gpu_model_runner.py 196 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.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 (
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    CompilationMode,
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    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
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from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
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from vllm.model_executor.model_loader import TensorizerLoader, get_model_loader
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from vllm.model_executor.models.interfaces import (
    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 (
    cdiv,
    check_use_alibi,
    is_pin_memory_available,
    length_from_prompt_token_ids_or_embeds,
    round_up,
)
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from vllm.utils.jsontree import json_map_leaves
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from vllm.utils.mem_constants import GiB_bytes
from vllm.utils.mem_utils import DeviceMemoryProfiler
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from vllm.utils.torch_utils import (
    get_dtype_size,
    kv_cache_dtype_str_to_dtype,
    supports_dynamo,
)
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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,
    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))
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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
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        self.kv_cache_dtype = kv_cache_dtype_str_to_dtype(
            cache_config.cache_dtype, self.model_config
        )
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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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        # Separate cuda stream for overlapping transfer of sampled token ids from
        # GPU to CPU when async scheduling is enabled.
        self.async_output_copy_stream: torch.cuda.Stream | None = None
        # cuda event to synchronize use of reused CPU tensors between steps
        # when async scheduling is enabled.
        self.prepare_inputs_event: torch.cuda.Event | None = None
        if self.use_async_scheduling:
            self.async_output_copy_stream = torch.cuda.Stream()
            self.prepare_inputs_event = torch.cuda.Event()
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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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        # 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.
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        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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            pin_memory=self.pin_memory,
        )
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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]

519
    def _make_buffer(
520
        self, *size: int | torch.SymInt, dtype: torch.dtype, numpy: bool = True
521
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528
    ) -> CpuGpuBuffer:
        return CpuGpuBuffer(
            *size,
            dtype=dtype,
            device=self.device,
            pin_memory=self.pin_memory,
            with_numpy=numpy,
        )
529

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532
    def _init_model_kwargs(self, num_tokens: int):
        model_kwargs = dict[str, Any]()

533
        if not self.is_pooling_model:
534
535
            return model_kwargs

536
537
        num_reqs = self.input_batch.num_reqs
        pooling_params = self.input_batch.get_pooling_params()
538
539
540

        token_type_id_requests = dict[int, Any]()
        for i, param in enumerate(pooling_params):
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544
545
            if (
                param.extra_kwargs is not None
                and (token_types := param.extra_kwargs.get("compressed_token_type_ids"))
                is not None
            ):
546
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550
                token_type_id_requests[i] = token_types

        if len(token_type_id_requests) == 0:
            return model_kwargs

551
        seq_lens = self.seq_lens.gpu[:num_reqs]
552
553
554
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557
558
559
        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(
560
561
            device=self.device
        )
562
563
        return model_kwargs

564
    def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
565
566
        """
        Update the order of requests in the batch based on the attention
567
        backend's needs. For example, some attention backends (namely MLA) may
568
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573
        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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581
        # 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

582
        if self.reorder_batch_threshold is not None:
583
584
585
            # 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.
586
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588
589
            if (
                self.dcp_world_size > 1
                and envs.VLLM_ATTENTION_BACKEND != "FLASH_ATTN_MLA"
            ):
590
                assert self.reorder_batch_threshold == 1, (
591
                    "DCP not support reorder_batch_threshold > 1 now."
592
                )
593
594
595
            reorder_batch_to_split_decodes_and_prefills(
                self.input_batch,
                scheduler_output,
596
597
                decode_threshold=self.reorder_batch_threshold,
            )
598

599
600
    # Note: used for model runner override.
    def _init_device_properties(self) -> None:
601
        """Initialize attributes from torch.cuda.get_device_properties"""
602
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604
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606
607
608
        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()

609
    def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
610
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615
        """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.

616
617
        The SamplingMetadata is updated and copied to the GPU if there is a
        new/resumed/paused/finished request in the batch.
618
619
        """
        # Remove finished requests from the cached states.
620
621
        for req_id in scheduler_output.finished_req_ids:
            self.requests.pop(req_id, None)
622
623
624
625
626
627
628
        # 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:
629
            self.input_batch.remove_request(req_id)
630
631

        # Free the cached encoder outputs.
632
633
        for mm_hash in scheduler_output.free_encoder_mm_hashes:
            self.encoder_cache.pop(mm_hash, None)
634

635
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637
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641
642
643
644
645
646
647
        # 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:
648
            self.input_batch.remove_request(req_id)
649

650
        reqs_to_add: list[CachedRequestState] = []
651
        # Add new requests to the cached states.
652
653
654
        for new_req_data in scheduler_output.scheduled_new_reqs:
            req_id = new_req_data.req_id
            sampling_params = new_req_data.sampling_params
655
            pooling_params = new_req_data.pooling_params
656

657
658
659
660
            if (
                sampling_params
                and sampling_params.sampling_type == SamplingType.RANDOM_SEED
            ):
661
662
663
664
665
                generator = torch.Generator(device=self.device)
                generator.manual_seed(sampling_params.seed)
            else:
                generator = None

666
667
            if self.is_pooling_model:
                assert pooling_params is not None
668
669
                task = pooling_params.task
                assert task is not None, "You did not set `task` in the API"
670

671
                model = cast(VllmModelForPooling, self.get_model())
672
                to_update = model.pooler.get_pooling_updates(task)
673
674
                to_update.apply(pooling_params)

675
            req_state = CachedRequestState(
676
                req_id=req_id,
677
                prompt_token_ids=new_req_data.prompt_token_ids,
678
                prompt_embeds=new_req_data.prompt_embeds,
679
                mm_features=new_req_data.mm_features,
680
                sampling_params=sampling_params,
681
                pooling_params=pooling_params,
682
                generator=generator,
683
684
                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
685
                output_token_ids=[],
686
                lora_request=new_req_data.lora_request,
687
            )
688
689
            self.requests[req_id] = req_state

690
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
691
            if self.uses_mrope:
692
                self._init_mrope_positions(req_state)
693

694
            reqs_to_add.append(req_state)
695

696
        # Update the states of the running/resumed requests.
697
        is_last_rank = get_pp_group().is_last_rank
698
699
        req_data = scheduler_output.scheduled_cached_reqs
        for i, req_id in enumerate(req_data.req_ids):
700
            req_state = self.requests[req_id]
701
702
703
            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]
704
            num_output_tokens = req_data.num_output_tokens[i]
705

706
            # Update the cached states.
707

708
            req_state.num_computed_tokens = num_computed_tokens
709
            req_index = self.input_batch.req_id_to_index.get(req_id)
710
711
712
713
714
715
716
717

            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.
718
719
720
                num_new_tokens = (
                    num_computed_tokens + len(new_token_ids) - req_state.num_tokens
                )
721
722
723
724
                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:
725
                    req_state.output_token_ids.extend(new_token_ids[-num_new_tokens:])
726
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729
730
            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:
731
732
733
734
                    end_idx = (
                        self.input_batch.num_prompt_tokens[req_index]
                        + num_output_tokens
                    )
735
736
                    self.input_batch.num_tokens[req_index] = end_idx
                    self.input_batch.num_tokens_no_spec[req_index] = end_idx
737

738
            # Update the block IDs.
739
            if not resumed_from_preemption:
740
741
                if new_block_ids is not None:
                    # Append the new blocks to the existing block IDs.
742
                    for block_ids, new_ids in zip(req_state.block_ids, new_block_ids):
743
                        block_ids.extend(new_ids)
744
            else:
745
                assert req_index is None
746
                assert new_block_ids is not None
747
748
                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
749
                req_state.block_ids = new_block_ids
750

751
752
753
754
755
756
                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:]
757
758
759
760
            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.
761
                reqs_to_add.append(req_state)
762
763
764
                continue

            # Update the persistent batch.
765
            self.input_batch.num_computed_tokens_cpu[req_index] = num_computed_tokens
766
            if new_block_ids is not None:
767
                self.input_batch.block_table.append_row(new_block_ids, req_index)
768
769
770
771
772
773
774

            # 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)
775
                self.input_batch.token_ids_cpu[
776
777
778
                    req_index, start_token_index:end_token_index
                ] = new_token_ids
                self.input_batch.num_tokens_no_spec[req_index] = end_token_index
779
                self.input_batch.num_tokens[req_index] = end_token_index
780

781
            # Add spec_token_ids to token_ids_cpu.
782
783
784
            spec_token_ids = scheduler_output.scheduled_spec_decode_tokens.get(
                req_id, ()
            )
785
786
787
788
789
            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[
790
791
                    req_index, start_index:end_token_index
                ] = spec_token_ids
792
793
                # NOTE(woosuk): `num_tokens` here may include spec tokens.
                self.input_batch.num_tokens[req_index] += num_spec_tokens
794
                self.input_batch.spec_token_ids[req_index] = spec_token_ids
795

796
797
        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
798
799
        for request in reqs_to_add:
            self.input_batch.add_request(request)
800

801
802
803
804
805
806
        # 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()
807

808
    def _update_states_after_model_execute(
809
810
        self, output_token_ids: torch.Tensor
    ) -> None:
811
812
813
814
815
816
817
818
819
820
821
822
        """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.
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
        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()
        )
843
844
845
        for i, num_tokens in enumerate(num_accepted_tokens):
            self.input_batch.num_accepted_tokens_cpu[i] = num_tokens

846
847
848
849
850
851
    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
852
853
854
855
        for mm_feature in req_state.mm_features:
            mm_item = mm_feature.data
            if mm_item is None:
                continue
856
857
858
859
860
861
862
863
864
865
866
867
            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

868
869
870
871
872
873
874
875
876
877
878
        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,
879
            )
880
        )
881

882
    def _extract_mm_kwargs(
883
        self,
884
885
        scheduler_output: "SchedulerOutput",
    ) -> BatchedTensorInputs:
886
        if not scheduler_output or not self.is_multimodal_raw_input_only_model:
887
            return {}
888

889
890
        mm_kwargs = list[MultiModalKwargsItem]()
        for req in scheduler_output.scheduled_new_reqs:
891
892
893
            for feature in req.mm_features:
                if feature.data is not None:
                    mm_kwargs.append(feature.data)
894

895
        # Input all modalities at once
896
        model = cast(SupportsMultiModal, self.model)
897
898
        mm_kwargs_combined: BatchedTensorInputs = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
899
900
901
902
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
903
904
        ):
            mm_kwargs_combined.update(mm_kwargs_group)
905

906
        return mm_kwargs_combined
907

908
    def _dummy_mm_kwargs(self, num_seqs: int) -> BatchedTensorInputs:
909
        if not self.is_multimodal_raw_input_only_model:
910
            return {}
911

912
913
914
915
916
        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)
917

918
919
920
    def _get_cumsum_and_arange(
        self,
        num_tokens: np.ndarray,
921
        cumsum_dtype: np.dtype | None = None,
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
    ) -> 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

938
939
940
    def _prepare_input_ids(
        self, total_num_scheduled_tokens: int, cu_num_tokens: np.ndarray
    ) -> None:
941
        """Prepare the input IDs for the current batch.
942

943
944
945
946
947
948
949
        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)
950
951
952
            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)
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
            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)
971
                indices_match &= prev_index == flattened_index
972
973
974
975
976
977
                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)
978
979
980
            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)
981
982
        if num_commmon_tokens == 0:
            # No requests in common with the previous iteration
983
            # So input_ids.cpu will have all the input ids.
984
985
986
987
988
989
990
            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_(
991
992
993
                self.input_batch.prev_sampled_token_ids[:num_commmon_tokens, 0],
                non_blocking=True,
            )
994
995
            if self.enable_prompt_embeds:
                self.is_token_ids.gpu[:num_commmon_tokens] = True
996
            return
997
        # Upload the index tensors asynchronously so the scatter can be non-blocking.
998
999
1000
        input_ids_index_tensor = torch.tensor(
            flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1001
        prev_common_req_indices_tensor = torch.tensor(
1002
1003
            prev_common_req_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1004
1005
1006
1007
        self.input_ids.gpu.scatter_(
            dim=0,
            index=input_ids_index_tensor,
            src=self.input_batch.prev_sampled_token_ids[
1008
1009
1010
                prev_common_req_indices_tensor, 0
            ],
        )
1011

1012
1013
1014
1015
1016
    def _get_encoder_seq_lens(
        self,
        scheduler_output: "SchedulerOutput",
        kv_cache_spec: KVCacheSpec,
        num_reqs: int,
1017
    ) -> np.ndarray | None:
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
        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

1030
    def _prepare_inputs(
1031
        self, scheduler_output: "SchedulerOutput"
1032
1033
1034
    ) -> tuple[
        PerLayerAttnMetadata,
        torch.Tensor,
1035
        SpecDecodeMetadata | None,
1036
        np.ndarray,
1037
        CommonAttentionMetadata | None,
1038
        int,
1039
1040
        UBatchSlices | None,
        torch.Tensor | None,
1041
1042
        bool,
    ]:
1043
1044
1045
        """
        :return: tuple[
            attn_metadata: layer-to-attention_metadata mapping,
1046
1047
1048
            logits_indices, spec_decode_metadata,
            num_scheduled_tokens, spec_decode_common_attn_metadata,
            max_num_scheduled_tokens, use_cascade_attn
1049
1050
        ]
        """
1051
1052
1053
1054
1055
1056
1057
        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.
1058
        self.input_batch.block_table.commit_block_table(num_reqs)
1059
1060

        # Get the number of scheduled tokens for each request.
1061
1062
1063
1064
        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)
1065
1066
1067

        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
1068
        req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens)
1069

1070
1071
        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
        # arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
1072
        cu_num_tokens, arange = self._get_cumsum_and_arange(num_scheduled_tokens)
1073
1074

        # Get positions.
1075
        positions_np = self.positions.np[:total_num_scheduled_tokens]
1076
1077
1078
1079
1080
        np.add(
            self.input_batch.num_computed_tokens_cpu[req_indices],
            arange,
            out=positions_np,
        )
1081

1082
1083
        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1084
        if self.uses_mrope:
1085
1086
            self._calc_mrope_positions(scheduler_output)

1087
1088
1089
1090
        # 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.
1091
1092
1093
        token_indices = (
            positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]
        )
1094
        token_indices_tensor = torch.from_numpy(token_indices)
1095

1096
1097
1098
        # 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.
1099
1100
1101
1102
1103
1104
        torch.index_select(
            self.input_batch.token_ids_cpu_tensor.flatten(),
            0,
            token_indices_tensor,
            out=self.input_ids.cpu[:total_num_scheduled_tokens],
        )
1105
1106
1107
1108
1109
1110
        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,
1111
1112
                out=self.is_token_ids.cpu[:total_num_scheduled_tokens],
            )
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145

        # 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:
1146
1147
1148
                    self.inputs_embeds.cpu[
                        output_idx : output_idx + actual_num_sched
                    ].copy_(req_embeds[start_pos:actual_end])
1149
1150

                output_idx += num_sched
1151

1152
1153
        self.input_batch.block_table.compute_slot_mapping(req_indices, positions_np)
        self.input_batch.block_table.commit_slot_mapping(total_num_scheduled_tokens)
1154
1155

        # Prepare the attention metadata.
1156
        self.query_start_loc.np[0] = 0
1157
        self.query_start_loc.np[1 : num_reqs + 1] = cu_num_tokens
1158
1159
        # Note: pad query_start_loc to be non-decreasing, as kernels
        # like FlashAttention requires that
1160
        self.query_start_loc.np[num_reqs + 1 :].fill(cu_num_tokens[-1])
1161
        self.query_start_loc.copy_to_gpu()
1162
        query_start_loc = self.query_start_loc.gpu[: num_reqs + 1]
1163

1164
        num_tokens_unpadded = scheduler_output.total_num_scheduled_tokens
1165
        num_tokens_padded = self._get_num_input_tokens(num_tokens_unpadded)
1166
1167
1168
        uniform_decode = (
            max_num_scheduled_tokens == self.uniform_decode_query_len
        ) and (total_num_scheduled_tokens == num_reqs * max_num_scheduled_tokens)
1169
1170
1171
1172
1173
1174
1175

        # 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

1176
        ubatch_slices, num_tokens_across_dp = coordinate_batch_across_dp(
1177
1178
1179
1180
1181
1182
1183
            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,
1184
        )
1185

1186
        self.seq_lens.np[:num_reqs] = (
1187
1188
            self.input_batch.num_computed_tokens_cpu[:num_reqs] + num_scheduled_tokens
        )
1189
        # Fill unused with 0 for full cuda graph mode.
1190
1191
1192
1193
        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()
1194

1195
        num_tokens = [self.requests[r].num_tokens for r in self.input_batch.req_ids]
1196
1197
1198
1199
1200
1201
1202
        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)
1203
1204
1205
        self.discard_request_indices.np[: self.num_discarded_requests] = (
            discard_request_indices
        )
1206
1207
1208

        self.discard_request_indices.copy_to_gpu(self.num_discarded_requests)

1209
        # Copy the tensors to the GPU.
1210
1211
        self._prepare_input_ids(total_num_scheduled_tokens, cu_num_tokens)

1212
        if self.uses_mrope:
1213
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1214
1215
            self.mrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.mrope_positions.cpu[:, :total_num_scheduled_tokens],
1216
1217
                non_blocking=True,
            )
1218
1219
        else:
            # Common case (1D positions)
1220
            self.positions.copy_to_gpu(total_num_scheduled_tokens)
1221

1222
        use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
1223
1224
1225
1226
1227
1228
1229
        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
1230
            num_draft_tokens = None
1231
1232
1233
1234
1235
1236
            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)
1237
1238
1239
            # 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)
1240
1241
1242
1243
            for (
                req_id,
                draft_token_ids,
            ) in scheduler_output.scheduled_spec_decode_tokens.items():
1244
1245
                req_idx = self.input_batch.req_id_to_index[req_id]
                num_draft_tokens[req_idx] = len(draft_token_ids)
1246
1247
1248
1249
1250
1251
1252
1253
                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
                )
1254
            spec_decode_metadata = self._calc_spec_decode_metadata(
1255
1256
                num_draft_tokens, cu_num_tokens
            )
1257
            logits_indices = spec_decode_metadata.logits_indices
1258
1259

            # For DECODE only cuda graph of some attention backends (e.g., GDN).
1260
            self.num_decode_draft_tokens.np[:num_reqs] = num_decode_draft_tokens
1261
1262
            self.num_decode_draft_tokens.np[num_reqs:].fill(-1)
            self.num_decode_draft_tokens.copy_to_gpu()
1263
1264
1265

        logits_indices_padded = None
        if self.cache_config.kv_sharing_fast_prefill:
1266
            logits_indices_padded = self._prepare_kv_sharing_fast_prefill(
1267
1268
                logits_indices
            )
1269

1270
1271
1272
        attn_metadata: PerLayerAttnMetadata = {}
        if ubatch_slices is not None:
            attn_metadata = [dict() for _ in range(len(ubatch_slices))]
1273
        use_cascade_attn = False
1274

1275
        # Used in the below loop.
1276
        query_start_loc_cpu = self.query_start_loc.cpu[: num_reqs + 1]
1277
        seq_lens_cpu = self.seq_lens.cpu[:num_reqs]
1278
1279
1280
        num_computed_tokens_cpu = self.input_batch.num_computed_tokens_cpu_tensor[
            :num_reqs
        ]
1281
        spec_decode_common_attn_metadata = None
1282
1283
        if use_spec_decode:
            self.num_accepted_tokens.np[:num_reqs] = (
1284
1285
                self.input_batch.num_accepted_tokens_cpu[:num_reqs]
            )
1286
1287
            self.num_accepted_tokens.np[num_reqs:].fill(1)
            self.num_accepted_tokens.copy_to_gpu()
1288

1289
1290
1291
        # 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(
1292
1293
            self.kv_cache_config.kv_cache_groups
        ):
1294
            encoder_seq_lens = self._get_encoder_seq_lens(
1295
1296
                scheduler_output, kv_cache_group_spec.kv_cache_spec, num_reqs
            )
1297

1298
            if isinstance(kv_cache_group_spec.kv_cache_spec, EncoderOnlyAttentionSpec):
1299
1300
1301
1302
1303
                # 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,
1304
1305
1306
                    device=self.device,
                )
                slot_mapping = torch.zeros(
1307
                    (total_num_scheduled_tokens,),
1308
1309
1310
                    dtype=torch.int64,
                    device=self.device,
                )
1311
1312
1313
                num_common_prefix_blocks = 0
            else:
                blk_table = self.input_batch.block_table[kv_cache_group_id]
1314
                blk_table_tensor = blk_table.get_device_tensor(num_reqs)
1315
                slot_mapping = blk_table.slot_mapping.gpu[:total_num_scheduled_tokens]
1316
1317
1318

                # Fill unused with -1. Needed for reshape_and_cache in full cuda
                # graph mode.
1319
1320
1321
1322
                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
                ]
1323

1324
            common_attn_metadata = CommonAttentionMetadata(
1325
1326
1327
1328
1329
                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,
1330
1331
1332
                num_reqs=num_reqs,
                num_actual_tokens=total_num_scheduled_tokens,
                max_query_len=max_num_scheduled_tokens,
1333
                max_seq_len=max_seq_len,
1334
1335
                block_table_tensor=blk_table_tensor,
                slot_mapping=slot_mapping,
1336
1337
                logits_indices_padded=logits_indices_padded,
                num_logits_indices=logits_indices.size(0),
1338
                causal=True,
1339
                encoder_seq_lens=encoder_seq_lens,
1340
1341
1342
                dcp_local_seq_lens=self.dcp_local_seq_lens.gpu[:num_reqs]
                if self.dcp_world_size > 1
                else None,
1343
1344
            )

1345
            if self.speculative_config and spec_decode_common_attn_metadata is None:
1346
                if isinstance(self.drafter, EagleProposer):
1347
1348
1349
1350
                    if (
                        self.drafter.attn_layer_names[0]
                        in kv_cache_group_spec.layer_names
                    ):
1351
1352
1353
                        spec_decode_common_attn_metadata = common_attn_metadata
                else:
                    spec_decode_common_attn_metadata = common_attn_metadata
1354

1355
1356
1357
            for attn_group in self.attn_groups[kv_cache_group_id]:
                # Prepare for cascade attention if enabled & beneficial.
                common_prefix_len = 0
1358
                builder = attn_group.get_metadata_builder()
1359
1360
1361
                if self.cascade_attn_enabled:
                    common_prefix_len = self._compute_cascade_attn_prefix_len(
                        num_scheduled_tokens,
1362
                        num_common_prefix_blocks,
1363
                        attn_group.kv_cache_spec,
1364
1365
                        builder,
                    )
1366

1367
                extra_attn_metadata_args = {}
1368
                if use_spec_decode and isinstance(builder, GDNAttentionMetadataBuilder):
1369
                    extra_attn_metadata_args = dict(
1370
1371
1372
1373
                        num_accepted_tokens=self.num_accepted_tokens.gpu[:num_reqs],
                        num_decode_draft_tokens_cpu=self.num_decode_draft_tokens.cpu[
                            :num_reqs
                        ],
1374
1375
                    )

1376
1377
                if ubatch_slices is not None:
                    common_attn_metadata_list = split_attn_metadata(
1378
1379
                        ubatch_slices, common_attn_metadata
                    )
1380
                    for ubid, common_attn_metadata in enumerate(
1381
1382
1383
1384
1385
1386
1387
1388
                        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,
                        )
1389
1390
1391
1392
1393
1394
1395
1396
                        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,
1397
1398
1399
                        **extra_attn_metadata_args,
                    )
                    use_cascade_attn |= getattr(attn_metadata_i, "use_cascade", False)
1400
1401
                    for layer_name in attn_group.layer_names:
                        attn_metadata[layer_name] = attn_metadata_i
1402

1403
1404
1405
1406
        # disable cascade attention when DBO
        if ubatch_slices is not None:
            use_cascade_attn = False

1407
1408
1409
1410
        # Hot-Swap lora model
        if self.lora_config:
            self.set_active_loras(self.input_batch, num_scheduled_tokens)

1411
1412
1413
1414
1415
1416
1417
1418
        return (
            attn_metadata,
            logits_indices,
            spec_decode_metadata,
            num_scheduled_tokens,
            spec_decode_common_attn_metadata,
            max_num_scheduled_tokens,
            ubatch_slices,
1419
            num_tokens_across_dp,
1420
1421
            use_cascade_attn,
        )
1422

1423
1424
1425
1426
    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
        num_common_prefix_blocks: int,
1427
1428
        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
    ) -> 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.
        """
1447
        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
1448
1449
1450
1451
1452
1453
1454
1455
1456
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
        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]
1485
        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
1486
1487
1488
1489
1490
1491
1492
        # 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(
1493
1494
            common_prefix_len, self.input_batch.num_computed_tokens_cpu[:num_reqs].min()
        )
1495
        # common_prefix_len should be a multiple of the block size.
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
        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
        )
1507
1508
        assert isinstance(kv_cache_spec, AttentionSpec)
        use_cascade = attn_metadata_builder.use_cascade_attention(
1509
1510
1511
            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
1512
            num_kv_heads=kv_cache_spec.num_kv_heads,
1513
            use_alibi=self.use_alibi,
1514
            use_sliding_window=use_sliding_window,
1515
            use_local_attention=use_local_attention,
1516
            num_sms=self.num_sms,
1517
            dcp_world_size=self.dcp_world_size,
1518
1519
1520
        )
        return common_prefix_len if use_cascade else 0

1521
1522
    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
1523
        for index, req_id in enumerate(self.input_batch.req_ids):
1524
1525
1526
            req = self.requests[req_id]
            assert req.mrope_positions is not None

1527
1528
            num_computed_tokens = self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1529
            num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
1530
1531
                req.prompt_token_ids, req.prompt_embeds
            )
1532
1533

            if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
1534
1535
                prompt_part_len = max(0, num_prompt_tokens - num_computed_tokens)
                completion_part_len = max(0, num_scheduled_tokens - prompt_part_len)
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
            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

1549
1550
1551
                self.mrope_positions.cpu[:, dst_start:dst_end] = req.mrope_positions[
                    :, src_start:src_end
                ]
1552
1553
1554
1555
1556
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1558
                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

1559
                MRotaryEmbedding.get_next_input_positions_tensor(
1560
                    out=self.mrope_positions.np,
1561
1562
1563
1564
1565
                    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,
                )
1566
1567
1568

                mrope_pos_ptr += completion_part_len

1569
1570
    def _calc_spec_decode_metadata(
        self,
1571
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1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
        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
1587
1588
1589
1590

        # 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(
1591
1592
            num_sampled_tokens, cumsum_dtype=np.int32
        )
1593
        # Step 2. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
1594
        logits_indices = np.repeat(
1595
1596
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens
        )
1597
        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
1598
1599
1600
1601
1602
1603
        logits_indices += arange

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

        # Compute the draft logits indices.
1604
1605
1606
        # 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(
1607
1608
            num_draft_tokens, cumsum_dtype=np.int32
        )
1609
1610
        # [0, 0, 0, 5, 5, 9]
        target_logits_indices = np.repeat(
1611
1612
            cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens
        )
1613
1614
1615
1616
1617
        # [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(
1618
1619
1620
1621
1622
            self.device, non_blocking=True
        )
        logits_indices = torch.from_numpy(logits_indices).to(
            self.device, non_blocking=True
        )
1623
        target_logits_indices = torch.from_numpy(target_logits_indices).to(
1624
1625
            self.device, non_blocking=True
        )
1626
        bonus_logits_indices = torch.from_numpy(bonus_logits_indices).to(
1627
1628
            self.device, non_blocking=True
        )
1629

1630
1631
        # Compute the draft token ids.
        # draft_token_indices:      [  1,   2,   3, 105, 106, 208]
1632
        draft_token_ids = self.input_ids.gpu[logits_indices]
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
        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

1645
1646
1647
1648
1649
1650
1651
    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
1652
        self.kv_sharing_fast_prefill_logits_indices[:num_logits].copy_(logits_indices)
1653
1654
1655
1656
1657
        # 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_(
1658
1659
1660
1661
1662
1663
            logits_indices[-1].item()
        )
        if (
            self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
            and num_logits <= self.cudagraph_batch_sizes[-1]
        ):
1664
1665
1666
1667
1668
            # 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
1669
1670
1671
        logits_indices_padded = self.kv_sharing_fast_prefill_logits_indices[
            :num_logits_padded
        ]
1672
1673
        return logits_indices_padded

1674
1675
1676
1677
1678
1679
1680
1681
    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
1682
                inputs.
1683
1684
1685
1686
1687
1688

        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
        """
1689
1690
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
1691
            return [], []
1692
        # Batch the multi-modal inputs.
1693
        mm_kwargs = list[MultiModalKwargsItem]()
1694
1695
        # list of tuple (mm_hash, position_info)
        mm_hashes_pos = list[tuple[str, PlaceholderRange]]()
1696
1697
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
1698
1699

            for mm_input_id in encoder_input_ids:
1700
1701
1702
1703
                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))
1704

1705
1706
1707
1708
1709
        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(
1710
1711
            scheduler_output
        )
1712
1713
1714
1715

        if not mm_kwargs:
            return

1716
1717
1718
1719
1720
1721
1722
        # 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.
1723
        model = cast(SupportsMultiModal, self.model)
1724
        encoder_outputs = []
1725
        for modality, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
1726
1727
1728
1729
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
1730
        ):
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
            # (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(
1743
1744
1745
                        (k, v[i : i + micro_batch_size])
                        for k, v in mm_kwargs_group.items()
                    )
1746
1747

                    micro_batch_outputs = model.get_multimodal_embeddings(
1748
1749
                        **micro_batch_mm_inputs
                    )
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759

                    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.
1760
                curr_group_outputs = model.get_multimodal_embeddings(**mm_kwargs_group)
1761

1762
1763
            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
1764
                expected_num_items=num_items,
1765
            )
1766
            encoder_outputs.extend(curr_group_outputs)
1767

1768
1769
1770
        # 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(
1771
1772
1773
1774
1775
                output,
                is_embed=pos_info.is_embed,
            )

    def _gather_mm_embeddings(
1776
1777
        self,
        scheduler_output: "SchedulerOutput",
1778
        shift_computed_tokens: int = 0,
1779
1780
1781
1782
1783
1784
1785
1786
    ) -> 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
1787
        should_sync_mrope_positions = False
1788

1789
        for req_id in self.input_batch.req_ids:
1790
1791
            mm_embeds_req: list[torch.Tensor] = []

1792
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1793
            req_state = self.requests[req_id]
1794
            num_computed_tokens = req_state.num_computed_tokens + shift_computed_tokens
1795

1796
1797
            for mm_feature in req_state.mm_features:
                pos_info = mm_feature.mm_position
1798
1799
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815

                # 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,
1816
1817
                    num_encoder_tokens,
                )
1818
                assert start_idx < end_idx
1819

1820
                mm_hash = mm_feature.identifier
1821
                encoder_output = self.encoder_cache.get(mm_hash, None)
1822
                assert encoder_output is not None, f"Encoder cache miss for {mm_hash}."
1823
1824
1825
1826

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

1827
                req_start_pos = req_start_idx + start_pos - num_computed_tokens
1828
1829
1830
                is_mm_embed[req_start_pos + start_idx : req_start_pos + end_idx] = (
                    True if is_embed is None else is_embed
                )
1831

1832
1833
1834
1835
                mm_embeds_item = gather_mm_placeholders(
                    encoder_output[start_idx:end_idx],
                    is_embed=is_embed,
                )
1836
1837
1838
                mm_embeds_req.append(mm_embeds_item)

            if self.is_multimodal_pruning_enabled and self.uses_mrope:
1839
                assert req_state.mrope_positions is not None
1840
1841
1842
1843
1844
1845
1846
                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,
1847
1848
                    )
                )
1849
1850
1851
1852
                req_state.mrope_positions.copy_(new_mrope_positions)
                req_state.mrope_position_delta = new_delta

            mm_embeds.extend(mm_embeds_req)
1853
1854
1855
            req_start_idx += num_scheduled_tokens

        is_mm_embed = self.is_mm_embed.copy_to_gpu(total_num_scheduled_tokens)
1856
1857
1858

        if should_sync_mrope_positions:
            self._calc_mrope_positions(scheduler_output)
1859
            self.mrope_positions.copy_to_gpu(total_num_scheduled_tokens)
1860

1861
        return mm_embeds, is_mm_embed
1862

1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
    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
1879
        model = cast(SupportsMultiModal, self.model)
1880
1881
        encoder_features = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
1882
1883
1884
1885
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
1886
1887
1888
1889
1890
1891
1892
1893
        ):
            # 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

1894
    def get_model(self) -> nn.Module:
1895
        # get raw model out of the cudagraph wrapper.
1896
        if isinstance(self.model, (CUDAGraphWrapper, UBatchWrapper)):
1897
            return self.model.unwrap()
1898
1899
        return self.model

1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
    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

1915
1916
1917
1918
1919
    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
        model = self.get_model()
        if not is_pooling_model(model):
            return []

1920
1921
        supported_tasks = list(model.pooler.get_supported_tasks())

1922
1923
1924
1925
1926
        if self.scheduler_config.chunked_prefill_enabled:
            if "token_embed" in supported_tasks:
                supported_tasks.remove("token_embed")
            if "token_classify" in supported_tasks:
                supported_tasks.remove("token_classify")
1927

1928
1929
            logger.debug_once(
                "Chunked prefill is not supported with "
1930
1931
                "token_embed and token_classify tasks "
                "which using ALL pooling. "
1932
1933
1934
                "Please turn off chunked prefill by "
                "`--no-enable-chunked-prefill` before using it."
            )
1935
1936
1937
1938
1939

        if "score" in supported_tasks:
            num_labels = getattr(self.model_config.hf_config, "num_labels", 0)
            if num_labels != 1:
                supported_tasks.remove("score")
1940
                logger.debug_once("Score API is only enabled for num_labels == 1.")
1941
1942

        return supported_tasks
1943

1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
    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)

1954
    def sync_and_slice_intermediate_tensors(
1955
1956
1957
1958
1959
        self,
        num_tokens: int,
        intermediate_tensors: IntermediateTensors,
        sync_self: bool,
    ) -> IntermediateTensors:
1960
1961
1962
        assert self.intermediate_tensors is not None

        tp = self.vllm_config.parallel_config.tensor_parallel_size
1963
        is_rs = is_residual_scattered_for_sp(self.vllm_config, num_tokens)
1964
1965
1966
1967
1968
1969

        # 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():
1970
                is_scattered = k == "residual" and is_rs
1971
                copy_len = num_tokens // tp if is_scattered else num_tokens
1972
                self.intermediate_tensors[k][:copy_len].copy_(
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
                    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:
1986
1987
1988
1989
1990
1991
1992
        """
        Step for the EPLB (Expert Parallelism Load Balancing) state.
        """
        if not self.parallel_config.enable_eplb:
            return

        assert self.eplb_state is not None
1993
1994
        model = self.get_model()
        assert is_mixture_of_experts(model)
1995
        self.eplb_state.step(
1996
            model,
1997
1998
            is_dummy,
            is_profile,
1999
            log_stats=self.parallel_config.eplb_config.log_balancedness,
2000
2001
        )

2002
2003
2004
2005
    # 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)
2006
2007
    @staticmethod
    def pad_out_ubatch_slice(ubatch_slices: UBatchSlices, num_total_tokens: int):
2008
2009
2010
2011
2012
2013
        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
        )
2014

2015
2016
2017
2018
2019
2020
    def _pool(
        self,
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
        num_scheduled_tokens_np: np.ndarray,
    ) -> ModelRunnerOutput:
2021
2022
2023
        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"
        )
2024

2025
        hidden_states = hidden_states[:num_scheduled_tokens]
2026
        pooling_metadata = self.input_batch.get_pooling_metadata()
2027
2028
2029
2030
        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]
2031

2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
        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()
2042

2043
        pooler_output: list[torch.Tensor | None] = []
2044
        for raw_output, seq_len, prompt_len in zip(
2045
2046
            raw_pooler_output, seq_lens_cpu, pooling_metadata.prompt_lens
        ):
2047
            output = raw_output if seq_len == prompt_len else None
2048
            pooler_output.append(output)
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058

        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,
        )

2059
    def _get_num_input_tokens(self, num_scheduled_tokens: int) -> int:
2060
2061
2062
2063
2064
2065
        if (
            self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
            and hasattr(self, "cudagraph_batch_sizes")
            and self.cudagraph_batch_sizes
            and num_scheduled_tokens <= self.cudagraph_batch_sizes[-1]
        ):
2066
2067
2068
2069
2070
2071
2072
2073
            # 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
2074
2075
2076
2077
        if (
            self.compilation_config.pass_config.enable_sequence_parallelism
            and tp_size > 1
        ):
2078
2079
2080
            return round_up(num_scheduled_tokens, tp_size)
        return num_scheduled_tokens

2081
    def _preprocess(
2082
2083
        self,
        scheduler_output: "SchedulerOutput",
2084
        num_input_tokens: int,  # Padded
2085
        intermediate_tensors: IntermediateTensors | None = None,
2086
2087
    ) -> tuple[
        int,
2088
2089
        torch.Tensor | None,
        torch.Tensor | None,
2090
        torch.Tensor,
2091
        IntermediateTensors | None,
2092
2093
        dict[str, Any],
    ]:
2094
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
2095
        is_first_rank = get_pp_group().is_first_rank
2096

2097
2098
        # _prepare_inputs may reorder the batch, so we must gather multi
        # modal outputs after that to ensure the correct order
2099
2100
        if (
            self.supports_mm_inputs
2101
            and is_first_rank
2102
2103
            and not self.model_config.is_encoder_decoder
        ):
2104
2105
            # Run the multimodal encoder if any.
            self._execute_mm_encoder(scheduler_output)
2106
            mm_embeds, is_mm_embed = self._gather_mm_embeddings(scheduler_output)
2107

2108
2109
2110
            # 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.
2111
            inputs_embeds_scheduled = self.model.get_input_embeddings(
2112
2113
2114
                self.input_ids.gpu[:num_scheduled_tokens],
                multimodal_embeddings=mm_embeds,
                is_multimodal=is_mm_embed,
2115
            )
2116

2117
            # TODO(woosuk): Avoid the copy. Optimize.
2118
            self.inputs_embeds.gpu[:num_scheduled_tokens].copy_(inputs_embeds_scheduled)
2119

2120
            input_ids = None
2121
            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
2122
2123
2124
2125
            model_kwargs = {
                **self._init_model_kwargs(num_scheduled_tokens),
                **self._extract_mm_kwargs(scheduler_output),
            }
2126
        elif self.enable_prompt_embeds and is_first_rank:
2127
2128
2129
2130
2131
2132
2133
2134
2135
2136
2137
2138
            # 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).
2139
2140
2141
            token_ids_idx = (
                self.is_token_ids.gpu[:num_scheduled_tokens]
                .nonzero(as_tuple=False)
2142
                .squeeze(1)
2143
            )
2144
2145
2146
            # Some tokens ids may need to become embeds
            if token_ids_idx.numel() > 0:
                token_ids = self.input_ids.gpu[token_ids_idx]
2147
                tokens_to_embeds = self.model.get_input_embeddings(input_ids=token_ids)
2148
2149
2150
2151
2152
                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
2153
        else:
2154
2155
2156
2157
            # 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.
2158
            input_ids = self.input_ids.gpu[:num_input_tokens]
2159
            inputs_embeds = None
2160
            model_kwargs = self._init_model_kwargs(num_input_tokens)
2161
        if self.uses_mrope:
2162
            positions = self.mrope_positions.gpu[:, :num_input_tokens]
2163
        else:
2164
            positions = self.positions.gpu[:num_input_tokens]
2165

2166
        if is_first_rank:
2167
2168
            intermediate_tensors = None
        else:
2169
            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
2170
2171
                num_input_tokens, intermediate_tensors, True
            )
2172

2173
2174
2175
2176
        if (
            self.model_config.is_encoder_decoder
            and scheduler_output.scheduled_encoder_inputs
        ):
2177
2178
2179
            encoder_inputs = self._extract_encoder_inputs(scheduler_output)
            model_kwargs.update(encoder_inputs)

2180
2181
2182
2183
2184
2185
2186
2187
        return (
            num_scheduled_tokens,
            input_ids,
            inputs_embeds,
            positions,
            intermediate_tensors,
            model_kwargs,
        )
2188

2189
    def _sample(
2190
        self,
2191
2192
        logits: torch.Tensor | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
2193
    ) -> SamplerOutput:
2194
        # Sample the next token and get logprobs if needed.
2195
        sampling_metadata = self.input_batch.sampling_metadata
2196
        if spec_decode_metadata is None:
2197
2198
2199
            # 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()
2200
            return self.sampler(
2201
2202
2203
                logits=logits,
                sampling_metadata=sampling_metadata,
            )
2204

2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2217
2218
2219
2220
2221
2222
2223
2224
2225
2226
2227
2228
2229
2230
        # 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)
2231
2232
2233
        return sampler_output

    def _bookkeeping_sync(
2234
2235
2236
        self,
        scheduler_output: "SchedulerOutput",
        sampler_output: SamplerOutput,
2237
        logits: torch.Tensor | None,
2238
2239
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
2240
    ) -> tuple[
2241
        dict[str, int],
2242
        LogprobsLists | None,
2243
        list[list[int]],
2244
        dict[str, LogprobsTensors | None],
2245
2246
2247
        list[str],
        dict[str, int],
        list[int],
2248
    ]:
2249
2250
2251
2252
        num_nans_in_logits = {}
        if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
            num_nans_in_logits = self._get_nans_in_logits(logits)

2253
2254
2255
        discard_sampled_tokens_req_indices = self.discard_request_indices.np[
            : self.num_discarded_requests
        ]
2256
2257
2258
2259
        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)
2260

2261
2262
2263
        # 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()
2264
        req_id_to_index_output_copy = self.input_batch.req_id_to_index.copy()
2265

2266
2267
        # NOTE: GPU -> CPU Sync happens here.
        # Move as many CPU operations as possible before this sync point.
2268
        logprobs_tensors = sampler_output.logprobs_tensors
2269
2270
2271
        logprobs_lists = (
            logprobs_tensors.tolists() if logprobs_tensors is not None else None
        )
2272
2273
2274

        # Compute prompt logprobs if needed.
        prompt_logprobs_dict = self._get_prompt_logprobs_dict(
2275
            hidden_states[:num_scheduled_tokens],
2276
            scheduler_output.num_scheduled_tokens,
2277
2278
        )

2279
        num_sampled_tokens = sampler_output.sampled_token_ids.shape[0]
2280
        sampled_token_ids = sampler_output.sampled_token_ids
2281
        invalid_req_indices = []
2282
2283
2284
2285
2286
2287
2288
2289
2290
2291
2292
2293
2294
2295
        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:
2296
                valid_sampled_token_ids[int(i)].clear()
2297
        else:
2298
            valid_sampled_token_ids = []
2299
            invalid_req_indices = discard_sampled_tokens_req_indices.tolist()
2300
2301
2302
2303
2304
2305
            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.
2306
            self.input_batch.prev_sampled_token_ids = sampled_token_ids
2307
2308
2309
2310
2311
            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
            }
2312

2313
2314
2315
2316
2317
        # 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.
2318
        req_ids = self.input_batch.req_ids
2319
2320
        for req_idx in range(num_sampled_tokens):
            if self.use_async_scheduling:
2321
                sampled_ids = [-1] if req_idx not in invalid_req_indices_set else None
2322
2323
            else:
                sampled_ids = valid_sampled_token_ids[req_idx]
2324
2325
2326
2327
2328
            if not sampled_ids:
                continue

            start_idx = self.input_batch.num_tokens_no_spec[req_idx]
            end_idx = start_idx + len(sampled_ids)
2329
2330
2331
2332
            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}"
2333
            )
2334

2335
2336
            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
2337
2338
            self.input_batch.num_tokens_no_spec[req_idx] = end_idx
            self.input_batch.num_tokens[req_idx] = end_idx
2339

2340
            req_id = req_ids[req_idx]
2341
2342
2343
            req_state = self.requests[req_id]
            req_state.output_token_ids.extend(sampled_ids)

2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
        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,
        )

2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
    @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()

2369
2370
    def _model_forward(
        self,
2371
2372
2373
2374
        input_ids: torch.Tensor | None = None,
        positions: torch.Tensor | None = None,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
2375
2376
2377
2378
2379
        **model_kwargs: dict[str, Any],
    ) -> Any:
        """Helper method to call the model forward pass.

        This method can be overridden by subclasses for model execution.
2380
        Motivation: We can inspect only this method versus
2381
2382
2383
2384
2385
2386
2387
2388
2389
2390
2391
2392
2393
2394
2395
2396
2397
2398
2399
2400
        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,
        )

2401
2402
2403
2404
    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
2405
2406
        intermediate_tensors: IntermediateTensors | None = None,
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput | IntermediateTensors:
2407
        with record_function_or_nullcontext("Preprocess"):
2408
2409
2410
2411
2412
2413
2414
2415
2416
            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(
2417
2418
                        scheduler_output, self.vllm_config
                    )
2419
2420
2421
2422
                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 "
2423
2424
                        "it when the requests need prompt logprobs"
                    )
2425

2426
                # Prepare the decoder inputs.
2427
2428
2429
2430
2431
2432
2433
2434
                (
                    attn_metadata,
                    logits_indices,
                    spec_decode_metadata,
                    num_scheduled_tokens_np,
                    spec_decode_common_attn_metadata,
                    max_query_len,
                    ubatch_slices,
2435
                    num_tokens_across_dp,
2436
2437
                    use_cascade_attn,
                ) = self._prepare_inputs(scheduler_output)
2438

2439
            dp_rank = self.parallel_config.data_parallel_rank
2440
2441
            if ubatch_slices:
                assert num_tokens_across_dp is not None
2442
                num_input_tokens = int(num_tokens_across_dp[dp_rank].item())
2443
2444
                self.pad_out_ubatch_slice(ubatch_slices, num_input_tokens)
            elif num_tokens_across_dp is not None:
2445
                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,
2458
            ) = self._preprocess(
2459
                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)
            )
2471

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        # Set cudagraph mode to none if calc_kv_scales is true.
        if attn_metadata is not None:
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            metadata_list = (
                attn_metadata.values()
                if isinstance(attn_metadata, dict)
                else [attn_metadata]
            )
2479
            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,
2494
                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,
        ):
2499
            model_output = self._model_forward(
2500
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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:
2509
                # True when EAGLE 3 is used.
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                hidden_states, aux_hidden_states = model_output
            else:
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                # 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
2523

2524
                if self.is_pooling_model:
2525
                    # 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]
2533
                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:
2539
                    all_gather_tensors = {
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                        "residual": not is_residual_scattered_for_sp(
                            self.vllm_config, num_input_tokens
                        )
2543
                    }
2544
                    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]
2552
                    logits = self.model.compute_logits(sample_hidden_states)
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2557

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

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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
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            if scheduler_output.structured_output_request_ids:
                apply_grammar_bitmask(scheduler_output, self.input_batch, logits)
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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
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2594
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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
        ):
2598
            effective_drafter_max_model_len = (
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                self.speculative_config.draft_model_config.max_model_len
            )
2601
        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
        )
2606
        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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2626
            ) = self._bookkeeping_sync(
                scheduler_output,
                sampler_output,
                logits,
                hidden_states,
                num_scheduled_tokens,
            )
2627

2628
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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)
2636

2637
2638
        with record_function_or_nullcontext("EPLB"):
            self.eplb_step()
2639

2640
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        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=[],
2647
            kv_connector_output=kv_connector_output,
2648
2649
2650
            num_nans_in_logits=num_nans_in_logits,
        )

2651
2652
2653
        if not self.use_async_scheduling:
            return output

2654
        async_output = AsyncGPUModelRunnerOutput(
2655
            model_runner_output=output,
2656
            sampled_token_ids=sampler_output.sampled_token_ids,
2657
2658
2659
2660
            invalid_req_indices=invalid_req_indices,
            async_output_copy_stream=self.async_output_copy_stream,
        )

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2663
2664
2665
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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

2670
    def take_draft_token_ids(self) -> DraftTokenIds | None:
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2673
2674
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2677
2678
2679
2680
        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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2683
    def propose_draft_token_ids(
        self,
        scheduler_output: "SchedulerOutput",
2684
        sampled_token_ids: torch.Tensor | list[list[int]],
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2686
2687
        sampling_metadata: SamplingMetadata,
        hidden_states: torch.Tensor,
        sample_hidden_states: torch.Tensor,
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2689
        aux_hidden_states: list[torch.Tensor] | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
2690
        common_attn_metadata: CommonAttentionMetadata,
2691
    ) -> list[list[int]] | torch.Tensor:
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2693
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
        if self.speculative_config.method == "ngram":
2694
            assert isinstance(sampled_token_ids, list)
2695
            assert isinstance(self.drafter, NgramProposer)
2696
            draft_token_ids = self.drafter.propose(
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2698
                sampled_token_ids,
                self.input_batch.req_ids,
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2700
                self.input_batch.num_tokens_no_spec,
                self.input_batch.token_ids_cpu,
2701
2702
                self.input_batch.spec_decode_unsupported_reqs,
            )
2703
        elif self.speculative_config.method == "medusa":
2704
            assert isinstance(sampled_token_ids, list)
2705
            assert isinstance(self.drafter, MedusaProposer)
2706

2707
2708
            if sample_hidden_states.shape[0] == len(sampled_token_ids):
                # The input to the target model does not include draft tokens.
2709
2710
2711
2712
                hidden_states = sample_hidden_states
            else:
                indices = []
                offset = 0
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Wentao Ye committed
2713
                assert spec_decode_metadata is not None
2714
                for num_draft, tokens in zip(
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                    spec_decode_metadata.num_draft_tokens, sampled_token_ids
                ):
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2718
                    indices.append(offset + len(tokens) - 1)
                    offset += num_draft + 1
2719
                indices = torch.tensor(indices, device=self.device)
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2721
                hidden_states = sample_hidden_states[indices]

2722
            draft_token_ids = self.drafter.propose(
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2724
2725
                target_hidden_states=hidden_states,
                sampling_metadata=sampling_metadata,
            )
2726
        elif self.speculative_config.use_eagle():
2727
            assert isinstance(self.drafter, EagleProposer)
2728
2729
2730
2731
2732

            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.
2733
2734
                assert isinstance(sampled_token_ids, list), (
                    "sampled_token_ids should be a python list when"
2735
                    "padded-batch is disabled."
2736
                )
2737
                next_token_ids = self.drafter.prepare_next_token_ids_cpu(
2738
2739
2740
2741
2742
                    sampled_token_ids,
                    self.requests,
                    self.input_batch,
                    scheduler_output.num_scheduled_tokens,
                )
2743
2744
2745
2746
2747
            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.
2748
2749
                assert isinstance(sampled_token_ids, torch.Tensor), (
                    "sampled_token_ids should be a torch.Tensor when"
2750
                    "padded-batch is enabled."
2751
2752
                )
                next_token_ids, valid_sampled_tokens_count = (
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2754
2755
2756
2757
2758
                    self.drafter.prepare_next_token_ids_padded(
                        common_attn_metadata,
                        sampled_token_ids,
                        self.requests,
                        self.input_batch,
                        self.discard_request_indices.gpu,
2759
                        self.num_discarded_requests,
2760
                    )
2761
                )
Jiayi Yao's avatar
Jiayi Yao committed
2762

2763
            if spec_decode_metadata is None:
2764
                token_indices_to_sample = None
2765
                # input_ids can be None for multimodal models.
2766
                target_token_ids = self.input_ids.gpu[:num_scheduled_tokens]
2767
                target_positions = self._get_positions(num_scheduled_tokens)
2768
                if self.use_aux_hidden_state_outputs:
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Wentao Ye committed
2769
                    assert aux_hidden_states is not None
2770
                    target_hidden_states = torch.cat(
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2772
                        [h[:num_scheduled_tokens] for h in aux_hidden_states], dim=-1
                    )
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2774
                else:
                    target_hidden_states = hidden_states[:num_scheduled_tokens]
2775
            else:
2776
2777
                if self.speculative_config.disable_padded_drafter_batch:
                    token_indices_to_sample = None
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2779
2780
2781
2782
                    common_attn_metadata, token_indices = self.drafter.prepare_inputs(
                        common_attn_metadata,
                        sampled_token_ids,
                        spec_decode_metadata.num_draft_tokens,
                    )
2783
                else:
2784
                    common_attn_metadata, token_indices, token_indices_to_sample = (
2785
2786
2787
                        self.drafter.prepare_inputs_padded(
                            common_attn_metadata,
                            spec_decode_metadata,
2788
2789
2790
                            valid_sampled_tokens_count,
                        )
                    )
2791

2792
                target_token_ids = self.input_ids.gpu[token_indices]
2793
                target_positions = self._get_positions(token_indices)
2794
                if self.use_aux_hidden_state_outputs:
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2795
                    assert aux_hidden_states is not None
2796
                    target_hidden_states = torch.cat(
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2798
                        [h[token_indices] for h in aux_hidden_states], dim=-1
                    )
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2800
                else:
                    target_hidden_states = hidden_states[token_indices]
2801

2802
            if self.supports_mm_inputs:
2803
2804
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2806
2807
2808
                mm_embed_inputs = self._gather_mm_embeddings(
                    scheduler_output,
                    shift_computed_tokens=1,
                )
            else:
                mm_embed_inputs = None
2809

2810
            draft_token_ids = self.drafter.propose(
2811
2812
2813
2814
                target_token_ids=target_token_ids,
                target_positions=target_positions,
                target_hidden_states=target_hidden_states,
                next_token_ids=next_token_ids,
2815
                last_token_indices=token_indices_to_sample,
2816
                sampling_metadata=sampling_metadata,
2817
                common_attn_metadata=common_attn_metadata,
2818
                mm_embed_inputs=mm_embed_inputs,
2819
            )
2820

2821
        return draft_token_ids
2822

2823
2824
2825
    def update_config(self, overrides: dict[str, Any]) -> None:
        allowed_config_names = {"load_config", "model_config"}
        for config_name, config_overrides in overrides.items():
2826
2827
            assert config_name in allowed_config_names, (
                f"Config `{config_name}` not supported. "
2828
                f"Allowed configs: {allowed_config_names}"
2829
            )
2830
2831
2832
2833
            config = getattr(self, config_name)
            new_config = update_config(config, config_overrides)
            setattr(self, config_name, new_config)

2834
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2836
2837
2838
    def load_model(self, eep_scale_up: bool = False) -> None:
        """
        Args:
            eep_scale_up: the model loading is for elastic EP scale up.
        """
2839
        logger.info("Starting to load model %s...", self.model_config.model)
2840
2841
        if eep_scale_up:
            from vllm.distributed.parallel_state import get_ep_group
2842
2843
2844
2845
2846

            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
            )
2847
2848
            num_local_physical_experts = int(num_local_physical_experts.item())
            new_ep_size = get_ep_group().world_size
2849
            global_expert_load, old_global_expert_indices = EplbState.recv_state()
2850
            num_logical_experts = global_expert_load.shape[1]
2851
            self.parallel_config.eplb_config.num_redundant_experts = (
2852
2853
2854
2855
2856
2857
                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
            )
2858
            rank_mapping = {
2859
                old_ep_rank: old_ep_rank for old_ep_rank in range(old_ep_size)
2860
2861
2862
2863
2864
2865
            }
        else:
            global_expert_load = None
            old_global_expert_indices = None
            rank_mapping = None

2866
        with DeviceMemoryProfiler() as m:
2867
            time_before_load = time.perf_counter()
2868
            model_loader = get_model_loader(self.load_config)
2869
            self.model = model_loader.load_model(
2870
2871
                vllm_config=self.vllm_config, model_config=self.model_config
            )
2872
            if self.lora_config:
2873
2874
2875
                self.model = self.load_lora_model(
                    self.model, self.vllm_config, self.device
                )
2876
2877
2878
            if hasattr(self, "drafter"):
                logger.info("Loading drafter model...")
                self.drafter.load_model(self.model)
2879
            if self.use_aux_hidden_state_outputs:
2880
                if not supports_eagle3(self.get_model()):
2881
2882
                    raise RuntimeError(
                        "Model does not support EAGLE3 interface but "
2883
2884
                        "aux_hidden_state_outputs was requested"
                    )
2885
2886
2887
2888
2889
2890
2891
2892
2893
2894
2895
2896
2897

                # 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)
2898
            time_after_load = time.perf_counter()
2899
        self.model_memory_usage = m.consumed_memory
2900
2901
2902
2903
2904
        logger.info(
            "Model loading took %.4f GiB and %.6f seconds",
            self.model_memory_usage / GiB_bytes,
            time_after_load - time_before_load,
        )
2905
        prepare_communication_buffer_for_model(self.model)
2906

2907
        self.is_multimodal_pruning_enabled = (
2908
            supports_multimodal_pruning(self.get_model())
2909
2910
            and self.model_config.multimodal_config.is_multimodal_pruning_enabled()
        )
2911

2912
2913
        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)
2914
2915
2916
2917
            self.eplb_state = EplbState.build(
                self.model,
                self.device,
                self.parallel_config,
2918
2919
2920
                global_expert_load,
                old_global_expert_indices,
                rank_mapping,
2921
2922
            )

2923
        if (
2924
2925
            self.vllm_config.compilation_config.mode
            == CompilationMode.STOCK_TORCH_COMPILE
2926
            and supports_dynamo()
2927
        ):
2928
            backend = self.vllm_config.compilation_config.init_backend(self.vllm_config)
2929
            compilation_counter.stock_torch_compile_count += 1
2930
            self.model.compile(fullgraph=True, backend=backend)
2931
            return
2932
        # for other compilation modes, cudagraph behavior is controlled by
2933
2934
2935
        # CudagraphWraper and CudagraphDispatcher of vllm.

        # wrap the model with full cudagraph wrapper if needed.
2936
2937
2938
2939
2940
2941
2942
        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
            )
2943
2944
        elif self.parallel_config.enable_dbo:
            if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
2945
2946
2947
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.FULL, self.device
                )
2948
            else:
2949
2950
2951
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.NONE, self.device
                )
2952

2953
    def _get_eagle3_aux_layers_from_config(self) -> tuple[int, ...] | None:
2954
2955
2956
2957
2958
2959
2960
2961
2962
2963
2964
2965
2966
2967
2968
2969
2970
2971
2972
2973
2974
2975
2976
        """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

2977
    def reload_weights(self) -> None:
2978
        assert getattr(self, "model", None) is not None, (
2979
            "Cannot reload weights before model is loaded."
2980
        )
2981
2982
        model_loader = get_model_loader(self.load_config)
        logger.info("Reloading weights inplace...")
2983
        model_loader.load_weights(self.get_model(), model_config=self.model_config)
2984

2985
2986
2987
2988
2989
    def save_tensorized_model(
        self,
        tensorizer_config: "TensorizerConfig",
    ) -> None:
        TensorizerLoader.save_model(
2990
            self.get_model(),
2991
            tensorizer_config=tensorizer_config,
2992
            model_config=self.model_config,
2993
2994
        )

2995
2996
2997
    def _get_prompt_logprobs_dict(
        self,
        hidden_states: torch.Tensor,
2998
        num_scheduled_tokens: dict[str, int],
2999
    ) -> dict[str, LogprobsTensors | None]:
3000
3001
3002
3003
        num_prompt_logprobs_dict = self.input_batch.num_prompt_logprobs
        if not num_prompt_logprobs_dict:
            return {}

3004
        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
3005
        prompt_logprobs_dict: dict[str, LogprobsTensors | None] = {}
3006
3007
3008
3009
3010

        # 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():
3011
            num_tokens = num_scheduled_tokens[req_id]
3012
3013
3014

            # Get metadata for this request.
            request = self.requests[req_id]
3015
3016
3017
3018
            if request.prompt_token_ids is None:
                # Prompt logprobs is incompatible with prompt embeddings
                continue

3019
3020
            num_prompt_tokens = len(request.prompt_token_ids)
            prompt_token_ids = torch.tensor(request.prompt_token_ids).to(
3021
3022
                self.device, non_blocking=True
            )
3023

3024
3025
3026
3027
3028
3029
            # 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(
3030
3031
                    num_prompt_tokens - 1, num_prompt_logprobs + 1
                )
3032
3033
                in_progress_dict[req_id] = logprobs_tensors

3034
            # Determine number of logits to retrieve.
3035
3036
            start_idx = request.num_computed_tokens
            start_tok = start_idx + 1
3037
            num_remaining_tokens = num_prompt_tokens - start_tok
3038
            if num_tokens <= num_remaining_tokens:
3039
                # This is a chunk, more tokens remain.
3040
3041
3042
                # 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.
3043
3044
3045
3046
3047
                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)
3048
3049
3050
3051
3052
3053
3054
                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
3055
3056
3057
3058
3059

            # 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]
3060
            offset = self.query_start_loc.np[req_idx].item()
3061
            prompt_hidden_states = hidden_states[offset : offset + num_logits]
3062
            logits = self.model.compute_logits(prompt_hidden_states)
3063
3064
3065
3066

            # 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.
3067
            tgt_token_ids = prompt_token_ids[start_tok : start_tok + num_logits]
3068
3069

            # Compute prompt logprobs.
3070
3071
            logprobs = self.sampler.compute_logprobs(logits)
            token_ids, logprobs, ranks = self.sampler.gather_logprobs(
3072
3073
                logprobs, num_prompt_logprobs, tgt_token_ids
            )
3074
3075

            # Transfer GPU->CPU async.
3076
3077
            chunk_slice = slice(start_idx, start_idx + num_logits)
            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
3078
3079
3080
                token_ids, non_blocking=True
            )
            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs, non_blocking=True)
3081
            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
3082
3083
                ranks, non_blocking=True
            )
3084
3085
3086
3087
3088

        # 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]
3089
            del in_progress_dict[req_id]
3090
3091

        # Must synchronize the non-blocking GPU->CPU transfers.
3092
        if prompt_logprobs_dict:
3093
            self._sync_device()
3094
3095
3096

        return prompt_logprobs_dict

3097
3098
    def _get_nans_in_logits(
        self,
3099
        logits: torch.Tensor | None,
3100
3101
3102
3103
3104
3105
3106
3107
3108
3109
3110
    ) -> 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])
3111
3112
3113
                    if num_nans_for_index is not None and req_index < logits.shape[0]
                    else 0
                )
3114
3115
3116
3117
            return num_nans_in_logits
        except IndexError:
            return {}

3118
3119
3120
3121
3122
3123
    @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
3124
         - during DP rank dummy run
3125
3126
3127
3128
3129
3130
3131
3132
3133
3134
3135
        """
        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(
3136
                    self.input_ids.gpu,
3137
3138
                    low=0,
                    high=self.model_config.get_vocab_size(),
3139
3140
                    dtype=input_ids.dtype,
                )
3141

3142
            logger.debug_once("Randomizing dummy data for DP Rank")
3143
            input_ids.copy_(rand_input_ids()[: input_ids.size(0)], non_blocking=True)
3144
3145
3146
            yield
            input_ids.fill_(0)

3147
3148
3149
3150
3151
3152
    def _get_mm_dummy_batch(
        self,
        modality: str,
        max_items_per_batch: int,
    ) -> BatchedTensorInputs:
        """Dummy data for profiling and precompiling multimodal models."""
3153
3154
        assert self.mm_budget is not None

3155
3156
        dummy_decoder_data = self.mm_registry.get_decoder_dummy_data(
            model_config=self.model_config,
3157
            seq_len=self.max_model_len,
3158
            mm_counts={modality: 1},
3159
            cache=self.mm_budget.cache,
3160
3161
3162
3163
        )
        dummy_mm_data = dummy_decoder_data.multi_modal_data

        # Result in the maximum GPU consumption of the model
3164
3165
        dummy_mm_item = dummy_mm_data[modality][0]
        dummy_mm_items = [dummy_mm_item] * max_items_per_batch
3166

3167
        model = cast(SupportsMultiModal, self.model)
3168
3169
3170
3171
3172
3173
3174
3175
3176
        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,
            )
        )
3177

3178
3179
3180
3181
    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
3182
        cudagraph_runtime_mode: CUDAGraphMode | None = None,
3183
3184
        force_attention: bool = False,
        uniform_decode: bool = False,
3185
        allow_microbatching: bool = True,
3186
3187
        skip_eplb: bool = False,
        is_profile: bool = False,
3188
        create_mixed_batch: bool = False,
3189
        remove_lora: bool = True,
3190
    ) -> tuple[torch.Tensor, torch.Tensor]:
3191
3192
3193
3194
3195
3196
3197
        """
        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.
3198
                - if not set will determine the cudagraph mode based on using
3199
                    the self.cudagraph_dispatcher.
3200
3201
3202
3203
                - CUDAGraphMode.NONE: No cudagraph, for warm up and profile run
                - CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
                - CUDAGraphMode.FULL: Full cudagraph, attention metadata is
                    needed.
3204
            force_attention: If True, always create attention metadata. Used to
3205
3206
3207
3208
                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.
3209
3210
            create_mixed_batch: If True, create a mixed batch with both decode
                (1 token) and prefill (multiple tokens) requests.
3211
            remove_lora: If False, dummy LoRAs are not destroyed after the run
3212
        """
3213
3214
3215
3216
        assert (
            cudagraph_runtime_mode is None
            or cudagraph_runtime_mode.valid_runtime_modes()
        )
3217

3218
        # If cudagraph_mode.decode_mode() == FULL and
3219
        # cudagraph_mode.separate_routine(). This means that we are using
3220
3221
3222
3223
3224
3225
3226
3227
3228
3229
3230
        # 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.
3231
        max_query_len = self.uniform_decode_query_len if uniform_decode else num_tokens
3232

3233
3234
3235
3236
3237
        # 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
3238
3239
3240
3241
        if create_mixed_batch:
            assert not uniform_decode
            # Create mixed batch:
            # first half decode tokens, second half one prefill
3242
            num_decode_tokens = min(max_num_reqs - 1, num_tokens // 2)
3243
3244
3245
3246
            num_prefill_tokens = num_tokens - num_decode_tokens
            num_reqs = num_decode_tokens + 1

            # Create decode requests (1 token each) followed by prefill request
3247
            num_scheduled_tokens_list = [1] * num_decode_tokens + [num_prefill_tokens]
3248
3249
3250
            # Note: Overriding max_query_len to be the prefill tokens
            max_query_len = num_prefill_tokens
        elif uniform_decode:
3251
            assert not create_mixed_batch
3252
            num_reqs = min(max_num_reqs, cdiv(num_tokens, max_query_len))
3253
3254
            num_scheduled_tokens_list = [max_query_len] * num_reqs
            if num_tokens % max_query_len != 0:
3255
                num_scheduled_tokens_list[-1] = num_tokens % max_query_len
3256
3257
3258
3259
3260
3261
        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

3262
3263
        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs
3264
        num_scheduled_tokens = np.array(num_scheduled_tokens_list, dtype=np.int32)
3265
        total_num_scheduled_tokens = int(num_scheduled_tokens.sum())
3266

3267
3268
3269
        # Disable DP padding when running eager
        allow_dp_padding = self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE

3270
3271
        # We currently only microbatch if the number of tokens is
        # over a certain threshold.
3272
        ubatch_slices, num_tokens_across_dp = coordinate_batch_across_dp(
3273
3274
3275
3276
3277
3278
3279
            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,
3280
3281
3282
        )
        num_tokens_after_padding = num_tokens
        if num_tokens_across_dp is not None:
3283
3284
            dp_rank = self.parallel_config.data_parallel_rank
            num_tokens_after_padding = int(num_tokens_across_dp[dp_rank])
3285

3286
        attn_metadata: PerLayerAttnMetadata | None = None
3287
3288
3289

        # If force_attention is True, we always capture attention. Otherwise,
        # it only happens for cudagraph_runtime_mode=FULL.
3290
        if force_attention or cudagraph_runtime_mode == CUDAGraphMode.FULL:
3291
            attn_metadata = {}
3292
3293
            if ubatch_slices is not None:
                attn_metadata = [dict() for _ in range(len(ubatch_slices))]
3294

3295
3296
3297
3298
3299
3300
            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:
3301
                seq_lens = max_query_len
3302
            self.seq_lens.np[:num_reqs] = seq_lens
3303
3304
            self.seq_lens.np[num_reqs:] = 0
            self.seq_lens.copy_to_gpu()
3305

3306
3307
            cum_num_tokens, _ = self._get_cumsum_and_arange(num_scheduled_tokens)
            self.query_start_loc.np[1 : num_reqs + 1] = cum_num_tokens
3308
3309
            self.query_start_loc.copy_to_gpu()

3310
            for kv_cache_group_id, kv_cache_group_spec in enumerate(
3311
3312
                self.kv_cache_config.kv_cache_groups
            ):
3313
                common_attn_metadata = CommonAttentionMetadata(
3314
3315
                    query_start_loc=self.query_start_loc.gpu[: num_reqs + 1],
                    query_start_loc_cpu=self.query_start_loc.cpu[: num_reqs + 1],
3316
3317
                    seq_lens=self.seq_lens.gpu[:num_reqs],
                    seq_lens_cpu=self.seq_lens.cpu[:num_reqs],
3318
3319
3320
                    num_computed_tokens_cpu=self.input_batch.num_computed_tokens_cpu_tensor[
                        :num_reqs
                    ],
3321
3322
                    num_reqs=num_reqs,
                    num_actual_tokens=num_tokens,
3323
                    max_query_len=max_query_len,
3324
                    max_seq_len=self.max_model_len,
3325
3326
3327
                    block_table_tensor=self.input_batch.block_table[
                        kv_cache_group_id
                    ].get_device_tensor(num_reqs),
3328
                    slot_mapping=self.input_batch.block_table[
3329
3330
3331
                        kv_cache_group_id
                    ].slot_mapping.gpu[:num_tokens],
                    causal=True,
3332
3333
3334
                    dcp_local_seq_lens=self.dcp_local_seq_lens.gpu[:num_reqs]
                    if self.dcp_world_size > 1
                    else None,
3335
                )
3336
                for attn_group in self.attn_groups[kv_cache_group_id]:
3337
3338
                    if ubatch_slices is not None:
                        common_attn_metadata_list = split_attn_metadata(
3339
3340
                            ubatch_slices, common_attn_metadata
                        )
3341
                        for ubid, common_attn_metadata in enumerate(
3342
3343
                            common_attn_metadata_list
                        ):
3344
                            assert common_attn_metadata.max_query_len == 1
3345
3346
3347
                            attn_metadata_i = attn_group.get_metadata_builder(
                                ubatch_id=ubid
                            ).build_for_cudagraph_capture(common_attn_metadata)
3348
                            for layer_name in attn_group.layer_names:
3349
                                assert type(attn_metadata) is list
3350
                                attn_metadata[ubid][layer_name] = attn_metadata_i
3351
3352
                    else:
                        assert type(attn_metadata) is dict
3353
3354
                        metadata_builder = attn_group.get_metadata_builder()
                        attn_metadata_i = metadata_builder.build_for_cudagraph_capture(
3355
3356
                            common_attn_metadata
                        )
3357
                        for layer_name in attn_group.layer_names:
3358
                            attn_metadata[layer_name] = attn_metadata_i
3359

3360
3361
3362
        with self.maybe_dummy_run_with_lora(
            self.lora_config, num_scheduled_tokens, remove_lora
        ):
3363
3364
3365
            # 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)
3366
            if self.supports_mm_inputs and not self.model_config.is_encoder_decoder:
3367
                input_ids = None
3368
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_after_padding]
3369
                model_kwargs = {
3370
                    **model_kwargs,
3371
3372
                    **self._dummy_mm_kwargs(num_reqs),
                }
3373
3374
            elif self.enable_prompt_embeds:
                input_ids = None
3375
3376
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_after_padding]
                model_kwargs = self._init_model_kwargs(num_tokens_after_padding)
3377
            else:
3378
                input_ids = self.input_ids.gpu[:num_tokens_after_padding]
3379
                inputs_embeds = None
3380

3381
            if self.uses_mrope:
3382
                positions = self.mrope_positions.gpu[:, :num_tokens_after_padding]
3383
            else:
3384
                positions = self.positions.gpu[:num_tokens_after_padding]
3385
3386
3387
3388
3389
3390
3391
3392
3393

            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,
3394
3395
3396
                            device=self.device,
                        )
                    )
3397
3398

                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
3399
                    num_tokens_after_padding, None, False
3400
                )
3401
3402

            # filter out the valid batch descriptor
3403
3404
3405
3406
3407
3408
3409
3410
3411
3412
            _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)
            )
3413
3414
3415
            if cudagraph_runtime_mode is not None:
                # we allow forcing NONE when the dispatcher disagrees to support
                # warm ups for cudagraph capture
3416
3417
3418
3419
                assert (
                    cudagraph_runtime_mode == CUDAGraphMode.NONE
                    or cudagraph_runtime_mode == _cg_mode
                ), (
3420
                    f"Cudagraph runtime mode mismatch at dummy_run. "
3421
3422
                    f"Expected {_cg_mode}, but got {cudagraph_runtime_mode}."
                )
3423
3424
            else:
                cudagraph_runtime_mode = _cg_mode
3425

3426
            if ubatch_slices is not None:
3427
3428
3429
3430
3431
3432
3433
                # 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

3434
3435
3436
            with (
                self.maybe_randomize_inputs(input_ids),
                set_forward_context(
3437
3438
                    attn_metadata,
                    self.vllm_config,
3439
                    num_tokens=num_tokens_after_padding,
3440
3441
                    num_tokens_across_dp=num_tokens_across_dp,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
3442
                    batch_descriptor=batch_descriptor,
3443
3444
3445
                    ubatch_slices=ubatch_slices,
                ),
            ):
3446
                outputs = self.model(
3447
3448
3449
3450
                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
3451
                    **model_kwargs,
3452
                )
3453

3454
3455
3456
3457
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
3458

3459
            if self.speculative_config and self.speculative_config.use_eagle():
3460
                assert isinstance(self.drafter, EagleProposer)
3461
3462
                use_cudagraphs = cudagraph_runtime_mode == CUDAGraphMode.PIECEWISE
                self.drafter.dummy_run(num_tokens, use_cudagraphs=use_cudagraphs)
3463

3464
3465
3466
3467
3468
3469
3470
3471
3472
3473
        # 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)

3474
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
3475
        return hidden_states, hidden_states[logit_indices]
3476
3477
3478
3479
3480
3481

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
3482
3483
3484
3485
        # 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)
3486

3487
        logits = self.model.compute_logits(hidden_states)
3488
3489
        num_reqs = logits.size(0)

3490
        dummy_tensors = lambda v: torch.full((num_reqs,), v, device=self.device)
3491
3492
3493
3494
3495
3496
3497
3498
3499
3500
3501
3502
3503
3504
3505

        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)],
3506
            spec_token_ids=[[] for _ in range(num_reqs)],
3507
3508
            allowed_token_ids_mask=None,
            bad_words_token_ids={},
3509
            logitsprocs=LogitsProcessors(),
3510
        )
3511
        try:
3512
3513
3514
            sampler_output = self.sampler(
                logits=logits, sampling_metadata=dummy_metadata
            )
3515
        except RuntimeError as e:
3516
            if "out of memory" in str(e):
3517
3518
3519
3520
                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 "
3521
3522
                    "initializing the engine."
                ) from e
3523
3524
            else:
                raise e
3525
        if self.speculative_config:
3526
3527
            draft_token_ids = [[0] for _ in range(num_reqs)]
            dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
3528
3529
                draft_token_ids, self.device
            )
3530
3531
3532
3533
3534
3535

            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
3536
3537
3538
            target_logits = torch.randn(
                num_tokens, logits.shape[-1], device=self.device, dtype=logits.dtype
            )
3539
3540
3541
            # 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.
3542
3543
3544
            bonus_token_ids = torch.zeros(
                num_reqs, device=self.device, dtype=torch.int32
            )
3545
3546
3547
3548
3549
3550
3551
            self.rejection_sampler(
                dummy_spec_decode_metadata,
                draft_probs,
                target_logits,
                bonus_token_ids,
                dummy_metadata,
            )
3552
        return sampler_output
3553

3554
    def _dummy_pooler_run_task(
3555
3556
        self,
        hidden_states: torch.Tensor,
3557
3558
        task: PoolingTask,
    ) -> PoolerOutput:
3559
3560
3561
3562
3563
3564
3565
3566
3567
3568
3569
        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

3570
        dummy_prompt_lens = torch.tensor(
3571
3572
            num_scheduled_tokens_list,
            device="cpu",
3573
        )
3574
3575
3576
        dummy_token_ids = torch.zeros(
            (num_reqs, req_num_tokens), dtype=torch.int32, device=self.device
        )
3577

3578
        model = cast(VllmModelForPooling, self.get_model())
3579
        dummy_pooling_params = PoolingParams(task=task)
3580
        dummy_pooling_params.verify(task=task, model_config=self.model_config)
3581
        to_update = model.pooler.get_pooling_updates(task)
3582
3583
        to_update.apply(dummy_pooling_params)

3584
        dummy_metadata = PoolingMetadata(
3585
3586
3587
3588
            prompt_lens=dummy_prompt_lens,
            prompt_token_ids=dummy_token_ids,
            pooling_params=[dummy_pooling_params] * num_reqs,
        )
3589

3590
3591
3592
        dummy_metadata.build_pooling_cursor(
            num_scheduled_tokens_list, device=hidden_states.device
        )
3593

3594
        try:
3595
3596
3597
            return model.pooler(
                hidden_states=hidden_states, pooling_metadata=dummy_metadata
            )
3598
        except RuntimeError as e:
3599
            if "out of memory" in str(e):
3600
                raise RuntimeError(
3601
3602
3603
                    "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 "
3604
3605
                    "initializing the engine."
                ) from e
3606
3607
            else:
                raise e
3608
3609
3610
3611
3612
3613
3614

    @torch.inference_mode()
    def _dummy_pooler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> PoolerOutput:
        # Find the task that has the largest output for subsequent steps
3615
3616
3617
3618
3619
3620
3621
3622
3623
3624
3625
3626
3627
3628
3629
3630
3631
3632
3633
3634
        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."
                )

3635
        output_size = dict[PoolingTask, float]()
3636
        for task in supported_pooling_tasks:
3637
3638
            # Run a full batch with each task to ensure none of them OOMs
            output = self._dummy_pooler_run_task(hidden_states, task)
3639
            output_size[task] = sum(o.nbytes for o in output)
3640
3641
3642
3643
            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)
3644

3645
    def profile_run(self) -> None:
3646
        # Profile with multimodal encoder & encoder cache.
3647
        if self.supports_mm_inputs:
3648
            if self.model_config.multimodal_config.skip_mm_profiling:
3649
                logger.info(
3650
                    "Skipping memory profiling for multimodal encoder and "
3651
3652
                    "encoder cache."
                )
3653
3654
3655
3656
3657
3658
3659
3660
            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.
3661
                    dummy_modality = mm_budget.get_modality_with_max_tokens()
3662
3663
3664
                    max_mm_items_per_batch = mm_budget.max_items_per_batch_by_modality[
                        dummy_modality
                    ]
3665
3666
3667
3668
3669
3670
3671
3672
3673

                    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,
                    )
3674

3675
3676
3677
3678
3679
                    # Create dummy batch of multimodal inputs.
                    batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                        dummy_modality,
                        max_mm_items_per_batch,
                    )
3680

3681
                    # Run multimodal encoder.
3682
3683
3684
                    dummy_encoder_outputs = self.model.get_multimodal_embeddings(
                        **batched_dummy_mm_inputs
                    )
3685

3686
3687
3688
3689
                    sanity_check_mm_encoder_outputs(
                        dummy_encoder_outputs,
                        expected_num_items=max_mm_items_per_batch,
                    )
3690

3691
3692
3693
3694
3695
3696
3697
3698
3699
3700
                    # 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(
3701
3702
                                (encoder_budget, encoder_output_shape[-1])
                            )
3703
3704
3705
3706
3707
3708
                            num_tokens = output.shape[0]
                            expanded[:num_tokens].copy_(output)
                            expanded_outputs.append(expanded)

                        dummy_encoder_outputs = expanded_outputs

3709
                    # Cache the dummy encoder outputs.
3710
                    self.encoder_cache["tmp"] = dict(enumerate(dummy_encoder_outputs))
3711

3712
        # Add `is_profile` here to pre-allocate communication buffers
3713
3714
3715
        hidden_states, last_hidden_states = self._dummy_run(
            self.max_num_tokens, is_profile=True
        )
3716
        if get_pp_group().is_last_rank:
3717
3718
3719
3720
            if self.is_pooling_model:
                output = self._dummy_pooler_run(hidden_states)
            else:
                output = self._dummy_sampler_run(last_hidden_states)
3721
        else:
3722
            output = None
3723
        self._sync_device()
3724
        del hidden_states, output
3725
        self.encoder_cache.clear()
3726
        gc.collect()
3727

3728
    def capture_model(self) -> int:
3729
        if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
3730
            logger.warning(
3731
                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
3732
3733
                "ensure `cudagraph_mode` was not manually set to `NONE`"
            )
3734
            return 0
3735
3736
        else:
            self.initialize_cudagraph_capture()
3737

3738
3739
        compilation_counter.num_gpu_runner_capture_triggers += 1

3740
3741
        start_time = time.perf_counter()

3742
3743
3744
3745
3746
3747
3748
3749
3750
3751
3752
3753
3754
3755
        @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()
3756
                    gc.collect()
3757

3758
3759
3760
        # 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.
3761
        set_cudagraph_capturing_enabled(True)
3762
        with freeze_gc(), graph_capture(device=self.device):
3763
            start_free_gpu_memory = torch.cuda.mem_get_info()[0]
3764
            cudagraph_mode = self.compilation_config.cudagraph_mode
3765
            assert cudagraph_mode is not None
3766
3767
3768
3769
3770
3771
3772
            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,
3773
3774
                    uniform_decode=False,
                )
3775

3776
3777
            # Capture full cudagraph for uniform decode batches if we
            # don't already have full mixed prefill-decode cudagraphs.
3778
3779
3780
3781
3782
3783
3784
            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
                )
3785
                decode_cudagraph_batch_sizes = [
3786
3787
                    x
                    for x in self.cudagraph_batch_sizes
3788
                    if max_num_tokens >= x >= self.uniform_decode_query_len
3789
                ]
3790
                compilation_cases_decode = list(reversed(decode_cudagraph_batch_sizes))
3791
3792
3793
                self._capture_cudagraphs(
                    compilation_cases=compilation_cases_decode,
                    cudagraph_runtime_mode=CUDAGraphMode.FULL,
3794
3795
                    uniform_decode=True,
                )
3796

3797
3798
3799
            torch.cuda.synchronize()
            end_free_gpu_memory = torch.cuda.mem_get_info()[0]

3800
3801
3802
        # 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
3803
        # we may do lazy capturing in future that still allows capturing
3804
3805
        # after here.
        set_cudagraph_capturing_enabled(False)
3806
3807
3808
3809
3810

        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.
3811
3812
3813
3814
3815
        logger.info(
            "Graph capturing finished in %.0f secs, took %.2f GiB",
            elapsed_time,
            cuda_graph_size / (1 << 30),
        )
3816
        return cuda_graph_size
3817

3818
3819
3820
3821
3822
3823
3824
3825
3826
3827
    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}"
3828
3829
3830
3831
3832
3833
3834
3835

        # 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",
3836
3837
3838
                    cudagraph_runtime_mode.name,
                ),
            )
3839

3840
3841
3842
        # 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
3843
3844
3845
            # 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
3846
3847
3848
3849
            allow_microbatching = (
                self.parallel_config.enable_dbo
                and cudagraph_runtime_mode == CUDAGraphMode.FULL
                and uniform_decode
3850
3851
3852
3853
3854
                and check_ubatch_thresholds(
                    config=self.vllm_config.parallel_config,
                    num_tokens=num_tokens,
                    uniform_decode=uniform_decode,
                )
3855
            )
3856

3857
3858
3859
3860
3861
3862
            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.
3863
3864
3865
3866
3867
3868
3869
3870
3871
3872
3873
3874
3875
3876
3877
3878
3879
3880
                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,
            )
3881
        self.maybe_remove_all_loras(self.lora_config)
3882

3883
3884
3885
3886
    def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize the attention backends and attention metadata builders.
        """
3887
        assert len(self.attn_groups) == 0, "Attention backends are already initialized"
3888

3889
3890
3891
3892
3893
3894
3895
3896
        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(
3897
3898
                self.vllm_config, AttentionLayerBase, kv_cache_group_spec.layer_names
            )
3899
3900
            attn_backends = {}
            attn_backend_layers = defaultdict(list)
3901
            # Dedupe based on full class name; this is a bit safer than
3902
3903
3904
3905
            # 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.
3906
            for layer_name in kv_cache_group_spec.layer_names:
3907
                attn_backend = layers[layer_name].get_attn_backend()
3908
3909
3910
3911
3912
3913
3914

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

3915
3916
3917
                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):
3918
                    layer_kv_cache_spec = layer_kv_cache_spec.kv_cache_specs[layer_name]
3919
                key = (full_cls_name, layer_kv_cache_spec)
3920
3921
3922
                attn_backends[key] = AttentionGroupKey(
                    attn_backend, layer_kv_cache_spec
                )
3923
                attn_backend_layers[key].append(layer_name)
3924
            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 mode is VLLM_COMPILE"
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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})"
            )
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            if self.compilation_config.mode == CompilationMode.VLLM_COMPILE and (
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                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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                    CUDAGraphMode.PIECEWISE
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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, "
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                "and make sure compilation mode is VLLM_COMPILE"
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            )
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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()
4082

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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,
4216
                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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            )

4224
    def _allocate_kv_cache_tensors(
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        self, kv_cache_config: KVCacheConfig
    ) -> dict[str, torch.Tensor]:
4227
        """
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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.

4231
        Args:
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            kv_cache_config: The KV cache config
4233
        Returns:
4234
            dict[str, torch.Tensor]: A map between layer names to their
4235
            corresponding memory buffer for KV cache.
4236
        """
4237
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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:
4247
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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)

4259
    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
4264

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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):
4289
                continue
4290
            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)
4300
            elif isinstance(kv_cache_spec, MambaSpec):
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                # This is likely Mamba or other non-attention cache,
                # no splitting.
4303
                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]:
4315
        """
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        Reshape the KV cache tensors to the desired shape and dtype.
4317

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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.
        """
4326
        kv_caches: dict[str, torch.Tensor] = {}
4327
        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
4336
                num_blocks = raw_tensor.numel() // kv_cache_spec.page_size_bytes
4337
                if isinstance(kv_cache_spec, AttentionSpec):
4338
                    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,
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                            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
4403
                else:
4404
                    raise NotImplementedError
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        if has_attn and has_mamba:
4407
            self._update_hybrid_attention_mamba_layout(kv_caches)
4408

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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.
        """

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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) and (
                kv_tgt_layer := attn_module.kv_sharing_target_layer_name
            ):
                # 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
            # Skip modules that don't need KV cache (eg encoder-only attention)
            if spec := attn_module.get_kv_cache_spec(self.vllm_config):
                kv_cache_spec[layer_name] = 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()