t5-very-small-random / t5-make-very-small-model.py

add t5-very-small-random

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	#!/usr/bin/env python
	# coding: utf-8
	# Copyright 2020 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/t5-very-small-random"

	from pathlib import Path
	import json
	import tempfile

	from transformers import T5Tokenizer, T5TokenizerFast, T5Config, T5ForConditionalGeneration
	from transformers.models.t5.tokenization_t5 import VOCAB_FILES_NAMES

	mname_from = "patrickvonplaten/t5-tiny-random"
	mname_very_small = "t5-very-small-random"

	tokenizer = T5Tokenizer.from_pretrained(mname_from)
	config = T5Config.from_pretrained(mname_from)
	tokenizer_fast = T5TokenizerFast.from_pretrained(mname_from)

	config.update(dict(
	vocab_size=32128,
	d_model=64,
	d_ff=256,
	d_kv=8,
	num_layers=8,
	num_decoder_layers=8,
	num_heads=4,
	relative_attention_num_buckets=32,
	))

	very_small_model = T5ForConditionalGeneration(config)
	print(f"num of params {very_small_model.num_parameters()}")

	# 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 t5-very-small-random
	# clone and add files