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third_party/tflite-micro/tensorflow/lite/kernels/internal/reference/select.h
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| /* Copyright 2022 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_SELECT_H_ | ||
| #define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SELECT_H_ | ||
|
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| #include <cmath> | ||
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| #include "ruy/profiler/instrumentation.h" // from @ruy | ||
| #include "tensorflow/lite/kernels/internal/common.h" | ||
| #include "tensorflow/lite/kernels/internal/types.h" | ||
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| namespace tflite { | ||
| namespace reference_ops { | ||
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| template <typename D, typename T> | ||
| void Select(const RuntimeShape& input_condition_shape, | ||
| const D* input_condition_data, const RuntimeShape& input_x_shape, | ||
| const T* input_x_data, const RuntimeShape& input_y_shape, | ||
| const T* input_y_data, const RuntimeShape& output_shape, | ||
| T* output_data) { | ||
| ruy::profiler::ScopeLabel label("Select"); | ||
| int64_t flatsize; | ||
| // Allow select operator executions on mixed scalar tensors and one element | ||
| // tensors. | ||
| if (input_condition_shape.FlatSize() == 1 && input_x_shape.FlatSize() == 1 && | ||
| input_y_shape.FlatSize() == 1 && output_shape.FlatSize() == 1) { | ||
| flatsize = 1; | ||
| } else { | ||
| flatsize = MatchingFlatSize(input_condition_shape, input_x_shape, | ||
| input_y_shape, output_shape); | ||
| } | ||
| for (int64_t i = 0; i < flatsize; ++i) { | ||
| output_data[i] = | ||
| input_condition_data[i] ? input_x_data[i] : input_y_data[i]; | ||
| } | ||
| } | ||
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| template <typename D, typename T> | ||
| void RankOneSelect(const RuntimeShape& input_condition_shape, | ||
| const D* input_condition_data, | ||
| const RuntimeShape& input_x_shape, const T* input_x_data, | ||
| const RuntimeShape& input_y_shape, const T* input_y_data, | ||
| const RuntimeShape& output_shape, T* output_data) { | ||
| ruy::profiler::ScopeLabel label("Select/RankOneSelect"); | ||
| const int64_t outer_size = input_condition_shape.FlatSize(); | ||
| int64_t inner_size; | ||
| if (input_condition_shape.DimensionsCount() == 0) { | ||
| inner_size = MatchingFlatSize(input_x_shape, input_y_shape, output_shape); | ||
| } else { | ||
| TFLITE_DCHECK_EQ( | ||
| MatchingDim(input_x_shape, 0, input_y_shape, 0, output_shape, 0), | ||
| outer_size); | ||
| inner_size = | ||
| MatchingFlatSizeSkipDim(input_x_shape, 0, input_y_shape, output_shape); | ||
| } | ||
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| int64_t offset = 0; | ||
| for (int64_t i = 0; i < outer_size; i++) { | ||
| const T* input_data = input_condition_data[i] ? input_x_data : input_y_data; | ||
| memcpy(output_data + offset, input_data + offset, inner_size * sizeof(T)); | ||
| offset += inner_size; | ||
| } | ||
| } | ||
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| template <typename D, typename T> | ||
| void BroadcastSelect5DSlow(const RuntimeShape& input_condition_shape, | ||
| const D* input_condition_data, | ||
| const RuntimeShape& input_x_shape, | ||
| const T* input_x_data, | ||
| const RuntimeShape& input_y_shape, | ||
| const T* input_y_data, | ||
| const RuntimeShape& output_shape, T* output_data) { | ||
| ruy::profiler::ScopeLabel label("Select/BroadcastSelectSlow"); | ||
| TFLITE_DCHECK_LE(input_condition_shape.DimensionsCount(), 5); | ||
| TFLITE_DCHECK_LE(input_x_shape.DimensionsCount(), 5); | ||
| TFLITE_DCHECK_LE(input_y_shape.DimensionsCount(), 5); | ||
| TFLITE_DCHECK_LE(output_shape.DimensionsCount(), 5); | ||
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| NdArrayDesc<5> desc_condition; | ||
| NdArrayDesc<5> desc_x; | ||
| NdArrayDesc<5> desc_y; | ||
| NdArrayDesc<5> desc_output; | ||
| const RuntimeShape extended_output_shape = | ||
| RuntimeShape::ExtendedShape(5, output_shape); | ||
| CopyDimsToDesc(extended_output_shape, &desc_output); | ||
| NdArrayDescsForElementwiseBroadcast(input_condition_shape, input_x_shape, | ||
| input_y_shape, &desc_condition, &desc_x, | ||
| &desc_y); | ||
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| // 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 n = 0; n < desc_output.extents[0]; ++n) { | ||
| int out_idx_n = desc_output.extents[1] * n; | ||
| int cond_idx_n = desc_condition.strides[0] * n; | ||
| int in_idx1_n = desc_x.strides[0] * n; | ||
| int in_idx2_n = desc_y.strides[0] * n; | ||
| for (int b = 0; b < desc_output.extents[1]; ++b) { | ||
| int out_idx_b = (out_idx_n + b) * desc_output.extents[2]; | ||
| int cond_idx_b = cond_idx_n + desc_condition.strides[1] * b; | ||
| int in_idx1_b = in_idx1_n + desc_x.strides[1] * b; | ||
| int in_idx2_b = in_idx2_n + desc_y.strides[1] * b; | ||
| for (int y = 0; y < desc_output.extents[2]; ++y) { | ||
| int out_idx_y = (out_idx_b + y) * desc_output.extents[3]; | ||
| int cond_idx_y = cond_idx_b + desc_condition.strides[2] * y; | ||
| int in_idx1_y = in_idx1_b + desc_x.strides[2] * y; | ||
| int in_idx2_y = in_idx2_b + desc_y.strides[2] * y; | ||
| for (int x = 0; x < desc_output.extents[3]; ++x) { | ||
| int out_idx = (out_idx_y + x) * desc_output.extents[4]; | ||
| int cond_idx = cond_idx_y + desc_condition.strides[3] * x; | ||
| int in_idx1 = in_idx1_y + desc_x.strides[3] * x; | ||
| int in_idx2 = in_idx2_y + desc_y.strides[3] * x; | ||
| for (int c = 0; c < desc_output.extents[4]; ++c) { | ||
| output_data[out_idx] = input_condition_data[cond_idx] | ||
| ? input_x_data[in_idx1] | ||
| : input_y_data[in_idx2]; | ||
| out_idx++; | ||
| cond_idx += desc_condition.strides[4]; | ||
| in_idx1 += desc_x.strides[4]; | ||
| in_idx2 += desc_y.strides[4]; | ||
| } | ||
| } | ||
| } | ||
| } | ||
| } | ||
| } | ||
|
|
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| } // namespace reference_ops | ||
| } // namespace tflite | ||
|
|
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| #endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SELECT_H_ |
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