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3095ee9dab
* Tokenizers should be framework agnostic * Run the slow tests * Not testing * Fix documentation * Apply suggestions from code review Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
75 lines
2.6 KiB
Python
Executable File
75 lines
2.6 KiB
Python
Executable File
#!/usr/bin/env python
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# coding: utf-8
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# This script creates a super tiny model that is useful inside tests, when we just want to test that
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# the machinery works, without needing to the check the quality of the outcomes.
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#
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# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
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# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
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# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
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# The latter is done by `fsmt-make-super-tiny-model.py`.
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#
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# It will be used then as "stas/tiny-wmt19-en-ru"
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from pathlib import Path
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import json
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import tempfile
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from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
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from transformers.tokenization_fsmt import VOCAB_FILES_NAMES
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mname_tiny = "tiny-wmt19-en-ru"
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# Build
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# borrowed from a test
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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>", ]
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vocab_tokens = dict(zip(vocab, range(len(vocab))))
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merges = ["l o 123", "lo w 1456", "e r</w> 1789", ""]
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with tempfile.TemporaryDirectory() as tmpdirname:
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build_dir = Path(tmpdirname)
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src_vocab_file = build_dir / VOCAB_FILES_NAMES["src_vocab_file"]
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tgt_vocab_file = build_dir / VOCAB_FILES_NAMES["tgt_vocab_file"]
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merges_file = build_dir / VOCAB_FILES_NAMES["merges_file"]
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with open(src_vocab_file, "w") as fp: fp.write(json.dumps(vocab_tokens))
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with open(tgt_vocab_file, "w") as fp: fp.write(json.dumps(vocab_tokens))
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with open(merges_file, "w") as fp : fp.write("\n".join(merges))
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tokenizer = FSMTTokenizer(
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langs=["en", "ru"],
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src_vocab_size = len(vocab),
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tgt_vocab_size = len(vocab),
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src_vocab_file=src_vocab_file,
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tgt_vocab_file=tgt_vocab_file,
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merges_file=merges_file,
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)
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config = FSMTConfig(
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langs=['ru', 'en'],
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src_vocab_size=1000, tgt_vocab_size=1000,
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d_model=4,
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encoder_layers=1, decoder_layers=1,
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encoder_ffn_dim=4, decoder_ffn_dim=4,
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encoder_attention_heads=1, decoder_attention_heads=1,
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)
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tiny_model = FSMTForConditionalGeneration(config)
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print(f"num of params {tiny_model.num_parameters()}")
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# Test
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batch = tokenizer.prepare_seq2seq_batch(["Making tiny model"], return_tensors="pt")
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outputs = tiny_model(**batch)
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print("test output:", len(outputs.logits[0]))
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# Save
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tiny_model.half() # makes it smaller
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tiny_model.save_pretrained(mname_tiny)
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tokenizer.save_pretrained(mname_tiny)
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print(f"Generated {mname_tiny}")
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# Upload
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# transformers-cli upload tiny-wmt19-en-ru
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