[v1.8.x] Fix Nightly Large Tensor test

src/operator/nn/batch_norm.cc

@@ -374,7 +374,7 @@ static bool BatchNormShape(const nnvm::NodeAttrs& attrs,
       : param.axis);
   CHECK_LT(channelAxis, dshape.ndim()) << "Channel axis out of range: " << param.axis;
 
-  const int channelCount = dshape[channelAxis];
+  const index_t channelCount = dshape[channelAxis];
 
   in_shape->at(batchnorm::kGamma) = mxnet::TShape(Shape1(channelCount));
   in_shape->at(batchnorm::kBeta) = mxnet::TShape(Shape1(channelCount));

src/operator/nn/layer_norm.cc

@@ -51,7 +51,7 @@ static bool LayerNormShape(const nnvm::NodeAttrs& attrs,
   CHECK(axis >= 0 && axis < dshape.ndim())
     << "Channel axis out of range: axis=" << param.axis;
 
-  const int channelCount = dshape[axis];
+  const index_t channelCount = dshape[axis];
 
   SHAPE_ASSIGN_CHECK(*in_shape,
                      layernorm::kGamma,

tests/nightly/test_large_array.py

@@ -27,9 +27,8 @@
 
 from mxnet.test_utils import rand_ndarray, assert_almost_equal, rand_coord_2d, default_context, check_symbolic_forward, create_2d_tensor, get_identity_mat, get_identity_mat_batch
 from mxnet import gluon, nd
-from common import with_seed, with_post_test_cleanup, assertRaises
+from common import with_seed, assertRaises
 from mxnet.base import MXNetError
-from nose.tools import with_setup
 import unittest
 
 # dimension constants

tests/nightly/test_large_vector.py

@@ -27,9 +27,8 @@
 
 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, assertRaises
+from common import with_seed, assertRaises
 from mxnet.base import MXNetError
-from nose.tools import with_setup
 import unittest
 
 # dimension constants

tests/python/unittest/common.py

@@ -314,26 +314,6 @@ def teardown():
     """
     mx.nd.waitall()
 
-
-def with_post_test_cleanup():
-    """
-    Helper function that cleans up memory by releasing it from memory pool
-    Required especially by large tensor tests that have memory footprints in GBs.
-    """
-    def test_helper(orig_test):
-        @make_decorator(orig_test)
-        def test_new(*args, **kwargs):
-            logger = default_logger()
-            try:
-                orig_test(*args, **kwargs)
-            except:
-                logger.info(test_msg)
-                raise
-            finally:
-                mx.nd.waitall()
-                mx.cpu().empty_cache()
-
-
 def with_environment(*args_):
     """
     Helper function that takes a dictionary of environment variables and their
@@ -349,7 +329,6 @@ def test_new(*args, **kwargs):
         return test_new
     return test_helper
 
-
 def run_in_spawned_process(func, env, *args):
     """
     Helper function to run a test in its own process.