adding llama70b instruct code example

examples/code_llama/code_instruct_example.py

@@ -0,0 +1,158 @@
+# 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 torch
+from transformers import AutoTokenizer
+
+from llama_recipes.inference.safety_utils import get_safety_checker
+from llama_recipes.inference.model_utils import load_model, load_peft_model
+
+
+def handle_safety_check(are_safe_user_prompt, user_prompt, safety_results_user_prompt, are_safe_system_prompt, system_prompt, safety_results_system_prompt):
+    """
+    Handles the output based on the safety check of both user and system prompts.
+
+    Parameters:
+    - are_safe_user_prompt (bool): Indicates whether the user prompt is safe.
+    - user_prompt (str): The user prompt that was checked for safety.
+    - safety_results_user_prompt (list of tuples): A list of tuples for the user prompt containing the method, safety status, and safety report.
+    - are_safe_system_prompt (bool): Indicates whether the system prompt is safe.
+    - system_prompt (str): The system prompt that was checked for safety.
+    - safety_results_system_prompt (list of tuples): A list of tuples for the system prompt containing the method, safety status, and safety report.
+    """
+    def print_safety_results(are_safe_prompt, prompt, safety_results, prompt_type="User"):
+        """
+        Prints the safety results for a prompt.
+
+        Parameters:
+        - are_safe_prompt (bool): Indicates whether the prompt is safe.
+        - prompt (str): The prompt that was checked for safety.
+        - safety_results (list of tuples): A list of tuples containing the method, safety status, and safety report.
+        - prompt_type (str): The type of prompt (User/System).
+        """
+        if are_safe_prompt:
+            print(f"{prompt_type} prompt deemed safe.")
+            print(f"{prompt_type} prompt:\n{prompt}")
+        else:
+            print(f"{prompt_type} prompt deemed unsafe.")
+            for method, is_safe, report in safety_results:
+                if not is_safe:
+                    print(method)
+                    print(report)
+            print(f"Skipping the inference as the {prompt_type.lower()} prompt is not safe.")
+            sys.exit(1)
+
+    # Check user prompt
+    print_safety_results(are_safe_user_prompt, user_prompt, safety_results_user_prompt, "User")
+    
+    # Check system prompt
+    print_safety_results(are_safe_system_prompt, system_prompt, safety_results_system_prompt, "System")
+
+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=False,  #[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=0.9, # [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=0.6, # [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, # Enable safety check with Llama-Guard
+    use_fast_kernels: bool = True, # Enable using SDPA from PyTroch Accelerated Transformers, make use Flash Attention and Xformer memory-efficient kernels
+    **kwargs
+):
+    system_prompt = input("Please insert your system prompt: ")
+    user_prompt = input("Please insert your prompt: ")
+    chat = [
+   {"role": "system", "content": system_prompt},
+   {"role": "user", "content": user_prompt},
+    ]       
+    # Set the seeds for reproducibility
+    torch.cuda.manual_seed(seed)
+    torch.manual_seed(seed)
+    
+    model = load_model(model_name, quantization)
+    if peft_model:
+        model = load_peft_model(model, peft_model)
+
+    model.eval()
+    
+    if use_fast_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.
+        """
+        try:
+            from optimum.bettertransformer import BetterTransformer
+            model = BetterTransformer.transform(model)    
+        except ImportError:
+            print("Module 'optimum' not found. Please install 'optimum' it before proceeding.")
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    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_user_prompt = [check(user_prompt) for check in safety_checker]
+    safety_results_system_prompt = [check(system_prompt) for check in safety_checker]
+    are_safe_user_prompt = all([r[1] for r in safety_results_user_prompt])
+    are_safe_system_prompt = all([r[1] for r in safety_results_system_prompt])
+    handle_safety_check(are_safe_user_prompt, user_prompt, safety_results_user_prompt, are_safe_system_prompt, system_prompt, safety_results_system_prompt)
+        
+    inputs = tokenizer.apply_chat_template(chat, return_tensors="pt").to("cuda")
+
+    start = time.perf_counter()
+    with torch.no_grad():
+        outputs = model.generate(
+            input_ids=inputs,
+            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) 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)
+                
+
+if __name__ == "__main__":
+    fire.Fire(main)