[LANGUAGE] Fixed issue with duplicates in large arrays of random uniform numbers (#338)

python/test/unit/language/test_random.py

@@ -132,34 +132,6 @@ def test_randint(size, seed, device='cuda'):
     out_ref = [gen.random_raw()[0] for _ in out_tri]
     assert out_tri == out_ref
 
-# test conversion of random uint32 into random float in [0, 1]
-def test_uint32_to_uniform_float():
-    @triton.jit
-    def kernel(SRC, TGT, N, **meta):
-        pid = tl.program_id(0)
-        offset = pid * BLOCK + tl.arange(0, BLOCK)
-        src = tl.load(SRC + offset)
-        tgt = tl.random.uint32_to_uniform_float(src)
-        tl.store(TGT + offset, tgt, mask=offset < N)
-    
-    def run(source):
-        target = -torch.ones(source.shape, dtype=torch.float32, device=source.device)
-        N = source.numel()
-        grid = lambda meta: (triton.cdiv(N, BLOCK),)
-        kernel[grid](source, target, N)
-        return target
-        
-    # check range of edge values
-    n = 100
-    source = torch.tensor(list(range(n)) + list(range(-n, 0)), dtype=torch.int32).cuda()
-    target = run(source).tolist()
-    assert target == sorted(target)
-    assert all(0.0 <= num < 1.0 for num in target)
-    # check distribution is uniform
-    source = torch.randint(-2**31, 2**31 - 1, dtype=torch.int32, size=(100000,)).cuda()
-    target = run(source).tolist()
-    assert scipy.stats.kstest(target, 'uniform', args=(0, 1)).statistic < 0.01
-
 # test uniform PRNG
 @pytest.mark.parametrize('size, seed',
     [(size, seed) for size in [1000000]\