esm-cpp 0.2.1

CPU-first C++ inference engine for ESM-2 protein language models

esm.cpp

A CPU-first C++ inference engine for ESM-2 protein language models, with Python bindings. Production throughput on commodity hardware, no GPU required.

Why

ESM-2 is the backbone of modern protein ML, but the reference stack is PyTorch + CUDA. That's the wrong shape for several real workloads:

• Deep mutational scanning (10⁴–10⁷ variants per protein) — embarrassingly parallel scoring across millions of mutants. CPU throughput matters more than per-call latency.
• Antibody developability screening (10⁵–10⁶ candidates) — rank by PLM likelihood for aggregation, solubility, immunogenicity prefiltering. Nightly batch jobs on lab CPUs.
• Embedding extraction at corpus scale — per-residue or per-sequence vectors for downstream classifiers, alignment, or retrieval.
• On-prem / regulated environments — clinical, compliance-restricted, or air-gapped pipelines that cannot reach a cloud GPU.

The defensible niche is the intersection no existing project occupies: production-grade CPU inference + ahead-of-time W8A8 quantization + variable-length packed-batch scheduling for encoder-only PLMs. esm.cpp targets it. v0.2 ships ESM-2 at 8M, 35M, 150M, 650M, and 3B, with W8A8 INT8 (SmoothQuant) for 150M and above and FP32 for the smaller two.

Performance

ESM-2-650M, esm.cpp vs HuggingFace eager FP32, p50 over 5 runs:

Host	Varlen 256-seq (OAS)	Uniform 8 × 256	Mechanism
Intel Xeon 8481C Sapphire Rapids (22 vCPU)	9.31× HF (12.4 s)	4.09× HF (0.92 s)	AMX-INT8, default
Apple M3 Pro (after one fetch step)	3.97× HF (37.8 s)	10.05× HF (459 ms)	Whole-graph CoreML → ANE + GPU
GCP C4A / Neoverse V2 (8 vCPU, Graviton-class)	5.04× HF (29.3 s)	2.30× HF (2.02 s)	NEON SDOT, default
Apple M3 Pro (default, no fetch)	3.97× HF (37.8 s)	1.79× HF (2.17 s)	NEON SDOT, default

Two structural wins drive these numbers.

Variable-length packed-batch scheduling (the x86 + Linux ARM headline). HuggingFace pads every sequence in a batch to max(len). esm.cpp packs sequences back-to-back along the token axis and isolates per-sequence attention via cu_seqlens. On antibody-shaped data (mean ~120 residues, max ~250) that saves roughly 3× of HF's attention compute and 2× of its FFN compute on top of the INT8 baseline.

Whole-graph CoreML compilation (the Apple uniform-shape headline). The entire ESM-2 forward — 33 encoder layers + LM head — is compiled into ONE .mlmodelc at convert time and dispatched through an Objective-C++ MLModel bridge. Keeps one op-fused fp16 graph hot on the Apple Neural Engine + GPU instead of the per-Linear pattern that thrashes the ANE compiled-state cache. Logit correlation vs HF FP32 is 0.999998 at 650M with strict pseudo-perplexity drift below 0.001 across the holdout subset.

How it works

• Kernels. Hand-written, runtime-dispatched per ISA. x86: AVX-512 + VNNI + AMX-INT8. ARM: NEON FMLA + SDOT + opt-in SMMLA/i8mm. Every vectorized kernel has a scalar-reference twin behind #ifdef ESM_KERNEL_REFERENCE; the same tests cross-check both with strict tolerances (FP32 rtol/atol=1e-6, INT8 rtol=1e-3 atol=1).
• Quantization. Ahead-of-time W8A8 INT8 with SmoothQuant, calibrated on UniRef50. Pseudo-perplexity drift < 0.1 and ProteinGym Spearman drift < 0.01 are non-negotiable gates.
• Scheduler. Variable-length sequences pack back-to-back along the token axis; attention isolates per-sequence via cu_seqlens; FFNs see one fused [ΣL, d] matmul instead of B padded [L_max, d] ones.
• Loaders. Both safetensors (HF native, zero-copy mmap) and GGUF (esm.cpp native, block-decoded INT8). Weight tensors are never copied into RAM at load.
• Apple path. On Apple Silicon, if pre-built artifacts are on disk, Model.load_* auto-engages them: a whole-graph CoreML model for registered uniform shapes, and a per-Linear AMX-fp16 BNNSGraph stack for everything else. With no artifacts installed, the engine falls back to the same NEON SDOT kernels Linux ARM uses. No Apple framework appears in the canonical kernel stack itself.

Install

pip install esm-cpp

That's the whole install on Linux x86, Linux ARM, and as a working baseline on Apple Silicon. On Apple Silicon, one extra step pulls the pre-built whole-graph + AMX artifacts that unlock the 10× headline:

esm-cpp-fetch-artifacts --model esm2_t33_650M

The artifacts land in ~/.cache/esm_cpp/<model>/ (~5 GB for 650M) and Model.load_* auto-discovers them on every load. The fetch CLI is pure stdlib — no coremltools, torch, or transformers needed at user time.

OS / arch	What pip install esm-cpp gets you	Extra step for headline
Linux x86_64 (Sapphire Rapids+)	AMX-INT8 → 9.31× HF varlen, 4.09× HF uniform	none
Linux x86_64 (Cascade Lake / Ice Lake)	AVX-512 + VNNI INT8 baseline	none
Linux ARM64 (Graviton 3/4, Axion, Ampere)	NEON SDOT → 5.04× HF varlen, 2.30× HF uniform	none
Apple Silicon (M1 / M2 / M3)	NEON SDOT → 3.97× HF varlen, 1.79× HF uniform	esm-cpp-fetch-artifacts → 10.05× HF uniform

Quick start

import esm_cpp

# Load FP32 weights from a HF safetensors file. On Apple Silicon, if you
# ran esm-cpp-fetch-artifacts, this auto-engages the whole-graph CoreML
# path — no env vars, no register calls.
model = esm_cpp.Model.load_from_safetensors(
    "/path/to/esm2_t33_650M_UR50D/model.safetensors")
tokenizer = esm_cpp.Tokenizer()

# Single sequence.
ids = tokenizer.encode("MKTGVAQRLELDSPMVLQKRSGE")
logits = model.forward(ids)  # [seq_len, vocab_size=33]

# Packed batch — variable-length sequences in one forward.
seqs = ["MKTGVA", "MAGAASPCANGCGPSAPS", "MSEEKRGGQATKLP"]
batch_ids = [tokenizer.encode(s) for s in seqs]
batch_logits = model.forward_scheduled(batch_ids)
# returns list of [L_i, vocab_size] arrays in input order

The same Model.load_from_safetensors (or Model.load_from_gguf for the quantized artifact) is the entry point on every supported OS; the correct kernel path is selected at load time from cpu_features plus whatever Apple artifacts are present.

Convert + quantize

# 1. HF safetensors -> esm-cpp GGUF (FP32).
esm-cpp-convert --hf facebook/esm2_t30_150M_UR50D --out weights/esm2_150m.gguf

# 2. Calibrate on UniRef50.
esm-cpp-quantize --calibrate \
    --model esm2_t30_150M \
    --calib data/uniref50_calib_v1.fasta \
    --out weights/esm2_150m_calib.json

# 3. Apply SmoothQuant + quantize to INT8.
esm-cpp-quantize --apply-smoothquant \
    --model esm2_t30_150M \
    --stats weights/esm2_150m_calib.json \
    --alpha 0.5 \
    --output weights/esm2_150m_q8.gguf

Then load the quantized artifact directly:

m = esm_cpp.Model.load_from_gguf("weights/esm2_150m_q8.gguf")
assert m.config.weights_quantized

Benchmark vs HuggingFace

esm-cpp-bench \
    --model facebook/esm2_t6_8M_UR50D \
    --dataset data/oas_sample_v1.fasta \
    --modes esm-cpp-fp32,hf-eager-fp32 \
    --output benchmarks/results/my_run.json

--modes accepts any subset of esm-cpp-fp32, esm-cpp-int8, hf-eager-fp32, hf-sdpa-fp32. The harness reports p50 / p90 / throughput on uniform and varlen workloads and writes a JSON record per run for tracking.

Scope

Inference only. No training, no LoRA, no backward pass — those belong upstream. Hardware targets are x86_64 (AVX-512 / VNNI / AMX) and AArch64 (NEON / SDOT / SMMLA, plus opt-in Apple ANE/AMX via CoreML artifacts on Apple Silicon). GPU, ARM SVE2, and RISC-V are out of scope for v0.x. ESM-2-15B is bandwidth-bound on CPU at FP32/INT8 and needs W4 quant before it makes sense; that's v2 work.