gpu_model_runner.py 127 KB
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import gc
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import time
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from contextlib import contextmanager
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from typing import TYPE_CHECKING, Any, Optional, Union, cast
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import numpy as np
import torch
import torch.distributed
import torch.nn as nn
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from tqdm import tqdm
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import vllm.envs as envs
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from vllm.attention import AttentionType, get_attn_backend
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from vllm.attention.backends.abstract import AttentionBackend
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from vllm.attention.layer import Attention
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from vllm.compilation.counter import compilation_counter
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from vllm.config import (CompilationLevel, VllmConfig,
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                         get_layers_from_vllm_config, update_config)
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from vllm.distributed.eplb.eplb_state import EplbState
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from vllm.distributed.kv_transfer import (get_kv_transfer_group,
                                          has_kv_transfer_group)
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from vllm.distributed.parallel_state import (
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    get_pp_group, get_tp_group, graph_capture, is_global_first_rank,
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    prepare_communication_buffer_for_model)
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from vllm.forward_context import DPMetadata, set_forward_context
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from vllm.logger import init_logger
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from vllm.model_executor.layers.mamba.mamba_mixer2 import MambaBase
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from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
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from vllm.model_executor.model_loader import TensorizerLoader, get_model_loader
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from vllm.model_executor.models.interfaces import is_mixture_of_experts
from vllm.model_executor.models.interfaces_base import (VllmModelForPooling,
                                                        is_pooling_model)
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from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import MultiModalKwargs, PlaceholderRange
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from vllm.multimodal.utils import group_mm_inputs_by_modality
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from vllm.pooling_params import PoolingParams, PoolingTask
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from vllm.sampling_params import SamplingType
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from vllm.sequence import IntermediateTensors, PoolerOutput
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from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, DeviceMemoryProfiler,
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                        GiB_bytes, LazyLoader, check_use_alibi, get_dtype_size,
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                        is_pin_memory_available, round_up)
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from vllm.v1.attention.backends.mamba_attn import Mamba2AttentionBackend
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from vllm.v1.attention.backends.utils import (
    AttentionMetadataBuilder, CommonAttentionMetadata,
    make_local_attention_virtual_batches)
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from vllm.v1.core.encoder_cache_manager import compute_encoder_budget
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from vllm.v1.kv_cache_interface import (AttentionSpec,
                                        ChunkedLocalAttentionSpec,
                                        FullAttentionSpec, KVCacheConfig,
                                        KVCacheSpec, MambaSpec,
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                                        SlidingWindowSpec)
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from vllm.v1.outputs import (EMPTY_MODEL_RUNNER_OUTPUT, LogprobsTensors,
                             ModelRunnerOutput)
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from vllm.v1.pool.metadata import PoolingMetadata
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from vllm.v1.sample.metadata import SamplingMetadata
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from vllm.v1.sample.rejection_sampler import RejectionSampler
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from vllm.v1.sample.sampler import Sampler
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from vllm.v1.spec_decode.eagle import EagleProposer
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from vllm.v1.spec_decode.medusa import MedusaProposer
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from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
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from vllm.v1.spec_decode.ngram_proposer import NgramProposer
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from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
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from vllm.v1.worker.kv_connector_model_runner_mixin import (
    KVConnectorModelRunnerMixin)
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from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
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from ..sample.logits_processor import LogitsProcessorManager
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from .utils import (bind_kv_cache, gather_mm_placeholders,
                    initialize_kv_cache_for_kv_sharing,
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                    sanity_check_mm_encoder_outputs, scatter_mm_placeholders)
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if TYPE_CHECKING:
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    import xgrammar as xgr
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    import xgrammar.kernels.apply_token_bitmask_inplace_torch_compile as xgr_torch_compile  # noqa: E501
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    from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
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    from vllm.v1.core.sched.output import SchedulerOutput
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else:
    xgr = LazyLoader("xgr", globals(), "xgrammar")
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    xgr_torch_compile = LazyLoader(
        "xgr_torch_compile", globals(),
        "xgrammar.kernels.apply_token_bitmask_inplace_torch_compile")
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logger = init_logger(__name__)


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class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
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    def __init__(
        self,
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        vllm_config: VllmConfig,
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        device: torch.device,
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    ):
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        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        self.cache_config = vllm_config.cache_config
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        self.compilation_config = vllm_config.compilation_config
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        self.lora_config = vllm_config.lora_config
        self.load_config = vllm_config.load_config
        self.parallel_config = vllm_config.parallel_config
        self.scheduler_config = vllm_config.scheduler_config
        self.speculative_config = vllm_config.speculative_config
        self.observability_config = vllm_config.observability_config
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        from vllm.model_executor.models.utils import set_cpu_offload_max_bytes
        set_cpu_offload_max_bytes(
            int(self.cache_config.cpu_offload_gb * 1024**3))

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        model_config = self.model_config
        cache_config = self.cache_config
        scheduler_config = self.scheduler_config
        parallel_config = self.parallel_config
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        self.device = device
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        self.pin_memory = is_pin_memory_available()
        self.dtype = self.model_config.dtype
        if cache_config.cache_dtype == "auto":
            self.kv_cache_dtype = self.dtype
        else:
            self.kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[
                cache_config.cache_dtype]

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        self.is_multimodal_model = model_config.is_multimodal_model
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        self.is_pooling_model = model_config.pooler_config is not None
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        self.model_supports_multimodal_raw_input = (
            model_config.model_supports_multimodal_raw_input)
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        self.max_model_len = model_config.max_model_len
        self.max_num_tokens = scheduler_config.max_num_batched_tokens
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        self.max_num_reqs = scheduler_config.max_num_seqs
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        # Model-related.
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        self.num_query_heads = model_config.get_num_attention_heads(
            parallel_config)
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        self.hidden_size = model_config.get_hidden_size()
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        self.attention_chunk_size = model_config.attention_chunk_size
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        self.cascade_attn_enabled = not self.model_config.disable_cascade_attn
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        # Multi-modal data support
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        self.mm_registry = MULTIMODAL_REGISTRY
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        self.uses_mrope = model_config.uses_mrope
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        encoder_compute_budget, encoder_cache_size = compute_encoder_budget(
            model_config=model_config,
            scheduler_config=scheduler_config,
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            mm_registry=self.mm_registry,
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        )
        self.max_num_encoder_input_tokens = encoder_compute_budget
        self.encoder_cache_size = encoder_cache_size
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        # Sampler
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        self.sampler = Sampler(logprobs_mode=self.model_config.logprobs_mode)
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        self.eplb_state: Optional[EplbState] = None
        """
        State of the expert parallelism load balancer.

        Will be lazily initialized when the model is loaded.
        """

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        # Lazy initializations
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        # self.model: nn.Module  # Set after load_model
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        # Initialize in initialize_kv_cache
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        self.kv_caches: list[torch.Tensor] = []
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        self.attn_metadata_builders: list[AttentionMetadataBuilder] = []
        self.attn_backends: list[type[AttentionBackend]] = []
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        # self.kv_cache_config: KVCacheConfig

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        # req_id -> (input_id -> encoder_output)
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        self.encoder_cache: dict[str, dict[int, torch.Tensor]] = {}
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        self.use_aux_hidden_state_outputs = False
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        # Set up speculative decoding.
        # NOTE(Jiayi): currently we put the entire draft model on
        # the last PP rank. This is not ideal if there are many
        # layers in the draft model.
        if self.speculative_config and get_pp_group().is_last_rank:
            if self.speculative_config.method == "ngram":
                self.drafter = NgramProposer(self.vllm_config)
            elif self.speculative_config.use_eagle():
                self.drafter = EagleProposer(self.vllm_config, self.device,
                                             self)  # type: ignore
                if self.speculative_config.method == "eagle3":
                    self.use_aux_hidden_state_outputs = True
            elif self.speculative_config.method == "medusa":
                self.drafter = MedusaProposer(
                    vllm_config=self.vllm_config,
                    device=self.device)  # type: ignore
            else:
                raise ValueError("Unknown speculative decoding method: "
                                 f"{self.speculative_config.method}")
            self.rejection_sampler = RejectionSampler()
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        # Request states.
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        self.requests: dict[str, CachedRequestState] = {}
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        # Input Batch
        # NOTE(Chen): Ideally, we should initialize the input batch inside
        # `initialize_kv_cache` based on the kv cache config. However, as in
        # https://github.com/vllm-project/vllm/pull/18298, due to some unknown
        # reasons, we have to initialize the input batch before `load_model`,
        # quantization + weight offloading will fail otherwise. As a temporary
        # solution, we initialize the input batch here, and re-initialize it
        # in `initialize_kv_cache` if the block_sizes here is different from
        # the block_sizes in the kv cache config.
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        self.input_batch = InputBatch(
            max_num_reqs=self.max_num_reqs,
            max_model_len=self.max_model_len,
            max_num_batched_tokens=self.max_num_tokens,
            device=self.device,
            pin_memory=self.pin_memory,
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            vocab_size=self.model_config.get_vocab_size(),
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            block_sizes=[self.cache_config.block_size],
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            is_spec_decode=bool(self.vllm_config.speculative_config),
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        )
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        self.use_cuda_graph = (
            self.vllm_config.compilation_config.level
            == CompilationLevel.PIECEWISE
            and self.vllm_config.compilation_config.use_cudagraph
            and not self.model_config.enforce_eager)
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        # TODO(woosuk): Provide an option to tune the max cudagraph batch size.
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        # The convention is different.
        # self.cudagraph_batch_sizes sorts in ascending order.
        # The batch sizes in the config are in descending order.
        self.cudagraph_batch_sizes = list(
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            reversed(self.compilation_config.cudagraph_capture_sizes))

        self.full_cuda_graph = self.compilation_config.full_cuda_graph
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        # Cache the device properties.
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        self._init_device_properties()
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        # Persistent buffers for CUDA graphs.
        self.input_ids = torch.zeros(self.max_num_tokens,
                                     dtype=torch.int32,
                                     device=self.device)
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        self.positions = torch.zeros(self.max_num_tokens,
                                     dtype=torch.int64,
                                     device=self.device)
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        self.query_start_loc = torch.zeros(self.max_num_reqs + 1,
                                           dtype=torch.int32,
                                           device=self.device)
        self.seq_lens = torch.zeros(self.max_num_reqs,
                                    dtype=torch.int32,
                                    device=self.device)
        self.slot_mapping = torch.zeros(self.max_num_tokens,
                                        dtype=torch.int64,
                                        device=self.device)

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        # None in the first PP rank. The rest are set after load_model.
        self.intermediate_tensors: Optional[IntermediateTensors] = None
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        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
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        if self.uses_mrope:
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            # NOTE: `mrope_positions` is implemented with one additional dummy
            # position on purpose to make it non-contiguous so that it can work
            # with torch compile.
            # See detailed explanation in https://github.com/vllm-project/vllm/pull/12128#discussion_r1926431923
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            # NOTE: When M-RoPE is enabled, position ids are 3D regardless of
            # the modality of inputs. For text-only inputs, each dimension has
            # identical position IDs, making M-RoPE functionally equivalent to
            # 1D-RoPE.
            # See page 5 of https://arxiv.org/abs/2409.12191
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            self.mrope_positions = torch.zeros((3, self.max_num_tokens + 1),
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                                               dtype=torch.int64,
                                               device=self.device)
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            self.mrope_positions_cpu = torch.zeros(
                (3, self.max_num_tokens + 1),
                dtype=torch.int64,
                device="cpu",
                pin_memory=self.pin_memory)
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            self.mrope_positions_np = self.mrope_positions_cpu.numpy()
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        # Only relevant for models using ALiBi (e.g, MPT)
        self.use_alibi = check_use_alibi(model_config)

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        self.inputs_embeds = torch.zeros(
            (self.max_num_tokens, self.hidden_size),
            dtype=self.dtype,
            device=self.device)
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        # OPTIMIZATION: Cache the tensors rather than creating them every step.
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        # Keep in int64 to avoid overflow with long context
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        self.arange_np = np.arange(max(self.max_num_reqs + 1,
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                                       self.max_model_len,
                                       self.max_num_tokens),
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                                   dtype=np.int64)
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        # NOTE(woosuk): These tensors are "stateless", i.e., they are literally
        # a faster version of creating a new tensor every time. Thus, we should
        # not make any assumptions about the values in these tensors.
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        self.input_ids_cpu = torch.zeros(self.max_num_tokens,
                                         dtype=torch.int32,
                                         device="cpu",
                                         pin_memory=self.pin_memory)
        self.positions_cpu = torch.zeros(self.max_num_tokens,
                                         dtype=torch.int64,
                                         device="cpu",
                                         pin_memory=self.pin_memory)
        self.positions_np = self.positions_cpu.numpy()
        self.query_start_loc_cpu = torch.zeros(self.max_num_reqs + 1,
                                               dtype=torch.int32,
                                               device="cpu",
                                               pin_memory=self.pin_memory)
        self.query_start_loc_np = self.query_start_loc_cpu.numpy()
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        self.seq_lens_cpu = torch.zeros(self.max_num_reqs,
                                        dtype=torch.int32,
                                        device="cpu",
                                        pin_memory=self.pin_memory)
        self.seq_lens_np = self.seq_lens_cpu.numpy()
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        # Layer pairings for cross-layer KV sharing.
        # If an Attention layer `layer_name` is in the keys of this dict, it
        # means this layer will perform attention using the keys and values
        # from the KV cache of `shared_kv_cache_layers[layer_name]`.
        self.shared_kv_cache_layers: dict[str, str] = {}

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    def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
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        """
        Update the order of requests in the batch based on the attention
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        backend's needs. For example, some attention backends (namely MLA) may
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        want to separate requests based on if the attention computation will be
        compute-bound or memory-bound.

        Args:
            scheduler_output: The scheduler output.
        """
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        # Attention free models have zero kv_cache_goups, however models
        # like Mamba are also attention free but use the kv_cache for
        # keeping its internal state. This is why we check the number
        # of kv_cache groups instead of solely checking
        # for self.model_config.is_attention_free.
        if len(self.kv_cache_config.kv_cache_groups) == 0:
            return

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        self.attn_metadata_builders[0].reorder_batch(self.input_batch,
                                                     scheduler_output)
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        # For models with multiple KV cache groups, the groups should agree on
        # the same order of requests. We ensure this by only allowing the first
        # group to reorder the batch and asserting that all other groups do not
        # reorder the batch.
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        # TODO(tdoublep): make this more flexible so that any group can
        # re-order the batch (not only the first).
        # TODO(tdoublep): verify this during engine init instead of at runtime
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        for i in range(1, len(self.kv_cache_config.kv_cache_groups)):
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            batch_reordered = self.attn_metadata_builders[i].reorder_batch(
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                self.input_batch, scheduler_output)
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            assert not batch_reordered
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    # Note: used for model runner override.
    def _init_device_properties(self) -> None:
        """Initialize attributes from torch.cuda.get_device_properties
        """
        self.device_properties = torch.cuda.get_device_properties(self.device)
        self.num_sms = self.device_properties.multi_processor_count

    # Note: used for model runner override.
    def _sync_device(self) -> None:
        torch.cuda.synchronize()

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    def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
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        """Update the cached states and the persistent batch with the scheduler
        output.

        The updated states are used by the `_prepare_inputs` function to create
        the input GPU tensors for the model.

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        The SamplingMetadata is updated and copied to the GPU if there is a
        new/resumed/paused/finished request in the batch.
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        """
        # Remove finished requests from the cached states.
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        for req_id in scheduler_output.finished_req_ids:
            self.requests.pop(req_id, None)
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            self.encoder_cache.pop(req_id, None)
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        # Remove the finished requests from the persistent batch.
        # NOTE(woosuk): There could be an edge case where finished_req_ids and
        # scheduled_req_ids overlap. This happens when a request is aborted and
        # then resubmitted with the same ID. In this case, we treat them as two
        # distinct requests - clearing the cached states for the first request
        # and handling the second as a new request.
        for req_id in scheduler_output.finished_req_ids:
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            self.input_batch.remove_request(req_id)
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        # Free the cached encoder outputs.
        for req_id, input_id in scheduler_output.free_encoder_input_ids:
            encoder_outputs = self.encoder_cache.get(req_id)
            if encoder_outputs is not None:
                encoder_outputs.pop(input_id, None)
                if not encoder_outputs:
                    self.encoder_cache.pop(req_id, None)
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        # Remove the unscheduled requests from the persistent batch.
        # NOTE(woosuk): The unscheduled requests are either preempted requests
        # or running requests that are not scheduled in this step. We remove
        # them from the persistent batch but keep their cached states since
        # they will be scheduled again sometime in the future.
        scheduled_req_ids = scheduler_output.num_scheduled_tokens.keys()
        cached_req_ids = self.input_batch.req_id_to_index.keys()
        unscheduled_req_ids = cached_req_ids - scheduled_req_ids
        # NOTE(woosuk): The persistent batch optimization assumes that
        # consecutive batches contain mostly the same requests. If batches
        # have low request overlap (e.g., alternating between two distinct
        # sets of requests), this optimization becomes very inefficient.
        for req_id in unscheduled_req_ids:
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            self.input_batch.remove_request(req_id)
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        req_ids_to_add: list[str] = []
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        # Add new requests to the cached states.
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        for new_req_data in scheduler_output.scheduled_new_reqs:
            req_id = new_req_data.req_id
            sampling_params = new_req_data.sampling_params
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            pooling_params = new_req_data.pooling_params
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            if sampling_params and \
                sampling_params.sampling_type == SamplingType.RANDOM_SEED:
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                generator = torch.Generator(device=self.device)
                generator.manual_seed(sampling_params.seed)
            else:
                generator = None

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            if pooling_params:
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                assert (task := pooling_params.task) is not None, (
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                    "You did not set `task` in the API")

                model = cast(VllmModelForPooling, self.model)
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                to_update = model.pooler.get_pooling_updates(task)
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                to_update.apply(pooling_params)

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            self.requests[req_id] = CachedRequestState(
                req_id=req_id,
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                prompt_token_ids=new_req_data.prompt_token_ids,
                mm_inputs=new_req_data.mm_inputs,
                mm_positions=new_req_data.mm_positions,
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                sampling_params=sampling_params,
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                pooling_params=pooling_params,
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                generator=generator,
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                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
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                output_token_ids=[],
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                lora_request=new_req_data.lora_request,
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            )
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            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
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            if self.uses_mrope:
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                image_grid_thw = []
                video_grid_thw = []
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                second_per_grid_ts = []
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                audio_feature_lengths = []
                use_audio_in_video = False
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                for mm_input in self.requests[req_id].mm_inputs:
                    if mm_input.get("image_grid_thw") is not None:
                        image_grid_thw.extend(
                            mm_input["image_grid_thw"].tolist())
                    if mm_input.get("video_grid_thw") is not None:
                        video_grid_thw.extend(
                            mm_input["video_grid_thw"].tolist())
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                    if mm_input.get("second_per_grid_ts") is not None:
                        second_per_grid_ts.extend(
                            mm_input["second_per_grid_ts"])
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                    if mm_input.get("audio_feature_lengths") is not None:
                        audio_feature_lengths.extend(
                            mm_input["audio_feature_lengths"])
                    if mm_input.get("use_audio_in_video") is True:
                        use_audio_in_video = True
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                hf_config = self.model_config.hf_config

                self.requests[req_id].mrope_positions, \
                    self.requests[req_id].mrope_position_delta = \
                    MRotaryEmbedding.get_input_positions_tensor(
                        self.requests[req_id].prompt_token_ids,
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                        hf_config=hf_config,
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                        image_grid_thw=image_grid_thw,
                        video_grid_thw=video_grid_thw,
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                        second_per_grid_ts=second_per_grid_ts,
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                        audio_feature_lengths=audio_feature_lengths,
                        use_audio_in_video=use_audio_in_video,
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                    )

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            req_ids_to_add.append(req_id)

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        # Update the states of the running/resumed requests.
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        is_last_rank = get_pp_group().is_last_rank
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        req_data = scheduler_output.scheduled_cached_reqs
        for i, req_id in enumerate(req_data.req_ids):
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            req_state = self.requests[req_id]
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            num_computed_tokens = req_data.num_computed_tokens[i]
            new_block_ids = req_data.new_block_ids[i]
            resumed_from_preemption = req_data.resumed_from_preemption[i]
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            # Update the cached states.
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            req_state.num_computed_tokens = num_computed_tokens
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            if not is_last_rank:
                # When using PP, the scheduler sends the sampled tokens back,
                # because there's no direct communication between the first-
                # stage worker and the last-stage worker.
                new_token_ids = req_data.new_token_ids[i]
                # Add the sampled token(s) from the previous step (if any).
                # This doesn't include "unverified" tokens like spec tokens.
                num_new_tokens = (num_computed_tokens + len(new_token_ids) -
                                  req_state.num_tokens)
                if num_new_tokens == 1:
                    # Avoid slicing list in most common case.
                    req_state.output_token_ids.append(new_token_ids[-1])
                elif num_new_tokens > 0:
                    req_state.output_token_ids.extend(
                        new_token_ids[-num_new_tokens:])

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            # Update the block IDs.
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            if not resumed_from_preemption:
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                # Append the new blocks to the existing block IDs.
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                for block_ids, new_ids in zip(req_state.block_ids,
                                              new_block_ids):
                    block_ids.extend(new_ids)
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            else:
                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
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                req_state.block_ids = new_block_ids
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            req_index = self.input_batch.req_id_to_index.get(req_id)
            if req_index is None:
                # The request is not in the persistent batch.
                # The request was either preempted and resumed later, or was not
                # scheduled in the previous step and needs to be added again.
                req_ids_to_add.append(req_id)
                continue

            # Update the persistent batch.
            self.input_batch.num_computed_tokens_cpu[req_index] = (
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                num_computed_tokens)
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            self.input_batch.block_table.append_row(new_block_ids, req_index)
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            # For the last rank, we don't need to update the token_ids_cpu
            # because the sampled tokens are already cached.
            if not is_last_rank:
                # Add new_token_ids to token_ids_cpu.
                start_token_index = num_computed_tokens
                end_token_index = num_computed_tokens + len(new_token_ids)
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                self.input_batch.token_ids_cpu[
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                    req_index,
                    start_token_index:end_token_index] = new_token_ids
                self.input_batch.num_tokens_no_spec[
                    req_index] = end_token_index
                self.input_batch.num_tokens[req_index] = end_token_index
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            # Add spec_token_ids to token_ids_cpu.
            spec_token_ids = (
                scheduler_output.scheduled_spec_decode_tokens.get(req_id, ()))
            if spec_token_ids:
                num_spec_tokens = len(spec_token_ids)
                start_index = self.input_batch.num_tokens_no_spec[req_index]
                end_token_index = start_index + num_spec_tokens
                self.input_batch.token_ids_cpu[
                    req_index, start_index:end_token_index] = spec_token_ids
                # NOTE(woosuk): `num_tokens` here may include spec tokens.
                self.input_batch.num_tokens[req_index] += num_spec_tokens

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        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
        for req_id in req_ids_to_add:
            req_state = self.requests[req_id]
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            self.input_batch.add_request(req_state)
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        # Condense the batched states if there are gaps left by removed requests
        self.input_batch.condense()
        # Allow attention backend to reorder the batch, potentially
        self._may_reorder_batch(scheduler_output)
        # Refresh batch metadata with any pending updates.
        self.input_batch.refresh_metadata()
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    def _init_model_kwargs_for_multimodal_model(
        self,
        scheduler_output: Optional["SchedulerOutput"] = None,
        num_reqs: int = -1,
    ) -> dict[str, Any]:

        model_kwargs: dict[str, Any] = {}
        if self.model_supports_multimodal_raw_input:
            # This model requires the raw multimodal data in input.
            if scheduler_output:
                multi_modal_kwargs_list = []
                for req in scheduler_output.scheduled_new_reqs:
                    req_mm_inputs = req.mm_inputs
                    if not isinstance(req_mm_inputs, list):
                        req_mm_inputs = list(req_mm_inputs)
                    multi_modal_kwargs_list.extend(req_mm_inputs)
                multi_modal_kwargs = MultiModalKwargs.batch(
                    multi_modal_kwargs_list)
            else:
                # The only case where SchedulerOutput is None is for
                # a dummy run let's get some dummy data.
                dummy_data = [
                    self.mm_registry.get_decoder_dummy_data(
                        model_config=self.model_config,
                        seq_len=1).multi_modal_data for i in range(num_reqs)
                ]
                multi_modal_kwargs = MultiModalKwargs.batch(dummy_data)

            model_kwargs.update(multi_modal_kwargs)

        return model_kwargs

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    def _get_cumsum_and_arange(
        self,
        num_tokens: np.ndarray,
        cumsum_dtype: Optional[np.dtype] = None,
    ) -> tuple[np.ndarray, np.ndarray]:
        """Get the cumulative sum and batched arange of the given array.
        # E.g., [2, 5, 3] -> ([2, 7, 10], [0, 1, 0, 1, 2, 3, 4, 0, 1, 2])
        # Equivalent to but faster than:
        # np.concatenate([np.arange(n) for n in num_tokens])
        """
        # Step 1. [2, 5, 3] -> [2, 7, 10]
        cu_num_tokens = np.cumsum(num_tokens, dtype=cumsum_dtype)
        total_num_tokens = cu_num_tokens[-1]
        # Step 2. [2, 7, 10] -> [0, 0, 2, 2, 2, 2, 2, 7, 7, 7]
        cumsums_offsets = np.repeat(cu_num_tokens - num_tokens, num_tokens)
        # Step 3. [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
        arange = self.arange_np[:total_num_tokens] - cumsums_offsets

        return cu_num_tokens, arange

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    def _prepare_inputs(
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        self,
        scheduler_output: "SchedulerOutput",
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    ) -> tuple[dict[str,
                    Any], bool, torch.Tensor, Optional[SpecDecodeMetadata],
               np.ndarray, Optional[CommonAttentionMetadata]]:
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        """
        :return: tuple[
            attn_metadata: layer-to-attention_metadata mapping,
            attention_cuda_graphs: whether attention can run in cudagraph
            logits_indices, spec_decode_metadata
        ]
        """
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        total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
        assert total_num_scheduled_tokens > 0
        num_reqs = self.input_batch.num_reqs
        assert num_reqs > 0

        # OPTIMIZATION: Start copying the block table first.
        # This way, we can overlap the copy with the following CPU operations.
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        self.input_batch.block_table.commit_block_table(num_reqs)
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        # Get the number of scheduled tokens for each request.
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        req_ids = self.input_batch.req_ids
        tokens = [scheduler_output.num_scheduled_tokens[i] for i in req_ids]
        num_scheduled_tokens = np.array(tokens, dtype=np.int32)
        max_num_scheduled_tokens = max(tokens)
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        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
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        req_indices = np.repeat(self.arange_np[:num_reqs],
                                num_scheduled_tokens)
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        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
        # arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
        cu_num_tokens, arange = self._get_cumsum_and_arange(
            num_scheduled_tokens)
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        # Get positions.
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        positions_np = self.positions_np[:total_num_scheduled_tokens]
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        np.add(self.input_batch.num_computed_tokens_cpu[req_indices],
               arange,
               out=positions_np)

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        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
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        if self.uses_mrope:
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            self._calc_mrope_positions(scheduler_output)

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        # Get token indices.
        # E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
        # -> [0, 1, M, M + 1, M + 2, M + 3, M + 4, 2 * M, 2 * M + 1, 2 * M + 2]
        # where M is the max_model_len.
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        token_indices = (positions_np +
                         req_indices * self.input_batch.token_ids_cpu.shape[1])
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        # NOTE(woosuk): We use torch.index_select instead of np.take here
        # because torch.index_select is much faster than np.take for large
        # tensors.
        torch.index_select(self.input_batch.token_ids_cpu_tensor.flatten(),
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                           0,
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                           torch.from_numpy(token_indices),
                           out=self.input_ids_cpu[:total_num_scheduled_tokens])
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        self.input_batch.block_table.compute_slot_mapping(
            req_indices, positions_np)
        self.input_batch.block_table.commit_slot_mapping(
            total_num_scheduled_tokens)
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        # Prepare the attention metadata.
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        self.query_start_loc_np[0] = 0
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        self.query_start_loc_np[1:num_reqs + 1] = cu_num_tokens
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        self.seq_lens_np[:num_reqs] = (
            self.input_batch.num_computed_tokens_cpu[:num_reqs] +
            num_scheduled_tokens)
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        # Copy the tensors to the GPU.
        self.input_ids[:total_num_scheduled_tokens].copy_(
            self.input_ids_cpu[:total_num_scheduled_tokens], non_blocking=True)
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        if self.uses_mrope:
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            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
            self.mrope_positions[:, :total_num_scheduled_tokens].copy_(
                self.mrope_positions_cpu[:, :total_num_scheduled_tokens],
                non_blocking=True)
        else:
            # Common case (1D positions)
            self.positions[:total_num_scheduled_tokens].copy_(
                self.positions_cpu[:total_num_scheduled_tokens],
                non_blocking=True)
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        self.query_start_loc[:num_reqs + 1].copy_(
            self.query_start_loc_cpu[:num_reqs + 1], non_blocking=True)
        self.seq_lens[:num_reqs].copy_(self.seq_lens_cpu[:num_reqs],
                                       non_blocking=True)

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        # Fill unused with 0 for full cuda graph mode.
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        self.seq_lens[num_reqs:].fill_(0)
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        # Note: pad query_start_loc to be non-decreasing, as kernels
        # like FlashAttention requires that
        self.query_start_loc[num_reqs + 1:].fill_(
            self.query_start_loc_cpu[num_reqs].item())
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        query_start_loc = self.query_start_loc[:num_reqs + 1]
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        spec_decode_common_attn_metadata = None
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        attn_metadata: dict[str, Any] = {}
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        # Prepare the attention metadata for each KV cache group and make layers
        # in the same group share the same metadata.
        for kv_cache_group_id, kv_cache_group_spec in enumerate(
                self.kv_cache_config.kv_cache_groups):

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            blk_table = self.input_batch.block_table[kv_cache_group_id]
            blk_table_tensor = blk_table.get_device_tensor()[:num_reqs]
            slot_mapping = blk_table.slot_mapping[:total_num_scheduled_tokens]
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            # Fill unused with -1. Needed for reshape_and_cache in full cuda
            # graph mode.
            blk_table.slot_mapping[total_num_scheduled_tokens:].fill_(-1)

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            common_attn_metadata = CommonAttentionMetadata(
                query_start_loc=self.query_start_loc[:num_reqs + 1],
                query_start_loc_cpu=self.query_start_loc_cpu[:num_reqs + 1],
                seq_lens=self.seq_lens[:num_reqs],
                seq_lens_cpu=self.seq_lens_cpu[:num_reqs],
                num_computed_tokens_cpu=self.input_batch.
                num_computed_tokens_cpu_tensor[:num_reqs],
                num_reqs=num_reqs,
                num_actual_tokens=total_num_scheduled_tokens,
                max_query_len=max_num_scheduled_tokens,
                block_table_tensor=blk_table_tensor,
                slot_mapping=slot_mapping,
            )

            if self.speculative_config and \
                spec_decode_common_attn_metadata is None:
                spec_decode_common_attn_metadata = common_attn_metadata

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            if isinstance(kv_cache_group_spec.kv_cache_spec,
                          ChunkedLocalAttentionSpec):
                common_attn_metadata = make_local_attention_virtual_batches(
                    kv_cache_group_spec.kv_cache_spec.attention_chunk_size,
                    common_attn_metadata, self.cache_config.block_size)

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            # Prepare for cascade attention if enabled & beneficial.
            common_prefix_len = 0
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            builder = self.attn_metadata_builders[kv_cache_group_id]
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            if self.cascade_attn_enabled:
                common_prefix_len = self._compute_cascade_attn_prefix_len(
                    num_scheduled_tokens,
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                    scheduler_output.
                    num_common_prefix_blocks[kv_cache_group_id],
                    kv_cache_group_spec.kv_cache_spec,
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                    builder,
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            attn_metadata_i = (builder.build(
                common_prefix_len=common_prefix_len,
                common_attn_metadata=common_attn_metadata,
            ))

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            for layer_name in kv_cache_group_spec.layer_names:
                attn_metadata[layer_name] = attn_metadata_i
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        attention_cuda_graphs = all(
            b.can_run_in_cudagraph(common_attn_metadata)
            for b in self.attn_metadata_builders)

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        use_spec_decode = len(
            scheduler_output.scheduled_spec_decode_tokens) > 0
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        if not use_spec_decode:
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            # NOTE(woosuk): Due to chunked prefills, the batch may contain
            # partial requests. While we should not sample any token
            # from these partial requests, we do so for simplicity.
            # We will ignore the sampled tokens from the partial requests.
            # TODO: Support prompt logprobs.
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            logits_indices = query_start_loc[1:] - 1
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            spec_decode_metadata = None
        else:
            # Get the number of draft tokens for each request.
            # Iterate over the dictionary rather than all requests since not all
            # requests have draft tokens.
            num_draft_tokens = np.zeros(num_reqs, dtype=np.int32)
            for req_id, draft_token_ids in (
                    scheduler_output.scheduled_spec_decode_tokens.items()):
                req_idx = self.input_batch.req_id_to_index[req_id]
                num_draft_tokens[req_idx] = len(draft_token_ids)

            spec_decode_metadata = self._calc_spec_decode_metadata(
                num_draft_tokens, cu_num_tokens)
            logits_indices = spec_decode_metadata.logits_indices
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        # Hot-Swap lora model
        if self.lora_config:
            self.set_active_loras(self.input_batch, num_scheduled_tokens)

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        return (attn_metadata, attention_cuda_graphs, logits_indices,
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                spec_decode_metadata, num_scheduled_tokens,
                spec_decode_common_attn_metadata)
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    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
        num_common_prefix_blocks: int,
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        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
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    ) -> int:
        """Compute the length of the common prefix for cascade attention.

        NOTE(woosuk): The common prefix length returned by this function
        represents the length used specifically for cascade attention, not the
        actual number of tokens shared between requests. When cascade attention
        is disabled (use_cascade=False), this function returns 0 even if
        requests share common tokens. Additionally, the common prefix length is
        truncated to a multiple of the block size and may be further truncated
        due to implementation details explained below.

        Args:
            num_scheduled_tokens: Number of tokens scheduled per request.
            num_common_prefix_blocks: Number of shared KV cache blocks.

        Returns:
            int: Length of common prefix in tokens.
        """
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        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
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        if common_prefix_len == 0:
            # Common case.
            return 0

        # NOTE(woosuk): Cascade attention uses two attention kernels: one
        # for the common prefix and the other for the rest. For the first
        # kernel, we concatenate all the query tokens (possibly from
        # different requests) and treat them as if they are from the same
        # request. Then, we use bi-directional attention to process the
        # common prefix in the KV cache. Importantly, this means that the
        # first kernel does not do any masking.

        # Consider the following example:
        # Request 1's input query: [D, E, X]
        # Request 1's kv cache: [A, B, C, D, E, X]
        # Request 1's num_computed_tokens: 3 (i.e., [A, B, C])
        # Request 2's input query: [E, Y]
        # Request 2's kv cache: [A, B, C, D, E, Y]
        # Request 2's num_computed_tokens: 4 (i.e., [A, B, C, D])

        # If we use [A, B, C, D, E] as the common prefix, then the
        # first kernel will compute the bi-directional attention between
        # input query [D, E, X, E, Y] and common prefix [A, B, C, D, E].
        # However, this is wrong because D in Request 1 should not attend to
        # E in the common prefix (i.e., we need masking).
        # To avoid this, [A, B, C, D] should be the common prefix.
        # That is, the common prefix should be capped by the minimum
        # num_computed_tokens among the requests, and plus one to include
        # the first token of the query.

        # In practice, we use [A, B, C] as the common prefix, instead of
        # [A, B, C, D] (i.e., the common prefix is capped by the minimum
        # num_computed_tokens, without plus one).
        # This is because of an implementation detail: We want to always
        # use two kernels for cascade attention. Let's imagine:
        # Request 3's input query: [D]
        # Request 3's kv cache: [A, B, C, D]
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        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
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        # If we use [A, B, C, D] as the common prefix for Request 1-3,
        # then Request 3 will be processed only by the first kernel,
        # and the second kernel will get an empty input. While this is not
        # a fundamental problem, our current implementation does not support
        # this case.
        num_reqs = len(num_scheduled_tokens)
        common_prefix_len = min(
            common_prefix_len,
            self.input_batch.num_computed_tokens_cpu[:num_reqs].min())
        # common_prefix_len should be a multiple of the block size.
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        common_prefix_len = (common_prefix_len // kv_cache_spec.block_size *
                             kv_cache_spec.block_size)
        use_sliding_window = (isinstance(kv_cache_spec, SlidingWindowSpec) or
                              (isinstance(kv_cache_spec, FullAttentionSpec)
                               and kv_cache_spec.sliding_window is not None))
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        use_local_attention = (
            isinstance(kv_cache_spec, ChunkedLocalAttentionSpec)
            or (isinstance(kv_cache_spec, FullAttentionSpec)
                and kv_cache_spec.attention_chunk_size is not None))
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        assert isinstance(kv_cache_spec, AttentionSpec)
        use_cascade = attn_metadata_builder.use_cascade_attention(
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            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
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            num_kv_heads=kv_cache_spec.num_kv_heads,
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            use_alibi=self.use_alibi,
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            use_sliding_window=use_sliding_window,
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            use_local_attention=use_local_attention,
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            num_sms=self.num_sms,
        )
        return common_prefix_len if use_cascade else 0

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    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
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        for index, req_id in enumerate(self.input_batch.req_ids):
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            req = self.requests[req_id]
            assert req.mrope_positions is not None

            num_computed_tokens = \
                self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = \
                scheduler_output.num_scheduled_tokens[req_id]
            num_prompt_tokens = len(req.prompt_token_ids)

            if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
                prompt_part_len = max(0,
                                      num_prompt_tokens - num_computed_tokens)
                completion_part_len = max(
                    0, num_scheduled_tokens - prompt_part_len)
            else:
                prompt_part_len = num_scheduled_tokens
                completion_part_len = 0

            assert num_scheduled_tokens == prompt_part_len + completion_part_len

            if prompt_part_len > 0:
                # prompt's mrope_positions are pre-computed
                dst_start = mrope_pos_ptr
                dst_end = mrope_pos_ptr + prompt_part_len
                src_start = num_computed_tokens
                src_end = num_computed_tokens + prompt_part_len

                self.mrope_positions_cpu[:, dst_start:dst_end] = \
                    req.mrope_positions[:,src_start:src_end]

                mrope_pos_ptr += prompt_part_len

            if completion_part_len > 0:
                # compute completion's mrope_positions on-the-fly
                dst_start = mrope_pos_ptr
                dst_end = mrope_pos_ptr + completion_part_len

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                MRotaryEmbedding.get_next_input_positions_tensor(
                    out=self.mrope_positions_np,
                    out_offset=dst_start,
                    mrope_position_delta=req.mrope_position_delta,
                    context_len=num_computed_tokens + prompt_part_len,
                    num_new_tokens=completion_part_len,
                )
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                mrope_pos_ptr += completion_part_len

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    def _calc_spec_decode_metadata(
        self,
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        num_draft_tokens: np.ndarray,
        cu_num_scheduled_tokens: np.ndarray,
    ) -> SpecDecodeMetadata:
        # Inputs:
        # cu_num_scheduled_tokens:  [  4, 104, 107, 207, 209]
        # num_draft_tokens:         [  3,   0,   2,   0,   1]
        # Outputs:
        # cu_num_draft_tokens:      [  3,   3,   5,   5,   6]
        # logits_indices:           [  0,   1,   2,   3, 103, 104, 105, 106,
        #                            206, 207, 208]
        # target_logits_indices:    [  0,   1,   2,   5,   6,   9]
        # bonus_logits_indices:     [  3,   4,   7,   8,  10]

        # Compute the logits indices.
        # [4, 1, 3, 1, 2]
        num_sampled_tokens = num_draft_tokens + 1
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        # Step 1. cu_num_sampled_tokens: [4, 5, 8, 9, 11]
        # arange: [0, 1, 2, 3, 0, 0, 1, 2, 0, 0, 1]
        cu_num_sampled_tokens, arange = self._get_cumsum_and_arange(
            num_sampled_tokens, cumsum_dtype=np.int32)
        # Step 2. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
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        logits_indices = np.repeat(
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens)
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        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
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        logits_indices += arange

        # Compute the bonus logits indices.
        bonus_logits_indices = cu_num_sampled_tokens - 1

        # Compute the draft logits indices.
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        # cu_num_draft_tokens: [3, 3, 5, 5, 6]
        # arange: [0, 1, 2, 0, 1, 0]
        cu_num_draft_tokens, arange = self._get_cumsum_and_arange(
            num_draft_tokens, cumsum_dtype=np.int32)
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        # [0, 0, 0, 5, 5, 9]
        target_logits_indices = np.repeat(
            cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens)
        # [0, 1, 2, 5, 6, 9]
        target_logits_indices += arange

        # TODO: Optimize the CPU -> GPU copy.
        cu_num_draft_tokens = torch.from_numpy(cu_num_draft_tokens).to(
            self.device, non_blocking=True)
        logits_indices = torch.from_numpy(logits_indices).to(self.device,
                                                             non_blocking=True)
        target_logits_indices = torch.from_numpy(target_logits_indices).to(
            self.device, non_blocking=True)
        bonus_logits_indices = torch.from_numpy(bonus_logits_indices).to(
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            self.device, non_blocking=True)

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        # Compute the draft token ids.
        # draft_token_indices:      [  1,   2,   3, 105, 106, 208]
        draft_token_ids = self.input_ids[logits_indices]
        draft_token_ids = draft_token_ids[target_logits_indices + 1]

        metadata = SpecDecodeMetadata(
            draft_token_ids=draft_token_ids,
            num_draft_tokens=num_draft_tokens.tolist(),
            cu_num_draft_tokens=cu_num_draft_tokens,
            target_logits_indices=target_logits_indices,
            bonus_logits_indices=bonus_logits_indices,
            logits_indices=logits_indices,
        )
        return metadata

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    def _execute_mm_encoder(self, scheduler_output: "SchedulerOutput"):
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        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
            return

        # Batch the multi-modal inputs.
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        mm_inputs = list[MultiModalKwargs]()
        req_ids_pos = list[tuple[str, int, PlaceholderRange]]()
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        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
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            for mm_input_id in encoder_input_ids:
                mm_inputs.append(req_state.mm_inputs[mm_input_id])
                req_ids_pos.append(
                    (req_id, mm_input_id, req_state.mm_positions[mm_input_id]))
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        # Batch mm inputs as much as we can: if a request in the batch has
        # multiple modalities or a different modality than the previous one,
        # we process it separately to preserve item order.
        # FIXME(ywang96): This is a hacky way to deal with multiple modalities
        # in the same batch while still being able to benefit from batching
        # multimodal inputs. The proper solution should be reordering the
        # encoder outputs.
        grouped_mm_inputs_list = group_mm_inputs_by_modality(mm_inputs)

        encoder_outputs = []
        for grouped_mm_inputs in grouped_mm_inputs_list:
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            batched_mm_inputs = MultiModalKwargs.batch(
                grouped_mm_inputs, pin_memory=self.pin_memory)
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            batched_mm_inputs = MultiModalKwargs.as_kwargs(
                batched_mm_inputs,
                device=self.device,
            )
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            # Run the encoder.
            # `curr_group_outputs` is either of the following:
            # 1. A tensor of shape (num_items, feature_size, hidden_size)
            # in case feature_size is fixed across all multimodal items.
            # 2. A list or tuple (length: num_items) of tensors, each of shape
            # (feature_size, hidden_size) in case the feature size is dynamic
            # depending on the input multimodal items.
            curr_group_outputs = self.model.get_multimodal_embeddings(
                **batched_mm_inputs)

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            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
                expected_num_items=len(grouped_mm_inputs),
            )

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            for output in curr_group_outputs:
                encoder_outputs.append(output)
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        # Cache the encoder outputs.
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        for (req_id, input_id, pos_info), output in zip(
                req_ids_pos,
                encoder_outputs,
        ):
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            if req_id not in self.encoder_cache:
                self.encoder_cache[req_id] = {}

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            self.encoder_cache[req_id][input_id] = scatter_mm_placeholders(
                output,
                is_embed=pos_info.is_embed,
            )

    def _gather_mm_embeddings(
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        self,
        scheduler_output: "SchedulerOutput",
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    ) -> list[torch.Tensor]:
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        mm_embeds: list[torch.Tensor] = []
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        for req_id in self.input_batch.req_ids:
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            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[
                req_id]
            req_state = self.requests[req_id]
            num_computed_tokens = req_state.num_computed_tokens
            mm_positions = req_state.mm_positions
            for i, pos_info in enumerate(mm_positions):
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                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
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                # The encoder output is needed if the two ranges overlap:
                # [num_computed_tokens,
                #  num_computed_tokens + num_scheduled_tokens) and
                # [start_pos, start_pos + num_encoder_tokens)
                if start_pos >= num_computed_tokens + num_scheduled_tokens:
                    # The encoder output is not needed in this step.
                    break
                if start_pos + num_encoder_tokens <= num_computed_tokens:
                    # The encoder output is already processed and stored
                    # in the decoder's KV cache.
                    continue

                start_idx = max(num_computed_tokens - start_pos, 0)
                end_idx = min(
                    num_computed_tokens - start_pos + num_scheduled_tokens,
                    num_encoder_tokens)
                assert start_idx < end_idx
                assert req_id in self.encoder_cache
                assert i in self.encoder_cache[req_id]
                encoder_output = self.encoder_cache[req_id][i]
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                if (is_embed := pos_info.is_embed) is not None:
                    is_embed = is_embed[start_idx:end_idx]

                mm_embeds_item = gather_mm_placeholders(
                    encoder_output[start_idx:end_idx],
                    is_embed=is_embed,
                )
                mm_embeds.append(mm_embeds_item)
        return mm_embeds
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    def get_model(self) -> nn.Module:
        return self.model

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    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
        model = self.get_model()
        if not is_pooling_model(model):
            return []

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        return list(model.pooler.get_supported_tasks())
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    def apply_grammar_bitmask(
        self,
        scheduler_output: "SchedulerOutput",
        logits: torch.Tensor,
    ):
        grammar_bitmask = scheduler_output.grammar_bitmask
        if grammar_bitmask is None:
            return

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        # We receive the structured output bitmask from the scheduler,
        # compacted to contain bitmasks only for structured output requests.
        # The order of the requests in the bitmask is not guaranteed to be the
        # same as the order of the requests in the gpu runner's batch. We need
        # to sort the bitmask to match the order of the requests used here.

        # Get the batch indices of the structured output requests.
        # Keep track of the number of speculative tokens scheduled for every
        # request in the batch, as the logit indices are offset by this amount.
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        struct_out_req_batch_indices: dict[str, int] = {}
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        cumulative_offset = 0
        seq = sorted(self.input_batch.req_id_to_index.items(),
                     key=lambda x: x[1])
        for req_id, batch_index in seq:
            logit_index = batch_index + cumulative_offset
            cumulative_offset += len(
                scheduler_output.scheduled_spec_decode_tokens.get(req_id, []))
            if req_id in scheduler_output.structured_output_request_ids:
                struct_out_req_batch_indices[req_id] = logit_index

        out_indices = []

        # Reorder the bitmask to match the order of the requests in the batch.
        sorted_bitmask = np.zeros_like(grammar_bitmask,
                                       shape=(logits.shape[0],
                                              grammar_bitmask.shape[1]))
        cumulative_index = 0
        seq = sorted(scheduler_output.structured_output_request_ids.items(),
                     key=lambda x: x[1])
        for req_id, _ in seq:
            logit_index = struct_out_req_batch_indices[req_id]
            num_spec_tokens = len(
                scheduler_output.scheduled_spec_decode_tokens.get(req_id, []))
            for i in range(1 + num_spec_tokens):
                sorted_bitmask[logit_index + i] = \
                    grammar_bitmask[cumulative_index + i]
                out_indices.append(logit_index + i)
            cumulative_index += 1 + num_spec_tokens
        grammar_bitmask = sorted_bitmask
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        # Serialization of np.ndarray is much more efficient than a tensor,
        # so we receive it in that format.
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        grammar_bitmask = torch.from_numpy(grammar_bitmask)

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        # Force use of the torch.compile implementation from xgrammar to work
        # around issues with the Triton kernel in concurrent structured output
        # scenarios. See PR #19565 and issues #19493, #18376 for details.
        xgr_torch_compile.apply_token_bitmask_inplace_torch_compile(
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            logits,
            grammar_bitmask.to(self.device, non_blocking=True),
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            indices=out_indices,
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        )

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    def sync_and_slice_intermediate_tensors(
            self, num_tokens: int, intermediate_tensors: IntermediateTensors,
            sync_self: bool) -> IntermediateTensors:

        assert self.intermediate_tensors is not None

        tp = self.vllm_config.parallel_config.tensor_parallel_size
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        enabled_sp = self.compilation_config.pass_config. \
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            enable_sequence_parallelism
        if enabled_sp:
            # When sequence parallelism is enabled, we always pad num_tokens
            # to be a multiple of tensor_parallel_size (tp) earlier
            assert num_tokens % tp == 0
        is_residual_scattered = tp > 1 and enabled_sp \
            and num_tokens % tp == 0

        # When sequence parallelism is enabled, the "residual" tensor is sharded
        # across tensor parallel ranks, so each rank only needs its own slice.
        if sync_self:
            assert intermediate_tensors is not None
            for k, v in intermediate_tensors.items():
                is_scattered = "residual" and is_residual_scattered
                copy_len = num_tokens // tp if is_scattered else \
                    num_tokens
                self.intermediate_tensors[k][:copy_len].copy_(
                    v[:copy_len], non_blocking=True)

        return IntermediateTensors({
            k:
            v[:num_tokens // tp]
            if k == "residual" and is_residual_scattered else v[:num_tokens]
            for k, v in self.intermediate_tensors.items()
        })

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    def eplb_step(self,
                  is_dummy: bool = False,
                  is_profile: bool = False) -> None:
        """
        Step for the EPLB (Expert Parallelism Load Balancing) state.
        """
        if not self.parallel_config.enable_eplb:
            return

        assert self.eplb_state is not None
        assert is_mixture_of_experts(self.model)
        self.eplb_state.step(
            self.model,
            is_dummy,
            is_profile,
            log_stats=self.parallel_config.eplb_log_balancedness,
        )

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    def get_dp_padding(self,
                       num_tokens: int) -> tuple[int, Optional[torch.Tensor]]:
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        dp_size = self.vllm_config.parallel_config.data_parallel_size
        dp_rank = self.vllm_config.parallel_config.data_parallel_rank
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        # For DP: Don't pad when setting enforce_eager.
        # This lets us set enforce_eager on the prefiller in a P/D setup and
        # still use CUDA graphs (enabled by this padding) on the decoder.
        #
        # TODO(tms) : There are many cases where padding is enabled for
        # prefills, causing unnecessary and excessive padding of activations.

        if dp_size == 1 or self.vllm_config.model_config.enforce_eager:
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            # Early exit.
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            return 0, None
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        num_tokens_across_dp = DPMetadata.num_tokens_across_dp(
            num_tokens, dp_size, dp_rank)
        max_tokens_across_dp_cpu = torch.max(num_tokens_across_dp).item()
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        num_tokens_after_padding = torch.tensor([max_tokens_across_dp_cpu] *
                                                dp_size,
                                                device="cpu",
                                                dtype=torch.int32)
        return max_tokens_across_dp_cpu - num_tokens, num_tokens_after_padding
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    def _pool(
        self,
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
        num_scheduled_tokens_np: np.ndarray,
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        finished_sending: Optional[set[str]],
        finished_recving: Optional[set[str]],
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    ) -> ModelRunnerOutput:
        assert self.input_batch.num_reqs ==\
            len(self.input_batch.pooling_params), \
        "Either all or none of the requests in" \
        " a batch must be pooling request"

        extracted_hidden_states = list(
            torch.split(hidden_states[:num_scheduled_tokens],
                        num_scheduled_tokens_np.tolist()))

        pooling_metadata = self.input_batch.pooling_metadata

        raw_pooler_output = self.model.pooler(
            hidden_states=extracted_hidden_states,
            pooling_metadata=pooling_metadata)

        pooler_output: list[Optional[torch.Tensor]] = []
        seq_lens = self.seq_lens[:self.input_batch.num_reqs]
        for raw_output, seq_len, prompt_len in zip(
                raw_pooler_output, seq_lens, pooling_metadata.prompt_lens):

            if seq_len == prompt_len:
                pooler_output.append(raw_output.data.cpu())
            else:
                pooler_output.append(None)

        return ModelRunnerOutput(
            req_ids=self.input_batch.req_ids,
            req_id_to_index=self.input_batch.req_id_to_index,
            sampled_token_ids=[],
            spec_token_ids=None,
            logprobs=None,
            prompt_logprobs_dict={},
            pooler_output=pooler_output,
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            finished_sending=finished_sending,
            finished_recving=finished_recving,
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        )

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    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
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        intermediate_tensors: Optional[IntermediateTensors] = None,
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    ) -> Union[ModelRunnerOutput, IntermediateTensors]:
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        self._update_states(scheduler_output)
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        if not scheduler_output.total_num_scheduled_tokens:
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            if not has_kv_transfer_group():
                # Return empty ModelRunnerOutput if there's no work to do.
                return EMPTY_MODEL_RUNNER_OUTPUT
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            return self.kv_connector_no_forward(scheduler_output,
                                                self.vllm_config)
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        # Prepare the decoder inputs.
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        (attn_metadata, attention_cuda_graphs, logits_indices,
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         spec_decode_metadata, num_scheduled_tokens_np,
         spec_decode_common_attn_metadata) = (
             self._prepare_inputs(scheduler_output))
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        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
        if (self.use_cuda_graph
                and num_scheduled_tokens <= self.cudagraph_batch_sizes[-1]):
            # Use piecewise CUDA graphs.
            # Add padding to the batch size.
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            num_input_tokens = self.vllm_config.pad_for_cudagraph(
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                num_scheduled_tokens)
        else:
            # Eager mode.
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            # Pad tokens to multiple of tensor_parallel_size when
            # enabled collective fusion for SP
            tp_size = self.vllm_config.parallel_config.tensor_parallel_size
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            if self.compilation_config.pass_config. \
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                enable_sequence_parallelism and tp_size > 1:
                num_input_tokens = round_up(num_scheduled_tokens, tp_size)
            else:
                num_input_tokens = num_scheduled_tokens
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        # Padding for DP
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        num_pad, num_tokens_across_dp = self.get_dp_padding(num_input_tokens)
        num_input_tokens += num_pad
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        # _prepare_inputs may reorder the batch, so we must gather multi
        # modal outputs after that to ensure the correct order
        if self.is_multimodal_model:
            # Run the multimodal encoder if any.
            self._execute_mm_encoder(scheduler_output)
            mm_embeds = self._gather_mm_embeddings(scheduler_output)
        else:
            mm_embeds = []

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        if self.is_multimodal_model and get_pp_group().is_first_rank:
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            # NOTE(woosuk): To unify token ids and soft tokens (vision
            # embeddings), we always use embeddings (rather than token ids)
            # as input to the multimodal model, even when the input is text.
            input_ids = self.input_ids[:num_scheduled_tokens]
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            model_kwargs = self._init_model_kwargs_for_multimodal_model(
                scheduler_output=scheduler_output)
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            inputs_embeds = self.model.get_input_embeddings(
                input_ids=input_ids,
                multimodal_embeddings=mm_embeds or None,
            )
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            # TODO(woosuk): Avoid the copy. Optimize.
            self.inputs_embeds[:num_scheduled_tokens].copy_(inputs_embeds)
            inputs_embeds = self.inputs_embeds[:num_input_tokens]
            input_ids = None
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        else:
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            # For text-only models, we use token ids as input.
            # While it is possible to use embeddings as input just like the
            # multimodal models, it is not desirable for performance since
            # then the embedding layer is not included in the CUDA graph.
            input_ids = self.input_ids[:num_input_tokens]
            inputs_embeds = None
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            model_kwargs = {}
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        if self.uses_mrope:
            positions = self.mrope_positions[:, :num_input_tokens]
        else:
            positions = self.positions[:num_input_tokens]
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        if get_pp_group().is_first_rank:
            intermediate_tensors = None
        else:
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            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
                num_input_tokens, intermediate_tensors, True)
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        # Some attention backends only support CUDA Graphs in pure decode.
        # If attention doesn't support CUDA Graphs for this batch, but we
        # compiled with full CUDA graphs, we have to skip them entirely.
        skip_cuda_graphs = self.full_cuda_graph and not attention_cuda_graphs

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        # Run the model.
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        # Use persistent buffers for CUDA graphs.
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        with set_forward_context(
                attn_metadata,
                self.vllm_config,
                num_tokens=num_input_tokens,
                num_tokens_across_dp=num_tokens_across_dp,
                skip_cuda_graphs=skip_cuda_graphs,
        ):
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            self.maybe_setup_kv_connector(scheduler_output)

            model_output = self.model(
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                input_ids=input_ids,
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                positions=positions,
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                intermediate_tensors=intermediate_tensors,
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                inputs_embeds=inputs_embeds,
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                **MultiModalKwargs.as_kwargs(
                    model_kwargs,
                    device=self.device,
                ),
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            )
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            self.maybe_wait_for_kv_save()
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            finished_sending, finished_recving = (
                self.get_finished_kv_transfers(scheduler_output))
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        if self.use_aux_hidden_state_outputs:
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            hidden_states, aux_hidden_states = model_output
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        else:
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            hidden_states = model_output
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            aux_hidden_states = None

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        # Broadcast PP output for external_launcher (torchrun)
        # to make sure we are synced across pp ranks
        # TODO: Support overlapping mirco-batches
        # https://github.com/vllm-project/vllm/issues/18019
        broadcast_pp_output = \
            self.parallel_config.distributed_executor_backend \
            == "external_launcher" and len(get_pp_group().ranks) > 0
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        if not get_pp_group().is_last_rank:
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            # For mid-pipeline stages, return the hidden states.
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            if not broadcast_pp_output:
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                if finished_sending or finished_recving:
                    hidden_states.finished_sending = finished_sending
                    hidden_states.finished_recving = finished_recving
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                return hidden_states
            assert isinstance(hidden_states, IntermediateTensors)
            get_pp_group().send_tensor_dict(hidden_states.tensors,
                                            all_gather_group=get_tp_group())
            logits = None
        else:
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            if self.input_batch.pooling_params:
                return self._pool(hidden_states, num_scheduled_tokens,
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                                  num_scheduled_tokens_np, finished_sending,
                                  finished_recving)
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            sample_hidden_states = hidden_states[logits_indices]
            logits = self.model.compute_logits(sample_hidden_states, None)
        if broadcast_pp_output:
            model_output_broadcast_data = {
                "logits": logits.contiguous(),
            } if logits is not None else {}
            model_output_broadcast_data = get_pp_group().broadcast_tensor_dict(
                model_output_broadcast_data, src=len(get_pp_group().ranks) - 1)
            assert model_output_broadcast_data is not None
            logits = model_output_broadcast_data["logits"]
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        # Apply structured output bitmasks if present
        if scheduler_output.grammar_bitmask is not None:
            self.apply_grammar_bitmask(scheduler_output, logits)

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        # Sample the next token and get logprobs if needed.
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        sampling_metadata = self.input_batch.sampling_metadata
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        if spec_decode_metadata is None:
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            sampler_output = self.sampler(
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                logits=logits,
                sampling_metadata=sampling_metadata,
            )
        else:
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            # When indexing with a tensor (bonus_logits_indices), PyTorch
            # creates a new tensor with separate storage from the original
            # logits tensor. This means any in-place operations on bonus_logits
            # won't affect the original logits tensor.
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            assert logits is not None
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            bonus_logits = logits[spec_decode_metadata.bonus_logits_indices]
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            sampler_output = self.sampler(
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                logits=bonus_logits,
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                sampling_metadata=sampling_metadata,
            )
            bonus_token_ids = sampler_output.sampled_token_ids
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            # Just like `bonus_logits`, `target_logits` is a new tensor with
            # separate storage from the original `logits` tensor. Therefore,
            # it is safe to update `target_logits` in place.
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            target_logits = logits[spec_decode_metadata.target_logits_indices]
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            output_token_ids = self.rejection_sampler(
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                spec_decode_metadata,
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                None,  # draft_probs
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                target_logits,
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                bonus_token_ids,
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                sampling_metadata,
            )
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            sampler_output.sampled_token_ids = output_token_ids
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        num_nans_in_logits = {}
        if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
            num_nans_in_logits = self._get_nans_in_logits(logits)

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        # TODO(woosuk): The following loop can be slow since it iterates over
        # the requests one by one. Optimize.
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        discard_sampled_tokens_req_indices = []
        for i, req_id in enumerate(self.input_batch.req_ids):
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            req_state = self.requests[req_id]
            seq_len = (req_state.num_computed_tokens +
                       scheduler_output.num_scheduled_tokens[req_id])
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            if seq_len < req_state.num_tokens:
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                # Ignore the sampled token for partial prefills.
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                # Rewind the generator state as if the token was not sampled.
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                # This relies on cuda-specific torch-internal impl details
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                generator = self.input_batch.generators.get(i)
                if generator is not None:
                    generator.set_offset(generator.get_offset() - 4)
                # Record the index of the request that should not be sampled,
                # so that we could clear the sampled tokens before returning.
                discard_sampled_tokens_req_indices.append(i)
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        # NOTE: GPU -> CPU Sync happens here.
        # Move as many CPU operations as possible before this sync point.
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        logprobs_tensors = sampler_output.logprobs_tensors
        logprobs_lists = logprobs_tensors.tolists() \
            if logprobs_tensors is not None else None

        # Compute prompt logprobs if needed.
        prompt_logprobs_dict = self._get_prompt_logprobs_dict(
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            hidden_states[:num_scheduled_tokens],
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            scheduler_output,
        )

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        # Get the valid generated tokens.
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        sampled_token_ids = sampler_output.sampled_token_ids
        max_gen_len = sampled_token_ids.shape[-1]
        if max_gen_len == 1:
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            # No spec decode tokens.
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            valid_sampled_token_ids = sampled_token_ids.tolist()
        else:
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            # Includes spec decode tokens.
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            valid_sampled_token_ids = self.rejection_sampler.parse_output(
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                sampled_token_ids,
                self.input_batch.vocab_size,
            )
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        # Mask out the sampled tokens that should not be sampled.
        for i in discard_sampled_tokens_req_indices:
            valid_sampled_token_ids[i].clear()
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        # Cache the sampled tokens in the model runner, so that the scheduler
        # doesn't need to send them back.
        # NOTE(woosuk): As an exception, when using PP, the scheduler sends
        # the sampled tokens back, because there's no direct communication
        # between the first-stage worker and the last-stage worker.
        for req_idx, sampled_ids in enumerate(valid_sampled_token_ids):
            if not sampled_ids:
                continue

            start_idx = self.input_batch.num_tokens_no_spec[req_idx]
            end_idx = start_idx + len(sampled_ids)
            assert end_idx <= self.max_model_len, (
                "Sampled token IDs exceed the max model length. "
                f"Total number of tokens: {end_idx} > max_model_len: "
                f"{self.max_model_len}")

            self.input_batch.token_ids_cpu[req_idx,
                                           start_idx:end_idx] = sampled_ids
            self.input_batch.num_tokens_no_spec[req_idx] = end_idx
            self.input_batch.num_tokens[req_idx] = end_idx
            req_id = self.input_batch.req_ids[req_idx]
            req_state = self.requests[req_id]
            req_state.output_token_ids.extend(sampled_ids)

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        if not self.speculative_config:
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            # Speculative decoding is not enabled.
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            spec_token_ids = None
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        else:
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            assert spec_decode_common_attn_metadata is not None
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            spec_token_ids = self.propose_draft_token_ids(
                scheduler_output,
                valid_sampled_token_ids,
                sampling_metadata,
                hidden_states,
                sample_hidden_states,
                aux_hidden_states,
                spec_decode_metadata,
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                spec_decode_common_attn_metadata,
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            )

        self.eplb_step()

        return ModelRunnerOutput(
            req_ids=self.input_batch.req_ids,
            req_id_to_index=self.input_batch.req_id_to_index,
            sampled_token_ids=valid_sampled_token_ids,
            spec_token_ids=spec_token_ids,
            logprobs=logprobs_lists,
            prompt_logprobs_dict=prompt_logprobs_dict,
            pooler_output=[],
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            finished_sending=finished_sending,
            finished_recving=finished_recving,
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            num_nans_in_logits=num_nans_in_logits,
        )

    def propose_draft_token_ids(
        self,
        scheduler_output: "SchedulerOutput",
        sampled_token_ids: list[list[int]],
        sampling_metadata: SamplingMetadata,
        hidden_states: torch.Tensor,
        sample_hidden_states: torch.Tensor,
        aux_hidden_states: Optional[torch.Tensor],
        spec_decode_metadata: Optional[SpecDecodeMetadata],
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        common_attn_metadata: CommonAttentionMetadata,
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    ) -> list[list[int]]:
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
        if self.speculative_config.method == "ngram":
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            assert isinstance(self.drafter, NgramProposer)
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            spec_token_ids = self.propose_ngram_draft_token_ids(
                sampled_token_ids)
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        elif self.speculative_config.method == "medusa":
            assert isinstance(self.drafter, MedusaProposer)
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            if sample_hidden_states.shape[0] == len(sampled_token_ids):
                # The input to the target model does not include draft tokens.
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                hidden_states = sample_hidden_states
            else:
                indices = []
                offset = 0
                for num_draft, tokens in zip(
                        spec_decode_metadata.num_draft_tokens,
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                        sampled_token_ids):
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                    indices.append(offset + len(tokens) - 1)
                    offset += num_draft + 1
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                indices = torch.tensor(indices, device=self.device)
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                hidden_states = sample_hidden_states[indices]

            spec_token_ids = self.drafter.propose(
                target_hidden_states=hidden_states,
                sampling_metadata=sampling_metadata,
            )
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        elif self.speculative_config.use_eagle():
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            assert isinstance(self.drafter, EagleProposer)
            # TODO(woosuk): Refactor the loop.
            next_token_ids: list[int] = []
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            for i, token_ids in enumerate(sampled_token_ids):
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                if token_ids:
                    # Common case.
                    next_token_id = token_ids[-1]
                else:
                    # Partial prefill (rare case).
                    # Get the next token id from the request state.
                    req_id = self.input_batch.req_ids[i]
                    req_state = self.requests[req_id]
                    seq_len = (req_state.num_computed_tokens +
                               scheduler_output.num_scheduled_tokens[req_id])
                    next_token_id = req_state.get_token_id(seq_len)
                next_token_ids.append(next_token_id)
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            next_token_ids = torch.tensor(next_token_ids,
                                          dtype=torch.int32,
                                          device=self.device)
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            if spec_decode_metadata is None:
                # input_ids can be None for multimodal models.
                target_token_ids = self.input_ids[:num_scheduled_tokens]
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                # TODO(woosuk): Support M-RoPE.
                target_positions = self.positions[:num_scheduled_tokens]
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                if self.use_aux_hidden_state_outputs:
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                    target_hidden_states = torch.cat(
                        [h[:num_scheduled_tokens] for h in aux_hidden_states],
                        dim=-1)
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                else:
                    target_hidden_states = hidden_states[:num_scheduled_tokens]
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            else:
                # TODO(woosuk): Refactor this.
                num_draft_tokens = spec_decode_metadata.num_draft_tokens
                num_rejected_tokens = [
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                    n + 1 - len(sampled_token_ids[i]) if n > 0 else 0
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                    for i, n in enumerate(num_draft_tokens)
                ]
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                num_rejected_tokens_cpu = torch.tensor(num_rejected_tokens,
                                                       dtype=torch.int32)
                common_attn_metadata, token_indices =\
                    self.drafter.prepare_inputs(
                    common_attn_metadata, num_rejected_tokens_cpu)

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                target_token_ids = self.input_ids[token_indices]
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                # TODO(woosuk): Support M-RoPE.
                target_positions = self.positions[token_indices]
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                if self.use_aux_hidden_state_outputs:
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                    target_hidden_states = torch.cat(
                        [h[token_indices] for h in aux_hidden_states], dim=-1)
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                else:
                    target_hidden_states = hidden_states[token_indices]
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            draft_token_ids = self.drafter.propose(
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                target_token_ids=target_token_ids,
                target_positions=target_positions,
                target_hidden_states=target_hidden_states,
                next_token_ids=next_token_ids,
                sampling_metadata=sampling_metadata,
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                common_attn_metadata=common_attn_metadata,
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            )
            spec_token_ids = draft_token_ids.tolist()
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        return spec_token_ids
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    def propose_ngram_draft_token_ids(
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        self,
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        sampled_token_ids: list[list[int]],
    ) -> list[list[int]]:
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        # TODO(woosuk): Optimize.
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        draft_token_ids: list[list[int]] = []
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        for i, sampled_ids in enumerate(sampled_token_ids):
            num_sampled_ids = len(sampled_ids)
            if not num_sampled_ids:
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                # Skip speculative decoding.
                draft_token_ids.append([])
                continue

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            # Skip requests that require sampling parameters that are not
            # supported with speculative decoding.
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            req_id = self.input_batch.req_ids[i]
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            if req_id in self.input_batch.spec_decode_unsupported_reqs:
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                draft_token_ids.append([])
                continue

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            num_tokens = self.input_batch.num_tokens_no_spec[i]
            if num_tokens >= self.max_model_len:
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                # Skip requests that have already reached the max model length.
                draft_token_ids.append([])
                continue

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            drafter_output = self.drafter.propose(
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                self.input_batch.token_ids_cpu[i, :num_tokens])
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            if drafter_output is None or len(drafter_output) == 0:
                draft_token_ids.append([])
            else:
                draft_token_ids.append(drafter_output.tolist())
        return draft_token_ids

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    def update_config(self, overrides: dict[str, Any]) -> None:
        allowed_config_names = {"load_config", "model_config"}
        for config_name, config_overrides in overrides.items():
            assert config_name in allowed_config_names, \
                f"Config `{config_name}` not supported. " \
                f"Allowed configs: {allowed_config_names}"
            config = getattr(self, config_name)
            new_config = update_config(config, config_overrides)
            setattr(self, config_name, new_config)

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    def load_model(self, eep_scale_up: bool = False) -> None:
        """
        Args:
            eep_scale_up: the model loading is for elastic EP scale up.
        """
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        logger.info("Starting to load model %s...", self.model_config.model)
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        if eep_scale_up:
            from vllm.distributed.parallel_state import get_ep_group
            num_local_physical_experts = torch.empty(1,
                                                     dtype=torch.int32,
                                                     device="cpu")
            torch.distributed.broadcast(num_local_physical_experts,
                                        group=get_ep_group().cpu_group,
                                        group_src=0)
            num_local_physical_experts = int(num_local_physical_experts.item())
            new_ep_size = get_ep_group().world_size
            global_expert_load, old_global_expert_indices = (
                EplbState.recv_state())
            num_logical_experts = global_expert_load.shape[1]
            self.parallel_config.num_redundant_experts = (
                num_local_physical_experts * new_ep_size - num_logical_experts)
            assert old_global_expert_indices.shape[
                1] % num_local_physical_experts == 0
            old_ep_size = old_global_expert_indices.shape[
                1] // num_local_physical_experts
            rank_mapping = {
                old_ep_rank: old_ep_rank
                for old_ep_rank in range(old_ep_size)
            }
        else:
            global_expert_load = None
            old_global_expert_indices = None
            rank_mapping = None

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        with DeviceMemoryProfiler() as m:
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            time_before_load = time.perf_counter()
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            model_loader = get_model_loader(self.load_config)
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            logger.info("Loading model from scratch...")
            self.model = model_loader.load_model(
                vllm_config=self.vllm_config, model_config=self.model_config)
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            if self.lora_config:
                self.model = self.load_lora_model(self.model,
                                                  self.model_config,
                                                  self.scheduler_config,
                                                  self.lora_config,
                                                  self.device)
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            if hasattr(self, "drafter"):
                logger.info("Loading drafter model...")
                self.drafter.load_model(self.model)
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            if self.use_aux_hidden_state_outputs:
                self.model.set_aux_hidden_state_layers(
                    self.model.get_eagle3_aux_hidden_state_layers())
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            time_after_load = time.perf_counter()
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        self.model_memory_usage = m.consumed_memory
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        logger.info("Model loading took %.4f GiB and %.6f seconds",
                    self.model_memory_usage / GiB_bytes,
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                    time_after_load - time_before_load)
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        prepare_communication_buffer_for_model(self.model)
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        if is_mixture_of_experts(
                self.model) and self.parallel_config.enable_eplb:
            logger.info("EPLB is enabled for model %s.",
                        self.model_config.model)
            self.eplb_state = EplbState.build(
                self.model,
                self.device,
                self.parallel_config,
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                global_expert_load,
                old_global_expert_indices,
                rank_mapping,
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            )

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    def reload_weights(self) -> None:
        assert getattr(self, "model", None) is not None, \
            "Cannot reload weights before model is loaded."
        model_loader = get_model_loader(self.load_config)
        logger.info("Reloading weights inplace...")
        model_loader.load_weights(self.model, model_config=self.model_config)

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    def save_tensorized_model(
        self,
        tensorizer_config: "TensorizerConfig",
    ) -> None:
        TensorizerLoader.save_model(
            self.model,
            tensorizer_config=tensorizer_config,
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            model_config=self.model_config,
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        )

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    def _get_prompt_logprobs_dict(
        self,
        hidden_states: torch.Tensor,
        scheduler_output: "SchedulerOutput",
1885
    ) -> dict[str, Optional[LogprobsTensors]]:
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        num_prompt_logprobs_dict = self.input_batch.num_prompt_logprobs
        if not num_prompt_logprobs_dict:
            return {}

1890
        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
1891
        prompt_logprobs_dict: dict[str, Optional[LogprobsTensors]] = {}
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        # Since prompt logprobs are a rare feature, prioritize simple,
        # maintainable loop over optimal performance.
        completed_prefill_reqs = []
        for req_id, num_prompt_logprobs in num_prompt_logprobs_dict.items():

            num_tokens = scheduler_output.num_scheduled_tokens[req_id]

            # Get metadata for this request.
            request = self.requests[req_id]
            num_prompt_tokens = len(request.prompt_token_ids)
            prompt_token_ids = torch.tensor(request.prompt_token_ids).to(
                self.device, non_blocking=True)

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            # Set up target LogprobsTensors object.
            logprobs_tensors = in_progress_dict.get(req_id)
            if not logprobs_tensors:
                # Create empty logprobs CPU tensors for the entire prompt.
                # If chunked, we'll copy in slice by slice.
                logprobs_tensors = LogprobsTensors.empty_cpu(
                    num_prompt_tokens - 1, num_prompt_logprobs + 1)
                in_progress_dict[req_id] = logprobs_tensors

1915
            # Determine number of logits to retrieve.
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            start_idx = request.num_computed_tokens
            start_tok = start_idx + 1
1918
            num_remaining_tokens = num_prompt_tokens - start_tok
1919
            if num_tokens <= num_remaining_tokens:
1920
                # This is a chunk, more tokens remain.
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                # In the == case, there are no more prompt logprobs to produce
                # but we want to defer returning them to the next step where we
                # have new generated tokens to return.
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                num_logits = num_tokens
            else:
                # This is the last chunk of prompt tokens to return.
                num_logits = num_remaining_tokens
                completed_prefill_reqs.append(req_id)
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                prompt_logprobs_dict[req_id] = logprobs_tensors

            if num_logits <= 0:
                # This can happen for the final chunk if we prefilled exactly
                # (num_prompt_tokens - 1) tokens for this request in the prior
                # step. There are no more prompt logprobs to produce.
                continue
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            # Get the logits corresponding to this req's prompt tokens.
            # If this is a partial request (i.e. chunked prefill),
            # then there is prompt logprob generated for each index.
            req_idx = self.input_batch.req_id_to_index[req_id]
            offset = self.query_start_loc_np[req_idx].item()
            prompt_hidden_states = hidden_states[offset:offset + num_logits]
            logits = self.model.compute_logits(prompt_hidden_states, None)

            # Get the "target" tokens for each index. For prompt at index i,
            # the token at prompt index i+1 is the "sampled" token we want
            # to gather the logprob for.
            tgt_token_ids = prompt_token_ids[start_tok:start_tok + num_logits]

            # Compute prompt logprobs.
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            logprobs = self.sampler.compute_logprobs(logits)
            token_ids, logprobs, ranks = self.sampler.gather_logprobs(
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                logprobs, num_prompt_logprobs, tgt_token_ids)

            # Transfer GPU->CPU async.
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            chunk_slice = slice(start_idx, start_idx + num_logits)
            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
                token_ids, non_blocking=True)
            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs,
                                                         non_blocking=True)
            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
                ranks, non_blocking=True)
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1965
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1967

        # Remove requests that have completed prefill from the batch
        # num_prompt_logprobs_dict.
        for req_id in completed_prefill_reqs:
            del num_prompt_logprobs_dict[req_id]
1968
            del in_progress_dict[req_id]
1969
1970

        # Must synchronize the non-blocking GPU->CPU transfers.
1971
        if prompt_logprobs_dict:
1972
            self._sync_device()
1973
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        return prompt_logprobs_dict

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    def _get_nans_in_logits(
        self,
        logits: Optional[torch.Tensor],
    ) -> dict[str, int]:
        try:
            if logits is None:
                return {req_id: 0 for req_id in self.input_batch.req_ids}

            num_nans_in_logits = {}
            num_nans_for_index = logits.isnan().sum(dim=-1).cpu().numpy()
            for req_id in self.input_batch.req_ids:
                req_index = self.input_batch.req_id_to_index[req_id]
                num_nans_in_logits[req_id] = (
                    int(num_nans_for_index[req_index])
                    if num_nans_for_index is not None
                    and req_index < logits.shape[0] else 0)
            return num_nans_in_logits
        except IndexError:
            return {}

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2001
    @contextmanager
    def maybe_randomize_inputs(self, input_ids: torch.Tensor):
        """
        Randomize input_ids if VLLM_RANDOMIZE_DP_DUMMY_INPUTS is set.
        This is to help balance expert-selection
         - during profile_run
2002
         - during DP rank dummy run
2003
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2007
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2009
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2011
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2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
        """
        dp_size = self.vllm_config.parallel_config.data_parallel_size
        randomize_inputs = envs.VLLM_RANDOMIZE_DP_DUMMY_INPUTS and dp_size > 1
        if not randomize_inputs:
            yield
        else:
            import functools

            @functools.cache
            def rand_input_ids() -> torch.Tensor:
                return torch.randint_like(
                    self.input_ids,
                    low=0,
                    high=self.model_config.get_vocab_size(),
                    dtype=input_ids.dtype)

            logger.debug("Randomizing dummy data for DP Rank")
            input_ids.copy_(rand_input_ids()[:input_ids.size(0)],
                            non_blocking=True)
            yield
            input_ids.fill_(0)

2025
2026
2027
2028
    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
2029
        capture_attn_cudagraph: bool = False,
2030
2031
        skip_eplb: bool = False,
        is_profile: bool = False,
2032
    ) -> tuple[torch.Tensor, torch.Tensor]:
2033

2034
        # Padding for DP
2035
2036
        num_pad, num_tokens_across_dp = self.get_dp_padding(num_tokens)
        num_tokens += num_pad
2037

2038
2039
2040
2041
2042
        # Set num_scheduled_tokens based on num_tokens and max_num_seqs
        # for dummy run with LoRA so that the num_reqs collectively
        # has num_tokens in total.
        assert num_tokens <= self.scheduler_config.max_num_batched_tokens
        max_num_reqs = self.scheduler_config.max_num_seqs
2043
        num_reqs = min(num_tokens, max_num_reqs)
2044
2045
2046
2047
2048
2049
2050
        min_tokens_per_req = num_tokens // num_reqs
        num_scheduled_tokens_list = [min_tokens_per_req] * num_reqs
        num_scheduled_tokens_list[-1] += num_tokens % num_reqs
        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs
        num_scheduled_tokens = np.array(num_scheduled_tokens_list,
                                        dtype=np.int32)
2051

2052
2053
2054
2055
        attn_metadata: Optional[dict[str, Any]] = None
        if capture_attn_cudagraph:
            attn_metadata = {}

2056
2057
2058
2059
2060
            # Make sure max_model_len is used at the graph capture time.
            self.seq_lens_np[:num_reqs] = self.max_model_len
            self.seq_lens_np[num_reqs:] = 0
            self.seq_lens[:num_reqs].copy_(self.seq_lens_cpu[:num_reqs],
                                           non_blocking=True)
2061

2062
2063
            for kv_cache_group_id, kv_cache_group_spec in enumerate(
                    self.kv_cache_config.kv_cache_groups):
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
                common_attn_metadata = CommonAttentionMetadata(
                    query_start_loc=self.query_start_loc[:num_reqs + 1],
                    query_start_loc_cpu=self.query_start_loc_cpu[:num_reqs +
                                                                 1],
                    seq_lens=self.seq_lens[:num_reqs],
                    seq_lens_cpu=self.seq_lens_cpu[:num_reqs],
                    num_computed_tokens_cpu=self.input_batch.
                    num_computed_tokens_cpu_tensor[:num_reqs],
                    num_reqs=num_reqs,
                    num_actual_tokens=num_tokens,
                    max_query_len=num_tokens,
                    block_table_tensor=self.input_batch.block_table[
                        kv_cache_group_id].get_device_tensor()[:num_reqs],
                    slot_mapping=self.input_batch.
2078
                    block_table[kv_cache_group_id].slot_mapping[:num_tokens])
2079
2080
2081
2082

                attn_metadata_i = self.attn_metadata_builders[
                    kv_cache_group_id].build_for_cudagraph_capture(
                        common_attn_metadata)
2083
2084
                for layer_name in kv_cache_group_spec.layer_names:
                    attn_metadata[layer_name] = attn_metadata_i
2085

2086
2087
2088
2089
        with self.maybe_dummy_run_with_lora(self.lora_config,
                                            num_scheduled_tokens):
            model = self.model
            if self.is_multimodal_model:
2090
2091
                model_kwargs = self._init_model_kwargs_for_multimodal_model(
                    num_reqs=num_reqs)
2092
2093
2094
2095
2096
                input_ids = None
                inputs_embeds = self.inputs_embeds[:num_tokens]
            else:
                input_ids = self.input_ids[:num_tokens]
                inputs_embeds = None
2097
2098
                model_kwargs = {}

2099
2100
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
2112
            if self.uses_mrope:
                positions = self.mrope_positions[:, :num_tokens]
            else:
                positions = self.positions[:num_tokens]

            if get_pp_group().is_first_rank:
                intermediate_tensors = None
            else:
                if self.intermediate_tensors is None:
                    self.intermediate_tensors = (
                        self.model.make_empty_intermediate_tensors(
                            batch_size=self.max_num_tokens,
                            dtype=self.model_config.dtype,
                            device=self.device))
2113
2114
2115

                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
                    num_tokens, None, False)
2116

2117
            with self.maybe_randomize_inputs(input_ids), set_forward_context(
2118
2119
2120
2121
                    attn_metadata,
                    self.vllm_config,
                    num_tokens=num_tokens,
                    num_tokens_across_dp=num_tokens_across_dp):
2122
                outputs = model(
2123
2124
2125
2126
                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
2127
2128
2129
2130
                    **MultiModalKwargs.as_kwargs(
                        model_kwargs,
                        device=self.device,
                    ),
2131
                )
2132

2133
2134
2135
2136
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
2137

2138
            if self.speculative_config and self.speculative_config.use_eagle():
2139
2140
2141
                assert isinstance(self.drafter, EagleProposer)
                self.drafter.dummy_run(num_tokens)

2142
2143
2144
2145
2146
2147
2148
2149
2150
2151
        # This is necessary to avoid blocking DP.
        # For dummy runs, we typically skip EPLB since we don't have any real
        # requests to process.
        # However, in DP settings, there may be cases when some DP ranks do
        # not have any requests to process, so they're executing dummy batches.
        # In such cases, we still have to trigger EPLB to make sure
        # ranks execute the rearrangement in synchronization.
        if not skip_eplb:
            self.eplb_step(is_dummy=True, is_profile=is_profile)

2152
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
2153
        return hidden_states, hidden_states[logit_indices]
2154
2155
2156
2157
2158
2159

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
2160
2161
2162
2163
        # The dummy hidden states may contain special values,
        # like `inf` or `nan`.
        # To avoid breaking the sampler, we use a random tensor here instead.
        hidden_states = torch.rand_like(hidden_states)
2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
2175
2176
2177
2178
2179
2180
2181
2182
2183
2184
2185
2186

        logits = self.model.compute_logits(hidden_states, None)
        num_reqs = logits.size(0)

        dummy_tensors = lambda v: torch.full(
            (num_reqs, ), v, device=self.device)

        dummy_metadata = SamplingMetadata(
            temperature=dummy_tensors(0.5),
            all_greedy=False,
            all_random=False,
            top_p=dummy_tensors(0.9),
            top_k=dummy_tensors(logits.size(1) - 1),
            generators={},
            max_num_logprobs=None,
            no_penalties=True,
            prompt_token_ids=None,
            frequency_penalties=dummy_tensors(0.1),
            presence_penalties=dummy_tensors(0.1),
            repetition_penalties=dummy_tensors(0.1),
            output_token_ids=[[] for _ in range(num_reqs)],
            allowed_token_ids_mask=None,
            bad_words_token_ids={},
2187
            logitsprocs=LogitsProcessorManager(),
2188
        )
2189
        try:
2190
2191
            sampler_output = self.sampler(logits=logits,
                                          sampling_metadata=dummy_metadata)
2192
2193
2194
2195
2196
2197
2198
2199
2200
        except RuntimeError as e:
            if 'out of memory' in str(e):
                raise RuntimeError(
                    "CUDA out of memory occurred when warming up sampler with "
                    f"{num_reqs} dummy requests. Please try lowering "
                    "`max_num_seqs` or `gpu_memory_utilization` when "
                    "initializing the engine.") from e
            else:
                raise e
2201
        if self.speculative_config:
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2217
2218
2219
2220
2221
2222
2223
2224
2225
2226
2227
            draft_token_ids = [[0] for _ in range(num_reqs)]
            dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
                draft_token_ids, self.device)

            num_tokens = sum(len(ids) for ids in draft_token_ids)
            # draft_probs = torch.randn(
            #     num_tokens, logits.shape[-1], device=self.device,
            #     dtype=logits.dtype)
            draft_probs = None
            target_logits = torch.randn(num_tokens,
                                        logits.shape[-1],
                                        device=self.device,
                                        dtype=logits.dtype)
            # NOTE(woosuk): Here, we should use int32 because the sampler uses
            # int32 for bonus_token_ids. If the dtype mismatches, re-compilation
            # will occur at runtime.
            bonus_token_ids = torch.zeros(num_reqs,
                                          device=self.device,
                                          dtype=torch.int32)
            self.rejection_sampler(
                dummy_spec_decode_metadata,
                draft_probs,
                target_logits,
                bonus_token_ids,
                dummy_metadata,
            )
2228
        return sampler_output
2229

2230
    def _dummy_pooler_run_task(
2231
2232
        self,
        hidden_states: torch.Tensor,
2233
2234
        task: PoolingTask,
    ) -> PoolerOutput:
2235
2236
2237
2238
2239
2240
2241
2242
2243
2244
2245
2246
2247
        num_tokens = hidden_states.shape[0]
        max_num_reqs = self.scheduler_config.max_num_seqs
        num_reqs = min(num_tokens, max_num_reqs)
        min_tokens_per_req = num_tokens // num_reqs
        num_scheduled_tokens_list = [min_tokens_per_req] * num_reqs
        num_scheduled_tokens_list[-1] += num_tokens % num_reqs
        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs

        hidden_states_list = list(
            torch.split(hidden_states, num_scheduled_tokens_list))
        req_num_tokens = num_tokens // num_reqs

2248
2249
2250
2251
2252
2253
2254
        dummy_prompt_lens = torch.tensor(
            [h.shape[0] for h in hidden_states_list],
            device=self.device,
        )
        dummy_token_ids = torch.zeros((num_reqs, req_num_tokens),
                                      dtype=torch.int32,
                                      device=self.device)
2255

2256
2257
2258
        model = cast(VllmModelForPooling, self.model)
        dummy_pooling_params = PoolingParams(task=task)
        to_update = model.pooler.get_pooling_updates(task)
2259
2260
        to_update.apply(dummy_pooling_params)

2261
        dummy_metadata = PoolingMetadata(
2262
2263
2264
2265
            prompt_lens=dummy_prompt_lens,
            prompt_token_ids=dummy_token_ids,
            pooling_params=[dummy_pooling_params] * num_reqs,
        )
2266
2267

        try:
2268
2269
            return model.pooler(hidden_states=hidden_states_list,
                                pooling_metadata=dummy_metadata)
2270
2271
2272
        except RuntimeError as e:
            if 'out of memory' in str(e):
                raise RuntimeError(
2273
2274
2275
                    "CUDA out of memory occurred when warming up pooler "
                    f"({task=}) with {num_reqs} dummy requests. Please try "
                    "lowering `max_num_seqs` or `gpu_memory_utilization` when "
2276
2277
2278
                    "initializing the engine.") from e
            else:
                raise e
2279
2280
2281
2282
2283
2284
2285
2286
2287
2288
2289
2290
2291
2292
2293
2294

    @torch.inference_mode()
    def _dummy_pooler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> PoolerOutput:
        # Find the task that has the largest output for subsequent steps
        output_size = dict[PoolingTask, float]()
        for task in self.get_supported_pooling_tasks():
            # Run a full batch with each task to ensure none of them OOMs
            output = self._dummy_pooler_run_task(hidden_states, task)
            output_size[task] = output.get_data_nbytes()
            del output  # Allow GC

        max_task = max(output_size.items(), key=lambda x: x[1])[0]
        return self._dummy_pooler_run_task(hidden_states, max_task)
2295

2296
    def profile_run(self) -> None:
2297
        # Profile with multimodal encoder & encoder cache.
2298
2299
2300
        # TODO: handle encoder-decoder models once we support them.
        if (self.is_multimodal_model and self.max_num_encoder_input_tokens > 0
                and self.encoder_cache_size > 0):
2301

2302
            # NOTE: Currently model is profiled with a single non-text
2303
2304
            # modality with the max possible input tokens even when
            # it supports multiple.
2305
2306
            max_tokens_by_modality_dict = self.mm_registry \
                .get_max_tokens_per_item_by_nonzero_modality(self.model_config)
2307
2308
2309
2310
            dummy_data_modality, max_tokens_per_mm_item = max(
                max_tokens_by_modality_dict.items(), key=lambda item: item[1])

            # Check how many items of this modality can be supported by
2311
2312
2313
2314
            # the encoder budget.
            encoder_budget = min(self.max_num_encoder_input_tokens,
                                 self.encoder_cache_size)

2315
2316
            max_num_mm_items_encoder_budget = encoder_budget // \
                max_tokens_per_mm_item
2317
2318
2319

            # Check how many items of this modality can be supported by
            # the decoder budget.
2320
2321
            max_mm_items_per_req = self.mm_registry.get_mm_limits_per_prompt(
                self.model_config)[dummy_data_modality]
2322
2323
2324
2325
2326
2327
2328

            # NOTE: We do not consider max_num_batched_tokens on purpose
            # because the multimodal embeddings can be generated in advance
            # and chunked prefilled.
            max_num_mm_items_decoder_budget = self.max_num_reqs * \
                max_mm_items_per_req

2329
2330
2331
2332
            max_num_mm_items = max(
                1,
                min(max_num_mm_items_encoder_budget,
                    max_num_mm_items_decoder_budget))
2333

2334
2335
2336
2337
2338
2339
            logger.info(
                "Encoder cache will be initialized with a budget of %s tokens,"
                " and profiled with %s %s items of the maximum feature size.",
                encoder_budget, max_num_mm_items, dummy_data_modality)

            # Create dummy batch of multimodal inputs.
2340
            dummy_mm_kwargs = self.mm_registry.get_decoder_dummy_data(
2341
                model_config=self.model_config,
2342
                seq_len=max_tokens_per_mm_item,
2343
2344
2345
2346
                mm_counts={
                    dummy_data_modality: 1
                },
            ).multi_modal_data
2347

2348
            batched_dummy_mm_inputs = MultiModalKwargs.batch(
2349
2350
                [dummy_mm_kwargs] * max_num_mm_items,
                pin_memory=self.pin_memory)
2351
            batched_dummy_mm_inputs = MultiModalKwargs.as_kwargs(
2352
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                batched_dummy_mm_inputs,
                device=self.device,
            )
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            # Run multimodal encoder.
            dummy_encoder_outputs = self.model.get_multimodal_embeddings(
                **batched_dummy_mm_inputs)
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            sanity_check_mm_encoder_outputs(
                dummy_encoder_outputs,
                expected_num_items=max_num_mm_items,
            )
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            # Cache the dummy encoder outputs.
            self.encoder_cache["tmp"] = dict(enumerate(dummy_encoder_outputs))

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        # Add `is_profile` here to pre-allocate communication buffers
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        hidden_states, last_hidden_states \
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            = self._dummy_run(self.max_num_tokens, is_profile=True)
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        if get_pp_group().is_last_rank:
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            if self.is_pooling_model:
                output = self._dummy_pooler_run(hidden_states)
            else:
                output = self._dummy_sampler_run(last_hidden_states)
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        else:
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            output = None
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        self._sync_device()
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        del hidden_states, output
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        self.encoder_cache.clear()
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        gc.collect()
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    def capture_model(self) -> None:
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        if not self.use_cuda_graph:
            logger.warning(
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                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
                "set -O %s and ensure `use_cudagraph` was not manually set to "
                "False", CompilationLevel.PIECEWISE)
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            return

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        compilation_counter.num_gpu_runner_capture_triggers += 1

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        start_time = time.perf_counter()
        start_free_gpu_memory = torch.cuda.mem_get_info()[0]

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        @contextmanager
        def freeze_gc():
            # Optimize garbage collection during CUDA graph capture.
            # Clean up, then freeze all remaining objects from being included
            # in future collections.
            gc.collect()
            should_freeze = not envs.VLLM_ENABLE_CUDAGRAPH_GC
            if should_freeze:
                gc.freeze()
            try:
                yield
            finally:
                if should_freeze:
                    gc.unfreeze()

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        # Trigger CUDA graph capture for specific shapes.
        # Capture the large shapes first so that the smaller shapes
        # can reuse the memory pool allocated for the large shapes.
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        with freeze_gc(), graph_capture(device=self.device):
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            full_cg = self.full_cuda_graph
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            # Only rank 0 should print progress bar during capture
            compilation_cases = reversed(self.cudagraph_batch_sizes)
            if is_global_first_rank():
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                compilation_cases = tqdm(
                    list(compilation_cases),
                    disable=not self.load_config.use_tqdm_on_load,
                    desc="Capturing CUDA graph shapes")
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            for num_tokens in compilation_cases:
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                # We skip EPLB here since we don't want to record dummy metrics
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                for _ in range(
                        self.compilation_config.cudagraph_num_of_warmups):
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                    self._dummy_run(num_tokens,
                                    capture_attn_cudagraph=full_cg,
                                    skip_eplb=True)
                self._dummy_run(num_tokens,
                                capture_attn_cudagraph=full_cg,
                                skip_eplb=True)
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        end_time = time.perf_counter()
        end_free_gpu_memory = torch.cuda.mem_get_info()[0]
        elapsed_time = end_time - start_time
        cuda_graph_size = start_free_gpu_memory - end_free_gpu_memory
        # This usually takes 5~20 seconds.
        logger.info("Graph capturing finished in %.0f secs, took %.2f GiB",
                    elapsed_time, cuda_graph_size / (1 << 30))
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    def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize the attention backends and attention metadata builders.
        """
        assert len(self.attn_backends) == 0 and len(
            self.attn_metadata_builders
        ) == 0, "Attention backends are already initialized"
        for i, kv_cache_group_spec in enumerate(
                kv_cache_config.kv_cache_groups):
            kv_cache_spec = kv_cache_group_spec.kv_cache_spec
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            if isinstance(kv_cache_spec, AttentionSpec):
                attn_backend_i = get_attn_backend(
                    kv_cache_spec.head_size,
                    self.dtype,
                    kv_cache_spec.dtype,
                    kv_cache_spec.block_size,
                    self.model_config.is_attention_free,
                    use_mla=kv_cache_spec.use_mla,
                )
                if attn_backend_i is None:
                    error_msg = (f"Error with get_attn_backend: "
                                 f"{kv_cache_spec.head_size=}, "
                                 f"{self.dtype=}, {kv_cache_spec.dtype=}, "
                                 f"{kv_cache_spec.block_size=}, "
                                 f"{self.model_config.is_attention_free=}, "
                                 f"{kv_cache_spec.use_mla=}")
                    logger.error(error_msg)
                    raise NotImplementedError(
                        "Non-Attention backend is not supported by V1 "
                        "GPUModelRunner.")
            elif isinstance(kv_cache_spec, MambaSpec):
                attn_backend_i = Mamba2AttentionBackend
            else:
                raise ValueError(
                    f"Unknown KV cache spec type: {type(kv_cache_spec)}")
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            attn_metadata_builder_i = attn_backend_i.get_builder_cls()(
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                kv_cache_spec,
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                self.vllm_config,
                self.device,
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            )

            if (self.full_cuda_graph
                    and not attn_metadata_builder_i.full_cudagraph_supported):
                raise ValueError(
                    f"Full CUDAGraph not supported for "
                    f"{attn_backend_i.__name__}. Turn off CompilationConfig."
                    f"full_cuda_graph or use a different attention backend.")

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            self.attn_backends.append(attn_backend_i)
            self.attn_metadata_builders.append(attn_metadata_builder_i)

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    def may_reinitialize_input_batch(self,
                                     kv_cache_config: KVCacheConfig) -> None:
        """
        Re-initialize the input batch if the block sizes are different from
        `[self.cache_config.block_size]`. This usually happens when there
        are multiple KV cache groups.

        Args:
            kv_cache_config: The KV cache configuration.
        """
        block_sizes = [
            kv_cache_group.kv_cache_spec.block_size
            for kv_cache_group in kv_cache_config.kv_cache_groups
        ]
        if block_sizes != [self.cache_config.block_size]:
            assert self.cache_config.cpu_offload_gb == 0, (
                "Cannot re-initialize the input batch when CPU weight "
                "offloading is enabled. See https://github.com/vllm-project/vllm/pull/18298 "  # noqa: E501
                "for more details.")
            self.input_batch = InputBatch(
                max_num_reqs=self.max_num_reqs,
                max_model_len=self.max_model_len,
                max_num_batched_tokens=self.max_num_tokens,
                device=self.device,
                pin_memory=self.pin_memory,
                vocab_size=self.model_config.get_vocab_size(),
                block_sizes=block_sizes,
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                is_spec_decode=bool(self.vllm_config.speculative_config),
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            )

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    def _allocate_kv_cache_tensors(
            self, kv_cache_config: KVCacheConfig) -> dict[str, torch.Tensor]:
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        """
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        Initializes the KV cache buffer with the correct size. The buffer needs
        to be reshaped to the desired shape before being used by the models.

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        Args:
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            kv_cache_config: The KV cache config
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        Returns:
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            dict[str, torch.Tensor]: A map between layer names to their
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            corresponding memory buffer for KV cache.
         """
        kv_cache_raw_tensors: dict[str, torch.Tensor] = {}
        for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
            tensor = torch.zeros(kv_cache_tensor.size,
                                 dtype=torch.int8,
                                 device=self.device)
            for layer_name in kv_cache_tensor.shared_by:
                kv_cache_raw_tensors[layer_name] = tensor

        layer_names = set()
        for group in kv_cache_config.kv_cache_groups:
            layer_names.update(group.layer_names)
        assert layer_names == set(kv_cache_raw_tensors.keys(
        )), "Some layers are not correctly initialized"
        return kv_cache_raw_tensors

    def _reshape_kv_cache_tensors(
        self,
        kv_cache_config: KVCacheConfig,
        kv_cache_raw_tensors: dict[str, torch.Tensor],
    ) -> dict[str, torch.Tensor]:
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        """
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        Reshape the KV cache tensors to the desired shape and dtype.
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        Args:
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            kv_cache_config: The KV cache config
            kv_cache_raw_tensors: The KV cache buffer of each layer, with
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            correct size but uninitialized shape.
        Returns:
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            Dict[str, torch.Tensor]: A map between layer names to their
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            corresponding memory buffer for KV cache.
        """
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        kv_caches: dict[str, torch.Tensor] = {}
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        has_attn, has_mamba = False, False
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        for i, kv_cache_group_spec in enumerate(
                kv_cache_config.kv_cache_groups):
            kv_cache_spec = kv_cache_group_spec.kv_cache_spec
            for layer_name in kv_cache_group_spec.layer_names:
                raw_tensor = kv_cache_raw_tensors[layer_name]
                assert raw_tensor.numel() % kv_cache_spec.page_size_bytes == 0
                num_blocks = (raw_tensor.numel() //
                              kv_cache_spec.page_size_bytes)
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                if isinstance(kv_cache_spec, AttentionSpec):
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                    has_attn = True
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                    kv_cache_shape = self.attn_backends[i].get_kv_cache_shape(
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                        num_blocks, kv_cache_spec.block_size,
                        kv_cache_spec.num_kv_heads, kv_cache_spec.head_size)
                    dtype = kv_cache_spec.dtype
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                    try:
                        kv_cache_stride_order = self.attn_backends[
                            i].get_kv_cache_stride_order()
                        assert len(kv_cache_stride_order) == len(
                            kv_cache_shape)
                    except (AttributeError, NotImplementedError):
                        kv_cache_stride_order = tuple(
                            range(len(kv_cache_shape)))
                    # The allocation respects the backend-defined stride order
                    # to ensure the semantic remains consistent for each
                    # backend. We first obtain the generic kv cache shape and
                    # then permute it according to the stride order which could
                    # result in a non-contiguous tensor.
                    kv_cache_shape = tuple(kv_cache_shape[i]
                                           for i in kv_cache_stride_order)
                    # Maintain original KV shape view.
                    inv_order = [
                        kv_cache_stride_order.index(i)
                        for i in range(len(kv_cache_stride_order))
                    ]
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                    kv_caches[layer_name] = kv_cache_raw_tensors[
                        layer_name].view(dtype).view(kv_cache_shape).permute(
                            *inv_order)
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                elif isinstance(kv_cache_spec, MambaSpec):
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                    has_mamba = True
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                    raw_tensor = kv_cache_raw_tensors[layer_name]
                    dtype = kv_cache_spec.dtype
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                    num_element_per_page = (kv_cache_spec.page_size_bytes //
                                            get_dtype_size(dtype))
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                    state_tensors = []
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                    storage_offset = 0
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                    for shape in kv_cache_spec.shapes:
                        target_shape = (num_blocks, *shape)
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                        stride = torch.empty(target_shape).stride()
                        target_stride = (num_element_per_page, *stride[1:])
                        tensor = torch.as_strided(
                            raw_tensor.view(dtype),
                            size=target_shape,
                            stride=target_stride,
                            storage_offset=storage_offset,
                        )
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                        state_tensors.append(tensor)
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                        storage_offset += stride[0]

                    kv_caches[layer_name] = state_tensors
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                else:
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                    raise NotImplementedError
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        if has_attn and has_mamba:
            self._verify_hybrid_attention_mamba_layout(kv_cache_config,
                                                       kv_cache_raw_tensors)

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        return kv_caches

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    def _verify_hybrid_attention_mamba_layout(
            self, kv_cache_config: KVCacheConfig,
            kv_cache_raw_tensors: dict[str, torch.Tensor]) -> None:
        """
        Verify that the KV cache memory layout is compatible for
        models with both attention and mamba KV cache groups.

        Args:
            kv_cache_config: The KV cache config
            kv_cache_raw_tensors: The KV cache buffer of each layer.
        """

        for i, kv_cache_group_spec in enumerate(
                kv_cache_config.kv_cache_groups):
            kv_cache_spec = kv_cache_group_spec.kv_cache_spec
            for layer_name in kv_cache_group_spec.layer_names:
                raw_tensor = kv_cache_raw_tensors[layer_name]
                num_blocks = (raw_tensor.numel() //
                              kv_cache_spec.page_size_bytes)
                if isinstance(kv_cache_spec, AttentionSpec):
                    kv_cache_shape = self.attn_backends[i].get_kv_cache_shape(
                        num_blocks, kv_cache_spec.block_size,
                        kv_cache_spec.num_kv_heads, kv_cache_spec.head_size)
                    if kv_cache_shape[0] != num_blocks or kv_cache_shape[
                            1] != 2:
                        raise ValueError(
                            "Hybrid models in V1 require an attention "
                            "backend with kv_cache_shape="
                            "(num_blocks, 2, ...). Please try setting "
                            "VLLM_ATTENTION_BACKEND=FLASHINFER")

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    def initialize_kv_cache_tensors(
            self, kv_cache_config: KVCacheConfig) -> dict[str, torch.Tensor]:
        """
        Initialize the memory buffer for KV cache.

        Args:
            kv_cache_config: The KV cache config
        Returns:
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            corresponding memory buffer for KV cache.
        """
        # Initialize the memory buffer for KV cache
        kv_cache_raw_tensors = self._allocate_kv_cache_tensors(kv_cache_config)
        # Change the memory buffer to the desired shape
        kv_caches = self._reshape_kv_cache_tensors(kv_cache_config,
                                                   kv_cache_raw_tensors)
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        # Setup `kv_cache_config` and `kv_caches` for models
        # with cross-layer KV sharing
        if self.shared_kv_cache_layers:
            initialize_kv_cache_for_kv_sharing(
                self.shared_kv_cache_layers,
                kv_cache_config.kv_cache_groups,
                kv_caches,
            )

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        bind_kv_cache(kv_caches,
                      self.compilation_config.static_forward_context,
                      self.kv_caches)
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        return kv_caches

    def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize KV cache based on `kv_cache_config`.
        Args:
            kv_cache_config: Configuration for the KV cache, including the KV
            cache size of each layer
        """
        self.kv_cache_config = kv_cache_config
        self.may_reinitialize_input_batch(kv_cache_config)
        self.initialize_attn_backend(kv_cache_config)
        kv_caches = self.initialize_kv_cache_tensors(kv_cache_config)

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        if self.speculative_config and self.speculative_config.use_eagle():
            assert isinstance(self.drafter, EagleProposer)
            # validate all draft model layers belong to the same kv cache
            # group
            self.drafter.validate_same_kv_cache_group(kv_cache_config)

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        if has_kv_transfer_group():
            get_kv_transfer_group().register_kv_caches(kv_caches)

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    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
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        """
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        Generates the KVCacheSpec by parsing the kv cache format from each
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        Attention module in the static forward context.
        Returns:
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            KVCacheSpec: A dictionary mapping layer names to their KV cache
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            format. Layers that do not need KV cache are not included.
        """

        block_size = self.vllm_config.cache_config.block_size
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        use_mla = self.vllm_config.model_config.use_mla
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        kv_cache_spec: dict[str, KVCacheSpec] = {}
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        attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention)
        for layer_name, attn_module in attn_layers.items():
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            if (kv_tgt_layer :=
                    attn_module.kv_sharing_target_layer_name) is not None:
                # The layer doesn't need its own KV cache and will use that of
                # the target layer. We skip creating a KVCacheSpec for it, so
                # that KV cache management logic will act as this layer does
                # not exist, and doesn't allocate KV cache for the layer. This
                # enables the memory saving of cross-layer kv sharing, allowing
                # a given amount of memory to accommodate longer context lengths
                # or enable more requests to be processed simultaneously.
                self.shared_kv_cache_layers[layer_name] = kv_tgt_layer
                continue

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            # TODO: Support other attention modules, e.g., cross-attention
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            if attn_module.attn_type == AttentionType.DECODER:
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                use_local_attention = (self.attention_chunk_size is not None
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                                       and attn_module.use_irope)
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                if attn_module.sliding_window is not None:
                    kv_cache_spec[layer_name] = SlidingWindowSpec(
                        block_size=block_size,
                        num_kv_heads=attn_module.num_kv_heads,
                        head_size=attn_module.head_size,
                        dtype=self.kv_cache_dtype,
                        sliding_window=attn_module.sliding_window,
                        use_mla=use_mla)
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                    assert not use_local_attention, (
                        "attention module can not be with ",
                        "both local attention and sliding window")
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                elif use_local_attention:
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                    kv_cache_spec[layer_name] = ChunkedLocalAttentionSpec(
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                        block_size=block_size,
                        num_kv_heads=attn_module.num_kv_heads,
                        head_size=attn_module.head_size,
                        dtype=self.kv_cache_dtype,
                        attention_chunk_size=self.attention_chunk_size,
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                        use_mla=use_mla)
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                else:
                    kv_cache_spec[layer_name] = FullAttentionSpec(
                        block_size=block_size,
                        num_kv_heads=attn_module.num_kv_heads,
                        head_size=attn_module.head_size,
                        dtype=self.kv_cache_dtype,
                        use_mla=use_mla)
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            elif attn_module.attn_type in (AttentionType.ENCODER,
                                           AttentionType.ENCODER_ONLY):
                # encoder-only attention does not need KV cache.
                continue
            elif attn_module.attn_type == AttentionType.ENCODER_DECODER:
                raise NotImplementedError
            else:
                raise ValueError(
                    f"Unknown attention type: {attn_module.attn_type}")

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        if len(mamba_layers) > 0:
            if self.vllm_config.speculative_config is not None:
                raise NotImplementedError(
                    "Mamba with speculative decoding is not supported yet.")
            if self.vllm_config.cache_config.enable_prefix_caching:
                raise NotImplementedError(
                    "Prefix caching is not supported for Mamba yet.")
            max_model_len = self.vllm_config.model_config.max_model_len
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            page_size_padded = (
                self.vllm_config.cache_config.mamba_page_size_padded)
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            # Set block_size to max_model_len, so that mamba model will always
            # have only one block in the KV cache.
            for layer_name, mamba_module in mamba_layers.items():
                kv_cache_spec[layer_name] = MambaSpec(
                    shapes=mamba_module.get_state_shape(),
                    dtype=self.kv_cache_dtype,
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                    block_size=max_model_len,
                    page_size_padded=page_size_padded)

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        return kv_cache_spec