gpu_model_runner.py 201 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 itertools
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import time
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from collections import defaultdict
from collections.abc import Iterator
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from contextlib import contextmanager
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from copy import deepcopy
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from typing import TYPE_CHECKING, Any, NamedTuple, TypeAlias, 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 Attention, AttentionType
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from vllm.attention.backends.abstract import AttentionBackend, MultipleOf
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from vllm.attention.layer import MLAAttention
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from vllm.attention.layers.chunked_local_attention import ChunkedLocalAttention
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from vllm.compilation.counter import compilation_counter
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from vllm.compilation.cuda_graph import CUDAGraphWrapper
from vllm.compilation.monitor import set_cudagraph_capturing_enabled
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from vllm.config import (
    CompilationLevel,
    CUDAGraphMode,
    VllmConfig,
    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.kv_transfer.kv_connector.utils import copy_kv_blocks
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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,
    prepare_communication_buffer_for_model,
)
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from vllm.forward_context import BatchDescriptor, set_forward_context
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from vllm.logger import init_logger
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from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
from vllm.model_executor.layers.mamba.abstract 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.deepseek_v2 import DeepseekV32IndexerCache
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from vllm.model_executor.models.interfaces import (
    SupportsMultiModal,
    is_mixture_of_experts,
    supports_eagle3,
    supports_mrope,
    supports_multimodal_pruning,
    supports_transcription,
)
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from vllm.model_executor.models.interfaces_base import (
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    VllmModelForPooling,
    is_pooling_model,
    is_text_generation_model,
)
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (
    BatchedTensorInputs,
    MultiModalKwargsItem,
    PlaceholderRange,
)
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from vllm.multimodal.utils import group_mm_kwargs_by_modality
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from vllm.pooling_params import PoolingParams
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from vllm.sampling_params import SamplingType
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from vllm.sequence import IntermediateTensors
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from vllm.tasks import GenerationTask, PoolingTask, SupportedTask
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from vllm.utils import (
    STR_DTYPE_TO_TORCH_DTYPE,
    DeviceMemoryProfiler,
    GiB_bytes,
    cdiv,
    check_use_alibi,
    get_dtype_size,
    is_pin_memory_available,
    length_from_prompt_token_ids_or_embeds,
    round_up,
    supports_dynamo,
)
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from vllm.utils.jsontree import json_map_leaves
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from vllm.v1.attention.backends.flash_attn import AttentionMetadata
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from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadataBuilder
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from vllm.v1.attention.backends.utils import (
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    AttentionCGSupport,
    AttentionMetadataBuilder,
    CommonAttentionMetadata,
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    create_fast_prefill_custom_backend,
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    reorder_batch_to_split_decodes_and_prefills,
    split_attn_metadata,
)
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from vllm.v1.cudagraph_dispatcher import CudagraphDispatcher
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from vllm.v1.kv_cache_interface import (
    AttentionSpec,
    ChunkedLocalAttentionSpec,
    CrossAttentionSpec,
    EncoderOnlyAttentionSpec,
    FullAttentionSpec,
    KVCacheConfig,
    KVCacheGroupSpec,
    KVCacheSpec,
    MambaSpec,
    MLAAttentionSpec,
    SlidingWindowSpec,
    UniformTypeKVCacheSpecs,
)
from vllm.v1.outputs import (
    EMPTY_MODEL_RUNNER_OUTPUT,
    AsyncModelRunnerOutput,
    DraftTokenIds,
    LogprobsLists,
    LogprobsTensors,
    ModelRunnerOutput,
    PoolerOutput,
    SamplerOutput,
)
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from vllm.v1.pool.metadata import PoolingMetadata
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from vllm.v1.sample.logits_processor import LogitsProcessors, build_logitsprocs
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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.structured_output.utils import apply_grammar_bitmask
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from vllm.v1.utils import CpuGpuBuffer, record_function_or_nullcontext
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from vllm.v1.worker.dp_utils import coordinate_batch_across_dp
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from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
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from vllm.v1.worker.gpu_ubatch_wrapper import UBatchWrapper
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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 vllm.v1.worker.ubatch_utils import (
    UBatchSlice,
    UBatchSlices,
    check_ubatch_thresholds,
)
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from vllm.v1.worker.utils import is_residual_scattered_for_sp
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from .utils import (
    AttentionGroup,
    MultiModalBudget,
    add_kv_sharing_layers_to_kv_cache_groups,
    bind_kv_cache,
    gather_mm_placeholders,
    sanity_check_mm_encoder_outputs,
    scatter_mm_placeholders,
)
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if TYPE_CHECKING:
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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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logger = init_logger(__name__)

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AttnMetadataDict: TypeAlias = dict[str, AttentionMetadata]
# list when ubatching is enabled
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PerLayerAttnMetadata: TypeAlias = list[AttnMetadataDict] | AttnMetadataDict
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# Wrapper for ModelRunnerOutput to support overlapped execution.
class AsyncGPUModelRunnerOutput(AsyncModelRunnerOutput):
    def __init__(
        self,
        model_runner_output: ModelRunnerOutput,
        sampled_token_ids: torch.Tensor,
        invalid_req_indices: list[int],
        async_output_copy_stream: torch.cuda.Stream,
    ):
        self._model_runner_output = model_runner_output
        self._invalid_req_indices = invalid_req_indices

        # Event on the copy stream so we can synchronize the non-blocking copy.
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        self.async_copy_ready_event = torch.cuda.Event()
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        # Keep a reference to the device tensor to avoid it being
        # deallocated until we finish copying it to the host.
        self._sampled_token_ids = sampled_token_ids

        # Initiate the copy on a separate stream, but do not synchronize it.
        default_stream = torch.cuda.current_stream()
        with torch.cuda.stream(async_output_copy_stream):
            async_output_copy_stream.wait_stream(default_stream)
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            self.sampled_token_ids_cpu = self._sampled_token_ids.to(
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                "cpu", non_blocking=True
            )
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            self.async_copy_ready_event.record()
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    def get_output(self) -> ModelRunnerOutput:
        """Copy the device tensors to the host and return a ModelRunnerOutput.
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        This function blocks until the copy is finished.
        """
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        self.async_copy_ready_event.synchronize()
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        # Release the device tensor once the copy has completed
        del self._sampled_token_ids

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        valid_sampled_token_ids = self.sampled_token_ids_cpu.tolist()
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        for i in self._invalid_req_indices:
            valid_sampled_token_ids[i].clear()

        output = self._model_runner_output
        output.sampled_token_ids = valid_sampled_token_ids
        return output


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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
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        set_cpu_offload_max_bytes(int(self.cache_config.cpu_offload_gb * 1024**3))
        from vllm.model_executor.layers.batch_invariant import init_batch_invariance

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        init_batch_invariance()
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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:
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            self.kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[cache_config.cache_dtype]
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        self.is_pooling_model = model_config.runner_type == "pooling"
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        self.enable_prompt_embeds = model_config.enable_prompt_embeds
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        self.is_multimodal_raw_input_only_model = (
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            model_config.is_multimodal_raw_input_only_model
        )
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        # This will be overridden in load_model()
        self.is_multimodal_pruning_enabled = False
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        self.max_model_len = model_config.max_model_len
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        self.dcp_world_size = self.parallel_config.decode_context_parallel_size
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        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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        # 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
        self.broadcast_pp_output = (
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            self.parallel_config.distributed_executor_backend == "external_launcher"
            and len(get_pp_group().ranks) > 0
        )
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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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        # Only relevant for models using ALiBi (e.g, MPT)
        self.use_alibi = check_use_alibi(model_config)
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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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        self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
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            model_config
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        if self.model_config.is_encoder_decoder:
            # Maximum length of the encoder input, only for encoder-decoder
            # models.
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            self.max_encoder_len = scheduler_config.max_num_encoder_input_tokens
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        else:
            self.max_encoder_len = 0

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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: EplbState | None = None
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        """
        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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        # indexes: [kv_cache_group_id][attn_group]
        self.attn_groups: list[list[AttentionGroup]] = []
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        # self.kv_cache_config: KVCacheConfig

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        # mm_hash ->  encoder_output
        self.encoder_cache: dict[str, 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():
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                self.drafter = EagleProposer(self.vllm_config, self.device, self)  # type: ignore
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                if self.speculative_config.method == "eagle3":
                    self.use_aux_hidden_state_outputs = True
            elif self.speculative_config.method == "medusa":
                self.drafter = MedusaProposer(
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                    vllm_config=self.vllm_config, device=self.device
                )  # type: ignore
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            else:
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                raise ValueError(
                    "Unknown speculative decoding method: "
                    f"{self.speculative_config.method}"
                )
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            self.rejection_sampler = RejectionSampler()
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        # Request states.
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        self.requests: dict[str, CachedRequestState] = {}
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        self.comm_stream = torch.cuda.Stream()
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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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        custom_logitsprocs = model_config.logits_processors
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        self.input_batch = InputBatch(
            max_num_reqs=self.max_num_reqs,
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            # We need to use the encoder length for encoder-decoer
            # because of KV cache for cross-attention.
            max_model_len=max(self.max_model_len, self.max_encoder_len),
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            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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            kernel_block_sizes=[self.cache_config.block_size],
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            is_spec_decode=bool(self.vllm_config.speculative_config),
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            logitsprocs=build_logitsprocs(
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                self.vllm_config,
                self.device,
                self.pin_memory,
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                self.is_pooling_model,
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                custom_logitsprocs,
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            ),
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            # We currently don't know whether a particular custom logits processor
            # uses output token ids so we set this conservatively.
            logitsprocs_need_output_token_ids=bool(custom_logitsprocs),
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            is_pooling_model=self.is_pooling_model,
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        )
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        self.use_async_scheduling = self.scheduler_config.async_scheduling
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        self.async_output_copy_stream = (
            torch.cuda.Stream() if self.use_async_scheduling else None
        )
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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.
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        if (
            self.compilation_config.cudagraph_capture_sizes
            and self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
        ):
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            self.cudagraph_batch_sizes = list(
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                reversed(self.compilation_config.cudagraph_capture_sizes)
            )
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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.
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        self.input_ids = self._make_buffer(self.max_num_tokens, dtype=torch.int32)
        self.positions = self._make_buffer(self.max_num_tokens, dtype=torch.int64)
        self.query_start_loc = self._make_buffer(
            self.max_num_reqs + 1, dtype=torch.int32
        )
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        self.seq_lens = self._make_buffer(self.max_num_reqs, dtype=torch.int32)
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        if self.dcp_world_size > 1:
            self.dcp_local_seq_lens = self._make_buffer(
                self.max_num_reqs, dtype=torch.int32
            )
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        # Because inputs_embeds may be bfloat16 and we don't need a numpy
        # version of this tensor, avoid a RuntimeError by not creating a
        # numpy buffer.
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        self.inputs_embeds = self._make_buffer(
            self.max_num_tokens, self.hidden_size, dtype=self.dtype, numpy=False
        )
        self.is_token_ids = self._make_buffer(self.max_num_tokens, dtype=torch.bool)
        self.discard_request_indices = self._make_buffer(
            self.max_num_reqs, dtype=torch.int64
        )
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        self.num_discarded_requests = 0

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        self.num_decode_draft_tokens = self._make_buffer(
            self.max_num_reqs, dtype=torch.int32
        )
        self.num_accepted_tokens = self._make_buffer(
            self.max_num_reqs, dtype=torch.int64
        )
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        # Only relevant for multimodal models
        if self.supports_mm_inputs:
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            self.is_mm_embed = self._make_buffer(self.max_num_tokens, dtype=torch.bool)
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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 = self._make_buffer(
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                (3, self.max_num_tokens + 1), dtype=torch.int64
            )
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        # CUDA event to synchronize use of reused CPU tensors between steps
        # when async scheduling is enabled.
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        self.prepare_inputs_event: torch.cuda.Event | None = None
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        if self.use_async_scheduling:
            self.prepare_inputs_event = torch.cuda.Event()
            # Start in a completed state.
            self.prepare_inputs_event.record(torch.cuda.default_stream())

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        # None in the first PP rank. The rest are set after load_model.
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        self.intermediate_tensors: IntermediateTensors | None = None
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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, self.max_model_len, self.max_num_tokens),
            dtype=np.int64,
        )
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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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        self.kv_sharing_fast_prefill_eligible_layers: set[str] = set()

        self.kv_sharing_fast_prefill_logits_indices = None
        if self.cache_config.kv_sharing_fast_prefill:
            self.kv_sharing_fast_prefill_logits_indices = torch.zeros(
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                self.max_num_tokens, dtype=torch.int32, device=self.device
            )
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        self.uniform_decode_query_len = (
            1
            if not self.speculative_config
            else 1 + self.speculative_config.num_speculative_tokens
        )
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        # Cudagraph dispatcher for runtime cudagraph dispatching.
        self.cudagraph_dispatcher = CudagraphDispatcher(self.vllm_config)

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        self.mm_budget = (
            MultiModalBudget(
                self.model_config,
                self.scheduler_config,
                self.mm_registry,
            )
            if self.supports_mm_inputs
            else None
        )
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        self.reorder_batch_threshold: int | None = None
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        # Attention layers that are only in the KVCacheConfig of the runner
        # (e.g., KV sharing, encoder-only attention), but not in the
        # KVCacheConfig of the scheduler.
        self.runner_only_attn_layers: set[str] = set()

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        # Cached outputs.
505
        self._draft_token_ids: list[list[int]] | torch.Tensor | None = None
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        self.transfer_event = torch.cuda.Event()
        self.sampled_token_ids_pinned_cpu = torch.empty(
            (self.max_model_len, 1),
            dtype=torch.int64,
            device="cpu",
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512
            pin_memory=self.pin_memory,
        )
513

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    def reset_mm_cache(self) -> None:
        if self.mm_budget:
            self.mm_budget.reset_cache()

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    def _get_positions(self, num_tokens: Any):
        if isinstance(num_tokens, int):
            if self.uses_mrope:
                return self.mrope_positions.gpu[:, :num_tokens]
            return self.positions.gpu[:num_tokens]
        else:
            if self.uses_mrope:
                return self.mrope_positions.gpu[:, num_tokens]
            return self.positions.gpu[num_tokens]

528
    def _make_buffer(
529
        self, *size: int | torch.SymInt, dtype: torch.dtype, numpy: bool = True
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    ) -> CpuGpuBuffer:
        return CpuGpuBuffer(
            *size,
            dtype=dtype,
            device=self.device,
            pin_memory=self.pin_memory,
            with_numpy=numpy,
        )
538

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    def _init_model_kwargs(self, num_tokens: int):
        model_kwargs = dict[str, Any]()

542
        if not self.is_pooling_model:
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            return model_kwargs

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        num_reqs = self.input_batch.num_reqs
        pooling_params = self.input_batch.get_pooling_params()
547
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549

        token_type_id_requests = dict[int, Any]()
        for i, param in enumerate(pooling_params):
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            if (
                param.extra_kwargs is not None
                and (token_types := param.extra_kwargs.get("compressed_token_type_ids"))
                is not None
            ):
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                token_type_id_requests[i] = token_types

        if len(token_type_id_requests) == 0:
            return model_kwargs

560
        seq_lens = self.seq_lens.gpu[:num_reqs]
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        token_type_ids = []

        for i in range(num_reqs):
            pos = token_type_id_requests.get(i, seq_lens[i])
            ids = (torch.arange(seq_lens[i]) >= pos).int()
            token_type_ids.append(ids)

        model_kwargs["token_type_ids"] = torch.concat(token_type_ids).to(
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            device=self.device
        )
571
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        return model_kwargs

573
    def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
574
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        """
        Update the order of requests in the batch based on the attention
576
        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

591
        if self.reorder_batch_threshold is not None:
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594
            # NOTE(lucas): currently no backend supports the custom masking
            #  required for DCP with q_len > 1, so we assert here. Remove this
            #  assert once the custom mask is support is added to FA3.
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            if (
                self.dcp_world_size > 1
                and envs.VLLM_ATTENTION_BACKEND != "FLASH_ATTN_MLA"
            ):
599
                assert self.reorder_batch_threshold == 1, (
600
                    "DCP not support reorder_batch_threshold > 1 now."
601
                )
602
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            reorder_batch_to_split_decodes_and_prefills(
                self.input_batch,
                scheduler_output,
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                decode_threshold=self.reorder_batch_threshold,
            )
607

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609
    # Note: used for model runner override.
    def _init_device_properties(self) -> None:
610
        """Initialize attributes from torch.cuda.get_device_properties"""
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617
        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()

618
    def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
619
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624
        """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.

625
626
        The SamplingMetadata is updated and copied to the GPU if there is a
        new/resumed/paused/finished request in the batch.
627
628
        """
        # Remove finished requests from the cached states.
629
630
        for req_id in scheduler_output.finished_req_ids:
            self.requests.pop(req_id, None)
631
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636
637
        # 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:
638
            self.input_batch.remove_request(req_id)
639
640

        # Free the cached encoder outputs.
641
642
        for mm_hash in scheduler_output.free_encoder_mm_hashes:
            self.encoder_cache.pop(mm_hash, None)
643

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655
656
        # 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:
657
            self.input_batch.remove_request(req_id)
658

659
        reqs_to_add: list[CachedRequestState] = []
660
        # Add new requests to the cached states.
661
662
663
        for new_req_data in scheduler_output.scheduled_new_reqs:
            req_id = new_req_data.req_id
            sampling_params = new_req_data.sampling_params
664
            pooling_params = new_req_data.pooling_params
665

666
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669
            if (
                sampling_params
                and sampling_params.sampling_type == SamplingType.RANDOM_SEED
            ):
670
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674
                generator = torch.Generator(device=self.device)
                generator.manual_seed(sampling_params.seed)
            else:
                generator = None

675
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            if self.is_pooling_model:
                assert pooling_params is not None
677
678
                task = pooling_params.task
                assert task is not None, "You did not set `task` in the API"
679

680
                model = cast(VllmModelForPooling, self.get_model())
681
                to_update = model.pooler.get_pooling_updates(task)
682
683
                to_update.apply(pooling_params)

684
            req_state = CachedRequestState(
685
                req_id=req_id,
686
                prompt_token_ids=new_req_data.prompt_token_ids,
687
                prompt_embeds=new_req_data.prompt_embeds,
688
                mm_features=new_req_data.mm_features,
689
                sampling_params=sampling_params,
690
                pooling_params=pooling_params,
691
                generator=generator,
692
693
                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
694
                output_token_ids=[],
695
                lora_request=new_req_data.lora_request,
696
            )
697
698
            self.requests[req_id] = req_state

699
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
700
            if self.uses_mrope:
701
                self._init_mrope_positions(req_state)
702

703
            reqs_to_add.append(req_state)
704

705
        # Update the states of the running/resumed requests.
706
        is_last_rank = get_pp_group().is_last_rank
707
708
        req_data = scheduler_output.scheduled_cached_reqs
        for i, req_id in enumerate(req_data.req_ids):
709
            req_state = self.requests[req_id]
710
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712
            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]
713
            num_output_tokens = req_data.num_output_tokens[i]
714

715
            # Update the cached states.
716

717
            req_state.num_computed_tokens = num_computed_tokens
718
            req_index = self.input_batch.req_id_to_index.get(req_id)
719
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721
722
723
724
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726

            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.
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729
                num_new_tokens = (
                    num_computed_tokens + len(new_token_ids) - req_state.num_tokens
                )
730
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732
733
                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:
734
                    req_state.output_token_ids.extend(new_token_ids[-num_new_tokens:])
735
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737
738
739
            elif num_output_tokens < len(req_state.output_token_ids):
                # Some output tokens were discarded due to a sync-KV-load
                # failure. Align the cached state.
                del req_state.output_token_ids[num_output_tokens:]
                if req_index is not None:
740
741
742
743
                    end_idx = (
                        self.input_batch.num_prompt_tokens[req_index]
                        + num_output_tokens
                    )
744
745
                    self.input_batch.num_tokens[req_index] = end_idx
                    self.input_batch.num_tokens_no_spec[req_index] = end_idx
746

747
            # Update the block IDs.
748
            if not resumed_from_preemption:
749
750
                if new_block_ids is not None:
                    # Append the new blocks to the existing block IDs.
751
                    for block_ids, new_ids in zip(req_state.block_ids, new_block_ids):
752
                        block_ids.extend(new_ids)
753
            else:
754
                assert req_index is None
755
                assert new_block_ids is not None
756
757
                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
758
                req_state.block_ids = new_block_ids
759

760
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764
765
                if self.use_async_scheduling and num_output_tokens > 0:
                    # We must recover the output token ids for resumed requests in the
                    # async scheduling case, so that correct input_ids are obtained.
                    resumed_token_ids = req_data.resumed_req_token_ids[i]
                    assert resumed_token_ids is not None
                    req_state.output_token_ids = resumed_token_ids[-num_output_tokens:]
766
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769
            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.
770
                reqs_to_add.append(req_state)
771
772
773
                continue

            # Update the persistent batch.
774
            self.input_batch.num_computed_tokens_cpu[req_index] = num_computed_tokens
775
            if new_block_ids is not None:
776
                self.input_batch.block_table.append_row(new_block_ids, req_index)
777
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779
780
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782
783

            # 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)
784
                self.input_batch.token_ids_cpu[
785
786
787
                    req_index, start_token_index:end_token_index
                ] = new_token_ids
                self.input_batch.num_tokens_no_spec[req_index] = end_token_index
788
                self.input_batch.num_tokens[req_index] = end_token_index
789

790
            # Add spec_token_ids to token_ids_cpu.
791
792
793
            spec_token_ids = scheduler_output.scheduled_spec_decode_tokens.get(
                req_id, ()
            )
794
795
796
797
798
            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[
799
800
                    req_index, start_index:end_token_index
                ] = spec_token_ids
801
802
                # NOTE(woosuk): `num_tokens` here may include spec tokens.
                self.input_batch.num_tokens[req_index] += num_spec_tokens
803
                self.input_batch.spec_token_ids[req_index] = spec_token_ids
804

805
806
        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
807
808
        for request in reqs_to_add:
            self.input_batch.add_request(request)
809

810
811
812
813
814
815
        # 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()
816

817
    def _update_states_after_model_execute(
818
819
        self, output_token_ids: torch.Tensor
    ) -> None:
820
821
822
823
824
825
826
827
828
829
830
831
        """Update the cached states after model execution.

        This is used for MTP/EAGLE for hybrid models, as in linear attention,
        only the last token's state is kept. In MTP/EAGLE, for draft tokens
        the state are kept util we decide how many tokens are accepted for
        each sequence, and a shifting is done during the next iteration
        based on the number of accepted tokens.
        """
        if not self.model_config.is_hybrid or not self.speculative_config:
            return

        # Find the number of accepted tokens for each sequence.
832
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834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
        num_accepted_tokens = (
            (
                torch.cat(
                    [
                        output_token_ids,
                        torch.full(
                            (output_token_ids.size(0), 1),
                            -1,
                            device=output_token_ids.device,
                        ),
                    ],
                    dim=1,
                )
                == -1
            )
            .int()
            .argmax(-1)
            .cpu()
            .numpy()
        )
852
853
854
        for i, num_tokens in enumerate(num_accepted_tokens):
            self.input_batch.num_accepted_tokens_cpu[i] = num_tokens

855
856
857
858
859
860
    def _init_mrope_positions(self, req_state: CachedRequestState):
        image_grid_thw = []
        video_grid_thw = []
        second_per_grid_ts = []
        audio_feature_lengths = []
        use_audio_in_video = False
861
862
863
864
        for mm_feature in req_state.mm_features:
            mm_item = mm_feature.data
            if mm_item is None:
                continue
865
866
867
868
869
870
871
872
873
874
875
876
            mm_input = mm_item.get_data()
            if (t := mm_input.get("image_grid_thw")) is not None:
                image_grid_thw.append(t.tolist())
            if (t := mm_input.get("video_grid_thw")) is not None:
                video_grid_thw.append(t.tolist())
            if (t := mm_input.get("second_per_grid_ts")) is not None:
                second_per_grid_ts.append(t)
            if (t := mm_input.get("audio_feature_lengths")) is not None:
                audio_feature_lengths.append(t)
            if mm_input.get("use_audio_in_video") is True:
                use_audio_in_video = True

877
878
879
880
881
882
883
884
885
886
887
        assert supports_mrope(self.get_model()), "M-RoPE support is not implemented."

        req_state.mrope_positions, req_state.mrope_position_delta = (
            self.model.get_mrope_input_positions(
                req_state.prompt_token_ids,
                hf_config=self.model_config.hf_config,
                image_grid_thw=image_grid_thw,
                video_grid_thw=video_grid_thw,
                second_per_grid_ts=second_per_grid_ts,
                audio_feature_lengths=audio_feature_lengths,
                use_audio_in_video=use_audio_in_video,
888
            )
889
        )
890

891
    def _extract_mm_kwargs(
892
        self,
893
894
        scheduler_output: "SchedulerOutput",
    ) -> BatchedTensorInputs:
895
        if not scheduler_output or not self.is_multimodal_raw_input_only_model:
896
            return {}
897

898
899
        mm_kwargs = list[MultiModalKwargsItem]()
        for req in scheduler_output.scheduled_new_reqs:
900
901
902
            for feature in req.mm_features:
                if feature.data is not None:
                    mm_kwargs.append(feature.data)
903

904
        # Input all modalities at once
905
        model = cast(SupportsMultiModal, self.model)
906
907
        mm_kwargs_combined: BatchedTensorInputs = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
908
909
910
911
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
912
913
        ):
            mm_kwargs_combined.update(mm_kwargs_group)
914

915
        return mm_kwargs_combined
916

917
    def _dummy_mm_kwargs(self, num_seqs: int) -> BatchedTensorInputs:
918
        if not self.is_multimodal_raw_input_only_model:
919
            return {}
920

921
922
923
924
925
        mm_budget = self.mm_budget
        assert mm_budget is not None

        dummy_modality = mm_budget.get_modality_with_max_tokens()
        return self._get_mm_dummy_batch(dummy_modality, num_seqs)
926

927
928
929
    def _get_cumsum_and_arange(
        self,
        num_tokens: np.ndarray,
930
        cumsum_dtype: np.dtype | None = None,
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
    ) -> 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

947
948
949
    def _prepare_input_ids(
        self, total_num_scheduled_tokens: int, cu_num_tokens: np.ndarray
    ) -> None:
950
        """Prepare the input IDs for the current batch.
951

952
953
954
955
956
957
958
        Carefully handles the `prev_sampled_token_ids` which can be cached
        from the previous engine iteration, in which case those tokens on the
        GPU need to be copied into the corresponding slots into input_ids."""

        if self.input_batch.prev_sampled_token_ids is None:
            # Normal scheduling case
            self.input_ids.copy_to_gpu(total_num_scheduled_tokens)
959
960
961
            if self.enable_prompt_embeds:
                self.inputs_embeds.copy_to_gpu(total_num_scheduled_tokens)
                self.is_token_ids.copy_to_gpu(total_num_scheduled_tokens)
962
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964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
            return

        # Async scheduling case, where some decode requests from the previous
        # iteration won't have entries in input_ids_cpu and need to be copied
        # on the GPU from prev_sampled_token_ids.
        prev_req_id_to_index = self.input_batch.prev_req_id_to_index
        assert prev_req_id_to_index is not None
        flattened_indices = []
        prev_common_req_indices = []
        indices_match = True
        max_flattened_index = -1
        for req_id, cur_index in self.input_batch.req_id_to_index.items():
            if (prev_index := prev_req_id_to_index.get(req_id)) is not None:
                prev_common_req_indices.append(prev_index)
                # We need to compute the flattened input_ids index of the
                # last token in each common request.
                flattened_index = cu_num_tokens[cur_index].item() - 1
                flattened_indices.append(flattened_index)
980
                indices_match &= prev_index == flattened_index
981
982
983
984
985
986
                max_flattened_index = max(max_flattened_index, flattened_index)
        num_commmon_tokens = len(flattened_indices)
        if num_commmon_tokens < total_num_scheduled_tokens:
            # If not all requests are decodes from the last iteration,
            # We need to copy the input_ids_cpu to the GPU first.
            self.input_ids.copy_to_gpu(total_num_scheduled_tokens)
987
988
989
            if self.enable_prompt_embeds:
                self.inputs_embeds.copy_to_gpu(total_num_scheduled_tokens)
                self.is_token_ids.copy_to_gpu(total_num_scheduled_tokens)
990
991
        if num_commmon_tokens == 0:
            # No requests in common with the previous iteration
992
            # So input_ids.cpu will have all the input ids.
993
994
995
996
997
998
999
            return
        if indices_match and max_flattened_index == (num_commmon_tokens - 1):
            # Common-case optimization: the batch is unchanged
            # and no reordering happened.
            # The indices are both the same permutation of 0..N-1 so
            # we can copy directly using a single slice.
            self.input_ids.gpu[:num_commmon_tokens].copy_(
1000
1001
1002
                self.input_batch.prev_sampled_token_ids[:num_commmon_tokens, 0],
                non_blocking=True,
            )
1003
1004
            if self.enable_prompt_embeds:
                self.is_token_ids.gpu[:num_commmon_tokens] = True
1005
            return
1006
        # Upload the index tensors asynchronously so the scatter can be non-blocking.
1007
1008
1009
        input_ids_index_tensor = torch.tensor(
            flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1010
        prev_common_req_indices_tensor = torch.tensor(
1011
1012
            prev_common_req_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1013
1014
1015
1016
        self.input_ids.gpu.scatter_(
            dim=0,
            index=input_ids_index_tensor,
            src=self.input_batch.prev_sampled_token_ids[
1017
1018
1019
                prev_common_req_indices_tensor, 0
            ],
        )
1020

1021
1022
1023
1024
1025
    def _get_encoder_seq_lens(
        self,
        scheduler_output: "SchedulerOutput",
        kv_cache_spec: KVCacheSpec,
        num_reqs: int,
1026
    ) -> np.ndarray | None:
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
        if not isinstance(kv_cache_spec, CrossAttentionSpec):
            return None

        # Build encoder_seq_lens array mapping request indices to
        # encoder lengths for inputs scheduled in this batch
        encoder_seq_lens = np.zeros(num_reqs, dtype=np.int32)
        for req_id in scheduler_output.scheduled_encoder_inputs:
            req_index = self.input_batch.req_id_to_index[req_id]
            encoder_seq_lens[req_index] = self.max_encoder_len

        return encoder_seq_lens

1039
    def _prepare_inputs(
1040
        self, scheduler_output: "SchedulerOutput"
1041
1042
1043
    ) -> tuple[
        PerLayerAttnMetadata,
        torch.Tensor,
1044
        SpecDecodeMetadata | None,
1045
        np.ndarray,
1046
        CommonAttentionMetadata | None,
1047
        int,
1048
1049
        UBatchSlices | None,
        torch.Tensor | None,
1050
1051
        bool,
    ]:
1052
1053
1054
        """
        :return: tuple[
            attn_metadata: layer-to-attention_metadata mapping,
1055
1056
1057
            logits_indices, spec_decode_metadata,
            num_scheduled_tokens, spec_decode_common_attn_metadata,
            max_num_scheduled_tokens, use_cascade_attn
1058
1059
        ]
        """
1060
1061
1062
1063
1064
1065
1066
        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.
1067
        self.input_batch.block_table.commit_block_table(num_reqs)
1068
1069

        # Get the number of scheduled tokens for each request.
1070
1071
1072
1073
        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)
1074
1075
1076

        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
1077
        req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens)
1078

1079
1080
        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
        # arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
1081
        cu_num_tokens, arange = self._get_cumsum_and_arange(num_scheduled_tokens)
1082
1083

        # Get positions.
1084
        positions_np = self.positions.np[:total_num_scheduled_tokens]
1085
1086
1087
1088
1089
        np.add(
            self.input_batch.num_computed_tokens_cpu[req_indices],
            arange,
            out=positions_np,
        )
1090

1091
1092
        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1093
        if self.uses_mrope:
1094
1095
            self._calc_mrope_positions(scheduler_output)

1096
1097
1098
1099
        # 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.
1100
1101
1102
        token_indices = (
            positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]
        )
1103
        token_indices_tensor = torch.from_numpy(token_indices)
1104

1105
1106
1107
        # 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.
1108
1109
1110
1111
1112
1113
        torch.index_select(
            self.input_batch.token_ids_cpu_tensor.flatten(),
            0,
            token_indices_tensor,
            out=self.input_ids.cpu[:total_num_scheduled_tokens],
        )
1114
1115
1116
1117
1118
1119
        if self.enable_prompt_embeds:
            is_token_ids = self.input_batch.is_token_ids.flatten()
            torch.index_select(
                is_token_ids,
                0,
                token_indices_tensor,
1120
1121
                out=self.is_token_ids.cpu[:total_num_scheduled_tokens],
            )
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154

        # Because we did not pre-allocate a massive prompt_embeds CPU tensor on
        # the InputBatch, we need to fill in the prompt embeds into the expected
        # spots in the GpuModelRunner's pre-allocated prompt_embeds tensor.
        if self.input_batch.req_prompt_embeds:
            output_idx = 0
            for req_idx in range(num_reqs):
                num_sched = num_scheduled_tokens[req_idx]

                # Skip if this request doesn't have embeddings
                if req_idx not in self.input_batch.req_prompt_embeds:
                    output_idx += num_sched
                    continue

                # Skip if no tokens scheduled
                if num_sched <= 0:
                    output_idx += num_sched
                    continue

                req_embeds = self.input_batch.req_prompt_embeds[req_idx]
                start_pos = self.input_batch.num_computed_tokens_cpu[req_idx]

                # Skip if trying to read beyond available embeddings
                if start_pos >= req_embeds.shape[0]:
                    output_idx += num_sched
                    continue

                # Copy available embeddings
                end_pos = start_pos + num_sched
                actual_end = min(end_pos, req_embeds.shape[0])
                actual_num_sched = actual_end - start_pos

                if actual_num_sched > 0:
1155
1156
1157
                    self.inputs_embeds.cpu[
                        output_idx : output_idx + actual_num_sched
                    ].copy_(req_embeds[start_pos:actual_end])
1158
1159

                output_idx += num_sched
1160

1161
1162
        self.input_batch.block_table.compute_slot_mapping(req_indices, positions_np)
        self.input_batch.block_table.commit_slot_mapping(total_num_scheduled_tokens)
1163
1164

        # Prepare the attention metadata.
1165
        self.query_start_loc.np[0] = 0
1166
        self.query_start_loc.np[1 : num_reqs + 1] = cu_num_tokens
1167
1168
        # Note: pad query_start_loc to be non-decreasing, as kernels
        # like FlashAttention requires that
1169
        self.query_start_loc.np[num_reqs + 1 :].fill(cu_num_tokens[-1])
1170
        self.query_start_loc.copy_to_gpu()
1171
        query_start_loc = self.query_start_loc.gpu[: num_reqs + 1]
1172

1173
        num_tokens_unpadded = scheduler_output.total_num_scheduled_tokens
1174
        num_tokens_padded = self._get_num_input_tokens(num_tokens_unpadded)
1175
1176
1177
        uniform_decode = (
            max_num_scheduled_tokens == self.uniform_decode_query_len
        ) and (total_num_scheduled_tokens == num_reqs * max_num_scheduled_tokens)
1178
1179
1180
1181
1182
1183
1184

        # Disable DP padding when running eager to avoid excessive padding when
        # running prefills. 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.
        allow_dp_padding = self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE

1185
        ubatch_slices, num_tokens_across_dp = coordinate_batch_across_dp(
1186
1187
1188
1189
1190
1191
1192
            num_tokens_unpadded=num_tokens_unpadded,
            parallel_config=self.parallel_config,
            allow_microbatching=True,
            allow_dp_padding=allow_dp_padding,
            num_tokens_padded=num_tokens_padded,
            uniform_decode=uniform_decode,
            num_scheduled_tokens_per_request=num_scheduled_tokens,
1193
        )
1194

1195
        self.seq_lens.np[:num_reqs] = (
1196
1197
            self.input_batch.num_computed_tokens_cpu[:num_reqs] + num_scheduled_tokens
        )
1198
        # Fill unused with 0 for full cuda graph mode.
1199
1200
1201
1202
        self.seq_lens.np[num_reqs:].fill(0)
        self.seq_lens.copy_to_gpu()
        seq_lens = self.seq_lens.gpu[:num_reqs]
        max_seq_len = self.seq_lens.np[:num_reqs].max().item()
1203

1204
        num_tokens = [self.requests[r].num_tokens for r in self.input_batch.req_ids]
1205
1206
1207
1208
1209
1210
1211
        num_tokens_np = np.array(num_tokens, dtype=np.int32)

        # Record the index of requests that should not be sampled,
        # so that we could clear the sampled tokens before returning
        discard_requests_mask = self.seq_lens.np[:num_reqs] < num_tokens_np
        discard_request_indices = np.nonzero(discard_requests_mask)[0]
        self.num_discarded_requests = len(discard_request_indices)
1212
1213
1214
        self.discard_request_indices.np[: self.num_discarded_requests] = (
            discard_request_indices
        )
1215
1216
1217

        self.discard_request_indices.copy_to_gpu(self.num_discarded_requests)

1218
        # Copy the tensors to the GPU.
1219
1220
        self._prepare_input_ids(total_num_scheduled_tokens, cu_num_tokens)

1221
        if self.uses_mrope:
1222
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1223
1224
            self.mrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.mrope_positions.cpu[:, :total_num_scheduled_tokens],
1225
1226
                non_blocking=True,
            )
1227
1228
        else:
            # Common case (1D positions)
1229
            self.positions.copy_to_gpu(total_num_scheduled_tokens)
1230

1231
        use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
1232
1233
1234
1235
1236
1237
1238
        if not use_spec_decode:
            # 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.
            logits_indices = query_start_loc[1:] - 1
1239
            num_draft_tokens = None
1240
1241
1242
1243
1244
1245
            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)
1246
1247
1248
            # For chunked prefills, use -1 as mask rather than 0, as guided
            # decoding may rollback speculative tokens.
            num_decode_draft_tokens = np.full(num_reqs, -1, dtype=np.int32)
1249
1250
1251
1252
            for (
                req_id,
                draft_token_ids,
            ) in scheduler_output.scheduled_spec_decode_tokens.items():
1253
1254
                req_idx = self.input_batch.req_id_to_index[req_id]
                num_draft_tokens[req_idx] = len(draft_token_ids)
1255
1256
1257
1258
1259
1260
1261
1262
                num_decode_draft_tokens[req_idx] = (
                    len(draft_token_ids)
                    if (
                        self.input_batch.num_computed_tokens_cpu[req_idx]
                        >= self.input_batch.num_prompt_tokens[req_idx]
                    )
                    else -1
                )
1263
            spec_decode_metadata = self._calc_spec_decode_metadata(
1264
1265
                num_draft_tokens, cu_num_tokens
            )
1266
            logits_indices = spec_decode_metadata.logits_indices
1267
1268

            # For DECODE only cuda graph of some attention backends (e.g., GDN).
1269
            self.num_decode_draft_tokens.np[:num_reqs] = num_decode_draft_tokens
1270
1271
            self.num_decode_draft_tokens.np[num_reqs:].fill(-1)
            self.num_decode_draft_tokens.copy_to_gpu()
1272
1273
1274

        logits_indices_padded = None
        if self.cache_config.kv_sharing_fast_prefill:
1275
            logits_indices_padded = self._prepare_kv_sharing_fast_prefill(
1276
1277
                logits_indices
            )
1278

1279
1280
1281
        attn_metadata: PerLayerAttnMetadata = {}
        if ubatch_slices is not None:
            attn_metadata = [dict() for _ in range(len(ubatch_slices))]
1282
        use_cascade_attn = False
1283

1284
        # Used in the below loop.
1285
        query_start_loc_cpu = self.query_start_loc.cpu[: num_reqs + 1]
1286
        seq_lens_cpu = self.seq_lens.cpu[:num_reqs]
1287
1288
1289
        num_computed_tokens_cpu = self.input_batch.num_computed_tokens_cpu_tensor[
            :num_reqs
        ]
1290
        spec_decode_common_attn_metadata = None
1291
1292
        if use_spec_decode:
            self.num_accepted_tokens.np[:num_reqs] = (
1293
1294
                self.input_batch.num_accepted_tokens_cpu[:num_reqs]
            )
1295
1296
            self.num_accepted_tokens.np[num_reqs:].fill(1)
            self.num_accepted_tokens.copy_to_gpu()
1297

1298
1299
1300
        # 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(
1301
1302
            self.kv_cache_config.kv_cache_groups
        ):
1303
            encoder_seq_lens = self._get_encoder_seq_lens(
1304
1305
                scheduler_output, kv_cache_group_spec.kv_cache_spec, num_reqs
            )
1306

1307
            if isinstance(kv_cache_group_spec.kv_cache_spec, EncoderOnlyAttentionSpec):
1308
1309
1310
1311
1312
                # Encoder-only layers do not have KV cache, so we need to
                # create a dummy block table and slot mapping for them.
                blk_table_tensor = torch.zeros(
                    (num_reqs, 1),
                    dtype=torch.int32,
1313
1314
1315
                    device=self.device,
                )
                slot_mapping = torch.zeros(
1316
                    (total_num_scheduled_tokens,),
1317
1318
1319
                    dtype=torch.int64,
                    device=self.device,
                )
1320
1321
1322
                num_common_prefix_blocks = 0
            else:
                blk_table = self.input_batch.block_table[kv_cache_group_id]
1323
                blk_table_tensor = blk_table.get_device_tensor(num_reqs)
1324
                slot_mapping = blk_table.slot_mapping.gpu[:total_num_scheduled_tokens]
1325
1326
1327

                # Fill unused with -1. Needed for reshape_and_cache in full cuda
                # graph mode.
1328
1329
1330
1331
                blk_table.slot_mapping.gpu[total_num_scheduled_tokens:].fill_(-1)
                num_common_prefix_blocks = scheduler_output.num_common_prefix_blocks[
                    kv_cache_group_id
                ]
1332

1333
            common_attn_metadata = CommonAttentionMetadata(
1334
1335
1336
1337
1338
                query_start_loc=query_start_loc,
                query_start_loc_cpu=query_start_loc_cpu,
                seq_lens=seq_lens,
                seq_lens_cpu=seq_lens_cpu,
                num_computed_tokens_cpu=num_computed_tokens_cpu,
1339
1340
1341
                num_reqs=num_reqs,
                num_actual_tokens=total_num_scheduled_tokens,
                max_query_len=max_num_scheduled_tokens,
1342
                max_seq_len=max_seq_len,
1343
1344
                block_table_tensor=blk_table_tensor,
                slot_mapping=slot_mapping,
1345
1346
                logits_indices_padded=logits_indices_padded,
                num_logits_indices=logits_indices.size(0),
1347
                causal=True,
1348
                encoder_seq_lens=encoder_seq_lens,
1349
1350
1351
                dcp_local_seq_lens=self.dcp_local_seq_lens.gpu[:num_reqs]
                if self.dcp_world_size > 1
                else None,
1352
1353
            )

1354
            if self.speculative_config and spec_decode_common_attn_metadata is None:
1355
                if isinstance(self.drafter, EagleProposer):
1356
1357
1358
1359
                    if (
                        self.drafter.attn_layer_names[0]
                        in kv_cache_group_spec.layer_names
                    ):
1360
1361
1362
                        spec_decode_common_attn_metadata = common_attn_metadata
                else:
                    spec_decode_common_attn_metadata = common_attn_metadata
1363

1364
1365
1366
            for attn_group in self.attn_groups[kv_cache_group_id]:
                # Prepare for cascade attention if enabled & beneficial.
                common_prefix_len = 0
1367
                builder = attn_group.get_metadata_builder()
1368
1369
1370
                if self.cascade_attn_enabled:
                    common_prefix_len = self._compute_cascade_attn_prefix_len(
                        num_scheduled_tokens,
1371
                        num_common_prefix_blocks,
1372
                        attn_group.kv_cache_spec,
1373
1374
                        builder,
                    )
1375

1376
                extra_attn_metadata_args = {}
1377
                if use_spec_decode and isinstance(builder, GDNAttentionMetadataBuilder):
1378
                    extra_attn_metadata_args = dict(
1379
1380
1381
1382
                        num_accepted_tokens=self.num_accepted_tokens.gpu[:num_reqs],
                        num_decode_draft_tokens_cpu=self.num_decode_draft_tokens.cpu[
                            :num_reqs
                        ],
1383
1384
                    )

1385
1386
                if ubatch_slices is not None:
                    common_attn_metadata_list = split_attn_metadata(
1387
1388
                        ubatch_slices, common_attn_metadata
                    )
1389
                    for ubid, common_attn_metadata in enumerate(
1390
1391
1392
1393
1394
1395
1396
1397
                        common_attn_metadata_list
                    ):
                        attn_metadata_i = attn_group.get_metadata_builder(
                            ubatch_id=ubid
                        ).build(
                            common_prefix_len=common_prefix_len,
                            common_attn_metadata=common_attn_metadata,
                        )
1398
1399
1400
1401
1402
1403
1404
1405
                        for layer_name in kv_cache_group_spec.layer_names:
                            assert type(attn_metadata) is list
                            attn_metadata[ubid][layer_name] = attn_metadata_i
                else:
                    assert isinstance(attn_metadata, dict)
                    attn_metadata_i = builder.build(
                        common_prefix_len=common_prefix_len,
                        common_attn_metadata=common_attn_metadata,
1406
1407
1408
                        **extra_attn_metadata_args,
                    )
                    use_cascade_attn |= getattr(attn_metadata_i, "use_cascade", False)
1409
1410
                    for layer_name in attn_group.layer_names:
                        attn_metadata[layer_name] = attn_metadata_i
1411

1412
1413
1414
1415
        # disable cascade attention when DBO
        if ubatch_slices is not None:
            use_cascade_attn = False

1416
1417
1418
1419
        # Hot-Swap lora model
        if self.lora_config:
            self.set_active_loras(self.input_batch, num_scheduled_tokens)

1420
1421
1422
1423
1424
1425
1426
1427
        return (
            attn_metadata,
            logits_indices,
            spec_decode_metadata,
            num_scheduled_tokens,
            spec_decode_common_attn_metadata,
            max_num_scheduled_tokens,
            ubatch_slices,
1428
            num_tokens_across_dp,
1429
1430
            use_cascade_attn,
        )
1431

1432
1433
1434
1435
    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
        num_common_prefix_blocks: int,
1436
1437
        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
    ) -> 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.
        """
1456
        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
        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]
1494
        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
1495
1496
1497
1498
1499
1500
1501
        # 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(
1502
1503
            common_prefix_len, self.input_batch.num_computed_tokens_cpu[:num_reqs].min()
        )
1504
        # common_prefix_len should be a multiple of the block size.
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
        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
        )
        use_local_attention = isinstance(kv_cache_spec, ChunkedLocalAttentionSpec) or (
            isinstance(kv_cache_spec, FullAttentionSpec)
            and kv_cache_spec.attention_chunk_size is not None
        )
1516
1517
        assert isinstance(kv_cache_spec, AttentionSpec)
        use_cascade = attn_metadata_builder.use_cascade_attention(
1518
1519
1520
            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
1521
            num_kv_heads=kv_cache_spec.num_kv_heads,
1522
            use_alibi=self.use_alibi,
1523
            use_sliding_window=use_sliding_window,
1524
            use_local_attention=use_local_attention,
1525
            num_sms=self.num_sms,
1526
            dcp_world_size=self.dcp_world_size,
1527
1528
1529
        )
        return common_prefix_len if use_cascade else 0

1530
1531
    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
1532
        for index, req_id in enumerate(self.input_batch.req_ids):
1533
1534
1535
            req = self.requests[req_id]
            assert req.mrope_positions is not None

1536
1537
            num_computed_tokens = self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1538
            num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
1539
1540
                req.prompt_token_ids, req.prompt_embeds
            )
1541
1542

            if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
1543
1544
                prompt_part_len = max(0, num_prompt_tokens - num_computed_tokens)
                completion_part_len = max(0, num_scheduled_tokens - prompt_part_len)
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
            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

1558
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1560
                self.mrope_positions.cpu[:, dst_start:dst_end] = req.mrope_positions[
                    :, src_start:src_end
                ]
1561
1562
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1565
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1567
                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

1568
                MRotaryEmbedding.get_next_input_positions_tensor(
1569
                    out=self.mrope_positions.np,
1570
1571
1572
1573
1574
                    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,
                )
1575
1576
1577

                mrope_pos_ptr += completion_part_len

1578
1579
    def _calc_spec_decode_metadata(
        self,
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1588
1589
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1591
1592
1593
1594
1595
        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
1596
1597
1598
1599

        # 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(
1600
1601
            num_sampled_tokens, cumsum_dtype=np.int32
        )
1602
        # Step 2. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
1603
        logits_indices = np.repeat(
1604
1605
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens
        )
1606
        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
1607
1608
1609
1610
1611
1612
        logits_indices += arange

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

        # Compute the draft logits indices.
1613
1614
1615
        # 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(
1616
1617
            num_draft_tokens, cumsum_dtype=np.int32
        )
1618
1619
        # [0, 0, 0, 5, 5, 9]
        target_logits_indices = np.repeat(
1620
1621
            cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens
        )
1622
1623
1624
1625
1626
        # [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(
1627
1628
1629
1630
1631
            self.device, non_blocking=True
        )
        logits_indices = torch.from_numpy(logits_indices).to(
            self.device, non_blocking=True
        )
1632
        target_logits_indices = torch.from_numpy(target_logits_indices).to(
1633
1634
            self.device, non_blocking=True
        )
1635
        bonus_logits_indices = torch.from_numpy(bonus_logits_indices).to(
1636
1637
            self.device, non_blocking=True
        )
1638

1639
1640
        # Compute the draft token ids.
        # draft_token_indices:      [  1,   2,   3, 105, 106, 208]
1641
        draft_token_ids = self.input_ids.gpu[logits_indices]
1642
1643
1644
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1652
1653
        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

1654
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1657
1658
1659
1660
    def _prepare_kv_sharing_fast_prefill(
        self,
        logits_indices: torch.Tensor,
    ) -> torch.Tensor:
        assert self.kv_sharing_fast_prefill_logits_indices is not None
        num_logits = logits_indices.shape[0]
        assert num_logits > 0
1661
        self.kv_sharing_fast_prefill_logits_indices[:num_logits].copy_(logits_indices)
1662
1663
1664
1665
1666
        # There might have leftover indices in logits_indices[num_logits:]
        # from previous iterations, whose values may be greater than the
        # batch size in the current iteration. To ensure indices are always
        # valid, we fill the padded indices with the last index.
        self.kv_sharing_fast_prefill_logits_indices[num_logits:].fill_(
1667
1668
1669
1670
1671
1672
            logits_indices[-1].item()
        )
        if (
            self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
            and num_logits <= self.cudagraph_batch_sizes[-1]
        ):
1673
1674
1675
1676
1677
            # Use piecewise CUDA graphs.
            # Add padding to the batch size.
            num_logits_padded = self.vllm_config.pad_for_cudagraph(num_logits)
        else:
            num_logits_padded = num_logits
1678
1679
1680
        logits_indices_padded = self.kv_sharing_fast_prefill_logits_indices[
            :num_logits_padded
        ]
1681
1682
        return logits_indices_padded

1683
1684
1685
1686
1687
1688
1689
1690
    def _batch_mm_kwargs_from_scheduler(
        self,
        scheduler_output: "SchedulerOutput",
    ) -> tuple[list[MultiModalKwargsItem], list[tuple[str, PlaceholderRange]]]:
        """Batch multimodal kwargs from scheduled encoder inputs.

        Args:
            scheduler_output: The scheduler output containing scheduled encoder
1691
                inputs.
1692
1693
1694
1695
1696
1697

        Returns:
            A tuple of (mm_kwargs, req_ids_pos) where:
            - mm_kwargs: List of multimodal kwargs items to be batched
            - mm_hashes_pos: List of (mm_hash, position_info) tuples
        """
1698
1699
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
1700
            return [], []
1701
        # Batch the multi-modal inputs.
1702
        mm_kwargs = list[MultiModalKwargsItem]()
1703
1704
        # list of tuple (mm_hash, position_info)
        mm_hashes_pos = list[tuple[str, PlaceholderRange]]()
1705
1706
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
1707
1708

            for mm_input_id in encoder_input_ids:
1709
1710
1711
1712
                mm_feature = req_state.mm_features[mm_input_id]
                mm_hash = mm_feature.identifier
                mm_kwargs.append(mm_feature.data)
                mm_hashes_pos.append((mm_hash, mm_feature.mm_position))
1713

1714
1715
1716
1717
1718
        return mm_kwargs, mm_hashes_pos

    def _execute_mm_encoder(self, scheduler_output: "SchedulerOutput"):
        # Batch the multi-modal inputs using the helper method.
        mm_kwargs, mm_hashes_pos = self._batch_mm_kwargs_from_scheduler(
1719
1720
            scheduler_output
        )
1721
1722
1723
1724

        if not mm_kwargs:
            return

1725
1726
1727
1728
1729
1730
1731
        # 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.
1732
        model = cast(SupportsMultiModal, self.model)
1733
        encoder_outputs = []
1734
        for modality, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
1735
1736
1737
1738
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
1739
        ):
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
            # (ekhvedchenia): Temporary hack to limit peak memory usage when
            # processing multimodal data.This solves the issue with scheduler
            # putting too many video samples into a single batch. Scheduler
            # uses pruned vision tokens count to compare it versus compute
            # budget which is incorrect (Either input media size or non-pruned
            # output vision tokens count should be considered)
            curr_group_outputs = []

            if self.is_multimodal_pruning_enabled and modality == "video":
                micro_batch_size = 1
                for i in range(0, num_items, micro_batch_size):
                    micro_batch_mm_inputs = dict(
1752
1753
1754
                        (k, v[i : i + micro_batch_size])
                        for k, v in mm_kwargs_group.items()
                    )
1755
1756

                    micro_batch_outputs = model.get_multimodal_embeddings(
1757
1758
                        **micro_batch_mm_inputs
                    )
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768

                    curr_group_outputs.extend(micro_batch_outputs)
            else:
                # 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.
1769
                curr_group_outputs = model.get_multimodal_embeddings(**mm_kwargs_group)
1770

1771
1772
            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
1773
                expected_num_items=num_items,
1774
            )
1775
            encoder_outputs.extend(curr_group_outputs)
1776

1777
1778
1779
        # Cache the encoder outputs by mm_hash
        for (mm_hash, pos_info), output in zip(mm_hashes_pos, encoder_outputs):
            self.encoder_cache[mm_hash] = scatter_mm_placeholders(
1780
1781
1782
1783
1784
                output,
                is_embed=pos_info.is_embed,
            )

    def _gather_mm_embeddings(
1785
1786
        self,
        scheduler_output: "SchedulerOutput",
1787
        shift_computed_tokens: int = 0,
1788
1789
1790
1791
1792
1793
1794
1795
    ) -> tuple[list[torch.Tensor], torch.Tensor]:
        total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens

        mm_embeds = list[torch.Tensor]()
        is_mm_embed = self.is_mm_embed.cpu
        is_mm_embed[:total_num_scheduled_tokens] = False

        req_start_idx = 0
1796
        should_sync_mrope_positions = False
1797

1798
        for req_id in self.input_batch.req_ids:
1799
1800
            mm_embeds_req: list[torch.Tensor] = []

1801
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1802
            req_state = self.requests[req_id]
1803
            num_computed_tokens = req_state.num_computed_tokens + shift_computed_tokens
1804

1805
1806
            for mm_feature in req_state.mm_features:
                pos_info = mm_feature.mm_position
1807
1808
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824

                # 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,
1825
1826
                    num_encoder_tokens,
                )
1827
                assert start_idx < end_idx
1828

1829
                mm_hash = mm_feature.identifier
1830
                encoder_output = self.encoder_cache.get(mm_hash, None)
1831
                assert encoder_output is not None, f"Encoder cache miss for {mm_hash}."
1832
1833
1834
1835

                if (is_embed := pos_info.is_embed) is not None:
                    is_embed = is_embed[start_idx:end_idx]

1836
                req_start_pos = req_start_idx + start_pos - num_computed_tokens
1837
1838
1839
                is_mm_embed[req_start_pos + start_idx : req_start_pos + end_idx] = (
                    True if is_embed is None else is_embed
                )
1840

1841
1842
1843
1844
                mm_embeds_item = gather_mm_placeholders(
                    encoder_output[start_idx:end_idx],
                    is_embed=is_embed,
                )
1845
1846
1847
                mm_embeds_req.append(mm_embeds_item)

            if self.is_multimodal_pruning_enabled and self.uses_mrope:
1848
                assert req_state.mrope_positions is not None
1849
1850
1851
1852
1853
1854
1855
                should_sync_mrope_positions = True
                mm_embeds_req, new_mrope_positions, new_delta = (
                    self.model.recompute_mrope_positions(
                        input_ids=req_state.prompt_token_ids,
                        multimodal_embeddings=mm_embeds_req,
                        mrope_positions=req_state.mrope_positions,
                        num_computed_tokens=req_state.num_computed_tokens,
1856
1857
                    )
                )
1858
1859
1860
1861
                req_state.mrope_positions.copy_(new_mrope_positions)
                req_state.mrope_position_delta = new_delta

            mm_embeds.extend(mm_embeds_req)
1862
1863
1864
            req_start_idx += num_scheduled_tokens

        is_mm_embed = self.is_mm_embed.copy_to_gpu(total_num_scheduled_tokens)
1865
1866
1867

        if should_sync_mrope_positions:
            self._calc_mrope_positions(scheduler_output)
1868
            self.mrope_positions.copy_to_gpu(total_num_scheduled_tokens)
1869

1870
        return mm_embeds, is_mm_embed
1871

1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
    def _extract_encoder_inputs(
        self,
        scheduler_output: "SchedulerOutput",
    ) -> dict[str, torch.Tensor]:
        """Extract encoder inputs for encoder-decoder models.

        This method extracts multimodal input features from scheduled encoder
        inputs and formats them for the encoder-decoder model forward pass.
        """
        # Batch the multi-modal inputs using the helper method.
        mm_kwargs, _ = self._batch_mm_kwargs_from_scheduler(scheduler_output)

        if not mm_kwargs:
            return {}

        # Group MM kwargs by modality and extract features
1888
        model = cast(SupportsMultiModal, self.model)
1889
1890
        encoder_features = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
1891
1892
1893
1894
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
1895
1896
1897
1898
1899
1900
1901
1902
        ):
            # Add the grouped features to encoder_features dict
            # This allows the model to receive them as kwargs (e.g.,
            # input_features=...)
            encoder_features.update(mm_kwargs_group)

        return encoder_features

1903
    def get_model(self) -> nn.Module:
1904
        # get raw model out of the cudagraph wrapper.
1905
        if isinstance(self.model, (CUDAGraphWrapper, UBatchWrapper)):
1906
            return self.model.unwrap()
1907
1908
        return self.model

1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
    def get_supported_generation_tasks(self) -> list[GenerationTask]:
        model = self.get_model()
        supported_tasks = list[GenerationTask]()

        if is_text_generation_model(model):
            supported_tasks.append("generate")

        if supports_transcription(model):
            if model.supports_transcription_only:
                return ["transcription"]

            supported_tasks.append("transcription")

        return supported_tasks

1924
1925
1926
1927
1928
    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
        model = self.get_model()
        if not is_pooling_model(model):
            return []

1929
1930
        supported_tasks = list(model.pooler.get_supported_tasks())

1931
1932
1933
1934
        if (
            self.scheduler_config.chunked_prefill_enabled
            and "encode" in supported_tasks
        ):
1935
1936
            supported_tasks.remove("encode")

1937
1938
1939
1940
1941
1942
            logger.debug_once(
                "Chunked prefill is not supported with "
                "encode task which using ALL pooling. "
                "Please turn off chunked prefill by "
                "`--no-enable-chunked-prefill` before using it."
            )
1943
1944
1945
1946
1947

        if "score" in supported_tasks:
            num_labels = getattr(self.model_config.hf_config, "num_labels", 0)
            if num_labels != 1:
                supported_tasks.remove("score")
1948
                logger.debug_once("Score API is only enabled for num_labels == 1.")
1949
1950

        return supported_tasks
1951

1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
    def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
        tasks = list[SupportedTask]()

        if self.model_config.runner_type == "generate":
            tasks.extend(self.get_supported_generation_tasks())
        if self.model_config.runner_type == "pooling":
            tasks.extend(self.get_supported_pooling_tasks())

        return tuple(tasks)

1962
    def sync_and_slice_intermediate_tensors(
1963
1964
1965
1966
1967
        self,
        num_tokens: int,
        intermediate_tensors: IntermediateTensors,
        sync_self: bool,
    ) -> IntermediateTensors:
1968
1969
1970
        assert self.intermediate_tensors is not None

        tp = self.vllm_config.parallel_config.tensor_parallel_size
1971
        is_rs = is_residual_scattered_for_sp(self.vllm_config, num_tokens)
1972
1973
1974
1975
1976
1977

        # 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():
1978
                is_scattered = k == "residual" and is_rs
1979
                copy_len = num_tokens // tp if is_scattered else num_tokens
1980
                self.intermediate_tensors[k][:copy_len].copy_(
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
                    v[:copy_len], non_blocking=True
                )

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

    def eplb_step(self, is_dummy: bool = False, is_profile: bool = False) -> None:
1994
1995
1996
1997
1998
1999
2000
        """
        Step for the EPLB (Expert Parallelism Load Balancing) state.
        """
        if not self.parallel_config.enable_eplb:
            return

        assert self.eplb_state is not None
2001
2002
        model = self.get_model()
        assert is_mixture_of_experts(model)
2003
        self.eplb_state.step(
2004
            model,
2005
2006
            is_dummy,
            is_profile,
2007
            log_stats=self.parallel_config.eplb_config.log_balancedness,
2008
2009
        )

2010
2011
2012
2013
    # This is where the second ubatch is adjusted to account for the padding.
    # Should be called after attention metadata creation. This just pads
    # the second ubatch slice out to the total number of tokens
    # (num_tokens + padding)
2014
2015
    @staticmethod
    def pad_out_ubatch_slice(ubatch_slices: UBatchSlices, num_total_tokens: int):
2016
2017
2018
2019
2020
2021
        padded_second_ubatch_slice = slice(
            ubatch_slices[1].token_slice.start, num_total_tokens
        )
        ubatch_slices[1] = UBatchSlice(
            padded_second_ubatch_slice, padded_second_ubatch_slice
        )
2022

2023
2024
2025
2026
2027
2028
    def _pool(
        self,
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
        num_scheduled_tokens_np: np.ndarray,
    ) -> ModelRunnerOutput:
2029
2030
2031
        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"
        )
2032

2033
        hidden_states = hidden_states[:num_scheduled_tokens]
2034
        pooling_metadata = self.input_batch.get_pooling_metadata()
2035
2036
2037
2038
        pooling_metadata.build_pooling_cursor(
            num_scheduled_tokens_np.tolist(), device=hidden_states.device
        )
        seq_lens_cpu = self.seq_lens.cpu[: self.input_batch.num_reqs]
2039

2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
        model = cast(VllmModelForPooling, self.model)
        raw_pooler_output: PoolerOutput = model.pooler(
            hidden_states=hidden_states,
            pooling_metadata=pooling_metadata,
        )
        raw_pooler_output = json_map_leaves(
            lambda x: x.to("cpu", non_blocking=True),
            raw_pooler_output,
        )
        self._sync_device()
2050

2051
        pooler_output: list[torch.Tensor | None] = []
2052
        for raw_output, seq_len, prompt_len in zip(
2053
2054
            raw_pooler_output, seq_lens_cpu, pooling_metadata.prompt_lens
        ):
2055
            output = raw_output if seq_len == prompt_len else None
2056
            pooler_output.append(output)
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066

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

2067
    def _get_num_input_tokens(self, num_scheduled_tokens: int) -> int:
2068
2069
2070
2071
2072
2073
2074
        if (
            self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
            and not envs.VLLM_DISABLE_PAD_FOR_CUDAGRAPH
            and hasattr(self, "cudagraph_batch_sizes")
            and self.cudagraph_batch_sizes
            and num_scheduled_tokens <= self.cudagraph_batch_sizes[-1]
        ):
2075
2076
2077
2078
2079
2080
2081
2082
            # Use CUDA graphs.
            # Add padding to the batch size.
            return self.vllm_config.pad_for_cudagraph(num_scheduled_tokens)

        # Eager mode.
        # Pad tokens to multiple of tensor_parallel_size when
        # enabled collective fusion for SP
        tp_size = self.vllm_config.parallel_config.tensor_parallel_size
2083
2084
2085
2086
        if (
            self.compilation_config.pass_config.enable_sequence_parallelism
            and tp_size > 1
        ):
2087
2088
2089
            return round_up(num_scheduled_tokens, tp_size)
        return num_scheduled_tokens

2090
    def _preprocess(
2091
2092
        self,
        scheduler_output: "SchedulerOutput",
2093
        num_input_tokens: int,  # Padded
2094
        intermediate_tensors: IntermediateTensors | None = None,
2095
2096
    ) -> tuple[
        int,
2097
2098
        torch.Tensor | None,
        torch.Tensor | None,
2099
        torch.Tensor,
2100
        IntermediateTensors | None,
2101
2102
        dict[str, Any],
    ]:
2103
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
2104
        is_first_rank = get_pp_group().is_first_rank
2105

2106
2107
        # _prepare_inputs may reorder the batch, so we must gather multi
        # modal outputs after that to ensure the correct order
2108
2109
        if (
            self.supports_mm_inputs
2110
            and is_first_rank
2111
2112
            and not self.model_config.is_encoder_decoder
        ):
2113
2114
            # Run the multimodal encoder if any.
            self._execute_mm_encoder(scheduler_output)
2115
            mm_embeds, is_mm_embed = self._gather_mm_embeddings(scheduler_output)
2116

2117
2118
2119
            # 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.
2120
            inputs_embeds_scheduled = self.model.get_input_embeddings(
2121
2122
2123
                self.input_ids.gpu[:num_scheduled_tokens],
                multimodal_embeddings=mm_embeds,
                is_multimodal=is_mm_embed,
2124
            )
2125

2126
            # TODO(woosuk): Avoid the copy. Optimize.
2127
            self.inputs_embeds.gpu[:num_scheduled_tokens].copy_(inputs_embeds_scheduled)
2128

2129
            input_ids = None
2130
            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
2131
2132
2133
2134
            model_kwargs = {
                **self._init_model_kwargs(num_scheduled_tokens),
                **self._extract_mm_kwargs(scheduler_output),
            }
2135
        elif self.enable_prompt_embeds and is_first_rank:
2136
2137
2138
2139
2140
2141
2142
2143
2144
2145
2146
2147
            # Get the input embeddings for the tokens that are not input embeds,
            # then put them into the appropriate positions.
            # TODO(qthequartermasterman): Since even when prompt embeds are
            # enabled, (a) not all requests will use prompt embeds, and (b)
            # after the initial prompt is processed, the rest of the generated
            # tokens will be token ids, it is not desirable to have the
            # embedding layer outside of the CUDA graph all the time. The v0
            # engine avoids this by "double compiling" the CUDA graph, once
            # with input_ids and again with inputs_embeds, for all num_tokens.
            # If a batch only has token ids, then including the embedding layer
            # in the CUDA graph will be more performant (like in the else case
            # below).
2148
2149
2150
            token_ids_idx = (
                self.is_token_ids.gpu[:num_scheduled_tokens]
                .nonzero(as_tuple=False)
2151
                .squeeze(1)
2152
            )
2153
2154
2155
            # Some tokens ids may need to become embeds
            if token_ids_idx.numel() > 0:
                token_ids = self.input_ids.gpu[token_ids_idx]
2156
                tokens_to_embeds = self.model.get_input_embeddings(input_ids=token_ids)
2157
2158
2159
2160
2161
                self.inputs_embeds.gpu[token_ids_idx] = tokens_to_embeds

            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
            model_kwargs = self._init_model_kwargs(num_input_tokens)
            input_ids = None
2162
        else:
2163
2164
2165
2166
            # 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.
2167
            input_ids = self.input_ids.gpu[:num_input_tokens]
2168
            inputs_embeds = None
2169
            model_kwargs = self._init_model_kwargs(num_input_tokens)
2170
        if self.uses_mrope:
2171
            positions = self.mrope_positions.gpu[:, :num_input_tokens]
2172
        else:
2173
            positions = self.positions.gpu[:num_input_tokens]
2174

2175
        if is_first_rank:
2176
2177
            intermediate_tensors = None
        else:
2178
            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
2179
2180
                num_input_tokens, intermediate_tensors, True
            )
2181

2182
2183
2184
2185
        if (
            self.model_config.is_encoder_decoder
            and scheduler_output.scheduled_encoder_inputs
        ):
2186
2187
2188
            encoder_inputs = self._extract_encoder_inputs(scheduler_output)
            model_kwargs.update(encoder_inputs)

2189
2190
2191
2192
2193
2194
2195
2196
        return (
            num_scheduled_tokens,
            input_ids,
            inputs_embeds,
            positions,
            intermediate_tensors,
            model_kwargs,
        )
2197

2198
    def _sample(
2199
        self,
2200
2201
        logits: torch.Tensor | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
2202
    ) -> SamplerOutput:
2203
        # Sample the next token and get logprobs if needed.
2204
        sampling_metadata = self.input_batch.sampling_metadata
2205
        if spec_decode_metadata is None:
2206
2207
2208
            # Update output token ids with tokens sampled in last step
            # if async scheduling and required by current sampling params.
            self.input_batch.update_async_output_token_ids()
2209
            return self.sampler(
2210
2211
2212
                logits=logits,
                sampling_metadata=sampling_metadata,
            )
2213

2214
2215
2216
2217
2218
2219
2220
2221
2222
2223
2224
2225
2226
2227
2228
2229
2230
2231
2232
2233
2234
2235
2236
2237
2238
2239
        # 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.
        assert logits is not None
        bonus_logits = logits[spec_decode_metadata.bonus_logits_indices]
        sampler_output = self.sampler(
            logits=bonus_logits,
            sampling_metadata=sampling_metadata,
            predict_bonus_token=True,
        )
        bonus_token_ids = sampler_output.sampled_token_ids

        # 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.
        target_logits = logits[spec_decode_metadata.target_logits_indices]
        output_token_ids = self.rejection_sampler(
            spec_decode_metadata,
            None,  # draft_probs
            target_logits,
            bonus_token_ids,
            sampling_metadata,
        )
        sampler_output.sampled_token_ids = output_token_ids
        self._update_states_after_model_execute(output_token_ids)
2240
2241
2242
        return sampler_output

    def _bookkeeping_sync(
2243
2244
2245
        self,
        scheduler_output: "SchedulerOutput",
        sampler_output: SamplerOutput,
2246
        logits: torch.Tensor | None,
2247
2248
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
2249
    ) -> tuple[
2250
        dict[str, int],
2251
        LogprobsLists | None,
2252
        list[list[int]],
2253
        dict[str, LogprobsTensors | None],
2254
2255
2256
        list[str],
        dict[str, int],
        list[int],
2257
    ]:
2258
2259
2260
2261
        num_nans_in_logits = {}
        if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
            num_nans_in_logits = self._get_nans_in_logits(logits)

2262
2263
2264
        discard_sampled_tokens_req_indices = self.discard_request_indices.np[
            : self.num_discarded_requests
        ]
2265
2266
2267
2268
        for i in discard_sampled_tokens_req_indices:
            gen = self.input_batch.generators.get(int(i))
            if gen is not None:
                gen.set_offset(gen.get_offset() - 4)
2269

2270
2271
2272
        # Copy some objects so they don't get modified after returning.
        # This is important when using async scheduling.
        req_ids_output_copy = self.input_batch.req_ids.copy()
2273
        req_id_to_index_output_copy = self.input_batch.req_id_to_index.copy()
2274

2275
2276
        # NOTE: GPU -> CPU Sync happens here.
        # Move as many CPU operations as possible before this sync point.
2277
        logprobs_tensors = sampler_output.logprobs_tensors
2278
2279
2280
        logprobs_lists = (
            logprobs_tensors.tolists() if logprobs_tensors is not None else None
        )
2281
2282
2283

        # Compute prompt logprobs if needed.
        prompt_logprobs_dict = self._get_prompt_logprobs_dict(
2284
            hidden_states[:num_scheduled_tokens],
2285
            scheduler_output.num_scheduled_tokens,
2286
2287
        )

2288
        num_sampled_tokens = sampler_output.sampled_token_ids.shape[0]
2289
        sampled_token_ids = sampler_output.sampled_token_ids
2290
        invalid_req_indices = []
2291
2292
2293
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
        if not self.use_async_scheduling:
            # Get the valid generated tokens.
            max_gen_len = sampled_token_ids.shape[-1]
            if max_gen_len == 1:
                # No spec decode tokens.
                valid_sampled_token_ids = self._to_list(sampled_token_ids)
            else:
                # Includes spec decode tokens.
                valid_sampled_token_ids = self.rejection_sampler.parse_output(
                    sampled_token_ids,
                    self.input_batch.vocab_size,
                )
            # Mask out the sampled tokens that should not be sampled.
            for i in discard_sampled_tokens_req_indices:
2305
                valid_sampled_token_ids[int(i)].clear()
2306
        else:
2307
            valid_sampled_token_ids = []
2308
            invalid_req_indices = discard_sampled_tokens_req_indices.tolist()
2309
2310
2311
2312
2313
2314
            invalid_req_indices_set = set(invalid_req_indices)
            assert sampled_token_ids.shape[-1] == 1

            # Cache the sampled tokens on the GPU and avoid CPU sync.
            # These will be copied into input_ids in the next step
            # when preparing inputs.
2315
            self.input_batch.prev_sampled_token_ids = sampled_token_ids
2316
2317
2318
2319
2320
            self.input_batch.prev_req_id_to_index = {
                req_id: i
                for i, req_id in enumerate(self.input_batch.req_ids)
                if i not in invalid_req_indices_set
            }
2321

2322
2323
2324
2325
2326
        # 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.
2327
        req_ids = self.input_batch.req_ids
2328
2329
        for req_idx in range(num_sampled_tokens):
            if self.use_async_scheduling:
2330
                sampled_ids = [-1] if req_idx not in invalid_req_indices_set else None
2331
2332
            else:
                sampled_ids = valid_sampled_token_ids[req_idx]
2333
2334
2335
2336
2337
            if not sampled_ids:
                continue

            start_idx = self.input_batch.num_tokens_no_spec[req_idx]
            end_idx = start_idx + len(sampled_ids)
2338
2339
2340
2341
            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}"
2342
            )
2343

2344
2345
            self.input_batch.token_ids_cpu[req_idx, start_idx:end_idx] = sampled_ids
            self.input_batch.is_token_ids[req_idx, start_idx:end_idx] = True
2346
2347
            self.input_batch.num_tokens_no_spec[req_idx] = end_idx
            self.input_batch.num_tokens[req_idx] = end_idx
2348

2349
            req_id = req_ids[req_idx]
2350
2351
2352
            req_state = self.requests[req_id]
            req_state.output_token_ids.extend(sampled_ids)

2353
2354
2355
2356
2357
2358
2359
2360
2361
2362
        return (
            num_nans_in_logits,
            logprobs_lists,
            valid_sampled_token_ids,
            prompt_logprobs_dict,
            req_ids_output_copy,
            req_id_to_index_output_copy,
            invalid_req_indices,
        )

2363
2364
2365
2366
2367
2368
2369
2370
2371
2372
2373
2374
2375
2376
2377
    @contextmanager
    def synchronize_input_prep(self):
        if self.prepare_inputs_event is None:
            yield
            return

        # Ensure prior step has finished with reused CPU tensors.
        # This is required in the async scheduling case because
        # the CPU->GPU transfer happens async.
        self.prepare_inputs_event.synchronize()
        try:
            yield
        finally:
            self.prepare_inputs_event.record()

2378
2379
    def _model_forward(
        self,
2380
2381
2382
2383
        input_ids: torch.Tensor | None = None,
        positions: torch.Tensor | None = None,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
2384
2385
2386
2387
2388
        **model_kwargs: dict[str, Any],
    ) -> Any:
        """Helper method to call the model forward pass.

        This method can be overridden by subclasses for model execution.
2389
        Motivation: We can inspect only this method versus
2390
2391
2392
2393
2394
2395
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
2408
2409
        the whole execute_model, which has additional logic.

        Args:
            input_ids: Input token IDs
            positions: Token positions
            intermediate_tensors: Tensors from previous pipeline stages
            inputs_embeds: Input embeddings (alternative to input_ids)
            **model_kwargs: Additional model arguments

        Returns:
            Model output tensor
        """
        return self.model(
            input_ids=input_ids,
            positions=positions,
            intermediate_tensors=intermediate_tensors,
            inputs_embeds=inputs_embeds,
            **model_kwargs,
        )

2410
2411
2412
2413
    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
2414
2415
        intermediate_tensors: IntermediateTensors | None = None,
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput | IntermediateTensors:
2416
        with record_function_or_nullcontext("Preprocess"):
2417
2418
2419
2420
2421
2422
2423
2424
2425
            with self.synchronize_input_prep():
                # Update persistent batch states.
                self._update_states(scheduler_output)

                if not scheduler_output.total_num_scheduled_tokens:
                    if not has_kv_transfer_group():
                        # Return empty ModelRunnerOutput if no work to do.
                        return EMPTY_MODEL_RUNNER_OUTPUT
                    return self.kv_connector_no_forward(
2426
2427
                        scheduler_output, self.vllm_config
                    )
2428
2429
2430
2431
                if self.cache_config.kv_sharing_fast_prefill:
                    assert not self.input_batch.num_prompt_logprobs, (
                        "--kv-sharing-fast-prefill produces incorrect "
                        "logprobs for prompt tokens, tokens, please disable "
2432
2433
                        "it when the requests need prompt logprobs"
                    )
2434

2435
                # Prepare the decoder inputs.
2436
2437
2438
2439
2440
2441
2442
2443
                (
                    attn_metadata,
                    logits_indices,
                    spec_decode_metadata,
                    num_scheduled_tokens_np,
                    spec_decode_common_attn_metadata,
                    max_query_len,
                    ubatch_slices,
2444
                    num_tokens_across_dp,
2445
2446
                    use_cascade_attn,
                ) = self._prepare_inputs(scheduler_output)
2447

2448
            dp_rank = self.parallel_config.data_parallel_rank
2449
2450
            if ubatch_slices:
                assert num_tokens_across_dp is not None
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                num_input_tokens = int(num_tokens_across_dp[dp_rank].item())
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                self.pad_out_ubatch_slice(ubatch_slices, num_input_tokens)
            elif num_tokens_across_dp is not None:
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                num_input_tokens = int(num_tokens_across_dp[dp_rank].item())
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            else:
                num_input_tokens = self._get_num_input_tokens(
                    scheduler_output.total_num_scheduled_tokens
                )

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            (
                num_scheduled_tokens,
                input_ids,
                inputs_embeds,
                positions,
                intermediate_tensors,
                model_kwargs,
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            ) = self._preprocess(
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                scheduler_output, num_input_tokens, intermediate_tensors
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            )

            uniform_decode = (max_query_len == self.uniform_decode_query_len) and (
                num_scheduled_tokens == self.input_batch.num_reqs * max_query_len
            )
            batch_descriptor = BatchDescriptor(
                num_tokens=num_input_tokens, uniform_decode=uniform_decode
            )
            cudagraph_runtime_mode, batch_descriptor = (
                self.cudagraph_dispatcher.dispatch(batch_descriptor, use_cascade_attn)
            )
2480

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        # Set cudagraph mode to none if calc_kv_scales is true.
        if attn_metadata is not None:
2483
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            metadata_list = (
                attn_metadata.values()
                if isinstance(attn_metadata, dict)
                else [attn_metadata]
            )
2488
            if any(
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                getattr(m, "enable_kv_scales_calculation", False) for m in metadata_list
            ):
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                cudagraph_runtime_mode = CUDAGraphMode.NONE

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        # Run the model.
        # Use persistent buffers for CUDA graphs.
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        with (
            set_forward_context(
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                attn_metadata,
                self.vllm_config,
                num_tokens=num_input_tokens,
                num_tokens_across_dp=num_tokens_across_dp,
                cudagraph_runtime_mode=cudagraph_runtime_mode,
                batch_descriptor=batch_descriptor,
2503
                ubatch_slices=ubatch_slices,
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            ),
            record_function_or_nullcontext("Forward"),
            self.maybe_get_kv_connector_output(scheduler_output) as kv_connector_output,
        ):
2508
            model_output = self._model_forward(
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                input_ids=input_ids,
                positions=positions,
                intermediate_tensors=intermediate_tensors,
                inputs_embeds=inputs_embeds,
                **model_kwargs,
            )

        with record_function_or_nullcontext("Postprocess"):
            if self.use_aux_hidden_state_outputs:
2518
                # True when EAGLE 3 is used.
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                hidden_states, aux_hidden_states = model_output
            else:
2521
                # Common case.
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                hidden_states = model_output
                aux_hidden_states = None

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            if not self.broadcast_pp_output:
                # Common case.
                if not get_pp_group().is_last_rank:
                    # Return the intermediate tensors.
                    assert isinstance(hidden_states, IntermediateTensors)
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                    hidden_states.kv_connector_output = kv_connector_output
                    return hidden_states
2532

2533
                if self.is_pooling_model:
2534
                    # Return the pooling output.
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                    output = self._pool(
                        hidden_states, num_scheduled_tokens, num_scheduled_tokens_np
                    )
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                    output.kv_connector_output = kv_connector_output
                    return output
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                sample_hidden_states = hidden_states[logits_indices]
2542
                logits = self.model.compute_logits(sample_hidden_states)
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            else:
                # Rare case.
                assert not self.is_pooling_model

                if not get_pp_group().is_last_rank:
2548
                    all_gather_tensors = {
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2551
                        "residual": not is_residual_scattered_for_sp(
                            self.vllm_config, num_input_tokens
                        )
2552
                    }
2553
                    get_pp_group().send_tensor_dict(
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                        hidden_states.tensors,
                        all_gather_group=get_tp_group(),
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                        all_gather_tensors=all_gather_tensors,
                    )
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                    logits = None
                else:
                    sample_hidden_states = hidden_states[logits_indices]
2561
                    logits = self.model.compute_logits(sample_hidden_states)
2562
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2566

                model_output_broadcast_data = {}
                if logits is not None:
                    model_output_broadcast_data["logits"] = logits.contiguous()

2567
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                model_output_broadcast_data = get_pp_group().broadcast_tensor_dict(
                    model_output_broadcast_data, src=len(get_pp_group().ranks) - 1
                )
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                assert model_output_broadcast_data is not None
                logits = model_output_broadcast_data["logits"]

            # Apply structured output bitmasks if present
            if scheduler_output.grammar_bitmask is not None:
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                apply_grammar_bitmask(
                    scheduler_output, self.input_batch, logits, self.device
                )
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        with record_function_or_nullcontext("Sample"):
            sampler_output = self._sample(logits, spec_decode_metadata)

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        def propose_draft_token_ids(sampled_token_ids):
            assert spec_decode_common_attn_metadata is not None
            with record_function_or_nullcontext("Draft"):
                self._draft_token_ids = self.propose_draft_token_ids(
                    scheduler_output,
                    sampled_token_ids,
                    self.input_batch.sampling_metadata,
                    hidden_states,
                    sample_hidden_states,
                    aux_hidden_states,
                    spec_decode_metadata,
                    spec_decode_common_attn_metadata,
                )

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        use_padded_batch_for_eagle = (
            self.speculative_config
            and self.speculative_config.use_eagle()
            and not self.speculative_config.disable_padded_drafter_batch
        )
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        effective_drafter_max_model_len = self.max_model_len
        if effective_drafter_max_model_len is None:
            effective_drafter_max_model_len = self.model_config.max_model_len
2604
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2606
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        if (
            self.speculative_config
            and self.speculative_config.draft_model_config is not None
            and self.speculative_config.draft_model_config.max_model_len is not None
        ):
2609
            effective_drafter_max_model_len = (
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                self.speculative_config.draft_model_config.max_model_len
            )
2612
        input_fits_in_drafter = spec_decode_common_attn_metadata and (
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            spec_decode_common_attn_metadata.max_seq_len
            + self.speculative_config.num_speculative_tokens
            <= effective_drafter_max_model_len
        )
2617
        if use_padded_batch_for_eagle and input_fits_in_drafter:
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            # EAGLE speculative decoding can use the GPU sampled tokens
            # as inputs, and does not need to wait for bookkeeping to finish.
            propose_draft_token_ids(sampler_output.sampled_token_ids)

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        with record_function_or_nullcontext("Bookkeep"):
            (
                num_nans_in_logits,
                logprobs_lists,
                valid_sampled_token_ids,
                prompt_logprobs_dict,
                req_ids_output_copy,
                req_id_to_index_output_copy,
                invalid_req_indices,
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2633
2634
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2637
            ) = self._bookkeeping_sync(
                scheduler_output,
                sampler_output,
                logits,
                hidden_states,
                num_scheduled_tokens,
            )
2638

2639
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        if (
            self.speculative_config
            and not use_padded_batch_for_eagle
            and input_fits_in_drafter
        ):
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            # ngram and other speculative decoding methods use the sampled
            # tokens on the CPU, so they are run after bookkeeping.
            propose_draft_token_ids(valid_sampled_token_ids)
2647

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2649
        with record_function_or_nullcontext("EPLB"):
            self.eplb_step()
2650

2651
2652
2653
        output = ModelRunnerOutput(
            req_ids=req_ids_output_copy,
            req_id_to_index=req_id_to_index_output_copy,
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            sampled_token_ids=valid_sampled_token_ids,
            logprobs=logprobs_lists,
            prompt_logprobs_dict=prompt_logprobs_dict,
            pooler_output=[],
2658
            kv_connector_output=kv_connector_output,
2659
2660
2661
            num_nans_in_logits=num_nans_in_logits,
        )

2662
2663
2664
        if not self.use_async_scheduling:
            return output

2665
        async_output = AsyncGPUModelRunnerOutput(
2666
            model_runner_output=output,
2667
            sampled_token_ids=sampler_output.sampled_token_ids,
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2670
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            invalid_req_indices=invalid_req_indices,
            async_output_copy_stream=self.async_output_copy_stream,
        )

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        # Save ref of sampled_token_ids CPU tensor if the batch contains
        # any requests with sampling params that that require output ids.
        self.input_batch.set_async_sampled_token_ids(
            async_output.sampled_token_ids_cpu,
            async_output.async_copy_ready_event,
        )

        return async_output

2681
    def take_draft_token_ids(self) -> DraftTokenIds | None:
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2684
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2688
2689
2690
2691
        if self._draft_token_ids is None:
            return None
        req_ids = self.input_batch.req_ids
        if isinstance(self._draft_token_ids, torch.Tensor):
            draft_token_ids = self._draft_token_ids.tolist()
        else:
            draft_token_ids = self._draft_token_ids
        self._draft_token_ids = None
        return DraftTokenIds(req_ids, draft_token_ids)

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    def propose_draft_token_ids(
        self,
        scheduler_output: "SchedulerOutput",
2695
        sampled_token_ids: torch.Tensor | list[list[int]],
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2698
        sampling_metadata: SamplingMetadata,
        hidden_states: torch.Tensor,
        sample_hidden_states: torch.Tensor,
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2700
        aux_hidden_states: list[torch.Tensor] | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
2701
        common_attn_metadata: CommonAttentionMetadata,
2702
    ) -> list[list[int]] | torch.Tensor:
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2704
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
        if self.speculative_config.method == "ngram":
2705
            assert isinstance(sampled_token_ids, list)
2706
            assert isinstance(self.drafter, NgramProposer)
2707
            draft_token_ids = self.drafter.propose(
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2709
                sampled_token_ids,
                self.input_batch.req_ids,
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2711
                self.input_batch.num_tokens_no_spec,
                self.input_batch.token_ids_cpu,
2712
2713
                self.input_batch.spec_decode_unsupported_reqs,
            )
2714
        elif self.speculative_config.method == "medusa":
2715
            assert isinstance(sampled_token_ids, list)
2716
            assert isinstance(self.drafter, MedusaProposer)
2717

2718
2719
            if sample_hidden_states.shape[0] == len(sampled_token_ids):
                # The input to the target model does not include draft tokens.
2720
2721
2722
2723
                hidden_states = sample_hidden_states
            else:
                indices = []
                offset = 0
Wentao Ye's avatar
Wentao Ye committed
2724
                assert spec_decode_metadata is not None
2725
                for num_draft, tokens in zip(
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                    spec_decode_metadata.num_draft_tokens, sampled_token_ids
                ):
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2729
                    indices.append(offset + len(tokens) - 1)
                    offset += num_draft + 1
2730
                indices = torch.tensor(indices, device=self.device)
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2732
                hidden_states = sample_hidden_states[indices]

2733
            draft_token_ids = self.drafter.propose(
2734
2735
2736
                target_hidden_states=hidden_states,
                sampling_metadata=sampling_metadata,
            )
2737
        elif self.speculative_config.use_eagle():
2738
            assert isinstance(self.drafter, EagleProposer)
2739
2740
2741
2742
2743

            if self.speculative_config.disable_padded_drafter_batch:
                # When padded-batch is disabled, the sampled_token_ids should be
                # the cpu-side list[list[int]] of valid sampled tokens for each
                # request, with invalid requests having empty lists.
2744
2745
                assert isinstance(sampled_token_ids, list), (
                    "sampled_token_ids should be a python list when"
2746
                    "padded-batch is disabled."
2747
                )
2748
                next_token_ids = self.drafter.prepare_next_token_ids_cpu(
2749
2750
2751
2752
2753
                    sampled_token_ids,
                    self.requests,
                    self.input_batch,
                    scheduler_output.num_scheduled_tokens,
                )
2754
2755
2756
2757
2758
            else:
                # When using padded-batch, the sampled_token_ids should be
                # the gpu tensor of sampled tokens for each request, of shape
                # (num_reqs, num_spec_tokens + 1) with rejected tokens having
                # value -1.
2759
2760
                assert isinstance(sampled_token_ids, torch.Tensor), (
                    "sampled_token_ids should be a torch.Tensor when"
2761
                    "padded-batch is enabled."
2762
2763
                )
                next_token_ids, valid_sampled_tokens_count = (
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2765
2766
2767
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2769
                    self.drafter.prepare_next_token_ids_padded(
                        common_attn_metadata,
                        sampled_token_ids,
                        self.requests,
                        self.input_batch,
                        self.discard_request_indices.gpu,
2770
                        self.num_discarded_requests,
2771
                    )
2772
                )
Jiayi Yao's avatar
Jiayi Yao committed
2773

2774
            if spec_decode_metadata is None:
2775
                token_indices_to_sample = None
2776
                # input_ids can be None for multimodal models.
2777
                target_token_ids = self.input_ids.gpu[:num_scheduled_tokens]
2778
                target_positions = self._get_positions(num_scheduled_tokens)
2779
                if self.use_aux_hidden_state_outputs:
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2780
                    assert aux_hidden_states is not None
2781
                    target_hidden_states = torch.cat(
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2783
                        [h[:num_scheduled_tokens] for h in aux_hidden_states], dim=-1
                    )
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2785
                else:
                    target_hidden_states = hidden_states[:num_scheduled_tokens]
2786
            else:
2787
2788
                if self.speculative_config.disable_padded_drafter_batch:
                    token_indices_to_sample = None
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2790
2791
2792
2793
                    common_attn_metadata, token_indices = self.drafter.prepare_inputs(
                        common_attn_metadata,
                        sampled_token_ids,
                        spec_decode_metadata.num_draft_tokens,
                    )
2794
                else:
2795
                    common_attn_metadata, token_indices, token_indices_to_sample = (
2796
2797
2798
                        self.drafter.prepare_inputs_padded(
                            common_attn_metadata,
                            spec_decode_metadata,
2799
2800
2801
                            valid_sampled_tokens_count,
                        )
                    )
2802

2803
                target_token_ids = self.input_ids.gpu[token_indices]
2804
                target_positions = self._get_positions(token_indices)
2805
                if self.use_aux_hidden_state_outputs:
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2806
                    assert aux_hidden_states is not None
2807
                    target_hidden_states = torch.cat(
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2809
                        [h[token_indices] for h in aux_hidden_states], dim=-1
                    )
2810
2811
                else:
                    target_hidden_states = hidden_states[token_indices]
2812

2813
            if self.supports_mm_inputs:
2814
2815
2816
2817
2818
2819
                mm_embed_inputs = self._gather_mm_embeddings(
                    scheduler_output,
                    shift_computed_tokens=1,
                )
            else:
                mm_embed_inputs = None
2820

2821
            draft_token_ids = self.drafter.propose(
2822
2823
2824
2825
                target_token_ids=target_token_ids,
                target_positions=target_positions,
                target_hidden_states=target_hidden_states,
                next_token_ids=next_token_ids,
2826
                last_token_indices=token_indices_to_sample,
2827
                sampling_metadata=sampling_metadata,
2828
                common_attn_metadata=common_attn_metadata,
2829
                mm_embed_inputs=mm_embed_inputs,
2830
            )
2831

2832
        return draft_token_ids
2833

2834
2835
2836
    def update_config(self, overrides: dict[str, Any]) -> None:
        allowed_config_names = {"load_config", "model_config"}
        for config_name, config_overrides in overrides.items():
2837
2838
            assert config_name in allowed_config_names, (
                f"Config `{config_name}` not supported. "
2839
                f"Allowed configs: {allowed_config_names}"
2840
            )
2841
2842
2843
2844
            config = getattr(self, config_name)
            new_config = update_config(config, config_overrides)
            setattr(self, config_name, new_config)

2845
2846
2847
2848
2849
    def load_model(self, eep_scale_up: bool = False) -> None:
        """
        Args:
            eep_scale_up: the model loading is for elastic EP scale up.
        """
2850
        logger.info("Starting to load model %s...", self.model_config.model)
2851
2852
        if eep_scale_up:
            from vllm.distributed.parallel_state import get_ep_group
2853
2854
2855
2856
2857

            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
            )
2858
2859
            num_local_physical_experts = int(num_local_physical_experts.item())
            new_ep_size = get_ep_group().world_size
2860
            global_expert_load, old_global_expert_indices = EplbState.recv_state()
2861
            num_logical_experts = global_expert_load.shape[1]
2862
            self.parallel_config.eplb_config.num_redundant_experts = (
2863
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2865
2866
2867
2868
                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
            )
2869
            rank_mapping = {
2870
                old_ep_rank: old_ep_rank for old_ep_rank in range(old_ep_size)
2871
2872
2873
2874
2875
2876
            }
        else:
            global_expert_load = None
            old_global_expert_indices = None
            rank_mapping = None

2877
        with DeviceMemoryProfiler() as m:
2878
            time_before_load = time.perf_counter()
2879
            model_loader = get_model_loader(self.load_config)
2880
            self.model = model_loader.load_model(
2881
2882
                vllm_config=self.vllm_config, model_config=self.model_config
            )
2883
            if self.lora_config:
2884
2885
2886
                self.model = self.load_lora_model(
                    self.model, self.vllm_config, self.device
                )
2887
2888
2889
            if hasattr(self, "drafter"):
                logger.info("Loading drafter model...")
                self.drafter.load_model(self.model)
2890
            if self.use_aux_hidden_state_outputs:
2891
                if not supports_eagle3(self.get_model()):
2892
2893
                    raise RuntimeError(
                        "Model does not support EAGLE3 interface but "
2894
2895
                        "aux_hidden_state_outputs was requested"
                    )
2896
2897
2898
2899
2900
2901
2902
2903
2904
2905
2906
2907
2908

                # Try to get auxiliary layers from speculative config,
                # otherwise use model's default layers
                aux_layers = self._get_eagle3_aux_layers_from_config()
                if aux_layers:
                    logger.info(
                        "Using auxiliary layers from speculative config: %s",
                        aux_layers,
                    )
                else:
                    aux_layers = self.model.get_eagle3_aux_hidden_state_layers()

                self.model.set_aux_hidden_state_layers(aux_layers)
2909
            time_after_load = time.perf_counter()
2910
        self.model_memory_usage = m.consumed_memory
2911
2912
2913
2914
2915
        logger.info(
            "Model loading took %.4f GiB and %.6f seconds",
            self.model_memory_usage / GiB_bytes,
            time_after_load - time_before_load,
        )
2916
        prepare_communication_buffer_for_model(self.model)
2917

2918
        self.is_multimodal_pruning_enabled = (
2919
            supports_multimodal_pruning(self.get_model())
2920
2921
            and self.model_config.multimodal_config.is_multimodal_pruning_enabled()
        )
2922

2923
2924
        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)
2925
2926
2927
2928
            self.eplb_state = EplbState.build(
                self.model,
                self.device,
                self.parallel_config,
2929
2930
2931
                global_expert_load,
                old_global_expert_indices,
                rank_mapping,
2932
2933
            )

2934
        if (
2935
2936
            self.vllm_config.compilation_config.level == CompilationLevel.DYNAMO_AS_IS
            and supports_dynamo()
2937
        ):
2938
            backend = self.vllm_config.compilation_config.init_backend(self.vllm_config)
2939
            compilation_counter.dynamo_as_is_count += 1
2940
            self.model.compile(fullgraph=True, backend=backend)
2941
2942
2943
2944
2945
            return
        # for other compilation levels, cudagraph behavior is controlled by
        # CudagraphWraper and CudagraphDispatcher of vllm.

        # wrap the model with full cudagraph wrapper if needed.
2946
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2948
2949
2950
2951
2952
        if (
            self.compilation_config.cudagraph_mode.has_full_cudagraphs()
            and not self.parallel_config.enable_dbo
        ):
            self.model = CUDAGraphWrapper(
                self.model, self.vllm_config, runtime_mode=CUDAGraphMode.FULL
            )
2953
2954
        elif self.parallel_config.enable_dbo:
            if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
2955
2956
2957
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.FULL, self.device
                )
2958
            else:
2959
2960
2961
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.NONE, self.device
                )
2962

2963
    def _get_eagle3_aux_layers_from_config(self) -> tuple[int, ...] | None:
2964
2965
2966
2967
2968
2969
2970
2971
2972
2973
2974
2975
2976
2977
2978
2979
2980
2981
2982
2983
2984
2985
2986
        """Extract Eagle3 auxiliary layer indices from speculative config.

        These indices specify which hidden states from the base model should
        be used as auxiliary inputs for the Eagle3 drafter model during
        speculative decoding.

        Returns:
            Tuple of layer indices if found in draft model config,
            None otherwise.
        """
        if not (self.speculative_config and self.speculative_config.draft_model_config):
            return None

        hf_config = self.speculative_config.draft_model_config.hf_config
        if not hasattr(hf_config, "eagle_aux_hidden_state_layer_ids"):
            return None

        layer_ids = hf_config.eagle_aux_hidden_state_layer_ids
        if layer_ids and isinstance(layer_ids, (list, tuple)):
            return tuple(layer_ids)

        return None

2987
    def reload_weights(self) -> None:
2988
        assert getattr(self, "model", None) is not None, (
2989
            "Cannot reload weights before model is loaded."
2990
        )
2991
2992
        model_loader = get_model_loader(self.load_config)
        logger.info("Reloading weights inplace...")
2993
        model_loader.load_weights(self.get_model(), model_config=self.model_config)
2994

2995
2996
2997
2998
2999
    def save_tensorized_model(
        self,
        tensorizer_config: "TensorizerConfig",
    ) -> None:
        TensorizerLoader.save_model(
3000
            self.get_model(),
3001
            tensorizer_config=tensorizer_config,
3002
            model_config=self.model_config,
3003
3004
        )

3005
3006
3007
    def _get_prompt_logprobs_dict(
        self,
        hidden_states: torch.Tensor,
3008
        num_scheduled_tokens: dict[str, int],
3009
    ) -> dict[str, LogprobsTensors | None]:
3010
3011
3012
3013
        num_prompt_logprobs_dict = self.input_batch.num_prompt_logprobs
        if not num_prompt_logprobs_dict:
            return {}

3014
        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
3015
        prompt_logprobs_dict: dict[str, LogprobsTensors | None] = {}
3016
3017
3018
3019
3020

        # 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():
3021
            num_tokens = num_scheduled_tokens[req_id]
3022
3023
3024

            # Get metadata for this request.
            request = self.requests[req_id]
3025
3026
3027
3028
            if request.prompt_token_ids is None:
                # Prompt logprobs is incompatible with prompt embeddings
                continue

3029
3030
            num_prompt_tokens = len(request.prompt_token_ids)
            prompt_token_ids = torch.tensor(request.prompt_token_ids).to(
3031
3032
                self.device, non_blocking=True
            )
3033

3034
3035
3036
3037
3038
3039
            # 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(
3040
3041
                    num_prompt_tokens - 1, num_prompt_logprobs + 1
                )
3042
3043
                in_progress_dict[req_id] = logprobs_tensors

3044
            # Determine number of logits to retrieve.
3045
3046
            start_idx = request.num_computed_tokens
            start_tok = start_idx + 1
3047
            num_remaining_tokens = num_prompt_tokens - start_tok
3048
            if num_tokens <= num_remaining_tokens:
3049
                # This is a chunk, more tokens remain.
3050
3051
3052
                # 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.
3053
3054
3055
3056
3057
                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)
3058
3059
3060
3061
3062
3063
3064
                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
3065
3066
3067
3068
3069

            # 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]
3070
            offset = self.query_start_loc.np[req_idx].item()
3071
            prompt_hidden_states = hidden_states[offset : offset + num_logits]
3072
            logits = self.model.compute_logits(prompt_hidden_states)
3073
3074
3075
3076

            # 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.
3077
            tgt_token_ids = prompt_token_ids[start_tok : start_tok + num_logits]
3078
3079

            # Compute prompt logprobs.
3080
3081
            logprobs = self.sampler.compute_logprobs(logits)
            token_ids, logprobs, ranks = self.sampler.gather_logprobs(
3082
3083
                logprobs, num_prompt_logprobs, tgt_token_ids
            )
3084
3085

            # Transfer GPU->CPU async.
3086
3087
            chunk_slice = slice(start_idx, start_idx + num_logits)
            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
3088
3089
3090
                token_ids, non_blocking=True
            )
            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs, non_blocking=True)
3091
            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
3092
3093
                ranks, non_blocking=True
            )
3094
3095
3096
3097
3098

        # 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]
3099
            del in_progress_dict[req_id]
3100
3101

        # Must synchronize the non-blocking GPU->CPU transfers.
3102
        if prompt_logprobs_dict:
3103
            self._sync_device()
3104
3105
3106

        return prompt_logprobs_dict

3107
3108
    def _get_nans_in_logits(
        self,
3109
        logits: torch.Tensor | None,
3110
3111
3112
3113
3114
3115
3116
3117
3118
3119
3120
    ) -> 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])
3121
3122
3123
                    if num_nans_for_index is not None and req_index < logits.shape[0]
                    else 0
                )
3124
3125
3126
3127
            return num_nans_in_logits
        except IndexError:
            return {}

3128
3129
3130
3131
3132
3133
    @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
3134
         - during DP rank dummy run
3135
3136
3137
3138
3139
3140
3141
3142
3143
3144
3145
        """
        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(
3146
                    self.input_ids.gpu,
3147
3148
                    low=0,
                    high=self.model_config.get_vocab_size(),
3149
3150
                    dtype=input_ids.dtype,
                )
3151

3152
            logger.debug_once("Randomizing dummy data for DP Rank")
3153
            input_ids.copy_(rand_input_ids()[: input_ids.size(0)], non_blocking=True)
3154
3155
3156
            yield
            input_ids.fill_(0)

3157
3158
3159
3160
3161
3162
    def _get_mm_dummy_batch(
        self,
        modality: str,
        max_items_per_batch: int,
    ) -> BatchedTensorInputs:
        """Dummy data for profiling and precompiling multimodal models."""
3163
3164
        assert self.mm_budget is not None

3165
3166
        dummy_decoder_data = self.mm_registry.get_decoder_dummy_data(
            model_config=self.model_config,
3167
            seq_len=self.max_model_len,
3168
            mm_counts={modality: 1},
3169
            cache=self.mm_budget.cache,
3170
3171
3172
3173
        )
        dummy_mm_data = dummy_decoder_data.multi_modal_data

        # Result in the maximum GPU consumption of the model
3174
3175
        dummy_mm_item = dummy_mm_data[modality][0]
        dummy_mm_items = [dummy_mm_item] * max_items_per_batch
3176

3177
        model = cast(SupportsMultiModal, self.model)
3178
3179
3180
3181
3182
3183
3184
3185
3186
        return next(
            mm_kwargs_group
            for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
                dummy_mm_items,
                device=self.device,
                pin_memory=self.pin_memory,
                merge_by_field_config=model.merge_by_field_config,
            )
        )
3187

3188
3189
3190
3191
    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
3192
        cudagraph_runtime_mode: CUDAGraphMode | None = None,
3193
3194
        force_attention: bool = False,
        uniform_decode: bool = False,
3195
        allow_microbatching: bool = True,
3196
3197
        skip_eplb: bool = False,
        is_profile: bool = False,
3198
        create_mixed_batch: bool = False,
3199
        remove_lora: bool = True,
3200
    ) -> tuple[torch.Tensor, torch.Tensor]:
3201
3202
3203
3204
3205
3206
3207
        """
        Run a dummy forward pass to warm up/profile run or capture the
        CUDA graph for the model.

        Args:
            num_tokens: Number of tokens to run the dummy forward pass.
            cudagraph_runtime_mode: used to control the behavior.
3208
                - if not set will determine the cudagraph mode based on using
3209
                    the self.cudagraph_dispatcher.
3210
3211
3212
3213
                - CUDAGraphMode.NONE: No cudagraph, for warm up and profile run
                - CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
                - CUDAGraphMode.FULL: Full cudagraph, attention metadata is
                    needed.
3214
            force_attention: If True, always create attention metadata. Used to
3215
3216
3217
3218
                warm up attention backend when mode is NONE.
            uniform_decode: If True, the batch is a uniform decode batch.
            skip_eplb: If True, skip EPLB state update.
            is_profile: If True, this is a profile run.
3219
3220
            create_mixed_batch: If True, create a mixed batch with both decode
                (1 token) and prefill (multiple tokens) requests.
3221
            remove_lora: If False, dummy LoRAs are not destroyed after the run
3222
        """
3223
3224
3225
3226
        assert (
            cudagraph_runtime_mode is None
            or cudagraph_runtime_mode.valid_runtime_modes()
        )
3227

3228
        # If cudagraph_mode.decode_mode() == FULL and
3229
        # cudagraph_mode.separate_routine(). This means that we are using
3230
3231
3232
3233
3234
3235
3236
3237
3238
3239
3240
        # different graphs and/or modes for mixed prefill-decode batches vs.
        # uniform decode batches. A uniform decode batch means that all
        # requests have identical query length, except a potential virtual
        # request (shorter) in the batch account for padding.
        # Uniform decode batch could either be common pure decode, where
        # max_query_len == 1, or speculative decode, where
        # max_query_len == 1 + num_spec_decode_tokens.

        # When setting max_query_len = 1, we switch to and capture the optimized
        # routine of FA2 for pure decode, i.e., Flashdecode + an optimization
        # for GQA/MQA.
3241
        max_query_len = self.uniform_decode_query_len if uniform_decode else num_tokens
3242

3243
3244
3245
3246
3247
        # 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
3248
3249
3250
3251
        if create_mixed_batch:
            assert not uniform_decode
            # Create mixed batch:
            # first half decode tokens, second half one prefill
3252
            num_decode_tokens = min(max_num_reqs - 1, num_tokens // 2)
3253
3254
3255
3256
            num_prefill_tokens = num_tokens - num_decode_tokens
            num_reqs = num_decode_tokens + 1

            # Create decode requests (1 token each) followed by prefill request
3257
            num_scheduled_tokens_list = [1] * num_decode_tokens + [num_prefill_tokens]
3258
3259
3260
            # Note: Overriding max_query_len to be the prefill tokens
            max_query_len = num_prefill_tokens
        elif uniform_decode:
3261
            assert not create_mixed_batch
3262
            num_reqs = min(max_num_reqs, cdiv(num_tokens, max_query_len))
3263
3264
            num_scheduled_tokens_list = [max_query_len] * num_reqs
            if num_tokens % max_query_len != 0:
3265
                num_scheduled_tokens_list[-1] = num_tokens % max_query_len
3266
3267
3268
3269
3270
3271
        else:
            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

3272
3273
        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs
3274
        num_scheduled_tokens = np.array(num_scheduled_tokens_list, dtype=np.int32)
3275
        total_num_scheduled_tokens = int(num_scheduled_tokens.sum())
3276

3277
3278
3279
        # Disable DP padding when running eager
        allow_dp_padding = self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE

3280
3281
        # We currently only microbatch if the number of tokens is
        # over a certain threshold.
3282
        ubatch_slices, num_tokens_across_dp = coordinate_batch_across_dp(
3283
3284
3285
3286
3287
3288
3289
            num_tokens_unpadded=total_num_scheduled_tokens,
            parallel_config=self.vllm_config.parallel_config,
            allow_microbatching=allow_microbatching,
            allow_dp_padding=allow_dp_padding,
            num_tokens_padded=total_num_scheduled_tokens,
            uniform_decode=uniform_decode,
            num_scheduled_tokens_per_request=num_scheduled_tokens,
3290
3291
3292
        )
        num_tokens_after_padding = num_tokens
        if num_tokens_across_dp is not None:
3293
3294
            dp_rank = self.parallel_config.data_parallel_rank
            num_tokens_after_padding = int(num_tokens_across_dp[dp_rank])
3295

3296
        attn_metadata: PerLayerAttnMetadata | None = None
3297
3298
3299

        # If force_attention is True, we always capture attention. Otherwise,
        # it only happens for cudagraph_runtime_mode=FULL.
3300
        if force_attention or cudagraph_runtime_mode == CUDAGraphMode.FULL:
3301
            attn_metadata = {}
3302
3303
            if ubatch_slices is not None:
                attn_metadata = [dict() for _ in range(len(ubatch_slices))]
3304

3305
3306
3307
3308
3309
3310
            if create_mixed_batch:
                # In the mixed batch mode (used for FI warmup), we use
                # shorter sequence lengths to run faster.
                # TODO(luka) better system for describing dummy batches
                seq_lens = [1] * num_decode_tokens + [num_prefill_tokens + 1]
            else:
3311
                seq_lens = max_query_len
3312
            self.seq_lens.np[:num_reqs] = seq_lens
3313
3314
            self.seq_lens.np[num_reqs:] = 0
            self.seq_lens.copy_to_gpu()
3315

3316
3317
            cum_num_tokens, _ = self._get_cumsum_and_arange(num_scheduled_tokens)
            self.query_start_loc.np[1 : num_reqs + 1] = cum_num_tokens
3318
3319
            self.query_start_loc.copy_to_gpu()

3320
            for kv_cache_group_id, kv_cache_group_spec in enumerate(
3321
3322
                self.kv_cache_config.kv_cache_groups
            ):
3323
                common_attn_metadata = CommonAttentionMetadata(
3324
3325
                    query_start_loc=self.query_start_loc.gpu[: num_reqs + 1],
                    query_start_loc_cpu=self.query_start_loc.cpu[: num_reqs + 1],
3326
3327
                    seq_lens=self.seq_lens.gpu[:num_reqs],
                    seq_lens_cpu=self.seq_lens.cpu[:num_reqs],
3328
3329
3330
                    num_computed_tokens_cpu=self.input_batch.num_computed_tokens_cpu_tensor[
                        :num_reqs
                    ],
3331
3332
                    num_reqs=num_reqs,
                    num_actual_tokens=num_tokens,
3333
                    max_query_len=max_query_len,
3334
                    max_seq_len=self.max_model_len,
3335
3336
3337
                    block_table_tensor=self.input_batch.block_table[
                        kv_cache_group_id
                    ].get_device_tensor(num_reqs),
3338
                    slot_mapping=self.input_batch.block_table[
3339
3340
3341
                        kv_cache_group_id
                    ].slot_mapping.gpu[:num_tokens],
                    causal=True,
3342
3343
3344
                    dcp_local_seq_lens=self.dcp_local_seq_lens.gpu[:num_reqs]
                    if self.dcp_world_size > 1
                    else None,
3345
                )
3346
                for attn_group in self.attn_groups[kv_cache_group_id]:
3347
3348
                    if ubatch_slices is not None:
                        common_attn_metadata_list = split_attn_metadata(
3349
3350
                            ubatch_slices, common_attn_metadata
                        )
3351
                        for ubid, common_attn_metadata in enumerate(
3352
3353
                            common_attn_metadata_list
                        ):
3354
                            assert common_attn_metadata.max_query_len == 1
3355
3356
3357
                            attn_metadata_i = attn_group.get_metadata_builder(
                                ubatch_id=ubid
                            ).build_for_cudagraph_capture(common_attn_metadata)
3358
                            for layer_name in attn_group.layer_names:
3359
                                assert type(attn_metadata) is list
3360
                                attn_metadata[ubid][layer_name] = attn_metadata_i
3361
3362
                    else:
                        assert type(attn_metadata) is dict
3363
3364
                        metadata_builder = attn_group.get_metadata_builder()
                        attn_metadata_i = metadata_builder.build_for_cudagraph_capture(
3365
3366
                            common_attn_metadata
                        )
3367
                        for layer_name in attn_group.layer_names:
3368
                            attn_metadata[layer_name] = attn_metadata_i
3369

3370
3371
3372
        with self.maybe_dummy_run_with_lora(
            self.lora_config, num_scheduled_tokens, remove_lora
        ):
3373
3374
3375
            # Make sure padding doesn't exceed max_num_tokens
            assert num_tokens_after_padding <= self.max_num_tokens
            model_kwargs = self._init_model_kwargs(num_tokens_after_padding)
3376
            if self.supports_mm_inputs and not self.model_config.is_encoder_decoder:
3377
                input_ids = None
3378
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_after_padding]
3379
                model_kwargs = {
3380
                    **model_kwargs,
3381
3382
                    **self._dummy_mm_kwargs(num_reqs),
                }
3383
3384
            elif self.enable_prompt_embeds:
                input_ids = None
3385
3386
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_after_padding]
                model_kwargs = self._init_model_kwargs(num_tokens_after_padding)
3387
            else:
3388
                input_ids = self.input_ids.gpu[:num_tokens_after_padding]
3389
                inputs_embeds = None
3390

3391
            if self.uses_mrope:
3392
                positions = self.mrope_positions.gpu[:, :num_tokens_after_padding]
3393
            else:
3394
                positions = self.positions.gpu[:num_tokens_after_padding]
3395
3396
3397
3398
3399
3400
3401
3402
3403

            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,
3404
3405
3406
                            device=self.device,
                        )
                    )
3407
3408

                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
3409
                    num_tokens_after_padding, None, False
3410
                )
3411
3412

            # filter out the valid batch descriptor
3413
3414
3415
3416
3417
3418
3419
3420
3421
3422
            _cg_mode, batch_descriptor = (
                self.cudagraph_dispatcher.dispatch(
                    BatchDescriptor(
                        num_tokens=num_tokens_after_padding,
                        uniform_decode=uniform_decode,
                    )
                )
                if not is_profile
                else (CUDAGraphMode.NONE, None)
            )
3423
3424
3425
            if cudagraph_runtime_mode is not None:
                # we allow forcing NONE when the dispatcher disagrees to support
                # warm ups for cudagraph capture
3426
3427
3428
3429
                assert (
                    cudagraph_runtime_mode == CUDAGraphMode.NONE
                    or cudagraph_runtime_mode == _cg_mode
                ), (
3430
                    f"Cudagraph runtime mode mismatch at dummy_run. "
3431
3432
                    f"Expected {_cg_mode}, but got {cudagraph_runtime_mode}."
                )
3433
3434
            else:
                cudagraph_runtime_mode = _cg_mode
3435

3436
            if ubatch_slices is not None:
3437
3438
3439
3440
3441
3442
3443
                # Adjust values to reflect a single ubatch.
                # TODO(sage,lucas): this is cruft that should be addressed in
                #  the padding refactor.
                num_tokens_after_padding = ubatch_slices[0].num_tokens
                if num_tokens_across_dp is not None:
                    num_tokens_across_dp[:] = num_tokens_after_padding

3444
3445
3446
            with (
                self.maybe_randomize_inputs(input_ids),
                set_forward_context(
3447
3448
                    attn_metadata,
                    self.vllm_config,
3449
                    num_tokens=num_tokens_after_padding,
3450
3451
                    num_tokens_across_dp=num_tokens_across_dp,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
3452
                    batch_descriptor=batch_descriptor,
3453
3454
3455
                    ubatch_slices=ubatch_slices,
                ),
            ):
3456
                outputs = self.model(
3457
3458
3459
3460
                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
3461
                    **model_kwargs,
3462
                )
3463

3464
3465
3466
3467
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
3468

3469
            if self.speculative_config and self.speculative_config.use_eagle():
3470
                assert isinstance(self.drafter, EagleProposer)
3471
3472
                use_cudagraphs = cudagraph_runtime_mode == CUDAGraphMode.PIECEWISE
                self.drafter.dummy_run(num_tokens, use_cudagraphs=use_cudagraphs)
3473

3474
3475
3476
3477
3478
3479
3480
3481
3482
3483
        # 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)

3484
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
3485
        return hidden_states, hidden_states[logit_indices]
3486
3487
3488
3489
3490
3491

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
3492
3493
3494
3495
        # 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)
3496

3497
        logits = self.model.compute_logits(hidden_states)
3498
3499
        num_reqs = logits.size(0)

3500
        dummy_tensors = lambda v: torch.full((num_reqs,), v, device=self.device)
3501
3502
3503
3504
3505
3506
3507
3508
3509
3510
3511
3512
3513
3514
3515

        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)],
3516
            spec_token_ids=[[] for _ in range(num_reqs)],
3517
3518
            allowed_token_ids_mask=None,
            bad_words_token_ids={},
3519
            logitsprocs=LogitsProcessors(),
3520
        )
3521
        try:
3522
3523
3524
            sampler_output = self.sampler(
                logits=logits, sampling_metadata=dummy_metadata
            )
3525
        except RuntimeError as e:
3526
            if "out of memory" in str(e):
3527
3528
3529
3530
                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 "
3531
3532
                    "initializing the engine."
                ) from e
3533
3534
            else:
                raise e
3535
        if self.speculative_config:
3536
3537
            draft_token_ids = [[0] for _ in range(num_reqs)]
            dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
3538
3539
                draft_token_ids, self.device
            )
3540
3541
3542
3543
3544
3545

            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
3546
3547
3548
            target_logits = torch.randn(
                num_tokens, logits.shape[-1], device=self.device, dtype=logits.dtype
            )
3549
3550
3551
            # 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.
3552
3553
3554
            bonus_token_ids = torch.zeros(
                num_reqs, device=self.device, dtype=torch.int32
            )
3555
3556
3557
3558
3559
3560
3561
            self.rejection_sampler(
                dummy_spec_decode_metadata,
                draft_probs,
                target_logits,
                bonus_token_ids,
                dummy_metadata,
            )
3562
        return sampler_output
3563

3564
    def _dummy_pooler_run_task(
3565
3566
        self,
        hidden_states: torch.Tensor,
3567
3568
        task: PoolingTask,
    ) -> PoolerOutput:
3569
3570
3571
3572
3573
3574
3575
3576
3577
3578
3579
        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

        req_num_tokens = num_tokens // num_reqs

3580
        dummy_prompt_lens = torch.tensor(
3581
3582
            num_scheduled_tokens_list,
            device="cpu",
3583
        )
3584
3585
3586
        dummy_token_ids = torch.zeros(
            (num_reqs, req_num_tokens), dtype=torch.int32, device=self.device
        )
3587

3588
        model = cast(VllmModelForPooling, self.get_model())
3589
        dummy_pooling_params = PoolingParams(task=task)
3590
        dummy_pooling_params.verify(task=task, model_config=self.model_config)
3591
        to_update = model.pooler.get_pooling_updates(task)
3592
3593
        to_update.apply(dummy_pooling_params)

3594
        dummy_metadata = PoolingMetadata(
3595
3596
3597
3598
            prompt_lens=dummy_prompt_lens,
            prompt_token_ids=dummy_token_ids,
            pooling_params=[dummy_pooling_params] * num_reqs,
        )
3599

3600
3601
3602
        dummy_metadata.build_pooling_cursor(
            num_scheduled_tokens_list, device=hidden_states.device
        )
3603

3604
        try:
3605
3606
3607
            return model.pooler(
                hidden_states=hidden_states, pooling_metadata=dummy_metadata
            )
3608
        except RuntimeError as e:
3609
            if "out of memory" in str(e):
3610
                raise RuntimeError(
3611
3612
3613
                    "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 "
3614
3615
                    "initializing the engine."
                ) from e
3616
3617
            else:
                raise e
3618
3619
3620
3621
3622
3623
3624

    @torch.inference_mode()
    def _dummy_pooler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> PoolerOutput:
        # Find the task that has the largest output for subsequent steps
3625
3626
3627
3628
3629
3630
3631
3632
3633
3634
3635
3636
3637
3638
3639
3640
3641
3642
3643
3644
        supported_pooling_tasks = self.get_supported_pooling_tasks()

        if not supported_pooling_tasks:
            if self.scheduler_config.chunked_prefill_enabled:
                raise RuntimeError(
                    f"Model {self.model_config.model} does not support "
                    "any pooling tasks with chunked prefill enabled. "
                    "Please add --no-enable-chunked-prefill to your "
                    "config or CLI args. See "
                    "https://docs.vllm.ai/en/latest/models/pooling_models.html "
                    "to learn more."
                )
            else:
                raise RuntimeError(
                    f"Model {self.model_config.model} does not support "
                    "any pooling tasks. See "
                    "https://docs.vllm.ai/en/latest/models/pooling_models.html "
                    "to learn more."
                )

3645
        output_size = dict[PoolingTask, float]()
3646
        for task in supported_pooling_tasks:
3647
3648
            # Run a full batch with each task to ensure none of them OOMs
            output = self._dummy_pooler_run_task(hidden_states, task)
3649
            output_size[task] = sum(o.nbytes for o in output)
3650
3651
3652
3653
            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)
3654

3655
    def profile_run(self) -> None:
3656
        # Profile with multimodal encoder & encoder cache.
3657
        if self.supports_mm_inputs:
3658
            if self.model_config.multimodal_config.skip_mm_profiling:
3659
                logger.info(
3660
                    "Skipping memory profiling for multimodal encoder and "
3661
3662
                    "encoder cache."
                )
3663
3664
3665
3666
3667
3668
3669
3670
            else:
                mm_budget = self.mm_budget
                assert mm_budget is not None

                if (encoder_budget := mm_budget.get_encoder_budget()) > 0:
                    # NOTE: Currently model is profiled with a single non-text
                    # modality with the max possible input tokens even when
                    # it supports multiple.
3671
                    dummy_modality = mm_budget.get_modality_with_max_tokens()
3672
3673
3674
                    max_mm_items_per_batch = mm_budget.max_items_per_batch_by_modality[
                        dummy_modality
                    ]
3675
3676
3677
3678
3679
3680
3681
3682
3683

                    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_mm_items_per_batch,
                        dummy_modality,
                    )
3684

3685
3686
3687
3688
3689
                    # Create dummy batch of multimodal inputs.
                    batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                        dummy_modality,
                        max_mm_items_per_batch,
                    )
3690

3691
                    # Run multimodal encoder.
3692
3693
3694
                    dummy_encoder_outputs = self.model.get_multimodal_embeddings(
                        **batched_dummy_mm_inputs
                    )
3695

3696
3697
3698
3699
                    sanity_check_mm_encoder_outputs(
                        dummy_encoder_outputs,
                        expected_num_items=max_mm_items_per_batch,
                    )
3700

3701
3702
3703
3704
3705
3706
3707
3708
3709
3710
                    # NOTE: This happens when encoder cache needs to store
                    # the embeddings that encoder outputs are scattered onto.
                    # In this case we create dummy embeddings of size
                    # (encode_budget, hidden_size) and scatter encoder
                    # output into it.
                    encoder_output_shape = dummy_encoder_outputs[0].shape
                    if encoder_output_shape[0] < encoder_budget:
                        expanded_outputs = []
                        for output in dummy_encoder_outputs:
                            expanded = output.new_zeros(
3711
3712
                                (encoder_budget, encoder_output_shape[-1])
                            )
3713
3714
3715
3716
3717
3718
                            num_tokens = output.shape[0]
                            expanded[:num_tokens].copy_(output)
                            expanded_outputs.append(expanded)

                        dummy_encoder_outputs = expanded_outputs

3719
                    # Cache the dummy encoder outputs.
3720
                    self.encoder_cache["tmp"] = dict(enumerate(dummy_encoder_outputs))
3721

3722
        # Add `is_profile` here to pre-allocate communication buffers
3723
3724
3725
        hidden_states, last_hidden_states = self._dummy_run(
            self.max_num_tokens, is_profile=True
        )
3726
        if get_pp_group().is_last_rank:
3727
3728
3729
3730
            if self.is_pooling_model:
                output = self._dummy_pooler_run(hidden_states)
            else:
                output = self._dummy_sampler_run(last_hidden_states)
3731
        else:
3732
            output = None
3733
        self._sync_device()
3734
        del hidden_states, output
3735
        self.encoder_cache.clear()
3736
        gc.collect()
3737

3738
    def capture_model(self) -> int:
3739
        if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
3740
            logger.warning(
3741
                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
3742
3743
                "ensure `cudagraph_mode` was not manually set to `NONE`"
            )
3744
            return 0
3745
3746
        else:
            self.initialize_cudagraph_capture()
3747

3748
3749
        compilation_counter.num_gpu_runner_capture_triggers += 1

3750
3751
        start_time = time.perf_counter()

3752
3753
3754
3755
3756
3757
3758
3759
3760
3761
3762
3763
3764
3765
        @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()
3766
                    gc.collect()
3767

3768
3769
3770
        # 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.
3771
        set_cudagraph_capturing_enabled(True)
3772
        with freeze_gc(), graph_capture(device=self.device):
3773
            start_free_gpu_memory = torch.cuda.mem_get_info()[0]
3774
            cudagraph_mode = self.compilation_config.cudagraph_mode
3775
            assert cudagraph_mode is not None
3776
3777
3778
3779
3780
3781
3782
            if cudagraph_mode.mixed_mode() != CUDAGraphMode.NONE:
                cudagraph_runtime_mode = cudagraph_mode.mixed_mode()

                compilation_cases = list(reversed(self.cudagraph_batch_sizes))
                self._capture_cudagraphs(
                    compilation_cases,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
3783
3784
                    uniform_decode=False,
                )
3785

3786
3787
            # Capture full cudagraph for uniform decode batches if we
            # don't already have full mixed prefill-decode cudagraphs.
3788
3789
3790
3791
3792
3793
3794
            if (
                cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
                and cudagraph_mode.separate_routine()
            ):
                max_num_tokens = (
                    self.scheduler_config.max_num_seqs * self.uniform_decode_query_len
                )
3795
                decode_cudagraph_batch_sizes = [
3796
3797
                    x
                    for x in self.cudagraph_batch_sizes
3798
                    if max_num_tokens >= x >= self.uniform_decode_query_len
3799
                ]
3800
                compilation_cases_decode = list(reversed(decode_cudagraph_batch_sizes))
3801
3802
3803
                self._capture_cudagraphs(
                    compilation_cases=compilation_cases_decode,
                    cudagraph_runtime_mode=CUDAGraphMode.FULL,
3804
3805
                    uniform_decode=True,
                )
3806

3807
3808
3809
            torch.cuda.synchronize()
            end_free_gpu_memory = torch.cuda.mem_get_info()[0]

3810
3811
3812
        # Disable cudagraph capturing globally, so any unexpected cudagraph
        # capturing will be detected and raise an error after here.
        # Note: We don't put it into graph_capture context manager because
3813
        # we may do lazy capturing in future that still allows capturing
3814
3815
        # after here.
        set_cudagraph_capturing_enabled(False)
3816
3817
3818
3819
3820

        end_time = time.perf_counter()
        elapsed_time = end_time - start_time
        cuda_graph_size = start_free_gpu_memory - end_free_gpu_memory
        # This usually takes 5~20 seconds.
3821
3822
3823
3824
3825
        logger.info(
            "Graph capturing finished in %.0f secs, took %.2f GiB",
            elapsed_time,
            cuda_graph_size / (1 << 30),
        )
3826
        return cuda_graph_size
3827

3828
3829
3830
3831
3832
3833
3834
3835
3836
3837
    def _capture_cudagraphs(
        self,
        compilation_cases: list[int],
        cudagraph_runtime_mode: CUDAGraphMode,
        uniform_decode: bool,
    ):
        assert (
            cudagraph_runtime_mode != CUDAGraphMode.NONE
            and cudagraph_runtime_mode.valid_runtime_modes()
        ), f"Invalid cudagraph runtime mode: {cudagraph_runtime_mode}"
3838
3839
3840
3841
3842
3843
3844
3845

        # Only rank 0 should print progress bar during capture
        if is_global_first_rank():
            compilation_cases = tqdm(
                compilation_cases,
                disable=not self.load_config.use_tqdm_on_load,
                desc="Capturing CUDA graphs ({}, {})".format(
                    "decode" if uniform_decode else "mixed prefill-decode",
3846
3847
3848
                    cudagraph_runtime_mode.name,
                ),
            )
3849

3850
3851
3852
        # We skip EPLB here since we don't want to record dummy metrics
        for num_tokens in compilation_cases:
            # We currently only capture ubatched graphs when its a FULL
3853
3854
3855
            # cudagraph, a uniform decode batch, and the number of tokens
            # is above the threshold. Otherwise we just capture a non-ubatched
            # version of the graph
3856
3857
3858
3859
            allow_microbatching = (
                self.parallel_config.enable_dbo
                and cudagraph_runtime_mode == CUDAGraphMode.FULL
                and uniform_decode
3860
3861
3862
3863
3864
                and check_ubatch_thresholds(
                    config=self.vllm_config.parallel_config,
                    num_tokens=num_tokens,
                    uniform_decode=uniform_decode,
                )
3865
            )
3866

3867
3868
3869
3870
3871
3872
            for _ in range(self.compilation_config.cudagraph_num_of_warmups):
                # Use CUDAGraphRuntimeStyle.NONE (default) for warmup.
                # But be careful, warm up with `NONE`is orthogonal to
                # if we want to warm up attention or not. This is
                # different from the case where `FULL` implies capture
                # attention while `PIECEWISE` implies no attention.
3873
3874
3875
3876
3877
3878
3879
3880
3881
3882
3883
3884
3885
3886
3887
3888
3889
3890
                force_attention = cudagraph_runtime_mode == CUDAGraphMode.FULL
                self._dummy_run(
                    num_tokens,
                    cudagraph_runtime_mode=CUDAGraphMode.NONE,
                    force_attention=force_attention,
                    uniform_decode=uniform_decode,
                    allow_microbatching=allow_microbatching,
                    skip_eplb=True,
                    remove_lora=False,
                )
            self._dummy_run(
                num_tokens,
                cudagraph_runtime_mode=cudagraph_runtime_mode,
                uniform_decode=uniform_decode,
                allow_microbatching=allow_microbatching,
                skip_eplb=True,
                remove_lora=False,
            )
3891
        self.maybe_remove_all_loras(self.lora_config)
3892

3893
3894
3895
3896
    def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize the attention backends and attention metadata builders.
        """
3897
        assert len(self.attn_groups) == 0, "Attention backends are already initialized"
3898

3899
3900
3901
3902
3903
3904
3905
3906
        class AttentionGroupKey(NamedTuple):
            attn_backend: type[AttentionBackend]
            kv_cache_spec: KVCacheSpec

        def get_attn_backends_for_group(
            kv_cache_group_spec: KVCacheGroupSpec,
        ) -> dict[AttentionGroupKey, list[str]]:
            layers = get_layers_from_vllm_config(
3907
3908
                self.vllm_config, AttentionLayerBase, kv_cache_group_spec.layer_names
            )
3909
3910
            attn_backends = {}
            attn_backend_layers = defaultdict(list)
3911
            # Dedupe based on full class name; this is a bit safer than
3912
3913
3914
3915
            # using the class itself as the key because when we create dynamic
            # attention backend subclasses (e.g. ChunkedLocalAttention) unless
            # they are cached correctly, there will be different objects per
            # layer.
3916
            for layer_name in kv_cache_group_spec.layer_names:
3917
                attn_backend = layers[layer_name].get_attn_backend()
3918
3919
3920
3921
3922
3923
3924

                if layer_name in self.kv_sharing_fast_prefill_eligible_layers:
                    attn_backend = create_fast_prefill_custom_backend(
                        "FastPrefill",
                        attn_backend,
                    )

3925
3926
3927
                full_cls_name = attn_backend.full_cls_name()
                layer_kv_cache_spec = kv_cache_group_spec.kv_cache_spec
                if isinstance(layer_kv_cache_spec, UniformTypeKVCacheSpecs):
3928
                    layer_kv_cache_spec = layer_kv_cache_spec.kv_cache_specs[layer_name]
3929
                key = (full_cls_name, layer_kv_cache_spec)
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                attn_backends[key] = AttentionGroupKey(
                    attn_backend, layer_kv_cache_spec
                )
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                attn_backend_layers[key].append(layer_name)
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            return {attn_backends[k]: v for k, v in attn_backend_layers.items()}
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        def create_attn_groups(
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            attn_backends_map: dict[AttentionGroupKey, list[str]],
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        ) -> list[AttentionGroup]:
            attn_groups: list[AttentionGroup] = []
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            for (attn_backend, kv_cache_spec), layer_names in attn_backends_map.items():
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                attn_group = AttentionGroup.create_with_metadata_builders(
                    attn_backend,
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                    layer_names,
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                    kv_cache_spec,
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                    self.vllm_config,
                    self.device,
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                    num_metadata_builders=1
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                    if not self.parallel_config.enable_dbo
                    else 2,
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                )

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                attn_groups.append(attn_group)
            return attn_groups

        for kv_cache_group_spec in kv_cache_config.kv_cache_groups:
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            attn_backends = get_attn_backends_for_group(kv_cache_group_spec)
            self.attn_groups.append(create_attn_groups(attn_backends))
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        # Calculate reorder batch threshold (if needed)
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        self.calculate_reorder_batch_threshold()

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    def initialize_cudagraph_capture(self) -> None:
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        """
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        Resolve the cudagraph_mode when there are multiple attention
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        backends with potential conflicting CUDA graph support.
        Then initialize the cudagraph_dispatcher based on the resolved
        cudagraph_mode.
        """
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        min_cg_support = AttentionCGSupport.ALWAYS
        min_cg_builder_name = None

        for attn_group in self._attn_group_iterator():
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            builder = attn_group.get_metadata_builder()
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            if builder.cudagraph_support.value < min_cg_support.value:
                min_cg_support = builder.cudagraph_support
                min_cg_builder_name = builder.__class__.__name__
        # Flexible resolve the cudagraph mode
        cudagraph_mode = self.compilation_config.cudagraph_mode
        # check cudagraph for mixed batch is supported
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        if (
            cudagraph_mode.mixed_mode() == CUDAGraphMode.FULL
            and min_cg_support != AttentionCGSupport.ALWAYS
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
                f"with {min_cg_builder_name} backend (support: "
                f"{min_cg_support})"
            )
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            if min_cg_support == AttentionCGSupport.NEVER:
                # if not supported any full cudagraphs, just raise it.
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                msg += (
                    "; please try cudagraph_mode=PIECEWISE, and "
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                    "make sure compilation level is piecewise"
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                )
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                raise ValueError(msg)

            # attempt to resolve the full cudagraph related mode
            if self.compilation_config.splitting_ops_contain_attention():
                msg += "; setting cudagraph_mode=FULL_AND_PIECEWISE"
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                cudagraph_mode = self.compilation_config.cudagraph_mode = (
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                    CUDAGraphMode.FULL_AND_PIECEWISE
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                )
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            else:
                msg += "; setting cudagraph_mode=FULL_DECODE_ONLY"
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                cudagraph_mode = self.compilation_config.cudagraph_mode = (
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                    CUDAGraphMode.FULL_DECODE_ONLY
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                )
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            logger.warning(msg)

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        # check that if we are doing decode full-cudagraphs it is supported
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        if (
            cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
            and min_cg_support == AttentionCGSupport.NEVER
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
                f"with {min_cg_builder_name} backend (support: "
                f"{min_cg_support})"
            )
            if self.compilation_config.level == CompilationLevel.PIECEWISE and (
                self.compilation_config.splitting_ops_contain_attention()
                or self.compilation_config.use_inductor_graph_partition
            ):
                msg += (
                    "; setting cudagraph_mode=PIECEWISE because "
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                    "attention is compiled piecewise"
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                )
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
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                )
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            else:
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                msg += (
                    "; setting cudagraph_mode=NONE because "
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                    "attention is not compiled piecewise"
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                )
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
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                    CUDAGraphMode.NONE
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                )
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            logger.warning(msg)

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        # check that if we are doing spec-decode + decode full-cudagraphs it is
        # supported
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        if (
            cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
            and self.uniform_decode_query_len > 1
            and min_cg_support.value < AttentionCGSupport.UNIFORM_BATCH.value
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported"
                f" with spec-decode for attention backend "
                f"{min_cg_builder_name} (support: {min_cg_support})"
            )
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            if self.compilation_config.splitting_ops_contain_attention():
                msg += "; setting cudagraph_mode=PIECEWISE"
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                cudagraph_mode = self.compilation_config.cudagraph_mode = (
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                    CUDAGraphMode.PIECEWISE
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                )
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            else:
                msg += "; setting cudagraph_mode=NONE"
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                cudagraph_mode = self.compilation_config.cudagraph_mode = (
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                    CUDAGraphMode.NONE
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                )
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            logger.warning(msg)

        # double check that we can support full cudagraph if they are requested
        # even after automatic downgrades
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        if (
            cudagraph_mode.has_full_cudagraphs()
            and min_cg_support == AttentionCGSupport.NEVER
        ):
            raise ValueError(
                f"CUDAGraphMode.{cudagraph_mode.name} is not "
                f"supported with {min_cg_builder_name} backend ("
                f"support:{min_cg_support}) "
                "; please try cudagraph_mode=PIECEWISE, "
                "and make sure compilation level is piecewise"
            )
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        # Trigger cudagraph dispatching keys initialization here (after
        # initializing attn backends).
        self.cudagraph_dispatcher.initialize_cudagraph_keys(
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            self.compilation_config.cudagraph_mode, self.uniform_decode_query_len
        )
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    def calculate_reorder_batch_threshold(self) -> None:
        """
        Check that if any backends reorder batches; that the reordering
        is compatible (e.g., decode threshold is the same)
        """
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        for group in self._attn_group_iterator():
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            attn_metadata_builder_i = group.get_metadata_builder()
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            # check that if any backends reorder batches; that the reordering
            # is compatible (e.g., decode threshold is the same)
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            reorder_batch_threshold_i = attn_metadata_builder_i.reorder_batch_threshold
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            if reorder_batch_threshold_i is not None:
                if self.reorder_batch_threshold is not None:
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                    if reorder_batch_threshold_i != self.reorder_batch_threshold:
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                        raise ValueError(
                            f"Attention backend reorders decodes with "
                            f"threshold {reorder_batch_threshold_i} but other "
                            f"backend uses threshold "
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                            f"{self.reorder_batch_threshold}"
                        )
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                else:
                    self.reorder_batch_threshold = reorder_batch_threshold_i

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    def _find_compatible_block_sizes(
        self,
        kv_manager_block_size: int,
        backend_cls: type[AttentionBackend],
        return_all: bool = False,
    ) -> list[int]:
        """
        Find compatible block sizes for a backend.

        Args:
            kv_manager_block_size: Physical block size of KV cache
            backend_cls: Attention backend class
            return_all: Return all compatible sizes if True, max size if False

        Returns:
            Compatible block size(s) based on return_all parameter

        Raises:
            ValueError: If no compatible block size found
        """
        supported_block_size = backend_cls.get_supported_kernel_block_size()
        compatible_sizes = []

        for block_size in supported_block_size:
            if isinstance(block_size, int):
                if kv_manager_block_size % block_size == 0:
                    compatible_sizes.append(block_size)
            elif (
                isinstance(block_size, MultipleOf)
                and kv_manager_block_size % block_size.base == 0
            ):
                compatible_sizes.append(kv_manager_block_size)

        if not compatible_sizes:
            raise ValueError(f"No compatible block size for {kv_manager_block_size}")

        return compatible_sizes if return_all else [max(compatible_sizes)]

    def _select_common_block_size(
        self, kv_manager_block_size: int, attn_groups: list[AttentionGroup]
    ) -> int:
        """
        Select common block size for all backends.

        Args:
            kv_manager_block_size: Block size of KV cache
            attn_groups: List of attention groups

        Returns:
            Block size supported by all backends,
            prioritizing cache_config.block_size

        Raises:
            ValueError: If no common block size found
        """
        all_backend_supports = []

        for attn_group in attn_groups:
            compatible_sizes = self._find_compatible_block_sizes(
                kv_manager_block_size, attn_group.backend, return_all=True
            )
            supported_sizes = sorted(list(set(compatible_sizes)), reverse=True)
            all_backend_supports.append(set(supported_sizes))

        common_supported_sizes = set.intersection(*all_backend_supports)

        if not common_supported_sizes:
            error_msg = f"No common block size for {kv_manager_block_size}. "
            for i, attn_group in enumerate(attn_groups):
                supported = all_backend_supports[i]
                error_msg += (
                    f"Backend {attn_group.backend} supports: {sorted(supported)}. "
                )
            raise ValueError(error_msg)

        if self.cache_config.block_size in common_supported_sizes:
            return self.cache_config.block_size

        return max(common_supported_sizes)

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    def may_reinitialize_input_batch(self, kv_cache_config: KVCacheConfig) -> None:
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        """
        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
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            if not isinstance(kv_cache_group.kv_cache_spec, EncoderOnlyAttentionSpec)
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        ]
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        # Generate kernel_block_sizes that matches each block_size
        kernel_block_sizes = self._prepare_kernel_block_sizes(kv_cache_config)

        if block_sizes != [self.cache_config.block_size] or kernel_block_sizes != [
            self.cache_config.block_size
        ]:
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            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
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                "for more details."
            )
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            self.input_batch = InputBatch(
                max_num_reqs=self.max_num_reqs,
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                max_model_len=max(self.max_model_len, self.max_encoder_len),
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                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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                kernel_block_sizes=kernel_block_sizes,
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                is_spec_decode=bool(self.vllm_config.speculative_config),
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                logitsprocs=self.input_batch.logitsprocs,
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                logitsprocs_need_output_token_ids=self.input_batch.logitsprocs_need_output_token_ids,
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                is_pooling_model=self.is_pooling_model,
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                num_speculative_tokens=(
                    self.vllm_config.speculative_config.num_speculative_tokens
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                    if self.vllm_config.speculative_config
                    else 0
                ),
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            )

4234
    def _allocate_kv_cache_tensors(
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        self, kv_cache_config: KVCacheConfig
    ) -> dict[str, torch.Tensor]:
4237
        """
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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.

4241
        Args:
4242
            kv_cache_config: The KV cache config
4243
        Returns:
4244
            dict[str, torch.Tensor]: A map between layer names to their
4245
            corresponding memory buffer for KV cache.
4246
        """
4247
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        kv_cache_raw_tensors: dict[str, torch.Tensor] = {}
        for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
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            tensor = torch.zeros(
                kv_cache_tensor.size, dtype=torch.int8, device=self.device
            )
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            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:
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            for layer_name in group.layer_names:
                if layer_name in self.runner_only_attn_layers:
                    continue
                layer_names.add(layer_name)
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        assert layer_names == set(kv_cache_raw_tensors.keys()), (
            "Some layers are not correctly initialized"
        )
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        return kv_cache_raw_tensors

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    def _attn_group_iterator(self) -> Iterator[AttentionGroup]:
        return itertools.chain.from_iterable(self.attn_groups)

4269
    def _kv_cache_spec_attn_group_iterator(self) -> Iterator[AttentionGroup]:
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        if not self.kv_cache_config.kv_cache_groups:
            return
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        for attn_groups in self.attn_groups:
            yield from attn_groups
4274

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    def _prepare_kernel_block_sizes(self, kv_cache_config: KVCacheConfig) -> list[int]:
        """
        Generate kernel_block_sizes that matches each block_size.

        For attention backends that support virtual block splitting,
        use the supported block sizes from the backend.
        For other backends (like Mamba), use the same block size (no splitting).

        Args:
            kv_cache_config: The KV cache configuration.

        Returns:
            list[int]: List of kernel block sizes for each cache group.
        """
        kernel_block_sizes = []
        for kv_cache_group_id, kv_cache_group in enumerate(
            kv_cache_config.kv_cache_groups
        ):
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            kv_cache_spec = kv_cache_group.kv_cache_spec
            if isinstance(kv_cache_spec, UniformTypeKVCacheSpecs):
                # All layers in the UniformTypeKVCacheSpecs have the same type,
                # Pick an arbitrary one to dispatch.
                kv_cache_spec = next(iter(kv_cache_spec.kv_cache_specs.values()))
            if isinstance(kv_cache_spec, EncoderOnlyAttentionSpec):
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                continue
4300
            elif isinstance(kv_cache_spec, AttentionSpec):
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                # This is an attention backend that supports virtual
                # block splitting. Get the supported block sizes from
                # all backends in the group.
                attn_groups = self.attn_groups[kv_cache_group_id]
                kv_manager_block_size = kv_cache_group.kv_cache_spec.block_size
                selected_kernel_size = self._select_common_block_size(
                    kv_manager_block_size, attn_groups
                )
                kernel_block_sizes.append(selected_kernel_size)
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            elif isinstance(kv_cache_spec, MambaSpec):
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                # This is likely Mamba or other non-attention cache,
                # no splitting.
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                kernel_block_sizes.append(kv_cache_spec.block_size)
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            else:
                raise NotImplementedError(
                    f"unknown kv cache spec {kv_cache_group.kv_cache_spec}"
                )
        return kernel_block_sizes

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    def _reshape_kv_cache_tensors(
        self,
        kv_cache_config: KVCacheConfig,
        kv_cache_raw_tensors: dict[str, torch.Tensor],
    ) -> dict[str, torch.Tensor]:
4325
        """
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        Reshape the KV cache tensors to the desired shape and dtype.
4327

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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.
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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.
        """
4336
        kv_caches: dict[str, torch.Tensor] = {}
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        has_attn, has_mamba = False, False
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        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
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            attn_backend = group.backend
            for layer_name in group.layer_names:
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                if layer_name in self.runner_only_attn_layers:
                    continue
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                raw_tensor = kv_cache_raw_tensors[layer_name]
                assert raw_tensor.numel() % kv_cache_spec.page_size_bytes == 0
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                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_manager_block_size = kv_cache_spec.block_size
                    kernel_size_list = self._find_compatible_block_sizes(
                        kv_manager_block_size, attn_backend, return_all=False
                    )
                    kernel_size = kernel_size_list[0]
                    num_blocks_per_kv_block = kv_manager_block_size // kernel_size
                    kernel_num_blocks = num_blocks * num_blocks_per_kv_block

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                    kv_cache_shape = attn_backend.get_kv_cache_shape(
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                        kernel_num_blocks,
                        kernel_size,
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                        kv_cache_spec.num_kv_heads,
                        kv_cache_spec.head_size,
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                        cache_dtype_str=self.cache_config.cache_dtype,
                    )
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                    dtype = kv_cache_spec.dtype
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                    try:
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                        kv_cache_stride_order = attn_backend.get_kv_cache_stride_order()  # noqa: E501
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                        assert len(kv_cache_stride_order) == len(kv_cache_shape)
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                    except (AttributeError, NotImplementedError):
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                        kv_cache_stride_order = tuple(range(len(kv_cache_shape)))
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                    # 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.
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                    kv_cache_shape = tuple(
                        kv_cache_shape[i] for i in kv_cache_stride_order
                    )
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                    # 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]
                    state_tensors = []
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                    storage_offset_bytes = 0
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                    for shape, dtype in zip(kv_cache_spec.shapes, kv_cache_spec.dtypes):
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                        dtype_size = get_dtype_size(dtype)
                        num_element_per_page = (
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                            kv_cache_spec.page_size_bytes // dtype_size
                        )
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                        target_shape = (num_blocks, *shape)
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                        stride = torch.empty(target_shape).stride()
                        target_stride = (num_element_per_page, *stride[1:])
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                        assert storage_offset_bytes % dtype_size == 0
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                        tensor = torch.as_strided(
                            raw_tensor.view(dtype),
                            size=target_shape,
                            stride=target_stride,
4407
                            storage_offset=storage_offset_bytes // dtype_size,
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                        )
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                        state_tensors.append(tensor)
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                        storage_offset_bytes += stride[0] * dtype_size
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                    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:
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            self._update_hybrid_attention_mamba_layout(kv_caches)
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        return kv_caches

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    def _update_hybrid_attention_mamba_layout(
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        self, kv_caches: dict[str, torch.Tensor]
    ) -> None:
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        """
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        Update the layout of attention layers from (2, num_blocks, ...) to
        (num_blocks, 2, ...).
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        Args:
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            kv_caches: The KV cache buffer of each layer.
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        """

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        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
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            for layer_name in group.layer_names:
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                kv_cache = kv_caches[layer_name]
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                if isinstance(kv_cache_spec, AttentionSpec) and kv_cache.shape[0] == 2:
                    assert kv_cache.shape[1] != 2, (
                        "Fail to determine whether the layout is "
                        "(2, num_blocks, ...) or (num_blocks, 2, ...) for "
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                        f"a tensor of shape {kv_cache.shape}"
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                    )
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                    hidden_size = kv_cache.shape[2:].numel()
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                    kv_cache.as_strided_(
                        size=kv_cache.shape,
                        stride=(hidden_size, 2 * hidden_size, *kv_cache.stride()[2:]),
                    )
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    def initialize_kv_cache_tensors(
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        self, kv_cache_config: KVCacheConfig
    ) -> dict[str, torch.Tensor]:
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        """
        Initialize the memory buffer for KV cache.

        Args:
            kv_cache_config: The KV cache config
        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.
        """
        # 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
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        kv_caches = self._reshape_kv_cache_tensors(
            kv_cache_config, kv_cache_raw_tensors
        )
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        # Set up cross-layer KV cache sharing
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        for layer_name, target_layer_name in self.shared_kv_cache_layers.items():
            logger.debug("%s reuses KV cache of %s", layer_name, target_layer_name)
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            kv_caches[layer_name] = kv_caches[target_layer_name]

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        num_attn_module = (
            2 if self.model_config.hf_config.model_type == "longcat_flash" else 1
        )
        bind_kv_cache(
            kv_caches,
            self.compilation_config.static_forward_context,
            self.kv_caches,
            num_attn_module,
        )
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        return kv_caches

    def maybe_add_kv_sharing_layers_to_kv_cache_groups(
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        self, kv_cache_config: KVCacheConfig
    ) -> None:
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        """
        Add layers that re-use KV cache to KV cache group of its target layer.
        Mapping of KV cache tensors happens in `initialize_kv_cache_tensors()`
        """
        if not self.shared_kv_cache_layers:
            # No cross-layer KV sharing, return
            return

        add_kv_sharing_layers_to_kv_cache_groups(
            self.shared_kv_cache_layers,
            kv_cache_config.kv_cache_groups,
            self.runner_only_attn_layers,
        )

        if self.cache_config.kv_sharing_fast_prefill:
            # In You Only Cache Once (https://arxiv.org/abs/2405.05254) or other
            # similar KV sharing setups, only the layers that generate KV caches
            # are involved in the prefill phase, enabling prefill to early exit.
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            attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention)
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            for layer_name in reversed(attn_layers):
                if layer_name in self.shared_kv_cache_layers:
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                    self.kv_sharing_fast_prefill_eligible_layers.add(layer_name)
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                else:
                    break
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    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
        """
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        kv_cache_config = deepcopy(kv_cache_config)
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        self.kv_cache_config = kv_cache_config
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        self.may_add_encoder_only_layers_to_kv_cache_config()
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        self.maybe_add_kv_sharing_layers_to_kv_cache_groups(kv_cache_config)
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        self.initialize_attn_backend(kv_cache_config)
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        # Reinitialize need to after initialize_attn_backend
        self.may_reinitialize_input_batch(kv_cache_config)
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        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():
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            kv_transfer_group = get_kv_transfer_group()
            kv_transfer_group.register_kv_caches(kv_caches)
            kv_transfer_group.set_host_xfer_buffer_ops(copy_kv_blocks)
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        if self.dcp_world_size > 1:
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            layer_names = self.attn_groups[0][0].layer_names
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            layers = get_layers_from_vllm_config(
                self.vllm_config, AttentionLayerBase, layer_names
            )
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            for layer in layers.values():
                assert layer.impl.need_to_return_lse_for_decode, (
                    "DCP requires attention impls to return"
                    " the softmax lse for decode, but the impl "
                    f"{layer.impl.__class__.__name__} "
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                    "does not return the softmax lse for decode."
                )
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    def may_add_encoder_only_layers_to_kv_cache_config(self) -> None:
        """
        Add encoder-only layers to the KV cache config.
        """
        block_size = self.vllm_config.cache_config.block_size
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        encoder_only_attn_specs: dict[AttentionSpec, list[str]] = defaultdict(list)
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        attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention)
        for layer_name, attn_module in attn_layers.items():
            if attn_module.attn_type == AttentionType.ENCODER_ONLY:
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                attn_spec: AttentionSpec = EncoderOnlyAttentionSpec(
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                    block_size=block_size,
                    num_kv_heads=attn_module.num_kv_heads,
                    head_size=attn_module.head_size,
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                    dtype=self.kv_cache_dtype,
                )
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                encoder_only_attn_specs[attn_spec].append(layer_name)
                self.runner_only_attn_layers.add(layer_name)
        if len(encoder_only_attn_specs) > 0:
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            assert len(encoder_only_attn_specs) == 1, (
                "Only support one encoder-only attention spec now"
            )
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            spec, layer_names = encoder_only_attn_specs.popitem()
            self.kv_cache_config.kv_cache_groups.append(
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                KVCacheGroupSpec(layer_names=layer_names, kv_cache_spec=spec)
            )
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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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        cache_dtype_str = self.vllm_config.cache_config.cache_dtype
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        kv_cache_spec: dict[str, KVCacheSpec] = {}
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        attn_layers = get_layers_from_vllm_config(self.vllm_config, AttentionLayerBase)
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        for layer_name, attn_module in attn_layers.items():
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            if isinstance(attn_module, Attention):
                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(lucas): move the attention specs into the model layers like
                # the attention backends
                if attn_module.attn_type == AttentionType.DECODER:
                    if attn_module.sliding_window is not None:
                        assert not use_mla, "MLA is not supported for slidingwindow"
                        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,
                        )
                    elif self.attention_chunk_size is not None and isinstance(
                        attn_module, ChunkedLocalAttention
                    ):
                        kv_cache_spec[layer_name] = ChunkedLocalAttentionSpec(
                            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,
                        )
                    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,
                        )
                elif attn_module.attn_type == AttentionType.ENCODER_DECODER:
                    kv_cache_spec[layer_name] = CrossAttentionSpec(
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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,
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                    )
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                elif attn_module.attn_type in (
                    AttentionType.ENCODER,
                    AttentionType.ENCODER_ONLY,
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                ):
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                    # encoder-only attention does not need KV cache.
                    continue
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                else:
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                    raise ValueError(f"Unknown attention type: {attn_module.attn_type}")

            elif isinstance(attn_module, MLAAttention):
                kv_cache_spec[layer_name] = MLAAttentionSpec(
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                    block_size=block_size,
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                    num_kv_heads=1,
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                    head_size=attn_module.head_size,
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                    dtype=self.kv_cache_dtype,
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                    cache_dtype_str=cache_dtype_str,
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                )
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            elif isinstance(attn_module, MambaBase):
                if (
                    self.vllm_config.speculative_config is not None
                    and self.vllm_config.model_config.hf_config.model_type
                    not in ["qwen3_next"]
                ):
                    raise NotImplementedError(
                        "Mamba with speculative decoding is not supported yet."
                    )
                mamba_block_size = self.vllm_config.cache_config.mamba_block_size
                page_size_padded = self.vllm_config.cache_config.mamba_page_size_padded
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                kv_cache_spec[layer_name] = MambaSpec(
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                    shapes=attn_module.get_state_shape(),
                    dtypes=attn_module.get_state_dtype(),
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                    block_size=mamba_block_size,
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                    page_size_padded=page_size_padded,
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                    mamba_type=attn_module.mamba_type,
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                    num_speculative_blocks=(
                        self.speculative_config.num_speculative_tokens
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                        if self.speculative_config
                        else 0
                    ),
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        ds_indexer_layers = get_layers_from_vllm_config(
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            self.vllm_config, DeepseekV32IndexerCache
        )
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        for layer_name, ds_indexer_module in ds_indexer_layers.items():
            kv_cache_spec[layer_name] = ds_indexer_module.get_kv_cache_spec()
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        return kv_cache_spec
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    def _to_list(self, sampled_token_ids: torch.Tensor) -> list[list[int]]:
        # This is a short term mitigation for issue mentioned in
        # https://github.com/vllm-project/vllm/issues/22754.
        # `tolist` would trigger a cuda wise stream sync, which
        # would block other copy ops from other cuda streams.
        # A cuda event sync would avoid such a situation. Since
        # this is in the critical path of every single model
        # forward loop, this has caused perf issue for a disagg
        # setup.
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        pinned = self.sampled_token_ids_pinned_cpu[: sampled_token_ids.shape[0]]
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        pinned.copy_(sampled_token_ids, non_blocking=True)
        self.transfer_event.record()
        self.transfer_event.synchronize()
        return pinned.tolist()