:rocket: Add dockerfile, app.py, and requirements.txt

Dockerfile

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+# Use the official PyTorch Docker image as a base (includes CUDA and PyTorch)
+FROM pytorch/pytorch:1.11.0-cuda11.3-cudnn8-runtime
+
+# Set a working directory in the container
+WORKDIR /workspace
+
+# Install Python dependencies
+COPY requirements.txt .
+RUN pip install --upgrade pip
+RUN pip install -r requirements.txt
+
+# Copy the training script
+COPY app.py .
+
+# Run the training script
+CMD ["python", "app.py"]

app.py

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+from typing import Any
+
+from pytorch_lightning import Trainer, LightningModule, LightningDataModule
+from pytorch_lightning.utilities.types import OptimizerLRScheduler, STEP_OUTPUT, EVAL_DATALOADERS
+from torch.utils.data import DataLoader, Dataset
+from torcheval.metrics import BinaryAccuracy, BinaryAUROC, BinaryF1Score, BinaryPrecision, BinaryRecall
+from transformers import BertModel, BatchEncoding, BertTokenizer, TrainingArguments
+from transformers.modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions
+import torch
+from torch import nn
+from datasets import load_dataset
+
+black = "\u001b[30m"
+red = "\u001b[31m"
+green = "\u001b[32m"
+yellow = "\u001b[33m"
+blue = "\u001b[34m"
+magenta = "\u001b[35m"
+cyan = "\u001b[36m"
+white = "\u001b[37m"
+
+FORWARD = "FORWARD_INPUT"
+BACKWARD = "BACKWARD_INPUT"
+
+DNA_BERT_6 = "zhihan1996/DNA_bert_6"
+
+
+class CommonAttentionLayer(nn.Module):
+  def __init__(self, hidden_size, *args, **kwargs):
+    super().__init__(*args, **kwargs)
+    self.attention_linear = nn.Linear(hidden_size, 1)
+    pass
+
+  def forward(self, hidden_states):
+    # Apply linear layer
+    attn_weights = self.attention_linear(hidden_states)
+    # Apply softmax to get attention scores
+    attn_weights = torch.softmax(attn_weights, dim=1)
+    # Apply attention weights to hidden states
+    context_vector = torch.sum(attn_weights * hidden_states, dim=1)
+    return context_vector, attn_weights
+
+
+class ReshapedBCEWithLogitsLoss(nn.BCEWithLogitsLoss):
+  def forward(self, input, target):
+    return super().forward(input.squeeze(), target.float())
+
+
+class MQtlDnaBERT6Classifier(nn.Module):
+  def __init__(self,
+               bert_model=BertModel.from_pretrained(pretrained_model_name_or_path=DNA_BERT_6),
+               hidden_size=768,
+               num_classes=1,
+               *args,
+               **kwargs
+               ):
+    super().__init__(*args, **kwargs)
+
+    self.model_name = "MQtlDnaBERT6Classifier"
+
+    self.bert_model = bert_model
+    self.attention = CommonAttentionLayer(hidden_size)
+    self.classifier = nn.Linear(hidden_size, num_classes)
+    pass
+
+  def forward(self, input_ids: torch.tensor, attention_mask: torch.tensor, token_type_ids):
+    """
+    # torch.Size([128, 1, 512]) --> [128, 512]
+    input_ids = input_ids.squeeze(dim=1).to(DEVICE)
+    # torch.Size([16, 1, 512]) --> [16, 512]
+    attention_mask = attention_mask.squeeze(dim=1).to(DEVICE)
+    token_type_ids = token_type_ids.squeeze(dim=1).to(DEVICE)
+    """
+    bert_output: BaseModelOutputWithPoolingAndCrossAttentions = self.bert_model(
+      input_ids=input_ids,
+      attention_mask=attention_mask,
+      token_type_ids=token_type_ids
+    )
+
+    last_hidden_state = bert_output.last_hidden_state
+    context_vector, ignore_attention_weight = self.attention(last_hidden_state)
+    y = self.classifier(context_vector)
+    return y
+
+
+class TorchMetrics:
+  def __init__(self):
+    self.binary_accuracy = BinaryAccuracy()  #.to(device)
+    self.binary_auc = BinaryAUROC()  # .to(device)
+    self.binary_f1_score = BinaryF1Score()  # .to(device)
+    self.binary_precision = BinaryPrecision()  # .to(device)
+    self.binary_recall = BinaryRecall()  # .to(device)
+    pass
+
+  def update_on_each_step(self, batch_predicted_labels, batch_actual_labels):  # todo: Add log if needed
+    # it looks like the library maintainers changed preds to input, ie, before: preds, now: input
+    self.binary_accuracy.update(input=batch_predicted_labels, target=batch_actual_labels)
+    self.binary_auc.update(input=batch_predicted_labels, target=batch_actual_labels)
+    self.binary_f1_score.update(input=batch_predicted_labels, target=batch_actual_labels)
+    self.binary_precision.update(input=batch_predicted_labels, target=batch_actual_labels)
+    self.binary_recall.update(input=batch_predicted_labels, target=batch_actual_labels)
+    pass
+
+  def compute_and_reset_on_epoch_end(self, log, log_prefix: str, log_color: str = green):
+    b_accuracy = self.binary_accuracy.compute()
+    b_auc = self.binary_auc.compute()
+    b_f1_score = self.binary_f1_score.compute()
+    b_precision = self.binary_precision.compute()
+    b_recall = self.binary_recall.compute()
+    # timber.info(  log_color + f"{log_prefix}_acc = {b_accuracy}, {log_prefix}_auc = {b_auc}, {log_prefix}_f1_score = {b_f1_score}, {log_prefix}_precision = {b_precision}, {log_prefix}_recall = {b_recall}")
+    log(f"{log_prefix}_accuracy", b_accuracy)
+    log(f"{log_prefix}_auc", b_auc)
+    log(f"{log_prefix}_f1_score", b_f1_score)
+    log(f"{log_prefix}_precision", b_precision)
+    log(f"{log_prefix}_recall", b_recall)
+
+    self.binary_accuracy.reset()
+    self.binary_auc.reset()
+    self.binary_f1_score.reset()
+    self.binary_precision.reset()
+    self.binary_recall.reset()
+    pass
+
+
+class MQtlBertClassifierLightningModule(LightningModule):
+  def __init__(self,
+               classifier: nn.Module,
+               criterion=None,  # nn.BCEWithLogitsLoss(),
+               regularization: int = 2,  # 1 == L1, 2 == L2, 3 (== 1 | 2) == both l1 and l2, else ignore / don't care
+               l1_lambda=0.001,
+               l2_wright_decay=0.001,
+               *args: Any,
+               **kwargs: Any):
+    super().__init__(*args, **kwargs)
+    self.classifier = classifier
+    self.criterion = criterion
+    self.train_metrics = TorchMetrics()
+    self.validate_metrics = TorchMetrics()
+    self.test_metrics = TorchMetrics()
+
+    self.regularization = regularization
+    self.l1_lambda = l1_lambda
+    self.l2_weight_decay = l2_wright_decay
+    pass
+
+  def forward(self, x, *args: Any, **kwargs: Any) -> Any:
+    input_ids: torch.tensor = x["input_ids"]
+    attention_mask: torch.tensor = x["attention_mask"]
+    token_type_ids: torch.tensor = x["token_type_ids"]
+    # print(f"\n{ type(input_ids) = }, {input_ids = }")
+    # print(f"{ type(attention_mask) = }, { attention_mask = }")
+    # print(f"{ type(token_type_ids) = }, { token_type_ids = }")
+
+    return self.classifier.forward(input_ids, attention_mask, token_type_ids)
+
+  def configure_optimizers(self) -> OptimizerLRScheduler:
+    # Here we add weight decay (L2 regularization) to the optimizer
+    weight_decay = 0.0
+    if self.regularization == 2 or self.regularization == 3:
+      weight_decay = self.l2_weight_decay
+    return torch.optim.Adam(self.parameters(), lr=1e-3, weight_decay=weight_decay)  # , weight_decay=0.005)
+
+  def training_step(self, batch, batch_idx, *args: Any, **kwargs: Any) -> STEP_OUTPUT:
+    # Accuracy on training batch data
+    x, y = batch
+    preds = self.forward(x)
+    loss = self.criterion(preds, y)
+
+    if self.regularization == 1 or self.regularization == 3:  # apply l1 regularization
+      l1_norm = sum(p.abs().sum() for p in self.parameters())
+      loss += self.l1_lambda * l1_norm
+
+    self.log("train_loss", loss)
+    # calculate the scores start
+    self.train_metrics.update_on_each_step(batch_predicted_labels=preds.squeeze(), batch_actual_labels=y)
+    # calculate the scores end
+    return loss
+
+  def on_train_epoch_end(self) -> None:
+    self.train_metrics.compute_and_reset_on_epoch_end(log=self.log, log_prefix="train")
+    pass
+
+  def validation_step(self, batch, batch_idx, *args: Any, **kwargs: Any) -> STEP_OUTPUT:
+    # Accuracy on validation batch data
+    # print(f"debug { batch = }")
+    x, y = batch
+    preds = self.forward(x)
+    loss = 0  # self.criterion(preds, y)
+    self.log("valid_loss", loss)
+    # calculate the scores start
+    self.validate_metrics.update_on_each_step(batch_predicted_labels=preds.squeeze(), batch_actual_labels=y)
+    # calculate the scores end
+    return loss
+
+  def on_validation_epoch_end(self) -> None:
+    self.validate_metrics.compute_and_reset_on_epoch_end(log=self.log, log_prefix="validate", log_color=blue)
+    return None
+
+  def test_step(self, batch, batch_idx, *args: Any, **kwargs: Any) -> STEP_OUTPUT:
+    # Accuracy on validation batch data
+    x, y = batch
+    preds = self.forward(x)
+    loss = self.criterion(preds, y)
+    self.log("test_loss", loss)  # do we need this?
+    # calculate the scores start
+    self.test_metrics.update_on_each_step(batch_predicted_labels=preds.squeeze(), batch_actual_labels=y)
+    # calculate the scores end
+    return loss
+
+  def on_test_epoch_end(self) -> None:
+    self.test_metrics.compute_and_reset_on_epoch_end(log=self.log, log_prefix="test", log_color=magenta)
+    return None
+
+  pass
+
+
+class DNABERTDataset(Dataset):
+  def __init__(self, dataset, tokenizer, max_length=512):
+    self.dataset = dataset
+    self.bert_tokenizer = tokenizer
+    self.max_length = max_length
+
+  def __len__(self):
+    return len(self.dataset)
+
+  def __getitem__(self, idx):
+    sequence = self.dataset[idx]['sequence']  # Fetch the 'sequence' column
+    label = self.dataset[idx]['label']  # Fetch the 'label' column (or whatever target you use)
+
+    # Tokenize the sequence
+    encoded_sequence: BatchEncoding = self.bert_tokenizer(
+      sequence,
+      truncation=True,
+      padding='max_length',
+      max_length=self.max_length,
+      return_tensors='pt'
+    )
+
+    encoded_sequence_squeezed = {key: val.squeeze() for key, val in encoded_sequence.items()}
+    return encoded_sequence_squeezed, label
+
+
+class DNABERTDataModule(LightningDataModule):
+  def __init__(self, model_name=DNA_BERT_6, batch_size=8):
+    super().__init__()
+    self.tokenized_dataset = None
+    self.dataset = None
+    self.train_dataset: DNABERTDataset = None
+    self.validate_dataset: DNABERTDataset = None
+    self.test_dataset: DNABERTDataset = None
+    self.tokenizer = BertTokenizer.from_pretrained(pretrained_model_name_or_path=DNA_BERT_6)
+    self.batch_size = batch_size
+
+  def prepare_data(self):
+    # Download and prepare dataset
+    self.dataset = load_dataset("fahimfarhan/mqtl-classification-dataset-binned-200")
+
+  def setup(self, stage=None):
+    self.train_dataset = DNABERTDataset(self.dataset['train'], self.tokenizer)
+    self.validate_dataset = DNABERTDataset(self.dataset['validate'], self.tokenizer)
+    self.test_dataset = DNABERTDataset(self.dataset['test'], self.tokenizer)
+
+  def train_dataloader(self):
+    return DataLoader(self.train_dataset, batch_size=self.batch_size, shuffle=True, num_workers=15)
+
+  def val_dataloader(self):
+    return DataLoader(self.validate_dataset, batch_size=self.batch_size, num_workers=15)
+
+  def test_dataloader(self) -> EVAL_DATALOADERS:
+    return DataLoader(self.test_dataset, batch_size=self.batch_size, num_workers=15)
+
+
+# Initialize DataModule
+model_name = "zhihan1996/DNABERT-6"
+data_module = DNABERTDataModule(model_name=model_name, batch_size=8)
+
+
+def start_bert(classifier_model, model_save_path, criterion, WINDOW=200, batch_size=4,
+               dataset_folder_prefix="inputdata/", is_binned=True, is_debug=False, max_epochs=10):
+  file_suffix = ""
+  if is_binned:
+    file_suffix = "_binned"
+
+  data_module = DNABERTDataModule(batch_size=batch_size)
+
+  # classifier_model = classifier_model.to(DEVICE)
+
+  classifier_module = MQtlBertClassifierLightningModule(
+    classifier=classifier_model,
+    regularization=2, criterion=criterion)
+
+  # if os.path.exists(model_save_path):
+  #   classifier_module.load_state_dict(torch.load(model_save_path))
+
+  classifier_module = classifier_module  # .double()
+
+  # Set up training arguments
+  training_args = TrainingArguments(
+    output_dir='./results',
+    evaluation_strategy="epoch",
+    per_device_train_batch_size=batch_size,
+    per_device_eval_batch_size=batch_size,
+    num_train_epochs=max_epochs,
+    logging_dir='./logs',
+    report_to="none",  # Disable reporting to WandB, etc.
+  )
+
+  # Prepare data using the DataModule
+  data_module.prepare_data()
+  data_module.setup()
+
+  # Initialize Trainer
+  # trainer = Trainer(
+  #   model=classifier_module,
+  #   args=training_args,
+  #   train_dataset=data_module.tokenized_dataset["train"],
+  #   eval_dataset=data_module.tokenized_dataset["test"],
+  # )
+
+  trainer = Trainer(max_epochs=max_epochs, precision="32")
+
+  # Train the model
+  trainer.fit(model=classifier_module, datamodule=data_module)
+  trainer.test(model=classifier_module, datamodule=data_module)
+  torch.save(classifier_module.state_dict(), model_save_path)
+
+  classifier_module.push_to_hub("fahimfarhan/mqtl-classifier-model")
+  pass
+
+
+if __name__ == "__main__":
+  dataset_folder_prefix = "inputdata/"
+  pytorch_model = MQtlDnaBERT6Classifier()
+  start_bert(classifier_model=pytorch_model, model_save_path=f"weights_{pytorch_model.model_name}.pth",
+             criterion=ReshapedBCEWithLogitsLoss(), WINDOW=200, batch_size=4,
+             dataset_folder_prefix=dataset_folder_prefix, max_epochs=2)
+  pass

requirements.txt

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+accelerate  # required by HayenaDNA
+datasets
+pandas
+polars
+numpy
+matplotlib
+scipy
+shap
+scikit-learn
+skorch==1.0.0
+six
+hyperopt
+requests
+pyyaml
+Bio
+plotly
+Levenshtein
+# pytorch
+captum
+torch==2.4.0
+torchvision
+torchaudio
+torchsummary
+torcheval
+pydot
+pydotplus
+PySide2  # matplotlib dependency on ubuntu. you may need sth else for other os/env setup
+torchviz
+gReLU @ git+https://github.com/Genentech/gReLU # @623fee8023aabcef89f0afeedbeafff4b71453af
+# lightning[extra]  # cz I got a stupid warning in the console logs
+torchmetrics