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)