from utils import DataFiles dataFiles = DataFiles(__file__) inputRaw = dataFiles.input inputRawEX = dataFiles.inputEX def calc_score(q1, q2): ans = 0 if (len(q1) > 0): for i in range(1, len(q1) + 1): ans += i * q1[-i] else: for i in range(1, len(q2) + 1): ans += i * q2[-i] return ans def part1(q1, q2): while (len(q1) > 0 and len(q2) > 0): c1 = q1[0] c2 = q2[0] if c1 > c2: q1 = q1[1:] + [c1, c2] q2 = q2[1:] else: q1 = q1[1:]
def main(): parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--data_dir", default=None, type=str, required=True, help= "The input data dir. Should contain the .tsv files (or other data files) for the task.", ) parser.add_argument( "--model_type", default=None, type=str, required=True, help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()), ) parser.add_argument( "--model_name_or_path", default=None, type=str, required=True, help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS), ) parser.add_argument( "--task_name", default=None, type=str, required=True, help="The name of the task to train selected in the list: " + ", ".join(processors.keys()), ) parser.add_argument( "--output_dir", default=None, type=str, required=True, help= "The output directory where the model predictions and checkpoints will be written.", ) parser.add_argument( "--bert_representation", default="pool", choices=["avg", "pool"], type=str, help="The BERT representation type", ) parser.add_argument( "--similarity_function", default="pool", choices=["dot"], type=str, help="The similarity scoring function", ) # Other parameters parser.add_argument( "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name", ) parser.add_argument( "--tokenizer_name", default="", type=str, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--cache_dir", default="", type=str, help= "Where do you want to store the pre-trained models downloaded from s3", ) parser.add_argument( "--max_seq_length", default=128, type=int, help= "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded.", ) parser.add_argument("--do_train", action="store_true", help="Whether to run training.") parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.") parser.add_argument( "--evaluate_during_training", action="store_true", help="Run evaluation during training at each logging step.", ) parser.add_argument( "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model.", ) parser.add_argument( "--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.", ) parser.add_argument( "--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help= "Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.") parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.") parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument( "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform.", ) parser.add_argument( "--max_steps", default=-1, type=int, help= "If > 0: set total number of training steps to perform. Override num_train_epochs.", ) parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.") parser.add_argument("--logging_steps", type=int, default=500, help="Log every X updates steps.") parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.") parser.add_argument( "--eval_all_checkpoints", action="store_true", help= "Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number", ) parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available") parser.add_argument( "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory", ) parser.add_argument( "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets", ) parser.add_argument("--seed", type=int, default=42, help="random seed for initialization") parser.add_argument( "--fp16", action="store_true", help= "Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit", ) parser.add_argument( "--fp16_opt_level", type=str, default="O1", help= "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']." "See details at https://nvidia.github.io/apex/amp.html", ) parser.add_argument( "--save_total_limit", type=int, default=None, help= "Limit the total amount of checkpoints, delete the older checkpoints in the output_dir, does not delete by default", ) parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") parser.add_argument("--server_ip", type=str, default="", help="For distant debugging.") parser.add_argument("--server_port", type=str, default="", help="For distant debugging.") args = parser.parse_args() logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN, ) if (os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir): # set to load the latest checkpoint for training args.model_name_or_path = args.output_dir all_model_checkpoints = [ ckpt for ckpt in os.listdir(args.model_name_or_path) if os.path.isdir(os.path.join(args.model_name_or_path, ckpt)) ] all_model_checkpoints = [(ckpt.split("-")[-1] if "-" in ckpt else -1, ckpt) for ckpt in all_model_checkpoints] all_model_checkpoints.sort(reverse=True) args.model_name_or_path = os.path.join(args.model_name_or_path, all_model_checkpoints[0][1]) logger.info("setting to load the model from %s", args.model_name_or_path) # Setup distant debugging if needed if args.server_ip and args.server_port: # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script import ptvsd print("Waiting for debugger attach") ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) ptvsd.wait_for_attach() # Setup CUDA, GPU & distributed training if args.local_rank == -1 or args.no_cuda: device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count() else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) torch.distributed.init_process_group(backend="nccl") args.n_gpu = 1 args.device = device # Setup logging logger.warning( "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16, ) # Set seed set_seed(args) # Prepare GLUE task args.task_name = args.task_name.lower() if args.task_name not in processors: raise ValueError("Task not found: %s" % (args.task_name)) processor = processors[args.task_name]() args.output_mode = output_modes[args.task_name] num_labels = 2 # Load pretrained model and tokenizer if args.local_rank not in [-1, 0]: torch.distributed.barrier( ) # Make sure only the first process in distributed training will download model & vocab args.model_type = args.model_type.lower() config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type] config = config_class.from_pretrained( args.config_name if args.config_name else args.model_name_or_path, num_labels=num_labels, finetuning_task=args.task_name, cache_dir=args.cache_dir if args.cache_dir else None, ) tokenizer = tokenizer_class.from_pretrained( args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case, cache_dir=args.cache_dir if args.cache_dir else None, ) model = model_class.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, cache_dir=args.cache_dir if args.cache_dir else None, ) if args.local_rank == 0: torch.distributed.barrier( ) # Make sure only the first process in distributed training will download model & vocab model.to(args.device) logger.info("Training/evaluation parameters %s", args) # Training if args.do_train: datafiles = DataFiles(args.data_dir) if os.path.isfile( os.path.join(args.model_name_or_path, "datafiles.txt")): datafiles.load( os.path.join(args.model_name_or_path, "datafiles.txt")) global_step = 0 shard_count = 0 if args.local_rank in [-1, 0]: tb_writer = SummaryWriter() while True: todo_file = datafiles.next() if not todo_file: break if args.local_rank == 0: torch.distributed.barrier() train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, todo_file) args.train_batch_size = args.per_gpu_train_batch_size * max( 1, args.n_gpu) train_sampler = RandomSampler( train_dataset ) if args.local_rank == -1 else DistributedSampler(train_dataset) train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size) if shard_count == 0: # if this is the first shard, create the optimizer or load from the previous checkpoint # Prepare optimizer and schedule (linear warmup and decay) no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [ p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay) ], "weight_decay": args.weight_decay, }, { "params": [ p for n, p in model.named_parameters() if any(nd in n for nd in no_decay) ], "weight_decay": 0.0 }, ] t_total = len( train_dataloader ) // args.gradient_accumulation_steps * args.num_train_epochs * len( datafiles.all_files) # 280 shards of data files in total optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon) scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total) # Check if saved optimizer or scheduler states exist if os.path.isfile( os.path.join(args.model_name_or_path, "optimizer.pt")) and os.path.isfile( os.path.join(args.model_name_or_path, "scheduler.pt")): logger.info("loading optimizer and scheduler from %s", args.model_name_or_path) # Load in optimizer and scheduler states optimizer.load_state_dict( torch.load( os.path.join(args.model_name_or_path, "optimizer.pt"))) scheduler.load_state_dict( torch.load( os.path.join(args.model_name_or_path, "scheduler.pt"))) if args.fp16: try: from apex import amp except ImportError: raise ImportError( "Please install apex from https://www.github.com/nvidia/apex to use fp16 training." ) model, optimizer = amp.initialize( model, optimizer, opt_level=args.fp16_opt_level) # multi-gpu training (should be after apex fp16 initialization) if args.n_gpu > 1: model = torch.nn.DataParallel(model) # Distributed training (should be after apex fp16 initialization) if args.local_rank != -1: model = torch.nn.parallel.DistributedDataParallel( model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True, ) # Train! logger.info("***** Running training *****") logger.info(" Num examples = %d", len(train_dataset)) logger.info(" Num Epochs = %d", args.num_train_epochs) logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size) logger.info( " Total train batch size (w. parallel, distributed & accumulation) = %d", args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1), ) logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps) logger.info(" Total optimization steps = %d", t_total) if shard_count == 0: # Check if continuing training from a checkpoint if os.path.exists(args.model_name_or_path): # set global_step to global_step of last saved checkpoint from model path try: global_step = int( args.model_name_or_path.split("-")[-1].split("/") [0]) except ValueError: global_step = 0 epochs_trained = global_step // ( len(train_dataloader) // args.gradient_accumulation_steps) logger.info(" Continuing training from checkpoint %s", args.model_name_or_path) logger.info(" Continuing training from global step %d", global_step) global_step, tr_loss, optimizer, scheduler = train( args, train_dataset, train_dataloader, model, tokenizer, optimizer, scheduler, tb_writer, global_step=global_step) logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) # Save model checkpoint output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step)) if not os.path.exists(output_dir): os.makedirs(output_dir) model_to_save = ( model.module if hasattr(model, "module") else model ) # Take care of distributed/parallel training model_to_save.save_pretrained(output_dir) tokenizer.save_pretrained(output_dir) torch.save(args, os.path.join(output_dir, "training_args.bin")) logger.info("Saving model checkpoint to %s", output_dir) torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt")) torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt")) datafiles.save(os.path.join(output_dir, "datafiles.txt")) logger.info("Saving optimizer and scheduler states to %s", output_dir) _rotate_checkpoints(args, "checkpoint") shard_count += 1 if args.local_rank in [-1, 0]: tb_writer.close() # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained() if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0): # Create output directory if needed if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]: os.makedirs(args.output_dir) logger.info("Saving model checkpoint to %s", args.output_dir) # Save a trained model, configuration and tokenizer using `save_pretrained()`. # They can then be reloaded using `from_pretrained()` model_to_save = (model.module if hasattr(model, "module") else model ) # Take care of distributed/parallel training model_to_save.save_pretrained(args.output_dir) tokenizer.save_pretrained(args.output_dir) # Good practice: save your training arguments together with the trained model torch.save(args, os.path.join(args.output_dir, "training_args.bin")) # Load a trained model and vocabulary that you have fine-tuned model = model_class.from_pretrained(args.output_dir) tokenizer = tokenizer_class.from_pretrained(args.output_dir) model.to(args.device) # Evaluation results = {} if args.do_eval and args.local_rank in [-1, 0]: tokenizer = tokenizer_class.from_pretrained( args.output_dir, do_lower_case=args.do_lower_case) checkpoints = [args.output_dir] if args.eval_all_checkpoints: checkpoints = list( os.path.dirname(c) for c in sorted( glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True))) logging.getLogger("transformers.modeling_utils").setLevel( logging.WARN) # Reduce logging logger.info("Evaluate the following checkpoints: %s", checkpoints) for checkpoint in checkpoints: global_step = checkpoint.split( "-")[-1] if len(checkpoints) > 1 else "" prefix = checkpoint.split( "/")[-1] if checkpoint.find("checkpoint") != -1 else "" model = model_class.from_pretrained(checkpoint) model.to(args.device) result = evaluate(args, model, tokenizer, prefix=prefix) result = dict( (k + "_{}".format(global_step), v) for k, v in result.items()) results.update(result) return results