fully_connected_layer.cpp
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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "../precomp.hpp"
#include "layers_common.hpp"
#include <opencv2/dnn/shape_utils.hpp>
namespace cv
{
namespace dnn
{
class FullyConnectedLayerInt8Impl CV_FINAL : public InnerProductLayerInt8
{
public:
enum { VEC_ALIGN = 32 };
FullyConnectedLayerInt8Impl(const LayerParams& params)
{
setParamsFrom(params);
output_zp = params.get<int>("zeropoints");
axis = params.get<int>("axis", 1);
if (blobs.size() == 3)
{
// blobs[0] - Weights
// blobs[1] - Bias fused with offset
// blobs[2] - Multipliers for output stage
int numOutput = params.get<int>("num_output");
int innerSize = (int)blobs[0].total() / numOutput;
CV_Assert(blobs[0].dims >= 2 && (size_t)(innerSize * numOutput) == blobs[0].total());
CV_Assert((size_t)numOutput == blobs[1].total());
weightsMat = blobs[0] = blobs[0].reshape(1, numOutput);
int vecsize = weightsMat.cols;
if (vecsize % VEC_ALIGN != 0)
{
int vecsize_aligned = (int)alignSize(vecsize, VEC_ALIGN);
Mat weightsBuf(weightsMat.rows, vecsize_aligned, weightsMat.type());
Mat wpadding = weightsBuf.colRange(vecsize, vecsize_aligned);
wpadding.setTo(Scalar::all(0));
weightsMat = weightsBuf.colRange(0, vecsize);
blobs[0].copyTo(weightsMat);
}
biasMat = blobs[1] = blobs[1].reshape(1, 1);
outputMultiplier = blobs[2];
}
}
bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &) const CV_OVERRIDE
{
int numOutput, cAxis;
CV_CheckEQ(inputs.size(), (size_t)1, "");
CV_CheckEQ(blobs[0].dims, 2, "");
numOutput = blobs[0].size[0];
CV_Assert((size_t)numOutput == blobs[1].total());
cAxis = normalize_axis(axis, inputs[0]);
MatShape outShape(cAxis + 1);
for (int i = 0; i < cAxis; ++i)
outShape[i] = inputs[0][i];
outShape.back() = numOutput;
outputs.resize(1, outShape);
return false;
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV;
}
virtual bool setActivation(const Ptr<ActivationLayer>& layer) CV_OVERRIDE
{
Ptr<ActivationLayerInt8> activ_int8 = layer.dynamicCast<ActivationLayerInt8>();
if (!activ_int8.empty())
{
activ = activ_int8;
if (!activ_int8->blobs.empty())
activ_int8->blobs[0].convertTo(activationLUT, CV_32S);
return true;
}
return false;
}
class FullyConnected : public ParallelLoopBody
{
public:
FullyConnected() : srcMat(0), weights(0), biasMat(0), outputMultiplier(0), activationLUT(0), activ(0),
dstMat(0), nstripes(0), outZp(0), useAVX2(false), useAVX512(false) {}
static void run(const Mat& srcMat, const Mat& weights, const Mat& biasMat, const Mat& outputMultiplier,
const Mat& activationLUT, Mat& dstMat, const ActivationLayerInt8* activ, int nstripes, int outZp)
{
CV_Assert( srcMat.dims == 2 && srcMat.cols == weights.cols &&
dstMat.rows == srcMat.rows && dstMat.cols == weights.rows &&
srcMat.type() == weights.type() && srcMat.type() == CV_8S &&
dstMat.type() == CV_32S && biasMat.type() == CV_32S &&
biasMat.isContinuous() && (int)biasMat.total() == dstMat.cols );
FullyConnected p;
p.srcMat = &srcMat;
p.weights = &weights;
p.biasMat = &biasMat;
p.outputMultiplier = &outputMultiplier;
p.activationLUT = &activationLUT;
p.dstMat = &dstMat;
p.nstripes = nstripes;
p.outZp = outZp;
p.activ = !activationLUT.empty() ? activ : 0;
p.useAVX2 = checkHardwareSupport(CPU_AVX2);
p.useAVX512 = CV_CPU_HAS_SUPPORT_AVX512_SKX;
parallel_for_(Range(0, nstripes), p, nstripes);
}
void operator()(const Range& r) const CV_OVERRIDE
{
int valign = FullyConnectedLayerInt8Impl::VEC_ALIGN;
int nsamples = srcMat->rows;
int nw0 = weights->rows;
int k, vecsize = srcMat->cols;
int vecsize_aligned = (int)alignSize(vecsize, VEC_ALIGN);
size_t total = (size_t)nsamples*nw0;
size_t stripeSize = (total + nstripes - 1)/nstripes;
size_t stripeStart = r.start*stripeSize;
size_t stripeEnd = r.end == nstripes ? total : std::min(r.end*stripeSize, total);
size_t wstep = weights->step1();
AutoBuffer<int8_t> srcbuf(vecsize_aligned + valign);
int8_t* sptr = alignPtr(srcbuf.data(), (int)(valign*sizeof(int8_t)));
const int* lutptr = !activationLUT->empty() ? activationLUT->ptr<int>() : 0;
for( k = vecsize; k < vecsize_aligned; k++ )
sptr[k] = 0;
for( size_t ofs = stripeStart; ofs < stripeEnd; )
{
int sampleIdx = (int)(ofs / nw0);
int delta = (int)(ofs - (size_t)sampleIdx*nw0);
const int8_t* sptr_ = srcMat->ptr<int8_t>(sampleIdx);
const int8_t* wptr = weights->ptr<int8_t>(delta);
int* dptr = dstMat->ptr<int>(sampleIdx) + delta;
const int* biasptr = biasMat->ptr<int>() + delta;
const float* multptr = outputMultiplier->ptr<float>() + delta;
int nw = std::min(nw0 - delta, (int)(stripeEnd - ofs));
memcpy(sptr, sptr_, vecsize*sizeof(sptr[0]));
#if CV_TRY_AVX512_SKX
if( useAVX512 )
opt_AVX512_SKX::fastGEMM1T( sptr, wptr, wstep, biasptr, multptr, dptr, nw, vecsize, outZp );
else
#endif
#if CV_TRY_AVX2
if( useAVX2 )
opt_AVX2::fastGEMM1T( sptr, wptr, wstep, biasptr, multptr, dptr, nw, vecsize, outZp );
else
#endif
{
int i = 0;
#if CV_SIMD
for( ; i <= nw - 4; i += 4, wptr += 4*wstep )
{
v_int32x4 vs0 = v_setzero_s32(), vs1 = v_setzero_s32(),
vs2 = v_setzero_s32(), vs3 = v_setzero_s32();
v_int32x4 outzp = v_setall_s32(outZp), outmin = v_setall_s32(-128), outmax = v_setall_s32(127);
v_int32x4 s = v_load(biasptr + i);
v_float32x4 mult = v_load(multptr + i);
for( k = 0; k < vecsize; k += 16 )
{
v_int8x16 v = v_load_aligned(sptr + k);
vs0 = v_dotprod_expand_fast(v, v_load_aligned(wptr + k), vs0);
vs1 = v_dotprod_expand_fast(v, v_load_aligned(wptr + wstep + k), vs1);
vs2 = v_dotprod_expand_fast(v, v_load_aligned(wptr + wstep*2 + k), vs2);
vs3 = v_dotprod_expand_fast(v, v_load_aligned(wptr + wstep*3 + k), vs3);
}
s += v_int32x4(v_reduce_sum(vs0), v_reduce_sum(vs1), v_reduce_sum(vs2), v_reduce_sum(vs3));
v_int32x4 out = outzp + v_round(v_cvt_f32(s)*mult);
v_store(dptr + i, v_min(v_max(out, outmin), outmax));
}
#endif
for( ; i < nw; i++, wptr += wstep )
{
int s0 = biasptr[i];
float mult0 = multptr[i];
for( k = 0; k < vecsize; k++ )
{
int8_t v = sptr[k];
s0 += (int)v*wptr[k];
}
int out0 = outZp + (int)std::round(s0*mult0);
dptr[i] = std::min(std::max(out0, -128), 127);
}
}
if(activ)
activ->forwardSlice(dptr, lutptr, dptr, 1, 1, delta, delta + nw);
ofs += nw;
}
}
const Mat *srcMat, *weights, *biasMat, *outputMultiplier, *activationLUT;
const ActivationLayerInt8* activ;
Mat* dstMat;
int nstripes, outZp;
bool useAVX2;
bool useAVX512;
};
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
std::vector<Mat> input, output;
inputs_arr.getMatVector(input);
outputs_arr.getMatVector(output);
int axisCan = normalize_axis(axis, input[0].dims);
int outerSize = input[0].total(0, axisCan);
Mat srcMat = input[0].reshape(1, outerSize);
Mat dstMat = output[0].reshape(1, outerSize);
Mat dstMatInt32= Mat(shape(dstMat), CV_32S);
const int nstripes = getNumThreads();
FullyConnected::run(srcMat, weightsMat, biasMat, outputMultiplier, activationLUT, dstMatInt32, activ.get(), nstripes, output_zp);
dstMatInt32.convertTo(dstMat, CV_8S);
}
virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
const std::vector<MatShape> &outputs) const CV_OVERRIDE
{
CV_UNUSED(inputs); // suppress unused variable warning
long flops = 0;
int innerSize = blobs[0].size[1];
for(int i = 0; i < outputs.size(); i++)
{
flops += CV_BIG_INT(3)*innerSize*total(outputs[i]);
}
return flops;
}
Mat weightsMat, biasMat, outputMultiplier, activationLUT;
Ptr<ActivationLayerInt8> activ;
};
Ptr<InnerProductLayerInt8> InnerProductLayerInt8::create(const LayerParams& params)
{
return Ptr<InnerProductLayerInt8>(new FullyConnectedLayerInt8Impl(params));
}
}
}