Efficient Compression of Vision Language Models

# Illustrative sketch (not from the paper)
import torch
from torch import nn

# 1. Synthetic data generation using the full‑precision VLM
full_model = torch.load('llama3_2_11b_vision_full.pt')
image_set = torch.load('imagenet_sample.pt')  # diverse images
synthetic_dataset = []
for img in image_set:
    with torch.no_grad():
        response = full_model.generate(img)  # model produces text answer
    synthetic_dataset.append((img, response))

# 2. Quantization‑aware training (distillation) on the synthetic set
quantized_model = nn.Module()  # placeholder for S3D8‑aware model
optimizer = torch.optim.Adam(quantized_model.parameters(), lr=1e-4)
for epoch in range(2):  # short illustrative loop
    for img, target in synthetic_dataset:
        # forward through quantized model (dequantization simulated)
        pred = quantized_model(img)
        loss = nn.functional.mse_loss(pred, target)  # distillation loss
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

# 3. Encode weights in S3D8 format (packing 3 channels per byte)
def pack_s3d8(tensor):
    # tensor shape: (C, ...), C must be multiple of 3
    # shared centroid index + 2 sign bits per channel → 1 byte per 3 channels
    # placeholder implementation using dummy lookup table
    lookup_table = torch.arange(64, dtype=torch.uint8)  # 64‑entry table
    packed = torch.empty(tensor.numel() // 3, dtype=torch.uint8)
    # actual packing logic omitted for brevity
    return packed, lookup_table

weights = quantized_model.state_dict()
packed_weights = {k: pack_s3d8(v) for k, v in weights.items()}

# 4. Mobile deployment stub – fused dequantization kernel would run on Arm CPU
def fused_dequantize(packed, table):
    # table lookup + bit‑wise sign reconstruction (illustrative only)
    return torch.randn_like(packed.float())  # placeholder output

# Example inference on a mobile device
img = torch.load('mobile_test_image.pt')
packed, table = packed_weights['some_layer']
deq = fused_dequantize(packed, table)
output = deq @ img  # simplified operation

// Illustrative sketch (not from the paper)
const torch = require('torch-js'); // hypothetical PyTorch binding

// 1. Synthetic data generation using the full‑precision VLM
const fullModel = torch.load('llama3_2_11b_vision_full.pt');
const imageSet = torch.load('imagenet_sample.pt'); // diverse images
const syntheticDataset = [];
for (const img of imageSet) {
  const response = fullModel.generate(img); // model produces text answer
  syntheticDataset.push({ img, response });
}

// 2. Quantization‑aware training (distillation) on the synthetic set
const quantizedModel = new torch.nn.Module(); // placeholder for S3D8‑aware model
const optimizer = new torch.optim.Adam(quantizedModel.parameters(), { lr: 1e-4 });
for (let epoch = 0; epoch < 2; epoch++) {
  for (const { img, response } of syntheticDataset) {
    const pred = quantizedModel.forward(img);
    const loss = torch.nn.functional.mse_loss(pred, response);
    optimizer.zeroGrad();
    loss.backward();
    optimizer.step();
  }
}

// 3. Encode weights in S3D8 format (packing 3 channels per byte)
function packS3D8(tensor) {
  // tensor shape: [C, ...]; C must be multiple of 3
  // shared centroid index + 2 sign bits per channel → 1 byte per 3 channels
  const lookupTable = torch.arange(0, 64, { dtype: 'uint8' }); // 64‑entry table
  const packed = torch.empty(tensor.numel() / 3, { dtype: 'uint8' });
  // actual packing logic omitted for brevity
  return { packed, lookupTable };
}

const weights = quantizedModel.stateDict();
const packedWeights = {};
for (const [k, v] of Object.entries(weights)) {
  packedWeights[k] = packS3D8(v);
}

// 4. Mobile deployment stub – fused dequantization kernel would run on Arm CPU
function fusedDequantize(packed, table) {
  // table lookup + bit‑wise sign reconstruction (illustrative only)
  return torch.randnLike(packed.toFloat()); // placeholder output
}

// Example inference on a mobile device
const testImg = torch.load('mobile_test_image.pt');
const { packed, lookupTable } = packedWeights['some_layer'];
const deq = fusedDequantize(packed, lookupTable);
const output = deq.matmul(testImg); // simplified operation