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d3378010ee
* schedule_with_vars -> linear_with_vars in tests * tests batch 1 * batch 2 * estimate_uop * simpler * rm
45 lines
1.6 KiB
Python
45 lines
1.6 KiB
Python
import unittest
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from tinygrad import Tensor, Device, Context
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from tinygrad.codegen import do_to_program
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from tinygrad.codegen.opt import Opt, OptOps
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from test.external.process_replay.process_replay import replay_to_program
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from test.helpers import replace_opts
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N = 16
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class TestProcessReplay(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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cls.ast = (Tensor.empty(N, N) @ Tensor.empty(N, N)).schedule_linear().src[-1].src[0]
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cls.renderer = Device[Device.DEFAULT].renderer
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def test_replay_no_opts(self):
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# opts=None means use default heuristic path
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p = do_to_program(self.ast, self.renderer)
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good, compare, _ = replay_to_program(p, self.ast, self.renderer)
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self.assertEqual(good, compare)
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def test_replay_empty_opts(self):
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# opts=[] means explicitly apply zero opts (unoptimized)
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ast = replace_opts(self.ast, [])
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p = do_to_program(ast, self.renderer)
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good, compare, _ = replay_to_program(p, ast, self.renderer)
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self.assertEqual(good, compare)
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def test_replay_with_opt(self):
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# opts=[Opt(...)] means apply a specific opt
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opts = [Opt(OptOps.UPCAST, 0, 4)]
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ast = replace_opts(self.ast, opts)
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p = do_to_program(ast, self.renderer)
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good, compare, _ = replay_to_program(p, ast, self.renderer)
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self.assertEqual(good, compare)
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def test_beam(self):
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with Context(BEAM=1):
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ast = (Tensor.empty(N, N) @ Tensor.empty(N, N)).schedule_linear().src[-1].src[0]
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p = do_to_program(ast, self.renderer)
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good, compare, _ = replay_to_program(p, ast, self.renderer)
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self.assertEqual(good, compare)
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if __name__ == '__main__':
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unittest.main(verbosity=2)
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