gpu_model_runner.py 302 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 functools
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import gc
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import itertools
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import threading
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import time
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from collections import defaultdict
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from collections.abc import Callable, Iterable, Iterator, Sequence
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from contextlib import contextmanager
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from copy import copy, deepcopy
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from dataclasses import dataclass, replace
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from functools import reduce
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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.compilation.counter import compilation_counter
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from vllm.compilation.cuda_graph import CUDAGraphStat, CUDAGraphWrapper
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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,
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    set_current_vllm_config,
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    update_config,
)
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from vllm.config.cache import CacheConfig
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from vllm.distributed.ec_transfer import get_ec_transfer, has_ec_transfer
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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_dcp_group,
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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.lora.layers import LoRAMapping, LoRAMappingType
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from vllm.model_executor.layers.attention import Attention, MLAAttention
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from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
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from vllm.model_executor.layers.fused_moe.routed_experts_capturer import (
    RoutedExpertsCapturer,
)
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from vllm.model_executor.layers.mamba.ops.ssu_dispatch import (
    initialize_mamba_ssu_backend,
)
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from vllm.model_executor.layers.rotary_embedding import (
    MRotaryEmbedding,
    XDRotaryEmbedding,
)
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from vllm.model_executor.model_loader import get_model_loader
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from vllm.model_executor.model_loader.reload import (
    finalize_layerwise_reload,
    initialize_layerwise_reload,
)
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from vllm.model_executor.models.interfaces import (
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    MultiModalEmbeddings,
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    SupportsMRoPE,
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    SupportsMultiModal,
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    SupportsXDRoPE,
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    is_mixture_of_experts,
    supports_eagle3,
    supports_mrope,
    supports_multimodal_pruning,
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    supports_realtime,
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    supports_transcription,
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    supports_xdrope,
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)
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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.model_executor.offloader import (
    create_offloader,
    get_offloader,
    set_offloader,
)
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.encoder_budget import MultiModalBudget
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from vllm.multimodal.inputs import (
    BatchedTensorInputs,
    MultiModalKwargsItem,
    PlaceholderRange,
)
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from vllm.multimodal.utils import group_and_batch_mm_kwargs
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from vllm.platforms import current_platform
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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.tracing import instrument
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from vllm.utils import length_from_prompt_token_ids_or_embeds
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from vllm.utils.math_utils import cdiv, round_up
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from vllm.utils.mem_utils import DeviceMemoryProfiler, format_gib
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from vllm.utils.nvtx_pytorch_hooks import PytHooks
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from vllm.utils.platform_utils import is_pin_memory_available, num_compute_units
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from vllm.utils.torch_utils import (
    get_dtype_size,
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    is_quantized_kv_cache,
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    kv_cache_dtype_str_to_dtype,
)
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from vllm.v1.attention.backend import (
    AttentionBackend,
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    AttentionCGSupport,
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    AttentionMetadata,
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    AttentionMetadataBuilder,
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    AttentionType,
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    CommonAttentionMetadata,
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)
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from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadataBuilder
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from vllm.v1.attention.backends.mamba2_attn import Mamba2AttentionMetadataBuilder
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from vllm.v1.attention.backends.utils import (
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    NULL_BLOCK_ID,
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    create_fast_prefill_custom_backend,
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    get_dcp_local_seq_lens,
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    reorder_batch_to_split_decodes_and_prefills,
)
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from vllm.v1.core.sched.output import NewRequestData
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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,
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    ECConnectorOutput,
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    KVConnectorOutput,
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    LogprobsLists,
    LogprobsTensors,
    ModelRunnerOutput,
    PoolerOutput,
    SamplerOutput,
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    make_empty_encoder_model_runner_output,
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)
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from vllm.v1.pool.metadata import PoolingMetadata, PoolingStates
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from vllm.v1.sample.logits_processor import LogitsProcessors, build_logitsprocs
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from vllm.v1.sample.logits_processor.interface import LogitsProcessor
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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.dflash import DFlashProposer
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from vllm.v1.spec_decode.draft_model import DraftModelProposer
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from vllm.v1.spec_decode.eagle import EagleProposer
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from vllm.v1.spec_decode.extract_hidden_states import ExtractHiddenStatesProposer
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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_gpu import (
    NgramProposerGPU,
    copy_num_valid_draft_tokens,
    update_ngram_gpu_tensors_incremental,
    update_scheduler_for_invalid_drafts,
)
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from vllm.v1.spec_decode.suffix_decoding import SuffixDecodingProposer
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from vllm.v1.spec_decode.utils import update_num_computed_tokens_for_batch_change
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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 import mamba_utils
from vllm.v1.worker.cp_utils import (
    check_attention_cp_compatibility,
    get_total_cp_world_size,
)
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from vllm.v1.worker.dp_utils import coordinate_batch_across_dp
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from vllm.v1.worker.ec_connector_model_runner_mixin import ECConnectorModelRunnerMixin
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from vllm.v1.worker.gpu.pool.late_interaction_runner import LateInteractionRunner
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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 (
    UBatchSlices,
    check_ubatch_thresholds,
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    maybe_create_ubatch_slices,
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    split_attn_metadata,
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)
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from vllm.v1.worker.utils import is_residual_scattered_for_sp
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from vllm.v1.worker.workspace import lock_workspace
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from .utils import (
    AttentionGroup,
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    KVBlockZeroer,
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    add_kv_sharing_layers_to_kv_cache_groups,
    bind_kv_cache,
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    prepare_kernel_block_sizes,
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    sanity_check_mm_encoder_outputs,
)
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if TYPE_CHECKING:
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    from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput
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    from vllm.v1.spec_decode.ngram_proposer import NgramProposer
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    from vllm.v1.worker.encoder_cudagraph import EncoderCudaGraphManager
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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,
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        logprobs_tensors: LogprobsTensors | None,
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        invalid_req_indices: list[int],
        async_output_copy_stream: torch.cuda.Stream,
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        vocab_size: int,
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    ):
        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.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
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        self.vocab_size = vocab_size
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        self._logprobs_tensors = logprobs_tensors
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        # 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._logprobs_tensors_cpu = (
                self._logprobs_tensors.to_cpu_nonblocking()
                if self._logprobs_tensors
                else None
            )
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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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        max_gen_len = self.sampled_token_ids_cpu.shape[-1]
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        self.async_copy_ready_event.synchronize()
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        # Release the device tensors once the copy has completed.
        del self._logprobs_tensors
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        del self._sampled_token_ids
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        if max_gen_len == 1:
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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()
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            logprobs_lists = None
            if self._logprobs_tensors_cpu is not None:
                logprobs_lists = self._logprobs_tensors_cpu.tolists()
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        else:
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            valid_sampled_token_ids, logprobs_lists = RejectionSampler.parse_output(
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                self.sampled_token_ids_cpu,
                self.vocab_size,
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                self._invalid_req_indices,
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                logprobs_tensors=self._logprobs_tensors_cpu,
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            )
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        output = self._model_runner_output
        output.sampled_token_ids = valid_sampled_token_ids
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        output.logprobs = logprobs_lists
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        return output


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def _copy_pooler_output_to_cpu(
    raw_pooler_output: PoolerOutput, finished_mask: list[bool]
) -> list[torch.Tensor | None]:
    num_reqs = len(finished_mask)

    if isinstance(raw_pooler_output, torch.Tensor):
        if raw_pooler_output.shape[0] != num_reqs:
            raise ValueError(
                "Pooler output batch size does not match finished mask size: "
                f"{raw_pooler_output.shape[0]} != {num_reqs}."
            )

        num_finished = sum(finished_mask)
        if num_finished == 0:
            return [None] * num_reqs
        if num_finished == num_reqs:
            return list(raw_pooler_output.to("cpu", non_blocking=True))

        # partial finished
        finished_indices = [i for i, include in enumerate(finished_mask) if include]
        index_tensor = torch.tensor(
            finished_indices, device=raw_pooler_output.device, dtype=torch.long
        )
        finished_outputs = raw_pooler_output.index_select(0, index_tensor).to(
            "cpu", non_blocking=True
        )
        partial_pooler_output: list[torch.Tensor | None] = [None] * num_reqs
        for i, out in zip(finished_indices, finished_outputs):
            partial_pooler_output[i] = out
        return partial_pooler_output

    assert isinstance(raw_pooler_output, list)
    if len(raw_pooler_output) != num_reqs:
        raise ValueError(
            "Pooler output batch size does not match finished mask size: "
            f"{len(raw_pooler_output)} != {num_reqs}."
        )

    pooler_output: list[torch.Tensor | None] = [None] * num_reqs
    for i, (out, include) in enumerate(zip(raw_pooler_output, finished_mask)):
        if include and out is not None:
            pooler_output[i] = out.to("cpu", non_blocking=True)
    return pooler_output


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class AsyncGPUPoolingModelRunnerOutput(AsyncModelRunnerOutput):
    def __init__(
        self,
        model_runner_output: ModelRunnerOutput,
        raw_pooler_output: PoolerOutput,
        finished_mask: list[bool],
        async_output_copy_stream: torch.cuda.Stream,
    ):
        self._model_runner_output = model_runner_output

        # Event on the copy stream so we can synchronize the non-blocking copy.
        self.async_copy_ready_event = torch.Event()

        # Keep a reference to the device tensors to avoid them being
        # deallocated until we finish copying it to the host.
        self._raw_pooler_output = raw_pooler_output

        # 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._model_runner_output.pooler_output = _copy_pooler_output_to_cpu(
                raw_pooler_output=self._raw_pooler_output,
                finished_mask=finished_mask,
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            )
            self.async_copy_ready_event.record()

    def get_output(self) -> ModelRunnerOutput:
        """Copy the device tensors to the host and return a ModelRunnerOutput.
        This function blocks until the copy is finished.
        """
        self.async_copy_ready_event.synchronize()

        # Release the device tensors once the copy has completed.
        del self._raw_pooler_output
        return self._model_runner_output


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class ExecuteModelState(NamedTuple):
    """Ephemeral cached state transferred between execute_model() and
    sample_tokens(), after execute_model() returns None."""

    scheduler_output: "SchedulerOutput"
    logits: torch.Tensor
    spec_decode_metadata: SpecDecodeMetadata | None
    spec_decode_common_attn_metadata: CommonAttentionMetadata | None
    hidden_states: torch.Tensor
    sample_hidden_states: torch.Tensor
    aux_hidden_states: list[torch.Tensor] | None
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    ec_connector_output: ECConnectorOutput | None
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    cudagraph_stats: CUDAGraphStat | None
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    slot_mappings: dict[str, torch.Tensor] | list[dict[str, torch.Tensor]] | None
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class GPUModelRunner(
    LoRAModelRunnerMixin, KVConnectorModelRunnerMixin, ECConnectorModelRunnerMixin
):
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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.offload_config = vllm_config.offload_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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        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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        # These will be overridden in load_model()
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        self.is_multimodal_pruning_enabled = False
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        self.requires_sequential_video_encoding = False
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        # Set to True after init_routed_experts_capturer() completes.
        # Prevents routed experts code from running during profiling/dummy run.
        self.routed_experts_initialized = False
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        self.max_model_len = model_config.max_model_len
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        # Always set to false after the first forward pass
        self.calculate_kv_scales = self.cache_config.calculate_kv_scales
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        self.dcp_world_size = self.parallel_config.decode_context_parallel_size
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        self.dcp_rank = 0 if self.dcp_world_size <= 1 else get_dcp_group().rank_in_group
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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
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        # TODO: Support overlapping micro-batches
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        # https://github.com/vllm-project/vllm/issues/18019
        self.broadcast_pp_output = (
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            self.parallel_config.distributed_executor_backend == "external_launcher"
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            and len(get_pp_group().ranks) > 1
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        )
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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.inputs_embeds_size = model_config.get_inputs_embeds_size()
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        # Only relevant for models using ALiBi (e.g, MPT)
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        self.use_alibi = model_config.uses_alibi
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        self.cascade_attn_enabled = not self.model_config.disable_cascade_attn
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        self.is_mm_prefix_lm = self.model_config.is_mm_prefix_lm
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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.uses_xdrope_dim = model_config.uses_xdrope_dim
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        self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
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            model_config
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        )
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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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        # Async scheduling
        self.use_async_scheduling = self.scheduler_config.async_scheduling

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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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        # NOTE(yongji): flag to temporarily disable EPLB during scaling up/down
        self.eep_eplb_suppressed = False
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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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        # Initialize in initialize_kv_cache_tensors
        self.cross_layers_kv_cache: torch.Tensor | None = None
        self.cross_layers_attn_backend: type[AttentionBackend] | None = None
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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.late_interaction_runner = LateInteractionRunner()
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        # Encoder CUDA graph manager (initialized after model load if enabled)
        self.encoder_cudagraph_manager: EncoderCudaGraphManager | None = None

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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:
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            self.drafter: (
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                NgramProposer  # noqa: F823
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                | NgramProposerGPU
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                | SuffixDecodingProposer
                | EagleProposer
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                | DFlashProposer
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                | DraftModelProposer
                | MedusaProposer
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                | ExtractHiddenStatesProposer
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            )
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            if self.speculative_config.method == "ngram":
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                from vllm.v1.spec_decode.ngram_proposer import NgramProposer

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                self.drafter = NgramProposer(self.vllm_config)
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            elif self.speculative_config.uses_draft_model():
                self.drafter = DraftModelProposer(
                    vllm_config=self.vllm_config,
                    device=self.device,
                    runner=self,
                )
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            elif self.speculative_config.use_ngram_gpu():
                self.drafter = NgramProposerGPU(self.vllm_config, self.device, self)
                self.num_tokens_no_spec_gpu = torch.zeros(
                    self.max_num_reqs, dtype=torch.int32, device=device
                )
                self.token_ids_gpu_tensor = torch.zeros(
                    self.max_num_reqs,
                    self.max_model_len,
                    dtype=torch.int32,
                    device=device,
                )
                self._ngram_pinned_idx_buf = torch.zeros(
                    self.max_num_reqs, dtype=torch.long, pin_memory=True
                )
                self._ngram_pinned_val_buf = torch.zeros(
                    self.max_num_reqs, dtype=torch.int32, pin_memory=True
                )
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            elif self.speculative_config.use_dflash():
                self.drafter = DFlashProposer(self.vllm_config, self.device, self)
                self.use_aux_hidden_state_outputs = True
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            elif self.speculative_config.method == "suffix":
                self.drafter = SuffixDecodingProposer(self.vllm_config)
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            elif self.speculative_config.use_eagle():
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                self.drafter = EagleProposer(self.vllm_config, self.device, self)
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                if self.speculative_config.method == "eagle3":
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                    self.use_aux_hidden_state_outputs = (
                        self.drafter.eagle3_use_aux_hidden_state
                    )
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            elif self.speculative_config.method == "medusa":
                self.drafter = MedusaProposer(
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                    vllm_config=self.vllm_config, device=self.device
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                )
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            elif self.speculative_config.method == "extract_hidden_states":
                self.drafter = ExtractHiddenStatesProposer(
                    vllm_config=self.vllm_config, device=self.device
                )
                self.use_aux_hidden_state_outputs = True
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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(
                self.sampler, self.speculative_config, self.device
            )
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        self.num_spec_tokens = 0
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        self.valid_sampled_token_count_gpu: torch.Tensor | None = None
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        if self.speculative_config:
            self.num_spec_tokens = self.speculative_config.num_speculative_tokens
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            draft_config = self.speculative_config.draft_model_config
            if draft_config is not None and draft_config.max_model_len is not None:
                self.effective_drafter_max_model_len = draft_config.max_model_len
            else:
                self.effective_drafter_max_model_len = self.max_model_len
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        self.use_async_spec_decode = (
            self.use_async_scheduling and self.num_spec_tokens > 0
        )
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        # Request states.
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        self.requests: dict[str, CachedRequestState] = {}
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        # NOTE(rob): num_prompt_logprobs only includes reqs
        # that are currently in the prefill phase.
        self.num_prompt_logprobs: dict[str, int] = {}
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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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        logits_processors = model_config.logits_processors
        custom_logitsprocs: Sequence[str | type[LogitsProcessor]] = (
            tuple(logits_processors) if logits_processors is not None else ()
        )
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        placeholder_block_size = (
            self.cache_config.block_size or CacheConfig.DEFAULT_BLOCK_SIZE
        )
        self._init_block_sizes = [placeholder_block_size]
        self._init_kernel_block_sizes = [placeholder_block_size]
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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-decoder
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            # 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=[placeholder_block_size],
            kernel_block_sizes=[placeholder_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.
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            # ThinkingTokenBudgetLogitsProcessor also needs output token ids to
            # correctly track think start/end token sequences in async scheduling.
            logitsprocs_need_output_token_ids=bool(custom_logitsprocs)
            or self.vllm_config.reasoning_config is not None,
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            is_pooling_model=self.is_pooling_model,
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            cp_kv_cache_interleave_size=self.parallel_config.cp_kv_cache_interleave_size,
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        )
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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.
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        self.prepare_inputs_event: torch.Event | None = None
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        if self.use_async_scheduling:
            self.async_output_copy_stream = torch.cuda.Stream()
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            self.prepare_inputs_event = torch.Event()
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        # self.cudagraph_batch_sizes sorts in ascending order.
        if (
            self.compilation_config.cudagraph_capture_sizes
            and self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
        ):
            self.cudagraph_batch_sizes = sorted(
                self.compilation_config.cudagraph_capture_sizes
            )
        else:
            self.cudagraph_batch_sizes = []

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        # Cache the device properties.
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        self._init_device_properties()
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        # Encoder timing registry for observability
        self.encoder_timing_registry: dict[str, EncoderTimingStats] = {}
        self._encoder_timing_lock = threading.Lock()

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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)
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        self.positions = torch.zeros(
            self.max_num_tokens, dtype=torch.int64, device=self.device
        )
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        self.query_start_loc = self._make_buffer(
            self.max_num_reqs + 1, dtype=torch.int32
        )
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        self.seq_lens = torch.zeros(
            self.max_num_reqs, dtype=torch.int32, device=self.device
        )
        self.optimistic_seq_lens_cpu = torch.zeros(
            self.max_num_reqs, dtype=torch.int32, pin_memory=self.pin_memory
        )
        self.num_computed_tokens = torch.zeros(
            self.max_num_reqs, dtype=torch.int32, device=self.device
        )
        self.prev_num_draft_tokens = self._make_buffer(
            self.max_num_reqs, dtype=torch.int32
        )
        self.req_indices = self._make_buffer(self.max_num_tokens, dtype=torch.int64)
        # Maps current batch position -> previous batch position (-1 for new reqs)
        self.prev_positions = self._make_buffer(self.max_num_reqs, dtype=torch.int64)
        self.num_scheduled_tokens = self._make_buffer(
            self.max_num_reqs, dtype=torch.int32
        )

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        self.encoder_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(
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            self.max_num_tokens, self.inputs_embeds_size, dtype=self.dtype, numpy=False
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        )
        self.is_token_ids = self._make_buffer(self.max_num_tokens, dtype=torch.bool)
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        self.discard_request_mask = self._make_buffer(
            self.max_num_reqs, dtype=torch.bool
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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(
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            self.max_num_reqs, dtype=torch.int32
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        )
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        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
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        if self.uses_mrope:
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            # 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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        # Only relevant for models using XD-RoPE (e.g, HunYuan-VL)
        if self.uses_xdrope_dim > 0:
            # Similar to mrope but use assigned dimension number for RoPE, 4 as default.
            self.xdrope_positions = self._make_buffer(
                (self.uses_xdrope_dim, 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 arange tensors rather than creating them
        # every step. Keep in int64 to avoid overflow with long context.
        # - arange_np: immutable [0, 1, 2, ...] used as source for batched computation
        # - query_pos: CpuGpuBuffer for the computed batched arange result
        arange_size = max(self.max_num_reqs + 1, self.max_num_tokens)
        self.arange_np = np.arange(arange_size, dtype=np.int64)
        self.query_pos = self._make_buffer(arange_size, dtype=torch.int64)
        self._arange_scratch = np.empty(arange_size, 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 + self.num_spec_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 = (
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            MultiModalBudget(self.vllm_config, self.mm_registry)
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            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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        # N-gram GPU path: async D2H buffer/event for per-request valid draft counts.
        self._num_valid_draft_tokens: torch.Tensor | None = None
        self._num_valid_draft_tokens_cpu: torch.Tensor | None = None
        self._num_valid_draft_tokens_event: torch.cuda.Event | None = None
        self._num_valid_draft_tokens_copy_stream: torch.cuda.Stream | None = None
        if (
            self.speculative_config is not None
            and self.speculative_config.use_ngram_gpu()
        ):
            self._num_valid_draft_tokens_cpu = torch.empty(
                self.max_num_reqs, dtype=torch.int32, pin_memory=self.pin_memory
            )
            self._num_valid_draft_tokens_event = torch.cuda.Event()
            self._num_valid_draft_tokens_copy_stream = torch.cuda.Stream()

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        self._draft_token_req_ids: list[str] | None = None
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        self.transfer_event = torch.Event()
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        self.sampled_token_ids_pinned_cpu = torch.empty(
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            (self.max_num_reqs, 1),
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            dtype=torch.int64,
            device="cpu",
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            pin_memory=self.pin_memory,
        )
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        # Pre-allocated tensor for copying valid sampled token counts to CPU,
        # with dedicated stream for overlapping and event for coordination.
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        self.valid_sampled_token_count_event: torch.Event | None = None
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        self.valid_sampled_token_count_copy_stream: torch.cuda.Stream | None = None
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        # We also copy the drafted tokens to the CPU asynchronously,
        # in case we need them for structured outputs.
        self.draft_token_ids_event: torch.Event | None = None
        self.draft_token_ids_copy_stream: torch.cuda.Stream | None = None
        self.valid_sampled_token_count_cpu: torch.Tensor | None = None
        self.draft_token_ids_cpu: torch.Tensor | None = None
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        self.num_accepted_tokens_event: torch.Event | None = None
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        if self.num_spec_tokens:
            self.draft_token_ids_event = torch.Event()
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            self.num_accepted_tokens_event = torch.Event()
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            self.draft_token_ids_copy_stream = torch.cuda.Stream()
            self.draft_token_ids_cpu = torch.empty(
                (self.max_num_reqs, self.num_spec_tokens),
                dtype=torch.int64,
                device="cpu",
                pin_memory=self.pin_memory,
            )
            if self.use_async_scheduling:
                self.valid_sampled_token_count_event = torch.Event()
                self.valid_sampled_token_count_copy_stream = torch.cuda.Stream()
                self.valid_sampled_token_count_cpu = torch.empty(
                    self.max_num_reqs,
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                    dtype=torch.int32,
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                    device="cpu",
                    pin_memory=self.pin_memory,
                )
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        # Model weight offloader
        # Make sure this is called before any get_offloader call
        set_offloader(create_offloader(self.offload_config))

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        # Ephemeral state transferred between execute_model() and sample_tokens().
        self.execute_model_state: ExecuteModelState | None = None
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        self.kv_connector_output: KVConnectorOutput | None = None
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        self.mamba_state_idx: dict[str, int] = {}
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        self._mamba_copy_bufs: mamba_utils.MambaCopyBuffers | None = None
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        self.layerwise_nvtx_hooks_registered = False
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    def update_max_model_len(self, max_model_len: int) -> None:
        self.max_model_len = max_model_len
        if self.speculative_config:
            draft_config = self.speculative_config.draft_model_config
            if draft_config is None or draft_config.max_model_len is None:
                self.effective_drafter_max_model_len = self.max_model_len

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    def reset_mm_cache(self) -> None:
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        """
        Clear the multi-modal cache that was used during profiling,
        but no longer needed during inference.
        """
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        if self.mm_budget:
            self.mm_budget.reset_cache()
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        self.late_interaction_runner.clear()
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    def reset_encoder_cache(self) -> None:
        """Clear the GPU-side encoder cache storing vision embeddings.

        This should be called when model weights are updated to ensure
        stale embeddings computed with old weights are not reused.
        """
        self.encoder_cache.clear()
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        self.late_interaction_runner.clear()
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    @torch.inference_mode()
    def init_fp8_kv_scales(self) -> None:
        """
        Re-initialize the KV cache and FP8 scales after waking from sleep.
        1. Zero out the KV cache tensors to remove garbage data from re-allocation.
        2. Reset Attention layer scaling factors (_k_scale, _v_scale) to 1.0.
          If these are left at 0.0 (default after wake_up), all KV cache values
          become effectively zero, causing gibberish output.
        """
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        if not is_quantized_kv_cache(self.cache_config.cache_dtype):
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            return

        kv_caches = getattr(self, "kv_caches", [])
        for cache_tensor in kv_caches:
            if cache_tensor is not None:
                cache_tensor.zero_()

        k_attr_names = ("_k_scale", "k_scale")
        v_attr_names = ("_v_scale", "v_scale")

        attn_layers = self.compilation_config.static_forward_context
        for name, module in attn_layers.items():
            if isinstance(module, (Attention, MLAAttention)):
                # TODO: Generally, scale is 1.0 if user uses on-the-fly fp8
                # kvcache quant. However, to get better accuracy, compression
                # frameworks like llm-compressors allow users to tune the
                # scale. We may need to restore the specific calibrated scales
                # here in the future.
                k_scale_val, v_scale_val = 1.0, 1.0

                # Processing K Scale
                for attr in k_attr_names:
                    if hasattr(module, attr):
                        param = getattr(module, attr)
                        if isinstance(param, torch.Tensor):
                            param.fill_(k_scale_val)

                # Processing V Scale
                for attr in v_attr_names:
                    if hasattr(module, attr):
                        param = getattr(module, attr)
                        if isinstance(param, torch.Tensor):
                            param.fill_(v_scale_val)

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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]
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            if self.uses_xdrope_dim > 0:
                return self.xdrope_positions.gpu[:, :num_tokens]
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            return self.positions[:num_tokens]
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        else:
            if self.uses_mrope:
                return self.mrope_positions.gpu[:, num_tokens]
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            if self.uses_xdrope_dim > 0:
                return self.xdrope_positions.gpu[:, num_tokens]
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            return self.positions[num_tokens]
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    def _make_buffer(
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        self, *size: int | torch.SymInt, dtype: torch.dtype, numpy: bool = True
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    ) -> CpuGpuBuffer:
        return CpuGpuBuffer(
            *size,
            dtype=dtype,
            device=self.device,
            pin_memory=self.pin_memory,
            with_numpy=numpy,
        )
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    def _get_mamba_copy_bufs(self) -> mamba_utils.MambaCopyBuffers:
        if self._mamba_copy_bufs is None:
            self._mamba_copy_bufs = mamba_utils.MambaCopyBuffers.create(
                self.max_num_reqs,
                self.kv_cache_config,
                self.model.get_mamba_state_copy_func(),
                self._make_buffer,
            )
        return self._mamba_copy_bufs

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

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        if not self.is_pooling_model:
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            return model_kwargs

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

        if len(token_type_id_requests) == 0:
            return model_kwargs

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

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

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

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

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

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        if self.reorder_batch_threshold is not None:
            reorder_batch_to_split_decodes_and_prefills(
                self.input_batch,
                scheduler_output,
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                decode_threshold=self.reorder_batch_threshold,
            )
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    def _init_kv_zero_meta(self) -> None:
        """One-time precomputation for _zero_block_ids.

        Delegates to KVBlockZeroer.init_meta with the runner's state.
        Called from gpu_worker.py outside the CuMem pool context.
        """
        self._kv_block_zeroer = KVBlockZeroer(self.device, self.pin_memory)
        self._kv_block_zeroer.init_meta(
            attn_groups_iter=self._kv_cache_spec_attn_group_iterator(),
            kernel_block_sizes=self._kernel_block_sizes,
            cache_dtype=self.cache_config.cache_dtype,
            runner_only_attn_layers=self.runner_only_attn_layers,
            static_forward_context=(self.compilation_config.static_forward_context),
        )

    def _zero_block_ids(self, block_ids: list[int]) -> None:
        """Zero the KV cache memory for the given block IDs."""
        if hasattr(self, "_kv_block_zeroer"):
            self._kv_block_zeroer.zero_block_ids(block_ids)

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    # Note: used for model runner override.
    def _init_device_properties(self) -> None:
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        """Initialize attributes from torch.cuda.get_device_properties"""
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        self.num_sms = num_compute_units(self.device.index)
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    # Note: used for model runner override.
    def _sync_device(self) -> None:
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        torch.accelerator.synchronize()
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    def _get_or_create_async_output_copy_stream(self) -> torch.cuda.Stream:
        stream = self.async_output_copy_stream
        if stream is None:
            stream = torch.cuda.Stream()
            self.async_output_copy_stream = stream
        return stream

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

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

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        The SamplingMetadata is updated and copied to the GPU if there is a
        new/resumed/paused/finished request in the batch.
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        """
        # Remove finished requests from the cached states.
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        for req_id in scheduler_output.finished_req_ids:
            self.requests.pop(req_id, None)
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            self.num_prompt_logprobs.pop(req_id, None)
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        self.late_interaction_runner.on_requests_finished(
            scheduler_output.finished_req_ids
        )
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        # Remove the finished requests from the persistent batch.
        # NOTE(woosuk): There could be an edge case where finished_req_ids and
        # scheduled_req_ids overlap. This happens when a request is aborted and
        # then resubmitted with the same ID. In this case, we treat them as two
        # distinct requests - clearing the cached states for the first request
        # and handling the second as a new request.
        for req_id in scheduler_output.finished_req_ids:
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            self.input_batch.remove_request(req_id)
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        # Zero GPU memory for freshly allocated cache blocks to prevent
        # stale NaN/data from corrupting attention or SSM computation.
        if scheduler_output.new_block_ids_to_zero:
            self._zero_block_ids(scheduler_output.new_block_ids_to_zero)

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        # Free the cached encoder outputs.
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        for mm_hash in scheduler_output.free_encoder_mm_hashes:
            self.encoder_cache.pop(mm_hash, None)
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        # Remove the unscheduled requests from the persistent batch.
        # NOTE(woosuk): The unscheduled requests are either preempted requests
        # or running requests that are not scheduled in this step. We remove
        # them from the persistent batch but keep their cached states since
        # they will be scheduled again sometime in the future.
        scheduled_req_ids = scheduler_output.num_scheduled_tokens.keys()
        cached_req_ids = self.input_batch.req_id_to_index.keys()
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        resumed_req_ids = scheduler_output.scheduled_cached_reqs.resumed_req_ids
        # NOTE(zhuohan): cached_req_ids and resumed_req_ids are usually disjoint,
        # so `(scheduled_req_ids - resumed_req_ids) == scheduled_req_ids` holds
        # apart from the forced-preemption case in reset_prefix_cache. And in
        # that case we include the resumed_req_ids in the unscheduled set so
        # that they get cleared from the persistent batch before being re-scheduled
        # in the normal resumed request path.
        unscheduled_req_ids = cached_req_ids - (scheduled_req_ids - resumed_req_ids)
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        # NOTE(woosuk): The persistent batch optimization assumes that
        # consecutive batches contain mostly the same requests. If batches
        # have low request overlap (e.g., alternating between two distinct
        # sets of requests), this optimization becomes very inefficient.
        for req_id in unscheduled_req_ids:
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            self.input_batch.remove_request(req_id)
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        is_ngram_gpu = (
            self.speculative_config is not None
            and self.speculative_config.use_ngram_gpu()
        )
        if is_ngram_gpu:
            ngram_gpu_new_reqs: list[CachedRequestState] = []

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        reqs_to_add: list[CachedRequestState] = []
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        deferred_spec_decode_corrections = []

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        # Add new requests to the cached states.
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        for new_req_data in scheduler_output.scheduled_new_reqs:
            req_id = new_req_data.req_id
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            if req_id in self.requests:
                # For streaming case only.
                req_state = self._update_streaming_request(req_id, new_req_data)
                reqs_to_add.append(req_state)
                continue

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            sampling_params = new_req_data.sampling_params
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            pooling_params = new_req_data.pooling_params
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            if (
                sampling_params
                and sampling_params.sampling_type == SamplingType.RANDOM_SEED
            ):
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                generator = torch.Generator(device=self.device)
                generator.manual_seed(sampling_params.seed)
            else:
                generator = None

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            if self.is_pooling_model:
                assert pooling_params is not None
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                task = pooling_params.task
                assert task is not None, "You did not set `task` in the API"
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                model = cast(VllmModelForPooling, self.get_model())
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                to_update = model.pooler.get_pooling_updates(task)
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                to_update.apply(pooling_params)

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            req_state = CachedRequestState(
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                req_id=req_id,
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                prompt_token_ids=new_req_data.prompt_token_ids,
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                prompt_embeds=new_req_data.prompt_embeds,
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                mm_features=new_req_data.mm_features,
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                sampling_params=sampling_params,
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                pooling_params=pooling_params,
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                generator=generator,
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                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
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                output_token_ids=[],
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                lora_request=new_req_data.lora_request,
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            )
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            self.requests[req_id] = req_state
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            self.late_interaction_runner.register_request(req_id, pooling_params)
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            if sampling_params and sampling_params.prompt_logprobs is not None:
                self.num_prompt_logprobs[req_id] = (
                    self.input_batch.vocab_size
                    if sampling_params.prompt_logprobs == -1
                    else sampling_params.prompt_logprobs
                )

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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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                self._init_mrope_positions(req_state)
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            # Only relevant for models using XD-RoPE (e.g, HunYuan-VL)
            if self.uses_xdrope_dim > 0:
                self._init_xdrope_positions(req_state)

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            reqs_to_add.append(req_state)
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            # Track new requests for ngram_gpu full tensor copy
            if is_ngram_gpu:
                ngram_gpu_new_reqs.append(req_state)
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        # Update the states of the running/resumed requests.
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        is_last_rank = get_pp_group().is_last_rank
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        req_data = scheduler_output.scheduled_cached_reqs
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        scheduled_spec_tokens = scheduler_output.scheduled_spec_decode_tokens
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        # Save scheduler-allocated spec lengths before trimming so
        # prev_num_draft_len keeps the optimistic count for rejection correction.
        original_num_spec_per_req: dict[str, int] = {}
        if (
            self.speculative_config is not None
            and self.speculative_config.use_ngram_gpu()
        ):
            for req_id, toks in scheduled_spec_tokens.items():
                original_num_spec_per_req[req_id] = len(toks)
            update_scheduler_for_invalid_drafts(
                self._num_valid_draft_tokens_event,
                self._num_valid_draft_tokens_cpu,
                scheduler_output,
                self.input_batch.req_id_to_index,
            )
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        if self.use_async_spec_decode:
            self.prev_num_draft_tokens.np.fill(0)
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        for i, req_id in enumerate(req_data.req_ids):
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            req_state = self.requests[req_id]
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            num_computed_tokens = req_data.num_computed_tokens[i]
            new_block_ids = req_data.new_block_ids[i]
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            resumed_from_preemption = req_id in req_data.resumed_req_ids
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            num_output_tokens = req_data.num_output_tokens[i]
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            req_index = self.input_batch.req_id_to_index.get(req_id)
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            if req_state.prev_num_draft_len and self.use_async_scheduling:
                # prev_num_draft_len is used in async scheduling mode with
                # spec decode. it indicates if need to update num_computed_tokens
                # of the request. for example:
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                # first step: num_computed_tokens = 0, spec_tokens = [],
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                # prev_num_draft_len = 0.
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                # second step: num_computed_tokens = 100(prompt length),
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                # spec_tokens = [a,b], prev_num_draft_len = 0.
                # third step: num_computed_tokens = 100 + 2, spec_tokens = [c,d],
                # prev_num_draft_len = 2.
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                # num_computed_tokens in first step and second step doesn't contain
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                # the spec tokens length, but in third step it contains the
                # spec tokens length. we only need to update num_computed_tokens
                # when prev_num_draft_len > 0.
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                if req_index is None:
                    req_state.prev_num_draft_len = 0
                else:
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                    # Optimistically assume all accepted; queue up a correction
                    # to be called after the model forward to preserve async
                    # scheduling. Corrected on GPU in _prepare_inputs.
                    optimistic_num_accepted = req_state.prev_num_draft_len
                    req_state.output_token_ids.extend([-1] * optimistic_num_accepted)

                    deferred_spec_decode_corrections.append(
                        (req_id, optimistic_num_accepted, req_state)
                    )

                    prev_req_index = (
                        self.input_batch.prev_req_id_to_index.get(req_id)
                        if self.input_batch.prev_req_id_to_index
                        else None
                    )
                    if prev_req_index is not None:
                        self.prev_num_draft_tokens.np[prev_req_index] = (
                            optimistic_num_accepted
                        )
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                    if is_ngram_gpu and optimistic_num_accepted > 0:
                        self.input_batch.num_tokens_no_spec[req_index] += (
                            optimistic_num_accepted
                        )
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            # Update the cached states.
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            req_state.num_computed_tokens = num_computed_tokens
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            if not is_last_rank:
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                if not req_data.new_token_ids:
                    # Async scheduled PP: Sampled tokens propagated via GPU broadcast.
                    new_token_ids: list[int] = []
                else:
                    # Non-async scheduling with PP: The scheduler sends
                    # sampled token ids back because there's no direct communication
                    # between the first-stage worker and the last-stage worker.
                    new_token_ids = req_data.new_token_ids[i]
                    # Add the sampled token(s) from the previous step (if any).
                    # This doesn't include "unverified" tokens like spec tokens.
                    num_new_tokens = (
                        num_computed_tokens + len(new_token_ids) - req_state.num_tokens
                    )
                    if num_new_tokens == 1:
                        # Avoid slicing list in most common case.
                        req_state.output_token_ids.append(new_token_ids[-1])
                    elif num_new_tokens > 0:
                        req_state.output_token_ids.extend(
                            new_token_ids[-num_new_tokens:]
                        )
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            elif num_output_tokens < len(req_state.output_token_ids):
                # Some output tokens were discarded due to a sync-KV-load
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                # failure, or output_token_ids was inflated by the optimistic
                # extend above (async spec decode). Align the cached state.
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                del req_state.output_token_ids[num_output_tokens:]
                if req_index is not None:
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                    end_idx = (
                        self.input_batch.num_prompt_tokens[req_index]
                        + num_output_tokens
                    )
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                    self.input_batch.num_tokens_no_spec[req_index] = end_idx
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            # Update the block IDs.
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            if not resumed_from_preemption:
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                if new_block_ids is not None:
                    # Append the new blocks to the existing block IDs.
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                    for block_ids, new_ids in zip(req_state.block_ids, new_block_ids):
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                        block_ids.extend(new_ids)
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            else:
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                assert req_index is None
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                assert new_block_ids is not None
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                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
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                req_state.block_ids = new_block_ids
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            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.
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                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.all_token_ids[req_id]
                    req_state.output_token_ids = resumed_token_ids[-num_output_tokens:]

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                reqs_to_add.append(req_state)
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                # Track resumed requests for ngram_gpu full tensor copy
                if is_ngram_gpu:
                    ngram_gpu_new_reqs.append(req_state)
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                continue

            # Update the persistent batch.
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            self.input_batch.num_computed_tokens_cpu[req_index] = num_computed_tokens
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            if new_block_ids is not None:
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                self.input_batch.block_table.append_row(new_block_ids, req_index)
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            # For the last rank, we don't need to update the token_ids_cpu
            # because the sampled tokens are already cached.
            if not is_last_rank:
                # Add new_token_ids to token_ids_cpu.
                start_token_index = num_computed_tokens
                end_token_index = num_computed_tokens + len(new_token_ids)
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                self.input_batch.token_ids_cpu[
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                    req_index, start_token_index:end_token_index
                ] = new_token_ids
                self.input_batch.num_tokens_no_spec[req_index] = end_token_index
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            # Add spec_token_ids to token_ids_cpu.
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            self.input_batch.update_req_spec_token_ids(req_state, scheduled_spec_tokens)
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            # Restore scheduler-side draft count after ngram trimming.
            if original_num_spec_per_req:
                orig = original_num_spec_per_req.get(req_id, 0)
                if orig != req_state.prev_num_draft_len:
                    req_state.prev_num_draft_len = orig
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        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
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        for request in reqs_to_add:
            self.input_batch.add_request(request)
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            self.input_batch.update_req_spec_token_ids(request, scheduled_spec_tokens)
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        # Condense the batched states if there are gaps left by removed requests
        self.input_batch.condense()
        # Allow attention backend to reorder the batch, potentially
        self._may_reorder_batch(scheduler_output)
        # Refresh batch metadata with any pending updates.
        self.input_batch.refresh_metadata()
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        # Incrementally update ngram_gpu tensors after batch is stable
        if is_ngram_gpu:
            update_ngram_gpu_tensors_incremental(
                self.input_batch,
                self.token_ids_gpu_tensor,
                self.num_tokens_no_spec_gpu,
                ngram_gpu_new_reqs,
                self.device,
                _pinned_idx_buf=self._ngram_pinned_idx_buf,
                _pinned_val_buf=self._ngram_pinned_val_buf,
            )

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        if deferred_spec_decode_corrections:

            def correct_spec_decode_token_counts():
                valid_sampled_token_count = self._get_valid_sampled_token_count()
                if not valid_sampled_token_count:
                    return
                prev_req_id_to_index = self.input_batch.prev_req_id_to_index
                if not prev_req_id_to_index:
                    return
                for (
                    req_id,
                    optimistic_num_accepted,
                    req_state,
                ) in deferred_spec_decode_corrections:
                    prev_req_index = prev_req_id_to_index.get(req_id)
                    if prev_req_index is None:
                        continue
                    num_accepted = valid_sampled_token_count[prev_req_index] - 1
                    correction = optimistic_num_accepted - num_accepted
                    req_state.num_computed_tokens -= correction
                    cur_req_index = self.input_batch.req_id_to_index.get(req_id)
                    if cur_req_index is None:
                        continue
                    self.input_batch.num_computed_tokens_cpu[cur_req_index] -= (
                        correction
                    )
                    if is_ngram_gpu and correction > 0:
                        self.input_batch.num_tokens_no_spec[cur_req_index] -= correction
                        self.num_tokens_no_spec_gpu[cur_req_index] -= correction

            return correct_spec_decode_token_counts
        else:
            return None

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    def _update_states_after_model_execute(
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        self, output_token_ids: torch.Tensor, scheduler_output: "SchedulerOutput"
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    ) -> None:
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        """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.
        """
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        if not self.speculative_config or not self.model_config.is_hybrid:
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            return

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        # TODO: Remove .cpu() sync to enable fully async for hybrid model;
        # Use num_computed_tokens.gpu instead of req.num_computed_tokens to
        # support aligned mamba cache mode.
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        # Count the number of accepted tokens for each sequence.
        # Valid tokens are contiguous from position 0, so counting non-(-1)
        # tokens gives us the first -1 position (i.e., number of accepted).
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        num_reqs = output_token_ids.size(0)
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        self.num_accepted_tokens.gpu[:num_reqs] = (output_token_ids != -1).sum(dim=1)
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        if self.cache_config.mamba_cache_mode == "align":
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            for i, num_tokens in enumerate(
                self.num_accepted_tokens.gpu[:num_reqs].cpu().numpy()
            ):
                self.input_batch.num_accepted_tokens_cpu[i] = num_tokens
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            mamba_utils.postprocess_mamba(
                scheduler_output,
                self.kv_cache_config,
                self.input_batch,
                self.requests,
                self.mamba_state_idx,
                self.compilation_config.static_forward_context,
                self.model.get_mamba_state_copy_func(),
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                self._get_mamba_copy_bufs(),
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            )
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        else:
            self.input_batch.num_accepted_tokens_cpu_tensor[:num_reqs].copy_(
                self.num_accepted_tokens.gpu[:num_reqs], non_blocking=True
            )
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            assert self.num_accepted_tokens_event is not None
            self.num_accepted_tokens_event.record()
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    def _update_streaming_request(
        self, req_id: str, new_req_data: NewRequestData
    ) -> CachedRequestState:
        """Updates streaming session request from `scheduled_new_reqs`.

        Removes the request from InputBatch (if present), updates the cached
        state, and prepares it for re-addition to the batch.

        NOTE: prompt_token_ids includes intermediate output tokens - tokens
        previously generated but now are input context (part of the prompt).
        """
        self.input_batch.remove_request(req_id)
        req_state = self.requests[req_id]

        req_state.prompt_token_ids = new_req_data.prompt_token_ids
        req_state.mm_features = new_req_data.mm_features
        req_state.prompt_embeds = new_req_data.prompt_embeds
        req_state.sampling_params = new_req_data.sampling_params
        req_state.pooling_params = new_req_data.pooling_params
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        self.late_interaction_runner.register_request(req_id, req_state.pooling_params)
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        req_state.block_ids = new_req_data.block_ids
        req_state.num_computed_tokens = new_req_data.num_computed_tokens
        req_state.num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
            req_state.prompt_token_ids, req_state.prompt_embeds
        )

        # Clear `output_token_ids` as previous output tokens are now part of
        # `prompt_token_ids`.
        req_state.output_token_ids.clear()

        if self.uses_mrope:
            self._init_mrope_positions(req_state)

        return req_state

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    def _init_mrope_positions(self, req_state: CachedRequestState):
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        model = self.get_model()
        assert supports_mrope(model), "M-RoPE support is not implemented."
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        assert req_state.prompt_token_ids is not None, (
            "M-RoPE requires prompt_token_ids to be available."
        )
        mrope_model = cast(SupportsMRoPE, model)
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        req_state.mrope_positions, req_state.mrope_position_delta = (
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            mrope_model.get_mrope_input_positions(
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                req_state.prompt_token_ids,
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                req_state.mm_features,
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            )
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        )
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    def _init_xdrope_positions(self, req_state: CachedRequestState):
        model = self.get_model()
        xdrope_model = cast(SupportsXDRoPE, model)
        assert req_state.prompt_token_ids is not None, (
            "XD-RoPE requires prompt_token_ids to be available."
        )
        assert supports_xdrope(model), "XD-RoPE support is not implemented."

        req_state.xdrope_positions = xdrope_model.get_xdrope_input_positions(
            req_state.prompt_token_ids,
            req_state.mm_features,
        )

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    def _extract_mm_kwargs(
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        self,
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        scheduler_output: "SchedulerOutput",
    ) -> BatchedTensorInputs:
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        if not scheduler_output or not self.is_multimodal_raw_input_only_model:
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            return {}
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        mm_kwargs = list[tuple[str, MultiModalKwargsItem]]()
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        for req in scheduler_output.scheduled_new_reqs:
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            for feature in req.mm_features:
                if feature.data is not None:
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                    mm_kwargs.append((feature.modality, feature.data))
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        # Input all modalities at once
        mm_kwargs_combined: BatchedTensorInputs = {}
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        for _, _, mm_kwargs_batch in group_and_batch_mm_kwargs(
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            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
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        ):
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            mm_kwargs_combined.update(mm_kwargs_batch)
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        return mm_kwargs_combined
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    def _dummy_mm_kwargs(self, num_seqs: int) -> BatchedTensorInputs:
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        if not self.is_multimodal_raw_input_only_model:
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            return {}
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        mm_budget = self.mm_budget
        assert mm_budget is not None

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        if not mm_budget.mm_max_toks_per_item:
            return {}  # No tower modalities (embed-only mode)

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        dummy_modality = mm_budget.get_modality_with_max_tokens()
        return self._get_mm_dummy_batch(dummy_modality, num_seqs)
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    def _get_cumsum_and_arange(
        self,
        num_tokens: np.ndarray,
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        arange_out: np.ndarray,
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        cumsum_dtype: np.dtype | None = None,
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    ) -> np.ndarray:
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        """Get the cumulative sum and batched arange of the given array.
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        E.g., [2, 5, 3] -> [2, 7, 10], arange written to
        arange_out[:10] as [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])
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        """
        # 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]
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        np.subtract(
            self.arange_np[:total_num_tokens],
            cumsums_offsets,
            out=arange_out[:total_num_tokens],
        )

        return cu_num_tokens

    def _compute_prev_positions(self, num_reqs: int) -> None:
        """Build prev_positions mapping: current pos -> previous pos (-1 if new).

        Populates self.prev_positions.np[:num_reqs] with the mapping.
        """
        prev_req_id_to_index = self.input_batch.prev_req_id_to_index
        prev_positions = self.prev_positions.np[:num_reqs]

        if not prev_req_id_to_index:
            prev_positions.fill(-1)
            return
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        for i, req_id in enumerate(self.input_batch.req_ids[:num_reqs]):
            prev_positions[i] = prev_req_id_to_index.get(req_id, -1)
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    def _prepare_input_ids(
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        self,
        scheduler_output: "SchedulerOutput",
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        num_reqs: int,
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        total_num_scheduled_tokens: int,
        cu_num_tokens: np.ndarray,
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    ) -> None:
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        """Prepare the input IDs for the current batch.
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        Carefully handles the `prev_sampled_token_ids` which can be cached
        from the previous engine iteration, in which case those tokens on the
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        GPU need to be copied into the corresponding slots into input_ids.

        Uses self.prev_positions[:num_reqs] which maps current pos -> prev pos
        (-1 for new requests).
        """
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        if self.input_batch.prev_sampled_token_ids is None:
            # Normal scheduling case
            self.input_ids.copy_to_gpu(total_num_scheduled_tokens)
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            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)
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            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.
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        prev_positions = self.prev_positions.np[:num_reqs]
        scheduled_spec_tokens = scheduler_output.scheduled_spec_decode_tokens
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        sample_flattened_indices: list[int] = []
        spec_flattened_indices: list[int] = []
        prev_draft_token_indices: list[int] = []
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        prev_indices: list[int] = []
        common_indices_match = True
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        max_flattened_index = -1
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        total_num_spec_tokens = 0

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        for cur_index in range(num_reqs):
            prev_index = prev_positions[cur_index]
            if prev_index < 0:
                continue
            prev_indices.append(prev_index)
            req_id = self.input_batch.req_ids[cur_index]
            # We need to compute the flattened input_ids index of the
            # last token in each common request.
            draft_len = len(scheduled_spec_tokens.get(req_id, ()))
            total_num_spec_tokens += draft_len
            flattened_index = cu_num_tokens[cur_index].item() - 1
            # example: cu_num_tokens = [2, 5, 8], draft_tokens = [1, 2, 2]
            # sample_flattened_indices = [0, 2, 5]
            # spec_flattened_indices = [1,   3, 4,    6, 7]
            sample_flattened_indices.append(flattened_index - draft_len)
            spec_flattened_indices.extend(
                range(flattened_index - draft_len + 1, flattened_index + 1)
            )
            start = prev_index * self.num_spec_tokens
            # prev_draft_token_indices is used to find which draft_tokens_id
            # should be copied to input_ids
            # example: prev draft_tokens_id [[1,2], [3,4], [5, 6]]
            # flatten draft_tokens_id [1,2,3,4,5,6]
            # draft_len of each request [1, 2, 1]
            # then prev_draft_token_indices is [0,   2, 3,   4]
            prev_draft_token_indices.extend(range(start, start + draft_len))
            common_indices_match &= prev_index == flattened_index
            max_flattened_index = max(max_flattened_index, flattened_index)

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        num_common_tokens = len(sample_flattened_indices)
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        total_without_spec = total_num_scheduled_tokens - total_num_spec_tokens
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        if num_common_tokens < total_without_spec:
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            # If not all requests are decodes from the last iteration,
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            # we need to copy the input_ids_cpu to the GPU first.
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            self.input_ids.copy_to_gpu(total_num_scheduled_tokens)
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            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)
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        if num_common_tokens == 0:
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            # No requests in common with the previous iteration
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            # So input_ids.cpu will have all the input ids.
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            return
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        if common_indices_match and max_flattened_index == (num_common_tokens - 1):
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            # 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.
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            self.input_ids.gpu[:num_common_tokens].copy_(
                self.input_batch.prev_sampled_token_ids[:num_common_tokens, 0],
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                non_blocking=True,
            )
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            if self.enable_prompt_embeds:
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                self.is_token_ids.gpu[:num_common_tokens] = True
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            return
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        # Upload the index tensors asynchronously so the scatter can be non-blocking.
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        sampled_tokens_index_tensor = torch.tensor(
            sample_flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
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        ).to(self.device, non_blocking=True)
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        prev_common_req_indices_tensor = torch.tensor(
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            prev_indices, dtype=torch.int64, pin_memory=self.pin_memory
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        ).to(self.device, non_blocking=True)
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        self.input_ids.gpu.scatter_(
            dim=0,
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            index=sampled_tokens_index_tensor,
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            src=self.input_batch.prev_sampled_token_ids[
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                prev_common_req_indices_tensor, 0
            ],
        )
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        # Scatter the draft tokens after the sampled tokens are scattered.
        if self._draft_token_ids is None or not spec_flattened_indices:
            return

        assert isinstance(self._draft_token_ids, torch.Tensor)
        draft_tokens_index_tensor = torch.tensor(
            spec_flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
        prev_draft_token_indices_tensor = torch.tensor(
            prev_draft_token_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)

        # because input_ids dtype is torch.int32,
        # so convert draft_token_ids to torch.int32 here.
        draft_token_ids = self._draft_token_ids.to(dtype=torch.int32)

        self.input_ids.gpu.scatter_(
            dim=0,
            index=draft_tokens_index_tensor,
            src=draft_token_ids.flatten()[prev_draft_token_indices_tensor],
        )

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    def _get_encoder_seq_lens(
        self,
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        num_scheduled_tokens: dict[str, int],
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        kv_cache_spec: KVCacheSpec,
        num_reqs: int,
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        for_cudagraph_capture: bool = False,
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    ) -> tuple[torch.Tensor | None, np.ndarray | None]:
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        if not isinstance(kv_cache_spec, CrossAttentionSpec):
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            return None, None
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        # Zero out buffer for padding requests that are not actually scheduled (CGs)
        self.encoder_seq_lens.np[:num_reqs] = 0
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        # Build encoder_seq_lens array mapping request indices to
        # encoder lengths for inputs scheduled in this batch
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        for req_id in num_scheduled_tokens:
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            req_index = self.input_batch.req_id_to_index[req_id]
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            req_state = self.requests[req_id]
            if req_state.mm_features is None:
                self.encoder_seq_lens.np[req_index] = 0
                continue

            # Get the total number of encoder input tokens for running encoder requests
            # whether encoding is finished or not so that cross-attention knows how
            # many encoder tokens to attend to.
            encoder_input_tokens = sum(
                feature.mm_position.length for feature in req_state.mm_features
            )
            self.encoder_seq_lens.np[req_index] = encoder_input_tokens
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        if for_cudagraph_capture:
            # During CUDA graph capture, we need to use realistic encoder lengths
            # so that max_seqlen_k is captured with the correct value.
            max_encoder_len = getattr(
                self.model_config.hf_config,
                "max_source_positions",
                self.max_encoder_len,
            )
            self.encoder_seq_lens.np[:num_reqs] = max_encoder_len
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        self.encoder_seq_lens.copy_to_gpu(num_reqs)
        encoder_seq_lens = self.encoder_seq_lens.gpu[:num_reqs]
        encoder_seq_lens_cpu = self.encoder_seq_lens.np[:num_reqs]
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        return encoder_seq_lens, encoder_seq_lens_cpu
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    def _prepare_inputs(
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        self,
        scheduler_output: "SchedulerOutput",
        num_scheduled_tokens: np.ndarray,
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    ) -> tuple[
        torch.Tensor,
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        SpecDecodeMetadata | None,
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    ]:
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        """
        :return: tuple[
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            logits_indices, spec_decode_metadata,
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        ]
        """
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        total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
        assert total_num_scheduled_tokens > 0
        num_reqs = self.input_batch.num_reqs
        assert num_reqs > 0

        # OPTIMIZATION: Start copying the block table first.
        # This way, we can overlap the copy with the following CPU operations.
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        self.input_batch.block_table.commit_block_table(num_reqs)
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        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
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        req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens)
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        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
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        # self.query_pos.np[:10]: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
        cu_num_tokens = self._get_cumsum_and_arange(
            num_scheduled_tokens, self.query_pos.np
        )
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        # Get positions.
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        positions_np = (
            self.input_batch.num_computed_tokens_cpu[req_indices]
            + self.query_pos.np[: cu_num_tokens[-1]]
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        )
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        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
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        if self.uses_mrope:
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            self._calc_mrope_positions(scheduler_output)

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        # Calculate XD-RoPE positions.
        # Only relevant for models using XD-RoPE (e.g, HunYuan-VL)
        if self.uses_xdrope_dim > 0:
            self._calc_xdrope_positions(scheduler_output)

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        # Get token indices.
        # E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
        # -> [0, 1, M, M + 1, M + 2, M + 3, M + 4, 2 * M, 2 * M + 1, 2 * M + 2]
        # where M is the max_model_len.
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        token_indices = (
            positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]
        )
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        token_indices_tensor = torch.from_numpy(token_indices)
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        # NOTE(woosuk): We use torch.index_select instead of np.take here
        # because torch.index_select is much faster than np.take for large
        # tensors.
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        torch.index_select(
            self.input_batch.token_ids_cpu_tensor.flatten(),
            0,
            token_indices_tensor,
            out=self.input_ids.cpu[:total_num_scheduled_tokens],
        )
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        if self.enable_prompt_embeds:
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            is_token_ids = self.input_batch.is_token_ids_tensor.flatten()
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            torch.index_select(
                is_token_ids,
                0,
                token_indices_tensor,
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                out=self.is_token_ids.cpu[:total_num_scheduled_tokens],
            )
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        # 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:
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                    self.inputs_embeds.cpu[
                        output_idx : output_idx + actual_num_sched
                    ].copy_(req_embeds[start_pos:actual_end])
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                output_idx += num_sched
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        # Prepare the attention metadata.
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        self.query_start_loc.np[0] = 0
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        self.query_start_loc.np[1 : num_reqs + 1] = cu_num_tokens
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        # Note: pad query_start_loc to be non-decreasing, as kernels
        # like FlashAttention requires that
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        self.query_start_loc.np[num_reqs + 1 :].fill(cu_num_tokens[-1])
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        self.query_start_loc.copy_to_gpu()
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        query_start_loc = self.query_start_loc.gpu[: num_reqs + 1]
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        # Compute optimistic seq_lens (assumes all draft tokens from previous
        # iteration accepted). Store in optimistic_seq_lens_cpu for use by
        # _build_attention_metadata (max_seq_len) and discard_request_mask.
        # seq_lens (GPU) will be computed later using the same optimistic values.
        torch.add(
            self.input_batch.num_computed_tokens_cpu_tensor[:num_reqs],
            torch.from_numpy(num_scheduled_tokens),
            out=self.optimistic_seq_lens_cpu[:num_reqs],
1906
        )
1907
1908
1909
1910
1911
1912
        self.optimistic_seq_lens_cpu[num_reqs:].fill_(0)

        # Build prev_positions mapping: current pos -> prev pos (-1 if new).
        # Used for gathering from previous iteration's GPU tensors.
        prev_req_id_to_index = self.input_batch.prev_req_id_to_index
        self._compute_prev_positions(num_reqs)
1913

1914
        num_tokens = [self.requests[r].num_tokens for r in self.input_batch.req_ids]
1915
1916
        num_tokens_np = np.array(num_tokens, dtype=np.int32)

1917
        # Record which requests should not be sampled,
1918
        # so that we could clear the sampled tokens before returning
1919
        self.discard_request_mask.np[:num_reqs] = (
1920
            self.optimistic_seq_lens_cpu[:num_reqs].numpy() < num_tokens_np
1921
        )
1922
        self.discard_request_mask.copy_to_gpu(num_reqs)
1923

1924
1925
1926
1927
        # Sync num_accepted_tokens from CPU (set by
        # _update_states_after_model_execute for hybrid models).
        if self.num_accepted_tokens_event is not None:
            self.num_accepted_tokens_event.synchronize()
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
            # Async mode: condense() reordered indices, use prev_positions mapping
            if self.use_async_scheduling and prev_req_id_to_index:
                prev_idx = self.prev_positions.np[:num_reqs]
                new_mask = prev_idx < 0
                self.num_accepted_tokens.np[:num_reqs] = (
                    self.input_batch.num_accepted_tokens_cpu[
                        np.where(new_mask, 0, prev_idx)
                    ]
                )
                self.num_accepted_tokens.np[:num_reqs][new_mask] = 1
                self.input_batch.num_accepted_tokens_cpu[:num_reqs] = (
                    self.num_accepted_tokens.np[:num_reqs]
                )
            else:
                # Non-async mode: use values directly
                self.num_accepted_tokens.np[:num_reqs] = (
                    self.input_batch.num_accepted_tokens_cpu[:num_reqs]
                )
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
            self.num_accepted_tokens.np[num_reqs:].fill(1)
            self.num_accepted_tokens.copy_to_gpu()
        else:
            self.num_accepted_tokens.np.fill(1)
            self.num_accepted_tokens.gpu.fill_(1)

        # Update num_computed_tokens on GPU. In async spec decode,
        # CPU values are optimistic (all drafts accepted). The kernel
        # corrects on GPU using the previous step's
        # valid_sampled_token_count_gpu. Otherwise, just copy from CPU.
        if (
            self.use_async_spec_decode
            and self.valid_sampled_token_count_gpu is not None
            and prev_req_id_to_index
        ):
            self.prev_positions.copy_to_gpu(num_reqs)
            self.prev_num_draft_tokens.copy_to_gpu()
            cpu_values = self.input_batch.num_computed_tokens_cpu_tensor[:num_reqs].to(
                device=self.device, non_blocking=True
            )
            update_num_computed_tokens_for_batch_change(
                self.num_computed_tokens,
                self.num_accepted_tokens.gpu[:num_reqs],
                self.prev_positions.gpu[:num_reqs],
                self.valid_sampled_token_count_gpu,
                self.prev_num_draft_tokens.gpu,
                cpu_values,
            )
        else:
            self.num_computed_tokens[:num_reqs].copy_(
                self.input_batch.num_computed_tokens_cpu_tensor[:num_reqs],
                non_blocking=True,
            )

        self.req_indices.np[:total_num_scheduled_tokens] = req_indices
        self.req_indices.copy_to_gpu(total_num_scheduled_tokens)
        req_indices_gpu = self.req_indices.gpu[:total_num_scheduled_tokens]

        self.query_pos.copy_to_gpu(total_num_scheduled_tokens)
        self.num_scheduled_tokens.np[:num_reqs] = num_scheduled_tokens
        self.num_scheduled_tokens.copy_to_gpu(num_reqs)
        num_scheduled_tokens_gpu = self.num_scheduled_tokens.gpu[:num_reqs]
        self.positions[:total_num_scheduled_tokens] = (
            self.num_computed_tokens[req_indices_gpu].to(torch.int64)
            + self.query_pos.gpu[:total_num_scheduled_tokens]
        )
        self.seq_lens[:num_reqs] = (
            self.num_computed_tokens[:num_reqs] + num_scheduled_tokens_gpu
        )
        self.seq_lens[num_reqs:].fill_(0)

        self.input_batch.block_table.compute_slot_mapping(
            num_reqs,
            self.query_start_loc.gpu[: num_reqs + 1],
            self.positions[:total_num_scheduled_tokens],
        )

2003
        # Copy the tensors to the GPU.
2004
2005
        self._prepare_input_ids(
            scheduler_output,
2006
            num_reqs,
2007
2008
2009
            total_num_scheduled_tokens,
            cu_num_tokens,
        )
2010

2011
        if self.uses_mrope:
2012
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
2013
2014
            self.mrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.mrope_positions.cpu[:, :total_num_scheduled_tokens],
2015
2016
                non_blocking=True,
            )
2017
2018
2019
2020
2021
2022
        elif self.uses_xdrope_dim > 0:
            # Only relevant for models using XD-RoPE (e.g, HunYuan-VL)
            self.xdrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.xdrope_positions.cpu[:, :total_num_scheduled_tokens],
                non_blocking=True,
            )
2023
2024
2025
2026
2027
2028
2029
2030
        if self.use_async_spec_decode and (self.uses_mrope or self.uses_xdrope_dim > 0):
            drift = self.num_computed_tokens[req_indices_gpu].to(
                torch.int64
            ) - self.input_batch.num_computed_tokens_cpu_tensor[req_indices].to(
                device=self.device, dtype=torch.int64, non_blocking=True
            )
            target = self.mrope_positions if self.uses_mrope else self.xdrope_positions
            target.gpu[:, :total_num_scheduled_tokens] += drift
2031

2032
        use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
2033
2034
2035
2036
2037
2038
2039
2040
        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
            spec_decode_metadata = None
2041
            num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
2042
2043
2044
2045
2046
        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)
2047
2048
2049
            # 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)
2050
2051
2052
2053
            for (
                req_id,
                draft_token_ids,
            ) in scheduler_output.scheduled_spec_decode_tokens.items():
2054
                req_idx = self.input_batch.req_id_to_index[req_id]
2055
2056
                draft_len = len(draft_token_ids)
                num_draft_tokens[req_idx] = draft_len
2057
2058
2059
2060
                if (
                    self.input_batch.num_computed_tokens_cpu[req_idx]
                    >= self.input_batch.num_prompt_tokens[req_idx]
                ):
2061
                    num_decode_draft_tokens[req_idx] = draft_len
2062
            spec_decode_metadata = self._calc_spec_decode_metadata(
2063
2064
                num_draft_tokens, cu_num_tokens
            )
2065
            logits_indices = spec_decode_metadata.logits_indices
2066
            num_sampled_tokens = num_draft_tokens + 1
2067
            # For DECODE only cuda graph of some attention backends (e.g., GDN).
2068
            self.num_decode_draft_tokens.np[:num_reqs] = num_decode_draft_tokens
2069
2070
            self.num_decode_draft_tokens.np[num_reqs:].fill(-1)
            self.num_decode_draft_tokens.copy_to_gpu()
2071

2072
2073
2074
2075
2076
        # Hot-Swap lora model
        if self.lora_config:
            assert (
                np.sum(num_sampled_tokens)
                <= self.vllm_config.scheduler_config.max_num_batched_tokens
2077
            )
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
2088
            self.set_active_loras(
                self.input_batch, num_scheduled_tokens, num_sampled_tokens
            )

        return (
            logits_indices,
            spec_decode_metadata,
        )

    def _build_attention_metadata(
        self,
2089
        num_tokens: int,
2090
        num_reqs: int,
2091
2092
2093
        max_query_len: int,
        num_tokens_padded: int | None = None,
        num_reqs_padded: int | None = None,
2094
2095
2096
2097
        ubatch_slices: UBatchSlices | None = None,
        logits_indices: torch.Tensor | None = None,
        use_spec_decode: bool = False,
        for_cudagraph_capture: bool = False,
2098
        num_scheduled_tokens: dict[str, int] | None = None,
2099
        cascade_attn_prefix_lens: list[list[int]] | None = None,
2100
        slot_mappings: dict[int, torch.Tensor] | None = None,
2101
2102
2103
2104
    ) -> tuple[PerLayerAttnMetadata, CommonAttentionMetadata | None]:
        """
        :return: tuple[attn_metadata, spec_decode_common_attn_metadata]
        """
2105
2106
2107
2108
        # Attention metadata is not needed for attention free models
        if len(self.kv_cache_config.kv_cache_groups) == 0:
            return {}, None

2109
2110
        num_tokens_padded = num_tokens_padded or num_tokens
        num_reqs_padded = num_reqs_padded or num_reqs
2111
        assert num_reqs_padded is not None and num_tokens_padded is not None
2112

2113
2114
2115
        attn_metadata: PerLayerAttnMetadata = {}
        if ubatch_slices is not None:
            attn_metadata = [dict() for _ in range(len(ubatch_slices))]
2116

2117
2118
2119
2120
2121
2122
        if for_cudagraph_capture:
            # For some attention backends (e.g. FA) with sliding window models we need
            # to make sure the backend see a max_seq_len that is larger to the sliding
            # window size when capturing to make sure the correct kernel is selected.
            max_seq_len = self.max_model_len
        else:
2123
            max_seq_len = self.optimistic_seq_lens_cpu.numpy()[:num_reqs].max().item()
2124

2125
        kv_cache_groups = self.kv_cache_config.kv_cache_groups
2126

2127
        def _get_block_table(kv_cache_gid: int):
2128
2129
2130
            assert num_reqs_padded is not None and num_tokens_padded is not None
            kv_cache_spec = kv_cache_groups[kv_cache_gid].kv_cache_spec
            if isinstance(kv_cache_spec, EncoderOnlyAttentionSpec):
2131
                blk_table_tensor = torch.zeros(
2132
                    (num_reqs_padded, 1),
2133
                    dtype=torch.int32,
2134
2135
                    device=self.device,
                )
2136
            else:
2137
                blk_table = self.input_batch.block_table[kv_cache_gid]
2138
                blk_table_tensor = blk_table.get_device_tensor(num_reqs_padded)
2139

2140
2141
2142
            # Fill unused block table entries with NULL_BLOCK_ID (null block)
            # for CUDAGraph padding. Block 0 is reserved for padding.
            blk_table_tensor[num_reqs:num_reqs_padded].fill_(NULL_BLOCK_ID)
2143
            return blk_table_tensor
2144

2145
2146
2147
        assert slot_mappings is not None
        block_table_gid_0 = _get_block_table(0)
        slot_mapping_gid_0 = slot_mappings[0]
2148

2149
2150
2151
2152
        if self.routed_experts_initialized:
            attn_gid = self.routed_experts_attn_gid
            slot_mapping_attn = slot_mappings[attn_gid]
            self.slot_mapping = slot_mapping_attn[:num_tokens].cpu().numpy()
2153
2154
2155
2156
2157
2158
        num_computed_tokens_cpu = self.input_batch.num_computed_tokens_cpu_tensor[
            :num_reqs_padded
        ]
        num_prompt_tokens_cpu = self.input_batch.num_prompt_tokens_cpu_tensor[
            :num_reqs_padded
        ]
2159
        seq_lens_cpu = self.optimistic_seq_lens_cpu[:num_reqs_padded]
2160
        seq_lens_cpu_upper_bound = seq_lens_cpu
2161
2162
2163
2164

        # is_prefilling: True if request is still in prefill phase.
        # Used by mamba backends to distinguish actual decodes from
        # short extends.
2165
2166
        is_prefilling = num_computed_tokens_cpu < num_prompt_tokens_cpu

2167
2168
2169
2170
2171
        if self.use_async_spec_decode:
            # GPU tensors are authoritative in async mode.
            seq_lens_cpu = None
            num_computed_tokens_cpu = None

2172
2173
2174
        cm_base = CommonAttentionMetadata(
            query_start_loc=self.query_start_loc.gpu[: num_reqs_padded + 1],
            query_start_loc_cpu=self.query_start_loc.cpu[: num_reqs_padded + 1],
2175
2176
            seq_lens=self.seq_lens[:num_reqs_padded],
            _seq_lens_cpu=seq_lens_cpu,
2177
            _num_computed_tokens_cpu=num_computed_tokens_cpu,
2178
            seq_lens_cpu_upper_bound=seq_lens_cpu_upper_bound,
2179
2180
2181
2182
2183
2184
2185
            num_reqs=num_reqs_padded,
            num_actual_tokens=num_tokens_padded,
            max_query_len=max_query_len,
            max_seq_len=max_seq_len,
            block_table_tensor=block_table_gid_0,
            slot_mapping=slot_mapping_gid_0,
            causal=True,
2186
            is_prefilling=is_prefilling,
2187
2188
2189
2190
        )

        if self.dcp_world_size > 1:
            self.dcp_local_seq_lens.cpu[:num_reqs] = get_dcp_local_seq_lens(
2191
                self.optimistic_seq_lens_cpu[:num_reqs],
2192
2193
2194
2195
2196
2197
2198
2199
2200
2201
2202
2203
2204
2205
2206
2207
2208
2209
                self.dcp_world_size,
                self.dcp_rank,
                self.parallel_config.cp_kv_cache_interleave_size,
            )
            self.dcp_local_seq_lens.cpu[num_reqs:].fill_(0)
            self.dcp_local_seq_lens.copy_to_gpu(num_reqs_padded)

            cm_base.dcp_local_seq_lens = self.dcp_local_seq_lens.gpu[:num_reqs_padded]
            cm_base.dcp_local_seq_lens_cpu = self.dcp_local_seq_lens.cpu[
                :num_reqs_padded
            ]

        if logits_indices is not None and self.cache_config.kv_sharing_fast_prefill:
            cm_base.num_logits_indices = logits_indices.size(0)
            cm_base.logits_indices_padded = self._prepare_kv_sharing_fast_prefill(
                logits_indices
            )

2210
2211
2212
2213
2214
2215
2216
2217
2218
        # Cache attention metadata builds across hybrid KV-cache groups
        # The only thing that changes between different hybrid KV-cache groups when the
        # same metadata builder and KVCacheSpec is the same is the block table, so we
        # can cache the attention metadata builds and just update the block table using
        # `builder.update_block_table` if the builder supports it.
        cached_attn_metadata: dict[
            tuple[KVCacheSpec, type[AttentionMetadataBuilder]], AttentionMetadata
        ] = {}

2219
2220
2221
2222
2223
2224
2225
        def _build_attn_group_metadata(
            kv_cache_gid: int,
            attn_gid: int,
            common_attn_metadata: CommonAttentionMetadata,
            ubid: int | None = None,
        ) -> None:
            attn_group = self.attn_groups[kv_cache_gid][attn_gid]
2226
            builder = attn_group.get_metadata_builder(ubid or 0)
2227
2228
2229
2230
            kv_cache_spec = kv_cache_groups[kv_cache_gid].kv_cache_spec
            if isinstance(kv_cache_spec, UniformTypeKVCacheSpecs):
                kv_cache_spec = kv_cache_spec.kv_cache_specs[attn_group.layer_names[0]]
            cache_key = (kv_cache_spec, type(builder))
2231

2232
2233
2234
2235
2236
2237
2238
            cascade_attn_prefix_len = (
                cascade_attn_prefix_lens[kv_cache_gid][attn_gid]
                if cascade_attn_prefix_lens
                else 0
            )

            extra_attn_metadata_args = {}
2239
2240
2241
            if use_spec_decode and isinstance(
                builder, (Mamba2AttentionMetadataBuilder, GDNAttentionMetadataBuilder)
            ):
2242
2243
2244
2245
2246
2247
2248
2249
2250
2251
2252
2253
                assert ubid is None, "UBatching not supported with GDN yet"
                extra_attn_metadata_args = dict(
                    num_accepted_tokens=self.num_accepted_tokens.gpu[:num_reqs_padded],
                    num_decode_draft_tokens_cpu=self.num_decode_draft_tokens.cpu[
                        :num_reqs_padded
                    ],
                )

            if for_cudagraph_capture:
                attn_metadata_i = builder.build_for_cudagraph_capture(
                    common_attn_metadata
                )
2254
2255
2256
2257
2258
2259
2260
2261
2262
            elif (
                cache_key in cached_attn_metadata
                and builder.supports_update_block_table
            ):
                attn_metadata_i = builder.update_block_table(
                    cached_attn_metadata[cache_key],
                    common_attn_metadata.block_table_tensor,
                    common_attn_metadata.slot_mapping,
                )
2263
2264
2265
2266
2267
2268
            else:
                attn_metadata_i = builder.build(
                    common_prefix_len=cascade_attn_prefix_len,
                    common_attn_metadata=common_attn_metadata,
                    **extra_attn_metadata_args,
                )
2269
2270
                if builder.supports_update_block_table:
                    cached_attn_metadata[cache_key] = attn_metadata_i
2271
2272
2273
2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
2287
2288
2289
2290
2291
2292
2293

            if ubid is None:
                assert isinstance(attn_metadata, dict)
                attn_metadata_dict = attn_metadata
            else:
                assert isinstance(attn_metadata, list)
                attn_metadata_dict = attn_metadata[ubid]

            for layer_name in attn_group.layer_names:
                attn_metadata_dict[layer_name] = attn_metadata_i

        # Prepare the attention metadata for each KV cache group and make layers
        # in the same group share the same metadata.
        spec_decode_common_attn_metadata = None
        for kv_cache_gid, kv_cache_group in enumerate(kv_cache_groups):
            cm = copy(cm_base)  # shallow copy

            # Basically only the encoder seq_lens, block_table and slot_mapping change
            # for each kv_cache_group.
            cm.encoder_seq_lens, cm.encoder_seq_lens_cpu = self._get_encoder_seq_lens(
                num_scheduled_tokens or {},
                kv_cache_group.kv_cache_spec,
                num_reqs_padded,
2294
                for_cudagraph_capture=for_cudagraph_capture,
2295
            )
2296
            if kv_cache_gid > 0:
2297
2298
                cm.block_table_tensor = _get_block_table(kv_cache_gid)
                cm.slot_mapping = slot_mappings[kv_cache_gid]
2299

2300
            if self.speculative_config and spec_decode_common_attn_metadata is None:
2301
                if isinstance(self.drafter, (EagleProposer, DFlashProposer)):
2302
                    if self.drafter.kv_cache_gid == kv_cache_gid:
2303
                        spec_decode_common_attn_metadata = cm
2304
                else:
2305
                    spec_decode_common_attn_metadata = cm
2306

2307
            for attn_gid in range(len(self.attn_groups[kv_cache_gid])):
2308
                if ubatch_slices is not None:
2309
2310
2311
                    for ubid, _cm in enumerate(split_attn_metadata(ubatch_slices, cm)):
                        _build_attn_group_metadata(kv_cache_gid, attn_gid, _cm, ubid)

2312
                else:
2313
                    _build_attn_group_metadata(kv_cache_gid, attn_gid, cm)
2314

2315
2316
        if self.is_mm_prefix_lm:
            req_doc_ranges = {}
2317
2318
2319
2320
2321
2322
2323
2324
2325
2326

            # Gemma4 bidi: skip ranges that exceed the sliding
            # window. When image tokens > sliding_window, bidi causes
            # early image tokens to attend to the entire image
            # (e.g. 6 → 1092 targets), degrading spatial precision.
            # Per-range filtering keeps bidi for small images/video
            # frames while skipping oversized images.
            hf_text_config = self.model_config.hf_text_config
            _bidi_sw = getattr(hf_text_config, "sliding_window", None)

2327
2328
2329
2330
2331
2332
            for req_id in self.input_batch.req_ids:
                image_doc_ranges = []
                req_state = self.requests[req_id]
                for mm_feature in req_state.mm_features:
                    pos_info = mm_feature.mm_position
                    img_doc_range = pos_info.extract_embeds_range()
2333
2334
2335
2336
                    for r in img_doc_range:
                        if _bidi_sw is not None and (r[1] - r[0] + 1) > _bidi_sw:
                            continue
                        image_doc_ranges.append(r)
2337
2338
2339
                req_idx = self.input_batch.req_id_to_index[req_id]
                req_doc_ranges[req_idx] = image_doc_ranges

2340
2341
            # Set mm_prefix_range for all attention metadata
            self._set_mm_prefix_range_for_metadata(attn_metadata, req_doc_ranges)
2342

2343
2344
2345
2346
2347
2348
2349
2350
2351
2352
        if spec_decode_common_attn_metadata is not None and (
            num_reqs != num_reqs_padded or num_tokens != num_tokens_padded
        ):
            # Currently the drafter still only uses piecewise cudagraphs (and modifies
            # the attention metadata in directly), and therefore does not want to use
            # padded attention metadata.
            spec_decode_common_attn_metadata = (
                spec_decode_common_attn_metadata.unpadded(num_tokens, num_reqs)
            )

2353
        return attn_metadata, spec_decode_common_attn_metadata
2354

2355
2356
2357
    def _compute_cascade_attn_prefix_lens(
        self,
        num_scheduled_tokens: np.ndarray,
2358
        num_computed_tokens: np.ndarray,
2359
2360
2361
2362
2363
2364
2365
        num_common_prefix_blocks: list[int],
    ) -> list[list[int]] | None:
        """
        :return: Optional[cascade_attn_prefix_lens]
            cascade_attn_prefix_lens is 2D: ``[kv_cache_group_id][attn_group_idx]``,
            None if we should not use cascade attention
        """
2366

2367
2368
2369
2370
2371
2372
2373
2374
2375
2376
2377
2378
2379
2380
        use_cascade_attn = False
        num_kv_cache_groups = len(self.kv_cache_config.kv_cache_groups)
        cascade_attn_prefix_lens: list[list[int]] = [
            [] for _ in range(num_kv_cache_groups)
        ]

        for kv_cache_gid in range(num_kv_cache_groups):
            for attn_group in self.attn_groups[kv_cache_gid]:
                if isinstance(attn_group.kv_cache_spec, EncoderOnlyAttentionSpec):
                    cascade_attn_prefix_len = 0
                else:
                    # 0 if cascade attention should not be used
                    cascade_attn_prefix_len = self._compute_cascade_attn_prefix_len(
                        num_scheduled_tokens,
2381
                        num_computed_tokens,
2382
2383
2384
2385
2386
2387
2388
2389
                        num_common_prefix_blocks[kv_cache_gid],
                        attn_group.kv_cache_spec,
                        attn_group.get_metadata_builder(),
                    )
                cascade_attn_prefix_lens[kv_cache_gid].append(cascade_attn_prefix_len)
                use_cascade_attn |= cascade_attn_prefix_len > 0

        return cascade_attn_prefix_lens if use_cascade_attn else None
2390

2391
2392
2393
    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
2394
        num_computed_tokens: np.ndarray,
2395
        num_common_prefix_blocks: int,
2396
2397
        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
    ) -> 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.
        """
2416

2417
        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
2418
2419
2420
2421
2422
2423
2424
2425
2426
2427
2428
2429
2430
2431
2432
2433
2434
2435
2436
2437
2438
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2442
2443
2444
2445
2446
2447
2448
2449
2450
2451
2452
2453
2454
        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]
2455
        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
2456
2457
2458
2459
2460
        # 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.
2461
        common_prefix_len = min(common_prefix_len, num_computed_tokens.min())
2462
        # common_prefix_len should be a multiple of the block size.
2463
2464
2465
2466
2467
2468
2469
2470
2471
2472
2473
        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
        )
2474
2475
        assert isinstance(kv_cache_spec, AttentionSpec)
        use_cascade = attn_metadata_builder.use_cascade_attention(
2476
2477
2478
            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
2479
            num_kv_heads=kv_cache_spec.num_kv_heads,
2480
            use_alibi=self.use_alibi,
2481
            use_sliding_window=use_sliding_window,
2482
            use_local_attention=use_local_attention,
2483
            num_sms=self.num_sms,
2484
            dcp_world_size=self.dcp_world_size,
2485
2486
2487
        )
        return common_prefix_len if use_cascade else 0

2488
2489
    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
2490
        for index, req_id in enumerate(self.input_batch.req_ids):
2491
2492
2493
            req = self.requests[req_id]
            assert req.mrope_positions is not None

2494
2495
            num_computed_tokens = self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
2496
            num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
2497
2498
                req.prompt_token_ids, req.prompt_embeds
            )
2499
2500

            if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
2501
2502
                prompt_part_len = max(0, num_prompt_tokens - num_computed_tokens)
                completion_part_len = max(0, num_scheduled_tokens - prompt_part_len)
2503
2504
2505
2506
2507
2508
2509
2510
2511
2512
2513
2514
2515
            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

2516
2517
2518
                self.mrope_positions.cpu[:, dst_start:dst_end] = req.mrope_positions[
                    :, src_start:src_end
                ]
2519
2520
2521
2522
2523
2524
2525
                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

2526
                assert req.mrope_position_delta is not None
2527
                MRotaryEmbedding.get_next_input_positions_tensor(
2528
                    out=self.mrope_positions.np,
2529
2530
2531
2532
2533
                    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,
                )
2534
2535
2536

                mrope_pos_ptr += completion_part_len

2537
2538
2539
2540
2541
2542
2543
2544
2545
2546
2547
2548
2549
2550
2551
2552
2553
2554
2555
2556
2557
2558
2559
2560
2561
2562
2563
2564
2565
2566
2567
2568
2569
2570
2571
2572
2573
2574
2575
2576
2577
2578
2579
2580
2581
2582
2583
    def _calc_xdrope_positions(self, scheduler_output: "SchedulerOutput"):
        xdrope_pos_ptr = 0
        for index, req_id in enumerate(self.input_batch.req_ids):
            req = self.requests[req_id]
            assert req.xdrope_positions is not None

            num_computed_tokens = self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
            num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
                req.prompt_token_ids, req.prompt_embeds
            )

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

            assert num_scheduled_tokens == prompt_part_len + completion_part_len

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

                self.xdrope_positions.cpu[:, dst_start:dst_end] = req.xdrope_positions[
                    :, src_start:src_end
                ]
                xdrope_pos_ptr += prompt_part_len

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

                XDRotaryEmbedding.get_next_input_positions_tensor(
                    out=self.xdrope_positions.np,
                    out_offset=dst_start,
                    context_len=num_computed_tokens + prompt_part_len,
                    num_new_tokens=completion_part_len,
                )

                xdrope_pos_ptr += completion_part_len

2584
2585
    def _calc_spec_decode_metadata(
        self,
2586
2587
2588
2589
2590
2591
2592
2593
2594
2595
2596
2597
2598
2599
2600
2601
        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
2602

2603
2604
2605
2606
2607
        # Step 1.
        # cu_num_sampled_tokens: [4, 5, 8, 9, 11]
        # _arange_scratch[:11]: [0, 1, 2, 3, 0, 0, 1, 2, 0, 0, 1]
        cu_num_sampled_tokens = self._get_cumsum_and_arange(
            num_sampled_tokens, self._arange_scratch, cumsum_dtype=np.int32
2608
        )
2609
        # Step 2. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
2610
        logits_indices = np.repeat(
2611
2612
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens
        )
2613
        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
2614
        logits_indices += self._arange_scratch[: cu_num_sampled_tokens[-1]]
2615
2616
2617
2618
2619

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

        # Compute the draft logits indices.
2620
        # cu_num_draft_tokens: [3, 3, 5, 5, 6]
2621
2622
2623
        # _arange_scratch[:6]: [0, 1, 2, 0, 1, 0]
        cu_num_draft_tokens = self._get_cumsum_and_arange(
            num_draft_tokens, self._arange_scratch, cumsum_dtype=np.int32
2624
        )
2625
2626
        # [0, 0, 0, 5, 5, 9]
        target_logits_indices = np.repeat(
2627
2628
            cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens
        )
2629
        # [0, 1, 2, 5, 6, 9]
2630
        target_logits_indices += self._arange_scratch[: cu_num_draft_tokens[-1]]
2631
2632
2633

        # TODO: Optimize the CPU -> GPU copy.
        cu_num_draft_tokens = torch.from_numpy(cu_num_draft_tokens).to(
2634
2635
            self.device, non_blocking=True
        )
2636
2637
2638
        cu_num_sampled_tokens = torch.from_numpy(cu_num_sampled_tokens).to(
            self.device, non_blocking=True
        )
2639
2640
2641
        logits_indices = torch.from_numpy(logits_indices).to(
            self.device, non_blocking=True
        )
2642
        target_logits_indices = torch.from_numpy(target_logits_indices).to(
2643
2644
            self.device, non_blocking=True
        )
2645
        bonus_logits_indices = torch.from_numpy(bonus_logits_indices).to(
2646
2647
            self.device, non_blocking=True
        )
2648

2649
2650
        # Compute the draft token ids.
        # draft_token_indices:      [  1,   2,   3, 105, 106, 208]
2651
        draft_token_ids = self.input_ids.gpu[logits_indices]
2652
2653
        draft_token_ids = draft_token_ids[target_logits_indices + 1]

2654
        return SpecDecodeMetadata(
2655
2656
2657
            draft_token_ids=draft_token_ids,
            num_draft_tokens=num_draft_tokens.tolist(),
            cu_num_draft_tokens=cu_num_draft_tokens,
2658
            cu_num_sampled_tokens=cu_num_sampled_tokens,
2659
2660
2661
2662
2663
            target_logits_indices=target_logits_indices,
            bonus_logits_indices=bonus_logits_indices,
            logits_indices=logits_indices,
        )

2664
2665
2666
2667
2668
2669
2670
    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
2671
        self.kv_sharing_fast_prefill_logits_indices[:num_logits].copy_(logits_indices)
2672
2673
2674
2675
2676
        # 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_(
2677
2678
            logits_indices[-1].item()
        )
2679
2680
        # Dispatch for the decoder portion of the model.
        _, batch_desc = self.cudagraph_dispatcher.dispatch(
2681
            num_logits, invalid_modes={CUDAGraphMode.FULL}
2682
2683
        )
        num_logits_padded = batch_desc.num_tokens
2684
2685
2686
        logits_indices_padded = self.kv_sharing_fast_prefill_logits_indices[
            :num_logits_padded
        ]
2687
2688
        return logits_indices_padded

2689
    def _batch_mm_inputs_from_scheduler(
2690
2691
        self,
        scheduler_output: "SchedulerOutput",
2692
2693
    ) -> tuple[
        list[str],
2694
        list[tuple[str, MultiModalKwargsItem]],
2695
2696
        list[tuple[str, PlaceholderRange]],
    ]:
2697
        """Batch multimodal inputs from scheduled encoder inputs.
2698
2699
2700

        Args:
            scheduler_output: The scheduler output containing scheduled encoder
2701
                inputs.
2702
2703

        Returns:
2704
            A tuple of (mm_hashes, mm_kwargs, mm_lora_refs) where:
2705
2706
            - mm_hashes: List of multimodal hashes for each item
            - mm_kwargs: List of multimodal kwargs for each item
2707
            - mm_lora_refs: List of (req_id, placeholder_range) for each item
2708
        """
2709
2710
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
2711
            return [], [], []
2712
2713

        mm_hashes = list[str]()
2714
        mm_kwargs = list[tuple[str, MultiModalKwargsItem]]()
2715
2716
2717
        # Multimodal LoRA reference info to map each multimodal item
        # back to its request & position
        mm_lora_refs = list[tuple[str, PlaceholderRange]]()
2718
2719
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
2720
2721

            for mm_input_id in encoder_input_ids:
2722
                mm_feature = req_state.mm_features[mm_input_id]
2723
2724
                if mm_feature.data is None:
                    continue
2725
2726

                mm_hashes.append(mm_feature.identifier)
2727
                mm_kwargs.append((mm_feature.modality, mm_feature.data))
2728
                mm_lora_refs.append((req_id, mm_feature.mm_position))
2729

2730
        return mm_hashes, mm_kwargs, mm_lora_refs
2731

2732
2733
2734
    def _execute_mm_encoder(
        self, scheduler_output: "SchedulerOutput"
    ) -> list[torch.Tensor]:
2735
2736
2737
        mm_hashes, mm_kwargs, mm_lora_refs = self._batch_mm_inputs_from_scheduler(
            scheduler_output
        )
2738
2739

        if not mm_kwargs:
2740
            return []
2741

2742
2743
2744
2745
2746
2747
        should_time = bool(
            self.observability_config
            and self.observability_config.enable_mm_processor_stats
            and scheduler_output.scheduled_encoder_inputs
        )

2748
2749
2750
2751
2752
2753
2754
        # 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.
2755
        model = cast(SupportsMultiModal, self.model)
2756
2757
2758
2759
2760
2761
2762
2763
2764
2765
2766
2767
2768
2769
2770

        if self.lora_config and self.lora_manager.supports_tower_connector_lora():
            # Build LoRA mappings independently for encoder inputs
            # (encoder batch structure is different from main batch)
            prompt_lora_mapping = []
            token_lora_mapping = []
            lora_requests = set()
            encoder_token_counts = []

            for req_id, pos_info in mm_lora_refs:
                req_idx = self.input_batch.req_id_to_index[req_id]
                lora_id = int(self.input_batch.request_lora_mapping[req_idx])

                # Prefer pos_info.get_num_embeds to count precise MM embedding tokens.
                num_tokens = self.model.get_num_mm_encoder_tokens(  # type: ignore[attr-defined]
2771
                    pos_info.get_num_embeds()
2772
2773
2774
2775
2776
2777
2778
2779
2780
2781
2782
2783
2784
2785
2786
2787
2788
2789
2790
                )
                prompt_lora_mapping.append(lora_id)
                token_lora_mapping.extend([lora_id] * num_tokens)
                encoder_token_counts.append(num_tokens)

                if lora_id > 0:
                    lora_request = self.input_batch.lora_id_to_lora_request.get(lora_id)
                    if lora_request is not None:
                        lora_requests.add(lora_request)

            # Set tower adapter mapping
            tower_mapping = LoRAMapping(
                tuple(token_lora_mapping),
                tuple(prompt_lora_mapping),
                is_prefill=True,
                type=LoRAMappingType.TOWER,
            )
            self.lora_manager.set_active_adapters(lora_requests, tower_mapping)

2791
2792
2793
2794
2795
2796
2797
2798
2799
2800
2801
2802
2803
2804
            # Only set connector mapping if the model actually has a connector.
            # Some multimodal models inherit a stub `get_num_mm_connector_tokens`
            # from `SupportsMultiModal`, which returns None and should not be
            # treated as a signal that connector LoRA is supported.
            mm_mapping = (
                self.model.get_mm_mapping()  # type: ignore[attr-defined]
                if hasattr(self.model, "get_mm_mapping")
                else None
            )
            if (
                mm_mapping is not None
                and mm_mapping.connector
                and hasattr(self.model, "get_num_mm_connector_tokens")
            ):
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                post_op_counts = [
                    self.model.get_num_mm_connector_tokens(num_tokens)  # type: ignore[attr-defined]
                    for num_tokens in encoder_token_counts
                ]

                connector_token_mapping = np.repeat(
                    np.array(prompt_lora_mapping, dtype=np.int32),
                    np.array(post_op_counts, dtype=np.int32),
                )
                connector_mapping = LoRAMapping(
                    index_mapping=tuple(connector_token_mapping.tolist()),
                    prompt_mapping=tuple(prompt_lora_mapping),
                    is_prefill=True,
                    type=LoRAMappingType.CONNECTOR,
                )

                self.lora_manager.set_active_adapters(
                    lora_requests,
                    connector_mapping,
                )

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        encoder_outputs: list[torch.Tensor] = []
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        # Track the current index in mm_kwargs/mm_lora_refs to map groups to request IDs
        current_item_idx = 0
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        for modality, num_items, mm_kwargs_batch in group_and_batch_mm_kwargs(
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            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
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        ):
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            batch_outputs: MultiModalEmbeddings
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            # EVS and dynamic res video related change.
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            # (ekhvedchenia): Temporary hack to limit peak memory usage when
2838
            # processing multimodal data. This solves the issue with scheduler
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            # 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)
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            # dynamic res video for nemotron temporarily uses this hack via
            # requires_sequential_video_encoding
            # because it doesn't yet support video batching.
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            # TODO(ywang96): Fix memory profiling to take EVS into account and
            # remove this hack.
            if (
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                (
                    self.is_multimodal_pruning_enabled
                    or self.requires_sequential_video_encoding
                )
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                and modality == "video"
                and num_items > 1
            ):
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                batch_outputs_lst = list[torch.Tensor]()
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                for video_idx in range(num_items):
                    video_mm_kwargs_item = mm_kwargs[current_item_idx + video_idx]
                    with self.timed_encoder_operation(
                        should_time, mm_lora_refs, current_item_idx + video_idx, 1
                    ):
                        _, _, micro_batch_mm_inputs = next(
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                            group_and_batch_mm_kwargs(
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                                [video_mm_kwargs_item],
                                device=self.device,
                                pin_memory=self.pin_memory,
                            )
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                        )
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                        micro_batch_outputs = model.embed_multimodal(
                            **micro_batch_mm_inputs
                        )
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                        batch_outputs_lst.extend(micro_batch_outputs)
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                batch_outputs = batch_outputs_lst
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            else:
                # Run the encoder.
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                # `batch_outputs` is either of the following:
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                # 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.
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                with self.timed_encoder_operation(
                    should_time, mm_lora_refs, current_item_idx, num_items
                ):
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                    cudagraph_output = None
                    if (
                        self.encoder_cudagraph_manager is not None
                        and self.encoder_cudagraph_manager.supports_modality(modality)
                    ):
                        cudagraph_output = self.encoder_cudagraph_manager.execute(
                            mm_kwargs_batch,
                        )

                    if cudagraph_output is not None:
                        batch_outputs = cudagraph_output
                    else:
                        batch_outputs = model.embed_multimodal(**mm_kwargs_batch)
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            sanity_check_mm_encoder_outputs(batch_outputs, expected_num_items=num_items)
            encoder_outputs.extend(batch_outputs)
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            current_item_idx += num_items

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        # Cache the encoder outputs by mm_hash
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        for mm_hash, output in zip(mm_hashes, encoder_outputs):
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            self.encoder_cache[mm_hash] = output
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            logger.debug("Finish execute for mm hash %s", mm_hash)
            self.maybe_save_ec_to_connector(self.encoder_cache, mm_hash)
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        return encoder_outputs

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    def _gather_mm_embeddings(
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        self,
        scheduler_output: "SchedulerOutput",
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        shift_computed_tokens: int = 0,
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    ) -> tuple[list[torch.Tensor], torch.Tensor]:
        total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens

        mm_embeds = list[torch.Tensor]()
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        is_mm_embed = torch.zeros(
            total_num_scheduled_tokens, dtype=torch.bool, device="cpu"
        )
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        req_start_idx = 0
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        should_sync_mrope_positions = False
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        should_sync_xdrope_positions = False
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        for req_id in self.input_batch.req_ids:
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            mm_embeds_req: list[torch.Tensor] = []

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            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
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            req_state = self.requests[req_id]
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            num_computed_tokens = req_state.num_computed_tokens + shift_computed_tokens
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            for mm_feature in req_state.mm_features:
                pos_info = mm_feature.mm_position
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                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
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                # The encoder output is needed if the two ranges overlap:
                # [num_computed_tokens,
                #  num_computed_tokens + num_scheduled_tokens) and
                # [start_pos, start_pos + num_encoder_tokens)
                if start_pos >= num_computed_tokens + num_scheduled_tokens:
                    # The encoder output is not needed in this step.
                    break
                if start_pos + num_encoder_tokens <= num_computed_tokens:
                    # The encoder output is already processed and stored
                    # in the decoder's KV cache.
                    continue

                start_idx = max(num_computed_tokens - start_pos, 0)
                end_idx = min(
                    num_computed_tokens - start_pos + num_scheduled_tokens,
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                    num_encoder_tokens,
                )
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                assert start_idx < end_idx
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                curr_embeds_start, curr_embeds_end = (
                    pos_info.get_embeds_indices_in_range(start_idx, end_idx)
                )
                # If there are no embeddings in the current range, we skip
                # gathering the embeddings.
                if curr_embeds_start == curr_embeds_end:
                    continue
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                mm_hash = mm_feature.identifier
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                encoder_output = self.encoder_cache.get(mm_hash, None)
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                assert encoder_output is not None, f"Encoder cache miss for {mm_hash}."
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                if (is_embed := pos_info.is_embed) is not None:
                    is_embed = is_embed[start_idx:end_idx]
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                    mm_embeds_item = encoder_output[curr_embeds_start:curr_embeds_end]
                else:
                    mm_embeds_item = encoder_output[start_idx:end_idx]
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                req_start_pos = req_start_idx + start_pos - num_computed_tokens
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                # OR mask for overlapping mm_features (use_audio_in_video)
                if is_embed is None:
                    is_mm_embed[req_start_pos + start_idx : req_start_pos + end_idx] = (
                        True
                    )
                else:
                    is_mm_embed[
                        req_start_pos + start_idx : req_start_pos + end_idx
                    ] |= is_embed
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                mm_embeds_req.append(mm_embeds_item)

            if self.is_multimodal_pruning_enabled and self.uses_mrope:
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                assert req_state.mrope_positions is not None
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                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,
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                    )
                )
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                req_state.mrope_positions.copy_(new_mrope_positions)
                req_state.mrope_position_delta = new_delta

            mm_embeds.extend(mm_embeds_req)
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            req_start_idx += num_scheduled_tokens

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        if should_sync_mrope_positions:
            self._calc_mrope_positions(scheduler_output)
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            self.mrope_positions.copy_to_gpu(total_num_scheduled_tokens)
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        if should_sync_xdrope_positions:
            self._calc_xdrope_positions(scheduler_output)
            self.xdrope_positions.copy_to_gpu(total_num_scheduled_tokens)

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        return mm_embeds, is_mm_embed
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    def get_model(self) -> nn.Module:
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        if not hasattr(self, "model"):
            raise ValueError("Cannot get model before model has been initialized")
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        if isinstance(self.model, (CUDAGraphWrapper, UBatchWrapper)):
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            # get raw model out of the cudagraph wrapper.
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            return self.model.unwrap()
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        return self.model

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    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")

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        if supports_realtime(model):
            supported_tasks.append("realtime")

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        return supported_tasks

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

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        return list(model.pooler.get_supported_tasks())
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    def 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)

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    def sync_and_slice_intermediate_tensors(
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        self,
        num_tokens: int,
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        intermediate_tensors: IntermediateTensors | None,
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        sync_self: bool,
    ) -> IntermediateTensors:
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        assert self.intermediate_tensors is not None

        tp = self.vllm_config.parallel_config.tensor_parallel_size
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        is_rs = is_residual_scattered_for_sp(self.vllm_config, num_tokens)
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        # 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():
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                is_scattered = k == "residual" and is_rs
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                copy_len = num_tokens // tp if is_scattered else num_tokens
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                self.intermediate_tensors[k][:copy_len].copy_(
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                    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:
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        """
        Step for the EPLB (Expert Parallelism Load Balancing) state.
        """
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        if not self.parallel_config.enable_eplb or self.eep_eplb_suppressed:
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            return

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

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    def setup_eplb_from_mapping(
        self,
        expanded_physical_to_logical: torch.Tensor,
        old_num_physical_experts: int,
    ) -> None:
        model = self.get_model()
        assert is_mixture_of_experts(model)

        self.eplb_state = EplbState.from_mapping(
            model=model,
            model_config=self.model_config,
            device=self.device,
            parallel_config=self.parallel_config,
            expanded_physical_to_logical=expanded_physical_to_logical,
            num_valid_physical_experts=old_num_physical_experts,
        )

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    def _pool(
        self,
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
        num_scheduled_tokens_np: np.ndarray,
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        kv_connector_output: KVConnectorOutput | None,
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput:
        num_reqs = self.input_batch.num_reqs
        assert num_reqs == len(self.input_batch.pooling_params), (
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            "Either all or none of the requests in a batch must be pooling request"
        )
3137

3138
        hidden_states = hidden_states[:num_scheduled_tokens]
3139
        seq_lens_cpu = self.optimistic_seq_lens_cpu[:num_reqs]
3140

3141
        pooling_metadata = self.input_batch.get_pooling_metadata()
3142
        pooling_metadata.build_pooling_cursor(
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            num_scheduled_tokens_np,
            seq_lens_cpu,
            device=hidden_states.device,
            query_start_loc_gpu=self.query_start_loc.gpu[: num_reqs + 1],
3147
        )
3148

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        model = cast(VllmModelForPooling, self.model)
        raw_pooler_output: PoolerOutput = model.pooler(
3151
            hidden_states=hidden_states, pooling_metadata=pooling_metadata
3152
        )
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        finished_mask = [
            seq_len == prompt_len
            for seq_len, prompt_len in zip(seq_lens_cpu, pooling_metadata.prompt_lens)
        ]
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        raw_pooler_output = self.late_interaction_runner.postprocess_pooler_output(
            raw_pooler_output=raw_pooler_output,
            pooling_params=pooling_metadata.pooling_params,
            req_ids=self.input_batch.req_ids,
            finished_mask=finished_mask,
        )
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        model_runner_output = ModelRunnerOutput(
            req_ids=self.input_batch.req_ids.copy(),
            req_id_to_index=self.input_batch.req_id_to_index.copy(),
            kv_connector_output=kv_connector_output,
        )

        if raw_pooler_output is None or not any(finished_mask):
            model_runner_output.pooler_output = [None] * num_reqs
            return model_runner_output

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        if not current_platform.is_cuda_alike():
            # cpu/xpu runners cannot use the CUDA stream/event-based wrapper.
            model_runner_output.pooler_output = _copy_pooler_output_to_cpu(
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                raw_pooler_output=raw_pooler_output,
                finished_mask=finished_mask,
            )
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            self._sync_device()
            return model_runner_output
3183

3184
3185
        return AsyncGPUPoolingModelRunnerOutput(
            model_runner_output=model_runner_output,
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            raw_pooler_output=raw_pooler_output,
            finished_mask=finished_mask,
3188
            async_output_copy_stream=self._get_or_create_async_output_copy_stream(),
3189
        )
3190

3191
    def _pad_for_sequence_parallelism(self, num_scheduled_tokens: int) -> int:
3192
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        # Pad tokens to multiple of tensor_parallel_size when
        # enabled collective fusion for SP
        tp_size = self.vllm_config.parallel_config.tensor_parallel_size
3195
        if self.compilation_config.pass_config.enable_sp and tp_size > 1:
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            return round_up(num_scheduled_tokens, tp_size)
        return num_scheduled_tokens

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Patrick von Platen committed
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    def _prepare_mm_inputs(
        self, num_tokens: int
    ) -> tuple[torch.Tensor | None, torch.Tensor]:
        if self.model.requires_raw_input_tokens:
            input_ids = self.input_ids.gpu[:num_tokens]
        else:
            input_ids = None

        inputs_embeds = self.inputs_embeds.gpu[:num_tokens]
        return input_ids, inputs_embeds

3210
    def _preprocess(
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        self,
        scheduler_output: "SchedulerOutput",
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        num_input_tokens: int,  # Padded
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        intermediate_tensors: IntermediateTensors | None = None,
3215
    ) -> tuple[
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        torch.Tensor | None,
        torch.Tensor | None,
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        torch.Tensor,
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        IntermediateTensors | None,
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        dict[str, Any],
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        ECConnectorOutput | None,
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    ]:
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        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
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        is_first_rank = get_pp_group().is_first_rank
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        is_encoder_decoder = self.model_config.is_encoder_decoder
3226

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        # _prepare_inputs may reorder the batch, so we must gather multi
        # modal outputs after that to ensure the correct order
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        ec_connector_output = None

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        if self.supports_mm_inputs and is_first_rank and not is_encoder_decoder:
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            # Run the multimodal encoder if any.
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            with self.maybe_get_ec_connector_output(
                scheduler_output,
                encoder_cache=self.encoder_cache,
            ) as ec_connector_output:
                self._execute_mm_encoder(scheduler_output)
                mm_embeds, is_mm_embed = self._gather_mm_embeddings(scheduler_output)
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            # NOTE(woosuk): To unify token ids and soft tokens (vision
            # embeddings), we always use embeddings (rather than token ids)
            # as input to the multimodal model, even when the input is text.
3243
            inputs_embeds_scheduled = self.model.embed_input_ids(
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                self.input_ids.gpu[:num_scheduled_tokens],
                multimodal_embeddings=mm_embeds,
                is_multimodal=is_mm_embed,
3247
            )
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3249
            # TODO(woosuk): Avoid the copy. Optimize.
3250
            self.inputs_embeds.gpu[:num_scheduled_tokens].copy_(inputs_embeds_scheduled)
3251

Patrick von Platen's avatar
Patrick von Platen committed
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            input_ids, inputs_embeds = self._prepare_mm_inputs(num_input_tokens)
3253
            model_kwargs = {
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                **self._init_model_kwargs(),
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                **self._extract_mm_kwargs(scheduler_output),
            }
3257
        elif self.enable_prompt_embeds and is_first_rank:
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            # 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).
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            token_ids_idx = (
                self.is_token_ids.gpu[:num_scheduled_tokens]
                .nonzero(as_tuple=False)
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                .squeeze(1)
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            )
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            # Some tokens ids may need to become embeds
            if token_ids_idx.numel() > 0:
                token_ids = self.input_ids.gpu[token_ids_idx]
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                tokens_to_embeds = self.model.embed_input_ids(input_ids=token_ids)
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                self.inputs_embeds.gpu[token_ids_idx] = tokens_to_embeds

            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
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            model_kwargs = self._init_model_kwargs()
3283
            input_ids = None
3284
        else:
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            # For text-only models, we use token ids as input.
            # While it is possible to use embeddings as input just like the
            # multimodal models, it is not desirable for performance since
            # then the embedding layer is not included in the CUDA graph.
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            input_ids = self.input_ids.gpu[:num_input_tokens]
3290
            inputs_embeds = None
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            model_kwargs = self._init_model_kwargs()
3292

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        if self.uses_mrope:
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            positions = self.mrope_positions.gpu[:, :num_input_tokens]
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        elif self.uses_xdrope_dim > 0:
            positions = self.xdrope_positions.gpu[:, :num_input_tokens]
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        else:
3298
            positions = self.positions[:num_input_tokens]
3299
            if num_input_tokens > num_scheduled_tokens:
3300
                self.positions[num_scheduled_tokens:num_input_tokens].zero_()
3301

3302
        if is_first_rank:
3303
3304
            intermediate_tensors = None
        else:
3305
            assert intermediate_tensors is not None
3306
            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
3307
3308
                num_input_tokens, intermediate_tensors, True
            )
3309

3310
        if is_encoder_decoder and scheduler_output.scheduled_encoder_inputs:
3311
3312
3313
3314
3315
3316
3317
            # Run the encoder, just like we do with other multimodal inputs.
            # For an encoder-decoder model, our processing here is a bit
            # simpler, because the outputs are just passed to the decoder.
            # We are not doing any prompt replacement. We also will only
            # ever have a single encoder input.
            encoder_outputs = self._execute_mm_encoder(scheduler_output)
            model_kwargs.update({"encoder_outputs": encoder_outputs})
3318

3319
3320
3321
3322
3323
3324
        return (
            input_ids,
            inputs_embeds,
            positions,
            intermediate_tensors,
            model_kwargs,
3325
            ec_connector_output,
3326
        )
3327

3328
    def _sample(
3329
        self,
3330
3331
        logits: torch.Tensor | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
3332
    ) -> SamplerOutput:
3333
        # Sample the next token and get logprobs if needed.
3334
        sampling_metadata = self.input_batch.sampling_metadata
3335
3336
3337
        # 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()
3338
        if spec_decode_metadata is None:
3339
            return self.sampler(
3340
3341
3342
                logits=logits,
                sampling_metadata=sampling_metadata,
            )
3343

3344
3345
3346
3347
3348
3349
        # Update spec_token_ids with real draft tokens from pre step only when
        # output_token_ids is needed (penalties or bad_words are in use).
        if self.use_async_scheduling and self._draft_token_req_ids is not None:
            draft_token_ids_cpu, _ = self._get_draft_token_ids_cpu()
            self.input_batch.update_async_spec_token_ids(draft_token_ids_cpu)

3350
        sampler_output = self.rejection_sampler(
3351
3352
            spec_decode_metadata,
            None,  # draft_probs
3353
            logits,
3354
3355
            sampling_metadata,
        )
3356
3357
3358
        return sampler_output

    def _bookkeeping_sync(
3359
3360
3361
        self,
        scheduler_output: "SchedulerOutput",
        sampler_output: SamplerOutput,
3362
        logits: torch.Tensor | None,
3363
3364
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
3365
    ) -> tuple[
3366
        dict[str, int],
3367
        LogprobsLists | None,
3368
        list[list[int]],
3369
        dict[str, LogprobsTensors | None],
3370
3371
3372
        list[str],
        dict[str, int],
        list[int],
3373
    ]:
3374
3375
3376
3377
        num_nans_in_logits = {}
        if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
            num_nans_in_logits = self._get_nans_in_logits(logits)

3378
3379
3380
3381
        num_reqs = self.input_batch.num_reqs
        discard_sampled_tokens_req_indices = np.nonzero(
            self.discard_request_mask.np[:num_reqs]
        )[0]
3382
3383
3384
3385
        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)
3386

3387
3388
3389
        # 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()
3390
        req_id_to_index_output_copy = self.input_batch.req_id_to_index.copy()
3391
3392

        num_sampled_tokens = sampler_output.sampled_token_ids.shape[0]
3393
        sampled_token_ids = sampler_output.sampled_token_ids
3394
        logprobs_tensors = sampler_output.logprobs_tensors
3395
        invalid_req_indices = []
3396
        logprobs_lists = None
3397
3398
3399
3400
3401
3402
        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)
3403
3404
3405
                # Mask out the sampled tokens that should not be sampled.
                for i in discard_sampled_tokens_req_indices:
                    valid_sampled_token_ids[int(i)].clear()
3406
3407
3408

                if logprobs_tensors is not None:
                    logprobs_lists = logprobs_tensors.tolists()
3409
3410
            else:
                # Includes spec decode tokens.
3411
                valid_sampled_token_ids, logprobs_lists = RejectionSampler.parse_output(
3412
3413
                    sampled_token_ids,
                    self.input_batch.vocab_size,
3414
                    discard_sampled_tokens_req_indices,
3415
                    logprobs_tensors=logprobs_tensors,
3416
                )
3417
        else:
3418
            valid_sampled_token_ids = []
3419
            invalid_req_indices = discard_sampled_tokens_req_indices.tolist()
3420
3421
3422
3423
3424
            invalid_req_indices_set = set(invalid_req_indices)

            # 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.
3425
3426
3427
3428
            # With spec decoding, this is done in propose_draft_token_ids().
            if self.input_batch.prev_sampled_token_ids is None:
                assert sampled_token_ids.shape[-1] == 1
                self.input_batch.prev_sampled_token_ids = sampled_token_ids
3429
3430
3431
3432
3433
            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
            }
3434

3435
3436
3437
3438
3439
        # 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.
3440
        req_ids = self.input_batch.req_ids
3441
3442
        for req_idx in range(num_sampled_tokens):
            if self.use_async_scheduling:
3443
                sampled_ids = [-1] if req_idx not in invalid_req_indices_set else None
3444
3445
            else:
                sampled_ids = valid_sampled_token_ids[req_idx]
3446

3447
            num_sampled_ids: int = len(sampled_ids) if sampled_ids else 0
3448

3449
            if not sampled_ids:
3450
3451
3452
                continue

            start_idx = self.input_batch.num_tokens_no_spec[req_idx]
3453
            end_idx = start_idx + num_sampled_ids
3454
3455
3456
3457
            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}"
3458
            )
3459

3460
3461
            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
3462
            self.input_batch.num_tokens_no_spec[req_idx] = end_idx
3463

3464
            req_id = req_ids[req_idx]
3465
3466
3467
            req_state = self.requests[req_id]
            req_state.output_token_ids.extend(sampled_ids)

3468
3469
3470
3471
3472
3473
        # Compute prompt logprobs if needed.
        prompt_logprobs_dict = self._get_prompt_logprobs_dict(
            hidden_states[:num_scheduled_tokens],
            scheduler_output.num_scheduled_tokens,
        )

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

3484
3485
3486
3487
3488
3489
3490
3491
3492
3493
3494
3495
3496
3497
3498
    @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()

3499
3500
    def _model_forward(
        self,
3501
3502
3503
3504
        input_ids: torch.Tensor | None = None,
        positions: torch.Tensor | None = None,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
3505
3506
3507
3508
3509
        **model_kwargs: dict[str, Any],
    ) -> Any:
        """Helper method to call the model forward pass.

        This method can be overridden by subclasses for model execution.
3510
        Motivation: We can inspect only this method versus
3511
3512
3513
3514
3515
3516
3517
3518
3519
3520
3521
3522
3523
3524
3525
3526
3527
3528
3529
3530
        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,
        )

3531
3532
3533
3534
3535
3536
3537
3538
3539
3540
3541
3542
3543
3544
3545
3546
3547
3548
3549
3550
3551
    @staticmethod
    def _is_uniform_decode(
        max_num_scheduled_tokens: int,
        uniform_decode_query_len: int,
        num_tokens: int,
        num_reqs: int,
        force_uniform_decode: bool | None = None,
    ) -> bool:
        """
        Checks if it's a decode batch with same amount scheduled tokens
        across all requests.
        """
        return (
            (
                (max_num_scheduled_tokens == uniform_decode_query_len)
                and (num_tokens == max_num_scheduled_tokens * num_reqs)
            )
            if force_uniform_decode is None
            else force_uniform_decode
        )

3552
3553
3554
3555
3556
3557
3558
3559
3560
3561
3562
3563
3564
    def _determine_batch_execution_and_padding(
        self,
        num_tokens: int,
        num_reqs: int,
        num_scheduled_tokens_np: np.ndarray,
        max_num_scheduled_tokens: int,
        use_cascade_attn: bool,
        allow_microbatching: bool = True,
        force_eager: bool = False,
        # For cudagraph capture TODO(lucas): Refactor how we capture cudagraphs (will
        # be improved in model runner v2)
        force_uniform_decode: bool | None = None,
        force_has_lora: bool | None = None,
3565
        force_num_active_loras: int | None = None,
3566
        num_encoder_reqs: int = 0,
3567
    ) -> tuple[
3568
3569
        CUDAGraphMode,
        BatchDescriptor,
3570
        bool,
3571
3572
        torch.Tensor | None,
        CUDAGraphStat | None,
3573
    ]:
3574
3575
3576
3577
3578
3579
        uniform_decode = self._is_uniform_decode(
            max_num_scheduled_tokens=max_num_scheduled_tokens,
            uniform_decode_query_len=self.uniform_decode_query_len,
            num_tokens=num_tokens,
            num_reqs=num_reqs,
            force_uniform_decode=force_uniform_decode,
3580
        )
3581
3582
3583
3584
3585
        # Encoder-decoder models only support CG for decoder_step > 0 (no enc_output
        # is present). Also, chunked-prefill is disabled, so batch are uniform.
        has_encoder_output = (
            self.model_config.is_encoder_decoder and num_encoder_reqs > 0
        )
3586

3587
3588
3589
3590
3591
        # Compute LoRA state for cudagraph dispatch
        num_active_loras = (
            force_num_active_loras
            if force_num_active_loras is not None
            else len(self.input_batch.lora_id_to_lora_request)
3592
        )
3593
        has_lora = num_active_loras > 0 if force_has_lora is None else force_has_lora
3594

3595
        num_tokens_padded = self._pad_for_sequence_parallelism(num_tokens)
3596
3597
3598

        def dispatch_cudagraph(num_tokens, disable_full=False, valid_modes=None):
            return self.cudagraph_dispatcher.dispatch(
3599
3600
3601
                num_tokens=num_tokens,
                has_lora=has_lora,
                uniform_decode=uniform_decode,
3602
                num_active_loras=num_active_loras,
3603
3604
                valid_modes={CUDAGraphMode.NONE} if force_eager else valid_modes,
                invalid_modes={CUDAGraphMode.FULL} if disable_full else None,
3605
3606
            )

3607
        cudagraph_mode, batch_descriptor = dispatch_cudagraph(
3608
            num_tokens_padded, disable_full=use_cascade_attn or has_encoder_output
3609
        )
3610
        num_tokens_padded = batch_descriptor.num_tokens
3611
3612
3613
3614
3615
3616
3617
3618
3619
        if self.compilation_config.pass_config.enable_sp:
            assert (
                batch_descriptor.num_tokens
                % self.vllm_config.parallel_config.tensor_parallel_size
                == 0
            ), (
                "Sequence parallelism requires num_tokens to be "
                "a multiple of tensor parallel size"
            )
3620
3621
3622

        # Extra coordination when running data-parallel since we need to coordinate
        # across ranks
3623
        should_ubatch, num_tokens_across_dp = False, None
3624
        if self.vllm_config.parallel_config.data_parallel_size > 1:
3625
3626
3627
3628
3629
3630
3631
3632
3633
            should_ubatch, num_tokens_across_dp, synced_cudagraph_mode = (
                coordinate_batch_across_dp(
                    num_tokens_unpadded=num_tokens,
                    parallel_config=self.parallel_config,
                    allow_microbatching=allow_microbatching,
                    num_tokens_padded=num_tokens_padded,
                    uniform_decode=uniform_decode,
                    cudagraph_mode=cudagraph_mode.value,
                )
3634
3635
            )

3636
            # Extract DP-synced values
3637
3638
3639
            if num_tokens_across_dp is not None:
                dp_rank = self.parallel_config.data_parallel_rank
                num_tokens_padded = int(num_tokens_across_dp[dp_rank].item())
3640
3641
3642
                # Re-dispatch with DP padding so we have the correct batch_descriptor
                cudagraph_mode, batch_descriptor = dispatch_cudagraph(
                    num_tokens_padded,
3643
                    valid_modes={CUDAGraphMode(synced_cudagraph_mode)},
3644
                )
3645
3646
3647
3648
                # Assert to make sure the agreed upon token count is correct otherwise
                # num_tokens_across_dp will no-longer be valid
                assert batch_descriptor.num_tokens == num_tokens_padded

3649
3650
3651
3652
3653
3654
3655
3656
3657
3658
3659
3660
        cudagraph_stats = None
        if self.vllm_config.observability_config.cudagraph_metrics:
            cudagraph_stats = CUDAGraphStat(
                num_unpadded_tokens=num_tokens,
                num_padded_tokens=batch_descriptor.num_tokens,
                num_paddings=batch_descriptor.num_tokens - num_tokens,
                runtime_mode=str(cudagraph_mode),
            )

        return (
            cudagraph_mode,
            batch_descriptor,
3661
            should_ubatch,
3662
3663
3664
            num_tokens_across_dp,
            cudagraph_stats,
        )
3665

3666
3667
3668
3669
3670
3671
3672
3673
3674
3675
3676
3677
3678
3679
3680
3681
3682
3683
3684
3685
3686
3687
3688
3689
3690
3691
3692
3693
3694
3695
3696
3697
3698
3699
3700
3701
    def _register_layerwise_nvtx_hooks(self) -> None:
        """
        Register layerwise NVTX hooks if --enable-layerwise-nvtx-tracing is enabled
        to trace detailed information of each layer or module in the model.
        """

        if (
            self.vllm_config.observability_config.enable_layerwise_nvtx_tracing
            and not self.layerwise_nvtx_hooks_registered
        ):
            if self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE:
                logger.debug_once(
                    "layerwise NVTX tracing is not supported when CUDA graph is "
                    "turned off; you may observe part or all of the model "
                    "missing NVTX markers"
                )

            # In STOCK_TORCH_COMPILE mode, after registering hooks here,
            # the __call__ function of nn.module will be recompiled with
            # fullgraph=True. Since nvtx.range_push/pop are not traceable
            # by torch dynamo, we can't register hook functions here
            # because hook functions will also be traced by torch dynamo.
            if (
                self.vllm_config.compilation_config.mode
                == CompilationMode.STOCK_TORCH_COMPILE
            ):
                logger.debug_once(
                    "layerwise NVTX tracing is not supported when "
                    "CompilationMode is STOCK_TORCH_COMPILE, skipping "
                    "function hooks registration"
                )
            else:
                pyt_hooks = PytHooks()
                pyt_hooks.register_hooks(self.model, self.model.__class__.__name__)
                self.layerwise_nvtx_hooks_registered = True

3702
3703
3704
3705
3706
3707
3708
3709
3710
3711
3712
3713
3714
3715
3716
3717
3718
3719
3720
3721
3722
3723
3724
3725
3726
3727
3728
3729
3730
3731
3732
3733
3734
3735
3736
3737
3738
3739
3740
3741
3742
3743
3744
3745
3746
3747
3748
3749
3750
3751
3752
3753
3754
3755
3756
3757
3758
3759
3760
3761
3762
3763
3764
3765
3766
3767
3768
3769
3770
3771
3772
3773
3774
3775
    def _get_slot_mappings(
        self,
        num_tokens_padded: int,
        num_reqs_padded: int,
        num_tokens_unpadded: int,
        ubatch_slices: "UBatchSlices | None" = None,
    ) -> tuple[
        dict[int, torch.Tensor] | None,
        dict[str, torch.Tensor] | list[dict[str, torch.Tensor]] | None,
    ]:
        """
        Build slot mappings in both formats needed by the system.

        Args:
            num_tokens_padded: Total number of tokens (padded)
            num_reqs_padded: Total number of requests (padded)
            num_tokens_unpadded: Actual number of tokens (unpadded)
            ubatch_slices: Optional ubatch slicing info for DBO

        Returns:
            A tuple of:
            - slot_mappings_by_gid: dict[int, torch.Tensor] for attention metadata
            - slot_mappings_by_layer: dict[str, torch.Tensor] or list for ForwardContext
        """
        if not (
            hasattr(self, "kv_cache_config")
            and self.kv_cache_config is not None
            and len(self.kv_cache_config.kv_cache_groups) > 0
        ):
            return None, None

        def _get_slot_mapping(kv_cache_gid: int):
            assert num_reqs_padded is not None and num_tokens_padded is not None
            kv_cache_spec = self.kv_cache_config.kv_cache_groups[
                kv_cache_gid
            ].kv_cache_spec
            if isinstance(kv_cache_spec, EncoderOnlyAttentionSpec):
                slot_mapping = torch.zeros(
                    (num_tokens_padded,),
                    dtype=torch.int64,
                    device=self.device,
                )
            else:
                blk_table = self.input_batch.block_table[kv_cache_gid]
                slot_mapping = blk_table.slot_mapping.gpu[:num_tokens_padded]

            # Fill unused with -1. Needed for reshape_and_cache in full cuda
            # graph mode. `blk_table_tensor` -1 to match mamba PAD_SLOT_ID
            slot_mapping[num_tokens_unpadded:num_tokens_padded].fill_(-1)

            return slot_mapping

        slot_mappings_by_gid = {
            gid: _get_slot_mapping(gid)
            for gid, _ in enumerate(self.kv_cache_config.kv_cache_groups)
        }

        slot_mappings_by_layer: dict[str, torch.Tensor] = {}
        for gid, kv_cache_group in enumerate(self.kv_cache_config.kv_cache_groups):
            slot_mapping = slot_mappings_by_gid[gid]
            for layer_name in kv_cache_group.layer_names:
                slot_mappings_by_layer[layer_name] = slot_mapping

        if ubatch_slices is not None:
            result: list[dict[str, torch.Tensor]] = []
            for ubatch in ubatch_slices:
                sliced_mappings: dict[str, torch.Tensor] = {}
                for layer_name, slot_mapping in slot_mappings_by_layer.items():
                    sliced_mappings[layer_name] = slot_mapping[ubatch.token_slice]
                result.append(sliced_mappings)
            return slot_mappings_by_gid, result

        return slot_mappings_by_gid, slot_mappings_by_layer

3776
3777
3778
3779
3780
3781
3782
3783
3784
    def _is_all_reqs_chunked_prefill(self) -> bool:
        """Check if all scheduled requests are marked to discard sampled tokens.

        This is true when `discard_request_mask` is set for every scheduled
        request (e.g., for chunked prefill requests that are not the last
        prefill chunk)."""
        num_reqs = self.input_batch.num_reqs
        return bool(self.discard_request_mask.np[:num_reqs].all())

3785
3786
3787
3788
    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
3789
        intermediate_tensors: IntermediateTensors | None = None,
3790
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput | IntermediateTensors | None:
3791
3792
3793
3794
3795
        if self.execute_model_state is not None:
            raise RuntimeError(
                "State error: sample_tokens() must be called "
                "after execute_model() returns None."
            )
3796

3797
        if self.routed_experts_initialized:
3798
3799
3800
3801
3802
3803
            capturer = RoutedExpertsCapturer.get_instance()
            if capturer is not None:
                capturer.clear_buffer()  # noqa
            else:
                logger.error("RoutedExpertsCapturer not initialized.")

3804
3805
3806
3807
3808
3809
3810
3811
3812
3813
3814
3815
3816
3817
3818
3819
3820
        # If ngram_gpu is used, we need to copy the scheduler_output to avoid
        # the modification has influence on the scheduler_output in engine core process.
        # The replace is much faster than deepcopy.
        if (
            self.speculative_config is not None
            and self.speculative_config.use_ngram_gpu()
        ):
            num_scheduled_tokens_copy = scheduler_output.num_scheduled_tokens.copy()
            spec_decode_tokens_copy = (
                scheduler_output.scheduled_spec_decode_tokens.copy()
            )
            scheduler_output = replace(
                scheduler_output,
                num_scheduled_tokens=num_scheduled_tokens_copy,
                scheduled_spec_decode_tokens=spec_decode_tokens_copy,
            )

3821
3822
3823
3824
        if has_kv_transfer_group():
            kv_connector_metadata = scheduler_output.kv_connector_metadata
            assert kv_connector_metadata is not None
            get_kv_transfer_group().handle_preemptions(kv_connector_metadata)
3825

3826
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
3827
3828
3829
3830
3831
        with (
            record_function_or_nullcontext("gpu_model_runner: preprocess"),
            self.synchronize_input_prep(),
        ):
            # Update persistent batch states.
3832
            deferred_state_corrections_fn = self._update_states(scheduler_output)
3833

3834
            if has_ec_transfer() and not get_ec_transfer().is_consumer:
3835
                with self.maybe_get_ec_connector_output(
3836
                    scheduler_output,
3837
3838
3839
3840
3841
3842
3843
3844
3845
3846
3847
3848
3849
3850
3851
3852
3853
3854
3855
3856
3857
3858
3859
3860
3861
3862
3863
3864
                    encoder_cache=self.encoder_cache,
                ) as ec_connector_output:
                    self._execute_mm_encoder(scheduler_output)
                    return make_empty_encoder_model_runner_output(scheduler_output)

            if not num_scheduled_tokens:
                if (
                    self.parallel_config.distributed_executor_backend
                    == "external_launcher"
                    and self.parallel_config.data_parallel_size > 1
                ):
                    # this is a corner case when both external launcher
                    # and DP are enabled, num_scheduled_tokens could be
                    # 0, and has_unfinished_requests in the outer loop
                    # returns True. before returning early here we call
                    # dummy run to ensure coordinate_batch_across_dp
                    # is called into to avoid out of sync issues.
                    self._dummy_run(1)
                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(scheduler_output, self.vllm_config)

            if self.cache_config.kv_sharing_fast_prefill:
                assert not self.num_prompt_logprobs, (
                    "--kv-sharing-fast-prefill produces incorrect "
                    "logprobs for prompt tokens, tokens, please disable "
                    "it when the requests need prompt logprobs"
3865
3866
                )

3867
3868
3869
3870
3871
3872
            num_reqs = self.input_batch.num_reqs
            req_ids = self.input_batch.req_ids
            tokens = [scheduler_output.num_scheduled_tokens[i] for i in req_ids]
            num_scheduled_tokens_np = np.array(tokens, dtype=np.int32)
            max_num_scheduled_tokens = int(num_scheduled_tokens_np.max())
            num_tokens_unpadded = scheduler_output.total_num_scheduled_tokens
3873

3874
3875
3876
3877
            logits_indices, spec_decode_metadata = self._prepare_inputs(
                scheduler_output,
                num_scheduled_tokens_np,
            )
3878

3879
3880
3881
3882
3883
            cascade_attn_prefix_lens = None
            # Disable cascade attention when using microbatching (DBO)
            if self.cascade_attn_enabled and not self.parallel_config.use_ubatching:
                # Pre-compute cascade attention prefix lengths
                cascade_attn_prefix_lens = self._compute_cascade_attn_prefix_lens(
3884
                    num_scheduled_tokens_np,
3885
3886
                    self.input_batch.num_computed_tokens_cpu[:num_reqs],
                    scheduler_output.num_common_prefix_blocks,
3887
3888
                )

3889
3890
3891
3892
3893
3894
3895
3896
3897
3898
3899
3900
3901
3902
            (
                cudagraph_mode,
                batch_desc,
                should_ubatch,
                num_tokens_across_dp,
                cudagraph_stats,
            ) = self._determine_batch_execution_and_padding(
                num_tokens=num_tokens_unpadded,
                num_reqs=num_reqs,
                num_scheduled_tokens_np=num_scheduled_tokens_np,
                max_num_scheduled_tokens=max_num_scheduled_tokens,
                use_cascade_attn=cascade_attn_prefix_lens is not None,
                num_encoder_reqs=len(scheduler_output.scheduled_encoder_inputs),
            )
3903

3904
3905
3906
3907
3908
3909
3910
3911
3912
3913
3914
3915
3916
3917
3918
3919
3920
3921
3922
3923
3924
3925
3926
3927
3928
3929
3930
            logger.debug(
                "Running batch with cudagraph_mode: %s, batch_descriptor: %s, "
                "should_ubatch: %s, num_tokens_across_dp: %s",
                cudagraph_mode,
                batch_desc,
                should_ubatch,
                num_tokens_across_dp,
            )

            num_tokens_padded = batch_desc.num_tokens
            num_reqs_padded = (
                batch_desc.num_reqs if batch_desc.num_reqs is not None else num_reqs
            )
            ubatch_slices, ubatch_slices_padded = maybe_create_ubatch_slices(
                should_ubatch,
                num_scheduled_tokens_np,
                num_tokens_padded,
                num_reqs_padded,
                self.parallel_config.num_ubatches,
            )

            logger.debug(
                "ubatch_slices: %s, ubatch_slices_padded: %s",
                ubatch_slices,
                ubatch_slices_padded,
            )

3931
3932
3933
3934
3935
3936
3937
3938
3939
3940
3941
            # True if any attention backend handles KV cache update separately
            # from forward() (i.e., forward_includes_kv_cache_update=False). When true,
            # slot_mappings must use padded dimensions to match the key/value tensors.
            has_separate_kv_update = not all(
                all(
                    g.backend.forward_includes_kv_cache_update
                    for g in self.attn_groups[id]
                )
                for id, spec in enumerate(self.kv_cache_config.kv_cache_groups)
                if not isinstance(spec.kv_cache_spec, EncoderOnlyAttentionSpec)
            )
3942
3943
            pad_attn = cudagraph_mode == CUDAGraphMode.FULL

3944
            if self.cache_config.mamba_cache_mode == "align":
3945
3946
3947
3948
3949
3950
                # preprocess_mamba reads req_state.num_computed_tokens (CPU)
                # to decide copy operations, so we must apply deferred
                # corrections before it runs.
                if deferred_state_corrections_fn:
                    deferred_state_corrections_fn()
                    deferred_state_corrections_fn = None
3951
3952
3953
3954
3955
3956
3957
3958
3959
                mamba_utils.preprocess_mamba(
                    scheduler_output,
                    self.kv_cache_config,
                    self.cache_config,
                    self.mamba_state_idx,
                    self.input_batch,
                    self.requests,
                    self.compilation_config.static_forward_context,
                    self.model.get_mamba_state_copy_func(),
3960
                    self._get_mamba_copy_bufs(),
3961
                )
3962
3963
3964
3965
3966
3967
3968
3969
                # preprocess_mamba resets num_accepted_tokens_cpu to 1
                # for requests whose state was copied to a new block.
                # Re-sync to GPU so the mamba kernel reads from the
                # correct initial state slot (init_token_idx = 0).
                self.num_accepted_tokens.np[:num_reqs] = (
                    self.input_batch.num_accepted_tokens_cpu[:num_reqs]
                )
                self.num_accepted_tokens.copy_to_gpu(num_reqs)
3970

3971
3972
3973
            use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
            ubatch_slices_attn = ubatch_slices_padded if pad_attn else ubatch_slices

3974
3975
3976
3977
3978
3979
3980
3981
3982
3983
3984
            slot_mappings_by_group, slot_mappings = self._get_slot_mappings(
                num_tokens_padded=num_tokens_padded
                if pad_attn or has_separate_kv_update
                else num_tokens_unpadded,
                num_reqs_padded=(
                    num_reqs_padded if pad_attn or has_separate_kv_update else num_reqs
                ),
                num_tokens_unpadded=num_tokens_unpadded,
                ubatch_slices=ubatch_slices_padded,
            )

3985
3986
3987
3988
3989
3990
3991
3992
3993
3994
3995
3996
            attn_metadata, spec_decode_common_attn_metadata = (
                self._build_attention_metadata(
                    num_tokens=num_tokens_unpadded,
                    num_tokens_padded=num_tokens_padded if pad_attn else None,
                    num_reqs=num_reqs,
                    num_reqs_padded=num_reqs_padded if pad_attn else None,
                    max_query_len=max_num_scheduled_tokens,
                    ubatch_slices=ubatch_slices_attn,
                    logits_indices=logits_indices,
                    use_spec_decode=use_spec_decode,
                    num_scheduled_tokens=scheduler_output.num_scheduled_tokens,
                    cascade_attn_prefix_lens=cascade_attn_prefix_lens,
3997
                    slot_mappings=slot_mappings_by_group,
3998
                )
3999
            )
4000

4001
4002
4003
4004
4005
4006
4007
4008
4009
            (
                input_ids,
                inputs_embeds,
                positions,
                intermediate_tensors,
                model_kwargs,
                ec_connector_output,
            ) = self._preprocess(
                scheduler_output, num_tokens_padded, intermediate_tensors
4010
            )
4011

4012
        # Set cudagraph mode to none if calc_kv_scales is true.
4013
4014
4015
        # KV scales calculation involves dynamic operations that are incompatible
        # with CUDA graph capture.
        if self.calculate_kv_scales:
4016
            cudagraph_mode = CUDAGraphMode.NONE
4017
4018
            # Mark KV scales as calculated after the first forward pass
            self.calculate_kv_scales = False
4019

4020
4021
4022
4023
4024
4025
4026
        # Encoder-decoder models can only compile the pure decode steps where no
        # encoder inputs are present. Use eager for the first pass.
        num_encoder_reqs = len(scheduler_output.scheduled_encoder_inputs)
        has_encoder_input = (
            self.model_config.is_encoder_decoder and num_encoder_reqs > 0
        )

4027
4028
        # Run the model.
        # Use persistent buffers for CUDA graphs.
4029
4030
4031
        # When spec decode is enabled, defer connector finalization
        # (wait_for_save + clear metadata) until after draft model runs.
        defer_kv_connector_finalize = self.speculative_config is not None
4032
4033
        with (
            set_forward_context(
4034
4035
                attn_metadata,
                self.vllm_config,
4036
                num_tokens=num_tokens_padded,
4037
                num_tokens_across_dp=num_tokens_across_dp,
4038
4039
                cudagraph_runtime_mode=cudagraph_mode,
                batch_descriptor=batch_desc,
4040
                ubatch_slices=ubatch_slices_padded,
4041
                slot_mapping=slot_mappings,
4042
                skip_compiled=has_encoder_input,
4043
            ),
4044
            record_function_or_nullcontext("gpu_model_runner: forward"),
4045
            self.maybe_get_kv_connector_output(
4046
4047
                scheduler_output,
                defer_finalize=defer_kv_connector_finalize,
4048
            ) as kv_connector_output,
4049
        ):
4050
            model_output = self._model_forward(
4051
4052
4053
4054
4055
4056
4057
                input_ids=input_ids,
                positions=positions,
                intermediate_tensors=intermediate_tensors,
                inputs_embeds=inputs_embeds,
                **model_kwargs,
            )

4058
        with record_function_or_nullcontext("gpu_model_runner: postprocess"):
4059
            if self.use_aux_hidden_state_outputs:
4060
                # True when EAGLE 3 is used.
4061
4062
                hidden_states, aux_hidden_states = model_output
            else:
4063
                # Common case.
4064
4065
4066
                hidden_states = model_output
                aux_hidden_states = None

4067
4068
4069
4070
4071
            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)
4072
                    hidden_states.kv_connector_output = kv_connector_output
4073
                    self.kv_connector_output = kv_connector_output
4074
                    return hidden_states
4075

4076
                if self.is_pooling_model:
4077
                    # Return the pooling output.
4078
4079
4080
4081
4082
                    return self._pool(
                        hidden_states,
                        num_scheduled_tokens,
                        num_scheduled_tokens_np,
                        kv_connector_output,
4083
                    )
4084
4085

                sample_hidden_states = hidden_states[logits_indices]
4086
                logits = self.model.compute_logits(sample_hidden_states)
4087
4088
4089
4090
            else:
                # Rare case.
                assert not self.is_pooling_model

4091
                sample_hidden_states = hidden_states[logits_indices]
4092
                if not get_pp_group().is_last_rank:
4093
                    all_gather_tensors = {
4094
                        "residual": not is_residual_scattered_for_sp(
4095
                            self.vllm_config, num_tokens_padded
4096
                        )
4097
                    }
4098
                    get_pp_group().send_tensor_dict(
4099
4100
                        hidden_states.tensors,
                        all_gather_group=get_tp_group(),
4101
4102
                        all_gather_tensors=all_gather_tensors,
                    )
4103
4104
                    logits = None
                else:
4105
                    logits = self.model.compute_logits(sample_hidden_states)
4106

4107
                model_output_broadcast_data: dict[str, Any] = {}
4108
4109
4110
                if logits is not None:
                    model_output_broadcast_data["logits"] = logits.contiguous()

4111
                broadcasted = get_pp_group().broadcast_tensor_dict(
4112
4113
                    model_output_broadcast_data, src=len(get_pp_group().ranks) - 1
                )
4114
4115
                assert broadcasted is not None
                logits = broadcasted["logits"]
4116

4117
4118
4119
4120
4121
4122
4123
4124
        self.execute_model_state = ExecuteModelState(
            scheduler_output,
            logits,
            spec_decode_metadata,
            spec_decode_common_attn_metadata,
            hidden_states,
            sample_hidden_states,
            aux_hidden_states,
4125
            ec_connector_output,
4126
            cudagraph_stats,
4127
            slot_mappings,
4128
        )
4129
        self.kv_connector_output = kv_connector_output
4130
4131
4132
4133
4134
4135

        # Now the batch has been launched we can wait for corrections from the
        # previous model forward without breaking async scheduling.
        if deferred_state_corrections_fn:
            deferred_state_corrections_fn()

4136
4137
4138
4139
4140
4141
4142
        return None

    @torch.inference_mode
    def sample_tokens(
        self, grammar_output: "GrammarOutput | None"
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput | IntermediateTensors:
        if self.execute_model_state is None:
4143
4144
            kv_connector_output = self.kv_connector_output
            self.kv_connector_output = None
4145
4146
4147
            # receive sampled token ids from the last PP rank.
            if self.use_async_scheduling and get_pp_group().world_size > 1:
                self._pp_receive_prev_sampled_token_ids_to_input_batch()
4148
            if not kv_connector_output:
4149
                return None  # type: ignore[return-value]
4150
4151
4152
4153
4154
4155
4156
4157
4158

            # In case of PP with kv transfer, we need to pass through the
            # kv_connector_output
            if kv_connector_output.is_empty():
                return EMPTY_MODEL_RUNNER_OUTPUT

            output = copy(EMPTY_MODEL_RUNNER_OUTPUT)
            output.kv_connector_output = kv_connector_output
            return output
4159
4160
4161
4162
4163
4164
4165
4166
4167
4168

        # Unpack ephemeral state.
        (
            scheduler_output,
            logits,
            spec_decode_metadata,
            spec_decode_common_attn_metadata,
            hidden_states,
            sample_hidden_states,
            aux_hidden_states,
4169
            ec_connector_output,
4170
            cudagraph_stats,
4171
            slot_mappings,
4172
4173
4174
4175
4176
4177
4178
4179
4180
        ) = self.execute_model_state
        # Clear ephemeral state.
        self.execute_model_state = None

        # Apply structured output bitmasks if present.
        if grammar_output is not None:
            apply_grammar_bitmask(
                scheduler_output, grammar_output, self.input_batch, logits
            )
4181

4182
        with record_function_or_nullcontext("gpu_model_runner: sample"):
4183
4184
            sampler_output = self._sample(logits, spec_decode_metadata)

4185
4186
4187
        self._update_states_after_model_execute(
            sampler_output.sampled_token_ids, scheduler_output
        )
4188
4189
        if self.use_async_scheduling:
            pp = get_pp_group()
4190
4191
4192
4193
            # For torchrun external_launcher PP mode with broadcast_pp_output=True,
            # PP outputs have been broadcasted to all ranks at logits computation.
            # Therefore, here is no need to send sampled token ids again in this case.
            if not self.broadcast_pp_output and pp.world_size > 1 and pp.is_last_rank:
4194
4195
4196
                self._pp_broadcast_prev_sampled_token_ids(
                    sampler_output.sampled_token_ids
                )
4197

4198
4199
        self._draft_token_ids = None
        self._draft_token_req_ids = None
4200
        self.valid_sampled_token_count_gpu = None
4201
4202
        self.input_batch.prev_sampled_token_ids = None

4203
        def propose_draft_token_ids(sampled_token_ids):
4204
            assert spec_decode_common_attn_metadata is not None
4205
            with record_function_or_nullcontext("gpu_model_runner: draft"):
4206
4207
4208
4209
4210
4211
4212
4213
4214
                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,
4215
                    slot_mappings,
4216
                )
4217
                self._copy_draft_token_ids_to_cpu(scheduler_output)
4218

4219
        spec_config = self.speculative_config
4220
4221
        propose_drafts_after_bookkeeping = False
        if spec_config is not None:
4222
            # Decide whether to run the drafter or zero out draft tokens.
4223
4224
4225
            input_fits_in_drafter = spec_decode_common_attn_metadata is not None and (
                spec_decode_common_attn_metadata.max_seq_len + self.num_spec_tokens
                <= self.effective_drafter_max_model_len
4226
            )
4227
            use_gpu_toks = (
4228
4229
4230
                spec_config.use_eagle()
                or spec_config.uses_draft_model()
                or spec_config.uses_extract_hidden_states()
4231
4232
4233
            ) and not spec_config.disable_padded_drafter_batch
            if use_gpu_toks:
                # EAGLE/DraftModel speculative decoding can use the GPU sampled tokens
4234
                # as inputs, and does not need to wait for bookkeeping to finish.
4235
4236
                assert isinstance(
                    self.drafter,
4237
4238
4239
4240
                    EagleProposer
                    | DFlashProposer
                    | DraftModelProposer
                    | ExtractHiddenStatesProposer,
4241
                )
4242
4243
4244
4245
4246
4247
4248
4249
4250
4251
4252
4253
                sampled_token_ids = sampler_output.sampled_token_ids
                if input_fits_in_drafter:
                    propose_draft_token_ids(sampled_token_ids)
                elif self.valid_sampled_token_count_event is not None:
                    assert spec_decode_common_attn_metadata is not None
                    next_token_ids, valid_sampled_tokens_count = (
                        self.drafter.prepare_next_token_ids_padded(
                            sampled_token_ids,
                            self.requests,
                            self.input_batch,
                            self.discard_request_mask.gpu,
                        )
4254
                    )
4255
4256
4257
                    self._copy_valid_sampled_token_count(
                        next_token_ids, valid_sampled_tokens_count
                    )
4258
4259
4260
4261
4262
4263
4264
4265
4266
4267
4268
4269
4270
4271
4272
4273
4274
4275
4276
4277
4278
4279
            elif (
                spec_config.use_ngram_gpu()
                and not spec_config.disable_padded_drafter_batch
            ):
                assert isinstance(self.drafter, NgramProposerGPU)
                sampled_token_ids = sampler_output.sampled_token_ids
                if input_fits_in_drafter:
                    propose_draft_token_ids(sampled_token_ids)
                elif self.valid_sampled_token_count_event is not None:
                    assert spec_decode_common_attn_metadata is not None
                    next_token_ids, valid_sampled_tokens_count, _ = (
                        self.drafter.update_token_ids_ngram(
                            sampled_token_ids,
                            self.input_batch,
                            self.token_ids_gpu_tensor,
                            self.num_tokens_no_spec_gpu,
                            self.discard_request_mask.gpu,
                        )
                    )
                    self._copy_valid_sampled_token_count(
                        next_token_ids, valid_sampled_tokens_count
                    )
4280
4281
            else:
                propose_drafts_after_bookkeeping = input_fits_in_drafter
4282

4283
4284
4285
4286
4287
4288
4289
4290
4291
4292
4293
            if not input_fits_in_drafter:
                # Zero out draft tokens so the scheduler doesn't schedule
                # stale drafts from the previous step.
                # For Nemotron-H: it is necessary to zero out the draft tokens,
                # otherwise the stale tokens will corrupt Mamba recurrent
                # state and logprobs for sequences near max_model_len.
                self._draft_token_ids = torch.zeros(
                    1, device=self.device, dtype=torch.int32
                ).expand(len(self.input_batch.req_ids), self.num_spec_tokens)
                self._copy_draft_token_ids_to_cpu(scheduler_output, zeros_only=True)

4294
        with record_function_or_nullcontext("gpu_model_runner: bookkeep"):
4295
4296
4297
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            (
                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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            ) = self._bookkeeping_sync(
                scheduler_output,
                sampler_output,
                logits,
                hidden_states,
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                scheduler_output.total_num_scheduled_tokens,
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            )
4310

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        if propose_drafts_after_bookkeeping:
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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)
4315

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        # Finalize KV connector (wait_for_save + clear metadata) after
        # draft model runs. Deferred from target model forward to allow
        # draft model to also save its KV cache.
        if spec_config is not None:
            self.finalize_kv_connector()
4321

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        with record_function_or_nullcontext("gpu_model_runner: eplb"):
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            self.eplb_step()
4324

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        # self.kv_connector_output may be modified during drafting
        kv_connector_output = self.kv_connector_output
        self.kv_connector_output = None

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        with record_function_or_nullcontext("gpu_model_runner: ModelRunnerOutput"):
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            if self.routed_experts_initialized:
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                capturer = RoutedExpertsCapturer.get_instance()
                if capturer is not None:
                    capturer.save_captured_experts(indices=self.slot_mapping)  # noqa
                else:
                    logger.error("RoutedExpertsCapturer not initialized.")

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            output = ModelRunnerOutput(
                req_ids=req_ids_output_copy,
                req_id_to_index=req_id_to_index_output_copy,
                sampled_token_ids=valid_sampled_token_ids,
                logprobs=logprobs_lists,
                prompt_logprobs_dict=prompt_logprobs_dict,
                kv_connector_output=kv_connector_output,
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                ec_connector_output=ec_connector_output
                if self.supports_mm_inputs
                else None,
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                num_nans_in_logits=num_nans_in_logits,
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                cudagraph_stats=cudagraph_stats,
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            )
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        if not self.use_async_scheduling:
            return output
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        with record_function_or_nullcontext(
            "gpu_model_runner: AsyncGPUModelRunnerOutput"
        ):
            async_output = AsyncGPUModelRunnerOutput(
                model_runner_output=output,
                sampled_token_ids=sampler_output.sampled_token_ids,
                logprobs_tensors=sampler_output.logprobs_tensors,
                invalid_req_indices=invalid_req_indices,
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                async_output_copy_stream=self._get_or_create_async_output_copy_stream(),
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                vocab_size=self.input_batch.vocab_size,
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            )
        with record_function_or_nullcontext(
            "gpu_model_runner: set_async_sampled_token_ids"
        ):
            # Save ref of sampled_token_ids CPU tensor if the batch contains
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            # any requests with sampling params that require output ids.
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            self.input_batch.set_async_sampled_token_ids(
                async_output.sampled_token_ids_cpu,
                async_output.async_copy_ready_event,
            )
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        return async_output

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    def _pp_broadcast_prev_sampled_token_ids(
        self, sampled_token_ids: torch.Tensor
    ) -> None:
        """Broadcast sampled token ids (GPU) from last PP stage"""
        pp = get_pp_group()
        assert pp.is_last_rank
        # `prev_sampled_token_ids` is expected to have shape [num_reqs, 1].
        assert sampled_token_ids.dim() == 2 and sampled_token_ids.shape[-1] == 1, (
            "PP+async expects sampled_token_ids to have shape [num_reqs, 1]"
        )
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        # Skip for chunked prefill: sampled tokens are dummy
        # and will be discarded, no need to broadcast.
        if not self._is_all_reqs_chunked_prefill():
            torch.distributed.broadcast(
                sampled_token_ids, src=pp.rank, group=pp.device_group
            )
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    def _pp_receive_prev_sampled_token_ids_to_input_batch(self) -> None:
        """Receive sampled token ids broadcast from last PP stage"""
        pp = get_pp_group()
        assert not pp.is_last_rank
        num_reqs = self.input_batch.num_reqs
        # `prev_sampled_token_ids` is expected to have shape [num_reqs, 1].
        recv = torch.empty((num_reqs, 1), dtype=torch.int32, device=self.device)
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        # skip for chunked prefill.
        if not self._is_all_reqs_chunked_prefill():
            torch.distributed.broadcast(recv, src=pp.last_rank, group=pp.device_group)
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        self.input_batch.prev_sampled_token_ids = recv

        # construct `prev_req_id_to_index` here so `_prepare_input_ids`
        # can map req_id -> previous batch row
        discard_req_indices = np.nonzero(self.discard_request_mask.np[:num_reqs])[0]
        discard_req_indices_set = set(discard_req_indices)
        prev_req_id_to_index: dict[str, int] = {}
        for i, req_id in enumerate(self.input_batch.req_ids):
            if i in discard_req_indices_set:
                continue
            prev_req_id_to_index[req_id] = i
            # PP+async scheduling: advance per-request local cached output length by
            # appending a placeholder (-1) token id.
            if (req_state := self.requests.get(req_id)) is not None:
                req_state.output_token_ids.append(-1)
        self.input_batch.prev_req_id_to_index = prev_req_id_to_index

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    def take_draft_token_ids(self) -> DraftTokenIds | None:
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        if not self.num_spec_tokens or not self._draft_token_req_ids:
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            return None
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        draft_token_ids, req_ids = self._get_draft_token_ids_cpu()
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        return DraftTokenIds(req_ids, draft_token_ids)
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    def _copy_draft_token_ids_to_cpu(
        self, scheduler_output: "SchedulerOutput", zeros_only: bool = False
    ) -> None:
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        # Check if we need to copy draft tokens to CPU. In async scheduling,
        # we only copy when needed for structured output, penalties or bad_words.
        if self.use_async_scheduling and not (
            scheduler_output.has_structured_output_requests
            or self.input_batch.sampling_metadata.output_token_ids
        ):
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            return
        # We must also set the corresponding request ids.
        self._draft_token_req_ids = self.input_batch.req_ids.copy()
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        draft_token_ids: torch.Tensor = self._draft_token_ids
        if not torch.is_tensor(draft_token_ids):
            return
        assert self.draft_token_ids_event is not None
        assert self.draft_token_ids_copy_stream is not None
        assert self.draft_token_ids_cpu is not None
        default_stream = torch.cuda.current_stream()
        num_reqs = draft_token_ids.shape[0]
        with torch.cuda.stream(self.draft_token_ids_copy_stream):
            if not zeros_only:
                # Trigger async copy of draft token ids to cpu.
                self.draft_token_ids_copy_stream.wait_stream(default_stream)
                self.draft_token_ids_cpu[:num_reqs].copy_(
                    draft_token_ids, non_blocking=True
                )
            else:
                # No copy needed, just zero-out cpu tensor.
                self.draft_token_ids_cpu[:num_reqs] = 0
            self.draft_token_ids_event.record()

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    def _get_draft_token_ids_cpu(self) -> tuple[list[list[int]], list[str]]:
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        if isinstance(self._draft_token_ids, list):
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            return self._draft_token_ids, self.input_batch.req_ids
        req_ids = self._draft_token_req_ids
        if req_ids is None:
            return [], []
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        assert self.draft_token_ids_event is not None
        assert self.draft_token_ids_cpu is not None
        self.draft_token_ids_event.synchronize()
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        return self.draft_token_ids_cpu[: len(req_ids)].tolist(), req_ids
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    def _copy_valid_sampled_token_count(
        self, next_token_ids: torch.Tensor, valid_sampled_tokens_count: torch.Tensor
    ) -> None:
        if self.valid_sampled_token_count_event is None:
            return

        default_stream = torch.cuda.current_stream()
        # Initialize a new stream to overlap the copy operation with
        # prepare_input of draft model.
        with torch.cuda.stream(self.valid_sampled_token_count_copy_stream):
            self.valid_sampled_token_count_copy_stream.wait_stream(default_stream)  # type: ignore
            counts = valid_sampled_tokens_count
            counts_cpu = self.valid_sampled_token_count_cpu
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            assert counts_cpu is not None
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            counts_cpu[: counts.shape[0]].copy_(counts, non_blocking=True)
            self.valid_sampled_token_count_event.record()

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        if self.use_async_spec_decode:
            # Stash for GPU-side correction in _prepare_inputs.
            self.valid_sampled_token_count_gpu = valid_sampled_tokens_count
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        self.input_batch.prev_sampled_token_ids = next_token_ids.unsqueeze(1)

    def _get_valid_sampled_token_count(self) -> list[int]:
        # Wait until valid_sampled_tokens_count is copied to cpu,
        prev_sampled_token_ids = self.input_batch.prev_sampled_token_ids
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        sampled_count_event = self.valid_sampled_token_count_event
        if sampled_count_event is None or prev_sampled_token_ids is None:
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            return []

        counts_cpu = self.valid_sampled_token_count_cpu
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        assert counts_cpu is not None
        sampled_count_event.synchronize()
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        return counts_cpu[: prev_sampled_token_ids.shape[0]].tolist()

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    def propose_draft_token_ids(
        self,
        scheduler_output: "SchedulerOutput",
4508
        sampled_token_ids: torch.Tensor | list[list[int]],
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        sampling_metadata: SamplingMetadata,
        hidden_states: torch.Tensor,
        sample_hidden_states: torch.Tensor,
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        aux_hidden_states: list[torch.Tensor] | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
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        common_attn_metadata: CommonAttentionMetadata,
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        slot_mappings: dict[str, torch.Tensor] | list[dict[str, torch.Tensor]] | None,
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    ) -> list[list[int]] | torch.Tensor:
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        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
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        spec_config = self.speculative_config
        assert spec_config is not None
        if spec_config.method == "ngram":
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            from vllm.v1.spec_decode.ngram_proposer import NgramProposer

4523
            assert isinstance(sampled_token_ids, list)
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            assert isinstance(self.drafter, NgramProposer)
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            draft_token_ids = self.drafter.propose(
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                sampled_token_ids,
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                self.input_batch.num_tokens_no_spec,
                self.input_batch.token_ids_cpu,
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                slot_mappings=slot_mappings,
4530
            )
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        elif spec_config.use_ngram_gpu():
            assert isinstance(self.drafter, NgramProposerGPU)
            (
                next_token_ids,
                valid_sampled_tokens_count,
                valid_sampled_token_ids_gpu,
            ) = self.drafter.update_token_ids_ngram(
                sampled_token_ids,
                self.input_batch,
                self.token_ids_gpu_tensor,
                self.num_tokens_no_spec_gpu,
                self.discard_request_mask.gpu,
            )
            self._copy_valid_sampled_token_count(
                next_token_ids, valid_sampled_tokens_count
            )

            batch_size = next_token_ids.shape[0]

            draft_token_ids, num_valid_draft_tokens = self.drafter.propose(
                self.num_tokens_no_spec_gpu[:batch_size],
                self.token_ids_gpu_tensor[:batch_size],
                valid_sampled_token_ids_gpu,
                valid_sampled_tokens_count,
            )

            # Cache valid draft counts for scheduler-side trimming.
            self._num_valid_draft_tokens = num_valid_draft_tokens

            # Async D2H copy on a dedicated stream.
            copy_num_valid_draft_tokens(
                self._num_valid_draft_tokens_cpu,
                self._num_valid_draft_tokens_copy_stream,
                self._num_valid_draft_tokens_event,
                self._num_valid_draft_tokens,
                self.input_batch.num_reqs,
            )
4568
        elif spec_config.method == "suffix":
4569
4570
            assert isinstance(sampled_token_ids, list)
            assert isinstance(self.drafter, SuffixDecodingProposer)
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            draft_token_ids = self.drafter.propose(
                self.input_batch, sampled_token_ids, slot_mappings=slot_mappings
            )
4574
        elif spec_config.method == "medusa":
4575
            assert isinstance(sampled_token_ids, list)
4576
            assert isinstance(self.drafter, MedusaProposer)
4577

4578
4579
            if sample_hidden_states.shape[0] == len(sampled_token_ids):
                # The input to the target model does not include draft tokens.
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4583
                hidden_states = sample_hidden_states
            else:
                indices = []
                offset = 0
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                assert spec_decode_metadata is not None, (
                    "No spec decode metadata for medusa"
                )
4587
                for num_draft, tokens in zip(
4588
4589
                    spec_decode_metadata.num_draft_tokens, sampled_token_ids
                ):
4590
                    indices.append(offset + len(tokens) - 1)
4591
                    offset += num_draft + 1
4592
                indices = torch.tensor(indices, device=self.device)
4593
4594
                hidden_states = sample_hidden_states[indices]

4595
            draft_token_ids = self.drafter.propose(
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                target_hidden_states=hidden_states,
                sampling_metadata=sampling_metadata,
4598
                slot_mappings=slot_mappings,
4599
            )
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        elif spec_config.uses_extract_hidden_states():
            assert isinstance(self.drafter, ExtractHiddenStatesProposer)
            assert isinstance(sampled_token_ids, torch.Tensor), (
                "sampled_token_ids should be a torch.Tensor for "
                "extract_hidden_states method."
            )
            if not self.use_aux_hidden_state_outputs or aux_hidden_states is None:
                raise ValueError(
                    "aux_hidden_states are required when using `extract_hidden_states`"
                )
            target_hidden_states = [h[:num_scheduled_tokens] for h in aux_hidden_states]

4612
            draft_token_ids = self.drafter.propose(
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                sampled_token_ids=sampled_token_ids,
                target_hidden_states=target_hidden_states,
                common_attn_metadata=common_attn_metadata,
                slot_mappings=slot_mappings,
            )
            next_token_ids, valid_sampled_tokens_count = (
                self.drafter.prepare_next_token_ids_padded(
                    sampled_token_ids,
                    self.requests,
                    self.input_batch,
                    self.discard_request_mask.gpu,
                )
            )
            self._copy_valid_sampled_token_count(
                next_token_ids, valid_sampled_tokens_count
            )

4630
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        elif (
            spec_config.use_eagle()
            or spec_config.use_dflash()
            or spec_config.uses_draft_model()
        ):
            assert isinstance(
                self.drafter, EagleProposer | DFlashProposer | DraftModelProposer
            )
4638

4639
            if spec_config.disable_padded_drafter_batch:
4640
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                # 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.
4643
4644
                assert isinstance(sampled_token_ids, list), (
                    "sampled_token_ids should be a python list when"
4645
                    "padded-batch is disabled."
4646
                )
4647
                next_token_ids = self.drafter.prepare_next_token_ids_cpu(
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                    sampled_token_ids,
                    self.requests,
                    self.input_batch,
                    scheduler_output.num_scheduled_tokens,
                )
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            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.
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                assert isinstance(sampled_token_ids, torch.Tensor), (
                    "sampled_token_ids should be a torch.Tensor when"
4660
                    "padded-batch is enabled."
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                )
                next_token_ids, valid_sampled_tokens_count = (
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                    self.drafter.prepare_next_token_ids_padded(
                        sampled_token_ids,
                        self.requests,
                        self.input_batch,
4667
                        self.discard_request_mask.gpu,
4668
                    )
4669
                )
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                self._copy_valid_sampled_token_count(
                    next_token_ids, valid_sampled_tokens_count
                )
Jiayi Yao's avatar
Jiayi Yao committed
4673

4674
            num_rejected_tokens_gpu = None
4675
            if spec_decode_metadata is None:
4676
                token_indices_to_sample = None
4677
                # input_ids can be None for multimodal models.
4678
                target_token_ids = self.input_ids.gpu[:num_scheduled_tokens]
4679
                target_positions = self._get_positions(num_scheduled_tokens)
4680
                if self.use_aux_hidden_state_outputs:
Wentao Ye's avatar
Wentao Ye committed
4681
                    assert aux_hidden_states is not None
4682
                    target_hidden_states = torch.cat(
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                        [h[:num_scheduled_tokens] for h in aux_hidden_states], dim=-1
                    )
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                else:
                    target_hidden_states = hidden_states[:num_scheduled_tokens]
4687
            else:
4688
                if spec_config.disable_padded_drafter_batch:
4689
                    token_indices_to_sample = None
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                    common_attn_metadata, token_indices = self.drafter.prepare_inputs(
                        common_attn_metadata,
                        sampled_token_ids,
                        spec_decode_metadata.num_draft_tokens,
                    )
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                    target_token_ids = self.input_ids.gpu[token_indices]
                    target_positions = self._get_positions(token_indices)
                    if self.use_aux_hidden_state_outputs:
                        assert aux_hidden_states is not None
                        target_hidden_states = torch.cat(
                            [h[token_indices] for h in aux_hidden_states], dim=-1
                        )
                    else:
                        target_hidden_states = hidden_states[token_indices]
4704
                else:
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                    (
                        common_attn_metadata,
                        token_indices_to_sample,
                        num_rejected_tokens_gpu,
                    ) = self.drafter.prepare_inputs_padded(
                        common_attn_metadata,
                        spec_decode_metadata,
                        valid_sampled_tokens_count,
4713
                    )
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                    total_num_tokens = common_attn_metadata.num_actual_tokens
                    # When padding the batch, token_indices is just a range
                    target_token_ids = self.input_ids.gpu[:total_num_tokens]
                    target_positions = self._get_positions(total_num_tokens)
                    if self.use_aux_hidden_state_outputs:
                        assert aux_hidden_states is not None
                        target_hidden_states = torch.cat(
                            [h[:total_num_tokens] for h in aux_hidden_states], dim=-1
                        )
                    else:
                        target_hidden_states = hidden_states[:total_num_tokens]
4725

4726
            if self.supports_mm_inputs and self.drafter.supports_mm_inputs:
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                mm_embed_inputs = self._gather_mm_embeddings(
                    scheduler_output,
                    shift_computed_tokens=1,
                )
            else:
                mm_embed_inputs = None
4733

4734
            draft_token_ids = self.drafter.propose(
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                target_token_ids=target_token_ids,
                target_positions=target_positions,
                target_hidden_states=target_hidden_states,
                next_token_ids=next_token_ids,
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                token_indices_to_sample=token_indices_to_sample,
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                sampling_metadata=sampling_metadata,
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                common_attn_metadata=common_attn_metadata,
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                mm_embed_inputs=mm_embed_inputs,
4743
                num_rejected_tokens_gpu=num_rejected_tokens_gpu,
4744
                slot_mappings=slot_mappings,
4745
            )
4746

4747
        return draft_token_ids
4748

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4751
    def update_config(self, overrides: dict[str, Any]) -> None:
        allowed_config_names = {"load_config", "model_config"}
        for config_name, config_overrides in overrides.items():
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            assert config_name in allowed_config_names, (
                f"Config `{config_name}` not supported. "
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                f"Allowed configs: {allowed_config_names}"
4755
            )
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            config = getattr(self, config_name)
            new_config = update_config(config, config_overrides)
            setattr(self, config_name, new_config)

4760
    @instrument(span_name="Loading (GPU)")
4761
    def load_model(self, load_dummy_weights: bool = False) -> None:
4762
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        """
        Args:
4764
            load_dummy_weights: load dummy weights instead of real weights.
4765
        """
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        logger.info_once(
            "Starting to load model %s...",
            self.model_config.model,
            scope="global",
        )
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        if self.parallel_config.enable_eplb:
            self.eplb_state = EplbState(self.parallel_config, self.device)
            eplb_models = 0

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        try:
            with DeviceMemoryProfiler() as m:
                time_before_load = time.perf_counter()
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                if load_dummy_weights:
                    self.load_config.load_format = "dummy"
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                model_loader = get_model_loader(self.load_config)
                self.model = model_loader.load_model(
                    vllm_config=self.vllm_config, model_config=self.model_config
                )
                if self.lora_config:
                    self.model = self.load_lora_model(
                        self.model, self.vllm_config, self.device
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                    )
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                if hasattr(self, "drafter"):
                    logger.info_once("Loading drafter model...")
                    self.drafter.load_model(self.model)
                    if (
                        hasattr(self.drafter, "model")
                        and is_mixture_of_experts(self.drafter.model)
                        and self.parallel_config.enable_eplb
                    ):
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                        assert not self.parallel_config.enable_elastic_ep, (
                            "Elastic EP is not supported with drafter model."
                        )
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                        spec_config = self.vllm_config.speculative_config
                        assert spec_config is not None
                        assert spec_config.draft_model_config is not None
                        logger.info_once(
                            "EPLB is enabled for drafter model %s.",
                            spec_config.draft_model_config.model,
                        )
                        if self.eplb_state is None:
                            self.eplb_state = EplbState(
                                self.parallel_config, self.device
                            )
                        self.eplb_state.add_model(
                            self.drafter.model,
                            spec_config.draft_model_config,
                        )
                        eplb_models += 1
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                if self.use_aux_hidden_state_outputs:
                    if not supports_eagle3(self.get_model()):
                        raise RuntimeError(
                            "Model does not support EAGLE3 interface but "
                            "aux_hidden_state_outputs was requested"
                        )
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                    # 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:
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                        aux_layers = (
                            self.model.get_eagle3_default_aux_hidden_state_layers()
                        )
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                    self.model.set_aux_hidden_state_layers(aux_layers)
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                if (
                    is_mixture_of_experts(self.model)
                    and self.parallel_config.enable_eplb
                    and not load_dummy_weights
                ):
                    logger.info_once(
                        "EPLB is enabled for model %s.",
                        self.model_config.model,
                    )
                    assert self.eplb_state is not None
                    self.eplb_state.add_model(
                        self.model,
                        self.model_config,
                    )
                    eplb_models += 1

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                time_after_load = time.perf_counter()
            self.model_memory_usage = m.consumed_memory
        except torch.cuda.OutOfMemoryError as e:
            msg = (
                "Failed to load model - not enough GPU memory. "
                "Try lowering --gpu-memory-utilization to free memory for weights, "
                "increasing --tensor-parallel-size, or using --quantization. "
                "See https://docs.vllm.ai/en/latest/configuration/conserving_memory/ "
                "for more tips."
            )
            combined_msg = f"{msg} (original error: {e})"
            logger.error(combined_msg)
            raise e
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        logger.info_once(
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            "Model loading took %s GiB memory and %.6f seconds",
            format_gib(self.model_memory_usage),
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            time_after_load - time_before_load,
        )
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        if not load_dummy_weights:
            prepare_communication_buffer_for_model(self.model)
            if (drafter := getattr(self, "drafter", None)) and (
                drafter_model := getattr(drafter, "model", None)
            ):
                prepare_communication_buffer_for_model(drafter_model)
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        mm_config = self.model_config.multimodal_config
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        self.is_multimodal_pruning_enabled = (
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            supports_multimodal_pruning(self.get_model())
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            and mm_config is not None
            and mm_config.is_multimodal_pruning_enabled()
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        )
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        self.requires_sequential_video_encoding = hasattr(
            self.get_model(), "requires_sequential_video_encoding"
        )  # Temporary hack for dynamic res video w/o support for bs>1 yet
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        if (
            is_mixture_of_experts(self.model)
            and self.parallel_config.enable_eplb
            and not load_dummy_weights
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            and self.eplb_state is not None
            and self.eplb_state.is_async
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        ):
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            self.eplb_state.start_async_loop()
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        if (
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            self.vllm_config.compilation_config.mode
            == CompilationMode.STOCK_TORCH_COMPILE
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        ):
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            from vllm.env_override import _apply_constrain_to_fx_strides_patch

            _apply_constrain_to_fx_strides_patch()
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            backend = self.vllm_config.compilation_config.init_backend(self.vllm_config)
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            compilation_counter.stock_torch_compile_count += 1
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            self.model.compile(fullgraph=True, backend=backend)
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            return
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        # for other compilation modes, cudagraph behavior is controlled by
Jiayi Yan's avatar
Jiayi Yan committed
4910
        # CudagraphWrapper and CudagraphDispatcher of vllm.
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        # wrap the model with full cudagraph wrapper if needed.
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        cudagraph_mode = self.compilation_config.cudagraph_mode
        assert cudagraph_mode is not None
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        if (
            cudagraph_mode.has_full_cudagraphs()
            and not self.parallel_config.use_ubatching
        ):
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            self.model = CUDAGraphWrapper(
                self.model, self.vllm_config, runtime_mode=CUDAGraphMode.FULL
            )
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        elif self.parallel_config.use_ubatching:
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            if cudagraph_mode.has_full_cudagraphs():
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                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.FULL, self.device
                )
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            else:
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                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.NONE, self.device
                )
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        get_offloader().post_init()

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    def _get_eagle3_aux_layers_from_config(self) -> tuple[int, ...] | None:
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        """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

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        layer_ids = getattr(hf_config, "eagle_aux_hidden_state_layer_ids", None)
        if not layer_ids:
            dflash_config = getattr(hf_config, "dflash_config", None)
            if dflash_config and isinstance(dflash_config, dict):
                layer_ids = dflash_config.get("target_layer_ids")

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        if layer_ids and isinstance(layer_ids, (list, tuple)):
            return tuple(layer_ids)

        return None

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    def reload_weights(
        self,
        weights_iterator: Iterable[tuple[str, torch.Tensor]] | None = None,
        weights_path: str | None = None,
        is_checkpoint_format: bool = True,
    ) -> None:
        """
        Reload weights from a weights iterator or from disk

        :param weights_iterator: weights to load into model
        :param weights_path: path to load weights from if weights_iterator is not
            provided. Use path of original model if neither is provided.
        :param is_checkpoint_format: set to False if weights have already been processed
Jiayi Yan's avatar
Jiayi Yan committed
4974
            into kernel format (repacking, renaming, etc.)
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        """
        # TODO(@kylesayrs): generalize to all runners and loaders
        # argument validation
        if weights_iterator is None and not is_checkpoint_format:
            logger.warning(
                "Reloading from disk means that weights will be in checkpoint format. "
                "Please use `is_checkpoint_format=True` "
                "to avoid weight reloading errors"
            )

        model = self.get_model()
        weights_to_load = {name for name, _ in model.named_parameters()}
        counter_before_reloading = time.perf_counter()

        # load weights from disk if none are provided
        if weights_iterator is None:
            model_loader = get_model_loader(self.load_config)
            if not hasattr(model_loader, "get_all_weights"):
                raise NotImplementedError(
                    f"Model reloading with `{self.load_config.load_format}` format"
                )

            if weights_path is not None:
                self.model_config.model = weights_path
            weights_iterator = model_loader.get_all_weights(self.model_config, model)
            weights_iterator = cast(
                Iterable[tuple[str, torch.Tensor]], weights_iterator
            )

        # begin loading weights
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        logger.info_once("Reloading weights inplace...")
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        if is_checkpoint_format:
            # load weights from checkpoint/ original model format
            initialize_layerwise_reload(model)
            loaded_weights = model.load_weights(weights_iterator)
            finalize_layerwise_reload(model, self.model_config)
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        else:
            # load weights from kernel format
            logger.warning_once(
                "Reloading with `is_checkpoint_format=True` requires that "
                "weights be in kernel format and already sharded",
            )
            loaded_weights = set()
            for name, loaded_weight in weights_iterator:
                param = model.get_parameter(name)  # TODO: buffers?
                param.copy_(loaded_weight)
                loaded_weights.add(name)
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        # logging and validation
        counter_after_reloading = time.perf_counter()
        diff_seconds = counter_after_reloading - counter_before_reloading
        logger.info_once(
            "Reloading and processing weights took %.2f seconds",
            diff_seconds,
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        )
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        if self.model_config.quantization is None and loaded_weights is not None:
            weights_not_loaded = weights_to_load - loaded_weights
            if weights_not_loaded:
                logger.warning(
                    "Following weights were not loaded from checkpoint: %s",
                    weights_not_loaded,
                )
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    def _get_prompt_logprobs_dict(
        self,
        hidden_states: torch.Tensor,
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        num_scheduled_tokens: dict[str, int],
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    ) -> dict[str, LogprobsTensors | None]:
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        num_prompt_logprobs_dict = self.num_prompt_logprobs
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        if not num_prompt_logprobs_dict:
            return {}

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        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
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        prompt_logprobs_dict: dict[str, LogprobsTensors | None] = {}
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        # Since prompt logprobs are a rare feature, prioritize simple,
        # maintainable loop over optimal performance.
        completed_prefill_reqs = []
        for req_id, num_prompt_logprobs in num_prompt_logprobs_dict.items():
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            num_tokens = num_scheduled_tokens.get(req_id)
            if num_tokens is None:
                # This can happen if the request was preempted in prefill stage.
                continue
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            # Get metadata for this request.
            request = self.requests[req_id]
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            if request.prompt_token_ids is None:
                # Prompt logprobs is incompatible with prompt embeddings
                continue

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

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

            if num_logits <= 0:
                # This can happen for the final chunk if we prefilled exactly
                # (num_prompt_tokens - 1) tokens for this request in the prior
                # step. There are no more prompt logprobs to produce.
                continue
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            # Get the logits corresponding to this req's prompt tokens.
            # If this is a partial request (i.e. chunked prefill),
            # then there is prompt logprob generated for each index.
            req_idx = self.input_batch.req_id_to_index[req_id]
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            offset = self.query_start_loc.np[req_idx].item()
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            prompt_hidden_states = hidden_states[offset : offset + num_logits]
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            logits = self.model.compute_logits(prompt_hidden_states)
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            # 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.
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            tgt_token_ids = prompt_token_ids[start_tok : start_tok + num_logits]
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            # Compute prompt logprobs.
5117
            logprobs = self.sampler.compute_logprobs(logits)
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            token_ids, logprobs, ranks, _ = self.sampler.gather_logprobs(
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                logprobs, num_prompt_logprobs, tgt_token_ids
            )
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            # Transfer GPU->CPU async.
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            chunk_slice = slice(start_idx, start_idx + num_logits)
            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
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                token_ids, non_blocking=True
            )
            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs, non_blocking=True)
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            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
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                ranks, non_blocking=True
            )
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        # 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]
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            del in_progress_dict[req_id]
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        # Must synchronize the non-blocking GPU->CPU transfers.
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        if prompt_logprobs_dict:
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            self._sync_device()
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        return prompt_logprobs_dict

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    def _get_nans_in_logits(
        self,
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        logits: torch.Tensor | None,
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    ) -> 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])
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                    if num_nans_for_index is not None and req_index < logits.shape[0]
                    else 0
                )
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            return num_nans_in_logits
        except IndexError:
            return {}

5165
    @contextmanager
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    def maybe_randomize_inputs(
        self, input_ids: torch.Tensor | None, inputs_embeds: torch.Tensor | None
    ):
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        """
        Randomize input_ids if VLLM_RANDOMIZE_DP_DUMMY_INPUTS is set.
        This is to help balance expert-selection
         - during profile_run
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         - during DP rank dummy run
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        """
5175

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        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
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        elif input_ids is not None:
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            @functools.cache
            def rand_input_ids() -> torch.Tensor:
                return torch.randint_like(
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                    self.input_ids.gpu,
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                    low=0,
                    high=self.model_config.get_vocab_size(),
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                )
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            logger.debug_once("Randomizing dummy input_ids for DP Rank")
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            input_ids.copy_(rand_input_ids()[: input_ids.size(0)], non_blocking=True)
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            yield
            input_ids.fill_(0)
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        else:

            @functools.cache
            def rand_inputs_embeds() -> torch.Tensor:
                return torch.randn_like(
                    self.inputs_embeds.gpu,
                )

            assert inputs_embeds is not None
            logger.debug_once("Randomizing dummy inputs_embeds for DP Rank")
            inputs_embeds.copy_(
                rand_inputs_embeds()[: inputs_embeds.size(0)], non_blocking=True
            )
            yield
            inputs_embeds.fill_(0)
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    def _get_mm_dummy_batch(
        self,
        modality: str,
        max_items_per_batch: int,
    ) -> BatchedTensorInputs:
        """Dummy data for profiling and precompiling multimodal models."""
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        assert self.mm_budget is not None

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        # Don't use `max_items_per_batch` here to avoid redundant computation
        dummy_mm_inputs = self.mm_registry.get_dummy_mm_inputs(
            self.model_config,
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            mm_counts={modality: 1},
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            cache=self.mm_budget.cache,
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        )
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        dummy_mm_item = dummy_mm_inputs["mm_kwargs"][modality][0]

        # We use the cache so that the item is saved to the cache,
        # but not read from the cache
        assert dummy_mm_item is not None, "Item should not already be cached"
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        return next(
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            mm_kwargs_batch
            for _, _, mm_kwargs_batch in group_and_batch_mm_kwargs(
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                [(modality, dummy_mm_item)] * max_items_per_batch,
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                device=self.device,
                pin_memory=self.pin_memory,
            )
        )
5238

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    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
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        cudagraph_runtime_mode: CUDAGraphMode | None = None,
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        force_attention: bool = False,
        uniform_decode: bool = False,
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        allow_microbatching: bool = True,
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        skip_eplb: bool = False,
        is_profile: bool = False,
5249
        create_mixed_batch: bool = False,
5250
        remove_lora: bool = True,
Rémi Delacourt's avatar
Rémi Delacourt committed
5251
        is_graph_capturing: bool = False,
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        num_active_loras: int = 0,
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        profile_seq_lens: int | None = None,
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    ) -> tuple[torch.Tensor, torch.Tensor]:
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        """
        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.
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                - if not set will determine the cudagraph mode based on using
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                    the self.cudagraph_dispatcher.
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                - CUDAGraphMode.NONE: No cudagraph, for warm up and profile run
                - CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
                - CUDAGraphMode.FULL: Full cudagraph, attention metadata is
                    needed.
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            force_attention: If True, always create attention metadata. Used to
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                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.
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            create_mixed_batch: If True, create a mixed batch with both decode
                (1 token) and prefill (multiple tokens) requests.
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            remove_lora: If False, dummy LoRAs are not destroyed after the run
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            num_active_loras: Number of distinct active LoRAs to capture for.
                LoRA is activated when num_active_loras > 0.
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            profile_seq_lens: If provided, use this value for seq_lens instead
                of max_query_len. Used to profile attention workspace that
                scales with context length.
5281
        """
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        mm_config = self.vllm_config.model_config.multimodal_config
        if mm_config and mm_config.mm_encoder_only:
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            # The current dummy run only covers LM execution, so we can skip it.
            # mm encoder dummy run may need to add in the future.
            return torch.tensor([]), torch.tensor([])

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        assert (
            cudagraph_runtime_mode is None
5290
            or cudagraph_runtime_mode.is_valid_runtime_mode()
5291
        )
5292

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        # If cudagraph_mode.decode_mode() == FULL and
5294
        # cudagraph_mode.separate_routine(). This means that we are using
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        # 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.
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        max_query_len = self.uniform_decode_query_len if uniform_decode else num_tokens
5307

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        # 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.
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        assert num_tokens <= self.max_num_tokens
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        max_num_reqs = self.scheduler_config.max_num_seqs
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        if create_mixed_batch:
            assert not uniform_decode
            # Create mixed batch:
            # first half decode tokens, second half one prefill
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            num_decode_tokens = min(max_num_reqs - 1, num_tokens // 2)
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            num_prefill_tokens = num_tokens - num_decode_tokens
            num_reqs = num_decode_tokens + 1

            # Create decode requests (1 token each) followed by prefill request
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            num_scheduled_tokens_list = [1] * num_decode_tokens + [num_prefill_tokens]
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            # Note: Overriding max_query_len to be the prefill tokens
            max_query_len = num_prefill_tokens
        elif uniform_decode:
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            assert not create_mixed_batch
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            num_reqs = min(max_num_reqs, cdiv(num_tokens, max_query_len))
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            num_scheduled_tokens_list = [max_query_len] * num_reqs
            if num_tokens % max_query_len != 0:
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                num_scheduled_tokens_list[-1] = num_tokens % max_query_len
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        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

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        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs
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        num_scheduled_tokens = np.array(num_scheduled_tokens_list, dtype=np.int32)
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        num_tokens_unpadded = int(num_scheduled_tokens.sum())

5342
        num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
5343

5344
        _cudagraph_mode, batch_desc, should_ubatch, num_tokens_across_dp, _ = (
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            self._determine_batch_execution_and_padding(
                num_tokens=num_tokens_unpadded,
                num_reqs=num_reqs,
                num_scheduled_tokens_np=num_scheduled_tokens,
                max_num_scheduled_tokens=max_query_len,
                use_cascade_attn=False,
                allow_microbatching=allow_microbatching,
                force_eager=is_profile
                or (cudagraph_runtime_mode == CUDAGraphMode.NONE),
                # `force_uniform_decode` is used for cudagraph capture; because for
                # capturing mixed prefill-decode batches, we sometimes use
                # num_tokens == num_reqs which looks like a uniform decode batch to the
                # dispatcher; but we actually want to capture a piecewise cudagraph
                force_uniform_decode=uniform_decode,
                # `force_has_lora` is used for cudagraph capture; because LoRA is
                # activated later in the context manager, but we need to know the
                # LoRA state when determining the batch descriptor for capture
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                force_has_lora=num_active_loras > 0,
                # `force_num_active_loras` is used for cudagraph capture; because we
                # need to capture graphs for specific num_active_loras counts
                force_num_active_loras=num_active_loras,
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            )
        )
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        if cudagraph_runtime_mode is None:
            cudagraph_runtime_mode = _cudagraph_mode
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        else:
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            assert cudagraph_runtime_mode == _cudagraph_mode, (
                f"Cudagraph runtime mode mismatch in dummy_run. "
                f"Expected {_cudagraph_mode}, but got {cudagraph_runtime_mode}."
            )

        num_tokens_padded = batch_desc.num_tokens
        num_reqs_padded = (
            batch_desc.num_reqs if batch_desc.num_reqs is not None else num_reqs
        )
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        ubatch_slices, ubatch_slices_padded = maybe_create_ubatch_slices(
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            should_ubatch,
            num_scheduled_tokens,
            num_tokens_padded,
            num_reqs_padded,
            self.vllm_config.parallel_config.num_ubatches,
        )
        logger.debug(
            "ubatch_slices: %s, ubatch_slices_padded: %s",
            ubatch_slices,
            ubatch_slices_padded,
5392
        )
5393

5394
        attn_metadata: PerLayerAttnMetadata | None = None
5395

5396
        slot_mappings_by_group, slot_mappings = self._get_slot_mappings(
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            num_tokens_padded=num_tokens_padded,
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            num_reqs_padded=num_reqs_padded,
            num_tokens_unpadded=num_tokens_unpadded,
            ubatch_slices=ubatch_slices_padded,
        )

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        # Dummy runs have no real slot assignments — fill with -1 so
        # concat_and_cache kernels skip the KV write.
        if slot_mappings_by_group is not None:
            for sm in slot_mappings_by_group.values():
                sm.fill_(-1)

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        # _dummy_run shares pinned CPU buffers (seq_lens, query_start_loc,
        # etc.) with execute_model.  It must participate in the same event
        # protocol so that back-to-back dummy/real steps don't overwrite
        # pinned memory while a prior non_blocking H2D DMA is still reading.
        with self.synchronize_input_prep():
            # If force_attention is True, we always capture attention.
            # Otherwise, it only happens for cudagraph_runtime_mode=FULL.
            if force_attention or cudagraph_runtime_mode == CUDAGraphMode.FULL:
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                if profile_seq_lens is not None:
                    seq_lens = profile_seq_lens  # type: ignore[assignment]
                elif create_mixed_batch:
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                    # 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
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                    seq_lens = torch.tensor(  # type: ignore[assignment]
                        [1] * num_decode_tokens + [num_prefill_tokens + 1],
                        dtype=torch.int,
                    )
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                else:
                    seq_lens = max_query_len  # type: ignore[assignment]
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                self.optimistic_seq_lens_cpu[:num_reqs] = seq_lens
                self.optimistic_seq_lens_cpu[num_reqs:].fill_(0)
                self.seq_lens.copy_(self.optimistic_seq_lens_cpu, non_blocking=True)
5432

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                cum_num_tokens = self._get_cumsum_and_arange(
                    num_scheduled_tokens, self.query_pos.np
                )
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                self.query_start_loc.np[1 : num_reqs + 1] = cum_num_tokens
                self.query_start_loc.copy_to_gpu()
5438

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                # Sync block table CPU->GPU so cleared rows from
                # remove_request() are visible to the attention metadata
                # builder. Without this, stale block IDs from finished
                # requests can corrupt Mamba state.
                self.input_batch.block_table.commit_block_table(num_reqs_padded)

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                pad_attn = cudagraph_runtime_mode == CUDAGraphMode.FULL
                attn_metadata, _ = self._build_attention_metadata(
                    num_tokens=num_tokens_unpadded,
                    num_tokens_padded=num_tokens_padded if pad_attn else None,
                    num_reqs=num_reqs_padded,
                    max_query_len=max_query_len,
                    ubatch_slices=(ubatch_slices_padded if pad_attn else ubatch_slices),
                    for_cudagraph_capture=is_graph_capturing,
                    slot_mappings=slot_mappings_by_group,
5454
                    use_spec_decode=self.speculative_config is not None,
5455
                )
5456

5457
        with self.maybe_dummy_run_with_lora(
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            self.lora_config,
            num_scheduled_tokens,
            num_sampled_tokens,
            remove_lora,
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            num_active_loras,
5463
        ):
5464
            # Make sure padding doesn't exceed max_num_tokens
5465
            assert num_tokens_padded <= self.max_num_tokens
5466
            model_kwargs = self._init_model_kwargs()
5467
            if self.supports_mm_inputs and not self.model_config.is_encoder_decoder:
Patrick von Platen's avatar
Patrick von Platen committed
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5469
                input_ids, inputs_embeds = self._prepare_mm_inputs(num_tokens_padded)

5470
                model_kwargs = {
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                    **model_kwargs,
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                    **self._dummy_mm_kwargs(num_reqs),
                }
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            elif self.enable_prompt_embeds:
                input_ids = None
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                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_padded]
5477
                model_kwargs = self._init_model_kwargs()
5478
            else:
5479
                input_ids = self.input_ids.gpu[:num_tokens_padded]
5480
                inputs_embeds = None
5481

5482
            if self.uses_mrope:
5483
                positions = self.mrope_positions.gpu[:, :num_tokens_padded]
5484
            elif self.uses_xdrope_dim > 0:
5485
                positions = self.xdrope_positions.gpu[:, :num_tokens_padded]
5486
            else:
5487
                positions = self.positions[:num_tokens_padded]
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            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,
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                            device=self.device,
                        )
                    )
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5501

                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
5502
                    num_tokens_padded, None, False
5503
                )
5504

5505
            if ubatch_slices_padded is not None:
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5508
                # Adjust values to reflect a single ubatch.
                # TODO(sage,lucas): this is cruft that should be addressed in
                #  the padding refactor.
5509
                num_tokens_padded = ubatch_slices_padded[0].num_tokens
5510
                if num_tokens_across_dp is not None:
5511
                    num_tokens_across_dp[:] = num_tokens_padded
5512

5513
            with (
5514
                self.maybe_randomize_inputs(input_ids, inputs_embeds),
5515
                set_forward_context(
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5517
                    attn_metadata,
                    self.vllm_config,
5518
                    num_tokens=num_tokens_padded,
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5520
                    num_tokens_across_dp=num_tokens_across_dp,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
5521
                    batch_descriptor=batch_desc,
5522
                    ubatch_slices=ubatch_slices_padded,
5523
                    slot_mapping=slot_mappings,
5524
5525
                ),
            ):
5526
                outputs = self.model(
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5530
                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
5531
                    **model_kwargs,
5532
                )
5533

5534
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5537
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
5538

5539
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            if self.speculative_config and (
                self.speculative_config.use_eagle()
                or self.speculative_config.uses_draft_model()
5542
                or self.speculative_config.uses_extract_hidden_states()
5543
            ):
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5545
                assert isinstance(
                    self.drafter,
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5549
                    EagleProposer
                    | DFlashProposer
                    | DraftModelProposer
                    | ExtractHiddenStatesProposer,
5550
                )
5551
                assert self.speculative_config is not None
5552
5553
5554
                # Eagle currently only supports PIECEWISE cudagraphs.
                # Therefore only use cudagraphs if the main model uses PIECEWISE
                # NOTE(lucas): this is a hack, need to clean up.
5555
                use_cudagraphs = (
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5564
                    (
                        is_graph_capturing
                        and cudagraph_runtime_mode == CUDAGraphMode.PIECEWISE
                    )
                    or (
                        not is_graph_capturing
                        and cudagraph_runtime_mode != CUDAGraphMode.NONE
                    )
                ) and not self.speculative_config.enforce_eager
5565
5566
5567
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5569

                # Note(gnovack) - We need to disable cudagraphs for one of the two
                # lora cases when cudagraph_specialize_lora is enabled. This is a
                # short term mitigation for issue mentioned in
                # https://github.com/vllm-project/vllm/issues/28334
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5573
                if (
                    self.compilation_config.cudagraph_specialize_lora
                    and num_active_loras > 0
                ):
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5578
                    use_cudagraphs = False

                self.drafter.dummy_run(
                    num_tokens,
                    use_cudagraphs=use_cudagraphs,
Rémi Delacourt's avatar
Rémi Delacourt committed
5579
                    is_graph_capturing=is_graph_capturing,
5580
                    slot_mappings=slot_mappings,
5581
                )
5582

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        # We register layerwise NVTX hooks here after the first dynamo tracing is
        # done to avoid nvtx operations in hook functions being traced by
        # torch dynamo and causing graph breaks.
        # Note that for DYNAMO_ONCE and VLLM_COMPILE mode,
        # compiled model's dynamo tracing is only done once and the compiled model's
        # __call__ function is replaced by calling the compiled function.
        # So it's safe to register hooks here. Hooks will be registered to
        # both compiled and uncompiled models but they will never
        # be called on the compiled model execution path.
        self._register_layerwise_nvtx_hooks()

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5603
        # 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)

5604
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
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5606
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5608
        logit_indices_device = torch.from_numpy(logit_indices).to(
            self.device, non_blocking=True
        )
        return hidden_states, hidden_states[logit_indices_device]
5609
5610
5611
5612
5613
5614

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
5615
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5617
        # The dummy hidden states may contain special values,
        # like `inf` or `nan`.
        # To avoid breaking the sampler, we use a random tensor here instead.
5618

5619
5620
        mm_config = self.vllm_config.model_config.multimodal_config
        if mm_config and mm_config.mm_encoder_only:
5621
5622
5623
            # MM Encoder only model no need to run sampler.
            return torch.tensor([])

5624
        hidden_states = torch.rand_like(hidden_states)
5625

5626
        logits = self.model.compute_logits(hidden_states)
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        num_reqs = logits.size(0)

5629
        dummy_tensors = lambda v: torch.full((num_reqs,), v, device=self.device)
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5638

        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,
Vedant V Jhaveri's avatar
Vedant V Jhaveri committed
5639
            logprob_token_ids=None,
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            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)],
5646
            spec_token_ids=[[] for _ in range(num_reqs)],
5647
5648
            allowed_token_ids_mask=None,
            bad_words_token_ids={},
5649
            logitsprocs=LogitsProcessors(),
5650
        )
5651
        try:
5652
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5654
            sampler_output = self.sampler(
                logits=logits, sampling_metadata=dummy_metadata
            )
5655
        except RuntimeError as e:
5656
            if "out of memory" in str(e):
5657
5658
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5660
                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 "
5661
5662
                    "initializing the engine."
                ) from e
5663
5664
            else:
                raise e
5665
        if self.speculative_config:
5666
5667
            draft_token_ids = [[0] for _ in range(num_reqs)]
            dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
5668
5669
                draft_token_ids, self.device
            )
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5671
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5675

            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
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5680
            logits = torch.randn(
                num_tokens + num_reqs,
                logits.shape[-1],
                device=self.device,
                dtype=logits.dtype,
5681
            )
5682
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5684
            self.rejection_sampler(
                dummy_spec_decode_metadata,
                draft_probs,
5685
                logits,
5686
5687
                dummy_metadata,
            )
5688
        return sampler_output
5689

5690
    def _dummy_pooler_run_task(
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5692
        self,
        hidden_states: torch.Tensor,
5693
5694
        task: PoolingTask,
    ) -> PoolerOutput:
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        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
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5702
        num_scheduled_tokens_np = np.full(num_reqs, min_tokens_per_req)
        num_scheduled_tokens_np[-1] += num_tokens % num_reqs
        assert np.sum(num_scheduled_tokens_np) == num_tokens
        assert len(num_scheduled_tokens_np) == num_reqs
5703
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5705

        req_num_tokens = num_tokens // num_reqs

5706
        dummy_prompt_lens = torch.from_numpy(num_scheduled_tokens_np)
5707
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5709
        dummy_token_ids = torch.zeros(
            (num_reqs, req_num_tokens), dtype=torch.int32, device=self.device
        )
5710

5711
        model = cast(VllmModelForPooling, self.get_model())
5712
        dummy_pooling_params = PoolingParams(task=task)
5713
        dummy_pooling_params.verify(self.model_config)
5714
        to_update = model.pooler.get_pooling_updates(task)
5715
5716
        to_update.apply(dummy_pooling_params)

5717
        dummy_metadata = PoolingMetadata(
5718
5719
            prompt_lens=dummy_prompt_lens,
            prompt_token_ids=dummy_token_ids,
5720
            prompt_token_ids_cpu=dummy_token_ids.cpu(),
5721
            pooling_params=[dummy_pooling_params] * num_reqs,
5722
            pooling_states=[PoolingStates() for i in range(num_reqs)],
5723
        )
5724

5725
        dummy_metadata.build_pooling_cursor(
5726
            num_scheduled_tokens_np,
5727
5728
            seq_lens_cpu=dummy_prompt_lens,
            device=hidden_states.device,
5729
        )
5730

5731
        try:
5732
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5734
            return model.pooler(
                hidden_states=hidden_states, pooling_metadata=dummy_metadata
            )
5735
        except RuntimeError as e:
5736
            if "out of memory" in str(e):
5737
                raise RuntimeError(
5738
5739
5740
                    "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 "
5741
5742
                    "initializing the engine."
                ) from e
5743
5744
            else:
                raise e
5745
5746
5747
5748
5749
5750

    @torch.inference_mode()
    def _dummy_pooler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> PoolerOutput:
5751
5752
        mm_config = self.vllm_config.model_config.multimodal_config
        if mm_config and mm_config.mm_encoder_only:
5753
5754
5755
            # MM Encoder only model not need to run pooler.
            return torch.tensor([])

5756
        # Find the task that has the largest output for subsequent steps
5757
5758
5759
        supported_pooling_tasks = self.get_supported_pooling_tasks()

        if not supported_pooling_tasks:
5760
5761
5762
5763
5764
5765
            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."
            )
5766

5767
        output_size = dict[PoolingTask, float]()
5768
        for task in supported_pooling_tasks:
5769
5770
            # Run a full batch with each task to ensure none of them OOMs
            output = self._dummy_pooler_run_task(hidden_states, task)
5771
            output_size[task] = sum(o.nbytes for o in output if o is not None)
5772
5773
5774
5775
            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)
5776

5777
    def profile_run(self) -> None:
5778
        # Profile with multimodal encoder & encoder cache.
5779
        if self.supports_mm_inputs:
5780
5781
            mm_config = self.model_config.multimodal_config
            if mm_config is not None and mm_config.skip_mm_profiling:
5782
                logger.info(
5783
                    "Skipping memory profiling for multimodal encoder and "
5784
5785
                    "encoder cache."
                )
5786
5787
5788
5789
5790
            else:
                mm_budget = self.mm_budget
                assert mm_budget is not None

                if (encoder_budget := mm_budget.get_encoder_budget()) > 0:
5791
5792
5793
5794
5795
5796
5797
5798
5799
5800
5801
5802
5803
5804
5805
5806
5807
                    if not mm_budget.mm_max_toks_per_item:
                        # All modality limits are 0 — embedding-only mode.
                        # Budget is non-zero for embedding storage, but
                        # there's no encoder to profile.
                        logger.info(
                            "Skipping encoder profiling for embedding-only "
                            "mode (all modality limits=0 with "
                            "enable_mm_embeds=True).",
                        )
                    else:
                        # NOTE: Currently model is profiled with a single
                        # non-text modality with the max possible input
                        # tokens even when it supports multiple.
                        dummy_modality = mm_budget.get_modality_with_max_tokens()
                        max_mm_items_per_batch = mm_budget.mm_max_items_per_batch[
                            dummy_modality
                        ]
5808

5809
                        logger.info_once(
5810
5811
5812
5813
5814
5815
5816
                            "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,
                        )
5817

5818
5819
5820
5821
5822
                        # Create dummy batch of multimodal inputs.
                        batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                            dummy_modality,
                            max_mm_items_per_batch,
                        )
5823

5824
5825
5826
5827
5828
5829
5830
5831
5832
5833
5834
                        # Run multimodal encoder.
                        dummy_encoder_outputs = self.model.embed_multimodal(
                            **batched_dummy_mm_inputs
                        )

                        sanity_check_mm_encoder_outputs(
                            dummy_encoder_outputs,
                            expected_num_items=max_mm_items_per_batch,
                        )
                        for i, output in enumerate(dummy_encoder_outputs):
                            self.encoder_cache[f"tmp_{i}"] = output
5835

5836
        # Add `is_profile` here to pre-allocate communication buffers
5837
5838
5839
        hidden_states, last_hidden_states = self._dummy_run(
            self.max_num_tokens, is_profile=True
        )
5840
        if get_pp_group().is_last_rank:
5841
5842
5843
5844
            if self.is_pooling_model:
                output = self._dummy_pooler_run(hidden_states)
            else:
                output = self._dummy_sampler_run(last_hidden_states)
5845
        else:
5846
            output = None
5847
        self._sync_device()
5848
        del hidden_states, output
5849
        self.encoder_cache.clear()
5850
        gc.collect()
5851

5852
5853
5854
5855
5856
5857
5858
5859
5860
5861
    def _init_minimal_kv_cache_for_profiling(self) -> None:
        from vllm.v1.core.kv_cache_utils import (
            get_kv_cache_config_from_groups,
            get_kv_cache_groups,
        )

        kv_cache_spec = self.get_kv_cache_spec()
        kv_cache_groups = get_kv_cache_groups(self.vllm_config, kv_cache_spec)
        min_blocks = self.compilation_config.max_cudagraph_capture_size or 1

5862
5863
5864
        # Temporarily change num_gpu_blocks_override to allocate a minimal KV cache
        saved_override = self.cache_config.num_gpu_blocks_override
        self.cache_config.num_gpu_blocks_override = min_blocks
5865
        minimal_config = get_kv_cache_config_from_groups(
5866
            self.vllm_config, kv_cache_groups, available_memory=0, suppress_log=True
5867
        )
5868
        self.cache_config.num_gpu_blocks_override = saved_override
5869

5870
        self.initialize_kv_cache(minimal_config, is_profiling=True)
5871
5872
5873
5874
5875
5876
5877
5878
5879
5880
5881
5882
5883
5884
5885
5886
5887
5888
        self.cache_config.num_gpu_blocks = minimal_config.num_blocks

        logger.debug("Initialized minimal KV cache for CUDA graph profiling")

    @staticmethod
    @contextmanager
    def _freeze_gc():
        gc.collect()
        should_freeze = not envs.VLLM_ENABLE_CUDAGRAPH_GC
        if should_freeze:
            gc.freeze()
        try:
            yield
        finally:
            if should_freeze:
                gc.unfreeze()
                gc.collect()

5889
5890
5891
5892
5893
5894
5895
5896
5897
5898
5899
5900
5901
5902
    def shutdown(self) -> None:
        """Release GPU tensors (model weights, KV caches, workspace) so that
        memory is reclaimable when running in the same process."""
        from vllm.model_executor.layers.rotary_embedding import _ROPE_DICT
        from vllm.v1.worker.workspace import reset_workspace_manager

        # Calls torch.accelerator.synchronize()
        self._cleanup_profiling_kv_cache()
        self.compilation_config.static_forward_context.clear()
        self.model = None  # type: ignore[assignment]
        _ROPE_DICT.clear()

        reset_workspace_manager()

5903
5904
5905
5906
5907
5908
5909
5910
5911
5912
5913
5914
5915
5916
5917
5918
5919
    def _cleanup_profiling_kv_cache(self) -> None:
        torch.accelerator.synchronize()
        if hasattr(self, "kv_caches") and self.kv_caches:
            for i in range(len(self.kv_caches)):
                self.kv_caches[i] = None  # type: ignore
            self.kv_caches.clear()
        if hasattr(self, "cross_layers_kv_cache"):
            self.cross_layers_kv_cache = None
            self.cross_layers_attn_backend = None
        if hasattr(self, "attn_groups"):
            self.attn_groups.clear()
        if hasattr(self, "kv_cache_config"):
            delattr(self, "kv_cache_config")
        self.cache_config.num_gpu_blocks = None

        for layer in self.compilation_config.static_forward_context.values():
            if hasattr(layer, "kv_cache"):
5920
5921
5922
5923
                kv_cache = layer.kv_cache
                layer.kv_cache = (
                    torch.tensor([]) if isinstance(kv_cache, torch.Tensor) else []
                )
5924
5925
5926
5927
5928
5929
5930
            # Clean up quantized KV cache scale views
            # (int8_per_token_head, fp8_per_token_head)
            if hasattr(layer, "impl"):
                if hasattr(layer.impl, "_k_scale_cache"):
                    layer.impl._k_scale_cache = None
                if hasattr(layer.impl, "_v_scale_cache"):
                    layer.impl._v_scale_cache = None
5931
5932
5933
5934
5935
5936
5937
5938
5939
5940
5941
5942
5943
5944
5945
5946
5947
5948
5949
5950
5951
5952
5953
5954
5955
5956
5957
5958
5959
5960
5961
5962
5963
5964
5965
5966
5967
5968
5969
5970
5971
5972
5973
5974
5975
5976
5977
5978
5979
5980
5981
5982
5983
5984
5985
5986
5987
5988
5989
5990
5991
5992
5993
5994
5995
5996
5997
5998
5999
6000
6001
6002
6003
6004
6005
6006
6007
6008
6009
6010
6011
6012
6013
6014
6015
6016
6017

        gc.collect()
        torch.accelerator.empty_cache()

        logger.debug("Cleaned up profiling KV cache and CUDA graphs")

    @torch.inference_mode()
    def profile_cudagraph_memory(self) -> int:
        with set_current_vllm_config(self.vllm_config):
            self._init_minimal_kv_cache_for_profiling()

        saved_num_cudagraph_captured = compilation_counter.num_cudagraph_captured

        capture_descs = self.cudagraph_dispatcher.get_capture_descs()

        total_graphs = sum(len(descs) for _, descs in capture_descs)
        if total_graphs == 0:
            logger.debug("No CUDA graphs will be captured, skipping profiling")
            self._cleanup_profiling_kv_cache()
            return 0

        logger.info(
            "Profiling CUDA graph memory: %s",
            ", ".join(
                f"{mode.name}={len(descs)} (largest={descs[0].num_tokens})"
                for mode, descs in capture_descs
                if descs
            ),
        )

        # Use a temporary pool for profiling to avoid fragmentation in the main pool.
        profiling_pool = current_platform.graph_pool_handle()
        original_pools: dict[int, Any] = {}
        for instance in list(CUDAGraphWrapper._all_instances):
            original_pools[id(instance)] = instance.graph_pool
            instance.graph_pool = profiling_pool

        set_cudagraph_capturing_enabled(True)
        with self._freeze_gc(), graph_capture(device=self.device):
            shared_memory_estimate = {}
            per_graph_estimate = {}
            torch.accelerator.synchronize()
            torch.accelerator.empty_cache()

            for mode, descs in capture_descs:
                profile_descs = descs[:2]
                mem_samples: list[int] = []

                for i, desc in enumerate(profile_descs):
                    mem_before = torch.cuda.mem_get_info()[0]
                    self._warmup_and_capture(
                        desc,
                        cudagraph_runtime_mode=mode,
                        profile_seq_lens=(
                            min(
                                self.max_model_len,
                                self.max_num_tokens // desc.num_tokens,
                            )
                            if mode == CUDAGraphMode.FULL and i == 0
                            else None
                        ),
                    )
                    torch.accelerator.synchronize()
                    free_after = torch.cuda.mem_get_info()[0]
                    mem_samples.append(mem_before - free_after)

                first_capture = mem_samples[0]
                # Use at least 1 MiB per graph for driver overhead
                per_graph = max(mem_samples[1] if len(mem_samples) > 1 else 0, 1 << 20)

                shared_memory_estimate[mode] = first_capture
                per_graph_estimate[mode] = per_graph * (len(descs) - 1)

                logger.debug(
                    "Estimated %s CUDA graph memory: "
                    "%.2f MiB first-capture + (%d-1) × %.2f MiB per-graph",
                    mode.name,
                    first_capture / (1 << 20),
                    len(descs),
                    per_graph / (1 << 20),
                )

        set_cudagraph_capturing_enabled(False)
        CUDAGraphWrapper.clear_all_graphs()
        for instance in list(CUDAGraphWrapper._all_instances):
            if id(instance) in original_pools:
                instance.graph_pool = original_pools[id(instance)]
6018
6019
6020
        for key_set in self.cudagraph_dispatcher.cudagraph_keys.values():
            key_set.clear()
        self.cudagraph_dispatcher.keys_initialized = False
6021
6022
6023
6024
6025
6026
6027
6028
6029
6030
6031
6032
6033
6034
6035
6036
6037
        self.maybe_remove_all_loras(self.lora_config)
        self._cleanup_profiling_kv_cache()
        compilation_counter.num_cudagraph_captured = saved_num_cudagraph_captured

        # FULL and PIECEWISE graphs share the global pool at runtime and are
        # never replayed concurrently, so the pool overlays their memory.
        # Take the max to avoid double-counting the overlap.
        total_estimate = max(shared_memory_estimate.values()) + sum(
            per_graph_estimate.values()
        )
        logger.info(
            "Estimated CUDA graph memory: %.2f GiB total",
            total_estimate / (1 << 30),
        )

        return int(total_estimate)

6038
    @instrument(span_name="Capture model")
6039
    def capture_model(self) -> int:
6040
        if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
6041
            logger.warning(
6042
                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
6043
6044
                "ensure `cudagraph_mode` was not manually set to `NONE`"
            )
6045
            return 0
6046

6047
6048
6049
6050
6051
6052
6053
6054
6055
6056
6057
6058
6059
        # Initialize encoder CUDA graph manager if enabled.
        # Use get_model() to unwrap CUDAGraphWrapper/UBatchWrapper,
        # because @runtime_checkable Protocol isinstance() checks do not
        # work through __getattr__ forwarding.
        if (
            self.compilation_config.cudagraph_mm_encoder
            and self.supports_mm_inputs
            and self.encoder_cudagraph_manager is None
        ):
            from vllm.model_executor.models.interfaces import (
                SupportsEncoderCudaGraph,
                supports_encoder_cudagraph,
            )
6060
            from vllm.v1.worker.encoder_cudagraph import (
6061
6062
                EncoderCudaGraphManager,
            )
6063
6064
6065
6066
6067
6068
6069
6070
6071
6072
6073

            raw_model = self.get_model()
            if supports_encoder_cudagraph(raw_model):
                self.encoder_cudagraph_manager = EncoderCudaGraphManager(
                    vllm_config=self.vllm_config,
                    device=self.device,
                    dtype=self.dtype,
                    model=cast(SupportsEncoderCudaGraph, raw_model),
                )
                logger.info("Initialized EncoderCudaGraphManager for vision encoder")

6074
6075
        compilation_counter.num_gpu_runner_capture_triggers += 1

6076
6077
        start_time = time.perf_counter()

6078
6079
6080
        # 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.
6081
        set_cudagraph_capturing_enabled(True)
6082
6083
6084
        with self._freeze_gc(), graph_capture(device=self.device):
            torch.accelerator.synchronize()
            torch.accelerator.empty_cache()
6085
            start_free_gpu_memory = torch.cuda.mem_get_info()[0]
6086

6087
6088
6089
6090
            for (
                runtime_mode,
                batch_descs,
            ) in self.cudagraph_dispatcher.get_capture_descs():
6091
                self._capture_cudagraphs(
6092
6093
                    batch_descriptors=batch_descs,
                    cudagraph_runtime_mode=runtime_mode,
6094
                )
6095
                torch.accelerator.synchronize()
6096

6097
6098
6099
6100
            # Capture encoder CUDA graphs if enabled
            if self.encoder_cudagraph_manager is not None:
                self.encoder_cudagraph_manager.capture()

6101
            torch.accelerator.synchronize()
6102
6103
            end_free_gpu_memory = torch.cuda.mem_get_info()[0]

6104
6105
6106
        # 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
6107
        # we may do lazy capturing in future that still allows capturing
6108
6109
        # after here.
        set_cudagraph_capturing_enabled(False)
6110

6111
6112
6113
        torch.accelerator.synchronize()
        torch.accelerator.empty_cache()

6114
6115
6116
6117
        # Lock workspace to prevent resizing during execution.
        # Max workspace sizes should have been captured during warmup/profiling.
        lock_workspace()

6118
6119
6120
6121
        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.
6122
        logger.info_once(
6123
6124
6125
6126
            "Graph capturing finished in %.0f secs, took %.2f GiB",
            elapsed_time,
            cuda_graph_size / (1 << 30),
        )
6127
        return cuda_graph_size
6128

6129
6130
6131
6132
6133
6134
6135
6136
6137
6138
6139
6140
6141
6142
6143
6144
6145
6146
6147
6148
6149
    def _warmup_and_capture(
        self,
        desc: BatchDescriptor,
        cudagraph_runtime_mode: CUDAGraphMode,
        profile_seq_lens: int | None = None,
        allow_microbatching: bool = False,
        num_warmups: int | None = None,
    ):
        if num_warmups is None:
            num_warmups = self.compilation_config.cudagraph_num_of_warmups
        force_attention = cudagraph_runtime_mode == CUDAGraphMode.FULL
        for _ in range(num_warmups):
            self._dummy_run(
                desc.num_tokens,
                cudagraph_runtime_mode=CUDAGraphMode.NONE,
                force_attention=force_attention,
                uniform_decode=desc.uniform,
                allow_microbatching=allow_microbatching,
                skip_eplb=True,
                remove_lora=False,
                num_active_loras=desc.num_active_loras,
6150
                profile_seq_lens=profile_seq_lens,
6151
6152
6153
6154
6155
6156
6157
6158
6159
6160
6161
6162
6163
            )
        self._dummy_run(
            desc.num_tokens,
            cudagraph_runtime_mode=cudagraph_runtime_mode,
            uniform_decode=desc.uniform,
            allow_microbatching=allow_microbatching,
            skip_eplb=True,
            remove_lora=False,
            num_active_loras=desc.num_active_loras,
            is_graph_capturing=True,
            profile_seq_lens=profile_seq_lens,
        )

6164
6165
    def _capture_cudagraphs(
        self,
6166
        batch_descriptors: list[BatchDescriptor],
6167
6168
6169
6170
        cudagraph_runtime_mode: CUDAGraphMode,
    ):
        assert (
            cudagraph_runtime_mode != CUDAGraphMode.NONE
6171
            and cudagraph_runtime_mode.is_valid_runtime_mode()
6172
        ), f"Invalid cudagraph runtime mode: {cudagraph_runtime_mode}"
6173

6174
6175
6176
6177
6178
        if not batch_descriptors:
            return

        uniform_decode = batch_descriptors[0].uniform

6179
6180
        # Only rank 0 should print progress bar during capture
        if is_global_first_rank():
6181
6182
            batch_descriptors = tqdm(
                batch_descriptors,
6183
6184
6185
                disable=not self.load_config.use_tqdm_on_load,
                desc="Capturing CUDA graphs ({}, {})".format(
                    "decode" if uniform_decode else "mixed prefill-decode",
6186
6187
6188
                    cudagraph_runtime_mode.name,
                ),
            )
6189

6190
        # We skip EPLB here since we don't want to record dummy metrics
6191
        for batch_desc in batch_descriptors:
6192
            # We currently only capture ubatched graphs when its a FULL
6193
6194
6195
            # 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
6196
            allow_microbatching = (
6197
                self.parallel_config.use_ubatching
6198
6199
                and cudagraph_runtime_mode == CUDAGraphMode.FULL
                and uniform_decode
6200
6201
                and check_ubatch_thresholds(
                    config=self.vllm_config.parallel_config,
6202
                    num_tokens=batch_desc.num_tokens,
6203
6204
                    uniform_decode=uniform_decode,
                )
6205
            )
6206
6207
            self._warmup_and_capture(
                batch_desc,
6208
6209
6210
                cudagraph_runtime_mode=cudagraph_runtime_mode,
                allow_microbatching=allow_microbatching,
            )
6211
            torch.accelerator.synchronize()
6212
        self.maybe_remove_all_loras(self.lora_config)
6213

6214
6215
6216
6217
6218
    def initialize_attn_backend(
        self,
        kv_cache_config: KVCacheConfig,
        is_profiling: bool = False,
    ) -> None:
6219
6220
6221
        """
        Initialize the attention backends and attention metadata builders.
        """
6222
        assert len(self.attn_groups) == 0, "Attention backends are already initialized"
6223

6224
6225
6226
6227
6228
6229
        class AttentionGroupKey(NamedTuple):
            attn_backend: type[AttentionBackend]
            kv_cache_spec: KVCacheSpec

        def get_attn_backends_for_group(
            kv_cache_group_spec: KVCacheGroupSpec,
6230
        ) -> tuple[dict[AttentionGroupKey, list[str]], set[type[AttentionBackend]]]:
6231
            layer_type = cast(type[Any], AttentionLayerBase)
6232
            layers = get_layers_from_vllm_config(
6233
                self.vllm_config, layer_type, kv_cache_group_spec.layer_names
6234
            )
6235
6236
            attn_backends = {}
            attn_backend_layers = defaultdict(list)
6237
            # Dedupe based on full class name; this is a bit safer than
6238
6239
6240
6241
            # 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.
6242
            for layer_name in kv_cache_group_spec.layer_names:
6243
                attn_backend = layers[layer_name].get_attn_backend()
6244
6245
6246
6247

                if layer_name in self.kv_sharing_fast_prefill_eligible_layers:
                    attn_backend = create_fast_prefill_custom_backend(
                        "FastPrefill",
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                        attn_backend,  # type: ignore[arg-type]
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                    )

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                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):
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                    layer_kv_cache_spec = layer_kv_cache_spec.kv_cache_specs[layer_name]
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                key = (full_cls_name, layer_kv_cache_spec)
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                attn_backends[key] = AttentionGroupKey(
                    attn_backend, layer_kv_cache_spec
                )
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                attn_backend_layers[key].append(layer_name)
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            return (
                {attn_backends[k]: v for k, v in attn_backend_layers.items()},
                set(group_key.attn_backend for group_key in attn_backends.values()),
            )
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        def create_attn_groups(
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            attn_backends_map: dict[AttentionGroupKey, list[str]],
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            kv_cache_group_id: int,
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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(
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                    attn_backend,
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                    layer_names,
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                    kv_cache_spec,
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                    kv_cache_group_id,
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                )

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                attn_groups.append(attn_group)
            return attn_groups

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        attention_backend_maps = []
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        attention_backend_list = []
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        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)
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            attention_backend_maps.append(attn_backends[0])
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            attention_backend_list.append(attn_backends[1])
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        # Resolve cudagraph_mode before actually initialize metadata_builders
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        self._check_and_update_cudagraph_mode(
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            attention_backend_list,
            kv_cache_config.kv_cache_groups,
            is_profiling=is_profiling,
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        )
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        # Check if attention backend supports PCP&DCP and related features.
        check_attention_cp_compatibility(self.vllm_config)

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        for i, attn_backend_map in enumerate(attention_backend_maps):
            self.attn_groups.append(create_attn_groups(attn_backend_map, i))
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    def initialize_metadata_builders(
        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
    ) -> None:
        """
        Create the metadata builders for all KV cache groups and attn groups.
        """
        for kv_cache_group_id in range(len(kv_cache_config.kv_cache_groups)):
            for attn_group in self.attn_groups[kv_cache_group_id]:
                attn_group.create_metadata_builders(
                    self.vllm_config,
                    self.device,
                    kernel_block_sizes[kv_cache_group_id]
                    if kv_cache_group_id < len(kernel_block_sizes)
                    else None,
                    num_metadata_builders=1
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                    if not self.parallel_config.use_ubatching
                    else self.parallel_config.num_ubatches,
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                )
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        # Calculate reorder batch threshold (if needed)
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        # Note (tdoublep): do this *after* constructing builders,
        # because some of them change the threshold at init time.
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        self.calculate_reorder_batch_threshold()

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        # Initialize drafter attention backend
        if self.speculative_config and (
            self.speculative_config.use_eagle()
            or self.speculative_config.uses_draft_model()
        ):
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            assert isinstance(
                self.drafter, EagleProposer | DFlashProposer | DraftModelProposer
            )
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            self.drafter.initialize_attn_backend(kv_cache_config, kernel_block_sizes)

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    def _check_and_update_cudagraph_mode(
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        self,
        attention_backends: list[set[type[AttentionBackend]]],
        kv_cache_groups: list[KVCacheGroupSpec],
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        is_profiling: bool = False,
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    ) -> None:
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        """
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        Resolve the cudagraph_mode when there are multiple attention
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        groups with potential conflicting CUDA graph support.
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        Then initialize the cudagraph_dispatcher based on the resolved
        cudagraph_mode.
        """
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        min_cg_support = AttentionCGSupport.ALWAYS
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        min_cg_attn_backend = None
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        for attn_backend_set, kv_cache_group in zip(
            attention_backends, kv_cache_groups
        ):
            for attn_backend in attn_backend_set:
                builder_cls = attn_backend.get_builder_cls()

                cg_support = builder_cls.get_cudagraph_support(
                    self.vllm_config, kv_cache_group.kv_cache_spec
                )
                if cg_support.value < min_cg_support.value:
                    min_cg_support = cg_support
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                    min_cg_attn_backend = attn_backend.__name__
        cudagraph_mode = self.compilation_config.resolve_cudagraph_mode_and_sizes(
            min_cg_support,
            min_cg_attn_backend,
            self.uniform_decode_query_len,
            self.parallel_config.tensor_parallel_size,
            self.kv_cache_config,
            self.max_num_reqs,
            is_profiling=is_profiling,
        )
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        # Trigger cudagraph dispatching keys initialization after
        # resolved cudagraph mode.
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        self.cudagraph_dispatcher.initialize_cudagraph_keys(
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            cudagraph_mode, self.uniform_decode_query_len
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        )
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        # Initialize drafter's cudagraph dispatcher if using spec decode.
        if self.speculative_config and (
            self.speculative_config.use_eagle()
            or self.speculative_config.uses_extract_hidden_states()
        ):
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            assert isinstance(
                self.drafter,
                EagleProposer | DFlashProposer | ExtractHiddenStatesProposer,
            )
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            self.drafter.initialize_cudagraph_keys(cudagraph_mode)

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    def calculate_reorder_batch_threshold(self) -> None:
        """
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        Choose the minimum reorder batch threshold from all attention groups.
        Backends should be able to support lower threshold then what they request
        just may have a performance penalty due to that backend treating decodes
        as prefills.
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        """
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        min_none_high = lambda a, b: a if b is None else b if a is None else min(a, b)

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        reorder_batch_thresholds: list[int | None] = [
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            group.get_metadata_builder().reorder_batch_threshold
            for group in self._attn_group_iterator()
        ]
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        # If there are no attention groups (attention-free model) or no backend
        # reports a threshold, leave reordering disabled.
        if len(reorder_batch_thresholds) == 0:
            self.reorder_batch_threshold = None
            return
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        self.reorder_batch_threshold = reduce(min_none_high, reorder_batch_thresholds)  # type: ignore[assignment]
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    def _set_mm_prefix_range_for_metadata(
        self,
        attn_metadata: Any,
        req_doc_ranges: dict[int, list[tuple[int, int]]],
    ) -> None:
        """Set mm_prefix_range for all attention metadata objects.

        This method handles both list and non-list attention metadata,
        computing mm_prefix_range_tensor once and sharing it across all
        metadata objects to avoid redundant host-to-device transfers.
        """
        from vllm.v1.attention.backends.triton_attn import (
            TritonAttentionMetadata,
        )

        # Get all metadata objects from either list or dict structure
        metadata_list = []
        if isinstance(attn_metadata, list):
            for ub_metadata in attn_metadata:
                metadata_list.extend(ub_metadata.values())
        else:
            metadata_list.extend(attn_metadata.values())

        # Set mm_prefix_range for all metadata and compute tensor once
        shared_tensor = None
        for metadata in metadata_list:
            metadata.mm_prefix_range = req_doc_ranges  # type: ignore[attr-defined]

            # Only compute tensor for TritonAttentionMetadata
            if isinstance(metadata, TritonAttentionMetadata):
                if shared_tensor is None:
                    shared_tensor = (
                        TritonAttentionMetadata.compute_mm_prefix_range_tensor(
                            req_doc_ranges,
                            metadata.seq_lens.shape[0],  # type: ignore[attr-defined]
                            metadata.seq_lens.device,  # type: ignore[attr-defined]
                        )
                    )
                metadata.mm_prefix_range_tensor = shared_tensor

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    def may_reinitialize_input_batch(
        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
    ) -> None:
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        """
        Re-initialize the input batch if the block sizes are different from
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        what it was originally created with. This happens when the final
        block size (determined after model loading) differs from the
        placeholder used during __init__, or when there are multiple
        KV cache groups.
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        Args:
            kv_cache_config: The KV cache configuration.
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            kernel_block_sizes: The kernel block sizes for each KV cache group.
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        """
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        block_sizes = []
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        max_num_blocks = []
        max_model_len = max(self.max_model_len, self.max_encoder_len)
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        for kv_cache_group in kv_cache_config.kv_cache_groups:
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            if isinstance(kv_cache_group.kv_cache_spec, EncoderOnlyAttentionSpec):
                continue
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            block_size = kv_cache_group.kv_cache_spec.block_size
            block_sizes.append(block_size)
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            max_num_blocks_per_req = cdiv(
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                max_model_len, block_size * get_total_cp_world_size()
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            )
            if isinstance(kv_cache_group.kv_cache_spec, MambaSpec):
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                max_num_blocks_per_req = (
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                    max_num_blocks_per_req
                    if self.cache_config.enable_prefix_caching
                    else 1
                ) + kv_cache_group.kv_cache_spec.num_speculative_blocks
            max_num_blocks.append(max_num_blocks_per_req)
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        if (
            block_sizes != self._init_block_sizes
            or kernel_block_sizes != self._init_kernel_block_sizes
        ):
            self._init_block_sizes = block_sizes
            self._init_kernel_block_sizes = kernel_block_sizes
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            self.input_batch = InputBatch(
                max_num_reqs=self.max_num_reqs,
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                max_model_len=max_model_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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                max_num_blocks_per_req=max_num_blocks,
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                is_spec_decode=bool(self.vllm_config.speculative_config),
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                logitsprocs=self.input_batch.logitsprocs,
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                logitsprocs_need_output_token_ids=self.input_batch.logitsprocs_need_output_token_ids,
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                is_pooling_model=self.is_pooling_model,
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            )

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        assert self._init_block_sizes == block_sizes, (
            f"InputBatch block_sizes {self._init_block_sizes} != "
            f"kv_cache block_sizes {block_sizes}"
        )
        assert self._init_kernel_block_sizes == kernel_block_sizes, (
            f"InputBatch kernel_block_sizes {self._init_kernel_block_sizes} "
            f"!= kv_cache kernel_block_sizes {kernel_block_sizes}"
        )

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    def _allocate_kv_cache_tensors(
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        self, kv_cache_config: KVCacheConfig
    ) -> dict[str, torch.Tensor]:
6514
        """
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        Initializes the KV cache buffer with the correct size. The buffer needs
        to be reshaped to the desired shape before being used by the models.

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        Args:
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            kv_cache_config: The KV cache config
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        Returns:
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            dict[str, torch.Tensor]: A map between layer names to their
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            corresponding memory buffer for KV cache.
6523
        """
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        kv_cache_raw_tensors: dict[str, torch.Tensor] = {}
        for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
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            tensor = torch.zeros(
                kv_cache_tensor.size, dtype=torch.int8, device=self.device
            )
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            for layer_name in kv_cache_tensor.shared_by:
                kv_cache_raw_tensors[layer_name] = tensor

        layer_names = set()
        for group in kv_cache_config.kv_cache_groups:
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            for layer_name in group.layer_names:
                if layer_name in self.runner_only_attn_layers:
                    continue
                layer_names.add(layer_name)
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        assert layer_names == set(kv_cache_raw_tensors.keys()), (
            "Some layers are not correctly initialized"
        )
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        return kv_cache_raw_tensors

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

6546
    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
6551

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    def _reshape_kv_cache_tensors(
        self,
        kv_cache_raw_tensors: dict[str, torch.Tensor],
6555
        kernel_block_sizes: list[int],
6556
    ) -> dict[str, torch.Tensor]:
6557
        """
6558
        Reshape the KV cache tensors to the desired shape and dtype.
6559

6560
        Args:
6561
            kv_cache_raw_tensors: The KV cache buffer of each layer, with
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                correct size but uninitialized shape.
6563
            kernel_block_sizes: The kernel block sizes for each KV cache group.
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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.
        """
6568
        kv_caches: dict[str, torch.Tensor] = {}
6569
        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
6572
            attn_backend = group.backend
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            if group.kv_cache_group_id == len(kernel_block_sizes):
                # There may be a last group for layers without kv cache.
                continue
            kernel_block_size = kernel_block_sizes[group.kv_cache_group_id]
6577
            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
6582
                num_blocks = raw_tensor.numel() // kv_cache_spec.page_size_bytes
6583
                if isinstance(kv_cache_spec, AttentionSpec):
6584
                    has_attn = True
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                    num_blocks_per_kv_block = (
                        kv_cache_spec.block_size // kernel_block_size
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                    )
                    kernel_num_blocks = num_blocks * num_blocks_per_kv_block

6590
                    kv_cache_shape = attn_backend.get_kv_cache_shape(
6591
                        kernel_num_blocks,
6592
                        kernel_block_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,
                    )
6597
                    dtype = kv_cache_spec.dtype
6598
                    try:
6599
                        kv_cache_stride_order = attn_backend.get_kv_cache_stride_order()
6600
                        assert len(kv_cache_stride_order) == len(kv_cache_shape)
6601
                    except (AttributeError, NotImplementedError):
6602
                        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,
6641
                        )
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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
6646
                else:
6647
                    raise NotImplementedError
6648
6649

        if has_attn and has_mamba:
6650
            self._update_hybrid_attention_mamba_layout(kv_caches, kernel_block_sizes)
6651

6652
6653
        return kv_caches

6654
    def _update_hybrid_attention_mamba_layout(
6655
        self, kv_caches: dict[str, torch.Tensor], kernel_block_sizes: list[int]
6656
    ) -> None:
6657
        """
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        Update the layout of attention layers from (2, num_blocks, ...) to
        (num_blocks, 2, ...).
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        Args:
6662
            kv_caches: The KV cache buffer of each layer.
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            kernel_block_sizes: The kernel block sizes for each KV cache group.
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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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            if not isinstance(kv_cache_spec, AttentionSpec):
                continue
            block_dim = group.backend.get_kv_cache_block_dim(
                kernel_block_sizes[group.kv_cache_group_id],
                kv_cache_spec.num_kv_heads,
                kv_cache_spec.head_size,
                cache_dtype_str=self.cache_config.cache_dtype,
            )
            # block_dim: 0 means (num_blocks, 2, ...); 1 means (2, num_blocks, ...).
            if block_dim == 0:
                continue
            assert block_dim == 1
6680
            for layer_name in group.layer_names:
6681
                kv_cache = kv_caches[layer_name]
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                hidden_size = kv_cache.shape[2:].numel()
                kv_cache.as_strided_(
                    size=kv_cache.shape,
                    stride=(hidden_size, 2 * hidden_size, *kv_cache.stride()[2:]),
                )
6687

6688
    def initialize_kv_cache_tensors(
6689
        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
6690
    ) -> dict[str, torch.Tensor]:
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        """
        Initialize the memory buffer for KV cache.

        Args:
            kv_cache_config: The KV cache config
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            kernel_block_sizes: The kernel block sizes for each KV cache group.

6698
        Returns:
6699
            Dict[str, torch.Tensor]: A map between layer names to their
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            corresponding memory buffer for KV cache.
        """
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        # Try creating KV caches optimized for kv-connector transfers
        cache_dtype = self.cache_config.cache_dtype
        if self.use_uniform_kv_cache(self.attn_groups, cache_dtype):
            kv_caches, cross_layers_kv_cache, attn_backend = (
                self.allocate_uniform_kv_caches(
                    kv_cache_config,
                    self.attn_groups,
                    cache_dtype,
                    self.device,
                    kernel_block_sizes,
                )
            )
            self.cross_layers_kv_cache = cross_layers_kv_cache
            self.cross_layers_attn_backend = attn_backend
        else:
            # Fallback to the general case
            # Initialize the memory buffer for KV cache
            kv_cache_raw_tensors = self._allocate_kv_cache_tensors(kv_cache_config)

            # Change the memory buffer to the desired shape
            kv_caches = self._reshape_kv_cache_tensors(
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                kv_cache_raw_tensors, kernel_block_sizes
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            )
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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,
        is_profiling: bool = False,
    ) -> None:
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        """
        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._mamba_copy_bufs = None
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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, is_profiling=is_profiling)
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        initialize_mamba_ssu_backend(
            self.vllm_config.mamba_config, self.kv_cache_config
        )
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        # The kernel block size for all KV cache groups. For example, if
        # kv_cache_manager uses block_size 256 for a given group, but the attention
        # backends for that group only supports block_size 64, we will return
        # kernel_block_size 64 and split the 256-token-block to 4 blocks with 64
        # tokens each.
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        kernel_block_sizes = prepare_kernel_block_sizes(
            kv_cache_config, self.attn_groups
        )
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        self._kernel_block_sizes = kernel_block_sizes
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        # create metadata builders
        self.initialize_metadata_builders(kv_cache_config, kernel_block_sizes)

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        # Reinitialize need to after initialize_attn_backend
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        self.may_reinitialize_input_batch(kv_cache_config, kernel_block_sizes)
        kv_caches = self.initialize_kv_cache_tensors(
            kv_cache_config, kernel_block_sizes
        )
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        if (
            self.speculative_config
            and self.speculative_config.uses_extract_hidden_states()
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        ):
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            assert isinstance(self.drafter, ExtractHiddenStatesProposer)
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            # 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() and not is_profiling:
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            kv_transfer_group = get_kv_transfer_group()
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            if self.cross_layers_kv_cache is not None:
                assert self.cross_layers_attn_backend is not None
                kv_transfer_group.register_cross_layers_kv_cache(
                    self.cross_layers_kv_cache, self.cross_layers_attn_backend
                )
            else:
                kv_transfer_group.register_kv_caches(kv_caches)
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            kv_transfer_group.set_host_xfer_buffer_ops(copy_kv_blocks)
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    def _get_attention_kv_cache_gid(self) -> int:
        """Find the KV cache group index for attention layers."""
        for gid, group in enumerate(self.kv_cache_config.kv_cache_groups):
            if isinstance(group.kv_cache_spec, AttentionSpec):
                return gid
        return 0
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    def init_routed_experts_capturer(self):
        logger.info(
            "Initializing routed experts capturer, enable_return_routed_experts: %s",
            self.model_config.enable_return_routed_experts,
        )
        routed_experts_capturer = RoutedExpertsCapturer.create()
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        self.routed_experts_attn_gid = self._get_attention_kv_cache_gid()
        min_block_size = min(
            [
                group.kv_cache_spec.block_size
                for group in self.kv_cache_config.kv_cache_groups
            ]
        )
        num_groups = len(self.kv_cache_config.kv_cache_groups)
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        self.max_num_kv_tokens = (
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            self.kv_cache_config.num_blocks // num_groups
        ) * min_block_size
        dcp_size = self.vllm_config.parallel_config.decode_context_parallel_size
        pcp_size = self.vllm_config.parallel_config.prefill_context_parallel_size
        if pcp_size * dcp_size > 1:
            self.max_num_kv_tokens *= pcp_size * dcp_size

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        routed_experts_capturer.init_buffer(
            max_num_batched_tokens=self.scheduler_config.max_num_batched_tokens,
            max_num_kv_tokens=self.max_num_kv_tokens,
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            vllm_config=self.vllm_config,
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        )
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        self._bind_routed_experts_capturer(routed_experts_capturer)
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        self.routed_experts_initialized = True
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    def _bind_routed_experts_capturer(self, capturer: RoutedExpertsCapturer) -> None:
        from vllm.model_executor.layers.fused_moe.layer import FusedMoE
        from vllm.model_executor.layers.fused_moe.router.base_router import (
            BaseRouter,
        )

        for module in self.compilation_config.static_forward_context.values():
            if isinstance(module, FusedMoE) and isinstance(module.router, BaseRouter):
                layer_id = module.layer_id

                def _capture_fn(topk_ids, _layer_id=layer_id, _capturer=capturer):
                    _capturer.capture(_layer_id, topk_ids)

                module.router.set_capture_fn(_capture_fn)
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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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        if has_ec_transfer() and not get_ec_transfer().is_consumer:
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            return {}
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        kv_cache_spec: dict[str, KVCacheSpec] = {}
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        layer_type = cast(type[Any], AttentionLayerBase)
        attn_layers = get_layers_from_vllm_config(self.vllm_config, layer_type)
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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]]:
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        # 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()
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        return pinned.tolist()
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    def get_encoder_timing_stats(self) -> dict[str, dict[str, float | int]]:
        """
        Get encoder timing stats for all requests and clear the registry.

        Returns:
            Dictionary mapping request_id to stats dict.
        """
        with self._encoder_timing_lock:
            stats = {
                req_id: stats_obj.to_dict()
                for req_id, stats_obj in self.encoder_timing_registry.items()
            }
            self.encoder_timing_registry.clear()
            return stats

    @contextmanager
    def timed_encoder_operation(
        self,
        should_time: bool,
        group_lora_refs: list[tuple[str, Any]],
        current_item_idx: int,
        num_items: int,
    ):
        """
        Context manager to time encoder forward operations.

        Args:
            should_time: Whether timing is enabled
            group_lora_refs: Full list of (request_id, pos_info) tuples
            current_item_idx: Starting index for this group
            num_items: Number of items in this group
        """
        if not should_time:
            yield
            return

        group_refs = group_lora_refs[current_item_idx : current_item_idx + num_items]
        group_request_ids = {req_id for req_id, _ in group_refs}

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        torch.accelerator.synchronize()
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        start_time = time.perf_counter()

        try:
            yield
        finally:
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            torch.accelerator.synchronize()
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            elapsed = time.perf_counter() - start_time

            per_request_time = elapsed / max(len(group_request_ids), 1)

            with self._encoder_timing_lock:
                for req_id in group_request_ids:
                    if req_id not in self.encoder_timing_registry:
                        self.encoder_timing_registry[req_id] = EncoderTimingStats()

                    stats = self.encoder_timing_registry[req_id]
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                    stats.encoder_forward_secs += per_request_time
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                    stats.num_encoder_calls += 1


@dataclass
class EncoderTimingStats:
    """Per-request timing statistics for encoder forward pass."""

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    encoder_forward_secs: float = 0.0
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    """Time spent in vision encoder forward pass (seconds)."""

    num_encoder_calls: int = 0
    """Number of times encoder was called for this request."""

    def to_dict(self) -> dict[str, float | int]:
        return {
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            "encoder_forward_secs": self.encoder_forward_secs,
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            "num_encoder_calls": self.num_encoder_calls,
        }