Create DLME.py

DLME.py

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+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)