Merge pull request #677 from pydata/spmv-example

Add `SpMV` example

Author: mtsokol

examples/spmv_add_example.py

@@ -0,0 +1,75 @@
+import time
+
+import sparse
+
+import numpy as np
+import scipy.sparse as sps
+
+LEN = 100000
+DENSITY = 0.000001
+ITERS = 3
+rng = np.random.default_rng(0)
+
+
+def benchmark(func, info, args):
+    print(info)
+    start = time.time()
+    for _ in range(ITERS):
+        func(*args)
+    elapsed = time.time() - start
+    print(f"Took {elapsed / ITERS} s.\n")
+
+
+if __name__ == "__main__":
+    print("SpMv_add Example:\n")
+
+    A_sps = sps.random(LEN - 10, LEN, format="csc", density=DENSITY, random_state=rng) * 10
+    x_sps = rng.random((LEN, 1)) * 10
+    y_sps = rng.random((LEN - 10, 1)) * 10
+
+    # Finch
+    with sparse.Backend(backend=sparse.BackendType.Finch):
+        A = sparse.asarray(A_sps)
+        x = sparse.asarray(np.array(x_sps, order="C"))
+        y = sparse.asarray(np.array(y_sps, order="C"))
+
+        @sparse.compiled
+        def spmv_finch(A, x, y):
+            return sparse.sum(A[:, None, :] * sparse.permute_dims(x, (1, 0))[None, :, :], axis=-1) + y
+
+        # Compile
+        result_finch = spmv_finch(A, x, y)
+        assert sparse.nonzero(result_finch)[0].size > 5
+        # Benchmark
+        benchmark(spmv_finch, info="Finch", args=[A, x, y])
+
+    # Numba
+    with sparse.Backend(backend=sparse.BackendType.Numba):
+        A = sparse.asarray(A_sps, format="csc")
+        x = x_sps
+        y = y_sps
+
+        def spmv_numba(A, x, y):
+            return A @ x + y
+
+        # Compile
+        result_numba = spmv_numba(A, x, y)
+        assert sparse.nonzero(result_numba)[0].size > 5
+        # Benchmark
+        benchmark(spmv_numba, info="Numba", args=[A, x, y])
+
+    # SciPy
+    def spmv_scipy(A, x, y):
+        return A @ x + y
+
+    A = A_sps
+    x = x_sps
+    y = y_sps
+
+    result_scipy = spmv_scipy(A, x, y)
+    # Benchmark
+    benchmark(spmv_scipy, info="SciPy", args=[A, x, y])
+
+    np.testing.assert_allclose(result_numba, result_scipy)
+    np.testing.assert_allclose(result_finch.todense(), result_numba)
+    np.testing.assert_allclose(result_finch.todense(), result_scipy)