Commit f94f5a0d authored by zihanl's avatar zihanl
Browse files

remove dynamic prompt argument

parent fd97dabd

tasks/knwl_dialo/prompt.py

+15 −10
Original line number Diff line number Diff line
@@ -42,7 +42,6 @@ def generate_samples_by_prompting_input_from_file(model): 
        fname = open(args.sample_input_file, "r")

        all_raw_text = fname.readlines()

        input_count = len(all_raw_text)

        input_pos = 0

        if args.sample_output_file is None:

            sample_output_file = args.sample_input_file + ".out"

            print('`sample-output-file` not specified, setting '
@@ -52,8 +51,13 @@ def generate_samples_by_prompting_input_from_file(model): 


        fname_out = open(sample_output_file, "w")



    # only two prompt types (i.e., knowledge and response) are allowed

    assert args.prompt_type in ["knowledge", "response"], \

                "Please input a correct prompt type!"



    # Read the prompt file

    if args.dynamic_prompt:

    if args.prompt_type == "knowledge":

        # read the prompts for the knowledge generation

        prompt_examples_dict = {}

        with open(args.prompt_file, "r") as f:

            for i, line in enumerate(f):
@@ -71,6 +75,7 @@ def generate_samples_by_prompting_input_from_file(model): 
                    prompt_examples_dict[key] = prompt



    else:

        # read the prompts for the response generation

        # prompts are fixed for all test samples

        with open(args.prompt_file, "r") as f:

            prompt_examples = f.readlines()
@@ -81,9 +86,8 @@ def generate_samples_by_prompting_input_from_file(model): 
                instance = instance.strip()

                prompt += instance + " \n"



    # only two prompt types (i.e., knowledge and response) are allowed

    assert args.prompt_type in ["knowledge", "response"]

    context_count = 0

    input_pos = 0

    model.eval()

    # perform prompting

    with torch.no_grad():
@@ -96,24 +100,25 @@ def generate_samples_by_prompting_input_from_file(model): 
                splits = input_str.split("\t")

                topic = splits[0]



                # first add the prompt into the inputs

                if args.dynamic_prompt:

                if args.prompt_type == "knowledge":

                    # first add the prompt into the raw_text

                    turns = splits[1].split(" [SEP] ")

                    last_turn = turns[-1]

                    key = topic + " " + last_turn

                    raw_text = prompt_examples_dict[key]

                else:

                    raw_text = prompt



                if args.prompt_type == "knowledge":

                    # construct inputs for knowledge generation

                    # then add the constructed inputs into the raw_text

                    turns = splits[1].split(" [SEP] ")

                    context = turns[-1]

                    raw_text += "( " + context + " ) " + topic + " =>"

                

                else:

                    # first add the prompt into the raw_text

                    raw_text = prompt



                    # construct inputs for response generation

                    # args.prompt_type == "response"

                    # then add the constructed inputs into the raw_text

                    turns = splits[1].split(" [SEP] ")

                    knowledge = splits[2]

                    last_turn = turns[-1]