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#include <torch/csrc/jit/codegen/cuda/executor.h>
#include <torch/csrc/jit/codegen/cuda/fusion.h>
#include <torch/csrc/jit/codegen/cuda/ir_all_nodes.h>
#include <torch/csrc/jit/codegen/cuda/ir_builder.h>
#include <torch/csrc/jit/codegen/cuda/ir_utils.h>
#include <torch/csrc/jit/codegen/cuda/lower2device.h>
#include <torch/csrc/jit/codegen/cuda/ops/all_ops.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 setupLayerNorm_BWD(Fusion* fusion, DataType dtype) {
FusionGuard fg(fusion);
TORCH_INTERNAL_ASSERT(dtype == DataType::Float || dtype == DataType::Half);
const int kReductionAxis = 1;
Double* eps_ptr = IrBuilder::create<Double>(1e-5);
// setup fusion
auto grad_out = makeContigTensor(2, dtype);
auto input = makeContigTensor(2, dtype);
auto weight = makeContigTensor(1, dtype);
auto bias = makeContigTensor(1, dtype);
auto mean = TensorViewBuilder()
.contiguity({false, false})
.shape({-1, 1})
.dtype(DataType::Float)
.build();
auto rstd = TensorViewBuilder()
.contiguity({false, false})
.shape({-1, 1})
.dtype(DataType::Float)
.build();
fusion->addInput(grad_out);
fusion->addInput(input);
fusion->addInput(weight);
fusion->addInput(bias);
fusion->addInput(mean);
fusion->addInput(rstd);
if (dtype == DataType::Half) {
grad_out = castOp(DataType::Float, grad_out);
input = castOp(DataType::Float, input);
weight = castOp(DataType::Float, weight);
bias = castOp(DataType::Float, bias);
}
auto layer_norm_results = layer_norm_backward(
grad_out, input, {1}, mean, rstd, weight, bias, {true, true, true});
if (dtype != DataType::Float) {
layer_norm_results.grad_input =
castOp(dtype, layer_norm_results.grad_input);
layer_norm_results.grad_bias = castOp(dtype, layer_norm_results.grad_bias);
layer_norm_results.grad_weight =
castOp(dtype, layer_norm_results.grad_weight);
}
fusion->addOutput(layer_norm_results.grad_input);
fusion->addOutput(layer_norm_results.grad_bias);
fusion->addOutput(layer_norm_results.grad_weight);
}
static void NvFuserScheduler_LayerNorm_BWD(
benchmark::State& benchmark_state,
FusionExecutorCache* fusion_executor_cache,
DataType dtype) {
TORCH_INTERNAL_ASSERT(dtype == DataType::Float || dtype == DataType::Half);
std::vector<int64_t> input_shape{
benchmark_state.range(0), benchmark_state.range(1)};
// inputs
at::manual_seed(0);
auto maybe_fp16_options =
at::TensorOptions().dtype(data_type_to_aten(dtype)).device(at::kCUDA, 0);
auto fp32_options =
at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor grad_out = at::randn(input_shape, maybe_fp16_options);
at::Tensor input = at::randn(input_shape, maybe_fp16_options);
at::Tensor weight = at::randn({input_shape[1]}, maybe_fp16_options);
at::Tensor bias = at::randn({input_shape[1]}, maybe_fp16_options);
at::Tensor mean = at::randn({input_shape[0], 1}, fp32_options);
at::Tensor rstd = at::randn({input_shape[0], 1}, fp32_options);
std::vector<c10::IValue> aten_inputs(
{grad_out, input, weight, bias, mean, rstd});
runBenchmarkIterations(benchmark_state, fusion_executor_cache, aten_inputs);
benchmark_state.SetBytesProcessed(
int64_t(benchmark_state.iterations()) *
(3 * input.numel() + weight.numel() + bias.numel() + mean.numel() +
rstd.numel()) *
int64_t(dataTypeSize(dtype)));
}
//------------------------------------------------------------------------------
static void Baseline_LayerNorm_BWD(
benchmark::State& benchmark_state,
DataType dtype) {
TORCH_INTERNAL_ASSERT(dtype == DataType::Float || dtype == DataType::Half);
std::vector<int64_t> input_shape{
benchmark_state.range(0), benchmark_state.range(1)};
const size_t kReductionAxis = 1;
std::vector<int64_t> norm_shape;
for (auto idx = kReductionAxis; idx < input_shape.size(); ++idx) {
norm_shape.push_back(input_shape[idx]);
}
// inputs
at::manual_seed(0);
auto maybe_fp16_options =
at::TensorOptions().dtype(data_type_to_aten(dtype)).device(at::kCUDA, 0);
auto fp32_options =
at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor grad_out = at::randn(input_shape, maybe_fp16_options);
at::Tensor input = at::randn(input_shape, maybe_fp16_options);
at::Tensor weight = at::randn({input_shape[1]}, maybe_fp16_options);
at::Tensor bias = at::randn({input_shape[1]}, maybe_fp16_options);
at::Tensor mean = at::randn({input_shape[0], 1}, fp32_options);
at::Tensor rstd = at::randn({input_shape[0], 1}, fp32_options);
std::array<bool, 3> output_mask = {true, true, true};
clearL2Cache();
C10_CUDA_CHECK(cudaDeviceSynchronize());
for (auto _ : benchmark_state) {
CudaKernelTimer timer;
at::native_layer_norm_backward(
grad_out, input, norm_shape, mean, rstd, weight, bias, output_mask);
auto output = at::layer_norm(input, norm_shape, weight, bias);
benchmark_state.SetIterationTime(timer.elapsed() / 1000.0);
C10_CUDA_CHECK(cudaDeviceSynchronize());
clearL2Cache();
C10_CUDA_CHECK(cudaDeviceSynchronize());
}
benchmark_state.SetBytesProcessed(
int64_t(benchmark_state.iterations()) *
(3 * input.numel() + weight.numel() + bias.numel() + mean.numel() +
rstd.numel()) *
int64_t(dataTypeSize(dtype)));
}
static void Baseline_LayerNorm_BWD_fp32(benchmark::State& benchmark_state) {
Baseline_LayerNorm_BWD(benchmark_state, DataType::Float);
}
static void Baseline_LayerNorm_BWD_fp16(benchmark::State& benchmark_state) {
Baseline_LayerNorm_BWD(benchmark_state, DataType::Half);
}
//------------------------------------------------------------------------------
NVFUSER_BENCHMARK_DEFINE(
NvFuserScheduler_LayerNorm_BWD_fp32,
setupLayerNorm_BWD,
NvFuserScheduler_LayerNorm_BWD,
DataType::Float);
NVFUSER_BENCHMARK_RUN(NvFuserScheduler_LayerNorm_BWD_fp32)
// ->RangeMultiplier(2)
->Ranges({{160, 320}, {2, 1024 * 1024}})
->Unit(benchmark::kMicrosecond)
->UseManualTime();
NVFUSER_BENCHMARK_RUN(NvFuserScheduler_LayerNorm_BWD_fp32)
// ->RangeMultiplier(2)
->Ranges({{2, 16}, {32768, 16 * 1024 * 1024}})
->Unit(benchmark::kMicrosecond)
->UseManualTime();
NVFUSER_BENCHMARK_RUN(NvFuserScheduler_LayerNorm_BWD_fp32)
// ->RangeMultiplier(2)
->Ranges({{32768, 16 * 1024 * 1024}, {2, 16}})
->Unit(benchmark::kMicrosecond)
->UseManualTime();
NVFUSER_BENCHMARK_RUN(NvFuserScheduler_LayerNorm_BWD_fp32)
// ->RangeMultiplier(2)
->Ranges({{128, 1024 * 16}, {128, 1024 * 16}})
->Unit(benchmark::kMicrosecond)
->UseManualTime();
NVFUSER_BENCHMARK_DEFINE(
NvFuserScheduler_LayerNorm_BWD_fp16,
setupLayerNorm_BWD,
NvFuserScheduler_LayerNorm_BWD,
DataType::Half);
NVFUSER_BENCHMARK_RUN(NvFuserScheduler_LayerNorm_BWD_fp16)
// ->RangeMultiplier(2)
->Ranges({{160, 320}, {2, 1024 * 1024}})
->Unit(benchmark::kMicrosecond)
->UseManualTime();
NVFUSER_BENCHMARK_RUN(NvFuserScheduler_LayerNorm_BWD_fp16)
// ->RangeMultiplier(2)
->Ranges({{2, 16}, {32768, 32 * 1024 * 1024}})
->Unit(benchmark::kMicrosecond)
->UseManualTime();
NVFUSER_BENCHMARK_RUN(NvFuserScheduler_LayerNorm_BWD_fp16)
// ->RangeMultiplier(2)
->Ranges({{32768, 32 * 1024 * 1024}, {2, 16}})
->Unit(benchmark::kMicrosecond)
->UseManualTime();
NVFUSER_BENCHMARK_RUN(NvFuserScheduler_LayerNorm_BWD_fp16)
// ->RangeMultiplier(2)
->Ranges({{128, 1024 * 16}, {128, 1024 * 16}})
->Unit(benchmark::kMicrosecond)
->UseManualTime();
//------------------------------------------------------------------------------
BENCHMARK(Baseline_LayerNorm_BWD_fp32)
// ->RangeMultiplier(2)
->Ranges({{160, 320}, {2, 1024 * 1024}})
->Unit(benchmark::kMicrosecond)
->UseManualTime();
BENCHMARK(Baseline_LayerNorm_BWD_fp32)
// ->RangeMultiplier(2)
->Ranges({{2, 16}, {32768, 16 * 1024 * 1024}})
->Unit(benchmark::kMicrosecond)
->UseManualTime();
BENCHMARK(Baseline_LayerNorm_BWD_fp32)
// ->RangeMultiplier(2)
->Ranges({{32768, 16 * 1024 * 1024}, {2, 16}})
->Unit(benchmark::kMicrosecond)
->UseManualTime();
BENCHMARK(Baseline_LayerNorm_BWD_fp32)
// ->RangeMultiplier(2)
->Ranges({{128, 1024 * 16}, {128, 1024 * 16}})
->Unit(benchmark::kMicrosecond)
->UseManualTime();
BENCHMARK(Baseline_LayerNorm_BWD_fp16)
// ->RangeMultiplier(2)
->Ranges({{160, 320}, {2, 1024 * 1024}})
->Unit(benchmark::kMicrosecond)
->UseManualTime();
BENCHMARK(Baseline_LayerNorm_BWD_fp16)
// ->RangeMultiplier(2)
->Ranges({{2, 16}, {32768, 32 * 1024 * 1024}})
->Unit(benchmark::kMicrosecond)
->UseManualTime();
BENCHMARK(Baseline_LayerNorm_BWD_fp16)
// ->RangeMultiplier(2)
->Ranges({{32768, 32 * 1024 * 1024}, {2, 16}})
->Unit(benchmark::kMicrosecond)
->UseManualTime();
BENCHMARK(Baseline_LayerNorm_BWD_fp16)
// ->RangeMultiplier(2)
->Ranges({{128, 1024 * 16}, {128, 1024 * 16}})
->Unit(benchmark::kMicrosecond)
->UseManualTime();
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