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48 lines
1.7 KiB
48 lines
1.7 KiB
import unittest
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from tinygrad import Tensor, Device, Variable
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from examples.gpt2 import Transformer
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from tinygrad.nn.state import get_state_dict
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class TestMethodCache(unittest.TestCase):
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def setUp(self):
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self.backup_compiler = Device[Device.DEFAULT].compiler
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def tearDown(self):
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Device[Device.DEFAULT].compiler = self.backup_compiler
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def test_simple_methodcache(self):
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a = Tensor([1])
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b = Tensor([2])
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c = Tensor([3])
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d = Tensor([4])
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(a+b).realize()
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Device[Device.DEFAULT].compiler = None
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(c+d).realize()
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def test_nested_methodcache(self):
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a,b,c,d = Tensor([1]), Tensor([2]), Tensor([3]), Tensor([4])
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((a+b)+(a+b)).realize()
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Device[Device.DEFAULT].compiler = None
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((c+d)+(c+d)).realize()
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def test_nested_methodcache_swap(self):
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a,b,c,d = Tensor([1]), Tensor([2]), Tensor([3]), Tensor([4])
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((a+b)+(c+d)).realize()
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Device[Device.DEFAULT].compiler = None
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((c+d)+(a+b)).realize()
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@unittest.skip("incorrect use of transformer")
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def test_small_transformer(self):
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args_tiny = {"dim": 16, "n_heads": 8, "n_layers": 8, "norm_eps": 1e-05, "vocab_size": 10}
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model = Transformer(**args_tiny)
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for v in get_state_dict(model).values(): v.assign(Tensor.empty(*v.shape, dtype=v.dtype).realize())
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# NOTE: you have to do this twice due to the k-v cache
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for i in range(3): model(Tensor([[1,2,3,4]]), Variable("start_pos", 0, 10).bind(i)).realize()
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for i in range(3): model(Tensor([[1,2,3,4]]), Variable("start_pos", 0, 10).bind(i)).realize()
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Device[Device.DEFAULT].compiler = None
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for i in range(3): model(Tensor([[1,2,3,4]]), Variable("start_pos", 0, 10).bind(i)).realize()
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if __name__ == '__main__':
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unittest.main()
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