mirror of
https://github.com/ml-explore/mlx.git
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c552ff2451
* Fix some cuda back-end bugs and enable corresponding tests * more fixes * enable more tests * format
3095 lines
107 KiB
Python
3095 lines
107 KiB
Python
# Copyright © 2023-2024 Apple Inc.
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import math
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import os
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import unittest
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from itertools import permutations, product
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import mlx.core as mx
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import mlx_tests
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import numpy as np
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def np_wrap_between(x, a):
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"""Wraps `x` between `[-a, a]`."""
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two_a = 2 * a
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zero = 0
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rem = np.remainder(np.add(x, a), two_a)
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if isinstance(rem, np.ndarray):
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rem = np.select(rem < zero, np.add(rem, two_a), rem)
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else:
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rem = np.add(rem, two_a) if rem < zero else rem
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return np.subtract(rem, a)
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def np_logaddexp(x1: np.ndarray, x2: np.ndarray):
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amax = np.maximum(x1, x2)
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if np.issubdtype(x1.dtype, np.floating):
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delta = np.subtract(x1, x2)
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if isinstance(delta, np.ndarray):
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return np.select(
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np.isnan(delta),
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np.add(x1, x2),
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np.add(amax, np.log1p(np.exp(np.negative(np.abs(delta))))),
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)
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else:
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return (
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np.add(x1, x2)
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if np.isnan(delta)
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else np.add(amax, np.log1p(np.exp(np.negative(np.abs(delta)))))
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)
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else:
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delta = np.subtract(np.add(x1, x2), np.multiply(amax, 2))
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out = np.add(amax, np.log1p(np.exp(delta)))
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return np.real(out) + 1j * np_wrap_between(np.imag(out), np.pi)
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def np_cumlogaddexp(x1: np.ndarray, axis: int = -1):
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out = x1.copy()
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for i in range(1, out.shape[axis]):
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out[i] = np_logaddexp(out[i], out[i - 1])
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return out
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class TestOps(mlx_tests.MLXTestCase):
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def test_full_ones_zeros(self):
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x = mx.full(2, 3.0)
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self.assertEqual(x.shape, (2,))
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self.assertEqual(x.tolist(), [3.0, 3.0])
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x = mx.full((2, 3), 2.0)
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self.assertEqual(x.dtype, mx.float32)
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self.assertEqual(x.shape, (2, 3))
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self.assertEqual(x.tolist(), [[2, 2, 2], [2, 2, 2]])
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x = mx.full([3, 2], mx.array([False, True]))
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self.assertEqual(x.dtype, mx.bool_)
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self.assertEqual(x.tolist(), [[False, True], [False, True], [False, True]])
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x = mx.full([3, 2], mx.array([2.0, 3.0]))
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self.assertEqual(x.tolist(), [[2, 3], [2, 3], [2, 3]])
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x = mx.zeros(2)
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self.assertEqual(x.shape, (2,))
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self.assertEqual(x.tolist(), [0.0, 0.0])
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x = mx.ones(2)
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self.assertEqual(x.shape, (2,))
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self.assertEqual(x.tolist(), [1.0, 1.0])
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for t in [mx.bool_, mx.int32, mx.float32]:
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x = mx.zeros([2, 2], t)
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self.assertEqual(x.dtype, t)
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self.assertTrue(mx.array_equal(x, mx.array([[0, 0], [0, 0]])))
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y = mx.zeros_like(x)
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self.assertEqual(y.dtype, t)
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self.assertTrue(mx.array_equal(y, x))
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x = mx.ones([2, 2], t)
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self.assertEqual(x.dtype, t)
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self.assertTrue(mx.array_equal(x, mx.array([[1, 1], [1, 1]])))
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y = mx.ones_like(x)
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self.assertEqual(y.dtype, t)
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self.assertTrue(mx.array_equal(y, x))
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def test_scalar_inputs(self):
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# Check combinations of python types
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a = mx.add(False, True)
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self.assertEqual(a.dtype, mx.bool_)
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self.assertEqual(a.item(), True)
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a = mx.add(1, 2)
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self.assertEqual(a.dtype, mx.int32)
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self.assertEqual(a.item(), 3)
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a = mx.add(1.0, 2.0)
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self.assertEqual(a.dtype, mx.float32)
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self.assertEqual(a.item(), 3.0)
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a = mx.add(True, 2)
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self.assertEqual(a.dtype, mx.int32)
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self.assertEqual(a.item(), 3)
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a = mx.add(True, 2.0)
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self.assertEqual(a.dtype, mx.float32)
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self.assertEqual(a.item(), 3.0)
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a = mx.add(1, 2.0)
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self.assertEqual(a.dtype, mx.float32)
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self.assertEqual(a.item(), 3.0)
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a = mx.add(2, True)
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self.assertEqual(a.dtype, mx.int32)
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self.assertEqual(a.item(), 3)
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a = mx.add(2.0, True)
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self.assertEqual(a.dtype, mx.float32)
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self.assertEqual(a.item(), 3.0)
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a = mx.add(2.0, 1)
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self.assertEqual(a.dtype, mx.float32)
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self.assertEqual(a.item(), 3.0)
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# Check combinations with mlx arrays
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a = mx.add(mx.array(True), False)
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self.assertEqual(a.dtype, mx.bool_)
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self.assertEqual(a.item(), True)
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a = mx.add(mx.array(1), False)
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self.assertEqual(a.dtype, mx.int32)
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self.assertEqual(a.item(), 1.0)
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# Edge case: take the type of the scalar
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a = mx.add(mx.array(True), 1)
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self.assertEqual(a.dtype, mx.int32)
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self.assertEqual(a.item(), 2)
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a = mx.add(mx.array(1.0), 1)
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self.assertEqual(a.dtype, mx.float32)
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self.assertEqual(a.item(), 2.0)
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a = mx.add(1, mx.array(1.0))
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self.assertEqual(a.dtype, mx.float32)
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self.assertEqual(a.item(), 2.0)
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binary_ops = [
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"add",
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"subtract",
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"multiply",
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"divide",
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"floor_divide",
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"remainder",
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"equal",
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"not_equal",
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"less",
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"greater",
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"less_equal",
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"greater_equal",
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"maximum",
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"minimum",
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]
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for op in binary_ops:
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npop = getattr(np, op)
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mlxop = getattr(mx, op)
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# Avoid subtract from bool and divide by 0
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for x in [-1, 0, 1, -1.0, 1.0]:
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for y in [True, -1, 1, -1.0, 1.0]:
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self.assertEqual(npop(x, y).item(), mlxop(x, y).item())
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def test_add(self):
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x = mx.array(1)
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y = mx.array(1)
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z = mx.add(x, y)
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self.assertEqual(z.item(), 2)
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x = mx.array(False, mx.bool_)
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z = x + 1
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self.assertEqual(z.dtype, mx.int32)
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self.assertEqual(z.item(), 1)
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z = 2 + x
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self.assertEqual(z.dtype, mx.int32)
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self.assertEqual(z.item(), 2)
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x = mx.array(1, mx.uint32)
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z = x + 3
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self.assertEqual(z.dtype, mx.uint32)
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self.assertEqual(z.item(), 4)
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z = 3 + x
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self.assertEqual(z.dtype, mx.uint32)
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self.assertEqual(z.item(), 4)
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z = x + 3.0
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self.assertEqual(z.dtype, mx.float32)
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self.assertEqual(z.item(), 4.0)
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z = 3.0 + x
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self.assertEqual(z.dtype, mx.float32)
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self.assertEqual(z.item(), 4.0)
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x = mx.array(1, mx.int64)
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z = x + 3
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self.assertEqual(z.dtype, mx.int64)
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self.assertEqual(z.item(), 4)
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z = 3 + x
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self.assertEqual(z.dtype, mx.int64)
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self.assertEqual(z.item(), 4)
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z = x + 3.0
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self.assertEqual(z.dtype, mx.float32)
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self.assertEqual(z.item(), 4.0)
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z = 3.0 + x
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self.assertEqual(z.dtype, mx.float32)
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self.assertEqual(z.item(), 4.0)
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x = mx.array(1, mx.float32)
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z = x + 3
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self.assertEqual(z.dtype, mx.float32)
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self.assertEqual(z.item(), 4)
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z = 3 + x
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self.assertEqual(z.dtype, mx.float32)
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self.assertEqual(z.item(), 4)
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def test_subtract(self):
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x = mx.array(4.0)
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y = mx.array(3.0)
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z = mx.subtract(x, y)
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self.assertEqual(z.dtype, mx.float32)
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self.assertEqual(z.item(), 1.0)
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z = x - 3.0
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self.assertEqual(z.dtype, mx.float32)
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self.assertEqual(z.item(), 1.0)
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z = 5.0 - x
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self.assertEqual(z.dtype, mx.float32)
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self.assertEqual(z.item(), 1.0)
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def test_multiply(self):
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x = mx.array(2.0)
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y = mx.array(3.0)
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z = mx.multiply(x, y)
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self.assertEqual(z.dtype, mx.float32)
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self.assertEqual(z.item(), 6.0)
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z = x * 3.0
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self.assertEqual(z.dtype, mx.float32)
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self.assertEqual(z.item(), 6.0)
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z = 3.0 * x
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self.assertEqual(z.dtype, mx.float32)
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self.assertEqual(z.item(), 6.0)
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def test_divide(self):
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x = mx.array(2.0)
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y = mx.array(4.0)
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z = mx.divide(x, y)
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self.assertEqual(z.dtype, mx.float32)
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self.assertEqual(z.item(), 0.5)
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z = x / 4.0
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self.assertEqual(z.dtype, mx.float32)
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self.assertEqual(z.item(), 0.5)
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z = 1.0 / x
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self.assertEqual(z.dtype, mx.float32)
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self.assertEqual(z.item(), 0.5)
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x = x.astype(mx.float16)
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z = x / 4.0
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self.assertEqual(z.dtype, mx.float16)
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x = x.astype(mx.float16)
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z = 4.0 / x
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self.assertEqual(z.dtype, mx.float16)
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x = mx.array(5)
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y = mx.array(2)
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z = x / y
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self.assertEqual(z.dtype, mx.float32)
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self.assertEqual(z.item(), 2.5)
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z = x // y
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self.assertEqual(z.dtype, mx.int32)
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self.assertEqual(z.item(), 2)
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def test_remainder(self):
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for dt in [mx.int32, mx.float32]:
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x = mx.array(2, dtype=dt)
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y = mx.array(4, dtype=dt)
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z1 = mx.remainder(x, y)
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z2 = mx.remainder(y, x)
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self.assertEqual(z1.dtype, dt)
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self.assertEqual(z1.item(), 2)
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self.assertEqual(z2.item(), 0)
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z = x % 4
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self.assertEqual(z.dtype, dt)
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self.assertEqual(z.item(), 2)
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z = 1 % x
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self.assertEqual(z.dtype, dt)
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self.assertEqual(z.item(), 1)
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z = -1 % x
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self.assertEqual(z.dtype, dt)
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self.assertEqual(z.item(), 1)
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z = -1 % -x
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self.assertEqual(z.dtype, dt)
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self.assertEqual(z.item(), -1)
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x = mx.arange(10).astype(dt) - 5
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y = x % 5
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z = x % -5
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self.assertEqual(y.tolist(), [0, 1, 2, 3, 4, 0, 1, 2, 3, 4])
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self.assertEqual(z.tolist(), [0, -4, -3, -2, -1, 0, -4, -3, -2, -1])
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z = -mx.ones(64) % mx.full(64, 2)
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self.assertTrue(mx.array_equal(z, mx.ones(64)))
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def test_comparisons(self):
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a = mx.array([0.0, 1.0, 5.0])
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b = mx.array([-1.0, 2.0, 5.0])
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self.assertEqual(mx.less(a, b).tolist(), [False, True, False])
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self.assertEqual(mx.less_equal(a, b).tolist(), [False, True, True])
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self.assertEqual(mx.greater(a, b).tolist(), [True, False, False])
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self.assertEqual(mx.greater_equal(a, b).tolist(), [True, False, True])
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self.assertEqual(mx.less(a, 5).tolist(), [True, True, False])
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self.assertEqual(mx.less(5, a).tolist(), [False, False, False])
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self.assertEqual(mx.less_equal(5, a).tolist(), [False, False, True])
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self.assertEqual(mx.greater(a, 1).tolist(), [False, False, True])
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self.assertEqual(mx.greater_equal(a, 1).tolist(), [False, True, True])
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a = mx.array([0.0, 1.0, 5.0, -1.0])
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b = mx.array([0.0, 2.0, 5.0, 3.0])
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self.assertEqual(mx.equal(a, b).tolist(), [True, False, True, False])
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self.assertEqual(mx.not_equal(a, b).tolist(), [False, True, False, True])
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def test_array_equal(self):
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x = mx.array([1, 2, 3, 4])
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y = mx.array([1, 2, 3, 4])
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self.assertTrue(mx.array_equal(x, y))
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y = mx.array([1, 2, 4, 5])
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self.assertFalse(mx.array_equal(x, y))
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y = mx.array([1, 2, 3])
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self.assertFalse(mx.array_equal(x, y))
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# Can still be equal with different types
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y = mx.array([1.0, 2.0, 3.0, 4.0])
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self.assertTrue(mx.array_equal(x, y))
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x = mx.array([0.0, float("nan")])
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y = mx.array([0.0, float("nan")])
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self.assertFalse(mx.array_equal(x, y))
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self.assertTrue(mx.array_equal(x, y, equal_nan=True))
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for t in [mx.float32, mx.float16, mx.bfloat16, mx.complex64]:
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with self.subTest(type=t):
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x = mx.array([0.0, float("nan")]).astype(t)
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y = mx.array([0.0, float("nan")]).astype(t)
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self.assertFalse(mx.array_equal(x, y))
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self.assertTrue(mx.array_equal(x, y, equal_nan=True))
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def test_isnan(self):
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x = mx.array([0.0, float("nan")])
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self.assertEqual(mx.isnan(x).tolist(), [False, True])
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x = mx.array([0.0, float("nan")]).astype(mx.float16)
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self.assertEqual(mx.isnan(x).tolist(), [False, True])
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x = mx.array([0.0, float("nan")]).astype(mx.bfloat16)
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self.assertEqual(mx.isnan(x).tolist(), [False, True])
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x = mx.array([0.0, float("nan")]).astype(mx.complex64)
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self.assertEqual(mx.isnan(x).tolist(), [False, True])
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self.assertEqual(mx.isnan(0 * mx.array(float("inf"))).tolist(), True)
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def test_isinf(self):
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x = mx.array([0.0, float("inf")])
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self.assertEqual(mx.isinf(x).tolist(), [False, True])
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x = mx.array([0.0, float("inf")]).astype(mx.float16)
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self.assertEqual(mx.isinf(x).tolist(), [False, True])
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x = mx.array([0.0, float("inf")]).astype(mx.bfloat16)
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self.assertEqual(mx.isinf(x).tolist(), [False, True])
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x = mx.array([0.0, float("inf")]).astype(mx.complex64)
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self.assertEqual(mx.isinf(x).tolist(), [False, True])
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self.assertEqual(mx.isinf(0 * mx.array(float("inf"))).tolist(), False)
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x = mx.array([-2147483648, 0, 2147483647], dtype=mx.int32)
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result = mx.isinf(x)
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self.assertEqual(result.tolist(), [False, False, False])
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x = mx.array([-32768, 0, 32767], dtype=mx.int16)
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result = mx.isinf(x)
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self.assertEqual(result.tolist(), [False, False, False])
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def test_isfinite(self):
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x = mx.array([0.0, float("inf"), float("nan")])
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self.assertEqual(mx.isfinite(x).tolist(), [True, False, False])
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x = x.astype(mx.float16)
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self.assertEqual(mx.isfinite(x).tolist(), [True, False, False])
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x = x.astype(mx.bfloat16)
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self.assertEqual(mx.isfinite(x).tolist(), [True, False, False])
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def test_tri(self):
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for shape in [[4], [4, 4], [2, 10]]:
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for diag in [-1, 0, 1, -2]:
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self.assertCmpNumpy(shape, mx.tri, np.tri, k=diag)
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self.assertEqual(mx.tri(1, 1).dtype, mx.float32)
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self.assertEqual(mx.tri(1, 1, dtype=mx.bfloat16).dtype, mx.bfloat16)
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def test_tril(self):
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for diag in [-1, 0, 1, -2]:
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self.assertCmpNumpy([(10, 10)], mx.tril, np.tril, k=diag)
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with self.assertRaises(Exception):
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mx.tril(mx.zeros((1)))
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def test_triu(self):
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for diag in [-1, 0, 1, -2]:
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self.assertCmpNumpy([(10, 10)], mx.triu, np.triu, k=diag)
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with self.assertRaises(Exception):
|
|
mx.triu(mx.zeros((1)))
|
|
|
|
def test_minimum(self):
|
|
x = mx.array([0.0, -5, 10.0])
|
|
y = mx.array([1.0, -7.0, 3.0])
|
|
|
|
expected = [0, -7, 3]
|
|
self.assertListEqual(mx.minimum(x, y).tolist(), expected)
|
|
|
|
a = mx.array([float("nan")])
|
|
b = mx.array([0.0])
|
|
self.assertTrue(math.isnan(mx.minimum(a, b).item()))
|
|
self.assertTrue(math.isnan(mx.minimum(b, a).item()))
|
|
|
|
def test_maximum(self):
|
|
x = mx.array([0.0, -5, 10.0])
|
|
y = mx.array([1.0, -7.0, 3.0])
|
|
|
|
expected = [1, -5, 10]
|
|
self.assertListEqual(mx.maximum(x, y).tolist(), expected)
|
|
|
|
a = mx.array([float("nan")])
|
|
b = mx.array([0.0])
|
|
self.assertTrue(math.isnan(mx.maximum(a, b).item()))
|
|
self.assertTrue(math.isnan(mx.maximum(b, a).item()))
|
|
|
|
def test_floor(self):
|
|
x = mx.array([-22.03, 19.98, -27, 9, 0.0, -np.inf, np.inf])
|
|
expected = [-23, 19, -27, 9, 0, -np.inf, np.inf]
|
|
self.assertListEqual(mx.floor(x).tolist(), expected)
|
|
|
|
with self.assertRaises(ValueError):
|
|
mx.floor(mx.array([22 + 3j, 19 + 98j]))
|
|
|
|
def test_ceil(self):
|
|
x = mx.array([-22.03, 19.98, -27, 9, 0.0, -np.inf, np.inf])
|
|
expected = [-22, 20, -27, 9, 0, -np.inf, np.inf]
|
|
self.assertListEqual(mx.ceil(x).tolist(), expected)
|
|
|
|
with self.assertRaises(ValueError):
|
|
mx.ceil(mx.array([22 + 3j, 19 + 98j]))
|
|
|
|
def test_isposinf(self):
|
|
x = mx.array([0.0, float("-inf")])
|
|
self.assertEqual(mx.isposinf(x).tolist(), [False, False])
|
|
|
|
x = mx.array([0.0, float("-inf")]).astype(mx.float16)
|
|
self.assertEqual(mx.isposinf(x).tolist(), [False, False])
|
|
|
|
x = mx.array([0.0, float("-inf")]).astype(mx.bfloat16)
|
|
self.assertEqual(mx.isposinf(x).tolist(), [False, False])
|
|
|
|
x = mx.array([0.0, float("-inf")]).astype(mx.complex64)
|
|
self.assertEqual(mx.isposinf(x).tolist(), [False, False])
|
|
|
|
self.assertEqual(mx.isposinf(0 * mx.array(float("inf"))).tolist(), False)
|
|
|
|
x = mx.array([-2147483648, 0, 2147483647], dtype=mx.int32)
|
|
result = mx.isposinf(x)
|
|
self.assertEqual(result.tolist(), [False, False, False])
|
|
|
|
x = mx.array([-32768, 0, 32767], dtype=mx.int16)
|
|
result = mx.isposinf(x)
|
|
self.assertEqual(result.tolist(), [False, False, False])
|
|
|
|
def test_isneginf(self):
|
|
x = mx.array([0.0, float("-inf")])
|
|
self.assertEqual(mx.isneginf(x).tolist(), [False, True])
|
|
|
|
x = mx.array([0.0, float("-inf")]).astype(mx.float16)
|
|
self.assertEqual(mx.isneginf(x).tolist(), [False, True])
|
|
|
|
x = mx.array([0.0, float("-inf")]).astype(mx.bfloat16)
|
|
self.assertEqual(mx.isneginf(x).tolist(), [False, True])
|
|
|
|
x = mx.array([0.0, float("-inf")]).astype(mx.complex64)
|
|
self.assertEqual(mx.isneginf(x).tolist(), [False, True])
|
|
|
|
self.assertEqual(mx.isneginf(0 * mx.array(float("inf"))).tolist(), False)
|
|
|
|
x = mx.array([-2147483648, 0, 2147483647], dtype=mx.int32)
|
|
result = mx.isneginf(x)
|
|
self.assertEqual(result.tolist(), [False, False, False])
|
|
|
|
x = mx.array([-32768, 0, 32767], dtype=mx.int16)
|
|
result = mx.isneginf(x)
|
|
self.assertEqual(result.tolist(), [False, False, False])
|
|
|
|
def test_round(self):
|
|
# float
|
|
x = mx.array(
|
|
[0.5, -0.5, 1.5, -1.5, -21.03, 19.98, -27, 9, 0.0, -np.inf, np.inf]
|
|
)
|
|
expected = [0, -0, 2, -2, -21, 20, -27, 9, 0, -np.inf, np.inf]
|
|
self.assertListEqual(mx.round(x).tolist(), expected)
|
|
|
|
# complex
|
|
y = mx.round(mx.array([22.2 + 3.6j, 18.5 + 98.2j]))
|
|
self.assertListEqual(y.tolist(), [22 + 4j, 18 + 98j])
|
|
|
|
# decimals
|
|
y0 = mx.round(mx.array([15, 122], mx.int32), decimals=0)
|
|
y1 = mx.round(mx.array([15, 122], mx.int32), decimals=-1)
|
|
y2 = mx.round(mx.array([15, 122], mx.int32), decimals=-2)
|
|
self.assertEqual(y0.dtype, mx.int32)
|
|
self.assertEqual(y1.dtype, mx.int32)
|
|
self.assertEqual(y2.dtype, mx.int32)
|
|
self.assertListEqual(y0.tolist(), [15, 122])
|
|
self.assertListEqual(y1.tolist(), [20, 120])
|
|
self.assertListEqual(y2.tolist(), [0, 100])
|
|
|
|
y1 = mx.round(mx.array([1.537, 1.471], mx.float32), decimals=1)
|
|
y2 = mx.round(mx.array([1.537, 1.471], mx.float32), decimals=2)
|
|
self.assertTrue(mx.allclose(y1, mx.array([1.5, 1.5])))
|
|
self.assertTrue(mx.allclose(y2, mx.array([1.54, 1.47])))
|
|
|
|
# check round to nearest for different types
|
|
dtypes = [mx.bfloat16, mx.float16, mx.float32]
|
|
for dtype in dtypes:
|
|
x = mx.arange(10, dtype=dtype) - 4.5
|
|
x = mx.round(x)
|
|
self.assertEqual(
|
|
x.astype(mx.float32).tolist(),
|
|
[-4.0, -4.0, -2.0, -2.0, -0.0, 0.0, 2.0, 2.0, 4.0, 4.0],
|
|
)
|
|
|
|
def test_transpose_noargs(self):
|
|
x = mx.array([[0, 1, 1], [1, 0, 0]])
|
|
|
|
expected = [
|
|
[0, 1],
|
|
[1, 0],
|
|
[1, 0],
|
|
]
|
|
|
|
self.assertListEqual(mx.transpose(x).tolist(), expected)
|
|
|
|
def test_transpose_axis(self):
|
|
x = mx.array(
|
|
[
|
|
[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]],
|
|
[[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]],
|
|
]
|
|
)
|
|
expected = [
|
|
[[0, 4, 8], [1, 5, 9], [2, 6, 10], [3, 7, 11]],
|
|
[[12, 16, 20], [13, 17, 21], [14, 18, 22], [15, 19, 23]],
|
|
]
|
|
|
|
self.assertListEqual(mx.transpose(x, axes=(0, 2, 1)).tolist(), expected)
|
|
|
|
def test_move_swap_axes(self):
|
|
x = mx.zeros((2, 3, 4))
|
|
self.assertEqual(mx.moveaxis(x, 0, 2).shape, (3, 4, 2))
|
|
self.assertEqual(x.moveaxis(0, 2).shape, (3, 4, 2))
|
|
self.assertEqual(mx.swapaxes(x, 0, 2).shape, (4, 3, 2))
|
|
self.assertEqual(x.swapaxes(0, 2).shape, (4, 3, 2))
|
|
|
|
def test_sum(self):
|
|
x = mx.array(
|
|
[
|
|
[1, 2],
|
|
[3, 3],
|
|
]
|
|
)
|
|
self.assertEqual(mx.sum(x).item(), 9)
|
|
y = mx.sum(x, keepdims=True)
|
|
self.assertEqual(y, mx.array(9))
|
|
self.assertEqual(y.shape, (1, 1))
|
|
|
|
self.assertEqual(mx.sum(x, axis=0).tolist(), [4, 5])
|
|
self.assertEqual(mx.sum(x, axis=1).tolist(), [3, 6])
|
|
|
|
x_npy = np.arange(3 * 5 * 4 * 7).astype(np.float32)
|
|
x_npy = np.reshape(x_npy, (3, 5, 4, 7))
|
|
x_mlx = mx.array(x_npy)
|
|
|
|
for axis in (None, 0, 1, 2, 3, (0, 1), (2, 3), (1, 2, 3)):
|
|
sum_npy = np.sum(x_npy, axis=axis)
|
|
sum_mlx = np.asarray(mx.sum(x_mlx, axis=axis))
|
|
self.assertListEqual(list(sum_npy.shape), list(sum_mlx.shape))
|
|
self.assertTrue(np.all(sum_npy == sum_mlx))
|
|
|
|
x_npy = np.array([1.0, 2.0, 3.0, 4.0]).astype(np.float32)
|
|
x_mlx = mx.array(x_npy)
|
|
|
|
y_npy = x_npy[0:4:2]
|
|
y_npy = np.broadcast_to(y_npy, (2, 2))
|
|
|
|
y_mlx = x_mlx[0:4:2]
|
|
y_mlx = mx.broadcast_to(y_mlx, (2, 2))
|
|
|
|
for axis in (None, 0, 1, (0, 1)):
|
|
sum_npy = np.sum(y_npy, axis=axis)
|
|
sum_mlx = np.asarray(mx.sum(y_mlx, axis=axis))
|
|
self.assertListEqual(list(sum_npy.shape), list(sum_mlx.shape))
|
|
self.assertTrue(np.all(sum_npy == sum_mlx))
|
|
|
|
x_npy = (
|
|
np.arange(3 * 2 * 3 * 3 * 3 * 3)
|
|
.reshape(3, 2, 3, 3, 3, 3)
|
|
.astype(np.float32)
|
|
)
|
|
x_mlx = mx.array(x_npy)
|
|
|
|
y_mlx = x_mlx.sum(axis=(0, 1, 3, 4, 5))
|
|
y_npy = x_npy.sum(axis=(0, 1, 3, 4, 5))
|
|
|
|
self.assertTrue(np.array_equal(y_mlx, y_npy))
|
|
|
|
def test_prod(self):
|
|
x = mx.array(
|
|
[
|
|
[1, 2],
|
|
[3, 3],
|
|
]
|
|
)
|
|
self.assertEqual(mx.prod(x).item(), 18)
|
|
y = mx.prod(x, keepdims=True)
|
|
self.assertEqual(y, mx.array(18))
|
|
self.assertEqual(y.shape, (1, 1))
|
|
|
|
self.assertEqual(mx.prod(x, axis=0).tolist(), [3, 6])
|
|
self.assertEqual(mx.prod(x, axis=1).tolist(), [2, 9])
|
|
|
|
def test_min_and_max(self):
|
|
x = mx.array(
|
|
[
|
|
[1, 2],
|
|
[3, 4],
|
|
]
|
|
)
|
|
self.assertEqual(mx.min(x).item(), 1)
|
|
self.assertEqual(mx.max(x).item(), 4)
|
|
y = mx.min(x, keepdims=True)
|
|
self.assertEqual(y.shape, (1, 1))
|
|
self.assertEqual(y, mx.array(1))
|
|
|
|
y = mx.max(x, keepdims=True)
|
|
self.assertEqual(y.shape, (1, 1))
|
|
self.assertEqual(y, mx.array(4))
|
|
|
|
self.assertEqual(mx.min(x, axis=0).tolist(), [1, 2])
|
|
self.assertEqual(mx.min(x, axis=1).tolist(), [1, 3])
|
|
self.assertEqual(mx.max(x, axis=0).tolist(), [3, 4])
|
|
self.assertEqual(mx.max(x, axis=1).tolist(), [2, 4])
|
|
|
|
def test_argmin_argmax(self):
|
|
data = np.random.rand(10, 12, 13)
|
|
x = mx.array(data)
|
|
for op in ["argmin", "argmax"]:
|
|
for axis in range(3):
|
|
for kd in [True, False]:
|
|
a = getattr(mx, op)(x, axis, kd)
|
|
b = getattr(np, op)(data, axis, keepdims=kd)
|
|
self.assertEqual(a.tolist(), b.tolist())
|
|
|
|
for op in ["argmin", "argmax"]:
|
|
a = getattr(mx, op)(x, keepdims=True)
|
|
b = getattr(np, op)(data, keepdims=True)
|
|
self.assertEqual(a.tolist(), b.tolist())
|
|
a = getattr(mx, op)(x)
|
|
b = getattr(np, op)(data)
|
|
self.assertEqual(a.item(), b)
|
|
|
|
def test_broadcast(self):
|
|
a_npy = np.reshape(np.arange(200), (10, 20))
|
|
a_mlx = mx.array(a_npy)
|
|
|
|
b_npy = np.broadcast_to(a_npy, (30, 10, 20))
|
|
b_mlx = mx.broadcast_to(a_mlx, (30, 10, 20))
|
|
self.assertListEqual(list(b_npy.shape), list(b_mlx.shape))
|
|
self.assertTrue(np.array_equal(b_npy, b_mlx))
|
|
|
|
b_npy = np.broadcast_to(a_npy, (1, 10, 20))
|
|
b_mlx = mx.broadcast_to(a_mlx, (1, 10, 20))
|
|
self.assertListEqual(list(b_npy.shape), list(b_mlx.shape))
|
|
self.assertTrue(np.array_equal(b_npy, b_mlx))
|
|
|
|
b_npy = np.broadcast_to(1, (10, 20))
|
|
b_mlx = mx.broadcast_to(1, (10, 20))
|
|
self.assertListEqual(list(b_npy.shape), list(b_mlx.shape))
|
|
self.assertTrue(np.array_equal(b_npy, b_mlx))
|
|
|
|
def test_logsumexp(self):
|
|
def logsumexp(x, axes=None):
|
|
maxs = mx.max(x, axis=axes, keepdims=True)
|
|
return mx.log(mx.sum(mx.exp(x - maxs), axis=axes, keepdims=True)) + maxs
|
|
|
|
x = mx.array(
|
|
[
|
|
[1.0, 2.0],
|
|
[3.0, 4.0],
|
|
]
|
|
)
|
|
self.assertTrue(math.isclose(mx.logsumexp(x).item(), logsumexp(x).item()))
|
|
|
|
x = mx.random.uniform(shape=(1025,))
|
|
self.assertTrue(mx.allclose(mx.logsumexp(x), logsumexp(x)))
|
|
|
|
# Transposed
|
|
x = mx.random.uniform(shape=(2, 2, 8))
|
|
x = x.swapaxes(0, 1)
|
|
self.assertTrue(mx.allclose(mx.logsumexp(x), logsumexp(x)))
|
|
|
|
# Broadcast
|
|
x = mx.broadcast_to(mx.random.uniform(shape=(2, 1, 8)), (2, 2, 8))
|
|
self.assertTrue(mx.allclose(mx.logsumexp(x), logsumexp(x)))
|
|
|
|
# Large
|
|
x = mx.random.uniform(shape=(1025,))
|
|
x = mx.broadcast_to(mx.random.uniform(shape=(2, 1, 8)), (2, 2, 8))
|
|
self.assertTrue(mx.allclose(mx.logsumexp(x), logsumexp(x)))
|
|
|
|
def test_mean(self):
|
|
x = mx.array(
|
|
[
|
|
[1, 2],
|
|
[3, 4],
|
|
]
|
|
)
|
|
self.assertEqual(mx.mean(x).item(), 2.5)
|
|
y = mx.mean(x, keepdims=True)
|
|
self.assertEqual(y, mx.array(2.5))
|
|
self.assertEqual(y.shape, (1, 1))
|
|
|
|
self.assertEqual(mx.mean(x, axis=0).tolist(), [2, 3])
|
|
self.assertEqual(mx.mean(x, axis=1).tolist(), [1.5, 3.5])
|
|
|
|
def test_var(self):
|
|
x = mx.array(
|
|
[
|
|
[1, 2],
|
|
[3, 4],
|
|
]
|
|
)
|
|
self.assertEqual(mx.var(x).item(), 1.25)
|
|
y = mx.var(x, keepdims=True)
|
|
self.assertEqual(y, mx.array(1.25))
|
|
self.assertEqual(y.shape, (1, 1))
|
|
|
|
self.assertEqual(mx.var(x, axis=0).tolist(), [1.0, 1.0])
|
|
self.assertEqual(mx.var(x, axis=1).tolist(), [0.25, 0.25])
|
|
|
|
x = mx.array([1.0, 2.0])
|
|
out = mx.var(x, ddof=2)
|
|
self.assertEqual(out.item(), float("inf"))
|
|
|
|
x = mx.array([1.0, 2.0])
|
|
out = mx.var(x, ddof=3)
|
|
self.assertEqual(out.item(), float("inf"))
|
|
|
|
def test_std(self):
|
|
x = mx.random.uniform(shape=(5, 5))
|
|
x_np = np.array(x)
|
|
self.assertAlmostEqual(mx.std(x).item(), x_np.std().item(), places=6)
|
|
|
|
def test_abs(self):
|
|
a = mx.array([-1.0, 1.0, -2.0, 3.0])
|
|
result = mx.abs(a)
|
|
expected = np.abs(a, dtype=np.float32)
|
|
self.assertTrue(np.allclose(result, expected))
|
|
|
|
self.assertTrue(np.allclose(a.abs(), abs(a)))
|
|
|
|
def test_negative(self):
|
|
a = mx.array([-1.0, 1.0, -2.0, 3.0])
|
|
result = mx.negative(a)
|
|
expected = np.negative(a, dtype=np.float32)
|
|
self.assertTrue(np.allclose(result, expected))
|
|
|
|
def test_sign(self):
|
|
a = mx.array([-1.0, 1.0, 0.0, -2.0, 3.0])
|
|
result = mx.sign(a)
|
|
expected = np.sign(a, dtype=np.float32)
|
|
self.assertTrue(np.allclose(result, expected))
|
|
|
|
a = mx.array([-1.0, 1.0, 0.0, -2.0, 3.0])
|
|
b = mx.array([-4.0, -3.0, 1.0, 0.0, 3.0])
|
|
c = a + b * 1j
|
|
result = mx.sign(c)
|
|
# np.sign differs in NumPy 1 and 2 so
|
|
# we manually implement the NumPy 2 version here.
|
|
expected = c / np.abs(c)
|
|
self.assertTrue(np.allclose(result, expected))
|
|
|
|
def test_logical_not(self):
|
|
a = mx.array([-1.0, 1.0, 0.0, 1.0, -2.0, 3.0])
|
|
result = mx.logical_not(a)
|
|
expected = np.logical_not(a)
|
|
self.assertTrue(np.array_equal(result, expected))
|
|
|
|
def test_logical_and(self):
|
|
a = mx.array([True, False, True, False])
|
|
b = mx.array([True, True, False, False])
|
|
result = mx.logical_and(a, b)
|
|
expected = np.logical_and(a, b)
|
|
self.assertTrue(np.array_equal(result, expected))
|
|
|
|
# test overloaded operator
|
|
result = a & b
|
|
self.assertTrue(np.array_equal(result, expected))
|
|
|
|
def test_logical_or(self):
|
|
a = mx.array([True, False, True, False])
|
|
b = mx.array([True, True, False, False])
|
|
result = mx.logical_or(a, b)
|
|
expected = np.logical_or(a, b)
|
|
self.assertTrue(np.array_equal(result, expected))
|
|
|
|
# test overloaded operator
|
|
result = a | b
|
|
self.assertTrue(np.array_equal(result, expected))
|
|
|
|
def test_square(self):
|
|
a = mx.array([0.1, 0.5, 1.0, 10.0])
|
|
result = mx.square(a)
|
|
expected = np.square(a, dtype=np.float32)
|
|
|
|
self.assertTrue(np.allclose(result, expected))
|
|
|
|
def test_sqrt(self):
|
|
a = mx.array([0.1, 0.5, 1.0, 10.0])
|
|
result = mx.sqrt(a)
|
|
expected = np.sqrt(a, dtype=np.float32)
|
|
self.assertTrue(np.allclose(result, expected))
|
|
|
|
def test_rsqrt(self):
|
|
a = mx.array([0.1, 0.5, 1.0, 10.0])
|
|
result = mx.rsqrt(a)
|
|
expected = 1.0 / np.sqrt(a, dtype=np.float32)
|
|
self.assertTrue(np.allclose(result, expected))
|
|
|
|
def test_reciprocal(self):
|
|
a = mx.array([0.1, 0.5, 1.0, 2.0])
|
|
result = mx.reciprocal(a)
|
|
expected = np.reciprocal(a, dtype=np.float32)
|
|
self.assertTrue(np.allclose(result, expected))
|
|
|
|
def test_logaddexp(self):
|
|
a = mx.array([0, 1, 2, 9.0])
|
|
b = mx.array([1, 0, 4, 2.5])
|
|
|
|
result = mx.logaddexp(a, b)
|
|
expected = np.logaddexp(a, b, dtype=np.float32)
|
|
|
|
self.assertTrue(np.allclose(result, expected))
|
|
|
|
# Complex test
|
|
|
|
a = mx.array([0, 1, 2, 9.0]) + 1j
|
|
b = mx.array([1, 0, 4, 2.5]) + 1j
|
|
|
|
result = mx.logaddexp(a, b)
|
|
expected = np_logaddexp(np.array(a), np.array(b))
|
|
|
|
self.assertTrue(np.allclose(result, expected))
|
|
|
|
a = mx.array([float("nan")])
|
|
b = mx.array([0.0])
|
|
self.assertTrue(math.isnan(mx.logaddexp(a, b).item()))
|
|
|
|
def test_log(self):
|
|
a = mx.array([1, 0.5, 10, 100])
|
|
result = mx.log(a)
|
|
expected = np.log(a, dtype=np.float32)
|
|
|
|
self.assertTrue(np.allclose(result, expected))
|
|
|
|
a = mx.array(1.0) + 1j * mx.array(2.0)
|
|
result = mx.log(a)
|
|
expected = np.log(np.array(a))
|
|
self.assertTrue(np.allclose(result, expected))
|
|
|
|
def test_log2(self):
|
|
a = mx.array([0.5, 1, 2, 10, 16])
|
|
result = mx.log2(a)
|
|
expected = np.log2(a, dtype=np.float32)
|
|
|
|
self.assertTrue(np.allclose(result, expected))
|
|
|
|
a = mx.array(1.0) + 1j * mx.array(2.0)
|
|
result = mx.log2(a)
|
|
expected = np.log2(np.array(a))
|
|
self.assertTrue(np.allclose(result, expected))
|
|
|
|
def test_log10(self):
|
|
a = mx.array([0.1, 1, 10, 20, 100])
|
|
result = mx.log10(a)
|
|
expected = np.log10(a, dtype=np.float32)
|
|
|
|
self.assertTrue(np.allclose(result, expected))
|
|
|
|
a = mx.array(1.0) + 1j * mx.array(2.0)
|
|
result = mx.log10(a)
|
|
expected = np.log10(np.array(a))
|
|
self.assertTrue(np.allclose(result, expected))
|
|
|
|
def test_exp(self):
|
|
a = mx.array([0, 0.5, -0.5, 5])
|
|
result = mx.exp(a)
|
|
expected = np.exp(a, dtype=np.float32)
|
|
|
|
self.assertTrue(np.allclose(result, expected))
|
|
|
|
def test_expm1(self):
|
|
a = mx.array([-88, -87, 0, 0.5, -0.5, 5, 87, 88, 89, 90])
|
|
result = mx.expm1(a)
|
|
errs = np.seterr(over="ignore")
|
|
expected = np.expm1(a)
|
|
np.seterr(over=errs["over"])
|
|
self.assertTrue(np.allclose(result, expected, rtol=1e-3, atol=1e-4))
|
|
|
|
def test_erf(self):
|
|
inputs = [-5, 0.0, 0.5, 1.0, 2.0, 10.0]
|
|
x = mx.array(inputs)
|
|
expected = np.array([math.erf(i) for i in inputs])
|
|
self.assertTrue(np.allclose(mx.erf(x), expected))
|
|
|
|
def test_erfinv(self):
|
|
inputs = [-5.0, -1.0, 0.5, 0.0, 0.5, 1.0, 5.0]
|
|
x = mx.array(inputs)
|
|
# Output of:
|
|
# scipy.special.erfinv([-5.0, -1.0, 0.5, 0.0, 0.5, 1.0, 5.0])
|
|
expected = np.array(
|
|
[
|
|
float("nan"),
|
|
-float("inf"),
|
|
0.47693628,
|
|
0.0,
|
|
0.47693628,
|
|
float("inf"),
|
|
float("nan"),
|
|
]
|
|
).astype(np.float32)
|
|
self.assertTrue(np.allclose(mx.erfinv(x), expected, equal_nan=True))
|
|
|
|
result = mx.erfinv(mx.array([0.9999999403953552] * 8))
|
|
expected = mx.array([3.8325066566467285] * 8)
|
|
self.assertTrue(mx.allclose(result, expected))
|
|
|
|
def test_sin(self):
|
|
a = mx.array(
|
|
[0, math.pi / 4, math.pi / 2, math.pi, 3 * math.pi / 4, 2 * math.pi]
|
|
)
|
|
result = mx.sin(a)
|
|
expected = np.sin(a, dtype=np.float32)
|
|
|
|
self.assertTrue(np.allclose(result, expected))
|
|
|
|
def test_cos(self):
|
|
a = mx.array(
|
|
[0, math.pi / 4, math.pi / 2, math.pi, 3 * math.pi / 4, 2 * math.pi]
|
|
)
|
|
result = mx.cos(a)
|
|
expected = np.cos(a, dtype=np.float32)
|
|
|
|
self.assertTrue(np.allclose(result, expected))
|
|
|
|
def test_degrees(self):
|
|
a = mx.array(
|
|
[0, math.pi / 4, math.pi / 2, math.pi, 3 * math.pi / 4, 2 * math.pi]
|
|
)
|
|
result = mx.degrees(a)
|
|
expected = np.degrees(a, dtype=np.float32)
|
|
|
|
self.assertTrue(np.allclose(result, expected))
|
|
|
|
def test_radians(self):
|
|
a = mx.array([0.0, 45.0, 90.0, 180.0, 270.0, 360.0])
|
|
result = mx.radians(a)
|
|
expected = np.radians(a, dtype=np.float32)
|
|
|
|
self.assertTrue(np.allclose(result, expected))
|
|
|
|
def test_log1p(self):
|
|
a = mx.array([1, 0.5, 10, 100])
|
|
result = mx.log1p(a)
|
|
expected = np.log1p(a, dtype=np.float32)
|
|
|
|
self.assertTrue(np.allclose(result, expected))
|
|
|
|
# Complex test
|
|
a = mx.array([1, 0.5, 10, 100]) + 1j
|
|
result = mx.log1p(a)
|
|
expected = np.log1p(a, dtype=np.complex64)
|
|
|
|
self.assertTrue(np.allclose(result, expected))
|
|
|
|
def test_sigmoid(self):
|
|
a = mx.array([0.0, 1.0, -1.0, 5.0, -5.0])
|
|
result = mx.sigmoid(a)
|
|
expected = 1 / (1 + np.exp(-a, dtype=np.float32))
|
|
self.assertTrue(np.allclose(result, expected))
|
|
|
|
def test_allclose(self):
|
|
a = mx.array(1.0)
|
|
b = mx.array(1.0)
|
|
|
|
self.assertTrue(mx.allclose(a, b).item())
|
|
|
|
b = mx.array(1.1)
|
|
self.assertFalse(mx.allclose(a, b).item())
|
|
self.assertTrue(mx.allclose(a, b, 0.1).item())
|
|
self.assertFalse(mx.allclose(a, b, 0.01).item())
|
|
self.assertTrue(mx.allclose(a, b, 0.01, 0.1).item())
|
|
|
|
c = mx.array(float("inf"))
|
|
self.assertTrue(mx.allclose(c, c).item())
|
|
|
|
def test_isclose(self):
|
|
a = mx.array([float("inf"), float("inf"), float("-inf")])
|
|
b = mx.array([float("inf"), float("-inf"), float("-inf")])
|
|
|
|
self.assertListEqual(mx.isclose(a, b).tolist(), [True, False, True])
|
|
|
|
a = mx.array([np.nan])
|
|
self.assertListEqual(mx.isclose(a, a).tolist(), [False])
|
|
|
|
a = mx.array([np.nan])
|
|
self.assertListEqual(mx.isclose(a, a, equal_nan=True).tolist(), [True])
|
|
|
|
def test_all(self):
|
|
a = mx.array([[True, False], [True, True]])
|
|
|
|
self.assertFalse(mx.all(a).item())
|
|
self.assertEqual(mx.all(a, keepdims=True).shape, (1, 1))
|
|
self.assertFalse(mx.all(a, axis=[0, 1]).item())
|
|
self.assertEqual(mx.all(a, axis=[0]).tolist(), [True, False])
|
|
self.assertEqual(mx.all(a, axis=[1]).tolist(), [False, True])
|
|
self.assertEqual(mx.all(a, axis=0).tolist(), [True, False])
|
|
self.assertEqual(mx.all(a, axis=1).tolist(), [False, True])
|
|
|
|
def test_any(self):
|
|
a = mx.array([[True, False], [False, False]])
|
|
|
|
self.assertTrue(mx.any(a).item())
|
|
self.assertEqual(mx.any(a, keepdims=True).shape, (1, 1))
|
|
self.assertTrue(mx.any(a, axis=[0, 1]).item())
|
|
self.assertEqual(mx.any(a, axis=[0]).tolist(), [True, False])
|
|
self.assertEqual(mx.any(a, axis=[1]).tolist(), [True, False])
|
|
self.assertEqual(mx.any(a, axis=0).tolist(), [True, False])
|
|
self.assertEqual(mx.any(a, axis=1).tolist(), [True, False])
|
|
|
|
def test_stop_gradient(self):
|
|
def func(x):
|
|
return mx.sum(2 * x + mx.stop_gradient(3 * x))
|
|
|
|
x = mx.array([0.0, 0.1, -3])
|
|
expected = [2, 2, 2]
|
|
|
|
self.assertListEqual(mx.grad(func)(x).tolist(), expected)
|
|
|
|
def test_kron(self):
|
|
# Basic vector test
|
|
x = mx.array([1, 2])
|
|
y = mx.array([3, 4])
|
|
z = mx.kron(x, y)
|
|
self.assertEqual(z.tolist(), [3, 4, 6, 8])
|
|
|
|
# Basic matrix test
|
|
x = mx.array([[1, 2], [3, 4]])
|
|
y = mx.array([[0, 5], [6, 7]])
|
|
z = mx.kron(x, y)
|
|
self.assertEqual(
|
|
z.tolist(),
|
|
[[0, 5, 0, 10], [6, 7, 12, 14], [0, 15, 0, 20], [18, 21, 24, 28]],
|
|
)
|
|
|
|
# Test with different dimensions
|
|
x = mx.array([1, 2]) # (2,)
|
|
y = mx.array([[3, 4], [5, 6]]) # (2, 2)
|
|
z = mx.kron(x, y)
|
|
self.assertEqual(z.tolist(), [[3, 4, 6, 8], [5, 6, 10, 12]])
|
|
|
|
# Test with empty array
|
|
x = mx.array([])
|
|
y = mx.array([1, 2])
|
|
with self.assertRaises(ValueError):
|
|
mx.kron(x, y)
|
|
|
|
def test_take(self):
|
|
# Shape: 4 x 3 x 2
|
|
l = [
|
|
[[1, 3], [-2, -2], [-3, -2]],
|
|
[[2, 4], [-3, 2], [-4, -2]],
|
|
[[2, 3], [2, 4], [2, 1]],
|
|
[[1, -5], [3, -1], [2, 3]],
|
|
]
|
|
|
|
a = mx.array(l)
|
|
a_npy = np.array(l)
|
|
|
|
indices = [0, -1]
|
|
flatten_take = mx.take(a, mx.array(indices)).tolist()
|
|
flatten_take_expected = np.take(a_npy, np.array(indices)).tolist()
|
|
self.assertListEqual(flatten_take, flatten_take_expected)
|
|
|
|
indices = [-1, 2, 0]
|
|
axis_take = mx.take(a, mx.array(indices), axis=0).tolist()
|
|
axis_take_expected = np.take(a_npy, np.array(indices), axis=0).tolist()
|
|
self.assertListEqual(axis_take, axis_take_expected)
|
|
|
|
indices = [0, 0, -2]
|
|
axis_take = mx.take(a, mx.array(indices), axis=1).tolist()
|
|
axis_take_expected = np.take(a_npy, np.array(indices), axis=1).tolist()
|
|
self.assertListEqual(axis_take, axis_take_expected)
|
|
|
|
indices = [0, -1, -1]
|
|
axis_take = mx.take(a, mx.array(indices), axis=-1).tolist()
|
|
axis_take_expected = np.take(a_npy, np.array(indices), axis=-1).tolist()
|
|
self.assertListEqual(axis_take, axis_take_expected)
|
|
|
|
a_npy = np.arange(8 * 8 * 8, dtype=np.int32)
|
|
a_npy = a_npy.reshape((8, 8, 8))
|
|
idx_npy = np.arange(6, dtype=np.uint32)
|
|
idx_npy = idx_npy.reshape((2, 3))
|
|
a_mlx = mx.array(a_npy)
|
|
idx_mlx = mx.array(idx_npy)
|
|
|
|
a_npy_taken = np.take(a_npy, idx_npy)
|
|
a_mlx_taken = mx.take(a_mlx, idx_mlx)
|
|
self.assertEqual(a_npy_taken.shape, a_mlx_taken.shape)
|
|
self.assertListEqual(a_npy_taken.tolist(), a_mlx_taken.tolist())
|
|
|
|
a_npy_taken = np.take(a_npy, idx_npy, axis=0)
|
|
a_mlx_taken = mx.take(a_mlx, idx_mlx, axis=0)
|
|
self.assertEqual(a_npy_taken.shape, a_mlx_taken.shape)
|
|
self.assertListEqual(a_npy_taken.tolist(), a_mlx_taken.tolist())
|
|
|
|
a_npy_taken = np.take(a_npy, idx_npy, axis=1)
|
|
a_mlx_taken = mx.take(a_mlx, idx_mlx, axis=1)
|
|
self.assertEqual(a_npy_taken.shape, a_mlx_taken.shape)
|
|
self.assertListEqual(a_npy_taken.tolist(), a_mlx_taken.tolist())
|
|
|
|
a_npy_taken = np.take(a_npy, idx_npy, axis=2)
|
|
a_mlx_taken = mx.take(a_mlx, idx_mlx, axis=2)
|
|
self.assertEqual(a_npy_taken.shape, a_mlx_taken.shape)
|
|
self.assertListEqual(a_npy_taken.tolist(), a_mlx_taken.tolist())
|
|
|
|
# Take with integer index
|
|
a = mx.arange(8).reshape(2, 4)
|
|
out = mx.take(a, 1, axis=0)
|
|
self.assertTrue(mx.array_equal(out, mx.array([4, 5, 6, 7])))
|
|
out = mx.take(a, 1, axis=1)
|
|
self.assertTrue(mx.array_equal(out, mx.array([1, 5])))
|
|
|
|
# Take with multi-dim scalar preserves dims
|
|
out = mx.take(a, mx.array(1), axis=0)
|
|
self.assertEqual(out.shape, (4,))
|
|
|
|
out = mx.take(a, mx.array([1]), axis=0)
|
|
self.assertEqual(out.shape, (1, 4))
|
|
|
|
out = mx.take(a, mx.array([[1]]), axis=0)
|
|
self.assertEqual(out.shape, (1, 1, 4))
|
|
|
|
def test_take_along_axis(self):
|
|
a_np = np.arange(8).reshape(2, 2, 2)
|
|
a_mlx = mx.array(a_np)
|
|
idx_np = np.array([1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0])
|
|
idx_mlx = mx.array(idx_np)
|
|
|
|
for ax in [None, 0, 1, 2]:
|
|
if ax == None:
|
|
shape = [-1]
|
|
else:
|
|
shape = [2] * 3
|
|
shape[ax] = 3
|
|
out_np = np.take_along_axis(a_np, idx_np.reshape(shape), axis=ax)
|
|
out_mlx = mx.take_along_axis(a_mlx, mx.reshape(idx_mlx, shape), axis=ax)
|
|
self.assertTrue(np.array_equal(out_np, np.array(out_mlx)))
|
|
|
|
def test_put_along_axis(self):
|
|
for ax in [None, 0, 1, 2]:
|
|
|
|
a_np = np.arange(16).reshape(2, 2, 4).astype(np.int32)
|
|
a_mlx = mx.array(a_np)
|
|
|
|
if ax == None:
|
|
idx_np = np.random.permutation(a_np.size)
|
|
values_np = np.random.randint(low=0, high=100, size=(16,))
|
|
else:
|
|
shape = list(a_np.shape)
|
|
shape[ax] = 2
|
|
idx_np = np.random.choice(a_np.shape[ax], replace=False, size=(2,))
|
|
idx_np = np.expand_dims(idx_np, list(range(1, 2 - ax + 1)))
|
|
idx_np = np.broadcast_to(idx_np, shape)
|
|
values_np = np.random.randint(low=0, high=100, size=shape)
|
|
|
|
idx_np.astype(np.int32)
|
|
values_np.astype(a_np.dtype)
|
|
|
|
idx_mlx = mx.array(idx_np)
|
|
values_mlx = mx.array(values_np)
|
|
|
|
np.put_along_axis(a_np, idx_np, values_np, axis=ax)
|
|
out_mlx = mx.put_along_axis(a_mlx, idx_mlx, values_mlx, axis=ax)
|
|
self.assertTrue(np.array_equal(a_np, out_mlx))
|
|
|
|
source = mx.zeros((1, 1, 8, 32))
|
|
indices = mx.array([0, 2, 4, 5]).reshape((1, 1, 4, 1))
|
|
update = mx.array(1.0)
|
|
|
|
out_mlx = mx.put_along_axis(source, indices, update, axis=-2)
|
|
out_np = np.array(source)
|
|
np.put_along_axis(out_np, np.array(indices), np.array(update), axis=-2)
|
|
self.assertTrue(np.array_equal(out_np, np.array(out_mlx)))
|
|
|
|
a = mx.array([], mx.float32)
|
|
b = mx.put_along_axis(a, a, a, axis=None)
|
|
mx.eval(b)
|
|
self.assertEqual(b.size, 0)
|
|
self.assertEqual(b.shape, a.shape)
|
|
|
|
def test_split(self):
|
|
a = mx.array([1, 2, 3])
|
|
splits = mx.split(a, 3)
|
|
for e, x in enumerate(splits):
|
|
self.assertEqual(x.item(), e + 1)
|
|
|
|
a = mx.array([[1, 2], [3, 4], [5, 6]])
|
|
x, y, z = mx.split(a, 3, axis=0)
|
|
self.assertEqual(x.tolist(), [[1, 2]])
|
|
self.assertEqual(y.tolist(), [[3, 4]])
|
|
self.assertEqual(z.tolist(), [[5, 6]])
|
|
|
|
with self.assertRaises(ValueError):
|
|
mx.split(a, 3, axis=2)
|
|
|
|
a = mx.arange(8)
|
|
x, y, z = mx.split(a, [1, 5])
|
|
self.assertEqual(x.tolist(), [0])
|
|
self.assertEqual(y.tolist(), [1, 2, 3, 4])
|
|
self.assertEqual(z.tolist(), [5, 6, 7])
|
|
|
|
def test_arange_overload_dispatch(self):
|
|
with self.assertRaises(ValueError):
|
|
a = mx.arange(float("nan"), 1, 5)
|
|
with self.assertRaises(ValueError):
|
|
a = mx.arange(0, float("nan"), 5)
|
|
with self.assertRaises(ValueError):
|
|
a = mx.arange(0, 2, float("nan"))
|
|
with self.assertRaises(ValueError):
|
|
a = mx.arange(0, float("inf"), float("inf"))
|
|
with self.assertRaises(ValueError):
|
|
a = mx.arange(float("inf"), 1, float("inf"))
|
|
with self.assertRaises(ValueError):
|
|
a = mx.arange(float("inf"), 1, 5)
|
|
with self.assertRaises(TypeError):
|
|
INT_MAX = 2147483647
|
|
a = mx.arange(0, INT_MAX + 1, 1)
|
|
|
|
a = mx.arange(5)
|
|
expected = [0, 1, 2, 3, 4]
|
|
self.assertListEqual(a.tolist(), expected)
|
|
|
|
a = mx.arange(1, 5)
|
|
expected = [1, 2, 3, 4]
|
|
self.assertListEqual(a.tolist(), expected)
|
|
|
|
a = mx.arange(-3, step=-1)
|
|
expected = [0, -1, -2]
|
|
self.assertListEqual(a.tolist(), expected)
|
|
|
|
a = mx.arange(stop=2, step=0.5)
|
|
expected = [0, 0.5, 1.0, 1.5]
|
|
self.assertListEqual(a.tolist(), expected)
|
|
|
|
with self.assertRaises(TypeError):
|
|
mx.arange(start=1, step=2)
|
|
|
|
a = mx.arange(stop=3)
|
|
expected = [0, 1, 2]
|
|
self.assertListEqual(a.tolist(), expected)
|
|
|
|
def test_arange_inferred_dtype(self):
|
|
a = mx.arange(5)
|
|
self.assertEqual(a.dtype, mx.int32)
|
|
|
|
a = mx.arange(5.0)
|
|
self.assertEqual(a.dtype, mx.float32)
|
|
|
|
a = mx.arange(1, 3.0)
|
|
self.assertEqual(a.dtype, mx.float32)
|
|
|
|
a = mx.arange(1, 3, dtype=mx.float32)
|
|
self.assertEqual(a.dtype, mx.float32)
|
|
|
|
a = mx.arange(1, 5, 1)
|
|
self.assertEqual(a.dtype, mx.int32)
|
|
|
|
a = mx.arange(1.0, 5, 1)
|
|
self.assertEqual(a.dtype, mx.float32)
|
|
|
|
a = mx.arange(1, 5.0, 1)
|
|
self.assertEqual(a.dtype, mx.float32)
|
|
|
|
a = mx.arange(1, 5, 1.0)
|
|
self.assertEqual(a.dtype, mx.float32)
|
|
|
|
a = mx.arange(1.0, 3.0, 0.2, dtype=mx.int32)
|
|
self.assertEqual(a.dtype, mx.int32)
|
|
|
|
def test_arange_corner_cases_cast(self):
|
|
a = mx.arange(0, 3, 0.2, dtype=mx.int32)
|
|
expected = [0] * 15
|
|
self.assertListEqual(a.tolist(), expected)
|
|
self.assertEqual(a.dtype, mx.int32)
|
|
|
|
a = mx.arange(-1, -4, -0.9, dtype=mx.int32)
|
|
expected = [-1] * 4
|
|
self.assertListEqual(a.tolist(), expected)
|
|
self.assertEqual(a.dtype, mx.int32)
|
|
|
|
a = mx.arange(-1, -20, -1.2, dtype=mx.int32)
|
|
expected = [
|
|
-1,
|
|
-2,
|
|
-3,
|
|
-4,
|
|
-5,
|
|
-6,
|
|
-7,
|
|
-8,
|
|
-9,
|
|
-10,
|
|
-11,
|
|
-12,
|
|
-13,
|
|
-14,
|
|
-15,
|
|
-16,
|
|
]
|
|
self.assertListEqual(a.tolist(), expected)
|
|
self.assertEqual(a.dtype, mx.int32)
|
|
|
|
a = mx.arange(0, 10, 100)
|
|
expected = [0]
|
|
self.assertListEqual(a.tolist(), expected)
|
|
self.assertEqual(a.dtype, mx.int32)
|
|
|
|
a = mx.arange(10, 0, 1)
|
|
expected = []
|
|
self.assertListEqual(a.tolist(), expected)
|
|
|
|
a = mx.arange(10, 0, float("inf"))
|
|
expected = []
|
|
self.assertListEqual(a.tolist(), expected)
|
|
|
|
a = mx.arange(0, 10, float("inf"))
|
|
expected = [0]
|
|
self.assertListEqual(a.tolist(), expected)
|
|
|
|
a = mx.arange(0, -10, float("-inf"))
|
|
expected = [0]
|
|
self.assertListEqual(a.tolist(), expected)
|
|
|
|
def test_unary_ops(self):
|
|
def test_ops(npop, mlxop, x, y, atol):
|
|
r_np = npop(x)
|
|
r_mlx = mlxop(y)
|
|
mx.eval(r_mlx)
|
|
|
|
self.assertTrue(np.allclose(r_np, r_mlx, atol=atol))
|
|
|
|
x = np.random.rand(18, 28, 38)
|
|
for op in ["abs", "exp", "log", "square", "sqrt"]:
|
|
with self.subTest(op=op):
|
|
float_dtypes = [("float16", 1e-3), ("float32", 1e-6)]
|
|
|
|
for dtype, atol in float_dtypes:
|
|
with self.subTest(dtype=dtype):
|
|
x_ = x.astype(getattr(np, dtype))
|
|
y_ = mx.array(x_)
|
|
test_ops(getattr(np, op), getattr(mx, op), x_, y_, atol)
|
|
|
|
def test_unary_ops_from_non_array(self):
|
|
unary_ops = [
|
|
"abs",
|
|
"exp",
|
|
"log",
|
|
"square",
|
|
"sqrt",
|
|
"sin",
|
|
"cos",
|
|
"tan",
|
|
"sinh",
|
|
"cosh",
|
|
"tanh",
|
|
"sign",
|
|
"negative",
|
|
"expm1",
|
|
"arcsin",
|
|
"arccos",
|
|
"arctan",
|
|
"arcsinh",
|
|
"arctanh",
|
|
"degrees",
|
|
"radians",
|
|
"log2",
|
|
"log10",
|
|
"log1p",
|
|
"floor",
|
|
"ceil",
|
|
"conjugate",
|
|
]
|
|
|
|
x = 0.5
|
|
x_np = np.random.rand(10).astype(np.float32)
|
|
for op in unary_ops:
|
|
with self.subTest(op=op):
|
|
# Test from scalar
|
|
expected = getattr(np, op)(x)
|
|
out = getattr(mx, op)(x)
|
|
|
|
# Check close
|
|
self.assertTrue(np.allclose(expected, out, equal_nan=True))
|
|
|
|
# Test from NumPy
|
|
expected = getattr(np, op)(x_np)
|
|
out = getattr(mx, op)(x_np)
|
|
|
|
# Check close
|
|
self.assertTrue(np.allclose(expected, np.array(out), equal_nan=True))
|
|
|
|
def test_trig_ops(self):
|
|
def test_ops(npop, mlxop, x, y, atol):
|
|
r_np = npop(x)
|
|
r_mlx = mlxop(y)
|
|
mx.eval(r_mlx)
|
|
|
|
self.assertTrue(np.allclose(r_np, r_mlx, atol=atol))
|
|
|
|
x = np.random.rand(9, 12, 18)
|
|
xi = np.random.rand(9, 12, 18)
|
|
base_ops = ["sin", "cos", "tan"]
|
|
hyperbolic_ops = ["sinh", "cosh", "tanh"]
|
|
all_fwd_ops = base_ops + hyperbolic_ops
|
|
|
|
for op in all_fwd_ops:
|
|
with self.subTest(op=op):
|
|
float_dtypes = [("float16", 1e-3), ("float32", 1e-6)]
|
|
|
|
for dtype, atol in float_dtypes:
|
|
with self.subTest(dtype=dtype):
|
|
x_ = x.astype(getattr(np, dtype))
|
|
y_ = mx.array(x_)
|
|
test_ops(getattr(np, op), getattr(mx, op), x_, y_, atol)
|
|
|
|
with self.subTest(op=op):
|
|
float_dtypes = [("complex64", 1e-5)]
|
|
|
|
for dtype, atol in float_dtypes:
|
|
with self.subTest(dtype=dtype):
|
|
x_ = x + 1.0j * xi
|
|
x_ = x_.astype(getattr(np, dtype))
|
|
y_ = mx.array(x_)
|
|
test_ops(getattr(np, op), getattr(mx, op), x_, y_, atol)
|
|
|
|
with self.subTest(op="arc" + op):
|
|
float_dtypes = [("float16", 1e-3), ("float32", 1e-6)]
|
|
op_inv = "arc" + op
|
|
|
|
for dtype, atol in float_dtypes:
|
|
with self.subTest(dtype=dtype):
|
|
np_op_fwd = getattr(np, op)
|
|
x_ = np_op_fwd(x).astype(getattr(np, dtype))
|
|
y_ = mx.array(x_)
|
|
test_ops(getattr(np, op_inv), getattr(mx, op_inv), x_, y_, atol)
|
|
|
|
# Test grads
|
|
np_vjp_funcs = {
|
|
"sin": lambda primal, cotan: cotan * np.cos(primal),
|
|
"cos": lambda primal, cotan: -cotan * np.sin(primal),
|
|
"tan": lambda primal, cotan: cotan / (np.cos(primal) ** 2),
|
|
"sinh": lambda primal, cotan: cotan * np.cosh(primal),
|
|
"cosh": lambda primal, cotan: cotan * np.sinh(primal),
|
|
"tanh": lambda primal, cotan: cotan / (np.cosh(primal) ** 2),
|
|
"arcsin": lambda primal, cotan: cotan / np.sqrt(1.0 - primal**2),
|
|
"arccos": lambda primal, cotan: -cotan / np.sqrt(1.0 - primal**2),
|
|
"arctan": lambda primal, cotan: cotan / (1.0 + primal**2),
|
|
"arctan2": lambda primal, cotan: cotan / (1.0 + primal**2),
|
|
"arcsinh": lambda primal, cotan: cotan / np.sqrt(primal**2 + 1),
|
|
"arccosh": lambda primal, cotan: cotan / np.sqrt(primal**2 - 1),
|
|
"arctanh": lambda primal, cotan: cotan / (1.0 - primal**2),
|
|
}
|
|
with self.subTest(name="grads"):
|
|
for op in all_fwd_ops:
|
|
with self.subTest(op=op):
|
|
primal_np = xi.astype(np.float32)
|
|
primal_mx = mx.array(primal_np)
|
|
x_ = x.astype(np.float32)
|
|
y_ = mx.array(x_)
|
|
op_ = op
|
|
atol_ = 1e-5
|
|
|
|
np_vjp = lambda x: np_vjp_funcs[op_](primal_np, x)
|
|
mx_vjp = lambda x: mx.vjp(getattr(mx, op_), [primal_mx], [x])[1][0]
|
|
test_ops(np_vjp, mx_vjp, x_, y_, atol_)
|
|
|
|
with self.subTest(op="arc" + op):
|
|
np_op_fwd = getattr(np, op)
|
|
primal_np = np_op_fwd(xi).astype(np.float32)
|
|
|
|
# To avoid divide by zero error
|
|
if op == "cosh":
|
|
primal_np[np.isclose(primal_np, 1.0)] += 1e-3
|
|
elif op == "cos":
|
|
primal_np[np.isclose(primal_np, 1.0)] -= 1e-3
|
|
|
|
primal_mx = mx.array(primal_np)
|
|
x_ = x.astype(np.float32)
|
|
y_ = mx.array(x_)
|
|
op_ = "arc" + op
|
|
atol_ = 1e-5
|
|
|
|
np_vjp = lambda x: np_vjp_funcs[op_](primal_np, x)
|
|
mx_vjp = lambda x: mx.vjp(getattr(mx, op_), [primal_mx], [x])[1][0]
|
|
test_ops(np_vjp, mx_vjp, x_, y_, atol_)
|
|
|
|
def test_binary_ops(self):
|
|
def test_ops(npop, mlxop, x1, x2, y1, y2, atol):
|
|
r_np = npop(x1, x2)
|
|
r_mlx = mlxop(y1, y2)
|
|
mx.eval(r_mlx)
|
|
self.assertTrue(np.allclose(r_np, r_mlx, atol=atol))
|
|
|
|
r_np = npop(x1[:1], x2)
|
|
r_mlx = mlxop(y1[:1], y2)
|
|
mx.eval(r_mlx)
|
|
self.assertTrue(np.allclose(r_np, r_mlx, atol=atol))
|
|
|
|
r_np = npop(x1[:, :1], x2)
|
|
r_mlx = mlxop(y1[:, :1], y2)
|
|
mx.eval(r_mlx)
|
|
self.assertTrue(np.allclose(r_np, r_mlx, atol=atol))
|
|
|
|
r_np = npop(x1[:, :, :1], x2)
|
|
r_mlx = mlxop(y1[:, :, :1], y2)
|
|
mx.eval(r_mlx)
|
|
self.assertTrue(np.allclose(r_np, r_mlx, atol=atol))
|
|
|
|
x1 = np.maximum(np.random.rand(18, 28, 38), 0.1)
|
|
x2 = np.maximum(np.random.rand(18, 28, 38), 0.1)
|
|
y1 = mx.array(x1)
|
|
y2 = mx.array(x2)
|
|
mx.eval(y1, y2)
|
|
for op in [
|
|
"add",
|
|
"subtract",
|
|
"multiply",
|
|
"divide",
|
|
"floor_divide",
|
|
"maximum",
|
|
"minimum",
|
|
"power",
|
|
]:
|
|
with self.subTest(op=op):
|
|
int_dtypes = [
|
|
"int8",
|
|
"int16",
|
|
"int32",
|
|
"int64",
|
|
"uint8",
|
|
"uint16",
|
|
"uint32",
|
|
"uint64",
|
|
]
|
|
float_dtypes = ["float16", "float32"]
|
|
|
|
dtypes = {
|
|
"divide": float_dtypes,
|
|
"power": float_dtypes,
|
|
"floor_divide": ["float32"] + int_dtypes,
|
|
}
|
|
dtypes = dtypes.get(op, int_dtypes + float_dtypes)
|
|
|
|
for dtype in dtypes:
|
|
atol = 1e-3 if dtype == "float16" else 1e-6
|
|
with self.subTest(dtype=dtype):
|
|
m = 10 if dtype in int_dtypes else 1
|
|
x1_ = (x1 * m).astype(getattr(np, dtype))
|
|
x2_ = (x2 * m).astype(getattr(np, dtype))
|
|
y1_ = mx.array(x1_)
|
|
y2_ = mx.array(x2_)
|
|
test_ops(
|
|
getattr(np, op), getattr(mx, op), x1_, x2_, y1_, y2_, atol
|
|
)
|
|
|
|
def test_irregular_binary_ops(self):
|
|
# Check transposed binary ops
|
|
dims = [2, 3, 4, 5]
|
|
size = 3
|
|
trial_mul = 2
|
|
np.random.seed(0)
|
|
for d in dims:
|
|
anp = np.random.randint(-20, 20, (size**d,)).reshape([size] * d)
|
|
bnp = np.random.randint(-20, 20, (size**d,)).reshape([size] * d)
|
|
for _ in range(trial_mul * d):
|
|
amlx = mx.array(anp)
|
|
bmlx = mx.array(bnp)
|
|
a_t = np.random.permutation(d).tolist()
|
|
b_t = np.random.permutation(d).tolist()
|
|
outnp = np.add(anp.transpose(a_t), bnp.transpose(b_t))
|
|
outmlx = mx.add(mx.transpose(amlx, a_t), mx.transpose(bmlx, b_t))
|
|
self.assertTrue(np.array_equal(outnp, outmlx))
|
|
|
|
# Check broadcast binary ops
|
|
for d in dims:
|
|
anp = np.random.randint(-20, 20, (size**d,)).reshape([size] * d)
|
|
for n_bsx in range(d):
|
|
bnp = np.random.randint(-20, 20, (size**n_bsx,)).reshape([size] * n_bsx)
|
|
for _ in range(trial_mul * d):
|
|
amlx = mx.array(anp)
|
|
bmlx = mx.array(bnp)
|
|
b_shape = [1] * (d - n_bsx) + [size] * n_bsx
|
|
np.random.shuffle(b_shape)
|
|
outnp = np.add(anp, bnp.reshape(b_shape))
|
|
outmlx = mx.add(amlx, mx.reshape(bmlx, b_shape))
|
|
self.assertTrue(np.array_equal(outnp, outmlx))
|
|
|
|
# Check strided binary ops
|
|
for d in dims:
|
|
a = np.random.randint(-20, 20, (10,) * d)
|
|
b = np.random.randint(-20, 20, (10,) * d)
|
|
a_ = mx.array(a)
|
|
b_ = mx.array(b)
|
|
for t in permutations(range(d)):
|
|
for s in range(d):
|
|
idx = tuple(
|
|
[slice(None)] * s
|
|
+ [slice(None, None, 2)]
|
|
+ [slice(None)] * (d - s - 1)
|
|
)
|
|
c = a.transpose(t)[idx] + b[idx]
|
|
c_ = mx.transpose(a_, t)[idx] + b_[idx]
|
|
self.assertTrue(np.array_equal(c, c_))
|
|
|
|
def test_softmax(self):
|
|
cases = [(np.float32, 1e-6), (np.float16, 1e-3)]
|
|
|
|
for dtype, atol in cases:
|
|
a_npy = np.random.randn(16, 8, 32).astype(dtype)
|
|
a_mlx = mx.array(a_npy)
|
|
|
|
def np_softmax(x, axis):
|
|
ex = np.exp(x - np.max(x, axis=axis, keepdims=True))
|
|
return ex / np.sum(ex, axis=axis, keepdims=True)
|
|
|
|
for axes in (None, 0, 1, 2, (0, 1), (1, 2), (0, 2), (0, 1, 2)):
|
|
b_npy = np_softmax(a_npy, axes)
|
|
b_mlx = mx.softmax(a_mlx, axes)
|
|
self.assertTrue(np.allclose(b_npy, b_mlx, atol=atol))
|
|
|
|
for s in [100, 2049, 4097, 8193]:
|
|
a = np.full(s, -np.inf)
|
|
a[-1] = 0.0
|
|
a = mx.softmax(mx.array(a))
|
|
self.assertFalse(np.any(np.isnan(a)))
|
|
self.assertTrue((a[:-1] < 1e-9).all())
|
|
self.assertEqual(a[-1], 1)
|
|
|
|
# Sliced inputs
|
|
y = mx.random.uniform(shape=(8, 4))
|
|
out = mx.softmax(y[:, 0:2], axis=-1)
|
|
self.assertAlmostEqual(out.sum().item(), 8.0, 5)
|
|
|
|
# Precise
|
|
for t in [mx.float16, mx.bfloat16]:
|
|
a = (10 * mx.random.normal(shape=(1024,))).astype(t)
|
|
out_expect = mx.softmax(a.astype(mx.float32)).astype(t)
|
|
out = mx.softmax(a, axis=-1, precise=True)
|
|
self.assertTrue(mx.allclose(out_expect, out))
|
|
|
|
# All Infs give NaNs
|
|
for n in [127, 128, 129]:
|
|
x = mx.full((n,), vals=-float("inf"))
|
|
self.assertTrue(mx.all(mx.isnan(mx.softmax(x))))
|
|
|
|
# Transposed inputs
|
|
a = mx.random.uniform(shape=(32, 32, 32))
|
|
b = mx.softmax(a, axis=-1)
|
|
c = mx.softmax(a.swapaxes(0, 1), axis=-1).swapaxes(0, 1)
|
|
self.assertEqual((b - c).abs().max().item(), 0.0)
|
|
|
|
with self.assertRaises(ValueError):
|
|
mx.softmax(mx.array(1.0), axis=-1)
|
|
|
|
def test_concatenate(self):
|
|
a_npy = np.random.randn(32, 32, 32)
|
|
b_npy = np.random.randn(32, 32, 32)
|
|
a_mlx = mx.array(a_npy)
|
|
b_mlx = mx.array(b_npy)
|
|
|
|
for axis in (None, 0, 1, 2):
|
|
for p in permutations([0, 1, 2]):
|
|
c_npy = np.concatenate([a_npy, np.transpose(b_npy, p)], axis=axis)
|
|
c_mlx = mx.concatenate([a_mlx, mx.transpose(b_mlx, p)], axis=axis)
|
|
self.assertEqual(list(c_npy.shape), list(c_mlx.shape))
|
|
self.assertTrue(np.allclose(c_npy, c_mlx, atol=1e-6))
|
|
|
|
with self.assertRaises(ValueError):
|
|
a = mx.array([[1, 2], [1, 2], [1, 2]])
|
|
b = mx.array([1, 2])
|
|
mx.concatenate([a, b], axis=0)
|
|
|
|
# Cocnatenate with 0-sized array
|
|
a = mx.zeros((2, 0, 2))
|
|
b = mx.zeros((2, 2, 2))
|
|
out = mx.concatenate([a, b], axis=1)
|
|
self.assertTrue(mx.array_equal(out, b))
|
|
|
|
def test_meshgrid(self):
|
|
x = mx.array([1, 2, 3], dtype=mx.int32)
|
|
y = np.array([1, 2, 3], dtype=np.int32)
|
|
|
|
# Test single input
|
|
a_mlx = mx.meshgrid(x)
|
|
a_np = np.meshgrid(y)
|
|
self.assertEqualArray(a_mlx[0], mx.array(a_np[0]))
|
|
|
|
# Test sparse
|
|
a_mlx, b_mlx, c_mlx = mx.meshgrid(x, x, x, sparse=True)
|
|
a_np, b_np, c_np = np.meshgrid(y, y, y, sparse=True)
|
|
self.assertEqualArray(a_mlx, mx.array(a_np))
|
|
self.assertEqualArray(b_mlx, mx.array(b_np))
|
|
self.assertEqualArray(c_mlx, mx.array(c_np))
|
|
|
|
# Test different lengths
|
|
x = mx.array([1, 2], dtype=mx.int32)
|
|
y = mx.array([1, 2, 3], dtype=mx.int32)
|
|
z = np.array([1, 2], dtype=np.int32)
|
|
w = np.array([1, 2, 3], dtype=np.int32)
|
|
a_mlx, b_mlx = mx.meshgrid(x, y)
|
|
a_np, b_np = np.meshgrid(z, w)
|
|
self.assertEqualArray(a_mlx, mx.array(a_np))
|
|
self.assertEqualArray(b_mlx, mx.array(b_np))
|
|
|
|
# Test empty input
|
|
x = mx.array([], dtype=mx.int32)
|
|
y = np.array([], dtype=np.int32)
|
|
a_mlx = mx.meshgrid(x)
|
|
a_np = np.meshgrid(y)
|
|
self.assertEqualArray(a_mlx[0], mx.array(a_np[0]))
|
|
|
|
# Test float32 input
|
|
x = mx.array([1.1, 2.2, 3.3], dtype=mx.float32)
|
|
y = np.array([1.1, 2.2, 3.3], dtype=np.float32)
|
|
a_mlx = mx.meshgrid(x, x, x)
|
|
a_np = np.meshgrid(y, y, y)
|
|
self.assertEqualArray(a_mlx[0], mx.array(a_np[0]))
|
|
self.assertEqualArray(a_mlx[1], mx.array(a_np[1]))
|
|
self.assertEqualArray(a_mlx[2], mx.array(a_np[2]))
|
|
|
|
# Test ij indexing
|
|
x = mx.array([1.1, 2.2, 3.3, 4.4, 5.5], dtype=mx.float32)
|
|
y = np.array([1.1, 2.2, 3.3, 4.4, 5.5], dtype=np.float32)
|
|
a_mlx = mx.meshgrid(x, x, indexing="ij")
|
|
a_np = np.meshgrid(y, y, indexing="ij")
|
|
self.assertEqualArray(a_mlx[0], mx.array(a_np[0]))
|
|
self.assertEqualArray(a_mlx[1], mx.array(a_np[1]))
|
|
|
|
# Test different lengths, sparse, and ij indexing
|
|
a = mx.array([1, 2], dtype=mx.int64)
|
|
b = mx.array([1, 2, 3], dtype=mx.int64)
|
|
c = mx.array([1, 2, 3, 4], dtype=mx.int64)
|
|
x = np.array([1, 2], dtype=np.int64)
|
|
y = np.array([1, 2, 3], dtype=np.int64)
|
|
z = np.array([1, 2, 3, 4], dtype=np.int64)
|
|
a_mlx, b_mlx, c_mlx = mx.meshgrid(a, b, c, sparse=True, indexing="ij")
|
|
a_np, b_np, c_np = np.meshgrid(x, y, z, sparse=True, indexing="ij")
|
|
self.assertEqualArray(a_mlx, mx.array(a_np))
|
|
self.assertEqualArray(b_mlx, mx.array(b_np))
|
|
self.assertEqualArray(c_mlx, mx.array(c_np))
|
|
|
|
def test_pad(self):
|
|
pad_width_and_values = [
|
|
([(1, 1), (1, 1), (1, 1)], 0),
|
|
([(1, 1), (1, 1), (1, 1)], 5),
|
|
([(3, 0), (0, 2), (5, 7)], 0),
|
|
([(3, 0), (0, 2), (5, 7)], -7),
|
|
([(0, 0), (0, 0), (0, 0)], 0),
|
|
]
|
|
|
|
for pw, v in pad_width_and_values:
|
|
with self.subTest(pad_width=pw, value=v):
|
|
a_npy = np.random.randn(16, 16, 16).astype(np.float32)
|
|
a_mlx = mx.array(a_npy)
|
|
|
|
b_npy = np.pad(a_npy, pw, constant_values=v)
|
|
b_mlx = mx.pad(a_mlx, pw, constant_values=v)
|
|
|
|
self.assertEqual(list(b_npy.shape), list(b_mlx.shape))
|
|
self.assertTrue(np.allclose(b_npy, b_mlx, atol=1e-6))
|
|
|
|
b_npy = np.pad(a_npy, pw, mode="edge")
|
|
b_mlx = mx.pad(a_mlx, pw, mode="edge")
|
|
|
|
self.assertEqual(list(b_npy.shape), list(b_mlx.shape))
|
|
self.assertTrue(np.allclose(b_npy, b_mlx, atol=1e-6))
|
|
|
|
a = mx.zeros((1, 1, 1))
|
|
self.assertEqual(mx.pad(a, 1).shape, (3, 3, 3))
|
|
self.assertEqual(mx.pad(a, (1,)).shape, (3, 3, 3))
|
|
self.assertEqual(mx.pad(a, [1]).shape, (3, 3, 3))
|
|
self.assertEqual(mx.pad(a, (1, 2)).shape, (4, 4, 4))
|
|
self.assertEqual(mx.pad(a, [(1, 2)]).shape, (4, 4, 4))
|
|
self.assertEqual(mx.pad(a, ((1, 2),)).shape, (4, 4, 4))
|
|
self.assertEqual(mx.pad(a, ((1, 2), (2, 1), (2, 2))).shape, (4, 4, 5))
|
|
|
|
# Test grads
|
|
a_fwd = mx.array(np.random.rand(16, 16).astype(np.float32))
|
|
a_bwd = mx.ones((22, 22))
|
|
f = lambda x: mx.pad(x, ((4, 2), (2, 4)))
|
|
|
|
_, df = mx.vjp(f, [a_fwd], [a_bwd])
|
|
self.assertTrue(mx.allclose(a_bwd[4:-2, 2:-4], df[0]).item())
|
|
|
|
def test_where(self):
|
|
self.assertCmpNumpy([True, mx.array([[1, 2], [3, 4]]), 1], mx.where, np.where)
|
|
self.assertCmpNumpy([True, 1, mx.array([[1, 2], [3, 4]])], mx.where, np.where)
|
|
self.assertCmpNumpy(
|
|
[
|
|
mx.array([[True, False], [False, True]]),
|
|
mx.array([[1, 2], [3, 4]]),
|
|
mx.array([5, 6]),
|
|
],
|
|
mx.where,
|
|
np.where,
|
|
)
|
|
|
|
# Check non-contiguous input with several dimensions
|
|
shape = [1, 2, 2, 3, 3, 1]
|
|
strides = [16, 4, 1, 4, 1, 1]
|
|
x = mx.ones(shape=(1, 4, 4, 1))
|
|
x = mx.as_strided(x, shape, strides)
|
|
out = mx.where(mx.isnan(x), mx.nan, x)
|
|
self.assertTrue(mx.allclose(out, mx.ones_like(out)))
|
|
|
|
def test_nan_to_num(self):
|
|
a = mx.array([6, float("inf"), 2, 0])
|
|
out_mx = mx.nan_to_num(a)
|
|
out_np = np.nan_to_num(a)
|
|
self.assertTrue(np.allclose(out_mx, out_np))
|
|
|
|
for t in [mx.float32, mx.float16]:
|
|
a = mx.array([float("inf"), 6.9, float("nan"), float("-inf")])
|
|
out_mx = mx.nan_to_num(a)
|
|
out_np = np.nan_to_num(a)
|
|
self.assertTrue(np.allclose(out_mx, out_np))
|
|
|
|
a = mx.array([float("inf"), 6.9, float("nan"), float("-inf")]).astype(t)
|
|
out_np = np.nan_to_num(a, nan=0.0, posinf=1000, neginf=-1000)
|
|
out_mx = mx.nan_to_num(a, nan=0.0, posinf=1000, neginf=-1000)
|
|
self.assertTrue(np.allclose(out_mx, out_np))
|
|
|
|
def test_as_strided(self):
|
|
x_npy = np.random.randn(128).astype(np.float32)
|
|
x_mlx = mx.array(x_npy)
|
|
|
|
shapes = [(10, 10), (5, 5), (2, 20), (10,)]
|
|
strides = [(3, 3), (7, 1), (1, 5), (4,)]
|
|
for shape, stride in zip(shapes, strides):
|
|
for offset in [0, 1, 3]:
|
|
y_npy = np.lib.stride_tricks.as_strided(
|
|
x_npy[offset:], shape, np.multiply(stride, 4)
|
|
)
|
|
y_mlx = mx.as_strided(x_mlx, shape, stride, offset)
|
|
self.assertTrue(np.array_equal(y_npy, y_mlx))
|
|
|
|
x = mx.random.uniform(shape=(32,))
|
|
y = mx.as_strided(x, (x.size,), (-1,), x.size - 1)
|
|
self.assertTrue(mx.array_equal(y, x[::-1]))
|
|
|
|
def test_logcumsumexp(self):
|
|
npop = np.logaddexp.accumulate
|
|
mxop = mx.logcumsumexp
|
|
|
|
a_npy = np.random.randn(32, 32, 32).astype(np.float32)
|
|
a_mlx = mx.array(a_npy)
|
|
|
|
for axis in (0, 1, 2):
|
|
c_npy = npop(a_npy, axis=axis)
|
|
c_mlx = mxop(a_mlx, axis=axis)
|
|
self.assertTrue(np.allclose(c_npy, c_mlx, rtol=1e-3, atol=1e-3))
|
|
|
|
edge_cases_npy = [
|
|
np.float32([-float("inf")] * 8),
|
|
np.float32([-float("inf"), 0, -float("inf")]),
|
|
np.float32([-float("inf"), float("inf"), -float("inf")]),
|
|
]
|
|
edge_cases_mlx = [mx.array(a) for a in edge_cases_npy]
|
|
|
|
for a_npy, a_mlx in zip(edge_cases_npy, edge_cases_mlx):
|
|
c_npy = npop(a_npy, axis=0)
|
|
c_mlx = mxop(a_mlx, axis=0)
|
|
self.assertTrue(np.allclose(c_npy, c_mlx, rtol=1e-3, atol=1e-3))
|
|
|
|
# Complex tests
|
|
|
|
a_npy = np.array([1, 2, 3]).astype(np.float32) + 1j
|
|
a_mlx = mx.array(a_npy)
|
|
c_npy = np_cumlogaddexp(a_npy, axis=-1)
|
|
c_mlx = mxop(a_mlx, axis=-1)
|
|
self.assertTrue(np.allclose(c_npy, c_mlx, rtol=1e-3, atol=1e-3))
|
|
|
|
def test_scans(self):
|
|
a_npy = np.random.randn(32, 32, 32).astype(np.float32)
|
|
a_mlx = mx.array(a_npy)
|
|
|
|
for op in ["cumsum", "cumprod"]:
|
|
npop = getattr(np, op)
|
|
mxop = getattr(mx, op)
|
|
for axis in (None, 0, 1, 2):
|
|
c_npy = npop(a_npy, axis=axis)
|
|
c_mlx = mxop(a_mlx, axis=axis)
|
|
self.assertTrue(np.allclose(c_npy, c_mlx, rtol=1e-3, atol=1e-3))
|
|
|
|
# Complex test
|
|
|
|
a_npy = np.random.randn(32, 32, 32).astype(np.float32) + 0.5j
|
|
a_mlx = mx.array(a_npy)
|
|
|
|
for op in ["cumsum", "cumprod"]:
|
|
npop = getattr(np, op)
|
|
mxop = getattr(mx, op)
|
|
for axis in (None, 0, 1, 2):
|
|
c_npy = npop(a_npy, axis=axis)
|
|
c_mlx = mxop(a_mlx, axis=axis)
|
|
self.assertTrue(np.allclose(c_npy, c_mlx, rtol=1e-3, atol=1e-3))
|
|
|
|
a_mlx = mx.random.randint(shape=(32, 32, 32), low=-100, high=100)
|
|
for dt in [mx.int32, mx.int64]:
|
|
mxx = a_mlx.astype(dt)
|
|
npx = np.array(mxx)
|
|
for op in ["cumsum", "cumprod"]:
|
|
npop = getattr(np, op)
|
|
mxop = getattr(mx, op)
|
|
for axis in (None, 0, 1, 2):
|
|
c_npy = npop(npx, axis=axis, dtype=npx.dtype)
|
|
c_mlx = mxop(mxx, axis=axis)
|
|
self.assertTrue(np.array_equal(c_npy, c_mlx))
|
|
|
|
a_mlx = mx.random.randint(shape=(32, 32, 32), low=-100, high=100)
|
|
for op in ["cumsum", "cumprod", "cummax", "cummin"]:
|
|
mxop = getattr(mx, op)
|
|
c1 = mxop(a_mlx, axis=2)
|
|
c2 = mxop(a_mlx, axis=2, inclusive=False, reverse=False)
|
|
self.assertTrue(mx.array_equal(c1[:, :, :-1], c2[:, :, 1:]))
|
|
c1 = mxop(a_mlx, axis=1)
|
|
c2 = mxop(a_mlx, axis=1, inclusive=False, reverse=False)
|
|
self.assertTrue(mx.array_equal(c1[:, :-1, :], c2[:, 1:, :]))
|
|
c1 = mxop(a_mlx, axis=0)
|
|
c2 = mxop(a_mlx, axis=0, inclusive=False, reverse=False)
|
|
self.assertTrue(mx.array_equal(c1[:-1, :, :], c2[1:, :, :]))
|
|
|
|
rev_idx = mx.arange(31, -1, -1)
|
|
c1 = mxop(a_mlx[:, :, rev_idx], axis=2)[:, :, rev_idx]
|
|
c2 = mxop(a_mlx, axis=2, inclusive=True, reverse=True)
|
|
self.assertTrue(mx.array_equal(c1, c2))
|
|
c1 = mxop(a_mlx[:, rev_idx, :], axis=1)[:, rev_idx, :]
|
|
c2 = mxop(a_mlx, axis=1, inclusive=True, reverse=True)
|
|
self.assertTrue(mx.array_equal(c1, c2))
|
|
c1 = mxop(a_mlx[rev_idx, :, :], axis=0)[rev_idx, :, :]
|
|
c2 = mxop(a_mlx, axis=0, inclusive=True, reverse=True)
|
|
self.assertTrue(mx.array_equal(c1, c2))
|
|
|
|
rev_idx = mx.arange(31, -1, -1)
|
|
c1 = mxop(a_mlx[:, :, rev_idx], axis=2)[:, :, rev_idx][:, :, 1:]
|
|
c2 = mxop(a_mlx, axis=2, inclusive=False, reverse=True)[:, :, :-1]
|
|
self.assertTrue(mx.array_equal(c1, c2))
|
|
c1 = mxop(a_mlx[:, rev_idx, :], axis=1)[:, rev_idx, :][:, 1:, :]
|
|
c2 = mxop(a_mlx, axis=1, inclusive=False, reverse=True)[:, :-1, :]
|
|
self.assertTrue(mx.array_equal(c1, c2))
|
|
c1 = mxop(a_mlx[rev_idx, :, :], axis=0)[rev_idx, :, :][1:, :, :]
|
|
c2 = mxop(a_mlx, axis=0, inclusive=False, reverse=True)[:-1, :, :]
|
|
self.assertTrue(mx.array_equal(c1, c2))
|
|
|
|
a = mx.random.uniform(shape=(8, 32))
|
|
mat = mx.tri(32)
|
|
for t in [mx.float16, mx.bfloat16]:
|
|
a_t = a.astype(t)
|
|
mat_t = mat.astype(t)
|
|
out = mx.cumsum(a_t, axis=-1)
|
|
expected = (mat_t * a_t[:, None, :]).sum(axis=-1)
|
|
self.assertTrue(mx.allclose(out, expected, rtol=0.02, atol=1e-3))
|
|
sizes = [1023, 1024, 1025, 2047, 2048, 2049]
|
|
for s in sizes:
|
|
a = mx.ones((s,), mx.int32)
|
|
out = mx.cumsum(a)
|
|
expected = mx.arange(1, s + 1, dtype=mx.int32)
|
|
self.assertTrue(mx.array_equal(expected, out))
|
|
|
|
# non-contiguous scan
|
|
a = mx.ones((s, 2), mx.int32)
|
|
out = mx.cumsum(a, axis=0)
|
|
expected = mx.repeat(expected[:, None], 2, axis=1)
|
|
self.assertTrue(mx.array_equal(expected, out))
|
|
|
|
# Test donation
|
|
def fn(its):
|
|
x = mx.ones((32,))
|
|
for _ in range(its):
|
|
x = mx.cumsum(x)
|
|
return x
|
|
|
|
mx.synchronize()
|
|
mx.eval(fn(2))
|
|
mx.synchronize()
|
|
mem2 = mx.get_peak_memory()
|
|
mx.eval(fn(4))
|
|
mx.synchronize()
|
|
mem4 = mx.get_peak_memory()
|
|
self.assertEqual(mem2, mem4)
|
|
|
|
def test_squeeze_expand(self):
|
|
a = mx.zeros((2, 1, 2, 1))
|
|
self.assertEqual(mx.squeeze(a).shape, (2, 2))
|
|
self.assertEqual(mx.squeeze(a, 1).shape, (2, 2, 1))
|
|
self.assertEqual(mx.squeeze(a, [1, 3]).shape, (2, 2))
|
|
self.assertEqual(a.squeeze().shape, (2, 2))
|
|
self.assertEqual(a.squeeze(1).shape, (2, 2, 1))
|
|
self.assertEqual(a.squeeze([1, 3]).shape, (2, 2))
|
|
|
|
a = mx.zeros((2, 2))
|
|
self.assertEqual(mx.squeeze(a).shape, (2, 2))
|
|
|
|
self.assertEqual(mx.expand_dims(a, 0).shape, (1, 2, 2))
|
|
self.assertEqual(mx.expand_dims(a, (0, 1)).shape, (1, 1, 2, 2))
|
|
self.assertEqual(mx.expand_dims(a, [0, -1]).shape, (1, 2, 2, 1))
|
|
|
|
def test_sort(self):
|
|
shape = (6, 4, 10)
|
|
tests = product(
|
|
("int32", "float32"), # type
|
|
(None, 0, 1, 2), # axis
|
|
(True, False), # strided
|
|
)
|
|
for dtype, axis, strided in tests:
|
|
with self.subTest(dtype=dtype, axis=axis, strided=strided):
|
|
np.random.seed(0)
|
|
np_dtype = getattr(np, dtype)
|
|
a_np = np.random.uniform(0, 100, size=shape).astype(np_dtype)
|
|
a_mx = mx.array(a_np)
|
|
if strided:
|
|
a_mx = a_mx[::2, :, ::2]
|
|
a_np = a_np[::2, :, ::2]
|
|
|
|
b_np = np.sort(a_np, axis=axis)
|
|
b_mx = mx.sort(a_mx, axis=axis)
|
|
|
|
self.assertTrue(np.array_equal(b_np, b_mx))
|
|
self.assertEqual(b_mx.dtype, a_mx.dtype)
|
|
|
|
c_np = np.argsort(a_np, axis=axis)
|
|
c_mx = mx.argsort(a_mx, axis=axis)
|
|
d_np = np.take_along_axis(a_np, c_np, axis=axis)
|
|
d_mx = mx.take_along_axis(a_mx, c_mx, axis=axis)
|
|
|
|
self.assertTrue(np.array_equal(d_np, d_mx))
|
|
self.assertEqual(c_mx.dtype, mx.uint32)
|
|
|
|
# Set random seed
|
|
np.random.seed(0)
|
|
|
|
# Test multi-block sort
|
|
for strided in (False, True):
|
|
with self.subTest(strided=strided):
|
|
a_np = np.random.normal(size=(32769,)).astype(np.float32)
|
|
a_mx = mx.array(a_np)
|
|
|
|
if strided:
|
|
a_mx = a_mx[::3]
|
|
a_np = a_np[::3]
|
|
|
|
b_np = np.sort(a_np)
|
|
b_mx = mx.sort(a_mx)
|
|
|
|
self.assertTrue(np.array_equal(b_np, b_mx))
|
|
self.assertEqual(b_mx.dtype, a_mx.dtype)
|
|
|
|
# Test multi-dum multi-block sort
|
|
a_np = np.random.normal(size=(2, 4, 32769)).astype(np.float32)
|
|
a_mx = mx.array(a_np)
|
|
|
|
if strided:
|
|
a_mx = a_mx[..., ::3]
|
|
a_np = a_np[..., ::3]
|
|
|
|
b_np = np.sort(a_np, axis=-1)
|
|
b_mx = mx.sort(a_mx, axis=-1)
|
|
|
|
self.assertTrue(np.array_equal(b_np, b_mx))
|
|
self.assertEqual(b_mx.dtype, a_mx.dtype)
|
|
|
|
a_np = np.random.normal(size=(2, 32769, 4)).astype(np.float32)
|
|
a_mx = mx.array(a_np)
|
|
|
|
if strided:
|
|
a_mx = a_mx[:, ::3]
|
|
a_np = a_np[:, ::3]
|
|
|
|
b_np = np.sort(a_np, axis=1)
|
|
b_mx = mx.sort(a_mx, axis=1)
|
|
|
|
self.assertTrue(np.array_equal(b_np, b_mx))
|
|
self.assertEqual(b_mx.dtype, a_mx.dtype)
|
|
|
|
# test 0 strides
|
|
a_np = np.array([1, 0, 2, 1, 3, 0, 4, 0])
|
|
a_mx = mx.array(a_np)
|
|
b_np = np.broadcast_to(a_np, (16, 8))
|
|
b_mx = mx.broadcast_to(a_mx, (16, 8))
|
|
mx.eval(b_mx)
|
|
for axis in (0, 1):
|
|
c_np = np.sort(b_np, axis=axis)
|
|
c_mx = mx.sort(b_mx, axis=axis)
|
|
self.assertTrue(np.array_equal(c_np, c_mx))
|
|
self.assertEqual(b_mx.dtype, c_mx.dtype)
|
|
|
|
# Test very large array
|
|
if mx.default_device() == mx.gpu:
|
|
a_np = np.random.normal(20, 20, size=(2**22)).astype(np.float32)
|
|
a_mx = mx.array(a_np)
|
|
|
|
b_np = np.sort(a_np)
|
|
b_mx = mx.sort(a_mx)
|
|
self.assertTrue(np.array_equal(b_np, b_mx))
|
|
|
|
# 1D strided sort
|
|
a = mx.array([[4, 3], [2, 1], [5, 4], [3, 2]])
|
|
out = mx.argsort(a[:, 1])
|
|
expected = mx.array([1, 3, 0, 2], dtype=mx.uint32)
|
|
self.assertTrue(mx.array_equal(out, expected))
|
|
|
|
# Test array with singleton dim
|
|
out = mx.sort(mx.array([1, 2, 3]), axis=0)
|
|
self.assertTrue(mx.array_equal(out, mx.array([1, 2, 3])))
|
|
|
|
x = np.random.uniform(size=(1, 4, 8, 1)).astype(np.float32)
|
|
y_np = np.sort(x, axis=-2)
|
|
y_mx = mx.sort(mx.array(x), axis=-2)
|
|
self.assertTrue(np.array_equal(y_np, y_mx))
|
|
|
|
def test_partition(self):
|
|
shape = (3, 4, 5)
|
|
for dtype in ("int32", "float32"):
|
|
for axis in (None, 0, 1, 2):
|
|
for kth in (-2, 0, 2):
|
|
with self.subTest(dtype=dtype, axis=axis, kth=kth):
|
|
np.random.seed(0)
|
|
np_dtype = getattr(np, dtype)
|
|
a_np = np.random.uniform(0, 100, size=shape).astype(np_dtype)
|
|
a_mx = mx.array(a_np)
|
|
|
|
b_np = np.partition(a_np, kth, axis=axis)
|
|
b_mx = mx.partition(a_mx, kth, axis=axis)
|
|
|
|
c_np = np.take(b_np, (kth,), axis=axis)
|
|
c_mx = np.take(np.array(b_mx), (kth,), axis=axis)
|
|
|
|
self.assertTrue(np.array_equal(c_np, c_mx))
|
|
self.assertEqual(b_mx.dtype, a_mx.dtype)
|
|
|
|
if kth >= 0:
|
|
top_k_mx = mx.topk(a_mx, kth, axis=axis)
|
|
top_k_np = np.take(
|
|
np.partition(a_np, -kth, axis=axis), (-kth,), axis=axis
|
|
)
|
|
self.assertTrue(np.all(top_k_np <= top_k_mx))
|
|
self.assertEqual(top_k_mx.dtype, a_mx.dtype)
|
|
N = a_mx.shape[axis] if axis is not None else a_mx.size
|
|
M = top_k_mx.shape[axis or 0]
|
|
self.assertEqual(M, (kth + N) % N)
|
|
|
|
def test_argpartition(self):
|
|
x = mx.broadcast_to(mx.array([1, 2, 3]), (2, 3))
|
|
out = mx.argpartition(x, kth=1, axis=0)
|
|
expected = mx.array([[0, 0, 0], [1, 1, 1]])
|
|
self.assertTrue(mx.array_equal(out, expected))
|
|
|
|
x = mx.array([[1, 2], [3, 4]]).T
|
|
out = mx.argpartition(x, kth=1, axis=0)
|
|
expected = mx.array([[0, 0], [1, 1]])
|
|
self.assertTrue(mx.array_equal(out, expected))
|
|
|
|
@unittest.skipIf(
|
|
os.getenv("LOW_MEMORY", None) is not None,
|
|
"This test requires a lot of memory",
|
|
)
|
|
def test_large_binary(self):
|
|
a = mx.ones([1000, 2147484], mx.int8)
|
|
b = mx.ones([2147484], mx.int8)
|
|
self.assertEqual((a + b)[0, 0].item(), 2)
|
|
|
|
def test_eye(self):
|
|
self.assertCmpNumpy([3], mx.eye, np.eye)
|
|
# Test for non-square matrix
|
|
self.assertCmpNumpy([3, 4], mx.eye, np.eye)
|
|
# Test with positive k parameter
|
|
self.assertCmpNumpy([3, 4], mx.eye, np.eye, k=1)
|
|
# Test with negative k parameter
|
|
self.assertCmpNumpy([5, 6], mx.eye, np.eye, k=-2)
|
|
|
|
def test_stack(self):
|
|
a = mx.ones((2,))
|
|
np_a = np.ones((2,))
|
|
b = mx.ones((2,))
|
|
np_b = np.ones((2,))
|
|
|
|
# One dimensional stack axis=0
|
|
c = mx.stack([a, b])
|
|
np_c = np.stack([np_a, np_b])
|
|
self.assertTrue(np.array_equal(c, np_c))
|
|
|
|
# One dimensional stack axis=1
|
|
c = mx.stack([a, b], axis=1)
|
|
np_c = np.stack([np_a, np_b], axis=1)
|
|
self.assertTrue(np.array_equal(c, np_c))
|
|
|
|
a = mx.ones((1, 2))
|
|
np_a = np.ones((1, 2))
|
|
b = mx.ones((1, 2))
|
|
np_b = np.ones((1, 2))
|
|
|
|
# Two dimensional stack axis=0
|
|
c = mx.stack([a, b])
|
|
np_c = np.stack([np_a, np_b])
|
|
self.assertTrue(np.array_equal(c, np_c))
|
|
|
|
# Two dimensional stack axis=1
|
|
c = mx.stack([a, b], axis=1)
|
|
np_c = np.stack([np_a, np_b], axis=1)
|
|
self.assertTrue(np.array_equal(c, np_c))
|
|
|
|
def test_flatten(self):
|
|
x = mx.zeros([2, 3, 4])
|
|
self.assertEqual(mx.flatten(x).shape, (2 * 3 * 4,))
|
|
self.assertEqual(mx.flatten(x, start_axis=1).shape, (2, 3 * 4))
|
|
self.assertEqual(mx.flatten(x, end_axis=1).shape, (2 * 3, 4))
|
|
self.assertEqual(x.flatten().shape, (2 * 3 * 4,))
|
|
self.assertEqual(x.flatten(start_axis=1).shape, (2, 3 * 4))
|
|
self.assertEqual(x.flatten(end_axis=1).shape, (2 * 3, 4))
|
|
|
|
def test_clip(self):
|
|
a = np.array([1, 4, 3, 8, 5], np.int32)
|
|
expected = np.clip(a, 2, 6)
|
|
clipped = mx.clip(mx.array(a), 2, 6)
|
|
self.assertTrue(np.array_equal(clipped, expected))
|
|
|
|
a = np.array([-1, 1, 0, 5], np.int32)
|
|
expected = np.clip(a, 0, None)
|
|
clipped = mx.clip(mx.array(a), 0, None)
|
|
self.assertTrue(np.array_equal(clipped, expected))
|
|
|
|
a = np.array([2, 3, 4, 5], np.int32)
|
|
expected = np.clip(a, None, 4)
|
|
clipped = mx.clip(mx.array(a), None, 4)
|
|
self.assertTrue(np.array_equal(clipped, expected))
|
|
|
|
mins = np.array([3, 1, 5, 5])
|
|
a = np.array([2, 3, 4, 5], np.int32)
|
|
expected = np.clip(a, mins, 4)
|
|
clipped = mx.clip(mx.array(a), mx.array(mins), 4)
|
|
self.assertTrue(np.array_equal(clipped, expected))
|
|
|
|
maxs = np.array([5, -1, 2, 9])
|
|
a = np.array([2, 3, 4, 5], np.int32)
|
|
expected = np.clip(a, mins, maxs)
|
|
clipped = mx.clip(mx.array(a), mx.array(mins), mx.array(maxs))
|
|
self.assertTrue(np.array_equal(clipped, expected))
|
|
|
|
# Check clip output types
|
|
a = mx.array([1, 2, 3], mx.int16)
|
|
out_t = mx.clip(a, a_min=0, a_max=5).dtype
|
|
self.assertEqual(out_t, mx.int16)
|
|
|
|
out_t = mx.clip(a, a_min=0.0, a_max=5).dtype
|
|
self.assertEqual(out_t, mx.float32)
|
|
|
|
a = mx.array([1, 2, 3], mx.float16)
|
|
out_t = mx.clip(a, a_min=0.0, a_max=5).dtype
|
|
self.assertEqual(out_t, mx.float16)
|
|
|
|
a = mx.array([1, 2, 3], mx.float16)
|
|
out_t = mx.clip(a, a_min=0.0, a_max=mx.array(1.0)).dtype
|
|
self.assertEqual(out_t, mx.float32)
|
|
|
|
def test_linspace(self):
|
|
# Test default num = 50
|
|
a = mx.linspace(0, 1)
|
|
expected = mx.array(np.linspace(0, 1))
|
|
self.assertEqualArray(a, expected)
|
|
|
|
# Test int64 dtype
|
|
b = mx.linspace(0, 10, 5, mx.int64)
|
|
expected = mx.array(np.linspace(0, 10, 5, dtype=int))
|
|
self.assertEqualArray(b, expected)
|
|
|
|
# Test negative sequence with float start and stop
|
|
c = mx.linspace(-2.7, -0.7, 7)
|
|
expected = mx.array(np.linspace(-2.7, -0.7, 7))
|
|
self.assertEqualArray(c, expected)
|
|
|
|
# Test irrational step size of 1/9
|
|
d = mx.linspace(0, 1, 10)
|
|
expected = mx.array(np.linspace(0, 1, 10))
|
|
self.assertEqualArray(d, expected)
|
|
|
|
# Test num equal to 1
|
|
d = mx.linspace(1, 10, 1)
|
|
expected = mx.array(np.linspace(1, 10, 1))
|
|
self.assertEqualArray(d, expected)
|
|
|
|
# Ensure that the start and stop are always the ones provided
|
|
ranges = mx.random.normal((16, 2)).tolist()
|
|
nums = (2 + mx.random.uniform(shape=(16,)) * 10).astype(mx.uint32).tolist()
|
|
for (a, b), n in zip(ranges, nums):
|
|
d = mx.linspace(a, b, n).tolist()
|
|
self.assertEqual(d[0], a)
|
|
self.assertEqual(d[-1], b)
|
|
|
|
def test_repeat(self):
|
|
# Setup data for the tests
|
|
data = mx.array([[[13, 3], [16, 6]], [[14, 4], [15, 5]], [[11, 1], [12, 2]]])
|
|
# Test repeat 0 times
|
|
self.assertCmpNumpy([data, 0], mx.repeat, np.repeat)
|
|
# Test repeat along axis 0
|
|
self.assertCmpNumpy([data, 2], mx.repeat, np.repeat, axis=0)
|
|
# Test repeat along axis 1
|
|
self.assertCmpNumpy([data, 2], mx.repeat, np.repeat, axis=1)
|
|
# Test repeat along the last axis (default)
|
|
self.assertCmpNumpy([data, 2], mx.repeat, np.repeat)
|
|
# Test repeat with a 1D array along axis 0
|
|
self.assertCmpNumpy([mx.array([1, 3, 2]), 3], mx.repeat, np.repeat, axis=0)
|
|
# Test repeat with a 2D array along axis 0
|
|
self.assertCmpNumpy(
|
|
[mx.array([[1, 2, 3], [4, 5, 4], [0, 1, 2]]), 2],
|
|
mx.repeat,
|
|
np.repeat,
|
|
axis=0,
|
|
)
|
|
|
|
def test_tensordot(self):
|
|
# No fp16 matmuls on common cpu backend
|
|
if not self.is_apple_silicon:
|
|
dtypes = [mx.float32]
|
|
else:
|
|
dtypes = [mx.float16, mx.float32]
|
|
for dtype in dtypes:
|
|
with self.subTest(dtype=dtype):
|
|
self.assertCmpNumpy(
|
|
[(3, 4, 5), (4, 3, 2)],
|
|
mx.tensordot,
|
|
np.tensordot,
|
|
dtype=dtype,
|
|
axes=([1, 0], [0, 1]),
|
|
)
|
|
self.assertCmpNumpy(
|
|
[(3, 4, 5), (4, 5, 6)],
|
|
mx.tensordot,
|
|
np.tensordot,
|
|
dtype=dtype,
|
|
axes=2,
|
|
)
|
|
self.assertCmpNumpy(
|
|
[(3, 5, 4, 6), (6, 4, 5, 3)],
|
|
mx.tensordot,
|
|
np.tensordot,
|
|
dtype=dtype,
|
|
axes=([2, 1, 3], [1, 2, 0]),
|
|
)
|
|
|
|
def test_inner(self):
|
|
self.assertCmpNumpy([(3,), (3,)], mx.inner, np.inner)
|
|
self.assertCmpNumpy([(1, 1, 2), (3, 2)], mx.inner, np.inner)
|
|
self.assertCmpNumpy([(2, 3, 4), (4,)], mx.inner, np.inner)
|
|
|
|
def test_outer(self):
|
|
self.assertCmpNumpy([(3,), (3,)], mx.outer, np.outer)
|
|
self.assertCmpNumpy(
|
|
[
|
|
mx.ones(
|
|
5,
|
|
),
|
|
mx.linspace(-2, 2, 5),
|
|
],
|
|
mx.outer,
|
|
np.outer,
|
|
)
|
|
self.assertCmpNumpy(
|
|
[
|
|
1j * mx.linspace(2, -2, 5),
|
|
mx.ones(
|
|
5,
|
|
),
|
|
],
|
|
mx.outer,
|
|
np.outer,
|
|
)
|
|
|
|
def test_divmod(self):
|
|
# A few sizes for the inputs with and without broadcasting
|
|
sizes = [
|
|
((1,), (1,)),
|
|
((1,), (10,)),
|
|
((10,), (1,)),
|
|
((3,), (3,)),
|
|
((2, 2, 2), (1, 2, 1)),
|
|
((2, 1, 2), (1, 2, 1)),
|
|
((2, 2, 2, 2), (2, 2, 2, 2)),
|
|
]
|
|
types = [np.uint16, np.uint32, np.int32, np.float16, np.float32]
|
|
for s1, s2 in sizes:
|
|
for t in types:
|
|
a_np = np.random.uniform(1, 100, size=s1).astype(t)
|
|
b_np = np.random.uniform(1, 100, size=s2).astype(t)
|
|
np_out = np.divmod(a_np, b_np)
|
|
mx_out = mx.divmod(mx.array(a_np), mx.array(b_np))
|
|
self.assertTrue(
|
|
np.allclose(np_out[0], mx_out[0]), msg=f"Shapes {s1} {s2}, Type {t}"
|
|
)
|
|
|
|
def test_tile(self):
|
|
self.assertCmpNumpy([(2,), [2]], mx.tile, np.tile)
|
|
self.assertCmpNumpy([(2, 3, 4), [2]], mx.tile, np.tile)
|
|
self.assertCmpNumpy([(2, 3, 4), [2, 1]], mx.tile, np.tile)
|
|
self.assertCmpNumpy(
|
|
[
|
|
(2, 3, 4),
|
|
[
|
|
2,
|
|
2,
|
|
],
|
|
],
|
|
mx.tile,
|
|
np.tile,
|
|
)
|
|
self.assertCmpNumpy([(3,), [2, 2, 2]], mx.tile, np.tile)
|
|
|
|
def test_empty_matmuls(self):
|
|
a = mx.array([])
|
|
b = mx.array([])
|
|
self.assertEqual(mx.inner(a, b).item(), 0.0)
|
|
|
|
a = mx.zeros((10, 0))
|
|
b = mx.zeros((0, 10))
|
|
out = a @ b
|
|
self.assertTrue(mx.array_equal(out, mx.zeros((10, 10))))
|
|
|
|
def test_diagonal(self):
|
|
x = mx.array(
|
|
[
|
|
[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]],
|
|
[[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]],
|
|
]
|
|
)
|
|
expected = [[0, 13], [4, 17], [8, 21]]
|
|
|
|
self.assertListEqual(mx.diagonal(x, 0, -1, 0).tolist(), expected)
|
|
|
|
expected = [[1, 14], [5, 18], [9, 22]]
|
|
self.assertListEqual(mx.diagonal(x, -1, 2, 0).tolist(), expected)
|
|
|
|
def test_diag(self):
|
|
# Test 1D input
|
|
x = mx.array([1, 2, 3, 4])
|
|
expected = mx.array([[1, 0, 0, 0], [0, 2, 0, 0], [0, 0, 3, 0], [0, 0, 0, 4]])
|
|
result = mx.diag(x)
|
|
self.assertTrue(mx.array_equal(result, expected))
|
|
|
|
# Test 1D with offset
|
|
x = mx.array([2, 6])
|
|
result = mx.diag(x, k=5)
|
|
expected = mx.array(np.diag(x, k=5))
|
|
self.assertTrue(mx.array_equal(result, expected))
|
|
|
|
# Test 2D input
|
|
x = mx.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
|
|
expected = mx.array([1, 5, 9])
|
|
result = mx.diag(x)
|
|
self.assertTrue(mx.array_equal(result, expected))
|
|
|
|
# Test with offset
|
|
expected = mx.array([2, 6])
|
|
result = mx.diag(x, 1)
|
|
self.assertTrue(mx.array_equal(result, expected))
|
|
|
|
# Test non-square
|
|
x = mx.array([[1, 2, 3], [4, 5, 6]])
|
|
result = mx.diag(x)
|
|
expected = mx.array(np.diag(x))
|
|
self.assertTrue(mx.array_equal(result, expected))
|
|
|
|
result = mx.diag(x, k=10)
|
|
expected = mx.array(np.diag(x, k=10))
|
|
self.assertTrue(mx.array_equal(result, expected))
|
|
|
|
result = mx.diag(x, k=-10)
|
|
expected = mx.array(np.diag(x, k=-10))
|
|
self.assertTrue(mx.array_equal(result, expected))
|
|
|
|
result = mx.diag(x, k=-1)
|
|
expected = mx.array(np.diag(x, k=-1))
|
|
self.assertTrue(mx.array_equal(result, expected))
|
|
|
|
def test_trace(self):
|
|
a_mx = mx.arange(9, dtype=mx.int64).reshape((3, 3))
|
|
a_np = np.arange(9, dtype=np.int64).reshape((3, 3))
|
|
|
|
# Test 2D array
|
|
result = mx.trace(a_mx)
|
|
expected = np.trace(a_np)
|
|
self.assertEqualArray(result, mx.array(expected))
|
|
|
|
# Test dtype
|
|
result = mx.trace(a_mx, dtype=mx.float16)
|
|
expected = np.trace(a_np, dtype=np.float16)
|
|
self.assertEqualArray(result, mx.array(expected))
|
|
|
|
# Test offset
|
|
result = mx.trace(a_mx, offset=1)
|
|
expected = np.trace(a_np, offset=1)
|
|
self.assertEqualArray(result, mx.array(expected))
|
|
|
|
# Test axis1 and axis2
|
|
b_mx = mx.arange(27, dtype=mx.int64).reshape(3, 3, 3)
|
|
b_np = np.arange(27, dtype=np.int64).reshape(3, 3, 3)
|
|
|
|
result = mx.trace(b_mx, axis1=1, axis2=2)
|
|
expected = np.trace(b_np, axis1=1, axis2=2)
|
|
self.assertEqualArray(result, mx.array(expected))
|
|
|
|
# Test offset, axis1, axis2, and dtype
|
|
result = mx.trace(b_mx, offset=1, axis1=1, axis2=2, dtype=mx.float32)
|
|
expected = np.trace(b_np, offset=1, axis1=1, axis2=2, dtype=np.float32)
|
|
self.assertEqualArray(result, mx.array(expected))
|
|
|
|
def test_atleast_1d(self):
|
|
# Test 1D input
|
|
arrays = [
|
|
[1],
|
|
[1, 2, 3],
|
|
[1, 2, 3, 4],
|
|
[[1], [2], [3]],
|
|
[[1, 2], [3, 4]],
|
|
[[1, 2, 3], [4, 5, 6]],
|
|
[[[[1]], [[2]], [[3]]]],
|
|
]
|
|
|
|
mx_arrays = [mx.atleast_1d(mx.array(x)) for x in arrays]
|
|
atleast_arrays = mx.atleast_1d(*mx_arrays)
|
|
|
|
for i, array in enumerate(arrays):
|
|
mx_res = mx.atleast_1d(mx.array(array))
|
|
np_res = np.atleast_1d(np.array(array))
|
|
self.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):
|
|
# Test 1D input
|
|
arrays = [
|
|
[1],
|
|
[1, 2, 3],
|
|
[1, 2, 3, 4],
|
|
[[1], [2], [3]],
|
|
[[1, 2], [3, 4]],
|
|
[[1, 2, 3], [4, 5, 6]],
|
|
[[[[1]], [[2]], [[3]]]],
|
|
]
|
|
|
|
mx_arrays = [mx.atleast_2d(mx.array(x)) for x in arrays]
|
|
atleast_arrays = mx.atleast_2d(*mx_arrays)
|
|
|
|
for i, array in enumerate(arrays):
|
|
mx_res = mx.atleast_2d(mx.array(array))
|
|
np_res = np.atleast_2d(np.array(array))
|
|
self.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):
|
|
# Test 1D input
|
|
arrays = [
|
|
[1],
|
|
[1, 2, 3],
|
|
[1, 2, 3, 4],
|
|
[[1], [2], [3]],
|
|
[[1, 2], [3, 4]],
|
|
[[1, 2, 3], [4, 5, 6]],
|
|
[[[[1]], [[2]], [[3]]]],
|
|
]
|
|
|
|
mx_arrays = [mx.atleast_3d(mx.array(x)) for x in arrays]
|
|
atleast_arrays = mx.atleast_3d(*mx_arrays)
|
|
|
|
for i, array in enumerate(arrays):
|
|
mx_res = mx.atleast_3d(mx.array(array))
|
|
np_res = np.atleast_3d(np.array(array))
|
|
self.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))
|
|
|
|
# Check broadcasting
|
|
a = mx.ones((3, 1, 5), dtype=mx.bool_)
|
|
b = mx.zeros((1, 2, 5), dtype=mx.bool_)
|
|
c = a | b
|
|
self.assertEqual(c.shape, (3, 2, 5))
|
|
self.assertTrue(mx.array_equal(c, mx.ones((3, 2, 5), dtype=mx.bool_)))
|
|
|
|
def test_bitwise_grad(self):
|
|
a = np.random.randint(0, 10, size=(4, 3))
|
|
b = np.random.randint(0, 10, size=(4, 3))
|
|
cotangent = np.random.randint(0, 10, size=(4, 3))
|
|
a = mx.array(a)
|
|
b = mx.array(b)
|
|
cotangent = mx.array(cotangent)
|
|
|
|
def bitwise(a, b):
|
|
return a.astype(mx.int32) & b.astype(mx.int32)
|
|
|
|
_, vjps = mx.vjp(bitwise, [a, b], [cotangent])
|
|
for vjp in vjps:
|
|
self.assertFalse(np.any(np.array(vjp)))
|
|
|
|
def test_conjugate(self):
|
|
shape = (3, 5, 7)
|
|
a = np.random.normal(size=shape) + 1j * np.random.normal(size=shape)
|
|
a = a.astype(np.complex64)
|
|
ops = ["conjugate", "conj"]
|
|
for op in ops:
|
|
out_mlx = getattr(mx, op)(mx.array(a))
|
|
out_np = getattr(np, op)(a)
|
|
self.assertTrue(np.array_equal(np.array(out_mlx), out_np))
|
|
out_mlx = mx.array(a).conj()
|
|
out_np = a.conj()
|
|
self.assertTrue(np.array_equal(np.array(out_mlx), out_np))
|
|
|
|
def test_view(self):
|
|
# Check scalar
|
|
out = mx.array(1, mx.int8).view(mx.uint8).item()
|
|
self.assertEqual(out, 1)
|
|
|
|
a = mx.random.randint(shape=(4, 2, 4), low=-100, high=100)
|
|
a_np = np.array(a)
|
|
|
|
for t in ["bool_", "int16", "float32", "int64"]:
|
|
out = a.view(getattr(mx, t))
|
|
expected = a_np.view(getattr(np, t))
|
|
self.assertTrue(np.array_equal(out, expected, equal_nan=True))
|
|
|
|
# Irregular strides
|
|
a = mx.random.randint(shape=(2, 4), low=-100, high=100)
|
|
a = mx.broadcast_to(a, shape=(4, 2, 4))
|
|
|
|
for t in ["bool_", "int16", "float32", "int64"]:
|
|
out = a.view(getattr(mx, t))
|
|
a_out = out.view(mx.int32)
|
|
self.assertTrue(mx.array_equal(a_out, a, equal_nan=True))
|
|
|
|
a = mx.random.randint(shape=(4, 4), low=-100, high=100).T
|
|
for t in ["bool_", "int16", "float32", "int64"]:
|
|
out = a.view(getattr(mx, t))
|
|
a_out = out.view(mx.int32)
|
|
self.assertTrue(mx.array_equal(a_out, a, equal_nan=True))
|
|
|
|
def _hadamard(self, N):
|
|
# Matches scipy.linalg.hadamard
|
|
H = np.array([[1]], dtype=np.int64)
|
|
for i in range(0, np.log2(N).astype(np.int64)):
|
|
H = np.vstack((np.hstack((H, H)), np.hstack((H, -H))))
|
|
return H
|
|
|
|
def test_hadamard(self):
|
|
with self.assertRaises(ValueError):
|
|
mx.hadamard_transform(mx.array([]))
|
|
|
|
h28_str = """
|
|
+------++----++-+--+-+--++--
|
|
-+-----+++-----+-+--+-+--++-
|
|
--+-----+++---+-+-+----+--++
|
|
---+-----+++---+-+-+-+--+--+
|
|
----+-----+++---+-+-+++--+--
|
|
-----+-----++++--+-+--++--+-
|
|
------++----++-+--+-+--++--+
|
|
--++++-+-------++--+++-+--+-
|
|
---++++-+-----+-++--+-+-+--+
|
|
+---+++--+----++-++--+-+-+--
|
|
++---++---+----++-++--+-+-+-
|
|
+++---+----+----++-++--+-+-+
|
|
++++--------+-+--++-++--+-+-
|
|
-++++--------+++--++--+--+-+
|
|
-+-++-++--++--+--------++++-
|
|
+-+-++--+--++--+--------++++
|
|
-+-+-++--+--++--+----+---+++
|
|
+-+-+-++--+--+---+---++---++
|
|
++-+-+-++--+------+--+++---+
|
|
-++-+-+-++--+------+-++++---
|
|
+-++-+---++--+------+-++++--
|
|
-++--++-+-++-+++----++------
|
|
+-++--++-+-++-+++-----+-----
|
|
++-++---+-+-++-+++-----+----
|
|
-++-++-+-+-+-+--+++-----+---
|
|
--++-++++-+-+----+++-----+--
|
|
+--++-+-++-+-+----+++-----+-
|
|
++--++-+-++-+-+----++------+
|
|
"""
|
|
|
|
def parse_h_string(h_str):
|
|
return np.array(
|
|
[[1 if s == "+" else -1 for s in row] for row in h_str.split()]
|
|
)
|
|
|
|
h28 = parse_h_string(h28_str)
|
|
|
|
x = mx.array(5)
|
|
y = mx.hadamard_transform(x)
|
|
self.assertEqual(y.item(), 5)
|
|
|
|
x = mx.array(5)
|
|
y = mx.hadamard_transform(x, scale=0.2)
|
|
self.assertEqual(y.item(), 1)
|
|
|
|
x = mx.random.normal((8, 8, 1))
|
|
y = mx.hadamard_transform(x)
|
|
self.assertTrue(mx.all(y == x).item())
|
|
|
|
# Too slow to compare to numpy so let's compare CPU to GPU
|
|
if mx.default_device() == mx.gpu:
|
|
rk = mx.random.key(42)
|
|
for k in range(14, 17):
|
|
for m in [1, 3, 5, 7]:
|
|
x = mx.random.normal((4, m * 2**k), key=rk)
|
|
y1 = mx.hadamard_transform(x, stream=mx.cpu)
|
|
y2 = mx.hadamard_transform(x, stream=mx.gpu)
|
|
self.assertLess(mx.abs(y1 - y2).max().item(), 5e-6)
|
|
|
|
np.random.seed(7)
|
|
tests = product([np.float32, np.float16, np.int32], [1, 28], range(1, 14))
|
|
for dtype, m, k in tests:
|
|
# skip large m=28 cases because they're very slow in NumPy
|
|
if m > 1 and k > 8:
|
|
continue
|
|
with self.subTest(dtype=dtype, m=m, k=k):
|
|
n = m * 2**k
|
|
b = 4
|
|
scale = 0.34
|
|
x = np.random.normal(size=(b, n)).astype(dtype)
|
|
# contiguity check
|
|
x = mx.array(x)[::2]
|
|
y = mx.hadamard_transform(x, scale=scale)
|
|
mx.eval(y)
|
|
h = (
|
|
self._hadamard(2**k)
|
|
if m == 1
|
|
else np.kron(h28, self._hadamard(2**k))
|
|
)
|
|
y_np = np.einsum("ij,bj->bi", h, x) * scale
|
|
atol = 2e-4 if dtype == np.float32 else 5e-2 * k
|
|
np.testing.assert_allclose(y, y_np, atol=atol)
|
|
|
|
# bfloat16 emulation on M1 means 2**14 doesn't fit in threadgroup memory
|
|
if dtype == np.float16 and k < 14:
|
|
y_bf16 = mx.hadamard_transform(x.astype(mx.bfloat16), scale=scale)
|
|
np.testing.assert_allclose(
|
|
y_bf16.astype(mx.float16), y, atol=atol * 2
|
|
)
|
|
|
|
def test_hadamard_grad_vmap(self):
|
|
np.random.seed(4)
|
|
|
|
for k in range(2, 8):
|
|
n = 2**k
|
|
x = np.random.normal(size=(n,))
|
|
h = self._hadamard(n)
|
|
c = np.random.normal(size=(n,))
|
|
x = mx.array(x).astype(mx.float32)
|
|
h = mx.array(h).astype(mx.float32)
|
|
c = mx.array(c).astype(mx.float32)
|
|
|
|
def hadamard_transform(x):
|
|
return h @ x / mx.sqrt(x.shape[-1])
|
|
|
|
out = mx.vjp(hadamard_transform, [x], [c])
|
|
out_t = mx.vjp(mx.hadamard_transform, [x], [c])
|
|
np.testing.assert_allclose(out, out_t, atol=1e-4)
|
|
|
|
for axis in (0, 1, 2):
|
|
vht = mx.vmap(mx.vmap(hadamard_transform, 0, 0), axis, axis)
|
|
vht_t = mx.vmap(mx.vmap(mx.hadamard_transform, 0, 0), axis, axis)
|
|
|
|
xb = mx.array(np.random.normal(size=(n, n, n)))
|
|
out = vht(xb)
|
|
out_t = vht_t(xb)
|
|
np.testing.assert_allclose(out, out_t, atol=1e-4)
|
|
|
|
def test_roll(self):
|
|
x = mx.arange(10).reshape(2, 5)
|
|
|
|
for s in [-2, -1, 0, 1, 2]:
|
|
y1 = np.roll(x, s)
|
|
y2 = mx.roll(x, s)
|
|
self.assertTrue(mx.array_equal(y1, y2).item())
|
|
|
|
y1 = np.roll(x, (s, s, s))
|
|
y2 = mx.roll(x, (s, s, s))
|
|
self.assertTrue(mx.array_equal(y1, y2).item())
|
|
|
|
shifts = [
|
|
1,
|
|
2,
|
|
-1,
|
|
-2,
|
|
(1, 1),
|
|
(-1, 2),
|
|
(33, 33),
|
|
]
|
|
axes = [
|
|
0,
|
|
1,
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(1, 0),
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|
(0, 1),
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|
(0, 0),
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(1, 1),
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|
]
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for s, a in product(shifts, axes):
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y1 = np.roll(x, s, a)
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y2 = mx.roll(x, s, a)
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self.assertTrue(mx.array_equal(y1, y2).item())
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|
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def test_roll_errors(self):
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x = mx.array([])
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result = mx.roll(x, [0], [0])
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self.assertTrue(mx.array_equal(result, x))
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|
|
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def test_real_imag(self):
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x = mx.random.uniform(shape=(4, 4))
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out = mx.real(x)
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self.assertTrue(mx.array_equal(x, out))
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|
|
|
out = mx.imag(x)
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|
self.assertTrue(mx.array_equal(mx.zeros_like(x), out))
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|
|
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y = mx.random.uniform(shape=(4, 4))
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|
z = x + 1j * y
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self.assertEqual(mx.real(z).dtype, mx.float32)
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self.assertTrue(mx.array_equal(mx.real(z), x))
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|
self.assertEqual(mx.imag(z).dtype, mx.float32)
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|
self.assertTrue(mx.array_equal(mx.imag(z), y))
|
|
|
|
def test_dynamic_slicing(self):
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|
x = mx.random.randint(0, 100, shape=(4, 4, 4))
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|
expected = x[1:, 2:, 3:]
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|
out = mx.slice(x, mx.array([1, 2, 3]), (0, 1, 2), (3, 2, 1))
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|
self.assertTrue(mx.array_equal(expected, out))
|
|
|
|
x = mx.zeros(shape=(4, 4, 4))
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|
update = mx.random.randint(0, 100, shape=(3, 2, 1))
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|
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))
|
|
|
|
|
|
class TestBroadcast(mlx_tests.MLXTestCase):
|
|
def test_broadcast_shapes(self):
|
|
# Basic broadcasting
|
|
self.assertEqual(mx.broadcast_shapes((1, 2, 3), (3,)), (1, 2, 3))
|
|
self.assertEqual(mx.broadcast_shapes((4, 1, 6), (5, 6)), (4, 5, 6))
|
|
self.assertEqual(mx.broadcast_shapes((5, 1, 4), (1, 3, 4)), (5, 3, 4))
|
|
|
|
# Multiple arguments
|
|
self.assertEqual(mx.broadcast_shapes((1, 1), (1, 8), (7, 1)), (7, 8))
|
|
self.assertEqual(
|
|
mx.broadcast_shapes((6, 1, 5), (1, 7, 1), (6, 7, 5)), (6, 7, 5)
|
|
)
|
|
|
|
# Same shapes
|
|
self.assertEqual(mx.broadcast_shapes((3, 4, 5), (3, 4, 5)), (3, 4, 5))
|
|
|
|
# Single argument
|
|
self.assertEqual(mx.broadcast_shapes((2, 3)), (2, 3))
|
|
|
|
# Empty shapes
|
|
self.assertEqual(mx.broadcast_shapes((), ()), ())
|
|
self.assertEqual(mx.broadcast_shapes((), (1,)), (1,))
|
|
self.assertEqual(mx.broadcast_shapes((1,), ()), (1,))
|
|
|
|
# Broadcasting with zeroes
|
|
self.assertEqual(mx.broadcast_shapes((0,), (0,)), (0,))
|
|
self.assertEqual(mx.broadcast_shapes((1, 0, 5), (3, 1, 5)), (3, 0, 5))
|
|
self.assertEqual(mx.broadcast_shapes((5, 0), (0, 5, 0)), (0, 5, 0))
|
|
|
|
# Error cases
|
|
with self.assertRaises(ValueError):
|
|
mx.broadcast_shapes((3, 4), (4, 3))
|
|
|
|
with self.assertRaises(ValueError):
|
|
mx.broadcast_shapes((2, 3, 4), (2, 5, 4))
|
|
|
|
with self.assertRaises(ValueError):
|
|
mx.broadcast_shapes()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
mlx_tests.MLXTestRunner()
|