tensorflow/lite/micro/kernels/xtensa_hifimini_staging/fully_connected.cc - platform/external/tensorflow - Git at Google

 /*******************************************************************************
  * Copyright (c) 2020 Cadence Design Systems, Inc.
  *
  * Permission is hereby granted, free of charge, to any person obtaining
  * a copy of this software and associated documentation files (the
  * "Software"), to use this Software with Cadence processor cores only and
  * not with any other processors and platforms, subject to
  * the following conditions:
  *
  * The above copyright notice and this permission notice shall be included
  * in all copies or substantial portions of the Software.
  *
  * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
  * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
  * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
  * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
  * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
  * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
  * SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
  ******************************************************************************/

 /* Copyright 2020 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/fully_connected.h"

 #include <xtensa/tie/xt_hifi2.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/fully_connected.h"
 #include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
 #include "tensorflow/lite/kernels/kernel_util.h"
 #include "tensorflow/lite/micro/kernels/xtensa_hifimini/fixedpoint_utils.h"
 #include "tensorflow/lite/micro/kernels/xtensa_hifimini_staging/xtensa_tf_micro_common.h"
 namespace tflite {
 namespace ops {
 namespace micro {

 namespace fully_connected {
 namespace {

 struct OpData {
   // The scaling factor from input to output (aka the 'real multiplier') can
   // be represented as a fixed point multiplier plus a left shift.
   int32_t output_multiplier;
   int output_shift;
   // The range of the fused activation layer. For example for kNone and
   // uint8_t these would be 0 and 255.
   int32_t output_activation_min;
   int32_t output_activation_max;
   // The index of the temporary tensor where the quantized inputs are cached.
   int input_quantized_index;
 };

 constexpr int kInputTensor = 0;
 constexpr int kWeightsTensor = 1;
 constexpr int kBiasTensor = 2;
 constexpr int kOutputTensor = 0;

 TfLiteStatus CalculateOpData(TfLiteContext* context,
                              TfLiteFusedActivation activation,
                              TfLiteType data_type, const TfLiteTensor* input,
                              const TfLiteTensor* filter,
                              const TfLiteTensor* bias, TfLiteTensor* output,
                              OpData* data) {
   if (data_type != kTfLiteInt8) {
     TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.",
                        TfLiteTypeGetName(data_type), data_type);
     return kTfLiteError;
   }

   double real_multiplier = 0.0;
   TF_LITE_ENSURE_STATUS(GetQuantizedConvolutionMultipler(
       context, input, filter, bias, output, &real_multiplier));
   xtensa::hifimini::QuantizeMultiplier(
       real_multiplier, &data->output_multiplier, &data->output_shift);
   return CalculateActivationRangeQuantized(context, activation, output,
                                            &data->output_activation_min,
                                            &data->output_activation_max);
 }

 }  // namespace

 void* Init(TfLiteContext* context, const char* buffer, size_t length) {
   TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
   return context->AllocatePersistentBuffer(context, sizeof(OpData));
 }

 TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TFLITE_DCHECK(node->user_data != nullptr);
   TFLITE_DCHECK(node->builtin_data != nullptr);

   OpData* data = static_cast<OpData*>(node->user_data);
   const auto* params =
       reinterpret_cast<TfLiteFullyConnectedParams*>(node->builtin_data);

   const TfLiteTensor* input = GetInput(context, node, kInputTensor);
   const TfLiteTensor* filter = GetInput(context, node, kWeightsTensor);
   const TfLiteTensor* bias = GetOptionalInputTensor(context, node, kBiasTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);

   return CalculateOpData(context, params->activation, input->type, input,
                          filter, bias, output, data);
 }

 TfLiteStatus EvalQuantizedInt8(TfLiteContext* context, TfLiteNode* node,
                                const OpData& data, const TfLiteTensor* input,
                                const TfLiteTensor* filter,
                                const TfLiteTensor* bias, TfLiteTensor* output) {
   // TODO(b/154032858): Investigate removing extra copies.
   FullyConnectedParams op_params;
   op_params.input_offset = -input->params.zero_point;
   op_params.weights_offset = -filter->params.zero_point;
   op_params.output_offset = output->params.zero_point;
   op_params.output_multiplier = data.output_multiplier;
   op_params.output_shift = data.output_shift;
   op_params.quantized_activation_min = data.output_activation_min;
   op_params.quantized_activation_max = data.output_activation_max;

   {
     int ret, b, weight_depth, out_depth, batches;
     int8_t* p_out = GetTensorData<int8_t>(output);
     weight_depth = GetTensorShape(filter).Dims(
         GetTensorShape(filter).DimensionsCount() - 1);
     out_depth = GetTensorShape(output).Dims(
         GetTensorShape(output).DimensionsCount() - 1);
     batches = FlatSizeSkipDim(GetTensorShape(output),
                               GetTensorShape(output).DimensionsCount() - 1);

     // TODO: Use xa_nn_fully_connected_sym8xasym8s_asym8s? the kernel tests fail
     // with it.
     for (b = 0; b < batches; b++) {
       ret = xa_nn_fully_connected_asym8sxasym8s_asym8s(
           (GetTensorData<int8_t>(output) + b * out_depth),
           GetTensorData<int8_t>(filter),
           (GetTensorData<int8_t>(input) + b * weight_depth),
           GetTensorData<int32_t>(bias), weight_depth, out_depth,
           op_params.weights_offset, op_params.input_offset,
           (op_params.output_multiplier << 8), op_params.output_shift,
           op_params.output_offset);
       CHECK_ERR_HIFI_NNLIB_KER(
           ret, "xa_nn_fully_connected_sym8xasym8s_asym8s failed");
     }
     ret = xa_nn_vec_activation_min_max_asym8s_asym8s(
         p_out, p_out, data.output_activation_min, data.output_activation_max,
         batches * out_depth);
     CHECK_ERR_HIFI_NNLIB_KER(
         ret,
         "fully_connected: xa_nn_vec_activation_min_max_asym8s_asym8s failed");
   }
   return kTfLiteOk;
 }

 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   TFLITE_DCHECK(node->user_data != nullptr);
   const OpData& data = *(static_cast<const OpData*>(node->user_data));

   const TfLiteTensor* input = GetInput(context, node, kInputTensor);
   const TfLiteTensor* filter = GetInput(context, node, kWeightsTensor);
   const TfLiteTensor* bias = GetOptionalInputTensor(context, node, kBiasTensor);
   TfLiteTensor* output = GetOutput(context, node, kOutputTensor);

   TFLITE_DCHECK(filter->type == kTfLiteInt8);
   return EvalQuantizedInt8(context, node, data, input, filter, bias, output);
 }

 }  // namespace fully_connected

 TfLiteRegistration Register_FULLY_CONNECTED() {
   return {/*init=*/fully_connected::Init,
           /*free=*/nullptr,
           /*prepare=*/fully_connected::Prepare,
           /*invoke=*/fully_connected::Eval,
           /*profiling_string=*/nullptr,
           /*builtin_code=*/0,
           /*custom_name=*/nullptr,
           /*version=*/0};
 }

 }  // namespace micro
 }  // namespace ops
 }  // namespace tflite
	/*******************************************************************************
	* Copyright (c) 2020 Cadence Design Systems, Inc.
	*
	* Permission is hereby granted, free of charge, to any person obtaining
	* a copy of this software and associated documentation files (the
	* "Software"), to use this Software with Cadence processor cores only and
	* not with any other processors and platforms, subject to
	* the following conditions:
	*
	* The above copyright notice and this permission notice shall be included
	* in all copies or substantial portions of the Software.
	*
	* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
	* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
	* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
	* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
	* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
	* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
	* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
	******************************************************************************/

	/* Copyright 2020 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/fully_connected.h"

	#include <xtensa/tie/xt_hifi2.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/fully_connected.h"
	#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
	#include "tensorflow/lite/kernels/kernel_util.h"
	#include "tensorflow/lite/micro/kernels/xtensa_hifimini/fixedpoint_utils.h"
	#include "tensorflow/lite/micro/kernels/xtensa_hifimini_staging/xtensa_tf_micro_common.h"
	namespace tflite {
	namespace ops {
	namespace micro {

	namespace fully_connected {
	namespace {

	struct OpData {
	// The scaling factor from input to output (aka the 'real multiplier') can
	// be represented as a fixed point multiplier plus a left shift.
	int32_t output_multiplier;
	int output_shift;
	// The range of the fused activation layer. For example for kNone and
	// uint8_t these would be 0 and 255.
	int32_t output_activation_min;
	int32_t output_activation_max;
	// The index of the temporary tensor where the quantized inputs are cached.
	int input_quantized_index;
	};

	constexpr int kInputTensor = 0;
	constexpr int kWeightsTensor = 1;
	constexpr int kBiasTensor = 2;
	constexpr int kOutputTensor = 0;

	TfLiteStatus CalculateOpData(TfLiteContext* context,
	TfLiteFusedActivation activation,
	TfLiteType data_type, const TfLiteTensor* input,
	const TfLiteTensor* filter,
	const TfLiteTensor* bias, TfLiteTensor* output,
	OpData* data) {
	if (data_type != kTfLiteInt8) {
	TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.",
	TfLiteTypeGetName(data_type), data_type);
	return kTfLiteError;
	}

	double real_multiplier = 0.0;
	TF_LITE_ENSURE_STATUS(GetQuantizedConvolutionMultipler(
	context, input, filter, bias, output, &real_multiplier));
	xtensa::hifimini::QuantizeMultiplier(
	real_multiplier, &data->output_multiplier, &data->output_shift);
	return CalculateActivationRangeQuantized(context, activation, output,
	&data->output_activation_min,
	&data->output_activation_max);
	}

	} // namespace

	void* Init(TfLiteContext* context, const char* buffer, size_t length) {
	TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
	return context->AllocatePersistentBuffer(context, sizeof(OpData));
	}

	TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
	TFLITE_DCHECK(node->user_data != nullptr);
	TFLITE_DCHECK(node->builtin_data != nullptr);

	OpData* data = static_cast<OpData*>(node->user_data);
	const auto* params =
	reinterpret_cast<TfLiteFullyConnectedParams*>(node->builtin_data);

	const TfLiteTensor* input = GetInput(context, node, kInputTensor);
	const TfLiteTensor* filter = GetInput(context, node, kWeightsTensor);
	const TfLiteTensor* bias = GetOptionalInputTensor(context, node, kBiasTensor);
	TfLiteTensor* output = GetOutput(context, node, kOutputTensor);

	return CalculateOpData(context, params->activation, input->type, input,
	filter, bias, output, data);
	}

	TfLiteStatus EvalQuantizedInt8(TfLiteContext* context, TfLiteNode* node,
	const OpData& data, const TfLiteTensor* input,
	const TfLiteTensor* filter,
	const TfLiteTensor* bias, TfLiteTensor* output) {
	// TODO(b/154032858): Investigate removing extra copies.
	FullyConnectedParams op_params;
	op_params.input_offset = -input->params.zero_point;
	op_params.weights_offset = -filter->params.zero_point;
	op_params.output_offset = output->params.zero_point;
	op_params.output_multiplier = data.output_multiplier;
	op_params.output_shift = data.output_shift;
	op_params.quantized_activation_min = data.output_activation_min;
	op_params.quantized_activation_max = data.output_activation_max;

	{
	int ret, b, weight_depth, out_depth, batches;
	int8_t* p_out = GetTensorData<int8_t>(output);
	weight_depth = GetTensorShape(filter).Dims(
	GetTensorShape(filter).DimensionsCount() - 1);
	out_depth = GetTensorShape(output).Dims(
	GetTensorShape(output).DimensionsCount() - 1);
	batches = FlatSizeSkipDim(GetTensorShape(output),
	GetTensorShape(output).DimensionsCount() - 1);

	// TODO: Use xa_nn_fully_connected_sym8xasym8s_asym8s? the kernel tests fail
	// with it.
	for (b = 0; b < batches; b++) {
	ret = xa_nn_fully_connected_asym8sxasym8s_asym8s(
	(GetTensorData<int8_t>(output) + b * out_depth),
	GetTensorData<int8_t>(filter),
	(GetTensorData<int8_t>(input) + b * weight_depth),
	GetTensorData<int32_t>(bias), weight_depth, out_depth,
	op_params.weights_offset, op_params.input_offset,
	(op_params.output_multiplier << 8), op_params.output_shift,
	op_params.output_offset);
	CHECK_ERR_HIFI_NNLIB_KER(
	ret, "xa_nn_fully_connected_sym8xasym8s_asym8s failed");
	}
	ret = xa_nn_vec_activation_min_max_asym8s_asym8s(
	p_out, p_out, data.output_activation_min, data.output_activation_max,
	batches * out_depth);
	CHECK_ERR_HIFI_NNLIB_KER(
	ret,
	"fully_connected: xa_nn_vec_activation_min_max_asym8s_asym8s failed");
	}
	return kTfLiteOk;
	}

	TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
	TFLITE_DCHECK(node->user_data != nullptr);
	const OpData& data = (static_cast<const OpData>(node->user_data));

	const TfLiteTensor* input = GetInput(context, node, kInputTensor);
	const TfLiteTensor* filter = GetInput(context, node, kWeightsTensor);
	const TfLiteTensor* bias = GetOptionalInputTensor(context, node, kBiasTensor);
	TfLiteTensor* output = GetOutput(context, node, kOutputTensor);

	TFLITE_DCHECK(filter->type == kTfLiteInt8);
	return EvalQuantizedInt8(context, node, data, input, filter, bias, output);
	}

	} // namespace fully_connected

	TfLiteRegistration Register_FULLY_CONNECTED() {
	return {/init=/fully_connected::Init,
	/free=/nullptr,
	/prepare=/fully_connected::Prepare,
	/invoke=/fully_connected::Eval,
	/profiling_string=/nullptr,
	/builtin_code=/0,
	/custom_name=/nullptr,
	/version=/0};
	}

	} // namespace micro
	} // namespace ops
	} // namespace tflite