TensorRT-LLMs/examples/high-level-api

High-level API

We are working on a Python high-level API(HLAPI) for LLM workflow, which is still in incubation and may change later. Here we show you a preview of how it works and how to use it.

Note that the APIs are not stable and we appreciate your patience and understanding as we improve this API.

HLAPI Supported Model

• LLaMA (including variants Mistral, Mixtral, InternLM)
• GPT (including variants Starcoder-1/2, Santacoder)
• Gemma-1/2
• Phi-1/2/3
• ChatGLM (including variants glm-10b, chatglm, chatglm2, chatglm3, glm4)
• QWen-1/1.5/2
• Falcon
• Baichuan-1/2
• GPT-J

Quick start

Please install the required packages first:

pip install -r requirements.txt

Here is a simple example to show how to use the HLAPI:

Firstly, import the LLM and SamplingParams from the tensorrt_llm package, and create an LLM object with a HuggingFace (HF) model directly. Here we use the TinyLlama model as an example, LLM will download the model from the HuggingFace model hub automatically. You can also specify local models, either in HF format, TensorRT-LLM engine format or TensorRT-LLM checkpoint format.

from tensorrt_llm import LLM, SamplingParams

llm = LLM(model="TinyLlama/TinyLlama-1.1B-Chat-v1.0")

Secondly, generate text with the generate method of the LLM object directly with a batch of prompts, the sampling_params is optional, and you can customize the sampling strategy with it.

prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]

sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

outputs = llm.generate(prompts, sampling_params)

# Print the outputs.
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

Please refer to the LLM quickstart for the complete example.

Examples

You can refer to llm_examples.py for all of the examples, and run it with the run_examples.py script, the command is as follows:

# To run examples with single GPU:
python3 ./run_examples.py run_single_gpu --model_dir <llama-model-path>

# Run the multi-GPU examples
python3 ./run_examples.py run_multi_gpu --model_dir <llama-model-path>

# Run the quantization examples
python3 ./run_examples.py run_quant --model_dir <llama-model-path>

For 7B, 13B models those could be held in a single GPU, it should run all the examples automatically and print the results.

For larger models, such as LLaMA v2 70B, at least two H100/A100 cards are required, the following command could be used to start a parallel task with Tensor Parallelism enabled.

python3 llm_examples.py --task run_llm_on_tensor_parallel \
    --prompt="<prompt>" \
    --hf_model_dir=<llama-model-path>

Model preparation

The LLM class supports four kinds of model inputs:

1. HuggingFace model name: triggers a download from the HuggingFace model hub, e.g. TinyLlama/TinyLlama-1.1B-Chat-v1.0 in the quickstart.
2. Local HuggingFace models: uses a locally stored HuggingFace model.
3. Local TensorRT-LLM engine: built by trtllm-build tool or saved by the HLAPI
4. Local TensorRT-LLM checkpoints: converted by convert_checkpoint.py script in the examples

All kinds of the model inputs can be seamlessly integrated with the HLAPI, and the LLM(model=<any-model-path>) construcotr can accommodate models in any of the above formats.

Let's delve into the preparation of the three kinds of local model formats.

Option 1: From HuggingFace models

Given its popularity, the TRT-LLM HLAPI chooses to support HuggingFace format as one of the start points, to use the HLAPI on LLaMA models, you need to run the following conversion script provided in transformers/llama or transformers/llama2 to convert the Meta checkpoint to HuggingFace format.

For instance, when targeting the LLaMA2 7B model, the official way to retrieve the model is to visit the LLaMA2 model page, normally you need to submit a request for the model file.

To convert the checkpoint files, a script from transformers is required, thus please also clone the transformers repo with the following code:

git clone https://github.com/huggingface/transformers.git

Finally, the command to convert the checkpoint files to HuggingFace format is as follows:

python <transformers-dir>/src/transformers/models/llama/convert_llama_weights_to_hf.py \
    --input_dir Llama-2-7b --model_size 7B --output_dir llama-hf-7b

That should produce a HuggingFace format model in ./llama-hf-7b, which could be used by the HLAPI.

Option 2: From TensorRT-LLM engine

There are two ways to build the TensorRT-LLM engine:

1. You can build the TensorRT-LLM engine from the HuggingFace model directly with the trtllm-build tool, and save the engine to disk for later use. Please consult the LLaMA's README.
2. Use the HLAPI to save one:

llm = LLM(<model-path>)

# Save engine to local disk
llm.save(<engine-dir>)

Option 3: From TensorRT-LLM checkpoint

In each model example, there is a convert_checkpoint.py to convert third-party models to TensorRT-LLM checkpoint for further usage. The HLAPI could seamlessly accept the checkpoint, and build the engine in the backend. For step-by-step guidance on checkpoint conversion, please refer to the LLaMA's README.

Basic usage

To use the API, import the LLM from the tensorrt_llm package and create an LLM object with a HuggingFace model directly. For example:

from tensorrt_llm import LLM

llm = LLM(model=<llama_model_path>)

It will trigger TRT-LLM engine building in the backend, and create a HuggingFace tokenizer by default to support an end-to-end generation.

To generate text, use the generate method of the LLM object directly with a batch of prompts, for example:

prompts = ["To tell a story"]
for output in llm.generate(prompts):
    print(output)

The output might be something like:

RequestOutput(request_id=2, prompt='To tell a story', prompt_token_ids=[1, 1763, 2649, 263, 5828], outputs=[CompletionOutput(index=0, text=', you need to have a beginning, a middle, and an end.\nThe beginning is the introduction of the characters and the setting.\nThe middle is', token_ids=[29892, 366, 817, 304, 505, 263, 6763, 29892, 263, 7256, 29892, 322, 385, 1095, 29889, 13, 1576, 6763, 338, 278, 18707, 310, 278, 4890, 322, 278, 4444, 29889, 13, 1576, 7256, 338], cumulative_logprob=None, logprobs=[])], finished=True)

You can also dump the runtime engine to disk, and load from the engine file directly in the next run to save the engine building time from the HuggingFace model.

# dump the llm
llm.save(<engine-path>)

# next time
llm = LLM(model=<engine-path>)

In other words, the model_dir could accept either a HugggingFace model, a built TensorRT-LLM engine, or a TensorRT-LLM checkpoint, and the LLM() will do the rest work silently for end-to-end execution.

Quantization

By simply setting several flags in the LLM, TensorRT-LLM can quantize the HuggingFace model automatically. For example, to perform an Int4 AWQ quantization, the following code will trigger the model quantization.

from tensorrt_llm.hlapi import QuantConfig, QuantAlgo

quant_config = QuantConfig(quant_algo=QuantAlgo.W4A16_AWQ)

llm = LLM(<model-dir>, quant_config=quant_config)

Parallelism

Tensor Parallelism

It is easy to enable Tensor Parallelism in the HLAPI. For example, setting parallel_config.tp_size=2 to perform a 2-way parallelism:

from tensorrt_llm.hlapi import LLM

llm = LLM(<llama_model_path>,
          tensor_parallel_size=2)

Pipeline Parallelism

Similar to Tensor Parallelism, you can enable Pipeline Parallelism in the HLAPI with following code:

llm = LLM(<llama_model_path>,
          pipeline_parallel_size=4)

Automatic Parallelism (in preview)

By simply enabling auto_parallel in the LLM class, TensorRT-LLM can parallelize the model automatically. For example, setting world_size to perform a 2-way parallelism:

from tensorrt_llm import LLM

llm = LLM(<llama_model_path>, auto_parallel=True, world_size=2)

Multi-GPU multi-node (MGMN) support

By default, the HLAPI will spawn MPI processes under the hood to support single-node-multi-gpu scenarios, what you need to do is to set the parallel_config.tp_size/pp_size to the number of GPUs you want to use.

But for MGMN scenarios, since the jobs are managed by some cluster management systems, such as Slurm, you need to submit HLAPI tasks differently.

Firstly, it is suggested to build the engine offline with the trtllm-build tools, please refer to LLaMA's README for more details.

Secondly, you need to prepare a Python file containing the HLAPI task, a naive example is as below, note that, this Python script will be executed only once on MPI rank0, and it looks nothing special compared to the single-node-multi-gpu scenario, such as TP or PP.

# Set the tensor_parallel_size and pipeline_parallel_size to the number of GPUs you want to use
llm = LLM(model=<llama_model_path>, tensor_parallel_size=4, pipeline_parallel_size=2)
for output in llm.generate([[32, 12]]):
    print(output)

Thirdly, you need to prepare a Slurm script to submit the task, the script contains the following lines:

#SBATCH -N 2                                 # number of nodes
#SBATCH --ntasks-per-node=4
#SBATCH -p <partition>
# more sbatch options here...

srun --container-image="<docker-image>" \
     --mpi=pmix \
     ... \ # more srun options here
     trtllm-hlapi-launch python3 <your-python-script>.py

The trtllm-hlapi-launch is a script provided by the HLAPI to launch the task and take care of the MPI runtime, you can find it in the local bin directory once you install the TensorRT-LLM package.

Finally, you can submit the task with sbatch <your-slurm-script>.sh.

Considering the Slurm or other cluster management systems may be highly customized and the task-submit command may be variant, the forementioned example is just a naive example. The key point is to submit the Python script with the MPI runtime, and the HLAPI will take care of the rest.

Generation

asyncio-based generation

With the high-level API, you can also perform asynchronous generation with the generate_async method. For example:

llm = LLM(model=<llama_model_path>)

async for output in llm.generate_async(<prompt>, streaming=True):
    print(output)

When the streaming flag is set to True, the generate_async method will return a generator that yields the token results as soon as they are available. Otherwise, it will return a generator that yields the final results only.

Future-style generation

The result of the generate_async method is a Future-like object, it doesn't block the thread unless the .result() is called.

# This will not block the main thread
generation = llm.generate_async(<prompt>)
# Do something else here
# call .result() to explicitly block the main thread and wait for the result when needed
output = generation.result()

The .result() method works like the result method in the Python Future, you can specify a timeout to wait for the result.

output = generation.result(timeout=10)

There is an async version, where the .aresult() is used.

generation = llm.generate_async(<prompt>)
output = await generation.aresult()

Customizing sampling with SamplingParams

With SamplingParams, you can customize the sampling strategy, such as beam search, temperature, and so on.

To enable beam search with a beam size of 4, set the sampling_params as follows:

from tensorrt_llm.hlapi import LLM, SamplingParams, BuildConfig

build_config = BuildConfig()
build_config.max_beam_width = 4

llm = LLM(<llama_model_path>, build_config=build_config)
# Let the LLM object generate text with the default sampling strategy, or
# you can create a SamplingParams object as well with several fields set manually
sampling_params = SamplingParams(beam_width=4) # current limitation: beam_width should be equal to max_beam_width

for output in llm.generate(<prompt>, sampling_params=sampling_params):
    print(output)

SamplingParams manages and dispatches fields to C++ classes including:

• SamplingConfig
• OutputConfig

Please refer to these classes for more details.

LLM pipeline configuration

Build configuration

Apart from the arguments mentioned above, you can also customize the build configuration with the build_config class and other arguments borrowed from the lower-level APIs. For example:

llm = LLM(<model-path>,
          build_config=BuildConfig(
            max_new_tokens=4096,
            max_batch_size=128,
            max_beam_width=4))

Runtime customization

Similar to build_config, you can also customize the runtime configuration with the runtime_config, peft_cache_config or other arguments borrowed from the lower-level APIs. For example:

from tensorrt_llm.hlapi import LLM, KvCacheConfig

llm = LLM(<llama_model_path>,
          kv_cache_config=KvCacheConfig(
            max_new_tokens=128,
            free_gpu_memory_fraction=0.8))

Tokenizer customization

By default, the high-level API uses transformers’ AutoTokenizer. You can override it with your own tokenizer by passing it when creating the LLM object. For example:

llm = LLM(<llama_model_path>, tokenizer=<my_faster_one>)

The LLM() workflow should use your tokenizer instead.

It is also possible to input token IDs directly without Tokenizers with the following code, note that the result will be also IDs without text since the tokenizer is not used.

llm = LLM(<llama_model_path>)

for output in llm.generate([32, 12]):
    ...

Disabling tokenizer

For performance considerations, you can disable the tokenizer by passing skip_tokenizer_init=True when creating LLM. In this case, LLM.generate and LLM.generate_async will expect prompt token ids as input. For example:

llm = LLM(<llama_model_path>)
for output in llm.generate([[32, 12]]):
    print(output)

You will get something like:

RequestOutput(request_id=1, prompt=None, prompt_token_ids=[1, 15043, 29892, 590, 1024, 338], outputs=[CompletionOutput(index=0, text='', token_ids=[518, 10858, 4408, 29962, 322, 306, 626, 263, 518, 10858, 20627, 29962, 472, 518, 10858, 6938, 1822, 306, 626, 5007, 304, 4653, 590, 4066, 297, 278, 518, 11947, 18527, 29962, 2602, 472], cumulative_logprob=None, logprobs=[])], finished=True)

Note that the text field in CompletionOutput is empty since the tokenizer is deactivated.

Build caching

Although the HLAPI runs the engine building in the background, you can also cache the built engine to disk and load it in the next run to save the engine building time.

To enable the build cache, there are two ways to do it:

1. Use the environment variable: export TLLM_HLAPI_BUILD_CACHE=1 to enable the build cache globally, and optionally export TLLM_HLAPI_BUILD_CACHE_ROOT to specify the cache root directory.
2. Pass the enable_build_cache to the LLM constructor

The build cache will reuse the built engine if all the building settings are the same, or it will rebuild the engine.

NOTE: The build cache monitors the model path and build settings, if you change the weights while keeping the same model path, the build cache will not detect the change and reuse the old engine.