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fr2en.cpp
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fr2en.cpp
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/**
* Copyright (c) Glow Contributors. See CONTRIBUTORS file.
*
* Licensed 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.
*/
#include "glow/ExecutionEngine/ExecutionEngine.h"
#include "glow/Graph/Graph.h"
#include "glow/Optimizer/GraphOptimizer/GraphOptimizer.h"
#include "glow/Quantization/Quantization.h"
#include "glow/Quantization/Serialization.h"
#include "llvm/Support/CommandLine.h"
#include "llvm/Support/Timer.h"
#include <glog/logging.h>
#include <algorithm>
#include <fstream>
#include <iostream>
#include <sstream>
#include <string>
#include <unordered_map>
#include <vector>
using namespace glow;
namespace {
/// Debugging options.
llvm::cl::OptionCategory debugCat("Glow Debugging Options");
llvm::cl::opt<std::string> dumpGraphDAGFileOpt(
"dump-graph-DAG",
llvm::cl::desc("Dump the graph to the given file in DOT format."),
llvm::cl::value_desc("file.dot"), llvm::cl::cat(debugCat));
/// Translator options.
llvm::cl::OptionCategory fr2enCat("French-to-English Translator Options");
llvm::cl::opt<unsigned> batchSizeOpt(
"batchsize", llvm::cl::desc("Process batches of N sentences at a time."),
llvm::cl::init(1), llvm::cl::value_desc("N"), llvm::cl::cat(fr2enCat));
llvm::cl::alias batchSizeA("b", llvm::cl::desc("Alias for -batchsize"),
llvm::cl::aliasopt(batchSizeOpt),
llvm::cl::cat(fr2enCat));
llvm::cl::opt<bool>
timeOpt("time",
llvm::cl::desc("Print timer data detailing how long it "
"takes for the program to execute translate phase. "
"This option will be useful if input is read from "
"the file directly."),
llvm::cl::Optional, llvm::cl::cat(fr2enCat));
llvm::cl::opt<std::string> ExecutionBackend(
"backend",
llvm::cl::desc("Backend to use, e.g., Interpreter, CPU, OpenCL:"),
llvm::cl::Optional, llvm::cl::init("Interpreter"), llvm::cl::cat(fr2enCat));
/// Quantization options.
llvm::cl::OptionCategory quantizationCat("Quantization Options");
llvm::cl::opt<std::string> dumpProfileFileOpt(
"dump-profile",
llvm::cl::desc("Perform quantization profiling for a given graph "
"and dump result to the file."),
llvm::cl::value_desc("profile.yaml"), llvm::cl::Optional,
llvm::cl::cat(quantizationCat));
llvm::cl::opt<std::string> loadProfileFileOpt(
"load-profile",
llvm::cl::desc("Load quantization profile file and quantize the graph"),
llvm::cl::value_desc("profile.yaml"), llvm::cl::Optional,
llvm::cl::cat(quantizationCat));
} // namespace
const unsigned MAX_LENGTH = 10;
const unsigned EMBEDDING_SIZE = 256;
const unsigned HIDDEN_SIZE = EMBEDDING_SIZE * 3;
/// Stores vocabulary of a language. Contains mapping from word to index and
/// vice versa.
struct Vocabulary {
std::vector<std::string> index2word_;
std::unordered_map<std::string, int64_t> word2index_;
void addWord(llvm::StringRef word) {
word2index_[word.str()] = index2word_.size();
index2word_.push_back(word.str());
}
Vocabulary() = default;
void loadVocabularyFromFile(llvm::StringRef filename) {
std::ifstream file(filename.str());
std::string word;
while (getline(file, word))
addWord(word);
}
};
/// Loads tensor of floats from binary file.
void loadMatrixFromFile(llvm::StringRef filename, Tensor &result) {
std::ifstream file(filename.str(), std::ios::binary);
if (!file.read(result.getUnsafePtr(), result.size() * sizeof(float))) {
std::cout
<< "Error reading file: " << filename.str() << '\n'
<< "Need to be downloaded by calling:\n"
<< "python ../glow/utils/download_datasets_and_models.py -d fr2en\n";
exit(1);
}
}
/// Represents a single RNN model: encoder combined with decoder.
/// Stores vocabulary, compiled Graph (ready to be executed), and
/// few references to input/output Variables.
struct Model {
unsigned batchSize_;
ExecutionEngine EE_{ExecutionBackend};
Function *F_;
Vocabulary en_, fr_;
Placeholder *input_;
Placeholder *seqLength_;
Placeholder *output_;
PlaceholderBindings bindings;
LoweredInfoMap loweredMap_;
void loadLanguages();
void loadEncoder();
void loadDecoder();
void translate(const std::vector<std::string> &batch);
Model(unsigned batchSize) : batchSize_(batchSize) {
F_ = EE_.getModule().createFunction("main");
}
void dumpGraphDAG(const char *filename) { F_->dumpDAG(filename); }
void compile() {
CompilationContext cctx{&bindings, &loweredMap_};
PrecisionConfiguration &precConfig = cctx.precisionConfig;
::glow::convertPlaceholdersToConstants(F_, bindings,
{input_, seqLength_, output_});
if (!dumpProfileFileOpt.empty()) {
precConfig.quantMode = QuantizationMode::Profile;
}
// Load the quantization profile and transform the graph.
if (!loadProfileFileOpt.empty()) {
precConfig.quantMode = QuantizationMode::Quantize;
deserializeProfilingInfosFromYaml(
loadProfileFileOpt, precConfig.quantConfig.graphPreLowerHash,
precConfig.quantConfig.infos);
precConfig.quantConfig.assertAllNodesQuantized = true;
}
EE_.compile(cctx);
// After compilation, the original function may be removed/replaced. Need to
// update F_.
F_ = EE_.getModule().getFunctions().front();
}
private:
Placeholder *embedding_fr_, *embedding_en_;
Node *encoderHiddenOutput_;
Placeholder *loadEmbedding(llvm::StringRef langPrefix, dim_t langSize) {
auto &mod = EE_.getModule();
auto *result =
mod.createPlaceholder(ElemKind::FloatTy, {langSize, EMBEDDING_SIZE},
"embedding." + langPrefix.str(), false);
loadMatrixFromFile("fr2en/" + langPrefix.str() + "_embedding.bin",
*bindings.allocate(result));
return result;
}
Node *createPyTorchGRUCell(Function *G, Node *input, Node *hidden,
Placeholder *wIh, Placeholder *bIh,
Placeholder *wHh, Placeholder *bHh) {
// reference implementation:
// https://github.com/pytorch/pytorch/blob/dd5c195646b941d3e20a72847ac48c41e272b8b2/torch/nn/_functions/rnn.py#L46
Node *gi = G->createFullyConnected("pytorch.GRU.gi", input, wIh, bIh);
Node *gh = G->createFullyConnected("pytorch.GRU.gh", hidden, wHh, bHh);
Node *i_r = G->createSlice("pytorch.GRU.i_r", gi, {0, 0},
{batchSize_, EMBEDDING_SIZE});
Node *i_i = G->createSlice("pytorch.GRU.i_i", gi, {0, EMBEDDING_SIZE},
{batchSize_, 2 * EMBEDDING_SIZE});
Node *i_n = G->createSlice("pytorch.GRU.i_n", gi, {0, 2 * EMBEDDING_SIZE},
{batchSize_, 3 * EMBEDDING_SIZE});
Node *h_r = G->createSlice("pytorch.GRU.h_r", gh, {0, 0},
{batchSize_, EMBEDDING_SIZE});
Node *h_i = G->createSlice("pytorch.GRU.h_i", gh, {0, EMBEDDING_SIZE},
{batchSize_, 2 * EMBEDDING_SIZE});
Node *h_n = G->createSlice("pytorch.GRU.h_n", gh, {0, 2 * EMBEDDING_SIZE},
{batchSize_, 3 * EMBEDDING_SIZE});
Node *resetgate = G->createSigmoid("pytorch.GRU.resetgate",
G->createAdd("i_r_plus_h_r", i_r, h_r));
Node *inputgate = G->createSigmoid("pytorch.GRU.inputgate",
G->createAdd("i_i_plus_h_i", i_i, h_i));
Node *newgate = G->createTanh(
"pytorch.GRU.newgate",
G->createAdd("i_n_plus_rg_mult_h_n", i_n,
G->createMul("rg_mult_h_n", resetgate, h_n)));
return G->createAdd(
"pytorch.GRU.hy", newgate,
G->createMul("ig_mult_hmng", inputgate,
G->createSub("hidden_minus_newgate", hidden, newgate)));
}
};
void Model::loadLanguages() {
fr_.loadVocabularyFromFile("fr2en/fr_vocabulary.txt");
en_.loadVocabularyFromFile("fr2en/en_vocabulary.txt");
embedding_fr_ = loadEmbedding("fr", fr_.index2word_.size());
embedding_en_ = loadEmbedding("en", en_.index2word_.size());
}
/// Model part representing Encoder. Remembers input sentence into hidden layer.
/// \p input is Variable representing the sentence.
/// \p seqLength is Variable representing the length of sentence.
/// \p encoderHiddenOutput saves resulting hidden layer.
void Model::loadEncoder() {
auto &mod = EE_.getModule();
input_ = mod.createPlaceholder(ElemKind::Int64ITy, {batchSize_, MAX_LENGTH},
"encoder.inputsentence", false);
bindings.allocate(input_);
seqLength_ = mod.createPlaceholder(ElemKind::Int64ITy, {batchSize_},
"encoder.seqLength", false);
bindings.allocate(seqLength_);
auto *hiddenInit =
mod.createPlaceholder(ElemKind::FloatTy, {batchSize_, EMBEDDING_SIZE},
"encoder.hiddenInit", false);
auto *hiddenInitTensor = bindings.allocate(hiddenInit);
hiddenInitTensor->zero();
Node *hidden = hiddenInit;
auto *wIh = mod.createPlaceholder(
ElemKind::FloatTy, {EMBEDDING_SIZE, HIDDEN_SIZE}, "encoder.w_ih", false);
auto *bIh = mod.createPlaceholder(ElemKind::FloatTy, {HIDDEN_SIZE},
"encoder.b_ih", false);
auto *wHh = mod.createPlaceholder(
ElemKind::FloatTy, {EMBEDDING_SIZE, HIDDEN_SIZE}, "encoder.w_hh", false);
auto *bHh = mod.createPlaceholder(ElemKind::FloatTy, {HIDDEN_SIZE},
"encoder.b_hh", false);
loadMatrixFromFile("fr2en/encoder_w_ih.bin", *bindings.allocate(wIh));
loadMatrixFromFile("fr2en/encoder_b_ih.bin", *bindings.allocate(bIh));
loadMatrixFromFile("fr2en/encoder_w_hh.bin", *bindings.allocate(wHh));
loadMatrixFromFile("fr2en/encoder_b_hh.bin", *bindings.allocate(bHh));
Node *inputEmbedded =
F_->createGather("encoder.embedding", embedding_fr_, input_);
// TODO: encoder does exactly MAX_LENGTH steps, while input size is smaller.
// We could use control flow here.
std::vector<NodeValue> outputs;
for (unsigned step = 0; step < MAX_LENGTH; step++) {
Node *inputSlice = F_->createSlice(
"encoder." + std::to_string(step) + ".inputSlice", inputEmbedded,
{0, step, 0}, {batchSize_, step + 1, EMBEDDING_SIZE});
Node *reshape =
F_->createReshape("encoder." + std::to_string(step) + ".reshape",
inputSlice, {batchSize_, EMBEDDING_SIZE}, ANY_LAYOUT);
hidden = createPyTorchGRUCell(F_, reshape, hidden, wIh, bIh, wHh, bHh);
outputs.push_back(hidden);
}
Node *output = F_->createConcat("encoder.output", outputs, 1);
Node *r2 =
F_->createReshape("encoder.output.r2", output,
{MAX_LENGTH * batchSize_, EMBEDDING_SIZE}, ANY_LAYOUT);
encoderHiddenOutput_ = F_->createGather("encoder.outputNth", r2, seqLength_);
}
/// Model part representing Decoder.
/// Uses \p encoderHiddenOutput as final state from Encoder.
/// Resulting translation is put into \p output Variable.
void Model::loadDecoder() {
auto &mod = EE_.getModule();
auto *input = mod.createPlaceholder(ElemKind::Int64ITy, {batchSize_},
"decoder.input", false);
auto *inputTensor = bindings.allocate(input);
for (dim_t i = 0; i < batchSize_; i++) {
inputTensor->getHandle<int64_t>().at({i}) = en_.word2index_["SOS"];
}
auto *wIh = mod.createPlaceholder(
ElemKind::FloatTy, {EMBEDDING_SIZE, HIDDEN_SIZE}, "decoder.w_ih", false);
auto *bIh = mod.createPlaceholder(ElemKind::FloatTy, {HIDDEN_SIZE},
"decoder.b_ih", false);
auto *wHh = mod.createPlaceholder(
ElemKind::FloatTy, {EMBEDDING_SIZE, HIDDEN_SIZE}, "decoder.w_hh", false);
auto *bHh = mod.createPlaceholder(ElemKind::FloatTy, {HIDDEN_SIZE},
"decoder.b_hh", false);
auto *outW = mod.createPlaceholder(
ElemKind::FloatTy, {EMBEDDING_SIZE, (dim_t)en_.index2word_.size()},
"decoder.out_w", false);
auto *outB =
mod.createPlaceholder(ElemKind::FloatTy, {(dim_t)en_.index2word_.size()},
"decoder.out_b", false);
loadMatrixFromFile("fr2en/decoder_w_ih.bin", *bindings.allocate(wIh));
loadMatrixFromFile("fr2en/decoder_b_ih.bin", *bindings.allocate(bIh));
loadMatrixFromFile("fr2en/decoder_w_hh.bin", *bindings.allocate(wHh));
loadMatrixFromFile("fr2en/decoder_b_hh.bin", *bindings.allocate(bHh));
loadMatrixFromFile("fr2en/decoder_out_w.bin", *bindings.allocate(outW));
loadMatrixFromFile("fr2en/decoder_out_b.bin", *bindings.allocate(outB));
Node *hidden = encoderHiddenOutput_;
Node *lastWordIdx = input;
std::vector<NodeValue> outputs;
// TODO: decoder does exactly MAX_LENGTH steps, while translation could be
// smaller. We could use control flow here.
for (unsigned step = 0; step < MAX_LENGTH; step++) {
// Use last translated word as an input at the current step.
Node *embedded =
F_->createGather("decoder.embedding." + std::to_string(step),
embedding_en_, lastWordIdx);
Node *relu = F_->createRELU("decoder.relu", embedded);
hidden = createPyTorchGRUCell(F_, relu, hidden, wIh, bIh, wHh, bHh);
Node *FC = F_->createFullyConnected("decoder.outFC", hidden, outW, outB);
auto *topK = F_->createTopK("decoder.topK", FC, 1);
lastWordIdx = F_->createReshape("decoder.reshape", topK->getIndices(),
{batchSize_}, "N");
outputs.push_back(lastWordIdx);
}
Node *concat = F_->createConcat("decoder.output.concat", outputs, 0);
Node *reshape = F_->createReshape("decoder.output.reshape", concat,
{MAX_LENGTH, batchSize_}, ANY_LAYOUT);
auto *save = F_->createSave("decoder.output", reshape);
output_ = save->getPlaceholder();
bindings.allocate(output_);
}
/// Translation has 2 stages:
/// 1) Input sentence is fed into Encoder word by word.
/// 2) "Memory" of Encoder is written into memory of Decoder.
/// Now Decoder streams resulting translation word by word.
void Model::translate(const std::vector<std::string> &batch) {
Tensor input(ElemKind::Int64ITy, {batchSize_, MAX_LENGTH});
Tensor seqLength(ElemKind::Int64ITy, {batchSize_});
input.zero();
for (dim_t j = 0; j < batch.size(); j++) {
std::istringstream iss(batch[j]);
std::vector<std::string> words;
std::string word;
while (iss >> word)
words.push_back(word);
words.push_back("EOS");
CHECK_LE(words.size(), MAX_LENGTH) << "sentence is too long.";
for (dim_t i = 0; i < words.size(); i++) {
auto iter = fr_.word2index_.find(words[i]);
CHECK(iter != fr_.word2index_.end()) << "Unknown word: " << words[i];
input.getHandle<int64_t>().at({j, i}) = iter->second;
}
seqLength.getHandle<int64_t>().at({j}) =
(words.size() - 1) + j * MAX_LENGTH;
}
updateInputPlaceholders(bindings, {input_, seqLength_}, {&input, &seqLength});
EE_.run(bindings);
auto OH = bindings.get(output_)->getHandle<int64_t>();
for (unsigned j = 0; j < batch.size(); j++) {
for (unsigned i = 0; i < MAX_LENGTH; i++) {
dim_t wordIdx = OH.at({i, j});
if (wordIdx == en_.word2index_["EOS"])
break;
if (i)
std::cout << ' ';
if (en_.index2word_.size() > (wordIdx))
std::cout << en_.index2word_[wordIdx];
else
std::cout << "[" << wordIdx << "]";
}
std::cout << "\n\n";
}
if (!dumpProfileFileOpt.empty()) {
std::vector<NodeProfilingInfo> PI =
quantization::generateNodeProfilingInfos(bindings, F_, loweredMap_);
serializeProfilingInfosToYaml(dumpProfileFileOpt,
/* graphPreLowerHash */ 0, PI);
}
}
int main(int argc, char **argv) {
std::array<const llvm::cl::OptionCategory *, 3> showCategories = {
{&debugCat, &quantizationCat, &fr2enCat}};
llvm::cl::HideUnrelatedOptions(showCategories);
llvm::cl::ParseCommandLineOptions(
argc, argv, "Translate sentences from French to English");
Model seq2seq(batchSizeOpt);
seq2seq.loadLanguages();
seq2seq.loadEncoder();
seq2seq.loadDecoder();
seq2seq.compile();
if (!dumpGraphDAGFileOpt.empty()) {
seq2seq.dumpGraphDAG(dumpGraphDAGFileOpt.c_str());
}
std::cout << "Please enter a sentence in French, such that its English "
<< "translation starts with one of the following:\n"
<< "\ti am\n"
<< "\the is\n"
<< "\tshe is\n"
<< "\tyou are\n"
<< "\twe are\n"
<< "\tthey are\n"
<< "\n"
<< "Here are some examples:\n"
<< "\tnous sommes desormais en securite .\n"
<< "\tvous etes puissantes .\n"
<< "\til etudie l histoire a l universite .\n"
<< "\tje ne suis pas timide .\n"
<< "\tj y songe encore .\n"
<< "\tje suis maintenant a l aeroport .\n\n";
llvm::Timer timer("Translate", "Translate");
if (timeOpt) {
timer.startTimer();
}
std::vector<std::string> batch;
do {
batch.clear();
for (size_t i = 0; i < batchSizeOpt; i++) {
std::string sentence;
if (!getline(std::cin, sentence)) {
break;
}
batch.push_back(sentence);
}
if (!batch.empty()) {
seq2seq.translate(batch);
}
} while (batch.size() == batchSizeOpt);
if (timeOpt) {
timer.stopTimer();
}
return 0;
}