Move the PDC release into CupOFMoCA-PDC/

06cc59e verified 8 days ago

17.2 kB

import argparse
import math
import os
from functools import partial
from collections import Counter

import torch
import torch.nn as nn
import torch.nn.functional as F
from datasets import load_from_disk
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from pytorch_lightning.callbacks import ModelCheckpoint, LearningRateMonitor
from pytorch_lightning.loggers import WandbLogger
from pytorch_lightning.strategies import DDPStrategy
from rdkit import Chem

from smiles.smiles_tokenizer.my_tokenizers import SMILES_SPE_Tokenizer
from smiles.evaluation.peptide_analyzer import PeptideAnalyzer
from smiles.models import dataloading_for_dynamic_batching as dynamic_dataloader


class RotaryPositionalEmbedding(nn.Module):
    def __init__(self, dim, max_position_embeddings=2048, base=10000):
        super().__init__()
        self.dim = dim
        self.max_position_embeddings = max_position_embeddings
        self.base = base

        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
        self.register_buffer("inv_freq", inv_freq)

    def forward(self, x, seq_len=None):
        if seq_len is None:
            seq_len = x.shape[1]
        
        t = torch.arange(seq_len, device=x.device, dtype=self.inv_freq.dtype)
        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
        emb = torch.cat((freqs, freqs), dim=-1)
        
        cos_emb = emb.cos()[None, :, :]
        sin_emb = emb.sin()[None, :, :]
        
        return cos_emb, sin_emb

def rotate_half(x):
    x1 = x[..., : x.shape[-1] // 2]
    x2 = x[..., x.shape[-1] // 2 :]
    return torch.cat((-x2, x1), dim=-1)

def apply_rotary_pos_emb(q, k, cos, sin):
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed

# --- Model Architecture with RoPE ---
def modulate(x, shift, scale):
    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)

class TimestepEmbedder(nn.Module):
    def __init__(self, hidden_size):
        super().__init__()
        self.mlp = nn.Sequential(
            nn.Linear(1, hidden_size, bias=True),
            nn.SiLU(),
            nn.Linear(hidden_size, hidden_size, bias=True),
        )

    def forward(self, t):
        return self.mlp(t.unsqueeze(-1))

class MultiHeadAttentionWithRoPE(nn.Module):
    def __init__(self, hidden_size, n_heads):
        super().__init__()
        self.hidden_size = hidden_size
        self.n_heads = n_heads
        self.head_dim = hidden_size // n_heads
        
        assert self.head_dim * n_heads == hidden_size, "hidden_size must be divisible by n_heads"
        
        self.q_proj = nn.Linear(hidden_size, hidden_size, bias=False)
        self.k_proj = nn.Linear(hidden_size, hidden_size, bias=False)
        self.v_proj = nn.Linear(hidden_size, hidden_size, bias=False)
        self.out_proj = nn.Linear(hidden_size, hidden_size)
        
        self.rope = RotaryPositionalEmbedding(self.head_dim)
        
    def forward(self, x):
        batch_size, seq_len, hidden_size = x.shape
        
        q = self.q_proj(x).view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
        k = self.k_proj(x).view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
        v = self.v_proj(x).view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
        
        cos, sin = self.rope(q, seq_len)
        q, k = apply_rotary_pos_emb(q, k, cos, sin)
        
        scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
        attn_weights = F.softmax(scores, dim=-1)
        attn_output = torch.matmul(attn_weights, v)
        
        attn_output = attn_output.transpose(1, 2).contiguous().view(batch_size, seq_len, hidden_size)
        output = self.out_proj(attn_output)
        
        return output

class DiTBlock(nn.Module):
    def __init__(self, hidden_size, n_heads):
        super().__init__()
        self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        self.attn = MultiHeadAttentionWithRoPE(hidden_size, n_heads)
        self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        self.mlp = nn.Sequential(
            nn.Linear(hidden_size, 4 * hidden_size),
            nn.GELU(),
            nn.Linear(4 * hidden_size, hidden_size)
        )
        self.adaLN_modulation = nn.Sequential(
            nn.SiLU(),
            nn.Linear(hidden_size, 6 * hidden_size, bias=True)
        )

    def forward(self, x, c):
        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1)
        x_norm1 = modulate(self.norm1(x), shift_msa, scale_msa)
        attn_output = self.attn(x_norm1)
        x = x + gate_msa.unsqueeze(1) * attn_output
        x_norm2 = modulate(self.norm2(x), shift_mlp, scale_mlp)
        mlp_output = self.mlp(x_norm2)
        x = x + gate_mlp.unsqueeze(1) * mlp_output
        return x

class MDLM(nn.Module):
    def __init__(self, vocab_size, model_dim, n_heads, n_layers):
        super().__init__()
        self.vocab_size = vocab_size
        self.model_dim = model_dim
        self.mask_token_id = vocab_size

        self.token_embedder = nn.Embedding(vocab_size, model_dim)
        self.time_embedder = TimestepEmbedder(model_dim)

        self.transformer_blocks = nn.ModuleList([
            DiTBlock(model_dim, n_heads) for _ in range(n_layers)
        ])

        self.final_norm = nn.LayerNorm(model_dim)
        self.lm_head = nn.Linear(model_dim, vocab_size)

        self.apply(self._init_weights)

    def _init_weights(self, module):
        if isinstance(module, (nn.Linear, nn.Embedding)):
            module.weight.data.normal_(mean=0.0, std=0.02)
            if isinstance(module, nn.Linear) and module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.LayerNorm):
            if module.bias is not None:
                module.bias.data.zero_()
            if module.weight is not None:
                module.weight.data.fill_(1.0)

    def forward(self, x, t):
        x_embed = self.token_embedder(x)
        t_embed = self.time_embedder(t)
        for block in self.transformer_blocks:
            x_embed = block(x_embed, t_embed)
        x_embed = self.final_norm(x_embed)
        logits = self.lm_head(x_embed)
        return logits

# --- PyTorch Lightning Module ---
class MDLMLightningModule(pl.LightningModule):
    def __init__(self, args, tokenizer):
        super().__init__()
        self.save_hyperparameters(ignore=['tokenizer'])
        self.args = args
        self.tokenizer = tokenizer
        self.peptide_analyzer = PeptideAnalyzer()
        
        # Initialize model
        self.model = MDLM(
            vocab_size=tokenizer.vocab_size,
            model_dim=args.model_dim,
            n_heads=args.n_heads,
            n_layers=args.n_layers
        )
        
        # For tracking steps
        self.automatic_optimization = True

        self.validation_step_outputs = []
        
    def forward(self, x, t):
        return self.model(x, t)

    def _compute_invalid_loss(self, logits):
        batch_token_ids = torch.argmax(logits, dim=-1)  # (batch_size, seq_length)

        sampled_sequences = self.tokenizer.batch_decode(batch_token_ids)
        
        penalties = torch.tensor(
            [1 if not self.peptide_analyzer.is_peptide(seq) else 0 for seq in sampled_sequences],
            dtype=torch.float32,
            device=self.device
        )  
        sampled_probs = torch.softmax(logits, dim=-1).gather(dim=-1, index=batch_token_ids.unsqueeze(-1)).squeeze(-1).to(self.device)

        scaled_penalty = penalties[:, None] * sampled_probs # (batch_size, seq_length)

        return scaled_penalty

    def _loss(self, logits, x_1, attn_mask):
        # Standard cross-entropy loss
        ce_loss = F.cross_entropy(
            logits.view(-1, self.model.vocab_size), 
            x_1.view(-1), 
            reduction='none'
        ).view(x_1.shape[0], -1)
        # ce_loss = (ce_loss * attn_mask).sum() / attn_mask.sum()
        
        # validity_weight = self.args.validity_weight * min(1.0, (self.current_epoch + 1) / self.trainer.max_epochs)
        
        invalid_loss = self._compute_invalid_loss(logits) # (batch_size, seq_length)
        
        loss = ce_loss + self.args.validity_weight * invalid_loss
        nlls = loss * attn_mask

        num_tokens = attn_mask.sum()
        batch_nll = nlls.sum()
        token_nll = batch_nll / num_tokens

        return token_nll, (ce_loss*attn_mask).sum() / num_tokens, (invalid_loss*attn_mask).sum() / num_tokens

    def training_step(self, batch, batch_idx):
        x_1 = batch['input_ids'].clone().detach().to(self.device)
        attn_mask = batch['attention_mask'].clone().detach().to(self.device)
        bond_mask = batch['bond_mask'].clone().detach().to(self.device).bool()
        batch_size, _ = x_1.shape

        # ReDi approach: random start -> target
        x_0 = torch.randint(0, self.model.vocab_size, x_1.shape, device=self.device)
        t_continuous = torch.rand(batch_size, device=self.device)
        # mask = torch.rand(x_1.shape, device=self.device) < t_continuous.view(-1, 1)
        # x_t = torch.where(mask, x_1, x_0)

        peptide_bond_prob = t_continuous.view(-1, 1) ** self.args.gamma  # slower increase
        non_peptide_prob = t_continuous.view(-1, 1)            # linear increase

        masking_prob = torch.where(bond_mask, peptide_bond_prob, non_peptide_prob)
        mask = torch.rand(x_1.shape, device=self.device) < masking_prob
        x_t = torch.where(mask, x_1, x_0)

        logits = self.model(x_t, t_continuous)
        
        token_nll, ce_loss, invalid_loss = self._loss(logits, x_1, attn_mask)
        
        # Logging
        self.log('train/token_nll', token_nll.item(), on_step=True, on_epoch=True, prog_bar=True, batch_size=x_1.size(0), sync_dist=True)
        self.log('train/ce_loss', ce_loss.item(), on_step=True, on_epoch=True, batch_size=x_1.size(0), sync_dist=True)
        self.log('train/invalid_loss', invalid_loss.item(), on_step=True, on_epoch=True, batch_size=x_1.size(0), sync_dist=True)
        # self.log('train/validity_weight', validity_weight, on_step=False, on_epoch=True, batch_size=x_1.size(0))
        
        return token_nll
    
    def validation_step(self, batch, batch_idx):
        x_1 = batch['input_ids'].clone().detach().to(self.device)
        attn_mask = batch['attention_mask'].clone().detach().to(self.device)
        bond_mask = batch['bond_mask'].clone().detach().to(self.device).bool()
        batch_size, _ = x_1.shape

        # ReDi approach: random start -> target
        x_0 = torch.randint(0, self.model.vocab_size, x_1.shape, device=self.device)
        t_continuous = torch.rand(batch_size, device=self.device)

        peptide_bond_prob = t_continuous.view(-1, 1) ** self.args.gamma  # slower increase
        non_peptide_prob = t_continuous.view(-1, 1)            # linear increase

        masking_prob = torch.where(bond_mask, peptide_bond_prob, non_peptide_prob)
        mask = torch.rand(x_1.shape, device=self.device) < masking_prob
        x_t = torch.where(mask, x_1, x_0)
        
        logits = self.model(x_t, t_continuous)
        
        token_nll, ce_loss, invalid_loss = self._loss(logits, x_1, attn_mask)
        self.log('val/token_nll', token_nll.item(), on_step=True, on_epoch=True, prog_bar=True, batch_size=x_1.size(0), sync_dist=True)
        self.log('val/ce_loss', ce_loss.item(), on_step=True, on_epoch=True, batch_size=x_1.size(0), sync_dist=True)
        self.log('val/invalid_loss', invalid_loss.item(), on_step=True, on_epoch=True, batch_size=x_1.size(0), sync_dist=True)

    def configure_optimizers(self):
        optimizer = AdamW(
            self.parameters(), 
            lr=self.args.learning_rate, 
            weight_decay=self.args.weight_decay
        )
        
        # Calculate total steps
        if hasattr(self.trainer, 'estimated_stepping_batches'):
            num_training_steps = self.trainer.estimated_stepping_batches
        else:
            # Fallback calculation
            num_training_steps = len(self.trainer.datamodule.train_dataloader()) * self.trainer.max_epochs
        
        warmup_steps = int(num_training_steps * 0.1)
        
        def lr_lambda(current_step):
            if current_step < warmup_steps:
                lr_range = self.args.learning_rate - (self.args.learning_rate * 0.1)
                lr = (self.args.learning_rate * 0.1) + lr_range * (current_step / warmup_steps)
                return lr / self.args.learning_rate
            else:
                progress = (current_step - warmup_steps) / (num_training_steps - warmup_steps)
                cosine_decay = 0.5 * (1.0 + math.cos(math.pi * progress))
                lr_range = self.args.learning_rate - (self.args.learning_rate * 0.1)
                lr = (self.args.learning_rate * 0.1) + lr_range * cosine_decay
                return lr / self.args.learning_rate

        scheduler = LambdaLR(optimizer, lr_lambda)
        
        return {
            "optimizer": optimizer,
            "lr_scheduler": {
                "scheduler": scheduler,
                "interval": "step",
                "frequency": 1,
            },
        }


# --- Main Execution ---
def main(args):
    # Set up checkpoint directory
    checkpoint_dir = (args.checkpoint_dir + 
                     f"correct_lr{args.learning_rate}_wd{args.weight_decay}_layer{args.n_layers}_"
                     f"head{args.n_heads}_valweight{args.validity_weight}")
    print(f"Saving to {checkpoint_dir}")
    os.makedirs(checkpoint_dir, exist_ok=True)

    print("Loading tokenizer...")
    tokenizer = SMILES_SPE_Tokenizer('./smiles/smiles_tokenizer/old_vocab.txt',
                                     './smiles/smiles_tokenizer/old_splits.txt')
    print(f"Tokenizer loaded. Vocab size: {tokenizer.vocab_size}")

    # Initialize data module
    data_module = dynamic_dataloader.CustomDataModule('./data/11M_smiles_old_tokenizer_no_limit/', tokenizer)
    
    # Initialize model
    model = MDLMLightningModule(args, tokenizer)
    
    # Set up logger
    logger = WandbLogger(
        project="smiles-redi-training",  # or your preferred project name
        entity="programmablebio",
        name=f"lr{args.learning_rate}_dim{args.model_dim}_head{args.n_heads}_layer{args.n_layers}",
        save_dir=checkpoint_dir
    )
    
    # Set up callbacks
    callbacks = [
        ModelCheckpoint(
            dirpath=checkpoint_dir,
            filename='best',
            monitor='val/token_nll',
            mode='min',
            save_top_k=1,
            save_last=True,
            # every_n_train_steps=10000  # This will save every 1000 steps AND when val/nll improves
        ),
        LearningRateMonitor(logging_interval='step')
    ]
    
    # Initialize trainer
    trainer = pl.Trainer(
        max_epochs=args.epochs,
        devices=torch.cuda.device_count(),
        accelerator='gpu',
        strategy=DDPStrategy(find_unused_parameters=False),
        num_nodes=int(os.environ.get("SLURM_NNODES", 1)),
        precision="bf16",
        gradient_clip_val=args.grad_clip if args.grad_clip > 0 else None,
        callbacks=callbacks,
        logger=logger,
        log_every_n_steps=100,
        check_val_every_n_epoch=True,
        # val_check_interval=10000,
        accumulate_grad_batches=8,
        enable_progress_bar=True,
        enable_model_summary=True
    )
    
    print(f"Model initialized with {sum(p.numel() for p in model.parameters()):,} parameters.")
    print("Starting training...")
    
    # Train the model
    trainer.fit(model, data_module)
    
    print("Training complete.")
    print(f"Best checkpoint saved at: {trainer.checkpoint_callback.best_model_path}")

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Train ReDi model for SMILES generation with RoPE using PyTorch Lightning")

    # Model arguments
    parser.add_argument("--model_dim", type=int, default=1024)
    parser.add_argument("--n_heads", type=int, default=8)
    parser.add_argument("--n_layers", type=int, default=6)

    # Training arguments
    parser.add_argument("--epochs", type=int, default=50)
    parser.add_argument("--learning_rate", type=float, default=1e-4)
    parser.add_argument("--weight_decay", type=float, default=1e-5)
    parser.add_argument("--label_smoothing", type=float, default=0)
    parser.add_argument("--grad_clip", type=float, default=1.0)
    parser.add_argument("--gamma", type=float, default=2.0)

    # Validity arguments
    parser.add_argument("--validity_weight", type=float, default=0.1)
    parser.add_argument("--validity_check_freq", type=int, default=10)
    parser.add_argument("--validity_eval_batches", type=int, default=20)

    # Logging arguments
    parser.add_argument("--checkpoint_dir", type=str, default="./checkpoints_smiles")

    args = parser.parse_args()
    main(args)

	import argparse
	import math
	import os
	from functools import partial
	from collections import Counter

	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	from datasets import load_from_disk
	from torch.optim import AdamW
	from torch.optim.lr_scheduler import LambdaLR
	from torch.utils.data import DataLoader
	import pytorch_lightning as pl
	from pytorch_lightning.callbacks import ModelCheckpoint, LearningRateMonitor
	from pytorch_lightning.loggers import WandbLogger
	from pytorch_lightning.strategies import DDPStrategy
	from rdkit import Chem

	from smiles.smiles_tokenizer.my_tokenizers import SMILES_SPE_Tokenizer
	from smiles.evaluation.peptide_analyzer import PeptideAnalyzer
	from smiles.models import dataloading_for_dynamic_batching as dynamic_dataloader


	class RotaryPositionalEmbedding(nn.Module):
	def __init__(self, dim, max_position_embeddings=2048, base=10000):
	super().__init__()
	self.dim = dim
	self.max_position_embeddings = max_position_embeddings
	self.base = base

	inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
	self.register_buffer("inv_freq", inv_freq)

	def forward(self, x, seq_len=None):
	if seq_len is None:
	seq_len = x.shape[1]

	t = torch.arange(seq_len, device=x.device, dtype=self.inv_freq.dtype)
	freqs = torch.einsum("i,j->ij", t, self.inv_freq)
	emb = torch.cat((freqs, freqs), dim=-1)

	cos_emb = emb.cos()[None, :, :]
	sin_emb = emb.sin()[None, :, :]

	return cos_emb, sin_emb

	def rotate_half(x):
	x1 = x[..., : x.shape[-1] // 2]
	x2 = x[..., x.shape[-1] // 2 :]
	return torch.cat((-x2, x1), dim=-1)

	def apply_rotary_pos_emb(q, k, cos, sin):
	q_embed = (q * cos) + (rotate_half(q) * sin)
	k_embed = (k * cos) + (rotate_half(k) * sin)
	return q_embed, k_embed

	# --- Model Architecture with RoPE ---
	def modulate(x, shift, scale):
	return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)

	class TimestepEmbedder(nn.Module):
	def __init__(self, hidden_size):
	super().__init__()
	self.mlp = nn.Sequential(
	nn.Linear(1, hidden_size, bias=True),
	nn.SiLU(),
	nn.Linear(hidden_size, hidden_size, bias=True),
	)

	def forward(self, t):
	return self.mlp(t.unsqueeze(-1))

	class MultiHeadAttentionWithRoPE(nn.Module):
	def __init__(self, hidden_size, n_heads):
	super().__init__()
	self.hidden_size = hidden_size
	self.n_heads = n_heads
	self.head_dim = hidden_size // n_heads

	assert self.head_dim * n_heads == hidden_size, "hidden_size must be divisible by n_heads"

	self.q_proj = nn.Linear(hidden_size, hidden_size, bias=False)
	self.k_proj = nn.Linear(hidden_size, hidden_size, bias=False)
	self.v_proj = nn.Linear(hidden_size, hidden_size, bias=False)
	self.out_proj = nn.Linear(hidden_size, hidden_size)

	self.rope = RotaryPositionalEmbedding(self.head_dim)

	def forward(self, x):
	batch_size, seq_len, hidden_size = x.shape

	q = self.q_proj(x).view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
	k = self.k_proj(x).view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
	v = self.v_proj(x).view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)

	cos, sin = self.rope(q, seq_len)
	q, k = apply_rotary_pos_emb(q, k, cos, sin)

	scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
	attn_weights = F.softmax(scores, dim=-1)
	attn_output = torch.matmul(attn_weights, v)

	attn_output = attn_output.transpose(1, 2).contiguous().view(batch_size, seq_len, hidden_size)
	output = self.out_proj(attn_output)

	return output

	class DiTBlock(nn.Module):
	def __init__(self, hidden_size, n_heads):
	super().__init__()
	self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
	self.attn = MultiHeadAttentionWithRoPE(hidden_size, n_heads)
	self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
	self.mlp = nn.Sequential(
	nn.Linear(hidden_size, 4 * hidden_size),
	nn.GELU(),
	nn.Linear(4 * hidden_size, hidden_size)
	)
	self.adaLN_modulation = nn.Sequential(
	nn.SiLU(),
	nn.Linear(hidden_size, 6 * hidden_size, bias=True)
	)

	def forward(self, x, c):
	shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1)
	x_norm1 = modulate(self.norm1(x), shift_msa, scale_msa)
	attn_output = self.attn(x_norm1)
	x = x + gate_msa.unsqueeze(1) * attn_output
	x_norm2 = modulate(self.norm2(x), shift_mlp, scale_mlp)
	mlp_output = self.mlp(x_norm2)
	x = x + gate_mlp.unsqueeze(1) * mlp_output
	return x

	class MDLM(nn.Module):
	def __init__(self, vocab_size, model_dim, n_heads, n_layers):
	super().__init__()
	self.vocab_size = vocab_size
	self.model_dim = model_dim
	self.mask_token_id = vocab_size

	self.token_embedder = nn.Embedding(vocab_size, model_dim)
	self.time_embedder = TimestepEmbedder(model_dim)

	self.transformer_blocks = nn.ModuleList([
	DiTBlock(model_dim, n_heads) for _ in range(n_layers)
	])

	self.final_norm = nn.LayerNorm(model_dim)
	self.lm_head = nn.Linear(model_dim, vocab_size)

	self.apply(self._init_weights)

	def _init_weights(self, module):
	if isinstance(module, (nn.Linear, nn.Embedding)):
	module.weight.data.normal_(mean=0.0, std=0.02)
	if isinstance(module, nn.Linear) and module.bias is not None:
	module.bias.data.zero_()
	elif isinstance(module, nn.LayerNorm):
	if module.bias is not None:
	module.bias.data.zero_()
	if module.weight is not None:
	module.weight.data.fill_(1.0)

	def forward(self, x, t):
	x_embed = self.token_embedder(x)
	t_embed = self.time_embedder(t)
	for block in self.transformer_blocks:
	x_embed = block(x_embed, t_embed)
	x_embed = self.final_norm(x_embed)
	logits = self.lm_head(x_embed)
	return logits

	# --- PyTorch Lightning Module ---
	class MDLMLightningModule(pl.LightningModule):
	def __init__(self, args, tokenizer):
	super().__init__()
	self.save_hyperparameters(ignore=['tokenizer'])
	self.args = args
	self.tokenizer = tokenizer
	self.peptide_analyzer = PeptideAnalyzer()

	# Initialize model
	self.model = MDLM(
	vocab_size=tokenizer.vocab_size,
	model_dim=args.model_dim,
	n_heads=args.n_heads,
	n_layers=args.n_layers
	)

	# For tracking steps
	self.automatic_optimization = True

	self.validation_step_outputs = []

	def forward(self, x, t):
	return self.model(x, t)

	def _compute_invalid_loss(self, logits):
	batch_token_ids = torch.argmax(logits, dim=-1) # (batch_size, seq_length)

	sampled_sequences = self.tokenizer.batch_decode(batch_token_ids)

	penalties = torch.tensor(
	[1 if not self.peptide_analyzer.is_peptide(seq) else 0 for seq in sampled_sequences],
	dtype=torch.float32,
	device=self.device
	)
	sampled_probs = torch.softmax(logits, dim=-1).gather(dim=-1, index=batch_token_ids.unsqueeze(-1)).squeeze(-1).to(self.device)

	scaled_penalty = penalties[:, None] * sampled_probs # (batch_size, seq_length)

	return scaled_penalty

	def _loss(self, logits, x_1, attn_mask):
	# Standard cross-entropy loss
	ce_loss = F.cross_entropy(
	logits.view(-1, self.model.vocab_size),
	x_1.view(-1),
	reduction='none'
	).view(x_1.shape[0], -1)
	# ce_loss = (ce_loss * attn_mask).sum() / attn_mask.sum()

	# validity_weight = self.args.validity_weight * min(1.0, (self.current_epoch + 1) / self.trainer.max_epochs)

	invalid_loss = self._compute_invalid_loss(logits) # (batch_size, seq_length)

	loss = ce_loss + self.args.validity_weight * invalid_loss
	nlls = loss * attn_mask

	num_tokens = attn_mask.sum()
	batch_nll = nlls.sum()
	token_nll = batch_nll / num_tokens

	return token_nll, (ce_lossattn_mask).sum() / num_tokens, (invalid_lossattn_mask).sum() / num_tokens

	def training_step(self, batch, batch_idx):
	x_1 = batch['input_ids'].clone().detach().to(self.device)
	attn_mask = batch['attention_mask'].clone().detach().to(self.device)
	bond_mask = batch['bond_mask'].clone().detach().to(self.device).bool()
	batch_size, _ = x_1.shape

	# ReDi approach: random start -> target
	x_0 = torch.randint(0, self.model.vocab_size, x_1.shape, device=self.device)
	t_continuous = torch.rand(batch_size, device=self.device)
	# mask = torch.rand(x_1.shape, device=self.device) < t_continuous.view(-1, 1)
	# x_t = torch.where(mask, x_1, x_0)

	peptide_bond_prob = t_continuous.view(-1, 1) ** self.args.gamma # slower increase
	non_peptide_prob = t_continuous.view(-1, 1) # linear increase

	masking_prob = torch.where(bond_mask, peptide_bond_prob, non_peptide_prob)
	mask = torch.rand(x_1.shape, device=self.device) < masking_prob
	x_t = torch.where(mask, x_1, x_0)

	logits = self.model(x_t, t_continuous)

	token_nll, ce_loss, invalid_loss = self._loss(logits, x_1, attn_mask)

	# Logging
	self.log('train/token_nll', token_nll.item(), on_step=True, on_epoch=True, prog_bar=True, batch_size=x_1.size(0), sync_dist=True)
	self.log('train/ce_loss', ce_loss.item(), on_step=True, on_epoch=True, batch_size=x_1.size(0), sync_dist=True)
	self.log('train/invalid_loss', invalid_loss.item(), on_step=True, on_epoch=True, batch_size=x_1.size(0), sync_dist=True)
	# self.log('train/validity_weight', validity_weight, on_step=False, on_epoch=True, batch_size=x_1.size(0))

	return token_nll

	def validation_step(self, batch, batch_idx):
	x_1 = batch['input_ids'].clone().detach().to(self.device)
	attn_mask = batch['attention_mask'].clone().detach().to(self.device)
	bond_mask = batch['bond_mask'].clone().detach().to(self.device).bool()
	batch_size, _ = x_1.shape

	# ReDi approach: random start -> target
	x_0 = torch.randint(0, self.model.vocab_size, x_1.shape, device=self.device)
	t_continuous = torch.rand(batch_size, device=self.device)

	peptide_bond_prob = t_continuous.view(-1, 1) ** self.args.gamma # slower increase
	non_peptide_prob = t_continuous.view(-1, 1) # linear increase

	masking_prob = torch.where(bond_mask, peptide_bond_prob, non_peptide_prob)
	mask = torch.rand(x_1.shape, device=self.device) < masking_prob
	x_t = torch.where(mask, x_1, x_0)

	logits = self.model(x_t, t_continuous)

	token_nll, ce_loss, invalid_loss = self._loss(logits, x_1, attn_mask)
	self.log('val/token_nll', token_nll.item(), on_step=True, on_epoch=True, prog_bar=True, batch_size=x_1.size(0), sync_dist=True)
	self.log('val/ce_loss', ce_loss.item(), on_step=True, on_epoch=True, batch_size=x_1.size(0), sync_dist=True)
	self.log('val/invalid_loss', invalid_loss.item(), on_step=True, on_epoch=True, batch_size=x_1.size(0), sync_dist=True)

	def configure_optimizers(self):
	optimizer = AdamW(
	self.parameters(),
	lr=self.args.learning_rate,
	weight_decay=self.args.weight_decay
	)

	# Calculate total steps
	if hasattr(self.trainer, 'estimated_stepping_batches'):
	num_training_steps = self.trainer.estimated_stepping_batches
	else:
	# Fallback calculation
	num_training_steps = len(self.trainer.datamodule.train_dataloader()) * self.trainer.max_epochs

	warmup_steps = int(num_training_steps * 0.1)

	def lr_lambda(current_step):
	if current_step < warmup_steps:
	lr_range = self.args.learning_rate - (self.args.learning_rate * 0.1)
	lr = (self.args.learning_rate * 0.1) + lr_range * (current_step / warmup_steps)
	return lr / self.args.learning_rate
	else:
	progress = (current_step - warmup_steps) / (num_training_steps - warmup_steps)
	cosine_decay = 0.5 * (1.0 + math.cos(math.pi * progress))
	lr_range = self.args.learning_rate - (self.args.learning_rate * 0.1)
	lr = (self.args.learning_rate * 0.1) + lr_range * cosine_decay
	return lr / self.args.learning_rate

	scheduler = LambdaLR(optimizer, lr_lambda)

	return {
	"optimizer": optimizer,
	"lr_scheduler": {
	"scheduler": scheduler,
	"interval": "step",
	"frequency": 1,
	},
	}


	# --- Main Execution ---
	def main(args):
	# Set up checkpoint directory
	checkpoint_dir = (args.checkpoint_dir +
	f"correct_lr{args.learning_rate}_wd{args.weight_decay}_layer{args.n_layers}_"
	f"head{args.n_heads}_valweight{args.validity_weight}")
	print(f"Saving to {checkpoint_dir}")
	os.makedirs(checkpoint_dir, exist_ok=True)

	print("Loading tokenizer...")
	tokenizer = SMILES_SPE_Tokenizer('./smiles/smiles_tokenizer/old_vocab.txt',
	'./smiles/smiles_tokenizer/old_splits.txt')
	print(f"Tokenizer loaded. Vocab size: {tokenizer.vocab_size}")

	# Initialize data module
	data_module = dynamic_dataloader.CustomDataModule('./data/11M_smiles_old_tokenizer_no_limit/', tokenizer)

	# Initialize model
	model = MDLMLightningModule(args, tokenizer)

	# Set up logger
	logger = WandbLogger(
	project="smiles-redi-training", # or your preferred project name
	entity="programmablebio",
	name=f"lr{args.learning_rate}_dim{args.model_dim}_head{args.n_heads}_layer{args.n_layers}",
	save_dir=checkpoint_dir
	)

	# Set up callbacks
	callbacks = [
	ModelCheckpoint(
	dirpath=checkpoint_dir,
	filename='best',
	monitor='val/token_nll',
	mode='min',
	save_top_k=1,
	save_last=True,
	# every_n_train_steps=10000 # This will save every 1000 steps AND when val/nll improves
	),
	LearningRateMonitor(logging_interval='step')
	]

	# Initialize trainer
	trainer = pl.Trainer(
	max_epochs=args.epochs,
	devices=torch.cuda.device_count(),
	accelerator='gpu',
	strategy=DDPStrategy(find_unused_parameters=False),
	num_nodes=int(os.environ.get("SLURM_NNODES", 1)),
	precision="bf16",
	gradient_clip_val=args.grad_clip if args.grad_clip > 0 else None,
	callbacks=callbacks,
	logger=logger,
	log_every_n_steps=100,
	check_val_every_n_epoch=True,
	# val_check_interval=10000,
	accumulate_grad_batches=8,
	enable_progress_bar=True,
	enable_model_summary=True
	)

	print(f"Model initialized with {sum(p.numel() for p in model.parameters()):,} parameters.")
	print("Starting training...")

	# Train the model
	trainer.fit(model, data_module)

	print("Training complete.")
	print(f"Best checkpoint saved at: {trainer.checkpoint_callback.best_model_path}")

	if __name__ == "__main__":
	parser = argparse.ArgumentParser(description="Train ReDi model for SMILES generation with RoPE using PyTorch Lightning")

	# Model arguments
	parser.add_argument("--model_dim", type=int, default=1024)
	parser.add_argument("--n_heads", type=int, default=8)
	parser.add_argument("--n_layers", type=int, default=6)

	# Training arguments
	parser.add_argument("--epochs", type=int, default=50)
	parser.add_argument("--learning_rate", type=float, default=1e-4)
	parser.add_argument("--weight_decay", type=float, default=1e-5)
	parser.add_argument("--label_smoothing", type=float, default=0)
	parser.add_argument("--grad_clip", type=float, default=1.0)
	parser.add_argument("--gamma", type=float, default=2.0)

	# Validity arguments
	parser.add_argument("--validity_weight", type=float, default=0.1)
	parser.add_argument("--validity_check_freq", type=int, default=10)
	parser.add_argument("--validity_eval_batches", type=int, default=20)

	# Logging arguments
	parser.add_argument("--checkpoint_dir", type=str, default="./checkpoints_smiles")

	args = parser.parse_args()
	main(args)