wehub-resource-sync
173 lines
7.3 KiB
Python
173 lines
7.3 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2021 The HuggingFace Inc. team.
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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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""" OPT Model Configuration"""
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from __future__ import annotations
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from typing import Dict
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from ..configuration_utils import PretrainedConfig
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__all__ = [
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"OPT_PRETRAINED_INIT_CONFIGURATION",
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"OPT_PRETRAINED_RESOURCE_FILES_MAP",
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"OPTConfig",
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]
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OPT_PRETRAINED_INIT_CONFIGURATION = {
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"facebook/opt-1.3b": {
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"init_args": [
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{
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"intermediate_size": 8192,
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"attention_probs_dropout_prob": 0.0,
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"hidden_dropout_prob": 0.1,
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"normalize_before": True,
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"word_embed_proj_dim": 2048,
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"num_attention_heads": 32,
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"bos_token_id": 2,
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"hidden_size": 2048,
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"eos_token_id": 2,
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"hidden_act": "relu",
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"initializer_range": 0.02,
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"max_position_embeddings": 2048,
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"num_hidden_layers": 24,
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"pad_token_id": 1,
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"vocab_size": 50272,
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"type_vocab_size": 16,
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"init_class": "OPTModel",
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}
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],
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"init_class": "OPTForCausalLM",
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},
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}
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OPT_PRETRAINED_RESOURCE_FILES_MAP = {
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"model_state": {
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"facebook/opt-1.3b": "https://bj.bcebos.com/paddlenlp/models/community/facebook/opt-1.3b/model_state.pdparams"
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}
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}
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class OPTConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`OPTModel`]. It is used to instantiate
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an OPT model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the OPT
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[facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 50272):
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Vocabulary size of the OPT model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`OPTModel`]
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hidden_size (`int`, *optional*, defaults to 2048):
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Dimensionality of the layers and the pooler layer.
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num_hidden_layers (`int`, *optional*, defaults to 24):
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Number of decoder layers.
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intermediate_size (`int`, *optional*, defaults to 8192):
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Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
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num_attention_heads (`int`, *optional*, defaults to 32):
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Number of attention heads for each attention layer in the Transformer decoder.
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hidden_act (`str` or `function`, *optional*, defaults to `"relu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"silu"` and `"gelu_new"` are supported.
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max_position_embeddings (`int`, *optional*, defaults to 2048):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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normalize_before (`bool`, *optional*, defaults to `True`):
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Whether to perform layer normalization before the attention block.
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word_embed_proj_dim (`int`, *optional*):
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`word_embed_proj_dim` can be set to down-project word embeddings, *e.g.* `opt-1.3b`. Defaults to
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`hidden_size`.
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hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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type_vocab_size (int, optional):
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The vocabulary size of the `token_type_ids`. Defaults to `16`.
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.. note::
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Please NOT using `type_vocab_size`, for it will be obsolete in the future..
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initializer_range (float, optional):
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The standard deviation of the normal initializer. Default to `0.02`.
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.. note::
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A normal_initializer initializes weight matrices as normal distributions.
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See :meth:`OPTPretrainedModel._init_weights()` for how weights are initialized in `OPTModel`.
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Example:
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```python
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>>> from paddlenlp.transformers import OPTModel, OPTConfig
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>>> # Initializing a OPT facebook/opt-1.3b style configuration
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>>> config = OPTConfig()
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>>> # Initializing a model from the facebook/opt-1.3b style configuration
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>>> model = OPTModel(config)
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>>> # Accessing the model config
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>>> config = model.config
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```"""
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attribute_map: Dict[str, str] = {
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"dropout": "classifier_dropout",
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"num_classes": "num_labels",
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"ffn_dim": "intermediate_size",
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"activation_function": "hidden_act",
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}
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pretrained_init_configuration = OPT_PRETRAINED_INIT_CONFIGURATION
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model_type = "opt"
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def __init__(
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self,
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vocab_size=50272,
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hidden_size=2048,
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num_hidden_layers=24,
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intermediate_size=8192,
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num_attention_heads=32,
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hidden_act="relu",
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max_position_embeddings=2048,
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normalize_before=True,
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word_embed_proj_dim=2048,
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.0,
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initializer_range=0.02,
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type_vocab_size=16,
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pad_token_id=1,
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bos_token_id=2,
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eos_token_id=2,
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enable_bias: bool = True,
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mp_degree: int = 1,
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fuse_attention_qkv=False,
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fuse_attention_ffn=False,
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**kwargs,
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):
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super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.intermediate_size = intermediate_size
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self.num_attention_heads = num_attention_heads
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self.hidden_act = hidden_act
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self.max_position_embeddings = max_position_embeddings
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self.normalize_before = normalize_before
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self.word_embed_proj_dim = word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.initializer_range = initializer_range
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self.type_vocab_size = type_vocab_size
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self.enable_bias = enable_bias
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self.mp_degree = mp_degree
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self.fuse_attention_qkv = fuse_attention_qkv
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self.fuse_attention_ffn = fuse_attention_ffn
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