Update rope_scaling to rope_parameters in preparation for Transformers v5 (vllm-project#28542)

Signed-off-by: Harry Mellor <[email protected]>

Author: hmellor

.buildkite/test-pipeline.yaml

@@ -872,12 +872,12 @@ steps:
   optional: true
   commands:
     - pip install --upgrade git+https://github.com/huggingface/transformers
-    - pytest -v -s tests/models/test_initialization.py -k 'not (Gemma3 or ModernBert or Qwen2_5_VL or Qwen2_5vl or Qwen2VL or TransformersMultiModalEmbeddingModel or TransformersMultiModalForSequenceClassification or Ultravox or Phi4Multimodal or LlavaNextVideo or MiniCPMO or Lfm2Moe or PaliGemma or RobertaForSequenceClassification or Ovis2_5 or Fuyu or DeepseekOCR or KimiVL)'
+    - pytest -v -s tests/models/test_initialization.py -k 'not (Ultravox or Phi4Multimodal or MiniCPMO or Lfm2Moe or RobertaForSequenceClassification or Ovis2_5 or DeepseekOCR or KimiVL)'
     - pytest -v -s tests/models/test_transformers.py
     # - pytest -v -s tests/models/multimodal/processing/
-    - pytest -v -s tests/models/multimodal/test_mapping.py -k 'not (Gemma3 or Qwen2VL or Qwen2_5_VL)'
+    - pytest -v -s tests/models/multimodal/test_mapping.py
     - python3 examples/offline_inference/basic/chat.py
-    # - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
+    - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
     # Whisper needs spawn method to avoid deadlock
     - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper
 

benchmarks/kernels/benchmark_mrope.py

@@ -6,7 +6,7 @@
 #
 # The CSV file (named with current date/time) contains these columns:
 # model_name, tp_size, num_tokens, num_heads, num_kv_heads, head_dim, max_position,
-# rope_theta, is_neox_style, rope_scaling, dtype, torch_mean, torch_median, torch_p99,
+# is_neox_style, rope_parameters, dtype, torch_mean, torch_median, torch_p99,
 # torch_min, torch_max, triton_mean, triton_median, triton_p99, triton_min, triton_max,
 # speedup
 #
@@ -86,9 +86,8 @@ def benchmark_mrope(
     num_heads: int,
     num_kv_heads: int,
     max_position: int = 8192,
-    rope_theta: float = 10000,
     is_neox_style: bool = True,
-    rope_scaling: dict[str, Any] = None,
+    rope_parameters: dict[str, Any] | None = None,
     dtype: torch.dtype = torch.bfloat16,
     seed: int = 0,
     warmup_iter: int = 10,
@@ -102,9 +101,8 @@ def benchmark_mrope(
         head_size=head_dim,
         rotary_dim=head_dim,
         max_position=max_position,
-        base=rope_theta,
         is_neox_style=is_neox_style,
-        rope_scaling=rope_scaling,
+        rope_parameters=rope_parameters,
         dtype=dtype,
     ).to(device=device)
 
@@ -203,9 +201,8 @@ def benchmark_mrope(
             num_kv_heads,
             head_dim,
             max_position,
-            rope_theta,
             is_neox_style,
-            str(rope_scaling),
+            str(rope_parameters),
             str(dtype).split(".")[-1],
             torch_stats["mean"],
             torch_stats["median"],
@@ -255,9 +252,8 @@ def benchmark_mrope(
             "num_kv_heads",
             "head_dim",
             "max_position",
-            "rope_theta",
             "is_neox_style",
-            "rope_scaling",
+            "rope_parameters",
             "dtype",
             "torch_mean",
             "torch_median",
@@ -303,7 +299,7 @@ def benchmark_mrope(
                 q_size = num_heads * head_dim
                 kv_size = num_kv_heads * head_dim
                 is_neox_style = True
-                rope_theta = config.rope_theta
+                rope_parameters = config.rope_parameters
                 max_position = config.max_position_embeddings
 
                 for num_tokens in num_tokens_list:
@@ -315,9 +311,8 @@ def benchmark_mrope(
                         num_heads=num_heads,
                         num_kv_heads=num_kv_heads,
                         max_position=max_position,
-                        rope_theta=rope_theta,
                         is_neox_style=is_neox_style,
-                        rope_scaling=config.rope_scaling,
+                        rope_parameters=rope_parameters,
                         dtype=getattr(torch, args.dtype),
                         seed=args.seed,
                         warmup_iter=args.warmup_iter,

examples/offline_inference/context_extension.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """
 This script demonstrates how to extend the context length
-of a Qwen model using the YARN method (rope_scaling)
+of a Qwen model using the YARN method (rope_parameters)
 and run a simple chat example.
 
 Usage:
@@ -19,8 +19,8 @@ def create_llm():
 
     # Use yarn to extend context
     hf_overrides = {
-        "rope_theta": rope_theta,
-        "rope_scaling": {
+        "rope_parameters": {
+            "rope_theta": rope_theta,
             "rope_type": "yarn",
             "factor": factor,
             "original_max_position_embeddings": original_max_position_embeddings,

tests/compile/test_functionalization.py

@@ -137,7 +137,7 @@ def __init__(self, head_dim=64, rotary_dim=None, max_position=2048, base=10000):
             self.head_dim,
             rotary_dim=self.rotary_dim,
             max_position=max_position,
-            base=base,
+            rope_parameters={"rope_type": "default", "rope_theta": base},
         )
 
     def forward(self, positions, q, k):
@@ -172,7 +172,7 @@ def __init__(self, head_dim=64, num_heads=4, max_position=2048, base=10000):
             self.head_dim,
             rotary_dim=self.head_dim,
             max_position=max_position,
-            base=base,
+            rope_parameters={"rope_type": "default", "rope_theta": base},
         )
 
     def forward(self, positions, hidden_states):

tests/kernels/core/test_mrope.py

@@ -5,11 +5,11 @@
 import pytest
 import torch
 from packaging.version import Version
-from transformers import AutoConfig
 from transformers import __version__ as TRANSFORMERS_VERSION
 
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.platforms import current_platform
+from vllm.transformers_utils.config import get_config
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
@@ -98,8 +98,7 @@ def test_mrope(
     atol = model_info.atol
     rtol = model_info.rtol
 
-    config = AutoConfig.from_pretrained(model_name)
-    config = config.get_text_config()
+    config = get_config(model_name, False).get_text_config()
 
     # get the model config
     total_num_kv_heads = config.num_key_value_heads
@@ -113,7 +112,6 @@ def test_mrope(
     )
     is_neox_style = True
 
-    rope_theta = config.rope_theta
     max_position = config.max_position_embeddings
     partial_rotary_factor = getattr(config, "partial_rotary_factor", 1.0)
     rotary_dim = int(head_dim * partial_rotary_factor)
@@ -122,9 +120,8 @@ def test_mrope(
         head_size=head_dim,
         rotary_dim=rotary_dim,
         max_position=max_position,
-        base=rope_theta,
         is_neox_style=is_neox_style,
-        rope_scaling=config.rope_scaling,
+        rope_parameters=config.rope_parameters,
         dtype=dtype,
     ).to(device=device)
 
@@ -173,8 +170,7 @@ def test_mrope_torch_compile_tracing(
     atol = model_info.atol
     rtol = model_info.rtol
 
-    config = AutoConfig.from_pretrained(model_name)
-    config = config.get_text_config()
+    config = get_config(model_name, False).get_text_config()
 
     # get the model config
     total_num_kv_heads = config.num_key_value_heads
@@ -187,7 +183,6 @@ def test_mrope_torch_compile_tracing(
         else config.hidden_size // total_num_heads
     )
     is_neox_style = True
-    rope_theta = config.rope_theta
     max_position = config.max_position_embeddings
     partial_rotary_factor = getattr(config, "partial_rotary_factor", 1.0)
     rotary_dim = int(head_dim * partial_rotary_factor)
@@ -196,9 +191,8 @@ def test_mrope_torch_compile_tracing(
         head_size=head_dim,
         rotary_dim=rotary_dim,
         max_position=max_position,
-        base=rope_theta,
         is_neox_style=is_neox_style,
-        rope_scaling=config.rope_scaling,
+        rope_parameters=config.rope_parameters,
         dtype=dtype,
     ).to(device=device)
 

tests/kernels/core/test_pos_encoding.py

@@ -74,7 +74,7 @@ def test_rotary_embedding(
     device: str,
     use_key: bool,
     max_position: int = 8192,
-    base: float = 10000,
+    rope_theta: float = 10000,
 ) -> None:
     if rotary_dim is None:
         rotary_dim = head_size
@@ -83,7 +83,8 @@ def test_rotary_embedding(
     torch.set_default_device(device)
     if rotary_dim is None:
         rotary_dim = head_size
-    rope = get_rope(head_size, rotary_dim, max_position, base, is_neox_style)
+    rope_parameters = {"rope_type": "default", "rope_theta": rope_theta}
+    rope = get_rope(head_size, rotary_dim, max_position, is_neox_style, rope_parameters)
     rope = rope.to(dtype=dtype, device=torch.get_default_device())
 
     positions = torch.randint(0, max_position, (batch_size, seq_len))
@@ -120,19 +121,19 @@ def test_rotary_embedding(
 @torch.inference_mode()
 def test_rope_module_cache():
     MAX_POSITIONS = [123, 1234]
-    BASES = [10000, 1000000]
-    ROPE_SCALINGS = (
-        None,
+    ROPE_THETAS = [10000, 1000000]
+    ROPE_PARAMETERS = (
+        {"rope_type": "default"},
         {"rope_type": "linear", "factor": (1,)},
         {"rope_type": "dynamic", "factor": 1},
     )
     settings = (
         HEAD_SIZES,
         ROTARY_DIMS,
         MAX_POSITIONS,
-        BASES,
+        ROPE_THETAS,
         IS_NEOX_STYLE,
-        ROPE_SCALINGS,
+        ROPE_PARAMETERS,
         DTYPES,
     )
     rope_setting_id_map: dict[str, int] = {}
@@ -141,20 +142,20 @@ def test_rope_module_cache():
             head_size,
             rotary_dim,
             max_position,
-            base,
-            is_neox_stype,
-            rope_scaling,
+            rope_theta,
+            is_neox_style,
+            rope_parameters,
             dtype,
         ) = setting
         if rotary_dim is None:
             rotary_dim = head_size
+        rope_parameters["rope_theta"] = rope_theta
         rope = get_rope(
             head_size,
             rotary_dim,
             max_position,
-            base,
-            is_neox_stype,
-            rope_scaling,
+            is_neox_style,
+            rope_parameters,
             dtype,
         )
         # different settings cannot share the same rope module
@@ -168,20 +169,20 @@ def test_rope_module_cache():
             head_size,
             rotary_dim,
             max_position,
-            base,
-            is_neox_stype,
-            rope_scaling,
+            rope_theta,
+            is_neox_style,
+            rope_parameters,
             dtype,
         ) = setting
         if rotary_dim is None:
             rotary_dim = head_size
+        rope_parameters["rope_theta"] = rope_theta
         rope = get_rope(
             head_size,
             rotary_dim,
             max_position,
-            base,
-            is_neox_stype,
-            rope_scaling,
+            is_neox_style,
+            rope_parameters,
             dtype,
         )
         # check if cache take effect

tests/kernels/moe/test_gpt_oss_triton_kernels.py

@@ -201,7 +201,7 @@ class ModelConfig:
     sliding_window: int = 128
     initial_context_length: int = 4096
     rope_theta: float = 150000.0
-    rope_scaling_factor: float = 32.0
+    rope_parameters_factor: float = 32.0
     rope_ntk_alpha: float = 1.0
     rope_ntk_beta: float = 32.0
 

tests/models/language/pooling/test_nomic_max_model_len.py

@@ -1,6 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 # ruff: noqa: SIM117
+from typing import Any
+
 import pytest
 
 from ...utils import EmbedModelInfo
@@ -79,8 +81,8 @@ def test_set_max_model_len_illegal(model_info, vllm_runner):
 @pytest.mark.parametrize("model_info", MODELS)
 def test_use_rope_scaling_legal(model_info, vllm_runner):
     hf_overrides = {
-        "rope_theta": rope_theta,
-        "rope_scaling": {
+        "rope_parameters": {
+            "rope_theta": rope_theta,
             "rope_type": "yarn",
             "factor": factor,
             "original_max_position_embeddings": original_max_position_embeddings,
@@ -96,9 +98,9 @@ def test_use_rope_scaling_legal(model_info, vllm_runner):
 
 @pytest.mark.parametrize("model_info", MODELS)
 def test_use_rope_scaling_illegal(model_info, vllm_runner):
-    hf_overrides = {
-        "rope_theta": rope_theta,
-        "rope_scaling": {
+    hf_overrides: dict[str, Any] = {
+        "rope_parameters": {
+            "rope_theta": rope_theta,
             "rope_type": "yarn",
             "factor": factor,
             "original_max_position_embeddings": original_max_position_embeddings,
@@ -115,8 +117,8 @@ def test_use_rope_scaling_illegal(model_info, vllm_runner):
             pass
 
     hf_overrides = {
-        "rope_theta": rope_theta,
-        "rope_scaling": {
+        "rope_parameters": {
+            "rope_theta": rope_theta,
             "rope_type": "yarn",
             "factor": factor,
             "original_max_position_embeddings": original_max_position_embeddings,