def __init__(self, args, dictionary, embed_tokens):
self.args = args
super().__init__(
TransformerConfig.from_namespace(args),
dictionary,
embed_tokens,
)
def build_self_attention(
self, embed_dim, args, add_bias_kv=False, add_zero_attn=False
):
return super().build_self_attention(
embed_dim,
TransformerConfig.from_namespace(args),
add_bias_kv=add_bias_kv,
add_zero_attn=add_zero_attn,
)
def __init__(
self, args, no_encoder_attn=False, add_bias_kv=False, add_zero_attn=False
):
super().__init__(
TransformerConfig.from_namespace(args),
no_encoder_attn=no_encoder_attn,
add_bias_kv=add_bias_kv,
add_zero_attn=add_zero_attn,
)
self.args = args
def __init__(
self,
args,
positional_embedding: Optional[RelativePositionalEmbedding] = None
):
super().__init__(
TransformerConfig.from_namespace(args),
positional_embedding=positional_embedding,
)
self.args = args
def __init__(
self,
args,
dictionary,
embed_tokens,
no_encoder_attn=False,
output_projection=None,
):
self.args = args
super().__init__(
TransformerConfig.from_namespace(args),
dictionary,
embed_tokens,
no_encoder_attn=no_encoder_attn,
output_projection=output_projection,
)
def __init__(
self,
args,
no_encoder_attn=False,
add_bias_kv=False,
add_zero_attn=False,
positional_embedding: Optional[RelativePositionalEmbedding] = None,
):
super().__init__(
TransformerConfig.from_namespace(args),
no_encoder_attn=no_encoder_attn,
add_bias_kv=add_bias_kv,
add_zero_attn=add_zero_attn,
positional_embedding=positional_embedding,
)
self.args = args
def build_encoder_attention(self, embed_dim, args):
return super().build_encoder_attention(
embed_dim,
TransformerConfig.from_namespace(args),
)
def __init__(self, args):
super().__init__(TransformerConfig.from_namespace(args))
self.args = args
def build_encoder_layer(self, args):
return super().build_encoder_layer(
TransformerConfig.from_namespace(args), )
def build_decoder_layer(self, args, no_encoder_attn=False):
return super().build_decoder_layer(
TransformerConfig.from_namespace(args),
no_encoder_attn=no_encoder_attn)
def build_output_projection(self, args, dictionary, embed_tokens):
super().build_output_projection(TransformerConfig.from_namespace(args),
dictionary, embed_tokens)
def build_self_attention(self, embed_dim, args, positional_embedding=None):
return super().build_self_attention(
embed_dim,
TransformerConfig.from_namespace(args),
positional_embedding=positional_embedding,
)
