TinyCeNN-LM: How to Replace Attention in LLMs Without Losing Your Mind

Architecture & Actionable Implementation

TinyCeNN-LM introduces a quality-gated post-training conversion framework that replaces attention mechanisms with CeNN-inspired cellular-recurrent layers. This approach aims to reduce memory and computational requirements while maintaining performance.

Topology

The TinyCeNN-LM architecture consists of three key components: Integrated Memory, MemoryFusion, and PDelta3-GDN2-CLVR+Local32. These components work together to ensure a seamless transition from attention mechanisms to cellular-recurrent layers.

Executable Code

import torch
import torch.nn as nn
import torch.nn.functional as F

class CeNNLayer(nn.Module):
    def __init__(self, input_dim, hidden_dim, kernel_size):
        super(CeNNLayer, self).__init__()
        self.conv1 = nn.Conv2d(input_dim, hidden_dim, kernel_size, padding=kernel_size//2)
        self.conv2 = nn.Conv2d(hidden_dim, input_dim, kernel_size, padding=kernel_size//2)
        
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = self.conv2(x)
        return x

class TinyCeNNLM(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, num_layers, kernel_size):
        super(TinyCeNNLM, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.layers = nn.ModuleList([CeNNLayer(embedding_dim, hidden_dim, kernel_size) for _ in range(num_layers)])
        self.fc = nn.Linear(embedding_dim, vocab_size)
        
    def forward(self, x):
        x = self.embedding(x)
        x = x.unsqueeze(2).unsqueeze(3)  # Add dummy dimensions for convolution
        for layer in self.layers:
            x = layer(x)
        x = x.squeeze(2).squeeze(2)  # Remove dummy dimensions
        x = self.fc(x)
        return x

# Example usage
vocab_size = 10000
embedding_dim = 256
hidden_dim = 512
num_layers = 6
kernel_size = 3

model = TinyCeNNLM(vocab_size, embedding_dim, hidden_dim, num_layers, kernel_size)
input_tensor = torch.randint(0, vocab_size, (32, 10))  # Batch of 32 sequences of length 10
output = model(input_tensor)
print(output.shape)  # Expected shape: (32, 10, 10000)

Hard Production Trade-Offs (The Cost of Scale)

Latency vs. Throughput

TinyCeNN-LM aims to reduce latency and improve throughput by replacing attention mechanisms with cellular-recurrent layers. However, the quality-gated conversion process introduces additional computational overhead, which must be carefully managed in production environments.

Unit Economics & TCO

The TinyCeNN-LM architecture reduces memory and computational requirements, which can lead to lower total cost of ownership (TCO) for deployment. However, the quality-gated conversion process may increase inference costs, particularly for large models.

Failure Domains

The TinyCeNN-LM architecture introduces new failure domains, including potential issues with representation fidelity and NLL criteria. These must be carefully monitored and managed in production to ensure reliable performance.

Benchmark Radar Capability Data