101 lines
4.3 KiB
C++
101 lines
4.3 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://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, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author sgazeos@gmail.com
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//
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#include <ops/declarable/helpers/fake_quantization.h>
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#include <NDArrayFactory.h>
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namespace nd4j {
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namespace ops {
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namespace helpers {
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template <typename T>
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void fakeQuantWithMinMaxVars_(NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output) {
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int lowIntBound = narrowed?1:0;
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int upperIntBound = 1 << numBits - 1;
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const float quant_min_float = static_cast<float>(lowIntBound);
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const float quant_max_float = static_cast<float>(upperIntBound);
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T scale = (max->t<T>(0) - min->t<T>(0)) / (quant_max_float - quant_min_float);
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const T zero_point_from_min = quant_min_float - min->e<T>(0) / scale;
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const uint16_t nudged_zero_point = [zero_point_from_min, lowIntBound,
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quant_min_float, upperIntBound,
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quant_max_float] {
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if (zero_point_from_min < quant_min_float) {
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return static_cast<uint16_t>(lowIntBound);
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}
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if (zero_point_from_min > quant_max_float) {
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return static_cast<uint16_t>(upperIntBound);
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}
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return static_cast<uint16_t>(roundf(zero_point_from_min));
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}();
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auto nudged_min = (quant_min_float - nudged_zero_point) * (scale);
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auto nudged_max = (quant_max_float - nudged_zero_point) * (scale);
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//input->applyScalar(scalar::CompareAndSet, nudged_max, clamped, nullptr); //.cwiseMin(nudged_max).cwiseMax(nudged_min);
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//input->applyScalar(scalar::CompareAndSet, nudged_min, clamped, nullptr); //.cwiseMin(nudged_max).cwiseMax(nudged_min);
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auto wiseMax = LAMBDA_T(x, nudged_min) {
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if (x < nudged_min) {
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return nudged_min;
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}
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return x;
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};
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auto wiseMin = LAMBDA_T(x, nudged_max) {
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if (x > nudged_max) {
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return nudged_max;
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}
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return x;
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};
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auto scaleTensor(*input); // = NDArrayFactory::create(input->ordering(), input->getShapeAsVector(), input->getWorkspace());
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auto clamped(*input); // = NDArrayFactory::create(input->ordering(), input->getShapeAsVector(), input->getWorkspace());
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scaleTensor.assign(scale);
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input->applyLambda<T>(wiseMin, &clamped);
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// const auto clamped = inputs.cwiseMin(nudged_max).cwiseMax(nudged_min);
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clamped.applyLambda<T>(wiseMax, output);
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// const auto clamped_shifted = clamped - nudged_min;
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*output -= nudged_min;
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// auto nudgedScale = scale;
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(*output) /= scaleTensor;
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(*output) += T(0.5f);
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output->applyTransform(transform::Floor, nullptr, nullptr);
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(*output) *= scaleTensor;
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(*output) += nudged_min;
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//output->printIndexedBuffer("FAKE QUANTED");
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/*
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const auto nudged_scale_repl = inputs.constant(nudged_scale);
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const auto clamped = inputs.cwiseMin(nudged_max).cwiseMax(nudged_min);
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const auto clamped_shifted = clamped - nudged_min;
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*output = (clamped_shifted / nudged_scale_repl + 0.5f).floor() *
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nudged_scale_repl +
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nudged_min;
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*/
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}
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void fakeQuantWithMinMaxVars(NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output) {
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BUILD_SINGLE_SELECTOR(input->dataType(), fakeQuantWithMinMaxVars_, (input, min, max, numBits, narrowed, output), FLOAT_TYPES);
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}
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BUILD_SINGLE_TEMPLATE(template void fakeQuantWithMinMaxVars_, (NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output), FLOAT_TYPES);
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}
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}
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}
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