[generate] do_sample default back to False (#3298)

* change do_samples back

* None better default as boolean

* adapt do_sample to True in test example

* make style

examples/summarization/bart/evaluate_cnn.py

@@ -35,7 +35,6 @@ def generate_summaries(lns, out_file, batch_size=8, device=DEFAULT_DEVICE):
             min_length=min_length + 1,  # +1 from original because we start at step=1
             no_repeat_ngram_size=3,
             early_stopping=True,
-            do_sample=False,
             decoder_start_token_id=model.config.eos_token_ids[0],
         )
         dec = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summaries]

src/transformers/modeling_tf_utils.py

@@ -460,8 +460,8 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
         input_ids=None,
         max_length=None,
         min_length=None,
-        do_sample=True,
-        early_stopping=False,
+        do_sample=None,
+        early_stopping=None,
         num_beams=None,
         temperature=None,
         top_k=None,
@@ -494,7 +494,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
                 The max length of the sequence to be generated.  Between 1 and infinity. Default to 20.
 
             do_sample: (`optional`) bool
-                If set to `False` greedy decoding is used. Otherwise sampling is used. Defaults to `True`.
+                If set to `False` greedy decoding is used. Otherwise sampling is used. Defaults to `False`.
 
             num_beams: (`optional`) int
                 Number of beams for beam search. Must be between 1 and infinity. 1 means no beam search. Default to 1.

src/transformers/modeling_utils.py

@@ -656,8 +656,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
         input_ids=None,
         max_length=None,
         min_length=None,
-        do_sample=True,
-        early_stopping=False,
+        do_sample=None,
+        early_stopping=None,
         num_beams=None,
         temperature=None,
         top_k=None,
@@ -691,7 +691,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
                 The max length of the sequence to be generated.  Between 1 and infinity. Default to 20.
 
             do_sample: (`optional`) bool
-                If set to `False` greedy decoding is used. Otherwise sampling is used. Defaults to `True`.
+                If set to `False` greedy decoding is used. Otherwise sampling is used. Defaults to `False`.
 
             num_beams: (`optional`) int
                 Number of beams for beam search. Must be between 1 and infinity. 1 means no beam search. Default to 1.

tests/test_modeling_bart.py

@@ -285,7 +285,12 @@ class BartHeadTests(unittest.TestCase):
 
         max_length = 5
         new_input_ids = lm_model.generate(
-            input_ids.clone(), num_return_sequences=1, num_beams=2, no_repeat_ngram_size=3, max_length=max_length
+            input_ids.clone(),
+            do_sample=True,
+            num_return_sequences=1,
+            num_beams=2,
+            no_repeat_ngram_size=3,
+            max_length=max_length,
         )
         self.assertEqual(new_input_ids.shape, (input_ids.shape[0], max_length - 1))
         # TODO(SS): uneven length batches, empty inputs

tests/test_modeling_common.py

@@ -638,16 +638,16 @@ class ModelTesterMixin:
 
             if config.bos_token_id is None:
                 with self.assertRaises(AssertionError):
-                    model.generate(max_length=5)
+                    model.generate(do_sample=True, max_length=5)
                 # batch_size = 1
-                self._check_generated_tokens(model.generate(input_ids))
+                self._check_generated_tokens(model.generate(input_ids, do_sample=True))
                 # batch_size = 1, num_beams > 1
-                self._check_generated_tokens(model.generate(input_ids, num_beams=3))
+                self._check_generated_tokens(model.generate(input_ids, do_sample=True, num_beams=3))
             else:
                 # batch_size = 1
-                self._check_generated_tokens(model.generate(max_length=5))
+                self._check_generated_tokens(model.generate(do_sample=True, max_length=5))
                 # batch_size = 1, num_beams > 1
-                self._check_generated_tokens(model.generate(max_length=5, num_beams=3))
+                self._check_generated_tokens(model.generate(do_sample=True, max_length=5, num_beams=3))
 
             with self.assertRaises(AssertionError):
                 # generating multiple sequences when greedy no beam generation
@@ -659,12 +659,14 @@ class ModelTesterMixin:
                 model.generate(input_ids, do_sample=False, num_return_sequences=3, num_beams=2)
 
             # batch_size > 1, sample
-            self._check_generated_tokens(model.generate(input_ids, num_return_sequences=3))
+            self._check_generated_tokens(model.generate(input_ids, do_sample=True, num_return_sequences=3))
             # batch_size > 1, greedy
             self._check_generated_tokens(model.generate(input_ids, do_sample=False))
 
             # batch_size > 1, num_beams > 1, sample
-            self._check_generated_tokens(model.generate(input_ids, num_beams=3, num_return_sequences=3,))
+            self._check_generated_tokens(
+                model.generate(input_ids, do_sample=True, num_beams=3, num_return_sequences=3,)
+            )
             # batch_size > 1, num_beams > 1, greedy
             self._check_generated_tokens(
                 model.generate(input_ids, do_sample=False, num_beams=3, num_return_sequences=3)

tests/test_modeling_tf_common.py

@@ -422,16 +422,16 @@ class TFModelTesterMixin:
 
             if config.bos_token_id is None:
                 with self.assertRaises(AssertionError):
-                    model.generate(max_length=5)
+                    model.generate(do_sample=True, max_length=5)
                 # batch_size = 1
-                self._check_generated_tokens(model.generate(input_ids))
+                self._check_generated_tokens(model.generate(input_ids, do_sample=True))
                 # batch_size = 1, num_beams > 1
-                self._check_generated_tokens(model.generate(input_ids, num_beams=3))
+                self._check_generated_tokens(model.generate(input_ids, do_sample=True, num_beams=3))
             else:
                 # batch_size = 1
-                self._check_generated_tokens(model.generate(max_length=5))
+                self._check_generated_tokens(model.generate(do_sample=True, max_length=5))
                 # batch_size = 1, num_beams > 1
-                self._check_generated_tokens(model.generate(max_length=5, num_beams=3))
+                self._check_generated_tokens(model.generate(do_sample=True, max_length=5, num_beams=3))
 
             with self.assertRaises(AssertionError):
                 # generating multiple sequences when greedy no beam generation
@@ -443,12 +443,14 @@ class TFModelTesterMixin:
                 model.generate(input_ids, do_sample=False, num_return_sequences=3, num_beams=2)
 
             # batch_size > 1, sample
-            self._check_generated_tokens(model.generate(input_ids, num_return_sequences=3))
+            self._check_generated_tokens(model.generate(input_ids, do_sample=True, num_return_sequences=3))
             # batch_size > 1, greedy
             self._check_generated_tokens(model.generate(input_ids, do_sample=False))
 
             # batch_size > 1, num_beams > 1, sample
-            self._check_generated_tokens(model.generate(input_ids, num_beams=3, num_return_sequences=3,))
+            self._check_generated_tokens(
+                model.generate(input_ids, do_sample=True, num_beams=3, num_return_sequences=3,)
+            )
             # batch_size > 1, num_beams > 1, greedy
             self._check_generated_tokens(
                 model.generate(input_ids, do_sample=False, num_beams=3, num_return_sequences=3)