From 2f70fb893e012f8b2218063c4bd69cdeac75f1be Mon Sep 17 00:00:00 2001
From: chenyu <chenyu@fastmail.com>
Date: Sun, 3 Nov 2024 12:36:50 -0500
Subject: [PATCH] move transcendental fuzzer test to test_transcendental
 (#7511)

---
 test/test_dtype_alu.py      | 40 ------------------------------
 test/test_transcendental.py | 49 ++++++++++++++++++++++++++++++++++---
 2 files changed, 45 insertions(+), 44 deletions(-)

diff --git a/test/test_dtype_alu.py b/test/test_dtype_alu.py
index d00e37c639..d4e407d5c6 100644
--- a/test/test_dtype_alu.py
+++ b/test/test_dtype_alu.py
@@ -173,45 +173,5 @@ class TestDTypeALU(unittest.TestCase):
   @given(ht.int32, strat.sampled_from(dtypes_float+dtypes_int+dtypes_bool))
   def test_int32_cast(self, a, dtype): universal_test_cast(a, dtypes.int32, dtype)
 
-class TestFromFuzzer(unittest.TestCase):
-  @given(strat.sampled_from(dtypes_float))
-  def test_sin(self, dtype):
-    if not is_dtype_supported(dtype): return
-    if dtype == dtypes.float64:
-      # crashes in CI CUDA
-      if getenv("MOCKGPU") and Device.DEFAULT == "NV": return
-    def _test_value(n: float, unit: float=1.0):
-      next_float = np.nextafter(1.0, 2.0, dtype=_to_np_dtype(dtype))
-      ulp = next_float - 1.0
-      ulp = unit * ulp
-      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)
-    _test_value(-35.0)
-    _test_value(-25.0)
-    _test_value(25.0)
-    _test_value(30.0) # 30.0 == switch_over
-    _test_value(35.0)
-    _test_value(0.0)
-    _test_value(np.pi / 2)
-     # worst case of ulp 1.5
-    _test_value(np.pi * 2, unit=1.5)
-
-  @given(strat.sampled_from(dtypes_float))
-  def test_log2(self, dtype):
-    if not is_dtype_supported(dtype): return
-    if dtype == dtypes.float64:
-      # crashes in CI CUDA
-      if getenv("MOCKGPU") and Device.DEFAULT == "NV": return
-    def _test_value(n: float, unit: float=1.0):
-      next_float = np.nextafter(1.0, 2.0, dtype=_to_np_dtype(dtype))
-      ulp = next_float - 1.0
-      ulp = unit * ulp
-      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)
-    fmin = np.finfo(_to_np_dtype(dtype)).tiny
-    for scale in [1.0, 1e10, 1e20, 1e30]:
-      _test_value(fmin * scale)
-      _test_value(-fmin * scale)
-    _test_value(0)
-    _test_value(0.0000009)
-
 if __name__ == '__main__':
   unittest.main()
diff --git a/test/test_transcendental.py b/test/test_transcendental.py
index 4d76029d03..8a1417db34 100644
--- a/test/test_transcendental.py
+++ b/test/test_transcendental.py
@@ -3,7 +3,7 @@ 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.test_dtype_alu import ht, dtypes_float
 from test.helpers import is_dtype_supported
 import numpy as np
 from hypothesis import given, settings, strategies as strat
@@ -40,10 +40,10 @@ class TestTranscendentalMath(unittest.TestCase):
                                  op[1](np.array([x], dtype=_to_np_dtype(dtypes.float16))),
                                  atol=1e-2, rtol=5e-3)  # exp can have bigger rtol
 
-  @unittest.skipIf(Device.DEFAULT=="LLVM", "FIXME: LLVM might change computer")
+  @unittest.skipIf(Device.DEFAULT=="LLVM", "FIXME: LLVM might change compute")
   @given(strat.sampled_from([(dtypes.float64, 709.5), (dtypes.float32, 88.7), (dtypes.float16, 11)]))
   def test_exp_near_inf(self, dtype_x):
-    # reordering compute might give inf result
+    # reordering compute might return inf
     dtype, x = dtype_x
     if not is_dtype_supported(dtype): return
     with Context(TRANSCENDENTAL=2):
@@ -51,8 +51,49 @@ class TestTranscendentalMath(unittest.TestCase):
       expected = np.exp(np.array([x], dtype=_to_np_dtype(dtype)))
       np.testing.assert_allclose(y, expected, rtol=5e-3)
 
+class TestFromFuzzer(unittest.TestCase):
+  @given(strat.sampled_from(dtypes_float))
+  def test_sin(self, dtype):
+    if not is_dtype_supported(dtype): return
+    if dtype == dtypes.float64:
+      # crashes in CI CUDA
+      if getenv("MOCKGPU") and Device.DEFAULT == "NV": return
+    def _test_value(n: float, unit: float=1.0):
+      next_float = np.nextafter(1.0, 2.0, dtype=_to_np_dtype(dtype))
+      ulp = next_float - 1.0
+      ulp = unit * ulp
+      with Context(TRANSCENDENTAL=2):
+        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)
+    _test_value(-35.0)
+    _test_value(-25.0)
+    _test_value(25.0)
+    _test_value(30.0) # 30.0 == switch_over
+    _test_value(35.0)
+    _test_value(0.0)
+    _test_value(np.pi / 2)
+     # worst case of ulp 1.5
+    _test_value(np.pi * 2, unit=1.5)
+
+  @given(strat.sampled_from(dtypes_float))
+  def test_log2(self, dtype):
+    if not is_dtype_supported(dtype): return
+    if dtype == dtypes.float64:
+      # crashes in CI CUDA
+      if getenv("MOCKGPU") and Device.DEFAULT == "NV": return
+    def _test_value(n: float, unit: float=1.0):
+      next_float = np.nextafter(1.0, 2.0, dtype=_to_np_dtype(dtype))
+      ulp = next_float - 1.0
+      ulp = unit * ulp
+      with Context(TRANSCENDENTAL=2):
+        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)
+    fmin = np.finfo(_to_np_dtype(dtype)).tiny
+    for scale in [1.0, 1e10, 1e20, 1e30]:
+      _test_value(fmin * scale)
+      _test_value(-fmin * scale)
+    _test_value(0)
+    _test_value(0.0000009)
+
 class TestTranscendentalSchedule(unittest.TestCase):
-  # w/ payne_hanek_reduction (fp32)
   def test_transcendental_sin_fusion(self):
     with Context(TRANSCENDENTAL=2):
       a = Tensor.empty(10)