pytorch/benchmarks/cpp/nvfuser/rms_norm_backward.cpp

#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 setupRMSNorm_BWD(Fusion* fusion, DataType dtype) {
  FusionGuard fg(fusion);

  TORCH_INTERNAL_ASSERT(
      dtype == DataType::Float || dtype == DataType::Half ||
      dtype == DataType::BFloat16);

  const int kReductionAxis = 2;
  Double* eps_ptr = IrBuilder::create<Double>(1e-6);

  // setup fusion
  auto grad_out = makeContigTensor(3, dtype);
  auto input = makeContigTensor(3, dtype);
  auto weight = makeContigTensor(1, dtype);
  auto rstd = TensorViewBuilder()
                  .contiguity({false, false, false})
                  .shape({-1, -1, 1})
                  .dtype(dtype)
                  .build();

  fusion->addInput(grad_out);
  fusion->addInput(input);
  fusion->addInput(weight);
  fusion->addInput(rstd);

  if (dtype == DataType::Half) {
    grad_out = castOp(DataType::Float, grad_out);
    input = castOp(DataType::Float, input);
    weight = castOp(DataType::Float, weight);
    rstd = castOp(DataType::Float, rstd);
  }

  auto rms_norm_results =
      rms_norm_backward(grad_out, input, {1}, rstd, weight, {true, true, true});

  if (dtype != DataType::Float) {
    rms_norm_results.grad_input = castOp(dtype, rms_norm_results.grad_input);
    rms_norm_results.grad_weight = castOp(dtype, rms_norm_results.grad_weight);
  }

  fusion->addOutput(rms_norm_results.grad_input);
  fusion->addOutput(rms_norm_results.grad_weight);
}

static void NvFuserScheduler_RMSNorm_BWD(
    benchmark::State& benchmark_state,
    FusionExecutorCache* fusion_executor_cache,
    DataType dtype) {
  TORCH_INTERNAL_ASSERT(
      dtype == DataType::Float || dtype == DataType::Half ||
      dtype == DataType::BFloat16);

  std::vector<int64_t> input_shape{8, benchmark_state.range(0), 1024};

  // inputs
  at::manual_seed(0);
  auto options =
      at::TensorOptions().dtype(data_type_to_aten(dtype)).device(at::kCUDA, 0);
  at::Tensor grad_out = at::randn(input_shape, options);
  at::Tensor input = at::randn(input_shape, options);
  at::Tensor weight = at::randn({input_shape[2]}, options);
  at::Tensor rstd = at::randn({input_shape[0], input_shape[1], 1}, options);

  std::vector<c10::IValue> aten_inputs({grad_out, input, weight, rstd});

  runBenchmarkIterations(benchmark_state, fusion_executor_cache, aten_inputs);

  benchmark_state.SetBytesProcessed(
      int64_t(benchmark_state.iterations()) *
      (3 * input.numel() + weight.numel() + rstd.numel()) *
      int64_t(dataTypeSize(dtype)));
}

//------------------------------------------------------------------------------

NVFUSER_BENCHMARK_DEFINE(
    NvFuserScheduler_RMSNorm_BWD_fp32,
    setupRMSNorm_BWD,
    NvFuserScheduler_RMSNorm_BWD,
    DataType::Float);

NVFUSER_BENCHMARK_RUN(NvFuserScheduler_RMSNorm_BWD_fp32)
    ->RangeMultiplier(2)
    ->Ranges({{16, 64}})
    ->Unit(benchmark::kMicrosecond)
    ->UseManualTime();

NVFUSER_BENCHMARK_RUN(NvFuserScheduler_RMSNorm_BWD_fp32)
    ->RangeMultiplier(2)
    ->Ranges({{28, 56}})
    ->Unit(benchmark::kMicrosecond)
    ->UseManualTime();

NVFUSER_BENCHMARK_RUN(NvFuserScheduler_RMSNorm_BWD_fp32)
    ->RangeMultiplier(2)
    ->Ranges({{24, 48}})
    ->Unit(benchmark::kMicrosecond)
    ->UseManualTime();

NVFUSER_BENCHMARK_DEFINE(
    NvFuserScheduler_RMSNorm_BWD_fp16,
    setupRMSNorm_BWD,
    NvFuserScheduler_RMSNorm_BWD,
    DataType::Half);

NVFUSER_BENCHMARK_RUN(NvFuserScheduler_RMSNorm_BWD_fp16)
    ->RangeMultiplier(2)
    ->Ranges({{16, 64}})
    ->Unit(benchmark::kMicrosecond)
    ->UseManualTime();

NVFUSER_BENCHMARK_RUN(NvFuserScheduler_RMSNorm_BWD_fp16)
    ->RangeMultiplier(2)
    ->Ranges({{28, 56}})
    ->Unit(benchmark::kMicrosecond)
    ->UseManualTime();

NVFUSER_BENCHMARK_RUN(NvFuserScheduler_RMSNorm_BWD_fp16)
    ->RangeMultiplier(2)
    ->Ranges({{24, 48}})
    ->Unit(benchmark::kMicrosecond)
    ->UseManualTime();

// TODO: Automatically disable/enable if bf16 is supported
// NVFUSER_BENCHMARK_DEFINE(
//     NvFuserScheduler_RMSNorm_BWD_bf16,
//     setupRMSNorm_BWD,
//     NvFuserScheduler_RMSNorm_BWD,
//     DataType::BFloat16);

// NVFUSER_BENCHMARK_RUN(NvFuserScheduler_RMSNorm_BWD_bf16)
//     ->RangeMultiplier(2)
//     ->Ranges({{16, 64}})
//     ->Unit(benchmark::kMicrosecond)
//     ->UseManualTime();

// NVFUSER_BENCHMARK_RUN(NvFuserScheduler_RMSNorm_BWD_bf16)
//     ->RangeMultiplier(2)
//     ->Ranges({{28, 56}})
//     ->Unit(benchmark::kMicrosecond)
//     ->UseManualTime();

// NVFUSER_BENCHMARK_RUN(NvFuserScheduler_RMSNorm_BWD_bf16)
//     ->RangeMultiplier(2)
//     ->Ranges({{24, 48}})
//     ->Unit(benchmark::kMicrosecond)
//     ->UseManualTime();