Merge remote-tracking branch 'origin/v0.15.1-dev' into v0.15.1-dev

# Conflicts:
#	vllm/model_executor/layers/fused_moe/config.py
#	vllm/model_executor/layers/fused_moe/layer.py
#	vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_marlin.py

vllm/model_executor/models/ernie45_vl.py

@@ -1650,7 +1650,7 @@ class Ernie4_5_VLMoeForConditionalGeneration(
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -1686,4 +1686,4 @@ class Ernie4_5_VLMoeForConditionalGeneration(
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         loader = AutoWeightsLoader(self)
-        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
\ No newline at end of file

vllm/model_executor/models/ernie45_vl_moe.py

@@ -565,7 +565,7 @@ class Ernie4_5_VLMoeModel(nn.Module):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -646,7 +646,7 @@ class Ernie4_5_VLMoeForCausalLM(nn.Module, SupportsPP):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -800,4 +800,4 @@ class Ernie4_5_VLMoeForCausalLM(nn.Module, SupportsPP):
                     )
                     weight_loader(param, loaded_weight)
             loaded_params.add(name)
-        return loaded_params
+        return loaded_params
\ No newline at end of file

vllm/model_executor/models/ernie_mtp.py

@@ -164,7 +164,7 @@ class ErnieMTP(nn.Module):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         hidden_states: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
@@ -275,4 +275,4 @@ class ErnieMTP(nn.Module):
         name = name.replace(
             "model.mtp_block.0.", f"model.layers.{layer_idx}.mtp_block."
         )
-        return name
+        return name
\ No newline at end of file

vllm/model_executor/models/exaone.py

@@ -496,7 +496,7 @@ class ExaoneForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -521,4 +521,4 @@ class ExaoneForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
             # processed with quantization, LoRA, fine-tuning, etc.
             skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
         )
-        return loader.load_weights(weights)
+        return loader.load_weights(weights)
\ No newline at end of file

vllm/model_executor/models/exaone4.py

@@ -490,7 +490,7 @@ class Exaone4ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -515,4 +515,4 @@ class Exaone4ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
             # processed with quantization, LoRA, fine-tuning, etc.
             skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
         )
-        return loader.load_weights(weights)
+        return loader.load_weights(weights)
\ No newline at end of file

vllm/model_executor/models/exaone_moe.py

@@ -549,7 +549,7 @@ class ExaoneMoeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -576,4 +576,4 @@ class ExaoneMoeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
                 ["lm_head.", "mtp."] if self.config.tie_word_embeddings else ["mtp."]
             ),
         )
-        return loader.load_weights(weights)
+        return loader.load_weights(weights)
\ No newline at end of file

vllm/model_executor/models/falcon.py

@@ -210,7 +210,7 @@ class FalconAttention(nn.Module):
                 quant_config=quant_config,
                 prefix=f"{prefix}.attn",
             )
-            self.quant_method = None
+        self.quant_method = None
         if quant_config is not None:
             self.quant_method=quant_config.get_name()
             self.quant_config=quant_config
@@ -423,7 +423,7 @@ class FalconModel(nn.Module):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None,
         inputs_embeds: torch.Tensor | None = None,

vllm/model_executor/models/falcon_h1.py

@@ -459,7 +459,7 @@ class FalconH1Model(nn.Module):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -602,7 +602,7 @@ class FalconH1ForCausalLM(
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -678,4 +678,4 @@ class FalconH1ForCausalLM(
 
         if self.tie_word_embeddings:
             loaded_params.add("lm_head.weight")
-        return loaded_params
+        return loaded_params
\ No newline at end of file

vllm/model_executor/models/fuyu.py

@@ -340,7 +340,7 @@ class FuyuForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -365,4 +365,4 @@ class FuyuForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         loader = AutoWeightsLoader(self)
-        return loader.load_weights(weights)
+        return loader.load_weights(weights)
\ No newline at end of file

vllm/model_executor/models/gemma.py

@@ -297,7 +297,7 @@ class GemmaModel(nn.Module):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None,
         inputs_embeds: torch.Tensor | None = None,
@@ -400,7 +400,7 @@ class GemmaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -422,4 +422,4 @@ class GemmaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
             self,
             skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
         )
-        return loader.load_weights(weights)
+        return loader.load_weights(weights)
\ No newline at end of file

vllm/model_executor/models/gemma2.py

@@ -410,7 +410,7 @@ class Gemma2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -432,4 +432,4 @@ class Gemma2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
             self,
             skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
         )
-        return loader.load_weights(weights)
+        return loader.load_weights(weights)
\ No newline at end of file

vllm/model_executor/models/gemma3.py

@@ -494,7 +494,7 @@ class Gemma3ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -517,4 +517,4 @@ class Gemma3ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
             self,
             skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
         )
-        return loader.load_weights(weights)
+        return loader.load_weights(weights)
\ No newline at end of file

vllm/model_executor/models/gemma3_mm.py

@@ -618,7 +618,7 @@ class Gemma3ForConditionalGeneration(
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -655,42 +655,4 @@ class Gemma3ForConditionalGeneration(
             language_model="language_model",
             connector="multi_modal_projector",
             tower_model="vision_tower",
-        )
-
+        )
\ No newline at end of file
-    def get_num_mm_encoder_tokens(self, num_image_tokens: int) -> int:
-        """
-        Calculate the number of tokens output by the vision encoder.
-
-        The vision encoder processes images into patch embeddings. For Gemma3,
-        the relationship between prompt placeholder tokens and actual vision
-        encoder output tokens depends on the patch grid size.
-
-        Args:
-            num_image_tokens: Number of image placeholder tokens in the prompt
-                              (typically mm_tokens_per_image per image)
-
-        Returns:
-            Number of tokens output by the vision encoder
-        """
-        # For Gemma3, the vision encoder outputs tokens_per_side x tokens_per_side
-        # tokens per image. Since num_image_tokens represents the number of
-        # connector output tokens (mm_tokens_per_image = 256), and tokens_per_side
-        # is sqrt(256) = 16, we need to account for the token expansion.
-        # Based on empirical testing, the multiplier of 16 works correctly.
-        return num_image_tokens * 16
-
-    def get_num_mm_connector_tokens(self, num_vision_tokens: int) -> int:
-        """
-        Calculate the number of tokens output by the multimodal connector.
-
-        The connector applies projection and normalization but maintains the
-        token count for Gemma3.
-
-        Args:
-            num_vision_tokens: Number of tokens from vision encoder
-
-        Returns:
-            Number of tokens after connector processing
-        """
-        # The Gemma3 connector maintains a 1:1 token mapping
-        return num_vision_tokens

vllm/model_executor/models/gemma3n.py

@@ -704,7 +704,7 @@ class Gemma3nSelfDecoder(nn.Module):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         inputs_embeds: torch.Tensor | None = None,
         per_layer_inputs: torch.Tensor | None = None,
@@ -887,7 +887,7 @@ class Gemma3nTextModel(nn.Module, SupportsQuant):
 
     def fast_prefill_forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         inputs_embeds: torch.Tensor | None = None,
         per_layer_inputs: torch.Tensor | None = None,
@@ -964,7 +964,7 @@ class Gemma3nTextModel(nn.Module, SupportsQuant):
 
     def normal_forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         inputs_embeds: torch.Tensor | None = None,
         per_layer_inputs: torch.Tensor | None = None,
@@ -1131,7 +1131,7 @@ class Gemma3nForCausalLM(nn.Module):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         *,
         per_layer_inputs: torch.Tensor | None = None,
@@ -1163,4 +1163,4 @@ class Gemma3nForCausalLM(nn.Module):
                 ["embed_audio.", "embed_vision.", "audio_tower.", "vision_tower."]
             ),
         )
-        return loader.load_weights(weights)
+        return loader.load_weights(weights)
\ No newline at end of file

vllm/model_executor/models/gemma3n_mm.py

@@ -707,7 +707,7 @@ class Gemma3nForConditionalGeneration(
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -815,4 +815,4 @@ class Gemma3nForConditionalGeneration(
             sample_rate=16000,
             # TODO enable chunking after more thorough testing.
             min_energy_split_window_size=None,
-        )
+        )
\ No newline at end of file

vllm/model_executor/models/glm4.py

@@ -41,22 +41,13 @@ from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
-from vllm.model_executor.model_loader.weight_utils import (
-    default_weight_loader,
-    maybe_remap_kv_scale_name,
-)
 from vllm.sequence import IntermediateTensors
 from vllm.v1.attention.backend import AttentionType
 
 from .interfaces import SupportsLoRA, SupportsPP
 from .llama import LlamaMLP as Glm4MLP
 from .llama import LlamaModel
-from .utils import (
-    AutoWeightsLoader,
-    PPMissingLayer,
-    is_pp_missing_parameter,
-    maybe_prefix,
-)
+from .utils import AutoWeightsLoader, PPMissingLayer, maybe_prefix
 
 from vllm.utils import W8a8GetCacheJSON
 from vllm import _custom_ops as ops
@@ -94,12 +85,7 @@ class Glm4Attention(nn.Module):
             assert tp_size % self.total_num_kv_heads == 0
 
         rope_params = getattr(config, "rope_parameters", None)
-        if isinstance(rope_params, dict) and "partial_rotary_factor" in rope_params:
-            config.rope_parameters.setdefault(
-                "partial_rotary_factor", rope_params["partial_rotary_factor"]
-            )
-        else:
-            config.rope_parameters.setdefault("partial_rotary_factor", 0.5)
+        config.rope_parameters.setdefault("partial_rotary_factor", 0.5)
 
         self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
         self.head_dim = head_dim or hidden_size // self.total_num_heads
@@ -242,73 +228,6 @@ class Glm4Model(LlamaModel):
             vllm_config=vllm_config, prefix=prefix, layer_type=Glm4DecoderLayer
         )
 
-    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
-        stacked_params_mapping = [
-            # (param_name, shard_name, shard_id)
-            (".qkv_proj", ".q_proj", "q"),
-            (".qkv_proj", ".k_proj", "k"),
-            (".qkv_proj", ".v_proj", "v"),
-            (".gate_up_proj", ".gate_proj", 0),
-            (".gate_up_proj", ".up_proj", 1),
-        ]
-        params_dict = dict(self.named_parameters())
-        loaded_params: set[str] = set()
-        for name, loaded_weight in weights:
-            spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
-            if spec_layer is not None:
-                continue
-            if "rotary_emb.inv_freq" in name:
-                continue
-            if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
-                # Models trained using ColossalAI may include these tensors in
-                # the checkpoint. Skip them.
-                continue
-            if self.quant_config is not None and (
-                scale_name := self.quant_config.get_cache_scale(name)
-            ):
-                # Loading kv cache quantization scales
-                param = params_dict[scale_name]
-                weight_loader = getattr(param, "weight_loader", default_weight_loader)
-                loaded_weight = (
-                    loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0]
-                )
-                weight_loader(param, loaded_weight)
-                loaded_params.add(scale_name)
-                continue
-            if "scale" in name or "zero_point" in name:
-                # Remapping the name of FP8 kv-scale or zero point.
-                name = maybe_remap_kv_scale_name(name, params_dict)
-                if name is None:
-                    continue
-            for param_name, weight_name, shard_id in stacked_params_mapping:
-                if weight_name not in name:
-                    continue
-                name = name.replace(weight_name, param_name)
-                # Skip loading extra bias for GPTQ models.
-                if name.endswith(".bias") and name not in params_dict:
-                    continue
-
-                if is_pp_missing_parameter(name, self):
-                    continue
-
-                param = params_dict[name]
-                weight_loader = param.weight_loader
-                weight_loader(param, loaded_weight, shard_id)
-                break
-            else:
-                # Skip loading extra bias for GPTQ models.
-                if name.endswith(".bias") and name not in params_dict:
-                    continue
-
-                if is_pp_missing_parameter(name, self):
-                    continue
-
-                param = params_dict[name]
-                weight_loader = getattr(param, "weight_loader", default_weight_loader)
-                weight_loader(param, loaded_weight)
-            loaded_params.add(name)
-        return loaded_params
-
 
 class Glm4ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
     packed_modules_mapping = {
@@ -371,7 +290,7 @@ class Glm4ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -394,16 +313,3 @@ class Glm4ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
             skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
         )
         return loader.load_weights(weights)
-
-
-def get_spec_layer_idx_from_weight_name(
-    config: Glm4Config, weight_name: str
-) -> int | None:
-    if hasattr(config, "num_nextn_predict_layers") and (
-        config.num_nextn_predict_layers > 0
-    ):
-        layer_idx = config.num_hidden_layers
-        for i in range(config.num_nextn_predict_layers):
-            if f"layers.{layer_idx + i}." in weight_name:
-                return layer_idx + i
-    return None

vllm/model_executor/models/glm4_1v.py

@@ -24,8 +24,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""Inference-only GLM-4.1V & GLM-4.6V-Flash, AutoGLM-Phone-9B model
-compatible with HuggingFace weights."""
+"""Inference-only GLM-4V model compatible with HuggingFace weights."""
 
 import os
 import itertools
@@ -1739,7 +1738,7 @@ class Glm4vForConditionalGeneration(
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,

vllm/model_executor/models/glm4_moe.py

@@ -451,7 +451,7 @@ class Glm4MoeModel(nn.Module):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -687,7 +687,7 @@ class Glm4MoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA, Glm4MixtureOfExper
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -722,4 +722,4 @@ def get_spec_layer_idx_from_weight_name(
         for i in range(config.num_nextn_predict_layers):
             if f"layers.{layer_idx + i}." in weight_name:
                 return layer_idx + i
-    return None
+    return None
\ No newline at end of file

vllm/model_executor/models/glm4_moe_lite.py

@@ -264,7 +264,7 @@ class Glm4MoeLiteModel(nn.Module):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -596,7 +596,7 @@ class Glm4MoeLiteForCausalLM(
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -639,4 +639,4 @@ def get_spec_layer_idx_from_weight_name(
         for i in range(config.num_nextn_predict_layers):
             if f"layers.{layer_idx + i}." in weight_name:
                 return layer_idx + i
-    return None
+    return None
\ No newline at end of file

vllm/model_executor/models/glm4_moe_lite_mtp.py

@@ -230,7 +230,7 @@ class Glm4MoeLiteMTP(nn.Module, SupportsPP, Glm4MixtureOfExperts):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         hidden_states: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
@@ -461,4 +461,4 @@ class Glm4MoeLiteMTP(nn.Module, SupportsPP, Glm4MixtureOfExperts):
         elif shared_weight:
             # treat shared weights as top level weights
             name = name.replace(f"model.layers.{spec_layer}.", "model.")
-        return name
+        return name
\ No newline at end of file