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756 lines
29 KiB
756 lines
29 KiB
/*
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* Copyright (C) 2017 The Android Open Source Project
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#define LOG_TAG "OperationsUtils"
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#include "OperationsUtils.h"
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#include <algorithm>
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#include <cmath>
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#include <limits>
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#include <sstream>
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#include <vector>
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#include "LegacyUtils.h"
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#include "Operations.h"
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namespace android {
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namespace nn {
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namespace {
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bool validateOperandTypes(const std::vector<OperandType>& expectedTypes, const char* tag,
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uint32_t operandCount,
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std::function<OperandType(uint32_t)> getOperandType) {
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NN_RET_CHECK_EQ(operandCount, expectedTypes.size());
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for (uint32_t i = 0; i < operandCount; ++i) {
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OperandType type = getOperandType(i);
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NN_RET_CHECK(type == expectedTypes[i])
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<< "Invalid " << tag << " tensor type " << type << " for " << tag << " " << i
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<< ", expected " << expectedTypes[i];
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}
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return true;
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}
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void CalculateActivationRangeImpl(int32_t activation, const Shape& outputShape, int32_t qmin,
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int32_t qmax, int32_t* act_min, int32_t* act_max) {
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const auto scale = outputShape.scale;
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const auto zero_point = outputShape.offset;
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auto quantize = [scale, zero_point](float f) {
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return zero_point + static_cast<int32_t>(std::round(f / scale));
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};
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if (activation == kActivationRelu) {
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*act_min = std::max(qmin, quantize(0.0));
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*act_max = qmax;
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} else if (activation == kActivationRelu6) {
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*act_min = std::max(qmin, quantize(0.0));
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*act_max = std::min(qmax, quantize(6.0));
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} else if (activation == kActivationRelu1) {
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*act_min = std::max(qmin, quantize(-1.0));
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*act_max = std::min(qmax, quantize(1.0));
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} else if (activation == kActivationNone) {
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*act_min = qmin;
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*act_max = qmax;
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} else {
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LOG(ERROR) << "Unsupported fused activation function.";
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}
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}
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} // namespace
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bool validateInputTypes(const IOperationValidationContext* context,
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const std::vector<OperandType>& expectedTypes) {
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return validateOperandTypes(expectedTypes, "input", context->getNumInputs(),
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[context](uint32_t index) { return context->getInputType(index); });
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}
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bool validateOutputTypes(const IOperationValidationContext* context,
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const std::vector<OperandType>& expectedTypes) {
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return validateOperandTypes(
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expectedTypes, "output", context->getNumOutputs(),
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[context](uint32_t index) { return context->getOutputType(index); });
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}
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bool validateVersion(const IOperationValidationContext* context, Version contextVersion,
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Version minSupportedVersion) {
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if (contextVersion < minSupportedVersion) {
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std::ostringstream message;
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message << "Operation " << context->getOperationName() << " with inputs {";
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for (uint32_t i = 0, n = context->getNumInputs(); i < n; ++i) {
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if (i != 0) {
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message << ", ";
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}
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message << context->getInputType(i);
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}
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message << "} and outputs {";
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for (uint32_t i = 0, n = context->getNumOutputs(); i < n; ++i) {
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if (i != 0) {
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message << ", ";
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}
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message << context->getOutputType(i);
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}
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message << "} is only supported since " << minSupportedVersion << " (validating using "
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<< contextVersion << ")";
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NN_RET_CHECK_FAIL() << message.str();
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}
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return true;
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}
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bool SameShape(const Shape& in1, const Shape& in2) {
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if (in1.type != in2.type || in1.dimensions.size() != in2.dimensions.size()) {
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return false;
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}
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for (size_t i = 0; i < in1.dimensions.size(); i++) {
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if (in1.dimensions[i] != in2.dimensions[i]) {
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return false;
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}
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}
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return true;
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}
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bool SetShape(const Shape& in, Shape* out) {
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if (in.type != out->type) {
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return false;
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}
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out->dimensions = in.dimensions;
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return true;
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}
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uint32_t getNumberOfElements(const Shape& shape) {
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uint32_t count = 1;
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for (size_t i = 0; i < shape.dimensions.size(); i++) {
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count *= shape.dimensions[i];
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}
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return count;
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}
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uint32_t getNumberOfElements(const Shape& shape, size_t firstAxisInclusive,
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size_t lastAxisExclusive) {
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nnAssert(0 <= firstAxisInclusive);
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nnAssert(firstAxisInclusive <= lastAxisExclusive);
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nnAssert(lastAxisExclusive <= shape.dimensions.size());
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uint32_t count = 1;
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for (size_t i = firstAxisInclusive; i < lastAxisExclusive; i++) {
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count *= shape.dimensions[i];
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}
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return count;
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}
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uint32_t getNumberOfDimensions(const Shape& shape) {
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return shape.dimensions.size();
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}
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uint32_t getSizeOfDimension(const Shape& shape, uint32_t dimensionIdx) {
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nnAssert(0 <= dimensionIdx && dimensionIdx < shape.dimensions.size());
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return shape.dimensions[dimensionIdx];
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}
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uint32_t hasKnownRank(const Shape& shape) {
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return !shape.dimensions.empty();
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}
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bool handleNegativeAxis(int32_t numberOfDimensions, int32_t* axis) {
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NN_CHECK(-numberOfDimensions <= *axis && *axis < numberOfDimensions);
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if (*axis < 0) {
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*axis += numberOfDimensions;
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}
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return true;
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}
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bool QuantizeMultiplier(double double_multiplier, int32_t* quantized_multiplier, int32_t* shift) {
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if (double_multiplier == 0.) {
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*quantized_multiplier = 0;
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*shift = 0;
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return true;
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}
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const double q = std::frexp(double_multiplier, shift);
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auto q_fixed = static_cast<int64_t>(std::round(q * (1ll << 31)));
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NN_RET_CHECK(q_fixed <= (1ll << 31));
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if (q_fixed == (1ll << 31)) {
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q_fixed /= 2;
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++*shift;
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}
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NN_RET_CHECK_LE(q_fixed, std::numeric_limits<int32_t>::max());
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// A shift amount smaller than -31 would cause all bits to be shifted out
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// and thus all results would be zero. We implement that instead with
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// q_fixed==0, so as to avoid hitting issues with right-shift
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// operations with shift amounts greater than 31. Note that this happens
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// roughly when abs(double_multiplier) < 2^-31 and the present handling means
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// that we're effectively flushing tiny double_multiplier's to zero.
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// We could conceivably handle values in the range (roughly) [32, 63]
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// as 'denormals' i.e. (shift==0, q_fixed < 2^30). In that point of view
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// the present handling is just doing 'flush denormals to zero'. We could
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// reconsider and actually generate nonzero denormals if a need arises.
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if (*shift < -31) {
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*shift = 0;
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q_fixed = 0;
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}
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*quantized_multiplier = static_cast<int32_t>(q_fixed);
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return true;
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}
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bool QuantizeMultiplierSmallerThanOneExp(double double_multiplier, int32_t* quantized_multiplier,
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int32_t* left_shift) {
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NN_RET_CHECK(double_multiplier > 0.);
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NN_RET_CHECK(double_multiplier < 1.);
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NN_RET_CHECK(QuantizeMultiplier(double_multiplier, quantized_multiplier, left_shift));
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NN_RET_CHECK(*left_shift <= 0);
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return true;
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}
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bool QuantizeMultiplierSmallerThanOne(double double_multiplier, int32_t* quantized_multiplier,
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int32_t* right_shift) {
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NN_OPS_CHECK(double_multiplier >= 0.);
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NN_OPS_CHECK(double_multiplier < 1.);
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if (double_multiplier == 0.) {
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*quantized_multiplier = 0;
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*right_shift = 0;
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return true;
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}
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NN_OPS_CHECK(double_multiplier > 0.);
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const double q = std::frexp(double_multiplier, right_shift);
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*right_shift *= -1;
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int64_t q_fixed = static_cast<int64_t>(std::round(q * (1LL << 31)));
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NN_OPS_CHECK(q_fixed <= (1LL << 31));
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if (q_fixed == (1LL << 31)) {
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q_fixed /= 2;
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--*right_shift;
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}
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NN_OPS_CHECK(*right_shift >= 0);
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NN_OPS_CHECK(q_fixed <= std::numeric_limits<int32_t>::max());
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*quantized_multiplier = static_cast<int32_t>(q_fixed);
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return true;
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}
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bool QuantizeMultiplierGreaterThanOne(double double_multiplier, int32_t* quantized_multiplier,
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int* left_shift) {
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NN_OPS_CHECK(double_multiplier > 1.);
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const double q = std::frexp(double_multiplier, left_shift);
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int64_t q_fixed = static_cast<int64_t>(std::round(q * (1LL << 31)));
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NN_OPS_CHECK(q_fixed <= (1LL << 31));
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if (q_fixed == (1LL << 31)) {
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q_fixed /= 2;
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++*left_shift;
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}
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NN_OPS_CHECK(*left_shift >= 0);
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NN_OPS_CHECK(q_fixed <= std::numeric_limits<int32_t>::max());
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*quantized_multiplier = static_cast<int32_t>(q_fixed);
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return true;
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}
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bool GetQuantizedConvolutionMultipler(const Shape& inputShape, const Shape& filterShape,
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const Shape& biasShape, const Shape& outputShape,
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double* multiplier) {
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// Upcast bias and input_product to double
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const double input_product_scale = inputShape.scale * filterShape.scale;
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const double bias_scale = biasShape.scale;
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// The following conditions must be guaranteed by the training pipeline.
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NN_OPS_CHECK(std::abs(input_product_scale - bias_scale) <=
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1e-6 * std::min(input_product_scale, bias_scale));
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NN_OPS_CHECK(input_product_scale >= 0);
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*multiplier = input_product_scale / outputShape.scale;
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return true;
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}
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void CalculateActivationRangeUint8(int32_t activation, const Shape& outputShape, int32_t* act_min,
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int32_t* act_max) {
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const int32_t qmin = std::numeric_limits<uint8_t>::min();
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const int32_t qmax = std::numeric_limits<uint8_t>::max();
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CalculateActivationRangeImpl(activation, outputShape, qmin, qmax, act_min, act_max);
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}
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void CalculateActivationRangeInt8(int32_t activation, const Shape& outputShape, int32_t* act_min,
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int32_t* act_max) {
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const int32_t qmin = std::numeric_limits<int8_t>::min();
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const int32_t qmax = std::numeric_limits<int8_t>::max();
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CalculateActivationRangeImpl(activation, outputShape, qmin, qmax, act_min, act_max);
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}
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void CalculateActivationRangeFloat(int32_t activation, float* activation_min,
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float* activation_max) {
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if (activation == kActivationRelu) {
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*activation_min = 0.f;
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*activation_max = std::numeric_limits<float>::max();
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} else if (activation == kActivationRelu6) {
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*activation_min = 0.f;
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*activation_max = 6.f;
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} else if (activation == kActivationRelu1) {
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*activation_min = -1.f;
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*activation_max = 1.f;
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} else if (activation == kActivationNone) {
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*activation_min = std::numeric_limits<float>::lowest();
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*activation_max = std::numeric_limits<float>::max();
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} else {
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LOG(ERROR) << "Unsupported fused activation function.";
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}
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}
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int32_t CalculateInputRadius(int input_integer_bits, int input_left_shift) {
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const double max_input_rescaled = 1.0 * ((1 << input_integer_bits) - 1) *
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(1LL << (31 - input_integer_bits)) /
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(1LL << input_left_shift);
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// Tighten bound using floor. Suppose that we could use the exact value.
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// After scaling the difference, the result would be at the maximum. Thus we
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// must ensure that our value has lower magnitude.
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return static_cast<int32_t>(std::floor(max_input_rescaled));
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}
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void calculateExplicitPaddingImpl(int32_t in_size, int32_t stride, int32_t dilation_factor,
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int32_t filter_size, int32_t padding_implicit,
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bool isTransposeConv, int32_t* padding_head,
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int32_t* padding_tail) {
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*padding_head = 0;
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*padding_tail = 0;
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int32_t effective_filter_size = (filter_size - 1) * dilation_factor + 1;
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if (padding_implicit == kPaddingSame) {
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int32_t out_size = (in_size + stride - 1) / stride;
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int32_t tmp = (out_size - 1) * stride + effective_filter_size;
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if (tmp > in_size) {
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*padding_head = (tmp - in_size) / 2;
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*padding_tail = (tmp - in_size) - *padding_head;
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}
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// For transpose conv, make padding tail fit tightly to the end of the last stride.
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if (isTransposeConv) {
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*padding_tail = (tmp - in_size) - *padding_head;
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}
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}
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}
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bool calculateBroadcastedShape(const Shape& in1, const Shape& in2, Shape* out) {
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NN_RET_CHECK(in1.type == in2.type);
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uint32_t numberOfDims1 = getNumberOfDimensions(in1);
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uint32_t numberOfDims2 = getNumberOfDimensions(in2);
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uint32_t maxDims = std::max(numberOfDims1, numberOfDims2);
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out->dimensions = std::vector<uint32_t>(maxDims);
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for (uint32_t i = 1; i <= maxDims; i++) {
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uint32_t dim1 = 1;
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if (i <= numberOfDims1) {
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dim1 = getSizeOfDimension(in1, numberOfDims1 - i);
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}
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uint32_t dim2 = 1;
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if (i <= numberOfDims2) {
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dim2 = getSizeOfDimension(in2, numberOfDims2 - i);
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}
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if (dim1 != dim2 && dim1 != 1 && dim2 != 1) {
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LOG(ERROR) << "Dimensions mismatch for broadcast:\n"
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<< "First tensor: dimension " << numberOfDims1 - i << " of size " << dim1
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<< "\nSecond tensor: dimension " << numberOfDims2 - i << " of size " << dim2;
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return false;
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}
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out->dimensions[maxDims - i] = (dim1 == 1) ? dim2 : dim1;
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}
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return true;
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}
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template <>
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uint8_t requantize<uint8_t>(uint8_t value, const Shape& oldShape, const Shape& newShape) {
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double doubleValue = (value - oldShape.offset) * oldShape.scale;
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double doubleRet = doubleValue / newShape.scale + newShape.offset;
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if (doubleRet < 0) return 0;
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if (doubleRet > 255) return 255;
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return static_cast<uint8_t>(std::round(doubleRet));
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}
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template <>
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int8_t requantize<int8_t>(int8_t value, const Shape& oldShape, const Shape& newShape) {
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double doubleValue = (value - oldShape.offset) * oldShape.scale;
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double doubleRet = doubleValue / newShape.scale + newShape.offset;
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if (doubleRet < -128) return -128;
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if (doubleRet > 127) return 127;
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return static_cast<int8_t>(std::round(doubleRet));
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}
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bool reshapePrepare(const Shape& input, const int32_t* targetDims, const int32_t targetDimsSize,
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Shape* output) {
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// Reshape allows one of the targetDims components to have the
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// special -1 value, meaning it will be calculated automatically based on the
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// input. Here we calculate what that dimension should be so that the number
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// of output elements in the same as the number of input elements.
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int32_t numInputElements = (int32_t)getNumberOfElements(input);
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std::vector<uint32_t> outDims(targetDimsSize);
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int32_t numOutputElements = 1;
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int32_t strechDim = -1;
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for (int32_t i = 0; i < targetDimsSize; ++i) {
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int32_t value = targetDims[i];
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if (value == -1) {
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NN_OPS_CHECK(strechDim == -1);
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strechDim = i;
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} else {
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numOutputElements *= value;
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outDims[i] = (uint32_t)value;
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}
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}
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if (strechDim != -1) {
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int32_t strechValue = numInputElements / numOutputElements;
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outDims[strechDim] = (uint32_t)strechValue;
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numOutputElements *= strechValue;
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}
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NN_OPS_CHECK(numInputElements == numOutputElements);
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output->type = input.type;
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output->dimensions = outDims;
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output->offset = input.offset;
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output->scale = input.scale;
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return true;
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}
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bool depthToSpacePrepare(const Shape& input, int32_t blockSize, Shape* output) {
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NN_OPS_CHECK(getNumberOfDimensions(input) == 4);
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NN_OPS_CHECK(blockSize > 0);
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uint32_t batches = getSizeOfDimension(input, 0);
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uint32_t height = getSizeOfDimension(input, 1);
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uint32_t width = getSizeOfDimension(input, 2);
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uint32_t channels = getSizeOfDimension(input, 3);
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NN_OPS_CHECK(channels % (blockSize * blockSize) == 0);
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output->type = input.type;
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output->dimensions = {batches, height * blockSize, width * blockSize,
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channels / (blockSize * blockSize)};
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output->offset = input.offset;
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output->scale = input.scale;
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return true;
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}
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bool spaceToDepthPrepare(const Shape& input, int32_t blockSize, Shape* output) {
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NN_OPS_CHECK(getNumberOfDimensions(input) == 4);
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NN_OPS_CHECK(blockSize > 0);
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uint32_t batches = getSizeOfDimension(input, 0);
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uint32_t height = getSizeOfDimension(input, 1);
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uint32_t width = getSizeOfDimension(input, 2);
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uint32_t channels = getSizeOfDimension(input, 3);
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NN_OPS_CHECK(height % blockSize == 0);
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NN_OPS_CHECK(width % blockSize == 0);
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|
|
|
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 embeddingLookupPrepare(const Shape& valueShape, const Shape& lookupShape, Shape* outputShape) {
|
|
NN_OPS_CHECK(getNumberOfDimensions(valueShape) >= 2);
|
|
NN_OPS_CHECK(getNumberOfDimensions(lookupShape) == 1);
|
|
|
|
const uint32_t columns = getSizeOfDimension(valueShape, 1);
|
|
const uint32_t lookups = getSizeOfDimension(lookupShape, 0);
|
|
|
|
outputShape->type = valueShape.type;
|
|
outputShape->dimensions = {lookups, columns};
|
|
for (uint32_t i = 2; i < getNumberOfDimensions(valueShape); i++) {
|
|
outputShape->dimensions.push_back(getSizeOfDimension(valueShape, i));
|
|
}
|
|
outputShape->offset = valueShape.offset;
|
|
outputShape->scale = valueShape.scale;
|
|
|
|
return true;
|
|
}
|
|
|
|
bool hashtableLookupPrepare(const Shape& lookupShape, const Shape& keyShape,
|
|
const Shape& valueShape, Shape* outputShape, Shape* hitShape) {
|
|
NN_OPS_CHECK(getNumberOfDimensions(lookupShape) == 1);
|
|
NN_OPS_CHECK(getNumberOfDimensions(keyShape) == 1);
|
|
NN_OPS_CHECK(getNumberOfDimensions(valueShape) >= 1);
|
|
|
|
const uint32_t lookups = getSizeOfDimension(lookupShape, 0);
|
|
outputShape->type = valueShape.type;
|
|
outputShape->dimensions = {lookups};
|
|
for (uint32_t i = 1; i < getNumberOfDimensions(valueShape); i++) {
|
|
outputShape->dimensions.push_back(getSizeOfDimension(valueShape, i));
|
|
}
|
|
outputShape->offset = valueShape.offset;
|
|
outputShape->scale = valueShape.scale;
|
|
|
|
hitShape->type = OperandType::TENSOR_QUANT8_ASYMM;
|
|
hitShape->dimensions = {lookups};
|
|
hitShape->offset = 0;
|
|
hitShape->scale = 1.f;
|
|
|
|
return true;
|
|
}
|
|
|
|
bool padPrepare(const Shape& input, const int32_t* paddingsData, const Shape& paddingsShape,
|
|
Shape* output) {
|
|
uint32_t numInputDims = getNumberOfDimensions(input);
|
|
|
|
// paddings need to be provided as a 2-D int32 tensor.
|
|
NN_OPS_CHECK(paddingsShape.type == OperandType::TENSOR_INT32);
|
|
NN_OPS_CHECK(getNumberOfDimensions(paddingsShape) == 2);
|
|
NN_OPS_CHECK(getSizeOfDimension(paddingsShape, 0) == numInputDims);
|
|
NN_OPS_CHECK(getSizeOfDimension(paddingsShape, 1) == 2);
|
|
|
|
std::vector<uint32_t> outDims(numInputDims);
|
|
for (uint32_t i = 0; i < numInputDims; ++i) {
|
|
int32_t beforePadding = *paddingsData++;
|
|
int32_t afterPadding = *paddingsData++;
|
|
// Pad value has to be greater than equal to 0.
|
|
NN_OPS_CHECK(beforePadding >= 0 && afterPadding >= 0);
|
|
outDims[i] = beforePadding + getSizeOfDimension(input, i) + afterPadding;
|
|
}
|
|
output->type = input.type;
|
|
output->dimensions = outDims;
|
|
output->offset = input.offset;
|
|
output->scale = input.scale;
|
|
|
|
return true;
|
|
}
|
|
|
|
bool batchToSpacePrepare(const Shape& input, const int32_t* blockSizeData,
|
|
const Shape& blockSizeShape, Shape* output) {
|
|
// Only 4D NHWC tensors are supported.
|
|
NN_OPS_CHECK(getNumberOfDimensions(input) == 4);
|
|
|
|
// blockSize need to be provided as a 1-D int32 tensor.
|
|
NN_OPS_CHECK(blockSizeShape.type == OperandType::TENSOR_INT32);
|
|
NN_OPS_CHECK(getNumberOfDimensions(blockSizeShape) == 1);
|
|
// Only applies to spatial dimensions.
|
|
NN_OPS_CHECK(getSizeOfDimension(blockSizeShape, 0) == 2);
|
|
|
|
uint32_t batches = getSizeOfDimension(input, 0);
|
|
uint32_t height = getSizeOfDimension(input, 1);
|
|
uint32_t width = getSizeOfDimension(input, 2);
|
|
uint32_t channels = getSizeOfDimension(input, 3);
|
|
|
|
NN_OPS_CHECK(batches % (blockSizeData[0] * blockSizeData[1]) == 0);
|
|
output->type = input.type;
|
|
output->dimensions = {batches / (blockSizeData[0] * blockSizeData[1]),
|
|
height * blockSizeData[0], width * blockSizeData[1], channels};
|
|
output->offset = input.offset;
|
|
output->scale = input.scale;
|
|
|
|
return true;
|
|
}
|
|
|
|
bool spaceToBatchPrepare(const Shape& input, const int32_t* blockSizeData,
|
|
const Shape& blockSizeShape, const int32_t* paddingsData,
|
|
const Shape& paddingsShape, Shape* output) {
|
|
// Only 4D NHWC tensors are supported.
|
|
NN_OPS_CHECK(getNumberOfDimensions(input) == 4);
|
|
|
|
// blockSize need to be provided as a 1-D int32 tensor.
|
|
NN_OPS_CHECK(blockSizeShape.type == OperandType::TENSOR_INT32);
|
|
NN_OPS_CHECK(getNumberOfDimensions(blockSizeShape) == 1);
|
|
// Only applies to spatial dimensions.
|
|
NN_OPS_CHECK(getSizeOfDimension(blockSizeShape, 0) == 2);
|
|
|
|
// paddings need to be provided as a 2-D int32 tensor.
|
|
NN_OPS_CHECK(paddingsShape.type == OperandType::TENSOR_INT32);
|
|
NN_OPS_CHECK(getNumberOfDimensions(paddingsShape) == 2);
|
|
NN_OPS_CHECK(getSizeOfDimension(paddingsShape, 0) == 2);
|
|
NN_OPS_CHECK(getSizeOfDimension(paddingsShape, 1) == 2);
|
|
|
|
uint32_t batches = getSizeOfDimension(input, 0);
|
|
uint32_t height = getSizeOfDimension(input, 1);
|
|
uint32_t width = getSizeOfDimension(input, 2);
|
|
uint32_t channels = getSizeOfDimension(input, 3);
|
|
|
|
uint32_t paddedHeight = paddingsData[0] + height + paddingsData[1];
|
|
uint32_t paddedWidth = paddingsData[2] + width + paddingsData[3];
|
|
|
|
NN_OPS_CHECK(paddedHeight % blockSizeData[0] == 0);
|
|
NN_OPS_CHECK(paddedWidth % blockSizeData[1] == 0);
|
|
|
|
output->type = input.type;
|
|
output->dimensions = {batches * (blockSizeData[0] * blockSizeData[1]),
|
|
paddedHeight / blockSizeData[0], paddedWidth / blockSizeData[1],
|
|
channels};
|
|
output->offset = input.offset;
|
|
output->scale = input.scale;
|
|
|
|
return true;
|
|
}
|
|
|
|
bool meanPrepare(const Shape& input, const int32_t* axisData, const Shape& axisShape, bool keepDims,
|
|
Shape* output) {
|
|
// perm need to be provided as a 1-D int32 tensor.
|
|
NN_OPS_CHECK(axisShape.type == OperandType::TENSOR_INT32);
|
|
NN_OPS_CHECK(getNumberOfDimensions(axisShape) == 1);
|
|
|
|
int32_t numInputDims = static_cast<int32_t>(getNumberOfDimensions(input));
|
|
int32_t axisSize = static_cast<int32_t>(getSizeOfDimension(axisShape, 0));
|
|
|
|
// Determines size of output tensor.
|
|
if (keepDims) {
|
|
std::vector<uint32_t> outDims(numInputDims);
|
|
for (int32_t idx = 0; idx < numInputDims; ++idx) {
|
|
bool isAxis = false;
|
|
for (int32_t axisIdx = 0; axisIdx < axisSize; ++axisIdx) {
|
|
if (axisData[axisIdx] == idx || axisData[axisIdx] + numInputDims == idx) {
|
|
isAxis = true;
|
|
break;
|
|
}
|
|
}
|
|
if (isAxis) {
|
|
outDims[idx] = 1;
|
|
} else {
|
|
outDims[idx] = getSizeOfDimension(input, idx);
|
|
}
|
|
}
|
|
output->dimensions = outDims;
|
|
} else {
|
|
// Calculates size of reducing axis.
|
|
int32_t numReduceAxis = axisSize;
|
|
for (int32_t i = 0; i < axisSize; ++i) {
|
|
int32_t current = axisData[i];
|
|
if (current < 0) {
|
|
current += numInputDims;
|
|
}
|
|
NN_OPS_CHECK(current >= 0 && current < numInputDims);
|
|
for (int32_t j = 0; j < i; ++j) {
|
|
int32_t previous = axisData[j];
|
|
if (previous < 0) {
|
|
previous += numInputDims;
|
|
}
|
|
if (current == previous) {
|
|
--numReduceAxis;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
// Determines output dimensions.
|
|
std::vector<uint32_t> outDims(numInputDims - numReduceAxis);
|
|
int32_t numSkipAxis = 0;
|
|
for (int32_t idx = 0; idx < numInputDims; ++idx) {
|
|
bool isAxis = false;
|
|
for (int32_t axisIdx = 0; axisIdx < axisSize; ++axisIdx) {
|
|
if (axisData[axisIdx] == idx || axisData[axisIdx] + numInputDims == idx) {
|
|
++numSkipAxis;
|
|
isAxis = true;
|
|
break;
|
|
}
|
|
}
|
|
if (!isAxis) {
|
|
outDims[idx - numSkipAxis] = getSizeOfDimension(input, idx);
|
|
}
|
|
}
|
|
// Handle the case when all dimensions are removed
|
|
if (outDims.empty()) {
|
|
outDims.push_back(1);
|
|
}
|
|
output->dimensions = outDims;
|
|
}
|
|
|
|
output->type = input.type;
|
|
output->offset = input.offset;
|
|
output->scale = input.scale;
|
|
|
|
return true;
|
|
}
|
|
|
|
bool argMinMaxPrepare(const Shape& input, int32_t axis, Shape* output) {
|
|
NN_CHECK(handleNegativeAxis(input, &axis));
|
|
|
|
output->type = OperandType::TENSOR_INT32;
|
|
|
|
// Copy the input dimensions, omitting the axis dimension.
|
|
output->dimensions.clear();
|
|
if (getNumberOfDimensions(input) > 1) {
|
|
output->dimensions.reserve(getNumberOfDimensions(input) - 1);
|
|
output->dimensions.insert(output->dimensions.end(), input.dimensions.begin(),
|
|
input.dimensions.begin() + axis);
|
|
output->dimensions.insert(output->dimensions.end(), input.dimensions.begin() + axis + 1,
|
|
input.dimensions.end());
|
|
} else {
|
|
output->dimensions.push_back(1);
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
bool splitPrepare(const Shape& input, int32_t axis, int32_t numOutputs,
|
|
std::vector<Shape>* output) {
|
|
NN_CHECK(handleNegativeAxis(input, &axis));
|
|
|
|
const int32_t sizeOfAxisToSplit = input.dimensions[axis];
|
|
NN_OPS_CHECK(sizeOfAxisToSplit % numOutputs == 0);
|
|
const int32_t sliceSize = sizeOfAxisToSplit / numOutputs;
|
|
|
|
for (int i = 0; i < numOutputs; ++i) {
|
|
output->at(i).type = input.type;
|
|
output->at(i).dimensions = input.dimensions;
|
|
output->at(i).dimensions[axis] = sliceSize;
|
|
output->at(i).offset = input.offset;
|
|
output->at(i).scale = input.scale;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
bool groupedConvPrepare(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,
|
|
int32_t numGroups, Shape* output) {
|
|
if (filter.type == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL) {
|
|
NN_OPS_CHECK(input.type == OperandType::TENSOR_QUANT8_ASYMM ||
|
|
input.type == OperandType::TENSOR_QUANT8_ASYMM_SIGNED);
|
|
} else {
|
|
NN_OPS_CHECK(input.type == filter.type);
|
|
}
|
|
if (input.type == OperandType::TENSOR_QUANT8_ASYMM ||
|
|
input.type == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
|
|
NN_OPS_CHECK(bias.type == OperandType::TENSOR_INT32);
|
|
} else {
|
|
NN_OPS_CHECK(input.type == bias.type);
|
|
}
|
|
NN_OPS_CHECK(getNumberOfDimensions(input) == 4);
|
|
NN_OPS_CHECK(getNumberOfDimensions(filter) == 4);
|
|
NN_OPS_CHECK(getNumberOfDimensions(bias) == 1);
|
|
|
|
NN_OPS_CHECK(getSizeOfDimension(filter, 0) == getSizeOfDimension(bias, 0));
|
|
|
|
NN_OPS_CHECK(getSizeOfDimension(filter, 3) * numGroups == getSizeOfDimension(input, 3));
|
|
NN_OPS_CHECK(getSizeOfDimension(filter, 0) % numGroups == 0);
|
|
|
|
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);
|
|
|
|
NN_RET_CHECK_GT(static_cast<int32_t>(filterWidth), padding_left);
|
|
NN_RET_CHECK_GT(static_cast<int32_t>(filterWidth), padding_right);
|
|
NN_RET_CHECK_GT(static_cast<int32_t>(filterHeight), padding_top);
|
|
NN_RET_CHECK_GT(static_cast<int32_t>(filterHeight), padding_bottom);
|
|
|
|
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;
|
|
}
|
|
|
|
} // namespace nn
|
|
} // namespace android
|