Merge branch 'main' into external/methylanvi

.github/workflows/test_linux_mps.yml

@@ -0,0 +1,77 @@
+name: test (mps)
+
+on:
+  push:
+    branches: [main, "[0-9]+.[0-9]+.x"]
+  pull_request:
+    branches: [main, "[0-9]+.[0-9]+.x"]
+    types: [labeled, synchronize, opened]
+  workflow_dispatch:
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  test:
+    # if PR has label "cuda tests" or "all tests" or if scheduled or manually triggered
+    if: >-
+      (
+        contains(github.event.pull_request.labels.*.name, 'mps') ||
+        contains(github.event.pull_request.labels.*.name, 'all tests') ||
+        contains(github.event_name, 'schedule') ||
+        contains(github.event_name, 'workflow_dispatch')
+      )
+
+    runs-on: [self-hosted, macOS, X64, MPS]
+
+    name: macos_integration
+
+    env:
+      OS: ${{ matrix.os }}
+      PYTHON: ${{ matrix.python }}
+
+    steps:
+      #- name: Get the current branch name
+      #  id: vars
+      #  run: echo "BRANCH_NAME=$(echo $GITHUB_REF | awk -F'/' '{print $3}')" >> $GITHUB_ENV
+
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python }}
+          cache: "pip"
+          cache-dependency-path: "**/pyproject.toml"
+
+      - name: Create Conda environment and install dependencies
+        run: |
+          conda init bash
+          source ~/.bash_profile
+          conda activate scvi
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip wheel uv
+          python -m pip install "scvi-tools[tests]"
+          python -m pip install jax-metal
+          python -m pip install coverage
+          python -m pip install pytest
+
+      - name: Run pytest
+        env:
+          MPLBACKEND: agg
+          PLATFORM: ${{ matrix.os }}
+          DISPLAY: :42
+          COLUMNS: 120
+          PYTORCH_MPS_HIGH_WATERMARK_RATIO: 0.0
+          PYTORCH_ENABLE_MPS_FALLBACK: 1.0
+        run: |
+          coverage run -m pytest -v --color=yes --accelerator mps --devices auto
+          coverage report
+
+      - uses: codecov/codecov-action@v4
+        with:
+          token: ${{ secrets.CODECOV_TOKEN }}

.github/workflows/test_macos.yml

@@ -55,13 +55,16 @@ jobs:
         run: |
           python -m pip install --upgrade pip wheel uv
           python -m uv pip install --system "scvi-tools[tests] @ ."
+          python -m pip install jax-metal
 
       - name: Run pytest
         env:
           MPLBACKEND: agg
           PLATFORM: ${{ matrix.os }}
           DISPLAY: :42
           COLUMNS: 120
+          PYTORCH_MPS_HIGH_WATERMARK_RATIO: 0.0
+          PYTORCH_ENABLE_MPS_FALLBACK: 1.0
         run: |
           coverage run -m pytest -v --color=yes
           coverage report

CHANGELOG.md

@@ -23,13 +23,17 @@ to [Semantic Versioning]. Full commit history is available in the
 
 #### Removed
 
-### 1.2.2 (2024-XX-XX)
+### 1.2.2 (2024-12-31)
 
 #### Added
 
 - Add MuData Minification option to {class}`~scvi.model.TOTALVI` {pr}`3061`.
+- Add Support for MPS usage in mac {pr}`3100`.
+- Add support for torch.compile before train (EXPERIMENTAL) {pr}`2931`.
 - Add support for Numpy 2.0 {pr}`2842`.
 - Changed scvi-hub ModelCard and add criticism metrics to the card {pr}`3078`.
+- MuData support for {class}`~scvi.model.MULTIVI` via the method
+    {meth}`~scvi.model.MULTIVI.setup_mudata` {pr}`3038`.
 
 #### Fixed
 
@@ -53,8 +57,6 @@ to [Semantic Versioning]. Full commit history is available in the
     validation set, if available. {pr}`3036`.
 - Add `batch_key` and `labels_key` to `scvi.external.SCAR.setup_anndata`. {pr}`3045`.
 - Implemented variance of ZINB distribution. {pr}`3044`.
-- MuData support for {class}`~scvi.model.MULTIVI` via the method
-    {meth}`~scvi.model.MULTIVI.setup_mudata` {pr}`3038`.
 - Support for minified mode while retaining counts to skip the encoder.
 - New Trainingplan argument `update_only_decoder` to use stored latent codes and skip training of
     the encoder.

pyproject.toml

@@ -4,7 +4,7 @@ requires = ["hatchling"]
 
 [project]
 name = "scvi-tools"
-version = "1.2.1"
+version = "1.2.2"
 description = "Deep probabilistic analysis of single-cell omics data."
 readme = "README.md"
 requires-python = ">=3.10"
@@ -62,6 +62,7 @@ editing = ["jupyter", "pre-commit"]
 dev = ["scvi-tools[editing,tests]"]
 test = ["scvi-tools[tests]"]
 cuda = ["torchvision","torchaudio","jax[cuda]"]
+metal = ["torchvision","torchaudio","jax-metal"]
 
 docs = [
     "docutils>=0.8,!=0.18.*,!=0.19.*", # see https://github.com/scverse/cookiecutter-scverse/pull/205
@@ -111,7 +112,7 @@ tutorials = [
     "squidpy",
 ]
 
-all = ["scvi-tools[cuda,dev,docs,tutorials]"]
+all = ["scvi-tools[dev,docs,tutorials]"]
 
 [tool.hatch.build.targets.wheel]
 packages = ['src/scvi']

src/scvi/data/_preprocessing.py

@@ -123,11 +123,12 @@ def poisson_gene_selection(
 
         # Calculate empirical statistics.
         sum_0 = np.asarray(data.sum(0)).ravel()
-        scaled_means = torch.from_numpy(sum_0 / sum_0.sum()).to(device)
         total_counts = torch.from_numpy(np.asarray(data.sum(1)).ravel()).to(device)
-
+        # in MPS we need to first change to float 32, as the MPS framework doesn't support float64.
+        # We will thus do it by default for all accelerators
+        scaled_means = torch.from_numpy(np.float32(sum_0 / sum_0.sum())).to(device)
         observed_fraction_zeros = torch.from_numpy(
-            np.asarray(1.0 - (data > 0).sum(0) / data.shape[0]).ravel()
+            np.float32(np.asarray(1.0 - (data > 0).sum(0) / data.shape[0]).ravel())
         ).to(device)
 
         # Calculate probability of zero for a Poisson model.
@@ -151,8 +152,13 @@ def poisson_gene_selection(
         expected_fraction_zeros /= data.shape[0]
 
         # Compute probability of enriched zeros through sampling from Binomial distributions.
-        observed_zero = torch.distributions.Binomial(probs=observed_fraction_zeros)
-        expected_zero = torch.distributions.Binomial(probs=expected_fraction_zeros)
+        # TODO:  TORCH MPS FIX - 'aten::binomial' is not currently implemented for the MPS device
+        if device.type == "mps":
+            observed_zero = torch.distributions.Binomial(probs=observed_fraction_zeros.to("cpu"))
+            expected_zero = torch.distributions.Binomial(probs=expected_fraction_zeros.to("cpu"))
+        else:
+            observed_zero = torch.distributions.Binomial(probs=observed_fraction_zeros)
+            expected_zero = torch.distributions.Binomial(probs=expected_fraction_zeros)
 
         extra_zeros = torch.zeros(expected_fraction_zeros.shape, device=device)
         for _ in track(
@@ -161,7 +167,8 @@ def poisson_gene_selection(
             disable=silent,
             style="tqdm",  # do not change
         ):
-            extra_zeros += observed_zero.sample() > expected_zero.sample()
+            obs_exp_bool_mat = observed_zero.sample() > expected_zero.sample()
+            extra_zeros += obs_exp_bool_mat.to("mps") if device.type == "mps" else obs_exp_bool_mat
 
         prob_zero_enrichment = (extra_zeros / n_samples).cpu().numpy()