/* Copyright 2023 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.
==============================================================================*/
#include "tensorflow/lite/kernels/internal/optimized/integer_ops/mul.h"

#include <stddef.h>
#include <stdint.h>

#include <algorithm>
#include <complex>

#include "tensorflow/lite/core/c/builtin_op_data.h"
#include "tensorflow/lite/core/c/c_api_types.h"
#include "tensorflow/lite/core/c/common.h"
#include "tensorflow/lite/kernels/internal/compatibility.h"
#include "tensorflow/lite/kernels/internal/optimized/cpu_check.h"
#include "tensorflow/lite/kernels/internal/optimized/neon_check.h"
#include "tensorflow/lite/kernels/internal/optimized/optimized_ops.h"
#include "tensorflow/lite/kernels/internal/quantization_util.h"
#include "tensorflow/lite/kernels/internal/reference/integer_ops/mul.h"
#include "tensorflow/lite/kernels/internal/reference/mul.h"
#include "tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h"
#include "tensorflow/lite/kernels/internal/reference/reference_ops.h"
#include "tensorflow/lite/kernels/internal/tensor.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/kernels/internal/types.h"
#include "tensorflow/lite/kernels/kernel_util.h"
#include "tensorflow/lite/types/half.h"

namespace tflite {
namespace ops {
namespace builtin {
namespace mul {

// This file has three implementation of Mul.
enum KernelType {
  kReference,
  kGenericOptimized,  // Neon-free
  kNeonOptimized,
};

constexpr int kInputTensor1 = 0;
constexpr int kInputTensor2 = 1;
constexpr int kOutputTensor = 0;

struct OpData {
  // Parameters used in the quantized paths where the output is 8bit
  int32 output_activation_min;
  int32 output_activation_max;

  // Parameters used in all quantized paths
  int32_t output_multiplier;
  int output_shift;
  // Indicates that 'Eval' is a noop as the output as written during 'Prepare'.
  bool noop;
};

void* Init(TfLiteContext* context, const char* buffer, size_t length) {
  auto* data = new OpData;
  return data;
}

void Free(TfLiteContext* context, void* buffer) {
  delete reinterpret_cast<OpData*>(buffer);
}

template <KernelType kernel_type>
TfLiteStatus EvalImpl(TfLiteContext* context, TfLiteNode* node, OpData* data,
                      TfLiteMulParams* params, const TfLiteTensor* input1,
                      const TfLiteTensor* input2, TfLiteTensor* output);

template <KernelType kernel_type>
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
  auto* params = reinterpret_cast<TfLiteMulParams*>(node->builtin_data);
  OpData* data = reinterpret_cast<OpData*>(node->user_data);
  data->noop = false;

  TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);

  const TfLiteTensor* input1;
  TF_LITE_ENSURE_OK(context,
                    GetInputSafe(context, node, kInputTensor1, &input1));
  const TfLiteTensor* input2;
  TF_LITE_ENSURE_OK(context,
                    GetInputSafe(context, node, kInputTensor2, &input2));
  TfLiteTensor* output;
  TF_LITE_ENSURE_OK(context,
                    GetOutputSafe(context, node, kOutputTensor, &output));

  TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type);

  if (output->type == kTfLiteComplex64 && params->activation) {
    TF_LITE_KERNEL_LOG(context,
                       "Activation is not allowed for COMPLEX64 input.");
    return kTfLiteError;
  }
  const bool requires_broadcast = !HaveSameShapes(input1, input2);

  TfLiteIntArray* output_size = nullptr;
  if (requires_broadcast) {
    TF_LITE_ENSURE_OK(context, CalculateShapeForBroadcast(
                                   context, input1, input2, &output_size));
  } else {
    output_size = TfLiteIntArrayCopy(input1->dims);
  }

  if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8 ||
      (output->quantization.type != kTfLiteNoQuantization &&
       output->type == kTfLiteInt16)) {
    TF_LITE_ENSURE_STATUS(CalculateActivationRangeQuantized(
        context, params->activation, output, &data->output_activation_min,
        &data->output_activation_max));
    double real_multiplier =
        input1->params.scale * input2->params.scale / output->params.scale;
    QuantizeMultiplier(real_multiplier, &data->output_multiplier,
                       &data->output_shift);
  }

  if (IsConstantOrPersistentTensor(input1) &&
      IsConstantOrPersistentTensor(input2)) {
    SetTensorToPersistentRo(output);
    data->noop = true;
    context->ResizeTensor(context, output, output_size);
    return EvalImpl<kernel_type>(context, node, data, params, input1, input2,
                                 output);
  }
  return context->ResizeTensor(context, output, output_size);
}

template <KernelType kernel_type>
void EvalMul(TfLiteContext* context, TfLiteNode* node, TfLiteMulParams* params,
             const OpData* data, const TfLiteTensor* input1,
             const TfLiteTensor* input2, TfLiteTensor* output) {
  tflite::ArithmeticParams op_params;
  const bool need_broadcast = optimized_ops::ProcessBroadcastShapes(
      GetTensorShape(input1), GetTensorShape(input2), &op_params);
#define TF_LITE_MUL(type, opname, data_type)                             \
  data_type output_activation_min, output_activation_max;                \
  CalculateActivationRange(params->activation, &output_activation_min,   \
                           &output_activation_max);                      \
  SetActivationParams(output_activation_min, output_activation_max,      \
                      &op_params);                                       \
  type::opname(op_params, GetTensorShape(input1),                        \
               GetTensorData<data_type>(input1), GetTensorShape(input2), \
               GetTensorData<data_type>(input2), GetTensorShape(output), \
               GetTensorData<data_type>(output))

  if (output->type == kTfLiteInt32) {
    if (kernel_type == kReference) {
      if (need_broadcast) {
        TF_LITE_MUL(reference_ops, BroadcastMul6DSlow, int32_t);
      } else {
        TF_LITE_MUL(reference_ops, Mul, int32_t);
      }
    } else {
      if (need_broadcast) {
        TF_LITE_MUL(optimized_ops, BroadcastMul6DSlow, int32_t);
      } else {
        TF_LITE_MUL(optimized_ops, Mul, int32_t);
      }
    }
  } else if (output->type == kTfLiteUInt32) {
    if (need_broadcast) {
      TF_LITE_MUL(reference_ops, BroadcastMul6DSlow, uint32_t);
    } else {
      TF_LITE_MUL(reference_ops, Mul, uint32_t);
    }
  } else if (output->type == kTfLiteFloat32) {
    if (kernel_type == kReference) {
      if (need_broadcast) {
        TF_LITE_MUL(reference_ops, BroadcastMul6DSlow, float);
      } else {
        TF_LITE_MUL(reference_ops, Mul, float);
      }
    } else {
      if (need_broadcast) {
        TF_LITE_MUL(optimized_ops, BroadcastMulDispatch, float);
      } else {
        TF_LITE_MUL(optimized_ops, Mul, float);
      }
    }
  } else if (output->type == kTfLiteFloat16) {
    if (need_broadcast) {
      TF_LITE_MUL(reference_ops, BroadcastMul6DSlow, half);
    } else {
      TF_LITE_MUL(reference_ops, Mul, half);
    }
  } else if (output->type == kTfLiteInt16) {
    int16_t output_activation_min, output_activation_max;
    CalculateActivationRange(params->activation, &output_activation_min,
                             &output_activation_max);
    SetActivationParams(output_activation_min, output_activation_max,
                        &op_params);
    if (need_broadcast) {
      reference_ops::BroadcastMul6DSlow<int16_t, true>(
          op_params, GetTensorShape(input1), GetTensorData<int16_t>(input1),
          GetTensorShape(input2), GetTensorData<int16_t>(input2),
          GetTensorShape(output), GetTensorData<int16_t>(output));
    } else {
      reference_ops::Mul<int16_t>(
          op_params, GetTensorShape(input1), GetTensorData<int16_t>(input1),
          GetTensorShape(input2), GetTensorData<int16_t>(input2),
          GetTensorShape(output), GetTensorData<int16_t>(output));
    }
  } else if (output->type == kTfLiteInt64) {
    if (need_broadcast) {
      TF_LITE_MUL(reference_ops, BroadcastMul6DSlow, int64_t);
    } else {
      TF_LITE_MUL(reference_ops, Mul, int64_t);
    }
#undef TF_LITE_MUL
  } else if (output->type == kTfLiteComplex64) {
#define TF_LITE_MUL_COMPLEX(op_name)                                      \
  reference_ops::op_name(                                                 \
      op_params, GetTensorShape(input1),                                  \
      GetTensorData<std::complex<float>>(input1), GetTensorShape(input2), \
      GetTensorData<std::complex<float>>(input2), GetTensorShape(output), \
      GetTensorData<std::complex<float>>(output));

    if (need_broadcast) {
      TF_LITE_MUL_COMPLEX(BroadcastMul6DSlow);
    } else {
      TF_LITE_MUL_COMPLEX(Mul);
    }
#undef TF_LITE_MUL_COMPLEX
  }
}

template <KernelType kernel_type>
TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node,
                           TfLiteMulParams* params, const OpData* data,
                           const TfLiteTensor* input1,
                           const TfLiteTensor* input2, TfLiteTensor* output) {
  if (input1->type == input2->type && input1->type == output->type &&
      (input1->type == kTfLiteUInt8 || input1->type == kTfLiteInt8 ||
       input1->type == kTfLiteInt16)) {
    tflite::ArithmeticParams op_params;
    SetActivationParams(data->output_activation_min,
                        data->output_activation_max, &op_params);
    op_params.input1_offset = -input1->params.zero_point;
    op_params.input2_offset = -input2->params.zero_point;
    op_params.output_offset = output->params.zero_point;
    op_params.output_multiplier = data->output_multiplier;
    op_params.output_shift = data->output_shift;
    bool need_broadcast = optimized_ops::ProcessBroadcastShapes(
        GetTensorShape(input1), GetTensorShape(input2), &op_params);
#define TF_LITE_MUL(type, opname, dtype)                             \
  type::opname(op_params, GetTensorShape(input1),                    \
               GetTensorData<dtype>(input1), GetTensorShape(input2), \
               GetTensorData<dtype>(input2), GetTensorShape(output), \
               GetTensorData<dtype>(output))
    if (input1->type == kTfLiteInt8) {
      if (kernel_type == kReference) {
        if (need_broadcast) {
          TF_LITE_MUL(reference_integer_ops, BroadcastMul6DSlow, int8_t);
        } else {
          TF_LITE_MUL(reference_integer_ops, Mul, int8_t);
        }
      } else {
        if (need_broadcast) {
          TF_LITE_MUL(optimized_integer_ops, BroadcastMulDispatch, int8_t);
        } else {
          TF_LITE_MUL(optimized_integer_ops, Mul, int8_t);
        }
      }
    } else if (input1->type == kTfLiteInt16) {
      // We have this check, because in case of int16
      // input1_val*input2_val can overflow int32:
      // see MulElementwise -
      // tensorflow/lite/kernels/internal/reference/integer_ops/mul.h in case of
      // 16-bit this function is used in symmetric quantization, so offset
      // should be zero.
      TF_LITE_ENSURE_EQ(context, op_params.input1_offset, 0.0);
      TF_LITE_ENSURE_EQ(context, op_params.input2_offset, 0.0);
      TF_LITE_ENSURE_EQ(context, op_params.output_offset, 0.0);

      if (need_broadcast) {
        TF_LITE_MUL(reference_integer_ops, BroadcastMul6DSlow, int16_t);
      } else {
        TF_LITE_MUL(reference_integer_ops, Mul, int16_t);
      }
    } else {
      // type == kTfLiteUInt8
      if (kernel_type == kReference) {
        if (need_broadcast) {
          TF_LITE_MUL(reference_ops, BroadcastMul6DSlow, uint8_t);
        } else {
          TF_LITE_MUL(reference_ops, Mul, uint8_t);
        }
      } else {
        if (need_broadcast) {
          TF_LITE_MUL(optimized_ops, BroadcastMulDispatch, uint8_t);
        } else {
          TF_LITE_MUL(optimized_ops, Mul, uint8_t);
        }
      }
    }
#undef TF_LITE_MUL
  } else if (input1->type == kTfLiteInt16 && input2->type == kTfLiteInt16 &&
             (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8)) {
#define TF_LITE_MUL(type, opname, output_dtype)                        \
  tflite::ArithmeticParams op_params;                                  \
  SetActivationParams(data->output_activation_min,                     \
                      data->output_activation_max, &op_params);        \
  op_params.output_offset = output->params.zero_point;                 \
  type::opname(op_params, GetTensorShape(input1),                      \
               GetTensorData<int16_t>(input1), GetTensorShape(input2), \
               GetTensorData<int16_t>(input2), GetTensorShape(output), \
               GetTensorData<output_dtype>(output))
    if (output->type == kTfLiteInt8) {
      TF_LITE_MUL(reference_integer_ops, Mul, int8_t);
    } else {
      if (kernel_type == kReference) {
        TF_LITE_MUL(reference_ops, Mul, uint8_t);
      } else {
        TF_LITE_MUL(optimized_ops, Mul, uint8_t);
      }
    }
#undef TF_LITE_MUL
  } else {
    TF_LITE_KERNEL_LOG(
        context, "Unsupported combination of input and output types in Mul.");
    return kTfLiteError;
  }
  return kTfLiteOk;
}

template <KernelType kernel_type>
TfLiteStatus EvalImpl(TfLiteContext* context, TfLiteNode* node, OpData* data,
                      TfLiteMulParams* params, const TfLiteTensor* input1,
                      const TfLiteTensor* input2, TfLiteTensor* output) {
  bool output_quantized = output->quantization.type != kTfLiteNoQuantization;
  if (output->type == kTfLiteFloat32 || output->type == kTfLiteFloat16 ||
      output->type == kTfLiteInt32 || output->type == kTfLiteInt64 ||
      output->type == kTfLiteComplex64 ||
      (!output_quantized && output->type == kTfLiteInt16) ||
      output->type == kTfLiteUInt32) {
    EvalMul<kernel_type>(context, node, params, data, input1, input2, output);
  } else if (output->type == kTfLiteUInt8 || output->type == kTfLiteInt8 ||
             output->type == kTfLiteInt16) {
    TF_LITE_ENSURE_OK(
        context, EvalQuantized<kernel_type>(context, node, params, data, input1,
                                            input2, output));
  } else {
    TF_LITE_KERNEL_LOG(context,
                       "Mul only supports FLOAT32, COMPLEX32, INT8, INT16,"
                       " INT32, INT64 and quantized UINT8 now, got %d.",
                       output->type);
    return kTfLiteError;
  }

  return kTfLiteOk;
}

template <KernelType kernel_type>
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
  auto* params = reinterpret_cast<TfLiteMulParams*>(node->builtin_data);
  OpData* data = reinterpret_cast<OpData*>(node->user_data);

  if (data->noop) {
    return kTfLiteOk;
  }
  const TfLiteTensor* input1;
  TF_LITE_ENSURE_OK(context,
                    GetInputSafe(context, node, kInputTensor1, &input1));
  const TfLiteTensor* input2;
  TF_LITE_ENSURE_OK(context,
                    GetInputSafe(context, node, kInputTensor2, &input2));
  TfLiteTensor* output;
  TF_LITE_ENSURE_OK(context,
                    GetOutputSafe(context, node, kOutputTensor, &output));
  return EvalImpl<kernel_type>(context, node, data, params, input1, input2,
                               output);
}
}  // namespace mul

TfLiteRegistration* Register_MUL_REF() {
  static TfLiteRegistration r = {
      mul::Init,
      mul::Free,
      mul::Prepare<mul::kReference>,
      mul::Eval<mul::kReference>,
      /*profiling_string=*/nullptr,
      /*builtin_code=*/0,
      /*custom_name=*/nullptr,
      /*version=*/0,
      /*registration_external=*/nullptr,
      /*async_kernel=*/nullptr,
      kTfLiteInplaceOpInput0Shared | kTfLiteInplaceOpInput1Shared};
  return &r;
}

TfLiteRegistration* Register_MUL_GENERIC_OPT() {
  static TfLiteRegistration r = {
      mul::Init,
      mul::Free,
      mul::Prepare<mul::kGenericOptimized>,
      mul::Eval<mul::kGenericOptimized>,
      /*profiling_string=*/nullptr,
      /*builtin_code=*/0,
      /*custom_name=*/nullptr,
      /*version=*/0,
      /*registration_external=*/nullptr,
      /*async_kernel=*/nullptr,
      kTfLiteInplaceOpInput0Shared | kTfLiteInplaceOpInput1Shared};
  return &r;
}

TfLiteRegistration* Register_MUL_NEON_OPT() {
  static TfLiteRegistration r = {
      mul::Init,
      mul::Free,
      mul::Prepare<mul::kNeonOptimized>,
      mul::Eval<mul::kNeonOptimized>,
      /*profiling_string=*/nullptr,
      /*builtin_code=*/0,
      /*custom_name=*/nullptr,
      /*version=*/0,
      /*registration_external=*/nullptr,
      /*async_kernel=*/nullptr,
      kTfLiteInplaceOpInput0Shared | kTfLiteInplaceOpInput1Shared};
  return &r;
}

TfLiteRegistration* Register_MUL() {
#ifdef USE_NEON
  return Register_MUL_NEON_OPT();
#else
  return Register_MUL_GENERIC_OPT();
#endif
}

}  // namespace builtin
}  // namespace ops
}  // namespace tflite