DLME.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import nn, optim
from torch.optim.lr_scheduler import ReduceLROnPlateau, CosineAnnealingLR
from transformers import Trainer, TrainingArguments, PreTrainedModel, PretrainedConfig
from datasets import load_dataset, load_metric
from transformers import BertTokenizerFast
from torchvision.transforms import Compose, Resize, ToTensor
import os
import matplotlib.pyplot as plt
import seaborn as sns
import logging

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Custom ZeusModel
class ZeusModel(PreTrainedModel):
    config_class = PretrainedConfig

    def __init__(self, config):
        super().__init__(config)
        self.hybrid_embedding = HybridEmbeddingLayer(config.vocab_size, config.embed_dim, config.image_dim, config.audio_dim)
        self.quantum_attention = QuantumAttentionLayer(config.embed_dim)
        self.dnp_layer = DNP(config.embed_dim, config.embed_dim)
        self.recursive_reflection = RecursiveSelfReflectionLayer(config.embed_dim, config.reflection_dim)
        self.mohe_layer = MoHELayer(config.embed_dim, config.num_experts)
        self.output_layer = nn.Linear(config.embed_dim, config.vocab_size)
        self._init_weights()

    def forward(self, text_input, image_input=None, audio_input=None):
        x = self.hybrid_embedding(text_input, image_input, audio_input)
        attention_output = self.quantum_attention(x)
        x = self.dnp_layer(attention_output)
        x = self.recursive_reflection(x)
        x = self.mohe_layer(x)
        output = self.output_layer(x)
        return output, attention_output
    
    def _init_weights(self, module=None):
        if module is None:
            module = self
        if isinstance(module, nn.Linear) or isinstance(module, nn.Embedding):
            nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
        if isinstance(module, nn.Linear) and module.bias is not None:
            nn.init.zeros_(module.bias)
        if isinstance(module, nn.LayerNorm):
            nn.init.ones_(module.weight)
            nn.init.zeros_(module.bias)
        for submodule in module.children():
            self._init_weights(submodule)

# Configuration class for ZeusModel
class ZeusConfig(PretrainedConfig):
    def __init__(self, vocab_size=50000, embed_dim=768, image_dim=256, audio_dim=128, reflection_dim=512, num_experts=4, initializer_range=0.02, **kwargs):
        super().__init__(**kwargs)
        self.vocab_size = vocab_size
        self.embed_dim = embed_dim
        self.image_dim = image_dim
        self.audio_dim = audio_dim
        self.reflection_dim = reflection_dim
        self.num_experts = num_experts
        self.initializer_range = initializer_range

# Hybrid Embedding Layer
class HybridEmbeddingLayer(nn.Module):
    def __init__(self, vocab_size, embed_dim, image_dim, audio_dim):
        super(HybridEmbeddingLayer, self).__init__()
        self.text_embedding = nn.Embedding(vocab_size, embed_dim)
        self.image_feature_extractor = nn.Conv2d(3, image_dim, kernel_size=3, stride=2)
        self.audio_feature_extractor = nn.Conv1d(1, audio_dim, kernel_size=3, stride=2)

    def forward(self, text_input, image_input=None, audio_input=None):
        text_emb = self.text_embedding(text_input)
        if image_input is not None:
            image_emb = self.image_feature_extractor(image_input)
            image_emb = image_emb.view(image_emb.size(0), -1)  # Flatten
        if audio_input is not None:
            audio_emb = self.audio_feature_extractor(audio_input)
            audio_emb = audio_emb.view(audio_emb.size(0), -1)  # Flatten
        combined_emb = text_emb + image_emb + audio_emb  # Hybrid combination
        return combined_emb

# Quantum-Inspired Attention Layer
class QuantumAttentionLayer(nn.Module):
    def __init__(self, embed_dim):
        super(QuantumAttentionLayer, self).__init__()
        self.attention_weights = nn.Parameter(torch.randn(embed_dim, embed_dim))

    def forward(self, x):
        # Quantum-inspired probabilistic attention
        attention_probs = F.softmax(torch.matmul(x, self.attention_weights), dim=-1)
        attention_output = torch.matmul(attention_probs, x)
        return attention_output

# Differentiable Neural Plasticity Layer
class DNP(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(DNP, self).__init__()
        self.fc = nn.Linear(input_dim, output_dim)
        self.plasticity_weights = nn.Parameter(torch.randn(output_dim))

    def forward(self, x):
        out = self.fc(x)
        plasticity_effect = torch.mul(out, self.plasticity_weights)
        return out + plasticity_effect

# Custom Recursive Self-Reflection Layer
class RecursiveSelfReflectionLayer(nn.Module):
    def __init__(self, input_dim, reflection_dim):
        super(RecursiveSelfReflectionLayer, self).__init__()
        self.reflection_layer = nn.Linear(input_dim, reflection_dim)

    def forward(self, x):
        reflection_output = F.relu(self.reflection_layer(x))
        self_adjusted_output = x + reflection_output  # Recursive adjustment
        return self_adjusted_output

# MoHE Layer
class MoHELayer(nn.Module):
    def __init__(self, embed_dim, num_experts):
        super(MoHELayer, self).__init__()
        self.experts = nn.ModuleList([nn.Linear(embed_dim, embed_dim) for _ in range(num_experts)])
        self.gate = nn.Linear(embed_dim, num_experts)

    def forward(self, x):
        gate_output = F.softmax(self.gate(x), dim=-1)
        expert_outputs = torch.stack([expert(x) for expert in self.experts], dim=1)
        output = torch.einsum("be,bec->bc", gate_output, expert_outputs)
        return output

# Complete Unique Model
class ZeusModel(nn.Module):
    def __init__(self, vocab_size, embed_dim, image_dim, audio_dim, num_heads, reflection_dim, num_experts):
        super(ZeusModel, self).__init__()
        self.hybrid_embedding = HybridEmbeddingLayer(vocab_size, embed_dim, image_dim, audio_dim)
        self.quantum_attention = QuantumAttentionLayer(embed_dim)
        self.dnp_layer = DNP(embed_dim, embed_dim)
        self.recursive_reflection = RecursiveSelfReflectionLayer(embed_dim, reflection_dim)
        self.mohe_layer = MoHELayer(embed_dim, num_experts)
        self.output_layer = nn.Linear(embed_dim, vocab_size)

    def forward_with_memory(self, text_input, image_input, audio_input, symbolic_input):
        x = self.hybrid_embedding(text_input, image_input, audio_input)
        x, memory_state = self.memory_augmented_layer(x, memory_state)
        x = self.quantum_attention(x)
        x = self.dnp_layer(x)
        x = self.recursive_reflection(x)
        x = self.mohe_layer(x)
        output = self.output_layer(x)
        return output

# Parameters
vocab_size = 50000
embed_dim = 768
image_dim = 256
audio_dim = 128
num_heads = 12
reflection_dim = 512
num_experts = 4

model = ZeusModel(vocab_size, embed_dim, image_dim, audio_dim, num_heads, reflection_dim, num_experts)