Delete model

model/codaBlock.py

@@ -1,14 +0,0 @@
-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

@@ -1,22 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from typing import Optional, Tuple
-from prelude_Block import PreludeBlock
-from recurrent_Block import RecurrentBlock
-from 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/multi_head_Attention.py

@@ -1,44 +0,0 @@
-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

@@ -1,28 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from typing import Optional, Tuple
-from 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

@@ -1,25 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from typing import Optional, Tuple
-from 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