Merge branch 'main' into spmv-example

Author: mtsokol

examples/mttkrp_example.py

@@ -0,0 +1,62 @@
+import time
+
+import sparse
+
+import numpy as np
+
+I_ = 1000
+J_ = 25
+K_ = 1000
+L_ = 100
+DENSITY = 0.0001
+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("MTTKRP Example:\n")
+
+    B_sps = sparse.random((I_, K_, L_), density=DENSITY, random_state=rng) * 10
+    D_sps = rng.random((L_, J_)) * 10
+    C_sps = rng.random((K_, J_)) * 10
+
+    # Finch
+    with sparse.Backend(backend=sparse.BackendType.Finch):
+        B = sparse.asarray(B_sps.todense(), format="csf")
+        D = sparse.asarray(np.array(D_sps, order="F"))
+        C = sparse.asarray(np.array(C_sps, order="F"))
+
+        @sparse.compiled
+        def mttkrp_finch(B, D, C):
+            return sparse.sum(B[:, :, :, None] * D[None, None, :, :] * C[None, :, None, :], axis=(1, 2))
+
+        # Compile
+        result_finch = mttkrp_finch(B, D, C)
+        assert sparse.nonzero(result_finch)[0].size > 5
+        # Benchmark
+        benchmark(mttkrp_finch, info="Finch", args=[B, D, C])
+
+    # Numba
+    with sparse.Backend(backend=sparse.BackendType.Numba):
+        B = sparse.asarray(B_sps, format="gcxs")
+        D = D_sps
+        C = C_sps
+
+        def mttkrp_numba(B, D, C):
+            return sparse.sum(B[:, :, :, None] * D[None, None, :, :] * C[None, :, None, :], axis=(1, 2))
+
+        # Compile
+        result_numba = mttkrp_numba(B, D, C)
+        # Benchmark
+        benchmark(mttkrp_numba, info="Numba", args=[B, D, C])
+
+    np.testing.assert_allclose(result_finch.todense(), result_numba.todense())

examples/sddmm_example.py

@@ -21,6 +21,8 @@ def benchmark(func, info, args):
 
 
 if __name__ == "__main__":
+    print("SDDMM Example:\n")
+
     a_sps = rng.random((LEN, LEN - 10)) * 10
     b_sps = rng.random((LEN - 10, LEN)) * 10
     s_sps = sps.random(LEN, LEN, format="coo", density=DENSITY, random_state=rng) * 10