Revert "Various Transformers v5 fixes" (#38127)

tests/entrypoints/offline_mode/test_offline_mode.py

@@ -112,7 +112,6 @@ 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

@@ -586,14 +586,6 @@ def __post_init__(
             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 @@
 
 import torch
 from torch import nn
-from transformers import Olmo2Config, Olmo3Config
+from transformers import Olmo2Config
 
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import VllmConfig
@@ -63,6 +63,7 @@
     maybe_prefix,
 )
 from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.configs.olmo3 import Olmo3Config
 
 
 class Olmo2Attention(nn.Module):

vllm/transformers_utils/config.py

@@ -105,6 +105,7 @@ def __getitem__(self, key):
     eagle="EAGLEConfig",
     speculators="SpeculatorsConfig",
     nemotron="NemotronConfig",
+    olmo3="Olmo3Config",
     olmo_hybrid="OlmoHybridConfig",
     ovis="OvisConfig",
     ultravox="UltravoxConfig",

vllm/transformers_utils/configs/__init__.py

@@ -50,6 +50,7 @@
     "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",
@@ -105,6 +106,7 @@
     "KimiK25Config",
     "NemotronConfig",
     "NemotronHConfig",
+    "Olmo3Config",
     "OlmoHybridConfig",
     "OvisConfig",
     "PixelShuffleSiglip2VisionConfig",

vllm/transformers_utils/configs/deepseek_vl2.py

@@ -114,9 +114,6 @@ def __init__(
         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

@@ -0,0 +1,83 @@
+# 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)
+            ]