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#include <benchmark/benchmark.h>
#include <torch/csrc/jit/jit_log.h>
#include <torch/csrc/jit/tensorexpr/ir.h>
#include <torch/csrc/jit/tensorexpr/ir_simplifier.h>
#include <torch/csrc/jit/tensorexpr/llvm_codegen.h>
#include <torch/csrc/jit/tensorexpr/loopnest.h>
#include <torch/csrc/jit/tensorexpr/operators/operators.h>
#include <torch/csrc/jit/tensorexpr/tensor.h>
#include <torch/torch.h>
using namespace torch::jit::tensorexpr;
namespace {
class SignedLog1pBench : public benchmark::Fixture {
public:
void SetUp(const benchmark::State& state) override {
input_size_ = {state.range(0), state.range(1)};
input_size_int_ = {state.range(0), state.range(1)};
input_ = torch::rand(input_size_);
ref_ = signedLog1p(input_);
}
void TearDown(benchmark::State& state) override {
TORCH_CHECK(at::allclose(ref_, output_, 1e-3, 1e-3));
state.counters["GB/s"] = benchmark::Counter(
uint64_t(state.iterations()) * 2 * output_.nbytes(),
benchmark::Counter::kIsRate);
}
at::Tensor signedLog1p(const at::Tensor& inp) {
auto sign = at::sign(inp);
auto log1p = at::log1p(at::abs(inp));
return sign * log1p;
}
void runATen(benchmark::State& state) {
for (auto _ : state) {
output_ = signedLog1p(input_);
}
}
void runNNC(benchmark::State& state) {
BufHandle input_ph(
"input", {input_size_int_[0], input_size_int_[1]}, kFloat);
Tensor abs_result = Compute(
"aten_abs",
{input_size_int_[0], input_size_int_[1]},
[&](const VarHandle& m, const VarHandle& n) {
return abs(input_ph.load(m, n));
});
Tensor log1p_result = Compute(
"aten_log1p",
{input_size_int_[0], input_size_int_[1]},
[&](const VarHandle& m, const VarHandle& n) {
return log1p(abs_result.load(m, n));
});
Tensor sign_result =
computeSign({input_ph}, {input_size_int_[0], input_size_int_[1]});
Tensor output = Compute(
"aten_mul",
{input_size_int_[0], input_size_int_[1]},
[&](const VarHandle& m, const VarHandle& n) {
return sign_result.load(m, n) * log1p_result.load(m, n);
});
LoopNest nest({output}, {abs_result, log1p_result, sign_result, output});
GRAPH_DEBUG("Original Stmt: ", *nest.root_stmt());
nest.inlineIntermediateBufs(true);
nest.prepareForCodegen();
nest.simplify();
nest.vectorizeInnerLoops();
nest.simplify();
GRAPH_DEBUG("Final stmt: ", *nest.root_stmt());
// StmtPtr s = IRSimplifier::simplify(nest.root_stmt());
std::vector<CodeGen::BufferArg> buf_args;
buf_args.emplace_back(input_ph);
buf_args.emplace_back(output);
LLVMCodeGen cg(nest.root_stmt(), buf_args);
std::vector<CodeGen::CallArg> call_args;
for (auto _ : state) {
output_ = at::empty_like(ref_);
call_args.clear();
call_args.emplace_back(input_.data_ptr<float>());
call_args.emplace_back(output_.data_ptr<float>());
cg.call(call_args);
}
}
void runNNCLogVml(benchmark::State& state) {
BufHandle input_ph(
"input", {input_size_int_[0], input_size_int_[1]}, kFloat);
Tensor abs_result = Compute(
"aten_abs",
{input_size_int_[0], input_size_int_[1]},
[&](const VarHandle& m, const VarHandle& n) {
return abs(input_ph.load(m, n));
});
Tensor log_vml_result = Compute(
"aten_log1p",
{input_size_int_[0], input_size_int_[1]},
[&](const VarHandle& m, const VarHandle& n) {
return log_vml(abs_result.load(m, n) + ExprHandle(1));
});
Tensor sign_result =
computeSign({input_ph}, {input_size_int_[0], input_size_int_[1]});
Tensor output = Compute(
"aten_mul",
{input_size_int_[0], input_size_int_[1]},
[&](const VarHandle& m, const VarHandle& n) {
return sign_result.load(m, n) * log_vml_result.load(m, n);
});
LoopNest nest({output}, {abs_result, log_vml_result, sign_result, output});
GRAPH_DEBUG("Original Stmt: ", *nest.root_stmt());
nest.inlineIntermediateBufs(true);
nest.prepareForCodegen();
nest.simplify();
nest.vectorizeInnerLoops();
nest.simplify();
GRAPH_DEBUG("Final stmt: ", *nest.root_stmt());
// StmtPtr s = IRSimplifier::simplify(nest.root_stmt());
std::vector<CodeGen::BufferArg> buf_args;
buf_args.emplace_back(input_ph);
buf_args.emplace_back(output);
LLVMCodeGen cg(nest.root_stmt(), buf_args);
std::vector<CodeGen::CallArg> call_args;
for (auto _ : state) {
output_ = at::empty_like(ref_);
call_args.clear();
call_args.emplace_back(input_.data_ptr<float>());
call_args.emplace_back(output_.data_ptr<float>());
cg.call(call_args);
}
}
private:
std::vector<long> input_size_;
std::vector<int> input_size_int_;
at::Tensor input_;
at::Tensor output_;
at::Tensor ref_;
};
} // namespace
BENCHMARK_DEFINE_F(SignedLog1pBench, ATen)(benchmark::State& state) {
runATen(state);
}
BENCHMARK_DEFINE_F(SignedLog1pBench, NNC)(benchmark::State& state) {
runNNC(state);
}
BENCHMARK_DEFINE_F(SignedLog1pBench, NNCLogVml)(benchmark::State& state) {
runNNCLogVml(state);
}
BENCHMARK_REGISTER_F(SignedLog1pBench, ATen)->Args({10, 1467});
BENCHMARK_REGISTER_F(SignedLog1pBench, NNC)->Args({10, 1467});
BENCHMARK_REGISTER_F(SignedLog1pBench, NNCLogVml)->Args({10, 1467});
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