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| /* Copyright 2024 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. | ||
| ==============================================================================*/ | ||
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|
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| #include <cstdint> | ||
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| #include "tensorflow/lite/kernels/internal/reference/transpose.h" | ||
| #include "tensorflow/lite/kernels/kernel_util.h" | ||
| #include "tensorflow/lite/micro/kernels/batch_matmul.h" | ||
| #include "tensorflow/lite/micro/kernels/kernel_util.h" | ||
| #include "tensorflow/lite/micro/micro_log.h" | ||
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| namespace tflite { | ||
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| const int kBatchMatmulInputLhsTensor = 0; | ||
| const int kBatchMatmulInputRhsTensor = 1; | ||
| const int kBatchMatmulOutputTensor = 0; | ||
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| TfLiteStatus ReshapeOutputTensor(TfLiteContext* context, TfLiteNode* node, | ||
| const RuntimeShape& extended_lhs_shape, | ||
| const RuntimeShape& extended_rhs_shape, | ||
| bool adj_x, bool adj_y, int output_rank, | ||
| TfLiteTensor* output) { | ||
| int64_t orig_size = NumElements(output); | ||
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| // make sure the new output dims rank does not exceed the original rank | ||
| TF_LITE_ENSURE(context, output_rank <= NumDimensions(output)); | ||
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| // make sure output tensor dims are not in the FlatBuffer | ||
| TfLiteEvalTensor* output_eval = | ||
| tflite::micro::GetEvalOutput(context, node, kBatchMatmulOutputTensor); | ||
| TF_LITE_ENSURE_OK(context, tflite::micro::CreateWritableTensorDimsWithCopy( | ||
| context, output, output_eval)); | ||
|
|
||
| // Fill in any broadcast dimensions. | ||
| for (int i = 0; i < output_rank - 2; ++i) { | ||
| const int lhs_dim = extended_lhs_shape.Dims(i); | ||
| const int rhs_dim = extended_rhs_shape.Dims(i); | ||
| int broadcast_dim = lhs_dim; | ||
| if ((lhs_dim != rhs_dim) && (lhs_dim == 1)) { | ||
| broadcast_dim = rhs_dim; | ||
| } | ||
| output->dims->data[i] = broadcast_dim; | ||
| } | ||
| // Fill in the matmul dimensions. | ||
| int lhs_rows_index = adj_x ? output_rank - 1 : output_rank - 2; | ||
| int rhs_cols_index = adj_y ? output_rank - 2 : output_rank - 1; | ||
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| output->dims->data[output_rank - 2] = extended_lhs_shape.Dims(lhs_rows_index); | ||
| output->dims->data[output_rank - 1] = extended_rhs_shape.Dims(rhs_cols_index); | ||
| output->dims->size = output_rank; | ||
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| // Check that output tensor has not been resized | ||
| // since TFLM doesn't support tensor resizing. | ||
| TF_LITE_ENSURE_EQ(context, orig_size, NumElements(output)); | ||
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| return kTfLiteOk; | ||
| } | ||
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| template <typename T> | ||
| void TransposeRowsColumnsImpl(const TfLiteEvalTensor& tensor_in, | ||
| TfLiteEvalTensor* tensor_out) { | ||
| const T* input = tflite::micro::GetTensorData<T>(&tensor_in); | ||
| T* output = tflite::micro::GetTensorData<T>(tensor_out); | ||
| RuntimeShape transposed_shape(tflite::micro::GetTensorShape(&tensor_in)); | ||
| RuntimeShape shape(transposed_shape); | ||
| TransposeParams params; | ||
| const int rank = shape.DimensionsCount(); | ||
| params.perm_count = rank; | ||
| for (int i = 0; i < rank - 2; ++i) { | ||
| params.perm[i] = i; | ||
| } | ||
| // Transpose the last two dimensions. | ||
| params.perm[rank - 2] = rank - 1; | ||
| params.perm[rank - 1] = rank - 2; | ||
| transposed_shape.SetDim(rank - 1, shape.Dims(rank - 2)); | ||
| transposed_shape.SetDim(rank - 2, shape.Dims(rank - 1)); | ||
| reference_ops::Transpose(params, shape, input, transposed_shape, output); | ||
| } | ||
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| TfLiteStatus TransposeRowsColumns(const TfLiteEvalTensor& tensor_in, | ||
| TfLiteEvalTensor* tensor_out) { | ||
| if (tensor_in.type == kTfLiteFloat32) { | ||
| TransposeRowsColumnsImpl<float>(tensor_in, tensor_out); | ||
| return kTfLiteOk; | ||
| } else if (tensor_in.type == kTfLiteInt8) { | ||
| TransposeRowsColumnsImpl<int8_t>(tensor_in, tensor_out); | ||
| return kTfLiteOk; | ||
| } else if (tensor_in.type == kTfLiteInt16) { | ||
| TransposeRowsColumnsImpl<int16_t>(tensor_in, tensor_out); | ||
| return kTfLiteOk; | ||
| } else { | ||
| MicroPrintf( | ||
| "BATCH_MATMUL can only transpose tensors with FLOAT32, INT8, INT16 " | ||
| "type."); | ||
| } | ||
| return kTfLiteError; | ||
| } | ||
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| RuntimeShape SwapRowColumnDims(const RuntimeShape& shape) { | ||
| RuntimeShape swapped_shape(shape); | ||
| const int32_t dims = shape.DimensionsCount(); | ||
| swapped_shape.SetDim(dims - 2, shape.Dims(dims - 1)); | ||
| swapped_shape.SetDim(dims - 1, shape.Dims(dims - 2)); | ||
| return swapped_shape; | ||
| } | ||
|
|
||
| } // namespace tflite |
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