| /* 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_MUL_H_ |
| #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MUL_H_ |
| |
| #include "tensorflow/lite/kernels/internal/common.h" |
| |
| namespace tflite { |
| |
| namespace reference_ops { |
| |
| // Element-wise mul that can often be used for inner loop of broadcast Mul as |
| // well as the non-broadcast Mul. |
| inline void MulElementwise(int size, const ArithmeticParams& params, |
| const uint8* input1_data, const uint8* input2_data, |
| uint8* output_data) { |
| for (int i = 0; i < size; ++i) { |
| const int32 input1_val = params.input1_offset + input1_data[i]; |
| const int32 input2_val = params.input2_offset + input2_data[i]; |
| const int32 unclamped_result = |
| params.output_offset + |
| MultiplyByQuantizedMultiplier(input1_val * input2_val, |
| params.output_multiplier, |
| params.output_shift); |
| const int32 clamped_output = |
| std::min(params.quantized_activation_max, |
| std::max(params.quantized_activation_min, unclamped_result)); |
| output_data[i] = static_cast<uint8>(clamped_output); |
| } |
| } |
| |
| template <typename T> |
| inline void Mul(const ArithmeticParams& params, |
| const RuntimeShape& input1_shape, const T* input1_data, |
| const RuntimeShape& input2_shape, const T* input2_data, |
| const RuntimeShape& output_shape, T* output_data) { |
| T output_activation_min; |
| T output_activation_max; |
| GetActivationParams(params, &output_activation_min, &output_activation_max); |
| |
| const int flat_size = |
| MatchingFlatSize(input1_shape, input2_shape, output_shape); |
| for (int i = 0; i < flat_size; ++i) { |
| output_data[i] = ActivationFunctionWithMinMax( |
| input1_data[i] * input2_data[i], output_activation_min, |
| output_activation_max); |
| } |
| } |
| |
| inline void Mul(const ArithmeticParams& params, |
| const RuntimeShape& input1_shape, const uint8* input1_data, |
| const RuntimeShape& input2_shape, const uint8* input2_data, |
| const RuntimeShape& output_shape, uint8* output_data) { |
| TFLITE_DCHECK_LE(params.quantized_activation_min, |
| params.quantized_activation_max); |
| const int flat_size = |
| MatchingFlatSize(input1_shape, input2_shape, output_shape); |
| |
| MulElementwise(flat_size, params, input1_data, input2_data, output_data); |
| } |
| |
| inline void BroadcastMul4DSlow(const ArithmeticParams& params, |
| const RuntimeShape& input1_shape, |
| const uint8* input1_data, |
| const RuntimeShape& input2_shape, |
| const uint8* input2_data, |
| const RuntimeShape& output_shape, |
| uint8* output_data) { |
| NdArrayDesc<4> desc1; |
| NdArrayDesc<4> desc2; |
| NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, |
| &desc2); |
| const RuntimeShape extended_output_shape = |
| RuntimeShape::ExtendedShape(4, output_shape); |
| |
| for (int b = 0; b < extended_output_shape.Dims(0); ++b) { |
| for (int y = 0; y < extended_output_shape.Dims(1); ++y) { |
| for (int x = 0; x < extended_output_shape.Dims(2); ++x) { |
| for (int c = 0; c < extended_output_shape.Dims(3); ++c) { |
| const int32 input1_val = |
| params.input1_offset + |
| input1_data[SubscriptToIndex(desc1, b, y, x, c)]; |
| const int32 input2_val = |
| params.input2_offset + |
| input2_data[SubscriptToIndex(desc2, b, y, x, c)]; |
| const int32 unclamped_result = |
| params.output_offset + |
| MultiplyByQuantizedMultiplier(input1_val * input2_val, |
| params.output_multiplier, |
| params.output_shift); |
| const int32 clamped_output = std::min( |
| params.quantized_activation_max, |
| std::max(params.quantized_activation_min, unclamped_result)); |
| output_data[Offset(extended_output_shape, b, y, x, c)] = |
| static_cast<uint8>(clamped_output); |
| } |
| } |
| } |
| } |
| } |
| |
| template <typename T> |
| void BroadcastMul4DSlow(const ArithmeticParams& params, |
| const RuntimeShape& unextended_input1_shape, |
| const T* input1_data, |
| const RuntimeShape& unextended_input2_shape, |
| const T* input2_data, |
| const RuntimeShape& unextended_output_shape, |
| T* output_data) { |
| T output_activation_min; |
| T output_activation_max; |
| GetActivationParams(params, &output_activation_min, &output_activation_max); |
| |
| TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), 4); |
| TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4); |
| const RuntimeShape output_shape = |
| RuntimeShape::ExtendedShape(4, unextended_output_shape); |
| |
| NdArrayDesc<4> desc1; |
| NdArrayDesc<4> desc2; |
| NdArrayDescsForElementwiseBroadcast(unextended_input1_shape, |
| unextended_input2_shape, &desc1, &desc2); |
| |
| // In Tensorflow, the dimensions are canonically named (batch_number, row, |
| // col, channel), with extents (batches, height, width, depth), with the |
| // trailing dimension changing most rapidly (channels has the smallest stride, |
| // typically 1 element). |
| // |
| // In generated C code, we store arrays with the dimensions reversed. The |
| // first dimension has smallest stride. |
| // |
| // We name our variables by their Tensorflow convention, but generate C code |
| // nesting loops such that the innermost loop has the smallest stride for the |
| // best cache behavior. |
| for (int b = 0; b < output_shape.Dims(0); ++b) { |
| for (int y = 0; y < output_shape.Dims(1); ++y) { |
| for (int x = 0; x < output_shape.Dims(2); ++x) { |
| for (int c = 0; c < output_shape.Dims(3); ++c) { |
| output_data[Offset(output_shape, b, y, x, c)] = |
| ActivationFunctionWithMinMax( |
| input1_data[SubscriptToIndex(desc1, b, y, x, c)] * |
| input2_data[SubscriptToIndex(desc2, b, y, x, c)], |
| output_activation_min, output_activation_max); |
| } |
| } |
| } |
| } |
| } |
| |
| } // namespace reference_ops |
| } // namespace tflite |
| |
| #endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MUL_H_ |