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from typing import Dict, List, Any
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
class EndpointHandler():
def __init__(self, path=""):
# Load model and tokenizer
self.tokenizer = AutoTokenizer.from_pretrained(path)
self.model = AutoModelForCausalLM.from_pretrained(
path,
device_map="auto",
torch_dtype=torch.bfloat16,
)
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:str): a string to be generated from
parameters (:dict): generation parameters
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
# Get the input text
inputs = data.pop("inputs", data)
parameters = data.pop("parameters", {})
# Set default parameters
max_new_tokens = parameters.get("max_new_tokens", 400)
temperature = parameters.get("temperature", 0.7)
do_sample = parameters.get("do_sample", True)
top_p = parameters.get("top_p", 0.9)
return_full_text = parameters.get("return_full_text", True)
# Tokenize the input
input_ids = self.tokenizer(inputs, return_tensors="pt").to(self.model.device)
# Generate text
with torch.no_grad():
generated_ids = self.model.generate(
**input_ids,
max_new_tokens=max_new_tokens,
temperature=temperature,
do_sample=do_sample,
top_p=top_p,
pad_token_id=self.tokenizer.eos_token_id,
eos_token_id=self.tokenizer.eos_token_id,
)
# Decode the generated text
if return_full_text:
generated_text = self.tokenizer.decode(generated_ids[0], skip_special_tokens=True)
else:
# Only return the newly generated part
new_tokens = generated_ids[0][input_ids["input_ids"].shape[1]:]
generated_text = self.tokenizer.decode(new_tokens, skip_special_tokens=True)
return [{"generated_text": generated_text}] |