# Copyright © 2023-2024 Apple Inc. import math import os import unittest from itertools import permutations, product import mlx.core as mx import mlx_tests import numpy as np def np_wrap_between(x, a): """Wraps `x` between `[-a, a]`.""" two_a = 2 * a zero = 0 rem = np.remainder(np.add(x, a), two_a) if isinstance(rem, np.ndarray): rem = np.select(rem < zero, np.add(rem, two_a), rem) else: rem = np.add(rem, two_a) if rem < zero else rem return np.subtract(rem, a) def np_logaddexp(x1: np.ndarray, x2: np.ndarray): amax = np.maximum(x1, x2) if np.issubdtype(x1.dtype, np.floating): delta = np.subtract(x1, x2) if isinstance(delta, np.ndarray): return np.select( np.isnan(delta), np.add(x1, x2), np.add(amax, np.log1p(np.exp(np.negative(np.abs(delta))))), ) else: return ( np.add(x1, x2) if np.isnan(delta) else np.add(amax, np.log1p(np.exp(np.negative(np.abs(delta))))) ) else: delta = np.subtract(np.add(x1, x2), np.multiply(amax, 2)) out = np.add(amax, np.log1p(np.exp(delta))) return np.real(out) + 1j * np_wrap_between(np.imag(out), np.pi) def np_cumlogaddexp(x1: np.ndarray, axis: int = -1): out = x1.copy() for i in range(1, out.shape[axis]): out[i] = np_logaddexp(out[i], out[i - 1]) return out class TestOps(mlx_tests.MLXTestCase): def test_full_ones_zeros(self): x = mx.full(2, 3.0) self.assertEqual(x.shape, (2,)) self.assertEqual(x.tolist(), [3.0, 3.0]) x = mx.full((2, 3), 2.0) self.assertEqual(x.dtype, mx.float32) self.assertEqual(x.shape, (2, 3)) self.assertEqual(x.tolist(), [[2, 2, 2], [2, 2, 2]]) x = mx.full([3, 2], mx.array([False, True])) self.assertEqual(x.dtype, mx.bool_) self.assertEqual(x.tolist(), [[False, True], [False, True], [False, True]]) x = mx.full([3, 2], mx.array([2.0, 3.0])) self.assertEqual(x.tolist(), [[2, 3], [2, 3], [2, 3]]) x = mx.zeros(2) self.assertEqual(x.shape, (2,)) self.assertEqual(x.tolist(), [0.0, 0.0]) x = mx.ones(2) self.assertEqual(x.shape, (2,)) self.assertEqual(x.tolist(), [1.0, 1.0]) for t in [mx.bool_, mx.int32, mx.float32]: x = mx.zeros([2, 2], t) self.assertEqual(x.dtype, t) self.assertTrue(mx.array_equal(x, mx.array([[0, 0], [0, 0]]))) y = mx.zeros_like(x) self.assertEqual(y.dtype, t) self.assertTrue(mx.array_equal(y, x)) x = mx.ones([2, 2], t) self.assertEqual(x.dtype, t) self.assertTrue(mx.array_equal(x, mx.array([[1, 1], [1, 1]]))) y = mx.ones_like(x) self.assertEqual(y.dtype, t) self.assertTrue(mx.array_equal(y, x)) def test_scalar_inputs(self): # Check combinations of python types a = mx.add(False, True) self.assertEqual(a.dtype, mx.bool_) self.assertEqual(a.item(), True) a = mx.add(1, 2) self.assertEqual(a.dtype, mx.int32) self.assertEqual(a.item(), 3) a = mx.add(1.0, 2.0) self.assertEqual(a.dtype, mx.float32) self.assertEqual(a.item(), 3.0) a = mx.add(True, 2) self.assertEqual(a.dtype, mx.int32) self.assertEqual(a.item(), 3) a = mx.add(True, 2.0) self.assertEqual(a.dtype, mx.float32) self.assertEqual(a.item(), 3.0) a = mx.add(1, 2.0) self.assertEqual(a.dtype, mx.float32) self.assertEqual(a.item(), 3.0) a = mx.add(2, True) self.assertEqual(a.dtype, mx.int32) self.assertEqual(a.item(), 3) a = mx.add(2.0, True) self.assertEqual(a.dtype, mx.float32) self.assertEqual(a.item(), 3.0) a = mx.add(2.0, 1) self.assertEqual(a.dtype, mx.float32) self.assertEqual(a.item(), 3.0) # Check combinations with mlx arrays a = mx.add(mx.array(True), False) self.assertEqual(a.dtype, mx.bool_) self.assertEqual(a.item(), True) a = mx.add(mx.array(1), False) self.assertEqual(a.dtype, mx.int32) self.assertEqual(a.item(), 1.0) # Edge case: take the type of the scalar a = mx.add(mx.array(True), 1) self.assertEqual(a.dtype, mx.int32) self.assertEqual(a.item(), 2) a = mx.add(mx.array(1.0), 1) self.assertEqual(a.dtype, mx.float32) self.assertEqual(a.item(), 2.0) a = mx.add(1, mx.array(1.0)) self.assertEqual(a.dtype, mx.float32) self.assertEqual(a.item(), 2.0) binary_ops = [ "add", "subtract", "multiply", "divide", "floor_divide", "remainder", "equal", "not_equal", "less", "greater", "less_equal", "greater_equal", "maximum", "minimum", ] for op in binary_ops: npop = getattr(np, op) mlxop = getattr(mx, op) # Avoid subtract from bool and divide by 0 for x in [-1, 0, 1, -1.0, 1.0]: for y in [True, -1, 1, -1.0, 1.0]: self.assertEqual(npop(x, y).item(), mlxop(x, y).item()) def test_add(self): x = mx.array(1) y = mx.array(1) z = mx.add(x, y) self.assertEqual(z.item(), 2) x = mx.array(False, mx.bool_) z = x + 1 self.assertEqual(z.dtype, mx.int32) self.assertEqual(z.item(), 1) z = 2 + x self.assertEqual(z.dtype, mx.int32) self.assertEqual(z.item(), 2) x = mx.array(1, mx.uint32) z = x + 3 self.assertEqual(z.dtype, mx.uint32) self.assertEqual(z.item(), 4) z = 3 + x self.assertEqual(z.dtype, mx.uint32) self.assertEqual(z.item(), 4) z = x + 3.0 self.assertEqual(z.dtype, mx.float32) self.assertEqual(z.item(), 4.0) z = 3.0 + x self.assertEqual(z.dtype, mx.float32) self.assertEqual(z.item(), 4.0) x = mx.array(1, mx.int64) z = x + 3 self.assertEqual(z.dtype, mx.int64) self.assertEqual(z.item(), 4) z = 3 + x self.assertEqual(z.dtype, mx.int64) self.assertEqual(z.item(), 4) z = x + 3.0 self.assertEqual(z.dtype, mx.float32) self.assertEqual(z.item(), 4.0) z = 3.0 + x self.assertEqual(z.dtype, mx.float32) self.assertEqual(z.item(), 4.0) x = mx.array(1, mx.float32) z = x + 3 self.assertEqual(z.dtype, mx.float32) self.assertEqual(z.item(), 4) z = 3 + x self.assertEqual(z.dtype, mx.float32) self.assertEqual(z.item(), 4) def test_subtract(self): x = mx.array(4.0) y = mx.array(3.0) z = mx.subtract(x, y) self.assertEqual(z.dtype, mx.float32) self.assertEqual(z.item(), 1.0) z = x - 3.0 self.assertEqual(z.dtype, mx.float32) self.assertEqual(z.item(), 1.0) z = 5.0 - x self.assertEqual(z.dtype, mx.float32) self.assertEqual(z.item(), 1.0) def test_multiply(self): x = mx.array(2.0) y = mx.array(3.0) z = mx.multiply(x, y) self.assertEqual(z.dtype, mx.float32) self.assertEqual(z.item(), 6.0) z = x * 3.0 self.assertEqual(z.dtype, mx.float32) self.assertEqual(z.item(), 6.0) z = 3.0 * x self.assertEqual(z.dtype, mx.float32) self.assertEqual(z.item(), 6.0) def test_divide(self): x = mx.array(2.0) y = mx.array(4.0) z = mx.divide(x, y) self.assertEqual(z.dtype, mx.float32) self.assertEqual(z.item(), 0.5) z = x / 4.0 self.assertEqual(z.dtype, mx.float32) self.assertEqual(z.item(), 0.5) z = 1.0 / x self.assertEqual(z.dtype, mx.float32) self.assertEqual(z.item(), 0.5) x = x.astype(mx.float16) z = x / 4.0 self.assertEqual(z.dtype, mx.float16) x = x.astype(mx.float16) z = 4.0 / x self.assertEqual(z.dtype, mx.float16) x = mx.array(5) y = mx.array(2) z = x / y self.assertEqual(z.dtype, mx.float32) self.assertEqual(z.item(), 2.5) z = x // y self.assertEqual(z.dtype, mx.int32) self.assertEqual(z.item(), 2) def test_remainder(self): for dt in [mx.int32, mx.float32]: x = mx.array(2, dtype=dt) y = mx.array(4, dtype=dt) z1 = mx.remainder(x, y) z2 = mx.remainder(y, x) self.assertEqual(z1.dtype, dt) self.assertEqual(z1.item(), 2) self.assertEqual(z2.item(), 0) z = x % 4 self.assertEqual(z.dtype, dt) self.assertEqual(z.item(), 2) z = 1 % x self.assertEqual(z.dtype, dt) self.assertEqual(z.item(), 1) z = -1 % x self.assertEqual(z.dtype, dt) self.assertEqual(z.item(), 1) z = -1 % -x self.assertEqual(z.dtype, dt) self.assertEqual(z.item(), -1) x = mx.arange(10).astype(dt) - 5 y = x % 5 z = x % -5 self.assertEqual(y.tolist(), [0, 1, 2, 3, 4, 0, 1, 2, 3, 4]) self.assertEqual(z.tolist(), [0, -4, -3, -2, -1, 0, -4, -3, -2, -1]) z = -mx.ones(64) % mx.full(64, 2) self.assertTrue(mx.array_equal(z, mx.ones(64))) def test_comparisons(self): a = mx.array([0.0, 1.0, 5.0]) b = mx.array([-1.0, 2.0, 5.0]) self.assertEqual(mx.less(a, b).tolist(), [False, True, False]) self.assertEqual(mx.less_equal(a, b).tolist(), [False, True, True]) self.assertEqual(mx.greater(a, b).tolist(), [True, False, False]) self.assertEqual(mx.greater_equal(a, b).tolist(), [True, False, True]) self.assertEqual(mx.less(a, 5).tolist(), [True, True, False]) self.assertEqual(mx.less(5, a).tolist(), [False, False, False]) self.assertEqual(mx.less_equal(5, a).tolist(), [False, False, True]) self.assertEqual(mx.greater(a, 1).tolist(), [False, False, True]) self.assertEqual(mx.greater_equal(a, 1).tolist(), [False, True, True]) a = mx.array([0.0, 1.0, 5.0, -1.0]) b = mx.array([0.0, 2.0, 5.0, 3.0]) self.assertEqual(mx.equal(a, b).tolist(), [True, False, True, False]) self.assertEqual(mx.not_equal(a, b).tolist(), [False, True, False, True]) def test_array_equal(self): x = mx.array([1, 2, 3, 4]) y = mx.array([1, 2, 3, 4]) self.assertTrue(mx.array_equal(x, y)) y = mx.array([1, 2, 4, 5]) self.assertFalse(mx.array_equal(x, y)) y = mx.array([1, 2, 3]) self.assertFalse(mx.array_equal(x, y)) # Can still be equal with different types y = mx.array([1.0, 2.0, 3.0, 4.0]) self.assertTrue(mx.array_equal(x, y)) x = mx.array([0.0, float("nan")]) y = mx.array([0.0, float("nan")]) self.assertFalse(mx.array_equal(x, y)) self.assertTrue(mx.array_equal(x, y, equal_nan=True)) for t in [mx.float32, mx.float16, mx.bfloat16, mx.complex64]: with self.subTest(type=t): x = mx.array([0.0, float("nan")]).astype(t) y = mx.array([0.0, float("nan")]).astype(t) self.assertFalse(mx.array_equal(x, y)) self.assertTrue(mx.array_equal(x, y, equal_nan=True)) def test_isnan(self): x = mx.array([0.0, float("nan")]) self.assertEqual(mx.isnan(x).tolist(), [False, True]) x = mx.array([0.0, float("nan")]).astype(mx.float16) self.assertEqual(mx.isnan(x).tolist(), [False, True]) x = mx.array([0.0, float("nan")]).astype(mx.bfloat16) self.assertEqual(mx.isnan(x).tolist(), [False, True]) x = mx.array([0.0, float("nan")]).astype(mx.complex64) self.assertEqual(mx.isnan(x).tolist(), [False, True]) self.assertEqual(mx.isnan(0 * mx.array(float("inf"))).tolist(), True) def test_isinf(self): x = mx.array([0.0, float("inf")]) self.assertEqual(mx.isinf(x).tolist(), [False, True]) x = mx.array([0.0, float("inf")]).astype(mx.float16) self.assertEqual(mx.isinf(x).tolist(), [False, True]) x = mx.array([0.0, float("inf")]).astype(mx.bfloat16) self.assertEqual(mx.isinf(x).tolist(), [False, True]) x = mx.array([0.0, float("inf")]).astype(mx.complex64) self.assertEqual(mx.isinf(x).tolist(), [False, True]) self.assertEqual(mx.isinf(0 * mx.array(float("inf"))).tolist(), False) x = mx.array([-2147483648, 0, 2147483647], dtype=mx.int32) result = mx.isinf(x) self.assertEqual(result.tolist(), [False, False, False]) x = mx.array([-32768, 0, 32767], dtype=mx.int16) result = mx.isinf(x) self.assertEqual(result.tolist(), [False, False, False]) def test_isfinite(self): x = mx.array([0.0, float("inf"), float("nan")]) self.assertEqual(mx.isfinite(x).tolist(), [True, False, False]) x = x.astype(mx.float16) self.assertEqual(mx.isfinite(x).tolist(), [True, False, False]) x = x.astype(mx.bfloat16) self.assertEqual(mx.isfinite(x).tolist(), [True, False, False]) def test_tri(self): for shape in [[4], [4, 4], [2, 10]]: for diag in [-1, 0, 1, -2]: self.assertCmpNumpy(shape, mx.tri, np.tri, k=diag) self.assertEqual(mx.tri(1, 1).dtype, mx.float32) self.assertEqual(mx.tri(1, 1, dtype=mx.bfloat16).dtype, mx.bfloat16) def test_tril(self): for diag in [-1, 0, 1, -2]: self.assertCmpNumpy([(10, 10)], mx.tril, np.tril, k=diag) with self.assertRaises(Exception): mx.tril(mx.zeros((1))) def test_triu(self): for diag in [-1, 0, 1, -2]: self.assertCmpNumpy([(10, 10)], mx.triu, np.triu, k=diag) with self.assertRaises(Exception): mx.triu(mx.zeros((1))) def test_minimum(self): x = mx.array([0.0, -5, 10.0]) y = mx.array([1.0, -7.0, 3.0]) expected = [0, -7, 3] self.assertListEqual(mx.minimum(x, y).tolist(), expected) a = mx.array([float("nan")]) b = mx.array([0.0]) self.assertTrue(math.isnan(mx.minimum(a, b).item())) self.assertTrue(math.isnan(mx.minimum(b, a).item())) def test_maximum(self): x = mx.array([0.0, -5, 10.0]) y = mx.array([1.0, -7.0, 3.0]) expected = [1, -5, 10] self.assertListEqual(mx.maximum(x, y).tolist(), expected) a = mx.array([float("nan")]) b = mx.array([0.0]) self.assertTrue(math.isnan(mx.maximum(a, b).item())) self.assertTrue(math.isnan(mx.maximum(b, a).item())) def test_floor(self): x = mx.array([-22.03, 19.98, -27, 9, 0.0, -np.inf, np.inf]) expected = [-23, 19, -27, 9, 0, -np.inf, np.inf] self.assertListEqual(mx.floor(x).tolist(), expected) with self.assertRaises(ValueError): mx.floor(mx.array([22 + 3j, 19 + 98j])) def test_ceil(self): x = mx.array([-22.03, 19.98, -27, 9, 0.0, -np.inf, np.inf]) expected = [-22, 20, -27, 9, 0, -np.inf, np.inf] self.assertListEqual(mx.ceil(x).tolist(), expected) with self.assertRaises(ValueError): mx.ceil(mx.array([22 + 3j, 19 + 98j])) def test_isposinf(self): x = mx.array([0.0, float("-inf")]) self.assertEqual(mx.isposinf(x).tolist(), [False, False]) x = mx.array([0.0, float("-inf")]).astype(mx.float16) self.assertEqual(mx.isposinf(x).tolist(), [False, False]) x = mx.array([0.0, float("-inf")]).astype(mx.bfloat16) self.assertEqual(mx.isposinf(x).tolist(), [False, False]) x = mx.array([0.0, float("-inf")]).astype(mx.complex64) self.assertEqual(mx.isposinf(x).tolist(), [False, False]) self.assertEqual(mx.isposinf(0 * mx.array(float("inf"))).tolist(), False) x = mx.array([-2147483648, 0, 2147483647], dtype=mx.int32) result = mx.isposinf(x) self.assertEqual(result.tolist(), [False, False, False]) x = mx.array([-32768, 0, 32767], dtype=mx.int16) result = mx.isposinf(x) self.assertEqual(result.tolist(), [False, False, False]) def test_isneginf(self): x = mx.array([0.0, float("-inf")]) self.assertEqual(mx.isneginf(x).tolist(), [False, True]) x = mx.array([0.0, float("-inf")]).astype(mx.float16) self.assertEqual(mx.isneginf(x).tolist(), [False, True]) x = mx.array([0.0, float("-inf")]).astype(mx.bfloat16) self.assertEqual(mx.isneginf(x).tolist(), [False, True]) x = mx.array([0.0, float("-inf")]).astype(mx.complex64) self.assertEqual(mx.isneginf(x).tolist(), [False, True]) self.assertEqual(mx.isneginf(0 * mx.array(float("inf"))).tolist(), False) x = mx.array([-2147483648, 0, 2147483647], dtype=mx.int32) result = mx.isneginf(x) self.assertEqual(result.tolist(), [False, False, False]) x = mx.array([-32768, 0, 32767], dtype=mx.int16) result = mx.isneginf(x) self.assertEqual(result.tolist(), [False, False, False]) def test_round(self): # float x = mx.array( [0.5, -0.5, 1.5, -1.5, -21.03, 19.98, -27, 9, 0.0, -np.inf, np.inf] ) expected = [0, -0, 2, -2, -21, 20, -27, 9, 0, -np.inf, np.inf] self.assertListEqual(mx.round(x).tolist(), expected) # complex y = mx.round(mx.array([22.2 + 3.6j, 18.5 + 98.2j])) self.assertListEqual(y.tolist(), [22 + 4j, 18 + 98j]) # decimals y0 = mx.round(mx.array([15, 122], mx.int32), decimals=0) y1 = mx.round(mx.array([15, 122], mx.int32), decimals=-1) y2 = mx.round(mx.array([15, 122], mx.int32), decimals=-2) self.assertEqual(y0.dtype, mx.int32) self.assertEqual(y1.dtype, mx.int32) self.assertEqual(y2.dtype, mx.int32) self.assertListEqual(y0.tolist(), [15, 122]) self.assertListEqual(y1.tolist(), [20, 120]) self.assertListEqual(y2.tolist(), [0, 100]) y1 = mx.round(mx.array([1.537, 1.471], mx.float32), decimals=1) y2 = mx.round(mx.array([1.537, 1.471], mx.float32), decimals=2) self.assertTrue(mx.allclose(y1, mx.array([1.5, 1.5]))) self.assertTrue(mx.allclose(y2, mx.array([1.54, 1.47]))) # check round to nearest for different types dtypes = [mx.bfloat16, mx.float16, mx.float32] for dtype in dtypes: x = mx.arange(10, dtype=dtype) - 4.5 x = mx.round(x) self.assertEqual( x.astype(mx.float32).tolist(), [-4.0, -4.0, -2.0, -2.0, -0.0, 0.0, 2.0, 2.0, 4.0, 4.0], ) def test_transpose_noargs(self): x = mx.array([[0, 1, 1], [1, 0, 0]]) expected = [ [0, 1], [1, 0], [1, 0], ] self.assertListEqual(mx.transpose(x).tolist(), expected) def test_transpose_axis(self): x = mx.array( [ [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]], [[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]], ] ) expected = [ [[0, 4, 8], [1, 5, 9], [2, 6, 10], [3, 7, 11]], [[12, 16, 20], [13, 17, 21], [14, 18, 22], [15, 19, 23]], ] self.assertListEqual(mx.transpose(x, axes=(0, 2, 1)).tolist(), expected) def test_move_swap_axes(self): x = mx.zeros((2, 3, 4)) self.assertEqual(mx.moveaxis(x, 0, 2).shape, (3, 4, 2)) self.assertEqual(x.moveaxis(0, 2).shape, (3, 4, 2)) self.assertEqual(mx.swapaxes(x, 0, 2).shape, (4, 3, 2)) self.assertEqual(x.swapaxes(0, 2).shape, (4, 3, 2)) def test_sum(self): x = mx.array( [ [1, 2], [3, 3], ] ) self.assertEqual(mx.sum(x).item(), 9) y = mx.sum(x, keepdims=True) self.assertEqual(y, mx.array(9)) self.assertEqual(y.shape, (1, 1)) self.assertEqual(mx.sum(x, axis=0).tolist(), [4, 5]) self.assertEqual(mx.sum(x, axis=1).tolist(), [3, 6]) x_npy = np.arange(3 * 5 * 4 * 7).astype(np.float32) x_npy = np.reshape(x_npy, (3, 5, 4, 7)) x_mlx = mx.array(x_npy) for axis in (None, 0, 1, 2, 3, (0, 1), (2, 3), (1, 2, 3)): sum_npy = np.sum(x_npy, axis=axis) sum_mlx = np.asarray(mx.sum(x_mlx, axis=axis)) self.assertListEqual(list(sum_npy.shape), list(sum_mlx.shape)) self.assertTrue(np.all(sum_npy == sum_mlx)) x_npy = np.array([1.0, 2.0, 3.0, 4.0]).astype(np.float32) x_mlx = mx.array(x_npy) y_npy = x_npy[0:4:2] y_npy = np.broadcast_to(y_npy, (2, 2)) y_mlx = x_mlx[0:4:2] y_mlx = mx.broadcast_to(y_mlx, (2, 2)) for axis in (None, 0, 1, (0, 1)): sum_npy = np.sum(y_npy, axis=axis) sum_mlx = np.asarray(mx.sum(y_mlx, axis=axis)) self.assertListEqual(list(sum_npy.shape), list(sum_mlx.shape)) self.assertTrue(np.all(sum_npy == sum_mlx)) x_npy = ( np.arange(3 * 2 * 3 * 3 * 3 * 3) .reshape(3, 2, 3, 3, 3, 3) .astype(np.float32) ) x_mlx = mx.array(x_npy) y_mlx = x_mlx.sum(axis=(0, 1, 3, 4, 5)) y_npy = x_npy.sum(axis=(0, 1, 3, 4, 5)) self.assertTrue(np.array_equal(y_mlx, y_npy)) def test_prod(self): x = mx.array( [ [1, 2], [3, 3], ] ) self.assertEqual(mx.prod(x).item(), 18) y = mx.prod(x, keepdims=True) self.assertEqual(y, mx.array(18)) self.assertEqual(y.shape, (1, 1)) self.assertEqual(mx.prod(x, axis=0).tolist(), [3, 6]) self.assertEqual(mx.prod(x, axis=1).tolist(), [2, 9]) def test_min_and_max(self): x = mx.array( [ [1, 2], [3, 4], ] ) self.assertEqual(mx.min(x).item(), 1) self.assertEqual(mx.max(x).item(), 4) y = mx.min(x, keepdims=True) self.assertEqual(y.shape, (1, 1)) self.assertEqual(y, mx.array(1)) y = mx.max(x, keepdims=True) self.assertEqual(y.shape, (1, 1)) self.assertEqual(y, mx.array(4)) self.assertEqual(mx.min(x, axis=0).tolist(), [1, 2]) self.assertEqual(mx.min(x, axis=1).tolist(), [1, 3]) self.assertEqual(mx.max(x, axis=0).tolist(), [3, 4]) self.assertEqual(mx.max(x, axis=1).tolist(), [2, 4]) def test_argmin_argmax(self): data = np.random.rand(10, 12, 13) x = mx.array(data) for op in ["argmin", "argmax"]: for axis in range(3): for kd in [True, False]: a = getattr(mx, op)(x, axis, kd) b = getattr(np, op)(data, axis, keepdims=kd) self.assertEqual(a.tolist(), b.tolist()) for op in ["argmin", "argmax"]: a = getattr(mx, op)(x, keepdims=True) b = getattr(np, op)(data, keepdims=True) self.assertEqual(a.tolist(), b.tolist()) a = getattr(mx, op)(x) b = getattr(np, op)(data) self.assertEqual(a.item(), b) def test_broadcast(self): a_npy = np.reshape(np.arange(200), (10, 20)) a_mlx = mx.array(a_npy) b_npy = np.broadcast_to(a_npy, (30, 10, 20)) b_mlx = mx.broadcast_to(a_mlx, (30, 10, 20)) self.assertListEqual(list(b_npy.shape), list(b_mlx.shape)) self.assertTrue(np.array_equal(b_npy, b_mlx)) b_npy = np.broadcast_to(a_npy, (1, 10, 20)) b_mlx = mx.broadcast_to(a_mlx, (1, 10, 20)) self.assertListEqual(list(b_npy.shape), list(b_mlx.shape)) self.assertTrue(np.array_equal(b_npy, b_mlx)) b_npy = np.broadcast_to(1, (10, 20)) b_mlx = mx.broadcast_to(1, (10, 20)) self.assertListEqual(list(b_npy.shape), list(b_mlx.shape)) self.assertTrue(np.array_equal(b_npy, b_mlx)) def test_logsumexp(self): def logsumexp(x, axes=None): maxs = mx.max(x, axis=axes, keepdims=True) return mx.log(mx.sum(mx.exp(x - maxs), axis=axes, keepdims=True)) + maxs x = mx.array( [ [1.0, 2.0], [3.0, 4.0], ] ) self.assertTrue(math.isclose(mx.logsumexp(x).item(), logsumexp(x).item())) x = mx.random.uniform(shape=(1025,)) self.assertTrue(mx.allclose(mx.logsumexp(x), logsumexp(x))) # Transposed x = mx.random.uniform(shape=(2, 2, 8)) x = x.swapaxes(0, 1) self.assertTrue(mx.allclose(mx.logsumexp(x), logsumexp(x))) # Broadcast x = mx.broadcast_to(mx.random.uniform(shape=(2, 1, 8)), (2, 2, 8)) self.assertTrue(mx.allclose(mx.logsumexp(x), logsumexp(x))) # Large x = mx.random.uniform(shape=(1025,)) x = mx.broadcast_to(mx.random.uniform(shape=(2, 1, 8)), (2, 2, 8)) self.assertTrue(mx.allclose(mx.logsumexp(x), logsumexp(x))) def test_mean(self): x = mx.array( [ [1, 2], [3, 4], ] ) self.assertEqual(mx.mean(x).item(), 2.5) y = mx.mean(x, keepdims=True) self.assertEqual(y, mx.array(2.5)) self.assertEqual(y.shape, (1, 1)) self.assertEqual(mx.mean(x, axis=0).tolist(), [2, 3]) self.assertEqual(mx.mean(x, axis=1).tolist(), [1.5, 3.5]) def test_var(self): x = mx.array( [ [1, 2], [3, 4], ] ) self.assertEqual(mx.var(x).item(), 1.25) y = mx.var(x, keepdims=True) self.assertEqual(y, mx.array(1.25)) self.assertEqual(y.shape, (1, 1)) self.assertEqual(mx.var(x, axis=0).tolist(), [1.0, 1.0]) self.assertEqual(mx.var(x, axis=1).tolist(), [0.25, 0.25]) x = mx.array([1.0, 2.0]) out = mx.var(x, ddof=2) self.assertEqual(out.item(), float("inf")) x = mx.array([1.0, 2.0]) out = mx.var(x, ddof=3) self.assertEqual(out.item(), float("inf")) def test_std(self): x = mx.random.uniform(shape=(5, 5)) x_np = np.array(x) self.assertAlmostEqual(mx.std(x).item(), x_np.std().item(), places=6) def test_abs(self): a = mx.array([-1.0, 1.0, -2.0, 3.0]) result = mx.abs(a) expected = np.abs(a, dtype=np.float32) self.assertTrue(np.allclose(result, expected)) self.assertTrue(np.allclose(a.abs(), abs(a))) def test_negative(self): a = mx.array([-1.0, 1.0, -2.0, 3.0]) result = mx.negative(a) expected = np.negative(a, dtype=np.float32) self.assertTrue(np.allclose(result, expected)) def test_sign(self): a = mx.array([-1.0, 1.0, 0.0, -2.0, 3.0]) result = mx.sign(a) expected = np.sign(a, dtype=np.float32) self.assertTrue(np.allclose(result, expected)) a = mx.array([-1.0, 1.0, 0.0, -2.0, 3.0]) b = mx.array([-4.0, -3.0, 1.0, 0.0, 3.0]) c = a + b * 1j result = mx.sign(c) # np.sign differs in NumPy 1 and 2 so # we manually implement the NumPy 2 version here. expected = c / np.abs(c) self.assertTrue(np.allclose(result, expected)) def test_logical_not(self): a = mx.array([-1.0, 1.0, 0.0, 1.0, -2.0, 3.0]) result = mx.logical_not(a) expected = np.logical_not(a) self.assertTrue(np.array_equal(result, expected)) def test_logical_and(self): a = mx.array([True, False, True, False]) b = mx.array([True, True, False, False]) result = mx.logical_and(a, b) expected = np.logical_and(a, b) self.assertTrue(np.array_equal(result, expected)) # test overloaded operator result = a & b self.assertTrue(np.array_equal(result, expected)) def test_logical_or(self): a = mx.array([True, False, True, False]) b = mx.array([True, True, False, False]) result = mx.logical_or(a, b) expected = np.logical_or(a, b) self.assertTrue(np.array_equal(result, expected)) # test overloaded operator result = a | b self.assertTrue(np.array_equal(result, expected)) def test_square(self): a = mx.array([0.1, 0.5, 1.0, 10.0]) result = mx.square(a) expected = np.square(a, dtype=np.float32) self.assertTrue(np.allclose(result, expected)) def test_sqrt(self): a = mx.array([0.1, 0.5, 1.0, 10.0]) result = mx.sqrt(a) expected = np.sqrt(a, dtype=np.float32) self.assertTrue(np.allclose(result, expected)) def test_rsqrt(self): a = mx.array([0.1, 0.5, 1.0, 10.0]) result = mx.rsqrt(a) expected = 1.0 / np.sqrt(a, dtype=np.float32) self.assertTrue(np.allclose(result, expected)) def test_reciprocal(self): a = mx.array([0.1, 0.5, 1.0, 2.0]) result = mx.reciprocal(a) expected = np.reciprocal(a, dtype=np.float32) self.assertTrue(np.allclose(result, expected)) def test_logaddexp(self): a = mx.array([0, 1, 2, 9.0]) b = mx.array([1, 0, 4, 2.5]) result = mx.logaddexp(a, b) expected = np.logaddexp(a, b, dtype=np.float32) self.assertTrue(np.allclose(result, expected)) # Complex test a = mx.array([0, 1, 2, 9.0]) + 1j b = mx.array([1, 0, 4, 2.5]) + 1j result = mx.logaddexp(a, b) expected = np_logaddexp(np.array(a), np.array(b)) self.assertTrue(np.allclose(result, expected)) a = mx.array([float("nan")]) b = mx.array([0.0]) self.assertTrue(math.isnan(mx.logaddexp(a, b).item())) def test_log(self): a = mx.array([1, 0.5, 10, 100]) result = mx.log(a) expected = np.log(a, dtype=np.float32) self.assertTrue(np.allclose(result, expected)) a = mx.array(1.0) + 1j * mx.array(2.0) result = mx.log(a) expected = np.log(np.array(a)) self.assertTrue(np.allclose(result, expected)) def test_log2(self): a = mx.array([0.5, 1, 2, 10, 16]) result = mx.log2(a) expected = np.log2(a, dtype=np.float32) self.assertTrue(np.allclose(result, expected)) a = mx.array(1.0) + 1j * mx.array(2.0) result = mx.log2(a) expected = np.log2(np.array(a)) self.assertTrue(np.allclose(result, expected)) def test_log10(self): a = mx.array([0.1, 1, 10, 20, 100]) result = mx.log10(a) expected = np.log10(a, dtype=np.float32) self.assertTrue(np.allclose(result, expected)) a = mx.array(1.0) + 1j * mx.array(2.0) result = mx.log10(a) expected = np.log10(np.array(a)) self.assertTrue(np.allclose(result, expected)) def test_exp(self): a = mx.array([0, 0.5, -0.5, 5]) result = mx.exp(a) expected = np.exp(a, dtype=np.float32) self.assertTrue(np.allclose(result, expected)) def test_expm1(self): a = mx.array([-88, -87, 0, 0.5, -0.5, 5, 87, 88, 89, 90]) result = mx.expm1(a) errs = np.seterr(over="ignore") expected = np.expm1(a) np.seterr(over=errs["over"]) self.assertTrue(np.allclose(result, expected, rtol=1e-3, atol=1e-4)) def test_erf(self): inputs = [-5, 0.0, 0.5, 1.0, 2.0, 10.0] x = mx.array(inputs) expected = np.array([math.erf(i) for i in inputs]) self.assertTrue(np.allclose(mx.erf(x), expected)) def test_erfinv(self): inputs = [-5.0, -1.0, 0.5, 0.0, 0.5, 1.0, 5.0] x = mx.array(inputs) # Output of: # scipy.special.erfinv([-5.0, -1.0, 0.5, 0.0, 0.5, 1.0, 5.0]) expected = np.array( [ float("nan"), -float("inf"), 0.47693628, 0.0, 0.47693628, float("inf"), float("nan"), ] ).astype(np.float32) self.assertTrue(np.allclose(mx.erfinv(x), expected, equal_nan=True)) result = mx.erfinv(mx.array([0.9999999403953552] * 8)) expected = mx.array([3.8325066566467285] * 8) self.assertTrue(mx.allclose(result, expected)) def test_sin(self): a = mx.array( [0, math.pi / 4, math.pi / 2, math.pi, 3 * math.pi / 4, 2 * math.pi] ) result = mx.sin(a) expected = np.sin(a, dtype=np.float32) self.assertTrue(np.allclose(result, expected)) def test_cos(self): a = mx.array( [0, math.pi / 4, math.pi / 2, math.pi, 3 * math.pi / 4, 2 * math.pi] ) result = mx.cos(a) expected = np.cos(a, dtype=np.float32) self.assertTrue(np.allclose(result, expected)) def test_degrees(self): a = mx.array( [0, math.pi / 4, math.pi / 2, math.pi, 3 * math.pi / 4, 2 * math.pi] ) result = mx.degrees(a) expected = np.degrees(a, dtype=np.float32) self.assertTrue(np.allclose(result, expected)) def test_radians(self): a = mx.array([0.0, 45.0, 90.0, 180.0, 270.0, 360.0]) result = mx.radians(a) expected = np.radians(a, dtype=np.float32) self.assertTrue(np.allclose(result, expected)) def test_log1p(self): a = mx.array([1, 0.5, 10, 100]) result = mx.log1p(a) expected = np.log1p(a, dtype=np.float32) self.assertTrue(np.allclose(result, expected)) # Complex test a = mx.array([1, 0.5, 10, 100]) + 1j result = mx.log1p(a) expected = np.log1p(a, dtype=np.complex64) self.assertTrue(np.allclose(result, expected)) def test_sigmoid(self): a = mx.array([0.0, 1.0, -1.0, 5.0, -5.0]) result = mx.sigmoid(a) expected = 1 / (1 + np.exp(-a, dtype=np.float32)) self.assertTrue(np.allclose(result, expected)) def test_allclose(self): a = mx.array(1.0) b = mx.array(1.0) self.assertTrue(mx.allclose(a, b).item()) b = mx.array(1.1) self.assertFalse(mx.allclose(a, b).item()) self.assertTrue(mx.allclose(a, b, 0.1).item()) self.assertFalse(mx.allclose(a, b, 0.01).item()) self.assertTrue(mx.allclose(a, b, 0.01, 0.1).item()) c = mx.array(float("inf")) self.assertTrue(mx.allclose(c, c).item()) def test_isclose(self): a = mx.array([float("inf"), float("inf"), float("-inf")]) b = mx.array([float("inf"), float("-inf"), float("-inf")]) self.assertListEqual(mx.isclose(a, b).tolist(), [True, False, True]) a = mx.array([np.nan]) self.assertListEqual(mx.isclose(a, a).tolist(), [False]) a = mx.array([np.nan]) self.assertListEqual(mx.isclose(a, a, equal_nan=True).tolist(), [True]) def test_all(self): a = mx.array([[True, False], [True, True]]) self.assertFalse(mx.all(a).item()) self.assertEqual(mx.all(a, keepdims=True).shape, (1, 1)) self.assertFalse(mx.all(a, axis=[0, 1]).item()) self.assertEqual(mx.all(a, axis=[0]).tolist(), [True, False]) self.assertEqual(mx.all(a, axis=[1]).tolist(), [False, True]) self.assertEqual(mx.all(a, axis=0).tolist(), [True, False]) self.assertEqual(mx.all(a, axis=1).tolist(), [False, True]) def test_any(self): a = mx.array([[True, False], [False, False]]) self.assertTrue(mx.any(a).item()) self.assertEqual(mx.any(a, keepdims=True).shape, (1, 1)) self.assertTrue(mx.any(a, axis=[0, 1]).item()) self.assertEqual(mx.any(a, axis=[0]).tolist(), [True, False]) self.assertEqual(mx.any(a, axis=[1]).tolist(), [True, False]) self.assertEqual(mx.any(a, axis=0).tolist(), [True, False]) self.assertEqual(mx.any(a, axis=1).tolist(), [True, False]) def test_stop_gradient(self): def func(x): return mx.sum(2 * x + mx.stop_gradient(3 * x)) x = mx.array([0.0, 0.1, -3]) expected = [2, 2, 2] self.assertListEqual(mx.grad(func)(x).tolist(), expected) def test_kron(self): # Basic vector test x = mx.array([1, 2]) y = mx.array([3, 4]) z = mx.kron(x, y) self.assertEqual(z.tolist(), [3, 4, 6, 8]) # Basic matrix test x = mx.array([[1, 2], [3, 4]]) y = mx.array([[0, 5], [6, 7]]) z = mx.kron(x, y) self.assertEqual( z.tolist(), [[0, 5, 0, 10], [6, 7, 12, 14], [0, 15, 0, 20], [18, 21, 24, 28]], ) # Test with different dimensions x = mx.array([1, 2]) # (2,) y = mx.array([[3, 4], [5, 6]]) # (2, 2) z = mx.kron(x, y) self.assertEqual(z.tolist(), [[3, 4, 6, 8], [5, 6, 10, 12]]) # Test with empty array x = mx.array([]) y = mx.array([1, 2]) with self.assertRaises(ValueError): mx.kron(x, y) def test_take(self): # Shape: 4 x 3 x 2 l = [ [[1, 3], [-2, -2], [-3, -2]], [[2, 4], [-3, 2], [-4, -2]], [[2, 3], [2, 4], [2, 1]], [[1, -5], [3, -1], [2, 3]], ] a = mx.array(l) a_npy = np.array(l) indices = [0, -1] flatten_take = mx.take(a, mx.array(indices)).tolist() flatten_take_expected = np.take(a_npy, np.array(indices)).tolist() self.assertListEqual(flatten_take, flatten_take_expected) indices = [-1, 2, 0] axis_take = mx.take(a, mx.array(indices), axis=0).tolist() axis_take_expected = np.take(a_npy, np.array(indices), axis=0).tolist() self.assertListEqual(axis_take, axis_take_expected) indices = [0, 0, -2] axis_take = mx.take(a, mx.array(indices), axis=1).tolist() axis_take_expected = np.take(a_npy, np.array(indices), axis=1).tolist() self.assertListEqual(axis_take, axis_take_expected) indices = [0, -1, -1] axis_take = mx.take(a, mx.array(indices), axis=-1).tolist() axis_take_expected = np.take(a_npy, np.array(indices), axis=-1).tolist() self.assertListEqual(axis_take, axis_take_expected) a_npy = np.arange(8 * 8 * 8, dtype=np.int32) a_npy = a_npy.reshape((8, 8, 8)) idx_npy = np.arange(6, dtype=np.uint32) idx_npy = idx_npy.reshape((2, 3)) a_mlx = mx.array(a_npy) idx_mlx = mx.array(idx_npy) a_npy_taken = np.take(a_npy, idx_npy) a_mlx_taken = mx.take(a_mlx, idx_mlx) self.assertEqual(a_npy_taken.shape, a_mlx_taken.shape) self.assertListEqual(a_npy_taken.tolist(), a_mlx_taken.tolist()) a_npy_taken = np.take(a_npy, idx_npy, axis=0) a_mlx_taken = mx.take(a_mlx, idx_mlx, axis=0) self.assertEqual(a_npy_taken.shape, a_mlx_taken.shape) self.assertListEqual(a_npy_taken.tolist(), a_mlx_taken.tolist()) a_npy_taken = np.take(a_npy, idx_npy, axis=1) a_mlx_taken = mx.take(a_mlx, idx_mlx, axis=1) self.assertEqual(a_npy_taken.shape, a_mlx_taken.shape) self.assertListEqual(a_npy_taken.tolist(), a_mlx_taken.tolist()) a_npy_taken = np.take(a_npy, idx_npy, axis=2) a_mlx_taken = mx.take(a_mlx, idx_mlx, axis=2) self.assertEqual(a_npy_taken.shape, a_mlx_taken.shape) self.assertListEqual(a_npy_taken.tolist(), a_mlx_taken.tolist()) # Take with integer index a = mx.arange(8).reshape(2, 4) out = mx.take(a, 1, axis=0) self.assertTrue(mx.array_equal(out, mx.array([4, 5, 6, 7]))) out = mx.take(a, 1, axis=1) self.assertTrue(mx.array_equal(out, mx.array([1, 5]))) # Take with multi-dim scalar preserves dims out = mx.take(a, mx.array(1), axis=0) self.assertEqual(out.shape, (4,)) out = mx.take(a, mx.array([1]), axis=0) self.assertEqual(out.shape, (1, 4)) out = mx.take(a, mx.array([[1]]), axis=0) self.assertEqual(out.shape, (1, 1, 4)) def test_take_along_axis(self): a_np = np.arange(8).reshape(2, 2, 2) a_mlx = mx.array(a_np) idx_np = np.array([1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0]) idx_mlx = mx.array(idx_np) for ax in [None, 0, 1, 2]: if ax == None: shape = [-1] else: shape = [2] * 3 shape[ax] = 3 out_np = np.take_along_axis(a_np, idx_np.reshape(shape), axis=ax) out_mlx = mx.take_along_axis(a_mlx, mx.reshape(idx_mlx, shape), axis=ax) self.assertTrue(np.array_equal(out_np, np.array(out_mlx))) def test_put_along_axis(self): for ax in [None, 0, 1, 2]: a_np = np.arange(16).reshape(2, 2, 4).astype(np.int32) a_mlx = mx.array(a_np) if ax == None: idx_np = np.random.permutation(a_np.size) values_np = np.random.randint(low=0, high=100, size=(16,)) else: shape = list(a_np.shape) shape[ax] = 2 idx_np = np.random.choice(a_np.shape[ax], replace=False, size=(2,)) idx_np = np.expand_dims(idx_np, list(range(1, 2 - ax + 1))) idx_np = np.broadcast_to(idx_np, shape) values_np = np.random.randint(low=0, high=100, size=shape) idx_np.astype(np.int32) values_np.astype(a_np.dtype) idx_mlx = mx.array(idx_np) values_mlx = mx.array(values_np) np.put_along_axis(a_np, idx_np, values_np, axis=ax) out_mlx = mx.put_along_axis(a_mlx, idx_mlx, values_mlx, axis=ax) self.assertTrue(np.array_equal(a_np, out_mlx)) source = mx.zeros((1, 1, 8, 32)) indices = mx.array([0, 2, 4, 5]).reshape((1, 1, 4, 1)) update = mx.array(1.0) out_mlx = mx.put_along_axis(source, indices, update, axis=-2) out_np = np.array(source) np.put_along_axis(out_np, np.array(indices), np.array(update), axis=-2) self.assertTrue(np.array_equal(out_np, np.array(out_mlx))) a = mx.array([], mx.float32) b = mx.put_along_axis(a, a, a, axis=None) mx.eval(b) self.assertEqual(b.size, 0) self.assertEqual(b.shape, a.shape) def test_split(self): a = mx.array([1, 2, 3]) splits = mx.split(a, 3) for e, x in enumerate(splits): self.assertEqual(x.item(), e + 1) a = mx.array([[1, 2], [3, 4], [5, 6]]) x, y, z = mx.split(a, 3, axis=0) self.assertEqual(x.tolist(), [[1, 2]]) self.assertEqual(y.tolist(), [[3, 4]]) self.assertEqual(z.tolist(), [[5, 6]]) with self.assertRaises(ValueError): mx.split(a, 3, axis=2) a = mx.arange(8) x, y, z = mx.split(a, [1, 5]) self.assertEqual(x.tolist(), [0]) self.assertEqual(y.tolist(), [1, 2, 3, 4]) self.assertEqual(z.tolist(), [5, 6, 7]) def test_arange_overload_dispatch(self): with self.assertRaises(ValueError): a = mx.arange(float("nan"), 1, 5) with self.assertRaises(ValueError): a = mx.arange(0, float("nan"), 5) with self.assertRaises(ValueError): a = mx.arange(0, 2, float("nan")) with self.assertRaises(ValueError): a = mx.arange(0, float("inf"), float("inf")) with self.assertRaises(ValueError): a = mx.arange(float("inf"), 1, float("inf")) with self.assertRaises(ValueError): a = mx.arange(float("inf"), 1, 5) with self.assertRaises(TypeError): INT_MAX = 2147483647 a = mx.arange(0, INT_MAX + 1, 1) a = mx.arange(5) expected = [0, 1, 2, 3, 4] self.assertListEqual(a.tolist(), expected) a = mx.arange(1, 5) expected = [1, 2, 3, 4] self.assertListEqual(a.tolist(), expected) a = mx.arange(-3, step=-1) expected = [0, -1, -2] self.assertListEqual(a.tolist(), expected) a = mx.arange(stop=2, step=0.5) expected = [0, 0.5, 1.0, 1.5] self.assertListEqual(a.tolist(), expected) with self.assertRaises(TypeError): mx.arange(start=1, step=2) a = mx.arange(stop=3) expected = [0, 1, 2] self.assertListEqual(a.tolist(), expected) def test_arange_inferred_dtype(self): a = mx.arange(5) self.assertEqual(a.dtype, mx.int32) a = mx.arange(5.0) self.assertEqual(a.dtype, mx.float32) a = mx.arange(1, 3.0) self.assertEqual(a.dtype, mx.float32) a = mx.arange(1, 3, dtype=mx.float32) self.assertEqual(a.dtype, mx.float32) a = mx.arange(1, 5, 1) self.assertEqual(a.dtype, mx.int32) a = mx.arange(1.0, 5, 1) self.assertEqual(a.dtype, mx.float32) a = mx.arange(1, 5.0, 1) self.assertEqual(a.dtype, mx.float32) a = mx.arange(1, 5, 1.0) self.assertEqual(a.dtype, mx.float32) a = mx.arange(1.0, 3.0, 0.2, dtype=mx.int32) self.assertEqual(a.dtype, mx.int32) def test_arange_corner_cases_cast(self): a = mx.arange(0, 3, 0.2, dtype=mx.int32) expected = [0] * 15 self.assertListEqual(a.tolist(), expected) self.assertEqual(a.dtype, mx.int32) a = mx.arange(-1, -4, -0.9, dtype=mx.int32) expected = [-1] * 4 self.assertListEqual(a.tolist(), expected) self.assertEqual(a.dtype, mx.int32) a = mx.arange(-1, -20, -1.2, dtype=mx.int32) expected = [ -1, -2, -3, -4, -5, -6, -7, -8, -9, -10, -11, -12, -13, -14, -15, -16, ] self.assertListEqual(a.tolist(), expected) self.assertEqual(a.dtype, mx.int32) a = mx.arange(0, 10, 100) expected = [0] self.assertListEqual(a.tolist(), expected) self.assertEqual(a.dtype, mx.int32) a = mx.arange(10, 0, 1) expected = [] self.assertListEqual(a.tolist(), expected) a = mx.arange(10, 0, float("inf")) expected = [] self.assertListEqual(a.tolist(), expected) a = mx.arange(0, 10, float("inf")) expected = [0] self.assertListEqual(a.tolist(), expected) a = mx.arange(0, -10, float("-inf")) expected = [0] self.assertListEqual(a.tolist(), expected) def test_unary_ops(self): def test_ops(npop, mlxop, x, y, atol): r_np = npop(x) r_mlx = mlxop(y) mx.eval(r_mlx) self.assertTrue(np.allclose(r_np, r_mlx, atol=atol)) x = np.random.rand(18, 28, 38) for op in ["abs", "exp", "log", "square", "sqrt"]: with self.subTest(op=op): float_dtypes = [("float16", 1e-3), ("float32", 1e-6)] for dtype, atol in float_dtypes: with self.subTest(dtype=dtype): x_ = x.astype(getattr(np, dtype)) y_ = mx.array(x_) test_ops(getattr(np, op), getattr(mx, op), x_, y_, atol) def test_unary_ops_from_non_array(self): unary_ops = [ "abs", "exp", "log", "square", "sqrt", "sin", "cos", "tan", "sinh", "cosh", "tanh", "sign", "negative", "expm1", "arcsin", "arccos", "arctan", "arcsinh", "arctanh", "degrees", "radians", "log2", "log10", "log1p", "floor", "ceil", "conjugate", ] x = 0.5 x_np = np.random.rand(10).astype(np.float32) for op in unary_ops: with self.subTest(op=op): # Test from scalar expected = getattr(np, op)(x) out = getattr(mx, op)(x) # Check close self.assertTrue(np.allclose(expected, out, equal_nan=True)) # Test from NumPy expected = getattr(np, op)(x_np) out = getattr(mx, op)(x_np) # Check close self.assertTrue(np.allclose(expected, np.array(out), equal_nan=True)) def test_trig_ops(self): def test_ops(npop, mlxop, x, y, atol): r_np = npop(x) r_mlx = mlxop(y) mx.eval(r_mlx) self.assertTrue(np.allclose(r_np, r_mlx, atol=atol)) x = np.random.rand(9, 12, 18) xi = np.random.rand(9, 12, 18) base_ops = ["sin", "cos", "tan"] hyperbolic_ops = ["sinh", "cosh", "tanh"] all_fwd_ops = base_ops + hyperbolic_ops for op in all_fwd_ops: with self.subTest(op=op): float_dtypes = [("float16", 1e-3), ("float32", 1e-6)] for dtype, atol in float_dtypes: with self.subTest(dtype=dtype): x_ = x.astype(getattr(np, dtype)) y_ = mx.array(x_) test_ops(getattr(np, op), getattr(mx, op), x_, y_, atol) with self.subTest(op=op): float_dtypes = [("complex64", 1e-5)] for dtype, atol in float_dtypes: with self.subTest(dtype=dtype): x_ = x + 1.0j * xi x_ = x_.astype(getattr(np, dtype)) y_ = mx.array(x_) test_ops(getattr(np, op), getattr(mx, op), x_, y_, atol) with self.subTest(op="arc" + op): float_dtypes = [("float16", 1e-3), ("float32", 1e-6)] op_inv = "arc" + op for dtype, atol in float_dtypes: with self.subTest(dtype=dtype): np_op_fwd = getattr(np, op) x_ = np_op_fwd(x).astype(getattr(np, dtype)) y_ = mx.array(x_) test_ops(getattr(np, op_inv), getattr(mx, op_inv), x_, y_, atol) # Test grads np_vjp_funcs = { "sin": lambda primal, cotan: cotan * np.cos(primal), "cos": lambda primal, cotan: -cotan * np.sin(primal), "tan": lambda primal, cotan: cotan / (np.cos(primal) ** 2), "sinh": lambda primal, cotan: cotan * np.cosh(primal), "cosh": lambda primal, cotan: cotan * np.sinh(primal), "tanh": lambda primal, cotan: cotan / (np.cosh(primal) ** 2), "arcsin": lambda primal, cotan: cotan / np.sqrt(1.0 - primal**2), "arccos": lambda primal, cotan: -cotan / np.sqrt(1.0 - primal**2), "arctan": lambda primal, cotan: cotan / (1.0 + primal**2), "arctan2": lambda primal, cotan: cotan / (1.0 + primal**2), "arcsinh": lambda primal, cotan: cotan / np.sqrt(primal**2 + 1), "arccosh": lambda primal, cotan: cotan / np.sqrt(primal**2 - 1), "arctanh": lambda primal, cotan: cotan / (1.0 - primal**2), } with self.subTest(name="grads"): for op in all_fwd_ops: with self.subTest(op=op): primal_np = xi.astype(np.float32) primal_mx = mx.array(primal_np) x_ = x.astype(np.float32) y_ = mx.array(x_) op_ = op atol_ = 1e-5 np_vjp = lambda x: np_vjp_funcs[op_](primal_np, x) mx_vjp = lambda x: mx.vjp(getattr(mx, op_), [primal_mx], [x])[1][0] test_ops(np_vjp, mx_vjp, x_, y_, atol_) with self.subTest(op="arc" + op): np_op_fwd = getattr(np, op) primal_np = np_op_fwd(xi).astype(np.float32) # To avoid divide by zero error if op == "cosh": primal_np[np.isclose(primal_np, 1.0)] += 1e-3 elif op == "cos": primal_np[np.isclose(primal_np, 1.0)] -= 1e-3 primal_mx = mx.array(primal_np) x_ = x.astype(np.float32) y_ = mx.array(x_) op_ = "arc" + op atol_ = 1e-5 np_vjp = lambda x: np_vjp_funcs[op_](primal_np, x) mx_vjp = lambda x: mx.vjp(getattr(mx, op_), [primal_mx], [x])[1][0] test_ops(np_vjp, mx_vjp, x_, y_, atol_) def test_binary_ops(self): def test_ops(npop, mlxop, x1, x2, y1, y2, atol): r_np = npop(x1, x2) r_mlx = mlxop(y1, y2) mx.eval(r_mlx) self.assertTrue(np.allclose(r_np, r_mlx, atol=atol)) r_np = npop(x1[:1], x2) r_mlx = mlxop(y1[:1], y2) mx.eval(r_mlx) self.assertTrue(np.allclose(r_np, r_mlx, atol=atol)) r_np = npop(x1[:, :1], x2) r_mlx = mlxop(y1[:, :1], y2) mx.eval(r_mlx) self.assertTrue(np.allclose(r_np, r_mlx, atol=atol)) r_np = npop(x1[:, :, :1], x2) r_mlx = mlxop(y1[:, :, :1], y2) mx.eval(r_mlx) self.assertTrue(np.allclose(r_np, r_mlx, atol=atol)) x1 = np.maximum(np.random.rand(18, 28, 38), 0.1) x2 = np.maximum(np.random.rand(18, 28, 38), 0.1) y1 = mx.array(x1) y2 = mx.array(x2) mx.eval(y1, y2) for op in [ "add", "subtract", "multiply", "divide", "floor_divide", "maximum", "minimum", "power", ]: with self.subTest(op=op): int_dtypes = [ "int8", "int16", "int32", "int64", "uint8", "uint16", "uint32", "uint64", ] float_dtypes = ["float16", "float32"] dtypes = { "divide": float_dtypes, "power": float_dtypes, "floor_divide": ["float32"] + int_dtypes, } dtypes = dtypes.get(op, int_dtypes + float_dtypes) for dtype in dtypes: atol = 1e-3 if dtype == "float16" else 1e-6 with self.subTest(dtype=dtype): m = 10 if dtype in int_dtypes else 1 x1_ = (x1 * m).astype(getattr(np, dtype)) x2_ = (x2 * m).astype(getattr(np, dtype)) y1_ = mx.array(x1_) y2_ = mx.array(x2_) test_ops( getattr(np, op), getattr(mx, op), x1_, x2_, y1_, y2_, atol ) def test_irregular_binary_ops(self): # Check transposed binary ops dims = [2, 3, 4, 5] size = 3 trial_mul = 2 np.random.seed(0) for d in dims: anp = np.random.randint(-20, 20, (size**d,)).reshape([size] * d) bnp = np.random.randint(-20, 20, (size**d,)).reshape([size] * d) for _ in range(trial_mul * d): amlx = mx.array(anp) bmlx = mx.array(bnp) a_t = np.random.permutation(d).tolist() b_t = np.random.permutation(d).tolist() outnp = np.add(anp.transpose(a_t), bnp.transpose(b_t)) outmlx = mx.add(mx.transpose(amlx, a_t), mx.transpose(bmlx, b_t)) self.assertTrue(np.array_equal(outnp, outmlx)) # Check broadcast binary ops for d in dims: anp = np.random.randint(-20, 20, (size**d,)).reshape([size] * d) for n_bsx in range(d): bnp = np.random.randint(-20, 20, (size**n_bsx,)).reshape([size] * n_bsx) for _ in range(trial_mul * d): amlx = mx.array(anp) bmlx = mx.array(bnp) b_shape = [1] * (d - n_bsx) + [size] * n_bsx np.random.shuffle(b_shape) outnp = np.add(anp, bnp.reshape(b_shape)) outmlx = mx.add(amlx, mx.reshape(bmlx, b_shape)) self.assertTrue(np.array_equal(outnp, outmlx)) # Check strided binary ops for d in dims: a = np.random.randint(-20, 20, (10,) * d) b = np.random.randint(-20, 20, (10,) * d) a_ = mx.array(a) b_ = mx.array(b) for t in permutations(range(d)): for s in range(d): idx = tuple( [slice(None)] * s + [slice(None, None, 2)] + [slice(None)] * (d - s - 1) ) c = a.transpose(t)[idx] + b[idx] c_ = mx.transpose(a_, t)[idx] + b_[idx] self.assertTrue(np.array_equal(c, c_)) def test_softmax(self): cases = [(np.float32, 1e-6), (np.float16, 1e-3)] for dtype, atol in cases: a_npy = np.random.randn(16, 8, 32).astype(dtype) a_mlx = mx.array(a_npy) def np_softmax(x, axis): ex = np.exp(x - np.max(x, axis=axis, keepdims=True)) return ex / np.sum(ex, axis=axis, keepdims=True) for axes in (None, 0, 1, 2, (0, 1), (1, 2), (0, 2), (0, 1, 2)): b_npy = np_softmax(a_npy, axes) b_mlx = mx.softmax(a_mlx, axes) self.assertTrue(np.allclose(b_npy, b_mlx, atol=atol)) for s in [100, 2049, 4097, 8193]: a = np.full(s, -np.inf) a[-1] = 0.0 a = mx.softmax(mx.array(a)) self.assertFalse(np.any(np.isnan(a))) self.assertTrue((a[:-1] < 1e-9).all()) self.assertEqual(a[-1], 1) # Sliced inputs y = mx.random.uniform(shape=(8, 4)) out = mx.softmax(y[:, 0:2], axis=-1) self.assertAlmostEqual(out.sum().item(), 8.0, 5) # Precise for t in [mx.float16, mx.bfloat16]: a = (10 * mx.random.normal(shape=(1024,))).astype(t) out_expect = mx.softmax(a.astype(mx.float32)).astype(t) out = mx.softmax(a, axis=-1, precise=True) self.assertTrue(mx.allclose(out_expect, out)) # All Infs give NaNs for n in [127, 128, 129]: x = mx.full((n,), vals=-float("inf")) self.assertTrue(mx.all(mx.isnan(mx.softmax(x)))) # Transposed inputs a = mx.random.uniform(shape=(32, 32, 32)) b = mx.softmax(a, axis=-1) c = mx.softmax(a.swapaxes(0, 1), axis=-1).swapaxes(0, 1) self.assertEqual((b - c).abs().max().item(), 0.0) with self.assertRaises(ValueError): mx.softmax(mx.array(1.0), axis=-1) def test_concatenate(self): a_npy = np.random.randn(32, 32, 32) b_npy = np.random.randn(32, 32, 32) a_mlx = mx.array(a_npy) b_mlx = mx.array(b_npy) for axis in (None, 0, 1, 2): for p in permutations([0, 1, 2]): c_npy = np.concatenate([a_npy, np.transpose(b_npy, p)], axis=axis) c_mlx = mx.concatenate([a_mlx, mx.transpose(b_mlx, p)], axis=axis) self.assertEqual(list(c_npy.shape), list(c_mlx.shape)) self.assertTrue(np.allclose(c_npy, c_mlx, atol=1e-6)) with self.assertRaises(ValueError): a = mx.array([[1, 2], [1, 2], [1, 2]]) b = mx.array([1, 2]) mx.concatenate([a, b], axis=0) # Cocnatenate with 0-sized array a = mx.zeros((2, 0, 2)) b = mx.zeros((2, 2, 2)) out = mx.concatenate([a, b], axis=1) self.assertTrue(mx.array_equal(out, b)) def test_meshgrid(self): x = mx.array([1, 2, 3], dtype=mx.int32) y = np.array([1, 2, 3], dtype=np.int32) # Test single input a_mlx = mx.meshgrid(x) a_np = np.meshgrid(y) self.assertEqualArray(a_mlx[0], mx.array(a_np[0])) # Test sparse a_mlx, b_mlx, c_mlx = mx.meshgrid(x, x, x, sparse=True) a_np, b_np, c_np = np.meshgrid(y, y, y, sparse=True) self.assertEqualArray(a_mlx, mx.array(a_np)) self.assertEqualArray(b_mlx, mx.array(b_np)) self.assertEqualArray(c_mlx, mx.array(c_np)) # Test different lengths x = mx.array([1, 2], dtype=mx.int32) y = mx.array([1, 2, 3], dtype=mx.int32) z = np.array([1, 2], dtype=np.int32) w = np.array([1, 2, 3], dtype=np.int32) a_mlx, b_mlx = mx.meshgrid(x, y) a_np, b_np = np.meshgrid(z, w) self.assertEqualArray(a_mlx, mx.array(a_np)) self.assertEqualArray(b_mlx, mx.array(b_np)) # Test empty input x = mx.array([], dtype=mx.int32) y = np.array([], dtype=np.int32) a_mlx = mx.meshgrid(x) a_np = np.meshgrid(y) self.assertEqualArray(a_mlx[0], mx.array(a_np[0])) # Test float32 input x = mx.array([1.1, 2.2, 3.3], dtype=mx.float32) y = np.array([1.1, 2.2, 3.3], dtype=np.float32) a_mlx = mx.meshgrid(x, x, x) a_np = np.meshgrid(y, y, y) self.assertEqualArray(a_mlx[0], mx.array(a_np[0])) self.assertEqualArray(a_mlx[1], mx.array(a_np[1])) self.assertEqualArray(a_mlx[2], mx.array(a_np[2])) # Test ij indexing x = mx.array([1.1, 2.2, 3.3, 4.4, 5.5], dtype=mx.float32) y = np.array([1.1, 2.2, 3.3, 4.4, 5.5], dtype=np.float32) a_mlx = mx.meshgrid(x, x, indexing="ij") a_np = np.meshgrid(y, y, indexing="ij") self.assertEqualArray(a_mlx[0], mx.array(a_np[0])) self.assertEqualArray(a_mlx[1], mx.array(a_np[1])) # Test different lengths, sparse, and ij indexing a = mx.array([1, 2], dtype=mx.int64) b = mx.array([1, 2, 3], dtype=mx.int64) c = mx.array([1, 2, 3, 4], dtype=mx.int64) x = np.array([1, 2], dtype=np.int64) y = np.array([1, 2, 3], dtype=np.int64) z = np.array([1, 2, 3, 4], dtype=np.int64) a_mlx, b_mlx, c_mlx = mx.meshgrid(a, b, c, sparse=True, indexing="ij") a_np, b_np, c_np = np.meshgrid(x, y, z, sparse=True, indexing="ij") self.assertEqualArray(a_mlx, mx.array(a_np)) self.assertEqualArray(b_mlx, mx.array(b_np)) self.assertEqualArray(c_mlx, mx.array(c_np)) def test_pad(self): pad_width_and_values = [ ([(1, 1), (1, 1), (1, 1)], 0), ([(1, 1), (1, 1), (1, 1)], 5), ([(3, 0), (0, 2), (5, 7)], 0), ([(3, 0), (0, 2), (5, 7)], -7), ([(0, 0), (0, 0), (0, 0)], 0), ] for pw, v in pad_width_and_values: with self.subTest(pad_width=pw, value=v): a_npy = np.random.randn(16, 16, 16).astype(np.float32) a_mlx = mx.array(a_npy) b_npy = np.pad(a_npy, pw, constant_values=v) b_mlx = mx.pad(a_mlx, pw, constant_values=v) self.assertEqual(list(b_npy.shape), list(b_mlx.shape)) self.assertTrue(np.allclose(b_npy, b_mlx, atol=1e-6)) b_npy = np.pad(a_npy, pw, mode="edge") b_mlx = mx.pad(a_mlx, pw, mode="edge") self.assertEqual(list(b_npy.shape), list(b_mlx.shape)) self.assertTrue(np.allclose(b_npy, b_mlx, atol=1e-6)) a = mx.zeros((1, 1, 1)) self.assertEqual(mx.pad(a, 1).shape, (3, 3, 3)) self.assertEqual(mx.pad(a, (1,)).shape, (3, 3, 3)) self.assertEqual(mx.pad(a, [1]).shape, (3, 3, 3)) self.assertEqual(mx.pad(a, (1, 2)).shape, (4, 4, 4)) self.assertEqual(mx.pad(a, [(1, 2)]).shape, (4, 4, 4)) self.assertEqual(mx.pad(a, ((1, 2),)).shape, (4, 4, 4)) self.assertEqual(mx.pad(a, ((1, 2), (2, 1), (2, 2))).shape, (4, 4, 5)) # Test grads a_fwd = mx.array(np.random.rand(16, 16).astype(np.float32)) a_bwd = mx.ones((22, 22)) f = lambda x: mx.pad(x, ((4, 2), (2, 4))) _, df = mx.vjp(f, [a_fwd], [a_bwd]) self.assertTrue(mx.allclose(a_bwd[4:-2, 2:-4], df[0]).item()) def test_where(self): self.assertCmpNumpy([True, mx.array([[1, 2], [3, 4]]), 1], mx.where, np.where) self.assertCmpNumpy([True, 1, mx.array([[1, 2], [3, 4]])], mx.where, np.where) self.assertCmpNumpy( [ mx.array([[True, False], [False, True]]), mx.array([[1, 2], [3, 4]]), mx.array([5, 6]), ], mx.where, np.where, ) # Check non-contiguous input with several dimensions shape = [1, 2, 2, 3, 3, 1] strides = [16, 4, 1, 4, 1, 1] x = mx.ones(shape=(1, 4, 4, 1)) x = mx.as_strided(x, shape, strides) out = mx.where(mx.isnan(x), mx.nan, x) self.assertTrue(mx.allclose(out, mx.ones_like(out))) def test_nan_to_num(self): a = mx.array([6, float("inf"), 2, 0]) out_mx = mx.nan_to_num(a) out_np = np.nan_to_num(a) self.assertTrue(np.allclose(out_mx, out_np)) for t in [mx.float32, mx.float16]: a = mx.array([float("inf"), 6.9, float("nan"), float("-inf")]) out_mx = mx.nan_to_num(a) out_np = np.nan_to_num(a) self.assertTrue(np.allclose(out_mx, out_np)) a = mx.array([float("inf"), 6.9, float("nan"), float("-inf")]).astype(t) out_np = np.nan_to_num(a, nan=0.0, posinf=1000, neginf=-1000) out_mx = mx.nan_to_num(a, nan=0.0, posinf=1000, neginf=-1000) self.assertTrue(np.allclose(out_mx, out_np)) def test_as_strided(self): x_npy = np.random.randn(128).astype(np.float32) x_mlx = mx.array(x_npy) shapes = [(10, 10), (5, 5), (2, 20), (10,)] strides = [(3, 3), (7, 1), (1, 5), (4,)] for shape, stride in zip(shapes, strides): for offset in [0, 1, 3]: y_npy = np.lib.stride_tricks.as_strided( x_npy[offset:], shape, np.multiply(stride, 4) ) y_mlx = mx.as_strided(x_mlx, shape, stride, offset) self.assertTrue(np.array_equal(y_npy, y_mlx)) x = mx.random.uniform(shape=(32,)) y = mx.as_strided(x, (x.size,), (-1,), x.size - 1) self.assertTrue(mx.array_equal(y, x[::-1])) def test_logcumsumexp(self): npop = np.logaddexp.accumulate mxop = mx.logcumsumexp a_npy = np.random.randn(32, 32, 32).astype(np.float32) a_mlx = mx.array(a_npy) for axis in (0, 1, 2): c_npy = npop(a_npy, axis=axis) c_mlx = mxop(a_mlx, axis=axis) self.assertTrue(np.allclose(c_npy, c_mlx, rtol=1e-3, atol=1e-3)) edge_cases_npy = [ np.float32([-float("inf")] * 8), np.float32([-float("inf"), 0, -float("inf")]), np.float32([-float("inf"), float("inf"), -float("inf")]), ] edge_cases_mlx = [mx.array(a) for a in edge_cases_npy] for a_npy, a_mlx in zip(edge_cases_npy, edge_cases_mlx): c_npy = npop(a_npy, axis=0) c_mlx = mxop(a_mlx, axis=0) self.assertTrue(np.allclose(c_npy, c_mlx, rtol=1e-3, atol=1e-3)) # Complex tests a_npy = np.array([1, 2, 3]).astype(np.float32) + 1j a_mlx = mx.array(a_npy) c_npy = np_cumlogaddexp(a_npy, axis=-1) c_mlx = mxop(a_mlx, axis=-1) self.assertTrue(np.allclose(c_npy, c_mlx, rtol=1e-3, atol=1e-3)) def test_scans(self): a_npy = np.random.randn(32, 32, 32).astype(np.float32) a_mlx = mx.array(a_npy) for op in ["cumsum", "cumprod"]: npop = getattr(np, op) mxop = getattr(mx, op) for axis in (None, 0, 1, 2): c_npy = npop(a_npy, axis=axis) c_mlx = mxop(a_mlx, axis=axis) self.assertTrue(np.allclose(c_npy, c_mlx, rtol=1e-3, atol=1e-3)) # Complex test a_npy = np.random.randn(32, 32, 32).astype(np.float32) + 0.5j a_mlx = mx.array(a_npy) for op in ["cumsum", "cumprod"]: npop = getattr(np, op) mxop = getattr(mx, op) for axis in (None, 0, 1, 2): c_npy = npop(a_npy, axis=axis) c_mlx = mxop(a_mlx, axis=axis) self.assertTrue(np.allclose(c_npy, c_mlx, rtol=1e-3, atol=1e-3)) a_mlx = mx.random.randint(shape=(32, 32, 32), low=-100, high=100) for dt in [mx.int32, mx.int64]: mxx = a_mlx.astype(dt) npx = np.array(mxx) for op in ["cumsum", "cumprod"]: npop = getattr(np, op) mxop = getattr(mx, op) for axis in (None, 0, 1, 2): c_npy = npop(npx, axis=axis, dtype=npx.dtype) c_mlx = mxop(mxx, axis=axis) self.assertTrue(np.array_equal(c_npy, c_mlx)) a_mlx = mx.random.randint(shape=(32, 32, 32), low=-100, high=100) for op in ["cumsum", "cumprod", "cummax", "cummin"]: mxop = getattr(mx, op) c1 = mxop(a_mlx, axis=2) c2 = mxop(a_mlx, axis=2, inclusive=False, reverse=False) self.assertTrue(mx.array_equal(c1[:, :, :-1], c2[:, :, 1:])) c1 = mxop(a_mlx, axis=1) c2 = mxop(a_mlx, axis=1, inclusive=False, reverse=False) self.assertTrue(mx.array_equal(c1[:, :-1, :], c2[:, 1:, :])) c1 = mxop(a_mlx, axis=0) c2 = mxop(a_mlx, axis=0, inclusive=False, reverse=False) self.assertTrue(mx.array_equal(c1[:-1, :, :], c2[1:, :, :])) rev_idx = mx.arange(31, -1, -1) c1 = mxop(a_mlx[:, :, rev_idx], axis=2)[:, :, rev_idx] c2 = mxop(a_mlx, axis=2, inclusive=True, reverse=True) self.assertTrue(mx.array_equal(c1, c2)) c1 = mxop(a_mlx[:, rev_idx, :], axis=1)[:, rev_idx, :] c2 = mxop(a_mlx, axis=1, inclusive=True, reverse=True) self.assertTrue(mx.array_equal(c1, c2)) c1 = mxop(a_mlx[rev_idx, :, :], axis=0)[rev_idx, :, :] c2 = mxop(a_mlx, axis=0, inclusive=True, reverse=True) self.assertTrue(mx.array_equal(c1, c2)) rev_idx = mx.arange(31, -1, -1) c1 = mxop(a_mlx[:, :, rev_idx], axis=2)[:, :, rev_idx][:, :, 1:] c2 = mxop(a_mlx, axis=2, inclusive=False, reverse=True)[:, :, :-1] self.assertTrue(mx.array_equal(c1, c2)) c1 = mxop(a_mlx[:, rev_idx, :], axis=1)[:, rev_idx, :][:, 1:, :] c2 = mxop(a_mlx, axis=1, inclusive=False, reverse=True)[:, :-1, :] self.assertTrue(mx.array_equal(c1, c2)) c1 = mxop(a_mlx[rev_idx, :, :], axis=0)[rev_idx, :, :][1:, :, :] c2 = mxop(a_mlx, axis=0, inclusive=False, reverse=True)[:-1, :, :] self.assertTrue(mx.array_equal(c1, c2)) a = mx.random.uniform(shape=(8, 32)) mat = mx.tri(32) for t in [mx.float16, mx.bfloat16]: a_t = a.astype(t) mat_t = mat.astype(t) out = mx.cumsum(a_t, axis=-1) expected = (mat_t * a_t[:, None, :]).sum(axis=-1) self.assertTrue(mx.allclose(out, expected, rtol=0.02, atol=1e-3)) sizes = [1023, 1024, 1025, 2047, 2048, 2049] for s in sizes: a = mx.ones((s,), mx.int32) out = mx.cumsum(a) expected = mx.arange(1, s + 1, dtype=mx.int32) self.assertTrue(mx.array_equal(expected, out)) # non-contiguous scan a = mx.ones((s, 2), mx.int32) out = mx.cumsum(a, axis=0) expected = mx.repeat(expected[:, None], 2, axis=1) self.assertTrue(mx.array_equal(expected, out)) # Test donation def fn(its): x = mx.ones((32,)) for _ in range(its): x = mx.cumsum(x) return x mx.synchronize() mx.eval(fn(2)) mx.synchronize() mem2 = mx.get_peak_memory() mx.eval(fn(4)) mx.synchronize() mem4 = mx.get_peak_memory() self.assertEqual(mem2, mem4) def test_squeeze_expand(self): a = mx.zeros((2, 1, 2, 1)) self.assertEqual(mx.squeeze(a).shape, (2, 2)) self.assertEqual(mx.squeeze(a, 1).shape, (2, 2, 1)) self.assertEqual(mx.squeeze(a, [1, 3]).shape, (2, 2)) self.assertEqual(a.squeeze().shape, (2, 2)) self.assertEqual(a.squeeze(1).shape, (2, 2, 1)) self.assertEqual(a.squeeze([1, 3]).shape, (2, 2)) a = mx.zeros((2, 2)) self.assertEqual(mx.squeeze(a).shape, (2, 2)) self.assertEqual(mx.expand_dims(a, 0).shape, (1, 2, 2)) self.assertEqual(mx.expand_dims(a, (0, 1)).shape, (1, 1, 2, 2)) self.assertEqual(mx.expand_dims(a, [0, -1]).shape, (1, 2, 2, 1)) def test_sort(self): shape = (6, 4, 10) tests = product( ("int32", "float32"), # type (None, 0, 1, 2), # axis (True, False), # strided ) for dtype, axis, strided in tests: with self.subTest(dtype=dtype, axis=axis, strided=strided): np.random.seed(0) np_dtype = getattr(np, dtype) a_np = np.random.uniform(0, 100, size=shape).astype(np_dtype) a_mx = mx.array(a_np) if strided: a_mx = a_mx[::2, :, ::2] a_np = a_np[::2, :, ::2] b_np = np.sort(a_np, axis=axis) b_mx = mx.sort(a_mx, axis=axis) self.assertTrue(np.array_equal(b_np, b_mx)) self.assertEqual(b_mx.dtype, a_mx.dtype) c_np = np.argsort(a_np, axis=axis) c_mx = mx.argsort(a_mx, axis=axis) d_np = np.take_along_axis(a_np, c_np, axis=axis) d_mx = mx.take_along_axis(a_mx, c_mx, axis=axis) self.assertTrue(np.array_equal(d_np, d_mx)) self.assertEqual(c_mx.dtype, mx.uint32) # Set random seed np.random.seed(0) # Test multi-block sort for strided in (False, True): with self.subTest(strided=strided): a_np = np.random.normal(size=(32769,)).astype(np.float32) a_mx = mx.array(a_np) if strided: a_mx = a_mx[::3] a_np = a_np[::3] b_np = np.sort(a_np) b_mx = mx.sort(a_mx) self.assertTrue(np.array_equal(b_np, b_mx)) self.assertEqual(b_mx.dtype, a_mx.dtype) # Test multi-dum multi-block sort a_np = np.random.normal(size=(2, 4, 32769)).astype(np.float32) a_mx = mx.array(a_np) if strided: a_mx = a_mx[..., ::3] a_np = a_np[..., ::3] b_np = np.sort(a_np, axis=-1) b_mx = mx.sort(a_mx, axis=-1) self.assertTrue(np.array_equal(b_np, b_mx)) self.assertEqual(b_mx.dtype, a_mx.dtype) a_np = np.random.normal(size=(2, 32769, 4)).astype(np.float32) a_mx = mx.array(a_np) if strided: a_mx = a_mx[:, ::3] a_np = a_np[:, ::3] b_np = np.sort(a_np, axis=1) b_mx = mx.sort(a_mx, axis=1) self.assertTrue(np.array_equal(b_np, b_mx)) self.assertEqual(b_mx.dtype, a_mx.dtype) # test 0 strides a_np = np.array([1, 0, 2, 1, 3, 0, 4, 0]) a_mx = mx.array(a_np) b_np = np.broadcast_to(a_np, (16, 8)) b_mx = mx.broadcast_to(a_mx, (16, 8)) mx.eval(b_mx) for axis in (0, 1): c_np = np.sort(b_np, axis=axis) c_mx = mx.sort(b_mx, axis=axis) self.assertTrue(np.array_equal(c_np, c_mx)) self.assertEqual(b_mx.dtype, c_mx.dtype) # Test very large array if mx.default_device() == mx.gpu: a_np = np.random.normal(20, 20, size=(2**22)).astype(np.float32) a_mx = mx.array(a_np) b_np = np.sort(a_np) b_mx = mx.sort(a_mx) self.assertTrue(np.array_equal(b_np, b_mx)) # 1D strided sort a = mx.array([[4, 3], [2, 1], [5, 4], [3, 2]]) out = mx.argsort(a[:, 1]) expected = mx.array([1, 3, 0, 2], dtype=mx.uint32) self.assertTrue(mx.array_equal(out, expected)) # Test array with singleton dim out = mx.sort(mx.array([1, 2, 3]), axis=0) self.assertTrue(mx.array_equal(out, mx.array([1, 2, 3]))) x = np.random.uniform(size=(1, 4, 8, 1)).astype(np.float32) y_np = np.sort(x, axis=-2) y_mx = mx.sort(mx.array(x), axis=-2) self.assertTrue(np.array_equal(y_np, y_mx)) def test_partition(self): shape = (3, 4, 5) for dtype in ("int32", "float32"): for axis in (None, 0, 1, 2): for kth in (-2, 0, 2): with self.subTest(dtype=dtype, axis=axis, kth=kth): np.random.seed(0) np_dtype = getattr(np, dtype) a_np = np.random.uniform(0, 100, size=shape).astype(np_dtype) a_mx = mx.array(a_np) b_np = np.partition(a_np, kth, axis=axis) b_mx = mx.partition(a_mx, kth, axis=axis) c_np = np.take(b_np, (kth,), axis=axis) c_mx = np.take(np.array(b_mx), (kth,), axis=axis) self.assertTrue(np.array_equal(c_np, c_mx)) self.assertEqual(b_mx.dtype, a_mx.dtype) if kth >= 0: top_k_mx = mx.topk(a_mx, kth, axis=axis) top_k_np = np.take( np.partition(a_np, -kth, axis=axis), (-kth,), axis=axis ) self.assertTrue(np.all(top_k_np <= top_k_mx)) self.assertEqual(top_k_mx.dtype, a_mx.dtype) N = a_mx.shape[axis] if axis is not None else a_mx.size M = top_k_mx.shape[axis or 0] self.assertEqual(M, (kth + N) % N) def test_argpartition(self): x = mx.broadcast_to(mx.array([1, 2, 3]), (2, 3)) out = mx.argpartition(x, kth=1, axis=0) expected = mx.array([[0, 0, 0], [1, 1, 1]]) self.assertTrue(mx.array_equal(out, expected)) x = mx.array([[1, 2], [3, 4]]).T out = mx.argpartition(x, kth=1, axis=0) expected = mx.array([[0, 0], [1, 1]]) self.assertTrue(mx.array_equal(out, expected)) @unittest.skipIf( os.getenv("LOW_MEMORY", None) is not None, "This test requires a lot of memory", ) def test_large_binary(self): a = mx.ones([1000, 2147484], mx.int8) b = mx.ones([2147484], mx.int8) self.assertEqual((a + b)[0, 0].item(), 2) def test_eye(self): self.assertCmpNumpy([3], mx.eye, np.eye) # Test for non-square matrix self.assertCmpNumpy([3, 4], mx.eye, np.eye) # Test with positive k parameter self.assertCmpNumpy([3, 4], mx.eye, np.eye, k=1) # Test with negative k parameter self.assertCmpNumpy([5, 6], mx.eye, np.eye, k=-2) def test_stack(self): a = mx.ones((2,)) np_a = np.ones((2,)) b = mx.ones((2,)) np_b = np.ones((2,)) # One dimensional stack axis=0 c = mx.stack([a, b]) np_c = np.stack([np_a, np_b]) self.assertTrue(np.array_equal(c, np_c)) # One dimensional stack axis=1 c = mx.stack([a, b], axis=1) np_c = np.stack([np_a, np_b], axis=1) self.assertTrue(np.array_equal(c, np_c)) a = mx.ones((1, 2)) np_a = np.ones((1, 2)) b = mx.ones((1, 2)) np_b = np.ones((1, 2)) # Two dimensional stack axis=0 c = mx.stack([a, b]) np_c = np.stack([np_a, np_b]) self.assertTrue(np.array_equal(c, np_c)) # Two dimensional stack axis=1 c = mx.stack([a, b], axis=1) np_c = np.stack([np_a, np_b], axis=1) self.assertTrue(np.array_equal(c, np_c)) def test_flatten(self): x = mx.zeros([2, 3, 4]) self.assertEqual(mx.flatten(x).shape, (2 * 3 * 4,)) self.assertEqual(mx.flatten(x, start_axis=1).shape, (2, 3 * 4)) self.assertEqual(mx.flatten(x, end_axis=1).shape, (2 * 3, 4)) self.assertEqual(x.flatten().shape, (2 * 3 * 4,)) self.assertEqual(x.flatten(start_axis=1).shape, (2, 3 * 4)) self.assertEqual(x.flatten(end_axis=1).shape, (2 * 3, 4)) def test_clip(self): a = np.array([1, 4, 3, 8, 5], np.int32) expected = np.clip(a, 2, 6) clipped = mx.clip(mx.array(a), 2, 6) self.assertTrue(np.array_equal(clipped, expected)) a = np.array([-1, 1, 0, 5], np.int32) expected = np.clip(a, 0, None) clipped = mx.clip(mx.array(a), 0, None) self.assertTrue(np.array_equal(clipped, expected)) a = np.array([2, 3, 4, 5], np.int32) expected = np.clip(a, None, 4) clipped = mx.clip(mx.array(a), None, 4) self.assertTrue(np.array_equal(clipped, expected)) mins = np.array([3, 1, 5, 5]) a = np.array([2, 3, 4, 5], np.int32) expected = np.clip(a, mins, 4) clipped = mx.clip(mx.array(a), mx.array(mins), 4) self.assertTrue(np.array_equal(clipped, expected)) maxs = np.array([5, -1, 2, 9]) a = np.array([2, 3, 4, 5], np.int32) expected = np.clip(a, mins, maxs) clipped = mx.clip(mx.array(a), mx.array(mins), mx.array(maxs)) self.assertTrue(np.array_equal(clipped, expected)) # Check clip output types a = mx.array([1, 2, 3], mx.int16) out_t = mx.clip(a, a_min=0, a_max=5).dtype self.assertEqual(out_t, mx.int16) out_t = mx.clip(a, a_min=0.0, a_max=5).dtype self.assertEqual(out_t, mx.float32) a = mx.array([1, 2, 3], mx.float16) out_t = mx.clip(a, a_min=0.0, a_max=5).dtype self.assertEqual(out_t, mx.float16) a = mx.array([1, 2, 3], mx.float16) out_t = mx.clip(a, a_min=0.0, a_max=mx.array(1.0)).dtype self.assertEqual(out_t, mx.float32) def test_linspace(self): # Test default num = 50 a = mx.linspace(0, 1) expected = mx.array(np.linspace(0, 1)) self.assertEqualArray(a, expected) # Test int64 dtype b = mx.linspace(0, 10, 5, mx.int64) expected = mx.array(np.linspace(0, 10, 5, dtype=int)) self.assertEqualArray(b, expected) # Test negative sequence with float start and stop c = mx.linspace(-2.7, -0.7, 7) expected = mx.array(np.linspace(-2.7, -0.7, 7)) self.assertEqualArray(c, expected) # Test irrational step size of 1/9 d = mx.linspace(0, 1, 10) expected = mx.array(np.linspace(0, 1, 10)) self.assertEqualArray(d, expected) # Test num equal to 1 d = mx.linspace(1, 10, 1) expected = mx.array(np.linspace(1, 10, 1)) self.assertEqualArray(d, expected) # Ensure that the start and stop are always the ones provided ranges = mx.random.normal((16, 2)).tolist() nums = (2 + mx.random.uniform(shape=(16,)) * 10).astype(mx.uint32).tolist() for (a, b), n in zip(ranges, nums): d = mx.linspace(a, b, n).tolist() self.assertEqual(d[0], a) self.assertEqual(d[-1], b) def test_repeat(self): # Setup data for the tests data = mx.array([[[13, 3], [16, 6]], [[14, 4], [15, 5]], [[11, 1], [12, 2]]]) # Test repeat 0 times self.assertCmpNumpy([data, 0], mx.repeat, np.repeat) # Test repeat along axis 0 self.assertCmpNumpy([data, 2], mx.repeat, np.repeat, axis=0) # Test repeat along axis 1 self.assertCmpNumpy([data, 2], mx.repeat, np.repeat, axis=1) # Test repeat along the last axis (default) self.assertCmpNumpy([data, 2], mx.repeat, np.repeat) # Test repeat with a 1D array along axis 0 self.assertCmpNumpy([mx.array([1, 3, 2]), 3], mx.repeat, np.repeat, axis=0) # Test repeat with a 2D array along axis 0 self.assertCmpNumpy( [mx.array([[1, 2, 3], [4, 5, 4], [0, 1, 2]]), 2], mx.repeat, np.repeat, axis=0, ) def test_tensordot(self): # No fp16 matmuls on common cpu backend if not self.is_apple_silicon: dtypes = [mx.float32] else: dtypes = [mx.float16, mx.float32] for dtype in dtypes: with self.subTest(dtype=dtype): self.assertCmpNumpy( [(3, 4, 5), (4, 3, 2)], mx.tensordot, np.tensordot, dtype=dtype, axes=([1, 0], [0, 1]), ) self.assertCmpNumpy( [(3, 4, 5), (4, 5, 6)], mx.tensordot, np.tensordot, dtype=dtype, axes=2, ) self.assertCmpNumpy( [(3, 5, 4, 6), (6, 4, 5, 3)], mx.tensordot, np.tensordot, dtype=dtype, axes=([2, 1, 3], [1, 2, 0]), ) def test_inner(self): self.assertCmpNumpy([(3,), (3,)], mx.inner, np.inner) self.assertCmpNumpy([(1, 1, 2), (3, 2)], mx.inner, np.inner) self.assertCmpNumpy([(2, 3, 4), (4,)], mx.inner, np.inner) def test_outer(self): self.assertCmpNumpy([(3,), (3,)], mx.outer, np.outer) self.assertCmpNumpy( [ mx.ones( 5, ), mx.linspace(-2, 2, 5), ], mx.outer, np.outer, ) self.assertCmpNumpy( [ 1j * mx.linspace(2, -2, 5), mx.ones( 5, ), ], mx.outer, np.outer, ) def test_divmod(self): # A few sizes for the inputs with and without broadcasting sizes = [ ((1,), (1,)), ((1,), (10,)), ((10,), (1,)), ((3,), (3,)), ((2, 2, 2), (1, 2, 1)), ((2, 1, 2), (1, 2, 1)), ((2, 2, 2, 2), (2, 2, 2, 2)), ] types = [np.uint16, np.uint32, np.int32, np.float16, np.float32] for s1, s2 in sizes: for t in types: a_np = np.random.uniform(1, 100, size=s1).astype(t) b_np = np.random.uniform(1, 100, size=s2).astype(t) np_out = np.divmod(a_np, b_np) mx_out = mx.divmod(mx.array(a_np), mx.array(b_np)) self.assertTrue( np.allclose(np_out[0], mx_out[0]), msg=f"Shapes {s1} {s2}, Type {t}" ) def test_tile(self): self.assertCmpNumpy([(2,), [2]], mx.tile, np.tile) self.assertCmpNumpy([(2, 3, 4), [2]], mx.tile, np.tile) self.assertCmpNumpy([(2, 3, 4), [2, 1]], mx.tile, np.tile) self.assertCmpNumpy( [ (2, 3, 4), [ 2, 2, ], ], mx.tile, np.tile, ) self.assertCmpNumpy([(3,), [2, 2, 2]], mx.tile, np.tile) def test_empty_matmuls(self): a = mx.array([]) b = mx.array([]) self.assertEqual(mx.inner(a, b).item(), 0.0) a = mx.zeros((10, 0)) b = mx.zeros((0, 10)) out = a @ b self.assertTrue(mx.array_equal(out, mx.zeros((10, 10)))) def test_diagonal(self): x = mx.array( [ [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]], [[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]], ] ) expected = [[0, 13], [4, 17], [8, 21]] self.assertListEqual(mx.diagonal(x, 0, -1, 0).tolist(), expected) expected = [[1, 14], [5, 18], [9, 22]] self.assertListEqual(mx.diagonal(x, -1, 2, 0).tolist(), expected) def test_diag(self): # Test 1D input x = mx.array([1, 2, 3, 4]) expected = mx.array([[1, 0, 0, 0], [0, 2, 0, 0], [0, 0, 3, 0], [0, 0, 0, 4]]) result = mx.diag(x) self.assertTrue(mx.array_equal(result, expected)) # Test 1D with offset x = mx.array([2, 6]) result = mx.diag(x, k=5) expected = mx.array(np.diag(x, k=5)) self.assertTrue(mx.array_equal(result, expected)) # Test 2D input x = mx.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) expected = mx.array([1, 5, 9]) result = mx.diag(x) self.assertTrue(mx.array_equal(result, expected)) # Test with offset expected = mx.array([2, 6]) result = mx.diag(x, 1) self.assertTrue(mx.array_equal(result, expected)) # Test non-square x = mx.array([[1, 2, 3], [4, 5, 6]]) result = mx.diag(x) expected = mx.array(np.diag(x)) self.assertTrue(mx.array_equal(result, expected)) result = mx.diag(x, k=10) expected = mx.array(np.diag(x, k=10)) self.assertTrue(mx.array_equal(result, expected)) result = mx.diag(x, k=-10) expected = mx.array(np.diag(x, k=-10)) self.assertTrue(mx.array_equal(result, expected)) result = mx.diag(x, k=-1) expected = mx.array(np.diag(x, k=-1)) self.assertTrue(mx.array_equal(result, expected)) def test_trace(self): a_mx = mx.arange(9, dtype=mx.int64).reshape((3, 3)) a_np = np.arange(9, dtype=np.int64).reshape((3, 3)) # Test 2D array result = mx.trace(a_mx) expected = np.trace(a_np) self.assertEqualArray(result, mx.array(expected)) # Test dtype result = mx.trace(a_mx, dtype=mx.float16) expected = np.trace(a_np, dtype=np.float16) self.assertEqualArray(result, mx.array(expected)) # Test offset result = mx.trace(a_mx, offset=1) expected = np.trace(a_np, offset=1) self.assertEqualArray(result, mx.array(expected)) # Test axis1 and axis2 b_mx = mx.arange(27, dtype=mx.int64).reshape(3, 3, 3) b_np = np.arange(27, dtype=np.int64).reshape(3, 3, 3) result = mx.trace(b_mx, axis1=1, axis2=2) expected = np.trace(b_np, axis1=1, axis2=2) self.assertEqualArray(result, mx.array(expected)) # Test offset, axis1, axis2, and dtype result = mx.trace(b_mx, offset=1, axis1=1, axis2=2, dtype=mx.float32) expected = np.trace(b_np, offset=1, axis1=1, axis2=2, dtype=np.float32) self.assertEqualArray(result, mx.array(expected)) def test_atleast_1d(self): def compare_nested_lists(x, y): if isinstance(x, list) and isinstance(y, list): if len(x) != len(y): return False for i in range(len(x)): if not compare_nested_lists(x[i], y[i]): return False return True else: return x == y # Test 1D input arrays = [ [1], [1, 2, 3], [1, 2, 3, 4], [[1], [2], [3]], [[1, 2], [3, 4]], [[1, 2, 3], [4, 5, 6]], [[[[1]], [[2]], [[3]]]], ] mx_arrays = [mx.atleast_1d(mx.array(x)) for x in arrays] atleast_arrays = mx.atleast_1d(*mx_arrays) for i, array in enumerate(arrays): mx_res = mx.atleast_1d(mx.array(array)) np_res = np.atleast_1d(np.array(array)) self.assertTrue(compare_nested_lists(mx_res.tolist(), np_res.tolist())) self.assertEqual(mx_res.shape, np_res.shape) self.assertEqual(mx_res.ndim, np_res.ndim) self.assertTrue(mx.all(mx.equal(mx_res, atleast_arrays[i]))) def test_atleast_2d(self): def compare_nested_lists(x, y): if isinstance(x, list) and isinstance(y, list): if len(x) != len(y): return False for i in range(len(x)): if not compare_nested_lists(x[i], y[i]): return False return True else: return x == y # Test 1D input arrays = [ [1], [1, 2, 3], [1, 2, 3, 4], [[1], [2], [3]], [[1, 2], [3, 4]], [[1, 2, 3], [4, 5, 6]], [[[[1]], [[2]], [[3]]]], ] mx_arrays = [mx.atleast_2d(mx.array(x)) for x in arrays] atleast_arrays = mx.atleast_2d(*mx_arrays) for i, array in enumerate(arrays): mx_res = mx.atleast_2d(mx.array(array)) np_res = np.atleast_2d(np.array(array)) self.assertTrue(compare_nested_lists(mx_res.tolist(), np_res.tolist())) self.assertEqual(mx_res.shape, np_res.shape) self.assertEqual(mx_res.ndim, np_res.ndim) self.assertTrue(mx.all(mx.equal(mx_res, atleast_arrays[i]))) def test_atleast_3d(self): def compare_nested_lists(x, y): if isinstance(x, list) and isinstance(y, list): if len(x) != len(y): return False for i in range(len(x)): if not compare_nested_lists(x[i], y[i]): return False return True else: return x == y # Test 1D input arrays = [ [1], [1, 2, 3], [1, 2, 3, 4], [[1], [2], [3]], [[1, 2], [3, 4]], [[1, 2, 3], [4, 5, 6]], [[[[1]], [[2]], [[3]]]], ] mx_arrays = [mx.atleast_3d(mx.array(x)) for x in arrays] atleast_arrays = mx.atleast_3d(*mx_arrays) for i, array in enumerate(arrays): mx_res = mx.atleast_3d(mx.array(array)) np_res = np.atleast_3d(np.array(array)) self.assertTrue(compare_nested_lists(mx_res.tolist(), np_res.tolist())) self.assertEqual(mx_res.shape, np_res.shape) self.assertEqual(mx_res.ndim, np_res.ndim) self.assertTrue(mx.all(mx.equal(mx_res, atleast_arrays[i]))) def test_issubdtype(self): self.assertTrue(mx.issubdtype(mx.bfloat16, mx.inexact)) cats = [ "complexfloating", "floating", "inexact", "signedinteger", "unsignedinteger", "integer", "number", "generic", "bool_", "uint8", "uint16", "uint32", "uint64", "int8", "int16", "int32", "int64", "float16", "float32", "complex64", ] for a in cats: for b in cats: self.assertEqual( mx.issubdtype(getattr(mx, a), getattr(mx, b)), np.issubdtype(getattr(np, a), getattr(np, b)), f"mx and np don't aggree on {a}, {b}", ) def test_bitwise_ops(self): types = [ mx.uint8, mx.uint16, mx.uint32, mx.uint64, mx.int8, mx.int16, mx.int32, mx.int64, ] a = mx.random.randint(0, 4096, (1000,)) b = mx.random.randint(0, 4096, (1000,)) for op in ["bitwise_and", "bitwise_or", "bitwise_xor"]: for t in types: a_mlx = a.astype(t) b_mlx = b.astype(t) a_np = np.array(a_mlx) b_np = np.array(b_mlx) out_mlx = getattr(mx, op)(a_mlx, b_mlx) out_np = getattr(np, op)(a_np, b_np) self.assertTrue(np.array_equal(np.array(out_mlx), out_np)) for op in ["left_shift", "right_shift"]: for t in types: a_mlx = a.astype(t) b_mlx = mx.random.randint(0, t.size, (1000,)).astype(t) a_np = np.array(a_mlx) b_np = np.array(b_mlx) out_mlx = getattr(mx, op)(a_mlx, b_mlx) out_np = getattr(np, op)(a_np, b_np) self.assertTrue(np.array_equal(np.array(out_mlx), out_np)) for t in types: a_mlx = a.astype(t) a_np = np.array(a_mlx) out_mlx = ~a_mlx out_np = ~a_np self.assertTrue(np.array_equal(np.array(out_mlx), out_np)) out_mlx = mx.bitwise_invert(a_mlx) out_np = mx.bitwise_invert(a_np) self.assertTrue(np.array_equal(np.array(out_mlx), out_np)) # Check broadcasting a = mx.ones((3, 1, 5), dtype=mx.bool_) b = mx.zeros((1, 2, 5), dtype=mx.bool_) c = a | b self.assertEqual(c.shape, (3, 2, 5)) self.assertTrue(mx.array_equal(c, mx.ones((3, 2, 5), dtype=mx.bool_))) def test_bitwise_grad(self): a = np.random.randint(0, 10, size=(4, 3)) b = np.random.randint(0, 10, size=(4, 3)) cotangent = np.random.randint(0, 10, size=(4, 3)) a = mx.array(a) b = mx.array(b) cotangent = mx.array(cotangent) def bitwise(a, b): return a.astype(mx.int32) & b.astype(mx.int32) _, vjps = mx.vjp(bitwise, [a, b], [cotangent]) for vjp in vjps: self.assertFalse(np.any(np.array(vjp))) def test_conjugate(self): shape = (3, 5, 7) a = np.random.normal(size=shape) + 1j * np.random.normal(size=shape) a = a.astype(np.complex64) ops = ["conjugate", "conj"] for op in ops: out_mlx = getattr(mx, op)(mx.array(a)) out_np = getattr(np, op)(a) self.assertTrue(np.array_equal(np.array(out_mlx), out_np)) out_mlx = mx.array(a).conj() out_np = a.conj() self.assertTrue(np.array_equal(np.array(out_mlx), out_np)) def test_view(self): # Check scalar out = mx.array(1, mx.int8).view(mx.uint8).item() self.assertEqual(out, 1) a = mx.random.randint(shape=(4, 2, 4), low=-100, high=100) a_np = np.array(a) for t in ["bool_", "int16", "float32", "int64"]: out = a.view(getattr(mx, t)) expected = a_np.view(getattr(np, t)) self.assertTrue(np.array_equal(out, expected, equal_nan=True)) # Irregular strides a = mx.random.randint(shape=(2, 4), low=-100, high=100) a = mx.broadcast_to(a, shape=(4, 2, 4)) for t in ["bool_", "int16", "float32", "int64"]: out = a.view(getattr(mx, t)) a_out = out.view(mx.int32) self.assertTrue(mx.array_equal(a_out, a, equal_nan=True)) a = mx.random.randint(shape=(4, 4), low=-100, high=100).T for t in ["bool_", "int16", "float32", "int64"]: out = a.view(getattr(mx, t)) a_out = out.view(mx.int32) self.assertTrue(mx.array_equal(a_out, a, equal_nan=True)) def _hadamard(self, N): # Matches scipy.linalg.hadamard H = np.array([[1]], dtype=np.int64) for i in range(0, np.log2(N).astype(np.int64)): H = np.vstack((np.hstack((H, H)), np.hstack((H, -H)))) return H def test_hadamard(self): with self.assertRaises(ValueError): mx.hadamard_transform(mx.array([])) h28_str = """ +------++----++-+--+-+--++-- -+-----+++-----+-+--+-+--++- --+-----+++---+-+-+----+--++ ---+-----+++---+-+-+-+--+--+ ----+-----+++---+-+-+++--+-- -----+-----++++--+-+--++--+- ------++----++-+--+-+--++--+ --++++-+-------++--+++-+--+- ---++++-+-----+-++--+-+-+--+ +---+++--+----++-++--+-+-+-- ++---++---+----++-++--+-+-+- +++---+----+----++-++--+-+-+ ++++--------+-+--++-++--+-+- -++++--------+++--++--+--+-+ -+-++-++--++--+--------++++- +-+-++--+--++--+--------++++ -+-+-++--+--++--+----+---+++ +-+-+-++--+--+---+---++---++ ++-+-+-++--+------+--+++---+ -++-+-+-++--+------+-++++--- +-++-+---++--+------+-++++-- -++--++-+-++-+++----++------ +-++--++-+-++-+++-----+----- ++-++---+-+-++-+++-----+---- -++-++-+-+-+-+--+++-----+--- --++-++++-+-+----+++-----+-- +--++-+-++-+-+----+++-----+- ++--++-+-++-+-+----++------+ """ def parse_h_string(h_str): return np.array( [[1 if s == "+" else -1 for s in row] for row in h_str.split()] ) h28 = parse_h_string(h28_str) x = mx.array(5) y = mx.hadamard_transform(x) self.assertEqual(y.item(), 5) x = mx.array(5) y = mx.hadamard_transform(x, scale=0.2) self.assertEqual(y.item(), 1) x = mx.random.normal((8, 8, 1)) y = mx.hadamard_transform(x) self.assertTrue(mx.all(y == x).item()) # Too slow to compare to numpy so let's compare CPU to GPU if mx.default_device() == mx.gpu: rk = mx.random.key(42) for k in range(14, 17): for m in [1, 3, 5, 7]: x = mx.random.normal((4, m * 2**k), key=rk) y1 = mx.hadamard_transform(x, stream=mx.cpu) y2 = mx.hadamard_transform(x, stream=mx.gpu) self.assertLess(mx.abs(y1 - y2).max().item(), 5e-6) np.random.seed(7) tests = product([np.float32, np.float16, np.int32], [1, 28], range(1, 14)) for dtype, m, k in tests: # skip large m=28 cases because they're very slow in NumPy if m > 1 and k > 8: continue with self.subTest(dtype=dtype, m=m, k=k): n = m * 2**k b = 4 scale = 0.34 x = np.random.normal(size=(b, n)).astype(dtype) # contiguity check x = mx.array(x)[::2] y = mx.hadamard_transform(x, scale=scale) mx.eval(y) h = ( self._hadamard(2**k) if m == 1 else np.kron(h28, self._hadamard(2**k)) ) y_np = np.einsum("ij,bj->bi", h, x) * scale atol = 2e-4 if dtype == np.float32 else 5e-2 * k np.testing.assert_allclose(y, y_np, atol=atol) # bfloat16 emulation on M1 means 2**14 doesn't fit in threadgroup memory if dtype == np.float16 and k < 14: y_bf16 = mx.hadamard_transform(x.astype(mx.bfloat16), scale=scale) np.testing.assert_allclose( y_bf16.astype(mx.float16), y, atol=atol * 2 ) def test_hadamard_grad_vmap(self): np.random.seed(4) for k in range(2, 8): n = 2**k x = np.random.normal(size=(n,)) h = self._hadamard(n) c = np.random.normal(size=(n,)) x = mx.array(x).astype(mx.float32) h = mx.array(h).astype(mx.float32) c = mx.array(c).astype(mx.float32) def hadamard_transform(x): return h @ x / mx.sqrt(x.shape[-1]) out = mx.vjp(hadamard_transform, [x], [c]) out_t = mx.vjp(mx.hadamard_transform, [x], [c]) np.testing.assert_allclose(out, out_t, atol=1e-4) for axis in (0, 1, 2): vht = mx.vmap(mx.vmap(hadamard_transform, 0, 0), axis, axis) vht_t = mx.vmap(mx.vmap(mx.hadamard_transform, 0, 0), axis, axis) xb = mx.array(np.random.normal(size=(n, n, n))) out = vht(xb) out_t = vht_t(xb) np.testing.assert_allclose(out, out_t, atol=1e-4) def test_roll(self): x = mx.arange(10).reshape(2, 5) for s in [-2, -1, 0, 1, 2]: y1 = np.roll(x, s) y2 = mx.roll(x, s) self.assertTrue(mx.array_equal(y1, y2).item()) y1 = np.roll(x, (s, s, s)) y2 = mx.roll(x, (s, s, s)) self.assertTrue(mx.array_equal(y1, y2).item()) shifts = [ 1, 2, -1, -2, (1, 1), (-1, 2), (33, 33), ] axes = [ 0, 1, (1, 0), (0, 1), (0, 0), (1, 1), ] for s, a in product(shifts, axes): y1 = np.roll(x, s, a) y2 = mx.roll(x, s, a) self.assertTrue(mx.array_equal(y1, y2).item()) def test_roll_errors(self): x = mx.array([]) result = mx.roll(x, [0], [0]) self.assertTrue(mx.array_equal(result, x)) def test_real_imag(self): x = mx.random.uniform(shape=(4, 4)) out = mx.real(x) self.assertTrue(mx.array_equal(x, out)) out = mx.imag(x) self.assertTrue(mx.array_equal(mx.zeros_like(x), out)) y = mx.random.uniform(shape=(4, 4)) z = x + 1j * y self.assertEqual(mx.real(z).dtype, mx.float32) self.assertTrue(mx.array_equal(mx.real(z), x)) self.assertEqual(mx.imag(z).dtype, mx.float32) self.assertTrue(mx.array_equal(mx.imag(z), y)) def test_dynamic_slicing(self): x = mx.random.randint(0, 100, shape=(4, 4, 4)) expected = x[1:, 2:, 3:] out = mx.slice(x, mx.array([1, 2, 3]), (0, 1, 2), (3, 2, 1)) self.assertTrue(mx.array_equal(expected, out)) x = mx.zeros(shape=(4, 4, 4)) update = mx.random.randint(0, 100, shape=(3, 2, 1)) out = mx.slice_update(x, update, mx.array([1, 2, 3]), (0, 1, 2)) expected = mx.zeros_like(x) expected[1:, 2:, 3:] = update self.assertTrue(mx.array_equal(expected, out)) def test_broadcast_arrays(self): a = mx.array(1) b = mx.array(1.0) a, b = mx.broadcast_arrays(a, b) self.assertEqual(a.shape, ()) self.assertEqual(a.dtype, mx.int32) self.assertEqual(b.shape, ()) self.assertEqual(b.dtype, mx.float32) a, b = mx.broadcast_arrays(mx.zeros((3, 1, 2)), mx.zeros((4, 1))) self.assertEqual(a.shape, (3, 4, 2)) self.assertEqual(b.shape, (3, 4, 2)) def test_slice_update_reversed(self): a = mx.array([1, 2, 3, 4]) b = a[::-1] b[::2] = 0 self.assertTrue(mx.array_equal(b, mx.array([0, 3, 0, 1]))) def test_slice_with_negative_stride(self): a = mx.random.uniform(shape=(128, 4)) out = a[::-1] self.assertTrue(mx.array_equal(out[-1, :], a[0, :])) def test_complex_ops(self): x = mx.array( [ 3.0 + 4.0j, -5.0 + 12.0j, -8.0 + 0.0j, 0.0 + 9.0j, 0.0 + 0.0j, ] ) ops = ["arccos", "arcsin", "arctan", "square", "sqrt"] for op in ops: with self.subTest(op=op): np_op = getattr(np, op) mx_op = getattr(mx, op) self.assertTrue(np.allclose(mx_op(x), np_op(x))) x = mx.array( [ 3.0 + 4.0j, -5.0 + 12.0j, -8.0 + 0.0j, 0.0 + 9.0j, 9.0 + 1.0j, ] ) self.assertTrue(np.allclose(mx.rsqrt(x), 1.0 / np.sqrt(x))) class TestBroadcast(mlx_tests.MLXTestCase): def test_broadcast_shapes(self): # Basic broadcasting self.assertEqual(mx.broadcast_shapes((1, 2, 3), (3,)), (1, 2, 3)) self.assertEqual(mx.broadcast_shapes((4, 1, 6), (5, 6)), (4, 5, 6)) self.assertEqual(mx.broadcast_shapes((5, 1, 4), (1, 3, 4)), (5, 3, 4)) # Multiple arguments self.assertEqual(mx.broadcast_shapes((1, 1), (1, 8), (7, 1)), (7, 8)) self.assertEqual( mx.broadcast_shapes((6, 1, 5), (1, 7, 1), (6, 7, 5)), (6, 7, 5) ) # Same shapes self.assertEqual(mx.broadcast_shapes((3, 4, 5), (3, 4, 5)), (3, 4, 5)) # Single argument self.assertEqual(mx.broadcast_shapes((2, 3)), (2, 3)) # Empty shapes self.assertEqual(mx.broadcast_shapes((), ()), ()) self.assertEqual(mx.broadcast_shapes((), (1,)), (1,)) self.assertEqual(mx.broadcast_shapes((1,), ()), (1,)) # Broadcasting with zeroes self.assertEqual(mx.broadcast_shapes((0,), (0,)), (0,)) self.assertEqual(mx.broadcast_shapes((1, 0, 5), (3, 1, 5)), (3, 0, 5)) self.assertEqual(mx.broadcast_shapes((5, 0), (0, 5, 0)), (0, 5, 0)) # Error cases with self.assertRaises(ValueError): mx.broadcast_shapes((3, 4), (4, 3)) with self.assertRaises(ValueError): mx.broadcast_shapes((2, 3, 4), (2, 5, 4)) with self.assertRaises(ValueError): mx.broadcast_shapes() if __name__ == "__main__": unittest.main()