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model.py

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+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+
+class RotaryPositionalEmbedding(nn.Module):
+    def __init__(self, dim, max_seq_len=2048, base=10000):
+        super().__init__()
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer('inv_freq', inv_freq)
+        self.max_seq_len = max_seq_len
+        
+    def forward(self, seq_len, device):
+        t = torch.arange(seq_len, device=device).type_as(self.inv_freq)
+        freqs = torch.einsum('i,j->ij', t, self.inv_freq)
+        emb = torch.cat((freqs, freqs), dim=-1)
+        return emb.cos(), emb.sin()
+
+def apply_rotary_pos_emb(q, k, cos, sin):
+    def rotate_half(x):
+        x1, x2 = x.chunk(2, dim=-1)
+        return torch.cat((-x2, x1), dim=-1)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+class MultiHeadSelfAttention(nn.Module):
+    def __init__(self, d_model, n_heads, dropout=0.1, max_seq_len=2048):
+        super().__init__()
+        assert d_model % n_heads == 0
+        self.d_model = d_model
+        self.n_heads = n_heads
+        self.d_k = d_model // n_heads
+        self.q_linear = nn.Linear(d_model, d_model, bias=False)
+        self.k_linear = nn.Linear(d_model, d_model, bias=False)
+        self.v_linear = nn.Linear(d_model, d_model, bias=False)
+        self.out_linear = nn.Linear(d_model, d_model, bias=False)
+        self.dropout = nn.Dropout(dropout)
+        self.attn_dropout = nn.Dropout(dropout)
+        self.rope = RotaryPositionalEmbedding(self.d_k, max_seq_len)
+        self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
+        
+    def forward(self, x, mask=None):
+        batch_size, seq_len, d_model = x.size()
+        Q = self.q_linear(x).view(batch_size, seq_len, self.n_heads, self.d_k).transpose(1, 2)
+        K = self.k_linear(x).view(batch_size, seq_len, self.n_heads, self.d_k).transpose(1, 2)
+        V = self.v_linear(x).view(batch_size, seq_len, self.n_heads, self.d_k).transpose(1, 2)
+        
+        cos, sin = self.rope(seq_len, x.device)
+        cos = cos[None, None, :, :]
+        sin = sin[None, None, :, :]
+        Q, K = apply_rotary_pos_emb(Q, K, cos, sin)
+        
+        if self.flash and mask is None:
+            context = F.scaled_dot_product_attention(Q, K, V, attn_mask=None, dropout_p=0.0, is_causal=True)
+        else:
+            scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(self.d_k)
+            if mask is not None:
+                scores = scores.masked_fill(mask == 0, float('-inf'))
+            attn_weights = F.softmax(scores, dim=-1)
+            attn_weights = self.attn_dropout(attn_weights)
+            context = torch.matmul(attn_weights, V)
+        
+        context = context.transpose(1, 2).contiguous().view(batch_size, seq_len, d_model)
+        output = self.out_linear(context)
+        return self.dropout(output)
+
+class FeedForward(nn.Module):
+    def __init__(self, d_model, d_ff, dropout=0.1):
+        super().__init__()
+        self.linear1 = nn.Linear(d_model, d_ff)
+        self.linear2 = nn.Linear(d_ff, d_model)
+        self.dropout = nn.Dropout(dropout)
+        
+    def forward(self, x):
+        return self.linear2(self.dropout(F.gelu(self.linear1(x))))
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim, eps=1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+        
+    def forward(self, x):
+        norm = torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+        return x * norm * self.weight
+
+class TransformerBlock(nn.Module):
+    def __init__(self, d_model, n_heads, d_ff, dropout=0.1, max_seq_len=2048, use_swiglu=False):
+        super().__init__()
+        self.attention = MultiHeadSelfAttention(d_model, n_heads, dropout, max_seq_len)
+        self.feed_forward = FeedForward(d_model, d_ff, dropout)
+        self.norm1 = RMSNorm(d_model)
+        self.norm2 = RMSNorm(d_model)
+        self.dropout = nn.Dropout(dropout)
+        
+    def forward(self, x, mask=None):
+        x = x + self.attention(self.norm1(x), mask)
+        x = x + self.feed_forward(self.norm2(x))
+        return x
+
+class MTPMiniModel(nn.Module):
+    def __init__(self, vocab_size, d_model=512, n_layers=8, n_heads=8, 
+                 d_ff=2048, max_seq_len=512, dropout=0.2, use_swiglu=False):
+        super().__init__()
+        self.vocab_size = vocab_size
+        self.d_model = d_model
+        self.max_seq_len = max_seq_len
+        self.token_embedding = nn.Embedding(vocab_size, d_model)
+        self.dropout = nn.Dropout(dropout)
+        self.blocks = nn.ModuleList([
+            TransformerBlock(d_model, n_heads, d_ff, dropout, max_seq_len, use_swiglu)
+            for _ in range(n_layers)
+        ])
+        self.norm_f = RMSNorm(d_model)
+        self.lm_head = nn.Linear(d_model, vocab_size, bias=False)
+        self.lm_head.weight = self.token_embedding.weight
+        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)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+    
+    def forward(self, input_ids, targets=None, use_eos_weight=False):
+        batch_size, seq_len = input_ids.size()
+        mask = torch.tril(torch.ones(seq_len, seq_len, device=input_ids.device)).view(1, 1, seq_len, seq_len)
+        x = self.dropout(self.token_embedding(input_ids))
+        for block in self.blocks:
+            x = block(x, mask)
+        x = self.norm_f(x)
+        logits = self.lm_head(x)
+        
+        loss = None
+        if targets is not None:
+            if use_eos_weight:
+                weights = torch.ones(self.vocab_size, device=logits.device)
+                weights[3] = 2.0 
+                loss = F.cross_entropy(logits.view(-1, self.vocab_size), targets.view(-1), weight=weights, label_smoothing=0.1)
+            else:
+                loss = F.cross_entropy(logits.view(-1, self.vocab_size), targets.view(-1), label_smoothing=0.1)
+        return logits, loss
+
+    def count_parameters(self):
+        return sum(p.numel() for p in self.parameters() if p.requires_grad)