mirror of https://github.com/commaai/tinygrad.git
322 lines
10 KiB
Python
322 lines
10 KiB
Python
#!/usr/bin/env python
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import unittest, functools
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import numpy as np
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from test.helpers import assert_jit_cache_len
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from tinygrad.tensor import Tensor
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from tinygrad.features.jit import TinyJit
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from tinygrad.device import Device
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from tinygrad.helpers import CI
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def _simple_test(add, extract=lambda x: x):
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for _ in range(5):
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a = Tensor.randn(10, 10)
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b = Tensor.randn(10, 10)
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c = add(a, b)
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np.testing.assert_allclose(extract(c).numpy(), a.numpy()+b.numpy(), atol=1e-4, rtol=1e-5)
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assert_jit_cache_len(add, 1)
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class TestJit(unittest.TestCase):
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def test_simple_jit(self):
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@TinyJit
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def add(a, b): return (a+b).realize()
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_simple_test(add)
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def test_simple_jit_norealize(self):
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@TinyJit
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def add(a, b): return (a+b)
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_simple_test(add)
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def test_simple_jit_norealize_list(self):
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@TinyJit
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def add(a, b): return [a+b]
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_simple_test(add, extract=lambda x: x[0])
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def test_simple_jit_norealize_dict(self):
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@TinyJit
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def add(a, b): return {"billy": a+b}
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_simple_test(add, extract=lambda x: x["billy"])
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def test_jit_multiple_outputs(self):
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@TinyJit
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def f(a, b): return (a+b).realize(), (a-b).realize(), (a*b).realize()
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for _ in range(5):
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a = Tensor.randn(10, 10)
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b = Tensor.randn(10, 10)
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c, d, e = f(a, b)
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np.testing.assert_allclose(c.numpy(), a.numpy()+b.numpy(), atol=1e-4, rtol=1e-5)
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np.testing.assert_allclose(d.numpy(), a.numpy()-b.numpy(), atol=1e-4, rtol=1e-5)
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np.testing.assert_allclose(e.numpy(), a.numpy()*b.numpy(), atol=1e-4, rtol=1e-5)
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assert_jit_cache_len(f, 3)
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def test_nothing_jitted(self):
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@TinyJit
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def add(a, b): return None
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with self.assertRaises(AssertionError):
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for _ in range(5):
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a = Tensor.randn(10, 10)
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b = Tensor.randn(10, 10)
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add(a, b)
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def test_jit_shape_mismatch(self):
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@TinyJit
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def add(a, b): return (a+b).realize()
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for _ in range(5):
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a = Tensor.randn(10, 10)
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b = Tensor.randn(10, 10)
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add(a, b)
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bad = Tensor.randn(20, 20)
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with self.assertRaises(AssertionError):
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add(a, bad)
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def test_jit_shape_views_mismatch(self):
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@TinyJit
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def add(a): return (a+1).realize()
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with self.assertRaises(AssertionError):
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for i in range(1,5):
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# a has an offset that the kernel doesn't know about
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a = Tensor.randn(10, 10).realize()[:, i:i+2]
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add(a)
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def test_jit_duplicate_fail(self):
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# the jit doesn't support duplicate arguments
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@TinyJit
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def add(a, b): return (a+b).realize()
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a = Tensor.randn(10, 10)
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with self.assertRaises(AssertionError):
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add(a, a)
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def test_kwargs_jit(self):
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@TinyJit
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def add_kwargs(first, second): return (first+second).realize()
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for _ in range(5):
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a = Tensor.randn(10, 10)
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b = Tensor.randn(10, 10)
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c = add_kwargs(first=a, second=b)
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np.testing.assert_allclose(c.numpy(), a.numpy()+b.numpy(), atol=1e-4, rtol=1e-5)
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assert_jit_cache_len(add_kwargs, 1)
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def test_array_jit(self):
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@TinyJit
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def add_array(a, arr): return (a+arr[0]).realize()
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for i in range(5):
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a = Tensor.randn(10, 10)
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b = Tensor.randn(10, 10)
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a.realize(), b.realize()
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c = add_array(a, [b])
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if i >= 2:
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# should fail once jitted since jit can't handle arrays
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np.testing.assert_allclose(np.any(np.not_equal(c.numpy(),a.numpy()+b.numpy())), True, atol=1e-4, rtol=1e-5)
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else:
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np.testing.assert_allclose(c.numpy(), a.numpy()+b.numpy(), atol=1e-4, rtol=1e-5)
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assert_jit_cache_len(add_array, 1)
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def test_method_jit(self):
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class Fun:
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def __init__(self):
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self.a = Tensor.randn(10, 10)
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@TinyJit
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def __call__(self, b:Tensor) -> Tensor:
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return (self.a+b).realize()
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fun = Fun()
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for _ in range(5):
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b = Tensor.randn(10, 10)
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c = fun(b)
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np.testing.assert_allclose(c.numpy(), fun.a.numpy()+b.numpy(), atol=1e-4, rtol=1e-5)
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assert_jit_cache_len(fun.__call__.func.__self__, 1)
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def test_jit_size1_input(self):
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@TinyJit
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def f(a, b): return (a+b).realize()
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a = Tensor([1, 2, 3])
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for i in range(5):
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np.testing.assert_allclose(f(a, Tensor([i])).numpy(), (a+i).numpy(), atol=1e-4, rtol=1e-5)
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assert_jit_cache_len(f, 1)
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def test_jit_output_non_tensor_fail(self):
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@TinyJit
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def f(a, b, i): return (a+b).realize(), i
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output1, output2 = [], []
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expect1, expect2 = [], []
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for i in range(5):
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a = Tensor.randn(10, 10)
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b = Tensor.randn(10, 10)
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o1, o2 = f(a, b, i)
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output1.append(o1.numpy().copy())
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output2.append(o2)
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expect1.append(a.numpy().copy()+b.numpy().copy())
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expect2.append(i)
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np.testing.assert_allclose(output1, expect1, atol=1e-4, rtol=1e-5)
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# the jit only works with Tensor outputs
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assert output2 != expect2
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assert_jit_cache_len(f, 1)
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def test_jit_random_regen(self):
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def f(a, b):
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rn = Tensor.randn(*a.shape)
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return ((a+b)*rn).realize()
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a = Tensor.randn(10, 10).realize() # realize these before resetting the random seed
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b = Tensor.randn(10, 10).realize()
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Tensor._seed = 1234
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jf = TinyJit(f)
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res = set()
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for _ in range(5):
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o1 = jf(a, b)
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res.add(o1.numpy()[0][0])
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assert len(res) == 5, "All values should be different, rand works in jit."
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Tensor._seed = 1234
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jf2 = TinyJit(f)
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res2 = set()
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for _ in range(5):
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o1 = jf2(a, b)
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res2.add(o1.numpy()[0][0])
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assert len(res2) == 5, "All values should be different, rand works in jit."
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assert res == res2, "Jit rand is not reproducible with the same seed"
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Tensor._seed = 3421
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jf3 = TinyJit(f)
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res3 = set()
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for _ in range(5):
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o1 = jf3(a, b)
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res3.add(o1.numpy()[0][0])
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assert len(res3) == 5, "All values should be different, rand works in jit."
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assert res3 != res2, "Jit rand is diff with diff seeds"
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def test_jit_realization_and_sampling(self):
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w = Tensor.eye(5)
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@TinyJit
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def foo (x): return w.dot(x).realize()
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arg = [
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Tensor([1,2,3,4,5]),
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Tensor([1,3,3,4,6]),
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Tensor([1,2,5,4,7]),
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Tensor([0,2,3,1,0]),
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]
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Y = [foo(e).numpy() for e in arg]
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foo(Tensor([7,7,7,7,7]))
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want = [[1., 2., 3., 4., 5.],
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[1., 3., 3., 4., 6.],
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[1., 2., 5., 4., 7.],
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[0., 2., 3., 1., 0.]]
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np.testing.assert_allclose(want, Y)
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def test_jitted_read_assign(self):
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class Cache:
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def __init__(self):
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self.good_cache = Tensor.zeros(1)
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self.bad_cache = Tensor.zeros(1)
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self.good_jitted = TinyJit(self.good)
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self.bad_jitted = TinyJit(self.bad)
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def good(self, y, cache_v=None):
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if cache_v is not None:
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self.good_cache.assign(cache_v+1-1).realize()
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return (self.good_cache + y).realize() # need + y to provide inputs to JIT
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def bad(self, y, cache_v=None):
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if cache_v is not None:
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self.bad_cache.assign(cache_v).realize()
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return (self.bad_cache + y).realize()
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cache = Cache()
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np.testing.assert_equal([0], cache.good_cache.numpy())
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np.testing.assert_equal([0], cache.bad_cache.numpy())
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zero = Tensor([0])
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one = Tensor([1])
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two = Tensor([2])
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# save [1] in the caches
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cache.good(zero, one)
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cache.bad(zero, one)
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np.testing.assert_equal([1], cache.good_cache.numpy())
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np.testing.assert_equal([1], cache.bad_cache.numpy())
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for i in range(5):
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x = Tensor([i]) # NOTE: if this doesn't change, it just hits the lazybuffer cache
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cache.good_jitted(x)
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cache.bad_jitted(x)
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# verify the jitted calls read 1 from the cache
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np.testing.assert_equal([1], cache.good_jitted(zero).numpy())
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np.testing.assert_equal([1], cache.bad_jitted(zero).numpy())
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# save [2] in the caches
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cache.good(zero, two)
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cache.bad(zero, two)
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np.testing.assert_equal([2], cache.good_cache.numpy())
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np.testing.assert_equal([2], cache.bad_cache.numpy())
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# verify the jitted calls read 2 from the cache
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np.testing.assert_equal([2], cache.good_jitted(zero).numpy())
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# but the bad_jitted doesn't!
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np.testing.assert_equal([1], cache.bad_jitted(zero).numpy())
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assert_jit_cache_len(cache.good_jitted, 1)
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assert_jit_cache_len(cache.bad_jitted, 1)
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def test_jit_buffer_behavior(self):
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@TinyJit
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def foo(x) -> Tensor: return x.sum().realize()
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result_1 = foo(Tensor([1] * 2))
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result_2 = foo(Tensor([2] * 2))
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result_3 = foo(Tensor([3] * 2))
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# expect the buffer to share underlying buffer
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np.testing.assert_allclose(result_1.numpy(), [2], atol=1e-4, rtol=1e-5)
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np.testing.assert_allclose(result_2.numpy(), [6], atol=1e-4, rtol=1e-5)
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np.testing.assert_allclose(result_3.numpy(), [6], atol=1e-4, rtol=1e-5)
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@unittest.skipIf(CI and Device.DEFAULT=="METAL", "no ICB in CI, creation of graph fails")
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def test_jit_batch_split(self):
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if Device[Device.DEFAULT].graph is None: raise unittest.SkipTest("only test graphs")
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# Create long jit with 83 kernels.
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def f(a, b, c, d, e):
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for _ in range(80):
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a = (a+b).realize()
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y = (a*c).realize()
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z = (y*d).realize()
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w = (z*e)
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return w.realize()
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a = Tensor.randn(10, 10).realize()
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b = Tensor.randn(10, 10).realize()
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c = Tensor.randn(10, 10).realize()
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d = Tensor.randn(10, 10).realize()
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e = Tensor.randn(10, 10).realize()
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jf = TinyJit(f)
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prev = None
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for _ in range(5):
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o = jf(a, b, c, d, e).numpy()
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if prev is not None: np.testing.assert_allclose(o, prev, atol=1e-4, rtol=1e-5)
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prev = o
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graph_t = Device[Device.DEFAULT].graph.func if isinstance(Device[Device.DEFAULT].graph, functools.partial) else Device[Device.DEFAULT].graph
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# Checking that 2 graphs are inited.
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assert isinstance(jf.jit_cache[0].prg, graph_t)
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assert isinstance(jf.jit_cache[1].prg, graph_t)
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def test_jit_const_inputs(self):
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@TinyJit
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def f(x,y): return (x+y).realize()
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for _ in range(5):
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np.testing.assert_equal(f(Tensor.ones(3), Tensor.zeros(3)).numpy(), np.ones(3))
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@TinyJit
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def g(x,y,z): return (x+y+z).realize()
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for i in range(5):
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np.testing.assert_equal(g(Tensor([i]*3), Tensor.ones(3), Tensor.zeros(3)).numpy(), np.array([i+1]*3))
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if __name__ == '__main__':
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unittest.main()
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