BLOOM minor changes on tokenizer (#17823)

* few fixes:

- hardcode tokenizer padding side
- remove unused args

* few fixes:

- added new attribute on TokenizerTesterMixin
- added new slow test
- remove unused arg on tokenizer class

* make style

* Update src/transformers/models/bloom/tokenization_bloom_fast.py

Co-authored-by: SaulLu <55560583+SaulLu@users.noreply.github.com>

* make quality

* apply changes

- remove new attribute
- redefine test on the class

* add comments

Co-authored-by: SaulLu <55560583+SaulLu@users.noreply.github.com>

src/transformers/models/bloom/tokenization_bloom_fast.py

@@ -45,16 +45,6 @@ PRETRAINED_VOCAB_FILES_MAP = {
     },
 }
 
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
-    "bigscience/tokenizer": 1024,
-    "bigscience/bloom-350m": 1024,
-    "bigscience/bloom-760m": 1024,
-    "bigscience/bloom-1b3": 1024,
-    "bigscience/bloom-2b5": 1024,
-    "bigscience/bloom-6b3": 1024,
-    "bigscience/bloom": 1024,
-}
-
 
 class BloomTokenizerFast(PreTrainedTokenizerFast):
     """
@@ -109,9 +99,9 @@ class BloomTokenizerFast(PreTrainedTokenizerFast):
 
     vocab_files_names = VOCAB_FILES_NAMES
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     model_input_names = ["input_ids", "attention_mask"]
     slow_tokenizer_class = None
+    # No `max_model_input_sizes` as BLOOM uses ALiBi positional embeddings
 
     def __init__(
         self,
@@ -136,7 +126,6 @@ class BloomTokenizerFast(PreTrainedTokenizerFast):
             add_prefix_space=add_prefix_space,
             **kwargs,
         )
-
         pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
         if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space:
             pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type"))

tests/models/bloom/test_modeling_bloom.py

@@ -441,6 +441,33 @@ class BloomModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase)
             tokenizer.decode(greedy_output_without_pad[0, :-3], skip_special_tokens=True),
         )
 
+    @slow
+    def test_right_left_batched_input(self):
+        path_1b3 = "bigscience/bloom-1b3"
+        model = BloomForCausalLM.from_pretrained(path_1b3, use_cache=True)
+        model = model.eval()
+
+        tokenizer = BloomTokenizerFast.from_pretrained(path_1b3)
+        tokenizer.padding_side = "right"
+
+        inputs = ["Hello there", "Joe Biden is the president of the"]
+        inputs_right = tokenizer(inputs, return_tensors="pt", padding=True)
+
+        tokenizer.padding_side = "left"
+        inputs_left = tokenizer(inputs, return_tensors="pt", padding=True)
+
+        # test token values are different
+        self.assertNotEqual(inputs_right["input_ids"].tolist(), inputs_left["input_ids"].tolist())
+
+        # test reconstructions are the same
+        outputs_right = model.generate(**inputs_right, max_length=10, do_sample=False)
+        outputs_left = model.generate(**inputs_left, max_length=10, do_sample=False)
+
+        self.assertEqual(
+            tokenizer.decode(outputs_right[0], skip_special_tokens=True),
+            tokenizer.decode(outputs_left[0], skip_special_tokens=True),
+        )
+
 
 @require_torch
 class BloomEmbeddingTest(unittest.TestCase):

tests/models/bloom/test_tokenization_bloom.py

@@ -127,3 +127,10 @@ class BloomTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
         output_tokens = list(map(tokenizer.encode, input_text))
         predicted_text = list(map(lambda x: tokenizer.decode(x, clean_up_tokenization_spaces=False), output_tokens))
         self.assertListEqual(predicted_text, input_text)
+
+    def test_pretrained_model_lists(self):
+        # The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
+        # any sequence length constraints. This test of the parent class will fail since it relies on the
+        # maximum sequence length of the positoonal embeddings.
+        self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map), 1)
+        self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0]), 1)