[xpu] update bert, ernie unittests (#4357)

lite/CMakeLists.txt

@@ -63,6 +63,7 @@ if (WITH_TESTING)
         lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL_FOR_UNITTESTS} "VGG19.tar.gz")
         # data
         lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL_FOR_UNITTESTS} "ILSVRC2012_small.tar.gz")
+        lite_download_and_uncompress(${LITE_MODEL_DIR} ${LITE_URL_FOR_UNITTESTS} "bert_data.tar.gz")
     endif()
 endif()
 

lite/tests/api/CMakeLists.txt

@@ -9,11 +9,18 @@ if(LITE_WITH_ARM)
 endif()
 
 function(xpu_x86_without_xtcl_test TARGET MODEL DATA)
-    lite_cc_test(${TARGET} SRCS ${TARGET}.cc
-        DEPS mir_passes lite_api_test_helper paddle_api_full paddle_api_light gflags utils
-        ${ops} ${host_kernels} ${x86_kernels} ${xpu_kernels}
-        ARGS --model_dir=${LITE_MODEL_DIR}/${MODEL}
-             --data_dir=${LITE_MODEL_DIR}/${DATA})
+    if(${DATA} STREQUAL "")
+        lite_cc_test(${TARGET} SRCS ${TARGET}.cc
+            DEPS mir_passes lite_api_test_helper paddle_api_full paddle_api_light gflags utils
+            ${ops} ${host_kernels} ${x86_kernels} ${xpu_kernels}
+            ARGS --model_dir=${LITE_MODEL_DIR}/${MODEL})
+    else()
+        lite_cc_test(${TARGET} SRCS ${TARGET}.cc
+            DEPS mir_passes lite_api_test_helper paddle_api_full paddle_api_light gflags utils
+            ${ops} ${host_kernels} ${x86_kernels} ${xpu_kernels}
+            ARGS --model_dir=${LITE_MODEL_DIR}/${MODEL} --data_dir=${LITE_MODEL_DIR}/${DATA})
+    endif()
+
     if(WITH_TESTING)
         add_dependencies(${TARGET} extern_lite_download_${MODEL}_tar_gz)
         if(NOT ${DATA} STREQUAL "")
@@ -26,8 +33,8 @@ if(LITE_WITH_XPU AND NOT LITE_WITH_XTCL)
     xpu_x86_without_xtcl_test(test_resnet50_fp32_xpu resnet50 ILSVRC2012_small)
     xpu_x86_without_xtcl_test(test_googlenet_fp32_xpu GoogLeNet ILSVRC2012_small)
     xpu_x86_without_xtcl_test(test_vgg19_fp32_xpu VGG19 ILSVRC2012_small)
-    xpu_x86_without_xtcl_test(test_ernie_fp32_xpu ernie "")
-    xpu_x86_without_xtcl_test(test_bert_fp32_xpu bert "")
+    xpu_x86_without_xtcl_test(test_ernie_fp32_xpu ernie bert_data)
+    xpu_x86_without_xtcl_test(test_bert_fp32_xpu bert bert_data)
 endif()
 
 if(LITE_WITH_RKNPU)

lite/tests/api/bert_utility.h

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// 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.
+
+#pragma once
+#include <algorithm>
+#include <iostream>
+#include <memory>
+#include <string>
+#include <vector>
+#include "lite/api/paddle_api.h"
+#include "lite/utils/cp_logging.h"
+#include "lite/utils/io.h"
+#include "lite/utils/string.h"
+
+namespace paddle {
+namespace lite {
+
+template <class T = int64_t>
+void ReadRawData(const std::string& input_data_dir,
+                 std::vector<std::vector<T>>* input0,
+                 std::vector<std::vector<T>>* input1,
+                 std::vector<std::vector<T>>* input2,
+                 std::vector<std::vector<T>>* input3,
+                 std::vector<std::vector<int64_t>>* input_shapes) {
+  auto lines = ReadLines(input_data_dir);
+  for (auto line : lines) {
+    std::vector<std::string> shape_and_data = Split(line, ";");
+    std::vector<int64_t> input_shape =
+        Split<int64_t>(Split(shape_and_data[0], ":")[0], " ");
+    input_shapes->emplace_back(input_shape);
+
+    std::vector<T> input0_data =
+        Split<T>(Split(shape_and_data[0], ":")[1], " ");
+    input0->emplace_back(input0_data);
+    std::vector<T> input1_data =
+        Split<T>(Split(shape_and_data[1], ":")[1], " ");
+    input1->emplace_back(input1_data);
+    std::vector<T> input2_data =
+        Split<T>(Split(shape_and_data[2], ":")[1], " ");
+    input2->emplace_back(input2_data);
+    std::vector<T> input3_data =
+        Split<T>(Split(shape_and_data[3], ":")[1], " ");
+    input3->emplace_back(input3_data);
+  }
+}
+
+template <class T = int64_t>
+void FillTensor(const std::shared_ptr<lite_api::PaddlePredictor>& predictor,
+                int tensor_id,
+                const std::vector<int64_t>& tensor_shape,
+                const std::vector<T>& tensor_value) {
+  predictor->GetInput(tensor_id)->Resize(tensor_shape);
+  int64_t tensor_size = 1;
+  for (size_t i = 0; i < tensor_shape.size(); i++) {
+    tensor_size *= tensor_shape[i];
+  }
+  CHECK_EQ(static_cast<size_t>(tensor_size), tensor_value.size());
+  memcpy(predictor->GetInput(tensor_id)->mutable_data<T>(),
+         tensor_value.data(),
+         sizeof(T) * tensor_size);
+}
+
+float CalBertOutAccuracy(const std::vector<std::vector<float>>& out,
+                         const std::string& out_file) {
+  auto lines = ReadLines(out_file);
+  std::vector<std::vector<float>> ref_out;
+  for (auto line : lines) {
+    ref_out.emplace_back(Split<float>(line, " "));
+  }
+
+  int right_num = 0;
+  for (size_t i = 0; i < out.size(); i++) {
+    std::vector<size_t> out_index{0, 1, 2};
+    std::vector<size_t> ref_out_index{0, 1, 2};
+
+    std::sort(out_index.begin(),
+              out_index.end(),
+              [&out, i](size_t a, size_t b) { return out[i][a] > out[i][b]; });
+    std::sort(ref_out_index.begin(),
+              ref_out_index.end(),
+              [&ref_out, i](size_t a, size_t b) {
+                return ref_out[i][a] > ref_out[i][b];
+              });
+    right_num += (out_index == ref_out_index);
+  }
+
+  return static_cast<float>(right_num) / static_cast<float>(out.size());
+}
+
+float CalErnieOutAccuracy(const std::vector<std::vector<float>>& out,
+                          const std::string& out_file) {
+  auto lines = ReadLines(out_file);
+  std::vector<std::vector<float>> ref_out;
+  for (auto line : lines) {
+    ref_out.emplace_back(Split<float>(line, " "));
+  }
+
+  int right_num = 0;
+  for (size_t i = 0; i < out.size(); i++) {
+    right_num += (std::fabs(out[i][0] - ref_out[i][0]) < 0.01f);
+  }
+
+  return static_cast<float>(right_num) / static_cast<float>(out.size());
+}
+
+}  // namespace lite
+}  // namespace paddle

lite/tests/api/test_bert_fp32_xpu.cc

@@ -21,23 +21,16 @@
 #include "lite/api/paddle_use_ops.h"
 #include "lite/api/paddle_use_passes.h"
 #include "lite/api/test_helper.h"
+#include "lite/tests/api/bert_utility.h"
 #include "lite/utils/cp_logging.h"
 
+DEFINE_string(data_dir, "", "data dir");
+DEFINE_int32(iteration, 9, "iteration times to run");
+
 namespace paddle {
 namespace lite {
 
-template <typename T>
-lite::Tensor GetTensorWithShape(std::vector<int64_t> shape) {
-  lite::Tensor ret;
-  ret.Resize(shape);
-  T* ptr = ret.mutable_data<T>();
-  for (int i = 0; i < ret.numel(); ++i) {
-    ptr[i] = (T)1;
-  }
-  return ret;
-}
-
-TEST(Ernie, test_ernie_fp32_xpu) {
+TEST(Bert, test_bert_fp32_xpu) {
   lite_api::CxxConfig config;
   config.set_model_dir(FLAGS_model_dir);
   config.set_valid_places({lite_api::Place{TARGET(kXPU), PRECISION(kFloat)},
@@ -46,56 +39,58 @@ TEST(Ernie, test_ernie_fp32_xpu) {
   config.set_xpu_workspace_l3_size_per_thread();
   auto predictor = lite_api::CreatePaddlePredictor(config);
 
-  int64_t batch_size = 1;
-  int64_t seq_len = 64;
-  Tensor sample_input = GetTensorWithShape<int64_t>({batch_size, seq_len, 1});
-  std::vector<int64_t> input_shape{batch_size, seq_len, 1};
-  predictor->GetInput(0)->Resize(input_shape);
-  predictor->GetInput(1)->Resize(input_shape);
-  predictor->GetInput(2)->Resize(input_shape);
-  predictor->GetInput(3)->Resize(input_shape);
-
-  memcpy(predictor->GetInput(0)->mutable_data<int64_t>(),
-         sample_input.raw_data(),
-         sizeof(int64_t) * batch_size * seq_len);
-  memcpy(predictor->GetInput(1)->mutable_data<int64_t>(),
-         sample_input.raw_data(),
-         sizeof(int64_t) * batch_size * seq_len);
-  memcpy(predictor->GetInput(2)->mutable_data<int64_t>(),
-         sample_input.raw_data(),
-         sizeof(int64_t) * batch_size * seq_len);
-  memcpy(predictor->GetInput(3)->mutable_data<int64_t>(),
-         sample_input.raw_data(),
-         sizeof(int64_t) * batch_size * seq_len);
+  std::string input_data_file = FLAGS_data_dir + std::string("/bert_in.txt");
+  std::vector<std::vector<int64_t>> input0;
+  std::vector<std::vector<int64_t>> input1;
+  std::vector<std::vector<int64_t>> input2;
+  std::vector<std::vector<int64_t>> input3;
+  std::vector<std::vector<int64_t>> input_shapes;
+  ReadRawData(
+      input_data_file, &input0, &input1, &input2, &input3, &input_shapes);
 
   for (int i = 0; i < FLAGS_warmup; ++i) {
+    std::vector<int64_t> shape = {1, 64, 1};
+    std::vector<int64_t> fill_value(64, 0);
+    for (int j = 0; j < 4; j++) {
+      FillTensor(predictor, j, shape, fill_value);
+    }
     predictor->Run();
   }
 
-  auto start = GetCurrentUS();
-  for (int i = 0; i < FLAGS_repeats; ++i) {
+  std::vector<std::vector<float>> out_rets;
+  out_rets.resize(FLAGS_iteration);
+  double cost_time = 0;
+  for (int i = 0; i < FLAGS_iteration; ++i) {
+    FillTensor(predictor, 0, input_shapes[i], input0[i]);
+    FillTensor(predictor, 1, input_shapes[i], input1[i]);
+    FillTensor(predictor, 2, input_shapes[i], input2[i]);
+    FillTensor(predictor, 3, input_shapes[i], input3[i]);
+
+    double start = GetCurrentUS();
     predictor->Run();
+    cost_time += GetCurrentUS() - start;
+
+    auto output_tensor = predictor->GetOutput(0);
+    auto output_shape = output_tensor->shape();
+    auto output_data = output_tensor->data<float>();
+    ASSERT_EQ(output_shape.size(), 2UL);
+    ASSERT_EQ(output_shape[0], 1);
+    ASSERT_EQ(output_shape[1], 3);
+
+    int output_size = output_shape[0] * output_shape[1];
+    out_rets[i].resize(output_size);
+    memcpy(&(out_rets[i].at(0)), output_data, sizeof(float) * output_size);
   }
 
   LOG(INFO) << "================== Speed Report ===================";
   LOG(INFO) << "Model: " << FLAGS_model_dir << ", threads num " << FLAGS_threads
-            << ", warmup: " << FLAGS_warmup << ", repeats: " << FLAGS_repeats
-            << ", spend " << (GetCurrentUS() - start) / FLAGS_repeats / 1000.0
-            << " ms in average.";
+            << ", warmup: " << FLAGS_warmup
+            << ", iteration: " << FLAGS_iteration << ", spend "
+            << cost_time / FLAGS_iteration / 1000.0 << " ms in average.";
 
-  std::vector<std::vector<float>> results;
-  results.emplace_back(std::vector<float>({0.278893, 0.330888, 0.39022}));
-  auto out = predictor->GetOutput(0);
-  ASSERT_EQ(out->shape().size(), 2);
-  ASSERT_EQ(out->shape()[0], 1);
-  ASSERT_EQ(out->shape()[1], 3);
-
-  for (size_t i = 0; i < results.size(); ++i) {
-    for (size_t j = 0; j < results[i].size(); ++j) {
-      EXPECT_NEAR(
-          out->data<float>()[j + (out->shape()[1] * i)], results[i][j], 3e-5);
-    }
-  }
+  std::string ref_out_file = FLAGS_data_dir + std::string("/bert_out.txt");
+  float out_accuracy = CalBertOutAccuracy(out_rets, ref_out_file);
+  ASSERT_GT(out_accuracy, 0.95f);
 }
 
 }  // namespace lite

lite/tests/api/test_ernie_fp32_xpu.cc

@@ -21,8 +21,12 @@
 #include "lite/api/paddle_use_ops.h"
 #include "lite/api/paddle_use_passes.h"
 #include "lite/api/test_helper.h"
+#include "lite/tests/api/bert_utility.h"
 #include "lite/utils/cp_logging.h"
 
+DEFINE_string(data_dir, "", "data dir");
+DEFINE_int32(iteration, 9, "iteration times to run");
+
 namespace paddle {
 namespace lite {
 
@@ -46,56 +50,58 @@ TEST(Ernie, test_ernie_fp32_xpu) {
   config.set_xpu_workspace_l3_size_per_thread();
   auto predictor = lite_api::CreatePaddlePredictor(config);
 
-  int64_t batch_size = 1;
-  int64_t seq_len = 64;
-  Tensor sample_input = GetTensorWithShape<int64_t>({batch_size, seq_len, 1});
-  std::vector<int64_t> input_shape{batch_size, seq_len, 1};
-  predictor->GetInput(0)->Resize(input_shape);
-  predictor->GetInput(1)->Resize(input_shape);
-  predictor->GetInput(2)->Resize(input_shape);
-  predictor->GetInput(3)->Resize(input_shape);
-
-  memcpy(predictor->GetInput(0)->mutable_data<int64_t>(),
-         sample_input.raw_data(),
-         sizeof(int64_t) * batch_size * seq_len);
-  memcpy(predictor->GetInput(1)->mutable_data<int64_t>(),
-         sample_input.raw_data(),
-         sizeof(int64_t) * batch_size * seq_len);
-  memcpy(predictor->GetInput(2)->mutable_data<int64_t>(),
-         sample_input.raw_data(),
-         sizeof(int64_t) * batch_size * seq_len);
-  memcpy(predictor->GetInput(3)->mutable_data<int64_t>(),
-         sample_input.raw_data(),
-         sizeof(int64_t) * batch_size * seq_len);
+  std::string input_data_file = FLAGS_data_dir + std::string("/bert_in.txt");
+  std::vector<std::vector<int64_t>> input0;
+  std::vector<std::vector<int64_t>> input1;
+  std::vector<std::vector<int64_t>> input2;
+  std::vector<std::vector<int64_t>> input3;
+  std::vector<std::vector<int64_t>> input_shapes;
+  ReadRawData(
+      input_data_file, &input0, &input1, &input2, &input3, &input_shapes);
 
   for (int i = 0; i < FLAGS_warmup; ++i) {
+    std::vector<int64_t> shape = {1, 64, 1};
+    std::vector<int64_t> fill_value(64, 0);
+    for (int j = 0; j < 4; j++) {
+      FillTensor(predictor, j, shape, fill_value);
+    }
     predictor->Run();
   }
 
-  auto start = GetCurrentUS();
-  for (int i = 0; i < FLAGS_repeats; ++i) {
+  std::vector<std::vector<float>> out_rets;
+  out_rets.resize(FLAGS_iteration);
+  double cost_time = 0;
+  for (int i = 0; i < FLAGS_iteration; ++i) {
+    FillTensor(predictor, 0, input_shapes[i], input0[i]);
+    FillTensor(predictor, 1, input_shapes[i], input1[i]);
+    FillTensor(predictor, 2, input_shapes[i], input2[i]);
+    FillTensor(predictor, 3, input_shapes[i], input3[i]);
+
+    double start = GetCurrentUS();
     predictor->Run();
+    cost_time += GetCurrentUS() - start;
+
+    auto output_tensor = predictor->GetOutput(0);
+    auto output_shape = output_tensor->shape();
+    auto output_data = output_tensor->data<float>();
+    ASSERT_EQ(output_shape.size(), 2UL);
+    ASSERT_EQ(output_shape[0], 1);
+    ASSERT_EQ(output_shape[1], 1);
+
+    int output_size = output_shape[0] * output_shape[1];
+    out_rets[i].resize(output_size);
+    memcpy(&(out_rets[i].at(0)), output_data, sizeof(float) * output_size);
   }
 
   LOG(INFO) << "================== Speed Report ===================";
   LOG(INFO) << "Model: " << FLAGS_model_dir << ", threads num " << FLAGS_threads
-            << ", warmup: " << FLAGS_warmup << ", repeats: " << FLAGS_repeats
-            << ", spend " << (GetCurrentUS() - start) / FLAGS_repeats / 1000.0
-            << " ms in average.";
-
-  std::vector<std::vector<float>> results;
-  results.emplace_back(std::vector<float>({0.108398}));
-  auto out = predictor->GetOutput(0);
-  ASSERT_EQ(out->shape().size(), 2);
-  ASSERT_EQ(out->shape()[0], 1);
-  ASSERT_EQ(out->shape()[1], 1);
+            << ", warmup: " << FLAGS_warmup
+            << ", iteration: " << FLAGS_iteration << ", spend "
+            << cost_time / FLAGS_iteration / 1000.0 << " ms in average.";
 
-  for (size_t i = 0; i < results.size(); ++i) {
-    for (size_t j = 0; j < results[i].size(); ++j) {
-      EXPECT_NEAR(
-          out->data<float>()[j + (out->shape()[1] * i)], results[i][j], 2e-5);
-    }
-  }
+  std::string ref_out_file = FLAGS_data_dir + std::string("/ernie_out.txt");
+  float out_accuracy = CalErnieOutAccuracy(out_rets, ref_out_file);
+  ASSERT_GT(out_accuracy, 0.95f);
 }
 
 }  // namespace lite

lite/tests/kernels/fc_compute_test.cc

@@ -121,9 +121,9 @@ class FcOPTest : public arena::TestCase {
     int k = wdims_[0];
     int n = wdims_[1];
 
-    LOG(INFO) << "M=" << m << ", N=" << n << ", K=" << k
-              << ", bias=" << flag_bias << ", with_relu=" << with_relu_
-              << ", padding_weights=" << padding_weights_;
+    VLOG(4) << "M=" << m << ", N=" << n << ", K=" << k << ", bias=" << flag_bias
+            << ", with_relu=" << with_relu_
+            << ", padding_weights=" << padding_weights_;
 
     if (m == 1) {
       basic_gemv(n,

lite/tests/kernels/prior_box_compute_test.cc

@@ -738,7 +738,7 @@ TEST(PriorBox, precision) {
 }
 
 TEST(DensityPriorBox, precision) {
-#ifdef LITE_WITH_X86
+#if defined(LITE_WITH_X86) && !defined(LITE_WITH_XPU)
   Place place(TARGET(kX86));
   test_density_prior_box(place);
 #endif

lite/tests/math/gemm_int8_compute_test.cc

@@ -104,11 +104,11 @@ bool test_gemm_int8(bool tra,
     scale_merge_int8[j] = scale_merge_fp32[j] / scale_c[0];
   }
 
-  LOG(INFO) << "gemm_int8 M: " << m << ", N: " << n << ", K: " << k
-            << ", transA: " << (tra ? "true" : "false")
-            << ", transB: " << (trb ? "true" : "false")
-            << ", relu: " << (has_relu ? "true" : "false")
-            << ", bias: " << (has_bias ? "true" : "false");
+  VLOG(4) << "gemm_int8 M: " << m << ", N: " << n << ", K: " << k
+          << ", transA: " << (tra ? "true" : "false")
+          << ", transB: " << (trb ? "true" : "false")
+          << ", relu: " << (has_relu ? "true" : "false")
+          << ", bias: " << (has_bias ? "true" : "false");
 #ifdef LITE_WITH_ARM
   int lda = tra ? m : k;
   int ldb = trb ? k : n;
@@ -344,13 +344,12 @@ TEST(TestLiteGemmInt8, gemm_prepacked_int8) {
                                                FLAGS_power_mode,
                                                th);
                     if (flag) {
-                      LOG(INFO) << "test m = " << m << ", n=" << n
-                                << ", k=" << k
-                                << ", bias: " << (has_bias ? "true" : "false")
-                                << ", relu: " << (has_relu ? "true" : "false")
-                                << ", trans A: " << (tra ? "true" : "false")
-                                << ", trans B: " << (trb ? "true" : "false")
-                                << " passed\n";
+                      VLOG(4) << "test m = " << m << ", n=" << n << ", k=" << k
+                              << ", bias: " << (has_bias ? "true" : "false")
+                              << ", relu: " << (has_relu ? "true" : "false")
+                              << ", trans A: " << (tra ? "true" : "false")
+                              << ", trans B: " << (trb ? "true" : "false")
+                              << " passed\n";
                     } else {
                       LOG(FATAL) << "test m = " << m << ", n=" << n
                                  << ", k=" << k

lite/tests/math/gemv_int8_compute_test.cc

@@ -97,9 +97,9 @@ bool test_gemv_int8(bool tra,
     scale_merge_int8[j] = scale_merge_fp32[j] / scale_c[0];
   }
 
-  LOG(INFO) << "gemv_int8 M: " << m << ", N: " << n
-            << ", transA: " << (tra ? "true" : "false") << ", act: " << flag_act
-            << ", bias: " << (has_bias ? "true" : "false");
+  VLOG(4) << "gemv_int8 M: " << m << ", N: " << n
+          << ", transA: " << (tra ? "true" : "false") << ", act: " << flag_act
+          << ", bias: " << (has_bias ? "true" : "false");
 #ifdef LITE_WITH_ARM
   auto da = ta.mutable_data<int8_t>();
   auto db = tb.mutable_data<int8_t>();
@@ -336,11 +336,11 @@ TEST(TestLiteGemvInt8, gemv_prepacked_int8) {
                                            six,
                                            alpha);
                 if (flag) {
-                  LOG(INFO) << "test m = " << m << ", n=" << n
-                            << ", bias: " << (has_bias ? "true" : "false")
-                            << ",  relu: " << (has_relu ? "true" : "false")
-                            << ", trans A: " << (tra ? "true" : "false")
-                            << " passed\n";
+                  VLOG(4) << "test m = " << m << ", n=" << n
+                          << ", bias: " << (has_bias ? "true" : "false")
+                          << ",  relu: " << (has_relu ? "true" : "false")
+                          << ", trans A: " << (tra ? "true" : "false")
+                          << " passed\n";
                 } else {
                   LOG(FATAL) << "test m = " << m << ", n=" << n
                              << ", bias: " << (has_bias ? "true" : "false")

lite/tests/math/sgemm_c4_compute_test.cc

@@ -98,9 +98,9 @@ bool test_sgemm_c4(
   basic_trans_mat_to_c4(da, da_c4, k, m, k, true);
   basic_trans_mat_to_c4(db, db_c4, n, k, n, false);
 
-  LOG(INFO) << "sgemm_c4 M: " << m << ", N: " << n << ", K: " << k
-            << ", relu: " << (has_relu ? "true" : "false")
-            << ", bias: " << (has_bias ? "true" : "false");
+  VLOG(4) << "sgemm_c4 M: " << m << ", N: " << n << ", K: " << k
+          << ", relu: " << (has_relu ? "true" : "false")
+          << ", bias: " << (has_bias ? "true" : "false");
 
   if (FLAGS_check_result) {
     basic_gemm_c4(false,
@@ -331,10 +331,10 @@ TEST(TestSgemmC4, test_func_sgemm_c4_prepacked) {
                 auto flag = test_sgemm_c4(
                     m, n, k, has_bias, has_relu, FLAGS_power_mode, th);
                 if (flag) {
-                  LOG(INFO) << "test m = " << m << ", n=" << n << ", k=" << k
-                            << ", bias: " << (has_bias ? "true" : "false")
-                            << ", relu: " << (has_relu ? "true" : "false")
-                            << " passed\n";
+                  VLOG(4) << "test m = " << m << ", n=" << n << ", k=" << k
+                          << ", bias: " << (has_bias ? "true" : "false")
+                          << ", relu: " << (has_relu ? "true" : "false")
+                          << " passed\n";
                 } else {
                   LOG(FATAL) << "test m = " << m << ", n=" << n << ", k=" << k
                              << ", bias: " << (has_bias ? "true" : "false")
@@ -364,10 +364,10 @@ TEST(TestSgemmC8, test_func_sgemm_c8_prepacked) {
                 auto flag = test_sgemm_c8(
                     m, n, k, has_bias, has_relu, FLAGS_power_mode, th);
                 if (flag) {
-                  LOG(INFO) << "test m = " << m << ", n=" << n << ", k=" << k
-                            << ", bias: " << (has_bias ? "true" : "false")
-                            << ", relu: " << (has_relu ? "true" : "false")
-                            << " passed\n";
+                  VLOG(4) << "test m = " << m << ", n=" << n << ", k=" << k
+                          << ", bias: " << (has_bias ? "true" : "false")
+                          << ", relu: " << (has_relu ? "true" : "false")
+                          << " passed\n";
                 } else {
                   LOG(FATAL) << "test m = " << m << ", n=" << n << ", k=" << k
                              << ", bias: " << (has_bias ? "true" : "false")

lite/tests/math/sgemv_compute_test.cc

@@ -75,9 +75,9 @@ bool test_sgemv(bool tra,
   // fill_tensor_const(tb, 1.f);
   fill_tensor_rand(tbias, -1.f, 1.f);
 
-  LOG(INFO) << "sgemv M: " << m << ", K: " << k
-            << ", transA: " << (tra ? "true" : "false") << ", act: " << flag_act
-            << ", bias: " << (has_bias ? "true" : "false");
+  VLOG(4) << "sgemv M: " << m << ", K: " << k
+          << ", transA: " << (tra ? "true" : "false") << ", act: " << flag_act
+          << ", bias: " << (has_bias ? "true" : "false");
 #ifdef LITE_WITH_ARM
 
   auto da = ta.mutable_data<float>();
@@ -209,11 +209,11 @@ TEST(TestLiteSgemv, Sgemv) {
                                        six,
                                        alpha);
                 if (flag) {
-                  LOG(INFO) << "test m = " << m << ", k=" << k
-                            << ", bias: " << (has_bias ? "true" : "false")
-                            << ", flag act: " << flag_act
-                            << ", trans A: " << (tra ? "true" : "false")
-                            << ", threads: " << th << " passed\n";
+                  VLOG(4) << "test m = " << m << ", k=" << k
+                          << ", bias: " << (has_bias ? "true" : "false")
+                          << ", flag act: " << flag_act
+                          << ", trans A: " << (tra ? "true" : "false")
+                          << ", threads: " << th << " passed\n";
                 } else {
                   LOG(FATAL) << "test m = " << m << ", k=" << k
                              << ", bias: " << (has_bias ? "true" : "false")