![]() ![]() supports_masking = True def call ( self, inputs, encoder_outputs, mask = None ): causal_mask = self. MultiHeadAttention ( num_heads = num_heads, key_dim = embed_dim ) self. Layer ): def _init_ ( self, embed_dim, latent_dim, num_heads, ** kwargs ): super (). not_equal ( inputs, 0 ) class TransformerDecoder ( layers. position_embeddings ( positions ) return embedded_tokens + embedded_positions def compute_mask ( self, inputs, mask = None ): return tf. token_embeddings ( inputs ) embedded_positions = self. range ( start = 0, limit = length, delta = 1 ) embedded_tokens = self. embed_dim = embed_dim def call ( self, inputs ): length = tf. Embedding ( input_dim = sequence_length, output_dim = embed_dim ) self. Embedding ( input_dim = vocab_size, output_dim = embed_dim ) self. Layer ): def _init_ ( self, sequence_length, vocab_size, embed_dim, ** kwargs ): super (). layernorm_2 ( proj_input + proj_output ) class PositionalEmbedding ( layers. layernorm_1 ( inputs + attention_output ) proj_output = self. attention ( query = inputs, value = inputs, key = inputs, attention_mask = padding_mask ) proj_input = self. cast ( mask, dtype = "int32" ) attention_output = self. supports_masking = True def call ( self, inputs, mask = None ): if mask is not None : padding_mask = tf. Layer ): def _init_ ( self, embed_dim, dense_dim, num_heads, ** kwargs ): super (). Result in a model that cannot be used at inference time).Ĭlass TransformerEncoder ( layers. (otherwise, it could use information from the future, which would Sure that it only uses information from target tokens 0 to N when predicting token N+1 The TransformerDecoder sees the entire sequences at once, and thus we must make (see method get_causal_attention_mask() on the TransformerDecoder). The TransformerDecoder will then seek to predict the next words in the target sequence (N+1 and beyond).Ī key detail that makes this possible is causal masking To the TransformerDecoder, together with the target sequence so far (target words 0 to N). This new representation will then be passed Which will produce a new representation of it. The source sequence will be pass to the TransformerEncoder, Our sequence-to-sequence Transformer consists of a TransformerEncoderĪnd a TransformerDecoder chained together. It provides the next words in the target sentence - what the model will try to predict.
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