Internal change

PiperOrigin-RevId: 466203688

official/nlp/modeling/ops/decoding_module.py

@@ -22,7 +22,7 @@ import tensorflow as tf
 from tensorflow.python.framework import dtypes
 from official.modeling import tf_utils
 
-Output = Tuple[tf.Tensor, tf.Tensor]
+Output = Tuple[tf.Tensor, tf.Tensor, Optional[tf.Tensor]]
 InternalState = Tuple[tf.Tensor, tf.Tensor, tf.Tensor, Dict]
 InitialState = Tuple[Dict[str, Any], Dict[str, Any]]
 
@@ -46,6 +46,10 @@ class StateKeys:
   # the previous iteration.
   ALIVE_CACHE = "ALIVE_CACHE"
 
+  # The initial model state/cache after model processing the initial token.
+  # The cache will be filled if extra_cache_output is true.
+  INITIAL_OUTPUT_CACHE = "INITIAL_OUTPUT_CACHE"
+
   # Top finished sequences for each batch item.
   # Has shape [batch_size, beam_size, CUR_INDEX + 1]. Sequences that are
   # shorter than CUR_INDEX + 1 are padded with 0s.
@@ -109,7 +113,8 @@ class DecodingModule(tf.Module, metaclass=abc.ABCMeta):
   def __init__(self,
                length_normalization_fn: Callable[[int, tf.DType], float],
                dtype: tf.DType = tf.float32,
-               decoding_name: Optional[str] = None):
+               decoding_name: Optional[str] = None,
+               extra_cache_output: bool = False):
     """Initialize the Decoding Module.
 
     Args:
@@ -118,24 +123,26 @@ class DecodingModule(tf.Module, metaclass=abc.ABCMeta):
       dtype: A tensorflow data type used for score computation. The default is
         tf.float32.
       decoding_name: an optional name for the decoding loop tensors.
+      extra_cache_output: If true, the first cache will be in the states.
     """
     self.length_normalization_fn = length_normalization_fn
     self.dtype = tf.as_dtype(dtype)
     self.decoding_name = decoding_name
 
-  def generate(self,
-               initial_ids: tf.Tensor,
+  def generate(self, initial_ids: tf.Tensor,
                initial_cache: Dict[str, tf.Tensor]) -> Output:
     """Implements the decoding strategy (beam_search or sampling).
 
     Args:
-      initial_ids: initial ids to pass into the symbols_to_logits_fn.
-                   int tensor with shape [batch_size, 1]
+      initial_ids: initial ids to pass into the symbols_to_logits_fn. int tensor
+        with shape [batch_size, 1]
       initial_cache: dictionary for caching model outputs from previous step.
+
     Returns:
       Tuple of tensors representing
         finished_sequence: shape [batch, max_seq_length]
         finished_scores: [batch]
+        first_cache: The cache after init token
     """
     batch_size = (
         initial_ids.shape.as_list()[0]
@@ -163,6 +170,17 @@ class DecodingModule(tf.Module, metaclass=abc.ABCMeta):
       }
       new_state.update(alive_state)
       new_state.update(finished_state)
+      if self.extra_cache_output:
+        i = state[StateKeys.CUR_INDEX]
+        old_cache = state[StateKeys.INITIAL_OUTPUT_CACHE]
+
+        def update_with_cache(new_state, cache):
+          """Updates new_state with cache."""
+          new_state.update({StateKeys.INITIAL_OUTPUT_CACHE: cache})
+
+        tf.cond(
+            tf.equal(i, 0), lambda: update_with_cache(new_state, new_cache),
+            lambda: update_with_cache(new_state, old_cache))
       return [new_state]
 
     finished_state = tf.nest.map_structure(

official/nlp/modeling/ops/decoding_module_test.py

@@ -29,6 +29,7 @@ class TestSubclass(decoding_module.DecodingModule, metaclass=abc.ABCMeta):
 
   def __init__(self,
                length_normalization_fn=length_normalization,
+               extra_cache_output=True,
                dtype=tf.float32):
     super(TestSubclass, self).__init__(
         length_normalization_fn=length_normalization, dtype=dtype)

official/nlp/modeling/ops/sampling_module.py

@@ -163,7 +163,8 @@ class SamplingModule(decoding_module.DecodingModule, metaclass=abc.ABCMeta):
                sample_temperature=0.0,
                enable_greedy: bool = True,
                dtype: tf.DType = tf.float32,
-               decoding_name: Optional[str] = None):
+               decoding_name: Optional[str] = None,
+               extra_cache_output: bool = False):
     """Initialize sampling module."""
     self.symbols_to_logits_fn = symbols_to_logits_fn
     self.length_normalization_fn = length_normalization_fn
@@ -178,10 +179,12 @@ class SamplingModule(decoding_module.DecodingModule, metaclass=abc.ABCMeta):
         sample_temperature, dtype=tf.float32)
     self.enable_greedy = enable_greedy
     self.decoding_name = decoding_name
+    self.extra_cache_output = extra_cache_output
     super(SamplingModule, self).__init__(
         length_normalization_fn=length_normalization_fn,
         dtype=dtype,
-        decoding_name=decoding_name)
+        decoding_name=decoding_name,
+        extra_cache_output=extra_cache_output)
 
   def _grow_alive_seq(self,
                       state: Dict[str, Any],
@@ -300,16 +303,14 @@ class SamplingModule(decoding_module.DecodingModule, metaclass=abc.ABCMeta):
           decoding_module.StateKeys.CUR_INDEX:
               tf.TensorShape([]),
           decoding_module.StateKeys.ALIVE_SEQ:
-              tf.TensorShape(
-                  [batch_size, self.max_decode_length + 1]),
+              tf.TensorShape([batch_size, self.max_decode_length + 1]),
           decoding_module.StateKeys.ALIVE_LOG_PROBS:
               tf.TensorShape([batch_size, 1]),
           decoding_module.StateKeys.ALIVE_CACHE:
               tf.nest.map_structure(lambda state: state.get_shape(),
                                     alive_cache),
           decoding_module.StateKeys.FINISHED_SEQ:
-              tf.TensorShape(
-                  [batch_size, self.max_decode_length + 1]),
+              tf.TensorShape([batch_size, self.max_decode_length + 1]),
           decoding_module.StateKeys.FINISHED_SCORES:
               tf.TensorShape([batch_size, 1]),
           decoding_module.StateKeys.FINISHED_FLAGS:
@@ -324,8 +325,7 @@ class SamplingModule(decoding_module.DecodingModule, metaclass=abc.ABCMeta):
           decoding_module.StateKeys.ALIVE_LOG_PROBS:
               tf.TensorShape([None, 1]),
           decoding_module.StateKeys.ALIVE_CACHE:
-              tf.nest.map_structure(
-                  decoding_module.get_shape_keep_last_dim,
+              tf.nest.map_structure(decoding_module.get_shape_keep_last_dim,
                                     alive_cache),
           decoding_module.StateKeys.FINISHED_SEQ:
               tf.TensorShape([None, None]),
@@ -335,6 +335,22 @@ class SamplingModule(decoding_module.DecodingModule, metaclass=abc.ABCMeta):
               tf.TensorShape([None, 1])
       }
 
+    if self.extra_cache_output:
+      state.update(
+          {decoding_module.StateKeys.INITIAL_OUTPUT_CACHE: alive_cache})
+      if self.padded_decode:
+        state_shape_invariants.update({
+            decoding_module.StateKeys.INITIAL_OUTPUT_CACHE:
+                tf.nest.map_structure(lambda state: state.get_shape(),
+                                      alive_cache)
+        })
+      else:
+        state_shape_invariants.update({
+            decoding_module.StateKeys.INITIAL_OUTPUT_CACHE:
+                tf.nest.map_structure(decoding_module.get_shape_keep_last_dim,
+                                      alive_cache),
+        })
+
     return state, state_shape_invariants
 
   def _get_new_alive_state(self, new_seq: tf.Tensor, new_log_probs: tf.Tensor,
@@ -428,6 +444,9 @@ class SamplingModule(decoding_module.DecodingModule, metaclass=abc.ABCMeta):
                                                      finished_scores)
     finished_seq = tf.where(seq_cond, finished_seq, alive_seq)
     finished_scores = tf.where(score_cond, finished_scores, alive_log_probs)
+    if self.extra_cache_output:
+      return finished_seq, finished_scores, finished_state[
+          decoding_module.StateKeys.INITIAL_OUTPUT_CACHE]
     return finished_seq, finished_scores
 
   def _continue_search(self, state) -> tf.Tensor: