#include "mace/core/quantize.h"
#include "mace/ops/delegator/gemv.h"

namespace mace {
namespace ops {
namespace ref {
namespace q8 {

template<typename T>
class Gemv : public delegator::Gemv {
 public:
  explicit Gemv(const DelegatorParam &param) : delegator::Gemv(param) {}
  ~Gemv() {}

  MaceStatus Compute(
      const OpContext *context,
      const Tensor *lhs,
      const Tensor *rhs,
      const Tensor *bias,
      const index_t batch,
      const index_t lhs_height,
      const index_t lhs_width,
      const bool lhs_batched,
      const bool rhs_batched,
      Tensor *output) override;
};

template<>
class Gemv<uint8_t> : public delegator::Gemv {
 public:
  explicit Gemv(const DelegatorParam &param) : delegator::Gemv(param) {}
  ~Gemv() {}

  MaceStatus Compute(
      const OpContext *context,
      const Tensor *lhs,
      const Tensor *rhs,
      const Tensor *bias,
      const index_t batch,
      const index_t lhs_height,
      const index_t lhs_width,
      const bool lhs_batched,
      const bool rhs_batched,
      Tensor *output) override;
};

template<>
class Gemv<int32_t> : public delegator::Gemv {
 public:
  explicit Gemv(const DelegatorParam &param) : delegator::Gemv(param) {}
  ~Gemv() {}

  MaceStatus Compute(
      const OpContext *context,
      const Tensor *lhs,
      const Tensor *rhs,
      const Tensor *bias,
      const index_t batch,
      const index_t lhs_height,
      const index_t lhs_width,
      const bool lhs_batched,
      const bool rhs_batched,
      Tensor *output) override;
};

MaceStatus Gemv<uint8_t>::Compute(const OpContext *context,
                                  const Tensor *lhs,
                                  const Tensor *rhs,
                                  const Tensor *bias,
                                  const index_t batch,
                                  const index_t lhs_height,
                                  const index_t lhs_width,
                                  const bool lhs_batched,
                                  const bool rhs_batched,
                                  Tensor *output) {
  MACE_UNUSED(context);
  const uint8_t *lhs_data = lhs->data<uint8_t>();
  const uint8_t *rhs_data = rhs->data<uint8_t>();
  const int32_t *bias_data = nullptr;
  if (bias) {
    bias_data = bias->data<int32_t>();
  }

  uint8_t *output_data = output->mutable_data<uint8_t>();

  MACE_CHECK(output->scale() > 0, "output scale must not be zero");
  const float
      output_multiplier_float = lhs->scale() * rhs->scale() / output->scale();
  int32_t lhs_zero = lhs->zero_point();
  int32_t rhs_zero = rhs->zero_point();

  for (index_t b = 0; b < batch; ++b) {
    for (index_t h = 0; h < lhs_height; ++h) {
      int32_t sum = bias ? bias_data[h] : 0;
      for (index_t w = 0; w < lhs_width; ++w) {
        sum += (lhs_data[
            static_cast<index_t>(lhs_batched) * b * lhs_height * lhs_width
                + h * lhs_width + w] - lhs_zero)
            * (rhs_data[static_cast<index_t>(rhs_batched) * b * lhs_width + w]
                - rhs_zero);
      }  

      output_data[b * lhs_height + h] =
          Saturate<uint8_t>(std::roundf(sum * output_multiplier_float));
    }  
  }   
  return MaceStatus::MACE_SUCCESS;
}

MaceStatus Gemv<int32_t>::Compute(const OpContext *context,
                                  const Tensor *lhs,
                                  const Tensor *rhs,
                                  const Tensor *bias,
                                  const index_t batch,
                                  const index_t lhs_height,
                                  const index_t lhs_width,
                                  const bool lhs_batched,
                                  const bool rhs_batched,
                                  Tensor *output) {
  MACE_UNUSED(context);
  const uint8_t *lhs_data = lhs->data<uint8_t>();
  const uint8_t *rhs_data = rhs->data<uint8_t>();
  const int32_t *bias_data = nullptr;
  if (bias) {
    bias_data = bias->data<int32_t>();
  }

  int32_t *output_data = output->mutable_data<int32_t>();

  int32_t lhs_zero = lhs->zero_point();
  int32_t rhs_zero = rhs->zero_point();

  for (index_t b = 0; b < batch; ++b) {
    for (index_t h = 0; h < lhs_height; ++h) {
      int32_t sum = bias ? bias_data[h] : 0;
      for (index_t w = 0; w < lhs_width; ++w) {
        sum += (lhs_data[
            static_cast<index_t>(lhs_batched) * b * lhs_height * lhs_width
                + h * lhs_width + w] - lhs_zero)
            * (rhs_data[static_cast<index_t>(rhs_batched) * b * lhs_width + w]
                - rhs_zero);
      }  

      output_data[b * lhs_height + h] = sum;
    }  
  }   
  return MaceStatus::MACE_SUCCESS;
}

void RegisterGemvDelegator(OpDelegatorRegistry *registry) {
  MACE_REGISTER_DELEGATOR(
      registry, Gemv<uint8_t>, DelegatorParam,
      MACE_DELEGATOR_KEY(Gemv, RuntimeType::RT_CPU, uint8_t, ImplType::REF));
  MACE_REGISTER_DELEGATOR(
      registry, Gemv<int32_t>, DelegatorParam,
      MACE_DELEGATOR_KEY(Gemv, RuntimeType::RT_CPU, int32_t, ImplType::REF));
}

}  
}  
}  
}
// Copyright 2019 The MACE 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 "mace/core/quantize.h"
#include "mace/ops/delegator/gemv.h"

namespace mace {
namespace ops {
namespace ref {
namespace q8 {

template<typename T>
class Gemv : public delegator::Gemv {
 public:
  explicit Gemv(const DelegatorParam &param) : delegator::Gemv(param) {}
  ~Gemv() {}
  // Always row-major after transpose
  MaceStatus Compute(
      const OpContext *context,
      const Tensor *lhs,
      const Tensor *rhs,
      const Tensor *bias,
      const index_t batch,
      const index_t lhs_height,
      const index_t lhs_width,
      const bool lhs_batched,
      const bool rhs_batched,
      Tensor *output) override;
};

template<>
class Gemv<uint8_t> : public delegator::Gemv {
 public:
  explicit Gemv(const DelegatorParam &param) : delegator::Gemv(param) {}
  ~Gemv() {}
  // Always row-major after transpose
  MaceStatus Compute(
      const OpContext *context,
      const Tensor *lhs,
      const Tensor *rhs,
      const Tensor *bias,
      const index_t batch,
      const index_t lhs_height,
      const index_t lhs_width,
      const bool lhs_batched,
      const bool rhs_batched,
      Tensor *output) override;
};

template<>
class Gemv<int32_t> : public delegator::Gemv {
 public:
  explicit Gemv(const DelegatorParam &param) : delegator::Gemv(param) {}
  ~Gemv() {}
  // Always row-major after transpose
  MaceStatus Compute(
      const OpContext *context,
      const Tensor *lhs,
      const Tensor *rhs,
      const Tensor *bias,
      const index_t batch,
      const index_t lhs_height,
      const index_t lhs_width,
      const bool lhs_batched,
      const bool rhs_batched,
      Tensor *output) override;
};

MaceStatus Gemv<uint8_t>::Compute(const OpContext *context,
                                  const Tensor *lhs,
                                  const Tensor *rhs,
                                  const Tensor *bias,
                                  const index_t batch,
                                  const index_t lhs_height,
                                  const index_t lhs_width,
                                  const bool lhs_batched,
                                  const bool rhs_batched,
                                  Tensor *output) {
  MACE_UNUSED(context);
  const uint8_t *lhs_data = lhs->data<uint8_t>();
  const uint8_t *rhs_data = rhs->data<uint8_t>();
  const int32_t *bias_data = nullptr;
  if (bias) {
    bias_data = bias->data<int32_t>();
  }

  uint8_t *output_data = output->mutable_data<uint8_t>();

  MACE_CHECK(output->scale() > 0, "output scale must not be zero");
  const float
      output_multiplier_float = lhs->scale() * rhs->scale() / output->scale();
  int32_t lhs_zero = lhs->zero_point();
  int32_t rhs_zero = rhs->zero_point();

  for (index_t b = 0; b < batch; ++b) {
    for (index_t h = 0; h < lhs_height; ++h) {
      int32_t sum = bias ? bias_data[h] : 0;
      for (index_t w = 0; w < lhs_width; ++w) {
        sum += (lhs_data[
            static_cast<index_t>(lhs_batched) * b * lhs_height * lhs_width
                + h * lhs_width + w] - lhs_zero)
            * (rhs_data[static_cast<index_t>(rhs_batched) * b * lhs_width + w]
                - rhs_zero);
      }  // w

      output_data[b * lhs_height + h] =
          Saturate<uint8_t>(std::roundf(sum * output_multiplier_float));
    }  // h
  }   // b
  return MaceStatus::MACE_SUCCESS;
}

MaceStatus Gemv<int32_t>::Compute(const OpContext *context,
                                  const Tensor *lhs,
                                  const Tensor *rhs,
                                  const Tensor *bias,
                                  const index_t batch,
                                  const index_t lhs_height,
                                  const index_t lhs_width,
                                  const bool lhs_batched,
                                  const bool rhs_batched,
                                  Tensor *output) {
  MACE_UNUSED(context);
  const uint8_t *lhs_data = lhs->data<uint8_t>();
  const uint8_t *rhs_data = rhs->data<uint8_t>();
  const int32_t *bias_data = nullptr;
  if (bias) {
    bias_data = bias->data<int32_t>();
  }

  int32_t *output_data = output->mutable_data<int32_t>();

  int32_t lhs_zero = lhs->zero_point();
  int32_t rhs_zero = rhs->zero_point();

  for (index_t b = 0; b < batch; ++b) {
    for (index_t h = 0; h < lhs_height; ++h) {
      int32_t sum = bias ? bias_data[h] : 0;
      for (index_t w = 0; w < lhs_width; ++w) {
        sum += (lhs_data[
            static_cast<index_t>(lhs_batched) * b * lhs_height * lhs_width
                + h * lhs_width + w] - lhs_zero)
            * (rhs_data[static_cast<index_t>(rhs_batched) * b * lhs_width + w]
                - rhs_zero);
      }  // w

      output_data[b * lhs_height + h] = sum;
    }  // h
  }   // b
  return MaceStatus::MACE_SUCCESS;
}

void RegisterGemvDelegator(OpDelegatorRegistry *registry) {
  MACE_REGISTER_DELEGATOR(
      registry, Gemv<uint8_t>, DelegatorParam,
      MACE_DELEGATOR_KEY(Gemv, RuntimeType::RT_CPU, uint8_t, ImplType::REF));
  MACE_REGISTER_DELEGATOR(
      registry, Gemv<int32_t>, DelegatorParam,
      MACE_DELEGATOR_KEY(Gemv, RuntimeType::RT_CPU, int32_t, ImplType::REF));
}

}  // namespace q8
}  // namespace ref
}  // namespace ops
}  // namespace mace
