transformers

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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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# 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 model through reduction of a normal pre-trained model, but keeping the
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# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
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# This gives ~3MB in total for all files.
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#
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# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
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#
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#
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# It will be used then as "stas/tiny-wmt19-en-de"
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# Build
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from transformers import FSMTConfig, FSMTForConditionalGeneration, FSMTTokenizer
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mname = "facebook/wmt19-en-de"
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tokenizer = FSMTTokenizer.from_pretrained(mname)
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# get the correct vocab sizes, etc. from the master model
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config = FSMTConfig.from_pretrained(mname)
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config.update({
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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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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(["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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mname_tiny = "tiny-wmt19-en-de"
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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-de
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