[CI/Build] Ensure compatability with Transformers v4.53

requirements/test.in

@@ -34,7 +34,7 @@ opencv-python-headless >= 4.11.0 # required for video test
 datamodel_code_generator # required for minicpm3 test
 lm-eval[api]==0.4.8 # required for model evaluation test
 mteb[bm25s]>=1.38.11, <2 # required for mteb test
-transformers==4.52.4
+transformers==4.53.2
 tokenizers==0.21.1
 huggingface-hub[hf_xet]>=0.33.0  # Required for Xet downloads.
 schemathesis>=3.39.15 # Required for openai schema test.

requirements/test.txt

@@ -800,7 +800,7 @@ tqdm==4.66.6
     #   transformers
 tqdm-multiprocess==0.0.11
     # via lm-eval
-transformers==4.52.4
+transformers==4.53.2
     # via
     #   -r requirements/test.in
     #   genai-perf

tests/models/multimodal/generation/test_common.py

@@ -318,6 +318,7 @@
         num_logprobs=10,
         image_size_factors=[(), (0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
         auto_cls=AutoModelForImageTextToText,
+        marks=[large_gpu_mark(min_gb=32)],
     ),
     "glm4_1v-video": VLMTestInfo(
         models=["THUDM/GLM-4.1V-9B-Thinking"],
@@ -331,8 +332,7 @@
             inputs=custom_inputs.video_with_metadata_glm4_1v(),
             limit_mm_per_prompt={"video": 1},
         )],
-        # This is needed to run on machine with 24GB VRAM
-        vllm_runner_kwargs={"gpu_memory_utilization": 0.95},
+        marks=[large_gpu_mark(min_gb=32)],
     ),
     "h2ovl": VLMTestInfo(
         models = [

tests/models/multimodal/processing/test_common.py

@@ -159,6 +159,7 @@ def _test_processing_correctness(
 _ADD_SPECIAL_TOKENS_OVERRIDES = {
     "mllama": False,
     "ovis": False,
+    "paligemma": False,
     "ultravox": False,
     "whisper": False,
 }

tests/models/test_initialization.py

@@ -31,7 +31,8 @@ def test_can_initialize(model_arch: str, monkeypatch: pytest.MonkeyPatch):
     model_info.check_transformers_version(on_fail="skip")
 
     # FIXME: Possible memory leak in the previous tests?
-    if model_arch in ("GraniteSpeechForConditionalGeneration",
+    if model_arch in ("Glm4vForConditionalGeneration",
+                      "GraniteSpeechForConditionalGeneration",
                       "KimiVLForConditionalGeneration"):
         pytest.skip("Avoid OOM")
 
@@ -46,16 +47,23 @@ def hf_overrides(hf_config: PretrainedConfig) -> PretrainedConfig:
         n_group = getattr(text_config, 'n_group', None)
         num_experts = n_group * 2 if n_group is not None else 2
 
+        # we use three layers for Gemma-3n to check
+        # both normal layer and kv_shared_layer
+        num_hidden_layers = (3 if model_arch
+                             == "Gemma3nForConditionalGeneration" else 1)
+
         text_config.update({
             "num_layers": 1,
-            "num_hidden_layers": 1,
+            "num_hidden_layers": num_hidden_layers,
             "num_experts": num_experts,
             "num_experts_per_tok": 2,
             "num_local_experts": num_experts,
             # Otherwise there will not be any expert layers
             "first_k_dense_replace": 0,
             # To avoid OOM on DeepSeek-V3
             "n_routed_experts": num_experts,
+            # For Gemma-3n
+            "num_kv_shared_layers": 1,
         })
 
         if hasattr(hf_config, "vision_config"):

vllm/inputs/registry.py

@@ -5,9 +5,7 @@
 from typing import TYPE_CHECKING, Any, NamedTuple, Optional, Union
 
 import torch
-from packaging.version import Version
 from transformers import BatchFeature, PretrainedConfig, ProcessorMixin
-from transformers import __version__ as TRANSFORMERS_VERSION
 from typing_extensions import TypeVar
 
 from vllm.jsontree import JSONTree, json_map_leaves
@@ -130,13 +128,9 @@ def get_hf_processor(
         /,
         **kwargs: object,
     ) -> _P:
-        # Transformers 4.53.0 has issue with passing tokenizer to
-        # initialize processor. We disable it for this version.
-        # See: https://github.com/vllm-project/vllm/issues/20224
-        if Version(TRANSFORMERS_VERSION) != Version("4.53.0"):
-            kwargs["tokenizer"] = self.tokenizer
         return super().get_hf_processor(
             typ,
+            tokenizer=self.tokenizer,
             **kwargs,
         )
 

vllm/model_executor/models/commandr.py

@@ -189,10 +189,13 @@ def __init__(
 
         layer_idx = extract_layer_index(prefix)
         layer_has_sliding_window = (
-            getattr(config, "sliding_window_pattern", False)
-            and (layer_idx + 1) % self.config.sliding_window_pattern != 0)
+            getattr(config, "sliding_window_pattern", False) and
+            (layer_idx + 1) % self.config.sliding_window_pattern
+            != 0) or (getattr(config, "layer_types", False)
+                      and config.layer_types[layer_idx] == "sliding_attention")
 
         self.sliding_window = (interleaved_sliding_window
+                               or config.sliding_window
                                if layer_has_sliding_window else None)
 
         self.attn = Attention(self.num_heads,

vllm/model_executor/models/fuyu.py

@@ -175,12 +175,21 @@ def _call_hf_processor(
 
             # Original output: (1, num_images, Pn, Px * Py * C)
             # New output: (num_images, Pn, Px * Py * C)
-            assert (isinstance(image_patches, list)
-                    and len(image_patches) == 1)
-            assert (isinstance(image_patches[0], torch.Tensor)
-                    and len(image_patches[0]) == len(images))
-
-            processed_outputs["image_patches"] = image_patches[0]
+            # image_patches is a list with shape:
+            # (1, num_images, Pn, Px * Py * C)
+            # before Transformers 4.53
+            if isinstance(image_patches, list):
+                assert len(image_patches) == 1
+                assert (isinstance(image_patches[0], torch.Tensor)
+                        and len(image_patches[0]) == len(images))
+                processed_outputs["image_patches"] = image_patches[0]
+            # image_patches is a tensor with shape:
+            # (num_images, Pn, Px * Py * C)
+            # after Transformers 4.53
+            elif isinstance(image_patches, torch.Tensor):
+                assert len(image_patches) == len(images)
+            else:
+                raise AssertionError("This line should be unreachable.")
 
         return processed_outputs
 
@@ -193,8 +202,10 @@ def _apply_hf_processor_tokens_only(
         vocab = tokenizer.get_vocab()
 
         boa_token_id = vocab["<0x04>"]
+        if prompt_tokens[-1] != boa_token_id:
+            prompt_tokens.append(boa_token_id)
 
-        return prompt_tokens + [boa_token_id]
+        return prompt_tokens
 
     def _get_mm_fields_config(
         self,

vllm/model_executor/models/gemma3.py

@@ -149,14 +149,17 @@ def __init__(self,
         # TODO(woosuk): Add reference to the original HF implementation.
         layer_idx = extract_layer_index(prefix)
         self.is_sliding = (getattr(
-            config, "interleaved_sliding_window", None) is not None and bool(
-                (layer_idx + 1) % config.sliding_window_pattern))
+            config, "interleaved_sliding_window", None) is not None and (bool(
+                (layer_idx + 1) % config.sliding_window_pattern))) or (
+                    getattr(config, "layer_types", None) is not None
+                    and config.layer_types[layer_idx] == "sliding_attention")
         # Initialize the rotary embedding.
         if self.is_sliding:
             # Local attention. Override the values in config.json.
             self.rope_theta = config.rope_local_base_freq
             self.rope_scaling = {"rope_type": "default"}
-            self.sliding_window = config.interleaved_sliding_window
+            self.sliding_window = (config.interleaved_sliding_window
+                                   or config.sliding_window)
         else:
             # Global attention. Use the values in config.json.
             self.rope_theta = config.rope_theta

vllm/model_executor/models/minicpmo.py

@@ -30,8 +30,10 @@
 from torch import nn
 from transformers import BatchFeature, PretrainedConfig
 from transformers.modeling_outputs import BaseModelOutputWithPast
-from transformers.models.whisper.modeling_whisper import (
-    ACT2FN, WHISPER_ATTENTION_CLASSES, WhisperConfig, WhisperEncoder)
+from transformers.models.whisper.modeling_whisper import (ACT2FN,
+                                                          WhisperAttention,
+                                                          WhisperConfig,
+                                                          WhisperEncoder)
 
 from vllm.config import VllmConfig
 from vllm.model_executor.layers.quantization import QuantizationConfig
@@ -378,14 +380,13 @@ class MiniCPMWhisperEncoderLayer(nn.Module):
     def __init__(self, config: WhisperConfig, layer_idx: int):
         super().__init__()
         self.embed_dim = config.d_model
-        self.self_attn = WHISPER_ATTENTION_CLASSES[
-            config._attn_implementation](
-                embed_dim=self.embed_dim,
-                num_heads=config.encoder_attention_heads,
-                dropout=config.attention_dropout,
-                config=config,
-                layer_idx=layer_idx,
-            )
+        self.self_attn = WhisperAttention(
+            embed_dim=self.embed_dim,
+            num_heads=config.encoder_attention_heads,
+            dropout=config.attention_dropout,
+            config=config,
+            layer_idx=layer_idx,
+        )
         self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
         self.dropout = config.dropout
         self.activation_fn = ACT2FN[config.activation_function]

vllm/model_executor/models/paligemma.py

@@ -125,7 +125,7 @@ def _call_hf_processor(
     ) -> BatchFeature:
         tokenizer = self.info.get_tokenizer()
         if not mm_data:
-            prompt_ids = tokenizer.encode(prompt)
+            prompt_ids = tokenizer.encode(prompt, add_special_tokens=False)
             return BatchFeature(dict(input_ids=[prompt_ids]), tensor_type="pt")
 
         return super()._call_hf_processor(

vllm/model_executor/models/qwen2_5_omni_thinker.py

@@ -144,8 +144,16 @@ def get_hf_processor(
     ) -> Qwen2_5OmniProcessor:
         if fps is not None:
             kwargs["fps"] = fps
+
+        # Monkey patch for Transformers v4.53
+        processor_class = Qwen2_5OmniProcessor
+        if processor_class.image_processor_class != "AutoImageProcessor":
+            processor_class.image_processor_class = "AutoImageProcessor"
+        if processor_class.video_processor_class != "AutoVideoProcessor":
+            processor_class.video_processor_class = "AutoVideoProcessor"
+
         processor = self.ctx.get_hf_processor(
-            Qwen2_5OmniProcessor,
+            processor_class,
             image_processor=self.get_image_processor(min_pixels=min_pixels,
                                                      max_pixels=max_pixels,
                                                      size=size,

vllm/model_executor/models/whisper.py

@@ -634,7 +634,14 @@ def get_hf_config(self) -> WhisperConfig:
     def get_hf_processor(self,
                          sampling_rate: Optional[int] = None
                          ) -> WhisperProcessor:
-        return self.ctx.get_hf_processor(WhisperProcessor)
+        # HACK: Transformers 4.53.0 has issue with whisper tokenizer to
+        # initialize processor. We use a monkeypatch to fix it here.
+        # See: https://github.com/vllm-project/vllm/issues/20224
+        processor_class = WhisperProcessor
+        tokenizer_class = ("WhisperTokenizer", "WhisperTokenizerFast")
+        if processor_class.tokenizer_class != tokenizer_class:
+            processor_class.tokenizer_class = tokenizer_class
+        return self.ctx.get_hf_processor(processor_class)
 
     def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
         return {"audio": 1}