[s2s] run_eval.py QOL improvements and cleanup(#6746)

examples/seq2seq/run_eval.py

@@ -1,6 +1,10 @@
 import argparse
 import json
+import time
+import warnings
+from logging import getLogger
 from pathlib import Path
+from typing import Dict, List
 
 import torch
 from tqdm import tqdm
@@ -8,10 +12,12 @@ from tqdm import tqdm
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
 
+logger = getLogger(__name__)
+
 try:
-    from .utils import calculate_bleu, calculate_rouge, trim_batch, use_task_specific_params
+    from .utils import calculate_bleu, calculate_rouge, use_task_specific_params
 except ImportError:
-    from utils import calculate_bleu, calculate_rouge, trim_batch, use_task_specific_params
+    from utils import calculate_bleu, calculate_rouge, use_task_specific_params
 
 DEFAULT_DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 
@@ -23,7 +29,7 @@ def chunks(lst, n):
 
 
 def generate_summaries_or_translations(
-    examples: list,
+    examples: List[str],
     out_file: str,
     model_name: str,
     batch_size: int = 8,
@@ -31,36 +37,39 @@ def generate_summaries_or_translations(
     fp16=False,
     task="summarization",
     decoder_start_token_id=None,
-    **gen_kwargs,
-) -> None:
+    **generate_kwargs,
+) -> Dict:
+    """Save model.generate results to <out_file>, and return how long it took."""
     fout = Path(out_file).open("w", encoding="utf-8")
     model_name = str(model_name)
     model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)
     if fp16:
         model = model.half()
-    if decoder_start_token_id is None:
-        decoder_start_token_id = gen_kwargs.pop("decoder_start_token_id", None)
 
     tokenizer = AutoTokenizer.from_pretrained(model_name)
+    logger.info(f"Inferred tokenizer type: {tokenizer.__class__}")  # if this is wrong, check config.model_type.
 
-    # update config with summarization specific params
+    start_time = time.time()
+    # update config with task specific params
     use_task_specific_params(model, task)
-
-    for batch in tqdm(list(chunks(examples, batch_size))):
+    for examples_chunk in tqdm(list(chunks(examples, batch_size))):
         if "t5" in model_name:
-            batch = [model.config.prefix + text for text in batch]
-        batch = tokenizer(batch, return_tensors="pt", truncation=True, padding="max_length").to(device)
-        input_ids, attention_mask = trim_batch(**batch, pad_token_id=tokenizer.pad_token_id)
+            examples_chunk = [model.config.prefix + text for text in examples_chunk]
+        batch = tokenizer(examples_chunk, return_tensors="pt", truncation=True, padding="longest").to(device)
         summaries = model.generate(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
+            input_ids=batch.input_ids,
+            attention_mask=batch.attention_mask,
             decoder_start_token_id=decoder_start_token_id,
-            **gen_kwargs,
+            **generate_kwargs,
         )
         dec = tokenizer.batch_decode(summaries, skip_special_tokens=True, clean_up_tokenization_spaces=False)
         for hypothesis in dec:
             fout.write(hypothesis + "\n")
             fout.flush()
+    fout.close()
+    runtime = time.time() - start_time
+    n_obs = len(examples)
+    return dict(n_obs=n_obs, runtime=runtime, seconds_per_sample=round(runtime / n_obs, 4))
 
 
 def run_generate():
@@ -70,7 +79,13 @@ def run_generate():
     parser.add_argument("save_path", type=str, help="where to save summaries")
 
     parser.add_argument("--reference_path", type=str, required=False, help="like cnn_dm/test_reference_summaries.txt")
-    parser.add_argument("--score_path", type=str, required=False, help="where to save the rouge score in json format")
+    parser.add_argument(
+        "--score_path",
+        type=str,
+        required=False,
+        default="metrics.json",
+        help="where to save the rouge score in json format",
+    )
     parser.add_argument("--device", type=str, required=False, default=DEFAULT_DEVICE, help="cuda, cuda:1, cpu etc.")
     parser.add_argument("--task", type=str, default="summarization", help="typically translation or summarization")
     parser.add_argument("--bs", type=int, default=8, required=False, help="batch size")
@@ -79,7 +94,7 @@ def run_generate():
         type=int,
         default=None,
         required=False,
-        help="decoder_start_token_id (otherwise will look at config)",
+        help="Defaults to using config",
     )
     parser.add_argument(
         "--n_obs", type=int, default=-1, required=False, help="How many observations. Defaults to all."
@@ -90,7 +105,9 @@ def run_generate():
     if args.n_obs > 0:
         examples = examples[: args.n_obs]
     Path(args.save_path).parent.mkdir(exist_ok=True)
-    generate_summaries_or_translations(
+    if args.reference_path is None and Path(args.score_path).exists():
+        warnings.warn(f"score_path {args.score_path} will be overwritten unless you type ctrl-c.")
+    runtime_metrics = generate_summaries_or_translations(
         examples,
         args.save_path,
         args.model_name,
@@ -107,9 +124,10 @@ def run_generate():
     output_lns = [x.rstrip() for x in open(args.save_path).readlines()]
     reference_lns = [x.rstrip() for x in open(args.reference_path).readlines()][: len(output_lns)]
     scores: dict = score_fn(output_lns, reference_lns)
+    scores.update(runtime_metrics)
     print(scores)
     if args.score_path is not None:
-        json.dump(scores, open(args.score_path, "w+"))
+        json.dump(scores, open(args.score_path, "w"))
     return scores
 
 

examples/seq2seq/test_seq2seq_examples.py

@@ -252,13 +252,24 @@ class TestSummarizationDistiller(unittest.TestCase):
 
 
 @pytest.mark.parametrize(["model"], [pytest.param(T5_TINY), pytest.param(BART_TINY), pytest.param(MBART_TINY)])
-def test_run_eval_bart(model):
+def test_run_eval(model):
     input_file_name = Path(tempfile.mkdtemp()) / "utest_input.source"
     output_file_name = input_file_name.parent / "utest_output.txt"
     assert not output_file_name.exists()
     articles = [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
     _dump_articles(input_file_name, articles)
-    testargs = ["run_eval.py", model, str(input_file_name), str(output_file_name)]  # TODO: test score_path
+    score_path = str(Path(tempfile.mkdtemp()) / "scores.json")
+    task = "translation_en_to_de" if model == T5_TINY else "summarization"
+    testargs = [
+        "run_eval.py",
+        model,
+        str(input_file_name),
+        str(output_file_name),
+        "--score_path",
+        score_path,
+        "--task",
+        task,
+    ]
     with patch.object(sys, "argv", testargs):
         run_generate()
         assert Path(output_file_name).exists()