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import json
import torch
from transformers import pipeline, AutoTokenizer, AutoConfig
from typing import Dict, List, Any, Optional, Union
class EndpointHandler:
def __init__(self, path=""):
# Initialize model and tokenizer
self.model_path = path if path else os.environ.get("MODEL_PATH", "")
# Fix RoPE scaling configuration
try:
config = AutoConfig.from_pretrained(self.model_path)
# Check if config has rope_scaling attribute and fix the short_factor length
if hasattr(config, "rope_scaling") and "short_factor" in config.rope_scaling:
short_factor = config.rope_scaling["short_factor"]
if len(short_factor) == 48: # If we have the problematic length
print("Fixing rope_scaling short_factor length from 48 to 64")
# Pad to length 64
padded_short_factor = list(short_factor) + [0.0] * (64 - len(short_factor))
config.rope_scaling["short_factor"] = padded_short_factor
# Save the fixed config
config.save_pretrained(self.model_path)
print("Fixed config saved")
except Exception as e:
print(f"Warning: Could not fix RoPE scaling configuration: {str(e)}")
# Load tokenizer
self.tokenizer = AutoTokenizer.from_pretrained(self.model_path)
# Create text generation pipeline
self.pipe = pipeline(
"text-generation",
model=self.model_path,
tokenizer=self.tokenizer,
torch_dtype=torch.float16,
device_map="auto",
return_full_text=False # Only return the generated text, not the prompt
)
def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
"""Handle inference request in OpenAI-like format"""
try:
# Parse input data
inputs = self._parse_input(data)
# Generate response
outputs = self._generate(inputs)
# Format response in OpenAI-like format
return self._format_response(outputs, inputs)
except Exception as e:
return {
"error": {
"message": str(e),
"type": "invalid_request_error",
"code": 400
}
}
def _parse_input(self, data: Dict[str, Any]) -> Dict[str, Any]:
"""Parse input data to extract generation parameters"""
# Extract messages
messages = data.get("messages", [])
if not messages:
raise ValueError("No messages provided")
# Convert messages to prompt
prompt = self._convert_messages_to_prompt(messages)
# Extract generation parameters with defaults
generation_params = {
"max_tokens": data.get("max_tokens", 256),
"temperature": data.get("temperature", 0.7),
"top_p": data.get("top_p", 1.0),
"n": data.get("n", 1),
"stream": data.get("stream", False),
"stop": data.get("stop", None),
"presence_penalty": data.get("presence_penalty", 0.0),
"frequency_penalty": data.get("frequency_penalty", 0.0),
}
return {
"prompt": prompt,
"messages": messages,
"generation_params": generation_params
}
def _convert_messages_to_prompt(self, messages: List[Dict[str, str]]) -> str:
"""Convert list of messages to a prompt string"""
prompt = ""
for message in messages:
role = message.get("role", "")
content = message.get("content", "")
if role == "system":
prompt += f"System: {content}\n\n"
elif role == "user":
prompt += f"User: {content}\n\n"
elif role == "assistant":
prompt += f"Assistant: {content}\n\n"
# Add final assistant prompt
prompt += "Assistant: "
return prompt
def _generate(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Generate response using the pipeline"""
prompt = inputs["prompt"]
params = inputs["generation_params"]
# Count input tokens
input_tokens = len(self.tokenizer.encode(prompt))
# Convert OpenAI-like parameters to pipeline parameters
generation_kwargs = {
"max_new_tokens": params["max_tokens"],
"temperature": params["temperature"],
"top_p": params["top_p"],
"num_return_sequences": params["n"],
"do_sample": params["temperature"] > 0,
}
# Add stopping criteria if provided
if params["stop"]:
generation_kwargs["stopping_criteria"] = params["stop"]
# Generate output using the pipeline
pipeline_outputs = self.pipe(
prompt,
**generation_kwargs
)
# Extract generated texts
generated_texts = []
for output in pipeline_outputs:
gen_text = output["generated_text"]
# Apply stop sequences if provided
if params["stop"]:
for stop in params["stop"]:
if stop in gen_text:
gen_text = gen_text[:gen_text.find(stop)]
generated_texts.append(gen_text)
# Count completion tokens
completion_tokens = [len(self.tokenizer.encode(text)) for text in generated_texts]
return {
"generated_texts": generated_texts,
"prompt_tokens": input_tokens,
"completion_tokens": completion_tokens,
}
def _format_response(self, outputs: Dict[str, Any], inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Format response in OpenAI-like format"""
generated_texts = outputs["generated_texts"]
prompt_tokens = outputs["prompt_tokens"]
completion_tokens = outputs["completion_tokens"]
choices = []
for i, text in enumerate(generated_texts):
choices.append({
"index": i,
"message": {
"role": "assistant",
"content": text
},
"finish_reason": "stop"
})
return {
"id": f"cmpl-{hash(inputs['prompt']) % 10000}",
"object": "chat.completion",
"created": int(torch.cuda.current_device()) if torch.cuda.is_available() else 0,
"model": os.path.basename(self.model_path),
"choices": choices,
"usage": {
"prompt_tokens": prompt_tokens,
"completion_tokens": sum(completion_tokens),
"total_tokens": prompt_tokens + sum(completion_tokens)
}
} |