Replace ExecutionEngine with ExecutionEngine2 [NFC]

examples/CMakeLists.txt

@@ -3,7 +3,7 @@ add_executable(cifar10
 target_link_libraries(cifar10
                       PRIVATE
                         Backends
-                        ExecutionEngine2
+                        ExecutionEngine
                         Graph
                         IR
                         Support
@@ -14,7 +14,7 @@ add_executable(mnist
 target_link_libraries(mnist
                       PRIVATE
                         Backends
-                        ExecutionEngine2
+                        ExecutionEngine
                         Graph
                         Importer
                         IR
@@ -27,7 +27,7 @@ add_executable(ptb
 target_link_libraries(ptb
                       PRIVATE
                         Backends
-                        ExecutionEngine2
+                        ExecutionEngine
                         Graph
                         IR
                         Support
@@ -38,7 +38,7 @@ add_executable(char-rnn
 target_link_libraries(char-rnn
                       PRIVATE
                         Backends
-                        ExecutionEngine2
+                        ExecutionEngine
                         Graph
                         IR
                         GraphOptimizer
@@ -49,7 +49,7 @@ add_executable(fr2en
 target_link_libraries(fr2en
                       PRIVATE
                         Base
-                        ExecutionEngine2
+                        ExecutionEngine
                         IR
                         GraphOptimizer
                         Quantization
@@ -60,7 +60,7 @@ add_executable(lenet-loader
                  lenet-loader.cpp)
 target_link_libraries(lenet-loader
                       PRIVATE
-                        ExecutionEngine2
+                        ExecutionEngine
                         Graph
                         Importer
                         Support)
@@ -70,7 +70,7 @@ if(GLOW_WITH_CPU)
                    resnet-verify.cpp)
   target_link_libraries(resnet-verify
                         PRIVATE
-                          ExecutionEngine2
+                          ExecutionEngine
                           Graph
                           Importer)
 
@@ -80,7 +80,7 @@ if(GLOW_WITH_CPU)
                         PRIVATE
                           Backends
                           ExecutionContext
-                          ExecutionEngine2
+                          ExecutionEngine
                           HostManager
                           Partitioner
                           Graph
@@ -93,7 +93,7 @@ if(GLOW_WITH_CPU)
                         PRIVATE
                           Backend
                           Backends
-                          ExecutionEngine2
+                          ExecutionEngine
                           Graph
                           Importer
                           GraphOptimizer)

examples/char-rnn.cpp

@@ -13,7 +13,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "glow/ExecutionEngine/ExecutionEngine2.h"
+#include "glow/ExecutionEngine/ExecutionEngine.h"
 #include "glow/Graph/Graph.h"
 #include "glow/IR/IR.h"
 #include "glow/Optimizer/GraphOptimizer/GraphOptimizer.h"
@@ -220,8 +220,7 @@ int main(int argc, char **argv) {
   CHECK_GT(text.size(), numSteps) << "Text is too short";
   TrainingConfig TC;
 
-  // ExecutionEngine2 EEI(executionBackend);
-  ExecutionEngine2 EET(executionBackend);
+  ExecutionEngine EET(executionBackend);
   TC.learningRate = 0.001;
   TC.momentum = 0.9;
   TC.batchSize = minibatchSize;
@@ -252,10 +251,10 @@ int main(int argc, char **argv) {
 
     // Train the network on the whole input.
     LOG(INFO) << "Iteration " << i + 1 << "/" << numEpochs;
-    runBatch2(EET, trainingBindings, batchSize / minibatchSize, sampleCounter,
-              {XT, YT}, {&thisCharTrain, &nextCharTrain});
+    runBatch(EET, trainingBindings, batchSize / minibatchSize, sampleCounter,
+             {XT, YT}, {&thisCharTrain, &nextCharTrain});
 
-    ExecutionEngine2 EEO(executionBackend);
+    ExecutionEngine EEO(executionBackend);
     inferBindings.clear();
     auto &mod = EEO.getModule();
     auto OF =
@@ -285,7 +284,7 @@ int main(int argc, char **argv) {
     // Generate a sentence by running inference over and over again.
     for (unsigned i = 0; i < generateChars; i++) {
       // Generate a char:
-      updateInputPlaceholders2(inferBindings, {X}, {&currCharInfer});
+      updateInputPlaceholders(inferBindings, {X}, {&currCharInfer});
       EEO.run(inferBindings);
 
       // Pick a char at random from the softmax distribution.

examples/cifar10.cpp

@@ -13,7 +13,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "glow/ExecutionEngine/ExecutionEngine2.h"
+#include "glow/ExecutionEngine/ExecutionEngine.h"
 #include "glow/Graph/Graph.h"
 #include "glow/Support/Support.h"
 
@@ -157,7 +157,7 @@ void testCIFAR10() {
   // Construct the network:
   TrainingConfig TC;
 
-  ExecutionEngine2 EE(executionBackend);
+  ExecutionEngine EE(executionBackend);
   PlaceholderBindings bindings;
 
   TC.learningRate = 0.001;
@@ -206,15 +206,15 @@ void testCIFAR10() {
 
     // Bind the images tensor to the input array A, and the labels tensor
     // to the softmax node SM.
-    runBatch2(EE, bindings, reportRate, sampleCounter, {A, E},
-              {&images, &labels}, tfName);
+    runBatch(EE, bindings, reportRate, sampleCounter, {A, E},
+             {&images, &labels}, tfName);
 
     unsigned score = 0;
 
     for (unsigned int i = 0; i < 100 / minibatchSize; i++) {
       Tensor sample(ElemKind::FloatTy, {minibatchSize, 32, 32, 3});
       sample.copyConsecutiveSlices(&images, minibatchSize * i);
-      updateInputPlaceholders2(bindings, {A}, {&sample});
+      updateInputPlaceholders(bindings, {A}, {&sample});
       EE.run(bindings);
 
       for (unsigned int iter = 0; iter < minibatchSize; iter++) {

examples/fr2en.cpp

@@ -13,7 +13,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "glow/ExecutionEngine/ExecutionEngine2.h"
+#include "glow/ExecutionEngine/ExecutionEngine.h"
 #include "glow/Graph/Graph.h"
 #include "glow/Optimizer/GraphOptimizer/GraphOptimizer.h"
 #include "glow/Quantization/Quantization.h"
@@ -126,7 +126,7 @@ void loadMatrixFromFile(llvm::StringRef filename, Tensor &result) {
 /// few references to input/output Variables.
 struct Model {
   unsigned batchSize_;
-  ExecutionEngine2 EE_{ExecutionBackend};
+  ExecutionEngine EE_{ExecutionBackend};
   Function *F_;
   Vocabulary en_, fr_;
   Placeholder *input_;
@@ -376,8 +376,7 @@ void Model::translate(const std::vector<std::string> &batch) {
         (words.size() - 1) + j * MAX_LENGTH;
   }
 
-  updateInputPlaceholders2(bindings, {input_, seqLength_},
-                           {&input, &seqLength});
+  updateInputPlaceholders(bindings, {input_, seqLength_}, {&input, &seqLength});
   EE_.run(bindings);
 
   auto OH = bindings.get(output_)->getHandle<int64_t>();

examples/lenet-loader.cpp

@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 #include "glow/Base/Image.h"
-#include "glow/ExecutionEngine/ExecutionEngine2.h"
+#include "glow/ExecutionEngine/ExecutionEngine.h"
 #include "glow/Importer/Caffe2ModelLoader.h"
 #include "glow/Support/Error.h"
 
@@ -24,7 +24,7 @@ using namespace glow;
 /// inference.
 int main() {
   glow::PlaceholderBindings bindings;
-  glow::ExecutionEngine2 EE;
+  glow::ExecutionEngine EE;
   auto &mod = EE.getModule();
   auto *F = mod.createFunction("lenet_mnist");
   auto *inputType = mod.uniqueType(glow::ElemKind::FloatTy, {1, 1, 28, 28});
@@ -50,7 +50,7 @@ int main() {
 
   // Allocate memory for input and bind it to the placeholders.
   bindings.allocate(mod.getPlaceholders());
-  glow::updateInputPlaceholders2(bindings, {input}, {&batch});
+  glow::updateInputPlaceholders(bindings, {input}, {&batch});
 
   // Perform inference.
   EE.run(bindings);

examples/mnist.cpp

@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 #include "glow/Base/Image.h"
-#include "glow/ExecutionEngine/ExecutionEngine2.h"
+#include "glow/ExecutionEngine/ExecutionEngine.h"
 #include "glow/Graph/Graph.h"
 #include "glow/Importer/Caffe2ModelLoader.h"
 #include "glow/Optimizer/GraphOptimizer/GraphOptimizer.h"
@@ -82,7 +82,7 @@ unsigned loadMNIST(Tensor &imageInputs, Tensor &labelInputs) {
   return numImages;
 }
 
-void createModel(ExecutionEngine2 &EE, Function *F,
+void createModel(ExecutionEngine &EE, Function *F,
                  PlaceholderBindings &bindings, unsigned minibatchSize,
                  Placeholder *&inputPH, Placeholder *&outputPH,
                  Placeholder *&selectedPH) {
@@ -107,8 +107,8 @@ void createModel(ExecutionEngine2 &EE, Function *F,
   outputPH = result->getPlaceholder();
 }
 
-void trainModel(ExecutionEngine2 &EE, PlaceholderBindings &bindings,
-                Function *F, unsigned minibatchSize, unsigned numIterations,
+void trainModel(ExecutionEngine &EE, PlaceholderBindings &bindings, Function *F,
+                unsigned minibatchSize, unsigned numIterations,
                 Tensor &imageInputs, Tensor &labelInputs, Placeholder *inputPH,
                 Placeholder *selectedPH) {
   llvm::Timer timer("Training", "Training");
@@ -141,14 +141,14 @@ void trainModel(ExecutionEngine2 &EE, PlaceholderBindings &bindings,
     // On each training iteration take a slice of imageInputs and labelInputs
     // and put them into variables A and B, then run forward and backward passes
     // and update weights.
-    runBatch2(EE, bindings, numIterations, sampleCounter, {inputPH, selectedPH},
-              {&imageInputs, &labelInputs}, tfName);
+    runBatch(EE, bindings, numIterations, sampleCounter, {inputPH, selectedPH},
+             {&imageInputs, &labelInputs}, tfName);
 
     timer.stopTimer();
   }
 }
 
-void validateModel(ExecutionEngine2 &EE, PlaceholderBindings &bindings,
+void validateModel(ExecutionEngine &EE, PlaceholderBindings &bindings,
                    Function *F, unsigned minibatchSize, unsigned numIterations,
                    Tensor &imageInputs, Tensor &labelInputs,
                    Placeholder *inputPH, Placeholder *outputPH,
@@ -215,13 +215,13 @@ void testMNIST() {
   PlaceholderBindings trainingBindings, inferBindings;
   Placeholder *A, *E, *selected;
 
-  ExecutionEngine2 EEI_(executionBackend);
+  ExecutionEngine EEI_(executionBackend);
   auto &inferMod = EEI_.getModule();
   Function *F = inferMod.createFunction("mnist");
   createModel(EEI_, F, inferBindings, minibatchSize, A, E, selected);
   inferBindings.allocate(inferMod.getPlaceholders());
 
-  ExecutionEngine2 EET_(executionBackend);
+  ExecutionEngine EET_(executionBackend);
   auto &trainMod = EET_.getModule();
   Function *TF = trainMod.createFunction("mnist");
   createModel(EET_, TF, trainingBindings, minibatchSize, A, E, selected);
@@ -247,7 +247,7 @@ void testMNISTLoadAndTraining() {
   imageInputsTransposed.transpose(&imageInputs, NHWC2NCHW);
 
   PlaceholderBindings trainingBindings, inferBindings;
-  ExecutionEngine2 EEI_(executionBackend);
+  ExecutionEngine EEI_(executionBackend);
   auto &inferMod = EEI_.getModule();
   auto *F = inferMod.createFunction("lenet_mnist");
   unsigned minibatchSize = 8;
@@ -280,7 +280,7 @@ void testMNISTLoadAndTraining() {
 
   // Load the model a second time for training.
   // TODO: remove once EE2 is able to compile in different modes.
-  ExecutionEngine2 EET_(executionBackend);
+  ExecutionEngine EET_(executionBackend);
   auto &trainMod = EET_.getModule();
   auto *TF = trainMod.createFunction("lenet_mnist_train");
   glow::Caffe2ModelLoader trainingLoader("lenet_mnist/predict_net.pb",

examples/ptb.cpp

@@ -13,7 +13,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "glow/ExecutionEngine/ExecutionEngine2.h"
+#include "glow/ExecutionEngine/ExecutionEngine.h"
 #include "glow/Graph/Graph.h"
 #include "glow/IR/IR.h"
 #include "glow/Support/Support.h"
@@ -182,7 +182,7 @@ void testPTB() {
   unsigned numWords = loadPTB(inputWords, targetWords, numSteps, vocabSize,
                               minibatchSize, maxNumWords);
   LOG(INFO) << "Loaded " << numWords << " words.";
-  ExecutionEngine2 EE(executionBackend);
+  ExecutionEngine EE(executionBackend);
   PlaceholderBindings bindings;
 
   // Construct the network:
@@ -270,8 +270,8 @@ void testPTB() {
       targetWordsBatch.copyConsecutiveSlices(&targetWords,
                                              minibatchSize * batch);
 
-      runBatch2(EE, bindings, 1, sampleCounter, {X, Y},
-                {&inputWordsBatch, &targetWordsBatch}, tfName);
+      runBatch(EE, bindings, 1, sampleCounter, {X, Y},
+               {&inputWordsBatch, &targetWordsBatch}, tfName);
       for (size_t step = 0; step < numSteps; step++) {
         for (unsigned int i = 0; i < minibatchSize; i++) {
           auto T =

examples/resnet-runtime.cpp

@@ -15,7 +15,7 @@
  */
 
 #include "glow/Base/Image.h"
-#include "glow/ExecutionEngine/ExecutionEngine2.h"
+#include "glow/ExecutionEngine/ExecutionEngine.h"
 #include "glow/Graph/Graph.h"
 #include "glow/Importer/Caffe2ModelLoader.h"
 #include "glow/Runtime/HostManager/HostManager.h"
@@ -200,8 +200,8 @@ int main(int argc, char **argv) {
 
     context->getPlaceholderBindings()->allocate(phList);
     Tensor batch = image.getUnowned(inputShape);
-    updateInputPlaceholders2(*(context->getPlaceholderBindings()), {input},
-                             {&batch});
+    updateInputPlaceholders(*(context->getPlaceholderBindings()), {input},
+                            {&batch});
 
     dispatchClassify(0, hostManager.get(), std::move(path), std::move(context),
                      returned, finished);

examples/resnet-verify.cpp

@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 #include "glow/Base/Image.h"
-#include "glow/ExecutionEngine/ExecutionEngine2.h"
+#include "glow/ExecutionEngine/ExecutionEngine.h"
 #include "glow/Graph/Hook.h"
 #include "glow/Importer/Caffe2ModelLoader.h"
 
@@ -27,7 +27,7 @@ const char inputName[] = "gpu_0/data";
 
 class Tester {
   PlaceholderBindings bindings, inferBindings;
-  ExecutionEngine2 EEI;
+  ExecutionEngine EEI;
   std::unique_ptr<Module> mod;
   Function *F;
   TypeRef inputType;
@@ -49,7 +49,7 @@ class Tester {
   void bindInput(Tensor *batch) {
     // Allocate memory for input and bind it to the placeholders.
     bindings.allocate(mod->getPlaceholders());
-    updateInputPlaceholders2(bindings, {input}, {batch});
+    updateInputPlaceholders(bindings, {input}, {batch});
   }
 
   TypeRef getInputType() const { return inputType; }

examples/tracing-compare.cpp

@@ -16,7 +16,7 @@
 
 #include "glow/Backends/DeviceManager.h"
 #include "glow/Base/Image.h"
-#include "glow/ExecutionEngine/ExecutionEngine2.h"
+#include "glow/ExecutionEngine/ExecutionEngine.h"
 #include "glow/Graph/Graph.h"
 #include "glow/Importer/Caffe2ModelLoader.h"
 #include "glow/Optimizer/GraphOptimizer/GraphOptimizer.h"
@@ -152,8 +152,8 @@ int main(int argc, char **argv) {
     context->setTraceContext(
         llvm::make_unique<TraceContext>(TraceLevel::STANDARD));
     context->getPlaceholderBindings()->allocate(module.getPlaceholders());
-    updateInputPlaceholders2(*(context->getPlaceholderBindings()), {input},
-                             {&batch});
+    updateInputPlaceholders(*(context->getPlaceholderBindings()), {input},
+                            {&batch});
 
     devices[i]->runFunction(
         "resnet50", std::move(context),