Fix some Transformers nightly tests (#29802)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

vllm/model_executor/models/jina_vl.py

@@ -29,7 +29,7 @@ logger = init_logger(__name__)
 class JinaVLScorer(nn.Module):
     def __init__(self, model_config: "ModelConfig"):
         super().__init__()
-        config = model_config.hf_config
+        config = model_config.hf_config.get_text_config()
         head_dtype = model_config.head_dtype
         self.dense = ColumnParallelLinear(
             config.hidden_size, config.hidden_size, params_dtype=head_dtype, bias=True

vllm/model_executor/models/modernbert.py

@@ -20,7 +20,7 @@ from vllm.model_executor.layers.pooler import (
     PoolingParamsUpdate,
     PoolingType,
 )
-from vllm.model_executor.layers.rotary_embedding import RotaryEmbedding
+from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.sequence import IntermediateTensors
@@ -62,19 +62,6 @@ class ModernBertEmbeddings(nn.Module):
             return embeddings
 
 
-class ModernBertRotaryEmbedding(RotaryEmbedding):
-    def __init__(self, config: ModernBertConfig, head_size: int, dim: int, base: float):
-        super().__init__(
-            head_size=head_size,
-            rotary_dim=dim,
-            max_position_embeddings=config.max_position_embeddings,
-            base=base,
-            is_neox_style=True,
-            dtype=torch.float16,
-        )
-        self.config = config
-
-
 class ModernBertAttention(nn.Module):
     def __init__(self, config: ModernBertConfig, layer_id: int | None = None):
         super().__init__()
@@ -95,19 +82,33 @@ class ModernBertAttention(nn.Module):
             bias=config.attention_bias,
         )
 
-        sliding_window = None
-        if layer_id % config.global_attn_every_n_layers != 0:
-            sliding_window = config.local_attention // 2
-            rope_theta = (
-                config.local_rope_theta
-                if config.local_rope_theta is not None
-                else config.global_rope_theta
-            )
+        if layer_types := getattr(config, "layer_types", None):
+            # Transformers v5
+            layer_type = layer_types[layer_id]
+            rope_parameters = config.rope_parameters[layer_type]
+            sliding_window: int | None = None
+            if layer_type == "sliding_attention":
+                sliding_window = config.local_attention // 2
         else:
-            rope_theta = config.global_rope_theta
+            # Transformers v4
+            sliding_window = None
+            if layer_id % config.global_attn_every_n_layers != 0:
+                sliding_window = config.local_attention // 2
+                rope_theta = (
+                    config.local_rope_theta
+                    if config.local_rope_theta is not None
+                    else config.global_rope_theta
+                )
+            else:
+                rope_theta = config.global_rope_theta
+            rope_parameters = {"rope_type": "default", "rope_theta": rope_theta}
 
-        self.rotary_emb = ModernBertRotaryEmbedding(
-            config=config, head_size=self.head_dim, dim=self.head_dim, base=rope_theta
+        self.rotary_emb = get_rope(
+            head_size=self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=config.max_position_embeddings,
+            rope_parameters=rope_parameters,
+            dtype=torch.float16,
         )
         self.attn = EncoderOnlyAttention(
             self.num_heads,

vllm/model_executor/models/qwen2.py

@@ -503,7 +503,7 @@ class Qwen2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsEagle3):
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
-        config = vllm_config.model_config.hf_config
+        config = vllm_config.model_config.hf_config.get_text_config()
         quant_config = vllm_config.quant_config
 
         self.config = config