Add Large Tensor Support for Sequence, NN Ops (apache#15807)

* sequence_last, sequence_reverse, sequence_mask

* working softmax_cross_entropy

* fix linting, add index_copy

* add softmax output

* add leaky relu

* add pooling

* add layernorm

* add dropout, activation, batchnorm and update layernorm

* address comments to remove some comments

* handling imports

Author: ChaiBapchya

tests/nightly/test_large_array.py

@@ -15,9 +15,11 @@
 # specific language governing permissions and limitations
 # under the License.
 
+import math
 import numpy as np
 import mxnet as mx
-from mxnet.test_utils import rand_ndarray, assert_almost_equal, rand_coord_2d
+
+from mxnet.test_utils import rand_ndarray, assert_almost_equal, rand_coord_2d, default_context
 from mxnet import gluon, nd
 from tests.python.unittest.common import with_seed
 
@@ -299,9 +301,11 @@ def test_pick():
 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.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])
+        y = np.reshape(tmp, [b, c // (blocksize**2), h * blocksize,
+                       w * blocksize])
         return y
 
     shape_inp = (LARGE_X, 8, 4, 2)
@@ -315,9 +319,11 @@ def numpy_depth_to_space(x, blocksize):
 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.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])
+        y = np.reshape(tmp, [b, c * (blocksize**2), h // blocksize,
+                       w // blocksize])
         return y
 
     shape_inp = (LARGE_X, 2, 8, 4)
@@ -327,6 +333,7 @@ def numpy_space_to_depth(x, blocksize):
     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, SMALL_Y)).astype(np.float32)
@@ -358,7 +365,8 @@ def test_ravel_multi_index():
     x2, y2 = rand_coord_2d((LARGE_X - 200), LARGE_X, 9, SMALL_Y)
     x3, y3 = rand_coord_2d((LARGE_X - 300), LARGE_X, 8, SMALL_Y)
     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, SMALL_Y))
+    idx = mx.nd.ravel_multi_index(mx.nd.array(indices_2d, dtype=np.int64),
+                                  shape=(LARGE_X, SMALL_Y))
     idx_numpy = np.ravel_multi_index(indices_2d, (LARGE_X, SMALL_Y))
     assert np.sum(1 for i in range(idx.size) if idx[i] == idx_numpy[i]) == 3
 
@@ -370,7 +378,8 @@ def test_unravel_index():
     x3, y3 = rand_coord_2d((LARGE_X - 300), LARGE_X, 8, SMALL_Y)
     original_2d_indices = [[x1, x2, x3], [y1, y2, y3]]
     idx_numpy = np.ravel_multi_index(original_2d_indices, (LARGE_X, SMALL_Y))
-    indices_2d = mx.nd.unravel_index(mx.nd.array(idx_numpy, dtype=np.int64), shape=(LARGE_X, SMALL_Y))
+    indices_2d = mx.nd.unravel_index(mx.nd.array(idx_numpy, dtype=np.int64),
+                                     shape=(LARGE_X, SMALL_Y))
     assert (indices_2d.asnumpy() == np.array(original_2d_indices)).all()
 
 
@@ -427,13 +436,288 @@ def test_topk():
     b = create_2d_tensor(rows=LARGE_X, columns=SMALL_Y)
     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)
+    ind, val = mx.nd.topk(b, k=3, axis=0, dtype=np.int64, ret_typ="both",
+                          is_ascend=False)
     assert np.all(ind == val)
     b = create_2d_tensor(rows=SMALL_Y, columns=LARGE_X)
     l = nd.topk(b, k=1, axis=-1, dtype=np.int64, ret_typ="value")
     assert l.sum() == np.sum(np.arange(0, SMALL_Y))
 
 
+def test_sequence_mask():
+    # Sequence Mask input [max_sequence_length, batch_size, other_feature_dims]
+    # test with input batch_size = 2
+    a = nd.arange(0, LARGE_X * SMALL_Y * 2).reshape(LARGE_X, 2, SMALL_Y)
+
+    # test as identity operator
+    b = nd.SequenceMask(a)
+    assert b[-1][0][1] == a[-1][0][1]
+    assert b.shape == a.shape
+
+    # test with default mask
+    b = nd.SequenceMask(a, sequence_length=nd.array([1, 1]),
+                        use_sequence_length=True)
+    assert b[0][1][-1] == a[0][1][-1]  # first sequence of each batch kept
+    assert b[-1][-1][-1] != a[-1][-1][-1]  # rest sequences masked
+    assert b[-1][-1][-1] == 0
+
+    # test with mask value
+    b = nd.SequenceMask(a, sequence_length=nd.array([1, 1]),
+                        use_sequence_length=True, value=-1)
+    assert b[-1][-1][-1] == -1
+
+
+def test_sequence_reverse():
+    a = nd.arange(0, LARGE_X * SMALL_Y * 2).reshape(LARGE_X, 2, SMALL_Y)
+    # test as reverse operator
+    b = nd.SequenceReverse(a)
+    assert b[-1][0][0] == a[0][0][0]
+    assert b.shape == a.shape
+
+    # test with sequence length
+    b = nd.SequenceReverse(a, sequence_length=[2, 3])
+    assert b[1][0][0] == a[0][0][0]  # check if reversed
+    assert b[-1][0][0] == a[-1][0][0]  # check if intact
+    assert b.shape == a.shape
+
+
+def test_sequence_last():
+    a = nd.arange(0, LARGE_X * SMALL_Y * 2).reshape(LARGE_X, 2, SMALL_Y)
+
+    # test if returns last sequence
+    b = nd.SequenceLast(a)
+    assert_almost_equal(b, a[-1])  # only checks for (2,SMALL_Y) tensor
+    assert b.shape == (2, SMALL_Y)
+
+    # test with sequence length
+    # parameter sequence_length - NDArray with shape (batch_size)
+    # (2,3) indicates 2nd sequence from batch 1 and 3rd sequence from batch 2
+    b = nd.SequenceLast(a, sequence_length=mx.nd.array([2, 3]),
+                        use_sequence_length=True)
+    # check if it takes 2nd sequence from the first batch
+    assert b[0][-1] == a[1][0][-1]
+
+
+def test_softmax_cross_entropy():
+    # dtype of input data, mxnet cross entropy set explicitly to float64
+    # numpy implicitly takes care of double precision
+    batch_size = SMALL_Y
+    num_labels = LARGE_X
+    input_data = mx.nd.ones((batch_size, num_labels), dtype="float64")
+    input_label = mx.nd.zeros((batch_size,), dtype="float64")
+
+    true_softmax = np.full((batch_size, num_labels), (1 / num_labels))
+    # use 1/batch_size when softmax axis=0
+    # here 1/num_labels since softmax_cross_entropy uses default axis
+    # by default axis=1
+    np_one_hot_label = np.zeros((batch_size, num_labels))
+    np_one_hot_label[:, 0] = 1
+
+    true_softmax_cross_entropy = np.sum(-np.log(true_softmax) *
+                                        np_one_hot_label)
+    mx_softmax_cross_entropy = mx.nd.softmax_cross_entropy(input_data,
+                                                           input_label,
+                                                           dtype="float64")
+    assert_almost_equal(mx_softmax_cross_entropy.asnumpy(),
+                        true_softmax_cross_entropy, rtol=1e-3, atol=1e-5)
+
+
+def test_index_copy():
+    x = mx.nd.zeros((LARGE_X, SMALL_Y))
+    t = mx.nd.arange(1, SMALL_Y + 1).reshape((1, SMALL_Y))
+    index = mx.nd.array([LARGE_X - 1])
+
+    x = mx.nd.contrib.index_copy(x, index, t)
+    assert x[-1][-1] == t[0][-1]
+
+
+def testSoftmaxOutput():
+    x = mx.sym.Variable('x')
+    label = mx.sym.Variable('label')
+    x_nd = mx.nd.ones((LARGE_X, SMALL_Y))
+    grad_x = mx.nd.zeros((LARGE_X, SMALL_Y))
+    label_nd = mx.nd.ones((LARGE_X))
+
+    sym = mx.sym.SoftmaxOutput(data=x, label=label, ignore_label=0,
+                               use_ignore=False)
+    ex = sym.bind(ctx=default_context(), args={'x': x_nd, 'label': label_nd},
+                  args_grad={'x': grad_x})
+
+    ex.forward(is_train=True)
+    softmax_out = ex.outputs[0][0].asnumpy()
+    expected_softmax_out = (1/SMALL_Y)*mx.nd.ones((SMALL_Y)).asnumpy()
+    assert np.isclose(softmax_out, expected_softmax_out).all()
+
+    ex.backward(is_train=True)
+    grad_out = ex.grad_arrays[0][0].asnumpy()
+    k = int(label_nd[0].asscalar())
+    expected_grad_out = np.zeros((SMALL_Y,))
+    expected_grad_out[k] = -1
+    assert np.isclose(grad_out - softmax_out, expected_grad_out).all()
+
+
+# TODO: correctness of prelu (currently flaky)
+def test_leaky_relu():
+    a = -1*mx.nd.ones((LARGE_X, SMALL_Y))
+
+    def test_leaky():
+        res = mx.nd.LeakyReLU(a, act_type="leaky", slope=0.3)
+        assert res[-1][-1].asnumpy() == 0.3*a[-1][-1].asnumpy()
+
+    def test_elu():
+        res = mx.nd.LeakyReLU(a, act_type="elu", slope=0.3)
+        assert res[-1][-1].asnumpy() == 0.3*(np.exp(a[-1][-1].asnumpy())-1)
+
+    def test_selu():
+        lam = 1.0507009873554804934193349852946
+        alpha = 1.6732632423543772848170429916717
+        res = mx.nd.LeakyReLU(a, act_type="selu")
+        assert res[-1][-1].asnumpy() == (lam * alpha * (np.exp(a[-1][-1].asnumpy())-1))
+
+    def test_rrelu():
+        lower = 0.125
+        upper = 0.333999991
+        res = mx.nd.LeakyReLU(a, act_type="rrelu")
+        assert res[-1][-1].asnumpy() == (lower + upper) / 2 * a[-1][-1].asnumpy()
+
+    test_leaky()
+    test_elu()
+    test_selu()
+    test_rrelu()
+
+
+def test_pooling():
+    a = mx.nd.ones((MEDIUM_X, MEDIUM_X, SMALL_Y, SMALL_Y))
+
+    def test_avg_pooling():
+        res = mx.nd.Pooling(a, kernel=(5, 5), pool_type='avg')
+        assert res[-1][-1][-1][-1] == 1.0000001
+        assert res.shape == SMALL_Y - 5 + 1
+
+    def test_max_pooling():
+        res = mx.nd.Pooling(a, kernel=(5, 5), pool_type='max')
+        assert res[-1][-1][-1][-1] == 1.
+        assert res.shape == SMALL_Y - 5 + 1
+
+    def test_sum_pooling():
+        res = mx.nd.Pooling(a, kernel=(5, 5), pool_type='sum')
+        assert res[-1][-1][-1][-1] == 25
+        assert res.shape == SMALL_Y - 5 + 1
+
+    def test_lp_pooling():
+        res = mx.nd.Pooling(a, kernel=(5, 5), pool_type='lp', p_value=2)
+        assert res[-1][-1][-1][-1] == 5.
+        assert res.shape == SMALL_Y - 5 + 1
+
+        res = mx.nd.Pooling(a, kernel=(5, 5), pool_type='lp', p_value=1)
+        assert res[-1][-1][-1][-1] == 25.
+        assert res.shape == SMALL_Y - 5 + 1
+
+    test_avg_pooling()
+    test_max_pooling()
+    test_sum_pooling()
+    test_lp_pooling()
+
+
+def test_layer_norm():
+    dtype = np.float32
+    forward_check_eps = 1E-3
+    axis = 1
+    eps = 1E-5
+    in_shape = (LARGE_X, SMALL_Y)
+    ctx = mx.cpu()
+
+    def npy_layer_norm(data, gamma, beta, axis=1, eps=1E-5):
+        if axis < 0:
+            axis += data.ndim
+        broadcast_shape = [1 for _ in range(data.ndim)]
+        broadcast_shape[axis] = data.shape[axis]
+        mean = data.mean(axis=axis, keepdims=True).astype(dtype)
+        var = data.var(axis=axis, keepdims=True).astype(dtype)
+        std = np.sqrt(var + dtype(eps)).astype(dtype)
+        out = np.reshape(gamma, broadcast_shape) * (data - mean) / std + \
+              np.reshape(beta, broadcast_shape)
+        return out
+    data = np.random.normal(0, 1, in_shape).astype(dtype)
+    gamma = np.random.normal(0, 1, (in_shape[axis],)).astype(dtype)
+    beta = np.random.normal(0, 1, (in_shape[axis],)).astype(dtype)
+    data_s = mx.symbol.Variable('data')
+    gamma_s = mx.symbol.Variable('gamma')
+    beta_s = mx.symbol.Variable('beta')
+    out_s = mx.symbol.LayerNorm(data=data_s, gamma=gamma_s, beta=beta_s,
+                                axis=axis, eps=eps)
+    exe = out_s.simple_bind(ctx, data=in_shape)
+    exe.arg_dict['data'][:] = data
+    exe.arg_dict['gamma'][:] = gamma
+    exe.arg_dict['beta'][:] = beta
+    out_nd = exe.forward()[0]
+    out = npy_layer_norm(data, gamma, beta, axis, eps)
+    assert_almost_equal(out, out_nd.asnumpy(), forward_check_eps,
+                        forward_check_eps)
+
+# TODO: correctness of dropout
+# currently only test for dropout to work
+# since testing for correctness involves flakiness issue #14288
+def test_dropout():
+    shape = (10, 10)
+    x = mx.sym.var('data')
+    y = mx.sym.Dropout(x, p=1, cudnn_off=True)
+    exe = y.simple_bind(ctx=default_context(), data=shape)
+    exe.arg_arrays[0][:] = 1
+    out = exe.forward(is_train=True)
+    out[0].wait_to_read()
+
+
+def test_activation():
+    a = mx.nd.ones((LARGE_X, SMALL_Y))
+    test_x = -2
+    a[-1, -1] = test_x
+
+    # Hyperbolic tangent (tanh)
+    # y = (exp(x)-exp(-x))/(exp(x)+exp(-x))
+    a = mx.nd.Activation(a, act_type="tanh")
+    tanh_x = (np.exp(-2)-np.exp(2))/(np.exp(-2)+np.exp(2))
+    assert a[-1][-1] == tanh_x
+
+    # Recitified Linear Unit (relu)
+    # y = max(x,0)
+    a = mx.nd.Activation(a, act_type="relu")
+    assert a[-1][-1] == 0
+
+    # Sigmoid
+    # y = x/(1+abs(x))
+    a = mx.nd.Activation(a, act_type="sigmoid")
+    sigmoid_x = 1/(1+math.exp(-test_x))
+    assert a[-1][-1] == sigmoid_x
+
+    # Soft Sign
+    # y = 1/(1+exp(-x))
+    a = mx.nd.Activation(a, act_type="softsign")
+    softsign_x = test_x/(1+abs(test_x))
+    assert a[-1][-1] == softsign_x
+
+
+# TODO: correctness of batchnorm
+# in future, we could test if mean, var of output
+# matches target output's mean, var
+def test_batchnorm():
+    shape = (LARGE_X, SMALL_Y)
+    axis = 1  # default
+    expand_shape = [1] * len(shape)
+    expand_shape[axis] = shape[axis]
+
+    nch = shape[axis]
+    data = mx.nd.ones(shape=shape)
+    bn_gamma = mx.nd.random.uniform(shape=(nch,))
+    bn_beta = mx.nd.random.uniform(shape=(nch,))
+    bn_running_mean = mx.nd.zeros(nch)
+    bn_running_var = mx.nd.ones(nch)
+
+    output = mx.nd.BatchNorm(data, bn_gamma, bn_beta,
+                             bn_running_mean, bn_running_var)
+    output.wait_to_read()
+
+
 def test_add():
     a = nd.ones(shape=(LARGE_X, SMALL_Y))
     b = nd.ones(shape=(LARGE_X, SMALL_Y))

tests/nightly/test_large_vector.py

@@ -17,6 +17,7 @@
 
 import numpy as np
 import mxnet as mx
+
 from mxnet.test_utils import rand_ndarray, assert_almost_equal, rand_coord_2d
 from mxnet import gluon, nd
 from tests.python.unittest.common import with_seed