Unified AGI-style System

import os
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
from torchvision import datasets, transforms, models
from transformers import GPT2Model
from fastapi import FastAPI, Request
from torch.optim import AdamW
from torch.amp import GradScaler, autocast  # Updated import
from torch.utils.checkpoint import checkpoint
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.distributed import init_process_group, destroy_process_group, is_initialized
from tqdm import tqdm

# --- Configuration ---
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dist_backend = "nccl" if torch.cuda.is_available() else "gloo"
use_distributed = int(os.environ.get("WORLD_SIZE", 1)) > 1

# Logging setup
logging.basicConfig(level=logging.INFO, format="%(asctime)s | %(levelname)s | %(message)s")

# --- Utility: Distributed setup ---
def setup_distributed():
    if use_distributed:
        init_process_group(backend=dist_backend)
        logging.info(f"Distributed training initialized on rank {os.environ['RANK']}.")
    else:
        logging.info("Running on a single GPU or CPU.")

def cleanup_distributed():
    if is_initialized():
        destroy_process_group()

# --- Text Dataset ---
class TextDataset(Dataset):
    def __init__(self, data, tokenizer, max_length=2048):
        self.data = data
        self.tokenizer = tokenizer
        self.max_length = max_length

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        item = self.data[idx]
        text = item["text"]
        encoding = self.tokenizer(
            text, padding="max_length", truncation=True, max_length=self.max_length, return_tensors="pt"
        )
        input_ids = encoding["input_ids"].squeeze()
        attention_mask = encoding["attention_mask"].squeeze()
        label = item.get("label", None)
        return input_ids, attention_mask, label

# --- Perception Module ---
class PerceptionModule(nn.Module):
    def __init__(self, text_dim, image_dim, sensor_dim, hidden_dim):
        super(PerceptionModule, self).__init__()
        self.text_model = GPT2Model.from_pretrained("gpt2")
        self.text_fc = nn.Linear(self.text_model.config.hidden_size, hidden_dim)

        self.image_model = models.efficientnet_b0(weights='IMAGENET1K_V1')
        num_ftrs = self.image_model.classifier[-1].in_features
        self.image_model.classifier = nn.Identity()
        self.image_fc = nn.Linear(num_ftrs, hidden_dim)

        self.sensor_fc = nn.Linear(sensor_dim, hidden_dim)
        self.fc = nn.Linear(hidden_dim * 3, hidden_dim)

    def forward(self, text, image, sensor):
        text_features = F.relu(self.text_fc(self.text_model(**text).last_hidden_state.mean(dim=1)))
        image_features = F.relu(self.image_fc(self.image_model(image)))
        sensor_features = F.relu(self.sensor_fc(sensor))
        combined_features = torch.cat((text_features, image_features, sensor_features), dim=1)
        return F.relu(self.fc(combined_features))

# --- Memory Module ---
class MemoryBank(nn.Module):
    def __init__(self, memory_size, memory_dim):
        super(MemoryBank, self).__init__()
        self.keys = nn.Parameter(torch.randn(memory_size, memory_dim))
        self.values = nn.Parameter(torch.randn(memory_size, memory_dim))
        self.attention = nn.MultiheadAttention(embed_dim=memory_dim, num_heads=4, batch_first=True)

    def write(self, key, value):
        with torch.no_grad():
            idx = torch.argmin(torch.norm(self.keys - key, dim=1))
            self.keys[idx].data.copy_(key)
            self.values[idx].data.copy_(value)

    def read(self, key):
        query = key.unsqueeze(0)
        attn_output, _ = self.attention(query, self.keys.unsqueeze(0), self.values.unsqueeze(0))
        return attn_output.squeeze(0)

# --- Decision Making Module ---
class DecisionMakingModule(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(DecisionMakingModule, self).__init__()
        self.fc = nn.Linear(input_dim, output_dim)

    def forward(self, features):
        return self.fc(features)

# --- Unified AGI System ---
class UnifiedAGISystem(nn.Module):
    def __init__(self, text_dim, image_dim, sensor_dim, hidden_dim, memory_size, output_dim):
        super(UnifiedAGISystem, self).__init__()
        self.perception = PerceptionModule(text_dim, image_dim, sensor_dim, hidden_dim)
        self.memory = MemoryBank(memory_size, hidden_dim)
        self.decision_making = DecisionMakingModule(hidden_dim, output_dim)

    def forward(self, text, image, sensor):
        features = checkpoint(self.perception, text, image, sensor)
        memory_output = self.memory.read(features)
        decision = self.decision_making(memory_output)
        return decision

    def perform_task(self, text_input, image_tensor, sensor_tensor):
        features = checkpoint(self.perception, text_input, image_tensor, sensor_tensor)
        self.memory.write(features.detach(), features.detach())
        decision = self.decision_making(features)
        return decision

# --- Training Function ---
def train(model, train_loader, criterion, optimizer, epochs=10, use_amp=True, accumulation_steps=2):
    scaler = GradScaler(enabled=use_amp)  # Updated line for GradScaler
    for epoch in range(epochs):
        model.train()
        running_loss = 0.0
        optimizer.zero_grad()

        for i, batch in enumerate(tqdm(train_loader)):
            images_, labels_ = batch

            dummy_text = {
                "input_ids": torch.randint(0, 100, (images_.size(0), 256)).to(device),
                "attention_mask": torch.ones((images_.size(0), 256)).to(device),
            }
            dummy_sensor = torch.randn(images_.size(0), 10).to(device)

            with autocast('cuda', enabled=use_amp):  # Updated line for autocast
                outputs_ = model(dummy_text, images_.to(device), dummy_sensor)
                loss_ = criterion(outputs_, labels_.to(device))

            scaler.scale(loss_ / accumulation_steps).backward()

            if (i + 1) % accumulation_steps == 0:
                scaler.step(optimizer)
                scaler.update()
                optimizer.zero_grad()

            running_loss += loss_.item()

        logging.info(f"Epoch [{epoch + 1}/{epochs}], Loss: {running_loss / len(train_loader):.4f}")

# --- Data Augmentation ---
transform = transforms.Compose([
    transforms.RandomHorizontalFlip(),
    transforms.RandomCrop(32),
    transforms.ToTensor(),
    transforms.Normalize(mean=(0.5,), std=(0.5,))
])

# --- Main Execution ---
if __name__ == "__main__":
    setup_distributed()
    try:
        text_dim = 100
        image_dim = (3, 32, 32)
        sensor_dim = 10
        hidden_dim = 64
        memory_size = 64
        output_dim = 10

        agi_system = UnifiedAGISystem(text_dim, image_dim, sensor_dim, hidden_dim, memory_size, output_dim).to(device)

        if use_distributed:
            agi_system = DDP(agi_system)

        train_dataset = datasets.CIFAR10(root="./data", train=True, download=True, transform=transform)
        train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)

        criterion = nn.CrossEntropyLoss()
        optimizer = AdamW(agi_system.parameters(), lr=0.001)

        train(agi_system, train_loader, criterion, optimizer, epochs=10)
    finally:
        cleanup_distributed()

# --- Deployment with FastAPI ---
app = FastAPI()

@app.post("/predict")
async def predict(request: Request):
    data = await request.json()
    text_input = {
        "input_ids": torch.tensor(data["text"]).to(device),
        "attention_mask": torch.tensor(data["mask"]).to(device)
    }
    image_tensor = torch.tensor(data["image"]).to(device)
    sensor_tensor = torch.tensor(data["sensor"]).float().to(device)

    decision = agi_system.perform_task(text_input, image_tensor, sensor_tensor)
    return {"decision": decision.tolist()}