stas
HF staff
commited on
Commit
2bd01f7
1 Parent(s): 37a0fb2

tiny mt5 random model

Browse files
config.json ADDED
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+ {
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+ "architectures": [
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+ "MT5ForConditionalGeneration"
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+ ],
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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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+ "decoder_start_token_id": 0,
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+ "dropout_rate": 0.1,
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+ "eos_token_id": 1,
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+ "feed_forward_proj": "gated-gelu",
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+ "initializer_factor": 1.0,
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+ "is_encoder_decoder": true,
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+ "layer_norm_epsilon": 1e-06,
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+ "model_type": "mt5",
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+ "num_decoder_layers": 8,
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+ "num_heads": 4,
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+ "num_layers": 8,
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+ "pad_token_id": 0,
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+ "relative_attention_num_buckets": 32,
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+ "tie_word_embeddings": false,
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+ "tokenizer_class": "T5Tokenizer",
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+ "transformers_version": "4.6.0.dev0",
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+ "use_cache": true,
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+ "vocab_size": 5100
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+ }
mt5-make-tiny-model.py ADDED
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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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+
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+ # This script creates a smallish random model, with a few layers to test things like MP/PP, where
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+ # tiny and tiner models are too too small
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+ #
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+ # It will be used then as "stas/mt5-tiny-random"
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+
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+ # To build:
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+ # 1. clone sentencepiece into this dir
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+ # git clone https://github.com/google/sentencepiece
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+ #
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+ # 2. run this script
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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 MT5Tokenizer, MT5TokenizerFast, MT5Config, MT5ForConditionalGeneration
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+ from transformers.models.t5.tokenization_t5 import VOCAB_FILES_NAMES
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+
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+ mname_from = "google/mt5-small"
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+ mname_very_small = "mt5-tiny-random"
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+
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+ tokenizer = MT5Tokenizer.from_pretrained(mname_from)
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+ config = MT5Config.from_pretrained(mname_from)
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+ #tokenizer_fast = MT5TokenizerFast.from_pretrained(mname_from)
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+
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+ # Shrink the vocab of mt5-small
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+ import sys
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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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+
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+ tmp_dir = "/tmp/mt5-small"
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+ tokenizer.save_pretrained(tmp_dir)
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+ file = tmp_dir + "/spiece.model"
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+ with open(file, 'rb') as f: data = f.read()
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+
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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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+
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+ keep_items = 5000
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+
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+ print("Shrinking vocab")
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+ print(f"original dict {len(m.pieces)}")
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+ for i in range(len(m.pieces)-keep_items): _ = m.pieces.pop()
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+ print(f"new dict {len(m.pieces)}")
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+
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+ with open(tmp_dir + "/spiece-short.model", 'wb') as f:
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+ f.write(m.SerializeToString())
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+
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+ tokenizer = MT5Tokenizer(vocab_file=tmp_dir + "/spiece-short.model")
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+
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+ config.update(dict(
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+ vocab_size=keep_items+12,
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+ d_model=64,
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+ d_ff=256,
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+ d_kv=8,
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+ num_layers=8,
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+ num_decoder_layers=8,
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+ num_heads=4,
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+ relative_attention_num_buckets=32,
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+ ))
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+ print("new config", config)
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+
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+ very_small_model = MT5ForConditionalGeneration(config)
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+ print(f"num of params {very_small_model.num_parameters()}")
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+ very_small_model.resize_token_embeddings(len(tokenizer))
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+
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+ # Test
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+ src_texts = ["A long paragraph for summarization.", "Another paragraph for summarization."]
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+ tgt_texts = ["Summary of the text.", "Another summary."]
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+
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+ batch = tokenizer.prepare_seq2seq_batch(src_texts, tgt_texts, return_tensors="pt")
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+ outputs = very_small_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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+ very_small_model.half() # makes it smaller
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+ very_small_model.save_pretrained(mname_very_small)
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+ config.save_pretrained(mname_very_small)
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+ tokenizer.save_pretrained(mname_very_small)
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+ #tokenizer_fast.save_pretrained(mname_very_small)
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+
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+ print(f"Generated {mname_very_small}")
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+
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+ # Upload
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+ # transformers-cli repo create mt5-tiny-random
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+ # clone and add files
pytorch_model.bin ADDED
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+ size 3342941
special_tokens_map.json ADDED
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spiece.model ADDED
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+ size 312867
tokenizer_config.json ADDED
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