🚨 [v5] Refactor RoPE for layer types

docs/source/en/internal/rope_utils.md

@@ -0,0 +1,89 @@
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+
+# Utilities for Rotary Embedding
+
+This page explains how the Rotary Embedding is computed and applied in Transformers and what types of RoPE are supported.
+
+
+## Overview
+
+Rotary Position Embeddings are a technique used to inject positional information into attention mechanisms without relying on explicit position encodings.  
+Instead of adding position vectors to token embeddings, RoPE rotates query and key vectors in the complex plane according to their positions enabling relative positional awareness and better extrapolation to unseen sequence lengths.
+
+The Transformers library provides a flexible and extensible implementation of various RoPE types defined in `[`~modeling_rope_utils.ROPE_VALIDATION_FUNCTIONS`]`, including both the default and scaled variants:
+
+| Rope Type | Description |
+|------------|-------------|
+| `"default"` | Standard rotary embedding as in LLaMA. |
+| `"linear"` | Linear-scaled RoPE which allows longer context windows. |
+| `"dynamic"` | NTK-aware scaling computed by rescaling frequency base (`θ`) for longer context. |
+| `"yarn"` | YaRN scaling variant providing smoother extrapolation and stability. |
+| `"longrope"` | [LongRoPE](https://github.com/microsoft/LongRoPE) scaling as in Phi-2 model series. |
+| `"llama3"` | RoPE scaling as in Llama3.1. |
+
+
+# Configuration in Model Configs
+
+To enable and customize rotary embeddings, add a `rope_scaling` field to your model’s configuration file (`config.json`). This field controls the RoPE behavior across model layers. Note that each RoPE variant defines its own set of expected keys and missing keys will raise an error. See the example below which creates a llama config with default RoPE parameters: 
+
+
+```python
+from transformers import LlamaConfig
+
+config = LlamaConfig()
+config.rope_scaling = {
+    "rope_type": "default", # type of RoPE to use
+    "rope_theta": 10000.0 # base frequency parameter
+}
+
+# If we want to apply a scaled RoPE type, we need to pass extra parameters
+config.rope_scaling = {
+    "rope_type": "linear",
+    "rope_theta": 10000.0,
+    "factor": 8.0  # scale factor for context extension
+}
+```
+
+## Per-Layer-Type RoPE Configuration
+
+Some models such as Gemma-3 use different layer types with different attention mechanisms, i.e. "full attention" in some blocks and "sliding-window attention" in others. Transformers supports specifying distinct RoPE parameters per layer type for these models. In this case, `rope_scaling` should be a nested dictionary, where top-level keys correspond to `config.layer_types` and values are per-type RoPE parameters. During model initialization, each decoder layer will automatically look up the matching RoPE configuration based on its declared layer type.
+
+
+```python
+from transformers import Gemma3Config
+
+config = Gemma3Config()
+config.rope_scaling = {
+    "full_attention": {
+        "rope_type": "dynamic",
+        "rope_theta": 1000000.0,
+        "factor": 8.0,
+        "original_max_position_embeddings": 8096,
+    },
+    "sliding_attention": {
+        "rope_type": "default",
+        "rope_theta": 10000.0,
+    }
+}
+```
+
+# Utilities
+
+[[autodoc]] RopeParameters
+    - __call__
+
+

docs/source/en/modular_transformers.md

@@ -288,7 +288,7 @@ class Olmo2DecoderLayer(OlmoDecoderLayer):
         output_attentions: Optional[bool] = False,
         use_cache: Optional[bool] = False,
         cache_position: Optional[torch.LongTensor] = None,
-        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
+        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
         **kwargs,
     ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
         residual = hidden_states

examples/modular-transformers/modeling_my_new_model2.py

@@ -218,7 +218,7 @@ def forward(
         past_key_values: Optional[Cache] = None,
         use_cache: Optional[bool] = False,
         cache_position: Optional[torch.LongTensor] = None,
-        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
+        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
         **kwargs: Unpack[TransformersKwargs],
     ) -> torch.Tensor:
         residual = hidden_states

examples/modular-transformers/modeling_super.py

@@ -260,7 +260,7 @@ def forward(
         past_key_values: Optional[Cache] = None,
         use_cache: Optional[bool] = False,
         cache_position: Optional[torch.LongTensor] = None,
-        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
+        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
         **kwargs: Unpack[TransformersKwargs],
     ) -> torch.Tensor:
         residual = hidden_states

src/transformers/configuration_utils.py

@@ -429,6 +429,22 @@ def torch_dtype(self, value):
         logger.warning_once("`torch_dtype` is deprecated! Use `dtype` instead!")
         self.dtype = value
 
+    @property
+    def rope_scaling(self):
+        logger.warning_once(
+            "`rope_scaling` has been deprecated. Please use `rope_parameters` instead, which contains all RoPE configuration "
+            "options, including `rope_theta`. Refer to `RopeParameters` for the full list of available keys."
+        )
+        return self.rope_parameters
+
+    @rope_scaling.setter
+    def rope_scaling(self, value):
+        logger.warning_once(
+            "`rope_scaling` has been deprecated. Please use `rope_parameters` instead, which contains all RoPE configuration "
+            "options, including `rope_theta`. Refer to `RopeParameters` for the full list of available keys."
+        )
+        self.rope_parameters = value
+
     def save_pretrained(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):
         """
         Save a configuration object to the directory `save_directory`, so that it can be re-loaded using the