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#include <benchmarks/cpp/nvfuser/utils.h>
#include <torch/csrc/jit/codegen/cuda/scheduler/all_schedulers.h>
#include <sstream>
using namespace torch::jit::fuser::cuda;
std::string toString(const ReductionParams& rparams) {
std::stringstream ss;
ss << (rparams.fastest_dim ? "Red On Fastest Dim // " : "Red On Slow Dim // ")
<< (rparams.persistent_kernel ? "Persistent Kernel // " : "")
<< (rparams.project_persistent_buffers ? "Project Persistent Buffers // "
: "");
if (rparams.schedule_3D) {
ss << "3D Schedule // "
<< "Outer Reduction: "
<< (rparams.cross_block_outer_reduction ? "cross block / " : "")
<< (rparams.cross_grid_outer_reduction ? "cross grid / " : "")
<< (rparams.split_grid_dim_outer_reduction ? "split grid dim / " : "");
if (rparams.batches_per_block_outer_reduction > 1 ||
rparams.persistent_kernel) {
ss << "persistent batch - " << rparams.batches_per_block_outer_reduction
<< " / ";
}
}
ss << " // Iteration Domain: "
<< (rparams.multiple_reds_per_blk ? "multiple reductions per block / "
: "")
<< (rparams.split_grid_dim_iter_dom ? "split grid dimension / " : "")
<< (rparams.vectorize_iter_dom ? "vectorize / " : "")
<< (rparams.unroll_factor_iter_dom > 1 && !rparams.vectorize_iter_dom
? "unroll / "
: "");
if (rparams.unroll_factor_iter_dom > 1 || rparams.vectorize_iter_dom) {
ss << "factor " << rparams.unroll_factor_iter_dom;
}
ss << " // Inner Reduction Domain: "
<< (rparams.cross_block_inner_reduction ? "cross block reduction / " : "")
<< (rparams.pad_inner_reduction_to_warp ? "pad to warp / " : "")
<< (rparams.cross_grid_inner_reduction ? "cross grid reduction / " : "");
if (rparams.batches_per_block_inner_reduction > 1 ||
rparams.persistent_kernel) {
ss << "persistent batch - " << rparams.batches_per_block_inner_reduction
<< " / ";
}
ss << (rparams.cross_grid_inner_reduction &&
rparams.split_grid_dim_inner_reduction
? "split grid dimension / "
: "")
<< (rparams.vectorize_inner_reduction ? "vectorize / " : "")
<< (rparams.unroll_factor_inner_reduction > 1 &&
!rparams.vectorize_inner_reduction
? "unroll / "
: "");
if (rparams.unroll_factor_inner_reduction > 1 ||
rparams.vectorize_inner_reduction) {
ss << "factor " << rparams.unroll_factor_inner_reduction;
}
return ss.str();
}
std::string toString(const PointwiseParams& params) {
std::stringstream ss;
if (params.break_point) {
ss << "2D Schedule at " << params.break_point << "/";
if (params.split_block) {
ss << " Split block into y-dim/";
}
if (params.split_grid_y_dim) {
ss << " Split y grid dim/";
}
} else {
ss << "1D"
<< "/";
}
if (params.unroll_factor > 1) {
if (params.vectorize) {
ss << "Vectorize, Factor: " << params.unroll_factor;
} else {
ss << "Unroll, Factor: " << params.unroll_factor;
}
}
return ss.str();
}
std::string toString(const TransposeParams& params) {
std::stringstream ss;
ss << "Tile size: (" << params.tile_size1 << "," << params.tile_size2
<< ")/";
ss << "Vectorize size: (" << params.vectorize_factor1 << ","
<< params.vectorize_factor2 << ")";
return ss.str();
}
std::string toString(const std::shared_ptr<HeuristicParams>& params) {
auto rparams = std::dynamic_pointer_cast<ReductionParams>(params);
if (rparams) {
return toString(*rparams);
}
auto pparams = std::dynamic_pointer_cast<PointwiseParams>(params);
if (pparams) {
return toString(*pparams);
}
auto tparams = std::dynamic_pointer_cast<TransposeParams>(params);
if (tparams) {
return toString(*tparams);
}
TORCH_INTERNAL_ASSERT(
false,
"Unknown heuristic parameter type. Did you just added a new heuristic parameter type but forget to update here?");
}
std::string toString(LaunchParams lparams) {
std::stringstream ss;
lparams.toString();
ss << "/Launch_Parameters["
<< "block(" << lparams.bdimz() << "/" << lparams.bdimy() << "/"
<< lparams.bdimx() << ")/grid(" << lparams.gdimz() << "/"
<< lparams.gdimy() << "/" << lparams.gdimx() << ")/" << lparams.smem()
<< "]";
return ss.str();
}
void clearL2Cache() {
torch::NoGradGuard no_grad;
auto l2_cache_size = at::cuda::getCurrentDeviceProperties()->l2CacheSize;
auto options =
torch::TensorOptions().dtype(torch::kFloat32).device(at::kCUDA, 0);
auto l2_elems = l2_cache_size / 4;
torch::Tensor t0 = torch::empty(l2_elems, options);
torch::Tensor t1 = torch::clone(t0);
};
TensorView* makeSymbolicTensor(size_t ndims, DataType dtype) {
return TensorViewBuilder().ndims(ndims).dtype(dtype).build();
}
TensorView* makeContigTensor(size_t ndims, DataType dtype) {
return TensorViewBuilder()
.ndims(ndims)
.dtype(dtype)
.contiguity(std::vector<bool>(ndims, true))
.build();
}
TensorView* makeConcreteTensor(std::vector<int64_t> shape, DataType dtype) {
return TensorViewBuilder().shape(shape).dtype(dtype).build();
}
TensorView* makeContigConcreteTensor(
std::vector<int64_t> shape,
DataType dtype) {
return TensorViewBuilder()
.shape(shape)
.dtype(dtype)
.contiguity(std::vector<bool>(shape.size(), true))
.build();
}
void runBenchmarkIterations(
benchmark::State& benchmark_state,
FusionExecutorCache* fusion_executor_cache,
std::vector<c10::IValue>& aten_inputs) {
fusion_executor_cache->runFusionWithInputs(aten_inputs);
bool segmented =
fusion_executor_cache->getMostRecentKernelRuntime()->isSegmented() &&
fusion_executor_cache->getMostRecentKernelRuntime()
->fusionSegments()
->groups()
.size() > 1;
if (!segmented) {
fusion_executor_cache->profile(true);
fusion_executor_cache->runFusionWithInputs(aten_inputs);
auto compile_log = fusion_executor_cache->getMostRecentExecutorInfo();
auto executor_instance = compile_log.fusion_executor;
auto params = toString(compile_log.params);
auto lparams = toString(compile_log.fusion_executor->lastLaunchParams());
benchmark_state.SetLabel(params + lparams);
executor_instance->setMeasureKernelTimeFlag(true);
// Sync everything up before we start
C10_CUDA_CHECK(cudaDeviceSynchronize());
for (auto _ : benchmark_state) {
clearL2Cache();
auto cg_outputs = fusion_executor_cache->runFusionWithInputs(aten_inputs);
benchmark_state.SetIterationTime(
executor_instance->kernelTimeMs() / 1000.0);
}
// Sync everything up before we're finished, don't want to run ahead on the
// cpu while benchmarking.
C10_CUDA_CHECK(cudaDeviceSynchronize());
} else {
// Segmented
// Sync everything up before we start
{
// Compile/warmup
auto cg_outputs = fusion_executor_cache->runFusionWithInputs(aten_inputs);
}
C10_CUDA_CHECK(cudaDeviceSynchronize());
CudaKernelTimer timer;
for (auto _ : benchmark_state) {
clearL2Cache();
timer.restart();
auto cg_outputs = fusion_executor_cache->runFusionWithInputs(aten_inputs);
benchmark_state.SetIterationTime(timer.elapsed() / 1000.0);
}
// Sync everything up before we're finished, don't want to run ahead on the
// cpu while benchmarking.
C10_CUDA_CHECK(cudaDeviceSynchronize());
}
}
namespace executorCache {
thread_local ExecutorMap executor_map_;
ExecutorMap& getGlobalMap() {
return executor_map_;
}
} // namespace executorCache
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