Generation refactor

keras_nlp/layers/modeling/transformer_decoder.py

@@ -250,6 +250,28 @@ 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,
@@ -325,7 +347,9 @@ 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/layers/preprocessing/start_end_packer.py

@@ -189,7 +189,7 @@ 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/models/bart/bart_backbone.py

@@ -254,5 +254,4 @@ def get_config(self):
  "max_sequence_length": self.max_sequence_length,
  }
  )
-
  return config

keras_nlp/models/bart/bart_seq_2_seq_lm.py

@@ -20,7 +20,6 @@
  BartSeq2SeqLMPreprocessor,
 )
 from keras_nlp.models.seq_2_seq_lm import Seq2SeqLM
-from keras_nlp.utils.tensor_utils import any_equal
 
 
 @keras_nlp_export("keras_nlp.models.BartSeq2SeqLM")
@@ -199,291 +198,68 @@ 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,
- }