{ "cells": [ { "cell_type": "markdown", "id": "15bb8c23", "metadata": { "id": "15bb8c23" }, "source": [ "# 🧬 Training a discrete diffusion model on biological sequences\n", "\n", "This notebook demonstrates how to **fine-tune NTv3** for controllable enhancer sequence generation using **Masked Diffusion Language Modeling (MDLM)** ([paper](https://arxiv.org/pdf/2406.07524)).\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, "referenced_widgets": [ "99c1e64a4752426a98777c831a5e1f5e", 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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", "5d298818dbcc47f1a31149896bb0cb66", "88a964e240094792ae2ecf9cae85df3f", "8e5a8320aa914efbaa1a7b1afd0a54ab", "19d28b94680d4185892b18bf1c2124db", "c9b48ba1d19146baa89c70dab620c453", 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"aaf47bf823fc4a489df06f88d7dbefbb", "2713575b044a4ffbb293523580b35fc6", "6c92c1f76f6247a79be6bfdd9b10dbc6", "a704bb8da0ce4262ae1104f713b371e1", "937c0e6464464364af85c3027ab52daa", "df29dd6eed4a433692bba36f0a21f273", "9498d3ac6e074fd9a1dc3cec2f299e98", "d268ed6f0da04554b13d551261822930", "803fa19b4dfe41c68dae567a135478c9", "49dc35db54a144bf8cb39b298e41aad2", "4bbc3577c8a4462daa616d5f569695d8", "fcd298ba7d194b16bfa9b16d57aeb488", "04cdd0e4a7e7472ba91759ee717bfe08", "90cb3f5b7c6f4623b288dea5b1ee8bd6", "3ae4dda5c6c446f485bd5e1c6637dfe8", "aa52d8678ff74350bc69f289678e55b3", "8c27b209aefd42a5b206d994057411a6", "cdd0c1d8f65543f494370ae118e01ea8", "8db1b3992a6a4e2db5573abf654e9f5f", "773f7b84ace94c1a9df2e95971e04aac", "d244fb08a91d4f0d9e54d8b427ed56c3", "11931a415f6c4c7986257f8f85106348", "e8cdddfe96a145d48261f42089684ac0", "3913ff46a8be4521ab00cd56cb41b35f", "e797eb31a8004b0fa248219a31081f01", "e003f9d8509b4faabef3f02191756d58", "bc52b7f4cc4040f8a7df56261dc01b72", "08f7d0286272451b96de80dff95174fb", "124d46a6e87a43ab85d69a99c53ffe6c", "a09035d76efc4d958095dcc6e4b2b311", "1773fefec3234ed2908b621babaf383a", "a15fdf07c3394c75ad96210bbeebb541", "af140d7e096145f6a88644f822539448", "8ecaae15a1e64e678e5540b3a6ab8ed2", "c88ed47f52c647f1b14eadc5dac2e9c0", "4d963aa1d9684265818bb599b4949be3" ] }, "collapsed": true, "id": "7f64da95", "outputId": "df7b082a-1484-48d4-f290-35fc8f2792ef" }, "outputs": [], "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(), 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