Load mlir(mhlo/lmhlo) and execute on GPU

main.cc

#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/Dialect/Arith/IR/Arith.h"  // from @llvm-project
#include "mlir/Dialect/MemRef/IR/MemRef.h"  // from @llvm-project
#include "mlir/Dialect/Bufferization/IR/Bufferization.h"


#include "llvm/Support/Debug.h"
#include "llvm/Support/SourceMgr.h"
#include "llvm/Support/FileUtilities.h"
#include "mlir/Support/FileUtilities.h"
#include "mlir/Parser/Parser.h"
#include "mlir/IR/MLIRContext.h"  // from @llvm-project
#include "mlir/Target/LLVMIR/Dialect/LLVMIR/LLVMToLLVMIRTranslation.h"  // from @llvm-project

#include "tensorflow/compiler/xla/stream_executor/device_memory.h"
#include "tensorflow/compiler/xla/service/backend.h"
#include "tensorflow/compiler/xla/service/executable.h"
#include "tensorflow/compiler/xla/service/gpu/gpu_compiler.h"
#include "tensorflow/compiler/xla/service/gpu/target_constants.h"
#include "tensorflow/compiler/xla/mlir_hlo/include/mlir-hlo/Dialect/mhlo/IR/hlo_ops.h"
#include "tensorflow/compiler/xla/mlir_hlo/include/mlir-hlo/Dialect/lhlo/IR/lhlo_ops.h"

// custom gpu compile library: mhlo -> hlo -> lmhlo -> GpuExecutable
#include "tensorflow/compiler/plugin/custom/gpu/gpu_backend.h"

namespace se = stream_executor;

void LoadMLIRDialects(mlir::MLIRContext& context) {
  context.loadDialect<
                      mlir::arith::ArithDialect,
                      mlir::memref::MemRefDialect,
                      mlir::mhlo::MhloDialect,
                      mlir::lmhlo::LmhloDialect,
                      mlir::func::FuncDialect,
                      mlir::bufferization::BufferizationDialect
                    >();
  mlir::registerLLVMDialectTranslation(context);
}

auto GetBackend(){
  se::Platform* platform = se::MultiPlatformManager::PlatformWithName("CUDA").value();
  xla::BackendOptions options;
  options.set_platform(platform);
  return xla::Backend::CreateBackend(options).value();
}

auto CachedBackend(){
  // init on use, can not be initialized as a global variable
  static std::shared_ptr<xla::Backend> backend = GetBackend();
  return backend;
}

mlir::OwningOpRef<mlir::ModuleOp> readFromMlirFile(mlir::MLIRContext&amp; context, const std::string&amp; mlir_path){
    std::string errorMessage;
    auto file = mlir::openInputFile(mlir_path, &amp;errorMessage);
    if (!file) {
        llvm::errs() << errorMessage << "n";
        return nullptr;
    }
    std::unique_ptr<llvm::MemoryBuffer> buffer = std::move(file);

    // Tell sourceMgr about this buffer, which is what the parser will pick up.
    llvm::SourceMgr sourceMgr;
    sourceMgr.AddNewSourceBuffer(std::move(buffer), llvm::SMLoc());
    // Prepare the parser config, and attach any useful/necessary resource handlers.
    mlir::ParserConfig config(&amp;context);

    // Parse the input file and reset the context threading state.
    mlir::OwningOpRef<mlir::ModuleOp> module(mlir::parseSourceFile<mlir::ModuleOp>(sourceMgr, config));
    module->dump();
    return module;
}

tsl::StatusOr<std::unique_ptr<xla::Executable>> CompileMlirModule(
  std::shared_ptr<xla::Backend> backend,
    mlir::ModuleOp module, se::Stream* stream) {
  llvm::LLVMContext llvm_context;
  auto llvm_module = std::make_unique<llvm::Module>("", llvm_context);
#if TENSORFLOW_USE_ROCM
  llvm_module->setTargetTriple(xla::gpu::amdgpu::TargetTriple());
  llvm_module->setDataLayout(xla::gpu::amdgpu::DataLayout());
#else
  llvm_module->setTargetTriple(xla::gpu::nvptx::TargetTriple());
  llvm_module->setDataLayout(xla::gpu::nvptx::DataLayout());
#endif

  se::StreamExecutor* stream_exec = stream->parent();
  xla::gpu::GpuDeviceInfo gpu_device_info = xla::gpu::GetGpuDeviceInfo(stream_exec);
  xla::gpu::IrEmitterContext ir_emitter_context(
      /*hlo_module=*/nullptr, /*buffer_assignment=*/nullptr,
      backend->platform()->Name(), gpu_device_info,
      stream_exec->GetDeviceDescription().cuda_compute_capability(),
      stream_exec->GetDeviceDescription().rocm_compute_capability(),
      /*mlir_context=*/nullptr, llvm_module.get());

  xla::HloModuleConfig module_config;
  module_config.set_debug_options(xla::GetDebugOptionsFromFlags());
  return CompileLmhloToExecutable(
      static_cast<xla::gpu::GpuCompiler*>(backend->compiler()), module, "TestModule",
      module_config, xla::Compiler::CompileOptions(), "main", stream_exec,
      std::move(llvm_module), &amp;ir_emitter_context);
}

tsl::StatusOr<xla::ExecutionOutput> RunMlirModule(
    std::shared_ptr<xla::Backend> backend,
    mlir::ModuleOp module, se::Stream* stream,
    absl::Span<const se::DeviceMemoryBase> arguments) {
  TF_ASSIGN_OR_RETURN(auto executable, CompileMlirModule(CachedBackend(), module, stream));
  std::cout << "CompileMlirModule endn";
  xla::ExecutableRunOptions executable_run_options;
  executable_run_options.set_stream(stream);
  executable_run_options.set_allocator(backend->memory_allocator());
  xla::ServiceExecutableRunOptions run_options(executable_run_options,
                                          backend->StreamBorrower());
  std::vector<xla::ExecutionInput> execution_inputs;
  execution_inputs.reserve(arguments.size());

  for (auto arg : arguments) {
    xla::Shape shape =
        xla::ShapeUtil::MakeShape(xla::U8, {static_cast<int64_t>(arg.size())});
    execution_inputs.emplace_back(shape);
    execution_inputs.back().SetBuffer({}, xla::MaybeOwningDeviceMemory(arg));
  }

  TF_ASSIGN_OR_RETURN(auto output,
                      executable->ExecuteAsyncOnStream(
                          &amp;run_options, std::move(execution_inputs),
                          /*hlo_execution_profile=*/nullptr));

  TF_CHECK_OK(stream->BlockHostUntilDone());

  return std::move(output);
}

tsl::StatusOr<std::vector<std::vector<uint8_t>>>
RunMlirModuleWithHostBuffers(
    std::shared_ptr<xla::Backend> backend,
    mlir::ModuleOp lmhlo_module, std::vector<absl::Span<uint8_t>> arguments) {
  auto* allocator = backend->memory_allocator();
  std::vector<se::OwningDeviceMemory> owning_memory;
  owning_memory.reserve(arguments.size());
  for (auto host_buffer : arguments) {
    owning_memory.push_back(
        allocator
            ->Allocate(backend->default_device_ordinal(), host_buffer.size())
            .value());
  }
  auto stream =
      backend->BorrowStream(backend->default_device_ordinal()).value();
  std::vector<se::DeviceMemoryBase> args;
  for (int i = 0; i < owning_memory.size(); i++) {
    se::DeviceMemoryBase memory(*owning_memory[i]);
    stream->ThenMemcpy(&amp;memory, static_cast<void*>(arguments[i].data()),
                       memory.size());
    args.push_back(memory);
  }
  TF_ASSIGN_OR_RETURN(xla::ExecutionOutput output,
                      RunMlirModule(CachedBackend(), lmhlo_module, stream.get(), args));
  std::cout << "RunMlirModule endn";

  std::vector<std::vector<uint8_t>> host_outputs;
  for (const auto&amp; result : output.Result().buffers().leaves()) {
    host_outputs.emplace_back();
    host_outputs.back().resize(result.second.size());
    stream->ThenMemcpy(static_cast<void*>(host_outputs.back().data()),
                       result.second, result.second.size());
  }
  TF_CHECK_OK(stream->BlockHostUntilDone());
  return host_outputs;
}

tsl::StatusOr<xla::ExecutionOutput> RunMlirModule(
    mlir::MLIRContext&amp; context,
    std::shared_ptr<xla::Backend> backend,
    mlir::OwningOpRef<mlir::ModuleOp> mhlo_module,
    se::Stream* stream,
    absl::Span<const se::DeviceMemoryBase> arguments) {
  // TF_ASSIGN_OR_RETURN(auto executable, CompileMlirModule(CachedBackend(), module, stream));
  TF_ASSIGN_OR_RETURN(auto executable, mygpu::custom_gpu_compile(nullptr, std::move(mhlo_module), context));

  std::cout << "CompileMlirModule endn";
  xla::ExecutableRunOptions executable_run_options;
  executable_run_options.set_stream(stream);
  executable_run_options.set_allocator(backend->memory_allocator());
  xla::ServiceExecutableRunOptions run_options(executable_run_options,
                                          backend->StreamBorrower());
  std::vector<xla::ExecutionInput> execution_inputs;
  execution_inputs.reserve(arguments.size());

  for (auto arg : arguments) {
    xla::Shape shape =
        xla::ShapeUtil::MakeShape(xla::U8, {static_cast<int64_t>(arg.size())});
    execution_inputs.emplace_back(shape);
    execution_inputs.back().SetBuffer({}, xla::MaybeOwningDeviceMemory(arg));
  }

  TF_ASSIGN_OR_RETURN(auto output,
                      executable->ExecuteAsyncOnStream(
                          &run_options, std::move(execution_inputs),
                          /*hlo_execution_profile=*/nullptr));

  TF_CHECK_OK(stream->BlockHostUntilDone());

  return std::move(output);
}

tsl::StatusOr<std::vector<std::vector<uint8_t>>>
RunMlirModuleWithHostBuffers(
    mlir::MLIRContext& context,
    std::shared_ptr<xla::Backend> backend,
    mlir::OwningOpRef<mlir::ModuleOp> mhlo_module,
    std::vector<absl::Span<uint8_t>> arguments
) {
  auto* allocator = backend->memory_allocator();
  std::vector<se::OwningDeviceMemory> owning_memory;
  owning_memory.reserve(arguments.size());
  for (auto host_buffer : arguments) {
    owning_memory.push_back(
        allocator
            ->Allocate(backend->default_device_ordinal(), host_buffer.size())
            .value());
  }
  auto stream =
      backend->BorrowStream(backend->default_device_ordinal()).value();
  std::vector<se::DeviceMemoryBase> args;
  for (int i = 0; i < owning_memory.size(); i++) {
    se::DeviceMemoryBase memory(*owning_memory[i]);
    stream->ThenMemcpy(&memory, static_cast<void*>(arguments[i].data()),
                       memory.size());
    args.push_back(memory);
  }
  TF_ASSIGN_OR_RETURN(xla::ExecutionOutput output,
                      RunMlirModule(context, CachedBackend(), std::move(mhlo_module), stream.get(), args));
  std::cout << "RunMlirModule endn";

  std::vector<std::vector<uint8_t>> host_outputs;
  for (const auto& result : output.Result().buffers().leaves()) {
    host_outputs.emplace_back();
    host_outputs.back().resize(result.second.size());
    stream->ThenMemcpy(static_cast<void*>(host_outputs.back().data()),
                       result.second, result.second.size());
  }
  TF_CHECK_OK(stream->BlockHostUntilDone());
  return host_outputs;
}

template <typename T>
static absl::Span<uint8_t> ToUint8Span(std::vector<T>* v) {
  return absl::Span<uint8_t>(reinterpret_cast<uint8_t*>(v->data()),
                              v->size() * sizeof(T));
}

template <typename T>
static absl::Span<const T> FromUint8Span(absl::Span<const uint8_t> span) {
  CHECK_EQ(0, span.size() % sizeof(T));
  return absl::Span<const T>(reinterpret_cast<const T*>(span.data()),
                              span.size() / sizeof(T));
}

int main(){
  mlir::MLIRContext context;
  LoadMLIRDialects(context);
  std::vector<float> arg0 = {1,2,3,4,5,6};

  auto path = "add-mhlo.mlir";
  mlir::OwningOpRef<mlir::ModuleOp> module = readFromMlirFile(context, path);
  auto outputs = RunMlirModuleWithHostBuffers(context, CachedBackend(), std::move(module), {ToUint8Span(&arg0), ToUint8Span(&arg0)}).value();
  
  // auto path = "add-lmhlo.mlir";
  // mlir::OwningOpRef<mlir::ModuleOp> module = readFromMlirFile(context, path);
  // auto outputs = RunMlirModuleWithHostBuffers(CachedBackend(), module.get(), {ToUint8Span(&arg0), ToUint8Span(&arg0)}).value();


  auto floats = FromUint8Span<float>(outputs[0]);
  std::cout << floats[0] << "n";
  std::cout << floats[1] << "n";
  std::cout << floats[2] << "n";
  std::cout << floats[3] << "n";
  std::cout << floats[4] << "n";
  std::cout << floats[5] << "n";
  std::cout << ">>>>>>>>>> Running gpu end to end bin" << "n";
  return 0;
}

bazel 配置：

tf_cc_binary(
    name = "gpu_bin",
    testonly = True,
    srcs = [
        "main.cc",
    ],
    deps = [
        ":gpu_backend",
        "@llvm-project//llvm:Support",
        "@llvm-project//mlir:AllPassesAndDialects",
        "@llvm-project//mlir:IR",
        "@llvm-project//mlir:MlirOptLib",
        "@llvm-project//mlir:Pass",
        "@llvm-project//mlir:Support",
    ],
)

代码007普通

打赏 收藏 海报 链接