Flan Ul2

🚀 Flan-UL2 Model Card

Flan-UL2 is an encoder-decoder model based on the T5 architecture. It uses the same configuration as the earlier released UL2 model. Fine-tuned with "Flan" prompt tuning and dataset collection, it offers notable improvements for NLP tasks.

Supported Languages

• English
• French
• Romanian
• German
• Multilingual

Widget Examples

Tags

• text2text-generation
• flan-ul2

Datasets

• svakulenk0/qrecc
• taskmaster2
• djaym7/wiki_dialog
• deepmind/code_contests
• lambada
• gsm8k
• aqua_rat
• esnli
• quasc
• qed
• c4

License

apache-2.0

🚀 Quick Start

Converting from T5x to huggingface

You can use the convert_t5x_checkpoint_to_pytorch.py script and pass the argument strict = False. The final layer norm is missing from the original dictionary, that's why we pass the strict = False argument.

python convert_t5x_checkpoint_to_pytorch.py --t5x_checkpoint_path PATH_TO_T5X_CHECKPOINTS --config_file PATH_TO_CONFIG --pytorch_dump_path PATH_TO_SAVE

We used the same config file as google/ul2.

Running the model

Basic Usage

For more efficient memory usage, we advise you to load the model in 8bit using load_in_8bit flag as follows (works only under GPU):

# pip install accelerate transformers bitsandbytes
from transformers import T5ForConditionalGeneration, AutoTokenizer
import torch
model = T5ForConditionalGeneration.from_pretrained("google/flan-ul2", device_map="auto", load_in_8bit=True)                                                                 
tokenizer = AutoTokenizer.from_pretrained("google/flan-ul2")

input_string = "Answer the following question by reasoning step by step. The cafeteria had 23 apples. If they used 20 for lunch, and bought 6 more, how many apple do they have?"                                               

inputs = tokenizer(input_string, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(inputs, max_length=200)

print(tokenizer.decode(outputs[0]))
# <pad> They have 23 - 20 = 3 apples left. They have 3 + 6 = 9 apples. Therefore, the answer is 9.</s>

Advanced Usage

Otherwise, you can load and run the model in bfloat16 as follows:

# pip install accelerate transformers
from transformers import T5ForConditionalGeneration, AutoTokenizer
import torch
model = T5ForConditionalGeneration.from_pretrained("google/flan-ul2", torch_dtype=torch.bfloat16, device_map="auto")                                                                 
tokenizer = AutoTokenizer.from_pretrained("google/flan-ul2")

input_string = "Answer the following question by reasoning step by step. The cafeteria had 23 apples. If they used 20 for lunch, and bought 6 more, how many apple do they have?"                                               

inputs = tokenizer(input_string, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(inputs, max_length=200)

print(tokenizer.decode(outputs[0]))
# <pad> They have 23 - 20 = 3 apples left. They have 3 + 6 = 9 apples. Therefore, the answer is 9.</s>

✨ Features

• Improved Receptive Field: The Flan-UL2 checkpoint uses a receptive field of 2048, making it more suitable for few-shot in-context learning compared to the original UL2 model with a receptive field of 512.
• No Mode Tokens Required: After additional training, the Flan-UL2 checkpoint does not require mode tokens, which were previously necessary for good performance but cumbersome during inference or fine-tuning.

📦 Installation

The installation involves converting the model from T5x to Hugging Face format. Please refer to the "Converting from T5x to huggingface" section in the "Quick Start" for the installation steps.

📚 Documentation

Results

Performance Improvement

The reported results are as follows:

Introduction to UL2

This entire section has been copied from the google/ul2 model card and might be subject to change with respect to flan-ul2.

UL2 is a unified framework for pretraining models that are universally effective across datasets and setups. UL2 uses Mixture-of-Denoisers (MoD), a pre-training objective that combines diverse pre-training paradigms together. UL2 introduces a notion of mode switching, wherein downstream fine-tuning is associated with specific pre-training schemes.

Abstract

Existing pre-trained models are generally geared towards a particular class of problems. To date, there seems to be still no consensus on what the right architecture and pre-training setup should be. This paper presents a unified framework for pre-training models that are universally effective across datasets and setups. We begin by disentangling architectural archetypes with pre-training objectives -- two concepts that are commonly conflated. Next, we present a generalized and unified perspective for self-supervision in NLP and show how different pre-training objectives can be cast as one another and how interpolating between different objectives can be effective. We then propose Mixture-of-Denoisers (MoD), a pre-training objective that combines diverse pre-training paradigms together. We furthermore introduce a notion of mode switching, wherein downstream fine-tuning is associated with specific pre-training schemes. We conduct extensive ablative experiments to compare multiple pre-training objectives and find that our method pushes the Pareto-frontier by outperforming T5 and/or GPT-like models across multiple diverse setups. Finally, by scaling our model up to 20B parameters, we achieve SOTA performance on 50 well-established supervised NLP tasks ranging from language generation (with automated and human evaluation), language understanding, text classification, question answering, commonsense reasoning, long text reasoning, structured knowledge grounding and information retrieval. Our model also achieve strong results at in-context learning, outperforming 175B GPT-3 on zero-shot SuperGLUE and tripling the performance of T5-XXL on one-shot summarization.

For more information, please take a look at the original paper.

Paper: Unifying Language Learning Paradigms

Authors: Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, Donald Metzler

Training

Flan UL2

The Flan-UL2 model was initialized using the UL2 checkpoints and then trained additionally using Flan Prompting. This means that the original training corpus is C4.

In “Scaling Instruction-Finetuned language models (Chung et al.)” (also referred to sometimes as the Flan2 paper), the key idea is to train a large language model on a collection of datasets. These datasets are phrased as instructions which enable generalization across diverse tasks. Flan has been primarily trained on academic tasks. In Flan2, we released a series of T5 models ranging from 200M to 11B parameters that have been instruction tuned with Flan.

The Flan datasets have also been open-sourced in “The Flan Collection: Designing Data and Methods for Effective Instruction Tuning” (Longpre et al.). See Google AI Blogpost: “The Flan Collection: Advancing Open Source Methods for Instruction Tuning”.

UL2 PreTraining

The model is pretrained on the C4 corpus. For pretraining, the model is trained on a total of 1 trillion tokens on C4 (2 million steps) with a batch size of 1024. The sequence length is set to 512/512 for inputs and targets. Dropout is set to 0 during pretraining. Pre-training took slightly more than one month for about 1 trillion tokens. The model has 32 encoder layers and 32 decoder layers, dmodel of 4096 and df of 16384. The dimension of each head is 256 for a total of 16 heads. Our model uses a model parallelism of 8. The same sentencepiece tokenizer as T5 of vocab size 32000 is used (click here for more information about the T5 tokenizer).

UL-20B can be interpreted as a model that is quite similar to T5 but trained with a different objective and slightly different scaling knobs. UL-20B was trained using the Jax and T5X infrastructure.

Mixture of Denoisers

To quote the paper:

We conjecture that a strong universal model has to be exposed to solving diverse set of problems during pre-training. Given that pre-training is done using self-supervision, we argue that such diversity should be injected to the objective of the model, otherwise the model might suffer from lack a certain ability, like long-coherent text generation. Motivated by this, as well as current class of objective functions, we define three main paradigms that are used during pre-training:

• R-Denoiser: The regular denoising is the standard span corruption introduced in T5 that uses a range of 2 to 5 tokens as the span length, which masks about 15% of input tokens. These spans are short and potentially useful to acquire knowledge instead of learning to generate fluent text.
• S-Denoiser: A specific case of denoising where we observe a strict sequential order when framing the inputs-to-targets task, i.e., prefix language modeling. To do so, we simply partition the input sequence into two sub-sequences of tokens as context and target such that the targets do not rely on future information. This is unlike standard span corruption where there could be a target token with an earlier position than a context token. Note that similar to the Prefix-LM setup, the context (prefix) retains a bidirectional receptive field. We note that S-Denoising with very short memory or no memory is in a similar spirit to standard causal language modeling.
• X-Denoiser: An extreme version of denoising where the model must recover a large part of the input, given a small to moderate part of it. This simulates a situation where a model needs to generate a long target from a memory with relatively limited information. To do so, we opt to include...

📄 License

The model is released under the apache-2.0 license.