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599bb35 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 | """Encoder-decoder Transformer written from scratch (Vaswani et al., 2017),
with Pre-LN sublayers, sinusoidal positions and tied embeddings.
No torch.nn.Transformer / nn.MultiheadAttention is used.
Masks are boolean tensors where True means "this key position may be attended to".
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class PositionalEncoding(nn.Module):
def __init__(self, d_model, max_len, dropout):
super().__init__()
if d_model % 2 != 0:
raise ValueError("d_model must be even for sinusoidal positional encoding")
pe = torch.zeros(max_len, d_model)
pos = torch.arange(0, max_len, dtype=torch.float32).unsqueeze(1)
div = torch.exp(torch.arange(0, d_model, 2, dtype=torch.float32) * (-math.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(pos * div)
pe[:, 1::2] = torch.cos(pos * div)
self.register_buffer("pe", pe.unsqueeze(0), persistent=False)
self.max_len = max_len
self.drop = nn.Dropout(dropout)
def forward(self, x):
if x.size(1) > self.max_len:
raise ValueError(f"Sequence length {x.size(1)} exceeds max_seq_len {self.max_len}")
return self.drop(x + self.pe[:, : x.size(1)].to(x.dtype))
class MultiHeadAttention(nn.Module):
def __init__(self, d_model, n_heads, dropout):
super().__init__()
if d_model % n_heads != 0:
raise ValueError("d_model must be divisible by n_heads")
self.h, self.dk = n_heads, d_model // n_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.drop = nn.Dropout(dropout)
def forward(self, q, k, v, mask=None):
B, Tq, _ = q.shape
Tk = k.size(1)
Q = self.q_proj(q).view(B, Tq, self.h, self.dk).transpose(1, 2)
K = self.k_proj(k).view(B, Tk, self.h, self.dk).transpose(1, 2)
V = self.v_proj(v).view(B, Tk, self.h, self.dk).transpose(1, 2)
scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(self.dk)
if mask is not None:
scores = scores.masked_fill(~mask, torch.finfo(scores.dtype).min)
attn = self.drop(F.softmax(scores, dim=-1))
out = torch.matmul(attn, V).transpose(1, 2).contiguous().view(B, Tq, self.h * self.dk)
return self.o_proj(out)
class FeedForward(nn.Module):
def __init__(self, d_model, d_ff, dropout):
super().__init__()
self.l1, self.l2 = nn.Linear(d_model, d_ff), nn.Linear(d_ff, d_model)
self.drop = nn.Dropout(dropout)
def forward(self, x):
return self.l2(self.drop(F.relu(self.l1(x))))
class EncoderLayer(nn.Module):
def __init__(self, d_model, n_heads, d_ff, dropout):
super().__init__()
self.attn = MultiHeadAttention(d_model, n_heads, dropout)
self.ff = FeedForward(d_model, d_ff, dropout)
self.n1, self.n2 = nn.LayerNorm(d_model), nn.LayerNorm(d_model)
self.drop = nn.Dropout(dropout)
def forward(self, x, src_mask):
h = self.n1(x)
x = x + self.drop(self.attn(h, h, h, src_mask))
return x + self.drop(self.ff(self.n2(x)))
class DecoderLayer(nn.Module):
def __init__(self, d_model, n_heads, d_ff, dropout):
super().__init__()
self.self_attn = MultiHeadAttention(d_model, n_heads, dropout)
self.cross_attn = MultiHeadAttention(d_model, n_heads, dropout)
self.ff = FeedForward(d_model, d_ff, dropout)
self.n1, self.n2, self.n3 = (nn.LayerNorm(d_model) for _ in range(3))
self.drop = nn.Dropout(dropout)
def forward(self, x, memory, src_mask, tgt_mask):
h = self.n1(x)
x = x + self.drop(self.self_attn(h, h, h, tgt_mask))
x = x + self.drop(self.cross_attn(self.n2(x), memory, memory, src_mask))
return x + self.drop(self.ff(self.n3(x)))
class TransformerMT(nn.Module):
def __init__(self, vocab_size, d_model=256, n_heads=4, n_encoder_layers=3, n_decoder_layers=3,
d_ff=1024, dropout=0.3, max_seq_len=320, pad_id=0, tie_embeddings=True):
super().__init__()
self.d_model, self.pad_id, self.max_seq_len = d_model, pad_id, max_seq_len
self.embed = nn.Embedding(vocab_size, d_model, padding_idx=pad_id) # shared src/tgt/output
self.pos = PositionalEncoding(d_model, max_seq_len, dropout)
self.enc_layers = nn.ModuleList(
[EncoderLayer(d_model, n_heads, d_ff, dropout) for _ in range(n_encoder_layers)])
self.dec_layers = nn.ModuleList(
[DecoderLayer(d_model, n_heads, d_ff, dropout) for _ in range(n_decoder_layers)])
self.enc_norm, self.dec_norm = nn.LayerNorm(d_model), nn.LayerNorm(d_model)
self.out_proj = nn.Linear(d_model, vocab_size, bias=False)
if tie_embeddings:
self.out_proj.weight = self.embed.weight
self._init_weights()
def _init_weights(self):
for m in self.modules():
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
nn.init.zeros_(m.bias)
nn.init.normal_(self.embed.weight, mean=0.0, std=self.d_model ** -0.5)
with torch.no_grad():
self.embed.weight[self.pad_id].zero_()
def num_parameters(self):
return sum(p.numel() for p in self.parameters())
def _embed(self, ids):
return self.pos(self.embed(ids) * math.sqrt(self.d_model))
def make_src_mask(self, src):
return (src != self.pad_id).unsqueeze(1).unsqueeze(2) # [B,1,1,Ts]
def make_tgt_mask(self, tgt):
T = tgt.size(1)
causal = torch.tril(torch.ones(T, T, dtype=torch.bool, device=tgt.device))
return (tgt != self.pad_id).unsqueeze(1).unsqueeze(2) & causal.unsqueeze(0).unsqueeze(0)
def encode(self, src, src_mask=None):
if src_mask is None:
src_mask = self.make_src_mask(src)
x = self._embed(src)
for layer in self.enc_layers:
x = layer(x, src_mask)
return self.enc_norm(x)
def decode(self, tgt_in, memory, src_mask, tgt_mask=None):
if tgt_mask is None:
tgt_mask = self.make_tgt_mask(tgt_in)
x = self._embed(tgt_in)
for layer in self.dec_layers:
x = layer(x, memory, src_mask, tgt_mask)
return self.out_proj(self.dec_norm(x)) # [B,Tt,V]
def forward(self, src, tgt_in):
src_mask = self.make_src_mask(src)
return self.decode(tgt_in, self.encode(src, src_mask), src_mask)
def load_model_from_checkpoint(path, device="cpu"):
"""Loads a best.pt written by src/train.py. Returns (model_in_eval_mode, meta_dict)."""
ckpt = torch.load(path, map_location=device, weights_only=True)
model = TransformerMT(**ckpt["model_args"]).to(device)
model.load_state_dict(ckpt["model_state"])
model.eval()
meta = {k: v for k, v in ckpt.items() if k != "model_state"}
return model, meta
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