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//===- UniformSolvers.cpp - Uniform type solver algorithms ----------------===//
//
// Copyright 2019 The MLIR Authors.
//
// 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 "mlir/Quantizer/Support/UniformSolvers.h"
#include "llvm/Support/raw_ostream.h"
#include <cmath>
using namespace mlir;
using namespace mlir::quantizer;
bool UniformParamsFromMinMaxSolver::compute() {
// Compute adjMin, adjMax, clamping to ensure that they straddle zero.
if (boundingMin > 0 && boundingMax >= boundingMin) {
// Lop-sided to the positive.
adjMin = 0;
adjMax = boundingMax;
} else if (boundingMax < 0 && boundingMin <= boundingMax) {
// Lop-sided to the negative.
adjMin = boundingMin;
adjMax = 0;
} else if (boundingMin <= 0 && boundingMax >= 0) {
adjMin = boundingMin;
adjMax = boundingMax;
} else {
// Illegal bounds.
return satisfied = false;
}
const double origMinAdj = adjMin;
const double origMaxAdj = adjMax;
const double numLevelsDouble = storageParams.numLevels;
struct fns {
static std::pair<double, double>
computeMinMax(double boundingMin, double numLevels, double delta) {
double adjMin = delta * std::floor(boundingMin / delta);
return std::make_pair(adjMin, adjMin + numLevels * delta);
}
static double overshoot(double boundingMin, double boundingMax,
double numLevels, double delta) {
auto adjMinMax = computeMinMax(boundingMin, numLevels, delta);
double maxOvershoot = adjMinMax.second - boundingMax;
double minOvershoot = boundingMin - adjMinMax.first;
// If undershooting on the min or max end, return that because it is
// to be unconditionally avoided. Otherwise return the end with the
// greateast magnitude of overshoot.
if (maxOvershoot < 0)
return maxOvershoot;
if (minOvershoot < 0)
return minOvershoot;
return std::max(maxOvershoot, minOvershoot);
}
};
// Bisect to find a suitable delta, starting with bounds of deltaInit
// and deltaMax.
double deltaInit = (adjMax - adjMin) / numLevelsDouble;
double deltaMax =
((numLevelsDouble * deltaInit) + 2 * deltaInit) / numLevelsDouble;
double deltaMid;
double prevDeltaMid = 0.0;
for (stepCount = 0; stepCount < 60; ++stepCount) {
deltaMid = (deltaInit + deltaMax) / 2.0;
auto fInit =
fns::overshoot(origMinAdj, origMaxAdj, numLevelsDouble, deltaInit);
auto fMid =
fns::overshoot(origMinAdj, origMaxAdj, numLevelsDouble, deltaMid);
if (fMid == 0 || (fMid > 0 && std::fabs(deltaMid - prevDeltaMid) < 1e-15)) {
// Solution found (or step size is infinitessimal and an overshoot).
// Empirically, this seems to terminate around 30-50 steps or so.
// This will find a zero point for exactly representable ranges and
// will terminate on a small step size for inexact, biasing towards
// overshooting.
delta = deltaMid;
break;
}
bool signMid = fMid > 0;
bool signInit = fInit > 0;
if (signMid == signInit) {
deltaInit = deltaMid;
} else {
deltaMax = deltaMid;
}
prevDeltaMid = deltaMid;
}
delta = deltaMid;
// Recalculate adjMin/adjMax based on new delta.
auto adjMinMax = fns::computeMinMax(origMinAdj, numLevelsDouble, delta);
adjMin = adjMinMax.first;
adjMax = adjMinMax.second;
satisfied = false;
zp = 0;
if (!std::isnan(delta) && !std::isnan(adjMin) && !std::isnan(adjMax)) {
satisfied = true;
// Finally, scale and zeroPoint. Since it casts to integer, only valid
// if the inputs are valid.
zp = std::round(storageParams.minValue - adjMin / delta);
}
return satisfied;
}
int64_t UniformParamsFromMinMaxSolver::quantize(double x) const {
int64_t xq = std::round(x / delta + zp);
return std::max<int64_t>(0, std::min<int64_t>(storageParams.numLevels, xq));
}
double UniformParamsFromMinMaxSolver::dequantize(int64_t xq) const {
return (xq - zp) * delta;
}
namespace mlir {
namespace quantizer {
llvm::raw_ostream &operator<<(llvm::raw_ostream &os,
const UniformStorageParams &p) {
os << "UniformStorageParams{" << p.numLevels << ", " << p.minValue << "}";
return os;
}
llvm::raw_ostream &operator<<(llvm::raw_ostream &os,
const UniformParamsFromMinMaxSolver &s) {
os << "UniformParamsFromMinMaxSolver(" << s.getStepCount() << "){";
os << "(" << s.getBoundingMin() << ":" << s.getBoundingMax() << ") -> ";
if (!s.isSatisfied()) {
os << "unsat}";
return os;
}
os << "(" << s.getAdjMin() << ":" << s.getAdjMax() << ")";
os << ", scale = " << s.getScale();
os << ", zp = " << s.getZp();
os << "}";
return os;
}
} // end namespace quantizer
} // end namespace mlir
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