Understanding VLLM Architecture: From Request to Response
Step Snap 1 [The Request Journey: High-Level Overview]
VLLM's architecture is designed for high-throughput, memory-efficient inference of large language models. Here's how it processes requests:
┌──────────────────┐ ┌───────────────┐ ┌────────────────┐ ┌──────────────┐
│ │ │ │ │ │ │ │
│ User Request │────▶│ LLM Engine │────▶│ Scheduler │────▶│ Worker │
│ (API/Interface) │ │ (Orchestrator)│ │ (Queue Manager)│ │(GPU Executor)│
│ │ │ │ │ │ │ │
└──────────────────┘ └───────────────┘ └────────────────┘ └──────────────┘
│ │
▼ ▼
┌────────────────┐ ┌──────────────┐
│ │ │ │
│ Block Manager │◀───▶│ Cache Engine │
│ (Memory Blocks)│ │(Memory Alloc.)│
│ │ │ │
└────────────────┘ └──────────────┘
When a request arrives, it flows through these components:
Entry Points: API server, CLI, or direct library calls
LLM Engine: Converts requests into SequenceGroups and orchestrates processing
Scheduler: Assigns priorities and manages request queues
Worker: Executes the actual model computations on GPU
Block Manager & Cache Engine: Handle memory allocation and KV cache management
The modular design allows VLLM to process multiple requests in parallel while efficiently utilizing GPU memory.
Step Snap 2 [Core Components: Under the Hood]
Diving deeper into the key components and their interactions:
┌─────────────────────────────────────────────────────────────────────────┐
│ LLM Engine │
└───────────────────────────────────┬─────────────────────────────────────┘
│ step()
▼
┌─────────────────────────────────────────────────────────────────────────┐
│ Scheduler │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ Waiting │ │ Running │ │ Swapped │ │
│ │ Queue │───▶│ Queue │◀──▶│ Queue │ │
│ └──────────────┘ └──────────────┘ └──────────────┘ │
│ │ │ │ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ Block Manager │ │
│ │ (Physical Blocks ↔ Logical Blocks Mapping) │ │
│ └──────────────────────────────────────────────────────┘ │
└─────────────────────────────────┬─────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────────┐
│ Worker │
│ ┌──────────────────┐ ┌──────────────────┐ ┌──────────────────┐ │
│ │ Model Runner │───▶│ KV Cache Manager│───▶│ Output Processor│ │
│ └──────────────────┘ └──────────────────┘ └──────────────────┘ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌──────────────────────────────────────────────────────────────────┐ │
│ │ Cache Engine │ │
│ │ (GPU Memory Management and Allocation) │ │
│ └──────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘
Key components and their roles:
Scheduler (core/scheduler.py):
Maintains three queues: waiting, running, and swapped
Implements scheduling policies (FIFO by default)
Decides which SequenceGroups to process in each step
Block Manager (core/block_manager.py):
Divides memory into equal-sized blocks
Maps logical blocks (sequence tokens) to physical memory blocks
Handles block allocation, deallocation, and swapping
Worker (worker/worker.py):
Abstracts GPU computation
Manages model execution and KV cache
One worker typically corresponds to one GPU
Cache Engine (worker/cache_engine.py):
Handles low-level memory allocation
Manages physical memory across devices (GPU and CPU)
Step Snap 3 [The Step Function: VLLM's Heartbeat]
The step() function in LLM Engine is the core execution unit of VLLM, orchestrating all components:
┌─────────────────────────────────────────────────────────────────┐
│ LLM Engine step() │
└──────────────────────────────┬──────────────────────────────────┘
│
┌──────────────┴──────────────┐
│ │
▼ ▼
┌───────────────────────────┐ ┌───────────────────────────┐
│ Scheduler step 1 │ │ Worker execute │
│ (Schedule next sequences) │──▶│ (Model computation) │
└───────────────────────────┘ └─────────────┬─────────────┘
│
▼
┌───────────────────────────┐
│ Scheduler step 2 │
│ (Process model outputs) │
└─────────────┬─────────────┘
│
▼
┌───────────────────────────┐
│ Return outputs/tokens │
│ (Back to user/API call) │
└───────────────────────────┘
The step function:
Planning Phase (Scheduler Step 1):
Determines which sequences to run in this iteration
Plans token swapping operations (in/out/copy)
May preempt/reorder sequences based on scheduling policy
Execution Phase (Worker Execute):
Performs the actual model forward pass
Processes batched inputs efficiently
Manages attention computation with the KV cache
Processing Phase (Scheduler Step 2):
Decodes model outputs
Updates scheduler state based on sampling results
Releases resources for completed requests
Output Phase:
Creates and returns generation results
Updates request status (complete/partial)
This step function executes repeatedly until all requests are fulfilled or timeout occurs.
Step Snap 4 [Memory Management: VLLM's Secret Sauce]
VLLM's exceptional performance comes from its innovative memory management techniques:
┌───────────────────────────────────────────────────────────────────┐
│ GPU Memory Organization │
│ │
│ ┌─────────────┐ ┌─────────────────────────────────────────────┐ │
│ │ Model Params│ │ KV Cache │ │
│ │ (weights) │ │ │ │
│ └─────────────┘ │ ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐ │ │
│ ┌─────────────┐ │ │Block │ │Block │ │Block │ .... │Block │ │ │
│ │ Activations │ │ │ 1 │ │ 2 │ │ 3 │ │ N │ │ │
│ └─────────────┘ │ └──────┘ └──────┘ └──────┘ └──────┘ │ │
│ └─────────────────────────────────────────────┘ │
└───────────────────────────────────────────────────────────────────┘
▲
│
▼
┌───────────────────────────────────────────────────────────────────┐
│ CPU Memory │
│ │
│ ┌─────────────────────────────────────────────────┐ │
│ │ Swap Space │ │
│ │ ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐ │ │
│ │ │Block │ │Block │ │Block │ .... │Block │ │ │
│ │ │ X │ │ Y │ │ Z │ │ M │ │ │
│ │ └──────┘ └──────┘ └──────┘ └──────┘ │ │
│ └─────────────────────────────────────────────────┘ │
└───────────────────────────────────────────────────────────────────┘
VLLM's memory management innovations include:
PagedAttention:
Divides KV cache into fixed-size blocks
Enables non-contiguous memory allocation for sequences
Eliminates memory fragmentation issues
Continuous Batching:
Dynamically adds new requests to ongoing batches
Maintains high GPU utilization
Eliminates need to wait for full batches
Block-level GPU-CPU Swapping:
Moves less active sequences to CPU memory
Prioritizes active sequences in GPU memory
Efficiently handles context switching
Prefix Caching:
Reuses computation for shared prefix tokens
Optimizes performance for similar prompts
Implemented via the Evictor component
These memory management techniques allow VLLM to handle more concurrent requests and longer sequences than traditional inference engines.
Step Snap 5 [Model Support: The Extensible Framework]
VLLM provides a flexible architecture to support various model types:
┌─────────────────────────────────────────────────────────────────┐
│ model_executor │
│ │
│ ┌─────────────────────────┐ ┌─────────────────────────────┐ │
│ │ layers │ │ models │ │
│ │ │ │ │ │
│ │ ┌─────────────────┐ │ │ ┌─────────────────────┐ │ │
│ │ │ Attention │ │ │ │ Llama │ │ │
│ │ ├─────────────────┤ │ │ ├─────────────────────┤ │ │
│ │ │ MLP │ │ │ │ Mistral │ │ │
│ │ ├─────────────────┤ │ │ ├─────────────────────┤ │ │
│ │ │ Embedding │────┼───▶│ │ Falcon │ │ │
│ │ ├─────────────────┤ │ │ ├─────────────────────┤ │ │
│ │ │ Normalization │ │ │ │ GPT-NeoX │ │ │
│ │ ├─────────────────┤ │ │ ├─────────────────────┤ │ │
│ │ │ Quantized Layers│ │ │ │ Qwen │ │ │
│ │ └─────────────────┘ │ │ └─────────────────────┘ │ │
│ └─────────────────────────┘ └─────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────┘
The model support framework consists of:
Layer Implementations (model_executor/layers/):
Optimized implementations of common model components
Special attention layers for PagedAttention
Support for various quantization methods
Model Architectures (model_executor/models/):
Architecture-specific implementations
Maps HuggingFace models to VLLM-optimized versions
Supports parameter adaptation for quantized models
Worker Abstraction:
Provides a consistent interface for different model types
Handles differences in input/output processing
Manages tensor parallelism for multi-GPU execution
To add support for a new model architecture, developers typically need to:
Create a new model implementation in model_executor/models/
Implement any unique layers in model_executor/layers/
Register the model with VLLM's model loader system
This extensible architecture has enabled VLLM to rapidly support a wide range of model families, including Llama, Mistral, Falcon, GPT-NeoX, and Qwen models.
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