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[RaySGD] revised existing transformer example to work with transformers>=3.0 #9661

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Jul 23, 2020
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[RaySGD] revised existing transformer example to work with transformers>=3.0 #9661

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20 changes: 11 additions & 9 deletions python/ray/util/sgd/torch/examples/transformers/transformers_example.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,10 +36,11 @@
from tqdm import trange
import torch.distributed as dist

from transformers import (MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, AdamW,
AutoConfig, AutoModelForSequenceClassification,
AutoTokenizer, get_linear_schedule_with_warmup,
HfArgumentParser, TrainingArguments)
from transformers import (MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, AdamW, AutoConfig,
AutoModelForSequenceClassification, AutoTokenizer,
get_linear_schedule_with_warmup, HfArgumentParser,
TrainingArguments)
from transformers import glue_output_modes as output_modes
from transformers import glue_processors as processors

Expand All @@ -58,7 +59,8 @@
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

ALL_MODELS = sum(
(tuple(conf.pretrained_config_archive_map.keys())
(tuple(key for key in ALL_PRETRAINED_CONFIG_ARCHIVE_MAP.keys()
if key.startswith(conf.model_type))
for conf in MODEL_CONFIG_CLASSES),
(),
)
Expand All @@ -78,11 +80,11 @@ def announce_training(args, dataset_len, t_total):
logger.info("***** Running training *****")
logger.info(" Num examples = %d", dataset_len)
logger.info(" Num Epochs = %d", args.num_train_epochs)
logger.info(" Instantaneous batch size per GPU = %d",
args.per_gpu_train_batch_size)
logger.info(" Instantaneous batch size per device = %d",
args.per_device_train_batch_size)
logger.info(
" Total train batch size (w. parallel, distributed & accum) = %d",
args.per_gpu_train_batch_size * args.gradient_accumulation_steps *
args.per_device_train_batch_size * args.gradient_accumulation_steps *
args.num_workers,
)
logger.info(" Gradient Accumulation steps = %d",
Expand Down Expand Up @@ -154,7 +156,7 @@ def data_creator(config):
return DataLoader(
train_dataset,
sampler=train_sampler,
batch_size=args.per_gpu_train_batch_size)
batch_size=args.per_device_train_batch_size)


class TransformerOperator(TrainingOperator):
Expand Down