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