seqmask

Composable MLM (masked language modeling) corruption strategies with curriculum schedulers, diffusion-style timestep schedules, and reference-driven position focusing. Pure NumPy on the inside; accepts and returns either NumPy arrays or CPU PyTorch tensors transparently.

Built primarily for protein-sequence pretraining but tokenizer-agnostic: the caller supplies mask_token_id, valid_token_ids, and special_token_ids, so the same machinery works for any vocabulary.

From source:

pip install seqmask

With dev dependencies (pytest):

pip install -e "seqmask[dev]"

Requires Python 3.9+. Depends on numpy and torch or jax (Whatever you are using).

import os
os.environ["SEQMASK_BACKEND"] = "torch" # or `jax`
import torch
from seqmask import BetaDistributionMasking
from seqmask import backend

seqmask = BetaDistributionMasking(
    mlm_prob=0.15,
    mlm_concentration=10.0,
    masking_prob=0.8,           # 80% of selected positions -> [MASK]
    mutation_prob=0.1,          # 10% -> random valid token
                                # remaining 10% -> kept as-is
    mask_token_id=103,
    valid_token_ids=list(range(10, 200)),
    special_token_ids=[0, 1, 2],
)

x = torch.randint(10, 200, (4, 128))
corrupted, labels = seqmask(x, seed=42)
# `corrupted` is the model input; `labels` is -100 outside selected
# positions and holds the original token id at selected positions.

All four implement the same __call__(sequences, seed=None, excluded_ids=None, focus_positions=None) contract (DiffusionMasking additionally takes timestep). Inputs and outputs match types — NumPy in, NumPy out; CPU torch in, CPU torch out.

Sampler-driven per-call rates. Use this when you want curriculum schedulers (LinearScheduler, EMAScheduler, StagedLinearScheduler) to steer the masking rate.

from seqmask import (
    CategoricalMasking, MLMProbabilitySampler, LinearScheduler,
)

scheduler = LinearScheduler(
    initial_weights=[1.0, 0.0, 0.0],
    final_weights=[0.0, 0.0, 1.0],
    total_steps=10_000,
)
sampler = MLMProbabilitySampler(
    mlm_probs=[0.15, 0.20, 0.30],
    masking_probs=[0.8, 0.8, 0.8],
    mutation_probs=[0.1, 0.1, 0.1],
    scheduler=scheduler,
)
seqmask = CategoricalMasking(
    mlm_prob_sampler=sampler,
    mask_token_id=103,
    valid_token_ids=list(range(200)),
    special_token_ids=[0, 1, 2],
)

corrupted, labels = seqmask(x)
sampler.step()   # advance the curriculum

Samples the per-call masking rate from Beta(mean, concentration). Simplest "randomize the rate" option — no scheduler needed.

Mix of scattered token masks and contiguous spans (SpanBERT-style). Per-call: Beta-sample the total rate and the span share alpha; fill the span budget with Geometric(span_length_p)-length spans clipped at specials, then top up with scattered positions to hit the target rate.

from seqmask import HybridSpanMasking

seqmask = HybridSpanMasking(
    mlm_prob=0.15,
    mlm_concentration=10.0,
    span_fraction_mean=0.5,         # ~half of masks come from spans
    span_fraction_concentration=10.0,
    span_length_p=0.2,              # mean span length = 1/p = 5
    max_span_length=10,
    masking_prob=0.8,
    mutation_prob=0.1,
    mask_token_id=103,
    valid_token_ids=list(range(200)),
    special_token_ids=[0, 1, 2],
)

Mask-only corruption (no 80/10/10 split) where the rate is a function of a per-call timestep. Used for discrete-diffusion MLM (D3PM / MaskGIT). Schedules are classes — they pickle cleanly under spawn-based multiprocessing (PyTorch DataLoader with num_workers > 0).

from seqmask import DiffusionMasking, CosineDiffusionScheduler

seqmask = DiffusionMasking(
    total_timesteps=100,
    mask_token_id=103,
    special_token_ids=[0, 1, 2],
    schedule=CosineDiffusionScheduler(),   # or "linear" / "cosine" / "sqrt"
)

t = 42
corrupted, labels = seqmask(x, timestep=t)

Available schedulers: LinearDiffusionScheduler, CosineDiffusionScheduler, SqrtDiffusionScheduler, or any DiffusionScheduler subclass (__call__(timestep, total_timesteps) -> float). Strings "linear" / "cosine" / "sqrt" are convenience aliases that resolve to the corresponding scheduler instance.

Bias masking toward specific positions — useful when fine-tuning on variants relative to a reference (wildtype proteins, edited code, etc.). All per-token strategies (Categorical, Beta, Diffusion) accept focus_positions as a (B, L) boolean mask per call.

from seqmask import (
    BetaDistributionMasking,
    focus_positions_from_reference,
)
from seqmask import backend

reference = backend.to_tensor([10, 20, 30, 40, 50])
variants  = backend.to_tensor([
    [10, 20, 30, 40, 50],   # unchanged
    [10, 99, 30, 99, 50],   # mutated at positions 1 and 3
])

focus = focus_positions_from_reference(variants, reference)
# tensor([[False, False, False, False, False],
#         [False,  True, False,  True, False]])

seqmask = BetaDistributionMasking(
    mlm_prob=0.1,
    mlm_concentration=10.0,
    masking_prob=0.8,
    mutation_prob=0.1,
    mask_token_id=103,
    valid_token_ids=list(range(200)),
    special_token_ids=[0],
    focus_strategy="multiplicative",   # or "force_include" / "weighted"
    focus_strength=3.0,
)
corrupted, labels = seqmask(variants, focus_positions=focus)

The three focus strategies:

focus_positions_from_reference(sequences, reference) handles broadcasting: (L,), (1, L), and (B, L) references all work; output type matches sequences (NumPy or torch).

Use these with MLMProbabilitySampler to advance the masking rate over training steps. They share a SharedCounter so multiprocessing workers see a consistent step count.

• LinearScheduler(initial_weights, final_weights, total_steps)
• EMAScheduler(initial_weights, final_weights, beta=0.999, multiplier=1.0)
• StagedLinearScheduler(num_stages, total_steps) — sequentially introduces each candidate, ramping its weight from 0 to equal share.

Call sampler.step() (or scheduler.step()) once per training step.

All seqmasks, samplers, and schedulers are picklable, so they can be attached to a Dataset and used with DataLoader(num_workers > 0) under both fork and spawn start methods. For DiffusionMasking, prefer the DiffusionScheduler subclasses (or string aliases) over lambdas — lambdas aren't picklable under spawn.

Every constructor that takes weights or probabilities calls ensure_non_negative from seqmask.utils. Negative values raise ValueError at construction time; out-of-range sampler outputs raise at call time. There's no silent clamping.

seqmask/
  scheduler/           # base Scheduler + Linear / StagedLinear / EMA
  masking/             # CategoricalMasking, BetaDistributionMasking,
                       # HybridSpanMasking, DiffusionMasking +
                       # DiffusionScheduler hierarchy + focus helper
  sampler.py           # MLMProbabilitySampler
  utils.py             # ensure_non_negative, seed_from_components,
                       # create_default_mlm_weights

pip install "seqmask[dev]"
pytest

If you use this software in your research, please cite it as follows:

@software{alsamkary2026seqmask,
  author       = {Hazem Alsamkary},
  title        = {seqmask: r1.0.1},
  month        = jun,
  year         = 2026,
  publisher    = {Zenodo},
  version      = {v1.0.1},
  doi          = {10.5281/zenodo.20532337},
  url          = {https://doi.org/10.5281/zenodo.20532337}
}

Apache 2.0 — see LICENSE.