# Mamba

![Mamba](assets/selection.png "Selective State Space")
> **Mamba: Linear-Time Sequence Modeling with Selective State Spaces**\
> Albert Gu*, Tri Dao*\
> Paper: https://arxiv.org/abs/2312.00752

## About

Mamba is a new state space model architecture showing promising performance on information-dense data such as language modeling, where previous subquadratic models fall short of Transformers.
It is based on the line of progress on [structured state space models](https://github.com/state-spaces/s4),
with an efficient hardware-aware design and implementation in the spirit of [FlashAttention](https://github.com/Dao-AILab/flash-attention).

## Installation

- `pip install causal-conv1d`: an efficient implementation of a simple causal Conv1d layer used inside the Mamba block.
- `pip install mamba-ssm`: the core Mamba package.

It can also be built from source with `pip install .` from this repository.

If `pip` complains about PyTorch versions, try passing `--no-build-isolation` to `pip`.

Other requirements:
- Linux
- NVIDIA GPU
- PyTorch 1.12+
- CUDA 11.6+

## Usage

We expose several levels of interface with the Mamba model.

### Selective SSM

Mamba is based on a selective SSM layer, which is the focus of the paper (Section 3; Algorithm 2).

Source: [ops/selective_scan_interface.py](mamba_ssm/ops/selective_scan_interface.py).

### Mamba Block

The main module of this repository is the Mamba architecture block wrapping the selective SSM.

Source: [modules/mamba_simple.py](mamba_ssm/modules/mamba_simple.py).

Usage:
```
from mamba_ssm import Mamba

batch, length, dim = 2, 64, 16
x = torch.randn(batch, length, dim).to("cuda")
model = Mamba(
    # This module uses roughly 3 * expand * d_model^2 parameters
    d_model=dim, # Model dimension d_model
    d_state=16,  # SSM state expansion factor
    d_conv=4,    # Local convolution width
    expand=2,    # Block expansion factor
).to("cuda")
y = model(x)
assert y.shape == x.shape
```

### Mamba Language Model

Finally, we provide an example of a complete language model: a deep sequence model backbone (with repeating Mamba blocks) + language model head.

Source: [models/mixer_seq_simple.py](mamba_ssm/models/mixer_seq_simple.py).

This is an example of how to integrate Mamba into an end-to-end neural network.
This example is used in the generation scripts below.



## Pretrained Models

Pretrained models are uploaded to
[HuggingFace](https://huggingface.co/state-spaces): `mamba-130m`, `mamba-370m`,
`mamba-790m`, `mamba-1.4b`, `mamba-2.8b`.

The models will be autodownloaded by the generation script below.

These models were trained on the [Pile](https://huggingface.co/datasets/EleutherAI/pile), and follow the standard model dimensions described by GPT-3 and followed by many open source models:

| Parameters | Layers | Model dim. | 
|------------|--------|------------|
| 130M       | 12     | 768        |
| 370M       | 24     | 1024       |
| 790M       | 24     | 1536       |
| 1.4B       | 24     | 2048       |
| 2.8B       | 32     | 2560       |

(The layer count of Mamba should be doubled, as two Mamba blocks are needed for each "layer" (MHA block + MLP block) of a Transformer.)

Note: these are base models trained only for 300B tokens, without any form of downstream modification (instruction tuning, etc.).
Performance is expected to be comparable or better than other architectures trained on similar data, but not to match larger or fine-tuned models.


## Evaluations

To run zero-shot evaluations of models (corresponding to Table 3 of the paper),
we use the
[lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/big-refactor)
library.

1. Pull the `lm-evaluation-harness` repo by `git submodule update --init
   --recursive`. We use the `big-refactor` branch.
2. Install `lm-evaluation-harness`: `pip install -e 3rdparty/lm-evaluation-harness`
3. Run evaluation with (more documentation at the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/big-refactor) repo):
```
python evals/lm_harness_eval.py --model mamba --model_args pretrained=state-spaces/mamba-130m --tasks lambada_openai,hellaswag,piqa,arc_easy,arc_challenge,winogrande --device cuda --batch_size 64
python evals/lm_harness_eval.py --model hf --model_args pretrained=EleutherAI/pythia-160m --tasks lambada_openai,hellaswag,piqa,arc_easy,arc_challenge,winogrande --device cuda --batch_size 64
```

Note that the result of each task might differ from reported values by 0.1-0.3 due to noise in the evaluation process.

## Inference

The script [benchmarks/benchmark_generation_mamba_simple.py](benchmarks/benchmark_generation_mamba_simple.py)
1. autoloads a model from the HuggingFace Hub,
2. generates completions of a user-specified prompt,
3. benchmarks the inference speed of this generation.

Other configurable options include the top-p (nucleus sampling) probability, and the softmax temperature.

### Examples

To test generation latency (e.g. batch size = 1) with different sampling strategies:

```
python benchmarks/benchmark_generation_mamba_simple.py --model-name "state-spaces/mamba-2.8b" --prompt "My cat wrote all this CUDA code for a new language model and" --topp 0.9 --temperature 0.5
python benchmarks/benchmark_generation_mamba_simple.py --model-name "EleutherAI/pythia-2.8b" --prompt "My cat wrote all this CUDA code for a new language model and" --topp 0.9 --temperature 0.5
```

To test generation throughput with random prompts (e.g. large batch size):
```
python benchmarks/benchmark_generation_mamba_simple.py --model-name "state-spaces/mamba-2.8b" --batch 128
python benchmarks/benchmark_generation_mamba_simple.py --model-name "EleutherAI/pythia-2.8b" --batch 128
```

## Citation

If you use this codebase, or otherwise found our work valuable, please cite Mamba:
```
@article{mamba,
  title={Mamba: Linear-Time Sequence Modeling with Selective State Spaces},
  author={Gu, Albert and Dao, Tri},
  journal={arXiv preprint arXiv:2312.00752},
  year={2023}
}
```