# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. 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. """ Code completion (both token level and line level) pipeline in CodeXGLUE """ from __future__ import absolute_import, division, print_function import argparse import glob import logging import os import pickle import random import re import shutil import json import numpy as np import torch from torch.utils.data import DataLoader, Dataset, SequentialSampler, RandomSampler,TensorDataset from torch.utils.data.distributed import DistributedSampler from dataset import TextDataset, finetuneDataset, EvalDataset, lineDataset from beam import Beam from transformers import (WEIGHTS_NAME, AdamW, get_linear_schedule_with_warmup, BertConfig, BertForMaskedLM, BertTokenizer, GPT2Config, GPT2LMHeadModel, GPT2Tokenizer, OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer, RobertaConfig, RobertaForMaskedLM, RobertaTokenizer, DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer) from model import RNNModel # logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', # datefmt='%m/%d/%Y %H:%M:%S', # level=logging.INFO) logger = logging.getLogger(__name__) MODEL_CLASSES = { 'gpt2': (GPT2Config, GPT2LMHeadModel, GPT2Tokenizer), 'rnn': (GPT2Config, RNNModel, GPT2Tokenizer), 'openai-gpt': (OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer), 'bert': (BertConfig, BertForMaskedLM, BertTokenizer), 'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer), 'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer) } def load_and_cache_examples(args, tokenizer, evaluate=False): if args.not_pretrain: dataset = finetuneDataset(tokenizer, args, logger, file_type='dev' if evaluate else 'train', block_size=args.block_size) else: dataset = TextDataset(tokenizer, args, logger, file_type='dev' if evaluate else 'train', block_size=args.block_size) return dataset def set_seed(args): random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if args.n_gpu > 0: torch.cuda.manual_seed_all(args.seed) def update_config(args, config): # config.n_positions = config.n_ctx = args.block_size config.vocab_size = args.vocab_size def get_special_tokens(path): lits = json.load(open(path)) tokens = ["", "", ""] for lit in lits["str"]: tokens.append(f"") for lit in lits["num"]: tokens.append(f"") for lit in lits["char"]: tokens.append(f"") return tokens def train(args, train_dataset, model, tokenizer, fh, pool): """ Train the model """ if args.local_rank in [-1, 0]: args.tensorboard_dir = os.path.join(args.output_dir, 'tensorboard') if not os.path.exists(args.tensorboard_dir): os.makedirs(args.tensorboard_dir) args.batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu) train_sampler = RandomSampler(train_dataset) train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.batch_size, drop_last=True) total_examples = len(train_dataset) * ( torch.distributed.get_world_size() if args.local_rank != -1 else 1) batch_size = args.batch_size * args.gradient_accumulation_steps * ( torch.distributed.get_world_size() if args.local_rank != -1 else 1) # if args.max_steps > 0: # t_total = args.max_steps # args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1 if args.num_train_epochs > 0: t_total = total_examples // batch_size * args.num_train_epochs args.max_steps = t_total model.to(args.device) if args.local_rank not in [-1, 0]: torch.distributed.barrier() # 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} ] 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) checkpoint_last = os.path.join(args.output_dir, 'checkpoint-last') # scheduler_last = os.path.join(checkpoint_last, 'scheduler.pt') optimizer_last = os.path.join(checkpoint_last, 'optimizer.pt') # if os.path.exists(scheduler_last): # scheduler.load_state_dict(torch.load(scheduler_last, map_location="cpu")) if os.path.exists(optimizer_last): logger.warning(f"Loading optimizer from {optimizer_last}") optimizer.load_state_dict(torch.load(optimizer_last, map_location="cpu")) if args.local_rank == 0: torch.distributed.barrier() 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%args.gpu_per_node], output_device=args.local_rank%args.gpu_per_node) # Train! logger.info("***** Running training *****") logger.info(" Num examples = %d", total_examples ) logger.info(" Num epoch = %d", t_total*batch_size//total_examples) 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", batch_size) logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps) logger.info(" Total optimization steps = %d", t_total) global_step = args.start_step tr_loss, logging_loss,avg_loss,tr_nb = 0.0, 0.0, 0.0, global_step # model.resize_token_embeddings(len(tokenizer)) model.zero_grad() set_seed(args) # Added here for reproducibility (even between python 2 and 3) for idx in range(args.start_epoch, int(args.num_train_epochs)): for step, batch in enumerate(train_dataloader): inputs, labels = (batch, batch) inputs = inputs.to(args.device) labels = labels.to(args.device) model.train() outputs = model(inputs, labels=labels) loss = outputs[0] if args.n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu parallel training if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps if args.fp16: with amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward() torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm) else: loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm) tr_loss += loss.item() if (step + 1) % args.gradient_accumulation_steps == 0: optimizer.step() optimizer.zero_grad() scheduler.step() global_step += 1 output_flag=True avg_loss=round(np.exp((tr_loss - logging_loss) /(global_step- tr_nb)),4) if global_step % args.logging_steps == 0: logger.info(" steps: %s ppl: %s lr: %s", global_step, round(avg_loss,5), scheduler.get_last_lr()[0]) if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: # Log metrics logging_loss = tr_loss tr_nb=global_step if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0: checkpoint_prefix = "checkpoint" # Save model checkpoint if args.evaluate_during_training: # Only evaluate when single GPU otherwise metrics may not average well results = evaluate(args, model, tokenizer, eval_when_training=True) for key, value in results.items(): logger.info(" %s = %s", key, round(value,4)) output_dir = os.path.join(args.output_dir, '{}-{}-{}'.format(checkpoint_prefix, global_step, round(results['perplexity'],4))) else: output_dir = os.path.join(args.output_dir, "{}-{}".format(checkpoint_prefix, 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 if args.model_type == "rnn": torch.save(model_to_save.state_dict(), os.path.join(output_dir, "model.pt")) else: 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) # _rotate_checkpoints(args, checkpoint_prefix) last_output_dir = os.path.join(args.output_dir, 'checkpoint-last') if not os.path.exists(last_output_dir): os.makedirs(last_output_dir) if args.model_type == "rnn": torch.save(model_to_save.state_dict(), os.path.join(last_output_dir, "model.pt")) else: model_to_save.save_pretrained(last_output_dir) tokenizer.save_pretrained(last_output_dir) idx_file = os.path.join(last_output_dir, 'idx_file.txt') with open(idx_file, 'w', encoding='utf-8') as idxf: idxf.write(str(0) + '\n') torch.save(optimizer.state_dict(), os.path.join(last_output_dir, "optimizer.pt")) # torch.save(scheduler.state_dict(), os.path.join(last_output_dir, "scheduler.pt")) logger.info("Saving optimizer and scheduler states to %s", last_output_dir) step_file = os.path.join(last_output_dir, 'step_file.txt') with open(step_file, 'w', encoding='utf-8') as stepf: stepf.write(str(global_step) + '\n') if args.max_steps > 0 and global_step > args.max_steps: break # 每一轮记录checkpoint output_dir = os.path.join(args.output_dir, 'epoch_{}'.format(idx+1)) if not os.path.exists(output_dir): os.makedirs(output_dir) model_to_save = model.module if hasattr(model, 'module') else model ckpt_output_path = os.path.join(output_dir, 'subject_model.pth') logger.info("Saving model checkpoint to %s", ckpt_output_path) torch.save(model_to_save.state_dict(), ckpt_output_path) if args.max_steps > 0 and global_step > args.max_steps: break return global_step, tr_loss / global_step def evaluate(args, model, tokenizer, prefix="", eval_when_training=False): # Loop to handle MNLI double evaluation (matched, mis-matched) eval_output_dir = args.output_dir eval_dataset = load_and_cache_examples(args, tokenizer, evaluate=True) if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]: os.makedirs(eval_output_dir) args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) # Note that DistributedSampler samples randomly eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset) eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, drop_last=True) # multi-gpu evaluate if args.n_gpu > 1 and eval_when_training is False: model = torch.nn.DataParallel(model) # Eval! #logger.info("***** Running evaluation {} *****".format(prefix)) #logger.info(" Num examples = %d", len(eval_dataset)) #logger.info(" Batch size = %d", args.eval_batch_size) eval_loss = 0.0 nb_eval_steps = 0 model.eval() for batch in eval_dataloader: inputs, labels = (batch, batch) inputs = inputs.to(args.device) labels = labels.to(args.device) with torch.no_grad(): outputs = model(inputs, labels=labels) lm_loss = outputs[0] eval_loss += lm_loss.mean().item() nb_eval_steps += 1 eval_loss = eval_loss / nb_eval_steps perplexity = torch.exp(torch.tensor(eval_loss)) result = { "perplexity": float(perplexity) } output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt") with open(output_eval_file, "w") as writer: #logger.info("***** Eval results {} *****".format(prefix)) for key in sorted(result.keys()): #logger.info(" %s = %s", key, str(result[key])) writer.write("%s = %s\n" % (key, str(result[key]))) return result def eval_acc(args, model, tokenizer, file_type='test'): """ Evaluate token level code completion on accuracy. This function can only used to evaluate accuracy, but not inference, because the inputs are previous sub-tokens but not tokens. But it can be guaranteed that the accuracy in this function is the same as the real token level completion. The reason is: Assuming the inputs are "context_len = 100 masks = np . zeros (", and the ground truth is "context_len". Due to our bpe encoding, the model have to outputs "context", "_" and "len" in 3 time step, i.e. gt0="context", gt1="_", gt2="len". In a real inference scenario: time step 0, inputs "context_len = 100 masks = np . zeros ( ", model outputs: out0; time step 1, inputs: in1=out0, outputs: out1 ... until the model outputs a complete token But in this function, no matter out0 is, in1=gt0="context". That is to say, in this function, we feed ground truth but not output sub-token when we predict the next token which is split by bpe. So obviouly we would get different predictions from the real token completion scenario. However, if we calculate token leval accuracy, if and only if the model predicts every sub-token correctly, the complete token can be seen correct. In this situation, out0==gt0, out1==gt1, so it doesn't matter we feed gt or output to model. In summary, this function can make models oupout the same complete token if this token equals to ground truth, if not, the model might predict a different token from the real completion scenario, but all wrong. So it would not affect the token level accuracy. I use this trick to speed up evaluation due to the large test set. """ eval_dataset = EvalDataset(tokenizer, args, logger, file_type=file_type, block_size=args.block_size) args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset) eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) model.to(args.device) # 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%args.gpu_per_node], output_device=args.local_rank%args.gpu_per_node) def DecodeIds(idxs): codes = "" for idx in idxs: to_add = tokenizer.convert_ids_to_tokens(idx) if tokenizer.convert_ids_to_tokens(idx)[0] == '\u0120': if not codes.endswith(" "): codes += " " + to_add[1:] else: codes += to_add[1:] elif ( idx in [tokenizer.bos_token_id, tokenizer.eos_token_id, tokenizer.sep_token_id, tokenizer.pad_token_id] or tokenizer.convert_ids_to_tokens(idx).startswith(" 0: try: all_pred.append(DecodeIds(now_pred).strip().split()[0]) except IndexError: all_pred.append("") all_gt.append(DecodeIds(now_gt).strip()) now_gt = [] now_pred = [] if y in [tokenizer.bos_token_id, tokenizer.eos_token_id, tokenizer.sep_token_id, tokenizer.pad_token_id] or tokenizer.convert_ids_to_tokens(y).startswith(" 0: try: all_pred.append(DecodeIds(now_pred).strip().split()[0]) except IndexError: all_pred.append("") all_gt.append(DecodeIds(now_gt).strip()) now_gt = [y] now_pred = [pred[i-1]] try: all_pred.append(DecodeIds(now_pred).strip().split()[0]) except IndexError: all_pred.append("") all_gt.append(DecodeIds(now_gt).strip()) now_gt = [] now_pred = [] continue now_gt.append(y) now_pred.append(pred[i-1]) assert len(all_pred) == len(all_gt) total_pred.extend(all_pred) total_gt.extend(all_gt) for x, y in zip(all_pred, all_gt): if y not in ["", "", "", ""]: total += 1 if x == y: correct += 1 if step % args.logging_steps == 0: logger.info(f"{step} are done!") logger.info(f"{total}, {correct/total}") # pickle.dump(total_pred, open(os.path.join(args.output_dir, "preds.pkl"), "wb")) # pickle.dump(total_gt, open(os.path.join(args.output_dir, "gts.pkl"), "wb")) saved_file = os.path.join(args.output_dir, "predictions.txt") total_samples = post_process(args, total_pred, total_gt, open(os.path.join(args.data_dir, f"{file_type}.txt")).readlines(), saved_file) logger.info(f"Eval on {total_samples}, saved at {saved_file}") return total, correct def post_process(args, preds, gts, true_gts, saved_file): wf = open(saved_file, "w") cnt = 0 new_gt = [] new_pred = [] for i, (pred,gt) in enumerate(zip(preds,gts)): if gt in ["", ""]: continue new_gt.append(gt) new_pred.append(pred.replace(" ", "")) if gt == "": gt_str = " ".join(new_gt) pred_str = " ".join(new_pred) assert gt_str == true_gts[cnt].strip(), f"{cnt} sample gt_str != true_gt" wf.write(pred_str+"\n") cnt += 1 new_gt = [] new_pred = [] return cnt def main(): parser = argparse.ArgumentParser() ## Required parameters parser.add_argument("--data_dir", default=None, type=str, required=True, help="The input data path.") parser.add_argument("--langs", default=None, type=str, required=True, help="Languages to train, if all, train all languages in data_dir") parser.add_argument("--output_dir", default=None, type=str, required=True, help="The output directory where the model predictions and checkpoints will be written.") ## Other parameters parser.add_argument("--model_type", default="gpt2", type=str, help="The model architecture to be fine-tuned.") parser.add_argument("--pretrain_dir", default="", type=str, help="The output directory where the model predictions and checkpoints will be written.") parser.add_argument("--config_dir", type=str, help="config name. Required when training from scratch") parser.add_argument("--tokenizer_dir", type=str, help="Pre-trained tokenizer dir. Required when training from scratch") parser.add_argument("--lit_file", type=str, help="literals json file") parser.add_argument("--load_name", type=str, default="pretrained", help="Load pretrained model name") parser.add_argument("--mlm", action='store_true', help="Train with masked-language modeling loss instead of language modeling.") parser.add_argument("--mlm_probability", type=float, default=0.15, help="Ratio of tokens to mask for masked language modeling loss") parser.add_argument("--cache_dir", default="", type=str, help="Optional directory to store the pre-trained models downloaded from s3 (instread of the default one)") parser.add_argument("--block_size", default=1024, type=int, help="Optional input sequence length after tokenization." "The training dataset will be truncated in block of this size for training." "Default to the model max input length for single sentence inputs (take into account special tokens).") 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=4, type=int, help="Batch size per GPU/CPU for training.") parser.add_argument("--per_gpu_eval_batch_size", default=12, 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 deay 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=1.0, 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=1000, help="Log every X updates steps.") parser.add_argument('--save_steps', type=int, default=5000, help="Save checkpoint every X updates steps.") 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("--eval_all_checkpoints", action='store_true', help="Evaluate all checkpoints starting with the same prefix as model_name_or_path 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('--not_pretrain', action='store_true', help="use different dataset") 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("--local_rank", type=int, default=-1, help="For distributed training: local_rank") parser.add_argument("--node_index", type=int, default=-1, help="node index if multi-node running") parser.add_argument("--gpu_per_node", type=int, default=-1, help="num of gpus per node") parser.add_argument('--server_ip', type=str, default='', help="For distant debugging.") parser.add_argument('--server_port', type=str, default='', help="For distant debugging.") parser.add_argument('--log_file', type=str, default='') parser.add_argument('--tensorboard_dir', type=str) pool = None args = parser.parse_args() # args.output_dir = os.path.join(args.output_dir, args.dataset) if args.model_type in ["bert", "roberta", "distilbert"] and not args.mlm: raise ValueError("BERT and RoBERTa do not have LM heads but masked LM heads. They must be run using the --mlm " "flag (masked language modeling).") if os.path.exists(args.output_dir) and os.listdir( args.output_dir) and args.do_train and not args.overwrite_output_dir: raise ValueError( "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format( args.output_dir)) # 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() logger.info("local_rank: %d, node_index: %d, gpu_per_node: %d"%(args.local_rank, args.node_index, args.gpu_per_node)) # 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 = 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.local_rank += args.node_index * args.gpu_per_node args.n_gpu = 1 args.device = device # args.batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu) # Setup logging 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) logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s, world size: %s", args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16, torch.distributed.get_world_size() if args.local_rank != -1 else 1) # 使用FileHandler输出到文件 fh = logging.FileHandler(args.log_file) logger.addHandler(fh) # Set seed set_seed(args) # Load pretrained model and tokenizer if args.local_rank not in [-1, 0]: torch.distributed.barrier() # Barrier to make sure only the first process in distributed training download model & vocab args.start_epoch = 0 args.start_step = 0 checkpoint_last = os.path.join(args.output_dir, 'checkpoint-last') if args.do_train and os.path.exists(checkpoint_last) and os.listdir(checkpoint_last): args.pretrain_dir = os.path.join(checkpoint_last) args.config_name = os.path.join(checkpoint_last, 'config.json') idx_file = os.path.join(checkpoint_last, 'idx_file.txt') with open(idx_file, encoding='utf-8') as idxf: args.start_epoch = int(idxf.readlines()[0].strip()) + 1 step_file = os.path.join(checkpoint_last, 'step_file.txt') if os.path.exists(step_file): with open(step_file, encoding='utf-8') as stepf: args.start_step = int(stepf.readlines()[0].strip()) logger.info("reload model from {}, resume from {} steps".format(checkpoint_last, args.start_step)) # get special tokens special_tokens = get_special_tokens(args.lit_file) # Load pre-trained model config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type] pretrained = checkpoint_last #args.pretrain_dir if pretrained: tokenizer = tokenizer_class.from_pretrained(pretrained, do_lower_case=args.do_lower_case, sep_token='', bos_token='', eos_token='', pad_token='', unk_token='<|UNKNOWN|>', additional_special_tokens=special_tokens) if args.model_type == "rnn": model = model_class(len(tokenizer), 768, 768, 1) model_last = os.path.join(pretrained, 'model.pt') if os.path.exists(model_last): logger.warning(f"Loading model from {model_last}") model.load_state_dict(torch.load(model_last, map_location="cpu")) else: model = model_class.from_pretrained(pretrained) model.resize_token_embeddings(len(tokenizer)) else: tokenizer = tokenizer_class.from_pretrained(args.tokenizer_dir, sep_token='', bos_token='', eos_token='', pad_token='', unk_token='<|UNKNOWN|>', additional_special_tokens=special_tokens) args.vocab_size = len(tokenizer) if args.model_type == "rnn": model = model_class(len(tokenizer), 768, 768, 1) else: config = config_class.from_pretrained(args.config_dir) model = model_class(config) model.resize_token_embeddings(len(tokenizer)) model_parameters = model.parameters() num_params = sum([np.prod(p.size()) for p in model_parameters]) logger.info(f"Model has a total of {num_params} trainable parameters") if args.local_rank == 0: torch.distributed.barrier() # End of barrier to make sure only the first process in distributed training download model & vocab logger.info("Training/evaluation parameters %s", args) # Training if args.do_train: train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False) global_step, tr_loss = train(args, train_dataset, model, tokenizer, fh, pool) logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) # Only works on single GPU if args.do_eval: checkpoint_prefix = 'epoch_5/subject_model.pth' output_dir = os.path.join(args.output_dir, '{}'.format(checkpoint_prefix)) model.load_state_dict(torch.load(output_dir)) model.to(args.device) # 不要用dev文件，否则会在EvalDataset的__init__中检测不通过，被exit # dev_total, dev_cr = eval_acc(args, model, tokenizer, 'dev') # logger.info(f"Dev total tokens: {dev_total}, accuracy: {dev_cr/dev_total}") test_total, test_cr = eval_acc(args, model, tokenizer, 'test') logger.info(f"Test total tokens: {test_total}, accuracy: {test_cr/test_total}") if __name__ == "__main__": main()