transformers/docs/source/en/model_doc/phi.md

Phi

Overview

The Phi-1 model was proposed in Textbooks Are All You Need by Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio César Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, Olli Saarikivi, Adil Salim, Shital Shah, Harkirat Singh Behl, Xin Wang, Sébastien Bubeck, Ronen Eldan, Adam Tauman Kalai, Yin Tat Lee and Yuanzhi Li.

The Phi-1.5 model was proposed in Textbooks Are All You Need II: phi-1.5 technical report by Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, Allie Del Giorno, Suriya Gunasekar and Yin Tat Lee.

Summary

In Phi-1 and Phi-1.5 papers, the authors showed how important the quality of the data is in training relative to the model size. They selected high quality "textbook" data alongside with synthetically generated data for training their small sized Transformer based model Phi-1 with 1.3B parameters. Despite this small scale, phi-1 attains pass@1 accuracy 50.6% on HumanEval and 55.5% on MBPP. They follow the same strategy for Phi-1.5 and created another 1.3B parameter model with performance on natural language tasks comparable to models 5x larger, and surpassing most non-frontier LLMs. Phi-1.5 exhibits many of the traits of much larger LLMs such as the ability to “think step by step” or perform some rudimentary in-context learning. With these two experiments the authors successfully showed the huge impact of quality of training data when training machine learning models.

The abstract from the Phi-1 paper is the following:

We introduce phi-1, a new large language model for code, with significantly smaller size than competing models: phi-1 is a Transformer-based model with 1.3B parameters, trained for 4 days on 8 A100s, using a selection of “textbook quality” data from the web (6B tokens) and synthetically generated textbooks and exercises with GPT-3.5 (1B tokens). Despite this small scale, phi-1 attains pass@1 accuracy 50.6% on HumanEval and 55.5% on MBPP. It also displays surprising emergent properties compared to phi-1-base, our model before our finetuning stage on a dataset of coding exercises, and phi-1-small, a smaller model with 350M parameters trained with the same pipeline as phi-1 that still achieves 45% on HumanEval.

The abstract from the Phi-1.5 paper is the following:

We continue the investigation into the power of smaller Transformer-based language models as initiated by TinyStories – a 10 million parameter model that can produce coherent English – and the follow-up work on phi-1, a 1.3 billion parameter model with Python coding performance close to the state-of-the-art. The latter work proposed to use existing Large Language Models (LLMs) to generate “textbook quality” data as a way to enhance the learning process compared to traditional web data. We follow the “Textbooks Are All You Need” approach, focusing this time on common sense reasoning in natural language, and create a new 1.3 billion parameter model named phi-1.5, with performance on natural language tasks comparable to models 5x larger, and surpassing most non-frontier LLMs on more complex reasoning tasks such as grade-school mathematics and basic coding. More generally, phi-1.5 exhibits many of the traits of much larger LLMs, both good –such as the ability to “think step by step” or perform some rudimentary in-context learning– and bad, including hallucinations and the potential for toxic and biased generations –encouragingly though, we are seeing improvement on that front thanks to the absence of web data. We open-source phi-1.5 to promote further research on these urgent topics.

This model was contributed by Susnato Dhar. The original code for Phi-1 and Phi-1.5 can be found here and here respectively.

Usage tips

• This model is quite similar to Llama with the main difference in [PhiDecoderLayer], where they used [PhiAttention] and [PhiMLP] layers in parallel configuration.
• The tokenizer used for this model is identical to the [CodeGenTokenizer].

Example :

>>> from transformers import PhiForCausalLM, AutoTokenizer

>>> # define the model and tokenizer.
>>> model = PhiForCausalLM.from_pretrained("susnato/phi-1_5_dev")
>>> tokenizer = AutoTokenizer.from_pretrained("susnato/phi-1_5_dev")

>>> # feel free to change the prompt to your liking.
>>> prompt = "If I were an AI that had just achieved"

>>> # apply the tokenizer.
>>> tokens = tokenizer(prompt, return_tensors="pt")

>>> # use the model to generate new tokens.
>>> generated_output = model.generate(**tokens, use_cache=True, max_new_tokens=10)

>>> tokenizer.batch_decode(generated_output)[0]
'If I were an AI that had just achieved a breakthrough in machine learning, I would be thrilled'

Combining Phi and Flash Attention 2

First, make sure to install the latest version of Flash Attention 2 to include the sliding window attention feature.

pip install -U flash-attn --no-build-isolation

Make also sure that you have a hardware that is compatible with Flash-Attention 2. Read more about it in the official documentation of flash-attn repository. Make also sure to load your model in half-precision (e.g. `torch.float16``)

To load and run a model using Flash Attention 2, refer to the snippet below:

>>> import torch
>>> from transformers import PhiForCausalLM, AutoTokenizer

>>> # define the model and tokenizer and push the model and tokens to the GPU.
>>> model = PhiForCausalLM.from_pretrained("susnato/phi-1_5_dev", torch_dtype=torch.float16, attn_implementation="flash_attention_2").to("cuda")
>>> tokenizer = AutoTokenizer.from_pretrained("susnato/phi-1_5_dev")

>>> # feel free to change the prompt to your liking.
>>> prompt = "If I were an AI that had just achieved"

>>> # apply the tokenizer.
>>> tokens = tokenizer(prompt, return_tensors="pt").to("cuda")

>>> # use the model to generate new tokens.
>>> generated_output = model.generate(**tokens, use_cache=True, max_new_tokens=10)

>>> tokenizer.batch_decode(generated_output)[0]
'If I were an AI that had just achieved a breakthrough in machine learning, I would be thrilled'

Expected speedups

Below is an expected speedup diagram that compares pure inference time between the native implementation in transformers using susnato/phi-1_dev checkpoint and the Flash Attention 2 version of the model using a sequence length of 2048.

PhiConfig

autodoc PhiConfig

PhiModel

autodoc PhiModel - forward

PhiForCausalLM

autodoc PhiForCausalLM - forward - generate

PhiForSequenceClassification

autodoc PhiForSequenceClassification - forward

PhiForTokenClassification

autodoc PhiForTokenClassification - forward