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import json
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from typing import Dict, List, Any
# Replace with actual GraniteMoeForCausalLM import if available
# from granitemoe import GraniteMoeForCausalLM
class EndpointHandler:
def __init__(self, path: str = ""):
self.tokenizer = AutoTokenizer.from_pretrained(path)
self.model = AutoModelForCausalLM.from_pretrained(
path,
torch_dtype=torch.bfloat16,
device_map="auto"
)
self.model.eval()
def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
inputs = data.get("inputs", "")
parameters = data.get("parameters", {})
input_ids = self.tokenizer(inputs, return_tensors="pt").input_ids.to(self.model.device)
max_length = parameters.get("max_length", 100)
temperature = parameters.get("temperature", 1.0)
top_p = parameters.get("top_p", 1.0)
do_sample = parameters.get("do_sample", True)
with torch.no_grad():
outputs = self.model.generate(
input_ids,
max_length=max_length,
temperature=temperature,
top_p=top_p,
do_sample=do_sample,
pad_token_id=self.tokenizer.pad_token_id,
eos_token_id=self.tokenizer.eos_token_id
)
generated_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
return {"generated_text": generated_text} |