Various Transformers v5 fixes (#38127)

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

tests/entrypoints/offline_mode/test_offline_mode.py

@@ -112,6 +112,7 @@ def _re_import_modules():
     aliased_module_patterns = [
         r".+\.tokenization_utils$",
         r".+\.tokenization_utils_fast$",
+        r".+\.image_processing_utils_fast$",
         r".+\.models\..+\.image_processing_.+_fast$",
     ]
 

vllm/config/model.py

@@ -586,6 +586,14 @@ class ModelConfig:
             config_format=self.config_format,
         )
 
+        # Some checkpoints set sliding_window to 0 to indicate that sliding window is
+        # disabled, but vLLM uses None for that. Convert 0 to None to avoid errors.
+        # Set before get_and_verify_max_len to ensure that max_model_len does not get
+        # capped to 0.
+        if self.get_sliding_window() == 0:
+            self.disable_sliding_window = True
+            self.hf_text_config.sliding_window = None
+
         self.original_max_model_len = self.max_model_len
         self.max_model_len = self.get_and_verify_max_len(self.max_model_len)
 

vllm/model_executor/models/olmo2.py

@@ -30,7 +30,7 @@ from itertools import islice
 
 import torch
 from torch import nn
-from transformers import Olmo2Config
+from transformers import Olmo2Config, Olmo3Config
 
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import VllmConfig
@@ -63,7 +63,6 @@ from vllm.model_executor.models.utils import (
     maybe_prefix,
 )
 from vllm.sequence import IntermediateTensors
-from vllm.transformers_utils.configs.olmo3 import Olmo3Config
 
 
 class Olmo2Attention(nn.Module):

vllm/transformers_utils/config.py

@@ -105,7 +105,6 @@ _CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = LazyConfigDict(
     eagle="EAGLEConfig",
     speculators="SpeculatorsConfig",
     nemotron="NemotronConfig",
-    olmo3="Olmo3Config",
     olmo_hybrid="OlmoHybridConfig",
     ovis="OvisConfig",
     ultravox="UltravoxConfig",

vllm/transformers_utils/configs/__init__.py

@@ -50,7 +50,6 @@ _CLASS_TO_MODULE: dict[str, str] = {
     "KimiK25Config": "vllm.transformers_utils.configs.kimi_k25",
     "NemotronConfig": "vllm.transformers_utils.configs.nemotron",
     "NemotronHConfig": "vllm.transformers_utils.configs.nemotron_h",
-    "Olmo3Config": "vllm.transformers_utils.configs.olmo3",
     "OlmoHybridConfig": "vllm.transformers_utils.configs.olmo_hybrid",
     "OvisConfig": "vllm.transformers_utils.configs.ovis",
     "PixelShuffleSiglip2VisionConfig": "vllm.transformers_utils.configs.isaac",
@@ -106,7 +105,6 @@ __all__ = [
     "KimiK25Config",
     "NemotronConfig",
     "NemotronHConfig",
-    "Olmo3Config",
     "OlmoHybridConfig",
     "OvisConfig",
     "PixelShuffleSiglip2VisionConfig",

vllm/transformers_utils/configs/deepseek_vl2.py

@@ -114,6 +114,9 @@ class DeepseekVLV2Config(PretrainedConfig):
         self.projector_config = MlpProjectorConfig(**projector_config)
 
         language_config = kwargs.get("language_config", {})
+        # remove kv_lora_rank if not specified, passing None is prohibited
+        if language_config.get("kv_lora_rank") is None:
+            language_config.pop("kv_lora_rank", None)
         self.text_config = DeepseekV2Config(**language_config)
 
         self.tile_tag = tile_tag

vllm/transformers_utils/configs/olmo3.py

-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from transformers.configuration_utils import PretrainedConfig
-
-
-class Olmo3Config(PretrainedConfig):
-    model_type = "olmo3"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    def __init__(
-        self,
-        vocab_size=50304,
-        hidden_size=4096,
-        intermediate_size=11008,
-        num_hidden_layers=32,
-        num_attention_heads=32,
-        num_key_value_heads=None,
-        hidden_act="silu",
-        max_position_embeddings=2048,
-        initializer_range=0.02,
-        use_cache=True,
-        pad_token_id=1,
-        bos_token_id=None,
-        eos_token_id=50279,
-        tie_word_embeddings=False,
-        rope_parameters=None,
-        attention_bias=False,
-        attention_dropout=0.0,
-        rms_norm_eps=1e-5,
-        sliding_window=4096,
-        layer_types=None,
-        **kwargs,
-    ):
-        # This model uses Olmo3ForCausalLM in transformers but Olmo2ForCausalLM
-        # in vLLM.
-        if "architectures" not in kwargs:
-            kwargs["architectures"] = ["Olmo2ForCausalLM"]
-        elif "Olmo3ForCausalLM" in kwargs["architectures"]:
-            kwargs["architectures"].remove("Olmo3ForCausalLM")
-            kwargs["architectures"].append("Olmo2ForCausalLM")
-
-        super().__init__(
-            pad_token_id=pad_token_id,
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )
-        self.vocab_size = vocab_size
-        self.max_position_embeddings = max_position_embeddings
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-
-        # for backward compatibility
-        if num_key_value_heads is None:
-            num_key_value_heads = num_attention_heads
-
-        self.num_key_value_heads = num_key_value_heads
-        self.hidden_act = hidden_act
-        self.initializer_range = initializer_range
-        self.use_cache = use_cache
-        # Try to set `rope_scaling` if available, otherwise use `rope_parameters`
-        rope_scaling = kwargs.pop("rope_scaling", None)
-        rope_parameters = rope_scaling or rope_parameters or {"rope_type": "default"}
-        rope_theta = kwargs.pop("rope_theta", 10000.0)
-        if "rope_theta" not in rope_parameters:
-            rope_parameters["rope_theta"] = rope_theta
-        self.rope_parameters = rope_parameters
-        self.attention_bias = attention_bias
-        self.attention_dropout = attention_dropout
-
-        self.rms_norm_eps = rms_norm_eps
-
-        self.sliding_window = sliding_window
-        self.layer_types = layer_types
-        if self.layer_types is None:
-            self.layer_types = [
-                "sliding_attention" if (i + 1) % 4 != 0 else "full_attention"
-                for i in range(self.num_hidden_layers)
-            ]