usrbinenv python 2020 the huggingface team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache orglicenseslicense2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license a table of supported tasks and the list of scores in the order of importance to be sorted by to add a new task simply list the score names that runeval rungenerate returns run parametric search over the desired hparam space with help of runeval py all the arguments except search are passed to runeval py as is the values inside of search are parsed reformatted and fed to runeval py as additional args the format for the search value is a simple string with hparams and colon separated values to try e g search numbeams5 10 lengthpenalty0 8 1 0 1 2 earlystoppingtrue false which will generate 12 232 searches for a product of each hparam for example the example that was just used will invoke runeval py repeatedly with numbeams 5 lengthpenalty 0 8 earlystopping true numbeams 5 lengthpenalty 0 8 earlystopping false numbeams 10 lengthpenalty 1 2 earlystopping false on completion this function prints a markdown table of the results sorted by the best bleu score and the winning arguments we share some of the args to support variations like translationentode xxx need to trap cuda oom and lower args bs if that happens and retry make sure scores are first in the table find widest entries usage normalrunevalsearch py cmd plus searchnumbeams1 5 10 lengthpenalty0 8 1 1 2 earlystoppingtrue false example pythonpathsrc examplesseq2seq python examplesseq2seqrunevalsearch py modelname datadirval source savedirtesttranslations txt referencepath datadirval target scorepath savedirtestbleu json bs bs task translation searchnumbeams1 5 10 lengthpenalty0 8 1 1 2 earlystoppingtrue false usr bin env python 2020 the huggingface team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license a table of supported tasks and the list of scores in the order of importance to be sorted by to add a new task simply list the score names that run_eval run_generate returns run parametric search over the desired hparam space with help of run_eval py all the arguments except search are passed to run_eval py as is the values inside of search are parsed reformatted and fed to run_eval py as additional args the format for the search value is a simple string with hparams and colon separated values to try e g search num_beams 5 10 length_penalty 0 8 1 0 1 2 early_stopping true false which will generate 12 2 3 2 searches for a product of each hparam for example the example that was just used will invoke run_eval py repeatedly with num_beams 5 length_penalty 0 8 early_stopping true num_beams 5 length_penalty 0 8 early_stopping false num_beams 10 length_penalty 1 2 early_stopping false on completion this function prints a markdown table of the results sorted by the best bleu score and the winning arguments we share some of the args to support variations like translation_en_to_de score cols first in case we need to reduce its size due to cuda oom xxx need to trap cuda oom and lower args bs if that happens and retry make sure scores are first in the table find widest entries usage normal run_eval_search py cmd plus search num_beams 1 5 10 length_penalty 0 8 1 1 2 early_stopping true false example pythonpath src examples seq2seq python examples seq2seq run_eval_search py model_name data_dir val source save_dir test_translations txt reference_path data_dir val target score_path save_dir test_bleu json bs bs task translation search num_beams 1 5 10 length_penalty 0 8 1 1 2 early_stopping true false
import argparse
import itertools
import operator
import sys
from collections import OrderedDict

from run_eval import datetime_now, run_generate

from utils import ROUGE_KEYS




task_score_names = {
    "translation": ["bleu"],
    "summarization": ROUGE_KEYS,
}


def parse_search_arg(search):
    groups = search.split()
    entries = dict((g.split("=") for g in groups))
    entry_names = list(entries.keys())
    sets = [[f"--{k} {v}" for v in vs.split(":")] for k, vs in entries.items()]
    matrix = [list(x) for x in itertools.product(*sets)]
    return matrix, entry_names


def run_search():
    
    prog = sys.argv[0]

    parser = argparse.ArgumentParser(
        usage=(
            "\n\nImportant: this script accepts all arguments `run_eval.py` accepts and then a few extra, therefore"
            " refer to `run_eval.py -h` for the complete list."
        )
    )
    parser.add_argument(
        "--search",
        type=str,
        required=False,
        help='param space to search, e.g. "num_beams=5:10 length_penalty=0.8:1.0:1.2"',
    )
    parser.add_argument(
        "--bs", type=int, default=8, required=False, help="initial batch size (may get reduced if it's too big)"
    )
    parser.add_argument("--task", type=str, help="used for task_specific_params + metrics")
    parser.add_argument(
        "--info",
        nargs="?",
        type=str,
        const=datetime_now(),
        help=(
            "add custom notes to be printed before the results table. If no value is passed, the current datetime"
            " string will be used."
        ),
    )
    args, args_main = parser.parse_known_args()
    
    args_main.extend(["--task", args.task])
    args_normal = [prog] + args_main

    
    task = "translation" if "translation" in args.task else "summarization"

    matrix, col_names = parse_search_arg(args.search)
    col_names[0:0] = task_score_names[task]  
    col_widths = {col: len(str(col)) for col in col_names}
    results = []
    for r in matrix:
        hparams = dict((x.replace("--", "").split() for x in r))
        args_exp = " ".join(r).split()
        args_exp.extend(["--bs", str(args.bs)])  
        sys.argv = args_normal + args_exp

        

        scores = run_generate(verbose=False)
        
        result = OrderedDict()
        for score in task_score_names[task]:
            result[score] = scores[score]
        result.update(hparams)
        results.append(result)

        
        for k, v in result.items():
            l = len(str(v))
            if l > col_widths[k]:
                col_widths[k] = l

    results_sorted = sorted(results, key=operator.itemgetter(*task_score_names[task]), reverse=True)
    print(" | ".join([f"{col:{col_widths[col]}}" for col in col_names]))
    print(" | ".join([f"{'-'*col_widths[col]}" for col in col_names]))
    for row in results_sorted:
        print(" | ".join([f"{row[col]:{col_widths[col]}}" for col in col_names]))

    best = results_sorted[0]
    for score in task_score_names[task]:
        del best[score]
    best_args = [f"--{k} {v}" for k, v in best.items()]
    dyn_args = ["--bs", str(args.bs)]
    if args.info:
        print(f"\nInfo: {args.info}")
    print("\nBest score args:")
    print(" ".join(args_main + best_args + dyn_args))

    return results_sorted


if __name__ == "__main__":
    
    
    
    
    
    
    
    
    
    run_search()