mullstm.32). Very wide cells (gru.1024-class) deliberately stay on the compiled-graph path, where batched matmul wins. Weight-gradient accumulations are handled by loop fission: the kernel stacks per-step operands and the einsum runs as one batched matmul after it. METALJAX_COMPILE=0 disables compilation, METALJAX_MSL=0 disables kernel codegen, METALJAX_TRACE_BUDGET (default 20000 ops) caps trace sizes, and METALJAX_DEBUG=1 logs loop/compile decisions.

On a 104-config language-model training suite (dense, GRU/LSTM-family, and linear-RNN cells from tens of weights to several million), 84 configs train faster on metal than on the M5's CPU cores; every config above 10k weights wins (median 3–6.6x faster), and 41 of 104 outpace an RTX 4090 running jax-CUDA. Only sub-10k-weight models remain CPU territory (kernel-dispatch floor). Every optimization is gated by a whole-model correctness sweep: one jitted training chunk per suite config executed on both backends from identical inputs, every output leaf compared.

openxla/xla benchmark suite

The single-device benchmarks from xla/tools/benchmarks (HLO converted to StableHLO with xla-translate, run via scripts/run_stablehlo_bench.py; ms per call, identical seeded inputs, outputs cross-checked against the CPU results):

¹ exceeds the 4090's 24 GB VRAM; the M5's 128 GB unified memory runs gemma3_12b (23.5 GB of bf16 weights) where the discrete GPU cannot. ² compiled whole-graph after working around an MLX limitation (equal constant-valued outputs break mx.compile); ~10× CPU.

Correctness vs CPU on identical inputs: gemma2/gemma4 outputs bit-exact; the gemma3 family diverges ≤3.6% in bf16 KV-cache tensors (a few bf16 ULPs across 26+ layers) — the 4090 shows the same divergence class vs CPU (≤4.2%), so that's cross-backend bf16 numerics, not a backend bug.

Known limitations

• Scan bodies that don't fit the kernel-codegen patterns (in-cell reductions, gather/scatter in the loop, non-affine indexing) fall back to per-timestep compiled-graph replay, which pays per-step dispatch.
• float64 is emulated in f32 (see METALJAX_F64); complex dtypes are unsupported.
• Not yet implemented (fail with a clear UnsupportedOpError): sort, general reduce_window (only cumsum/cumprod/cummax/cummin patterns), partial-window scatter, send/recv, multi-device anything.
• Buffer donation is ignored (correct, but no memory savings).

License / provenance

Experimental personal project; builds against public JAX/OpenXLA (PJRT header vendored from openxla/xla) and Apple's MLX.