From df0c81b008315ba2e3fc66e823f060c972b134f2 Mon Sep 17 00:00:00 2001
From: zqwerty <zhuq96@hotmail.com>
Date: Fri, 22 Apr 2022 14:43:58 +0800
Subject: [PATCH] add t5nlu few-shot script, 1% multiwoz21: acc 54, f1 72.1;
 10% multiwoz21: acc73.3, f1 84.6

---
 convlab2/base_models/t5/create_data.py        |  9 +-
 .../base_models/t5/nlu/merge_predict_res.py   |  3 +-
 .../base_models/t5/nlu/run_nlu_fewshot.sh     | 83 +++++++++++++++++++
 3 files changed, 92 insertions(+), 3 deletions(-)
 create mode 100644 convlab2/base_models/t5/nlu/run_nlu_fewshot.sh

diff --git a/convlab2/base_models/t5/create_data.py b/convlab2/base_models/t5/create_data.py
index c2f3da96..b2091f52 100644
--- a/convlab2/base_models/t5/create_data.py
+++ b/convlab2/base_models/t5/create_data.py
@@ -138,14 +138,19 @@ if __name__ == '__main__':
     parser.add_argument('--speaker', '-s', type=str, choices=['user', 'system', 'all'], help='speaker(s)')
     parser.add_argument('--context_window_size', '-c', type=int, default=0, help='how many contextual utterances are considered')
     parser.add_argument('--len_tokenizer', '-l', type=str, default=None, help='name or path of tokenizer that used to get seq len')
+    parser.add_argument('--ratio', '-r', type=float, default=None, help='how many data is used for training and evaluation')
+    parser.add_argument('--dial_ids_order', '-o', type=int, default=None, help='which data order is used for experiments')
     args = parser.parse_args()
     print(args)
     if args.len_tokenizer:
         tokenizer = AutoTokenizer.from_pretrained(args.len_tokenizer)
     for dataset_name in tqdm(args.datasets, desc='datasets'):
-        dataset = load_dataset(dataset_name)
+        dataset = load_dataset(dataset_name, args.dial_ids_order)
+        if args.ratio:
+            dataset['train'] = dataset['train'][:round(len(dataset['train'])*args.ratio)]
+            dataset['validation'] = dataset['validation'][:round(len(dataset['validation'])*args.ratio)]
         for task_name in tqdm(args.tasks, desc='tasks', leave=False):
-            data_dir = os.path.join('data', task_name, dataset_name)
+            data_dir = os.path.join('data', task_name, (dataset_name if not args.ratio else f'{dataset_name}_{args.ratio}_order{args.dial_ids_order}'))
             data_by_split = eval(f"create_{task_name}_data")(dataset, data_dir, args)
             if args.len_tokenizer:
                 get_max_len(data_by_split, tokenizer)
diff --git a/convlab2/base_models/t5/nlu/merge_predict_res.py b/convlab2/base_models/t5/nlu/merge_predict_res.py
index f3386b21..cc7c9913 100755
--- a/convlab2/base_models/t5/nlu/merge_predict_res.py
+++ b/convlab2/base_models/t5/nlu/merge_predict_res.py
@@ -6,7 +6,7 @@ from convlab2.base_models.t5.nlu.serialization import deserialize_dialogue_acts
 
 def merge(dataset_name, speaker, save_dir, context_window_size, predict_result):
     assert os.path.exists(predict_result)
-    dataset = load_dataset(dataset_name)
+    dataset = load_dataset(dataset_name, args.dial_ids_order)
     data = load_nlu_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:
@@ -29,6 +29,7 @@ if __name__ == '__main__':
     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')
+    parser.add_argument('--dial_ids_order', '-o', type=int, default=None, help='which data order is used for experiments')
     args = parser.parse_args()
     print(args)
     merge(args.dataset, args.speaker, args.save_dir, args.context_window_size, args.predict_result)
diff --git a/convlab2/base_models/t5/nlu/run_nlu_fewshot.sh b/convlab2/base_models/t5/nlu/run_nlu_fewshot.sh
new file mode 100644
index 00000000..026e50aa
--- /dev/null
+++ b/convlab2/base_models/t5/nlu/run_nlu_fewshot.sh
@@ -0,0 +1,83 @@
+n_gpus=1
+task_name="nlu"
+dataset_name=$1
+speaker="user"
+context_window_size=$2
+ratio=$3
+dial_ids_order=$4
+data_dir="data/${task_name}/${dataset_name}_${ratio}_order${dial_ids_order}/${speaker}/context_${context_window_size}"
+output_dir="output/${task_name}/${dataset_name}_${ratio}_order${dial_ids_order}/${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="nlu_metric.py"
+metric_for_best_model="overall_f1"
+source_column="context"
+target_column="dialogue_acts_seq"
+truncation_side="left"
+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=2
+lr=1e-3
+num_train_epochs=100
+
+python ../create_data.py -t ${task_name} -d ${dataset_name} -s ${speaker} -c ${context_window_size} -r ${ratio} -o ${dial_ids_order}
+
+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 \
+    --save_total_limit 3 \
+    --prediction_loss_only \
+    --load_best_model_at_end \
+    --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 -o ${dial_ids_order}
+
+python ../../../nlu/evaluate_unified_datasets.py -p ${output_dir}/predictions.json
-- 
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