| /* Copyright 2018 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 "tensorflow/lite/kernels/internal/reference/softmax.h" |
| |
| #include "tensorflow/lite/c/builtin_op_data.h" |
| #include "tensorflow/lite/c/common.h" |
| #include "tensorflow/lite/kernels/internal/common.h" |
| #include "tensorflow/lite/kernels/internal/quantization_util.h" |
| #include "tensorflow/lite/kernels/internal/reference/integer_ops/softmax.h" |
| #include "tensorflow/lite/kernels/internal/tensor_ctypes.h" |
| #include "tensorflow/lite/kernels/kernel_util.h" |
| #include "tensorflow/lite/kernels/op_macros.h" |
| |
| namespace tflite { |
| namespace ops { |
| namespace micro { |
| namespace activations { |
| namespace { |
| |
| struct OpData { |
| int32_t input_multiplier = 0; |
| int input_left_shift = 0; |
| int32_t input_range_radius = 0; |
| int diff_min = 0; |
| }; |
| |
| TfLiteStatus CalculateSoftmaxOpData(TfLiteContext* context, |
| const TfLiteTensor* input, |
| TfLiteTensor* output, |
| const TfLiteSoftmaxParams* params, |
| OpData* data) { |
| if (input->type == kTfLiteUInt8 || input->type == kTfLiteInt8) { |
| if (input->type == kTfLiteUInt8) { |
| TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0); |
| } else { |
| if (output->type == kTfLiteInt16) { |
| TF_LITE_ENSURE_EQ(context, output->params.zero_point, -32768); |
| // NOTE: Current int16 softmax output does not require symmetric scaling |
| // - so no need to verify scale here. |
| } else { |
| TF_LITE_ENSURE_EQ(context, output->params.zero_point, -128); |
| TF_LITE_ENSURE(context, output->params.scale == 1.f / 256); |
| } |
| } |
| |
| static const int kScaledDiffIntegerBits = 5; |
| |
| tflite::PreprocessSoftmaxScaling( |
| static_cast<double>(params->beta), |
| static_cast<double>(input->params.scale), kScaledDiffIntegerBits, |
| &data->input_multiplier, &data->input_left_shift); |
| data->diff_min = -1.0 * tflite::CalculateInputRadius( |
| kScaledDiffIntegerBits, data->input_left_shift); |
| } |
| return kTfLiteOk; |
| } |
| |
| } // namespace |
| |
| void* Init(TfLiteContext* context, const char* buffer, size_t length) { |
| return nullptr; |
| } |
| |
| void Free(TfLiteContext* context, void* buffer) {} |
| |
| TfLiteStatus SoftmaxPrepare(TfLiteContext* context, TfLiteNode* node) { |
| return kTfLiteOk; |
| } |
| |
| // Takes a 1D tensor and performs softmax along it. |
| void Softmax1DFloat(const TfLiteTensor* input, TfLiteTensor* output, |
| TfLiteSoftmaxParams* params) { |
| const int input_size = input->dims->data[0]; |
| tflite::reference_ops::Softmax(input->data.f, input_size, 1, params->beta, |
| output->data.f); |
| } |
| |
| // Takes a 2D tensor and perform softmax along the last dimension. |
| void Softmax2DFloat(const TfLiteTensor* input, TfLiteTensor* output, |
| TfLiteSoftmaxParams* params) { |
| const int batch_size = input->dims->data[0]; |
| const int input_size = input->dims->data[1]; |
| tflite::reference_ops::Softmax(input->data.f, input_size, batch_size, |
| params->beta, output->data.f); |
| } |
| |
| void Softmax1DQuantized(const TfLiteTensor* input, TfLiteTensor* output, |
| TfLiteSoftmaxParams* params, OpData* data) { |
| // TODO(ahentz): this is arguably a dirty trick. Since the implementation |
| // always traverses the last dimension of a 4D tensor, we will pretend our 1D |
| // tensor is 4D in a special way. We will convert a (Y) shape into a (1, |
| // 1, 1, Y) shape. |
| const int input_size = input->dims->data[0]; |
| const int32_t shape_data[4] = {1, 1, 1, input_size}; |
| RuntimeShape shape(4, shape_data); |
| SoftmaxParams op_params; |
| op_params.input_multiplier = data->input_multiplier; |
| op_params.input_left_shift = data->input_left_shift; |
| op_params.diff_min = data->diff_min; |
| if (input->type == kTfLiteUInt8) { |
| tflite::reference_ops::Softmax(op_params, shape, |
| GetTensorData<uint8_t>(input), shape, |
| GetTensorData<uint8_t>(output)); |
| } else { |
| if (output->type == kTfLiteInt16) { |
| tflite::reference_integer_ops::Softmax( |
| op_params, shape, GetTensorData<int8_t>(input), shape, |
| GetTensorData<int16_t>(output)); |
| } else { |
| tflite::reference_integer_ops::Softmax( |
| op_params, shape, GetTensorData<int8_t>(input), shape, |
| GetTensorData<int8_t>(output)); |
| } |
| } |
| } |
| |
| void Softmax2DQuantized(const TfLiteTensor* input, TfLiteTensor* output, |
| TfLiteSoftmaxParams* params, OpData* data) { |
| // TODO(ahentz): this is arguably a dirty trick. Since the implementation |
| // always traverses the last dimension of a 4D tensor, we will pretend our 2D |
| // tensor is 4D in a special way. We will convert a (X, Y) shape into a (X, |
| // 1, 1, Y) shape. |
| const int batch_size = input->dims->data[0]; |
| const int input_size = input->dims->data[1]; |
| const int32_t shape_data[4] = {batch_size, 1, 1, input_size}; |
| RuntimeShape shape(4, shape_data); |
| SoftmaxParams op_params; |
| op_params.input_multiplier = data->input_multiplier; |
| op_params.input_left_shift = data->input_left_shift; |
| op_params.diff_min = data->diff_min; |
| if (input->type == kTfLiteUInt8) { |
| tflite::reference_ops::Softmax(op_params, shape, |
| GetTensorData<uint8_t>(input), shape, |
| GetTensorData<uint8_t>(output)); |
| } else { |
| if (output->type == kTfLiteInt16) { |
| tflite::reference_integer_ops::Softmax( |
| op_params, shape, GetTensorData<int8_t>(input), shape, |
| GetTensorData<int16_t>(output)); |
| } else { |
| tflite::reference_integer_ops::Softmax( |
| op_params, shape, GetTensorData<int8_t>(input), shape, |
| GetTensorData<int8_t>(output)); |
| } |
| } |
| } |
| |
| // Takes a 4D tensor and perform softmax along the forth dimension. |
| void Softmax4DFloat(const TfLiteTensor* input, TfLiteTensor* output, |
| TfLiteSoftmaxParams* params) { |
| SoftmaxParams op_params; |
| op_params.beta = static_cast<double>(params->beta); |
| tflite::reference_ops::Softmax( |
| op_params, GetTensorShape(input), GetTensorData<float>(input), |
| GetTensorShape(output), GetTensorData<float>(output)); |
| } |
| |
| void Softmax4DQuantized(const TfLiteTensor* input, TfLiteTensor* output, |
| TfLiteSoftmaxParams* params, OpData* data) { |
| SoftmaxParams op_params; |
| op_params.input_multiplier = data->input_multiplier; |
| op_params.input_left_shift = data->input_left_shift; |
| op_params.diff_min = data->diff_min; |
| if (input->type == kTfLiteUInt8) { |
| tflite::reference_ops::Softmax( |
| op_params, GetTensorShape(input), GetTensorData<uint8_t>(input), |
| GetTensorShape(output), GetTensorData<uint8_t>(output)); |
| } else { |
| if (output->type == kTfLiteInt16) { |
| tflite::reference_integer_ops::Softmax( |
| op_params, GetTensorShape(input), GetTensorData<int8_t>(input), |
| GetTensorShape(output), GetTensorData<int16_t>(output)); |
| } else { |
| tflite::reference_integer_ops::Softmax( |
| op_params, GetTensorShape(input), GetTensorData<int8_t>(input), |
| GetTensorShape(output), GetTensorData<int8_t>(output)); |
| } |
| } |
| } |
| |
| TfLiteStatus SoftmaxEval(TfLiteContext* context, TfLiteNode* node) { |
| auto* params = reinterpret_cast<TfLiteSoftmaxParams*>(node->builtin_data); |
| |
| const TfLiteTensor* input = GetInput(context, node, 0); |
| TfLiteTensor* output = GetOutput(context, node, 0); |
| |
| OpData local_data_object; |
| OpData* data = &local_data_object; |
| TF_LITE_ENSURE_STATUS( |
| CalculateSoftmaxOpData(context, input, output, params, data)); |
| |
| // TODO(ahentz): consider an implementation that works for many (all?) |
| // dimensions. |
| switch (input->type) { |
| case kTfLiteFloat32: { |
| if (NumDimensions(input) == 1) { |
| Softmax1DFloat(input, output, params); |
| return kTfLiteOk; |
| } |
| if (NumDimensions(input) == 2) { |
| Softmax2DFloat(input, output, params); |
| return kTfLiteOk; |
| } |
| if (NumDimensions(input) == 4) { |
| Softmax4DFloat(input, output, params); |
| return kTfLiteOk; |
| } |
| TF_LITE_KERNEL_LOG( |
| context, "Only 1D, 2D and 4D tensors supported currently, got %dD.", |
| NumDimensions(input)); |
| return kTfLiteError; |
| } |
| case kTfLiteInt8: |
| case kTfLiteUInt8: { |
| if (NumDimensions(input) == 1) { |
| Softmax1DQuantized(input, output, params, data); |
| return kTfLiteOk; |
| } |
| if (NumDimensions(input) == 2) { |
| Softmax2DQuantized(input, output, params, data); |
| return kTfLiteOk; |
| } |
| if (NumDimensions(input) == 4) { |
| Softmax4DQuantized(input, output, params, data); |
| return kTfLiteOk; |
| } |
| TF_LITE_KERNEL_LOG(context, |
| "Only 1D, 2D and 4D tensors supported currently, got %dD.", |
| NumDimensions(input)); |
| return kTfLiteError; |
| } |
| default: |
| TF_LITE_KERNEL_LOG( |
| context, |
| "Only float32, uint8_t and int8_t supported currently, got %d.", |
| input->type); |
| return kTfLiteError; |
| } |
| } |
| } // namespace activations |
| |
| TfLiteRegistration* Register_SOFTMAX() { |
| static TfLiteRegistration r = {}; |
| r.init = activations::Init; |
| r.free = activations::Free; |
| r.prepare = activations::SoftmaxPrepare; |
| r.invoke = activations::SoftmaxEval; |
| return &r; |
| } |
| |
| } // namespace micro |
| } // namespace ops |
| } // namespace tflite |