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// Based on NVFuserTest.FusionBiasGeluBwd_CUDA
#include <torch/csrc/jit/codegen/cuda/arith.h>
#include <torch/csrc/jit/codegen/cuda/executor.h>
#include <torch/csrc/jit/codegen/cuda/fusion.h>
#include <torch/csrc/jit/codegen/cuda/ir_builder.h>
#include <torch/csrc/jit/codegen/cuda/lower2device.h>
#include <torch/csrc/jit/codegen/cuda/scheduler/all_schedulers.h>
#include <benchmark/benchmark.h>
#include <cuda_runtime.h>
#include <benchmarks/cpp/nvfuser/utils.h>
using namespace torch::jit::fuser::cuda;
static void setupFusion(Fusion* fusion) {
FusionGuard fg(fusion);
const float k_079 = 0.79788456;
const float k_004 = 0.044715;
const float k_010 = 0.1070322243;
// gradient tensor
auto t0 = makeContigTensor(3, DataType::Half);
fusion->addInput(t0);
auto t1 = castOp(DataType::Float, t0);
// bias tensor
auto t2 = makeContigTensor(1, DataType::Half);
fusion->addInput(t2);
auto t3 = castOp(DataType::Float, t2);
// input tensor
auto t4 = makeContigTensor(3, DataType::Half);
fusion->addInput(t4);
auto t5 = castOp(DataType::Float, t4);
auto t6 = broadcast(t3, {true, true, false});
auto t7 = add(t6, t5);
auto t8 = mul(t7, IrBuilder::create<Double>(k_079));
auto t9 = mul(t7, IrBuilder::create<Double>(k_004));
auto t10 = mul(t9, t7);
auto t11 = add(t10, IrBuilder::create<Int>(1));
auto t12 = mul(t8, t11);
auto t13 = unaryOp(UnaryOpType::Tanh, t12);
auto t14 = mul(t7, IrBuilder::create<Double>(0.5));
auto t15 = mul(t13, t13);
auto t16 = unaryOp(UnaryOpType::Neg, t15);
auto t17 = add(t16, IrBuilder::create<Int>(1));
auto t18 = mul(t7, IrBuilder::create<Double>(k_010));
auto t19 = mul(t18, t7);
auto t20 = add(t19, IrBuilder::create<Double>(k_079));
auto t21 = mul(t17, t20);
auto t22 = mul(t14, t21);
auto t23 = add(t13, IrBuilder::create<Int>(1));
auto t24 = mul(t23, IrBuilder::create<Double>(0.5));
auto t25 = add(t22, t24);
auto t26 = mul(t25, t1);
// Save float output for validation
fusion->addOutput(t26);
auto t27 = castOp(DataType::Half, t26);
fusion->addOutput(t27);
}
static std::vector<c10::IValue> setupInputs() {
at::manual_seed(0);
auto options = at::TensorOptions().dtype(at::kHalf).device(at::kCUDA, 0);
std::vector<int64_t> input_shape{6, 512, 4096};
std::vector<int64_t> bias_shape{4096};
auto at_input = at::randn(input_shape, options);
auto at_bias = at::randn(bias_shape, options);
auto at_grad = at::randn(input_shape, options);
return {at_grad, at_bias, at_input};
}
//------------------------------------------------------------------------------
static void GeluBackward_SetupFusion(benchmark::State& benchmark_state) {
for (auto _ : benchmark_state) {
Fusion fusion;
setupFusion(&fusion);
}
}
BENCHMARK(GeluBackward_SetupFusion)->Unit(benchmark::kMicrosecond);
//------------------------------------------------------------------------------
static void GeluBackward_AutoSchedule(benchmark::State& benchmark_state) {
for (auto _ : benchmark_state) {
// Setup (not included in the measurement)
benchmark_state.PauseTiming();
Fusion fusion;
setupFusion(&fusion);
std::vector<c10::IValue> inputs = setupInputs();
benchmark_state.ResumeTiming();
// Auto-schedule
schedulePointwise(&fusion, c10::ArrayRef<c10::IValue>(inputs));
}
}
BENCHMARK(GeluBackward_AutoSchedule)->Unit(benchmark::kMicrosecond);
//------------------------------------------------------------------------------
static void GeluBackward_Lower(benchmark::State& benchmark_state) {
constexpr int kHiddenFeatures = 512;
constexpr int kBatchSize = 64;
Fusion fusion;
// setup fusion
setupFusion(&fusion);
// inputs
std::vector<c10::IValue> inputs = setupInputs();
schedulePointwise(&fusion, c10::ArrayRef<c10::IValue>(inputs));
for (auto _ : benchmark_state) {
GpuLower gpu_lower(&fusion);
}
}
BENCHMARK(GeluBackward_Lower)->Unit(benchmark::kMillisecond);
//------------------------------------------------------------------------------
static void GeluBackward_Compile(benchmark::State& benchmark_state) {
Fusion fusion;
// setup fusion
setupFusion(&fusion);
// inputs
std::vector<c10::IValue> inputs = setupInputs();
schedulePointwise(&fusion, c10::ArrayRef<c10::IValue>(inputs));
for (auto _ : benchmark_state) {
FusionExecutor executor;
executor.compileFusion(&fusion);
}
}
BENCHMARK(GeluBackward_Compile)->Unit(benchmark::kMillisecond);
//------------------------------------------------------------------------------
static void GeluBackward_RunFusion(benchmark::State& benchmark_state) {
Fusion fusion;
// setup fusion
setupFusion(&fusion);
// inputs
std::vector<c10::IValue> inputs = setupInputs();
// outputs
std::vector<at::Tensor> outputs;
auto lparams = schedulePointwise(&fusion, c10::ArrayRef<c10::IValue>(inputs));
FusionExecutor executor;
executor.compileFusion(&fusion);
C10_CUDA_CHECK(cudaDeviceSynchronize());
for (auto _ : benchmark_state) {
outputs = executor.runFusion(c10::ArrayRef<c10::IValue>(inputs), lparams);
C10_CUDA_CHECK(cudaDeviceSynchronize());
clearL2Cache();
}
}
BENCHMARK(GeluBackward_RunFusion)->Unit(benchmark::kMicrosecond);
//------------------------------------------------------------------------------
static void GeluBackward_RunFusion_GpuOnly(benchmark::State& benchmark_state) {
Fusion fusion;
// setup fusion
setupFusion(&fusion);
// inputs
std::vector<c10::IValue> inputs = setupInputs();
// outputs
std::vector<at::Tensor> outputs;
auto lparams = schedulePointwise(&fusion, c10::ArrayRef<c10::IValue>(inputs));
FusionExecutor executor;
executor.setMeasureKernelTimeFlag(true);
executor.compileFusion(&fusion);
C10_CUDA_CHECK(cudaDeviceSynchronize());
for (auto _ : benchmark_state) {
outputs = executor.runFusion(c10::ArrayRef<c10::IValue>(inputs), lparams);
benchmark_state.SetIterationTime(executor.kernelTimeMs() / 1000.0);
clearL2Cache();
}
}
BENCHMARK(GeluBackward_RunFusion_GpuOnly)
->Unit(benchmark::kMicrosecond)
->UseManualTime();
//------------------------------------------------------------------------------
static void GeluBackward_RunFusion_CpuOnly(benchmark::State& benchmark_state) {
Fusion fusion;
// setup fusion
setupFusion(&fusion);
// inputs
std::vector<c10::IValue> inputs = setupInputs();
// outputs
std::vector<at::Tensor> outputs;
auto lparams = schedulePointwise(&fusion, c10::ArrayRef<c10::IValue>(inputs));
FusionExecutor executor;
executor.setExecuteKernelFlag(false);
executor.compileFusion(&fusion);
for (auto _ : benchmark_state) {
outputs = executor.runFusion(c10::ArrayRef<c10::IValue>(inputs), lparams);
}
}
BENCHMARK(GeluBackward_RunFusion_CpuOnly)->Unit(benchmark::kMicrosecond);
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