# Copyright (c) Meta Platforms, Inc. and affiliates. # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. # from accelerate import init_empty_weights, load_checkpoint_and_dispatch import fire import os import sys import time import gradio as gr import torch from transformers import AutoTokenizer from llama_recipes.inference.safety_utils import get_safety_checker, AgentType from llama_recipes.inference.model_utils import load_model, load_peft_model from accelerate.utils import is_xpu_available def main( model_name, peft_model: str=None, quantization: bool=False, max_new_tokens =100, #The maximum numbers of tokens to generate prompt_file: str=None, seed: int=42, #seed value for reproducibility do_sample: bool=True, #Whether or not to use sampling ; use greedy decoding otherwise. min_length: int=None, #The minimum length of the sequence to be generated, input prompt + min_new_tokens use_cache: bool=True, #[optional] Whether or not the model should use the past last key/values attentions Whether or not the model should use the past last key/values attentions (if applicable to the model) to speed up decoding. top_p: float=1.0, # [optional] If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation. temperature: float=1.0, # [optional] The value used to modulate the next token probabilities. top_k: int=50, # [optional] The number of highest probability vocabulary tokens to keep for top-k-filtering. repetition_penalty: float=1.0, #The parameter for repetition penalty. 1.0 means no penalty. length_penalty: int=1, #[optional] Exponential penalty to the length that is used with beam-based generation. enable_azure_content_safety: bool=False, # Enable safety check with Azure content safety api enable_sensitive_topics: bool=False, # Enable check for sensitive topics using AuditNLG APIs enable_salesforce_content_safety: bool=True, # Enable safety check with Salesforce safety flan t5 enable_llamaguard_content_safety: bool=False, max_padding_length: int=None, # the max padding length to be used with tokenizer padding the prompts. use_fast_kernels: bool = False, # Enable using SDPA from PyTroch Accelerated Transformers, make use Flash Attention and Xformer memory-efficient kernels **kwargs ): def inference(user_prompt, temperature, top_p, top_k, max_new_tokens, **kwargs,): safety_checker = get_safety_checker(enable_azure_content_safety, enable_sensitive_topics, enable_salesforce_content_safety, enable_llamaguard_content_safety ) # Safety check of the user prompt safety_results = [check(user_prompt) for check in safety_checker] are_safe = all([r[1] for r in safety_results]) if are_safe: print("User prompt deemed safe.") print(f"User prompt:\n{user_prompt}") else: print("User prompt deemed unsafe.") for method, is_safe, report in safety_results: if not is_safe: print(method) print(report) print("Skipping the inference as the prompt is not safe.") sys.exit(1) # Exit the program with an error status # Set the seeds for reproducibility if is_xpu_available(): torch.xpu.manual_seed(seed) else: torch.cuda.manual_seed(seed) torch.manual_seed(seed) model = load_model(model_name, quantization, use_fast_kernels) if peft_model: model = load_peft_model(model, peft_model) model.eval() tokenizer = AutoTokenizer.from_pretrained(model_name) tokenizer.pad_token = tokenizer.eos_token batch = tokenizer(user_prompt, padding='max_length', truncation=True, max_length=max_padding_length, return_tensors="pt") if is_xpu_available(): batch = {k: v.to("xpu") for k, v in batch.items()} else: batch = {k: v.to("cuda") for k, v in batch.items()} start = time.perf_counter() with torch.no_grad(): outputs = model.generate( **batch, max_new_tokens=max_new_tokens, do_sample=do_sample, top_p=top_p, temperature=temperature, min_length=min_length, use_cache=use_cache, top_k=top_k, repetition_penalty=repetition_penalty, length_penalty=length_penalty, **kwargs ) e2e_inference_time = (time.perf_counter()-start)*1000 print(f"the inference time is {e2e_inference_time} ms") output_text = tokenizer.decode(outputs[0], skip_special_tokens=True) # Safety check of the model output safety_results = [check(output_text, agent_type=AgentType.AGENT, user_prompt=user_prompt) for check in safety_checker] are_safe = all([r[1] for r in safety_results]) if are_safe: print("User input and model output deemed safe.") print(f"Model output:\n{output_text}") else: print("Model output deemed unsafe.") for method, is_safe, report in safety_results: if not is_safe: print(method) print(report) return output_text if prompt_file is not None: assert os.path.exists( prompt_file ), f"Provided Prompt file does not exist {prompt_file}" with open(prompt_file, "r") as f: user_prompt = "\n".join(f.readlines()) inference(user_prompt, temperature, top_p, top_k, max_new_tokens) elif not sys.stdin.isatty(): user_prompt = "\n".join(sys.stdin.readlines()) inference(user_prompt, temperature, top_p, top_k, max_new_tokens) else: gr.Interface( fn=inference, inputs=[ gr.components.Textbox( lines=9, label="User Prompt", placeholder="none", ), gr.components.Slider( minimum=0, maximum=1, value=1.0, label="Temperature" ), gr.components.Slider( minimum=0, maximum=1, value=1.0, label="Top p" ), gr.components.Slider( minimum=0, maximum=100, step=1, value=50, label="Top k" ), gr.components.Slider( minimum=1, maximum=2000, step=1, value=200, label="Max tokens" ), ], outputs=[ gr.components.Textbox( lines=5, label="Output", ) ], title="Meta Llama3 Playground", description="https://github.com/facebookresearch/llama-recipes", ).queue().launch(server_name="0.0.0.0", share=True) if __name__ == "__main__": fire.Fire(main)