[GitHub] [incubator-mxnet] ciyongch commented on a change in pull request #15950: [MKLDNN]Support fullyconnected and element-wise ops fusion

[GitBox]

ciyongch commented on a change in pull request #15950: [MKLDNN]Support 
fullyconnected and element-wise ops fusion
URL: https://github.com/apache/incubator-mxnet/pull/15950#discussion_r316002034
 
 

 ##
 File path: src/operator/subgraph/mkldnn/mkldnn_fc_property.h
 ##
 @@ -68,44 +73,70 @@ class SgMKLDNNFCSelector : public SubgraphSelector {
   }
 
   bool SelectOutput(const nnvm::Node , const nnvm::Node _node) override {
-if (status == kFail || status == kSuccess || new_node.is_variable())
+if (status_ == kFail || status_ == kSuccess || new_node.is_variable())
   return false;
 
 // If n isn't the last matched node, then we encoutered a internal
 // branch, we should pop out the node behind n and stop fusion.
-if (matched_list.back() != ) {
-  if (std::find(matched_list.begin(), matched_list.end(), ) !=
-matched_list.end()) {
-while (matched_list.back() != ) {
-  matched_list.pop_back();
+if (matched_list_.back() != ) {
+  if (std::find(matched_list_.begin(), matched_list_.end(), ) !=
+matched_list_.end()) {
+while (matched_list_.back() != ) {
+  matched_list_.pop_back();
 }
   }
 
-  status = kSuccess;
+  status_ = kSuccess;
   return false;
 }
 
-switch (status) {
+switch (status_) {
   case kStart:
-if (new_node.op() == Op::Get("Activation") &&
-new_node.attrs.dict.at("act_type") == "relu") {
-  matched_list.push_back(_node);
-  status = kSuccess;
+// Currently, For INT8 FC fusion, only supports 
relu/bounded_relu(clip)/abs.
+if (new_node.op() == Op::Get("Activation")) {
+  const ActivationParam  = 
nnvm::get(new_node.attrs.parsed);
+  if ((quantized_ && SupportQuantizedMKLDNNAct(param)) ||
+  (!quantized_ && SupportMKLDNNAct(param))) {
+matched_list_.push_back(_node);
+status_ = kSuccess;
+return true;
+  }
+}
+if (!quantized_ && (new_node.op() == Op::Get("square") ||
+new_node.op() == Op::Get("sqrt") ||
+new_node.op() == Op::Get("exp"))) {
+  matched_list_.push_back(_node);
+  status_ = kSuccess;
+  return true;
+}
+if (new_node.op() == Op::Get("abs")) {
+  matched_list_.push_back(_node);
+  status_ = kSuccess;
   return true;
 }
+if (new_node.op() == Op::Get("clip")) {
+  const ClipParam  = nnvm::get(new_node.attrs.parsed);
+  if (param.a_min == 0.f && param.a_max == 1.0f) {
 
 Review comment:
   Good catch, will remove this check for a_max for `bounded_relu`.


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[GitHub] [incubator-mxnet] hzfan commented on a change in pull request #15938: Tvm broadcast backward

[GitBox]

hzfan commented on a change in pull request #15938: Tvm broadcast backward
URL: https://github.com/apache/incubator-mxnet/pull/15938#discussion_r316000342
 
 

 ##
 File path: src/operator/contrib/tvmop/ufunc.cc
 ##
 @@ -37,29 +38,88 @@ namespace op {
 
 static constexpr char func_vadd_cpu[] = "vadd";
 static constexpr char func_vadd_gpu[] = "cuda_vadd";
+static constexpr char func_bakcward_vadd_cpu[] = "backward_vadd";
+static constexpr char func_bakcward_vadd_gpu[] = "cuda_backward_vadd";
 
 template
-void TVMBroadcastCompute(const nnvm::NodeAttrs& attrs,
- const mxnet::OpContext& ctx,
- const std::vector& inputs,
- const std::vector& req,
- const std::vector& outputs) {
+void TVMBinaryCompute(const nnvm::NodeAttrs& attrs,
+  const mxnet::OpContext& ctx,
+  const std::vector& inputs,
+  const std::vector& req,
+  const std::vector& outputs) {
   CHECK_EQ(inputs.size(), 2U);
   CHECK_EQ(outputs.size(), 1U);
   tvm::runtime::TVMOpModule::Get()->Call(func, ctx, {inputs[0], inputs[1], 
outputs[0]});
 }
 
+template
+void TVMBinaryBackwardComputeUseNone(const nnvm::NodeAttrs& attrs,
+ const mxnet::OpContext& ctx,
+ const std::vector& inputs,
+ const std::vector& req,
+ const std::vector& outputs) {
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 2U);
+  int ndim = inputs[0].shape_.ndim();
+  for (int k = 0; k < 2; ++k) {
+// dispatch by backward
+std::vector ov, iv;
+const TBlob& ograd = inputs[0], igrad = outputs[k];
+bool flag = ograd.size(0) != igrad.size(0);
+for (int i = 0; i < ndim; ++i) {
 
 Review comment:
   I think we can pad the shape after dimension collapse. In this case, the 
`tblob` will be reshaped into `(2, 12, 30, 1, 1)` and then reduce on `axis=[1, 
3]`.


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[GitHub] [incubator-mxnet] apeforest commented on issue #15545: Softmax optimization for GPU

[GitBox]

apeforest commented on issue #15545: Softmax optimization for GPU
URL: https://github.com/apache/incubator-mxnet/pull/15545#issuecomment-523292097
 
 
   Thanks for refactoring the Softmax functions to make it into one.


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[GitHub] [incubator-mxnet] reminisce commented on a change in pull request #15938: Tvm broadcast backward

[GitBox]

reminisce commented on a change in pull request #15938: Tvm broadcast backward
URL: https://github.com/apache/incubator-mxnet/pull/15938#discussion_r315995425
 
 

 ##
 File path: src/operator/contrib/tvmop/ufunc.cc
 ##
 @@ -37,29 +38,88 @@ namespace op {
 
 static constexpr char func_vadd_cpu[] = "vadd";
 static constexpr char func_vadd_gpu[] = "cuda_vadd";
+static constexpr char func_bakcward_vadd_cpu[] = "backward_vadd";
+static constexpr char func_bakcward_vadd_gpu[] = "cuda_backward_vadd";
 
 template
-void TVMBroadcastCompute(const nnvm::NodeAttrs& attrs,
- const mxnet::OpContext& ctx,
- const std::vector& inputs,
- const std::vector& req,
- const std::vector& outputs) {
+void TVMBinaryCompute(const nnvm::NodeAttrs& attrs,
+  const mxnet::OpContext& ctx,
+  const std::vector& inputs,
+  const std::vector& req,
+  const std::vector& outputs) {
   CHECK_EQ(inputs.size(), 2U);
   CHECK_EQ(outputs.size(), 1U);
   tvm::runtime::TVMOpModule::Get()->Call(func, ctx, {inputs[0], inputs[1], 
outputs[0]});
 }
 
+template
+void TVMBinaryBackwardComputeUseNone(const nnvm::NodeAttrs& attrs,
+ const mxnet::OpContext& ctx,
+ const std::vector& inputs,
+ const std::vector& req,
+ const std::vector& outputs) {
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 2U);
+  int ndim = inputs[0].shape_.ndim();
+  for (int k = 0; k < 2; ++k) {
+// dispatch by backward
+std::vector ov, iv;
+const TBlob& ograd = inputs[0], igrad = outputs[k];
+bool flag = ograd.size(0) != igrad.size(0);
+for (int i = 0; i < ndim; ++i) {
 
 Review comment:
   Please correct me if my understanding is wrong, but don't you still need 
kernels generated for `ndims < 5` since you will collapse consecutive 
dimensions where reduction is performed? For example, given a 5d shape `(2, 3, 
4, 5, 6)`, and perform reduction on `axis=(1, 2)`, the `tblob` will be first 
reshaped into `(2, 12, 30)`, and then reduce on `axis=1`.  In this case, do you 
need a kernel generated for 3D shapes?


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[GitHub] [incubator-mxnet] apeforest commented on a change in pull request #15545: Softmax optimization for GPU

[GitBox]

apeforest commented on a change in pull request #15545: Softmax optimization 
for GPU
URL: https://github.com/apache/incubator-mxnet/pull/15545#discussion_r315994975
 
 

 ##
 File path: src/operator/nn/softmax-inl.h
 ##
 @@ -188,89 +180,77 @@ struct log_softmax_bwd {
   }
 };
 
-
 template
+ typename AType, typename DType, typename OType, typename IType, int 
ndim>
 inline void SoftmaxGrad(Stream *s, OType *out, OType *ograd,
-DType *igrad, Shape shape, int axis,
-const DType temperature) {
+DType *igrad, IType *length, Shape shape,
+int axis, const DType temperature) {
   index_t M = shape[axis];
   index_t N = shape.Size()/M;
   Shape stride = calc_stride(shape);
   Shape sshape = shape;
   sshape[axis] = 1;
   index_t sa = stride[axis];
 
-  #pragma omp parallel for
-  for (index_t i = 0; i < N; ++i) {
-index_t base = unravel_dot(i, sshape, stride);
+  if (length != nullptr) {
+#pragma omp parallel for
+for (index_t i = 0; i < N; ++i) {
+  index_t base = unravel_dot(i, sshape, stride);
+  index_t len = static_cast(length[i]);
 
-AType sum = AType(0);
-for (index_t j = 0; j < M; ++j) {
-  sum += OP1::Map(ograd[base + j*sa], out[base + j*sa]);
-}
-
-// By default temperature is 1.0.
-// Adding a branch here to save the CPU 'divide-by-1' computation at 
runtime
-DType final_result;
-if (temperature == 1.0) {
-  for (index_t j = 0; j < M; ++j) {
-final_result = negate ?
-   -OP2::Map(ograd[base + j*sa], out[base + j*sa], sum) :
-   OP2::Map(ograd[base + j*sa], out[base + j*sa], sum);
-KERNEL_ASSIGN(igrad[base + j*sa], Req, final_result);
-  }
-} else {
-  for (index_t j = 0; j < M; ++j) {
-final_result = negate ?
-   -OP2::Map(ograd[base + j*sa], out[base + j*sa], sum) / 
temperature :
-   OP2::Map(ograd[base + j*sa], out[base + j*sa], sum) / 
temperature;
-KERNEL_ASSIGN(igrad[base + j*sa], Req, final_result);
+  AType sum = AType(0);
+  for (index_t j = 0; j < len; ++j) {
+sum += OP1::Map(ograd[base + j*sa], out[base + j*sa]);
   }
-}
-  }
-}
 
-template
-inline void SoftmaxWithLengthGrad(Stream *s, OType *out, OType *ograd,
-  DType *igrad, IType *length, Shape 
shape,
-  int axis, const DType temperature) {
-  index_t M = shape[axis];
-  index_t N = shape.Size()/M;
-  Shape stride = calc_stride(shape);
-  Shape sshape = shape;
-  sshape[axis] = 1;
-  index_t sa = stride[axis];
-
-  #pragma omp parallel for
-  for (index_t i = 0; i < N; ++i) {
-index_t base = unravel_dot(i, sshape, stride);
-index_t len = static_cast(length[i]);
-
-AType sum = AType(0);
-for (index_t j = 0; j < len; ++j) {
-  sum += OP1::Map(ograd[base + j*sa], out[base + j*sa]);
+  // By default temperature is 1.0.
+  // Adding a branch here to save the CPU 'divide-by-1' computation at 
runtime
+  DType final_result;
+  if (temperature == 1.0) {
 
 Review comment:
   Yes, I have done performance comparison earlier. This check speed up the 
operator by 30%


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[GitHub] [incubator-mxnet] mahmoodn opened a new issue #15957: error: call of overloaded is ambiguous

[GitBox]

mahmoodn opened a new issue #15957: error: call of overloaded is ambiguous
URL: https://github.com/apache/incubator-mxnet/issues/15957
 
 
   With gcc 7.4 and cuda 10 and mxnet-1.4.1, I get this compilation error
   ```
   include/mxnet/././ndarray.h:169:78: error: call of overloaded 
‘NodeEntry()’ is ambiguous
   dtype_(data.type_flag_), storage_type_(stype), entry_({nullptr, 0, 
0}) {
  
   ```
   details:
   ```
   g++ -std=c++11 -c -DMSHADOW_FORCE_STREAM -Wall -Wsign-compare -O3 -DNDEBUG=1 
-I/home/mh.naderan/mx/mxnet-1.4.1/3rdparty/mshadow/ 
-I/home/mh.naderan/mx/mxnet-1.4.1/3rdparty/dmlc-core/include -fPIC 
-I/home/mh.naderan/mx/mxnet-1.4.1/3rdparty/tvm/nnvm/include 
-I/home/mh.naderan/mx/mxnet-1.4.1/3rdparty/dlpack/include 
-I/home/mh.naderan/mx/mxnet-1.4.1/3rdparty/tvm/include -Iinclude -funroll-loops 
-Wno-unused-parameter -Wno-unknown-pragmas -Wno-unused-local-typedefs -msse3 
-DMSHADOW_USE_F16C=0 -I/usr/local/cuda/include -DMSHADOW_USE_CBLAS=1 
-DMSHADOW_USE_MKL=0 -DMSHADOW_RABIT_PS=0 -DMSHADOW_DIST_PS=0 
-DMSHADOW_USE_PASCAL=0 -DMXNET_USE_OPENCV=1 -I/usr/include/opencv -fopenmp 
-DMXNET_USE_OPERATOR_TUNING=1 -DMSHADOW_USE_CUDNN=1  
-I/home/mh.naderan/mx/mxnet-1.4.1/warp-ctc/include 
-I/home/mh.naderan/mx/mxnet-1.4.1/3rdparty/cub -DMXNET_ENABLE_CUDA_RTC=1 
-DMXNET_USE_NCCL=0 -DMXNET_USE_LIBJPEG_TURBO=0 -MMD -c 
src/operator/nn/mkldnn/mkldnn_act.cc -o 
build/src/operator/nn/mkldnn/mkldnn_act.o
   In file included from include/mxnet/./base.h:432:0,
from include/mxnet/operator.h:38,
from src/operator/nn/mkldnn/mkldnn_act.cc:28:
   include/mxnet/././tensor_blob.h: In member function ‘virtual void 
dmlc::parameter::FieldEntry::Check(void*) const’:
   include/mxnet/././tensor_blob.h:438:39: warning: comparison between signed 
and unsigned integer expressions [-Wsign-compare]
if (expect_ndim_ != 0 && v.ndim() != expect_ndim_) {
 ~^~~
   include/mxnet/././tensor_blob.h:445:36: warning: comparison between signed 
and unsigned integer expressions [-Wsign-compare]
  for (mxnet::index_t i = 0; i < v.ndim(); ++i) {
 ~~^~
   In file included from include/mxnet/./op_attr_types.h:36:0,
from include/mxnet/operator.h:40,
from src/operator/nn/mkldnn/mkldnn_act.cc:28:
   include/mxnet/././ndarray.h: In constructor ‘mxnet::NDArray::NDArray(const 
TShape&, mxnet::Context, bool, int)’:
   include/mxnet/././ndarray.h:98:31: error: call of overloaded 
‘NodeEntry()’ is ambiguous
entry_({nullptr, 0, 0}) {
  ^
   In file included from include/mxnet/operator.h:33:0,
from src/operator/nn/mkldnn/mkldnn_act.cc:28:
   /home/mh.naderan/mx/mxnet-1.4.1/3rdparty/tvm/nnvm/include/nnvm/node.h:59:12: 
note: candidate: nnvm::NodeEntry::NodeEntry(nnvm::NodePtr)
  explicit NodeEntry(NodePtr node):
   ^
   /home/mh.naderan/mx/mxnet-1.4.1/3rdparty/tvm/nnvm/include/nnvm/node.h:52:8: 
note: candidate: nnvm::NodeEntry::NodeEntry(const nnvm::NodeEntry&)
struct NodeEntry {
   ^
   /home/mh.naderan/mx/mxnet-1.4.1/3rdparty/tvm/nnvm/include/nnvm/node.h:52:8: 
note: candidate: nnvm::NodeEntry::NodeEntry(nnvm::NodeEntry&&)
   In file included from include/mxnet/./op_attr_types.h:36:0,
from include/mxnet/operator.h:40,
from src/operator/nn/mkldnn/mkldnn_act.cc:28:
   include/mxnet/././ndarray.h: In constructor ‘mxnet::NDArray::NDArray(const 
mxnet::TBlob&, int)’:
   include/mxnet/././ndarray.h:128:31: error: call of overloaded 
‘NodeEntry()’ is ambiguous
entry_({nullptr, 0, 0}) {
  ^
   In file included from include/mxnet/operator.h:33:0,
from src/operator/nn/mkldnn/mkldnn_act.cc:28:
   /home/mh.naderan/mx/mxnet-1.4.1/3rdparty/tvm/nnvm/include/nnvm/node.h:59:12: 
note: candidate: nnvm::NodeEntry::NodeEntry(nnvm::NodePtr)
  explicit NodeEntry(NodePtr node):
   ^
   /home/mh.naderan/mx/mxnet-1.4.1/3rdparty/tvm/nnvm/include/nnvm/node.h:52:8: 
note: candidate: nnvm::NodeEntry::NodeEntry(const nnvm::NodeEntry&)
struct NodeEntry {
   ^
   /home/mh.naderan/mx/mxnet-1.4.1/3rdparty/tvm/nnvm/include/nnvm/node.h:52:8: 
note: candidate: nnvm::NodeEntry::NodeEntry(nnvm::NodeEntry&&)
   In file included from include/mxnet/./op_attr_types.h:36:0,
from include/mxnet/operator.h:40,
from src/operator/nn/mkldnn/mkldnn_act.cc:28:
   include/mxnet/././ndarray.h: In constructor ‘mxnet::NDArray::NDArray(const 
mxnet::TBlob&, int, const std::function&)’:
   include/mxnet/././ndarray.h:147:31: error: call of overloaded 
‘NodeEntry()’ is ambiguous
entry_({nullptr, 0, 0}) {
  ^
   In file included from 

[GitHub] [incubator-mxnet] xidulu opened a new pull request #15956: [Numpy] random.randint() implemented

[GitBox]

xidulu opened a new pull request #15956: [Numpy] random.randint() implemented
URL: https://github.com/apache/incubator-mxnet/pull/15956
 
 
   ## Description ##
   np.random.randint()
   
https://docs.scipy.org/doc/numpy-1.15.1/reference/generated/numpy.random.randint.html
   
   ## 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] [incubator-mxnet] hzfan commented on a change in pull request #15938: Tvm broadcast backward

[GitBox]

hzfan commented on a change in pull request #15938: Tvm broadcast backward
URL: https://github.com/apache/incubator-mxnet/pull/15938#discussion_r315983914
 
 

 ##
 File path: src/operator/contrib/tvmop/ufunc.cc
 ##
 @@ -37,29 +38,88 @@ namespace op {
 
 static constexpr char func_vadd_cpu[] = "vadd";
 static constexpr char func_vadd_gpu[] = "cuda_vadd";
+static constexpr char func_bakcward_vadd_cpu[] = "backward_vadd";
+static constexpr char func_bakcward_vadd_gpu[] = "cuda_backward_vadd";
 
 template
-void TVMBroadcastCompute(const nnvm::NodeAttrs& attrs,
- const mxnet::OpContext& ctx,
- const std::vector& inputs,
- const std::vector& req,
- const std::vector& outputs) {
+void TVMBinaryCompute(const nnvm::NodeAttrs& attrs,
+  const mxnet::OpContext& ctx,
+  const std::vector& inputs,
+  const std::vector& req,
+  const std::vector& outputs) {
   CHECK_EQ(inputs.size(), 2U);
   CHECK_EQ(outputs.size(), 1U);
   tvm::runtime::TVMOpModule::Get()->Call(func, ctx, {inputs[0], inputs[1], 
outputs[0]});
 }
 
+template
+void TVMBinaryBackwardComputeUseNone(const nnvm::NodeAttrs& attrs,
+ const mxnet::OpContext& ctx,
+ const std::vector& inputs,
+ const std::vector& req,
+ const std::vector& outputs) {
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 2U);
+  int ndim = inputs[0].shape_.ndim();
+  for (int k = 0; k < 2; ++k) {
+// dispatch by backward
+std::vector ov, iv;
+const TBlob& ograd = inputs[0], igrad = outputs[k];
+bool flag = ograd.size(0) != igrad.size(0);
 
 Review comment:
   What about expand it into a if-else?


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[GitHub] [incubator-mxnet] hzfan commented on a change in pull request #15938: Tvm broadcast backward

[GitBox]

hzfan commented on a change in pull request #15938: Tvm broadcast backward
URL: https://github.com/apache/incubator-mxnet/pull/15938#discussion_r315983703
 
 

 ##
 File path: contrib/tvmop/basic/ufunc.py
 ##
 @@ -48,3 +50,71 @@ def vadd_gpu(dtype, ndim):
 s[C].bind(bx, tvm.thread_axis("blockIdx.x"))
 s[C].bind(tx, tvm.thread_axis("threadIdx.x"))
 return s, [A, B, C]
+
+
+def assign_by_req(a, req):
 
 Review comment:
   Shall we use the existing contrib/tvmop/utils.py or create a 
contrib/tvmop/basic/common.py?


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[GitHub] [incubator-mxnet] hzfan commented on a change in pull request #15938: Tvm broadcast backward

[GitBox]

hzfan commented on a change in pull request #15938: Tvm broadcast backward
URL: https://github.com/apache/incubator-mxnet/pull/15938#discussion_r315980043
 
 

 ##
 File path: src/operator/contrib/tvmop/ufunc.cc
 ##
 @@ -37,29 +38,88 @@ namespace op {
 
 static constexpr char func_vadd_cpu[] = "vadd";
 static constexpr char func_vadd_gpu[] = "cuda_vadd";
+static constexpr char func_bakcward_vadd_cpu[] = "backward_vadd";
+static constexpr char func_bakcward_vadd_gpu[] = "cuda_backward_vadd";
 
 template
-void TVMBroadcastCompute(const nnvm::NodeAttrs& attrs,
- const mxnet::OpContext& ctx,
- const std::vector& inputs,
- const std::vector& req,
- const std::vector& outputs) {
+void TVMBinaryCompute(const nnvm::NodeAttrs& attrs,
+  const mxnet::OpContext& ctx,
+  const std::vector& inputs,
+  const std::vector& req,
+  const std::vector& outputs) {
   CHECK_EQ(inputs.size(), 2U);
   CHECK_EQ(outputs.size(), 1U);
   tvm::runtime::TVMOpModule::Get()->Call(func, ctx, {inputs[0], inputs[1], 
outputs[0]});
 }
 
+template
+void TVMBinaryBackwardComputeUseNone(const nnvm::NodeAttrs& attrs,
+ const mxnet::OpContext& ctx,
+ const std::vector& inputs,
+ const std::vector& req,
+ const std::vector& outputs) {
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 2U);
+  int ndim = inputs[0].shape_.ndim();
+  for (int k = 0; k < 2; ++k) {
+// dispatch by backward
+std::vector ov, iv;
+const TBlob& ograd = inputs[0], igrad = outputs[k];
+bool flag = ograd.size(0) != igrad.size(0);
+for (int i = 0; i < ndim; ++i) {
 
 Review comment:
   Yes, `igrad.ndim = ograd.ndim` is assumed.
   
   @yzhliu suggests padding the input to 5-dim, which is the largest possible 
dim supported by this op. The padding will 1) reduce the number of kernels (by 
a factor of 5) 2) handle the `igrad.ndim < ograd.ndim` issue. But there may be 
loss in performance.
   
   I think prepending axes before `igrad` to make it `ograd.dim` requires more 
kernels, but the performance is better. It is a tradeoff.


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[GitHub] [incubator-mxnet] DickJC123 opened a new pull request #15955: Debug laop 6

[GitBox]

DickJC123 opened a new pull request #15955: Debug laop 6
URL: https://github.com/apache/incubator-mxnet/pull/15955
 
 
   ## Description ##
   Do not merge this PR.  This PR is experimenting with seeds of the 
test_laop_6 unittest.  The PR is seeking to learn if errors seen with the 
64-bit windows build toolchain are observable on the 32-bit windows build
   
   ## 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] [incubator-mxnet] hzfan commented on a change in pull request #15938: Tvm broadcast backward

[GitBox]

hzfan commented on a change in pull request #15938: Tvm broadcast backward
URL: https://github.com/apache/incubator-mxnet/pull/15938#discussion_r315980043
 
 

 ##
 File path: src/operator/contrib/tvmop/ufunc.cc
 ##
 @@ -37,29 +38,88 @@ namespace op {
 
 static constexpr char func_vadd_cpu[] = "vadd";
 static constexpr char func_vadd_gpu[] = "cuda_vadd";
+static constexpr char func_bakcward_vadd_cpu[] = "backward_vadd";
+static constexpr char func_bakcward_vadd_gpu[] = "cuda_backward_vadd";
 
 template
-void TVMBroadcastCompute(const nnvm::NodeAttrs& attrs,
- const mxnet::OpContext& ctx,
- const std::vector& inputs,
- const std::vector& req,
- const std::vector& outputs) {
+void TVMBinaryCompute(const nnvm::NodeAttrs& attrs,
+  const mxnet::OpContext& ctx,
+  const std::vector& inputs,
+  const std::vector& req,
+  const std::vector& outputs) {
   CHECK_EQ(inputs.size(), 2U);
   CHECK_EQ(outputs.size(), 1U);
   tvm::runtime::TVMOpModule::Get()->Call(func, ctx, {inputs[0], inputs[1], 
outputs[0]});
 }
 
+template
+void TVMBinaryBackwardComputeUseNone(const nnvm::NodeAttrs& attrs,
+ const mxnet::OpContext& ctx,
+ const std::vector& inputs,
+ const std::vector& req,
+ const std::vector& outputs) {
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 2U);
+  int ndim = inputs[0].shape_.ndim();
+  for (int k = 0; k < 2; ++k) {
+// dispatch by backward
+std::vector ov, iv;
+const TBlob& ograd = inputs[0], igrad = outputs[k];
+bool flag = ograd.size(0) != igrad.size(0);
+for (int i = 0; i < ndim; ++i) {
 
 Review comment:
   Yes, `igrad.ndim = ograd.ndim` is assumed.
   
   @yzhliu suggests padding the input to 5-dim, which is the largest possible 
dim supported by this op. The padding will 1) reduce the number of kernels 2) 
handle the `igrad.ndim < ograd.ndim` issue. But there may be loss in 
performance.
   
   I think prepending axes before `igrad` to make it `ograd.dim` requires more 
kernels, but the performance is better. It is a tradeoff.


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

[marcoabreu]

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

marcoabreu 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 7fb8860  Bump the publish timestamp.
7fb8860 is described below

commit 7fb886057d57b4dd6f0e17a73906a0468ed95868
Author: mxnet-ci 
AuthorDate: Wed Aug 21 01:38:35 2019 +

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

diff --git a/date.txt b/date.txt
new file mode 100644
index 000..29f87f1
--- /dev/null
+++ b/date.txt
@@ -0,0 +1 @@
+Wed Aug 21 01:38:35 UTC 2019

[GitHub] [incubator-mxnet] marcoabreu commented on issue #15167: Pointwise fusion for GPU

[GitBox]

marcoabreu commented on issue #15167: Pointwise fusion for GPU
URL: https://github.com/apache/incubator-mxnet/pull/15167#issuecomment-523250049
 
 
   Okay, so lets have a discussion on dev@ and then agree on a style.


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[GitHub] [incubator-mxnet] larroy commented on a change in pull request #15063: Rename np_compat to np_shape

[GitBox]

larroy commented on a change in pull request #15063: Rename np_compat to 
np_shape
URL: https://github.com/apache/incubator-mxnet/pull/15063#discussion_r315958800
 
 

 ##
 File path: include/mxnet/c_api.h
 ##
 @@ -1067,14 +1067,14 @@ MXNET_DLL int MXAutogradIsTraining(bool* curr);
  * \param curr returns the current status
  * \return 0 when success, -1 when failure happens
  */
-MXNET_DLL int MXIsNumpyCompatible(bool* curr);
+MXNET_DLL int MXIsNumpyShape(bool* curr);
 /*!
  * \brief set numpy compatibility switch
- * \param is_np_comp 1 when numpy compatibility is on, 0 when off
+ * \param is_np_shape 1 when numpy shape semantics is on, 0 when off
  * \param prev returns the previous status before this set
  * \return 0 when success, -1 when failure happens
  */
-MXNET_DLL int MXSetIsNumpyCompatible(int is_np_comp, int* prev);
+MXNET_DLL int MXSetIsNumpyShape(int is_np_shape, int* prev);
 
 Review comment:
   This changes how array format is expected when loading from disk. It would 
be good to document this on the function call. If NumpyShape is on, then 
Loading ndarray will fail. Just happened to me now. I think this is a side 
effect which should be clearly documented, or can we add additional arguments 
to Load with the different semantics?


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[GitHub] [incubator-mxnet] anirudh2290 merged pull request #15574: fix naive engine for multi-threaded inference

[GitBox]

anirudh2290 merged pull request #15574: fix naive engine for multi-threaded 
inference
URL: https://github.com/apache/incubator-mxnet/pull/15574
 
 
   


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[incubator-mxnet] branch master updated (308e4ac -> d225074)

[anirudh2290]

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

anirudh2290 pushed a change to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git.


from 308e4ac  Adding tests to verify support for Large Tensors in 
additional Ops along with new C_Apis supporting 64bit indexing (#15895)
 add d225074  fix naive engine for multi-threaded inference (#15574)

No new revisions were added by this update.

Summary of changes:
 src/engine/naive_engine.cc | 12 ++--
 1 file changed, 6 insertions(+), 6 deletions(-)

[GitHub] [incubator-mxnet] haojin2 commented on issue #15951: Revert "Numpy-compatible concatenate upstream"

[GitBox]

haojin2 commented on issue #15951: Revert "Numpy-compatible concatenate 
upstream"
URL: https://github.com/apache/incubator-mxnet/pull/15951#issuecomment-523243342
 
 
   @larroy I think the author of #13818 is @larroy , isn't it?
   I was quoting your comment: 
https://github.com/apache/incubator-mxnet/pull/13818#issuecomment-452919438 


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[GitHub] [incubator-mxnet] larroy commented on a change in pull request #15545: Softmax optimization for GPU

[GitBox]

larroy commented on a change in pull request #15545: Softmax optimization for 
GPU
URL: https://github.com/apache/incubator-mxnet/pull/15545#discussion_r315954114
 
 

 ##
 File path: src/operator/nn/softmax-inl.h
 ##
 @@ -313,71 +294,134 @@ __global__ void softmax_compute_kernel(DType *in, OType 
*out, index_t M, int axi
 
   for (index_t i = x; i < M; i += x_size) {
 val = negate ? -in[base + i*sa] : in[base + i*sa];
-out[base + i*sa] = OP::Map((val - smax)/static_cast(temperature), 
ssum);
+out[base + i*sa] =
+  (i < len) ? OType(OP::Map((val - smax)/static_cast(temperature), 
ssum)) : OType(0.0f);
   }
 }
 
-template
-inline void Softmax(Stream *s, DType *in, OType *out,
-Shape shape, int axis, const double temperature) {
-  const int x_bits = 7;
-  const int x_size = 1 << x_bits;
-  index_t M = shape[axis];
-  index_t N = shape.Size()/M;
-  Shape stride = calc_stride(shape);
-  Shape sshape = shape;
-  sshape[axis] = 1;
+const int softmax_threads_per_block = 512;
+
+template
+__global__ void softmax_stride1_compute_kernel(const DType *in, OType *out, 
IType *length,
+   const index_t M, const double 
temperature,
+   const int rows_per_block, const 
index_t total_rows) {
+  __shared__ AType scratch[softmax_threads_per_block];
+  __shared__ LType persistent_storage[20 * 1024 / sizeof(LType)];
+  const int warp_size = 32;
+  const int threads_per_row = softmax_threads_per_block / rows_per_block;
+  const int my_local_row = threadIdx.x / threads_per_row;
+  const int my_row = blockIdx.x * rows_per_block + my_local_row;
+  if (my_row >= total_rows) return;
+  const int my_id = threadIdx.x % threads_per_row;
+  const int entries_per_load = sizeof(LType)/sizeof(DType);
+  const index_t len = length == nullptr ? M : 
static_cast(length[my_row]);
+  // Due to usage of MSHADOW_TYPE_SWITCH macro we are generating
+  // kernels where sizeof(LType) may be less than sizeof(DType),
+  // resulting in entries_per_load being 0.
+  // This is not a valid combination and is being checked against
+  // in the launcher code. This switch here is just to silence
+  // the division by zero warning generated for such invalid cases.
+  const int row_length = entries_per_load > 0 ? M / entries_per_load : 0;
+
+  const LType* in_aligned = reinterpret_cast(in);
+  size_t base = my_row * row_length;
+
+  for (index_t i = my_id; i < row_length; i += threads_per_row) {
+persistent_storage[my_local_row * row_length + i] = in_aligned[base + i];
+  }
+  DType * row = reinterpret_cast(persistent_storage + my_local_row * 
row_length);
+  __syncthreads();
 
-  softmax_compute_kernel
-<<::GetStream(s)>>>(
-  in, out, M, axis, sshape, stride, temperature);
-  MSHADOW_CUDA_POST_KERNEL_CHECK(softmax_compute_kernel);
-}
+  DType my_max_value;
+  red::maximum::SetInitValue(my_max_value);
 
-template
-__global__ void softmax_with_length_kernel(DType *in, OType *out, IType 
*length,
-   index_t M, int axis, Shape 
sshape,
-   Shape stride, const double 
temperature) {
-  const unsigned x_size = 1 << x_bits;
-  __shared__ AType smem[x_size];
-  index_t sa = stride[axis];
-  index_t base = unravel_dot(blockIdx.x, sshape, stride);
-  index_t x = threadIdx.x;
-  index_t len = static_cast(length[blockIdx.x]);
-
-  red::maximum::SetInitValue(smem[x]);
-  for (index_t i = x; i < len; i += x_size) {
-smem[x] = ::max(smem[x], negate ? -in[base + i*sa] : in[base + i*sa]);
+  for (index_t i = my_id; i < len; i += threads_per_row) {
+my_max_value = ::max(my_max_value, negate ? -row[i] : row[i]);
   }
+  scratch[threadIdx.x] = my_max_value;
   __syncthreads();
-  cuda::Reduce1D(smem);
+  for (int size = threads_per_row / 2; size >= warp_size; size /= 2) {
+if (my_id < size) {
+  scratch[threadIdx.x] = ::max(scratch[threadIdx.x], scratch[threadIdx.x + 
size]);
+}
+__syncthreads();
+  }
+  if (my_id < warp_size) {
+AType my_value = warp_reduce(scratch[threadIdx.x],
+ [](AType x, AType y) { return ::max(x, y); });
+scratch[threadIdx.x] = my_value;
+  }
   __syncthreads();
-  DType smax = smem[0];
+  DType smax = scratch[threadIdx.x - threadIdx.x % threads_per_row];
   __syncthreads();
 
-  red::sum::SetInitValue(smem[x]);
-  DType val;
-  for (index_t i = x; i < len; i += x_size) {
-val = negate ? -in[base + i*sa]:in[base + i*sa];
-smem[x] += static_cast(expf((val - smax) / 
static_cast(temperature)));
+  AType my_sum;
+  red::sum::SetInitValue(my_sum);
+
+  for (index_t i = my_id; i < len; i += threads_per_row) {
+const DType val = negate ? -row[i] : row[i];
+my_sum += static_cast(expf((val - smax) / 
static_cast(temperature)));
   }
+  scratch[threadIdx.x] = my_sum;
   __syncthreads();
-  cuda::Reduce1D(smem);
+  for (int size = threads_per_row / 2; 

[GitHub] [incubator-mxnet] larroy commented on a change in pull request #15545: Softmax optimization for GPU

[GitBox]

larroy commented on a change in pull request #15545: Softmax optimization for 
GPU
URL: https://github.com/apache/incubator-mxnet/pull/15545#discussion_r315953722
 
 

 ##
 File path: src/operator/nn/softmax-inl.h
 ##
 @@ -313,71 +294,134 @@ __global__ void softmax_compute_kernel(DType *in, OType 
*out, index_t M, int axi
 
   for (index_t i = x; i < M; i += x_size) {
 val = negate ? -in[base + i*sa] : in[base + i*sa];
-out[base + i*sa] = OP::Map((val - smax)/static_cast(temperature), 
ssum);
+out[base + i*sa] =
+  (i < len) ? OType(OP::Map((val - smax)/static_cast(temperature), 
ssum)) : OType(0.0f);
   }
 }
 
-template
-inline void Softmax(Stream *s, DType *in, OType *out,
-Shape shape, int axis, const double temperature) {
-  const int x_bits = 7;
-  const int x_size = 1 << x_bits;
-  index_t M = shape[axis];
-  index_t N = shape.Size()/M;
-  Shape stride = calc_stride(shape);
-  Shape sshape = shape;
-  sshape[axis] = 1;
+const int softmax_threads_per_block = 512;
+
+template
+__global__ void softmax_stride1_compute_kernel(const DType *in, OType *out, 
IType *length,
+   const index_t M, const double 
temperature,
+   const int rows_per_block, const 
index_t total_rows) {
+  __shared__ AType scratch[softmax_threads_per_block];
+  __shared__ LType persistent_storage[20 * 1024 / sizeof(LType)];
+  const int warp_size = 32;
+  const int threads_per_row = softmax_threads_per_block / rows_per_block;
+  const int my_local_row = threadIdx.x / threads_per_row;
+  const int my_row = blockIdx.x * rows_per_block + my_local_row;
+  if (my_row >= total_rows) return;
+  const int my_id = threadIdx.x % threads_per_row;
+  const int entries_per_load = sizeof(LType)/sizeof(DType);
+  const index_t len = length == nullptr ? M : 
static_cast(length[my_row]);
+  // Due to usage of MSHADOW_TYPE_SWITCH macro we are generating
+  // kernels where sizeof(LType) may be less than sizeof(DType),
+  // resulting in entries_per_load being 0.
+  // This is not a valid combination and is being checked against
+  // in the launcher code. This switch here is just to silence
+  // the division by zero warning generated for such invalid cases.
+  const int row_length = entries_per_load > 0 ? M / entries_per_load : 0;
+
+  const LType* in_aligned = reinterpret_cast(in);
+  size_t base = my_row * row_length;
+
+  for (index_t i = my_id; i < row_length; i += threads_per_row) {
+persistent_storage[my_local_row * row_length + i] = in_aligned[base + i];
+  }
+  DType * row = reinterpret_cast(persistent_storage + my_local_row * 
row_length);
+  __syncthreads();
 
-  softmax_compute_kernel
-<<::GetStream(s)>>>(
-  in, out, M, axis, sshape, stride, temperature);
-  MSHADOW_CUDA_POST_KERNEL_CHECK(softmax_compute_kernel);
-}
+  DType my_max_value;
 
 Review comment:
   Can we add a comment or maybe a more descriptive name? is this the max of 
the stride? 


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[GitHub] [incubator-mxnet] larroy commented on a change in pull request #15545: Softmax optimization for GPU

[GitBox]

larroy commented on a change in pull request #15545: Softmax optimization for 
GPU
URL: https://github.com/apache/incubator-mxnet/pull/15545#discussion_r315953443
 
 

 ##
 File path: src/operator/nn/softmax-inl.h
 ##
 @@ -301,7 +282,7 @@ __global__ void softmax_compute_kernel(DType *in, OType 
*out, index_t M, int axi
 
   red::sum::SetInitValue(smem[x]);
 
 Review comment:
   Does have multiple max values affect numerical accuracy? Or are they reduced 
at some other point to a final max?


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[GitHub] [incubator-mxnet] apeforest commented on issue #15613: [Discussion] 1.5.1 Patch Release

[GitBox]

apeforest commented on issue #15613: [Discussion] 1.5.1 Patch Release
URL: 
https://github.com/apache/incubator-mxnet/issues/15613#issuecomment-523242083
 
 
   This PR fixes a memory misalignment bug in topk operator introduced 
recently. Please add it to 1.5.1 patch release:
   https://github.com/apache/incubator-mxnet/pull/15948
   
   Thanks


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[GitHub] [incubator-mxnet] samskalicky commented on issue #15613: [Discussion] 1.5.1 Patch Release

[GitBox]

samskalicky commented on issue #15613: [Discussion] 1.5.1 Patch Release
URL: 
https://github.com/apache/incubator-mxnet/issues/15613#issuecomment-523240695
 
 
   @TaoLv I'd like to pull in the following PRs, they are necessary fixes for 
some of my use-cases:
   https://github.com/apache/incubator-mxnet/pull/15245
   https://github.com/apache/incubator-mxnet/pull/15917


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[GitHub] [incubator-mxnet] larroy commented on a change in pull request #15545: Softmax optimization for GPU

[GitBox]

larroy commented on a change in pull request #15545: Softmax optimization for 
GPU
URL: https://github.com/apache/incubator-mxnet/pull/15545#discussion_r315952016
 
 

 ##
 File path: src/operator/nn/softmax-inl.h
 ##
 @@ -188,89 +180,77 @@ struct log_softmax_bwd {
   }
 };
 
-
 template
+ typename AType, typename DType, typename OType, typename IType, int 
ndim>
 inline void SoftmaxGrad(Stream *s, OType *out, OType *ograd,
-DType *igrad, Shape shape, int axis,
-const DType temperature) {
+DType *igrad, IType *length, Shape shape,
+int axis, const DType temperature) {
   index_t M = shape[axis];
   index_t N = shape.Size()/M;
   Shape stride = calc_stride(shape);
   Shape sshape = shape;
   sshape[axis] = 1;
   index_t sa = stride[axis];
 
-  #pragma omp parallel for
-  for (index_t i = 0; i < N; ++i) {
-index_t base = unravel_dot(i, sshape, stride);
+  if (length != nullptr) {
+#pragma omp parallel for
+for (index_t i = 0; i < N; ++i) {
+  index_t base = unravel_dot(i, sshape, stride);
+  index_t len = static_cast(length[i]);
 
-AType sum = AType(0);
-for (index_t j = 0; j < M; ++j) {
-  sum += OP1::Map(ograd[base + j*sa], out[base + j*sa]);
-}
-
-// By default temperature is 1.0.
-// Adding a branch here to save the CPU 'divide-by-1' computation at 
runtime
-DType final_result;
-if (temperature == 1.0) {
-  for (index_t j = 0; j < M; ++j) {
-final_result = negate ?
-   -OP2::Map(ograd[base + j*sa], out[base + j*sa], sum) :
-   OP2::Map(ograd[base + j*sa], out[base + j*sa], sum);
-KERNEL_ASSIGN(igrad[base + j*sa], Req, final_result);
-  }
-} else {
-  for (index_t j = 0; j < M; ++j) {
-final_result = negate ?
-   -OP2::Map(ograd[base + j*sa], out[base + j*sa], sum) / 
temperature :
-   OP2::Map(ograd[base + j*sa], out[base + j*sa], sum) / 
temperature;
-KERNEL_ASSIGN(igrad[base + j*sa], Req, final_result);
+  AType sum = AType(0);
+  for (index_t j = 0; j < len; ++j) {
+sum += OP1::Map(ograd[base + j*sa], out[base + j*sa]);
   }
-}
-  }
-}
 
-template
-inline void SoftmaxWithLengthGrad(Stream *s, OType *out, OType *ograd,
-  DType *igrad, IType *length, Shape 
shape,
-  int axis, const DType temperature) {
-  index_t M = shape[axis];
-  index_t N = shape.Size()/M;
-  Shape stride = calc_stride(shape);
-  Shape sshape = shape;
-  sshape[axis] = 1;
-  index_t sa = stride[axis];
-
-  #pragma omp parallel for
-  for (index_t i = 0; i < N; ++i) {
-index_t base = unravel_dot(i, sshape, stride);
-index_t len = static_cast(length[i]);
-
-AType sum = AType(0);
-for (index_t j = 0; j < len; ++j) {
-  sum += OP1::Map(ograd[base + j*sa], out[base + j*sa]);
+  // By default temperature is 1.0.
+  // Adding a branch here to save the CPU 'divide-by-1' computation at 
runtime
+  DType final_result;
+  if (temperature == 1.0) {
 
 Review comment:
   is this micro-opt really making things better?


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[GitHub] [incubator-mxnet] larroy commented on issue #15951: Revert "Numpy-compatible concatenate upstream"

[GitBox]

larroy commented on issue #15951: Revert "Numpy-compatible concatenate upstream"
URL: https://github.com/apache/incubator-mxnet/pull/15951#issuecomment-523239737
 
 
   @marcoabreu  I think you can close this empty PR, since the conversation 
here is not constructive and there's no code change.


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[GitHub] [incubator-mxnet] larroy edited a comment on issue #15951: Revert "Numpy-compatible concatenate upstream"

[GitBox]

larroy edited a comment on issue #15951: Revert "Numpy-compatible concatenate 
upstream"
URL: https://github.com/apache/incubator-mxnet/pull/15951#issuecomment-523238480
 
 
   @haojin2 I think you are confusing me with @marcoabreu. I didn't make the 
change you mention. My handle is @larroy.  Please followup with @marcoabreu on 
this topic or through the dev@ list. Thanks.


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[GitHub] [incubator-mxnet] larroy commented on issue #15951: Revert "Numpy-compatible concatenate upstream"

[GitBox]

larroy commented on issue #15951: Revert "Numpy-compatible concatenate upstream"
URL: https://github.com/apache/incubator-mxnet/pull/15951#issuecomment-523238480
 
 
   @haojin2 I think you are confusing me with @marcoabreu. I didn't make the 
change you mention. My handle is @larroy.


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[GitHub] [incubator-mxnet] ptrendx commented on a change in pull request #15545: Softmax optimization for GPU

[GitBox]

ptrendx commented on a change in pull request #15545: Softmax optimization for 
GPU
URL: https://github.com/apache/incubator-mxnet/pull/15545#discussion_r315948857
 
 

 ##
 File path: src/operator/nn/softmax-inl.h
 ##
 @@ -218,6 +219,157 @@ __global__ void softmax_compute_kernel(DType *in, OType 
*out, index_t M, int axi
   }
 }
 
+const int softmax_threads_per_block = 512;
+
+template 
+__device__ inline T warp_reduce(T value, OP redfun) {
+  value = redfun(value, __shfl_down_sync(0x, value, 16));
+  value = redfun(value, __shfl_down_sync(0x, value, 8));
+  value = redfun(value, __shfl_down_sync(0x, value, 4));
+  value = redfun(value, __shfl_down_sync(0x, value, 2));
+  value = redfun(value, __shfl_down_sync(0x, value, 1));
+  return value;
+}
+
+template 
+__device__ inline mshadow::half::half_t warp_reduce(mshadow::half::half_t 
value, OP redfun) {
+  float v = static_cast(value);
+  v = redfun(v, __shfl_down_sync(0x, v, 16));
+  v = redfun(v, __shfl_down_sync(0x, v, 8));
+  v = redfun(v, __shfl_down_sync(0x, v, 4));
+  v = redfun(v, __shfl_down_sync(0x, v, 2));
+  v = redfun(v, __shfl_down_sync(0x, v, 1));
+  return mshadow::half::half_t(v);
+}
+
+template
+__global__ void softmax_compute_kernel2(const DType *in, OType *out, const 
index_t M,
+   const double temperature, int 
rows_per_block,
+   const index_t total_rows) {
+  __shared__ AType scratch[softmax_threads_per_block];
+  __shared__ LType persistent_storage[20*1024 / sizeof(LType)];
+  const int warp_size = 32;
+  const int threads_per_row = softmax_threads_per_block / rows_per_block;
+  const int my_local_row = threadIdx.x / threads_per_row;
+  const int my_row = blockIdx.x * rows_per_block + my_local_row;
+  if (my_row >= total_rows) return;
+  const int my_id = threadIdx.x % threads_per_row;
+  const int entries_per_load = sizeof(LType)/sizeof(DType);
+  // Due to usage of MSHADOW_TYPE_SWITCH macro we are generating
+  // kernels where sizeof(LType) may be less than sizeof(DType),
+  // resulting in entries_per_load being 0.
+  // This is not a valid combination and is being checked against
+  // in the launcher code. This switch here is just to silence
+  // the division by zero warning generated for such invalid cases.
+  const int row_length = entries_per_load > 0 ? M / entries_per_load : 0;
+
+  const LType * in_aligned = reinterpret_cast(in);
+  size_t base = my_row * row_length;
+
+  for (index_t i = my_id; i < row_length; i += threads_per_row) {
+persistent_storage[my_local_row * row_length + i] = in_aligned[base + i];
+  }
+  DType * row = reinterpret_cast(persistent_storage + my_local_row * 
row_length);
+  __syncthreads();
+
+  DType my_max_value;
+  red::maximum::SetInitValue(my_max_value);
+
+  for (index_t i = my_id; i < M; i += threads_per_row) {
+my_max_value = ::max(my_max_value, negate ? -row[i] : row[i]);
+  }
+  scratch[threadIdx.x] = my_max_value;
+  __syncthreads();
+  for (int size = threads_per_row / 2; size >= warp_size; size /= 2) {
+if (my_id < size) {
+  scratch[threadIdx.x] = ::max(scratch[threadIdx.x], scratch[threadIdx.x + 
size]);
+}
+__syncthreads();
+  }
+  if (my_id < warp_size) {
+AType my_value = warp_reduce(scratch[threadIdx.x],
+ [](AType x, AType y) { return ::max(x, y); });
+scratch[threadIdx.x] = my_value;
+  }
+  __syncthreads();
+  DType smax = scratch[threadIdx.x - threadIdx.x % threads_per_row];
+  __syncthreads();
+
+  AType my_sum;
+  red::sum::SetInitValue(my_sum);
+
+  for (index_t i = my_id; i < M; i += threads_per_row) {
+const DType val = negate ? -row[i] : row[i];
+my_sum += static_cast(expf((val - smax) / 
static_cast(temperature)));
+  }
+  scratch[threadIdx.x] = my_sum;
+  __syncthreads();
+  for (int size = threads_per_row / 2; size >= warp_size; size /= 2) {
+if (my_id < size) {
+  scratch[threadIdx.x] += scratch[threadIdx.x + size];
+}
+__syncthreads();
+  }
+  if (my_id < warp_size) {
+AType my_value = warp_reduce(scratch[threadIdx.x],
+ [](AType x, AType y) { return x + y;});
+scratch[threadIdx.x] = my_value;
+  }
+  __syncthreads();
+
+  AType ssum = scratch[threadIdx.x - threadIdx.x % threads_per_row];
+  __syncthreads();
+
+  for (index_t i = my_id; i < M; i += threads_per_row) {
+const DType val = negate ? -row[i] : row[i];
+row[i] = OP::Map((val - smax)/static_cast(temperature), ssum);
+  }
+  __syncthreads();
+
+  LType * out_aligned = reinterpret_cast(out);
+
+  for (index_t i = my_id; i < row_length; i += threads_per_row) {
+out_aligned[base + i] = persistent_storage[my_local_row * row_length + i];
+  }
+}
+
+namespace {
+
+int get_load_type(size_t N) {
+  if (N % 8 == 0) {
+return kFloat64;
+  } else if (N % 4 == 0) {
+return kFloat32;
+  } else if (N % 2 == 

[GitHub] [incubator-mxnet] eric-haibin-lin edited a comment on issue #15930: Fix dtype inference in arange_like operator

[GitBox]

eric-haibin-lin edited a comment on issue #15930: Fix dtype inference in 
arange_like operator
URL: https://github.com/apache/incubator-mxnet/pull/15930#issuecomment-523225836
 
 
   Yes. Could you also check the forwward output with [0, 1, 2,.. ] etc? 


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[GitHub] [incubator-mxnet] eric-haibin-lin commented on issue #15930: Fix dtype inference in arange_like operator

[GitBox]

eric-haibin-lin commented on issue #15930: Fix dtype inference in arange_like 
operator
URL: https://github.com/apache/incubator-mxnet/pull/15930#issuecomment-523225836
 
 
   Yes


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[GitHub] [incubator-mxnet] eric-haibin-lin commented on a change in pull request #15545: Softmax optimization for GPU

[GitBox]

eric-haibin-lin commented on a change in pull request #15545: Softmax 
optimization for GPU
URL: https://github.com/apache/incubator-mxnet/pull/15545#discussion_r315936560
 
 

 ##
 File path: src/operator/nn/softmax-inl.h
 ##
 @@ -218,6 +219,157 @@ __global__ void softmax_compute_kernel(DType *in, OType 
*out, index_t M, int axi
   }
 }
 
+const int softmax_threads_per_block = 512;
+
+template 
+__device__ inline T warp_reduce(T value, OP redfun) {
+  value = redfun(value, __shfl_down_sync(0x, value, 16));
+  value = redfun(value, __shfl_down_sync(0x, value, 8));
+  value = redfun(value, __shfl_down_sync(0x, value, 4));
+  value = redfun(value, __shfl_down_sync(0x, value, 2));
+  value = redfun(value, __shfl_down_sync(0x, value, 1));
+  return value;
+}
+
+template 
+__device__ inline mshadow::half::half_t warp_reduce(mshadow::half::half_t 
value, OP redfun) {
+  float v = static_cast(value);
+  v = redfun(v, __shfl_down_sync(0x, v, 16));
+  v = redfun(v, __shfl_down_sync(0x, v, 8));
+  v = redfun(v, __shfl_down_sync(0x, v, 4));
+  v = redfun(v, __shfl_down_sync(0x, v, 2));
+  v = redfun(v, __shfl_down_sync(0x, v, 1));
+  return mshadow::half::half_t(v);
+}
+
+template
+__global__ void softmax_compute_kernel2(const DType *in, OType *out, const 
index_t M,
+   const double temperature, int 
rows_per_block,
+   const index_t total_rows) {
+  __shared__ AType scratch[softmax_threads_per_block];
+  __shared__ LType persistent_storage[20*1024 / sizeof(LType)];
+  const int warp_size = 32;
+  const int threads_per_row = softmax_threads_per_block / rows_per_block;
+  const int my_local_row = threadIdx.x / threads_per_row;
+  const int my_row = blockIdx.x * rows_per_block + my_local_row;
+  if (my_row >= total_rows) return;
+  const int my_id = threadIdx.x % threads_per_row;
+  const int entries_per_load = sizeof(LType)/sizeof(DType);
+  // Due to usage of MSHADOW_TYPE_SWITCH macro we are generating
+  // kernels where sizeof(LType) may be less than sizeof(DType),
+  // resulting in entries_per_load being 0.
+  // This is not a valid combination and is being checked against
+  // in the launcher code. This switch here is just to silence
+  // the division by zero warning generated for such invalid cases.
+  const int row_length = entries_per_load > 0 ? M / entries_per_load : 0;
+
+  const LType * in_aligned = reinterpret_cast(in);
+  size_t base = my_row * row_length;
+
+  for (index_t i = my_id; i < row_length; i += threads_per_row) {
+persistent_storage[my_local_row * row_length + i] = in_aligned[base + i];
+  }
+  DType * row = reinterpret_cast(persistent_storage + my_local_row * 
row_length);
+  __syncthreads();
+
+  DType my_max_value;
+  red::maximum::SetInitValue(my_max_value);
+
+  for (index_t i = my_id; i < M; i += threads_per_row) {
+my_max_value = ::max(my_max_value, negate ? -row[i] : row[i]);
+  }
+  scratch[threadIdx.x] = my_max_value;
+  __syncthreads();
+  for (int size = threads_per_row / 2; size >= warp_size; size /= 2) {
+if (my_id < size) {
+  scratch[threadIdx.x] = ::max(scratch[threadIdx.x], scratch[threadIdx.x + 
size]);
+}
+__syncthreads();
+  }
+  if (my_id < warp_size) {
+AType my_value = warp_reduce(scratch[threadIdx.x],
+ [](AType x, AType y) { return ::max(x, y); });
+scratch[threadIdx.x] = my_value;
+  }
+  __syncthreads();
+  DType smax = scratch[threadIdx.x - threadIdx.x % threads_per_row];
+  __syncthreads();
+
+  AType my_sum;
+  red::sum::SetInitValue(my_sum);
+
+  for (index_t i = my_id; i < M; i += threads_per_row) {
+const DType val = negate ? -row[i] : row[i];
+my_sum += static_cast(expf((val - smax) / 
static_cast(temperature)));
+  }
+  scratch[threadIdx.x] = my_sum;
+  __syncthreads();
+  for (int size = threads_per_row / 2; size >= warp_size; size /= 2) {
+if (my_id < size) {
+  scratch[threadIdx.x] += scratch[threadIdx.x + size];
+}
+__syncthreads();
+  }
+  if (my_id < warp_size) {
+AType my_value = warp_reduce(scratch[threadIdx.x],
+ [](AType x, AType y) { return x + y;});
+scratch[threadIdx.x] = my_value;
+  }
+  __syncthreads();
+
+  AType ssum = scratch[threadIdx.x - threadIdx.x % threads_per_row];
+  __syncthreads();
+
+  for (index_t i = my_id; i < M; i += threads_per_row) {
+const DType val = negate ? -row[i] : row[i];
+row[i] = OP::Map((val - smax)/static_cast(temperature), ssum);
+  }
+  __syncthreads();
+
+  LType * out_aligned = reinterpret_cast(out);
+
+  for (index_t i = my_id; i < row_length; i += threads_per_row) {
+out_aligned[base + i] = persistent_storage[my_local_row * row_length + i];
+  }
+}
+
+namespace {
+
+int get_load_type(size_t N) {
+  if (N % 8 == 0) {
+return kFloat64;
+  } else if (N % 4 == 0) {
+return kFloat32;
+  } else if 

[GitHub] [incubator-mxnet] larroy closed pull request #14601: [WIP] Add test for gemm overflow.

[GitBox]

larroy closed pull request #14601: [WIP] Add test for gemm overflow.
URL: https://github.com/apache/incubator-mxnet/pull/14601
 
 
   


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[GitHub] [incubator-mxnet] haojin2 commented on issue #15951: Revert "Numpy-compatible concatenate upstream"

[GitBox]

haojin2 commented on issue #15951: Revert "Numpy-compatible concatenate 
upstream"
URL: https://github.com/apache/incubator-mxnet/pull/15951#issuecomment-523207697
 
 
   @larroy Please don't avoid my question, in #15952 you claimed that I shall 
tag you on any changes to files under CI folder, so are you actively working on 
the CI? Would you help with root causing the CI issues? If you have increased 
the CI timeout once before [from 2:00 to 
3:00](https://github.com/apache/incubator-mxnet/pull/13818/files) because you 
"restarted 4 PRs on the issue", have you investigated the root cause back then? 
TBH I've restarted more than 4 PRs on this issue, does that justify my change 
here then? Please provide answers to those questions, thanks.


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[GitHub] [incubator-mxnet] ZhennanQin commented on a change in pull request #15950: [MKLDNN]Support fullyconnected and element-wise ops fusion

[GitBox]

ZhennanQin commented on a change in pull request #15950: [MKLDNN]Support 
fullyconnected and element-wise ops fusion
URL: https://github.com/apache/incubator-mxnet/pull/15950#discussion_r315916256
 
 

 ##
 File path: src/operator/subgraph/mkldnn/mkldnn_fc_property.h
 ##
 @@ -68,44 +73,70 @@ class SgMKLDNNFCSelector : public SubgraphSelector {
   }
 
   bool SelectOutput(const nnvm::Node , const nnvm::Node _node) override {
-if (status == kFail || status == kSuccess || new_node.is_variable())
+if (status_ == kFail || status_ == kSuccess || new_node.is_variable())
   return false;
 
 // If n isn't the last matched node, then we encoutered a internal
 // branch, we should pop out the node behind n and stop fusion.
-if (matched_list.back() != ) {
-  if (std::find(matched_list.begin(), matched_list.end(), ) !=
-matched_list.end()) {
-while (matched_list.back() != ) {
-  matched_list.pop_back();
+if (matched_list_.back() != ) {
+  if (std::find(matched_list_.begin(), matched_list_.end(), ) !=
+matched_list_.end()) {
+while (matched_list_.back() != ) {
+  matched_list_.pop_back();
 }
   }
 
-  status = kSuccess;
+  status_ = kSuccess;
   return false;
 }
 
-switch (status) {
+switch (status_) {
   case kStart:
-if (new_node.op() == Op::Get("Activation") &&
-new_node.attrs.dict.at("act_type") == "relu") {
-  matched_list.push_back(_node);
-  status = kSuccess;
+// Currently, For INT8 FC fusion, only supports 
relu/bounded_relu(clip)/abs.
+if (new_node.op() == Op::Get("Activation")) {
+  const ActivationParam  = 
nnvm::get(new_node.attrs.parsed);
+  if ((quantized_ && SupportQuantizedMKLDNNAct(param)) ||
+  (!quantized_ && SupportMKLDNNAct(param))) {
+matched_list_.push_back(_node);
+status_ = kSuccess;
+return true;
+  }
+}
+if (!quantized_ && (new_node.op() == Op::Get("square") ||
+new_node.op() == Op::Get("sqrt") ||
+new_node.op() == Op::Get("exp"))) {
+  matched_list_.push_back(_node);
+  status_ = kSuccess;
+  return true;
+}
+if (new_node.op() == Op::Get("abs")) {
+  matched_list_.push_back(_node);
+  status_ = kSuccess;
   return true;
 }
+if (new_node.op() == Op::Get("clip")) {
+  const ClipParam  = nnvm::get(new_node.attrs.parsed);
+  if (param.a_min == 0.f && param.a_max == 1.0f) {
 
 Review comment:
   why a_max have to be 1.0f? I think it's not necessary.


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[GitHub] [incubator-mxnet] haojin2 commented on issue #15951: Revert "Numpy-compatible concatenate upstream"

[GitBox]

haojin2 commented on issue #15951: Revert "Numpy-compatible concatenate 
upstream"
URL: https://github.com/apache/incubator-mxnet/pull/15951#issuecomment-523204837
 
 
   @marcoabreu Maybe let's take a look at one unix-cpu 
[build](http://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/mxnet-validation%2Funix-cpu/detail/PR-15887/5/pipeline/279)
 triggered 15 hours ago based on the latest master (cause I performed rebase).
   BlueOcean shows that the whole test finished using about 2hr55min (< 180 
mins obviously), now you can see that the CI infra itself could also has some 
ups and downs too, cause 6 days ago we sometimes even had difficulties getting 
CI machines up (which was why this increase was performed). With those being 
said, I think of your timeout more of a safe net, which means you should also 
account for the flaky CI infra performance when you set it.
   Again as I said the total time for CI is only going to increase, cause we're 
only adding more to it. Putting a hard limit on the total time does not work 
and make sense, I think you should pay more attention to the time consumed by a 
single test, so that the "40-minute long unit test" you mentioned somewhere 
else could be detected and rejected by CI, not a 3-second one.


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[incubator-mxnet] branch master updated (cd397a3 -> 308e4ac)

[apeforest]

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

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


from cd397a3  Benchmark doc fix (#15769)
 add 308e4ac  Adding tests to verify support for Large Tensors in 
additional Ops along with new C_Apis supporting 64bit indexing (#15895)

No new revisions were added by this update.

Summary of changes:
 include/mxnet/c_api.h |  10 ++
 python/mxnet/ndarray/ndarray.py   |  32 ++--
 src/c_api/c_api.cc|  33 +++-
 src/operator/tensor/dot-inl.h |   2 +-
 tests/nightly/test_large_array.py | 316 +-
 5 files changed, 378 insertions(+), 15 deletions(-)

[GitHub] [incubator-mxnet] apeforest merged pull request #15895: Adding tests and C APIs for Large Tensors

[GitBox]

apeforest merged pull request #15895: Adding tests and C APIs for Large Tensors
URL: https://github.com/apache/incubator-mxnet/pull/15895
 
 
   


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[GitHub] [incubator-mxnet] larroy commented on issue #15929: ./build.py -p ubuntu_tpu_tensorrt fails with error

[GitBox]

larroy commented on issue #15929: ./build.py -p ubuntu_tpu_tensorrt fails with 
error
URL: 
https://github.com/apache/incubator-mxnet/issues/15929#issuecomment-523203768
 
 
   Hi @arsdragonfly 
   You have a dirty working directory, you should remove the build folder or 
really clean the repository. See dev_menu.py option 10, be careful because it 
will discard your local modifications.


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[GitHub] [incubator-mxnet] apeforest commented on issue #15953: Add Median, p50, p99 to python profiler

[GitBox]

apeforest commented on issue #15953: Add Median,p50,p99 to python profiler
URL: https://github.com/apache/incubator-mxnet/pull/15953#issuecomment-523200903
 
 
   Thanks for the contribution. How to enable the percentile output for users? 
Or it comes as default?


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[GitHub] [incubator-mxnet] apeforest commented on a change in pull request #15953: Add Median, p50, p99 to python profiler

[GitBox]

apeforest commented on a change in pull request #15953: Add Median,p50,p99 to 
python profiler
URL: https://github.com/apache/incubator-mxnet/pull/15953#discussion_r315909903
 
 

 ##
 File path: benchmark/opperf/utils/profiler_utils.py
 ##
 @@ -228,10 +229,16 @@ def python_profile(func):
 
 @functools.wraps(func)
 def python_profile_it(*args, **kwargs):
-start_time = time.perf_counter()# 1
-res = func(*args, **kwargs)
-end_time = time.perf_counter()  # 2
-run_time = end_time - start_time# 3
+runs = args[1]
+modified_args = (args[0], 1)
 
 Review comment:
   what is this for?


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[GitHub] [incubator-mxnet] larroy edited a comment on issue #15951: Revert "Numpy-compatible concatenate upstream"

[GitBox]

larroy edited a comment on issue #15951: Revert "Numpy-compatible concatenate 
upstream"
URL: https://github.com/apache/incubator-mxnet/pull/15951#issuecomment-523200224
 
 
   @haojin2 I just helped Marco revert the PR since due the way it was merged 
it was not trivial, please don't kill the messenger. Increasing CI timeout 
should be at least done in a separate PR.


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[GitHub] [incubator-mxnet] larroy commented on issue #15951: Revert "Numpy-compatible concatenate upstream"

[GitBox]

larroy commented on issue #15951: Revert "Numpy-compatible concatenate upstream"
URL: https://github.com/apache/incubator-mxnet/pull/15951#issuecomment-523200224
 
 
   @haojin2 I just helped Marco revert the PR since due the way it was merged 
it was not trivial, please don't kill the messenger. Increasing CI timeout 
should be done in a separate PR and discussed before.


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[GitHub] [incubator-mxnet] apeforest commented on issue #15895: Adding tests and C APIs for Large Tensors

[GitBox]

apeforest commented on issue #15895: Adding tests and C APIs for Large Tensors
URL: https://github.com/apache/incubator-mxnet/pull/15895#issuecomment-523197438
 
 
   @ChaiBapchya could you please review?


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[GitHub] [incubator-mxnet] marcoabreu commented on issue #15951: Revert "Numpy-compatible concatenate upstream"

[GitBox]

marcoabreu commented on issue #15951: Revert "Numpy-compatible concatenate 
upstream"
URL: https://github.com/apache/incubator-mxnet/pull/15951#issuecomment-523195164
 
 
   Hi Hao,
   
   this is not a personal affront towards you or your PR and I understand your 
frustration. The maximum time acts as a forcing function since within the past 
9 months, the test duration more than doubled. Thus, increasing the timeout is 
not an option since it worsens the contributor experience further and further - 
of course I understand that running into timeouts also does. But that's exactly 
the forcing function we'd like to achieve until the community improves the 
situation.
   
   So it's quite unfortunate that it did hit your PR and that I only caught it 
now after the PR was merged.


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

[marcoabreu]

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marcoabreu 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 9724c7a  Bump the publish timestamp.
9724c7a is described below

commit 9724c7af8b5c489b832baf6a484276071a83487f
Author: mxnet-ci 
AuthorDate: Tue Aug 20 21:00:41 2019 +

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

diff --git a/date.txt b/date.txt
new file mode 100644
index 000..c133180
--- /dev/null
+++ b/date.txt
@@ -0,0 +1 @@
+Tue Aug 20 21:00:41 UTC 2019

[GitHub] [incubator-mxnet] reminisce commented on a change in pull request #15938: Tvm broadcast backward

[GitBox]

reminisce commented on a change in pull request #15938: Tvm broadcast backward
URL: https://github.com/apache/incubator-mxnet/pull/15938#discussion_r315894807
 
 

 ##
 File path: src/operator/contrib/tvmop/ufunc.cc
 ##
 @@ -37,29 +38,88 @@ namespace op {
 
 static constexpr char func_vadd_cpu[] = "vadd";
 static constexpr char func_vadd_gpu[] = "cuda_vadd";
+static constexpr char func_bakcward_vadd_cpu[] = "backward_vadd";
+static constexpr char func_bakcward_vadd_gpu[] = "cuda_backward_vadd";
 
 template
-void TVMBroadcastCompute(const nnvm::NodeAttrs& attrs,
- const mxnet::OpContext& ctx,
- const std::vector& inputs,
- const std::vector& req,
- const std::vector& outputs) {
+void TVMBinaryCompute(const nnvm::NodeAttrs& attrs,
+  const mxnet::OpContext& ctx,
+  const std::vector& inputs,
+  const std::vector& req,
+  const std::vector& outputs) {
   CHECK_EQ(inputs.size(), 2U);
   CHECK_EQ(outputs.size(), 1U);
   tvm::runtime::TVMOpModule::Get()->Call(func, ctx, {inputs[0], inputs[1], 
outputs[0]});
 }
 
+template
+void TVMBinaryBackwardComputeUseNone(const nnvm::NodeAttrs& attrs,
+ const mxnet::OpContext& ctx,
+ const std::vector& inputs,
+ const std::vector& req,
+ const std::vector& outputs) {
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 2U);
+  int ndim = inputs[0].shape_.ndim();
+  for (int k = 0; k < 2; ++k) {
+// dispatch by backward
+std::vector ov, iv;
+const TBlob& ograd = inputs[0], igrad = outputs[k];
+bool flag = ograd.size(0) != igrad.size(0);
+for (int i = 0; i < ndim; ++i) {
 
 Review comment:
   If my understanding is correct, there seems to be an assumption that 
`ograd.ndim = igrad.ndim`, which is not necessarily true. I think you need to 
prepend axes before `igrad` if `igrad.ndim < ograd.ndim` and then use the logic 
here.


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[GitHub] [incubator-mxnet] haojin2 opened a new pull request #15954: Revert unix-cpu timeout increase

[GitBox]

haojin2 opened a new pull request #15954: Revert unix-cpu timeout increase
URL: https://github.com/apache/incubator-mxnet/pull/15954
 
 
   ## Description ##
   As title.
   
   ## Checklist ##
   ### Essentials ###
   Please feel free to remove inapplicable items for your PR.
   - [ ] The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to 
the relevant [JIRA issue](https://issues.apache.org/jira/projects/MXNET/issues) 
created (except PRs with tiny changes)
   - [ ] Changes are complete (i.e. I finished coding on this PR)
   - [ ] All changes have test coverage:
   - Unit tests are added for small changes to verify correctness (e.g. adding 
a new operator)
   - Nightly tests are added for complicated/long-running ones (e.g. changing 
distributed kvstore)
   - Build tests will be added for build configuration changes (e.g. adding a 
new build option with NCCL)
   - [ ] Code is well-documented: 
   - For user-facing API changes, API doc string has been updated. 
   - For new C++ functions in header files, their functionalities and arguments 
are documented. 
   - For new examples, README.md is added to explain the what the example does, 
the source of the dataset, expected performance on test set and reference to 
the original paper if applicable
   - Check the API doc at 
http://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$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 ##
   @larroy @marcoabreu 
   


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[GitHub] [incubator-mxnet] PatriciaXiao commented on issue #15629: DataLoader Error using Multi processing

[GitBox]

PatriciaXiao commented on issue #15629: DataLoader Error using Multi processing
URL: 
https://github.com/apache/incubator-mxnet/issues/15629#issuecomment-523183266
 
 
   It isn't always like this: normally it won't happen, only after I tried to 
upgrade Python to 3.7 on server it becomes like this. Also, my laptop has local 
Python3.7 installed but nothing is wrong here, simply slow (without GPU). For 
now I couldn't reproduce the error anymore, since I terminated that buggy 
environment to make my life easier. But it is for sure that the above-mentioned 
code isn't causing any error on my local mac environment, neither on the other 
instances I've launched.


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[GitHub] [incubator-mxnet] haojin2 edited a comment on issue #15951: Revert "Numpy-compatible concatenate upstream"

[GitBox]

haojin2 edited a comment on issue #15951: Revert "Numpy-compatible concatenate 
upstream"
URL: https://github.com/apache/incubator-mxnet/pull/15951#issuecomment-523179089
 
 
   @larroy So if you're really that actively in charge of CI stuff then why 
don't you root cause it while this very PR you guys are trying to revert only 
adds ~3 extra seconds to the whole unit test? Previous CI time limit increases 
were not caused by this commit, so simply reverting this PR does not help us 
find the root cause, and only blocks more people who depend on it (not to 
mention most of them are summer interns who are about to leave and desperately 
want to submit their PRs).
   
   On the other hand, why were the previous increases of CI timeout reasonable 
while mine is not? I actually expect this kind of increase to happen as we add 
more things to MXNet, each of which requires one or more unit tests (which is 
the right thing to do). If you guys did already find a unit test that costs 
more than 40 mins, please deal with it instead of targeting at my PR.
   
   Actually, I'm already aware of time costs of unit tests, and every single 
one of my unit tests try to reuse the generated mock test data while covering 
the minimal necessary test cases. Most of the tests in my PRs, including the 
one you guys are trying to revert here, cost less than 5 seconds to finish. I 
don't think I'm the right one to be blamed on the status quo here, neither 
should my commit be.


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[GitHub] [incubator-mxnet] haojin2 commented on issue #15951: Revert "Numpy-compatible concatenate upstream"

[GitBox]

haojin2 commented on issue #15951: Revert "Numpy-compatible concatenate 
upstream"
URL: https://github.com/apache/incubator-mxnet/pull/15951#issuecomment-523179089
 
 
   @larroy So if you're really that actively in charge of CI stuff then why 
don't you root cause it while this very PR you guys are trying to revert only 
adds ~3 extra seconds to the whole unit test? Previous CI time limit increases 
were not caused by this commit, so simply reverting this PR does not help us 
find the root cause, and only blocks more people who depend on it (not to 
mention most of them are summer interns who are about to leave and desperately 
want to submit their PRs).
   On the other hand, why were the previous increases of CI timeout reasonable 
while mine is not? I actually expect this kind of increase to happen as we add 
more things to MXNet, each of which requires one or more unit tests (which is 
the right thing to do). If you guys did already find a unit test that costs 
more than 40 mins, please deal with it instead of targeting at my PR.
   Actually, I'm already aware of time costs of unit tests, and every single 
one of my unit tests try to reuse the generated mock test data while covering 
the minimal necessary test cases. Most of the tests in my PRs, including the 
one you guys are trying to revert here, cost less than 5 seconds to finish. I 
don't think I'm the right one to be blamed on the status quo here, neither 
should my commit be.


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[GitHub] [incubator-mxnet] haojin2 closed pull request #15952: Revert #15842 and #15894. These PRs should not modify CI timeouts.

[GitBox]

haojin2 closed pull request #15952: Revert #15842 and #15894. These PRs should 
not modify CI timeouts.
URL: https://github.com/apache/incubator-mxnet/pull/15952
 
 
   


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[GitHub] [incubator-mxnet] haojin2 commented on issue #15952: Revert #15842 and #15894. These PRs should not modify CI timeouts.

[GitBox]

haojin2 commented on issue #15952: Revert #15842 and #15894. These PRs should 
not modify CI timeouts.
URL: https://github.com/apache/incubator-mxnet/pull/15952#issuecomment-523172593
 
 
   I'll revert the CI time limit in a separate PR myself, closing this one now.


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[GitHub] [incubator-mxnet] ChaiBapchya opened a new pull request #15953: Add Median, p50, p99 to python profiler

[GitBox]

ChaiBapchya opened a new pull request #15953: Add Median,p50,p99 to python 
profiler
URL: https://github.com/apache/incubator-mxnet/pull/15953
 
 
   ## Description ##
   Profile more metrics (than just average; also fix incorrect avg calculation)
   
   ## 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] [incubator-mxnet] larroy commented on issue #15951: Revert "Numpy-compatible concatenate upstream"

[GitBox]

larroy commented on issue #15951: Revert "Numpy-compatible concatenate upstream"
URL: https://github.com/apache/incubator-mxnet/pull/15951#issuecomment-523171026
 
 
   @haojin2 increase on CI limit should be discussed first and be done in a 
separate PR. We have gone from 1:20 to 3h, we should find the root cause and 
not just increase the limit without bounds.


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[GitHub] [incubator-mxnet] larroy commented on issue #15952: Revert #15842 and #15894. These PRs should not modify CI timeouts.

[GitBox]

larroy commented on issue #15952: Revert #15842 and #15894. These PRs should 
not modify CI timeouts.
URL: https://github.com/apache/incubator-mxnet/pull/15952#issuecomment-523170610
 
 
   Is not really a duplicate as the PR you linked has an empty patch.


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[GitHub] [incubator-mxnet] larroy commented on issue #15952: Revert #15842 and #15894. These PRs should not modify CI timeouts.

[GitBox]

larroy commented on issue #15952: Revert #15842 and #15894. These PRs should 
not modify CI timeouts.
URL: https://github.com/apache/incubator-mxnet/pull/15952#issuecomment-523170205
 
 
   I couldn't revert just one, as the other builds on top and creates a 
conflict while reverting. Feel free to revert yourself and close this PR if you 
so prefer.


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[GitHub] [incubator-mxnet] larroy commented on issue #15951: Revert "Numpy-compatible concatenate upstream"

[GitBox]

larroy commented on issue #15951: Revert "Numpy-compatible concatenate upstream"
URL: https://github.com/apache/incubator-mxnet/pull/15951#issuecomment-523170377
 
 
   This PR has an empty patch as they were several empty commits. @marcoabreu  
I think you should close this one.


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[GitHub] [incubator-mxnet] haojin2 commented on issue #15952: Revert #15842 and #15894. These PRs should not modify CI timeouts.

[GitBox]

haojin2 commented on issue #15952: Revert #15842 and #15894. These PRs should 
not modify CI timeouts.
URL: https://github.com/apache/incubator-mxnet/pull/15952#issuecomment-523164907
 
 
   Please let me know why #15842 also has to be reverted, seems like it has 
nothing to do with CI?


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[GitHub] [incubator-mxnet] larroy opened a new pull request #15952: Revert #15842 and #15894. These PRs should not modify CI timeouts.

[GitBox]

larroy opened a new pull request #15952: Revert #15842 and #15894. These PRs 
should not modify CI timeouts.
URL: https://github.com/apache/incubator-mxnet/pull/15952
 
 
   ## Description ##
   
   These PRs introduced an unrelated increase of the CI time limit. This should 
be in a separate PR and discussed with the community and CI maintainers before.
   
   @haojin2 Please reintroduce your PRs without unrelated changes to the CI 
infrastructure and tag @marcoabreu and me on changes to CI.
   
   Thanks.


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

[marcoabreu]

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

marcoabreu 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 f73a540  Bump the publish timestamp.
f73a540 is described below

commit f73a540628faff449b4e93dfd558f6ad210c4f73
Author: mxnet-ci 
AuthorDate: Tue Aug 20 19:26:51 2019 +

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

diff --git a/date.txt b/date.txt
new file mode 100644
index 000..43a7340
--- /dev/null
+++ b/date.txt
@@ -0,0 +1 @@
+Tue Aug 20 19:26:51 UTC 2019

[GitHub] [incubator-mxnet] haojin2 commented on issue #15951: Revert "Numpy-compatible concatenate upstream"

[GitBox]

haojin2 commented on issue #15951: Revert "Numpy-compatible concatenate 
upstream"
URL: https://github.com/apache/incubator-mxnet/pull/15951#issuecomment-523142933
 
 
   I don't think that's a reasonable assumption. Firstly this PR passed CI 
several times already (since I rebase with master daily) until some day it gets 
timeout all the time. I remember for some reason there was a unit test in 
test_random (probably test_shuffle if I remember correctly) that was taking a 
very long time so CI was stuck on it. Obviously that was not related with my 
PR, which was the reason why I'm increasing the CI time limit since I want to 
unblock several summer interns on their tasks. On the other hand, the only unit 
test introduced in my PR only takes less than 3 seconds, so I don't think my PR 
is the cause for CI time regression:
   ```
   nosetests tests/python/unittest/test_numpy_op.py:test_np_concat
   .
   --
   Ran 1 test in 2.998s
   
   OK
   ```


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[GitHub] [incubator-mxnet] reminisce commented on issue #15951: Revert "Numpy-compatible concatenate upstream"

[GitBox]

reminisce commented on issue #15951: Revert "Numpy-compatible concatenate 
upstream"
URL: https://github.com/apache/incubator-mxnet/pull/15951#issuecomment-523141806
 
 
   @marcoabreu Could you elaborate what urges to revert the PR? I am expecting 
more time duration since we have been adding a lot of operators and unit tests. 
Thanks.


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[incubator-mxnet] branch revert-15894-np_concatenate_master created (now 3afc7c0)

[marcoabreu]

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

marcoabreu pushed a change to branch revert-15894-np_concatenate_master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git.


  at 3afc7c0  Revert "Numpy-compatible concatenate upstream (#15894)"

No new revisions were added by this update.

[GitHub] [incubator-mxnet] larroy commented on a change in pull request #15894: Numpy-compatible concatenate upstream

[GitBox]

larroy commented on a change in pull request #15894: Numpy-compatible 
concatenate upstream
URL: https://github.com/apache/incubator-mxnet/pull/15894#discussion_r315836185
 
 

 ##
 File path: ci/jenkins/Jenkinsfile_unix_cpu
 ##
 @@ -21,7 +21,7 @@
 // See documents at https://jenkins.io/doc/book/pipeline/jenkinsfile/
 
 // timeout in minutes
-max_time = 180
+max_time = 240
 
 Review comment:
   why wasn't this discussed before? Sneaking this change here is not ok, this 
should go in a separate PR and clearly marked as such.


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[GitHub] [incubator-mxnet] marcoabreu opened a new pull request #15951: Revert "Numpy-compatible concatenate upstream"

[GitBox]

marcoabreu opened a new pull request #15951: Revert "Numpy-compatible 
concatenate upstream"
URL: https://github.com/apache/incubator-mxnet/pull/15951
 
 
   Reverts apache/incubator-mxnet#15894
   
   This PR increased the CI timeout. I assume that it did not pass without 
increasing the limit, thus I'm reverting the PR with the request to improve the 
test duration.


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[GitHub] [incubator-mxnet] apeforest commented on a change in pull request #15948: Fix a memory misalignment in topk operator

[GitBox]

apeforest commented on a change in pull request #15948: Fix a memory 
misalignment in topk operator
URL: https://github.com/apache/incubator-mxnet/pull/15948#discussion_r315824638
 
 

 ##
 File path: 3rdparty/mshadow/mshadow/tensor.h
 ##
 @@ -69,15 +69,15 @@ struct Shape {
* \param idx dimension index
* \return the corresponding dimension size
*/
-  MSHADOW_XINLINE index_t [](index_t idx) {
+  MSHADOW_XINLINE index_t [](int idx) {
 
 Review comment:
   This is not directly fixing this bug. However, while checking the tensor 
struct, I noticed this data type should be int because it is indexing the 
dimension (which is set to int)


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[GitHub] [incubator-mxnet] apeforest commented on a change in pull request #15948: Fix a memory misalignment in topk operator

[GitBox]

apeforest commented on a change in pull request #15948: Fix a memory 
misalignment in topk operator
URL: https://github.com/apache/incubator-mxnet/pull/15948#discussion_r315824135
 
 

 ##
 File path: src/operator/tensor/ordering_op-inl.h
 ##
 @@ -414,30 +414,23 @@ void TopKImpl(const RunContext ,
 << element_num << ", but the selected index_t can only represent "
 << mxnet::common::MaxIntegerValue() << " elements";
   Tensor dat = src.FlatTo3D(axis, axis, s);
-  size_t temp_size = 0;
-  // Temp space needed by the gpu-based full sorts.
-  temp_size = std::max(temp_size,
-mxnet::op::SortByKeyWorkspaceSize(src.Size()));
-  temp_size = std::max(temp_size,
-mxnet::op::SortByKeyWorkspaceSize(src.Size()));
-  temp_size = std::max(temp_size,
-mxnet::op::SortByKeyWorkspaceSize(src.Size()));
-  // Additional temp space for gpu full sorts for batch ids.
-  temp_size += PadBytes(sizeof(index_t) * src.Size(), alignment);
-  // Temp space for cpu sorts.
-  temp_size = std::max(temp_size, static_cast(sizeof(DType) * 
src.Size()));
+  // Temp space needed by the full sorts.
+  size_t temp_size = std::max(
+  mxnet::op::SortByKeyWorkspaceSize(src.Size()),
+  mxnet::op::SortByKeyWorkspaceSize(src.Size()));
+
   size_t workspace_size = temp_size + PadBytes(sizeof(DType) * src.Size(), 
alignment)
 + PadBytes(sizeof(index_t) * src.Size(), 
alignment);
   if (param.ret_typ == topk_enum::kReturnMask) {
-workspace_size += PadBytes(sizeof(int) * batch_size * k, alignment);
+workspace_size += PadBytes(sizeof(index_t) * batch_size * k, alignment);
   }
   workspace = resource.get_space_typed(Shape1(workspace_size), 
s);
   char* workspace_curr_ptr = workspace.dptr_;
   sorted_dat = Tensor(reinterpret_cast(workspace_curr_ptr),
-  Shape1(src.Size()), s);  // contain 
sorted dat
+  Shape1(src.Size()), s);  // contain sorted dat
 
 Review comment:
   it's 4 space continuation indent set by Google Style Guide


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[GitHub] [incubator-mxnet] apeforest edited a comment on issue #15703: Storage manager / memory usage regression in v1.5

[GitBox]

apeforest edited a comment on issue #15703: Storage manager / memory usage 
regression in v1.5
URL: 
https://github.com/apache/incubator-mxnet/issues/15703#issuecomment-523119231
 
 
   @TaoLv This is not an issue (bug per se) but limitation of int32_t data 
types we used in MXNet. As I pointed to the line 
https://github.com/apache/incubator-mxnet/blob/master/src/operator/tensor/ordering_op-inl.h#L434
 the workspace is created using a 1D mshadow::Shape object, whose length is 
bounded by `index_t` which is int32_t by default. When the workspace size 
required is larger than 2^31, there will be overflow and causing OOM.
   
   @leezu #15948 is a partial fix because it only fixed the memory misalignment 
but not the OOM caused by int overflow. To really fix this issue, we need to 
support int64_t in mxnet by default.


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[GitHub] [incubator-mxnet] apeforest edited a comment on issue #15703: Storage manager / memory usage regression in v1.5

[GitBox]

apeforest edited a comment on issue #15703: Storage manager / memory usage 
regression in v1.5
URL: 
https://github.com/apache/incubator-mxnet/issues/15703#issuecomment-523119231
 
 
   @TaoLv This is not an issue (bug per se) but limitation of int32_t data 
types we used in MXNet. As I pointed to the line 
https://github.com/apache/incubator-mxnet/blob/master/src/operator/tensor/ordering_op-inl.h#L434
 the workspace is created using a 1D mshadow::Shape object, whose length is 
bounded by `index_t` which is int32_t by default. When the workspace size 
required is larger than 2^31, there will be overflow and causing OOM.
   
   #15948 is a partial fix because it only fixed the memory misalignment but 
not the OOM caused by int overflow. To really fix this issue, we need to 
support int64_t in mxnet by default.


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[GitHub] [incubator-mxnet] apeforest commented on issue #15703: Storage manager / memory usage regression in v1.5

[GitBox]

apeforest commented on issue #15703: Storage manager / memory usage regression 
in v1.5
URL: 
https://github.com/apache/incubator-mxnet/issues/15703#issuecomment-523119231
 
 
   @TaoLv This is not an issue (bug per se) but limitation of int32_t data 
types we used in MXNet. As I pointed to the line 
https://github.com/apache/incubator-mxnet/blob/master/src/operator/tensor/ordering_op-inl.h#L434
 the workspace is created using a 1D mshadow::Shape object, whose length is 
bounded by `index_t` which is int32_t by default. When the workspace size 
required is larger than 2^31, there will be overflow and causing OOM.


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[GitHub] [incubator-mxnet] ChaiBapchya commented on a change in pull request #15772: Add symbol api for randn and fix shape issue for randn ndarray and symbol api

[GitBox]

ChaiBapchya commented on a change in pull request #15772: Add symbol api for 
randn and fix shape issue for randn ndarray and symbol api
URL: https://github.com/apache/incubator-mxnet/pull/15772#discussion_r315816843
 
 

 ##
 File path: mxnet_py3/include/python3.6m
 ##
 @@ -0,0 +1 @@
+/Users/bapac/anaconda3/include/python3.6m
 
 Review comment:
   bummer! removing it  


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[GitHub] [incubator-mxnet] access2rohit commented on a change in pull request #15899: Typedef cleanup

[GitBox]

access2rohit commented on a change in pull request #15899: Typedef cleanup
URL: https://github.com/apache/incubator-mxnet/pull/15899#discussion_r315794983
 
 

 ##
 File path: include/mxnet/c_predict_api.h
 ##
 @@ -43,8 +43,6 @@ extern "C" {
 
 /*! \brief manually define unsigned int */
 typedef uint32_t mx_uint;
 
 Review comment:
   java, C++, R, Scala


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[GitHub] [incubator-mxnet] access2rohit commented on a change in pull request #15899: Typedef cleanup

[GitBox]

access2rohit commented on a change in pull request #15899: Typedef cleanup
URL: https://github.com/apache/incubator-mxnet/pull/15899#discussion_r315795007
 
 

 ##
 File path: include/mxnet/c_predict_api.h
 ##
 @@ -43,8 +43,6 @@ extern "C" {
 
 /*! \brief manually define unsigned int */
 typedef uint32_t mx_uint;
-/*! \brief manually define 64-bit int */
-typedef int64_t mx_int64;
 /*! \brief manually define float */
 typedef float mx_float;
 
 Review comment:
   java, C++, R, Scala


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[GitHub] [incubator-mxnet] kshitij12345 commented on a change in pull request #15772: Add symbol api for randn and fix shape issue for randn ndarray and symbol api

[GitBox]

kshitij12345 commented on a change in pull request #15772: Add symbol api for 
randn and fix shape issue for randn ndarray and symbol api
URL: https://github.com/apache/incubator-mxnet/pull/15772#discussion_r315790759
 
 

 ##
 File path: mxnet_py3/include/python3.6m
 ##
 @@ -0,0 +1 @@
+/Users/bapac/anaconda3/include/python3.6m
 
 Review comment:
   Stray file?


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[GitHub] [incubator-mxnet] kshitij12345 commented on issue #15474: [MXNET-978] Higher Order Gradient Support `sqrt`, `cbrt`.

[GitBox]

kshitij12345 commented on issue #15474: [MXNET-978] Higher Order Gradient 
Support `sqrt`, `cbrt`.
URL: https://github.com/apache/incubator-mxnet/pull/15474#issuecomment-523094624
 
 
   @roywei Have retriggered and successfully run.


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[GitHub] [incubator-mxnet] kshitij12345 commented on issue #15746: [MXNET-978] Higher Order Gradient Support `clip`, `dropout`.

[GitBox]

kshitij12345 commented on issue #15746: [MXNET-978] Higher Order Gradient 
Support `clip`, `dropout`.
URL: https://github.com/apache/incubator-mxnet/pull/15746#issuecomment-523094418
 
 
   @roywei @apeforest Have retriggered and the tests have ran successfully.


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[GitHub] [incubator-mxnet] leezu commented on issue #12795: Deserialization problem with gluon `ValueError: There are multiple outputs with name ...`

[GitBox]

leezu commented on issue #12795: Deserialization problem with gluon 
`ValueError: There are multiple outputs with name ...`
URL: 
https://github.com/apache/incubator-mxnet/issues/12795#issuecomment-523090915
 
 
   The "Minimum reproducible example" works for me on 1.5 and current master. 
This can probably be closed?


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[GitHub] [incubator-mxnet] access2rohit commented on a change in pull request #15948: Fix a memory misalignment in topk operator

[GitBox]

access2rohit commented on a change in pull request #15948: Fix a memory 
misalignment in topk operator
URL: https://github.com/apache/incubator-mxnet/pull/15948#discussion_r315777314
 
 

 ##
 File path: src/operator/tensor/ordering_op-inl.h
 ##
 @@ -414,30 +414,23 @@ void TopKImpl(const RunContext ,
 << element_num << ", but the selected index_t can only represent "
 << mxnet::common::MaxIntegerValue() << " elements";
   Tensor dat = src.FlatTo3D(axis, axis, s);
-  size_t temp_size = 0;
-  // Temp space needed by the gpu-based full sorts.
-  temp_size = std::max(temp_size,
-mxnet::op::SortByKeyWorkspaceSize(src.Size()));
-  temp_size = std::max(temp_size,
-mxnet::op::SortByKeyWorkspaceSize(src.Size()));
-  temp_size = std::max(temp_size,
-mxnet::op::SortByKeyWorkspaceSize(src.Size()));
-  // Additional temp space for gpu full sorts for batch ids.
-  temp_size += PadBytes(sizeof(index_t) * src.Size(), alignment);
-  // Temp space for cpu sorts.
-  temp_size = std::max(temp_size, static_cast(sizeof(DType) * 
src.Size()));
+  // Temp space needed by the full sorts.
+  size_t temp_size = std::max(
+  mxnet::op::SortByKeyWorkspaceSize(src.Size()),
+  mxnet::op::SortByKeyWorkspaceSize(src.Size()));
+
   size_t workspace_size = temp_size + PadBytes(sizeof(DType) * src.Size(), 
alignment)
 + PadBytes(sizeof(index_t) * src.Size(), 
alignment);
   if (param.ret_typ == topk_enum::kReturnMask) {
-workspace_size += PadBytes(sizeof(int) * batch_size * k, alignment);
+workspace_size += PadBytes(sizeof(index_t) * batch_size * k, alignment);
   }
   workspace = resource.get_space_typed(Shape1(workspace_size), 
s);
   char* workspace_curr_ptr = workspace.dptr_;
   sorted_dat = Tensor(reinterpret_cast(workspace_curr_ptr),
-  Shape1(src.Size()), s);  // contain 
sorted dat
+  Shape1(src.Size()), s);  // contain sorted dat
 
 Review comment:
   nit: indentation ?


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[GitHub] [incubator-mxnet] access2rohit commented on a change in pull request #15948: Fix a memory misalignment in topk operator

[GitBox]

access2rohit commented on a change in pull request #15948: Fix a memory 
misalignment in topk operator
URL: https://github.com/apache/incubator-mxnet/pull/15948#discussion_r315777529
 
 

 ##
 File path: src/operator/tensor/ordering_op-inl.h
 ##
 @@ -414,30 +414,23 @@ void TopKImpl(const RunContext ,
 << element_num << ", but the selected index_t can only represent "
 << mxnet::common::MaxIntegerValue() << " elements";
   Tensor dat = src.FlatTo3D(axis, axis, s);
-  size_t temp_size = 0;
-  // Temp space needed by the gpu-based full sorts.
-  temp_size = std::max(temp_size,
-mxnet::op::SortByKeyWorkspaceSize(src.Size()));
-  temp_size = std::max(temp_size,
-mxnet::op::SortByKeyWorkspaceSize(src.Size()));
-  temp_size = std::max(temp_size,
-mxnet::op::SortByKeyWorkspaceSize(src.Size()));
-  // Additional temp space for gpu full sorts for batch ids.
-  temp_size += PadBytes(sizeof(index_t) * src.Size(), alignment);
-  // Temp space for cpu sorts.
-  temp_size = std::max(temp_size, static_cast(sizeof(DType) * 
src.Size()));
+  // Temp space needed by the full sorts.
+  size_t temp_size = std::max(
+  mxnet::op::SortByKeyWorkspaceSize(src.Size()),
+  mxnet::op::SortByKeyWorkspaceSize(src.Size()));
+
   size_t workspace_size = temp_size + PadBytes(sizeof(DType) * src.Size(), 
alignment)
 + PadBytes(sizeof(index_t) * src.Size(), 
alignment);
   if (param.ret_typ == topk_enum::kReturnMask) {
-workspace_size += PadBytes(sizeof(int) * batch_size * k, alignment);
+workspace_size += PadBytes(sizeof(index_t) * batch_size * k, alignment);
   }
   workspace = resource.get_space_typed(Shape1(workspace_size), 
s);
   char* workspace_curr_ptr = workspace.dptr_;
   sorted_dat = Tensor(reinterpret_cast(workspace_curr_ptr),
-  Shape1(src.Size()), s);  // contain 
sorted dat
+  Shape1(src.Size()), s);  // contain sorted dat
   workspace_curr_ptr += PadBytes(sizeof(DType) * src.Size(), alignment);
   indices = Tensor(reinterpret_cast(workspace_curr_ptr),
-Shape1(src.Size()), s);  // indices in the 
original matrix
+  Shape1(src.Size()), s);  // indices in the original matrix
 
 Review comment:
   nit: indentation ?


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[GitHub] [incubator-mxnet] access2rohit commented on a change in pull request #15948: Fix a memory misalignment in topk operator

[GitBox]

access2rohit commented on a change in pull request #15948: Fix a memory 
misalignment in topk operator
URL: https://github.com/apache/incubator-mxnet/pull/15948#discussion_r315777314
 
 

 ##
 File path: src/operator/tensor/ordering_op-inl.h
 ##
 @@ -414,30 +414,23 @@ void TopKImpl(const RunContext ,
 << element_num << ", but the selected index_t can only represent "
 << mxnet::common::MaxIntegerValue() << " elements";
   Tensor dat = src.FlatTo3D(axis, axis, s);
-  size_t temp_size = 0;
-  // Temp space needed by the gpu-based full sorts.
-  temp_size = std::max(temp_size,
-mxnet::op::SortByKeyWorkspaceSize(src.Size()));
-  temp_size = std::max(temp_size,
-mxnet::op::SortByKeyWorkspaceSize(src.Size()));
-  temp_size = std::max(temp_size,
-mxnet::op::SortByKeyWorkspaceSize(src.Size()));
-  // Additional temp space for gpu full sorts for batch ids.
-  temp_size += PadBytes(sizeof(index_t) * src.Size(), alignment);
-  // Temp space for cpu sorts.
-  temp_size = std::max(temp_size, static_cast(sizeof(DType) * 
src.Size()));
+  // Temp space needed by the full sorts.
+  size_t temp_size = std::max(
+  mxnet::op::SortByKeyWorkspaceSize(src.Size()),
+  mxnet::op::SortByKeyWorkspaceSize(src.Size()));
+
   size_t workspace_size = temp_size + PadBytes(sizeof(DType) * src.Size(), 
alignment)
 + PadBytes(sizeof(index_t) * src.Size(), 
alignment);
   if (param.ret_typ == topk_enum::kReturnMask) {
-workspace_size += PadBytes(sizeof(int) * batch_size * k, alignment);
+workspace_size += PadBytes(sizeof(index_t) * batch_size * k, alignment);
   }
   workspace = resource.get_space_typed(Shape1(workspace_size), 
s);
   char* workspace_curr_ptr = workspace.dptr_;
   sorted_dat = Tensor(reinterpret_cast(workspace_curr_ptr),
-  Shape1(src.Size()), s);  // contain 
sorted dat
+  Shape1(src.Size()), s);  // contain sorted dat
 
 Review comment:
   nit: indentation 


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[GitHub] [incubator-mxnet] access2rohit commented on a change in pull request #15948: Fix a memory misalignment in topk operator

[GitBox]

access2rohit commented on a change in pull request #15948: Fix a memory 
misalignment in topk operator
URL: https://github.com/apache/incubator-mxnet/pull/15948#discussion_r315777158
 
 

 ##
 File path: 3rdparty/mshadow/mshadow/tensor.h
 ##
 @@ -69,15 +69,15 @@ struct Shape {
* \param idx dimension index
* \return the corresponding dimension size
*/
-  MSHADOW_XINLINE index_t [](index_t idx) {
+  MSHADOW_XINLINE index_t [](int idx) {
 
 Review comment:
   Can you describe this change a bit more in detail ? Why is this required ?


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[GitHub] [incubator-mxnet] ptrendx commented on issue #15545: Softmax optimization for GPU

[GitBox]

ptrendx commented on issue #15545: Softmax optimization for GPU
URL: https://github.com/apache/incubator-mxnet/pull/15545#issuecomment-523070340
 
 
   Ok, it seems that splitting softmax.cc into 3 files, 1 for each operator 
(softmax, softmin and log_softmax) did the trick fortunately.


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[GitHub] [incubator-mxnet] TaoLv commented on issue #15930: Fix dtype inference in arange_like operator

[GitBox]

TaoLv commented on issue #15930: Fix dtype inference in arange_like operator
URL: https://github.com/apache/incubator-mxnet/pull/15930#issuecomment-523068244
 
 
   @eric-haibin-lin  do you think the below code snippet can be used as a test 
case?
   
   ```python
   import mxnet as mx
   import numpy as np
   
   dtypes = [np.float16, np.float32, np.float64]
   
   for t in dtypes:
   x = mx.sym.Variable('x', dtype=t)
   y = mx.sym.reshape(x, shape=(0, 0, -1))
   z = mx.sym.contrib.arange_like(y, axis=-1)
   
   mod = z.simple_bind(ctx=mx.gpu(0), x=(3, 4, 5, 6), graph_req='null')
   
   mod.arg_arrays[0][:] = 
np.random.normal(size=mod.arg_arrays[0].shape).astype(t)
   out = mod.forward(is_train=False)
   assert out[0].dtype == np.float32
   ```


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[GitHub] [incubator-mxnet] TaoLv commented on issue #15703: Storage manager / memory usage regression in v1.5

[GitBox]

TaoLv commented on issue #15703: Storage manager / memory usage regression in 
v1.5
URL: 
https://github.com/apache/incubator-mxnet/issues/15703#issuecomment-523037374
 
 
   @apeforest Thank you for the analysis. What's the blocker to get this issue 
fixed on the v1.5.x branch?


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[GitHub] [incubator-mxnet] ciyongch opened a new pull request #15950: [MKLDNN]Support fullyconnected and element-wise ops fusion

[GitBox]

ciyongch opened a new pull request #15950: [MKLDNN]Support fullyconnected and 
element-wise ops fusion
URL: https://github.com/apache/incubator-mxnet/pull/15950
 
 
   ## Description ##
   This PR is to add the support for fullyconnected and some element-wise 
(including activation/square/sqrt/exp/abs/clip) ops fusion.
   
   ## 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
   @pengzhao-intel @TaoLv @ZhennanQin 


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

[marcoabreu]

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

marcoabreu 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 c798770  Bump the publish timestamp.
c798770 is described below

commit c798770e49f96b40b14f06bf328f06a8c82c5c08
Author: mxnet-ci 
AuthorDate: Tue Aug 20 13:34:35 2019 +

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

diff --git a/date.txt b/date.txt
new file mode 100644
index 000..ef652a0
--- /dev/null
+++ b/date.txt
@@ -0,0 +1 @@
+Tue Aug 20 13:34:35 UTC 2019

[GitHub] [incubator-mxnet] zoeygxy commented on issue #15942: Refines NDArray indexing and adds numpy ndarray indexing [DO NOT MERGE YET]

[GitBox]

zoeygxy commented on issue #15942: Refines NDArray indexing and adds numpy 
ndarray indexing [DO NOT MERGE YET]
URL: https://github.com/apache/incubator-mxnet/pull/15942#issuecomment-523009739
 
 
   Waiting for CI result. Still fixing style.


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[GitHub] [incubator-mxnet] zixuanweeei commented on issue #15741: MKL-DNN LBR-GRU Inference Integration (FP32 LBR-GRU)

[GitBox]

zixuanweeei commented on issue #15741: MKL-DNN LBR-GRU Inference Integration 
(FP32 LBR-GRU)
URL: https://github.com/apache/incubator-mxnet/pull/15741#issuecomment-522955864
 
 
   Cherry picked from commit 1cf63e1 according to 
https://github.com/apache/incubator-mxnet/pull/15847#issuecomment-522840815


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[GitHub] [incubator-mxnet] yzhliu commented on a change in pull request #15938: Tvm broadcast backward

[GitBox]

yzhliu commented on a change in pull request #15938: Tvm broadcast backward
URL: https://github.com/apache/incubator-mxnet/pull/15938#discussion_r315582881
 
 

 ##
 File path: contrib/tvmop/basic/ufunc.py
 ##
 @@ -48,3 +50,71 @@ def vadd_gpu(dtype, ndim):
 s[C].bind(bx, tvm.thread_axis("blockIdx.x"))
 s[C].bind(tx, tvm.thread_axis("threadIdx.x"))
 return s, [A, B, C]
+
+
+def assign_by_req(a, req):
 
 Review comment:
   move to sth like common.py?


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[GitHub] [incubator-mxnet] yzhliu commented on a change in pull request #15938: Tvm broadcast backward

[GitBox]

yzhliu commented on a change in pull request #15938: Tvm broadcast backward
URL: https://github.com/apache/incubator-mxnet/pull/15938#discussion_r315584826
 
 

 ##
 File path: src/operator/contrib/tvmop/ufunc.cc
 ##
 @@ -37,29 +38,88 @@ namespace op {
 
 static constexpr char func_vadd_cpu[] = "vadd";
 static constexpr char func_vadd_gpu[] = "cuda_vadd";
+static constexpr char func_bakcward_vadd_cpu[] = "backward_vadd";
+static constexpr char func_bakcward_vadd_gpu[] = "cuda_backward_vadd";
 
 template
-void TVMBroadcastCompute(const nnvm::NodeAttrs& attrs,
- const mxnet::OpContext& ctx,
- const std::vector& inputs,
- const std::vector& req,
- const std::vector& outputs) {
+void TVMBinaryCompute(const nnvm::NodeAttrs& attrs,
+  const mxnet::OpContext& ctx,
+  const std::vector& inputs,
+  const std::vector& req,
+  const std::vector& outputs) {
   CHECK_EQ(inputs.size(), 2U);
   CHECK_EQ(outputs.size(), 1U);
   tvm::runtime::TVMOpModule::Get()->Call(func, ctx, {inputs[0], inputs[1], 
outputs[0]});
 }
 
+template
+void TVMBinaryBackwardComputeUseNone(const nnvm::NodeAttrs& attrs,
+ const mxnet::OpContext& ctx,
+ const std::vector& inputs,
+ const std::vector& req,
+ const std::vector& outputs) {
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 2U);
+  int ndim = inputs[0].shape_.ndim();
+  for (int k = 0; k < 2; ++k) {
+// dispatch by backward
+std::vector ov, iv;
+const TBlob& ograd = inputs[0], igrad = outputs[k];
+bool flag = ograd.size(0) != igrad.size(0);
 
 Review comment:
   better to use int and explicitly assign the value.


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[GitHub] [incubator-mxnet] yzhliu commented on a change in pull request #15938: Tvm broadcast backward

[GitBox]

yzhliu commented on a change in pull request #15938: Tvm broadcast backward
URL: https://github.com/apache/incubator-mxnet/pull/15938#discussion_r315583132
 
 

 ##
 File path: src/operator/contrib/tvmop/ufunc.cc
 ##
 @@ -37,29 +38,88 @@ namespace op {
 
 static constexpr char func_vadd_cpu[] = "vadd";
 static constexpr char func_vadd_gpu[] = "cuda_vadd";
+static constexpr char func_bakcward_vadd_cpu[] = "backward_vadd";
+static constexpr char func_bakcward_vadd_gpu[] = "cuda_backward_vadd";
 
 template
-void TVMBroadcastCompute(const nnvm::NodeAttrs& attrs,
- const mxnet::OpContext& ctx,
- const std::vector& inputs,
- const std::vector& req,
- const std::vector& outputs) {
+void TVMBinaryCompute(const nnvm::NodeAttrs& attrs,
+  const mxnet::OpContext& ctx,
+  const std::vector& inputs,
+  const std::vector& req,
+  const std::vector& outputs) {
   CHECK_EQ(inputs.size(), 2U);
   CHECK_EQ(outputs.size(), 1U);
   tvm::runtime::TVMOpModule::Get()->Call(func, ctx, {inputs[0], inputs[1], 
outputs[0]});
 }
 
+template
+void TVMBinaryBackwardComputeUseNone(const nnvm::NodeAttrs& attrs,
+ const mxnet::OpContext& ctx,
+ const std::vector& inputs,
+ const std::vector& req,
+ const std::vector& outputs) {
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 2U);
+  int ndim = inputs[0].shape_.ndim();
+  for (int k = 0; k < 2; ++k) {
+// dispatch by backward
+std::vector ov, iv;
+const TBlob& ograd = inputs[0], igrad = outputs[k];
+bool flag = ograd.size(0) != igrad.size(0);
+for (int i = 0; i < ndim; ++i) {
+  if (i == 0 || (ograd.size(i) != igrad.size(i)) != (ograd.size(i - 1) != 
igrad.size(i - 1))) {
+ov.push_back(ograd.size(i));
+  } else {
+ov.back() *= ograd.size(i);
+  }
+}
+for (int i = flag; i < ov.size(); i += 2) {
+  iv.push_back(ov[i]);
+}
+TShape oshape(ov.begin(), ov.end()), ishape(iv.begin(), iv.end());
+TBlob ograd_tvm(ograd.reshape(oshape).dltensor());
+TBlob igrad_tvm(igrad.reshape(ishape).dltensor());
+std::string funcname = std::string(func) + "reduce1st_" + 
std::to_string(flag);
+// dispatch by req
+funcname += "req_";
+MXNET_ASSIGN_REQ_SWITCH(req[k], req_type, {
+  if (req_type == kWriteTo) {
+funcname += "kWriteTo";
 
 Review comment:
   alignment


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[GitHub] [incubator-mxnet] yzhliu commented on a change in pull request #15938: Tvm broadcast backward

[GitBox]

yzhliu commented on a change in pull request #15938: Tvm broadcast backward
URL: https://github.com/apache/incubator-mxnet/pull/15938#discussion_r315585709
 
 

 ##
 File path: src/operator/contrib/tvmop/ufunc.cc
 ##
 @@ -37,29 +38,88 @@ namespace op {
 
 static constexpr char func_vadd_cpu[] = "vadd";
 static constexpr char func_vadd_gpu[] = "cuda_vadd";
+static constexpr char func_bakcward_vadd_cpu[] = "backward_vadd";
+static constexpr char func_bakcward_vadd_gpu[] = "cuda_backward_vadd";
 
 template
-void TVMBroadcastCompute(const nnvm::NodeAttrs& attrs,
- const mxnet::OpContext& ctx,
- const std::vector& inputs,
- const std::vector& req,
- const std::vector& outputs) {
+void TVMBinaryCompute(const nnvm::NodeAttrs& attrs,
+  const mxnet::OpContext& ctx,
+  const std::vector& inputs,
+  const std::vector& req,
+  const std::vector& outputs) {
   CHECK_EQ(inputs.size(), 2U);
   CHECK_EQ(outputs.size(), 1U);
   tvm::runtime::TVMOpModule::Get()->Call(func, ctx, {inputs[0], inputs[1], 
outputs[0]});
 }
 
+template
+void TVMBinaryBackwardComputeUseNone(const nnvm::NodeAttrs& attrs,
+ const mxnet::OpContext& ctx,
+ const std::vector& inputs,
+ const std::vector& req,
+ const std::vector& outputs) {
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 2U);
+  int ndim = inputs[0].shape_.ndim();
+  for (int k = 0; k < 2; ++k) {
+// dispatch by backward
+std::vector ov, iv;
+const TBlob& ograd = inputs[0], igrad = outputs[k];
+bool flag = ograd.size(0) != igrad.size(0);
+for (int i = 0; i < ndim; ++i) {
+  if (i == 0 || (ograd.size(i) != igrad.size(i)) != (ograd.size(i - 1) != 
igrad.size(i - 1))) {
+ov.push_back(ograd.size(i));
+  } else {
+ov.back() *= ograd.size(i);
+  }
+}
+for (int i = flag; i < ov.size(); i += 2) {
+  iv.push_back(ov[i]);
+}
+TShape oshape(ov.begin(), ov.end()), ishape(iv.begin(), iv.end());
+TBlob ograd_tvm(ograd.reshape(oshape).dltensor());
+TBlob igrad_tvm(igrad.reshape(ishape).dltensor());
 
 Review comment:
   please add some comments to elaborate the ideas.


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[GitHub] [incubator-mxnet] yzhliu commented on a change in pull request #15938: Tvm broadcast backward

[GitBox]

yzhliu commented on a change in pull request #15938: Tvm broadcast backward
URL: https://github.com/apache/incubator-mxnet/pull/15938#discussion_r315579522
 
 

 ##
 File path: contrib/tvmop/basic/ufunc.py
 ##
 @@ -48,3 +50,71 @@ def vadd_gpu(dtype, ndim):
 s[C].bind(bx, tvm.thread_axis("blockIdx.x"))
 s[C].bind(tx, tvm.thread_axis("threadIdx.x"))
 return s, [A, B, C]
+
+
+def assign_by_req(a, req):
+b = tvm.placeholder(a.shape, name='assign_by_req_b', dtype=a.dtype)
+if (req == "kAddTo"):
+c = tvm.compute(a.shape, lambda *idx: a[idx] + b[idx])
+else:
+c = tvm.compute(a.shape, lambda *idx: a[idx])
+return b, c
+
+
+def reduce_axes(X, axes, reducer):
 
 Review comment:
   can we add some comments to elaborate the idea? e.g., meaning of axes. also 
can we move it to somewhere else so that other operators can reuse?


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[GitHub] [incubator-mxnet] leezu commented on issue #15703: Storage manager / memory usage regression in v1.5

[GitBox]

leezu commented on issue #15703: Storage manager / memory usage regression in 
v1.5
URL: 
https://github.com/apache/incubator-mxnet/issues/15703#issuecomment-522917231
 
 
   Thank you for diving deep to find the root cause! I'm not blocked by this 
fix having to wait for MXNet 1.6, but we may wan't ask @TaoLv as release 
manager. If the fix has to wait for 1.6 the regression should be documented, 
for example in the "known issues" section of the release notes? 
https://github.com/apache/incubator-mxnet/releases
   
   What do you mean with "**partially** fixes" in #15948? Could that fix in 
principle be cherry-picked for 1.5.X?


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[GitHub] [incubator-mxnet] pengzhao-intel commented on issue #15853: Float64 fallback for mkldnn subgraph and rnn op

[GitBox]

pengzhao-intel commented on issue #15853: Float64 fallback for mkldnn subgraph 
and rnn op
URL: https://github.com/apache/incubator-mxnet/pull/15853#issuecomment-522908818
 
 
   @ZhennanQin could you try CI again?


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[GitHub] [incubator-mxnet] ElaineBao commented on issue #15884: [WIP] New Website: New Docs [1/3]

[GitBox]

ElaineBao commented on issue #15884: [WIP] New Website: New Docs [1/3]
URL: https://github.com/apache/incubator-mxnet/pull/15884#issuecomment-522898751
 
 
   Hi, is it possible to make API docs easier to find ? I have to click many 
times to get to the function page: Main page -> Docs & Tutorials -> Python API 
Reference -> Python API -> mxnet.ndarray -> NDArray -> 
mxnet.ndarray.NDArray.dtype. 


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

[marcoabreu]

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

marcoabreu 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 9d8c1d9  Bump the publish timestamp.
9d8c1d9 is described below

commit 9d8c1d950a602b1463e429c2ff1ddac51509dfa2
Author: mxnet-ci 
AuthorDate: Tue Aug 20 07:35:09 2019 +

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

diff --git a/date.txt b/date.txt
new file mode 100644
index 000..6ea3551
--- /dev/null
+++ b/date.txt
@@ -0,0 +1 @@
+Tue Aug 20 07:35:09 UTC 2019

[GitHub] [incubator-mxnet] samskalicky commented on issue #15921: [WIP] dynamic custom operator support

[GitBox]

samskalicky commented on issue #15921: [WIP] dynamic custom operator support
URL: https://github.com/apache/incubator-mxnet/pull/15921#issuecomment-522889129
 
 
   @wkcn while this PR is not quite done yet, it would be great to get some 
early feedback since the design/implementation has changed since our initial 
discussion. Let me know what you think, thanks!


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[GitHub] [incubator-mxnet] gyshi opened a new pull request #15949: Numpy op exp2

[GitBox]

gyshi opened a new pull request #15949: Numpy op  exp2 
URL: https://github.com/apache/incubator-mxnet/pull/15949
 
 
   this op exp2 test in numpy branch.
   (https://www.numpy.org/doc/1.17/reference/generated/numpy.exp2.html)


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[GitHub] [incubator-mxnet] apeforest commented on issue #15948: Fix a memory misalignment in topk operator

[GitBox]

apeforest commented on issue #15948: Fix a memory misalignment in topk operator
URL: https://github.com/apache/incubator-mxnet/pull/15948#issuecomment-522877070
 
 
   @access2rohit @ChaiBapchya Please also help review. Thanks


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