Porting Reverse_V2 operator from TFLite #3123
+633
−0
There are no files selected for viewing
This file contains hidden or 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 hidden or 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 hidden or 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 hidden or 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
|
There was a problem hiding this comment. Please add kTfLiteInt64 and kTfLiteBool support, as per TfLite. |
This file contains hidden or 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 +1,201 @@ | ||
| /* Copyright 2025 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 <stdint.h> | ||
|
|
||
|
There was a problem hiding this comment. Add |
||
| #include "tensorflow/lite/c/common.h" | ||
| #include "tensorflow/lite/kernels/internal/tensor_ctypes.h" | ||
| #include "tensorflow/lite/kernels/kernel_util.h" | ||
| #include "tensorflow/lite/micro/kernels/kernel_util.h" | ||
| #include "tensorflow/lite/micro/micro_log.h" | ||
| #include "tensorflow/lite/micro/micro_utils.h" | ||
|
|
||
| namespace tflite { | ||
|
|
||
| constexpr int kMaxDimensions = 8; | ||
|
There was a problem hiding this comment. Should be in the anonymous namespace. Also, this should be set to the value of |
||
|
|
||
| namespace { | ||
|
|
||
| constexpr int kInputTensor = 0; | ||
| constexpr int kAxisTensor = 1; | ||
| constexpr int kOutputTensor = 0; | ||
|
|
||
| int comp(const void* a, const void* b) { return (*(int*)a - *(int*)b); } | ||
|
|
||
|
|
||
| TfLiteStatus ReverseV2Prepare(TfLiteContext* context, TfLiteNode* node) { | ||
| MicroContext* micro_context = GetMicroContext(context); | ||
|
|
||
| TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); | ||
| TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); | ||
|
|
||
| // Ensure inputs and outputs exist. | ||
| TfLiteTensor* input = | ||
| micro_context->AllocateTempInputTensor(node, kInputTensor); | ||
| TF_LITE_ENSURE(context, input != nullptr); | ||
| TfLiteTensor* axis = | ||
| micro_context->AllocateTempInputTensor(node, kAxisTensor); | ||
| TF_LITE_ENSURE(context, axis != nullptr); | ||
| TfLiteTensor* output = | ||
| micro_context->AllocateTempOutputTensor(node, kOutputTensor); | ||
| TF_LITE_ENSURE(context, output != nullptr); | ||
| TF_LITE_ENSURE_EQ(context, NumDimensions(axis), 1); | ||
| TF_LITE_ENSURE(context, NumDimensions(input) <= 8); | ||
|
|
||
| TF_LITE_ENSURE(context, NumDimensions(input) >= NumElements(axis)); | ||
|
|
||
| if (input->type != kTfLiteInt32 && input->type != kTfLiteFloat32 && | ||
| input->type != kTfLiteUInt8 && input->type != kTfLiteInt8 && | ||
| input->type != kTfLiteInt16) { | ||
| MicroPrintf("Type '%s' is not supported by reverse.", | ||
| TfLiteTypeGetName(input->type)); | ||
| return kTfLiteError; | ||
| } | ||
|
|
||
| if (axis->type != kTfLiteInt32) { | ||
| MicroPrintf("Axis Type '%s' is not supported by reverse.", | ||
| TfLiteTypeGetName(axis->type)); | ||
| return kTfLiteError; | ||
| } | ||
| // The value type and output type must match. | ||
| TF_LITE_ENSURE_EQ(context, input->type, output->type); | ||
|
|
||
| micro_context->DeallocateTempTfLiteTensor(input); | ||
| micro_context->DeallocateTempTfLiteTensor(axis); | ||
| micro_context->DeallocateTempTfLiteTensor(output); | ||
| return kTfLiteOk; | ||
| } | ||
|
|
||
| template <typename T> | ||
| void ReverseImpl(int32_t* axes, int num_axes, const RuntimeShape& input_shape, | ||
|
Comment on lines
+79
to
+80
There was a problem hiding this comment. This method should not be implemented. Instead please use tflite::reference_ops::Reverse(). |
||
| const T* input_data, T* output_data) { | ||
| bool is_upper = (axes[num_axes - 1] == input_shape.DimensionsCount() - 1); | ||
| bool is_lower = (axes[0] == 0); | ||
| int rank = input_shape.DimensionsCount(); | ||
| if (is_upper && is_lower) { | ||
| std::reverse_copy(input_data, input_data + input_shape.FlatSize(), | ||
| output_data); | ||
| return; | ||
| } else { | ||
| int32_t min_dim = axes[0]; | ||
| int32_t max_dim = axes[num_axes - 1]; | ||
| int upper_size = 1; | ||
| for (int i = 0; i < min_dim; ++i) { | ||
| upper_size *= input_shape.Dims(i); | ||
| } | ||
| int lower_size = 1; | ||
| for (int i = max_dim + 1; i < rank; ++i) { | ||
| lower_size *= input_shape.Dims(i); | ||
| } | ||
| int middle_size = 1; | ||
| for (int i = min_dim; i <= max_dim; ++i) { | ||
| middle_size *= input_shape.Dims(i); | ||
| } | ||
|
|
||
| if (lower_size > 1) { | ||
| for (int i = 0; i < upper_size; ++i) { | ||
| for (int j = 0; j < middle_size; ++j) { | ||
| T* src = (T*)input_data + (i * (middle_size) + j) * lower_size; | ||
| T* dst = (T*)output_data + | ||
| (i * (middle_size) + (middle_size - j - 1)) * lower_size; | ||
| memcpy(dst, src, lower_size * sizeof(T)); | ||
| } | ||
| } | ||
| } else { | ||
| for (int i = 0; i < upper_size; ++i) { | ||
| std::reverse_copy(input_data + i * (middle_size), | ||
| input_data + i * middle_size + middle_size, | ||
| output_data + i * (middle_size)); | ||
| } | ||
| } | ||
| } | ||
| } | ||
|
|
||
| TfLiteStatus ReverseV2Eval(TfLiteContext* context, TfLiteNode* node) { | ||
| const TfLiteEvalTensor* input = | ||
| micro::GetEvalInput(context, node, kInputTensor); | ||
| const TfLiteEvalTensor* axis = | ||
| micro::GetEvalInput(context, node, kAxisTensor); | ||
| TfLiteEvalTensor* output = micro::GetEvalOutput(context, node, kOutputTensor); | ||
|
|
||
| TF_LITE_ENSURE_EQ(context, axis->type, kTfLiteInt32); | ||
|
There was a problem hiding this comment. This check is not required (already done in Prepare phase). |
||
| const int num_axes = static_cast<int>(ElementCount(*axis->dims)); | ||
| TF_LITE_ENSURE(context, num_axes <= 8); | ||
|
There was a problem hiding this comment. This check is not required (already done in Prepare phase). |
||
|
|
||
| int32_t axes_data[kMaxDimensions]; | ||
|
There was a problem hiding this comment. Use of |
||
| std::memcpy(axes_data, axis->data.i32, sizeof(int32_t) * num_axes); | ||
|
There was a problem hiding this comment. This should be |
||
| const int rank = tflite::micro::GetTensorShape(input).DimensionsCount(); | ||
| for (int i = 0; i < num_axes; ++i) { | ||
| if (axes_data[i] < 0) { | ||
| axes_data[i] += rank; | ||
| } | ||
| TF_LITE_ENSURE(context, axes_data[i] >= 0 && axes_data[i] < rank); | ||
| } | ||
| qsort(axes_data, num_axes, sizeof(int32_t), comp); | ||
|
There was a problem hiding this comment. Please use |
||
|
|
||
| bool is_contiguous = true; | ||
| for (int i = 1; i < num_axes; ++i) { | ||
| if (axes_data[i - 1] + 1 != axes_data[i]) { | ||
| is_contiguous = false; | ||
| break; | ||
| } | ||
| } | ||
| if (!is_contiguous) { | ||
| MicroPrintf("Non-contiguous `axes` not supported"); | ||
| return kTfLiteError; | ||
| } | ||
|
|
||
| switch (output->type) { | ||
| case kTfLiteFloat32: | ||
| ReverseImpl<float>(axes_data, num_axes, | ||
| tflite::micro::GetTensorShape(input), | ||
| tflite::micro::GetTensorData<float>(input), | ||
| tflite::micro::GetTensorData<float>(output)); | ||
| break; | ||
| case kTfLiteInt32: | ||
| ReverseImpl<int32_t>(axes_data, num_axes, | ||
| tflite::micro::GetTensorShape(input), | ||
| tflite::micro::GetTensorData<int32_t>(input), | ||
| tflite::micro::GetTensorData<int32_t>(output)); | ||
| break; | ||
| case kTfLiteInt16: | ||
| ReverseImpl<int16_t>(axes_data, num_axes, | ||
| tflite::micro::GetTensorShape(input), | ||
| tflite::micro::GetTensorData<int16_t>(input), | ||
| tflite::micro::GetTensorData<int16_t>(output)); | ||
| break; | ||
| case kTfLiteInt8: | ||
| case kTfLiteUInt8: | ||
| ReverseImpl<uint8_t>(axes_data, num_axes, | ||
| tflite::micro::GetTensorShape(input), | ||
| tflite::micro::GetTensorData<uint8_t>(input), | ||
| tflite::micro::GetTensorData<uint8_t>(output)); | ||
|
Comment on lines
+160
to
+182
There was a problem hiding this comment. Please use |
||
| break; | ||
| default: | ||
| MicroPrintf( | ||
| "Reverse currently supports float32, int16, " | ||
| "int8 and uint8 for output, got %d.", | ||
|
Comment on lines
+186
to
+187
There was a problem hiding this comment. Please change this to |
||
| TfLiteTypeGetName(output->type)); | ||
| return kTfLiteError; | ||
| } | ||
|
|
||
| return kTfLiteOk; | ||
| } | ||
|
|
||
| } // namespace | ||
|
|
||
| TFLMRegistration Register_REVERSE_V2() { | ||
| return tflite::micro::RegisterOp(nullptr, ReverseV2Prepare, ReverseV2Eval); | ||
| } | ||
|
|
||
| } // namespace tflite | ||
Oops, something went wrong.
Oops, something went wrong.
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Update copyright year.