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[general] a bunch of fixes in anticipation of proper triton vs cudnn

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benchmarks

* DNN: Added partial auto-tuning mode and skeleton for heuristics
* Examples: Moduralized benchmarking and now evaluating ResNet-18 shapes
This commit is contained in:
Philippe Tillet
2019-07-21 20:17:56 -07:00
parent b1d81a5802
commit ead368d1ed
10 changed files with 221 additions and 147 deletions
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65
examples/cpp/shift.cpp
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@@ -10,7 +10,7 @@
double do_bench(triton::driver::context* context,
int32_t R, int32_t S, int32_t B, int32_t F, int32_t H, int32_t W, int32_t C,
triton::dnn::shift::op_t op, triton::dnn::shift::layout_t layout,
triton::dnn::op_t op, triton::dnn::layout_t layout,
std::string numeric_t) {
typedef float NumericT;
@@ -25,14 +25,14 @@ double do_bench(triton::driver::context* context,
triton::dnn::shift shift(B, C, 1, H, W, 1, R, S, F, 1, 1,
shift_h.data(), shift_w.data(),
numeric_t, numeric_t,
op, false, triton::dnn::shift::CHWN);
op, false, layout);
// host buffers
size_t a_size = B*C*H*W;
size_t b_size = C*F;
size_t c_size = B*F*H*W;
if(op == triton::dnn::shift::BPROP)
if(op == triton::dnn::BPROP)
std::swap(a_size, c_size);
if(op == triton::dnn::shift::WGRAD){
if(op == triton::dnn::WGRAD){
std::swap(b_size, c_size);
std::swap(a_size, b_size);
}
@@ -58,20 +58,57 @@ double do_bench(triton::driver::context* context,
stream->write(db, true, 0, hb);
stream->write(dc, true, 0, hc);
stream->synchronize();
shift.enqueue(stream, {da, db, dc}, true);
double tns = triton::tools::bench([&]() { shift.enqueue(stream, {da, db, dc}, true);}, stream);
std::cout << tns << std::endl;
double nanosec = triton::tools::bench([&]() { shift.enqueue(stream, {da, db, dc});}, stream);
return shift.num_flops() / nanosec * 1e-3;
}
int main() {
using triton::dnn::op_t;
using triton::dnn::layout_t;
struct config_t{
int32_t B;
int32_t C;
int32_t H;
int32_t W;
int32_t R;
int32_t S;
int32_t F;
int32_t stride_h;
int32_t stride_w;
op_t op;
layout_t layout;
std::string ty;
std::string repr() {
std::ostringstream oss;
oss << B << ", " << C << ", " << H << ", " << W << ", " << R << ", " << S << ", " << F << ", " << op << ", " << layout << ", " << ty;
return oss.str();
}
double perf(triton::driver::context *context){
return do_bench(context, R, S, B, F, H, W, C, op, layout, ty);
}
};
// shapes to benchmark
std::vector<config_t> configs;
std::vector<config_t> resnet18 = {
{128, 128, 32, 32, 3, 3, 128, 1, 1},
{128, 128, 32, 32, 3, 3, 256, 2, 2},
{128, 256, 16, 16, 3, 3, 256, 1, 1},
{128, 256, 16, 16, 3, 3, 512, 2, 2},
{128, 512, 8, 8, 3, 3, 512, 1, 1},
{128, 512, 8, 8, 3, 3, 1024, 1, 1},
{128, 1024, 8, 8, 3, 3, 1024, 1, 1}
};
for(config_t c: resnet18){
for(op_t op: {op_t::FPROP, op_t::BPROP, op_t::WGRAD})
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"});
}
// initialize default compute device
auto context = triton::driver::backend::contexts::get_default();
// shapes
int32_t R = 3, S = 3;
int32_t B = 16, F = 4096;
int32_t H = 32, W = 32;
int32_t C = 4096;
// benchmark
do_bench(context, R, S, B, F, H, W, C, triton::dnn::shift::FPROP, triton::dnn::shift::CHWN, "fp16");
for(config_t c: configs)
std::cout << c.repr() << ", " << c.perf(context) << std::endl;
}