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https://github.com/NVIDIA/TensorRT-LLM.git
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db4edea1e1
* Update TensorRT-LLM --------- Co-authored-by: Kota Tsuyuzaki <bloodeagle40234@gmail.com> Co-authored-by: Pzzzzz <hello-cd.plus@hotmail.com> Co-authored-by: Patrick Reiter Horn <patrick.horn@gmail.com>
78 lines
3.2 KiB
Python
78 lines
3.2 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Optional
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from .lora_manager import LoraConfig
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from .mapping import Mapping
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from .plugin.plugin import PluginConfig
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class TopModelMixin:
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'''
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The Module class are reused between building blocks (like Attention, MLP) and the top level model (like LLaMAForCausalLM)
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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.
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So top level model class like: LLaMAForCausalLM shall inherit this class.
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'''
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def __init__(self) -> None:
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pass
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@classmethod
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def from_hugging_face(cls,
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hf_model_dir: str,
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dtype: Optional[str] = 'float16',
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mapping: Optional[Mapping] = None,
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**kwargs):
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'''
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Create LLM object and load weights from hugging face
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Parameters:
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hf_model_dir: the hugging face model directory
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dtype: str, the default weights data type when loading from the hugging face model
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mapping: Mapping, specify the multi-gpu parallel strategy, when it's None, single GPU is used
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'''
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raise NotImplementedError("Subclass shall override this")
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@classmethod
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def convert_hf_checkpoint(cls,
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hf_model_dir: str,
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dtype: Optional[str] = "float16",
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output_dir: Optional[str] = None,
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**kwargs):
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'''
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Convert Huggingface checkpoint to TRT-LLM checkpoint
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'''
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raise NotImplementedError("Subclass shall override this")
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def use_lora(self, lora_config: LoraConfig):
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'''
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Load lora weights from the give config to the module
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Parameters:
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lora_config: the lora config
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'''
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raise NotImplementedError("Subclass shall override this")
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def use_prompt_tuning(self, max_prompt_embedding_table_size: str,
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prompt_table_path: str):
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'''Enable p tuning when build the TRT engine, call this before to_trt
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'''
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raise NotImplementedError
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def default_plugin_config(self, **kwargs) -> PluginConfig:
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'''Return the default plugin config for this model, when the plugin_config value is not given in to_trt() call.
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If users need to set different plugin configs, they can start from the return object and change it.
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'''
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return PluginConfig.from_dict(kwargs)
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