139 lines
6.2 KiB
Plaintext
139 lines
6.2 KiB
Plaintext
/*******************************************************************************
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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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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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// fakeQuantWithMinMaxVars_
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// input - input tensor
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// min - min scalar tensor
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// max - max scalar tensor
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// numBits - (default 16bit)
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// narrowed - shrink is true
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// output - output tensor
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//
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template <typename T>
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static __host__ __device__ void
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nudge(T min, T max, int quantMin, int quantMax, T* scale, T* nudgedMin, T* nudgedMax) {
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T quantMaxF = static_cast<T>(quantMax);
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T quantMinF = static_cast<T>(quantMin);
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*scale = (max - min) / (quantMaxF - quantMinF);
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auto zeroPointFromMin = quantMinF - min / *scale;
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uint16_t const nudgedZeroPoint = [zeroPointFromMin, quantMin, quantMax, quantMaxF, quantMinF] {
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if (zeroPointFromMin < quantMinF) {
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return static_cast<uint16_t>(quantMin);
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}
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if (zeroPointFromMin > quantMaxF) {
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return static_cast<uint16_t>(quantMax);
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}
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return nd4j::math::nd4j_round<T,uint16_t>(zeroPointFromMin);
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}();
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*nudgedMin = (quantMinF - nudgedZeroPoint) * (*scale);
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*nudgedMax = (quantMaxF - nudgedZeroPoint) * (*scale);
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}
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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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min->syncToHost(); // these are scalars, so nothing much happened
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max->syncToHost();
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T scale, nudgedMin, nudgedMax;
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nudge(min->t<T>(0), max->t<T>(0), lowIntBound, upperIntBound, &scale, &nudgedMin, &nudgedMax);
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auto wiseMinMaxAndSoOn = LAMBDA_T(x, nudgedMin, nudgedMax, scale) {
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T val = x;
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if (x < nudgedMin) {
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val = nudgedMin;
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}
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else if (x > nudgedMax) {
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val = nudgedMax;
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}
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else
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val = x;
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return (math::nd4j_floor<T,T>((val - nudgedMin) / scale + T(0.5)) * scale + nudgedMin);
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};
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input->applyLambda(wiseMinMaxAndSoOn, output);
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}
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template <typename T>
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static __global__ void fakeQuantWithMinMaxKernel(T* input, Nd4jLong* inputShape, T* min, T* max,
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int lowIntBound, int upperIntBound, Nd4jLong channels,
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T* output, Nd4jLong* outputShape, Nd4jLong length) {
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__shared__ int block;
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if (threadIdx.x == 0) {
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block = length / channels; // to loop with last dimension as block
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}
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__syncthreads();
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for (auto i = blockIdx.x; i < (int)channels; i += gridDim.x) {
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T scale, nudgedMin, nudgedMax;
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nudge(min[i], max[i], lowIntBound, upperIntBound, &scale, &nudgedMin, &nudgedMax);
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// loop over blocks to quantization between nudged min and max
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for (auto b = threadIdx.x; b < block; b += blockDim.x) {
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T val = input[shape::getIndexOffset(b * channels + i, inputShape)];
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if (val < nudgedMin) {
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val = nudgedMin;
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} else if (val > nudgedMax) {
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val = nudgedMax;
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}
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output[shape::getIndexOffset(b * channels + i, outputShape)] =
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(math::nd4j_floor<T, T>((val - nudgedMin) / scale + T(0.5)) * scale + nudgedMin);
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};
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}
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}
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template <typename T>
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void fakeQuantWithMinMaxVarsPerChannel_(LaunchContext* context, 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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auto channels = min->lengthOf();
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auto length = input->lengthOf();
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NDArray::prepareSpecialUse({output}, {min, max, input});
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auto stream = context->getCudaStream();
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T* inputBuf = input->dataBuffer()->specialAsT<T>();
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T* outputBuf = output->dataBuffer()->specialAsT<T>();
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T* minBuf = min->dataBuffer()->specialAsT<T>();
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T* maxBuf = max->dataBuffer()->specialAsT<T>();
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fakeQuantWithMinMaxKernel<<<128, 256, 256, *stream>>>(inputBuf, input->specialShapeInfo(),
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minBuf, maxBuf, lowIntBound, upperIntBound, channels, outputBuf, output->specialShapeInfo(), length);
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NDArray::registerSpecialUse({output}, {min, max, input});
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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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void fakeQuantWithMinMaxVarsPerChannel(LaunchContext* context, NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output) {
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BUILD_SINGLE_SELECTOR(input->dataType(), fakeQuantWithMinMaxVarsPerChannel_, (context, 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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BUILD_SINGLE_TEMPLATE(template void fakeQuantWithMinMaxVarsPerChannel_, (LaunchContext* context, 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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