The Multiscale Byte Language Model is a model-agnostic, hierarchical architecture for causal byte-level language modeling that scales to million-length sequences.
MBLM is tested against Python versions 3.10, 3.11, 3.12 and 3.13.
Install from PyPI:
pip install mblm
For uv:
uv add mblm
You will need to install a recent PyTorch version manually. We use >=2.6.0. It is best to do this after installing the package since some sub-dependencies might install their own (CPU) PyTorch version.
pip install 'torch>=2.6.0' --index-url https://download.pytorch.org/whl/cu124
For uv:
uv pip install 'torch>=2.6.0' --index-url https://download.pytorch.org/whl/cu124
Finally, in order to use the efficient Mamba-SSM, follow their instructions on the homepage. You'll need Linux and a GPU available during installation.
pip install "mamba-ssm>=2.2.2" "causal-conv1d>=1.4.0" --no-build-isolation
For uv:
uv pip install "mamba-ssm>=2.2.2" "causal-conv1d>=1.4.0" --no-build-isolation
If mamba-ssm is not available, we fall back to using mambapy, which is written in pure PyTorch.
MBLM can be used with the default Transformer Decoder or Mamba block. The below model is a 2D MBLM with a global Mamba and local Transformer model.
import torch
from mblm import (
MBLM,
MambaBlock,
MBLMModelConfig,
MBLMReturnType,
TransformerBlock,
)
mblm = MBLM(
MBLMModelConfig(
num_tokens=257,
hidden_dims=[1024, 1024],
seq_lens=[1024, 8],
num_layers=[5, 5],
pad_token_id=256,
train_checkpoint_chunks=None,
block=[
MambaBlock(
d_state=128,
d_conv=4,
expand=2,
headdim=64,
pos_emb_type=None,
),
TransformerBlock(
attn_head_dims=64,
attn_num_heads=16,
attn_use_rot_embs=True,
use_flash_attn=True,
pos_emb_type="fixed",
),
],
)
)
x = torch.randint(0, 258, (1, 12)).long()
# Choose between any of the return types
logits = mblm.forward(x, return_type=MBLMReturnType.LOGITS)
loss = mblm.forward(x, return_type=MBLMReturnType.LOSS)
loss, logits = mblm.forward(x, return_type=MBLMReturnType.LOSS_LOGITS)
assert logits.shape == (1, 12, 257)
assert loss.ndim == 0Alternatively, you can read configuration from a YAML string (or file):
import torch
import yaml
from mblm import MBLM, MBLMModelConfig, MBLMReturnType
yml_model_config = """
num_tokens: 257
hidden_dims: [1024, 1024]
seq_lens: [1024, 8]
num_layers: [5, 5]
pad_token_id: 256
train_checkpoint_chunks: null
block:
- d_state: 128
d_conv: 4
expand: 2
headdim: 64
pos_emb_type: null
- attn_head_dims: 64
attn_num_heads: 16
attn_use_rot_embs: true
use_flash_attn: true
pos_emb_type: fixed
"""
parsed_config = yaml.safe_load(yml_model_config)
mblm = MBLM(MBLMModelConfig.model_validate(parsed_config))
x = torch.randint(0, 258, (1, 12)).long()
mblm.forward(x, return_type=MBLMReturnType.LOSS)You can define custom stage blocks for MBLM as follows. A stageblock must provide a block_type field as well as a to_model function with the signature below that returns a torch.nn.Module. Other than that, specify whatever other parameters you might need. Note that the default blocks (Transformer and Mamba) are already registered.
import torch
from mblm import MBLM, MBLMModelConfig, MBLMReturnType, TransformerBlock
from mblm.model.block import StageBlock
# Define any custom model
class LSTM(torch.nn.Module):
def __init__(self, lstm: torch.nn.LSTM):
super().__init__()
self.lstm = lstm
def forward(self, input_ids: torch.Tensor) -> torch.Tensor:
# Wrap the LSTM forward to extract the output
out, _ = self.lstm(input_ids)
return out
# Add a block config and inherit from StageBlock
class LSTMBlock(StageBlock):
block_type: str = "lstm"
# Add whatever is needed
dropout: float
def to_model(self, model_dim: int, num_layers: int) -> torch.nn.Module:
return LSTM(
torch.nn.LSTM(
input_size=model_dim,
hidden_size=model_dim,
batch_first=True,
dropout=self.dropout,
num_layers=num_layers,
)
)
mblm = MBLM(
MBLMModelConfig(
num_tokens=257,
hidden_dims=[1024, 1024],
seq_lens=[1024, 8],
num_layers=[5, 5],
pad_token_id=256,
train_checkpoint_chunks=None,
block=[
LSTMBlock(
dropout=0.1,
pos_emb_type=None,
),
TransformerBlock(
attn_head_dims=64,
attn_num_heads=16,
attn_use_rot_embs=True,
use_flash_attn=True,
pos_emb_type="fixed",
),
],
)
)
x = torch.randint(0, 258, (1, 12)).long()
mblm.forward(x, return_type=MBLMReturnType.LOSS)If you want to parse a YAML config to a custom block, register the block before creating the model:
import torch
import yaml
from mblm import MBLM, MBLMModelConfig, MBLMReturnType
from mblm.model.block import StageBlock
from mblm.model.config import block_registry # Import this!
# Define any custom model
class LSTM(torch.nn.Module):
def __init__(self, lstm: torch.nn.LSTM):
super().__init__()
self.lstm = lstm
def forward(self, input_ids: torch.Tensor) -> torch.Tensor:
# Wrap the LSTM forward to extract the output
out, _ = self.lstm(input_ids)
return out
# Add a block config and inherit from StageBlock
@block_registry.register()
class LSTMBlock(StageBlock):
block_type: str = "lstm"
# Add whatever is needed
dropout: float
my_property: int
def to_model(self, model_dim: int, num_layers: int) -> torch.nn.Module:
return LSTM(
torch.nn.LSTM(
input_size=model_dim,
hidden_size=model_dim,
batch_first=True,
dropout=self.dropout,
num_layers=num_layers,
)
)
yml_model_config = """
num_tokens: 257
hidden_dims: [1024, 1024]
seq_lens: [1024, 8]
num_layers: [5, 5]
pad_token_id: 256
train_checkpoint_chunks: null
block:
- dropout: 0.1
my_property: 1
pos_emb_type: null
- attn_head_dims: 64
attn_num_heads: 16
attn_use_rot_embs: true
use_flash_attn: true
pos_emb_type: fixed
"""
parsed_config = yaml.safe_load(yml_model_config)
mblm = MBLM(MBLMModelConfig.model_validate(parsed_config))
x = torch.randint(0, 258, (1, 12)).long()
mblm.forward(x, return_type=MBLMReturnType.LOSS)If you want to use the MBLM trainer with torchrun with a custom dataset, you will need to add a few special methods. Here is an end-to-end example where you launch training on your own:
# Filename: train_my_mblm.py
import torch
from typing_extensions import Unpack
from mblm import MambaBlock, TransformerBlock
from mblm.data.datasets import DistributedDataset, DistributedDatasetConfig
from mblm.data.types import BatchWithLossMask, ModelMode
from mblm.train.core.config import CoreTrainConfig
from mblm.train.mblm import (
TrainEntryConfig,
TrainMBLMIoConfig,
TrainMBLMParams,
dataset_registry,
train_mblm,
)
# Register dataset with a unique ID
@dataset_registry.register("mydataset")
class MyDataset(DistributedDataset[BatchWithLossMask]):
def __init__(
self,
mode: ModelMode,
dataset_dir: str,
**args: Unpack[DistributedDatasetConfig],
):
# Dummy example - Get data from anywhere, e.g., the disk
print(f"Reading dataset from {dataset_dir}")
if mode == ModelMode.TRAIN:
data = list(range(10_000))
else:
data = list(range(2_000))
self._data = data
super().__init__(
data_size=len(data),
is_sequential=True, # We have a sequential dataset
**args,
)
def get_sample(self, from_idx: int):
"""
Tell the superclass how to get a single sample - here, a sequence of
the specified length.
"""
data = torch.tensor(self._data[from_idx : from_idx + self.seq_len])
return torch.ones_like(data), data
@staticmethod
def from_train_entry_config(
config: TrainEntryConfig,
mode: ModelMode,
worker_id: int,
num_workers: int,
) -> DistributedDataset[BatchWithLossMask]:
"""
How to parse a training config to a dataset.
"""
return MyDataset(
dataset_dir=config.io.dataset_dir,
mode=mode,
seq_len=config.params.input_seq_len,
num_workers=num_workers,
worker_id=worker_id,
)
@staticmethod
def supports_test_mode() -> bool:
"""
Whether or not this dataset supports a test mode. Some datasets might not
expose the answers in their test set so we cannot evaluate a model on it.
Override if necessary
"""
return True
config = TrainEntryConfig(
io=TrainMBLMIoConfig(
dataset_dir="data/datasets/my-dataset",
dataset_id="mydataset", # Must match the ID above
name_model="my-model",
output_dir="data/outputs",
num_models_to_save=3,
validate_amount=20,
log_train_loss_amount=100,
),
train=CoreTrainConfig(
batch_size=1,
target_elements=1000,
target_elements_strategy="sequence",
learning_rate=0.001,
gradient_accumulate_every=4,
gradient_clipping=1,
shuffle_train=True,
shuffle_eval=False,
),
params=TrainMBLMParams(
input_seq_len=128,
num_tokens=257,
hidden_dims=[512, 512],
seq_lens=[16, 8],
num_layers=[5, 5],
pad_token_id=256,
train_checkpoint_chunks=None,
block=[
MambaBlock(
d_state=128,
d_conv=4,
expand=2,
headdim=64,
pos_emb_type=None,
),
TransformerBlock(
attn_head_dims=64,
attn_num_heads=16,
attn_use_rot_embs=True,
use_flash_attn=True,
pos_emb_type="fixed",
),
],
),
)
if __name__ == "__main__":
train_mblm(config)Then, run the above file with:
OMP_NUM_THREADS=1 uv run torchrun --standalone \
--nproc_per_node=gpu train_my_mblm.pyGenerally, training is started from a config file in YAML format. The above is just to give an idea of how everything works together.
Check the example configs - they should look very similar to the config above - and how we launch training (with scripts/train_mblm.py). With any config, simply run:
bash scripts/train_mblm.py -c <your-config>Which will launch torchrun with all the necessary configuration.
Alternatively, you can always subclass the core trainer and do things you way. There are many examples in the source dir and the end-to-end tests.
As a byte language model, MBLM generates integer representations of bytes. We can hook into the generation process and stream all generated bytes directly to a file object such as sys.stdout (for debugging or interactive sessions) or io.TextIO and io.BinaryIO stream interfaces.
Let's assume our model is conditioned to generate the following text string:
👉🏽 bytes generated by a 🤖
In UTF-8 bytes, this corresponds to:
# hex representation
f0 9f 91 89 f0 9f 8f bd 20 62 79 74 65 73 20 67 65 6e 65 72 61 74 65 64 20 62 79 20 61 20 f0 9f a4 96
# integer representation
240 159 145 137 240 159 143 189 32 98 121 116 101 115 32 103 101 110 101 114 97 116 101 100 32 98 121 32 97 32 240 159 164 150Internally, these integers are what the model generates. However, maybe you have trained to output a different modality such as a PNG file or an MP4 video - the possibilities are endless.
For simplicity, let's assume we have some root_dir and a function create_mblm to create an MBLM module.
We can stream the response directly to a file - no need to specify the encoding. All we need to do is open a file in binary mode. In this example, the output corresponds to UTF-8.
from pathlib import Path
from mblm.utils.stream import ByteStreamer
mblm = create_mblm(...)
# any modality that the model learns to output - .png, .txt, .bin, etc.
file_path = Path(root_dir) / "output.txt"
# open in binary mode and write raw bytes
with Path(file_path).open("wb") as file:
with ByteStreamer(stream=file) as streamer:
mblm.generate(streamer)
# we can open the file and interprete its content as UTF-8
with Path(file_path).open("r", encoding="utf8") as file:
assert file.read() == "👉🏽 bytes generated by a 🤖"For developing and interactive sessions, we can stream the response directly to the terminal. We can either decode the bytes from UTF-8 on the fly or stream the raw integer bytes to the terminal when the bytes represent something other than text.
import sys
from mblm.utils.stream import ByteStreamer
mblm = create_mblm(...)
# approach 1: stream to stdout and decode on the fly
with ByteStreamer(stream=sys.stdout, decode_utf8=True) as streamer:
mblm.generate(streamer)
# streams the decoded bytes to the terminal:
# 👉🏽 bytes generated by a 🤖
# approach 2: stream raw output to stdout
with ByteStreamer(stream=sys.stdout) as streamer:
mblm.generate(streamer)
# streams the bytes as integers to the terminal:
# 240 159 145 ... 159 164 150Our approach of decoding from UTF-8 uses the replace strategy for dealing with malformed data, which enables continuous decoding even for partially corrupted sequences. Whenever decode_utf8 is False, raw bytes are streamed and you'll need to deal with corrupted UTF-8 sequences on your own.
We use uv for packaging and dependency management. Before proceeding, install a recent version (>= 0.5) via the instructions on the homepage.
- With CUDA:
make install_cuda - CPU only (e.g., MacOS):
make install_cpu
If you've noticed, there are two SSM/Mamba dependencies:
mambapy, defined inpyproject.tomlmamba-ssm(withcausal-conv1d), defined inMakefile
Because the official Mamba implementation mamba-ssm requires a Linux machine and a GPU available during installation, we shim the dependencies. mambapy is used as a fallback for all unsupported platforms or when mamba-ssm is not installed. Because mamba-ssm is so delicate, it needs to be installed manually:
make install_mambaFor any experiments, we wish to use the new Mamba 2 block from mamba-ssm. If the import of this module fails, we fall back to a Mamba 1 block from mambapy, which is written in pure PyTorch.
- Project-related tasks (e.g., installing dependencies, running tests) are defined in the Makefile
Before every commit, we lint the staged Python and Jupyter Notebook files and check if they are formatted correctly. Doing this locally speeds up development because one does not have to wait for the CI to catch issues. Errors of these checks are not fixed automatically, instead, you will have to fix the files yourself before committing. You may bypass hooks with git commit -m <message> --no-verify. However, the CI will likely fail in this case.
All Pre-commit hooks can be run manually as well:
pre-commit run lintpre-commit run check-format
Note that:
- The
lintcommand is similar to themake lintcommand, but themakecommand operates on all files in the project and not just the staged files - While
check-formatsimply checks the format,make formatwill actually format the files
TBD.
