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#!/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).
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