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[test] Fix previous discovered flaky test_sparse_linear_solver.py. #8110

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Jun 1, 2023
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[test] Fix previous discovered flaky test_sparse_linear_solver.py. #8110

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636c746
Fix previous discovered flaky test_sparse_linear_solver.py.
houkensjtu May 31, 2023
d220b25
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] May 31, 2023
a26e733
Use to_numpy_type() instead of manual conversion.
houkensjtu May 31, 2023
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39 changes: 22 additions & 17 deletions tests/python/test_sparse_linear_solver.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,15 +4,24 @@
import taichi as ti
from tests import test_utils

"""
A_psd used in the tests is a random positive definite matrix with a given number of rows and columns:
A_psd = A * A^T
Reference: https://stackoverflow.com/questions/619335/a-simple-algorithm-for-generating-positive-semidefinite-matrices
2023.5.31 qbao: It's observed that the matrix generated above is semi-definite, and it fails about 5% of the tests.
Therefore, A_psd is modified from A * A^T to A * A^T + np.eye(n) to improve stability.
"""


@pytest.mark.parametrize("dtype", [ti.f32, ti.f64])
@pytest.mark.parametrize("solver_type", ["LLT", "LDLT", "LU"])
@pytest.mark.parametrize("ordering", ["AMD", "COLAMD"])
@test_utils.test(arch=ti.x64)
def test_sparse_LLT_solver(dtype, solver_type, ordering):
np_dtype = ti.lang.util.to_numpy_type(dtype)
n = 10
A = np.random.rand(n, n)
A_psd = np.dot(A, A.transpose())
A_psd = (np.dot(A, A.transpose()) + np.eye(n)).astype(np_dtype)
Abuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100, dtype=dtype)
b = ti.field(dtype=dtype, shape=n)

Expand Down Expand Up @@ -44,10 +53,11 @@ def fill(
@pytest.mark.parametrize("ordering", ["AMD", "COLAMD"])
@test_utils.test(arch=ti.cpu)
def test_sparse_solver_ndarray_vector(dtype, solver_type, ordering):
np_dtype = ti.lang.util.to_numpy_type(dtype)
n = 10
A = np.random.rand(n, n)
A_psd = np.dot(A, A.transpose())
Abuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=300)
A_psd = (np.dot(A, A.transpose()) + np.eye(n)).astype(np_dtype)
Abuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=300, dtype=dtype)
b = ti.ndarray(ti.f32, shape=n)

@ti.kernel
Expand All @@ -73,7 +83,6 @@ def fill(
assert x[i] == test_utils.approx(res[i], rel=1.0)


@pytest.mark.skip(reason="Flaky; Reason to be investigated. 2023.5.18 qbao")
@test_utils.test(arch=ti.cuda)
def test_gpu_sparse_solver():
from scipy.sparse import coo_matrix
Expand All @@ -83,13 +92,9 @@ def init_b(b: ti.types.ndarray(), nrows: ti.i32):
for i in range(nrows):
b[i] = 1.0 + i / nrows

"""
Generate a positive definite matrix with a given number of rows and columns.
Reference: https://stackoverflow.com/questions/619335/a-simple-algorithm-for-generating-positive-semidefinite-matrices
"""
matrixSize = 10
A = np.random.rand(matrixSize, matrixSize)
A_psd = np.dot(A, A.transpose())
n = 10
A = np.random.rand(n, n)
A_psd = (np.dot(A, A.transpose()) + np.eye(n)).astype(np.float32)

A_raw_coo = coo_matrix(A_psd)
nrows, ncols = A_raw_coo.shape
Expand Down Expand Up @@ -122,31 +127,31 @@ def fill(
x_np = np.linalg.solve(A_psd, b_np)

# solve Ax=b using cusolver refectorization
solver = ti.linalg.SparseSolver()
solver = ti.linalg.SparseSolver(dtype=ti.f32)
solver.analyze_pattern(A_ti)
solver.factorize(A_ti)
x_ti = solver.solve(b)
ti.sync()
assert np.allclose(x_ti.to_numpy(), x_np, rtol=5.0e-3)

# solve Ax = b using compute function
solver = ti.linalg.SparseSolver()
solver = ti.linalg.SparseSolver(dtype=ti.f32)
solver.compute(A_ti)
x_cti = solver.solve(b)
ti.sync()
assert np.allclose(x_cti.to_numpy(), x_np, rtol=5.0e-3)


@pytest.mark.skip(reason="Flaky; Reason to be investigated. 2023.5.18 qbao")
@pytest.mark.parametrize("dtype", [ti.f32])
@pytest.mark.parametrize("solver_type", ["LLT", "LU"])
@test_utils.test(arch=ti.cuda)
def test_gpu_sparse_solver2(dtype, solver_type):
np_dtype = ti.lang.util.to_numpy_type(dtype)
n = 10
A = np.random.rand(n, n)
A_psd = np.dot(A, A.transpose())
Abuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=300)
b = ti.ndarray(ti.f32, shape=n)
A_psd = (np.dot(A, A.transpose()) + np.eye(n)).astype(np_dtype)
Abuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=300, dtype=dtype)
b = ti.ndarray(dtype, shape=n)

@ti.kernel
def fill(
Expand Down