File size: 4,146 Bytes
d2659b8 412fa1c d2659b8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | 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)
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