mirror of https://github.com/commaai/tinygrad.git
126 lines
4.0 KiB
Python
126 lines
4.0 KiB
Python
#!/usr/bin/env python
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import unittest
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import numpy as np
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from tinygrad.tensor import Tensor, Device
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from tinygrad.jit import TinyJit, JIT_SUPPORTED_DEVICE
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# NOTE: METAL fails, might be platform and optimization options dependent.
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@unittest.skipUnless(Device.DEFAULT in JIT_SUPPORTED_DEVICE and Device.DEFAULT not in ["METAL", "WEBGPU"], f"no JIT on {Device.DEFAULT}")
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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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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_equal(c.numpy(), a.numpy()+b.numpy())
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assert len(add.jit_cache) == 1
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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_equal(c.numpy(), a.numpy()+b.numpy())
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np.testing.assert_equal(d.numpy(), a.numpy()-b.numpy())
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np.testing.assert_equal(e.numpy(), a.numpy()*b.numpy())
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assert len(f.jit_cache) == 3
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def test_nothing_jitted(self):
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@TinyJit
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def add(a, b): return a+b
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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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c = 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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c = 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_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_equal(c.numpy(), a.numpy()+b.numpy())
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assert len(add_kwargs.jit_cache) == 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_equal(np.any(np.not_equal(c.numpy(),a.numpy()+b.numpy())), True)
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else:
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np.testing.assert_equal(c.numpy(), a.numpy()+b.numpy())
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assert len(add_array.jit_cache) == 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_equal(c.numpy(), fun.a.numpy()+b.numpy())
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assert len(fun.__call__.func.__self__.jit_cache) == 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_equal(f(a, Tensor([i])).cpu().numpy(), (a+i).cpu().numpy())
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assert len(f.jit_cache) == 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_equal(output1, expect1)
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# the jit only works with Tensor outputs
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assert output2 != expect2
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assert len(f.jit_cache) == 1
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if __name__ == '__main__':
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unittest.main() |