I got following error in colab when start a training.
CUDA error: CUBLAS_STATUS_NOT_INITIALIZED when calling
cublasCreate(handle)
This is my code:
from datasets import load_dataset, Dataset, DatasetDict
tds = Dataset.from_pandas(splittrain)
vds = Dataset.from_pandas(splitvalidation)
raw_datasets = DatasetDict()
raw_datasets['train'] = tds
raw_datasets['validation'] = vds
print(raw_datasets)
from transformers import AutoTokenizer
model_checkpoint = "bert-base-cased"
tokenizer_checkpoint = "/content/Tokenizer"
tokenizer = AutoTokenizer.from_pretrained(tokenizer_checkpoint)
max_length = 384
stride = 128
def preprocess_training_examples(examples):
questions = [q.strip() for q in examples["question"]]
inputs = tokenizer(
questions,
examples["context"],
max_length=max_length,
truncation="only_second",
stride=stride,
return_overflowing_tokens=True,
return_offsets_mapping=True,
padding="max_length",
)
offset_mapping = inputs.pop("offset_mapping")
sample_map = inputs.pop("overflow_to_sample_mapping")
answers = examples["answers"]
start_positions = []
end_positions = []
for i, offset in enumerate(offset_mapping):
sample_idx = sample_map[i]
answer = answers[sample_idx]
start_char = answer["answer_start"][0]
end_char = answer["answer_start"][0] + len(answer["text"][0])
sequence_ids = inputs.sequence_ids(i)
# Find the start and end of the context
idx = 0
while sequence_ids[idx] != 1:
idx += 1
context_start = idx
while sequence_ids[idx] == 1:
idx += 1
context_end = idx - 1
# If the answer is not fully inside the context, label is (0, 0)
if offset[context_start][0] > start_char or offset[context_end][1] < end_char:
start_positions.append(0)
end_positions.append(0)
else:
# Otherwise it's the start and end token positions
idx = context_start
while idx <= context_end and offset[idx][0] <= start_char:
idx += 1
start_positions.append(idx - 1)
idx = context_end
while idx >= context_start and offset[idx][1] >= end_char:
idx -= 1
end_positions.append(idx + 1)
inputs["start_positions"] = start_positions
inputs["end_positions"] = end_positions
return inputs
train_dataset = raw_datasets["train"].map(
preprocess_training_examples,
batched=True,
remove_columns=raw_datasets["train"].column_names,
)
len(raw_datasets["train"]), len(train_dataset)
def preprocess_validation_examples(examples):
questions = [q.strip() for q in examples["question"]]
inputs = tokenizer(
questions,
examples["context"],
max_length=max_length,
truncation="only_second",
stride=stride,
return_overflowing_tokens=True,
return_offsets_mapping=True,
padding="max_length",
)
sample_map = inputs.pop("overflow_to_sample_mapping")
example_ids = []
for i in range(len(inputs["input_ids"])):
sample_idx = sample_map[i]
example_ids.append(examples["id"][sample_idx])
sequence_ids = inputs.sequence_ids(i)
offset = inputs["offset_mapping"][i]
inputs["offset_mapping"][i] = [
o if sequence_ids[k] == 1 else None for k, o in enumerate(offset)
]
inputs["example_id"] = example_ids
return inputs
validation_dataset = raw_datasets["validation"].map(
preprocess_validation_examples,
batched=True,
remove_columns=raw_datasets["validation"].column_names,
)
len(raw_datasets["validation"]), len(validation_dataset)
import evaluate
metric = evaluate.load("squad")
from tqdm.auto import tqdm
import collections
n_best = 20
max_answer_length = 30
def compute_metrics(start_logits, end_logits, features, examples):
example_to_features = collections.defaultdict(list)
for idx, feature in enumerate(features):
example_to_features[feature["example_id"]].append(idx)
predicted_answers = []
for example in tqdm(examples):
example_id = example["id"]
context = example["context"]
answers = []
# Loop through all features associated with that example
for feature_index in example_to_features[example_id]:
start_logit = start_logits[feature_index]
end_logit = end_logits[feature_index]
offsets = features[feature_index]["offset_mapping"]
start_indexes = np.argsort(start_logit)[-1 : -n_best - 1 : -1].tolist()
end_indexes = np.argsort(end_logit)[-1 : -n_best - 1 : -1].tolist()
for start_index in start_indexes:
for end_index in end_indexes:
# Skip answers that are not fully in the context
if offsets[start_index] is None or offsets[end_index] is None:
continue
# Skip answers with a length that is either < 0 or > max_answer_length
if (
end_index < start_index
or end_index - start_index + 1 > max_answer_length
):
continue
answer = {
"text": context[offsets[start_index][0] : offsets[end_index][1]],
"logit_score": start_logit[start_index] + end_logit[end_index],
}
answers.append(answer)
# Select the answer with the best score
if len(answers) > 0:
best_answer = max(answers, key=lambda x: x["logit_score"])
predicted_answers.append(
{"id": example_id, "prediction_text": best_answer["text"]}
)
else:
predicted_answers.append({"id": example_id, "prediction_text": ""})
theoretical_answers = [{"id": ex["id"], "answers": ex["answers"]} for ex in examples]
return metric.compute(predictions=predicted_answers, references=theoretical_answers)
# Code with Logging for Tensorboard Logging
from transformers import AutoModelForQuestionAnswering
model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)
from transformers import TrainingArguments
args = TrainingArguments(
"bert-finetuned-squad",
evaluation_strategy="steps", # "no" to steps
eval_steps=500, # new add
logging_strategy="steps", # new add
logging_steps=500, # new add
save_strategy="epoch",
learning_rate=2e-5,
num_train_epochs=3,
weight_decay=0.01,
fp16=True,
report_to="tensorboard", # Change or Add
)
from transformers import Trainer
trainer = Trainer(
model=model,
args=args,
train_dataset=train_dataset,
eval_dataset=validation_dataset,
tokenizer=tokenizer,
)
trainer.train()
