-
Notifications
You must be signed in to change notification settings - Fork 853
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Fix quant specific op registration for some ops (#2770)
BUG=Quantization specific registration for BatchMatmul, SVDF and LSTM were not working correctly.
- Loading branch information
Showing
6 changed files
with
140 additions
and
91 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,119 @@ | ||
| /* 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. | ||
| ==============================================================================*/ | ||
|
|
||
| #include <cstdint> | ||
|
|
||
| #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" | ||
|
|
||
| namespace tflite { | ||
|
|
||
| const int kBatchMatmulInputLhsTensor = 0; | ||
| const int kBatchMatmulInputRhsTensor = 1; | ||
| const int kBatchMatmulOutputTensor = 0; | ||
|
|
||
| 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); | ||
|
|
||
| // make sure the new output dims rank does not exceed the original rank | ||
| TF_LITE_ENSURE(context, output_rank <= NumDimensions(output)); | ||
|
|
||
| // 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; | ||
|
|
||
| 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; | ||
|
|
||
| // Check that output tensor has not been resized | ||
| // since TFLM doesn't support tensor resizing. | ||
| TF_LITE_ENSURE_EQ(context, orig_size, NumElements(output)); | ||
|
|
||
| return kTfLiteOk; | ||
| } | ||
|
|
||
| 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); | ||
| } | ||
|
|
||
| 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; | ||
| } | ||
|
|
||
| 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 |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters