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Model/codaBlock.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional, Tuple
+
+# Final Projection Block
+class CodaBlock(nn.Module):
+    def __init__(self, d_model: int, vocab_size: int):
+        super().__init__()
+        self.norm = nn.LayerNorm(d_model)
+        self.output_proj = nn.Linear(d_model, vocab_size)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.output_proj(self.norm(x))

Model/latent_Recurrent.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional, Tuple
+from Model.prelude_Block import PreludeBlock
+from Model.recurrent_Block import RecurrentBlock
+from Model.codaBlock import CodaBlock
+
+# Full Latent Recurrent Depth Model
+class LatentRecurrentDepthLM(nn.Module):
+    def __init__(self, vocab_size: int, d_model: int, num_heads: int, dropout: float = 0.1):
+        super().__init__()
+        self.prelude = PreludeBlock(vocab_size, d_model, num_heads, dropout)
+        self.recurrent = RecurrentBlock(d_model, num_heads, dropout)
+        self.coda = CodaBlock(d_model, vocab_size)
+
+    def forward(self, x: torch.Tensor, num_iterations: int, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        hidden = self.prelude(x, mask)
+        recurrent_state = torch.zeros_like(hidden)
+        for _ in range(num_iterations):
+            hidden, recurrent_state = self.recurrent(hidden, recurrent_state, mask)
+        return self.coda(hidden)

Model/model.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional, Tuple
+import math
+from transformers import PretrainedConfig, PreTrainedModel
+from Model.latent_Recurrent import LatentRecurrentDepthLM
+
+# Configuration for the Latent Recurrent Depth Model
+class LatentRecurrentDepthConfig(PretrainedConfig):
+    model_type = "latent_recurrent_depth"
+
+    def __init__(self, vocab_size=50257, d_model=768, num_heads=12, dropout=0.1, **kwargs):
+        super().__init__(**kwargs)
+        self.vocab_size = vocab_size
+        self.d_model = d_model
+        self.num_heads = num_heads
+        self.dropout = dropout
+
+
+# Hugging Face-Compatible Model Wrapper
+class LatentRecurrentDepthModel(PreTrainedModel):
+    config_class = LatentRecurrentDepthConfig
+    base_model_prefix = "latent_recurrent_depth"
+
+    def __init__(self, config: LatentRecurrentDepthConfig):
+        super().__init__(config)
+        self.latent_model = LatentRecurrentDepthLM(config.vocab_size, config.d_model, config.num_heads, config.dropout)
+        self.init_weights()
+
+    def forward(self, input_ids: torch.Tensor, num_iterations: int, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        return self.latent_model(input_ids, num_iterations, mask)
+
+    def generate(
+        self,
+        input_ids: torch.Tensor,
+        max_length: int = 20,
+        num_iterations: int = 3,
+        temperature: float = 1.0,
+        top_k: Optional[int] = 50,
+    ) -> torch.Tensor:
+        """
+        Generate a sequence of tokens given input_ids.
+
+        Args:
+          input_ids: torch.Tensor of shape (batch, seq_length) containing the prompt.
+          max_length: The number of tokens to generate.
+          num_iterations: The number of recurrent iterations to use in each forward pass.
+          temperature: Temperature for scaling logits.
+          top_k: If set, only sample from the top k tokens.
+
+        Returns:
+          generated: torch.Tensor containing the generated sequence.
+        """
+        generated = input_ids.clone()
+        self.eval()
+        with torch.no_grad():
+            for _ in range(max_length):
+                # Get logits from the model for the current sequence.
+                logits = self.forward(generated, num_iterations=num_iterations)
+                # Use only the logits for the last token in the sequence.
+                next_token_logits = logits[:, -1, :] / temperature
+                if top_k is not None:
+                    # Top-k filtering
+                    top_k_logits, top_k_indices = torch.topk(next_token_logits, top_k)
+                    probabilities = F.softmax(top_k_logits, dim=-1)
+                    next_token = top_k_indices.gather(-1, torch.multinomial(probabilities, num_samples=1))
+                else:
+                    probabilities = F.softmax(next_token_logits, dim=-1)
+                    next_token = torch.multinomial(probabilities, num_samples=1)
+                generated = torch.cat([generated, next_token], dim=1)
+                # Optionally, break if the EOS token is generated.
+                if next_token.item() == self.config.eos_token_id:
+                    break
+        return generated

Model/multi_head_Attention.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional, Tuple
+
+# Multi-Head Attention Mechanism
+class MultiHeadAttention(nn.Module):
+    def __init__(self, d_model: int, num_heads: int, dropout: float = 0.1):
+        super().__init__()
+        assert d_model % num_heads == 0
+
+        self.d_model = d_model
+        self.num_heads = num_heads
+        self.head_dim = d_model // num_heads
+
+        self.q_proj = nn.Linear(d_model, d_model)
+        self.k_proj = nn.Linear(d_model, d_model)
+        self.v_proj = nn.Linear(d_model, d_model)
+        self.o_proj = nn.Linear(d_model, d_model)
+
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x: torch.Tensor, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        batch_size, seq_len, d_model = x.shape
+        
+        # Project and reshape for multi-head attention
+        q = self.q_proj(x).reshape(batch_size, seq_len, self.num_heads, self.head_dim)
+        k = self.k_proj(x).reshape(batch_size, seq_len, self.num_heads, self.head_dim)
+        v = self.v_proj(x).reshape(batch_size, seq_len, self.num_heads, self.head_dim)
+
+        # Transpose for attention computation
+        q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
+
+        # Compute attention scores
+        scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
+        if mask is not None:
+            scores = scores.masked_fill(mask == 0, float('-inf'))
+        
+        attn_weights = F.softmax(scores, dim=-1)
+        attn_weights = self.dropout(attn_weights)
+        
+        # Apply attention to values
+        out = torch.matmul(attn_weights, v).transpose(1, 2).reshape(batch_size, seq_len, d_model)
+        return self.o_proj(out)

Model/prelude_Block.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional, Tuple
+from Model.multi_head_Attention import MultiHeadAttention
+
+
+# Prelude Block (Initial Processing)
+class PreludeBlock(nn.Module):
+    def __init__(self, vocab_size: int, d_model: int, num_heads: int, dropout: float = 0.1):
+        super().__init__()
+        self.token_embedding = nn.Embedding(vocab_size, d_model)
+        self.pos_encoding = nn.Parameter(torch.zeros(1, 1024, d_model))
+        self.attention = MultiHeadAttention(d_model, num_heads, dropout)
+        self.norm1, self.norm2 = nn.LayerNorm(d_model), nn.LayerNorm(d_model)
+        self.feed_forward = nn.Sequential(
+            nn.Linear(d_model, 4 * d_model),
+            nn.GELU(),
+            nn.Linear(4 * d_model, d_model),
+            nn.Dropout(dropout)
+        )
+
+    def forward(self, x: torch.Tensor, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        seq_len = x.size(1)
+        x = self.token_embedding(x) + self.pos_encoding[:, :seq_len, :]
+        attended = self.attention(self.norm1(x), mask)
+        x = x + attended
+        return x + self.feed_forward(self.norm2(x))

Model/recurrent_Block.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional, Tuple
+from Model.multi_head_Attention import MultiHeadAttention
+
+# Recurrent Block (Processing Over Time)
+class RecurrentBlock(nn.Module):
+    def __init__(self, d_model: int, num_heads: int, dropout: float = 0.1):
+        super().__init__()
+        self.attention = MultiHeadAttention(d_model, num_heads, dropout)
+        self.norm1, self.norm2 = nn.LayerNorm(d_model), nn.LayerNorm(d_model)
+        self.feed_forward = nn.Sequential(
+            nn.Linear(d_model, 4 * d_model),
+            nn.GELU(),
+            nn.Linear(4 * d_model, d_model),
+            nn.Dropout(dropout)
+        )
+        self.state_proj = nn.Linear(d_model, d_model)
+
+    def forward(self, x: torch.Tensor, recurrent_state: torch.Tensor, mask: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor]:
+        recurrent_state = self.state_proj(recurrent_state)
+        x = x + recurrent_state
+        attended = self.attention(self.norm1(x), mask)
+        return x + attended + self.feed_forward(self.norm2(x)), x