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Understanding VLLM Architecture: From Request to Response

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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.

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Step Snap 2 [Core Components: Under the Hood]

Diving deeper into the key components and their interactions:

Key components and their roles:

  1. 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

  2. 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

  3. Worker (worker/worker.py):

    • Abstracts GPU computation

    • Manages model execution and KV cache

    • One worker typically corresponds to one GPU

  4. Cache Engine (worker/cache_engine.py):

    • Handles low-level memory allocation

    • Manages physical memory across devices (GPU and CPU)

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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:

The step function:

  1. 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

  2. Execution Phase (Worker Execute):

    • Performs the actual model forward pass

    • Processes batched inputs efficiently

    • Manages attention computation with the KV cache

  3. Processing Phase (Scheduler Step 2):

    • Decodes model outputs

    • Updates scheduler state based on sampling results

    • Releases resources for completed requests

  4. 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.

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Step Snap 4 [Memory Management: VLLM's Secret Sauce]

VLLM's exceptional performance comes from its innovative memory management techniques:

VLLM's memory management innovations include:

  1. PagedAttention:

    • Divides KV cache into fixed-size blocks

    • Enables non-contiguous memory allocation for sequences

    • Eliminates memory fragmentation issues

  2. Continuous Batching:

    • Dynamically adds new requests to ongoing batches

    • Maintains high GPU utilization

    • Eliminates need to wait for full batches

  3. Block-level GPU-CPU Swapping:

    • Moves less active sequences to CPU memory

    • Prioritizes active sequences in GPU memory

    • Efficiently handles context switching

  4. 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.

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Step Snap 5 [Model Support: The Extensible Framework]

VLLM provides a flexible architecture to support various model types:

The model support framework consists of:

  1. Layer Implementations (model_executor/layers/):

    • Optimized implementations of common model components

    • Special attention layers for PagedAttention

    • Support for various quantization methods

  2. Model Architectures (model_executor/models/):

    • Architecture-specific implementations

    • Maps HuggingFace models to VLLM-optimized versions

    • Supports parameter adaptation for quantized models

  3. 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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