mlx-gen-parity 0.1.3

MLX generation parity utils with HF/Torch-compatible sampling, processors, and steering hooks

mlx-gen-parity

Small, reusable MLX decoding and training library that brings HF/Torch generate() feature parity and clean persona steering to Apple Silicon. It reuses mlx-lm primitives (caches, projections, speculative) and fills the missing parity pieces.

Features

• HF-style GenerationConfig with processors/warpers: repetition penalty, no-repeat-ngrams, frequency/presence, bad-words, min_new_tokens, typical_p, epsilon_cutoff.
• Constraints: force_words_ids (strict start + continuation), suppress_tokens, begin_suppress_tokens, multiple eos_token_ids, forced BOS/EOS and per-position forced_decoder_ids.
• Modes: sampling (fast path via mlx-lm), beam (num_beams, length_penalty, early_stopping), speculative (mlx-lm), sliding KV (max_kv_size).
• Hooks: ResidualInjectionHook (sampling) and LogitBiasHook (sampling/beam); SoftPromptHook for training.
• Training (MLX): loss_forward, xent_loss (label smoothing), mixed-precision compute (bf16) with fp32 master weights.
• Training utilities: sequence_logprob, token_kl for scoring and policy KL.
• Model helpers: ema_update, build_action_mask, stable_softmax; best-effort clone_reference.

Install

• From PyPI (recommended):

pip install mlx-gen-parity

• Dependencies (if not already installed):

pip install mlx mlx-lm transformers

• From source (editable):

pip install -e .

Models from Hugging Face

• If the repo provides MLX weights (e.g., in mlx-community), you can load directly: load('mlx-community/<model>').
• For standard HF (PyTorch) repos, convert once using mlx-lm:
  • Python: from mlx_gen_parity.interop import convert_hf_to_mlx; convert_hf_to_mlx('Qwen/Qwen3-0.6B', quantize=False, local_out='mlx_qwen3_0_6b')
  • CLI: mlx_lm.convert --hf-path Qwen/Qwen3-0.6B --mlx-path mlx_qwen3_0_6b
  • Then load with load('mlx_qwen3_0_6b').

Basic usage

from mlx_gen_parity import GenerationConfig, generate
from mlx_lm import load

model, tokenizer = load('mlx_qwen3_0_6b')
cfg = GenerationConfig(max_tokens=64, temperature=0.7, top_p=0.95, seed=17)
out = generate(model, tokenizer, 'Hello MLX parity', cfg)
print(out['text'])

Beam and constraints

cfg = GenerationConfig(max_tokens=64, temperature=0.0, num_beams=4, early_stopping=True, length_penalty=0.2,
                       force_words_ids=[tokenizer.encode(' cat')], min_new_tokens=8,
                       bad_words_ids=[[tokenizer.eos_token_id]], suppress_tokens=[tokenizer.eos_token_id])
out = generate(model, tokenizer, 'The', cfg)

Speculative decoding

cfg = GenerationConfig(max_tokens=64, temperature=0.7, top_p=0.95,
                       use_speculative=True, draft_model_id='mlx_qwen3_0_6b', num_draft_tokens=3)
out = generate(model, tokenizer, 'Speculative test', cfg)

Persona steering

import mlx.core as mx
from mlx_gen_parity import LogitBiasHook
H = model.args.hidden_size
model['_persona_v'] = mx.random.normal((H,)) * (1.0/(H**0.5))
cfg = GenerationConfig(max_tokens=64, temperature=0.7)
out = generate(model, tokenizer, 'Summarize MLX', cfg, hooks=[LogitBiasHook(param_key='_persona_v', alpha=1.2)])

Training (MLX)

from mlx_gen_parity import TrainingConfig, train_step, SoftPromptHook
from mlx.optimizers import AdamW
pad_id = getattr(tokenizer, 'pad_token_id', -100) or -100
opt = AdamW(learning_rate=2e-4)
batch = {'tokens': ...}  # mx.array [B, T]
cfg = TrainingConfig(dtype='bf16', loss_scale=1024.0)
loss = train_step(model, batch, opt, cfg, hooks=[SoftPromptHook(n_virtual=10, param_key='_soft_prompt')], pad_id=pad_id)

Utilities

from mlx_gen_parity import sequence_logprob, token_kl, ema_update, build_action_mask

# Per-sample mean log-prob on supervised positions (labels == -100 are ignored)
lp = sequence_logprob(model, batch_tokens, labels)  # [B]

# KL(pi || pref) averaged over supervised positions
kl = token_kl(model, ref_model, batch_tokens, labels)  # [B]

# EMA update of a target model from a source model
ema_update(target_model, model, decay=0.999)

# Supervised mask after prompt
mask = build_action_mask(prompt_lens=[12, 20], seq_len=T)  # [B, T] bool

Parity testing

• Torch vs MLX: python -m mlx_gen_parity.tests.parity_hf --hf-model Qwen/Qwen3-0.6B --mlx-model ./mlx_qwen3_0_6b --prompt 'hello'
• Suite (8 prompts): python -m mlx_gen_parity.tests.parity_suite --hf-model Qwen/Qwen3-0.6B --mlx-model ./mlx_qwen3_0_6b

CLI wrapper

mlxgp-generate \
  --model Qwen/Qwen3-0.6B \
  --prompt "Hello MLX" \
  --max-tokens 64 --temp 0.7 --top-p 0.95 \
  --num-beams 1 --no-repeat-ngram-size 2

Performance bench

python -m mlx_gen_parity.tests.perf_bench --hf-model Qwen/Qwen3-0.6B --mlx-model ./mlx_qwen3_0_6b --prompt "Hello performance" --max-tokens 64

Releases

• Bump version across files:
  • make bump-version PART=patch (or minor/major)
• Create and push a git tag (vX.Y.Z):
  • make git-release
  • This tags and pushes the repo; PyPI packaging can be added later.

Notes

• Parity targets control‑surface equivalence: constraints, stops, finish reasons, determinism; token streams may differ across frameworks/devices.
• Sampling fast path reuses mlx-lm’s decoding loop and caches for best performance on Apple Silicon.