"""Finetuning the library models for sequence classification on GLUE."""
import dataclasses
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Callable, Dict, Optional
import torch
import numpy as np
import transformers
from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, EvalPrediction
from transformers import GlueDataTrainingArguments as DataTrainingArguments
from transformers import HfArgumentParser, TrainingArguments, set_seed
from src.dataset import FewShotDataset
from src.models import BertForPromptFinetuning, RobertaForPromptFinetuning, resize_token_type_embeddings
from src.trainer import Trainer
from src.processors import processors_mapping, num_labels_mapping, output_modes_mapping, compute_metrics_mapping, bound_mapping
from filelock import FileLock
from datetime import datetime
from copy import deepcopy
from tqdm import tqdm
import json
logger = logging.getLogger(__name__)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
)
# Few-shot type
# - finetune: standard fine-tuning
# - prompt: prompt-based fine-tuning
# - prompt-demo: prompt-based fine-tuning with demonstrations
few_shot_type: str = field(
default='prompt-demo',
metadata={"help": "Few-shot learning model type. Choice: finetune, prompt, prompt-demo"}
)
# Only for BERT-type model
random_segment: bool = field(
default=False,
metadata={"help": "Whether to reinitialize the token type embeddings (only for BERT)."}
)
@dataclass
class DynamicDataTrainingArguments(DataTrainingArguments):
"""
Arguments for dynamic training.
"""
num_k: Optional[int] = field(
default=16,
metadata={"help": "Number of training instances per class"}
)
num_sample: Optional[int] = field(
default=16,
metadata={"help": "Number of samples (for inference) in fine-tuning with demonstrations"}
)
num_demo: Optional[int] = field(
default=1,
metadata={"help": "Number of demonstrations from each class"}
)
auto_demo: bool = field(
default=True,
metadata={"help": "Automatically generate template for using demonstrations"}
)
# For prompting
template: str = field(
default=None,
metadata={"help": "Template"}
)
mapping: str = field(
default=None,
metadata={"help": "Label word mapping"}
)
template_path: str = field(
default=None,
metadata={"help": "Path to a txt file that stores all the templates, one per line. Do not set this when prompt_path is used"}
)
mapping_path: str = field(
default=None,
metadata={"help": "Path to a txt file that stores all the label word mappings, one per line. Do not set this when prompt_path is used"}
)
prompt_path: str = field(
default=None,
metadata={"help": "Path to a txt file that stores all the prompts (templates and mappings), one per line"}
)
template_id: int = field(
default=None,
metadata={"help": "Template id if using template_path"}
)
mapping_id: int = field(
default=None,
metadata={"help": "Mapping id if using template_path"}
)
prompt_id: int = field(
default=None,
metadata={"help": "Prompt id if using prompt_path"}
)
top_n_template: int = field(
default=None,
metadata={"help": "Use top-n template in the template path"}
)
# For logging
tag: str = field(
default='',
metadata={"help": "Set the tag and find the result easier in the log."}
)
# For filtering when using demonstrations
demo_filter: bool = field(
default=False,
metadata={"help": "Only use similar instances in demonstrations"}
)
demo_filter_rate: float = field(
default=0.5,
metadata={"help": "Only use top-x\% similar instances in demonstrations"}
)
demo_filter_model: str = field(
default=None,
metadata={"help": "Model name for demonstration filter embeddings. Will load embeddings based on the model name."}
)
debug_mode: bool = field(
default=False,
metadata={"help": "Debug mode"}
)
# For max length
double_demo: bool = field(
default=False,
metadata={"help": "Use double length for using demonstrations"}
)
first_sent_limit: int = field(
default=None,
metadata={"help": "Limit the length of the first sentence (i.e., sent_0)"}
)
other_sent_limit: int = field(
default=None,
metadata={"help": "Limit the length of sentences other than the first sentence"}
)
use_full_length: bool = field(
default=None,
metadata={"help": "Use the full length (512)"}
)
# GPT-3's in-context learning
gpt3_in_context_head: bool = field(
default=False,
metadata={"help": "GPT-3's in-context learning (context at the beginning)"}
)
gpt3_in_context_tail: bool = field(
default=False,
metadata={"help": "GPT-3's in-context learning (context at the end)"}
)
gpt3_in_context_num: int = field(
default=32,
metadata={"help": "Number of context examples"}
)
truncate_head: bool = field(
default=False,
metadata={"help": "When exceeding the maximum length, truncate the head instead of the tail."}
)
# Do not set up the following fields. They are set up automatically.
prompt: bool = field(
default=False,
metadata={"help": "Whether to use prompt-based fine-tuning"}
)
template_list: list = field(
default=None,
metadata={"help": "(DO NOT List of templates (only initialized after the program starts."}
)
@dataclass
class DynamicTrainingArguments(TrainingArguments):
# For ensemble
array_id: int = field(
default=-1,
metadata={"help": "Array ID (contains seed and hyper-paramter search) to idenfity the model"}
)
model_id: int = field(
default=-1,
metadata={"help": "Model ID (contains template information) to identify the model"}
)
save_logit: bool = field(
default=False,
metadata={"help": "Save test file logit with name $TASK-$MODEL_ID-$ARRAY_ID.npy"}
)
save_logit_dir: str = field(
default=None,
metadata={"help": "Where to save the prediction result"}
)
# Regularization
fix_layers: int = field(
default=0,
metadata={"help": "Fix bottom-n layers when optimizing"}
)
# Training
save_at_last: bool = field(
default=False,
metadata={"help": "Instead of saving the best (dev performance) checkpoint, save the last checkpoint"}
)
# Turn off train/test
no_train: bool = field(
default=False,
metadata={"help": "No training"}
)
no_predict: bool = field(
default=False,
metadata={"help": "No test"}
)
def main():
parser = HfArgumentParser((ModelArguments, DynamicDataTrainingArguments, DynamicTrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
if 'prompt' in model_args.few_shot_type:
data_args.prompt = True
if training_args.no_train:
training_args.do_train = False
if training_args.no_predict:
training_args.do_predict = False
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,
)
# Load prompt/template/mapping file
if data_args.prompt:
if data_args.prompt_path is not None:
assert data_args.prompt_id is not None
prompt_list = []
with open(data_args.prompt_path) as f:
for line in f:
line = line.strip()
template, mapping = line.split('\t')
prompt_list.append((template, mapping))
data_args.template, data_args.mapping = prompt_list[data_args.prompt_id]
logger.info("Specify load the %d-th prompt: %s | %s" % (data_args.prompt_id, data_args.template, data_args.mapping))
else:
if data_args.template_path is not None:
with open(data_args.template_path) as f:
data_args.template_list = []
for line in f:
line = line.strip()
if len(line) > 0:
data_args.template_list.append(line)
# Load top-n templates
if data_args.top_n_template is not None:
data_args.template_list = data_args.template_list[:data_args.top_n_template]
logger.info("Load top-%d templates from %s" % (len(data_args.template_list), data_args.template_path))
# ... or load i-th template
if data_args.template_id is not None:
data_args.template = data_args.template_list[data_args.template_id]
data_args.template_list = None
logger.info("Specify load the %d-th template: %s" % (data_args.template_id, data_args.template))
if data_args.mapping_path is not None:
assert data_args.mapping_id is not None # Only can use one label word mapping
with open(data_args.mapping_path) as f:
mapping_list = []
for line in f:
line = line.strip()
mapping_list.append(line)
data_args.mapping = mapping_list[data_args.mapping_id]
logger.info("Specify using the %d-th mapping: %s" % (data_args.mapping_id, data_args.mapping))
# Check save path
if (
os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir)
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(f"Output directory ({training_args.output_dir}) already exists.")
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
training_args.local_rank,
training_args.device,
training_args.n_gpu,
bool(training_args.local_rank != -1),
training_args.fp16,
)
logger.info("Training/evaluation parameters %s", training_args)
# Set seed
set_seed(training_args.seed)
try:
num_labels = num_labels_mapping[data_args.task_name]
output_mode = output_modes_mapping[data_args.task_name]
logger.info("Task name: {}, number of labels: {}, output mode: {}".format(data_args.task_name, num_labels, output_mode))
except KeyError:
raise ValueError("Task not found: %s" % (data_args.task_name))
# Automatically generate template for using demonstrations
if data_args.auto_demo and model_args.few_shot_type == 'prompt-demo':
# GPT-3's in-context learning
if data_args.gpt3_in_context_head or data_args.gpt3_in_context_tail:
logger.info("Automatically convert the template to GPT-3's in-context learning.")
assert data_args.template_list is None
old_template = data_args.template
new_template = old_template + ''
old_template = old_template.replace('*cls*', '')
# Single sentence or sentence pair?
sent_num = 1
if "_1" in old_template:
sent_num = 2
for instance_id in range(data_args.gpt3_in_context_num):
sub_template = old_template + ''
# Replace sent_id
for sent_id in range(sent_num):
sub_template = sub_template.replace("_{}*".format(sent_id), "_{}*".format(sent_num + sent_num * instance_id + sent_id))
# Replace mask
sub_template = sub_template.replace("*mask*", "*labelx_{}*".format(instance_id))
if data_args.gpt3_in_context_tail:
new_template = new_template + sub_template # Put context at the end
else:
new_template = sub_template + new_template # Put context at the beginning
logger.info("| {} => {}".format(data_args.template, new_template))
data_args.template = new_template
else:
logger.info("Automatically convert the template to using demonstrations.")
if data_args.template_list is not None:
for i in range(len(data_args.template_list)):
old_template = data_args.template_list[i]
new_template = old_template + ''
old_template = old_template.replace('*cls*', '')
# Single sentence or sentence pair?
sent_num = 1
if "_1" in old_template:
sent_num = 2
for label_id in range(num_labels):
sub_template = old_template + ''
# Replace sent id
for sent_id in range(sent_num):
sub_template = sub_template.replace("_{}*".format(sent_id), "_{}*".format(sent_num + sent_num * label_id + sent_id))
# Replace mask
sub_template = sub_template.replace("*mask*", "*label_{}*".format(label_id))
new_template = new_template + sub_template
logger.info("| {} => {}".format(data_args.template_list[i], new_template))
data_args.template_list[i] = new_template
else:
old_template = data_args.template
new_template = old_template + ''
old_template = old_template.replace('*cls*', '')
# Single sentence or sentence pair?
sent_num = 1
if "_1" in old_template:
sent_num = 2
for label_id in range(num_labels):
sub_template = old_template + ''
# Replace sent id
for sent_id in range(sent_num):
sub_template = sub_template.replace("_{}".format(sent_id), "_{}".format(sent_num + sent_num * label_id + sent_id))
# Replace mask
sub_template = sub_template.replace("*mask*", "*label_{}*".format(label_id))
new_template = new_template + sub_template
logger.info("| {} => {}".format(data_args.template, new_template))
data_args.template = new_template
# Create config
config = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
num_labels=num_labels,
finetuning_task=data_args.task_name,
cache_dir=model_args.cache_dir,
)
if 'prompt' in model_args.few_shot_type:
if config.model_type == 'roberta':
model_fn = RobertaForPromptFinetuning
elif config.model_type == 'bert':
model_fn = BertForPromptFinetuning
else:
raise NotImplementedError
elif model_args.few_shot_type == 'finetune':
model_fn = AutoModelForSequenceClassification
else:
raise NotImplementedError
special_tokens = []
# Create tokenizer
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
additional_special_tokens=special_tokens,
cache_dir=model_args.cache_dir,
)
# Get our special datasets.
train_dataset = (
FewShotDataset(data_args, tokenizer=tokenizer, mode="train", use_demo=("demo" in model_args.few_shot_type))
)
eval_dataset = (
FewShotDataset(data_args, tokenizer=tokenizer, mode="dev", use_demo=("demo" in model_args.few_shot_type))
if training_args.do_eval
else None
)
test_dataset = (
FewShotDataset(data_args, tokenizer=tokenizer, mode="test", use_demo=("demo" in model_args.few_shot_type))
if training_args.do_predict
else None
)
set_seed(training_args.seed)
model = model_fn.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
)
# For BERT, increase the size of the segment (token type) embeddings
if config.model_type == 'bert':
model.resize_token_embeddings(len(tokenizer))
resize_token_type_embeddings(model, new_num_types=10, random_segment=model_args.random_segment)
# Pass dataset and argument information to the model
if data_args.prompt:
model.label_word_list = torch.tensor(train_dataset.label_word_list).long().cuda()
if output_modes_mapping[data_args.task_name] == 'regression':
# lower / upper bounds
model.lb, model.ub = bound_mapping[data_args.task_name]
model.model_args = model_args
model.data_args = data_args
model.tokenizer = tokenizer
# Build metric
def build_compute_metrics_fn(task_name: str) -> Callable[[EvalPrediction], Dict]:
def compute_metrics_fn(p: EvalPrediction):
# Note: the eval dataloader is sequential, so the examples are in order.
# We average the logits over each sample for using demonstrations.
predictions = p.predictions
num_logits = predictions.shape[-1]
logits = predictions.reshape([eval_dataset.num_sample, -1, num_logits])
logits = logits.mean(axis=0)
if num_logits == 1:
preds = np.squeeze(logits)
else:
preds = np.argmax(logits, axis=1)
# Just for sanity, assert label ids are the same.
label_ids = p.label_ids.reshape([eval_dataset.num_sample, -1])
label_ids_avg = label_ids.mean(axis=0)
label_ids_avg = label_ids_avg.astype(p.label_ids.dtype)
assert (label_ids_avg - label_ids[0]).mean() < 1e-2
label_ids = label_ids[0]
return compute_metrics_mapping[task_name](task_name, preds, label_ids)
return compute_metrics_fn
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
compute_metrics=build_compute_metrics_fn(data_args.task_name)
)
# Training
if training_args.do_train:
trainer.train(model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None)
# Use the early stop, so do not save the model in the end (unless specify save_at_last)
if training_args.save_at_last:
trainer.save_model(training_args.output_dir)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir)
torch.save(model_args, os.path.join(training_args.output_dir, "model_args.bin"))
torch.save(data_args, os.path.join(training_args.output_dir, "data_args.bin"))
# Reload the best checkpoint (for eval)
model = model_fn.from_pretrained(training_args.output_dir)
model = model.to(training_args.device)
trainer.model = model
if data_args.prompt:
model.label_word_list = torch.tensor(train_dataset.label_word_list).long().cuda()
if output_modes_mapping[data_args.task_name] == 'regression':
# lower / upper bounds
model.lb, model.ub = bound_mapping[data_args.task_name]
model.model_args = model_args
model.data_args = data_args
model.tokenizer = tokenizer
# Evaluation
final_result = {
'time': str(datetime.today()),
}
eval_results = {}
if training_args.do_eval:
logger.info("*** Validate ***")
eval_datasets = [eval_dataset]
for eval_dataset in eval_datasets:
trainer.compute_metrics = build_compute_metrics_fn(eval_dataset.args.task_name)
output = trainer.evaluate(eval_dataset=eval_dataset)
eval_result = output.metrics
output_eval_file = os.path.join(
training_args.output_dir, f"eval_results_{eval_dataset.args.task_name}.txt"
)
if trainer.is_world_master():
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results {} *****".format(eval_dataset.args.task_name))
for key, value in eval_result.items():
logger.info(" %s = %s", key, value)
writer.write("%s = %s\n" % (key, value))
final_result[eval_dataset.args.task_name + '_dev_' + key] = value
eval_results.update(eval_result)
test_results = {}
if training_args.do_predict:
logging.info("*** Test ***")
test_datasets = [test_dataset]
if data_args.task_name == "mnli":
mnli_mm_data_args = dataclasses.replace(data_args, task_name="mnli-mm")
test_datasets.append(
FewShotDataset(mnli_mm_data_args, tokenizer=tokenizer, mode="test", use_demo=('demo' in model_args.few_shot_type))
)
for test_dataset in test_datasets:
trainer.compute_metrics = build_compute_metrics_fn(test_dataset.args.task_name)
output = trainer.evaluate(eval_dataset=test_dataset)
test_result = output.metrics
output_test_file = os.path.join(
training_args.output_dir, f"test_results_{test_dataset.args.task_name}.txt"
)
if trainer.is_world_master():
with open(output_test_file, "w") as writer:
logger.info("***** Test results {} *****".format(test_dataset.args.task_name))
for key, value in test_result.items():
logger.info(" %s = %s", key, value)
writer.write("%s = %s\n" % (key, value))
final_result[test_dataset.args.task_name + '_test_' + key] = value
if training_args.save_logit:
predictions = output.predictions
num_logits = predictions.shape[-1]
logits = predictions.reshape([test_dataset.num_sample, -1, num_logits]).mean(axis=0)
np.save(os.path.join(training_args.save_logit_dir, "{}-{}-{}.npy".format(test_dataset.task_name, training_args.model_id, training_args.array_id)), logits)
test_results.update(test_result)
with FileLock('log.lock'):
with open('log', 'a') as f:
final_result.update(vars(model_args))
final_result.update(vars(training_args))
final_result.update(vars(data_args))
if 'evaluation_strategy' in final_result:
final_result.pop('evaluation_strategy')
f.write(str(final_result) + '\n')
return eval_results
if __name__ == "__main__":
main()