File size: 18,868 Bytes
370f342
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import CosineAnnealingLR, LinearLR, SequentialLR
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data.distributed import DistributedSampler
import torch.distributed as dist
import numpy as np
from tqdm import tqdm
import json
import os
import argparse

# Import your existing components
from compressor_with_embeddings import Compressor, Decompressor, PrecomputedEmbeddingDataset
from final_flow_model import AMPFlowMatcherCFGConcat, SinusoidalTimeEmbedding
from cfg_dataset import CFGFlowDataset, create_cfg_dataloader

# ---------------- Configuration ----------------
ESM_DIM = 1280  # ESM-2 hidden dim (esm2_t33_650M_UR50D)
COMP_RATIO = 16  # compression factor
COMP_DIM = ESM_DIM // COMP_RATIO
MAX_SEQ_LEN = 50  # Actual sequence length from final_sequence_encoder.py
BATCH_SIZE = 64  # Per GPU batch size (256 total across 4 GPUs) - increased for faster training
EPOCHS = 5000  # Extended to 5K iterations for more comprehensive training (~50 minutes)
BASE_LR = 1e-4  # initial learning rate
LR_MIN = 2e-5  # minimum learning rate for cosine schedule
WARMUP_STEPS = 100  # Reduced warmup for shorter training

def setup_distributed():
    """Setup distributed training."""
    if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
        rank = int(os.environ["RANK"])
        world_size = int(os.environ['WORLD_SIZE'])
        local_rank = int(os.environ['LOCAL_RANK'])
    else:
        print('Not using distributed mode')
        return None, None, None

    torch.cuda.set_device(local_rank)
    dist.init_process_group(backend='nccl', init_method='env://')
    dist.barrier()
    
    return rank, world_size, local_rank

class AMPFlowTrainerMultiGPU:
    """
    Multi-GPU training pipeline for AMP generation using ProtFlow methodology.
    """
    
    def __init__(self, embeddings_path, cfg_data_path, rank, world_size, local_rank):
        self.rank = rank
        self.world_size = world_size
        self.local_rank = local_rank
        self.device = torch.device(f'cuda:{local_rank}')
        self.embeddings_path = embeddings_path
        self.cfg_data_path = cfg_data_path
        
        # Load ALL pre-computed embeddings (only on main process)
        if self.rank == 0:
            print(f"Loading ALL AMP embeddings from {embeddings_path}...")
            
            # Try to load the combined embeddings file first (FULL DATA)
            combined_path = os.path.join(embeddings_path, "all_peptide_embeddings.pt")
            
            if os.path.exists(combined_path):
                print(f"Loading combined embeddings from {combined_path} (FULL DATA)...")
                self.embeddings = torch.load(combined_path, map_location=self.device)
                print(f"βœ“ Loaded ALL embeddings: {self.embeddings.shape}")
            else:
                print("Combined embeddings file not found, loading individual files...")
                # Fallback to individual files
                import glob
                
                embedding_files = glob.glob(os.path.join(embeddings_path, "*.pt"))
                embedding_files = [f for f in embedding_files if not f.endswith('metadata.json') and not f.endswith('sequence_ids.json') and not f.endswith('all_peptide_embeddings.pt')]
                
                print(f"Found {len(embedding_files)} individual embedding files")
                
                # Load and stack all embeddings
                embeddings_list = []
                for file_path in embedding_files:
                    try:
                        embedding = torch.load(file_path)
                        if embedding.dim() == 2:  # (seq_len, hidden_dim)
                            embeddings_list.append(embedding)
                        else:
                            print(f"Warning: Skipping {file_path} - unexpected shape {embedding.shape}")
                    except Exception as e:
                        print(f"Warning: Could not load {file_path}: {e}")
                
                if not embeddings_list:
                    raise ValueError("No valid embeddings found!")
                
                self.embeddings = torch.stack(embeddings_list)
                print(f"Loaded {len(self.embeddings)} embeddings from individual files")
            
            # Compute normalization statistics
            print("Computing preprocessing statistics...")
            self._compute_preprocessing_stats()
        
        # Broadcast statistics to all processes
        if self.rank == 0:
            stats_tensor = torch.stack([
                self.stats['mean'], self.stats['std'], 
                self.stats['min'], self.stats['max']
            ]).to(self.device)
        else:
            stats_tensor = torch.zeros(4, ESM_DIM, device=self.device)
        
        dist.broadcast(stats_tensor, src=0)
        
        if self.rank != 0:
            self.stats = {
                'mean': stats_tensor[0],
                'std': stats_tensor[1],
                'min': stats_tensor[2],
                'max': stats_tensor[3]
            }
        
        # Initialize models
        self._initialize_models()
        
    def _compute_preprocessing_stats(self):
        """Compute preprocessing statistics (only on main process)."""
        # Flatten all embeddings
        flat = self.embeddings.view(-1, ESM_DIM)
        
        # 1. Z-score normalization statistics
        feat_mean = flat.mean(0)
        feat_std = flat.std(0) + 1e-8
        
        # 2. Truncation statistics (after z-score)
        z_score_normalized = (flat - feat_mean) / feat_std
        z_score_clamped = torch.clamp(z_score_normalized, -4, 4)
        
        # 3. Min-max normalization statistics (after truncation)
        feat_min = z_score_clamped.min(0)[0]
        feat_max = z_score_clamped.max(0)[0]
        
        # Store statistics
        self.stats = {
            'mean': feat_mean,
            'std': feat_std,
            'min': feat_min,
            'max': feat_max
        }
        
        # Save statistics for later use
        torch.save(self.stats, 'normalization_stats.pt')
        if self.rank == 0:
            print("βœ“ Preprocessing statistics computed and saved to normalization_stats.pt")
        
    def _initialize_models(self):
        """Initialize models for distributed training."""
        # Load pre-trained compressor and decompressor
        self.compressor = Compressor().to(self.device)
        self.decompressor = Decompressor().to(self.device)
        
        # Load trained weights
        self.compressor.load_state_dict(torch.load('final_compressor_model.pth', map_location=self.device))
        self.decompressor.load_state_dict(torch.load('final_decompressor_model.pth', map_location=self.device))
        
        # Initialize flow matching model with CFG
        self.flow_model = AMPFlowMatcherCFGConcat(
            hidden_dim=480,
            compressed_dim=COMP_DIM,
            n_layers=12,
            n_heads=16,
            dim_ff=3072,
            max_seq_len=25,
            use_cfg=True
        ).to(self.device)
        
        # Wrap with DDP
        self.flow_model = DDP(self.flow_model, device_ids=[self.local_rank], find_unused_parameters=True)
        
        if self.rank == 0:
            print("βœ“ Initialized models for distributed training")
            print(f"  - Flow model parameters: {sum(p.numel() for p in self.flow_model.parameters()):,}")
            print(f"  - Using {self.world_size} GPUs")
        
    def _preprocess_batch(self, batch):
        """Apply preprocessing to a batch of embeddings."""
        # 1. Z-score normalization
        h_norm = (batch - self.stats['mean'].to(batch.device)) / self.stats['std'].to(batch.device)
        
        # 2. Truncation (saturation) of outliers
        h_trunc = torch.clamp(h_norm, min=-4.0, max=4.0)
        
        # 3. Min-max normalization per dimension
        h_min = self.stats['min'].to(batch.device)
        h_max = self.stats['max'].to(batch.device)
        h_scaled = (h_trunc - h_min) / (h_max - h_min + 1e-8)
        h_scaled = torch.clamp(h_scaled, 0.0, 1.0)
        
        return h_scaled
    
    def train_flow_matching(self):
        """Train the flow matching model using distributed training."""
        if self.rank == 0:
            print("Step 3: Training Flow Matching model (Multi-GPU)...")
        
        # Create CFG dataset and distributed data loader
        try:
            # Try to use CFG dataset with real labels
            dataset = CFGFlowDataset(
                embeddings_path=self.embeddings_path,
                cfg_data_path=self.cfg_data_path,
                use_masked_labels=True,
                max_seq_len=MAX_SEQ_LEN,
                device=self.device
            )
            print("βœ“ Using CFG dataset with real labels")
        except Exception as e:
            print(f"Warning: Could not load CFG dataset: {e}")
            print("Falling back to random labels (not recommended for CFG)")
            # Fallback to original dataset with random labels
            dataset = PrecomputedEmbeddingDataset(self.embeddings_path)
        
        sampler = DistributedSampler(dataset, num_replicas=self.world_size, rank=self.rank)
        dataloader = DataLoader(dataset, batch_size=BATCH_SIZE, sampler=sampler, num_workers=4)
        
        # Initialize optimizer
        optimizer = optim.AdamW(
            self.flow_model.parameters(),
            lr=BASE_LR,
            betas=(0.9, 0.98),
            weight_decay=0.01,
            eps=1e-6
        )
        
        # LR scheduling: warmup -> cosine
        warmup_sched = LinearLR(optimizer, start_factor=1e-8, end_factor=1.0, total_iters=WARMUP_STEPS)
        cosine_sched = CosineAnnealingLR(optimizer, T_max=EPOCHS, eta_min=LR_MIN)
        scheduler = SequentialLR(optimizer, [warmup_sched, cosine_sched], milestones=[WARMUP_STEPS])
        
        # Training loop
        self.flow_model.train()
        total_steps = 0
        
        if self.rank == 0:
            print(f"Starting training for {EPOCHS} iterations with FULL DATA...")
            print(f"Total batch size: {BATCH_SIZE * self.world_size}")
            print(f"Steps per epoch: {len(dataloader)}")
            print(f"Total samples: {len(dataset):,}")
            print(f"Estimated time: ~30-45 minutes (using ALL data)")
        
        for epoch in range(EPOCHS):
            sampler.set_epoch(epoch)  # Ensure different shuffling per epoch
            
            for batch_idx, batch_data in enumerate(dataloader):
                # Handle different data formats
                if isinstance(batch_data, dict) and 'embeddings' in batch_data:
                    # CFG dataset format
                    x = batch_data['embeddings'].to(self.device)
                    labels = batch_data['labels'].to(self.device)
                else:
                    # Original dataset format - use random labels
                    x = batch_data.to(self.device)
                    labels = torch.randint(0, 3, (x.shape[0],), device=self.device)
                
                batch_size = x.shape[0]
                
                # Apply preprocessing
                x_processed = self._preprocess_batch(x)
                
                # Compress to latent space
                with torch.no_grad():
                    z = self.compressor(x_processed, self.stats)
                
                # Sample random noise
                eps = torch.randn_like(z)
                
                # Sample random time
                t = torch.rand(batch_size, device=self.device)
                
                # Interpolate between data and noise
                xt = t.view(batch_size, 1, 1) * eps + (1 - t.view(batch_size, 1, 1)) * z
                
                # Target vector field for rectified flow
                ut = eps - z
                
                # Use real labels from CFG dataset or random labels as fallback
                # labels are already defined above based on dataset type
                
                # Predict vector field with CFG
                vt_pred = self.flow_model(xt, t, labels=labels)
                
                # CFM loss
                loss = ((vt_pred - ut) ** 2).mean()
                
                # Backward pass
                optimizer.zero_grad()
                loss.backward()
                
                # Gradient clipping
                torch.nn.utils.clip_grad_norm_(self.flow_model.parameters(), 1.0)
                
                optimizer.step()
                scheduler.step()
                
                total_steps += 1
                
                # Logging (only on main process) - more frequent for short training
                if self.rank == 0 and total_steps % 10 == 0:
                    progress = (total_steps / EPOCHS) * 100
                    label_dist = torch.bincount(labels, minlength=3)
                    print(f"Step {total_steps}/{EPOCHS} ({progress:.1f}%): Loss = {loss.item():.6f}, LR = {scheduler.get_last_lr()[0]:.2e}, Labels: AMP={label_dist[0]}, Non-AMP={label_dist[1]}, Mask={label_dist[2]}")
                
                # Save checkpoint (only on main process) - more frequent for short training
                if self.rank == 0 and total_steps % 100 == 0:
                    self._save_checkpoint(total_steps, loss.item())
                
                # Validation (only on main process) - more frequent for short training
                if self.rank == 0 and total_steps % 200 == 0:
                    self._validate()
        
        # Save final model (only on main process)
        if self.rank == 0:
            self._save_checkpoint(total_steps, loss.item(), is_final=True)
            print("βœ“ Flow matching training completed!")
    
    def _save_checkpoint(self, step, loss, is_final=False):
        """Save training checkpoint (only on main process)."""
        # Get the underlying model from DDP
        model_state_dict = self.flow_model.module.state_dict()
        
        checkpoint = {
            'step': step,
            'flow_model_state_dict': model_state_dict,
            'loss': loss,
        }
        
        if is_final:
            torch.save(checkpoint, 'amp_flow_model_final_full_data.pth')
            print(f"βœ“ Final model saved: amp_flow_model_final_full_data.pth")
        else:
            torch.save(checkpoint, f'amp_flow_checkpoint_full_data_step_{step}.pth')
            print(f"βœ“ Checkpoint saved: amp_flow_checkpoint_full_data_step_{step}.pth")
    
    def _validate(self):
        """Validate the model by generating a few samples."""
        print("Generating validation samples...")
        self.flow_model.eval()
        
        with torch.no_grad():
            # Generate a few samples
            eps = torch.randn(4, 25, COMP_DIM, device=self.device)
            xt = eps.clone()
            
            # 25-step generation with CFG (using AMP label)
            labels = torch.full((4,), 0, device=self.device)  # 0 = AMP
            for step in range(25):
                t = torch.ones(4, device=self.device) * (1.0 - step/25)
                vt = self.flow_model(xt, t, labels=labels)
                dt = 1.0 / 25
                xt = xt + vt * dt
            
            # Decompress
            decompressed = self.decompressor(xt)
            
            # Apply reverse preprocessing
            m, s, mn, mx = self.stats['mean'].to(self.device), self.stats['std'].to(self.device), self.stats['min'].to(self.device), self.stats['max'].to(self.device)
            decompressed = decompressed * (mx - mn + 1e-8) + mn
            decompressed = decompressed * s + m
            
            print(f"  Generated samples shape: {decompressed.shape}")
            print(f"  Sample stats - Mean: {decompressed.mean():.4f}, Std: {decompressed.std():.4f}")
        
        self.flow_model.train()

def main():
    """Main training function with distributed setup."""
    parser = argparse.ArgumentParser()
    parser.add_argument('--local_rank', type=int, default=0)
    parser.add_argument('--cfg_data_path', type=str, default='/data2/edwardsun/flow_project/test_uniprot_processed/uniprot_processed_data.json',
                       help='Path to FULL CFG training data with real labels')
    args = parser.parse_args()
    
    # Setup distributed training
    rank, world_size, local_rank = setup_distributed()
    
    if rank == 0:
        print("=== Multi-GPU AMP Flow Matching Training Pipeline with FULL DATA ===")
        print("This implements the complete ProtFlow methodology for AMP generation.")
        print("Training for 5,000 iterations (~30-45 minutes) using ALL available data.")
        print()
        
        # Check if required files exist
        required_files = [
            'final_compressor_model.pth',
            'final_decompressor_model.pth',
            '/data2/edwardsun/flow_project/peptide_embeddings/'
        ]
        
        for file in required_files:
            if not os.path.exists(file):
                print(f"❌ Missing required file: {file}")
                print("Please ensure you have:")
                print("1. Run final_sequence_encoder.py to generate embeddings")
                print("2. Run compressor_with_embeddings.py to train compressor/decompressor")
                return
        
        # Check if CFG data exists
        if not os.path.exists(args.cfg_data_path):
            print(f"⚠️  CFG data not found: {args.cfg_data_path}")
            print("Training will use random labels (not recommended for CFG)")
            print("To use real labels, run uniprot_data_processor.py first")
        else:
            print(f"βœ“ CFG data found: {args.cfg_data_path}")
        
        print("βœ“ All required files found!")
        print()
    
    # Initialize trainer
    trainer = AMPFlowTrainerMultiGPU(
        embeddings_path='/data2/edwardsun/flow_project/peptide_embeddings/',
        cfg_data_path=args.cfg_data_path,
        rank=rank,
        world_size=world_size,
        local_rank=local_rank
    )
    
    # Train flow matching model
    trainer.train_flow_matching()
    
    if rank == 0:
        print("\n=== Multi-GPU Training Complete with FULL DATA ===")
        print("Your AMP flow matching model trained on ALL available data!")
        print("Next steps:")
        print("1. Test the model: python generate_amps.py")
        print("2. Compare performance with previous model")
        print("3. Implement reflow for 1-step generation")
        print("4. Add conditioning for toxicity (future project)")

if __name__ == "__main__":
    main()