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Inference

For inference we have provided an inference script. Depending on the type of finetuning performed during training the inference script takes different arguments. To finetune all model parameters the output dir of the training has to be given as --model_name argument. In the case of a parameter efficient method like lora the base model has to be given as --model_name and the output dir of the training has to be given as --peft_model argument. Additionally, a prompt for the model in the form of a text file has to be provided. The prompt file can either be piped through standard input or given as --prompt_file parameter.

Content Safety The inference script also supports safety checks for both user prompt and model outputs. In particular, we use two packages, AuditNLG and Azure content safety.

Note If using Azure content Safety, please make sure to get the endpoint and API key as described here and add them as the following environment variables,CONTENT_SAFETY_ENDPOINT and CONTENT_SAFETY_KEY.

Examples:

# Full finetuning of all parameters
cat <test_prompt_file> | python examples/inference.py --model_name <training_config.output_dir> --use_auditnlg
# PEFT method
cat <test_prompt_file> | python examples/inference.py --model_name <training_config.model_name> --peft_model <training_config.output_dir> --use_auditnlg
# prompt as parameter
python examples/inference.py --model_name <training_config.output_dir> --prompt_file <test_prompt_file> --use_auditnlg

The example folder contains test prompts for summarization use-case:

examples/samsum_prompt.txt
...

Note Currently pad token by default in HuggingFace Tokenizer is None. We add the padding token as a special token to the tokenizer, which in this case requires to resize the token_embeddings as shown below:

tokenizer.add_special_tokens(
        {

            "pad_token": "<PAD>",
        }
    )
model.resize_token_embeddings(model.config.vocab_size + 1)

Padding would be required for batch inference. In this this example, batch size = 1 so essentially padding is not required. However,We added the code pointer as an example in case of batch inference.

Chat completion

The inference folder also includes a chat completion example, that adds built-in safety features in fine-tuned models to the prompt tokens. To run the example:

python examples/chat_completion/chat_completion.py --model_name "PATH/TO/MODEL/7B/" --prompt_file examples/chat_completion/chats.json  --quantization --use_auditnlg

Code Llama

Code llama was recently released with three flavors, base-model that support multiple programming languages, Python fine-tuned model and an instruction fine-tuned and aligned variation of Code Llama, please read more here. Also note that the Python fine-tuned model and 34B models are not trained on infilling objective, hence can not be used for infilling use-case.

Find the scripts to run Code Llama here, where there are two examples of running code completion and infilling.

Note Please find the right model on HF side here.

Make sure to install Transformers from source for now

pip install git+https://github.com/huggingface/transformers

To run the code completion example:

python examples/code_llama/code_completion_example.py --model_name MODEL_NAME  --prompt_file examples/code_llama/code_completion_prompt.txt --temperature 0.2 --top_p 0.9

To run the code infilling example:

python examples/code_llama/code_infilling_example.py --model_name MODEL_NAME --prompt_file examples/code_llama/code_infilling_prompt.txt --temperature 0.2 --top_p 0.9

To run the 70B Instruct model example run the following (you'll need to enter the system and user prompts to instruct the model):

python examples/code_llama/code_instruct_example.py --model_name codellama/CodeLlama-70b-Instruct-hf --temperature 0.2 --top_p 0.9

You can learn more about the chat prompt template on HF and original Code Llama repository. HF tokenizer has already taken care of the chat template as shown in this example.

Llama Guard

Llama Guard is a new experimental model that provides input and output guardrails for LLM deployments. For more details, please visit the main repository.

Find the inference script for Llama Guard here.

Note Please find the right model on HF side here.

Edit inference.py to add test prompts for Llama Guard and execute it with this command:

python examples/llama_guard/inference.py

Flash Attention and Xformer Memory Efficient Kernels

Setting use_fast_kernels will enable using of Flash Attention or Xformer memory-efficient kernels based on the hardware being used. This would speed up inference when used for batched inputs. This has been enabled in optimum library from HuggingFace as a one-liner API, please read more here.

python examples/chat_completion/chat_completion.py --model_name "PATH/TO/MODEL/7B/" --prompt_file examples/chat_completion/chats.json  --quantization --use_auditnlg --use_fast_kernels

python examples/inference.py --model_name <training_config.output_dir> --peft_model <training_config.output_dir> --prompt_file <test_prompt_file> --use_auditnlg --use_fast_kernels

Loading back FSDP checkpoints

In case you have fine-tuned your model with pure FSDP and saved the checkpoints with "SHARDED_STATE_DICT" as shown here, you can use this converter script to convert the FSDP Sharded checkpoints into HuggingFace checkpoints. This enables you to use the inference script normally as mentioned above. To convert the checkpoint use the following command:

This is helpful if you have fine-tuned you model using FSDP only as follows:

torchrun --nnodes 1 --nproc_per_node 8  examples/finetuning.py --enable_fsdp --model_name /patht_of_model_folder/7B --dist_checkpoint_root_folder model_checkpoints --dist_checkpoint_folder fine-tuned --pure_bf16

Then convert your FSDP checkpoint to HuggingFace checkpoints using:

 python -m llama_recipes.inference.checkpoint_converter_fsdp_hf --fsdp_checkpoint_path  PATH/to/FSDP/Checkpoints --consolidated_model_path PATH/to/save/checkpoints --HF_model_path_or_name PATH/or/HF/model_name

 # --HF_model_path_or_name specifies the HF Llama model name or path where it has config.json and tokenizer.json

By default, training parameter are saved in train_params.yaml in the path where FSDP checkpoints are saved, in the converter script we frist try to find the HugingFace model name used in the fine-tuning to load the model with configs from there, if not found user need to provide it.

Then run inference using:

python examples/inference.py --model_name <training_config.output_dir> --prompt_file <test_prompt_file>

Prompt Llama 2

As outlined by this blog by Hugging Face, you can use the template below to prompt Llama 2 chat models. Review the blog article for more information.

<s>[INST] <<SYS>>
{{ system_prompt }}
<</SYS>>

{{ user_message }} [/INST]

Other Inference Options

Alternate inference options include:

vLLM: To use vLLM you will need to install it using the instructions here. Once installed, you can use the vllm/inference.py script provided here.

Below is an example of how to run the vLLM_inference.py script found within the inference folder.

python examples/vllm/inference.py --model_name <PATH/TO/MODEL/7B>

TGI: Text Generation Inference (TGI) is another inference option available to you. For more information on how to set up and use TGI see here.

Here is a complete tutorial on how to use vLLM and TGI to deploy Llama 2 on-prem and interact with the Llama API services.