Estimating Memory Consumption of LLMs for Inference and Fine-Tuning for Cohere Command-R+

Community Article Published April 26, 2024

Introduction

In the realm of AI, the advent of LLMs has revolutionized how we interact with language. Command-R+, Mixtral-8x22b, and Llama 3 70B, standing as titans with billions of parameters, have brought us closer to the brink of what's possible in language modeling. However, with great power comes great demands, particularly in terms of memory consumption. Understanding and optimizing the memory footprint of these LLMs are imperative for their widespread deployment and utilization across various applications.

Definitions:

Inference: The process of using a pre-trained model to make predictions or generate text based on input data.
Fine-tuning: The process of further training a pre-trained model on a specific dataset to adapt it to a particular task.
Memory Consumption: The amount of computer memory required to store and process data during LLM inference and fine-tuning.

Benefits:

Understanding the memory consumption of LLMs is crucial for several reasons:

Efficient Resource Allocation: By accurately estimating memory requirements, developers can allocate resources optimally, ensuring smooth execution of NLP tasks.
Cost Optimization: Efficient memory consumption translates to lower hardware requirements and reduced operational costs, making LLM deployment more economically viable for businesses and organizations.
Model Deployment: Optimal memory usage enables smoother deployment of LLMs in resource-constrained environments, such as edge devices and cloud servers, expanding their accessibility and applicability.
Environmental Impact:Streamlined memory usage contributes to reduced energy consumption and carbon footprint, aligning with sustainability goals and environmental consciousness.

In the pursuit of maximizing the potential of LLMs while minimizing their memory footprint, researchers and practitioners have delved into various optimization techniques. From data-level manipulations to system-level enhancements, a diverse array of approaches has been explored to streamline memory consumption without compromising performance.

As we navigate through the intricate landscape of LLM memory consumption, it becomes evident that a holistic understanding of the underlying mechanisms and optimization strategies is paramount. By unraveling the complexities and nuances of memory utilization in LLMs, we pave the way for more efficient, sustainable, and impactful AI systems that empower humanity in unprecedented ways.

Code Implementation

To further understand and analyze the memory consumption of LLMs during inference and fine-tuning processes,lets delve into the code implementation

from transformers import AutoConfig
model_name = "CohereForAI/c4ai-command-r-plus" # @param {type: "string"}

model_config = AutoConfig.from_pretrained(model_name)

hidden_layers = model_config.num_hidden_layers
hidden_size = model_config.hidden_size
attention_heads = model_config.num_attention_heads

print("Model: "+str(model_name))
print("Hidden Layers (L): "+str(hidden_layers))
print("Hidden Size (h): "+str(hidden_size))
print("Attention Heads (a): "+str(attention_heads))

Model: CohereForAI/c4ai-command-r-plus
Hidden Layers (L): 64
Hidden Size (h): 12288
Attention Heads (a): 96

#Number of parameters in the model (in billions)
nb_billion_parameters = 104 # @param {type:"number"}
print("Number of parameters in the model (n): "+str(nb_billion_parameters)+"B")

#Precision of the parameters in the model
bitwidth_model = 16 # @param {type:"integer"}
print("Bitwidth of the model's parameters (p): "+str(bitwidth_model)+"-bit")

#Precision of the parameters in the optimizer
bitwidth_optimizer = 32 # @param {type:"integer"}
print("Bitwidth of the optimizer's parameters (o): "+str(bitwidth_optimizer)+"-bit")

#The maximum number of tokens in a sequence
seqlen = 512 # @param {type:"integer"}
print("Sequence length (s): "+str(seqlen))

#The batch size
batch_size = 8 # @param {type:"integer"}
print("Batch size (b): "+str(batch_size))

Number of parameters in the model (n): 104B
Bitwidth of the model's parameters (p): 16-bit
Bitwidth of the optimizer's parameters (o): 32-bit
Sequence length (s): 512
Batch size (b): 8

def estimate_consumption():
  #34 sbh + 5as²b
  return round((34*seqlen*batch_size*hidden_size + 5*attention_heads*seqlen*seqlen*batch_size)*2/(1024**3),2)

def estimate_optimizer_size():
  return round((2*nb_billion_parameters*bitwidth_optimizer/8*(1000**3))/(1024**3),2)

def estimate_model_size():
  return round(nb_billion_parameters*bitwidth_model/8*(1000**3)/(1024**3),2)

activation_consumption = estimate_consumption()
model_consumption = estimate_model_size()
optimizer_consumption = estimate_optimizer_size()

print("Memory consumption of the model: "+str(model_consumption)+" GB\n")

print("Memory consumption of the optimizer: "+str(optimizer_consumption)+" GB")
print("Memory consumption of activations for fine-tuning: "+str(activation_consumption*hidden_layers)+" GB")
print("Total memory consumption for fine-tuning: "+str(model_consumption+optimizer_consumption+activation_consumption*hidden_layers)+" GB\n")

print("Memory consumption of activations for inference: "+str(activation_consumption)+" GB")
print("Total memory consumption for inference: "+str(model_consumption+activation_consumption)+" GB")

Memory consumption of the model: 193.72 GB

Memory consumption of the optimizer: 774.86 GB
Memory consumption of activations for fine-tuning: 323.84 GB
Total memory consumption for fine-tuning: 1292.42 GB

Memory consumption of activations for inference: 5.06 GB
Total memory consumption for inference: 198.78 GB

Conclusion

In conclusion, optimizing memory usage is crucial for the efficient deployment of Large Language Models (LLMs) like Command-R+, Mixtral-8x22b, and Llama 3 70B. By understanding and addressing inefficiencies in model size, attention operations, and decoding approaches, we can improve LLM inference efficiency. Through ongoing research and collaboration, we can unlock the full potential of LLMs across various applications, driving innovation and societal impact.

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