chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,724 @@
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# coding=utf-8
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from __future__ import absolute_import, division, print_function
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import argparse
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import glob
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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
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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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BertTokenizerFast,
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RobertaConfig,
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RobertaForSequenceClassification,
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RobertaTokenizer,
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get_linear_schedule_with_warmup,
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)
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from layoutlm import LayoutlmConfig, LayoutlmForSequenceClassification
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from layoutlm.data.rvl_cdip import CdipProcessor, load_and_cache_examples
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try:
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from torch.utils.tensorboard import SummaryWriter
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except:
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from tensorboardX import SummaryWriter
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logger = logging.getLogger(__name__)
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ALL_MODELS = sum(
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(
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tuple(conf.pretrained_config_archive_map.keys())
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for conf in (BertConfig, RobertaConfig, LayoutlmConfig)
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),
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(),
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)
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MODEL_CLASSES = {
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"bert": (BertConfig, BertForSequenceClassification, BertTokenizerFast),
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"roberta": (RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer),
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"layoutlm": (LayoutlmConfig, LayoutlmForSequenceClassification, BertTokenizerFast),
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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 simple_accuracy(preds, labels):
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return (preds == labels).mean()
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def train(args, train_dataset, model, tokenizer): # noqa C901
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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(comment="_" + os.path.basename(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 = (
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RandomSampler(train_dataset)
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if args.local_rank == -1
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else DistributedSampler(train_dataset)
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)
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train_dataloader = DataLoader(
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train_dataset, sampler=train_sampler, batch_size=args.train_batch_size
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)
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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 = (
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args.max_steps
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// (len(train_dataloader) // args.gradient_accumulation_steps)
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+ 1
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)
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else:
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t_total = (
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len(train_dataloader)
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// args.gradient_accumulation_steps
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* args.num_train_epochs
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)
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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": [
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p
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for n, p in model.named_parameters()
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if not any(nd in n for nd in no_decay)
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],
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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 any(nd in n for nd in no_decay)
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],
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"weight_decay": 0.0,
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},
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]
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optimizer = AdamW(
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optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon
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)
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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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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(
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"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
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)
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model, optimizer = amp.initialize(
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model, optimizer, opt_level=args.fp16_opt_level
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)
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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,
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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(
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" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size
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)
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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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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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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 (even between python 2 and 3)
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for _ in train_iterator:
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epoch_iterator = tqdm(
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train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0]
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)
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for step, batch in enumerate(epoch_iterator):
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model.train()
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if args.model_type != "layoutlm":
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batch = batch[:4]
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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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if args.model_type == "layoutlm":
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inputs["bbox"] = batch[4]
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inputs["token_type_ids"] = (
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batch[2] if args.model_type in ["bert", "layoutlm"] else None
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) # RoBERTa don't use segment_ids
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outputs = model(**inputs)
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loss = outputs[
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0
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] # 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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torch.nn.utils.clip_grad_norm_(
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amp.master_params(optimizer), args.max_grad_norm
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)
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else:
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loss.backward()
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torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
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tr_loss += loss.item()
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if (step + 1) % args.gradient_accumulation_steps == 0:
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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 (
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args.local_rank in [-1, 0]
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and args.logging_steps > 0
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and global_step % args.logging_steps == 0
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):
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# Log metrics
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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, "val")
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for key, value in results.items():
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tb_writer.add_scalar(
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"eval_{}".format(key), value, global_step
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)
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tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step)
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tb_writer.add_scalar(
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"loss",
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(tr_loss - logging_loss) / args.logging_steps,
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global_step,
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)
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logging_loss = tr_loss
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if (
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args.local_rank in [-1, 0]
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and args.save_steps > 0
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and global_step % args.save_steps == 0
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):
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# Save model checkpoint
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output_dir = os.path.join(
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args.output_dir, "checkpoint-{}".format(global_step)
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)
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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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torch.save(args, os.path.join(output_dir, "training_args.bin"))
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tokenizer.save_pretrained(output_dir)
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logger.info("Saving model checkpoint 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, mode, prefix=""):
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results = {}
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eval_dataset = load_and_cache_examples(args, tokenizer, mode=mode)
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if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
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os.makedirs(args.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(
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eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size
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)
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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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if args.model_type != "layoutlm":
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batch = batch[:4]
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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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if args.model_type == "layoutlm":
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inputs["bbox"] = batch[4]
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inputs["token_type_ids"] = (
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batch[2] if args.model_type in ["bert", "layoutlm"] else None
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) # RoBERTa don"t use segment_ids
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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(
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out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0
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)
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eval_loss = eval_loss / nb_eval_steps
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preds = np.argmax(preds, axis=1)
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result = {"acc": simple_accuracy(preds=preds, labels=out_label_ids)}
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results.update(result)
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output_eval_file = os.path.join(
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args.output_dir, prefix, "{}_results.txt".format(mode)
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)
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with open(output_eval_file, "w") as writer:
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logger.info("***** {} results {} *****".format(mode, 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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writer.write("%s = %s\n" % (key, str(result[key])))
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output_eval_file = os.path.join(
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args.output_dir, prefix, "{}_compare.txt".format(mode)
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)
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with open(output_eval_file, "w") as writer:
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for p, l in zip(preds, out_label_ids):
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writer.write("%s %s\n" % (p, l))
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return results
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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 selected in the list: "
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+ ", ".join(ALL_MODELS),
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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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## Other parameters
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parser.add_argument(
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"--config_name",
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default="",
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type=str,
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help="Pretrained config name or path if not the same as model_name",
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)
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parser.add_argument(
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"--tokenizer_name",
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default="",
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type=str,
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help="Pretrained tokenizer name or path if not the same as model_name",
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)
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parser.add_argument(
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"--cache_dir",
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default="",
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type=str,
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help="Where do you want to store the pre-trained models downloaded from s3",
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)
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parser.add_argument(
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"--max_seq_length",
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default=512,
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type=int,
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help="The maximum total input sequence length after tokenization. Sequences longer "
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"than this will be truncated, sequences shorter will be padded.",
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)
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parser.add_argument(
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"--do_train", action="store_true", help="Whether to run training."
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)
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parser.add_argument(
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"--do_eval", action="store_true", help="Whether to run eval on the dev set."
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)
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parser.add_argument(
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"--do_test", action="store_true", help="Whether to run test on the test set."
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)
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parser.add_argument(
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"--evaluate_during_training",
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action="store_true",
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help="Rul evaluation during training at each logging step.",
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)
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parser.add_argument(
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"--do_lower_case",
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action="store_true",
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help="Set this flag if you are using an uncased model.",
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)
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parser.add_argument(
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"--per_gpu_train_batch_size",
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default=8,
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type=int,
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help="Batch size per GPU/CPU for training.",
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)
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parser.add_argument(
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"--per_gpu_eval_batch_size",
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default=8,
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type=int,
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help="Batch size per GPU/CPU for evaluation.",
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)
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parser.add_argument(
|
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"--gradient_accumulation_steps",
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type=int,
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default=1,
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help="Number of updates steps to accumulate before performing a backward/update pass.",
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)
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parser.add_argument(
|
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"--learning_rate",
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default=5e-5,
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||||
type=float,
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help="The initial learning rate for Adam.",
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)
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parser.add_argument(
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"--weight_decay", default=0.0, type=float, help="Weight deay if we apply some."
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)
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parser.add_argument(
|
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"--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer."
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)
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parser.add_argument(
|
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"--max_grad_norm", default=1.0, type=float, help="Max gradient norm."
|
||||
)
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||||
parser.add_argument(
|
||||
"--num_train_epochs",
|
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default=3.0,
|
||||
type=float,
|
||||
help="Total number of training epochs to perform.",
|
||||
)
|
||||
parser.add_argument(
|
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"--max_steps",
|
||||
default=-1,
|
||||
type=int,
|
||||
help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
|
||||
)
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||||
parser.add_argument(
|
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"--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(
|
||||
"--tpu",
|
||||
action="store_true",
|
||||
help="Whether to run on the TPU defined in the environment variables",
|
||||
)
|
||||
|
||||
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:0" if torch.cuda.is_available() and not args.no_cuda else "cpu"
|
||||
)
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.set_device(device)
|
||||
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)
|
||||
|
||||
processor = CdipProcessor()
|
||||
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,
|
||||
)
|
||||
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,
|
||||
)
|
||||
model = model_class.from_pretrained(
|
||||
args.model_name_or_path,
|
||||
from_tf=bool(".ckpt" in args.model_name_or_path),
|
||||
config=config,
|
||||
)
|
||||
|
||||
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, tokenizer, mode="train")
|
||||
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):
|
||||
# Create output directory if needed
|
||||
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
|
||||
os.makedirs(args.output_dir)
|
||||
|
||||
logger.info("Saving model checkpoint to %s", args.output_dir)
|
||||
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
|
||||
# They can then be reloaded using `from_pretrained()`
|
||||
model_to_save = (
|
||||
model.module if hasattr(model, "module") else model
|
||||
) # Take care of distributed/parallel training
|
||||
model_to_save.save_pretrained(args.output_dir)
|
||||
tokenizer.save_pretrained(args.output_dir)
|
||||
|
||||
# Good practice: save your training arguments together with the trained model
|
||||
torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
|
||||
|
||||
# Load a trained model and vocabulary that you have fine-tuned
|
||||
model = model_class.from_pretrained(args.output_dir)
|
||||
tokenizer = tokenizer_class.from_pretrained(
|
||||
args.output_dir, do_lower_case=args.do_lower_case
|
||||
)
|
||||
model.to(args.device)
|
||||
|
||||
# Evaluation
|
||||
results = {}
|
||||
if args.do_eval and args.local_rank in [-1, 0]:
|
||||
tokenizer = tokenizer_class.from_pretrained(
|
||||
args.output_dir, do_lower_case=args.do_lower_case
|
||||
)
|
||||
checkpoints = [args.output_dir]
|
||||
if args.eval_all_checkpoints:
|
||||
checkpoints = list(
|
||||
os.path.dirname(c)
|
||||
for c in sorted(
|
||||
glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True)
|
||||
)
|
||||
)
|
||||
logging.getLogger("transformers.modeling_utils").setLevel(
|
||||
logging.WARN
|
||||
) # Reduce logging
|
||||
logger.info("Evaluate the following checkpoints: %s", checkpoints)
|
||||
for checkpoint in checkpoints:
|
||||
global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
|
||||
prefix = (
|
||||
checkpoint.split("/")[-1]
|
||||
if checkpoint.find("checkpoint") != -1 and args.eval_all_checkpoints
|
||||
else ""
|
||||
)
|
||||
|
||||
model = model_class.from_pretrained(checkpoint)
|
||||
model.to(args.device)
|
||||
result = evaluate(args, model, tokenizer, mode="val", prefix=prefix)
|
||||
result = dict(
|
||||
("val_" + k + "_{}".format(global_step), v) for k, v in result.items()
|
||||
)
|
||||
results.update(result)
|
||||
|
||||
if args.do_test and args.local_rank in [-1, 0]:
|
||||
tokenizer = tokenizer_class.from_pretrained(
|
||||
args.output_dir, do_lower_case=args.do_lower_case
|
||||
)
|
||||
checkpoints = [args.output_dir]
|
||||
if args.eval_all_checkpoints:
|
||||
checkpoints = list(
|
||||
os.path.dirname(c)
|
||||
for c in sorted(
|
||||
glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True)
|
||||
)
|
||||
)
|
||||
logging.getLogger("transformers.modeling_utils").setLevel(
|
||||
logging.WARN
|
||||
) # Reduce logging
|
||||
logger.info("Evaluate the following checkpoints: %s", checkpoints)
|
||||
for checkpoint in checkpoints:
|
||||
global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
|
||||
prefix = (
|
||||
checkpoint.split("/")[-1]
|
||||
if checkpoint.find("checkpoint") != -1 and args.eval_all_checkpoints
|
||||
else ""
|
||||
)
|
||||
|
||||
model = model_class.from_pretrained(checkpoint)
|
||||
model.to(args.device)
|
||||
result = evaluate(args, model, tokenizer, mode="test", prefix=prefix)
|
||||
result = dict(
|
||||
("test_" + k + "_{}".format(global_step), v) for k, v in result.items()
|
||||
)
|
||||
results.update(result)
|
||||
|
||||
return results
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user