diff --git a/test/test_transcendental.py b/test/test_transcendental.py
index 785c8ae6a9..ef4b2a7d16 100644
--- a/test/test_transcendental.py
+++ b/test/test_transcendental.py
@@ -1,7 +1,45 @@
 import unittest
-from tinygrad.tensor import Tensor
-from tinygrad.helpers import Context
+from tinygrad import Tensor, Device, dtypes
+from tinygrad.tensor import _to_np_dtype
+from tinygrad.helpers import Context, getenv
 from test.test_schedule import check_schedule
+from test.test_dtype_alu import ht
+from test.helpers import is_dtype_supported
+import numpy as np
+from hypothesis import given, settings, strategies as strat
+
+settings.register_profile("my_profile", max_examples=200, deadline=None, derandomize=getenv("DERANDOMIZE_CI", False))
+settings.load_profile("my_profile")
+
+class TestTranscendentalMath(unittest.TestCase):
+  @unittest.skipUnless(is_dtype_supported(dtypes.float64, Device.DEFAULT), f"no float64 on {Device.DEFAULT}")
+  @unittest.skipIf(getenv("CUDACPU") or (getenv("MOCKGPU") and Device.DEFAULT == "NV"), "crashed")
+  @given(ht.float64, strat.sampled_from([(Tensor.exp, np.exp), (Tensor.log, np.log), (Tensor.sin, np.sin)]))
+  def test_float64(self, x, op):
+    if op[0] == Tensor.sin:
+      # TODO: reduction does not work
+      if abs(x) > 2 ** 32: return
+
+    with Context(TRANSCENDENTAL=2):
+      np.testing.assert_allclose(op[0](Tensor([x], dtype=dtypes.float64)).numpy(),
+                                 op[1](np.array([x], dtype=_to_np_dtype(dtypes.float64))),
+                                 atol=1e-5)
+
+  @unittest.skipIf(getenv("CUDACPU") or (getenv("MOCKGPU") and Device.DEFAULT == "NV"), "crashed")
+  @given(ht.float32, strat.sampled_from([(Tensor.exp, np.exp), (Tensor.log, np.log), (Tensor.sin, np.sin)]))
+  def test_float32(self, x, op):
+    with Context(TRANSCENDENTAL=2):
+      np.testing.assert_allclose(op[0](Tensor([x], dtype=dtypes.float32)).numpy(),
+                                 op[1](np.array([x], dtype=_to_np_dtype(dtypes.float32))),
+                                 atol=1e-5)
+
+  @unittest.skipUnless(is_dtype_supported(dtypes.float16, Device.DEFAULT), f"no float16 on {Device.DEFAULT}")
+  @given(ht.float16, strat.sampled_from([(Tensor.exp, np.exp), (Tensor.log, np.log), (Tensor.sin, np.sin)]))
+  def test_float16(self, x, op):
+    with Context(TRANSCENDENTAL=2):
+      np.testing.assert_allclose(op[0](Tensor([x], dtype=dtypes.float16)).numpy(),
+                                 op[1](np.array([x], dtype=_to_np_dtype(dtypes.float16))),
+                                 atol=1e-2, rtol=2e-3)
 
 class TestTranscendentalSchedule(unittest.TestCase):
   # w/ payne_hanek_reduction (fp32)