[Refactor] Remove utils dead code

tests/models/multimodal/generation/test_memory_leak.py

@@ -15,7 +15,8 @@
 from vllm.distributed import cleanup_dist_env_and_memory
 from vllm.entrypoints.chat_utils import ChatCompletionMessageParam
 from vllm.platforms import current_platform
-from vllm.utils.mem_utils import KiB_bytes, MiB_bytes, format_mib
+from vllm.utils.mem_constants import KiB_bytes, MiB_bytes
+from vllm.utils.mem_utils import format_mib
 
 MODEL_NAME = "Qwen/Qwen3-VL-4B-Instruct"
 RANDOM_PREFIX_LEN = 100

vllm/compilation/passes/fx_utils.py

@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-import operator
 from collections.abc import Iterable, Iterator
 
 from torch import fx
@@ -29,15 +28,6 @@ def find_auto_fn(nodes: Iterable[fx.Node], op: OpOverload) -> fx.Node:
     return node
 
 
-# Returns the getitem node that extracts the idx-th element from node
-# (if it exists)
-def find_getitem_maybe(node: fx.Node, idx: int) -> fx.Node | None:
-    for user in node.users:
-        if is_func(user, operator.getitem) and user.args[1] == idx:
-            return user
-    return None
-
-
 # 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(

vllm/distributed/kv_transfer/kv_connector/v1/lmcache_integration/utils.py

@@ -23,11 +23,6 @@
 _config_lock = threading.Lock()
 
 
-def is_false(value: str) -> bool:
-    """Check if the given string value is equivalent to 'false'."""
-    return value.lower() in ("false", "0", "no", "n", "off")
-
-
 def lmcache_get_or_create_config() -> V1Config:
     """Get the LMCache configuration from the environment variable
     `LMCACHE_CONFIG_FILE`. If the environment variable is not set, this
@@ -97,75 +92,6 @@ def mla_enabled(model_config: "ModelConfig") -> bool:
     )
 
 
-def create_lmcache_metadata(
-    vllm_config=None, model_config=None, parallel_config=None, cache_config=None
-):
-    """
-    Create LMCacheEngineMetadata from vLLM configuration.
-
-    This function extracts common metadata creation logic that was duplicated
-    across multiple files.
-
-    Args:
-        vllm_config (VllmConfig): vLLM configuration object containing model,
-                                  parallel, and cache configs (alternative to
-                                  individual config parameters)
-        model_config (ModelConfig): Model configuration (alternative to
-                                    vllm_config)
-        parallel_config (ParallelConfig): Parallel configuration (alternative
-                                          to vllm_config)
-        cache_config (CacheConfig): Cache configuration (alternative to
-                                    vllm_config)
-    """
-    # Third Party
-    # First Party
-    from lmcache.config import LMCacheEngineMetadata
-
-    from vllm.utils.torch_utils import get_kv_cache_torch_dtype
-
-    config = lmcache_get_or_create_config()
-    # Support both vllm_config object and individual config parameters
-    if vllm_config is not None:
-        model_cfg = vllm_config.model_config
-        parallel_cfg = vllm_config.parallel_config
-        cache_cfg = vllm_config.cache_config
-    else:
-        if model_config is None or parallel_config is None or cache_config is None:
-            raise ValueError(
-                "Either vllm_config must be provided, or all of "
-                "model_config, parallel_config, and cache_config must be provided."
-            )
-        model_cfg = model_config
-        parallel_cfg = parallel_config
-        cache_cfg = cache_config
-
-    # Get KV cache dtype
-    kv_dtype = get_kv_cache_torch_dtype(cache_cfg.cache_dtype, model_cfg.dtype)
-
-    # Check if MLA is enabled
-    use_mla = mla_enabled(model_cfg)
-
-    # Construct KV shape (for memory pool)
-    num_layer = model_cfg.get_num_layers(parallel_cfg)
-    chunk_size = config.chunk_size
-    num_kv_head = model_cfg.get_num_kv_heads(parallel_cfg)
-    head_size = model_cfg.get_head_size()
-    kv_shape = (num_layer, 1 if use_mla else 2, chunk_size, num_kv_head, head_size)
-
-    # Create metadata
-    metadata = LMCacheEngineMetadata(
-        model_cfg.model,
-        parallel_cfg.world_size,
-        parallel_cfg.rank,
-        "vllm",
-        kv_dtype,
-        kv_shape,
-        use_mla,
-    )
-
-    return metadata, config
-
-
 def extract_mm_features(
     request: Union["Request", "NewRequestData"], modify: bool = False
 ) -> tuple[list[str], list["PlaceholderRange"]]:

vllm/model_executor/layers/utils.py

@@ -22,14 +22,6 @@
 
 logger = init_logger(__name__)
 
-MOE_LAYER_ROUTER_GATE_SUFFIXES = {
-    "gate",
-    "router",
-    "router_gate",
-    "shared_expert_gate",
-    "expert_gate",
-}
-
 
 def get_token_bin_counts_and_mask(
     tokens: torch.Tensor,

vllm/model_executor/model_loader/weight_utils.py

@@ -1209,21 +1209,6 @@ def _load_file(bin_file: str):
             del state
 
 
-def convert_pyslice_to_tensor(x: Any) -> torch.Tensor:
-    """convert PySafeSlice object from safetensors to torch.Tensor
-
-    PySafeSlice object supports indexing, which is done before loading the
-    actual tensor and can reduce the amount of memory being read into the
-    memory. However, it does not support more advanced functionalities
-    like `.view()` or `.t()`. Therefore, if we need to modify the loaded
-    tensor with these more complicated operators, we need to convert to
-    tensor first.
-    """
-    if not isinstance(x, torch.Tensor):
-        x = x[:]
-    return x
-
-
 def default_weight_loader(param: torch.Tensor, loaded_weight: torch.Tensor) -> None:
     """Default weight loader."""
     try:

vllm/utils/flashinfer.py

@@ -918,44 +918,6 @@ def mm_mxfp8_fake(
         return torch.empty(A.shape[0], B.shape[1], dtype=out_dtype, device=A.device)
 
 
-def flashinfer_mm_mxfp8(
-    a: torch.Tensor,
-    b: torch.Tensor,
-    block_scale_a: torch.Tensor,
-    block_scale_b: torch.Tensor,
-    out_dtype: torch.dtype,
-    backend: str = "cutlass",
-) -> torch.Tensor:
-    """MXFP8 MM helper - mirrors flashinfer_scaled_fp4_mm API.
-
-    Takes non-transposed weights and handles transpose internally.
-
-    CRITICAL: mm_mxfp8 CUTLASS kernel requires SWIZZLED 1D scales for optimal
-    performance and accuracy. Both input and weight scales should be in
-    swizzled format from FlashInfer's mxfp8_quantize(is_sf_swizzled_layout=True).
-    """
-    # a shape [M, K]
-    # b shape [K, N]
-    assert a.ndim == 2 and b.ndim == 2
-    assert a.shape[1] == b.shape[1]  # K dimension must match
-
-    if block_scale_b.ndim != 1:
-        raise ValueError(
-            "mm_mxfp8 expects 1D swizzled weight scales for CUTLASS; "
-            f"got shape={tuple(block_scale_b.shape)}"
-        )
-
-    # Output tensor [M, N]
-    return mm_mxfp8(
-        a,
-        b.t(),  # Transpose weight: [N, K] -> [K, N]
-        block_scale_a,
-        block_scale_b,
-        out_dtype,
-        backend=backend,
-    )
-
-
 def flashinfer_scaled_fp4_mm(
     a: torch.Tensor,
     b: torch.Tensor,

vllm/utils/hpc.py

@@ -76,27 +76,6 @@ def fuse_moe_impl(
     )
 
 
-# @torch.library.register_fake(
-#     "vllm::fuse_moe_impl",
-# )
-def fuse_moe_impl_fake(
-    x: torch.Tensor,
-    gate_up_weight: torch.Tensor,
-    down_weight: torch.Tensor,
-    gate_up_scale: torch.Tensor,
-    down_scale: torch.Tensor,
-    act_and_mul_scale: torch.Tensor,
-    topk_ids: torch.Tensor,
-    topk_scale: torch.Tensor,
-    rank_ep: int,
-    num_expert_total: int,
-    use_bf16_mul: bool = True,
-    shared_output: torch.Tensor = None,
-    output: torch.Tensor = None,
-) -> torch.Tensor:
-    return torch.empty_like(x)
-
-
 def hpc_fuse_moe(
     x: torch.Tensor,
     gate_up_weight: torch.Tensor,
@@ -166,26 +145,6 @@ def fuse_moe_blockwise_impl(
     )
 
 
-# @torch.library.register_fake(
-#     "vllm::fuse_moe_blockwise_impl",
-# )
-def fuse_moe_blockwise_impl_fake(
-    x: torch.Tensor,
-    x_scale: torch.Tensor,
-    gate_up_weight: torch.Tensor,
-    gate_up_weight_scale: torch.Tensor,
-    down_weight: torch.Tensor,
-    down_weight_scale: torch.Tensor,
-    topk_ids: torch.Tensor,
-    topk_scale: torch.Tensor,
-    rank_ep: int,
-    num_expert_total: int,
-    shared_output: torch.Tensor = None,
-    output: torch.Tensor = None,
-) -> torch.Tensor:
-    return torch.empty_like(x)
-
-
 def hpc_fuse_moe_blockwise(
     x: torch.Tensor,
     x_scale: torch.Tensor,

vllm/utils/mem_utils.py

@@ -43,11 +43,6 @@ def get_max_shared_memory_bytes(gpu: int = 0) -> int:
     return int(max_shared_mem)
 
 
-def get_cpu_memory() -> int:
-    """Returns the total CPU memory of the node in bytes."""
-    return psutil.virtual_memory().total
-
-
 _UMA_PRESSURE_THRESHOLD = 0.8
 _UMA_MIN_RELEASE_BYTES = 512 * MiB_bytes
 

vllm/utils/torch_utils.py

@@ -497,15 +497,6 @@ def get_kv_cache_quant_algo_string(quant_cfg: dict[str, Any]) -> str | None:
     return None
 
 
-def get_kv_cache_quant_algo_dtype(quant_cfg: dict[str, Any]) -> torch.dtype | None:
-    """Get the KV cache quantization algorithm dtype from the quantization config."""
-    kv_algo_str = get_kv_cache_quant_algo_string(quant_cfg)
-    if kv_algo_str is not None and kv_algo_str != "auto":
-        # Only convert if we have a valid dtype string (not "auto" fallback)
-        return STR_DTYPE_TO_TORCH_DTYPE[kv_algo_str]
-    return None
-
-
 def resolve_kv_cache_dtype_string(
     kv_cache_dtype: str, model_config: ModelConfig
 ) -> str:

vllm/v1/attention/backends/utils.py

@@ -3,7 +3,7 @@
 import math
 from collections import defaultdict
 from collections.abc import Callable, Iterable, Sequence
-from dataclasses import dataclass, field, fields, make_dataclass
+from dataclasses import dataclass, field, fields
 from typing import (
     TYPE_CHECKING,
     Any,
@@ -975,19 +975,6 @@ def reshape_attn_output_for_spec_decode(attn_output: torch.Tensor) -> torch.Tens
     return attn_output.view(total_tokens, attn_output.shape[2], attn_output.shape[3])
 
 
-def subclass_attention_metadata(
-    name_prefix: str,
-    metadata_cls: Any,
-    fields: list[tuple[str, Any, Any]],
-) -> Any:
-    """
-    Return a new subclass of `metadata_cls` with additional fields
-    """
-    name: str = name_prefix + metadata_cls.__name__  # type: ignore
-    Wrapped = make_dataclass(name, fields, bases=(metadata_cls,))
-    return Wrapped
-
-
 @runtime_checkable
 class KVSharingFastPrefillMetadata(Protocol):
     logits_indices_padded: torch.Tensor | None = None

vllm/v1/core/kv_cache_coordinator.py

@@ -85,8 +85,6 @@ def __init__(
         num_prefill_lookahead: int = 0,
     ):
         self.kv_cache_config = kv_cache_config
-        self.max_model_len = max_model_len
-        self.enable_caching = enable_caching
         # The scheduling granularity (LCM of all group block sizes), must be a multiple
         # of the hash_block_size and the block size of each group.
         assert scheduler_block_size % hash_block_size == 0 and all(

vllm/v1/core/single_type_kv_cache_manager.py

@@ -2013,8 +2013,6 @@ def __init__(
         )
         sink_len = kv_cache_spec.sink_len
         assert sink_len is not None and sink_len > 0 and sink_len % self.block_size == 0
-        num_sink_block = sink_len // self.block_size
-        self.sink_blocks = self.block_pool.free_block_queue.popleft_n(num_sink_block)
 
 
 def get_manager_for_kv_cache_spec(

vllm/v1/engine/core.py

@@ -85,7 +85,7 @@
     EngineCoreSentinel,
     fault_tolerant_wrapper,
 )
-from vllm.v1.kv_cache_interface import KVCacheConfig, get_kv_cache_spec_kind
+from vllm.v1.kv_cache_interface import KVCacheConfig
 from vllm.v1.metrics.stats import SchedulerIterationDetails, SchedulerStats
 from vllm.v1.outputs import ModelRunnerOutput
 from vllm.v1.request import Request, RequestStatus
@@ -430,25 +430,6 @@ def log_field(name: str, field: str) -> str:
             supported_pooling_tasks,
         )
 
-    def get_kv_cache_group_metadata(self) -> list[dict[str, int | str | None]]:
-        """Return msgspec-serializable metadata for scheduler KV cache groups."""
-        kv_cache_config = getattr(self.scheduler, "kv_cache_config", None)
-        if kv_cache_config is None:
-            return []
-
-        metadata: list[dict[str, int | str | None]] = []
-        for group_idx, group in enumerate(kv_cache_config.kv_cache_groups):
-            spec = group.kv_cache_spec
-            metadata.append(
-                {
-                    "group_idx": group_idx,
-                    "kind": get_kv_cache_spec_kind(spec).value,
-                    "block_size": spec.block_size,
-                    "sliding_window": getattr(spec, "sliding_window", None),
-                }
-            )
-        return metadata
-
     def add_request(self, request: Request, request_wave: int = 0):
         """Add request to the scheduler.