[marian] Automate Tatoeba-Challenge conversion (#7709)

.gitignore

@@ -12,6 +12,7 @@ __pycache__/
 tests/fixtures
 logs/
 lightning_logs/
+lang_code_data/
 
 # Distribution / packaging
 .Python

examples/seq2seq/test_tatoeba_conversion.py

@@ -0,0 +1,22 @@
+import tempfile
+import unittest
+
+from transformers.convert_marian_tatoeba_to_pytorch import TatoebaConverter
+from transformers.file_utils import cached_property
+from transformers.testing_utils import slow
+
+
+class TatoebaConversionTester(unittest.TestCase):
+    @cached_property
+    def resolver(self):
+        tmp_dir = tempfile.mkdtemp()
+        return TatoebaConverter(save_dir=tmp_dir)
+
+    @slow
+    def test_resolver(self):
+        self.resolver.convert_models(["heb-eng"])
+
+    @slow
+    def test_model_card(self):
+        content, mmeta = self.resolver.write_model_card("opus-mt-he-en", dry_run=True)
+        assert mmeta["long_pair"] == "heb-eng"

scripts/tatoeba/README.md

@@ -0,0 +1,44 @@
+Setup transformers following instructions in README.md, (I would fork first).
+```bash
+git clone git@github.com:huggingface/transformers.git
+cd transformers
+pip install -e .
+pip install pandas
+```
+
+Get required metadata
+```
+curl https://cdn-datasets.huggingface.co/language_codes/language-codes-3b2.csv  > language-codes-3b2.csv
+curl https://cdn-datasets.huggingface.co/language_codes/iso-639-3.csv > iso-639-3.csv
+```
+
+Install Tatoeba-Challenge repo inside transformers
+```bash
+git clone git@github.com:Helsinki-NLP/Tatoeba-Challenge.git
+```
+
+To convert a few models, call the conversion script from command line:
+```bash
+python src/transformers/convert_marian_tatoeba_to_pytorch.py --models heb-eng eng-heb --save_dir converted
+```
+
+To convert lots of models you can pass your list of Tatoeba model names to `resolver.convert_models` in a python client or script.
+
+```python
+from transformers.convert_marian_tatoeba_to_pytorch import TatoebaConverter
+resolver = TatoebaConverter(save_dir='converted')
+resolver.convert_models(['heb-eng', 'eng-heb'])
+```
+
+
+### Upload converted models
+```bash
+cd converted
+transformers-cli login
+for FILE in *; do transformers-cli upload $FILE; done
+```
+
+
+### Modifications
+- To change naming logic, change the code near `os.rename`. The model card creation code may also need to change.
+- To change model card content, you must modify `TatoebaCodeResolver.write_model_card`

src/transformers/convert_marian_to_pytorch.py

@@ -1,12 +1,11 @@
 import argparse
 import json
 import os
-import shutil
 import socket
 import time
 import warnings
 from pathlib import Path
-from typing import Dict, List, Tuple, Union
+from typing import Dict, List, Union
 from zipfile import ZipFile
 
 import numpy as np
@@ -23,85 +22,6 @@ def remove_suffix(text: str, suffix: str):
     return text  # or whatever
 
 
-def _process_benchmark_table_row(x):
-    fields = lmap(str.strip, x.replace("\t", "").split("|")[1:-1])
-    assert len(fields) == 3
-    return (fields[0], float(fields[1]), float(fields[2]))
-
-
-def process_last_benchmark_table(readme_path) -> List[Tuple[str, float, float]]:
-    md_content = Path(readme_path).open().read()
-    entries = md_content.split("## Benchmarks")[-1].strip().split("\n")[2:]
-    data = lmap(_process_benchmark_table_row, entries)
-    return data
-
-
-def check_if_models_are_dominated(old_repo_path="OPUS-MT-train/models", new_repo_path="Tatoeba-Challenge/models/"):
-    """Make a blacklist for models where we have already ported the same language pair, and the ported model has higher BLEU score."""
-    import pandas as pd
-
-    newest_released, old_reg, released = get_released_df(new_repo_path, old_repo_path)
-
-    short_to_new_bleu = newest_released.set_index("short_pair").bleu
-
-    assert released.groupby("short_pair").pair.nunique().max() == 1
-
-    short_to_long = released.groupby("short_pair").pair.first().to_dict()
-
-    overlap_short = old_reg.index.intersection(released.short_pair.unique())
-    overlap_long = [short_to_long[o] for o in overlap_short]
-    new_reported_bleu = [short_to_new_bleu[o] for o in overlap_short]
-
-    def get_old_bleu(o) -> float:
-        pat = old_repo_path + "/{}/README.md"
-        bm_data = process_last_benchmark_table(pat.format(o))
-        tab = pd.DataFrame(bm_data, columns=["testset", "bleu", "chr-f"])
-        tato_bleu = tab.loc[lambda x: x.testset.str.startswith("Tato")].bleu
-        if tato_bleu.shape[0] > 0:
-            return tato_bleu.iloc[0]
-        else:
-            return np.nan
-
-    old_bleu = [get_old_bleu(o) for o in overlap_short]
-    cmp_df = pd.DataFrame(
-        dict(short=overlap_short, long=overlap_long, old_bleu=old_bleu, new_bleu=new_reported_bleu)
-    ).fillna(-1)
-
-    dominated = cmp_df[cmp_df.old_bleu > cmp_df.new_bleu]
-    whitelist_df = cmp_df[cmp_df.old_bleu <= cmp_df.new_bleu]
-    blacklist = dominated.long.unique().tolist()  # 3 letter codes
-    return whitelist_df, dominated, blacklist
-
-
-def get_released_df(new_repo_path, old_repo_path):
-    import pandas as pd
-
-    released_cols = [
-        "url_base",
-        "pair",  # (ISO639-3/ISO639-5 codes),
-        "short_pair",  # (reduced codes),
-        "chrF2_score",
-        "bleu",
-        "brevity_penalty",
-        "ref_len",
-        "src_name",
-        "tgt_name",
-    ]
-    released = pd.read_csv(f"{new_repo_path}/released-models.txt", sep="\t", header=None).iloc[:-1]
-    released.columns = released_cols
-    old_reg = make_registry(repo_path=old_repo_path)
-    old_reg = pd.DataFrame(old_reg, columns=["id", "prepro", "url_model", "url_test_set"])
-    assert old_reg.id.value_counts().max() == 1
-    old_reg = old_reg.set_index("id")
-    released["fname"] = released["url_base"].apply(
-        lambda x: remove_suffix(remove_prefix(x, "https://object.pouta.csc.fi/Tatoeba-Challenge/opus"), ".zip")
-    )
-    released["2m"] = released.fname.str.startswith("2m")
-    released["date"] = pd.to_datetime(released["fname"].apply(lambda x: remove_prefix(remove_prefix(x, "2m-"), "-")))
-    newest_released = released.dsort("date").drop_duplicates(["short_pair"], keep="first")
-    return newest_released, old_reg, released
-
-
 def remove_prefix(text: str, prefix: str):
     if text.startswith(prefix):
         return text[len(prefix) :]
@@ -183,7 +103,11 @@ def find_model_file(dest_dir):  # this one better
 
 
 # Group Names Logic: change long opus model names to something shorter, like opus-mt-en-ROMANCE
-ROM_GROUP = "fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO+es_EC+es_ES+es_GT+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR+pt_PT+gl+lad+an+mwl+it+it_IT+co+nap+scn+vec+sc+ro+la"
+ROM_GROUP = (
+    "fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO+es_EC+es_ES+es_GT"
+    "+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR+pt_PT+gl+lad+an+mwl+it+it_IT+co"
+    "+nap+scn+vec+sc+ro+la"
+)
 GROUPS = [
     ("cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh", "ZH"),
     (ROM_GROUP, "ROMANCE"),
@@ -221,13 +145,15 @@ ORG_NAME = "Helsinki-NLP/"
 
 
 def convert_opus_name_to_hf_name(x):
+    """For OPUS-MT-Train/ DEPRECATED"""
     for substr, grp_name in GROUPS:
         x = x.replace(substr, grp_name)
     return x.replace("+", "_")
 
 
 def convert_hf_name_to_opus_name(hf_model_name):
-    """Relies on the assumption that there are no language codes like pt_br in models that are not in GROUP_TO_OPUS_NAME."""
+    """Relies on the assumption that there are no language codes like pt_br in models that are not in
+    GROUP_TO_OPUS_NAME."""
     hf_model_name = remove_prefix(hf_model_name, ORG_NAME)
     if hf_model_name in GROUP_TO_OPUS_NAME:
         opus_w_prefix = GROUP_TO_OPUS_NAME[hf_model_name]
@@ -247,8 +173,9 @@ def get_system_metadata(repo_root):
     )
 
 
-front_matter = """---
-language: {}
+FRONT_MATTER_TEMPLATE = """---
+language:
+{}
 tags:
 - translation
 
@@ -256,11 +183,13 @@ license: apache-2.0
 ---
 
 """
+DEFAULT_REPO = "Tatoeba-Challenge"
+DEFAULT_MODEL_DIR = os.path.join(DEFAULT_REPO, "models")
 
 
 def write_model_card(
     hf_model_name: str,
-    repo_root="OPUS-MT-train",
+    repo_root=DEFAULT_REPO,
     save_dir=Path("marian_converted"),
     dry_run=False,
     extra_metadata={},
@@ -294,7 +223,10 @@ def write_model_card(
 
     # combine with opus markdown
 
-    extra_markdown = f"### {hf_model_name}\n\n* source group: {metadata['src_name']} \n* target group: {metadata['tgt_name']} \n*  OPUS readme: [{opus_name}]({readme_url})\n"
+    extra_markdown = (
+        f"### {hf_model_name}\n\n* source group: {metadata['src_name']} \n* target group: "
+        f"{metadata['tgt_name']} \n*  OPUS readme: [{opus_name}]({readme_url})\n"
+    )
 
     content = opus_readme_path.open().read()
     content = content.split("\n# ")[-1]  # Get the lowest level 1 header in the README -- the most recent model.
@@ -302,7 +234,7 @@ def write_model_card(
     print(splat[3])
     content = "*".join(splat)
     content = (
-        front_matter.format(metadata["src_alpha2"])
+        FRONT_MATTER_TEMPLATE.format(metadata["src_alpha2"])
         + extra_markdown
         + "\n* "
         + content.replace("download", "download original weights")
@@ -323,48 +255,6 @@ def write_model_card(
     return content, metadata
 
 
-def get_clean_model_id_mapping(multiling_model_ids):
-    return {x: convert_opus_name_to_hf_name(x) for x in multiling_model_ids}
-
-
-def expand_group_to_two_letter_codes(grp_name):
-    raise NotImplementedError()
-
-
-def get_two_letter_code(three_letter_code):
-    raise NotImplementedError()
-    # return two_letter_code
-
-
-def get_tags(code, ref_name):
-    if len(code) == 2:
-        assert "languages" not in ref_name, f"{code}: {ref_name}"
-        return [code], False
-    elif "languages" in ref_name:
-        group = expand_group_to_two_letter_codes(code)
-        group.append(code)
-        return group, True
-    else:  # zho-> zh
-        raise ValueError(f"Three letter monolingual code: {code}")
-
-
-def resolve_lang_code(r):
-    """R is a row in ported"""
-    short_pair = r.short_pair
-    src, tgt = short_pair.split("-")
-    src_tags, src_multilingual = get_tags(src, r.src_name)
-    assert isinstance(src_tags, list)
-    tgt_tags, tgt_multilingual = get_tags(src, r.tgt_name)
-    assert isinstance(tgt_tags, list)
-    if src_multilingual:
-        src_tags.append("multilingual_src")
-    if tgt_multilingual:
-        tgt_tags.append("multilingual_tgt")
-    return src_tags + tgt_tags
-
-    # process target
-
-
 def make_registry(repo_path="Opus-MT-train/models"):
     if not (Path(repo_path) / "fr-en" / "README.md").exists():
         raise ValueError(
@@ -382,36 +272,25 @@ def make_registry(repo_path="Opus-MT-train/models"):
     return [(k, v["pre-processing"], v["download"], v["download"][:-4] + ".test.txt") for k, v in results.items()]
 
 
-def make_tatoeba_registry(repo_path="Tatoeba-Challenge/models"):
-    if not (Path(repo_path) / "zho-eng" / "README.md").exists():
-        raise ValueError(
-            f"repo_path:{repo_path} does not exist: "
-            "You must run: git clone git@github.com:Helsinki-NLP/Tatoeba-Challenge.git before calling."
-        )
-    results = {}
-    for p in Path(repo_path).iterdir():
-        if len(p.name) != 7:
-            continue
-        lns = list(open(p / "README.md").readlines())
-        results[p.name] = _parse_readme(lns)
-    return [(k, v["pre-processing"], v["download"], v["download"][:-4] + ".test.txt") for k, v in results.items()]
-
-
-def convert_all_sentencepiece_models(model_list=None, repo_path=None):
+def convert_all_sentencepiece_models(model_list=None, repo_path=None, dest_dir=Path("marian_converted")):
     """Requires 300GB"""
     save_dir = Path("marian_ckpt")
-    dest_dir = Path("marian_converted")
+    dest_dir = Path(dest_dir)
     dest_dir.mkdir(exist_ok=True)
+    save_paths = []
     if model_list is None:
         model_list: list = make_registry(repo_path=repo_path)
     for k, prepro, download, test_set_url in tqdm(model_list):
         if "SentencePiece" not in prepro:  # dont convert BPE models.
             continue
-        if not os.path.exists(save_dir / k / "pytorch_model.bin"):
+        if not os.path.exists(save_dir / k):
             download_and_unzip(download, save_dir / k)
         pair_name = convert_opus_name_to_hf_name(k)
         convert(save_dir / k, dest_dir / f"opus-mt-{pair_name}")
 
+        save_paths.append(dest_dir / f"opus-mt-{pair_name}")
+    return save_paths
+
 
 def lmap(f, x) -> List:
     return list(map(f, x))
@@ -493,15 +372,6 @@ def add_special_tokens_to_vocab(model_dir: Path) -> None:
     save_tokenizer_config(model_dir)
 
 
-def save_tokenizer(self, save_directory):
-    dest = Path(save_directory)
-    src_path = Path(self.init_kwargs["source_spm"])
-
-    for dest_name in {"source.spm", "target.spm", "tokenizer_config.json"}:
-        shutil.copyfile(src_path.parent / dest_name, dest / dest_name)
-    save_json(self.encoder, dest / "vocab.json")
-
-
 def check_equal(marian_cfg, k1, k2):
     v1, v2 = marian_cfg[k1], marian_cfg[k2]
     assert v1 == v2, f"hparams {k1},{k2} differ: {v1} != {v2}"
@@ -698,14 +568,14 @@ def convert(source_dir: Path, dest_dir):
 
     add_special_tokens_to_vocab(source_dir)
     tokenizer = MarianTokenizer.from_pretrained(str(source_dir))
-    save_tokenizer(tokenizer, dest_dir)
+    tokenizer.save_pretrained(dest_dir)
 
     opus_state = OpusState(source_dir)
     assert opus_state.cfg["vocab_size"] == len(
         tokenizer.encoder
     ), f"Original vocab size {opus_state.cfg['vocab_size']} and new vocab size {len(tokenizer.encoder)} mismatched"
     # save_json(opus_state.cfg, dest_dir / "marian_original_config.json")
-    # ^^ Save human readable marian config for debugging
+    # ^^ Uncomment to save human readable marian config for debugging
 
     model = opus_state.load_marian_model()
     model = model.half()
@@ -732,15 +602,11 @@ def unzip(zip_path: str, dest_dir: str) -> None:
 
 if __name__ == "__main__":
     """
-    To bulk convert, run
-    >>> from transformers.convert_marian_to_pytorch import make_tatoeba_registry, convert_all_sentencepiece_models
-    >>> reg = make_tatoeba_registry()
-    >>> convert_all_sentencepiece_models(model_list=reg)  # saves to marian_converted
-    (bash) aws s3 sync marian_converted s3://models.huggingface.co/bert/Helsinki-NLP/ --dryrun
+    Tatoeba conversion instructions in scripts/tatoeba/README.md
     """
     parser = argparse.ArgumentParser()
     # Required parameters
-    parser.add_argument("--src", type=str, help="path to marian model dir", default="en-de")
+    parser.add_argument("--src", type=str, help="path to marian model sub dir", default="en-de")
     parser.add_argument("--dest", type=str, default=None, help="Path to the output PyTorch model.")
     args = parser.parse_args()