Add torch-backed scikit-learn solver with array api dispatch

soda-inria · Oct 13, 2023 · dea87bc · dea87bc
1 parent 434a7ba
commit dea87bc
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diff --git a/.github/workflows/run_benchmark_results_file_sanity_checks.yaml b/.github/workflows/run_benchmark_results_file_sanity_checks.yaml
@@ -18,3 +18,4 @@ jobs:
     - name: Check sanity of benchmark files
       run: |
         python ./benchmarks/kmeans/consolidate_result_csv.py ./benchmarks/kmeans/results.csv --check-csv
+        python ./benchmarks/pca/consolidate_result_csv.py ./benchmarks/pca/results.csv --check-csv
diff --git a/.github/workflows/sync_benchmark_files_to_gsheet.yaml b/.github/workflows/sync_benchmark_files_to_gsheet.yaml
@@ -23,6 +23,9 @@ jobs:
         GSPREAD_URL: ${{vars.GSPREAD_URL}}
       run: |
         python ./benchmarks/kmeans/consolidate_result_csv.py ./benchmarks/kmeans/results.csv --check-csv
+        python ./benchmarks/pca/consolidate_result_csv.py ./benchmarks/pca/results.csv --check-csv
         echo "$GSPREAD_SERVICE_ACCOUNT_AUTH_KEY" > service_account.json
         python ./benchmarks/kmeans/consolidate_result_csv.py ./benchmarks/kmeans/results.csv \
           --sync-to-gspread --gspread-url $GSPREAD_URL --gspread-auth-key ./service_account.json
+        python ./benchmarks/pca/consolidate_result_csv.py ./benchmarks/pca/results.csv \
+          --sync-to-gspread --gspread-url $GSPREAD_URL --gspread-auth-key ./service_account.json
diff --git a/.github/workflows/test_cpu_benchmarks.yaml b/.github/workflows/test_cpu_benchmarks.yaml
@@ -141,3 +141,5 @@ jobs:
       run: |
         cd benchmarks/kmeans
         PYTHONPATH=$PYTHONPATH:$(realpath ../../kmeans_dpcpp/) benchopt run --no-plot -l -d Simulated_correlated_data[n_samples=1000,n_features=14]
+        cd ../pca
+        benchopt run --no-plot -l -d Simulated_correlated_data[n_samples=100,n_features=100]
diff --git a/benchmarks/kmeans/solvers/sklearn_pytorch_engine.py b/benchmarks/kmeans/solvers/sklearn_pytorch_engine.py
@@ -54,7 +54,7 @@ def set_objective(
         device = self.device
         # Copy the data before running the benchmark to ensure that no unfortunate side
         # effects can happen
-        self.X = torch.asarray(X, copy=True, device=self.device)
+        self.X = torch.asarray(X, copy=True, device=device)
 
         if hasattr(sample_weight, "copy"):
             sample_weight = torch.asarray(sample_weight, copy=True, device=device)