add knwl_dialo scripts

examples/knwl_dialo/data_processing.sh

+#!/bin/bash
+
+# Data preparation for our framework: preprocessing the WoW and WoI datasets
+# The datasets can be downloaded through the following links:
+# WoW: https://parl.ai/projects/wizard_of_wikipedia/
+# WoI: https://parl.ai/projects/sea/
+
+DIR=`pwd`
+# Before running the preprocessing, please download 
+# the wizard of wikipedia and wizard datasets
+WOW_DATA_FOLDER=<PATH_OF_WIZARD_OF_WIKIPEDIA_DATA_FOLDER>
+WOI_DATA_FOLDER=<PATH_OF_WIZARD_OF_INTERNET_DATA_FOLDER>
+
+# We provide examples for processing the raw data from Wizard of Wikipedia
+# Processing the train dataset (train.json)
+python ${DIR}/tasks/knwl_dialo/preprocessing.py \
+        --func process_wow_dataset \
+        --raw_file ${WOW_DATA_FOLDER}/train.json \
+        --processed_file ${WOW_DATA_FOLDER}/train_processed.txt
+
+# Processing test seen dataset (test_random_split.json)
+python ${DIR}/tasks/knwl_dialo/preprocessing.py \
+        --func process_wow_dataset \
+        --raw_file ${WOW_DATA_FOLDER}/test_random_split.json \
+        --processed_file ${WOW_DATA_FOLDER}/testseen_processed.txt \
+        --knwl_ref_file ${WOW_DATA_FOLDER}/output_testseen_knowledge_reference.txt \
+        --resp_ref_file ${WOW_DATA_FOLDER}/output_testseen_response_reference.txt
+
+# processing test unseen dataset (test_topic_split.json)
+python ${DIR}/tasks/knwl_dialo/preprocessing.py \
+        --func process_wow_dataset \
+        --raw_file ${WOW_DATA_FOLDER}/test_topic_split.json \
+        --processed_file ${WOW_DATA_FOLDER}/testunseen_processed.txt \
+        --knwl_ref_file ${WOW_DATA_FOLDER}/output_testunseen_knowledge_reference.txt \
+        --resp_ref_file ${WOW_DATA_FOLDER}/output_testunseen_response_reference.txt
+
+
+# We provide the following script to process the raw data from Wizard of Internet
+# Processing the test dataset (test.jsonl)
+python ${DIR}/tasks/knwl_dialo/preprocessing.py \
+        --func process_woi_dataset \
+        --raw_file ${WOI_DATA_FOLDER}/test.jsonl \
+        --processed_file ${WOI_DATA_FOLDER}/test_processed.txt \
+        --knwl_ref_file ${WOI_DATA_FOLDER}/output_test_knowledge_reference.txt \
+        --resp_ref_file ${WOI_DATA_FOLDER}/output_test_response_reference.txt
+
+
+# Get the knowledge generation prompts for the each test dataset in WoW and WoI
+MODEL_FILE=<PATH_OF_THE_FINETUNED_DPR_MODEL> 
+# WoW test seen
+python ${DIR}/tasks/knwl_dialo/preprocessing.py \
+        --func get_knwl_gen_prompts \
+        --test_file ${WOW_DATA_FOLDER}/testseen_processed.txt \
+        --train_file ${WOW_DATA_FOLDER}/train_processed.txt \
+        --model_file ${MODEL_FILE} \
+        --processed_file ${WOW_DATA_FOLDER}/output_testseen_knowledge_prompts.json \
+        --data_type wow_seen
+
+# WoW test unseen
+python ${DIR}/tasks/knwl_dialo/preprocessing.py \
+        --func get_knwl_gen_prompts \
+        --test_file ${WOW_DATA_FOLDER}/testunseen_processed.txt \
+        --train_file ${WOW_DATA_FOLDER}/train_processed.txt \
+        --model_file ${MODEL_FILE} \
+        --processed_file ${WOW_DATA_FOLDER}/output_testunseen_knowledge_prompts.json \
+        --data_type wow_unseen
+
+# WoI
+python ${DIR}/tasks/knwl_dialo/preprocessing.py \
+        --func get_knwl_gen_prompts \
+        --test_file ${WOI_DATA_FOLDER}/test_processed.txt \
+        --train_file ${WOW_DATA_FOLDER}/train_processed.txt \
+        --model_file ${MODEL_FILE} \
+        --processed_file ${WOI_DATA_FOLDER}/output_test_knowledge_prompts.json \
+        --data_type woi
+
+
+# Get the response generation prompts (can be applied for all the test datasets)
+python ${DIR}/tasks/knwl_dialo/preprocessing.py \
+        --func get_resp_gen_prompts \
+        --train_file ${WOW_DATA_FOLDER}/train_processed.txt \
+        --processed_file ${WOW_DATA_FOLDER}/output_response_prompts.txt
+

examples/knwl_dialo/eval_knwl_generation.sh

+#!/bin/bash
+
+#########################
+# Evaluate the F1 scores.
+#########################
+
+WORLD_SIZE=1
+DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
+                  --nnodes 1 \
+                  --node_rank 0 \
+                  --master_addr localhost \
+                  --master_port 6000"
+                  
+MODEL_GEN_PATH=<PATH_OF_THE_KNOWLEDGE_GENERATION> \ 
+        (e.g., /testseen_knowledge_generations.txt)
+GROUND_TRUTH_PATH=<PATH_OF_THE_GROUND_TRUTH_KNOWLEDGE> \ 
+        (e.g., /testseen_knowledge_reference.txt)
+
+python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \
+        --num-layers 24 \
+        --hidden-size 1024 \
+        --num-attention-heads 16 \
+        --seq-length 2048 \
+        --max-position-embeddings 2048 \
+        --micro-batch-size 4 \
+        --task KNWL-DIALO-EVAL-F1 \
+        --guess-file ${MODEL_GEN_PATH} \
+        --answer-file ${GROUND_TRUTH_PATH}
+
+
+############################################
+# Evaluate BLEU, METEOR, and ROUGE-L scores.
+############################################
+
+# We follow the nlg-eval (https://github.com/Maluuba/nlg-eval) to 
+# evaluate the BLEU, METEOR, and ROUGE-L scores. 
+
+# To evaluate on these metrics, please setup the environments based on 
+# the nlg-eval github, and run the corresponding evaluation commands.
+
+nlg-eval \
+    --hypothesis=<PATH_OF_THE_KNOWLEDGE_GENERATION> \
+    --references=<PATH_OF_THE_GROUND_TRUTH_KNOWLEDGE>

examples/knwl_dialo/eval_resp_generation.sh

+#!/bin/bash
+
+#########################
+# Evaluate the F1 scores.
+#########################
+
+WORLD_SIZE=1
+DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
+                  --nnodes 1 \
+                  --node_rank 0 \
+                  --master_addr localhost \
+                  --master_port 6000"
+                  
+MODEL_GEN_PATH=<PATH_OF_THE_RESPONSE_GENERATION> \ 
+        (e.g., /testseen_response_generations.txt)
+GROUND_TRUTH_PATH=<PATH_OF_THE_GROUND_TRUTH_RESPONSE> \ 
+        (e.g., /testseen_response_reference.txt)
+
+python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \
+        --num-layers 24 \
+        --hidden-size 1024 \
+        --num-attention-heads 16 \
+        --seq-length 2048 \
+        --max-position-embeddings 2048 \
+        --micro-batch-size 4 \
+        --task KNWL-DIALO-EVAL-F1 \
+        --guess-file ${MODEL_GEN_PATH} \
+        --answer-file ${GROUND_TRUTH_PATH}
+
+
+##########################
+# Evaluate the KF1 scores.
+##########################
+                  
+MODEL_GEN_PATH=<PATH_OF_THE_RESPONSE_GENERATION> \ 
+        (e.g., /testseen_response_generations.txt)
+GROUND_TRUTH_PATH=<PATH_OF_THE_GROUND_TRUTH_KNOWLEDGE> \ 
+        (e.g., /testseen_knowledge_reference.txt)
+
+python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \
+        --num-layers 24 \
+        --hidden-size 1024 \
+        --num-attention-heads 16 \
+        --seq-length 2048 \
+        --max-position-embeddings 2048 \
+        --micro-batch-size 4 \
+        --task KNWL-DIALO-EVAL-F1 \
+        --guess-file ${MODEL_GEN_PATH} \
+        --answer-file ${GROUND_TRUTH_PATH}
+
+
+############################################
+# Evaluate BLEU, METEOR, and ROUGE-L scores.
+############################################
+
+# We follow the nlg-eval (https://github.com/Maluuba/nlg-eval) to 
+# evaluate the BLEU, METEOR, and ROUGE-L scores. 
+
+# To evaluate on these metrics, please setup the environments based on 
+# the nlg-eval github, and run the corresponding evaluation commands.
+
+nlg-eval \
+    --hypothesis=<PATH_OF_THE_RESPONSE_GENERATION> \
+    --references=<PATH_OF_THE_GROUND_TRUTH_RESPONSE>

examples/knwl_dialo/prep_resp_gen.sh

+#!/bin/bash
+
+# Preparing the input file for the response generation (second-stage prompting)
+
+DIR=`pwd`
+
+TEST_FILE=<PATH_OF_PROCESSED_TEST_DATA> \
+        (e.g., /testseen_processed.txt)
+KNOWLEDGE_FILE=<PATH_OF_GENERATED_KNOWLEDGE_DATA> \
+        (e.g., /testseen_knowledge_generations.txt)
+PROCESSED_FILE=<PATH_OF_INPUT_FILE_FOR_RESPONSE_GENERATION> \
+        (e.g., /testseen_processed_with_generated_knowledge.txt)
+
+python ${DIR}/tasks/knwl_dialo/preprocessing.py \
+        --func prepare_input \
+        --test_file ${TEST_FILE} \
+        --knowledge_gen_file ${KNOWLEDGE_FILE} \
+        --processed_file ${PROCESSED_FILE}

examples/knwl_dialo/prompt_knwl_gen.sh

+#!/bin/bash
+
+# Stage-1: Prompt a pretrained language model to generate the context-relevant knowledge
+# The input contains prompts and current dialogue context, the output is the relevant knowledge
+# The size of the pretrained language model is 357M
+
+WORLD_SIZE=8
+
+DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
+                  --nnodes 1 \
+                  --node_rank 0 \
+                  --master_addr localhost \
+                  --master_port 6000"
+
+CHECKPOINT_PATH=<PATH_OF_LANGUAGE_MODEL> (e.g., /357m)
+VOCAB_PATH=<PATH_OF_VOCAB_FILE> (e.g., /gpt2-vocab.json)
+MERGE_PATH=<PATH_OF_MERGE_FILE> (e.g., /gpt2-merges.txt)
+INPUT_PATH=<PATH_OF_PROCESSED_TEST_DATA_FILE> \ 
+        (e.g., /testseen_processed.txt)
+PROMPT_PATH=<PATH_OF_KNOWLEDGE_GENERATION_PROMPTS> \
+        (e.g., /testseen_knowledge_prompts.json)
+OUTPUT_PATH=<PATH_OF_OUTPUT_GENERATION_FILE> \
+        (e.g., /testseen_knowledge_generations.txt)
+
+python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \
+        --num-layers 24 \
+        --hidden-size 1024 \
+        --num-attention-heads 16 \
+        --seq-length 2048 \
+        --max-position-embeddings 2048 \
+        --micro-batch-size 1 \
+        --vocab-file ${VOCAB_PATH} \
+        --merge-file ${MERGE_PATH} \
+        --load ${CHECKPOINT_PATH} \
+        --fp16 \
+        --DDP-impl torch \
+        --tokenizer-type GPT2BPETokenizer \
+        --sample-input-file ${INPUT_PATH} \
+        --sample-output-file ${OUTPUT_PATH} \
+        --prompt-file ${PROMPT_PATH} \
+        --prompt-type knowledge \
+        --num-prompt-examples 10 \
+        --task KNWL-DIALO-PROMPT 
+
+# NOTE: If you use api for the model generation, please use 
+# the "--api-prompt" flag (setting this value as True). 

examples/knwl_dialo/prompt_resp_gen.sh

+#!/bin/bash
+
+# Stage-2: Prompt a pretrained language model to generate the corresponding response
+# The input contains prompts, current dialogue context, and generated knowledge in Stage-1
+# The output is the corresponding response.
+# The size of the pretrained language model is 357M
+
+WORLD_SIZE=8
+
+DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
+                  --nnodes 1 \
+                  --node_rank 0 \
+                  --master_addr localhost \
+                  --master_port 6000"
+
+CHECKPOINT_PATH=<PATH_OF_LANGUAGE_MODEL> (e.g., /357m)
+VOCAB_PATH=<PATH_OF_VOCAB_FILE> (e.g., /gpt2-vocab.json)
+MERGE_PATH=<PATH_OF_MERGE_FILE> (e.g., /gpt2-merges.txt)
+INPUT_PATH=<PATH_OF_INPUT_TEST_DATA_FILE> (e.g., /testseen_processed.txt)
+PROMPT_PATH=<PATH_OF_RESPONSE_GENERATION_PROMPTS> \
+        (e.g., /response_prompts.txt)
+OUTPUT_PATH=<PATH_OF_OUTPUT_GENERATION_FILE> \
+        (e.g., /output_testseen_response_generations.txt)
+
+python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \
+        --num-layers 24 \
+        --hidden-size 1024 \
+        --num-attention-heads 16 \
+        --seq-length 2048 \
+        --max-position-embeddings 2048 \
+        --micro-batch-size 1 \
+        --vocab-file ${VOCAB_PATH} \
+        --merge-file ${MERGE_PATH} \
+        --load ${CHECKPOINT_PATH} \
+        --fp16 \
+        --DDP-impl torch \
+        --tokenizer-type GPT2BPETokenizer \
+        --sample-input-file ${INPUT_PATH} \
+        --sample-output-file ${OUTPUT_PATH} \
+        --prompt-file ${PROMPT_PATH} \
+        --prompt-type response \
+        --num-prompt-examples 20 \
+        --task KNWL-DIALO-PROMPT 
+
+# NOTE: If you use api for the model generation, please use 
+# the "--api-prompt" flag (setting this value as True).