usrbinenv python coding utf8 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 this script creates a super tiny model that is useful inside tests when we just want to test that the machinery works without needing to the check the quality of the outcomes this version creates a tiny vocab first and then a tiny model so the outcome is truly tiny all files 60kb as compared to taking a fullsize model reducing to the minimum its layers and emb dimensions but keeping the full vocab merges files leading to 3mb in total for all files the latter is done by fsmtmakesupertinymodel py it will be used then as stastinywmt19enru build borrowed from a test test save upload transformerscli upload tinywmt19enru usr bin env python coding utf 8 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 this script creates a super tiny model that is useful inside tests when we just want to test that the machinery works without needing to the check the quality of the outcomes this version creates a tiny vocab first and then a tiny model so the outcome is truly tiny all files 60kb as compared to taking a full size model reducing to the minimum its layers and emb dimensions but keeping the full vocab merges files leading to 3mb in total for all files the latter is done by fsmt make super tiny model py it will be used then as stas tiny wmt19 en ru build borrowed from a test test save makes it smaller upload transformers cli upload tiny wmt19 en ru
import json
import tempfile
from pathlib import Path

from transformers import FSMTConfig, FSMTForConditionalGeneration, FSMTTokenizer
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES


mname_tiny = "tiny-wmt19-en-ru"




vocab = [ "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "w</w>", "r</w>", "t</w>", "lo", "low", "er</w>", "low</w>", "lowest</w>", "newer</w>", "wider</w>", "<unk>", ]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["l o 123", "lo w 1456", "e r</w> 1789", ""]

with tempfile.TemporaryDirectory() as tmpdirname:
    build_dir = Path(tmpdirname)
    src_vocab_file = build_dir / VOCAB_FILES_NAMES["src_vocab_file"]
    tgt_vocab_file = build_dir / VOCAB_FILES_NAMES["tgt_vocab_file"]
    merges_file = build_dir / VOCAB_FILES_NAMES["merges_file"]
    with open(src_vocab_file, "w") as fp: fp.write(json.dumps(vocab_tokens))
    with open(tgt_vocab_file, "w") as fp: fp.write(json.dumps(vocab_tokens))
    with open(merges_file, "w") as fp   : fp.write("\n".join(merges))

    tokenizer = FSMTTokenizer(
        langs=["en", "ru"],
        src_vocab_size = len(vocab),
        tgt_vocab_size = len(vocab),
        src_vocab_file=src_vocab_file,
        tgt_vocab_file=tgt_vocab_file,
        merges_file=merges_file,
    )

config = FSMTConfig(
    langs=['ru', 'en'],
    src_vocab_size=1000, tgt_vocab_size=1000,
    d_model=4,
    encoder_layers=1, decoder_layers=1,
    encoder_ffn_dim=4, decoder_ffn_dim=4,
    encoder_attention_heads=1, decoder_attention_heads=1,
)

tiny_model = FSMTForConditionalGeneration(config)
print(f"num of params {tiny_model.num_parameters()}")


batch = tokenizer(["Making tiny model"], return_tensors="pt")
outputs = tiny_model(**batch)

print("test output:", len(outputs.logits[0]))


tiny_model.half() 
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f"Generated {mname_tiny}")