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Fix build issue with USE_CUDNN=0 #11470

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Jul 12, 2018
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Fix build issue with USE_CUDNN=0 #11470

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6961a20
Fix build issue with CUDNN=0
anirudh2290 Jun 28, 2018
7bdf6c7
Fix nocudnn func name
anirudh2290 Jun 28, 2018
06815b4
Remove python2 tests
anirudh2290 Jun 28, 2018
def0842
Remove CPP package test
anirudh2290 Jun 28, 2018
e7e6084
Check assert raises when cudnn disabled for op tests on gpu
anirudh2290 Jul 2, 2018
a488e43
Merge with master
anirudh2290 Jul 2, 2018
0631682
Add line
anirudh2290 Jul 2, 2018
2d11ade
Remove whitespace
anirudh2290 Jul 2, 2018
f338866
add decorator for other ops
anirudh2290 Jul 2, 2018
2c7453a
Fix tests
anirudh2290 Jul 5, 2018
a5c2c79
Add and remove assert
anirudh2290 Jul 5, 2018
71d7d72
Fix op and common
anirudh2290 Jul 5, 2018
65d01e0
Fix merge issue
anirudh2290 Jul 5, 2018
4ce776c
Remove C API
anirudh2290 Jul 10, 2018
556dee1
Fix
anirudh2290 Jul 10, 2018
67d00a3
Fix lint
anirudh2290 Jul 10, 2018
266ba86
Add init git
anirudh2290 Jul 10, 2018
e837777
Rename CUDNN_DISABLED env variable
anirudh2290 Jul 10, 2018
2f590ee
Add a runtime function for nocudnn
anirudh2290 Jul 10, 2018
a2fc5f5
Resolve conflicts
anirudh2290 Jul 11, 2018
749981d
Remove MXCudnnIsenabled
anirudh2290 Jul 11, 2018
9573913
Add comment for disabled test
anirudh2290 Jul 11, 2018
e8d87dd
Add full link in comment
anirudh2290 Jul 11, 2018
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25 changes: 25 additions & 0 deletions Jenkinsfile
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Original file line number Diff line number Diff line change
Expand Up @@ -281,6 +281,17 @@ 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') {
Expand Down Expand Up @@ -645,6 +656,20 @@ try {
}
}
},
'Python3: MKLDNN-GPU-NOCUDNN': {
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Is it needed to test python2 and python3 or is one enough?

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one is enough , i will remove python2.

node('mxnetlinux-gpu') {
ws('workspace/ut-python3-mkldnn-gpu-nocudnn') {
try {
init_git()
unpack_lib('mkldnn_gpu_nocudnn', mx_mkldnn_lib)
python3_gpu_ut('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') {
Expand Down
18 changes: 18 additions & 0 deletions ci/docker/runtime_functions.sh
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Expand Up @@ -457,6 +457,24 @@ build_ubuntu_gpu_mkldnn() {
report_ccache_usage
}

build_ubuntu_gpu_mkldnn_nocudnn() {
set -ex

build_ccache_wrappers

make \
DEV=1 \
USE_CPP_PACKAGE=1 \
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No need for the cpp package here since we don't run the cpp tests

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not sure about this . shouldnt we test if it builds fine with CUDNN with CPP_PACKAGE just in case cpp package depends on the flag to use cudnn specific functionality (unlikely) ?

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The CPP package just generates wrappers around the C-API and should not rely on backend-specific things like CUDA or CUDNN. Thus, I'd say that we should keep it separate and only build the CPP package if we're actually going to run it (which we do in another test)

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okay, removed!

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
Expand Down
12 changes: 6 additions & 6 deletions src/operator/nn/convolution.cu
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Expand Up @@ -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;
Expand Down Expand Up @@ -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;
Expand Down