File size: 6,379 Bytes
a31420b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 |
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)
|