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# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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)
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