diff --git "a/notebooks_tutorials/06_NTv3_generative_training.ipynb" "b/notebooks_tutorials/06_NTv3_generative_training.ipynb" new file mode 100644--- /dev/null +++ "b/notebooks_tutorials/06_NTv3_generative_training.ipynb" @@ -0,0 +1,10079 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "15bb8c23", + "metadata": { + "id": "15bb8c23" + }, + "source": [ + "# 🧬 NTv3-Generative: Training Tutorial\n", + "\n", + "This notebook demonstrates how to **fine-tune NTv3** for controllable enhancer sequence generation using **Masked Diffusion Language Modeling (MDLM)**.\n", + "\n", + "---\n", + "\n", + "## 🎯 What You'll Learn\n", + "\n", + "This tutorial covers:\n", + "1. How to extend a post-trained NTv3 model with new conditioning signals\n", + "2. The fundamentals of Masked Diffusion Language Modeling (MDLM) for DNA generation\n", + "3. Best practices for training generative DNA models with EMA stabilization\n", + "\n", + "---\n", + "\n", + "## πŸ§ͺ Background: Generative DNA Modeling\n", + "\n", + "### What is MDLM?\n", + "\n", + "**Masked Diffusion Language Modeling (MDLM)** is a diffusion-based approach adapted for discrete sequences. Unlike continuous diffusion models that add Gaussian noise, MDLM progressively masks tokens:\n", + "\n", + "- **Forward process**: Gradually replace tokens with `[MASK]` according to a noise schedule\n", + "- **Reverse process**: Model learns to predict original tokens from masked sequences\n", + "- **Sampling**: Start from fully masked sequence and iteratively unmask\n", + "\n", + "This approach combines the strengths of diffusion models (iterative refinement, stable training) with the discrete nature of DNA sequences.\n", + "\n", + "### Why Conditional Generation?\n", + "\n", + "We want to generate enhancer sequences with **specific properties**:\n", + "- **Species**: Maintain species-specific sequence patterns (e.g., codon usage, regulatory motifs)\n", + "- **Activity level**: Control how \"strong\" the enhancer should be (low β†’ high activity)\n", + "- **Sequence context**: Provided by the input sequences, allow the enhancer design to consider promoter-enhancer interaction.\n", + "\n", + "By conditioning on these attributes, we can generate enhancers that are both biologically realistic and functionally targeted.\n", + "\n", + "---\n", + "\n", + "## πŸ“Š Overview\n", + "\n", + "We fine-tune **NTv3_650M_post** to generate enhancer sequences conditioned on:\n", + "\n", + "| Condition | Source | Training |\n", + "|-----------|--------|----------|\n", + "| **Species** | Pre-trained embeddings | Frozen (transfer learning) |\n", + "| **Activity Level** | New embeddings (bins 0-4) | Trainable |\n", + "\n", + "The training uses MDLM-style diffusion with:\n", + "- **Linear noise schedule** for sequence masking (uniform sampling of noise levels)\n", + "- **Classifier-free guidance** through activity dropout (10% of samples)\n", + "- **Exponential Moving Average (EMA)** for stable inference\n", + "\n", + "---\n", + "\n", + "## πŸ“š Notebook Structure\n", + "\n", + "1. **Setup**: Load data and base model from HuggingFace\n", + "2. **Model Architecture**: Add activity conditioning to NTv3\n", + "3. **Training Components**: MDLM masking, loss functions, EMA\n", + "4. **Training Loop**: Full training pipeline with LR scheduling\n", + "5. **Dataset**: Prepare enhancer sequences with activity labels\n", + "6. **Run Training**: Execute training on enhancer dataset\n", + "\n", + "---\n", + "\n", + "## πŸ’» Hardware Requirements\n", + "In order to train the 650M parameter model, we recommend using a GPU with at least 40GB of memory, such as the NVIDIA A100.\n", + "The hyperparameters in this notebook is set to run a full training that's expected to take around 80hr on a single GPU. \n", + "\n", + "\n", + "> πŸ“ **Note for Google Colab users:** This notebook is compatible with Colab! Enable GPU for faster training: Runtime β†’ Change runtime type β†’ GPU.\n", + "> However, due to memory limit, the full scale training for the 650M model needs to be ran with A100 or better GPUs." + ] + }, + { + "cell_type": "markdown", + "id": "903d8e2c", + "metadata": { + "id": "903d8e2c" + }, + "source": [ + "## 0) πŸ“¦ Setup\n", + "\n", + "First, we import the required libraries and authenticate with HuggingFace to access the pre-trained model and dataset.\n", + "\n", + "### Dependencies\n", + "\n", + "| Library | Purpose |\n", + "|---------|---------|\n", + "| `torch` | Deep learning framework |\n", + "| `transformers` | Load pre-trained NTv3 model and tokenizer |\n", + "| `datasets` | Load enhancer generation dataset from HuggingFace |\n", + "| `huggingface_hub` | Authentication for gated models |" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7423eb86", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17, + 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numpy as np\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "from datasets import load_dataset\n", + "from huggingface_hub import login\n", + "from torch.optim import AdamW\n", + "from torch.optim.lr_scheduler import LambdaLR\n", + "from torch.utils.data import DataLoader, Dataset\n", + "from tqdm import tqdm\n", + "from transformers import AutoConfig, AutoModel, AutoTokenizer, PreTrainedTokenizer\n", + "\n", + "login() # Authenticate with HuggingFace" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f64da95", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "63b0953251d74fd3b983def6f02f3c13", + "1e81075cd60f4c4fa88c88e44766b6f4", + "46a71589b44a42e3b711eecf8d8d9b8f", + "cf339ec50ebb4b32b9d74dcf32105392", + "1e8d7f99fec84179b8114a660323cea9", + "e759333cdacf429283b21ef888b8932b", + "4997bedb6c8e4515a9d1a98da99db56e", + 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"source": [ + "# Load training dataset and base model\n", + "dataset = load_dataset(\"InstaDeepAI/NTv3_enhancer_generation\", split='train')\n", + "print(f\"Dataset size: {len(dataset)} samples\")\n", + "\n", + "model_repo = \"InstaDeepAI/NTv3_650M_post\"\n", + "ntv3_base = AutoModel.from_pretrained(model_repo, trust_remote_code=True)\n", + "tokenizer = AutoTokenizer.from_pretrained(model_repo, trust_remote_code=True)\n", + "print(f\"Base model loaded from {model_repo}\")" + ] + }, + { + "cell_type": "markdown", + "id": "c23293cd", + "metadata": { + "id": "c23293cd" + }, + "source": [ + "### πŸ“Š Understanding the Training Data\n", + "\n", + "The dataset `InstaDeepAI/NTv3_enhancer_generation` contains synthetic enhancer sequences with measured activity levels from STARR-seq experiments:\n", + "\n", + "| Field | Description |\n", + "|-------|-------------|\n", + "| `full_sequence` | Complete construct sequence (promoter + enhancer + reporter) |\n", + "| `discrete_label` | Activity bin (0-4): 0 = low activity, 4 = high activity |\n", + "| `promoter` | Promoter type used (DSCP or RpS12) |\n", + "\n", + "**Why Activity Bins?** \n", + "Continuous activity values are discretized into 5 bins for conditioning. This makes the conditioning signal more robust and enables discrete classifier-free guidance during generation.\n", + "\n", + "**Enhancer Region Masking** \n", + "During training, we only mask the enhancer region (not the promoter/reporter), as this is the region we want the model to learn to generate. The promoter context is provided as input but not predicted.\n", + "\n", + "---\n", + "\n", + "## 1) πŸ—οΈ Model Architecture\n", + "\n", + "### How NTv3 Conditioning Works\n", + "\n", + "NTv3 supports **discrete conditioning** through learned embedding tables. For each condition:\n", + "1. An embedding table maps condition values (e.g., species ID, activity bin) to vectors\n", + "2. These vectors modulate the model's hidden states via adaptive layer norm layers\n", + "3. A prediction head can optionally predict the condition from the sequence\n", + "\n", + "### Extending with New Conditions\n", + "\n", + "We extend the pre-trained model by adding a **new activity condition**:\n", + "- **Species** (index 0): Uses pre-trained embeddings β†’ **frozen**\n", + "- **Activity** (index 1): New embeddings initialized randomly β†’ **trainable**\n", + "\n", + "This approach leverages transfer learning: the model retains its species-specific knowledge while learning the new activity conditioning from scratch.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8cd3c410", + "metadata": { + "id": "8cd3c410" + }, + "outputs": [], + "source": [ + "# Configure model with activity conditioning\n", + "# - conditions_names[0] = 'species' (frozen, from post-training)\n", + "# - conditions_names[1] = 'activity' (trainable, 5 bins)\n", + "\n", + "generation_config = AutoConfig.from_pretrained(\n", + " model_repo,\n", + " conditions_names=['species', 'activity'],\n", + " conditions_vocab_size=[30, 5], # 30 species, 5 activity bins\n", + " conditions_dims=[16, 16],\n", + " bigwigs_per_species={},\n", + " bed_elements_names=[],\n", + " trust_remote_code=True\n", + ")\n", + "\n", + "\n", + "class GenerativeNTv3(nn.Module):\n", + " \"\"\"NTv3 model with additional activity conditioning for generation.\"\"\"\n", + "\n", + " def __init__(self, config: AutoConfig, base_model: AutoModel):\n", + " super().__init__()\n", + " self.config = config\n", + " # Get the conditioned model class from the base model\n", + " discrete_conditioned_model = type(base_model.core).__bases__[0]\n", + " self.core = discrete_conditioned_model(config)\n", + " # Load pre-trained weights (strict=False allows new conditioning layers)\n", + " self.load_state_dict(base_model.state_dict(), strict=False)\n", + "\n", + " def forward(\n", + " self,\n", + " input_ids: torch.LongTensor = None,\n", + " condition_ids: List[torch.Tensor] = None,\n", + " conditions_masks: List[torch.Tensor] = None,\n", + " inputs_embeds: torch.FloatTensor = None,\n", + " output_hidden_states: bool = False,\n", + " output_attentions: bool = False,\n", + " ):\n", + " return self.core(\n", + " input_ids=input_ids,\n", + " condition_ids=condition_ids,\n", + " conditions_masks=conditions_masks,\n", + " inputs_embeds=inputs_embeds,\n", + " output_hidden_states=output_hidden_states,\n", + " output_attentions=output_attentions\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b2c78ba6", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "b2c78ba6", + "outputId": "6c0de322-1f35-4630-de39-187a0db68f23" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Trainable parameters: 655,231,760 / 658,672,910 (99.5%)\n" + ] + } + ], + "source": [ + "# Instantiate generative model and freeze species conditioning\n", + "generation_model = GenerativeNTv3(generation_config, ntv3_base)\n", + "del ntv3_base # Free memory\n", + "\n", + "# Freeze species conditioning layers (index 0) - only train activity (index 1)\n", + "for name, param in generation_model.named_parameters():\n", + " if any(x in name for x in ['modulation_layers.0', 'cond_tables.0', 'conditions_heads.0']):\n", + " param.requires_grad = False\n", + "\n", + "trainable_params = sum(p.numel() for p in generation_model.parameters() if p.requires_grad)\n", + "total_params = sum(p.numel() for p in generation_model.parameters())\n", + "print(f\"Trainable parameters: {trainable_params:,} / {total_params:,} ({100*trainable_params/total_params:.1f}%)\")" + ] + }, + { + "cell_type": "markdown", + "id": "8a6219ed", + "metadata": { + "id": "8a6219ed" + }, + "source": [ + "## 2) πŸ”§ Training Components\n", + "\n", + "This section defines the core training components for MDLM training.\n", + "\n", + "---\n", + "\n", + "### MDLM Training Strategy\n", + "\n", + "The model is trained to predict masked tokens using:\n", + "\n", + "| Component | Strategy |\n", + "|-----------|----------|\n", + "| **Sequence loss** | MDLM-style masking with linear noise schedule, weighted CE loss |\n", + "| **Activity loss** | Dropout-style masking (10%), CE loss on masked samples only |\n", + "| **Species** | Frozen (input conditioning only, no loss) |\n", + "\n", + "> πŸ’‘ **Key insight:** Activity is masked during training so the model learns to predict it from the sequence, also enabling classifier-free guidance at inference. However the species logits were never trained and therefore should not be used\n", + "\n", + "---\n", + "\n", + "### The Noise Schedule\n", + "\n", + "In MDLM, the **noise level** $t \\in (0, 1]$ determines what fraction of tokens to mask:\n", + "- $t = 0$: No masking (original sequence)\n", + "- $t = 1$: All tokens masked\n", + "\n", + "During training, we sample $t$ uniformly for each sequence. The loss is weighted by $1/t$ (MDLM weighting) to account for the varying difficulty of different noise levels.\n", + "\n", + "### Classifier-Free Guidance (CFG)\n", + "\n", + "To enable CFG at inference time, we need the model to work both **with** and **without** the activity condition:\n", + "- During training: Randomly drop the activity condition with 10% probability\n", + "- The model learns: $P(\\text{sequence})$ and $P(\\text{sequence} | \\text{activity})$\n", + "- At inference: Use both predictions to amplify the conditioning signal\n", + "\n", + "---\n", + "\n", + "### Masking Function\n", + "\n", + "The `mask_by_noise_level` function implements the forward diffusion process:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "236f52f1", + "metadata": { + "id": "236f52f1" + }, + "outputs": [], + "source": [ + "def mask_by_noise_level(\n", + " tokens: torch.Tensor,\n", + " noise_level: torch.Tensor,\n", + " mask_token_id: int,\n", + " pad_token_id: int,\n", + " mask_vector: Optional[torch.Tensor] = None,\n", + ") -> Tuple[torch.Tensor, torch.Tensor]:\n", + " \"\"\"\n", + " Mask tokens with probability = noise_level (MDLM diffusion-style).\n", + "\n", + " Returns:\n", + " masked_tokens: (B, L) with some tokens replaced by mask_token_id\n", + " targets: (B, L) original tokens where masked, pad_token_id elsewhere\n", + " \"\"\"\n", + " B, L = tokens.shape\n", + "\n", + " # Mask each position with probability = noise_level\n", + " mask = torch.rand(B, L, device=tokens.device) < noise_level.view(-1, 1)\n", + " mask = mask & (tokens != pad_token_id) # Don't mask padding\n", + "\n", + " # Only mask positions specified by mask_vector (if provided)\n", + " if mask_vector is not None:\n", + " mask = mask & mask_vector\n", + "\n", + " masked_tokens = torch.where(mask, mask_token_id, tokens)\n", + " targets = torch.where(mask, tokens, pad_token_id)\n", + " return masked_tokens, targets\n", + "\n", + "\n", + "def compute_sequence_loss(\n", + " logits: torch.Tensor,\n", + " targets: torch.Tensor,\n", + " weights: torch.Tensor,\n", + " pad_token_id: int,\n", + " input_tokens: torch.Tensor,\n", + ") -> torch.Tensor:\n", + " \"\"\"MDLM weighted cross-entropy loss, normalized by all non-padding tokens.\"\"\"\n", + " B, L, V = logits.shape\n", + " sequence_mask = targets != pad_token_id\n", + " loss_denominator_mask = input_tokens != pad_token_id\n", + "\n", + " ce = F.cross_entropy(\n", + " logits.view(-1, V),\n", + " targets.view(-1),\n", + " reduction='none',\n", + " ignore_index=pad_token_id\n", + " ).view(B, L)\n", + "\n", + " weighted_ce = ce * weights.unsqueeze(1)\n", + " running_sum_loss = (weighted_ce * sequence_mask).sum(dim=1)\n", + " per_sample_loss = running_sum_loss / loss_denominator_mask.sum(dim=1).clamp(min=1)\n", + " return per_sample_loss.mean()\n", + "\n", + "\n", + "def compute_activity_loss(\n", + " logits: torch.Tensor,\n", + " targets: torch.Tensor,\n", + " mask: torch.Tensor,\n", + ") -> torch.Tensor:\n", + " \"\"\"Cross-entropy loss for activity prediction (only on masked samples).\"\"\"\n", + " if not mask.any():\n", + " return torch.tensor(0.0, device=logits.device)\n", + " return F.cross_entropy(logits[mask], targets[mask])" + ] + }, + { + "cell_type": "markdown", + "id": "d2793cc4", + "metadata": { + "id": "d2793cc4" + }, + "source": [ + "### Exponential Moving Average (EMA)\n", + "\n", + "EMA maintains a smoothed version of model weights throughout training:\n", + "\n", + "$$\\theta_{\\text{EMA}} = \\beta \\cdot \\theta_{\\text{EMA}} + (1 - \\beta) \\cdot \\theta_{\\text{current}}$$\n", + "\n", + "where $\\beta = 0.999$ (decay rate).\n", + "\n", + "**Why use EMA for generation?**\n", + "- Training weights can be noisy due to stochastic gradient descent\n", + "- EMA provides a \"averaged\" model that's more stable for inference\n", + "- Particularly important for generative models where output quality matters\n", + "- Adpated from existing literature\n", + "\n", + "At inference time, we use the **EMA model** instead of the training model. This has large impact on the sequence generation quality." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4c653278", + "metadata": { + "id": "4c653278" + }, + "outputs": [], + "source": [ + "class EMAModel:\n", + " \"\"\"\n", + " Exponential Moving Average of model parameters.\n", + " Formula: ema_param = decay * ema_param + (1 - decay) * current_param\n", + " \"\"\"\n", + "\n", + " def __init__(self, model: nn.Module, decay: float = 0.999):\n", + " self.decay = decay\n", + " self.ema_model = copy.deepcopy(model)\n", + " self.ema_model.eval()\n", + " for param in self.ema_model.parameters():\n", + " param.requires_grad = False\n", + "\n", + " @torch.no_grad()\n", + " def update(self, model: nn.Module):\n", + " \"\"\"Update EMA parameters after each training step.\"\"\"\n", + " for ema_param, model_param in zip(self.ema_model.parameters(), model.parameters()):\n", + " ema_param.data.mul_(self.decay).add_(model_param.data, alpha=1.0 - self.decay)\n", + "\n", + " def get_model(self) -> nn.Module:\n", + " return self.ema_model\n", + "\n", + " def state_dict(self) -> Dict[str, Any]:\n", + " return self.ema_model.state_dict()\n", + "\n", + " def load_state_dict(self, state_dict: Dict[str, Any]):\n", + " self.ema_model.load_state_dict(state_dict)" + ] + }, + { + "cell_type": "markdown", + "id": "5a5979ba", + "metadata": { + "id": "5a5979ba" + }, + "source": [ + "### Training Step\n", + "\n", + "The `train_step` function implements a single MDLM training iteration:\n", + "\n", + "1. **Sample noise level**: Random $t \\sim \\text{Uniform}(\\epsilon, 1)$ for each sequence\n", + "2. **Mask tokens**: Replace tokens with `[MASK]` with probability $t$\n", + "3. **Apply condition dropout**: Mask activity with 10% probability (for CFG)\n", + "4. **Forward pass**: Model predicts original tokens and activity\n", + "5. **Compute losses**: Weighted sequence loss + activity prediction loss\n", + "6. **Track metrics**: Accuracy for both sequence and activity predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3c06d4e6", + "metadata": { + "id": "3c06d4e6" + }, + "outputs": [], + "source": [ + "def train_step(\n", + " model: nn.Module,\n", + " tokens: torch.Tensor,\n", + " activity: torch.Tensor,\n", + " species_ids: torch.Tensor,\n", + " mask_vector: Optional[torch.Tensor],\n", + " mask_token_id: int,\n", + " pad_token_id: int,\n", + " activity_masking_prob: float = 0.1,\n", + " activity_loss_weight: float = 1.0,\n", + ") -> Dict[str, Any]:\n", + " \"\"\"Single MDLM training step with sequence and activity losses.\"\"\"\n", + " B = tokens.shape[0]\n", + " device = tokens.device\n", + "\n", + " # 1. Sample noise level (linear schedule, avoid t=0 for stability)\n", + " eps = 0.001\n", + " t = torch.rand(B, device=device) * (1 - eps) + eps\n", + " seq_weights = (1.0 / t).clamp(max=100.0) # MDLM weight = 1/t\n", + "\n", + " # 2. Mask sequence tokens\n", + " masked_tokens, seq_targets = mask_by_noise_level(\n", + " tokens, t, mask_token_id, pad_token_id, mask_vector\n", + " )\n", + "\n", + " # 3. Condition masking (dropout for classifier-free guidance)\n", + " species_mask = torch.ones(B, dtype=torch.bool, device=device) # Always use species\n", + " activity_mask = torch.rand(B, device=device) >= activity_masking_prob # 10% dropout\n", + "\n", + " # 4. Forward pass\n", + " outputs = model(\n", + " input_ids=masked_tokens,\n", + " condition_ids=[species_ids, activity], # Fixed: was conditions_ids\n", + " conditions_masks=[species_mask, activity_mask]\n", + " )\n", + "\n", + " # 5. Compute losses\n", + " seq_loss = compute_sequence_loss(\n", + " outputs['logits'], seq_targets, seq_weights, pad_token_id, tokens\n", + " )\n", + " activity_loss = compute_activity_loss(\n", + " outputs['condition_1_logits'], activity, ~activity_mask\n", + " )\n", + " total_loss = seq_loss + activity_loss_weight * activity_loss\n", + "\n", + " # 6. Compute metrics\n", + " seq_mask = seq_targets != pad_token_id\n", + " masked_tokens_count = seq_mask.sum().item()\n", + " total_tokens_count = (tokens != pad_token_id).sum().item()\n", + " masked_activity_count = (~activity_mask).sum().item()\n", + "\n", + " with torch.no_grad():\n", + " seq_preds = outputs['logits'].argmax(dim=-1)\n", + " seq_correct = (seq_preds == seq_targets) & seq_mask\n", + " seq_accuracy = seq_correct.sum().item() / max(1, masked_tokens_count)\n", + "\n", + " if masked_activity_count > 0:\n", + " act_preds = outputs['condition_1_logits'].argmax(dim=-1)\n", + " act_correct = (act_preds == activity) & (~activity_mask)\n", + " act_accuracy = act_correct.sum().item() / masked_activity_count\n", + " else:\n", + " act_accuracy = 0.0\n", + "\n", + " return {\n", + " 'loss': total_loss,\n", + " 'seq_loss': seq_loss.item(),\n", + " 'activity_loss': activity_loss.item(),\n", + " 'seq_accuracy': seq_accuracy,\n", + " 'act_accuracy': act_accuracy,\n", + " 'masked_tokens': masked_tokens_count,\n", + " 'total_tokens': total_tokens_count,\n", + " 'masked_activity': masked_activity_count,\n", + " 'batch_size': B,\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "dfa1613c", + "metadata": { + "id": "dfa1613c" + }, + "source": [ + "## 3) πŸ”„ Training Loop\n", + "\n", + "This section implements the main training loop with learning rate scheduling.\n", + "\n", + "---\n", + "\n", + "### Learning Rate Schedule\n", + "\n", + "We use a **modified square-root decay** schedule with linear warmup:\n", + "\n", + "1. **Warmup phase**: Linear increase from `initial_lr` to `peak_lr`\n", + "2. **Decay phase**: Power-law decay: $\\text{LR}(t) = \\text{peak\\_lr} \\times \\left(\\frac{\\text{warmup\\_steps}}{t}\\right)^\\alpha$\n", + "\n", + "This schedule provides:\n", + "- Stable early training (warmup prevents large initial updates)\n", + "- Gradual refinement (decay allows fine-tuning without overshooting)\n", + "\n", + "### Training Configuration\n", + "\n", + "| Parameter | Recommended Value | Description |\n", + "|-----------|------------------|-------------|\n", + "| `num_steps` | 500,000+ | Dataset dependent convergence time |\n", + "| `lr` | 1e-4 | Peak learning rate |\n", + "| `warmup_steps` | 25,000 | ~5% of total steps |\n", + "| `ema_decay` | 0.999 | Higher = smoother EMA |\n", + "| `activity_masking_prob` | 0.1 | Activity conditioning dropout rate |" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b3be2336", + "metadata": { + "id": "b3be2336" + }, + "outputs": [], + "source": [ + "def get_modified_sqrt_decay_scheduler(\n", + " optimizer: torch.optim.Optimizer,\n", + " warmup_steps: int,\n", + " total_steps: int,\n", + " initial_lr: float,\n", + " peak_lr: float,\n", + " final_ratio: float = 0.5,\n", + ") -> LambdaLR:\n", + " \"\"\"Linear warmup β†’ power-law decay scheduler.\"\"\"\n", + " alpha = np.log(1 / final_ratio) / np.log(total_steps / max(1, warmup_steps))\n", + "\n", + " def lr_lambda(step):\n", + " if step < warmup_steps:\n", + " return (initial_lr + (peak_lr - initial_lr) * step / max(1, warmup_steps)) / peak_lr\n", + " return (warmup_steps / (step + 1)) ** alpha\n", + "\n", + " return LambdaLR(optimizer, lr_lambda)\n", + "\n", + "\n", + "def train_mdlm(\n", + " model: nn.Module,\n", + " train_dataloader: DataLoader,\n", + " tokenizer: PreTrainedTokenizer,\n", + " num_steps: int = 10000,\n", + " lr: float = 5e-5,\n", + " initial_lr: float = 0.0,\n", + " device: str = 'cuda',\n", + " activity_masking_prob: float = 0.1,\n", + " activity_loss_weight: float = 1.0,\n", + " warmup_steps: int = 0,\n", + " scheduler_type: Optional[str] = None,\n", + " final_lr_ratio: float = 0.5,\n", + " log_every: int = 100,\n", + " ema_decay: float = 0.999,\n", + " use_ema: bool = True,\n", + ") -> Tuple[nn.Module, nn.Module]:\n", + " \"\"\"MDLM training loop. Returns (trained_model, ema_model).\"\"\"\n", + " model = model.to(device)\n", + " optimizer = AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=lr)\n", + "\n", + " ema = EMAModel(model, decay=ema_decay) if use_ema else None\n", + " scheduler = get_modified_sqrt_decay_scheduler(\n", + " optimizer, warmup_steps, num_steps, initial_lr, lr, final_lr_ratio\n", + " ) if scheduler_type == 'modified_sqrt' else None\n", + "\n", + " mask_token_id = tokenizer.mask_token_id\n", + " pad_token_id = tokenizer.pad_token_id\n", + "\n", + " model.train()\n", + " train_iter = iter(train_dataloader)\n", + " metrics = {'loss': 0, 'seq': 0, 'act': 0, 'seq_acc': 0, 'act_acc': 0,\n", + " 'masked': 0, 'total': 0, 'masked_act': 0, 'batch': 0}\n", + "\n", + " pbar = tqdm(range(1, num_steps + 1), desc=\"Training\")\n", + " for step in pbar:\n", + " try:\n", + " batch = next(train_iter)\n", + " except StopIteration:\n", + " train_iter = iter(train_dataloader)\n", + " batch = next(train_iter)\n", + "\n", + " tokens = batch['input_ids'].to(device)\n", + " species_ids = batch['species_ids'].to(device)\n", + " activity = batch['activity_labels'].to(device)\n", + " mask_vector = batch['masking_vector'].to(device)\n", + "\n", + " losses = train_step(\n", + " model, tokens, activity, species_ids, mask_vector,\n", + " mask_token_id, pad_token_id, activity_masking_prob, activity_loss_weight\n", + " )\n", + "\n", + " optimizer.zero_grad()\n", + " losses['loss'].backward()\n", + " optimizer.step()\n", + "\n", + " if ema: ema.update(model)\n", + " if scheduler: scheduler.step()\n", + "\n", + " # Track metrics\n", + " metrics['loss'] += losses['loss'].item()\n", + " metrics['seq'] += losses['seq_loss']\n", + " metrics['act'] += losses['activity_loss']\n", + " metrics['seq_acc'] += losses['seq_accuracy']\n", + " metrics['act_acc'] += losses['act_accuracy'] * losses['masked_activity']\n", + " metrics['masked'] += losses['masked_tokens']\n", + " metrics['total'] += losses['total_tokens']\n", + " metrics['masked_act'] += losses['masked_activity']\n", + " metrics['batch'] += losses['batch_size']\n", + "\n", + " if step % log_every == 0:\n", + " avg_act_acc = metrics['act_acc'] / max(1, metrics['masked_act'])\n", + " tqdm.write(\n", + " f\"Step {step:5d} | loss: {metrics['loss']/log_every:.4f} | \"\n", + " f\"seq_loss: {metrics['seq']/log_every:.4f} | seq_acc: {metrics['seq_acc']/log_every:.3f} | \"\n", + " f\"act_loss: {metrics['act']/log_every:.4f} | act_acc: {avg_act_acc:.3f} | \"\n", + " f\"lr: {optimizer.param_groups[0]['lr']:.2e}\"\n", + " )\n", + " metrics = {k: 0 for k in metrics}\n", + "\n", + " return (model, ema.get_model()) if use_ema else (model, model)" + ] + }, + { + "cell_type": "markdown", + "id": "505a8ec5", + "metadata": { + "id": "505a8ec5" + }, + "source": [ + "## 4) πŸ“Š Dataset\n", + "\n", + "This section defines the PyTorch Dataset class for loading enhancer sequences.\n", + "\n", + "---\n", + "\n", + "### Dataset Structure\n", + "\n", + "The `EnhancerDataset` class handles:\n", + "1. **Tokenization**: Convert DNA sequences to token IDs using the NTv3 tokenizer\n", + "2. **Activity labels**: Extract discrete activity bins (0-4)\n", + "3. **Masking vectors**: Create binary masks indicating which positions to train on\n", + "\n", + "### Enhancer Insertion Positions\n", + "\n", + "Different promoter types have the enhancer inserted at different positions:\n", + "\n", + "| Promoter | Enhancer Start | Enhancer End | Length |\n", + "|----------|---------------|--------------|--------|\n", + "| DSCP | 1018 | 1267 | 249 bp |\n", + "| RpS12 | 968 | 1217 | 249 bp |\n", + "\n", + "The `masking_vector` ensures we only apply MDLM loss to the enhancer region, not the flanking promoter/reporter sequences." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "20b82fb9", + "metadata": { + "id": "20b82fb9" + }, + "outputs": [], + "source": [ + "class EnhancerDataset(Dataset):\n", + " \"\"\"Dataset for enhancer generation training.\"\"\"\n", + "\n", + " # Enhancer insertion positions for each promoter type\n", + " PROMOTER_INSERTIONS = {\n", + " 'DSCP': [1018, 1267],\n", + " 'RpS12': [968, 1217],\n", + " }\n", + "\n", + " def __init__(self, hf_dataset, tokenizer: PreTrainedTokenizer, species_dict: Dict[str, int]):\n", + " self.data = hf_dataset\n", + " self.tokenizer = tokenizer\n", + " self.species_dict = species_dict\n", + "\n", + " def __len__(self) -> int:\n", + " return len(self.data)\n", + "\n", + " def __getitem__(self, idx: int) -> Dict[str, Any]:\n", + " item = self.data[idx]\n", + "\n", + " # Tokenize sequence\n", + " tokens = self.tokenizer(\n", + " item['full_sequence'], add_special_tokens=False, return_tensors='pt'\n", + " )['input_ids'].squeeze(0)\n", + "\n", + " # Activity label (0-4)\n", + " activity = torch.tensor(item.get('discrete_label', 0), dtype=torch.long)\n", + "\n", + " # Create masking vector for enhancer region only\n", + " promoter_type = item.get('promoter')\n", + " mask_interval = self.PROMOTER_INSERTIONS[promoter_type]\n", + " masking_vector = torch.zeros(len(tokens), dtype=torch.bool)\n", + " masking_vector[mask_interval[0]:mask_interval[1]] = True\n", + "\n", + " return {\n", + " 'input_ids': tokens,\n", + " 'species_ids': self.species_dict['drosophila_melanogaster'],\n", + " 'activity_labels': activity,\n", + " 'masking_vector': masking_vector\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "b5d1f96d", + "metadata": { + "id": "b5d1f96d" + }, + "source": [ + "## 5) πŸš€ Run Training\n", + "\n", + "Now we instantiate the dataset and run the training loop.\n", + "\n", + "---\n", + "\n", + "### Training Parameters\n", + "\n", + "The configuration below is set for a **full training run**. For quick demo, decrease `num_steps` and adjust batch size according to your GPU memory:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cfb3aa86", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cfb3aa86", + "outputId": "bdb773dd-658b-42d4-ba82-98507c31e3d5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training on 804592 samples\n" + ] + } + ], + "source": [ + "# Create dataset and dataloader\n", + "train_dataset = EnhancerDataset(dataset, tokenizer, generation_config.species_to_token_id)\n", + "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=0)\n", + "\n", + "print(f\"Training on {len(train_dataset)} samples\")" + ] + }, + { + "cell_type": "markdown", + "id": "02d6fff1", + "metadata": { + "id": "02d6fff1" + }, + "source": [ + "### Understanding Training Metrics\n", + "\n", + "During training, monitor these metrics:\n", + "\n", + "| Metric | What to Watch |\n", + "|--------|---------------|\n", + "| `loss` | Total loss (seq + activity) | \n", + "| `seq_loss` | Sequence prediction loss | \n", + "| `seq_acc` | Token prediction accuracy | \n", + "| `act_loss` | Activity prediction loss | \n", + "| `act_acc` | Activity classification accuracy |\n", + "\n", + "**Interpreting Results:**\n", + "- **seq_acc** measures how well the model predicts masked DNA tokens\n", + "- **act_acc** measures if the model learns the activity-sequence relationship" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0fe43871", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0fe43871", + "outputId": "68a7ad30-ec30-45cb-a610-bdd9f88f6aff" + }, + "outputs": [], + "source": [ + "# Train the model\n", + "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", + "print(f\"Training on: {device}\")\n", + "\n", + "trained_model, ema_model = train_mdlm(\n", + " model=generation_model,\n", + " train_dataloader=train_loader,\n", + " tokenizer=tokenizer,\n", + " num_steps=500000, # Increase for real training (e.g., 50000)\n", + " lr=1e-4, # Peak LR\n", + " initial_lr=5e-5, # Starting LR for warmup\n", + " device=device,\n", + " activity_masking_prob=0.1, # 10% activity dropout for CFG\n", + " activity_loss_weight=0.1,\n", + " warmup_steps=25000, # Increase for real training\n", + " scheduler_type='modified_sqrt',\n", + " final_lr_ratio=0.5,\n", + " log_every=2000, # Increase for real training (e.g., 1000)\n", + " ema_decay=0.999,\n", + " use_ema=True\n", + ")\n", + "\n", + "# Use EMA model for generation (smoother, more stable)\n", + "generation_model = ema_model\n", + "print(\"Training complete!\")" + ] + }, + { + "cell_type": "markdown", + "id": "e48a2658", + "metadata": { + "id": "e48a2658" + }, + "source": [ + "### Saving Checkpoints\n", + "\n", + "After training, save both the training model and the EMA model. **Use the EMA model for generation** as it produces smoother, more stable outputs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "528236cd", + "metadata": { + "id": "528236cd" + }, + "outputs": [], + "source": [ + "#Save checkpoint\n", + "torch.save(trained_model.state_dict(), 'ntv3_generative_finetuned.pt')\n", + "torch.save(ema_model.state_dict(), 'ntv3_generative_finetuned_ema.pt')\n", + "print(\"Checkpoints saved!\")" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "A100", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "00c99b6cadc34fbaaf4b8e1286844dbc": { + "model_module": "@jupyter-widgets/controls", + 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