Added tests to verify Large Vector Support for initial set of ops

python/mxnet/test_utils.py

@@ -262,6 +262,17 @@ def assign_each2(input1, input2, function):
 
     return output
 
+# For testing Large Tensors having total size > 2^32 elements
+def create_2d_tensor(rows, columns, dtype=np.int64):
+    a = mx.nd.arange(0, rows, dtype=dtype).reshape(rows, 1)
+    b = mx.nd.broadcast_to(a, shape=(a.shape[0], columns))
+    return b
+
+# For testing Large Vectors having total size > 2^32 elements
+def create_vector(size, dtype=np.int64):
+    a = mx.nd.arange(0, size, dtype=dtype)
+    return a
+
 def rand_sparse_ndarray(shape, stype, density=None, dtype=None, distribution=None,
                         data_init=None, rsp_indices=None, modifier_func=None,
                         shuffle_csr_indices=False, ctx=None):

src/operator/softmax_output-inl.h

@@ -117,9 +117,9 @@ class SoftmaxOutputOp : public Operator {
     CHECK_EQ(out_data.size(), 1U) << "SoftmaxOutput Output: [output]";
     Stream<xpu> *s = ctx.get_stream<xpu>();
     if (param_.multi_output) {
-      int n = in_data[softmaxout_enum::kData].size(0);
-      int k = in_data[softmaxout_enum::kData].size(1);
-      Shape<3> s3 = Shape3(n, k, static_cast<int>(in_data[softmaxout_enum::kData].Size()/n/k));
+      index_t n = in_data[softmaxout_enum::kData].size(0);
+      index_t k = in_data[softmaxout_enum::kData].size(1);
+      Shape<3> s3 = Shape3(n, k, static_cast<index_t>(in_data[softmaxout_enum::kData].Size()/n/k));
       Tensor<xpu, 3, DType> data =
           in_data[softmaxout_enum::kData].get_with_shape<xpu, 3, DType>(s3, s);
       Tensor<xpu, 3, DType> out =
@@ -131,8 +131,8 @@ class SoftmaxOutputOp : public Operator {
         Tensor<xpu, 2, DType> out = out_data[softmaxout_enum::kOut].FlatTo2D<xpu, DType>(s);
         Softmax(out, data);
       } else {
-        int n = in_data[softmaxout_enum::kData].size(0);
-        int k = in_data[softmaxout_enum::kData].Size()/n;
+        index_t n = in_data[softmaxout_enum::kData].size(0);
+        index_t k = in_data[softmaxout_enum::kData].Size()/n;
         Shape<2> s2 = Shape2(n, k);
         Tensor<xpu, 2, DType> data =
             in_data[softmaxout_enum::kData].get_with_shape<xpu, 2, DType>(s2, s);
@@ -171,9 +171,9 @@ class SoftmaxOutputOp : public Operator {
         grad = (out - label) * scalar<DType>(param_.grad_scale);
       }
     } else if (param_.multi_output) {
-      int n = out_data[softmaxout_enum::kOut].size(0);
-      int k = out_data[softmaxout_enum::kOut].size(1);
-      Shape<3> s3 = Shape3(n, k, static_cast<int>(out_data[softmaxout_enum::kOut].Size()/n/k));
+      index_t n = out_data[softmaxout_enum::kOut].size(0);
+      index_t k = out_data[softmaxout_enum::kOut].size(1);
+      Shape<3> s3 = Shape3(n, k, static_cast<index_t>(out_data[softmaxout_enum::kOut].Size()/n/k));
       Shape<2> s2 = Shape2(s3[0], s3[2]);
       Tensor<xpu, 2, DType> label =
           in_data[softmaxout_enum::kLabel].get_with_shape<xpu, 2, DType>(s2, s);
@@ -224,7 +224,7 @@ class SoftmaxOutputOp : public Operator {
 //        Tensor<xpu, 2, DType> out = out_data[softmaxout_enum::kOut].FlatTo2D<xpu, DType>(s);
 //        Tensor<xpu, 2, DType> grad = in_grad[softmaxout_enum::kData].FlatTo2D<xpu, DType>(s);
       } else {
-        int n = out_data[softmaxout_enum::kOut].size(0);
+        index_t n = out_data[softmaxout_enum::kOut].size(0);
         data_shape = Shape2(n, out_data[softmaxout_enum::kOut].Size()/n);
       }
       Tensor<xpu, 1, DType> label = in_data[softmaxout_enum::kLabel].get_with_shape<xpu, 1, DType>(

src/operator/tensor/matrix_op-inl.h

@@ -732,8 +732,8 @@ inline void GetIndexRange(const mxnet::TShape& dshape,
 }
 
 inline void SetSliceOpOutputDimSize(const mxnet::TShape& dshape,
-                                    const index_t i, const int b,
-                                    const int e, const int s,
+                                    const index_t i, const index_t b,
+                                    const index_t e, const index_t s,
                                     mxnet::TShape* oshape) {
   if (!mxnet::dim_size_is_known(dshape, i)) {
     (*oshape)[i] = -1;
@@ -765,7 +765,7 @@ inline bool SliceOpShape(const nnvm::NodeAttrs& attrs,
     common::StaticArray<index_t, ndim> begin, end, step;
     GetIndexRange(dshape, param.begin, param.end, param.step, &begin, &end, &step);
     for (int i = 0; i < param.begin.ndim(); ++i) {
-      const int b = begin[i], e = end[i], s = step[i];
+      const index_t b = begin[i], e = end[i], s = step[i];
       SetSliceOpOutputDimSize(dshape, i, b, e, s, &oshape);
     }
   })

tests/nightly/test_large_array.py

@@ -19,7 +19,7 @@
 import numpy as np
 import mxnet as mx
 
-from mxnet.test_utils import rand_ndarray, assert_almost_equal, rand_coord_2d, default_context, check_symbolic_forward
+from mxnet.test_utils import rand_ndarray, assert_almost_equal, rand_coord_2d, default_context, check_symbolic_forward, create_2d_tensor
 from mxnet import gluon, nd
 from tests.python.unittest.common import with_seed
 
@@ -31,12 +31,6 @@
 LARGE_SIZE = LARGE_X * SMALL_Y
 
 
-def create_2d_tensor(rows, columns, dtype=np.int64):
-    a = nd.arange(0, rows, dtype=dtype).reshape(rows, 1)
-    b = nd.broadcast_to(a, shape=(a.shape[0], columns))
-    return nd.array(b, dtype=dtype)
-
-
 def test_gluon_embedding():
     m = gluon.nn.Embedding(SMALL_Y, MEDIUM_X)
     m.initialize()

tests/nightly/test_large_vector.py

@@ -18,7 +18,7 @@
 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, create_vector
 from mxnet import gluon, nd
 from tests.python.unittest.common import with_seed
 
@@ -30,9 +30,146 @@
 def test_slice():
     a = nd.ones(LARGE_X)
     res = nd.slice(a, begin=(LARGE_X - MEDIUM_X), end=LARGE_X)
+    assert a[0] == 1
     assert res.shape[0] == MEDIUM_X
 
 
+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] == 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
+
+
+@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 > low  and a < 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 res[-1].asnumpy() == 2
+    res = a + 1
+    assert res[-1].asnumpy() == 2
+    res = nd.sqrt(a + 8)
+    assert res[-1].asnumpy() == 3
+
+
+def test_reduce():
+    a = nd.ones(shape=(LARGE_X, 1))
+    assert nd.sum(a).asnumpy() == a.shape[0] * a.shape[1]
+
+
+def test_clip():
+    a = create_vector(LARGE_X)
+    res = nd.clip(a, a_min=100, a_max=1000)
+    assert np.sum(res[-1].asnumpy() == 1000) == 1
+
+
+def test_argmin():
+    a = create_vector(LARGE_X, dtype=np.float32)
+    assert a[0] == 0
+    idx = mx.nd.argmin(a, axis=0)
+    assert idx[0] == 0
+    assert idx.shape[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_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_argsort():
+    b = create_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_vector(size=LARGE_X)
+    s = nd.sort(b, axis=0, is_ascend=False)
+    assert np.sum(s[-1].asnumpy() == 0).all()
+    s = nd.sort(b, is_ascend=True)
+    assert np.sum(s[0].asnumpy() == 0).all()
+
+
+def test_topk():
+    b = create_vector(size=LARGE_X)
+    ind = nd.topk(b, k=10, axis=0, dtype=np.int64)
+    assert np.sum(ind.asnumpy() == (LARGE_X - 1)) == 1
+    ind, val = mx.nd.topk(b, k=3, axis=0, dtype=np.int64, ret_typ="both", is_ascend=False)
+    assert np.all(ind == val)
+    val = nd.topk(b, k=1, axis=0, dtype=np.int64, ret_typ="value")
+    assert val.sum() == (LARGE_X - 1)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()