mirror of
https://github.com/clearml/clearml-serving
synced 2025-06-26 18:16:00 +00:00
55 lines
2.0 KiB
Python
55 lines
2.0 KiB
Python
"""Hugginface preprocessing module for ClearML Serving."""
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from typing import Any, Optional, List, Callable, Union
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class Preprocess:
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"""Processing class will be run by the ClearML inference services before and after each request."""
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def __init__(self):
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"""Set internal state, this will be called only once. (i.e. not per request)."""
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self.model_endpoint = None
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def load(self, local_file_name: str) -> Optional[Any]: # noqa
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vllm_model_config = {
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"lora_modules": None, # [LoRAModulePath(name=a, path=b)]
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"prompt_adapters": None, # [PromptAdapterPath(name=a, path=b)]
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"response_role": "assistant",
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"chat_template": None,
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"return_tokens_as_token_ids": False,
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"max_log_len": None
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}
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chat_settings = {
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"enable_reasoning": False,
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"reasoning_parser": None,
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"enable_auto_tools": False,
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"tool_parser": None,
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"enable_prompt_tokens_details": False,
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"chat_template_content_format": "auto"
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}
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return {
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"vllm_model_config": vllm_model_config,
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"chat_settings": chat_settings
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}
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def remove_extra_system_prompts(self, messages: List) -> List:
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system_messages_indices = []
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for i, msg in enumerate(messages):
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if msg["role"] == "system":
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system_messages_indices.append(i)
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else:
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break
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if len(system_messages_indices) > 1:
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last_system_index = system_messages_indices[-1]
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messages = [msg for i, msg in enumerate(messages) if msg["role"] != "system" or i == last_system_index]
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return messages
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async def preprocess(
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self,
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body: Union[bytes, dict],
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state: dict,
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collect_custom_statistics_fn: Optional[Callable[[dict], None]],
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) -> Any: # noqa
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if "messages" in body["request"]:
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body["request"]["messages"] = self.remove_extra_system_prompts(body["request"]["messages"])
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return body
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