From 0a3d87008f03d85e2c81d5f98551337d1fbc8960 Mon Sep 17 00:00:00 2001 From: Matthew Wood <62712722+mattwoodx@users.noreply.github.com> Date: Mon, 7 Jul 2025 17:17:58 +0200 Subject: [PATCH 01/17] Update typo to include regression as an option for HyenaDNA fine tuning (#242) * Update typo to include regression as an option for HyenaDNA fine tuning * Upgrade datasets version to fix loading issue * fixup! Upgrade datasets version to fix loading issue --- examples/notebooks/Cell-Type-Annotation.ipynb | 8 +- examples/notebooks/Hyena-DNA-Inference.ipynb | 884 +++++++++++++++--- examples/notebooks/HyenaDNA-Fine-Tuning.ipynb | 8 +- helical/models/hyena_dna/fine_tuning_model.py | 4 +- pyproject.toml | 2 +- 5 files changed, 777 insertions(+), 129 deletions(-) diff --git a/examples/notebooks/Cell-Type-Annotation.ipynb b/examples/notebooks/Cell-Type-Annotation.ipynb index 95877c6f..e2edfb7f 100644 --- a/examples/notebooks/Cell-Type-Annotation.ipynb +++ b/examples/notebooks/Cell-Type-Annotation.ipynb @@ -1148,8 +1148,14 @@ } ], "metadata": { + "kernelspec": { + "display_name": "matt-helical", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "name": "python", + "version": "3.11.13" } }, "nbformat": 4, diff --git a/examples/notebooks/Hyena-DNA-Inference.ipynb b/examples/notebooks/Hyena-DNA-Inference.ipynb index baa6b41a..8e709cf8 100644 --- a/examples/notebooks/Hyena-DNA-Inference.ipynb +++ b/examples/notebooks/Hyena-DNA-Inference.ipynb @@ -30,14 +30,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/benoit/miniconda3/envs/helical-package/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + "/home/matthew/miniconda3/envs/matt-helical/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] } @@ -72,20 +72,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:helical.utils.downloader:File: '/home/benoit/.cache/helical/models/hyena_dna/hyenadna-tiny-1k-seqlen-d256.ckpt' exists already. File is not overwritten and nothing is downloaded.\n", - "INFO:helical.utils.downloader:File saved to: '/home/benoit/.cache/helical/models/hyena_dna/hyenadna-tiny-1k-seqlen-d256.ckpt'\n", - "INFO:helical.models.hyena_dna.pretrained_model:Loaded pretrained weights ok!\n", - "INFO:helical.models.hyena_dna.model:Model finished initializing.\n" - ] - } - ], + "outputs": [], "source": [ "from helical.models.hyena_dna import HyenaDNA, HyenaDNAConfig \n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", @@ -98,65 +87,29 @@ "metadata": {}, "source": [ "### Download the dataset\n", - "Several datasets are available from the [Nucleotide Transformer](https://arxiv.org/abs/2306.15794). Using the `get_dataset_config_names()` function, we get a list of the available the datasets for the downstream tasks." + "Several datasets are available from the [Nucleotide Transformer](https://arxiv.org/abs/2306.15794).\n", + "We can select any of the 18 downstream tasks. Let us take the `promoter_tata` as an example." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "['H4ac',\n", - " 'H3K36me3',\n", - " 'splice_sites_donors',\n", - " 'splice_sites_acceptors',\n", - " 'H3',\n", - " 'H4',\n", - " 'H3K4me3',\n", - " 'splice_sites_all',\n", - " 'H3K4me1',\n", - " 'H3K14ac',\n", - " 'enhancers_types',\n", - " 'promoter_no_tata',\n", - " 'H3K79me3',\n", - " 'H3K4me2',\n", - " 'promoter_tata',\n", - " 'enhancers',\n", - " 'H3K9ac',\n", - " 'promoter_all']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" + "name": "stderr", + "output_type": "stream", + "text": [ + "Filter: 100%|██████████| 461850/461850 [00:00<00:00, 477649.20 examples/s]\n", + "Filter: 100%|██████████| 48797/48797 [00:00<00:00, 492263.67 examples/s]\n" + ] } ], - "source": [ - "from datasets import get_dataset_config_names\n", - "\n", - "configs = get_dataset_config_names(\"InstaDeepAI/nucleotide_transformer_downstream_tasks\", trust_remote_code=True)\n", - "configs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can select any of the 18 downstream tasks. Let us take the `promoter_tata` as an example." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], "source": [ "from datasets import load_dataset\n", "label = \"promoter_tata\"\n", - "dataset = load_dataset(\"InstaDeepAI/nucleotide_transformer_downstream_tasks_revised\", label, trust_remote_code=True)" + "\n", + "dataset = load_dataset(\"InstaDeepAI/nucleotide_transformer_downstream_tasks\", trust_remote_code=True).filter(lambda x: x[\"task\"] == \"promoter_tata\")" ] }, { @@ -168,15 +121,15 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Nucleotide sequence: ATGTGGAACT ...\n", - "Label name: promoter_tata and value: 1\n", + "Nucleotide sequence: CGCTCCCCCA ...\n", + "Label name: default and value: 0\n", "Number of classes: 2\n" ] } @@ -203,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -240,18 +193,24 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "INFO:helical.models.hyena_dna.model:Processing data\n", - "Processing sequences: 100%|██████████| 5062/5062 [00:00<00:00, 10420.00it/s]\n", - "INFO:helical.models.hyena_dna.model:Data processing finished.\n", - "INFO:helical.models.hyena_dna.model:Inference started\n", - "Getting embeddings: 100%|██████████| 1013/1013 [00:01<00:00, 510.49it/s]\n" + "INFO:helical.models.hyena_dna.model:Processing data for HyenaDNA.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:helical.models.hyena_dna.model:Succesfully prepared the HyenaDNA Dataset.\n", + "INFO:helical.models.hyena_dna.model:Started getting embeddings:\n", + "Getting embeddings: 100%|██████████| 1102/1102 [00:03<00:00, 337.42it/s]\n", + "INFO:helical.models.hyena_dna.model:Finished getting embeddings.\n" ] } ], @@ -274,22 +233,22 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "tensor([[0., 1.],\n", - " [0., 1.],\n", + "tensor([[1., 0.],\n", + " [1., 0.],\n", " [0., 1.],\n", " ...,\n", " [1., 0.],\n", - " [1., 0.],\n", + " [0., 1.],\n", " [1., 0.]])" ] }, - "execution_count": 9, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -307,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -316,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -368,7 +327,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -436,38 +395,38 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1, Validation Loss: 0.4806538000702858\n", - "Epoch 2, Validation Loss: 0.48406418040394783\n", - "Epoch 3, Validation Loss: 0.4835537150502205\n", - "Epoch 4, Validation Loss: 0.48033208027482033\n", - "Epoch 5, Validation Loss: 0.49775027111172676\n", - "Epoch 6, Validation Loss: 0.47879376634955406\n", - "Epoch 7, Validation Loss: 0.47708773985505104\n", - "Epoch 8, Validation Loss: 0.4774513877928257\n", - "Epoch 9, Validation Loss: 0.4782363697886467\n", - "Epoch 10, Validation Loss: 0.4791817106306553\n", - "Epoch 11, Validation Loss: 0.47751539573073387\n", - "Epoch 12, Validation Loss: 0.4845072031021118\n", - "Epoch 13, Validation Loss: 0.4801477864384651\n", - "Epoch 14, Validation Loss: 0.4804844334721565\n", - "Epoch 15, Validation Loss: 0.47806529700756073\n", - "Epoch 16, Validation Loss: 0.4759875535964966\n", - "Epoch 17, Validation Loss: 0.48205339536070824\n", - "Epoch 18, Validation Loss: 0.4805366061627865\n", - "Epoch 19, Validation Loss: 0.4773087054491043\n", - "Epoch 20, Validation Loss: 0.47644931450486183\n", - "Epoch 21, Validation Loss: 0.476366750895977\n", - "Epoch 22, Validation Loss: 0.4779576063156128\n", - "Epoch 23, Validation Loss: 0.4803308770060539\n", - "Epoch 24, Validation Loss: 0.4766850620508194\n", - "Epoch 25, Validation Loss: 0.4757780134677887\n" + "Epoch 1, Validation Loss: 0.6822912096977234\n", + "Epoch 2, Validation Loss: 0.5138157142533196\n", + "Epoch 3, Validation Loss: 0.4389282796117995\n", + "Epoch 4, Validation Loss: 0.45615750551223755\n", + "Epoch 5, Validation Loss: 0.45748764938778347\n", + "Epoch 6, Validation Loss: 0.43795546558168197\n", + "Epoch 7, Validation Loss: 0.43910149070951676\n", + "Epoch 8, Validation Loss: 0.42771519554985893\n", + "Epoch 9, Validation Loss: 0.41031453013420105\n", + "Epoch 10, Validation Loss: 0.4496387706862556\n", + "Epoch 11, Validation Loss: 0.4233228928512997\n", + "Epoch 12, Validation Loss: 0.4684457348452674\n", + "Epoch 13, Validation Loss: 0.41786743534935844\n", + "Epoch 14, Validation Loss: 0.4579118854469723\n", + "Epoch 15, Validation Loss: 0.41921072867181564\n", + "Epoch 16, Validation Loss: 0.4725731909275055\n", + "Epoch 17, Validation Loss: 0.4140022893746694\n", + "Epoch 18, Validation Loss: 0.41290755073229474\n", + "Epoch 19, Validation Loss: 0.4361809558338589\n", + "Epoch 20, Validation Loss: 0.42776555485195583\n", + "Epoch 21, Validation Loss: 0.4498215946886275\n", + "Epoch 22, Validation Loss: 0.40431450804074603\n", + "Epoch 23, Validation Loss: 0.4069192674424913\n", + "Epoch 24, Validation Loss: 0.40002157621913487\n", + "Epoch 25, Validation Loss: 0.4215077956517537\n" ] } ], @@ -483,18 +442,18 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "INFO:helical.models.hyena_dna.model:Processing data\n", - "Processing sequences: 100%|██████████| 212/212 [00:00<00:00, 9593.29it/s]\n", - "INFO:helical.models.hyena_dna.model:Data processing finished.\n", - "INFO:helical.models.hyena_dna.model:Inference started\n", - "Getting embeddings: 100%|██████████| 43/43 [00:00<00:00, 1005.46it/s]\n" + "INFO:helical.models.hyena_dna.model:Processing data for HyenaDNA.\n", + "INFO:helical.models.hyena_dna.model:Succesfully prepared the HyenaDNA Dataset.\n", + "INFO:helical.models.hyena_dna.model:Started getting embeddings:\n", + "Getting embeddings: 100%|██████████| 125/125 [00:00<00:00, 439.64it/s]\n", + "INFO:helical.models.hyena_dna.model:Finished getting embeddings.\n" ] } ], @@ -513,14 +472,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Correct predictions: 86.32%\n" + "Correct predictions: 88.08%\n" ] } ], @@ -551,19 +510,698 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
SVC(C=1)
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On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + "
SVC(C=1)
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" ], "text/plain": [ "SVC(C=1)" ] }, - "execution_count": 16, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -577,14 +1215,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Test accuracy: 87.3%\n" + "Test accuracy: 89.7%\n" ] } ], @@ -598,12 +1236,12 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -639,7 +1277,7 @@ ], "metadata": { "kernelspec": { - "display_name": "helical-package", + "display_name": "matt-helical", "language": "python", "name": "python3" }, @@ -653,7 +1291,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.8" + "version": "3.11.13" } }, "nbformat": 4, diff --git a/examples/notebooks/HyenaDNA-Fine-Tuning.ipynb b/examples/notebooks/HyenaDNA-Fine-Tuning.ipynb index 6b3fdb39..7478b5e4 100644 --- a/examples/notebooks/HyenaDNA-Fine-Tuning.ipynb +++ b/examples/notebooks/HyenaDNA-Fine-Tuning.ipynb @@ -91,13 +91,15 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "label = \"promoter_tata\"\n", - "dataset_train = load_dataset(\"InstaDeepAI/nucleotide_transformer_downstream_tasks_revised\", label, split=\"train\", trust_remote_code=True)\n", - "dataset_test = load_dataset(\"InstaDeepAI/nucleotide_transformer_downstream_tasks_revised\", label, split=\"test\", trust_remote_code=True)" + "\n", + "dataset = load_dataset(\"InstaDeepAI/nucleotide_transformer_downstream_tasks\", trust_remote_code=True).filter(lambda x: x[\"task\"] == \"promoter_tata\")\n", + "dataset_train = dataset[\"train\"]\n", + "dataset_test = dataset[\"test\"]" ] }, { diff --git a/helical/models/hyena_dna/fine_tuning_model.py b/helical/models/hyena_dna/fine_tuning_model.py index 521d1610..e3c9e7b0 100644 --- a/helical/models/hyena_dna/fine_tuning_model.py +++ b/helical/models/hyena_dna/fine_tuning_model.py @@ -65,7 +65,9 @@ class HyenaDNAFineTuningModel(HelicalBaseFineTuningModel, HyenaDNA): def __init__( self, hyena_config: HyenaDNAConfig, - fine_tuning_head: Literal["classification"] | HelicalBaseFineTuningHead, + fine_tuning_head: ( + Literal["classification", "regression"] | HelicalBaseFineTuningHead + ), output_size: Optional[int] = None, ): HelicalBaseFineTuningModel.__init__(self, fine_tuning_head, output_size) diff --git a/pyproject.toml b/pyproject.toml index 10797c56..3edb65f3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -42,7 +42,7 @@ dependencies = [ 'hydra-core==1.3.2', 'louvain==0.8.2', 'pyensembl', - 'datasets==2.20.0' + 'datasets==3.6.0' ] From 1815c7c6975e02b0cce682f88dcda38e82a36481 Mon Sep 17 00:00:00 2001 From: Matthew Wood Date: Tue, 15 Jul 2025 17:19:22 +0200 Subject: [PATCH 02/17] Update the helix model files in the case that all hidden states should be accumulated --- helical/models/helix_mrna/modeling_helix_mrna.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/helical/models/helix_mrna/modeling_helix_mrna.py b/helical/models/helix_mrna/modeling_helix_mrna.py index ff01501f..4e7e3e87 100644 --- a/helical/models/helix_mrna/modeling_helix_mrna.py +++ b/helical/models/helix_mrna/modeling_helix_mrna.py @@ -1666,7 +1666,13 @@ def forward( cache_position=cache_position, ) - hidden_states = layer_outputs[0] + if output_hidden_states: + all_hidden_states += (layer_outputs[0],) + + hidden_states = self.norm_f(layer_outputs[0]) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) if output_attentions: if layer_outputs[1] is not None: @@ -1676,11 +1682,6 @@ def forward( if use_cache: cache_params.seqlen_offset += inputs_embeds.shape[1] - hidden_states = self.norm_f(hidden_states) - - if output_hidden_states: - all_hidden_states += (hidden_states,) - if not return_dict: return tuple( v From 14449168b709059d20bf9f709084907e12f20de1 Mon Sep 17 00:00:00 2001 From: Maxime Date: Thu, 17 Jul 2025 12:56:07 +0200 Subject: [PATCH 03/17] updated downloader --- helical/constants/hash_values.py | 89 ---------------- helical/utils/downloader.py | 172 +++++++++++++------------------ pyproject.toml | 3 +- 3 files changed, 74 insertions(+), 190 deletions(-) delete mode 100644 helical/constants/hash_values.py diff --git a/helical/constants/hash_values.py b/helical/constants/hash_values.py deleted file mode 100644 index 3c994fb4..00000000 --- a/helical/constants/hash_values.py +++ /dev/null @@ -1,89 +0,0 @@ -HASH_DICT = { - 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"transcriptformer/tf_exemplar/config.json": "695c50832a608078bc3b136a0776f8d795532369bc249a83c07db2b28bb97b24", - "transcriptformer/tf_exemplar/model_weights.pt": "1adbb8671c00ea9d66ac0a1be8261c7e96824eaf8435ae17b048570372d4f8a1", - "transcriptformer/tf_exemplar/vocabs/assay_vocab.json": "0c405e4ead45a4b8350d8e874f834273efdd3f7ad4669b52d3e3727fb4fe70af", - "transcriptformer/tf_exemplar/vocabs/caenorhabditis_elegans_gene.h5": "a18f234ce456614b1c6d1fb57f6d1ba031a71539c2c658b0dcbb87af4e7efb64", - "transcriptformer/tf_exemplar/vocabs/danio_rerio_gene.h5": "e2b34013c1a0779361b3aaac05cdba5062cb117ce903779232a43f73a99c78e3", - "transcriptformer/tf_exemplar/vocabs/drosophila_melanogaster_gene.h5": "fa3915cf36ed457a162719b2172367a1066cde47677afb2a3b78bc1c51da0ac2", - "transcriptformer/tf_exemplar/vocabs/homo_sapiens_gene.h5": "74366d4f45c4bd60983c3d1d1c406d7d58d30a798455c239eb2691eaa162e2dc", - "transcriptformer/tf_exemplar/vocabs/mus_musculus_gene.h5": "e63c25b8d2ad87d696d8f7dfe4bcb307eee8a7a69dec62a33bb8469dbd75e3e1", - "text_embeddings/gene_prot_embeddings_granite.json": "8fb51d28986fa24e4b7b63bc28f86789db184f902259838cf7f7aa9ae4942ffd", - "mrna_embeddings/sequences_helix_genes_cds_go.json": "364c744b845572b6900820f031c88a24a9ed5b12d2b9c49fa9e5855b1b7abdbc", -} diff --git a/helical/utils/downloader.py b/helical/utils/downloader.py index 29c4909e..44be2f9c 100644 --- a/helical/utils/downloader.py +++ b/helical/utils/downloader.py @@ -8,8 +8,10 @@ from tqdm import tqdm from azure.storage.blob import BlobClient from helical.constants.paths import CACHE_DIR_HELICAL -from helical.constants.hash_values import HASH_DICT import hashlib +import boto3 +from botocore import UNSIGNED +from botocore.config import Config LOGGER = logging.getLogger(__name__) INTERVAL = 1000 # interval to get gene mappings @@ -88,35 +90,14 @@ def _display_download_progress(self, data_chunk_size: int) -> None: sys.stdout.write("\r[%s%s]" % ("=" * done, " " * (LOADING_BAR_LENGTH - done))) sys.stdout.flush() - def calculate_partial_file_hash( - self, file_path: str, chunk_size: int = 8192, algorithm: str = "sha256" - ) -> str: - """ - Calculate the hash of the first and last chunks of a file. - - Args: - file_path (str): Path to the file. - chunk_size (int): Size of the chunks to hash (in bytes). - algorithm (str): Hashing algorithm (e.g., 'sha256', 'blake2b', 'md5'), default is 'sha256'. - - Returns: - str: Combined hash of the first and last chunks. - """ - hash_func = hashlib.new(algorithm) - file_size = os.path.getsize(file_path) - - with open(file_path, "rb") as f: - # Read the first chunk - first_chunk = f.read(chunk_size) - hash_func.update(first_chunk) - - # Read the last chunk - if file_size > chunk_size: - f.seek(-chunk_size, os.SEEK_END) - last_chunk = f.read(chunk_size) - hash_func.update(last_chunk) - - return hash_func.hexdigest() + def s3_download_with_progress(self, s3client, bucket, key, output): + with open(output, "wb") as f: + s3client.download_fileobj( + Bucket=bucket, + Key=key, + Fileobj=f, + Callback=S3ProgressPercentage(bucket, key, s3client), + ) def download_via_name(self, name: str) -> None: """ @@ -128,90 +109,81 @@ def download_via_name(self, name: str) -> None: Returns: None """ - - main_link = "https://helicalpackage.blob.core.windows.net/helicalpackage/data" + s3 = boto3.client("s3", config=Config(signature_version=UNSIGNED)) + # Example usage + bucket_name = "helicalpackage" + s3_key = name output = os.path.join(CACHE_DIR_HELICAL, name) - blob_url = f"{main_link}/{name}" - - # Create a BlobClient object for the specified blob - blob_client = BlobClient.from_blob_url( - blob_url, - max_single_get_size=1024 * 1024 * 32, - max_chunk_get_size=1024 * 1024 * 4, - session=self.session, - ) - if not os.path.exists(os.path.dirname(output)): os.makedirs(os.path.dirname(output), exist_ok=True) LOGGER.info(f"Creating Folder {os.path.dirname(output)}") - if ( - Path(output).is_file() - and self.calculate_partial_file_hash(output) == HASH_DICT[name] - ): - LOGGER.debug( - f"File: '{output}' exists already. File is not overwritten and nothing is downloaded." - ) - - else: + if not Path(output).is_file() or not self.etag_compare(s3_key, output, s3): LOGGER.info( - f"File does not exist or has incorrect hash. Starting to download: '{blob_url}'" + f"File does not exist or has incorrect hash. Starting to download: '{s3_key}'" ) # temporarily disable INFO logging from Azure package original_level = logging.getLogger().getEffectiveLevel() logging.getLogger().setLevel(logging.WARNING) - self.display_azure_download_progress(blob_client, blob_url, output) + self.s3_download_with_progress(s3, bucket_name, s3_key, output) # restore original logging level logging.getLogger().setLevel(original_level) - assert ( - self.calculate_partial_file_hash(output) == HASH_DICT[name] + assert self.etag_compare( + s3_key, output, s3 ), f"Hash of downloaded file '{output}' does not match the expected hash." LOGGER.info(f"File saved to: '{output}'") - - def display_azure_download_progress( - self, blob_client: BlobClient, blob_url: str, output: Path - ) -> None: - """ - Displays the progress of an Azure blob download and saves the downloaded file. - - Args: - blob_client (BlobClient): The BlobClient object used to download the blob. - blob_url (str): The URL of the blob to be downloaded. - output (Path): The path where the downloaded file will be saved. - - Returns: - None - """ - # Resetting for visualization - self.data_length = 0 - total_length = blob_client.get_blob_properties().size - - # handle displaying download progress or not - if self.display: - pbar = tqdm( - total=total_length, unit="B", unit_scale=True, desc="Downloading" - ) - - def progress_callback(bytes_transferred, total_bytes): - pbar.update(bytes_transferred - pbar.n) - else: - pbar = None - - def progress_callback(bytes_transferred, total_bytes): - pass - - # actual download - try: - with open(output, "wb") as sample_blob: - download_stream = blob_client.download_blob( - max_concurrency=100, progress_hook=progress_callback - ) - - sample_blob.write(download_stream.readall()) - except: - LOGGER.error(f"Failed downloading file from '{blob_url}'") + LOGGER.debug( + f"File: '{output}' exists already. File is not overwritten and nothing is downloaded." + ) - if self.display: - pbar.close() + def etag_compare(self, s3_key, filename, s3client, bucket_name="helicalpackage"): + + # obj = s3client.get_object(Bucket=bucket_name, Key=filename) + # Get the object's metadata + response = s3client.head_object(Bucket=bucket_name, Key=s3_key) + + # Extract the ETag + etag = response["ETag"].strip('"') # Remove quotes if needed + # etag = obj.e_tag + + def md5_checksum(filename): + m = hashlib.md5() + with open(filename, "rb") as f: + for data in iter(lambda: f.read(1024 * 1024), b""): + m.update(data) + return m.hexdigest() + + def etag_checksum(filename, chunk_size=8 * 1024 * 1024): + md5s = [] + with open(filename, "rb") as f: + for data in iter(lambda: f.read(chunk_size), b""): + md5s.append(hashlib.md5(data).digest()) + m = hashlib.md5(b"".join(md5s)) + return "{}-{}".format(m.hexdigest(), len(md5s)) + + et = etag # [1:-1] # strip quotes + if "-" in et and et == etag_checksum(filename): + return True + if "-" not in et and et == md5_checksum(filename): + return True + return False + + +class S3ProgressPercentage: + def __init__(self, bucket, key, s3_client): + self._key = key + self._size = s3_client.head_object(Bucket=bucket, Key=key)["ContentLength"] + self._seen_so_far = 0 + self._tqdm = tqdm(total=self._size, unit="B", unit_scale=True, desc=key) + + def __call__(self, bytes_amount): + self._seen_so_far += bytes_amount + self._tqdm.update(bytes_amount) + + +if __name__ == "__main__": + # Example usage + downloader = Downloader() + downloader.download_via_name("10k_pbmcs_proc.h5ad") diff --git a/pyproject.toml b/pyproject.toml index 3edb65f3..2dae849a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -42,7 +42,8 @@ dependencies = [ 'hydra-core==1.3.2', 'louvain==0.8.2', 'pyensembl', - 'datasets==3.6.0' + 'datasets==3.6.0', + 'boto3' ] From 3a58ea4f0246fbba5aad5daf4f6ccadcbe947455 Mon Sep 17 00:00:00 2001 From: TitouanCh Date: Thu, 17 Jul 2025 14:34:36 +0200 Subject: [PATCH 04/17] `anndata` 0.12 allowed in `pyproject.toml`. --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 3edb65f3..08162ac2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,7 +22,7 @@ license = {file = "LICENSE"} dependencies = [ 'requests>=2.32.3', 'pandas==2.2.2', - 'anndata==0.11', + 'anndata>=0.11', 'numpy==1.26.4', 'scikit-learn>=1.5.0', 'scipy==1.13.1', From 3895f8222f8fdbd365f3f18ce89d9b3cac0c783c Mon Sep 17 00:00:00 2001 From: bputzeys Date: Mon, 28 Jul 2025 14:08:13 +0200 Subject: [PATCH 05/17] Remove flash attention and its error message as we never used it --- examples/run_models/configs/scgpt_config.yaml | 2 - helical/models/scgpt/model_dir/model.py | 47 ++----------------- .../models/scgpt/model_dir/multiomic_model.py | 30 ++---------- helical/models/scgpt/scgpt_config.py | 10 +--- helical/models/scgpt/scgpt_utils.py | 11 ++--- 5 files changed, 15 insertions(+), 85 deletions(-) diff --git a/examples/run_models/configs/scgpt_config.yaml b/examples/run_models/configs/scgpt_config.yaml index 01d40f94..2876f0ed 100644 --- a/examples/run_models/configs/scgpt_config.yaml +++ b/examples/run_models/configs/scgpt_config.yaml @@ -1,7 +1,6 @@ pad_token: "" batch_size: 24 emb_mode: "gene" -fast_transformer: True nlayers: 12 nheads: 8 embsize: 512 @@ -13,5 +12,4 @@ pad_value: -2 world_size: 8 accelerator: False device: "cpu" -use_fast_transformer: False binning_seed: 123 diff --git a/helical/models/scgpt/model_dir/model.py b/helical/models/scgpt/model_dir/model.py index ca9ad90d..d9a3717f 100644 --- a/helical/models/scgpt/model_dir/model.py +++ b/helical/models/scgpt/model_dir/model.py @@ -1,6 +1,5 @@ -import gc import math -from typing import Dict, Mapping, Optional, Tuple, Any, Union +from typing import Dict, Mapping, Optional, Any, Union import torch import numpy as np @@ -14,15 +13,6 @@ from torch.distributions import Bernoulli from tqdm import trange -try: - from flash_attn.flash_attention import FlashMHA - - flash_attn_available = True -except ImportError: - import warnings - - warnings.warn("flash_attn is not installed") - flash_attn_available = False from .dsbn import DomainSpecificBatchNorm1d from .grad_reverse import grad_reverse @@ -53,8 +43,6 @@ def __init__( mvc_decoder_style: str = "inner product", ecs_threshold: float = 0.3, explicit_zero_prob: bool = False, - use_fast_transformer: bool = False, - fast_transformer_backend: str = "flash", pre_norm: bool = False, ): super().__init__() @@ -75,15 +63,6 @@ def __init__( ) if cell_emb_style not in ["cls", "avg-pool", "w-pool"]: raise ValueError(f"Unknown cell_emb_style: {cell_emb_style}") - if use_fast_transformer: - if not flash_attn_available: - warnings.warn( - "flash-attn is not installed, using pytorch transformer instead. " - "Set use_fast_transformer=False to avoid this warning. " - "Installing flash-attn is highly recommended." - ) - use_fast_transformer = False - self.use_fast_transformer = use_fast_transformer # TODO: add dropout in the GeneEncoder self.encoder = GeneEncoder(ntoken, d_model, padding_idx=vocab[pad_token]) @@ -115,26 +94,10 @@ def __init__( print("Using simple batchnorm instead of domain specific batchnorm") self.bn = nn.BatchNorm1d(d_model, eps=6.1e-5) - if use_fast_transformer: - if fast_transformer_backend == "linear": - self.transformer_encoder = FastTransformerEncoderWrapper( - d_model, nhead, d_hid, nlayers, dropout - ) - elif fast_transformer_backend == "flash": - encoder_layers = FlashTransformerEncoderLayer( - d_model, - nhead, - d_hid, - dropout, - batch_first=True, - norm_scheme=self.norm_scheme, - ) - self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers) - else: - encoder_layers = TransformerEncoderLayer( - d_model, nhead, d_hid, dropout, batch_first=True - ) - self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers) + encoder_layers = TransformerEncoderLayer( + d_model, nhead, d_hid, dropout, batch_first=True + ) + self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers) self.decoder = ExprDecoder( d_model, diff --git a/helical/models/scgpt/model_dir/multiomic_model.py b/helical/models/scgpt/model_dir/multiomic_model.py index 8ac6cc9f..006ef490 100644 --- a/helical/models/scgpt/model_dir/multiomic_model.py +++ b/helical/models/scgpt/model_dir/multiomic_model.py @@ -14,10 +14,6 @@ LOGGER = logging.getLogger(__name__) -try: - from flash_attn.flash_attention import FlashMHA -except ImportError: - LOGGER.warning("flash_attn is not installed.") from .dsbn import DomainSpecificBatchNorm1d from .grad_reverse import grad_reverse @@ -48,8 +44,6 @@ def __init__( mvc_decoder_style: str = "inner product", ecs_threshold: float = 0.3, explicit_zero_prob: bool = False, - use_fast_transformer: bool = False, - fast_transformer_backend: str = "flash", pre_norm: bool = False, use_mod: bool = False, ntokens_mod: Optional[int] = None, @@ -111,26 +105,10 @@ def __init__( print("Using simple batchnorm instead of domain specific batchnorm") self.bn = nn.BatchNorm1d(d_model, eps=6.1e-5) - if use_fast_transformer: - if fast_transformer_backend == "linear": - self.transformer_encoder = FastTransformerEncoderWrapper( - d_model, nhead, d_hid, nlayers, dropout - ) - elif fast_transformer_backend == "flash": - encoder_layers = FlashTransformerEncoderLayer( - d_model, - nhead, - d_hid, - dropout, - batch_first=True, - norm_scheme=self.norm_scheme, - ) - self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers) - else: - encoder_layers = TransformerEncoderLayer( - d_model, nhead, d_hid, dropout, batch_first=True - ) - self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers) + encoder_layers = TransformerEncoderLayer( + d_model, nhead, d_hid, dropout, batch_first=True + ) + self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers) self.decoder = ExprDecoder( d_model, diff --git a/helical/models/scgpt/scgpt_config.py b/helical/models/scgpt/scgpt_config.py index a5d1a68d..4f47beec 100644 --- a/helical/models/scgpt/scgpt_config.py +++ b/helical/models/scgpt/scgpt_config.py @@ -15,8 +15,6 @@ class scGPTConfig: The batch size emb_mode : Literal["cls", "cell", "gene"], optional, default="cell" The embedding mode to use. "cls" uses the cls token, "cell" uses the cell token, "gene" uses the gene token. - fast_transformer : bool, optional, default=True - Whether to use fast transformer or not nlayers : int, optional, default=12 The number of layers nheads : int, optional, default=8 @@ -39,8 +37,8 @@ class scGPTConfig: The accelerator configuration. By default same device as model. device : Literal["cpu", "cuda"], optional, default="cpu" The device to use. Either use "cuda" or "cpu". - use_fast_transformer : bool, optional, default=False - Wheter to use fast transformer or nots + binning_seed : int, optional, default=123 + The seed for the binning Returns ------- @@ -58,7 +56,6 @@ def __init__( pad_token: str = "", batch_size: int = 24, emb_mode: Literal["cls", "cell", "gene"] = "cell", - fast_transformer: bool = True, nlayers: int = 12, nheads: int = 8, embsize: int = 512, @@ -70,7 +67,6 @@ def __init__( world_size: int = 8, accelerator: Optional[bool] = False, device: Literal["cpu", "cuda"] = "cpu", - use_fast_transformer: bool = False, binning_seed: int = 123, ): @@ -87,7 +83,6 @@ def __init__( "pad_token": pad_token, "batch_size": batch_size, "emb_mode": emb_mode, - "fast_transformer": fast_transformer, "nlayers": nlayers, "nheads": nheads, "embsize": embsize, @@ -99,7 +94,6 @@ def __init__( "world_size": world_size, "accelerator": accelerator, "device": device, - "use_fast_transformer": use_fast_transformer, "MAX_LENGTH": 1200, "binning_seed": binning_seed, } diff --git a/helical/models/scgpt/scgpt_utils.py b/helical/models/scgpt/scgpt_utils.py index 723f2d21..34e5ad52 100644 --- a/helical/models/scgpt/scgpt_utils.py +++ b/helical/models/scgpt/scgpt_utils.py @@ -31,11 +31,10 @@ def load_pretrained( torch.nn.Module: The model with pretrained weights. """ - use_flash_attn = getattr(model, "use_fast_transformer", True) - if not use_flash_attn: - pretrained_params = { - k.replace("Wqkv.", "in_proj_"): v for k, v in pretrained_params.items() - } + # as we do not use fast transformer, we need to rename the keys + pretrained_params = { + k.replace("Wqkv.", "in_proj_"): v for k, v in pretrained_params.items() + } if prefix is not None and len(prefix) > 0: if isinstance(prefix, str): @@ -96,8 +95,6 @@ def load_model(model_configs: scGPTConfig): use_batch_labels=False, domain_spec_batchnorm=False, explicit_zero_prob=False, - use_fast_transformer=model_configs["use_fast_transformer"], - fast_transformer_backend="flash", pre_norm=False, ) From 4962d85d0cad5e9aeddd44378b30b1a147df458f Mon Sep 17 00:00:00 2001 From: Benoit Putzeys <157973952+bputzeys@users.noreply.github.com> Date: Tue, 29 Jul 2025 16:01:35 +0200 Subject: [PATCH 06/17] Update pyproject.toml --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 08162ac2..646ac438 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "hatchling.build" [project] name = "helical" -version = "1.3.0" +version = "1.4.0" authors = [ { name="Helical Team", email="support@helical-ai.com" }, ] From cb3a5594618fcd8c96c4357f9682612fe4fda525 Mon Sep 17 00:00:00 2001 From: Matthew Wood <62712722+mattwoodx@users.noreply.github.com> Date: Tue, 29 Jul 2025 16:03:29 +0200 Subject: [PATCH 07/17] Add new large Geneformer models (#251) * Addition of new geneformer v2 models Called them v3 because we already had new models added a while ago which we named v2. Hashes currently for one of the models * Add notebook comparing Geneformer scalings * Rename older geneformer models to have model size instead of number of pretraining cells * Update model names to be number of parameters to match newer model Keep names the same for downloads to ensure older installs of the package still work * Update readme * Fix test download names * fixup! Fix test download names * fixup! fixup! Fix test download names * Models updated on azure so no need for download names * Make test naming up to date with model name changes * fixup! Make test naming up to date with model name changes * Do not run the comparison notebook as it is too large * Remove accelerator from Geneformer config * UPdate readme and docs * Add what's new section to main readme --- .github/workflows/main.yml | 9 +- .github/workflows/release.yml | 8 +- README.md | 5 + ci/download_all.py | 4 +- .../test_geneformer/test_geneformer_model.py | 26 +- docs/index.md | 6 +- .../Geneformer-Series-Comparison.ipynb | 479 ++++++++++++++++++ examples/notebooks/Cell-Type-Annotation.ipynb | 12 +- ...Cell-Type-Classification-Fine-Tuning.ipynb | 2 +- .../Geneformer-Series-Comparison.ipynb | 479 ++++++++++++++++++ .../run_models/configs/geneformer_config.yaml | 5 +- helical/constants/hash_values.py | 48 +- helical/models/geneformer/README.md | 29 +- .../models/geneformer/fine_tuning_model.py | 4 +- .../models/geneformer/geneformer_config.py | 103 ++-- helical/models/geneformer/model.py | 19 +- 16 files changed, 1135 insertions(+), 103 deletions(-) create mode 100644 docs/notebooks/Geneformer-Series-Comparison.ipynb create mode 100644 examples/notebooks/Geneformer-Series-Comparison.ipynb diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index bf430217..c321fd26 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -63,19 +63,19 @@ jobs: - name: Execute Geneformer v1 run: | - python examples/run_models/run_geneformer.py ++model_name="gf-12L-30M-i2048" ++device="cuda" + python examples/run_models/run_geneformer.py ++model_name="gf-12L-40M-i2048" ++device="cuda" - name: Fine-tune Geneformer v1 run: | - python examples/fine_tune_models/fine_tune_geneformer.py ++model_name="gf-12L-30M-i2048" ++device="cuda" + python examples/fine_tune_models/fine_tune_geneformer.py ++model_name="gf-12L-40M-i2048" ++device="cuda" - name: Execute Geneformer v2 run: | - python examples/run_models/run_geneformer.py ++model_name="gf-12L-95M-i4096" ++device="cuda" + python examples/run_models/run_geneformer.py ++model_name="gf-12L-38M-i4096" ++device="cuda" - name: Fine-tune Geneformer v2 run: | - python examples/fine_tune_models/fine_tune_geneformer.py ++model_name="gf-12L-30M-i2048" ++device="cuda" + python examples/fine_tune_models/fine_tune_geneformer.py ++model_name="gf-12L-40M-i2048" ++device="cuda" - name: Execute scGPT run: | @@ -157,6 +157,7 @@ jobs: sed -i 's/list(np.array(train_dataset.obs\[\\"LVL1\\"].tolist()))/list(np.array(train_dataset.obs\[\\"LVL1\\"].tolist()))[:100]/g' ./examples/notebooks/Cell-Type-Classification-Fine-Tuning.ipynb sed -i 's/list(np.array(test_dataset.obs\[\\"LVL1\\"].tolist()))/list(np.array(test_dataset.obs\[\\"LVL1\\"].tolist()))[:10]/g' ./examples/notebooks/Cell-Type-Classification-Fine-Tuning.ipynb rm ./examples/notebooks/Evo-2.ipynb + rm ./examples/notebooks/Geneformer-Series-Comparison.ipynb - name: Run Notebooks run: | diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 5d71ab9a..c31b24c5 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -81,19 +81,19 @@ jobs: - name: Execute Geneformer v1 run: | - python examples/run_models/run_geneformer.py ++model_name="gf-12L-30M-i2048" ++device="cuda" + python examples/run_models/run_geneformer.py ++model_name="gf-12L-40M-i2048" ++device="cuda" - name: Fine-tune Geneformer v1 run: | - python examples/fine_tune_models/fine_tune_geneformer.py ++model_name="gf-12L-30M-i2048" ++device="cuda" + python examples/fine_tune_models/fine_tune_geneformer.py ++model_name="gf-12L-40M-i2048" ++device="cuda" - name: Execute Geneformer v2 run: | - python examples/run_models/run_geneformer.py ++model_name="gf-12L-95M-i4096" ++device="cuda" + python examples/run_models/run_geneformer.py ++model_name="gf-12L-38M-i4096" ++device="cuda" - name: Fine-tune Geneformer v2 run: | - python examples/fine_tune_models/fine_tune_geneformer.py ++model_name="gf-12L-30M-i2048" ++device="cuda" + python examples/fine_tune_models/fine_tune_geneformer.py ++model_name="gf-12L-40M-i2048" ++device="cuda" - name: Execute scGPT run: | diff --git a/README.md b/README.md index 5eede69a..4ede89c4 100644 --- a/README.md +++ b/README.md @@ -31,6 +31,10 @@ Let’s build the most exciting AI-for-Bio community together! ## What's new? +### New Larger Geneformer Models +We have integrated the new Geneformer models which are larger and have been trained on more data. Find out which models have been integrated into the Geneformer suite in the [model card](./helical/models/geneformer/README.md). Check out the our notebook on drug perturbation prediction using different Geneformer scalings [here](./examples/notebooks/Geneformer-Series-Comparison.ipynb). + + ### TranscriptFormer We have integrated [TranscriptFormer](https://github.com/czi-ai/transcriptformer) into our helical package and have made a model card for it in our [Transcriptformer model folder](helical/models/transcriptformer/README.md). If you would like to test the model, take a look at our [example notebook](examples/notebooks/Geneformer-vs-TranscriptFormer.ipynb)! @@ -132,6 +136,7 @@ Within the `examples/notebooks` folder, open the notebook of your choice. We rec |[Cell-Type-Classification-Fine-Tuning.ipynb](./examples/notebooks/Cell-Type-Classification-Fine-Tuning.ipynb)|An example how to fine-tune different models on classification tasks.|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/helicalAI/helical/blob/main/examples/notebooks/Cell-Type-Classification-Fine-Tuning.ipynb) | |[HyenaDNA-Fine-Tuning.ipynb](./examples/notebooks/HyenaDNA-Fine-Tuning.ipynb)|An example of how to fine-tune the HyenaDNA model on downstream benchmarks.|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/helicalAI/helical/blob/main/examples/notebooks/HyenaDNA-Fine-Tuning.ipynb) | |[Cell-Gene-Cls-embedding-generation.ipynb](./examples/notebooks/Cell-Gene-Cls-embedding-generation.ipynb)|A notebook explaining the different embedding modes of single cell RNA models.|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/helicalAI/helical/blob/main/examples/notebooks/Cell-Gene-Cls-embedding-generation.ipynb) | +|[Geneformer-Series-Comparison.ipynb](./examples/notebooks/Geneformer-Series-Comparison.ipynb)|A zero shot comparison between Geneformer model scaling on drug perturbation prediction|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/helicalAI/helical/blob/main/examples/notebooks/Geneformer-Series-Comparison.ipynb) | ## Stuck somewhere ? Other ideas ? We are eager to help you and interact with you: diff --git a/ci/download_all.py b/ci/download_all.py index 9eefeb25..96c918dd 100644 --- a/ci/download_all.py +++ b/ci/download_all.py @@ -11,8 +11,8 @@ def download_geneformer_models(): # We can decide to download more models by simply adding the model names from the full list as reported in geneformer_config.py version_models_dict = { - "v1": ["gf-12L-30M-i2048", "gf-6L-30M-i2048"], - "v2": ["gf-12L-95M-i4096", "gf-12L-95M-i4096-CLcancer", "gf-20L-95M-i4096"], + "v1": ["gf-12L-40M-i2048", "gf-6L-10M-i2048"], + "v2": ["gf-12L-38M-i4096", "gf-12L-38M-i4096-CLcancer", "gf-20L-151M-i4096"], } for version in versions: diff --git a/ci/tests/test_geneformer/test_geneformer_model.py b/ci/tests/test_geneformer/test_geneformer_model.py index 4da74359..3fbda7ac 100644 --- a/ci/tests/test_geneformer/test_geneformer_model.py +++ b/ci/tests/test_geneformer/test_geneformer_model.py @@ -51,7 +51,7 @@ def mock_embeddings_v2(self, mocker): ).repeat(12, 1, 1, 1) return embs - @pytest.fixture(params=["gf-12L-30M-i2048", "gf-12L-95M-i4096"]) + @pytest.fixture(params=["gf-12L-40M-i2048", "gf-12L-38M-i4096"]) def geneformer(self, request): config = GeneformerConfig(model_name=request.param, batch_size=5) geneformer = Geneformer(config) @@ -132,14 +132,14 @@ def test_cls_mode_with_v1_model_config(self, geneformer): "This test is only for v1 models and should thus be only executed once." ) with pytest.raises(ValueError): - GeneformerConfig(model_name="gf-12L-30M-i2048", emb_mode="cls") + GeneformerConfig(model_name="gf-12L-40M-i2048", emb_mode="cls") @pytest.mark.parametrize("emb_mode", ["cell", "gene"]) def test_get_embeddings_of_different_modes_v1( self, emb_mode, mock_data, mock_embeddings_v1, mocker ): config = GeneformerConfig( - model_name="gf-12L-30M-i2048", batch_size=5, emb_mode=emb_mode + model_name="gf-12L-40M-i2048", batch_size=5, emb_mode=emb_mode ) geneformer = Geneformer(config) mocker.patch.object( @@ -168,7 +168,7 @@ def test_get_embeddings_of_different_modes_v2( self, emb_mode, mock_data, mock_embeddings_v2, mocker ): config = GeneformerConfig( - model_name="gf-12L-95M-i4096", batch_size=5, emb_mode=emb_mode + model_name="gf-12L-38M-i4096", batch_size=5, emb_mode=emb_mode ) geneformer = Geneformer(config) mocker.patch.object( @@ -213,7 +213,7 @@ def test_fine_tune_classifier_returns_correct_shape(self, emb_mode, mock_data): def test_fine_tune_classifier_cls_returns_correct_shape(self, mock_data): fine_tuned_model = GeneformerFineTuningModel( - GeneformerConfig(model_name="gf-12L-95M-i4096", emb_mode="cls"), + GeneformerConfig(model_name="gf-12L-38M-i4096", emb_mode="cls"), fine_tuning_head="classification", output_size=1, ) @@ -230,10 +230,10 @@ def test_fine_tune_classifier_cls_returns_correct_shape(self, mock_data): @pytest.mark.parametrize( "model_name,emb_layer,expected_error", [ - ("gf-6L-30M-i2048", -1, "No Error"), - ("gf-6L-30M-i2048", 7, "Error"), - ("gf-12L-30M-i2048", 6, "No Error"), - ("gf-20L-95M-i4096", 23, "Error"), + ("gf-6L-10M-i2048", -1, "No Error"), + ("gf-6L-10M-i2048", 7, "Error"), + ("gf-12L-40M-i2048", 6, "No Error"), + ("gf-20L-151M-i4096", 23, "Error"), ], ) def test_embedding_layer_error(self, model_name, emb_layer, expected_error): @@ -253,10 +253,10 @@ def test_embedding_layer_error(self, model_name, emb_layer, expected_error): @pytest.mark.parametrize( "model_name,emb_layer", [ - ("gf-6L-30M-i2048", -1), - ("gf-6L-30M-i2048", 3), - ("gf-12L-30M-i2048", 6), - ("gf-20L-95M-i4096", -1), + ("gf-6L-10M-i2048", -1), + ("gf-6L-10M-i2048", 3), + ("gf-12L-40M-i2048", 6), + ("gf-20L-151M-i4096", -1), ], ) def test_layer_to_quant(self, model_name, emb_layer): diff --git a/docs/index.md b/docs/index.md index 212a341d..46244aa8 100644 --- a/docs/index.md +++ b/docs/index.md @@ -11,8 +11,11 @@ We will update this repo on a regular basis with new models, benchmarks, modalit Let’s build the most exciting AI-for-Bio community together! ## What's new? +### New Larger Geneformer Models +We have integrated the new Geneformer models which are larger and have been trained on more data. Find out which models have been integrated into the Geneformer suite in the [model card](./model_cards/geneformer.md). Check out the our notebook on drug perturbation prediction using different Geneformer scalings [here](./notebooks/Geneformer-Series-Comparison.ipynb). + ### TranscriptFormer -We have integrated [TranscriptFormer](https://github.com/czi-ai/transcriptformer) into our helical package and have made a model card for it in our [Transcriptformer model folder](helical/models/transcriptformer/README.md). If you would like to test the model, take a look at our [example notebook](examples/notebooks/Geneformer-vs-TranscriptFormer.ipynb)! +We have integrated [TranscriptFormer](https://github.com/czi-ai/transcriptformer) into our helical package and have made a model card for it in our [Transcriptformer model folder](./model_cards/transcriptformer.md). If you would like to test the model, take a look at our [example notebook](./notebooks/Geneformer-vs-TranscriptFormer.ipynb)! ### 🧬 Introducing Helix-mRNA-v0: Unlocking new frontiers & use cases in mRNA therapy 🧬 We’re thrilled to announce the release of our first-ever mRNA Bio Foundation Model, designed to: @@ -107,6 +110,7 @@ Within the `example/notebooks` folder, open the notebook of your choice. We reco |[Cell-Type-Classification-Fine-Tuning.ipynb](./notebooks/Cell-Type-Classification-Fine-Tuning.ipynb)|An example how to fine-tune different models on classification tasks.|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/helicalAI/helical/blob/main/examples/notebooks/Cell-Type-Classification-Fine-Tuning.ipynb) | |[HyenaDNA-Fine-Tuning.ipynb](./notebooks/HyenaDNA-Fine-Tuning.ipynb)|An example of how to fine-tune the HyenaDNA model on downstream benchmarks.|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/helicalAI/helical/blob/main/examples/notebooks/HyenaDNA-Fine-Tuning.ipynb) | |[Cell-Gene-Cls-embedding-generation.ipynb](./examples/notebooks/Cell-Gene-Cls-embedding-generation.ipynb)|A notebook explaining the different embedding modes of single cell RNA models.|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/helicalAI/helical/blob/main/examples/notebooks/Cell-Gene-Cls-embedding-generation.ipynb) | +|[Geneformer-Series-Comparison.ipynb](./notebooks/Geneformer-Series-Comparison.ipynb)|A zero shot comparison between Geneformer model scaling on drug perturbation prediction|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/helicalAI/helical/blob/main/examples/notebooks/Geneformer-Series-Comparison.ipynb) | ## Stuck somewhere ? Other ideas ? We are eager to help you and interact with you: diff --git a/docs/notebooks/Geneformer-Series-Comparison.ipynb b/docs/notebooks/Geneformer-Series-Comparison.ipynb new file mode 100644 index 00000000..e7c76ce5 --- /dev/null +++ b/docs/notebooks/Geneformer-Series-Comparison.ipynb @@ -0,0 +1,479 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "25ea64e5", + "metadata": {}, + "source": [ + "# 🔬 Geneformer Series Showdown 🚀 \n", + "### 📊 Zero-Shot Scaling on SciPlex 2 Drug Perturbations \n", + "\n", + "Welcome to an exploration of the **Geneformer** model series, where we evaluate how model scaling influences performance on the **SciPlex 2** drug perturbation dataset — all in a **zero-shot** setting. \n", + "\n", + "This notebook dives into: \n", + "- 🧠 How different Geneformer model sizes perform \n", + "- 💥 Zero-shot inference on high-dimensional drug response data \n", + "- 📈 Insights into biological signal capture vs. model scale \n", + "\n", + "Let’s see how far scaling takes us! 🧬" + ] + }, + { + "cell_type": "markdown", + "id": "814a9b78", + "metadata": {}, + "source": [ + "### Imports ⇅" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0e0215a0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:datasets:PyTorch version 2.6.0 available.\n" + ] + } + ], + "source": [ + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "from helical.models.geneformer import Geneformer, GeneformerConfig\n", + "import anndata\n", + "import os\n", + "import urllib\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.gridspec as gridspec\n", + "import matplotlib.cm as cm\n", + "import umap.umap_ as umap\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score, f1_score\n", + "from sklearn.linear_model import RidgeClassifier" + ] + }, + { + "cell_type": "markdown", + "id": "dbe31f0c", + "metadata": {}, + "source": [ + "# Download the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "29659763", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File already exists: SrivatsanTrapnell2020_sciplex2.h5ad\n" + ] + } + ], + "source": [ + "url = \"https://zenodo.org/record/10044268/files/SrivatsanTrapnell2020_sciplex2.h5ad?download=1\"\n", + "output_path = \"SrivatsanTrapnell2020_sciplex2.h5ad\"\n", + "\n", + "if not os.path.exists(output_path):\n", + " urllib.request.urlretrieve(url, output_path)\n", + " print(f\"Downloaded to {output_path}\")\n", + "else:\n", + " print(f\"File already exists: {output_path}\")\n", + "\n", + "sciplex2_adata = anndata.read_h5ad(output_path)" + ] + }, + { + "cell_type": "markdown", + "id": "0d94d555", + "metadata": {}, + "source": [ + "## 🧬 Extracting Geneformer Embeddings \n", + "### 🔧 Selected Models for Zero-Shot Evaluation\n", + "\n", + "To evaluate how model scaling impacts representation quality, we extract embeddings from the following **Geneformer** model variants:\n", + "\n", + "| Model Name | Layers | Parameters | Input Dim |\n", + "|------------------------|--------|------------|-----------|\n", + "| `gf-6L-10M-i2048` | 6 | 10M | 2048 |\n", + "| `gf-12L-38M-i4096` | 12 | 38M | 4096 |\n", + "| `gf-12L-104M-i4096` | 12 | 104M | 4096 |\n", + "| `gf-18L-316M-i4096` | 18 | 316M | 4096 |\n", + "\n", + "We evaluate each of these in their `cell` embedding mode which aggregates all gene level embeddings.\n", + "\n", + "📥 Below, we generate embeddings for the SciPlex 2 perturbation data in a **zero-shot** setting.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "38995b83", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Embeddings for 6L-10M model already exist. Skipping processing.\n" + ] + } + ], + "source": [ + "if not os.path.exists(\"embeddings_6l_10m.npz\"):\n", + " gf_6l_30m_model = Geneformer(GeneformerConfig(model_name=\"gf-6L-10M-i2048\", device=\"cuda\", batch_size=8))\n", + "\n", + " num = gf_6l_30m_model.model.num_parameters(only_trainable=False)\n", + " print(f\"Number of parameters: {num:_}\".replace(\"_\", \" \"))\n", + " \n", + " print(\"Processing data and generating embeddings...\")\n", + " dataset = gf_6l_30m_model.process_data(sciplex2_adata)\n", + " embeddings = gf_6l_30m_model.get_embeddings(dataset)\n", + "\n", + " with open(\"embeddings_6l_10m.npz\", \"wb\") as f:\n", + " np.save(f, embeddings)\n", + "else:\n", + " print(\"Embeddings for 6L-10M model already exist. Skipping processing.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "da572cbf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Embeddings for 12L-38M model already exist. Skipping processing.\n" + ] + } + ], + "source": [ + "if not os.path.exists(\"embeddings_12l_38m.npz\"):\n", + " gf_12l_95m_model = Geneformer(GeneformerConfig(model_name=\"gf-12L-38M-i4096\", device=\"cuda\", batch_size=8))\n", + "\n", + " num = gf_12l_95m_model.model.num_parameters(only_trainable=False)\n", + " print(f\"Number of parameters: {num:_}\".replace(\"_\", \" \"))\n", + " \n", + " print(\"Processing data and generating embeddings...\")\n", + " dataset = gf_12l_95m_model.process_data(sciplex2_adata)\n", + " embeddings = gf_12l_95m_model.get_embeddings(dataset)\n", + "\n", + " with open(\"embeddings_12l_38m.npz\", \"wb\") as f:\n", + " np.save(f, embeddings)\n", + "else:\n", + " print(\"Embeddings for 12L-38M model already exist. Skipping processing.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ba5d1ee0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Embeddings for 12L-104M model already exist. Skipping processing.\n" + ] + } + ], + "source": [ + "if not os.path.exists(\"embeddings_12l_104m.npz\"):\n", + " gf_12l_104m_model = Geneformer(GeneformerConfig(model_name=\"gf-12L-104M-i4096\", device=\"cuda\", batch_size=1))\n", + "\n", + " num = gf_12l_104m_model.model.num_parameters(only_trainable=False)\n", + " print(f\"Number of parameters: {num:_}\".replace(\"_\", \" \"))\n", + " \n", + " print(\"Processing data and generating embeddings...\")\n", + " dataset = gf_12l_104m_model.process_data(sciplex2_adata)\n", + " embeddings = gf_12l_104m_model.get_embeddings(dataset)\n", + "\n", + " with open(\"embeddings_12l_104m.npz\", \"wb\") as f:\n", + " np.save(f, embeddings)\n", + "else:\n", + " print(\"Embeddings for 12L-104M model already exist. Skipping processing.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e579b0d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Embeddings for 18L-316M model already exist. Skipping processing.\n" + ] + } + ], + "source": [ + "if not os.path.exists(\"embeddings_18l_316m.npz\"):\n", + " gf_18l_316m_model = Geneformer(GeneformerConfig(model_name=\"gf-18L-316M-i4096\", device=\"cuda\", batch_size=8))\n", + "\n", + " num = gf_18l_316m_model.model.num_parameters(only_trainable=False)\n", + " print(f\"Number of parameters: {num:_}\".replace(\"_\", \" \"))\n", + "\n", + " print(\"Processing data and generating embeddings...\")\n", + " dataset = gf_18l_316m_model.process_data(sciplex2_adata)\n", + " embeddings = gf_18l_316m_model.get_embeddings(dataset)\n", + "\n", + " with open(\"embeddings_18l_316m.npz\", \"wb\") as f:\n", + " np.save(f, embeddings)\n", + "else:\n", + " print(\"Embeddings for 18L-316M model already exist. Skipping processing.\")" + ] + }, + { + "cell_type": "markdown", + "id": "35953fc0", + "metadata": {}, + "source": [ + "## 🌌 Visualizing Geneformer Embeddings with UMAP \n", + "### 🧭 How Do Different Model Scales Separate Drug Perturbations?\n", + "\n", + "We use **UMAP** to project high-dimensional embeddings into 2D space for visual comparison across model scales. Each point represents a cell, colored by its **perturbation label** from the SciPlex 2 dataset.\n", + "\n", + "🔍 Models visualized:\n", + "- **6L 10M**\n", + "- **12L 38M**\n", + "- **12L 104M**\n", + "- **18L 316M**\n", + "\n", + "This visualization helps reveal how well each model separates different perturbation classes in a zero-shot setting — giving us insight into **representation quality** and **semantic clustering** as model capacity increases.\n", + "\n", + "🎨 Legend shows unique perturbation labels encoded with consistent colors." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a9276080", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "embeddings_list = [\n", + " np.load(\"embeddings_6l_10m.npz\"),\n", + " np.load(\"embeddings_12l_38m.npz\"),\n", + " np.load(\"embeddings_12l_104m.npz\"),\n", + " np.load(\"embeddings_18l_316m.npz\"),\n", + "]\n", + "\n", + "labels = sciplex2_adata.obs[\"perturbation\"].values\n", + "titles = [\"6L 10M\", \"12L 38M\", \"12L 104M\", \"18L 316M\"]\n", + "\n", + "label_encoder = LabelEncoder()\n", + "labels_encoded = label_encoder.fit_transform(labels)\n", + "unique_labels = label_encoder.classes_\n", + "n_classes = len(unique_labels)\n", + "\n", + "cmap = cm.get_cmap('tab10', n_classes)\n", + "colors = [cmap(i) for i in range(n_classes)]\n", + "\n", + "fig = plt.figure(figsize=(22, 5))\n", + "gs = gridspec.GridSpec(1, 5, width_ratios=[1, 1, 1, 1, 0.3])\n", + "\n", + "reducer = umap.UMAP(random_state=42)\n", + "\n", + "for i, (embeddings, title) in enumerate(zip(embeddings_list, titles)):\n", + " ax = fig.add_subplot(gs[i])\n", + " umap_result = reducer.fit_transform(embeddings)\n", + " for class_idx in range(n_classes):\n", + " ax.scatter(\n", + " umap_result[labels_encoded == class_idx, 0],\n", + " umap_result[labels_encoded == class_idx, 1],\n", + " s=5, color=colors[class_idx], label=unique_labels[class_idx], alpha=0.8\n", + " )\n", + " ax.set_title(title)\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + "\n", + "ax_legend = fig.add_subplot(gs[4])\n", + "ax_legend.axis('off')\n", + "handles = [plt.Line2D([0], [0], marker='o', color='w',\n", + " markerfacecolor=colors[i], markersize=8,\n", + " label=unique_labels[i]) for i in range(n_classes)]\n", + "ax_legend.legend(handles=handles, loc='center left', fontsize=10, title=\"Labels\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "059d9489", + "metadata": {}, + "source": [ + "## 🧪 Zero-Shot Classification with RidgeClassifier \n", + "### 🧠 How Well Do Geneformer Embeddings Support Supervised Tasks?\n", + "\n", + "To assess the **semantic quality** of embeddings across model scales, we train a simple **RidgeClassifier** on top of each embedding set to predict **perturbation labels**. This lightweight classifier helps us understand how **linearly separable** the embeddings are — a useful proxy for their expressive power.\n", + "\n", + "🧰 **Evaluation Setup:**\n", + "- Train/test split: 80/20 \n", + "- Classifier: `RidgeClassifier` (no hyperparameter tuning) \n", + "- Metrics: `Accuracy` and `F1 Score (weighted)` \n", + "\n", + "📊 **Results:**\n", + "\n", + "| Model | Accuracy | F1 Score |\n", + "|--------------|----------|----------|\n", + "| GF 6L 10M | 0.658 | 0.642 |\n", + "| GF 12L 38M | 0.698 | 0.685 |\n", + "| GF 12L 104M | 0.724 | 0.712 |\n", + "| GF 18L 316M | 0.726 | 0.716 |\n", + "\n", + "📈 Below, we visualize these results to compare performance across models — showing a clear trend: **larger Geneformer models produce more discriminative embeddings** in this zero-shot drug perturbation setting.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "dce70dab", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Model Accuracy F1 Score\n", + "0 GF 6L 10M 0.658150 0.642452\n", + "1 GF 12L 38M 0.697713 0.685367\n", + "2 GF 12L 104M 0.724294 0.712239\n", + "3 GF 18L 316M 0.726355 0.715880\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "embedding_sets = [\n", + " (\"GF 6L 10M\", embeddings_list[0]),\n", + " (\"GF 12L 38M\", embeddings_list[1]),\n", + " (\"GF 12L 104M\", embeddings_list[2]),\n", + " (\"GF 18L 316M\", embeddings_list[3]),\n", + "]\n", + "\n", + "results = []\n", + "\n", + "for name, embeddings in embedding_sets:\n", + " X_train, X_test, y_train, y_test = train_test_split(\n", + " embeddings, sciplex2_adata.obs[\"perturbation\"].values,\n", + " test_size=0.2, random_state=42\n", + " )\n", + " clf = RidgeClassifier()\n", + " clf.fit(X_train, y_train)\n", + " y_pred = clf.predict(X_test)\n", + " \n", + " accuracy = accuracy_score(y_test, y_pred)\n", + " f1 = f1_score(y_test, y_pred, average='weighted')\n", + " \n", + " results.append({\n", + " \"Model\": name,\n", + " \"Accuracy\": accuracy,\n", + " \"F1 Score\": f1\n", + " })\n", + "\n", + "# Convert to DataFrame for table display\n", + "results_df = pd.DataFrame(results)\n", + "print(results_df)\n", + "\n", + "plot_df = results_df.melt(id_vars=\"Model\", var_name=\"Metric\", value_name=\"Score\")\n", + "\n", + "sns.set_theme(style=\"whitegrid\")\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "palette = sns.color_palette(\"Set2\", n_colors=2)\n", + "\n", + "bar_plot = sns.barplot(\n", + " data=plot_df,\n", + " x=\"Model\",\n", + " y=\"Score\",\n", + " hue=\"Metric\",\n", + " palette=palette,\n", + " edgecolor=\"0.2\"\n", + ")\n", + "\n", + "bar_plot.set_title(\"RidgeClassifier Performance per Embedding\", fontsize=16, weight=\"bold\")\n", + "bar_plot.set_ylabel(\"Score\", fontsize=12)\n", + "bar_plot.set_xlabel(\"Model\", fontsize=12)\n", + "bar_plot.set_ylim(0, 1.0)\n", + "bar_plot.set_xticklabels(bar_plot.get_xticklabels(), rotation=15, ha='right')\n", + "\n", + "bar_plot.legend(title=\"Metric\", loc=\"upper left\", fontsize=10, title_fontsize=11)\n", + "\n", + "for p in bar_plot.patches:\n", + " height = p.get_height()\n", + " if height > 0.01:\n", + " bar_plot.annotate(\n", + " f'{height:.2f}',\n", + " (p.get_x() + p.get_width() / 2., height),\n", + " ha='center', va='bottom',\n", + " fontsize=9, color='black',\n", + " xytext=(0, 5), textcoords='offset points'\n", + " )\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "helical-matt", + "language": "python", + "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": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/Cell-Type-Annotation.ipynb b/examples/notebooks/Cell-Type-Annotation.ipynb index e2edfb7f..13148260 100644 --- a/examples/notebooks/Cell-Type-Annotation.ipynb +++ b/examples/notebooks/Cell-Type-Annotation.ipynb @@ -852,12 +852,12 @@ "INFO:helical.utils.downloader:File saved to: '/home/benoit/.cache/helical/models/geneformer/v1/token_dictionary.pkl'\n", "INFO:helical.utils.downloader:File: '/home/benoit/.cache/helical/models/geneformer/v1/ensembl_mapping_dict.pkl' exists already. File is not overwritten and nothing is downloaded.\n", "INFO:helical.utils.downloader:File saved to: '/home/benoit/.cache/helical/models/geneformer/v1/ensembl_mapping_dict.pkl'\n", - "INFO:helical.utils.downloader:File: '/home/benoit/.cache/helical/models/geneformer/v1/gf-12L-30M-i2048/config.json' exists already. File is not overwritten and nothing is downloaded.\n", - "INFO:helical.utils.downloader:File saved to: '/home/benoit/.cache/helical/models/geneformer/v1/gf-12L-30M-i2048/config.json'\n", - "INFO:helical.utils.downloader:File: '/home/benoit/.cache/helical/models/geneformer/v1/gf-12L-30M-i2048/training_args.bin' exists already. File is not overwritten and nothing is downloaded.\n", - "INFO:helical.utils.downloader:File saved to: '/home/benoit/.cache/helical/models/geneformer/v1/gf-12L-30M-i2048/training_args.bin'\n", - "INFO:helical.utils.downloader:File: '/home/benoit/.cache/helical/models/geneformer/v1/gf-12L-30M-i2048/pytorch_model.bin' exists already. File is not overwritten and nothing is downloaded.\n", - "INFO:helical.utils.downloader:File saved to: '/home/benoit/.cache/helical/models/geneformer/v1/gf-12L-30M-i2048/pytorch_model.bin'\n", + "INFO:helical.utils.downloader:File: '/home/benoit/.cache/helical/models/geneformer/v1/gf-12L-40M-i2048/config.json' exists already. File is not overwritten and nothing is downloaded.\n", + "INFO:helical.utils.downloader:File saved to: '/home/benoit/.cache/helical/models/geneformer/v1/gf-12L-40M-i2048/config.json'\n", + "INFO:helical.utils.downloader:File: '/home/benoit/.cache/helical/models/geneformer/v1/gf-12L-40M-i2048/training_args.bin' exists already. File is not overwritten and nothing is downloaded.\n", + "INFO:helical.utils.downloader:File saved to: '/home/benoit/.cache/helical/models/geneformer/v1/gf-12L-40M-i2048/training_args.bin'\n", + "INFO:helical.utils.downloader:File: '/home/benoit/.cache/helical/models/geneformer/v1/gf-12L-40M-i2048/pytorch_model.bin' exists already. File is not overwritten and nothing is downloaded.\n", + "INFO:helical.utils.downloader:File saved to: '/home/benoit/.cache/helical/models/geneformer/v1/gf-12L-40M-i2048/pytorch_model.bin'\n", "INFO:helical.models.geneformer.model:Model finished initializing.\n", "INFO:pyensembl.sequence_data:Loaded sequence dictionary from /home/benoit/.cache/pyensembl/GRCh38/ensembl110/Homo_sapiens.GRCh38.cdna.all.fa.gz.pickle\n", "INFO:pyensembl.sequence_data:Loaded sequence dictionary from /home/benoit/.cache/pyensembl/GRCh38/ensembl110/Homo_sapiens.GRCh38.ncrna.fa.gz.pickle\n", diff --git a/examples/notebooks/Cell-Type-Classification-Fine-Tuning.ipynb b/examples/notebooks/Cell-Type-Classification-Fine-Tuning.ipynb index 21ea93fe..9120bcfe 100644 --- a/examples/notebooks/Cell-Type-Classification-Fine-Tuning.ipynb +++ b/examples/notebooks/Cell-Type-Classification-Fine-Tuning.ipynb @@ -172,7 +172,7 @@ "metadata": {}, "outputs": [], "source": [ - "geneformer_config = GeneformerConfig(device=device, batch_size=10, model_name=\"gf-6L-30M-i2048\")\n", + "geneformer_config = GeneformerConfig(device=device, batch_size=10, model_name=\"gf-6L-10M-i2048\")\n", "geneformer_fine_tune = GeneformerFineTuningModel(geneformer_config=geneformer_config, fine_tuning_head=\"classification\", output_size=len(label_set))" ] }, diff --git a/examples/notebooks/Geneformer-Series-Comparison.ipynb b/examples/notebooks/Geneformer-Series-Comparison.ipynb new file mode 100644 index 00000000..e7c76ce5 --- /dev/null +++ b/examples/notebooks/Geneformer-Series-Comparison.ipynb @@ -0,0 +1,479 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "25ea64e5", + "metadata": {}, + "source": [ + "# 🔬 Geneformer Series Showdown 🚀 \n", + "### 📊 Zero-Shot Scaling on SciPlex 2 Drug Perturbations \n", + "\n", + "Welcome to an exploration of the **Geneformer** model series, where we evaluate how model scaling influences performance on the **SciPlex 2** drug perturbation dataset — all in a **zero-shot** setting. \n", + "\n", + "This notebook dives into: \n", + "- 🧠 How different Geneformer model sizes perform \n", + "- 💥 Zero-shot inference on high-dimensional drug response data \n", + "- 📈 Insights into biological signal capture vs. model scale \n", + "\n", + "Let’s see how far scaling takes us! 🧬" + ] + }, + { + "cell_type": "markdown", + "id": "814a9b78", + "metadata": {}, + "source": [ + "### Imports ⇅" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0e0215a0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:datasets:PyTorch version 2.6.0 available.\n" + ] + } + ], + "source": [ + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "from helical.models.geneformer import Geneformer, GeneformerConfig\n", + "import anndata\n", + "import os\n", + "import urllib\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.gridspec as gridspec\n", + "import matplotlib.cm as cm\n", + "import umap.umap_ as umap\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score, f1_score\n", + "from sklearn.linear_model import RidgeClassifier" + ] + }, + { + "cell_type": "markdown", + "id": "dbe31f0c", + "metadata": {}, + "source": [ + "# Download the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "29659763", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File already exists: SrivatsanTrapnell2020_sciplex2.h5ad\n" + ] + } + ], + "source": [ + "url = \"https://zenodo.org/record/10044268/files/SrivatsanTrapnell2020_sciplex2.h5ad?download=1\"\n", + "output_path = \"SrivatsanTrapnell2020_sciplex2.h5ad\"\n", + "\n", + "if not os.path.exists(output_path):\n", + " urllib.request.urlretrieve(url, output_path)\n", + " print(f\"Downloaded to {output_path}\")\n", + "else:\n", + " print(f\"File already exists: {output_path}\")\n", + "\n", + "sciplex2_adata = anndata.read_h5ad(output_path)" + ] + }, + { + "cell_type": "markdown", + "id": "0d94d555", + "metadata": {}, + "source": [ + "## 🧬 Extracting Geneformer Embeddings \n", + "### 🔧 Selected Models for Zero-Shot Evaluation\n", + "\n", + "To evaluate how model scaling impacts representation quality, we extract embeddings from the following **Geneformer** model variants:\n", + "\n", + "| Model Name | Layers | Parameters | Input Dim |\n", + "|------------------------|--------|------------|-----------|\n", + "| `gf-6L-10M-i2048` | 6 | 10M | 2048 |\n", + "| `gf-12L-38M-i4096` | 12 | 38M | 4096 |\n", + "| `gf-12L-104M-i4096` | 12 | 104M | 4096 |\n", + "| `gf-18L-316M-i4096` | 18 | 316M | 4096 |\n", + "\n", + "We evaluate each of these in their `cell` embedding mode which aggregates all gene level embeddings.\n", + "\n", + "📥 Below, we generate embeddings for the SciPlex 2 perturbation data in a **zero-shot** setting.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "38995b83", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Embeddings for 6L-10M model already exist. Skipping processing.\n" + ] + } + ], + "source": [ + "if not os.path.exists(\"embeddings_6l_10m.npz\"):\n", + " gf_6l_30m_model = Geneformer(GeneformerConfig(model_name=\"gf-6L-10M-i2048\", device=\"cuda\", batch_size=8))\n", + "\n", + " num = gf_6l_30m_model.model.num_parameters(only_trainable=False)\n", + " print(f\"Number of parameters: {num:_}\".replace(\"_\", \" \"))\n", + " \n", + " print(\"Processing data and generating embeddings...\")\n", + " dataset = gf_6l_30m_model.process_data(sciplex2_adata)\n", + " embeddings = gf_6l_30m_model.get_embeddings(dataset)\n", + "\n", + " with open(\"embeddings_6l_10m.npz\", \"wb\") as f:\n", + " np.save(f, embeddings)\n", + "else:\n", + " print(\"Embeddings for 6L-10M model already exist. Skipping processing.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "da572cbf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Embeddings for 12L-38M model already exist. Skipping processing.\n" + ] + } + ], + "source": [ + "if not os.path.exists(\"embeddings_12l_38m.npz\"):\n", + " gf_12l_95m_model = Geneformer(GeneformerConfig(model_name=\"gf-12L-38M-i4096\", device=\"cuda\", batch_size=8))\n", + "\n", + " num = gf_12l_95m_model.model.num_parameters(only_trainable=False)\n", + " print(f\"Number of parameters: {num:_}\".replace(\"_\", \" \"))\n", + " \n", + " print(\"Processing data and generating embeddings...\")\n", + " dataset = gf_12l_95m_model.process_data(sciplex2_adata)\n", + " embeddings = gf_12l_95m_model.get_embeddings(dataset)\n", + "\n", + " with open(\"embeddings_12l_38m.npz\", \"wb\") as f:\n", + " np.save(f, embeddings)\n", + "else:\n", + " print(\"Embeddings for 12L-38M model already exist. Skipping processing.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ba5d1ee0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Embeddings for 12L-104M model already exist. Skipping processing.\n" + ] + } + ], + "source": [ + "if not os.path.exists(\"embeddings_12l_104m.npz\"):\n", + " gf_12l_104m_model = Geneformer(GeneformerConfig(model_name=\"gf-12L-104M-i4096\", device=\"cuda\", batch_size=1))\n", + "\n", + " num = gf_12l_104m_model.model.num_parameters(only_trainable=False)\n", + " print(f\"Number of parameters: {num:_}\".replace(\"_\", \" \"))\n", + " \n", + " print(\"Processing data and generating embeddings...\")\n", + " dataset = gf_12l_104m_model.process_data(sciplex2_adata)\n", + " embeddings = gf_12l_104m_model.get_embeddings(dataset)\n", + "\n", + " with open(\"embeddings_12l_104m.npz\", \"wb\") as f:\n", + " np.save(f, embeddings)\n", + "else:\n", + " print(\"Embeddings for 12L-104M model already exist. Skipping processing.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e579b0d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Embeddings for 18L-316M model already exist. Skipping processing.\n" + ] + } + ], + "source": [ + "if not os.path.exists(\"embeddings_18l_316m.npz\"):\n", + " gf_18l_316m_model = Geneformer(GeneformerConfig(model_name=\"gf-18L-316M-i4096\", device=\"cuda\", batch_size=8))\n", + "\n", + " num = gf_18l_316m_model.model.num_parameters(only_trainable=False)\n", + " print(f\"Number of parameters: {num:_}\".replace(\"_\", \" \"))\n", + "\n", + " print(\"Processing data and generating embeddings...\")\n", + " dataset = gf_18l_316m_model.process_data(sciplex2_adata)\n", + " embeddings = gf_18l_316m_model.get_embeddings(dataset)\n", + "\n", + " with open(\"embeddings_18l_316m.npz\", \"wb\") as f:\n", + " np.save(f, embeddings)\n", + "else:\n", + " print(\"Embeddings for 18L-316M model already exist. Skipping processing.\")" + ] + }, + { + "cell_type": "markdown", + "id": "35953fc0", + "metadata": {}, + "source": [ + "## 🌌 Visualizing Geneformer Embeddings with UMAP \n", + "### 🧭 How Do Different Model Scales Separate Drug Perturbations?\n", + "\n", + "We use **UMAP** to project high-dimensional embeddings into 2D space for visual comparison across model scales. Each point represents a cell, colored by its **perturbation label** from the SciPlex 2 dataset.\n", + "\n", + "🔍 Models visualized:\n", + "- **6L 10M**\n", + "- **12L 38M**\n", + "- **12L 104M**\n", + "- **18L 316M**\n", + "\n", + "This visualization helps reveal how well each model separates different perturbation classes in a zero-shot setting — giving us insight into **representation quality** and **semantic clustering** as model capacity increases.\n", + "\n", + "🎨 Legend shows unique perturbation labels encoded with consistent colors." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a9276080", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "embeddings_list = [\n", + " np.load(\"embeddings_6l_10m.npz\"),\n", + " np.load(\"embeddings_12l_38m.npz\"),\n", + " np.load(\"embeddings_12l_104m.npz\"),\n", + " np.load(\"embeddings_18l_316m.npz\"),\n", + "]\n", + "\n", + "labels = sciplex2_adata.obs[\"perturbation\"].values\n", + "titles = [\"6L 10M\", \"12L 38M\", \"12L 104M\", \"18L 316M\"]\n", + "\n", + "label_encoder = LabelEncoder()\n", + "labels_encoded = label_encoder.fit_transform(labels)\n", + "unique_labels = label_encoder.classes_\n", + "n_classes = len(unique_labels)\n", + "\n", + "cmap = cm.get_cmap('tab10', n_classes)\n", + "colors = [cmap(i) for i in range(n_classes)]\n", + "\n", + "fig = plt.figure(figsize=(22, 5))\n", + "gs = gridspec.GridSpec(1, 5, width_ratios=[1, 1, 1, 1, 0.3])\n", + "\n", + "reducer = umap.UMAP(random_state=42)\n", + "\n", + "for i, (embeddings, title) in enumerate(zip(embeddings_list, titles)):\n", + " ax = fig.add_subplot(gs[i])\n", + " umap_result = reducer.fit_transform(embeddings)\n", + " for class_idx in range(n_classes):\n", + " ax.scatter(\n", + " umap_result[labels_encoded == class_idx, 0],\n", + " umap_result[labels_encoded == class_idx, 1],\n", + " s=5, color=colors[class_idx], label=unique_labels[class_idx], alpha=0.8\n", + " )\n", + " ax.set_title(title)\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + "\n", + "ax_legend = fig.add_subplot(gs[4])\n", + "ax_legend.axis('off')\n", + "handles = [plt.Line2D([0], [0], marker='o', color='w',\n", + " markerfacecolor=colors[i], markersize=8,\n", + " label=unique_labels[i]) for i in range(n_classes)]\n", + "ax_legend.legend(handles=handles, loc='center left', fontsize=10, title=\"Labels\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "059d9489", + "metadata": {}, + "source": [ + "## 🧪 Zero-Shot Classification with RidgeClassifier \n", + "### 🧠 How Well Do Geneformer Embeddings Support Supervised Tasks?\n", + "\n", + "To assess the **semantic quality** of embeddings across model scales, we train a simple **RidgeClassifier** on top of each embedding set to predict **perturbation labels**. This lightweight classifier helps us understand how **linearly separable** the embeddings are — a useful proxy for their expressive power.\n", + "\n", + "🧰 **Evaluation Setup:**\n", + "- Train/test split: 80/20 \n", + "- Classifier: `RidgeClassifier` (no hyperparameter tuning) \n", + "- Metrics: `Accuracy` and `F1 Score (weighted)` \n", + "\n", + "📊 **Results:**\n", + "\n", + "| Model | Accuracy | F1 Score |\n", + "|--------------|----------|----------|\n", + "| GF 6L 10M | 0.658 | 0.642 |\n", + "| GF 12L 38M | 0.698 | 0.685 |\n", + "| GF 12L 104M | 0.724 | 0.712 |\n", + "| GF 18L 316M | 0.726 | 0.716 |\n", + "\n", + "📈 Below, we visualize these results to compare performance across models — showing a clear trend: **larger Geneformer models produce more discriminative embeddings** in this zero-shot drug perturbation setting.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "dce70dab", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Model Accuracy F1 Score\n", + "0 GF 6L 10M 0.658150 0.642452\n", + "1 GF 12L 38M 0.697713 0.685367\n", + "2 GF 12L 104M 0.724294 0.712239\n", + "3 GF 18L 316M 0.726355 0.715880\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "embedding_sets = [\n", + " (\"GF 6L 10M\", embeddings_list[0]),\n", + " (\"GF 12L 38M\", embeddings_list[1]),\n", + " (\"GF 12L 104M\", embeddings_list[2]),\n", + " (\"GF 18L 316M\", embeddings_list[3]),\n", + "]\n", + "\n", + "results = []\n", + "\n", + "for name, embeddings in embedding_sets:\n", + " X_train, X_test, y_train, y_test = train_test_split(\n", + " embeddings, sciplex2_adata.obs[\"perturbation\"].values,\n", + " test_size=0.2, random_state=42\n", + " )\n", + " clf = RidgeClassifier()\n", + " clf.fit(X_train, y_train)\n", + " y_pred = clf.predict(X_test)\n", + " \n", + " accuracy = accuracy_score(y_test, y_pred)\n", + " f1 = f1_score(y_test, y_pred, average='weighted')\n", + " \n", + " results.append({\n", + " \"Model\": name,\n", + " \"Accuracy\": accuracy,\n", + " \"F1 Score\": f1\n", + " })\n", + "\n", + "# Convert to DataFrame for table display\n", + "results_df = pd.DataFrame(results)\n", + "print(results_df)\n", + "\n", + "plot_df = results_df.melt(id_vars=\"Model\", var_name=\"Metric\", value_name=\"Score\")\n", + "\n", + "sns.set_theme(style=\"whitegrid\")\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "palette = sns.color_palette(\"Set2\", n_colors=2)\n", + "\n", + "bar_plot = sns.barplot(\n", + " data=plot_df,\n", + " x=\"Model\",\n", + " y=\"Score\",\n", + " hue=\"Metric\",\n", + " palette=palette,\n", + " edgecolor=\"0.2\"\n", + ")\n", + "\n", + "bar_plot.set_title(\"RidgeClassifier Performance per Embedding\", fontsize=16, weight=\"bold\")\n", + "bar_plot.set_ylabel(\"Score\", fontsize=12)\n", + "bar_plot.set_xlabel(\"Model\", fontsize=12)\n", + "bar_plot.set_ylim(0, 1.0)\n", + "bar_plot.set_xticklabels(bar_plot.get_xticklabels(), rotation=15, ha='right')\n", + "\n", + "bar_plot.legend(title=\"Metric\", loc=\"upper left\", fontsize=10, title_fontsize=11)\n", + "\n", + "for p in bar_plot.patches:\n", + " height = p.get_height()\n", + " if height > 0.01:\n", + " bar_plot.annotate(\n", + " f'{height:.2f}',\n", + " (p.get_x() + p.get_width() / 2., height),\n", + " ha='center', va='bottom',\n", + " fontsize=9, color='black',\n", + " xytext=(0, 5), textcoords='offset points'\n", + " )\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "helical-matt", + "language": "python", + "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": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/run_models/configs/geneformer_config.yaml b/examples/run_models/configs/geneformer_config.yaml index 7ee91173..fffdd40e 100644 --- a/examples/run_models/configs/geneformer_config.yaml +++ b/examples/run_models/configs/geneformer_config.yaml @@ -1,6 +1,5 @@ -model_name: "gf-12L-30M-i2048" +model_name: "gf-12L-40M-i2048" batch_size: 24 emb_layer: -1 emb_mode: "cell" -device: "cpu" -accelerator: False \ No newline at end of file +device: "cpu" \ No newline at end of file diff --git a/helical/constants/hash_values.py b/helical/constants/hash_values.py index 3c994fb4..19aa6e0f 100644 --- a/helical/constants/hash_values.py +++ b/helical/constants/hash_values.py @@ -17,27 +17,39 @@ "geneformer/v1/gene_median_dictionary.pkl": "b509e0d0227acf223c72ca4f604ce47a1f7af84eb1d53d1466c1c340103e9a2b", "geneformer/v1/token_dictionary.pkl": "e2c77b0f292c3e3a98ae0a0b562d5feb6d31373c655cd3b4a61a0c748794de24", "geneformer/v1/ensembl_mapping_dict.pkl": "28391ff889e406ee580ace21eb40fb73072dd69a2059b5204f17c4efa2d3bbf0", - "geneformer/v1/gf-6L-30M-i2048/config.json": "9cf69ca3bdb0215c4188b54c451b6f02adfe68b8f66011a57d0f32845133fd4b", - "geneformer/v1/gf-6L-30M-i2048/training_args.bin": "f0ec3459454205174c9d2e4d6c6930f6b0fbf3364fc03a6f4d99c4d3add2012b", - "geneformer/v1/gf-6L-30M-i2048/pytorch_model.bin": "9dc411f9667850bd6bb76e9e8cf2f0b923d7501780fb4c277adae55965c476d5", - "geneformer/v1/gf-12L-30M-i2048/config.json": "a82b9cf2cb830be227bfbbe5a4c5d62723f49fac892ca37c541d0ef40a0e1de9", - "geneformer/v1/gf-12L-30M-i2048/training_args.bin": "259cf6067211e24e198690d00f0a222ee5550ad57e23d04ced0d0ca2e1b3738e", - "geneformer/v1/gf-12L-30M-i2048/pytorch_model.bin": "cea6f8480b6267c3622d57b9c1d53d0f2fa4df38379cf64ec49a35e075afa09d", + "geneformer/v1/gf-6L-10M-i2048/config.json": "9cf69ca3bdb0215c4188b54c451b6f02adfe68b8f66011a57d0f32845133fd4b", + "geneformer/v1/gf-6L-10M-i2048/training_args.bin": "f0ec3459454205174c9d2e4d6c6930f6b0fbf3364fc03a6f4d99c4d3add2012b", + "geneformer/v1/gf-6L-10M-i2048/pytorch_model.bin": "9dc411f9667850bd6bb76e9e8cf2f0b923d7501780fb4c277adae55965c476d5", + "geneformer/v1/gf-12L-40M-i2048/config.json": "a82b9cf2cb830be227bfbbe5a4c5d62723f49fac892ca37c541d0ef40a0e1de9", + "geneformer/v1/gf-12L-40M-i2048/training_args.bin": "259cf6067211e24e198690d00f0a222ee5550ad57e23d04ced0d0ca2e1b3738e", + "geneformer/v1/gf-12L-40M-i2048/pytorch_model.bin": "cea6f8480b6267c3622d57b9c1d53d0f2fa4df38379cf64ec49a35e075afa09d", "geneformer/v2/gene_median_dictionary.pkl": "2be7704fd679a43720011fa0337a5a34d2cf3cb48768c656680dca3dd0653b75", "geneformer/v2/token_dictionary.pkl": "12b094814a5764310a2be428b81748fe0af1b246832384a2b187923481b93c8c", "geneformer/v2/ensembl_mapping_dict.pkl": "28391ff889e406ee580ace21eb40fb73072dd69a2059b5204f17c4efa2d3bbf0", - "geneformer/v2/gf-20L-95M-i4096/training_args.bin": "5afed602918d6f0c4916c1b9335bcdb619bca2c6fd6c7e0dd2a86d195264b8cc", - "geneformer/v2/gf-20L-95M-i4096/config.json": "915948b8161a15747647c9dd04d4bd3a950ca5fb145a14d9bc1157948a4cb9e7", - "geneformer/v2/gf-20L-95M-i4096/generation_config.json": "07f46e7e174ffc98dd5072d6a7e0df8935f44046258d30dbf7b78edadcd44af4", - "geneformer/v2/gf-20L-95M-i4096/model.safetensors": "5109b987c2e390b7bc46f77675bf020f94125ed36e2ba968b52cee7674106669", - "geneformer/v2/gf-12L-95M-i4096-CLcancer/training_args.bin": "37074f3ea62a6ba0a312c38526c20c2dccbb068a2c7ee8c7c73b435dd90ab7b1", - "geneformer/v2/gf-12L-95M-i4096-CLcancer/config.json": "7d3720eb553238849f7b3b3fd874e2bafb59c97b0e70afd7ca90132c43b8d5b1", - "geneformer/v2/gf-12L-95M-i4096-CLcancer/generation_config.json": "07f46e7e174ffc98dd5072d6a7e0df8935f44046258d30dbf7b78edadcd44af4", - "geneformer/v2/gf-12L-95M-i4096-CLcancer/model.safetensors": "b5add9f834ee85a3ed10416b34acab3815f3f8f1b045d83274618f54b6667bb3", - "geneformer/v2/gf-12L-95M-i4096/config.json": "f56780389d8c89c1b6c4084e2e6ee1f736558e4b3bb8ce7473159e83465de401", - "geneformer/v2/gf-12L-95M-i4096/training_args.bin": "21a45980734b138029422e95a5601def858821a9ec02cd473938b9f525ac108d", - "geneformer/v2/gf-12L-95M-i4096/generation_config.json": "07f46e7e174ffc98dd5072d6a7e0df8935f44046258d30dbf7b78edadcd44af4", - "geneformer/v2/gf-12L-95M-i4096/model.safetensors": "25e191c9b3e1762a967260894df12e6d6f0daf2c0dc6f8f0fa055ea77bf8c8ba", + "geneformer/v2/gf-20L-151M-i4096/training_args.bin": "5afed602918d6f0c4916c1b9335bcdb619bca2c6fd6c7e0dd2a86d195264b8cc", + "geneformer/v2/gf-20L-151M-i4096/config.json": "915948b8161a15747647c9dd04d4bd3a950ca5fb145a14d9bc1157948a4cb9e7", + "geneformer/v2/gf-20L-151M-i4096/generation_config.json": "07f46e7e174ffc98dd5072d6a7e0df8935f44046258d30dbf7b78edadcd44af4", + "geneformer/v2/gf-20L-151M-i4096/model.safetensors": "5109b987c2e390b7bc46f77675bf020f94125ed36e2ba968b52cee7674106669", + "geneformer/v2/gf-12L-38M-i4096-CLcancer/training_args.bin": "37074f3ea62a6ba0a312c38526c20c2dccbb068a2c7ee8c7c73b435dd90ab7b1", + "geneformer/v2/gf-12L-38M-i4096-CLcancer/config.json": "7d3720eb553238849f7b3b3fd874e2bafb59c97b0e70afd7ca90132c43b8d5b1", + "geneformer/v2/gf-12L-38M-i4096-CLcancer/generation_config.json": "07f46e7e174ffc98dd5072d6a7e0df8935f44046258d30dbf7b78edadcd44af4", + "geneformer/v2/gf-12L-38M-i4096-CLcancer/model.safetensors": "b5add9f834ee85a3ed10416b34acab3815f3f8f1b045d83274618f54b6667bb3", + "geneformer/v2/gf-12L-38M-i4096/config.json": "f56780389d8c89c1b6c4084e2e6ee1f736558e4b3bb8ce7473159e83465de401", + "geneformer/v2/gf-12L-38M-i4096/training_args.bin": "21a45980734b138029422e95a5601def858821a9ec02cd473938b9f525ac108d", + "geneformer/v2/gf-12L-38M-i4096/generation_config.json": "07f46e7e174ffc98dd5072d6a7e0df8935f44046258d30dbf7b78edadcd44af4", + "geneformer/v2/gf-12L-38M-i4096/model.safetensors": "25e191c9b3e1762a967260894df12e6d6f0daf2c0dc6f8f0fa055ea77bf8c8ba", + "geneformer/v3/gene_median_dictionary.pkl": "2be7704fd679a43720011fa0337a5a34d2cf3cb48768c656680dca3dd0653b75", + "geneformer/v3/token_dictionary.pkl": "12b094814a5764310a2be428b81748fe0af1b246832384a2b187923481b93c8c", + "geneformer/v3/ensembl_mapping_dict.pkl": "28391ff889e406ee580ace21eb40fb73072dd69a2059b5204f17c4efa2d3bbf0", + "geneformer/v3/gf-12L-104M-i4096/config.json": "467d4492f0dd53b4d60afffe20812db484ca1cf9fdbeb6a6e060e93564f70859", + "geneformer/v3/gf-12L-104M-i4096/training_args.bin": "0d8ddd9e4f35b5fe23a3adaae03aa4480705ca82eed546a488f970adb3752d9d", + "geneformer/v3/gf-12L-104M-i4096/model.safetensors": "abfd504df843877db245444179c0f4a72639ca59ab21312bb32bcceb6bff759c", + "geneformer/v3/gf-12L-104M-i4096-CLcancer/config.json": "b3e1f3e71b1df7238f0f1d6dda258d560ea84e5e9698351a1588cfad06321e18", + "geneformer/v3/gf-12L-104M-i4096-CLcancer/training_args.bin": "8cf8ce52b498253adc6df53197a99821fa145c19b8ae5eeb8d15be76b8b7ddb3", + "geneformer/v3/gf-12L-104M-i4096-CLcancer/model.safetensors": "16f9da57b2f5e13c57c437884c1eb5958292c6ad31ff19bdc6502c7a25b3067d", + "geneformer/v3/gf-18L-316M-i4096/config.json": "2cc4af3442644e84af71814a607b61c958d7b4e5ebde6791ab77b5f534ac6f6e", + "geneformer/v3/gf-18L-316M-i4096/training_args.bin": "e45150f9a4ca34cb4e91ce79f65f3d99d9d66df9f66a37517a352d291008e0b8", + "geneformer/v3/gf-18L-316M-i4096/model.safetensors": "e7429684c45cbf9bb731533f0bc30ae690d6ef079b6cb7163118f99dcf862c0b", "hyena_dna/hyenadna-tiny-1k-seqlen.ckpt": "dc6a481cbebe567ed6c68da856479dc69e66ea2f4cb0a59f6da16a75c91785d9", "hyena_dna/hyenadna-tiny-1k-seqlen-d256.ckpt": "381e95e5b1c6ad2ce19b4d00ef2bbadeb15548cad7401e07a4f17ea4953407f1", "hyena_dna/hyenadna-small-32k-seqlen.ckpt": "72af6f9bd5a04fcecc38a19c89eac3259baa8474b34252ed8faeeabceae020ab", diff --git a/helical/models/geneformer/README.md b/helical/models/geneformer/README.md index 3bf75779..34e694eb 100644 --- a/helical/models/geneformer/README.md +++ b/helical/models/geneformer/README.md @@ -41,29 +41,42 @@ Key improvements in v2.0: ## Available Models for each Version ### Version 1.0 (30M dataset) -- **gf-6L-30M-i2048** +- **gf-6L-10M-i2048** - 6 layers - 2048 input size - Trained on ~30 million cells -- **gf-12L-30M-i2048** +- **gf-12L-40M-i2048** - 12 layers - 2048 input size - Trained on ~30 million cells ### Version 2.0 (95M dataset) -- **gf-12L-95M-i4096** +- **gf-12L-38M-i4096** - 12 layers - 4096 input size - Trained on ~95 million cells -- **gf-20L-95M-i4096** +- **gf-20L-151M-i4096** - 20 layers - 4096 input size - Trained on ~95 million cells -- **gf-12L-95M-i4096-CLcancer** +- **gf-12L-38M-i4096-CLcancer** - 12 layers - 4096 input size - Initially trained on ~95 million cells - Further tuned on ~14 million cancer cells +- **gf-12L-104M-i4096** + - 12 layers + - 4096 input size + - Initially trained on ~104M human single cell transcriptomes +- **gf-12L-104M-i4096-CLcancer** + - 12 layers + - 4096 input size + - Initially trained on ~104M human single cell transcriptomes + - Further tuned on ~14 million cancer cells +- **gf-18L-316M-i4096** + - 18 layers + - 4096 input size + - Initially trained on ~104M human single cell transcriptomes ## Model Developers @@ -191,7 +204,7 @@ from helical.models.geneformer import Geneformer, GeneformerConfig import anndata as ad # Example configuration -model_config = GeneformerConfig(model_name="gf-12L-95M-i4096", batch_size=10) +model_config = GeneformerConfig(model_name="gf-12L-38M-i4096", batch_size=10) geneformer_v2 = Geneformer(model_config) # Example usage for base pretrained model @@ -201,7 +214,7 @@ embeddings = geneformer_v2.get_embeddings(dataset) print("Base model embeddings shape:", embeddings.shape) # Example usage for cancer-tuned model -model_config_cancer = GeneformerConfig(model_name="gf-12L-95M-i4096-CLcancer", batch_size=10) +model_config_cancer = GeneformerConfig(model_name="gf-12L-38M-i4096-CLcancer", batch_size=10) geneformer_v2_cancer = Geneformer(model_config) cancer_ann_data = ad.read_h5ad("anndata_file.h5ad") @@ -224,7 +237,7 @@ cell_types = list(ann_data.obs["cell_types"]) label_set = set(cell_types) # Create a GeneformerConfig object -geneformer_config = GeneformerConfig(model_name="gf-12L-95M-i4096", batch_size=10) +geneformer_config = GeneformerConfig(model_name="gf-12L-38M-i4096", batch_size=10) # Create a GeneformerFineTuningModel object geneformer_fine_tune = GeneformerFineTuningModel(geneformer_config=geneformer_config, fine_tuning_head="classification", output_size=len(label_set)) diff --git a/helical/models/geneformer/fine_tuning_model.py b/helical/models/geneformer/fine_tuning_model.py index c4c488f6..d240ba0c 100644 --- a/helical/models/geneformer/fine_tuning_model.py +++ b/helical/models/geneformer/fine_tuning_model.py @@ -42,7 +42,7 @@ class GeneformerFineTuningModel(HelicalBaseFineTuningModel, Geneformer): label_set = set(cell_types) # Create a GeneformerConfig object - geneformer_config = GeneformerConfig(model_name="gf-12L-95M-i4096", batch_size=10) + geneformer_config = GeneformerConfig(model_name="gf-12L-38M-i4096", batch_size=10) # Create a GeneformerFineTuningModel object geneformer_fine_tune = GeneformerFineTuningModel(geneformer_config=geneformer_config, fine_tuning_head="classification", output_size=len(label_set)) @@ -342,7 +342,7 @@ def get_outputs(self, dataset: Dataset, silent=False): """ self.model.eval() self.fine_tuning_head.eval() - + model_input_size = get_model_input_size(self.model) self.to(self.device) diff --git a/helical/models/geneformer/geneformer_config.py b/helical/models/geneformer/geneformer_config.py index 33abaa82..85221936 100644 --- a/helical/models/geneformer/geneformer_config.py +++ b/helical/models/geneformer/geneformer_config.py @@ -9,7 +9,16 @@ class GeneformerConfig: Parameters ---------- - model_name : Literal["gf-6L-30M-i2048", "gf-12L-30M-i2048", "gf-12L-95M-i4096", "gf-20L-95M-i4096", "gf-12L-95M-i4096-CLcancer"], optional, default="gf-12L-30M-i2048" + model_name : Literal[ + "gf-6L-10M-i2048", + "gf-12L-38M-i4096", + "gf-12L-38M-i4096-CLcancer", + "gf-12L-40M-i2048", + "gf-12L-104M-i4096", + "gf-12L-104M-i4096-CLcancer", + "gf-20L-151M-i4096", + "gf-18L-316M-i4096", + ], default="gf-12L-38M-i4096" The name of the model. batch_size : int, optional, default = 24 The batch size @@ -21,8 +30,6 @@ class GeneformerConfig: For gene level embeddings, each gene token embedding is returned along with the corresponding ensembl ID. device : Literal["cpu", "cuda"], optional, default="cpu" The device to use. Either use "cuda" or "cpu". - accelerator : bool, optional, default=False - The accelerator configuration. By default same device as model. nproc: int, optional, default=1 Number of processes to use for data processing. custom_attr_name_dict : dict, optional, default=None @@ -40,94 +47,123 @@ class GeneformerConfig: def __init__( self, model_name: Literal[ - "gf-6L-30M-i2048", - "gf-12L-30M-i2048", - "gf-12L-95M-i4096", - "gf-20L-95M-i4096", - "gf-12L-95M-i4096-CLcancer", - ] = "gf-12L-30M-i2048", + "gf-6L-10M-i2048", + "gf-12L-38M-i4096", + "gf-12L-38M-i4096-CLcancer", + "gf-12L-40M-i2048", + "gf-12L-104M-i4096", + "gf-12L-104M-i4096-CLcancer", + "gf-20L-151M-i4096", + "gf-18L-316M-i4096", + ] = "gf-12L-38M-i4096", batch_size: int = 24, emb_layer: int = -1, emb_mode: Literal["cls", "cell", "gene"] = "cell", device: Literal["cpu", "cuda"] = "cpu", - accelerator: Optional[bool] = False, nproc: int = 1, custom_attr_name_dict: Optional[dict] = None, ): # model specific parameters self.model_map = { - "gf-12L-95M-i4096": { + "gf-12L-38M-i4096": { "input_size": 4096, "special_token": True, "embsize": 512, + "model_version": "v2", }, - "gf-12L-95M-i4096-CLcancer": { + "gf-12L-38M-i4096-CLcancer": { "input_size": 4096, "special_token": True, "embsize": 512, + "model_version": "v2", }, - "gf-20L-95M-i4096": { + "gf-20L-151M-i4096": { "input_size": 4096, "special_token": True, "embsize": 896, + "model_version": "v2", }, - "gf-12L-30M-i2048": { + "gf-12L-40M-i2048": { "input_size": 2048, "special_token": False, "embsize": 512, + "model_version": "v1", }, - "gf-6L-30M-i2048": { + "gf-6L-10M-i2048": { "input_size": 2048, "special_token": False, "embsize": 256, + "model_version": "v1", + }, + "gf-12L-104M-i4096": { + "input_size": 4096, + "special_token": True, + "embsize": 768, + "model_version": "v3", + }, + "gf-12L-104M-i4096-CLcancer": { + "input_size": 4096, + "special_token": True, + "embsize": 768, + "model_version": "v3", + }, + "gf-18L-316M-i4096": { + "input_size": 4096, + "special_token": True, + "embsize": 1152, + "model_version": "v3", }, } + if model_name not in self.model_map: raise ValueError( f"Model name {model_name} not found in available models: {self.model_map.keys()}" ) - model_version = "v2" if "95M" in model_name else "v1" - - if model_version == "v1" and emb_mode == "cls": + if self.model_map[model_name]["model_version"] == "v1" and emb_mode == "cls": raise ValueError( f"Verson 1 Geneformer models do not support the CLS embedding mode" ) self.list_of_files_to_download = [ - f"geneformer/{model_version}/gene_median_dictionary.pkl", - f"geneformer/{model_version}/token_dictionary.pkl", - f"geneformer/{model_version}/ensembl_mapping_dict.pkl", - f"geneformer/{model_version}/{model_name}/config.json", - f"geneformer/{model_version}/{model_name}/training_args.bin", + f"geneformer/{self.model_map[model_name]['model_version']}/gene_median_dictionary.pkl", + f"geneformer/{self.model_map[model_name]['model_version']}/token_dictionary.pkl", + f"geneformer/{self.model_map[model_name]['model_version']}/ensembl_mapping_dict.pkl", + f"geneformer/{self.model_map[model_name]['model_version']}/{model_name}/config.json", + f"geneformer/{self.model_map[model_name]['model_version']}/{model_name}/training_args.bin", ] # Add model weight files to download based on the model version (v1 or v2) - if model_version == "v2": - self.list_of_files_to_download.append( - f"geneformer/{model_version}/{model_name}/generation_config.json" - ) + if ( + self.model_map[model_name]["model_version"] == "v2" + or self.model_map[model_name]["model_version"] == "v3" + ): self.list_of_files_to_download.append( - f"geneformer/{model_version}/{model_name}/model.safetensors" + f"geneformer/{self.model_map[model_name]['model_version']}/{model_name}/model.safetensors" ) else: self.list_of_files_to_download.append( - f"geneformer/{model_version}/{model_name}/pytorch_model.bin" + f"geneformer/{self.model_map[model_name]['model_version']}/{model_name}/pytorch_model.bin" ) self.model_dir = Path(CACHE_DIR_HELICAL, "geneformer") self.files_config = { "model_files_dir": Path( - CACHE_DIR_HELICAL, "geneformer", model_version, model_name + CACHE_DIR_HELICAL, + "geneformer", + self.model_map[model_name]["model_version"], + model_name, ), "gene_median_path": self.model_dir - / model_version + / self.model_map[model_name]["model_version"] / "gene_median_dictionary.pkl", - "token_path": self.model_dir / model_version / "token_dictionary.pkl", + "token_path": self.model_dir + / self.model_map[model_name]["model_version"] + / "token_dictionary.pkl", "ensembl_dict_path": self.model_dir - / model_version + / self.model_map[model_name]["model_version"] / "ensembl_mapping_dict.pkl", } @@ -137,7 +173,6 @@ def __init__( "emb_layer": emb_layer, "emb_mode": emb_mode, "device": device, - "accelerator": accelerator, "input_size": self.model_map[model_name]["input_size"], "special_token": self.model_map[model_name]["special_token"], "embsize": self.model_map[model_name]["embsize"], diff --git a/helical/models/geneformer/model.py b/helical/models/geneformer/model.py index 4a6135a6..4f0f29a1 100644 --- a/helical/models/geneformer/model.py +++ b/helical/models/geneformer/model.py @@ -21,13 +21,18 @@ class Geneformer(HelicalRNAModel): Both versions of Geneformer (v1 and v2) have different sub-models with varying numbers of layers, context size and pretraining set. The available models are the following: Version 1.0: - - gf-12L-30M-i2048 - - gf-6L-30M-i2048 + - gf-12L-40M-i2048 + - gf-6L-10M-i2048 Version 2.0: - - gf-12L-95M-i4096 - - gf-12L-95M-i4096-CLcancer - - gf-20L-95M-i4096 + - gf-12L-38M-i4096 + - gf-12L-38M-i4096-CLcancer + - gf-20L-151M-i4096 + + Version 3.0: + - gf-12L-104M-i4096 + - gf-12L-104M-i4096-CLcancer + - gf-18L-316M-i4096 For a detailed explanation of the differences between these models and versions, please refer to the Geneformer model card: https://helical.readthedocs.io/en/latest/model_cards/geneformer/ @@ -38,7 +43,7 @@ class Geneformer(HelicalRNAModel): import anndata as ad # Example configuration - model_config = GeneformerConfig(model_name="gf-12L-95M-i4096", batch_size=10) + model_config = GeneformerConfig(model_name="gf-12L-38M-i4096", batch_size=10) geneformer_v2 = Geneformer(model_config) # Example usage for base pretrained model @@ -48,7 +53,7 @@ class Geneformer(HelicalRNAModel): print("Base model embeddings shape:", embeddings.shape) # Example usage for cancer-tuned model - model_config_cancer = GeneformerConfig(model_name="gf-12L-95M-i4096-CLcancer", batch_size=10) + model_config_cancer = GeneformerConfig(model_name="gf-12L-38M-i4096-CLcancer", batch_size=10) geneformer_v2_cancer = Geneformer(model_config) cancer_ann_data = ad.read_h5ad("anndata_file.h5ad") From 55e055ac1fa9da29d9676f120da43435fbf1e09a Mon Sep 17 00:00:00 2001 From: Izumi Ando Date: Wed, 30 Jul 2025 13:43:47 -0400 Subject: [PATCH 08/17] fix for issue 243 --- helical/models/uce/uce_dataset.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/helical/models/uce/uce_dataset.py b/helical/models/uce/uce_dataset.py index ed21029e..86bb9de3 100644 --- a/helical/models/uce/uce_dataset.py +++ b/helical/models/uce/uce_dataset.py @@ -54,7 +54,13 @@ def __getitem__(self, idx): counts = cts[idx] counts = torch.tensor(counts).unsqueeze(0) weights = torch.log1p(counts) - weights = weights / torch.sum(weights) + # weights = weights / torch.sum(weights) + weights_sum = torch.sum(weights) + # Changes made on July 15th to avoid numpy errors + if weights_sum==0: + weights = torch.ones_like(weights) / len(weights) + else: + weights = weights / weights_sum batch_sentences, mask, seq_len, cell_sentences = ( self.sample_cell_sentences(counts, weights) ) From 8b95bf1c3919c48bf8af155d2479ca90576c564e Mon Sep 17 00:00:00 2001 From: Izumi Ando Date: Wed, 30 Jul 2025 13:45:24 -0400 Subject: [PATCH 09/17] fix for issue 243 --- helical/models/uce/uce_dataset.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/helical/models/uce/uce_dataset.py b/helical/models/uce/uce_dataset.py index 86bb9de3..39f8b37f 100644 --- a/helical/models/uce/uce_dataset.py +++ b/helical/models/uce/uce_dataset.py @@ -56,7 +56,7 @@ def __getitem__(self, idx): weights = torch.log1p(counts) # weights = weights / torch.sum(weights) weights_sum = torch.sum(weights) - # Changes made on July 15th to avoid numpy errors + # Changes made on July 15th, 2025 to avoid numpy error ValueError: probabilities contain NaN if weights_sum==0: weights = torch.ones_like(weights) / len(weights) else: From 16c3b3c56b8c3da7c50359c3a34e99134ab660fb Mon Sep 17 00:00:00 2001 From: Matthew Wood <62712722+mattwoodx@users.noreply.github.com> Date: Thu, 31 Jul 2025 08:56:38 +0200 Subject: [PATCH 10/17] Address Different Size Attention Maps and Genes In Context (#252) * Make output attentions for Geneformer a list to account for different sizes Add output_genes option to scGPT and Geneformer which when set to true for cls and cell it will return a tuple which contains the list of genes from the context window for each embedding * Add testing for returning genes * fixup! Add testing for returning genes * Remove shape from list print * Update model names to match changes in main for new tests --- .../test_geneformer/test_geneformer_model.py | 22 ++++++++ ci/tests/test_scgpt/test_scgpt_model.py | 46 ++++++++++++++++ examples/run_models/configs/scgpt_config.yaml | 2 +- examples/run_models/run_geneformer.py | 4 +- examples/run_models/run_scgpt.py | 4 +- helical/models/geneformer/geneformer_utils.py | 17 +++++- helical/models/geneformer/model.py | 11 +++- helical/models/scgpt/model.py | 54 ++++++++++++++++--- 8 files changed, 145 insertions(+), 15 deletions(-) diff --git a/ci/tests/test_geneformer/test_geneformer_model.py b/ci/tests/test_geneformer/test_geneformer_model.py index 3fbda7ac..a325e853 100644 --- a/ci/tests/test_geneformer/test_geneformer_model.py +++ b/ci/tests/test_geneformer/test_geneformer_model.py @@ -163,6 +163,28 @@ def test_get_embeddings_of_different_modes_v1( expected = np.array([[4, 4.333333, 4.666667, 4.333333, 4]]) np.testing.assert_allclose(embeddings, expected, rtol=1e-4, atol=1e-4) + def test_get_embeddings_with_output_genes( + self, mock_data, mock_embeddings_v1, mocker + ): + config = GeneformerConfig( + model_name="gf-12L-40M-i2048", batch_size=5, emb_mode="cell" + ) + geneformer = Geneformer(config) + mocker.patch.object( + geneformer.model, "forward", return_value=mock_embeddings_v1 + ) + + dataset = geneformer.process_data(mock_data, gene_names="gene_symbols") + embeddings, genes = geneformer.get_embeddings(dataset, output_genes=True) + + expected = np.array([[4, 4.333333, 4.666667, 4.333333, 4]]) + np.testing.assert_allclose(embeddings, expected, rtol=1e-4, atol=1e-4) + for gene_list in genes: + assert len(gene_list) == 3 + assert "ENSG00000187583" in gene_list + assert "ENSG00000187634" in gene_list + assert "ENSG00000188290" in gene_list + @pytest.mark.parametrize("emb_mode", ["cell", "gene", "cls"]) def test_get_embeddings_of_different_modes_v2( self, emb_mode, mock_data, mock_embeddings_v2, mocker diff --git a/ci/tests/test_scgpt/test_scgpt_model.py b/ci/tests/test_scgpt/test_scgpt_model.py index 36eed3c7..8a641480 100644 --- a/ci/tests/test_scgpt/test_scgpt_model.py +++ b/ci/tests/test_scgpt/test_scgpt_model.py @@ -170,6 +170,52 @@ def test_get_embeddings_of_different_modes(self, mocker, emb_mode): atol=1e-4, # absolute tolerance ) + @pytest.mark.parametrize("emb_mode", ["cell", "cls"]) + def test_get_embeddings_with_gene_outputs(self, mocker, emb_mode): + self.scgpt.config["emb_mode"] = emb_mode + self.scgpt.config["embsize"] = 5 + + # Mock the method directly on the instance + mocked_embeddings = torch.tensor( + [ + [ + [1.0, 1.0, 1.0, 1.0, 1.0], + [5.0, 5.0, 5.0, 5.0, 5.0], + [1.0, 2.0, 3.0, 2.0, 1.0], + [6.0, 6.0, 6.0, 6.0, 6.0], + ], + ] + ) + mocker.patch.object(self.scgpt.model, "_encode", return_value=mocked_embeddings) + + # mocking the normalization of embeddings makes it easier to test the output + mocker.patch.object( + self.scgpt, "_normalize_embeddings", side_effect=lambda x: x + ) + + dataset = self.scgpt.process_data(self.data, gene_names="gene_names") + embeddings, genes = self.scgpt.get_embeddings(dataset, output_genes=True) + + if emb_mode == "cls": + assert (embeddings == np.array([1.0, 1.0, 1.0, 1.0, 1.0])).all() + assert len(genes) == len(embeddings) + for gene_list in genes: + for gene in gene_list: + assert gene in ["SAMD11", "PLEKHN1", "HES4"] + if emb_mode == "cell": + # average column wise excluding first row + expected = np.array([[4.0, 4.3333335, 4.6666665, 4.3333335, 4.0]]) + np.testing.assert_allclose( + embeddings, + expected, + rtol=1e-4, # relative tolerance + atol=1e-4, # absolute tolerance + ) + assert len(genes) == len(embeddings) + for gene_list in genes: + for gene in gene_list: + assert gene in ["SAMD11", "PLEKHN1", "HES4"] + @pytest.mark.parametrize("emb_mode", ["cls", "cell"]) def test_normalization_cell_and_cls(self, emb_mode): mocked_embeddings = np.array( diff --git a/examples/run_models/configs/scgpt_config.yaml b/examples/run_models/configs/scgpt_config.yaml index 2876f0ed..74ca9271 100644 --- a/examples/run_models/configs/scgpt_config.yaml +++ b/examples/run_models/configs/scgpt_config.yaml @@ -1,6 +1,6 @@ pad_token: "" batch_size: 24 -emb_mode: "gene" +emb_mode: "cell" nlayers: 12 nheads: 8 embsize: 512 diff --git a/examples/run_models/run_geneformer.py b/examples/run_models/run_geneformer.py index 318ff472..d8696352 100644 --- a/examples/run_models/run_geneformer.py +++ b/examples/run_models/run_geneformer.py @@ -24,7 +24,7 @@ def run(cfg: DictConfig): ann_data = ad.read_h5ad("./yolksac_human.h5ad") dataset = geneformer.process_data(ann_data[:10, :100]) - embeddings = geneformer.get_embeddings(dataset) + embeddings = geneformer.get_embeddings(dataset, output_genes=True) print(embeddings) embeddings, attention_weights = geneformer.get_embeddings( @@ -32,7 +32,7 @@ def run(cfg: DictConfig): ) print(embeddings) - print(attention_weights.shape) + print(attention_weights) if __name__ == "__main__": diff --git a/examples/run_models/run_scgpt.py b/examples/run_models/run_scgpt.py index 923bf34f..d1a96e91 100644 --- a/examples/run_models/run_scgpt.py +++ b/examples/run_models/run_scgpt.py @@ -24,8 +24,8 @@ def run(cfg: DictConfig): ann_data = ad.read_h5ad("./yolksac_human.h5ad") data = scgpt.process_data(ann_data[:10]) - embeddings = scgpt.get_embeddings(data, output_attentions=False) - print(embeddings) + embeddings, input_genes = scgpt.get_embeddings(data, output_genes=True) + print(embeddings, input_genes) embeddings, attn_weights = scgpt.get_embeddings(data, output_attentions=True) print(embeddings) diff --git a/helical/models/geneformer/geneformer_utils.py b/helical/models/geneformer/geneformer_utils.py index 173ae350..3da680b0 100644 --- a/helical/models/geneformer/geneformer_utils.py +++ b/helical/models/geneformer/geneformer_utils.py @@ -155,11 +155,13 @@ def get_embs( device, silent=False, output_attentions=False, + output_genes=False ): model_input_size = get_model_input_size(model) total_batch_length = len(filtered_input_data) embs_list = [] attn_list = [] + input_genes = [] _check_for_expected_special_tokens( filtered_input_data, emb_mode, cls_present, eos_present, gene_token_dict @@ -194,6 +196,13 @@ def get_embs( # attn_i = torch.mean(attn_i, dim=1).cpu().numpy() # average over heads attn_list.extend(attn_i.cpu().numpy()) + if output_genes: + for input_ids in minibatch["input_ids"]: + gene_list = [] + for id in input_ids: + gene_list.append(token_to_ensembl_dict[id.item()]) + input_genes.append(gene_list) + embs_list.extend( _compute_embeddings_depending_on_mode( embs_i, @@ -216,7 +225,13 @@ def get_embs( embs_list = np.array(embs_list) if output_attentions: - return embs_list, np.array(attn_list) + if output_genes: + return embs_list, attn_list, input_genes + return embs_list, attn_list + + if output_genes: + return embs_list, input_genes + return embs_list diff --git a/helical/models/geneformer/model.py b/helical/models/geneformer/model.py index 4f0f29a1..f7a4f910 100644 --- a/helical/models/geneformer/model.py +++ b/helical/models/geneformer/model.py @@ -208,7 +208,7 @@ def process_data( return tokenized_dataset def get_embeddings( - self, dataset: Dataset, output_attentions: bool = False + self, dataset: Dataset, output_attentions: bool = False, output_genes: bool = False ) -> np.array: """Gets the gene embeddings from the Geneformer model @@ -219,15 +219,21 @@ def get_embeddings( output_attentions : bool, optional, default=False Whether to output attentions from the model. This is useful for debugging or analysis purposes. The attention maps are averaged across heads and use the same layer as the embeddings. + output_genes : bool, optional, default=False + Whether to output the genes corresponding to the embeddings. Returns ------- np.array The gene embeddings in the form of a numpy array - np.array, optional + list, optional The attention weights from the model, if `output_attentions` is set to True. The shape of the attention weights is (batch_size, num_heads, seq_length, seq_length). If `output_attentions` is False, this will not be returned. + list, optional + The list of genes corresponding to the embeddings, if `output_genes` is set to True. + Each element in the list corresponds to the genes for each input in the dataset. + If `output_genes` is False, this will not be returned. """ LOGGER.info(f"Started getting embeddings:") embeddings = get_embs( @@ -243,6 +249,7 @@ def get_embeddings( self.eos_present, self.device, output_attentions=output_attentions, + output_genes=output_genes, ) LOGGER.info(f"Finished getting embeddings.") diff --git a/helical/models/scgpt/model.py b/helical/models/scgpt/model.py index 1604c300..05552da5 100644 --- a/helical/models/scgpt/model.py +++ b/helical/models/scgpt/model.py @@ -87,7 +87,7 @@ def __init__(self, configurer: scGPTConfig = configurer) -> None: ) def get_embeddings( - self, dataset: Dataset, output_attentions: bool = False + self, dataset: Dataset, output_attentions: bool = False, output_genes: bool = False ) -> np.array: """Gets the gene embeddings @@ -98,6 +98,8 @@ def get_embeddings( output_attentions : bool, optional, default=False Whether to output the attention maps from the model. If set to True, the attention maps will be returned along with the embeddings. If set to False, only the embeddings will be returned. **Note**: This will increase the memory usage of the model significantly, so use it only if you need the attention maps. + output_genes : bool, optional, default=False + Whether to output the genes corresponding to the embeddings. If set to True, the genes will be returned as a list of strings corresponding to the embeddings. Returns ------- @@ -105,9 +107,12 @@ def get_embeddings( The embeddings produced by the model. The return type depends on the `emb_mode` parameter in the configuration. If `emb_mode` is set to "gene", the embeddings are returned as a list of pd.Series which contain a mapping of gene_name:embedding for each cell. - np.ndarray If `output_attentions` is set to True, the attention maps will be returned as a numpy array of shape (n_layers, n_heads, n_cells, n_tokens, n_tokens). + list, optional + If `output_genes` is set to True, the genes corresponding to the embeddings will be returned as a list of strings. + Each element in the list corresponds to the genes for each input in the dataset. + If `output_genes` is False, this will not be returned """ LOGGER.info(f"Started getting embeddings:") @@ -145,6 +150,7 @@ def get_embeddings( resulting_embeddings = [] resulting_attn_maps = [] + input_genes = [] with ( torch.no_grad(), @@ -181,15 +187,25 @@ def get_embeddings( ), ) - resulting_embeddings.extend( - self._compute_embeddings_depending_on_mode(embeddings, data_dict) - ) + if output_genes and self.config["emb_mode"] != "gene": + embeddings_batch, input_genes_batch = self._compute_embeddings_depending_on_mode( + embeddings, data_dict, output_genes=output_genes) + resulting_embeddings.extend(embeddings_batch) + input_genes.extend(input_genes_batch) + else: + resulting_embeddings.extend( + self._compute_embeddings_depending_on_mode(embeddings, data_dict, output_genes=output_genes) + ) resulting_embeddings = self._normalize_embeddings(resulting_embeddings) LOGGER.info(f"Finished getting embeddings.") - if output_attentions: + if output_attentions and output_genes: + return resulting_embeddings, torch.stack(resulting_attn_maps).cpu().numpy(), input_genes + elif output_attentions: return resulting_embeddings, torch.stack(resulting_attn_maps).cpu().numpy() + elif output_genes: + return resulting_embeddings, input_genes else: return resulting_embeddings @@ -209,7 +225,7 @@ def _normalize_embeddings(self, resulting_embeddings: torch.tensor) -> np.ndarra return resulting_embeddings def _compute_embeddings_depending_on_mode( - self, embeddings: torch.tensor, data_dict: dict + self, embeddings: torch.tensor, data_dict: dict, output_genes: bool = False ) -> np.ndarray: """ Compute the embeddings depending on the mode set in the configuration. @@ -220,15 +236,36 @@ def _compute_embeddings_depending_on_mode( The embeddings to be processed. data_dict : dict The data dictionary containing the data to be processed. + output_genes : bool, optional, default=False + Whether to output the genes corresponding to the embeddings. Returns ------- np.ndarray The embeddings corresponding to the mode selected + list, optional + If `output_genes` is set to True, the genes corresponding to the embeddings will be returned as a list of strings. + Each element in the list corresponds to the genes for each input in the dataset. + If `output_genes` is False, this will not be returned """ + input_genes = [] + if output_genes and self.config["emb_mode"] != "gene": + gene_ids = data_dict["gene"].cpu().numpy() + + batch_embeddings = [] + for ids in gene_ids: + gene_list = [] + for id in ids[1:]: # skip the token + if id != self.vocab[self.config["pad_token"]]: + gene_list.append(self.vocab_id_to_str[id]) + input_genes.append(gene_list) + if self.config["emb_mode"] == "cls": embeddings = embeddings[:, 0, :] # get the position embedding embeddings = embeddings.cpu().numpy() + if output_genes: + # if we output genes, we return the embeddings and the genes + return embeddings, input_genes return embeddings elif self.config["emb_mode"] == "cell": @@ -239,6 +276,9 @@ def _compute_embeddings_depending_on_mode( embeddings, dim=1 ) # mean embeddings to get cell embedding embeddings = embeddings.cpu().numpy() + if output_genes: + # if we output genes, we return the embeddings and the genes + return embeddings, input_genes return embeddings elif self.config["emb_mode"] == "gene": From ed48d8ec7a2602318bccea3ab5c3adbe73be991a Mon Sep 17 00:00:00 2001 From: Matthew Wood <62712722+mattwoodx@users.noreply.github.com> Date: Thu, 31 Jul 2025 17:39:36 +0200 Subject: [PATCH 11/17] CZI Fine Tuned Geneformer Integration (#257) * Add CZI fine-tuned Geneformer * Add to CZI Geneformer to model card * fixup! Add CZI fine-tuned Geneformer --- examples/run_models/run_geneformer.py | 3 +++ helical/constants/hash_values.py | 3 +++ helical/models/geneformer/README.md | 8 ++++++++ helical/models/geneformer/geneformer_config.py | 11 +++++++++-- 4 files changed, 23 insertions(+), 2 deletions(-) diff --git a/examples/run_models/run_geneformer.py b/examples/run_models/run_geneformer.py index d8696352..74d551b2 100644 --- a/examples/run_models/run_geneformer.py +++ b/examples/run_models/run_geneformer.py @@ -11,6 +11,9 @@ def run(cfg: DictConfig): geneformer_config = GeneformerConfig(**cfg) geneformer = Geneformer(configurer=geneformer_config) + num = geneformer.model.num_parameters(only_trainable=False) + print(f"Number of parameters: {num:_}".replace("_", " ")) + # either load via huggingface # hf_dataset = load_dataset( # "helical-ai/yolksac_human", diff --git a/helical/constants/hash_values.py b/helical/constants/hash_values.py index 19aa6e0f..3035f9ad 100644 --- a/helical/constants/hash_values.py +++ b/helical/constants/hash_values.py @@ -23,6 +23,9 @@ "geneformer/v1/gf-12L-40M-i2048/config.json": "a82b9cf2cb830be227bfbbe5a4c5d62723f49fac892ca37c541d0ef40a0e1de9", "geneformer/v1/gf-12L-40M-i2048/training_args.bin": "259cf6067211e24e198690d00f0a222ee5550ad57e23d04ced0d0ca2e1b3738e", "geneformer/v1/gf-12L-40M-i2048/pytorch_model.bin": "cea6f8480b6267c3622d57b9c1d53d0f2fa4df38379cf64ec49a35e075afa09d", + "geneformer/v1/gf-12L-40M-i2048-CZI-CellxGene/config.json": "74f8b1f2f5ac2054d5251b5b926023b501283c93e726594bf768bbe04de93ed0", + "geneformer/v1/gf-12L-40M-i2048-CZI-CellxGene/training_args.bin": "ecf08bf21af01892f5e1e57a8b959d01f6a1294b2e58f46d567fd2db745ec183", + "geneformer/v1/gf-12L-40M-i2048-CZI-CellxGene/model.safetensors": "0bcef778cb1786d674bced77bdf53d30397e94e9e195cc4dba7c70bb18170be9", "geneformer/v2/gene_median_dictionary.pkl": "2be7704fd679a43720011fa0337a5a34d2cf3cb48768c656680dca3dd0653b75", "geneformer/v2/token_dictionary.pkl": "12b094814a5764310a2be428b81748fe0af1b246832384a2b187923481b93c8c", "geneformer/v2/ensembl_mapping_dict.pkl": "28391ff889e406ee580ace21eb40fb73072dd69a2059b5204f17c4efa2d3bbf0", diff --git a/helical/models/geneformer/README.md b/helical/models/geneformer/README.md index 34e694eb..53d3f9de 100644 --- a/helical/models/geneformer/README.md +++ b/helical/models/geneformer/README.md @@ -49,6 +49,14 @@ Key improvements in v2.0: - 12 layers - 2048 input size - Trained on ~30 million cells +- **gf-12L-40M-i2048-CZI-CellxGene** + - 12 layers + - 2048 input size + - Pretrained on ~30 million cells + - Fine-tuned by the CELLxGENE Discover Team at CZI using multi-task learning on the CELLxGENE Census + - Optimized to distinguish context-specific cell states across cell types, tissues, developmental stages, and diseases + - [CC BY 4.0 License](https://creativecommons.org/licenses/by/4.0/) + - [CELLxGENE Discover Team](mailto:soma@chanzuckerberg.com) ### Version 2.0 (95M dataset) - **gf-12L-38M-i4096** diff --git a/helical/models/geneformer/geneformer_config.py b/helical/models/geneformer/geneformer_config.py index 85221936..b040bf60 100644 --- a/helical/models/geneformer/geneformer_config.py +++ b/helical/models/geneformer/geneformer_config.py @@ -14,6 +14,7 @@ class GeneformerConfig: "gf-12L-38M-i4096", "gf-12L-38M-i4096-CLcancer", "gf-12L-40M-i2048", + "gf-12L-40M-i2048-CZI-CellxGene", "gf-12L-104M-i4096", "gf-12L-104M-i4096-CLcancer", "gf-20L-151M-i4096", @@ -55,6 +56,7 @@ def __init__( "gf-12L-104M-i4096-CLcancer", "gf-20L-151M-i4096", "gf-18L-316M-i4096", + "gf-12L-40M-i2048-CZI-CellxGene", ] = "gf-12L-38M-i4096", batch_size: int = 24, emb_layer: int = -1, @@ -114,6 +116,12 @@ def __init__( "embsize": 1152, "model_version": "v3", }, + "gf-12L-40M-i2048-CZI-CellxGene": { + "input_size": 2048, + "special_token": False, + "embsize": 512, + "model_version": "v1", + }, } if model_name not in self.model_map: @@ -136,8 +144,7 @@ def __init__( # Add model weight files to download based on the model version (v1 or v2) if ( - self.model_map[model_name]["model_version"] == "v2" - or self.model_map[model_name]["model_version"] == "v3" + self.model_map[model_name]["model_version"] != "v1" or model_name == "gf-12L-40M-i2048-CZI-CellxGene" ): self.list_of_files_to_download.append( f"geneformer/{self.model_map[model_name]['model_version']}/{model_name}/model.safetensors" From 6c0fd213a9c88e53d71f0b381e0402420945b4f7 Mon Sep 17 00:00:00 2001 From: Izumi Ando Date: Thu, 31 Jul 2025 15:51:40 -0400 Subject: [PATCH 12/17] removed commented out code --- helical/models/uce/uce_dataset.py | 1 - 1 file changed, 1 deletion(-) diff --git a/helical/models/uce/uce_dataset.py b/helical/models/uce/uce_dataset.py index 39f8b37f..0ab1dfdf 100644 --- a/helical/models/uce/uce_dataset.py +++ b/helical/models/uce/uce_dataset.py @@ -54,7 +54,6 @@ def __getitem__(self, idx): counts = cts[idx] counts = torch.tensor(counts).unsqueeze(0) weights = torch.log1p(counts) - # weights = weights / torch.sum(weights) weights_sum = torch.sum(weights) # Changes made on July 15th, 2025 to avoid numpy error ValueError: probabilities contain NaN if weights_sum==0: From 4cbecfc0a8018cb0e5b7a7ff8b8b30f41f00d343 Mon Sep 17 00:00:00 2001 From: Giovanni Ortolani Date: Fri, 1 Aug 2025 07:49:23 +0000 Subject: [PATCH 13/17] Add contributions section in readme (#256) --- CONTRIBUTING.md | 31 +++++++++++++++++++++++++++++++ README.md | 3 +++ 2 files changed, 34 insertions(+) create mode 100644 CONTRIBUTING.md diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 00000000..09ecdfd5 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,31 @@ +# Contributing to Helical + +We welcome all kinds of contributions, including code, documentation, bug reports, and feature suggestions. Please read the following guidelines to help us keep the project organized and collaborative. + +## Support expectations + +The Helical team aims to be responsive and engaged with the community. While we do our best to reply promptly to issues, pull requests, and questions, there may be times when responses take some time, as we balance open source contributions with other work. We appreciate your patience and understanding, and we always welcome community support and collaboration. + +## New features + +If you'd like to see a new feature added, the best way to move it forward is to implement it and open a pull request. For more substantial changes, especially those that affect core functionality or involve design decisions, please start by opening an issue to discuss the idea first. This helps ensure alignment with the project's direction before significant work is done. + +While the Helical team actively works to support and integrate new foundation models as quickly as possible, decisions about which models to add and when will be made at the team's discretion, based on technical fit, demand, and project priorities. + +## Submitting a Pull Request + +1. Ensure your code builds and passes existing tests. +2. Link related issue(s) in your PR description, if applicable. +3. Be ready for constructive feedback and revision requests. +4. Squash commits if needed before final merge. +5. Make sure to open your pull request against the `main` branch. By default, GitHub may select the `release` branch as the base, so you'll need to manually switch it to `main`. The `release` branch is updated periodically by the team and should not be used for contributions. + +## Reporting Bugs + +When reporting a bug, please provide a clear description of the issue, the steps to reproduce it, and any relevant environment details (e.g., OS, version, browser). If the problem can't be easily reproduced, the team may ask for a minimal code snippet or example that demonstrates the issue end to end. + +## Before You Start + +1. Look through existing issues to see if your idea or bug is already being addressed. +2. If you want to propose a major change or fix a bug, open an issue first to discuss. +3. Write clear, descriptive commit messages. \ No newline at end of file diff --git a/README.md b/README.md index 4ede89c4..c4bdf71f 100644 --- a/README.md +++ b/README.md @@ -153,6 +153,9 @@ If you are (or plan to) working with bio foundation models s.a. Geneformer or UC We will continuously upload the latest model, publish benchmarks and make our code more efficient. +## Contributing + +We welcome all kinds of contributions, including code, documentation, bug reports, and feature suggestions. Please read our [Contributing Guidelines](CONTRIBUTING.md) to help us keep the project organized and collaborative. ## Acknowledgements From 26f915a1860df863aa3bab1a55f09d2265811d79 Mon Sep 17 00:00:00 2001 From: Benoit Putzeys <157973952+bputzeys@users.noreply.github.com> Date: Fri, 1 Aug 2025 09:59:48 +0200 Subject: [PATCH 14/17] Update python to 3.11.13 dependency --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c4bdf71f..228e01b8 100644 --- a/README.md +++ b/README.md @@ -51,7 +51,7 @@ Check out our None: - """ - Download a file via a link. - - Args: - output: Path to the output file. - link: URL to download the file from. - - Raises: - Exception: If the download fails. - """ - if output.is_file(): LOGGER.debug( f"File: '{output}' exists already. File is not overwritten and nothing is downloaded." ) + return - else: - LOGGER.info(f"Starting to download: '{link}'") - response = requests.get(link, stream=True) + LOGGER.info(f"Starting to download: '{link}'") + response = self.session.get(link, stream=True) - if response.status_code != 200: - message = f"Failed downloading file from '{link}' with status code: {response.status_code}" - LOGGER.error(message) - raise Exception(message) + if response.status_code != 200: + message = f"Failed downloading file from '{link}' with status code: {response.status_code}" + LOGGER.error(message) + raise Exception(message) - total_length = response.headers.get("content-length") + total_length = response.headers.get("content-length") + self.data_length = 0 + self.total_length = int(total_length) if total_length else None - # Resetting for visualization - self.data_length = 0 - self.total_length = int(total_length) + output.parent.mkdir(parents=True, exist_ok=True) + if not self.total_length: + # If total length unknown, just write content directly without progress bar with open(output, "wb") as f: - if total_length is None: # no content length header - f.write(response.content) - else: - try: - for data in response.iter_content(chunk_size=CHUNK_SIZE): - if self.display: - self._display_download_progress(len(data)) - f.write(data) - except: - LOGGER.error(f"Failed downloading file from '{link}'") - LOGGER.info(f"File saved to: '{output}'") - - def _display_download_progress(self, data_chunk_size: int) -> None: - """ - Display the download progress in console. - - Args: - data_chunk_size: Integer of size of the newly downloaded data chunk. - """ - self.data_length += data_chunk_size - done = int(LOADING_BAR_LENGTH * self.data_length / self.total_length) - sys.stdout.write("\r[%s%s]" % ("=" * done, " " * (LOADING_BAR_LENGTH - done))) - sys.stdout.flush() - - def s3_download_with_progress(self, s3client, bucket, key, output): - with open(output, "wb") as f: - s3client.download_fileobj( - Bucket=bucket, - Key=key, - Fileobj=f, - Callback=S3ProgressPercentage(bucket, key, s3client), - ) + f.write(response.content) + else: + with ( + open(output, "wb") as f, + tqdm( + total=self.total_length, unit="B", unit_scale=True, desc=output.name + ) as pbar, + ): + for data in response.iter_content(chunk_size=CHUNK_SIZE): + f.write(data) + pbar.update(len(data)) def download_via_name(self, name: str) -> None: - """ - Download a file via a link. - - Args: - name (str): The name of the file to be downloaded. - - Returns: - None - """ - s3 = boto3.client("s3", config=Config(signature_version=UNSIGNED)) - # Example usage bucket_name = "helicalpackage" + region = "eu-west-2" s3_key = name - output = os.path.join(CACHE_DIR_HELICAL, name) - - if not os.path.exists(os.path.dirname(output)): - os.makedirs(os.path.dirname(output), exist_ok=True) - LOGGER.info(f"Creating Folder {os.path.dirname(output)}") - - if not Path(output).is_file() or not self.etag_compare(s3_key, output, s3): - LOGGER.info( - f"File does not exist or has incorrect hash. Starting to download: '{s3_key}'" - ) - # temporarily disable INFO logging from Azure package - original_level = logging.getLogger().getEffectiveLevel() - logging.getLogger().setLevel(logging.WARNING) - self.s3_download_with_progress(s3, bucket_name, s3_key, output) - # restore original logging level - logging.getLogger().setLevel(original_level) - assert self.etag_compare( - s3_key, output, s3 - ), f"Hash of downloaded file '{output}' does not match the expected hash." - LOGGER.info(f"File saved to: '{output}'") + output = Path(CACHE_DIR_HELICAL) / name + url = f"https://{bucket_name}.s3.{region}.amazonaws.com/{s3_key}" + + output.parent.mkdir(parents=True, exist_ok=True) + + if not self.check_file_valid(url, output): + if output.is_file(): + LOGGER.warning( + f"File '{output}' is corrupted or invalid. Deleting and re-downloading." + ) + output.unlink() # delete corrupted file + + LOGGER.info(f"Downloading '{name}'") + self.download_via_link(output, url) + + # Validate again after download + if not self.check_file_valid(url, output): + raise ValueError(f"File '{output}' failed validation after download.") + else: + LOGGER.info(f"File saved to: '{output}'") else: - LOGGER.debug( - f"File: '{output}' exists already. File is not overwritten and nothing is downloaded." - ) + LOGGER.debug(f"File '{output}' already exists and is valid.") - def etag_compare(self, s3_key, filename, s3client, bucket_name="helicalpackage"): - - # obj = s3client.get_object(Bucket=bucket_name, Key=filename) - # Get the object's metadata - response = s3client.head_object(Bucket=bucket_name, Key=s3_key) - - # Extract the ETag - etag = response["ETag"].strip('"') # Remove quotes if needed - # etag = obj.e_tag - - def md5_checksum(filename): - m = hashlib.md5() - with open(filename, "rb") as f: - for data in iter(lambda: f.read(1024 * 1024), b""): - m.update(data) - return m.hexdigest() - - def etag_checksum(filename, chunk_size=8 * 1024 * 1024): - md5s = [] - with open(filename, "rb") as f: - for data in iter(lambda: f.read(chunk_size), b""): - md5s.append(hashlib.md5(data).digest()) - m = hashlib.md5(b"".join(md5s)) - return "{}-{}".format(m.hexdigest(), len(md5s)) - - et = etag # [1:-1] # strip quotes - if "-" in et and et == etag_checksum(filename): - return True - if "-" not in et and et == md5_checksum(filename): + def check_file_valid(self, url: str, file_path: Path) -> bool: + """ + Validates a local file against the remote object's ETag or Content-Length. + Returns True if file exists and is considered valid. + """ + try: + if not file_path.is_file(): + return False + + head = self.session.head(url) + etag = head.headers.get("ETag", "").strip('"') + remote_size = int(head.headers.get("Content-Length", 0)) + local_size = file_path.stat().st_size + + if local_size != remote_size: + LOGGER.warning( + f"File size mismatch: local {local_size}, remote {remote_size}" + ) + return False + + # Handle single-part ETag (true MD5) + if "-" not in etag: + local_md5 = self._md5_checksum(file_path) + if etag != local_md5: + LOGGER.warning( + f"MD5 checksum mismatch: local {local_md5}, remote {etag}" + ) + return False + + # For multi-part uploads (ETag has "-"), assume size check is sufficient return True - return False - - -class S3ProgressPercentage: - def __init__(self, bucket, key, s3_client): - self._key = key - self._size = s3_client.head_object(Bucket=bucket, Key=key)["ContentLength"] - self._seen_so_far = 0 - self._tqdm = tqdm(total=self._size, unit="B", unit_scale=True, desc=key) - - def __call__(self, bytes_amount): - self._seen_so_far += bytes_amount - self._tqdm.update(bytes_amount) + except Exception as e: + LOGGER.warning(f"Validation failed for {file_path}: {e}") + return False -if __name__ == "__main__": - # Example usage - downloader = Downloader() - downloader.download_via_name("10k_pbmcs_proc.h5ad") + def _md5_checksum(self, filename: Path, chunk_size: int = 1024 * 1024) -> str: + m = hashlib.md5() + with open(filename, "rb") as f: + for chunk in iter(lambda: f.read(chunk_size), b""): + m.update(chunk) + return m.hexdigest() diff --git a/pyproject.toml b/pyproject.toml index 952b8ead..646ac438 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -42,8 +42,7 @@ dependencies = [ 'hydra-core==1.3.2', 'louvain==0.8.2', 'pyensembl', - 'datasets==3.6.0', - 'boto3' + 'datasets==3.6.0' ] From a02138f277d3e00b4575ce98fed320f31965a1e1 Mon Sep 17 00:00:00 2001 From: bputzeys Date: Wed, 6 Aug 2025 13:56:49 +0200 Subject: [PATCH 17/17] Remove GF comparison notebook on release action --- .github/workflows/release.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index c31b24c5..8fed8c75 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -175,6 +175,7 @@ jobs: sed -i 's/list(np.array(train_dataset.obs\[\\"LVL1\\"].tolist()))/list(np.array(train_dataset.obs\[\\"LVL1\\"].tolist()))[:100]/g' ./examples/notebooks/Cell-Type-Classification-Fine-Tuning.ipynb sed -i 's/list(np.array(test_dataset.obs\[\\"LVL1\\"].tolist()))/list(np.array(test_dataset.obs\[\\"LVL1\\"].tolist()))[:10]/g' ./examples/notebooks/Cell-Type-Classification-Fine-Tuning.ipynb rm ./examples/notebooks/Evo-2.ipynb + rm ./examples/notebooks/Geneformer-Series-Comparison.ipynb - name: Run Notebooks run: |