[GitHub] pengzhao-intel commented on a change in pull request #11129: [MXNET-497] fix bugs in MKLDNN operators to handle the kAddTo request
pengzhao-intel commented on a change in pull request #11129: [MXNET-497] fix
bugs in MKLDNN operators to handle the kAddTo request
URL: https://github.com/apache/incubator-mxnet/pull/11129#discussion_r198020569
##
File path: src/operator/nn/mkldnn/mkldnn_base.cc
##
@@ -141,13 +212,16 @@ void CommitOutput(const NDArray , const
mkldnn_output_t ) {
if (res.first == CopyBack) {
const_cast(arr).CopyFrom(*res.second);
} else if (res.first == AddBack) {
+auto res_memory = res.second;
+auto target_pd = arr.GetMKLDNNData()->get_primitive_desc();
auto mem = arr.GetMKLDNNData(res.second->get_primitive_desc());
-CHECK(mem != nullptr);
-// We have to allocate new memory for the sum result.
-auto sum_res = TmpMemMgr::Get()->Alloc(
-res.second->get_primitive_desc());
-op::MKLDNNSum(*res.second, *mem, *sum_res);
-const_cast(arr).CopyFrom(*sum_res);
+if (mem == nullptr) {
+ auto tmp_memory = TmpMemMgr::Get()->Alloc(target_pd);
+ MKLDNNCopy(*res_memory, tmp_memory);
+ res_memory = tmp_memory;
Review comment:
As my understanding, `MKLDNNCopy` already reordered the `res_memory` with
`tmp_memory`, so why we need to assign `tmp_memory` to `res_memory` again?
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[GitHub] zhanghang1989 commented on issue #9989: Cannot train example gluon style transfer
zhanghang1989 commented on issue #9989: Cannot train example gluon style transfer URL: https://github.com/apache/incubator-mxnet/issues/9989#issuecomment-400175517 The main problem stops hybridizing is the Gram Matrix calculation, which reads the shape. https://github.com/zhanghang1989/MXNet-Gluon-Style-Transfer/blob/master/net.py#L159-L164 This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] jeremiedb commented on issue #11374: [MXNET-563] Refactor R optimizers to fix memory leak
jeremiedb commented on issue #11374: [MXNET-563] Refactor R optimizers to fix memory leak URL: https://github.com/apache/incubator-mxnet/pull/11374#issuecomment-400175274 @anirudhacharya Sure, I'll add tests. Would be great if you could jump in as well. I was expecting to add the missing Adagrad and Adadelta optimizers within a week in order to match existing functionnalities as soon as possible. Would you be disposed looking at the non-mutatble NDArrays which was actually the root cause leading to refactor optimizers into symbolic execution? Thanks! This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] reminisce commented on a change in pull request #11325: Added TensorRT runtime integration
reminisce commented on a change in pull request #11325: Added TensorRT runtime
integration
URL: https://github.com/apache/incubator-mxnet/pull/11325#discussion_r198010854
##
File path: src/operator/contrib/tensorrt-inl.h
##
@@ -0,0 +1,140 @@
+#ifndef MXNET_OPERATOR_CONTRIB_TENSORRT_INL_H_
+#define MXNET_OPERATOR_CONTRIB_TENSORRT_INL_H_
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2018 by Contributors
+ * \file tensorrt-inl.h
+ * \brief TensorRT Operator
+ * \author Marek Kolodziej, Clement Fuji Tsang
+*/
+
+#if MXNET_USE_TENSORRT
+
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+
+#include
+#include
+
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+
+#include "../operator_common.h"
+#include "../../common/utils.h"
+#include "../../common/serialization.h"
+#include "../../executor/exec_pass.h"
+#include "../../executor/graph_executor.h"
+#include "../../executor/onnx_to_tensorrt.h"
+
+namespace mxnet {
+namespace op {
+
+using namespace nnvm;
+using namespace ::onnx;
+using int64 = ::google::protobuf::int64;
+
+namespace tensorrt {
+ enum class TypeIO { Inputs = 0, Outputs = 1 };
+ using NameToIdx_t = std::map;
+ using InferenceTuple_t = std::tuple;
+ using InferenceMap_t = std::map;
+} // namespace tensorrt
+
+using trt_name_to_idx = std::map;
+
+struct TRTParam : public dmlc::Parameter {
+ std::string serialized_onnx_graph;
+ std::string serialized_input_map;
+ std::string serialized_output_map;
+ tensorrt::NameToIdx_t input_map;
+ tensorrt::InferenceMap_t output_map;
+ ::onnx::ModelProto onnx_pb_graph;
+
+ TRTParam() {}
+
+ TRTParam(const ::onnx::ModelProto& onnx_graph,
+ const tensorrt::InferenceMap_t& input_map,
+ const tensorrt::NameToIdx_t& output_map) {
+common::Serialize(input_map, _input_map);
+common::Serialize(output_map, _output_map);
+onnx_graph.SerializeToString(_onnx_graph);
+ }
+
+DMLC_DECLARE_PARAMETER(TRTParam) {
+DMLC_DECLARE_FIELD(serialized_onnx_graph)
+.describe("Serialized ONNX graph");
+DMLC_DECLARE_FIELD(serialized_input_map)
+.describe("Map from inputs to topological order as input.");
+DMLC_DECLARE_FIELD(serialized_output_map)
+.describe("Map from outputs to order in g.outputs.");
+ }
+};
+
+struct TRTEngineParam {
+ nvinfer1::IExecutionContext* trt_executor;
+ std::vector > binding_map;
+};
+
+OpStatePtr TRTCreateState(const nnvm::NodeAttrs& attrs, Context ctx,
+ const std::vector& ishape,
+ const std::vector& itype);
+
+template
+void TRTCompute(const OpStatePtr& state, const OpContext& ctx,
Review comment:
IMHO, it's framework's job to throw error messages like this. Registering a
CPU version of stateful FCompute for TRT doesn't sound semantically correct,
even though it would print an error message in the Forward function. If the
framework's error message is not informative enough, we can always improve it.
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[GitHub] nicklhy opened a new issue #11400: how to train models with multiple gpus in C++
nicklhy opened a new issue #11400: how to train models with multiple gpus in C++ URL: https://github.com/apache/incubator-mxnet/issues/11400 The `mx.mod.Module` provides a convenient high level api for model training in python. But due to some reasons, I need to train my models in pure C++ environment. I was wondering if it is also possible to support multiple gpu device with the interfaces in `cpp-package/include/mxnet-cpp`. Currently, I can only find the `Executor` in `cpp-package/include/mxnet-cpp/executor.h` which can set a single context variable each time. This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] haojin2 commented on a change in pull request #11389: [MXNET-566] Fix flaky test_operator_gpu.test_sparse_dot
haojin2 commented on a change in pull request #11389: [MXNET-566] Fix flaky
test_operator_gpu.test_sparse_dot
URL: https://github.com/apache/incubator-mxnet/pull/11389#discussion_r198005802
##
File path: src/operator/tensor/dot-inl.cuh
##
@@ -1053,7 +1053,7 @@ inline void DotDnsCsrDnsImpl(const OpContext& ctx, const
gpu& gpu_dev,
TBlob csr_indices = rhs.aux_data(csr::kIdx);
TBlob csr_indptr = rhs.aux_data(csr::kIndPtr);
if (!rhs.storage_initialized()) {
-FillZerosCsrImpl(s, *ret);
Review comment:
Maybe it's failing in set_aux_shape() here:
https://github.com/apache/incubator-mxnet/blob/b2ccd34ad2801b6c87c957c28ad718562a4c5b6e/src/operator/tensor/init_op.h#L359?
I can do a check quickly and let you know the result.
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[GitHub] eric-haibin-lin commented on a change in pull request #11330: [MXNET-537] add_n(dense, csr, dense) = dense and add_n([dense, csr, rsp]*, dense, [dense, csr, rsp]*) = dense on CPU & GPU
eric-haibin-lin commented on a change in pull request #11330: [MXNET-537]
add_n(dense, csr, dense) = dense and add_n([dense, csr, rsp]*, dense, [dense,
csr, rsp]*) = dense on CPU & GPU
URL: https://github.com/apache/incubator-mxnet/pull/11330#discussion_r198001502
##
File path: src/ndarray/ndarray_function.cu
##
@@ -185,6 +187,101 @@ void ElementwiseSumRspImpl(mshadow::Stream* s,
});
}
+void ElementwiseSumDnsCsrDnsImpl(mshadow::Stream* s,
+ const Resource& rsc,
+ const std::vector& nds,
+ NDArray* out) {
+ using namespace mxnet::op;
+ using namespace mxnet::op::mxnet_op;
+ const TBlob& out_data = out->data();
+ MSHADOW_TYPE_SWITCH(out->dtype(), DType, { // data type
+Kernel::Launch(
+ s, out_data.Size(), out_data.dptr(), kWriteTo,
nds[0].data().dptr(),
+ nds[2].data().dptr());
+const TBlob& csr_data = nds[1].data();
+const TBlob& csr_indices = nds[1].aux_data(csr::kIdx);
+const TBlob& csr_indptr = nds[1].aux_data(csr::kIndPtr);
+const nnvm::dim_t num_rows = nds[1].shape()[0];
+const nnvm::dim_t num_cols = nds[1].shape()[1];
+MSHADOW_IDX_TYPE_SWITCH(csr_indices.type_flag_, IType, { // indices type
+ MSHADOW_IDX_TYPE_SWITCH(csr_indptr.type_flag_, CType, { // indptr type
+if (nds[1].storage_initialized()) {
+ Kernel,
gpu>::Launch(
+s, 32 * num_rows, out_data.dptr(), out_data.dptr(),
+csr_data.dptr(), csr_indices.dptr(),
+csr_indptr.dptr(), num_rows, num_cols);
+}
+ });
+});
+ });
+}
+
+void ElementwiseSumContainsDnsImpl(mshadow::Stream* s,
+ const Resource& rsc,
+ const std::vector& nds,
+ NDArray* out) {
+ using namespace mxnet::op;
+ using namespace mxnet::op::mxnet_op;
+ const TBlob& out_data = out->data();
+ MSHADOW_TYPE_SWITCH(out->dtype(), DType, { // data type
+for (size_t i = 0; i < nds.size(); ++i) {
+ const NDArray& nd = nds[i];
+ const nnvm::dim_t num_rows = nd.shape()[0];
+ const nnvm::dim_t num_cols = nd.shape()[1];
+ const TBlob& nd_data = nd.data();
+
+ if (i == 0) {
+if (nd.storage_type() == kDefaultStorage) {
+ Kernel, gpu>::Launch(
+s, out_data.Size(), out_data.dptr(), nd_data.dptr());
+ continue;
+} else {
+ Kernel::Launch(s, out_data.Size(),
out_data.dptr());
+}
+ }
+
+ switch (nd.storage_type()) {
+case kDefaultStorage: {
+ Kernel, gpu>::Launch(
+s, out_data.Size(), out_data.dptr(), out_data.dptr(),
+nd_data.dptr());
+ break;
+}
+case kCSRStorage: {
+ const TBlob& nd_indices = nd.aux_data(csr::kIdx);
+ const TBlob& nd_indptr = nd.aux_data(csr::kIndPtr);
+ MSHADOW_IDX_TYPE_SWITCH(nd_indices.type_flag_, IType, { // indices
type
+MSHADOW_IDX_TYPE_SWITCH(nd_indptr.type_flag_, CType, { // indptr
type
+ if (nd.storage_initialized()) {
+Kernel, gpu>::Launch(
+ s, 32 * num_rows, out_data.dptr(),
out_data.dptr(),
Review comment:
Suggest use a const var with meaningful name instead of 32
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[GitHub] mkolod commented on issue #11380: Add ability to query cuDNN BatchNorm min. epsilon. Allow ONNX importer to use cuDNN BN if chosen eps >= cuDNN min. eps.
mkolod commented on issue #11380: Add ability to query cuDNN BatchNorm min. epsilon. Allow ONNX importer to use cuDNN BN if chosen eps >= cuDNN min. eps. URL: https://github.com/apache/incubator-mxnet/pull/11380#issuecomment-400164515 @marcoabreu It seems like all tests pass on all plaforms, except for Windows-GPU, which is failing on all tests with `CUDA: unspecified launch failure`. It seems like the issue may be with the Windows runner. This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] samhodge commented on issue #9989: Cannot train example gluon style transfer
samhodge commented on issue #9989: Cannot train example gluon style transfer URL: https://github.com/apache/incubator-mxnet/issues/9989#issuecomment-400163481 Thanks for the swift response @zhanghang1989 I was hoping to serialise the model and run in C++ as a hybrid model. Do you have any suggestions as how to run the model in C++, besides pybind11? This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] eric-haibin-lin commented on a change in pull request #11389: [MXNET-566] Fix flaky test_operator_gpu.test_sparse_dot
eric-haibin-lin commented on a change in pull request #11389: [MXNET-566] Fix
flaky test_operator_gpu.test_sparse_dot
URL: https://github.com/apache/incubator-mxnet/pull/11389#discussion_r198000365
##
File path: src/operator/tensor/dot-inl.cuh
##
@@ -1053,7 +1053,7 @@ inline void DotDnsCsrDnsImpl(const OpContext& ctx, const
gpu& gpu_dev,
TBlob csr_indices = rhs.aux_data(csr::kIdx);
TBlob csr_indptr = rhs.aux_data(csr::kIndPtr);
if (!rhs.storage_initialized()) {
-FillZerosCsrImpl(s, *ret);
Review comment:
Why is there segmentation fault instead of log fatal err msg? The
CheckAndAllocData function used by FillZerosCsrImpl does check stype:
https://github.com/apache/incubator-mxnet/blob/master/include/mxnet/ndarray.h#L621
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[GitHub] larroy commented on issue #11359: Flaky test test_io:test_ImageRecordIter_seed_augmentation
larroy commented on issue #11359: Flaky test test_io:test_ImageRecordIter_seed_augmentation URL: https://github.com/apache/incubator-mxnet/issues/11359#issuecomment-400157621 http://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/incubator-mxnet/detail/PR-11382/6/pipeline This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] ijkguo commented on issue #11373: update rcnn example
ijkguo commented on issue #11373: update rcnn example URL: https://github.com/apache/incubator-mxnet/pull/11373#issuecomment-400157323 @ZiyueHuang MutableModule now gone. This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] larroy edited a comment on issue #11205: Clojure Contrib Package
larroy edited a comment on issue #11205: Clojure Contrib Package
URL: https://github.com/apache/incubator-mxnet/pull/11205#issuecomment-400152945
Hi @gigasquid CI runs stuff in docker containers. There's the Dockerfile
which defines the platform, in your case you should use ubuntu_cpu
The steps to add your code to CI would be the following:
1. Add your clojure dependencies (jvm, lein etc) to
`ci/docker/install/ubuntu_clojure.sh` (check how it's done with scala in
`ci/docker/install/ubuntu_scala.sh` These are used to build the container
2. You need to add one function to
`ci/docker/runtime_functions.sh` that builds your package. This is the entry
point that is triggered inside the docker container.
and then add a stage into the Jenkinsfile:
See for example this PR which adds an additional Android stage:
https://github.com/apache/incubator-mxnet/pull/11382/files#diff-58231b16fdee45a03a4ee3cf94a9f2c3L486
To test locally run:
ci/build.py --platform ubuntu_cpu --shm-size 500m /work/runtime_functions.sh
unittest_ubuntu_cpu_scala
but just use the function for clojure that was just created.
ci/build.py --platform ubuntu_cpu -i will put you inside the container,
so you can check what steps you need to take and add to the
runtime_functions.sh script.
You can also run build.py from osx with docker, you might need to increase
the memory in preferences. Let me know if you have any issues.
PD: to install docker in an ubuntu machine you can do the following:
```
function install_docker_ubuntu() {
#apt-get -y install docker docker.io
export DEBIAN_FRONTEND=noninteractive
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add
-
add-apt-repository \
"deb [arch=amd64] https://download.docker.com/linux/ubuntu \
$(lsb_release -cs) \
stable"
apt-get update
apt-get -y install docker-ce
# Nvidia docker
wget -P /tmp
https://github.com/NVIDIA/nvidia-docker/releases/download/v1.0.1/nvidia-docker_1.0.1-1_amd64.deb
dpkg -i /tmp/nvidia-docker*.deb && rm /tmp/nvidia-docker*.deb
# Restart docker
service docker restart
# Add ubuntu to docker group
usermod -a -G docker ubuntu
}
```
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[GitHub] larroy edited a comment on issue #11205: Clojure Contrib Package
larroy edited a comment on issue #11205: Clojure Contrib Package
URL: https://github.com/apache/incubator-mxnet/pull/11205#issuecomment-400152945
Hi @gigasquid CI runs stuff in docker containers.
The steps to add your code to CI would be the following:
1. Add your clojure dependencies to `ci/docker/install/ubuntu_clojure.sh`
(check how it's done with scala)
2. You need to add one function to
`ci/docker/runtime_functions.sh` that builds your package
and then add a stage into the Jenkinsfile:
See for example this PR which adds an additional Android stage:
https://github.com/apache/incubator-mxnet/pull/11382/files#diff-58231b16fdee45a03a4ee3cf94a9f2c3L486
To test locally run:
ci/build.py --platform ubuntu_cpu --shm-size 500m /work/runtime_functions.sh
unittest_ubuntu_cpu_scala
but just use the function for clojure that was just created.
ci/build.py --platform ubuntu_cpu -i will put you inside the container,
so you can check what steps you need to take and add to the
runtime_functions.sh script.
Let me know if you have any issues.
PD: to install docker in an ubuntu machine you can do the following:
```
function install_docker_ubuntu() {
#apt-get -y install docker docker.io
export DEBIAN_FRONTEND=noninteractive
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add
-
add-apt-repository \
"deb [arch=amd64] https://download.docker.com/linux/ubuntu \
$(lsb_release -cs) \
stable"
apt-get update
apt-get -y install docker-ce
# Nvidia docker
wget -P /tmp
https://github.com/NVIDIA/nvidia-docker/releases/download/v1.0.1/nvidia-docker_1.0.1-1_amd64.deb
dpkg -i /tmp/nvidia-docker*.deb && rm /tmp/nvidia-docker*.deb
# Restart docker
service docker restart
# Add ubuntu to docker group
usermod -a -G docker ubuntu
}
```
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[GitHub] larroy commented on issue #11205: Clojure Contrib Package
larroy commented on issue #11205: Clojure Contrib Package URL: https://github.com/apache/incubator-mxnet/pull/11205#issuecomment-400152945 Hi @gigasquid CI runs stuff in docker containers. The steps to add your code to CI would be the following: 1. Add your clojure dependencies to `ci/docker/install/ubuntu_clojure.sh` (check how it's done with scala) 2. You need to add one function to `ci/docker/runtime_functions.sh` that builds your package and then add a stage into the Jenkinsfile: See for example this PR which adds an additional Android stage: https://github.com/apache/incubator-mxnet/pull/11382/files#diff-58231b16fdee45a03a4ee3cf94a9f2c3L486 To test locally run: ci/build.py --platform ubuntu_cpu --shm-size 500m /work/runtime_functions.sh unittest_ubuntu_cpu_scala but just use the function for clojure that was just created. ci/build.py --platform ubuntu_cpu -i will put you inside the container, so you can check what steps you need to take and add to the runtime_functions.sh script. Let me know if you have any issues. This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-mxnet-site] branch asf-site updated: Bump the publish timestamp.
This is an automated email from the ASF dual-hosted git repository. zhasheng pushed a commit to branch asf-site in repository https://gitbox.apache.org/repos/asf/incubator-mxnet-site.git The following commit(s) were added to refs/heads/asf-site by this push: new 987250c Bump the publish timestamp. 987250c is described below commit 987250c67fabe01012540acd9181ec4dd0992730 Author: mxnet-ci AuthorDate: Tue Jun 26 01:37:16 2018 + Bump the publish timestamp. --- date.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/date.txt b/date.txt new file mode 100644 index 000..6164f23 --- /dev/null +++ b/date.txt @@ -0,0 +1 @@ +Tue Jun 26 01:37:16 UTC 2018
[GitHub] azai91 commented on issue #11129: [MXNET-497] fix bugs in MKLDNN operators to handle the kAddTo request
azai91 commented on issue #11129: [MXNET-497] fix bugs in MKLDNN operators to handle the kAddTo request URL: https://github.com/apache/incubator-mxnet/pull/11129#issuecomment-400145869 @zheng-da @pengzhao-intel updated PR. please take a look when you have time. This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] TaoLv commented on issue #11399: [WIP] Add Fused Vanilla RNN and dropout
TaoLv commented on issue #11399: [WIP] Add Fused Vanilla RNN and dropout URL: https://github.com/apache/incubator-mxnet/pull/11399#issuecomment-400145788 Please remove [WIP] from the title and add the JIRA number to it. https://issues.apache.org/jira/browse/MXNET-107 This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] lihaofd opened a new pull request #11399: [WIP] Add Fused Vanilla RNN and dropout
lihaofd opened a new pull request #11399: [WIP] Add Fused Vanilla RNN and dropout URL: https://github.com/apache/incubator-mxnet/pull/11399 ## Description ## In this PR, it creates Fused Vanilla RNN(tanh/relu) operator and dropout of GRU/LSTM/vRNN for CPU. @pengzhao-intel, @TaoLv ## Feature changes ## ### New features ### - Single layer/Multiple layer and unidirectional/bidirectional Vanilla RNN(tanh/relu), including both forward and backward computation. - Support dropout of GRU/LSTM/vRNN ### Unit-test changes ### - Create new testcase in tests/python/unittests/test_operator.py. - update testcase in example/rnn/bucketing/cudnn_rnn_bucketing.py - Check consistency with original RNNCell implementation. ### Performance ### We have tested performance of FusedRNN and NonFused RNNCell on our local Skylake-8180 with 2 Sockets and 56 cores. Use MKL as blas lib in this performance test. Test input size is from DS2 default parameters(seq_length = 300, batch_size = 20, input_size = 800, hidden_size = 800). Layer=1 bidirectional = False | API | Inference time(fwd, samples/sec) | Training time(fwd + bwd, samples/sec) | | | :-: | :: | | rnn.RNNCell - NoFusedRNN(Tanh, CPU) | 492.61 | 198.02| | this PR - FusedRNN(Tanh, CPU) | 952.38 | 318.98 | | speedup | 1.93x | 1.61x | | API | Inference time(fwd, samples/sec) | Training time(fwd + bwd, samples/sec) | | | :-: | :: | | rnn.RNNCell - NoFusedRNN(Relu, CPU) | 277.78 | 104.17| | this PR - FusedRNN(Relu, CPU) | 740.74 | 177 | | speedup | 2.67x | 1.7x | Layer=5 bidirectional = True | API | Inference time(fwd, samples/sec) | Training time(fwd + bwd, samples/sec) | | | :-: | :: | | rnn.RNNCell - NoFusedRNN(Tanh, CPU) | 38.91 | 22.73 | | rnn.RNNCell (Tanh, cuda) | 47.85 | 26.95 | | rnn.RNNCell (Tanh, cudnn) | 208.33 | 81.63 | | this PR - FusedRNN(Tanh, CPU) | 104.17 | 34.01 | | speedup -this PR/RNNCell (Tanh, CPU) | 267.7% | 149.7%| | speedup -this PR/RNNCell (Tanh, cuda)| 217.7% | 126.2%| | speedup -this PR/RNNCell (Tanh, cudnn)| 50% | 41.7% | | API | Inference time(fwd, samples/sec) | Training time(fwd + bwd, samples/sec) | | | :-: | :: | | rnn.RNNCell - NoFusedRNN(Relu, CPU) | 40.73 | 22.6 | | rnn.RNNCell (Relu, cuda) | 52.91 | 26.81 | | rnn.RNNCell (Relu, cudnn) | 206.83 | 82.64 | | this PR - FusedRNN(Relu, CPU) | 134.23 | 35.97 | | speedup -this PR/RNNCell (Relu, CPU) | 329.5% | 159.2%| | speedup -this PR/RNNCell (Relu, cuda)| 253.7% | 134.2%| | speedup -this PR/RNNCell (Relu, cudnn)| 64.9% | 43.5% | ### Convergency Curve ### We have tested Convergency of FusedGRU/LSTM(dropout = 0.5) on our CPU-Skylake-8180 with 2 Sockets and 56 cores and GPU-P100 by using example/rnn/bucketing/cudnn_rnn_bucketing.py Test input size is layer = 3, batch_size = 32, num-embed = 800, num-hidden = 800, num-epochs 20 This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at:
[GitHub] gigasquid commented on a change in pull request #11205: Clojure Contrib Package
gigasquid commented on a change in pull request #11205: Clojure Contrib Package
URL: https://github.com/apache/incubator-mxnet/pull/11205#discussion_r197981747
##
File path:
contrib/clojure-package/examples/cnn-text-classification/src/cnn_text_classification/classifier.clj
##
@@ -0,0 +1,112 @@
+;;
+;; Licensed to the Apache Software Foundation (ASF) under one or more
+;; contributor license agreements. See the NOTICE file distributed with
+;; this work for additional information regarding copyright ownership.
+;; The ASF licenses this file to You under the Apache License, Version 2.0
+;; (the "License"); you may not use this file except in compliance with
+;; the License. You may obtain a copy of the License at
+;;
+;;http://www.apache.org/licenses/LICENSE-2.0
+;;
+;; Unless required by applicable law or agreed to in writing, software
+;; distributed under the License is distributed on an "AS IS" BASIS,
+;; WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+;; See the License for the specific language governing permissions and
+;; limitations under the License.
+;;
+
+(ns cnn-text-classification.classifier
+ (:require [cnn-text-classification.data-helper :as data-helper]
+[org.apache.clojure-mxnet.eval-metric :as eval-metric]
+[org.apache.clojure-mxnet.io :as mx-io]
+[org.apache.clojure-mxnet.module :as m]
+[org.apache.clojure-mxnet.ndarray :as ndarray]
+[org.apache.clojure-mxnet.optimizer :as optimizer]
+[org.apache.clojure-mxnet.symbol :as sym]
+[org.apache.clojure-mxnet.context :as context])
+ (:gen-class))
+
+(def mr-dataset-path "data/mr-data") ;; the MR polarity dataset path
+(def glove-file-path "data/glove/glove.6B.50d.txt")
+
+(defn shuffle-data [test-num {:keys [data label sentence-count sentence-size
embedding-size]}]
+ (println "Shuffling the data and splitting into training and test sets")
+ (println {:sentence-count sentence-count
+:sentence-size sentence-size
+:embedding-size embedding-size})
+ (let [shuffled (shuffle (map (fn [d l] [d l]) data label))
+train-num (- (count shuffled) test-num)
+training (into [] (take train-num shuffled))
+test (into [] (drop train-num shuffled))]
+{:training {:data (ndarray/array (into [] (flatten (mapv (fn [v] (first
v)) training)))
+ [train-num 1 sentence-size
embedding-size]) ;; has to be channel x y
+:label (ndarray/array (into [] (flatten (mapv (fn [v] (last v)
) training)))
+ [train-num])}
+ :test {:data (ndarray/array (into [] (flatten (mapv (fn [v] (first v))
test)))
+ [test-num 1 sentence-size embedding-size])
;; has to be channel x y
+:label (ndarray/array (into [] (flatten (mapv (fn [v] (last v) )
test)))
+ [test-num])}}))
+
+;;; convnet with multiple filter sizes
+;; from Convolutional Neural Networks for Sentence Classification by Yoon Kim
+(defn get-multi-filter-convnet [num-embed sentence-size batch-size]
+ (let [filter-list [3 4 5]
+num-filter 100
+num-label 2
+dropout 0.5
+input-x (sym/variable "data")
+polled-outputs (mapv (fn [filter-size]
+ (as-> (sym/convolution {:data input-x
+ :kernel [filter-size
num-embed]
+ :num-filter
num-filter}) data
+ (sym/activation {:data data :act-type "relu"})
+ (sym/pooling {:data data
+ :pool-type "max"
Review comment:
not sure exactly what you are proposing..
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[GitHub] toddsundsted opened a new issue #11398: Floating Point Exception after Array Creation
toddsundsted opened a new issue #11398: Floating Point Exception after Array
Creation
URL: https://github.com/apache/incubator-mxnet/issues/11398
## Description
Expressions like `nd.random_uniform(shape=[5, 5, -2])` and
`nd.random_uniform(shape=[5, 5, 0])` cause the runtime to crash (the former
with `std::bad_alloc`, the latter with `Floating point exception: 8`. It's a
problem in versions 1.1.0 to 1.1.3 (master).
## Environment info (Required)
```
--Python Info--
Version : 3.6.4
Compiler : GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)
Build: ('default', 'Jan 16 2018 12:04:33')
Arch : ('64bit', '')
Pip Info---
Version : 10.0.1
Directory: /Users/tsundsted/miniconda3/lib/python3.6/site-packages/pip
--MXNet Info---
Version : 1.1.0
Directory: /Users/tsundsted/miniconda3/lib/python3.6/site-packages/mxnet
Hashtag not found. Not installed from pre-built package.
--System Info--
Platform : Darwin-16.7.0-x86_64-i386-64bit
system : Darwin
node : Todds-MacBook-Pro.local
release : 16.7.0
version : Darwin Kernel Version 16.7.0: Fri Apr 27 17:59:46 PDT 2018;
root:xnu-3789.73.13~1/RELEASE_X86_64
--Hardware Info--
machine : x86_64
processor: i386
b'machdep.cpu.extfeatures: SYSCALL XD 1GBPAGE EM64T LAHF LZCNT RDTSCP TSCI'
b'machdep.cpu.leaf7_features: SMEP ERMS RDWRFSGS TSC_THREAD_OFFSET BMI1 AVX2
BMI2 INVPCID FPU_CSDS'
b'machdep.cpu.features: FPU VME DE PSE TSC MSR PAE MCE CX8 APIC SEP MTRR PGE
MCA CMOV PAT PSE36 CLFSH DS ACPI MMX FXSR SSE SSE2 SS HTT TM PBE SSE3 PCLMULQDQ
DTES64 MON DSCPL VMX SMX EST TM2 SSSE3 FMA CX16 TPR PDCM SSE4.1 SSE4.2 x2APIC
MOVBE POPCNT AES PCID XSAVE OSXSAVE SEGLIM64 TSCTMR AVX1.0 RDRAND F16C'
b'machdep.cpu.brand_string: Intel(R) Core(TM) i7-4960HQ CPU @ 2.60GHz'
--Network Test--
Setting timeout: 10
Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0176
sec, LOAD: 0.4752 sec.
Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.4092 sec, LOAD:
0.6132 sec.
Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 3.4790 sec, LOAD:
0.7560 sec.
Timing for FashionMNIST:
https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz,
DNS: 0.1225 sec, LOAD: 0.8626 sec.
Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0179 sec, LOAD:
0.4299 sec.
Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0157 sec,
LOAD: 0.3484 sec.
```
I'm using Python.
## Error Message:
```
$ python
Python 3.6.4 |Anaconda, Inc.| (default, Jan 16 2018, 12:04:33)
[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import mxnet as mx
>>> import mxnet.ndarray as nd
>>> nd.random_uniform(shape=[5, 5, -2])
Traceback (most recent call last):
File "", line 1, in
File
"/Users/tsundsted/miniconda3/lib/python3.6/site-packages/mxnet/ndarray/ndarray.py",
line 182, in __repr__
return '\n%s\n<%s %s @%s>' % (str(self.asnumpy()),
File
"/Users/tsundsted/miniconda3/lib/python3.6/site-packages/mxnet/ndarray/ndarray.py",
line 1793, in asnumpy
libc++abi.dylib: terminating with uncaught exception of type std::bad_alloc:
std::bad_alloc
Abort trap: 6
```
```
$ python
Python 3.6.4 |Anaconda, Inc.| (default, Jan 16 2018, 12:04:33)
[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import mxnet as mx
>>> import mxnet.ndarray as nd
>>> nd.random_uniform(shape=[5, 5, 0])
Floating point exception: 8
```
## Minimum reproducible example
Any shape with a non-positive dimension size: for example,
`nd.random_uniform(shape=[5, 5, -2])` and `nd.random_uniform(shape=[5, 5, 0])`.
## Steps to reproduce
1. run python
2. `import mxnet as mx`
3. `import mxnet.ndarray as nd`
4. `nd.random_uniform(shape=[5, 5, 0])`
## What have you tried to solve it?
Created https://github.com/apache/incubator-mxnet/pull/11397
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[GitHub] gigasquid commented on a change in pull request #11205: Clojure Contrib Package
gigasquid commented on a change in pull request #11205: Clojure Contrib Package
URL: https://github.com/apache/incubator-mxnet/pull/11205#discussion_r197981330
##
File path:
contrib/clojure-package/examples/cnn-text-classification/src/cnn_text_classification/classifier.clj
##
@@ -0,0 +1,112 @@
+;;
+;; Licensed to the Apache Software Foundation (ASF) under one or more
+;; contributor license agreements. See the NOTICE file distributed with
+;; this work for additional information regarding copyright ownership.
+;; The ASF licenses this file to You under the Apache License, Version 2.0
+;; (the "License"); you may not use this file except in compliance with
+;; the License. You may obtain a copy of the License at
+;;
+;;http://www.apache.org/licenses/LICENSE-2.0
+;;
+;; Unless required by applicable law or agreed to in writing, software
+;; distributed under the License is distributed on an "AS IS" BASIS,
+;; WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+;; See the License for the specific language governing permissions and
+;; limitations under the License.
+;;
+
+(ns cnn-text-classification.classifier
+ (:require [cnn-text-classification.data-helper :as data-helper]
+[org.apache.clojure-mxnet.eval-metric :as eval-metric]
+[org.apache.clojure-mxnet.io :as mx-io]
+[org.apache.clojure-mxnet.module :as m]
+[org.apache.clojure-mxnet.ndarray :as ndarray]
+[org.apache.clojure-mxnet.optimizer :as optimizer]
+[org.apache.clojure-mxnet.symbol :as sym]
+[org.apache.clojure-mxnet.context :as context])
+ (:gen-class))
+
+(def mr-dataset-path "data/mr-data") ;; the MR polarity dataset path
+(def glove-file-path "data/glove/glove.6B.50d.txt")
+
+(defn shuffle-data [test-num {:keys [data label sentence-count sentence-size
embedding-size]}]
+ (println "Shuffling the data and splitting into training and test sets")
+ (println {:sentence-count sentence-count
+:sentence-size sentence-size
+:embedding-size embedding-size})
+ (let [shuffled (shuffle (map (fn [d l] [d l]) data label))
+train-num (- (count shuffled) test-num)
+training (into [] (take train-num shuffled))
+test (into [] (drop train-num shuffled))]
+{:training {:data (ndarray/array (into [] (flatten (mapv (fn [v] (first
v)) training)))
Review comment:
I think `#()` here is preferable. Sometimes, when I spent too much time
translating Scala code to Clojure, my brain got a bit fuzzy - will fix
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[GitHub] gigasquid commented on a change in pull request #11205: Clojure Contrib Package
gigasquid commented on a change in pull request #11205: Clojure Contrib Package URL: https://github.com/apache/incubator-mxnet/pull/11205#discussion_r197980344 ## File path: contrib/clojure-package/examples/cnn-text-classification/README.md ## @@ -0,0 +1,38 @@ +# cnn-text-classification + +An example of text classification using CNN + +To use you must download the MR polarity dataset and put it in the path specified in the mr-dataset-path +The dataset can be obtained here: [https://github.com/yoonkim/CNN_sentence](https://github.com/yoonkim/CNN_sentence). The two files `rt-polarity.neg` +and `rt-polarity.pos` must be put in a directory. For example, `data/mr-data/rt-polarity.neg`. + +You also must download the glove word embeddings. The suggested one to use is the smaller 50 dimension one Review comment: Word2vec is available in the demo as well - but I haven't been able to test that yet. I can put that on the Needs Help page https://cwiki.apache.org/confluence/display/MXNET/Clojure+Package+Contribution+Needs This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] gigasquid commented on a change in pull request #11205: Clojure Contrib Package
gigasquid commented on a change in pull request #11205: Clojure Contrib Package URL: https://github.com/apache/incubator-mxnet/pull/11205#discussion_r197979848 ## File path: contrib/clojure-package/examples/cnn-text-classification/README.md ## @@ -0,0 +1,38 @@ +# cnn-text-classification + +An example of text classification using CNN + +To use you must download the MR polarity dataset and put it in the path specified in the mr-dataset-path +The dataset can be obtained here: [https://github.com/yoonkim/CNN_sentence](https://github.com/yoonkim/CNN_sentence). The two files `rt-polarity.neg` +and `rt-polarity.pos` must be put in a directory. For example, `data/mr-data/rt-polarity.neg`. + +You also must download the glove word embeddings. The suggested one to use is the smaller 50 dimension one Review comment: It was smaller and could fit into my laptop memory :) This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] gigasquid commented on a change in pull request #11205: Clojure Contrib Package
gigasquid commented on a change in pull request #11205: Clojure Contrib Package URL: https://github.com/apache/incubator-mxnet/pull/11205#discussion_r197979725 ## File path: contrib/clojure-package/README.md ## @@ -0,0 +1,203 @@ +# Clojure MXNet + +A clojure package to the MXNet Deep Learning library + +## Introduction + +MXNet is a first class, modern deep learning library that AWS has officially picked as its chosen library. It supports multiple languages on a first class basis and is incubating as an Apache project. + +The motivation for creating a Clojure package is to be able to open the deep learning library to the Clojure ecosystem and build bridges for future development and innovation for the community. It provides all the needed tools including low level and high level apis, dynamic graphs, and things like GAN and natural language support. + +For high leverage, the Clojure package has been built on the existing Scala package using interop. This has allowed rapid development and close parity with the Scala functionality. This also leaves the door open to directly developing code against the jni-bindings with Clojure in the future in an incremental fashion, using the test suites as a refactoring guide. + +## Current State and Plans + +The Clojure package is nearing the end of its first development milestone which is to achieve a close parity with the Scala package and to potentially be included into the main project for official Clojure language support. + +What is needed now is alpha testing on both OSX and Linux to discover any bugs, rough edges, and generally harden it before an official PR is opened on the main project. + +Help with this effort is greatly appreciated and contributors will be recognized in the project README. + +Testing instructions can be found in the Testing.md + +## Getting Started + +The following systems are supported: + +- OSX cpu +- Linux cpu +- Linux gpu + +There are two ways of getting going. The first way is the easiest and that is to use the pre-built jars from Maven. The second way is to build from source. In both cases, you will need to load the prereqs and dependencies, (like opencv). + +It's been tested on AWS Deep Learning AMI and OSX High Sierra 10.13.4 + + +### Prerequisites + +**If you are using the AWS Deep Learning Ubuntu or Linux AMI you should be good to go without doing anything on this step.** + + +Follow the instructions from https://mxnet.incubator.apache.org/install/osx_setup.html or https://mxnet.incubator.apache.org/install/ubuntu_setup.html +about _Prepare Environment for GPU Installation_ +and _Install MXNet dependencies_ + + + Cloning the repo and running from source + +To use the prebuilt jars, you will need to replace the native version of the line in the project dependencies with your configuration. + +`[org.apache.mxnet/mxnet-full_2.11-linux-x86_64-gpu "1.2.0"]` +or +`[org.apache.mxnet/mxnet-full_2.11-linux-x86_64-cpu "1.2.0"]` +or +`[org.apache.mxnet/mxnet-full_2.11-osx-x86_64-cpu "1.2.0"]` + + +```clojure + +(ns tutorial.ndarray + (:require [org.apache.clojure-mxnet.ndarray :as ndarray] +[org.apache.clojure-mxnet.context :as context])) + +;;Create NDArray +(def a (ndarray/zeros [100 50])) ;;all zero arrray of dimension 100 x 50 +(def b (ndarray/ones [256 32 128 1])) ;; all one array of dimension +(def c (ndarray/array [1 2 3 4 5 6] [2 3])) ;; array with contents of a shape 2 x 3 + +;;; There are also ways to convert to a vec or get the shape as an object or vec +(ndarray/->vec c) ;=> [1.0 2.0 3.0 4.0 5.0 6.0] +``` + +See the examples/tutorial section for more. + + +The jars from maven with the needed MXNet native binaries in it. On startup, the native libraries are extracted from the jar and copied into a temporary location on your path. On termination, they are deleted. + +If you want details on the flags (opencv verison and cuda version of the jars), they are documented here https://cwiki.apache.org/confluence/display/MXNET/MXNet-Scala+Release+Process + + +### Build from MXNET Source + +Checkout the latest sha from the main package + +`git clone --recursive https://github.com/dmlc/mxnet ~/mxnet` +`cd ~/mxnet` + + +`git checkout tags/1.2.0 -b release-1.2.0` + +`git submodule update --init --recursive` + +Sometimes it useful to use this script to clean hard +https://gist.github.com/nicktoumpelis/11214362 + + +Go here to do the base package installation https://mxnet.incubator.apache.org/install/index.html + + Run `make scalapkg` then `make scalainstall` + +then replace the correct jar for your architecture in the project.clj, example `[ml.dmlc.mxnet/mxnet-full_2.11-osx-x86_64-cpu "1.0.1-SNAPSHOT"]` + + Test your installation + +To test your installation, you should run `lein test`. This will run the test suite (CPU) for the clojure package. + + + Generation of NDArray and Symbol apis + +The bulk of the ndarray and symbol apis are generated via java reflection
[GitHub] toddsundsted opened a new pull request #11397: Check Shape
toddsundsted opened a new pull request #11397: Check Shape URL: https://github.com/apache/incubator-mxnet/pull/11397 ## Description ## The `NDArray` constructors do not ensure that shape dimensions are all positive numbers. In Python, at least, expressions like `nd.random_uniform(shape=[5, 5, -2])` and `nd.random_uniform(shape=[5, 5, 0])` cause the runtime to crash. ## Checklist ## ### Essentials ### - [X] Changes are complete (i.e. I finished coding on this PR) - [X] Code is well-documented: - [X] To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change ### Changes ### - Create a method `CheckShape()` and invoke it in every constructor. ## Comments ## I didn't see unit tests for `NDArray`. I'd be happy to use or create unit tests, if that is desired. This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] gigasquid commented on a change in pull request #11205: Clojure Contrib Package
gigasquid commented on a change in pull request #11205: Clojure Contrib Package URL: https://github.com/apache/incubator-mxnet/pull/11205#discussion_r197979462 ## File path: contrib/clojure-package/README.md ## @@ -0,0 +1,203 @@ +# Clojure MXNet + +A clojure package to the MXNet Deep Learning library + +## Introduction + +MXNet is a first class, modern deep learning library that AWS has officially picked as its chosen library. It supports multiple languages on a first class basis and is incubating as an Apache project. + +The motivation for creating a Clojure package is to be able to open the deep learning library to the Clojure ecosystem and build bridges for future development and innovation for the community. It provides all the needed tools including low level and high level apis, dynamic graphs, and things like GAN and natural language support. + +For high leverage, the Clojure package has been built on the existing Scala package using interop. This has allowed rapid development and close parity with the Scala functionality. This also leaves the door open to directly developing code against the jni-bindings with Clojure in the future in an incremental fashion, using the test suites as a refactoring guide. + +## Current State and Plans + +The Clojure package is nearing the end of its first development milestone which is to achieve a close parity with the Scala package and to potentially be included into the main project for official Clojure language support. + +What is needed now is alpha testing on both OSX and Linux to discover any bugs, rough edges, and generally harden it before an official PR is opened on the main project. + +Help with this effort is greatly appreciated and contributors will be recognized in the project README. + +Testing instructions can be found in the Testing.md + +## Getting Started + +The following systems are supported: + +- OSX cpu +- Linux cpu +- Linux gpu + +There are two ways of getting going. The first way is the easiest and that is to use the pre-built jars from Maven. The second way is to build from source. In both cases, you will need to load the prereqs and dependencies, (like opencv). + +It's been tested on AWS Deep Learning AMI and OSX High Sierra 10.13.4 + + +### Prerequisites + +**If you are using the AWS Deep Learning Ubuntu or Linux AMI you should be good to go without doing anything on this step.** + + +Follow the instructions from https://mxnet.incubator.apache.org/install/osx_setup.html or https://mxnet.incubator.apache.org/install/ubuntu_setup.html +about _Prepare Environment for GPU Installation_ +and _Install MXNet dependencies_ + + + Cloning the repo and running from source + +To use the prebuilt jars, you will need to replace the native version of the line in the project dependencies with your configuration. + +`[org.apache.mxnet/mxnet-full_2.11-linux-x86_64-gpu "1.2.0"]` +or +`[org.apache.mxnet/mxnet-full_2.11-linux-x86_64-cpu "1.2.0"]` +or +`[org.apache.mxnet/mxnet-full_2.11-osx-x86_64-cpu "1.2.0"]` + + +```clojure + +(ns tutorial.ndarray + (:require [org.apache.clojure-mxnet.ndarray :as ndarray] +[org.apache.clojure-mxnet.context :as context])) + +;;Create NDArray +(def a (ndarray/zeros [100 50])) ;;all zero arrray of dimension 100 x 50 +(def b (ndarray/ones [256 32 128 1])) ;; all one array of dimension +(def c (ndarray/array [1 2 3 4 5 6] [2 3])) ;; array with contents of a shape 2 x 3 + +;;; There are also ways to convert to a vec or get the shape as an object or vec +(ndarray/->vec c) ;=> [1.0 2.0 3.0 4.0 5.0 6.0] +``` + +See the examples/tutorial section for more. + + +The jars from maven with the needed MXNet native binaries in it. On startup, the native libraries are extracted from the jar and copied into a temporary location on your path. On termination, they are deleted. + +If you want details on the flags (opencv verison and cuda version of the jars), they are documented here https://cwiki.apache.org/confluence/display/MXNET/MXNet-Scala+Release+Process + + +### Build from MXNET Source + +Checkout the latest sha from the main package + +`git clone --recursive https://github.com/dmlc/mxnet ~/mxnet` +`cd ~/mxnet` + + +`git checkout tags/1.2.0 -b release-1.2.0` + +`git submodule update --init --recursive` + +Sometimes it useful to use this script to clean hard +https://gist.github.com/nicktoumpelis/11214362 + + +Go here to do the base package installation https://mxnet.incubator.apache.org/install/index.html + + Run `make scalapkg` then `make scalainstall` + +then replace the correct jar for your architecture in the project.clj, example `[ml.dmlc.mxnet/mxnet-full_2.11-osx-x86_64-cpu "1.0.1-SNAPSHOT"]` + + Test your installation + +To test your installation, you should run `lein test`. This will run the test suite (CPU) for the clojure package. + + + Generation of NDArray and Symbol apis + +The bulk of the ndarray and symbol apis are generated via java reflection
[GitHub] spidyDev commented on issue #9857: C++ test Core dump DROPOUT_PERF.TimingGPU
spidyDev commented on issue #9857: C++ test Core dump DROPOUT_PERF.TimingGPU URL: https://github.com/apache/incubator-mxnet/issues/9857#issuecomment-400130297 @marcoabreu I ran this test ~1000 times, couldnt replicate the failure. Can we close this issue ? This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] piiswrong closed pull request #10931: [MXNET-349] Histogram Operator
piiswrong closed pull request #10931: [MXNET-349] Histogram Operator
URL: https://github.com/apache/incubator-mxnet/pull/10931
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As GitHub hides the original diff on merge, it is displayed below for
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diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py
index f017d7e65e7..002ce3ebbc2 100644
--- a/python/mxnet/ndarray/ndarray.py
+++ b/python/mxnet/ndarray/ndarray.py
@@ -46,7 +46,7 @@
"ones", "add", "arange", "eye", "divide", "equal", "full",
"greater", "greater_equal",
"imdecode", "lesser", "lesser_equal", "logical_and", "logical_or",
"logical_xor",
"maximum", "minimum", "moveaxis", "modulo", "multiply",
"not_equal", "onehot_encode",
- "power", "subtract", "true_divide", "waitall", "_new_empty_handle"]
+ "power", "subtract", "true_divide", "waitall", "_new_empty_handle",
"histogram"]
_STORAGE_TYPE_UNDEFINED = -1
_STORAGE_TYPE_DEFAULT = 0
@@ -3740,3 +3740,36 @@ def empty(shape, ctx=None, dtype=None):
if dtype is None:
dtype = mx_real_t
return NDArray(handle=_new_alloc_handle(shape, ctx, False, dtype))
+
+
+# pylint: disable= redefined-builtin
+def histogram(a, bins=10, range=None):
+"""Compute the histogram of the input data.
+
+Parameters
+--
+a : NDArray
+Input data. The histogram is computed over the flattened array.
+bins : int or sequence of scalars
+If bins is an int, it defines the number of equal-width bins in the
+given range (10, by default). If bins is a sequence, it defines the
bin edges,
+including the rightmost edge, allowing for non-uniform bin widths.
+range : (float, float), optional
+The lower and upper range of the bins. If not provided, range is
simply (a.min(), a.max()).
+Values outside the range are ignored. The first element of the range
must be less than or
+equal to the second. range affects the automatic bin computation as
well, the range will
+be equally divided by the number of bins.
+"""
+
+# pylint: disable= no-member, protected-access
+if isinstance(bins, NDArray):
+return _internal._histogram(data=a, bins=bins)
+elif isinstance(bins, integer_types):
+if range is None:
+warnings.warn("range is not specified, using numpy's result "
+ "to ensure consistency with numpy")
+res, bin_bounds = np.histogram(a.asnumpy(), bins=bins)
+return array(res), array(bin_bounds)
+return _internal._histogram(data=a, bin_cnt=bins, range=range)
+raise ValueError("bins argument should be either an integer or an NDArray")
+# pylint: enable= no-member, protected-access, redefined-builtin
diff --git a/python/mxnet/symbol/symbol.py b/python/mxnet/symbol/symbol.py
index 7e5b52770fe..c5e2f5cb77d 100644
--- a/python/mxnet/symbol/symbol.py
+++ b/python/mxnet/symbol/symbol.py
@@ -34,7 +34,7 @@
from ..attribute import AttrScope
from ..base import _LIB, numeric_types, c_array, c_array_buf, c_str,
c_str_array, c_handle_array
-from ..base import mx_uint, py_str, string_types
+from ..base import mx_uint, py_str, string_types, integer_types
from ..base import NDArrayHandle, ExecutorHandle, SymbolHandle
from ..base import check_call, MXNetError, NotImplementedForSymbol
from ..context import Context, current_context
@@ -47,7 +47,8 @@
from ._internal import SymbolBase, _set_symbol_class
__all__ = ["Symbol", "var", "Variable", "Group", "load", "load_json",
- "pow", "maximum", "minimum", "hypot", "eye", "zeros", "ones",
"full", "arange"]
+ "pow", "maximum", "minimum", "hypot", "eye", "zeros", "ones",
"full", "arange",
+ "histogram"]
class Symbol(SymbolBase):
@@ -2864,4 +2865,29 @@ def arange(start, stop=None, step=1.0, repeat=1,
name=None, dtype=None):
return _internal._arange(start=start, stop=stop, step=step, repeat=repeat,
name=name, dtype=dtype)
+def histogram(a, bins=10, range=None, **kwargs):
+"""Compute the histogram of the input data.
+
+Parameters
+--
+a : NDArray
+Input data. The histogram is computed over the flattened array.
+bins : int or sequence of scalars
+If bins is an int, it defines the number of equal-width bins in the
+given range (10, by default). If bins is a sequence, it defines the
bin edges,
+including the rightmost edge, allowing for non-uniform bin widths.
+range : (float, float), required if bins is an integer
+The lower and upper range of the bins. If not provided, range is
simply (a.min(), a.max()).
+Values outside the range are ignored. The first element of the range
must be less than or
+equal to the
[incubator-mxnet] branch master updated: [MXNET-349] Histogram Operator (#10931)
This is an automated email from the ASF dual-hosted git repository.
jxie pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/master by this push:
new ed7e360 [MXNET-349] Histogram Operator (#10931)
ed7e360 is described below
commit ed7e3602a8046646582c0c681b70d9556f5fa0a4
Author: Hao Jin
AuthorDate: Mon Jun 25 16:45:32 2018 -0700
[MXNET-349] Histogram Operator (#10931)
* implementation of histogram operator
* address code reviews and code re-design
* add exception for invalid inputs
* address code reviews
* add symbol and symbolic forward check for histogram
---
python/mxnet/ndarray/ndarray.py | 35 +-
python/mxnet/symbol/symbol.py| 30 -
src/common/cuda_utils.h | 30 +
src/operator/tensor/histogram-inl.h | 172 +++
src/operator/tensor/histogram.cc | 159 +
src/operator/tensor/histogram.cu | 111 +
src/operator/tensor/util/tensor_util-inl.cuh | 4 +-
tests/python/unittest/test_operator.py | 34 ++
8 files changed, 571 insertions(+), 4 deletions(-)
diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py
index f017d7e..002ce3e 100644
--- a/python/mxnet/ndarray/ndarray.py
+++ b/python/mxnet/ndarray/ndarray.py
@@ -46,7 +46,7 @@ __all__ = ["NDArray", "concatenate", "_DTYPE_NP_TO_MX",
"_DTYPE_MX_TO_NP", "_GRA
"ones", "add", "arange", "eye", "divide", "equal", "full",
"greater", "greater_equal",
"imdecode", "lesser", "lesser_equal", "logical_and", "logical_or",
"logical_xor",
"maximum", "minimum", "moveaxis", "modulo", "multiply",
"not_equal", "onehot_encode",
- "power", "subtract", "true_divide", "waitall", "_new_empty_handle"]
+ "power", "subtract", "true_divide", "waitall", "_new_empty_handle",
"histogram"]
_STORAGE_TYPE_UNDEFINED = -1
_STORAGE_TYPE_DEFAULT = 0
@@ -3740,3 +3740,36 @@ def empty(shape, ctx=None, dtype=None):
if dtype is None:
dtype = mx_real_t
return NDArray(handle=_new_alloc_handle(shape, ctx, False, dtype))
+
+
+# pylint: disable= redefined-builtin
+def histogram(a, bins=10, range=None):
+"""Compute the histogram of the input data.
+
+Parameters
+--
+a : NDArray
+Input data. The histogram is computed over the flattened array.
+bins : int or sequence of scalars
+If bins is an int, it defines the number of equal-width bins in the
+given range (10, by default). If bins is a sequence, it defines the
bin edges,
+including the rightmost edge, allowing for non-uniform bin widths.
+range : (float, float), optional
+The lower and upper range of the bins. If not provided, range is
simply (a.min(), a.max()).
+Values outside the range are ignored. The first element of the range
must be less than or
+equal to the second. range affects the automatic bin computation as
well, the range will
+be equally divided by the number of bins.
+"""
+
+# pylint: disable= no-member, protected-access
+if isinstance(bins, NDArray):
+return _internal._histogram(data=a, bins=bins)
+elif isinstance(bins, integer_types):
+if range is None:
+warnings.warn("range is not specified, using numpy's result "
+ "to ensure consistency with numpy")
+res, bin_bounds = np.histogram(a.asnumpy(), bins=bins)
+return array(res), array(bin_bounds)
+return _internal._histogram(data=a, bin_cnt=bins, range=range)
+raise ValueError("bins argument should be either an integer or an NDArray")
+# pylint: enable= no-member, protected-access, redefined-builtin
diff --git a/python/mxnet/symbol/symbol.py b/python/mxnet/symbol/symbol.py
index 7e5b527..c5e2f5c 100644
--- a/python/mxnet/symbol/symbol.py
+++ b/python/mxnet/symbol/symbol.py
@@ -34,7 +34,7 @@ import numpy as _numpy
from ..attribute import AttrScope
from ..base import _LIB, numeric_types, c_array, c_array_buf, c_str,
c_str_array, c_handle_array
-from ..base import mx_uint, py_str, string_types
+from ..base import mx_uint, py_str, string_types, integer_types
from ..base import NDArrayHandle, ExecutorHandle, SymbolHandle
from ..base import check_call, MXNetError, NotImplementedForSymbol
from ..context import Context, current_context
@@ -47,7 +47,8 @@ from . import op
from ._internal import SymbolBase, _set_symbol_class
__all__ = ["Symbol", "var", "Variable", "Group", "load", "load_json",
- "pow", "maximum", "minimum", "hypot", "eye", "zeros", "ones",
"full", "arange"]
+ "pow", "maximum", "minimum", "hypot", "eye", "zeros", "ones",
"full", "arange",
+ "histogram"]
class Symbol(SymbolBase):
@@ -2864,4 +2865,29
[GitHub] azai91 edited a comment on issue #11371: [MXNET-486] Create CPP test for concat MKLDNN operator
azai91 edited a comment on issue #11371: [MXNET-486] Create CPP test for concat MKLDNN operator URL: https://github.com/apache/incubator-mxnet/pull/11371#issuecomment-400086783 @zheng-da @pengzhao-intel please review when you have time. This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-mxnet] branch master updated: Fix flaky test test_operator.test_binary_op due to numerical errors (#11259)
This is an automated email from the ASF dual-hosted git repository.
zhasheng pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/master by this push:
new 21ff36b Fix flaky test test_operator.test_binary_op due to numerical
errors (#11259)
21ff36b is described below
commit 21ff36b06bf47ff2ac4145ce60ec1fe5dd14ce1d
Author: Pedro Larroy <928489+lar...@users.noreply.github.com>
AuthorDate: Mon Jun 25 16:35:23 2018 -0700
Fix flaky test test_operator.test_binary_op due to numerical errors (#11259)
Use float64 computations as the reference numpy implementation operates in
double and not float.
f64(f32(f64(.))) % f64(f32(f64(.))) is not the same as f64(.) % f64(.) due
to limited precission.
fixes #9853
---
tests/python/unittest/test_operator.py | 50 --
1 file changed, 36 insertions(+), 14 deletions(-)
diff --git a/tests/python/unittest/test_operator.py
b/tests/python/unittest/test_operator.py
index fbd3886..287d830 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -1550,6 +1550,7 @@ def gen_broadcast_data_int(idx):
def gen_binary_data(dummy):
ndim = np.random.randint(1, 6)
shape = np.random.randint(1, 6, size=(ndim,))
+#print("gen shape {}".format(shape))
return [np.random.random(shape), np.random.random(shape)]
@@ -1562,27 +1563,46 @@ def check_binary_op_forward(symbol, baseline, gen_data,
rtol=1e-3, atol=1e-5, mx
sample_num = 200
for i in range(sample_num):
d = gen_data(i)
-x = baseline(d[0], d[1])
y = symbol.bind(default_context(), args={'a': mx.nd.array(d[0]), 'b':
mx.nd.array(d[1])})
y.forward(is_train=True)
y = y.outputs[0].asnumpy()
+x = baseline(d[0], d[1]).astype(y.dtype)
+
+#np.set_printoptions(precision=20)
+
+a = d[0]
+b = d[1]
+#print("a: {} {}".format(a.dtype, a))
+#print("a: {} {}".format(b.dtype, b))
+
+#print("x: {} {}".format(x.dtype, x))
+#print("y: {} {}".format(y.dtype, y))
if mx_nd_func is not None:
d0 = mx.nd.array(d[0], dtype=d[0].dtype)
d1 = mx.nd.array(d[1], dtype=d[1].dtype)
assert_almost_equal(y, mx_nd_func(d0, d1).asnumpy(), rtol=rtol,
atol=atol)
idx = np.abs(x-y) > atol+rtol*np.abs(x)
if idx.any():
-print('found precision problem')
+import binascii
+np.set_printoptions(precision=20)
+logging.error('found precision problem:')
d[0] = np.broadcast_to(d[0], x.shape)
d[1] = np.broadcast_to(d[1], x.shape)
-print('a: {}'.format(d[0][idx]))
-print('b: {}'.format(d[1][idx]))
-import struct
-print('a hex: {}'.format(struct.pack('d',
d[0][idx]).encode('hex')))
-print('b hex: {}'.format(struct.pack('d', np.broadcast_to(d[1],
x.shape)[idx]).encode('hex')))
-print('in baseline(a, b): {}'.format(x[idx]))
-print('in symbol(a, b): {}'.format(y[idx]))
-print('diff: {}'.format(np.abs(x-y)[idx] -
atol-rtol*np.abs(x)[idx]))
+logging.error('input a: {}'.format(d[0][idx]))
+logging.error('input b: {}'.format(d[1][idx]))
+logging.error("output x: {} {}".format(x.dtype, x))
+logging.error("output y: {} {}".format(y.dtype, y))
+def ftohex(xs):
+import struct
+return list(map(lambda x: binascii.hexlify(struct.pack('d',
x)), xs.flatten()))
+logging.error('output x in baseline(a, b): {}'.format(x[idx]))
+logging.error('output y in symbol(a, b): {}'.format(y[idx]))
+logging.error('output x in baseline(a,b) hex:
{}'.format(ftohex(x[idx])))
+logging.error('output y in symbol(a,b) hex:
{}'.format(ftohex(y[idx])))
+logging.error('input a hex: {}'.format(ftohex(d[0][idx])))
+logging.error('input a hex: {}'.format(ftohex(d[1][idx])))
+
+logging.error('diff: {}'.format(np.abs(x-y)[idx] -
atol-rtol*np.abs(x)[idx]))
assert_allclose(y, x, rtol=rtol, atol=atol)
@@ -1641,10 +1661,13 @@ def test_binary_op():
check_binary_op_backward(c, lambda g_out, a, b: (g_out / b, - g_out *
a / (b * b)), gen_binary_data)
def test_bmod(a, b):
-c = a % b
+# Python and numpy operate only in double so to avoid numerical errors
we have to use
+# doubles as well. This was a flaky test before when using float32.
seed 1688524483, 1768433044
+#c = a % b
+c = mx.sym.cast(a, dtype='float64') % mx.sym.cast(b, dtype='float64')
# '%' is sensitive to the precision of the calculation. Force numpy
to match mxnet's float32.
-# Issue exposed with seed 1768433044
-
[GitHub] szha closed issue #9853: Flaky test_operator.test_binary_op
szha closed issue #9853: Flaky test_operator.test_binary_op URL: https://github.com/apache/incubator-mxnet/issues/9853 This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] szha closed pull request #11259: [MXNET-184] Fix flaky test test_operator.test_binary_op due to numerical errors
szha closed pull request #11259: [MXNET-184] Fix flaky test
test_operator.test_binary_op due to numerical errors
URL: https://github.com/apache/incubator-mxnet/pull/11259
This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:
As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):
diff --git a/tests/python/unittest/test_operator.py
b/tests/python/unittest/test_operator.py
index 67426693436..c1f6ba0dcf9 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -60,14 +60,14 @@ def check_rnn_consistency(cell1, cell2, T, N, I, H,
grad_req):
dy = mx.random.uniform(shape=mod1.get_outputs()[0].shape)
mod1.backward(out_grads=[dy])
-mod2.backward(out_grads=[dy])
+mod2.backward(out_grads=[dy])
if grad_req != 'null':
assert_allclose(mod1.get_input_grads()[0].asnumpy(),
mod2.get_input_grads()[0].asnumpy(), rtol=1e-2, atol=1e-4)
else:
assert(mod1.get_input_grads()[0] == None)
assert(mod2.get_input_grads()[0] == None)
-
-
+
+
@with_seed()
def test_lstm_sym():
@@ -77,7 +77,7 @@ def test_lstm_sym():
stack.add(mx.rnn.LSTMCell(H, prefix='l0_'))
stack.add(mx.rnn.LSTMCell(H, prefix='l1_'))
stack.add(mx.rnn.LSTMCell(H, prefix='l2_'))
-
+
check_rnn_consistency(fused, stack, T, N, I, H, 'write')
check_rnn_consistency(fused, stack, T, N, I, H, 'add')
check_rnn_consistency(fused, stack, T, N, I, H, 'null')
@@ -120,21 +120,21 @@ def test_gru_sym():
@with_seed()
def test_gru_bidirectional():
T, N, I, H = 5, 20, 800, 800
-
+
fused = mx.rnn.FusedRNNCell(H, num_layers=2, mode='gru',
bidirectional=True, get_next_state=True,
prefix='')
-
+
stack = mx.rnn.SequentialRNNCell()
stack.add(mx.rnn.BidirectionalCell(
mx.rnn.GRUCell(H, prefix='l0_'),
mx.rnn.GRUCell(H, prefix='r0_'),
-output_prefix='bi_gru_0_'))
-
+output_prefix='bi_gru_0_'))
+
stack.add(mx.rnn.BidirectionalCell(
mx.rnn.GRUCell(H, prefix='l1_'),
mx.rnn.GRUCell(H, prefix='r1_'),
output_prefix='bi_gru_1_'))
-
+
check_rnn_consistency(fused, stack, T, N, I, H, 'write')
check_rnn_consistency(fused, stack, T, N, I, H, 'add')
check_rnn_consistency(fused, stack, T, N, I, H, 'null')
@@ -1553,6 +1553,7 @@ def gen_broadcast_data_int(idx):
def gen_binary_data(dummy):
ndim = np.random.randint(1, 6)
shape = np.random.randint(1, 6, size=(ndim,))
+#print("gen shape {}".format(shape))
return [np.random.random(shape), np.random.random(shape)]
@@ -1565,27 +1566,46 @@ def check_binary_op_forward(symbol, baseline, gen_data,
rtol=1e-3, atol=1e-5, mx
sample_num = 200
for i in range(sample_num):
d = gen_data(i)
-x = baseline(d[0], d[1])
y = symbol.bind(default_context(), args={'a': mx.nd.array(d[0]), 'b':
mx.nd.array(d[1])})
y.forward(is_train=True)
y = y.outputs[0].asnumpy()
+x = baseline(d[0], d[1]).astype(y.dtype)
+
+#np.set_printoptions(precision=20)
+
+a = d[0]
+b = d[1]
+#print("a: {} {}".format(a.dtype, a))
+#print("a: {} {}".format(b.dtype, b))
+
+#print("x: {} {}".format(x.dtype, x))
+#print("y: {} {}".format(y.dtype, y))
if mx_nd_func is not None:
d0 = mx.nd.array(d[0], dtype=d[0].dtype)
d1 = mx.nd.array(d[1], dtype=d[1].dtype)
assert_almost_equal(y, mx_nd_func(d0, d1).asnumpy(), rtol=rtol,
atol=atol)
idx = np.abs(x-y) > atol+rtol*np.abs(x)
if idx.any():
-print('found precision problem')
+import binascii
+np.set_printoptions(precision=20)
+logging.error('found precision problem:')
d[0] = np.broadcast_to(d[0], x.shape)
d[1] = np.broadcast_to(d[1], x.shape)
-print('a: {}'.format(d[0][idx]))
-print('b: {}'.format(d[1][idx]))
-import struct
-print('a hex: {}'.format(struct.pack('d',
d[0][idx]).encode('hex')))
-print('b hex: {}'.format(struct.pack('d', np.broadcast_to(d[1],
x.shape)[idx]).encode('hex')))
-print('in baseline(a, b): {}'.format(x[idx]))
-print('in symbol(a, b): {}'.format(y[idx]))
-print('diff: {}'.format(np.abs(x-y)[idx] -
atol-rtol*np.abs(x)[idx]))
+logging.error('input a: {}'.format(d[0][idx]))
+logging.error('input b: {}'.format(d[1][idx]))
+logging.error("output x: {} {}".format(x.dtype, x))
+logging.error("output y: {} {}".format(y.dtype, y))
+def ftohex(xs):
+import struct
+return
[GitHub] gigasquid commented on a change in pull request #11205: Clojure Contrib Package
gigasquid commented on a change in pull request #11205: Clojure Contrib Package URL: https://github.com/apache/incubator-mxnet/pull/11205#discussion_r197973457 ## File path: contrib/clojure-package/README.md ## @@ -0,0 +1,203 @@ +# Clojure MXNet + +A clojure package to the MXNet Deep Learning library + +## Introduction + +MXNet is a first class, modern deep learning library that AWS has officially picked as its chosen library. It supports multiple languages on a first class basis and is incubating as an Apache project. + +The motivation for creating a Clojure package is to be able to open the deep learning library to the Clojure ecosystem and build bridges for future development and innovation for the community. It provides all the needed tools including low level and high level apis, dynamic graphs, and things like GAN and natural language support. + +For high leverage, the Clojure package has been built on the existing Scala package using interop. This has allowed rapid development and close parity with the Scala functionality. This also leaves the door open to directly developing code against the jni-bindings with Clojure in the future in an incremental fashion, using the test suites as a refactoring guide. + +## Current State and Plans + +The Clojure package is nearing the end of its first development milestone which is to achieve a close parity with the Scala package and to potentially be included into the main project for official Clojure language support. + +What is needed now is alpha testing on both OSX and Linux to discover any bugs, rough edges, and generally harden it before an official PR is opened on the main project. + +Help with this effort is greatly appreciated and contributors will be recognized in the project README. + +Testing instructions can be found in the Testing.md + +## Getting Started + +The following systems are supported: + +- OSX cpu +- Linux cpu +- Linux gpu + +There are two ways of getting going. The first way is the easiest and that is to use the pre-built jars from Maven. The second way is to build from source. In both cases, you will need to load the prereqs and dependencies, (like opencv). + +It's been tested on AWS Deep Learning AMI and OSX High Sierra 10.13.4 + + +### Prerequisites + +**If you are using the AWS Deep Learning Ubuntu or Linux AMI you should be good to go without doing anything on this step.** + + +Follow the instructions from https://mxnet.incubator.apache.org/install/osx_setup.html or https://mxnet.incubator.apache.org/install/ubuntu_setup.html +about _Prepare Environment for GPU Installation_ +and _Install MXNet dependencies_ + + + Cloning the repo and running from source + +To use the prebuilt jars, you will need to replace the native version of the line in the project dependencies with your configuration. + +`[org.apache.mxnet/mxnet-full_2.11-linux-x86_64-gpu "1.2.0"]` +or +`[org.apache.mxnet/mxnet-full_2.11-linux-x86_64-cpu "1.2.0"]` +or +`[org.apache.mxnet/mxnet-full_2.11-osx-x86_64-cpu "1.2.0"]` + + +```clojure + +(ns tutorial.ndarray + (:require [org.apache.clojure-mxnet.ndarray :as ndarray] +[org.apache.clojure-mxnet.context :as context])) + +;;Create NDArray +(def a (ndarray/zeros [100 50])) ;;all zero arrray of dimension 100 x 50 +(def b (ndarray/ones [256 32 128 1])) ;; all one array of dimension +(def c (ndarray/array [1 2 3 4 5 6] [2 3])) ;; array with contents of a shape 2 x 3 + +;;; There are also ways to convert to a vec or get the shape as an object or vec +(ndarray/->vec c) ;=> [1.0 2.0 3.0 4.0 5.0 6.0] +``` + +See the examples/tutorial section for more. + + +The jars from maven with the needed MXNet native binaries in it. On startup, the native libraries are extracted from the jar and copied into a temporary location on your path. On termination, they are deleted. + +If you want details on the flags (opencv verison and cuda version of the jars), they are documented here https://cwiki.apache.org/confluence/display/MXNET/MXNet-Scala+Release+Process + + +### Build from MXNET Source + +Checkout the latest sha from the main package + +`git clone --recursive https://github.com/dmlc/mxnet ~/mxnet` +`cd ~/mxnet` + + +`git checkout tags/1.2.0 -b release-1.2.0` + +`git submodule update --init --recursive` + +Sometimes it useful to use this script to clean hard +https://gist.github.com/nicktoumpelis/11214362 + + +Go here to do the base package installation https://mxnet.incubator.apache.org/install/index.html + + Run `make scalapkg` then `make scalainstall` + +then replace the correct jar for your architecture in the project.clj, example `[ml.dmlc.mxnet/mxnet-full_2.11-osx-x86_64-cpu "1.0.1-SNAPSHOT"]` + + Test your installation + +To test your installation, you should run `lein test`. This will run the test suite (CPU) for the clojure package. + + + Generation of NDArray and Symbol apis + +The bulk of the ndarray and symbol apis are generated via java reflection
[GitHub] mkolod commented on a change in pull request #11325: Added TensorRT runtime integration
mkolod commented on a change in pull request #11325: Added TensorRT runtime integration URL: https://github.com/apache/incubator-mxnet/pull/11325#discussion_r197972947 ## File path: Makefile ## @@ -94,6 +94,14 @@ else endif CFLAGS += -I$(TPARTYDIR)/mshadow/ -I$(TPARTYDIR)/dmlc-core/include -fPIC -I$(NNVM_PATH)/include -I$(DLPACK_PATH)/include -I$(TPARTYDIR)/tvm/include -Iinclude $(MSHADOW_CFLAGS) LDFLAGS = -pthread $(MSHADOW_LDFLAGS) $(DMLC_LDFLAGS) + + +ifeq ($(USE_TENSORRT), 1) Review comment: @KellenSunderland I agree. Should the CMake build be part of the initial PR or a subsequent one? This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] mkolod commented on a change in pull request #11325: Added TensorRT runtime integration
mkolod commented on a change in pull request #11325: Added TensorRT runtime
integration
URL: https://github.com/apache/incubator-mxnet/pull/11325#discussion_r197972245
##
File path: src/executor/tensorrt_pass.cc
##
@@ -0,0 +1,583 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2018 by Contributors
+ * \file tensorrt_pass.cc
+ * \brief Replace TRT compatible subgraphs by TRT engines
+ * \author Clement Fuji Tsang
+ */
+
+#if MXNET_USE_TENSORRT
+
+#include
+#include
+#include
+#include
+#include
+#include
+
+#include "./onnx_to_tensorrt.h"
+#include "./exec_pass.h"
+#include "../operator/contrib/nnvm_to_onnx-inl.h"
+
+namespace mxnet {
+namespace exec {
+
+using NodePtr = nnvm::NodePtr;
+
+/*!
+ * \brief Custom graph class, which will contain bi-directional nodes
+ * we need to compute DFS and reverse DFS for graph partitioning
+ */
+class BidirectionalGraph {
+ public:
+ struct Node {
+nnvm::Node* nnvmptr;
+std::vector inputs;
+std::vector outputs;
+ };
+ std::vector nodes;
+ std::unordered_map nnvm2nid;
+ std::vector outputs;
+ static const std::unordered_set unconditionalTRTop;
+
+ explicit BidirectionalGraph(const Graph ) {
+auto& idx = g.indexed_graph();
+auto num_nodes = idx.num_nodes();
+nodes.reserve(num_nodes);
+nnvm2nid.reserve(num_nodes);
+outputs.reserve(idx.outputs().size());
+DFSVisit(g.outputs, [this](const nnvm::NodePtr& n) {
+ BidirectionalGraph::Node new_node;
+ new_node.nnvmptr = n.get();
+ nnvm2nid[n.get()] = static_cast(nodes.size());
+ nodes.emplace_back(std::move(new_node));
+});
+for (const auto& it : nnvm2nid) {
+ nnvm::Node* nnvmnode = it.first;
+ uint32_t nid = it.second;
+ for (auto& n : nnvmnode->inputs) {
+uint32_t input_nid = nnvm2nid[n.node.get()];
+nodes[input_nid].outputs.emplace_back([nid]);
+nodes[nid].inputs.emplace_back([input_nid]);
+ }
+}
+for (auto& e : g.outputs) {
+ uint32_t nid = nnvm2nid[e.node.get()];
+ outputs.emplace_back([nid]);
+}
+ }
+
+ template
+ void DFS(const std::vector& heads, bool reverse, FVisit fvisit) {
+std::unordered_set visited;
+std::deque stack(heads.begin(), heads.end());
+visited.reserve(heads.size());
+while (!stack.empty()) {
+ Node* vertex = stack.back();
+ stack.pop_back();
+ if (visited.count(vertex) == 0) {
+visited.insert(vertex);
+fvisit(vertex);
+std::vector nexts = reverse ? vertex->inputs : vertex->outputs;
+for (Node* node : nexts) {
+ if (visited.count(node) == 0) {
+stack.emplace_back(node);
+ }
+}
+ }
+}
+ }
+
+ using t_pairset = std::pair,
std::unordered_set>;
+ using t_pairvec = std::pair, std::vector>;
+ using t_uncomp_map = std::unordered_map>;
+
+ std::unordered_set naive_grow_subgraph(Node* head,
+std::unordered_set*
set_unused,
+t_uncomp_map* uncomp_map) {
+std::unordered_set subgraph;
+std::unordered_set uncomp_set;
+std::deque stack;
+stack.emplace_back(head);
+while (!stack.empty()) {
+ Node* vertex = stack.back();
+ stack.pop_back();
+ if (set_unused->count(vertex) && !uncomp_set.count(vertex)) {
+set_unused->erase(vertex);
+subgraph.insert(vertex);
+uncomp_set.insert((*uncomp_map)[vertex].begin(),
(*uncomp_map)[vertex].end());
+for (Node* input : vertex->inputs) {
+ if (set_unused->count(input) && !uncomp_set.count(input)) {
+stack.emplace_back(input);
+ }
+}
+for (Node* output : vertex->outputs) {
+ if (set_unused->count(output) && !uncomp_set.count(output)) {
+stack.emplace_back(output);
+ }
+}
+ }
+}
+return subgraph;
+ }
+
+ std::vector> get_subsets(
+std::unordered_map* const params_map) {
+std::vector> subgraphs;
+std::unordered_set set_nonTRTnodes;
+std::unordered_set set_allnodes(nodes.size());
+std::vector separation_sets;
+for (Node& node : nodes)
[GitHub] mkolod commented on a change in pull request #11325: Added TensorRT runtime integration
mkolod commented on a change in pull request #11325: Added TensorRT runtime
integration
URL: https://github.com/apache/incubator-mxnet/pull/11325#discussion_r197972135
##
File path: src/executor/tensorrt_pass.cc
##
@@ -0,0 +1,583 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2018 by Contributors
+ * \file tensorrt_pass.cc
+ * \brief Replace TRT compatible subgraphs by TRT engines
+ * \author Clement Fuji Tsang
+ */
+
+#if MXNET_USE_TENSORRT
+
+#include
+#include
+#include
+#include
+#include
+#include
+
+#include "./onnx_to_tensorrt.h"
+#include "./exec_pass.h"
+#include "../operator/contrib/nnvm_to_onnx-inl.h"
+
+namespace mxnet {
+namespace exec {
+
+using NodePtr = nnvm::NodePtr;
+
+/*!
+ * \brief Custom graph class, which will contain bi-directional nodes
+ * we need to compute DFS and reverse DFS for graph partitioning
+ */
+class BidirectionalGraph {
+ public:
+ struct Node {
+nnvm::Node* nnvmptr;
+std::vector inputs;
+std::vector outputs;
+ };
+ std::vector nodes;
+ std::unordered_map nnvm2nid;
+ std::vector outputs;
+ static const std::unordered_set unconditionalTRTop;
+
+ explicit BidirectionalGraph(const Graph ) {
+auto& idx = g.indexed_graph();
+auto num_nodes = idx.num_nodes();
+nodes.reserve(num_nodes);
+nnvm2nid.reserve(num_nodes);
+outputs.reserve(idx.outputs().size());
+DFSVisit(g.outputs, [this](const nnvm::NodePtr& n) {
+ BidirectionalGraph::Node new_node;
+ new_node.nnvmptr = n.get();
+ nnvm2nid[n.get()] = static_cast(nodes.size());
+ nodes.emplace_back(std::move(new_node));
+});
+for (const auto& it : nnvm2nid) {
+ nnvm::Node* nnvmnode = it.first;
+ uint32_t nid = it.second;
+ for (auto& n : nnvmnode->inputs) {
+uint32_t input_nid = nnvm2nid[n.node.get()];
+nodes[input_nid].outputs.emplace_back([nid]);
+nodes[nid].inputs.emplace_back([input_nid]);
+ }
+}
+for (auto& e : g.outputs) {
+ uint32_t nid = nnvm2nid[e.node.get()];
+ outputs.emplace_back([nid]);
+}
+ }
+
+ template
+ void DFS(const std::vector& heads, bool reverse, FVisit fvisit) {
+std::unordered_set visited;
+std::deque stack(heads.begin(), heads.end());
+visited.reserve(heads.size());
+while (!stack.empty()) {
+ Node* vertex = stack.back();
+ stack.pop_back();
+ if (visited.count(vertex) == 0) {
+visited.insert(vertex);
+fvisit(vertex);
+std::vector nexts = reverse ? vertex->inputs : vertex->outputs;
+for (Node* node : nexts) {
+ if (visited.count(node) == 0) {
+stack.emplace_back(node);
+ }
+}
+ }
+}
+ }
+
+ using t_pairset = std::pair,
std::unordered_set>;
+ using t_pairvec = std::pair, std::vector>;
+ using t_uncomp_map = std::unordered_map>;
+
+ std::unordered_set naive_grow_subgraph(Node* head,
+std::unordered_set*
set_unused,
+t_uncomp_map* uncomp_map) {
+std::unordered_set subgraph;
+std::unordered_set uncomp_set;
+std::deque stack;
+stack.emplace_back(head);
+while (!stack.empty()) {
+ Node* vertex = stack.back();
+ stack.pop_back();
+ if (set_unused->count(vertex) && !uncomp_set.count(vertex)) {
+set_unused->erase(vertex);
+subgraph.insert(vertex);
+uncomp_set.insert((*uncomp_map)[vertex].begin(),
(*uncomp_map)[vertex].end());
+for (Node* input : vertex->inputs) {
+ if (set_unused->count(input) && !uncomp_set.count(input)) {
+stack.emplace_back(input);
+ }
+}
+for (Node* output : vertex->outputs) {
+ if (set_unused->count(output) && !uncomp_set.count(output)) {
+stack.emplace_back(output);
+ }
+}
+ }
+}
+return subgraph;
+ }
+
+ std::vector> get_subsets(
+std::unordered_map* const params_map) {
+std::vector> subgraphs;
+std::unordered_set set_nonTRTnodes;
+std::unordered_set set_allnodes(nodes.size());
+std::vector separation_sets;
+for (Node& node : nodes)
[GitHub] mkolod commented on a change in pull request #11325: Added TensorRT runtime integration
mkolod commented on a change in pull request #11325: Added TensorRT runtime
integration
URL: https://github.com/apache/incubator-mxnet/pull/11325#discussion_r197971193
##
File path: src/executor/tensorrt_pass.cc
##
@@ -0,0 +1,583 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2018 by Contributors
+ * \file tensorrt_pass.cc
+ * \brief Replace TRT compatible subgraphs by TRT engines
+ * \author Clement Fuji Tsang
+ */
+
+#if MXNET_USE_TENSORRT
+
+#include
+#include
+#include
+#include
+#include
+#include
+
+#include "./onnx_to_tensorrt.h"
+#include "./exec_pass.h"
+#include "../operator/contrib/nnvm_to_onnx-inl.h"
+
+namespace mxnet {
+namespace exec {
+
+using NodePtr = nnvm::NodePtr;
+
+/*!
+ * \brief Custom graph class, which will contain bi-directional nodes
+ * we need to compute DFS and reverse DFS for graph partitioning
+ */
+class BidirectionalGraph {
+ public:
+ struct Node {
+nnvm::Node* nnvmptr;
+std::vector inputs;
+std::vector outputs;
+ };
+ std::vector nodes;
+ std::unordered_map nnvm2nid;
+ std::vector outputs;
+ static const std::unordered_set unconditionalTRTop;
+
+ explicit BidirectionalGraph(const Graph ) {
+auto& idx = g.indexed_graph();
+auto num_nodes = idx.num_nodes();
+nodes.reserve(num_nodes);
+nnvm2nid.reserve(num_nodes);
+outputs.reserve(idx.outputs().size());
+DFSVisit(g.outputs, [this](const nnvm::NodePtr& n) {
+ BidirectionalGraph::Node new_node;
+ new_node.nnvmptr = n.get();
+ nnvm2nid[n.get()] = static_cast(nodes.size());
+ nodes.emplace_back(std::move(new_node));
+});
+for (const auto& it : nnvm2nid) {
+ nnvm::Node* nnvmnode = it.first;
+ uint32_t nid = it.second;
+ for (auto& n : nnvmnode->inputs) {
+uint32_t input_nid = nnvm2nid[n.node.get()];
+nodes[input_nid].outputs.emplace_back([nid]);
+nodes[nid].inputs.emplace_back([input_nid]);
+ }
+}
+for (auto& e : g.outputs) {
+ uint32_t nid = nnvm2nid[e.node.get()];
+ outputs.emplace_back([nid]);
+}
+ }
+
+ template
+ void DFS(const std::vector& heads, bool reverse, FVisit fvisit) {
+std::unordered_set visited;
+std::deque stack(heads.begin(), heads.end());
+visited.reserve(heads.size());
+while (!stack.empty()) {
+ Node* vertex = stack.back();
+ stack.pop_back();
+ if (visited.count(vertex) == 0) {
+visited.insert(vertex);
+fvisit(vertex);
+std::vector nexts = reverse ? vertex->inputs : vertex->outputs;
+for (Node* node : nexts) {
+ if (visited.count(node) == 0) {
+stack.emplace_back(node);
+ }
+}
+ }
+}
+ }
+
+ using t_pairset = std::pair,
std::unordered_set>;
+ using t_pairvec = std::pair, std::vector>;
+ using t_uncomp_map = std::unordered_map>;
+
+ std::unordered_set naive_grow_subgraph(Node* head,
+std::unordered_set*
set_unused,
+t_uncomp_map* uncomp_map) {
+std::unordered_set subgraph;
+std::unordered_set uncomp_set;
+std::deque stack;
+stack.emplace_back(head);
+while (!stack.empty()) {
+ Node* vertex = stack.back();
+ stack.pop_back();
+ if (set_unused->count(vertex) && !uncomp_set.count(vertex)) {
+set_unused->erase(vertex);
+subgraph.insert(vertex);
+uncomp_set.insert((*uncomp_map)[vertex].begin(),
(*uncomp_map)[vertex].end());
+for (Node* input : vertex->inputs) {
+ if (set_unused->count(input) && !uncomp_set.count(input)) {
+stack.emplace_back(input);
+ }
+}
+for (Node* output : vertex->outputs) {
+ if (set_unused->count(output) && !uncomp_set.count(output)) {
+stack.emplace_back(output);
+ }
+}
+ }
+}
+return subgraph;
+ }
+
+ std::vector> get_subsets(
+std::unordered_map* const params_map) {
+std::vector> subgraphs;
+std::unordered_set set_nonTRTnodes;
+std::unordered_set set_allnodes(nodes.size());
+std::vector separation_sets;
+for (Node& node : nodes)
[GitHub] mkolod commented on a change in pull request #11325: Added TensorRT runtime integration
mkolod commented on a change in pull request #11325: Added TensorRT runtime
integration
URL: https://github.com/apache/incubator-mxnet/pull/11325#discussion_r197969467
##
File path: include/mxnet/executor.h
##
@@ -152,19 +152,19 @@ class Executor {
static Executor* SimpleBind(nnvm::Symbol symbol,
const Context& default_ctx,
const std::map& group2ctx,
- const std::vector& in_arg_ctxes,
- const std::vector& arg_grad_ctxes,
- const std::vector& aux_state_ctxes,
- const std::unordered_map&
arg_shape_map,
- const std::unordered_map&
arg_dtype_map,
- const std::unordered_map&
arg_stype_map,
- const std::vector& grad_req_types,
- const std::unordered_set&
param_names,
+ std::vector* in_arg_ctxes,
Review comment:
@reminisce Because if things are to be mutated, they need to be pointers,
not non-const references (per the linter rules). Given your earlier comments
about SimpleBindEx rather than modifying SimpleBind, this will be addressed
there rather than modifying it here.
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[GitHub] mkolod commented on a change in pull request #11325: Added TensorRT runtime integration
mkolod commented on a change in pull request #11325: Added TensorRT runtime
integration
URL: https://github.com/apache/incubator-mxnet/pull/11325#discussion_r197969124
##
File path: include/mxnet/executor.h
##
@@ -152,19 +152,19 @@ class Executor {
static Executor* SimpleBind(nnvm::Symbol symbol,
const Context& default_ctx,
const std::map& group2ctx,
- const std::vector& in_arg_ctxes,
- const std::vector& arg_grad_ctxes,
- const std::vector& aux_state_ctxes,
- const std::unordered_map&
arg_shape_map,
- const std::unordered_map&
arg_dtype_map,
- const std::unordered_map&
arg_stype_map,
- const std::vector& grad_req_types,
- const std::unordered_set&
param_names,
+ std::vector* in_arg_ctxes,
+ std::vector* arg_grad_ctxes,
+ std::vector* aux_state_ctxes,
+ std::unordered_map*
arg_shape_map,
+ std::unordered_map*
arg_dtype_map,
+ std::unordered_map*
arg_stype_map,
+ std::vector* grad_req_types,
+ std::unordered_set* param_names,
std::vector* in_args,
std::vector* arg_grads,
std::vector* aux_states,
std::unordered_map*
-shared_data_arrays = nullptr,
+ shared_data_arrays = nullptr,
Review comment:
OK
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[GitHub] mkolod commented on a change in pull request #11325: Added TensorRT runtime integration
mkolod commented on a change in pull request #11325: Added TensorRT runtime
integration
URL: https://github.com/apache/incubator-mxnet/pull/11325#discussion_r197967101
##
File path: src/common/serialization.h
##
@@ -0,0 +1,526 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2015 by Contributors
+ * \file serialization.h
+ * \brief Serialization of some STL and nnvm data-structures
+ * \author Clement Fuji Tsang
+ */
+
+#ifndef MXNET_COMMON_SERIALIZATION_H_
+#define MXNET_COMMON_SERIALIZATION_H_
+
+#include
+#include
+#include
+#include
+
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+
+
+namespace mxnet {
+namespace common {
+
+template
+inline size_t serialized_size(const T& obj);
+
+template
+inline size_t serialized_size(const nnvm::Tuple& obj);
+
+template
+inline size_t serialized_size(const std::vector& obj);
+
+template
+inline size_t serialized_size(const std::pair& obj);
+
+template
+inline size_t serialized_size(const std::map& obj);
+
+template
+inline size_t serialized_size(const std::unordered_map& obj);
+
+template
+inline size_t serialized_size(const std::set& obj);
+
+template
+inline size_t serialized_size(const std::unordered_set& obj);
+
+template<>
+inline size_t serialized_size(const std::string& obj);
+
+template
+inline size_t serialized_size(const std::tuple& obj);
+
+template
+inline void serialize(const T& obj, char** buffer);
+
+template
+inline void serialize(const nnvm::Tuple& obj, char** buffer);
+
+template
+inline void serialize(const std::vector& obj, char** buffer);
+
+template
+inline void serialize(const std::pair& obj, char** buffer);
+
+template
+inline void serialize(const std::map& obj, char** buffer);
+
+template
+inline void serialize(const std::unordered_map& obj, char** buffer);
+
+template
+inline void serialize(const std::set& obj, char** buffer);
+
+template
+inline void serialize(const std::unordered_set& obj, char** buffer);
+
+template<>
+inline void serialize(const std::string& obj, char** buffer);
+
+template
+inline void serialize(const std::tuple& obj, char** buffer);
+
+template
+inline void deserialize(T* obj, const std::string& buffer, size_t* curr_pos);
+
+template
+inline void deserialize(nnvm::Tuple* obj, const std::string& buffer,
size_t* curr_pos);
+
+template
+inline void deserialize(std::vector* obj, const std::string& buffer,
size_t* curr_pos);
+
+template
+inline void deserialize(std::pair* obj, const std::string& buffer,
size_t* curr_pos);
+
+template
+inline void deserialize(std::map* obj, const std::string& buffer,
size_t* curr_pos);
+
+template
+inline void deserialize(std::unordered_map* obj, const std::string&
buffer, size_t* curr_pos);
+
+template
+inline void deserialize(std::set* obj, const std::string& buffer, size_t*
curr_pos);
+
+template
+inline void deserialize(std::unordered_set* obj, const std::string& buffer,
size_t* curr_pos);
+
+template<>
+inline void deserialize(std::string* obj, const std::string& buffer, size_t*
curr_pos);
+
+template
+inline void deserialize(std::tuple* obj, const std::string& buffer,
size_t* curr_pos);
+
+
+template
+struct is_cont {
+ static const bool value = !std::is_pod::value;
+};
+
+template
+inline size_t serialized_size(const T& obj) {
+ return sizeof(T);
+}
+
+template
+inline size_t serialized_size(const nnvm::Tuple& obj) {
+ if (is_cont::value) {
+size_t sum_val = 4;
+for (auto& el : obj) {
+ sum_val += serialized_size(el);
+}
+return sum_val;
+ } else {
+return 4 + (obj.ndim() * sizeof(T));
+ }
+}
+
+template
+inline size_t serialized_size(const std::vector& obj) {
+ if (is_cont::value) {
+size_t sum_val = 4;
+for (T i : obj) {
+ sum_val += serialized_size(i);
+}
+return sum_val;
+ } else {
+return sizeof(T) * obj.size() + 4;
+ }
+}
+
+template
+inline size_t serialized_size(const std::pair& obj) {
+ return serialized_size(obj.first) + serialized_size(obj.second);
+}
+
+template
+inline size_t serialized_size(const std::map& obj) {
+ size_t sum_val = 4;
+ if (is_cont::value && is_cont::value) {
+for (auto p : obj) {
+ sum_val +=
[GitHub] mkolod commented on a change in pull request #11325: Added TensorRT runtime integration
mkolod commented on a change in pull request #11325: Added TensorRT runtime integration URL: https://github.com/apache/incubator-mxnet/pull/11325#discussion_r197966899 ## File path: docs/api/python/contrib/tensorrt.md ## @@ -0,0 +1,117 @@ +# MxNet-TensorRT Runtime Integration +## What is this? + +This document described how to use the [MxNet](http://mxnet.incubator.apache.org/)-[TensorRT](https://developer.nvidia.com/tensorrt) runtime integration to accelerate model inference. + +## Why is TensorRT integration useful? + +TensorRT can greatly speed up inference of deep learning models. One experiment on a Titan V (V100) GPU shows that with MxNet 1.2, we can get an approximately 3x speed-up when running inference of the ResNet-50 model on the CIFAR-10 dataset in single precision (fp32). As batch sizes and image sizes go up (for CNN inference), the benefit may be less, but in general, TensorRT helps especially in cases which have: +- many bandwidth-bound layers (e.g. pointwise operations) that benefit from GPU kernel fusion +- inference use cases which have tight latency requirements and where the client application can't wait for large batches to be queued up +- embedded systems, where memory constraints are tighter than on servers +- when performing inference in reduced precision, especially for integer (e.g. int8) inference. + +In the past, the main hindrance for the user wishing to benefit from TensorRT was the fact that the model needed to be exported from the framework first. Once the model got exported through some means (NNVM to TensorRT graph rewrite, via ONNX, etc.), one had to then write a TensorRT client application, which would feed the data into the TensorRT engine. Since at that point the model was independent of the original framework, and since TensorRT could only compute the neural network layers but the user had to bring their own data pipeline, this increased the burden on the user and reduced the likelihood of reproducibility (e.g. different frameworks may have slightly different data pipelines, or flexibility of data pipeline operation ordering). Moreover, since frameworks typically support more operators than TensorRT, one could have to resort to TensorRT plugins for operations that aren't already available via the TensorRT graph API. + +The current experimental runtime integration of TensorRT with MxNet resolves the above concerns by ensuring that: +- the graph is still executed by MxNet +- the MxNet data pipeline is preserved +- the TensorRT runtime integration logic partitions the graph into subgraphs that are either TensorRT compatible or incompatible +- the graph partitioner collects the TensorRT-compatible subgraphs, hands them over to TensorRT, and substitutes the TensorRT compatible subgraph with a TensorRT library call, represented as a TensorRT node in NNVM. +- if a node is not TensorRT compatible, it won't be extracted and substituted with a TensorRT call, and will still execute within MxNet + +The above points ensure that we find a compromise between the flexibility of MxNet, and fast inference in TensorRT, without putting a burden on the user to learn how TensorRT APIs work, without the need to write one's own client application and data pipeline, etc. + +## How do I build MxNet with TensorRT integration? + +Building MxNet together with TensorRT is somewhat complex. The recipe will hopefully be simplified in the near future, but for now, it's easiest to build a Docker container with a Ubuntu 16.04 base. This Dockerfile can be found under the ci subdirectory of the MxNet repository. You can build the container as follows: + +``` +docker build -t ci/docker/Dockerfile.build.ubuntu_gpu_tensorrt mxnet_with_tensorrt +``` + +Next, we can run this container as follows (don't forget to install [nvidia-docker](https://github.com/NVIDIA/nvidia-docker)): + +```no-highlight +nvidia-docker run -ti --rm mxnet_with_tensorrt +``` + +After starting the container, you will find yourself in the /opt/mxnet directory by default. + +## Running a "hello, world" model / unit test: + +You can then run the LeNet-5 unit test, which will train LeNet-5 on MNIST, and subsequently run inference in MxNet, as well as using the MxNet-TensorRT runtime integration, and compare the results. The test can be run as follows: + +```no-highlight +python tests/python/tensorrt/test_tensorrt_lenet5.py +``` + +You should get a result similar to the following: + +```no-highlight +Running inference in MxNet +[03:31:18] src/operator/nn/./cudnn/./cudnn_algoreg-inl.h:107: Running performance tests to find the best convolution algorithm, this can take a while... (setting env variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable) +Running inference in MxNet-TensorRT +[03:31:18] src/operator/contrib/nnvm_to_onnx.cc:152: ONNX graph construction complete. +Building TensorRT engine, FP16 available:1 +Max batch size: 1024 +Max workspace size: 1024
[GitHub] mkolod commented on a change in pull request #11325: Added TensorRT runtime integration
mkolod commented on a change in pull request #11325: Added TensorRT runtime
integration
URL: https://github.com/apache/incubator-mxnet/pull/11325#discussion_r197966796
##
File path: src/operator/contrib/nnvm_to_onnx-inl.h
##
@@ -0,0 +1,156 @@
+#ifndef MXNET_OPERATOR_CONTRIB_NNVM_TO_ONNX_INL_H_
+#define MXNET_OPERATOR_CONTRIB_NNVM_TO_ONNX_INL_H_
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2018 by Contributors
+ * \file tensorrt-inl.h
+ * \brief TensorRT Operator
+ * \author Marek Kolodziej, Clement Fuji Tsang
+*/
+
+#if MXNET_USE_TENSORRT
+
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+
+#include
+#include
+
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+
+#include "./tensorrt-inl.h"
+#include "../operator_common.h"
+#include "../../common/utils.h"
+#include "../../common/serialization.h"
+
+namespace mxnet {
+namespace op {
+namespace nnvm_to_onnx {
+
+using namespace nnvm;
+using namespace ::onnx;
+using int64 = ::google::protobuf::int64;
+
+std::unordered_map GetPlaceholderShapes(const
ShapeVector& shape_inputs,
+const nnvm::IndexedGraph& ig);
+
+std::unordered_map GetOutputLookup(const
nnvm::IndexedGraph& ig);
+
+void ConvertPlaceholder(
+ const std::string& node_name,
+ const std::unordered_map& placeholder_shapes,
+ GraphProto* const graph_proto);
+
+void ConvertConstant(GraphProto* const graph_proto,
+ const std::string& node_name,
+ std::unordered_map* const shared_buffer);
+
+void ConvertOutput(op::tensorrt::InferenceMap_t* const trt_output_map,
+ GraphProto* const graph_proto,
+ const std::unordered_map::iterator&
out_iter,
+ const std::string& node_name,
+ const nnvm::Graph& g,
+ const StorageTypeVector& storage_types,
+ const DTypeVector& dtypes);
+
+typedef void (*ConverterFunction)(NodeProto *node_proto,
+ const NodeAttrs ,
+ const nnvm::IndexedGraph ,
+ const array_view
);
+
+
+// Forward declarations
+void ConvertConvolution(
+NodeProto *node_proto,
+const NodeAttrs ,
+const nnvm::IndexedGraph ,
+const array_view );
+
+
+void ConvertPooling(NodeProto *node_proto,
+const NodeAttrs ,
+const nnvm::IndexedGraph ,
+const array_view );
+
+void ConvertActivation(NodeProto *node_proto,
+ const NodeAttrs ,
+ const nnvm::IndexedGraph ,
+ const array_view );
+
+void ConvertFullyConnected(NodeProto *node_proto,
+ const NodeAttrs ,
+ const nnvm::IndexedGraph ,
+ const array_view );
+
+void ConvertSoftmaxOutput(NodeProto *node_proto,
+ const NodeAttrs ,
+ const nnvm::IndexedGraph ,
+ const array_view );
+
+void ConvertFlatten(NodeProto *node_proto,
+const NodeAttrs ,
+const nnvm::IndexedGraph ,
+const array_view );
+
+void ConvertBatchNorm(NodeProto *node_proto,
+const NodeAttrs ,
+const nnvm::IndexedGraph ,
+const array_view );
+
+void ConvertElementwiseAdd(NodeProto *node_proto,
+const NodeAttrs ,
+const nnvm::IndexedGraph ,
+const array_view );
+
+TRTParam ConvertNnvmGraphToOnnx(
+const nnvm::Graph ,
+std::unordered_map *const shared_buffer);
+
+static const std::unordered_map converter_map
= {
Review comment:
@eric-haibin-lin Yes, so far. TensorRT supports more operators, so the list
will be expanded once the initial integration is in place.
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[GitHub] mkolod commented on a change in pull request #11325: Added TensorRT runtime integration
mkolod commented on a change in pull request #11325: Added TensorRT runtime
integration
URL: https://github.com/apache/incubator-mxnet/pull/11325#discussion_r197966636
##
File path: src/common/serialization.h
##
@@ -0,0 +1,526 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * Copyright (c) 2015 by Contributors
+ * \file serialization.h
+ * \brief Serialization of some STL and nnvm data-structures
+ * \author Clement Fuji Tsang
+ */
+
+#ifndef MXNET_COMMON_SERIALIZATION_H_
+#define MXNET_COMMON_SERIALIZATION_H_
+
+#include
+#include
+#include
+#include
+
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+
+
+namespace mxnet {
+namespace common {
+
+template
+inline size_t serialized_size(const T& obj);
Review comment:
@eric-haibin-lin It would make sense to increase test coverage for this
independently. Will add it to the to-do list for polishing up the PR.
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[GitHub] mkolod commented on a change in pull request #11325: Added TensorRT runtime integration
mkolod commented on a change in pull request #11325: Added TensorRT runtime integration URL: https://github.com/apache/incubator-mxnet/pull/11325#discussion_r197966380 ## File path: python/mxnet/cuda_utils.py ## @@ -0,0 +1,90 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +# Copyright (c) 2015 by Contributors +# File: serialization.h +# Purpose: Functions to query GPU count, arch, etc. +# Author: Dick Carter + +"""Provides information on the visible CUDA GPUs on the system.""" +# pylint: disable=broad-except +# As a stand-alone program, it prints a list of unique cuda SM architectures +import ctypes as C +from ctypes.util import find_library + +def cint(init_val=0): Review comment: @eric-haibin-lin Good point, the Ctypes utils could just be moved to base, and then reused in cuda_utils. This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] szha commented on issue #11340: [MXNET-559] Scripts for running the Broken link checker job
szha commented on issue #11340: [MXNET-559] Scripts for running the Broken link checker job URL: https://github.com/apache/incubator-mxnet/pull/11340#issuecomment-400118911 @marcoabreu CI passed. Should this be merged? This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] szha closed issue #11353: Flaky test test_gluon_trainer.test_trainer_reset_kv
szha closed issue #11353: Flaky test test_gluon_trainer.test_trainer_reset_kv URL: https://github.com/apache/incubator-mxnet/issues/11353 This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-mxnet] branch master updated: Fix #11353 (#11360)
This is an automated email from the ASF dual-hosted git repository.
zhasheng pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/master by this push:
new 619e4bd Fix #11353 (#11360)
619e4bd is described below
commit 619e4bded058e4bf77029bc30b395275d72f7907
Author: Haibin Lin
AuthorDate: Mon Jun 25 15:34:54 2018 -0700
Fix #11353 (#11360)
* Update test_gluon_trainer.py
* Update test_gluon_trainer.py
* Update test_gluon_trainer.py
* Update test_gluon_trainer.py
* Update test_gluon_trainer.py
* trigger
* Run 10 times
* Update test_gluon_trainer.py
* run 10K times
* test_trainer_reset_kv didn't fail for 10K time . 2nd Trigger.
* test_trainer_reset_kv didn't fail for 10K times. 3rd Trigger.
* remove for loop
---
tests/python/unittest/test_gluon_trainer.py | 1 +
1 file changed, 1 insertion(+)
diff --git a/tests/python/unittest/test_gluon_trainer.py
b/tests/python/unittest/test_gluon_trainer.py
index 1c59cea..eac9fad 100644
--- a/tests/python/unittest/test_gluon_trainer.py
+++ b/tests/python/unittest/test_gluon_trainer.py
@@ -190,6 +190,7 @@ def test_trainer_reset_kv():
trainer.step(1)
assert trainer._kvstore.type == kv
# load would reset kvstore
+mx.nd.waitall()
params.load('test_trainer_reset_kv.params')
assert trainer._kvstore is None
assert trainer._kv_initialized is False
[GitHub] szha closed pull request #11360: Fix #11353
szha closed pull request #11360: Fix #11353
URL: https://github.com/apache/incubator-mxnet/pull/11360
This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:
As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):
diff --git a/tests/python/unittest/test_gluon_trainer.py
b/tests/python/unittest/test_gluon_trainer.py
index 1c59ceaa093..eac9fad45f5 100644
--- a/tests/python/unittest/test_gluon_trainer.py
+++ b/tests/python/unittest/test_gluon_trainer.py
@@ -190,6 +190,7 @@ def check_trainer_reset_kv(kv):
trainer.step(1)
assert trainer._kvstore.type == kv
# load would reset kvstore
+mx.nd.waitall()
params.load('test_trainer_reset_kv.params')
assert trainer._kvstore is None
assert trainer._kv_initialized is False
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[GitHub] ankkhedia edited a comment on issue #10274: test_ndarray.test_reduce fails in v1.0.0
ankkhedia edited a comment on issue #10274: test_ndarray.test_reduce fails in v1.0.0 URL: https://github.com/apache/incubator-mxnet/issues/10274#issuecomment-400113596 Tested on master for d6813efa2206afb5be98c2da16dd6e2efaf44cda using gcc-6 (Ubuntu 6.4.0-17ubuntu1~16.04) 6.4.0 20180424, could not reproduce. It's probably ARM specific issue on edge devices(RaspBerry PI) This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] hcho3 opened a new pull request #11396: Fix flaky test test_operator_gpu.test_batchnorm_with_type
hcho3 opened a new pull request #11396: Fix flaky test test_operator_gpu.test_batchnorm_with_type URL: https://github.com/apache/incubator-mxnet/pull/11396 ## Description ## Addresses #10087. See [#9916 (comment-371736378)](https://github.com/apache/incubator-mxnet/issues/9916#issuecomment-371736378) for a justification for this change. ## 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) - [x] Changes are complete (i.e. I finished coding on this PR) This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-mxnet] branch master updated: Don't fail storing test results if test suite got aborted (#11363) (#11391)
This is an automated email from the ASF dual-hosted git repository.
zhasheng pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/master by this push:
new cdb01fc Don't fail storing test results if test suite got aborted
(#11363) (#11391)
cdb01fc is described below
commit cdb01fc72ec5c8973a5ed48076380721db50ffa8
Author: Marco de Abreu
AuthorDate: Tue Jun 26 00:26:41 2018 +0200
Don't fail storing test results if test suite got aborted (#11363) (#11391)
* Dont fail during artifact storage
* Update Jenkinsfile
* Update Jenkinsfile
---
Jenkinsfile | 21 +
1 file changed, 13 insertions(+), 8 deletions(-)
diff --git a/Jenkinsfile b/Jenkinsfile
index 44aad8e..10fdf1d 100644
--- a/Jenkinsfile
+++ b/Jenkinsfile
@@ -97,18 +97,23 @@ def publish_test_coverage() {
}
def collect_test_results_unix(original_file_name, new_file_name) {
-echo 'Saving python test results for ' + new_file_name
-// Rename file to make it distinguishable. Unfortunately, it's not
possible to get STAGE_NAME in a parallel stage
-sh 'cp ' + original_file_name + ' ' + new_file_name
-archiveArtifacts artifacts: new_file_name
+if (fileExists(original_file_name)) {
+// Rename file to make it distinguishable. Unfortunately, it's not
possible to get STAGE_NAME in a parallel stage
+// Thus, we have to pick a name manually and rename the files so that
they can be stored separately.
+sh 'cp ' + original_file_name + ' ' + new_file_name
+archiveArtifacts artifacts: new_file_name
+}
}
def collect_test_results_windows(original_file_name, new_file_name) {
-echo 'Saving python test results for ' + new_file_name
// Rename file to make it distinguishable. Unfortunately, it's not
possible to get STAGE_NAME in a parallel stage
-bat 'xcopy ' + original_file_name + ' ' + new_file_name + '*'
-archiveArtifacts artifacts: new_file_name
-}
+// Thus, we have to pick a name manually and rename the files so that they
can be stored separately.
+if (fileExists(original_file_name)) {
+bat 'xcopy ' + original_file_name + ' ' + new_file_name + '*'
+archiveArtifacts artifacts: new_file_name
+}
+}
+
def docker_run(platform, function_name, use_nvidia, shared_mem = '500m') {
def command = "ci/build.py --docker-registry ${env.DOCKER_CACHE_REGISTRY}
%USE_NVIDIA% --platform %PLATFORM% --shm-size %SHARED_MEM%
/work/runtime_functions.sh %FUNCTION_NAME%"
[GitHub] szha closed pull request #11391: Don't fail storing test results if test suite got aborted (#11363)
szha closed pull request #11391: Don't fail storing test results if test suite
got aborted (#11363)
URL: https://github.com/apache/incubator-mxnet/pull/11391
This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:
As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):
diff --git a/Jenkinsfile b/Jenkinsfile
index 44aad8e006e..10fdf1d6cfa 100644
--- a/Jenkinsfile
+++ b/Jenkinsfile
@@ -97,18 +97,23 @@ def publish_test_coverage() {
}
def collect_test_results_unix(original_file_name, new_file_name) {
-echo 'Saving python test results for ' + new_file_name
-// Rename file to make it distinguishable. Unfortunately, it's not
possible to get STAGE_NAME in a parallel stage
-sh 'cp ' + original_file_name + ' ' + new_file_name
-archiveArtifacts artifacts: new_file_name
+if (fileExists(original_file_name)) {
+// Rename file to make it distinguishable. Unfortunately, it's not
possible to get STAGE_NAME in a parallel stage
+// Thus, we have to pick a name manually and rename the files so that
they can be stored separately.
+sh 'cp ' + original_file_name + ' ' + new_file_name
+archiveArtifacts artifacts: new_file_name
+}
}
def collect_test_results_windows(original_file_name, new_file_name) {
-echo 'Saving python test results for ' + new_file_name
// Rename file to make it distinguishable. Unfortunately, it's not
possible to get STAGE_NAME in a parallel stage
-bat 'xcopy ' + original_file_name + ' ' + new_file_name + '*'
-archiveArtifacts artifacts: new_file_name
-}
+// Thus, we have to pick a name manually and rename the files so that they
can be stored separately.
+if (fileExists(original_file_name)) {
+bat 'xcopy ' + original_file_name + ' ' + new_file_name + '*'
+archiveArtifacts artifacts: new_file_name
+}
+}
+
def docker_run(platform, function_name, use_nvidia, shared_mem = '500m') {
def command = "ci/build.py --docker-registry ${env.DOCKER_CACHE_REGISTRY}
%USE_NVIDIA% --platform %PLATFORM% --shm-size %SHARED_MEM%
/work/runtime_functions.sh %FUNCTION_NAME%"
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[GitHub] ankkhedia commented on issue #10274: test_ndarray.test_reduce fails in v1.0.0
ankkhedia commented on issue #10274: test_ndarray.test_reduce fails in v1.0.0 URL: https://github.com/apache/incubator-mxnet/issues/10274#issuecomment-400113596 Tested on master for d6813efa2206afb5be98c2da16dd6e2efaf44cda using gcc-6 (Ubuntu 6.4.0-17ubuntu1~16.04) 6.4.0 20180424, could not reproduce This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] vrakesh edited a comment on issue #11367: Segfault when running test_operator_gpu.test_sparse_dot many times
vrakesh edited a comment on issue #11367: Segfault when running test_operator_gpu.test_sparse_dot many times URL: https://github.com/apache/incubator-mxnet/issues/11367#issuecomment-400102780 @haojin2 sounds good thanks This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] vrakesh commented on issue #11367: Segfault when running test_operator_gpu.test_sparse_dot many times
vrakesh commented on issue #11367: Segfault when running test_operator_gpu.test_sparse_dot many times URL: https://github.com/apache/incubator-mxnet/issues/11367#issuecomment-400102780 @haojin2 ah sounds good :) This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] zhreshold commented on issue #9974: DataLoader with workers not compatible with ImageRecordDataset
zhreshold commented on issue #9974: DataLoader with workers not compatible with ImageRecordDataset URL: https://github.com/apache/incubator-mxnet/issues/9974#issuecomment-400101752 https://github.com/apache/incubator-mxnet/pull/11370 This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] DickJC123 commented on issue #11395: Check failed: e == cudaSuccess CUDA: unspecified launch failure
DickJC123 commented on issue #11395: Check failed: e == cudaSuccess CUDA: unspecified launch failure URL: https://github.com/apache/incubator-mxnet/issues/11395#issuecomment-400101599 In a private communication, you indicated this was seen on all platforms. Here you tag it as 'Windows'. Please clarify. This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] DickJC123 commented on issue #11341: Deterministic cudnn algorithms
DickJC123 commented on issue #11341: Deterministic cudnn algorithms URL: https://github.com/apache/incubator-mxnet/issues/11341#issuecomment-400098491 I often use tests/jenkins/run_test_ubuntu.sh to compile MXNet and run the regression tests. You may need to set DEV=0 in that script to get past compile warnings treated as errors. This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] marcoabreu commented on issue #11394: Flaky test on Python2 Windows
marcoabreu commented on issue #11394: Flaky test on Python2 Windows URL: https://github.com/apache/incubator-mxnet/issues/11394#issuecomment-400094588 Ah, sorry. I have documented it at https://github.com/apache/incubator-mxnet/issues/11395. I already engaged with Nvidia about that one but forgot to create GitHub issue. This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] marcoabreu commented on issue #11395: Check failed: e == cudaSuccess CUDA: unspecified launch failure
marcoabreu commented on issue #11395: Check failed: e == cudaSuccess CUDA: unspecified launch failure URL: https://github.com/apache/incubator-mxnet/issues/11395#issuecomment-400094621 @DickJC123 This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] marcoabreu opened a new issue #11395: Check failed: e == cudaSuccess CUDA: unspecified launch failure
marcoabreu opened a new issue #11395: Check failed: e == cudaSuccess CUDA:
unspecified launch failure
URL: https://github.com/apache/incubator-mxnet/issues/11395
Sometimes, our slaves get corrupted and suddenly all test start to fail.
This is unrelated to the tests directly.
http://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/incubator-mxnet/detail/PR-11377/5/pipeline/
```
==
ERROR: test_operator_gpu.test_op_roi_align
--
Traceback (most recent call last):
File "C:\Anaconda3\envs\py2\lib\site-packages\nose\case.py", line 197, in
runTest
self.test(*self.arg)
File "C:\Anaconda3\envs\py2\lib\site-packages\nose\util.py", line 620, in
newfunc
return func(*arg, **kw)
File
"C:\jenkins_slave\workspace\ut-python-gpu\tests\python\gpu\../unittest\common.py",
line 157, in test_new
orig_test(*args, **kwargs)
File
"C:\jenkins_slave\workspace\ut-python-gpu\tests\python\gpu\../unittest\test_operator.py",
line 6269, in test_op_roi_align
test_roi_align_value()
File
"C:\jenkins_slave\workspace\ut-python-gpu\tests\python\gpu\../unittest\test_operator.py",
line 6230, in test_roi_align_value
data = mx.nd.array(np.arange(N*C*W*H).reshape((N,C,H,W)), ctx=ctx, dtype
= dtype)
File
"C:\jenkins_slave\workspace\ut-python-gpu\pkg_vc14_gpu\python\mxnet\ndarray\utils.py",
line 146, in array
return _array(source_array, ctx=ctx, dtype=dtype)
File
"C:\jenkins_slave\workspace\ut-python-gpu\pkg_vc14_gpu\python\mxnet\ndarray\ndarray.py",
line 2357, in array
arr[:] = source_array
File
"C:\jenkins_slave\workspace\ut-python-gpu\pkg_vc14_gpu\python\mxnet\ndarray\ndarray.py",
line 444, in __setitem__
self._set_nd_basic_indexing(key, value)
File
"C:\jenkins_slave\workspace\ut-python-gpu\pkg_vc14_gpu\python\mxnet\ndarray\ndarray.py",
line 710, in _set_nd_basic_indexing
self._sync_copyfrom(value)
File
"C:\jenkins_slave\workspace\ut-python-gpu\pkg_vc14_gpu\python\mxnet\ndarray\ndarray.py",
line 876, in _sync_copyfrom
ctypes.c_size_t(source_array.size)))
File
"C:\jenkins_slave\workspace\ut-python-gpu\pkg_vc14_gpu\python\mxnet\base.py",
line 210, in check_call
raise MXNetError(py_str(_LIB.MXGetLastError()))
MXNetError: [06:35:08]
c:\jenkins_slave\workspace\build-gpu\3rdparty\mshadow\mshadow\./tensor_gpu-inl.h:69:
Check failed: e == cudaSuccess CUDA: unspecified launch failure
>> begin captured logging <<
common: INFO: Setting test np/mx/python random seeds, use
MXNET_TEST_SEED=1046236735 to reproduce.
- >> end captured logging << -
```
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[GitHub] marcoabreu closed issue #11064: Flaky test: test_operator.test_op_roi_align
marcoabreu closed issue #11064: Flaky test: test_operator.test_op_roi_align URL: https://github.com/apache/incubator-mxnet/issues/11064 This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] marcoabreu removed a comment on issue #11394: Flaky test on Python2 Windows
marcoabreu removed a comment on issue #11394: Flaky test on Python2 Windows URL: https://github.com/apache/incubator-mxnet/issues/11394#issuecomment-400093569 Oh sorry, that run is a duplicate of https://github.com/apache/incubator-mxnet/issues/11064 I have reopened the issue for you. Please document your findings there. P.S. In future, please paste the log into the ticket to prevent people from having to access our website. This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] eric-haibin-lin opened a new issue #11064: Flaky test: test_operator.test_op_roi_align
eric-haibin-lin opened a new issue #11064: Flaky test:
test_operator.test_op_roi_align
URL: https://github.com/apache/incubator-mxnet/issues/11064
```
==
FAIL: test_operator.test_op_roi_align
--
Traceback (most recent call last):
File "/usr/local/lib/python3.5/dist-packages/nose/case.py", line 198, in
runTest
self.test(*self.arg)
File "/work/mxnet/tests/python/unittest/common.py", line 157, in test_new
orig_test(*args, **kwargs)
File "/work/mxnet/tests/python/unittest/test_operator.py", line 6170, in
test_op_roi_align
test_roi_align_value()
File "/work/mxnet/tests/python/unittest/test_operator.py", line 6149, in
test_roi_align_value
assert np.allclose(data.grad.asnumpy(), dx, atol = 1e-6),
np.abs(data.grad.asnumpy() - dx).max()
AssertionError: 1.3150275e-06
>> begin captured logging <<
common: INFO: Setting test np/mx/python random seeds, use
MXNET_TEST_SEED=1619190489 to reproduce.
- >> end captured logging << -
```
http://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/incubator-mxnet/detail/PR-11058/1/pipeline
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[GitHub] marcoabreu commented on issue #11394: Flaky test on Python2 Windows
marcoabreu commented on issue #11394: Flaky test on Python2 Windows URL: https://github.com/apache/incubator-mxnet/issues/11394#issuecomment-400093569 Oh sorry, that run is a duplicate of https://github.com/apache/incubator-mxnet/issues/11064 I have reopened the issue for you. Please document your findings there. P.S. In future, please paste the log into the ticket to prevent people from having to access our website. This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] szha commented on a change in pull request #11370: fix recordfile dataset with multi worker
szha commented on a change in pull request #11370: fix recordfile dataset with multi worker URL: https://github.com/apache/incubator-mxnet/pull/11370#discussion_r197938947 ## File path: tests/python/unittest/test_gluon_data.py ## @@ -72,6 +72,18 @@ def test_recordimage_dataset(): assert x.shape[0] == 1 and x.shape[3] == 3 assert y.asscalar() == i +with_seed() Review comment: @with_seed This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] lanking520 commented on issue #11394: Flaky test on Python2 Windows
lanking520 commented on issue #11394: Flaky test on Python2 Windows URL: https://github.com/apache/incubator-mxnet/issues/11394#issuecomment-400091572 @marcoabreu this one is not dupes: http://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/incubator-mxnet/detail/PR-11377/5/pipeline This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] nswamy commented on issue #11315: Support Double precision type in Scala
nswamy commented on issue #11315: Support Double precision type in Scala URL: https://github.com/apache/incubator-mxnet/issues/11315#issuecomment-400090838 This is both a bug and feature request, since the accuracy will drop using Float32 on a model vs Float64 This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] marcoabreu commented on issue #11394: Flaky test on Python2 Windows
marcoabreu commented on issue #11394: Flaky test on Python2 Windows URL: https://github.com/apache/incubator-mxnet/issues/11394#issuecomment-400090376 Duplicate of https://github.com/apache/incubator-mxnet/issues/11353 This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] marcoabreu closed issue #11394: Flaky test on Python2 Windows
marcoabreu closed issue #11394: Flaky test on Python2 Windows URL: https://github.com/apache/incubator-mxnet/issues/11394 This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] aaronmarkham commented on issue #11180: [MXNET-503] Website landing page for MMS, PR II
aaronmarkham commented on issue #11180: [MXNET-503] Website landing page for MMS, PR II URL: https://github.com/apache/incubator-mxnet/pull/11180#issuecomment-400090248 Closing for now. Will add this to an ecosystem page soon. This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] azai91 commented on issue #11371: [MXNET-486] Create CPP test for concat MKLDNN operator
azai91 commented on issue #11371: [MXNET-486] Create CPP test for concat MKLDNN operator URL: https://github.com/apache/incubator-mxnet/pull/11371#issuecomment-400086783 @zheng-da please review when you have time. This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] wenyangchu edited a comment on issue #11341: Deterministic cudnn algorithms
wenyangchu edited a comment on issue #11341: Deterministic cudnn algorithms URL: https://github.com/apache/incubator-mxnet/issues/11341#issuecomment-400075286 Hi @DickJC123 , I have little knowledge on the regression test in mxnet. Could you please let me know how you ran the test? Thank you! This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] frankfliu commented on issue #11394: Flaky test on Python2 Windows
frankfliu commented on issue #11394: Flaky test on Python2 Windows URL: https://github.com/apache/incubator-mxnet/issues/11394#issuecomment-400084661 Thanks for the question. @sandeep-krishnamurthy requesting this be labeled under Flaky and Test. This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] frankfliu commented on issue #11393: Validation Accuracy is higher than training accuracy.
frankfliu commented on issue #11393: Validation Accuracy is higher than training accuracy. URL: https://github.com/apache/incubator-mxnet/issues/11393#issuecomment-400083102 Hi @absalama thanks for the question, @sandeep-krishnamurthy requesting this be labeled under Question This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] azai91 closed pull request #11328: [MXNET-549] MKLDNNSum can handle variable number of inputs
azai91 closed pull request #11328: [MXNET-549] MKLDNNSum can handle variable
number of inputs
URL: https://github.com/apache/incubator-mxnet/pull/11328
This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:
As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):
diff --git a/src/operator/nn/mkldnn/mkldnn_base.cc
b/src/operator/nn/mkldnn/mkldnn_base.cc
index b182aa0b68d..a05a3218911 100644
--- a/src/operator/nn/mkldnn/mkldnn_base.cc
+++ b/src/operator/nn/mkldnn/mkldnn_base.cc
@@ -146,7 +146,8 @@ void CommitOutput(const NDArray , const mkldnn_output_t
) {
// We have to allocate new memory for the sum result.
auto sum_res = TmpMemMgr::Get()->Alloc(
res.second->get_primitive_desc());
-op::MKLDNNSum(*res.second, *mem, *sum_res);
+std::vector in_mems = {*res.second, *mem};
+op::MKLDNNSum(in_mems, *sum_res);
const_cast(arr).CopyFrom(*sum_res);
}
}
diff --git a/src/operator/nn/mkldnn/mkldnn_copy.cc
b/src/operator/nn/mkldnn/mkldnn_copy.cc
index 75e51aff006..d6a12f01610 100644
--- a/src/operator/nn/mkldnn/mkldnn_copy.cc
+++ b/src/operator/nn/mkldnn/mkldnn_copy.cc
@@ -50,7 +50,8 @@ void MKLDNNCopy(const nnvm::NodeAttrs& attrs, const OpContext
,
if (out_mem == nullptr)
out_mem = out_data.GetMKLDNNData();
auto sum_res = TmpMemMgr::Get()->Alloc(out_mem->get_primitive_desc());
-MKLDNNSum(*in_mem, *out_mem, *sum_res);
+std::vector in_mems = {in_mem, out_mem};
+MKLDNNSum(in_mems, *sum_res);
const_cast(out_data).CopyFrom(*sum_res);
} else {
const_cast(out_data).CopyFrom(*in_mem);
diff --git a/src/operator/nn/mkldnn/mkldnn_ops-inl.h
b/src/operator/nn/mkldnn/mkldnn_ops-inl.h
index 50937706d93..850fc509d23 100644
--- a/src/operator/nn/mkldnn/mkldnn_ops-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_ops-inl.h
@@ -104,8 +104,8 @@ void MKLDNNActivationBackward(const nnvm::NodeAttrs& attrs,
const OpContext
const NDArray _grad, const NDArray _data,
const OpReqType , const NDArray _grad);
-void MKLDNNSum(const mkldnn::memory , const mkldnn::memory ,
- const mkldnn::memory );
+void MKLDNNSum(std::vector _mems,
+ const mkldnn::memory );
} // namespace op
} // namespace mxnet
diff --git a/src/operator/nn/mkldnn/mkldnn_sum.cc
b/src/operator/nn/mkldnn/mkldnn_sum.cc
index c51e1081d69..00f2a323510 100644
--- a/src/operator/nn/mkldnn/mkldnn_sum.cc
+++ b/src/operator/nn/mkldnn/mkldnn_sum.cc
@@ -23,6 +23,7 @@
* \author Da Zheng
*/
#include
+#include
#include "./mkldnn_ops-inl.h"
#include "./mkldnn_base-inl.h"
@@ -31,16 +32,16 @@
namespace mxnet {
namespace op {
-void MKLDNNSum(const mkldnn::memory , const mkldnn::memory ,
+void MKLDNNSum(std::vector _mems,
const mkldnn::memory ) {
- std::vector input_pds(2);
- std::vector scales(2, 1);
+ std::vector input_pds(in_mems.size());
+ std::vector scales(in_mems.size(), 1);
std::vector inputs;
- input_pds[0] = arr1.get_primitive_desc();
- input_pds[1] = arr2.get_primitive_desc();
- CHECK(input_pds[0] == input_pds[1]);
- inputs.push_back(arr1);
- inputs.push_back(arr2);
+ for (int i = 0; i < in_mems.size(); i++) {
+input_pds[i] = in_mems[i].get_primitive_desc();
+inputs.push_back(in_mems[i]);
+if (i > 0) CHECK(input_pds[i] == input_pds[i-1]);
+ }
// TODO(zhengda) I need to reorder memory here.
mkldnn::sum::primitive_desc sum_pd(scales, input_pds);
MKLDNNStream::Get()->RegisterPrim(mkldnn::sum(sum_pd, inputs, out));
@@ -54,7 +55,7 @@ void MKLDNNSumForward(const nnvm::NodeAttrs& attrs, const
OpContext ,
}
TmpMemMgr::Get()->Init(ctx.requested[0]);
- std::vector in_prims;
+ std::vector in_prims;
std::vector in_pds(inputs.size());
std::vector scales(inputs.size(), 1);
in_prims.reserve(inputs.size());
@@ -70,11 +71,10 @@ void MKLDNNSumForward(const nnvm::NodeAttrs& attrs, const
OpContext ,
in_prims.push_back(*in_mem);
in_pds[i] = in_mem->get_primitive_desc();
}
-
mkldnn::sum::primitive_desc pdesc(scales, in_pds);
auto mem = CreateMKLDNNMem(out_data, pdesc.dst_primitive_desc(), req,
[0]);
MKLDNNStream *stream = MKLDNNStream::Get();
- stream->RegisterPrim(mkldnn::sum(pdesc, in_prims, *mem.second));
+ MKLDNNSum(in_prims, *mem.second);
CommitOutput(out_data, mem);
stream->Submit();
}
diff --git a/tests/cpp/operator/mkldnn.cc b/tests/cpp/operator/mkldnn.cc
index 82fee67b114..45c03d61e62 100644
--- a/tests/cpp/operator/mkldnn.cc
+++ b/tests/cpp/operator/mkldnn.cc
@@ -799,7 +799,8 @@ TEST(MKLDNN_BASE, MKLDNNSum) {
if (out_mem == nullptr)
continue;
PrintVerifyMsg(in_arr, in_arr);
- op::MKLDNNSum(*in_mem1, *in_mem2, *out_mem);
+ std::vector in_mems = {*in_mem1, *in_mem2};
+ op::MKLDNNSum(in_mems, *out_mem);
[GitHub] lanking520 opened a new issue #11394: Flaky test on Python2 Windows
lanking520 opened a new issue #11394: Flaky test on Python2 Windows URL: https://github.com/apache/incubator-mxnet/issues/11394 ## Description CI Flaky on Python and Windows @haojin2 @marcoabreu http://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/incubator-mxnet/detail/PR-11377/6/pipeline http://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/incubator-mxnet/detail/PR-11377/5/pipeline This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] haojin2 commented on a change in pull request #11356: [MXNET-560][WIP] Add temperature parameter in Softmax and SoftmaxOutput operator
haojin2 commented on a change in pull request #11356: [MXNET-560][WIP] Add
temperature parameter in Softmax and SoftmaxOutput operator
URL: https://github.com/apache/incubator-mxnet/pull/11356#discussion_r197924316
##
File path: src/operator/nn/softmax-inl.h
##
@@ -127,7 +137,7 @@ inline void SoftmaxGrad(Stream *s, DType *out, DType
*ograd,
#ifdef __CUDACC__
template
__global__ void softmax_compute_kernel(DType *in, DType *out, index_t M, int
axis,
- Shape sshape, Shape stride)
{
+ Shape sshape, Shape stride,
float temperature) {
Review comment:
adding `const` qualifier could be a safe net by explicitly telling the
compiler about your assumption that you're not changing the input value at all
in the function.
Say if you have a function like this:
```c++
void foo(int a) {
return a;
}
```
Here your assumption that the original input should be returned without any
changes, and that is essential for correct behavior of this function.
Now if someone happens to change the function to:
```c++
void foo(int a) {
a++; // <- new code that happens to change the value of a, and will
affect correctness
return a;
}
```
compiler will not complain about it as you do not have a `const` qualifier
here, so an extra `const` qualifier is not necessary, but it could be helpful.
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[GitHub] wenyangchu commented on issue #11341: Deterministic cudnn algorithms
wenyangchu commented on issue #11341: Deterministic cudnn algorithms URL: https://github.com/apache/incubator-mxnet/issues/11341#issuecomment-400075286 Hi @DickJC123 , I have little knowledge on the regression test in mxnet. Could you please let me know how you ran the test? This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] absalama opened a new issue #11393: Validation Accuracy is higher than training accuracy.
absalama opened a new issue #11393: Validation Accuracy is higher than training
accuracy.
URL: https://github.com/apache/incubator-mxnet/issues/11393
I am training Imagenet (1k) on alexnet. I used the im2rec tool to split 5%
of the training data to be used by the validation phase. The results are two
set of records files (I use chunks) one set for training and on set for
validation.
The log shows the following:
```
top_k_accuracy_5=0.159258cross-entropy=5.465672
INFO:root:Epoch[0] Batch [9400] Speed: 395.94 samples/sec
accuracy=0.052461 top_k_accuracy_5=0.160195 cross-entropy=5.454822
**INFO:root:Epoch[0] Train-accuracy=0.056324
INFO:root:Epoch[0] Train-top_k_accuracy_5=0.165848
INFO:root:Epoch[0] Train-cross-entropy=5.416635
INFO:root:Epoch[0] Time cost=3079.019
INFO:root:Saved checkpoint to
"mxnet_alexnet_single_gpu_all_data_set_256-0001.params"
INFO:root:Epoch[0] `Validation-accuracy=0.078869
INFO:root:Epoch[0] Validation-top_k_accuracy_5=0.216859
INFO:root:Epoch[0] Validation-cross-entropy=5.142231**
```
The validation here is higher than training accuracy and this increases with
further epochs (Until the time I write this issue the epoch 10 , the the
validation is higher than the training accuracy with around 7%).
**The commands used for data preprocessing:**
`python3 im2rec.py --list --recursive --chunks 1024 --train-ratio 0.95
${IMAGENET_ROOT}/record_io_all_raw_data/metadata-train256/imagenet1k
${IMAGENET_EXTRACTED}/train
`
`python3 im2rec.py --resize 256 --quality 95 --num-thread 16
${IMAGENET_ROOT}/record_io_all_raw_data/metadata-train256/imagenet1k
${IMAGENET_EXTRACTED}/train`
python3 im2rec.py --resize 256 --quality 95 --num-thread 16
`${IMAGENET_ROOT}/record_io_all_raw_data/metadata-val256/imagenet1k
${IMAGENET_EXTRACTED}/train`
**The arguments used for training:**
```
Namespace(batch_size=128, benchmark=0, data_nthreads=4,
data_train='/work/projects/Project00755/datasets/imagenet/record_io_all_raw_data/train256/',
data_train_
idx='',
data_val='/work/projects/Project00755/datasets/imagenet/record_io_all_raw_data/val256/',
data_val_idx='', disp_batches=200, dtype='float32', gc_threshold=0.5,
gc_type='none', gpus='0'
, image_shape='3,227,227', initializer='default', kv_store='device',
load_epoch=None, loss='ce', lr=0.01, lr_factor=0.1, lr_step_epochs='30,60',
macrobatch_size=0, max_random_aspect_ratio=0.2
5, max_random_h=36, max_random_l=50, max_random_rotate_angle=10,
max_random_s=50, max_random_scale=1, max_random_shear_ratio=0.1,
min_random_scale=1, model_prefix='mxnet_alexnet_single_gpu_al
l_data_set_256', mom=0.9, monitor=0, network='alexnet', num_classes=1000,
num_epochs=80, num_examples=1216718, num_layers=8, optimizer='sgd', pad_size=0,
random_crop=1, random_mirror=1, rgb_m
ean='123.68,116.779,103.939', save_period=1, test_io=0, top_k=5,
warmup_epochs=5, warmup_strategy='linear', wd=0.0005)
```
Any help will be appreciated.
Thanks
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[GitHub] marcoabreu commented on issue #11359: Flaky test test_io:test_ImageRecordIter_seed_augmentation
marcoabreu commented on issue #11359: Flaky test test_io:test_ImageRecordIter_seed_augmentation URL: https://github.com/apache/incubator-mxnet/issues/11359#issuecomment-400069936 I would have to check, but that could be a good start for further investigation. This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] KellenSunderland closed pull request #11362: WIP
KellenSunderland closed pull request #11362: WIP
URL: https://github.com/apache/incubator-mxnet/pull/11362
This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:
As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):
diff --git a/.gitmodules b/.gitmodules
index 9aeb1c75498..836d824a6f5 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -26,3 +26,6 @@
[submodule "3rdparty/tvm"]
path = 3rdparty/tvm
url = https://github.com/dmlc/tvm
+[submodule "3rdparty/onnx-tensorrt"]
+ path = 3rdparty/onnx-tensorrt
+ url = https://github.com/onnx/onnx-tensorrt.git
diff --git a/3rdparty/onnx-tensorrt b/3rdparty/onnx-tensorrt
new file mode 16
index 000..e7be19cff37
--- /dev/null
+++ b/3rdparty/onnx-tensorrt
@@ -0,0 +1 @@
+Subproject commit e7be19cff377a95817503e8525e20de34cdc574a
diff --git a/Jenkinsfile b/Jenkinsfile
index cc839171f86..452c002fd3c 100644
--- a/Jenkinsfile
+++ b/Jenkinsfile
@@ -340,6 +340,17 @@ try {
}
}
},
+'TensorRT': {
+ node('mxnetlinux-cpu') {
+ws('workspace/build-tensorrt') {
+ timeout(time: max_time, unit: 'MINUTES') {
+init_git()
+docker_run('ubuntu_gpu_tensorrt', 'build_ubuntu_gpu_tensorrt',
false)
+pack_lib('tensorrt')
+ }
+}
+ }
+},
'Build CPU windows':{
node('mxnetwindows-cpu') {
timeout(time: max_time, unit: 'MINUTES') {
diff --git a/Makefile b/Makefile
index 67aaa7cf707..6a9b34cccf1 100644
--- a/Makefile
+++ b/Makefile
@@ -94,6 +94,14 @@ else
endif
CFLAGS += -I$(TPARTYDIR)/mshadow/ -I$(TPARTYDIR)/dmlc-core/include -fPIC
-I$(NNVM_PATH)/include -I$(DLPACK_PATH)/include -I$(TPARTYDIR)/tvm/include
-Iinclude $(MSHADOW_CFLAGS)
LDFLAGS = -pthread $(MSHADOW_LDFLAGS) $(DMLC_LDFLAGS)
+
+
+ifeq ($(USE_TENSORRT), 1)
+ CFLAGS += -I$(ROOTDIR) -I$(TPARTYDIR)
-DONNX_NAMESPACE=$(ONNX_NAMESPACE) -DMXNET_USE_TENSORRT=1
+ LDFLAGS += -lprotobuf -pthread -lonnx -lonnx_proto -lnvonnxparser
-lnvonnxparser_runtime -lnvinfer -lnvinfer_plugin
+endif
+# -L/usr/local/lib
+
ifeq ($(DEBUG), 1)
NVCCFLAGS += -std=c++11 -Xcompiler -D_FORCE_INLINES -g -G -O0 -ccbin
$(CXX) $(MSHADOW_NVCCFLAGS)
else
diff --git a/amalgamation/amalgamation.py b/amalgamation/amalgamation.py
index 52d775b7692..a3c28f7118e 100644
--- a/amalgamation/amalgamation.py
+++ b/amalgamation/amalgamation.py
@@ -23,13 +23,12 @@
import platform
blacklist = [
-'Windows.h', 'cublas_v2.h', 'cuda/tensor_gpu-inl.cuh',
-'cuda_runtime.h', 'cudnn.h', 'cudnn_lrn-inl.h', 'curand.h',
'curand_kernel.h',
-'glog/logging.h', 'io/azure_filesys.h', 'io/hdfs_filesys.h',
'io/s3_filesys.h',
-'kvstore_dist.h', 'mach/clock.h', 'mach/mach.h',
-'malloc.h', 'mkl.h', 'mkl_cblas.h', 'mkl_vsl.h', 'mkl_vsl_functions.h',
-'nvml.h', 'opencv2/opencv.hpp', 'sys/stat.h', 'sys/types.h', 'cuda.h',
'cuda_fp16.h',
-'omp.h', 'execinfo.h', 'packet/sse-inl.h', 'emmintrin.h',
'thrust/device_vector.h',
+'Windows.h', 'cublas_v2.h', 'cuda/tensor_gpu-inl.cuh', 'cuda_runtime.h',
'cudnn.h',
+'cudnn_lrn-inl.h', 'curand.h', 'curand_kernel.h', 'glog/logging.h',
'io/azure_filesys.h',
+'io/hdfs_filesys.h', 'io/s3_filesys.h', 'kvstore_dist.h', 'mach/clock.h',
'mach/mach.h',
+'malloc.h', 'mkl.h', 'mkl_cblas.h', 'mkl_vsl.h', 'mkl_vsl_functions.h',
'NvInfer.h', 'nvml.h',
+'opencv2/opencv.hpp', 'sys/stat.h', 'sys/types.h', 'cuda.h',
'cuda_fp16.h', 'omp.h',
+'onnx/onnx.pb.h', 'execinfo.h', 'packet/sse-inl.h', 'emmintrin.h',
'thrust/device_vector.h',
'cusolverDn.h', 'internal/concurrentqueue_internal_debug.h',
'relacy/relacy_std.hpp',
'relacy_shims.h', 'ittnotify.h', 'shared_mutex'
]
@@ -150,6 +149,7 @@ def expand(x, pending, stage):
h not in sysheaders and
'mkl' not in h and
'nnpack' not in h and
+'tensorrt' not in h and
not h.endswith('.cuh')): sysheaders.append(h)
else:
expand.treeDepth += 1
diff --git a/ci/docker/Dockerfile.build.ubuntu_gpu_tensorrt
b/ci/docker/Dockerfile.build.ubuntu_gpu_tensorrt
new file mode 100755
index 000..255da316041
--- /dev/null
+++ b/ci/docker/Dockerfile.build.ubuntu_gpu_tensorrt
@@ -0,0 +1,41 @@
+# -*- mode: dockerfile -*-
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by
[GitHub] marcoabreu commented on issue #11390: [MXNET-23] add README for test directory
marcoabreu commented on issue #11390: [MXNET-23] add README for test directory URL: https://github.com/apache/incubator-mxnet/pull/11390#issuecomment-400068052 See https://cwiki.apache.org/confluence/display/MXNET/Reproducing+test+results for reference This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] marcoabreu commented on issue #11390: [MXNET-23] add README for test directory
marcoabreu commented on issue #11390: [MXNET-23] add README for test directory URL: https://github.com/apache/incubator-mxnet/pull/11390#issuecomment-400066497 Hi, thanks for improving the instructions! Could you please also include the Docker-based method? This way, people don't have to set up local dependencies but can simply use the Docker container. This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] spidyDev commented on a change in pull request #11390: [MXNET-23] add README for test directory
spidyDev commented on a change in pull request #11390: [MXNET-23] add README
for test directory
URL: https://github.com/apache/incubator-mxnet/pull/11390#discussion_r197912493
##
File path: tests/README.md
##
@@ -0,0 +1,45 @@
+# Testing MXNET
+
+## Running CPP Tests
+
+1. Install [cmake](https://cmake.org/install/)
+1. Create a build directory in the root of the mxnet project
+```
+mkdir build
+cd build
+```
+1. Generate your Makefile and build along with the tests with cmake (specify
appropraite flags)
+```
+cmake -DUSE_CUDNN=ON -DUSE_CUDA=ON -DUSE_MKLDNN=ON -DBLAS=Open
-DCMAKE_BUILD_TYPE=Debug .. && make
+```
+1. Run tests
+```
+ctest --verbose
+```
+
+1. The following will run all the tests the in `cpp` directory. To run just
your test file replace the following in your `tests/CMakeLists.txt`
+```
+file(GLOB_RECURSE UNIT_TEST_SOURCE "cpp/*.cc" "cpp/*.h")
+```
+with
+```
+file(GLOB_RECURSE UNIT_TEST_SOURCE "cpp/test_main.cc" "cpp/{YOUR TEST
FILE}")
Review comment:
Just to be clear :
{COMPLETE PATH TO YOUR TEST FILE}
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[GitHub] anirudhacharya removed a comment on issue #11380: Add ability to query cuDNN BatchNorm min. epsilon. Allow ONNX importer to use cuDNN BN if chosen eps >= cuDNN min. eps.
anirudhacharya removed a comment on issue #11380: Add ability to query cuDNN BatchNorm min. epsilon. Allow ONNX importer to use cuDNN BN if chosen eps >= cuDNN min. eps. URL: https://github.com/apache/incubator-mxnet/pull/11380#issuecomment-400063961 yes, I can make this change. This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] anirudhacharya commented on issue #11380: Add ability to query cuDNN BatchNorm min. epsilon. Allow ONNX importer to use cuDNN BN if chosen eps >= cuDNN min. eps.
anirudhacharya commented on issue #11380: Add ability to query cuDNN BatchNorm min. epsilon. Allow ONNX importer to use cuDNN BN if chosen eps >= cuDNN min. eps. URL: https://github.com/apache/incubator-mxnet/pull/11380#issuecomment-400063961 yes, I can make this change. This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] leezu opened a new pull request #11392: Document AdaGrad eps as initial history accumulator value
leezu opened a new pull request #11392: Document AdaGrad eps as initial history accumulator value URL: https://github.com/apache/incubator-mxnet/pull/11392 See #11223 This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] marcoabreu commented on issue #11340: [MXNET-559] Scripts for running the Broken link checker job
marcoabreu commented on issue #11340: [MXNET-559] Scripts for running the Broken link checker job URL: https://github.com/apache/incubator-mxnet/pull/11340#issuecomment-400062796 I have retriggered CI. Please force push your PR until CI passes. This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] marcoabreu opened a new pull request #11391: Don't fail storing test results if test suite got aborted (#11363)
marcoabreu opened a new pull request #11391: Don't fail storing test results if test suite got aborted (#11363) URL: https://github.com/apache/incubator-mxnet/pull/11391 ## Description ## Address https://github.com/apache/incubator-mxnet/issues/11363 Don't fail if test result file does not exist. This might happen if a test suite failed and thus the following test suits got aborted. ## Checklist ## ### Essentials ### Please feel free to remove inapplicable items for your PR. - [ ] The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to the relevant [JIRA issue](https://issues.apache.org/jira/projects/MXNET/issues) created (except PRs with tiny changes) - [x] Changes are complete (i.e. I finished coding on this PR) - [x] All changes have test coverage: - Unit tests are added for small changes to verify correctness (e.g. adding a new operator) - Nightly tests are added for complicated/long-running ones (e.g. changing distributed kvstore) - Build tests will be added for build configuration changes (e.g. adding a new build option with NCCL) - [x] Code is well-documented: - For user-facing API changes, API doc string has been updated. - For new C++ functions in header files, their functionalities and arguments are documented. - For new examples, README.md is added to explain the what the example does, the source of the dataset, expected performance on test set and reference to the original paper if applicable - Check the API doc at http://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html - [x] To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change ### Changes ### ## Comments ## This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] haojin2 commented on a change in pull request #11356: [MXNET-560][WIP] Add temperature parameter in Softmax and SoftmaxOutput operator
haojin2 commented on a change in pull request #11356: [MXNET-560][WIP] Add
temperature parameter in Softmax and SoftmaxOutput operator
URL: https://github.com/apache/incubator-mxnet/pull/11356#discussion_r197905846
##
File path: src/operator/nn/softmax-inl.h
##
@@ -145,23 +155,36 @@ __global__ void softmax_compute_kernel(DType *in, DType
*out, index_t M, int axi
__syncthreads();
red::sum::SetInitValue(smem[x]);
- for (index_t i = x; i < M; i += x_size) {
-red::sum::Reduce(smem[x], static_cast(expf(in[base + i*sa] -
smax)));
+ if (temperature == 1.0) {
+for (index_t i = x; i < M; i += x_size) {
+ red::sum::Reduce(smem[x], static_cast(expf(in[base + i*sa] -
smax)));
+}
+ } else {
+for (index_t i = x; i < M; i += x_size) {
+ red::sum::Reduce(smem[x], static_cast(expf((in[base + i*sa] -
smax)/temperature)));
+}
}
+
__syncthreads();
cuda::Reduce1D(smem);
__syncthreads();
DType ssum = smem[0];
__syncthreads();
- for (index_t i = x; i < M; i += x_size) {
-out[base + i*sa] = OP::Map(in[base + i*sa] - smax, ssum);
+ if (temperature == 1.0) {
Review comment:
@apeforest Could you provide data on which version is faster? We have
`test_speed` function:
https://github.com/apache/incubator-mxnet/blob/a7952f0b3218363a9520aa606f43db94a34c55b8/python/mxnet/test_utils.py#L1133
ready for you to test the speed.
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[incubator-mxnet] branch master updated: [MXNET-538] Add XUnit test result publishing to windows (#11348)
This is an automated email from the ASF dual-hosted git repository.
marcoabreu pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/master by this push:
new 225f71f [MXNET-538] Add XUnit test result publishing to windows
(#11348)
225f71f is described below
commit 225f71f744ac5e7bd29868b6d3ba0e4fe2527c43
Author: Marco de Abreu
AuthorDate: Mon Jun 25 20:57:39 2018 +0200
[MXNET-538] Add XUnit test result publishing to windows (#11348)
* Add test result publishing to windows
* Fix names of files
* Fix syntax of xcopy on Windows
---
Jenkinsfile | 20 +---
1 file changed, 9 insertions(+), 11 deletions(-)
diff --git a/Jenkinsfile b/Jenkinsfile
index cc83917..44aad8e 100644
--- a/Jenkinsfile
+++ b/Jenkinsfile
@@ -106,7 +106,7 @@ def collect_test_results_unix(original_file_name,
new_file_name) {
def collect_test_results_windows(original_file_name, new_file_name) {
echo 'Saving python test results for ' + new_file_name
// Rename file to make it distinguishable. Unfortunately, it's not
possible to get STAGE_NAME in a parallel stage
-bat 'xcopy ' + original_file_name + ' ' + new_file_name
+bat 'xcopy ' + original_file_name + ' ' + new_file_name + '*'
archiveArtifacts artifacts: new_file_name
}
@@ -786,8 +786,7 @@ try {
del /S /Q ${env.WORKSPACE}\\pkg_vc14_cpu\\python\\*.pyc
C:\\mxnet\\test_cpu.bat"""
} finally {
- // We are unable to modify test_cpu.bat, so we can't track test
failures on Windows
- // collect_test_results_windows('nosetests.xml',
'nosetests_windows_python2_cpu.xml')
+ collect_test_results_windows('nosetests_unittest.xml',
'nosetests_unittest_windows_python2_cpu.xml')
}
}
}
@@ -809,8 +808,7 @@ try {
del /S /Q ${env.WORKSPACE}\\pkg_vc14_cpu\\python\\*.pyc
C:\\mxnet\\test_cpu.bat"""
} finally {
- // We are unable to modify test_cpu.bat, so we can't track test
failures on Windows
- // collect_test_results_windows('nosetests.xml',
'nosetests_windows_python3_cpu.xml')
+ collect_test_results_windows('nosetests_unittest.xml',
'nosetests_unittest_windows_python3_cpu.xml')
}
}
}
@@ -832,8 +830,8 @@ try {
del /S /Q ${env.WORKSPACE}\\pkg_vc14_gpu\\python\\*.pyc
C:\\mxnet\\test_gpu.bat"""
} finally {
- // We are unable to modify test_cpu.bat, so we can't track test
failures on Windows
- // collect_test_results_windows('nosetests.xml',
'nosetests_windows_python2_gpu.xml')
+ collect_test_results_windows('nosetests_gpu_forward.xml',
'nosetests_gpu_forward_windows_python2_gpu.xml')
+ collect_test_results_windows('nosetests_gpu_operator.xml',
'nosetests_gpu_operator_windows_python2_gpu.xml')
}
}
}
@@ -855,8 +853,8 @@ try {
del /S /Q ${env.WORKSPACE}\\pkg_vc14_gpu\\python\\*.pyc
C:\\mxnet\\test_gpu.bat"""
} finally {
- // We are unable to modify test_cpu.bat, so we can't track test
failures on Windows
- // collect_test_results_windows('nosetests.xml',
'nosetests_windows_python3_gpu.xml')
+ collect_test_results_windows('nosetests_gpu_forward.xml',
'nosetests_gpu_forward_windows_python3_gpu.xml')
+ collect_test_results_windows('nosetests_gpu_operator.xml',
'nosetests_gpu_operator_windows_python3_gpu.xml')
}
}
}
@@ -878,8 +876,8 @@ try {
del /S /Q ${env.WORKSPACE}\\pkg_vc14_gpu_mkldnn\\python\\*.pyc
C:\\mxnet\\test_gpu.bat"""
} finally {
- // We are unable to modify test_cpu.bat, so we can't track test
failures on Windows
- // collect_test_results_windows('nosetests.xml',
'nosetests_windows_python3_mkldnn_Gpu.xml')
+ collect_test_results_windows('nosetests_gpu_forward.xml',
'nosetests_gpu_forward_windows_python3_gpu_mkldnn.xml')
+ collect_test_results_windows('nosetests_gpu_operator.xml',
'nosetests_gpu_operator_windows_python3_gpu_mkldnn.xml')
}
}
}
[GitHub] marcoabreu closed pull request #11348: [MXNET-538] Add XUnit test result publishing to windows
marcoabreu closed pull request #11348: [MXNET-538] Add XUnit test result
publishing to windows
URL: https://github.com/apache/incubator-mxnet/pull/11348
This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:
As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):
diff --git a/Jenkinsfile b/Jenkinsfile
index cc839171f86..44aad8e006e 100644
--- a/Jenkinsfile
+++ b/Jenkinsfile
@@ -106,7 +106,7 @@ def collect_test_results_unix(original_file_name,
new_file_name) {
def collect_test_results_windows(original_file_name, new_file_name) {
echo 'Saving python test results for ' + new_file_name
// Rename file to make it distinguishable. Unfortunately, it's not
possible to get STAGE_NAME in a parallel stage
-bat 'xcopy ' + original_file_name + ' ' + new_file_name
+bat 'xcopy ' + original_file_name + ' ' + new_file_name + '*'
archiveArtifacts artifacts: new_file_name
}
@@ -786,8 +786,7 @@ try {
del /S /Q ${env.WORKSPACE}\\pkg_vc14_cpu\\python\\*.pyc
C:\\mxnet\\test_cpu.bat"""
} finally {
- // We are unable to modify test_cpu.bat, so we can't track test
failures on Windows
- // collect_test_results_windows('nosetests.xml',
'nosetests_windows_python2_cpu.xml')
+ collect_test_results_windows('nosetests_unittest.xml',
'nosetests_unittest_windows_python2_cpu.xml')
}
}
}
@@ -809,8 +808,7 @@ try {
del /S /Q ${env.WORKSPACE}\\pkg_vc14_cpu\\python\\*.pyc
C:\\mxnet\\test_cpu.bat"""
} finally {
- // We are unable to modify test_cpu.bat, so we can't track test
failures on Windows
- // collect_test_results_windows('nosetests.xml',
'nosetests_windows_python3_cpu.xml')
+ collect_test_results_windows('nosetests_unittest.xml',
'nosetests_unittest_windows_python3_cpu.xml')
}
}
}
@@ -832,8 +830,8 @@ try {
del /S /Q ${env.WORKSPACE}\\pkg_vc14_gpu\\python\\*.pyc
C:\\mxnet\\test_gpu.bat"""
} finally {
- // We are unable to modify test_cpu.bat, so we can't track test
failures on Windows
- // collect_test_results_windows('nosetests.xml',
'nosetests_windows_python2_gpu.xml')
+ collect_test_results_windows('nosetests_gpu_forward.xml',
'nosetests_gpu_forward_windows_python2_gpu.xml')
+ collect_test_results_windows('nosetests_gpu_operator.xml',
'nosetests_gpu_operator_windows_python2_gpu.xml')
}
}
}
@@ -855,8 +853,8 @@ try {
del /S /Q ${env.WORKSPACE}\\pkg_vc14_gpu\\python\\*.pyc
C:\\mxnet\\test_gpu.bat"""
} finally {
- // We are unable to modify test_cpu.bat, so we can't track test
failures on Windows
- // collect_test_results_windows('nosetests.xml',
'nosetests_windows_python3_gpu.xml')
+ collect_test_results_windows('nosetests_gpu_forward.xml',
'nosetests_gpu_forward_windows_python3_gpu.xml')
+ collect_test_results_windows('nosetests_gpu_operator.xml',
'nosetests_gpu_operator_windows_python3_gpu.xml')
}
}
}
@@ -878,8 +876,8 @@ try {
del /S /Q ${env.WORKSPACE}\\pkg_vc14_gpu_mkldnn\\python\\*.pyc
C:\\mxnet\\test_gpu.bat"""
} finally {
- // We are unable to modify test_cpu.bat, so we can't track test
failures on Windows
- // collect_test_results_windows('nosetests.xml',
'nosetests_windows_python3_mkldnn_Gpu.xml')
+ collect_test_results_windows('nosetests_gpu_forward.xml',
'nosetests_gpu_forward_windows_python3_gpu_mkldnn.xml')
+ collect_test_results_windows('nosetests_gpu_operator.xml',
'nosetests_gpu_operator_windows_python3_gpu_mkldnn.xml')
}
}
}
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[GitHub] azai91 opened a new pull request #11390: [MXNET-23] add README for test directory
azai91 opened a new pull request #11390: [MXNET-23] add README for test directory URL: https://github.com/apache/incubator-mxnet/pull/11390 ## Description ## add readme to onboard developers for building / running CPP tests ## Checklist ## ### Essentials ### Please feel free to remove inapplicable items for your PR. - [ ] The PR title starts with [MXNET-$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 ### - [ ] add readme ## Comments ## - If this change is a backward incompatible change, why must this change be made. - Interesting edge cases to note here This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] leleamol commented on issue #11340: [MXNET-559] Scripts for running the Broken link checker job
leleamol commented on issue #11340: [MXNET-559] Scripts for running the Broken link checker job URL: https://github.com/apache/incubator-mxnet/pull/11340#issuecomment-400058320 @marcoabreu @sandeep-krishnamurthy @bhavinthaker @eric-haibin-lin Hi everybody, The PR is approved for merge. However, can you please merge this PR if you have committers access. The CI integration reports failure in 2 tests (python2: GPU Win and Python3: MKLDNN CPU) that are not related to changes in this PR. These checks are blocking this PR from merging. This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] leezu closed pull request #11223: Allow specifying AdaGrad initial accumulator value
leezu closed pull request #11223: Allow specifying AdaGrad initial accumulator
value
URL: https://github.com/apache/incubator-mxnet/pull/11223
This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:
As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):
diff --git a/python/mxnet/optimizer.py b/python/mxnet/optimizer.py
index 0c3fc904fb1..e7727b7e586 100644
--- a/python/mxnet/optimizer.py
+++ b/python/mxnet/optimizer.py
@@ -1091,14 +1091,20 @@ class AdaGrad(Optimizer):
--
eps: float, optional
Small value to avoid division by 0.
+initial_accumulator_value: float, default 0
+The Adagrad state is initially set to this value.
"""
-def __init__(self, eps=1e-7, **kwargs):
+def __init__(self, eps=1e-7, initial_accumulator_value=0, **kwargs):
super(AdaGrad, self).__init__(**kwargs)
self.float_stable_eps = eps
+self.initial_accumulator_value = initial_accumulator_value
def create_state(self, index, weight):
-return zeros(weight.shape, weight.context, stype=weight.stype) #
history
+history = zeros(weight.shape, weight.context, stype=weight.stype)
+if self.initial_accumulator_value:
+history[:] = self.initial_accumulator_value
+return history
def update(self, index, weight, grad, state):
assert(isinstance(weight, NDArray))
diff --git a/tests/python/unittest/test_optimizer.py
b/tests/python/unittest/test_optimizer.py
index fba10fb522a..cd516738130 100644
--- a/tests/python/unittest/test_optimizer.py
+++ b/tests/python/unittest/test_optimizer.py
@@ -15,6 +15,7 @@
# specific language governing permissions and limitations
# under the License.
+import itertools
import numpy as np
import mxnet as mx
import mxnet.lr_scheduler as lr_scheduler
@@ -991,12 +992,16 @@ class PyAdaGrad(mx.optimizer.Optimizer):
Small value to avoid division by 0.
"""
-def __init__(self, eps=1e-7, **kwargs):
+def __init__(self, eps=1e-7, initial_accumulator_value=0, **kwargs):
super(PyAdaGrad, self).__init__(**kwargs)
self.float_stable_eps = eps
+self.initial_accumulator_value = initial_accumulator_value
def create_state(self, index, weight):
-return mx.nd.zeros(weight.shape, weight.context, stype=weight.stype)
+history = mx.nd.zeros(weight.shape, weight.context, stype=weight.stype)
+if self.initial_accumulator_value:
+history[:] = self.initial_accumulator_value
+return history
def update(self, index, weight, grad, state):
self._update_count(index)
@@ -1020,21 +1025,21 @@ def test_adagrad():
cg_options = [{}, {'clip_gradient': 0.4}, {'clip_gradient': 0.5}]
rg_options = [{}, {'rescale_grad': 0.14}, {'rescale_grad': 0.8}]
wd_options = [{}, {'wd': 0.0}]
-for dtype in [np.float32]:
-for eps_option in eps_options:
-for cg_option in cg_options:
-for rg_option in rg_options:
-for wd_option in wd_options:
-kwarg = {}
-kwarg.update(eps_option)
-kwarg.update(cg_option)
-kwarg.update(rg_option)
-kwarg.update(wd_option)
-compare_optimizer(opt1(**kwarg), opt2(**kwarg), shape,
dtype)
-if wd_option.get('wd', 0.0) == 0.0:
-compare_optimizer(opt1(**kwarg), opt2(**kwarg),
shape, dtype,
- w_stype='row_sparse',
g_stype='row_sparse')
+acc_options = [{}, {'initial_accumulator_value': 1.0}]
+for dtype in [np.float32]:
+for eps_option, cg_option, rg_option, wd_option, acc_option in
itertools.product(
+eps_options, cg_options, rg_options, wd_options, acc_options):
+kwarg = {}
+kwarg.update(eps_option)
+kwarg.update(cg_option)
+kwarg.update(rg_option)
+kwarg.update(wd_option)
+kwarg.update(acc_option)
+compare_optimizer(opt1(**kwarg), opt2(**kwarg), shape, dtype)
+if wd_option.get('wd', 0.0) == 0.0:
+compare_optimizer(opt1(**kwarg), opt2(**kwarg), shape, dtype,
+ w_stype='row_sparse', g_stype='row_sparse')
if __name__ == '__main__':
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[GitHub] Roshrini commented on issue #11380: Add ability to query cuDNN BatchNorm min. epsilon. Allow ONNX importer to use cuDNN BN if chosen eps >= cuDNN min. eps.
Roshrini commented on issue #11380: Add ability to query cuDNN BatchNorm min. epsilon. Allow ONNX importer to use cuDNN BN if chosen eps >= cuDNN min. eps. URL: https://github.com/apache/incubator-mxnet/pull/11380#issuecomment-400052737 Thanks @mkolod for making this change. This will definitely be helpful. @anirudhacharya @spidyDev This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] haojin2 commented on issue #11367: Segfault when running test_operator_gpu.test_sparse_dot many times
haojin2 commented on issue #11367: Segfault when running test_operator_gpu.test_sparse_dot many times URL: https://github.com/apache/incubator-mxnet/issues/11367#issuecomment-400050189 I already submitted a fix @vrakesh This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
