Enhancing Judicial Data Analysis with ChatGLM Quantization and Efficient Inferencing

Objectives

• Reduce Model Size: Apply quantization techniques to decrease ChatGLM’s memory footprint without sacrificing performance.
• Accelerate Inferencing Speed: Achieve faster response times to facilitate real-time data analysis and iterative research.
• Minimize Operational Costs: Optimize the use of computational resources to stay within the project’s budget constraints.
• Preserve Model Accuracy: Ensure that optimization efforts do not significantly degrade ChatGLM’s ability to understand and analyze legal texts.
• Enable Scalability: Develop a solution capable of handling increasing data volumes and complex queries as the research evolves.
• Adaptability to Other Domains: While the current focus is on legal casesummarization, the system can be adapted for other domains requiring structured information extraction from large textual datasets.

Challenges

1. Dataset Complexity: The judicial records contained intricate legal language, necessitating a highly accurate and context-aware LLM.
2. Resource Limitations: The existing computational infrastructure at Stanford Law School was insufficient for high-performance AI workloads.
3. Optimization Trade-offs: Balancing the reduction in model size and computational load with the necessity to maintain high accuracy in legal interpretations.
4. Data Security and Compliance: Ensuring that the processing of sensitive judicial records adhered to data privacy and compliance standards.

Technical Architecture and Flow

1. System Architecture Overview

The optimized system architecture comprises the following layers:

• Data Ingestion Layer: Handles the ingestion and preprocessing of judicial records.
• Model Deployment Layer: Hosts the quantized ChatGLM model for inferencing.
• Inferencing Optimization Layer: Implements batching, caching, and asynchronous processing.
• Infrastructure Layer: Manages cloud and edge deployments with security protocols.
• Monitoring and Feedback Layer: Continuously monitors performance and facilitates iterative improvements.

Diagram 1: High-Level System Architecture

2. Data Flow Process

• Data Ingestion: Judicial records are ingested from various sources, including databases and file systems, and preprocessed to ensure consistency and quality.
• Model Deployment: The quantized ChatGLM model is deployed on optimized hardware infrastructure.
• Inferencing Optimization: Incoming queries are batched, cached, and processed asynchronously to enhance performance.
• Infrastructure Management: The system leverages cloud resources for scalability and edge devices for low-latency processing.
• Monitoring and Feedback: Real-time monitoring dashboards track performance metrics, enabling continuous optimization.

Diagram 2: Data Flow Process

Solution Provided by GenAI Solutions

GenAI Solutions implemented a comprehensive strategy to optimize ChatGLM through quantization and efficient inferencing tailored to the unique needs of legal research. The solution encompassed four main components:

1. Model Quantization

Objective: Reduce the size of ChatGLM from 6 GB to approximately 1.5 GB (75% reduction) while maintaining 98% of its original accuracy.

Techniques Applied

• Post-Training Quantization (PTQ)
• Dynamic Range Quantization
• Bias Correction

Technical Steps

1. Model Evaluation: Assessed the original ChatGLM model’s architecture and performance metrics.
2. Framework Utilization: Utilized TensorFlow Lite and PyTorch’s quantization toolkits for the quantization process.
3. Quantization Pipeline:
   
   • Weight Quantization: Converted model weights from 32-bit floating-point to 8-bit integers.
   • Activation Quantization: Applied dynamic range quantization to activations during inferencing.
   • Bias Correction: Applied calibration techniques to adjust biases post-quantization, ensuring minimal performance degradation.
   • Model pruning:  Explored to reduce size and computation load, but it led to significant losses in summarization accuracy and reasoning. Due to performance degradation, pruning was deemed unsuitable.

Code Snippet: Post-Training Quantization with PyTorch

import torch
from torch.quantization import quantize_dynamic
# Load the original ChatGLM model
model = torch.load('chatglm_model.pth')
model.eval()
# Apply dynamic quantization
quantized_model = quantize_dynamic(
    model, 
    {torch.nn.Linear}, 
    dtype=torch.qint8
)
# Save the quantized model
torch.save(quantized_model.state_dict(), 'chatglm_quantized.pth')

Performance Metrics

2. Inferencing Optimization

Objective: Enhance inferencing speed by 60%, reduce latency, and improve throughput.

Techniques Applied

• Hardware Acceleration
• DDP(Data Distributed Parallel)
• Batching Strategies
• Prompt Engineering

Technical Steps

1. Hardware Setup:
   • Deployed quantized ChatGLM on NVIDIA GPUs configured with TensorRT for optimized inferencing.
   • Integrated Google TPUs for additional processing power.
2. Batching Implementation:
   • Configured dynamic batching parameters to aggregate incoming requests based on current load and processing capacity.
   • Tuned batch sizes to maximize GPU utilization while minimizing latency.

    total_records = len(df)
    chunk_size = total_records // 4
        if gpu_id == 3:  # Last GPU handles the remainder
        batch_df = df.iloc[gpu_id * chunk_size:]
    else:
        batch_df = df.iloc[gpu_id * chunk_size:(gpu_id + 1) * chunk_size]

    # Process the batch on the assigned GPU
    results = process_batch_on_gpu(batch_df, gpu_id,input_csv_path, tokenizer, model)

def process_batch_on_gpu(batch_df, gpu_id,input_csv_path, tokenizer, model):
    file_name = os.path.splitext(os.path.basename(input_csv_path))[0]
    device = torch.device(f"cuda:{gpu_id}")
    model.to(device)
    model.eval()
    torch.set_grad_enabled(False)

3. Infrastructure Enhancement

Objective: Develop a scalable and secure infrastructure capable of handling increasing data volumes and complex queries.

Techniques Applied

• Cloud Integration
• Edge Deployment
• Data Security Measures

Technical Steps

1. Cloud Migration:
   • Platform Selection: Choose AWS for its comprehensive AI services and scalability.
   • Model Deployment: Utilized Amazon SageMaker to deploy the quantized ChatGLM model, enabling seamless scaling based on demand.
   • Auto Scaling: Configured Auto Scaling groups to dynamically adjust computational resources in response to fluctuating workloads.
2. Edge Deployment:
   • Model Distillation: Created distilled versions of ChatGLM optimized for edge devices with limited computational resources.
   • Deployment Strategy: Deployed these models on secure edge devices within the university’s network to enable immediate local inferencing, reducing dependency on centralized servers.
3. Data Security and Compliance:
   • Encryption: Implemented TLS for data in transit and AES-256 for data at rest.
   • Access Control: Utilized AWS Identity and Access Management (IAM) to enforce strict access controls.
   • Compliance: Ensured adherence to GDPR and other relevant data protection regulations by conducting regular security audits and implementing necessary safeguards

4. Continuous Monitoring and Iteration

Objective: Ensure sustained optimal performance through real-time monitoring and iterative optimizations.

Techniques Applied

• Monitoring Dashboards
• Automated Feedback Loops
• Performance Audits

Technical Steps

1. Monitoring Setup:
   • gpustat: A Python-based command-line utility that monitors NVIDIA GPUs in real   time. It provides a summary of GPU utilization, memory usage, temperature, and other metrics. 
   •  NVIDIA DCGM: A set of tools for managing and monitoring NVIDIA GPUs in Linux-based cluster environments. It includes APIs for gathering GPU telemetry, such as GPU utilization metrics, memory metrics, and interconnect traffic metrics.
2. Performance Audits:
   • Regular Reviews: Scheduled monthly performance audits to assess system health and identify areas for improvement.
   • Optimization Cycles: Incorporated findings from audits into the optimization cycle, refining quantization parameters and inferencing strategies as needed.
   • Code Snippet: Nvidia Configuration for Monitoring GPU Utilization
   • GPU Utilization: Displays real-time GPU usage percentages using nvidia-smi.
   • Inferencing Latency: Tracks average and peak inferencing response times.
   • Cache Hit Rate: Monitors the effectiveness of the caching layer.
   • Auto Scaling Activity: Visualizes scaling events and resource adjustments.

Performance Metrics

Results

The collaboration between Stanford University Law School and GenAI Solutions yielded remarkable outcomes that significantly advanced the PhD research project:

1. Reduced Model Size: Achieved a 75% reduction in ChatGLM’s memory footprint, decreasing from 6 GB to 1.5 GB, facilitating easier deployment and management of the model within the university’s infrastructure.
2. Accelerated Inferencing Speed: Realized a 60% decrease in average inferencing latency, enabling real-time analysis and allowing researchers to iterate more rapidly on their hypotheses.
3. Preserved Model Accuracy: Maintained ChatGLM’s accuracy at 98% of its original performance, ensuring that the legal insights and analyses remained precise and reliable.
4. Enhanced Scalability: Established a scalable infrastructure capable of handling a 200% increase in data volume and concurrent inferencing requests, accommodating the expanding scope of the research project.
5. Enhanced Research Efficiency: Enabled the research team to perform complex analyses more efficiently, accelerating the pace of discovery and contributing to the project’s academic success.

Conclusion

By implementing advanced quantization techniques and optimizing inferencing pipelines specifically for ChatGLM, GenAI Solutions empowered Stanford University Law School to overcome the technical challenges associated with deploying a Large Language Model for extensive legal research. The project not only delivered significant cost savings and performance enhancements but also provided the research team with the computational tools necessary to conduct in-depth analysis of judicial records. This collaboration exemplifies how specialized generative AI-focused IT services can drive academic innovation and support groundbreaking research initiatives.

About GenAI Solutions

GenAI Solutions is a leading generative AI-focused IT services and consulting firm specializing in artificial intelligence optimization, machine learning deployment, and scalable infrastructure solutions. With a team of seasoned experts, GenAI Solutions empowers academic institutions, research organizations, and businesses to harness the full potential of their AI initiatives, driving innovation, efficiency, and excellence.

‍