[Lang] Support basic sparse matrix operations on GPU. (#6082)

Issue: #2906

Implement the basic operations like `+`, `-`, `*`, `@`, and transpose,
etc. in this pr.

Co-authored-by: FantasyVR <y2505418927@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Yi Xu <xy_xuyi@foxmail.com>

misc/test_gpu_sm_op.py

@@ -0,0 +1,84 @@
+import numpy as np
+import scipy
+from numpy.random import default_rng
+from scipy import stats
+from scipy.sparse import random
+
+import taichi as ti
+
+ti.init(arch=ti.cuda)
+
+idx_dt = ti.i32
+val_dt = ti.f32
+
+seed = 2
+np.random.seed(seed)
+
+rng = default_rng(seed)
+rvs = stats.poisson(3, loc=1).rvs
+N = 5
+np_dtype = np.float32
+rows = N
+cols = N - 1
+
+S1 = random(rows, cols, density=0.5, random_state=rng,
+            data_rvs=rvs).astype(np_dtype)
+S2 = random(rows, cols, density=0.5, random_state=rng,
+            data_rvs=rvs).astype(np_dtype)
+
+nnz_A = len(S1.data)
+nnz_B = len(S2.data)
+
+coo_S1 = scipy.sparse.coo_matrix(S1)
+coo_S2 = scipy.sparse.coo_matrix(S2)
+
+row_coo_A = ti.ndarray(shape=nnz_A, dtype=idx_dt)
+col_coo_A = ti.ndarray(shape=nnz_A, dtype=idx_dt)
+value_coo_A = ti.ndarray(shape=nnz_A, dtype=val_dt)
+row_coo_A.from_numpy(coo_S1.row)
+col_coo_A.from_numpy(coo_S1.col)
+value_coo_A.from_numpy(coo_S1.data)
+
+row_coo_B = ti.ndarray(shape=nnz_B, dtype=idx_dt)
+col_coo_B = ti.ndarray(shape=nnz_B, dtype=idx_dt)
+value_coo_B = ti.ndarray(shape=nnz_B, dtype=val_dt)
+row_coo_B.from_numpy(coo_S2.row)
+col_coo_B.from_numpy(coo_S2.col)
+value_coo_B.from_numpy(coo_S2.data)
+
+A = ti.linalg.SparseMatrix(n=rows, m=cols, dtype=ti.f32)
+B = ti.linalg.SparseMatrix(n=rows, m=cols, dtype=ti.f32)
+A.build_coo(row_coo_A, col_coo_A, value_coo_A)
+B.build_coo(row_coo_B, col_coo_B, value_coo_B)
+
+print('>>>> A:')
+print(A)
+print('>>>> B:')
+print(B)
+
+print('>>>> C = A + B:')
+C = A + B
+print(C)
+print('>>>> verify:')
+S3 = S1 + S2
+print(S3.A)
+print('>>>> C - A:')
+D = C - A
+print(D)
+print('>>>> verify:')
+print((S3 - S1).A)
+print('>>>> A * 2.5:')
+E = A * 2.5
+print(E)
+print('>>>> verify:')
+print((2.5 * S1).A)
+print('>>>> A.T:')
+F = A.transpose()
+print(F)
+print('>>>> verify:')
+print(S1.T.A)
+print('>>>> A @ B.T:')
+G = A @ B.transpose()
+print(G)
+print('>>>> verify:')
+print((S1 @ S2.T).A)