180 lines
8.2 KiB
Python
180 lines
8.2 KiB
Python
|
# coding=utf-8
|
||
|
|
# Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
|
||
|
|
#
|
||
|
|
# 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.
|
||
|
|
""" MVP model configuration"""
|
||
|
|
import warnings
|
||
|
|
|
||
|
|
from ...configuration_utils import PretrainedConfig
|
||
|
|
from ...utils import logging
|
||
|
|
|
||
|
|
|
||
|
|
logger = logging.get_logger(__name__)
|
||
|
|
|
||
|
|
|
||
|
|
class MvpConfig(PretrainedConfig):
|
||
|
|
r"""
|
||
|
|
This is the configuration class to store the configuration of a [`MvpModel`]. It is used to instantiate a MVP model
|
||
|
|
according to the specified arguments, defining the model architecture. Instantiating a configuration with the
|
||
|
|
defaults will yield a similar configuration to that of the MVP [RUCAIBox/mvp](https://huggingface.co/RUCAIBox/mvp)
|
||
|
|
architecture.
|
||
|
|
|
||
|
|
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||
|
|
documentation from [`PretrainedConfig`] for more information.
|
||
|
|
|
||
|
|
|
||
|
|
Args:
|
||
|
|
vocab_size (`int`, *optional*, defaults to 50267):
|
||
|
|
Vocabulary size of the MVP model. Defines the number of different tokens that can be represented by the
|
||
|
|
`inputs_ids` passed when calling [`MvpModel`].
|
||
|
|
d_model (`int`, *optional*, defaults to 1024):
|
||
|
|
Dimensionality of the layers and the pooler layer.
|
||
|
|
encoder_layers (`int`, *optional*, defaults to 12):
|
||
|
|
Number of encoder layers.
|
||
|
|
decoder_layers (`int`, *optional*, defaults to 12):
|
||
|
|
Number of decoder layers.
|
||
|
|
encoder_attention_heads (`int`, *optional*, defaults to 16):
|
||
|
|
Number of attention heads for each attention layer in the Transformer encoder.
|
||
|
|
decoder_attention_heads (`int`, *optional*, defaults to 16):
|
||
|
|
Number of attention heads for each attention layer in the Transformer decoder.
|
||
|
|
decoder_ffn_dim (`int`, *optional*, defaults to 4096):
|
||
|
|
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
|
||
|
|
encoder_ffn_dim (`int`, *optional*, defaults to 4096):
|
||
|
|
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
|
||
|
|
activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
|
||
|
|
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
|
||
|
|
`"relu"`, `"silu"` and `"gelu_new"` are supported.
|
||
|
|
dropout (`float`, *optional*, defaults to 0.1):
|
||
|
|
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
|
||
|
|
attention_dropout (`float`, *optional*, defaults to 0.0):
|
||
|
|
The dropout ratio for the attention probabilities.
|
||
|
|
activation_dropout (`float`, *optional*, defaults to 0.0):
|
||
|
|
The dropout ratio for activations inside the fully connected layer.
|
||
|
|
classifier_dropout (`float`, *optional*, defaults to 0.0):
|
||
|
|
The dropout ratio for classifier.
|
||
|
|
max_position_embeddings (`int`, *optional*, defaults to 1024):
|
||
|
|
The maximum sequence length that this model might ever be used with. Typically set this to something large
|
||
|
|
just in case (e.g., 512 or 1024 or 2048).
|
||
|
|
init_std (`float`, *optional*, defaults to 0.02):
|
||
|
|
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
||
|
|
encoder_layerdrop (`float`, *optional*, defaults to 0.0):
|
||
|
|
The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
|
||
|
|
for more details.
|
||
|
|
decoder_layerdrop (`float`, *optional*, defaults to 0.0):
|
||
|
|
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
|
||
|
|
for more details.
|
||
|
|
scale_embedding (`bool`, *optional*, defaults to `False`):
|
||
|
|
Scale embeddings by diving by sqrt(d_model).
|
||
|
|
use_cache (`bool`, *optional*, defaults to `True`):
|
||
|
|
Whether or not the model should return the last key/values attentions (not used by all models).
|
||
|
|
forced_eos_token_id (`int`, *optional*, defaults to 2):
|
||
|
|
The id of the token to force as the last generated token when `max_length` is reached. Usually set to
|
||
|
|
`eos_token_id`.
|
||
|
|
use_prompt (`bool`, *optional*, defaults to `False`):
|
||
|
|
Whether or not to use prompt.
|
||
|
|
prompt_length (`int`, *optional*, defaults to 100):
|
||
|
|
The length of prompt.
|
||
|
|
prompt_mid_dim (`int`, *optional*, defaults to 800):
|
||
|
|
Dimensionality of the "intermediate" layer in prompt.
|
||
|
|
Example:
|
||
|
|
|
||
|
|
```python
|
||
|
|
>>> from transformers import MvpConfig, MvpModel
|
||
|
|
|
||
|
|
>>> # Initializing a MVP RUCAIBox/mvp style configuration
|
||
|
|
>>> configuration = MvpConfig()
|
||
|
|
|
||
|
|
>>> # Initializing a model (with random weights) from the RUCAIBox/mvp style configuration
|
||
|
|
>>> model = MvpModel(configuration)
|
||
|
|
|
||
|
|
>>> # Accessing the model configuration
|
||
|
|
>>> configuration = model.config
|
||
|
|
```"""
|
||
|
|
|
||
|
|
model_type = "mvp"
|
||
|
|
keys_to_ignore_at_inference = ["past_key_values"]
|
||
|
|
attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
vocab_size=50267,
|
||
|
|
max_position_embeddings=1024,
|
||
|
|
encoder_layers=12,
|
||
|
|
encoder_ffn_dim=4096,
|
||
|
|
encoder_attention_heads=16,
|
||
|
|
decoder_layers=12,
|
||
|
|
decoder_ffn_dim=4096,
|
||
|
|
decoder_attention_heads=16,
|
||
|
|
encoder_layerdrop=0.0,
|
||
|
|
decoder_layerdrop=0.0,
|
||
|
|
activation_function="gelu",
|
||
|
|
d_model=1024,
|
||
|
|
dropout=0.1,
|
||
|
|
attention_dropout=0.0,
|
||
|
|
activation_dropout=0.0,
|
||
|
|
init_std=0.02,
|
||
|
|
classifier_dropout=0.0,
|
||
|
|
scale_embedding=False,
|
||
|
|
use_cache=True,
|
||
|
|
pad_token_id=1,
|
||
|
|
bos_token_id=0,
|
||
|
|
eos_token_id=2,
|
||
|
|
is_encoder_decoder=True,
|
||
|
|
decoder_start_token_id=2,
|
||
|
|
forced_eos_token_id=2,
|
||
|
|
use_prompt=False,
|
||
|
|
prompt_length=100,
|
||
|
|
prompt_mid_dim=800,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
self.vocab_size = vocab_size
|
||
|
|
self.max_position_embeddings = max_position_embeddings
|
||
|
|
self.d_model = d_model
|
||
|
|
self.encoder_ffn_dim = encoder_ffn_dim
|
||
|
|
self.encoder_layers = encoder_layers
|
||
|
|
self.encoder_attention_heads = encoder_attention_heads
|
||
|
|
self.decoder_ffn_dim = decoder_ffn_dim
|
||
|
|
self.decoder_layers = decoder_layers
|
||
|
|
self.decoder_attention_heads = decoder_attention_heads
|
||
|
|
self.dropout = dropout
|
||
|
|
self.attention_dropout = attention_dropout
|
||
|
|
self.activation_dropout = activation_dropout
|
||
|
|
self.activation_function = activation_function
|
||
|
|
self.init_std = init_std
|
||
|
|
self.encoder_layerdrop = encoder_layerdrop
|
||
|
|
self.decoder_layerdrop = decoder_layerdrop
|
||
|
|
self.classifier_dropout = classifier_dropout
|
||
|
|
self.use_cache = use_cache
|
||
|
|
self.num_hidden_layers = encoder_layers
|
||
|
|
self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
|
||
|
|
self.use_prompt = use_prompt
|
||
|
|
self.prompt_length = prompt_length
|
||
|
|
self.prompt_mid_dim = prompt_mid_dim
|
||
|
|
|
||
|
|
super().__init__(
|
||
|
|
pad_token_id=pad_token_id,
|
||
|
|
bos_token_id=bos_token_id,
|
||
|
|
eos_token_id=eos_token_id,
|
||
|
|
is_encoder_decoder=is_encoder_decoder,
|
||
|
|
decoder_start_token_id=decoder_start_token_id,
|
||
|
|
forced_eos_token_id=forced_eos_token_id,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False):
|
||
|
|
self.forced_bos_token_id = self.bos_token_id
|
||
|
|
warnings.warn(
|
||
|
|
f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
|
||
|
|
"The config can simply be saved and uploaded again to be fixed."
|
||
|
|
)
|