Large Language Model Inference in Beam
In Apache Beam 2.40.0, Beam introduced the RunInference API, which lets you deploy a machine learning model in a Beam pipeline. A
RunInference transform performs inference on a
PCollection of examples using a machine learning (ML) model. The transform outputs a PCollection that contains the input examples and output predictions. For more information, see RunInference here. You can also find inference examples on GitHub.
Using RunInference with very large models
RunInference works well on arbitrarily large models as long as they can fit on your hardware.
This example demonstrates running inference with a
T5 language model using
RunInference in a pipeline.
T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks. Each task is converted into a text-to-text format. The example uses
T5-11B, which contains 11 billion parameters and is 45 GB in size. In order to work well on a variety of tasks,
T5 prepends a different prefix to the input corresponding to each task. For example, for translation, the input would be:
translate English to German: … and for summarization, it would be:
summarize: …. For more information about
T5 see the T5 overiew in the HuggingFace documentation.
Run the Pipeline ?
apache-beam 2.40 or greater:
pip install apache-beam -U
Next, install the required packages listed in requirements.txt and pass the required arguments. You can download the
T5-11b model from Hugging Face Hub with the following steps:
- Install Git LFS following the instructions here
git lfs install
git clone https://huggingface.co/t5-11b(this may take a long time). This will download the checkpoint, then you need to convert it to the model state dict as described here:
import torch from transformers import T5ForConditionalGeneration model = T5ForConditionalGeneration.from_pretrained("path/to/cloned/t5-11b") torch.save(model.state_dict(), "path/to/save/state_dict.pth")
You can view the code on GitHub
- Locally on your machine:
python main.py --runner DirectRunner \ --model_state_dict_path <local or remote path to state_dict> \ --model_name t5-11b
You need to have 45 GB of disk space available to run this example.
- On Google Cloud using Dataflow:
python main.py --runner DataflowRunner \ --model_state_dict_path <gs://path/to/saved/state_dict.pth> \ --model_name t5-11b \ --project <PROJECT_ID> \ --region <REGION> \ --requirements_file requirements.txt \ --staging_location <gs://path/to/staging/location> --temp_location <gs://path/to/temp/location> \ --experiments "use_runner_v2,no_use_multiple_sdk_containers" \ --machine_type=n1-highmem-16 \ --disk_size_gb=200
You can also pass other configuration parameters as described here.
The pipeline contains the following steps:
- Read the inputs.
- Encode the text into transformer-readable token ID integers using a tokenizer.
- Use RunInference to get the output.
- Decode the RunInference output and print it.
The following code snippet contains the four steps:
with beam.Pipeline(options=pipeline_options) as pipeline: _ = ( pipeline | "CreateInputs" >> beam.Create(task_sentences) | "Preprocess" >> beam.ParDo(Preprocess(tokenizer=tokenizer)) | "RunInference" >> RunInference(model_handler=model_handler) | "PostProcess" >> beam.ParDo(Postprocess(tokenizer=tokenizer)) )
In the third step of pipeline we use
In order to use it, you must first define a
ModelHandler. RunInference provides model handlers for
Scikit-Learn. Because the example uses a
PyTorch model, it uses the
PyTorchModelHandlerTensor model handler.
ModelHandler requires parameters like:
state_dict_path– The path to the saved dictionary of the model state.
model_class– The class of the Pytorch model that defines the model structure.
model_params– A dictionary of arguments required to instantiate the model class.
device– The device on which you wish to run the model. If device = GPU then a GPU device will be used if it is available. Otherwise, it will be CPU.
inference_fn- The inference function to use during RunInference.
Last updated on 2023/03/28
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