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README.md

Mamba-based Language Models

Introduction

This document is an entrypoint into the code used for An Empirical Study of Mamba-based Language Models.

We are releasing the parameters for some of the models described in that technical report via HuggingFace.

Installation

Create and run a Docker container using the Dockerfile.

docker build -t your_image_name:your_tag .
docker run --gpus all -it --rm \
  -v /path/to/megatron:/workspace/megatron \
  -v /path/to/dataset:/workspace/dataset \
  -v /path/to/checkpoints:/workspace/checkpoints \
  -w /workspace/megatron/examples/mamba \
  your_image_name:your_tag

Train

train.sh is an example pretraining script, showing how to run on a single node. Select between 800M-scale and 8B-scale models by setting the MODEL_SCALE variable. The 8B-scale hybrid model architecture is the same as the one described in the technical report.

Text Generation

Use run_text_gen_server_8b.sh to start a text generation server using an 8B hybrid checkpoint. This is configured to run the 8B hybrid model described in the technical report, with tensor model parallel set to 1.

The arguments in the script will need to be changed if using a checkpoint with a different model parallel configuration or other differences, such as model architecture. For example, to run the 8B pure Mamba-2 model, change --hybrid-attention-ratio and --hybrid-mlp-ratio to 0.0, or remove them.

Use run_text_gen_server_8b_gpt3.sh to start a text generation server using the 8B reference Transformer checkpoint.

Checkpoint Formats

For inference, the model must be configured to match the checkpoint file used, including the hybrid layer configuration and model parallel configuration.

If you need to convert a hybrid checkpoint file to a different tensor parallel or pipeline parallel size, use the hybrid conversion script. There is an example run command at the end of that file.

Before running that script, you will need to set PYTHONPATH to include the root directory of your Megatron-LM repository clone.

export PYTHONPATH=<path-to-megatron>:PYTHONPATH

Hybrid Options

--hybrid-attention-ratio ATT specifies a target ratio of attention layers to total layers. For example, 4 attention layers out of 48 total layers is specified by --hybrid-attention-ratio 0.08.

--hybrid-mlp-ratio MLP specifies a target ratio of MLP layers to total layers. For example, 24 MLP layers out of 48 total layers is specified by --hybrid-mlp-ratio 0.5.

  • (ATT + MLP) must be less than or equal to 1.0.
  • (1.0 - ATT - MLP) is the hybrid mamba ratio, the ratio of mamba layers to total layers.
  • ATT = MLP = 0 is a pure Mamba model.
  • ATT = MLP = 0.5 is a transfomer model.

If either ATT or MLP is greater than 0.0 or if --hybrid-override-pattern is specified, the logfile will include information about the hybrid layer pattern used. --hybrid-override-pattern can be used to specify a different pattern than the default, algorithmically-generated one.

Mamba vs Mamba-2

This codebase currently only supports Mamba-2, and not the original version of Mamba. However, the fixed snapshot of the code used for the technical report can be configured to run the original version of Mamba.