xref: /frameworks/ml/nn/common/operations/LSHProjectionTest.cpp

/*
 * Copyright (C) 2017 The Android Open Source Project
 *
 * 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 "LSHProjection.h"

#include "NeuralNetworksWrapper.h"
#include "gmock/gmock-generated-matchers.h"
#include "gmock/gmock-matchers.h"
#include "gtest/gtest.h"

using ::testing::FloatNear;
using ::testing::Matcher;

namespace android {
namespace nn {
namespace wrapper {

using ::testing::ElementsAre;

#define FOR_ALL_INPUT_AND_WEIGHT_TENSORS(ACTION) \
  ACTION(Hash, float)                            \
  ACTION(Input, int)                             \
  ACTION(Weight, float)

// For all output and intermediate states
#define FOR_ALL_OUTPUT_TENSORS(ACTION) \
  ACTION(Output, int)

class LSHProjectionOpModel {
 public:
  LSHProjectionOpModel(LSHProjectionType type,
                       std::initializer_list<uint32_t> hash_shape,
                       std::initializer_list<uint32_t> input_shape,
                       std::initializer_list<uint32_t> weight_shape)
      : type_(type) {
    std::vector<uint32_t> inputs;

    OperandType HashTy(Type::TENSOR_FLOAT32, hash_shape);
    inputs.push_back(model_.addOperand(&HashTy));
    OperandType InputTy(Type::TENSOR_INT32, input_shape);
    inputs.push_back(model_.addOperand(&InputTy));
    OperandType WeightTy(Type::TENSOR_FLOAT32, weight_shape);
    inputs.push_back(model_.addOperand(&WeightTy));

    OperandType TypeParamTy(Type::INT32, {});
    inputs.push_back(model_.addOperand(&TypeParamTy));

    std::vector<uint32_t> outputs;

    auto multiAll = [](const std::vector<uint32_t> &dims) -> uint32_t {
      uint32_t sz = 1;
      for (uint32_t d : dims) {
        sz *= d;
      }
      return sz;
    };

    uint32_t outShapeDimension = 0;
    if (type == LSHProjectionType_SPARSE) {
      auto it = hash_shape.begin();
      Output_.insert(Output_.end(), *it, 0.f);
      outShapeDimension = *it;
    } else {
      Output_.insert(Output_.end(), multiAll(hash_shape), 0.f);
      outShapeDimension = multiAll(hash_shape);
    }

    OperandType OutputTy(Type::TENSOR_INT32, {outShapeDimension});
    outputs.push_back(model_.addOperand(&OutputTy));

    model_.addOperation(ANEURALNETWORKS_LSH_PROJECTION, inputs, outputs);
    model_.identifyInputsAndOutputs(inputs, outputs);

    model_.finish();
  }

#define DefineSetter(X, T)                       \
  void Set##X(const std::vector<T> &f) {         \
    X##_.insert(X##_.end(), f.begin(), f.end()); \
  }

  FOR_ALL_INPUT_AND_WEIGHT_TENSORS(DefineSetter);

#undef DefineSetter

  const std::vector<int> &GetOutput() const { return Output_; }

  void Invoke() {
    ASSERT_TRUE(model_.isValid());

    Compilation compilation(&model_);
    compilation.finish();
    Execution execution(&compilation);

#define SetInputOrWeight(X, T)                                             \
    ASSERT_EQ(execution.setInput(LSHProjection::k##X##Tensor, X##_.data(), \
                                 sizeof(T) * X##_.size()),                 \
              Result::NO_ERROR);

    FOR_ALL_INPUT_AND_WEIGHT_TENSORS(SetInputOrWeight);

#undef SetInputOrWeight

#define SetOutput(X, T)                                                   \
  ASSERT_EQ(execution.setOutput(LSHProjection::k##X##Tensor, X##_.data(), \
                                sizeof(T) * X##_.size()),                 \
            Result::NO_ERROR);

    FOR_ALL_OUTPUT_TENSORS(SetOutput);

#undef SetOutput

    ASSERT_EQ(
        execution.setInput(LSHProjection::kTypeParam, &type_, sizeof(type_)),
        Result::NO_ERROR);

    ASSERT_EQ(execution.compute(), Result::NO_ERROR);
  }

 private:
  Model model_;
  LSHProjectionType type_;

  std::vector<float> Hash_;
  std::vector<int> Input_;
  std::vector<float> Weight_;
  std::vector<int> Output_;
};  // namespace wrapper

TEST(LSHProjectionOpTest2, DenseWithThreeInputs) {
  LSHProjectionOpModel m(LSHProjectionType_DENSE, {4, 2}, {3, 2}, {3});

  m.SetInput({12345, 54321, 67890, 9876, -12345678, -87654321});
  m.SetHash({0.123, 0.456, -0.321, -0.654, 1.234, 5.678, -4.321, -8.765});
  m.SetWeight({0.12, 0.34, 0.56});

  m.Invoke();

  EXPECT_THAT(m.GetOutput(), ElementsAre(1, 1, 1, 0, 1, 1, 1, 0));
}

TEST(LSHProjectionOpTest2, SparseWithTwoInputs) {
  LSHProjectionOpModel m(LSHProjectionType_SPARSE, {4, 2}, {3, 2}, {});

  m.SetInput({12345, 54321, 67890, 9876, -12345678, -87654321});
  m.SetHash({0.123, 0.456, -0.321, -0.654, 1.234, 5.678, -4.321, -8.765});

  m.Invoke();

  EXPECT_THAT(m.GetOutput(), ElementsAre(1, 2, 2, 0));
}

}  // namespace wrapper
}  // namespace nn
}  // namespace android