287 lines
8.3 KiB
C++
287 lines
8.3 KiB
C++
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#include <vector>
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#include <chrono>
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#include "triton/driver/device.h"
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#include <algorithm>
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template<class T, bool AT, bool BT>
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void simple_gemm(std::vector<T> &c, const std::vector<T> &a, const std::vector<T> &b, size_t M, size_t N, size_t K){
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for(size_t m = 0; m < M; m++)
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for(size_t n = 0; n < N; n++){
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T acc = 0;
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for(size_t k = 0; k < K; k++)
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acc += (AT?a[k + m*K]:a[m + k*M]) * (BT?b[n + k*N]:b[k + n*K]);
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c[m + n*M] = acc;
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}
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}
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class timer{
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typedef std::chrono::high_resolution_clock high_resolution_clock;
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typedef std::chrono::nanoseconds nanoseconds;
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public:
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explicit timer(bool run = false)
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{ if (run) start(); }
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void start()
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{ _start = high_resolution_clock::now(); }
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nanoseconds get() const
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{ return std::chrono::duration_cast<nanoseconds>(high_resolution_clock::now() - _start); }
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private:
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high_resolution_clock::time_point _start;
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};
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template<class T>
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T min(std::vector<T> x)
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{ return *std::min_element(x.begin(), x.end()); }
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template<class OP, class SYNC>
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double bench(OP const & op, SYNC const & sync, triton::driver::device const & device)
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{
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timer tmr;
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std::vector<size_t> times;
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double total_time = 0;
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op();
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sync();
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while(total_time*1e-9 < 1e-3){
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float norm = 1;
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// normalize clock if possible to get roughly constant result
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if(auto cu_device = dynamic_cast<const triton::driver::cu_device*>(&device))
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norm = (float)cu_device->current_sm_clock()/cu_device->max_sm_clock();
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tmr.start();
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op();
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sync();
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times.push_back(norm*tmr.get().count());
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total_time+=times.back();
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}
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return min(times);
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}
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//
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void build_conv_lut(int TK,
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int stride_d, int stride_h, int stride_w, int stride_c,
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int pad_d, int pad_h, int pad_w,
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int T, int R, int S,
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std::vector<int>& res, std::vector<int>& masks) {
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/* convolution parameters */
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int F = T * R * S;
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int Nlut = (TK + F - 1) / F * F;
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int upsample_w = 1;
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int upsample_h = 1;
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int upsample_d = 1;
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/* unpack index wrt filters */
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auto unpack = [&](int32_t trs){
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int32_t tr = trs / S;
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int32_t s = trs - tr*S;
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int32_t t = tr / R;
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int32_t r = tr - t*R;
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return std::make_tuple(t, r, s);
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};
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/* increments */
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for(size_t i = 0; i < Nlut; ++i)
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res[i] = (((i + TK) % Nlut) - i);
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/* deltas */
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size_t Ds0 = Nlut;
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size_t Ds1 = upsample_w;
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size_t Ds2 = upsample_h;
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size_t Ds3 = upsample_d;
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for(size_t pd = 0; pd < Ds3; ++pd)
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for(size_t ph = 0; ph < Ds2; ++ph)
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for(size_t pw = 0; pw < Ds1; ++pw){
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int32_t* deltas_ptr = &res[Nlut + pw*Ds0 + ph*Ds0*Ds1 + pd*Ds0*Ds1*Ds2];
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// cumulative increments
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for(size_t i = 0; i < Ds0; ++i){
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int32_t ctrs = i;
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int32_t c = ctrs / F;
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int32_t t, r, s;
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std::tie(t, r, s) = unpack(ctrs % F);
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// next indices
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int32_t nextctrs = ctrs + TK;
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int32_t nextc = nextctrs / F;
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int32_t nextt, nextr, nexts;
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std::tie(nextt, nextr, nexts) = unpack(nextctrs % F);
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// diffs
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int32_t cdiff = nextc - c;
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int32_t tdiff = (nextt + pd)/upsample_d - (t + pd)/upsample_d;
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int32_t rdiff = (nextr + ph)/upsample_h - (r + ph)/upsample_h;
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int32_t sdiff = (nexts + pw)/upsample_w - (s + pw)/upsample_w;
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// delta pointers
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deltas_ptr[i] = cdiff*stride_c + sdiff*stride_w + rdiff*stride_h + tdiff*stride_d;
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}
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}
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/* Masks */
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size_t Ms0 = Nlut;
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size_t Ms1 = 2*pad_w + 1;
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size_t Ms2 = 2*pad_h + 1;
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size_t Ms3 = 2*pad_d + 1;
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for(size_t pd = 0; pd < Ms3; ++pd)
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for(size_t ph = 0; ph < Ms2; ++ph)
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for(size_t pw = 0; pw < Ms1; ++pw){
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int32_t* masks_ptr = &masks[Nlut + pw*Ms0 + ph*Ms0*Ms1 + pd*Ms0*Ms1*Ms2];
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for(size_t i = 0; i < Ms0; ++i){
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int32_t t, r, s;
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int32_t mask = 0x0;
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for(size_t j = 0; j < TK; ++j){
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std::tie(t, r, s) = unpack((i + j) % F);
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bool in_bounds_d = (t + pd) >= pad_d && (t + pd) < (T + pad_d);
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bool in_bounds_h = (r + ph) >= pad_h && (r + ph) < (R + pad_h);
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bool in_bounds_w = (s + pw) >= pad_w && (s + pw) < (S + pad_w);
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mask |= (in_bounds_d && in_bounds_h && in_bounds_w) << j;
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}
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masks_ptr[i] = mask;
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}
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}
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for(size_t i = 0; i < Nlut; ++i)
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masks[i] = 0x0;
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}
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// Index computation
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inline int32_t idx(int32_t x, int32_t y, int32_t z, int32_t w, int32_t u,
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int32_t /*s0*/, int32_t s1, int32_t s2, int32_t s3, int32_t s4)
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{ return u + w*s4 + z*s4*s3 + y*s4*s3*s2 + x*s4*s3*s2*s1; }
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// Pack
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template <class T> T clamp(T x, T lo, T hi){
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return std::max<T>(lo, std::min<T>(x, hi));
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}
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template<class T, class U>
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T pack(U* tmp, U scale);
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template<>
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double pack<double, double>(double* tmp, double scale)
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{ return tmp[0]*scale; }
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template<>
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float pack<float, float>(float* tmp, float scale)
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{ return tmp[0]*scale; }
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template<>
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int pack<int, float>(float* tmp, float scale)
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{
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int res = 0;
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for(int i = 0; i < 4; i++){
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int8_t clamped = std::round(clamp(tmp[i]*scale, (float)-128, (float)127));
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res |= (clamped & 0xFF) << (8*i);
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}
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return res;
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}
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template<class T> struct pack_increment
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{ enum{ VALUE = 1}; };
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template<> struct pack_increment<int>
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{ enum{ VALUE = 4}; };
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// Dot
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template<class T>
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inline T dot(T x, T y, T z)
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{
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return std::fma(x, y, z);
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}
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inline int dot(int x, int y, int z){
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int res = 0;
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for(int i = 0; i < 4; i++){
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int32_t a = ((x >> (8*i)) & 0x000000FF);
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int32_t b = ((y >> (8*i)) & 0x000000FF);
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res += (*(int8_t*)(&a)) * (*(int8_t*)(&b));
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}
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return res + z;
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}
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template<class IN_DTYPE, class OUT_DTYPE>
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void cpp_conv_nchw(int32_t C, int32_t N, int32_t K,
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int32_t D, int32_t H, int32_t W,
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int32_t T, int32_t R, int32_t S,
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int32_t pad_d, int32_t pad_h, int32_t pad_w,
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int32_t stride_d, int32_t stride_h, int32_t stride_w,
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int32_t M, int32_t P, int32_t Q,
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std::vector<OUT_DTYPE>& O,
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const std::vector<IN_DTYPE>& I,
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const std::vector<IN_DTYPE>& F)
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{
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static const int PACK_IN = pack_increment<IN_DTYPE>::VALUE;
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static const int PACK_OUT = pack_increment<OUT_DTYPE>::VALUE;
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if(C % PACK_IN != 0) throw std::runtime_error("Number of input channels must be a multiple of 4");
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if(K % PACK_OUT != 0) throw std::runtime_error("Number of output channels must be a multiple of 4");
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C /= PACK_IN;
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K /= PACK_OUT;
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int32_t Kout = K;
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IN_DTYPE accs[PACK_OUT];
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float tmp[PACK_OUT];
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for(int32_t m = 0 ; m < M; ++m)
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for(int32_t p = 0 ; p < P; ++p)
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for(int32_t q = 0; q < Q; ++q)
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for(int32_t n = 0; n < N; ++n)
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for(int32_t k = 0; k < Kout ; ++k)
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{
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for(int32_t i = 0; i < PACK_OUT; ++i)
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accs[i] = 0;
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int32_t mm = m*stride_d - pad_d;
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int32_t pp = p*stride_h - pad_h;
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int32_t qq = q*stride_w - pad_w;
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for(int32_t kk = 0; kk < PACK_OUT; ++kk)
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for(int32_t c = 0; c < C; ++c)
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for(int32_t t = 0; t < T; ++t)
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for(int32_t r = 0; r < R; ++r)
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for(int32_t s = 0; s < S; ++s){
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int32_t d = mm + t;
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int32_t h = pp + r;
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int32_t w = qq + s;
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bool in_bounds = (d >= 0 && h >= 0 && w >= 0 && d < D && h < H && w < W);
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IN_DTYPE i = in_bounds?I[idx(n, c, d, h, w, N, C, D, H, W)]:0;
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IN_DTYPE f = F[idx(c, t, r, s, k*PACK_OUT + kk, C, T, R, S, K*PACK_OUT)];
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accs[kk] = dot(i, f, accs[kk]);
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}
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for(int32_t kk = 0; kk < PACK_OUT; ++kk){
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tmp[kk] = accs[kk];
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}
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O[idx(n, k, m, p, q, N, K, M, P, Q)] = tmp[0];
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}
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}
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// input layout: C, H, W, BS
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// filter layout: C, K
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// output layout: K, H, W, BS
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template<class IN_DTYPE, class OUT_DTYPE>
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void shift_conv(int32_t C, int32_t H, int32_t W, int32_t BS,
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int32_t K,
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std::vector<OUT_DTYPE>& O,
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const std::vector<IN_DTYPE>& I,
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const std::vector<IN_DTYPE>& F,
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const std::vector<int32_t> shift_h,
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const std::vector<int32_t> shift_w)
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{
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OUT_DTYPE acc;
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for(int32_t p = 0; p < H; ++p)
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for(int32_t q = 0; q < W; ++q)
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for(int32_t bs = 0; bs < BS; ++bs)
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for(int32_t k = 0; k < K; ++k)
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{
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acc = 0;
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for(int32_t c = 0; c < C; ++c){
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int32_t h = p + shift_h[c];
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int32_t w = q + shift_w[c];
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bool in_bounds = (h >= 0 && w >= 0 && h < H && w < W);
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IN_DTYPE a = in_bounds?I[bs + w*BS + h*BS*W + c*BS*H*W]:0;
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IN_DTYPE b = F[k + c*K];
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acc = dot(a, b, acc);
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}
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O[bs + q*BS + p*BS*W + k*BS*H*W] = acc;
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}
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}
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