Omni-modal Understanding for E-commerce Live Streaming

# Illustrative sketch (not from the paper)
import torch
from torch.utils.data import DataLoader

# Placeholder backbone and audio encoder (Qwen3.5 and AuT)
class QwenVisionLanguage(torch.nn.Module):
    def forward(self, tokens, vision):
        # fuse language and vision tokens
        return torch.randn(tokens.size(0), 768)  # mock representation

class AudioEncoder(torch.nn.Module):
    def forward(self, audio_wave):
        return torch.randn(audio_wave.size(0), 512)  # mock audio embedding

# Light‑weight audio aligner (aligns audio to video grids)
class AudioAligner(torch.nn.Module):
    def forward(self, audio_emb, grid_times):
        # simple linear projection as placeholder
        return audio_emb

# Per‑vGrid token organizer
def per_vgrid(video_frames, audio_emb, timestamps):
    # Insert boundary tokens and prepend timestamps (illustrative)
    tokens = []
    for i, frame in enumerate(video_frames):
        tokens.append(f"<BND_{i}>")
        tokens.append(f"<TS_{timestamps[i]}>")
        tokens.append(frame)          # visual token placeholder
        tokens.append(audio_emb[i])   # aligned audio token
    return tokens

# Three‑stage supervised fine‑tuning (pseudo‑loop)
def supervised_finetune(model, dataloader, stage):
    optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
    for batch in dataloader:
        # stage‑specific loss would be computed here
        loss = torch.randn(1)  # placeholder
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()

# Faithful‑RFT reinforcement step (high‑level sketch)
def faithful_rft(model, env):
    # GRPO loop placeholder
    for episode in range(10):
        state = env.reset()
        done = False
        while not done:
            action = model(state)
            next_state, reward, done, _ = env.step(action)
            # policy update with task‑conditioned reward routing
            # (details omitted)
            state = next_state

# Synchronized length‑grouped sampler (illustrative)
class LengthGroupedSampler:
    def __init__(self, dataset, batch_size):
        self.dataset = dataset
        self.batch_size = batch_size
    def __iter__(self):
        # group by modality length (mock implementation)
        for i in range(0, len(self.dataset), self.batch_size):
            yield self.dataset[i:i+self.batch_size]

# Example usage (mock objects)
vision_lang = QwenVisionLanguage()
audio_enc = AudioEncoder()
aligner = AudioAligner()

# Mock data
video_frames = ["frame"] * 5
audio_wave = torch.randn(5, 16000)
audio_emb = audio_enc(audio_wave)
timestamps = [0.0, 1.0, 2.0, 3.0, 4.0]

tokens = per_vgrid(video_frames, audio_emb, timestamps)

# DataLoader with length‑grouped sampler
dataset = list(range(100))
loader = DataLoader(dataset, batch_sampler=LengthGroupedSampler(dataset, batch_size=8))

supervised_finetune(vision_lang, loader, stage=1)
faithful_rft(vision_lang, env=None)  # env placeholder

// Illustrative sketch (not from the paper)
const torch = require('torch-js'); // placeholder for tensor ops

// Mock Qwen3.5 vision‑language backbone
class QwenVisionLanguage {
  forward(tokens, vision) {
    // fuse language and vision tokens (mock output)
    return torch.randn([tokens.shape[0], 768]);
  }
}

// Mock audio encoder (AuT)
class AudioEncoder {
  forward(audioWave) {
    return torch.randn([audioWave.shape[0], 512]); // audio embedding
  }
}

// Light‑weight audio aligner (placeholder)
class AudioAligner {
  forward(audioEmb, gridTimes) {
    return audioEmb; // identity alignment for illustration
  }
}

// Per‑vGrid token organization
function perVGrid(videoFrames, audioEmb, timestamps) {
  const tokens = [];
  for (let i = 0; i < videoFrames.length; i++) {
    tokens.push(`<BND_${i}>`);
    tokens.push(`<TS_${timestamps[i]}>`);
    tokens.push(videoFrames[i]); // visual token placeholder
    tokens.push(audioEmb[i]);    // aligned audio token
  }
  return tokens;
}

// Three‑stage supervised fine‑tuning loop (simplified)
async function supervisedFinetune(model, dataloader, stage) {
  const optimizer = new torch.optim.Adam(model.parameters(), { lr: 1e-4 });
  for await (const batch of dataloader) {
    const loss = torch.randn([1]); // placeholder loss
    loss.backward();
    optimizer.step();
    optimizer.zeroGrad();
  }
}

// Faithful‑RFT reinforcement fine‑tuning (high‑level sketch)
async function faithfulRFT(model, env) {
  for (let episode = 0; episode < 10; episode++) {
    let state = env.reset();
    let done = false;
    while (!done) {
      const action = model(state);
      const { nextState, reward, done: stepDone } = env.step(action);
      // GRPO update with task‑conditioned reward (details omitted)
      state = nextState;
      done = stepDone;
    }
  }
}

// Synchronized length‑grouped sampler (illustrative)
class LengthGroupedSampler {
  constructor(dataset, batchSize) {
    this.dataset = dataset;
    this.batchSize = batchSize;
  }
  *[Symbol.iterator]() {
    for (let i = 0; i < this.dataset.length; i += this.batchSize) {
      yield this.dataset.slice(i, i + this.batchSize);
    }
  }
}

// Example usage (mock data)
const visionLang = new QwenVisionLanguage();
const audioEnc = new AudioEncoder();
const aligner = new AudioAligner();

const videoFrames = Array(5).fill('frame');
const audioWave = torch.randn([5, 16000]);
const audioEmb = audioEnc.forward(audioWave);
const timestamps = [0.0, 1.0, 2.0, 3.0, 4.0];

const tokens = perVGrid(videoFrames, audioEmb, timestamps);

// Mock dataset and loader
const dataset = Array.from({ length: 100 }, (_, i) => i);
const loader = new LengthGroupedSampler(dataset, 8);

supervisedFinetune(visionLang, loader, 1);
faithfulRFT(visionLang, null); // env placeholder