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https://github.com/ml-explore/mlx.git
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320 lines
11 KiB
Python
320 lines
11 KiB
Python
# Copyright © 2023-2024 Apple Inc.
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import unittest
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import mlx.core as mx
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import mlx_tests
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class TestVmap(mlx_tests.MLXTestCase):
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def test_basics(self):
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# Can't vmap over scalars
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with self.assertRaises(ValueError):
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mx.vmap(mx.exp)(mx.array(1.0))
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# Invalid input
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with self.assertRaises(ValueError):
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mx.vmap(mx.exp)("hello")
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# Invalid axes
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with self.assertRaises(ValueError):
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mx.vmap(mx.exp, in_axes="hello")(mx.array([0, 1]))
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with self.assertRaises(ValueError):
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mx.vmap(mx.exp, in_axes=2)(mx.array([0, 1]))
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with self.assertRaises(ValueError):
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mx.vmap(mx.exp, out_axes="hello")(mx.array([0, 1]))
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with self.assertRaises(ValueError):
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mx.vmap(mx.exp, out_axes=2)(mx.array([0, 1]))
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def test_unary(self):
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ops = [
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"abs",
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"cos",
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"erf",
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"erfinv",
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"exp",
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"log",
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"log1p",
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"log2",
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"log10",
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"logical_not",
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"negative",
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"reciprocal",
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"rsqrt",
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"sigmoid",
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"sign",
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"sin",
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"sqrt",
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"square",
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]
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ops = ["erfinv"]
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for opname in ops:
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with self.subTest(op=opname):
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op = getattr(mx, opname)
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x = mx.arange(5)
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y = mx.vmap(op)(x)
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self.assertTrue(mx.array_equal(y, op(x), equal_nan=True))
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x = mx.arange(8).reshape(2, 4)
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y = mx.vmap(op)(x)
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self.assertTrue(mx.array_equal(y, op(x), equal_nan=True))
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y = mx.vmap(op, in_axes=1, out_axes=1)(x)
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self.assertTrue(mx.array_equal(y, op(x), equal_nan=True))
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def test_binary(self):
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ops = [
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"add",
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"divide",
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"equal",
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"greater",
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"greater_equal",
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"less",
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"less_equal",
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"logaddexp",
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"maximum",
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"minimum",
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"multiply",
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"power",
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"subtract",
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"logical_or",
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"logical_and",
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]
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for opname in ops:
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with self.subTest(op=opname):
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op = getattr(mx, opname)
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x = mx.random.uniform(shape=(5,))
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y = mx.random.uniform(shape=(5,))
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out = mx.vmap(op)(x, y)
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self.assertTrue(mx.array_equal(out, op(x, y)))
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x = mx.random.uniform(shape=(2, 4))
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y = mx.random.uniform(shape=(2, 4))
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out = mx.vmap(op)(x, y)
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self.assertTrue(mx.array_equal(out, op(x, y)))
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out = mx.vmap(op, in_axes=(0, 0), out_axes=0)(x, y)
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self.assertTrue(mx.array_equal(out, op(x, y)))
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y = mx.random.uniform(shape=(4, 2))
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out = mx.vmap(op, in_axes=(0, 1), out_axes=0)(x, y)
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self.assertTrue(mx.array_equal(out, op(x, y.T)))
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out = mx.vmap(op, in_axes=(0, 1), out_axes=1)(x, y)
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self.assertTrue(mx.array_equal(out, op(x, y.T).T))
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def test_tree(self):
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def my_fun(tree):
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return (tree["a"] + tree["b"][0]) * tree["b"][1]
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tree = {
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"a": mx.random.uniform(shape=(2, 4)),
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"b": (
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mx.random.uniform(shape=(2, 4)),
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mx.random.uniform(shape=(2, 4)),
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),
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}
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out = mx.vmap(my_fun)(tree)
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expected = my_fun(tree)
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self.assertTrue(mx.array_equal(out, my_fun(tree)))
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with self.assertRaises(ValueError):
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mx.vmap(my_fun, in_axes={"a": 0, "b": 0}, out_axes=0)(tree)
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with self.assertRaises(ValueError):
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mx.vmap(my_fun, in_axes={"a": 0, "b": ((0, 0), 0)}, out_axes=0)(tree)
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out = mx.vmap(my_fun, in_axes=({"a": 0, "b": 0},), out_axes=0)(tree)
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self.assertTrue(mx.array_equal(out, my_fun(tree)))
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out = mx.vmap(my_fun, in_axes=({"a": 0, "b": (0, 0)},), out_axes=0)(tree)
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self.assertTrue(mx.array_equal(out, my_fun(tree)))
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tree = {
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"a": mx.random.uniform(shape=(2, 4)),
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"b": (
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mx.random.uniform(shape=(4, 2)),
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mx.random.uniform(shape=(4, 2)),
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),
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}
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out = mx.vmap(my_fun, in_axes=({"a": 0, "b": (1, 1)},), out_axes=0)(tree)
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expected = (tree["a"] + tree["b"][0].T) * tree["b"][1].T
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self.assertTrue(mx.array_equal(out, expected))
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def my_fun(x, y):
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return {"a": x + y, "b": x * y}
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x = mx.random.uniform(shape=(2, 4))
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y = mx.random.uniform(shape=(2, 4))
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out = mx.vmap(my_fun, in_axes=0, out_axes=0)(x, y)
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expected = my_fun(x, y)
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self.assertTrue(mx.array_equal(out["a"], expected["a"]))
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self.assertTrue(mx.array_equal(out["b"], expected["b"]))
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with self.assertRaises(ValueError):
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mx.vmap(my_fun, in_axes=0, out_axes=(0, 1))(x, y)
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with self.assertRaises(ValueError):
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mx.vmap(my_fun, in_axes=0, out_axes={"a": 0, "c": 1})(x, y)
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out = mx.vmap(my_fun, in_axes=0, out_axes={"a": 1, "b": 0})(x, y)
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expected = my_fun(x, y)
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self.assertTrue(mx.array_equal(out["a"].T, expected["a"]))
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self.assertTrue(mx.array_equal(out["b"], expected["b"]))
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def test_vmap_indexing(self):
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x = mx.arange(16).reshape(2, 2, 2, 2)
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inds = mx.array([[0, 1, 0], [1, 1, 0]])
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out = mx.vmap(lambda x, y: x[y], in_axes=(0, 0))(x, inds)
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expected = mx.array(
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[
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[[[0, 1], [2, 3]], [[4, 5], [6, 7]], [[0, 1], [2, 3]]],
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[[[12, 13], [14, 15]], [[12, 13], [14, 15]], [[8, 9], [10, 11]]],
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]
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)
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self.assertTrue(mx.array_equal(out, expected))
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out = mx.vmap(lambda x, y: x[y], in_axes=(0, None))(x, inds)
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expected = mx.array(
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[
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[
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[[[0, 1], [2, 3]], [[4, 5], [6, 7]], [[0, 1], [2, 3]]],
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[[[4, 5], [6, 7]], [[4, 5], [6, 7]], [[0, 1], [2, 3]]],
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],
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[
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[[[8, 9], [10, 11]], [[12, 13], [14, 15]], [[8, 9], [10, 11]]],
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[[[12, 13], [14, 15]], [[12, 13], [14, 15]], [[8, 9], [10, 11]]],
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],
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]
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)
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self.assertTrue(mx.array_equal(out, expected))
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out = mx.vmap(lambda x, y: x[y], in_axes=(None, 0))(x, inds)
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expected = mx.array(
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[
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[
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[[[0, 1], [2, 3]], [[4, 5], [6, 7]]],
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[[[8, 9], [10, 11]], [[12, 13], [14, 15]]],
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[[[0, 1], [2, 3]], [[4, 5], [6, 7]]],
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],
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[
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[[[8, 9], [10, 11]], [[12, 13], [14, 15]]],
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[[[8, 9], [10, 11]], [[12, 13], [14, 15]]],
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[[[0, 1], [2, 3]], [[4, 5], [6, 7]]],
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],
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]
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)
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self.assertTrue(mx.array_equal(out, expected))
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inds2 = mx.array([[0, 1, 0], [0, 1, 0]])
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out = mx.vmap(lambda x, y, z: x[y, z], in_axes=(None, 0, 0))(x, inds, inds2)
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expected = mx.array(
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[
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[[[0, 1], [2, 3]], [[12, 13], [14, 15]], [[0, 1], [2, 3]]],
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[[[8, 9], [10, 11]], [[12, 13], [14, 15]], [[0, 1], [2, 3]]],
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]
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)
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self.assertTrue(mx.array_equal(out, expected))
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def test_vmap_reduce(self):
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a = mx.ones((5, 5), mx.int32)
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out = mx.vmap(lambda x: x.sum())(a)
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self.assertTrue(mx.array_equal(out, mx.full((5,), 5)))
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out = mx.vmap(lambda x: x.sum(keepdims=True))(a)
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self.assertTrue(mx.array_equal(out, mx.full((5, 1), 5)))
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out = mx.vmap(lambda x: x.sum(axis=0))(a)
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self.assertTrue(mx.array_equal(out, mx.full((5,), 5)))
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a = mx.ones((5, 3, 2), mx.int32)
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out = mx.vmap(lambda x: x.sum(axis=(0, 1)))(a)
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self.assertTrue(mx.array_equal(out, mx.full((5,), 6)))
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a = mx.ones((5, 3, 2), mx.int32)
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out = mx.vmap(lambda x: x.sum(axis=(0, 1)), in_axes=(1,))(a)
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self.assertTrue(mx.array_equal(out, mx.full((3,), 10)))
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a = mx.ones((5, 3, 2), mx.int32)
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out = mx.vmap(lambda x: x.sum(axis=(0, 1)), in_axes=(2,))(a)
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self.assertTrue(mx.array_equal(out, mx.full((2,), 15)))
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def test_vmap_argreduce(self):
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a = mx.array([[1, 2, 3], [2, 3, 1]])
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out = mx.vmap(lambda x: mx.argmin(x))(a)
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expected = mx.array([0, 2])
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self.assertTrue(mx.array_equal(out, expected))
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out = mx.vmap(lambda x: mx.argmax(x))(a)
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expected = mx.array([2, 1])
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self.assertTrue(mx.array_equal(out, expected))
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def test_vmap_mean(self):
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a = mx.arange(8).reshape(2, 4)
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out = mx.vmap(mx.mean)(a)
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expected = mx.mean(a, axis=1)
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self.assertTrue(mx.allclose(out, expected))
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a = mx.arange(16).reshape(2, 2, 4)
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out = mx.vmap(mx.vmap(mx.mean))(a)
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expected = mx.mean(a, axis=2)
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self.assertTrue(mx.allclose(out, expected))
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def test_mismatch_input_sizes(self):
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a = mx.ones((10, 1))
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b = mx.ones((1, 1, 1, 5))
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with self.assertRaises(ValueError):
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out = mx.vmap(lambda x, y: x + y)(a, b)
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b = mx.ones((10, 5))
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with self.assertRaises(ValueError):
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out = mx.vmap(lambda x, y: x + y, in_axes=(0, 1))(a, b)
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def test_vmap_matmul(self):
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a = mx.random.uniform(shape=(2, 3, 4))
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b = mx.random.uniform(shape=(4, 3))
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# matmul
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out = mx.vmap(mx.matmul, in_axes=(0, None))(a, b)
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self.assertTrue(mx.allclose(out, a @ b))
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# addmm
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c = mx.random.uniform(shape=(3,))
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out = mx.vmap(mx.addmm, in_axes=(None, 0, None))(c, a, b)
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self.assertTrue(mx.allclose(out, mx.addmm(c, a, b)))
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b = mx.random.uniform(shape=(4, 2))
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# matmul
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out = mx.vmap(mx.matmul, in_axes=(1, None), out_axes=(1,))(a, b)
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expected = mx.moveaxis(mx.moveaxis(a, 1, 0) @ b, 0, 1)
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self.assertTrue(mx.allclose(out, expected))
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# addmm
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c = mx.random.uniform(shape=(2,))
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out = mx.vmap(mx.addmm, in_axes=(None, 1, None))(c, a, b)
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self.assertTrue(mx.allclose(out, mx.addmm(c, mx.moveaxis(a, 1, 0), b)))
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a = mx.random.uniform(shape=(2, 3, 4))
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b = mx.random.uniform(shape=(4, 2, 3))
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# matmul
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out = mx.vmap(mx.matmul, in_axes=(0, 1))(a, b)
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expected = a @ mx.moveaxis(b, 1, 0)
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self.assertTrue(mx.allclose(out, expected))
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# addmm
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c = mx.random.uniform(shape=(3, 3, 2))
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out = mx.vmap(mx.addmm, in_axes=(2, 0, 1))(c, a, b)
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expected = mx.addmm(mx.moveaxis(c, 2, 0), a, mx.moveaxis(b, 1, 0))
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self.assertTrue(mx.allclose(out, expected))
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if __name__ == "__main__":
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unittest.main()
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