| /* Copyright 2019 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. |
| ==============================================================================*/ |
| #ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TANH_H_ |
| #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TANH_H_ |
| |
| #include <limits> |
| |
| #include "fixedpoint/fixedpoint.h" |
| #include "tensorflow/lite/kernels/internal/common.h" |
| |
| namespace tflite { |
| namespace reference_integer_ops { |
| |
| inline void Tanh(int32_t input_zero_point, int32_t input_range_radius, |
| int32_t input_multiplier, int32_t input_shift, |
| int32_t input_size, const int8_t* input_data, |
| int8_t* output_data) { |
| // Integer bits must be in sync with Prepare() function. |
| static constexpr int32_t kInputIntegerBits = 4; |
| static constexpr int32_t kOutputScale = 7; |
| static constexpr int32_t kMinInt8 = std::numeric_limits<int8_t>::min(); |
| static constexpr int32_t kMaxInt8 = std::numeric_limits<int8_t>::max(); |
| using F4 = gemmlowp::FixedPoint<int32_t, kInputIntegerBits>; |
| |
| for (int i = 0; i < input_size; ++i) { |
| const int32_t input = |
| static_cast<int32_t>(input_data[i]) - input_zero_point; |
| if (input <= -input_range_radius) { |
| output_data[i] = kMinInt8; |
| } else if (input >= input_range_radius) { |
| output_data[i] = kMaxInt8; |
| } else { |
| const int32_t input_in_q4 = |
| MultiplyByQuantizedMultiplier(input, input_multiplier, input_shift); |
| const int32_t output_in_q0 = |
| gemmlowp::tanh(F4::FromRaw(input_in_q4)).raw(); |
| |
| // Rescale and downcast. |
| using gemmlowp::RoundingDivideByPOT; |
| int32_t output_in_q24 = |
| RoundingDivideByPOT(output_in_q0, 31 - kOutputScale); |
| output_in_q24 = std::min(std::max(output_in_q24, kMinInt8), kMaxInt8); |
| output_data[i] = static_cast<int8_t>(output_in_q24); |
| } |
| } |
| } |
| |
| inline void Tanh(int32_t input_multiplier, int32_t input_left_shift, |
| int32_t input_size, const int16_t* ptr_input_data, |
| int16_t* ptr_output_data) { |
| // We use the LUT for sigmoid and take into account, that |
| // tanh(x) = 2*sigmoid(2*x) - 1 |
| |
| int32_t input_data_mul = (input_multiplier > 0) ? input_multiplier : 1; |
| |
| for (int i = 0; i < input_size; ++i, ptr_input_data++, ptr_output_data++) { |
| int32_t input_data = (*ptr_input_data) * input_data_mul; |
| |
| if (input_left_shift == 1) { |
| input_data <<= 1; |
| } |
| |
| // Scale by 3/4 to expand range [-8,8]->[-10.7,10.7]. |
| uint32_t abs_input_data = 3 * abs(input_data); |
| uint32_t uh = abs_input_data >> 8; |
| int32_t result; |
| |
| if (uh >= 255) { |
| // Saturate to maximum. |
| result = 0xFFFF << 8; |
| } else { |
| uint32_t ua = sigmoid_table_uint16[uh]; |
| uint32_t ub = sigmoid_table_uint16[uh + 1]; |
| |
| uint8_t ut = abs_input_data & 0xFF; |
| |
| result = (ua << 8) + ut * (ub - ua); |
| } |
| |
| result = (input_data >= 0) |
| ? (result - (1 << (14 + 9)) + (1 << (9 - 2))) |
| : (-result + (1 << (14 + 9)) + (1 << (9 - 2)) - 1); |
| |
| // Convert back to 16-bit. |
| result >>= (9 - 1); |
| |
| *ptr_output_data = result; |
| } |
| } |
| |
| } // namespace reference_integer_ops |
| } // namespace tflite |
| |
| #endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TANH_H_ |