#!/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/msdp/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 MSDP-PROMPT 

# NOTE: If you use api for the model generation, please use 
# the "--api_prompt" flag (setting this value as True).