new tiny model
Browse files- config.json +34 -0
- make-tiny-xlm-roberta.py +140 -0
- pytorch_model.bin +3 -0
- sentencepiece.bpe.model +3 -0
- special_tokens_map.json +1 -0
- tokenizer.json +0 -0
- tokenizer_config.json +1 -0
config.json
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{
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"architectures": [
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"XLMRobertaForCausalLM"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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"d_ff": 256,
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"d_kv": 8,
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"d_model": 64,
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"eos_token_id": 2,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 256,
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"initializer_range": 0.02,
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"intermediate_size": 256,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 64,
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"model_type": "xlm-roberta",
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"num_attention_heads": 2,
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"num_decoder_layers": 2,
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"num_heads": 2,
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"num_hidden_layers": 2,
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"num_layers": 2,
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"output_past": true,
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"relative_attention_num_buckets": 32,
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"torch_dtype": "float16",
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"transformers_version": "4.9.0.dev0",
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 5002
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}
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make-tiny-xlm-roberta.py
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#!/usr/bin/env python
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# coding: utf-8
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# Copyright 2021 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 tiny random model
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#
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# It will be used then as "hf-internal-testing/tiny-xlm-roberta"
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# ***To build from scratch***
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#
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# 1. clone sentencepiece into a parent dir
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# git clone https://github.com/google/sentencepiece
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#
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# 2. create a new repo at https://huggingface.co/new
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# make sure to choose 'hf-internal-testing' as the Owner
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#
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# 3. clone
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# git clone https://huggingface.co/hf-internal-testing/tiny-xlm-roberta
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# cd tiny-xlm-roberta
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# 4. start with some pre-existing script from one of the https://huggingface.co/hf-internal-testing/ tiny model repos, e.g.
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# wget https://huggingface.co/hf-internal-testing/tiny-bert/raw/main/make-tiny-xlm-roberta.py
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# chmod a+x ./make-tiny-xlm-roberta.py
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#
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# 5. edit and re-run this script while fixing it up
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# ./make-tiny-xlm-roberta.py .
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#
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# 6. add/commit/push
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# git add *
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# git commit -m "new tiny model"
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# git push
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# ***To update***
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#
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# 1. clone the existing repo
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# git clone https://huggingface.co/hf-internal-testing/tiny-xlm-roberta
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# cd tiny-xlm-roberta
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#
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# 2. edit and re-run this script after doing whatever changes are needed
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# ./make-tiny-xlm-roberta.py .
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#
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# 3. commit/push
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# git commit -m "new tiny model"
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# git push
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from pathlib import Path
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import json
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import tempfile
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import sys
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import os
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from transformers import XLMRobertaTokenizer, XLMRobertaTokenizerFast, XLMRobertaConfig, XLMRobertaForCausalLM
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# workaround for fast tokenizer protobuffer issue, and it's much faster too!
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os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
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mname_orig = "xlm-roberta-base"
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mname_tiny = "tiny-xlm-roberta"
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tmp_dir = f"/tmp/{mname_tiny}"
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### Tokenizer
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# Shrink the orig vocab to keep things small
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vocab_keep_items = 5000
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vocab_orig_path = f"{tmp_dir}/sentencepiece.bpe.model"
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vocab_short_path = f"{tmp_dir}/spiece-short.model"
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if 1: # set to 0 to skip this after running once to speed things up during tune up
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# HACK: need the sentencepiece source to get sentencepiece_model_pb2, as it doesn't get installed
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sys.path.append("../sentencepiece/python/src/sentencepiece")
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import sentencepiece_model_pb2 as model
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tokenizer_orig = XLMRobertaTokenizer.from_pretrained(mname_orig)
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tokenizer_orig.save_pretrained(tmp_dir)
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with open(vocab_orig_path, 'rb') as f: data = f.read()
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# adapted from https://blog.ceshine.net/post/trim-down-sentencepiece-vocabulary/
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m = model.ModelProto()
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m.ParseFromString(data)
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print(f"Shrinking vocab from original {len(m.pieces)} dict items")
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for i in range(len(m.pieces) - vocab_keep_items): _ = m.pieces.pop()
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print(f"new dict {len(m.pieces)}")
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with open(vocab_short_path, 'wb') as f: f.write(m.SerializeToString())
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m = None
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tokenizer_fast_tiny = XLMRobertaTokenizerFast(vocab_file=vocab_short_path)
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tokenizer_tiny = XLMRobertaTokenizer(vocab_file=vocab_short_path)
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### Config
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config_tiny = XLMRobertaConfig.from_pretrained(mname_orig)
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# remember to update this to the actual config as each model is different and then shrink the numbers
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config_tiny.update(dict(
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vocab_size=vocab_keep_items+12,
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d_ff=256,
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d_kv=8,
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d_model=64,
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hidden_size=256,
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intermediate_size=256,
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max_position_embeddings=64,
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num_attention_heads=2,
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num_decoder_layers=2,
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num_heads=2,
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num_hidden_layers=2,
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num_layers=2,
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relative_attention_num_buckets=32,
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))
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print("New config", config_tiny)
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### Model
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model_tiny = XLMRobertaForCausalLM(config_tiny)
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print(f"{mname_tiny}: num of params {model_tiny.num_parameters()}")
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model_tiny.resize_token_embeddings(len(tokenizer_tiny))
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inputs = tokenizer_tiny("hello", return_tensors="pt")
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outputs = model_tiny(**inputs)
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print("Test with normal tokenizer:", len(outputs.logits[0]))
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inputs = tokenizer_fast_tiny("hello", return_tensors="pt")
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outputs = model_tiny(**inputs)
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print("Test with fast tokenizer:", len(outputs.logits[0]))
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# Save
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model_tiny.half() # makes it smaller
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model_tiny.save_pretrained(".")
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tokenizer_tiny.save_pretrained(".")
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tokenizer_fast_tiny.save_pretrained(".")
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print(f"Generated {mname_tiny}")
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:f27b39bb93be6fc20581fd2cce5f6db55ee7770c428ebac0df7f01d9e7aac311
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size 4334436
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sentencepiece.bpe.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:ef67a60b933d0da430d4b839301301ada0179b0c71102b0eef4567386faa1588
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size 309222
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special_tokens_map.json
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{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true}}
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tokenizer.json
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tokenizer_config.json
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{"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "sp_model_kwargs": {}, "tokenizer_class": "XLMRobertaTokenizer"}
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