| /* Copyright 2020 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_SUB_H_ |
| #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SUB_H_ |
| |
| #include <stdint.h> |
| |
| #include <algorithm> |
| #include <limits> |
| |
| #include "ruy/profiler/instrumentation.h" // from @ruy |
| #include "tensorflow/lite/kernels/internal/common.h" |
| #include "tensorflow/lite/kernels/internal/compatibility.h" |
| #include "tensorflow/lite/kernels/internal/types.h" |
| |
| namespace tflite { |
| |
| namespace reference_ops { |
| |
| inline void SubNonBroadcast(const ArithmeticParams& params, |
| const RuntimeShape& input1_shape, |
| const float* input1_data, |
| const RuntimeShape& input2_shape, |
| const float* input2_data, |
| const RuntimeShape& output_shape, |
| float* output_data) { |
| const int flat_size = |
| MatchingElementsSize(input1_shape, input2_shape, output_shape); |
| for (int i = 0; i < flat_size; ++i) { |
| output_data[i] = ActivationFunctionWithMinMax( |
| input1_data[i] - input2_data[i], params.float_activation_min, |
| params.float_activation_max); |
| } |
| } |
| |
| inline void SubNonBroadcast(const ArithmeticParams& params, |
| const RuntimeShape& input1_shape, |
| const int32* input1_data, |
| const RuntimeShape& input2_shape, |
| const int32* input2_data, |
| const RuntimeShape& output_shape, |
| int32* output_data) { |
| const int flat_size = |
| MatchingElementsSize(input1_shape, input2_shape, output_shape); |
| for (int i = 0; i < flat_size; ++i) { |
| output_data[i] = ActivationFunctionWithMinMax( |
| input1_data[i] - input2_data[i], params.quantized_activation_min, |
| params.quantized_activation_max); |
| } |
| } |
| |
| // TODO(b/151345304): We can implement BroadcastSub on buffers of arbitrary |
| // dimensionality if the runtime code does a single loop over one dimension |
| // that handles broadcasting as the base case. The code generator would then |
| // generate max(D1, D2) nested for loops. |
| // TODO(b/151345101): BroadcastSub is intentionally duplicated from |
| // reference_ops.h. Once an optimized version is implemented and NdArrayDesc<T> |
| // is no longer referenced in this file, move NdArrayDesc<T> from types.h to |
| // reference_ops.h. |
| template <int N = 5> |
| inline void BroadcastSubSlow(const ArithmeticParams& params, |
| const RuntimeShape& input1_shape, |
| const float* input1_data, |
| const RuntimeShape& input2_shape, |
| const float* input2_data, |
| const RuntimeShape& output_shape, |
| float* output_data) { |
| ruy::profiler::ScopeLabel label("BroadcastSubSlow/float"); |
| TFLITE_DCHECK_LE(input1_shape.DimensionsCount(), N); |
| TFLITE_DCHECK_LE(input2_shape.DimensionsCount(), N); |
| TFLITE_DCHECK_LE(output_shape.DimensionsCount(), N); |
| NdArrayDesc<N> desc1; |
| NdArrayDesc<N> desc2; |
| NdArrayDesc<N> output_desc; |
| NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, |
| &desc2); |
| CopyDimsToDesc(RuntimeShape::ExtendedShape(N, output_shape), &output_desc); |
| |
| // 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. |
| auto sub_func = [&](int indexes[N]) { |
| output_data[SubscriptToIndex(output_desc, indexes)] = |
| ActivationFunctionWithMinMax( |
| input1_data[SubscriptToIndex(desc1, indexes)] - |
| input2_data[SubscriptToIndex(desc2, indexes)], |
| params.float_activation_min, params.float_activation_max); |
| }; |
| NDOpsHelper<N>(output_desc, sub_func); |
| } |
| |
| template <int N = 5> |
| inline void BroadcastSubSlow(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) { |
| ruy::profiler::ScopeLabel label("BroadcastSubSlow/uint8"); |
| TFLITE_DCHECK_LE(input1_shape.DimensionsCount(), N); |
| TFLITE_DCHECK_LE(input2_shape.DimensionsCount(), N); |
| TFLITE_DCHECK_LE(output_shape.DimensionsCount(), N); |
| NdArrayDesc<N> desc1; |
| NdArrayDesc<N> desc2; |
| NdArrayDesc<N> output_desc; |
| NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, |
| &desc2); |
| CopyDimsToDesc(RuntimeShape::ExtendedShape(N, output_shape), &output_desc); |
| |
| // 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. |
| auto sub_func = [&](int indexes[N]) { |
| const int32 input1_val = |
| params.input1_offset + input1_data[SubscriptToIndex(desc1, indexes)]; |
| const int32 input2_val = |
| params.input2_offset + input2_data[SubscriptToIndex(desc2, indexes)]; |
| const int32 shifted_input1_val = input1_val * (1 << params.left_shift); |
| const int32 shifted_input2_val = input2_val * (1 << params.left_shift); |
| const int32 scaled_input1_val = |
| MultiplyByQuantizedMultiplierSmallerThanOneExp( |
| shifted_input1_val, params.input1_multiplier, params.input1_shift); |
| const int32 scaled_input2_val = |
| MultiplyByQuantizedMultiplierSmallerThanOneExp( |
| shifted_input2_val, params.input2_multiplier, params.input2_shift); |
| const int32 raw_sub = scaled_input1_val - scaled_input2_val; |
| const int32 raw_output = |
| MultiplyByQuantizedMultiplierSmallerThanOneExp( |
| raw_sub, params.output_multiplier, params.output_shift) + |
| params.output_offset; |
| const int32 clamped_output = |
| std::min(params.quantized_activation_max, |
| std::max(params.quantized_activation_min, raw_output)); |
| output_data[SubscriptToIndex(output_desc, indexes)] = |
| static_cast<uint8>(clamped_output); |
| }; |
| NDOpsHelper<N>(output_desc, sub_func); |
| } |
| |
| template <int N = 5> |
| inline void BroadcastSubSlow(const ArithmeticParams& params, |
| const RuntimeShape& input1_shape, |
| const int32* input1_data, |
| const RuntimeShape& input2_shape, |
| const int32* input2_data, |
| const RuntimeShape& output_shape, |
| int32* output_data) { |
| ruy::profiler::ScopeLabel label("BroadcastSubSlow/int32"); |
| TFLITE_DCHECK_LE(input1_shape.DimensionsCount(), N); |
| TFLITE_DCHECK_LE(input2_shape.DimensionsCount(), N); |
| TFLITE_DCHECK_LE(output_shape.DimensionsCount(), N); |
| NdArrayDesc<N> desc1; |
| NdArrayDesc<N> desc2; |
| NdArrayDesc<N> output_desc; |
| NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, |
| &desc2); |
| CopyDimsToDesc(RuntimeShape::ExtendedShape(N, output_shape), &output_desc); |
| |
| // 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. |
| auto sub_func = [&](int indexes[N]) { |
| output_data[SubscriptToIndex(output_desc, indexes)] = |
| ActivationFunctionWithMinMax( |
| input1_data[SubscriptToIndex(desc1, indexes)] - |
| input2_data[SubscriptToIndex(desc2, indexes)], |
| params.quantized_activation_min, params.quantized_activation_max); |
| }; |
| NDOpsHelper<N>(output_desc, sub_func); |
| } |
| |
| template <int N = 5> |
| inline void BroadcastSubSlow(const ArithmeticParams& params, |
| const RuntimeShape& input1_shape, |
| const int8_t* input1_data, |
| const RuntimeShape& input2_shape, |
| const int8_t* input2_data, |
| const RuntimeShape& output_shape, |
| int8_t* output_data) { |
| ruy::profiler::ScopeLabel label("BroadcastSubSlow/int8"); |
| NdArrayDesc<N> desc1; |
| NdArrayDesc<N> desc2; |
| NdArrayDesc<N> output_desc; |
| NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, |
| &desc2); |
| CopyDimsToDesc(RuntimeShape::ExtendedShape(N, output_shape), &output_desc); |
| |
| // 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. |
| auto sub_func = [&](int indexes[N]) { |
| const int32_t input1_val = |
| params.input1_offset + input1_data[SubscriptToIndex(desc1, indexes)]; |
| const int32_t input2_val = |
| params.input2_offset + input2_data[SubscriptToIndex(desc2, indexes)]; |
| const int32_t shifted_input1_val = input1_val * (1 << params.left_shift); |
| const int32_t shifted_input2_val = input2_val * (1 << params.left_shift); |
| const int32_t scaled_input1_val = |
| MultiplyByQuantizedMultiplierSmallerThanOneExp( |
| shifted_input1_val, params.input1_multiplier, params.input1_shift); |
| const int32_t scaled_input2_val = |
| MultiplyByQuantizedMultiplierSmallerThanOneExp( |
| shifted_input2_val, params.input2_multiplier, params.input2_shift); |
| const int32_t raw_sub = scaled_input1_val - scaled_input2_val; |
| const int32_t raw_output = |
| MultiplyByQuantizedMultiplierSmallerThanOneExp( |
| raw_sub, params.output_multiplier, params.output_shift) + |
| params.output_offset; |
| const int32_t clamped_output = |
| std::min(params.quantized_activation_max, |
| std::max(params.quantized_activation_min, raw_output)); |
| output_data[SubscriptToIndex(output_desc, indexes)] = |
| static_cast<int8_t>(clamped_output); |
| }; |
| NDOpsHelper<N>(output_desc, sub_func); |
| } |
| |
| template <typename T, int N = 5> |
| void BroadcastSubSlow(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) { |
| ruy::profiler::ScopeLabel label("BroadcastSubSlow/templated"); |
| TFLITE_DCHECK_LE(input1_shape.DimensionsCount(), N); |
| TFLITE_DCHECK_LE(input2_shape.DimensionsCount(), N); |
| TFLITE_DCHECK_LE(output_shape.DimensionsCount(), N); |
| NdArrayDesc<N> desc1; |
| NdArrayDesc<N> desc2; |
| NdArrayDesc<N> output_desc; |
| NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, |
| &desc2); |
| CopyDimsToDesc(RuntimeShape::ExtendedShape(N, output_shape), &output_desc); |
| |
| // 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. |
| auto sub_func = [&](int indexes[N]) { |
| output_data[SubscriptToIndex(output_desc, indexes)] = |
| ActivationFunctionWithMinMax( |
| input1_data[SubscriptToIndex(desc1, indexes)] - |
| input2_data[SubscriptToIndex(desc2, indexes)], |
| params.quantized_activation_min, params.quantized_activation_max); |
| }; |
| NDOpsHelper<N>(output_desc, sub_func); |
| } |
| |
| // Element-wise Sub that can often be used for inner loop of broadcast sub as |
| // well as the non-broadcast sub. |
| inline void SubElementwise(int size, const ArithmeticParams& params, |
| const uint8* input1_data, const uint8* input2_data, |
| uint8* output_data) { |
| TFLITE_DCHECK_GT(params.input1_offset, -256); |
| TFLITE_DCHECK_GT(params.input2_offset, -256); |
| TFLITE_DCHECK_LT(params.input1_offset, 256); |
| TFLITE_DCHECK_LT(params.input2_offset, 256); |
| |
| 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 shifted_input1_val = input1_val * (1 << params.left_shift); |
| const int32 shifted_input2_val = input2_val * (1 << params.left_shift); |
| const int32 scaled_input1_val = |
| MultiplyByQuantizedMultiplierSmallerThanOneExp( |
| shifted_input1_val, params.input1_multiplier, params.input1_shift); |
| const int32 scaled_input2_val = |
| MultiplyByQuantizedMultiplierSmallerThanOneExp( |
| shifted_input2_val, params.input2_multiplier, params.input2_shift); |
| const int32 raw_sub = scaled_input1_val - scaled_input2_val; |
| const int32 raw_output = |
| MultiplyByQuantizedMultiplierSmallerThanOneExp( |
| raw_sub, params.output_multiplier, params.output_shift) + |
| params.output_offset; |
| const int32 clamped_output = |
| std::min(params.quantized_activation_max, |
| std::max(params.quantized_activation_min, raw_output)); |
| output_data[i] = static_cast<uint8>(clamped_output); |
| } |
| } |
| |
| // Element-wise add that can often be used for inner loop of broadcast add as |
| // well as the non-broadcast add. |
| inline void SubElementwise(int size, const ArithmeticParams& params, |
| const int8_t* input1_data, const int8_t* input2_data, |
| int8_t* output_data) { |
| const int32_t int8_max_value = std::numeric_limits<int8_t>::max(); |
| TFLITE_DCHECK_GE(params.input1_offset, -1 * int8_max_value); |
| TFLITE_DCHECK_GE(params.input2_offset, -1 * int8_max_value); |
| TFLITE_DCHECK_LE(params.input1_offset, int8_max_value); |
| TFLITE_DCHECK_LE(params.input2_offset, int8_max_value); |
| |
| 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 shifted_input1_val = input1_val * (1 << params.left_shift); |
| const int32 shifted_input2_val = input2_val * (1 << params.left_shift); |
| const int32 scaled_input1_val = |
| MultiplyByQuantizedMultiplierSmallerThanOneExp( |
| shifted_input1_val, params.input1_multiplier, params.input1_shift); |
| const int32 scaled_input2_val = |
| MultiplyByQuantizedMultiplierSmallerThanOneExp( |
| shifted_input2_val, params.input2_multiplier, params.input2_shift); |
| const int32 raw_sub = scaled_input1_val - scaled_input2_val; |
| const int32 raw_output = |
| MultiplyByQuantizedMultiplierSmallerThanOneExp( |
| raw_sub, params.output_multiplier, params.output_shift) + |
| params.output_offset; |
| const int32 clamped_output = |
| std::min(params.quantized_activation_max, |
| std::max(params.quantized_activation_min, raw_output)); |
| output_data[i] = static_cast<int8_t>(clamped_output); |
| } |
| } |
| |
| inline void Sub(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 = |
| MatchingElementsSize(input1_shape, input2_shape, output_shape); |
| |
| TFLITE_DCHECK_GT(params.input1_offset, -256); |
| TFLITE_DCHECK_GT(params.input2_offset, -256); |
| TFLITE_DCHECK_LT(params.input1_offset, 256); |
| TFLITE_DCHECK_LT(params.input2_offset, 256); |
| SubElementwise(flat_size, params, input1_data, input2_data, output_data); |
| } |
| |
| inline void Sub(const ArithmeticParams& params, |
| const RuntimeShape& input1_shape, const int8_t* input1_data, |
| const RuntimeShape& input2_shape, const int8_t* input2_data, |
| const RuntimeShape& output_shape, int8_t* output_data) { |
| TFLITE_DCHECK_LE(params.quantized_activation_min, |
| params.quantized_activation_max); |
| |
| const int flat_size = |
| MatchingElementsSize(input1_shape, input2_shape, output_shape); |
| |
| const int32_t int8_max_value = std::numeric_limits<int8_t>::max(); |
| TFLITE_DCHECK_GE(params.input1_offset, -1 * int8_max_value); |
| TFLITE_DCHECK_GE(params.input2_offset, -1 * int8_max_value); |
| TFLITE_DCHECK_LE(params.input1_offset, int8_max_value); |
| TFLITE_DCHECK_LE(params.input2_offset, int8_max_value); |
| SubElementwise(flat_size, params, input1_data, input2_data, output_data); |
| } |
| |
| template <typename T> |
| void Sub(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) { |
| NdArrayDesc<4> desc1; |
| NdArrayDesc<4> desc2; |
| NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, |
| &desc2); |
| const RuntimeShape extended_output_shape = |
| RuntimeShape::ExtendedShape(4, output_shape); |
| |
| // 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 < 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) { |
| output_data[Offset(extended_output_shape, b, y, x, c)] = |
| input1_data[SubscriptToIndex(desc1, b, y, x, c)] - |
| input2_data[SubscriptToIndex(desc2, b, y, x, c)]; |
| } |
| } |
| } |
| } |
| } |
| |
| inline void SubWithActivation(const ArithmeticParams& params, |
| const RuntimeShape& input1_shape, |
| const int32* input1_data, |
| const RuntimeShape& input2_shape, |
| const int32* input2_data, |
| const RuntimeShape& output_shape, |
| int32* output_data) { |
| ruy::profiler::ScopeLabel label("SubWithActivation"); |
| const int flat_size = |
| MatchingElementsSize(input1_shape, input2_shape, output_shape); |
| for (int i = 0; i < flat_size; ++i) { |
| output_data[i] = ActivationFunctionWithMinMax( |
| input1_data[i] - input2_data[i], params.quantized_activation_min, |
| params.quantized_activation_max); |
| } |
| } |
| |
| inline void SubWithActivation(const ArithmeticParams& params, |
| const RuntimeShape& input1_shape, |
| const float* input1_data, |
| const RuntimeShape& input2_shape, |
| const float* input2_data, |
| const RuntimeShape& output_shape, |
| float* output_data) { |
| const int flat_size = |
| MatchingElementsSize(input1_shape, input2_shape, output_shape); |
| for (int i = 0; i < flat_size; ++i) { |
| output_data[i] = ActivationFunctionWithMinMax( |
| input1_data[i] - input2_data[i], params.float_activation_min, |
| params.float_activation_max); |
| } |
| } |
| |
| |
| } // namespace reference_ops |
| } // namespace tflite |
| |
| #endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SUB_H_ |