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#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/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 setupSBR(Fusion* fusion, DataType dtype) {
TORCH_INTERNAL_ASSERT(dtype == DataType::Float || dtype == DataType::Half);
FusionGuard fg(fusion);
const size_t kNumberOfDims = 4;
std::vector<int64_t> bcast_shape(kNumberOfDims, 1);
bcast_shape[bcast_shape.size() - 1] = -1;
std::vector<bool> bcast_contig(kNumberOfDims, false);
bcast_contig[bcast_contig.size() - 1] = true;
auto x = makeContigTensor(kNumberOfDims, dtype);
auto scale = TensorViewBuilder()
.contiguity(bcast_contig)
.shape(bcast_shape)
.dtype(dtype)
.build();
auto bias = TensorViewBuilder()
.contiguity(bcast_contig)
.shape(bcast_shape)
.dtype(dtype)
.build();
fusion->addInput(x);
fusion->addInput(scale);
fusion->addInput(bias);
if (dtype == DataType::Half) {
x = castOp(DataType::Float, x);
scale = castOp(DataType::Float, scale);
bias = castOp(DataType::Float, bias);
}
auto scale_bias = add(mul(x, scale), bias);
auto scale_bias_relu = unaryOp(UnaryOpType::Relu, scale_bias);
if (dtype == DataType::Half) {
scale_bias_relu = castOp(DataType::Half, scale_bias_relu);
}
fusion->addOutput(scale_bias_relu);
}
static void setupSBRNorm(Fusion* fusion, DataType dtype) {
TORCH_INTERNAL_ASSERT(dtype == DataType::Float || dtype == DataType::Half);
FusionGuard fg(fusion);
const size_t kNumberOfDims = 4;
auto x = makeContigTensor(kNumberOfDims, dtype);
auto weight = makeContigTensor(1, dtype);
auto bias = makeContigTensor(1, dtype);
auto mean = makeContigTensor(1, dtype);
auto var = makeContigTensor(1, dtype);
fusion->addInput(x);
fusion->addInput(weight);
fusion->addInput(bias);
fusion->addInput(mean);
fusion->addInput(var);
std::vector<bool> broadcast_mask(kNumberOfDims, true);
broadcast_mask[broadcast_mask.size() - 1] = false;
if (dtype == DataType::Half) {
x = castOp(DataType::Float, x);
weight = castOp(DataType::Float, weight);
bias = castOp(DataType::Float, bias);
mean = castOp(DataType::Float, mean);
var = castOp(DataType::Float, var);
}
auto rsqrt = unaryOp(UnaryOpType::Rsqrt, var);
auto this_scale = mul(weight, rsqrt);
auto this_bias = mul(sub(bias, mean), this_scale);
auto bcast_scale = broadcast(this_scale, broadcast_mask);
auto bcast_bias = broadcast(this_bias, broadcast_mask);
auto scale_bias = add(mul(x, bcast_scale), bcast_bias);
auto scale_bias_relu = unaryOp(UnaryOpType::Relu, scale_bias);
if (dtype == DataType::Half) {
scale_bias_relu = castOp(DataType::Half, scale_bias_relu);
}
fusion->addOutput(scale_bias_relu);
}
//------------------------------------------------------------------------------
static void NvFuserScheduler_SBR(
benchmark::State& benchmark_state,
FusionExecutorCache* fusion_executor_cache,
DataType dtype) {
// N, H, W, C format
std::vector<int64_t> input_shape{
benchmark_state.range(0),
benchmark_state.range(1),
benchmark_state.range(1),
benchmark_state.range(2)};
std::vector<int64_t> bcast_shape{1, 1, 1, -1};
// inputs
at::manual_seed(0);
std::vector<int64_t> static_bcast_shape{1, 1, 1, benchmark_state.range(2)};
auto options =
at::TensorOptions().dtype(data_type_to_aten(dtype)).device(at::kCUDA, 0);
at::Tensor at_x = at::randn(input_shape, options);
at::Tensor at_scale = at::ones(static_bcast_shape, options);
at::Tensor at_bias = at::zeros(static_bcast_shape, options);
// inputs
std::vector<c10::IValue> aten_inputs = {at_x, at_scale, at_bias};
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);
benchmark_state.SetLabel(lparams);
fusion_executor_cache->profile(false);
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());
const size_t size =
input_shape[0] * input_shape[1] * input_shape[2] * input_shape[3];
const size_t channels = input_shape[3];
benchmark_state.SetBytesProcessed(
int64_t(benchmark_state.iterations()) * (channels * 2 + size * 2) *
int64_t(dataTypeSize(dtype)));
}
static void Baseline_SBR(benchmark::State& benchmark_state, DataType dtype) {
// N, H, W, C format
std::vector<int64_t> input_shape{
benchmark_state.range(0),
benchmark_state.range(1),
benchmark_state.range(1),
benchmark_state.range(2)};
std::vector<int64_t> bcast_shape{benchmark_state.range(2)};
// inputs
at::manual_seed(0);
auto options =
at::TensorOptions().dtype(data_type_to_aten(dtype)).device(at::kCUDA, 0);
at::Tensor at_x = at::randn(input_shape, options);
at::Tensor at_y = at::randn(input_shape, options);
at::Tensor at_scale = at::ones(bcast_shape, options);
at::Tensor at_bias = at::zeros(bcast_shape, options);
clearL2Cache();
C10_CUDA_CHECK(cudaDeviceSynchronize());
for (auto _ : benchmark_state) {
CudaKernelTimer timer;
auto scale = at::mul(at_x, at_scale);
auto bias = at::add(scale, at_bias);
auto output = at::relu(bias);
benchmark_state.SetIterationTime(timer.elapsed() / 1000.0);
C10_CUDA_CHECK(cudaDeviceSynchronize());
clearL2Cache();
C10_CUDA_CHECK(cudaDeviceSynchronize());
}
const size_t size =
input_shape[0] * input_shape[1] * input_shape[2] * input_shape[3];
const size_t channels = input_shape[3];
benchmark_state.SetBytesProcessed(
int64_t(benchmark_state.iterations()) * (channels * 2 + size * 2) *
int64_t(dataTypeSize(dtype)));
}
//------------------------------------------------------------------------------
static void NvFuserScheduler_SBR_Norm(
benchmark::State& benchmark_state,
FusionExecutorCache* fusion_executor_cache,
DataType dtype) {
// N, H, W, C format
std::vector<int64_t> input_shape{
benchmark_state.range(0),
benchmark_state.range(1),
benchmark_state.range(1),
benchmark_state.range(2)};
std::vector<int64_t> bcast_shape{benchmark_state.range(2)};
// inputs
at::manual_seed(0);
auto options =
at::TensorOptions().dtype(data_type_to_aten(dtype)).device(at::kCUDA, 0);
at::Tensor at_x = at::randn(input_shape, options);
at::Tensor at_weight = at::ones(bcast_shape, options);
at::Tensor at_bias = at::zeros(bcast_shape, options);
at::Tensor at_mean = at::zeros(bcast_shape, options);
at::Tensor at_var = at::ones(bcast_shape, options);
// inputs
std::vector<c10::IValue> aten_inputs = {
at_x, at_weight, at_bias, at_mean, at_var};
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);
fusion_executor_cache->profile(false);
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());
const size_t size =
input_shape[0] * input_shape[1] * input_shape[2] * input_shape[3];
const size_t channels = input_shape[3];
benchmark_state.SetBytesProcessed(
int64_t(benchmark_state.iterations()) * (channels * 4 + size * 2) *
int64_t(dataTypeSize(dtype)));
}
static void Baseline_SBR_Norm(
benchmark::State& benchmark_state,
DataType dtype) {
// N, H, W, C format
std::vector<int64_t> input_shape{
benchmark_state.range(0),
benchmark_state.range(1),
benchmark_state.range(1),
benchmark_state.range(2)};
std::vector<int64_t> bcast_shape{1, 1, 1, benchmark_state.range(2)};
// inputs
at::manual_seed(0);
auto options =
at::TensorOptions().dtype(data_type_to_aten(dtype)).device(at::kCUDA, 0);
at::Tensor at_x = at::randn(input_shape, options);
at::Tensor at_weight = at::ones(bcast_shape, options);
at::Tensor at_bias = at::zeros(bcast_shape, options);
at::Tensor at_mean = at::zeros(bcast_shape, options);
at::Tensor at_var = at::ones(bcast_shape, options);
C10_CUDA_CHECK(cudaDeviceSynchronize());
for (auto _ : benchmark_state) {
CudaKernelTimer timer;
auto this_scale = at::mul(at_weight, at::rsqrt(at_var));
auto this_bias = at::mul(at::sub(at_bias, at_mean), this_scale);
auto scale = at::mul(at_x, this_scale);
auto bias = at::add(scale, this_bias);
auto output = at::relu(bias);
benchmark_state.SetIterationTime(timer.elapsed() / 1000.0);
C10_CUDA_CHECK(cudaDeviceSynchronize());
}
const size_t size =
input_shape[0] * input_shape[1] * input_shape[2] * input_shape[3];
const size_t channels = input_shape[3];
benchmark_state.SetBytesProcessed(
int64_t(benchmark_state.iterations()) * (channels * 4 + size * 2) *
int64_t(dataTypeSize(dtype)));
}
//------------------------------------------------------------------------------
NVFUSER_BENCHMARK_DEFINE(
NvFuserScheduler_SBR_fp32,
setupSBR,
NvFuserScheduler_SBR,
DataType::Float);
NVFUSER_BENCHMARK_RUN(NvFuserScheduler_SBR_fp32)
// ->RangeMultiplier(2)
->Ranges({{8, 8}, {640, 640}, {64, 128}})
->Unit(benchmark::kMicrosecond)
->UseManualTime();
NVFUSER_BENCHMARK_DEFINE(
NvFuserScheduler_SBR_fp16,
setupSBR,
NvFuserScheduler_SBR,
DataType::Half);
NVFUSER_BENCHMARK_RUN(NvFuserScheduler_SBR_fp16)
// ->RangeMultiplier(2)
->Ranges({{8, 8}, {640, 640}, {64, 128}})
->Unit(benchmark::kMicrosecond)
->UseManualTime();
//------------------------------------------------------------------------------
NVFUSER_BENCHMARK_DEFINE(
NvFuserScheduler_SBR_Norm_fp32,
setupSBRNorm,
NvFuserScheduler_SBR_Norm,
DataType::Float);
NVFUSER_BENCHMARK_RUN(NvFuserScheduler_SBR_Norm_fp32)
// ->RangeMultiplier(2)
->Ranges({{8, 8}, {640, 640}, {64, 128}})
->Unit(benchmark::kMicrosecond)
->UseManualTime();
NVFUSER_BENCHMARK_DEFINE(
NvFuserScheduler_SBR_Norm_fp16,
setupSBRNorm,
NvFuserScheduler_SBR_Norm,
DataType::Half);
NVFUSER_BENCHMARK_RUN(NvFuserScheduler_SBR_Norm_fp16)
// ->RangeMultiplier(2)
->Ranges({{8, 8}, {640, 640}, {64, 128}})
->Unit(benchmark::kMicrosecond)
->UseManualTime();
//------------------------------------------------------------------------------
static void Baseline_SBR_fp32(benchmark::State& benchmark_state) {
Baseline_SBR(benchmark_state, DataType::Float);
}
BENCHMARK(Baseline_SBR_fp32)
// ->RangeMultiplier(2)
->Ranges({{8, 8}, {640, 640}, {64, 128}})
->Unit(benchmark::kMicrosecond)
->UseManualTime();
static void Baseline_SBR_fp16(benchmark::State& benchmark_state) {
Baseline_SBR(benchmark_state, DataType::Half);
}
BENCHMARK(Baseline_SBR_fp16)
// ->RangeMultiplier(2)
->Ranges({{8, 8}, {640, 640}, {64, 128}})
->Unit(benchmark::kMicrosecond)
->UseManualTime();
//------------------------------------------------------------------------------
static void Baseline_SBR_Norm_fp32(benchmark::State& benchmark_state) {
Baseline_SBR_Norm(benchmark_state, DataType::Float);
}
BENCHMARK(Baseline_SBR_Norm_fp32)
// ->RangeMultiplier(2)
->Ranges({{8, 8}, {640, 640}, {64, 128}})
->Unit(benchmark::kMicrosecond)
->UseManualTime();
static void Baseline_SBR_Norm_fp16(benchmark::State& benchmark_state) {
Baseline_SBR_Norm(benchmark_state, DataType::Half);
}
BENCHMARK(Baseline_SBR_Norm_fp16)
// ->RangeMultiplier(2)
->Ranges({{8, 8}, {640, 640}, {64, 128}})
->Unit(benchmark::kMicrosecond)
->UseManualTime();
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