remove multiple dead codes

Signed-off-by: yewentao256 <zhyanwentao@126.com>

vllm/compilation/passes/fx_utils.py

@@ -14,10 +14,6 @@ def is_func(node: fx.Node, target: Target) -> bool:
     return bool(node.op == "call_function" and node.target == target)
 
 
-def is_auto_func(node: fx.Node, op: OpOverload) -> bool:
-    return is_func(node, auto_functionalized) and node.args[0] == op
-
-
 # Returns the first auto_functionalized node with the given op (if it exists)
 def find_auto_fn_maybe(nodes: Iterable[fx.Node], op: OpOverload) -> fx.Node | None:
     for node in nodes:
@@ -42,13 +38,6 @@ def find_getitem_maybe(node: fx.Node, idx: int) -> fx.Node | None:
     return None
 
 
-# Returns the getitem node that extracts the idx-th element from node
-def find_getitem(node: fx.Node, idx: int) -> fx.Node:
-    ret = find_getitem_maybe(node, idx)
-    assert ret is not None, f"Could not find getitem {idx} in node {node}"
-    return ret
-
-
 # An auto-functionalization-aware utility for finding nodes with a specific op
 # Also handles op overload packets and finds all overloads
 def find_op_nodes(
@@ -67,11 +56,3 @@ def find_op_nodes(
     for n in graph.find_nodes(op="call_function", target=auto_functionalized):
         if n.args[0] == op:
             yield n
-
-
-# Asserts that the node only has one user and returns it
-# Even if a node has only 1 user, it might share storage with another node,
-# which might need to be taken into account.
-def get_only_user(node: fx.Node) -> fx.Node:
-    assert len(node.users) == 1
-    return next(iter(node.users))

vllm/compilation/passes/vllm_inductor_pass.py

@@ -333,12 +333,3 @@ class VllmFusionPatternMatcherPass(VllmPatternMatcherPass):
     def __call__(self, graph: torch.fx.Graph) -> None:
         self.matched_count = self.pm_pass.apply(graph)
         VllmPatternMatcherPass.match_table[self.pass_name] += self.matched_count
-
-
-class PrinterInductorPass(VllmInductorPass):
-    def __init__(self, name: str, config: VllmConfig) -> None:
-        super().__init__(config)
-        self.name = name
-
-    def __call__(self, graph: torch.fx.Graph) -> None:
-        self.dump_graph(graph, self.name)

vllm/config/utils.py

@@ -415,31 +415,6 @@ class Range:
         return self.__str__()
 
 
-def handle_deprecated(
-    config: ConfigT,
-    old_name: str,
-    new_name_or_names: str | list[str],
-    removal_version: str,
-) -> None:
-    old_val = getattr(config, old_name)
-    if old_val is None:
-        return
-
-    if isinstance(new_name_or_names, str):
-        new_names = [new_name_or_names]
-    else:
-        new_names = new_name_or_names
-
-    msg = (
-        f"{old_name} is deprecated and will be removed in {removal_version}. "
-        f"Use {', '.join(new_names)} instead."
-    )
-    logger.warning(msg)
-
-    for new_name in new_names:
-        setattr(config, new_name, old_val)
-
-
 def get_from_deprecated_env_if_set(
     env_name: str,
     removal_version: str,

vllm/distributed/parallel_state.py

@@ -2081,9 +2081,6 @@ def model_parallel_is_initialized():
     return _TP is not None and _PP is not None
 
 
-_TP_STATE_PATCHED = False
-
-
 def get_tensor_model_parallel_world_size() -> int:
     """Return world size for the tensor model parallel group."""
     return get_tp_group().world_size

vllm/entrypoints/chat_utils.py

@@ -2015,15 +2015,6 @@ async def parse_chat_messages_async(
     return conversation, mm_data, mm_uuids
 
 
-def get_history_tool_calls_cnt(conversation: list[ConversationMessage]):
-    idx = 0
-    for msg in conversation:
-        if msg["role"] == "assistant":
-            tool_calls = msg.get("tool_calls")
-            idx += len(list(tool_calls)) if tool_calls is not None else 0  # noqa
-    return idx
-
-
 _KIMI_MODEL_TYPES = ("kimi_k2", "kimi_k25", "kimi_k3")
 
 

vllm/model_executor/layers/activation.py

@@ -8,14 +8,8 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from vllm.distributed import (
-    divide,
-    get_tensor_model_parallel_rank,
-    get_tensor_model_parallel_world_size,
-)
 from vllm.logger import init_logger
 from vllm.model_executor.custom_op import CustomOp
-from vllm.model_executor.utils import set_weight_attrs
 from vllm.platforms import CpuArchEnum, current_platform
 from vllm.triton_utils import tl, triton
 from vllm.utils.collection_utils import LazyDict
@@ -788,48 +782,6 @@ class XIELU(CustomOp):
         return self.forward_native(input)
 
 
-class ScaledActivation(nn.Module):
-    """An activation function with post-scale parameters.
-
-    This is used for some quantization methods like AWQ.
-    """
-
-    def __init__(
-        self,
-        act_module: nn.Module,
-        intermediate_size: int,
-        input_is_parallel: bool = True,
-        params_dtype: torch.dtype | None = None,
-    ):
-        super().__init__()
-        self.act = act_module
-        self.input_is_parallel = input_is_parallel
-        if input_is_parallel:
-            tp_size = get_tensor_model_parallel_world_size()
-            intermediate_size_per_partition = divide(intermediate_size, tp_size)
-        else:
-            intermediate_size_per_partition = intermediate_size
-        if params_dtype is None:
-            params_dtype = torch.get_default_dtype()
-        self.scales = nn.Parameter(
-            torch.empty(intermediate_size_per_partition, dtype=params_dtype)
-        )
-        set_weight_attrs(self.scales, {"weight_loader": self.weight_loader})
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return self.act(x) / self.scales
-
-    def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor):
-        param_data = param.data
-        if self.input_is_parallel:
-            tp_rank = get_tensor_model_parallel_rank()
-            shard_size = param_data.shape[0]
-            start_idx = tp_rank * shard_size
-            loaded_weight = loaded_weight.narrow(0, start_idx, shard_size)
-        assert param_data.shape == loaded_weight.shape
-        param_data.copy_(loaded_weight)
-
-
 _ACTIVATION_REGISTRY = LazyDict(
     {
         "gelu": lambda: GELU(),

vllm/model_executor/layers/conv.py

@@ -155,58 +155,6 @@ class Conv2dLayer(ConvLayerBase):
         return self._forward_conv(x)
 
 
-class CausalConv2dLayer(Conv2dLayer):
-    """
-    A causal version of nn.Conv2d where each location in the 2D matrix would
-    have no access to locations on its right or down
-    All arguments are the same as nn.Conv2d except padding which should be
-    set as None
-    """
-
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        kernel_size: int,
-        stride: int,
-        padding: int = 0,
-        dilation: int = 1,
-        groups: int = 1,
-        bias: bool = True,
-        padding_mode: str = "zeros",
-        *,
-        params_dtype: torch.dtype | None = None,
-    ) -> None:
-        if padding is not None:
-            raise ValueError(
-                "Argument padding should be set to None for CausalConv2dLayer."
-            )
-        self._left_padding: int = kernel_size - 1
-        self._right_padding: int = stride - 1
-        padding = 0
-
-        super().__init__(
-            in_channels,
-            out_channels,
-            kernel_size,
-            stride,
-            padding,
-            dilation,
-            groups,
-            bias,
-            padding_mode,
-            params_dtype=params_dtype,
-        )
-
-    def forward(
-        self,
-        x: torch.Tensor,
-    ) -> torch.Tensor:
-        x = F.pad(x, pad=(self._left_padding, self._right_padding, 0, 0))
-        x = super().forward(x)
-        return x
-
-
 # --8<-- [start:conv3d]
 @CustomOp.register("conv3d")
 class Conv3dLayer(ConvLayerBase):

vllm/model_executor/layers/quantization/fp8.py

@@ -4,7 +4,6 @@
 from typing import TYPE_CHECKING, Any
 
 import torch
-from torch.utils._python_dispatch import TorchDispatchMode
 
 import vllm.envs as envs
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
@@ -234,26 +233,6 @@ class Fp8Config(QuantizationConfig):
         return cache_scale_mapper | QuantizationConfig.get_cache_scale_mapper()
 
 
-class CopyNumelCounter(TorchDispatchMode):
-    """
-    Tracks total number of elements modified with `copy_`. Useful for keeping
-    track of weight loading where underlying weights can be arbitrarily
-    transformed (such as with `narrow`) before calling copy.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.copied_numel = 0
-
-    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
-        if kwargs is None:
-            kwargs = {}
-        out = func(*args, **kwargs)
-        if func == torch.ops.aten.copy_.default:
-            self.copied_numel += args[0].numel()
-        return out
-
-
 class Fp8LinearMethod(LinearMethodBase):
     """Linear method for FP8.
     Supports loading FP8 checkpoints with static weight scale and

vllm/model_executor/layers/utils.py

@@ -25,12 +25,6 @@ MOE_LAYER_ROUTER_GATE_SUFFIXES = {
 }
 
 
-def is_layer_moe_router_gate(prefix: str) -> bool:
-    if not prefix:
-        return False
-    return prefix.rsplit(".", 1)[-1] in MOE_LAYER_ROUTER_GATE_SUFFIXES
-
-
 def get_token_bin_counts_and_mask(
     tokens: torch.Tensor,
     vocab_size: int,

vllm/model_executor/models/glm_ocr.py

@@ -242,10 +242,6 @@ class GlmOcrVisionBlock(Glm4vVisionBlock):
         )
 
 
-class GlmOcrVisionPatchEmbed(Glm4vVisionPatchEmbed):
-    pass
-
-
 class GlmOcrPatchMerger(Glm4vPatchMerger):
     pass
 

vllm/model_executor/models/idefics3.py

@@ -103,41 +103,6 @@ class Idefics3ProcessingInfo(BaseProcessingInfo):
     def get_supported_mm_limits(self) -> Mapping[str, int | None]:
         return {"image": None}
 
-    def _resize_output_size(
-        self,
-        *,
-        height: int,
-        width: int,
-        max_len: int | None = None,
-        min_len: int = 1,
-        max_size: int | None = None,
-    ) -> tuple[int, int]:
-        # Set default value for max_len if not provided
-        max_len = max(height, width) if max_len is None else max_len
-        aspect_ratio = width / height
-
-        # Handle the maximum size constraint
-        if max_size is not None:
-            max_len = min(max_len, max_size)
-
-        # Adjust dimensions according to the aspect ratio
-        if width >= height:
-            width = max_len
-            height = int(width / aspect_ratio)
-        else:
-            height = max_len
-            width = int(height * aspect_ratio)
-
-        # Ensure both width and height are even (if needed)
-        height += height % 2
-        width += width % 2
-
-        # Ensure dimensions are not smaller than the minimum length
-        height = max(height, min_len)
-        width = max(width, min_len)
-
-        return height, width
-
     def _get_image_feature_grid_size(
         self,
         *,

vllm/model_executor/models/llava_onevision2.py

@@ -1531,13 +1531,6 @@ class LlavaOnevision2MultiModalDataParser(MultiModalDataParser):
 class LlavaOnevision2MultiModalProcessor(
     BaseMultiModalProcessor[LlavaOnevision2ProcessingInfo]
 ):
-    def _get_data_parser(self) -> MultiModalDataParser:
-        # Retained for symmetry; vLLM actually fetches the parser via
-        # info.get_data_parser() (see ProcessingInfo override above).
-        return LlavaOnevision2MultiModalDataParser(
-            self.info.get_hf_config().vision_config.spatial_merge_size
-        )
-
     def _call_hf_processor(
         self,
         prompt: str,

vllm/model_executor/models/nemotron_parse.py

@@ -77,24 +77,6 @@ class BartScaledWordEmbedding(VocabParallelEmbedding):
         return super().forward(input_ids) * self.embed_scale
 
 
-class BartParallelLMHead(ParallelLMHead):
-    """
-    This module overrides ParallelLMHead's
-    forward by dividing by embeddings scale,
-    yielding effectively the inverse of
-    BartScaledWordEmbedding
-    """
-
-    def __init__(
-        self, num_embeddings: int, embedding_dim: int, embed_scale: float = 1.0
-    ):
-        super().__init__(num_embeddings, embedding_dim)
-        self.embed_scale = embed_scale
-
-    def forward(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return super().forward(input_ids) / self.embed_scale
-
-
 class BartDecoderLayer(nn.Module):
     def __init__(
         self,

vllm/model_executor/models/phi4mm.py

@@ -521,32 +521,6 @@ class Phi4MMAudioEmbeddingInputs(TensorSchema):
 Phi4MMAudioInputs: TypeAlias = Phi4MMAudioFeatureInputs | Phi4MMAudioEmbeddingInputs
 
 
-def cat_with_pad(tensors, dim, padding_value=0):
-    """
-    cat along dim, while pad to max for all other dims
-    """
-    ndim = tensors[0].dim()
-    assert all(t.dim() == ndim for t in tensors[1:]), (
-        "All tensors must have the same number of dimensions"
-    )
-
-    out_size = [max(t.shape[i] for t in tensors) for i in range(ndim)]
-    out_size[dim] = sum(t.shape[dim] for t in tensors)
-    output = tensors[0].new_full(out_size, padding_value)
-
-    index = 0
-    for t in tensors:
-        # Create a slice list where every dimension except dim is full slice
-        slices = [slice(0, t.shape[d]) for d in range(ndim)]
-        # Update only the concat dimension slice
-        slices[dim] = slice(index, index + t.shape[dim])
-
-        output[slices] = t
-        index += t.shape[dim]
-
-    return output
-
-
 def stack_with_pad(
     tensors: torch.Tensor | list[torch.Tensor],
     padding_value: int | float = 0,

vllm/model_executor/models/phi4mm_utils.py

@@ -1595,14 +1595,6 @@ class AttModule(nn.Module):
         return x, memory, pos_emb, att_mask
 
 
-class AttBlock(BlockBase, AttModule):
-    """Attention Block module to support both Attention and Block module."""
-
-    def memory_dims(self, max_len: bool = False) -> tuple[int, int]:
-        """memory dimensions"""
-        return (1, self.input_size)
-
-
 def masked_softmax(
     scores: Tensor,
     mask: Tensor | None,

vllm/model_executor/models/skyworkr1v.py

@@ -7,7 +7,7 @@
 # Copyright (c) 2025 Skywork
 # Licensed under The MIT License [see LICENSE for details]
 # --------------------------------------------------------
-from collections.abc import Iterable, Mapping
+from collections.abc import Iterable
 from typing import Annotated, Literal, TypeAlias
 
 import torch
@@ -15,8 +15,6 @@ import torch.nn as nn
 from transformers import PretrainedConfig
 
 from vllm.config import VllmConfig
-from vllm.config.multimodal import BaseDummyOptions
-from vllm.inputs import MultiModalDataDict
 from vllm.model_executor.layers.linear import ReplicatedLinear
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.quantization.auto_awq import AutoAWQConfig
@@ -24,7 +22,6 @@ from vllm.model_executor.models.intern_vit import (
     InternVisionModel,
 )
 from vllm.multimodal import MULTIMODAL_REGISTRY
-from vllm.multimodal.processing import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.processors.internvl import (
     InternVLImageProcessor,
@@ -117,33 +114,6 @@ class SkyworkR1VProcessingInfo(BaseInternVLProcessingInfo):
         )
 
 
-class SkyworkR1VDummyInputsBuilder(BaseDummyInputsBuilder[SkyworkR1VProcessingInfo]):
-    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
-        num_images = mm_counts.get("image", 0)
-
-        return "<image>" * num_images
-
-    def get_dummy_mm_data(
-        self,
-        seq_len: int,
-        mm_counts: Mapping[str, int],
-        mm_options: Mapping[str, BaseDummyOptions],
-    ) -> MultiModalDataDict:
-        target_width, target_height = self.info.get_image_size_with_most_features()
-        num_images = mm_counts.get("image", 0)
-
-        image_overrides = mm_options.get("image")
-
-        return {
-            "image": self._get_dummy_images(
-                width=target_width,
-                height=target_height,
-                num_images=num_images,
-                overrides=image_overrides,
-            )
-        }
-
-
 @MULTIMODAL_REGISTRY.register_processor(
     BaseInternVLMultiModalProcessor,
     info=SkyworkR1VProcessingInfo,

vllm/model_executor/models/utils.py

@@ -27,7 +27,6 @@ from vllm.multimodal import NestedTensors
 from vllm.sequence import IntermediateTensors
 from vllm.utils.math_utils import cdiv
 from vllm.utils.torch_utils import (
-    async_tensor_h2d,
     direct_register_custom_op,
 )
 
@@ -673,17 +672,6 @@ def _merge_multimodal_embeddings(
     return inputs_embeds
 
 
-def isin_list(
-    elements: torch.Tensor,
-    test_elements_list: list[int],
-) -> torch.Tensor:
-    test_elements = async_tensor_h2d(
-        test_elements_list, dtype=torch.int64, device=elements.device
-    )
-
-    return torch.isin(elements, test_elements)
-
-
 class StageMissingLayer(nn.Module):
     def __init__(self, stage_name: str, module: nn.Module | None = None) -> None:
         super().__init__()

vllm/model_executor/models/vision.py

@@ -580,40 +580,3 @@ def run_dp_sharded_mrope_vision_model(
         "Found unassigned embeddings"
     )
     return out_embeddings
-
-
-def get_llm_pos_ids_for_vision(
-    start_idx: int,
-    vision_idx: int,
-    spatial_merge_size: int,
-    t_index: list[int],
-    grid_hs: torch.Tensor,
-    grid_ws: torch.Tensor,
-) -> torch.Tensor:
-    llm_pos_ids_list = []
-    llm_grid_h = grid_hs[vision_idx] // spatial_merge_size
-    llm_grid_w = grid_ws[vision_idx] // spatial_merge_size
-    h_index = (
-        torch.arange(llm_grid_h)
-        .view(1, -1, 1)
-        .expand(len(t_index), -1, llm_grid_w)
-        .flatten()
-    )
-    w_index = (
-        torch.arange(llm_grid_w)
-        .view(1, 1, -1)
-        .expand(len(t_index), llm_grid_h, -1)
-        .flatten()
-    )
-    t_index_tensor = (
-        torch.Tensor(t_index)
-        .to(llm_grid_h.device)
-        .view(-1, 1)
-        .expand(-1, llm_grid_h * llm_grid_w)
-        .long()
-        .flatten()
-    )
-    _llm_pos_ids = torch.stack([t_index_tensor, h_index, w_index])
-    llm_pos_ids_list.append(_llm_pos_ids + start_idx)
-    llm_pos_ids = torch.cat(llm_pos_ids_list, dim=1)
-    return llm_pos_ids

vllm/profiler/utils.py

@@ -12,15 +12,6 @@ from torch._C._profiler import _EventType, _ProfilerEvent, _TensorMetadata
 #
 
 
-def trim_string_front(string: str, width: int) -> str:
-    if len(string) > width:
-        offset = len(string) - width + 3
-        string = string[offset:]
-        if len(string) > 3:
-            string = "..." + string[3:]
-    return string
-
-
 def trim_string_back(string: str, width: int) -> str:
     if len(string) > width:
         offset = len(string) - width + 3

vllm/utils/cache.py

@@ -182,10 +182,6 @@ class LRUCache(cachetools.LRUCache[_K, _V]):
 
         self.popitem(remove_pinned=remove_pinned)
 
-    def _remove_old_if_needed(self) -> None:
-        while self.currsize > self.capacity:
-            self.remove_oldest()
-
     def popitem(self, remove_pinned: bool = False):
         """Remove and return the `(key, value)` pair least recently used."""
         if not remove_pinned:

vllm/utils/multi_stream_utils.py

@@ -8,10 +8,6 @@ from typing import Any
 import torch
 
 
-class AuxStreamType(Enum):
-    Attention = 1
-
-
 class EventType(Enum):
     Main = 0
     Attention = 1

vllm/v1/core/kv_cache_utils.py

@@ -427,26 +427,6 @@ class FreeKVCacheBlockQueue:
             curr_block = curr_block.next_free_block
 
 
-def need_extra_keys(request: Request) -> bool:
-    """Check whether the blocks allocated to this request need extra hash keys.
-
-    Args:
-        request (Request): The request.
-
-    Returns:
-        bool: Whether blocks allocated to this request need extra hash keys.
-    """
-
-    # Multimodal requests need to include the MM hash.
-    # LoRA requests need to include the LoRA name.
-    # Request with provided cache salt need to include the salt.
-    return (
-        bool(request.mm_features)
-        or (request.lora_request is not None)
-        or (request.cache_salt is not None)
-    )
-
-
 def _gen_mm_extra_hash_keys(
     request: Request, start_token_idx: int, end_token_idx: int, start_mm_idx: int
 ) -> tuple[list[Any], int]:
@@ -1053,20 +1033,6 @@ def _get_kv_cache_groups_uniform_type(
     return [KVCacheGroupSpec(list(spec.kv_cache_specs.keys()), spec)]
 
 
-def is_kv_cache_page_size_uniform(kv_cache_spec: dict[str, KVCacheSpec]) -> bool:
-    """
-    Whether all layers in the given KVCacheSpec have the same page size.
-    Args:
-        kv_cache_spec: The KVCacheSpec of each attention layer in the model
-
-    Returns:
-        True if all layers have the same page size, False otherwise.
-    """
-
-    page_sizes = {layer.page_size_bytes for layer in kv_cache_spec.values()}
-    return len(page_sizes) == 1
-
-
 def unify_kv_cache_spec_page_size(
     kv_cache_spec: dict[str, KVCacheSpec],
 ) -> dict[str, KVCacheSpec]: