[GitHub] eric-haibin-lin commented on a change in pull request #11357: [MXNET-331] Single machine All Reduce Topology-aware Communication

[GitBox]

eric-haibin-lin commented on a change in pull request #11357: [MXNET-331] 
Single machine All Reduce Topology-aware Communication
URL: https://github.com/apache/incubator-mxnet/pull/11357#discussion_r199057710
 
 

 ##
 File path: src/kvstore/gpu_topology.h
 ##
 @@ -0,0 +1,1043 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/**
+ * Copyright (c) 2015 by Contributors
+ */
+#ifndef MXNET_KVSTORE_GPU_TOPOLOGY_H_
+#define MXNET_KVSTORE_GPU_TOPOLOGY_H_
+#if MXNET_USE_CUDA
+  #include 
+  #include 
+#endif
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+#define MXNET_KVSTORE_MAXDEPTH 16
+
+namespace mxnet {
+namespace kvstore {
+
+template 
+inline void PrintVector(const std::string& str, const std::vector& vec) {
+  std::cout << str << ":\n";
+  for (unsigned i = 0; i < vec.size(); ++i)
+std::cout << vec[i] << " ";
+  std::cout << std::endl;
+}
+
+template 
+inline void PrintMatrix(const std::string& str, const std::vector& matrix,
+int num_rows, int num_cols) {
+  std::cout << str << ":\n";
+  int count = 0;
+  for (int row = 0; row < num_rows; ++row) {
+for (int col = 0; col < num_cols; ++col) {
+  std::cout << matrix[count++] << " ";
+}
+std::cout << std::endl;
+  }
+}
+
+inline void PrintTopo(const std::string& str, const std::vector& 
topo_row,
+std::vector scan_row) {
+  PrintVector("Topo vector", topo_row);
+  PrintVector("Scan vector", scan_row);
+  std::cout << str << ":\n";
+  int depth = scan_row.size()-1;
+  for (int row = 0; row < depth; ++row) {
+int start = scan_row[row];
+int end   = scan_row[row+1];
+for (; start < end; start++) {
+  for (int i = 0; i < (2 << (depth-row-2))+1; ++i) {
+std::cout << " ";
+  }
+  std::cout << topo_row[start];
+}
+std::cout << std::endl;
+  }
+}
+
+// Generate adjacency matrix with row/col numbering from 0, 1, ..., n_gpu
+// @input:  devs is a vector of GPU contexts
+// @output: matrix is adjacency matrix of link topology graph
+//  where edge weight represents relative performance of NVIDIA GPUs
+//0: Self-connection
+//1: PCI-E
+//2: 1 NVLink connection
+//3: 2 NVLink connections
+template 
+inline void GetP2PWeight(const std::vector& devs,
+  std::vector* matrix) {
+  int num_gpus = devs.size();
+  int count= 0;
+  std::vector zero_dev_id(num_gpus, -1);
+  for (auto d : devs) {
+zero_dev_id[count] = d.dev_id;
+count++;
+  }
+
+#if MXNET_USE_CUDA
+  cudaDeviceP2PAttr attr;
+  attr = cudaDevP2PAttrPerformanceRank;
+  std::vector max(num_gpus, 0);
+
+  for (int row = 0; row < num_gpus; ++row) {
+for (int col = 0; col < num_gpus; ++col) {
+  if (row == col) {
+(*matrix)[row*num_gpus+col] = 0;
+  } else {
+int value;
+int row_gpu = zero_dev_id[row];
+int col_gpu = zero_dev_id[col];
+cudaDeviceGetP2PAttribute(, attr, row_gpu, col_gpu);
+if (value > max[row])
+  max[row] = value;
+(*matrix)[row*num_gpus+col] = static_cast(value)+1;
+  }
+}
+  }
+
+  // Check that all GPUs have at least 1 NVLink connection
+  int max_value = 0;
+  for (unsigned int i = 0; i < max.size(); ++i) {
+if (max[i] > max_value)
+  max_value = max[i];
+  }
+
+  // If all GPUs have at least 1 NVLink connection, then we can use NVLink only
+  // to communicate instead of going over PCI-E
+  if (max_value > 0) {
+for (auto& matrix_value : *matrix) {
+  matrix_value = (matrix_value == 1) ? 0 : matrix_value;
+}
+  }
+  PrintMatrix("Weight W", *matrix, num_gpus, num_gpus);
+#else
+  LOG(WARNING) << "GPU required for link topology";
+#endif
+}
+
+// Dense matrix-vector multiplication
+// Assume: matrix is square
+//   y = A*x (no accumulate)
+template 
+inline void gemv(const std::vector&   A,
+  const std::vector& x,
+  std::vector* y) {
+  int nrows = x.size();
+  int count = 0;
+  for (int row=0; row < nrows; ++row) {
+(*y)[row] = 0;
+for (int col=0; col < nrows; ++col) {
+  (*y)[row] += A[count]*static_cast(x[col]);
+  count++;
+}
+  }
+}
+

[GitHub] eric-haibin-lin commented on a change in pull request #11357: [MXNET-331] Single machine All Reduce Topology-aware Communication

[GitBox]

eric-haibin-lin commented on a change in pull request #11357: [MXNET-331] 
Single machine All Reduce Topology-aware Communication
URL: https://github.com/apache/incubator-mxnet/pull/11357#discussion_r199058554
 
 

 ##
 File path: tests/python/gpu/test_kvstore_gpu.py
 ##
 @@ -88,34 +89,48 @@ def check_rsp_pull(kv, count, ctxs, is_same_rowid=False, 
use_slice=False):
 
 # test fails intermittently. temporarily disabled till it gets fixed. 
tracked at https://github.com/apache/incubator-mxnet/issues/9384
 # check_rsp_push_pull('local')
+os.environ["MXNET_KVSTORE_USETREE"] = ""
+check_rsp_push_pull('device')
+check_rsp_push_pull('device', is_push_cpu=False)
+os.environ["MXNET_KVSTORE_USETREE"] = "1"
+logging.info("Setting env to use tree reduce...")
 check_rsp_push_pull('device')
 check_rsp_push_pull('device', is_push_cpu=False)
 
 
 def test_row_sparse_pull_single_device():
-kvstore = mx.kv.create('device')
-copy = mx.nd.random_normal(shape=(4,4), ctx=mx.gpu(0))
-grad = copy.tostype("row_sparse")
+envs = ["","1"]
 
 Review comment:
   minor suggestion: we could add some util class like 
https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/autograd.py#L93-L119
 which manages the scope of such env var. It sets the env var to some var when 
entering the scope, and reset the env var when exiting the scope. 


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[GitHub] eric-haibin-lin commented on a change in pull request #11357: [MXNET-331] Single machine All Reduce Topology-aware Communication

[GitBox]

eric-haibin-lin commented on a change in pull request #11357: [MXNET-331] 
Single machine All Reduce Topology-aware Communication
URL: https://github.com/apache/incubator-mxnet/pull/11357#discussion_r199057204
 
 

 ##
 File path: src/kvstore/comm.h
 ##
 @@ -750,6 +762,8 @@ class CommDevice : public Comm {
 std::vector compressed_send_buf;
 /// \brief the small buffer for compressed data in receiver
 std::vector compressed_recv_buf;
+/// \brief size of allocation in case we do not actually allocate merged
+TShape merged_size;
 
 Review comment:
   Is this being used?


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[GitHub] hxhxhx88 opened a new issue #11480: Image classfication example has wrong accuracy metric.

[GitBox]

hxhxhx88 opened a new issue #11480: Image classfication example has wrong 
accuracy metric.
URL: https://github.com/apache/incubator-mxnet/issues/11480
 
 
   In file 
[example/image-classification/common/fit.py](https://github.com/apache/incubator-mxnet/blob/master/example/image-classification/common/fit.py)
 at [line 
295](https://github.com/apache/incubator-mxnet/blob/master/example/image-classification/common/fit.py#L295),
 the `mx.callback.Speedometer` is initialized with `auto_reset` unset, which 
will be its [default 
value](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/callback.py#L142),
 i.e. `True`. This will make the logged **epoch accuracy** incorrect, since the 
module and the speedometer share the same metric.
   
   Following is the current log for 1 epoch:
   ```
   INFO:root:Epoch[1] Batch [20]Speed: 1375.76 samples/sec  
accuracy=0.519345
   INFO:root:Epoch[1] Batch [40]Speed: 1374.71 samples/sec  
accuracy=0.515625
   INFO:root:Epoch[1] Batch [60]Speed: 1371.57 samples/sec  
accuracy=0.521094
   INFO:root:Epoch[1] Batch [80]Speed: 1376.55 samples/sec  
accuracy=0.533203
   INFO:root:Epoch[1] Batch [100]   Speed: 1375.78 samples/sec  
accuracy=0.548828
   INFO:root:Epoch[1] Batch [120]   Speed: 1370.05 samples/sec  
accuracy=0.544141
   INFO:root:Epoch[1] Batch [140]   Speed: 1375.61 samples/sec  
accuracy=0.566797
   INFO:root:Epoch[1] Batch [160]   Speed: 1372.94 samples/sec  
accuracy=0.571094
   INFO:root:Epoch[1] Batch [180]   Speed: 1328.99 samples/sec  
accuracy=0.566016
   INFO:root:Epoch[1] Batch [200]   Speed: 1319.79 samples/sec  
accuracy=0.576562
   INFO:root:Epoch[1] Batch [220]   Speed: 1322.05 samples/sec  
accuracy=0.576172
   INFO:root:Epoch[1] Batch [240]   Speed: 1318.98 samples/sec  
accuracy=0.597656
   INFO:root:Epoch[1] Batch [260]   Speed: 1323.49 samples/sec  
accuracy=0.593359
   INFO:root:Epoch[1] Batch [280]   Speed: 1301.38 samples/sec  
accuracy=0.611719
   INFO:root:Epoch[1] Batch [300]   Speed: 1299.34 samples/sec  
accuracy=0.615234
   INFO:root:Epoch[1] Batch [320]   Speed: 1300.00 samples/sec  
accuracy=0.622656
   INFO:root:Epoch[1] Batch [340]   Speed: 1302.01 samples/sec  
accuracy=0.641406
   INFO:root:Epoch[1] Batch [360]   Speed: 1300.59 samples/sec  
accuracy=0.632812
   INFO:root:Epoch[1] Batch [380]   Speed: 1301.89 samples/sec  
accuracy=0.623047
   INFO:root:Epoch[1] Train-accuracy=0.642969
   ```
   
   Following is the log with `auto_reset` set to `False`:
   ```
   INFO:root:Epoch[1] Batch [20]Speed: 1366.77 samples/sec  
accuracy=0.518601
   INFO:root:Epoch[1] Batch [40]Speed: 1373.52 samples/sec  
accuracy=0.518293
   INFO:root:Epoch[1] Batch [60]Speed: 1369.00 samples/sec  
accuracy=0.518315
   INFO:root:Epoch[1] Batch [80]Speed: 1372.24 samples/sec  
accuracy=0.521991
   INFO:root:Epoch[1] Batch [100]   Speed: 1377.56 samples/sec  
accuracy=0.526609
   INFO:root:Epoch[1] Batch [120]   Speed: 1373.20 samples/sec  
accuracy=0.530475
   INFO:root:Epoch[1] Batch [140]   Speed: 1371.03 samples/sec  
accuracy=0.536070
   INFO:root:Epoch[1] Batch [160]   Speed: 1367.66 samples/sec  
accuracy=0.541295
   INFO:root:Epoch[1] Batch [180]   Speed: 1371.29 samples/sec  
accuracy=0.543854
   INFO:root:Epoch[1] Batch [200]   Speed: 1364.62 samples/sec  
accuracy=0.547069
   INFO:root:Epoch[1] Batch [220]   Speed: 1370.96 samples/sec  
accuracy=0.549456
   INFO:root:Epoch[1] Batch [240]   Speed: 1363.94 samples/sec  
accuracy=0.553488
   INFO:root:Epoch[1] Batch [260]   Speed: 1371.96 samples/sec  
accuracy=0.556454
   INFO:root:Epoch[1] Batch [280]   Speed: 1368.86 samples/sec  
accuracy=0.560582
   INFO:root:Epoch[1] Batch [300]   Speed: 1360.55 samples/sec  
accuracy=0.564654
   INFO:root:Epoch[1] Batch [320]   Speed: 1366.20 samples/sec  
accuracy=0.567465
   INFO:root:Epoch[1] Batch [340]   Speed: 1366.99 samples/sec  
accuracy=0.571527
   INFO:root:Epoch[1] Batch [360]   Speed: 1367.97 samples/sec  
accuracy=0.575160
   INFO:root:Epoch[1] Batch [380]   Speed: 1367.14 samples/sec  
accuracy=0.578043
   INFO:root:Epoch[1] Train-accuracy=0.579803
   ```
   
   Note that when `auto_reset=True`, the batch-wise accuracy is correct, but 
epoch-wise is wrong. When `auto_reset=False`, the batch-wise one is incorrect, 
but epoch-wise is correct.
   
   I believe there are two fixes:
   1. Manually set `auto_reset=False` to log accumulated accuracy for each 
batch.
   2. Set an independent metric for the speedometer.
   


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[GitHub] zheng-da commented on issue #10921: [MXNET-500]Test cases improvement for MKLDNN on Gluon

[GitBox]

zheng-da commented on issue #10921: [MXNET-500]Test cases improvement for 
MKLDNN on Gluon
URL: https://github.com/apache/incubator-mxnet/pull/10921#issuecomment-401248185
 
 
   can you try rebase and see if it works?


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[GitHub] leezu commented on issue #11314: Embedding Backward (AddTakeGradLargeBatchCaller) non-deterministic nan values

[GitBox]

leezu commented on issue #11314: Embedding Backward 
(AddTakeGradLargeBatchCaller) non-deterministic nan values
URL: 
https://github.com/apache/incubator-mxnet/issues/11314#issuecomment-401248199
 
 
   There is some evidence that `nan` values can still occur with above 
workaround applied, though the root cause is still not determined.


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[GitHub] Hui-Li commented on issue #9267: compiling from source failed

[GitBox]

Hui-Li commented on issue #9267: compiling from source failed
URL: 
https://github.com/apache/incubator-mxnet/issues/9267#issuecomment-401245124
 
 
   In case you are using Mac OS, it is probably that you use GCC as the 
compiler. Change  to clang in **config.mk**:
   
   ```
   export CC = clang
   export CXX = clang++
   export NVCC = nvcc
   ```


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[GitHub] szha commented on issue #11287: [MXNET-548] fixed path for auto_module_index.js

[GitBox]

szha commented on issue #11287: [MXNET-548] fixed path for auto_module_index.js
URL: https://github.com/apache/incubator-mxnet/pull/11287#issuecomment-401239592
 
 
   @aaronmarkham could you do a rebase? some tests are already fixed recently.


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[GitHub] szha commented on issue #11152: updating Scala IntelliJ tutorial & installation instructions

[GitBox]

szha commented on issue #11152: updating Scala IntelliJ tutorial & installation 
instructions
URL: https://github.com/apache/incubator-mxnet/pull/11152#issuecomment-401239498
 
 
   @aaronmarkham lol. you can use interactive rebase or squash to squash/fix-up 
all commits into one before rebasing, so that if there's any conflict you only 
need to resolve it once.


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[GitHub] szha commented on issue #11442: No module named 'dmlc_tracker'

[GitBox]

szha commented on issue #11442: No module named 'dmlc_tracker'
URL: 
https://github.com/apache/incubator-mxnet/issues/11442#issuecomment-401239133
 
 
   dmlc_tracker will be included in nightly builds starting from tonight.


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[GitHub] zhulihuacmss commented on issue #7146: How to compile Amalgamation for android?

[GitBox]

zhulihuacmss commented on issue #7146: How to compile Amalgamation for android?
URL: 
https://github.com/apache/incubator-mxnet/issues/7146#issuecomment-401237164
 
 
   @Corea i have met the same problem 'can not find openblas.so', how did you 
solve it? thanks 


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[GitHub] marcoabreu closed pull request #11452: [Nightly Tests] Disable some nightly tests for installation guide

[GitBox]

marcoabreu closed pull request #11452: [Nightly Tests] Disable some nightly 
tests for installation guide
URL: https://github.com/apache/incubator-mxnet/pull/11452
 
 
   

This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:

As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):

diff --git a/tests/nightly/Jenkinsfile b/tests/nightly/Jenkinsfile
index 4b7a0943701..fb9fbfd0521 100755
--- a/tests/nightly/Jenkinsfile
+++ b/tests/nightly/Jenkinsfile
@@ -89,11 +89,13 @@ try {
   node('mxnetlinux-cpu') {
 ws('workspace/nt-Installation-cpu') {
   init_git()
+  //Some install guide tests are currently diabled and tracked here:
+  //1. https://github.com/apache/incubator-mxnet/issues/11369
+  //2. https://github.com/apache/incubator-mxnet/issues/11288
   docker_run('ubuntu_base_cpu', 'nightly_test_installation 
ubuntu_python_cpu_virtualenv', false)
-  docker_run('ubuntu_base_cpu', 'nightly_test_installation 
ubuntu_python_cpu_pip', false)
-  //Docker Install Test is currently disabled and tracked here: 
https://github.com/apache/incubator-mxnet/issues/11288
+  //docker_run('ubuntu_base_cpu', 'nightly_test_installation 
ubuntu_python_cpu_pip', false)
   //docker_run('ubuntu_base_cpu', 'nightly_test_installation 
ubuntu_python_cpu_docker', false)
-  docker_run('ubuntu_base_cpu', 'nightly_test_installation 
ubuntu_python_cpu_source', false)
+  //docker_run('ubuntu_base_cpu', 'nightly_test_installation 
ubuntu_python_cpu_source', false)
 }
   }
 },
@@ -101,9 +103,11 @@ try {
   node('mxnetlinux-gpu') {
 ws('workspace/nt-Installation-gpu') {
   init_git()
+  //Some install guide tests are currently diabled and tracked here:
+  //1. https://github.com/apache/incubator-mxnet/issues/11369
+  //2. https://github.com/apache/incubator-mxnet/issues/11288
   docker_run('ubuntu_base_gpu', 'nightly_test_installation 
ubuntu_python_gpu_virtualenv', true)
-  docker_run('ubuntu_base_gpu', 'nightly_test_installation 
ubuntu_python_gpu_pip', true)
-  //Docker Install Test is currently disabled and tracked here: 
https://github.com/apache/incubator-mxnet/issues/11288
+  //docker_run('ubuntu_base_gpu', 'nightly_test_installation 
ubuntu_python_gpu_pip', true)
   //docker_run('ubuntu_base_gpu', 'nightly_test_installation 
ubuntu_python_gpu_docker', true)
   docker_run('ubuntu_base_gpu', 'nightly_test_installation 
ubuntu_python_gpu_source', true)
 }


 


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[incubator-mxnet] branch master updated: [Nightly Tests] Disable some nightly tests for installation guide (#11452)

[marcoabreu]

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

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


The following commit(s) were added to refs/heads/master by this push:
 new a0669d0  [Nightly Tests] Disable some nightly tests for installation 
guide (#11452)
a0669d0 is described below

commit a0669d07d12f7dcad47e8db8befd1e0a99a8203f
Author: mbaijal <30911248+mbai...@users.noreply.github.com>
AuthorDate: Thu Jun 28 20:27:14 2018 -0700

[Nightly Tests] Disable some nightly tests for installation guide (#11452)

* Disable nightly install guide test for pip and build from source on cpu

* add link to github issue
---
 tests/nightly/Jenkinsfile | 14 +-
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/tests/nightly/Jenkinsfile b/tests/nightly/Jenkinsfile
index 4b7a094..fb9fbfd 100755
--- a/tests/nightly/Jenkinsfile
+++ b/tests/nightly/Jenkinsfile
@@ -89,11 +89,13 @@ try {
   node('mxnetlinux-cpu') {
 ws('workspace/nt-Installation-cpu') {
   init_git()
+  //Some install guide tests are currently diabled and tracked here:
+  //1. https://github.com/apache/incubator-mxnet/issues/11369
+  //2. https://github.com/apache/incubator-mxnet/issues/11288
   docker_run('ubuntu_base_cpu', 'nightly_test_installation 
ubuntu_python_cpu_virtualenv', false)
-  docker_run('ubuntu_base_cpu', 'nightly_test_installation 
ubuntu_python_cpu_pip', false)
-  //Docker Install Test is currently disabled and tracked here: 
https://github.com/apache/incubator-mxnet/issues/11288
+  //docker_run('ubuntu_base_cpu', 'nightly_test_installation 
ubuntu_python_cpu_pip', false)
   //docker_run('ubuntu_base_cpu', 'nightly_test_installation 
ubuntu_python_cpu_docker', false)
-  docker_run('ubuntu_base_cpu', 'nightly_test_installation 
ubuntu_python_cpu_source', false)
+  //docker_run('ubuntu_base_cpu', 'nightly_test_installation 
ubuntu_python_cpu_source', false)
 }
   }
 },
@@ -101,9 +103,11 @@ try {
   node('mxnetlinux-gpu') {
 ws('workspace/nt-Installation-gpu') {
   init_git()
+  //Some install guide tests are currently diabled and tracked here:
+  //1. https://github.com/apache/incubator-mxnet/issues/11369
+  //2. https://github.com/apache/incubator-mxnet/issues/11288
   docker_run('ubuntu_base_gpu', 'nightly_test_installation 
ubuntu_python_gpu_virtualenv', true)
-  docker_run('ubuntu_base_gpu', 'nightly_test_installation 
ubuntu_python_gpu_pip', true)
-  //Docker Install Test is currently disabled and tracked here: 
https://github.com/apache/incubator-mxnet/issues/11288
+  //docker_run('ubuntu_base_gpu', 'nightly_test_installation 
ubuntu_python_gpu_pip', true)
   //docker_run('ubuntu_base_gpu', 'nightly_test_installation 
ubuntu_python_gpu_docker', true)
   docker_run('ubuntu_base_gpu', 'nightly_test_installation 
ubuntu_python_gpu_source', true)
 }

[incubator-mxnet] branch master updated: [MXNET-607] Fix the broken reported by the new BLC (#11465)

[marcoabreu]

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

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


The following commit(s) were added to refs/heads/master by this push:
 new a4054cd  [MXNET-607] Fix the broken reported by the new BLC (#11465)
a4054cd is described below

commit a4054cd5b20ebc12409effa398b1a32329bb91bf
Author: kpmurali <37911926+kpmur...@users.noreply.github.com>
AuthorDate: Thu Jun 28 20:25:14 2018 -0700

[MXNET-607] Fix the broken reported by the new BLC (#11465)

* Fixing the broken for the moved directories in ap/python and scala 
imageclassifier and SSDClassifier

* Fixing the broken for the moved directories in ap/python and scala 
imageclassifier and SSDClassifier
---
 docs/tutorials/gluon/mnist.md| 12 ++--
 .../infer/imageclassifier/ImageClassifierExample.scala   |  6 +++---
 .../infer/objectdetector/SSDClassifierExample.scala  |  6 +++---
 3 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/docs/tutorials/gluon/mnist.md b/docs/tutorials/gluon/mnist.md
index 3a2a2cb..5b8a98a 100644
--- a/docs/tutorials/gluon/mnist.md
+++ b/docs/tutorials/gluon/mnist.md
@@ -77,7 +77,7 @@ In an MLP, the outputs of most FC layers are fed into an 
activation function, wh
 The following code declares three fully connected layers with 128, 64 and 10 
neurons each.
 The last fully connected layer often has its hidden size equal to the number 
of output classes in the dataset. Furthermore, these FC layers uses ReLU 
activation for performing an element-wise ReLU transformation on the FC layer 
output.
 
-To do this, we will use [Sequential 
layer](http://mxnet.io/api/python/gluon.html#mxnet.gluon.nn.Sequential) type. 
This is simply a linear stack of neural network layers. `nn.Dense` layers are 
nothing but the fully connected layers we discussed above.
+To do this, we will use [Sequential 
layer](http://mxnet.io/api/python/gluon/gluon.html#mxnet.gluon.nn.Sequential) 
type. This is simply a linear stack of neural network layers. `nn.Dense` layers 
are nothing but the fully connected layers we discussed above.
 
 ```python
 # define network
@@ -90,13 +90,13 @@ with net.name_scope():
 
  Initialize parameters and optimizer
 
-The following source code initializes all parameters received from parameter 
dict using 
[Xavier](http://mxnet.io/api/python/optimization.html#mxnet.initializer.Xavier) 
initializer
+The following source code initializes all parameters received from parameter 
dict using 
[Xavier](http://mxnet.io/api/python/optimization/optimization.html#mxnet.initializer.Xavier)
 initializer
 to train the MLP network we defined above.
 
 For our training, we will make use of the stochastic gradient descent (SGD) 
optimizer. In particular, we'll be using mini-batch SGD. Standard SGD processes 
train data one example at a time. In practice, this is very slow and one can 
speed up the process by processing examples in small batches. In this case, our 
batch size will be 100, which is a reasonable choice. Another parameter we 
select here is the learning rate, which controls the step size the optimizer 
takes in search of a soluti [...]
 
-We will use [Trainer](http://mxnet.io/api/python/gluon.html#trainer) class to 
apply the
-[SGD 
optimizer](http://mxnet.io/api/python/optimization.html#mxnet.optimizer.SGD) on 
the
+We will use [Trainer](http://mxnet.io/api/python/gluon/gluon.html#trainer) 
class to apply the
+[SGD 
optimizer](http://mxnet.io/api/python/optimization/optimization.html#mxnet.optimizer.SGD)
 on the
 initialized parameters.
 
 ```python
@@ -112,7 +112,7 @@ Typically, one runs the training until convergence, which 
means that we have lea
 
 We will take following steps for training:
 
-- Define [Accuracy evaluation 
metric](http://mxnet.io/api/python/metric.html#mxnet.metric.Accuracy) over 
training data.
+- Define [Accuracy evaluation 
metric](http://mxnet.io/api/python/metric/metric.html#mxnet.metric.Accuracy) 
over training data.
 - Loop over inputs for every epoch.
 - Forward input through network to get output.
 - Compute loss with output and label inside record scope.
@@ -121,7 +121,7 @@ We will take following steps for training:
 
 Loss function takes (output, label) pairs and computes a scalar loss for each 
sample in the mini-batch. The scalars measure how far each output is from the 
label.
 There are many predefined loss functions in gluon.loss. Here we use
-[softmax_cross_entropy_loss](http://mxnet.io/api/python/gluon.html#mxnet.gluon.loss.softmax_cross_entropy_loss)
 for digit classification. We will compute loss and do backward propagation 
inside
+[softmax_cross_entropy_loss](http://mxnet.io/api/python/gluon/gluon.html#mxnet.gluon.loss.softmax_cross_entropy_loss)
 for digit classification. We will compute loss and do backward propagation 
inside
 training scope which is defined by `autograd.record()`.
 

[GitHub] marcoabreu closed pull request #11465: [MXNET-607] Fix the broken reported by the new BLC

[GitBox]

marcoabreu closed pull request #11465: [MXNET-607] Fix the broken reported by 
the new BLC
URL: https://github.com/apache/incubator-mxnet/pull/11465
 
 
   

This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:

As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):

diff --git a/docs/tutorials/gluon/mnist.md b/docs/tutorials/gluon/mnist.md
index 3a2a2cbe01b..5b8a98a3d66 100644
--- a/docs/tutorials/gluon/mnist.md
+++ b/docs/tutorials/gluon/mnist.md
@@ -77,7 +77,7 @@ In an MLP, the outputs of most FC layers are fed into an 
activation function, wh
 The following code declares three fully connected layers with 128, 64 and 10 
neurons each.
 The last fully connected layer often has its hidden size equal to the number 
of output classes in the dataset. Furthermore, these FC layers uses ReLU 
activation for performing an element-wise ReLU transformation on the FC layer 
output.
 
-To do this, we will use [Sequential 
layer](http://mxnet.io/api/python/gluon.html#mxnet.gluon.nn.Sequential) type. 
This is simply a linear stack of neural network layers. `nn.Dense` layers are 
nothing but the fully connected layers we discussed above.
+To do this, we will use [Sequential 
layer](http://mxnet.io/api/python/gluon/gluon.html#mxnet.gluon.nn.Sequential) 
type. This is simply a linear stack of neural network layers. `nn.Dense` layers 
are nothing but the fully connected layers we discussed above.
 
 ```python
 # define network
@@ -90,13 +90,13 @@ with net.name_scope():
 
  Initialize parameters and optimizer
 
-The following source code initializes all parameters received from parameter 
dict using 
[Xavier](http://mxnet.io/api/python/optimization.html#mxnet.initializer.Xavier) 
initializer
+The following source code initializes all parameters received from parameter 
dict using 
[Xavier](http://mxnet.io/api/python/optimization/optimization.html#mxnet.initializer.Xavier)
 initializer
 to train the MLP network we defined above.
 
 For our training, we will make use of the stochastic gradient descent (SGD) 
optimizer. In particular, we'll be using mini-batch SGD. Standard SGD processes 
train data one example at a time. In practice, this is very slow and one can 
speed up the process by processing examples in small batches. In this case, our 
batch size will be 100, which is a reasonable choice. Another parameter we 
select here is the learning rate, which controls the step size the optimizer 
takes in search of a solution. We'll pick a learning rate of 0.02, again a 
reasonable choice. Settings such as batch size and learning rate are what are 
usually referred to as hyper-parameters. What values we give them can have a 
great impact on training performance.
 
-We will use [Trainer](http://mxnet.io/api/python/gluon.html#trainer) class to 
apply the
-[SGD 
optimizer](http://mxnet.io/api/python/optimization.html#mxnet.optimizer.SGD) on 
the
+We will use [Trainer](http://mxnet.io/api/python/gluon/gluon.html#trainer) 
class to apply the
+[SGD 
optimizer](http://mxnet.io/api/python/optimization/optimization.html#mxnet.optimizer.SGD)
 on the
 initialized parameters.
 
 ```python
@@ -112,7 +112,7 @@ Typically, one runs the training until convergence, which 
means that we have lea
 
 We will take following steps for training:
 
-- Define [Accuracy evaluation 
metric](http://mxnet.io/api/python/metric.html#mxnet.metric.Accuracy) over 
training data.
+- Define [Accuracy evaluation 
metric](http://mxnet.io/api/python/metric/metric.html#mxnet.metric.Accuracy) 
over training data.
 - Loop over inputs for every epoch.
 - Forward input through network to get output.
 - Compute loss with output and label inside record scope.
@@ -121,7 +121,7 @@ We will take following steps for training:
 
 Loss function takes (output, label) pairs and computes a scalar loss for each 
sample in the mini-batch. The scalars measure how far each output is from the 
label.
 There are many predefined loss functions in gluon.loss. Here we use
-[softmax_cross_entropy_loss](http://mxnet.io/api/python/gluon.html#mxnet.gluon.loss.softmax_cross_entropy_loss)
 for digit classification. We will compute loss and do backward propagation 
inside
+[softmax_cross_entropy_loss](http://mxnet.io/api/python/gluon/gluon.html#mxnet.gluon.loss.softmax_cross_entropy_loss)
 for digit classification. We will compute loss and do backward propagation 
inside
 training scope which is defined by `autograd.record()`.
 
 ```python
diff --git 
a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/imageclassifier/ImageClassifierExample.scala
 
b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/imageclassifier/ImageClassifierExample.scala
index 8a57527f355..e886b908ba2 100644
--- 
a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/imageclassifier/ImageClassifierExample.scala

[incubator-mxnet] branch master updated: fix regex which processes RAT check output (#11476)

[marcoabreu]

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

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


The following commit(s) were added to refs/heads/master by this push:
 new f02e627  fix regex which processes RAT check output (#11476)
f02e627 is described below

commit f02e627109d54f1cee32ef20a14462d0b4878f49
Author: mbaijal <30911248+mbai...@users.noreply.github.com>
AuthorDate: Thu Jun 28 20:23:03 2018 -0700

fix regex which processes RAT check output (#11476)
---
 tests/nightly/apache_rat_license_check/license_check.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/nightly/apache_rat_license_check/license_check.sh 
b/tests/nightly/apache_rat_license_check/license_check.sh
index 84accf9..79e86c0 100755
--- a/tests/nightly/apache_rat_license_check/license_check.sh
+++ b/tests/nightly/apache_rat_license_check/license_check.sh
@@ -43,7 +43,7 @@ SOURCE="^0 Unknown Licenses"
 
 echo "---Process The Output---"
 
-if [[ "$OUTPUT" =~ "$SOURCE" ]]; then
+if [[ "$OUTPUT" =~ $SOURCE ]]; then
   echo "SUCCESS: There are no files with an Unknown License.";
 else
   echo "ERROR: RAT Check detected files with unknown licenses. Please fix 
and run test again!";

[GitHub] marcoabreu closed pull request #11476: [Nightly Tests] Fix regex which processes Apache RAT check output

[GitBox]

marcoabreu closed pull request #11476: [Nightly Tests] Fix regex which 
processes Apache RAT check output
URL: https://github.com/apache/incubator-mxnet/pull/11476
 
 
   

This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:

As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):

diff --git a/tests/nightly/apache_rat_license_check/license_check.sh 
b/tests/nightly/apache_rat_license_check/license_check.sh
index 84accf93f70..79e86c03e59 100755
--- a/tests/nightly/apache_rat_license_check/license_check.sh
+++ b/tests/nightly/apache_rat_license_check/license_check.sh
@@ -43,7 +43,7 @@ SOURCE="^0 Unknown Licenses"
 
 echo "---Process The Output---"
 
-if [[ "$OUTPUT" =~ "$SOURCE" ]]; then
+if [[ "$OUTPUT" =~ $SOURCE ]]; then
   echo "SUCCESS: There are no files with an Unknown License.";
 else
   echo "ERROR: RAT Check detected files with unknown licenses. Please fix 
and run test again!";


 


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[GitHub] freshcoderman closed issue #11479: gamma require 2d

[GitBox]

freshcoderman closed issue #11479: gamma require 2d
URL: https://github.com/apache/incubator-mxnet/issues/11479
 
 
   


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[GitHub] freshcoderman opened a new issue #11479: gamma require 2d

[GitBox]

freshcoderman opened a new issue #11479: gamma require 2d
URL: https://github.com/apache/incubator-mxnet/issues/11479
 
 
   Note: Providing complete information in the most concise form is the best 
way to get help. This issue template serves as the checklist for essential 
information to most of the technical issues and bug reports. For non-technical 
issues and feature requests, feel free to present the information in what you 
believe is the best form.
   
   For Q & A and discussion, please start a discussion thread at 
https://discuss.mxnet.io 
   
   ## Description
   (Brief description of the problem in no more than 2 sentences.)
   
   ## Environment info (Required)
   
   ```
   What to do:
   1. Download the diagnosis script from 
https://raw.githubusercontent.com/apache/incubator-mxnet/master/tools/diagnose.py
   2. Run the script using `python diagnose.py` and paste its output here.
   
   ```
   
   Package used (Python/R/Scala/Julia):
   (I'm using ...)
   
   For Scala user, please provide:
   1. Java version: (`java -version`)
   2. Maven version: (`mvn -version`)
   3. Scala runtime if applicable: (`scala -version`)
   
   For R user, please provide R `sessionInfo()`:
   
   ## Build info (Required if built from source)
   
   Compiler (gcc/clang/mingw/visual studio):
   
   MXNet commit hash:
   (Paste the output of `git rev-parse HEAD` here.)
   
   Build config:
   (Paste the content of config.mk, or the build command.)
   
   ## Error Message:
   (Paste the complete error message, including stack trace.)
   
   ## Minimum reproducible example
   (If you are using your own code, please provide a short script that 
reproduces the error. Otherwise, please provide link to the existing example.)
   
   ## Steps to reproduce
   (Paste the commands you ran that produced the error.)
   
   1.
   2.
   
   ## What have you tried to solve it?
   
   1.
   2.
   


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[GitHub] rahul003 closed issue #11469: Performance regression in augmentation

[GitBox]

rahul003 closed issue #11469: Performance regression in augmentation
URL: https://github.com/apache/incubator-mxnet/issues/11469
 
 
   


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[incubator-mxnet] branch master updated: fix record io augmentation speed (#11474)

[zhasheng]

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

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


The following commit(s) were added to refs/heads/master by this push:
 new b344d89  fix record io augmentation speed (#11474)
b344d89 is described below

commit b344d89a80f87fce01a964347ff0b5f54e62f374
Author: Tong He 
AuthorDate: Thu Jun 28 19:24:00 2018 -0700

fix record io augmentation speed (#11474)
---
 example/image-classification/common/data.py | 2 +-
 src/io/image_aug_default.cc | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/example/image-classification/common/data.py 
b/example/image-classification/common/data.py
index bfaadb3..c1dfcf5 100755
--- a/example/image-classification/common/data.py
+++ b/example/image-classification/common/data.py
@@ -86,7 +86,7 @@ def add_data_aug_args(parser):
 
 def set_resnet_aug(aug):
 # standard data augmentation setting for resnet training
-aug.set_defaults(random_crop=1, random_resized_crop=1)
+aug.set_defaults(random_crop=0, random_resized_crop=1)
 aug.set_defaults(min_random_area=0.08)
 aug.set_defaults(max_random_aspect_ratio=4./3., 
min_random_aspect_ratio=3./4.)
 aug.set_defaults(brightness=0.4, contrast=0.4, saturation=0.4, 
pca_noise=0.1)
diff --git a/src/io/image_aug_default.cc b/src/io/image_aug_default.cc
index ce9c79c..5b28aa1 100644
--- a/src/io/image_aug_default.cc
+++ b/src/io/image_aug_default.cc
@@ -293,7 +293,7 @@ class DefaultImageAugmenter : public ImageAugmenter {
 if (param_.max_rotate_angle > 0 || param_.max_shear_ratio > 0.0f
 || param_.rotate > 0 || rotate_list_.size() > 0
 || param_.max_random_scale != 1.0f || param_.min_random_scale != 1.0
-|| min_aspect_ratio != 1.0f || max_aspect_ratio != 1.0f
+|| (!param_.random_resized_crop && (min_aspect_ratio != 1.0f || 
max_aspect_ratio != 1.0f))
 || param_.max_img_size != 1e10f || param_.min_img_size != 0.0f) {
   std::uniform_real_distribution rand_uniform(0, 1);
   // shear

[GitHub] szha closed pull request #11474: Fix record io augmentation speed

[GitBox]

szha closed pull request #11474: Fix record io augmentation speed
URL: https://github.com/apache/incubator-mxnet/pull/11474
 
 
   

This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:

As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):

diff --git a/example/image-classification/common/data.py 
b/example/image-classification/common/data.py
index bfaadb3ff6b..c1dfcf56546 100755
--- a/example/image-classification/common/data.py
+++ b/example/image-classification/common/data.py
@@ -86,7 +86,7 @@ def add_data_aug_args(parser):
 
 def set_resnet_aug(aug):
 # standard data augmentation setting for resnet training
-aug.set_defaults(random_crop=1, random_resized_crop=1)
+aug.set_defaults(random_crop=0, random_resized_crop=1)
 aug.set_defaults(min_random_area=0.08)
 aug.set_defaults(max_random_aspect_ratio=4./3., 
min_random_aspect_ratio=3./4.)
 aug.set_defaults(brightness=0.4, contrast=0.4, saturation=0.4, 
pca_noise=0.1)
diff --git a/src/io/image_aug_default.cc b/src/io/image_aug_default.cc
index ce9c79c403a..5b28aa18913 100644
--- a/src/io/image_aug_default.cc
+++ b/src/io/image_aug_default.cc
@@ -293,7 +293,7 @@ class DefaultImageAugmenter : public ImageAugmenter {
 if (param_.max_rotate_angle > 0 || param_.max_shear_ratio > 0.0f
 || param_.rotate > 0 || rotate_list_.size() > 0
 || param_.max_random_scale != 1.0f || param_.min_random_scale != 1.0
-|| min_aspect_ratio != 1.0f || max_aspect_ratio != 1.0f
+|| (!param_.random_resized_crop && (min_aspect_ratio != 1.0f || 
max_aspect_ratio != 1.0f))
 || param_.max_img_size != 1e10f || param_.min_img_size != 0.0f) {
   std::uniform_real_distribution rand_uniform(0, 1);
   // shear


 


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[GitHub] szha closed pull request #11379: support shared parameter in summary

[GitBox]

szha closed pull request #11379: support shared parameter in summary
URL: https://github.com/apache/incubator-mxnet/pull/11379
 
 
   

This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:

As this is a foreign pull request (from a fork), the diff is supplied
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diff --git a/python/mxnet/gluon/block.py b/python/mxnet/gluon/block.py
index 0ef28496c20..731d5414973 100644
--- a/python/mxnet/gluon/block.py
+++ b/python/mxnet/gluon/block.py
@@ -563,6 +563,7 @@ def summary(self, *inputs):
 :class:`mxnet.ndarray.NDArray` is supported.
 """
 summary = OrderedDict()
+seen = set()
 hooks = []
 
 def _get_shape_str(args):
@@ -611,9 +612,14 @@ def _summary_hook(block, _, outputs):
 
 params = 0
 summary[m_key]['trainable'] = 0
+summary[m_key]['shared'] = 0
 for p in block._reg_params.values():
 params += p.data().size
 summary[m_key]['trainable'] += 0 if p.grad_req == 'null' 
else p.data().size
+if p in seen:
+summary[m_key]['shared'] += p.data().size
+else:
+seen.add(p)
 summary[m_key]['n_params'] = params
 
 from .nn.basic_layers import Sequential, HybridSequential
@@ -624,6 +630,7 @@ def _summary_hook(block, _, outputs):
 summary['Input']['output_shape'] = _get_shape_str(inputs)
 summary['Input']['n_params'] = 0
 summary['Input']['trainable'] = 0
+summary['Input']['shared'] = 0
 
 try:
 self.apply(_register_summary_hook)
@@ -635,16 +642,19 @@ def _summary_hook(block, _, outputs):
 print('='*80)
 total_params = 0
 trainable_params = 0
+shared_params = 0
 for layer in summary:
 print(line_format.format(layer,
  str(summary[layer]['output_shape']),
  summary[layer]['n_params']))
 total_params += summary[layer]['n_params']
 trainable_params += summary[layer]['trainable']
+shared_params += summary[layer]['shared']
 print('='*80)
 print('Total params: ' + str(total_params))
 print('Trainable params: ' + str(trainable_params))
 print('Non-trainable params: ' + str(total_params - 
trainable_params))
+print('Shared params: ' + str(shared_params))
 print('-'*80)
 finally:
 for h in hooks:
diff --git a/tests/python/unittest/test_gluon.py 
b/tests/python/unittest/test_gluon.py
index 7fff6b8c1f5..d275cd61029 100644
--- a/tests/python/unittest/test_gluon.py
+++ b/tests/python/unittest/test_gluon.py
@@ -1264,9 +1264,9 @@ def test_summary():
 
 net2 = nn.Sequential()
 with net2.name_scope():
-net2.add(nn.Embedding(10, 20))
+net2.add(nn.Embedding(40, 30))
 net2.add(gluon.rnn.LSTM(30))
-net2.add(nn.Dense(40, flatten=False))
+net2.add(nn.Dense(40, flatten=False, params=net2[0].params))
 net2.initialize()
 net2.summary(mx.nd.ones((80, 32)))
 


 


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[incubator-mxnet] branch master updated: support shared parameter in summary (#11379)

[zhasheng]

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

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


The following commit(s) were added to refs/heads/master by this push:
 new 36f2aae  support shared parameter in summary (#11379)
36f2aae is described below

commit 36f2aae9fb97159d226f24c9b1f829eccc318c14
Author: Sheng Zha 
AuthorDate: Thu Jun 28 19:15:39 2018 -0700

support shared parameter in summary (#11379)
---
 python/mxnet/gluon/block.py | 10 ++
 tests/python/unittest/test_gluon.py |  4 ++--
 2 files changed, 12 insertions(+), 2 deletions(-)

diff --git a/python/mxnet/gluon/block.py b/python/mxnet/gluon/block.py
index 0ef2849..731d541 100644
--- a/python/mxnet/gluon/block.py
+++ b/python/mxnet/gluon/block.py
@@ -563,6 +563,7 @@ class Block(object):
 :class:`mxnet.ndarray.NDArray` is supported.
 """
 summary = OrderedDict()
+seen = set()
 hooks = []
 
 def _get_shape_str(args):
@@ -611,9 +612,14 @@ class Block(object):
 
 params = 0
 summary[m_key]['trainable'] = 0
+summary[m_key]['shared'] = 0
 for p in block._reg_params.values():
 params += p.data().size
 summary[m_key]['trainable'] += 0 if p.grad_req == 'null' 
else p.data().size
+if p in seen:
+summary[m_key]['shared'] += p.data().size
+else:
+seen.add(p)
 summary[m_key]['n_params'] = params
 
 from .nn.basic_layers import Sequential, HybridSequential
@@ -624,6 +630,7 @@ class Block(object):
 summary['Input']['output_shape'] = _get_shape_str(inputs)
 summary['Input']['n_params'] = 0
 summary['Input']['trainable'] = 0
+summary['Input']['shared'] = 0
 
 try:
 self.apply(_register_summary_hook)
@@ -635,16 +642,19 @@ class Block(object):
 print('='*80)
 total_params = 0
 trainable_params = 0
+shared_params = 0
 for layer in summary:
 print(line_format.format(layer,
  str(summary[layer]['output_shape']),
  summary[layer]['n_params']))
 total_params += summary[layer]['n_params']
 trainable_params += summary[layer]['trainable']
+shared_params += summary[layer]['shared']
 print('='*80)
 print('Total params: ' + str(total_params))
 print('Trainable params: ' + str(trainable_params))
 print('Non-trainable params: ' + str(total_params - 
trainable_params))
+print('Shared params: ' + str(shared_params))
 print('-'*80)
 finally:
 for h in hooks:
diff --git a/tests/python/unittest/test_gluon.py 
b/tests/python/unittest/test_gluon.py
index 7fff6b8..d275cd6 100644
--- a/tests/python/unittest/test_gluon.py
+++ b/tests/python/unittest/test_gluon.py
@@ -1264,9 +1264,9 @@ def test_summary():
 
 net2 = nn.Sequential()
 with net2.name_scope():
-net2.add(nn.Embedding(10, 20))
+net2.add(nn.Embedding(40, 30))
 net2.add(gluon.rnn.LSTM(30))
-net2.add(nn.Dense(40, flatten=False))
+net2.add(nn.Dense(40, flatten=False, params=net2[0].params))
 net2.initialize()
 net2.summary(mx.nd.ones((80, 32)))
 

[GitHub] szha commented on issue #11003: rnn_cell little bug fixed

[GitBox]

szha commented on issue #11003: rnn_cell little bug fixed
URL: https://github.com/apache/incubator-mxnet/pull/11003#issuecomment-401226598
 
 
   suppose you have a git remote called "upstream" (which you can get by doing 
`git remote add upstream https://github.com/apache/incubator-mxnet`), you can 
do `git pull upstream master --rebase` and then do a force push. You don't need 
to reopen the PR


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[GitHub] pengzhao-intel commented on issue #11448: fail to fall back when sparse arrays are passed to MKLDNN-enabled operators.

[GitBox]

pengzhao-intel commented on issue #11448: fail to fall back when sparse arrays 
are passed to MKLDNN-enabled operators.
URL: 
https://github.com/apache/incubator-mxnet/issues/11448#issuecomment-401225029
 
 
   what else need to cover? @zouluobao can help


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[GitHub] anirudh2290 commented on issue #11478: 1.2.1 release notes

[GitBox]

anirudh2290 commented on issue #11478: 1.2.1 release notes
URL: https://github.com/apache/incubator-mxnet/pull/11478#issuecomment-401224690
 
 
   @aaronmarkham can you please take a look.


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[GitHub] haojin2 commented on a change in pull request #11229: [MXNET-379] L1 Normalization

[GitBox]

haojin2 commented on a change in pull request #11229: [MXNET-379] L1 
Normalization
URL: https://github.com/apache/incubator-mxnet/pull/11229#discussion_r199034382
 
 

 ##
 File path: tests/python/unittest/test_operator.py
 ##
 @@ -3009,6 +3009,49 @@ def npy_layer_norm(data, gamma, beta, axis=1, eps=1E-5):
grad_nodes={'data': req, 'gamma': req, 'beta': 
req},
numeric_eps=1e-2, rtol=1e-2, atol=1e-2)
 
+@with_seed()
+def test_norm():
+def l1norm(input_data, axis=0, keepdims=True):
+return np.sum(abs(input_data), axis=axis, keepdims=keepdims)
+def l2norm(input_data, axis=0, keepdims=True): 
+return np.linalg.norm(input_data, axis=axis, keepdims=keepdims)
+
+ctx = default_context()
+data = mx.symbol.Variable('data')
+in_data_dim = random_sample([4,5,6], 1)[0]
+in_shape = rand_shape_nd(in_data_dim)
+for order in [1, 2]:
+for dtype in [np.float16, np.float32, np.float64]:
+in_data = np.random.uniform(-1, 1, in_shape).astype(dtype)
+in_data[abs(in_data) < 1e-2] = 1e-2
+for i in range(in_data_dim):
+norm_sym = mx.symbol.norm(data=data, ord=order, axis=i, 
keepdims=True)
+npy_out = l1norm(in_data, i) if order==1 else l2norm(in_data, 
i)
 
 Review comment:
   Okay, so maybe you can go ahead and change all
   ```Python
   1e-x if dtype is np.float16 else 1e-y
   ```
   in this function to use `==`?


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

[zhasheng]

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

zhasheng 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 35b9788  Bump the publish timestamp.
35b9788 is described below

commit 35b978860e88bc09017fdf583de8e2b7a4a35dec
Author: mxnet-ci 
AuthorDate: Fri Jun 29 01:37:52 2018 +

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..b2fb3cb
--- /dev/null
+++ b/date.txt
@@ -0,0 +1 @@
+Fri Jun 29 01:37:52 UTC 2018

[GitHub] aaronmarkham commented on issue #11404: [MXNET-595] Install page not loading properly in Internet Explorer

[GitBox]

aaronmarkham commented on issue #11404: [MXNET-595] Install page not loading 
properly in Internet Explorer
URL: https://github.com/apache/incubator-mxnet/pull/11404#issuecomment-401219131
 
 
   @indhub this should be the fix for the issue you reported... can you merge?


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[GitHub] anirudhacharya commented on a change in pull request #11229: [MXNET-379] L1 Normalization

[GitBox]

anirudhacharya commented on a change in pull request #11229: [MXNET-379] L1 
Normalization
URL: https://github.com/apache/incubator-mxnet/pull/11229#discussion_r199029890
 
 

 ##
 File path: src/operator/tensor/broadcast_reduce_op.h
 ##
 @@ -880,27 +880,24 @@ inline bool L2NormStorageType(const nnvm::NodeAttrs& 
attrs,
   int& out_stype = out_attrs->at(0);
   const NormParam& param = nnvm::get(attrs.parsed);
   bool dispatched = false;
-  // l2 norm on a particular axis only supports cpu
-  const bool invalid_ctx = dev_mask != mshadow::cpu::kDevMask;
-  const auto dispatch_ex =
+  if (param.ord == 2) {
+// l2 norm on a particular axis only supports cpu
+const bool invalid_ctx = dev_mask != mshadow::cpu::kDevMask;
+const auto dispatch_ex =
   invalid_ctx ? DispatchMode::kFComputeFallback : 
DispatchMode::kFComputeEx;
-  if (!dispatched && in_stype == kDefaultStorage) {
-// dns -> dns
-dispatched = storage_type_assign(_stype, kDefaultStorage, 
dispatch_mode,
- DispatchMode::kFCompute);
-  }
-  const TShape axis = param.axis.has_value() ? param.axis.value() : TShape();
-  if (!dispatched && (in_stype == kRowSparseStorage || in_stype == 
kCSRStorage) &&
-  axis.ndim() == 0 && param.ord == 2) {
-// l2 norm: rsp/csr, axis = () -> dns
-dispatched = storage_type_assign(_stype, kDefaultStorage, 
dispatch_mode,
- DispatchMode::kFComputeEx);
-  }
-  if (!dispatched && in_stype == kCSRStorage && axis.ndim() == 1 && 
!param.keepdims &&
-  (axis[0] == 0 || axis[0] == 1) && param.ord == 2) {
-// l2 norm: csr, axis = 0/1 -> dns
-dispatched = storage_type_assign(_stype, kDefaultStorage, 
dispatch_mode,
- dispatch_ex);
+const TShape axis = param.axis.has_value() ? param.axis.value() : TShape();
+if (!dispatched && (in_stype == kRowSparseStorage || in_stype == 
kCSRStorage) &&
+axis.ndim() == 0 && param.ord == 2) {
+  // l2 norm: rsp/csr, axis = () -> dns
+  dispatched = storage_type_assign(_stype, kDefaultStorage, 
dispatch_mode,
+   DispatchMode::kFComputeEx);
+}
+if (!dispatched && in_stype == kCSRStorage && axis.ndim() == 1 && 
!param.keepdims &&
+(axis[0] == 0 || axis[0] == 1) && param.ord == 2) {
+  // l2 norm: csr, axis = 0/1 -> dns
+  dispatched = storage_type_assign(_stype, kDefaultStorage, 
dispatch_mode,
+   dispatch_ex);
+}
   }
   if (!dispatched) {
 dispatched = dispatch_fallback(out_attrs, dispatch_mode);
 
 Review comment:
   i will fix this


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[GitHub] leezu commented on issue #11403: L2 norm for fp16 goes to nan, needs to be fixed

[GitBox]

leezu commented on issue #11403: L2 norm for fp16 goes to nan, needs to be fixed
URL: 
https://github.com/apache/incubator-mxnet/issues/11403#issuecomment-401217193
 
 
   How about using a numerically stable L2 norm algorithm by scaling the 
elements? Ie. [BLAS nrm2](http://www.netlib.org/blas/snrm2.f)


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[GitHub] chinakook commented on issue #11003: rnn_cell little bug fixed

[GitBox]

chinakook commented on issue #11003: rnn_cell little bug fixed
URL: https://github.com/apache/incubator-mxnet/pull/11003#issuecomment-401216200
 
 
   Rebasing like this? I've not very familiar with rebasing.


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[GitHub] haojin2 commented on a change in pull request #11229: [MXNET-379] L1 Normalization

[GitBox]

haojin2 commented on a change in pull request #11229: [MXNET-379] L1 
Normalization
URL: https://github.com/apache/incubator-mxnet/pull/11229#discussion_r199028398
 
 

 ##
 File path: tests/python/unittest/test_operator.py
 ##
 @@ -3009,6 +3009,49 @@ def npy_layer_norm(data, gamma, beta, axis=1, eps=1E-5):
grad_nodes={'data': req, 'gamma': req, 'beta': 
req},
numeric_eps=1e-2, rtol=1e-2, atol=1e-2)
 
+@with_seed()
+def test_norm():
+def l1norm(input_data, axis=0, keepdims=True):
+return np.sum(abs(input_data), axis=axis, keepdims=keepdims)
+def l2norm(input_data, axis=0, keepdims=True): 
+return np.linalg.norm(input_data, axis=axis, keepdims=keepdims)
+
+ctx = default_context()
+data = mx.symbol.Variable('data')
+in_data_dim = random_sample([4,5,6], 1)[0]
+in_shape = rand_shape_nd(in_data_dim)
+for order in [1, 2]:
+for dtype in [np.float16, np.float32, np.float64]:
+in_data = np.random.uniform(-1, 1, in_shape).astype(dtype)
+in_data[abs(in_data) < 1e-2] = 1e-2
+for i in range(in_data_dim):
+norm_sym = mx.symbol.norm(data=data, ord=order, axis=i, 
keepdims=True)
+npy_out = l1norm(in_data, i) if order==1 else l2norm(in_data, 
i)
 
 Review comment:
   ```python
   >>> a = 1
   >>> a is 1
   True
   ```


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[GitHub] anirudhacharya commented on a change in pull request #11229: [MXNET-379] L1 Normalization

[GitBox]

anirudhacharya commented on a change in pull request #11229: [MXNET-379] L1 
Normalization
URL: https://github.com/apache/incubator-mxnet/pull/11229#discussion_r199028391
 
 

 ##
 File path: src/operator/tensor/broadcast_reduce_op_value.cc
 ##
 @@ -284,10 +284,16 @@ array.
 
 Examples::
 
-  x = [[1, 2],
-   [3, 4]]
+  x = [[[1,2],
+[3,4]],
+   [[2,2],
+[5,6]]]
 
 Review comment:
   sure i can do that


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[GitHub] anirudhacharya commented on a change in pull request #11229: [MXNET-379] L1 Normalization

[GitBox]

anirudhacharya commented on a change in pull request #11229: [MXNET-379] L1 
Normalization
URL: https://github.com/apache/incubator-mxnet/pull/11229#discussion_r199028119
 
 

 ##
 File path: tests/python/unittest/test_operator.py
 ##
 @@ -3009,6 +3009,49 @@ def npy_layer_norm(data, gamma, beta, axis=1, eps=1E-5):
grad_nodes={'data': req, 'gamma': req, 'beta': 
req},
numeric_eps=1e-2, rtol=1e-2, atol=1e-2)
 
+@with_seed()
+def test_norm():
+def l1norm(input_data, axis=0, keepdims=True):
+return np.sum(abs(input_data), axis=axis, keepdims=keepdims)
+def l2norm(input_data, axis=0, keepdims=True): 
+return np.linalg.norm(input_data, axis=axis, keepdims=keepdims)
+
+ctx = default_context()
+data = mx.symbol.Variable('data')
+in_data_dim = random_sample([4,5,6], 1)[0]
+in_shape = rand_shape_nd(in_data_dim)
+for order in [1, 2]:
+for dtype in [np.float16, np.float32, np.float64]:
+in_data = np.random.uniform(-1, 1, in_shape).astype(dtype)
+in_data[abs(in_data) < 1e-2] = 1e-2
 
 Review comment:
   sure, i will use 1e-3. that is the epsilon i am using in check_gradient


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[GitHub] haojin2 commented on a change in pull request #11229: [MXNET-379] L1 Normalization

[GitBox]

haojin2 commented on a change in pull request #11229: [MXNET-379] L1 
Normalization
URL: https://github.com/apache/incubator-mxnet/pull/11229#discussion_r199028037
 
 

 ##
 File path: src/operator/tensor/broadcast_reduce_op_value.cc
 ##
 @@ -284,10 +284,16 @@ array.
 
 Examples::
 
-  x = [[1, 2],
-   [3, 4]]
+  x = [[[1,2],
+[3,4]],
+   [[2,2],
+[5,6]]]
 
 Review comment:
   Then would you please fix those too?


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[GitHub] anirudhacharya commented on a change in pull request #11229: [MXNET-379] L1 Normalization

[GitBox]

anirudhacharya commented on a change in pull request #11229: [MXNET-379] L1 
Normalization
URL: https://github.com/apache/incubator-mxnet/pull/11229#discussion_r199027961
 
 

 ##
 File path: tests/python/unittest/test_operator.py
 ##
 @@ -3009,6 +3009,49 @@ def npy_layer_norm(data, gamma, beta, axis=1, eps=1E-5):
grad_nodes={'data': req, 'gamma': req, 'beta': 
req},
numeric_eps=1e-2, rtol=1e-2, atol=1e-2)
 
+@with_seed()
+def test_norm():
+def l1norm(input_data, axis=0, keepdims=True):
+return np.sum(abs(input_data), axis=axis, keepdims=keepdims)
+def l2norm(input_data, axis=0, keepdims=True): 
+return np.linalg.norm(input_data, axis=axis, keepdims=keepdims)
+
+ctx = default_context()
+data = mx.symbol.Variable('data')
+in_data_dim = random_sample([4,5,6], 1)[0]
+in_shape = rand_shape_nd(in_data_dim)
+for order in [1, 2]:
+for dtype in [np.float16, np.float32, np.float64]:
+in_data = np.random.uniform(-1, 1, in_shape).astype(dtype)
+in_data[abs(in_data) < 1e-2] = 1e-2
+for i in range(in_data_dim):
+norm_sym = mx.symbol.norm(data=data, ord=order, axis=i, 
keepdims=True)
+npy_out = l1norm(in_data, i) if order==1 else l2norm(in_data, 
i)
 
 Review comment:
   python's ``is`` compares object reference whereas == compares for value. 
Here we want to compare the values and not object references. Using ``is`` 
might make the test fail. 
   
   For example - 
   ```python
   >>> 625 is 25**2
   False
   >>> 625 == 25**2
   True
   ```


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[GitHub] haojin2 commented on a change in pull request #11229: [MXNET-379] L1 Normalization

[GitBox]

haojin2 commented on a change in pull request #11229: [MXNET-379] L1 
Normalization
URL: https://github.com/apache/incubator-mxnet/pull/11229#discussion_r199027684
 
 

 ##
 File path: src/operator/tensor/broadcast_reduce_op_value.cc
 ##
 @@ -284,10 +284,16 @@ array.
 
 Examples::
 
-  x = [[1, 2],
-   [3, 4]]
+  x = [[[1,2],
+[3,4]],
+   [[2,2],
+[5,6]]]
 
-  norm(x) = [5.47722578]
+  norm(x, ord=2, axis=1) = [[3.1622777 4.472136 ]
+[5.3851647 6.3245554]]
+
+  norm(x, ord=1, axis=1) = [[4., 6.],
+[7., 8.]]
 
   rsp = x.cast_storage('row_sparse')
 
 Review comment:
   the lines below have examples for sparse, and you should update them with 
the latest changes to the behavior of this op, you should also add that only 
"ord=2" supports sparse ndarrays after your changes.


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[GitHub] haojin2 commented on a change in pull request #11229: [MXNET-379] L1 Normalization

[GitBox]

haojin2 commented on a change in pull request #11229: [MXNET-379] L1 
Normalization
URL: https://github.com/apache/incubator-mxnet/pull/11229#discussion_r199027435
 
 

 ##
 File path: src/operator/tensor/broadcast_reduce_op.h
 ##
 @@ -1021,8 +1023,38 @@ void L2NormGradCompute(const nnvm::NodeAttrs& attrs,
   } else {
 small = ReduceAxesShapeImpl(outputs[0].shape_, param.axis, true, false);
   }
-  ReduceAxesBackwardUseInOutImpl(ctx, small, 
inputs,
+  if (param.ord == 1) {
+using namespace mshadow;
+using namespace mshadow::expr;
+TShape src_shape, dst_shape;
+BroadcastReduceShapeCompact(outputs[0].shape_, small, _shape, 
_shape);
+Stream *s = ctx.get_stream();
+MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+  if (dst_shape.ndim() == 2) {
+Tensor ograd =
+  inputs[0].get_with_shape(dst_shape.get<2>(), s);
+Tensor igrad =
+  outputs[0].get_with_shape(src_shape.get<2>(), s);
+Tensor data =
+  inputs[1].get_with_shape(src_shape.get<2>(), s);
+ASSIGN_DISPATCH(igrad, req[0],
+  broadcast_to(ograd, src_shape)*F(data));
+  } else {
+const int ndim = MXNET_SPECIAL_MAX_NDIM;
+Tensor igrad =
+  outputs[0].get_with_shape(src_shape.get(), 
s);
+Tensor ograd =
+  inputs[0].get_with_shape(dst_shape.get(), s);
+Tensor data =
+  inputs[1].get_with_shape(src_shape.get(), s);
+ASSIGN_DISPATCH(igrad, req[0],
+  broadcast_to(ograd, src_shape)*F(data));
+  }
+});
+  } else if (param.ord == 2) {
+ReduceAxesBackwardUseInOutImpl(ctx, small, 
inputs,
   req, outputs);
 
 Review comment:
   No, you should have `req` aligned with `ctx` above.


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[GitHub] haojin2 commented on a change in pull request #11229: [MXNET-379] L1 Normalization

[GitBox]

haojin2 commented on a change in pull request #11229: [MXNET-379] L1 
Normalization
URL: https://github.com/apache/incubator-mxnet/pull/11229#discussion_r199027220
 
 

 ##
 File path: src/operator/tensor/broadcast_reduce_op.h
 ##
 @@ -880,27 +880,24 @@ inline bool L2NormStorageType(const nnvm::NodeAttrs& 
attrs,
   int& out_stype = out_attrs->at(0);
   const NormParam& param = nnvm::get(attrs.parsed);
   bool dispatched = false;
-  // l2 norm on a particular axis only supports cpu
-  const bool invalid_ctx = dev_mask != mshadow::cpu::kDevMask;
-  const auto dispatch_ex =
+  if (param.ord == 2) {
+// l2 norm on a particular axis only supports cpu
+const bool invalid_ctx = dev_mask != mshadow::cpu::kDevMask;
+const auto dispatch_ex =
   invalid_ctx ? DispatchMode::kFComputeFallback : 
DispatchMode::kFComputeEx;
-  if (!dispatched && in_stype == kDefaultStorage) {
-// dns -> dns
-dispatched = storage_type_assign(_stype, kDefaultStorage, 
dispatch_mode,
- DispatchMode::kFCompute);
-  }
-  const TShape axis = param.axis.has_value() ? param.axis.value() : TShape();
-  if (!dispatched && (in_stype == kRowSparseStorage || in_stype == 
kCSRStorage) &&
-  axis.ndim() == 0 && param.ord == 2) {
-// l2 norm: rsp/csr, axis = () -> dns
-dispatched = storage_type_assign(_stype, kDefaultStorage, 
dispatch_mode,
- DispatchMode::kFComputeEx);
-  }
-  if (!dispatched && in_stype == kCSRStorage && axis.ndim() == 1 && 
!param.keepdims &&
-  (axis[0] == 0 || axis[0] == 1) && param.ord == 2) {
-// l2 norm: csr, axis = 0/1 -> dns
-dispatched = storage_type_assign(_stype, kDefaultStorage, 
dispatch_mode,
- dispatch_ex);
+const TShape axis = param.axis.has_value() ? param.axis.value() : TShape();
+if (!dispatched && (in_stype == kRowSparseStorage || in_stype == 
kCSRStorage) &&
+axis.ndim() == 0 && param.ord == 2) {
+  // l2 norm: rsp/csr, axis = () -> dns
+  dispatched = storage_type_assign(_stype, kDefaultStorage, 
dispatch_mode,
+   DispatchMode::kFComputeEx);
+}
+if (!dispatched && in_stype == kCSRStorage && axis.ndim() == 1 && 
!param.keepdims &&
+(axis[0] == 0 || axis[0] == 1) && param.ord == 2) {
+  // l2 norm: csr, axis = 0/1 -> dns
+  dispatched = storage_type_assign(_stype, kDefaultStorage, 
dispatch_mode,
+   dispatch_ex);
+}
   }
   if (!dispatched) {
 dispatched = dispatch_fallback(out_attrs, dispatch_mode);
 
 Review comment:
   @anirudhacharya I think you missed the default case when copying the 
previous code over.


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[GitHub] anirudhacharya commented on a change in pull request #11229: [MXNET-379] L1 Normalization

[GitBox]

anirudhacharya commented on a change in pull request #11229: [MXNET-379] L1 
Normalization
URL: https://github.com/apache/incubator-mxnet/pull/11229#discussion_r199026763
 
 

 ##
 File path: src/operator/tensor/broadcast_reduce_op_value.cc
 ##
 @@ -284,10 +284,16 @@ array.
 
 Examples::
 
-  x = [[1, 2],
-   [3, 4]]
+  x = [[[1,2],
+[3,4]],
+   [[2,2],
+[5,6]]]
 
-  norm(x) = [5.47722578]
+  norm(x, ord=2, axis=1) = [[3.1622777 4.472136 ]
+[5.3851647 6.3245554]]
+
+  norm(x, ord=1, axis=1) = [[4., 6.],
+[7., 8.]]
 
   rsp = x.cast_storage('row_sparse')
 
 Review comment:
   I do not understand what you mean by "specification for sparse-related 
cases" could you please elaborate?


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[GitHub] anirudhacharya commented on a change in pull request #11229: [MXNET-379] L1 Normalization

[GitBox]

anirudhacharya commented on a change in pull request #11229: [MXNET-379] L1 
Normalization
URL: https://github.com/apache/incubator-mxnet/pull/11229#discussion_r199026810
 
 

 ##
 File path: src/operator/tensor/broadcast_reduce_op_value.cc
 ##
 @@ -322,7 +328,7 @@ NNVM_REGISTER_OP(_backward_norm)
   [](const NodeAttrs& attrs) {
 return std::vector{ResourceRequest::kTempSpace};
   })
-.set_attr("FCompute", L2NormGradCompute);
+.set_attr("FCompute", LpNormGradCompute);
 
 Review comment:
   this is done


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[GitHub] haojin2 commented on a change in pull request #11229: [MXNET-379] L1 Normalization

[GitBox]

haojin2 commented on a change in pull request #11229: [MXNET-379] L1 
Normalization
URL: https://github.com/apache/incubator-mxnet/pull/11229#discussion_r199026778
 
 

 ##
 File path: tests/python/unittest/test_operator.py
 ##
 @@ -3009,6 +3009,49 @@ def npy_layer_norm(data, gamma, beta, axis=1, eps=1E-5):
grad_nodes={'data': req, 'gamma': req, 'beta': 
req},
numeric_eps=1e-2, rtol=1e-2, atol=1e-2)
 
+@with_seed()
+def test_norm():
+def l1norm(input_data, axis=0, keepdims=True):
+return np.sum(abs(input_data), axis=axis, keepdims=keepdims)
+def l2norm(input_data, axis=0, keepdims=True): 
+return np.linalg.norm(input_data, axis=axis, keepdims=keepdims)
+
+ctx = default_context()
+data = mx.symbol.Variable('data')
+in_data_dim = random_sample([4,5,6], 1)[0]
+in_shape = rand_shape_nd(in_data_dim)
+for order in [1, 2]:
+for dtype in [np.float16, np.float32, np.float64]:
+in_data = np.random.uniform(-1, 1, in_shape).astype(dtype)
+in_data[abs(in_data) < 1e-2] = 1e-2
+for i in range(in_data_dim):
+norm_sym = mx.symbol.norm(data=data, ord=order, axis=i, 
keepdims=True)
+npy_out = l1norm(in_data, i) if order==1 else l2norm(in_data, 
i)
 
 Review comment:
   maybe use `is` instead of `==`


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[GitHub] eric-haibin-lin commented on a change in pull request #11229: [MXNET-379] L1 Normalization

[GitBox]

eric-haibin-lin commented on a change in pull request #11229: [MXNET-379] L1 
Normalization
URL: https://github.com/apache/incubator-mxnet/pull/11229#discussion_r199026685
 
 

 ##
 File path: src/operator/tensor/broadcast_reduce_op.h
 ##
 @@ -880,27 +880,24 @@ inline bool L2NormStorageType(const nnvm::NodeAttrs& 
attrs,
   int& out_stype = out_attrs->at(0);
   const NormParam& param = nnvm::get(attrs.parsed);
   bool dispatched = false;
-  // l2 norm on a particular axis only supports cpu
-  const bool invalid_ctx = dev_mask != mshadow::cpu::kDevMask;
-  const auto dispatch_ex =
+  if (param.ord == 2) {
+// l2 norm on a particular axis only supports cpu
+const bool invalid_ctx = dev_mask != mshadow::cpu::kDevMask;
+const auto dispatch_ex =
   invalid_ctx ? DispatchMode::kFComputeFallback : 
DispatchMode::kFComputeEx;
-  if (!dispatched && in_stype == kDefaultStorage) {
-// dns -> dns
-dispatched = storage_type_assign(_stype, kDefaultStorage, 
dispatch_mode,
- DispatchMode::kFCompute);
-  }
-  const TShape axis = param.axis.has_value() ? param.axis.value() : TShape();
-  if (!dispatched && (in_stype == kRowSparseStorage || in_stype == 
kCSRStorage) &&
-  axis.ndim() == 0 && param.ord == 2) {
-// l2 norm: rsp/csr, axis = () -> dns
-dispatched = storage_type_assign(_stype, kDefaultStorage, 
dispatch_mode,
- DispatchMode::kFComputeEx);
-  }
-  if (!dispatched && in_stype == kCSRStorage && axis.ndim() == 1 && 
!param.keepdims &&
-  (axis[0] == 0 || axis[0] == 1) && param.ord == 2) {
-// l2 norm: csr, axis = 0/1 -> dns
-dispatched = storage_type_assign(_stype, kDefaultStorage, 
dispatch_mode,
- dispatch_ex);
+const TShape axis = param.axis.has_value() ? param.axis.value() : TShape();
+if (!dispatched && (in_stype == kRowSparseStorage || in_stype == 
kCSRStorage) &&
+axis.ndim() == 0 && param.ord == 2) {
+  // l2 norm: rsp/csr, axis = () -> dns
+  dispatched = storage_type_assign(_stype, kDefaultStorage, 
dispatch_mode,
+   DispatchMode::kFComputeEx);
+}
+if (!dispatched && in_stype == kCSRStorage && axis.ndim() == 1 && 
!param.keepdims &&
+(axis[0] == 0 || axis[0] == 1) && param.ord == 2) {
+  // l2 norm: csr, axis = 0/1 -> dns
+  dispatched = storage_type_assign(_stype, kDefaultStorage, 
dispatch_mode,
+   dispatch_ex);
+}
   }
   if (!dispatched) {
 dispatched = dispatch_fallback(out_attrs, dispatch_mode);
 
 Review comment:
   Do not dispatch to fallback if all your inputs are dense. 


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[GitHub] andrewfayres commented on issue #11477: Copy profiler examples to new location for CI

[GitBox]

andrewfayres commented on issue #11477: Copy profiler examples to new location 
for CI
URL: https://github.com/apache/incubator-mxnet/pull/11477#issuecomment-401214009
 
 
   Odd, I know I updated these earlier. I'll take a look when I get home.


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[GitHub] haojin2 commented on a change in pull request #11229: [MXNET-379] L1 Normalization

[GitBox]

haojin2 commented on a change in pull request #11229: [MXNET-379] L1 
Normalization
URL: https://github.com/apache/incubator-mxnet/pull/11229#discussion_r199026636
 
 

 ##
 File path: tests/python/unittest/test_operator.py
 ##
 @@ -3009,6 +3009,49 @@ def npy_layer_norm(data, gamma, beta, axis=1, eps=1E-5):
grad_nodes={'data': req, 'gamma': req, 'beta': 
req},
numeric_eps=1e-2, rtol=1e-2, atol=1e-2)
 
+@with_seed()
+def test_norm():
+def l1norm(input_data, axis=0, keepdims=True):
+return np.sum(abs(input_data), axis=axis, keepdims=keepdims)
+def l2norm(input_data, axis=0, keepdims=True): 
+return np.linalg.norm(input_data, axis=axis, keepdims=keepdims)
+
+ctx = default_context()
+data = mx.symbol.Variable('data')
+in_data_dim = random_sample([4,5,6], 1)[0]
+in_shape = rand_shape_nd(in_data_dim)
+for order in [1, 2]:
+for dtype in [np.float16, np.float32, np.float64]:
+in_data = np.random.uniform(-1, 1, in_shape).astype(dtype)
+in_data[abs(in_data) < 1e-2] = 1e-2
 
 Review comment:
   Please only do this for abs(in_data) < eps, where eps is the eps you're 
using for check_numeric_gradient


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[GitHub] threeleafzerg commented on issue #10696: [MXNET-366]Extend MXNet Distributed Training by AllReduce

[GitBox]

threeleafzerg commented on issue #10696: [MXNET-366]Extend MXNet Distributed 
Training by AllReduce
URL: https://github.com/apache/incubator-mxnet/pull/10696#issuecomment-401213560
 
 
   Hi
   yang,
   I checked your error txt. 
   Your build failure should be unrelated with my PR.
   e.g.
   build/src/operator/custom/native_op.o: In function 
`mxnet::op::NativeOpProp::CreateOperator(mxnet::Context) const [clone 
.localalias.447]':
   native_op.cc:(.text+0x410): undefined reference to `mxnet::Operator* 
mxnet::op::CreateOp(mxnet::op::NativeOpParam)'
   
   Can you have a clean git clone and rebuild it again? It should be caused by 
environment issue.
   This is the jenkens result of Commit 1444a84:
   
http://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/incubator-mxnet/detail/PR-10696/28/pipeline
   
   You can see that all build test pass and it failed at test cases unrelated 
with kvstore.


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[GitHub] haojin2 commented on a change in pull request #11229: [MXNET-379] L1 Normalization

[GitBox]

haojin2 commented on a change in pull request #11229: [MXNET-379] L1 
Normalization
URL: https://github.com/apache/incubator-mxnet/pull/11229#discussion_r199025996
 
 

 ##
 File path: src/operator/tensor/broadcast_reduce_op_value.cc
 ##
 @@ -284,10 +284,16 @@ array.
 
 Examples::
 
-  x = [[1, 2],
-   [3, 4]]
+  x = [[[1,2],
+[3,4]],
+   [[2,2],
+[5,6]]]
 
 Review comment:
   Please insert spaces just like the format before.


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[GitHub] haojin2 commented on a change in pull request #11229: [MXNET-379] L1 Normalization

[GitBox]

haojin2 commented on a change in pull request #11229: [MXNET-379] L1 
Normalization
URL: https://github.com/apache/incubator-mxnet/pull/11229#discussion_r199025950
 
 

 ##
 File path: src/operator/tensor/broadcast_reduce_op_value.cc
 ##
 @@ -284,10 +284,16 @@ array.
 
 Examples::
 
-  x = [[1, 2],
-   [3, 4]]
+  x = [[[1,2],
+[3,4]],
+   [[2,2],
+[5,6]]]
 
-  norm(x) = [5.47722578]
+  norm(x, ord=2, axis=1) = [[3.1622777 4.472136 ]
+[5.3851647 6.3245554]]
+
+  norm(x, ord=1, axis=1) = [[4., 6.],
+[7., 8.]]
 
   rsp = x.cast_storage('row_sparse')
 
 Review comment:
   Please also add specification for sparse-related cases below.


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[GitHub] haojin2 commented on a change in pull request #11229: [MXNET-379] L1 Normalization

[GitBox]

haojin2 commented on a change in pull request #11229: [MXNET-379] L1 
Normalization
URL: https://github.com/apache/incubator-mxnet/pull/11229#discussion_r199025732
 
 

 ##
 File path: src/operator/tensor/broadcast_reduce_op.h
 ##
 @@ -1021,8 +1023,38 @@ void L2NormGradCompute(const nnvm::NodeAttrs& attrs,
   } else {
 small = ReduceAxesShapeImpl(outputs[0].shape_, param.axis, true, false);
   }
-  ReduceAxesBackwardUseInOutImpl(ctx, small, 
inputs,
+  if (param.ord == 1) {
+using namespace mshadow;
+using namespace mshadow::expr;
+TShape src_shape, dst_shape;
+BroadcastReduceShapeCompact(outputs[0].shape_, small, _shape, 
_shape);
+Stream *s = ctx.get_stream();
+MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+  if (dst_shape.ndim() == 2) {
+Tensor ograd =
+  inputs[0].get_with_shape(dst_shape.get<2>(), s);
+Tensor igrad =
+  outputs[0].get_with_shape(src_shape.get<2>(), s);
+Tensor data =
+  inputs[1].get_with_shape(src_shape.get<2>(), s);
+ASSIGN_DISPATCH(igrad, req[0],
+  broadcast_to(ograd, src_shape)*F(data));
+  } else {
+const int ndim = MXNET_SPECIAL_MAX_NDIM;
+Tensor igrad =
+  outputs[0].get_with_shape(src_shape.get(), 
s);
+Tensor ograd =
+  inputs[0].get_with_shape(dst_shape.get(), s);
+Tensor data =
+  inputs[1].get_with_shape(src_shape.get(), s);
+ASSIGN_DISPATCH(igrad, req[0],
+  broadcast_to(ograd, src_shape)*F(data));
+  }
+});
+  } else if (param.ord == 2) {
+ReduceAxesBackwardUseInOutImpl(ctx, small, 
inputs,
   req, outputs);
 
 Review comment:
   Fix the alignment here.


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[GitHub] haojin2 commented on a change in pull request #11229: [MXNET-379] L1 Normalization

[GitBox]

haojin2 commented on a change in pull request #11229: [MXNET-379] L1 
Normalization
URL: https://github.com/apache/incubator-mxnet/pull/11229#discussion_r199025529
 
 

 ##
 File path: src/operator/tensor/broadcast_reduce_op.h
 ##
 @@ -1021,8 +1023,38 @@ void L2NormGradCompute(const nnvm::NodeAttrs& attrs,
   } else {
 small = ReduceAxesShapeImpl(outputs[0].shape_, param.axis, true, false);
   }
-  ReduceAxesBackwardUseInOutImpl(ctx, small, 
inputs,
+  if (param.ord == 1) {
+using namespace mshadow;
+using namespace mshadow::expr;
 
 Review comment:
   These to `using namespace` line are redundant. 


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[GitHub] haojin2 commented on a change in pull request #11229: [MXNET-379] L1 Normalization

[GitBox]

haojin2 commented on a change in pull request #11229: [MXNET-379] L1 
Normalization
URL: https://github.com/apache/incubator-mxnet/pull/11229#discussion_r199025529
 
 

 ##
 File path: src/operator/tensor/broadcast_reduce_op.h
 ##
 @@ -1021,8 +1023,38 @@ void L2NormGradCompute(const nnvm::NodeAttrs& attrs,
   } else {
 small = ReduceAxesShapeImpl(outputs[0].shape_, param.axis, true, false);
   }
-  ReduceAxesBackwardUseInOutImpl(ctx, small, 
inputs,
+  if (param.ord == 1) {
+using namespace mshadow;
+using namespace mshadow::expr;
 
 Review comment:
   These two `using namespace` line are redundant. 


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[GitHub] anirudhacharya edited a comment on issue #11229: [MXNET-379] L1 Normalization

[GitBox]

anirudhacharya edited a comment on issue #11229: [MXNET-379] L1 Normalization
URL: https://github.com/apache/incubator-mxnet/pull/11229#issuecomment-400887360
 
 
   @haojin2 @eric-haibin-lin @piiswrong ping for review. 


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[GitHub] anirudhacharya commented on issue #11475: Amending ONNX importer/exporter #11213

[GitBox]

anirudhacharya commented on issue #11475: Amending ONNX importer/exporter #11213
URL: 
https://github.com/apache/incubator-mxnet/issues/11475#issuecomment-401211603
 
 
   Hi @zhreshold thanks for the feedback - 
   I will make a PR to change ``python-pytest`` to ``onnx``.
   
   But the other changes could be hard to accommodate - 
   - onnx2mx/mx2onnx, from the perspective of inside mxnet python package, it 
is more reasonable to use name from_onnx/to_onnx - 
   
   This change was made in the recent 
[PR](https://github.com/apache/incubator-mxnet/pull/11213#discussion_r195585421)
 and I think it makes things more clear and unambiguous because ``onnx2mx`` 
says a lot more about what the module does compared to ``from_onnx``. But I am 
still open to discussion here.
   - Please be considerate about modules and their purposes before spreading to 
many files. For example, 
https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/contrib/onnx/mx2onnx/_export_helper.py
 don't necessarily need to be a separate module, a private function in 
export_onnx.py is more appropriate.
   - import_model/import_onnx/import_to_gluon.py are basically doing similar 
things, just put them in import_onnx.py. Same story for 
export_model/export_onnx.
   
   What is the advantage we derive by merging all these into one file now. Lets 
discuss this over a call/in-person
   - Name of highest level APIs, e.g., import_model, get_model_metadata, 
import_to_gluon, export_model are totally dummy and missing valid info. I 
suggest to use import_onnx, get_onnx_metadata, export_onnx, import_to_gluon can 
be deleted and merged as an option for example import_onnx(to_gluon=True)
   
   This is API breakage. the ``import_model`` API was already shipped as part 
of v1.2 breaking it now would not be very customer friendly. There are blog 
posts and tutorials published based on this.
   
   
   Also as an aside, I think all this is very valuable feedback and I thank you 
for that but my perception is that it just came a bit late. It is hard to 
incorporate them after actually shipping the product. We had sent out design 
proposal for this module on the mxnet dev list in early March and then again we 
asked for one more round of feedback in the month of April. We created 
confluence wiki with [API Design 
Document](https://cwiki.apache.org/confluence/display/MXNET/ONNX-MXNet+API+Design)
 and sent it out on the dev list for review. We also solicited reviews on all 
the PRs. Going ahead it would be great if we could get the feedback during 
design and review phase rather than after merging and shipping the code.
   


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[GitHub] ArmageddonKnight commented on a change in pull request #11364: [MXNET-490] Added OpenLSTMRNN together with benchmarks and Tensorboard callback routines.

[GitBox]

ArmageddonKnight commented on a change in pull request #11364: [MXNET-490] 
Added OpenLSTMRNN together with benchmarks and Tensorboard callback routines.
URL: https://github.com/apache/incubator-mxnet/pull/11364#discussion_r199024323
 
 

 ##
 File path: example/rnn-backends/bucketing/dataset/download_ptb.sh
 ##
 @@ -0,0 +1,27 @@
+#!/bin/sh
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# This file downloads the PTB dataset.
+
+mkdir -p $(cd $(dirname $0) && pwd)/ptb
+
+cd $(cd $(dirname $0) && pwd)/ptb && \
 
 Review comment:
   Thank you for your comments. I have made changes accordingly.


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[incubator-mxnet] branch master updated: Document AdaGrad eps as initial history accumulator value (#11392)

[zhasheng]

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

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


The following commit(s) were added to refs/heads/master by this push:
 new 2594fca  Document AdaGrad eps as initial history accumulator value 
(#11392)
2594fca is described below

commit 2594fcabdd63aaf26e112ad337dcb711c7783bc2
Author: Leonard Lausen 
AuthorDate: Fri Jun 29 00:20:37 2018 +

Document AdaGrad eps as initial history accumulator value (#11392)
---
 python/mxnet/optimizer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/mxnet/optimizer.py b/python/mxnet/optimizer.py
index 267a402..e73a45f 100644
--- a/python/mxnet/optimizer.py
+++ b/python/mxnet/optimizer.py
@@ -1090,7 +1090,7 @@ class AdaGrad(Optimizer):
 Parameters
 --
 eps: float, optional
-Small value to avoid division by 0.
+Initial value of the history accumulator. Avoids division by 0.
 
 """
 def __init__(self, eps=1e-7, **kwargs):

[GitHub] szha closed pull request #11392: Document AdaGrad eps as initial history accumulator value

[GitBox]

szha closed pull request #11392: Document AdaGrad eps as initial history 
accumulator value
URL: https://github.com/apache/incubator-mxnet/pull/11392
 
 
   

This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:

As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):

diff --git a/python/mxnet/optimizer.py b/python/mxnet/optimizer.py
index 267a402f246..e73a45f74b0 100644
--- a/python/mxnet/optimizer.py
+++ b/python/mxnet/optimizer.py
@@ -1090,7 +1090,7 @@ class AdaGrad(Optimizer):
 Parameters
 --
 eps: float, optional
-Small value to avoid division by 0.
+Initial value of the history accumulator. Avoids division by 0.
 
 """
 def __init__(self, eps=1e-7, **kwargs):


 


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[GitHub] szha commented on issue #11003: rnn_cell little bug fixed

[GitBox]

szha commented on issue #11003: rnn_cell little bug fixed
URL: https://github.com/apache/incubator-mxnet/pull/11003#issuecomment-401210997
 
 
   @chinakook would you mind doing a rebase? we have a couple of flaky tests 
that are already fixed in the upstream.


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[GitHub] azai91 commented on issue #11118: [MXNET-431] Get physical cores

[GitBox]

azai91 commented on issue #8: [MXNET-431] Get physical cores
URL: https://github.com/apache/incubator-mxnet/pull/8#issuecomment-401210597
 
 
   just chatted with @szha and the plan is to by default use cmake to build 
this dependency in the makefile (similar to what we do with MKLDNN). if the 
developer does not have cmake, then we will instruct them to enable to flag to 
turn off this feature and we will default to what we had before.
   


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[GitHub] lanking520 commented on a change in pull request #11477: Copy profiler examples to new location for CI

[GitBox]

lanking520 commented on a change in pull request #11477: Copy profiler examples 
to new location for CI
URL: https://github.com/apache/incubator-mxnet/pull/11477#discussion_r199023063
 
 

 ##
 File path: 
scala-package/examples/src/main/scala/org/apache/mxnet/examples/profiler/ProfilerNDArray.scala
 ##
 @@ -0,0 +1,252 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.mxnet.examples.profiler
+
+import org.kohsuke.args4j.{CmdLineParser, Option}
+import org.slf4j.LoggerFactory
+import scala.collection.JavaConverters._
+import java.io.File
+import org.apache.mxnet.Profiler
+import org.apache.mxnet.Random
+import org.apache.mxnet.Shape
+import org.apache.mxnet.NDArray
+import org.apache.mxnet.Context
+
+/**
+ * @author Depeng Liang
+ */
+object ProfilerNDArray {
+  private val logger = LoggerFactory.getLogger(classOf[ProfilerNDArray])
+
+  def testBroadcast(): Unit = {
+val sampleNum = 1000
+def testBroadcastTo(): Unit = {
+  for (i <- 0 until sampleNum) {
+val nDim = scala.util.Random.nextInt(2) + 1
+val targetShape = Shape((0 until nDim).map(i => 
scala.util.Random.nextInt(10) + 1))
+val shape = targetShape.toArray.map { s =>
+if (scala.util.Random.nextInt(2) == 1) 1
+else s
+}
+val dat = NDArray.empty(shape: _*)
+val randomRet = (0 until shape.product)
+  .map(r => scala.util.Random.nextFloat() - 0.5f).toArray
+dat.set(randomRet)
+val ndArrayRet = NDArray.broadcast_to(Map("shape" -> 
targetShape))(dat).get
+require(ndArrayRet.shape == targetShape)
+val err = {
+  // implementation of broadcast
+  val ret = {
+(randomRet /: shape.zipWithIndex.reverse){ (acc, elem) => elem 
match { case (s, i) =>
+  if (s != targetShape(i)) {
+acc.grouped(shape.takeRight(shape.length - i).product).map {g 
=>
+  (0 until targetShape(i)).map(x => g).flatten
+}.flatten.toArray
+  } else acc
+}}
+  }
+  val tmp = ndArrayRet.toArray.zip(ret).map{ case (l, r) => Math.pow(l 
- r, 2) }
+  tmp.sum / tmp.length
+}
+require(err < 1E-8)
+ndArrayRet.dispose()
+dat.dispose()
+  }
+}
+testBroadcastTo()
+  }
+
+  def randomNDArray(dim: Int): NDArray = {
+val tmp = Math.pow(1000, 1.0 / dim).toInt
+val shape = Shape((0 until dim).map(d => scala.util.Random.nextInt(tmp) + 
1))
+Random.uniform(-10f, 10f, shape)
+  }
+
+  def testNDArraySaveload(): Unit = {
+val maxDim = 5
+val nRepeat = 10
+val fileName = s"${System.getProperty("java.io.tmpdir")}/tmpList.bin"
+for (repeat <- 0 until nRepeat) {
+  try {
+val data = (0 until 10).map(i => 
randomNDArray(scala.util.Random.nextInt(4) + 1))
+NDArray.save(fileName, data)
+val data2 = NDArray.load2Array(fileName)
+require(data.length == data2.length)
+for ((x, y) <- data.zip(data2)) {
+  val tmp = x - y
+  require(tmp.toArray.sum == 0)
+  tmp.dispose()
+}
+val dMap = data.zipWithIndex.map { case (arr, i) =>
+  s"NDArray xx $i" -> arr
+}.toMap
+NDArray.save(fileName, dMap)
+ val dMap2 = NDArray.load2Map(fileName)
+ require(dMap.size == dMap2.size)
+ for ((k, x) <- dMap) {
+   val y = dMap2(k)
+   val tmp = x - y
+   require(tmp.toArray.sum == 0)
+   tmp.dispose()
+ }
+data.foreach(_.dispose())
+  } finally {
+val file = new File(fileName)
+file.delete()
+  }
+}
+  }
+
+  def testNDArrayCopy(): Unit = {
+val c = Random.uniform(-10f, 10f, Shape(10, 10))
+val d = c.copyTo(Context.cpu(0))
+val tmp = c - d
+require(tmp.toArray.map(Math.abs).sum == 0)
+c.dispose()
+d.dispose()
+  }
+
+  def reldiff(a: NDArray, b: NDArray): Float = {
+val diff = NDArray.sum(NDArray.abs(a - b)).toScalar
+val norm = NDArray.sum(NDArray.abs(a)).toScalar
 
 Review comment:
   Same in here with `NDArray.api`


This 

[GitHub] lanking520 commented on a change in pull request #11477: Copy profiler examples to new location for CI

[GitBox]

lanking520 commented on a change in pull request #11477: Copy profiler examples 
to new location for CI
URL: https://github.com/apache/incubator-mxnet/pull/11477#discussion_r199022241
 
 

 ##
 File path: 
scala-package/examples/src/main/scala/org/apache/mxnet/examples/profiler/ProfilerNDArray.scala
 ##
 @@ -0,0 +1,252 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.mxnet.examples.profiler
+
+import org.kohsuke.args4j.{CmdLineParser, Option}
+import org.slf4j.LoggerFactory
+import scala.collection.JavaConverters._
+import java.io.File
+import org.apache.mxnet.Profiler
+import org.apache.mxnet.Random
+import org.apache.mxnet.Shape
+import org.apache.mxnet.NDArray
+import org.apache.mxnet.Context
+
+/**
+ * @author Depeng Liang
+ */
+object ProfilerNDArray {
+  private val logger = LoggerFactory.getLogger(classOf[ProfilerNDArray])
+
+  def testBroadcast(): Unit = {
+val sampleNum = 1000
+def testBroadcastTo(): Unit = {
+  for (i <- 0 until sampleNum) {
+val nDim = scala.util.Random.nextInt(2) + 1
+val targetShape = Shape((0 until nDim).map(i => 
scala.util.Random.nextInt(10) + 1))
+val shape = targetShape.toArray.map { s =>
+if (scala.util.Random.nextInt(2) == 1) 1
+else s
+}
+val dat = NDArray.empty(shape: _*)
+val randomRet = (0 until shape.product)
+  .map(r => scala.util.Random.nextFloat() - 0.5f).toArray
+dat.set(randomRet)
+val ndArrayRet = NDArray.broadcast_to(Map("shape" -> 
targetShape))(dat).get
 
 Review comment:
   Can you try to use NDArray.api.broadcast_to in here?


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[GitHub] lanking520 commented on a change in pull request #11477: Copy profiler examples to new location for CI

[GitBox]

lanking520 commented on a change in pull request #11477: Copy profiler examples 
to new location for CI
URL: https://github.com/apache/incubator-mxnet/pull/11477#discussion_r199023103
 
 

 ##
 File path: 
scala-package/examples/src/main/scala/org/apache/mxnet/examples/profiler/ProfilerNDArray.scala
 ##
 @@ -0,0 +1,252 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.mxnet.examples.profiler
+
+import org.kohsuke.args4j.{CmdLineParser, Option}
+import org.slf4j.LoggerFactory
+import scala.collection.JavaConverters._
+import java.io.File
+import org.apache.mxnet.Profiler
+import org.apache.mxnet.Random
+import org.apache.mxnet.Shape
+import org.apache.mxnet.NDArray
+import org.apache.mxnet.Context
+
+/**
+ * @author Depeng Liang
+ */
+object ProfilerNDArray {
+  private val logger = LoggerFactory.getLogger(classOf[ProfilerNDArray])
+
+  def testBroadcast(): Unit = {
+val sampleNum = 1000
+def testBroadcastTo(): Unit = {
+  for (i <- 0 until sampleNum) {
+val nDim = scala.util.Random.nextInt(2) + 1
+val targetShape = Shape((0 until nDim).map(i => 
scala.util.Random.nextInt(10) + 1))
+val shape = targetShape.toArray.map { s =>
+if (scala.util.Random.nextInt(2) == 1) 1
+else s
+}
+val dat = NDArray.empty(shape: _*)
+val randomRet = (0 until shape.product)
+  .map(r => scala.util.Random.nextFloat() - 0.5f).toArray
+dat.set(randomRet)
+val ndArrayRet = NDArray.broadcast_to(Map("shape" -> 
targetShape))(dat).get
+require(ndArrayRet.shape == targetShape)
+val err = {
+  // implementation of broadcast
+  val ret = {
+(randomRet /: shape.zipWithIndex.reverse){ (acc, elem) => elem 
match { case (s, i) =>
+  if (s != targetShape(i)) {
+acc.grouped(shape.takeRight(shape.length - i).product).map {g 
=>
+  (0 until targetShape(i)).map(x => g).flatten
+}.flatten.toArray
+  } else acc
+}}
+  }
+  val tmp = ndArrayRet.toArray.zip(ret).map{ case (l, r) => Math.pow(l 
- r, 2) }
+  tmp.sum / tmp.length
+}
+require(err < 1E-8)
+ndArrayRet.dispose()
+dat.dispose()
+  }
+}
+testBroadcastTo()
+  }
+
+  def randomNDArray(dim: Int): NDArray = {
+val tmp = Math.pow(1000, 1.0 / dim).toInt
+val shape = Shape((0 until dim).map(d => scala.util.Random.nextInt(tmp) + 
1))
+Random.uniform(-10f, 10f, shape)
+  }
+
+  def testNDArraySaveload(): Unit = {
+val maxDim = 5
+val nRepeat = 10
+val fileName = s"${System.getProperty("java.io.tmpdir")}/tmpList.bin"
+for (repeat <- 0 until nRepeat) {
+  try {
+val data = (0 until 10).map(i => 
randomNDArray(scala.util.Random.nextInt(4) + 1))
+NDArray.save(fileName, data)
+val data2 = NDArray.load2Array(fileName)
+require(data.length == data2.length)
+for ((x, y) <- data.zip(data2)) {
+  val tmp = x - y
+  require(tmp.toArray.sum == 0)
+  tmp.dispose()
+}
+val dMap = data.zipWithIndex.map { case (arr, i) =>
+  s"NDArray xx $i" -> arr
+}.toMap
+NDArray.save(fileName, dMap)
+ val dMap2 = NDArray.load2Map(fileName)
+ require(dMap.size == dMap2.size)
+ for ((k, x) <- dMap) {
+   val y = dMap2(k)
+   val tmp = x - y
+   require(tmp.toArray.sum == 0)
+   tmp.dispose()
+ }
+data.foreach(_.dispose())
+  } finally {
+val file = new File(fileName)
+file.delete()
+  }
+}
+  }
+
+  def testNDArrayCopy(): Unit = {
+val c = Random.uniform(-10f, 10f, Shape(10, 10))
+val d = c.copyTo(Context.cpu(0))
+val tmp = c - d
+require(tmp.toArray.map(Math.abs).sum == 0)
+c.dispose()
+d.dispose()
+  }
+
+  def reldiff(a: NDArray, b: NDArray): Float = {
+val diff = NDArray.sum(NDArray.abs(a - b)).toScalar
+val norm = NDArray.sum(NDArray.abs(a)).toScalar
+diff / norm
+  }
+
+  def reldiff(a: Array[Float], b: Array[Float]): Float = {
+val diff =
+  (a zip b).map { 

[GitHub] lanking520 commented on a change in pull request #11477: Copy profiler examples to new location for CI

[GitBox]

lanking520 commented on a change in pull request #11477: Copy profiler examples 
to new location for CI
URL: https://github.com/apache/incubator-mxnet/pull/11477#discussion_r199023126
 
 

 ##
 File path: 
scala-package/examples/src/main/scala/org/apache/mxnet/examples/profiler/ProfilerNDArray.scala
 ##
 @@ -0,0 +1,252 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.mxnet.examples.profiler
+
+import org.kohsuke.args4j.{CmdLineParser, Option}
+import org.slf4j.LoggerFactory
+import scala.collection.JavaConverters._
+import java.io.File
+import org.apache.mxnet.Profiler
+import org.apache.mxnet.Random
+import org.apache.mxnet.Shape
+import org.apache.mxnet.NDArray
+import org.apache.mxnet.Context
+
+/**
+ * @author Depeng Liang
+ */
+object ProfilerNDArray {
+  private val logger = LoggerFactory.getLogger(classOf[ProfilerNDArray])
+
+  def testBroadcast(): Unit = {
+val sampleNum = 1000
+def testBroadcastTo(): Unit = {
+  for (i <- 0 until sampleNum) {
+val nDim = scala.util.Random.nextInt(2) + 1
+val targetShape = Shape((0 until nDim).map(i => 
scala.util.Random.nextInt(10) + 1))
+val shape = targetShape.toArray.map { s =>
+if (scala.util.Random.nextInt(2) == 1) 1
+else s
+}
+val dat = NDArray.empty(shape: _*)
+val randomRet = (0 until shape.product)
+  .map(r => scala.util.Random.nextFloat() - 0.5f).toArray
+dat.set(randomRet)
+val ndArrayRet = NDArray.broadcast_to(Map("shape" -> 
targetShape))(dat).get
+require(ndArrayRet.shape == targetShape)
+val err = {
+  // implementation of broadcast
+  val ret = {
+(randomRet /: shape.zipWithIndex.reverse){ (acc, elem) => elem 
match { case (s, i) =>
+  if (s != targetShape(i)) {
+acc.grouped(shape.takeRight(shape.length - i).product).map {g 
=>
+  (0 until targetShape(i)).map(x => g).flatten
+}.flatten.toArray
+  } else acc
+}}
+  }
+  val tmp = ndArrayRet.toArray.zip(ret).map{ case (l, r) => Math.pow(l 
- r, 2) }
+  tmp.sum / tmp.length
+}
+require(err < 1E-8)
+ndArrayRet.dispose()
+dat.dispose()
+  }
+}
+testBroadcastTo()
+  }
+
+  def randomNDArray(dim: Int): NDArray = {
+val tmp = Math.pow(1000, 1.0 / dim).toInt
+val shape = Shape((0 until dim).map(d => scala.util.Random.nextInt(tmp) + 
1))
+Random.uniform(-10f, 10f, shape)
+  }
+
+  def testNDArraySaveload(): Unit = {
+val maxDim = 5
+val nRepeat = 10
+val fileName = s"${System.getProperty("java.io.tmpdir")}/tmpList.bin"
+for (repeat <- 0 until nRepeat) {
+  try {
+val data = (0 until 10).map(i => 
randomNDArray(scala.util.Random.nextInt(4) + 1))
+NDArray.save(fileName, data)
+val data2 = NDArray.load2Array(fileName)
+require(data.length == data2.length)
+for ((x, y) <- data.zip(data2)) {
+  val tmp = x - y
+  require(tmp.toArray.sum == 0)
+  tmp.dispose()
+}
+val dMap = data.zipWithIndex.map { case (arr, i) =>
+  s"NDArray xx $i" -> arr
+}.toMap
+NDArray.save(fileName, dMap)
+ val dMap2 = NDArray.load2Map(fileName)
+ require(dMap.size == dMap2.size)
+ for ((k, x) <- dMap) {
+   val y = dMap2(k)
+   val tmp = x - y
+   require(tmp.toArray.sum == 0)
+   tmp.dispose()
+ }
+data.foreach(_.dispose())
+  } finally {
+val file = new File(fileName)
+file.delete()
+  }
+}
+  }
+
+  def testNDArrayCopy(): Unit = {
+val c = Random.uniform(-10f, 10f, Shape(10, 10))
+val d = c.copyTo(Context.cpu(0))
+val tmp = c - d
+require(tmp.toArray.map(Math.abs).sum == 0)
+c.dispose()
+d.dispose()
+  }
+
+  def reldiff(a: NDArray, b: NDArray): Float = {
+val diff = NDArray.sum(NDArray.abs(a - b)).toScalar
+val norm = NDArray.sum(NDArray.abs(a)).toScalar
+diff / norm
+  }
+
+  def reldiff(a: Array[Float], b: Array[Float]): Float = {
+val diff =
+  (a zip b).map { 

[GitHub] lanking520 commented on a change in pull request #11477: Copy profiler examples to new location for CI

[GitBox]

lanking520 commented on a change in pull request #11477: Copy profiler examples 
to new location for CI
URL: https://github.com/apache/incubator-mxnet/pull/11477#discussion_r199022352
 
 

 ##
 File path: 
scala-package/examples/src/main/scala/org/apache/mxnet/examples/profiler/ProfilerMatMul.scala
 ##
 @@ -0,0 +1,107 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.mxnet.examples.profiler
+
+import org.kohsuke.args4j.{CmdLineParser, Option}
+import org.slf4j.LoggerFactory
+import scala.collection.JavaConverters._
+import org.apache.mxnet.Context
+import org.apache.mxnet.Profiler
+import java.io.File
+import org.apache.mxnet.Symbol
+import org.apache.mxnet.Shape
+import org.apache.mxnet.Random
+
+/**
+ * @author Depeng Liang
+ */
+object ProfilerMatMul {
+  private val logger = LoggerFactory.getLogger(classOf[ProfilerMatMul])
+
+  def main(args: Array[String]): Unit = {
+val erul = new ProfilerMatMul
+val parser: CmdLineParser = new CmdLineParser(erul)
+try {
+  parser.parseArgument(args.toList.asJava)
+  val ctx = if (erul.gpu >= 0) Context.gpu(erul.gpu) else Context.cpu()
+
+  val path = s"${erul.outputPath}${File.separator}${erul.profilerName}"
+  val kwargs = Map("file_name" -> path, "profile_" + erul.profilerMode -> 
"1")
+  Profiler.profilerSetConfig(kwargs)
+  logger.info(s"profile file save to $path")
+
+  val A = Symbol.Variable("A")
+  val B = Symbol.Variable("B")
+  val C = Symbol.dot()(A, B)()
 
 Review comment:
   Please use `Symbol.api.dot()`


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[GitHub] anirudh2290 opened a new pull request #11478: 1.2.1 release notes

[GitBox]

anirudh2290 opened a new pull request #11478: 1.2.1 release notes
URL: https://github.com/apache/incubator-mxnet/pull/11478
 
 
   ## Description ##
   I have added more info to 1.2.1 release notes from what @mli sent on the 
mailing list.
   
   Please help review: Once this is approved , I will make the new release with 
the same content.
   
   @mli @piiswrong @ThomasDelteil @szha @marcoabreu 
   
   ## Checklist ##
   ### Essentials ###
   Please feel free to remove inapplicable items for your PR.
   - [ ] The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to 
the relevant [JIRA issue](https://issues.apache.org/jira/projects/MXNET/issues) 
created (except PRs with tiny changes)
   - [ ] Changes are complete (i.e. I finished coding on this PR)
   - [ ] All changes have test coverage:
   - Unit tests are added for small changes to verify correctness (e.g. adding 
a new operator)
   - Nightly tests are added for complicated/long-running ones (e.g. changing 
distributed kvstore)
   - Build tests will be added for build configuration changes (e.g. adding a 
new build option with NCCL)
   - [ ] Code is well-documented: 
   - For user-facing API changes, API doc string has been updated. 
   - For new C++ functions in header files, their functionalities and arguments 
are documented. 
   - For new examples, README.md is added to explain the what the example does, 
the source of the dataset, expected performance on test set and reference to 
the original paper if applicable
   - Check the API doc at 
http://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$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] haojin2 commented on issue #11473: batchnorm falls back for sparse arrays.

[GitBox]

haojin2 commented on issue #11473: batchnorm falls back for sparse arrays.
URL: https://github.com/apache/incubator-mxnet/pull/11473#issuecomment-401208891
 
 
   I think we do need some extra checks for this to verify that this works


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[GitHub] andrewfayres opened a new pull request #11477: Copy profiler examples to new location for CI

[GitBox]

andrewfayres opened a new pull request #11477: Copy profiler examples to new 
location for CI
URL: https://github.com/apache/incubator-mxnet/pull/11477
 
 
   ## Description ##
   As part of the effort to update models to the new inference API we're also 
moving to a new folder. This example isn't an inference one but still needs to 
be moved.
   
   ## Checklist ##
   ### Essentials ###
   Please feel free to remove inapplicable items for your PR.
   - [ ] The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to 
the relevant [JIRA issue](https://issues.apache.org/jira/projects/MXNET/issues) 
created (except PRs with tiny changes)
   - [ ] Changes are complete (i.e. I finished coding on this PR)
   - [ ] All changes have test coverage:
   - Unit tests are added for small changes to verify correctness (e.g. adding 
a new operator)
   - Nightly tests are added for complicated/long-running ones (e.g. changing 
distributed kvstore)
   - Build tests will be added for build configuration changes (e.g. adding a 
new build option with NCCL)
   - [ ] Code is well-documented: 
   - For user-facing API changes, API doc string has been updated. 
   - For new C++ functions in header files, their functionalities and arguments 
are documented. 
   - For new examples, README.md is added to explain the what the example does, 
the source of the dataset, expected performance on test set and reference to 
the original paper if applicable
   - Check the API doc at 
http://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$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
   
   @lanking520 @nswamy 


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[GitHub] mbaijal opened a new pull request #11476: [Nightly Tests] Fix regex which processes Apache RAT check output

[GitBox]

mbaijal opened a new pull request #11476: [Nightly Tests] Fix regex which 
processes Apache RAT check output
URL: https://github.com/apache/incubator-mxnet/pull/11476
 
 
   ## Description ##
   I had recently updated the `license_check.sh` script to process the output 
correctly for an edge case (`20 unknown licenses` was being successfully parsed 
as `0 unknown license`)
   
   However, this was not working as expected due to unnecessary quotes. This PR 
fixes this. 


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[GitHub] ctcyang edited a comment on issue #10696: [MXNET-366]Extend MXNet Distributed Training by AllReduce

[GitBox]

ctcyang edited a comment on issue #10696: [MXNET-366]Extend MXNet Distributed 
Training by AllReduce
URL: https://github.com/apache/incubator-mxnet/pull/10696#issuecomment-401201724
 
 
   I am having trouble building 1444a84 with GPU support, so I was hoping you 
could help me. `make` seems to cause the following 
[error](https://github.com/apache/incubator-mxnet/files/2147258/error.txt) 
during linking phase.
   
   Ubuntu 16.04
   mpich 3.2
   g++ 5.4.0


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[GitHub] toddsundsted edited a comment on issue #11398: Floating Point Exception after Array Creation

[GitBox]

toddsundsted edited a comment on issue #11398: Floating Point Exception after 
Array Creation
URL: 
https://github.com/apache/incubator-mxnet/issues/11398#issuecomment-400872858
 
 
   @frankfliu I have a proposed fix here: 
https://github.com/apache/incubator-mxnet/pull/11397
   
   this small change ensures that `random_uniform(..)` and similar behave 
consistently with other methods for `NDArray` creation, like `zeros(...)`. I 
would love a review and feedback on the proposal.
   


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[GitHub] hetong007 commented on issue #11474: Fix record io augmentation speed

[GitBox]

hetong007 commented on issue #11474: Fix record io augmentation speed
URL: https://github.com/apache/incubator-mxnet/pull/11474#issuecomment-401206035
 
 
   Confirmed that this brings back the speed on both Symbol and Gluon training.


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[GitHub] szha commented on a change in pull request #11364: [MXNET-490] Added OpenLSTMRNN together with benchmarks and Tensorboard callback routines.

[GitBox]

szha commented on a change in pull request #11364: [MXNET-490] Added 
OpenLSTMRNN together with benchmarks and Tensorboard callback routines.
URL: https://github.com/apache/incubator-mxnet/pull/11364#discussion_r199017711
 
 

 ##
 File path: example/rnn-backends/bucketing/dataset/download_ptb.sh
 ##
 @@ -0,0 +1,27 @@
+#!/bin/sh
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# This file downloads the PTB dataset.
+
+mkdir -p $(cd $(dirname $0) && pwd)/ptb
+
+cd $(cd $(dirname $0) && pwd)/ptb && \
 
 Review comment:
   we're moving away from ptb. see 
https://github.com/apache/incubator-mxnet/pull/11435


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[GitHub] piiswrong closed pull request #10524: [MXNET-312] Added Matthew's Correlation Coefficient to metrics

[GitBox]

piiswrong closed pull request #10524: [MXNET-312] Added Matthew's Correlation 
Coefficient to metrics
URL: https://github.com/apache/incubator-mxnet/pull/10524
 
 
   

This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:

As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):

diff --git a/python/mxnet/metric.py b/python/mxnet/metric.py
index aa3ab44c48a..aa378cae509 100644
--- a/python/mxnet/metric.py
+++ b/python/mxnet/metric.py
@@ -568,6 +568,27 @@ def fscore(self):
 else:
 return 0.
 
+@property
+def matthewscc(self):
+"""
+Calculate the Matthew's Correlation Coefficent
+"""
+if not self.total_examples:
+return 0.
+
+true_pos = float(self.true_positives)
+false_pos = float(self.false_positives)
+false_neg = float(self.false_negatives)
+true_neg = float(self.true_negatives)
+terms = [(true_pos + false_pos),
+ (true_pos + false_neg),
+ (true_neg + false_pos),
+ (true_neg + false_neg)]
+denom = 1.
+for t in filter(lambda t: t != 0., terms):
+denom *= t
+return ((true_pos * true_neg) - (false_pos * false_neg)) / 
math.sqrt(denom)
+
 @property
 def total_examples(self):
 return self.false_negatives + self.false_positives + \
@@ -584,7 +605,7 @@ def reset_stats(self):
 class F1(EvalMetric):
 """Computes the F1 score of a binary classification problem.
 
-The F1 score is equivalent to weighted average of the precision and recall,
+The F1 score is equivalent to harmonic mean of the precision and recall,
 where the best value is 1.0 and the worst value is 0.0. The formula for F1 
score is::
 
 F1 = 2 * (precision * recall) / (precision + recall)
@@ -661,6 +682,107 @@ def reset(self):
 self.metrics.reset_stats()
 
 
+@register
+class MCC(EvalMetric):
+"""Computes the Matthews Correlation Coefficient of a binary 
classification problem.
+
+While slower to compute than F1 the MCC can give insight that F1 or 
Accuracy cannot.
+For instance, if the network always predicts the same result
+then the MCC will immeadiately show this. The MCC is also symetric with 
respect
+to positive and negative categorization, however, there needs to be both
+positive and negative examples in the labels or it will always return 0.
+MCC of 0 is uncorrelated, 1 is completely correlated, and -1 is negatively 
correlated.
+
+.. math::
+\\text{MCC} = \\frac{ TP \\times TN - FP \\times FN }
+{\\sqrt{ (TP + FP) ( TP + FN ) ( TN + FP ) ( TN + FN ) } }
+
+where 0 terms in the denominator are replaced by 1.
+
+.. note::
+
+This version of MCC only supports binary classification.
+
+Parameters
+--
+name : str
+Name of this metric instance for display.
+output_names : list of str, or None
+Name of predictions that should be used when updating with update_dict.
+By default include all predictions.
+label_names : list of str, or None
+Name of labels that should be used when updating with update_dict.
+By default include all labels.
+average : str, default 'macro'
+Strategy to be used for aggregating across mini-batches.
+"macro": average the MCC for each batch.
+"micro": compute a single MCC across all batches.
+
+Examples
+
+In this example the network almost always predicts positive
+>>> false_positives = 1000
+>>> false_negatives = 1
+>>> true_positives = 1
+>>> true_negatives = 1
+>>> predicts = [mx.nd.array(
+[[.3, .7]]*false_positives +
+[[.7, .3]]*true_negatives +
+[[.7, .3]]*false_negatives +
+[[.3, .7]]*true_positives
+)]
+>>> labels  = [mx.nd.array(
+[0.]*(false_positives + true_negatives) +
+[1.]*(false_negatives + true_positives)
+)]
+>>> f1 = mx.metric.F1()
+>>> f1.update(preds = predicts, labels = labels)
+>>> mcc = mx.metric.MCC()
+>>> mcc.update(preds = predicts, labels = labels)
+>>> print f1.get()
+('f1', 0.95233560306652054)
+>>> print mcc.get()
+('mcc', 0.01917751877733392)
+"""
+
+def __init__(self, name='mcc',
+ output_names=None, label_names=None, average="macro"):
+self._average = average
+self._metrics = _BinaryClassificationMetrics()
+EvalMetric.__init__(self, name=name,
+output_names=output_names, label_names=label_names)
+
+def update(self, labels, preds):
+"""Updates the internal evaluation result.
+
+Parameters
+--
+labels : list of `NDArray`
+The labels of the data.
+
+preds : list of 

[GitHub] anirudhacharya commented on issue #11475: Amending ONNX importer/exporter #11213

[GitBox]

anirudhacharya commented on issue #11475: Amending ONNX importer/exporter #11213
URL: 
https://github.com/apache/incubator-mxnet/issues/11475#issuecomment-401203574
 
 
   @spidyDev @Roshrini 


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[GitHub] szha closed pull request #11447: [documentation] Update doc for contrib.rand_zipfian

[GitBox]

szha closed pull request #11447: [documentation] Update doc for 
contrib.rand_zipfian
URL: https://github.com/apache/incubator-mxnet/pull/11447
 
 
   

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As GitHub hides the original diff on merge, it is displayed below for
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diff --git a/python/mxnet/ndarray/contrib.py b/python/mxnet/ndarray/contrib.py
index ba402e6f3f8..cc66483f00b 100644
--- a/python/mxnet/ndarray/contrib.py
+++ b/python/mxnet/ndarray/contrib.py
@@ -55,8 +55,7 @@ def rand_zipfian(true_classes, num_sampled, range_max, 
ctx=None):
 range_max: int
 The number of possible classes.
 ctx : Context
-Device context of output. Default is current context. Overridden by
-`mu.context` when `mu` is an NDArray.
+Device context of output. Default is current context.
 
 Returns
 ---


 


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[GitHub] szha commented on a change in pull request #10524: [MXNET-312] Added Matthew's Correlation Coefficient to metrics

[GitBox]

szha commented on a change in pull request #10524: [MXNET-312] Added Matthew's 
Correlation Coefficient to metrics
URL: https://github.com/apache/incubator-mxnet/pull/10524#discussion_r199016678
 
 

 ##
 File path: tests/python/unittest/test_metric.py
 ##
 @@ -122,6 +123,54 @@ def test_f1():
 np.testing.assert_almost_equal(microF1.get()[1], fscore_total)
 np.testing.assert_almost_equal(macroF1.get()[1], (fscore1 + fscore2) / 2.)
 
+def test_mcc():
 
 Review comment:
   I tested that this implementation of micro mcc is consistent with 
http://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html


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[GitHub] ctcyang commented on issue #10696: [MXNET-366]Extend MXNet Distributed Training by AllReduce

[GitBox]

ctcyang commented on issue #10696: [MXNET-366]Extend MXNet Distributed Training 
by AllReduce
URL: https://github.com/apache/incubator-mxnet/pull/10696#issuecomment-401201724
 
 
   I am having trouble building 1444a84 with GPU support, so I was hoping your 
could help me. `make` seems to cause the following 
[error](https://github.com/apache/incubator-mxnet/files/2147258/error.txt) 
during linking phase.
   
   Ubuntu 16.04
   mpich 3.2
   g++ 5.4.0


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[incubator-mxnet] branch master updated: fix. (#11471)

[zhasheng]

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

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


The following commit(s) were added to refs/heads/master by this push:
 new 4cdc150  fix. (#11471)
4cdc150 is described below

commit 4cdc150af8250665b7880f8fbda830eb26476fb7
Author: Da Zheng 
AuthorDate: Thu Jun 28 16:17:17 2018 -0700

fix. (#11471)
---
 mkldnn.mk | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mkldnn.mk b/mkldnn.mk
index f17fdbc..1be0704 100644
--- a/mkldnn.mk
+++ b/mkldnn.mk
@@ -32,9 +32,9 @@ endif
 
 .PHONY: mkldnn mkldnn_clean
 
-mkldnn_build: $(MKLDNNROOT)/lib/libmkldnn.so
+mkldnn_build: $(MKLDNN_LIBFILE)
 
-$(MKLDNNROOT)/lib/libmkldnn.so:
+$(MKLDNN_LIBFILE):
mkdir -p $(MKLDNNROOT)
cd $(MKLDNN_SUBMODDIR) && rm -rf external && cd scripts && 
./prepare_mkl.sh && cd .. && cp -a external/*/* $(MKLDNNROOT)/.
cmake $(MKLDNN_SUBMODDIR) -DCMAKE_INSTALL_PREFIX=$(MKLDNNROOT) 
-B$(MKLDNN_BUILDDIR) -DARCH_OPT_FLAGS="-mtune=generic" -DWITH_TEST=OFF 
-DWITH_EXAMPLE=OFF

[GitHub] szha closed pull request #11471: Fix MKLDNN build for Mac.

[GitBox]

szha closed pull request #11471: Fix MKLDNN build for Mac.
URL: https://github.com/apache/incubator-mxnet/pull/11471
 
 
   

This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
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diff --git a/mkldnn.mk b/mkldnn.mk
index f17fdbce084..1be0704dcde 100644
--- a/mkldnn.mk
+++ b/mkldnn.mk
@@ -32,9 +32,9 @@ endif
 
 .PHONY: mkldnn mkldnn_clean
 
-mkldnn_build: $(MKLDNNROOT)/lib/libmkldnn.so
+mkldnn_build: $(MKLDNN_LIBFILE)
 
-$(MKLDNNROOT)/lib/libmkldnn.so:
+$(MKLDNN_LIBFILE):
mkdir -p $(MKLDNNROOT)
cd $(MKLDNN_SUBMODDIR) && rm -rf external && cd scripts && 
./prepare_mkl.sh && cd .. && cp -a external/*/* $(MKLDNNROOT)/.
cmake $(MKLDNN_SUBMODDIR) -DCMAKE_INSTALL_PREFIX=$(MKLDNNROOT) 
-B$(MKLDNN_BUILDDIR) -DARCH_OPT_FLAGS="-mtune=generic" -DWITH_TEST=OFF 
-DWITH_EXAMPLE=OFF


 


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[GitHub] szha closed pull request #11444: Fix flaky test test_operator_gpu.test_spatial_transformer_with_type (…

[GitBox]

szha closed pull request #11444: Fix flaky test 
test_operator_gpu.test_spatial_transformer_with_type (…
URL: https://github.com/apache/incubator-mxnet/pull/11444
 
 
   

This is a PR merged from a forked repository.
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diff --git a/tests/python/gpu/test_operator_gpu.py 
b/tests/python/gpu/test_operator_gpu.py
index 45f52b32a34..bc2ed31c9b5 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -697,8 +697,7 @@ def test_grid_generator_with_type():
 check_consistency(sym, ctx_list, grad_req="add")
 
 
-@unittest.skip("test fails intermittently. temporarily disabled till it gets 
fixed. tracked at https://github.com/apache/incubator-mxnet/issues/7645;)
-@with_seed(1234)
+@with_seed()
 def test_spatial_transformer_with_type():
 data = mx.sym.Variable('data')
 loc = mx.sym.Flatten(data)
@@ -707,8 +706,8 @@ def test_spatial_transformer_with_type():
 loc = mx.sym.FullyConnected(data=loc, num_hidden=6)
 sym = mx.sym.SpatialTransformer(data=data, loc=loc, target_shape=(10, 10),
 transform_type="affine", 
sampler_type="bilinear")
-ctx_list = [{'ctx': mx.gpu(0), 'data': (1, 5, 10, 10), 'type_dict': 
{'data': np.float32}},
-{'ctx': mx.cpu(0), 'data': (1, 5, 10, 10), 'type_dict': 
{'data': np.float32}}]
+ctx_list = [{'ctx': mx.gpu(0), 'data': (1, 5, 10, 10), 'type_dict': 
{'data': np.float64}},
+{'ctx': mx.cpu(0), 'data': (1, 5, 10, 10), 'type_dict': 
{'data': np.float64}}]
 check_consistency(sym, ctx_list)
 check_consistency(sym, ctx_list, grad_req="add")
 


 


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[incubator-mxnet] branch master updated: Fix flaky test test_operator_gpu.test_spatial_transformer_with_type (#7645) (#11444)

[zhasheng]

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

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


The following commit(s) were added to refs/heads/master by this push:
 new f7a0025  Fix flaky test 
test_operator_gpu.test_spatial_transformer_with_type (#7645) (#11444)
f7a0025 is described below

commit f7a00250cf83ba34779b7c6ffc02532d20795f2f
Author: ddavydenko 
AuthorDate: Thu Jun 28 16:16:29 2018 -0700

Fix flaky test test_operator_gpu.test_spatial_transformer_with_type (#7645) 
(#11444)
---
 tests/python/gpu/test_operator_gpu.py | 7 +++
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/tests/python/gpu/test_operator_gpu.py 
b/tests/python/gpu/test_operator_gpu.py
index fd76990..3efc284 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -701,8 +701,7 @@ def test_grid_generator_with_type():
 check_consistency(sym, ctx_list, grad_req="add")
 
 
-@unittest.skip("test fails intermittently. temporarily disabled till it gets 
fixed. tracked at https://github.com/apache/incubator-mxnet/issues/7645;)
-@with_seed(1234)
+@with_seed()
 def test_spatial_transformer_with_type():
 data = mx.sym.Variable('data')
 loc = mx.sym.Flatten(data)
@@ -711,8 +710,8 @@ def test_spatial_transformer_with_type():
 loc = mx.sym.FullyConnected(data=loc, num_hidden=6)
 sym = mx.sym.SpatialTransformer(data=data, loc=loc, target_shape=(10, 10),
 transform_type="affine", 
sampler_type="bilinear")
-ctx_list = [{'ctx': mx.gpu(0), 'data': (1, 5, 10, 10), 'type_dict': 
{'data': np.float32}},
-{'ctx': mx.cpu(0), 'data': (1, 5, 10, 10), 'type_dict': 
{'data': np.float32}}]
+ctx_list = [{'ctx': mx.gpu(0), 'data': (1, 5, 10, 10), 'type_dict': 
{'data': np.float64}},
+{'ctx': mx.cpu(0), 'data': (1, 5, 10, 10), 'type_dict': 
{'data': np.float64}}]
 check_consistency(sym, ctx_list)
 check_consistency(sym, ctx_list, grad_req="add")
 

[GitHub] szha commented on issue #11213: [MXNET-533] MXNet-ONNX export

[GitBox]

szha commented on issue #11213: [MXNET-533] MXNet-ONNX export
URL: https://github.com/apache/incubator-mxnet/pull/11213#issuecomment-401200760
 
 
   @sandeep-krishnamurthy thanks for the efforts. Please respect "request 
changes" as vetos nonetheless and try and reach @zhreshold, especially given 
that you sit in the same office. Much appreciated.


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[GitHub] Caenorst commented on a change in pull request #11325: Added TensorRT runtime integration

[GitBox]

Caenorst commented on a change in pull request #11325: Added TensorRT runtime 
integration
URL: https://github.com/apache/incubator-mxnet/pull/11325#discussion_r199014063
 
 

 ##
 File path: src/executor/tensorrt_pass.cc
 ##
 @@ -0,0 +1,583 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2018 by Contributors
+ * \file tensorrt_pass.cc
+ * \brief Replace TRT compatible subgraphs by TRT engines
+ * \author Clement Fuji Tsang
+ */
+
+#if MXNET_USE_TENSORRT
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+#include "./onnx_to_tensorrt.h"
+#include "./exec_pass.h"
+#include "../operator/contrib/nnvm_to_onnx-inl.h"
+
+namespace mxnet {
+namespace exec {
+
+using NodePtr = nnvm::NodePtr;
+
+/*!
+ * \brief Custom graph class, which will contain bi-directional nodes
+ * we need to compute DFS and reverse DFS for graph partitioning
+ */
+class BidirectionalGraph {
+ public:
+  struct Node {
+nnvm::Node* nnvmptr;
+std::vector inputs;
+std::vector outputs;
+  };
+  std::vector nodes;
+  std::unordered_map nnvm2nid;
+  std::vector outputs;
+  static const std::unordered_set unconditionalTRTop;
+
+  explicit BidirectionalGraph(const Graph ) {
+auto& idx = g.indexed_graph();
+auto num_nodes = idx.num_nodes();
+nodes.reserve(num_nodes);
+nnvm2nid.reserve(num_nodes);
 
 Review comment:
   It's actually not node id in the IndexedGraph but the position in the 
BidirectionalGraph `std::vector` attribute `node` (position 


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[GitHub] zhreshold commented on issue #11213: [MXNET-533] MXNet-ONNX export

[GitBox]

zhreshold commented on issue #11213: [MXNET-533] MXNet-ONNX export
URL: https://github.com/apache/incubator-mxnet/pull/11213#issuecomment-401197110
 
 
   I opened a new issue regarding my concerns in #11475 


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[GitHub] zheng-da commented on a change in pull request #11371: [MXNET-486] Create CPP test for concat MKLDNN operator

[GitBox]

zheng-da commented on a change in pull request #11371: [MXNET-486] Create CPP 
test for concat MKLDNN operator
URL: https://github.com/apache/incubator-mxnet/pull/11371#discussion_r199010760
 
 

 ##
 File path: tests/cpp/operator/mkldnn.cc
 ##
 @@ -804,6 +1012,24 @@ TEST(IMPERATIVE, SumBackwardsOp) {
   TestOp(attrs, VerifySumBackwardsResult);
 }
 
+TEST(IMPERATIVE, ConcatOp) {
+  for (int num_inputs = 2; num_inputs < 4; num_inputs++) {
+for (int dim = 0; dim < 5; dim++) {
+  OpAttrs attrs = GetConcatOp(num_inputs, dim);
+  TestConcatOp(attrs, VerifyConcatResult);
+}
+  }
+}
+
+TEST(IMPERATIVE, ConcatBackwardsOp) {
+  for (int num_inputs = 2; num_inputs < 3; num_inputs++) {
 
 Review comment:
   the same comment for here.


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[GitHub] aaronmarkham commented on a change in pull request #11304: [MXNET-594] Added Learning Rate Finder tutorial

[GitBox]

aaronmarkham commented on a change in pull request #11304: [MXNET-594] Added 
Learning Rate Finder tutorial
URL: https://github.com/apache/incubator-mxnet/pull/11304#discussion_r199007868
 
 

 ##
 File path: docs/tutorials/gluon/learning_rate_finder.md
 ##
 @@ -0,0 +1,322 @@
+
+# Learning Rate Finder
+
+Setting the learning rate for stochastic gradient descent (SGD) is crucially 
important when training neural network because it controls both the speed of 
convergence and the ultimate performance of the network. Set the learning too 
low and you could be twiddling your thumbs for quite some time as the 
parameters update very slowly. Set it too high and the updates will skip over 
optimal solutions, or worse the optimizer might not converge at all!
+
+Leslie Smith from the U.S. Naval Research Laboratory presented a method for 
finding a good learning rate in a paper called ["Cyclical Learning Rates for 
Training Neural Networks"](https://arxiv.org/abs/1506.01186). We take a look at 
the central idea of the paper, cyclical learning rate schedules, in the 
tutorial called 'Advanced Learning Rate Schedules', but in this tutorial we 
implement a 'Learning Rate Finder' in MXNet with the Gluon API that you can use 
while training your own networks.
+
+## Simple Idea
+
+Given an initialized network, a defined loss and a training dataset we take 
the following steps:
+
+1. train one batch at a time (a.k.a. an iteration)
 
 Review comment:
   nit:
   Train
   Start
   Record


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[GitHub] aaronmarkham commented on a change in pull request #11304: [MXNET-594] Added Learning Rate Finder tutorial

[GitBox]

aaronmarkham commented on a change in pull request #11304: [MXNET-594] Added 
Learning Rate Finder tutorial
URL: https://github.com/apache/incubator-mxnet/pull/11304#discussion_r199008464
 
 

 ##
 File path: docs/tutorials/gluon/learning_rate_finder.md
 ##
 @@ -0,0 +1,322 @@
+
+# Learning Rate Finder
+
+Setting the learning rate for stochastic gradient descent (SGD) is crucially 
important when training neural network because it controls both the speed of 
convergence and the ultimate performance of the network. Set the learning too 
low and you could be twiddling your thumbs for quite some time as the 
parameters update very slowly. Set it too high and the updates will skip over 
optimal solutions, or worse the optimizer might not converge at all!
+
+Leslie Smith from the U.S. Naval Research Laboratory presented a method for 
finding a good learning rate in a paper called ["Cyclical Learning Rates for 
Training Neural Networks"](https://arxiv.org/abs/1506.01186). We take a look at 
the central idea of the paper, cyclical learning rate schedules, in the 
tutorial called 'Advanced Learning Rate Schedules', but in this tutorial we 
implement a 'Learning Rate Finder' in MXNet with the Gluon API that you can use 
while training your own networks.
+
+## Simple Idea
+
+Given an initialized network, a defined loss and a training dataset we take 
the following steps:
+
+1. train one batch at a time (a.k.a. an iteration)
+2. start with a very small learning rate (e.g. 0.01) and slowly increase 
it every iteration
+3. record the training loss and continue until we see the training loss diverge
+
+We then analyse the results by plotting a graph of the learning rate against 
the training loss as seen below (taking note of the log scales).
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_finder/finder_plot.png)
 
+
+As expected, for very small learning rates we don't see much change in the 
loss as the parameter updates are negligible. At a learning rate of 0.001, we 
start to see the loss fall. Setting the initial learning rate here is 
reasonable, but we still have the potential to learn faster. We observe a drop 
in the loss up until 0.1 where the loss appears to diverge. We want to set the 
initial learning rate as high as possible before the loss becomes unstable, so 
we choose a learning rate of 0.05.
 
 Review comment:
   Could you add event pointers on the chart for increased clarity. 
--> at 0.001 loss falls
--> at 0.1 divergence (loss increases)
   --> at 0.05 loss seems lowest (right?)


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[GitHub] zheng-da commented on issue #11448: fail to fall back when sparse arrays are passed to MKLDNN-enabled operators.

[GitBox]

zheng-da commented on issue #11448: fail to fall back when sparse arrays are 
passed to MKLDNN-enabled operators.
URL: 
https://github.com/apache/incubator-mxnet/issues/11448#issuecomment-401195601
 
 
   i created a PR for batchnorm.


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[GitHub] hetong007 opened a new pull request #11474: Fix record io augmentation speed

[GitBox]

hetong007 opened a new pull request #11474: Fix record io augmentation speed
URL: https://github.com/apache/incubator-mxnet/pull/11474
 
 
   ## Description ##
   
   1. Fixing https://github.com/apache/incubator-mxnet/issues/11469
   2. Fixing default augmentation setting in current image classification 
example.
   
   ## 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] To the my best knowledge, examples are either not affected by this 
change, or have been fixed to be compatible with this change
   


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[GitHub] zheng-da opened a new pull request #11473: batchnorm falls back for sparse arrays.

[GitBox]

zheng-da opened a new pull request #11473: batchnorm falls back for sparse 
arrays.
URL: https://github.com/apache/incubator-mxnet/pull/11473
 
 
   ## 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 $JIRA_ID refers to 
the relevant [JIRA issue](https://issues.apache.org/jira/projects/MXNET/issues) 
created (except PRs with tiny changes)
   - [ ] Changes are complete (i.e. I finished coding on this PR)
   - [ ] All changes have test coverage:
   - Unit tests are added for small changes to verify correctness (e.g. adding 
a new operator)
   - Nightly tests are added for complicated/long-running ones (e.g. changing 
distributed kvstore)
   - Build tests will be added for build configuration changes (e.g. adding a 
new build option with NCCL)
   - [ ] Code is well-documented: 
   - For user-facing API changes, API doc string has been updated. 
   - For new C++ functions in header files, their functionalities and arguments 
are documented. 
   - For new examples, README.md is added to explain the what the example does, 
the source of the dataset, expected performance on test set and reference to 
the original paper if applicable
   - Check the API doc at 
http://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$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
   


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[GitHub] rahul003 commented on issue #11469: Performance regression in augmentation

[GitBox]

rahul003 commented on issue #11469: Performance regression in augmentation
URL: 
https://github.com/apache/incubator-mxnet/issues/11469#issuecomment-401194743
 
 
   That was it. Was accidentally performing an unnecessary affine transform 
before cropping. 
   Tong is sending out a PR to fix it. 
   Will close that issue after that PR goes in. 


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[GitHub] rahul003 edited a comment on issue #11469: Performance regression in augmentation

[GitBox]

rahul003 edited a comment on issue #11469: Performance regression in 
augmentation
URL: 
https://github.com/apache/incubator-mxnet/issues/11469#issuecomment-401194743
 
 
   That was it. Was accidentally performing an unnecessary affine transform 
before cropping. 
   Tong is sending out a PR to fix it. 
   Will close the issue after that PR goes in. 


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[GitHub] gigasquid commented on a change in pull request #11205: Clojure Contrib Package

[GitBox]

gigasquid commented on a change in pull request #11205: Clojure Contrib Package
URL: https://github.com/apache/incubator-mxnet/pull/11205#discussion_r199008534
 
 

 ##
 File path: contrib/clojure-package/README.md
 ##
 @@ -0,0 +1,204 @@
+# Clojure MXNet
+
+A clojure package to the MXNet Deep Learning library
+
+## Introduction
+
+MXNet is a first class, modern deep learning library that AWS has officially 
picked as its chosen library. It supports multiple languages on a first class 
basis and is incubating as an Apache project.
+
+The motivation for creating a Clojure package is to be able to open the deep 
learning library to the Clojure ecosystem and build bridges for future 
development and innovation for the community. It provides all the needed tools 
including low level and high level apis, dynamic graphs, and things like GAN 
and natural language support.
+
+For high leverage, the Clojure package has been built on the existing Scala 
package using interop. This has allowed rapid development and close parity with 
the Scala functionality. This also leaves the door open to directly developing 
code against the jni-bindings with Clojure in the future in an incremental 
fashion, using the test suites as a refactoring guide.
+
+## Current State and Plans
+
+The Clojure package is nearing the end of its first development milestone 
which is to achieve a close parity with the Scala package and to potentially be 
included into the main project for official Clojure language support.
+
+What is needed now is alpha testing on both OSX and Linux to discover any 
bugs, rough edges, and generally harden it before an official PR is opened on 
the main project.
+
+Help with this effort is greatly appreciated and contributors will be 
recognized in the project README.
+
+Testing instructions can be found in the testing.md
+
+## Getting Started
+
+The following systems are supported:
+
+- OSX cpu
+- Linux cpu
+- Linux gpu
+
+There are two ways of getting going. The first way is the easiest and that is 
to use the pre-built jars from Maven. The second way is to build from source. 
In both cases, you will need to load the prereqs and dependencies, (like 
opencv).
+
+It's been tested on AWS Deep Learning AMI and OSX High Sierra 10.13.4
+
+
+### Prerequisites
+
+**If you are using the AWS Deep Learning Ubuntu or Linux AMI you should be 
good to go without doing anything on this step.**
+
+
+Follow the instructions from 
https://mxnet.incubator.apache.org/install/osx_setup.html or 
https://mxnet.incubator.apache.org/install/ubuntu_setup.html
+about _Prepare Environment for GPU Installation_
+and _Install MXNet dependencies_
+
+
+ Cloning the repo and running from source
+
+To use the prebuilt jars, you will need to replace the native version of the 
line in the project dependencies with your configuration.
+
+`[org.apache.mxnet/mxnet-full_2.11-linux-x86_64-gpu "1.2.0"]`
+or
+`[org.apache.mxnet/mxnet-full_2.11-linux-x86_64-cpu "1.2.0"]`
+or
+`[org.apache.mxnet/mxnet-full_2.11-osx-x86_64-cpu "1.2.0"]`
+
+
+```clojure
+
+(ns tutorial.ndarray
+  (:require [org.apache.clojure-mxnet.ndarray :as ndarray]
+[org.apache.clojure-mxnet.context :as context]))
+
+;;Create NDArray
+(def a (ndarray/zeros [100 50])) ;;all zero arrray of dimension 100 x 50
+(def b (ndarray/ones [256 32 128 1])) ;; all one array of dimension
+(def c (ndarray/array [1 2 3 4 5 6] [2 3])) ;; array with contents of a shape 
2 x 3
+
+;;; There are also ways to convert to a vec or get the shape as an object or 
vec
+(ndarray/->vec c) ;=> [1.0 2.0 3.0 4.0 5.0 6.0]
+```
+
+See the examples/tutorial section for more.
+
+
+The jars from maven with the needed MXNet native binaries in it. On startup, 
the native libraries are extracted from the jar and copied into a temporary 
location on your path. On termination, they are deleted.
+
+If you want details on the flags (opencv verison and cuda version of the 
jars), they are documented here 
https://cwiki.apache.org/confluence/display/MXNET/MXNet-Scala+Release+Process
+
+
+### Build from MXNET Source
+
+Checkout the latest sha from the main package
+
+`git clone --recursive https://github.com/dmlc/mxnet ~/mxnet`
+`cd ~/mxnet`
+
+
+`git checkout tags/1.2.0 -b release-1.2.0`
+
+`git submodule update --init --recursive`
+
+Sometimes it useful to use this script to clean hard
+https://gist.github.com/nicktoumpelis/11214362
+
+
+Go here to do the base package installation 
https://mxnet.incubator.apache.org/install/index.html
+
+ Run `make scalapkg` then `make scalainstall`
+
+then replace the correct jar for your architecture in the project.clj, example 
`[ml.dmlc.mxnet/mxnet-full_2.11-osx-x86_64-cpu "1.0.1-SNAPSHOT"]`
+
+ Test your installation
+
+To test your installation, you should run `lein test`. This will run the test 
suite (CPU) for the clojure package.
+
+
+ Generation of NDArray and Symbol apis
+
+The bulk of the ndarray and symbol apis are generated via java reflection 

[GitHub] sandeep-krishnamurthy edited a comment on issue #11406: [MXNET-599] Partial shape infer for Slice

[GitBox]

sandeep-krishnamurthy edited a comment on issue #11406: [MXNET-599] Partial 
shape infer for Slice
URL: https://github.com/apache/incubator-mxnet/pull/11406#issuecomment-401194229
 
 
   Thanks a lot @rahul003 - This is very useful fix for all Keras-MXNet users 
using slice operation. Overall looks good to me, except 1 change that Jun 
mentioned about unknown dimension should be unknown.


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[GitHub] sandeep-krishnamurthy commented on issue #11406: [MXNET-599] Partial shape infer for Slice

[GitBox]

sandeep-krishnamurthy commented on issue #11406: [MXNET-599] Partial shape 
infer for Slice
URL: https://github.com/apache/incubator-mxnet/pull/11406#issuecomment-401194229
 
 
   Thanks a lot @rahul003 - This is very useful fix for all Keras-MXNet users 
using slice operation.


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[GitHub] mkolod commented on a change in pull request #11325: Added TensorRT runtime integration

[GitBox]

mkolod commented on a change in pull request #11325: Added TensorRT runtime 
integration
URL: https://github.com/apache/incubator-mxnet/pull/11325#discussion_r199008230
 
 

 ##
 File path: src/executor/tensorrt_pass.cc
 ##
 @@ -0,0 +1,583 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2018 by Contributors
+ * \file tensorrt_pass.cc
+ * \brief Replace TRT compatible subgraphs by TRT engines
+ * \author Clement Fuji Tsang
+ */
+
+#if MXNET_USE_TENSORRT
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+#include "./onnx_to_tensorrt.h"
+#include "./exec_pass.h"
+#include "../operator/contrib/nnvm_to_onnx-inl.h"
+
+namespace mxnet {
+namespace exec {
+
+using NodePtr = nnvm::NodePtr;
+
+/*!
+ * \brief Custom graph class, which will contain bi-directional nodes
+ * we need to compute DFS and reverse DFS for graph partitioning
+ */
+class BidirectionalGraph {
+ public:
+  struct Node {
+nnvm::Node* nnvmptr;
+std::vector inputs;
+std::vector outputs;
+  };
+  std::vector nodes;
+  std::unordered_map nnvm2nid;
+  std::vector outputs;
+  static const std::unordered_set unconditionalTRTop;
+
+  explicit BidirectionalGraph(const Graph ) {
+auto& idx = g.indexed_graph();
+auto num_nodes = idx.num_nodes();
+nodes.reserve(num_nodes);
+nnvm2nid.reserve(num_nodes);
+outputs.reserve(idx.outputs().size());
+DFSVisit(g.outputs, [this](const nnvm::NodePtr& n) {
+  BidirectionalGraph::Node new_node;
+  new_node.nnvmptr = n.get();
+  nnvm2nid[n.get()] = static_cast(nodes.size());
+  nodes.emplace_back(std::move(new_node));
+});
+for (const auto& it : nnvm2nid) {
+  nnvm::Node* nnvmnode = it.first;
+  uint32_t nid = it.second;
+  for (auto& n : nnvmnode->inputs) {
+uint32_t input_nid = nnvm2nid[n.node.get()];
+nodes[input_nid].outputs.emplace_back([nid]);
+nodes[nid].inputs.emplace_back([input_nid]);
+  }
+}
+for (auto& e : g.outputs) {
+  uint32_t nid = nnvm2nid[e.node.get()];
+  outputs.emplace_back([nid]);
+}
+  }
+
+  template 
+  void DFS(const std::vector& heads, bool reverse, FVisit fvisit) {
+std::unordered_set visited;
+std::deque stack(heads.begin(), heads.end());
 
 Review comment:
   Good catch, will change to std::stack in this case. There were other cases 
for std::deque in the code, but this wasn't one of them.


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[GitHub] sandeep-krishnamurthy commented on a change in pull request #11406: [MXNET-599] Partial shape infer for Slice

[GitBox]

sandeep-krishnamurthy commented on a change in pull request #11406: [MXNET-599] 
Partial shape infer for Slice
URL: https://github.com/apache/incubator-mxnet/pull/11406#discussion_r199007699
 
 

 ##
 File path: src/operator/tensor/matrix_op-inl.h
 ##
 @@ -674,13 +675,23 @@ inline void SetSliceOpOutputDimSize(const index_t i, 
const int b,
 const int e, const int s,
 TShape* oshape) {
   if (s > 0) {
-CHECK_LT(b, e) << "slicing with begin=[" << i << "]=" << b << ", end[" << 
i << "]="
+CHECK_LE(b, e) << "slicing with begin=[" << i << "]=" << b << ", end[" << 
i << "]="
<< e << ", and step[" << i << "]=" << s << " is invalid";
-(*oshape)[i] = (e - b - 1) / s + 1;
+if (e == b) {
+  // for partial shape infer
+  (*oshape)[i] = 0;
+} else {
+  (*oshape)[i] = (e - b - 1) / s + 1;
 
 Review comment:
   Yes that is correct. Even if one of the shape is unknown (0) we 
cannot/should not infer output shape. 


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[GitHub] mkolod commented on a change in pull request #11325: Added TensorRT runtime integration

[GitBox]

mkolod commented on a change in pull request #11325: Added TensorRT runtime 
integration
URL: https://github.com/apache/incubator-mxnet/pull/11325#discussion_r199006909
 
 

 ##
 File path: src/executor/graph_executor.cc
 ##
 @@ -940,6 +968,91 @@ void GraphExecutor::FinishInitGraph(nnvm::Symbol symbol,
   this->InitOpSegs();
 }
 
+
+Graph GraphExecutor::ReinitGraph(Graph&& g, const Context _ctx,
 
 Review comment:
   Will do.


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[GitHub] aaronmarkham commented on issue #11287: [MXNET-548] fixed path for auto_module_index.js

[GitBox]

aaronmarkham commented on issue #11287: [MXNET-548] fixed path for 
auto_module_index.js
URL: https://github.com/apache/incubator-mxnet/pull/11287#issuecomment-401192228
 
 
   5th restart due to flaky CI...


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