1 files changed, 124 insertions, 0 deletions
diff --git a/compiler/luci-interpreter/src/kernels/LogSoftmax.test.cpp b/compiler/luci-interpreter/src/kernels/LogSoftmax.test.cpp
new file mode 100644
index 000000000..50dcd5c28
--- /dev/null
+++ b/compiler/luci-interpreter/src/kernels/LogSoftmax.test.cpp
@@ -0,0 +1,124 @@
+/*
+ * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
+ * Copyright 2017 The TensorFlow 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.
+ */
+
+#include "kernels/LogSoftmax.h"
+#include "kernels/TestUtils.h"
+#include "luci_interpreter/TestMemoryManager.h"
+
+namespace luci_interpreter
+{
+namespace kernels
+{
+namespace
+{
+
+using namespace testing;
+
+class LogSoftmaxTest : public ::testing::Test
+{
+protected:
+  void SetUp() override { _memory_manager = std::make_unique<TestMemoryManager>(); }
+
+  std::unique_ptr<IMemoryManager> _memory_manager;
+};
+
+TEST_F(LogSoftmaxTest, Float)
+{
+  Shape input_shape{2, 4};
+  std::vector<float> input_data{
+    0, -6, 2,  4, //
+    3, -2, 10, 1, //
+  };
+  Tensor input_tensor =
+    makeInputTensor<DataType::FLOAT32>(input_shape, input_data, _memory_manager.get());
+  Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
+
+  LogSoftmax kernel(&input_tensor, &output_tensor);
+  kernel.configure();
+  _memory_manager->allocate_memory(output_tensor);
+  kernel.execute();
+
+  std::vector<float> ref_output_data{
+    -4.14297, -10.14297, -2.14297,   -.142971, //
+    -7.00104, -12.00104, -.00104087, -9.00104, //
+  };
+  EXPECT_THAT(extractTensorData<float>(output_tensor), FloatArrayNear(ref_output_data));
+}
+
+TEST_F(LogSoftmaxTest, Uint8)
+{
+  float kMin = -10;
+  float kMax = 10;
+  float kLogSoftmaxQuantizedTolerance = 16. / 256;
+  std::pair<float, int32_t> quant_param = quantizationParams<uint8_t>(kMin, kMax);
+  std::vector<float> input_data{
+    0, -6, 2,  4, //
+    3, -2, 10, 1, //
+  };
+  Tensor input_tensor = makeInputTensor<DataType::U8>({2, 4}, quant_param.first, quant_param.second,
+                                                      input_data, _memory_manager.get());
+  Tensor output_tensor = makeOutputTensor(DataType::U8, 16. / 256, 255);
+
+  LogSoftmax kernel(&input_tensor, &output_tensor);
+  kernel.configure();
+  _memory_manager->allocate_memory(output_tensor);
+  kernel.execute();
+
+  std::vector<float> ref_output_data{
+    -4.14297, -10.14297, -2.14297,   -.142971, //
+    -7.00104, -12.00104, -.00104087, -9.00104, //
+  };
+  std::vector<int32_t> ref_output_shape{2, 4};
+  EXPECT_THAT(dequantizeTensorData(output_tensor),
+              FloatArrayNear(ref_output_data, kLogSoftmaxQuantizedTolerance));
+  EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape));
+  EXPECT_THAT(extractTensorData<uint8_t>(output_tensor),
+              ::testing::ElementsAreArray({189, 93, 221, 253, 142, 63, 255, 111}));
+}
+
+TEST_F(LogSoftmaxTest, InvalidInputOutputType_NEG)
+{
+  std::vector<float> input_data{
+    0, -6, 2,  4, //
+    3, -2, 10, 1, //
+  };
+  Tensor input_tensor =
+    makeInputTensor<DataType::FLOAT32>({2, 4}, input_data, _memory_manager.get());
+  Tensor output_tensor = makeOutputTensor(DataType::U8, 16. / 256, 255);
+
+  LogSoftmax kernel(&input_tensor, &output_tensor);
+  EXPECT_ANY_THROW(kernel.configure());
+}
+
+TEST_F(LogSoftmaxTest, InvalidOutputQuantParam_NEG)
+{
+  std::pair<float, int32_t> quant_param = quantizationParams<uint8_t>(-10, 10);
+  std::vector<float> input_data{
+    0, -6, 2,  4, //
+    3, -2, 10, 1, //
+  };
+  Tensor input_tensor = makeInputTensor<DataType::U8>({2, 4}, quant_param.first, quant_param.second,
+                                                      input_data, _memory_manager.get());
+  Tensor output_tensor = makeOutputTensor(DataType::U8, 20. / 256, 255);
+
+  LogSoftmax kernel(&input_tensor, &output_tensor);
+  EXPECT_ANY_THROW(kernel.configure());
+}
+
+} // namespace
+} // namespace kernels
+} // namespace luci_interpreter