common/OperationsUtils.cpp - platform/packages/modules/NeuralNetworks - Git at Google

 /*
  * Copyright (C) 2017 The Android Open Source Project
  *
  * 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.
  */

 #define LOG_TAG "OperationsUtils"

 #include "OperationsUtils.h"
 #include "Operations.h"
 #include "Utils.h"

 #include <cmath>

 namespace android {
 namespace nn {

 bool SameShape(const Shape& in1, const Shape& in2) {
     if (in1.type != in2.type || in1.dimensions.size() != in2.dimensions.size()) {
         return false;
     }
     for (size_t i = 0; i < in1.dimensions.size(); i++) {
         if (in1.dimensions[i] != in2.dimensions[i]) {
             return false;
         }
     }
     return true;
 }

 bool SetShape(const Shape& in, Shape* out) {
     if (in.type != out->type || in.dimensions.size() != out->dimensions.size()) {
         return false;
     }
     out->dimensions = in.dimensions;
     return true;
 }

 uint32_t getNumberOfElements(const Shape& shape) {
     uint32_t count = 1;
     for (size_t i = 0; i < shape.dimensions.size(); i++) {
         count *= shape.dimensions[i];
     }
     return count;
 }

 uint32_t getNumberOfDimensions(const Shape& shape) {
     return shape.dimensions.size();
 }

 uint32_t getSizeOfDimension(const Shape& shape, uint32_t dimensionIdx) {
     if (dimensionIdx >= shape.dimensions.size()) {
         // TODO, log the error
         return 0;
     }
     return shape.dimensions[dimensionIdx];
 }


 void QuantizeMultiplierSmallerThanOne(double double_multiplier,
                                       int32_t* quantized_multiplier,
                                       int32_t* right_shift) {
     CHECK(double_multiplier >= 0.);
     CHECK(double_multiplier < 1.);
     if (double_multiplier == 0.) {
         *quantized_multiplier = 0;
         *right_shift = 0;
         return;
     }
     CHECK(double_multiplier > 0.);
     const double q = std::frexp(double_multiplier, right_shift);
     *right_shift *= -1;
     int64_t q_fixed = static_cast<int64_t>(std::round(q * (1ll << 31)));
     CHECK(q_fixed <= (1ll << 31));
     if (q_fixed == (1ll << 31)) {
         q_fixed /= 2;
         --*right_shift;
     }
     CHECK_GE(*right_shift, 0);
     CHECK_LE(q_fixed, std::numeric_limits<int32_t>::max());
     *quantized_multiplier = static_cast<int32_t>(q_fixed);
 }

 void QuantizeMultiplierGreaterThanOne(double double_multiplier,
                                       int32_t* quantized_multiplier,
                                       int* left_shift) {
     CHECK(double_multiplier > 1.);
     const double q = std::frexp(double_multiplier, left_shift);
     int64_t q_fixed = static_cast<int64_t>(std::round(q * (1ll << 31)));
     CHECK(q_fixed <= (1ll << 31));
     if (q_fixed == (1ll << 31)) {
         q_fixed /= 2;
         ++*left_shift;
     }
     CHECK_GE(*left_shift, 0);
     CHECK_LE(q_fixed, std::numeric_limits<int32_t>::max());
     *quantized_multiplier = static_cast<int32_t>(q_fixed);
 }

 void GetQuantizedConvolutionMultipler(const Shape& inputShape,
                                       const Shape& filterShape,
                                       const Shape& biasShape,
                                       const Shape& outputShape,
                                       float* multiplier) {
     const float input_product_scale = inputShape.scale * filterShape.scale;
     const float bias_scale = biasShape.scale;
     const float output_scale = outputShape.scale;

     // The following conditions must be guaranteed by the training pipeline.
     CHECK(std::abs(input_product_scale - bias_scale) <=
               1e-6 * std::min(input_product_scale, bias_scale));
     CHECK(input_product_scale >= 0);
     CHECK(input_product_scale < output_scale);
     *multiplier = input_product_scale / output_scale;
 }

 void CalculateActivationRangeUint8(int32_t activation,
                                    const Shape& outputShape,
                                    int32_t* act_min,
                                    int32_t* act_max) {
     const int32_t qmin = std::numeric_limits<uint8_t>::min();
     const int32_t qmax = std::numeric_limits<uint8_t>::max();

     const auto scale = outputShape.scale;
     const auto zero_point = outputShape.offset;

     auto quantize = [scale, zero_point](float f) {
         return zero_point + static_cast<int32_t>(std::round(f / scale));
     };

     if (activation == kActivationRelu) {
         *act_min = std::max(qmin, quantize(0.0));
         *act_max = qmax;
     } else if (activation == kActivationRelu6) {
         *act_min = std::max(qmin, quantize(0.0));
         *act_max = std::min(qmax, quantize(6.0));
     } else if (activation == kActivationRelu1) {
         *act_min = std::max(qmin, quantize(-1.0));
         *act_max = std::min(qmax, quantize(1.0));
     } else {
         *act_min = qmin;
         *act_max = qmax;
     }
 }

 int32_t CalculateInputRadius(int input_integer_bits, int input_left_shift) {
     const double max_input_rescaled = 1.0 * ((1 << input_integer_bits) - 1) *
                                       (1ll << (31 - input_integer_bits)) /
                                       (1ll << input_left_shift);
     // Tighten bound using floor.  Suppose that we could use the exact value.
     // After scaling the difference, the result would be at the maximum.  Thus we
     // must ensure that our value has lower magnitude.
     return static_cast<int32_t>(std::floor(max_input_rescaled));
 }


 // Macro to check if the input parameters for operation are valid or not.
 #define nnOpsCheck(v)                                                                      \
     if (!(v)) {                                                                            \
         LOG(ERROR) << "nnOpsCheck failed: "  << #v << "'\n";                               \
         return false;                                                                      \
     }

 bool addMulPrepare(const Shape& in1, const Shape& in2, Shape* out) {
     nnOpsCheck(getNumberOfDimensions(in1) <= 4 && getNumberOfDimensions(in2) <= 4);
     if (SameShape(in1, in2)) {
         return SetShape(in1, out);
     } else {
         // BroadcastAdd needed
         uint32_t numberOfDims1 = getNumberOfDimensions(in1);
         uint32_t numberOfDims2 = getNumberOfDimensions(in2);
         uint32_t maxDims = std::max(numberOfDims1, numberOfDims2);
         out->dimensions = std::vector<uint32_t>(maxDims);
         for (uint32_t i = 1; i <= maxDims; i++) {
             uint32_t dim1 = 1;
             if (i <= numberOfDims1) {
                 dim1 = getSizeOfDimension(in1, numberOfDims1 - i);
             }
             uint32_t dim2 = 1;
             if (i <= numberOfDims2) {
                 dim2 = getSizeOfDimension(in2, numberOfDims2 - i);
             }
             if (dim1 != dim2 && dim1 != 1 && dim2 != 1) {
                 LOG(ERROR) << "Dimensions mismatch for BroadcastAdd";
                 return false;
             }
             out->dimensions[maxDims - i] = std::max(dim1, dim2);
         }
     }
     return true;
 }

 bool floorPrepare(const Shape& input, Shape* output) {
     return SetShape(input, output);
 }

 bool dequantizePrepare(const Shape& input, Shape* output) {
     if (input.type != OperandType::TENSOR_QUANT8_ASYMM ||
             output->type != OperandType::TENSOR_FLOAT32) {
         LOG(ERROR) << "bad input / output operand type.";
         return false;
     }
     if (input.dimensions.size() != output->dimensions.size()) {
         LOG(ERROR) << "input and output tensors don't have the same rank.";
         return false;
     }
     output->dimensions = input.dimensions;
     return true;
 }

 bool convPrepare(const Shape& input,
                  const Shape& filter,
                  const Shape& bias,
                  int32_t padding_left, int32_t padding_right,
                  int32_t padding_top, int32_t padding_bottom,
                  int32_t stride_width, int32_t stride_height,
                  Shape* output) {
     nnOpsCheck(getNumberOfDimensions(input) == 4);
     nnOpsCheck(getNumberOfDimensions(filter) == 4);
     nnOpsCheck(getNumberOfDimensions(bias) == 1);

     nnOpsCheck(getSizeOfDimension(filter, 0) == getSizeOfDimension(bias, 0));
     nnOpsCheck(getSizeOfDimension(filter, 3) == getSizeOfDimension(input, 3));

     nnOpsCheck(stride_width == stride_height);

     uint32_t channels_out = getSizeOfDimension(filter, 0);
     uint32_t width        = getSizeOfDimension(input, 2);
     uint32_t height       = getSizeOfDimension(input, 1);
     uint32_t filterWidth  = getSizeOfDimension(filter, 2);
     uint32_t filterHeight = getSizeOfDimension(filter, 1);
     uint32_t batches      = getSizeOfDimension(input, 0);

     uint32_t outWidth = computeOutSize(width, filterWidth, stride_width,
                                        padding_left, padding_right);
     uint32_t outHeight = computeOutSize(height, filterHeight, stride_height,
                                         padding_top, padding_bottom);

     output->type = input.type;
     output->dimensions = {batches, outHeight, outWidth, channels_out};
     return true;
 }

 bool depthwiseConvPrepare(const Shape& input,
                           const Shape& filter,
                           const Shape& bias,
                           int32_t padding_left, int32_t padding_right,
                           int32_t padding_top, int32_t padding_bottom,
                           int32_t stride_width, int32_t stride_height,
                           Shape* output) {
     nnOpsCheck(getNumberOfDimensions(input) == 4);
     nnOpsCheck(getNumberOfDimensions(filter) == 4);
     nnOpsCheck(getNumberOfDimensions(bias) == 1);

     nnOpsCheck(getSizeOfDimension(filter, 3) == getSizeOfDimension(bias, 0));

     nnOpsCheck(stride_width == stride_height);

     uint32_t channels_out = getSizeOfDimension(filter, 3);
     uint32_t width        = getSizeOfDimension(input, 2);
     uint32_t height       = getSizeOfDimension(input, 1);
     uint32_t filterWidth  = getSizeOfDimension(filter, 2);
     uint32_t filterHeight = getSizeOfDimension(filter, 1);
     uint32_t batches      = getSizeOfDimension(input, 0);

     uint32_t outWidth = computeOutSize(width, filterWidth, stride_width,
                                        padding_left, padding_right);
     uint32_t outHeight = computeOutSize(height, filterHeight, stride_height,
                                         padding_top, padding_bottom);

     output->type = input.type;
     output->dimensions = {batches, outHeight, outWidth, channels_out};
     return true;
 }


 bool genericPoolingPrepare(const Shape& input,
                            int32_t padding_left, int32_t padding_right,
                            int32_t padding_top, int32_t padding_bottom,
                            int32_t stride_width, int32_t stride_height,
                            int32_t filter_width, int32_t filter_height,
                            Shape* output) {
     nnOpsCheck(getNumberOfDimensions(input) == 4);
     nnOpsCheck(stride_width == stride_height);

     uint32_t batches      = getSizeOfDimension(input, 0);
     uint32_t width        = getSizeOfDimension(input, 2);
     uint32_t height       = getSizeOfDimension(input, 1);
     uint32_t channels_out = getSizeOfDimension(input, 3);

     uint32_t outWidth = computeOutSize(width, filter_width, stride_width,
                                        padding_left, padding_right);
     uint32_t outHeight = computeOutSize(height, filter_height, stride_height,
                                         padding_top, padding_bottom);

     output->type = input.type;
     output->dimensions = {batches, outHeight, outWidth, channels_out};
     return true;
 }


 bool genericActivationPrepare(const Shape& input,
                               Shape* output) {
     nnOpsCheck(getNumberOfDimensions(input) <= 4);
     return SetShape(input, output);
 }

 bool fullyConnectedPrepare(const Shape& input,
                            const Shape& weights,
                            const Shape& bias,
                            Shape* output) {
     // Check all the parameters of tensor match within themselves and match the
     // input configuration.
     uint32_t input_size = getNumberOfElements(input);
     uint32_t num_units  = getSizeOfDimension(weights, 0);
     uint32_t batch_size = input_size / getSizeOfDimension(weights, 1);

     nnOpsCheck(getSizeOfDimension(bias, 0) == num_units);
     nnOpsCheck(getSizeOfDimension(weights, 1) * batch_size == input_size);
     nnOpsCheck(getNumberOfDimensions(weights) == 2);

     output->type = input.type;
     output->dimensions = {batch_size, num_units};

     return true;
 }

 bool concatenationPrepare(const std::vector<Shape>& inputShapes,
                           int32_t axis,
                           Shape* output) {

     int num_inputs = inputShapes.size();
     OperandType input_type = inputShapes[0].type;
     uint32_t num_dimensions = getNumberOfDimensions(inputShapes[0]);

     nnOpsCheck(axis >= 0);
     nnOpsCheck(axis < (int32_t)num_dimensions);

     int sum_axis = getSizeOfDimension(inputShapes[0], axis);
     for (int i = 1; i < num_inputs; ++i) {
         nnOpsCheck(getNumberOfDimensions(inputShapes[i]) == num_dimensions);
         nnOpsCheck(inputShapes[i].type == inputShapes[0].type);
         if (input_type == OperandType::TENSOR_QUANT8_ASYMM) {
             nnOpsCheck(inputShapes[0].offset == inputShapes[i].offset);
             nnOpsCheck(inputShapes[0].scale == inputShapes[i].scale);
         }
         for (int d = 0; d < (int32_t)num_dimensions; ++d) {
             if (d == axis) {
                 sum_axis += getSizeOfDimension(inputShapes[i], axis);
             } else {
                 nnOpsCheck(getSizeOfDimension(inputShapes[0], d) ==
                            getSizeOfDimension(inputShapes[i], d));
             }
         }
     }

     output->type = input_type;
     output->dimensions = inputShapes[0].dimensions;
     output->dimensions[axis] = sum_axis;

     if (input_type == OperandType::TENSOR_QUANT8_ASYMM) {
         nnOpsCheck(inputShapes[0].offset == output->offset);
         nnOpsCheck(inputShapes[0].scale == output->scale);
     }

     return true;
 }


 bool genericNormalizationPrepare(const Shape& input, Shape* output) {
     nnOpsCheck(getNumberOfDimensions(input) == 4);
     return SetShape(input, output);
 }

 bool reshapePrepare(const Shape& input,
                     const int32_t* targetDims,
                     const int32_t targetDimsSize,
                     Shape* output) {
     // Reshape allows one of the targetDims components to have the
     // special -1 value, meaning it will be calculated automatically based on the
     // input. Here we calculate what that dimension should be so that the number
     // of output elements in the same as the number of input elements.
     int32_t numInputElements = (int32_t) getNumberOfElements(input);

     std::vector<uint32_t> outDims(targetDimsSize);
     int32_t numOutputElements = 1;
     int32_t strechDim = -1;
     for (int32_t i = 0; i < targetDimsSize; ++i) {
         int32_t value = targetDims[i];
         if (value == -1) {
             nnOpsCheck(strechDim == -1);
             strechDim = i;
         } else {
             numOutputElements *= value;
             outDims[i] = (uint32_t)value;
         }
     }
     if (strechDim != -1) {
         int32_t strechValue = numInputElements / numOutputElements;
         outDims[strechDim] = (uint32_t) strechValue;
         numOutputElements *= strechValue;
     }

     nnOpsCheck(numInputElements == numOutputElements);

     output->type = input.type;
     output->dimensions = outDims;
     output->offset = input.offset;
     output->scale = input.scale;

     return true;
 }

 bool resizeBilinearPrepare(const Shape& input,
                            int32_t width,
                            int32_t height,
                            Shape* output) {
     nnOpsCheck(getNumberOfDimensions(input) == 4);
     uint32_t batches  = getSizeOfDimension(input, 0);
     uint32_t channels = getSizeOfDimension(input, 3);

     output->type = input.type;
     output->dimensions = {batches, (uint32_t)height, (uint32_t)width, channels};

     return true;
 }

 bool depthToSpacePrepare(const Shape& input,
                          int32_t blockSize,
                          Shape* output) {
     nnOpsCheck(getNumberOfDimensions(input) == 4);
     nnOpsCheck(blockSize > 0);

     uint32_t batches  = getSizeOfDimension(input, 0);
     uint32_t height   = getSizeOfDimension(input, 1);
     uint32_t width    = getSizeOfDimension(input, 2);
     uint32_t channels = getSizeOfDimension(input, 3);

     nnOpsCheck(channels % (blockSize * blockSize) == 0);
     output->type = input.type;
     output->dimensions = {batches,
                           height * blockSize,
                           width * blockSize,
                           channels / (blockSize * blockSize)};
     output->offset = input.offset;
     output->scale = input.scale;

     return true;
 }

 bool spaceToDepthPrepare(const Shape& input,
                          int32_t blockSize,
                          Shape* output) {
     nnOpsCheck(getNumberOfDimensions(input) == 4);
     nnOpsCheck(blockSize > 0);

     uint32_t batches  = getSizeOfDimension(input, 0);
     uint32_t height   = getSizeOfDimension(input, 1);
     uint32_t width    = getSizeOfDimension(input, 2);
     uint32_t channels = getSizeOfDimension(input, 3);

     nnOpsCheck(height % blockSize == 0);
     nnOpsCheck(width % blockSize == 0);

     output->type = input.type;
     output->dimensions = {batches,
                           height / blockSize,
                           width / blockSize,
                           channels * (blockSize * blockSize)};
     output->offset = input.offset;
     output->scale = input.scale;

     return true;
 }

 } // namespace nn
 } // namespace android
	/*
	* Copyright (C) 2017 The Android Open Source Project
	*
	* 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.
	*/

	#define LOG_TAG "OperationsUtils"

	#include "OperationsUtils.h"
	#include "Operations.h"
	#include "Utils.h"

	#include <cmath>

	namespace android {
	namespace nn {

	bool SameShape(const Shape& in1, const Shape& in2) {
	if (in1.type != in2.type \|\| in1.dimensions.size() != in2.dimensions.size()) {
	return false;
	}
	for (size_t i = 0; i < in1.dimensions.size(); i++) {
	if (in1.dimensions[i] != in2.dimensions[i]) {
	return false;
	}
	}
	return true;
	}

	bool SetShape(const Shape& in, Shape* out) {
	if (in.type != out->type \|\| in.dimensions.size() != out->dimensions.size()) {
	return false;
	}
	out->dimensions = in.dimensions;
	return true;
	}

	uint32_t getNumberOfElements(const Shape& shape) {
	uint32_t count = 1;
	for (size_t i = 0; i < shape.dimensions.size(); i++) {
	count *= shape.dimensions[i];
	}
	return count;
	}

	uint32_t getNumberOfDimensions(const Shape& shape) {
	return shape.dimensions.size();
	}

	uint32_t getSizeOfDimension(const Shape& shape, uint32_t dimensionIdx) {
	if (dimensionIdx >= shape.dimensions.size()) {
	// TODO, log the error
	return 0;
	}
	return shape.dimensions[dimensionIdx];
	}


	void QuantizeMultiplierSmallerThanOne(double double_multiplier,
	int32_t* quantized_multiplier,
	int32_t* right_shift) {
	CHECK(double_multiplier >= 0.);
	CHECK(double_multiplier < 1.);
	if (double_multiplier == 0.) {
	*quantized_multiplier = 0;
	*right_shift = 0;
	return;
	}
	CHECK(double_multiplier > 0.);
	const double q = std::frexp(double_multiplier, right_shift);
	right_shift = -1;
	int64_t q_fixed = static_cast<int64_t>(std::round(q * (1ll << 31)));
	CHECK(q_fixed <= (1ll << 31));
	if (q_fixed == (1ll << 31)) {
	q_fixed /= 2;
	--*right_shift;
	}
	CHECK_GE(*right_shift, 0);
	CHECK_LE(q_fixed, std::numeric_limits<int32_t>::max());
	*quantized_multiplier = static_cast<int32_t>(q_fixed);
	}

	void QuantizeMultiplierGreaterThanOne(double double_multiplier,
	int32_t* quantized_multiplier,
	int* left_shift) {
	CHECK(double_multiplier > 1.);
	const double q = std::frexp(double_multiplier, left_shift);
	int64_t q_fixed = static_cast<int64_t>(std::round(q * (1ll << 31)));
	CHECK(q_fixed <= (1ll << 31));
	if (q_fixed == (1ll << 31)) {
	q_fixed /= 2;
	++*left_shift;
	}
	CHECK_GE(*left_shift, 0);
	CHECK_LE(q_fixed, std::numeric_limits<int32_t>::max());
	*quantized_multiplier = static_cast<int32_t>(q_fixed);
	}

	void GetQuantizedConvolutionMultipler(const Shape& inputShape,
	const Shape& filterShape,
	const Shape& biasShape,
	const Shape& outputShape,
	float* multiplier) {
	const float input_product_scale = inputShape.scale * filterShape.scale;
	const float bias_scale = biasShape.scale;
	const float output_scale = outputShape.scale;

	// The following conditions must be guaranteed by the training pipeline.
	CHECK(std::abs(input_product_scale - bias_scale) <=
	1e-6 * std::min(input_product_scale, bias_scale));
	CHECK(input_product_scale >= 0);
	CHECK(input_product_scale < output_scale);
	*multiplier = input_product_scale / output_scale;
	}

	void CalculateActivationRangeUint8(int32_t activation,
	const Shape& outputShape,
	int32_t* act_min,
	int32_t* act_max) {
	const int32_t qmin = std::numeric_limits<uint8_t>::min();
	const int32_t qmax = std::numeric_limits<uint8_t>::max();

	const auto scale = outputShape.scale;
	const auto zero_point = outputShape.offset;

	auto quantize = [scale, zero_point](float f) {
	return zero_point + static_cast<int32_t>(std::round(f / scale));
	};

	if (activation == kActivationRelu) {
	*act_min = std::max(qmin, quantize(0.0));
	*act_max = qmax;
	} else if (activation == kActivationRelu6) {
	*act_min = std::max(qmin, quantize(0.0));
	*act_max = std::min(qmax, quantize(6.0));
	} else if (activation == kActivationRelu1) {
	*act_min = std::max(qmin, quantize(-1.0));
	*act_max = std::min(qmax, quantize(1.0));
	} else {
	*act_min = qmin;
	*act_max = qmax;
	}
	}

	int32_t CalculateInputRadius(int input_integer_bits, int input_left_shift) {
	const double max_input_rescaled = 1.0 * ((1 << input_integer_bits) - 1) *
	(1ll << (31 - input_integer_bits)) /
	(1ll << input_left_shift);
	// Tighten bound using floor. Suppose that we could use the exact value.
	// After scaling the difference, the result would be at the maximum. Thus we
	// must ensure that our value has lower magnitude.
	return static_cast<int32_t>(std::floor(max_input_rescaled));
	}


	// Macro to check if the input parameters for operation are valid or not.
	#define nnOpsCheck(v) \
	if (!(v)) { \
	LOG(ERROR) << "nnOpsCheck failed: " << #v << "'\n"; \
	return false; \
	}

	bool addMulPrepare(const Shape& in1, const Shape& in2, Shape* out) {
	nnOpsCheck(getNumberOfDimensions(in1) <= 4 && getNumberOfDimensions(in2) <= 4);
	if (SameShape(in1, in2)) {
	return SetShape(in1, out);
	} else {
	// BroadcastAdd needed
	uint32_t numberOfDims1 = getNumberOfDimensions(in1);
	uint32_t numberOfDims2 = getNumberOfDimensions(in2);
	uint32_t maxDims = std::max(numberOfDims1, numberOfDims2);
	out->dimensions = std::vector<uint32_t>(maxDims);
	for (uint32_t i = 1; i <= maxDims; i++) {
	uint32_t dim1 = 1;
	if (i <= numberOfDims1) {
	dim1 = getSizeOfDimension(in1, numberOfDims1 - i);
	}
	uint32_t dim2 = 1;
	if (i <= numberOfDims2) {
	dim2 = getSizeOfDimension(in2, numberOfDims2 - i);
	}
	if (dim1 != dim2 && dim1 != 1 && dim2 != 1) {
	LOG(ERROR) << "Dimensions mismatch for BroadcastAdd";
	return false;
	}
	out->dimensions[maxDims - i] = std::max(dim1, dim2);
	}
	}
	return true;
	}

	bool floorPrepare(const Shape& input, Shape* output) {
	return SetShape(input, output);
	}

	bool dequantizePrepare(const Shape& input, Shape* output) {
	if (input.type != OperandType::TENSOR_QUANT8_ASYMM \|\|
	output->type != OperandType::TENSOR_FLOAT32) {
	LOG(ERROR) << "bad input / output operand type.";
	return false;
	}
	if (input.dimensions.size() != output->dimensions.size()) {
	LOG(ERROR) << "input and output tensors don't have the same rank.";
	return false;
	}
	output->dimensions = input.dimensions;
	return true;
	}

	bool convPrepare(const Shape& input,
	const Shape& filter,
	const Shape& bias,
	int32_t padding_left, int32_t padding_right,
	int32_t padding_top, int32_t padding_bottom,
	int32_t stride_width, int32_t stride_height,
	Shape* output) {
	nnOpsCheck(getNumberOfDimensions(input) == 4);
	nnOpsCheck(getNumberOfDimensions(filter) == 4);
	nnOpsCheck(getNumberOfDimensions(bias) == 1);

	nnOpsCheck(getSizeOfDimension(filter, 0) == getSizeOfDimension(bias, 0));
	nnOpsCheck(getSizeOfDimension(filter, 3) == getSizeOfDimension(input, 3));

	nnOpsCheck(stride_width == stride_height);

	uint32_t channels_out = getSizeOfDimension(filter, 0);
	uint32_t width = getSizeOfDimension(input, 2);
	uint32_t height = getSizeOfDimension(input, 1);
	uint32_t filterWidth = getSizeOfDimension(filter, 2);
	uint32_t filterHeight = getSizeOfDimension(filter, 1);
	uint32_t batches = getSizeOfDimension(input, 0);

	uint32_t outWidth = computeOutSize(width, filterWidth, stride_width,
	padding_left, padding_right);
	uint32_t outHeight = computeOutSize(height, filterHeight, stride_height,
	padding_top, padding_bottom);

	output->type = input.type;
	output->dimensions = {batches, outHeight, outWidth, channels_out};
	return true;
	}

	bool depthwiseConvPrepare(const Shape& input,
	const Shape& filter,
	const Shape& bias,
	int32_t padding_left, int32_t padding_right,
	int32_t padding_top, int32_t padding_bottom,
	int32_t stride_width, int32_t stride_height,
	Shape* output) {
	nnOpsCheck(getNumberOfDimensions(input) == 4);
	nnOpsCheck(getNumberOfDimensions(filter) == 4);
	nnOpsCheck(getNumberOfDimensions(bias) == 1);

	nnOpsCheck(getSizeOfDimension(filter, 3) == getSizeOfDimension(bias, 0));

	nnOpsCheck(stride_width == stride_height);

	uint32_t channels_out = getSizeOfDimension(filter, 3);
	uint32_t width = getSizeOfDimension(input, 2);
	uint32_t height = getSizeOfDimension(input, 1);
	uint32_t filterWidth = getSizeOfDimension(filter, 2);
	uint32_t filterHeight = getSizeOfDimension(filter, 1);
	uint32_t batches = getSizeOfDimension(input, 0);

	uint32_t outWidth = computeOutSize(width, filterWidth, stride_width,
	padding_left, padding_right);
	uint32_t outHeight = computeOutSize(height, filterHeight, stride_height,
	padding_top, padding_bottom);

	output->type = input.type;
	output->dimensions = {batches, outHeight, outWidth, channels_out};
	return true;
	}


	bool genericPoolingPrepare(const Shape& input,
	int32_t padding_left, int32_t padding_right,
	int32_t padding_top, int32_t padding_bottom,
	int32_t stride_width, int32_t stride_height,
	int32_t filter_width, int32_t filter_height,
	Shape* output) {
	nnOpsCheck(getNumberOfDimensions(input) == 4);
	nnOpsCheck(stride_width == stride_height);

	uint32_t batches = getSizeOfDimension(input, 0);
	uint32_t width = getSizeOfDimension(input, 2);
	uint32_t height = getSizeOfDimension(input, 1);
	uint32_t channels_out = getSizeOfDimension(input, 3);

	uint32_t outWidth = computeOutSize(width, filter_width, stride_width,
	padding_left, padding_right);
	uint32_t outHeight = computeOutSize(height, filter_height, stride_height,
	padding_top, padding_bottom);

	output->type = input.type;
	output->dimensions = {batches, outHeight, outWidth, channels_out};
	return true;
	}


	bool genericActivationPrepare(const Shape& input,
	Shape* output) {
	nnOpsCheck(getNumberOfDimensions(input) <= 4);
	return SetShape(input, output);
	}

	bool fullyConnectedPrepare(const Shape& input,
	const Shape& weights,
	const Shape& bias,
	Shape* output) {
	// Check all the parameters of tensor match within themselves and match the
	// input configuration.
	uint32_t input_size = getNumberOfElements(input);
	uint32_t num_units = getSizeOfDimension(weights, 0);
	uint32_t batch_size = input_size / getSizeOfDimension(weights, 1);

	nnOpsCheck(getSizeOfDimension(bias, 0) == num_units);
	nnOpsCheck(getSizeOfDimension(weights, 1) * batch_size == input_size);
	nnOpsCheck(getNumberOfDimensions(weights) == 2);

	output->type = input.type;
	output->dimensions = {batch_size, num_units};

	return true;
	}

	bool concatenationPrepare(const std::vector<Shape>& inputShapes,
	int32_t axis,
	Shape* output) {

	int num_inputs = inputShapes.size();
	OperandType input_type = inputShapes[0].type;
	uint32_t num_dimensions = getNumberOfDimensions(inputShapes[0]);

	nnOpsCheck(axis >= 0);
	nnOpsCheck(axis < (int32_t)num_dimensions);

	int sum_axis = getSizeOfDimension(inputShapes[0], axis);
	for (int i = 1; i < num_inputs; ++i) {
	nnOpsCheck(getNumberOfDimensions(inputShapes[i]) == num_dimensions);
	nnOpsCheck(inputShapes[i].type == inputShapes[0].type);
	if (input_type == OperandType::TENSOR_QUANT8_ASYMM) {
	nnOpsCheck(inputShapes[0].offset == inputShapes[i].offset);
	nnOpsCheck(inputShapes[0].scale == inputShapes[i].scale);
	}
	for (int d = 0; d < (int32_t)num_dimensions; ++d) {
	if (d == axis) {
	sum_axis += getSizeOfDimension(inputShapes[i], axis);
	} else {
	nnOpsCheck(getSizeOfDimension(inputShapes[0], d) ==
	getSizeOfDimension(inputShapes[i], d));
	}
	}
	}

	output->type = input_type;
	output->dimensions = inputShapes[0].dimensions;
	output->dimensions[axis] = sum_axis;

	if (input_type == OperandType::TENSOR_QUANT8_ASYMM) {
	nnOpsCheck(inputShapes[0].offset == output->offset);
	nnOpsCheck(inputShapes[0].scale == output->scale);
	}

	return true;
	}


	bool genericNormalizationPrepare(const Shape& input, Shape* output) {
	nnOpsCheck(getNumberOfDimensions(input) == 4);
	return SetShape(input, output);
	}

	bool reshapePrepare(const Shape& input,
	const int32_t* targetDims,
	const int32_t targetDimsSize,
	Shape* output) {
	// Reshape allows one of the targetDims components to have the
	// special -1 value, meaning it will be calculated automatically based on the
	// input. Here we calculate what that dimension should be so that the number
	// of output elements in the same as the number of input elements.
	int32_t numInputElements = (int32_t) getNumberOfElements(input);

	std::vector<uint32_t> outDims(targetDimsSize);
	int32_t numOutputElements = 1;
	int32_t strechDim = -1;
	for (int32_t i = 0; i < targetDimsSize; ++i) {
	int32_t value = targetDims[i];
	if (value == -1) {
	nnOpsCheck(strechDim == -1);
	strechDim = i;
	} else {
	numOutputElements *= value;
	outDims[i] = (uint32_t)value;
	}
	}
	if (strechDim != -1) {
	int32_t strechValue = numInputElements / numOutputElements;
	outDims[strechDim] = (uint32_t) strechValue;
	numOutputElements *= strechValue;
	}

	nnOpsCheck(numInputElements == numOutputElements);

	output->type = input.type;
	output->dimensions = outDims;
	output->offset = input.offset;
	output->scale = input.scale;

	return true;
	}

	bool resizeBilinearPrepare(const Shape& input,
	int32_t width,
	int32_t height,
	Shape* output) {
	nnOpsCheck(getNumberOfDimensions(input) == 4);
	uint32_t batches = getSizeOfDimension(input, 0);
	uint32_t channels = getSizeOfDimension(input, 3);

	output->type = input.type;
	output->dimensions = {batches, (uint32_t)height, (uint32_t)width, channels};

	return true;
	}

	bool depthToSpacePrepare(const Shape& input,
	int32_t blockSize,
	Shape* output) {
	nnOpsCheck(getNumberOfDimensions(input) == 4);
	nnOpsCheck(blockSize > 0);

	uint32_t batches = getSizeOfDimension(input, 0);
	uint32_t height = getSizeOfDimension(input, 1);
	uint32_t width = getSizeOfDimension(input, 2);
	uint32_t channels = getSizeOfDimension(input, 3);

	nnOpsCheck(channels % (blockSize * blockSize) == 0);
	output->type = input.type;
	output->dimensions = {batches,
	height * blockSize,
	width * blockSize,
	channels / (blockSize * blockSize)};
	output->offset = input.offset;
	output->scale = input.scale;

	return true;
	}

	bool spaceToDepthPrepare(const Shape& input,
	int32_t blockSize,
	Shape* output) {
	nnOpsCheck(getNumberOfDimensions(input) == 4);
	nnOpsCheck(blockSize > 0);

	uint32_t batches = getSizeOfDimension(input, 0);
	uint32_t height = getSizeOfDimension(input, 1);
	uint32_t width = getSizeOfDimension(input, 2);
	uint32_t channels = getSizeOfDimension(input, 3);

	nnOpsCheck(height % blockSize == 0);
	nnOpsCheck(width % blockSize == 0);

	output->type = input.type;
	output->dimensions = {batches,
	height / blockSize,
	width / blockSize,
	channels * (blockSize * blockSize)};
	output->offset = input.offset;
	output->scale = input.scale;

	return true;
	}

	} // namespace nn
	} // namespace android