File size: 2,862 Bytes
42fd383 10f9067 42fd383 10f9067 42fd383 | 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 | import os
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
class EndpointHandler:
def __init__(self, path: str = ""):
model_dir = path or "/repository"
self.tokenizer = AutoTokenizer.from_pretrained(
model_dir,
trust_remote_code=True,
)
if self.tokenizer.pad_token_id is None:
self.tokenizer.pad_token = self.tokenizer.eos_token
self.model = AutoModelForCausalLM.from_pretrained(
model_dir,
trust_remote_code=True,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
device_map="auto",
)
self.model.eval()
def _messages_to_prompt(self, inputs):
# Use chat template only if both the method and a non-empty template exist
if hasattr(self.tokenizer, "apply_chat_template") and getattr(
self.tokenizer, "chat_template", None
):
return self.tokenizer.apply_chat_template(
inputs,
tokenize=False,
add_generation_prompt=True,
)
# Fallback for plain causal LMs with no chat_template (e.g. MedAlpaca)
parts = []
for msg in inputs:
role = (msg.get("role") or "user").upper()
content = msg.get("content", "")
parts.append(f"[{role}]\n{content}")
parts.append("[ASSISTANT]\n")
return "\n\n".join(parts)
def __call__(self, data):
inputs = data.get("inputs", "")
params = data.get("parameters", {}) or {}
max_new_tokens = int(params.get("max_new_tokens", 128))
temperature = float(params.get("temperature", 0.0))
top_p = float(params.get("top_p", 1.0))
do_sample = bool(params.get("do_sample", temperature > 0))
repetition_penalty = float(params.get("repetition_penalty", 1.0))
no_repeat_ngram_size = int(params.get("no_repeat_ngram_size", 0))
if isinstance(inputs, list):
prompt = self._messages_to_prompt(inputs)
else:
prompt = str(inputs)
enc = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
with torch.no_grad():
out = self.model.generate(
**enc,
max_new_tokens=max_new_tokens,
temperature=temperature,
top_p=top_p,
do_sample=do_sample,
repetition_penalty=repetition_penalty,
no_repeat_ngram_size=no_repeat_ngram_size,
pad_token_id=self.tokenizer.pad_token_id,
eos_token_id=self.tokenizer.eos_token_id,
)
generated_ids = out[0][enc["input_ids"].shape[-1]:]
text = self.tokenizer.decode(generated_ids, skip_special_tokens=True)
return {"generated_text": text}
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