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How to use _get_encoder_class method in autotest

Best Python code snippet using autotest_python

transformer_builders.py

Source:transformer_builders.py

...111    RecurrentTransformerEncoder.112    Inheriting classes should implement the following:113    - _get_attention_builder()114    - _get_attention_layer_class()115    - _get_encoder_class()116    - _get_encoder_layer_class()117    """118    def __init__(self):119        super(BaseTransformerEncoderBuilder, self).__init__()120        self._attention_builder = self._get_attention_builder()121        self._attention_type = "full"122    def _get_attention_builder(self):123        """Return an instance of the appropriate attention builder."""124        raise NotImplementedError()125    def _get_attention_layer_class(self):126        """Return the class for the layer that projects queries keys and127        values."""128        raise NotImplementedError()129    def _get_encoder_class(self):130        """Return the class for the transformer encoder."""131        raise NotImplementedError()132    def _get_encoder_layer_class(self):133        """Return the class for the transformer encoder layer."""134        raise NotImplementedError()135    @property136    def attention(self):137        """The attention builder instance."""138        return self._attention_builder139    @property140    def attention_type(self):141        """The attention implementation chosen."""142        return self._attention_type143    @attention_type.setter144    def attention_type(self, val):145        if not self._attention_builder.validate_attention_type(val):146            raise ValueError(("{!r} is not an available attention "147                              "type").format(val))148        self._attention_type = val149    def __setattr__(self, key, val):150        # "protected" attributes are settable (probably from withing the class)151        if key[0] == "_":152            return super().__setattr__(key, val)153        # Existing attributes are settable but they might also be attention154        # parameters so try that as well155        fail_on_exception = True156        if hasattr(self, key):157            super().__setattr__(key, val)158            fail_on_exception = False159        # Non-existing "public" attributes may be attention parameters160        try:161            setattr(self._attention_builder, key, val)162        except:163            if fail_on_exception:164                raise165    def get(self):166        """Build the transformer and return it."""167        # Set the event dispatcher to the attention builder168        self.attention.event_dispatcher = self.event_dispatcher169        # Extract into local variables the classes to be used170        Encoder = self._get_encoder_class()171        EncoderLayer = self._get_encoder_layer_class()172        Attention = self._get_attention_layer_class()173        model_dimensions = self.value_dimensions*self.n_heads174        return Encoder(175            [176                EncoderLayer(177                    Attention(178                        self.attention.get(self.attention_type),179                        model_dimensions,180                        self.n_heads,181                        d_keys=self.query_dimensions,182                        d_values=self.value_dimensions,183                        event_dispatcher=self.event_dispatcher184                    ),185                    model_dimensions,186                    self.feed_forward_dimensions,187                    self.dropout,188                    self.activation,189                    event_dispatcher=self.event_dispatcher190                )191                for _ in range(self.n_layers)192            ],193            (LayerNorm(model_dimensions) if self.final_normalization else None),194            event_dispatcher=self.event_dispatcher195        )196class TransformerEncoderBuilder(BaseTransformerEncoderBuilder):197    """Build a batch transformer encoder for training or processing of198    sequences all elements at a time.199    Example usage:200        builder = TransformerEncoderBuilder()201        builder.n_layers = 12202        builder.n_heads = 8203        builder.feed_forward_dimensions = 1024204        builder.query_dimensions = 64205        builder.value_dimensions = 64206        builder.dropout = 0.1207        builder.attention_dropout = 0.1208        builder.attention_type = "linear"209        transformer = builder.get()210    """211    def _get_attention_builder(self):212        """Return an instance of the appropriate attention builder."""213        return AttentionBuilder()214    def _get_attention_layer_class(self):215        """Return the class for the layer that projects queries keys and216        values."""217        return AttentionLayer218    def _get_encoder_class(self):219        """Return the class for the transformer encoder."""220        return TransformerEncoder221    def _get_encoder_layer_class(self):222        """Return the class for the transformer encoder layer."""223        return TransformerEncoderLayer224class RecurrentEncoderBuilder(BaseTransformerEncoderBuilder):225    """Build a transformer encoder for autoregressive processing of sequences.226    Example usage:227        builder = RecurrentEncoderBuilder()228        builder.n_layers = 12229        builder.n_heads = 8230        builder.feed_forward_dimensions = 1024231        builder.query_dimensions = 64232        builder.value_dimensions = 64233        builder.dropout = 0.1234        builder.attention_dropout = 0.1235        builder.attention_type = "linear"236        transformer = builder.get()237    """238    def _get_attention_builder(self):239        """Return an attention builder for recurrent attention."""240        return RecurrentAttentionBuilder()241    def _get_attention_layer_class(self):242        """Return the class for the recurrent layer that projects queries keys243        and values."""244        return RecurrentAttentionLayer245    def _get_encoder_class(self):246        """Return the class for the recurrent transformer encoder."""247        return RecurrentTransformerEncoder248    def _get_encoder_layer_class(self):249        """Return the class for the recurrent transformer encoder layer."""250        return RecurrentTransformerEncoderLayer251class BaseTransformerDecoderBuilder(BaseTransformerBuilder):252    """Similar to BaseTransformerEncoderBuilder implement the logic of253    building the transformer decoder without defining concrete layers.254    Inheriting classes should implement the following:255    - _get_self_attention_builder() and _get_cross_attention_builder()256    - _get_self_attention_layer_class() and _get_cross_attention_layer_class()257    - _get_decoder_class()258    - _get_decoder_layer_class()259    """...

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