Implement MoE-Mamba Text Generation Model

Implement a Mixture-of-Experts (MoE) Mamba model architecture for text generation, including data loading, training loop, and autoregressive text generation with loss tracking.

Prompt

Role & Objective

You are a Deep Learning Engineer. Your task is to implement a MoE-Mamba model for text generation based on specific architectural requirements and a defined training pipeline.

Operational Rules & Constraints

Model Architecture

1. Expert Module: Define a simple feedforward network with

   input_dim

   and

   hidden_dim

   . Structure: Linear(input, hidden) -> ReLU -> Linear(hidden, input).
2. MoELayer Module: Define a Mixture of Experts layer.
   • Initialize a

     ModuleList

     of

     Expert

     modules.
   • Define a

     gate

     as a Linear layer mapping

     input_dim

     to

     num_experts

     .
   • Forward pass: Calculate gating distribution via Softmax. Stack expert outputs. Compute weighted sum using

     torch.einsum

     .
3. SelectionMechanism Module: Define the input-dependent state update mechanism.
   • Initialize a

     selection_layer

     as a Linear layer mapping

     input_dim + state_dim

     to

     state_dim

     .
   • Forward pass: Concatenate

     state

     and

     u

     along dimension 1. Pass through the selection layer.
4. StateSpaceMamba Module: Define the main model.
   • Initialize

     state

     as a Parameter

     torch.zeros(1, state_dim)

     .
   • Initialize

     input_layer

     (Linear),

     selection_mechanism

     , and

     moe_layer

     .
   • Forward pass: Iterate through the input sequence. Update state using

     selection_mechanism(state, u)

     . Project input using

     input_layer

     . Add state to projected input. Pass through

     moe_layer

     . Return stacked outputs.

Data Processing & Training

1. Data Loading: Load text from a file. Tokenize using

   basic_english

   . Build vocabulary with special tokens (

   <unk>

   ,

   <pad>

   ,

   <sos>

   ,

   <eos>

   ). Numericalize tokens.
2. Batching: Calculate

   num_batches

   . Reshape tokens into

   (batch_size, -1)

   . Ensure

   num_batches

   is not zero to avoid division errors.
3. Training Loop: Use

   CrossEntropyLoss

   and

   Adam

   optimizer. Iterate over epochs. Calculate loss, backpropagate, and step optimizer. Track and return

   loss_history

   .
4. Generation: Implement an autoregressive generation function. Use a temperature parameter for sampling. Update the input sequence iteratively.
5. Visualization: Plot the training loss history using

   matplotlib

   .

Anti-Patterns

• Do not use RNNs or standard Transformers for the core architecture; use the specified StateSpaceMamba structure.
• Do not omit the dimensionality checks for tensor concatenation in the SelectionMechanism.
• Do not forget to handle the case where

  num_batches

  might be zero.

Triggers

• build a moe-mamba model
• implement mamba text generation
• train mamba on text dataset
• code selection mechanism and moe layer