File size: 4,042 Bytes
a3ce164 | 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 99 100 101 102 103 104 105 106 | from typing import Dict, List, Any
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import os
class EndpointHandler():
def __init__(self, path=""):
# Look for checkpoint-100 folder
checkpoint_path = None
if not path or path == "/repository":
base_path = "."
else:
base_path = path
# Check different possible locations
possible_paths = [
os.path.join(base_path, "checkpoint-100"),
os.path.join(".", "checkpoint-100"),
os.path.join("/repository", "checkpoint-100"),
"checkpoint-100"
]
for check_path in possible_paths:
if os.path.exists(check_path) and os.path.isdir(check_path):
# Verify it contains model files
files = os.listdir(check_path)
if any(f in files for f in ['config.json', 'pytorch_model.bin', 'model.safetensors']):
checkpoint_path = check_path
break
if checkpoint_path is None:
print(f"Available files in base path: {os.listdir(base_path) if os.path.exists(base_path) else 'Path does not exist'}")
raise ValueError("Could not find checkpoint-100 folder with model files")
print(f"Loading model from: {checkpoint_path}")
print(f"Files in checkpoint: {os.listdir(checkpoint_path)}")
# Load model and tokenizer from checkpoint-100
self.tokenizer = AutoTokenizer.from_pretrained(checkpoint_path, trust_remote_code=True)
self.model = AutoModelForCausalLM.from_pretrained(
checkpoint_path,
device_map="auto",
torch_dtype=torch.bfloat16,
trust_remote_code=True,
)
# Set pad token if not exists
if self.tokenizer.pad_token is None:
self.tokenizer.pad_token = self.tokenizer.eos_token
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", {})
# Handle string input directly
if isinstance(inputs, str):
input_text = inputs
else:
input_text = str(inputs)
# Set default parameters
max_new_tokens = parameters.get("max_new_tokens", 1000)
temperature = parameters.get("temperature", 0.1)
do_sample = parameters.get("do_sample", True)
top_p = parameters.get("top_p", 0.9)
return_full_text = parameters.get("return_full_text", False)
# Tokenize the input
input_ids = self.tokenizer(
input_text,
return_tensors="pt",
padding=True,
truncation=True,
max_length=2048
).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.pad_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}] |