Fix build issue with USE_CUDNN=0

Jenkinsfile

@@ -163,6 +163,13 @@ def python3_gpu_ut(docker_container_name) {
   }
 }
 
+// Python 3 NOCUDNN
+def python3_gpu_ut_nocudnn(docker_container_name) {
+  timeout(time: max_time, unit: 'MINUTES') {
+    docker_run(docker_container_name, 'unittest_ubuntu_python3_gpu_nocudnn', true)
+  }
+}
+
 try {
   stage('Sanity Check') {
     parallel 'Lint': {
@@ -292,6 +299,17 @@ try {
         }
       }
     },
+    'GPU: MKLDNN_CUDNNOFF': {
+       node('mxnetlinux-cpu') {
+         ws('workspace/build-mkldnn-gpu-nocudnn') {
+           timeout(time: max_time, unit: 'MINUTES') {
+             init_git()
+             docker_run('ubuntu_build_cuda', 'build_ubuntu_gpu_mkldnn_nocudnn', false)
+             pack_lib('mkldnn_gpu_nocudnn', mx_mkldnn_lib)
+           }
+         }
+       }
+    },
     'GPU: CUDA9.1+cuDNN7': {
       node('mxnetlinux-cpu') {
         ws('workspace/build-gpu') {
@@ -667,6 +685,20 @@ try {
         }
       }
     },
+    'Python3: MKLDNN-GPU-NOCUDNN': {
+      node('mxnetlinux-gpu') {
+        ws('workspace/ut-python3-mkldnn-gpu-nocudnn') {
+          try {
+            init_git()
+            unpack_lib('mkldnn_gpu_nocudnn', mx_mkldnn_lib)
+            python3_gpu_ut_nocudnn('ubuntu_gpu')
+            publish_test_coverage()
+          } finally {
+            collect_test_results_unix('nosetests_gpu.xml', 'nosetests_python3_mkldnn_gpu_nocudnn.xml')
+          }
+        }
+      }
+    },
     'Python3: CentOS 7 CPU': {
       node('mxnetlinux-cpu') {
         ws('workspace/build-centos7-cpu') {

ci/docker/runtime_functions.sh

@@ -452,6 +452,23 @@ build_ubuntu_gpu_mkldnn() {
     report_ccache_usage
 }
 
+build_ubuntu_gpu_mkldnn_nocudnn() {
+    set -ex
+
+    build_ccache_wrappers
+
+    make  \
+        DEV=1                         \
+        USE_BLAS=openblas             \
+        USE_MKLDNN=1                  \
+        USE_CUDA=1                    \
+        USE_CUDA_PATH=/usr/local/cuda \
+        USE_CUDNN=0                   \
+        -j$(nproc)
+
+    report_ccache_usage
+}
+
 build_ubuntu_gpu_cuda91_cudnn7() {
     set -ex
     # unfortunately this build has problems in 3rdparty dependencies with ccache and make
@@ -611,6 +628,14 @@ unittest_ubuntu_python3_gpu() {
     nosetests-3.4 $NOSE_COVERAGE_ARGUMENTS --with-xunit --xunit-file nosetests_gpu.xml --verbose tests/python/gpu
 }
 
+unittest_ubuntu_python3_gpu_nocudnn() {
+    set -ex
+    export PYTHONPATH=./python/
+    export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
+    export CUDNN_OFF_TEST_ONLY=true
+    nosetests-3.4 $NOSE_COVERAGE_ARGUMENTS --with-xunit --xunit-file nosetests_gpu.xml --verbose tests/python/gpu
+}
+
 # quantization gpu currently only runs on P3 instances
 # need to separte it from unittest_ubuntu_python2_gpu()
 unittest_ubuntu_python2_quantization_gpu() {

src/c_api/c_api.cc

@@ -1319,7 +1319,6 @@ int MXRtcCudaKernelCall(CudaKernelHandle handle, int dev_id, void** args,
   API_END();
 }
 
-
 int MXNDArrayGetSharedMemHandle(NDArrayHandle handle, int* shared_pid, int* shared_id) {
   API_BEGIN();
   NDArray* arr = reinterpret_cast<NDArray*>(handle);

src/operator/nn/convolution.cu

@@ -90,9 +90,9 @@ void ConvolutionCompute<gpu>(const nnvm::NodeAttrs& attrs,
   int dtype = inputs[conv::kData].type_flag_;
 
 #if CUDNN_MAJOR < 5
-  if (param_.layout.value() != kNCW &&
-      param_.layout.value() != kNCHW &&
-      param_.layout.value() != kNCDHW) {
+  if (param.layout.value() != kNCW &&
+      param.layout.value() != kNCHW &&
+      param.layout.value() != kNCDHW) {
     // Need CuDNN > 5.0 for layout support. use MXNet implementation
     MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
       ConvolutionOp<gpu, DType> op;
@@ -168,9 +168,9 @@ void ConvolutionGradCompute<gpu>(const nnvm::NodeAttrs& attrs,
   int dtype = out_grad.type_flag_;
 
 #if CUDNN_MAJOR < 5
-  if (param_.layout.value() != kNCW &&
-      param_.layout.value() != kNCHW &&
-      param_.layout.value() != kNCDHW) {
+  if (param.layout.value() != kNCW &&
+      param.layout.value() != kNCHW &&
+      param.layout.value() != kNCDHW) {
     // Need CuDNN > 5.0 for layout support. use MXNet implementation
     MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
       ConvolutionOp<gpu, DType> op;

tests/python/gpu/test_operator_gpu.py

@@ -32,7 +32,7 @@
 
 curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
 sys.path.insert(0, os.path.join(curr_path, '../unittest'))
-from common import setup_module, with_seed, teardown
+from common import setup_module, with_seed, teardown, assert_raises_cudnn_disabled
 from test_operator import *
 from test_optimizer import *
 from test_random import *
@@ -422,6 +422,7 @@ def test_3d_batchnorm(fix_gamma, use_global_stats):
 
 
 @with_seed(1234)
+@assert_raises_cudnn_disabled()
 def test_convolution_with_type():
     sym1 = mx.sym.Convolution(num_filter=3, kernel=(3,3), name='conv')
 
@@ -1316,6 +1317,7 @@ def check_rnn_consistency(cell1, cell2):
     assert_allclose(mod1.get_outputs()[0].asnumpy(), mod2.get_outputs()[0].asnumpy(), rtol=1e-2, atol=1e-4)
 
 @with_seed()
+@assert_raises_cudnn_disabled()
 def test_rnn():
     fused = mx.rnn.FusedRNNCell(100, num_layers=2, mode='rnn_relu', prefix='')
 
@@ -1327,6 +1329,7 @@ def test_rnn():
     check_rnn_consistency(stack, fused)
 
 @with_seed()
+@assert_raises_cudnn_disabled()
 def test_lstm_forget_bias():
     forget_bias = 2.0
     fused = mx.rnn.FusedRNNCell(10, forget_bias=forget_bias, num_layers=2, mode='lstm', prefix='')
@@ -1348,6 +1351,7 @@ def test_lstm_forget_bias():
     assert_allclose(args[bias_name].asnumpy(), expected_bias)
 
 @with_seed()
+@assert_raises_cudnn_disabled()
 def test_gru():
     fused = mx.rnn.FusedRNNCell(100, num_layers=2, mode='gru', prefix='')
 
@@ -1359,6 +1363,7 @@ def test_gru():
     check_rnn_consistency(stack, fused)
 
 @with_seed()
+@assert_raises_cudnn_disabled()
 def test_bidirectional():
     fused = mx.rnn.FusedRNNCell(100, num_layers=2, mode='gru', prefix='',
             bidirectional=True)
@@ -1377,6 +1382,7 @@ def test_bidirectional():
     check_rnn_consistency(stack, fused)
 
 @with_seed()
+@assert_raises_cudnn_disabled()
 def test_unfuse():
     for mode in ['rnn_tanh', 'rnn_relu', 'lstm', 'gru']:
         fused = mx.rnn.FusedRNNCell(
@@ -1548,6 +1554,7 @@ def test_deformable_convolution_options():
                                                name='deformable_conv')
 
 @with_seed()
+@assert_raises_cudnn_disabled()
 def test_residual_fused():
     cell = mx.rnn.ResidualCell(
             mx.rnn.FusedRNNCell(50, num_layers=3, mode='lstm',
@@ -1603,6 +1610,7 @@ def check_rnn_layer_w_rand_inputs(layer):
         assert_almost_equal(g.asnumpy(), c.asnumpy(), rtol=1e-2, atol=1e-6)
 
 @with_seed()
+@assert_raises_cudnn_disabled()
 def test_rnn_layer():
     check_rnn_layer(gluon.rnn.RNN(100, num_layers=3))
     check_rnn_layer(gluon.rnn.RNN(100, activation='tanh', num_layers=3))

tests/python/unittest/common.py

@@ -20,13 +20,14 @@
 import numpy as np
 import random
 import shutil
+from mxnet.base import MXNetError
 curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
 sys.path.append(os.path.join(curr_path, '../common/'))
 sys.path.insert(0, os.path.join(curr_path, '../../../python'))
 
 import models
 from contextlib import contextmanager
-from nose.tools import make_decorator
+from nose.tools import make_decorator, assert_raises
 import tempfile
 
 def assertRaises(expected_exception, func, *args, **kwargs):
@@ -94,6 +95,23 @@ def random_seed(seed=None):
         random.seed(next_seed)
 
 
+def assert_raises_cudnn_disabled(assertion_error=False):
+    def test_helper(orig_test):
+        @make_decorator(orig_test)
+        def test_new(*args, **kwargs):
+            cudnn_disabled = (os.getenv('CUDNN_OFF_TEST_ONLY') == "true")
+            if not cudnn_disabled or mx.context.current_context().device_type == 'cpu':
+                orig_test(*args, **kwargs)
+            else:
+                if assertion_error:
+                    errors = (MXNetError, RuntimeError, AssertionError)
+                else:
+                    errors = (MXNetError, RuntimeError)
+                assert_raises(errors, orig_test, *args, **kwargs)
+        return test_new
+    return test_helper
+
+
 def with_seed(seed=None):
     """
     A decorator for nosetests test functions that manages rng seeds.

tests/python/unittest/test_gluon.py

@@ -20,7 +20,7 @@
 from mxnet.gluon import nn
 from mxnet.test_utils import assert_almost_equal
 from mxnet.ndarray.ndarray import _STORAGE_TYPE_STR_TO_ID
-from common import setup_module, with_seed, assertRaises, teardown
+from common import setup_module, with_seed, assertRaises, teardown, assert_raises_cudnn_disabled
 import numpy as np
 from numpy.testing import assert_array_equal
 from nose.tools import raises, assert_raises
@@ -1259,6 +1259,7 @@ def record_name(block):
 
 
 @with_seed()
+@assert_raises_cudnn_disabled()
 def test_summary():
     net = gluon.model_zoo.vision.resnet50_v1()
     net.initialize()

tests/python/unittest/test_gluon_rnn.py

@@ -22,8 +22,10 @@
 from numpy.testing import assert_allclose
 import unittest
 from mxnet.test_utils import almost_equal, assert_almost_equal
+from common import assert_raises_cudnn_disabled
 
 
+@assert_raises_cudnn_disabled()
 def test_rnn():
     cell = gluon.rnn.RNNCell(100, prefix='rnn_')
     inputs = [mx.sym.Variable('rnn_t%d_data'%i) for i in range(3)]
@@ -104,6 +106,7 @@ def test_lstm():
     assert outs == [(10, 100), (10, 100), (10, 100)]
 
 
+@assert_raises_cudnn_disabled()
 def test_lstm_forget_bias():
     forget_bias = 2.0
     stack = gluon.rnn.SequentialRNNCell()
@@ -124,6 +127,8 @@ def test_lstm_forget_bias():
                                forget_bias * np.ones(100, ), np.zeros((2 * 100,))])
     assert_allclose(mod.get_params()[0][bias_argument].asnumpy(), expected_bias)
 
+
+@assert_raises_cudnn_disabled()
 def test_lstm_cpu_inference():
     # should behave the same as lstm cell
     EXPECTED_LSTM_OUTPUT = np.array([[[0.72045636, 0.72045636, 0.95215213, 0.95215213],
@@ -139,6 +144,7 @@ def test_lstm_cpu_inference():
                                       rtol=1e-3, atol=1e-5)
 
 
+@assert_raises_cudnn_disabled()
 def test_gru():
     cell = gluon.rnn.GRUCell(100, prefix='rnn_')
     inputs = [mx.sym.Variable('rnn_t%d_data'%i) for i in range(3)]
@@ -419,6 +425,7 @@ def check_rnn_layer_forward(layer, inputs, states=None, run_only=False):
         mx.test_utils.assert_almost_equal(np_dx, inputs.grad.asnumpy(), rtol=1e-3, atol=1e-5)
 
 
+@assert_raises_cudnn_disabled()
 def test_rnn_layers():
     check_rnn_layer_forward(gluon.rnn.RNN(10, 2), mx.nd.ones((8, 3, 20)))
     check_rnn_layer_forward(gluon.rnn.RNN(10, 2, bidirectional=True), mx.nd.ones((8, 3, 20)), mx.nd.ones((4, 3, 10)))
@@ -531,6 +538,8 @@ def test_cell_fill_shape():
     check_rnn_forward(cell, mx.nd.ones((2, 3, 7)))
     assert cell.i2h_weight.shape[1] == 7, cell.i2h_weight.shape[1]
 
+
+@assert_raises_cudnn_disabled()
 def test_layer_fill_shape():
     layer = gluon.rnn.LSTM(10)
     layer.hybridize()