File size: 9,882 Bytes
712dbf0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 |
# Copyright © 2024 Apple Inc.
import math
import os
import unittest
import mlx.core as mx
import mlx_tests
import numpy as np
class TestDouble(mlx_tests.MLXTestCase):
def test_unary_ops(self):
shape = (3, 3)
x = mx.random.normal(shape=shape)
if mx.default_device() == mx.gpu:
with self.assertRaises(ValueError):
x.astype(mx.float64)
x_double = x.astype(mx.float64, stream=mx.cpu)
ops = [
mx.abs,
mx.arccos,
mx.arccosh,
mx.arcsin,
mx.arcsinh,
mx.arctan,
mx.arctanh,
mx.ceil,
mx.erf,
mx.erfinv,
mx.exp,
mx.expm1,
mx.floor,
mx.log,
mx.logical_not,
mx.negative,
mx.round,
mx.sin,
mx.sinh,
mx.sqrt,
mx.rsqrt,
mx.tan,
mx.tanh,
]
for op in ops:
if mx.default_device() == mx.gpu:
with self.assertRaises(ValueError):
op(x_double)
continue
y = op(x)
y_double = op(x_double)
self.assertTrue(
mx.allclose(y, y_double.astype(mx.float32, mx.cpu), equal_nan=True)
)
def test_binary_ops(self):
shape = (3, 3)
a = mx.random.normal(shape=shape)
b = mx.random.normal(shape=shape)
a_double = a.astype(mx.float64, stream=mx.cpu)
b_double = b.astype(mx.float64, stream=mx.cpu)
ops = [
mx.add,
mx.arctan2,
mx.divide,
mx.multiply,
mx.subtract,
mx.logical_and,
mx.logical_or,
mx.remainder,
mx.maximum,
mx.minimum,
mx.power,
mx.equal,
mx.greater,
mx.greater_equal,
mx.less,
mx.less_equal,
mx.not_equal,
mx.logaddexp,
]
for op in ops:
if mx.default_device() == mx.gpu:
with self.assertRaises(ValueError):
op(a_double, b_double)
continue
y = op(a, b)
y_double = op(a_double, b_double)
self.assertTrue(
mx.allclose(y, y_double.astype(mx.float32, mx.cpu), equal_nan=True)
)
def test_where(self):
shape = (3, 3)
cond = mx.random.uniform(shape=shape) > 0.5
a = mx.random.normal(shape=shape)
b = mx.random.normal(shape=shape)
a_double = a.astype(mx.float64, stream=mx.cpu)
b_double = b.astype(mx.float64, stream=mx.cpu)
if mx.default_device() == mx.gpu:
with self.assertRaises(ValueError):
mx.where(cond, a_double, b_double)
return
y = mx.where(cond, a, b)
y_double = mx.where(cond, a_double, b_double)
self.assertTrue(mx.allclose(y, y_double.astype(mx.float32, mx.cpu)))
def test_reductions(self):
shape = (32, 32)
a = mx.random.normal(shape=shape)
a_double = a.astype(mx.float64, stream=mx.cpu)
axes = [0, 1, (0, 1)]
ops = [mx.sum, mx.prod, mx.min, mx.max, mx.any, mx.all]
for op in ops:
for ax in axes:
if mx.default_device() == mx.gpu:
with self.assertRaises(ValueError):
op(a_double, axis=ax)
continue
y = op(a)
y_double = op(a_double)
self.assertTrue(mx.allclose(y, y_double.astype(mx.float32, mx.cpu)))
def test_get_and_set_item(self):
shape = (3, 3)
a = mx.random.normal(shape=shape)
b = mx.random.normal(shape=(2,))
a_double = a.astype(mx.float64, stream=mx.cpu)
b_double = b.astype(mx.float64, stream=mx.cpu)
idx_i = mx.array([0, 2])
idx_j = mx.array([0, 2])
if mx.default_device() == mx.gpu:
with self.assertRaises(ValueError):
a_double[idx_i, idx_j]
else:
y = a[idx_i, idx_j]
y_double = a_double[idx_i, idx_j]
self.assertTrue(mx.allclose(y, y_double.astype(mx.float32, mx.cpu)))
if mx.default_device() == mx.gpu:
with self.assertRaises(ValueError):
a_double[idx_i, idx_j] = b_double
else:
a[idx_i, idx_j] = b
a_double[idx_i, idx_j] = b_double
self.assertTrue(mx.allclose(a, a_double.astype(mx.float32, mx.cpu)))
def test_gemm(self):
shape = (8, 8)
a = mx.random.normal(shape=shape)
b = mx.random.normal(shape=shape)
a_double = a.astype(mx.float64, stream=mx.cpu)
b_double = b.astype(mx.float64, stream=mx.cpu)
if mx.default_device() == mx.gpu:
with self.assertRaises(ValueError):
a_double @ b_double
return
y = a @ b
y_double = a_double @ b_double
self.assertTrue(
mx.allclose(y, y_double.astype(mx.float32, mx.cpu), equal_nan=True)
)
def test_type_promotion(self):
import mlx.core as mx
a = mx.array([4, 8], mx.float64)
b = mx.array([4, 8], mx.int32)
with mx.stream(mx.cpu):
c = a + b
self.assertEqual(c.dtype, mx.float64)
def test_lapack(self):
with mx.stream(mx.cpu):
# QRF
A = mx.array([[2.0, 3.0], [1.0, 2.0]], dtype=mx.float64)
Q, R = mx.linalg.qr(A)
out = Q @ R
self.assertTrue(mx.allclose(out, A))
out = Q.T @ Q
self.assertTrue(mx.allclose(out, mx.eye(2)))
self.assertTrue(mx.allclose(mx.tril(R, -1), mx.zeros_like(R)))
self.assertEqual(Q.dtype, mx.float64)
self.assertEqual(R.dtype, mx.float64)
# SVD
A = mx.array(
[[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]], dtype=mx.float64
)
U, S, Vt = mx.linalg.svd(A)
self.assertTrue(mx.allclose(U[:, : len(S)] @ mx.diag(S) @ Vt, A))
# Inverse
A = mx.array([[1, 2, 3], [6, -5, 4], [-9, 8, 7]], dtype=mx.float64)
A_inv = mx.linalg.inv(A)
self.assertTrue(mx.allclose(A @ A_inv, mx.eye(A.shape[0])))
# Tri inv
A = mx.array([[1, 0, 0], [6, -5, 0], [-9, 8, 7]], dtype=mx.float64)
B = mx.array([[7, 0, 0], [3, -2, 0], [1, 8, 3]], dtype=mx.float64)
AB = mx.stack([A, B])
invs = mx.linalg.tri_inv(AB, upper=False)
for M, M_inv in zip(AB, invs):
self.assertTrue(mx.allclose(M @ M_inv, mx.eye(M.shape[0])))
# Cholesky
sqrtA = mx.array(
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]], dtype=mx.float64
)
A = sqrtA.T @ sqrtA / 81
L = mx.linalg.cholesky(A)
U = mx.linalg.cholesky(A, upper=True)
self.assertTrue(mx.allclose(L @ L.T, A))
self.assertTrue(mx.allclose(U.T @ U, A))
# Psueod inverse
A = mx.array([[1, 2, 3], [6, -5, 4], [-9, 8, 7]], dtype=mx.float64)
A_plus = mx.linalg.pinv(A)
self.assertTrue(mx.allclose(A @ A_plus @ A, A))
# Eigh
def check_eigs_and_vecs(A_np, kwargs={}):
A = mx.array(A_np, dtype=mx.float64)
eig_vals, eig_vecs = mx.linalg.eigh(A, **kwargs)
eig_vals_np, _ = np.linalg.eigh(A_np, **kwargs)
self.assertTrue(np.allclose(eig_vals, eig_vals_np))
self.assertTrue(
mx.allclose(A @ eig_vecs, eig_vals[..., None, :] * eig_vecs)
)
eig_vals_only = mx.linalg.eigvalsh(A, **kwargs)
self.assertTrue(mx.allclose(eig_vals, eig_vals_only))
# Test a simple 2x2 symmetric matrix
A_np = np.array([[1.0, 2.0], [2.0, 4.0]], dtype=np.float64)
check_eigs_and_vecs(A_np)
# Test a larger random symmetric matrix
n = 5
np.random.seed(1)
A_np = np.random.randn(n, n).astype(np.float64)
A_np = (A_np + A_np.T) / 2
check_eigs_and_vecs(A_np)
# Test with upper triangle
check_eigs_and_vecs(A_np, {"UPLO": "U"})
# LU factorization
# Test 3x3 matrix
a = mx.array(
[[3.0, 1.0, 2.0], [1.0, 8.0, 6.0], [9.0, 2.0, 5.0]], dtype=mx.float64
)
P, L, U = mx.linalg.lu(a)
self.assertTrue(mx.allclose(L[P, :] @ U, a))
# Solve triangular
# Test lower triangular matrix
a = mx.array(
[[4.0, 0.0, 0.0], [2.0, 3.0, 0.0], [1.0, -2.0, 5.0]], dtype=mx.float64
)
b = mx.array([8.0, 14.0, 3.0], dtype=mx.float64)
result = mx.linalg.solve_triangular(a, b, upper=False)
expected = np.linalg.solve(np.array(a), np.array(b))
self.assertTrue(np.allclose(result, expected))
# Test upper triangular matrix
a = mx.array(
[[3.0, 2.0, 1.0], [0.0, 5.0, 4.0], [0.0, 0.0, 6.0]], dtype=mx.float64
)
b = mx.array([13.0, 33.0, 18.0], dtype=mx.float64)
result = mx.linalg.solve_triangular(a, b, upper=True)
expected = np.linalg.solve(np.array(a), np.array(b))
self.assertTrue(np.allclose(result, expected))
def test_conversion(self):
a = mx.array([1.0, 2.0], mx.float64)
b = np.array(a)
self.assertTrue(np.array_equal(a, b))
if __name__ == "__main__":
mlx_tests.MLXTestRunner()
|