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124 lines
4.2 KiB
124 lines
4.2 KiB
import datasets
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from transformers import AutoTokenizer, PreTrainedTokenizer
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from colossalai.booster.plugin.dp_plugin_base import DPPluginBase
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class GLUEDataBuilder:
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task_text_field_map = {
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"cola": ["sentence"],
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"sst2": ["sentence"],
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"mrpc": ["sentence1", "sentence2"],
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"qqp": ["question1", "question2"],
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"stsb": ["sentence1", "sentence2"],
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"mnli": ["premise", "hypothesis"],
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"qnli": ["question", "sentence"],
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"rte": ["sentence1", "sentence2"],
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"wnli": ["sentence1", "sentence2"],
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"ax": ["premise", "hypothesis"],
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}
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glue_task_num_labels = {
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"cola": 2,
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"sst2": 2,
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"mrpc": 2,
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"qqp": 2,
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"stsb": 1,
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"mnli": 3,
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"qnli": 2,
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"rte": 2,
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"wnli": 2,
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"ax": 3,
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}
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loader_columns = [
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"datasets_idx",
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"input_ids",
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"token_type_ids",
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"attention_mask",
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"start_positions",
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"end_positions",
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"labels",
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]
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def __init__(
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self,
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model_name_or_path: str,
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plugin: DPPluginBase,
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task_name: str = "mrpc",
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max_seq_length: int = 128,
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train_batch_size: int = 32,
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eval_batch_size: int = 32,
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**kwargs,
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):
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super().__init__()
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self.model_name_or_path = model_name_or_path
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self.task_name = task_name
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self.max_seq_length = max_seq_length
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self.train_batch_size = train_batch_size
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self.eval_batch_size = eval_batch_size
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self.plugin = plugin
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self.text_fields = self.task_text_field_map[task_name]
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self.num_labels = self.glue_task_num_labels[task_name]
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self.tokenizer: PreTrainedTokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True)
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self.setup()
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def setup(self):
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self.dataset = datasets.load_dataset("glue", self.task_name)
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for split in self.dataset.keys():
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self.dataset[split] = self.dataset[split].map(
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self.convert_to_features,
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batched=True,
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remove_columns=["label"],
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)
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self.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns]
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self.dataset[split].set_format(type="torch", columns=self.columns)
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self.eval_splits = [x for x in self.dataset.keys() if "validation" in x]
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def prepare_data(self):
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datasets.load_dataset("glue", self.task_name)
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AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True)
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def train_dataloader(self):
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return self.plugin.prepare_dataloader(
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self.dataset["train"], batch_size=self.train_batch_size, shuffle=True, drop_last=True
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)
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def val_dataloader(self):
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if len(self.eval_splits) == 1:
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return self.plugin.prepare_dataloader(self.dataset["validation"], batch_size=self.eval_batch_size)
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elif len(self.eval_splits) > 1:
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return [
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self.plugin.prepare_dataloader(self.dataset[x], batch_size=self.eval_batch_size)
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for x in self.eval_splits
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]
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def test_dataloader(self):
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if len(self.eval_splits) == 1:
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return self.plugin.prepare_dataloader(self.dataset["test"], batch_size=self.eval_batch_size)
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elif len(self.eval_splits) > 1:
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return [
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self.plugin.prepare_dataloader(self.dataset[x], batch_size=self.eval_batch_size)
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for x in self.eval_splits
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]
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def convert_to_features(self, example_batch):
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# Either encode single sentence or sentence pairs
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if len(self.text_fields) > 1:
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texts_or_text_pairs = list(zip(example_batch[self.text_fields[0]], example_batch[self.text_fields[1]]))
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else:
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texts_or_text_pairs = example_batch[self.text_fields[0]]
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# Tokenize the text/text pairs
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features = self.tokenizer.batch_encode_plus(
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texts_or_text_pairs, max_length=self.max_seq_length, padding="max_length", truncation=True
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)
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# Rename label to labels to make it easier to pass to model forward
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features["labels"] = example_batch["label"]
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return features
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