stas
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2d61f25
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add the script

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  1. fsmt-make-super-tiny-model.py +87 -0
fsmt-make-super-tiny-model.py ADDED
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+ #!/usr/bin/env python
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+ # coding: utf-8
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+ # Copyright 2020 The HuggingFace Team. All rights reserved.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+
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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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+
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+ from pathlib import Path
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+ import json
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+ import tempfile
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+
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+ from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
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+ from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
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+
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+ mname_tiny = "tiny-wmt19-en-ru"
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+
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+ # Build
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ # Test
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+ batch = tokenizer(["Making tiny model"], return_tensors="pt")
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+ outputs = tiny_model(**batch)
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+
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+ print("test output:", len(outputs.logits[0]))
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+
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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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+
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+ print(f"Generated {mname_tiny}")
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+
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+ # Upload
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+ # transformers-cli upload tiny-wmt19-en-ru