File size: 30,213 Bytes
476c25f | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 | """
Stage 4 β model.py
A full encoder-decoder transformer, in the shape of "Attention Is All You Need".
Design choice: the encoder and decoder *layers* are hand-written so you can see
exactly which tensor is the query and which are the keys/values in each of the
three attention calls β that is the whole point of this project. The attention
operation itself uses nn.MultiheadAttention rather than a hand-rolled
softmax(QK^T/sqrt(d))V, because that part you have already studied and it is
where subtle numerical bugs hide. Swap it out later if you want to write the
scaled dot-product by hand; the surrounding code will not change.
THE THREE ATTENTION CALLS
-------------------------
1. Encoder self-attention Q,K,V = source NOT causal (bidirectional)
2. Decoder self-attention Q,K,V = target CAUSAL
3. Decoder cross-attention Q = target
K,V = encoder output NOT causal
Call 3 is the new piece. The decoder asks, at each target position, "which
source tokens are relevant to the word I am about to produce?" β so the query
comes from the decoder and the keys/values come from the encoder's final
output (`memory`). It is NOT causally masked in either direction: target
position 0 is allowed to look at the whole source sentence. Only *target*
positions are restricted, and only in call 2.
MASK CONVENTIONS (kept identical everywhere on purpose)
-------------------------------------------------------
key_padding_mask : (B, S) bool. True = that key position is <pad>, ignore it.
attn_mask : (T, T) bool. True = that (query, key) pair is FORBIDDEN.
Both are "True means blocked". PyTorch uses this polarity for
nn.MultiheadAttention; flipping it silently trains a model that attends only
to padding, which does not crash and does not produce NaNs β it just produces
garbage. Hence: one convention, stated once, used everywhere.
Padding must be masked on BOTH sides:
- source padding -> encoder self-attention keys (call 1)
- source padding -> cross-attention keys (call 3) <- easy to forget
- target padding -> decoder self-attention keys (call 2)
Pre-LN vs post-LN: the 2017 paper puts LayerNorm *after* each residual
addition. Pre-LN (norm on the branch input, as written below) is what most
modern implementations use because it trains stably without a carefully tuned
warmup. We still use warmup, but this makes the model far more forgiving if
you change the learning rate. Each stack gets a final LayerNorm, which pre-LN
requires.
============================================================================
THIS FILE'S PLACE IN THE CHAIN
============================================================================
model.py has ONE project-internal dependency: config.py (for ModelConfig).
It does not know about tokenizers, datasets, or files on disk β it is pure
architecture, operating on already-tokenized integer id tensors.
Who calls into this file:
- train.py's main(): cfg = ModelConfig(...); model = build_model(cfg, pad_id, device)
then, every step, run_epoch() calls model(src, tgt_in, ...) which routes
to TransformerTranslator.forward() below.
- decoding.py's greedy_decode()/beam_search_decode(): call model.encode(...)
ONCE, then model.decode(...) repeatedly in a loop β this is why encode()
and decode() are exposed as separate methods instead of only forward().
- evaluate.py's main(): build_model(cfg, ...) again from a saved
ModelConfig, then model.load_state_dict(ckpt["model"]) to restore trained weights.
- sanity_checks.py: imports build_model and causal_mask directly to probe
the model's masking behavior with hand-crafted inputs.
Internal call chain within this file (top to bottom of what actually runs
during a training step):
TransformerTranslator.forward(src, tgt_in, ...)
-> self.encode(src, ...)
-> self.embed(src) * sqrt(d_model) [token ids -> vectors]
-> self.pos_enc(x) [add position information]
-> for each layer in self.encoder_layers: EncoderLayer.forward(x, ...)
-> ATTENTION CALL 1 (self.self_attn, not masked causally)
-> self.ff(x) (FeedForward)
-> self.enc_norm(x) [final pre-LN norm]
=> returns `memory`, shape (B, S, d)
-> self.decode(tgt_in, memory, ...)
-> self.embed(tgt_in) * sqrt(d_model) [SAME embedding table as above]
-> self.pos_enc(x)
-> cm = causal_mask(T, device) [built once per decode() call]
-> for each layer in self.decoder_layers: DecoderLayer.forward(x, memory, cm, ...)
-> ATTENTION CALL 2 (self.self_attn, WITH causal_mask)
-> ATTENTION CALL 3 (self.cross_attn, query=decoder, key/value=memory)
-> self.ff(x)
-> self.dec_norm(x)
-> self.output_proj(x) [-> logits over the vocab]
"""
import math
import torch
import torch.nn as nn
from config import ModelConfig
# ---------------------------------------------------------------------------
# Positional encodings
# ---------------------------------------------------------------------------
class SinusoidalPositionalEncoding(nn.Module):
"""Fixed sin/cos encodings from the original paper.
PE[pos, 2i] = sin(pos / 10000^(2i/d))
PE[pos, 2i+1] = cos(pos / 10000^(2i/d))
No parameters, and it extrapolates (sort of) to lengths never seen in
training β which matters here because translation output length is not
bounded by anything the model saw.
Instantiated once inside TransformerTranslator.__init__ (via
build_positional_encoding below) and called from BOTH encode() and
decode() β the SAME module instance handles source and target positions,
since "position 3" means the same thing (a fixed sin/cos vector) in
either sequence.
"""
def __init__(self, d_model, max_len=512, dropout=0.1):
super().__init__()
self.dropout = nn.Dropout(dropout)
# Precompute the ENTIRE table up to max_len positions, once, at
# construction time β forward() below just slices into it.
pe = torch.zeros(max_len, d_model)
position = torch.arange(max_len, dtype=torch.float).unsqueeze(1) # (max_len, 1)
# Computed in log space for numerical stability.
# div[i] = 10000^(-2i/d_model), one value per even dimension index.
div = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
# Broadcasting: position (max_len,1) * div (d_model/2,) -> (max_len, d_model/2)
pe[:, 0::2] = torch.sin(position * div) # even dims: sin
pe[:, 1::2] = torch.cos(position * div) # odd dims: cos
# register_buffer: saved with the model, moved by .to(device), not a parameter.
# (i.e. it's part of state_dict()/checkpoint, and follows model.to(device),
# but torch.optim never updates it β it's fixed math, not learned.)
self.register_buffer("pe", pe.unsqueeze(0)) # (1, max_len, d)
def forward(self, x): # x: (B, L, d)
# Slice the precomputed table down to this sequence's actual length L,
# broadcast-add across the batch dimension, then apply dropout.
# Called from TransformerTranslator.encode()/decode() right after the
# scaled embedding lookup β see the module docstring's call chain.
return self.dropout(x + self.pe[:, : x.size(1)])
class LearnedPositionalEncoding(nn.Module):
"""An ordinary embedding table indexed by position. Ablation 6.
Selected via ModelConfig.pos_encoding="learned" instead of "sinusoidal" β
see build_positional_encoding() below, which is the only place that
chooses between this class and SinusoidalPositionalEncoding above.
Unlike the sinusoidal version, this one has trainable parameters
(self.emb.weight) and CANNOT extrapolate past max_len (see the assert).
"""
def __init__(self, d_model, max_len=512, dropout=0.1):
super().__init__()
self.dropout = nn.Dropout(dropout)
self.emb = nn.Embedding(max_len, d_model) # one learned vector per position 0..max_len-1
nn.init.normal_(self.emb.weight, mean=0.0, std=0.02)
self.max_len = max_len
def forward(self, x):
L = x.size(1)
assert L <= self.max_len, f"sequence of {L} exceeds learned max_len {self.max_len}"
# arange(L) -> position indices [0, 1, ..., L-1], broadcast to batch
# dim 1 so nn.Embedding can look each one up.
pos = torch.arange(L, device=x.device).unsqueeze(0) # (1, L)
return self.dropout(x + self.emb(pos))
def build_positional_encoding(kind, d_model, max_len, dropout):
"""Factory function: picks which positional-encoding class to instantiate
based on ModelConfig.pos_encoding. Called exactly once, from
TransformerTranslator.__init__, to build self.pos_enc."""
if kind == "sinusoidal":
return SinusoidalPositionalEncoding(d_model, max_len, dropout)
if kind == "learned":
return LearnedPositionalEncoding(d_model, max_len, dropout)
raise ValueError(f"unknown pos_encoding: {kind}")
# ---------------------------------------------------------------------------
# Layers
# ---------------------------------------------------------------------------
class FeedForward(nn.Module):
"""Position-wise FFN: d_model -> d_ff -> d_model, applied identically at
every position (it is where most of the parameters live).
"Position-wise" means: the SAME two Linear layers are applied
independently to every one of the L positions in (B, L, d_model) β there
is no mixing ACROSS positions here (that's what attention is for). This
is why nn.Linear works directly on a 3D tensor: PyTorch applies it to the
last dimension only, batching over everything else automatically.
Instantiated once per EncoderLayer and once per DecoderLayer below
(self.ff), so with 4+4 default layers there are 8 independent
FeedForward modules, each with its own weights.
"""
def __init__(self, d_model, d_ff, dropout):
super().__init__()
self.net = nn.Sequential(
nn.Linear(d_model, d_ff), # expand
nn.ReLU(), # nonlinearity β this is what makes the FFN
# do more than a linear projection
nn.Dropout(dropout),
nn.Linear(d_ff, d_model), # project back down to d_model so residual add works
)
def forward(self, x):
# Called from EncoderLayer.forward() and DecoderLayer.forward(), each
# time wrapped in a residual connection: x = x + self.ff(norm(x)).
return self.net(x)
class EncoderLayer(nn.Module):
"""self-attention (bidirectional) -> feed-forward, both residual + pre-LN.
Stacked cfg.n_encoder_layers times inside TransformerTranslator.__init__
(self.encoder_layers). TransformerTranslator.encode() runs input `x`
through each of these in sequence, output of one feeding the next.
"""
def __init__(self, d_model, n_heads, d_ff, dropout):
super().__init__()
# nn.MultiheadAttention does the actual softmax(QK^T/sqrt(d))V math
# (scaled dot-product attention across n_heads parallel heads) β
# see the module docstring for why this project doesn't hand-roll it.
# batch_first=True: tensors are (batch, seq, feature), matching every
# other shape convention in this file (the PyTorch default is
# (seq, batch, feature), which would be inconsistent here).
self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout,
batch_first=True)
self.ff = FeedForward(d_model, d_ff, dropout)
self.norm1 = nn.LayerNorm(d_model) # pre-LN before the attention sublayer
self.norm2 = nn.LayerNorm(d_model) # pre-LN before the feed-forward sublayer
self.drop = nn.Dropout(dropout) # applied to each sublayer's OUTPUT before the residual add
def forward(self, x, src_key_padding_mask=None):
# x: (B, S, d) β called from TransformerTranslator.encode(), once per
# layer in self.encoder_layers, x is this layer's input AND (after
# this function returns) the next layer's input.
# ATTENTION CALL 1: query = key = value = the source itself.
# No attn_mask -> every source token sees every other source token in
# both directions. That is the point of the encoder.
h = self.norm1(x) # pre-LN: normalize BEFORE feeding into attention
attn, _ = self.self_attn(h, h, h, # (query, key, value) β all three are `h`
key_padding_mask=src_key_padding_mask,
# ^ (B, S) bool, True = ignore this source
# position (it's <pad>) β this is
# dataset.py's "src_key_padding_mask" batch
# key, passed down unchanged from
# TransformerTranslator.forward()/encode().
need_weights=False) # we never inspect attention
# weights, so skip computing them (speed)
x = x + self.drop(attn) # residual connection: original x + (dropped) attention output
h = self.norm2(x) # pre-LN before the FFN sublayer
x = x + self.drop(self.ff(h)) # second residual connection
return x
class DecoderLayer(nn.Module):
"""causal self-attention -> cross-attention -> feed-forward.
Stacked cfg.n_decoder_layers times inside TransformerTranslator.__init__
(self.decoder_layers). TransformerTranslator.decode() runs decoder state
`x` AND the fixed `memory` (encoder output) through each of these in
sequence β `memory` itself is NOT modified by the decoder stack, only
`x` is.
"""
def __init__(self, d_model, n_heads, d_ff, dropout):
super().__init__()
self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout,
batch_first=True)
# ^ ATTENTION CALL 2's engine β target attends to target, causally masked below.
self.cross_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout,
batch_first=True)
# ^ ATTENTION CALL 3's engine β target attends to encoder memory.
# A SEPARATE nn.MultiheadAttention instance from self_attn above β
# different learned weights, even though the shapes look similar.
self.ff = FeedForward(d_model, d_ff, dropout)
self.norm1 = nn.LayerNorm(d_model) # before self-attention
self.norm2 = nn.LayerNorm(d_model) # before cross-attention
self.norm3 = nn.LayerNorm(d_model) # before feed-forward
self.drop = nn.Dropout(dropout)
def forward(self, x, memory, causal_mask=None,
tgt_key_padding_mask=None, memory_key_padding_mask=None):
# x : (B, T, d) decoder states (the target so far)
# memory : (B, S, d) encoder output (the whole source, always)
# Called from TransformerTranslator.decode(), once per layer in
# self.decoder_layers, with the SAME `memory` tensor passed to every
# layer (memory is computed once by encode() and reused).
# ATTENTION CALL 2 β decoder self-attention, CAUSAL.
# attn_mask is the (T, T) upper-triangular block: position i may not
# look at any j > i. Without it, the model can read the answer during
# teacher forcing and will look perfect in training and useless at
# inference, where the future genuinely does not exist yet.
h = self.norm1(x)
sa, _ = self.self_attn(h, h, h, # query=key=value=decoder states
attn_mask=causal_mask,
# ^ (T, T) bool β built by the standalone
# causal_mask() function (below in this
# file) and passed down from
# TransformerTranslator.decode(). Same
# causal_mask tensor is reused for every
# decoder layer within one decode() call.
key_padding_mask=tgt_key_padding_mask,
# ^ (B, T) bool β masks <pad> positions WITHIN
# the target itself (relevant during
# training with a padded batch; None during
# greedy/beam generation, where there are no
# pads yet in the growing prefix).
need_weights=False)
x = x + self.drop(sa)
# ATTENTION CALL 3 β CROSS-ATTENTION. The new piece.
# query <- decoder states (T positions: "what am I writing?")
# key <- encoder memory (S positions: "what's available?")
# value <- encoder memory (S positions: "what do I copy across?")
# Deliberately NO attn_mask: target position 0 may attend to the whole
# source, including its last word. Word order differs between Russian
# and English, so restricting this would make translation impossible.
# key_padding_mask IS passed: <pad> in the source must not be attended
# to. Forgetting it does not crash β it quietly costs a few BLEU.
h = self.norm2(x)
ca, _ = self.cross_attn(query=h, key=memory, value=memory,
key_padding_mask=memory_key_padding_mask,
# ^ (B, S) bool β this is the SAME
# src_key_padding_mask used in encoder call
# 1, just renamed "memory_key_padding_mask"
# here because from the decoder's point of
# view it's masking the KEYS (memory), not
# its own sequence.
need_weights=False)
x = x + self.drop(ca)
h = self.norm3(x)
x = x + self.drop(self.ff(h))
return x
# ---------------------------------------------------------------------------
# Full model
# ---------------------------------------------------------------------------
def causal_mask(size, device):
"""(size, size) bool, True = forbidden. Row i has True for all columns j>i.
[[F, T, T],
[F, F, T],
[F, F, F]]
Called from TransformerTranslator.decode() every time it runs (built
fresh from the CURRENT target length T each call β cheap, so no need to
cache it). Also called directly by sanity_checks.py's
test_causal_mask_shape() to verify this exact shape/polarity in isolation.
torch.triu(..., diagonal=1): keeps only the STRICTLY upper triangle
(diagonal=1 excludes the main diagonal itself), which is exactly "j > i".
That's why position i CAN attend to itself (j == i is False, i.e. allowed)
but not to anything after it.
"""
return torch.triu(torch.ones(size, size, dtype=torch.bool, device=device),
diagonal=1)
class TransformerTranslator(nn.Module):
"""The whole model. Constructed via build_model() below (never
instantiated directly outside this file/sanity_checks.py)."""
def __init__(self, cfg: ModelConfig, pad_id=0):
super().__init__()
self.cfg = cfg
self.pad_id = pad_id
self.d_model = cfg.d_model
# ONE embedding table, shared by:
# - the encoder input (Russian tokens)
# - the decoder input (English tokens)
# - the output projection (via weight tying, below)
# This is only possible because the BPE vocabulary is shared.
# padding_idx=pad_id: tells nn.Embedding that row `pad_id` should
# never receive a gradient update (kept pinned at whatever it's
# initialized to β see the explicit zeroing right below).
self.embed = nn.Embedding(cfg.vocab_size, cfg.d_model, padding_idx=pad_id)
nn.init.normal_(self.embed.weight, mean=0.0, std=cfg.d_model ** -0.5)
# ^ std=d_model^-0.5 matches the scale expected by the
# "* sqrt(d_model)" multiplication done in encode()/decode() below
# β see the comment there for why.
with torch.no_grad():
self.embed.weight[pad_id].zero_() # belt-and-suspenders: <pad>'s row starts at exactly 0
# Builds either SinusoidalPositionalEncoding or
# LearnedPositionalEncoding depending on cfg.pos_encoding β see
# build_positional_encoding() above.
self.pos_enc = build_positional_encoding(cfg.pos_encoding, cfg.d_model,
cfg.max_len, cfg.dropout)
# nn.ModuleList: a Python-list-like container that PyTorch still
# recognizes as holding submodules (a plain Python list would NOT
# register these layers' parameters, and .to(device)/state_dict()
# would silently miss them).
self.encoder_layers = nn.ModuleList([
EncoderLayer(cfg.d_model, cfg.n_heads, cfg.d_ff, cfg.dropout)
for _ in range(cfg.n_encoder_layers)])
self.decoder_layers = nn.ModuleList([
DecoderLayer(cfg.d_model, cfg.n_heads, cfg.d_ff, cfg.dropout)
for _ in range(cfg.n_decoder_layers)])
# Pre-LN needs a final norm at the top of each stack, otherwise the
# output of the last residual branch is never normalized.
self.enc_norm = nn.LayerNorm(cfg.d_model) # applied at the end of encode()
self.dec_norm = nn.LayerNorm(cfg.d_model) # applied at the end of decode(), before output_proj
self.output_proj = nn.Linear(cfg.d_model, cfg.vocab_size, bias=False)
# ^ turns each decoder position's d_model-dim vector into a
# vocab_size-dim vector of LOGITS (unnormalized scores per token) β
# this is what decode()'s return value actually is.
if cfg.tie_embeddings:
# Weight tying: the output layer reuses the embedding matrix, so
# logit[v] = <decoder_state, embedding[v]>. Saves vocab*d_model
# parameters (16000*256 = 4.1M here, a large share of the model)
# and usually helps a low-resource model generalize.
# NOTE: this line REPLACES self.output_proj.weight with a
# reference to the SAME tensor object as self.embed.weight β from
# this point on, training self.embed.weight via backprop also
# changes self.output_proj's behavior, and vice versa, because
# they are literally one tensor with two names.
self.output_proj.weight = self.embed.weight
# -- encoder ------------------------------------------------------------
def encode(self, src, src_key_padding_mask=None):
"""src: (B, S) -> memory: (B, S, d)
Called from: TransformerTranslator.forward() below (training path),
AND directly by decoding.py's greedy_decode()/beam_search_decode()
(generation path) β those call it ONCE per source sentence, then
reuse the returned `memory` across every decode() step, which is the
whole computational payoff of splitting encoder/decoder into
separate methods instead of only exposing forward().
"""
# Scale by sqrt(d_model) as in the paper: embeddings are initialized
# with std d^-0.5, so this puts them on the same scale as the
# positional encoding (which has unit-ish amplitude) rather than being
# swamped by it.
x = self.embed(src) * math.sqrt(self.d_model) # (B, S) ids -> (B, S, d) vectors, rescaled
x = self.pos_enc(x) # add positional information, (B, S, d)
for layer in self.encoder_layers:
x = layer(x, src_key_padding_mask=src_key_padding_mask)
# ^ EncoderLayer.forward() β see that class for ATTENTION CALL 1.
# Output of layer i becomes input to layer i+1.
return self.enc_norm(x) # final pre-LN normalization required by the pre-LN convention
# -- decoder ------------------------------------------------------------
def decode(self, tgt_in, memory, memory_key_padding_mask=None,
tgt_key_padding_mask=None):
"""tgt_in: (B, T) decoder INPUT (already shifted, starts with <bos>)
memory: (B, S, d) -> logits (B, T, vocab)
Called from: TransformerTranslator.forward() below (training path,
ONE call covering the whole target sequence at once thanks to
teacher forcing + the causal mask), AND from decoding.py's
greedy_decode()/beam_search_decode() in a LOOP β each generation
step re-runs decode() over the WHOLE prefix generated so far (see
decoding.py's module docstring "Efficiency note" for why this is
O(T^2) and deliberately not KV-cached).
"""
T = tgt_in.size(1)
x = self.embed(tgt_in) * math.sqrt(self.d_model) # SAME embedding table as encode() used
x = self.pos_enc(x) # SAME positional-encoding module as encode()
cm = causal_mask(T, tgt_in.device) # (T, T), built fresh each call β see causal_mask() above
for layer in self.decoder_layers:
x = layer(x, memory,
causal_mask=cm,
tgt_key_padding_mask=tgt_key_padding_mask,
memory_key_padding_mask=memory_key_padding_mask)
# ^ DecoderLayer.forward() β see that class for ATTENTION CALLS 2 & 3.
# `memory` is passed unchanged to every layer; only `x` accumulates.
x = self.dec_norm(x) # final pre-LN normalization
return self.output_proj(x) # (B, T, d) -> (B, T, vocab_size) logits
# -- both ---------------------------------------------------------------
def forward(self, src, tgt_in, src_key_padding_mask=None,
tgt_key_padding_mask=None):
"""Training forward pass.
src : (B, S) source token ids
tgt_in : (B, T) decoder input = target shifted right (<bos> w1 w2 w3)
returns: (B, T, vocab) logits, where logits[:, i] predicts the token
that should follow tgt_in[:, i]
Called from train.py's run_epoch()/validate_loss() as
`logits = model(src, tgt_in, src_key_padding_mask=src_mask, tgt_key_padding_mask=tgt_mask)`
β this is the ONLY place forward() is used; generation (decoding.py)
calls encode()/decode() directly instead, precisely to avoid
recomputing `memory` at every generation step (see encode()'s docstring).
"""
memory = self.encode(src, src_key_padding_mask)
return self.decode(tgt_in, memory,
memory_key_padding_mask=src_key_padding_mask,
# ^ reuses the SAME mask tensor for both "encoder
# self-attn keys" (inside encode(), call 1) and
# "cross-attn keys" (here, call 3) β both are
# masking the same source padding pattern.
tgt_key_padding_mask=tgt_key_padding_mask)
def num_parameters(self, trainable_only=True):
"""Called by train.py/evaluate.py purely for the printed
"X trainable parameters" log line."""
ps = self.parameters()
return sum(p.numel() for p in ps if p.requires_grad or not trainable_only)
def build_model(cfg: ModelConfig, pad_id=0, device="cpu"):
"""Thin factory wrapping TransformerTranslator(cfg).to(device).
Called from: train.py's main(), evaluate.py's main(), sanity_checks.py's
_model(), and this file's own __main__ block below. Centralizing
construction+device-placement here means every caller gets identical
behavior instead of each remembering to call .to(device) itself.
"""
model = TransformerTranslator(cfg, pad_id=pad_id).to(device)
return model
# ---------------------------------------------------------------------------
# Quick self-check: python model.py
# ---------------------------------------------------------------------------
if __name__ == "__main__":
# Runs only when this file is executed directly. Builds a tiny toy model
# (small vocab/d_model so it's instant) and runs one forward pass with
# RANDOM token ids (no real tokenizer/data needed) just to confirm the
# architecture is wired correctly end to end β shapes match, weight
# tying is really the same tensor. For actual correctness properties
# (causality, mask polarity) see sanity_checks.py instead.
torch.manual_seed(0)
cfg = ModelConfig(vocab_size=500, d_model=64, n_heads=4,
n_encoder_layers=2, n_decoder_layers=2, d_ff=128, dropout=0.0)
m = build_model(cfg)
m.eval() # disables dropout for a deterministic check
B, S, T = 3, 7, 5
src = torch.randint(4, 500, (B, S)) # random "sentences", ids 4..499 (avoiding specials 0-3)
tgt_in = torch.randint(4, 500, (B, T))
logits = m(src, tgt_in) # exercises forward() -> encode() -> decode()
print("logits:", tuple(logits.shape), "(expected", (B, T, cfg.vocab_size), ")")
print("parameters:", f"{m.num_parameters():,}")
print("embedding tied to output:", m.output_proj.weight is m.embed.weight)
# ^ `is` checks OBJECT IDENTITY, not just equal values β confirms the
# weight-tying assignment in __init__ really did share one tensor.
print("\nRun `python sanity_checks.py` for the causality / masking tests.")
|