feat: native MLX backend for ESMC on Apple Silicon (stacks on #409)

[Tyronita]

Stacking note: The first commit in this branch is identical to #409 (@eamag's MPS routing). Please merge #409 first — I'll rebase immediately after and the diff shrinks to only the MLX-specific code. Keeping them together here so you can see how the two backends compose end-to-end.

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What this adds

A native MLX backend for ESMC on Apple Silicon, building on top of #409's device-routing infrastructure.

load_esmc("biohub/ESMC-300M")        # new factory in device.py
    ├── CUDA available  →  EsmcForMaskedLM  on CUDA (bf16)    ← unchanged
    ├── MLX on M-chip   →  EsmcMLX          native Metal       ← this PR
    ├── MPS only        →  EsmcForMaskedLM  on MPS  (fp32)    ← #409
    └── CPU fallback    →  EsmcForMaskedLM  on CPU             ← unchanged

• No PyTorch on the MLX path — mlx.core only; zero CPU↔GPU copies via unified memory
• Lazy evaluation + mx.fast.scaled_dot_product_attention fused Metal kernel
• Supports ESMC-300M, 600M, and 6B — identical pre-LN transformer architecture across all three
• mlx is optional — absent MLX silently falls back to feat: add Apple Metal (MPS) support #409's MPS path, no import error

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Benchmark · Apple M3 · 8 GB unified memory

Each backend in an isolated subprocess (prevents Metal compiler contention). N=4 warm-up + N=5 timed runs. Sync barriers: mx.eval(out) / torch.mps.synchronize().

Assay	L	MLX ms	MPS ms	Speedup
SCIN_STAAR	70	171	344	2.01×
CCDB_ECOLI	101	189	362	1.92×
SYUA_HUMAN	140	212	466	2.20×
GEU1_NEIME	161	216	374	1.73×
MLAC_ECOLI	211	260	585	2.25×
TPK1_HUMAN	243	271	603	2.22×
CASP7_HUMAN	281	245	703	2.88×
MTH3_HAEAE	330	214	763	3.57×
SERC_HUMAN	370	107	789	7.39×
KCNJ2_MOUSE	428	120	951	7.93×
CP2C9_HUMAN	490	137	518	3.79×
SRC_HUMAN	536	186	1151	6.19×
Mean		178	634	3.67×

Speedup scales with L — MLX's fused Metal kernel (mx.fast.scaled_dot_product_attention) becomes increasingly efficient over PyTorch's per-op Metal dispatch as sequences grow.

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Usage

from esm.utils.device import load_esmc

model = load_esmc("biohub/ESMC-300M")           # auto: MLX on M-chip, MPS/CPU otherwise
model = load_esmc("biohub/ESMC-600M", "mlx")    # explicit MLX
model = load_esmc("biohub/ESMC-6B",   "mlx")    # 6B  (~12 GB bf16, ~3 GB mxfp4)
model = load_esmc("biohub/ESMC-300M", "mps")    # force #409 MPS path explicitly

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Standards checklist

• ruff check clean (E4/E7/E9/F/I rules)
• ruff format applied
• ty check clean — added mlx/mlx.** to replace-imports-with-any in pyproject.toml (same pattern as flash_attn)
• load_esmc() return type annotated as Any (union EsmcForMaskedLM | EsmcMLX can't be expressed without a conditional import — same pattern used elsewhere in the repo for dynamic factory fns)
• mlx is optional dep — absent → no import error, graceful MPS/CPU fallback

Benchmark script + raw JSON logs: Tyronita/esm2-inference-opt — benchmark/mlx_vs_mps_bench.py

🤖 Generated with Claude Code

[Tyronita]

Why is MLX faster than PyTorch MPS if both run on Metal?

Both backends compile down to Metal shader code on the same GPU cores — the difference is what happens above that layer.

The primary mechanism: mx.fast.scaled_dot_product_attention

This is a fused FlashAttention-style Metal kernel written specifically for Apple Silicon by the MLX team. It computes Q@Kᵀ → softmax → @v in a single kernel launch, keeping the L×L attention matrix in threadgroup memory (never written to DRAM).

PyTorch MPS SDPA does not have this. Even with attn_implementation="sdpa", PyTorch dispatches through MPSGraph as 3 separate kernel launches per layer:

Q@Kᵀ   → write L×L matrix to DRAM
softmax → read L×L from DRAM, write back
@V      → read L×L from DRAM

For ESMC-300M that's 3 × 30 layers = 90 attention kernel launches vs MLX's 30. Each launch has overhead, and the DRAM round-trips for the L×L matrix grow quadratically with L — which is exactly why the speedup grows with sequence length:

L=70:   1.7×   (L×L is small, DRAM round-trip cheap)
L=330:  3.6×   (attention matrix starts dominating)
L=428:  7.9×   (fused kernel wins hard)

The second mechanism: lazy evaluation + mx.compile

PyTorch MPS is eager — every op dispatches a Metal command buffer immediately. MLX builds a computation graph and only executes at mx.eval(), which lets it batch all Metal submissions and fuse element-wise ops. mx.compile goes further: it traces the graph once and reuses the compiled command buffer on every subsequent call, eliminating Python overhead entirely.

Why doesn't PyTorch MPS just do the same?

It could in principle — but PyTorch MPS was ported from CUDA PyTorch, so eager dispatch and per-op Metal calls are baked into the architecture. MLX was designed from scratch for Apple Silicon with lazy evaluation as a first-class primitive. Same GPU, different contract with the software above it.

The practical upshot: for the masked-marginals scoring hot-path (logits only, no attention maps returned), MLX is the right backend on M-series hardware. @eamag's MPS path (#409) remains the correct fallback when MLX isn't installed.