BinaryArithmeticLayer.cc

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/*
 * Copyright (c) 2020 Samsung Electronics Co., Ltd. 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 "BinaryArithmeticLayer.h"
 
#include <cker/operation/BinaryArithmeticOps.h>
 
namespace onert::backend::cpu::ops
{
 
namespace
{
 
template <nnfw::cker::BinaryArithmeticOpType arithmetic_type, typename T> struct Eval
{
  nnfw::cker::Shape _lhs_shape;
  nnfw::cker::Shape _rhs_shape;
  nnfw::cker::Shape _output_shape;
  nnfw::cker::BinaryArithmeticOpParam _op_params;
  bool _need_broadcast;
 
  Eval(const IPortableTensor *lhs, const IPortableTensor *rhs, IPortableTensor *output,
       nnfw::cker::BinaryArithmeticOpParam op_params)
    : _op_params(std::move(op_params)), _need_broadcast(false)
  {
    if (!output->is_dynamic())
      updateCache(lhs, rhs, output);
  }
 
  void updateCache(const IPortableTensor *lhs, const IPortableTensor *rhs, IPortableTensor *output)
  {
    _lhs_shape.ReplaceWith(getShape(lhs));
    _rhs_shape.ReplaceWith(getShape(rhs));
    _output_shape.ReplaceWith(getShape(output));
    _need_broadcast = nnfw::cker::ProcessBroadcastShapes(_lhs_shape, _rhs_shape, &_op_params);
  }
 
  void operator()(const IPortableTensor *lhs, const IPortableTensor *rhs, IPortableTensor *output)
  {
    // Assume dynamic tensors never become static and static ones never change shape since
    // configure()
    if (output->is_dynamic())
      updateCache(lhs, rhs, output);
    else
      assert(_lhs_shape == getShape(lhs) && _rhs_shape == getShape(rhs) &&
             _output_shape == getShape(output));
    auto lhs_buffer = getBuffer<T>(lhs);
    auto rhs_buffer = getBuffer<T>(rhs);
    auto output_buffer = getBuffer<T>(output);
    if (_need_broadcast)
    {
      nnfw::cker::BroadcastBinaryArithmeticOp<arithmetic_type>(
        _op_params, _lhs_shape, lhs_buffer, _rhs_shape, rhs_buffer, _output_shape, output_buffer);
    }
    else
    {
      nnfw::cker::BinaryArithmeticOp<arithmetic_type>(
        _op_params, _lhs_shape, lhs_buffer, _rhs_shape, rhs_buffer, _output_shape, output_buffer);
    }
  }
};
 
template <nnfw::cker::BinaryArithmeticOpType arithmetic_type>
std::function<void(const IPortableTensor *, const IPortableTensor *, IPortableTensor *)>
generateKernelGeneric(const IPortableTensor *lhs, const IPortableTensor *rhs,
                      IPortableTensor *output, const ir::Activation activation,
                      nnfw::cker::BinaryArithmeticOpParam &op_params)
{
  switch (lhs->data_type())
  {
    case OperandType::FLOAT32:
    {
      float output_activation_min = 0, output_activation_max = 0;
      CalculateActivationRange(activation, &output_activation_min, &output_activation_max);
      op_params.float_activation_max = output_activation_max;
      op_params.float_activation_min = output_activation_min;
      return Eval<arithmetic_type, float>(lhs, rhs, output, op_params);
      break;
    }
    case OperandType::INT32:
    {
      int32_t output_activation_min = 0, output_activation_max = 0;
      CalculateActivationRange(activation, &output_activation_min, &output_activation_max);
      op_params.quantized_activation_max = output_activation_max;
      op_params.quantized_activation_min = output_activation_min;
      return Eval<arithmetic_type, int32_t>(lhs, rhs, output, op_params);
      break;
    }
    case OperandType::INT64:
    {
      int64_t output_activation_min = 0, output_activation_max = 0;
      CalculateActivationRange(activation, &output_activation_min, &output_activation_max);
      op_params.int64_activation_max = output_activation_max;
      op_params.int64_activation_min = output_activation_min;
      return Eval<arithmetic_type, int64_t>(lhs, rhs, output, op_params);
      break;
    }
    case OperandType::BOOL8:
    {
      if (activation != ir::Activation::NONE)
        throw std::runtime_error(
          "BinaryArithmetic(generic): Fused activation is not supported with bool8 type");
      int32_t output_activation_min = 0, output_activation_max = 0;
      CalculateActivationRange(activation, &output_activation_min, &output_activation_max);
      static_assert(sizeof(bool) == 1, "cpu backend supports bool type which is 1 byte");
      return Eval<arithmetic_type, bool>(lhs, rhs, output, op_params);
      break;
    }
    default:
      throw std::runtime_error{"BinaryArithmetic(generic): Unsupported data type"};
  }
}
 
void setAddOrSubQuant8Params(const IPortableTensor *lhs, const IPortableTensor *rhs,
                             IPortableTensor *output, ir::Activation activation,
                             nnfw::cker::BinaryArithmeticOpParam *params)
{
  int32_t output_activation_min, output_activation_max;
  CalculateActivationRangeQuantized(activation, output, &output_activation_min,
                                    &output_activation_max);
  nnfw::cker::BinaryArithmeticOpParam &op_params = *params;
  op_params.quantized_activation_max = output_activation_max;
  op_params.quantized_activation_min = output_activation_min;
  // Parameters for scaled quantized computation
  op_params.left_shift = 20;
  // Zero-points of input and output tensors
  op_params.input1_offset = -lhs->data_zero_point();
  op_params.input2_offset = -rhs->data_zero_point();
  op_params.output_offset = output->data_zero_point();
 
  // Compute normalized scale for _lhs and _rhs values,
  // and represent in 32-bit fixed point
  const double norm_max_scale = 2 * std::max(lhs->data_scale(), rhs->data_scale());
  const double real_lhs_scale = lhs->data_scale() / norm_max_scale;
  const double real_rhs_scale = rhs->data_scale() / norm_max_scale;
  // output scale is used to normalize final result, so we invert the scale here
  const double real_output_scale =
    norm_max_scale / (output->data_scale() * (1 << op_params.left_shift));
 
  // Represent the scales as fixed int32_t multipliers, and int32_t shifts
  QuantizeMultiplier(real_lhs_scale, &op_params.input1_multiplier, &op_params.input1_shift);
  QuantizeMultiplier(real_rhs_scale, &op_params.input2_multiplier, &op_params.input2_shift);
  QuantizeMultiplier(real_output_scale, &op_params.output_multiplier, &op_params.output_shift);
}
 
void setMulQuant8Params(const IPortableTensor *lhs, const IPortableTensor *rhs,
                        IPortableTensor *output, ir::Activation activation,
                        nnfw::cker::BinaryArithmeticOpParam *params)
{
  int32_t output_activation_min, output_activation_max;
  CalculateActivationRangeQuantized(activation, output, &output_activation_min,
                                    &output_activation_max);
  nnfw::cker::BinaryArithmeticOpParam &op_params = *params;
 
  op_params.quantized_activation_max = output_activation_max;
  op_params.quantized_activation_min = output_activation_min;
  op_params.input1_offset = -lhs->data_zero_point();
  op_params.input2_offset = -rhs->data_zero_point();
  op_params.output_offset = output->data_zero_point();
 
  double real_multiplier = lhs->data_scale() * rhs->data_scale() / output->data_scale();
  QuantizeMultiplier(real_multiplier, &op_params.output_multiplier, &op_params.output_shift);
}
 
} // namespace
 
void BinaryArithmeticLayer::configure(const IPortableTensor *lhs, const IPortableTensor *rhs,
                                      IPortableTensor *output, const ir::Activation activation,
                                      const ArithmeticType arithmetic_type)
{
  assert(lhs != nullptr);
  assert(rhs != nullptr);
  assert(output != nullptr);
 
  _lhs = lhs;
  _rhs = rhs;
  _output = output;
 
  nnfw::cker::BinaryArithmeticOpParam op_params;
  switch (arithmetic_type)
  {
    case ArithmeticType::kAdd:
      if (_lhs->data_type() == OperandType::QUANT_UINT8_ASYMM)
      {
        setAddOrSubQuant8Params(_lhs, _rhs, _output, activation, &op_params);
        _kernel =
          Eval<nnfw::cker::BinaryArithmeticOpType::ADD, uint8_t>(_lhs, _rhs, _output, op_params);
      }
      else if (_lhs->data_type() == OperandType::QUANT_INT8_ASYMM)
      {
        setAddOrSubQuant8Params(_lhs, _rhs, _output, activation, &op_params);
        _kernel =
          Eval<nnfw::cker::BinaryArithmeticOpType::ADD, int8_t>(_lhs, _rhs, _output, op_params);
      }
 
      else
      {
        _kernel = generateKernelGeneric<nnfw::cker::BinaryArithmeticOpType::ADD>(
          _lhs, _rhs, _output, activation, op_params);
      }
      break;
    case ArithmeticType::kSub:
      if (_lhs->data_type() == OperandType::QUANT_UINT8_ASYMM)
      {
        setAddOrSubQuant8Params(_lhs, _rhs, _output, activation, &op_params);
        op_params.input2_multiplier *= -1;
        _kernel =
          Eval<nnfw::cker::BinaryArithmeticOpType::SUB, uint8_t>(_lhs, _rhs, _output, op_params);
      }
      else if (_lhs->data_type() == OperandType::QUANT_INT8_ASYMM)
      {
        setAddOrSubQuant8Params(_lhs, _rhs, _output, activation, &op_params);
        op_params.input2_multiplier *= -1;
        _kernel =
          Eval<nnfw::cker::BinaryArithmeticOpType::SUB, int8_t>(_lhs, _rhs, _output, op_params);
      }
 
      else
      {
        _kernel = generateKernelGeneric<nnfw::cker::BinaryArithmeticOpType::SUB>(
          _lhs, _rhs, _output, activation, op_params);
      }
      break;
    case ArithmeticType::kMul:
      if (_lhs->data_type() == OperandType::QUANT_UINT8_ASYMM)
      {
        nnfw::cker::BinaryArithmeticOpParam op_params;
        setMulQuant8Params(_lhs, _rhs, _output, activation, &op_params);
        _kernel =
          Eval<nnfw::cker::BinaryArithmeticOpType::MUL, uint8_t>(_lhs, _rhs, _output, op_params);
      }
      else if (_lhs->data_type() == OperandType::QUANT_INT8_ASYMM)
      {
        nnfw::cker::BinaryArithmeticOpParam op_params;
        setMulQuant8Params(_lhs, _rhs, _output, activation, &op_params);
        _kernel =
          Eval<nnfw::cker::BinaryArithmeticOpType::MUL, int8_t>(_lhs, _rhs, _output, op_params);
      }
      else
      {
        _kernel = generateKernelGeneric<nnfw::cker::BinaryArithmeticOpType::MUL>(
          _lhs, _rhs, _output, activation, op_params);
      }
      break;
    case ArithmeticType::kDiv:
      if (_lhs->data_type() == OperandType::FLOAT32)
      {
        _kernel = generateKernelGeneric<nnfw::cker::BinaryArithmeticOpType::DIV>(
          _lhs, _rhs, _output, activation, op_params);
      }
      else
      {
        // TODO Support quantized type
        // TODO Support integer type with zero check
        throw std::runtime_error{
          "BinaryArithmetic(Div): Div operation does not support non-float data types yet"};
      }
      break;
    default:
      throw std::runtime_error{"BinaryArithmetic: Unsupported BinaryArithmetic type"};
  }
}
void BinaryArithmeticLayer::configure(const IPortableTensor *lhs, const IPortableTensor *rhs, {…}
 
void BinaryArithmeticLayer::run() { _kernel(_lhs, _rhs, _output); }
 
} // namespace onert::backend::cpu::ops