Basic LogSoftmax support

PiperOrigin-RevId: 186357933

1 files changed, 112 insertions, 0 deletions

diff --git a/tensorflow/contrib/lite/kernels/log_softmax_test.cc b/tensorflow/contrib/lite/kernels/log_softmax_test.cc
new file mode 100644
index 0000000000..62820a2f51
--- /dev/null
+++ b/tensorflow/contrib/lite/kernels/log_softmax_test.cc
@@ -0,0 +1,112 @@
+/* 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.
+==============================================================================*/
+// Unit test for TFLite LOG_SOFTMAX op.
+
+#include <iomanip>
+#include <memory>
+#include <vector>
+
+#include <gmock/gmock.h>
+#include <gtest/gtest.h>
+#include "tensorflow/contrib/lite/interpreter.h"
+#include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h"
+#include "tensorflow/contrib/lite/kernels/register.h"
+#include "tensorflow/contrib/lite/kernels/test_util.h"
+#include "tensorflow/contrib/lite/model.h"
+
+namespace tflite {
+namespace {
+
+class LogSoftmaxOpModel : public SingleOpModel {
+ public:
+  LogSoftmaxOpModel(int batches, int size)
+      : batches_(batches), input_size_(size) {
+    input_ = AddInput(TensorType_FLOAT32);
+    output_ = AddOutput(TensorType_FLOAT32);
+    SetBuiltinOp(BuiltinOperator_LOG_SOFTMAX, BuiltinOptions_LogSoftmaxOptions,
+                 CreateLogSoftmaxOptions(builder_).Union());
+    BuildInterpreter({{batches_, input_size_}});
+  }
+
+  void SetInput(std::initializer_list<float> data) {
+    PopulateTensor(input_, data);
+  }
+
+  void SetInput(int offset, float* begin, float* end) {
+    PopulateTensor(input_, offset, begin, end);
+  }
+
+  std::vector<float> GetOutput() { return ExtractVector<float>(output_); }
+
+ private:
+  int input_;
+  int output_;
+
+  int batches_;
+  int input_size_;
+};
+
+TEST(LogSoftmaxOpTest, SimpleTest) {
+  LogSoftmaxOpModel m(/*batches=*/2, /*size=*/5);
+  m.SetInput({
+      1.0, 2.0, 3.0, 4.0, 5.0,       // b = 0
+      -1.0, -2.0, -3.0, -4.0, -5.0,  // b = 1
+  });
+
+  m.Invoke();
+
+  EXPECT_THAT(
+      m.GetOutput(),
+      ElementsAreArray(ArrayFloatNear(
+          {-4.45191431, -3.45191431, -2.45191431, -1.45191443, -0.4519144,
+           -0.4519144, -1.45191443, -2.45191431, -3.45191431, -4.45191431},
+          1e-6)));
+}
+
+TEST(LogSoftmaxOpTest, CompareWithTFmini) {
+  const int batch_size = 2;
+  const int input_size = 5;
+  static float input_buffer[] = {
+      1.0,  2.0,  3.0,  4.0,  5.0,   // b = 0
+      -1.0, -2.0, -3.0, -4.0, -5.0,  // b = 1
+  };
+
+  LogSoftmaxOpModel m(batch_size, input_size);
+
+  m.SetInput(0, input_buffer, input_buffer + input_size * batch_size);
+
+  m.Invoke();
+
+  std::unique_ptr<float[]> output_buffer(new float[input_size * batch_size]);
+  static tflite::Dims<4> input_dims = {{input_size, 1, 1, batch_size},
+                                       {1, 0, 0, input_size}};
+  tflite::reference_ops::LogSoftmax(input_buffer, input_dims,
+                                    output_buffer.get(), input_dims);
+
+  std::vector<float> expected;
+  expected.insert(expected.end(), output_buffer.get(),
+                  output_buffer.get() + input_size * batch_size);
+
+  EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear(expected, 1e-6)));
+}
+
+}  // namespace
+}  // namespace tflite
+
+int main(int argc, char** argv) {
+  ::tflite::LogToStderr();
+  ::testing::InitGoogleTest(&argc, argv);
+  return RUN_ALL_TESTS();
+}