Delete modeling_snowflake.py.txt

modeling_snowflake.py.txt

@@ -1,112 +0,0 @@
-import torch
-import torch.nn as nn
-import math
-
-# --- Submodule: FusedQKVAttention ---
-class FusedQKVAttention(nn.Module):
-    def __init__(self, d_model, num_heads):
-        super().__init__()
-        self.d_model = d_model
-        self.num_heads = num_heads
-        self.head_dim = d_model // num_heads
-        # Fused QKV projection
-        self.qkv_proj = nn.Linear(d_model, 3 * d_model)
-        self.wo = nn.Linear(d_model, d_model)
-        # Initialize weights for better training stability
-        nn.init.xavier_uniform_(self.qkv_proj.weight)
-        nn.init.xavier_uniform_(self.wo.weight)
-        nn.init.zeros_(self.qkv_proj.bias)
-        nn.init.zeros_(self.wo.bias)
-
-    def forward(self, x, attention_mask=None):
-        batch_size, seq_len, _ = x.shape
-        # Fused projection and reshape
-        qkv = self.qkv_proj(x).reshape(batch_size, seq_len, 3, self.num_heads, self.head_dim)
-        qkv = qkv.permute(2, 0, 3, 1, 4)  # [3, batch, heads, seq_len, head_dim]
-        q, k, v = qkv[0], qkv[1], qkv[2]
-        # Compute attention with memory efficiency
-        attention_scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
-        if attention_mask is not None:
-            attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
-            attention_scores = attention_scores.masked_fill(attention_mask == 0, float('-inf'))
-        attention_weights = torch.softmax(attention_scores, dim=-1)
-        # Apply attention and reshape
-        context = torch.matmul(attention_weights, v)
-        context = context.transpose(1, 2).reshape(batch_size, seq_len, self.d_model)
-        return self.wo(context)
-
-
-# --- Submodule: EnhancedFeedForward ---
-class EnhancedFeedForward(nn.Module):
-    def __init__(self, d_model, ff_dim, dropout=0.1):
-        super().__init__()
-        self.linear1 = nn.Linear(d_model, ff_dim)
-        self.dropout1 = nn.Dropout(dropout)
-        self.linear2 = nn.Linear(ff_dim, d_model)
-        self.dropout2 = nn.Dropout(dropout)
-        self.activation = nn.GELU()
-        # Initialize weights for better training
-        nn.init.xavier_uniform_(self.linear1.weight)
-        nn.init.xavier_uniform_(self.linear2.weight)
-        nn.init.zeros_(self.linear1.bias)
-        nn.init.zeros_(self.linear2.bias)
-
-    def forward(self, x):
-        return self.dropout2(self.linear2(self.dropout1(self.activation(self.linear1(x)))))
-
-
-# --- Submodule: EnhancedTransformerBlock ---
-class EnhancedTransformerBlock(nn.Module):
-    def __init__(self, d_model, num_heads, ff_dim, dropout=0.1):
-        super().__init__()
-        self.attention = FusedQKVAttention(d_model, num_heads)
-        self.norm1 = nn.LayerNorm(d_model, eps=1e-6)
-        self.dropout1 = nn.Dropout(dropout)
-        self.feed_forward = EnhancedFeedForward(d_model, ff_dim, dropout)
-        self.norm2 = nn.LayerNorm(d_model, eps=1e-6)
-        self.dropout2 = nn.Dropout(dropout)
-
-    def forward(self, x, attention_mask=None):
-        # Pre-norm architecture
-        attn_input = self.norm1(x)
-        attn_output = self.attention(attn_input, attention_mask)
-        x = x + self.dropout1(attn_output)
-        ff_input = self.norm2(x)
-        ff_output = self.feed_forward(ff_input)
-        x = x + self.dropout2(ff_output)
-        return x
-
-
-# --- Main Model Class: Snowflake4CausalLM ---
-class Snowflake4CausalLM(nn.Module):
-    def __init__(self, vocab_size, max_seq_length, d_model, num_heads, num_layers, ff_dim, dropout=0.1):
-        super().__init__()
-        self.embedding = nn.Embedding(vocab_size, d_model)
-        # Initialize positional encodings without in-place modification
-        self.pos_encoding = nn.Parameter(torch.zeros(1, max_seq_length, d_model))
-        position = torch.arange(max_seq_length).unsqueeze(1).float()
-        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
-        pos_enc = torch.zeros(1, max_seq_length, d_model)
-        pos_enc[0, :, 0::2] = torch.sin(position * div_term)
-        pos_enc[0, :, 1::2] = torch.cos(position * div_term)
-        self.pos_encoding.data = pos_enc.data
-        self.layers = nn.ModuleList([
-            EnhancedTransformerBlock(d_model, num_heads, ff_dim, dropout)
-            for _ in range(num_layers)
-        ])
-        self.final_norm = nn.LayerNorm(d_model, eps=1e-6)
-        self.dropout = nn.Dropout(dropout)
-        self.fc_out = nn.Linear(d_model, vocab_size)
-        # Tie embedding and output weights for memory efficiency and better generalization
-        self.fc_out.weight = self.embedding.weight
-        # Initialize embedding weights
-        nn.init.normal_(self.embedding.weight, mean=0, std=0.02)
-
-    def forward(self, input_ids, attention_mask=None):
-        seq_length = input_ids.size(1)
-        x = self.embedding(input_ids) + self.pos_encoding[:, :seq_length, :]
-        x = self.dropout(x)
-        for layer in self.layers:
-            x = layer(x, attention_mask)
-        x = self.final_norm(x)
-        return self.fc_out(x)