added new model file, still making some modifications

base/new.py

@@ -0,0 +1,252 @@
+import json
+import os
+current_directory = os.path.dirname(os.path.abspath(__file__))
+os.chdir(current_directory)
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+with open('config_enigma.json', 'r', encoding='utf-8') as file:
+  params = json.load(file)
+
+batch_size = params['batch_size']
+block_size = params['block_size']
+n_head = params['n_head']
+d_model = params['d_model']
+n_layers = params['n_layer']
+dropout = params['dropout']
+norm_eps = params['norm_eps']
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+class MaskedHead(nn.Module):
+  def __init__(self, d_model, head_size, dropout, block_size):
+    super().__init__()
+    self.key = nn.Linear(d_model, head_size, bias=True)
+    self.query = nn.Linear(d_model, head_size, bias=True)
+    self.value = nn.Linear(d_model, head_size, bias=False)
+    self.dropout = nn.Dropout(dropout)
+    self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
+
+  def forward(self, x):
+    B, T, C = x.shape
+    key = self.key(x)
+    query = self.query(x)
+
+    weights = torch.matmul(query, key.transpose(-2, -1)) / (key.shape[-1]**-0.5)
+    weights = weights.masked_fill(self.tril[:T, :T] == 0, float('-inf'))
+    weights = F.softmax(weights, dim=-1)
+    weights = self.dropout(weights)
+
+    value = self.value(x)
+    out = torch.matmul(weights, value)
+    return out
+
+class MultiMaskedAttention(nn.Module):
+  def __init__(self, d_model, n_head, dropout, block_size):
+    head_size = d_model // n_head
+    super().__init__()
+    self.heads = nn.ModuleList([MaskedHead(d_model=d_model, dropout=dropout, head_size=head_size, block_size=block_size) for _ in range(n_head)])
+    self.dropout = nn.Dropout(dropout)
+
+  def forward(self, x):
+    out = torch.cat([h(x) for h in self.heads], dim=-1)
+    out = self.dropout(out)
+    return out
+
+class UnMaskedHead(nn.Module):
+  def __init__(self, d_model, head_size, dropout, block_size):
+    super().__init__()
+    self.key = nn.Linear(d_model, head_size, bias=True)
+    self.query = nn.Linear(d_model, head_size, bias=True)
+    self.value = nn.Linear(d_model, head_size, bias=False)
+    self.dropout = nn.Dropout(dropout)
+
+  def forward(self, x):
+    key = self.key(x)
+    query = self.query(x)
+
+    weights = torch.matmul(query, key.transpose(-2, -1)) / (key.shape[-1]**-0.5)
+    weights = F.softmax(weights, dim=-1)
+    weights = self.dropout(weights)
+
+    value = self.value(x)
+    out = torch.matmul(weights, value)
+
+    return out, weights
+
+class EncoderAttention(nn.Module):
+  def __init__(self, d_model, n_head, dropout, block_size):
+    head_size = d_model // n_head
+    super().__init__()
+    self.heads = nn.ModuleList([UnMaskedHead(d_model=d_model, dropout=dropout, head_size=head_size, block_size=block_size) for _ in range(n_head)])
+    self.dropout = nn.Dropout(dropout)
+
+  def forward(self, src):
+    out_list = []
+    weights_list = []
+    for h in self.heads:
+      out, weights = h(src)
+      out_list.append(out)
+      weights_list.append(weights)
+
+    out = torch.cat(out_list, dim=-1)
+    weights = torch.cat(weights_list, dim=-1)
+    out = self.dropout(out)
+    return out, weights
+
+class FinalHead(nn.Module):
+  def __init__(self, d_model, head_size, dropout, block_size):
+    super().__init__()
+    self.key = nn.Linear(d_model, head_size, bias=True)
+    self.query = nn.Linear(d_model, head_size, bias=True)
+    self.value = nn.Linear(d_model, head_size, bias=False)
+    self.proj = nn.Linear(head_size, d_model)
+    self.dropout = nn.Dropout(dropout)
+
+  def forward(self, wei, val):
+    wei = self.proj(wei)
+    return torch.matmul(wei, val.transpose(-2, -1))
+
+class DecoderAttention(nn.Module):
+  def __init__(self, d_model, n_head, dropout, block_size):
+    head_size = d_model // n_head
+    super().__init__()
+    self.heads = nn.ModuleList([FinalHead(d_model=d_model, dropout=dropout, head_size=head_size, block_size=block_size) for _ in range(n_head)])
+    self.proj = nn.Linear(n_head * head_size, d_model)
+    self.dropout = nn.Dropout(dropout)
+
+  def forward(self, wei, val):
+    out= torch.cat([h(wei, val) for h in self.heads], dim=-1)
+    out = self.dropout(self.proj(out))
+    return out
+
+class FeedForward(nn.Module):
+  def __init__(self, d_model, dropout):
+    super().__init__()
+    self.ln1 = nn.Linear(d_model, 5*d_model)
+    self.gelu = nn.GELU()
+    self.ln2 = nn.Linear(5*d_model, d_model)
+    self.drop = nn.Dropout(dropout)
+
+  def forward(self, x):
+    return self.drop(self.ln2(self.gelu(self.ln1(x))))
+
+class EncoderNetwork(nn.Module):
+  def __init__(self, d_model, n_head, norm_eps, dropout, block_size):
+    super().__init__()
+    self.s_att = EncoderAttention(n_head=n_head, d_model=d_model, dropout=dropout, block_size=block_size)
+    self.ffwd = FeedForward(d_model, dropout)
+    self.dropout = nn.Dropout(dropout)
+    self.norm1 = nn.LayerNorm(d_model, eps=norm_eps)
+    self.norm2 = nn.LayerNorm(d_model, eps=norm_eps)
+
+  def forward(self, src):
+    src2, att = self.s_att(src)
+    src = src + self.dropout(src2)
+    src = self.norm1(src)
+
+    src2 = self.ffwd(src)
+    src = src + self.dropout(src2)
+    src = self.norm2(src)
+
+    return src, att
+
+class DecoderNetwork(nn.Module):
+  def __init__(self, d_model, n_head, norm_eps, dropout, block_size):
+    super().__init__()
+    self.m_att = MultiMaskedAttention(n_head=n_head, d_model=d_model, dropout=dropout, block_size=block_size)
+    self.d_att = DecoderAttention(n_head=n_head, d_model=d_model, dropout=dropout, block_size=block_size)
+    self.ffwd = FeedForward(d_model, dropout)
+    self.dropout = nn.Dropout(dropout)
+    self.norm1 = nn.LayerNorm(d_model, eps=norm_eps)
+    self.norm2 = nn.LayerNorm(d_model, eps=norm_eps)
+
+  def forward(self, src, att):
+    src2 = self.m_att(src)
+    src = src + self.dropout(src2)
+    src = src + self.norm1(src)
+
+    trg2 = self.d_att(att, src)
+    trg = trg + self.dropout(trg2)
+    trg_f = trg + self.norm1(trg)
+
+    src_f2 = self.ffwd(self.norm2(trg_f))
+    src_f = src_f + self.dropout(src_f2)
+    src_f = self.norm2(src_f)
+
+    return src_f
+
+class Transformer(nn.Module):
+  def __init__(self, vocab_size):
+    super().__init__()
+    self.toked_model = nn.Embedding(vocab_size, d_model)
+    self.pos_encod = nn.Embedding(block_size, d_model)
+    self.enc_layer = nn.ModuleList([EncoderNetwork(n_head=n_head, norm_eps=norm_eps, block_size=block_size, dropout=dropout, d_model=d_model) for _ in range(n_layers)])
+    self.dec_layer = nn.ModuleList([DecoderNetwork(n_head=n_head, norm_eps=norm_eps, block_size=block_size, dropout=dropout, d_model=d_model) for _ in range(n_layers)])
+
+    self.norm_final = nn.LayerNorm(d_model)
+    self.linear_final = nn.Linear(d_model, vocab_size)
+    self.dropout = nn.Dropout(dropout)
+    self.apply(self._init_weights)
+
+  def _init_weights(self, module):
+    if isinstance(module, nn.Linear):
+      torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+      if module.bias is not None:
+        torch.nn.init.zeros_(module.bias.data)
+    elif isinstance(module, nn.Embedding):
+      torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+
+  def forward(self, idx, targets=None):
+    B, T = idx.shape
+
+    toked_model = self.toked_model(idx)
+    pos_encod = self.pos_encod(torch.arange(T, device=device))
+    x = toked_model + pos_encod
+
+    for layer in self.enc_layer:
+      wei, att = layer(x)
+
+    for layer in self.dec_layer:
+      x = layer(x, att)
+
+    x = self.norm_final(x)
+    logits = self.linear_final(x)
+
+    if targets is None:
+      loss = None
+
+    else:
+      B, T, C = logits.shape
+      logits = logits.view(B*T, C)
+      targets = targets.view(B*T)
+      loss = F.cross_entropy(logits, targets)
+
+    return logits, loss
+
+  def generate(self, idx, max_new_tokens, temperature=1.0, top_k=0):
+    generated_tokens = []
+
+    for _ in range(max_new_tokens):
+      idx_cond = idx[:, -block_size:]
+      logits, _ = self(idx_cond)
+      logits = logits[:, -1, :]
+
+      scaled_logits = logits / temperature
+      if top_k > 0:
+        scaled_logits = self._top_k_filtering(scaled_logits, top_k)
+
+      probs = F.softmax(scaled_logits, dim=-1)
+      sampled_idx = torch.multinomial(probs, num_samples=1)
+      generated_tokens.append(sampled_idx.item())
+      idx = torch.cat((idx, sampled_idx), dim=1)
+
+    return generated_tokens
+
+  def _top_k_filtering(self, logits, top_k):
+    values, indices = torch.topk(logits, top_k, dim=-1)
+    min_value = values[:, -1].unsqueeze(-1).expand_as(logits)
+    filtered_logits = torch.where(logits < min_value, torch.ones_like(logits) * -float('inf'), logits)
+
+    return filtered_logits

llama/model.py

@@ -94,8 +94,8 @@ def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
     Returns:
         torch.Tensor: Precomputed frequency tensor with complex exponentials.
 
-    
-        
+
+
 
     """
     freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
@@ -151,7 +151,7 @@ def apply_rotary_emb(
     Returns:
         Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings.
 
-        
+
 
     """
     xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
@@ -176,6 +176,7 @@ def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
 
 class Attention(nn.Module):
     """Multi-head attention module."""
+
     def __init__(self, args: ModelArgs):
         """
         Initialize the Attention module.
@@ -290,12 +291,18 @@ def forward(
         values = self.cache_v[:bsz, : start_pos + seqlen]
 
         # repeat k/v heads if n_kv_heads < n_heads
-        keys = repeat_kv(keys, self.n_rep)  # (bs, cache_len + seqlen, n_local_heads, head_dim)
-        values = repeat_kv(values, self.n_rep)  # (bs, cache_len + seqlen, n_local_heads, head_dim)
+        keys = repeat_kv(
+            keys, self.n_rep
+        )  # (bs, cache_len + seqlen, n_local_heads, head_dim)
+        values = repeat_kv(
+            values, self.n_rep
+        )  # (bs, cache_len + seqlen, n_local_heads, head_dim)
 
         xq = xq.transpose(1, 2)  # (bs, n_local_heads, seqlen, head_dim)
-        keys = keys.transpose(1, 2) # (bs, n_local_heads, cache_len + seqlen, head_dim)
-        values = values.transpose(1, 2) # (bs, n_local_heads, cache_len + seqlen, head_dim)
+        keys = keys.transpose(1, 2)  # (bs, n_local_heads, cache_len + seqlen, head_dim)
+        values = values.transpose(
+            1, 2
+        )  # (bs, n_local_heads, cache_len + seqlen, head_dim)
         scores = torch.matmul(xq, keys.transpose(2, 3)) / math.sqrt(self.head_dim)
         if mask is not None:
             scores = scores + mask  # (bs, n_local_heads, seqlen, cache_len + seqlen)
@@ -449,9 +456,10 @@ def __init__(self, params: ModelArgs):
         )
 
         self.freqs_cis = precompute_freqs_cis(
-            # Note that self.params.max_seq_len is multiplied by 2 because the token limit for the Llama 2 generation of models is 4096. 
+            # Note that self.params.max_seq_len is multiplied by 2 because the token limit for the Llama 2 generation of models is 4096.
             # Adding this multiplier instead of using 4096 directly allows for dynamism of token lengths while training or fine-tuning.
-            self.params.dim // self.params.n_heads, self.params.max_seq_len * 2
+            self.params.dim // self.params.n_heads,
+            self.params.max_seq_len * 2,
         )
 
     @torch.inference_mode()
@@ -474,23 +482,20 @@ def forward(self, tokens: torch.Tensor, start_pos: int):
 
         mask = None
         if seqlen > 1:
-            mask = torch.full(
-                (seqlen, seqlen), float("-inf"), device=tokens.device
-            )
+            mask = torch.full((seqlen, seqlen), float("-inf"), device=tokens.device)
 
             mask = torch.triu(mask, diagonal=1)
 
             # When performing key-value caching, we compute the attention scores
             # only for the new sequence. Thus, the matrix of scores is of size
             # (seqlen, cache_len + seqlen), and the only masked entries are (i, j) for
             # j > cache_len + i, since row i corresponds to token cache_len + i.
-            mask = torch.hstack([
-                torch.zeros((seqlen, start_pos), device=tokens.device),
-                mask
-            ]).type_as(h)
+            mask = torch.hstack(
+                [torch.zeros((seqlen, start_pos), device=tokens.device), mask]
+            ).type_as(h)
 
         for layer in self.layers:
             h = layer(h, start_pos, freqs_cis, mask)
         h = self.norm(h)
         output = self.output(h).float()
-        return output
+        return output