mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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fe540f6caa
* support custom model&&model_runner * fix merge * add test && update doc * fix codestyle * fix unittest * load model in rl
127 lines
3.9 KiB
Python
127 lines
3.9 KiB
Python
"""
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# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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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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"""
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from abc import ABC, abstractmethod
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from typing import Dict, Union
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import numpy as np
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import paddle
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from paddle import nn
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from paddleformers.transformers import PretrainedModel
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class ModelRegistry:
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"""
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Used to register and retrieve model classes.
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"""
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_arch_to_model_cls = {}
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_arch_to_pretrained_model_cls = {}
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@classmethod
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def register_model_class(cls, model_class):
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"""register model class"""
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if issubclass(model_class, ModelForCasualLM) and model_class is not ModelForCasualLM:
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cls._arch_to_model_cls[model_class.name()] = model_class
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return model_class
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@classmethod
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def register_pretrained_model(cls, pretrained_model):
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"""register pretrained model class"""
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if (
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issubclass(pretrained_model, PretrainedModel)
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and pretrained_model is not PretrainedModel
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and hasattr(pretrained_model, "arch_name")
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):
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cls._arch_to_pretrained_model_cls[pretrained_model.arch_name()] = pretrained_model
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return pretrained_model
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@classmethod
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def get_pretrain_cls(cls, architectures: str):
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"""get_pretrain_cls"""
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return cls._arch_to_pretrained_model_cls[architectures]
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@classmethod
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def get_class(cls, name):
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"""get model class"""
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if name not in cls._arch_to_model_cls:
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raise ValueError(f"Model '{name}' is not registered!")
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return cls._arch_to_model_cls[name]
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@classmethod
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def get_supported_archs(cls):
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assert len(cls._arch_to_model_cls) >= len(
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cls._arch_to_pretrained_model_cls
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), "model class num is more than pretrained model registry num"
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return [key for key in cls._arch_to_model_cls.keys()]
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class ModelForCasualLM(nn.Layer, ABC):
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"""
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Base class for LM
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"""
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def __init__(self, configs):
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"""
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Args:
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configs (dict): Configurations including parameters such as max_dec_len, min_dec_len, decode_strategy,
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vocab_size, use_topp_sampling, etc.
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"""
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super(ModelForCasualLM, self).__init__()
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self.fd_config = configs
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@abstractmethod
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def set_state_dict(self, state_dict: Dict[str, Union[np.ndarray, paddle.Tensor]]):
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"""
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Load model parameters from a given state dictionary.
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Args:
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state_dict (dict[str, np.ndarray | paddle.Tensor]):
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A dictionary containing model parameters, where keys are parameter names
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and values are NumPy arrays or PaddlePaddle tensors.
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"""
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raise NotImplementedError
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@abstractmethod
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def forward(
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self,
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input_ids=None,
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pos_emb=None,
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**model_kwargs,
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):
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"""
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Defines the forward pass of the model for generating text.
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Args:
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input_ids (Tensor, optional): The input token ids to the model.
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pos_emb (Tensor, optional): position Embeddings for model.
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**model_kwargs: Additional keyword arguments for the model.
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Returns:
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Tensor or list of Tensors: Generated tokens or decoded outputs.
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"""
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raise NotImplementedError
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@abstractmethod
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def compute_logits(self, hidden_state, **logits_prosessor_kwargs):
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raise NotImplementedError
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@classmethod
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@abstractmethod
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def name(self):
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raise NotImplementedError
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