Bug fix: HyenaDNA variable length input sequences

ci/tests/test_hyena_dna/test_hyena_dna_fine_tuning.py

@@ -12,7 +12,7 @@ def hyenaDNAFineTune(self, request):
     @pytest.fixture
     def mock_data(self, hyenaDNAFineTune):
         input_sequences = ["AAAA", "CCCC", "TTTT", "ACGT", "ACGN", "ANNT"]
-        labels = [0, 0, 0, 0, 0, 0, 0]
+        labels = [0, 0, 0, 0, 0, 0]
         tokenized_sequences = hyenaDNAFineTune.process_data(input_sequences)
         return tokenized_sequences, labels
 

ci/tests/test_hyena_dna/test_hyena_dna_model.py

@@ -1,8 +1,7 @@
 from helical.models.hyena_dna.model import HyenaDNAConfig
 import pytest
-import torch
 from helical.models.hyena_dna.model import HyenaDNA
-from helical.models.hyena_dna.hyena_dna_utils import HyenaDNADataset
+import numpy as np
 
 @pytest.mark.parametrize("model_name, d_model, d_inner", [
     ("hyenadna-tiny-1k-seqlen", 128, 512),
@@ -39,39 +38,28 @@ def test_hyena_dna__invalid_model_names(model_name):
     with pytest.raises(ValueError):
         HyenaDNAConfig(model_name=model_name)
 
-@pytest.mark.parametrize("input_sequence, expected_output, raise_error", [
+@pytest.mark.parametrize("input_sequence, expected_output", [
     # Valid DNA sequences
-    ("", [0, 1], False),
-    ("A", [0, 7, 1], False),
-    ("CC", [0, 8, 8, 1], False),
-    ("TTTT", [0, 10, 10, 10, 10, 1], False),
-    ("ACGTN", [0, 7, 8, 9, 10, 11, 1], False),
-    ("ACGT" * 256, [0] + [7, 8, 9, 10] * 256 + [1], False),
-    # Invalid sequences / sequences with uncertain 'N' nucleodites 
-    ("BHIK", [0, 6, 6, 6, 6, 1], True),
-    ("ANNTBH", [0, 7, 11, 11, 10, 6, 6, 1], True),
-
+    ("", [0, 1]),
+    ("A", [0, 7, 1]),
+    ("CC", [0, 8, 8, 1]),
+    ("TTTT", [0, 10, 10, 10, 10, 1]),
+    ("ACGTN", [0, 7, 8, 9, 10, 11, 1]),
+    ("ACGT" * 256, [0] + [7, 8, 9, 10] * 256 + [1])
 ])
-def test_hyena_dna_process_data(input_sequence, expected_output, raise_error):
-    """
-    Test the process_data method of the HyenaDNA model.
-    The input DNA sequence is tokenized and the output is compared to the expected output.
-
-    Args:
-        input_sequence (str): The input DNA sequence to be processed.
-        expected_output (int): The expected output of the process_data method.
-
-    Returns:
-        None
+def test_hyena_dna_process_data(input_sequence, expected_output):
+    model = HyenaDNA()
+    output = model.process_data([input_sequence])
+    expected = np.array(expected_output)
+    assert np.all(np.equal(np.array(output["input_ids"][0]), expected))
 
-    Raises:
-        AssertionError: If the output of the process_data method does not match the expected output.
-    """
+@pytest.mark.parametrize("input_sequence, expected_output", [
+    (
+        ["A", "CC", "TTTT", "ACGTN", "ACGT"],
+        [[4, 4, 4, 4, 0, 7, 1], [4, 4, 4, 0, 8, 8, 1], [4, 0, 10, 10, 10, 10, 1], [0, 7, 8, 9, 10, 11, 1], [4, 0, 7, 8, 9, 10, 1]]
+    )
+])
+def test_hyena_dna_process_data_variable_length_sequences(input_sequence, expected_output):
     model = HyenaDNA()
-    if raise_error:
-        with pytest.raises(ValueError):
-            model.process_data([input_sequence])
-    else:
-        output = model.process_data([input_sequence])
-        expected = torch.tensor([expected_output])
-        assert torch.equal(output.sequences, expected)
+    dataset = model.process_data(input_sequence)
+    assert np.all(np.equal(np.array(expected_output), np.array(dataset["input_ids"])))

examples/fine_tune_models/fine_tune_hyena_dna.py

@@ -4,7 +4,7 @@
 
 @hydra.main(version_base=None, config_path="../run_models/configs", config_name="hyena_dna_config")
 def run_fine_tuning(cfg: DictConfig):
-    input_sequences = ["ACT"*20, "ATG"*20, "ATG"*20, "ACT"*20, "ATT"*20]
+    input_sequences = ["ACT"*20, "ATG"*10, "ATG"*20, "ACT"*10, "ATT"*20]
     labels = [0, 2, 2, 0, 1]
 
     hyena_dna_config = HyenaDNAConfig(**cfg)

examples/run_models/run_hyena_dna.py

@@ -4,12 +4,13 @@
 
 @hydra.main(version_base=None, config_path="configs", config_name="hyena_dna_config")
 def run(cfg: DictConfig):
-
     hyena_config = HyenaDNAConfig(**cfg)
     model = HyenaDNA(configurer = hyena_config)   
-    sequence = ['ACTG' * int(1024/4)]
-    tokenized_sequence = model.process_data(sequence)
-    embeddings = model.get_embeddings(tokenized_sequence)
+
+    sequence = ["A", "CC", "TTTT", "ACGTN", "ACGT"]
+
+    dataset = model.process_data(sequence)
+    embeddings = model.get_embeddings(dataset)
     print(embeddings.shape)
 
 if __name__ == "__main__":

helical/models/hyena_dna/fine_tuning_model.py

@@ -4,7 +4,7 @@
 from torch import optim
 import torch
 from torch.nn.modules import loss
-from .hyena_dna_utils import HyenaDNADataset
+from datasets import Dataset
 from torch.utils.data import DataLoader
 from tqdm import tqdm
 from transformers import get_scheduler
@@ -75,15 +75,16 @@ def _forward(self, x):
 
     def train(        
         self,
-        train_dataset: HyenaDNADataset,
+        train_dataset: Dataset,
         train_labels: list[int],     
-        validation_dataset: HyenaDNADataset = None,
+        validation_dataset: Dataset = None,
         validation_labels: list[int] = None,
         optimizer: optim = optim.AdamW,
         optimizer_params: dict = {'lr': 0.0001}, 
         loss_function: loss = loss.CrossEntropyLoss(), 
         epochs: int = 1,
-        lr_scheduler_params: Optional[dict] = None):
+        lr_scheduler_params: Optional[dict] = None,
+        shuffle: bool = True):
         """Fine-tunes the Hyena-DNA model with different head modules. 
 
         Parameters
@@ -108,13 +109,15 @@ def train(
         lr_scheduler_params : dict, default=None
             The learning rate scheduler parameters for the transformers get_scheduler method. The optimizer will be taken from the optimizer input and should not be included in the learning scheduler parameters. If not specified, a constant learning rate will be used.
             e.g. lr_scheduler_params = { 'name': 'linear', 'num_warmup_steps': 0 }. num_steps will be calculated based on the number of epochs and the length of the training dataset.
+        shuffle : bool, default=True
+            Whether to shuffle the training data or not.
         """
-        train_dataset.set_labels(train_labels)
-        train_dataloader = DataLoader(train_dataset, batch_size=self.config["batch_size"])
+        train_dataset = self._add_data_column(train_dataset, np.array(train_labels))
+        train_dataloader = DataLoader(train_dataset, collate_fn=self._collate_fn, batch_size=self.config["batch_size"], shuffle=shuffle)
 
         if validation_dataset is not None and validation_labels is not None:
-            validation_dataset.set_labels(validation_labels)
-            validation_dataloader = DataLoader(validation_dataset, batch_size=self.config["batch_size"])
+            validation_dataset = self._add_data_column(validation_dataset, np.array(validation_labels))
+            validation_dataloader = DataLoader(validation_dataset, collate_fn=self._collate_fn, batch_size=self.config["batch_size"])
 
         self.to(self.config["device"])
         self.model.train()
@@ -163,7 +166,7 @@ def train(
 
     def get_outputs(
             self, 
-            dataset: HyenaDNADataset) -> np.ndarray:
+            dataset: Dataset) -> np.ndarray:
         """Get the outputs of the fine-tuned model.
 
         Parameters
@@ -176,7 +179,7 @@ def get_outputs(
         np.ndarray
             The outputs of the model
         """
-        data_loader = DataLoader(dataset, batch_size=self.config["batch_size"])
+        data_loader = DataLoader(dataset, collate_fn=self._collate_fn, batch_size=self.config["batch_size"])
 
         self.to(self.config["device"])
         self.model.eval()
@@ -189,4 +192,24 @@ def get_outputs(
             output = self._forward(input_data)
             outputs.append(output.detach().cpu().numpy())
 
-        return np.vstack(outputs)
+        return np.vstack(outputs)
+
+    def _add_data_column(self, dataset: Dataset, data: list, column_name: str="labels") -> Dataset:
+        """
+        Add a column to the dataset.
+
+        Parameters
+        ----------
+        dataset : Dataset
+            The dataset to add the column to.
+        data : list
+            The data to add to the column.
+        column_name : str, optional, default="labels"
+            The name of the column to add.
+        """
+        if len(data.shape) > 1:
+            for i in range(len(data[0])):  # Assume all inner lists are the same length
+                dataset = dataset.add_column(f"{column_name}", [row[i] for row in data])
+        else:  # If 1D
+            dataset = dataset.add_column(column_name, data)
+        return dataset

helical/models/hyena_dna/model.py

@@ -2,7 +2,7 @@
 from helical.models.hyena_dna.hyena_dna_config import HyenaDNAConfig
 from helical.models.base_models import HelicalDNAModel
 from tqdm import tqdm
-from .hyena_dna_utils import HyenaDNADataset
+from datasets import Dataset
 from helical.models.hyena_dna.pretrained_model import HyenaDNAPreTrainedModel
 import torch
 from .standalone_hyenadna import CharacterTokenizer
@@ -69,44 +69,44 @@ def __init__(self, configurer: HyenaDNAConfig = default_configurer) -> None:
         self.model.eval()
         LOGGER.info(f"Model finished initializing.")
 
-    def process_data(self, sequence: list[str]) -> HyenaDNADataset:
-        """Process the input DNA sequences.
+    def process_data(self, sequences: list[str], return_tensors: str="pt", padding: str="max_length", truncation: bool=True) -> Dataset:
+        """Process the input DNA sequence.
 
         Parameters 
         ----------
         sequences : list[str]
             The input DNA sequences to be processed.
+        return_tensors : str, optional, default="pt"
+            The return type of the processed data.
+        padding : str, optional, default="max_length"
+            The padding strategy to be used.
+        truncation : bool, optional, default=True
+            Whether to truncate the sequences or not.
 
         Returns
         -------
-        HyenaDNADataset
+        Dataset
             Containing processed DNA sequences.
 
         """
-        LOGGER.info(f"Processing data")
+        LOGGER.info("Processing data")
 
-        self.ensure_dna_sequence_validity(sequence)
+        self.ensure_dna_sequence_validity(sequences)
 
-        processed_sequences = []
-        for seq in tqdm(sequence, desc="Processing sequences"):
-            tok_seq = self.tokenizer(seq)
-            tok_seq_input_ids = tok_seq["input_ids"]
+        max_length = len(max(sequences, key=len))+2 # +2 for special tokens at the beginning and end of sequences
 
-            tensor = torch.LongTensor(tok_seq_input_ids)
-            tensor = tensor.to(self.device)
-            processed_sequences.append(tensor)
+        tokenized_sequences = self.tokenizer(sequences, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length)
 
-        dataset = HyenaDNADataset(torch.stack(processed_sequences))
+        dataset = Dataset.from_dict(tokenized_sequences)
         LOGGER.info(f"Data processing finished.")
-
         return dataset
 
-    def get_embeddings(self, dataset: HyenaDNADataset) -> torch.Tensor:
+    def get_embeddings(self, dataset: Dataset) -> torch.Tensor:
         """Get the embeddings for the tokenized sequence.
 
         Parameters
         ----------
-        dataset : HyenaDNADataset
+        dataset : Dataset
             The output dataset from `process_data`.
 
         Returns
@@ -117,11 +117,23 @@ def get_embeddings(self, dataset: HyenaDNADataset) -> torch.Tensor:
         """
         LOGGER.info(f"Inference started")
 
-        train_data_loader = DataLoader(dataset, batch_size=self.config["batch_size"])
+        train_data_loader = DataLoader(dataset, collate_fn=self._collate_fn, batch_size=self.config["batch_size"])
         with torch.inference_mode():
             embeddings = []
             for batch in tqdm(train_data_loader, desc="Getting embeddings"):
                 input_data = batch["input_ids"].to(self.device)
                 embeddings.append(self.model(input_data).detach().cpu().numpy())
 
         return np.vstack(embeddings)
+
+
+    def _collate_fn(self, batch):
+        input_ids = torch.tensor([item["input_ids"] for item in batch])
+        batch_dict = {
+            "input_ids": input_ids,
+        }
+
+        if "labels" in batch[0]:
+            batch_dict["labels"] = torch.tensor([item["labels"] for item in batch])
+
+        return batch_dict