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Support for torch-lightning in NER examples (#2890)

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* initial pytorch lightning commit

* tested multigpu

* Fix learning rate schedule

* black formatting

* fix flake8

* isort

* isort

* .

Co-authored-by: Check your git settings! <chris@chris-laptop>
This commit is contained in:
srush
2020-02-20 11:50:05 -05:00
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parent ab1238393c
commit b662f0e625
10 changed files with 753 additions and 196 deletions
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examples/ner/run_pl_ner.py Normal file
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import argparse
import glob
import logging
import os
import numpy as np
import torch
from seqeval.metrics import f1_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from torch.utils.data.distributed import DistributedSampler
from transformer_base import BaseTransformer, add_generic_args, generic_train
from utils_ner import convert_examples_to_features, get_labels, read_examples_from_file
logger = logging.getLogger(__name__)
class NERTransformer(BaseTransformer):
"""
A training module for NER. See BaseTransformer for the core options.
"""
def __init__(self, hparams):
self.labels = get_labels(hparams.labels)
num_labels = len(self.labels)
super(NERTransformer, self).__init__(hparams, num_labels)
def forward(self, **inputs):
return self.model(**inputs)
def training_step(self, batch, batch_num):
"Compute loss"
inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
if self.hparams.model_type != "distilbert":
inputs["token_type_ids"] = (
batch[2] if self.hparams.model_type in ["bert", "xlnet"] else None
) # XLM and RoBERTa don"t use segment_ids
outputs = self.forward(**inputs)
loss = outputs[0]
tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def load_dataset(self, mode, batch_size):
labels = get_labels(self.hparams.labels)
self.pad_token_label_id = CrossEntropyLoss().ignore_index
dataset = self.load_and_cache_examples(labels, self.pad_token_label_id, mode)
if mode == "train":
if self.hparams.n_gpu > 1:
sampler = DistributedSampler(dataset)
else:
sampler = RandomSampler(dataset)
else:
sampler = SequentialSampler(dataset)
dataloader = DataLoader(dataset, sampler=sampler, batch_size=batch_size)
return dataloader
def validation_step(self, batch, batch_nb):
inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
if self.hparams.model_type != "distilbert":
inputs["token_type_ids"] = (
batch[2] if self.hparams.model_type in ["bert", "xlnet"] else None
) # XLM and RoBERTa don"t use segment_ids
outputs = self.forward(**inputs)
tmp_eval_loss, logits = outputs[:2]
preds = logits.detach().cpu().numpy()
out_label_ids = inputs["labels"].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss, "pred": preds, "target": out_label_ids}
def _eval_end(self, outputs):
"Task specific validation"
val_loss_mean = torch.stack([x["val_loss"] for x in outputs]).mean()
preds = np.concatenate([x["pred"] for x in outputs], axis=0)
preds = np.argmax(preds, axis=2)
out_label_ids = np.concatenate([x["target"] for x in outputs], axis=0)
label_map = {i: label for i, label in enumerate(self.labels)}
out_label_list = [[] for _ in range(out_label_ids.shape[0])]
preds_list = [[] for _ in range(out_label_ids.shape[0])]
for i in range(out_label_ids.shape[0]):
for j in range(out_label_ids.shape[1]):
if out_label_ids[i, j] != self.pad_token_label_id:
out_label_list[i].append(label_map[out_label_ids[i][j]])
preds_list[i].append(label_map[preds[i][j]])
results = {
"val_loss": val_loss_mean,
"precision": precision_score(out_label_list, preds_list),
"recall": recall_score(out_label_list, preds_list),
"f1": f1_score(out_label_list, preds_list),
}
if self.is_logger():
logger.info(self.proc_rank)
logger.info("***** Eval results *****")
for key in sorted(results.keys()):
logger.info(" %s = %s", key, str(results[key]))
tensorboard_logs = results
ret = {k: v for k, v in results.items()}
ret["log"] = tensorboard_logs
return ret, preds_list, out_label_list
def validation_end(self, outputs):
ret, preds, targets = self._eval_end(outputs)
return ret
def test_end(self, outputs):
ret, predictions, targets = self._eval_end(outputs)
if self.is_logger():
# Write output to a file:
# Save results
output_test_results_file = os.path.join(self.hparams.output_dir, "test_results.txt")
with open(output_test_results_file, "w") as writer:
for key in sorted(ret.keys()):
if key != "log":
writer.write("{} = {}\n".format(key, str(ret[key])))
# Save predictions
output_test_predictions_file = os.path.join(self.hparams.output_dir, "test_predictions.txt")
with open(output_test_predictions_file, "w") as writer:
with open(os.path.join(self.hparams.data_dir, "test.txt"), "r") as f:
example_id = 0
for line in f:
if line.startswith("-DOCSTART-") or line == "" or line == "\n":
writer.write(line)
if not predictions[example_id]:
example_id += 1
elif predictions[example_id]:
output_line = line.split()[0] + " " + predictions[example_id].pop(0) + "\n"
writer.write(output_line)
else:
logger.warning(
"Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0]
)
return ret
def load_and_cache_examples(self, labels, pad_token_label_id, mode):
args = self.hparams
tokenizer = self.tokenizer
if self.proc_rank not in [-1, 0] and mode == "train":
torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache
# Load data features from cache or dataset file
cached_features_file = os.path.join(
args.data_dir,
"cached_{}_{}_{}".format(
mode, list(filter(None, args.model_name_or_path.split("/"))).pop(), str(args.max_seq_length)
),
)
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)
examples = read_examples_from_file(args.data_dir, mode)
features = convert_examples_to_features(
examples,
labels,
args.max_seq_length,
tokenizer,
cls_token_at_end=bool(args.model_type in ["xlnet"]),
cls_token=tokenizer.cls_token,
cls_token_segment_id=2 if args.model_type in ["xlnet"] else 0,
sep_token=tokenizer.sep_token,
sep_token_extra=bool(args.model_type in ["roberta"]),
pad_on_left=bool(args.model_type in ["xlnet"]),
pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],
pad_token_segment_id=4 if args.model_type in ["xlnet"] else 0,
pad_token_label_id=pad_token_label_id,
)
if self.proc_rank in [-1, 0]:
logger.info("Saving features into cached file %s", cached_features_file)
torch.save(features, cached_features_file)
if self.proc_rank == 0 and mode == "train":
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_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
all_label_ids = torch.tensor([f.label_ids for f in features], dtype=torch.long)
dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
return dataset
@staticmethod
def add_model_specific_args(parser, root_dir):
# Add NER specific options
BaseTransformer.add_model_specific_args(parser, root_dir)
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(
"--labels",
default="",
type=str,
help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.",
)
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help="The input data dir. Should contain the training files for the CoNLL-2003 NER task.",
)
parser.add_argument(
"--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
)
return parser
if __name__ == "__main__":
parser = argparse.ArgumentParser()
add_generic_args(parser, os.getcwd())
parser = NERTransformer.add_model_specific_args(parser, os.getcwd())
args = parser.parse_args()
model = NERTransformer(args)
trainer = generic_train(model, args)
if args.do_predict:
checkpoints = list(sorted(glob.glob(args.output_dir + "/checkpoint_*.ckpt", recursive=True)))
NERTransformer.load_from_checkpoint(checkpoints[-1])
trainer.test(model)