698 строки
29 KiB
Python
Executable File
698 строки
29 KiB
Python
Executable File
# coding=utf-8
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# Copyright 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and Microsoft Corporation.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Training and inference using the library models for sequence classification on GLUE (Bert, Albert) with PABEE."""
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import argparse
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import glob
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import json
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import logging
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import os
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import random
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import numpy as np
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import torch
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from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
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from torch.utils.data.distributed import DistributedSampler
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from tqdm import tqdm, trange
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from pabee.modeling_pabee_albert import AlbertForSequenceClassificationWithPabee
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from pabee.modeling_pabee_bert import BertForSequenceClassificationWithPabee
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from transformers import (
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WEIGHTS_NAME,
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AdamW,
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AlbertConfig,
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AlbertTokenizer,
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BertConfig,
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BertTokenizer,
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get_linear_schedule_with_warmup,
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)
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from transformers import glue_compute_metrics as compute_metrics
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from transformers import glue_convert_examples_to_features as convert_examples_to_features
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from transformers import glue_output_modes as output_modes
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from transformers import glue_processors as processors
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try:
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from torch.utils.tensorboard import SummaryWriter
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except ImportError:
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from tensorboardX import SummaryWriter
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logger = logging.getLogger(__name__)
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MODEL_CLASSES = {
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"bert": (BertConfig, BertForSequenceClassificationWithPabee, BertTokenizer),
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"albert": (AlbertConfig, AlbertForSequenceClassificationWithPabee, AlbertTokenizer),
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}
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def set_seed(args):
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random.seed(args.seed)
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np.random.seed(args.seed)
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torch.manual_seed(args.seed)
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if args.n_gpu > 0:
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torch.cuda.manual_seed_all(args.seed)
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def train(args, train_dataset, model, tokenizer):
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""" Train the model """
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if args.local_rank in [-1, 0]:
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tb_writer = SummaryWriter()
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args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
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train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
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train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)
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if args.max_steps > 0:
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t_total = args.max_steps
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args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
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else:
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t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
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# Prepare optimizer and schedule (linear warmup and decay)
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no_decay = ["bias", "LayerNorm.weight"]
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optimizer_grouped_parameters = [
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{
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"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
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"weight_decay": args.weight_decay,
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},
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{"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
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]
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optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
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scheduler = get_linear_schedule_with_warmup(
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optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
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)
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# Check if saved optimizer or scheduler states exist
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if os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt")) and os.path.isfile(
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os.path.join(args.model_name_or_path, "scheduler.pt")
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):
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# Load in optimizer and scheduler states
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optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt")))
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scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt")))
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if args.fp16:
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try:
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from apex import amp
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except ImportError:
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raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
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model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
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# multi-gpu training (should be after apex fp16 initialization)
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if args.n_gpu > 1:
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model = torch.nn.DataParallel(model)
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# Distributed training (should be after apex fp16 initialization)
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if args.local_rank != -1:
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model = torch.nn.parallel.DistributedDataParallel(
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model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True,
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)
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# Train!
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logger.info("***** Running training *****")
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logger.info(" Num examples = %d", len(train_dataset))
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logger.info(" Num Epochs = %d", args.num_train_epochs)
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logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
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logger.info(
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" Total train batch size (w. parallel, distributed & accumulation) = %d",
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args.train_batch_size
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* args.gradient_accumulation_steps
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* (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
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)
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logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
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logger.info(" Total optimization steps = %d", t_total)
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global_step = 0
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epochs_trained = 0
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steps_trained_in_current_epoch = 0
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# Check if continuing training from a checkpoint
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if os.path.exists(args.model_name_or_path):
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# set global_step to gobal_step of last saved checkpoint from model path
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global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0])
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epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps)
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steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps)
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logger.info(" Continuing training from checkpoint, will skip to saved global_step")
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logger.info(" Continuing training from epoch %d", epochs_trained)
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logger.info(" Continuing training from global step %d", global_step)
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logger.info(
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" Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch,
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)
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tr_loss, logging_loss = 0.0, 0.0
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model.zero_grad()
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train_iterator = trange(
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epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0],
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)
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set_seed(args) # Added here for reproductibility
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for _ in train_iterator:
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epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
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for step, batch in enumerate(epoch_iterator):
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# Skip past any already trained steps if resuming training
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if steps_trained_in_current_epoch > 0:
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steps_trained_in_current_epoch -= 1
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continue
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model.train()
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batch = tuple(t.to(args.device) for t in batch)
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inputs = {
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"input_ids": batch[0],
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"attention_mask": batch[1],
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"labels": batch[3],
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}
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inputs["token_type_ids"] = batch[2]
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outputs = model(**inputs)
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loss = outputs[0] # model outputs are always tuple in transformers (see doc)
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if args.n_gpu > 1:
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loss = loss.mean() # mean() to average on multi-gpu parallel training
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if args.gradient_accumulation_steps > 1:
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loss = loss / args.gradient_accumulation_steps
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if args.fp16:
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with amp.scale_loss(loss, optimizer) as scaled_loss:
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scaled_loss.backward()
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else:
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loss.backward()
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tr_loss += loss.item()
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if (step + 1) % args.gradient_accumulation_steps == 0:
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if args.fp16:
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torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
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else:
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torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
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optimizer.step()
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scheduler.step() # Update learning rate schedule
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model.zero_grad()
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global_step += 1
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if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
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logs = {}
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if (
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args.local_rank == -1 and args.evaluate_during_training
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): # Only evaluate when single GPU otherwise metrics may not average well
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results = evaluate(args, model, tokenizer)
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for key, value in results.items():
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eval_key = "eval_{}".format(key)
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logs[eval_key] = value
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loss_scalar = (tr_loss - logging_loss) / args.logging_steps
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learning_rate_scalar = scheduler.get_lr()[0]
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logs["learning_rate"] = learning_rate_scalar
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logs["loss"] = loss_scalar
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logging_loss = tr_loss
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for key, value in logs.items():
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tb_writer.add_scalar(key, value, global_step)
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print(json.dumps({**logs, **{"step": global_step}}))
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if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
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# Save model checkpoint
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output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
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model_to_save = (
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model.module if hasattr(model, "module") else model
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) # Take care of distributed/parallel training
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model_to_save.save_pretrained(output_dir)
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tokenizer.save_pretrained(output_dir)
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torch.save(args, os.path.join(output_dir, "training_args.bin"))
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logger.info("Saving model checkpoint to %s", output_dir)
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torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
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torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
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logger.info("Saving optimizer and scheduler states to %s", output_dir)
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if args.max_steps > 0 and global_step > args.max_steps:
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epoch_iterator.close()
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break
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if args.max_steps > 0 and global_step > args.max_steps:
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train_iterator.close()
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break
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if args.local_rank in [-1, 0]:
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tb_writer.close()
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return global_step, tr_loss / global_step
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def evaluate(args, model, tokenizer, prefix="", patience=0):
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if args.model_type == "albert":
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model.albert.set_regression_threshold(args.regression_threshold)
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model.albert.set_patience(patience)
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model.albert.reset_stats()
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elif args.model_type == "bert":
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model.bert.set_regression_threshold(args.regression_threshold)
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model.bert.set_patience(patience)
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model.bert.reset_stats()
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else:
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raise NotImplementedError()
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# Loop to handle MNLI double evaluation (matched, mis-matched)
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eval_task_names = ("mnli", "mnli-mm") if args.task_name == "mnli" else (args.task_name,)
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eval_outputs_dirs = (args.output_dir, args.output_dir + "-MM") if args.task_name == "mnli" else (args.output_dir,)
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results = {}
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for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs):
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eval_dataset = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True)
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if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
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os.makedirs(eval_output_dir)
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args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
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# Note that DistributedSampler samples randomly
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eval_sampler = SequentialSampler(eval_dataset)
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eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
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# multi-gpu eval
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if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
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model = torch.nn.DataParallel(model)
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# Eval!
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logger.info("***** Running evaluation {} *****".format(prefix))
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logger.info(" Num examples = %d", len(eval_dataset))
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logger.info(" Batch size = %d", args.eval_batch_size)
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eval_loss = 0.0
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nb_eval_steps = 0
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preds = None
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out_label_ids = None
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for batch in tqdm(eval_dataloader, desc="Evaluating"):
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model.eval()
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batch = tuple(t.to(args.device) for t in batch)
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with torch.no_grad():
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inputs = {
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"input_ids": batch[0],
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"attention_mask": batch[1],
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"labels": batch[3],
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}
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inputs["token_type_ids"] = batch[2]
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outputs = model(**inputs)
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tmp_eval_loss, logits = outputs[:2]
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eval_loss += tmp_eval_loss.mean().item()
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nb_eval_steps += 1
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if preds is None:
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preds = logits.detach().cpu().numpy()
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out_label_ids = inputs["labels"].detach().cpu().numpy()
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else:
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preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
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out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0)
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eval_loss = eval_loss / nb_eval_steps
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if args.output_mode == "classification":
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preds = np.argmax(preds, axis=1)
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elif args.output_mode == "regression":
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preds = np.squeeze(preds)
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result = compute_metrics(eval_task, preds, out_label_ids)
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results.update(result)
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output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt")
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with open(output_eval_file, "w") as writer:
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logger.info("***** Eval results {} *****".format(prefix))
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for key in sorted(result.keys()):
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logger.info(" %s = %s", key, str(result[key]))
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print(" %s = %s" % (key, str(result[key])))
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writer.write("%s = %s\n" % (key, str(result[key])))
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if args.eval_all_checkpoints and patience != 0:
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if args.model_type == "albert":
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model.albert.log_stats()
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elif args.model_type == "bert":
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model.bert.log_stats()
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else:
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raise NotImplementedError()
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return results
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def load_and_cache_examples(args, task, tokenizer, evaluate=False):
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if args.local_rank not in [-1, 0] and not evaluate:
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torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache
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processor = processors[task]()
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output_mode = output_modes[task]
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# Load data features from cache or dataset file
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cached_features_file = os.path.join(
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args.data_dir,
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"cached_{}_{}_{}_{}".format(
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"dev" if evaluate else "train",
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list(filter(None, args.model_name_or_path.split("/"))).pop(),
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str(args.max_seq_length),
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str(task),
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),
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)
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if os.path.exists(cached_features_file) and not args.overwrite_cache:
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logger.info("Loading features from cached file %s", cached_features_file)
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features = torch.load(cached_features_file)
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else:
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logger.info("Creating features from dataset file at %s", args.data_dir)
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label_list = processor.get_labels()
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if task in ["mnli", "mnli-mm"] and args.model_type in ["roberta", "xlmroberta"]:
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# HACK(label indices are swapped in RoBERTa pretrained model)
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label_list[1], label_list[2] = label_list[2], label_list[1]
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examples = (
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processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir)
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)
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features = convert_examples_to_features(
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examples, tokenizer, label_list=label_list, max_length=args.max_seq_length, output_mode=output_mode,
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)
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if args.local_rank in [-1, 0]:
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logger.info("Saving features into cached file %s", cached_features_file)
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torch.save(features, cached_features_file)
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if args.local_rank == 0 and not evaluate:
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torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache
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# Convert to Tensors and build dataset
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all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
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all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
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all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)
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if output_mode == "classification":
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all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
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elif output_mode == "regression":
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all_labels = torch.tensor([f.label for f in features], dtype=torch.float)
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dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels)
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return dataset
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def main():
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parser = argparse.ArgumentParser()
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# Required parameters
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parser.add_argument(
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"--data_dir",
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default=None,
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type=str,
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required=True,
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help="The input data dir. Should contain the .tsv files (or other data files) for the task.",
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)
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parser.add_argument(
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"--model_type",
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default=None,
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type=str,
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required=True,
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help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
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)
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parser.add_argument(
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"--model_name_or_path",
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default=None,
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type=str,
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required=True,
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help="Path to pre-trained model or shortcut name.",
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)
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parser.add_argument(
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"--task_name",
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default=None,
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type=str,
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required=True,
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help="The name of the task to train selected in the list: " + ", ".join(processors.keys()),
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)
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parser.add_argument(
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"--output_dir",
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default=None,
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type=str,
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required=True,
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help="The output directory where the model predictions and checkpoints will be written.",
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)
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parser.add_argument(
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"--patience", default="0", type=str, required=False,
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)
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parser.add_argument(
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"--regression_threshold", default=0, type=float, required=False,
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)
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# Other parameters
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parser.add_argument(
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"--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name",
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|
)
|
|
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=1, 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(
|
|
"--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()
|
|
|
|
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()
|
|
|
|
# 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.n_gpu = 1
|
|
args.device = device
|
|
|
|
# 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",
|
|
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]
|
|
label_list = processor.get_labels()
|
|
num_labels = len(label_list)
|
|
|
|
if args.patience != "0" and args.per_gpu_eval_batch_size != 1:
|
|
raise ValueError("The eval batch size must be 1 with PABEE inference on.")
|
|
|
|
# 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)
|
|
|
|
print("Total Model Parameters:", sum(param.numel() for param in model.parameters()))
|
|
output_layers_param_num = sum(param.numel() for param in model.classifiers.parameters())
|
|
print("Output Layers Parameters:", output_layers_param_num)
|
|
single_output_layer_param_num = sum(param.numel() for param in model.classifiers[0].parameters())
|
|
print(
|
|
"Added Output Layers Parameters:", output_layers_param_num - single_output_layer_param_num,
|
|
)
|
|
|
|
logger.info("Training/evaluation parameters %s", args)
|
|
|
|
# Training
|
|
if args.do_train:
|
|
train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False)
|
|
global_step, tr_loss = train(args, train_dataset, model, tokenizer)
|
|
logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
|
|
|
|
# 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):
|
|
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]:
|
|
patience_list = [int(x) for x in args.patience.split(",")]
|
|
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)
|
|
|
|
print(f"Evaluation for checkpoint {prefix}")
|
|
for patience in patience_list:
|
|
result = evaluate(args, model, tokenizer, prefix=prefix, patience=patience)
|
|
result = dict((k + "_{}".format(global_step), v) for k, v in result.items())
|
|
results.update(result)
|
|
return results
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|