Falcon BetterTransformer requires transformers>=4.34 (#1431)

* falcon BT requires transformers>=4.34

* more fix

optimum/bettertransformer/models/attention.py

@@ -913,6 +913,7 @@ def falcon_forward(
     alibi: Optional[torch.Tensor],
     attention_mask: torch.Tensor,
     layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    position_ids: Optional[torch.LongTensor] = None,
     head_mask: Optional[torch.Tensor] = None,
     use_cache: bool = False,
     output_attentions: bool = False,
@@ -937,7 +938,7 @@ def falcon_forward(
     value_layer = value_layer.transpose(1, 2).reshape(batch_size * num_kv_heads, query_length, self.head_dim)
 
     past_kv_length = 0 if layer_past is None else layer_past[0].shape[1]
-    query_layer, key_layer = self.maybe_rotary(query_layer, key_layer, past_kv_length)
+    query_layer, key_layer = self.maybe_rotary(query_layer, key_layer, past_kv_length, position_ids)
 
     if layer_past is not None:
         past_key, past_value = layer_past

optimum/bettertransformer/models/decoder_models.py

@@ -44,7 +44,7 @@
 else:
     from ...utils.dummy_bettertransformer_objects import BarkSelfAttention
 
-if check_if_transformers_greater("4.32"):
+if check_if_transformers_greater("4.34"):
     from transformers.models.falcon.modeling_falcon import FalconAttention
 else:
     from ...utils.dummy_bettertransformer_objects import FalconAttention

optimum/utils/dummy_bettertransformer_objects.py

@@ -16,10 +16,10 @@ def __init__(self, *args, **kwargs):
 
 
 class FalconAttention(metaclass=DummyObject):
-    _backends = ["transformers_432"]
+    _backends = ["transformers_434"]
 
     def __init__(self, *args, **kwargs):
-        requires_backends(self, ["transformers_432"])
+        requires_backends(self, ["transformers_434"])
 
 
 def _llama_prepare_decoder_attention_mask(*args, **kwargs):

optimum/utils/import_utils.py

@@ -201,6 +201,10 @@ def require_numpy_strictly_lower(version: str, message: str):
             "transformers_432",
             (lambda: check_if_transformers_greater("4.32"), "{0} " + TRANSFORMERS_IMPORT_ERROR.format("4.32")),
         ),
+        (
+            "transformers_434",
+            (lambda: check_if_transformers_greater("4.34"), "{0} " + TRANSFORMERS_IMPORT_ERROR.format("4.34")),
+        ),
     ]
 )
 

optimum/utils/input_generators.py

@@ -862,21 +862,44 @@ def generate(self, input_name: str, framework: str = "pt", int_dtype: str = "int
 
 
 class FalconDummyPastKeyValuesGenerator(DummyPastKeyValuesGenerator):
-    def generate(self, input_name: str, framework: str = "pt", int_dtype: str = "int64", float_dtype: str = "fp32"):
-        self.num_kv_heads = 1
-        head_dim = self.hidden_size // self.num_attention_heads
+    def __init__(
+        self,
+        task: str,
+        normalized_config: NormalizedTextConfig,
+        batch_size: int = DEFAULT_DUMMY_SHAPES["batch_size"],
+        sequence_length: int = DEFAULT_DUMMY_SHAPES["sequence_length"],
+        random_batch_size_range: Optional[Tuple[int, int]] = None,
+        random_sequence_length_range: Optional[Tuple[int, int]] = None,
+        **kwargs,
+    ):
+        super().__init__(
+            task=task,
+            normalized_config=normalized_config,
+            batch_size=batch_size,
+            sequence_length=sequence_length,
+            random_batch_size_range=random_batch_size_range,
+            random_sequence_length_range=random_sequence_length_range,
+            **kwargs,
+        )
+        self.num_kv_heads = self.num_kv_heads = (
+            normalized_config.num_kv_heads
+            if (normalized_config.new_decoder_architecture or not normalized_config.multi_query)
+            else 1
+        )
+        self.head_dim = self.hidden_size // self.num_attention_heads
 
+    def generate(self, input_name: str, framework: str = "pt", int_dtype: str = "int64", float_dtype: str = "fp32"):
         past_key_shape = (
             self.batch_size,
             self.num_kv_heads,
             self.sequence_length,
-            head_dim,
+            self.head_dim,
         )
         past_value_shape = (
             self.batch_size,
             self.num_kv_heads,
             self.sequence_length,
-            head_dim,
+            self.head_dim,
         )
         return [
             (

optimum/utils/normalized_config.py

@@ -211,7 +211,9 @@ class NormalizedConfigManager:
         "blenderbot": BartLikeNormalizedTextConfig,
         "blenderbot-small": BartLikeNormalizedTextConfig,
         "bloom": NormalizedTextConfig.with_args(num_layers="n_layer"),
-        "falcon": NormalizedTextConfig.with_args(num_layers="num_hidden_layers", num_attention_heads="num_kv_heads"),
+        "falcon": NormalizedTextConfig.with_args(
+            num_layers="num_hidden_layers", num_attention_heads="num_attention_heads"
+        ),
         "camembert": NormalizedTextConfig,
         "codegen": GPT2LikeNormalizedTextConfig,
         "cvt": NormalizedVisionConfig,

tests/bettertransformer/testing_utils.py

@@ -43,7 +43,7 @@
     "distilbert": "hf-internal-testing/tiny-random-DistilBertModel",
     "electra": "hf-internal-testing/tiny-random-ElectraModel",
     "ernie": "hf-internal-testing/tiny-random-ErnieModel",
-    "falcon": "Rocketknight1/tiny-random-falcon-7b",
+    "falcon": "fxmarty/really-tiny-falcon-testing",
     "fsmt": "hf-internal-testing/tiny-random-FSMTModel",
     "gpt2": "hf-internal-testing/tiny-random-GPT2Model",
     # NOTE: this tiny model does not use attention_softmax_in_fp32=True (contrary to e.g. starcoder)