963 lines
40 KiB
Python
963 lines
40 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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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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""" Fine-pruning Masked BERT on sequence classification on GLUE."""
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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 emmental import MaskedBertConfig, MaskedBertForSequenceClassification
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from torch import nn
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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 transformers import (
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WEIGHTS_NAME,
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AdamW,
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BertConfig,
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BertForSequenceClassification,
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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, BertForSequenceClassification, BertTokenizer),
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"masked_bert": (MaskedBertConfig, MaskedBertForSequenceClassification, BertTokenizer),
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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 schedule_threshold(
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step: int,
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total_step: int,
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warmup_steps: int,
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initial_threshold: float,
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final_threshold: float,
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initial_warmup: int,
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final_warmup: int,
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final_lambda: float,
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):
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if step <= initial_warmup * warmup_steps:
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threshold = initial_threshold
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elif step > (total_step - final_warmup * warmup_steps):
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threshold = final_threshold
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else:
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spars_warmup_steps = initial_warmup * warmup_steps
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spars_schedu_steps = (final_warmup + initial_warmup) * warmup_steps
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mul_coeff = 1 - (step - spars_warmup_steps) / (total_step - spars_schedu_steps)
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threshold = final_threshold + (initial_threshold - final_threshold) * (mul_coeff**3)
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regu_lambda = final_lambda * threshold / final_threshold
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return threshold, regu_lambda
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def regularization(model: nn.Module, mode: str):
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regu, counter = 0, 0
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for name, param in model.named_parameters():
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if "mask_scores" in name:
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if mode == "l1":
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regu += torch.norm(torch.sigmoid(param), p=1) / param.numel()
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elif mode == "l0":
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regu += torch.sigmoid(param - 2 / 3 * np.log(0.1 / 1.1)).sum() / param.numel()
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else:
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ValueError("Don't know this mode.")
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counter += 1
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return regu / counter
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def train(args, train_dataset, model, tokenizer, teacher=None):
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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(log_dir=args.output_dir)
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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 "mask_score" in n and p.requires_grad],
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"lr": args.mask_scores_learning_rate,
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},
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{
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"params": [
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p
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for n, p in model.named_parameters()
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if "mask_score" not in n and p.requires_grad and not any(nd in n for nd in no_decay)
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],
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"lr": args.learning_rate,
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"weight_decay": args.weight_decay,
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},
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{
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"params": [
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p
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for n, p in model.named_parameters()
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if "mask_score" not in n and p.requires_grad and any(nd in n for nd in no_decay)
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],
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"lr": args.learning_rate,
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"weight_decay": 0.0,
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},
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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 = 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 = nn.parallel.DistributedDataParallel(
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model,
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device_ids=[args.local_rank],
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output_device=args.local_rank,
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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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# Distillation
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if teacher is not None:
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logger.info(" Training with distillation")
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global_step = 0
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# Global TopK
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if args.global_topk:
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threshold_mem = None
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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 global_step of last saved checkpoint from model path
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try:
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global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0])
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except ValueError:
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global_step = 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(" Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)
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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,
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int(args.num_train_epochs),
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desc="Epoch",
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disable=args.local_rank not in [-1, 0],
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)
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set_seed(args) # Added here for reproducibility
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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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threshold, regu_lambda = schedule_threshold(
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step=global_step,
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total_step=t_total,
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warmup_steps=args.warmup_steps,
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final_threshold=args.final_threshold,
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initial_threshold=args.initial_threshold,
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final_warmup=args.final_warmup,
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initial_warmup=args.initial_warmup,
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final_lambda=args.final_lambda,
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)
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# Global TopK
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if args.global_topk:
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if threshold == 1.0:
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threshold = -1e2 # Or an indefinitely low quantity
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else:
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if (threshold_mem is None) or (global_step % args.global_topk_frequency_compute == 0):
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# Sort all the values to get the global topK
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concat = torch.cat(
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[param.view(-1) for name, param in model.named_parameters() if "mask_scores" in name]
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)
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n = concat.numel()
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kth = max(n - (int(n * threshold) + 1), 1)
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threshold_mem = concat.kthvalue(kth).values.item()
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threshold = threshold_mem
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else:
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threshold = threshold_mem
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inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
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if args.model_type != "distilbert":
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inputs["token_type_ids"] = (
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batch[2] if args.model_type in ["bert", "masked_bert", "xlnet", "albert"] else None
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) # XLM, DistilBERT, RoBERTa, and XLM-RoBERTa don't use segment_ids
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if "masked" in args.model_type:
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inputs["threshold"] = threshold
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outputs = model(**inputs)
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loss, logits_stu = outputs # model outputs are always tuple in transformers (see doc)
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# Distillation loss
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if teacher is not None:
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if "token_type_ids" not in inputs:
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inputs["token_type_ids"] = None if args.teacher_type == "xlm" else batch[2]
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with torch.no_grad():
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(logits_tea,) = teacher(
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input_ids=inputs["input_ids"],
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token_type_ids=inputs["token_type_ids"],
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attention_mask=inputs["attention_mask"],
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)
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loss_logits = nn.functional.kl_div(
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input=nn.functional.log_softmax(logits_stu / args.temperature, dim=-1),
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target=nn.functional.softmax(logits_tea / args.temperature, dim=-1),
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reduction="batchmean",
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) * (args.temperature**2)
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loss = args.alpha_distil * loss_logits + args.alpha_ce * loss
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# Regularization
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if args.regularization is not None:
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regu_ = regularization(model=model, mode=args.regularization)
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loss = loss + regu_lambda * regu_
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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 or (
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# last step in epoch but step is always smaller than gradient_accumulation_steps
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len(epoch_iterator) <= args.gradient_accumulation_steps and (step + 1) == len(epoch_iterator)
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):
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if args.fp16:
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nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
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else:
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nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
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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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tb_writer.add_scalar("threshold", threshold, global_step)
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for name, param in model.named_parameters():
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if not param.requires_grad:
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continue
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tb_writer.add_scalar("parameter_mean/" + name, param.data.mean(), global_step)
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tb_writer.add_scalar("parameter_std/" + name, param.data.std(), global_step)
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tb_writer.add_scalar("parameter_min/" + name, param.data.min(), global_step)
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tb_writer.add_scalar("parameter_max/" + name, param.data.max(), global_step)
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tb_writer.add_scalar("grad_mean/" + name, param.grad.data.mean(), global_step)
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tb_writer.add_scalar("grad_std/" + name, param.grad.data.std(), global_step)
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if args.regularization is not None and "mask_scores" in name:
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if args.regularization == "l1":
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perc = (torch.sigmoid(param) > threshold).sum().item() / param.numel()
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elif args.regularization == "l0":
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perc = (torch.sigmoid(param - 2 / 3 * np.log(0.1 / 1.1))).sum().item() / param.numel()
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tb_writer.add_scalar("retained_weights_perc/" + name, perc, global_step)
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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()
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logs["learning_rate"] = learning_rate_scalar[0]
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if len(learning_rate_scalar) > 1:
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for idx, lr in enumerate(learning_rate_scalar[1:]):
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logs[f"learning_rate/{idx+1}"] = lr
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logs["loss"] = loss_scalar
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if teacher is not None:
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logs["loss/distil"] = loss_logits.item()
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if args.regularization is not None:
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logs["loss/regularization"] = regu_.item()
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if (teacher is not None) or (args.regularization is not None):
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if (teacher is not None) and (args.regularization is not None):
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logs["loss/instant_ce"] = (
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loss.item()
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- regu_lambda * logs["loss/regularization"]
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- args.alpha_distil * logs["loss/distil"]
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) / args.alpha_ce
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elif teacher is not None:
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logs["loss/instant_ce"] = (
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loss.item() - args.alpha_distil * logs["loss/distil"]
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) / args.alpha_ce
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else:
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logs["loss/instant_ce"] = loss.item() - regu_lambda * logs["loss/regularization"]
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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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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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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=""):
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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, nn.DataParallel):
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model = 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)
|
|
eval_loss = 0.0
|
|
nb_eval_steps = 0
|
|
preds = None
|
|
out_label_ids = None
|
|
|
|
# Global TopK
|
|
if args.global_topk:
|
|
threshold_mem = None
|
|
|
|
for batch in tqdm(eval_dataloader, desc="Evaluating"):
|
|
model.eval()
|
|
batch = tuple(t.to(args.device) for t in batch)
|
|
|
|
with torch.no_grad():
|
|
inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
|
|
if args.model_type != "distilbert":
|
|
inputs["token_type_ids"] = (
|
|
batch[2] if args.model_type in ["bert", "masked_bert", "xlnet", "albert"] else None
|
|
) # XLM, DistilBERT, RoBERTa, and XLM-RoBERTa don't use segment_ids
|
|
if "masked" in args.model_type:
|
|
inputs["threshold"] = args.final_threshold
|
|
if args.global_topk:
|
|
if threshold_mem is None:
|
|
concat = torch.cat(
|
|
[param.view(-1) for name, param in model.named_parameters() if "mask_scores" in name]
|
|
)
|
|
n = concat.numel()
|
|
kth = max(n - (int(n * args.final_threshold) + 1), 1)
|
|
threshold_mem = concat.kthvalue(kth).values.item()
|
|
inputs["threshold"] = threshold_mem
|
|
outputs = model(**inputs)
|
|
tmp_eval_loss, logits = outputs[:2]
|
|
|
|
eval_loss += tmp_eval_loss.mean().item()
|
|
nb_eval_steps += 1
|
|
if preds is None:
|
|
preds = logits.detach().cpu().numpy()
|
|
out_label_ids = inputs["labels"].detach().cpu().numpy()
|
|
else:
|
|
preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
|
|
out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0)
|
|
|
|
eval_loss = eval_loss / nb_eval_steps
|
|
if args.output_mode == "classification":
|
|
from scipy.special import softmax
|
|
|
|
probs = softmax(preds, axis=-1)
|
|
entropy = np.exp((-probs * np.log(probs)).sum(axis=-1).mean())
|
|
preds = np.argmax(preds, axis=1)
|
|
elif args.output_mode == "regression":
|
|
preds = np.squeeze(preds)
|
|
result = compute_metrics(eval_task, preds, out_label_ids)
|
|
results.update(result)
|
|
if entropy is not None:
|
|
result["eval_avg_entropy"] = entropy
|
|
|
|
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 results
|
|
|
|
|
|
def load_and_cache_examples(args, task, tokenizer, evaluate=False):
|
|
if args.local_rank not in [-1, 0] and not evaluate:
|
|
torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache
|
|
|
|
processor = processors[task]()
|
|
output_mode = output_modes[task]
|
|
# Load data features from cache or dataset file
|
|
cached_features_file = os.path.join(
|
|
args.data_dir,
|
|
"cached_{}_{}_{}_{}".format(
|
|
"dev" if evaluate else "train",
|
|
list(filter(None, args.model_name_or_path.split("/"))).pop(),
|
|
str(args.max_seq_length),
|
|
str(task),
|
|
),
|
|
)
|
|
if os.path.exists(cached_features_file) and not args.overwrite_cache:
|
|
logger.info("Loading features from cached file %s", cached_features_file)
|
|
features = torch.load(cached_features_file)
|
|
else:
|
|
logger.info("Creating features from dataset file at %s", args.data_dir)
|
|
label_list = processor.get_labels()
|
|
if task in ["mnli", "mnli-mm"] and args.model_type in ["roberta", "xlmroberta"]:
|
|
# HACK(label indices are swapped in RoBERTa pretrained model)
|
|
label_list[1], label_list[2] = label_list[2], label_list[1]
|
|
examples = (
|
|
processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir)
|
|
)
|
|
features = convert_examples_to_features(
|
|
examples,
|
|
tokenizer,
|
|
max_length=args.max_seq_length,
|
|
label_list=label_list,
|
|
output_mode=output_mode,
|
|
)
|
|
if args.local_rank in [-1, 0]:
|
|
logger.info("Saving features into cached file %s", cached_features_file)
|
|
torch.save(features, cached_features_file)
|
|
|
|
if args.local_rank == 0 and not evaluate:
|
|
torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache
|
|
|
|
# Convert to Tensors and build dataset
|
|
all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
|
|
all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
|
|
all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)
|
|
if output_mode == "classification":
|
|
all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
|
|
elif output_mode == "regression":
|
|
all_labels = torch.tensor([f.label for f in features], dtype=torch.float)
|
|
|
|
dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels)
|
|
return dataset
|
|
|
|
|
|
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 pretrained model or model identifier from huggingface.co/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.",
|
|
)
|
|
# 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 huggingface.co",
|
|
)
|
|
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("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
|
|
|
|
# Pruning parameters
|
|
parser.add_argument(
|
|
"--mask_scores_learning_rate",
|
|
default=1e-2,
|
|
type=float,
|
|
help="The Adam initial learning rate of the mask scores.",
|
|
)
|
|
parser.add_argument(
|
|
"--initial_threshold", default=1.0, type=float, help="Initial value of the threshold (for scheduling)."
|
|
)
|
|
parser.add_argument(
|
|
"--final_threshold", default=0.7, type=float, help="Final value of the threshold (for scheduling)."
|
|
)
|
|
parser.add_argument(
|
|
"--initial_warmup",
|
|
default=1,
|
|
type=int,
|
|
help=(
|
|
"Run `initial_warmup` * `warmup_steps` steps of threshold warmup during which threshold stays "
|
|
"at its `initial_threshold` value (sparsity schedule)."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--final_warmup",
|
|
default=2,
|
|
type=int,
|
|
help=(
|
|
"Run `final_warmup` * `warmup_steps` steps of threshold cool-down during which threshold stays "
|
|
"at its final_threshold value (sparsity schedule)."
|
|
),
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--pruning_method",
|
|
default="topK",
|
|
type=str,
|
|
help=(
|
|
"Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"
|
|
" sigmoied_threshold = Soft movement pruning)."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--mask_init",
|
|
default="constant",
|
|
type=str,
|
|
help="Initialization method for the mask scores. Choices: constant, uniform, kaiming.",
|
|
)
|
|
parser.add_argument(
|
|
"--mask_scale", default=0.0, type=float, help="Initialization parameter for the chosen initialization method."
|
|
)
|
|
|
|
parser.add_argument("--regularization", default=None, help="Add L0 or L1 regularization to the mask scores.")
|
|
parser.add_argument(
|
|
"--final_lambda",
|
|
default=0.0,
|
|
type=float,
|
|
help="Regularization intensity (used in conjunction with `regularization`.",
|
|
)
|
|
|
|
parser.add_argument("--global_topk", action="store_true", help="Global TopK on the Scores.")
|
|
parser.add_argument(
|
|
"--global_topk_frequency_compute",
|
|
default=25,
|
|
type=int,
|
|
help="Frequency at which we compute the TopK global threshold.",
|
|
)
|
|
|
|
# Distillation parameters (optional)
|
|
parser.add_argument(
|
|
"--teacher_type",
|
|
default=None,
|
|
type=str,
|
|
help=(
|
|
"Teacher type. Teacher tokenizer and student (model) tokenizer must output the same tokenization. Only for"
|
|
" distillation."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--teacher_name_or_path",
|
|
default=None,
|
|
type=str,
|
|
help="Path to the already fine-tuned teacher model. Only for distillation.",
|
|
)
|
|
parser.add_argument(
|
|
"--alpha_ce", default=0.5, type=float, help="Cross entropy loss linear weight. Only for distillation."
|
|
)
|
|
parser.add_argument(
|
|
"--alpha_distil", default=0.5, type=float, help="Distillation loss linear weight. Only for distillation."
|
|
)
|
|
parser.add_argument(
|
|
"--temperature", default=2.0, type=float, help="Distillation temperature. Only for distillation."
|
|
)
|
|
|
|
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("--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=50, help="Log every X updates steps.")
|
|
parser.add_argument("--save_steps", type=int, default=50, 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")
|
|
|
|
args = parser.parse_args()
|
|
|
|
# Regularization
|
|
if args.regularization == "null":
|
|
args.regularization = None
|
|
|
|
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(
|
|
f"Output directory ({args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to"
|
|
" overcome."
|
|
)
|
|
|
|
# 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 synchronizing 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)
|
|
|
|
# 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,
|
|
pruning_method=args.pruning_method,
|
|
mask_init=args.mask_init,
|
|
mask_scale=args.mask_scale,
|
|
)
|
|
tokenizer = tokenizer_class.from_pretrained(
|
|
args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
|
|
cache_dir=args.cache_dir if args.cache_dir else None,
|
|
do_lower_case=args.do_lower_case,
|
|
)
|
|
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.teacher_type is not None:
|
|
assert args.teacher_name_or_path is not None
|
|
assert args.alpha_distil > 0.0
|
|
assert args.alpha_distil + args.alpha_ce > 0.0
|
|
teacher_config_class, teacher_model_class, _ = MODEL_CLASSES[args.teacher_type]
|
|
teacher_config = teacher_config_class.from_pretrained(args.teacher_name_or_path)
|
|
teacher = teacher_model_class.from_pretrained(
|
|
args.teacher_name_or_path,
|
|
from_tf=False,
|
|
config=teacher_config,
|
|
cache_dir=args.cache_dir if args.cache_dir else None,
|
|
)
|
|
teacher.to(args.device)
|
|
else:
|
|
teacher = 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:
|
|
train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False)
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global_step, tr_loss = train(args, train_dataset, model, tokenizer, teacher=teacher)
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logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
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|
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# Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
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if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
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logger.info("Saving model checkpoint to %s", args.output_dir)
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# Save a trained model, configuration and tokenizer using `save_pretrained()`.
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# They can then be reloaded using `from_pretrained()`
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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(args.output_dir)
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tokenizer.save_pretrained(args.output_dir)
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|
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# Good practice: save your training arguments together with the trained model
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torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
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|
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# Load a trained model and vocabulary that you have fine-tuned
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model = model_class.from_pretrained(args.output_dir)
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tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
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model.to(args.device)
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|
|
|
# Evaluation
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|
results = {}
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|
if args.do_eval and args.local_rank in [-1, 0]:
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tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
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|
checkpoints = [args.output_dir]
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if args.eval_all_checkpoints:
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|
checkpoints = [
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os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True))
|
|
]
|
|
|
|
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 = {k + "_{}".format(global_step): v for k, v in result.items()}
|
|
results.update(result)
|
|
|
|
return results
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|