| #!/usr/bin/env python | |
| # coding: utf-8 | |
| # Copyright 2021 The HuggingFace Team. All rights reserved. | |
| # | |
| # 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 tiny random model | |
| # | |
| # It will be used then as "hf-internal-testing/tiny-xlm-roberta" | |
| # ***To build from scratch*** | |
| # | |
| # 1. clone sentencepiece into a parent dir | |
| # git clone https://github.com/google/sentencepiece | |
| # | |
| # 2. create a new repo at https://huggingface.co/new | |
| # make sure to choose 'hf-internal-testing' as the Owner | |
| # | |
| # 3. clone | |
| # git clone https://huggingface.co/hf-internal-testing/tiny-xlm-roberta | |
| # cd tiny-xlm-roberta | |
| # | |
| # 4. start with some pre-existing script from one of the https://huggingface.co/hf-internal-testing/ tiny model repos, e.g. | |
| # wget https://huggingface.co/hf-internal-testing/tiny-albert/raw/main/make-tiny-albert.py | |
| # chmod a+x ./make-tiny-albert.py | |
| # mv ./make-tiny-albert.py ./make-tiny-xlm-roberta.py | |
| # | |
| # 5. automatically rename things from the old names to new ones | |
| # perl -pi -e 's|Albert|XLMRoberta|g' make-tiny-xlm-roberta.py | |
| # perl -pi -e 's|albert|xlm-roberta|g' make-tiny-xlm-roberta.py | |
| # | |
| # 6. edit and re-run this script while fixing it up | |
| # ./make-tiny-xlm-roberta.py | |
| # | |
| # 7. add/commit/push | |
| # git add * | |
| # git commit -m "new tiny model" | |
| # git push | |
| # ***To update*** | |
| # | |
| # 1. clone the existing repo | |
| # git clone https://huggingface.co/hf-internal-testing/tiny-xlm-roberta | |
| # cd tiny-xlm-roberta | |
| # | |
| # 2. edit and re-run this script after doing whatever changes are needed | |
| # ./make-tiny-xlm-roberta.py | |
| # | |
| # 3. commit/push | |
| # git commit -m "new tiny model" | |
| # git push | |
| import sys | |
| import os | |
| # workaround for fast tokenizer protobuf issue, and it's much faster too! | |
| os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python" | |
| from transformers import XLMRobertaTokenizerFast, XLMRobertaConfig, XLMRobertaForCausalLM | |
| mname_orig = "xlm-roberta-base" | |
| mname_tiny = "tiny-xlm-roberta" | |
| ### Tokenizer | |
| # Shrink the orig vocab to keep things small | |
| vocab_keep_items = 5000 | |
| tmp_dir = f"/tmp/{mname_tiny}" | |
| vocab_orig_path = f"{tmp_dir}/sentencepiece.bpe.model" | |
| vocab_short_path = f"{tmp_dir}/spiece-short.model" | |
| if 1: # set to 0 to skip this after running once to speed things up during tune up | |
| # HACK: need the sentencepiece source to get sentencepiece_model_pb2, as it doesn't get installed | |
| sys.path.append("../sentencepiece/python/src/sentencepiece") | |
| import sentencepiece_model_pb2 as model | |
| tokenizer_orig = XLMRobertaTokenizerFast.from_pretrained(mname_orig) | |
| tokenizer_orig.save_pretrained(tmp_dir) | |
| with open(vocab_orig_path, 'rb') as f: data = f.read() | |
| # adapted from https://blog.ceshine.net/post/trim-down-sentencepiece-vocabulary/ | |
| m = model.ModelProto() | |
| m.ParseFromString(data) | |
| print(f"Shrinking vocab from original {len(m.pieces)} dict items") | |
| for i in range(len(m.pieces) - vocab_keep_items): _ = m.pieces.pop() | |
| print(f"new dict {len(m.pieces)}") | |
| with open(vocab_short_path, 'wb') as f: f.write(m.SerializeToString()) | |
| m = None | |
| tokenizer_fast_tiny = XLMRobertaTokenizerFast(vocab_file=vocab_short_path) | |
| ### Config | |
| config_tiny = XLMRobertaConfig.from_pretrained(mname_orig) | |
| print(config_tiny) | |
| # remember to update this to the actual config as each model is different and then shrink the numbers | |
| config_tiny.update(dict( | |
| vocab_size=vocab_keep_items+12, | |
| d_ff=256, | |
| d_kv=8, | |
| d_model=64, | |
| hidden_size=256, | |
| intermediate_size=256, | |
| max_position_embeddings=64, | |
| num_attention_heads=2, | |
| num_decoder_layers=2, | |
| num_heads=2, | |
| num_hidden_layers=2, | |
| num_layers=2, | |
| relative_attention_num_buckets=32, | |
| )) | |
| print("New config", config_tiny) | |
| ### Model | |
| model_tiny = XLMRobertaForCausalLM(config_tiny) | |
| print(f"{mname_tiny}: num of params {model_tiny.num_parameters()}") | |
| model_tiny.resize_token_embeddings(len(tokenizer_fast_tiny)) | |
| # Test | |
| inputs = tokenizer_fast_tiny("hello", return_tensors="pt") | |
| outputs = model_tiny(**inputs) | |
| print("Test with fast tokenizer:", len(outputs.logits[0])) | |
| # Save | |
| model_tiny.half() # makes it smaller | |
| model_tiny.save_pretrained(".") | |
| tokenizer_fast_tiny.save_pretrained(".") | |
| readme = "README.md" | |
| if not os.path.exists(readme): | |
| with open(readme, "w") as f: | |
| f.write(f"This is a {mname_tiny} random model to be used for basic testing.\n") | |
| print(f"Generated {mname_tiny}") | |