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c407ed8 | 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 | from __future__ import annotations
import math
import torch
from torch import nn
from torch.nn import functional as F
from tiny_transformer.config import ModelConfig
class CausalSelfAttention(nn.Module):
def __init__(self, config: ModelConfig) -> None:
super().__init__()
self.n_head = config.n_head
self.head_dim = config.n_embd // config.n_head
self.qkv = nn.Linear(config.n_embd, 3 * config.n_embd)
self.proj = nn.Linear(config.n_embd, config.n_embd)
self.attn_dropout = nn.Dropout(config.dropout)
self.resid_dropout = nn.Dropout(config.dropout)
mask = torch.tril(torch.ones(config.block_size, config.block_size))
self.register_buffer("causal_mask", mask.view(1, 1, config.block_size, config.block_size))
def forward(
self, x: torch.Tensor, return_attention: bool = False
) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
batch, seq_len, channels = x.shape
qkv = self.qkv(x)
query, key, value = qkv.split(channels, dim=2)
query = query.view(batch, seq_len, self.n_head, self.head_dim).transpose(1, 2)
key = key.view(batch, seq_len, self.n_head, self.head_dim).transpose(1, 2)
value = value.view(batch, seq_len, self.n_head, self.head_dim).transpose(1, 2)
scores = query @ key.transpose(-2, -1) / math.sqrt(self.head_dim)
scores = scores.masked_fill(self.causal_mask[:, :, :seq_len, :seq_len] == 0, float("-inf"))
weights = F.softmax(scores, dim=-1)
weights = self.attn_dropout(weights)
out = weights @ value
out = out.transpose(1, 2).contiguous().view(batch, seq_len, channels)
out = self.resid_dropout(self.proj(out))
if return_attention:
return out, weights
return out
class FeedForward(nn.Module):
def __init__(self, config: ModelConfig) -> None:
super().__init__()
self.net = nn.Sequential(
nn.Linear(config.n_embd, 4 * config.n_embd),
nn.GELU(),
nn.Linear(4 * config.n_embd, config.n_embd),
nn.Dropout(config.dropout),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.net(x)
class TransformerBlock(nn.Module):
def __init__(self, config: ModelConfig) -> None:
super().__init__()
self.ln_1 = nn.LayerNorm(config.n_embd)
self.attn = CausalSelfAttention(config)
self.ln_2 = nn.LayerNorm(config.n_embd)
self.ffwd = FeedForward(config)
def forward(
self, x: torch.Tensor, return_attention: bool = False
) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
if return_attention:
attn_out, weights = self.attn(self.ln_1(x), return_attention=True)
x = x + attn_out
x = x + self.ffwd(self.ln_2(x))
return x, weights
x = x + self.attn(self.ln_1(x))
x = x + self.ffwd(self.ln_2(x))
return x
class TinyTransformer(nn.Module):
def __init__(self, config: ModelConfig) -> None:
super().__init__()
self.config = config
self.token_embedding = nn.Embedding(config.vocab_size, config.n_embd)
self.position_embedding = nn.Embedding(config.block_size, config.n_embd)
self.dropout = nn.Dropout(config.dropout)
self.blocks = nn.ModuleList([TransformerBlock(config) for _ in range(config.n_layer)])
self.ln_f = nn.LayerNorm(config.n_embd)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size)
self.apply(self._init_weights)
def _init_weights(self, module: nn.Module) -> None:
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
def forward(
self, idx: torch.Tensor, targets: torch.Tensor | None = None
) -> tuple[torch.Tensor, torch.Tensor | None]:
logits, loss, _ = self._forward(idx, targets, capture_attention=False)
return logits, loss
def _forward(
self,
idx: torch.Tensor,
targets: torch.Tensor | None = None,
capture_attention: bool = False,
) -> tuple[torch.Tensor, torch.Tensor | None, list[torch.Tensor]]:
batch, seq_len = idx.shape
if seq_len > self.config.block_size:
raise ValueError("Sequence length exceeds block_size")
attentions: list[torch.Tensor] = []
positions = torch.arange(seq_len, device=idx.device)
x = self.token_embedding(idx) + self.position_embedding(positions)
x = self.dropout(x)
for block in self.blocks:
if capture_attention:
x, weights = block(x, return_attention=True)
attentions.append(weights)
else:
x = block(x)
x = self.ln_f(x)
logits = self.lm_head(x)
loss = None
if targets is not None:
loss = F.cross_entropy(logits.view(batch * seq_len, -1), targets.view(batch * seq_len))
return logits, loss, attentions
@torch.no_grad()
def attention_maps(self, idx: torch.Tensor) -> list[torch.Tensor]:
self.eval()
_, _, attentions = self._forward(idx, capture_attention=True)
return attentions
@torch.no_grad()
def generate(
self,
idx: torch.Tensor,
max_new_tokens: int,
temperature: float = 1.0,
top_k: int | None = None,
) -> torch.Tensor:
if temperature <= 0:
raise ValueError("temperature must be positive")
for _ in range(max_new_tokens):
idx_cond = idx[:, -self.config.block_size :]
logits, _ = self(idx_cond)
logits = logits[:, -1, :] / temperature
if top_k is not None:
values, _ = torch.topk(logits, min(top_k, logits.size(-1)))
logits[logits < values[:, [-1]]] = -float("inf")
probs = F.softmax(logits, dim=-1)
next_idx = torch.multinomial(probs, num_samples=1)
idx = torch.cat((idx, next_idx), dim=1)
return idx
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