nucleus sampling

src/generate_unconditional_samples.py

@@ -16,6 +16,7 @@ def sample_model(
     length=None,
     temperature=1,
     top_k=0,
+    top_p=1,
     models_dir='models',
 ):
     """
@@ -58,7 +59,7 @@ def sample_model(
             hparams=hparams, length=length,
             start_token=enc.encoder['<|endoftext|>'],
             batch_size=batch_size,
-            temperature=temperature, top_k=top_k
+            temperature=temperature, top_k=top_k, top_p=top_p
         )[:, 1:]
 
         saver = tf.train.Saver()

src/interactive_conditional_samples.py

@@ -16,6 +16,7 @@ def interact_model(
     length=None,
     temperature=1,
     top_k=0,
+    top_p=1,
     models_dir='models',
 ):
     """
@@ -61,7 +62,7 @@ def interact_model(
             hparams=hparams, length=length,
             context=context,
             batch_size=batch_size,
-            temperature=temperature, top_k=top_k
+            temperature=temperature, top_k=top_k, top_p=top_p
         )
 
         saver = tf.train.Saver()

src/sample.py

@@ -22,7 +22,25 @@ def top_k_logits(logits, k):
     )
 
 
-def sample_sequence(*, hparams, length, start_token=None, batch_size=None, context=None, temperature=1, top_k=0):
+def top_p_logits(logits, p):
+    """Nucleus sampling"""
+    batch, _ = logits.shape.as_list()
+    sorted_logits = tf.sort(logits, direction='DESCENDING', axis=-1)
+    cumulative_probs = tf.cumsum(tf.nn.softmax(sorted_logits, axis=-1), axis=-1)
+    indices = tf.stack([
+        tf.range(0, batch),
+        # number of indices to include
+        tf.maximum(tf.reduce_sum(tf.cast(cumulative_probs <= p, tf.int32), axis=-1) - 1, 0),
+    ], axis=-1)
+    min_values = tf.gather_nd(sorted_logits, indices)
+    return tf.where(
+        logits < min_values,
+        tf.ones_like(logits) * -1e10,
+        logits,
+    )
+
+
+def sample_sequence(*, hparams, length, start_token=None, batch_size=None, context=None, temperature=1, top_k=0, top_p=1):
     if start_token is None:
         assert context is not None, 'Specify exactly one of start_token and context!'
     else:
@@ -45,6 +63,7 @@ def sample_sequence(*, hparams, length, start_token=None, batch_size=None, conte
             next_outputs = step(hparams, prev, past=past)
             logits = next_outputs['logits'][:, -1, :]  / tf.to_float(temperature)
             logits = top_k_logits(logits, k=top_k)
+            logits = top_p_logits(logits, p=top_p)
             samples = tf.multinomial(logits, num_samples=1, output_dtype=tf.int32)
             return [
                 next_outputs['presents'] if past is None else tf.concat([past, next_outputs['presents']], axis=-2),