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test_sparse_matrix.py
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import numpy as np
import pytest
import taichi as ti
from tests import test_utils
@pytest.mark.parametrize('dtype, storage_format', [(ti.f32, 'col_major'),
(ti.f32, 'row_major'),
(ti.f64, 'col_major'),
(ti.f64, 'row_major')])
@test_utils.test(arch=ti.cpu)
def test_sparse_matrix_builder_deprecated_anno(dtype, storage_format):
n = 8
Abuilder = ti.linalg.SparseMatrixBuilder(n,
n,
max_num_triplets=100,
dtype=dtype,
storage_format=storage_format)
@ti.kernel
def fill(Abuilder: ti.types.sparse_matrix_builder()):
for i, j in ti.ndrange(n, n):
Abuilder[i, j] += i + j
fill(Abuilder)
A = Abuilder.build()
for i in range(n):
for j in range(n):
assert A[i, j] == i + j
@pytest.mark.parametrize('dtype, storage_format', [(ti.f32, 'col_major'),
(ti.f32, 'row_major'),
(ti.f64, 'col_major'),
(ti.f64, 'row_major')])
@test_utils.test(arch=ti.cpu)
def test_sparse_matrix_builder(dtype, storage_format):
n = 8
Abuilder = ti.linalg.SparseMatrixBuilder(n,
n,
max_num_triplets=100,
dtype=dtype,
storage_format=storage_format)
@ti.kernel
def fill(Abuilder: ti.types.sparse_matrix_builder()):
for i, j in ti.ndrange(n, n):
Abuilder[i, j] += i + j
fill(Abuilder)
A = Abuilder.build()
for i in range(n):
for j in range(n):
assert A[i, j] == i + j
@pytest.mark.parametrize('dtype, storage_format', [(ti.f32, 'col_major'),
(ti.f32, 'row_major'),
(ti.f64, 'col_major'),
(ti.f64, 'row_major')])
@test_utils.test(arch=ti.cpu)
def test_build_sparse_matrix_frome_ndarray(dtype, storage_format):
n = 8
triplets = ti.Vector.ndarray(n=3, dtype=ti.f32, shape=n)
A = ti.linalg.SparseMatrix(n=10,
m=10,
dtype=ti.f32,
storage_format=storage_format)
@ti.kernel
def fill(triplets: ti.types.ndarray()):
for i in range(n):
triplet = ti.Vector([i, i, i], dt=ti.f32)
triplets[i] = triplet
fill(triplets)
A.build_from_ndarray(triplets)
for i in range(n):
assert A[i, i] == i
@pytest.mark.parametrize('dtype, storage_format', [(ti.f32, 'col_major'),
(ti.f32, 'row_major'),
(ti.f64, 'col_major'),
(ti.f64, 'row_major')])
@test_utils.test(arch=ti.cpu)
def test_sparse_matrix_shape(dtype, storage_format):
n, m = 8, 9
Abuilder = ti.linalg.SparseMatrixBuilder(n,
m,
max_num_triplets=100,
dtype=dtype,
storage_format=storage_format)
@ti.kernel
def fill(Abuilder: ti.types.sparse_matrix_builder()):
for i, j in ti.ndrange(n, m):
Abuilder[i, j] += i + j
fill(Abuilder)
A = Abuilder.build()
assert A.shape == (n, m)
@pytest.mark.parametrize('dtype, storage_format', [(ti.f32, 'col_major'),
(ti.f32, 'row_major'),
(ti.f64, 'col_major'),
(ti.f64, 'row_major')])
@test_utils.test(arch=ti.cpu)
def test_sparse_matrix_element_access(dtype, storage_format):
n = 8
Abuilder = ti.linalg.SparseMatrixBuilder(n,
n,
max_num_triplets=100,
dtype=dtype,
storage_format=storage_format)
@ti.kernel
def fill(Abuilder: ti.types.sparse_matrix_builder()):
for i in range(n):
Abuilder[i, i] += i
fill(Abuilder)
A = Abuilder.build()
for i in range(n):
assert A[i, i] == i
@pytest.mark.parametrize('dtype, storage_format', [(ti.f32, 'col_major'),
(ti.f32, 'row_major'),
(ti.f64, 'col_major'),
(ti.f64, 'row_major')])
@test_utils.test(arch=ti.cpu)
def test_sparse_matrix_element_modify(dtype, storage_format):
n = 8
Abuilder = ti.linalg.SparseMatrixBuilder(n,
n,
max_num_triplets=100,
dtype=dtype,
storage_format=storage_format)
@ti.kernel
def fill(Abuilder: ti.types.sparse_matrix_builder()):
for i in range(n):
Abuilder[i, i] += i
fill(Abuilder)
A = Abuilder.build()
A[0, 0] = 1024.0
assert A[0, 0] == 1024.0
@pytest.mark.parametrize('dtype, storage_format', [(ti.f32, 'col_major'),
(ti.f32, 'row_major'),
(ti.f64, 'col_major'),
(ti.f64, 'row_major')])
@test_utils.test(arch=ti.cpu)
def test_sparse_matrix_addition(dtype, storage_format):
n = 8
Abuilder = ti.linalg.SparseMatrixBuilder(n,
n,
max_num_triplets=100,
dtype=dtype,
storage_format=storage_format)
Bbuilder = ti.linalg.SparseMatrixBuilder(n,
n,
max_num_triplets=100,
dtype=dtype,
storage_format=storage_format)
@ti.kernel
def fill(Abuilder: ti.types.sparse_matrix_builder(),
Bbuilder: ti.types.sparse_matrix_builder()):
for i, j in ti.ndrange(n, n):
Abuilder[i, j] += i + j
Bbuilder[i, j] += i - j
fill(Abuilder, Bbuilder)
A = Abuilder.build()
B = Bbuilder.build()
C = A + B
for i in range(n):
for j in range(n):
assert C[i, j] == 2 * i
@pytest.mark.parametrize('dtype, storage_format', [(ti.f32, 'col_major'),
(ti.f32, 'row_major'),
(ti.f64, 'col_major'),
(ti.f64, 'row_major')])
@test_utils.test(arch=ti.cpu)
def test_sparse_matrix_subtraction(dtype, storage_format):
n = 8
Abuilder = ti.linalg.SparseMatrixBuilder(n,
n,
max_num_triplets=100,
dtype=dtype,
storage_format=storage_format)
Bbuilder = ti.linalg.SparseMatrixBuilder(n,
n,
max_num_triplets=100,
dtype=dtype,
storage_format=storage_format)
@ti.kernel
def fill(Abuilder: ti.types.sparse_matrix_builder(),
Bbuilder: ti.types.sparse_matrix_builder()):
for i, j in ti.ndrange(n, n):
Abuilder[i, j] += i + j
Bbuilder[i, j] += i - j
fill(Abuilder, Bbuilder)
A = Abuilder.build()
B = Bbuilder.build()
C = A - B
for i in range(n):
for j in range(n):
assert C[i, j] == 2 * j
@pytest.mark.parametrize('dtype, storage_format', [(ti.f32, 'col_major'),
(ti.f32, 'row_major'),
(ti.f64, 'col_major'),
(ti.f64, 'row_major')])
@test_utils.test(arch=ti.cpu)
def test_sparse_matrix_scalar_multiplication(dtype, storage_format):
n = 8
Abuilder = ti.linalg.SparseMatrixBuilder(n,
n,
max_num_triplets=100,
dtype=dtype,
storage_format=storage_format)
@ti.kernel
def fill(Abuilder: ti.types.sparse_matrix_builder()):
for i, j in ti.ndrange(n, n):
Abuilder[i, j] += i + j
fill(Abuilder)
A = Abuilder.build()
B = A * 3.0
for i in range(n):
for j in range(n):
assert B[i, j] == 3 * (i + j)
@pytest.mark.parametrize('dtype, storage_format', [(ti.f32, 'col_major'),
(ti.f32, 'row_major'),
(ti.f64, 'col_major'),
(ti.f64, 'row_major')])
@test_utils.test(arch=ti.cpu)
def test_sparse_matrix_transpose(dtype, storage_format):
n = 8
Abuilder = ti.linalg.SparseMatrixBuilder(n,
n,
max_num_triplets=100,
dtype=dtype,
storage_format=storage_format)
@ti.kernel
def fill(Abuilder: ti.types.sparse_matrix_builder()):
for i, j in ti.ndrange(n, n):
Abuilder[i, j] += i + j
fill(Abuilder)
A = Abuilder.build()
B = A.transpose()
for i in range(n):
for j in range(n):
assert B[i, j] == A[j, i]
@pytest.mark.parametrize('dtype, storage_format', [(ti.f32, 'col_major'),
(ti.f32, 'row_major'),
(ti.f64, 'col_major'),
(ti.f64, 'row_major')])
@test_utils.test(arch=ti.cpu)
def test_sparse_matrix_elementwise_multiplication(dtype, storage_format):
n = 8
Abuilder = ti.linalg.SparseMatrixBuilder(n,
n,
max_num_triplets=100,
dtype=dtype,
storage_format=storage_format)
Bbuilder = ti.linalg.SparseMatrixBuilder(n,
n,
max_num_triplets=100,
dtype=dtype,
storage_format=storage_format)
@ti.kernel
def fill(Abuilder: ti.types.sparse_matrix_builder(),
Bbuilder: ti.types.sparse_matrix_builder()):
for i, j in ti.ndrange(n, n):
Abuilder[i, j] += i + j
Bbuilder[i, j] += i - j
fill(Abuilder, Bbuilder)
A = Abuilder.build()
B = Bbuilder.build()
C = A * B
for i in range(n):
for j in range(n):
assert C[i, j] == (i + j) * (i - j)
@pytest.mark.parametrize('dtype, storage_format', [(ti.f32, 'col_major'),
(ti.f32, 'row_major'),
(ti.f64, 'col_major'),
(ti.f64, 'row_major')])
@test_utils.test(arch=ti.cpu)
def test_sparse_matrix_multiplication(dtype, storage_format):
n = 2
Abuilder = ti.linalg.SparseMatrixBuilder(n,
n,
max_num_triplets=100,
dtype=dtype,
storage_format=storage_format)
Bbuilder = ti.linalg.SparseMatrixBuilder(n,
n,
max_num_triplets=100,
dtype=dtype,
storage_format=storage_format)
@ti.kernel
def fill(Abuilder: ti.types.sparse_matrix_builder(),
Bbuilder: ti.types.sparse_matrix_builder()):
for i, j in ti.ndrange(n, n):
Abuilder[i, j] += i + j
Bbuilder[i, j] += i - j
fill(Abuilder, Bbuilder)
A = Abuilder.build()
B = Bbuilder.build()
C = A @ B
assert C[0, 0] == 1.0
assert C[0, 1] == 0.0
assert C[1, 0] == 2.0
assert C[1, 1] == -1.0
@pytest.mark.parametrize('dtype, storage_format', [(ti.f32, 'col_major'),
(ti.f32, 'row_major'),
(ti.f64, 'col_major'),
(ti.f64, 'row_major')])
@test_utils.test(arch=ti.cpu)
def test_sparse_matrix_nonsymmetric_multiplication(dtype, storage_format):
n, k, m = 2, 3, 4
Abuilder = ti.linalg.SparseMatrixBuilder(n,
k,
max_num_triplets=100,
dtype=dtype,
storage_format=storage_format)
Bbuilder = ti.linalg.SparseMatrixBuilder(k,
m,
max_num_triplets=100,
dtype=dtype,
storage_format=storage_format)
@ti.kernel
def fill(Abuilder: ti.types.sparse_matrix_builder(),
Bbuilder: ti.types.sparse_matrix_builder()):
for i, j in ti.ndrange(n, k):
Abuilder[i, j] += i + j
for i, j in ti.ndrange(k, m):
Bbuilder[i, j] -= i + j
fill(Abuilder, Bbuilder)
A = Abuilder.build()
B = Bbuilder.build()
C = A @ B
GT = [[-5, -8, -11, -14], [-8, -14, -20, -26]]
for i in range(n):
for j in range(m):
assert C[i, j] == GT[i][j]
@test_utils.test(arch=ti.cuda)
def test_gpu_sparse_matrix():
h_coo_row = np.asarray([1, 0, 0, 0, 2, 2, 2, 3, 3], dtype=np.int32)
h_coo_col = np.asarray([1, 0, 2, 3, 0, 2, 3, 1, 3], dtype=np.int32)
h_coo_val = np.asarray([4.0, 1.0, 2.0, 3.0, 5.0, 6.0, 7.0, 8.0, 9.0],
dtype=np.float32)
h_X = np.asarray([1.0, 2.0, 3.0, 4.0], dtype=np.float32)
h_Y = np.asarray([19.0, 8.0, 51.0, 52.0], dtype=np.float32)
# Data structure for building the CSR matrix A using Taichi Sparse Matrix
idx_dt = ti.int32
val_dt = ti.f32
d_coo_row = ti.ndarray(shape=9, dtype=idx_dt)
d_coo_col = ti.ndarray(shape=9, dtype=idx_dt)
d_coo_val = ti.ndarray(shape=9, dtype=val_dt)
# Dense vector x
X = ti.ndarray(shape=4, dtype=val_dt)
# Results for A @ x
Y = ti.ndarray(shape=4, dtype=val_dt)
# Initialize the CSR matrix and vectors with numpy array
d_coo_row.from_numpy(h_coo_row)
d_coo_col.from_numpy(h_coo_col)
d_coo_val.from_numpy(h_coo_val)
X.from_numpy(h_X)
Y.fill(0.0)
# Define the CSR matrix A
A = ti.linalg.SparseMatrix(n=4, m=4, dtype=ti.f32)
# Build the CSR matrix A with Taichi ndarray
A.build_coo(d_coo_row, d_coo_col, d_coo_val)
# Compute Y = A @ X
A.spmv(X, Y)
for i in range(4):
assert Y[i] == h_Y[i]