date:20191015

reminisce commented on issue #16450: Add test pipeline for USE_TVM_OP=OFF on 
Unix
URL: https://github.com/apache/incubator-mxnet/pull/16450#issuecomment-542507363
 
 
   dist-kvstore tests GPU failure has shown up in quite a few PRs recently. 
Wonder what the root cause is.


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[GitHub] [incubator-mxnet] reminisce commented on issue #16436: Add sum for boolean type when not built with TVM

reminisce commented on issue #16436: Add sum for boolean type when not built 
with TVM
URL: https://github.com/apache/incubator-mxnet/pull/16436#issuecomment-542506826
 
 
   `dist-kvstore tests GPU` failure has shown up in quite a few PRs recently. 
Wonder what's the root cause.


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[GitHub] [incubator-mxnet] TaoLv opened a new pull request #16501: [Doc] Use mirror link in the download page

TaoLv opened a new pull request #16501: [Doc] Use mirror link in the download 
page
URL: https://github.com/apache/incubator-mxnet/pull/16501
 
 
   ## Description ##
   
   1. Use mirror link in the download page. According to 
http://www.apache.org/dev/release-publishing.html#distribution_dist.
   2. Add a link for the KEY file.
   
   ## Checklist ##
   ### Essentials ###
   Please feel free to remove inapplicable items for your PR.
   - [ ] The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to 
the relevant [JIRA issue](https://issues.apache.org/jira/projects/MXNET/issues) 
created (except PRs with tiny changes)
   - [ ] Changes are complete (i.e. I finished coding on this PR)
   - [ ] All changes have test coverage:
   - Unit tests are added for small changes to verify correctness (e.g. adding 
a new operator)
   - Nightly tests are added for complicated/long-running ones (e.g. changing 
distributed kvstore)
   - Build tests will be added for build configuration changes (e.g. adding a 
new build option with NCCL)
   - [ ] Code is well-documented: 
   - For user-facing API changes, API doc string has been updated. 
   - For new C++ functions in header files, their functionalities and arguments 
are documented. 
   - For new examples, README.md is added to explain the what the example does, 
the source of the dataset, expected performance on test set and reference to 
the original paper if applicable
   - Check the API doc at 
https://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html
   - [ ] To the my best knowledge, examples are either not affected by this 
change, or have been fixed to be compatible with this change
   
   ### Changes ###
   - [ ] Feature1, tests, (and when applicable, API doc)
   - [ ] Feature2, tests, (and when applicable, API doc)
   
   ## Comments ##
   - If this change is a backward incompatible change, why must this change be 
made.
   - Interesting edge cases to note here
   


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[GitHub] [incubator-mxnet] wkcn commented on issue #16234: [MXNET-1426] Fix the wrong result of sum, mean, argmin, argmax when inputs contain inf or nan

wkcn commented on issue #16234: [MXNET-1426] Fix the wrong result of sum, mean, 
argmin, argmax when inputs contain inf or nan
URL: https://github.com/apache/incubator-mxnet/pull/16234#issuecomment-542495435
 
 
   Hi @eric-haibin-lin , could you please help take a review?
   It is a bug, which outputs a wrong result or an inconsistent result with 
that of NumPy.


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[GitHub] [incubator-mxnet] wkcn edited a comment on issue #16234: [MXNET-1426] Fix the wrong result of sum, mean, argmin, argmax when inputs contain inf or nan

wkcn edited a comment on issue #16234: [MXNET-1426] Fix the wrong result of 
sum, mean, argmin, argmax when inputs contain inf or nan
URL: https://github.com/apache/incubator-mxnet/pull/16234#issuecomment-542495435
 
 
   Hi @eric-haibin-lin , could you please help take a review?
   It is a bug, which outputs a wrong result or an inconsistent result with 
that of NumPy.
   
   Thank you so much!


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[GitHub] [incubator-mxnet] rongzha1 commented on issue #16468: [mkldnn-1.0] add mkldnn subgraph fc

rongzha1 commented on issue #16468: [mkldnn-1.0] add mkldnn subgraph fc
URL: https://github.com/apache/incubator-mxnet/pull/16468#issuecomment-542491120
 
 
   I didn't change lint failed file in sanity test and it passed in my local 
branch. So please merge directly if other CI test passed.


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[GitHub] [incubator-mxnet] vexilligera commented on issue #16469: Tests of NumPy interoperability

vexilligera commented on issue #16469: Tests of NumPy interoperability
URL: https://github.com/apache/incubator-mxnet/pull/16469#issuecomment-542477763
 
 
   > Please make sure the test can pass CI.
   
   Hi, CI tests have passed.


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[GitHub] [incubator-mxnet] aaronmarkham opened a new pull request #16500: Fixing broken links

aaronmarkham opened a new pull request #16500: Fixing broken links
URL: https://github.com/apache/incubator-mxnet/pull/16500
 
 
   Lots more to do, but let's get some fixes in there!


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[GitHub] [incubator-mxnet] starimpact commented on issue #15102: the grad of lars should be scaled in lbsgd

starimpact commented on issue #15102: the grad of lars should be scaled in lbsgd
URL: 
https://github.com/apache/incubator-mxnet/issues/15102#issuecomment-542476256
 
 
   _l2norm is time consuming for a large parameter, so I suggest it should be:
   ```python
   def _l2norm(self, v):
   "inner product implementation"
   #for big local parameter
   v = v.reshape(-1)
   if len(v) > 10:
   step = len(v)/10+1
   v = v[::step]
   norm = multiply(v, v).asnumpy().sum()
   #norm = (multiply(v, v).sum()).asnumpy()
   norm = math.sqrt(norm)
   return norm
   ```


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[GitHub] [incubator-mxnet] pengzhao-intel commented on issue #16477: added more tests to verify support for large vector

pengzhao-intel commented on issue #16477: added more tests to verify support 
for large vector
URL: https://github.com/apache/incubator-mxnet/pull/16477#issuecomment-542472043
 
 
   @wuxun-zhang maybe take a look for new tests :)


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[GitHub] [incubator-mxnet] aaronmarkham commented on issue #15925: [CI] illegal memory access

aaronmarkham commented on issue #15925: [CI] illegal memory access
URL: 
https://github.com/apache/incubator-mxnet/issues/15925#issuecomment-542466581
 
 
   Happened here too:
   
http://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/mxnet-validation%2Funix-gpu/detail/PR-16496/1/pipeline/294


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[GitHub] [incubator-mxnet] leezu commented on issue #16499: mxnet/tensorRT docker image coredumps

leezu commented on issue #16499: mxnet/tensorRT docker image coredumps 
URL: 
https://github.com/apache/incubator-mxnet/issues/16499#issuecomment-542463446
 
 
   Who owns the Docker images at https://hub.docker.com/u/mxnet ? The tensorRT 
hasn't been updated since a year.


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[GitHub] [incubator-mxnet] anirudh2290 commented on a change in pull request #16477: added more tests to verify support for large vector

anirudh2290 commented on a change in pull request #16477: added more tests to 
verify support for large vector
URL: https://github.com/apache/incubator-mxnet/pull/16477#discussion_r335235795
 
 

 ##
 File path: tests/nightly/test_large_vector.py
 ##
 @@ -708,6 +708,182 @@ def test_full():
 assert a[-1] == 3
 
 
+def test_astype():
+x = create_vector(size=LARGE_X//4)
+x = nd.tile(x, 4)
+y = x.astype('int32')
+assert y.dtype == np.int32
+assert y[-1] == LARGE_X//4-1
+
+
+def test_cast():
+x = create_vector(size=LARGE_X//4)
+x = nd.tile(x, 4)
+y = nd.cast(x, np.int32)
+assert y.dtype == np.int32
+assert y[-1] == LARGE_X//4-1
+
+
+def test_repeat():
+x = create_vector(size=LARGE_X//2)
+y = nd.repeat(x, repeats=2, axis = 0)
+assert y.shape[0] == LARGE_X
+assert y[1] == 0
+assert y[LARGE_X-1] == LARGE_X//2-1
+
+
+def create_input_for_rounding_ops():
+inp = nd.arange(-LARGE_X//2, LARGE_X//2, dtype=np.float64)
+inp = inp/2
+return inp
+
+
+def test_ceil():
+x = create_input_for_rounding_ops()
+y = nd.ceil(x)
+assert y[LARGE_X//2-2] == -1
+assert y[LARGE_X//2-1] == 0
+assert y[LARGE_X//2] == 0
+assert y[LARGE_X//2+1] == 1
+assert y[LARGE_X//2+2] == 1
+
+
+def test_fix():
+x = create_input_for_rounding_ops()
+y = nd.fix(x)
+assert y[LARGE_X//2-2] == -1
 
 Review comment:
   can you add some comment of what it is testing, here and for other tests.


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[GitHub] [incubator-mxnet] anirudh2290 commented on a change in pull request #16477: added more tests to verify support for large vector

anirudh2290 commented on a change in pull request #16477: added more tests to 
verify support for large vector
URL: https://github.com/apache/incubator-mxnet/pull/16477#discussion_r335234920
 
 

 ##
 File path: tests/nightly/test_large_vector.py
 ##
 @@ -708,6 +708,182 @@ def test_full():
 assert a[-1] == 3
 
 
+def test_astype():
+x = create_vector(size=LARGE_X//4)
+x = nd.tile(x, 4)
+y = x.astype('int32')
+assert y.dtype == np.int32
+assert y[-1] == LARGE_X//4-1
+
+
+def test_cast():
+x = create_vector(size=LARGE_X//4)
+x = nd.tile(x, 4)
+y = nd.cast(x, np.int32)
+assert y.dtype == np.int32
+assert y[-1] == LARGE_X//4-1
+
+
+def test_repeat():
+x = create_vector(size=LARGE_X//2)
+y = nd.repeat(x, repeats=2, axis = 0)
+assert y.shape[0] == LARGE_X
+assert y[1] == 0
+assert y[LARGE_X-1] == LARGE_X//2-1
+
+
+def create_input_for_rounding_ops():
+inp = nd.arange(-LARGE_X//2, LARGE_X//2, dtype=np.float64)
+inp = inp/2
+return inp
+
+
+def test_ceil():
+x = create_input_for_rounding_ops()
+y = nd.ceil(x)
+assert y[LARGE_X//2-2] == -1
 
 Review comment:
   what is this testing, can you add some comment here.


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[GitHub] [incubator-mxnet] anirudh2290 commented on a change in pull request #16477: added more tests to verify support for large vector

anirudh2290 commented on a change in pull request #16477: added more tests to 
verify support for large vector
URL: https://github.com/apache/incubator-mxnet/pull/16477#discussion_r335235628
 
 

 ##
 File path: tests/nightly/test_large_vector.py
 ##
 @@ -708,6 +708,182 @@ def test_full():
 assert a[-1] == 3
 
 
+def test_astype():
+x = create_vector(size=LARGE_X//4)
+x = nd.tile(x, 4)
+y = x.astype('int32')
+assert y.dtype == np.int32
+assert y[-1] == LARGE_X//4-1
+
+
+def test_cast():
+x = create_vector(size=LARGE_X//4)
+x = nd.tile(x, 4)
+y = nd.cast(x, np.int32)
+assert y.dtype == np.int32
+assert y[-1] == LARGE_X//4-1
+
+
+def test_repeat():
+x = create_vector(size=LARGE_X//2)
+y = nd.repeat(x, repeats=2, axis = 0)
+assert y.shape[0] == LARGE_X
+assert y[1] == 0
+assert y[LARGE_X-1] == LARGE_X//2-1
+
+
+def create_input_for_rounding_ops():
+inp = nd.arange(-LARGE_X//2, LARGE_X//2, dtype=np.float64)
+inp = inp/2
+return inp
+
+
+def test_ceil():
+x = create_input_for_rounding_ops()
+y = nd.ceil(x)
+assert y[LARGE_X//2-2] == -1
+assert y[LARGE_X//2-1] == 0
+assert y[LARGE_X//2] == 0
+assert y[LARGE_X//2+1] == 1
+assert y[LARGE_X//2+2] == 1
+
+
+def test_fix():
+x = create_input_for_rounding_ops()
+y = nd.fix(x)
+assert y[LARGE_X//2-2] == -1
+assert y[LARGE_X//2-1] == 0
+assert y[LARGE_X//2] == 0
+assert y[LARGE_X//2+1] == 0
+assert y[LARGE_X//2+2] == 1
+
+
+def test_floor():
+x = create_input_for_rounding_ops()
+y = nd.floor(x)
+assert y[LARGE_X//2-2] == -1
+assert y[LARGE_X//2-1] == -1
+assert y[LARGE_X//2] == 0
+assert y[LARGE_X//2+1] == 0
+assert y[LARGE_X//2+2] == 1
+
+
+def test_rint():
+x = create_input_for_rounding_ops()
+y = nd.rint(x)
+assert y[LARGE_X//2-2] == -1
+assert y[LARGE_X//2-1] == -1
+assert y[LARGE_X//2] == 0
+assert y[LARGE_X//2+1] == 0
+assert y[LARGE_X//2+2] == 1
+
+
+def test_round():
+x = create_input_for_rounding_ops()
+y = nd.round(x)
+assert y[LARGE_X//2-2] == -1
+assert y[LARGE_X//2-1] == -1
+assert y[LARGE_X//2] == 0
+assert y[LARGE_X//2+1] == 1
+assert y[LARGE_X//2+2] == 1
+
+
+def test_trunc():
+x = create_input_for_rounding_ops()
+y = nd.trunc(x)
+assert y[LARGE_X//2-2] == -1
+assert y[LARGE_X//2-1] == 0
+assert y[LARGE_X//2] == 0
+assert y[LARGE_X//2+1] == 0
 
 Review comment:
   looks like a bunch of repeated code that can be reused in these 5 tests.


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[GitHub] [incubator-mxnet] anirudh2290 commented on a change in pull request #16477: added more tests to verify support for large vector

anirudh2290 commented on a change in pull request #16477: added more tests to 
verify support for large vector
URL: https://github.com/apache/incubator-mxnet/pull/16477#discussion_r335236059
 
 

 ##
 File path: tests/nightly/test_large_vector.py
 ##
 @@ -708,6 +708,182 @@ def test_full():
 assert a[-1] == 3
 
 
+def test_astype():
+x = create_vector(size=LARGE_X//4)
+x = nd.tile(x, 4)
+y = x.astype('int32')
+assert y.dtype == np.int32
+assert y[-1] == LARGE_X//4-1
+
+
+def test_cast():
+x = create_vector(size=LARGE_X//4)
+x = nd.tile(x, 4)
+y = nd.cast(x, np.int32)
+assert y.dtype == np.int32
+assert y[-1] == LARGE_X//4-1
+
+
+def test_repeat():
+x = create_vector(size=LARGE_X//2)
+y = nd.repeat(x, repeats=2, axis = 0)
+assert y.shape[0] == LARGE_X
+assert y[1] == 0
+assert y[LARGE_X-1] == LARGE_X//2-1
+
+
+def create_input_for_rounding_ops():
+inp = nd.arange(-LARGE_X//2, LARGE_X//2, dtype=np.float64)
+inp = inp/2
+return inp
+
+
+def test_ceil():
+x = create_input_for_rounding_ops()
+y = nd.ceil(x)
+assert y[LARGE_X//2-2] == -1
+assert y[LARGE_X//2-1] == 0
+assert y[LARGE_X//2] == 0
+assert y[LARGE_X//2+1] == 1
+assert y[LARGE_X//2+2] == 1
+
+
+def test_fix():
+x = create_input_for_rounding_ops()
+y = nd.fix(x)
+assert y[LARGE_X//2-2] == -1
+assert y[LARGE_X//2-1] == 0
+assert y[LARGE_X//2] == 0
+assert y[LARGE_X//2+1] == 0
+assert y[LARGE_X//2+2] == 1
+
+
+def test_floor():
+x = create_input_for_rounding_ops()
+y = nd.floor(x)
+assert y[LARGE_X//2-2] == -1
+assert y[LARGE_X//2-1] == -1
+assert y[LARGE_X//2] == 0
+assert y[LARGE_X//2+1] == 0
+assert y[LARGE_X//2+2] == 1
+
+
+def test_rint():
+x = create_input_for_rounding_ops()
+y = nd.rint(x)
+assert y[LARGE_X//2-2] == -1
+assert y[LARGE_X//2-1] == -1
+assert y[LARGE_X//2] == 0
+assert y[LARGE_X//2+1] == 0
+assert y[LARGE_X//2+2] == 1
+
+
+def test_round():
+x = create_input_for_rounding_ops()
+y = nd.round(x)
+assert y[LARGE_X//2-2] == -1
+assert y[LARGE_X//2-1] == -1
+assert y[LARGE_X//2] == 0
+assert y[LARGE_X//2+1] == 1
+assert y[LARGE_X//2+2] == 1
+
+
+def test_trunc():
+x = create_input_for_rounding_ops()
+y = nd.trunc(x)
+assert y[LARGE_X//2-2] == -1
+assert y[LARGE_X//2-1] == 0
+assert y[LARGE_X//2] == 0
+assert y[LARGE_X//2+1] == 0
+assert y[LARGE_X//2+2] == 1
+
+
+def test_arcsin():
+x = nd.array([-1, -.707, 0, .707, 1])
+x = nd.tile(x, LARGE_X//5)
+y = nd.arcsin(x)
+assert_almost_equal(y[0].asnumpy(), -np.pi/2, atol=1e-3)
+assert_almost_equal(y[1].asnumpy(), -np.pi/4, atol=1e-3)
+assert_almost_equal(y[-3].asnumpy(), 0, atol=1e-3)
+assert_almost_equal(y[-2].asnumpy(), np.pi/4, atol=1e-3)
+assert_almost_equal(y[-1].asnumpy(), np.pi/2, atol=1e-3)
+
+
+def test_arccos():
+x = nd.array([-1, -.707, 0, .707, 1])
+x = nd.tile(x, LARGE_X//5)
+y = nd.arccos(x)
+assert_almost_equal(y[0].asnumpy(), np.pi, atol=1e-3)
+assert_almost_equal(y[1].asnumpy(), 3*np.pi/4, atol=1e-3)
+assert_almost_equal(y[-3].asnumpy(), np.pi/2, atol=1e-3)
+assert_almost_equal(y[-2].asnumpy(), np.pi/4, atol=1e-3)
+assert_almost_equal(y[-1].asnumpy(), 0, atol=1e-3)
+
+
+def test_arctan():
+x = nd.array([-np.Inf, -1, 0, 1, np.Inf])
+x = nd.tile(x, LARGE_X//5)
+y = nd.arctan(x)
+assert_almost_equal(y[0].asnumpy(), -np.pi/2, atol=1e-3)
+assert_almost_equal(y[1].asnumpy(), -np.pi/4, atol=1e-3)
+assert_almost_equal(y[-3].asnumpy(), 0, atol=1e-3)
+assert_almost_equal(y[-2].asnumpy(), np.pi/4, atol=1e-3)
+assert_almost_equal(y[-1].asnumpy(), np.pi/2, atol=1e-3)
+
+
+def test_sin():
+x = nd.array([-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2])
+x = nd.tile(x, LARGE_X//5)
+y = nd.sin(x)
+assert_almost_equal(y[0].asnumpy(), -1, atol=1e-3)
+assert_almost_equal(y[1].asnumpy(), -.707, atol=1e-3)
+assert_almost_equal(y[-3].asnumpy(), 0, atol=1e-3)
+assert_almost_equal(y[-2].asnumpy(), .707, atol=1e-3)
+assert_almost_equal(y[-1].asnumpy(), 1, atol=1e-3)
+
+
+def test_cos():
+x = nd.array([0, np.pi/4, np.pi/2, 3*np.pi/4, np.pi])
+x = nd.tile(x, LARGE_X//5)
+y = nd.cos(x)
+assert_almost_equal(y[0].asnumpy(), 1, atol=1e-3)
+assert_almost_equal(y[1].asnumpy(), .707, atol=1e-3)
+assert_almost_equal(y[-3].asnumpy(), 0, atol=1e-3)
+assert_almost_equal(y[-2].asnumpy(), -.707, atol=1e-3)
+assert_almost_equal(y[-1].asnumpy(), -1, atol=1e-3)
+
+
+def test_tan():
+x = nd.array([-np.pi/4, 0, np.pi/4])
+x = nd.tile(x, LARGE_X//3)
+y = nd.tan(x)
+assert y[0] == -1
+assert y[1] == 0
+assert y[-1] == 1
+
+
+def test_radians():
+x = nd.array([0, 90, 180, 270, 360])
+x = nd.tile(x, LARGE_X//5)
+y = nd.radians(x)
+assert_almost_equal(y[0].asnumpy(), 0, atol=1e-3)

[GitHub] [incubator-mxnet] wx3000 opened a new issue #16499: mxnet/tensorRT docker image coredumps

wx3000 opened a new issue #16499: mxnet/tensorRT docker image coredumps
URL: https://github.com/apache/incubator-mxnet/issues/16499

## Description

The docker image mxnet/tensorrt which integrates mxnet with tensorrt core
dumps. docker image has TRT 4.0, python3.5, cuda9 and MxNet 1.3.

## Environment info (Required)
AWS base DLAMI on G4 instance. DLAMI version is 19.2 and OS is ubuntu.
Installed docker 19.2 and nvidia docker on top of it.

first install docker:
https://docs.docker.com/install/linux/docker-ce/ubuntu/

there is one issue with a workaround:
https://github.com/docker/for-linux/issues/813
sudo apt-get install runc=1.0.0~rc7+git20190403.029124da-0ubuntu1~16.04.4
sudo apt-get install docker-ce

then I installed latest nvidia docker (which requires docker 19.03)
https://github.com/NVIDIA/nvidia-docker

then I pulled the image
$ docker run --gpus all -it mxnet/tensorrt bash

I followed the python scripts on this page for mxnet/tensorRT integration.

https://github.com/apache/incubator-mxnet/blob/8004a027ad6a73f8f6eae102de8d249fbdfb9a2d/docs/python_docs/python/tutorials/performance/backend/tensorrt/tensorrt.md

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[GitHub] [incubator-mxnet] mxnet-label-bot commented on issue #16499: mxnet/tensorRT docker image coredumps

mxnet-label-bot commented on issue #16499: mxnet/tensorRT docker image 
coredumps 
URL: 
https://github.com/apache/incubator-mxnet/issues/16499#issuecomment-542460604
 
 
   Hey, this is the MXNet Label Bot. 
Thank you for submitting the issue! I will try and suggest some labels so 
that the appropriate MXNet community members can help resolve it. 
Here are my recommended label(s): Feature, Performance


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[incubator-mxnet] branch master updated (06438ab -> 0c00a79)

2019-10-15 Thread haibin

This is an automated email from the ASF dual-hosted git repository.

haibin pushed a change to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git.


from 06438ab  Mxnet allclose (#14443)
 add 0c00a79  Fix optimizer bug for np attribute (#16494)

No new revisions were added by this update.

Summary of changes:
 python/mxnet/optimizer/optimizer.py   | 2 +-
 tests/python/unittest/test_numpy_gluon.py | 9 +
 2 files changed, 10 insertions(+), 1 deletion(-)

[incubator-mxnet] branch master updated (06438ab -> 0c00a79)

2019-10-15 Thread haibin

This is an automated email from the ASF dual-hosted git repository.

haibin pushed a change to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git.


from 06438ab  Mxnet allclose (#14443)
 add 0c00a79  Fix optimizer bug for np attribute (#16494)

No new revisions were added by this update.

Summary of changes:
 python/mxnet/optimizer/optimizer.py   | 2 +-
 tests/python/unittest/test_numpy_gluon.py | 9 +
 2 files changed, 10 insertions(+), 1 deletion(-)

[GitHub] [incubator-mxnet] eric-haibin-lin merged pull request #16494: Proper handling of "allow_np_array" attribute in optimizer

eric-haibin-lin merged pull request #16494: Proper handling of "allow_np_array" 
attribute in optimizer
URL: https://github.com/apache/incubator-mxnet/pull/16494
 
 
   


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[GitHub] [incubator-mxnet] ChaiBapchya opened a new issue #16498: [CI] Centos GPU Seg Fault

ChaiBapchya opened a new issue #16498: [CI] Centos GPU Seg Fault
URL: https://github.com/apache/incubator-mxnet/issues/16498
 
 
   Seg Fault
   Unrelated PR - #16497 
   Pipeline - 
http://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/mxnet-validation%2Fcentos-gpu/detail/PR-16497/1/pipeline
   ```
   src/operator/numpy/../tensor/broadcast_reduce-inl.cuh: In function 'void 
mxnet::op::broadcast::ReduceImpl(cudaStream_t, const mxnet::TBlob&, const 
mxnet::TBlob&, const mxnet::TBlob&, mxnet::OpReqType, const mxnet::TBlob&, 
const mshadow::Tensor&, const 
mxnet::op::broadcast::ReduceImplConfig&) [with Reducer = 
mshadow::red::sum; int ndim = 4; DType = long int; OP1 = 
mxnet::op::mshadow_op::mul; OP2 = mxnet::op::mshadow_op::hypot_grad_left; 
cudaStream_t = CUstream_st*]':
   src/operator/numpy/../tensor/broadcast_reduce-inl.cuh:564:1: internal 
compiler error: Segmentation fault
void ReduceImpl(cudaStream_t stream, const TBlob& small, const TBlob& lhs, 
const TBlob& rhs,
^
   ```


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[GitHub] [incubator-mxnet] mxnet-label-bot commented on issue #16498: [CI] Centos GPU Seg Fault

mxnet-label-bot commented on issue #16498: [CI] Centos GPU Seg Fault
URL: 
https://github.com/apache/incubator-mxnet/issues/16498#issuecomment-542454688
 
 
   Hey, this is the MXNet Label Bot. 
Thank you for submitting the issue! I will try and suggest some labels so 
that the appropriate MXNet community members can help resolve it. 
Here are my recommended label(s): Bug, CI


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[GitHub] [incubator-mxnet] ChaiBapchya opened a new pull request #16497: Large Vector tests for DGL Ops Part 2

ChaiBapchya opened a new pull request #16497: Large Vector tests for DGL Ops 
Part 2
URL: https://github.com/apache/incubator-mxnet/pull/16497
 
 
   ## Description ##
   add hyperbolic, logical, sign and regression tests for large vector
   
   ## Checklist ##
   ### Essentials ###
   Please feel free to remove inapplicable items for your PR.
   - [x] Changes are complete (i.e. I finished coding on this PR)
   - [x] All changes have test coverage:
   - [x] Code is well-documented: 
   - [x] To the my best knowledge, examples are either not affected by this 
change, or have been fixed to be compatible with this change
   
   ### Changes ###
   - [x] Add tests to test_large_array.py
   - [x] Fix issue with test_concat introduced in PR #15960 


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[GitHub] [incubator-mxnet] anandj91 commented on issue #15124: [MXNET-1294] Priority-based parameter propagation for improved data parallel training throughput

anandj91 commented on issue #15124: [MXNET-1294] Priority-based parameter 
propagation for improved data parallel training throughput
URL: https://github.com/apache/incubator-mxnet/pull/15124#issuecomment-542440290
 
 
   I'm facing some design level challenges to properly implement Priority based 
update (P3) on top of PushPull API. MXNet does a simple load balancing before 
pushing or pulling key-values by splitting NDArrays equally to the parameter 
servers. P3 requires a round-robin style parameter distribution which means 
slicing a large NDArray into thousands of smaller ones. Much more granular than 
current default distribution strategy and each PS would get more than one slice.
   
   With the way mxnet and ps-lite designed right now, ps-lite assumes a single 
ZPush/ZPull/ZPushPull belongs to a single layer/NDArray. It also assumes that 
one slice only belong to one PS. These assumption need to be broken for 
implementing P3. What I have done right now is to add round-robin (RR) 
distribution strategy along with the default one and use a boolean flag to 
switch between these two. When user chooses to use RR, KVStore consider each 
slice as separate key-value pair. Otherwise fallback to the default mode.


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[incubator-mxnet] branch master updated (b1932c0 -> 06438ab)

This is an automated email from the ASF dual-hosted git repository.

ptrendx pushed a change to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git.


from b1932c0  Move MRCNNMaskTarget op to contrib (#16486)
 add 06438ab  Mxnet allclose (#14443)

No new revisions were added by this update.

Summary of changes:
 python/mxnet/test_utils.py| 188 ++--
 src/operator/contrib/allclose_op-inl.h| 160 +++
 src/operator/contrib/allclose_op.cc   |  86 
 src/operator/contrib/allclose_op.cu   |  58 +++
 tests/python-pytest/onnx/mxnet_export_test.py |   2 +-
 tests/python/gpu/test_gluon_gpu.py| 136 --
 tests/python/gpu/test_gluon_model_zoo_gpu.py  |  14 +-
 tests/python/gpu/test_operator_gpu.py |  59 ++-
 tests/python/mkl/test_mkldnn.py   |  12 +-
 tests/python/unittest/test_gluon_contrib.py   |  21 +-
 tests/python/unittest/test_loss.py|  31 +-
 tests/python/unittest/test_ndarray.py |  27 +-
 tests/python/unittest/test_operator.py| 663 +++---
 tests/python/unittest/test_random.py  |  16 +-
 tests/python/unittest/test_sparse_operator.py |   4 +-
 tests/python/unittest/test_subgraph.py|   7 -
 16 files changed, 938 insertions(+), 546 deletions(-)
 create mode 100644 src/operator/contrib/allclose_op-inl.h
 create mode 100644 src/operator/contrib/allclose_op.cc
 create mode 100644 src/operator/contrib/allclose_op.cu

[incubator-mxnet] branch master updated (b1932c0 -> 06438ab)

This is an automated email from the ASF dual-hosted git repository.

ptrendx pushed a change to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git.


from b1932c0  Move MRCNNMaskTarget op to contrib (#16486)
 add 06438ab  Mxnet allclose (#14443)

No new revisions were added by this update.

Summary of changes:
 python/mxnet/test_utils.py| 188 ++--
 src/operator/contrib/allclose_op-inl.h| 160 +++
 src/operator/contrib/allclose_op.cc   |  86 
 src/operator/contrib/allclose_op.cu   |  58 +++
 tests/python-pytest/onnx/mxnet_export_test.py |   2 +-
 tests/python/gpu/test_gluon_gpu.py| 136 --
 tests/python/gpu/test_gluon_model_zoo_gpu.py  |  14 +-
 tests/python/gpu/test_operator_gpu.py |  59 ++-
 tests/python/mkl/test_mkldnn.py   |  12 +-
 tests/python/unittest/test_gluon_contrib.py   |  21 +-
 tests/python/unittest/test_loss.py|  31 +-
 tests/python/unittest/test_ndarray.py |  27 +-
 tests/python/unittest/test_operator.py| 663 +++---
 tests/python/unittest/test_random.py  |  16 +-
 tests/python/unittest/test_sparse_operator.py |   4 +-
 tests/python/unittest/test_subgraph.py|   7 -
 16 files changed, 938 insertions(+), 546 deletions(-)
 create mode 100644 src/operator/contrib/allclose_op-inl.h
 create mode 100644 src/operator/contrib/allclose_op.cc
 create mode 100644 src/operator/contrib/allclose_op.cu

[GitHub] [incubator-mxnet] ptrendx merged pull request #14443: Mxnet allclose

ptrendx merged pull request #14443: Mxnet allclose
URL: https://github.com/apache/incubator-mxnet/pull/14443
 
 
   


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[GitHub] [incubator-mxnet] ChaiBapchya commented on issue #16477: added more tests to verify support for large vector

ChaiBapchya commented on issue #16477: added more tests to verify support for 
large vector
URL: https://github.com/apache/incubator-mxnet/pull/16477#issuecomment-542438790
 
 
   Randint has been flaky.
   Tracked here https://github.com/apache/incubator-mxnet/issues/16172


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[GitHub] [incubator-mxnet] reminisce commented on issue #16450: Add test pipeline for USE_TVM_OP=OFF on Unix

reminisce commented on issue #16450: Add test pipeline for USE_TVM_OP=OFF on 
Unix
URL: https://github.com/apache/incubator-mxnet/pull/16450#issuecomment-542438460
 
 
   @mseth10 I have made changes as you suggested. Could you review again? 
Thanks.


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[GitHub] [incubator-mxnet] sojiadeshina opened a new pull request #16496: fix missing docs due to git add issues

sojiadeshina opened a new pull request #16496: fix missing docs due to git add 
issues
URL: https://github.com/apache/incubator-mxnet/pull/16496
 
 
   ## Description ##
   Some files were missing from the #16392 pr due to gitignore issue. They are 
now included here. 
   
   This should allow the pages for gluon.data to be rendered properly.
   
   


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[GitHub] [incubator-mxnet] aaronmarkham opened a new issue #16495: docs for gluon.data.* are missing

aaronmarkham opened a new issue #16495: docs for gluon.data.* are missing
URL: https://github.com/apache/incubator-mxnet/issues/16495
 
 
   Probably related to git filters when adding the files.
   
https://stackoverflow.com/questions/8006393/force-add-despite-the-gitignore-file
   
   
   
   


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[GitHub] [incubator-mxnet] mxnet-label-bot commented on issue #16495: docs for gluon.data.* are missing

mxnet-label-bot commented on issue #16495: docs for gluon.data.* are missing
URL: 
https://github.com/apache/incubator-mxnet/issues/16495#issuecomment-542433508
 
 
   Hey, this is the MXNet Label Bot. 
Thank you for submitting the issue! I will try and suggest some labels so 
that the appropriate MXNet community members can help resolve it. 
Here are my recommended label(s): Gluon, Doc


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[GitHub] [incubator-mxnet] stu1130 commented on a change in pull request #16464: Image crop gpu support

stu1130 commented on a change in pull request #16464: Image crop gpu support
URL: https://github.com/apache/incubator-mxnet/pull/16464#discussion_r335204117
 
 

 ##
 File path: tests/python/gpu/test_gluon_transforms.py
 ##
 @@ -28,76 +28,19 @@
 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 assertRaises, setup_module, with_seed, teardown
-
+from test_gluon_data_vision import test_to_tensor, test_normalize, 
test_crop_resize
 
 set_default_context(mx.gpu(0))
 
 @with_seed()
-def test_normalize():
-# 3D Input
-data_in_3d = nd.random.uniform(0, 1, (3, 300, 300))
-out_nd_3d = transforms.Normalize(mean=(0, 1, 2), std=(3, 2, 1))(data_in_3d)
-data_expected_3d = data_in_3d.asnumpy()
-data_expected_3d[:][:][0] = data_expected_3d[:][:][0] / 3.0
-data_expected_3d[:][:][1] = (data_expected_3d[:][:][1] - 1.0) / 2.0
-data_expected_3d[:][:][2] = data_expected_3d[:][:][2] - 2.0
-assert_almost_equal(data_expected_3d, out_nd_3d.asnumpy())
-
-# 4D Input
-data_in_4d = nd.random.uniform(0, 1, (2, 3, 300, 300))
-out_nd_4d = transforms.Normalize(mean=(0, 1, 2), std=(3, 2, 1))(data_in_4d)
-data_expected_4d = data_in_4d.asnumpy()
-data_expected_4d[0][:][:][0] = data_expected_4d[0][:][:][0] / 3.0
-data_expected_4d[0][:][:][1] = (data_expected_4d[0][:][:][1] - 1.0) / 2.0
-data_expected_4d[0][:][:][2] = data_expected_4d[0][:][:][2] - 2.0
-data_expected_4d[1][:][:][0] = data_expected_4d[1][:][:][0] / 3.0
-data_expected_4d[1][:][:][1] = (data_expected_4d[1][:][:][1] - 1.0) / 2.0
-data_expected_4d[1][:][:][2] = data_expected_4d[1][:][:][2] - 2.0
-assert_almost_equal(data_expected_4d, out_nd_4d.asnumpy())
-
-# Default normalize values i.e., mean=0, std=1
-data_in_3d_def = nd.random.uniform(0, 1, (3, 300, 300))
-out_nd_3d_def = transforms.Normalize()(data_in_3d_def)
-data_expected_3d_def = data_in_3d_def.asnumpy()
-assert_almost_equal(data_expected_3d_def, out_nd_3d_def.asnumpy())
+def test_normalize_gpu():
+test_normalize()
 
-# Invalid Input - Neither 3D or 4D input
-invalid_data_in = nd.random.uniform(0, 1, (5, 5, 3, 300, 300))
-normalize_transformer = transforms.Normalize(mean=(0, 1, 2), std=(3, 2, 1))
-assertRaises(MXNetError, normalize_transformer, invalid_data_in)
-
-# Invalid Input - Channel neither 1 or 3
-invalid_data_in = nd.random.uniform(0, 1, (5, 4, 300, 300))
-normalize_transformer = transforms.Normalize(mean=(0, 1, 2), std=(3, 2, 1))
-assertRaises(MXNetError, normalize_transformer, invalid_data_in)
 
 @with_seed()
-def test_to_tensor():
-# 3D Input
-data_in = np.random.uniform(0, 255, (300, 300, 3)).astype(dtype=np.uint8)
-out_nd = transforms.ToTensor()(nd.array(data_in, dtype='uint8'))
-assert_almost_equal(out_nd.asnumpy(), np.transpose(
-data_in.astype(dtype=np.float32) / 255.0, (2, 0, 1)))
-
-# 4D Input
-data_in = np.random.uniform(0, 255, (5, 300, 300, 
3)).astype(dtype=np.uint8)
-out_nd = transforms.ToTensor()(nd.array(data_in, dtype='uint8'))
-assert_almost_equal(out_nd.asnumpy(), np.transpose(
-data_in.astype(dtype=np.float32) / 255.0, (0, 3, 1, 
2)))
+def test_to_tensor_gpu():
+test_to_tensor()
 
-# Invalid Input
-invalid_data_in = nd.random.uniform(0, 255, (5, 5, 300, 300, 
3)).astype(dtype=np.uint8)
-transformer = transforms.ToTensor()
-assertRaises(MXNetError, transformer, invalid_data_in)
-
-# Bounds (0->0, 255->1)
-data_in = np.zeros((10, 20, 3)).astype(dtype=np.uint8)
-out_nd = transforms.ToTensor()(nd.array(data_in, dtype='uint8'))
-assert same(out_nd.asnumpy(), np.transpose(np.zeros(data_in.shape, 
dtype=np.float32), (2, 0, 1)))
-
-data_in = np.full((10, 20, 3), 255).astype(dtype=np.uint8)
-out_nd = transforms.ToTensor()(nd.array(data_in, dtype='uint8'))
-assert same(out_nd.asnumpy(), np.transpose(np.ones(data_in.shape, 
dtype=np.float32), (2, 0, 1)))
 
 @with_seed()
 def test_resize():
 
 Review comment:
   done


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[GitHub] [incubator-mxnet] roywei commented on a change in pull request #16464: Image crop gpu support

roywei commented on a change in pull request #16464: Image crop gpu support
URL: https://github.com/apache/incubator-mxnet/pull/16464#discussion_r335201026
 
 

 ##
 File path: tests/python/gpu/test_gluon_transforms.py
 ##
 @@ -28,76 +28,19 @@
 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 assertRaises, setup_module, with_seed, teardown
-
+from test_gluon_data_vision import test_to_tensor, test_normalize, 
test_crop_resize
 
 set_default_context(mx.gpu(0))
 
 @with_seed()
-def test_normalize():
-# 3D Input
-data_in_3d = nd.random.uniform(0, 1, (3, 300, 300))
-out_nd_3d = transforms.Normalize(mean=(0, 1, 2), std=(3, 2, 1))(data_in_3d)
-data_expected_3d = data_in_3d.asnumpy()
-data_expected_3d[:][:][0] = data_expected_3d[:][:][0] / 3.0
-data_expected_3d[:][:][1] = (data_expected_3d[:][:][1] - 1.0) / 2.0
-data_expected_3d[:][:][2] = data_expected_3d[:][:][2] - 2.0
-assert_almost_equal(data_expected_3d, out_nd_3d.asnumpy())
-
-# 4D Input
-data_in_4d = nd.random.uniform(0, 1, (2, 3, 300, 300))
-out_nd_4d = transforms.Normalize(mean=(0, 1, 2), std=(3, 2, 1))(data_in_4d)
-data_expected_4d = data_in_4d.asnumpy()
-data_expected_4d[0][:][:][0] = data_expected_4d[0][:][:][0] / 3.0
-data_expected_4d[0][:][:][1] = (data_expected_4d[0][:][:][1] - 1.0) / 2.0
-data_expected_4d[0][:][:][2] = data_expected_4d[0][:][:][2] - 2.0
-data_expected_4d[1][:][:][0] = data_expected_4d[1][:][:][0] / 3.0
-data_expected_4d[1][:][:][1] = (data_expected_4d[1][:][:][1] - 1.0) / 2.0
-data_expected_4d[1][:][:][2] = data_expected_4d[1][:][:][2] - 2.0
-assert_almost_equal(data_expected_4d, out_nd_4d.asnumpy())
-
-# Default normalize values i.e., mean=0, std=1
-data_in_3d_def = nd.random.uniform(0, 1, (3, 300, 300))
-out_nd_3d_def = transforms.Normalize()(data_in_3d_def)
-data_expected_3d_def = data_in_3d_def.asnumpy()
-assert_almost_equal(data_expected_3d_def, out_nd_3d_def.asnumpy())
+def test_normalize_gpu():
+test_normalize()
 
-# Invalid Input - Neither 3D or 4D input
-invalid_data_in = nd.random.uniform(0, 1, (5, 5, 3, 300, 300))
-normalize_transformer = transforms.Normalize(mean=(0, 1, 2), std=(3, 2, 1))
-assertRaises(MXNetError, normalize_transformer, invalid_data_in)
-
-# Invalid Input - Channel neither 1 or 3
-invalid_data_in = nd.random.uniform(0, 1, (5, 4, 300, 300))
-normalize_transformer = transforms.Normalize(mean=(0, 1, 2), std=(3, 2, 1))
-assertRaises(MXNetError, normalize_transformer, invalid_data_in)
 
 @with_seed()
-def test_to_tensor():
-# 3D Input
-data_in = np.random.uniform(0, 255, (300, 300, 3)).astype(dtype=np.uint8)
-out_nd = transforms.ToTensor()(nd.array(data_in, dtype='uint8'))
-assert_almost_equal(out_nd.asnumpy(), np.transpose(
-data_in.astype(dtype=np.float32) / 255.0, (2, 0, 1)))
-
-# 4D Input
-data_in = np.random.uniform(0, 255, (5, 300, 300, 
3)).astype(dtype=np.uint8)
-out_nd = transforms.ToTensor()(nd.array(data_in, dtype='uint8'))
-assert_almost_equal(out_nd.asnumpy(), np.transpose(
-data_in.astype(dtype=np.float32) / 255.0, (0, 3, 1, 
2)))
+def test_to_tensor_gpu():
+test_to_tensor()
 
-# Invalid Input
-invalid_data_in = nd.random.uniform(0, 255, (5, 5, 300, 300, 
3)).astype(dtype=np.uint8)
-transformer = transforms.ToTensor()
-assertRaises(MXNetError, transformer, invalid_data_in)
-
-# Bounds (0->0, 255->1)
-data_in = np.zeros((10, 20, 3)).astype(dtype=np.uint8)
-out_nd = transforms.ToTensor()(nd.array(data_in, dtype='uint8'))
-assert same(out_nd.asnumpy(), np.transpose(np.zeros(data_in.shape, 
dtype=np.float32), (2, 0, 1)))
-
-data_in = np.full((10, 20, 3), 255).astype(dtype=np.uint8)
-out_nd = transforms.ToTensor()(nd.array(data_in, dtype='uint8'))
-assert same(out_nd.asnumpy(), np.transpose(np.ones(data_in.shape, 
dtype=np.float32), (2, 0, 1)))
 
 @with_seed()
 def test_resize():
 
 Review comment:
   can you rename this to test_resize_gpu for consistency, so people know there 
is a difference from test_resize()


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[GitHub] [incubator-mxnet] mseth10 commented on a change in pull request #16450: Add test pipeline for USE_TVM_OP=OFF on Unix

mseth10 commented on a change in pull request #16450: Add test pipeline for 
USE_TVM_OP=OFF on Unix
URL: https://github.com/apache/incubator-mxnet/pull/16450#discussion_r335190974
 
 

 ##
 File path: ci/jenkins/Jenkins_steps.groovy
 ##
 @@ -25,19 +25,23 @@ utils = load('ci/Jenkinsfile_utils.groovy')
 // mxnet libraries
 mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, 
lib/libtvmop.so, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 
3rdparty/tvm/nnvm/lib/libnnvm.a'
 mx_lib_cython = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, 
lib/libtvmop.so, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 
3rdparty/tvm/nnvm/lib/libnnvm.a, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
+mx_lib_cython_no_tvm_op = 'lib/libmxnet.so, lib/libmxnet.a, libsample_lib.so, 
3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, 
python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
 
 Review comment:
   We do not need this library set, as a result of above comment:
   https://github.com/apache/incubator-mxnet/pull/16450/files#r335190763


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[GitHub] [incubator-mxnet] mseth10 commented on a change in pull request #16450: Add test pipeline for USE_TVM_OP=OFF on Unix

mseth10 commented on a change in pull request #16450: Add test pipeline for 
USE_TVM_OP=OFF on Unix
URL: https://github.com/apache/incubator-mxnet/pull/16450#discussion_r335188815
 
 

 ##
 File path: ci/jenkins/Jenkins_steps.groovy
 ##
 @@ -25,19 +25,23 @@ utils = load('ci/Jenkinsfile_utils.groovy')
 // mxnet libraries
 mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, 
lib/libtvmop.so, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 
3rdparty/tvm/nnvm/lib/libnnvm.a'
 mx_lib_cython = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, 
lib/libtvmop.so, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 
3rdparty/tvm/nnvm/lib/libnnvm.a, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
+mx_lib_cython_no_tvm_op = 'lib/libmxnet.so, lib/libmxnet.a, libsample_lib.so, 
3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, 
python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
 
 // Python wheels
 mx_pip = 'build/*.whl'
 
 // mxnet cmake libraries, in cmake builds we do not produce a libnvvm static 
library by default.
 mx_cmake_lib = 'build/libmxnet.so, build/libmxnet.a, 
build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, 
build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, 
build/3rdparty/openmp/runtime/src/libomp.so'
+mx_cmake_lib_no_tvm_op = 'build/libmxnet.so, build/libmxnet.a, 
build/libsample_lib.so, build/3rdparty/dmlc-core/libdmlc.a, 
build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so'
 mx_cmake_lib_cython = 'build/libmxnet.so, build/libmxnet.a, 
build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, 
build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, 
build/3rdparty/openmp/runtime/src/libomp.so, python/mxnet/_cy2/*.so, 
python/mxnet/_cy3/*.so'
+mx_cmake_lib_cython_no_tvm_op = 'build/libmxnet.so, build/libmxnet.a, 
build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, 
build/3rdparty/openmp/runtime/src/libomp.so, python/mxnet/_cy2/*.so, 
python/mxnet/_cy3/*.so'
 
 Review comment:
   We do not need this library set, as a result of above comment:
   https://github.com/apache/incubator-mxnet/pull/16450/files#r335187907


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[GitHub] [incubator-mxnet] mseth10 commented on a change in pull request #16450: Add test pipeline for USE_TVM_OP=OFF on Unix

mseth10 commented on a change in pull request #16450: Add test pipeline for 
USE_TVM_OP=OFF on Unix
URL: https://github.com/apache/incubator-mxnet/pull/16450#discussion_r335190974
 
 

 ##
 File path: ci/jenkins/Jenkins_steps.groovy
 ##
 @@ -25,19 +25,23 @@ utils = load('ci/Jenkinsfile_utils.groovy')
 // mxnet libraries
 mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, 
lib/libtvmop.so, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 
3rdparty/tvm/nnvm/lib/libnnvm.a'
 mx_lib_cython = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, 
lib/libtvmop.so, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 
3rdparty/tvm/nnvm/lib/libnnvm.a, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
+mx_lib_cython_no_tvm_op = 'lib/libmxnet.so, lib/libmxnet.a, libsample_lib.so, 
3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, 
python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
 
 Review comment:
   We do not need this library set, as a result of this:
   https://github.com/apache/incubator-mxnet/pull/16450/files#r335190763


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[GitHub] [incubator-mxnet] mseth10 commented on a change in pull request #16450: Add test pipeline for USE_TVM_OP=OFF on Unix

mseth10 commented on a change in pull request #16450: Add test pipeline for 
USE_TVM_OP=OFF on Unix
URL: https://github.com/apache/incubator-mxnet/pull/16450#discussion_r335190763
 
 

 ##
 File path: ci/jenkins/Jenkins_steps.groovy
 ##
 @@ -756,6 +802,22 @@ def test_unix_python3_gpu() {
 }]
 }
 
+def test_unix_python3_gpu_no_tvm_op() {
+return ['Python3: GPU TVM_OP OFF': {
+  node(NODE_LINUX_GPU) {
+ws('workspace/ut-python3-gpu-no-tvm-op') {
+  try {
+utils.unpack_and_init('gpu_no_tvm_op', mx_lib_cython_no_tvm_op)
 
 Review comment:
   The build corresponding to this test packs library set 
`mx_lib_cpp_examples_no_tvm_op `
   
https://github.com/apache/incubator-mxnet/pull/16450/files#diff-bb61a49bf10098c4c42879f1632fdb40R273
   
   We should use the same library set while unpacking. A new library set is not 
required.
   Suggested action: Replace `mx_lib_cython_no_tvm_op ` with 
`mx_lib_cpp_examples_no_tvm_op `


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[incubator-mxnet] branch numpy_pr_merge updated (63500c5 -> b1932c0)

This is an automated email from the ASF dual-hosted git repository.

haoj pushed a change to branch numpy_pr_merge
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git.


omit 63500c5  add numpy op logspace (#15825)
omit bf8bd40  Numpy compatible vsplit; minor changes to split (#15983)
omit 80982ec  numpy eye op (#16132)
omit 974327e  [Numpy] Numpy compatible dstack (#15871)
 add 812e504  fix autodoc for spurrious toggles (#16452)
 add 7ce  Fix dtype bug (#16467)
 add 9ab428e  [Doc] Update the download page with 1.5.1 release (#16442)
 add 6e0b1a5  [Numpy] Numpy compatible dstack (#15871)
 add ceebcaf  numpy eye op (#16132)
 add 8222979  Numpy compatible vsplit; minor changes to split (#15983)
 add 8562adc  add numpy op logspace (#15825)
 add 9681197  add numpy op bitwise_xor, hsplit, moveaxis, rot90 (#16257)
 add f9359c3  Fix flakey pylint CI failures (#16462)
 add 67e1e68  Aggregated zero grad (#16446)
 add b1932c0  Move MRCNNMaskTarget op to contrib (#16486)

This update added new revisions after undoing existing revisions.
That is to say, some revisions that were in the old version of the
branch are not in the new version.  This situation occurs
when a user --force pushes a change and generates a repository
containing something like this:

 * -- * -- B -- O -- O -- O   (63500c5)
\
 N -- N -- N   refs/heads/numpy_pr_merge (b1932c0)

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Any revisions marked "omit" are not gone; other references still
refer to them.  Any revisions marked "discard" are gone forever.

No new revisions were added by this update.

Summary of changes:
 ci/other/pylintrc  |   7 +-
 docs/python_docs/_static/autodoc.js|  34 +-
 .../src/_includes/get_started/get_started.html |   6 +-
 .../src/_includes/get_started/pip_snippet.md   |   2 +-
 docs/static_site/src/pages/get_started/download.md |   1 +
 python/mxnet/_numpy_op_doc.py  |  44 +++
 python/mxnet/gluon/parameter.py|  21 +-
 python/mxnet/ndarray/numpy/_op.py  | 164 -
 python/mxnet/numpy/multiarray.py   | 161 -
 python/mxnet/numpy_op_signature.py |   5 +-
 python/mxnet/symbol/numpy/_symbol.py   | 151 -
 ...{mrcnn_target-inl.h => mrcnn_mask_target-inl.h} |  44 +--
 .../{mrcnn_target.cu => mrcnn_mask_target.cu}  |  56 ++--
 src/operator/contrib/reset_arrays-inl.h|  92 ++
 src/operator/contrib/reset_arrays.cc   |  74 +
 .../contrib/{multi_lars.cu => reset_arrays.cu} |  18 +-
 src/operator/mshadow_op.h  |   2 +
 src/operator/numpy/np_elemwise_broadcast_op.cu |   4 +
 src/operator/numpy/np_matrix_op-inl.h  | 367 +
 src/operator/numpy/np_matrix_op.cc | 168 ++
 src/operator/numpy/np_matrix_op.cu |  12 +
 src/operator/operator_tune.cc  |   1 +
 src/operator/tensor/matrix_op-inl.h| 115 ---
 tests/python/unittest/test_contrib_operator.py |  58 
 tests/python/unittest/test_gluon.py|  66 +++-
 tests/python/unittest/test_numpy_ndarray.py|  23 ++
 tests/python/unittest/test_numpy_op.py | 148 +
 tests/python/unittest/test_operator.py |  57 
 28 files changed, 1705 insertions(+), 196 deletions(-)
 rename src/operator/contrib/{mrcnn_target-inl.h => mrcnn_mask_target-inl.h} 
(70%)
 rename src/operator/contrib/{mrcnn_target.cu => mrcnn_mask_target.cu} (83%)
 create mode 100644 src/operator/contrib/reset_arrays-inl.h
 create mode 100644 src/operator/contrib/reset_arrays.cc
 copy src/operator/contrib/{multi_lars.cu => reset_arrays.cu} (69%)

[GitHub] [incubator-mxnet] mseth10 commented on a change in pull request #16450: Add test pipeline for USE_TVM_OP=OFF on Unix

mseth10 commented on a change in pull request #16450: Add test pipeline for 
USE_TVM_OP=OFF on Unix
URL: https://github.com/apache/incubator-mxnet/pull/16450#discussion_r335188815
 
 

 ##
 File path: ci/jenkins/Jenkins_steps.groovy
 ##
 @@ -25,19 +25,23 @@ utils = load('ci/Jenkinsfile_utils.groovy')
 // mxnet libraries
 mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, 
lib/libtvmop.so, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 
3rdparty/tvm/nnvm/lib/libnnvm.a'
 mx_lib_cython = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, 
lib/libtvmop.so, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 
3rdparty/tvm/nnvm/lib/libnnvm.a, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
+mx_lib_cython_no_tvm_op = 'lib/libmxnet.so, lib/libmxnet.a, libsample_lib.so, 
3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, 
python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
 
 // Python wheels
 mx_pip = 'build/*.whl'
 
 // mxnet cmake libraries, in cmake builds we do not produce a libnvvm static 
library by default.
 mx_cmake_lib = 'build/libmxnet.so, build/libmxnet.a, 
build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, 
build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, 
build/3rdparty/openmp/runtime/src/libomp.so'
+mx_cmake_lib_no_tvm_op = 'build/libmxnet.so, build/libmxnet.a, 
build/libsample_lib.so, build/3rdparty/dmlc-core/libdmlc.a, 
build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so'
 mx_cmake_lib_cython = 'build/libmxnet.so, build/libmxnet.a, 
build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, 
build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, 
build/3rdparty/openmp/runtime/src/libomp.so, python/mxnet/_cy2/*.so, 
python/mxnet/_cy3/*.so'
+mx_cmake_lib_cython_no_tvm_op = 'build/libmxnet.so, build/libmxnet.a, 
build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, 
build/3rdparty/openmp/runtime/src/libomp.so, python/mxnet/_cy2/*.so, 
python/mxnet/_cy3/*.so'
 
 Review comment:
   We do not need this library set, as a result of this:
   https://github.com/apache/incubator-mxnet/pull/16450/files#r335187907


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[incubator-mxnet] branch numpy_pr_merge updated (63500c5 -> b1932c0)

This is an automated email from the ASF dual-hosted git repository.

haoj pushed a change to branch numpy_pr_merge
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git.


omit 63500c5  add numpy op logspace (#15825)
omit bf8bd40  Numpy compatible vsplit; minor changes to split (#15983)
omit 80982ec  numpy eye op (#16132)
omit 974327e  [Numpy] Numpy compatible dstack (#15871)
 add 812e504  fix autodoc for spurrious toggles (#16452)
 add 7ce  Fix dtype bug (#16467)
 add 9ab428e  [Doc] Update the download page with 1.5.1 release (#16442)
 add 6e0b1a5  [Numpy] Numpy compatible dstack (#15871)
 add ceebcaf  numpy eye op (#16132)
 add 8222979  Numpy compatible vsplit; minor changes to split (#15983)
 add 8562adc  add numpy op logspace (#15825)
 add 9681197  add numpy op bitwise_xor, hsplit, moveaxis, rot90 (#16257)
 add f9359c3  Fix flakey pylint CI failures (#16462)
 add 67e1e68  Aggregated zero grad (#16446)
 add b1932c0  Move MRCNNMaskTarget op to contrib (#16486)

This update added new revisions after undoing existing revisions.
That is to say, some revisions that were in the old version of the
branch are not in the new version.  This situation occurs
when a user --force pushes a change and generates a repository
containing something like this:

 * -- * -- B -- O -- O -- O   (63500c5)
\
 N -- N -- N   refs/heads/numpy_pr_merge (b1932c0)

You should already have received notification emails for all of the O
revisions, and so the following emails describe only the N revisions
from the common base, B.

Any revisions marked "omit" are not gone; other references still
refer to them.  Any revisions marked "discard" are gone forever.

No new revisions were added by this update.

Summary of changes:
 ci/other/pylintrc  |   7 +-
 docs/python_docs/_static/autodoc.js|  34 +-
 .../src/_includes/get_started/get_started.html |   6 +-
 .../src/_includes/get_started/pip_snippet.md   |   2 +-
 docs/static_site/src/pages/get_started/download.md |   1 +
 python/mxnet/_numpy_op_doc.py  |  44 +++
 python/mxnet/gluon/parameter.py|  21 +-
 python/mxnet/ndarray/numpy/_op.py  | 164 -
 python/mxnet/numpy/multiarray.py   | 161 -
 python/mxnet/numpy_op_signature.py |   5 +-
 python/mxnet/symbol/numpy/_symbol.py   | 151 -
 ...{mrcnn_target-inl.h => mrcnn_mask_target-inl.h} |  44 +--
 .../{mrcnn_target.cu => mrcnn_mask_target.cu}  |  56 ++--
 src/operator/contrib/reset_arrays-inl.h|  92 ++
 src/operator/contrib/reset_arrays.cc   |  74 +
 .../contrib/{multi_lars.cu => reset_arrays.cu} |  18 +-
 src/operator/mshadow_op.h  |   2 +
 src/operator/numpy/np_elemwise_broadcast_op.cu |   4 +
 src/operator/numpy/np_matrix_op-inl.h  | 367 +
 src/operator/numpy/np_matrix_op.cc | 168 ++
 src/operator/numpy/np_matrix_op.cu |  12 +
 src/operator/operator_tune.cc  |   1 +
 src/operator/tensor/matrix_op-inl.h| 115 ---
 tests/python/unittest/test_contrib_operator.py |  58 
 tests/python/unittest/test_gluon.py|  66 +++-
 tests/python/unittest/test_numpy_ndarray.py|  23 ++
 tests/python/unittest/test_numpy_op.py | 148 +
 tests/python/unittest/test_operator.py |  57 
 28 files changed, 1705 insertions(+), 196 deletions(-)
 rename src/operator/contrib/{mrcnn_target-inl.h => mrcnn_mask_target-inl.h} 
(70%)
 rename src/operator/contrib/{mrcnn_target.cu => mrcnn_mask_target.cu} (83%)
 create mode 100644 src/operator/contrib/reset_arrays-inl.h
 create mode 100644 src/operator/contrib/reset_arrays.cc
 copy src/operator/contrib/{multi_lars.cu => reset_arrays.cu} (69%)

[incubator-mxnet] branch numpy_pr_merge updated (63500c5 -> b1932c0)

This is an automated email from the ASF dual-hosted git repository.

haoj pushed a change to branch numpy_pr_merge
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git.


omit 63500c5  add numpy op logspace (#15825)
omit bf8bd40  Numpy compatible vsplit; minor changes to split (#15983)
omit 80982ec  numpy eye op (#16132)
omit 974327e  [Numpy] Numpy compatible dstack (#15871)
 add 812e504  fix autodoc for spurrious toggles (#16452)
 add 7ce  Fix dtype bug (#16467)
 add 9ab428e  [Doc] Update the download page with 1.5.1 release (#16442)
 add 6e0b1a5  [Numpy] Numpy compatible dstack (#15871)
 add ceebcaf  numpy eye op (#16132)
 add 8222979  Numpy compatible vsplit; minor changes to split (#15983)
 add 8562adc  add numpy op logspace (#15825)
 add 9681197  add numpy op bitwise_xor, hsplit, moveaxis, rot90 (#16257)
 add f9359c3  Fix flakey pylint CI failures (#16462)
 add 67e1e68  Aggregated zero grad (#16446)
 add b1932c0  Move MRCNNMaskTarget op to contrib (#16486)

This update added new revisions after undoing existing revisions.
That is to say, some revisions that were in the old version of the
branch are not in the new version.  This situation occurs
when a user --force pushes a change and generates a repository
containing something like this:

 * -- * -- B -- O -- O -- O   (63500c5)
\
 N -- N -- N   refs/heads/numpy_pr_merge (b1932c0)

You should already have received notification emails for all of the O
revisions, and so the following emails describe only the N revisions
from the common base, B.

Any revisions marked "omit" are not gone; other references still
refer to them.  Any revisions marked "discard" are gone forever.

No new revisions were added by this update.

Summary of changes:
 ci/other/pylintrc  |   7 +-
 docs/python_docs/_static/autodoc.js|  34 +-
 .../src/_includes/get_started/get_started.html |   6 +-
 .../src/_includes/get_started/pip_snippet.md   |   2 +-
 docs/static_site/src/pages/get_started/download.md |   1 +
 python/mxnet/_numpy_op_doc.py  |  44 +++
 python/mxnet/gluon/parameter.py|  21 +-
 python/mxnet/ndarray/numpy/_op.py  | 164 -
 python/mxnet/numpy/multiarray.py   | 161 -
 python/mxnet/numpy_op_signature.py |   5 +-
 python/mxnet/symbol/numpy/_symbol.py   | 151 -
 ...{mrcnn_target-inl.h => mrcnn_mask_target-inl.h} |  44 +--
 .../{mrcnn_target.cu => mrcnn_mask_target.cu}  |  56 ++--
 src/operator/contrib/reset_arrays-inl.h|  92 ++
 src/operator/contrib/reset_arrays.cc   |  74 +
 .../contrib/{multi_lars.cu => reset_arrays.cu} |  18 +-
 src/operator/mshadow_op.h  |   2 +
 src/operator/numpy/np_elemwise_broadcast_op.cu |   4 +
 src/operator/numpy/np_matrix_op-inl.h  | 367 +
 src/operator/numpy/np_matrix_op.cc | 168 ++
 src/operator/numpy/np_matrix_op.cu |  12 +
 src/operator/operator_tune.cc  |   1 +
 src/operator/tensor/matrix_op-inl.h| 115 ---
 tests/python/unittest/test_contrib_operator.py |  58 
 tests/python/unittest/test_gluon.py|  66 +++-
 tests/python/unittest/test_numpy_ndarray.py|  23 ++
 tests/python/unittest/test_numpy_op.py | 148 +
 tests/python/unittest/test_operator.py |  57 
 28 files changed, 1705 insertions(+), 196 deletions(-)
 rename src/operator/contrib/{mrcnn_target-inl.h => mrcnn_mask_target-inl.h} 
(70%)
 rename src/operator/contrib/{mrcnn_target.cu => mrcnn_mask_target.cu} (83%)
 create mode 100644 src/operator/contrib/reset_arrays-inl.h
 create mode 100644 src/operator/contrib/reset_arrays.cc
 copy src/operator/contrib/{multi_lars.cu => reset_arrays.cu} (69%)

[GitHub] [incubator-mxnet] mseth10 commented on a change in pull request #16450: Add test pipeline for USE_TVM_OP=OFF on Unix

mseth10 commented on a change in pull request #16450: Add test pipeline for 
USE_TVM_OP=OFF on Unix
URL: https://github.com/apache/incubator-mxnet/pull/16450#discussion_r335187907
 
 

 ##
 File path: ci/jenkins/Jenkins_steps.groovy
 ##
 @@ -273,6 +305,20 @@ def compile_unix_cmake_gpu() {
 }]
 }
 
+def compile_unix_cmake_gpu_no_tvm_op() {
+return ['GPU: CMake TVM_OP OFF': {
+  node(NODE_LINUX_CPU) {
+ws('workspace/build-cmake-gpu-no-tvm-op') {
+  timeout(time: max_time, unit: 'MINUTES') {
+utils.init_git()
+utils.docker_run('ubuntu_gpu_cu101', 
'build_ubuntu_gpu_cmake_no_tvm_op', false)
+utils.pack_lib('cmake_gpu_no_tvm_op', 
mx_cmake_lib_cython_no_tvm_op)
 
 Review comment:
   We don't need to pack libraries here, as there is no test stage that uses it.


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[incubator-mxnet] branch numpy_pr_merge updated (63500c5 -> b1932c0)

This is an automated email from the ASF dual-hosted git repository.

haoj pushed a change to branch numpy_pr_merge
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git.


omit 63500c5  add numpy op logspace (#15825)
omit bf8bd40  Numpy compatible vsplit; minor changes to split (#15983)
omit 80982ec  numpy eye op (#16132)
omit 974327e  [Numpy] Numpy compatible dstack (#15871)
 add 812e504  fix autodoc for spurrious toggles (#16452)
 add 7ce  Fix dtype bug (#16467)
 add 9ab428e  [Doc] Update the download page with 1.5.1 release (#16442)
 add 6e0b1a5  [Numpy] Numpy compatible dstack (#15871)
 add ceebcaf  numpy eye op (#16132)
 add 8222979  Numpy compatible vsplit; minor changes to split (#15983)
 add 8562adc  add numpy op logspace (#15825)
 add 9681197  add numpy op bitwise_xor, hsplit, moveaxis, rot90 (#16257)
 add f9359c3  Fix flakey pylint CI failures (#16462)
 add 67e1e68  Aggregated zero grad (#16446)
 add b1932c0  Move MRCNNMaskTarget op to contrib (#16486)

This update added new revisions after undoing existing revisions.
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branch are not in the new version.  This situation occurs
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Summary of changes:
 ci/other/pylintrc  |   7 +-
 docs/python_docs/_static/autodoc.js|  34 +-
 .../src/_includes/get_started/get_started.html |   6 +-
 .../src/_includes/get_started/pip_snippet.md   |   2 +-
 docs/static_site/src/pages/get_started/download.md |   1 +
 python/mxnet/_numpy_op_doc.py  |  44 +++
 python/mxnet/gluon/parameter.py|  21 +-
 python/mxnet/ndarray/numpy/_op.py  | 164 -
 python/mxnet/numpy/multiarray.py   | 161 -
 python/mxnet/numpy_op_signature.py |   5 +-
 python/mxnet/symbol/numpy/_symbol.py   | 151 -
 ...{mrcnn_target-inl.h => mrcnn_mask_target-inl.h} |  44 +--
 .../{mrcnn_target.cu => mrcnn_mask_target.cu}  |  56 ++--
 src/operator/contrib/reset_arrays-inl.h|  92 ++
 src/operator/contrib/reset_arrays.cc   |  74 +
 .../contrib/{multi_lars.cu => reset_arrays.cu} |  18 +-
 src/operator/mshadow_op.h  |   2 +
 src/operator/numpy/np_elemwise_broadcast_op.cu |   4 +
 src/operator/numpy/np_matrix_op-inl.h  | 367 +
 src/operator/numpy/np_matrix_op.cc | 168 ++
 src/operator/numpy/np_matrix_op.cu |  12 +
 src/operator/operator_tune.cc  |   1 +
 src/operator/tensor/matrix_op-inl.h| 115 ---
 tests/python/unittest/test_contrib_operator.py |  58 
 tests/python/unittest/test_gluon.py|  66 +++-
 tests/python/unittest/test_numpy_ndarray.py|  23 ++
 tests/python/unittest/test_numpy_op.py | 148 +
 tests/python/unittest/test_operator.py |  57 
 28 files changed, 1705 insertions(+), 196 deletions(-)
 rename src/operator/contrib/{mrcnn_target-inl.h => mrcnn_mask_target-inl.h} 
(70%)
 rename src/operator/contrib/{mrcnn_target.cu => mrcnn_mask_target.cu} (83%)
 create mode 100644 src/operator/contrib/reset_arrays-inl.h
 create mode 100644 src/operator/contrib/reset_arrays.cc
 copy src/operator/contrib/{multi_lars.cu => reset_arrays.cu} (69%)

[incubator-mxnet] branch numpy_pr_merge updated (63500c5 -> b1932c0)

This is an automated email from the ASF dual-hosted git repository.

haoj pushed a change to branch numpy_pr_merge
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git.


omit 63500c5  add numpy op logspace (#15825)
omit bf8bd40  Numpy compatible vsplit; minor changes to split (#15983)
omit 80982ec  numpy eye op (#16132)
omit 974327e  [Numpy] Numpy compatible dstack (#15871)
 add 812e504  fix autodoc for spurrious toggles (#16452)
 add 7ce  Fix dtype bug (#16467)
 add 9ab428e  [Doc] Update the download page with 1.5.1 release (#16442)
 add 6e0b1a5  [Numpy] Numpy compatible dstack (#15871)
 add ceebcaf  numpy eye op (#16132)
 add 8222979  Numpy compatible vsplit; minor changes to split (#15983)
 add 8562adc  add numpy op logspace (#15825)
 add 9681197  add numpy op bitwise_xor, hsplit, moveaxis, rot90 (#16257)
 add f9359c3  Fix flakey pylint CI failures (#16462)
 add 67e1e68  Aggregated zero grad (#16446)
 add b1932c0  Move MRCNNMaskTarget op to contrib (#16486)

This update added new revisions after undoing existing revisions.
That is to say, some revisions that were in the old version of the
branch are not in the new version.  This situation occurs
when a user --force pushes a change and generates a repository
containing something like this:

 * -- * -- B -- O -- O -- O   (63500c5)
\
 N -- N -- N   refs/heads/numpy_pr_merge (b1932c0)

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Any revisions marked "omit" are not gone; other references still
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No new revisions were added by this update.

Summary of changes:
 ci/other/pylintrc  |   7 +-
 docs/python_docs/_static/autodoc.js|  34 +-
 .../src/_includes/get_started/get_started.html |   6 +-
 .../src/_includes/get_started/pip_snippet.md   |   2 +-
 docs/static_site/src/pages/get_started/download.md |   1 +
 python/mxnet/_numpy_op_doc.py  |  44 +++
 python/mxnet/gluon/parameter.py|  21 +-
 python/mxnet/ndarray/numpy/_op.py  | 164 -
 python/mxnet/numpy/multiarray.py   | 161 -
 python/mxnet/numpy_op_signature.py |   5 +-
 python/mxnet/symbol/numpy/_symbol.py   | 151 -
 ...{mrcnn_target-inl.h => mrcnn_mask_target-inl.h} |  44 +--
 .../{mrcnn_target.cu => mrcnn_mask_target.cu}  |  56 ++--
 src/operator/contrib/reset_arrays-inl.h|  92 ++
 src/operator/contrib/reset_arrays.cc   |  74 +
 .../contrib/{multi_lars.cu => reset_arrays.cu} |  18 +-
 src/operator/mshadow_op.h  |   2 +
 src/operator/numpy/np_elemwise_broadcast_op.cu |   4 +
 src/operator/numpy/np_matrix_op-inl.h  | 367 +
 src/operator/numpy/np_matrix_op.cc | 168 ++
 src/operator/numpy/np_matrix_op.cu |  12 +
 src/operator/operator_tune.cc  |   1 +
 src/operator/tensor/matrix_op-inl.h| 115 ---
 tests/python/unittest/test_contrib_operator.py |  58 
 tests/python/unittest/test_gluon.py|  66 +++-
 tests/python/unittest/test_numpy_ndarray.py|  23 ++
 tests/python/unittest/test_numpy_op.py | 148 +
 tests/python/unittest/test_operator.py |  57 
 28 files changed, 1705 insertions(+), 196 deletions(-)
 rename src/operator/contrib/{mrcnn_target-inl.h => mrcnn_mask_target-inl.h} 
(70%)
 rename src/operator/contrib/{mrcnn_target.cu => mrcnn_mask_target.cu} (83%)
 create mode 100644 src/operator/contrib/reset_arrays-inl.h
 create mode 100644 src/operator/contrib/reset_arrays.cc
 copy src/operator/contrib/{multi_lars.cu => reset_arrays.cu} (69%)

[GitHub] [incubator-mxnet] ChaiBapchya commented on issue #16416: Dgl ops 2

ChaiBapchya commented on issue #16416: Dgl ops 2
URL: https://github.com/apache/incubator-mxnet/pull/16416#issuecomment-542411988
 
 
   Batch_dot operator hasn't been tested for large array before. Atleast not in 
the test_large_array.py file


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[GitHub] [incubator-mxnet] haojin2 commented on issue #16493: Tests of interoperability of numpy dispatch

haojin2 commented on issue #16493: Tests of interoperability of numpy dispatch
URL: https://github.com/apache/incubator-mxnet/pull/16493#issuecomment-542411471
 
 
   @xiezhq-hermann please fix the CI errors.


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[GitHub] [incubator-mxnet] sxjscience edited a comment on issue #16416: Dgl ops 2

sxjscience edited a comment on issue #16416: Dgl ops 2
URL: https://github.com/apache/incubator-mxnet/pull/16416#issuecomment-542409048
 
 
   ~~I think we've tested for batch_dot before~~
   
   Okay, this is specific to large array.


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[GitHub] [incubator-mxnet] sxjscience commented on issue #16416: Dgl ops 2

sxjscience commented on issue #16416: Dgl ops 2
URL: https://github.com/apache/incubator-mxnet/pull/16416#issuecomment-542409048
 
 
   I think we've tested for batch_dot before


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[GitHub] [incubator-mxnet] haojin2 opened a new pull request #16494: Proper handling of "allow_np_array" attribute in optimizer

haojin2 opened a new pull request #16494: Proper handling of "allow_np_array" 
attribute in optimizer
URL: https://github.com/apache/incubator-mxnet/pull/16494
 
 
   ## Description ##
   As title.
   
   ## Checklist ##
   ### Essentials ###
   Please feel free to remove inapplicable items for your PR.
   - [ ] The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to 
the relevant [JIRA issue](https://issues.apache.org/jira/projects/MXNET/issues) 
created (except PRs with tiny changes)
   - [ ] Changes are complete (i.e. I finished coding on this PR)
   - [ ] All changes have test coverage:
   - Unit tests are added for small changes to verify correctness (e.g. adding 
a new operator)
   - Nightly tests are added for complicated/long-running ones (e.g. changing 
distributed kvstore)
   - Build tests will be added for build configuration changes (e.g. adding a 
new build option with NCCL)
   - [ ] Code is well-documented: 
   - For user-facing API changes, API doc string has been updated. 
   - For new C++ functions in header files, their functionalities and arguments 
are documented. 
   - For new examples, README.md is added to explain the what the example does, 
the source of the dataset, expected performance on test set and reference to 
the original paper if applicable
   - Check the API doc at 
https://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html
   - [ ] To the my best knowledge, examples are either not affected by this 
change, or have been fixed to be compatible with this change
   
   ### Changes ###
   - [x] Fix of the logic
   - [x] Unit test
   
   ## Comments ##
   @eric-haibin-lin @reminisce 


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[GitHub] [incubator-mxnet] DickJC123 commented on issue #16401: CI showing random pylint failures

DickJC123 commented on issue #16401: CI showing random pylint failures
URL: 
https://github.com/apache/incubator-mxnet/issues/16401#issuecomment-542400603
 
 
   Issue fixed by now-merged 
https://github.com/apache/incubator-mxnet/pull/16462.


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[GitHub] [incubator-mxnet] DickJC123 closed issue #16401: CI showing random pylint failures

DickJC123 closed issue #16401: CI showing random pylint failures
URL: https://github.com/apache/incubator-mxnet/issues/16401
 
 
   


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[GitHub] [incubator-mxnet] xiezhq-hermann opened a new pull request #16493: Tests of interoperability of numpy dispatch

xiezhq-hermann opened a new pull request #16493: Tests of interoperability of 
numpy dispatch
URL: https://github.com/apache/incubator-mxnet/pull/16493
 
 
   ## Description ##
   Tests of interoperability of numpy dispatch, this PR covers these OPs:
   - concatenate
   - copy
   - expand_dims
   - expm1
   - norm
   - svd
   - split
   - squeeze
   - std
   - swapaxes
   - tensorfot
   - tile
   - trace
   - transpose
   - tril
   ## Checklist ##
   ### Essentials ###
   Please feel free to remove inapplicable items for your PR.
   - [ ] The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to 
the relevant [JIRA issue](https://issues.apache.org/jira/projects/MXNET/issues) 
created (except PRs with tiny changes)
   - [ ] Changes are complete (i.e. I finished coding on this PR)
   - [ ] All changes have test coverage:
   - Unit tests are added for small changes to verify correctness (e.g. adding 
a new operator)
   - Nightly tests are added for complicated/long-running ones (e.g. changing 
distributed kvstore)
   - Build tests will be added for build configuration changes (e.g. adding a 
new build option with NCCL)
   - [ ] Code is well-documented: 
   - For user-facing API changes, API doc string has been updated. 
   - For new C++ functions in header files, their functionalities and arguments 
are documented. 
   - For new examples, README.md is added to explain the what the example does, 
the source of the dataset, expected performance on test set and reference to 
the original paper if applicable
   - Check the API doc at 
https://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html
   - [ ] To the my best knowledge, examples are either not affected by this 
change, or have been fixed to be compatible with this change
   
   ### Changes ###
   - [ ] Feature1, tests, (and when applicable, API doc)
   - [ ] Feature2, tests, (and when applicable, API doc)
   
   ## Comments ##
   Known common issues:
   - data type coverage
   - some module (like masked array, np.matrix, etc) not supported
   - python native scalar and list objects are not dispatchable
   
   @reminisce Thanks for your effort to review the test codes, please feedback 
me any problems.


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[GitHub] [incubator-mxnet] ChaiBapchya commented on a change in pull request #16416: Dgl ops 2

ChaiBapchya commented on a change in pull request #16416: Dgl ops 2
URL: https://github.com/apache/incubator-mxnet/pull/16416#discussion_r335171324
 
 

 ##
 File path: tests/nightly/test_large_array.py
 ##
 @@ -1212,6 +1214,80 @@ def test_full():
 assert a[-1][-1] == 3
 
 
+def test_hyperbolic():
+def test_arccosh(a):
 
 Review comment:
   We are doing the last element check to save on time. But this is being done 
for each test (>50) so doesn't make sense to add for this specific test. In 
that case, should i add it in beginning of the file?


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[GitHub] [incubator-mxnet] ChaiBapchya commented on a change in pull request #16416: Dgl ops 2

ChaiBapchya commented on a change in pull request #16416: Dgl ops 2
URL: https://github.com/apache/incubator-mxnet/pull/16416#discussion_r335171324
 
 

 ##
 File path: tests/nightly/test_large_array.py
 ##
 @@ -1212,6 +1214,80 @@ def test_full():
 assert a[-1][-1] == 3
 
 
+def test_hyperbolic():
+def test_arccosh(a):
 
 Review comment:
   We are doing the last element check to save on time. But this is being done 
for each test (>50) so doesn't make sense to add for this specific test. In 
that case, should i add it to the top of the function?


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[GitHub] [incubator-mxnet] stu1130 closed issue #16491: CI failed due to DockerHub outage

stu1130 closed issue #16491: CI failed due to DockerHub outage
URL: https://github.com/apache/incubator-mxnet/issues/16491
 
 
   


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[GitHub] [incubator-mxnet] leezu commented on a change in pull request #16465: [Gluon] [Fix] [WIP] Fix HybridBlock when hybridize is not called

leezu commented on a change in pull request #16465: [Gluon] [Fix] [WIP] Fix 
HybridBlock when hybridize is not called
URL: https://github.com/apache/incubator-mxnet/pull/16465#discussion_r335161765
 
 

 ##
 File path: python/mxnet/gluon/block.py
 ##
 @@ -1054,34 +1098,16 @@ def register_op_hook(self, callback, 
monitor_all=False):
 def forward(self, x, *args):
 """Defines the forward computation. Arguments can be either
 :py:class:`NDArray` or :py:class:`Symbol`."""
-flatten_args = _flatten([x] + list(args), 'inputs')[0]
-is_ndarray = None
-ctx = None
-exist_sym_nd = False
-for ele in flatten_args:
-if isinstance(ele, NDArray):
-if is_ndarray is False:
-raise ValueError('In HybridBlock, we do not support mixed 
NDArrays and Symbols'
- ' types for the input.\n'
- 'Received types are: {}.'
- .format([type(ele) for ele in 
flatten_args]))
-is_ndarray = True
-exist_sym_nd = True
-ctx = ele.context
 
 Review comment:
   We should get rid of choosing one array and using it's context as default 
context. For parameters, users should get the array via 
`self.weight.data(ctx)`. For the time being I suggest not to break the 
behaviour, to avoid unintended consequences


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[GitHub] [incubator-mxnet] access2rohit commented on issue #16409: adding tests to verify large tensor support for more operators

access2rohit commented on issue #16409: adding tests to verify large tensor 
support for more operators
URL: https://github.com/apache/incubator-mxnet/pull/16409#issuecomment-542389259
 
 
   test_large_array.test_gluon_embedding ... ok
   test_large_array.test_ndarray_zeros ... ok
   test_large_array.test_ndarray_ones ... ok
   test_large_array.test_ndarray_convert ... ok
   test_large_array.test_ndarray_random_uniform ... [DEBUG] Setting test 
np/mx/python random seeds, use MXNET_TEST_SEED=583459780 to reproduce.
   ok
   test_large_array.test_ndarray_random_randint ... [DEBUG] Setting test 
np/mx/python random seeds, use MXNET_TEST_SEED=1561187177 to reproduce.
   ok
   test_large_array.test_ndarray_random_exponential ... [DEBUG] Setting test 
np/mx/python random seeds, use MXNET_TEST_SEED=488723450 to reproduce.
   ok
   test_large_array.test_ndarray_random_gamma ... [DEBUG] Setting test 
np/mx/python random seeds, use MXNET_TEST_SEED=1246538300 to reproduce.
   ok
   test_large_array.test_ndarray_random_multinomial ... [DEBUG] Setting test 
np/mx/python random seeds, use MXNET_TEST_SEED=279077978 to reproduce.
   
   
   ok
   test_large_array.test_ndarray_random_generalized_negative_binomial ... 
[DEBUG] Setting test np/mx/python random seeds, use MXNET_TEST_SEED=582809580 
to reproduce.
   ok
   test_large_array.test_ndarray_random_negative_binomial ... [DEBUG] Setting 
test np/mx/python random seeds, use MXNET_TEST_SEED=1682432459 to reproduce.
   ok
   test_large_array.test_ndarray_random_normal ... [DEBUG] Setting test 
np/mx/python random seeds, use MXNET_TEST_SEED=721628740 to reproduce.
   ok
   test_large_array.test_ndarray_random_poisson ... [DEBUG] Setting test 
np/mx/python random seeds, use MXNET_TEST_SEED=1614267541 to reproduce.
   ok
   test_large_array.test_ndarray_random_randn ... [DEBUG] Setting test 
np/mx/python random seeds, use MXNET_TEST_SEED=1931064888 to reproduce.
   ok
   test_large_array.test_ndarray_random_shuffle ... [DEBUG] Setting test 
np/mx/python random seeds, use MXNET_TEST_SEED=887032379 to reproduce.
   ok
   test_large_array.test_ndarray_empty ... ok
   test_large_array.test_elementwise ... ok
   test_large_array.test_reduce ... ok
   test_large_array.test_dot ... ok
   test_large_array.test_FullyConnected ... ok
   test_large_array.test_broadcast ... ok
   test_large_array.test_clip ... ok
   test_large_array.test_split ... ok
   test_large_array.test_argmin ... ok
   test_large_array.test_tile ... ok
   test_large_array.test_take ... ok
   test_large_array.test_slice ... ok
   test_large_array.test_slice_assign ... ok
   test_large_array.test_expand_dims ... ok
   Helper function that cleans up memory by releasing it from memory pool ... ok
   test_large_array.test_squeeze ... ok
   test_large_array.test_broadcast_div ... ok
   test_large_array.test_Dense ... ok
   test_large_array.test_where ... ok
   test_large_array.test_pick ... ok
   test_large_array.test_depthtospace ... ok
   test_large_array.test_spacetodepth ... ok
   test_large_array.test_diag ... [DEBUG] Setting test np/mx/python random 
seeds, use MXNET_TEST_SEED=1737100945 to reproduce.
   ok
   test_large_array.test_ravel_multi_index ... [DEBUG] Setting test 
np/mx/python random seeds, use MXNET_TEST_SEED=1577038190 to reproduce.
   ok
   test_large_array.test_unravel_index ... [DEBUG] Setting test np/mx/python 
random seeds, use MXNET_TEST_SEED=754939684 to reproduce.
   ok
   test_large_array.test_transpose ... ok
   test_large_array.test_swapaxes ... ok
   test_large_array.test_flip ... ok
   
   
   
   test_large_array.test_softmax ... ok
   test_large_array.test_argsort ... ok
   test_large_array.test_sort ... ok
   test_large_array.test_topk ... ok
   test_large_array.test_exponent_logarithm_operators ... ok
   test_large_array.test_power_operators ... ok
   
   
   test_large_array.test_sequence_mask ... ok
   test_large_array.test_sequence_reverse ... ok
   test_large_array.test_sequence_last ... ok
   test_large_array.test_softmax_cross_entropy ... ok
   test_large_array.test_index_copy ... ok
   test_large_array.testSoftmaxOutput ... ok
   test_large_array.test_leaky_relu ... ok
   test_large_array.test_pooling ... ok
   test_large_array.test_layer_norm ... ok
   
   test_large_array.test_dropout ... ok
   test_large_array.test_activation ... ok
   test_large_array.test_batchnorm ... ok
   test_large_array.test_add ... ok
   test_large_array.test_sub ... ok
   test_large_array.test_rsub ... ok
   test_large_array.test_neg ... ok
   test_large_array.test_mul ... ok
   test_large_array.test_div ... ok
   test_large_array.test_rdiv ... ok
   test_large_array.test_mod ... ok
   test_large_array.test_rmod ... ok
   test_large_array.test_imod ... ok
   test_large_array.test_pow ... ok
   test_large_array.test_rpow ... ok
   test_large_array.test_shape ... ok
   test_large_array.test_size ... ok
   test_large_array.test_copy ... ok
   test_large_array.test_copy_to ... ok

[GitHub] [incubator-mxnet] anirudh2290 commented on issue #16477: added more tests to verify support for large vector

anirudh2290 commented on issue #16477: added more tests to verify support for 
large vector
URL: https://github.com/apache/incubator-mxnet/pull/16477#issuecomment-542380621
 
 
   randint not failing now ?


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[incubator-mxnet] branch master updated (67e1e68 -> b1932c0)

2019-10-15 Thread zhreshold

This is an automated email from the ASF dual-hosted git repository.

zhreshold pushed a change to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git.


from 67e1e68  Aggregated zero grad (#16446)
 add b1932c0  Move MRCNNMaskTarget op to contrib (#16486)

No new revisions were added by this update.

Summary of changes:
 ...{mrcnn_target-inl.h => mrcnn_mask_target-inl.h} | 44 
 .../{mrcnn_target.cu => mrcnn_mask_target.cu}  | 56 ++---
 tests/python/unittest/test_contrib_operator.py | 58 ++
 tests/python/unittest/test_operator.py | 57 -
 4 files changed, 108 insertions(+), 107 deletions(-)
 rename src/operator/contrib/{mrcnn_target-inl.h => mrcnn_mask_target-inl.h} 
(70%)
 rename src/operator/contrib/{mrcnn_target.cu => mrcnn_mask_target.cu} (83%)

[incubator-mxnet] branch master updated (67e1e68 -> b1932c0)

2019-10-15 Thread zhreshold

This is an automated email from the ASF dual-hosted git repository.

zhreshold pushed a change to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git.


from 67e1e68  Aggregated zero grad (#16446)
 add b1932c0  Move MRCNNMaskTarget op to contrib (#16486)

No new revisions were added by this update.

Summary of changes:
 ...{mrcnn_target-inl.h => mrcnn_mask_target-inl.h} | 44 
 .../{mrcnn_target.cu => mrcnn_mask_target.cu}  | 56 ++---
 tests/python/unittest/test_contrib_operator.py | 58 ++
 tests/python/unittest/test_operator.py | 57 -
 4 files changed, 108 insertions(+), 107 deletions(-)
 rename src/operator/contrib/{mrcnn_target-inl.h => mrcnn_mask_target-inl.h} 
(70%)
 rename src/operator/contrib/{mrcnn_target.cu => mrcnn_mask_target.cu} (83%)

[GitHub] [incubator-mxnet] zhreshold merged pull request #16486: Move mrcnn_mask_target op to contrib

zhreshold merged pull request #16486: Move mrcnn_mask_target op to contrib
URL: https://github.com/apache/incubator-mxnet/pull/16486
 
 
   


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[GitHub] [incubator-mxnet] zhreshold commented on issue #16486: Move mrcnn_mask_target op to contrib

zhreshold commented on issue #16486: Move mrcnn_mask_target op to contrib
URL: https://github.com/apache/incubator-mxnet/pull/16486#issuecomment-542357930
 
 
   thanks for the fix!


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[GitHub] [incubator-mxnet] mseth10 commented on a change in pull request #16450: Add test pipeline for USE_TVM_OP=OFF on Unix

mseth10 commented on a change in pull request #16450: Add test pipeline for 
USE_TVM_OP=OFF on Unix
URL: https://github.com/apache/incubator-mxnet/pull/16450#discussion_r335124096
 
 

 ##
 File path: ci/jenkins/Jenkins_steps.groovy
 ##
 @@ -25,19 +25,23 @@ utils = load('ci/Jenkinsfile_utils.groovy')
 // mxnet libraries
 mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, 
lib/libtvmop.so, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 
3rdparty/tvm/nnvm/lib/libnnvm.a'
 mx_lib_cython = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, 
lib/libtvmop.so, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 
3rdparty/tvm/nnvm/lib/libnnvm.a, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
+mx_lib_cython_no_tvm_op = 'lib/libmxnet.so, lib/libmxnet.a, libsample_lib.so, 
3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, 
python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
 
 // Python wheels
 mx_pip = 'build/*.whl'
 
 // mxnet cmake libraries, in cmake builds we do not produce a libnvvm static 
library by default.
 mx_cmake_lib = 'build/libmxnet.so, build/libmxnet.a, 
build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, 
build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, 
build/3rdparty/openmp/runtime/src/libomp.so'
+mx_cmake_lib_no_tvm_op = 'build/libmxnet.so, build/libmxnet.a, 
libsample_lib.so, build/3rdparty/dmlc-core/libdmlc.a, 
build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so'
 mx_cmake_lib_cython = 'build/libmxnet.so, build/libmxnet.a, 
build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, 
build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, 
build/3rdparty/openmp/runtime/src/libomp.so, python/mxnet/_cy2/*.so, 
python/mxnet/_cy3/*.so'
+mx_cmake_lib_cython_no_tvm_op = 'build/libmxnet.so, build/libmxnet.a, 
build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, 
build/3rdparty/openmp/runtime/src/libomp.so, python/mxnet/_cy2/*.so, 
python/mxnet/_cy3/*.so'
 // mxnet cmake libraries, in cmake builds we do not produce a libnvvm static 
library by default.
 mx_cmake_lib_debug = 'build/libmxnet.so, build/libmxnet.a, 
build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, 
build/libsample_lib.so, build/3rdparty/dmlc-core/libdmlc.a, 
build/tests/mxnet_unit_tests'
 mx_cmake_mkldnn_lib = 'build/libmxnet.so, build/libmxnet.a, 
build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, 
build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, 
build/3rdparty/openmp/runtime/src/libomp.so, 
build/3rdparty/mkldnn/src/libmkldnn.so.0'
 mx_mkldnn_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, 
lib/libtvmop.so, libsample_lib.so, lib/libiomp5.so, lib/libmkldnn.so.0, 
lib/libmklml_intel.so, 3rdparty/dmlc-core/libdmlc.a, 
3rdparty/tvm/nnvm/lib/libnnvm.a'
 mx_tensorrt_lib = 'build/libmxnet.so, build/3rdparty/tvm/libtvm_runtime.so, 
build/libtvmop.so, lib/libnvonnxparser_runtime.so.0, lib/libnvonnxparser.so.0, 
lib/libonnx_proto.so, lib/libonnx.so'
 mx_lib_cpp_examples = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, 
lib/libtvmop.so, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 
3rdparty/tvm/nnvm/lib/libnnvm.a, 3rdparty/ps-lite/build/libps.a, 
deps/lib/libprotobuf-lite.a, deps/lib/libzmq.a, build/cpp-package/example/*, 
python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
+mx_lib_cpp_examples_no_tvm_op = 'lib/libmxnet.so, lib/libmxnet.a, 
libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 
3rdparty/tvm/nnvm/lib/libnnvm.a, 3rdparty/ps-lite/build/libps.a, 
deps/lib/libprotobuf-lite.a, deps/lib/libzmq.a, build/cpp-package/example/*, 
python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
 mx_lib_cpp_examples_cpu = 'build/libmxnet.so, 
build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, 
build/cpp-package/example/*'
 
 Review comment:
   I took a look and let @reminisce know offline. `mx_cmake_lib_no_tvm_op` is 
the binary set corresponding to a cmake build and hence we need to put it in a 
`build/` directory.


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[GitHub] [incubator-mxnet] mseth10 commented on a change in pull request #16450: Add test pipeline for USE_TVM_OP=OFF on Unix

mseth10 commented on a change in pull request #16450: Add test pipeline for 
USE_TVM_OP=OFF on Unix
URL: https://github.com/apache/incubator-mxnet/pull/16450#discussion_r335124096
 
 

 ##
 File path: ci/jenkins/Jenkins_steps.groovy
 ##
 @@ -25,19 +25,23 @@ utils = load('ci/Jenkinsfile_utils.groovy')
 // mxnet libraries
 mx_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, 
lib/libtvmop.so, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 
3rdparty/tvm/nnvm/lib/libnnvm.a'
 mx_lib_cython = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, 
lib/libtvmop.so, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 
3rdparty/tvm/nnvm/lib/libnnvm.a, python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
+mx_lib_cython_no_tvm_op = 'lib/libmxnet.so, lib/libmxnet.a, libsample_lib.so, 
3rdparty/dmlc-core/libdmlc.a, 3rdparty/tvm/nnvm/lib/libnnvm.a, 
python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
 
 // Python wheels
 mx_pip = 'build/*.whl'
 
 // mxnet cmake libraries, in cmake builds we do not produce a libnvvm static 
library by default.
 mx_cmake_lib = 'build/libmxnet.so, build/libmxnet.a, 
build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, 
build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, 
build/3rdparty/openmp/runtime/src/libomp.so'
+mx_cmake_lib_no_tvm_op = 'build/libmxnet.so, build/libmxnet.a, 
libsample_lib.so, build/3rdparty/dmlc-core/libdmlc.a, 
build/tests/mxnet_unit_tests, build/3rdparty/openmp/runtime/src/libomp.so'
 mx_cmake_lib_cython = 'build/libmxnet.so, build/libmxnet.a, 
build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, 
build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, 
build/3rdparty/openmp/runtime/src/libomp.so, python/mxnet/_cy2/*.so, 
python/mxnet/_cy3/*.so'
+mx_cmake_lib_cython_no_tvm_op = 'build/libmxnet.so, build/libmxnet.a, 
build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, 
build/3rdparty/openmp/runtime/src/libomp.so, python/mxnet/_cy2/*.so, 
python/mxnet/_cy3/*.so'
 // mxnet cmake libraries, in cmake builds we do not produce a libnvvm static 
library by default.
 mx_cmake_lib_debug = 'build/libmxnet.so, build/libmxnet.a, 
build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, 
build/libsample_lib.so, build/3rdparty/dmlc-core/libdmlc.a, 
build/tests/mxnet_unit_tests'
 mx_cmake_mkldnn_lib = 'build/libmxnet.so, build/libmxnet.a, 
build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, 
build/3rdparty/dmlc-core/libdmlc.a, build/tests/mxnet_unit_tests, 
build/3rdparty/openmp/runtime/src/libomp.so, 
build/3rdparty/mkldnn/src/libmkldnn.so.0'
 mx_mkldnn_lib = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, 
lib/libtvmop.so, libsample_lib.so, lib/libiomp5.so, lib/libmkldnn.so.0, 
lib/libmklml_intel.so, 3rdparty/dmlc-core/libdmlc.a, 
3rdparty/tvm/nnvm/lib/libnnvm.a'
 mx_tensorrt_lib = 'build/libmxnet.so, build/3rdparty/tvm/libtvm_runtime.so, 
build/libtvmop.so, lib/libnvonnxparser_runtime.so.0, lib/libnvonnxparser.so.0, 
lib/libonnx_proto.so, lib/libonnx.so'
 mx_lib_cpp_examples = 'lib/libmxnet.so, lib/libmxnet.a, lib/libtvm_runtime.so, 
lib/libtvmop.so, libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 
3rdparty/tvm/nnvm/lib/libnnvm.a, 3rdparty/ps-lite/build/libps.a, 
deps/lib/libprotobuf-lite.a, deps/lib/libzmq.a, build/cpp-package/example/*, 
python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
+mx_lib_cpp_examples_no_tvm_op = 'lib/libmxnet.so, lib/libmxnet.a, 
libsample_lib.so, 3rdparty/dmlc-core/libdmlc.a, 
3rdparty/tvm/nnvm/lib/libnnvm.a, 3rdparty/ps-lite/build/libps.a, 
deps/lib/libprotobuf-lite.a, deps/lib/libzmq.a, build/cpp-package/example/*, 
python/mxnet/_cy2/*.so, python/mxnet/_cy3/*.so'
 mx_lib_cpp_examples_cpu = 'build/libmxnet.so, 
build/3rdparty/tvm/libtvm_runtime.so, build/libtvmop.so, 
build/cpp-package/example/*'
 
 Review comment:
   I took a look and let @reminisce know offline. `mx_cmake_lib_no_tvm_op ` is 
the binary set corresponding to a cmake build and hence we need to put it in a 
`build/` directory.


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[GitHub] [incubator-mxnet] mxnet-label-bot commented on issue #16492: Broken Links - Python Tutorial | Getting Started

mxnet-label-bot commented on issue #16492: Broken Links - Python Tutorial | 
Getting Started 
URL: 
https://github.com/apache/incubator-mxnet/issues/16492#issuecomment-542355915
 
 
   Hey, this is the MXNet Label Bot. 
Thank you for submitting the issue! I will try and suggest some labels so 
that the appropriate MXNet community members can help resolve it. 
Here are my recommended label(s): Doc


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[GitHub] [incubator-mxnet] TEChopra1000 opened a new issue #16492: Broken Links - Python Tutorial | Getting Started

TEChopra1000 opened a new issue #16492: Broken Links - Python Tutorial | 
Getting Started 
URL: https://github.com/apache/incubator-mxnet/issues/16492
 
 
   **Broken Links Found in the [Crash 
Course](https://mxnet.apache.org/api/python/docs/tutorials/getting-started/crash-course/index.html#)**
   
   * previous tutorial | [found here 
](https://mxnet.incubator.apache.org/api/python/docs/tutorials/getting-started/crash-course/5-predict.html)
   * previous tutorial | [found 
here](https://mxnet.incubator.apache.org/api/python/docs/tutorials/getting-started/crash-course/6-use_gpus.html)
   
   **Broken links found in [Moving to MXNet from Other 
Frameworks](https://mxnet.apache.org/api/python/docs/tutorials/getting-started/to-mxnet/index.html)**
   
   * Caffe to MXNet box
   *  Caffe to MXNet does not show up on left-menu under Moving to MXNet from 
Other Frameworks
   
   **Broken links found in [PyTorch vs Apache 
MXNet](https://mxnet.apache.org/api/python/docs/tutorials/getting-started/to-mxnet/pytorch.html)**
   
   A majority of links are broken on this page and so I will not list them all 
here. 
   
   **Broken links found in [Gluon: from experiment to 
deployment](https://mxnet.apache.org/api/python/docs/tutorials/getting-started/gluon_from_experiment_to_deployment.html)**
   
   A majority of links are broken on this page and so I will not list them all 
here. 
   
   **Broken Links found in [Logistic Regression 
Explained](https://mxnet.apache.org/api/python/docs/tutorials/getting-started/logistic_regression_explained.html)**
   
   All of the links in this paragraph are broken:
   
   To work with data, Apache MXNet provides 
[Dataset](https://mxnet.apache.org/api/python/gluon/data.html#mxnet.gluon.data.Dataset)
 and 
[DataLoader](https://mxnet.apache.org/api/python/gluon/data.html#mxnet.gluon.data.DataLoader)
 classes. The former is used to provide an indexed access to the data, the 
latter is used to shuffle and batchify the data. To learn more about working 
with data in Gluon, please refer to [Gluon Datasets and 
Dataloaders](https://mxnet.apache.org/tutorials/gluon/datasets.html) tutorial.
   
   
   I think that the link in the word Xavier further down the page is meant to 
point 
[here](https://mxnet.apache.org/api/python/docs/api/initializer/index.html#mxnet.initializer.Xavier)
 or [here] 
(https://mxnet.apache.org/api/python/docs/api/initializer/index.html).
   
   **Incorrect links found in [Logistic Regression 
Explained](https://mxnet.apache.org/api/python/docs/tutorials/getting-started/logistic_regression_explained.html)**
   
   HybridSequential
   Sigmoid
   LogisticRegressionOutput


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[incubator-mxnet-site] branch asf-site updated: Bump the publish timestamp.

2019-10-15 Thread aaronmarkham

This is an automated email from the ASF dual-hosted git repository.

aaronmarkham pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet-site.git


The following commit(s) were added to refs/heads/asf-site by this push:
 new 0fdc502  Bump the publish timestamp.
0fdc502 is described below

commit 0fdc5023907506b5ab814d17f4923d5a4b1ba574
Author: mxnet-ci 
AuthorDate: Tue Oct 15 18:43:10 2019 +

Bump the publish timestamp.
---
 date.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/date.txt b/date.txt
new file mode 100644
index 000..48c17e8
--- /dev/null
+++ b/date.txt
@@ -0,0 +1 @@
+Tue Oct 15 18:43:10 UTC 2019

[GitHub] [incubator-mxnet] aaronmarkham closed issue #16322: [website] rendering issue on implementing ops faq

aaronmarkham closed issue #16322: [website] rendering issue on implementing ops 
faq
URL: https://github.com/apache/incubator-mxnet/issues/16322
 
 
   


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[GitHub] [incubator-mxnet] TEChopra1000 closed issue #16482: Broken Links: Handwritten Digit Recognition (Python)

TEChopra1000 closed issue #16482: Broken Links: Handwritten Digit Recognition 
(Python)
URL: https://github.com/apache/incubator-mxnet/issues/16482
 
 
   


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[GitHub] [incubator-mxnet] TEChopra1000 commented on issue #16482: Broken Links: Handwritten Digit Recognition (Python)

TEChopra1000 commented on issue #16482: Broken Links: Handwritten Digit 
Recognition (Python)
URL: 
https://github.com/apache/incubator-mxnet/issues/16482#issuecomment-542339846
 
 
   I'm going to put all broken links from the python tutorial into one ticket 
and so I'll close this one. 


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[GitHub] [incubator-mxnet] ThomasDelteil commented on issue #16490: Correct Google Analytics Tracker

ThomasDelteil commented on issue #16490: Correct Google Analytics Tracker
URL: https://github.com/apache/incubator-mxnet/pull/16490#issuecomment-542339412
 
 
   @aaronmarkham the python docs header is done through the theme which is 
loading the analytics file that @szha changed 


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[GitHub] [incubator-mxnet] stu1130 commented on issue #16491: CI failed due to DockerHub outage

stu1130 commented on issue #16491: CI failed due to DockerHub outage
URL: 
https://github.com/apache/incubator-mxnet/issues/16491#issuecomment-542338273
 
 
   looks like it's working now, will close it if my PR passed the CI


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[GitHub] [incubator-mxnet] stu1130 edited a comment on issue #16491: CI failed due to DockerHub outage

stu1130 edited a comment on issue #16491: CI failed due to DockerHub outage
URL: 
https://github.com/apache/incubator-mxnet/issues/16491#issuecomment-542338273
 
 
   looks like it's working now, will close it if my PR pass the CI


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[GitHub] [incubator-mxnet] roywei commented on issue #16491: CI failed due to DockerHub outage

roywei commented on issue #16491: CI failed due to DockerHub outage
URL: 
https://github.com/apache/incubator-mxnet/issues/16491#issuecomment-542336802
 
 
   tracked here on docker: https://github.com/docker/hub-feedback/issues/1897


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[GitHub] [incubator-mxnet] sxjscience commented on a change in pull request #16465: [Gluon] [Fix] [WIP] Fix HybridBlock when hybridize is not called

sxjscience commented on a change in pull request #16465: [Gluon] [Fix] [WIP] 
Fix HybridBlock when hybridize is not called
URL: https://github.com/apache/incubator-mxnet/pull/16465#discussion_r335100583
 
 

 ##
 File path: python/mxnet/gluon/block.py
 ##
 @@ -1054,34 +1098,16 @@ def register_op_hook(self, callback, 
monitor_all=False):
 def forward(self, x, *args):
 """Defines the forward computation. Arguments can be either
 :py:class:`NDArray` or :py:class:`Symbol`."""
-flatten_args = _flatten([x] + list(args), 'inputs')[0]
-is_ndarray = None
-ctx = None
-exist_sym_nd = False
-for ele in flatten_args:
-if isinstance(ele, NDArray):
-if is_ndarray is False:
-raise ValueError('In HybridBlock, we do not support mixed 
NDArrays and Symbols'
- ' types for the input.\n'
- 'Received types are: {}.'
- .format([type(ele) for ele in 
flatten_args]))
-is_ndarray = True
-exist_sym_nd = True
-ctx = ele.context
 
 Review comment:
   @leezu I agree that the backward-compatible issue is valid. Let me first 
make it to be backward-compatible. However, this does not fix the issue of the 
cpu, cpu_pinned, cpu_shared combination.


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[GitHub] [incubator-mxnet] mxnet-label-bot commented on issue #16491: CI failed due to DockerHub outage

mxnet-label-bot commented on issue #16491: CI failed due to DockerHub outage
URL: 
https://github.com/apache/incubator-mxnet/issues/16491#issuecomment-542336621
 
 
   Hey, this is the MXNet Label Bot. 
Thank you for submitting the issue! I will try and suggest some labels so 
that the appropriate MXNet community members can help resolve it. 
Here are my recommended label(s): Test, CI, Build


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[GitHub] [incubator-mxnet] stu1130 opened a new issue #16491: CI failed due to DockerHub outage

stu1130 opened a new issue #16491: CI failed due to DockerHub outage
URL: https://github.com/apache/incubator-mxnet/issues/16491
 
 
   some example of CI failure
   
http://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/mxnet-validation%2Fcentos-cpu/detail/PR-16490/1/pipeline
   
   The latest status of DockerHub
   https://status.docker.com/pages/533c6539221ae15e3f31


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[GitHub] [incubator-mxnet] ptrendx commented on a change in pull request #16408: Add MXNet Ops for fast multihead attention

ptrendx commented on a change in pull request #16408: Add MXNet Ops for fast 
multihead attention
URL: https://github.com/apache/incubator-mxnet/pull/16408#discussion_r335095248
 
 

 ##
 File path: src/operator/contrib/transformer.cu
 ##
 @@ -22,12 +22,568 @@
  * \file transformer.cu
  * \brief GPU implementation of the operators used in Transformer
  */
+
+#include 
+#include 
+#include 
+#include 
+
 #include 
 #include "./transformer-inl.h"
+#include "../../common/cuda_utils.h"
 
 namespace mxnet {
 namespace op {
 
+// gemm_switch_fp32accum and the functions called are almost fully copied from:
+// MLPerf v0.6 submission repository from NVIDIA by 
https://github.com/kevinstephano
 
 Review comment:
   This is no longer the case, right?


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[GitHub] [incubator-mxnet] ptrendx commented on a change in pull request #16408: Add MXNet Ops for fast multihead attention

ptrendx commented on a change in pull request #16408: Add MXNet Ops for fast 
multihead attention
URL: https://github.com/apache/incubator-mxnet/pull/16408#discussion_r335094420
 
 

 ##
 File path: src/operator/contrib/transformer.cc
 ##
 @@ -29,6 +29,163 @@
 namespace mxnet {
 namespace op {
 
+DMLC_REGISTER_PARAMETER(InterleavedMatMulParam);
+
+static bool InterleavedMatMulSelfAttQKShape(const NodeAttrs& attrs,
+mxnet::ShapeVector* in_shape,
+mxnet::ShapeVector* out_shape) {
+  const auto& params = nnvm::get(attrs.parsed);
+  CHECK_EQ(in_shape->size(), 1);
+  auto qkv_shape = in_shape->at(0);
+  CHECK_EQ(qkv_shape.ndim(), 3);
+  out_shape->resize(1);
+  SHAPE_ASSIGN_CHECK(*out_shape, 0,
+mxnet::TShape({params.heads * qkv_shape[1], qkv_shape[0], qkv_shape[0]}));
+  return true;
+}
+
+static bool InterleavedMatMulSelfAttValAttShape(const NodeAttrs& attrs,
+mxnet::ShapeVector* in_shape,
+mxnet::ShapeVector* out_shape) 
{
+  CHECK_EQ(in_shape->size(), 2);
+  auto qkv_shape = in_shape->at(0);
+  auto att_shape = in_shape->at(1);
+  CHECK_EQ(qkv_shape.ndim(), 3);
+  CHECK_EQ(att_shape.ndim(), 3);
+  CHECK_EQ(qkv_shape[0], att_shape[1]);
+  CHECK_EQ(qkv_shape[0], att_shape[2]);
+  CHECK_EQ(qkv_shape[2] % 3, 0);
 
 Review comment:
   Could you make some meaningful error messages when the shape does not match 
your expectation?


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[GitHub] [incubator-mxnet] sxjscience commented on a change in pull request #16465: [Gluon] [Fix] [WIP] Fix HybridBlock when hybridize is not called

sxjscience commented on a change in pull request #16465: [Gluon] [Fix] [WIP] 
Fix HybridBlock when hybridize is not called
URL: https://github.com/apache/incubator-mxnet/pull/16465#discussion_r335094454
 
 

 ##
 File path: python/mxnet/gluon/block.py
 ##
 @@ -1054,34 +1098,16 @@ def register_op_hook(self, callback, 
monitor_all=False):
 def forward(self, x, *args):
 """Defines the forward computation. Arguments can be either
 :py:class:`NDArray` or :py:class:`Symbol`."""
-flatten_args = _flatten([x] + list(args), 'inputs')[0]
-is_ndarray = None
-ctx = None
-exist_sym_nd = False
-for ele in flatten_args:
-if isinstance(ele, NDArray):
-if is_ndarray is False:
-raise ValueError('In HybridBlock, we do not support mixed 
NDArrays and Symbols'
- ' types for the input.\n'
- 'Received types are: {}.'
- .format([type(ele) for ele in 
flatten_args]))
-is_ndarray = True
-exist_sym_nd = True
-ctx = ele.context
 
 Review comment:
   @leezu It's also possible that, previously cpu_pinned is picked as the 
default argument and after the change, the correct cpu context is picked as the 
default. My point is we need to probably give special treatment of the `cpu, 
cpu_pinned, cpu_shared`. What's your opinion?


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[GitHub] [incubator-mxnet] ptrendx commented on a change in pull request #16408: Add MXNet Ops for fast multihead attention

ptrendx commented on a change in pull request #16408: Add MXNet Ops for fast 
multihead attention
URL: https://github.com/apache/incubator-mxnet/pull/16408#discussion_r335094585
 
 

 ##
 File path: src/operator/contrib/transformer.cc
 ##
 @@ -29,6 +29,163 @@
 namespace mxnet {
 namespace op {
 
+DMLC_REGISTER_PARAMETER(InterleavedMatMulParam);
+
+static bool InterleavedMatMulSelfAttQKShape(const NodeAttrs& attrs,
+mxnet::ShapeVector* in_shape,
+mxnet::ShapeVector* out_shape) {
+  const auto& params = nnvm::get(attrs.parsed);
+  CHECK_EQ(in_shape->size(), 1);
+  auto qkv_shape = in_shape->at(0);
+  CHECK_EQ(qkv_shape.ndim(), 3);
+  out_shape->resize(1);
+  SHAPE_ASSIGN_CHECK(*out_shape, 0,
+mxnet::TShape({params.heads * qkv_shape[1], qkv_shape[0], qkv_shape[0]}));
+  return true;
+}
+
+static bool InterleavedMatMulSelfAttValAttShape(const NodeAttrs& attrs,
+mxnet::ShapeVector* in_shape,
+mxnet::ShapeVector* out_shape) 
{
+  CHECK_EQ(in_shape->size(), 2);
+  auto qkv_shape = in_shape->at(0);
+  auto att_shape = in_shape->at(1);
+  CHECK_EQ(qkv_shape.ndim(), 3);
+  CHECK_EQ(att_shape.ndim(), 3);
+  CHECK_EQ(qkv_shape[0], att_shape[1]);
+  CHECK_EQ(qkv_shape[0], att_shape[2]);
+  CHECK_EQ(qkv_shape[2] % 3, 0);
+  SHAPE_ASSIGN_CHECK(*out_shape, 0,
+mxnet::TShape({qkv_shape[0], qkv_shape[1], qkv_shape[2] / 3}));
+  return true;
+}
+
+static bool InterleavedMatMulEncDecQKShape(const NodeAttrs& attrs,
+   mxnet::ShapeVector* in_shape,
+   mxnet::ShapeVector* out_shape) {
+  const auto& params = nnvm::get(attrs.parsed);
+  CHECK_EQ(in_shape->size(), 2);
+  auto q_shape = in_shape->at(0);
+  auto kv_shape = in_shape->at(1);
+  CHECK_EQ(q_shape.ndim(), 3);
+  CHECK_EQ(kv_shape.ndim(), 3);
+  CHECK_EQ(q_shape[2] * 2, kv_shape[2]);
+  CHECK_EQ(q_shape[1], kv_shape[1]);
+  SHAPE_ASSIGN_CHECK(*out_shape, 0,
+  mxnet::TShape({q_shape[1] * params.heads, q_shape[0], kv_shape[0]}));
+  return true;
+}
+
+static bool InterleavedMatMulEncDecValAttShape(const NodeAttrs& attrs,
+   mxnet::ShapeVector* in_shape,
+   mxnet::ShapeVector* out_shape) {
+  const auto& params = nnvm::get(attrs.parsed);
+  CHECK_EQ(in_shape->size(), 2);
+  auto kv_shape = in_shape->at(0);
+  auto att_shape = in_shape->at(1);
+  CHECK_EQ(kv_shape[0], att_shape[2]);
+  CHECK_EQ(kv_shape[1] * params.heads, att_shape[0]);
 
 Review comment:
   And here


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[GitHub] [incubator-mxnet] ptrendx commented on a change in pull request #16408: Add MXNet Ops for fast multihead attention

ptrendx commented on a change in pull request #16408: Add MXNet Ops for fast 
multihead attention
URL: https://github.com/apache/incubator-mxnet/pull/16408#discussion_r335094502
 
 

 ##
 File path: src/operator/contrib/transformer.cc
 ##
 @@ -29,6 +29,163 @@
 namespace mxnet {
 namespace op {
 
+DMLC_REGISTER_PARAMETER(InterleavedMatMulParam);
+
+static bool InterleavedMatMulSelfAttQKShape(const NodeAttrs& attrs,
+mxnet::ShapeVector* in_shape,
+mxnet::ShapeVector* out_shape) {
+  const auto& params = nnvm::get(attrs.parsed);
+  CHECK_EQ(in_shape->size(), 1);
+  auto qkv_shape = in_shape->at(0);
+  CHECK_EQ(qkv_shape.ndim(), 3);
+  out_shape->resize(1);
+  SHAPE_ASSIGN_CHECK(*out_shape, 0,
+mxnet::TShape({params.heads * qkv_shape[1], qkv_shape[0], qkv_shape[0]}));
+  return true;
+}
+
+static bool InterleavedMatMulSelfAttValAttShape(const NodeAttrs& attrs,
+mxnet::ShapeVector* in_shape,
+mxnet::ShapeVector* out_shape) 
{
+  CHECK_EQ(in_shape->size(), 2);
+  auto qkv_shape = in_shape->at(0);
+  auto att_shape = in_shape->at(1);
+  CHECK_EQ(qkv_shape.ndim(), 3);
+  CHECK_EQ(att_shape.ndim(), 3);
+  CHECK_EQ(qkv_shape[0], att_shape[1]);
+  CHECK_EQ(qkv_shape[0], att_shape[2]);
+  CHECK_EQ(qkv_shape[2] % 3, 0);
+  SHAPE_ASSIGN_CHECK(*out_shape, 0,
+mxnet::TShape({qkv_shape[0], qkv_shape[1], qkv_shape[2] / 3}));
+  return true;
+}
+
+static bool InterleavedMatMulEncDecQKShape(const NodeAttrs& attrs,
+   mxnet::ShapeVector* in_shape,
+   mxnet::ShapeVector* out_shape) {
+  const auto& params = nnvm::get(attrs.parsed);
+  CHECK_EQ(in_shape->size(), 2);
+  auto q_shape = in_shape->at(0);
+  auto kv_shape = in_shape->at(1);
+  CHECK_EQ(q_shape.ndim(), 3);
+  CHECK_EQ(kv_shape.ndim(), 3);
+  CHECK_EQ(q_shape[2] * 2, kv_shape[2]);
+  CHECK_EQ(q_shape[1], kv_shape[1]);
 
 Review comment:
   Same here


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[GitHub] [incubator-mxnet] ptrendx commented on a change in pull request #16408: Add MXNet Ops for fast multihead attention

ptrendx commented on a change in pull request #16408: Add MXNet Ops for fast 
multihead attention
URL: https://github.com/apache/incubator-mxnet/pull/16408#discussion_r335093967
 
 

 ##
 File path: Makefile
 ##
 @@ -106,7 +106,7 @@ ifeq ($(DEBUG), 1)
 else
CFLAGS += -O3 -DNDEBUG=1
 endif
-CFLAGS += -I$(TPARTYDIR)/mshadow/ -I$(TPARTYDIR)/dmlc-core/include -fPIC 
-I$(NNVM_PATH)/include -I$(DLPACK_PATH)/include -I$(TPARTYDIR)/tvm/include 
-Iinclude $(MSHADOW_CFLAGS)
+CFLAGS += -I$(TPARTYDIR)/mshadow/ -I$(TPARTYDIR)/dmlc-core/include -fPIC 
-I$(NNVM_PATH)/include -I$(DLPACK_PATH)/include -I$(TPARTYDIR)/tvm/include 
-Iinclude $(MSHADOW_CFLAGS) -I$(TPARTYDIR)/cutlass/
 
 Review comment:
   Remove cutlass from here.


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[GitHub] [incubator-mxnet] leezu commented on a change in pull request #16465: [Gluon] [Fix] [WIP] Fix HybridBlock when hybridize is not called

leezu commented on a change in pull request #16465: [Gluon] [Fix] [WIP] Fix 
HybridBlock when hybridize is not called
URL: https://github.com/apache/incubator-mxnet/pull/16465#discussion_r335090536
 
 

 ##
 File path: python/mxnet/gluon/block.py
 ##
 @@ -1054,34 +1098,16 @@ def register_op_hook(self, callback, 
monitor_all=False):
 def forward(self, x, *args):
 """Defines the forward computation. Arguments can be either
 :py:class:`NDArray` or :py:class:`Symbol`."""
-flatten_args = _flatten([x] + list(args), 'inputs')[0]
-is_ndarray = None
-ctx = None
-exist_sym_nd = False
-for ele in flatten_args:
-if isinstance(ele, NDArray):
-if is_ndarray is False:
-raise ValueError('In HybridBlock, we do not support mixed 
NDArrays and Symbols'
- ' types for the input.\n'
- 'Received types are: {}.'
- .format([type(ele) for ele in 
flatten_args]))
-is_ndarray = True
-exist_sym_nd = True
-ctx = ele.context
 
 Review comment:
   As the previous implementation hasn't enforced all contexts being equal, we 
shouldn't start picking a different array to determine the context. As you 
stated above, it's valid to use a mix of  `cpu, cpu_pinned, cpu_shared` 
contexts.
   For example, after your change, `cpu_pinned` or `cpu_shared` may be picked 
as default context instead of `cpu` if the user passed a  `cpu_pinned` or 
`cpu_shared`  as last argument. The extra overhead could cause a performance 
regression (all parameters will be made available under default context).
   No need to risk this given there is no advantage?


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[GitHub] [incubator-mxnet] aaronmarkham commented on issue #16490: Correct Google Analytics Tracker

aaronmarkham commented on issue #16490: Correct Google Analytics Tracker
URL: https://github.com/apache/incubator-mxnet/pull/16490#issuecomment-542327294
 
 
   It is setup to use it here: 
https://github.com/apache/incubator-mxnet/blame/master/docs/static_site/src/_includes/google-analytics.html
   
   I'm not sure why there are two analytics files. Maybe this one should be 
deleted. I think the config is setup correctly because if you view source the 
ID is `-1`.


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[GitHub] [incubator-mxnet] aaronmarkham commented on issue #16490: Correct Google Analytics Tracker

aaronmarkham commented on issue #16490: Correct Google Analytics Tracker
URL: https://github.com/apache/incubator-mxnet/pull/16490#issuecomment-542323233
 
 
   Hi @szha - So the config for the ID should be here:
   
https://github.com/apache/incubator-mxnet/blob/master/docs/static_site/src/_config_prod.yml
   It is there, so any mention of the tracker ID should be using the variable, 
not be statically defined.


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[incubator-mxnet] branch szha-patch-1 created (now da0bf19)

2019-10-15 Thread zhasheng

This is an automated email from the ASF dual-hosted git repository.

zhasheng pushed a change to branch szha-patch-1
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git.


  at da0bf19  Correct Google Analytics Tracker

No new revisions were added by this update.

[incubator-mxnet] branch szha-patch-1 created (now da0bf19)

2019-10-15 Thread zhasheng

This is an automated email from the ASF dual-hosted git repository.

zhasheng pushed a change to branch szha-patch-1
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git.


  at da0bf19  Correct Google Analytics Tracker

No new revisions were added by this update.

[GitHub] [incubator-mxnet] szha opened a new pull request #16490: Correct Google Analytics Tracker

szha opened a new pull request #16490: Correct Google Analytics Tracker
URL: https://github.com/apache/incubator-mxnet/pull/16490
 
 
   ## Description ##
   (Brief description on what this PR is about)
   
   ## Checklist ##
   ### Essentials ###
   Please feel free to remove inapplicable items for your PR.
   - [ ] The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to 
the relevant [JIRA issue](https://issues.apache.org/jira/projects/MXNET/issues) 
created (except PRs with tiny changes)
   - [ ] Changes are complete (i.e. I finished coding on this PR)
   - [ ] All changes have test coverage:
   - Unit tests are added for small changes to verify correctness (e.g. adding 
a new operator)
   - Nightly tests are added for complicated/long-running ones (e.g. changing 
distributed kvstore)
   - Build tests will be added for build configuration changes (e.g. adding a 
new build option with NCCL)
   - [ ] Code is well-documented: 
   - For user-facing API changes, API doc string has been updated. 
   - For new C++ functions in header files, their functionalities and arguments 
are documented. 
   - For new examples, README.md is added to explain the what the example does, 
the source of the dataset, expected performance on test set and reference to 
the original paper if applicable
   - Check the API doc at 
https://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html
   - [ ] To the my best knowledge, examples are either not affected by this 
change, or have been fixed to be compatible with this change
   
   ### Changes ###
   - [ ] Feature1, tests, (and when applicable, API doc)
   - [ ] Feature2, tests, (and when applicable, API doc)
   
   ## Comments ##
   - If this change is a backward incompatible change, why must this change be 
made.
   - Interesting edge cases to note here
   


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[GitHub] [incubator-mxnet] TEChopra1000 commented on issue #16481: Broken / Incorrect Links - Gluon: from experiment to deployment (DOCS)

TEChopra1000 commented on issue #16481: Broken / Incorrect Links - Gluon: from 
experiment to deployment (DOCS)
URL: 
https://github.com/apache/incubator-mxnet/issues/16481#issuecomment-542310643
 
 
   I'm going to move all broken links from the Python Tutorials into one 
ticket. Closing this ticket. 


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[GitHub] [incubator-mxnet] TEChopra1000 closed issue #16481: Broken / Incorrect Links - Gluon: from experiment to deployment (DOCS)

TEChopra1000 closed issue #16481: Broken / Incorrect Links - Gluon: from 
experiment to deployment (DOCS)
URL: https://github.com/apache/incubator-mxnet/issues/16481
 
 
   


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[GitHub] [incubator-mxnet] TEChopra1000 closed issue #16480: Broken Links - Logistic Regression Explained (DOCS)

TEChopra1000 closed issue #16480: Broken Links - Logistic Regression Explained 
(DOCS)
URL: https://github.com/apache/incubator-mxnet/issues/16480
 
 
   


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[GitHub] [incubator-mxnet] TEChopra1000 commented on issue #16480: Broken Links - Logistic Regression Explained (DOCS)

TEChopra1000 commented on issue #16480: Broken Links - Logistic Regression 
Explained (DOCS)
URL: 
https://github.com/apache/incubator-mxnet/issues/16480#issuecomment-542310176
 
 
   I'm going to move all of the broken links from the Python Tutorials section 
into one ticket. Closing this one. 


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[GitHub] [incubator-mxnet] hetong007 commented on a change in pull request #16487: Fix learning rate scheduler being unexpectedly overwritten by optimizer's default value

hetong007 commented on a change in pull request #16487: Fix learning rate 
scheduler being unexpectedly overwritten by optimizer's default value
URL: https://github.com/apache/incubator-mxnet/pull/16487#discussion_r335070091
 
 

 ##
 File path: python/mxnet/optimizer/optimizer.py
 ##
 @@ -63,8 +63,10 @@ class Optimizer(object):
 clip_gradient : float, optional, default None
 Clip the gradient by projecting onto the box ``[-clip_gradient, 
clip_gradient]``.
 
-learning_rate : float, optional, default 0.01
-The initial learning rate.
+learning_rate : float, optional, default None
+The initial learning rate. If None, the optimization will use the
+learning rate from ``lr_scheduler``. If not None, it will overwrite
 
 Review comment:
   It is technically `None` by default, but effectively it is `0.01`. I'll 
include it in the params' doc.


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[GitHub] [incubator-mxnet] Caenorst commented on a change in pull request #16408: Add MXNet Ops for fast multihead attention

Caenorst commented on a change in pull request #16408: Add MXNet Ops for fast 
multihead attention
URL: https://github.com/apache/incubator-mxnet/pull/16408#discussion_r335057869
 
 

 ##
 File path: .gitmodules
 ##
 @@ -26,3 +26,6 @@
 [submodule "3rdparty/nvidia_cub"]
path = 3rdparty/nvidia_cub
url = https://github.com/NVlabs/cub.git
+[submodule "3rdparty/cutlass"]
 
 Review comment:
   Update: We decided to drop Cutlass from now, there are some cases on which 
Cublas is actually working better and it make the PR more simple


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[incubator-mxnet] branch master updated (f9359c3 -> 67e1e68)

This is an automated email from the ASF dual-hosted git repository.

ptrendx pushed a change to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git.


from f9359c3  Fix flakey pylint CI failures (#16462)
 add 67e1e68  Aggregated zero grad (#16446)

No new revisions were added by this update.

Summary of changes:
 python/mxnet/gluon/parameter.py| 21 -
 src/operator/contrib/reset_arrays-inl.h| 92 ++
 src/operator/contrib/reset_arrays.cc   | 74 +
 .../contrib/{multi_lars.cu => reset_arrays.cu} | 18 +++--
 tests/python/unittest/test_gluon.py| 66 +---
 5 files changed, 252 insertions(+), 19 deletions(-)
 create mode 100644 src/operator/contrib/reset_arrays-inl.h
 create mode 100644 src/operator/contrib/reset_arrays.cc
 copy src/operator/contrib/{multi_lars.cu => reset_arrays.cu} (69%)

[incubator-mxnet] branch master updated (f9359c3 -> 67e1e68)

This is an automated email from the ASF dual-hosted git repository.

ptrendx pushed a change to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git.


from f9359c3  Fix flakey pylint CI failures (#16462)
 add 67e1e68  Aggregated zero grad (#16446)

No new revisions were added by this update.

Summary of changes:
 python/mxnet/gluon/parameter.py| 21 -
 src/operator/contrib/reset_arrays-inl.h| 92 ++
 src/operator/contrib/reset_arrays.cc   | 74 +
 .../contrib/{multi_lars.cu => reset_arrays.cu} | 18 +++--
 tests/python/unittest/test_gluon.py| 66 +---
 5 files changed, 252 insertions(+), 19 deletions(-)
 create mode 100644 src/operator/contrib/reset_arrays-inl.h
 create mode 100644 src/operator/contrib/reset_arrays.cc
 copy src/operator/contrib/{multi_lars.cu => reset_arrays.cu} (69%)

[GitHub] [incubator-mxnet] ptrendx edited a comment on issue #15589: [Discussion] 1.6.0 Roadmap