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Hieu Hoang
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README.md
31
README.md
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# AmuNMT
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[](https://gitter.im/amunmt/amunmt?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
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[](http://37.247.57.181:8000/job/amunmt_compilation_cuda/)
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[](http://37.247.57.181:8000/job/amunmt_compilation_cpu/)
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[](http://vali.inf.ed.ac.uk/jenkins/job/amunmt_compilation_cuda/)
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[](http://vali.inf.ed.ac.uk/jenkins/job/amunmt_compilation_cpu/)
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A C++ inference engine for Neural Machine Translation (NMT) models trained with Theano-based scripts from
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Nematus (https://github.com/rsennrich/nematus) or DL4MT (https://github.com/nyu-dl/dl4mt-tutorial)
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@ -92,14 +94,35 @@ AmuNMT has integrated support for [BPE encoding](https://github.com/rsennrich/su
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bpe: bpe.codes
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debpe: true
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## Python Bindings
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Python bindings allow to run AmuNMT decoder in python scripts. The compilation of the bindings requires `python-dev` package. To compile the bindings run:
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```
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make python
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```
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The Python bindings consist of 2 function: `init` and `translate`:
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```python
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import libamunmt
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libamunmt.init('-c config.yml')
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print libamunmt.translate(['this is a little test .'])
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```
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The `init` function init the decoder and the syntax is the same as in command line. The `translate`
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function takes a list of sentences to translate. For real-world example, see the `scripts/amunmt_erver.py`
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script, which uses python bindings to run REST server.
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## Using GPU/CPU threads
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AmuNMT can use GPUs, CPUs, or both, to distribute translation of different sentences.
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AmuNMT can use GPUs, CPUs, or both, to distribute translation of different sentences. **However, it is unlikely that CPUs used together with GPUs yield any performance improvement. It is probably better to only use the GPU if one or more are available.**
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cpu-threads: 8
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gpu-threads: 2
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devices: [0, 1]
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The setting above uses 8 CPU threads and 4 GPU threads (2 GPUs x 2 threads). The `gpu-threads` and `devices` options are only available when AmuNMT has been compiled with CUDA support. Multiple GPU threads can be used to increase GPU saturation, but will likely not result in a large performance boost. By default, `gpu-threads` is set to `1` and `cpu-threads` to `0` if CUDA is available. Otherwise `cpu-threads` is set to `1`. To disable the GPU set `gpu-threads` to `0`. Setting both `gpu-threads` and `cpu-threads` to `0` will result in an exception.
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The setting above uses 8 CPU threads and 4 GPU threads (2 GPUs x 2 threads). The `gpu-threads` and `devices` options are only available when AmuNMT has been compiled with CUDA support. Multiple GPU threads can be used to increase GPU saturation, but will likely not result in a large performance boost. By default, `gpu-threads` is set to `1` and `cpu-threads` to `0` if CUDA is available. Otherwise `cpu-threads` is set to `1`. To disable the GPU set `gpu-threads` to `0`. Setting both `gpu-threads` and `cpu-threads` to `0` will result in an exception.
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## Example usage
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@ -64,7 +64,7 @@ cuda_add_executable(
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)
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if(PYTHONLIBS_FOUND)
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cuda_add_library(amunmt SHARED
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cuda_add_library(python SHARED
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python/amunmt.cpp
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# gpu/decoder/ape_penalty.cu
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gpu/decoder/encoder_decoder.cu
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@ -80,7 +80,9 @@ cuda_add_library(amunmt SHARED
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$<TARGET_OBJECTS:cpumode>
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$<TARGET_OBJECTS:libyaml-cpp>
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)
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set_target_properties("amunmt" PROPERTIES EXCLUDE_FROM_ALL 1)
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set_target_properties("python" PROPERTIES EXCLUDE_FROM_ALL 1)
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set_target_properties("python" PROPERTIES OUTPUT_NAME "amunmt")
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endif(PYTHONLIBS_FOUND)
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cuda_add_library(mosesplugin STATIC
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@ -116,7 +118,7 @@ add_executable(
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)
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if(PYTHONLIBS_FOUND)
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add_library(amunmt SHARED
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add_library(python SHARED
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python/amunmt.cpp
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common/loader_factory.cpp
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$<TARGET_OBJECTS:libcnpy>
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@ -124,14 +126,15 @@ add_library(amunmt SHARED
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$<TARGET_OBJECTS:libcommon>
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$<TARGET_OBJECTS:libyaml-cpp>
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)
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set_target_properties("amunmt" PROPERTIES EXCLUDE_FROM_ALL 1)
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set_target_properties("python" PROPERTIES EXCLUDE_FROM_ALL 1)
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set_target_properties("python" PROPERTIES OUTPUT_NAME "amunmt")
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endif(PYTHONLIBS_FOUND)
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endif(CUDA_FOUND)
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SET(EXES "amun")
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if(PYTHONLIBS_FOUND)
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SET(EXES ${EXES} "amunmt")
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SET(EXES ${EXES} "python")
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endif(PYTHONLIBS_FOUND)
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foreach(exec ${EXES})
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@ -22,20 +22,22 @@ using namespace std;
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namespace amunmt {
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God::God()
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:threadIncr_(0)
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: threadIncr_(0)
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{
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}
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God::~God() {}
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God::~God()
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{
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}
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God& God::Init(const std::string& options) {
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std::vector<std::string> args = boost::program_options::split_unix(options);
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int argc = args.size() + 1;
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char* argv[argc];
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argv[0] = const_cast<char*>("bogus");
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for(int i = 1; i < argc; i++)
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for (int i = 1; i < argc; ++i) {
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argv[i] = const_cast<char*>(args[i-1].c_str());
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}
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return Init(argc, argv);
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}
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@ -49,35 +51,35 @@ God& God::Init(int argc, char** argv) {
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config_.AddOptions(argc, argv);
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config_.LogOptions();
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if(Get("source-vocab").IsSequence()) {
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for(auto sourceVocabPath : Get<std::vector<std::string>>("source-vocab"))
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sourceVocabs_.emplace_back(new Vocab(sourceVocabPath));
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}
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else {
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sourceVocabs_.emplace_back(new Vocab(Get<std::string>("source-vocab")));
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if (Get("source-vocab").IsSequence()) {
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for (auto sourceVocabPath : Get<std::vector<std::string>>("source-vocab")) {
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sourceVocabs_.emplace_back(new Vocab(sourceVocabPath));
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}
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} else {
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sourceVocabs_.emplace_back(new Vocab(Get<std::string>("source-vocab")));
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}
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targetVocab_.reset(new Vocab(Get<std::string>("target-vocab")));
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weights_ = Get<std::map<std::string, float>>("weights");
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if(Get<bool>("show-weights")) {
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LOG(info) << "Outputting weights and exiting";
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for(auto && pair : weights_) {
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std::cout << pair.first << "= " << pair.second << std::endl;
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}
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exit(0);
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LOG(info) << "Outputting weights and exiting";
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for(auto && pair : weights_) {
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std::cout << pair.first << "= " << pair.second << std::endl;
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}
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exit(0);
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}
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LoadScorers();
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LoadFiltering();
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if (Has("input-file")) {
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LOG(info) << "Reading from " << Get<std::string>("input-file");
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inputStream_.reset(new InputFileStream(Get<std::string>("input-file")));
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LOG(info) << "Reading from " << Get<std::string>("input-file");
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inputStream_.reset(new InputFileStream(Get<std::string>("input-file")));
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}
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else {
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LOG(info) << "Reading from stdin";
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inputStream_.reset(new InputFileStream(std::cin));
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LOG(info) << "Reading from stdin";
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inputStream_.reset(new InputFileStream(std::cin));
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}
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LoadPrePostProcessing();
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@ -184,11 +186,9 @@ std::vector<ScorerPtr> God::GetScorers(const DeviceInfo &deviceInfo) const {
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std::vector<ScorerPtr> scorers;
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if (deviceInfo.deviceType == CPUDevice) {
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//cerr << "CPU GetScorers" << endl;
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for (auto&& loader : cpuLoaders_ | boost::adaptors::map_values)
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scorers.emplace_back(loader->NewScorer(*this, deviceInfo));
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} else {
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//cerr << "GPU GetScorers" << endl;
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for (auto&& loader : gpuLoaders_ | boost::adaptors::map_values)
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scorers.emplace_back(loader->NewScorer(*this, deviceInfo));
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}
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@ -233,14 +233,12 @@ std::vector<std::string> God::Postprocess(const std::vector<std::string>& input)
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}
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return processed;
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}
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// clean up cuda vectors before cuda context goes out of scope
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void God::CleanUp() {
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for (Loaders::value_type& loader : cpuLoaders_) {
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//cerr << "cpu loader=" << loader.first << endl;
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loader.second.reset(nullptr);
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}
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for (Loaders::value_type& loader : gpuLoaders_) {
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//cerr << "gpu loader=" << loader.first << endl;
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loader.second.reset(nullptr);
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}
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}
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@ -274,7 +272,6 @@ DeviceInfo God::GetNextDevice() const
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++threadIncr_;
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//cerr << "GetNextDevice=" << ret << endl;
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return ret;
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}
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