#
# Author: penhe@microsoft.com
# Date: 01/25/2019
#
"""
Pooling functions
"""
from torch import nn
import copy
import json
from .bert import ACT2FN
from .ops import StableDropout
from .config import AbsModelConfig
__all__ = ['PoolConfig', 'ContextPooler']
[docs]class PoolConfig(AbsModelConfig):
"""Configuration class to store the configuration of `pool layer`.
Parameters:
config (:class:`~DeBERTa.deberta.ModelConfig`): The model config. The field of pool config will be initalized with the `pooling` field in model config.
Attributes:
hidden_size (int): Size of the encoder layers and the pooler layer, default: `768`.
dropout (float): The dropout rate applied on the output of `[CLS]` token,
hidden_act (:obj:`str`): The activation function of the projection layer, it can be one of ['gelu', 'tanh'].
Example::
# Here is the content of an exmple model config file in json format
{
"hidden_size": 768,
"num_hidden_layers" 12,
"num_attention_heads": 12,
"intermediate_size": 3072,
...
"pooling": {
"hidden_size": 768,
"hidden_act": "gelu",
"dropout": 0.1
}
}
"""
def __init__(self, config=None):
"""Constructs PoolConfig.
Args:
`config`: the config of the model. The field of pool config will be initalized with the 'pooling' field in model config.
"""
self.hidden_size = 768
self.dropout = 0
self.hidden_act = 'gelu'
if config:
pool_config = getattr(config, 'pooling', config)
if isinstance(pool_config, dict):
pool_config = AbsModelConfig.from_dict(pool_config)
self.hidden_size = getattr(pool_config, 'hidden_size', config.hidden_size)
self.dropout = getattr(pool_config, 'dropout', 0)
self.hidden_act = getattr(pool_config, 'hidden_act', 'gelu')
[docs]class ContextPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = StableDropout(config.dropout)
self.config = config
def forward(self, hidden_states, mask = None):
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
context_token = hidden_states[:, 0]
context_token = self.dropout(context_token)
pooled_output = self.dense(context_token)
pooled_output = ACT2FN[self.config.hidden_act](pooled_output)
return pooled_output
def output_dim(self):
return self.config.hidden_size