@@ -57,7 +57,7 @@ def test_floor_ceil_trunc_usm_type(np_call, dpt_call, usm_type):
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q = get_queue_or_skip ()
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arg_dt = np .dtype ("f4" )
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- input_shape = (10 , 10 , 10 , 10 )
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+ input_shape = (2 , 2 , 2 , 10 )
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X = dpt .empty (input_shape , dtype = arg_dt , usm_type = usm_type , sycl_queue = q )
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X [..., 0 ::2 ] = - 0.4
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X [..., 1 ::2 ] = 0.7
@@ -80,16 +80,16 @@ def test_floor_ceil_trunc_order(np_call, dpt_call, dtype):
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skip_if_dtype_not_supported (dtype , q )
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arg_dt = np .dtype (dtype )
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- input_shape = (10 , 10 , 10 , 10 )
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+ input_shape = (4 , 4 , 4 , 4 )
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X = dpt .empty (input_shape , dtype = arg_dt , sycl_queue = q )
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X [..., 0 ::2 ] = - 0.4
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X [..., 1 ::2 ] = 0.7
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- for ord in ["C" , "F" , "A" , "K" ]:
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- for perms in itertools .permutations (range (4 )):
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- U = dpt .permute_dims (X [:, ::- 1 , ::- 1 , :], perms )
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+ for perms in itertools .permutations (range (4 )):
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+ U = dpt .permute_dims (X [:, ::- 1 , ::- 1 , :], perms )
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+ expected_Y = np_call (dpt .asnumpy (U ))
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+ for ord in ["C" , "F" , "A" , "K" ]:
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Y = dpt_call (U , order = ord )
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- expected_Y = np_call (dpt .asnumpy (U ))
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assert_allclose (dpt .asnumpy (Y ), expected_Y )
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@@ -180,7 +180,7 @@ def test_floor_ceil_trunc_strided(np_call, dpt_call, dtype):
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np .random .seed (42 )
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strides = np .array ([- 4 , - 3 , - 2 , - 1 , 1 , 2 , 3 , 4 ])
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- sizes = np . arange ( 2 , 100 )
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+ sizes = [ 2 , 4 , 6 , 8 , 24 , 32 , 72 ]
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for ii in sizes :
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Xnp = np .random .uniform (low = - 99.9 , high = 99.9 , size = ii )
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