mirror of
https://github.com/ml-explore/mlx.git
synced 2025-10-31 16:21:27 +08:00
2040 lines
66 KiB
Python
2040 lines
66 KiB
Python
# Copyright © 2023-2024 Apple Inc.
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import gc
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import operator
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import os
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import pickle
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import platform
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import sys
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import unittest
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import weakref
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from copy import copy, deepcopy
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from itertools import permutations
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if platform.system() == "Windows":
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import psutil
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else:
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import resource
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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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try:
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import tensorflow as tf
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has_tf = True
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except ImportError as e:
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has_tf = False
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class TestVersion(mlx_tests.MLXTestCase):
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def test_version(self):
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v = mx.__version__
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vnums = v.split(".")
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self.assertGreaterEqual(len(vnums), 3)
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v = ".".join(str(int(vn)) for vn in vnums[:3])
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self.assertEqual(v, mx.__version__[: len(v)])
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class TestDtypes(mlx_tests.MLXTestCase):
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def test_dtypes(self):
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self.assertEqual(mx.bool_.size, 1)
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self.assertEqual(mx.uint8.size, 1)
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self.assertEqual(mx.uint16.size, 2)
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self.assertEqual(mx.uint32.size, 4)
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self.assertEqual(mx.uint64.size, 8)
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self.assertEqual(mx.int8.size, 1)
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self.assertEqual(mx.int16.size, 2)
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self.assertEqual(mx.int32.size, 4)
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self.assertEqual(mx.int64.size, 8)
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self.assertEqual(mx.float16.size, 2)
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self.assertEqual(mx.float32.size, 4)
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self.assertEqual(mx.bfloat16.size, 2)
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self.assertEqual(mx.complex64.size, 8)
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self.assertEqual(str(mx.bool_), "mlx.core.bool")
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self.assertEqual(str(mx.uint8), "mlx.core.uint8")
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self.assertEqual(str(mx.uint16), "mlx.core.uint16")
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self.assertEqual(str(mx.uint32), "mlx.core.uint32")
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self.assertEqual(str(mx.uint64), "mlx.core.uint64")
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self.assertEqual(str(mx.int8), "mlx.core.int8")
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self.assertEqual(str(mx.int16), "mlx.core.int16")
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self.assertEqual(str(mx.int32), "mlx.core.int32")
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self.assertEqual(str(mx.int64), "mlx.core.int64")
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self.assertEqual(str(mx.float16), "mlx.core.float16")
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self.assertEqual(str(mx.float32), "mlx.core.float32")
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self.assertEqual(str(mx.bfloat16), "mlx.core.bfloat16")
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self.assertEqual(str(mx.complex64), "mlx.core.complex64")
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def test_scalar_conversion(self):
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dtypes = [
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"uint8",
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"uint16",
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"uint32",
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"uint64",
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"int8",
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"int16",
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"int32",
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"int64",
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"float16",
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"float32",
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"complex64",
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]
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for dtype in dtypes:
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with self.subTest(dtype=dtype):
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x = np.array(2, dtype=getattr(np, dtype))
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y = np.min(x)
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self.assertEqual(x.dtype, y.dtype)
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self.assertTupleEqual(x.shape, y.shape)
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z = mx.array(y)
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self.assertEqual(np.array(z), x)
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self.assertEqual(np.array(z), y)
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self.assertEqual(z.dtype, getattr(mx, dtype))
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self.assertListEqual(list(z.shape), list(x.shape))
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self.assertListEqual(list(z.shape), list(y.shape))
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def test_finfo(self):
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with self.assertRaises(ValueError):
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mx.finfo(mx.int32)
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self.assertEqual(mx.finfo(mx.float32).min, np.finfo(np.float32).min)
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self.assertEqual(mx.finfo(mx.float32).max, np.finfo(np.float32).max)
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self.assertEqual(mx.finfo(mx.float32).eps, np.finfo(np.float32).eps)
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self.assertEqual(mx.finfo(mx.float32).dtype, mx.float32)
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self.assertEqual(mx.finfo(mx.float16).min, np.finfo(np.float16).min)
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self.assertEqual(mx.finfo(mx.float16).max, np.finfo(np.float16).max)
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self.assertEqual(mx.finfo(mx.float16).eps, np.finfo(np.float16).eps)
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self.assertEqual(mx.finfo(mx.float16).dtype, mx.float16)
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def test_iinfo(self):
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with self.assertRaises(ValueError):
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mx.iinfo(mx.float32)
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self.assertEqual(mx.iinfo(mx.int32).min, np.iinfo(np.int32).min)
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self.assertEqual(mx.iinfo(mx.int32).max, np.iinfo(np.int32).max)
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self.assertEqual(mx.iinfo(mx.int32).dtype, mx.int32)
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self.assertEqual(mx.iinfo(mx.uint32).min, np.iinfo(np.uint32).min)
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self.assertEqual(mx.iinfo(mx.uint32).max, np.iinfo(np.uint32).max)
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self.assertEqual(mx.iinfo(mx.int8).dtype, mx.int8)
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class TestEquality(mlx_tests.MLXTestCase):
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def test_array_eq_array(self):
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a = mx.array([1, 2, 3])
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b = mx.array([1, 2, 3])
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c = mx.array([1, 2, 4])
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self.assertTrue(mx.all(a == b))
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self.assertFalse(mx.all(a == c))
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def test_array_eq_scalar(self):
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a = mx.array([1, 2, 3])
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b = 1
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c = 4
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d = 2.5
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e = mx.array([1, 2.5, 3.25])
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self.assertTrue(mx.any(a == b))
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self.assertFalse(mx.all(a == c))
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self.assertFalse(mx.all(a == d))
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self.assertTrue(mx.any(a == e))
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def test_list_equals_array(self):
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a = mx.array([1, 2, 3])
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b = [1, 2, 3]
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c = [1, 2, 4]
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# mlx array equality returns false if is compared with any kind of
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# object which is not an mlx array
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self.assertFalse(a == b)
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self.assertFalse(a == c)
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def test_tuple_equals_array(self):
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a = mx.array([1, 2, 3])
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b = (1, 2, 3)
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c = (1, 2, 4)
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# mlx array equality returns false if is compared with any kind of
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# object which is not an mlx array
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self.assertFalse(a == b)
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self.assertFalse(a == c)
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class TestInequality(mlx_tests.MLXTestCase):
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def test_array_ne_array(self):
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a = mx.array([1, 2, 3])
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b = mx.array([1, 2, 3])
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c = mx.array([1, 2, 4])
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self.assertFalse(mx.any(a != b))
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self.assertTrue(mx.any(a != c))
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def test_array_ne_scalar(self):
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a = mx.array([1, 2, 3])
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b = 1
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c = 4
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d = 1.5
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e = 2.5
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f = mx.array([1, 2.5, 3.25])
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self.assertFalse(mx.all(a != b))
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self.assertTrue(mx.any(a != c))
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self.assertTrue(mx.any(a != d))
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self.assertTrue(mx.any(a != e))
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self.assertFalse(mx.all(a != f))
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def test_list_not_equals_array(self):
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a = mx.array([1, 2, 3])
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b = [1, 2, 3]
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c = [1, 2, 4]
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# mlx array inequality returns true if is compared with any kind of
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# object which is not an mlx array
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self.assertTrue(a != b)
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self.assertTrue(a != c)
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def test_dlx_device_type(self):
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a = mx.array([1, 2, 3])
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device_type, device_id = a.__dlpack_device__()
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self.assertIn(device_type, [1, 8, 13])
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self.assertEqual(device_id, 0)
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if device_type == 8:
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# Additional check if Metal is supposed to be available
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self.assertTrue(mx.metal.is_available())
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elif device_type == 1:
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# Additional check if CPU is the fallback
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self.assertFalse(mx.metal.is_available())
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def test_tuple_not_equals_array(self):
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a = mx.array([1, 2, 3])
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b = (1, 2, 3)
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c = (1, 2, 4)
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# mlx array inequality returns true if is compared with any kind of
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# object which is not an mlx array
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self.assertTrue(a != b)
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self.assertTrue(a != c)
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def test_obj_inequality_array(self):
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str_ = "hello"
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a = mx.array([1, 2, 3])
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lst_ = [1, 2, 3]
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tpl_ = (1, 2, 3)
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# check if object comparison(</>/<=/>=) with mlx array should throw an exception
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# if not, the tests will fail
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with self.assertRaises(ValueError):
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a < str_
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with self.assertRaises(ValueError):
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a > str_
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with self.assertRaises(ValueError):
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a <= str_
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with self.assertRaises(ValueError):
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a >= str_
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with self.assertRaises(ValueError):
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a < lst_
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with self.assertRaises(ValueError):
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a > lst_
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with self.assertRaises(ValueError):
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a <= lst_
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with self.assertRaises(ValueError):
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a >= lst_
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with self.assertRaises(ValueError):
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a < tpl_
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with self.assertRaises(ValueError):
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a > tpl_
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with self.assertRaises(ValueError):
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a <= tpl_
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with self.assertRaises(ValueError):
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a >= tpl_
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def test_invalid_op_on_array(self):
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str_ = "hello"
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a = mx.array([1, 2.5, 3.25])
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lst_ = [1, 2.1, 3.25]
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tpl_ = (1, 2.5, 3.25)
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with self.assertRaises(ValueError):
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a * str_
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with self.assertRaises(ValueError):
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a *= str_
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with self.assertRaises(ValueError):
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a /= lst_
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with self.assertRaises(ValueError):
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a // lst_
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with self.assertRaises(ValueError):
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a % lst_
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with self.assertRaises(ValueError):
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a**tpl_
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with self.assertRaises(ValueError):
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a & tpl_
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with self.assertRaises(ValueError):
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a | str_
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class TestArray(mlx_tests.MLXTestCase):
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def test_array_basics(self):
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x = mx.array(1)
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self.assertEqual(x.size, 1)
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self.assertEqual(x.ndim, 0)
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self.assertEqual(x.itemsize, 4)
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self.assertEqual(x.nbytes, 4)
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self.assertEqual(x.shape, ())
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self.assertEqual(x.dtype, mx.int32)
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self.assertEqual(x.item(), 1)
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self.assertTrue(isinstance(x.item(), int))
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with self.assertRaises(TypeError):
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len(x)
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x = mx.array(1, mx.uint32)
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self.assertEqual(x.item(), 1)
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self.assertTrue(isinstance(x.item(), int))
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x = mx.array(1, mx.int64)
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self.assertEqual(x.item(), 1)
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self.assertTrue(isinstance(x.item(), int))
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x = mx.array(1, mx.bfloat16)
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self.assertEqual(x.item(), 1.0)
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x = mx.array(1.0)
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self.assertEqual(x.size, 1)
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self.assertEqual(x.ndim, 0)
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self.assertEqual(x.shape, ())
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self.assertEqual(x.dtype, mx.float32)
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self.assertEqual(x.item(), 1.0)
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self.assertTrue(isinstance(x.item(), float))
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x = mx.array(False)
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self.assertEqual(x.size, 1)
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self.assertEqual(x.ndim, 0)
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self.assertEqual(x.shape, ())
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self.assertEqual(x.dtype, mx.bool_)
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self.assertEqual(x.item(), False)
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self.assertTrue(isinstance(x.item(), bool))
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x = mx.array(complex(1, 1))
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self.assertEqual(x.ndim, 0)
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self.assertEqual(x.shape, ())
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self.assertEqual(x.dtype, mx.complex64)
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self.assertEqual(x.item(), complex(1, 1))
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self.assertTrue(isinstance(x.item(), complex))
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x = mx.array([True, False, True])
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self.assertEqual(x.dtype, mx.bool_)
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self.assertEqual(x.ndim, 1)
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self.assertEqual(x.shape, (3,))
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self.assertEqual(len(x), 3)
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x = mx.array([True, False, True], mx.float32)
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self.assertEqual(x.dtype, mx.float32)
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x = mx.array([0, 1, 2])
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self.assertEqual(x.dtype, mx.int32)
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self.assertEqual(x.ndim, 1)
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self.assertEqual(x.shape, (3,))
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x = mx.array([0, 1, 2], mx.float32)
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self.assertEqual(x.dtype, mx.float32)
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x = mx.array([0.0, 1.0, 2.0])
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self.assertEqual(x.dtype, mx.float32)
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self.assertEqual(x.ndim, 1)
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self.assertEqual(x.shape, (3,))
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x = mx.array([1j, 1 + 0j])
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self.assertEqual(x.dtype, mx.complex64)
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self.assertEqual(x.ndim, 1)
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self.assertEqual(x.shape, (2,))
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# From tuple
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x = mx.array((1, 2, 3), mx.int32)
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self.assertEqual(x.dtype, mx.int32)
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self.assertEqual(x.tolist(), [1, 2, 3])
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def test_bool_conversion(self):
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x = mx.array(True)
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self.assertTrue(x)
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x = mx.array(False)
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self.assertFalse(x)
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x = mx.array(1.0)
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self.assertTrue(x)
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x = mx.array(0.0)
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self.assertFalse(x)
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def test_int_type(self):
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x = mx.array(1)
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self.assertTrue(x.dtype == mx.int32)
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x = mx.array(2**32 - 1)
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self.assertTrue(x.dtype == mx.int64)
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x = mx.array(2**40)
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self.assertTrue(x.dtype == mx.int64)
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x = mx.array(2**32 - 1, dtype=mx.uint32)
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self.assertTrue(x.dtype == mx.uint32)
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x = mx.array([1, 2], dtype=mx.int64) + 0x80000000
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self.assertTrue(x.dtype == mx.int64)
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def test_construction_from_lists(self):
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x = mx.array([])
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self.assertEqual(x.size, 0)
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self.assertEqual(x.shape, (0,))
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self.assertEqual(x.dtype, mx.float32)
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x = mx.array([[], [], []])
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self.assertEqual(x.size, 0)
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self.assertEqual(x.shape, (3, 0))
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self.assertEqual(x.dtype, mx.float32)
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x = mx.array([[[], []], [[], []], [[], []]])
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self.assertEqual(x.size, 0)
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self.assertEqual(x.shape, (3, 2, 0))
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self.assertEqual(x.dtype, mx.float32)
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# Check failure cases
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with self.assertRaises(ValueError):
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x = mx.array([[[], []], [[]], [[], []]])
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with self.assertRaises(ValueError):
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x = mx.array([[[], []], [[1.0, 2.0], []], [[], []]])
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with self.assertRaises(ValueError):
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x = mx.array([[0, 1], [[0, 1], 1]])
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with self.assertRaises(ValueError):
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x = mx.array([[0, 1], ["hello", 1]])
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x = mx.array([True, False, 3])
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self.assertEqual(x.dtype, mx.int32)
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x = mx.array([True, False, 3, 4.0])
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self.assertEqual(x.dtype, mx.float32)
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x = mx.array([[True, False], [1, 3], [2, 4.0]])
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self.assertEqual(x.dtype, mx.float32)
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x = mx.array([[1.0, 2.0], [0.0, 3.9]], mx.bool_)
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self.assertEqual(x.dtype, mx.bool_)
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self.assertTrue(mx.array_equal(x, mx.array([[True, True], [False, True]])))
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x = mx.array([[1.0, 2.0], [0.0, 3.9]], mx.int32)
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self.assertTrue(mx.array_equal(x, mx.array([[1, 2], [0, 3]])))
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x = mx.array([1 + 0j, 2j, True, 0], mx.complex64)
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self.assertEqual(x.tolist(), [1 + 0j, 2j, 1 + 0j, 0j])
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xnp = np.array([0, 4294967295], dtype=np.uint32)
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x = mx.array([0, 4294967295], dtype=mx.uint32)
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self.assertTrue(np.array_equal(x, xnp))
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xnp = np.array([0, 4294967295], dtype=np.float32)
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x = mx.array([0, 4294967295], dtype=mx.float32)
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self.assertTrue(np.array_equal(x, xnp))
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def test_construction_from_lists_of_mlx_arrays(self):
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dtypes = [
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mx.bool_,
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mx.uint8,
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mx.uint16,
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mx.uint32,
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mx.uint64,
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mx.int8,
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mx.int16,
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mx.int32,
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mx.int64,
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mx.float16,
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mx.float32,
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mx.bfloat16,
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mx.complex64,
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]
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for x_t, y_t in permutations(dtypes, 2):
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# check type promotion and numeric correctness
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x, y = mx.array([1.0], x_t), mx.array([2.0], y_t)
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z = mx.array([x, y])
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expected = mx.stack([x, y], axis=0)
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self.assertEqualArray(z, expected)
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# check heterogeneous construction with mlx arrays and python primitive types
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x, y = mx.array([True], x_t), mx.array([False], y_t)
|
|
z = mx.array([[x, [2.0]], [[3.0], y]])
|
|
expected = mx.array([[[x.item()], [2.0]], [[3.0], [y.item()]]], z.dtype)
|
|
self.assertEqualArray(z, expected)
|
|
|
|
# check when create from an array which does not contain memory to the raw data
|
|
x = mx.array([1.0]).astype(mx.bfloat16) # x does not hold raw data
|
|
for y_t in dtypes:
|
|
y = mx.array([2.0], y_t)
|
|
z = mx.array([x, y])
|
|
expected = mx.stack([x, y], axis=0)
|
|
self.assertEqualArray(z, expected)
|
|
|
|
# shape check from `stack()`
|
|
with self.assertRaises(ValueError) as e:
|
|
mx.array([x, 1.0])
|
|
self.assertEqual(
|
|
str(e.exception), "Initialization encountered non-uniform length."
|
|
)
|
|
|
|
# shape check from `validate_shape`
|
|
with self.assertRaises(ValueError) as e:
|
|
mx.array([1.0, x])
|
|
self.assertEqual(
|
|
str(e.exception), "Initialization encountered non-uniform length."
|
|
)
|
|
|
|
# check that `[mx.array, ...]` retains the `mx.array` in the graph
|
|
def f(x):
|
|
y = mx.array([x, mx.array([2.0])])
|
|
return (2 * y).sum()
|
|
|
|
x = mx.array([1.0])
|
|
dfdx = mx.grad(f)
|
|
self.assertEqual(dfdx(x).item(), 2.0)
|
|
|
|
def test_init_from_array(self):
|
|
x = mx.array(3.0)
|
|
y = mx.array(x)
|
|
|
|
self.assertTrue(mx.array_equal(x, y))
|
|
|
|
y = mx.array(x, mx.int32)
|
|
self.assertEqual(y.dtype, mx.int32)
|
|
self.assertEqual(y.item(), 3)
|
|
|
|
y = mx.array(x, mx.bool_)
|
|
self.assertEqual(y.dtype, mx.bool_)
|
|
self.assertEqual(y.item(), True)
|
|
|
|
y = mx.array(x, mx.complex64)
|
|
self.assertEqual(y.dtype, mx.complex64)
|
|
self.assertEqual(y.item(), 3.0 + 0j)
|
|
|
|
def test_array_repr(self):
|
|
x = mx.array(True)
|
|
self.assertEqual(str(x), "array(True, dtype=bool)")
|
|
x = mx.array(1)
|
|
self.assertEqual(str(x), "array(1, dtype=int32)")
|
|
x = mx.array(1.0)
|
|
self.assertEqual(str(x), "array(1, dtype=float32)")
|
|
|
|
x = mx.array([1, 0, 1])
|
|
self.assertEqual(str(x), "array([1, 0, 1], dtype=int32)")
|
|
|
|
x = mx.array([1] * 6)
|
|
expected = "array([1, 1, 1, 1, 1, 1], dtype=int32)"
|
|
self.assertEqual(str(x), expected)
|
|
|
|
x = mx.array([1] * 7)
|
|
expected = "array([1, 1, 1, ..., 1, 1, 1], dtype=int32)"
|
|
self.assertEqual(str(x), expected)
|
|
|
|
x = mx.array([[1, 2], [1, 2], [1, 2]])
|
|
expected = "array([[1, 2],\n" " [1, 2],\n" " [1, 2]], dtype=int32)"
|
|
self.assertEqual(str(x), expected)
|
|
|
|
x = mx.array([[[1, 2], [1, 2]], [[1, 2], [1, 2]]])
|
|
expected = (
|
|
"array([[[1, 2],\n"
|
|
" [1, 2]],\n"
|
|
" [[1, 2],\n"
|
|
" [1, 2]]], dtype=int32)"
|
|
)
|
|
self.assertEqual(str(x), expected)
|
|
|
|
x = mx.array([[1, 2]] * 6)
|
|
expected = (
|
|
"array([[1, 2],\n"
|
|
" [1, 2],\n"
|
|
" [1, 2],\n"
|
|
" [1, 2],\n"
|
|
" [1, 2],\n"
|
|
" [1, 2]], dtype=int32)"
|
|
)
|
|
self.assertEqual(str(x), expected)
|
|
x = mx.array([[1, 2]] * 7)
|
|
expected = (
|
|
"array([[1, 2],\n"
|
|
" [1, 2],\n"
|
|
" [1, 2],\n"
|
|
" ...,\n"
|
|
" [1, 2],\n"
|
|
" [1, 2],\n"
|
|
" [1, 2]], dtype=int32)"
|
|
)
|
|
self.assertEqual(str(x), expected)
|
|
|
|
x = mx.array([1], dtype=mx.int8)
|
|
expected = "array([1], dtype=int8)"
|
|
self.assertEqual(str(x), expected)
|
|
x = mx.array([1], dtype=mx.int16)
|
|
expected = "array([1], dtype=int16)"
|
|
self.assertEqual(str(x), expected)
|
|
x = mx.array([1], dtype=mx.uint8)
|
|
expected = "array([1], dtype=uint8)"
|
|
self.assertEqual(str(x), expected)
|
|
|
|
# Fp16 is not supported in all platforms
|
|
x = mx.array([1.2], dtype=mx.float16)
|
|
expected = "array([1.2002], dtype=float16)"
|
|
self.assertEqual(str(x), expected)
|
|
|
|
x = mx.array([1 + 1j], dtype=mx.complex64)
|
|
expected = "array([1+1j], dtype=complex64)"
|
|
self.assertEqual(str(x), expected)
|
|
x = mx.array([1 - 1j], dtype=mx.complex64)
|
|
expected = "array([1-1j], dtype=complex64)"
|
|
|
|
x = mx.array([1 + 1j], dtype=mx.complex64)
|
|
expected = "array([1+1j], dtype=complex64)"
|
|
self.assertEqual(str(x), expected)
|
|
x = mx.array([1 - 1j], dtype=mx.complex64)
|
|
expected = "array([1-1j], dtype=complex64)"
|
|
|
|
def test_array_to_list(self):
|
|
types = [mx.bool_, mx.uint32, mx.int32, mx.int64, mx.float32]
|
|
for t in types:
|
|
x = mx.array(1, t)
|
|
self.assertEqual(x.tolist(), 1)
|
|
|
|
vals = [1, 2, 3, 4]
|
|
x = mx.array(vals)
|
|
self.assertEqual(x.tolist(), vals)
|
|
|
|
vals = [[1, 2], [3, 4]]
|
|
x = mx.array(vals)
|
|
self.assertEqual(x.tolist(), vals)
|
|
|
|
vals = [[1, 0], [0, 1]]
|
|
x = mx.array(vals, mx.bool_)
|
|
self.assertEqual(x.tolist(), vals)
|
|
|
|
vals = [[1.5, 2.5], [3.5, 4.5]]
|
|
x = mx.array(vals)
|
|
self.assertEqual(x.tolist(), vals)
|
|
|
|
vals = [[[0.5, 1.5], [2.5, 3.5]], [[4.5, 5.5], [6.5, 7.5]]]
|
|
x = mx.array(vals)
|
|
self.assertEqual(x.tolist(), vals)
|
|
|
|
# Empty arrays
|
|
vals = []
|
|
x = mx.array(vals)
|
|
self.assertEqual(x.tolist(), vals)
|
|
|
|
vals = [[], []]
|
|
x = mx.array(vals)
|
|
self.assertEqual(x.tolist(), vals)
|
|
|
|
# Complex arrays
|
|
vals = [0.5 + 0j, 1.5 + 1j, 2.5 + 0j, 3.5 + 1j]
|
|
x = mx.array(vals)
|
|
self.assertEqual(x.tolist(), vals)
|
|
|
|
# Half types
|
|
vals = [1.0, 2.0, 3.0, 4.0, 5.0]
|
|
x = mx.array(vals, dtype=mx.float16)
|
|
self.assertEqual(x.tolist(), vals)
|
|
|
|
x = mx.array(vals, dtype=mx.bfloat16)
|
|
self.assertEqual(x.tolist(), vals)
|
|
|
|
def test_array_np_conversion(self):
|
|
# Shape test
|
|
a = np.array([])
|
|
x = mx.array(a)
|
|
self.assertEqual(x.size, 0)
|
|
self.assertEqual(x.shape, (0,))
|
|
self.assertEqual(x.dtype, mx.float32)
|
|
|
|
a = np.array([[], [], []])
|
|
x = mx.array(a)
|
|
self.assertEqual(x.size, 0)
|
|
self.assertEqual(x.shape, (3, 0))
|
|
self.assertEqual(x.dtype, mx.float32)
|
|
|
|
a = np.array([[[], []], [[], []], [[], []]])
|
|
x = mx.array(a)
|
|
self.assertEqual(x.size, 0)
|
|
self.assertEqual(x.shape, (3, 2, 0))
|
|
self.assertEqual(x.dtype, mx.float32)
|
|
|
|
# Content test
|
|
a = 2.0 * np.ones((3, 5, 4))
|
|
x = mx.array(a)
|
|
self.assertEqual(x.dtype, mx.float32)
|
|
self.assertEqual(x.ndim, 3)
|
|
self.assertEqual(x.shape, (3, 5, 4))
|
|
|
|
y = np.asarray(x)
|
|
self.assertTrue(np.allclose(a, y))
|
|
|
|
a = np.array(3, dtype=np.int32)
|
|
x = mx.array(a)
|
|
self.assertEqual(x.dtype, mx.int32)
|
|
self.assertEqual(x.ndim, 0)
|
|
self.assertEqual(x.shape, ())
|
|
self.assertEqual(x.item(), 3)
|
|
|
|
# mlx to numpy test
|
|
x = mx.array([True, False, True])
|
|
y = np.asarray(x)
|
|
self.assertEqual(y.dtype, np.bool_)
|
|
self.assertEqual(y.ndim, 1)
|
|
self.assertEqual(y.shape, (3,))
|
|
self.assertEqual(y[0], True)
|
|
self.assertEqual(y[1], False)
|
|
self.assertEqual(y[2], True)
|
|
|
|
# complex64 mx <-> np
|
|
cvals = [0j, 1, 1 + 1j]
|
|
x = np.array(cvals)
|
|
y = mx.array(x)
|
|
self.assertEqual(y.dtype, mx.complex64)
|
|
self.assertEqual(y.shape, (3,))
|
|
self.assertEqual(y.tolist(), cvals)
|
|
|
|
y = mx.array([0j, 1, 1 + 1j])
|
|
x = np.asarray(y)
|
|
self.assertEqual(x.dtype, np.complex64)
|
|
self.assertEqual(x.shape, (3,))
|
|
self.assertEqual(x.tolist(), cvals)
|
|
|
|
def test_array_np_dtype_conversion(self):
|
|
dtypes_list = [
|
|
(mx.bool_, np.bool_),
|
|
(mx.uint8, np.uint8),
|
|
(mx.uint16, np.uint16),
|
|
(mx.uint32, np.uint32),
|
|
(mx.uint64, np.uint64),
|
|
(mx.int8, np.int8),
|
|
(mx.int16, np.int16),
|
|
(mx.int32, np.int32),
|
|
(mx.int64, np.int64),
|
|
(mx.float16, np.float16),
|
|
(mx.float32, np.float32),
|
|
(mx.complex64, np.complex64),
|
|
]
|
|
|
|
for mlx_dtype, np_dtype in dtypes_list:
|
|
a_npy = np.random.uniform(low=0, high=100, size=(32,)).astype(np_dtype)
|
|
a_mlx = mx.array(a_npy)
|
|
|
|
self.assertEqual(a_mlx.dtype, mlx_dtype)
|
|
self.assertTrue(np.allclose(a_mlx, a_npy))
|
|
|
|
b_mlx = mx.random.uniform(
|
|
low=0,
|
|
high=10,
|
|
shape=(32,),
|
|
).astype(mlx_dtype)
|
|
b_npy = np.array(b_mlx)
|
|
|
|
self.assertEqual(b_npy.dtype, np_dtype)
|
|
|
|
def test_array_from_noncontiguous_np(self):
|
|
for t in [np.int8, np.int32, np.float16, np.float32, np.complex64]:
|
|
np_arr = np.random.uniform(size=(10, 10)).astype(np.complex64)
|
|
np_arr = np_arr.T
|
|
mx_arr = mx.array(np_arr)
|
|
self.assertTrue(mx.array_equal(np_arr, mx_arr))
|
|
|
|
def test_array_np_shape_dim_check(self):
|
|
a_npy = np.empty(2**31, dtype=np.bool_)
|
|
with self.assertRaises(ValueError) as e:
|
|
mx.array(a_npy)
|
|
self.assertEqual(
|
|
str(e.exception), "Shape dimension falls outside supported `int` range."
|
|
)
|
|
|
|
def test_dtype_promotion(self):
|
|
dtypes_list = [
|
|
(mx.bool_, np.bool_),
|
|
(mx.uint8, np.uint8),
|
|
(mx.uint16, np.uint16),
|
|
(mx.uint32, np.uint32),
|
|
(mx.uint64, np.uint64),
|
|
(mx.int8, np.int8),
|
|
(mx.int16, np.int16),
|
|
(mx.int32, np.int32),
|
|
(mx.int64, np.int64),
|
|
(mx.float32, np.float32),
|
|
]
|
|
|
|
promotion_pairs = permutations(dtypes_list, 2)
|
|
|
|
for (mlx_dt_1, np_dt_1), (mlx_dt_2, np_dt_2) in promotion_pairs:
|
|
with self.subTest(dtype1=np_dt_1, dtype2=np_dt_2):
|
|
a_npy = np.ones((3,), dtype=np_dt_1)
|
|
b_npy = np.ones((3,), dtype=np_dt_2)
|
|
|
|
c_npy = a_npy + b_npy
|
|
|
|
a_mlx = mx.ones((3,), dtype=mlx_dt_1)
|
|
b_mlx = mx.ones((3,), dtype=mlx_dt_2)
|
|
|
|
c_mlx = a_mlx + b_mlx
|
|
|
|
self.assertEqual(c_mlx.dtype, mx.array(c_npy).dtype)
|
|
|
|
a_mlx = mx.ones((3,), dtype=mx.float16)
|
|
b_mlx = mx.ones((3,), dtype=mx.float32)
|
|
c_mlx = a_mlx + b_mlx
|
|
|
|
self.assertEqual(c_mlx.dtype, mx.float32)
|
|
|
|
b_mlx = mx.ones((3,), dtype=mx.int32)
|
|
c_mlx = a_mlx + b_mlx
|
|
|
|
self.assertEqual(c_mlx.dtype, mx.float16)
|
|
|
|
def test_dtype_python_scalar_promotion(self):
|
|
tests = [
|
|
(mx.bool_, operator.mul, False, mx.bool_),
|
|
(mx.bool_, operator.mul, 0, mx.int32),
|
|
(mx.bool_, operator.mul, 1.0, mx.float32),
|
|
(mx.int8, operator.mul, False, mx.int8),
|
|
(mx.int8, operator.mul, 0, mx.int8),
|
|
(mx.int8, operator.mul, 1.0, mx.float32),
|
|
(mx.int16, operator.mul, False, mx.int16),
|
|
(mx.int16, operator.mul, 0, mx.int16),
|
|
(mx.int16, operator.mul, 1.0, mx.float32),
|
|
(mx.int32, operator.mul, False, mx.int32),
|
|
(mx.int32, operator.mul, 0, mx.int32),
|
|
(mx.int32, operator.mul, 1.0, mx.float32),
|
|
(mx.int64, operator.mul, False, mx.int64),
|
|
(mx.int64, operator.mul, 0, mx.int64),
|
|
(mx.int64, operator.mul, 1.0, mx.float32),
|
|
(mx.uint8, operator.mul, False, mx.uint8),
|
|
(mx.uint8, operator.mul, 0, mx.uint8),
|
|
(mx.uint8, operator.mul, 1.0, mx.float32),
|
|
(mx.uint16, operator.mul, False, mx.uint16),
|
|
(mx.uint16, operator.mul, 0, mx.uint16),
|
|
(mx.uint16, operator.mul, 1.0, mx.float32),
|
|
(mx.uint32, operator.mul, False, mx.uint32),
|
|
(mx.uint32, operator.mul, 0, mx.uint32),
|
|
(mx.uint32, operator.mul, 1.0, mx.float32),
|
|
(mx.uint64, operator.mul, False, mx.uint64),
|
|
(mx.uint64, operator.mul, 0, mx.uint64),
|
|
(mx.uint64, operator.mul, 1.0, mx.float32),
|
|
(mx.float32, operator.mul, False, mx.float32),
|
|
(mx.float32, operator.mul, 0, mx.float32),
|
|
(mx.float32, operator.mul, 1.0, mx.float32),
|
|
(mx.float16, operator.mul, False, mx.float16),
|
|
(mx.float16, operator.mul, 0, mx.float16),
|
|
(mx.float16, operator.mul, 1.0, mx.float16),
|
|
]
|
|
|
|
for dtype_in, f, v, dtype_out in tests:
|
|
x = mx.array(0, dtype_in)
|
|
y = f(x, v)
|
|
self.assertEqual(y.dtype, dtype_out)
|
|
|
|
def test_array_comparison(self):
|
|
a = mx.array([0.0, 1.0, 5.0])
|
|
b = mx.array([-1.0, 2.0, 5.0])
|
|
|
|
self.assertEqual((a < b).tolist(), [False, True, False])
|
|
self.assertEqual((a <= b).tolist(), [False, True, True])
|
|
self.assertEqual((a > b).tolist(), [True, False, False])
|
|
self.assertEqual((a >= b).tolist(), [True, False, True])
|
|
|
|
self.assertEqual((a < 5).tolist(), [True, True, False])
|
|
self.assertEqual((5 < a).tolist(), [False, False, False])
|
|
self.assertEqual((5 <= a).tolist(), [False, False, True])
|
|
self.assertEqual((a > 1).tolist(), [False, False, True])
|
|
self.assertEqual((a >= 1).tolist(), [False, True, True])
|
|
|
|
def test_array_neg(self):
|
|
a = mx.array([-1.0, 4.0, 0.0])
|
|
|
|
self.assertEqual((-a).tolist(), [1.0, -4.0, 0.0])
|
|
|
|
def test_array_type_cast(self):
|
|
a = mx.array([0.1, 2.3, -1.3])
|
|
b = [0, 2, -1]
|
|
|
|
self.assertEqual(a.astype(mx.int32).tolist(), b)
|
|
self.assertEqual(a.astype(mx.int32).dtype, mx.int32)
|
|
|
|
b = mx.array(b).astype(mx.float32)
|
|
self.assertEqual(b.dtype, mx.float32)
|
|
|
|
def test_array_iteration(self):
|
|
a = mx.array([0, 1, 2])
|
|
|
|
for i, x in enumerate(a):
|
|
self.assertEqual(x.item(), i)
|
|
|
|
a = mx.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
|
|
x, y, z = a
|
|
self.assertEqual(x.tolist(), [1.0, 2.0])
|
|
self.assertEqual(y.tolist(), [3.0, 4.0])
|
|
self.assertEqual(z.tolist(), [5.0, 6.0])
|
|
|
|
def test_array_pickle(self):
|
|
dtypes = [
|
|
mx.int8,
|
|
mx.int16,
|
|
mx.int32,
|
|
mx.int64,
|
|
mx.uint8,
|
|
mx.uint16,
|
|
mx.uint32,
|
|
mx.uint64,
|
|
mx.float16,
|
|
mx.float32,
|
|
mx.complex64,
|
|
]
|
|
|
|
for dtype in dtypes:
|
|
x = mx.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=dtype)
|
|
state = pickle.dumps(x)
|
|
y = pickle.loads(state)
|
|
self.assertEqualArray(y, x)
|
|
|
|
# check if it throws an error when dtype is not supported (bfloat16)
|
|
x = mx.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=mx.bfloat16)
|
|
with self.assertRaises(TypeError):
|
|
pickle.dumps(x)
|
|
|
|
def test_array_copy(self):
|
|
dtypes = [
|
|
mx.int8,
|
|
mx.int16,
|
|
mx.int32,
|
|
mx.int64,
|
|
mx.uint8,
|
|
mx.uint16,
|
|
mx.uint32,
|
|
mx.uint64,
|
|
mx.float16,
|
|
mx.float32,
|
|
mx.bfloat16,
|
|
mx.complex64,
|
|
]
|
|
|
|
for copy_function in [copy, deepcopy]:
|
|
for dtype in dtypes:
|
|
x = mx.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=dtype)
|
|
y = copy_function(x)
|
|
self.assertEqualArray(y, x)
|
|
|
|
y -= 1
|
|
self.assertEqualArray(y, x - 1)
|
|
|
|
def test_indexing(self):
|
|
# Only ellipsis is a no-op
|
|
a_mlx = mx.array([1])[...]
|
|
self.assertEqual(a_mlx.shape, (1,))
|
|
self.assertEqual(a_mlx.item(), 1)
|
|
|
|
# Basic content check, slice indexing
|
|
a_npy = np.arange(64, dtype=np.float32)
|
|
a_mlx = mx.array(a_npy)
|
|
a_sliced_mlx = a_mlx[2:50:4]
|
|
a_sliced_npy = np.asarray(a_sliced_mlx)
|
|
self.assertTrue(np.array_equal(a_sliced_npy, a_npy[2:50:4]))
|
|
|
|
# Basic content check, mlx array indexing
|
|
a_npy = np.arange(64, dtype=np.int32)
|
|
a_npy = a_npy.reshape((8, 8))
|
|
a_mlx = mx.array(a_npy)
|
|
idx_npy = np.array([0, 1, 2, 7, 5], dtype=np.uint32)
|
|
idx_mlx = mx.array(idx_npy)
|
|
a_sliced_mlx = a_mlx[idx_mlx]
|
|
a_sliced_npy = np.asarray(a_sliced_mlx)
|
|
self.assertTrue(np.array_equal(a_sliced_npy, a_npy[idx_npy]))
|
|
|
|
# Basic content check, int indexing
|
|
a_sliced_mlx = a_mlx[5]
|
|
a_sliced_npy = np.asarray(a_sliced_mlx)
|
|
self.assertTrue(np.array_equal(a_sliced_npy, a_npy[5]))
|
|
self.assertEqual(len(a_sliced_npy.shape), len(a_npy[5].shape))
|
|
self.assertEqual(len(a_sliced_npy.shape), 1)
|
|
self.assertEqual(a_sliced_npy.shape[0], a_npy[5].shape[0])
|
|
|
|
# Basic content check, negative indexing
|
|
a_sliced_mlx = a_mlx[-1]
|
|
self.assertTrue(np.array_equal(a_sliced_mlx, a_npy[-1]))
|
|
|
|
# Basic content check, empty index
|
|
a_sliced_mlx = a_mlx[()]
|
|
a_sliced_npy = np.asarray(a_sliced_mlx)
|
|
self.assertTrue(np.array_equal(a_sliced_npy, a_npy[()]))
|
|
|
|
# Basic content check, new axis
|
|
a_sliced_mlx = a_mlx[None]
|
|
a_sliced_npy = np.asarray(a_sliced_mlx)
|
|
self.assertTrue(np.array_equal(a_sliced_npy, a_npy[None]))
|
|
|
|
a_sliced_mlx = a_mlx[:, None]
|
|
a_sliced_npy = np.asarray(a_sliced_mlx)
|
|
self.assertTrue(np.array_equal(a_sliced_npy, a_npy[:, None]))
|
|
|
|
# Multi dim indexing, all ints
|
|
self.assertEqual(a_mlx[0, 0].item(), 0)
|
|
self.assertEqual(a_mlx[0, 0].ndim, 0)
|
|
|
|
# Multi dim indexing, all slices
|
|
a_sliced_mlx = a_mlx[2:4, 5:]
|
|
a_sliced_npy = np.asarray(a_sliced_mlx)
|
|
self.assertTrue(np.array_equal(a_sliced_npy, a_npy[2:4, 5:]))
|
|
|
|
a_sliced_mlx = a_mlx[:, 0:5]
|
|
a_sliced_npy = np.asarray(a_sliced_mlx)
|
|
self.assertTrue(np.array_equal(a_sliced_npy, a_npy[:, 0:5]))
|
|
|
|
# Slicing, strides
|
|
a_sliced_mlx = a_mlx[:, ::2]
|
|
a_sliced_npy = np.asarray(a_sliced_mlx)
|
|
self.assertTrue(np.array_equal(a_sliced_npy, a_npy[:, ::2]))
|
|
|
|
# Slicing, -ve index
|
|
a_sliced_mlx = a_mlx[-2:, :-1]
|
|
a_sliced_npy = np.asarray(a_sliced_mlx)
|
|
self.assertTrue(np.array_equal(a_sliced_npy, a_npy[-2:, :-1]))
|
|
|
|
# Slicing, start > end
|
|
a_sliced_mlx = a_mlx[8:3]
|
|
self.assertEqual(a_sliced_mlx.size, 0)
|
|
|
|
# Slicing, Clipping past the end
|
|
a_sliced_mlx = a_mlx[7:10]
|
|
a_sliced_npy = np.asarray(a_sliced_mlx)
|
|
self.assertTrue(np.array_equal(a_sliced_npy, a_npy[7:10]))
|
|
|
|
# Multi dim indexing, int and slices
|
|
a_sliced_mlx = a_mlx[0, :5]
|
|
a_sliced_npy = np.asarray(a_sliced_mlx)
|
|
self.assertTrue(np.array_equal(a_sliced_npy, a_npy[0, :5]))
|
|
|
|
a_sliced_mlx = a_mlx[:, -1]
|
|
a_sliced_npy = np.asarray(a_sliced_mlx)
|
|
self.assertTrue(np.array_equal(a_sliced_npy, a_npy[:, -1]))
|
|
|
|
# Multi dim indexing, int and array
|
|
a_sliced_mlx = a_mlx[idx_mlx, 0]
|
|
a_sliced_npy = np.asarray(a_sliced_mlx)
|
|
self.assertTrue(np.array_equal(a_sliced_npy, a_npy[idx_npy, 0]))
|
|
|
|
# Multi dim indexing, array and slices
|
|
a_sliced_mlx = a_mlx[idx_mlx, :5]
|
|
a_sliced_npy = np.asarray(a_sliced_mlx)
|
|
self.assertTrue(np.array_equal(a_sliced_npy, a_npy[idx_npy, :5]))
|
|
|
|
a_sliced_mlx = a_mlx[:, idx_mlx]
|
|
a_sliced_npy = np.asarray(a_sliced_mlx)
|
|
self.assertTrue(np.array_equal(a_sliced_npy, a_npy[:, idx_npy]))
|
|
|
|
# Multi dim indexing with multiple arrays
|
|
def check_slices(arr_np, *idx_np):
|
|
arr_mlx = mx.array(arr_np)
|
|
idx_mlx = [
|
|
mx.array(idx) if isinstance(idx, np.ndarray) else idx for idx in idx_np
|
|
]
|
|
slice_mlx = arr_mlx[tuple(idx_mlx)]
|
|
self.assertTrue(
|
|
np.array_equal(arr_np[tuple(idx_np)], arr_mlx[tuple(idx_mlx)])
|
|
)
|
|
|
|
a_np = np.arange(16).reshape(4, 4)
|
|
check_slices(a_np, np.array([0, 1, 2, 3]), np.array([0, 1, 2, 3]))
|
|
check_slices(a_np, np.array([0, 1, 2, 3]), np.array([1, 0, 3, 3]))
|
|
check_slices(a_np, np.array([[0, 1]]), np.array([[0], [1], [3]]))
|
|
|
|
a_np = np.arange(64).reshape(2, 4, 2, 4)
|
|
check_slices(a_np, 0, np.array([0, 1, 2]))
|
|
check_slices(a_np, slice(0, 1), np.array([0, 1, 2]))
|
|
check_slices(
|
|
a_np, slice(0, 1), np.array([0, 1, 2]), slice(None), slice(0, 4, 2)
|
|
)
|
|
check_slices(
|
|
a_np, slice(0, 1), np.array([0, 1, 2]), slice(None), np.array([1, 2, 0])
|
|
)
|
|
check_slices(a_np, slice(0, 1), np.array([0, 1, 2]), 1, np.array([1, 2, 0]))
|
|
check_slices(
|
|
a_np, slice(0, 1), np.array([0, 1, 2]), np.array([1, 0, 0]), slice(0, 1)
|
|
)
|
|
check_slices(
|
|
a_np,
|
|
slice(0, 1),
|
|
np.array([[0], [1], [2]]),
|
|
np.array([[1, 0, 0]]),
|
|
slice(0, 1),
|
|
)
|
|
check_slices(
|
|
a_np,
|
|
slice(0, 2),
|
|
np.array([[0], [1], [2]]),
|
|
slice(0, 2),
|
|
np.array([[1, 0, 0]]),
|
|
)
|
|
for p in permutations([slice(None), slice(None), 0, np.array([1, 0])]):
|
|
check_slices(a_np, *p)
|
|
for p in permutations(
|
|
[slice(None), slice(None), 0, np.array([1, 0]), None, None]
|
|
):
|
|
check_slices(a_np, *p)
|
|
for p in permutations([0, np.array([1, 0]), None, Ellipsis, slice(None)]):
|
|
check_slices(a_np, *p)
|
|
|
|
# Non-contiguous arrays in slicing
|
|
a_mlx = mx.reshape(mx.arange(128), (16, 8))
|
|
a_mlx = a_mlx[::2, :]
|
|
a_np = np.array(a_mlx)
|
|
idx_np = np.arange(8)[::2]
|
|
idx_mlx = mx.arange(8)[::2]
|
|
self.assertTrue(
|
|
np.array_equal(a_np[idx_np, idx_np], np.array(a_mlx[idx_mlx, idx_mlx]))
|
|
)
|
|
|
|
# Slicing with negative indices and integer
|
|
a_np = np.arange(10).reshape(5, 2)
|
|
a_mlx = mx.array(a_np)
|
|
self.assertTrue(np.array_equal(a_np[2:-1, 0], np.array(a_mlx[2:-1, 0])))
|
|
|
|
def test_indexing_grad(self):
|
|
x = mx.array([[1, 2], [3, 4]]).astype(mx.float32)
|
|
ind = mx.array([0, 1, 0]).astype(mx.float32)
|
|
|
|
def index_fn(x, ind):
|
|
return x[ind.astype(mx.int32)].sum()
|
|
|
|
grad_x, grad_ind = mx.grad(index_fn, argnums=(0, 1))(x, ind)
|
|
expected = mx.array([[2, 2], [1, 1]])
|
|
|
|
self.assertTrue(mx.array_equal(grad_x, expected))
|
|
self.assertTrue(mx.array_equal(grad_ind, mx.zeros(ind.shape)))
|
|
|
|
def test_setitem(self):
|
|
a = mx.array(0)
|
|
a[None] = 1
|
|
self.assertEqual(a.item(), 1)
|
|
|
|
a = mx.array([1, 2, 3])
|
|
a[0] = 2
|
|
self.assertEqual(a.tolist(), [2, 2, 3])
|
|
|
|
a[-1] = 2
|
|
self.assertEqual(a.tolist(), [2, 2, 2])
|
|
|
|
a[0] = mx.array([[[1]]])
|
|
self.assertEqual(a.tolist(), [1, 2, 2])
|
|
|
|
a[:] = 0
|
|
self.assertEqual(a.tolist(), [0, 0, 0])
|
|
|
|
a[None] = 1
|
|
self.assertEqual(a.tolist(), [1, 1, 1])
|
|
|
|
a[0:1] = 2
|
|
self.assertEqual(a.tolist(), [2, 1, 1])
|
|
|
|
a[0:2] = 3
|
|
self.assertEqual(a.tolist(), [3, 3, 1])
|
|
|
|
a[0:3] = 4
|
|
self.assertEqual(a.tolist(), [4, 4, 4])
|
|
|
|
a[0:1] = mx.array(0)
|
|
self.assertEqual(a.tolist(), [0, 4, 4])
|
|
|
|
a[0:1] = mx.array([1])
|
|
self.assertEqual(a.tolist(), [1, 4, 4])
|
|
|
|
with self.assertRaises(ValueError):
|
|
a[0:1] = mx.array([2, 3])
|
|
|
|
a[0:2] = mx.array([2, 2])
|
|
self.assertEqual(a.tolist(), [2, 2, 4])
|
|
|
|
a[:] = mx.array([[[[1, 1, 1]]]])
|
|
self.assertEqual(a.tolist(), [1, 1, 1])
|
|
|
|
# Array slices
|
|
def check_slices(arr_np, update_np, *idx_np):
|
|
arr_mlx = mx.array(arr_np)
|
|
update_mlx = mx.array(update_np)
|
|
idx_mlx = [
|
|
mx.array(idx) if isinstance(idx, np.ndarray) else idx for idx in idx_np
|
|
]
|
|
if len(idx_np) > 1:
|
|
idx_np = tuple(idx_np)
|
|
idx_mlx = tuple(idx_mlx)
|
|
else:
|
|
idx_np = idx_np[0]
|
|
idx_mlx = idx_mlx[0]
|
|
arr_np[idx_np] = update_np
|
|
arr_mlx[idx_mlx] = update_mlx
|
|
self.assertTrue(np.array_equal(arr_np, arr_mlx))
|
|
|
|
check_slices(np.zeros((3, 3)), 1, 0)
|
|
check_slices(np.zeros((3, 3)), 1, -1)
|
|
check_slices(np.zeros((3, 3)), 1, slice(0, 2))
|
|
check_slices(np.zeros((3, 3)), np.array([[0, 1, 2], [3, 4, 5]]), slice(0, 2))
|
|
|
|
with self.assertRaises(ValueError):
|
|
a = mx.array(0)
|
|
a[0] = mx.array(1)
|
|
|
|
check_slices(np.zeros((3, 3)), 1, np.array([0, 1, 2]))
|
|
check_slices(np.zeros((3, 3)), np.array(3), np.array([0, 1, 2]))
|
|
check_slices(np.zeros((3, 3)), np.array([3]), np.array([0, 1, 2]))
|
|
check_slices(np.zeros((3, 3)), np.array([3]), np.array([0, 1]))
|
|
check_slices(np.zeros((3, 2)), np.array([[3, 3], [4, 4]]), np.array([0, 1]))
|
|
check_slices(np.zeros((3, 2)), np.array([[3, 3], [4, 4]]), np.array([0, 1]))
|
|
check_slices(
|
|
np.zeros((3, 2)), np.array([[3, 3], [4, 4], [5, 5]]), np.array([0, 2, 1])
|
|
)
|
|
|
|
# Multiple slices
|
|
a = mx.array(0)
|
|
a[None, None] = 1
|
|
self.assertEqual(a.item(), 1)
|
|
|
|
a[None, None] = mx.array(2)
|
|
self.assertEqual(a.item(), 2)
|
|
|
|
a[None, None] = mx.array([[[3]]])
|
|
self.assertEqual(a.item(), 3)
|
|
|
|
a[()] = 4
|
|
self.assertEqual(a.item(), 4)
|
|
|
|
a_np = np.zeros((2, 3, 4, 5))
|
|
check_slices(a_np, 1, np.array([0, 0]), slice(0, 2), slice(0, 3), 4)
|
|
check_slices(
|
|
a_np,
|
|
np.arange(10).reshape(2, 5),
|
|
np.array([0, 0]),
|
|
np.array([0, 1]),
|
|
np.array([2, 3]),
|
|
)
|
|
check_slices(
|
|
a_np,
|
|
np.array([[3], [4]]),
|
|
np.array([0, 0]),
|
|
np.array([0, 1]),
|
|
np.array([2, 3]),
|
|
)
|
|
check_slices(
|
|
a_np, np.arange(5), np.array([0, 0]), np.array([0, 1]), np.array([2, 3])
|
|
)
|
|
check_slices(np.zeros(5), np.arange(2), None, None, np.array([2, 3]))
|
|
check_slices(
|
|
np.zeros((4, 3, 4)),
|
|
np.arange(3),
|
|
np.array([2, 3]),
|
|
slice(0, 3),
|
|
np.array([2, 3]),
|
|
)
|
|
|
|
with self.assertRaises(ValueError):
|
|
a = mx.zeros((4, 3, 4))
|
|
a[mx.array([2, 3]), None, mx.array([2, 3])] = mx.arange(2)
|
|
|
|
with self.assertRaises(ValueError):
|
|
a = mx.zeros((4, 3, 4))
|
|
a[mx.array([2, 3]), None, mx.array([2, 3])] = mx.arange(3)
|
|
|
|
check_slices(np.zeros((4, 3, 4)), 1, np.array([2, 3]), None, np.array([2, 1]))
|
|
check_slices(
|
|
np.zeros((4, 3, 4)), np.arange(4), np.array([2, 3]), None, np.array([2, 1])
|
|
)
|
|
check_slices(
|
|
np.zeros((4, 3, 4)),
|
|
np.arange(2 * 4).reshape(2, 1, 4),
|
|
np.array([2, 3]),
|
|
None,
|
|
np.array([2, 1]),
|
|
)
|
|
|
|
check_slices(np.zeros((4, 4)), 1, slice(0, 2), slice(0, 2))
|
|
check_slices(np.zeros((4, 4)), np.arange(2), slice(0, 2), slice(0, 2))
|
|
check_slices(
|
|
np.zeros((4, 4)), np.arange(2).reshape(2, 1), slice(0, 2), slice(0, 2)
|
|
)
|
|
check_slices(
|
|
np.zeros((4, 4)), np.arange(4).reshape(2, 2), slice(0, 2), slice(0, 2)
|
|
)
|
|
|
|
with self.assertRaises(ValueError):
|
|
a = mx.zeros((2, 2, 2))
|
|
a[..., ...] = 1
|
|
|
|
with self.assertRaises(ValueError):
|
|
a = mx.zeros((2, 2, 2, 2, 2))
|
|
a[0, ..., 0, ..., 0] = 1
|
|
|
|
with self.assertRaises(ValueError):
|
|
a = mx.zeros((2, 2))
|
|
a[0, 0, 0] = 1
|
|
|
|
with self.assertRaises(ValueError):
|
|
a = mx.zeros((5, 4, 3))
|
|
a[:, 0] = mx.ones((5, 1, 3))
|
|
|
|
check_slices(np.zeros((2, 2, 2, 2)), 1, None, Ellipsis, None)
|
|
check_slices(
|
|
np.zeros((2, 2, 2, 2)), 1, np.array([0, 1]), Ellipsis, np.array([0, 1])
|
|
)
|
|
check_slices(
|
|
np.zeros((2, 2, 2, 2)),
|
|
np.arange(2 * 2 * 2).reshape(2, 2, 2),
|
|
np.array([0, 1]),
|
|
Ellipsis,
|
|
np.array([0, 1]),
|
|
)
|
|
|
|
# Check slice assign with negative indices works
|
|
a = mx.zeros((5, 5), mx.int32)
|
|
a[2:-2, 2:-2] = 4
|
|
self.assertEqual(a[2, 2].item(), 4)
|
|
|
|
# Check slice array slice
|
|
check_slices(
|
|
np.zeros((5, 4, 4)),
|
|
np.arange(4 * 2 * 3).reshape(4, 2, 3),
|
|
slice(0, 4),
|
|
np.array([1, 3]),
|
|
slice(None, -1),
|
|
)
|
|
check_slices(
|
|
np.zeros((5, 4, 4)),
|
|
np.arange(4 * 2 * 2).reshape(4, 2, 2),
|
|
slice(0, 4),
|
|
np.array([1, 3]),
|
|
slice(0, 4, 2),
|
|
)
|
|
|
|
check_slices(
|
|
np.zeros((1, 10, 4)),
|
|
np.arange(2 * 4).reshape(1, 2, 4),
|
|
slice(None, None, None),
|
|
np.array([1, 3]),
|
|
)
|
|
|
|
check_slices(
|
|
np.zeros((3, 4, 5, 3)),
|
|
np.arange(2 * 4 * 3 * 3).reshape(2, 4, 3, 3),
|
|
np.array([2, 1]),
|
|
slice(None, None, None),
|
|
slice(None, None, 2),
|
|
slice(None, None, None),
|
|
)
|
|
|
|
check_slices(
|
|
np.zeros((3, 4, 5, 3)),
|
|
np.arange(2 * 4 * 3 * 3).reshape(2, 4, 3, 3),
|
|
np.array([2, 1]),
|
|
slice(None, None, None),
|
|
slice(None, None, 2),
|
|
)
|
|
|
|
check_slices(np.zeros((5, 4, 3)), np.ones((5, 3)), slice(None), 0)
|
|
|
|
check_slices(np.zeros((5, 4, 3)), np.ones((5, 1, 3)), slice(None), slice(0, 1))
|
|
check_slices(
|
|
np.ones((3, 4, 4, 4)), np.zeros((4, 4)), 0, slice(0, 4), 3, slice(0, 4)
|
|
)
|
|
|
|
x = mx.zeros((2, 3, 4, 5, 3))
|
|
x[..., 0] = 1.0
|
|
self.assertTrue(mx.array_equal(x[..., 0], mx.ones((2, 3, 4, 5))))
|
|
|
|
x = mx.zeros((2, 3, 4, 5, 3))
|
|
x[:, 0] = 1.0
|
|
self.assertTrue(mx.array_equal(x[:, 0], mx.ones((2, 4, 5, 3))))
|
|
|
|
x = mx.zeros((2, 2, 2, 2, 2, 2))
|
|
x[0, 0] = 1
|
|
self.assertTrue(mx.array_equal(x[0, 0], mx.ones((2, 2, 2, 2))))
|
|
|
|
a = mx.zeros((2, 2, 2))
|
|
with self.assertRaises(ValueError):
|
|
a[:, None, :] = mx.ones((2, 2, 2))
|
|
|
|
# Ok, doesn't throw
|
|
a[:, None, :] = mx.ones((2, 1, 2, 2))
|
|
a[:, None, :] = mx.ones((2, 2))
|
|
a[:, None, 0] = mx.ones((2,))
|
|
a[:, None, 0] = mx.ones((1, 2))
|
|
|
|
def test_array_at(self):
|
|
a = mx.array(1)
|
|
with self.assertRaises(ValueError):
|
|
a.at.add(1)
|
|
|
|
a = a.at[None].add(1)
|
|
self.assertEqual(a.item(), 2)
|
|
|
|
a = mx.array([0, 1, 2])
|
|
a = a.at[1].add(2)
|
|
self.assertEqual(a.tolist(), [0, 3, 2])
|
|
|
|
a = a.at[mx.array([0, 0, 0, 0])].add(1)
|
|
self.assertEqual(a.tolist(), [4, 3, 2])
|
|
|
|
a = mx.zeros((10, 10))
|
|
a = a.at[0].add(mx.arange(10))
|
|
self.assertEqual(a[0].tolist(), list(range(10)))
|
|
|
|
a = mx.zeros((10, 10))
|
|
index_x = mx.array([0, 2, 3, 7])
|
|
index_y = mx.array([3, 3, 1, 2])
|
|
u = mx.random.uniform(shape=(4,))
|
|
a = a.at[index_x, index_y].add(u)
|
|
self.assertTrue(mx.allclose(a.sum(), u.sum()))
|
|
self.assertEqualArray(a.sum(), u.sum(), atol=1e-6, rtol=1e-5)
|
|
self.assertEqual(a[index_x, index_y].tolist(), u.tolist())
|
|
|
|
# Test all array.at ops
|
|
a = mx.random.uniform(shape=(10, 5, 2))
|
|
idx_x = mx.array([0, 4])
|
|
update = mx.ones((2, 5))
|
|
a[idx_x, :, 0] = 0
|
|
a = a.at[idx_x, :, 0].add(update)
|
|
self.assertEqualArray(a[idx_x, :, 0], update)
|
|
a = a.at[idx_x, :, 0].subtract(update)
|
|
self.assertEqualArray(a[idx_x, :, 0], mx.zeros_like(update))
|
|
a = a.at[idx_x, :, 0].add(2 * update)
|
|
self.assertEqualArray(a[idx_x, :, 0], 2 * update)
|
|
a = a.at[idx_x, :, 0].multiply(2 * update)
|
|
self.assertEqualArray(a[idx_x, :, 0], 4 * update)
|
|
a = a.at[idx_x, :, 0].divide(3 * update)
|
|
self.assertEqualArray(a[idx_x, :, 0], (4 / 3) * update)
|
|
a[idx_x, :, 0] = 5
|
|
update = mx.arange(10).reshape(2, 5)
|
|
a = a.at[idx_x, :, 0].maximum(update)
|
|
self.assertEqualArray(a[idx_x, :, 0], mx.maximum(a[idx_x, :, 0], update))
|
|
a[idx_x, :, 0] = 5
|
|
a = a.at[idx_x, :, 0].minimum(update)
|
|
self.assertEqualArray(a[idx_x, :, 0], mx.minimum(a[idx_x, :, 0], update))
|
|
|
|
update = mx.array([1.0, 2.0])[None, None, None]
|
|
src = mx.array([1.0, 2.0])[None, :]
|
|
src = src.at[0:1].add(update)
|
|
self.assertTrue(mx.array_equal(src, mx.array([[2.0, 4.0]])))
|
|
|
|
def test_slice_negative_step(self):
|
|
a_np = np.arange(20)
|
|
a_mx = mx.array(a_np)
|
|
|
|
# Basic negative slice
|
|
b_np = a_np[::-1]
|
|
b_mx = a_mx[::-1]
|
|
self.assertTrue(np.array_equal(b_np, b_mx))
|
|
|
|
# Bounds negative slice
|
|
b_np = a_np[-3:3:-1]
|
|
b_mx = a_mx[-3:3:-1]
|
|
self.assertTrue(np.array_equal(b_np, b_mx))
|
|
|
|
# Bounds negative slice
|
|
b_np = a_np[25:-50:-1]
|
|
b_mx = a_mx[25:-50:-1]
|
|
self.assertTrue(np.array_equal(b_np, b_mx))
|
|
|
|
# Jumping negative slice
|
|
b_np = a_np[::-3]
|
|
b_mx = a_mx[::-3]
|
|
self.assertTrue(np.array_equal(b_np, b_mx))
|
|
|
|
# Bounds and negative slice
|
|
b_np = a_np[-3:3:-3]
|
|
b_mx = a_mx[-3:3:-3]
|
|
self.assertTrue(np.array_equal(b_np, b_mx))
|
|
|
|
# Bounds and negative slice
|
|
b_np = a_np[25:-50:-3]
|
|
b_mx = a_mx[25:-50:-3]
|
|
self.assertTrue(np.array_equal(b_np, b_mx))
|
|
|
|
# Negative slice and ascending bounds
|
|
b_np = a_np[0:20:-3]
|
|
b_mx = a_mx[0:20:-3]
|
|
self.assertTrue(np.array_equal(b_np, b_mx))
|
|
|
|
# Multi-dim negative slices
|
|
a_np = np.arange(3 * 6 * 4).reshape(3, 6, 4)
|
|
a_mx = mx.array(a_np)
|
|
|
|
# Flip each dim
|
|
b_np = a_np[..., ::-1]
|
|
b_mx = a_mx[..., ::-1]
|
|
self.assertTrue(np.array_equal(b_np, b_mx))
|
|
|
|
b_np = a_np[:, ::-1, :]
|
|
b_mx = a_mx[:, ::-1, :]
|
|
self.assertTrue(np.array_equal(b_np, b_mx))
|
|
|
|
b_np = a_np[::-1, ...]
|
|
b_mx = a_mx[::-1, ...]
|
|
self.assertTrue(np.array_equal(b_np, b_mx))
|
|
|
|
# Flip pairs of dims
|
|
b_np = a_np[::-1, 1:5:2, ::-2]
|
|
b_mx = a_mx[::-1, 1:5:2, ::-2]
|
|
self.assertTrue(np.array_equal(b_np, b_mx))
|
|
|
|
b_np = a_np[::-1, ::-2, 1:5:2]
|
|
b_mx = a_mx[::-1, ::-2, 1:5:2]
|
|
self.assertTrue(np.array_equal(b_np, b_mx))
|
|
|
|
# Flip all dims
|
|
b_np = a_np[::-1, ::-3, ::-2]
|
|
b_mx = a_mx[::-1, ::-3, ::-2]
|
|
self.assertTrue(np.array_equal(b_np, b_mx))
|
|
|
|
def test_api(self):
|
|
x = mx.array(np.random.rand(10, 10, 10))
|
|
ops = [
|
|
("reshape", (100, -1)),
|
|
"square",
|
|
"sqrt",
|
|
"rsqrt",
|
|
"reciprocal",
|
|
"exp",
|
|
"log",
|
|
"sin",
|
|
"cos",
|
|
"log1p",
|
|
"abs",
|
|
"log10",
|
|
"log2",
|
|
"conj",
|
|
("all", 1),
|
|
("any", 1),
|
|
("transpose", (0, 2, 1)),
|
|
("sum", 1),
|
|
("prod", 1),
|
|
("min", 1),
|
|
("max", 1),
|
|
("logcumsumexp", 1),
|
|
("logsumexp", 1),
|
|
("mean", 1),
|
|
("var", 1),
|
|
("argmin", 1),
|
|
("argmax", 1),
|
|
("cummax", 1),
|
|
("cummin", 1),
|
|
("cumprod", 1),
|
|
("cumsum", 1),
|
|
("diagonal", 0, 0, 1),
|
|
("flatten", 0, -1),
|
|
("moveaxis", 1, 2),
|
|
("round", 2),
|
|
("std", 1, True, 0),
|
|
("swapaxes", 1, 2),
|
|
]
|
|
for op in ops:
|
|
if isinstance(op, tuple):
|
|
op, *args = op
|
|
else:
|
|
args = tuple()
|
|
y1 = getattr(mx, op)(x, *args)
|
|
y2 = getattr(x, op)(*args)
|
|
self.assertEqual(y1.dtype, y2.dtype)
|
|
self.assertEqual(y1.shape, y2.shape)
|
|
self.assertTrue(mx.array_equal(y1, y2))
|
|
|
|
y1 = mx.split(x, 2)
|
|
y2 = x.split(2)
|
|
self.assertEqual(len(y1), 2)
|
|
self.assertEqual(len(y1), len(y2))
|
|
self.assertTrue(mx.array_equal(y1[0], y2[0]))
|
|
self.assertTrue(mx.array_equal(y1[1], y2[1]))
|
|
x = mx.array(np.random.rand(10, 10, 1))
|
|
y1 = mx.squeeze(x, axis=2)
|
|
y2 = x.squeeze(axis=2)
|
|
self.assertEqual(y1.shape, y2.shape)
|
|
self.assertTrue(mx.array_equal(y1, y2))
|
|
|
|
def test_memoryless_copy(self):
|
|
a_mx = mx.ones((2, 2))
|
|
b_mx = mx.broadcast_to(a_mx, (5, 2, 2))
|
|
|
|
# Make np arrays without copy
|
|
a_np = np.array(a_mx, copy=False)
|
|
b_np = np.array(b_mx, copy=False)
|
|
|
|
# Check that we get read-only array that does not own the underlying data
|
|
self.assertFalse(a_np.flags.owndata)
|
|
self.assertTrue(a_np.flags.writeable)
|
|
|
|
# Check contents
|
|
self.assertTrue(np.array_equal(np.ones((2, 2), dtype=np.float32), a_np))
|
|
self.assertTrue(np.array_equal(np.ones((5, 2, 2), dtype=np.float32), b_np))
|
|
|
|
# Check strides
|
|
self.assertSequenceEqual(b_np.strides, (0, 8, 4))
|
|
|
|
def test_np_array_conversion_copies_by_default(self):
|
|
a_mx = mx.ones((2, 2))
|
|
a_np = np.array(a_mx)
|
|
self.assertTrue(a_np.flags.owndata)
|
|
self.assertTrue(a_np.flags.writeable)
|
|
|
|
def test_buffer_protocol(self):
|
|
dtypes_list = [
|
|
(mx.bool_, np.bool_, None),
|
|
(mx.uint8, np.uint8, np.iinfo),
|
|
(mx.uint16, np.uint16, np.iinfo),
|
|
(mx.uint32, np.uint32, np.iinfo),
|
|
(mx.uint64, np.uint64, np.iinfo),
|
|
(mx.int8, np.int8, np.iinfo),
|
|
(mx.int16, np.int16, np.iinfo),
|
|
(mx.int32, np.int32, np.iinfo),
|
|
(mx.int64, np.int64, np.iinfo),
|
|
(mx.float16, np.float16, np.finfo),
|
|
(mx.float32, np.float32, np.finfo),
|
|
(mx.complex64, np.complex64, np.finfo),
|
|
]
|
|
|
|
for mlx_dtype, np_dtype, info_fn in dtypes_list:
|
|
a_np = np.random.uniform(low=0, high=100, size=(3, 4)).astype(np_dtype)
|
|
if info_fn is not None:
|
|
info = info_fn(np_dtype)
|
|
a_np[0, 0] = info.min
|
|
a_np[0, 1] = info.max
|
|
a_mx = mx.array(a_np)
|
|
for f in [lambda x: x, lambda x: x.T]:
|
|
mv_mx = memoryview(f(a_mx))
|
|
mv_np = memoryview(f(a_np))
|
|
self.assertEqual(mv_mx.strides, mv_np.strides, f"{mlx_dtype}{np_dtype}")
|
|
self.assertEqual(mv_mx.shape, mv_np.shape, f"{mlx_dtype}{np_dtype}")
|
|
# correct buffer format for 8 byte (unsigned) 'long long' is Q/q, see
|
|
# https://docs.python.org/3.10/library/struct.html#format-characters
|
|
# numpy returns L/l, as 'long' is equivalent to 'long long' on 64bit machines, so q and l are equivalent
|
|
# see https://github.com/pybind/pybind11/issues/1908
|
|
if np_dtype == np.uint64:
|
|
self.assertEqual(mv_mx.format, "Q", f"{mlx_dtype}{np_dtype}")
|
|
elif np_dtype == np.int64:
|
|
self.assertEqual(mv_mx.format, "q", f"{mlx_dtype}{np_dtype}")
|
|
else:
|
|
self.assertEqual(
|
|
mv_mx.format, mv_np.format, f"{mlx_dtype}{np_dtype}"
|
|
)
|
|
self.assertFalse(mv_mx.readonly)
|
|
back_to_npy = np.array(mv_mx, copy=False)
|
|
self.assertEqualArray(
|
|
back_to_npy,
|
|
f(a_np),
|
|
atol=0,
|
|
rtol=0,
|
|
)
|
|
|
|
# extra test for bfloat16, which is not numpy convertible
|
|
a_mx = mx.random.uniform(low=0, high=100, shape=(3, 4), dtype=mx.bfloat16)
|
|
mv_mx = memoryview(a_mx)
|
|
self.assertEqual(mv_mx.strides, (8, 2))
|
|
self.assertEqual(mv_mx.shape, (3, 4))
|
|
self.assertEqual(mv_mx.format, "B")
|
|
with self.assertRaises(RuntimeError) as cm:
|
|
np.array(a_mx)
|
|
e = cm.exception
|
|
self.assertTrue("Item size 2 for PEP 3118 buffer format string" in str(e))
|
|
|
|
# Test buffer protocol with non-arrays ie bytes
|
|
a = ord("a") * 257 + mx.arange(10).astype(mx.int16)
|
|
ab = bytes(a)
|
|
self.assertEqual(len(ab), 20)
|
|
if sys.byteorder == "little":
|
|
self.assertEqual(b"aaaaaaaaaa", ab[1::2])
|
|
self.assertEqual(b"abcdefghij", ab[::2])
|
|
else:
|
|
self.assertEqual(b"aaaaaaaaaa", ab[::2])
|
|
self.assertEqual(b"abcdefghij", ab[1::2])
|
|
|
|
def test_buffer_protocol_ref_counting(self):
|
|
a = mx.arange(3)
|
|
wr = weakref.ref(a)
|
|
self.assertIsNotNone(wr())
|
|
mv = memoryview(a)
|
|
a = None
|
|
self.assertIsNotNone(wr())
|
|
mv = None
|
|
self.assertIsNone(wr())
|
|
|
|
def test_array_view_ref_counting(self):
|
|
a = mx.arange(3)
|
|
wr = weakref.ref(a)
|
|
self.assertIsNotNone(wr())
|
|
a_np = np.array(a, copy=False)
|
|
a = None
|
|
self.assertIsNotNone(wr())
|
|
a_np = None
|
|
self.assertIsNone(wr())
|
|
|
|
@unittest.skipIf(not has_tf, "requires TensorFlow")
|
|
def test_buffer_protocol_tf(self):
|
|
dtypes_list = [
|
|
(
|
|
mx.bool_,
|
|
tf.bool,
|
|
np.bool_,
|
|
),
|
|
(
|
|
mx.uint8,
|
|
tf.uint8,
|
|
np.uint8,
|
|
),
|
|
(
|
|
mx.uint16,
|
|
tf.uint16,
|
|
np.uint16,
|
|
),
|
|
(
|
|
mx.uint32,
|
|
tf.uint32,
|
|
np.uint32,
|
|
),
|
|
(mx.uint64, tf.uint64, np.uint64),
|
|
(mx.int8, tf.int8, np.int8),
|
|
(mx.int16, tf.int16, np.int16),
|
|
(mx.int32, tf.int32, np.int32),
|
|
(mx.int64, tf.int64, np.int64),
|
|
(mx.float16, tf.float16, np.float16),
|
|
(mx.float32, tf.float32, np.float32),
|
|
(
|
|
mx.complex64,
|
|
tf.complex64,
|
|
np.complex64,
|
|
),
|
|
]
|
|
|
|
for mlx_dtype, tf_dtype, np_dtype in dtypes_list:
|
|
a_np = np.random.uniform(low=0, high=100, size=(3, 4)).astype(np_dtype)
|
|
a_tf = tf.constant(a_np, dtype=tf_dtype)
|
|
a_mx = mx.array(np.array(a_tf))
|
|
for f in [
|
|
lambda x: x,
|
|
lambda x: tf.transpose(x) if isinstance(x, tf.Tensor) else x.T,
|
|
]:
|
|
mv_mx = memoryview(f(a_mx))
|
|
mv_tf = memoryview(f(a_tf))
|
|
if (mv_mx.c_contiguous and mv_tf.c_contiguous) or (
|
|
mv_mx.f_contiguous and mv_tf.f_contiguous
|
|
):
|
|
self.assertEqual(
|
|
mv_mx.strides, mv_tf.strides, f"{mlx_dtype}{tf_dtype}"
|
|
)
|
|
self.assertEqual(mv_mx.shape, mv_tf.shape, f"{mlx_dtype}{tf_dtype}")
|
|
self.assertFalse(mv_mx.readonly)
|
|
back_to_npy = np.array(mv_mx)
|
|
self.assertEqualArray(
|
|
back_to_npy,
|
|
f(a_tf),
|
|
atol=0,
|
|
rtol=0,
|
|
)
|
|
|
|
def test_logical_overloads(self):
|
|
with self.assertRaises(ValueError):
|
|
mx.array(1.0) & mx.array(1)
|
|
with self.assertRaises(ValueError):
|
|
mx.array(1.0) | mx.array(1)
|
|
|
|
self.assertEqual((mx.array(True) & True).item(), True)
|
|
self.assertEqual((mx.array(True) & False).item(), False)
|
|
self.assertEqual((mx.array(True) | False).item(), True)
|
|
self.assertEqual((mx.array(False) | False).item(), False)
|
|
self.assertEqual((~mx.array(False)).item(), True)
|
|
self.assertEqual((mx.array(False) ^ True).item(), True)
|
|
|
|
def test_inplace(self):
|
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iops = [
|
|
"__iadd__",
|
|
"__isub__",
|
|
"__imul__",
|
|
"__ifloordiv__",
|
|
"__imod__",
|
|
"__ipow__",
|
|
"__ixor__",
|
|
]
|
|
|
|
for op in iops:
|
|
a = mx.array([1, 2, 3])
|
|
a_np = np.array(a)
|
|
b = a
|
|
b = getattr(a, op)(3)
|
|
self.assertTrue(mx.array_equal(a, b))
|
|
out_np = getattr(a_np, op)(3)
|
|
self.assertTrue(np.array_equal(out_np, a))
|
|
|
|
with self.assertRaises(ValueError):
|
|
a = mx.array([1])
|
|
a /= 1
|
|
|
|
a = mx.array([2.0])
|
|
b = a
|
|
b /= 2
|
|
self.assertEqual(b.item(), 1.0)
|
|
self.assertEqual(b.item(), a.item())
|
|
|
|
a = mx.array(True)
|
|
b = a
|
|
b &= False
|
|
self.assertEqual(b.item(), False)
|
|
self.assertEqual(b.item(), a.item())
|
|
|
|
a = mx.array(False)
|
|
b = a
|
|
b |= True
|
|
self.assertEqual(b.item(), True)
|
|
self.assertEqual(b.item(), a.item())
|
|
|
|
# In-place matmul on its own
|
|
a = mx.array([[1.0, 2.0], [3.0, 4.0]])
|
|
b = a
|
|
b @= a
|
|
self.assertTrue(mx.array_equal(a, b))
|
|
|
|
a = mx.array(False)
|
|
a ^= True
|
|
self.assertEqual(a.item(), True)
|
|
|
|
def test_inplace_preserves_ids(self):
|
|
a = mx.array([1.0])
|
|
orig_id = id(a)
|
|
a += mx.array(2.0)
|
|
self.assertEqual(id(a), orig_id)
|
|
|
|
a[0] = 2.0
|
|
self.assertEqual(id(a), orig_id)
|
|
|
|
a -= mx.array(3.0)
|
|
self.assertEqual(id(a), orig_id)
|
|
|
|
a *= mx.array(3.0)
|
|
self.assertEqual(id(a), orig_id)
|
|
|
|
def test_load_from_pickled_np(self):
|
|
a = np.array([1, 2, 3], dtype=np.int32)
|
|
b = pickle.loads(pickle.dumps(a))
|
|
self.assertTrue(mx.array_equal(mx.array(a), mx.array(b)))
|
|
|
|
a = np.array([1.0, 2.0, 3.0], dtype=np.float16)
|
|
b = pickle.loads(pickle.dumps(a))
|
|
self.assertTrue(mx.array_equal(mx.array(a), mx.array(b)))
|
|
|
|
def test_multi_output_leak(self):
|
|
def fun():
|
|
a = mx.zeros((2**20))
|
|
mx.eval(a)
|
|
b, c = mx.divmod(a, a)
|
|
del b, c
|
|
|
|
fun()
|
|
mx.synchronize()
|
|
peak_1 = mx.get_peak_memory()
|
|
fun()
|
|
mx.synchronize()
|
|
peak_2 = mx.get_peak_memory()
|
|
self.assertEqual(peak_1, peak_2)
|
|
|
|
def fun():
|
|
a = mx.array([1.0, 2.0, 3.0, 4.0])
|
|
b, _ = mx.divmod(a, a)
|
|
return mx.log(b)
|
|
|
|
fun()
|
|
mx.synchronize()
|
|
peak_1 = mx.get_peak_memory()
|
|
fun()
|
|
mx.synchronize()
|
|
peak_2 = mx.get_peak_memory()
|
|
self.assertEqual(peak_1, peak_2)
|
|
|
|
def test_add_numpy(self):
|
|
x = mx.array(1)
|
|
y = np.array(2, dtype=np.int32)
|
|
z = x + y
|
|
self.assertEqual(z.dtype, mx.int32)
|
|
self.assertEqual(z.item(), 3)
|
|
|
|
def test_dlpack(self):
|
|
x = mx.array(1, dtype=mx.int32)
|
|
y = np.from_dlpack(x)
|
|
self.assertTrue(mx.array_equal(y, x))
|
|
|
|
x = mx.array([[1.0, 2.0], [3.0, 4.0]])
|
|
y = np.from_dlpack(x)
|
|
self.assertTrue(mx.array_equal(y, x))
|
|
|
|
x = mx.arange(16).reshape(4, 4)
|
|
x = x[::2, ::2]
|
|
y = np.from_dlpack(x)
|
|
self.assertTrue(mx.array_equal(y, x))
|
|
|
|
def test_getitem_with_list(self):
|
|
a = mx.array([1, 2, 3, 4, 5])
|
|
idx = [0, 2, 4]
|
|
self.assertTrue(np.array_equal(a[idx], np.array(a)[idx]))
|
|
|
|
a = mx.array([[1, 2], [3, 4], [5, 6]])
|
|
idx = [0, 2]
|
|
self.assertTrue(np.array_equal(a[idx], np.array(a)[idx]))
|
|
|
|
a = mx.arange(10).reshape(5, 2)
|
|
idx = [0, 2, 4]
|
|
self.assertTrue(np.array_equal(a[idx], np.array(a)[idx]))
|
|
|
|
idx = [0, 2]
|
|
a = mx.arange(16).reshape(4, 4)
|
|
anp = np.array(a)
|
|
self.assertTrue(np.array_equal(a[idx, 0], anp[idx, 0]))
|
|
self.assertTrue(np.array_equal(a[idx, :], anp[idx, :]))
|
|
self.assertTrue(np.array_equal(a[0, idx], anp[0, idx]))
|
|
self.assertTrue(np.array_equal(a[:, idx], anp[:, idx]))
|
|
|
|
def test_setitem_with_list(self):
|
|
a = mx.array([1, 2, 3, 4, 5])
|
|
anp = np.array(a)
|
|
idx = [0, 2, 4]
|
|
a[idx] = 3
|
|
anp[idx] = 3
|
|
self.assertTrue(np.array_equal(a, anp))
|
|
|
|
a = mx.array([[1, 2], [3, 4], [5, 6]])
|
|
idx = [0, 2]
|
|
anp = np.array(a)
|
|
a[idx] = 3
|
|
anp[idx] = 3
|
|
self.assertTrue(np.array_equal(a, anp))
|
|
|
|
a = mx.arange(10).reshape(5, 2)
|
|
idx = [0, 2, 4]
|
|
anp = np.array(a)
|
|
a[idx] = 3
|
|
anp[idx] = 3
|
|
self.assertTrue(np.array_equal(a, anp))
|
|
|
|
idx = [0, 2]
|
|
a = mx.arange(16).reshape(4, 4)
|
|
anp = np.array(a)
|
|
a[idx, 0] = 1
|
|
anp[idx, 0] = 1
|
|
self.assertTrue(np.array_equal(a, anp))
|
|
|
|
a[idx, :] = 2
|
|
anp[idx, :] = 2
|
|
self.assertTrue(np.array_equal(a, anp))
|
|
|
|
a[0, idx] = 3
|
|
anp[0, idx] = 3
|
|
self.assertTrue(np.array_equal(a, anp))
|
|
|
|
a[:, idx] = 4
|
|
anp[:, idx] = 4
|
|
self.assertTrue(np.array_equal(a, anp))
|
|
|
|
def test_array_namespace(self):
|
|
a = mx.array(1.0)
|
|
api = a.__array_namespace__()
|
|
self.assertTrue(hasattr(api, "array"))
|
|
self.assertTrue(hasattr(api, "add"))
|
|
|
|
def test_to_scalar(self):
|
|
a = mx.array(1)
|
|
self.assertEqual(int(a), 1)
|
|
self.assertEqual(float(a), 1)
|
|
|
|
a = mx.array(1.5)
|
|
self.assertEqual(float(a), 1.5)
|
|
self.assertEqual(int(a), 1)
|
|
|
|
a = mx.zeros((2, 1))
|
|
with self.assertRaises(ValueError):
|
|
float(a)
|
|
with self.assertRaises(ValueError):
|
|
int(a)
|
|
|
|
def test_format(self):
|
|
a = mx.arange(3)
|
|
self.assertEqual(f"{a[0]:.2f}", "0.00")
|
|
|
|
b = mx.array(0.35487)
|
|
self.assertEqual(f"{b:.1f}", "0.4")
|
|
|
|
with self.assertRaises(TypeError):
|
|
s = f"{a:.2f}"
|
|
|
|
a = mx.array([1, 2, 3])
|
|
self.assertEqual(f"{a}", "array([1, 2, 3], dtype=int32)")
|
|
|
|
def test_deep_graphs(self):
|
|
# The following tests should simply run cleanly without a segfault or
|
|
# crash due to exceeding recursion depth limits.
|
|
|
|
# Deep graph destroyed without eval
|
|
x = mx.array([1.0, 2.0])
|
|
for _ in range(100_000):
|
|
x = mx.sin(x)
|
|
del x
|
|
|
|
# Duplicate input deep graph destroyed without eval
|
|
x = mx.array([1.0, 2.0])
|
|
for _ in range(100_000):
|
|
x = x + x
|
|
|
|
# Deep graph with siblings destroyed without eval
|
|
x = mx.array([1, 2])
|
|
for _ in range(100_000):
|
|
x = mx.concatenate(mx.split(x, 2))
|
|
del x
|
|
|
|
# Deep graph with eval
|
|
x = mx.array([1.0, 2.0])
|
|
for _ in range(100_000):
|
|
x = mx.sin(x)
|
|
mx.eval(x)
|
|
|
|
def test_siblings_without_eval(self):
|
|
def get_mem():
|
|
if platform.system() == "Windows":
|
|
process = psutil.Process(os.getpid())
|
|
return process.memory_info().peak_wset
|
|
else:
|
|
return resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
|
|
|
|
key = mx.array([1, 2])
|
|
|
|
def t():
|
|
a, b = mx.split(key, 2)
|
|
a = mx.reshape(a, [])
|
|
b = mx.reshape(b, [])
|
|
return b
|
|
|
|
t()
|
|
gc.collect()
|
|
expected = get_mem()
|
|
for _ in range(100):
|
|
t()
|
|
used = get_mem()
|
|
self.assertEqual(expected, used)
|
|
|
|
def test_scalar_integer_conversion_overflow(self):
|
|
y = mx.array(2000000000, dtype=mx.int32)
|
|
x = 3000000000
|
|
with self.assertRaises(ValueError):
|
|
y + x
|
|
with self.assertRaises(ValueError):
|
|
mx.add(y, x)
|
|
|
|
def test_real_imag(self):
|
|
x = mx.array([1.0])
|
|
self.assertEqual(x.real.item(), 1.0)
|
|
self.assertEqual(x.imag.item(), 0.0)
|
|
|
|
x = mx.array([1.0 + 1.0j])
|
|
self.assertEqual(x.imag.item(), 1.0)
|
|
self.assertEqual(x.real.item(), 1.0)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
mlx_tests.MLXTestRunner()
|