#!/usr/bin/env python import argparse import shutil import time from json import JSONDecodeError from logging import getLogger from pathlib import Path from typing import Dict, List import torch from torch.utils.data import DataLoader from tqdm import tqdm from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from seq2seq_utils import ( Seq2SeqDataset, calculate_bleu, calculate_rouge, chunks, lmap, load_json, parse_numeric_n_bool_cl_kwargs, save_json, use_task_specific_params, write_txt_file, ) logger = getLogger(__name__) def eval_data_dir( data_dir, save_dir: str, model_name: str, bs: int = 8, max_source_length: int = 1024, type_path="val", n_obs=None, fp16=False, task="summarization", local_rank=None, num_return_sequences=1, dataset_kwargs: Dict = None, prefix="", **generate_kwargs, ) -> Dict: """Run evaluation on part of the data for one gpu and save to {save_dir}/rank_{rank}_output.json""" model_name = str(model_name) assert local_rank is not None torch.distributed.init_process_group(backend="nccl", rank=local_rank) save_dir = Path(save_dir) save_path = save_dir.joinpath(f"rank_{local_rank}_output.json") torch.cuda.set_device(local_rank) model = AutoModelForSeq2SeqLM.from_pretrained(model_name).cuda() if fp16: model = model.half() # determine if we need to increase num_beams use_task_specific_params(model, task) # update config with task specific params num_beams = generate_kwargs.pop("num_beams", model.config.num_beams) # AttributeError risk? if num_return_sequences > num_beams: num_beams = num_return_sequences tokenizer = AutoTokenizer.from_pretrained(model_name) logger.info(f"Inferred tokenizer type: {tokenizer.__class__}") # if this is wrong, check config.model_type. if max_source_length is None: max_source_length = tokenizer.model_max_length if prefix is None: prefix = prefix or getattr(model.config, "prefix", "") or "" ds = Seq2SeqDataset( tokenizer, data_dir, max_source_length, max_target_length=1024, type_path=type_path, n_obs=n_obs, prefix=prefix, **dataset_kwargs, ) # I set shuffle=True for a more accurate progress bar. # If all the longest samples are first, the prog bar estimate is too high at the beginning. sampler = ds.make_sortish_sampler(bs, distributed=True, add_extra_examples=False, shuffle=True) data_loader = DataLoader(ds, sampler=sampler, batch_size=bs, collate_fn=ds.collate_fn) results = [] for batch in tqdm(data_loader): summaries = model.generate( input_ids=batch["input_ids"].to(model.device), attention_mask=batch["attention_mask"].to(model.device), num_return_sequences=num_return_sequences, num_beams=num_beams, **generate_kwargs, ) preds = tokenizer.batch_decode(summaries, skip_special_tokens=True, clean_up_tokenization_spaces=False) ids = batch["ids"] if num_return_sequences > 1: preds = chunks(preds, num_return_sequences) # batch size chunks, each of size num_return_seq for i, pred in enumerate(preds): results.append(dict(pred=pred, id=ids[i].item())) save_json(results, save_path) return results, sampler.num_replicas def run_generate(): parser = argparse.ArgumentParser( epilog="Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate" ) parser.add_argument("--data_dir", type=str, help="like cnn_dm/test.source") parser.add_argument( "--model_name", type=str, help="like facebook/bart-large-cnn,t5-base, etc.", default="sshleifer/distilbart-xsum-12-3", ) parser.add_argument("--save_dir", type=str, help="where to save", default="tmp_gen") parser.add_argument("--max_source_length", type=int, default=None) parser.add_argument( "--type_path", type=str, default="test", help="which subset to evaluate typically train/val/test" ) parser.add_argument("--task", type=str, default="summarization", help="used for task_specific_params + metrics") parser.add_argument("--bs", type=int, default=8, required=False, help="batch size") parser.add_argument( "--local_rank", type=int, default=-1, required=False, help="should be passed by distributed.launch" ) parser.add_argument( "--n_obs", type=int, default=None, required=False, help="How many observations. Defaults to all." ) parser.add_argument( "--num_return_sequences", type=int, default=1, required=False, help="How many sequences to return" ) parser.add_argument( "--sync_timeout", type=int, default=600, required=False, help="How long should master process wait for other processes to finish.", ) parser.add_argument("--src_lang", type=str, default=None, required=False) parser.add_argument("--tgt_lang", type=str, default=None, required=False) parser.add_argument( "--prefix", type=str, required=False, default=None, help="will be added to the begininng of src examples" ) parser.add_argument("--fp16", action="store_true") parser.add_argument("--debug", action="store_true") start_time = time.time() args, rest = parser.parse_known_args() generate_kwargs = parse_numeric_n_bool_cl_kwargs(rest) if generate_kwargs and args.local_rank <= 0: print(f"parsed the following generate kwargs: {generate_kwargs}") json_save_dir = Path(args.save_dir + "_tmp") Path(json_save_dir).mkdir(exist_ok=True) # this handles locking. intermediate_files = list(json_save_dir.glob("rank_*.json")) if intermediate_files: raise ValueError(f"Found files at {json_save_dir} please move or remove them.") # In theory, a node could finish and save before another node hits this. If this happens, we can address later. dataset_kwargs = {} if args.src_lang is not None: dataset_kwargs["src_lang"] = args.src_lang if args.tgt_lang is not None: dataset_kwargs["tgt_lang"] = args.tgt_lang Path(args.save_dir).mkdir(exist_ok=True) results, num_replicas = eval_data_dir( args.data_dir, json_save_dir, args.model_name, type_path=args.type_path, bs=args.bs, fp16=args.fp16, task=args.task, local_rank=args.local_rank, n_obs=args.n_obs, max_source_length=args.max_source_length, num_return_sequences=args.num_return_sequences, prefix=args.prefix, dataset_kwargs=dataset_kwargs, **generate_kwargs, ) if args.local_rank <= 0: save_dir = Path(args.save_dir) save_dir.mkdir(exist_ok=True) partial_results = gather_results_from_each_node(num_replicas, json_save_dir, args.sync_timeout) preds = combine_partial_results(partial_results) if args.num_return_sequences > 1: save_path = save_dir.joinpath("pseudolabel_results.json") print(f"Saving aggregated results at {save_path}, intermediate in {json_save_dir}/") save_json(preds, save_path) return tgt_file = Path(args.data_dir).joinpath(args.type_path + ".target") labels = [x.rstrip() for x in open(tgt_file).readlines()][: len(preds)] # Calculate metrics, save metrics, and save _generations.txt calc_bleu = "translation" in args.task score_fn = calculate_bleu if calc_bleu else calculate_rouge metric_name = "bleu" if calc_bleu else "rouge" metrics: Dict = score_fn(preds, labels) metrics["n_obs"] = len(preds) runtime = time.time() - start_time metrics["seconds_per_sample"] = round(runtime / metrics["n_obs"], 4) metrics["n_gpus"] = num_replicas # TODO(@stas00): add whatever metadata to metrics metrics_save_path = save_dir.joinpath(f"{args.type_path}_{metric_name}.json") save_json(metrics, metrics_save_path, indent=None) print(metrics) write_txt_file(preds, save_dir.joinpath(f"{args.type_path}_generations.txt")) if args.debug: write_txt_file(labels, save_dir.joinpath(f"{args.type_path}.target")) else: shutil.rmtree(json_save_dir) def combine_partial_results(partial_results) -> List: """Concatenate partial results into one file, then sort it by id.""" records = [] for partial_result in partial_results: records.extend(partial_result) records = list(sorted(records, key=lambda x: x["id"])) preds = [x["pred"] for x in records] return preds def gather_results_from_each_node(num_replicas, save_dir, timeout) -> List[Dict[str, List]]: # WAIT FOR lots of .json files start_wait = time.time() logger.info("waiting for all nodes to finish") json_data = None while (time.time() - start_wait) < timeout: json_files = list(save_dir.glob("rank_*.json")) if len(json_files) < num_replicas: continue try: # make sure all json files are fully saved json_data = lmap(load_json, json_files) return json_data except JSONDecodeError: continue else: raise TimeoutError("Rank 0 gave up on waiting for other processes") # Unreachable if __name__ == "__main__": # Usage for MT: run_generate()