Avoid dynamic shape computations in MultiHeadedAttention/PackSource.

PiperOrigin-RevId: 687059900

lingvo/core/attention.py

@@ -1697,6 +1697,18 @@ def PackSource(
     # [time_steps, batch_size, source_dim]
     source_vecs = py_utils.HasRank(source_vecs, 3)
     time_steps, batch_size = py_utils.GetShape(source_vecs, 2)
+
+    # calculate time_steps and batch_size * num_heads, to be used as time and
+    # batch size for the internal attention, but avoid dynamic size calculations
+    # whenever possible.
+    if source_vecs.shape.ndims is None or source_vecs.shape[1] is None:
+      # batch_size is dynamic; avoid multiplication with num_heads
+      time_steps_for_internal_attn = time_steps
+      batch_size_for_internal_attn = -1
+    else:
+      time_steps_for_internal_attn = -1
+      batch_size_for_internal_attn = batch_size * num_heads
+
     # [time_steps, batch_size, context_dim]
     source_contexts = py_utils.HasShape(
         source_contexts, [time_steps, batch_size, -1]
@@ -1739,8 +1751,8 @@ def PackSource(
       source_vecs = tf.reshape(
           source_vecs,
           [
-              -1,
-              batch_size * num_heads,
+              time_steps_for_internal_attn,
+              batch_size_for_internal_attn,
               symbolic.ToStatic(p.hidden_dim // num_heads),
           ],
       )
@@ -1781,7 +1793,11 @@ def PackSource(
         # => [time_steps, batch_size * num_heads, context_dim / num_heads]
         source_contexts = tf.reshape(
             source_contexts,
-            [-1, batch_size * num_heads, context_dim // num_heads],
+            [
+                time_steps_for_internal_attn,
+                batch_size_for_internal_attn,
+                context_dim // num_heads,
+            ],
         )
         source_contexts = gshard_utils.MeshSplit(
             source_contexts, p.device_mesh, p.activation_split_dims_mapping
@@ -1798,7 +1814,8 @@ def PackSource(
         source_padding = tf.tile(source_padding, [1, 1, num_heads])
         # => [time_steps, batch_size * num_heads]
         source_padding = tf.reshape(
-            source_padding, [-1, batch_size * num_heads]
+            source_padding,
+            [time_steps_for_internal_attn, batch_size_for_internal_attn],
         )
 
     with tf.name_scope('segment_id'):
@@ -1809,7 +1826,8 @@ def PackSource(
         source_segment_id = tf.tile(source_segment_id, [1, 1, num_heads])
         # => [time_steps, batch_size * num_heads]
         source_segment_id = tf.reshape(
-            source_segment_id, [-1, batch_size * num_heads]
+            source_segment_id,
+            [time_steps_for_internal_attn, batch_size_for_internal_attn],
         )
 
     return self.atten.PackSource(