Merge pull request #54 from ConvLab/pre-training

t5 nlg, multiwoz21 context=0: bleu 35.3; context=3: bleu 35.7

convlab2/base_models/t5/nlg/merge_predict_res.py

+import json
+import os
+from convlab2.util import load_dataset, load_nlg_data
+
+
+def merge(dataset_name, speaker, save_dir, context_window_size, predict_result):
+    assert os.path.exists(predict_result)
+    dataset = load_dataset(dataset_name)
+    data = load_nlg_data(dataset, data_split='test', speaker=speaker, use_context=context_window_size>0, context_window_size=context_window_size)['test']
+    
+    if save_dir is None:
+        save_dir = os.path.dirname(predict_result)
+    else:
+        os.makedirs(save_dir, exist_ok=True)
+    predict_result = [json.loads(x)['predictions'].strip() for x in open(predict_result)]
+
+    for sample, prediction in zip(data, predict_result):
+        sample['predictions'] = {'utterance': prediction}
+
+    json.dump(data, open(os.path.join(save_dir, 'predictions.json'), 'w', encoding='utf-8'), indent=2, ensure_ascii=False)
+
+
+if __name__ == '__main__':
+    from argparse import ArgumentParser
+    parser = ArgumentParser(description="merge predict results with original data for unified NLU evaluation")
+    parser.add_argument('--dataset', '-d', metavar='dataset_name', type=str, help='name of the unified dataset')
+    parser.add_argument('--speaker', '-s', type=str, choices=['user', 'system', 'all'], help='speaker(s) of utterances')
+    parser.add_argument('--save_dir', type=str, help='merged data will be saved as $save_dir/predictions.json. default: on the same directory as predict_result')
+    parser.add_argument('--context_window_size', '-c', type=int, default=0, help='how many contextual utterances are considered')
+    parser.add_argument('--predict_result', '-p', type=str, required=True, help='path to the output file generated_predictions.json')
+    args = parser.parse_args()
+    print(args)
+    merge(args.dataset, args.speaker, args.save_dir, args.context_window_size, args.predict_result)

convlab2/base_models/t5/nlg/run_nlg.sh

+n_gpus=1
+task_name="nlg"
+dataset_name=$1
+speaker="system"
+context_window_size=$2
+data_dir="data/${task_name}/${dataset_name}/${speaker}/context_${context_window_size}"
+output_dir="output/${task_name}/${dataset_name}/${speaker}/context_${context_window_size}"
+cache_dir="../cache"
+logging_dir="${output_dir}/runs"
+train_file="${data_dir}/train.json"
+validation_file="${data_dir}/validation.json"
+test_file="${data_dir}/test.json"
+metric_name_or_path="nlg_metric.py"
+metric_for_best_model="bleu"
+source_column="context+da"
+target_column="response"
+truncation_side="right"
+max_source_length=512
+max_target_length=512
+model_name_or_path="t5-small"
+per_device_train_batch_size=128
+per_device_eval_batch_size=64
+gradient_accumulation_steps=4
+lr=1e-3
+num_train_epochs=10
+
+python ../create_data.py -t ${task_name} -d ${dataset_name} -s ${speaker} -c ${context_window_size}
+
+python ../run_seq2seq.py \
+    --task_name ${task_name} \
+    --train_file ${train_file} \
+    --validation_file ${validation_file} \
+    --source_column ${source_column} \
+    --target_column ${target_column} \
+    --max_source_length ${max_source_length} \
+    --max_target_length ${max_target_length} \
+    --truncation_side ${truncation_side} \
+    --model_name_or_path ${model_name_or_path} \
+    --do_train \
+    --do_eval \
+    --save_strategy epoch \
+    --evaluation_strategy epoch \
+    --prediction_loss_only \
+    --cache_dir ${cache_dir} \
+    --output_dir ${output_dir} \
+    --logging_dir ${logging_dir} \
+    --overwrite_output_dir \
+    --preprocessing_num_workers 4 \
+    --per_device_train_batch_size ${per_device_train_batch_size} \
+    --per_device_eval_batch_size ${per_device_eval_batch_size} \
+    --gradient_accumulation_steps ${gradient_accumulation_steps} \
+    --learning_rate ${lr} \
+    --num_train_epochs ${num_train_epochs} \
+    --debug underflow_overflow \
+    --adafactor \
+    --gradient_checkpointing
+
+python ../run_seq2seq.py \
+    --task_name ${task_name} \
+    --test_file ${test_file} \
+    --source_column ${source_column} \
+    --target_column ${target_column} \
+    --max_source_length ${max_source_length} \
+    --max_target_length ${max_target_length} \
+    --truncation_side ${truncation_side} \
+    --model_name_or_path ${output_dir} \
+    --do_predict \
+    --predict_with_generate \
+    --metric_name_or_path ${metric_name_or_path} \
+    --cache_dir ${cache_dir} \
+    --output_dir ${output_dir} \
+    --logging_dir ${logging_dir} \
+    --overwrite_output_dir \
+    --preprocessing_num_workers 4 \
+    --per_device_eval_batch_size ${per_device_eval_batch_size}
+
+python merge_predict_res.py -d ${dataset_name} -s ${speaker} -c ${context_window_size} -p ${output_dir}/generated_predictions.json
+
+python ../../../nlg/evaluate_unified_datasets.py -p ${output_dir}/predictions.json

convlab2/nlg/evaluate_unified_datasets.py

+import json
+from pprint import pprint
+import sacrebleu
+
+
+def evaluate(predict_result):
+    predict_result = json.load(open(predict_result))
+
+    metrics = {}
+    predictions, references = [], []
+    for sample in predict_result:
+        references.append(sample['utterance'])
+        predictions.append(sample['predictions']['utterance'])
+
+    metrics['bleu'] = sacrebleu.corpus_bleu(predictions, [references], lowercase=True).score
+
+    return metrics
+
+
+if __name__ == '__main__':
+    from argparse import ArgumentParser
+    parser = ArgumentParser(description="calculate NLU metrics for unified datasets")
+    parser.add_argument('--predict_result', '-p', type=str, required=True, help='path to the prediction file that in the unified data format')
+    args = parser.parse_args()
+    print(args)
+    metrics = evaluate(args.predict_result)
+    pprint(metrics)