more cleanup

Author: lucidrains

src/dalle_mtf/sample.py

@@ -5,14 +5,10 @@
 
 def sample_autoregressive(inputs,
                           model,
-                          stop_at_token=50256,
                           max_steps=None,
                           temperature=0.9,
-                          padding_id = 0,
-                          min_start_pos = None,
                           variable_dtype=mtf.VariableDType(tf.float32),
                           has_partial_sequences=True,
-                          remove_partial_sequences=False,
                           sampling_keep_top_k=-1,
                           ):
     """Sample randomly one token at a time.
@@ -87,25 +83,10 @@ def sample_autoregressive(inputs,
     if not has_partial_sequences:
         partial_sequences_eos_count = 0
 
-    if stop_at_token is not None:
-        partial_sequences_eos_count = mtf.reduce_sum(
-            mtf.to_int32(mtf.equal(inputs, stop_at_token)),
-            reduced_dim=length_dim)
-
     def cond_fn(position, ids, *unused_states):
         """Should we run another loop iteration?"""
         past_end = mtf.greater_equal(position, length_dim.size)
-        if max_steps:
-            past_end = mtf.logical_or(
-                past_end, mtf.greater_equal(position - initial_position, max_steps))
-
         is_done = past_end
-        if stop_at_token is not None:
-            eos_count = mtf.reduce_sum(
-                mtf.to_int32(mtf.equal(ids, stop_at_token)),
-                reduced_dim=length_dim)
-            has_additional_eos = mtf.greater(eos_count, partial_sequences_eos_count)
-            is_done = mtf.logical_or(is_done, has_additional_eos)
         all_done = mtf.reduce_all(is_done)
         return mtf.logical_not(all_done)
 
@@ -169,11 +150,4 @@ def body_fn(position, ids, *states):
     final_position, outputs = mtf.while_loop(
         cond_fn, body_fn, while_loop_inputs)[:2]
     del final_position
-    if has_partial_sequences and remove_partial_sequences:
-        # Remove partial sequences from outputs
-        partial_length = mtf.reduce_sum(
-            mtf.to_int32(mtf.not_equal(inputs, padding_id)),
-            reduced_dim=length_dim)
-        outputs = mtf.dynamic_shift(
-            outputs, -partial_length, length_dim, wrap=False)
     return outputs

src/model_fns.py

@@ -152,14 +152,10 @@ def dalle_model_fn(features, labels, mode, params):
 
         mtf_samples = sample_autoregressive(inputs,
                                             model,
-                                            max_steps=model.total_seq_dim, # will always run until the full image is produced
-                                            stop_at_token=None,
                                             temperature=0.9,
-                                            padding_id = 0,
                                             variable_dtype=model.variable_dtype,
                                             has_partial_sequences=True,
-                                            remove_partial_sequences=True,
-                                            sampling_keep_top_k=-1,
+                                            sampling_keep_top_k=-2,
                                             )
 
         mtf_samples = mtf.anonymize(mtf_samples)

test.py

@@ -73,10 +73,7 @@ def test_sampling():
             inputs,
             model,
             variable_dtype=mtf.VariableDType(),
-            max_steps = sequence_dim.size,
-            remove_partial_sequences=False,
-            stop_at_token=None,
-            min_start_pos=model.text_seq_len
+            max_steps = sequence_dim.size
         )
 
         mesh_impl = placement_mesh_impl.PlacementMeshImpl(shape=[], layout={}, devices=[""])