mamba2-jax 0.1.0

Implementation of Mamba2 SSM on JAX

Mamba2-JAX: Pure JAX Implementation of Mamba2

Introduction

This is an experimental JAX/Flax implementation of Mamba2 [1] inspired by vasqu's exquisite PyTorch version [2]. The implementation provides a pure JAX alternative for researchers and practitioners who prefer the JAX ecosystem for its functional programming paradigm, automatic differentiation, and seamless integration with TPU hardware.

Current Status: Alpha (Stable) Release

This alpha version focuses on numerical correctness and stability. The implementation has been tested against the PyTorch version and shows equivalent numerical behavior (see Numerical Validation below).

NOTE: This is an early-stage implementation that currently supports:

• Pure JAX/Flax implementation (no Triton kernels)
• Causal language modeling with Mamba2ForCausalLM
• Time series forecasting with Mamba2Forecaster
• Full forward and backward passes with gradient computation
• Small to medium-scale experimentation

Why JAX?

While vasqu's excellent PyTorch implementation provides multiple optimization paths including Triton kernels, this JAX version offers several unique advantages:

• Functional Programming: JAX's functional approach makes it easier to reason about model behavior and transformations
• Hardware Flexibility: Seamless support for TPUs alongside GPUs through XLA compilation
• Research-Friendly: JAX's transformation system (jit, grad, vmap, pmap) enables elegant experimentation
• Ecosystem Integration: Natural fit for projects already using JAX (Flax, Optax, Haiku)
• Educational Value: Cleaner implementation for understanding Mamba2 internals without CUDA complexity

This implementation prioritizes clarity and correctness over raw performance, making it ideal for:

• Understanding Mamba2 architecture
• Rapid prototyping of variants
• Integration into JAX-based research codebases
• TPU-based training workflows

Installation

Stable Version

Automatically download from PyPI using pip

pip install mamba2-jax

Development Version

Clone the repository and install as a package:

git clone https://github.com/yourusername/mamba2-jax.git
cd mamba2-jax
pip install -e .

Requirements

pip install jax jaxlib flax optax einops

GPU (CUDA) & TPU support

CUDA support will be released in an upcoming version with plans to optimise for triton kernel.

For TPU support, follow the official JAX TPU guide.

WARN! TPU support has not been validated as of this release.

Usage

Basic Language Modeling Example

This complete example shows how to create a Mamba2 language model, initialize it, and run a forward pass. You can copy and paste this entire block to get started:

import jax
import jax.numpy as jnp
from mamba2_jax import Mamba2Config, Mamba2ForCausalLM

# Create a small configuration for testing
# You can scale these up for real applications
config = Mamba2Config(
    vocab_size=1024,        # Small vocabulary for demo
    hidden_size=256,        # Hidden dimension
    state_size=64,          # SSM state size
    head_dim=32,            # Dimension per head
    num_hidden_layers=4,    # Number of Mamba2 blocks
    chunk_size=64,          # Chunk size for SSD computation
)

# Initialize the model
model = Mamba2ForCausalLM(config)

# Create some random input tokens
key = jax.random.PRNGKey(42)
batch_size, seq_len = 2, 64
input_ids = jax.random.randint(
    key, 
    (batch_size, seq_len), 
    minval=0, 
    maxval=config.vocab_size
)

# Initialize model parameters with the input shape
print("Initializing model parameters...")
variables = model.init(key, input_ids=input_ids)
params = variables["params"]

# Run forward pass with loss computation
print("Running forward pass...")
outputs = model.apply(
    {"params": params},
    input_ids=input_ids,
    labels=input_ids,  # Using same tokens as labels for demo
)

# Check outputs
print(f"Logits shape: {outputs['logits'].shape}")  # Should be (2, 64, 1024)
print(f"Loss: {float(outputs['loss']):.4f}")
print("Forward pass completed successfully!")

Time Series Forecasting Example

This example shows how to use Mamba2 for time series prediction. The model takes a historical sequence and predicts future values:

import jax
import jax.numpy as jnp
import optax

from mamba2_jax import Mamba2Forecaster

# Suppose we have univariate timeseries windows of length L
batch_size = 8
input_length = 32
forecast_horizon = 12
input_dim = 1
output_dim = 1

model = Mamba2Forecaster(
    input_dim=input_dim,
    d_model=256,
    n_layers=4,
    output_dim=output_dim,
    forecast_horizon=forecast_horizon,
)

key = jax.random.PRNGKey(0)
x = jax.random.normal(key, (batch_size, input_length, input_dim))

variables = model.init(key, x)
params = variables["params"]

y_pred = model.apply({"params": params}, x)  # (B, H, D_out)
print("Timeseries output shape:", y_pred.shape)

Advanced Features

The core Mamba2Model exposes the same SSM hooks as the PyTorch implementation:

• Stateful / streaming inference via initial_states and output_last_ssm_states.
• Layer-wise analysis via output_hidden_states=True.

See the runnable scripts in the examples/ directory:

• 04_streaming_states_demo.py – carry SSM state across chunks for streaming generation or very long sequences.
• 05_inspect_hidden_states.py – retrieve per-layer hidden states for analysis or auxiliary losses.

Examples

For more end-to-end, runnable scripts (tiny training loops, sine-wave forecasting, streaming state demos, etc.), see the examples/ directory in this repository.

Numerical Validation with PyTorch

The implementation has been validated against the reference PyTorch version [2] to ensure numerical correctness on CPU. Further tests will investigate GPU (CUDA) and TPU performance once enabled post alpha release.

Methodology

• A small Mamba2 model is instantiated in both PyTorch and JAX with identical hyperparameters (hidden size, state size, number of layers, sequence length, etc.).
• A simple synthetic MSE regression task is constructed and shared between the two frameworks using the same random seed.
• On CPU only, both models are trained side-by-side on this task for a short number of optimisation steps.
• At each training step we record:
  • the PyTorch loss,
  • the JAX loss,
  • the absolute difference |L_torch - L_jax|,
  • and the per-step wall-clock time for each framework.
• All experiments are run in float32 with no mixed precision or framework-specific numerical tricks, to keep the comparison as fair as possible.

The whole procedure is implemented in the standalone script test-parity.py, which can be run on any CPU-only machine.

The key experiment configuration is summarised below:

Category	Parameter	PyTorch	JAX
Backend	Framework	PyTorch	JAX + Optax + Flax
	Device	CPU	CPU
	Dtype	float32	float32
Data / task	Task	Synthetic time-series forecasting (MSE regression)	Same dataset via shared NumPy arrays
	Batch size	2	2
	Context length	32 time steps	32 time steps
	Input dimension	10	10
	Forecast horizon	16 time steps	16 time steps
Model	Model wrapper	Mamba2Model + linear head	Mamba2Forecaster
	d_model	768	768
	n_layers	1	1
	d_state	128 (Mamba2Config default)	128
	headdim	64 (Mamba2Config default)	64
	expand	2	2
	d_conv	4	4
Training	Optimiser	Manual SGD (param -= lr * grad)	optax.sgd(lr)
	Loss function	Mean squared error	Mean squared error
	Learning rate	0.001	0.001
	Training steps	16	16
	Random seed	Shared seed for data + initialisation where possible	Shared seed for data + initialisation where possible

Test Results

See the Mamba2 PyTorch vs JAX parity test appendix for full details.

The figure below summarises the comparison:

• Training loss (left panel). The PyTorch and JAX MSE losses follow almost identical learning curves. Both decrease smoothly over time, and by the final steps the two curves are visually indistinguishable.
• Loss difference (middle panel). The absolute difference |L_torch - L_jax| starts around ~2×10⁻¹ and decays monotonically during training, reaching the ~10⁻² range after ~15–16 steps. This level of discrepancy is well within normal numerical noise between different backends and confirms that the JAX implementation closely tracks the PyTorch reference.
• CPU wall-clock time (right panel). On this micro-benchmark the JAX implementation is roughly 2× faster per step on CPU, with typical step times around ~0.08–0.09 s versus ~0.18–0.20 s for PyTorch.

Overall, these experiments indicate that the JAX implementation is numerically consistent with the PyTorch model while offering competitive (and often better) CPU performance for this class of workloads.

Summary metrics

Category	Metric	PyTorch	JAX	Notes
Training loss	Initial MSE (step 0)	1.5894	2.1382	Different random inits, both converge
Training loss	Final MSE (step 15)	0.0249	0.0371	Final diff ≈ 0.0121
Training loss	Mean abs. diff (all steps)	–	–	0.1606 (mean), 0.5487 (max)
Training loss	Mean rel. diff (all steps)	–	–	≈ 47 % mean, ≈ 51 % max
Prediction parity	Pearson correlation	–	–	0.992 between PyTorch and JAX predictions
Prediction parity	MAE / std(torch)	–	–	≈ 0.10 (~10 %)
Prediction parity	RMSE / std(torch)	–	–	≈ 0.13 (~13 %)
Timing (CPU)	Mean step time	0.1935 s	0.0879 s	JAX is ≈ 2.2× faster per step on CPU
Timing (CPU)	JIT compile (train_step)	–	0.97 s	One-off JIT cost before steady-state steps

In short, both implementations learn very similar functions: the loss curves track each other closely, the final losses differ by only ≈ 0.012, and the final predictions have a Pearson correlation of ~0.99 with discrepancies on the order of 10–13 % of the PyTorch signal scale. On CPU, the JAX version achieves roughly a 2.2× lower per-step wall-clock time once the one-off JIT compilation cost is paid, while remaining numerically consistent with the PyTorch reference.

Project Structure

Roadmap

Beta Release (Coming Soon)

• GPU Optimisation: Profile and optimize performance on modern GPUs
• Expanded Test Suite: Comprehensive unit tests and integration tests
• Model Conversion Scripts: Tools to convert pretrained PyTorch weights to JAX
• Benchmarking Suite: Systematic performance comparison across hardware
• Documentation: Detailed API documentation and architecture guide

Future Releases

• Triton Kernel Support: Custom kernels for improved performance
• Pretrained Models: Host converted models on Hugging Face Hub
• Mixed Precision Training: BF16/FP16 support with proper loss scaling
• Model Parallelism: Support for large-scale training with pmap/pjit
• Advanced Caching: Efficient KV-like caching for generation
• Hybrid Variants: Attention and MLP hybrid architectures

Known Limitations

This alpha release has several known limitations:

• No Triton Kernels: Uses naive SSD implementation, slower than optimized PyTorch version
• No Pretrained Weights: No conversion scripts yet (coming in beta)
• Limited Generation Support: Basic generation only, no advanced sampling methods
• No Hybrid Architectures: Pure Mamba2 blocks only (no attention/MLP variants)
• Memory Usage: Not optimized for very long sequences (>4096 tokens)

We're actively working on addressing these limitations in upcoming releases.

Contributing

Contributions are welcome! Areas where help would be particularly valuable:

• Performance optimization and profiling
• Test coverage expansion
• Documentation improvements
• Bug reports and feature requests

Please open an issue or submit a pull request on GitHub.

Acknowledgments

This implementation builds upon the excellent work of many researchers and engineers:

Original Mamba2 Authors [1] :

• Tri Dao and Albert Gu for the Mamba2 architecture and original implementation
• The entire State Spaces team for advancing SSM research

PyTorch Implementation [2] :

• vasqu for the clean PyTorch implementation that served as a reference
• The implementation structure and many design decisions were inspired by mamba2-torch

JAX Ecosystem [3] [4] :

• The JAX, Flax, and Optax teams at Google for the excellent frameworks
• The broader JAX community for tools and support

NOTE: I (Cosmo Santoni) am not affiliated with the original authors of Mamba2 paper nor PyTorch, HuggingFace, Google, JAX or Flax teams. I am an independent researcher at Imperial College London.s

References

[1] Mamba2
@inproceedings{mamba2,
  title={Transformers are {SSM}s: Generalized Models and Efficient Algorithms Through Structured State Space Duality},
  author={Dao, Tri and Gu, Albert},
  booktitle={International Conference on Machine Learning (ICML)},
  year={2024}
}

[2] mamba2-torch (PyTorch Implementation)
@software{vasqu2024mamba2torch,
  author = {vasqu},
  title = {mamba2-torch: HuggingFace Compatible Mamba2},
  year = {2024},
  url = {https://github.com/vasqu/mamba2-torch}
}

[3] JAX
@software{jax2018github,
  author = {James Bradbury and Roy Frostig and Peter Hawkins and Matthew James Johnson and Chris Leary and Dougal Maclaurin and George Necula and Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao Zhang},
  title = {{JAX}: composable transformations of {P}ython+{N}um{P}y programs},
  url = {http://github.com/google/jax},
  version = {0.3.13},
  year = {2018},
}

[4] Flax
@software{flax2020github,
  author = {Jonathan Heek and Anselm Levskaya and Avital Oliver and Marvin Ritter and Bertrand Rondepierre and Andreas Steiner and Marc van {Z}ee},
  title = {{F}lax: A neural network library and ecosystem for {JAX}},
  url = {http://github.com/google/flax},
  version = {0.7.0},
  year = {2020},
}

License

MIT

Citation

If you use this implementation in your research, please cite both the original Mamba2 paper and acknowledge this JAX implementation:

@software{mamba2jax2024,
  author = {[Cosmo Santoni]},
  title = {mamba2-jax: Pure JAX Implementation of Mamba2},
  year = {2024},
  url = {https://github.com/CosmoNaught/mamba2-jax}
}

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Questions or Issues? Please open an issue on GitHub or reach out through discussions.

Want to Contribute? PRs are welcome! See the Contributing section above.