122 lines
5.3 KiB
Python
122 lines
5.3 KiB
Python
import unittest
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from tinygrad import Tensor, Device, dtypes
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from tinygrad.tensor import _to_np_dtype
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from tinygrad.helpers import Context, getenv
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from test.test_schedule import check_schedule
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from test.test_dtype_alu import ht, dtypes_float
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from tinygrad.device import is_dtype_supported
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import numpy as np
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from hypothesis import given, settings, strategies as strat
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settings.register_profile("my_profile", max_examples=200, deadline=None, derandomize=getenv("DERANDOMIZE_CI", False))
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settings.load_profile("my_profile")
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class TestTranscendentalMath(unittest.TestCase):
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@unittest.skipUnless(is_dtype_supported(dtypes.float64, Device.DEFAULT), f"no float64 on {Device.DEFAULT}")
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@unittest.skipIf(getenv("MOCKGPU") and Device.DEFAULT == "NV", "crashed")
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@given(ht.float64, strat.sampled_from([(Tensor.exp, np.exp), (Tensor.log, np.log), (Tensor.sin, np.sin)]))
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def test_float64(self, x, op):
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if op[0] == Tensor.sin:
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# TODO: reduction does not work # 536870912.125 # 2914593.01171875 # 134217728.03125 # 230581075.65625 # 139216373.71875
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if abs(x) > 100_000_000: return
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with Context(TRANSCENDENTAL=2), np.errstate(all='ignore'):
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np.testing.assert_allclose(op[0](Tensor([x], dtype=dtypes.float64)).numpy(),
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op[1](np.array([x], dtype=_to_np_dtype(dtypes.float64))),
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atol=3e-2, rtol=1e-5) # sin can have bigger atol for very big x
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@unittest.skipIf(getenv("MOCKGPU") and Device.DEFAULT == "NV", "crashed")
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@given(ht.float32, strat.sampled_from([(Tensor.exp, np.exp), (Tensor.log, np.log), (Tensor.sin, np.sin)]))
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def test_float32(self, x, op):
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with Context(TRANSCENDENTAL=2), np.errstate(all='ignore'):
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np.testing.assert_allclose(op[0](Tensor([x], dtype=dtypes.float32)).numpy(),
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op[1](np.array([x], dtype=_to_np_dtype(dtypes.float32))),
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atol=2e-5, rtol=1e-5)
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@unittest.skipUnless(is_dtype_supported(dtypes.float16, Device.DEFAULT), f"no float16 on {Device.DEFAULT}")
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@given(ht.float16, strat.sampled_from([(Tensor.exp, np.exp), (Tensor.log, np.log), (Tensor.sin, np.sin)]))
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def test_float16(self, x, op):
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with Context(TRANSCENDENTAL=2), np.errstate(all='ignore'):
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np.testing.assert_allclose(op[0](Tensor([x], dtype=dtypes.float16)).numpy(),
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op[1](np.array([x], dtype=_to_np_dtype(dtypes.float16))),
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atol=1e-2, rtol=5e-3) # exp can have bigger rtol
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@given(strat.sampled_from([(dtypes.float64, 709.5), (dtypes.float32, 88.7), (dtypes.float16, 11)]))
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def test_exp_near_inf(self, dtype_x):
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# reordering compute might return inf
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dtype, x = dtype_x
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if not is_dtype_supported(dtype): return
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with Context(TRANSCENDENTAL=2):
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y = Tensor([x], dtype=dtype).exp().numpy()
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expected = np.exp(np.array([x], dtype=_to_np_dtype(dtype)))
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np.testing.assert_allclose(y, expected, rtol=5e-3)
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class TestFromFuzzer(unittest.TestCase):
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@given(strat.sampled_from(dtypes_float))
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def test_sin(self, dtype):
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if not is_dtype_supported(dtype): return
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if dtype == dtypes.float64:
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# crashes in CI CUDA
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if getenv("MOCKGPU") and Device.DEFAULT == "NV": return
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def _test_value(n: float, unit: float=1.0):
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next_float = np.nextafter(1.0, 2.0, dtype=_to_np_dtype(dtype))
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ulp = next_float - 1.0
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ulp = unit * ulp
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with Context(TRANSCENDENTAL=2):
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np.testing.assert_allclose(Tensor([n], dtype=dtype).sin().numpy(), np.sin(np.array([n], dtype=_to_np_dtype(dtype))), atol=ulp, rtol=1e-5)
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_test_value(-35.0)
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_test_value(-25.0)
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_test_value(25.0)
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_test_value(30.0) # 30.0 == switch_over
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_test_value(35.0)
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_test_value(0.0)
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_test_value(np.pi / 2)
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# worst case of ulp 1.5
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_test_value(np.pi * 2, unit=1.5)
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@given(strat.sampled_from(dtypes_float))
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def test_log2(self, dtype):
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if not is_dtype_supported(dtype): return
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if dtype == dtypes.float64:
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# crashes in CI CUDA
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if getenv("MOCKGPU") and Device.DEFAULT == "NV": return
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def _test_value(n: float, unit: float=1.0):
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next_float = np.nextafter(1.0, 2.0, dtype=_to_np_dtype(dtype))
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ulp = next_float - 1.0
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ulp = unit * ulp
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with Context(TRANSCENDENTAL=2):
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np.testing.assert_allclose(Tensor([n], dtype=dtype).log2().numpy(), np.log2(np.array([n], dtype=_to_np_dtype(dtype))), atol=ulp, rtol=1e-5)
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fmin = np.finfo(_to_np_dtype(dtype)).tiny
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for scale in [1.0, 1e10, 1e20, 1e30]:
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_test_value(fmin * scale)
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_test_value(-fmin * scale)
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_test_value(0)
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_test_value(0.0000009)
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class TestTranscendentalSchedule(unittest.TestCase):
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def test_transcendental_sin_fusion(self):
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with Context(TRANSCENDENTAL=2):
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a = Tensor.empty(10)
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b = Tensor.empty(10)
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c = a.sin() + b.sin()
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c = c.sin()
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check_schedule(c, 1)
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def test_transcendental_log2_fusion(self):
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with Context(TRANSCENDENTAL=2):
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a = Tensor.empty(10)
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b = Tensor.empty(10)
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c = a.log2() + b.log2()
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c = c.log2()
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check_schedule(c, 1)
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def test_transcendental_exp2_fusion(self):
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with Context(TRANSCENDENTAL=2):
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a = Tensor.empty(10)
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b = Tensor.empty(10)
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c = a.exp2() + b.exp2()
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c = c.exp2()
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check_schedule(c, 1)
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if __name__ == '__main__':
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unittest.main()
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