ead368d1ed
benchmarks * DNN: Added partial auto-tuning mode and skeleton for heuristics * Examples: Moduralized benchmarking and now evaluating ResNet-18 shapes
115 lines
3.7 KiB
C++
115 lines
3.7 KiB
C++
#include <cstring>
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#include <cstdio>
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#include <sstream>
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#include "triton/runtime/jit.h"
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#include "triton/driver/backend.h"
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#include "triton/driver/stream.h"
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#include "triton/tools/bench.hpp"
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#include "triton/dnn/shift.h"
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#include "triton/external/half.hpp"
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double do_bench(triton::driver::context* context,
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int32_t R, int32_t S, int32_t B, int32_t F, int32_t H, int32_t W, int32_t C,
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triton::dnn::op_t op, triton::dnn::layout_t layout,
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std::string numeric_t) {
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typedef float NumericT;
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// random shifts
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std::vector<int32_t> shift_h(C);
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std::vector<int32_t> shift_w(C);
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for(int32_t c = 0; c < C; c++){
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shift_h[c] = rand() % R - R / 2;
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shift_w[c] = rand() % S - S / 2;
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}
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// configuration
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triton::dnn::shift shift(B, C, 1, H, W, 1, R, S, F, 1, 1,
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shift_h.data(), shift_w.data(),
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numeric_t, numeric_t,
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op, false, layout);
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// host buffers
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size_t a_size = B*C*H*W;
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size_t b_size = C*F;
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size_t c_size = B*F*H*W;
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if(op == triton::dnn::BPROP)
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std::swap(a_size, c_size);
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if(op == triton::dnn::WGRAD){
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std::swap(b_size, c_size);
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std::swap(a_size, b_size);
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}
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std::vector<NumericT> ha(a_size);
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std::vector<NumericT> hb(b_size);
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std::vector<float> hc(c_size);
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std::vector<float> rc(hc.size());
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// device buffers
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triton::driver::buffer* dc = triton::driver::buffer::create(context, hc.size()*4);
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triton::driver::buffer* da = triton::driver::buffer::create(context, ha.size()*sizeof(NumericT));
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triton::driver::buffer* db = triton::driver::buffer::create(context, hb.size()*sizeof(NumericT));
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triton::driver::stream* stream = triton::driver::stream::create(context);
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// initialize host
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srand(0);
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for(size_t i = 0; i < ha.size(); i++)
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ha[i] = (NumericT)rand() / RAND_MAX;
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for(size_t i = 0; i < hb.size(); i++)
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hb[i] = (NumericT)rand() / RAND_MAX;
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for(size_t i = 0; i < hc.size(); i++)
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hc[i] = 0;
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// initialize device
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stream->write(da, true, 0, ha);
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stream->write(db, true, 0, hb);
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stream->write(dc, true, 0, hc);
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stream->synchronize();
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double nanosec = triton::tools::bench([&]() { shift.enqueue(stream, {da, db, dc});}, stream);
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return shift.num_flops() / nanosec * 1e-3;
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}
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int main() {
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using triton::dnn::op_t;
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using triton::dnn::layout_t;
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struct config_t{
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int32_t B;
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int32_t C;
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int32_t H;
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int32_t W;
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int32_t R;
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int32_t S;
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int32_t F;
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int32_t stride_h;
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int32_t stride_w;
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op_t op;
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layout_t layout;
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std::string ty;
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std::string repr() {
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std::ostringstream oss;
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oss << B << ", " << C << ", " << H << ", " << W << ", " << R << ", " << S << ", " << F << ", " << op << ", " << layout << ", " << ty;
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return oss.str();
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}
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double perf(triton::driver::context *context){
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return do_bench(context, R, S, B, F, H, W, C, op, layout, ty);
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}
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};
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// shapes to benchmark
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std::vector<config_t> configs;
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std::vector<config_t> resnet18 = {
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{128, 128, 32, 32, 3, 3, 128, 1, 1},
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{128, 128, 32, 32, 3, 3, 256, 2, 2},
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{128, 256, 16, 16, 3, 3, 256, 1, 1},
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{128, 256, 16, 16, 3, 3, 512, 2, 2},
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{128, 512, 8, 8, 3, 3, 512, 1, 1},
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{128, 512, 8, 8, 3, 3, 1024, 1, 1},
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{128, 1024, 8, 8, 3, 3, 1024, 1, 1}
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};
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for(config_t c: resnet18){
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for(op_t op: {op_t::FPROP, op_t::BPROP, op_t::WGRAD})
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configs.push_back({c.B, c.C, c.H, c.W, c.R, c.S, c.F, c.stride_h, c.stride_w, op, layout_t::CHWN, "fp16"});
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}
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// initialize default compute device
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auto context = triton::driver::backend::contexts::get_default();
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for(config_t c: configs)
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std::cout << c.repr() << ", " << c.perf(context) << std::endl;
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}
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