lib.linux-x86_64-cpython-310/tensorrt_llm/top_model_mixin.py

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# SPDX-License-Identifier: Apache-2.0
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
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from typing import Optional

from .lora_manager import LoraConfig
from .mapping import Mapping
from .plugin.plugin import PluginConfig


class TopModelMixin:
    '''
        The Module class are reused between building blocks (like Attention, MLP) and the top level model (like LLaMAForCausalLM)
        While there are some functions, like the loading hf/ft weights, or build/load trt engines are only supported by the top level model, not the building blocks.
        So top level model class like: LLaMAForCausalLM shall inherit this class.
    '''

    def __init__(self) -> None:
        pass

    @classmethod
    def from_hugging_face(cls,
                          hf_model_dir: str,
                          dtype: Optional[str] = 'float16',
                          mapping: Optional[Mapping] = None,
                          **kwargs):
        '''
        Create LLM object and load weights from hugging face
        Parameters:
            hf_model_dir: the hugging face model directory
            dtype: str, the default weights data type when loading from the hugging face model
            mapping: Mapping, specify the multi-gpu parallel strategy, when it's None, single GPU is used
        '''
        raise NotImplementedError("Subclass shall override this")

    @classmethod
    def convert_hf_checkpoint(cls,
                              hf_model_dir: str,
                              dtype: Optional[str] = "float16",
                              output_dir: Optional[str] = None,
                              **kwargs):
        '''
        Convert Huggingface checkpoint to TRT-LLM checkpoint
        '''
        raise NotImplementedError("Subclass shall override this")

    def use_lora(self, lora_config: LoraConfig):
        '''
        Load lora weights from the give config to the module
        Parameters:
           lora_config: the lora config
        '''
        raise NotImplementedError("Subclass shall override this")

    def use_prompt_tuning(self, max_prompt_embedding_table_size: str,
                          prompt_table_path: str):
        '''Enable p tuning when build the TRT engine, call this before to_trt
        '''
        raise NotImplementedError

    def default_plugin_config(self, **kwargs) -> PluginConfig:
        '''Return the default plugin config for this model, when the plugin_config value is not given in to_trt() call.
           If users need to set different plugin configs, they can start from the return object and change it.
        '''
        return PluginConfig.from_dict(kwargs)