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mt5-tiny-random

Text2Text Generation Transformers PyTorch JAX mt5 Inference Endpoints text-generation-inference

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mt5-tiny-random / mt5-make-tiny-model.py

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tiny mt5 random model

2bd01f7 almost 3 years ago

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	#!/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 smallish random model, with a few layers to test things like MP/PP, where
	# tiny and tiner models are too too small
	#
	# It will be used then as "stas/mt5-tiny-random"

	# To build:
	# 1. clone sentencepiece into this dir
	# git clone https://github.com/google/sentencepiece
	#
	# 2. run this script

	from pathlib import Path
	import json
	import tempfile

	from transformers import MT5Tokenizer, MT5TokenizerFast, MT5Config, MT5ForConditionalGeneration
	from transformers.models.t5.tokenization_t5 import VOCAB_FILES_NAMES

	mname_from = "google/mt5-small"
	mname_very_small = "mt5-tiny-random"

	tokenizer = MT5Tokenizer.from_pretrained(mname_from)
	config = MT5Config.from_pretrained(mname_from)
	#tokenizer_fast = MT5TokenizerFast.from_pretrained(mname_from)

	# Shrink the vocab of mt5-small
	import sys
	# 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

	tmp_dir = "/tmp/mt5-small"
	tokenizer.save_pretrained(tmp_dir)
	file = tmp_dir + "/spiece.model"
	with open(file, 'rb') as f: data = f.read()

	# adapted from https://blog.ceshine.net/post/trim-down-sentencepiece-vocabulary/
	m = model.ModelProto()
	m.ParseFromString(data)

	keep_items = 5000

	print("Shrinking vocab")
	print(f"original dict {len(m.pieces)}")
	for i in range(len(m.pieces)-keep_items): _ = m.pieces.pop()
	print(f"new dict {len(m.pieces)}")

	with open(tmp_dir + "/spiece-short.model", 'wb') as f:
	f.write(m.SerializeToString())

	tokenizer = MT5Tokenizer(vocab_file=tmp_dir + "/spiece-short.model")

	config.update(dict(
	vocab_size=keep_items+12,
	d_model=64,
	d_ff=256,
	d_kv=8,
	num_layers=8,
	num_decoder_layers=8,
	num_heads=4,
	relative_attention_num_buckets=32,
	))
	print("new config", config)

	very_small_model = MT5ForConditionalGeneration(config)
	print(f"num of params {very_small_model.num_parameters()}")
	very_small_model.resize_token_embeddings(len(tokenizer))

	# Test
	src_texts = ["A long paragraph for summarization.", "Another paragraph for summarization."]
	tgt_texts = ["Summary of the text.", "Another summary."]

	batch = tokenizer.prepare_seq2seq_batch(src_texts, tgt_texts, return_tensors="pt")
	outputs = very_small_model(**batch)

	print("test output:", len(outputs.logits[0]))

	# Save
	very_small_model.half() # makes it smaller
	very_small_model.save_pretrained(mname_very_small)
	config.save_pretrained(mname_very_small)
	tokenizer.save_pretrained(mname_very_small)
	#tokenizer_fast.save_pretrained(mname_very_small)

	print(f"Generated {mname_very_small}")

	# Upload
	# transformers-cli repo create mt5-tiny-random
	# clone and add files