Spaces:
Sleeping
Sleeping
File size: 10,159 Bytes
c407ed8 8098a3f c407ed8 8098a3f c407ed8 8098a3f c407ed8 8098a3f c407ed8 8098a3f c407ed8 8098a3f c407ed8 8098a3f c407ed8 8098a3f c407ed8 8098a3f 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 | 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
def forward_cached(
self,
x: torch.Tensor,
past_key_value: tuple[torch.Tensor, torch.Tensor] | None = None,
) -> tuple[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
batch, seq_len, channels = x.shape
query, key, value = self.qkv(x).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)
past_len = 0
if past_key_value is not None:
past_key, past_value = past_key_value
past_len = past_key.size(2)
key = torch.cat((past_key, key), dim=2)
value = torch.cat((past_value, value), dim=2)
total_len = key.size(2)
scores = query @ key.transpose(-2, -1) / math.sqrt(self.head_dim)
mask = self.causal_mask[:, :, past_len : past_len + seq_len, :total_len]
scores = scores.masked_fill(mask == 0, float("-inf"))
weights = self.attn_dropout(F.softmax(scores, dim=-1))
out = weights @ value
out = out.transpose(1, 2).contiguous().view(batch, seq_len, channels)
return self.resid_dropout(self.proj(out)), (key, value)
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
def forward_cached(
self,
x: torch.Tensor,
past_key_value: tuple[torch.Tensor, torch.Tensor] | None = None,
) -> tuple[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
attn_out, present = self.attn.forward_cached(self.ln_1(x), past_key_value)
x = x + attn_out
x = x + self.ffwd(self.ln_2(x))
return x, present
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)
self.lm_head.weight = self.token_embedding.weight
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
def _forward_cached(
self,
idx: torch.Tensor,
past_key_values: list[tuple[torch.Tensor, torch.Tensor]] | None = None,
) -> tuple[torch.Tensor, list[tuple[torch.Tensor, torch.Tensor]]]:
_, seq_len = idx.shape
past_len = 0 if past_key_values is None else past_key_values[0][0].size(2)
if past_len + seq_len > self.config.block_size:
raise ValueError("Cached sequence length exceeds block_size")
positions = torch.arange(past_len, past_len + seq_len, device=idx.device)
x = self.dropout(self.token_embedding(idx) + self.position_embedding(positions))
presents = []
for layer, block in enumerate(self.blocks):
past = None if past_key_values is None else past_key_values[layer]
x, present = block.forward_cached(x, past)
presents.append(present)
return self.lm_head(self.ln_f(x)), presents
def parameter_count(self, trainable_only: bool = True) -> int:
parameters = self.parameters()
if trainable_only:
parameters = (parameter for parameter in parameters if parameter.requires_grad)
return sum(parameter.numel() for parameter in parameters)
@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,
use_cache: bool = True,
) -> torch.Tensor:
if max_new_tokens < 0:
raise ValueError("max_new_tokens must be non-negative")
if temperature <= 0:
raise ValueError("temperature must be positive")
if top_k is not None and top_k <= 0:
raise ValueError("top_k must be positive")
past_key_values = None
for _ in range(max_new_tokens):
if not use_cache:
idx_cond = idx[:, -self.config.block_size :]
logits, _ = self(idx_cond)
elif past_key_values is None:
idx_cond = idx[:, -self.config.block_size :]
logits, past_key_values = self._forward_cached(idx_cond)
else:
cached_length = past_key_values[0][0].size(2)
if cached_length >= self.config.block_size:
idx_cond = idx[:, -self.config.block_size :]
logits, past_key_values = self._forward_cached(idx_cond)
else:
logits, past_key_values = self._forward_cached(
idx[:, -1:], past_key_values
)
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
|