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# Copyright © 2023 Apple Inc.
import os
import tempfile
import unittest
from pathlib import Path
import mlx.core as mx
import mlx_tests
import numpy as np
class TestLoad(mlx_tests.MLXTestCase):
dtypes = [
"uint8",
"uint16",
"uint32",
"uint64",
"int8",
"int16",
"int32",
"int64",
"float32",
"float16",
"complex64",
]
@classmethod
def setUpClass(cls):
cls.test_dir_fid = tempfile.TemporaryDirectory()
cls.test_dir = cls.test_dir_fid.name
if not os.path.isdir(cls.test_dir):
os.mkdir(cls.test_dir)
@classmethod
def tearDownClass(cls):
cls.test_dir_fid.cleanup()
def test_save_and_load(self):
for dt in self.dtypes:
with self.subTest(dtype=dt):
for i, shape in enumerate([(1,), (23,), (1024, 1024), (4, 6, 3, 1, 2)]):
with self.subTest(shape=shape):
save_file_mlx = os.path.join(self.test_dir, f"mlx_{dt}_{i}.npy")
save_file_npy = os.path.join(self.test_dir, f"npy_{dt}_{i}.npy")
save_arr = np.random.uniform(0.0, 32.0, size=shape)
save_arr_npy = save_arr.astype(getattr(np, dt))
save_arr_mlx = mx.array(save_arr_npy)
mx.save(save_file_mlx, save_arr_mlx)
np.save(save_file_npy, save_arr_npy)
# Load array saved by mlx as mlx array
load_arr_mlx_mlx = mx.load(save_file_mlx)
self.assertTrue(mx.array_equal(load_arr_mlx_mlx, save_arr_mlx))
# Load array saved by numpy as mlx array
load_arr_npy_mlx = mx.load(save_file_npy)
self.assertTrue(mx.array_equal(load_arr_npy_mlx, save_arr_mlx))
# Load array saved by mlx as numpy array
load_arr_mlx_npy = np.load(save_file_mlx)
self.assertTrue(np.array_equal(load_arr_mlx_npy, save_arr_npy))
save_file = os.path.join(self.test_dir, f"mlx_path.npy")
save_arr = mx.ones((32,))
mx.save(Path(save_file), save_arr)
# Load array saved by mlx as mlx array
load_arr = mx.load(Path(save_file))
self.assertTrue(mx.array_equal(load_arr, save_arr))
def test_save_and_load_safetensors(self):
test_file = os.path.join(self.test_dir, "test.safetensors")
with self.assertRaises(Exception):
mx.save_safetensors(test_file, {"a": mx.ones((4, 4))}, {"testing": 0})
for obj in [str, Path]:
mx.save_safetensors(
obj(test_file),
{"test": mx.ones((2, 2))},
{"testing": "test", "format": "mlx"},
)
res = mx.load(obj(test_file), return_metadata=True)
self.assertEqual(len(res), 2)
self.assertEqual(res[1], {"testing": "test", "format": "mlx"})
for dt in self.dtypes + ["bfloat16"]:
with self.subTest(dtype=dt):
for i, shape in enumerate([(1,), (23,), (1024, 1024), (4, 6, 3, 1, 2)]):
with self.subTest(shape=shape):
save_file_mlx = os.path.join(
self.test_dir, f"mlx_{dt}_{i}_fs.safetensors"
)
save_dict = {
"test": (
mx.random.normal(shape=shape, dtype=getattr(mx, dt))
if dt in ["float32", "float16", "bfloat16"]
else mx.ones(shape, dtype=getattr(mx, dt))
)
}
with open(save_file_mlx, "wb") as f:
mx.save_safetensors(f, save_dict)
with open(save_file_mlx, "rb") as f:
load_dict = mx.load(f)
self.assertTrue("test" in load_dict)
self.assertTrue(
mx.array_equal(load_dict["test"], save_dict["test"])
)
def test_save_and_load_gguf(self):
if not os.path.isdir(self.test_dir):
os.mkdir(self.test_dir)
# TODO: Add support for other dtypes (self.dtypes + ["bfloat16"])
supported_dtypes = ["float16", "float32", "int8", "int16", "int32"]
for dt in supported_dtypes:
with self.subTest(dtype=dt):
for i, shape in enumerate([(1,), (23,), (1024, 1024), (4, 6, 3, 1, 2)]):
with self.subTest(shape=shape):
save_file_mlx = os.path.join(
self.test_dir, f"mlx_{dt}_{i}_fs.gguf"
)
save_dict = {
"test": (
mx.random.normal(shape=shape, dtype=getattr(mx, dt))
if dt in ["float32", "float16", "bfloat16"]
else mx.ones(shape, dtype=getattr(mx, dt))
)
}
mx.save_gguf(save_file_mlx, save_dict)
load_dict = mx.load(save_file_mlx)
self.assertTrue("test" in load_dict)
self.assertTrue(
mx.array_equal(load_dict["test"], save_dict["test"])
)
save_file_mlx = os.path.join(self.test_dir, f"mlx_path_test_fs.gguf")
save_dict = {"test": mx.ones(shape)}
mx.save_gguf(Path(save_file_mlx), save_dict)
load_dict = mx.load(Path(save_file_mlx))
self.assertTrue("test" in load_dict)
self.assertTrue(mx.array_equal(load_dict["test"], save_dict["test"]))
def test_load_f8_e4m3(self):
if not os.path.isdir(self.test_dir):
os.mkdir(self.test_dir)
expected = [
0,
mx.nan,
mx.nan,
-0.875,
0.4375,
-0.005859,
-1.25,
-1.25,
-1.5,
-0.0039,
]
expected = mx.array(expected, dtype=mx.bfloat16)
contents = b'H\x00\x00\x00\x00\x00\x00\x00{"tensor":{"dtype":"F8_E4M3","shape":[10],"data_offsets":[0,10]}} \x00\x7f\xff\xb6.\x83\xba\xba\xbc\x82'
with tempfile.NamedTemporaryFile(suffix=".safetensors") as f:
f.write(contents)
f.seek(0)
out = mx.load(f)["tensor"]
self.assertTrue(mx.allclose(out[0], expected[0], equal_nan=True))
def test_save_and_load_gguf_metadata_basic(self):
if not os.path.isdir(self.test_dir):
os.mkdir(self.test_dir)
save_file_mlx = os.path.join(self.test_dir, f"mlx_gguf_with_metadata.gguf")
save_dict = {"test": mx.ones((4, 4), dtype=mx.int32)}
metadata = {}
# Empty works
mx.save_gguf(save_file_mlx, save_dict, metadata)
# Loads without the metadata
load_dict = mx.load(save_file_mlx)
self.assertTrue("test" in load_dict)
self.assertTrue(mx.array_equal(load_dict["test"], save_dict["test"]))
# Loads empty metadata
load_dict, meta_load_dict = mx.load(save_file_mlx, return_metadata=True)
self.assertTrue("test" in load_dict)
self.assertTrue(mx.array_equal(load_dict["test"], save_dict["test"]))
self.assertEqual(len(meta_load_dict), 0)
# Loads string metadata
metadata = {"meta": "data"}
mx.save_gguf(save_file_mlx, save_dict, metadata)
load_dict, meta_load_dict = mx.load(save_file_mlx, return_metadata=True)
self.assertTrue("test" in load_dict)
self.assertTrue(mx.array_equal(load_dict["test"], save_dict["test"]))
self.assertEqual(len(meta_load_dict), 1)
self.assertTrue("meta" in meta_load_dict)
self.assertEqual(meta_load_dict["meta"], "data")
def test_save_and_load_gguf_metadata_arrays(self):
if not os.path.isdir(self.test_dir):
os.mkdir(self.test_dir)
save_file_mlx = os.path.join(self.test_dir, f"mlx_gguf_with_metadata.gguf")
save_dict = {"test": mx.ones((4, 4), dtype=mx.int32)}
# Test scalars and one dimensional arrays
for t in [
mx.uint8,
mx.int8,
mx.uint16,
mx.int16,
mx.uint32,
mx.int32,
mx.uint64,
mx.int64,
mx.float32,
]:
for shape in [(), (2,)]:
arr = mx.random.uniform(shape=shape).astype(t)
metadata = {"meta": arr}
mx.save_gguf(save_file_mlx, save_dict, metadata)
_, meta_load_dict = mx.load(save_file_mlx, return_metadata=True)
self.assertEqual(len(meta_load_dict), 1)
self.assertTrue("meta" in meta_load_dict)
self.assertTrue(mx.array_equal(meta_load_dict["meta"], arr))
self.assertEqual(meta_load_dict["meta"].dtype, arr.dtype)
for t in [mx.float16, mx.bfloat16, mx.complex64]:
with self.assertRaises(ValueError):
arr = mx.array(1, t)
metadata = {"meta": arr}
mx.save_gguf(save_file_mlx, save_dict, metadata)
def test_save_and_load_gguf_metadata_mixed(self):
if not os.path.isdir(self.test_dir):
os.mkdir(self.test_dir)
save_file_mlx = os.path.join(self.test_dir, f"mlx_gguf_with_metadata.gguf")
save_dict = {"test": mx.ones((4, 4), dtype=mx.int32)}
# Test string and array
arr = mx.array(1.5)
metadata = {"meta1": arr, "meta2": "data"}
mx.save_gguf(save_file_mlx, save_dict, metadata)
_, meta_load_dict = mx.load(save_file_mlx, return_metadata=True)
self.assertEqual(len(meta_load_dict), 2)
self.assertTrue("meta1" in meta_load_dict)
self.assertTrue(mx.array_equal(meta_load_dict["meta1"], arr))
self.assertEqual(meta_load_dict["meta1"].dtype, arr.dtype)
self.assertTrue("meta2" in meta_load_dict)
self.assertEqual(meta_load_dict["meta2"], "data")
# Test list of strings
metadata = {"meta": ["data1", "data2", "data345"]}
mx.save_gguf(save_file_mlx, save_dict, metadata)
_, meta_load_dict = mx.load(save_file_mlx, return_metadata=True)
self.assertEqual(len(meta_load_dict), 1)
self.assertEqual(meta_load_dict["meta"], metadata["meta"])
# Test a combination of stuff
metadata = {
"meta1": ["data1", "data2", "data345"],
"meta2": mx.array([1, 2, 3, 4]),
"meta3": "data",
"meta4": mx.array(1.5),
}
mx.save_gguf(save_file_mlx, save_dict, metadata)
_, meta_load_dict = mx.load(save_file_mlx, return_metadata=True)
self.assertEqual(len(meta_load_dict), 4)
for k, v in metadata.items():
if isinstance(v, mx.array):
self.assertTrue(mx.array_equal(meta_load_dict[k], v))
else:
self.assertEqual(meta_load_dict[k], v)
def test_save_and_load_fs(self):
if not os.path.isdir(self.test_dir):
os.mkdir(self.test_dir)
for dt in self.dtypes:
with self.subTest(dtype=dt):
for i, shape in enumerate([(1,), (23,), (1024, 1024), (4, 6, 3, 1, 2)]):
with self.subTest(shape=shape):
save_file_mlx = os.path.join(
self.test_dir, f"mlx_{dt}_{i}_fs.npy"
)
save_file_npy = os.path.join(
self.test_dir, f"npy_{dt}_{i}_fs.npy"
)
save_arr = np.random.uniform(0.0, 32.0, size=shape)
save_arr_npy = save_arr.astype(getattr(np, dt))
save_arr_mlx = mx.array(save_arr_npy)
with open(save_file_mlx, "wb") as f:
mx.save(f, save_arr_mlx)
np.save(save_file_npy, save_arr_npy)
# Load array saved by mlx as mlx array
with open(save_file_mlx, "rb") as f:
load_arr_mlx_mlx = mx.load(f)
self.assertTrue(mx.array_equal(load_arr_mlx_mlx, save_arr_mlx))
# Load array saved by numpy as mlx array
with open(save_file_npy, "rb") as f:
load_arr_npy_mlx = mx.load(f)
self.assertTrue(mx.array_equal(load_arr_npy_mlx, save_arr_mlx))
# Load array saved by mlx as numpy array
load_arr_mlx_npy = np.load(save_file_mlx)
self.assertTrue(np.array_equal(load_arr_mlx_npy, save_arr_npy))
def test_savez_and_loadz(self):
if not os.path.isdir(self.test_dir):
os.mkdir(self.test_dir)
for dt in self.dtypes:
with self.subTest(dtype=dt):
shapes = [(6,), (6, 6), (4, 1, 3, 1, 2)]
save_file_mlx_uncomp = os.path.join(
self.test_dir, f"mlx_{dt}_uncomp.npz"
)
save_file_npy_uncomp = os.path.join(
self.test_dir, f"npy_{dt}_uncomp.npz"
)
save_file_mlx_comp = os.path.join(self.test_dir, f"mlx_{dt}_comp.npz")
save_file_npy_comp = os.path.join(self.test_dir, f"npy_{dt}_comp.npz")
# Make dictionary of multiple
save_arrs_npy = {
f"save_arr_{i}": np.random.uniform(
0.0, 32.0, size=shapes[i]
).astype(getattr(np, dt))
for i in range(len(shapes))
}
save_arrs_mlx = {k: mx.array(v) for k, v in save_arrs_npy.items()}
# Save as npz files
np.savez(save_file_npy_uncomp, **save_arrs_npy)
mx.savez(save_file_mlx_uncomp, **save_arrs_mlx)
np.savez_compressed(save_file_npy_comp, **save_arrs_npy)
mx.savez_compressed(save_file_mlx_comp, **save_arrs_mlx)
for save_file_npy, save_file_mlx in (
(save_file_npy_uncomp, save_file_mlx_uncomp),
(save_file_npy_comp, save_file_mlx_comp),
):
# Load array saved by mlx as mlx array
load_arr_mlx_mlx = mx.load(save_file_mlx)
for k, v in load_arr_mlx_mlx.items():
self.assertTrue(mx.array_equal(save_arrs_mlx[k], v))
# Load arrays saved by numpy as mlx arrays
load_arr_npy_mlx = mx.load(save_file_npy)
for k, v in load_arr_npy_mlx.items():
self.assertTrue(mx.array_equal(save_arrs_mlx[k], v))
# Load array saved by mlx as numpy array
load_arr_mlx_npy = np.load(save_file_mlx)
for k, v in load_arr_mlx_npy.items():
self.assertTrue(np.array_equal(save_arrs_npy[k], v))
def test_non_contiguous(self):
a = mx.broadcast_to(mx.array([1, 2]), [4, 2])
save_file = os.path.join(self.test_dir, "a.npy")
mx.save(save_file, a)
aload = mx.load(save_file)
self.assertTrue(mx.array_equal(a, aload))
save_file = os.path.join(self.test_dir, "a.safetensors")
mx.save_safetensors(save_file, {"a": a})
aload = mx.load(save_file)["a"]
self.assertTrue(mx.array_equal(a, aload))
save_file = os.path.join(self.test_dir, "a.gguf")
mx.save_gguf(save_file, {"a": a})
aload = mx.load(save_file)["a"]
self.assertTrue(mx.array_equal(a, aload))
# safetensors and gguf only work with row contiguous
# make sure col contiguous is handled properly
save_file = os.path.join(self.test_dir, "a.safetensors")
a = mx.arange(4).reshape(2, 2).T
mx.save_safetensors(save_file, {"a": a})
aload = mx.load(save_file)["a"]
self.assertTrue(mx.array_equal(a, aload))
save_file = os.path.join(self.test_dir, "a.gguf")
mx.save_gguf(save_file, {"a": a})
aload = mx.load(save_file)["a"]
self.assertTrue(mx.array_equal(a, aload))
def test_load_donation(self):
x = mx.random.normal((1024,))
mx.eval(x)
save_file = os.path.join(self.test_dir, "donation.npy")
mx.save(save_file, x)
mx.synchronize()
mx.reset_peak_memory()
scale = mx.array(2.0)
y = mx.load(save_file)
mx.eval(y)
mx.synchronize()
load_only = mx.get_peak_memory()
y = mx.load(save_file) * scale
mx.eval(y)
mx.synchronize()
load_with_binary = mx.get_peak_memory()
self.assertEqual(load_only, load_with_binary)
if __name__ == "__main__":
mlx_tests.MLXTestRunner()
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