Generation refactor

Author: mattdangerw

keras_nlp/src/layers/modeling/transformer_decoder.py

@@ -251,6 +251,28 @@ def build(
         # Create layers based on input shape.
         self.built = True
 
+    def compute_self_attention_cache(
+        self,
+        decoder_sequence,
+    ):
+        x = decoder_sequence
+        if self.normalize_first:
+            x = self._self_attention_layer_norm(x)
+        key = self._self_attention_layer._key_dense(x)
+        value = self._self_attention_layer._value_dense(x)
+        return ops.stack((key, value), axis=1)
+
+    def compute_cross_attention_cache(
+        self,
+        encoder_sequence,
+    ):
+        x = encoder_sequence
+        if self.normalize_first:
+            x = self._cross_attention_layer_norm(x)
+        key = self._cross_attention_layer._key_dense(x)
+        value = self._cross_attention_layer._value_dense(x)
+        return ops.stack((key, value), axis=1)
+
     def call(
         self,
         decoder_sequence,
@@ -314,7 +336,9 @@ def call(
               the layer has cross-attention.
         """
 
-        has_encoder_sequence = encoder_sequence is not None
+        has_encoder_sequence = (
+            encoder_sequence is not None or cross_attention_cache is not None
+        )
 
         has_cross_attention = self._cross_attention_layer is not None
         if not has_cross_attention and has_encoder_sequence:

keras_nlp/src/layers/preprocessing/start_end_packer.py

@@ -193,7 +193,7 @@ def call(
         outputs = tf.squeeze(outputs, axis=0) if unbatched else outputs
 
         if self.return_padding_mask:
-            mask = tf.ones_like(x, dtype="bool")
+            mask = tf.ones_like(x, dtype="int32")
             mask = mask.to_tensor(shape=(batch_size, sequence_length))
             mask = tf.squeeze(mask, axis=0) if unbatched else mask
             return outputs, mask

keras_nlp/src/models/bart/bart_backbone.py

@@ -257,5 +257,4 @@ def get_config(self):
                 "max_sequence_length": self.max_sequence_length,
             }
         )
-
         return config

keras_nlp/src/models/bart/bart_seq_2_seq_lm.py

@@ -21,7 +21,6 @@
     BartSeq2SeqLMPreprocessor,
 )
 from keras_nlp.src.models.seq_2_seq_lm import Seq2SeqLM
-from keras_nlp.src.utils.tensor_utils import any_equal
 
 
 @keras_nlp_export("keras_nlp.models.BartSeq2SeqLM")
@@ -200,291 +199,68 @@ def __init__(
             **kwargs,
         )
 
-    def call_decoder_with_cache(
+    def build_cache(self, batch_size, max_length):
+        num_layers = self.backbone.num_layers
+        num_heads = self.backbone.num_heads
+        head_dim = self.backbone.hidden_dim // self.backbone.num_heads
+        shape = [batch_size, num_layers, 2, max_length, num_heads, head_dim]
+        return ops.zeros(shape, dtype=self.compute_dtype)
+
+    def compute_cross_attention_cache(
+        self, encoder_token_ids, encoder_padding_mask
+    ):
+        """Does a forward pass on the encoder and returns the encoder output."""
+        # Embedding layers.
+        tokens = self.backbone.token_embedding(encoder_token_ids)
+        positions = self.backbone.encoder_position_embedding(tokens)
+        # Sum, normalize and apply dropout to embeddings.
+        x = self.backbone.encoder_embeddings_add((tokens, positions))
+        x = self.backbone.encoder_embeddings_layer_norm(x)
+        x = self.backbone.encoder_embeddings_dropout(x)
+        # Transformer encoder layers.
+        for layer in self.backbone.encoder_transformer_layers:
+            x = layer(x, padding_mask=encoder_padding_mask)
+        # Transformer encoder layers.
+        caches = []
+        for layer in self.backbone.decoder_transformer_layers:
+            caches.append(layer.compute_cross_attention_cache(x))
+        return ops.stack(caches, axis=1)
+
+    def call_with_cache(
         self,
-        encoder_hidden_states,
+        token_ids,
+        cache,
+        index,
+        *,
         encoder_padding_mask,
-        decoder_token_ids,
-        self_attention_cache=None,
-        self_attention_cache_update_index=None,
-        cross_attention_cache=None,
-        cross_attention_cache_update_index=None,
+        cross_attention_cache,
     ):
-        """Forward pass with a key/value caches for generative decoding..
-
-        `call_decoder_with_cache` adds an additional inference-time forward pass
-        for the model for seq2seq text generation. Unlike calling the model
-        directly, this method does two things to optimize text generation:
-
-        - Allows caching previous key/value tensors in the decoder's
-          self-attention layer to avoid recomputing the outputs of seen tokens.
-        - Allows caching key/value tensors in the decoder's cross-attention
-          layer to avoid recomputing the encoder outputs.
-
-        Args:
-            encoder_hidden_states: a dense float Tensor of shape
-                `(batch_size, encoder_sequence_length, hidden_dim)`. The
-                sequence of hidden states at the output of the encoder's last
-                layer.
-            encoder_padding_mask: a dense float Tensor of shape
-                `(batch_size, encoder_sequence_length)`. The padding mask for
-                the encoder input.
-            decoder_token_ids: a dense int Tensor of shape
-                `(batch_size, max_length)`. Input token ids to be fed to
-                the decoder.
-            self_attention_cache: a dense float Tensor of shape
-                `(batch_size, num_layers, 2, max_length, num_heads, key_dims)`.
-                The cached key/value tensors of previously seen tokens in the
-                decoder's self-attention layer.
-            self_attention_cache_update_index: an int or int Tensor, the index
-                at which to update the `self_attention_cache`. Usually, this is
-                the index of the current token being processed during decoding.
-            cross_attention_cache: a dense float Tensor of shape
-                `(batch_size, num_layers, 2, encoder_sequence_length, num_heads, key_dims)`.
-                The cached key/value tensors of the encoder outputs in the
-                decoder's cross-attention layer.
-            cross_attention_cache_update_index: an int or int Tensor, the index
-                at which to update the `cross_attention_cache`. Usually, this is
-                either `0` (compute the entire `cross_attention_cache`), or
-                `None` (reuse a previously computed `cross_attention_cache`).
-
-        Returns:
-            A `(logits, hidden_states, self_attention_cache, cross_attention_cache)`
-            tuple, where `logits` is the language model logits for the input
-            `decoder_token_ids`, `hidden_states` is the final hidden
-            representation of the input tokens, `self_attention_cache` is the
-            key/value cache in the decoder's self-attention layer and
-            `cross_attention_cache` is the key/value cache in the decoder's
-            cross-attention layer.
-        """
-        # Embedding layers.
-        tokens = self.backbone.token_embedding(decoder_token_ids)
+        tokens = self.backbone.token_embedding(token_ids)
         positions = self.backbone.decoder_position_embedding(
-            tokens,
-            start_index=self_attention_cache_update_index,
+            tokens, start_index=index
         )
         # Sum, normalize and apply dropout to embeddings.
         x = self.backbone.decoder_embeddings_add((tokens, positions))
         x = self.backbone.decoder_embeddings_layer_norm(x)
         x = self.backbone.decoder_embeddings_dropout(x)
-
-        # Every decoder layer has a separate cache for the self-attention layer
-        # and the cross-attention layer. We update all of them separately.
-        self_attention_caches = []
-        cross_attention_caches = []
+        # Each decoder layer has a cache; we update them separately.
+        caches = []
         for i, layer in enumerate(self.backbone.decoder_transformer_layers):
-            current_self_attention_cache = self_attention_cache[:, i, ...]
+            current_self_attention_cache = cache[:, i, ...]
             current_cross_attention_cache = cross_attention_cache[:, i, ...]
-            (
-                x,
-                next_self_attention_cache,
-                next_cross_attention_cache,
-            ) = layer(
+            x, next_cache, _ = layer(
                 decoder_sequence=x,
-                encoder_sequence=encoder_hidden_states,
                 encoder_padding_mask=encoder_padding_mask,
                 self_attention_cache=current_self_attention_cache,
-                self_attention_cache_update_index=self_attention_cache_update_index,
+                self_attention_cache_update_index=index,
                 cross_attention_cache=current_cross_attention_cache,
-                cross_attention_cache_update_index=cross_attention_cache_update_index,
             )
-            if self_attention_cache_update_index is not None:
-                self_attention_caches.append(next_self_attention_cache)
-            if cross_attention_cache_update_index is not None:
-                cross_attention_caches.append(next_cross_attention_cache)
-
-        if self_attention_cache_update_index is not None:
-            self_attention_cache = ops.stack(self_attention_caches, axis=1)
-        if cross_attention_cache_update_index is not None:
-            cross_attention_cache = ops.stack(cross_attention_caches, axis=1)
-
+            caches.append(next_cache)
+        cache = ops.stack(caches, axis=1)
         hidden_states = x
         logits = self.backbone.token_embedding(hidden_states, reverse=True)
         return (
             logits,
             hidden_states,
-            self_attention_cache,
-            cross_attention_cache,
+            cache,
         )
-
-    def call_encoder(self, token_ids, padding_mask):
-        """Does a forward pass on the encoder and returns the encoder output."""
-        tokens = self.backbone.token_embedding(token_ids)
-        positions = self.backbone.encoder_position_embedding(tokens)
-        x = self.backbone.decoder_embeddings_add((tokens, positions))
-        x = self.backbone.encoder_embeddings_layer_norm(x)
-        x = self.backbone.encoder_embeddings_dropout(x)
-        for transformer_layer in self.backbone.encoder_transformer_layers:
-            x = transformer_layer(x, padding_mask=padding_mask)
-        return x
-
-    def _initialize_cache(self, encoder_token_ids, decoder_token_ids):
-        """Initializes empty self-attention cache and cross-attention cache."""
-        batch_size = ops.shape(encoder_token_ids)[0]
-        encoder_max_length = ops.shape(encoder_token_ids)[1]
-        decoder_max_length = ops.shape(decoder_token_ids)[1]
-
-        num_layers = self.backbone.num_layers
-        num_heads = self.backbone.num_heads
-        head_dim = self.backbone.hidden_dim // self.backbone.num_heads
-
-        shape = [
-            batch_size,
-            num_layers,
-            2,
-            decoder_max_length,
-            num_heads,
-            head_dim,
-        ]
-        self_attention_cache = ops.zeros(shape, dtype=self.compute_dtype)
-
-        shape[3] = encoder_max_length
-        cross_attention_cache = ops.zeros(shape, dtype=self.compute_dtype)
-
-        return (self_attention_cache, cross_attention_cache)
-
-    def _build_cache(
-        self, encoder_token_ids, encoder_padding_mask, decoder_token_ids
-    ):
-        """Builds the self-attention cache and the cross-attention cache (key/value pairs)."""
-        encoder_hidden_states = self.call_encoder(
-            token_ids=encoder_token_ids, padding_mask=encoder_padding_mask
-        )
-        self_attention_cache, cross_attention_cache = self._initialize_cache(
-            encoder_token_ids, decoder_token_ids
-        )
-
-        # Seed the self-attention cache and the cross-attention cache.
-        (
-            _,
-            hidden_states,
-            self_attention_cache,
-            cross_attention_cache,
-        ) = self.call_decoder_with_cache(
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_padding_mask=encoder_padding_mask,
-            decoder_token_ids=decoder_token_ids,
-            self_attention_cache=self_attention_cache,
-            self_attention_cache_update_index=0,
-            cross_attention_cache=cross_attention_cache,
-            cross_attention_cache_update_index=0,
-        )
-        return (
-            hidden_states,
-            encoder_hidden_states,
-            self_attention_cache,
-            cross_attention_cache,
-        )
-
-    def generate_step(
-        self,
-        inputs,
-        stop_token_ids=None,
-    ):
-        """A compilable generation function for a batch of inputs.
-
-        This function represents the inner, XLA-compilable, generation function
-        for a single batch of inputs. Inputs should have the same structure as
-        model inputs, a dictionary with keys `"encoder_token_ids"`,
-        `"encoder_padding_mask"`, `"decoder_token_ids"` and
-        `"decoder_padding_mask"`.
-
-        Args:
-            inputs: A dictionary with four keys - `"encoder_token_ids"`,
-                `"encoder_padding_mask"`, `"decoder_token_ids"` and
-                `"decoder_padding_mask"`, with batched tensor values.
-            stop_token_ids: Tuple of id's of end token's to stop on. If all
-                sequences have produced a new stop token, generation
-                will stop.
-        """
-        (
-            encoder_token_ids,
-            encoder_padding_mask,
-            decoder_token_ids,
-            decoder_padding_mask,
-        ) = (
-            inputs["encoder_token_ids"],
-            inputs["encoder_padding_mask"],
-            inputs["decoder_token_ids"],
-            inputs["decoder_padding_mask"],
-        )
-
-        batch_size = ops.shape(encoder_token_ids)[0]
-
-        # Create and seed cache with a single forward pass.
-        (
-            hidden_states,
-            encoder_hidden_states,
-            self_attention_cache,
-            cross_attention_cache,
-        ) = self._build_cache(
-            encoder_token_ids, encoder_padding_mask, decoder_token_ids
-        )
-        # Compute the lengths of all user inputted tokens ids.
-        row_lengths = ops.sum(ops.cast(decoder_padding_mask, "int32"), axis=-1)
-        # Start at the first index that has no user inputted id.
-        index = ops.min(row_lengths)
-
-        def next(prompt, cache, index):
-            # The cache index is the index of our previous token.
-            cache_index = index - 1
-            num_samples = ops.shape(prompt)[0]
-            prompt = ops.slice(prompt, [0, cache_index], [num_samples, 1])
-
-            def repeat_tensor(x):
-                """Repeats tensors along batch axis to match dim for beam search."""
-                if ops.shape(x)[0] == num_samples:
-                    return x
-                return ops.repeat(x, repeats=num_samples // batch_size, axis=0)
-
-            logits, hidden_states, cache, _ = self.call_decoder_with_cache(
-                encoder_hidden_states=repeat_tensor(encoder_hidden_states),
-                encoder_padding_mask=repeat_tensor(encoder_padding_mask),
-                decoder_token_ids=prompt,
-                self_attention_cache=cache,
-                self_attention_cache_update_index=cache_index,
-                cross_attention_cache=repeat_tensor(cross_attention_cache),
-                cross_attention_cache_update_index=None,
-            )
-            return (
-                ops.squeeze(logits, axis=1),
-                ops.squeeze(hidden_states, axis=1),
-                cache,
-            )
-
-        decoder_token_ids = self.sampler(
-            next=next,
-            prompt=decoder_token_ids,
-            cache=self_attention_cache,
-            index=index,
-            mask=decoder_padding_mask,
-            stop_token_ids=stop_token_ids,
-            hidden_states=hidden_states,
-            model=self,
-        )
-
-        # Compute an output padding mask with the token ids we updated.
-        if stop_token_ids is not None:
-            # Build a mask of `stop_token_ids` locations not in the original
-            # prompt (not in locations where `decoder_padding_mask` is True).
-            end_locations = any_equal(
-                decoder_token_ids,
-                stop_token_ids,
-                ops.logical_not(decoder_padding_mask),
-            )
-            end_locations = ops.cast(end_locations, "int32")
-            # Use cumsum to get ones in all locations after `end_locations`.
-            cumsum = ops.cast(ops.cumsum(end_locations, axis=-1), "int32")
-            overflow = cumsum - end_locations
-            # Our padding mask is the inverse of these overflow locations.
-            decoder_padding_mask = ops.logical_not(ops.cast(overflow, "bool"))
-        else:
-            # Without early stopping, all locations will have been updated.
-            decoder_padding_mask = ops.ones_like(
-                decoder_token_ids, dtype="bool"
-            )
-
-        return {
-            "decoder_token_ids": decoder_token_ids,
-            "decoder_padding_mask": decoder_padding_mask,
-        }