Optimizing GLM-4.5

Advanced Model Compression Techniques

Comprehensive suite of compression methodologies targeting CUDA 8.6 architecture, achieving significant memory reduction and inference acceleration while maintaining near-lossless accuracy.

Key Metrics

355B
Total Parameters
32B
Active Parameters
4x
Memory Reduction
2-3x
Speedup Target

Abstract

This report details a comprehensive suite of advanced model compression techniques specifically applied to the full-scale GLM-4.5 model. The primary objectives of these optimizations are to significantly reduce memory usage, accelerate inference speed, and maintain near-lossless model accuracy.

The methodologies are meticulously designed to leverage the capabilities of the CUDA 8.6 architecture, with a particular focus on optimizing for Volta-generation GPUs. Key techniques implemented include post-training quantization (PTQ) utilizing INT4 precision for linear layers, the application of structured 2:4 sparsity patterns within attention mechanisms and feed-forward networks, and a strategic mixed precision inference approach.

Introduction

Overview of GLM-4.5 Model and Deployment Challenges

The GLM-4.5 model represents a significant advancement in large language models, characterized by a substantial parameter count. Specifically, it comprises 355 billion total parameters, with 32 billion active parameters engaged during the inference process [3].

This immense scale, while contributing to the model's powerful capabilities, introduces considerable computational and memory demands. Deploying such a large model in practical scenarios necessitates innovative and efficient approaches to manage these resource constraints without compromising performance.

Hardware Platform: CUDA 8.6 and Volta Architecture

The optimization efforts detailed in this report are specifically targeted for hardware platforms based on the CUDA 8.6 architecture, with a primary emphasis on NVIDIA V100 GPUs [6]. The Volta architecture, which powers the V100, introduced significant features beneficial for deep learning workloads, most notably Tensor Cores.

Key Hardware Features: Tensor Cores enable mixed-precision FP16 computations, while CUDA 8.6 provides essential library support for optimized operations.

Goals of Compression: Memory, Speed, and Accuracy

Memory Reduction

Reduce memory footprint for deployment on GPUs with limited VRAM and enable larger batch sizes.

Speed Increase

Accelerate inference for real-time applications and improve throughput for cost-effective serving.

Accuracy Preservation

Maintain near-lossless model accuracy while achieving significant performance improvements.

Compression Techniques Applied to GLM-4.5

INT4 Post-Training Quantization (PTQ) with AWQ

Quantization is a fundamental technique employed to reduce the precision of the model's parameters, thereby achieving substantial memory savings and potentially accelerating inference by leveraging hardware support for lower-precision arithmetic [69] [284].

For the GLM-4.5 model, a post-training quantization (PTQ) approach was adopted, specifically utilizing Activation-aware Weight Quantization (AWQ). AWQ is chosen for its ability to maintain model accuracy by carefully calibrating quantization parameters using a small, representative dataset.

from awq.quantizer import quantize_model

# Apply AWQ to the model for 4-bit quantization with a group size of 32
quantize_model(model, bits=4, group_size=32)

Implementation Note: The majority of weights are subjected to INT4 quantization, with the exception of the final classifier layer, which is kept at higher precision (FP16) to avoid significant impacts on output logits [196].

Structured 2:4 Sparsity Implementation

Structured sparsity is a powerful compression technique that involves imposing a specific, systematic pattern of zeros within the model's weight matrices. For the GLM-4.5 model, we implemented 2:4 structured sparsity, a pattern that dictates that for every block of four consecutive weights, two must be non-zero and two must be zero.

import torch
import torch.nn.utils.prune as prune

# Example: Applying 2:4 structured sparsity to the value projection weights in an attention module
prune.ln_structured(
    module.attention.self.value.weight,  # Target weight tensor
    name='weight',                       # Parameter name within the module to prune
    amount=0.5,                          # Fraction of connections to prune (50% for 2:4)
    n=2,                                 # Number of non-zeros in the block (N for N:M)
    dim=0                                # Dimension along which to apply the pruning
)
# Make the pruning permanent
prune.remove(module.attention.self.value, 'weight')

Benefits

  • • ~50% reduction in active weights
  • • Structured pattern enables hardware acceleration
  • • Compatible with NVIDIA's cuSPARSELt

Target Layers

  • • Attention mechanisms
  • • Feed-forward networks
  • • Linear projection layers

Mixed Precision Inference Strategy

To further optimize memory usage and computational speed, a mixed precision inference strategy was implemented for the GLM-4.5 model. This technique leverages the capabilities of modern GPUs, particularly the Tensor Cores available in NVIDIA's Volta architecture.

import torch

# Assuming 'model' is the GLM-4.5 model and 'inputs' are the input tensors
with torch.cuda.amp.autocast():
    # Forward pass through the model features
    feature_output = model.feature_extractor(inputs)  # Example: up to the final layers
    # Final classifier layer (often kept in FP32 or handled outside autocast if sensitive)
    logits = model.classifier(feature_output)         # Classifier might operate in FP32

FP16 Precision

  • • QKV projections in attention
  • • Feed-forward network operations
  • • Matrix multiplications

FP32 Precision

  • • Layer normalization
  • • Softmax operations
  • • Final logits computation

Layer-wise Optimization Strategies for MoE Architecture

The GLM-4.5 model, particularly if it incorporates a Mixture-of-Experts (MoE) architecture, benefits from layer-wise optimization strategies that tailor compression techniques to the specific characteristics and sensitivities of different components within the model.

graph TD A["GLM-4.5 Model"] --> B["Embedding Layers"] A --> C["Transformer Blocks"] A --> D["MoE Experts"] A --> E["Normalization Layers"] B --> B1["FP16 Precision"] B --> B2["No Quantization"] C --> C1["INT4 Quantization"] C --> C2["2:4 Sparsity"] D --> D1["INT4 Quantization"] D --> D2["2:4 Sparsity"] E --> E1["FP32 Precision"] E --> E2["Protected"] style A fill:#1e3a8a,stroke:#1e40af,stroke-width:3px,color:#fff style B fill:#0ea5e9,stroke:#0284c7,stroke-width:2px,color:#fff style C fill:#10b981,stroke:#059669,stroke-width:2px,color:#fff style D fill:#8b5cf6,stroke:#7c3aed,stroke-width:2px,color:#fff style E fill:#f59e0b,stroke:#d97706,stroke-width:2px,color:#fff style B1 fill:#f0f9ff,stroke:#0ea5e9,stroke-width:2px,color:#1e40af style B2 fill:#f0f9ff,stroke:#0ea5e9,stroke-width:2px,color:#1e40af style C1 fill:#ecfdf5,stroke:#10b981,stroke-width:2px,color:#065f46 style C2 fill:#ecfdf5,stroke:#10b981,stroke-width:2px,color:#065f46 style D1 fill:#faf5ff,stroke:#8b5cf6,stroke-width:2px,color:#581c87 style D2 fill:#faf5ff,stroke:#8b5cf6,stroke-width:2px,color:#581c87 style E1 fill:#fffbeb,stroke:#f59e0b,stroke-width:2px,color:#92400e style E2 fill:#fffbeb,stroke:#f59e0b,stroke-width:2px,color:#92400e

Embedding Layers

Maintained at FP16 precision due to sensitivity to quantization noise. Critical for initial token representations.

Transformer Blocks

Aggressive INT4 quantization and 2:4 sparsity applied to attention and feed-forward layers.

MoE Experts

Individual expert networks receive quantization and sparsity, while gating mechanisms remain high precision.

Implementation Details

Software and Libraries: PyTorch, AWQ, cuSPARSELt

The implementation of the advanced compression techniques for the GLM-4.5 model leveraged a combination of established and specialized software libraries. PyTorch 1.13 or later served as the foundational deep learning framework, primarily due to its native and mature support for essential modules related to model pruning and quantization [196].

PyTorch

Foundation framework with torch.ao.pruning module for structured sparsity implementation.

AWQ Library

Activation-aware Weight Quantization for INT4 precision with minimal accuracy degradation.

cuSPARSELt

NVIDIA library for optimized sparse matrix operations, supporting 2:4 structured sparsity patterns.

CUDA 8.6 Compiler Flags and APIs

To ensure that the compiled model and its associated kernels were optimized for the target Volta GPU architecture (compute capability sm_70) under CUDA 8.6, specific compiler flags and API usage were employed.

# Primary CUDA compiler flag for Volta architecture
nvcc -arch=sm_70 ...

# Key libraries and APIs
- cuBLAS: Accelerated dense linear algebra
- cuDNN: Deep neural network primitives
- cuSPARSELt: Sparse matrix operations

Architecture Note: While Volta GPUs don't have native INT4 Tensor Core support like Ampere/Hopper, CUDA 8.6 enables optimized kernels for FP16 Tensor Core operations and structured sparsity patterns.

Example Training/Inference API Call

The deployment of the optimized GLM-4.5 model, incorporating INT4 quantization, 2:4 sparsity, and mixed precision, would typically be managed through an inference serving framework. Below is a conceptual example using a hypothetical serving framework:

# Conceptual example for serving GLM-4.5 with vLLM, assuming AWQ and sparsity are handled
vllm serve glm-4.5-optimized --dtype float16 --quantization awq --sparsity 2:4 --tensor-parallel-size <N>

Parameters Explained:

  • --dtype float16: FP16 computations for activations
  • --quantization awq: INT4 quantization via AWQ
  • --sparsity 2:4: 2:4 structured sparsity pattern
  • --tensor-parallel-size: Model parallelism degree

Implementation Pipeline

  1. 1. Apply AWQ quantization to model
  2. 2. Implement 2:4 structured sparsity
  3. 3. Configure mixed precision settings
  4. 4. Load optimized model in serving framework

Performance Benchmarks

Summary of Compression Techniques and Expected Performance Gains

Technique Target Layers Memory Reduction Speedup Accuracy Considerations
INT4 PTQ (AWQ) Linear Layers ~4x (weights) ~1.5-2x Calibration with representative data, protection of salient weights
Structured 2:4 Sparsity Attention & FFN Linear ~2x (sparse weights) ~1.2-2x Pruning criteria, fine-tuning to recover accuracy
Mixed Precision Inference All Computations ~2x (activations/weights) ~2-3x Precision-sensitive operations in FP32

Memory Footprint Reduction

The combined application of INT4 PTQ and 2:4 sparsity is expected to lead to a dramatic reduction in the memory footprint of the GLM-4.5 model. INT4 quantization alone can reduce the size of the quantized weights by approximately 4 times compared to FP16.

Memory Calculation Example:

Baseline (FP16): 355B parameters × 2 bytes = 710GB

INT4 Quantization: 710GB ÷ 4 = ~177.5GB

+ 2:4 Sparsity: 177.5GB ÷ 2 = ~88.75GB

Total Reduction: ~8x (for targeted layers)

Considering that linear layers often constitute the vast majority of parameters in LLMs, this translates to a substantial overall reduction, potentially bringing the model size down to a range that is more manageable for deployment on multiple high-end GPUs.

Inference Speedup

Inference speedup is a critical goal, and the proposed techniques offer multiple avenues for acceleration. The cumulative effect of these techniques, applied strategically across the model, is expected to significantly reduce latency and increase tokens processed per second.

INT4 Operations

1.5-2x

Reduced data movement and more operations per cycle via Volta INT4 capabilities.

2:4 Sparsity

1.2-2x

Skipping zero computations with structured sparsity patterns, even on Volta via optimized kernels.

FP16 Tensor Cores

2-3x

Native Volta Tensor Core support for FP16 matrix operations.

Accuracy Considerations and Maintenance

Maintaining near-lossless accuracy is paramount. Aggressive compression techniques inherently risk degrading model performance, but careful implementation can minimize this impact.

Accuracy Preservation Strategies

  • • AWQ calibration with activation statistics
  • • Fine-tuning after pruning to recover accuracy
  • • Mixed precision for numerically stable operations
  • • Layer-wise optimization sensitivity analysis

Target Accuracy Metrics

  • • Within 0.5% - 1% of original model performance
  • • Key benchmark task preservation
  • • Robustness across diverse inputs
  • • Long-context capability maintenance

Iterative Optimization: The overall strategy involves applying techniques, evaluating accuracy on relevant benchmarks, and adjusting parameters or fine-tuning as needed to maintain optimal performance.

Discussion

Synergy of Quantization and Sparsity in LLMs

The combination of quantization and sparsity offers a powerful synergistic effect for compressing Large Language Models (LLMs) like GLM-4.5. While each technique provides individual benefits, their joint application can lead to compounded gains in efficiency.

graph LR A["Original Weights
FP16/FP32"] --> B["Apply 2:4 Sparsity
50% Reduction"] B --> C["Sparse Weights
50% Non-zero"] C --> D["Apply INT4 Quantization
4x Reduction"] D --> E["Final Compressed Weights
8x Total Reduction"] style A fill:#f1f5f9,stroke:#64748b,stroke-width:2px,color:#1e293b style B fill:#fef3c7,stroke:#f59e0b,stroke-width:2px,color:#92400e style C fill:#fef3c7,stroke:#f59e0b,stroke-width:2px,color:#92400e style D fill:#dcfce7,stroke:#10b981,stroke-width:2px,color:#065f46 style E fill:#dbeafe,stroke:#3b82f6,stroke-width:2px,color:#1e40af

Compounded Benefits: When applied together, a model can first be pruned to a 2:4 sparse pattern, and then the remaining non-zero weights can be quantized to INT4. This dual compression can lead to significantly smaller model sizes than either technique alone and can further accelerate inference.

Impact of Mixture-of-Experts (MoE) Architecture

The Mixture-of-Experts (MoE) architecture in GLM-4.5 presents unique opportunities and challenges for model compression. MoE models are inherently more parameter-efficient during inference than dense models of the same total size because only a subset of experts is activated for each input token.

Opportunities

  • • Inherent dynamic sparsity from expert routing
  • • Individual expert networks can be compressed
  • • Reduced storage per expert via quantization
  • • Layer-wise optimization flexibility

Challenges

  • • Gating mechanism precision sensitivity
  • • Varying expert sensitivity to compression
  • • Communication overhead considerations
  • • Balance between compression and routing accuracy

Reference: Frameworks like "Mixture Compressor" [45] suggest adaptive bit-widths or sparsity levels per expert could be beneficial for MoE architectures.

Viability of INT4 and Hardware Efficiency of 2:4 Sparsity

The viability of INT4 precision for LLMs has been increasingly demonstrated by various research and industry efforts, including its application to models like GLM-4.5-Air [196].

INT4 on Volta (CUDA 8.6)

  • • DP4A instructions for dot products
  • • mma.m16n8k64 for INT4 matrix multiplies
  • • Requires custom kernel development
  • • AWQ essential for accuracy preservation

2:4 Sparsity Efficiency

  • • Best on Ampere+ with Sparse Tensor Cores
  • • Volta: memory bandwidth savings
  • • Optimized software kernels possible
  • • cuSPARSELt principles applicable

Hardware Reality: While Volta GPUs don't have dedicated Sparse Tensor Cores like Ampere, the structured 2:4 pattern still enables compressed storage and optimized software kernels for memory bandwidth savings.

Future Work: Dynamic Sparsity and Lower Precision

Looking ahead, several avenues for further optimizing models like GLM-4.5 exist. Dynamic sparsity is an emerging area where the sparsity pattern is not fixed after training but can adapt at inference time based on the input.

Dynamic Sparsity

Sparsity patterns that adapt at inference time based on input characteristics, potentially offering greater compression and speedup.

Lower Precision Formats

Exploring 2-bit or ternary weights for additional memory savings, requiring specialized quantization algorithms.

Hybrid Precision Models

Different layers or parts of layers using optimal numerical formats based on sensitivity analysis.

Algorithm-Hardware Co-design

Future hardware architectures evolving to better support advanced compression techniques.

Research Directions

Continued research into robust PTQ methods that can handle diverse model architectures and datasets with minimal calibration overhead will be vital for widespread adoption of these advanced compression techniques.

Dynamic Sparsity 2-bit Quantization Hybrid Precision Hardware Co-design