forked from M-Labs/nac3
standalone/ndarray: add and organize view function tests
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@ -68,6 +68,19 @@ def output_ndarray_float_2(n: ndarray[float, Literal[2]]):
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for c in range(len(n[r])):
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for c in range(len(n[r])):
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output_float64(n[r][c])
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output_float64(n[r][c])
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def output_ndarray_float_3(n: ndarray[float, Literal[3]]):
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for d in range(len(n)):
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for r in range(len(n[d])):
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for c in range(len(n[d][r])):
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output_float64(n[d][r][c])
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def output_ndarray_float_4(n: ndarray[float, Literal[4]]):
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for x in range(len(n)):
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for y in range(len(n[x])):
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for z in range(len(n[x][y])):
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for w in range(len(n[x][y][z])):
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output_float64(n[x][y][z][w])
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def consume_ndarray_1(n: ndarray[float, Literal[1]]):
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def consume_ndarray_1(n: ndarray[float, Literal[1]]):
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pass
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pass
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@ -196,6 +209,68 @@ def test_ndarray_nd_idx():
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output_float64(x[1, 0])
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output_float64(x[1, 0])
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output_float64(x[1, 1])
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output_float64(x[1, 1])
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def test_ndarray_transpose():
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x: ndarray[float, 2] = np_array([[1., 2., 3.], [4., 5., 6.]])
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y = np_transpose(x)
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z = np_transpose(y)
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output_ndarray_float_2(x)
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output_ndarray_float_2(y)
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def test_ndarray_reshape():
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w: ndarray[float, 1] = np_array([1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])
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x = np_reshape(w, (1, 2, 1, -1))
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y = np_reshape(x, [2, -1])
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z = np_reshape(y, 10)
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x1: ndarray[int32, 1] = np_array([1, 2, 3, 4])
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x2: ndarray[int32, 2] = np_reshape(x1, (2, 2))
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output_ndarray_float_1(w)
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output_ndarray_float_2(y)
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output_ndarray_float_1(z)
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def test_ndarray_broadcast_to():
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xs = np_array([1.0, 2.0, 3.0])
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ys = np_broadcast_to(xs, (1, 3))
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zs = np_broadcast_to(ys, (2, 4, 3))
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output_ndarray_float_1(xs)
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output_ndarray_float_2(ys)
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output_ndarray_float_3(zs)
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def test_ndarray_subscript_assignment():
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xs = np_array([[11.0, 22.0, 33.0, 44.0], [55.0, 66.0, 77.0, 88.0]])
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xs[0, 0] = 99.0
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output_ndarray_float_2(xs)
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xs[0] = 100.0
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output_ndarray_float_2(xs)
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xs[:, ::2] = 101.0
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output_ndarray_float_2(xs)
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xs[1:, 0] = 102.0
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output_ndarray_float_2(xs)
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xs[0] = np_array([-1.0, -2.0, -3.0, -4.0])
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output_ndarray_float_2(xs)
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xs[:] = np_array([-5.0, -6.0, -7.0, -8.0])
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output_ndarray_float_2(xs)
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# Test assignment with memory sharing
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ys1 = np_reshape(xs, (2, 4))
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ys2 = np_transpose(ys1)
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ys3 = ys2[::-1, 0]
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ys3[0] = -999.0
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output_ndarray_float_2(xs)
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output_ndarray_float_2(ys1)
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output_ndarray_float_2(ys2)
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output_ndarray_float_1(ys3)
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def test_ndarray_add():
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def test_ndarray_add():
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x = np_identity(2)
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x = np_identity(2)
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y = x + np_ones([2, 2])
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y = x + np_ones([2, 2])
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@ -540,11 +615,59 @@ def test_ndarray_ipow_broadcast_scalar():
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output_ndarray_float_2(x)
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output_ndarray_float_2(x)
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def test_ndarray_matmul():
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def test_ndarray_matmul():
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x = np_identity(2)
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# 2D @ 2D -> 2D
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y = x @ np_ones([2, 2])
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a1 = np_array([[2.0, 3.0], [5.0, 7.0]])
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b1 = np_array([[11.0, 13.0], [17.0, 23.0]])
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c1 = a1 @ b1
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output_int32(np_shape(c1)[0])
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output_int32(np_shape(c1)[1])
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output_ndarray_float_2(c1)
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output_ndarray_float_2(x)
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# 1D @ 1D -> Scalar
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output_ndarray_float_2(y)
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a2 = np_array([2.0, 3.0, 5.0])
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b2 = np_array([7.0, 11.0, 13.0])
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c2 = a2 @ b2
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output_float64(c2)
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# 2D @ 1D -> 1D
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a3 = np_array([[1.0, 2.0, 3.0], [7.0, 8.0, 9.0]])
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b3 = np_array([4.0, 5.0, 6.0])
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c3 = a3 @ b3
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output_int32(np_shape(c3)[0])
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output_ndarray_float_1(c3)
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# 1D @ 2D -> 1D
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a4 = np_array([1.0, 2.0, 3.0])
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b4 = np_array([[4.0, 5.0], [6.0, 7.0], [8.0, 9.0]])
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c4 = a4 @ b4
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output_int32(np_shape(c4)[0])
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output_ndarray_float_1(c4)
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# Broadcasting
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a5 = np_array([
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[[ 0.0, 1.0, 2.0, 3.0],
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[ 4.0, 5.0, 6.0, 7.0]],
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[[ 8.0, 9.0, 10.0, 11.0],
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[12.0, 13.0, 14.0, 15.0]],
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[[16.0, 17.0, 18.0, 19.0],
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[20.0, 21.0, 22.0, 23.0]]
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])
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b5 = np_array([
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[[[ 0.0, 1.0, 2.0],
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[ 3.0, 4.0, 5.0],
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[ 6.0, 7.0, 8.0],
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[ 9.0, 10.0, 11.0]]],
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[[[12.0, 13.0, 14.0],
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[15.0, 16.0, 17.0],
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[18.0, 19.0, 20.0],
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[21.0, 22.0, 23.0]]]
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])
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c5 = a5 @ b5
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output_int32(np_shape(c5)[0])
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output_int32(np_shape(c5)[1])
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output_int32(np_shape(c5)[2])
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output_int32(np_shape(c5)[3])
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output_ndarray_float_4(c5)
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def test_ndarray_imatmul():
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def test_ndarray_imatmul():
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x = np_identity(2)
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x = np_identity(2)
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@ -1439,27 +1562,6 @@ def test_ndarray_nextafter_broadcast_rhs_scalar():
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output_ndarray_float_2(nextafter_x_zeros)
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output_ndarray_float_2(nextafter_x_zeros)
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output_ndarray_float_2(nextafter_x_ones)
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output_ndarray_float_2(nextafter_x_ones)
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def test_ndarray_transpose():
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x: ndarray[float, 2] = np_array([[1., 2., 3.], [4., 5., 6.]])
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y = np_transpose(x)
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z = np_transpose(y)
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output_ndarray_float_2(x)
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output_ndarray_float_2(y)
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def test_ndarray_reshape():
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w: ndarray[float, 1] = np_array([1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])
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x = np_reshape(w, (1, 2, 1, -1))
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y = np_reshape(x, [2, -1])
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z = np_reshape(y, 10)
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x1: ndarray[int32, 1] = np_array([1, 2, 3, 4])
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x2: ndarray[int32, 2] = np_reshape(x1, (2, 2))
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output_ndarray_float_1(w)
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output_ndarray_float_2(y)
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output_ndarray_float_1(z)
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def test_ndarray_dot():
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def test_ndarray_dot():
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x1: ndarray[float, 1] = np_array([5.0, 1.0, 4.0, 2.0])
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x1: ndarray[float, 1] = np_array([5.0, 1.0, 4.0, 2.0])
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y1: ndarray[float, 1] = np_array([5.0, 1.0, 6.0, 6.0])
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y1: ndarray[float, 1] = np_array([5.0, 1.0, 6.0, 6.0])
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@ -1591,6 +1693,11 @@ def run() -> int32:
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test_ndarray_slices()
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test_ndarray_slices()
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test_ndarray_nd_idx()
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test_ndarray_nd_idx()
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test_ndarray_transpose()
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test_ndarray_reshape()
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test_ndarray_broadcast_to()
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test_ndarray_subscript_assignment()
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test_ndarray_add()
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test_ndarray_add()
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test_ndarray_add_broadcast()
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test_ndarray_add_broadcast()
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test_ndarray_add_broadcast_lhs_scalar()
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test_ndarray_add_broadcast_lhs_scalar()
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@ -1754,8 +1861,6 @@ def run() -> int32:
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test_ndarray_nextafter_broadcast()
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test_ndarray_nextafter_broadcast()
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test_ndarray_nextafter_broadcast_lhs_scalar()
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test_ndarray_nextafter_broadcast_lhs_scalar()
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test_ndarray_nextafter_broadcast_rhs_scalar()
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test_ndarray_nextafter_broadcast_rhs_scalar()
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test_ndarray_transpose()
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test_ndarray_reshape()
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test_ndarray_dot()
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test_ndarray_dot()
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test_ndarray_cholesky()
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test_ndarray_cholesky()
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