import gc
import os
import tempfile
import unittest
import mlx.core as mx
import mlx.nn as nn
import mlx_tests
class TestExportImport(mlx_tests.MLXTestCase):
@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_basic_export_import(self):
path = os.path.join(self.test_dir, "fn.mlxfn")
def fun():
return mx.zeros((3, 3))
mx.export_function(path, fun)
imported = mx.import_function(path)
expected = fun()
(out,) = imported()
self.assertTrue(mx.array_equal(out, expected))
def fun(x):
return mx.abs(mx.sin(x))
inputs = mx.array([1.0, 2.0, 3.0, 4.0, 5.0])
mx.export_function(path, fun, inputs)
imported = mx.import_function(path)
expected = fun(inputs)
(out,) = imported(inputs)
self.assertTrue(mx.allclose(out, expected))
def fun(x):
x = mx.abs(mx.sin(x))
return x
mx.export_function(path, fun, [inputs])
imported = mx.import_function(path)
expected = fun(inputs)
(out,) = imported([inputs])
self.assertTrue(mx.allclose(out, expected))
(out,) = imported(inputs)
self.assertTrue(mx.allclose(out, expected))
mx.export_function(path, fun, (inputs,))
imported = mx.import_function(path)
(out,) = imported((inputs,))
self.assertTrue(mx.allclose(out, expected))
def fun(x):
return [mx.abs(mx.sin(x))]
mx.export_function(path, fun, inputs)
imported = mx.import_function(path)
(out,) = imported(inputs)
self.assertTrue(mx.allclose(out, expected))
def fun(x):
return (mx.abs(mx.sin(x)),)
mx.export_function(path, fun, inputs)
imported = mx.import_function(path)
(out,) = imported(inputs)
self.assertTrue(mx.allclose(out, expected))
def fun(x):
return mx.abs(x)
with self.assertRaises(ValueError):
mx.export_function(path, fun, "hi")
with self.assertRaises(ValueError):
mx.export_function(path, fun, mx.array(1.0), "hi")
def fun(x):
return mx.abs(x[0][0])
with self.assertRaises(ValueError):
mx.export_function(path, fun, [[mx.array(1.0)]])
def fun():
return (mx.zeros((3, 3)), 1)
with self.assertRaises(ValueError):
mx.export_function(path, fun)
def fun():
return (mx.zeros((3, 3)), [mx.zeros((3, 3))])
with self.assertRaises(ValueError):
mx.export_function(path, fun)
def fun(x, y):
return x + y
mx.export_function(path, fun, mx.array(1.0), mx.array(1.0))
imported = mx.import_function(path)
with self.assertRaises(ValueError):
imported(mx.array(1.0), 1.0)
with self.assertRaises(ValueError):
imported(mx.array(1.0), mx.array(1.0), mx.array(1.0))
with self.assertRaises(ValueError):
imported(mx.array(1.0), [mx.array(1.0)])
def test_export_random_sample(self):
path = os.path.join(self.test_dir, "fn.mlxfn")
mx.random.seed(5)
def fun():
return mx.random.uniform(shape=(3,))
mx.export_function(path, fun)
imported = mx.import_function(path)
(out,) = imported()
mx.random.seed(5)
expected = fun()
self.assertTrue(mx.array_equal(out, expected))
def test_export_with_kwargs(self):
path = os.path.join(self.test_dir, "fn.mlxfn")
def fun(x, z=None):
out = x
if z is not None:
out += z
return out
x = mx.array([1, 2, 3])
y = mx.array([1, 1, 0])
z = mx.array([2, 2, 2])
mx.export_function(path, fun, (x,), {"z": z})
imported_fun = mx.import_function(path)
with self.assertRaises(ValueError):
imported_fun(x, z)
with self.assertRaises(ValueError):
imported_fun(x, y=z)
with self.assertRaises(ValueError):
imported_fun((x,), {"y": z})
out = imported_fun(x, z=z)[0]
self.assertTrue(mx.array_equal(out, mx.array([3, 4, 5])))
out = imported_fun((x,), {"z": z})[0]
self.assertTrue(mx.array_equal(out, mx.array([3, 4, 5])))
mx.export_function(path, fun, x, z=z)
imported_fun = mx.import_function(path)
out = imported_fun(x, z=z)[0]
self.assertTrue(mx.array_equal(out, mx.array([3, 4, 5])))
out = imported_fun((x,), {"z": z})[0]
self.assertTrue(mx.array_equal(out, mx.array([3, 4, 5])))
mx.export_function(path, fun, x=x, z=z)
imported_fun = mx.import_function(path)
with self.assertRaises(ValueError):
out = imported_fun(x, z=z)[0]
out = imported_fun(x=x, z=z)[0]
self.assertTrue(mx.array_equal(out, mx.array([3, 4, 5])))
out = imported_fun({"x": x, "z": z})[0]
self.assertTrue(mx.array_equal(out, mx.array([3, 4, 5])))
def test_export_variable_inputs(self):
path = os.path.join(self.test_dir, "fn.mlxfn")
def fun(x, y, z=None):
out = x + y
if z is not None:
out += z
return out
with mx.exporter(path, fun) as exporter:
exporter(mx.array([1, 2, 3]), mx.array([1, 1, 1]))
exporter(mx.array([1, 2, 3]), mx.array([1, 1, 1]), z=mx.array([2]))
with self.assertRaises(RuntimeError):
exporter(mx.array([1, 2, 3, 4]), mx.array([1, 1, 1, 1]))
imported_fun = mx.import_function(path)
out = imported_fun(mx.array([1, 2, 3]), mx.array([1, 1, 1]))[0]
self.assertTrue(mx.array_equal(out, mx.array([2, 3, 4])))
out = imported_fun(mx.array([1, 2, 3]), mx.array([1, 1, 1]), z=mx.array([2]))[0]
self.assertTrue(mx.array_equal(out, mx.array([4, 5, 6])))
with self.assertRaises(ValueError):
imported_fun(mx.array([1, 2, 3, 4]), mx.array([1, 1, 1, 1]))
constant = mx.zeros((16, 2048))
mx.eval(constant)
def fun(*args):
return constant + sum(args)
with mx.exporter(path, fun) as exporter:
for i in range(5):
exporter(*[mx.array(1)] * i)
constants_size = constant.nbytes + 8192
self.assertTrue(os.path.getsize(path) < constants_size)
def test_leaks(self):
path = os.path.join(self.test_dir, "fn.mlxfn")
mx.synchronize()
if mx.metal.is_available():
mem_pre = mx.get_active_memory()
else:
mem_pre = 0
def outer():
d = {}
def f(x):
return d["x"]
d["f"] = mx.exporter(path, f)
d["x"] = mx.array([0] * 1000)
for _ in range(5):
outer()
gc.collect()
if mx.metal.is_available():
mem_post = mx.get_active_memory()
else:
mem_post = 0
self.assertEqual(mem_pre, mem_post)
def test_export_import_shapeless(self):
path = os.path.join(self.test_dir, "fn.mlxfn")
def fun(*args):
return sum(args)
with mx.exporter(path, fun, shapeless=True) as exporter:
exporter(mx.array(1))
exporter(mx.array(1), mx.array(2))
exporter(mx.array(1), mx.array(2), mx.array(3))
f2 = mx.import_function(path)
self.assertEqual(f2(mx.array(1))[0].item(), 1)
self.assertEqual(f2(mx.array(1), mx.array(1))[0].item(), 2)
self.assertEqual(f2(mx.array(1), mx.array(1), mx.array(1))[0].item(), 3)
with self.assertRaises(ValueError):
f2(mx.array(10), mx.array([5, 10, 20]))
def test_export_scatter_gather(self):
path = os.path.join(self.test_dir, "fn.mlxfn")
def fun(a, b):
return mx.take_along_axis(a, b, axis=0)
x = mx.random.uniform(shape=(4, 4))
y = mx.array([[0, 1, 2, 3], [1, 2, 0, 3]])
mx.export_function(path, fun, (x, y))
imported_fun = mx.import_function(path)
expected = fun(x, y)
out = imported_fun(x, y)[0]
self.assertTrue(mx.array_equal(expected, out))
def fun(a, b, c):
return mx.put_along_axis(a, b, c, axis=0)
x = mx.random.uniform(shape=(4, 4))
y = mx.array([[0, 1, 2, 3], [1, 2, 0, 3]])
z = mx.random.uniform(shape=(2, 4))
mx.export_function(path, fun, (x, y, z))
imported_fun = mx.import_function(path)
expected = fun(x, y, z)
out = imported_fun(x, y, z)[0]
self.assertTrue(mx.array_equal(expected, out))
def test_export_conv(self):
path = os.path.join(self.test_dir, "fn.mlxfn")
class Model(nn.Module):
def __init__(self):
super().__init__()
self.c1 = nn.Conv2d(
3, 16, kernel_size=3, stride=1, padding=1, bias=False
)
self.c2 = nn.Conv2d(
16, 16, kernel_size=3, stride=2, padding=1, bias=False
)
self.c3 = nn.Conv2d(
16, 16, kernel_size=3, stride=1, padding=2, bias=False
)
def __call__(self, x):
return self.c3(self.c2(self.c1(x)))
model = Model()
mx.eval(model.parameters())
def forward(x):
return model(x)
input_data = mx.random.normal(shape=(4, 32, 32, 3))
mx.export_function(path, forward, input_data)
imported_fn = mx.import_function(path)
out = imported_fn(input_data)[0]
expected = forward(input_data)
self.assertTrue(mx.allclose(expected, out))
def test_export_conv_shapeless(self):
path = os.path.join(self.test_dir, "conv1d.mlxfn")
class M1(nn.Module):
def __init__(self):
super().__init__()
self.c = nn.Conv1d(3, 8, kernel_size=3, stride=2, padding=1, bias=False)
def __call__(self, x):
return self.c(x)
m1 = M1()
mx.eval(m1.parameters())
def f1(x):
return m1(x)
x = mx.random.normal(shape=(4, 64, 3))
mx.export_function(path, f1, x, shapeless=True)
f1_imp = mx.import_function(path)
for shape in [(4, 64, 3), (1, 33, 3), (2, 128, 3)]:
xt = mx.random.normal(shape=shape)
self.assertTrue(mx.allclose(f1_imp(xt)[0], f1(xt)))
path = os.path.join(self.test_dir, "conv2d.mlxfn")
class M2(nn.Module):
def __init__(self):
super().__init__()
self.c = nn.Conv2d(3, 6, kernel_size=3, stride=2, padding=1, bias=False)
def __call__(self, x):
return self.c(x)
m2 = M2()
mx.eval(m2.parameters())
def f2(x):
return m2(x)
x = mx.random.normal(shape=(2, 32, 32, 3))
mx.export_function(path, f2, x, shapeless=True)
f2_imp = mx.import_function(path)
for shape in [(2, 32, 32, 3), (1, 31, 31, 3), (4, 64, 48, 3)]:
xt = mx.random.normal(shape=shape)
self.assertTrue(mx.allclose(f2_imp(xt)[0], f2(xt)))
path = os.path.join(self.test_dir, "conv3d.mlxfn")
class M3(nn.Module):
def __init__(self):
super().__init__()
self.c = nn.Conv3d(2, 4, kernel_size=3, stride=2, padding=1, bias=False)
def __call__(self, x):
return self.c(x)
m3 = M3()
mx.eval(m3.parameters())
def f3(x):
return m3(x)
x = mx.random.normal(shape=(1, 8, 8, 8, 2))
mx.export_function(path, f3, x, shapeless=True)
f3_imp = mx.import_function(path)
for shape in [(1, 8, 8, 8, 2), (2, 7, 8, 9, 2), (1, 16, 16, 4, 2)]:
xt = mx.random.normal(shape=shape)
self.assertTrue(mx.allclose(f3_imp(xt)[0], f3(xt)))
path = os.path.join(self.test_dir, "conv2d_grouped.mlxfn")
class MG(nn.Module):
def __init__(self):
super().__init__()
self.c = nn.Conv2d(
4, 6, kernel_size=3, stride=2, padding=1, groups=2, bias=False
)
def __call__(self, x):
return self.c(x)
mg = MG()
mx.eval(mg.parameters())
def fg(x):
return mg(x)
x = mx.random.normal(shape=(2, 32, 32, 4))
mx.export_function(path, fg, x, shapeless=True)
fg_imp = mx.import_function(path)
for shape in [(2, 32, 32, 4), (1, 32, 32, 4), (3, 15, 20, 4)]:
xt = mx.random.normal(shape=shape)
self.assertTrue(mx.allclose(fg_imp(xt)[0], fg(xt)))
def test_export_control_flow(self):
def fun(x, y):
if y.shape[0] <= 2:
return x + y
else:
return x + 2 * y
for y in (mx.array([1, 2, 3]), mx.array([1, 2])):
for shapeless in (True, False):
with self.subTest(y=y, shapeless=shapeless):
x = mx.array(1)
export_path = os.path.join(self.test_dir, "control_flow.mlxfn")
mx.export_function(export_path, fun, x, y, shapeless=shapeless)
imported_fn = mx.import_function(export_path)
self.assertTrue(mx.array_equal(imported_fn(x, y)[0], fun(x, y)))
def test_export_quantized_model(self):
for shapeless in (True, False):
with self.subTest(shapeless=shapeless):
model = nn.Sequential(
nn.Linear(1024, 512), nn.ReLU(), nn.Linear(512, 1024)
)
model.eval()
mx.eval(model.parameters())
input_data = mx.ones(shape=(512, 1024))
nn.quantize(model)
self.assertTrue(isinstance(model.layers[0], nn.QuantizedLinear))
self.assertTrue(isinstance(model.layers[2], nn.QuantizedLinear))
mx.eval(model.parameters())
export_path = os.path.join(self.test_dir, "quantized_linear.mlxfn")
mx.export_function(export_path, model, input_data, shapeless=shapeless)
imported_fn = mx.import_function(export_path)
self.assertTrue(
mx.array_equal(imported_fn(input_data)[0], model(input_data))
)
def test_export_kwarg_ordering(self):
path = os.path.join(self.test_dir, "fun.mlxfn")
def fn(x, y):
return x - y
mx.export_function(path, fn, x=mx.array(1.0), y=mx.array(1.0))
imported = mx.import_function(path)
out = imported(x=mx.array(2.0), y=mx.array(3.0))[0]
self.assertEqual(out.item(), -1.0)
out = imported(y=mx.array(2.0), x=mx.array(3.0))[0]
self.assertEqual(out.item(), 1.0)
def test_export_with_callback(self):
def fn(x, y):
return mx.log(mx.abs(x - y)).astype(mx.int32)
n_in = None
n_out = None
n_const = None
keywords = None
primitives = []
primitive_args = []
def callback(args):
nonlocal n_in, n_out, n_const, keywords, primitives
t = args["type"]
if t == "inputs":
n_in = len(args["inputs"])
elif args["type"] == "outputs":
n_out = len(args["outputs"])
elif args["type"] == "keyword_inputs":
keywords = args["keywords"]
elif t == "constants":
n_const = len(args["constants"])
elif t == "primitive":
primitives.append(args["name"])
primitive_args.append(args["arguments"])
mx.export_function(callback, fn, mx.array(1.0), y=mx.array(1.0))
self.assertEqual(n_in, 2)
self.assertEqual(n_out, 1)
self.assertEqual(n_const, 0)
self.assertEqual(len(keywords), 1)
self.assertEqual(keywords[0][0], "y")
self.assertEqual(primitives, ["Subtract", "Abs", "Log", "AsType"])
self.assertEqual(primitive_args[0], [])
self.assertEqual(primitive_args[1], [])
self.assertEqual(primitive_args[2], [2])
self.assertEqual(primitive_args[3], [mx.int32])
@unittest.skipIf(not mx.is_available(mx.gpu), "No GPU available")
def test_export_import_custom_kernel(self):
if mx.metal.is_available():
source = """
uint elem = thread_position_in_grid.x;
out1[elem] = a[elem];
"""
custom_kernel = mx.fast.metal_kernel
elif mx.cuda.is_available():
source = """
auto elem = cooperative_groups::this_grid().thread_rank();
out1[elem] = a[elem];
"""
custom_kernel = mx.fast.cuda_kernel
kernel = custom_kernel(
name="basic",
input_names=["a"],
output_names=["out1"],
source=source,
)
def call(a):
return kernel(
inputs=[a],
grid=(4, 1, 1),
threadgroup=(2, 1, 1),
output_shapes=[(2, 2)],
output_dtypes=[mx.float32],
stream=mx.gpu,
)[0]
mx.random.seed(7)
a = mx.random.normal(shape=(2, 2))
path = os.path.join(self.test_dir, "fn.mlxfn")
expected = call(a)
mx.export_function(path, call, a)
imported = mx.import_function(path)
out = imported(a)[0]
self.assertTrue(mx.allclose(expected, out))
def test_export_import_multi_with_constants(self):
path = os.path.join(self.test_dir, "fn.mlxfn")
def fun(y):
i = y.shape[0]
x = mx.array(i)
for j in range(10):
x = x + mx.array(i + j)
return x * y.sum()
ys = [mx.array([1]), mx.array([1, 1]), mx.array([1, 1, 1])]
with mx.exporter(path, fun) as exporter:
for y in ys:
exporter(y)
imported = mx.import_function(path)
for y in ys:
self.assertEqual(imported(y)[0].item(), fun(y).item())
def test_export_import_scatter_sum(self):
def fun(x, y, z):
return x.at[y].add(z)
x = mx.array([1, 2, 3])
y = mx.array([0, 0, 1])
z = mx.array([1, 1, 1])
path = os.path.join(self.test_dir, "fn.mlxfn")
mx.export_function(path, fun, x, y, z)
imported = mx.import_function(path)
self.assertTrue(mx.array_equal(imported(x, y, z)[0], fun(x, y, z)))
if __name__ == "__main__":
mlx_tests.MLXTestRunner()