File size: 21,261 Bytes
ff85374 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 |
"""
Intelligent Loss Functions for v6.2.0
Multi-objective loss with GPT-5 suggested improvements
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Dict, Optional, Tuple
import math
class IntelligentLoss(nn.Module):
"""
Comprehensive loss function for progressive splitting tokenizer
Combines multiple objectives with dynamic weighting
"""
def __init__(self, config: Optional[Dict] = None):
super().__init__()
# Default configuration
self.config = config or {}
# Special tokens (must match tokenizer)
self.PAD = 256
self.BOS = 257
self.EOS = 258
self.MASK = 259
# Loss components
self.reconstruction_loss = ReconstructionLoss(self.PAD)
self.compression_loss = CompressionLoss()
self.boundary_loss = BoundaryLoss()
self.language_loss = LanguageLoss()
self.consistency_loss = ConsistencyLoss()
# Dynamic weight adjustment
self.use_dynamic_weights = True
self.weight_history = {
'reconstruction': [],
'compression': [],
'boundary': [],
'language': [],
'consistency': []
}
def estimate_language_difficulty(self, targets: Dict) -> float:
"""Estimate language difficulty based on input characteristics"""
if 'input_ids' not in targets:
return 1.0
input_ids = targets['input_ids']
if input_ids.numel() == 0:
return 1.0
# Higher entropy = more complex language
unique_tokens = input_ids.unique().numel()
total_tokens = input_ids.numel()
diversity = min(1.0, (unique_tokens / total_tokens) * 2)
return diversity
def forward(self,
outputs: Dict[str, torch.Tensor],
targets: Dict[str, torch.Tensor],
weights: Optional[Dict[str, float]] = None) -> Dict[str, torch.Tensor]:
"""
Compute combined loss with all objectives
Args:
outputs: Model outputs dictionary
targets: Target values dictionary
weights: Optional weight overrides
Returns:
Dictionary with total loss and individual components
"""
losses = {}
# 1. Reconstruction loss (primary objective)
if 'logits' in outputs and 'input_ids' in targets:
losses['reconstruction'] = self.reconstruction_loss(
outputs['logits'],
targets['input_ids'],
targets.get('attention_mask')
)
# 2. Compression loss (encourage optimal compression)
if 'compression_ratio' in outputs:
losses['compression'] = self.compression_loss(
outputs['compression_ratio'],
outputs.get('num_tokens')
)
# 3. Boundary loss (learn meaningful boundaries)
if 'boundaries' in outputs and 'boundary_targets' in targets:
losses['boundary'] = self.boundary_loss(
outputs['boundaries'],
targets['boundary_targets'],
targets.get('boundary_mask')
)
# 4. Language loss (language identification/clustering)
if 'language_clusters' in outputs and 'language_targets' in targets:
losses['language'] = self.language_loss(
outputs['language_clusters'],
targets['language_targets']
)
# 5. Consistency loss (encoder-decoder consistency)
if 'encoder_hidden' in outputs and 'decoder_hidden' in outputs:
losses['consistency'] = self.consistency_loss(
outputs['encoder_hidden'],
outputs['decoder_hidden']
)
# Apply weights (either provided or dynamic)
if weights is None and self.use_dynamic_weights:
weights = self.compute_dynamic_weights(losses)
elif weights is None:
weights = {
'reconstruction': 1.0,
'compression': 1.0,
'boundary': 1.0,
'language': 0.5,
'consistency': 0.5
}
# Weighted sum
total_loss = torch.tensor(0.0, device=next(iter(losses.values())).device)
for key, loss in losses.items():
weight = weights.get(key, 1.0)
total_loss = total_loss + weight * loss
losses[f'{key}_weighted'] = weight * loss
losses['total'] = total_loss
# Update weight history
for key in self.weight_history:
if key in losses:
self.weight_history[key].append(losses[key].item())
return losses
def compute_dynamic_weights(self, losses: Dict[str, torch.Tensor]) -> Dict[str, float]:
"""
Dynamically adjust weights based on loss magnitudes and progress
GPT-5 suggestion: balance loss magnitudes for stable training
"""
weights = {}
eps = 1e-8 # GPT fix: prevent division by zero
# Get loss magnitudes with NaN protection
magnitudes = {}
for k, v in losses.items():
if torch.isnan(v) or torch.isinf(v):
magnitudes[k] = 1.0 # Default safe value
else:
magnitudes[k] = v.item()
# Compute relative scales (GPT fix: add epsilon)
avg_magnitude = max(eps, sum(magnitudes.values()) / len(magnitudes))
for key, magnitude in magnitudes.items():
# Inverse scaling to balance magnitudes (GPT fix: add epsilon)
weights[key] = avg_magnitude / max(eps, magnitude)
# Dynamic adjustment based on loss ratios
if 'reconstruction' in magnitudes and 'compression' in magnitudes:
recon_loss = magnitudes['reconstruction']
comp_loss = magnitudes['compression']
# If reconstruction loss is too high relative to compression
if recon_loss > comp_loss * 10:
# Drastically reduce compression pressure
weights['compression'] *= 0.1
weights['reconstruction'] *= 5.0
elif recon_loss > comp_loss * 5:
# Moderate adjustment
weights['compression'] *= 0.5
weights['reconstruction'] *= 2.0
elif recon_loss < comp_loss * 0.5:
# Good reconstruction, can push compression
weights['compression'] *= 2.0
weights['reconstruction'] *= 0.5
# Normalize weights to prevent explosion
total_weight = sum(weights.values())
if total_weight > 0:
weights = {k: min(10.0, v / total_weight * len(weights)) for k, v in weights.items()}
return weights
class ReconstructionLoss(nn.Module):
"""
Cross-entropy loss for sequence reconstruction
With label smoothing and focal loss options
"""
def __init__(self, pad_token: int = 256, label_smoothing: float = 0.1):
super().__init__()
self.pad_token = pad_token
self.label_smoothing = label_smoothing
self.focal_alpha = 0.25
self.focal_gamma = 2.0
self.use_focal = False
def forward(self,
logits: torch.Tensor,
targets: torch.Tensor,
mask: Optional[torch.Tensor] = None) -> torch.Tensor:
"""
Compute reconstruction loss
Args:
logits: [batch, seq_len, vocab_size]
targets: [batch, seq_len]
mask: [batch, seq_len] attention mask
"""
batch_size, seq_len, vocab_size = logits.shape
# Reshape for loss computation
logits_flat = logits.reshape(-1, vocab_size)
targets_flat = targets.reshape(-1)
if self.use_focal:
# Focal loss for hard examples
ce_loss = F.cross_entropy(logits_flat, targets_flat, reduction='none')
pt = torch.exp(-ce_loss)
focal_loss = self.focal_alpha * (1 - pt) ** self.focal_gamma * ce_loss
if mask is not None:
mask_flat = mask.reshape(-1)
focal_loss = focal_loss * mask_flat
loss = focal_loss.sum() / mask_flat.sum()
else:
loss = focal_loss.mean()
else:
# Standard cross-entropy with label smoothing
if mask is not None:
mask_flat = mask.reshape(-1).bool() # GPT fix: ensure bool dtype
loss = F.cross_entropy(
logits_flat[mask_flat],
targets_flat[mask_flat],
ignore_index=self.pad_token,
label_smoothing=self.label_smoothing
)
else:
loss = F.cross_entropy(
logits_flat,
targets_flat,
ignore_index=self.pad_token,
label_smoothing=self.label_smoothing
)
return loss
class CompressionLoss(nn.Module):
"""
Aggressive compression loss - push for high compression
Must beat existing tokenizers (4 bytes/token = 4:1)
"""
def __init__(self):
super().__init__()
# Dynamic compression based on token count
# 1 token = 48:1, 2 = 24:1, 3 = 16:1, 4 = 12:1
self.min_ratio = 12.0 # 4 tokens (worst case, still 3x better than BPE)
self.target_ratio = 24.0 # 2 tokens (optimal balance)
self.max_ratio = 48.0 # 1 token (best compression)
def forward(self,
compression_ratio: torch.Tensor,
num_tokens: Optional[torch.Tensor] = None) -> torch.Tensor:
"""
Compute compression loss (GPT fix: fully vectorized)
Args:
compression_ratio: Current compression ratio (scalar or batch)
num_tokens: Number of tokens used (for additional penalty)
"""
# Ensure tensor (GPT fix: handle device properly)
if not torch.is_tensor(compression_ratio):
device = num_tokens.device if torch.is_tensor(num_tokens) else torch.device('cpu')
compression_ratio = torch.tensor(compression_ratio, dtype=torch.float32, device=device)
# Aggressive compression enforcement
# MUST achieve at least 16:1 to be viable
if compression_ratio < self.min_ratio:
# Moderate penalty for falling below minimum (reduced for stability)
under_loss = ((self.min_ratio - compression_ratio) / self.min_ratio) * 0.5
else:
under_loss = torch.tensor(0.0, dtype=compression_ratio.dtype, device=compression_ratio.device)
# Reward getting close to target (24:1)
if self.min_ratio <= compression_ratio < self.target_ratio:
# Encourage reaching target
target_loss = ((self.target_ratio - compression_ratio) / self.target_ratio) * 0.5
elif compression_ratio >= self.target_ratio:
# Excellent compression - small reward for going higher
target_loss = -0.1 * torch.log(compression_ratio / self.target_ratio + 1.0)
else:
target_loss = torch.tensor(0.0, dtype=compression_ratio.dtype, device=compression_ratio.device)
# Only mild penalty for extreme compression (>48:1)
if compression_ratio > self.max_ratio:
over_loss = ((compression_ratio - self.max_ratio) / self.max_ratio) * 0.2
else:
over_loss = torch.tensor(0.0, dtype=compression_ratio.dtype, device=compression_ratio.device)
loss = under_loss + target_loss + over_loss
# Additional penalty based on token count (GPT fix: vectorized)
if num_tokens is not None:
if not torch.is_tensor(num_tokens):
num_tokens = torch.tensor(num_tokens, dtype=torch.float32, device=compression_ratio.device)
token_penalty = 0.1 * torch.clamp(num_tokens - 8, min=0.0) ** 2
loss = loss + token_penalty
return loss.mean() if loss.dim() > 0 else loss
class BoundaryLoss(nn.Module):
"""
Learn meaningful chunk boundaries
Combines multiple boundary objectives
"""
def __init__(self):
super().__init__()
self.bce_loss = nn.BCEWithLogitsLoss(reduction='none')
def forward(self,
predicted: torch.Tensor,
target: torch.Tensor,
mask: Optional[torch.Tensor] = None) -> torch.Tensor:
"""
Compute boundary loss
Args:
predicted: [batch, seq_len, boundary_classes] predicted boundaries
target: [batch, seq_len, boundary_classes] target boundaries
mask: [batch, seq_len] valid positions mask
"""
# Binary cross-entropy for boundary prediction
loss = self.bce_loss(predicted, target.float())
if mask is not None:
# Apply mask
mask_expanded = mask.unsqueeze(-1).expand_as(loss)
loss = loss * mask_expanded
loss = loss.sum() / mask_expanded.sum()
else:
loss = loss.mean()
# Add regularization for boundary sparsity
# (boundaries should be relatively rare)
boundary_probs = torch.sigmoid(predicted)
sparsity_loss = 0.01 * boundary_probs.mean()
# Add smoothness regularization
# (boundaries should be somewhat smooth/continuous)
if predicted.size(1) > 1:
diff = predicted[:, 1:] - predicted[:, :-1]
smoothness_loss = 0.01 * (diff ** 2).mean()
else:
smoothness_loss = 0.0
total_loss = loss + sparsity_loss + smoothness_loss
return total_loss
class LanguageLoss(nn.Module):
"""
Language identification/clustering loss
Supports both classification and clustering objectives
"""
def __init__(self, num_languages: int = 128, temperature: float = 0.07):
super().__init__()
self.num_languages = num_languages
self.temperature = temperature
# For supervised language classification
self.ce_loss = nn.CrossEntropyLoss()
def forward(self,
predicted: torch.Tensor,
target: torch.Tensor,
mode: str = 'classification') -> torch.Tensor:
"""
Compute language loss
Args:
predicted: [batch, seq_len, num_languages] or [batch, num_languages]
target: Language labels or cluster assignments
mode: 'classification' or 'clustering'
"""
if mode == 'classification':
# Standard classification loss
if predicted.dim() == 3:
# Sequence-level predictions
batch_size, seq_len, _ = predicted.shape
predicted = predicted.reshape(-1, self.num_languages)
target = target.reshape(-1)
loss = self.ce_loss(predicted, target)
elif mode == 'clustering':
# Contrastive clustering loss (similar to SimCLR)
# Normalize embeddings
predicted = F.normalize(predicted, dim=-1)
# Compute similarity matrix
sim_matrix = torch.matmul(predicted, predicted.t()) / self.temperature
# Create labels (assuming batch contains similar samples)
batch_size = predicted.size(0)
labels = torch.arange(batch_size, device=predicted.device)
# Contrastive loss
loss = F.cross_entropy(sim_matrix, labels)
else:
raise ValueError(f"Unknown mode: {mode}")
return loss
class ConsistencyLoss(nn.Module):
"""
Ensure consistency between encoder and decoder representations
GPT-5 suggestion: helps with training stability
"""
def __init__(self, margin: float = 0.5):
super().__init__()
self.margin = margin
def forward(self,
encoder_hidden: torch.Tensor,
decoder_hidden: torch.Tensor) -> torch.Tensor:
"""
Compute consistency loss between encoder and decoder
Args:
encoder_hidden: [batch, seq_len, hidden_dim]
decoder_hidden: [batch, seq_len, hidden_dim]
"""
# Ensure same shape
if encoder_hidden.shape != decoder_hidden.shape:
# Align sequence lengths if different
min_len = min(encoder_hidden.size(1), decoder_hidden.size(1))
encoder_hidden = encoder_hidden[:, :min_len]
decoder_hidden = decoder_hidden[:, :min_len]
# L2 distance
l2_loss = F.mse_loss(encoder_hidden, decoder_hidden)
# Cosine similarity loss
encoder_norm = F.normalize(encoder_hidden, dim=-1)
decoder_norm = F.normalize(decoder_hidden, dim=-1)
cosine_sim = (encoder_norm * decoder_norm).sum(dim=-1)
cosine_loss = 1.0 - cosine_sim.mean()
# Combined loss
loss = l2_loss + 0.5 * cosine_loss
return loss
class AdaptiveLossScheduler:
"""
Dynamically adjust loss weights during training
Based on training progress and performance
"""
def __init__(self, config: Dict):
self.config = config
self.current_phase = 0
self.phase_epochs = [30, 60, 100] # Phase transition points
# Define phase-specific weights
self.phase_weights = [
# Phase 1: Boundary mastery
{
'reconstruction': 2.0,
'compression': 0.5,
'boundary': 3.0,
'language': 0.5,
'consistency': 0.5
},
# Phase 2: Compression focus
{
'reconstruction': 2.0,
'compression': 3.0,
'boundary': 1.0,
'language': 1.0,
'consistency': 1.0
},
# Phase 3: Balanced optimization
{
'reconstruction': 3.0,
'compression': 2.0,
'boundary': 1.0,
'language': 1.0,
'consistency': 1.5
}
]
def get_weights(self, epoch: int, metrics: Optional[Dict] = None) -> Dict[str, float]:
"""
Get current loss weights based on training phase
Args:
epoch: Current training epoch
metrics: Optional performance metrics for adaptive adjustment
"""
# Determine current phase
for i, phase_end in enumerate(self.phase_epochs):
if epoch <= phase_end:
self.current_phase = i
break
weights = self.phase_weights[self.current_phase].copy()
# Adaptive adjustments based on metrics
if metrics:
# If reconstruction is poor, increase its weight
if metrics.get('reconstruction_accuracy', 1.0) < 0.9:
weights['reconstruction'] *= 1.5
# If compression is off target, adjust weight
compression_ratio = metrics.get('compression_ratio', 16.0)
if compression_ratio < 8.0 or compression_ratio > 20.0:
weights['compression'] *= 1.5
return weights
if __name__ == "__main__":
# Test losses
print("Testing Intelligent Loss Functions")
# Create loss module
loss_fn = IntelligentLoss()
# Create dummy data
batch_size = 2
seq_len = 48
vocab_size = 260
hidden_dim = 1280
outputs = {
'logits': torch.randn(batch_size, seq_len, vocab_size),
'compression_ratio': torch.tensor(16.0),
'num_tokens': torch.tensor(3),
'boundaries': torch.randn(batch_size, seq_len, 4),
'language_clusters': torch.randn(batch_size, 128),
'encoder_hidden': torch.randn(batch_size, seq_len, hidden_dim),
'decoder_hidden': torch.randn(batch_size, seq_len, hidden_dim)
}
targets = {
'input_ids': torch.randint(0, 256, (batch_size, seq_len)),
'attention_mask': torch.ones(batch_size, seq_len),
'boundary_targets': torch.zeros(batch_size, seq_len, 4),
'language_targets': torch.randint(0, 128, (batch_size,))
}
# Compute losses
losses = loss_fn(outputs, targets)
print("\nLoss components:")
for key, value in losses.items():
if isinstance(value, torch.Tensor):
print(f" {key}: {value.item():.4f}")
# Test adaptive scheduler
scheduler = AdaptiveLossScheduler({})
print("\nPhase weights:")
for epoch in [10, 40, 70]:
weights = scheduler.get_weights(epoch)
print(f" Epoch {epoch}: {weights}") |