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tests.py
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import sys
from functools import wraps
import jax
import numpy as np
import pytest
jax.config.update("jax_enable_x64", True)
jax.config.update("jax_platform_name", "cpu")
import jax.numpy as jnp
import jax.scipy as jsp
from jax import lax
from jaxlib.xla_extension import XlaRuntimeError
import klujax
def log_test_name(f):
@wraps(f)
def new(*args, **kwargs):
print(f"\n{f.__name__}", file=sys.stderr)
if args:
print(f"args={args}", file=sys.stderr)
if kwargs:
print(f"kwargs={kwargs}", file=sys.stderr)
return f(*args, **kwargs)
return new
@log_test_name
@pytest.mark.parametrize("dtype", [np.float64, np.complex128])
@pytest.mark.parametrize("ops", [(klujax.solve, jsp.linalg.solve), (klujax.coo_mul_vec, lax.dot)]) # fmt: skip
def test_1d(dtype, ops):
op_sparse, op_dense = ops
Ai, Aj, Ax, b = _get_rand_arrs_1d(8, (n_col := 5), dtype=dtype)
x_sp = op_sparse(Ai, Aj, Ax, b)
A = jnp.zeros((n_col, n_col), dtype=Ax.dtype).at[Ai, Aj].add(Ax)
x = op_dense(A, b)
_log_and_test_equality(x, x_sp)
@log_test_name
@pytest.mark.parametrize("dtype", [np.float64, np.complex128])
@pytest.mark.parametrize("ops", [(klujax.solve, jsp.linalg.solve), (klujax.coo_mul_vec, lax.dot)]) # fmt: skip
def test_2d(dtype, ops):
op_sparse, op_dense = ops
Ai, Aj, Ax, b = _get_rand_arrs_2d((n_lhs := 3), 8, (n_col := 5), dtype=dtype)
x_sp = op_sparse(Ai, Aj, Ax, b)
A = jnp.zeros((n_lhs, n_col, n_col), dtype=dtype).at[:, Ai, Aj].add(Ax)
x = jax.vmap(op_dense, (0, 0), 0)(A, b)
_log_and_test_equality(x, x_sp)
@log_test_name
@pytest.mark.parametrize("dtype", [np.float64, np.complex128])
@pytest.mark.parametrize("ops", [(klujax.solve, jsp.linalg.solve), (klujax.coo_mul_vec, lax.dot)]) # fmt: skip
def test_2d_vmap(dtype, ops):
op_sparse, op_dense = ops
Ai, Aj, Ax, b = _get_rand_arrs_2d((n_lhs := 3), 8, (n_col := 5), dtype=dtype)
x_sp = jax.vmap(op_sparse, (None, None, 1, 1), 0)(Ai, Aj, Ax.T, b.T)
A = jnp.zeros((n_lhs, n_col, n_col), dtype=dtype).at[:, Ai, Aj].add(Ax)
x = jax.vmap(op_dense, (0, 0), 0)(A, b)
_log_and_test_equality(x, x_sp)
@log_test_name
@pytest.mark.parametrize("dtype", [np.float64, np.complex128])
@pytest.mark.parametrize("ops", [(klujax.solve, jsp.linalg.solve), (klujax.coo_mul_vec, lax.dot)]) # fmt: skip
def test_3d(dtype, ops):
op_sparse, op_dense = ops
Ai, Aj, Ax, b = _get_rand_arrs_3d((n_lhs := 3), 8, (n_col := 5), 2, dtype=dtype)
x_sp = op_sparse(Ai, Aj, Ax, b)
A = jnp.zeros((n_lhs, n_col, n_col), dtype=dtype).at[:, Ai, Aj].add(Ax)
x = jax.vmap(op_dense, (0, 0), 0)(A, b)
_log_and_test_equality(x, x_sp)
@log_test_name
@pytest.mark.parametrize("dtype", [np.float64, np.complex128])
@pytest.mark.parametrize("ops", [(klujax.solve, jsp.linalg.solve), (klujax.coo_mul_vec, lax.dot)]) # fmt: skip
def test_3d_vmap(dtype, ops):
op_sparse, op_dense = ops
Ai, Aj, Ax, b = _get_rand_arrs_3d((n_lhs := 3), 8, (n_col := 5), 2, dtype=dtype)
_log(Ai_shape=Ai.shape, Aj_shape=Aj.shape, Ax_shape=Ax.shape, b_shape=b.shape)
x_sp = jax.vmap(op_sparse, (None, None, None, -1), -1)(Ai, Aj, Ax, b)
A = jnp.zeros((n_lhs, n_col, n_col), dtype=dtype).at[:, Ai, Aj].add(Ax)
x = jax.vmap(op_dense, (0, 0), 0)(A, b)
_log_and_test_equality(x, x_sp)
@log_test_name
@pytest.mark.parametrize("dtype", [np.float64, np.complex128])
@pytest.mark.parametrize("op", [klujax.solve, klujax.coo_mul_vec])
def test_4d(dtype, op):
Ai, Aj, Ax, b = _get_rand_arrs_3d(3, 8, 5, 2, dtype=dtype)
with pytest.raises(ValueError):
op(Ai, Aj, Ax, b[None])
with pytest.raises(ValueError):
op(Ai, Aj, Ax[None], b)
with pytest.raises(ValueError):
op(Ai, Aj, Ax[None], b[None])
@log_test_name
@pytest.mark.parametrize("dtype", [np.float64, np.complex128])
@pytest.mark.parametrize("op", [klujax.solve, klujax.coo_mul_vec])
def test_4d_vmap(dtype, op):
Ai, Aj, Ax, b = _get_rand_arrs_3d(3, 8, 5, 2, dtype=dtype)
jax.vmap(op, (None, None, None, 1), 0)(Ai, Aj, Ax, b[:, None])
# TODO: compare with dense result
@log_test_name
@pytest.mark.skipif(sys.platform in ("win32", "darwin"), reason="FIXME: known to still segfault on Windows/MacOS!") # fmt: skip
@pytest.mark.parametrize("dtype", [np.float64, np.complex128])
@pytest.mark.parametrize("op", [klujax.solve, klujax.coo_mul_vec])
def test_vmap_fail(dtype, op):
n_lhs = 23
n_nz = 8
n_col = 5
n_rhs = 1
Axkey, Aikey, Ajkey, bkey = jax.random.split(jax.random.PRNGKey(33), 4)
Ax = jax.random.normal(Axkey, (n_nz,), dtype=dtype)
Ai = jax.random.randint(Aikey, (n_nz,), 0, n_col, jnp.int32)
Aj = jax.random.randint(Ajkey, (n_nz,), 0, n_col, jnp.int32)
b = jax.random.normal(bkey, (n_lhs, n_col, n_rhs), dtype=dtype)
with pytest.raises(XlaRuntimeError):
jax.vmap(op, in_axes=(None, None, None, 0), out_axes=0)(Ai, Aj, Ax, b)
def _get_rand_arrs_1d(n_nz, n_col, *, dtype, seed=33):
Axkey, Aikey, Ajkey, bkey = jax.random.split(jax.random.PRNGKey(seed), 4)
Ai = jax.random.randint(Aikey, (n_nz,), 0, n_col, jnp.int32)
Aj = jax.random.randint(Ajkey, (n_nz,), 0, n_col, jnp.int32)
Ax = jax.random.normal(Axkey, (n_nz,), dtype=dtype)
b = jax.random.normal(bkey, (n_col,), dtype=dtype)
return Ai, Aj, Ax, b
def _get_rand_arrs_2d(n_lhs, n_nz, n_col, *, dtype, seed=33):
Axkey, Aikey, Ajkey, bkey = jax.random.split(jax.random.PRNGKey(seed), 4)
Ai = jax.random.randint(Aikey, (n_nz,), 0, n_col, jnp.int32)
Aj = jax.random.randint(Ajkey, (n_nz,), 0, n_col, jnp.int32)
Ax = jax.random.normal(
Axkey,
(
n_lhs,
n_nz,
),
dtype=dtype,
)
b = jax.random.normal(bkey, (n_lhs, n_col), dtype=dtype)
return Ai, Aj, Ax, b
def _get_rand_arrs_3d(n_lhs, n_nz, n_col, n_rhs, *, dtype, seed=33):
Axkey, Aikey, Ajkey, bkey = jax.random.split(jax.random.PRNGKey(seed), 4)
Ai = jax.random.randint(Aikey, (n_nz,), 0, n_col, jnp.int32)
Aj = jax.random.randint(Ajkey, (n_nz,), 0, n_col, jnp.int32)
Ax = jax.random.normal(
Axkey,
(
n_lhs,
n_nz,
),
dtype=dtype,
)
b = jax.random.normal(bkey, (n_lhs, n_col, n_rhs), dtype=dtype)
return Ai, Aj, Ax, b
def _log_and_test_equality(x, x_sp):
print(f"\nx_sp=\n{x_sp}")
print(f"\nx=\n{x}")
print(f"\ndiff=\n{np.round(x_sp - x, 9)}")
print(f"\nis_equal=\n{_is_almost_equal(x_sp, x)}")
np.testing.assert_array_almost_equal(x_sp, x)
def _log(**kwargs):
for k, v in kwargs.items():
print(f"{k}={v}")
def _is_almost_equal(arr1, arr2):
try:
np.testing.assert_array_almost_equal(arr1, arr2)
return True
except AssertionError:
return False