Custom dataloader registry support (#2932)

Co-authored-by: Can Ergen <canergen.ac@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: 3 people

.github/workflows/test_linux_custom_dataloader.yml

@@ -0,0 +1,72 @@
+name: test (custom dataloaders)
+
+on:
+  push:
+    branches: [main, "[0-9]+.[0-9]+.x"]
+  pull_request:
+    branches: [main, "[0-9]+.[0-9]+.x"]
+    types: [labeled, synchronize, opened]
+  schedule:
+    - cron: "0 10 * * *" # runs at 10:00 UTC (03:00 PST) every day
+  workflow_dispatch:
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  test:
+    # if PR has label "custom_dataloader" or "all tests" or if scheduled or manually triggered
+    if: >-
+      (
+        contains(github.event.pull_request.labels.*.name, 'custom_dataloader') ||
+        contains(github.event.pull_request.labels.*.name, 'all tests') ||
+        contains(github.event_name, 'schedule') ||
+        contains(github.event_name, 'workflow_dispatch')
+      )
+
+    runs-on: ${{ matrix.os }}
+
+    defaults:
+      run:
+        shell: bash -e {0} # -e to fail on error
+
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest]
+        python: ["3.12"]
+
+    name: integration
+
+    env:
+      OS: ${{ matrix.os }}
+      PYTHON: ${{ matrix.python }}
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python }}
+          cache: "pip"
+          cache-dependency-path: "**/pyproject.toml"
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip wheel uv
+          python -m uv pip install --system "scvi-tools[tests] @ ."
+
+      - name: Run specific custom dataloader pytest
+        env:
+          MPLBACKEND: agg
+          PLATFORM: ${{ matrix.os }}
+          DISPLAY: :42
+          COLUMNS: 120
+        run: |
+          coverage run -m pytest -v --color=yes --custom-dataloader-tests
+          coverage report
+
+      - uses: codecov/codecov-action@v4
+        with:
+          token: ${{ secrets.CODECOV_TOKEN }}

CHANGELOG.md

@@ -16,6 +16,9 @@ to [Semantic Versioning]. Full commit history is available in the
 - Add get normalized function model property for any generative model {pr}`3238` and changed
     get_accessibility_estimates to get_normalized_accessibility, where needed.
 - Add {class}`scvi.external.TOTALANVI`. {pr}`3259`.
+- Add Custom Dataloaders registry support, {pr}`2932`.
+- Add support for using Census and LaminAI custom dataloaders for {class}`scvi.model.SCVI`
+    and {class}`scvi.model.SCANVI`, {pr}`2932`.
 - Add Early stopping KL warmup steps. {pr}`3262`.
 - Add Minification option to {class}`~scvi.model.LinearSCVI` {pr}`3294`.
 

docs/tutorials/index_use_cases.md

@@ -6,4 +6,6 @@
 notebooks/use_cases/autotune_scvi
 notebooks/use_cases/minification
 notebooks/use_cases/interpretability
+notebooks/use_cases/custom_dl/tiledb
+notebooks/use_cases/custom_dl/lamin
 ```

docs/tutorials/notebooks

@@ -21,25 +21,34 @@ Pros:
 - Optimized for ML Workflows: If your dataset is structured as tables (rows and columns), LamindDB’s format aligns well with SCVI's expectations, potentially reducing the need for complex transformations.
 
 ```python
-os.system("lamin init --storage ./test-registries")
 import lamindb as ln
+from scvi.dataloaders import MappedCollectionDataModule
+import scvi
+import os
+
+os.system("lamin init --storage ./test-registries")
 
 ln.setup.init(name="lamindb_instance_name", storage=save_path)
 
 # a test for mapped collection
-collection = ln.Collection.get(name="covid_normal_lung")
+collection = ln.Collection.using("laminlabs/cellxgene").get(name="covid_normal_lung")
 artifacts = collection.artifacts.all()
 artifacts.df()
 
 datamodule = MappedCollectionDataModule(
-    collection, batch_key="assay", batch_size=1024, join="inner"
+    collection,
+    batch_key="assay",
+    batch_size=1024,
+    join="inner",
+    shuffle=True,
 )
 model = scvi.model.SCVI(adata=None, registry=datamodule.registry)
+model.train(max_epochs=1, batch_size=1024, datamodule=datamodule.inference_dataloader())
 ...
 ```
 LamindDB may not be as efficient or flexible as TileDB for handling complex multi-dimensional data
 
-2. [CZI](https://chanzuckerberg.com/) based [tiledb](https://tiledb.com/) custom dataloader is based on CensusSCVIDataModule and can run a large multi-dimensional datasets that are stored in TileDB’s format.
+2. [CZI](https://chanzuckerberg.com/) based [tiledb](https://tiledb.com/) custom dataloader is based on TileDBDataModule and can run a large multi-dimensional datasets that are stored in TileDB’s format.
 
 TileDB is a general-purpose, multi-dimensional array storage engine designed for high-performance, scalable data access. It supports various data types, including dense and sparse arrays, and is optimized for handling large datasets efficiently. TileDB’s strength lies in its ability to store and query data across multiple dimensions and scale efficiently with large volumes of data.
 
@@ -52,9 +61,10 @@ Scalability: Handles large datasets that exceed your system's memory capacity, m
 ```python
 import cellxgene_census
 import tiledbsoma as soma
-from cellxgene_census.experimental.ml import experiment_dataloader
-from cellxgene_census.experimental.ml.datamodule import CensusSCVIDataModule
+import tiledbsoma_ml
+from scvi.dataloaders import TileDBDataModule
 import numpy as np
+import scvi
 
 # this test checks the local custom dataloder made by CZI and run several tests with it
 census = cellxgene_census.open_soma(census_version="stable")
@@ -66,25 +76,48 @@ obs_value_filter = (
 
 hv_idx = np.arange(100)  # just ot make it smaller and faster for debug
 
-# this is CZI part to be taken once all is ready
-batch_keys = ["dataset_id", "assay", "suspension_type", "donor_id"]
-datamodule = CensusSCVIDataModule(
-    census["census_data"][experiment_name],
+# For HVG, we can use the highly_variable_genes function provided in cellxgene_census,
+# which can compute HVGs in constant memory:
+hvg_query = census["census_data"][experiment_name].axis_query(
     measurement_name="RNA",
-    X_name="raw",
     obs_query=soma.AxisQuery(value_filter=obs_value_filter),
     var_query=soma.AxisQuery(coords=(list(hv_idx),)),
+)
+
+# this is CZI part to be taken once all is ready
+batch_keys = ["dataset_id", "assay", "suspension_type", "donor_id"]
+label_keys = ["tissue_general"]
+datamodule = TileDBDataModule(
+    hvg_query,
+    layer_name="raw",
     batch_size=1024,
     shuffle=True,
-    batch_keys=batch_keys,
+    seed=42,
+    batch_column_names=batch_keys,
+    label_keys=label_keys,
+    train_size=0.9,
+    unlabeled_category="label_0",
     dataloader_kwargs={"num_workers": 0, "persistent_workers": False},
 )
 
+# We can now create the scVI model object and train it:
+model = scvi.model.SCVI(
+    adata=None,
+    registry=datamodule.registry,
+    gene_likelihood="nb",
+    encode_covariates=False,
+)
+
+# creating the dataloader for trainset
+datamodule.setup()
 
-# basicaly we should mimiC everything below to any model census in scvi
-adata_orig = synthetic_iid()
-scvi.model.SCVI.setup_anndata(adata_orig, batch_key="batch")
-model = scvi.model.SCVI(adata_orig)
+model.train(
+    datamodule=datamodule,
+    max_epochs=1,
+    batch_size=1024,
+    train_size=0.9,
+    early_stopping=False,
+)
 ...
 ```
 Key Differences between them in terms of Custom Dataloaders:
@@ -110,6 +143,8 @@ When to Use Each:
 Writing custom dataloaders requires a good understanding of PyTorch’s DataLoader class and how to integrate it with SCVI, which may be difficult for beginners.
 It will also requite maintenance: If the data format or preprocessing needs change, you’ll have to modify and maintain the custom dataloader code, But it can be a greate addition to the model pipeline, in terms of runtime and how much data we can digest.
 
+See relevant tutorials in this subject for further examples.
+
 :::{note}
-As for SCVI-Tools v1.3.0 Custom Dataloaders are experimental.
+As for SCVI-Tools v1.3.0 Custom Dataloaders are experimental and only supported for adata and SCVI and SCANVI models
 :::

docs/user_guide/use_case/custom_dataloaders.md

@@ -82,11 +82,9 @@ docs = [
 docsbuild = ["scvi-tools[docs,optional]"]
 
 # scvi.autotune
-autotune = ["hyperopt>=0.2", "ray[tune]","scib-metrics"]
+autotune = ["hyperopt>=0.2", "ray[tune]", "scib-metrics"]
 # scvi.hub.HubModel.pull_from_s3
 aws = ["boto3"]
-# scvi.data.cellxgene
-census = ["cellxgene-census", "numpy<2.0"]
 # scvi.hub dependencies
 hub = ["huggingface_hub", "igraph", "leidenalg", "dvc[s3]"]
 # scvi.data.add_dna_sequence
@@ -96,13 +94,15 @@ scanpy = ["scanpy>=1.10", "scikit-misc"]
 # for convinient files sharing
 file_sharing = ["pooch", "cellxgene-census"]
 # for parallelization engine
-parallel = ["dask[array]>=2023.5.1,<2024.8.0"]
+parallel = ["dask[array]>=2023.5.1,<2024.8.0", "zarr<3.0.0"]
 # for supervised models interpretability
-interpretability = ["captum","shap"]
+interpretability = ["captum", "shap"]
+# for custom dataloders
+dataloaders = ["lamindb>=1.3.0", "biomart", "bionty", "cellxgene_lamin", "cellxgene-census", "numpy<2.0", "tiledbsoma", "tiledb", "tiledbsoma_ml", "torchdata==0.9.0"]
 
 
 optional = [
-    "scvi-tools[autotune,aws,hub,file_sharing,regseq,scanpy,parallel,interpretability]"
+    "scvi-tools[autotune,aws,hub,file_sharing,regseq,scanpy,parallel,interpretability,dataloaders]"
 ]
 tutorials = [
     "cell2location",
@@ -137,6 +137,7 @@ markers = [
     "private: mark tests that uses private keys, like HF",
     "multigpu: mark tests that are used to check multi GPU performance",
     "autotune: mark tests that are used to check ray autotune capabilities",
+    "custom dataloaders: mark tests that are used to check different custom data loaders",
 ]
 
 [tool.ruff]

pyproject.toml

@@ -21,6 +21,8 @@
 from . import _constants
 
 if TYPE_CHECKING:
+    from collections.abc import Iterator
+
     import numpy.typing as npt
     from pandas.api.types import CategoricalDtype
     from torch import Tensor
@@ -361,3 +363,21 @@ def _check_fragment_counts(
     )  # True if there are more 2s than 1s
     ret = not (non_fragments or binary)
     return ret
+
+
+def _validate_adata_dataloader_input(
+    model,
+    adata: AnnOrMuData | None = None,
+    dataloader: Iterator[dict[str, Tensor | None]] | None = None,
+):
+    """Validate that model uses adata or custom dataloader"""
+    if adata is not None and dataloader is not None:
+        raise ValueError("Only one of `adata` or `dataloader` can be provided.")
+    elif (
+        hasattr(model, "registry")
+        and "setup_method_name" in model.registry.keys()
+        and model.registry["setup_method_name"] == "setup_datamodule"
+        and dataloader is None
+    ):
+        raise ValueError("`dataloader` must be provided.")
+    return

src/scvi/data/_utils.py

@@ -3,6 +3,7 @@
 
 from ._ann_dataloader import AnnDataLoader
 from ._concat_dataloader import ConcatDataLoader
+from ._custom_dataloders import MappedCollectionDataModule, TileDBDataModule
 from ._data_splitting import (
     DataSplitter,
     DeviceBackedDataSplitter,
@@ -20,4 +21,6 @@
     "DataSplitter",
     "SemiSupervisedDataSplitter",
     "BatchDistributedSampler",
+    "MappedCollectionDataModule",
+    "TileDBDataModule",
 ]