| | |
| |
|
| | 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): |
| | |
| | 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) |
| |
|
| | |
| | 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) |
| |
|
| | |
| | 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) |
| |
|
| | |
| | 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)) |
| |
|
| | |
| | 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): |
| | |
| | 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) |
| |
|
| | |
| | y = mx.round(mx.array([22.2 + 3.6j, 18.5 + 98.2j])) |
| | self.assertListEqual(y.tolist(), [22 + 4j, 18 + 98j]) |
| |
|
| | |
| | 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]))) |
| |
|
| | |
| | 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))) |
| |
|
| | |
| | x = mx.random.uniform(shape=(2, 2, 8)) |
| | x = x.swapaxes(0, 1) |
| | self.assertTrue(mx.allclose(mx.logsumexp(x), logsumexp(x))) |
| |
|
| | |
| | x = mx.broadcast_to(mx.random.uniform(shape=(2, 1, 8)), (2, 2, 8)) |
| | self.assertTrue(mx.allclose(mx.logsumexp(x), logsumexp(x))) |
| |
|
| | |
| | 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) |
| | |
| | |
| | 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)) |
| |
|
| | |
| | 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)) |
| |
|
| | |
| | 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)) |
| |
|
| | |
| |
|
| | 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) |
| | |
| | |
| | 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)) |
| |
|
| | |
| | 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): |
| | |
| | x = mx.array([1, 2]) |
| | y = mx.array([3, 4]) |
| | z = mx.kron(x, y) |
| | self.assertEqual(z.tolist(), [3, 4, 6, 8]) |
| |
|
| | |
| | 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]], |
| | ) |
| |
|
| | |
| | x = mx.array([1, 2]) |
| | y = mx.array([[3, 4], [5, 6]]) |
| | z = mx.kron(x, y) |
| | self.assertEqual(z.tolist(), [[3, 4, 6, 8], [5, 6, 10, 12]]) |
| |
|
| | |
| | x = mx.array([]) |
| | y = mx.array([1, 2]) |
| | with self.assertRaises(ValueError): |
| | mx.kron(x, y) |
| |
|
| | def test_take(self): |
| | |
| | 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()) |
| |
|
| | |
| | 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]))) |
| |
|
| | |
| | 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): |
| | |
| | expected = getattr(np, op)(x) |
| | out = getattr(mx, op)(x) |
| |
|
| | |
| | self.assertTrue(np.allclose(expected, out, equal_nan=True)) |
| |
|
| | |
| | expected = getattr(np, op)(x_np) |
| | out = getattr(mx, op)(x_np) |
| |
|
| | |
| | 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, equal_nan=True)) |
| |
|
| | 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) |
| |
|
| | |
| | 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) |
| |
|
| | |
| | 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): |
| | |
| | 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)) |
| |
|
| | |
| | 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)) |
| |
|
| | |
| | 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) |
| |
|
| | |
| | y = mx.random.uniform(shape=(8, 4)) |
| | out = mx.softmax(y[:, 0:2], axis=-1) |
| | self.assertAlmostEqual(out.sum().item(), 8.0, 5) |
| |
|
| | |
| | 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)) |
| |
|
| | |
| | for n in [127, 128, 129]: |
| | x = mx.full((n,), vals=-float("inf")) |
| | self.assertTrue(mx.all(mx.isnan(mx.softmax(x)))) |
| |
|
| | |
| | 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) |
| |
|
| | |
| | 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) |
| |
|
| | |
| | a_mlx = mx.meshgrid(x) |
| | a_np = np.meshgrid(y) |
| | self.assertEqualArray(a_mlx[0], mx.array(a_np[0])) |
| |
|
| | |
| | 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)) |
| |
|
| | |
| | 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)) |
| |
|
| | |
| | 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])) |
| |
|
| | |
| | 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])) |
| |
|
| | |
| | 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])) |
| |
|
| | |
| | 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)) |
| |
|
| | |
| | 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, |
| | ) |
| |
|
| | |
| | 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)) |
| |
|
| | |
| |
|
| | 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)) |
| |
|
| | |
| |
|
| | 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)) |
| |
|
| | |
| | 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)) |
| |
|
| | |
| | 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"), |
| | (None, 0, 1, 2), |
| | (True, False), |
| | ) |
| | 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) |
| |
|
| | |
| | np.random.seed(0) |
| |
|
| | |
| | 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) |
| |
|
| | |
| | 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) |
| |
|
| | |
| | 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) |
| |
|
| | |
| | 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)) |
| |
|
| | |
| | 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)) |
| |
|
| | |
| | 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)) |
| |
|
| | |
| | a = mx.random.uniform(shape=(512, 128)) |
| | y_mx = mx.sort(a, axis=-1) |
| | y_np = np.sort(np.array(a), axis=-1) |
| | 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) |
| | |
| | self.assertCmpNumpy([3, 4], mx.eye, np.eye) |
| | |
| | self.assertCmpNumpy([3, 4], mx.eye, np.eye, k=1) |
| | |
| | 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,)) |
| |
|
| | |
| | c = mx.stack([a, b]) |
| | np_c = np.stack([np_a, np_b]) |
| | self.assertTrue(np.array_equal(c, np_c)) |
| |
|
| | |
| | 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)) |
| |
|
| | |
| | c = mx.stack([a, b]) |
| | np_c = np.stack([np_a, np_b]) |
| | self.assertTrue(np.array_equal(c, np_c)) |
| |
|
| | |
| | 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)) |
| |
|
| | |
| | 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): |
| | |
| | a = mx.linspace(0, 1) |
| | expected = mx.array(np.linspace(0, 1)) |
| | self.assertEqualArray(a, expected) |
| |
|
| | |
| | b = mx.linspace(0, 10, 5, mx.int64) |
| | expected = mx.array(np.linspace(0, 10, 5, dtype=int)) |
| | self.assertEqualArray(b, expected) |
| |
|
| | |
| | c = mx.linspace(-2.7, -0.7, 7) |
| | expected = mx.array(np.linspace(-2.7, -0.7, 7)) |
| | self.assertEqualArray(c, expected) |
| |
|
| | |
| | d = mx.linspace(0, 1, 10) |
| | expected = mx.array(np.linspace(0, 1, 10)) |
| | self.assertEqualArray(d, expected) |
| |
|
| | |
| | d = mx.linspace(1, 10, 1) |
| | expected = mx.array(np.linspace(1, 10, 1)) |
| | self.assertEqualArray(d, expected) |
| |
|
| | |
| | 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): |
| | |
| | data = mx.array([[[13, 3], [16, 6]], [[14, 4], [15, 5]], [[11, 1], [12, 2]]]) |
| | |
| | self.assertCmpNumpy([data, 0], mx.repeat, np.repeat) |
| | |
| | self.assertCmpNumpy([data, 2], mx.repeat, np.repeat, axis=0) |
| | |
| | self.assertCmpNumpy([data, 2], mx.repeat, np.repeat, axis=1) |
| | |
| | self.assertCmpNumpy([data, 2], mx.repeat, np.repeat) |
| | |
| | self.assertCmpNumpy([mx.array([1, 3, 2]), 3], mx.repeat, np.repeat, 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): |
| | |
| | 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): |
| | |
| | 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): |
| | |
| | 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)) |
| |
|
| | |
| | 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)) |
| |
|
| | |
| | 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)) |
| |
|
| | |
| | expected = mx.array([2, 6]) |
| | result = mx.diag(x, 1) |
| | self.assertTrue(mx.array_equal(result, expected)) |
| |
|
| | |
| | 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)) |
| |
|
| | |
| | result = mx.trace(a_mx) |
| | expected = np.trace(a_np) |
| | self.assertEqualArray(result, mx.array(expected)) |
| |
|
| | |
| | result = mx.trace(a_mx, dtype=mx.float16) |
| | expected = np.trace(a_np, dtype=np.float16) |
| | self.assertEqualArray(result, mx.array(expected)) |
| |
|
| | |
| | result = mx.trace(a_mx, offset=1) |
| | expected = np.trace(a_np, offset=1) |
| | self.assertEqualArray(result, mx.array(expected)) |
| |
|
| | |
| | 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)) |
| |
|
| | |
| | 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): |
| | |
| | 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.assertEqual(mx_res.shape, np_res.shape) |
| | self.assertEqual(mx_res.ndim, np_res.ndim) |
| | self.assertTrue(mx.array_equal(mx_res, atleast_arrays[i])) |
| |
|
| | def test_atleast_2d(self): |
| | |
| | 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.assertEqual(mx_res.shape, np_res.shape) |
| | self.assertEqual(mx_res.ndim, np_res.ndim) |
| | self.assertTrue(mx.array_equal(mx_res, atleast_arrays[i])) |
| |
|
| | def test_atleast_3d(self): |
| | |
| | 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.assertEqual(mx_res.shape, np_res.shape) |
| | self.assertEqual(mx_res.ndim, np_res.ndim) |
| | self.assertTrue(mx.array_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)) |
| |
|
| | |
| | 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): |
| | |
| | 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)) |
| |
|
| | |
| | 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): |
| | |
| | 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()) |
| |
|
| | |
| | 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: |
| | |
| | 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) |
| | |
| | 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) |
| |
|
| | |
| | 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))) |
| |
|
| | def test_complex_power(self): |
| | out = mx.power(mx.array(0j), 2) |
| | self.assertEqual(out.item(), 0j) |
| |
|
| | out = mx.power(mx.array(0j), float("nan")) |
| | self.assertTrue(mx.isnan(out)) |
| |
|
| | def test_irregular_alignments(self): |
| | |
| | a = mx.ones((64, 1)) |
| | b = -a[1:] |
| | self.assertTrue(mx.all(b == -1.0)) |
| |
|
| | |
| | a = mx.ones((64, 1)) |
| | b = a[1:] |
| | c = b + b |
| | self.assertTrue(mx.all(c == 2.0)) |
| |
|
| | |
| | a = mx.ones((64, 1)) |
| | b = mx.zeros((63, 1)) |
| | c = mx.ones((63, 1)).astype(mx.bool_) |
| | d = mx.where(c, a[1:], b) |
| | self.assertTrue(mx.all(d == 1.0)) |
| |
|
| | def test_integer_power(self): |
| | x = mx.power(2, mx.array([8, 8, 8, 8, 8, 8, 8, 8])) |
| | self.assertTrue(mx.all(x == 256)) |
| |
|
| | |
| | x = mx.power(2, -1) |
| |
|
| | def test_depends(self): |
| | a = mx.array([1.0, 2.0, 3.0]) |
| | b = mx.exp(a) |
| | c = mx.log(a) |
| | out = mx.depends([b], [c])[0] |
| | self.assertTrue(mx.array_equal(out, b)) |
| |
|
| | a = mx.array([1.0, 2.0, 3.0]) |
| | b = mx.exp(a) |
| | c = mx.log(a) |
| | out = mx.depends(b, c) |
| | self.assertTrue(mx.array_equal(out, b)) |
| |
|
| |
|
| | class TestBroadcast(mlx_tests.MLXTestCase): |
| | def test_broadcast_shapes(self): |
| | |
| | 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)) |
| |
|
| | |
| | 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) |
| | ) |
| |
|
| | |
| | self.assertEqual(mx.broadcast_shapes((3, 4, 5), (3, 4, 5)), (3, 4, 5)) |
| |
|
| | |
| | self.assertEqual(mx.broadcast_shapes((2, 3)), (2, 3)) |
| |
|
| | |
| | self.assertEqual(mx.broadcast_shapes((), ()), ()) |
| | self.assertEqual(mx.broadcast_shapes((), (1,)), (1,)) |
| | self.assertEqual(mx.broadcast_shapes((1,), ()), (1,)) |
| |
|
| | |
| | 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)) |
| |
|
| | |
| | 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__": |
| | mlx_tests.MLXTestRunner() |
| |
|
