chore: fix typos in language models

src/transformers/models/aria/modeling_aria.py

@@ -1094,7 +1094,7 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
             dtype (`torch.dtype`):
                 The dtype to use for the 4D attention mask.
             device (`torch.device`):
-                The device to plcae the 4D attention mask on.
+                The device to place the 4D attention mask on.
             cache_position (`torch.Tensor`):
                 Indices depicting the position of the input sequence tokens in the sequence.
             batch_size (`torch.Tensor`):

src/transformers/models/bamba/modeling_bamba.py

@@ -1399,7 +1399,7 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
             dtype (`torch.dtype`):
                 The dtype to use for the 4D attention mask.
             device (`torch.device`):
-                The device to plcae the 4D attention mask on.
+                The device to place the 4D attention mask on.
             cache_position (`torch.Tensor`):
                 Indices depicting the position of the input sequence tokens in the sequence.
             batch_size (`torch.Tensor`):

src/transformers/models/bamba/modular_bamba.py

@@ -1140,7 +1140,7 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
             dtype (`torch.dtype`):
                 The dtype to use for the 4D attention mask.
             device (`torch.device`):
-                The device to plcae the 4D attention mask on.
+                The device to place the 4D attention mask on.
             cache_position (`torch.Tensor`):
                 Indices depicting the position of the input sequence tokens in the sequence.
             batch_size (`torch.Tensor`):

src/transformers/models/bark/modeling_bark.py

@@ -201,7 +201,7 @@ def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
         # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
-        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
         # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
         self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
 

src/transformers/models/bart/modeling_bart.py

@@ -298,7 +298,7 @@ def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
         # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
-        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
         # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
         self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
 

src/transformers/models/bart/modeling_flax_bart.py

@@ -274,7 +274,7 @@ def _merge_heads(self, hidden_states):
     def _concatenate_to_cache(self, key, value, query, attention_mask):
         """
         This function takes projected key, value states from a single input token and concatenates the states to cached
-        states from previous steps. This function is slighly adapted from the official Flax repository:
+        states from previous steps. This function is slightly adapted from the official Flax repository:
         https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
         """
         # detect if we're initializing by absence of existing cache data.

src/transformers/models/bert/modeling_flax_bert.py

@@ -263,7 +263,7 @@ def _merge_heads(self, hidden_states):
     def _concatenate_to_cache(self, key, value, query, attention_mask):
         """
         This function takes projected key, value states from a single input token and concatenates the states to cached
-        states from previous steps. This function is slighly adapted from the official Flax repository:
+        states from previous steps. This function is slightly adapted from the official Flax repository:
         https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
         """
         # detect if we're initializing by absence of existing cache data.

src/transformers/models/big_bird/modeling_flax_big_bird.py

@@ -284,7 +284,7 @@ def _merge_heads(self, hidden_states):
     def _concatenate_to_cache(self, key, value, query, attention_mask):
         """
         This function takes projected key, value states from a single input token and concatenates the states to cached
-        states from previous steps. This function is slighly adapted from the official Flax repository:
+        states from previous steps. This function is slightly adapted from the official Flax repository:
         https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
         """
         # detect if we're initializing by absence of existing cache data.

src/transformers/models/blenderbot/modeling_flax_blenderbot.py

@@ -262,7 +262,7 @@ def _merge_heads(self, hidden_states):
     def _concatenate_to_cache(self, key, value, query, attention_mask):
         """
         This function takes projected key, value states from a single input token and concatenates the states to cached
-        states from previous steps. This function is slighly adapted from the official Flax repository:
+        states from previous steps. This function is slightly adapted from the official Flax repository:
         https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
         """
         # detect if we're initializing by absence of existing cache data.

src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py

@@ -273,7 +273,7 @@ def _merge_heads(self, hidden_states):
     def _concatenate_to_cache(self, key, value, query, attention_mask):
         """
         This function takes projected key, value states from a single input token and concatenates the states to cached
-        states from previous steps. This function is slighly adapted from the official Flax repository:
+        states from previous steps. This function is slightly adapted from the official Flax repository:
         https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
         """
         # detect if we're initializing by absence of existing cache data.

src/transformers/models/bloom/modeling_bloom.py

@@ -824,7 +824,7 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
             dtype (`torch.dtype`):
                 The dtype to use for the 4D attention mask.
             device (`torch.device`):
-                The device to plcae the 4D attention mask on.
+                The device to place the 4D attention mask on.
             cache_position (`torch.Tensor`):
                 Indices depicting the position of the input sequence tokens in the sequence.
             batch_size (`torch.Tensor`):

src/transformers/models/bloom/modeling_flax_bloom.py

@@ -187,7 +187,7 @@ def _merge_heads(self, hidden_states):
     def _concatenate_to_cache(self, key, value, query, attention_mask):
         """
         This function takes projected key, value states from a single input token and concatenates the states to cached
-        states from previous steps. This function is slighly adapted from the official Flax repository:
+        states from previous steps. This function is slightly adapted from the official Flax repository:
         https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
         """
         # detect if we're initializing by absence of existing cache data.

src/transformers/models/chameleon/modeling_chameleon.py

@@ -376,7 +376,7 @@ def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
         # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
-        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
         # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
         self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
 
@@ -1470,7 +1470,7 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
             dtype (`torch.dtype`):
                 The dtype to use for the 4D attention mask.
             device (`torch.device`):
-                The device to plcae the 4D attention mask on.
+                The device to place the 4D attention mask on.
             cache_position (`torch.Tensor`):
                 Indices depicting the position of the input sequence tokens in the sequence.
             batch_size (`torch.Tensor`):

src/transformers/models/clip/modeling_clip.py

@@ -412,7 +412,7 @@ def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
         # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
-        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
         # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
         self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
 

src/transformers/models/code_llama/tokenization_code_llama_fast.py

@@ -82,7 +82,7 @@ class CodeLlamaTokenizerFast(PreTrainedTokenizerFast):
             [tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that
             contains everything needed to load the tokenizer.
         clean_up_tokenization_spaces (`str`, *optional*, defaults to `False`):
-            Wether to cleanup spaces after decoding, cleanup consists in removing potential artifacts like extra
+            Whether to cleanup spaces after decoding, cleanup consists in removing potential artifacts like extra
             spaces.
         unk_token (`str`, *optional*, defaults to `"<unk>"`):
             The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this

src/transformers/models/codegen/modeling_codegen.py

@@ -667,7 +667,7 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
             dtype (`torch.dtype`):
                 The dtype to use for the 4D attention mask.
             device (`torch.device`):
-                The device to plcae the 4D attention mask on.
+                The device to place the 4D attention mask on.
             cache_position (`torch.Tensor`):
                 Indices depicting the position of the input sequence tokens in the sequence.
             batch_size (`torch.Tensor`):

src/transformers/models/cohere/modeling_cohere.py

@@ -744,7 +744,7 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
             dtype (`torch.dtype`):
                 The dtype to use for the 4D attention mask.
             device (`torch.device`):
-                The device to plcae the 4D attention mask on.
+                The device to place the 4D attention mask on.
             cache_position (`torch.Tensor`):
                 Indices depicting the position of the input sequence tokens in the sequence.
             batch_size (`torch.Tensor`):

src/transformers/models/cohere/tokenization_cohere_fast.py

@@ -406,7 +406,7 @@ def apply_grounded_generation_template(
             conversation (Union[List[Dict[str, str]]]): A list of dicts
                 with "role" and "content" keys, representing the chat history so far.
             documents (List[Dict[str, str]): A list of dicts, representing documents or tool outputs to ground your
-                generation on. A document is a semistructured dict, wiht a string to string mapping. Common fields are
+                generation on. A document is a semistructured dict, with a string to string mapping. Common fields are
                 `url`, `title`, `snippet` etc but should be descriptive of the key. They will get rendered into the prompt.
             citation_mode: either "accurate" (prompt the model to generate an answer first, then rewrite it with citation
                 spans in) or "fast", where the prompt instructs the model to generate an answer with citations in directly.

src/transformers/models/cohere2/modeling_cohere2.py

@@ -745,7 +745,7 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
             dtype (`torch.dtype`):
                 The dtype to use for the 4D attention mask.
             device (`torch.device`):
-                The device to plcae the 4D attention mask on.
+                The device to place the 4D attention mask on.
             cache_position (`torch.Tensor`):
                 Indices depicting the position of the input sequence tokens in the sequence.
             batch_size (`torch.Tensor`):

src/transformers/models/data2vec/modeling_data2vec_audio.py

@@ -493,7 +493,7 @@ def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
         # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
-        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
         # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
         self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
 

src/transformers/models/dbrx/modeling_dbrx.py

@@ -322,7 +322,7 @@ def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
         # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
-        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
         # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
         self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
 
@@ -1199,7 +1199,7 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
             dtype (`torch.dtype`):
                 The dtype to use for the 4D attention mask.
             device (`torch.device`):
-                The device to plcae the 4D attention mask on.
+                The device to place the 4D attention mask on.
             cache_position (`torch.Tensor`):
                 Indices depicting the position of the input sequence tokens in the sequence.
             batch_size (`torch.Tensor`):

src/transformers/models/diffllama/modeling_diffllama.py

@@ -244,7 +244,7 @@ def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
         # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
-        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
         # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
         self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
 
@@ -983,7 +983,7 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
             dtype (`torch.dtype`):
                 The dtype to use for the 4D attention mask.
             device (`torch.device`):
-                The device to plcae the 4D attention mask on.
+                The device to place the 4D attention mask on.
             cache_position (`torch.Tensor`):
                 Indices depicting the position of the input sequence tokens in the sequence.
             batch_size (`torch.Tensor`):

src/transformers/models/diffllama/modular_diffllama.py

@@ -174,7 +174,7 @@ def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
         # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
-        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
         # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
         self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
 

src/transformers/models/distilbert/modeling_distilbert.py

@@ -249,7 +249,7 @@ def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
         # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
-        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
         # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
         self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()