From 1a373fbb0d0a4e46c8d6cd810f6f49db9cd0e148 Mon Sep 17 00:00:00 2001
From: luotao1 <luotao02@baidu.com>
Date: Wed, 5 Sep 2018 12:03:00 +0800
Subject: [PATCH] add result check for multi-thread UT

---
 .../inference/analysis/analyzer_tester.cc     | 64 ++++++++++---------
 paddle/fluid/inference/api/helper.h           |  4 +-
 2 files changed, 37 insertions(+), 31 deletions(-)

diff --git a/paddle/fluid/inference/analysis/analyzer_tester.cc b/paddle/fluid/inference/analysis/analyzer_tester.cc
index 3aa28479af1..d19ae257746 100644
--- a/paddle/fluid/inference/analysis/analyzer_tester.cc
+++ b/paddle/fluid/inference/analysis/analyzer_tester.cc
@@ -234,6 +234,26 @@ const float ditu_rnn_target_data[] = {
     10.7286, 12.0595, 10.6672, 0,       0,       0,       0,       0,
     93.5771, 3.84641, 0,       0,       0,       0,       0,       0,
     169.426, 0,       0,       0,       0,       0,       0,       0};
+void CompareResult(const std::vector<PaddleTensor> &outputs,
+                   const std::vector<PaddleTensor> &base_outputs) {
+  PADDLE_ENFORCE_GT(outputs.size(), 0);
+  PADDLE_ENFORCE_EQ(outputs.size(), base_outputs.size());
+  for (size_t i = 0; i < outputs.size(); i++) {
+    auto &out = outputs[i];
+    auto &base_out = base_outputs[i];
+    size_t size = std::accumulate(out.shape.begin(), out.shape.end(), 1,
+                                  [](int a, int b) { return a * b; });
+    size_t size1 = std::accumulate(base_out.shape.begin(), base_out.shape.end(),
+                                   1, [](int a, int b) { return a * b; });
+    PADDLE_ENFORCE_EQ(size, size1);
+    PADDLE_ENFORCE_GT(size, 0);
+    float *data = static_cast<float *>(out.data.data());
+    float *base_data = static_cast<float *>(base_out.data.data());
+    for (size_t i = 0; i < size; i++) {
+      EXPECT_NEAR(data[i], base_data[i], 1e-3);
+    }
+  }
+}
 // Test with a really complicate model.
 void TestDituRNNPrediction(bool use_analysis_and_activate_ir = false,
                            int num_threads = FLAGS_num_threads) {
@@ -266,7 +286,8 @@ void TestDituRNNPrediction(bool use_analysis_and_activate_ir = false,
     for (int i = 0; i < num_times; i++) {
       predictor->Run(input_slots, &outputs);
     }
-    print_time(batch_size, num_times, 1, 0, timer.toc() / num_times);
+    PrintTime(batch_size, num_times, 1, 0, timer.toc() / num_times);
+    CompareResult(outputs, base_outputs);
   } else {
     std::vector<std::thread> threads;
     std::vector<std::unique_ptr<PaddlePredictor>> predictors;
@@ -279,13 +300,19 @@ void TestDituRNNPrediction(bool use_analysis_and_activate_ir = false,
     }
     for (int tid = 0; tid < num_threads; ++tid) {
       threads.emplace_back([&, tid]() {
+        // Each thread should have local input_slots and outputs.
+        std::vector<PaddleTensor> input_slots;
+        DataRecord data(FLAGS_infer_ditu_rnn_data, batch_size);
+        PrepareInputs(&input_slots, &data, batch_size);
+        std::vector<PaddleTensor> outputs;
         Timer timer;
         timer.tic();
         for (int i = 0; i < num_times; i++) {
           predictors[tid]->Run(input_slots, &outputs);
         }
-        print_time(batch_size, num_times, num_threads, tid,
-                   timer.toc() / num_times);
+        PrintTime(batch_size, num_times, num_threads, tid,
+                  timer.toc() / num_times);
+        CompareResult(outputs, base_outputs);
       });
     }
     for (int i = 0; i < num_threads; ++i) {
@@ -294,27 +321,6 @@ void TestDituRNNPrediction(bool use_analysis_and_activate_ir = false,
   }
   LOG(INFO) << "=====================================";
 
-  if (num_threads == 1) {
-    PADDLE_ENFORCE_GT(outputs.size(), 0);
-    PADDLE_ENFORCE_EQ(outputs.size(), base_outputs.size());
-    for (size_t i = 0; i < outputs.size(); i++) {
-      auto &out = outputs[i];
-      auto &base_out = base_outputs[i];
-      size_t size = std::accumulate(out.shape.begin(), out.shape.end(), 1,
-                                    [](int a, int b) { return a * b; });
-      size_t size1 =
-          std::accumulate(base_out.shape.begin(), base_out.shape.end(), 1,
-                          [](int a, int b) { return a * b; });
-      PADDLE_ENFORCE_EQ(size, size1);
-      PADDLE_ENFORCE_GT(size, 0);
-      float *data = static_cast<float *>(out.data.data());
-      float *base_data = static_cast<float *>(base_out.data.data());
-      for (size_t i = 0; i < size; i++) {
-        EXPECT_NEAR(data[i], base_data[i], 1e-3);
-      }
-    }
-  }
-
   if (use_analysis_and_activate_ir) {
     AnalysisPredictor *analysis_predictor =
         dynamic_cast<AnalysisPredictor *>(predictor.get());
@@ -342,13 +348,13 @@ void TestDituRNNPrediction(bool use_analysis_and_activate_ir = false,
   }
 }
 
-TEST(Analyzer, DituRNN) {
-  // default FLAGS_num_threads = 1
-  TestDituRNNPrediction(false, FLAGS_num_threads);
-  TestDituRNNPrediction(true, FLAGS_num_threads);
-}
+// basic unit-test of DituRNN, easy for profiling independently.
+TEST(Analyzer, DituRNN) { TestDituRNNPrediction(false, FLAGS_num_threads); }
 
+// advance unit-test of DituRNN, test use_analysis_and_activate_ir and
+// multi-threads.
 TEST(Analyzer, DituRNN_multi_thread) {
+  TestDituRNNPrediction(true, 1);
   TestDituRNNPrediction(false, 4);
   TestDituRNNPrediction(true, 4);
 }
diff --git a/paddle/fluid/inference/api/helper.h b/paddle/fluid/inference/api/helper.h
index f98fe2d7887..2c2ac656e80 100644
--- a/paddle/fluid/inference/api/helper.h
+++ b/paddle/fluid/inference/api/helper.h
@@ -122,8 +122,8 @@ std::string DescribeTensor(const PaddleTensor &tensor) {
   return os.str();
 }
 
-void print_time(int batch_size, int repeat, int num_threads, int tid,
-                double latency) {
+void PrintTime(int batch_size, int repeat, int num_threads, int tid,
+               double latency) {
   LOG(INFO) << "batch_size: " << batch_size << ", repeat: " << repeat
             << ", threads: " << num_threads << ", thread id: " << tid
             << ", latency: " << latency << "ms";
-- 
GitLab