Fix RoPE config validation for FalconConfig + various config typos

src/transformers/models/deprecated/open_llama/configuration_open_llama.py

@@ -69,8 +69,8 @@ class OpenLlamaConfig(PretrainedConfig):
             Whether to tie weight embeddings
         rope_scaling (`Dict`, *optional*):
             Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
-            strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
-            is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
             `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
             these scaling strategies behave:
             https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
@@ -164,4 +164,4 @@ def _rope_scaling_validation(self):
                 f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
             )
         if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
-            raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")
+            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")

src/transformers/models/falcon/configuration_falcon.py

@@ -79,8 +79,8 @@ class FalconConfig(PretrainedConfig):
             The base period of the RoPE embeddings.
         rope_scaling (`Dict`, *optional*):
             Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
-            strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
-            is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
             `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
             these scaling strategies behave:
             https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
@@ -92,7 +92,7 @@ class FalconConfig(PretrainedConfig):
 
     Example:
 
-    ```pytho
+    ```python
     >>> from transformers import FalconModel, FalconConfig
 
     >>> # Initializing a small (2-layer) Falcon configuration
@@ -173,7 +173,7 @@ def _rope_scaling_validation(self):
         if self.rope_scaling is None:
             return
 
-        if self.rotary:
+        if self.alibi:
             raise ValueError("`rope_scaling` is not supported when `alibi` is `True`.")
 
         if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
@@ -188,4 +188,4 @@ def _rope_scaling_validation(self):
                 f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
             )
         if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
-            raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")
+            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")

src/transformers/models/fuyu/configuration_fuyu.py

@@ -72,8 +72,8 @@ class FuyuConfig(PretrainedConfig):
             The base period of the RoPE embeddings.
         rope_scaling (`Dict`, *optional*):
             Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
-            strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
-            is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
             `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
             these scaling strategies behave:
             https://www.reddit.com/r/LocalFuyu/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
@@ -189,6 +189,7 @@ def __init__(
             **kwargs,
         )
 
+    # Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
     def _rope_scaling_validation(self):
         """
         Validate the `rope_scaling` configuration.
@@ -208,4 +209,4 @@ def _rope_scaling_validation(self):
                 f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
             )
         if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
-            raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")
+            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")

src/transformers/models/gpt_neox/configuration_gpt_neox.py

@@ -80,8 +80,8 @@ class GPTNeoXConfig(PretrainedConfig):
             speedup at large scales (e.g. 20B).
         rope_scaling (`Dict`, *optional*):
             Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
-            strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
-            is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
             `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
             these scaling strategies behave:
             https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
@@ -173,4 +173,4 @@ def _rope_scaling_validation(self):
                 f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
             )
         if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
-            raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")
+            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")

src/transformers/models/llama/configuration_llama.py

@@ -87,8 +87,8 @@ class LlamaConfig(PretrainedConfig):
             The base period of the RoPE embeddings.
         rope_scaling (`Dict`, *optional*):
             Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
-            strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
-            is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
             `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
             these scaling strategies behave:
             https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
@@ -184,4 +184,4 @@ def _rope_scaling_validation(self):
                 f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
             )
         if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
-            raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")
+            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")

src/transformers/models/persimmon/configuration_persimmon.py

@@ -65,8 +65,8 @@ class PersimmonConfig(PretrainedConfig):
             The base period of the RoPE embeddings.
         rope_scaling (`Dict`, *optional*):
             Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
-            strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
-            is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
             `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
             these scaling strategies behave:
             https://www.reddit.com/r/LocalPersimmon/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This
@@ -141,6 +141,7 @@ def __init__(
             **kwargs,
         )
 
+    # Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
     def _rope_scaling_validation(self):
         """
         Validate the `rope_scaling` configuration.
@@ -160,4 +161,4 @@ def _rope_scaling_validation(self):
                 f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
             )
         if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
-            raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")
+            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")