Upload handler.py

handler.py

@@ -0,0 +1,104 @@
+from typing import Dict, Any
+import logging
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftConfig, PeftModel
+import torch.cuda
+
+
+LOGGER = logging.getLogger(__name__)
+logging.basicConfig(level=logging.INFO)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        config = PeftConfig.from_pretrained(path)
+        model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, load_in_8bit=True, device_map='auto')
+        self.tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+        # Load the Lora model
+        self.model = PeftModel.from_pretrained(model, path)
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        LOGGER.info(f"Received data: {data}")
+        # Get inputs
+        # Extract required parameters from data
+        message = data.get("message")
+        chat_history = data.get("chat_history", [])
+        system_prompt = data.get("system_prompt", "")
+
+        # Extract optional parameters for the generate function logic
+        instruction = data.get("instruction")
+        conclusions = data.get("conclusions")
+        context = data.get("context")
+
+        # Optional parameters with default values
+        max_new_tokens = data.get("max_new_tokens", 1024)
+        temperature = data.get("temperature", 0.6)
+        top_p = data.get("top_p", 0.9)
+        top_k = data.get("top_k", 50)
+        repetition_penalty = data.get("repetition_penalty", 1.2)
+
+        if message is None or system_prompt is None:
+            raise ValueError("Missing required parameters.")
+
+        # Call the generate function
+        output = generate(
+            message=message,
+            chat_history=chat_history,
+            system_prompt=system_prompt,
+            instruction=instruction,
+            conclusions=conclusions,
+            context=context
+            max_new_tokens=max_new_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            top_k=top_k,
+            repetition_penalty=repetition_penalty
+        )
+
+        # Postprocess
+        prediction = self.tokenizer.decode(output[0])
+        LOGGER.info(f"Generated text: {prediction}")
+        return {"generated_text": prediction}
+
+def generate(
+    message: str,
+    chat_history: list[tuple[str, str]],
+    system_prompt: str = None,
+    instruction: str = None,
+    conclusions: list[tuple[str, str]] = None,
+    context: list[str] = None,
+    max_new_tokens: int = 1024,
+    temperature: float = 0.6,
+    top_p: float = 0.9,
+    top_k: int = 50,
+    repetition_penalty: float = 1.2,
+) -> Iterator[str]:
+    
+    # Check if the system_prompt is provided, else construct it from instruction, conclusions, and context
+    if system_prompt is None and instruction is not None and conclusions is not None and context is not None:
+        system_prompt = "Instruction: {}\nConclusions:\n".format(instruction)
+        for idx, (conclusion, conclusion_key) in enumerate(conclusions):
+            system_prompt += "{}: {}\n".format(conclusion_key, conclusion)
+        system_prompt += "\nContext:\n"
+        for idx, ctx in enumerate(context):
+            system_prompt += "{}: [{}]\n".format(ctx, idx + 1)
+
+    # Construct conversation history
+    conversation = [{"role": "system", "content": system_prompt}]
+    for user, assistant in chat_history:
+        if user is not None:
+            conversation.extend([{"role": "user", "content": user}])
+        conversation.extend([{"role": "assistant", "content": assistant}])
+    conversation.append({"role": "user", "content": message})
+
+    # Tokenize and process the conversation
+    input_ids = self.tokenizer.apply_chat_template(conversation, return_tensors="pt")
+    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
+        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
+        gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
+    input_ids = input_ids.to(model.device)
+
+    # Generate the response
+    return model.generate(input_ids.input_ids, max_new_tokens=max_new_tokens)