# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import cProfile import time import os import numpy as np import paddle.fluid as fluid import paddle.fluid.core as core import paddle.fluid.profiler as profiler import paddle.fluid.transpiler.distribute_transpiler as distribute_transpiler from args import * def append_nccl2_prepare(trainer_id): if trainer_id >= 0: # append gen_nccl_id at the end of startup program trainer_id = int(os.getenv("PADDLE_TRAINER_ID")) port = os.getenv("PADDLE_PSERVER_PORT") worker_ips = os.getenv("PADDLE_TRAINER_IPS") worker_endpoints = [] for ip in worker_ips.split(","): worker_endpoints.append(':'.join([ip, port])) num_trainers = len(worker_endpoints) current_endpoint = os.getenv("PADDLE_CURRENT_IP") + ":" + port worker_endpoints.remove(current_endpoint) nccl_id_var = fluid.default_startup_program().global_block().create_var( name="NCCLID", persistable=True, type=fluid.core.VarDesc.VarType.RAW) fluid.default_startup_program().global_block().append_op( type="gen_nccl_id", inputs={}, outputs={"NCCLID": nccl_id_var}, attrs={ "endpoint": current_endpoint, "endpoint_list": worker_endpoints, "trainer_id": trainer_id }) return nccl_id_var, num_trainers, trainer_id else: raise Exception("must set positive PADDLE_TRAINER_ID env variables for " "nccl-based dist train.") def dist_transpile(trainer_id, args): if trainer_id < 0: return None, None # the port of all pservers, needed by both trainer and pserver port = os.getenv("PADDLE_PSERVER_PORT", "6174") # comma separated ips of all pservers, needed by trainer and # pserver pserver_ips = os.getenv("PADDLE_PSERVER_IPS", "") eplist = [] for ip in pserver_ips.split(","): eplist.append(':'.join([ip, port])) pserver_endpoints = ",".join(eplist) # total number of workers/trainers in the job, needed by # trainer and pserver trainers = int(os.getenv("PADDLE_TRAINERS")) # the IP of the local machine, needed by pserver only current_endpoint = os.getenv("PADDLE_CURRENT_IP", "") + ":" + port # the role, should be either PSERVER or TRAINER training_role = os.getenv("PADDLE_TRAINING_ROLE") t = distribute_transpiler.DistributeTranspiler() t.transpile( trainer_id, pservers=pserver_endpoints, trainers=trainers, sync_mode=not args.async_mode, slice_var_up=not args.no_split_var) if training_role == "PSERVER": pserver_program = t.get_pserver_program(current_endpoint) pserver_startup_program = t.get_startup_program(current_endpoint, pserver_program) return pserver_program, pserver_startup_program elif training_role == "TRAINER": train_program = t.get_trainer_program() return train_program, fluid.default_startup_program() else: raise ValueError( 'TRAINING_ROLE environment variable must be either TRAINER or PSERVER' ) def test(exe, inference_program, test_reader, feeder, batch_acc): accuracy_evaluator = fluid.metrics.Accuracy() for batch_id, data in enumerate(test_reader()): acc = exe.run(inference_program, feed=feeder.feed(data), fetch_list=[batch_acc]) accuracy_evaluator.update(value=np.array(acc), weight=len(data)) return accuracy_evaluator.eval() # TODO(wuyi): replace train, train_parallel, test functions with new trainer # API once it is ready. def train(avg_loss, infer_prog, optimizer, train_reader, test_reader, batch_acc, args, train_prog, startup_prog): if os.getenv("PADDLE_TRAINING_ROLE") == "PSERVER": place = core.CPUPlace() exe = fluid.Executor(place) exe.run(startup_prog) exe.run(train_prog) return if args.use_fake_data: raise Exception( "fake data is not supported in single GPU test for now.") place = core.CPUPlace() if args.device == 'CPU' else core.CUDAPlace(0) exe = fluid.Executor(place) exe.run(startup_prog) if not args.use_reader_op: feed_var_list = [ var for var in train_prog.global_block().vars.itervalues() if var.is_data ] feeder = fluid.DataFeeder(feed_var_list, place) iters, num_samples, start_time = 0, 0, time.time() for pass_id in range(args.pass_num): train_losses = [] if not args.use_reader_op: reader_generator = train_reader() batch_id = 0 data = None while True: if not args.use_reader_op: data = next(reader_generator, None) if data == None: break if iters == args.iterations: break if iters == args.skip_batch_num: start_time = time.time() num_samples = 0 if arg.profile and pass_id == 0 and batch_id == 5: profiler.start_profiler("All") elif args.profile and pass_id == 0 and batch_id == 10: profiler.stop_profiler("total", "/tmp/profile") if args.use_reader_op: try: loss = exe.run(train_prog, fetch_list=[avg_loss], use_program_cache=True) except fluid.core.EnforceNotMet as ex: break else: loss = exe.run(train_prog, feed=feeder.feed(data), fetch_list=[avg_loss], use_program_cache=True) iters += 1 batch_id += 1 # FIXME(wuyi): For use_reader_op, if the current # pass is not the last, the last batch of this pass # is also equal to args.batch_size. if args.use_reader_op: num_samples += args.batch_size * args.gpus else: num_samples += len(data) train_losses.append(loss) print("Pass: %d, Iter: %d, Loss: %f\n" % (pass_id, iters, np.mean(train_losses))) print_train_time(start_time, time.time(), num_samples) print("Pass: %d, Loss: %f" % (pass_id, np.mean(train_losses))), # evaluation if not args.no_test and batch_acc: pass_test_acc = test(exe, infer_prog, test_reader, feeder, batch_acc) print(", Test Accuracy: %f" % pass_test_acc) print("\n") # TODO(wuyi): add warmup passes to get better perf data. exit(0) # TODO(wuyi): replace train, train_parallel, test functions with new trainer # API once it is ready. def train_parallel(avg_loss, infer_prog, optimizer, train_reader, test_reader, batch_acc, args, train_prog, startup_prog, nccl_id_var, num_trainers, trainer_id): place = core.CPUPlace() if args.device == 'CPU' else core.CUDAPlace(0) if not args.use_reader_op: feed_var_list = [ var for var in train_prog.global_block().vars.itervalues() if var.is_data ] feeder = fluid.DataFeeder(feed_var_list, place) # generate fake: if args.use_fake_data: for var in feed_var_list: v = startup_prog.global_block().clone_variable(var) var.persistable = True v.persistable = True real_shape = list(var.shape) real_shape[0] = args.batch_size / args.gpus startup_prog.global_block().append_op( outputs={"Out": v}, type="fill_constant", attrs={"shape": real_shape, "value": 1.0, "dtype": var.dtype}) if nccl_id_var and trainer_id == 0: #FIXME(wuyi): wait other trainer to start listening time.sleep(30) startup_exe = fluid.Executor(place) startup_exe.run(startup_prog) strategy = fluid.ExecutionStrategy() strategy.num_threads = 1 strategy.allow_op_delay = False exe = fluid.ParallelExecutor( True, avg_loss.name, exec_strategy=strategy, num_trainers=num_trainers, trainer_id=trainer_id) for pass_id in range(args.pass_num): num_samples = 0 iters = 0 start_time = time.time() if not args.use_reader_op: reader_generator = train_reader() batch_id = 0 data = None while True: if not args.use_reader_op: data = next(reader_generator, None) if data == None: break if iters == args.iterations: break if args.profile and pass_id == 0 and batch_id == 5: profiler.start_profiler("All") elif args.profile and pass_id == 0 and batch_id == 10: profiler.stop_profiler("total", "/tmp/profile_%d" % trainer_id) if iters == args.skip_batch_num: start_time = time.time() num_samples = 0 if args.use_fake_data or args.use_reader_op: try: loss, = exe.run([avg_loss.name]) except fluid.core.EnforceNotMet as ex: break else: loss, = exe.run([avg_loss.name], feed=feeder.feed(data)) if args.update_method == "pserver": exe.bcast_params() if args.use_reader_op: num_samples += args.batch_size * args.gpus else: num_samples += len(data) iters += 1 if batch_id % 1 == 0: print("Pass %d, batch %d, loss %s" % (pass_id, batch_id, np.array(loss))) batch_id += 1 print_train_time(start_time, time.time(), num_samples) if not args.no_test and batch_acc: test_acc = test(startup_exe, infer_prog, test_reader, feeder, batch_acc) print("Pass: %d, Test Accuracy: %f\n" % (pass_id, test_acc)) exit(0) def print_arguments(args): vars(args)['use_nvprof'] = (vars(args)['use_nvprof'] and vars(args)['device'] == 'GPU') print('----------- Configuration Arguments -----------') for arg, value in sorted(vars(args).iteritems()): print('%s: %s' % (arg, value)) print('------------------------------------------------') def print_train_time(start_time, end_time, num_samples): train_elapsed = end_time - start_time examples_per_sec = num_samples / train_elapsed print('\nTotal examples: %d, total time: %.5f, %.5f examples/sed\n' % (num_samples, train_elapsed, examples_per_sec)) def main(): args = parse_args() print_arguments(args) # the unique trainer id, starting from 0, needed by trainer # only nccl_id_var, num_trainers, trainer_id = ( None, 1, int(os.getenv("PADDLE_TRAINER_ID", "0"))) if args.use_cprof: pr = cProfile.Profile() pr.enable() model_def = __import__("models.%s" % args.model, fromlist=["models"]) train_args = list(model_def.get_model(args)) train_args.append(args) # Run optimizer.minimize(avg_loss) train_args[2].minimize(train_args[0]) if args.memory_optimize: fluid.memory_optimize(fluid.default_main_program()) if args.update_method == "pserver": train_prog, startup_prog = dist_transpile(trainer_id, args) if not train_prog: raise Exception( "Must configure correct environments to run dist train.") train_args.extend([train_prog, startup_prog]) if args.gpus > 1 and os.getenv("PADDLE_TRAINING_ROLE") == "TRAINER": train_args.extend([nccl_id_var, num_trainers, trainer_id]) train_parallel(*train_args) train(*train_args) exit(0) # for other update methods, use default programs train_args.append(fluid.default_main_program()) train_args.append(fluid.default_startup_program()) if args.update_method == "nccl2": nccl_id_var, num_trainers, trainer_id = append_nccl2_prepare(trainer_id) if args.gpus == 1: # NOTE: parallel executor use profiler interanlly if args.use_nvprof and args.device == 'GPU': with profiler.cuda_profiler("cuda_profiler.txt", 'csv') as nvprof: train(*train_args) else: train(*train_args) else: if args.device == "CPU": raise Exception("Only support GPU perf with parallel exe") train_args.extend([nccl_id_var, num_trainers, trainer_id]) train_parallel(*train_args) if __name__ == "__main__": main()