mirror of
https://github.com/ml-explore/mlx.git
synced 2025-10-31 16:21:27 +08:00
1043 lines
29 KiB
Python
1043 lines
29 KiB
Python
# Copyright © 2023-2024 Apple Inc.
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import gc
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import inspect
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import io
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import math
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import unittest
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from functools import partial, wraps
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from io import StringIO
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import mlx.core as mx
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import mlx_tests
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class TestCompile(mlx_tests.MLXTestCase):
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def test_simple_compile(self):
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def fun(x, y):
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return x + y
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compiled_fn = mx.compile(fun)
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compiled_fn = mx.compile(fun)
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x = mx.array(1.0)
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y = mx.array(1.0)
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out = compiled_fn(x, y)
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self.assertEqual(out.item(), 2.0)
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# Try again
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out = compiled_fn(x, y)
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self.assertEqual(out.item(), 2.0)
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# Change sizes
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x = mx.array([1.0, 2.0])
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out = compiled_fn(x, y)
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self.assertTrue(mx.array_equal(out, mx.array([2.0, 3.0])))
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y = mx.array([1.0, 2.0])
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out = compiled_fn(x, y)
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self.assertTrue(mx.array_equal(out, mx.array([2.0, 4.0])))
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# Change types
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x = mx.array([1, 2], mx.int32)
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y = mx.array([1, 2], mx.int32)
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out = compiled_fn(x, y)
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self.assertEqual(out.dtype, mx.int32)
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self.assertTrue(mx.array_equal(out, mx.array([2, 4])))
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def test_compile_grad(self):
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def loss_fn(x):
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return mx.exp(x).sum()
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grad_fn = mx.grad(loss_fn)
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x = mx.array([0.5, -0.5, 1.2])
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dfdx = grad_fn(x)
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compile_grad_fn = mx.compile(grad_fn)
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c_dfdx = grad_fn(x)
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self.assertTrue(mx.allclose(c_dfdx, dfdx))
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# Run it again without calling compile
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c_dfdx = compile_grad_fn(x)
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self.assertTrue(mx.allclose(c_dfdx, dfdx))
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# Run it again with calling compile
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c_dfdx = mx.compile(grad_fn)(x)
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self.assertTrue(mx.allclose(c_dfdx, dfdx))
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# Value and grad
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def loss_fn(x):
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return mx.exp(x).sum(), mx.sin(x)
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val_and_grad_fn = mx.value_and_grad(loss_fn)
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(loss, val), dfdx = val_and_grad_fn(x)
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(c_loss, c_val), c_dfdx = mx.compile(val_and_grad_fn)(x)
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self.assertTrue(mx.allclose(c_dfdx, dfdx))
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self.assertTrue(mx.allclose(c_loss, loss))
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self.assertTrue(mx.allclose(c_val, val))
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def test_compile_inputs_with_primitives(self):
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x = mx.array([1, 2, 3])
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y = mx.array([1, 2, 3])
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for _ in range(5):
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x = x + y
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y = y + 1
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def fun(x, y):
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return x * y
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out = fun(x, y)
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x = mx.array([1, 2, 3])
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y = mx.array([1, 2, 3])
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for _ in range(5):
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x = x + y
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y = y + 1
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c_out = mx.compile(fun)(x, y)
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self.assertTrue(mx.array_equal(out, c_out))
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# Try again
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c_out = mx.compile(fun)(x, y)
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self.assertTrue(mx.array_equal(out, c_out))
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def test_compile_with_closure(self):
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x = mx.array(1)
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def closure(y):
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return x + y
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compiled = mx.compile(closure)
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out = compiled(mx.array(1))
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self.assertEqual(out.item(), 2)
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# Try again
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out = compiled(mx.array(1))
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self.assertEqual(out.item(), 2)
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# Change the shape of the enclosed variable
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x = mx.array([1, 2])
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out = compiled(mx.array(1))
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# We still get the original input (closures are not updated)
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self.assertEqual(out.item(), 2)
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# Try with a tree of enclosed variables
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x = {"a": mx.array(1), "b": mx.array(2)}
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def closure(y):
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return x["a"] + y + x["b"]
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compiled = mx.compile(closure)
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out = compiled(mx.array(1))
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self.assertEqual(out.item(), 4)
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# Change the shape of one input
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x["a"] = mx.array([4, 5])
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out = compiled(mx.array(1))
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self.assertEqual(out.item(), 4)
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x["b"] = mx.array([-6, -8])
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out = compiled(mx.array(1))
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self.assertEqual(out.item(), 4)
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# Enclosed variable is not evaluated yet
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x = mx.array(1)
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x = x + x
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def closure(y):
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return x + y
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compiled = mx.compile(closure)
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out = compiled(mx.array(2))
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self.assertEqual(out.item(), 4)
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# And again
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out = compiled(mx.array(2))
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self.assertEqual(out.item(), 4)
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def test_function_creates_array(self):
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def fun(x):
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return x + mx.array(1)
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cfun = mx.compile(fun)
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out = cfun(mx.array(3))
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self.assertEqual(out.item(), 4)
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# And again
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out = cfun(mx.array(3))
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self.assertEqual(out.item(), 4)
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def test_enable_disable(self):
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def fun(x):
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y = x + 1
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z = x + 1
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return y + z
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def count_prims(outputs):
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buf = io.StringIO()
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mx.export_to_dot(buf, outputs)
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buf.seek(0)
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return len([l for l in buf.read().split() if "label" in l])
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x = mx.array(1.0)
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cfun = mx.compile(fun)
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n_compiled = count_prims(cfun(x))
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# Check disabled
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mx.disable_compile()
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n_uncompiled = count_prims(cfun(x))
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self.assertTrue(n_compiled < n_uncompiled)
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# Check renabled
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mx.enable_compile()
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n_enable_compiled = count_prims(cfun(x))
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self.assertEqual(n_compiled, n_enable_compiled)
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def test_compile_two_input_grad(self):
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def loss(w, x):
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y = x * w
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return (y * mx.exp(y)).sum()
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x = mx.array([1.0, 0.5, 2.0, -0.5])
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w = mx.array([-1.0, 0.3, 1.0, -0.9])
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expected_grad = mx.grad(loss)(w, x)
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compiled_grad = mx.compile(mx.grad(loss))(w, x)
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self.assertTrue(mx.allclose(expected_grad, compiled_grad))
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def test_vmap_compiled(self):
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def simple_unary(x):
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return -mx.exp(x)
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x = mx.array([[1.0, 2.0], [2.0, 3.0]])
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expected_out = mx.vmap(simple_unary)(x)
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out = mx.vmap(mx.compile(simple_unary))(x)
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self.assertTrue(mx.allclose(expected_out, out))
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def simple_binary(x, y):
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return mx.abs(mx.exp(x + y) + y)
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x = mx.array([[1.0, -3.0], [0.5, -0.5]])
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y = mx.array([[2.0, -1.0], [0.25, -0.25]])
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expected_out = mx.vmap(simple_binary)(x, y)
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out = mx.vmap(mx.compile(simple_binary))(x, y)
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self.assertTrue(mx.allclose(expected_out, out))
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expected_out = mx.vmap(simple_binary, in_axes=(0, 1))(x, y)
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out = mx.vmap(mx.compile(simple_binary), in_axes=(0, 1))(x, y)
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self.assertTrue(mx.allclose(expected_out, out))
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y = mx.array([0.25, -0.25])
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expected_out = mx.vmap(simple_binary, in_axes=(0, None))(x, y)
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out = mx.vmap(mx.compile(simple_binary), in_axes=(0, None))(x, y)
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self.assertTrue(mx.allclose(expected_out, out))
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def simple_unary_outer(x):
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x = mx.abs(x)
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@mx.compile
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def simple_unary_inner(z):
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return -mx.exp(x)
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return simple_unary_inner(x)
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expected_out = -mx.exp(mx.abs(x))
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out = mx.vmap(simple_unary_outer)(x)
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self.assertTrue(mx.allclose(expected_out, out))
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def test_vjp_vjp_compiled(self):
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def simple_unary(x):
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return -mx.exp(x)
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x = mx.array([[1.0, 2.0], [2.0, 3.0]])
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y = mx.array([[1.0, 1.0], [1.0, 1.0]])
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expected_out, expected_vjp_out = mx.vjp(simple_unary, (x,), (y,))
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out, vjp_out = mx.vjp(mx.compile(simple_unary), (x,), (y,))
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self.assertTrue(mx.allclose(expected_vjp_out[0], vjp_out[0]))
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self.assertTrue(mx.allclose(expected_out[0], out[0]))
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expected_out, expected_jvp_out = mx.jvp(simple_unary, (x,), (y,))
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out, jvp_out = mx.jvp(mx.compile(simple_unary), (x,), (y,))
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self.assertTrue(mx.allclose(expected_jvp_out[0], jvp_out[0]))
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self.assertTrue(mx.allclose(expected_out[0], out[0]))
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def simple_binary(x, y):
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return mx.abs(mx.exp(x + y) + y)
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x = mx.array([[1.0, -3.0], [0.5, -0.5]])
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y = mx.array([[2.0, -1.0], [0.25, -0.25]])
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cotans = mx.ones_like(x)
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expected_out, expected_vjp_out = mx.vjp(simple_binary, (x, y), (cotans,))
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out, vjp_out = mx.vjp(mx.compile(simple_binary), (x, y), (cotans,))
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self.assertTrue(mx.allclose(expected_out[0], out[0]))
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self.assertTrue(mx.allclose(expected_vjp_out[0], vjp_out[0]))
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self.assertTrue(mx.allclose(expected_vjp_out[1], vjp_out[1]))
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tans = (mx.ones_like(x), mx.ones_like(y))
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expected_out, expected_jvp_out = mx.jvp(simple_binary, (x, y), tans)
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out, jvp_out = mx.jvp(mx.compile(simple_binary), (x, y), tans)
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self.assertTrue(mx.allclose(expected_jvp_out[0], jvp_out[0]))
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self.assertTrue(mx.allclose(expected_out[0], out[0]))
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def test_transform_over_eval_compiled(self):
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def outer(x):
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y = mx.exp(mx.abs(x))
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mx.eval(y)
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return y.sum()
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x = mx.array([2.0, -1.0, 0.5])
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dfdx = mx.grad(outer)(x)
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@mx.compile
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def simple_unary(x):
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return mx.exp(mx.abs(x))
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def outer(x):
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y = simple_unary(x)
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mx.eval(y)
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return y.sum()
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cdfdx = mx.grad(outer)(x)
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self.assertTrue(mx.allclose(dfdx, cdfdx))
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def test_compile_capture(self):
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# Test update captured state outside compiled function
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state = {"y": mx.array(2)}
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@partial(mx.compile, inputs=state)
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def test_state(x):
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x = x + state["y"]
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return x
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test_state(mx.array(1))
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# Check the state is unchanged
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self.assertEqual(state["y"], 2)
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# Check the udpated state is used
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state["y"] = mx.array(3)
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out = test_state(mx.array(1))
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self.assertEqual(out.item(), 4)
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# Capture list
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state = [mx.array(2)]
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@partial(mx.compile, inputs=state)
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def test_state(x):
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x = x + state[0]
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return x
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out = test_state(mx.array(1))
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self.assertEqual(out.item(), 3)
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state[0] = mx.array(3)
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out = test_state(mx.array(1))
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self.assertEqual(out.item(), 4)
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# Capture tuple of list
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state = ([mx.array(2)],)
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@partial(mx.compile, inputs=state)
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def test_state(x):
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x = x + state[0][0]
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return x
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out = test_state(mx.array(1))
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self.assertEqual(out.item(), 3)
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state[0][0] = mx.array(3)
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out = test_state(mx.array(1))
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self.assertEqual(out.item(), 4)
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# Test state updated inside compiled function
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state = {}
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@partial(mx.compile, outputs=state)
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def test_state(x):
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state["y"] = x + 3
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return mx.abs(x)
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test_state(mx.array(-1))
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self.assertEqual(state["y"].item(), 2)
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# Test state changed inside compiled function
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# triggers recompile
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state = {}
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@partial(mx.compile, inputs=state, outputs=state)
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def test_state(x):
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y = state.get("y", mx.array(0))
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state["y"] = x + y
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return x + 2 * y
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test_state(mx.array(1))
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self.assertEqual(state["y"].item(), 1)
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test_state(mx.array(1))
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self.assertEqual(state["y"].item(), 2)
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def test_compile_rng(self):
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@partial(mx.compile, inputs=mx.random.state, outputs=mx.random.state)
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def fun():
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return mx.random.uniform(shape=(10, 10))
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self.assertFalse(mx.allclose(fun(), fun(), 1e-2, 1e-2))
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def test_compile_kwargs(self):
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@mx.compile
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def fun(x, y, z):
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return x + y + z
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x = mx.array(1)
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y = mx.array(2)
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z = mx.array(3)
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out = fun(x, y=y, z=z)
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self.assertEqual(out.item(), 6)
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def test_shapeless_compile(self):
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y = 1
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@partial(mx.compile, shapeless=True)
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def fun(x):
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return x + y
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x = mx.array([1, 2])
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self.assertTrue(mx.array_equal(fun(x), mx.array([2, 3])))
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# The function is not recompiled, so the change
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# to y should not be reflected in the output
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y = 2
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x = mx.array([1, 2, 3])
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self.assertTrue(mx.array_equal(fun(x), mx.array([2, 3, 4])))
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# Type change recompiles
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x = mx.array([1.0, 2.0, 3.0])
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self.assertTrue(mx.array_equal(fun(x), mx.array([3.0, 4.0, 5.0])))
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# Dim change recompiles
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x = mx.array([[1, 2, 3]])
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self.assertTrue(mx.array_equal(fun(x), mx.array([[3, 4, 5]])))
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def test_shapeless_compile_with_broadcasts(self):
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x = mx.ones((2, 2))
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y = mx.array([2, 2])
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def fun(x, y):
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return x * y
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cfun = mx.compile(fun, shapeless=True)
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self.assertTrue(mx.array_equal(cfun(x, y), fun(x, y)))
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self.assertTrue(mx.array_equal(cfun(y, x), fun(y, x)))
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y = mx.array([[3]])
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self.assertTrue(mx.array_equal(cfun(x, y), fun(x, y)))
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self.assertTrue(mx.array_equal(cfun(y, x), fun(y, x)))
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def test_shapeless_compile_with_reduction(self):
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# Test shapeless compile with a reduction
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z = 1
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@partial(mx.compile, shapeless=True)
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def fun(x, y):
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return x + y.sum(0, keepdims=True) + z
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x = mx.ones((2, 2), mx.int32)
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y = mx.ones((2, 2), mx.int32)
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self.assertTrue(mx.array_equal(fun(x, y), mx.full(shape=(2, 2), vals=4)))
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x = mx.ones((3, 3), mx.int32)
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y = mx.ones((3, 3), mx.int32)
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z = 2
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self.assertTrue(mx.array_equal(fun(x, y), mx.full(shape=(3, 3), vals=5)))
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x1 = mx.array([[1, 2], [3, 4], [5, 6]])
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x2 = mx.array([[1, 2]])
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def fun(x):
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return x * x.sum(-1, keepdims=True)
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cfun = mx.compile(fun, shapeless=True)
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mx.eval(cfun(x1))
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self.assertTrue(mx.array_equal(fun(x2), cfun(x2)))
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def fun(x):
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return x * x.sum(-1, keepdims=False)
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cfun = mx.compile(fun, shapeless=True)
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self.assertTrue(mx.array_equal(fun(x2), cfun(x2)))
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def test_shapeless_compile_unflatten(self):
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x = mx.zeros((1, 1, 4 * 32))
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def fun(x):
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return mx.unflatten(x, -1, (4, -1))
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self.assertEqual(mx.compile(fun, shapeless=True)(x).shape, (1, 1, 4, 32))
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def test_shapeless_compile_gather(self):
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x = mx.zeros((1, 1, 32))
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|
def fun(x):
|
|
return x[:, -1, :]
|
|
|
|
self.assertEqual(mx.compile(fun, shapeless=True)(x).shape, (1, 32))
|
|
|
|
def test_compile_with_constant(self):
|
|
# Test float
|
|
@partial(mx.compile)
|
|
def fun(x, y):
|
|
return x + y
|
|
|
|
z = fun(mx.array(1.0), 1.0)
|
|
self.assertEqual(z.item(), 2.0)
|
|
|
|
z = fun(mx.array(1.0), 2.0)
|
|
self.assertEqual(z.item(), 3.0)
|
|
|
|
z = fun(mx.array(1.0), y=1.0)
|
|
self.assertEqual(z.item(), 2.0)
|
|
|
|
z = fun(mx.array(1.0), y=3.0)
|
|
self.assertEqual(z.item(), 4.0)
|
|
|
|
# Test tuple
|
|
@partial(mx.compile)
|
|
def fun(x, y=(1, 2)):
|
|
return x + y[0] + y[1]
|
|
|
|
z = fun(mx.array(1))
|
|
self.assertEqual(z.item(), 4)
|
|
|
|
z = fun(mx.array(1), (2, 2))
|
|
self.assertEqual(z.item(), 5)
|
|
|
|
z = fun(mx.array(1), (2, 1))
|
|
self.assertEqual(z.item(), 4)
|
|
|
|
# Test bool
|
|
@partial(mx.compile)
|
|
def fun(x, y):
|
|
if y:
|
|
return x + 1
|
|
else:
|
|
return x + 2
|
|
|
|
z = fun(mx.array(1), True)
|
|
self.assertEqual(z.item(), 2)
|
|
|
|
z = fun(mx.array(1), False)
|
|
self.assertEqual(z.item(), 3)
|
|
|
|
# Test string
|
|
@partial(mx.compile)
|
|
def fun(x, y):
|
|
if y == "one":
|
|
return x + 1
|
|
else:
|
|
return x + 2
|
|
|
|
z = fun(mx.array(1), "one")
|
|
self.assertEqual(z.item(), 2)
|
|
|
|
z = fun(mx.array(1), "two")
|
|
self.assertEqual(z.item(), 3)
|
|
|
|
# Test nested constant
|
|
@partial(mx.compile)
|
|
def fun(x, y):
|
|
if y[0][0] == 1:
|
|
return x + 1
|
|
else:
|
|
return x + 2
|
|
|
|
z = fun(mx.array(1), [[1]])
|
|
self.assertEqual(z.item(), 2)
|
|
|
|
z = fun(mx.array(1), [[0]])
|
|
self.assertEqual(z.item(), 3)
|
|
|
|
@partial(mx.compile)
|
|
def fun(x, a, b):
|
|
for ai in a:
|
|
for bi in b:
|
|
x = bi * x + ai
|
|
return x
|
|
|
|
z = fun(mx.array(1), [1, 1], [2])
|
|
self.assertEqual(z.item(), 7)
|
|
|
|
z = fun(mx.array(1), [1], [1, 2])
|
|
self.assertEqual(z.item(), 5)
|
|
|
|
counter = [0]
|
|
|
|
@partial(mx.compile)
|
|
def fun(x, y):
|
|
counter[0] += 1
|
|
return x + y
|
|
|
|
z = fun(mx.array(1), 1)
|
|
self.assertEqual(z.item(), 2)
|
|
|
|
z = fun(1, mx.array(1))
|
|
self.assertEqual(z.item(), 2)
|
|
|
|
self.assertEqual(counter[0], 2)
|
|
|
|
y = 1.0
|
|
|
|
@mx.compile
|
|
def fun(x, constant):
|
|
return x + y
|
|
|
|
constant1 = "abc"
|
|
out = fun(mx.array(0.0), constant1)
|
|
self.assertEqual(out, mx.array(1.0))
|
|
|
|
# new object, same value, no recompilation
|
|
y = 2.0
|
|
constant2 = "abc".encode("utf-8").decode("utf-8")
|
|
out = fun(mx.array(0.0), constant2)
|
|
self.assertEqual(out, mx.array(1.0))
|
|
|
|
# same object, new value, recompilation
|
|
constant2 = "xyz"
|
|
out = fun(mx.array(0.0), constant2)
|
|
self.assertEqual(out, mx.array(2.0))
|
|
|
|
def test_compile_inf(self):
|
|
@mx.compile
|
|
def fun(x):
|
|
return mx.isinf(x + 2)
|
|
|
|
out = fun(mx.array([0.0]))
|
|
self.assertEqual(out.item(), False)
|
|
|
|
def test_unsupported_input_types(self):
|
|
class MyClass:
|
|
value = 1
|
|
|
|
@mx.compile
|
|
def fun(x, y):
|
|
return x + y.value
|
|
|
|
with self.assertRaises(ValueError):
|
|
out = fun(mx.array(0.0), MyClass())
|
|
|
|
with self.assertRaises(ValueError):
|
|
out = fun(mx.array(0.0), y=MyClass())
|
|
|
|
def test_compile_create_list(self):
|
|
@mx.compile
|
|
def fun():
|
|
return [0.1 * mx.zeros((2,)), 0.1 * mx.zeros((2,))]
|
|
|
|
out = fun()
|
|
mx.eval(out)
|
|
|
|
def test_compile_vjp(self):
|
|
def fun(w):
|
|
w1 = w + w
|
|
w2 = w + w
|
|
return w @ w1 + w2 @ w2
|
|
|
|
def step(w):
|
|
out, grad = mx.vjp(fun, (w,), (mx.array([[1.0, 1.0], [1.0, 1.0]]),))
|
|
return out[0], grad[0]
|
|
|
|
w = mx.zeros((2, 2))
|
|
mx.eval(w)
|
|
|
|
expected = step(w)
|
|
out = mx.compile(step)(w)
|
|
self.assertTrue(mx.allclose(expected[0], out[0]))
|
|
self.assertTrue(mx.allclose(expected[1], out[1]))
|
|
|
|
def fun(w1, w2, x):
|
|
x = x @ w1
|
|
y = x @ w2
|
|
x = x + y * y
|
|
return (x * x).sum()
|
|
|
|
w1 = mx.zeros((4, 4))
|
|
w2 = mx.zeros((4, 4))
|
|
x = mx.zeros((4, 4))
|
|
|
|
def step(w1, w2, x):
|
|
loss, gradient = mx.value_and_grad(fun)(w1, w2, x)
|
|
w1 = w1 + gradient
|
|
return loss, w1
|
|
|
|
mx.eval(x, w1, w2)
|
|
expected = step(w1, w2, x)
|
|
out = mx.compile(step)(w1, w2, x)
|
|
|
|
self.assertTrue(mx.allclose(expected[0], out[0]))
|
|
self.assertTrue(mx.allclose(expected[1], out[1]))
|
|
|
|
def test_shapeless_mean(self):
|
|
def mean(x):
|
|
return mx.mean(x, keepdims=True)
|
|
|
|
cfun = mx.compile(mean)
|
|
out = cfun(mx.ones((5, 5)))
|
|
self.assertTrue(mx.allclose(out, mx.array(1.0)))
|
|
|
|
cmean = mx.compile(mean, shapeless=True)
|
|
|
|
x = mx.ones(2)
|
|
out = cmean(x)
|
|
self.assertTrue(mx.allclose(out, mean(x)))
|
|
|
|
x = mx.ones(4)
|
|
out = cmean(x)
|
|
self.assertTrue(mx.allclose(out, mean(x)))
|
|
|
|
x = mx.ones(7)
|
|
out = cmean(x)
|
|
self.assertTrue(mx.allclose(out, mean(x)))
|
|
|
|
def test_compile_broadcast_only(self):
|
|
def fn(a):
|
|
a = mx.broadcast_to(a, (1,))
|
|
return a + a
|
|
|
|
out = mx.compile(fn)(mx.array(2.0))
|
|
# Make sure repr can be called
|
|
self.assertTrue(repr(out) is not None)
|
|
self.assertTrue(mx.array_equal(out, mx.array([4.0])))
|
|
|
|
def test_compile_with_long_name(self):
|
|
def fn(a, b):
|
|
for _ in range(10):
|
|
a = a - 1.0
|
|
b = b - 1.0
|
|
return a + b
|
|
|
|
out = mx.compile(fn)(mx.array(10.0), mx.array(20.0))
|
|
self.assertEqual(out.item(), 10.0)
|
|
|
|
def test_compile_multi_output(self):
|
|
def fn(x):
|
|
ys = [x]
|
|
for i in range(5):
|
|
ys.append(ys[-1] + x)
|
|
return ys, mx.sum(ys[-1])
|
|
|
|
x = mx.ones(1, dtype=mx.int32)
|
|
y1 = mx.compile(fn)(x)[1]
|
|
y2 = fn(x)[1]
|
|
self.assertEqual(y1.item(), y2.item())
|
|
self.assertEqual(y1.item(), 6)
|
|
|
|
def test_inf_constant(self):
|
|
def fn(x):
|
|
return mx.where(mx.isinf(x), 0, 1)
|
|
|
|
x = mx.array([0, float("inf"), 1], dtype=mx.bfloat16)
|
|
self.assertTrue(mx.array_equal(mx.compile(fn)(x), fn(x)))
|
|
|
|
def test_max_into_equal(self):
|
|
x = mx.random.uniform(shape=(1, 2, 2))
|
|
mx.eval(x)
|
|
|
|
def fn():
|
|
maxes = mx.max(x, axis=(1, 2), keepdims=True)
|
|
return x == maxes
|
|
|
|
out = mx.compile(fn)()
|
|
expected = fn()
|
|
self.assertTrue(mx.array_equal(expected, out))
|
|
|
|
def test_dtypes(self):
|
|
x = mx.array([0, 1, 2, 3])
|
|
dtypes = [mx.bool_, mx.int8, mx.uint8, mx.int16, mx.uint16]
|
|
for dtype in dtypes:
|
|
x = x.astype(dtype)
|
|
mx.eval(x)
|
|
|
|
def fn(x):
|
|
return x * 1 + 0
|
|
|
|
out = mx.compile(fn)(x)
|
|
expected = fn(x)
|
|
self.assertTrue(mx.array_equal(expected, out))
|
|
|
|
def test_compile_without_captured_inputs(self):
|
|
x = mx.array([1, 2, 3]) + 2
|
|
|
|
def fn(a):
|
|
y = x + 1
|
|
return a + y
|
|
|
|
with self.assertRaises(ValueError):
|
|
y = mx.compile(fn)(x)
|
|
|
|
x = mx.array([1.0, 2.0]) + mx.array([1.0, 2.0])
|
|
y = None
|
|
|
|
def fn(x):
|
|
nonlocal y
|
|
if y is None:
|
|
y = mx.array([1.0, 2.0])
|
|
|
|
y = y + x
|
|
return y
|
|
|
|
fn(x)
|
|
with self.assertRaises(ValueError):
|
|
y = mx.compile(fn)(x)
|
|
|
|
def test_compile_dynamic_dims(self):
|
|
a = mx.random.uniform(shape=(2,) * 10)
|
|
b = mx.random.uniform(shape=(2,) * 10)
|
|
a = a.T
|
|
mx.eval(a, b)
|
|
|
|
def fn(a, b):
|
|
return mx.abs(a + b)
|
|
|
|
out = mx.compile(fn)(a, b)
|
|
expected = fn(a, b)
|
|
self.assertTrue(mx.allclose(out, expected))
|
|
|
|
def test_compile_many_inputs(self):
|
|
inputs = [mx.ones((2, 2, 2, 2)) for _ in range(20)]
|
|
inputs[0] = inputs[0].T
|
|
|
|
@mx.compile
|
|
def fun(*inputs):
|
|
x = inputs[0]
|
|
for y in inputs[1:10]:
|
|
x = x + y
|
|
a = inputs[10]
|
|
for b in inputs[11:]:
|
|
a = a + b
|
|
return x + a
|
|
|
|
out = fun(*inputs)
|
|
self.assertTrue(mx.allclose(out, mx.full((2, 2), 20)))
|
|
|
|
@mx.compile
|
|
def fun(arrs):
|
|
for _ in range(6):
|
|
arrs = [x + y for x, y in zip(arrs[::2], arrs[1::2])]
|
|
return arrs[0]
|
|
|
|
arrs = [mx.array([1.0, 2.0]) for _ in range(64)]
|
|
out = fun(arrs)
|
|
self.assertTrue(mx.allclose(out, mx.array([64.0, 128.0])))
|
|
|
|
def test_compile_many_outputs(self):
|
|
|
|
@mx.compile
|
|
def fun(arr):
|
|
arrs = [arr] * 64
|
|
first_arrs = None
|
|
for _ in range(6):
|
|
arrs = [x + y for x, y in zip(arrs[::2], arrs[1::2])]
|
|
if first_arrs is None:
|
|
first_arrs = arrs
|
|
return arrs[0], first_arrs
|
|
|
|
out = fun(mx.array([1.0, 2.0]))
|
|
self.assertTrue(mx.allclose(out[0], mx.array([64.0, 128.0])))
|
|
|
|
def test_shapeless_compile_matmul(self):
|
|
a = mx.array([0.0, 1.0, 2.0])
|
|
b = mx.array([0.0, 1.0, 2.0])
|
|
|
|
fun = mx.compile(lambda a, b: a @ b, shapeless=True)
|
|
self.assertTrue(mx.allclose(fun(a, b), a @ b))
|
|
|
|
def test_shapeless_compile_slice_update(self):
|
|
def fun(x):
|
|
x[2] = mx.array([3.0])
|
|
return x
|
|
|
|
cfun = mx.compile(fun, shapeless=True)
|
|
|
|
a = mx.array([0.0, 1.0, 2.0, 3.0])
|
|
self.assertTrue(mx.allclose(cfun(a), fun(a)))
|
|
|
|
a = mx.array([0.0, 1.0, 2.0, 3.0, 4.0])
|
|
self.assertTrue(mx.allclose(cfun(a), fun(a)))
|
|
|
|
def test_shapeless_compile_with_reshape(self):
|
|
def fun(x):
|
|
return x.reshape(x.shape[0] * x.shape[1], -1)
|
|
|
|
compiled_fun = mx.compile(fun, shapeless=True)
|
|
|
|
x = mx.zeros(shape=(2, 3, 4))
|
|
out = compiled_fun(x)
|
|
self.assertEqual(out.shape, (6, 4))
|
|
|
|
x = mx.zeros(shape=(2, 3, 8))
|
|
out = compiled_fun(x)
|
|
self.assertEqual(out.shape, (6, 8))
|
|
|
|
x = mx.zeros(shape=(5, 5, 5))
|
|
|
|
with self.assertRaises(ValueError):
|
|
compiled_fun(x)
|
|
|
|
def test_compile_shapeless_with_broadcast(self):
|
|
a = mx.array(0.0)
|
|
b = mx.ones((2, 2))
|
|
|
|
def fun(a):
|
|
return mx.broadcast_to(a, b.shape)
|
|
|
|
cfun = mx.compile(fun, shapeless=True)
|
|
# Works on the first shape
|
|
cfun(a)
|
|
|
|
# Fails on a different shape
|
|
with self.assertRaises(ValueError):
|
|
cfun(mx.array(0.0).reshape(1, 1, 1))
|
|
|
|
def fun(a, b):
|
|
return mx.broadcast_arrays(a, b)
|
|
|
|
cfun = mx.compile(fun, shapeless=True)
|
|
a, b = cfun(a, b)
|
|
self.assertEqual(a.shape, (2, 2))
|
|
self.assertEqual(b.shape, (2, 2))
|
|
|
|
# Batched matmul
|
|
a = mx.zeros((2, 1, 4, 2))
|
|
b = mx.zeros((3, 2, 5))
|
|
|
|
def fun(a, b):
|
|
return a @ b
|
|
|
|
cfun = mx.compile(fun, shapeless=True)
|
|
out = cfun(a, b)
|
|
self.assertEqual(out.shape, (2, 3, 4, 5))
|
|
|
|
# Shapeless compile should be preserved over vjp, jvp, vmap
|
|
def fun(args):
|
|
return sum(args).sum()
|
|
|
|
a = mx.array(0.0)
|
|
b = mx.ones((2, 2))
|
|
|
|
cfun = mx.compile(mx.grad(fun), shapeless=True)
|
|
out = cfun((a, b))
|
|
|
|
self.assertEqual(out[0].shape, ())
|
|
self.assertEqual(out[1].shape, (2, 2))
|
|
|
|
out = cfun((b, a))
|
|
|
|
self.assertEqual(out[0].shape, (2, 2))
|
|
self.assertEqual(out[1].shape, ())
|
|
|
|
# Shapeless compile should be preserved over vjp, jvp, vmap
|
|
def fun(args):
|
|
return (args[0] @ args[1]).sum()
|
|
|
|
a = mx.zeros((2, 1, 4, 2))
|
|
b = mx.zeros((3, 2, 5))
|
|
|
|
cfun = mx.compile(mx.grad(fun), shapeless=True)
|
|
out = cfun((a, b))
|
|
|
|
self.assertEqual(out[0].shape, (2, 1, 4, 2))
|
|
self.assertEqual(out[1].shape, (3, 2, 5))
|
|
|
|
a = mx.zeros((3, 1, 4, 2))
|
|
b = mx.zeros((2, 2, 5))
|
|
|
|
out = cfun((a, b))
|
|
|
|
self.assertEqual(out[0].shape, (3, 1, 4, 2))
|
|
self.assertEqual(out[1].shape, (2, 2, 5))
|
|
|
|
def test_leaks(self):
|
|
gc.collect()
|
|
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.compile(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_double_constant(self):
|
|
with mx.stream(mx.cpu):
|
|
x = mx.array(1.0, dtype=mx.float64)
|
|
|
|
def fun(x):
|
|
return (x + math.pi) * 2.0
|
|
|
|
y = fun(x).item()
|
|
y_compiled = mx.compile(fun)(x).item()
|
|
self.assertEqual(y, y_compiled)
|
|
|
|
def test_shared_broadcast(self):
|
|
def fun(x, y, z):
|
|
yy = mx.broadcast_to(y, z.shape)
|
|
return (x + yy * z), yy.sum()
|
|
|
|
a = mx.random.normal((10, 10))
|
|
b = mx.array(0.1)
|
|
c = mx.random.normal((10, 10))
|
|
mx.eval(a, b, c)
|
|
fc = mx.compile(fun)
|
|
d = fc(a, b, c)
|
|
|
|
s = StringIO()
|
|
mx.export_to_dot(s, a=a, b=b, c=c, d1=d[0], d2=d[1])
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s.seek(0)
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s = s.read()
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|
|
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self.assertTrue("CompiledBroadcastMultiplyAdd" in s)
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d_hat = fun(a, b, c)
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self.assertTrue(mx.allclose(d[0], d_hat[0]))
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self.assertTrue(mx.allclose(d[1], d_hat[1]))
|
|
|
|
def test_wrap_compiled(self):
|
|
@mx.compile
|
|
def inner():
|
|
pass
|
|
|
|
@wraps(inner)
|
|
def wrapper():
|
|
pass
|
|
|
|
def test_compiled_preserves_attributes(self):
|
|
def inner(x: mx.array, y: str):
|
|
"""
|
|
A useful function.
|
|
"""
|
|
pass
|
|
|
|
c_inner = mx.compile(inner)
|
|
self.assertEqual(inner.__name__, c_inner.__name__)
|
|
self.assertEqual(inner.__qualname__, c_inner.__qualname__)
|
|
self.assertEqual(inner.__doc__, c_inner.__doc__)
|
|
self.assertEqual(inspect.signature(inner), inspect.signature(c_inner))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
mlx_tests.MLXTestRunner()
|