From 44b217b62da7aacbf05d447f0c35e1475c31e36f Mon Sep 17 00:00:00 2001 From: Rohit Kumar Srivastava Date: Wed, 14 Aug 2019 22:39:11 +0000 Subject: [PATCH] Adding tests to verify support for Large Tensors in additional Ops along with new C_Apis supporting 64bit indexing --- tests/nightly/test_large_vector.py | 295 +++++++++++++++++++++++++++++ 1 file changed, 295 insertions(+) diff --git a/tests/nightly/test_large_vector.py b/tests/nightly/test_large_vector.py index 3a66500957e0..24bd46886f8a 100644 --- a/tests/nightly/test_large_vector.py +++ b/tests/nightly/test_large_vector.py @@ -25,6 +25,7 @@ # dimension constants LARGE_X = 5000000000 MEDIUM_X = 1000000000 +SMALL_Y = 1 def test_slice(): @@ -33,6 +34,300 @@ def test_slice(): assert res.shape[0] == MEDIUM_X +def test_gluon_embedding(): + m = gluon.nn.Embedding(SMALL_Y, MEDIUM_X) + m.initialize() + a = nd.zeros((MEDIUM_X, SMALL_Y)) + b = m(a) + assert b.shape == (MEDIUM_X, SMALL_Y, MEDIUM_X) + assert b.asnumpy().size == LARGE_SIZE + + +def test_ndarray_zeros(): + a = nd.zeros(shape=LARGE_X) + assert a[-1] == 0 + assert a.shape == (LARGE_X,) + assert a.size == LARGE_X + + +def test_ndarray_ones(): + a = nd.ones(shape=(LARGE_X)) + assert a[-1][0] == 1 + assert nd.sum(a).asnumpy() == LARGE_X + + +@with_seed() +def test_ndarray_random_uniform(): + a = nd.random.uniform(shape=LARGE_X) + assert a[-1][0] != 0 + + +@with_seed() +def test_ndarray_random_randint(): + a = nd.random.randint(100, 10000, shape=LARGE_X) + assert a.shape == (LARGE_X,) + # check if randint can generate value greater than 2**32 (large) + low_large_value = 2**32 + high_large_value = 2**34 + a = nd.random.randint(low_large_value, high_large_value, dtype=np.int64) + low = mx.nd.array([low_large_value], dtype='int64') + high = mx.nd.array([high_large_value], dtype='int64') + assert a.__gt__(low) and a.__lt__(high) + + +def test_ndarray_empty(): + a = nd.empty(LARGE_X) + assert a.shape == (LARGE_X,) + + +def test_elementwise(): + a = nd.ones(shape=LARGE_X) + b = nd.ones(shape=LARGE_X) + res = a + b + assert np.sum(res[-1].asnumpy() == 2) == a.shape[1] + res = a + 1 + assert np.sum(res[-1].asnumpy() == 2) == a.shape[1] + res = nd.sqrt(a + 3) + assert np.sum(res[-1].asnumpy() == 2) == a.shape[1] + + +def test_reduce(): + a = nd.ones(shape=(LARGE_X, SMALL_Y)) + assert nd.sum(a).asnumpy() == a.shape[0] * a.shape[1] + + +def test_dot(): + a = nd.ones(shape=(LARGE_X, 5)) + b = nd.ones(shape=(5, 5)) + res = nd.dot(a, b) + assert res[0][0] == 5 + + +def test_FullyConnected(): + a = nd.ones(shape=(LARGE_X, SMALL_Y)) + b = nd.ones(shape=(SMALL_Y, SMALL_Y)) + res = nd.FullyConnected(a, b, num_hidden=b.shape[1], no_bias=True) + assert np.sum(res[-1].asnumpy() == SMALL_Y) == b.shape[1] + + +def test_broadcast(): + a = nd.ones(shape=(LARGE_X, SMALL_Y*2)) + b = nd.arange(0, LARGE_X).reshape(LARGE_X, 1) + res = nd.broadcast_to(b, shape=(b.shape[0], SMALL_Y*2)) + assert np.sum(res[-1].asnumpy() == LARGE_X) == res.shape[1] + res = mx.nd.broadcast_like(b, a) + assert np.sum(res[-1].asnumpy() == LARGE_X) == res.shape[1] + + +def test_clip(): + a = nd.arange(0, LARGE_X) + res = nd.clip(a, a_min=100, a_max=1000) + assert np.sum(res[-1].asnumpy() == 1000) == 101 + + +def test_argmin(): + a = nd.arange(0, LARGE_X) + idx = mx.nd.argmin(a, axis=0) + assert idx.shape[0] == SMALL_Y + + +def test_tile(): + a = nd.arange(0, LARGE_X) + b = nd.tile(a, reps=(1,2)) + assert b[0][LARGE_X] == b[0][0] + assert b[0][LARGE_X-1] == b[0][-1] + + +def test_take(): + a = nd.ones(shape=LARGE_X) + idx = nd.arange(LARGE_X - 1000, LARGE_X) + res = nd.take(a, idx) + assert np.sum(res.asnumpy() == 1) == res.shape[0] + + +def test_slice(): + a = nd.ones(shape=(2, LARGE_X)) + res = nd.slice(a, begin=(1, LARGE_X-1000000000), end=(2, LARGE_X)) + assert np.sum(res[-1].asnumpy() == 1) == res.shape[1] + + +def test_slice_assign(): + a = nd.ones(shape=LARGE_X) + a[LARGE_X-1:LARGE_X] = 1000 + assert np.sum(a[-1].asnumpy() == 1000) == 1 + + +def test_expand_dims(): + a = nd.ones(shape=LARGE_X) + res = nd.expand_dims(a, axis=0) + assert res[0][0] == 1 + assert res.shape == (1, a.shape[0]) + + +def test_squeeze(): + a = nd.ones(shape=LARGE_X) + data = nd.expand_dims(a, axis=0) + res = nd.squeeze(data) + assert a[0] == res[0] + assert res.shape == a.shape + + +def test_broadcast_div(): + a = nd.ones(shape=LARGE_X) + b = nd.ones(shape=LARGE_X) * 2 + res = a / b + assert np.sum(res.asnumpy() == 0.5) == a.shape[0] + + +def test_Dense(ctx=mx.cpu(0)): + data = mx.nd.ones(shape=LARGE_X) + linear = gluon.nn.Dense(2) + linear.initialize(ctx=ctx) + res = linear(data) + res.wait_to_read() + assert res.shape == (LARGE_X, 2) + + +def test_pick(): + a = mx.nd.ones(shape=(LARGE_X, 2)) + b = mx.nd.ones(shape=LARGE_X) + res = mx.nd.pick(a, b) + assert res.shape == b.shape + + +def test_depthtospace(): + def numpy_depth_to_space(x, blocksize): + b, c, h, w = x.shape[0], x.shape[1], x.shape[2], x.shape[3] + tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize**2), h, w]) + tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2]) + y = np.reshape(tmp, [b, c // (blocksize**2), h * blocksize, w * blocksize]) + return y + + shape_inp = (LARGE_X, 4, 1, 1) + data = rand_ndarray(shape_inp, 'default') + data_np = data.asnumpy() + expected = numpy_depth_to_space(data_np, 2) + output = mx.nd.depth_to_space(data, 2) + assert_almost_equal(output.asnumpy(), expected, atol=1e-3, rtol=1e-3) + + +def test_spacetodepth(): + def numpy_space_to_depth(x, blocksize): + b, c, h, w = x.shape[0], x.shape[1], x.shape[2], x.shape[3] + tmp = np.reshape(x, [b, c, h // blocksize, blocksize, w // blocksize, blocksize]) + tmp = np.transpose(tmp, [0, 3, 5, 1, 2, 4]) + y = np.reshape(tmp, [b, c * (blocksize**2), h // blocksize, w // blocksize]) + return y + + shape_inp = (LARGE_X, 1, 2, 2) + data = rand_ndarray(shape_inp, 'default') + data_np = data.asnumpy() + expected = numpy_space_to_depth(data_np, 2) + output = mx.nd.space_to_depth(data, 2) + assert_almost_equal(output.asnumpy(), expected, atol=1e-3, rtol=1e-3) + +@with_seed() +def test_diag(): + a_np = np.random.random((LARGE_X, 2)).astype(np.float32) + a = mx.nd.array(a_np) + + # k == 0 + r = mx.nd.diag(a) + assert_almost_equal(r.asnumpy(), np.diag(a_np)) + + # k == 1 + k = 1 + r = mx.nd.diag(a, k=k) + assert_almost_equal(r.asnumpy(), np.diag(a_np, k=k)) + + # k == -1 + k = -1 + r = mx.nd.diag(a, k=k) + assert_almost_equal(r.asnumpy(), np.diag(a_np, k=k)) + + +@with_seed() +def test_ravel_multi_index(): + x1, y1 = rand_coord_2d((LARGE_X - 100), LARGE_X, SMALL_Y, 4) + x2, y2 = rand_coord_2d((LARGE_X - 200), LARGE_X, SMALL_Y, 3) + x3, y3 = rand_coord_2d((LARGE_X - 300), LARGE_X, SMALL_Y, 2) + indices_2d = [[x1, x2, x3], [y1, y2, y3]] + idx = mx.nd.ravel_multi_index(mx.nd.array(indices_2d, dtype=np.int64), shape=(LARGE_X, 5)) + idx_numpy = np.ravel_multi_index(indices_2d, (LARGE_X, 5)) + assert np.sum(1 for i in range(idx.size) if idx[i] == idx_numpy[i]) == 3 + + +@with_seed() +def test_unravel_index(): + x1, y1 = rand_coord_2d((LARGE_X - 100), LARGE_X, SMALL_Y, 4) + x2, y2 = rand_coord_2d((LARGE_X - 200), LARGE_X, SMALL_Y, 3) + x3, y3 = rand_coord_2d((LARGE_X - 300), LARGE_X, SMALL_Y, 2) + original_2d_indices = [[x1, x2, x3], [y1, y2, y3]] + idx_numpy = np.ravel_multi_index(original_2d_indices, (LARGE_X, 5)) + indices_2d = mx.nd.unravel_index(mx.nd.array(idx_numpy, dtype=np.int64), shape=(LARGE_X, 5)) + assert (indices_2d.asnumpy() == np.array(original_2d_indices)).all() + + +def create_large_vector(size, dtype=np.int64): + a = nd.arange(0, size, dtype=dtype) + # Implicitly calling nd.waitall() + assert a[0] == 0 + return a + + +def test_transpose(): + b = nd.arange(0, LARGE_X, dtype=np.int64).reshape(1, LARGE_X) + t = b.T + assert t.shape == (LARGE_X, 1) + assert t[-1, 0].asnumpy() == (LARGE_X - 1) + + +def test_swapaxes(): + b = nd.arange(0, LARGE_X, dtype=np.int64).reshape(LARGE_X, 1) + t = nd.swapaxes(b, dim1=0, dim2=1) + assert t.shape == (1, LARGE_X) + assert t[0, -1].asnumpy() == (LARGE_X - 1) + + +def test_flip(): + b = nd.arange(0, LARGE_X, dtype=np.int64).reshape(1, LARGE_X) + t = nd.flip(b, axis=0) + assert t.shape == (LARGE_X, 1) + assert t[-1, :].asnumpy() == 0 + + +def test_softmax(): + input_data = mx.nd.ones(2, LARGE_X) + true_output = np.full(LARGE_X, 0.5) + output = nd.softmax(input_data, axis=0) + assert_almost_equal(output.asnumpy(), true_output, rtol=1e-5, atol=1e-5) + + +def test_argsort(): + b = create_large_vector(size=LARGE_X) + s = nd.argsort(b, axis=0, is_ascend=False, dtype=np.int64) + mx.nd.waitall() + assert (s[0].asnumpy() == (LARGE_X - 1)).all() + + +def test_sort(): + b = create_large_vector(size=LARGE_X) + s = nd.sort(b, axis=0, is_ascend=False) + assert np.sum(s[-1][SMALL_Y//2:SMALL_Y].asnumpy() == 0).all() + s = nd.sort(b, is_ascend=True) + assert np.sum(s[0].asnumpy() == 0).all() + + +def test_topk(): + b = create_large_vector(size=LARGE_X) + k = nd.topk(b, k=10, axis=0, dtype=np.int64) + assert np.sum(k.asnumpy() == (LARGE_X - 1)) == SMALL_Y + ind, val = mx.nd.topk(b, k=3, axis=0, dtype=np.int64, ret_typ="both", is_ascend=False) + assert np.all(ind == val) + l = nd.topk(b, k=1, axis=0, dtype=np.int64, ret_typ="value") + assert l.sum() == (LARGE_X - 1) + + if __name__ == '__main__': import nose nose.runmodule()