TensorRT-LLMs/examples/auto_deploy/README.md

🔥🚀⚡ AutoDeploy

Seamless Model Deployment from PyTorch to TRT-LLM

AutoDeploy is a prototype feature in beta stage designed to simplify and accelerate the deployment of PyTorch models, including off-the-shelf models like those from Hugging Face, to TensorRT-LLM. It automates graph transformations to integrate inference optimizations such as tensor parallelism, KV-caching and quantization. AutoDeploy supports optimized in-framework deployment, minimizing the amount of manual modification needed.

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Motivation & Approach

Deploying large language models (LLMs) can be challenging, especially when balancing ease of use with high performance. Teams need simple, intuitive deployment solutions that reduce engineering effort, speed up the integration of new models, and support rapid experimentation without compromising performance.

AutoDeploy addresses these challenges with a streamlined, (semi-)automated pipeline that transforms in-framework PyTorch models, including Hugging Face models, into optimized inference-ready models for TRT-LLM. It simplifies deployment, optimizes models for efficient inference, and bridges the gap between simplicity and performance.

Key Features:

• Seamless Model Transition: Automatically converts PyTorch/Hugging Face models to TRT-LLM without manual rewrites.
• Unified Model Definition: Maintain a single source of truth with your original PyTorch/Hugging Face model.
• Optimized Inference: Built-in transformations for sharding, quantization, KV-cache integration, MHA fusion, and CudaGraph optimization.
• Immediate Deployment: Day-0 support for models with continuous performance enhancements.
• Quick Setup & Prototyping: Lightweight pip package for easy installation with a demo environment for fast testing.

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Get Started

1. Install AutoDeploy:

AutoDeploy is accessible through TRT-LLM installation.

sudo apt-get -y install libopenmpi-dev && pip3 install --upgrade pip setuptools && pip3 install tensorrt_llm

You can refer to TRT-LLM installation guide for more information.

2. Run Llama Example:

You are ready to run an in-framework LLama Demo now.

The general entrypoint to run the auto-deploy demo is the build_and_run_ad.py script, Checkpoints are loaded directly from Huggingface (HF) or a local HF-like directory:

cd examples/auto_deploy
python build_and_run_ad.py --model "TinyLlama/TinyLlama-1.1B-Chat-v1.0"

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Support Matrix

AutoDeploy streamlines the model deployment process through an automated workflow designed for efficiency and performance. The workflow begins with a PyTorch model, which is exported using torch.export to generate a standard Torch graph. This graph contains core PyTorch ATen operations alongside custom attention operations, determined by the attention backend specified in the configuration.

The exported graph then undergoes a series of automated transformations, including graph sharding, KV-cache insertion, and GEMM fusion, to optimize model performance. After these transformations, the graph is compiled using one of the supported compile backends (like torch-opt), followed by deploying it via the TRT-LLM runtime.

Supported Models

Bring Your Own Model: AutoDeploy leverages torch.export and dynamic graph pattern matching, enabling seamless integration for a wide variety of models without relying on hard-coded architectures.

Additionally, we have officially verified support for the following models:

Click to expand supported models list

Model Series	HF Model Card	Model Factory	Precision	World Size	Runtime	Compile Backend			Attention Backend
						torch-simple	torch-compile	torch-opt	triton	flashinfer	MultiHeadLatentAttention
LLaMA	meta-llama/Llama-2-7b-chat-hf meta-llama/Meta-Llama-3.1-8B-Instruct meta-llama/Llama-3.1-70B-Instruct codellama/CodeLlama-13b-Instruct-hf	AutoModelForCausalLM	BF16	1,2,4	demollm, trtllm	✅	✅	✅	✅	✅	n/a
LLaMA-4	meta-llama/Llama-4-Scout-17B-16E-Instruct meta-llama/Llama-4-Maverick-17B-128E-Instruct	AutoModelForImageTextToText	BF16	1,2,4,8	demollm, trtllm	✅	✅	❌	✅	✅	n/a
Nvidia Minitron	nvidia/Llama-3_1-Nemotron-51B-Instruct nvidia/Llama-3.1-Minitron-4B-Width-Base nvidia/Llama-3.1-Minitron-4B-Depth-Base	AutoModelForCausalLM	BF16	1,2,4	demollm, trtllm	✅	✅	✅	✅	✅	n/a
Nvidia Model Optimizer	nvidia/Llama-3.1-8B-Instruct-FP8 nvidia/Llama-3.1-405B-Instruct-FP8	AutoModelForCausalLM	FP8	1,2,4	demollm, trtllm	✅	✅	✅	✅	✅	n/a
DeepSeek	deepseek-ai/DeepSeek-R1-Distill-Llama-70B	AutoModelForCausalLM	BF16	1,2,4	demollm, trtllm	✅	✅	✅	✅	✅	n/a
Mistral	mistralai/Mixtral-8x7B-Instruct-v0.1 mistralai/Mistral-7B-Instruct-v0.3	AutoModelForCausalLM	BF16	1,2,4	demollm, trtllm	✅	✅	✅	✅	✅	n/a
BigCode	bigcode/starcoder2-15b	AutoModelForCausalLM	FP32	1,2,4	demollm, trtllm	✅	✅	✅	✅	✅	n/a
Deepseek-V3	deepseek-ai/DeepSeek-V3	AutoModelForCausalLM	BF16	1,2,4	demollm	✅	❌	❌	n/a	n/a	✅
Phi4	microsoft/phi-4 microsoft/Phi-4-reasoning microsoft/Phi-4-reasoning-plus	AutoModelForCausalLM	BF16	1,2,4	demollm, trtllm	✅	✅	✅	✅	✅	n/a
Phi3/2	microsoft/Phi-3-mini-4k-instruct microsoft/Phi-3-mini-128k-instruct microsoft/Phi-3-medium-4k-instruct microsoft/Phi-3-medium-128k-instruct microsoft/Phi-3.5-mini-instruct	AutoModelForCausalLM	BF16	1,2,4	demollm, trtllm	✅	✅	✅(partly)	✅	❌	n/a

Runtime Integrations

AutoDeploy runs natively with the entire TRT-LLM stack via the LLM API. In addition, we provide a light-weight wrapper of the LLM API for onboarding and debugging new models:

"runtime"	Description
trtllm	A robust, production-grade runtime optimized for high-performance inference.
demollm	A lightweight runtime wrapper designed for development and testing, featuring a naive scheduler and KV-cache manager for simplified debugging and testing.

Compile Backends

AutoDeploy supports multiple backends for compiling the exported Torch graph:

"compile_backend"	Description
torch-simple	Exports the graph without additional optimizations.
torch-compile	Applies torch.compile to the graph after all AutoDeploy transformations have been completed.
torch-cudagraph	Performs CUDA graph capture (without torch.compile).
torch-opt	Uses torch.compile along with CUDA Graph capture to enhance inference performance.

Attention backends

Optimize attention operations using different attention kernel implementations:

"attn_backend"	Description
triton	Custom fused multi-head attention (MHA) with KV Cache kernels for efficient attention processing.
flashinfer	Uses off-the-shelf optimized attention kernels with KV Cache from the flashinfer library.

Precision Support

AutoDeploy supports a range of precision formats to enhance model performance, including:

• BF16, FP32
• Quantization formats like FP8.

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Advanced Usage

Example Run Script (build_and_run_ad.py)

To build and run AutoDeploy example, use the build_and_run_ad.py script:

cd examples/auto_deploy
python build_and_run_ad.py --model "TinyLlama/TinyLlama-1.1B-Chat-v1.0"

You can arbitrarily configure your experiment. Use the -h/--help flag to see available options:

python build_and_run_ad.py --help

Below is a non-exhaustive list of common config options:

Configuration Key	Description
--model	The HF model card or path to a HF checkpoint folder
--args.model-factory	Choose model factory implementation ("AutoModelForCausalLM", ...)
--args.skip-loading-weights	Only load the architecture, not the weights
--args.model-kwargs	Extra kwargs that are being passed to the model initializer in the model factory
--args.tokenizer-kwargs	Extra kwargs that are being passed to the tokenizer initializer in the model factory
--args.world-size	The number of GPUs used for auto-sharding the model
--args.runtime	Specifies which type of Engine to use during runtime ("demollm" or "trtllm")
--args.compile-backend	Specifies how to compile the graph at the end
--args.attn-backend	Specifies kernel implementation for attention
--args.mla-backend	Specifies implementation for multi-head latent attention
--args.max-seq-len	Maximum sequence length for inference/cache
--args.max-batch-size	Maximum dimension for statically allocated KV cache
--args.attn-page-size	Page size for attention
--prompt.batch-size	Number of queries to generate
--benchmark.enabled	Whether to run the built-in benchmark (true/false)

For default values and additional configuration options, refer to the ExperimentConfig class in build_and_run_ad.py file.

Here is a more complete example of using the script:

cd examples/auto_deploy
python build_and_run_ad.py \
--model "TinyLlama/TinyLlama-1.1B-Chat-v1.0" \
--args.world-size 2 \
--args.runtime "demollm" \
--args.compile-backend "torch-compile" \
--args.attn-backend "flashinfer" \
--benchmark.enabled True

Logging Level

Use the following env variable to specify the logging level of our built-in logger ordered by decreasing verbosity;

AUTO_DEPLOY_LOG_LEVEL=DEBUG
AUTO_DEPLOY_LOG_LEVEL=INFO
AUTO_DEPLOY_LOG_LEVEL=WARNING
AUTO_DEPLOY_LOG_LEVEL=ERROR
AUTO_DEPLOY_LOG_LEVEL=INTERNAL_ERROR

The default level is INFO.

Model Evaluation with LM Evaluation Harness

lm-evaluation-harness is supported. To run the evaluation, please use the following command:

# model is defined the same as above. Other config args can also be specified in the model_args (comma separated).
# You can specify any tasks supported with lm-evaluation-harness.
cd examples/auto_deploy
python lm_eval_ad.py \
--model autodeploy --model_args model=meta-llama/Meta-Llama-3.1-8B-Instruct,world_size=2 --tasks mmlu

Mixed-precision Quantization using TensorRT Model Optimizer

TensorRT Model Optimizer AutoQuantize algorithm is a PTQ algorithm from ModelOpt which quantizes a model by searching for the best quantization format per-layer while meeting the performance constraint specified by the user. This way, AutoQuantize enables to trade-off model accuracy for performance.

Currently AutoQuantize supports only effective_bits as the performance constraint (for both weight-only quantization and weight & activation quantization). See AutoQuantize documentation for more details.

1. Quantize a model with ModelOpt

Refer to NVIDIA TensorRT Model Optimizer for generating quantized model checkpoint.

2. Deploy the quantized model with AutoDeploy

cd examples/auto_deploy
python build_and_run_ad.py --model "<MODELOPT_CKPT_PATH>" --args.world-size 1

Incorporating auto_deploy into your own workflow

AutoDeploy can be seamlessly integrated into your existing workflows using TRT-LLM's LLM high-level API. This section provides a blueprint for configuring and invoking AutoDeploy within your custom applications.

Here is an example of how you can build an LLM object with AutoDeploy integration:

from tensorrt_llm._torch.auto_deploy import LLM


# Construct the LLM high-level interface object with autodeploy as backend
llm = LLM(
    model=<HF_MODEL_CARD_OR_DIR>,
    world_size=<DESIRED_WORLD_SIZE>,
    compile_backend="torch-compile",
    model_kwargs={"num_hidden_layers": 2}, # test with smaller model configuration
    attn_backend="flashinfer", # choose between "triton" and "flashinfer"
    attn_page_size=64, # page size for attention (tokens_per_block, should be == max_seq_len for triton)
    skip_loading_weights=False,
    model_factory="AutoModelForCausalLM", # choose appropriate model factory
    mla_backend="MultiHeadLatentAttention", # for models that support MLA
    free_mem_ratio=0.8, # fraction of available memory for cache
    simple_shard_only=False, # tensor parallelism sharding strategy
    max_seq_len=<MAX_SEQ_LEN>,
    max_batch_size=<MAX_BATCH_SIZE>,
)

Please consult the AutoDeploy LLM API and the AutoDeployConfig class for more detail on how AutoDeploy is configured via the **kwargs of the LLM API.

Expert Configuration of LLM API

For expert TensorRT-LLM users, we also expose the full set of LlmArgs at your own risk (the argument list diverges from TRT-LLM's argument list):

Click to expand for more details on using LlmArgs directly

• All config fields that are used by the AutoDeploy core pipeline (i.e. the InferenceOptimizer) are exclusively exposed in the AutoDeployConfig class. Please make sure to refer to those first.
• For expert users we expose the full set of LlmArgs that can be used to configure the AutoDeploy LLM API including runtime options.
• Note that some fields in the full LlmArgs object are overlapping, duplicated, and/or ignored in AutoDeploy, particularly arguments pertaining to configuring the model itself since AutoDeploy's model ingestion+optimize pipeline significantly differs from the default manual workflow in TensorRT-LLM.
• However, with the proper care the full LlmArgs objects can be used to configure advanced runtime options in TensorRT-LLM.
• Note that any valid field can be simply provided as keyword argument ("**kwargs") to the AutoDeploy LLM API.

Expert Configuration of build_and_run_ad.py

For expert users, build_and_run_ad.py provides advanced configuration capabilities through a flexible argument parser powered by PyDantic Settings and OmegaConf. You can use dot notation for CLI arguments, provide multiple YAML configuration files, and leverage sophisticated configuration precedence rules to create complex deployment configurations.

Click to expand for detailed configuration examples

CLI Arguments with Dot Notation

The script supports flexible CLI argument parsing using dot notation to modify nested configurations dynamically. You can target any field in both the ExperimentConfig and nested AutoDeployConfig/LlmArgs objects:

# Configure model parameters
# NOTE: config values like num_hidden_layers are automatically resolved into the appropriate nested
# dict value ``{"args": {"model_kwargs": {"num_hidden_layers": 10}}}`` although not explicitly
# specified as CLI arg
python build_and_run_ad.py \
  --model "meta-llama/Meta-Llama-3.1-8B-Instruct" \
  --args.model-kwargs.num-hidden-layers=10 \
  --args.model-kwargs.hidden-size=2048 \
  --args.tokenizer-kwargs.padding-side=left

# Configure runtime and backend settings
python build_and_run_ad.py \
  --model "TinyLlama/TinyLlama-1.1B-Chat-v1.0" \
  --args.world-size=2 \
  --args.compile-backend=torch-opt \
  --args.attn-backend=flashinfer

# Configure prompting and benchmarking
python build_and_run_ad.py \
  --model "microsoft/phi-4" \
  --prompt.batch-size=4 \
  --prompt.sp-kwargs.max-tokens=200 \
  --prompt.sp-kwargs.temperature=0.7 \
  --benchmark.enabled=true \
  --benchmark.bs=8 \
  --benchmark.isl=1024

YAML Configuration Files

Both ExperimentConfig and AutoDeployConfig/LlmArgs inherit from DynamicYamlMixInForSettings, enabling you to provide multiple YAML configuration files that are automatically deep-merged at runtime.

Create a YAML configuration file (e.g., my_config.yaml):

# my_config.yaml
args:
  model_kwargs:
    num_hidden_layers: 12
    hidden_size: 1024
  world_size: 4
  compile_backend: torch-compile
  attn_backend: triton
  max_seq_len: 2048
  max_batch_size: 16
  transforms:
    sharding:
      strategy: auto
    quantization:
      enabled: false

prompt:
  batch_size: 8
  sp_kwargs:
    max_tokens: 150
    temperature: 0.8
    top_k: 50

benchmark:
  enabled: true
  num: 20
  bs: 4
  isl: 1024
  osl: 256

Create an additional override file (e.g., production.yaml):

# production.yaml
args:
  world_size: 8
  compile_backend: torch-opt
  max_batch_size: 32

benchmark:
  enabled: false

Then use these configurations:

# Using single YAML config
python build_and_run_ad.py \
  --model "meta-llama/Meta-Llama-3.1-8B-Instruct" \
  --yaml-configs my_config.yaml

# Using multiple YAML configs (deep merged in order, later files have higher priority)
python build_and_run_ad.py \
  --model "meta-llama/Meta-Llama-3.1-8B-Instruct" \
  --yaml-configs my_config.yaml production.yaml

# Targeting nested AutoDeployConfig with separate YAML
python build_and_run_ad.py \
  --model "meta-llama/Meta-Llama-3.1-8B-Instruct" \
  --yaml-configs my_config.yaml \
  --args.yaml-configs autodeploy_overrides.yaml

Configuration Precedence and Deep Merging

The configuration system follows a strict precedence order where higher priority sources override lower priority ones:

1. CLI Arguments (highest priority) - Direct command line arguments
2. YAML Configs - Files specified via --yaml-configs and --args.yaml-configs
3. Default Settings (lowest priority) - Built-in defaults from the config classes

Deep Merging: Unlike simple overwriting, deep merging intelligently combines nested dictionaries recursively. For example:

# Base config
args:
  model_kwargs:
    num_hidden_layers: 10
    hidden_size: 1024
  max_seq_len: 2048

# Override config
args:
  model_kwargs:
    hidden_size: 2048  # This will override
    # num_hidden_layers: 10 remains unchanged
  world_size: 4  # This gets added

Nested Config Behavior: When using nested configurations, outer YAML configs become init settings for inner objects, giving them higher precedence:

# The outer yaml-configs affects the entire ExperimentConfig
# The inner args.yaml-configs affects only the AutoDeployConfig
python build_and_run_ad.py \
  --model "meta-llama/Meta-Llama-3.1-8B-Instruct" \
  --yaml-configs experiment_config.yaml \
  --args.yaml-configs autodeploy_config.yaml \
  --args.world-size=8  # CLI override beats both YAML configs

Built-in Default Configuration

Both AutoDeployConfig and LlmArgs classes automatically load a built-in default.yaml configuration file that provides sensible defaults for the AutoDeploy inference optimizer pipeline. This file is specified in the _get_config_dict() function and defines default transform configurations for graph optimization stages.

The built-in defaults are automatically merged with your configurations at the lowest priority level, ensuring that your custom settings always override the defaults. You can inspect the current default configuration to understand the baseline transform pipeline:

# View the default configuration
cat tensorrt_llm/_torch/auto_deploy/config/default.yaml

# Override specific transform settings
python build_and_run_ad.py \
  --model "TinyLlama/TinyLlama-1.1B-Chat-v1.0" \
  --args.transforms.export-to-gm.strict=true

Roadmap

Check out our Github Project Board to learn more about the current progress in AutoDeploy and where you can help.

Disclaimer

This project is in active development and is currently in an early (beta) stage. The code is in prototype, subject to change, and may include backward-incompatible updates. While we strive for correctness, we provide no guarantees regarding functionality, stability, or reliability. Use at your own risk.