LLMs Downcasting

Why Downcast Your LLMs?

Large Language Models (LLMs) are revolutionizing the way we interact with technology, powering everything from chatbots to code generators. However, their immense size often presents challenges in terms of deployment and efficiency. This is where downcasting comes in.

Downcasting, in the context of LLMs, refers to converting the model's data type to a lower precision, such as from float32 (the standard for training) to float16 or even lower ,this seemingly simple change can unlock significant benefits:

1. Reduced Memory Footprint:

Imagine trying to fit a massive LLM onto a resource-constrained device like a smartphone ,it's like trying to squeeze an elephant🐘 into a mini cooper🚗 ,downcasting helps by significantly reducing the model's size. Lower precision data types require fewer bits to represent each number, leading to a smaller overall memory footprint. This allows you to run larger models on the same hardware or deploy models on devices with limited memory.

2. Faster Inference:

Think of inference as the process of the LLM "thinking" and generating a response, with lower precision data types, the mathematical operations involved in inference become faster ,this translates to quicker response times, making your applications feel snappier and more responsive.

3. Lower Power Consumption:

Processing large amounts of data in high precision is energy-intensive. Downcasting reduces the computational load, leading to lower power consumption. This is particularly crucial for mobile and edge devices where battery life is a major concern.

In essence, downcasting allows you to deploy LLMs more efficiently, making them faster, smaller, and less power-hungry. This opens up new possibilities for running LLMs on a wider range of devices and making them more accessible to users.

Think of it like this: You wouldn't use a high-resolution image when a lower-resolution one would suffice for a specific task. Similarly, downcasting allows you to use a "lower-resolution" version of your LLM without significantly sacrificing performance, while reaping the benefits of reduced size and increased speed.

So, if you're looking to optimize your LLM deployment and unlock its full potential, downcasting is a technique worth exploring.

Now let's dive into some practical examples of how to downcast your LLMs using the popular Transformers library and PyTorch , we'll explore three different approaches to achieve this, each with its own nuances and advantages ,we'll be using the Qwen2-0.5B model as a case study, but these techniques can be applied to other LLMs as well ,pay close attention to the different methods for loading and converting the model to the desired lower precision data type (bfloat16 in this case). Understanding these variations will empower you to choose the most suitable approach for your specific needs and environment.

Method 1: Deep Copy and Conversion

This method first loads the model in the default precision (likely float32) ,then it creates a deep copy of the model and uses the .to(torch.bfloat16) method to convert the copy's parameters to bfloat16 ,this leaves the original model untouched.

from transformers import AutoTokenizer, AutoModelForCausalLM
from copy import deepcopy
import torch


def print_param_dtype(model):
    for name, param in model.named_parameters():
        print(f"{name} is loaded in {param.dtype}")

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B")

# check that the model is in float32
print_param_dtype(model)

model_bf16 = deepcopy(model)
model_bf16 = model_bf16.to(torch.bfloat16)

# check that the model_bf16 is in bfloat16
print_param_dtype(model_bf16)

# get memory footprint
model.get_memory_footprint()

Advantages: Preserves the original model in its initial precision, allowing for easy comparison or switching back if needed.
Considerations: Requires more memory as it essentially duplicates the model in memory.

Method 2: Loading with Specified Data Type

This approach leverages the torch_dtype argument within the from_pretrained function ,this directly loads the model in the specified data type (bfloat16 in this case) during initialization.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the Tokenizer
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B")

# Load the model in bfloat16
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B",
                                              torch_dtype=torch.bfloat16)
# Check that the model is in bfloat16
print_param_dtype(model)

# get memory footprint
model.get_memory_footprint()

Advantages: More memory-efficient as it avoids creating a duplicate copy of the model.
Considerations: The model is loaded directly in the lower precision, so you won't have the original float32 version readily available.

Method 3: Setting the Default Data Type

This method sets the default data type for PyTorch tensors to bfloat16 ,consequently the model will be loaded using this data type.

Remember to set the default data type back to float32 afterward to avoid affecting other parts of your code.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# set the default data type to bfloat16
torch.set_default_dtype(torch.bfloat16)

# Load the model (it is loaded in bfloat16 by default now)
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B")

# Change the default data type back to float32
torch.set_default_dtype(torch.float32)

Advantages: Simple and concise way to load the model in the desired precision.
Considerations: Potentially affects other parts of your code that rely on the default data type,it's crucial to revert the default type back to float32 after loading the model.

Each method offers a different way to downcast your LLM ,the best approach depends on your specific needs and priorities. Consider factors like memory usagethe need to preserve the original model, and the potential impact on other parts of your code when making your choice.