mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-01-13 00:48:21 -05:00
ea1be2e4cd
* Modify tests and start work towards removing symbolic reshape * Refactor symbolic reshape * fix small error * much cleaner + fix more tests * Can remove this now * Update test_symbolic_ops and test_tiny * Couple more tests * Unused import * More tests and add EXPAND to Tensor.empty * Fix test beam search * all int * Fix rangeify by adding shrink * Remove OOB check and so fix test_symbolic_jit * test_symbolic_jit doesn't need OOB Context anymore either * Should remove that test now * Cleanups part 1 * fix linters * Final cleanups * Don't reassign inside for loop --------- Co-authored-by: chenyu <chenyu@fastmail.com>
80 lines
2.4 KiB
Python
80 lines
2.4 KiB
Python
import unittest
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import numpy as np
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from tinygrad.helpers import BEAM, Timing, CI, Context
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from tinygrad import Variable, Tensor
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from tinygrad.nn import Conv2d
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def rand(*shape):
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return Tensor(np.random.rand(*shape).astype(np.float32))
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class TestBeamSearch(unittest.TestCase):
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def setUp(self):
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self.old_beam = BEAM.value
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BEAM.value = 2
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def tearDown(self):
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BEAM.value = self.old_beam
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def test_variable_ast_beam(self):
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vi = Variable("a", 1, 10).bind(3)
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a = rand(10, 3)[:vi]
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a = (a+1).realize()
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def test_big_prime_number(self):
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a = rand(367, 367)
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b = rand(367, 367)
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c = (a@b).realize()
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np.testing.assert_allclose(c.numpy(), a.numpy() @ b.numpy(), atol=1e-4, rtol=1e-4)
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def test_big_prime_number_max(self):
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a = -rand(367, 367)
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b = rand(367, 367)
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# if incorrectly padded 0, the max would be 0 instead of a negative number
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c = (a*b).max(1)
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np.testing.assert_allclose(c.numpy(), (a.numpy() * b.numpy()).max(1), atol=1e-4, rtol=1e-4)
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def test_big_prime_number_sum(self):
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a = rand(367, 367)
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b = rand(367, 367)
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# if incorrectly padded 0, the sum would be inf
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c = (a/b).sum(1).realize()
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np.testing.assert_allclose(c.numpy(), (a.numpy() / b.numpy()).sum(1), atol=1e-4, rtol=1e-4)
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def test_variable_big_prime_number(self):
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v = Variable("v", 1, 400).bind(367)
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a = rand(367, 400)
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b = rand(400, 367)
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c = (a[:, :v] @ b[:v, :]).realize()
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np.testing.assert_allclose(c.numpy(), a[:, :367].numpy() @ b[:367, :].numpy(), atol=1e-4, rtol=1e-4)
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def test_variable_shrink_prime_number(self):
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v = Variable("v", 1, 400).bind(367)
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a = rand(400, 367)
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b = (a.shrink(((0,v), None))+1).reshape(367,367).realize()
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np.testing.assert_allclose(b.numpy(), a.numpy()[:367]+1, atol=1e-4, rtol=1e-4)
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def test_no_mutate_rawbuffers(self):
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a = rand(3, 3).realize()
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desired = a.numpy() + 1
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a.assign(a+1)
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actual = a.numpy()
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np.testing.assert_allclose(actual, desired)
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@unittest.skipIf(CI, "flaky. CL_OUT_OF_RESOURCES")
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def test_conv_beam(self):
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c = Conv2d(3, 16, (3,3))
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x = rand(1,3,32,32)
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with Timing():
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c(x).realize()
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@unittest.skip("flaky, Fatal Python error: Floating point exception")
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def test_large_ast(self):
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a = Tensor.rand(3, 3)
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for _ in range(5):
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for _ in range(4):
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a = (a + a) * a
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a.realize()
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if __name__ == '__main__':
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unittest.main()
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