Implementation:Vllm project Vllm Model Inspection

Knowledge Sources	vllm
Domains	Model_Inspection, Debugging, Quantization
Last Updated	2026-02-08 00:00 GMT

Overview

Provides utilities for inspecting and formatting PyTorch model architecture into human-readable hierarchical string representations.

Description

This module contains functions to recursively traverse a torch.nn.Module tree and produce a formatted string representation similar to Hugging Face Transformers model summaries. It groups identical numbered children (such as repeated decoder layers) using range notation (e.g., "0-27, 29-47: 47 x LlamaDecoderLayer"), extracts quantization method and scheme information from modules, and formats the output with proper indentation. The main entry point is format_model_inspection, which returns the complete formatted string.

Usage

Use this when you need to inspect or log the architecture of a loaded vLLM model, particularly to verify quantization schemes applied to specific layers, debug model loading issues, or document the structure of a deployed model configuration.

Code Reference

Source Location

• Repository: vllm
• File: vllm/model_inspection.py
• Lines: 1-136

Signature

def _get_module_info(module: nn.Module) -> str:
    """Get info string for a module, including quantization details."""

def _get_child_signature(child: nn.Module) -> str:
    """Get a signature for a child module to detect duplicates."""

def _format_index_ranges(indices: list[int]) -> str:
    """Format indices into range notation (e.g., [0,1,2,4,5,6] -> '0-2, 4-6')."""

def _format_module_tree(
    module: nn.Module, name: str = "", indent: int = 0,
) -> list[str]:
    """Format a module tree with indentation, grouping identical layers."""

def format_model_inspection(model: nn.Module) -> str:
    """Format a model into a transformers-style hierarchical string."""

Import

from vllm.model_inspection import format_model_inspection

I/O Contract

Inputs

Name	Type	Required	Description
model	torch.nn.Module	Yes	The PyTorch model to inspect and format
module	torch.nn.Module	Yes	Individual module for info extraction (internal helpers)
name	str	No	Name prefix for the current module in the tree (default empty)
indent	int	No	Current indentation level (default 0)

Outputs

Name	Type	Description
formatted_string	str	Hierarchical string representation of the model architecture with quantization info and grouped layers

Usage Examples

from vllm.model_inspection import format_model_inspection
import torch.nn as nn

# Inspect a loaded vLLM model
# model = vllm_engine.model_executor.driver_worker.model_runner.model
formatted = format_model_inspection(model)
print(formatted)

# Example output:
# LlamaForCausalLM(
#   (model): LlamaModel(
#     (embed_tokens): VocabParallelEmbedding(...)
#     (layers): ModuleList(
#       (0-31): 32 x LlamaDecoderLayer(
#         (self_attn): LlamaAttention(quant=AWQLinearMethod, ...)
#         (mlp): LlamaMLP(...)
#       )
#     )
#     (norm): LlamaRMSNorm(...)
#   )
#   (lm_head): ParallelLMHead(...)
# )

Related Pages

• Environment:Vllm_project_Vllm_Python