add tiny test for randomness + remove ulong buffers (#7648)

* add tiny test for randomness

* Tensor._device_seeds is a Tuple

* no tuple, just a 2 element tensor

* no more longs

* fix tests, and maybe ocelot works now

* NV still doesn't work. cleanup rules

* test + two more rules

test/test_randomness.py

@@ -6,6 +6,7 @@ import torch
 from tinygrad import nn, dtypes, Tensor, Device, TinyJit
 from tinygrad.helpers import getenv, CI
 from tinygrad.device import is_dtype_supported
+from tinygrad.engine.realize import lower_schedule, CompiledRunner
 from hypothesis import given, settings, strategies as strat
 
 settings.register_profile("my_profile", max_examples=200, deadline=None, derandomize=getenv("DERANDOMIZE_CI", False))
@@ -92,10 +93,16 @@ class TestRandomness(unittest.TestCase):
 
     counts = Tensor.arange(20, dtype=dtypes.uint32)
     counts0, counts1 = counts.chunk(2)
-    r = Tensor._threefry_random_bits(1337 << 32, counts0, counts1).numpy()
+    r = Tensor._threefry_random_bits(Tensor([0, 1337], dtype='uint32'), counts0, counts1).numpy()
 
     np.testing.assert_allclose(jr, r)
 
+  def test_threefry_doesnt_use_long(self):
+    for ei in lower_schedule(Tensor.rand(20).schedule()):
+      if isinstance(ei.prg, CompiledRunner):
+        for u in ei.prg.p.uops:
+          self.assertNotIn(u.dtype, {dtypes.long, dtypes.ulong}, msg=f"long found in {ei.prg.p.name}")
+
   def test_threefry_against_reference_full(self):
     Tensor.manual_seed(1337)
 

test/test_tiny.py

@@ -29,6 +29,14 @@ class TestTiny(unittest.TestCase):
     self.assertListEqual((out:=a@b).flatten().tolist(), [1.0]*(N*N))
     if IMAGE < 2: self.assertEqual(out.dtype, out_dtype)
 
+  # *** randomness ***
+
+  def test_random(self):
+    out = Tensor.rand(10)
+    for x in out.tolist():
+      self.assertGreaterEqual(x, 0.0)
+      self.assertLessEqual(x, 1.0)
+
   # *** JIT (for Python speed) ***
 
   def test_jit(self):