Tensorflow XLA Service 详解 I

compiler/aot/ 以AOT的方式将tf2xla/接入TF引擎
compiler/jit/以JIT的方式将tf2xla/接入TF引擎, 核心是9个优化器和3个tfop,其中XlaCompileOp调用tf2xla的“编译”入口完成功能封装,XlaRunOp调用xla/client完成“运行”功能。
compiler/tf2xla/对上提供xla_compiler.cc:XlaCompiler::CompileFunction()供jit:compile_fn()使用将cluster转化为XlaComputation。核心是利用xla/client提供的接口,实现对XlaOpKernel的“Symbolic Execution”功能。每个XlaOpKernel子类均做的以下工作: **从XlaOpKernelContext中取出XlaExpression或XlaOp, 调用xla/client/xla_buidler.h提供的方法完成计算, 将计算结果的XlaOp存入XlaKernelContext.**
compiler/xla/client/ 对上提供xla_builder.cc:Builder等供CompileFunction()使用,将Graph由Op表达转化为HloModuleProto:HloComputationProto:HloInstructionProto表达并保存在XlaComputation中。
对上提供local_client.cc:LocalClient::Compile(),作为编译入口供jit:BuildExecutable()使用,将已经得到的XlaComputation交给service并进一步编译为二进制。
对上提供local_client.cc:LocalExecutable::Run(),作为运行入口供jit/kernels/xla_ops.cc:XlaRunOp使用,通过Key找到相应的二进制交给service层处理
compiler/xla/service/ 对上提供local_service.cc:LocalService::BuildExecutable()供LocalClient::Compile()使用实现真正的编译,承接XlaComputation封装的HloProto, 将其转化为HloModule:HloComputation:HloInstruction表达, 对其进行优化之后, 使用LLVM后端将其编译为相应Executable后端的二进制代码
对上提供executable.cc:Executable::ExecuteOnStream()供LocalExecutable::Run()使用实现真正的执行二进制。

编译cubin

调用栈:

e->tensorflow::XlaCompilationCache::BuildExecutable(entry->compilation_result, &entry->executable)
  compile_result = xla::LocalClient::Compile()
    executable = xla::LocalService::CompileExecutable()
      execution_options = xla::CreateExecutionOptions()
      xla::Service::CreateModuleConfig() 
      executor = execute_backend_->stream_executor()
      xla::Service::BuildExecutable()
        module = xla::gpu::CreateModuleFromProto()
          module = HloModule::CreateFromProto()
          xla::gpu::NVPTXCompiler::RunHloPasses() 
        executable = xla::gpu::NVPTXCompiler::RunBackend()  
          llvm::Module llvm_module(module->name().c_str(), llvm_context);
          std::unique_ptr<StreamAssignment> stream_assignment = AssignStreams(*module);
          hlo_schedule = GpuHloSchedule::Build()
          buffer_assignment = BufferAssigner::Run() 
          IrEmitterUnnested ir_emitter();
          entry_computation->Accept(&ir_emitter)
          llvm_ir::DumpIrIfEnabled(*module, llvm_module, /*optimized=*/false);
          ptx = CompileToPtx(&llvm_module, {cc_major, cc_minor}, module->config(), libdevice_dir)  
            ptx =  CompileModuleToPtx()
              target_machine = GetTargetMachine()
              module_passes.add(...
              module_passes.run(*module);
              return EmitModuleToPTX(module, target_machine.get());
                llvm::raw_string_ostream stream(ptx);
                llvm::buffer_ostream pstream(stream);
                codegen_passes.add(new llvm::TargetLibraryInfoWrapperPas)
                target_machine->addPassesToEmitFile(codegen_passes, pstream)
                  codegen_passes.run() //dump_ir_pass.cc
                    for i in passes_.size():
                      llvm::legacy::PassManager::add(P);
                    llvm::legacy::PassManager::run(module); 
          cubin = CompilePtxOrGetCachedResult(ptx, module->config())
            XLA_SCOPED_LOGGING_TIMER("NVPTXCompiler::CompilePtxOrGetCachedResult");
            if !ptx.empty():
              //本质就是把string 的ptx变成uint8
              StatusOr<std::vector<uint8>> maybe_cubin = se::cuda::CompilePtx(stream_exec->device_ordinal(), cache_ptx->c_str(),PtxOptsFromConfig(hlo_module_config));
                tensorflow::WriteStringToFile(env, ptx_path, ptx_contents);
                std::vector<string> ptxas_args = {ptxas_path, ptx_path, "-o", cubin_path, absl::StrCat("-arch=sm_", cc_major, cc_minor)};
                ptxas_info_dumper.SetProgram(ptxas_path, ptxas_args);
                tensorflow::ReadFileToString(tensorflow::Env::Default(),cubin_path, &cubin));
          module->entry_computation()->Accept(&cost_analysis);   --> 这个是啥, 性能分析吗???
          auto thunk_schedule = absl::make_unique<ThunkSchedule>(ir_emitter)
          gpu_executable = new GpuExecutable(cubin, thunk_schedule)
          return gpu_executable
    return new LocalExecutable(executable)
  executable = std::move(compile_result.ValueOrDie()
  out_compilation_result = &entry->compilation_result 

-2- Client端Graph编译入口
-3- LocalService端Graph编译入口
-7- Service的Graph编译入口
-8- 根据Client端生成的HloProto表示的Graph转换为Hlo格式
-10- 优化HloModule, backend是XlaOp过滤逻辑用到的, 这里是service/gpu/nvptx_compiler.cc-11- 编译HloModule入口   nvptx_compiler.cc
-12- 构造最终提交到LLVM的llvm::Module对象
-13- 按照PostOrder的顺序依次给HloInstruction分配stream number, 根据是否是GEMM, 决定是否复用operand的stream number. 
-14- 分配Stream number, 决定了HloInstruction最终的处理顺序, 核心工作时确定了thunk_launch_order_以及据此构造的hlo_ordering_. 如果是配置为单Stream, 就是PostOrder, 否则使用BFSLaunchOrder, 会根据HloInstruction之间的依赖关系, 以及一共可用的Stream number数量来给每个HloInstruction分配Stream Number, 原则上, 会将不存在依赖关系的HloInstruction尽量分配到不同的Stream number, 如果有依赖, 那么这个HloInstruction的Stream number会和某个它所依赖的HloInstruction使用相同的Stream number, 所谓的launch order 并不是最终执行的顺序
-15- **显存优化**
-16- visitor 也可以是别的visitor, 之前优化HloInstruction的时候就用到很多
-17- 遍历每一个HloInstruction 构造相应的Thunk, 除了全图的Accept, Instruction也有自己的Accept()用于局部遍历
-19- nvptx_backend_lib.cc  根据HloInstruction 生成ptx
-33- 根据ptx生成cubin, 即把string 的ptx变成uint8-39- 这个有没有优化空间
-42- 这个是性能分析???

执行cubin

调用栈:

tensorflow::XlaRunOp::Compute()
  run_result = xla::LocalExecutable::Run() //friend class LocalClient
    return executable_->ExecuteOnStreamWrapper()
      return_value = ExecuteOnStream()    //GpuExecutable, gpu_executable.cc
        return Execute()
          globals = ResolveConstantGlobals(executor)
            if !cubin().empty():
              module_spec.AddCudaCubinInMemory(cubin());
              module_spec.AddCudaPtxInMemory(ptx().c_str());
              module_handles_.emplace()
          buffer_allocations = buffer_allocations_builder.Build()
          ExecuteThunks(buffer_allocations)    
            se::Stream* main_stream = run_options->stream();
            se::StreamExecutor* executor = main_stream->parent();
            HloExecutionProfiler profiler()
            for thunk in thunk_schedule_->TotalOrder():  
              thunk->Initialize(*this, executor);      
                kernel = CreateKernel(executable.ptx(), executable.cubin()) //xla::gpu::CreateKernel
                  loader_spec.AddCudaPtxInMemory(ptx, kernel_name)
                  loader_spec.AddCudaCubinInMemory(cubin_data.data(), kernel_name)
                    cuda_cubin_in_memory_.reset(new CudaCubinInMemory{bytes, kernelname});
                  stream_exec->GetKernel()
                kernel_cache_.emplace(executor, kernel)
              int32 stream_no = thunk_schedule_->StreamNumberForHlo(*thunk->hlo_instruction())
              se::Stream* stream =(stream_no == 0 ? main_stream : sub_streams[stream_no - 1].get());
              thunk->ExecuteOnStream()            //xla::gpu::KernelThunk::ExecuteOnStream
                it = kernel_cache_.find(executor)
                kernel = it->second.get()
                ExecuteKernelOnStream(*kernel)
                  for buf in args:
                    kernel_args->add_device_memory_argument(buf);
                  stream->parent()->Launch(kernel_args)
                    implementation_->Launch(kernel, args) 
                      CUstream custream = AsGpuStreamValue(stream);
                      const GpuKernel* cuda_kernel = AsGpuKernel(&kernel);
                      CUfunction cufunc = cuda_kernel->AsGpuFunctionHandle();
                      void **kernel_params = const_cast<void **>(args.argument_addresses().data());
                      GpuDriver::LaunchKernel(context_, cufunc, custream, kernel_params, ...)
                        cuLaunchKernel()
          root = hlo_module_->entry_computation()->root_instruction()
          return std::move(shaped_buffer);
      return return_value
  launch_context.PopulateOutputs(ctx, run_result)
    output.set_buffer(se::OwningDeviceMemory(), {output_num});
    ctx->set_output(i, output_tensor);  

-23- 将kernel加入kernel_cache, cache的作用是防止load 的时间占用了执行的时间, 让execute的统计更准确
-34- 获取custream
-39- 加载kernel到GPU执行, 注意, 加载时机和实际执行时机不是一回事, CPU端只需批量加载, GPU负责顺序执行stream上两个kernel




Related:
Tensorflow XLA HLO I — BufferLiveness
Tensorflow XLA Service 详解 II
Tensorflow XLA Service 详解 I
Tensorflow XLA Client | HloModuleProto 详解
Tensorflow XlaOpKernel | tf2xla 机制详解
Tensorflow JIT 技术详解
Tensorflow JIT/XLA UML
Tensorflow OpKernel机制详解
Tensorflow Op机制详解
Tensorflow Optimization机制详解
Tensorflow 图计算引擎概述

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