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https://github.com/ml-explore/mlx.git
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8b25ce62d5
* Add tests for export, including control flow export and quantized model export. * Skip quantization related test for CUDA backend.
392 lines
12 KiB
Python
392 lines
12 KiB
Python
# Copyright © 2024 Apple Inc.
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import gc
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import os
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import tempfile
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import unittest
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import mlx.core as mx
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import mlx.nn as nn
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import mlx_tests
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class TestExportImport(mlx_tests.MLXTestCase):
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@classmethod
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def setUpClass(cls):
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cls.test_dir_fid = tempfile.TemporaryDirectory()
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cls.test_dir = cls.test_dir_fid.name
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if not os.path.isdir(cls.test_dir):
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os.mkdir(cls.test_dir)
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@classmethod
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def tearDownClass(cls):
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cls.test_dir_fid.cleanup()
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def test_basic_export_import(self):
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path = os.path.join(self.test_dir, "fn.mlxfn")
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# Function with no inputs
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def fun():
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return mx.zeros((3, 3))
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mx.export_function(path, fun)
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imported = mx.import_function(path)
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expected = fun()
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(out,) = imported()
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self.assertTrue(mx.array_equal(out, expected))
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# Simple function with inputs
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def fun(x):
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return mx.abs(mx.sin(x))
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inputs = mx.array([1.0, 2.0, 3.0, 4.0, 5.0])
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mx.export_function(path, fun, inputs)
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imported = mx.import_function(path)
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expected = fun(inputs)
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(out,) = imported(inputs)
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self.assertTrue(mx.allclose(out, expected))
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# Inputs in a list or tuple
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def fun(x):
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x = mx.abs(mx.sin(x))
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return x
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mx.export_function(path, fun, [inputs])
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imported = mx.import_function(path)
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expected = fun(inputs)
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(out,) = imported([inputs])
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self.assertTrue(mx.allclose(out, expected))
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(out,) = imported(inputs)
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self.assertTrue(mx.allclose(out, expected))
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mx.export_function(path, fun, (inputs,))
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imported = mx.import_function(path)
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(out,) = imported((inputs,))
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self.assertTrue(mx.allclose(out, expected))
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# Outputs in a list
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def fun(x):
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return [mx.abs(mx.sin(x))]
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mx.export_function(path, fun, inputs)
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imported = mx.import_function(path)
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(out,) = imported(inputs)
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self.assertTrue(mx.allclose(out, expected))
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# Outputs in a tuple
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def fun(x):
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return (mx.abs(mx.sin(x)),)
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mx.export_function(path, fun, inputs)
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imported = mx.import_function(path)
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(out,) = imported(inputs)
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self.assertTrue(mx.allclose(out, expected))
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# Check throws on invalid inputs / outputs
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def fun(x):
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return mx.abs(x)
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with self.assertRaises(ValueError):
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mx.export_function(path, fun, "hi")
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with self.assertRaises(ValueError):
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mx.export_function(path, fun, mx.array(1.0), "hi")
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def fun(x):
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return mx.abs(x[0][0])
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with self.assertRaises(ValueError):
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mx.export_function(path, fun, [[mx.array(1.0)]])
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def fun():
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return (mx.zeros((3, 3)), 1)
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with self.assertRaises(ValueError):
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mx.export_function(path, fun)
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def fun():
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return (mx.zeros((3, 3)), [mx.zeros((3, 3))])
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with self.assertRaises(ValueError):
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mx.export_function(path, fun)
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def fun(x, y):
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return x + y
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mx.export_function(path, fun, mx.array(1.0), mx.array(1.0))
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imported = mx.import_function(path)
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with self.assertRaises(ValueError):
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imported(mx.array(1.0), 1.0)
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with self.assertRaises(ValueError):
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imported(mx.array(1.0), mx.array(1.0), mx.array(1.0))
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with self.assertRaises(ValueError):
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imported(mx.array(1.0), [mx.array(1.0)])
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def test_export_random_sample(self):
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path = os.path.join(self.test_dir, "fn.mlxfn")
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mx.random.seed(5)
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def fun():
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return mx.random.uniform(shape=(3,))
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mx.export_function(path, fun)
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imported = mx.import_function(path)
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(out,) = imported()
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mx.random.seed(5)
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expected = fun()
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self.assertTrue(mx.array_equal(out, expected))
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def test_export_with_kwargs(self):
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path = os.path.join(self.test_dir, "fn.mlxfn")
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def fun(x, z=None):
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out = x
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if z is not None:
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out += z
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return out
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x = mx.array([1, 2, 3])
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y = mx.array([1, 1, 0])
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z = mx.array([2, 2, 2])
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mx.export_function(path, fun, (x,), {"z": z})
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imported_fun = mx.import_function(path)
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with self.assertRaises(ValueError):
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imported_fun(x, z)
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with self.assertRaises(ValueError):
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imported_fun(x, y=z)
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with self.assertRaises(ValueError):
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imported_fun((x,), {"y": z})
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out = imported_fun(x, z=z)[0]
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self.assertTrue(mx.array_equal(out, mx.array([3, 4, 5])))
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out = imported_fun((x,), {"z": z})[0]
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self.assertTrue(mx.array_equal(out, mx.array([3, 4, 5])))
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mx.export_function(path, fun, x, z=z)
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imported_fun = mx.import_function(path)
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out = imported_fun(x, z=z)[0]
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self.assertTrue(mx.array_equal(out, mx.array([3, 4, 5])))
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out = imported_fun((x,), {"z": z})[0]
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self.assertTrue(mx.array_equal(out, mx.array([3, 4, 5])))
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# Only specify kwargs
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mx.export_function(path, fun, x=x, z=z)
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imported_fun = mx.import_function(path)
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with self.assertRaises(ValueError):
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out = imported_fun(x, z=z)[0]
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out = imported_fun(x=x, z=z)[0]
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self.assertTrue(mx.array_equal(out, mx.array([3, 4, 5])))
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out = imported_fun({"x": x, "z": z})[0]
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self.assertTrue(mx.array_equal(out, mx.array([3, 4, 5])))
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def test_export_variable_inputs(self):
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path = os.path.join(self.test_dir, "fn.mlxfn")
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def fun(x, y, z=None):
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out = x + y
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if z is not None:
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out += z
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return out
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with mx.exporter(path, fun) as exporter:
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exporter(mx.array([1, 2, 3]), mx.array([1, 1, 1]))
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exporter(mx.array([1, 2, 3]), mx.array([1, 1, 1]), z=mx.array([2]))
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with self.assertRaises(RuntimeError):
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exporter(mx.array([1, 2, 3, 4]), mx.array([1, 1, 1, 1]))
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imported_fun = mx.import_function(path)
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out = imported_fun(mx.array([1, 2, 3]), mx.array([1, 1, 1]))[0]
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self.assertTrue(mx.array_equal(out, mx.array([2, 3, 4])))
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out = imported_fun(mx.array([1, 2, 3]), mx.array([1, 1, 1]), z=mx.array([2]))[0]
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self.assertTrue(mx.array_equal(out, mx.array([4, 5, 6])))
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with self.assertRaises(ValueError):
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imported_fun(mx.array([1, 2, 3, 4]), mx.array([1, 1, 1, 1]))
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# A function with a large constant
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constant = mx.zeros((16, 2048))
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mx.eval(constant)
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def fun(*args):
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return constant + sum(args)
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with mx.exporter(path, fun) as exporter:
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for i in range(5):
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exporter(*[mx.array(1)] * i)
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# Check the exported file size < constant size + small amount
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constants_size = constant.nbytes + 8192
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self.assertTrue(os.path.getsize(path) < constants_size)
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def test_leaks(self):
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path = os.path.join(self.test_dir, "fn.mlxfn")
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mx.synchronize()
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if mx.metal.is_available():
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mem_pre = mx.get_active_memory()
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else:
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mem_pre = 0
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def outer():
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d = {}
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def f(x):
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return d["x"]
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d["f"] = mx.exporter(path, f)
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d["x"] = mx.array([0] * 1000)
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for _ in range(5):
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outer()
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gc.collect()
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if mx.metal.is_available():
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mem_post = mx.get_active_memory()
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else:
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mem_post = 0
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self.assertEqual(mem_pre, mem_post)
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def test_export_import_shapeless(self):
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path = os.path.join(self.test_dir, "fn.mlxfn")
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def fun(*args):
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return sum(args)
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with mx.exporter(path, fun, shapeless=True) as exporter:
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exporter(mx.array(1))
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exporter(mx.array(1), mx.array(2))
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exporter(mx.array(1), mx.array(2), mx.array(3))
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f2 = mx.import_function(path)
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self.assertEqual(f2(mx.array(1))[0].item(), 1)
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self.assertEqual(f2(mx.array(1), mx.array(1))[0].item(), 2)
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self.assertEqual(f2(mx.array(1), mx.array(1), mx.array(1))[0].item(), 3)
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with self.assertRaises(ValueError):
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f2(mx.array(10), mx.array([5, 10, 20]))
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def test_export_scatter_gather(self):
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path = os.path.join(self.test_dir, "fn.mlxfn")
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def fun(a, b):
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return mx.take_along_axis(a, b, axis=0)
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x = mx.random.uniform(shape=(4, 4))
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y = mx.array([[0, 1, 2, 3], [1, 2, 0, 3]])
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mx.export_function(path, fun, (x, y))
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imported_fun = mx.import_function(path)
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expected = fun(x, y)
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out = imported_fun(x, y)[0]
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self.assertTrue(mx.array_equal(expected, out))
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def fun(a, b, c):
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return mx.put_along_axis(a, b, c, axis=0)
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x = mx.random.uniform(shape=(4, 4))
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y = mx.array([[0, 1, 2, 3], [1, 2, 0, 3]])
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z = mx.random.uniform(shape=(2, 4))
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mx.export_function(path, fun, (x, y, z))
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imported_fun = mx.import_function(path)
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expected = fun(x, y, z)
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out = imported_fun(x, y, z)[0]
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self.assertTrue(mx.array_equal(expected, out))
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def test_export_conv(self):
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path = os.path.join(self.test_dir, "fn.mlxfn")
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class Model(nn.Module):
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def __init__(self):
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super().__init__()
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self.c1 = nn.Conv2d(
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3, 16, kernel_size=3, stride=1, padding=1, bias=False
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)
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self.c2 = nn.Conv2d(
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16, 16, kernel_size=3, stride=2, padding=1, bias=False
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)
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self.c3 = nn.Conv2d(
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16, 16, kernel_size=3, stride=1, padding=2, bias=False
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)
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def __call__(self, x):
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return self.c3(self.c2(self.c1(x)))
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model = Model()
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mx.eval(model.parameters())
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def forward(x):
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return model(x)
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input_data = mx.random.normal(shape=(4, 32, 32, 3))
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mx.export_function(path, forward, input_data)
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imported_fn = mx.import_function(path)
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out = imported_fn(input_data)[0]
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expected = forward(input_data)
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self.assertTrue(mx.allclose(expected, out))
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def test_export_control_flow(self):
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def fun(x, y):
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if y.shape[0] <= 2:
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return x + y
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else:
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return x + 2 * y
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for y in (mx.array([1, 2, 3]), mx.array([1, 2])):
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for shapeless in (True, False):
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with self.subTest(y=y, shapeless=shapeless):
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x = mx.array(1)
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export_path = os.path.join(self.test_dir, "control_flow.mlxfn")
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mx.export_function(export_path, fun, x, y, shapeless=shapeless)
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imported_fn = mx.import_function(export_path)
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self.assertTrue(mx.array_equal(imported_fn(x, y)[0], fun(x, y)))
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def test_export_quantized_model(self):
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for shapeless in (True, False):
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with self.subTest(shapeless=shapeless):
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model = nn.Sequential(
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nn.Linear(1024, 512), nn.ReLU(), nn.Linear(512, 1024)
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)
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model.eval()
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mx.eval(model.parameters())
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input_data = mx.ones(shape=(512, 1024))
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nn.quantize(model)
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self.assertTrue(isinstance(model.layers[0], nn.QuantizedLinear))
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self.assertTrue(isinstance(model.layers[2], nn.QuantizedLinear))
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mx.eval(model.parameters())
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export_path = os.path.join(self.test_dir, "quantized_linear.mlxfn")
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mx.export_function(export_path, model, input_data, shapeless=shapeless)
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imported_fn = mx.import_function(export_path)
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self.assertTrue(
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mx.array_equal(imported_fn(input_data)[0], model(input_data))
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)
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if __name__ == "__main__":
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mlx_tests.MLXTestRunner()
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