lite-mamba 1.0.2

Pure-TensorFlow lightweight Mamba with multi-dilated causal conv front-end

lite-mamba

A minimal, pure-TensorFlow implementation of Mamba with a multi-dilated causal depthwise conv front-end. No custom C++ or Triton kernels needed; works seamlessly on CPU, GPU, or TPU with standard TensorFlow ops.

Install

pip install lite-mamba

Usage

from lite_mamba import TFPTCNMamba
import tensorflow as tf

x = tf.random.normal((2, 128, 512))  # (batch, seq, d_model)
m = TFPTCNMamba(d_model=512, d_conv=3, conv_dilations=(1, 2, 4, 8))
y = m(x)
print(y.shape)  # (2, 128, 512)

Conv front-end variants

• TFPTCNMamba: default Mamba variant, mixes parallel dilated depthwise conv branches via learned softmax gates.
• TFSTCNMamba: runs the same depthwise conv layers in sequence (no gating); each branch output feeds the next to create a deterministic dilation stack.
• TFDPWCMamba: pairs each depthwise branch with a pointwise (1x1) conv before the gating mix, adding extra channel mixing without stacking more layers.
• TFBaselineMamba: single-branch baseline matching the reference Mamba architecture from state-spaces.

All variants expose the same constructor signature (d_model, d_state, conv_dilations, etc.) and streaming helpers (allocate_inference_cache, step). Swap them simply by changing the imported class name:

from lite_mamba import TFSTCNMamba

m = TFSTCNMamba(d_model=512, d_state=16, conv_dilations=(1, 2, 4))

API quick reference

TFMamba(d_model, d_state=16, d_conv=4, conv_dilations=(1,), expand=2, dt_rank="auto", dt_min=0.001, dt_max=0.1, dt_init="random", dt_scale=1.0, dt_init_floor=1e-4, conv_bias=True, bias=False, layer_idx=None)

• d_model (int, required): input/output embedding size.
• d_state (int, default 16): SSM state dimension per channel. Larger gives longer memory; increases compute.
• d_conv (int, default 4): depthwise conv kernel size for each branch.
• conv_dilations (tuple[int], default (1,)): dilation per branch. Multiple values create parallel dilated convs; effective receptive field is (d_conv-1)*dilation.
• expand (float, default 2): inner width multiplier; sets d_inner = expand * d_model.
• dt_rank (int or "auto", default "auto"): rank of delta projection. "auto" sets ceil(d_model/16).
• dt_min, dt_max (float, defaults 1e-3 / 1e-1): log-uniform range for delta initialization.
• dt_init ("random" | "constant", default "random") and dt_scale, dt_init_floor: control delta init magnitude/stability.
• conv_bias (bool, default True): include bias in depthwise convs.
• bias (bool, default False): include bias in input/output linear projections.
• layer_idx (int | None): identifier for streaming cache registration.

Inference / streaming helpers

• allocate_inference_cache(batch_size, max_seqlen, dtype=None): preallocates conv and SSM state buffers for step-wise decoding.
• step(hidden_states, conv_state, ssm_state): single-token forward (expects hidden_states with shape (B, 1, d_model)).

Highlights

• Multi-branch Architecture: Parallel or stacked causal dilated convolutions with learned gating.
• Pure TensorFlow: No custom C++/CUDA kernels required. Compatible with XLA compilation (tf.function(jit_compile=True)).
• Streaming Support: Per-branch conv states and SSM state caching for autoregressive generation.

License

Apache-2.0