[s2s] distributed eval cleanup (#7110)

examples/seq2seq/run_distributed_eval.py

@@ -1,5 +1,4 @@
 import argparse
-import warnings
 from logging import getLogger
 from pathlib import Path
 from typing import Dict
@@ -18,6 +17,7 @@ try:
 except ImportError:
     from utils import Seq2SeqDataset, parse_numeric_cl_kwargs, save_json, use_task_specific_params
 
+
 DEFAULT_DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 
 
@@ -51,6 +51,8 @@ def eval_data_dir(
     tokenizer = AutoTokenizer.from_pretrained(model_name)
     logger.info(f"Inferred tokenizer type: {tokenizer.__class__}")  # if this is wrong, check config.model_type.
     use_task_specific_params(model, task)  # update config with task specific params
+    if max_source_length is None:
+        max_source_length = tokenizer.model_max_length
     ds = Seq2SeqDataset(
         tokenizer,
         data_dir,
@@ -97,9 +99,11 @@ def run_generate():
         default="sshleifer/distilbart-xsum-12-3",
     )
     parser.add_argument("--save_dir", type=str, help="where to save", default="tmp_gen")
-    parser.add_argument("--prefix", type=str, default="test", help="which subset to evaluate typically train/val/test")
+    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("--reference_path", type=str, required=False, help="like cnn_dm/test.target")
-    parser.add_argument("--score_path", type=str, required=False, default="metrics.json", help="where to save metrics")
     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(
@@ -113,24 +117,23 @@ def run_generate():
     parser.add_argument("--save_source", action="store_true")
 
     args, rest = parser.parse_known_args()
-    parsed = parse_numeric_cl_kwargs(rest)
+    generate_kwargs = parse_numeric_cl_kwargs(rest)
-    if parsed:
+    if generate_kwargs:
-        print(f"parsed the following generate kwargs: {parsed}")
+        print(f"parsed the following generate kwargs: {generate_kwargs}")
     Path(args.save_dir).mkdir(exist_ok=True)
-    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.")
     eval_data_dir(
         args.input_path,
         args.save_dir,
         args.model_name,
-        prefix=args.prefix,
+        type_path=args.type_path,
         batch_size=args.bs,
         fp16=args.fp16,
         task=args.task,
         local_rank=args.local_rank,
         n_obs=args.n_obs,
         save_source=args.save_source,
-        **parsed,
+        max_source_length=args.max_source_length,
+        **generate_kwargs,
     )
 
 

examples/seq2seq/utils.py

@@ -98,7 +98,8 @@ class AbstractSeq2SeqDataset(Dataset):
         self.max_target_length = max_target_length
         assert min(self.src_lens) > 0, f"found empty line in {self.src_file}"
         self.tokenizer = tokenizer
-        self.prefix = prefix
+        self.prefix = prefix if prefix is not None else ""
+
         if n_obs is not None:
             self.src_lens = self.src_lens[:n_obs]
         self.pad_token_id = self.tokenizer.pad_token_id