First off, what they do: Phi is a neural network model that can generate text based on input prompts (like “Write a thrilling sci-fi story”), and Flash Attention 2 is a fancy new way to speed up the training process for this model.
Now, here comes the technical stuff. When you train a neural network, it involves feeding in data and adjusting the weights of various connections between different parts of the network (called “neurons”) until it can accurately predict what output should come out based on that input. This is called “backpropagation” because we’re essentially going backwards through time to figure out how to improve our predictions for future inputs.
The problem with backpropagation, though, is that it can be really slow and memory-intensive (especially when dealing with large datasets). That’s where Flash Attention 2 comes in: by reordering the attention computation and using classical techniques like tiling and work partitioning, we can significantly speed up training times while also reducing memory usage from quadratic to linear.
Here’s an example of how this might look in code (using Python):
# Import necessary libraries
import torch
from transformers import PhiForCausalLM, AutoTokenizer
# Load the model and tokenizer using Flash Attention 2
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")
# Define a prompt and convert it to tokenized input format using the tokenizer
prompt = "If I were an AI that had just achieved"
tokens = tokenizer(prompt, return_tensors="pt").to("cuda")
# Generate output based on the model's predictions for this input
generated_output = model.generate(**tokens, use_cache=True, max_new_tokens=10)
# Convert generated tokens back to human-readable text format using the tokenizer
output_text = tokenizer.batch_decode(generated_output)[0]
# Print the generated output
print(output_text)
# Explanation:
# The first line imports the necessary libraries for the script to run.
# The second line loads the PhiForCausalLM model from the "susnato/phi-1_5_dev" pretrained model, specifying the torch data type as float16 and using the Flash Attention 2 implementation. It is then moved to the "cuda" device for faster processing.
# The third line loads the AutoTokenizer from the same pretrained model.
# The fifth line defines a prompt for the model to generate output for.
# The sixth line uses the tokenizer to convert the prompt into tokenized input format and moves it to the "cuda" device.
# The eighth line uses the model to generate output based on the tokenized input, specifying to use cache and limiting the maximum number of new tokens to 10.
# The tenth line uses the tokenizer to convert the generated tokens back to human-readable text format.
# The twelfth line prints the generated output.
And that’s it! With Flash Attention 2 and Phi, you can train your neural network models faster than ever before while also reducing memory usage and improving overall performance. So go ahead and give them a try who knows what kind of amazing results you might achieve?