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from typing import List, Dict
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
class EndpointHandler:
def __init__(self, path: str):
# Load model and tokenizer
self.tokenizer = AutoTokenizer.from_pretrained(path)
self.model = AutoModelForCausalLM.from_pretrained(
path,
torch_dtype=torch.float32, # Use float32 for CPU
device_map="auto"
)
# Set up generation parameters
self.default_params = {
"max_length": 1000,
"temperature": 0.7,
"top_p": 0.7,
"top_k": 50,
"repetition_penalty": 1.0,
"do_sample": True,
"pad_token_id": self.tokenizer.pad_token_id,
"eos_token_id": self.tokenizer.eos_token_id
}
def __call__(self, data: Dict):
try:
# Handle input
if isinstance(data.get("inputs"), str):
input_text = data["inputs"]
else:
input_text = data.get("inputs")[0] if isinstance(data.get("inputs"), list) else str(data.get("inputs"))
# Print debug information
print(f"Input text: {input_text}")
# Tokenize with fixed dimensions
tokenizer_output = self.tokenizer(
input_text,
padding='max_length', # Changed to max_length
truncation=True,
max_length=512, # Fixed length
return_tensors="pt",
return_attention_mask=True
)
# Print tensor shapes for debugging
print(f"Input ids shape: {tokenizer_output['input_ids'].shape}")
print(f"Attention mask shape: {tokenizer_output['attention_mask'].shape}")
# Generate response
with torch.no_grad():
outputs = self.model.generate(
tokenizer_output["input_ids"],
attention_mask=tokenizer_output["attention_mask"],
max_new_tokens=1024,
pad_token_id=self.tokenizer.pad_token_id,
do_sample=True,
temperature=0.7,
top_p=0.7,
top_k=50
)
# Decode response
generated_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
return [{"generated_text": generated_text}]
except Exception as e:
print(f"Error in generation: {str(e)}")
print(f"Model config: {self.model.config}")
return {"error": str(e)}
def preprocess(self, request):
"""
Prepare request for inference
"""
if request.content_type != "application/json":
raise ValueError("Content type must be application/json")
data = request.json
return data
def postprocess(self, data):
"""
Post-process model output
"""
return data |