micro: prepare to port operator FLOOR_DIV kernel from lite with test
Implement skeleton (non-working) code for operator and test. Header files changed. Namespaces changed. Some original code deleted. Some original code modified. This represents PR step 4 of the work to port operator FLOOR_DIV as tracked in Issue #45657
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ddavis-2015
ddavis-2015
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9ee7896d22
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5bddd09921
@ -1,4 +1,4 @@
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/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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@ -12,22 +12,18 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include <math.h>
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#include <stddef.h>
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#include <stdint.h>
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#include <functional>
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/kernels/internal/reference/binary_function.h"
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#include "tensorflow/lite/kernels/internal/reference/reference_ops.h"
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#include "tensorflow/lite/kernels/internal/tensor.h"
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#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
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#include "tensorflow/lite/kernels/internal/quantization_util.h"
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#include "tensorflow/lite/kernels/internal/reference/div.h"
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#include "tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h"
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#include "tensorflow/lite/kernels/internal/types.h"
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#include "tensorflow/lite/kernels/kernel_util.h"
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#include "tensorflow/lite/micro/kernels/kernel_util.h"
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namespace tflite {
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namespace ops {
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namespace builtin {
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namespace micro {
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namespace floor_div {
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namespace {
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@ -36,28 +32,14 @@ constexpr int kInputTensor1 = 0;
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constexpr int kInputTensor2 = 1;
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constexpr int kOutputTensor = 0;
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// Op data for floor_div op.
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struct OpData {
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bool requires_broadcast;
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};
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void* Init(TfLiteContext* context, const char* buffer, size_t length) {
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auto* data = new OpData;
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data->requires_broadcast = false;
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return data;
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}
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void Free(TfLiteContext* context, void* buffer) {
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delete reinterpret_cast<OpData*>(buffer);
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return nullptr;
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}
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TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
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TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
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// Reinterprete the opaque data provided by user.
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OpData* data = reinterpret_cast<OpData*>(node->user_data);
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const TfLiteTensor* input1;
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TF_LITE_ENSURE_OK(context,
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GetInputSafe(context, node, kInputTensor1, &input1));
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@ -82,17 +64,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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}
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output->type = type;
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data->requires_broadcast = !HaveSameShapes(input1, input2);
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TfLiteIntArray* output_size = nullptr;
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if (data->requires_broadcast) {
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TF_LITE_ENSURE_OK(context, CalculateShapeForBroadcast(
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context, input1, input2, &output_size));
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} else {
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output_size = TfLiteIntArrayCopy(input1->dims);
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}
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return context->ResizeTensor(context, output, output_size);
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return kTfLiteError;
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}
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template <typename T>
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@ -125,8 +97,6 @@ TfLiteStatus EvalImpl(TfLiteContext* context, bool requires_broadcast,
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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OpData* data = reinterpret_cast<OpData*>(node->user_data);
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const TfLiteTensor* input1;
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TF_LITE_ENSURE_OK(context,
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GetInputSafe(context, node, kInputTensor1, &input1));
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@ -137,13 +107,15 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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TF_LITE_ENSURE_OK(context,
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GetOutputSafe(context, node, kOutputTensor, &output));
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bool requires_broadcast = false;
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switch (input1->type) {
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case kTfLiteInt32: {
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return EvalImpl<int32_t>(context, data->requires_broadcast, input1,
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input2, output);
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return EvalImpl<int32_t>(context, requires_broadcast, input1, input2,
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output);
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}
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case kTfLiteFloat32: {
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return EvalImpl<float>(context, data->requires_broadcast, input1, input2,
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return EvalImpl<float>(context, requires_broadcast, input1, input2,
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output);
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}
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default: {
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@ -157,14 +129,8 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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} // namespace
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} // namespace floor_div
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TfLiteRegistration* Register_FLOOR_DIV() {
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// Init, Free, Prepare, Eval are satisfying the Interface required by
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// TfLiteRegistration.
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static TfLiteRegistration r = {floor_div::Init, floor_div::Free,
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floor_div::Prepare, floor_div::Eval};
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return &r;
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}
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TfLiteRegistration* Register_FLOOR_DIV() { return nullptr; }
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} // namespace builtin
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} // namespace micro
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} // namespace ops
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} // namespace tflite
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@ -1,4 +1,4 @@
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/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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@ -12,106 +12,88 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include <stdint.h>
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#include <vector>
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#include <type_traits>
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#include "tensorflow/lite/kernels/test_util.h"
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#include "tensorflow/lite/schema/schema_generated.h"
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#include "tensorflow/lite/c/builtin_op_data.h"
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/micro/kernels/kernel_runner.h"
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#include "tensorflow/lite/micro/test_helpers.h"
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#include "tensorflow/lite/micro/testing/micro_test.h"
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namespace tflite {
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namespace testing {
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namespace {
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using ::testing::ElementsAre;
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TF_LITE_MICRO_TESTS_BEGIN
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template <typename T>
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class FloorDivModel : public SingleOpModel {
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public:
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FloorDivModel(const TensorData& input1, const TensorData& input2,
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const TensorData& output) {
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input1_ = AddInput(input1);
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input2_ = AddInput(input2);
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output_ = AddOutput(output);
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SetBuiltinOp(BuiltinOperator_FLOOR_DIV, BuiltinOptions_FloorDivOptions,
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CreateFloorDivOptions(builder_).Union());
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BuildInterpreter({GetShape(input1_), GetShape(input2_)});
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}
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int input1() { return input1_; }
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int input2() { return input2_; }
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std::vector<T> GetOutput() { return ExtractVector<T>(output_); }
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std::vector<int> GetOutputShape() { return GetTensorShape(output_); }
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private:
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int input1_;
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int input2_;
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int output_;
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};
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TEST(FloorDivModel, Simple) {
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TF_LITE_MICRO_TEST(FloorDivModelSimple) {
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#ifdef notdef
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FloorDivModel<int32_t> model({TensorType_INT32, {1, 2, 2, 1}},
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{TensorType_INT32, {1, 2, 2, 1}},
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{TensorType_INT32, {}});
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model.PopulateTensor<int32_t>(model.input1(), {10, 9, 11, 3});
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model.PopulateTensor<int32_t>(model.input2(), {2, 2, 3, 4});
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model.Invoke();
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1));
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EXPECT_THAT(model.GetOutput(), ElementsAre(5, 4, 3, 0));
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#endif
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}
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TEST(FloorDivModel, NegativeValue) {
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TF_LITE_MICRO_TEST(FloorDivModelNegativeValue) {
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#ifdef notdef
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FloorDivModel<int32_t> model({TensorType_INT32, {1, 2, 2, 1}},
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{TensorType_INT32, {1, 2, 2, 1}},
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{TensorType_INT32, {}});
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model.PopulateTensor<int32_t>(model.input1(), {10, -9, -11, 7});
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model.PopulateTensor<int32_t>(model.input2(), {2, 2, -3, -4});
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model.Invoke();
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1));
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EXPECT_THAT(model.GetOutput(), ElementsAre(5, -5, 3, -2));
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#endif
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}
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TEST(FloorDivModel, BroadcastFloorDiv) {
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TF_LITE_MICRO_TEST(FloorDivModelBroadcastFloorDiv) {
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#ifdef notdef
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FloorDivModel<int32_t> model({TensorType_INT32, {1, 2, 2, 1}},
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{TensorType_INT32, {1}}, {TensorType_INT32, {}});
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model.PopulateTensor<int32_t>(model.input1(), {10, -9, -11, 7});
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model.PopulateTensor<int32_t>(model.input2(), {-3});
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model.Invoke();
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1));
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EXPECT_THAT(model.GetOutput(), ElementsAre(-4, 3, 3, -3));
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#endif
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}
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TEST(FloorDivModel, SimpleFloat) {
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TF_LITE_MICRO_TEST(FloorDivModelSimpleFloat) {
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#ifdef notdef
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FloorDivModel<float> model({TensorType_FLOAT32, {1, 2, 2, 1}},
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{TensorType_FLOAT32, {1, 2, 2, 1}},
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{TensorType_FLOAT32, {}});
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model.PopulateTensor<float>(model.input1(), {10.05, 9.09, 11.9, 3.01});
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model.PopulateTensor<float>(model.input2(), {2.05, 2.03, 3.03, 4.03});
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model.Invoke();
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1));
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EXPECT_THAT(model.GetOutput(), ElementsAre(4.0, 4.0, 3.0, 0.0));
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#endif
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}
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TEST(FloorDivModel, NegativeValueFloat) {
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TF_LITE_MICRO_TEST(FloorDivModelNegativeValueFloat) {
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#ifdef notdef
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FloorDivModel<float> model({TensorType_FLOAT32, {1, 2, 2, 1}},
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{TensorType_FLOAT32, {1, 2, 2, 1}},
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{TensorType_FLOAT32, {}});
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model.PopulateTensor<float>(model.input1(), {10.03, -9.9, -11.0, 7.0});
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model.PopulateTensor<float>(model.input2(), {2.0, 2.3, -3.0, -4.1});
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model.Invoke();
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1));
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EXPECT_THAT(model.GetOutput(), ElementsAre(5.0, -5.0, 3.0, -2.0));
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#endif
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}
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TEST(FloorDivModel, BroadcastFloorDivFloat) {
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TF_LITE_MICRO_TEST(FloorDivModelBroadcastFloorDivFloat) {
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#ifdef notdef
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FloorDivModel<float> model({TensorType_FLOAT32, {1, 2, 2, 1}},
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{TensorType_FLOAT32, {1}},
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{TensorType_FLOAT32, {}});
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model.PopulateTensor<float>(model.input1(), {10.03, -9.9, -11.0, 7.0});
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model.PopulateTensor<float>(model.input2(), {-3.3});
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model.Invoke();
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1));
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EXPECT_THAT(model.GetOutput(), ElementsAre(-4.0, 2.0, 3.0, -3.0));
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#endif
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}
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TF_LITE_MICRO_TESTS_END
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} // namespace
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} // namespace testing
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} // namespace tflite
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