gpu_model_runner.py 198 KB
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import gc
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import itertools
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import time
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from collections import defaultdict
from collections.abc import Iterator
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from contextlib import contextmanager
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from copy import deepcopy
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from typing import TYPE_CHECKING, Any, NamedTuple, Optional, Union, cast
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import numpy as np
import torch
import torch.distributed
import torch.nn as nn
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from tqdm import tqdm
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from typing_extensions import TypeAlias
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import vllm.envs as envs
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from vllm.attention import Attention, AttentionType
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from vllm.attention.backends.abstract import AttentionBackend, MultipleOf
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from vllm.attention.layer import MLAAttention
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from vllm.attention.layers.chunked_local_attention import ChunkedLocalAttention
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from vllm.compilation.counter import compilation_counter
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from vllm.compilation.cuda_graph import CUDAGraphWrapper
from vllm.compilation.monitor import set_cudagraph_capturing_enabled
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from vllm.config import (
    CompilationLevel,
    CUDAGraphMode,
    VllmConfig,
    get_layers_from_vllm_config,
    update_config,
)
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from vllm.distributed.eplb.eplb_state import EplbState
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from vllm.distributed.kv_transfer import get_kv_transfer_group, has_kv_transfer_group
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from vllm.distributed.kv_transfer.kv_connector.utils import copy_kv_blocks
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from vllm.distributed.parallel_state import (
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    get_pp_group,
    get_tp_group,
    graph_capture,
    is_global_first_rank,
    prepare_communication_buffer_for_model,
)
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from vllm.forward_context import BatchDescriptor, set_forward_context
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from vllm.logger import init_logger
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from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
from vllm.model_executor.layers.mamba.abstract import MambaBase
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from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
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from vllm.model_executor.model_loader import TensorizerLoader, get_model_loader
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from vllm.model_executor.models.deepseek_v2 import DeepseekV32IndexerCache
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from vllm.model_executor.models.interfaces import (
    SupportsMultiModal,
    is_mixture_of_experts,
    supports_eagle3,
    supports_mrope,
    supports_multimodal_pruning,
    supports_transcription,
)
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from vllm.model_executor.models.interfaces_base import (
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    VllmModelForPooling,
    is_pooling_model,
    is_text_generation_model,
)
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (
    BatchedTensorInputs,
    MultiModalKwargsItem,
    PlaceholderRange,
)
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from vllm.multimodal.utils import group_mm_kwargs_by_modality
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from vllm.pooling_params import PoolingParams
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from vllm.sampling_params import SamplingType
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from vllm.sequence import IntermediateTensors
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from vllm.tasks import GenerationTask, PoolingTask, SupportedTask
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from vllm.utils import (
    STR_DTYPE_TO_TORCH_DTYPE,
    DeviceMemoryProfiler,
    GiB_bytes,
    cdiv,
    check_use_alibi,
    get_dtype_size,
    is_pin_memory_available,
    length_from_prompt_token_ids_or_embeds,
    round_up,
    supports_dynamo,
)
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from vllm.utils.jsontree import json_map_leaves
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from vllm.v1.attention.backends.flash_attn import AttentionMetadata
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from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadataBuilder
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from vllm.v1.attention.backends.utils import (
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    AttentionCGSupport,
    AttentionMetadataBuilder,
    CommonAttentionMetadata,
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    create_fast_prefill_custom_backend,
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    reorder_batch_to_split_decodes_and_prefills,
    split_attn_metadata,
)
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from vllm.v1.cudagraph_dispatcher import CudagraphDispatcher
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from vllm.v1.kv_cache_interface import (
    AttentionSpec,
    ChunkedLocalAttentionSpec,
    CrossAttentionSpec,
    EncoderOnlyAttentionSpec,
    FullAttentionSpec,
    KVCacheConfig,
    KVCacheGroupSpec,
    KVCacheSpec,
    MambaSpec,
    MLAAttentionSpec,
    SlidingWindowSpec,
    UniformTypeKVCacheSpecs,
)
from vllm.v1.outputs import (
    EMPTY_MODEL_RUNNER_OUTPUT,
    AsyncModelRunnerOutput,
    DraftTokenIds,
    LogprobsLists,
    LogprobsTensors,
    ModelRunnerOutput,
    PoolerOutput,
    SamplerOutput,
)
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from vllm.v1.pool.metadata import PoolingMetadata
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from vllm.v1.sample.logits_processor import LogitsProcessors, build_logitsprocs
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from vllm.v1.sample.metadata import SamplingMetadata
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from vllm.v1.sample.rejection_sampler import RejectionSampler
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from vllm.v1.sample.sampler import Sampler
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from vllm.v1.spec_decode.eagle import EagleProposer
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from vllm.v1.spec_decode.medusa import MedusaProposer
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from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
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from vllm.v1.spec_decode.ngram_proposer import NgramProposer
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from vllm.v1.structured_output.utils import apply_grammar_bitmask
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from vllm.v1.utils import CpuGpuBuffer, record_function_or_nullcontext
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from vllm.v1.worker.dp_utils import coordinate_batch_across_dp
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from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
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from vllm.v1.worker.gpu_ubatch_wrapper import UBatchWrapper
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from vllm.v1.worker.kv_connector_model_runner_mixin import KVConnectorModelRunnerMixin
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from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
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from vllm.v1.worker.ubatch_utils import (
    UBatchSlice,
    UBatchSlices,
    check_ubatch_thresholds,
)
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from vllm.v1.worker.utils import is_residual_scattered_for_sp
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from .utils import (
    AttentionGroup,
    MultiModalBudget,
    add_kv_sharing_layers_to_kv_cache_groups,
    bind_kv_cache,
    gather_mm_placeholders,
    sanity_check_mm_encoder_outputs,
    scatter_mm_placeholders,
)
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if TYPE_CHECKING:
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    from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
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    from vllm.v1.core.sched.output import SchedulerOutput
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logger = init_logger(__name__)

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

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

        # Keep a reference to the device tensor to avoid it being
        # deallocated until we finish copying it to the host.
        self._sampled_token_ids = sampled_token_ids

        # Initiate the copy on a separate stream, but do not synchronize it.
        default_stream = torch.cuda.current_stream()
        with torch.cuda.stream(async_output_copy_stream):
            async_output_copy_stream.wait_stream(default_stream)
            self._sampled_token_ids_cpu = self._sampled_token_ids.to(
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                "cpu", non_blocking=True
            )
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            self._async_copy_ready_event.record()

    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.
        """
        self._async_copy_ready_event.synchronize()

        # Release the device tensor once the copy has completed
        del self._sampled_token_ids

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

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


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

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        init_batch_invariance()
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        model_config = self.model_config
        cache_config = self.cache_config
        scheduler_config = self.scheduler_config
        parallel_config = self.parallel_config
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        self.device = device
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        self.pin_memory = is_pin_memory_available()
        self.dtype = self.model_config.dtype
        if cache_config.cache_dtype == "auto":
            self.kv_cache_dtype = self.dtype
        else:
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            self.kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[cache_config.cache_dtype]
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        self.is_pooling_model = model_config.runner_type == "pooling"
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        self.enable_prompt_embeds = model_config.enable_prompt_embeds
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        self.is_multimodal_raw_input_only_model = (
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            model_config.is_multimodal_raw_input_only_model
        )
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        # This will be overridden in load_model()
        self.is_multimodal_pruning_enabled = False
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        self.max_model_len = model_config.max_model_len
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        self.dcp_world_size = self.parallel_config.decode_context_parallel_size
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        self.max_num_tokens = scheduler_config.max_num_batched_tokens
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        self.max_num_reqs = scheduler_config.max_num_seqs
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        # Broadcast PP output for external_launcher (torchrun)
        # to make sure we are synced across pp ranks
        # TODO: Support overlapping mirco-batches
        # https://github.com/vllm-project/vllm/issues/18019
        self.broadcast_pp_output = (
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            self.parallel_config.distributed_executor_backend == "external_launcher"
            and len(get_pp_group().ranks) > 0
        )
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        # Model-related.
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        self.num_query_heads = model_config.get_num_attention_heads(parallel_config)
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        self.hidden_size = model_config.get_hidden_size()
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        self.attention_chunk_size = model_config.attention_chunk_size
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        # Only relevant for models using ALiBi (e.g, MPT)
        self.use_alibi = check_use_alibi(model_config)
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        self.cascade_attn_enabled = not self.model_config.disable_cascade_attn
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        # Multi-modal data support
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        self.mm_registry = MULTIMODAL_REGISTRY
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        self.uses_mrope = model_config.uses_mrope
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        self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
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            model_config
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        if self.model_config.is_encoder_decoder:
            # Maximum length of the encoder input, only for encoder-decoder
            # models.
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            self.max_encoder_len = scheduler_config.max_num_encoder_input_tokens
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        else:
            self.max_encoder_len = 0

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        # Sampler
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        self.sampler = Sampler(logprobs_mode=self.model_config.logprobs_mode)
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        self.eplb_state: Optional[EplbState] = None
        """
        State of the expert parallelism load balancer.

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

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        # Lazy initializations
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        # self.model: nn.Module  # Set after load_model
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        # Initialize in initialize_kv_cache
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        self.kv_caches: list[torch.Tensor] = []
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        # indexes: [kv_cache_group_id][attn_group]
        self.attn_groups: list[list[AttentionGroup]] = []
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        # self.kv_cache_config: KVCacheConfig

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        # mm_hash ->  encoder_output
        self.encoder_cache: dict[str, torch.Tensor] = {}
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        self.use_aux_hidden_state_outputs = False
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        # Set up speculative decoding.
        # NOTE(Jiayi): currently we put the entire draft model on
        # the last PP rank. This is not ideal if there are many
        # layers in the draft model.
        if self.speculative_config and get_pp_group().is_last_rank:
            if self.speculative_config.method == "ngram":
                self.drafter = NgramProposer(self.vllm_config)
            elif self.speculative_config.use_eagle():
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                self.drafter = EagleProposer(self.vllm_config, self.device, self)  # type: ignore
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                if self.speculative_config.method == "eagle3":
                    self.use_aux_hidden_state_outputs = True
            elif self.speculative_config.method == "medusa":
                self.drafter = MedusaProposer(
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                    vllm_config=self.vllm_config, device=self.device
                )  # type: ignore
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            else:
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                raise ValueError(
                    "Unknown speculative decoding method: "
                    f"{self.speculative_config.method}"
                )
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            self.rejection_sampler = RejectionSampler()
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        # Request states.
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        self.requests: dict[str, CachedRequestState] = {}
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        self.comm_stream = torch.cuda.Stream()
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        # Input Batch
        # NOTE(Chen): Ideally, we should initialize the input batch inside
        # `initialize_kv_cache` based on the kv cache config. However, as in
        # https://github.com/vllm-project/vllm/pull/18298, due to some unknown
        # reasons, we have to initialize the input batch before `load_model`,
        # quantization + weight offloading will fail otherwise. As a temporary
        # solution, we initialize the input batch here, and re-initialize it
        # in `initialize_kv_cache` if the block_sizes here is different from
        # the block_sizes in the kv cache config.
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        self.input_batch = InputBatch(
            max_num_reqs=self.max_num_reqs,
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            # We need to use the encoder length for encoder-decoer
            # because of KV cache for cross-attention.
            max_model_len=max(self.max_model_len, self.max_encoder_len),
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            max_num_batched_tokens=self.max_num_tokens,
            device=self.device,
            pin_memory=self.pin_memory,
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            vocab_size=self.model_config.get_vocab_size(),
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            block_sizes=[self.cache_config.block_size],
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            kernel_block_sizes=[self.cache_config.block_size],
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            is_spec_decode=bool(self.vllm_config.speculative_config),
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            logitsprocs=build_logitsprocs(
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                self.vllm_config,
                self.device,
                self.pin_memory,
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                self.is_pooling_model,
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                self.vllm_config.model_config.logits_processors,
            ),
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            is_pooling_model=self.is_pooling_model,
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        )
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        self.use_async_scheduling = self.scheduler_config.async_scheduling
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        self.async_output_copy_stream = (
            torch.cuda.Stream() if self.use_async_scheduling else None
        )
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        # TODO(woosuk): Provide an option to tune the max cudagraph batch size.
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        # The convention is different.
        # self.cudagraph_batch_sizes sorts in ascending order.
        # The batch sizes in the config are in descending order.
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        if (
            self.compilation_config.cudagraph_capture_sizes
            and self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
        ):
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            self.cudagraph_batch_sizes = list(
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                reversed(self.compilation_config.cudagraph_capture_sizes)
            )
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        # Cache the device properties.
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        self._init_device_properties()
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        # Persistent buffers for CUDA graphs.
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        self.input_ids = self._make_buffer(self.max_num_tokens, dtype=torch.int32)
        self.positions = self._make_buffer(self.max_num_tokens, dtype=torch.int64)
        self.query_start_loc = self._make_buffer(
            self.max_num_reqs + 1, dtype=torch.int32
        )
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        self.seq_lens = self._make_buffer(self.max_num_reqs, dtype=torch.int32)
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        if self.dcp_world_size > 1:
            self.dcp_local_seq_lens = self._make_buffer(
                self.max_num_reqs, dtype=torch.int32
            )
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        # Because inputs_embeds may be bfloat16 and we don't need a numpy
        # version of this tensor, avoid a RuntimeError by not creating a
        # numpy buffer.
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        self.inputs_embeds = self._make_buffer(
            self.max_num_tokens, self.hidden_size, dtype=self.dtype, numpy=False
        )
        self.is_token_ids = self._make_buffer(self.max_num_tokens, dtype=torch.bool)
        self.discard_request_indices = self._make_buffer(
            self.max_num_reqs, dtype=torch.int64
        )
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        self.num_discarded_requests = 0

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        self.num_decode_draft_tokens = self._make_buffer(
            self.max_num_reqs, dtype=torch.int32
        )
        self.num_accepted_tokens = self._make_buffer(
            self.max_num_reqs, dtype=torch.int64
        )
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        # Only relevant for multimodal models
        if self.supports_mm_inputs:
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            self.is_mm_embed = self._make_buffer(self.max_num_tokens, dtype=torch.bool)
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        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
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        if self.uses_mrope:
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            # NOTE: `mrope_positions` is implemented with one additional dummy
            # position on purpose to make it non-contiguous so that it can work
            # with torch compile.
            # See detailed explanation in https://github.com/vllm-project/vllm/pull/12128#discussion_r1926431923
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            # NOTE: When M-RoPE is enabled, position ids are 3D regardless of
            # the modality of inputs. For text-only inputs, each dimension has
            # identical position IDs, making M-RoPE functionally equivalent to
            # 1D-RoPE.
            # See page 5 of https://arxiv.org/abs/2409.12191
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            self.mrope_positions = self._make_buffer(
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                (3, self.max_num_tokens + 1), dtype=torch.int64
            )
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        # CUDA event to synchronize use of reused CPU tensors between steps
        # when async scheduling is enabled.
        self.prepare_inputs_event: Optional[torch.cuda.Event] = None
        if self.use_async_scheduling:
            self.prepare_inputs_event = torch.cuda.Event()
            # Start in a completed state.
            self.prepare_inputs_event.record(torch.cuda.default_stream())

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        # None in the first PP rank. The rest are set after load_model.
        self.intermediate_tensors: Optional[IntermediateTensors] = None
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        # OPTIMIZATION: Cache the tensors rather than creating them every step.
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        # Keep in int64 to avoid overflow with long context
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        self.arange_np = np.arange(
            max(self.max_num_reqs + 1, self.max_model_len, self.max_num_tokens),
            dtype=np.int64,
        )
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        # Layer pairings for cross-layer KV sharing.
        # If an Attention layer `layer_name` is in the keys of this dict, it
        # means this layer will perform attention using the keys and values
        # from the KV cache of `shared_kv_cache_layers[layer_name]`.
        self.shared_kv_cache_layers: dict[str, str] = {}
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        self.kv_sharing_fast_prefill_eligible_layers: set[str] = set()

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

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        self.mm_budget = (
            MultiModalBudget(
                self.model_config,
                self.scheduler_config,
                self.mm_registry,
            )
            if self.supports_mm_inputs
            else None
        )
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        self.reorder_batch_threshold: Optional[int] = 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: Optional[Union[list[list[int]], torch.Tensor]] = None
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        self.transfer_event = torch.cuda.Event()
        self.sampled_token_ids_pinned_cpu = torch.empty(
            (self.max_model_len, 1),
            dtype=torch.int64,
            device="cpu",
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            pin_memory=self.pin_memory,
        )
510

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

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    def _make_buffer(
        self, *size: Union[int, torch.SymInt], dtype: torch.dtype, numpy: bool = True
    ) -> CpuGpuBuffer:
        return CpuGpuBuffer(
            *size,
            dtype=dtype,
            device=self.device,
            pin_memory=self.pin_memory,
            with_numpy=numpy,
        )
531

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

535
        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()
540
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542

        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

553
        seq_lens = self.seq_lens.gpu[:num_reqs]
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        token_type_ids = []

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

        model_kwargs["token_type_ids"] = torch.concat(token_type_ids).to(
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            device=self.device
        )
564
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        return model_kwargs

566
    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
569
        backend's needs. For example, some attention backends (namely MLA) may
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        want to separate requests based on if the attention computation will be
        compute-bound or memory-bound.

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

584
        if self.reorder_batch_threshold is not None:
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587
            # NOTE(lucas): currently no backend supports the custom masking
            #  required for DCP with q_len > 1, so we assert here. Remove this
            #  assert once the custom mask is support is added to FA3.
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            if (
                self.dcp_world_size > 1
                and envs.VLLM_ATTENTION_BACKEND != "FLASH_ATTN_MLA"
            ):
592
                assert self.reorder_batch_threshold == 1, (
593
                    "DCP not support reorder_batch_threshold > 1 now."
594
                )
595
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            reorder_batch_to_split_decodes_and_prefills(
                self.input_batch,
                scheduler_output,
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                decode_threshold=self.reorder_batch_threshold,
            )
600

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    # Note: used for model runner override.
    def _init_device_properties(self) -> None:
603
        """Initialize attributes from torch.cuda.get_device_properties"""
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        self.device_properties = torch.cuda.get_device_properties(self.device)
        self.num_sms = self.device_properties.multi_processor_count

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

611
    def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
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        """Update the cached states and the persistent batch with the scheduler
        output.

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

618
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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.
620
621
        """
        # Remove finished requests from the cached states.
622
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        for req_id in scheduler_output.finished_req_ids:
            self.requests.pop(req_id, None)
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        # Remove the finished requests from the persistent batch.
        # NOTE(woosuk): There could be an edge case where finished_req_ids and
        # scheduled_req_ids overlap. This happens when a request is aborted and
        # then resubmitted with the same ID. In this case, we treat them as two
        # distinct requests - clearing the cached states for the first request
        # and handling the second as a new request.
        for req_id in scheduler_output.finished_req_ids:
631
            self.input_batch.remove_request(req_id)
632
633

        # Free the cached encoder outputs.
634
635
        for mm_hash in scheduler_output.free_encoder_mm_hashes:
            self.encoder_cache.pop(mm_hash, None)
636

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648
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        # Remove the unscheduled requests from the persistent batch.
        # NOTE(woosuk): The unscheduled requests are either preempted requests
        # or running requests that are not scheduled in this step. We remove
        # them from the persistent batch but keep their cached states since
        # they will be scheduled again sometime in the future.
        scheduled_req_ids = scheduler_output.num_scheduled_tokens.keys()
        cached_req_ids = self.input_batch.req_id_to_index.keys()
        unscheduled_req_ids = cached_req_ids - scheduled_req_ids
        # NOTE(woosuk): The persistent batch optimization assumes that
        # consecutive batches contain mostly the same requests. If batches
        # have low request overlap (e.g., alternating between two distinct
        # sets of requests), this optimization becomes very inefficient.
        for req_id in unscheduled_req_ids:
650
            self.input_batch.remove_request(req_id)
651

652
        reqs_to_add: list[CachedRequestState] = []
653
        # Add new requests to the cached states.
654
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656
        for new_req_data in scheduler_output.scheduled_new_reqs:
            req_id = new_req_data.req_id
            sampling_params = new_req_data.sampling_params
657
            pooling_params = new_req_data.pooling_params
658

659
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662
            if (
                sampling_params
                and sampling_params.sampling_type == SamplingType.RANDOM_SEED
            ):
663
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667
                generator = torch.Generator(device=self.device)
                generator.manual_seed(sampling_params.seed)
            else:
                generator = None

668
669
            if self.is_pooling_model:
                assert pooling_params is not None
670
671
                task = pooling_params.task
                assert task is not None, "You did not set `task` in the API"
672

673
                model = cast(VllmModelForPooling, self.get_model())
674
                to_update = model.pooler.get_pooling_updates(task)
675
676
                to_update.apply(pooling_params)

677
            req_state = CachedRequestState(
678
                req_id=req_id,
679
                prompt_token_ids=new_req_data.prompt_token_ids,
680
                prompt_embeds=new_req_data.prompt_embeds,
681
                mm_features=new_req_data.mm_features,
682
                sampling_params=sampling_params,
683
                pooling_params=pooling_params,
684
                generator=generator,
685
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                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
687
                output_token_ids=[],
688
                lora_request=new_req_data.lora_request,
689
            )
690
691
            self.requests[req_id] = req_state

692
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
693
            if self.uses_mrope:
694
                self._init_mrope_positions(req_state)
695

696
            reqs_to_add.append(req_state)
697

698
        # Update the states of the running/resumed requests.
699
        is_last_rank = get_pp_group().is_last_rank
700
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        req_data = scheduler_output.scheduled_cached_reqs
        for i, req_id in enumerate(req_data.req_ids):
702
            req_state = self.requests[req_id]
703
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705
            num_computed_tokens = req_data.num_computed_tokens[i]
            new_block_ids = req_data.new_block_ids[i]
            resumed_from_preemption = req_data.resumed_from_preemption[i]
706
            num_output_tokens = req_data.num_output_tokens[i]
707

708
            # Update the cached states.
709

710
            req_state.num_computed_tokens = num_computed_tokens
711
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718

            if not is_last_rank:
                # When using PP, the scheduler sends the sampled tokens back,
                # because there's no direct communication between the first-
                # stage worker and the last-stage worker.
                new_token_ids = req_data.new_token_ids[i]
                # Add the sampled token(s) from the previous step (if any).
                # This doesn't include "unverified" tokens like spec tokens.
719
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                num_new_tokens = (
                    num_computed_tokens + len(new_token_ids) - req_state.num_tokens
                )
722
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                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:
726
                    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
                # failure. Align the cached state.
                del req_state.output_token_ids[num_output_tokens:]

                req_index = self.input_batch.req_id_to_index.get(req_id)
                if req_index is not None:
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                    old_end_idx = self.input_batch.num_tokens_no_spec[req_index]
                    end_idx = (
                        self.input_batch.num_prompt_tokens[req_index]
                        + num_output_tokens
                    )
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                    self.input_batch.num_tokens[req_index] = end_idx
                    self.input_batch.num_tokens_no_spec[req_index] = end_idx
741
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743
                    self.input_batch.is_token_ids[req_index, end_idx:old_end_idx] = (
                        False
                    )
744

745
            # Update the block IDs.
746
            if not resumed_from_preemption:
747
748
                if new_block_ids is not None:
                    # Append the new blocks to the existing block IDs.
749
                    for block_ids, new_ids in zip(req_state.block_ids, new_block_ids):
750
                        block_ids.extend(new_ids)
751
            else:
752
                assert new_block_ids is not None
753
754
                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
755
                req_state.block_ids = new_block_ids
756
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761

            req_index = self.input_batch.req_id_to_index.get(req_id)
            if req_index is None:
                # The request is not in the persistent batch.
                # The request was either preempted and resumed later, or was not
                # scheduled in the previous step and needs to be added again.
762
                reqs_to_add.append(req_state)
763
764
765
                continue

            # Update the persistent batch.
766
            self.input_batch.num_computed_tokens_cpu[req_index] = num_computed_tokens
767
            if new_block_ids is not None:
768
                self.input_batch.block_table.append_row(new_block_ids, req_index)
769
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774
775

            # 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)
776
                self.input_batch.token_ids_cpu[
777
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779
                    req_index, start_token_index:end_token_index
                ] = new_token_ids
                self.input_batch.num_tokens_no_spec[req_index] = end_token_index
780
                self.input_batch.num_tokens[req_index] = end_token_index
781

782
            # Add spec_token_ids to token_ids_cpu.
783
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785
            spec_token_ids = scheduler_output.scheduled_spec_decode_tokens.get(
                req_id, ()
            )
786
787
788
789
790
            if spec_token_ids:
                num_spec_tokens = len(spec_token_ids)
                start_index = self.input_batch.num_tokens_no_spec[req_index]
                end_token_index = start_index + num_spec_tokens
                self.input_batch.token_ids_cpu[
791
792
                    req_index, start_index:end_token_index
                ] = spec_token_ids
793
794
                # NOTE(woosuk): `num_tokens` here may include spec tokens.
                self.input_batch.num_tokens[req_index] += num_spec_tokens
795
                self.input_batch.spec_token_ids[req_index] = spec_token_ids
796

797
798
        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
799
800
        for request in reqs_to_add:
            self.input_batch.add_request(request)
801

802
803
804
805
806
807
        # 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()
808

809
    def _update_states_after_model_execute(
810
811
        self, output_token_ids: torch.Tensor
    ) -> None:
812
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815
816
817
818
819
820
821
822
823
        """Update the cached states after model execution.

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

        # Find the number of accepted tokens for each sequence.
824
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827
828
829
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831
832
833
834
835
836
837
838
839
840
841
842
843
        num_accepted_tokens = (
            (
                torch.cat(
                    [
                        output_token_ids,
                        torch.full(
                            (output_token_ids.size(0), 1),
                            -1,
                            device=output_token_ids.device,
                        ),
                    ],
                    dim=1,
                )
                == -1
            )
            .int()
            .argmax(-1)
            .cpu()
            .numpy()
        )
844
845
846
        for i, num_tokens in enumerate(num_accepted_tokens):
            self.input_batch.num_accepted_tokens_cpu[i] = num_tokens

847
848
849
850
851
852
    def _init_mrope_positions(self, req_state: CachedRequestState):
        image_grid_thw = []
        video_grid_thw = []
        second_per_grid_ts = []
        audio_feature_lengths = []
        use_audio_in_video = False
853
854
855
856
        for mm_feature in req_state.mm_features:
            mm_item = mm_feature.data
            if mm_item is None:
                continue
857
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859
860
861
862
863
864
865
866
867
868
            mm_input = mm_item.get_data()
            if (t := mm_input.get("image_grid_thw")) is not None:
                image_grid_thw.append(t.tolist())
            if (t := mm_input.get("video_grid_thw")) is not None:
                video_grid_thw.append(t.tolist())
            if (t := mm_input.get("second_per_grid_ts")) is not None:
                second_per_grid_ts.append(t)
            if (t := mm_input.get("audio_feature_lengths")) is not None:
                audio_feature_lengths.append(t)
            if mm_input.get("use_audio_in_video") is True:
                use_audio_in_video = True

869
        if supports_mrope(self.model):
870
            req_state.mrope_positions, req_state.mrope_position_delta = (
871
872
873
874
875
876
877
878
879
                self.model.get_mrope_input_positions(
                    req_state.prompt_token_ids,
                    hf_config=self.model_config.hf_config,
                    image_grid_thw=image_grid_thw,
                    video_grid_thw=video_grid_thw,
                    second_per_grid_ts=second_per_grid_ts,
                    audio_feature_lengths=audio_feature_lengths,
                    use_audio_in_video=use_audio_in_video,
                )
880
            )
881
        else:
882
            req_state.mrope_positions, req_state.mrope_position_delta = (
883
884
885
886
887
888
889
890
891
                MRotaryEmbedding.get_input_positions_tensor(
                    req_state.prompt_token_ids,
                    hf_config=self.model_config.hf_config,
                    image_grid_thw=image_grid_thw,
                    video_grid_thw=video_grid_thw,
                    second_per_grid_ts=second_per_grid_ts,
                    audio_feature_lengths=audio_feature_lengths,
                    use_audio_in_video=use_audio_in_video,
                )
892
            )
893

894
    def _extract_mm_kwargs(
895
        self,
896
897
        scheduler_output: "SchedulerOutput",
    ) -> BatchedTensorInputs:
898
        if not scheduler_output or not self.is_multimodal_raw_input_only_model:
899
            return {}
900

901
902
        mm_kwargs = list[MultiModalKwargsItem]()
        for req in scheduler_output.scheduled_new_reqs:
903
904
905
            for feature in req.mm_features:
                if feature.data is not None:
                    mm_kwargs.append(feature.data)
906

907
        # Input all modalities at once
908
        model = cast(SupportsMultiModal, self.model)
909
910
        mm_kwargs_combined: BatchedTensorInputs = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
911
912
913
914
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
915
916
        ):
            mm_kwargs_combined.update(mm_kwargs_group)
917

918
        return mm_kwargs_combined
919

920
    def _dummy_mm_kwargs(self, num_seqs: int) -> BatchedTensorInputs:
921
        if not self.is_multimodal_raw_input_only_model:
922
            return {}
923

924
925
926
927
928
        mm_budget = self.mm_budget
        assert mm_budget is not None

        dummy_modality = mm_budget.get_modality_with_max_tokens()
        return self._get_mm_dummy_batch(dummy_modality, num_seqs)
929

930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
    def _get_cumsum_and_arange(
        self,
        num_tokens: np.ndarray,
        cumsum_dtype: Optional[np.dtype] = None,
    ) -> tuple[np.ndarray, np.ndarray]:
        """Get the cumulative sum and batched arange of the given array.
        # E.g., [2, 5, 3] -> ([2, 7, 10], [0, 1, 0, 1, 2, 3, 4, 0, 1, 2])
        # Equivalent to but faster than:
        # np.concatenate([np.arange(n) for n in num_tokens])
        """
        # Step 1. [2, 5, 3] -> [2, 7, 10]
        cu_num_tokens = np.cumsum(num_tokens, dtype=cumsum_dtype)
        total_num_tokens = cu_num_tokens[-1]
        # Step 2. [2, 7, 10] -> [0, 0, 2, 2, 2, 2, 2, 7, 7, 7]
        cumsums_offsets = np.repeat(cu_num_tokens - num_tokens, num_tokens)
        # Step 3. [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
        arange = self.arange_np[:total_num_tokens] - cumsums_offsets

        return cu_num_tokens, arange

950
951
952
    def _prepare_input_ids(
        self, total_num_scheduled_tokens: int, cu_num_tokens: np.ndarray
    ) -> None:
953
        """Prepare the input IDs for the current batch.
954

955
956
957
958
959
960
961
        Carefully handles the `prev_sampled_token_ids` which can be cached
        from the previous engine iteration, in which case those tokens on the
        GPU need to be copied into the corresponding slots into input_ids."""

        if self.input_batch.prev_sampled_token_ids is None:
            # Normal scheduling case
            self.input_ids.copy_to_gpu(total_num_scheduled_tokens)
962
963
964
            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)
965
966
967
968
969
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971
972
973
974
975
976
977
978
979
980
981
982
            return

        # Async scheduling case, where some decode requests from the previous
        # iteration won't have entries in input_ids_cpu and need to be copied
        # on the GPU from prev_sampled_token_ids.
        prev_req_id_to_index = self.input_batch.prev_req_id_to_index
        assert prev_req_id_to_index is not None
        flattened_indices = []
        prev_common_req_indices = []
        indices_match = True
        max_flattened_index = -1
        for req_id, cur_index in self.input_batch.req_id_to_index.items():
            if (prev_index := prev_req_id_to_index.get(req_id)) is not None:
                prev_common_req_indices.append(prev_index)
                # We need to compute the flattened input_ids index of the
                # last token in each common request.
                flattened_index = cu_num_tokens[cur_index].item() - 1
                flattened_indices.append(flattened_index)
983
                indices_match &= prev_index == flattened_index
984
985
986
987
988
989
                max_flattened_index = max(max_flattened_index, flattened_index)
        num_commmon_tokens = len(flattened_indices)
        if num_commmon_tokens < total_num_scheduled_tokens:
            # If not all requests are decodes from the last iteration,
            # We need to copy the input_ids_cpu to the GPU first.
            self.input_ids.copy_to_gpu(total_num_scheduled_tokens)
990
991
992
            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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999
1000
1001
1002
        if num_commmon_tokens == 0:
            # No requests in common with the previous iteration
            # So input_ids_cpu will have all the input ids.
            return
        if indices_match and max_flattened_index == (num_commmon_tokens - 1):
            # Common-case optimization: the batch is unchanged
            # and no reordering happened.
            # The indices are both the same permutation of 0..N-1 so
            # we can copy directly using a single slice.
            self.input_ids.gpu[:num_commmon_tokens].copy_(
1003
1004
1005
                self.input_batch.prev_sampled_token_ids[:num_commmon_tokens, 0],
                non_blocking=True,
            )
1006
1007
            if self.enable_prompt_embeds:
                self.is_token_ids.gpu[:num_commmon_tokens] = True
1008
1009
1010
            return
        # Upload the index tensors asynchronously
        # so the scatter can be non-blocking.
1011
1012
1013
        input_ids_index_tensor = torch.tensor(
            flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1014
        prev_common_req_indices_tensor = torch.tensor(
1015
1016
            prev_common_req_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1017
1018
1019
1020
        self.input_ids.gpu.scatter_(
            dim=0,
            index=input_ids_index_tensor,
            src=self.input_batch.prev_sampled_token_ids[
1021
1022
1023
                prev_common_req_indices_tensor, 0
            ],
        )
1024

1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
    def _get_encoder_seq_lens(
        self,
        scheduler_output: "SchedulerOutput",
        kv_cache_spec: KVCacheSpec,
        num_reqs: int,
    ) -> Optional[np.ndarray]:
        if not isinstance(kv_cache_spec, CrossAttentionSpec):
            return None

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

        return encoder_seq_lens

1043
    def _prepare_inputs(
1044
        self, scheduler_output: "SchedulerOutput"
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
    ) -> tuple[
        PerLayerAttnMetadata,
        torch.Tensor,
        Optional[SpecDecodeMetadata],
        np.ndarray,
        Optional[CommonAttentionMetadata],
        int,
        Optional[UBatchSlices],
        Optional[torch.Tensor],
        bool,
    ]:
1056
1057
1058
        """
        :return: tuple[
            attn_metadata: layer-to-attention_metadata mapping,
1059
1060
1061
            logits_indices, spec_decode_metadata,
            num_scheduled_tokens, spec_decode_common_attn_metadata,
            max_num_scheduled_tokens, use_cascade_attn
1062
1063
        ]
        """
1064
1065
1066
1067
1068
1069
1070
        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.
1071
        self.input_batch.block_table.commit_block_table(num_reqs)
1072
1073

        # Get the number of scheduled tokens for each request.
1074
1075
1076
1077
        req_ids = self.input_batch.req_ids
        tokens = [scheduler_output.num_scheduled_tokens[i] for i in req_ids]
        num_scheduled_tokens = np.array(tokens, dtype=np.int32)
        max_num_scheduled_tokens = max(tokens)
1078
1079
1080

        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
1081
        req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens)
1082

1083
1084
        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
        # arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
1085
        cu_num_tokens, arange = self._get_cumsum_and_arange(num_scheduled_tokens)
1086
1087

        # Get positions.
1088
        positions_np = self.positions.np[:total_num_scheduled_tokens]
1089
1090
1091
1092
1093
        np.add(
            self.input_batch.num_computed_tokens_cpu[req_indices],
            arange,
            out=positions_np,
        )
1094

1095
1096
        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1097
        if self.uses_mrope:
1098
1099
            self._calc_mrope_positions(scheduler_output)

1100
1101
1102
1103
        # 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.
1104
1105
1106
        token_indices = (
            positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]
        )
1107
        token_indices_tensor = torch.from_numpy(token_indices)
1108

1109
1110
1111
        # 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.
1112
1113
1114
1115
1116
1117
        torch.index_select(
            self.input_batch.token_ids_cpu_tensor.flatten(),
            0,
            token_indices_tensor,
            out=self.input_ids.cpu[:total_num_scheduled_tokens],
        )
1118
1119
1120
1121
1122
1123
        if self.enable_prompt_embeds:
            is_token_ids = self.input_batch.is_token_ids.flatten()
            torch.index_select(
                is_token_ids,
                0,
                token_indices_tensor,
1124
1125
                out=self.is_token_ids.cpu[:total_num_scheduled_tokens],
            )
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
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1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158

        # 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:
1159
1160
1161
                    self.inputs_embeds.cpu[
                        output_idx : output_idx + actual_num_sched
                    ].copy_(req_embeds[start_pos:actual_end])
1162
1163

                output_idx += num_sched
1164

1165
1166
        self.input_batch.block_table.compute_slot_mapping(req_indices, positions_np)
        self.input_batch.block_table.commit_slot_mapping(total_num_scheduled_tokens)
1167
1168

        # Prepare the attention metadata.
1169
        self.query_start_loc.np[0] = 0
1170
        self.query_start_loc.np[1 : num_reqs + 1] = cu_num_tokens
1171
1172
        # Note: pad query_start_loc to be non-decreasing, as kernels
        # like FlashAttention requires that
1173
        self.query_start_loc.np[num_reqs + 1 :].fill(cu_num_tokens[-1])
1174
        self.query_start_loc.copy_to_gpu()
1175
        query_start_loc = self.query_start_loc.gpu[: num_reqs + 1]
1176

1177
        num_tokens_unpadded = scheduler_output.total_num_scheduled_tokens
1178
        num_tokens_padded = self._get_num_input_tokens(num_tokens_unpadded)
1179
1180
1181
        uniform_decode = (
            max_num_scheduled_tokens == self.uniform_decode_query_len
        ) and (total_num_scheduled_tokens == num_reqs * max_num_scheduled_tokens)
1182
        ubatch_slices, num_tokens_across_dp = coordinate_batch_across_dp(
1183
1184
1185
            num_scheduled_tokens,
            num_tokens_unpadded,
            num_tokens_padded,
1186
1187
1188
            self.parallel_config,
            True,
            uniform_decode,
1189
        )
1190

1191
        self.seq_lens.np[:num_reqs] = (
1192
1193
            self.input_batch.num_computed_tokens_cpu[:num_reqs] + num_scheduled_tokens
        )
1194
        # Fill unused with 0 for full cuda graph mode.
1195
1196
1197
1198
        self.seq_lens.np[num_reqs:].fill(0)
        self.seq_lens.copy_to_gpu()
        seq_lens = self.seq_lens.gpu[:num_reqs]
        max_seq_len = self.seq_lens.np[:num_reqs].max().item()
1199

1200
        num_tokens = [self.requests[r].num_tokens for r in self.input_batch.req_ids]
1201
1202
1203
1204
1205
1206
1207
        num_tokens_np = np.array(num_tokens, dtype=np.int32)

        # Record the index of requests that should not be sampled,
        # so that we could clear the sampled tokens before returning
        discard_requests_mask = self.seq_lens.np[:num_reqs] < num_tokens_np
        discard_request_indices = np.nonzero(discard_requests_mask)[0]
        self.num_discarded_requests = len(discard_request_indices)
1208
1209
1210
        self.discard_request_indices.np[: self.num_discarded_requests] = (
            discard_request_indices
        )
1211
1212
1213

        self.discard_request_indices.copy_to_gpu(self.num_discarded_requests)

1214
        # Copy the tensors to the GPU.
1215
1216
        self._prepare_input_ids(total_num_scheduled_tokens, cu_num_tokens)

1217
        if self.uses_mrope:
1218
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1219
1220
            self.mrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.mrope_positions.cpu[:, :total_num_scheduled_tokens],
1221
1222
                non_blocking=True,
            )
1223
1224
        else:
            # Common case (1D positions)
1225
            self.positions.copy_to_gpu(total_num_scheduled_tokens)
1226

1227
        use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
1228
1229
1230
1231
1232
1233
1234
        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
1235
            num_draft_tokens = None
1236
1237
1238
1239
1240
1241
            spec_decode_metadata = None
        else:
            # Get the number of draft tokens for each request.
            # Iterate over the dictionary rather than all requests since not all
            # requests have draft tokens.
            num_draft_tokens = np.zeros(num_reqs, dtype=np.int32)
1242
1243
1244
            # 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)
1245
1246
1247
1248
            for (
                req_id,
                draft_token_ids,
            ) in scheduler_output.scheduled_spec_decode_tokens.items():
1249
1250
                req_idx = self.input_batch.req_id_to_index[req_id]
                num_draft_tokens[req_idx] = len(draft_token_ids)
1251
1252
1253
1254
1255
1256
1257
1258
                num_decode_draft_tokens[req_idx] = (
                    len(draft_token_ids)
                    if (
                        self.input_batch.num_computed_tokens_cpu[req_idx]
                        >= self.input_batch.num_prompt_tokens[req_idx]
                    )
                    else -1
                )
1259
            spec_decode_metadata = self._calc_spec_decode_metadata(
1260
1261
                num_draft_tokens, cu_num_tokens
            )
1262
            logits_indices = spec_decode_metadata.logits_indices
1263
1264

            # For DECODE only cuda graph of some attention backends (e.g., GDN).
1265
            self.num_decode_draft_tokens.np[:num_reqs] = num_decode_draft_tokens
1266
1267
            self.num_decode_draft_tokens.np[num_reqs:].fill(-1)
            self.num_decode_draft_tokens.copy_to_gpu()
1268
1269
1270

        logits_indices_padded = None
        if self.cache_config.kv_sharing_fast_prefill:
1271
            logits_indices_padded = self._prepare_kv_sharing_fast_prefill(
1272
1273
                logits_indices
            )
1274

1275
1276
1277
        attn_metadata: PerLayerAttnMetadata = {}
        if ubatch_slices is not None:
            attn_metadata = [dict() for _ in range(len(ubatch_slices))]
1278
        use_cascade_attn = False
1279

1280
        # Used in the below loop.
1281
        query_start_loc_cpu = self.query_start_loc.cpu[: num_reqs + 1]
1282
        seq_lens_cpu = self.seq_lens.cpu[:num_reqs]
1283
1284
1285
        num_computed_tokens_cpu = self.input_batch.num_computed_tokens_cpu_tensor[
            :num_reqs
        ]
1286
        spec_decode_common_attn_metadata = None
1287
1288
        if use_spec_decode:
            self.num_accepted_tokens.np[:num_reqs] = (
1289
1290
                self.input_batch.num_accepted_tokens_cpu[:num_reqs]
            )
1291
1292
            self.num_accepted_tokens.np[num_reqs:].fill(1)
            self.num_accepted_tokens.copy_to_gpu()
1293

1294
1295
1296
        # Prepare the attention metadata for each KV cache group and make layers
        # in the same group share the same metadata.
        for kv_cache_group_id, kv_cache_group_spec in enumerate(
1297
1298
            self.kv_cache_config.kv_cache_groups
        ):
1299
            encoder_seq_lens = self._get_encoder_seq_lens(
1300
1301
                scheduler_output, kv_cache_group_spec.kv_cache_spec, num_reqs
            )
1302

1303
            if isinstance(kv_cache_group_spec.kv_cache_spec, EncoderOnlyAttentionSpec):
1304
1305
1306
1307
1308
                # Encoder-only layers do not have KV cache, so we need to
                # create a dummy block table and slot mapping for them.
                blk_table_tensor = torch.zeros(
                    (num_reqs, 1),
                    dtype=torch.int32,
1309
1310
1311
                    device=self.device,
                )
                slot_mapping = torch.zeros(
1312
                    (total_num_scheduled_tokens,),
1313
1314
1315
                    dtype=torch.int64,
                    device=self.device,
                )
1316
1317
1318
                num_common_prefix_blocks = 0
            else:
                blk_table = self.input_batch.block_table[kv_cache_group_id]
1319
                blk_table_tensor = blk_table.get_device_tensor(num_reqs)
1320
                slot_mapping = blk_table.slot_mapping.gpu[:total_num_scheduled_tokens]
1321
1322
1323

                # Fill unused with -1. Needed for reshape_and_cache in full cuda
                # graph mode.
1324
1325
1326
1327
                blk_table.slot_mapping.gpu[total_num_scheduled_tokens:].fill_(-1)
                num_common_prefix_blocks = scheduler_output.num_common_prefix_blocks[
                    kv_cache_group_id
                ]
1328

1329
            common_attn_metadata = CommonAttentionMetadata(
1330
1331
1332
1333
1334
                query_start_loc=query_start_loc,
                query_start_loc_cpu=query_start_loc_cpu,
                seq_lens=seq_lens,
                seq_lens_cpu=seq_lens_cpu,
                num_computed_tokens_cpu=num_computed_tokens_cpu,
1335
1336
1337
                num_reqs=num_reqs,
                num_actual_tokens=total_num_scheduled_tokens,
                max_query_len=max_num_scheduled_tokens,
1338
                max_seq_len=max_seq_len,
1339
1340
                block_table_tensor=blk_table_tensor,
                slot_mapping=slot_mapping,
1341
1342
                logits_indices_padded=logits_indices_padded,
                num_logits_indices=logits_indices.size(0),
1343
                causal=True,
1344
                encoder_seq_lens=encoder_seq_lens,
1345
1346
1347
                dcp_local_seq_lens=self.dcp_local_seq_lens.gpu[:num_reqs]
                if self.dcp_world_size > 1
                else None,
1348
1349
            )

1350
            if self.speculative_config and spec_decode_common_attn_metadata is None:
1351
                if isinstance(self.drafter, EagleProposer):
1352
1353
1354
1355
                    if (
                        self.drafter.attn_layer_names[0]
                        in kv_cache_group_spec.layer_names
                    ):
1356
1357
1358
                        spec_decode_common_attn_metadata = common_attn_metadata
                else:
                    spec_decode_common_attn_metadata = common_attn_metadata
1359

1360
1361
1362
            for attn_group in self.attn_groups[kv_cache_group_id]:
                # Prepare for cascade attention if enabled & beneficial.
                common_prefix_len = 0
1363
                builder = attn_group.get_metadata_builder()
1364
1365
1366
                if self.cascade_attn_enabled:
                    common_prefix_len = self._compute_cascade_attn_prefix_len(
                        num_scheduled_tokens,
1367
                        num_common_prefix_blocks,
1368
                        attn_group.kv_cache_spec,
1369
1370
                        builder,
                    )
1371

1372
                extra_attn_metadata_args = {}
1373
                if use_spec_decode and isinstance(builder, GDNAttentionMetadataBuilder):
1374
                    extra_attn_metadata_args = dict(
1375
1376
1377
1378
                        num_accepted_tokens=self.num_accepted_tokens.gpu[:num_reqs],
                        num_decode_draft_tokens_cpu=self.num_decode_draft_tokens.cpu[
                            :num_reqs
                        ],
1379
1380
                    )

1381
1382
                if ubatch_slices is not None:
                    common_attn_metadata_list = split_attn_metadata(
1383
1384
                        ubatch_slices, common_attn_metadata
                    )
1385
                    for ubid, common_attn_metadata in enumerate(
1386
1387
1388
1389
1390
1391
1392
1393
                        common_attn_metadata_list
                    ):
                        attn_metadata_i = attn_group.get_metadata_builder(
                            ubatch_id=ubid
                        ).build(
                            common_prefix_len=common_prefix_len,
                            common_attn_metadata=common_attn_metadata,
                        )
1394
1395
1396
1397
1398
1399
1400
1401
                        for layer_name in kv_cache_group_spec.layer_names:
                            assert type(attn_metadata) is list
                            attn_metadata[ubid][layer_name] = attn_metadata_i
                else:
                    assert isinstance(attn_metadata, dict)
                    attn_metadata_i = builder.build(
                        common_prefix_len=common_prefix_len,
                        common_attn_metadata=common_attn_metadata,
1402
1403
1404
                        **extra_attn_metadata_args,
                    )
                    use_cascade_attn |= getattr(attn_metadata_i, "use_cascade", False)
1405
1406
                    for layer_name in attn_group.layer_names:
                        attn_metadata[layer_name] = attn_metadata_i
1407

1408
1409
1410
1411
        # disable cascade attention when DBO
        if ubatch_slices is not None:
            use_cascade_attn = False

1412
1413
1414
1415
        # Hot-Swap lora model
        if self.lora_config:
            self.set_active_loras(self.input_batch, num_scheduled_tokens)

1416
1417
1418
1419
1420
1421
1422
1423
        return (
            attn_metadata,
            logits_indices,
            spec_decode_metadata,
            num_scheduled_tokens,
            spec_decode_common_attn_metadata,
            max_num_scheduled_tokens,
            ubatch_slices,
1424
            num_tokens_across_dp,
1425
1426
            use_cascade_attn,
        )
1427

1428
1429
1430
1431
    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
        num_common_prefix_blocks: int,
1432
1433
        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
    ) -> 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.
        """
1452
        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
        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]
1490
        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
1491
1492
1493
1494
1495
1496
1497
        # If we use [A, B, C, D] as the common prefix for Request 1-3,
        # then Request 3 will be processed only by the first kernel,
        # and the second kernel will get an empty input. While this is not
        # a fundamental problem, our current implementation does not support
        # this case.
        num_reqs = len(num_scheduled_tokens)
        common_prefix_len = min(
1498
1499
            common_prefix_len, self.input_batch.num_computed_tokens_cpu[:num_reqs].min()
        )
1500
        # common_prefix_len should be a multiple of the block size.
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
        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
        )
1512
1513
        assert isinstance(kv_cache_spec, AttentionSpec)
        use_cascade = attn_metadata_builder.use_cascade_attention(
1514
1515
1516
            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
1517
            num_kv_heads=kv_cache_spec.num_kv_heads,
1518
            use_alibi=self.use_alibi,
1519
            use_sliding_window=use_sliding_window,
1520
            use_local_attention=use_local_attention,
1521
1522
1523
1524
            num_sms=self.num_sms,
        )
        return common_prefix_len if use_cascade else 0

1525
1526
    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
1527
        for index, req_id in enumerate(self.input_batch.req_ids):
1528
1529
1530
            req = self.requests[req_id]
            assert req.mrope_positions is not None

1531
1532
            num_computed_tokens = self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1533
            num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
1534
1535
                req.prompt_token_ids, req.prompt_embeds
            )
1536
1537

            if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
1538
1539
                prompt_part_len = max(0, num_prompt_tokens - num_computed_tokens)
                completion_part_len = max(0, num_scheduled_tokens - prompt_part_len)
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
            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

1553
1554
1555
                self.mrope_positions.cpu[:, dst_start:dst_end] = req.mrope_positions[
                    :, src_start:src_end
                ]
1556
1557
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1559
1560
1561
1562
                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

1563
                MRotaryEmbedding.get_next_input_positions_tensor(
1564
                    out=self.mrope_positions.np,
1565
1566
1567
1568
1569
                    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,
                )
1570
1571
1572

                mrope_pos_ptr += completion_part_len

1573
1574
    def _calc_spec_decode_metadata(
        self,
1575
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1577
1578
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1580
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1582
1583
1584
1585
1586
1587
1588
1589
1590
        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
1591
1592
1593
1594

        # Step 1. cu_num_sampled_tokens: [4, 5, 8, 9, 11]
        # arange: [0, 1, 2, 3, 0, 0, 1, 2, 0, 0, 1]
        cu_num_sampled_tokens, arange = self._get_cumsum_and_arange(
1595
1596
            num_sampled_tokens, cumsum_dtype=np.int32
        )
1597
        # Step 2. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
1598
        logits_indices = np.repeat(
1599
1600
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens
        )
1601
        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
1602
1603
1604
1605
1606
1607
        logits_indices += arange

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

        # Compute the draft logits indices.
1608
1609
1610
        # cu_num_draft_tokens: [3, 3, 5, 5, 6]
        # arange: [0, 1, 2, 0, 1, 0]
        cu_num_draft_tokens, arange = self._get_cumsum_and_arange(
1611
1612
            num_draft_tokens, cumsum_dtype=np.int32
        )
1613
1614
        # [0, 0, 0, 5, 5, 9]
        target_logits_indices = np.repeat(
1615
1616
            cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens
        )
1617
1618
1619
1620
1621
        # [0, 1, 2, 5, 6, 9]
        target_logits_indices += arange

        # TODO: Optimize the CPU -> GPU copy.
        cu_num_draft_tokens = torch.from_numpy(cu_num_draft_tokens).to(
1622
1623
1624
1625
1626
            self.device, non_blocking=True
        )
        logits_indices = torch.from_numpy(logits_indices).to(
            self.device, non_blocking=True
        )
1627
        target_logits_indices = torch.from_numpy(target_logits_indices).to(
1628
1629
            self.device, non_blocking=True
        )
1630
        bonus_logits_indices = torch.from_numpy(bonus_logits_indices).to(
1631
1632
            self.device, non_blocking=True
        )
1633

1634
1635
        # Compute the draft token ids.
        # draft_token_indices:      [  1,   2,   3, 105, 106, 208]
1636
        draft_token_ids = self.input_ids.gpu[logits_indices]
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
        draft_token_ids = draft_token_ids[target_logits_indices + 1]

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

1649
1650
1651
1652
1653
1654
1655
    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
1656
        self.kv_sharing_fast_prefill_logits_indices[:num_logits].copy_(logits_indices)
1657
1658
1659
1660
1661
        # 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_(
1662
1663
1664
1665
1666
1667
            logits_indices[-1].item()
        )
        if (
            self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
            and num_logits <= self.cudagraph_batch_sizes[-1]
        ):
1668
1669
1670
1671
1672
            # Use piecewise CUDA graphs.
            # Add padding to the batch size.
            num_logits_padded = self.vllm_config.pad_for_cudagraph(num_logits)
        else:
            num_logits_padded = num_logits
1673
1674
1675
        logits_indices_padded = self.kv_sharing_fast_prefill_logits_indices[
            :num_logits_padded
        ]
1676
1677
        return logits_indices_padded

1678
1679
1680
1681
1682
1683
1684
1685
    def _batch_mm_kwargs_from_scheduler(
        self,
        scheduler_output: "SchedulerOutput",
    ) -> tuple[list[MultiModalKwargsItem], list[tuple[str, PlaceholderRange]]]:
        """Batch multimodal kwargs from scheduled encoder inputs.

        Args:
            scheduler_output: The scheduler output containing scheduled encoder
1686
                inputs.
1687
1688
1689
1690
1691
1692

        Returns:
            A tuple of (mm_kwargs, req_ids_pos) where:
            - mm_kwargs: List of multimodal kwargs items to be batched
            - mm_hashes_pos: List of (mm_hash, position_info) tuples
        """
1693
1694
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
1695
            return [], []
1696
        # Batch the multi-modal inputs.
1697
        mm_kwargs = list[MultiModalKwargsItem]()
1698
1699
        # list of tuple (mm_hash, position_info)
        mm_hashes_pos = list[tuple[str, PlaceholderRange]]()
1700
1701
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
1702
1703

            for mm_input_id in encoder_input_ids:
1704
1705
1706
1707
                mm_feature = req_state.mm_features[mm_input_id]
                mm_hash = mm_feature.identifier
                mm_kwargs.append(mm_feature.data)
                mm_hashes_pos.append((mm_hash, mm_feature.mm_position))
1708

1709
1710
1711
1712
1713
        return mm_kwargs, mm_hashes_pos

    def _execute_mm_encoder(self, scheduler_output: "SchedulerOutput"):
        # Batch the multi-modal inputs using the helper method.
        mm_kwargs, mm_hashes_pos = self._batch_mm_kwargs_from_scheduler(
1714
1715
            scheduler_output
        )
1716
1717
1718
1719

        if not mm_kwargs:
            return

1720
1721
1722
1723
1724
1725
1726
        # 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.
1727
        model = cast(SupportsMultiModal, self.model)
1728
        encoder_outputs = []
1729
        for modality, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
1730
1731
1732
1733
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
1734
        ):
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
            # (ekhvedchenia): Temporary hack to limit peak memory usage when
            # processing multimodal data.This solves the issue with scheduler
            # putting too many video samples into a single batch. Scheduler
            # uses pruned vision tokens count to compare it versus compute
            # budget which is incorrect (Either input media size or non-pruned
            # output vision tokens count should be considered)
            curr_group_outputs = []

            if self.is_multimodal_pruning_enabled and modality == "video":
                micro_batch_size = 1
                for i in range(0, num_items, micro_batch_size):
                    micro_batch_mm_inputs = dict(
1747
1748
1749
                        (k, v[i : i + micro_batch_size])
                        for k, v in mm_kwargs_group.items()
                    )
1750
1751

                    micro_batch_outputs = model.get_multimodal_embeddings(
1752
1753
                        **micro_batch_mm_inputs
                    )
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763

                    curr_group_outputs.extend(micro_batch_outputs)
            else:
                # Run the encoder.
                # `curr_group_outputs` is either of the following:
                # 1. A tensor of shape (num_items, feature_size, hidden_size)
                # in case feature_size is fixed across all multimodal items.
                # 2. A list or tuple (length: num_items) of tensors,
                # each of shape (feature_size, hidden_size) in case the feature
                # size is dynamic depending on the input multimodal items.
1764
                curr_group_outputs = model.get_multimodal_embeddings(**mm_kwargs_group)
1765

1766
1767
            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
1768
                expected_num_items=num_items,
1769
            )
1770
            encoder_outputs.extend(curr_group_outputs)
1771

1772
1773
1774
        # Cache the encoder outputs by mm_hash
        for (mm_hash, pos_info), output in zip(mm_hashes_pos, encoder_outputs):
            self.encoder_cache[mm_hash] = scatter_mm_placeholders(
1775
1776
1777
1778
1779
                output,
                is_embed=pos_info.is_embed,
            )

    def _gather_mm_embeddings(
1780
1781
        self,
        scheduler_output: "SchedulerOutput",
1782
        shift_computed_tokens: int = 0,
1783
1784
1785
1786
1787
1788
1789
1790
    ) -> tuple[list[torch.Tensor], torch.Tensor]:
        total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens

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

        req_start_idx = 0
1791
        should_sync_mrope_positions = False
1792

1793
        for req_id in self.input_batch.req_ids:
1794
1795
            mm_embeds_req: list[torch.Tensor] = []

1796
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1797
            req_state = self.requests[req_id]
1798
            num_computed_tokens = req_state.num_computed_tokens + shift_computed_tokens
1799

1800
1801
            for mm_feature in req_state.mm_features:
                pos_info = mm_feature.mm_position
1802
1803
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819

                # 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,
1820
1821
                    num_encoder_tokens,
                )
1822
                assert start_idx < end_idx
1823

1824
                mm_hash = mm_feature.identifier
1825
                encoder_output = self.encoder_cache.get(mm_hash, None)
1826
                assert encoder_output is not None, f"Encoder cache miss for {mm_hash}."
1827
1828
1829
1830

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

1831
                req_start_pos = req_start_idx + start_pos - num_computed_tokens
1832
1833
1834
                is_mm_embed[req_start_pos + start_idx : req_start_pos + end_idx] = (
                    True if is_embed is None else is_embed
                )
1835

1836
1837
1838
1839
                mm_embeds_item = gather_mm_placeholders(
                    encoder_output[start_idx:end_idx],
                    is_embed=is_embed,
                )
1840
1841
1842
                mm_embeds_req.append(mm_embeds_item)

            if self.is_multimodal_pruning_enabled and self.uses_mrope:
1843
                assert req_state.mrope_positions is not None
1844
1845
1846
1847
1848
1849
1850
                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,
1851
1852
                    )
                )
1853
1854
1855
1856
                req_state.mrope_positions.copy_(new_mrope_positions)
                req_state.mrope_position_delta = new_delta

            mm_embeds.extend(mm_embeds_req)
1857
1858
1859
            req_start_idx += num_scheduled_tokens

        is_mm_embed = self.is_mm_embed.copy_to_gpu(total_num_scheduled_tokens)
1860
1861
1862

        if should_sync_mrope_positions:
            self._calc_mrope_positions(scheduler_output)
1863
            self.mrope_positions.copy_to_gpu(total_num_scheduled_tokens)
1864

1865
        return mm_embeds, is_mm_embed
1866

1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
    def _extract_encoder_inputs(
        self,
        scheduler_output: "SchedulerOutput",
    ) -> dict[str, torch.Tensor]:
        """Extract encoder inputs for encoder-decoder models.

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

        if not mm_kwargs:
            return {}

        # Group MM kwargs by modality and extract features
1883
        model = cast(SupportsMultiModal, self.model)
1884
1885
        encoder_features = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
1886
1887
1888
1889
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
1890
1891
1892
1893
1894
1895
1896
1897
        ):
            # Add the grouped features to encoder_features dict
            # This allows the model to receive them as kwargs (e.g.,
            # input_features=...)
            encoder_features.update(mm_kwargs_group)

        return encoder_features

1898
    def get_model(self) -> nn.Module:
1899
        # get raw model out of the cudagraph wrapper.
1900
        if isinstance(self.model, (CUDAGraphWrapper, UBatchWrapper)):
1901
            return self.model.unwrap()
1902
1903
        return self.model

1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
    def get_supported_generation_tasks(self) -> list[GenerationTask]:
        model = self.get_model()
        supported_tasks = list[GenerationTask]()

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

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

            supported_tasks.append("transcription")

        return supported_tasks

1919
1920
1921
1922
1923
    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
        model = self.get_model()
        if not is_pooling_model(model):
            return []

1924
1925
        supported_tasks = list(model.pooler.get_supported_tasks())

1926
1927
1928
1929
        if (
            self.scheduler_config.chunked_prefill_enabled
            and "encode" in supported_tasks
        ):
1930
1931
            supported_tasks.remove("encode")

1932
1933
1934
1935
1936
1937
            logger.debug_once(
                "Chunked prefill is not supported with "
                "encode task which using ALL pooling. "
                "Please turn off chunked prefill by "
                "`--no-enable-chunked-prefill` before using it."
            )
1938
1939
1940
1941
1942

        if "score" in supported_tasks:
            num_labels = getattr(self.model_config.hf_config, "num_labels", 0)
            if num_labels != 1:
                supported_tasks.remove("score")
1943
                logger.debug_once("Score API is only enabled for num_labels == 1.")
1944
1945

        return supported_tasks
1946

1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
    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)

1957
    def sync_and_slice_intermediate_tensors(
1958
1959
1960
1961
1962
        self,
        num_tokens: int,
        intermediate_tensors: IntermediateTensors,
        sync_self: bool,
    ) -> IntermediateTensors:
1963
1964
1965
        assert self.intermediate_tensors is not None

        tp = self.vllm_config.parallel_config.tensor_parallel_size
1966
        is_rs = is_residual_scattered_for_sp(self.vllm_config, num_tokens)
1967
1968
1969
1970
1971
1972

        # 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():
1973
                is_scattered = k == "residual" and is_rs
1974
                copy_len = num_tokens // tp if is_scattered else num_tokens
1975
                self.intermediate_tensors[k][:copy_len].copy_(
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
                    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:
1989
1990
1991
1992
1993
1994
1995
        """
        Step for the EPLB (Expert Parallelism Load Balancing) state.
        """
        if not self.parallel_config.enable_eplb:
            return

        assert self.eplb_state is not None
1996
1997
        model = self.get_model()
        assert is_mixture_of_experts(model)
1998
        self.eplb_state.step(
1999
            model,
2000
2001
            is_dummy,
            is_profile,
2002
            log_stats=self.parallel_config.eplb_config.log_balancedness,
2003
2004
        )

2005
2006
2007
2008
    # This is where the second ubatch is adjusted to account for the padding.
    # Should be called after attention metadata creation. This just pads
    # the second ubatch slice out to the total number of tokens
    # (num_tokens + padding)
2009
2010
    @staticmethod
    def pad_out_ubatch_slice(ubatch_slices: UBatchSlices, num_total_tokens: int):
2011
2012
2013
2014
2015
2016
        padded_second_ubatch_slice = slice(
            ubatch_slices[1].token_slice.start, num_total_tokens
        )
        ubatch_slices[1] = UBatchSlice(
            padded_second_ubatch_slice, padded_second_ubatch_slice
        )
2017

2018
2019
2020
2021
2022
2023
    def _pool(
        self,
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
        num_scheduled_tokens_np: np.ndarray,
    ) -> ModelRunnerOutput:
2024
2025
2026
        assert self.input_batch.num_reqs == len(self.input_batch.pooling_params), (
            "Either all or none of the requests in a batch must be pooling request"
        )
2027

2028
        hidden_states = hidden_states[:num_scheduled_tokens]
2029
        pooling_metadata = self.input_batch.get_pooling_metadata()
2030
2031
2032
2033
        pooling_metadata.build_pooling_cursor(
            num_scheduled_tokens_np.tolist(), device=hidden_states.device
        )
        seq_lens_cpu = self.seq_lens.cpu[: self.input_batch.num_reqs]
2034

2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
        model = cast(VllmModelForPooling, self.model)
        raw_pooler_output: PoolerOutput = model.pooler(
            hidden_states=hidden_states,
            pooling_metadata=pooling_metadata,
        )
        raw_pooler_output = json_map_leaves(
            lambda x: x.to("cpu", non_blocking=True),
            raw_pooler_output,
        )
        self._sync_device()
2045
2046
2047

        pooler_output: list[Optional[torch.Tensor]] = []
        for raw_output, seq_len, prompt_len in zip(
2048
2049
            raw_pooler_output, seq_lens_cpu, pooling_metadata.prompt_lens
        ):
2050
            output = raw_output if seq_len == prompt_len else None
2051
            pooler_output.append(output)
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061

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

2062
    def _get_num_input_tokens(self, num_scheduled_tokens: int) -> int:
2063
2064
2065
2066
2067
2068
2069
        if (
            self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
            and not envs.VLLM_DISABLE_PAD_FOR_CUDAGRAPH
            and hasattr(self, "cudagraph_batch_sizes")
            and self.cudagraph_batch_sizes
            and num_scheduled_tokens <= self.cudagraph_batch_sizes[-1]
        ):
2070
2071
2072
2073
2074
2075
2076
2077
            # Use CUDA graphs.
            # Add padding to the batch size.
            return self.vllm_config.pad_for_cudagraph(num_scheduled_tokens)

        # Eager mode.
        # Pad tokens to multiple of tensor_parallel_size when
        # enabled collective fusion for SP
        tp_size = self.vllm_config.parallel_config.tensor_parallel_size
2078
2079
2080
2081
        if (
            self.compilation_config.pass_config.enable_sequence_parallelism
            and tp_size > 1
        ):
2082
2083
2084
            return round_up(num_scheduled_tokens, tp_size)
        return num_scheduled_tokens

2085
    def _preprocess(
2086
2087
        self,
        scheduler_output: "SchedulerOutput",
2088
        num_input_tokens: int,  # Padded
2089
        intermediate_tensors: Optional[IntermediateTensors] = None,
2090
2091
2092
2093
2094
2095
2096
2097
    ) -> tuple[
        int,
        Optional[torch.Tensor],
        Optional[torch.Tensor],
        torch.Tensor,
        Optional[IntermediateTensors],
        dict[str, Any],
    ]:
2098
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
2099
        is_first_rank = get_pp_group().is_first_rank
2100

2101
2102
        # _prepare_inputs may reorder the batch, so we must gather multi
        # modal outputs after that to ensure the correct order
2103
2104
        if (
            self.supports_mm_inputs
2105
            and is_first_rank
2106
2107
            and not self.model_config.is_encoder_decoder
        ):
2108
2109
            # Run the multimodal encoder if any.
            self._execute_mm_encoder(scheduler_output)
2110
            mm_embeds, is_mm_embed = self._gather_mm_embeddings(scheduler_output)
2111

2112
2113
2114
            # 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.
2115
            inputs_embeds_scheduled = self.model.get_input_embeddings(
2116
2117
2118
                self.input_ids.gpu[:num_scheduled_tokens],
                multimodal_embeddings=mm_embeds,
                is_multimodal=is_mm_embed,
2119
            )
2120

2121
            # TODO(woosuk): Avoid the copy. Optimize.
2122
            self.inputs_embeds.gpu[:num_scheduled_tokens].copy_(inputs_embeds_scheduled)
2123

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

            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
            model_kwargs = self._init_model_kwargs(num_input_tokens)
            input_ids = None
2157
        else:
2158
2159
2160
2161
            # 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.
2162
            input_ids = self.input_ids.gpu[:num_input_tokens]
2163
            inputs_embeds = None
2164
            model_kwargs = self._init_model_kwargs(num_input_tokens)
2165
        if self.uses_mrope:
2166
            positions = self.mrope_positions.gpu[:, :num_input_tokens]
2167
        else:
2168
            positions = self.positions.gpu[:num_input_tokens]
2169

2170
        if is_first_rank:
2171
2172
            intermediate_tensors = None
        else:
2173
            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
2174
2175
                num_input_tokens, intermediate_tensors, True
            )
2176

2177
2178
2179
2180
        if (
            self.model_config.is_encoder_decoder
            and scheduler_output.scheduled_encoder_inputs
        ):
2181
2182
2183
            encoder_inputs = self._extract_encoder_inputs(scheduler_output)
            model_kwargs.update(encoder_inputs)

2184
2185
2186
2187
2188
2189
2190
2191
        return (
            num_scheduled_tokens,
            input_ids,
            inputs_embeds,
            positions,
            intermediate_tensors,
            model_kwargs,
        )
2192

2193
    def _sample(
2194
2195
2196
        self,
        logits: Optional[torch.Tensor],
        spec_decode_metadata: Optional[SpecDecodeMetadata],
2197
    ) -> SamplerOutput:
2198
        # Sample the next token and get logprobs if needed.
2199
        sampling_metadata = self.input_batch.sampling_metadata
2200
        if spec_decode_metadata is None:
2201
            return self.sampler(
2202
2203
2204
                logits=logits,
                sampling_metadata=sampling_metadata,
            )
2205

2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2217
2218
2219
2220
2221
2222
2223
2224
2225
2226
2227
2228
2229
2230
2231
        # When indexing with a tensor (bonus_logits_indices), PyTorch
        # creates a new tensor with separate storage from the original
        # logits tensor. This means any in-place operations on bonus_logits
        # won't affect the original logits tensor.
        assert logits is not None
        bonus_logits = logits[spec_decode_metadata.bonus_logits_indices]
        sampler_output = self.sampler(
            logits=bonus_logits,
            sampling_metadata=sampling_metadata,
            predict_bonus_token=True,
        )
        bonus_token_ids = sampler_output.sampled_token_ids

        # Just like `bonus_logits`, `target_logits` is a new tensor with
        # separate storage from the original `logits` tensor. Therefore,
        # it is safe to update `target_logits` in place.
        target_logits = logits[spec_decode_metadata.target_logits_indices]
        output_token_ids = self.rejection_sampler(
            spec_decode_metadata,
            None,  # draft_probs
            target_logits,
            bonus_token_ids,
            sampling_metadata,
        )
        sampler_output.sampled_token_ids = output_token_ids
        self._update_states_after_model_execute(output_token_ids)
2232
2233
2234
        return sampler_output

    def _bookkeeping_sync(
2235
2236
2237
2238
2239
2240
        self,
        scheduler_output: "SchedulerOutput",
        sampler_output: SamplerOutput,
        logits: Optional[torch.Tensor],
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
2241
    ) -> tuple[
2242
2243
2244
2245
2246
2247
2248
        dict[str, int],
        Optional[LogprobsLists],
        list[list[int]],
        dict[str, Optional[LogprobsTensors]],
        list[str],
        dict[str, int],
        list[int],
2249
    ]:
2250
2251
2252
2253
        num_nans_in_logits = {}
        if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
            num_nans_in_logits = self._get_nans_in_logits(logits)

2254
2255
2256
        discard_sampled_tokens_req_indices = self.discard_request_indices.np[
            : self.num_discarded_requests
        ]
2257
2258
2259
2260
        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)
2261

2262
2263
2264
        # 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()
2265
        req_id_to_index_output_copy = self.input_batch.req_id_to_index.copy()
2266

2267
2268
        # NOTE: GPU -> CPU Sync happens here.
        # Move as many CPU operations as possible before this sync point.
2269
        logprobs_tensors = sampler_output.logprobs_tensors
2270
2271
2272
        logprobs_lists = (
            logprobs_tensors.tolists() if logprobs_tensors is not None else None
        )
2273
2274
2275

        # Compute prompt logprobs if needed.
        prompt_logprobs_dict = self._get_prompt_logprobs_dict(
2276
            hidden_states[:num_scheduled_tokens],
2277
            scheduler_output.num_scheduled_tokens,
2278
2279
        )

2280
        num_sampled_tokens = sampler_output.sampled_token_ids.shape[0]
2281
        sampled_token_ids = sampler_output.sampled_token_ids
2282
        invalid_req_indices = []
2283
2284
2285
2286
2287
2288
2289
2290
2291
2292
2293
2294
2295
2296
        if not self.use_async_scheduling:
            # Get the valid generated tokens.
            max_gen_len = sampled_token_ids.shape[-1]
            if max_gen_len == 1:
                # No spec decode tokens.
                valid_sampled_token_ids = self._to_list(sampled_token_ids)
            else:
                # Includes spec decode tokens.
                valid_sampled_token_ids = self.rejection_sampler.parse_output(
                    sampled_token_ids,
                    self.input_batch.vocab_size,
                )
            # Mask out the sampled tokens that should not be sampled.
            for i in discard_sampled_tokens_req_indices:
2297
                valid_sampled_token_ids[int(i)].clear()
2298
        else:
2299
            valid_sampled_token_ids = []
2300
            invalid_req_indices = discard_sampled_tokens_req_indices.tolist()
2301
2302
2303
2304
2305
2306
            invalid_req_indices_set = set(invalid_req_indices)
            assert sampled_token_ids.shape[-1] == 1

            # Cache the sampled tokens on the GPU and avoid CPU sync.
            # These will be copied into input_ids in the next step
            # when preparing inputs.
2307
            self.input_batch.prev_sampled_token_ids = sampled_token_ids
2308
2309
2310
2311
2312
            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
            }
2313

2314
2315
2316
2317
2318
        # 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.
2319
        req_ids = self.input_batch.req_ids
2320
2321
        for req_idx in range(num_sampled_tokens):
            if self.use_async_scheduling:
2322
                sampled_ids = [-1] if req_idx not in invalid_req_indices_set else None
2323
2324
            else:
                sampled_ids = valid_sampled_token_ids[req_idx]
2325
2326
2327
2328
2329
            if not sampled_ids:
                continue

            start_idx = self.input_batch.num_tokens_no_spec[req_idx]
            end_idx = start_idx + len(sampled_ids)
2330
2331
2332
2333
            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}"
2334
            )
2335

2336
2337
            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
2338
2339
            self.input_batch.num_tokens_no_spec[req_idx] = end_idx
            self.input_batch.num_tokens[req_idx] = end_idx
2340

2341
            req_id = req_ids[req_idx]
2342
2343
2344
            req_state = self.requests[req_id]
            req_state.output_token_ids.extend(sampled_ids)

2345
2346
2347
2348
2349
2350
2351
2352
2353
2354
        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,
        )

2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
    @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()

2370
2371
2372
2373
2374
2375
2376
2377
2378
2379
2380
    def _model_forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        positions: Optional[torch.Tensor] = None,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        **model_kwargs: dict[str, Any],
    ) -> Any:
        """Helper method to call the model forward pass.

        This method can be overridden by subclasses for model execution.
2381
        Motivation: We can inspect only this method versus
2382
2383
2384
2385
2386
2387
2388
2389
2390
2391
2392
2393
2394
2395
2396
2397
2398
2399
2400
2401
        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,
        )

2402
2403
2404
2405
2406
2407
2408
    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
        intermediate_tensors: Optional[IntermediateTensors] = None,
    ) -> Union[ModelRunnerOutput, AsyncModelRunnerOutput, IntermediateTensors]:
        with record_function_or_nullcontext("Preprocess"):
2409
2410
2411
2412
2413
2414
2415
2416
2417
            with self.synchronize_input_prep():
                # Update persistent batch states.
                self._update_states(scheduler_output)

                if not scheduler_output.total_num_scheduled_tokens:
                    if not has_kv_transfer_group():
                        # Return empty ModelRunnerOutput if no work to do.
                        return EMPTY_MODEL_RUNNER_OUTPUT
                    return self.kv_connector_no_forward(
2418
2419
                        scheduler_output, self.vllm_config
                    )
2420
2421
2422
2423
                if self.cache_config.kv_sharing_fast_prefill:
                    assert not self.input_batch.num_prompt_logprobs, (
                        "--kv-sharing-fast-prefill produces incorrect "
                        "logprobs for prompt tokens, tokens, please disable "
2424
2425
                        "it when the requests need prompt logprobs"
                    )
2426

2427
                # Prepare the decoder inputs.
2428
2429
2430
2431
2432
2433
2434
2435
                (
                    attn_metadata,
                    logits_indices,
                    spec_decode_metadata,
                    num_scheduled_tokens_np,
                    spec_decode_common_attn_metadata,
                    max_query_len,
                    ubatch_slices,
2436
                    num_tokens_across_dp,
2437
2438
                    use_cascade_attn,
                ) = self._prepare_inputs(scheduler_output)
2439

2440
2441
2442
2443
2444
2445
2446
2447
2448
2449
2450
            if ubatch_slices:
                assert num_tokens_across_dp is not None
                num_input_tokens = int(num_tokens_across_dp[0].item())
                self.pad_out_ubatch_slice(ubatch_slices, num_input_tokens)
            elif num_tokens_across_dp is not None:
                num_input_tokens = int(num_tokens_across_dp[0].item())
            else:
                num_input_tokens = self._get_num_input_tokens(
                    scheduler_output.total_num_scheduled_tokens
                )

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

            uniform_decode = (max_query_len == self.uniform_decode_query_len) and (
                num_scheduled_tokens == self.input_batch.num_reqs * max_query_len
            )
            batch_descriptor = BatchDescriptor(
                num_tokens=num_input_tokens, uniform_decode=uniform_decode
            )
            cudagraph_runtime_mode, batch_descriptor = (
                self.cudagraph_dispatcher.dispatch(batch_descriptor, use_cascade_attn)
            )
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        # Set cudagraph mode to none if calc_kv_scales is true.
        if attn_metadata is not None:
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            metadata_list = (
                attn_metadata.values()
                if isinstance(attn_metadata, dict)
                else [attn_metadata]
            )
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            if any(
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                getattr(m, "enable_kv_scales_calculation", False) for m in metadata_list
            ):
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                cudagraph_runtime_mode = CUDAGraphMode.NONE

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

        with record_function_or_nullcontext("Postprocess"):
            if self.use_aux_hidden_state_outputs:
2509
                # True when EAGLE 3 is used.
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                hidden_states, aux_hidden_states = model_output
            else:
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                # Common case.
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                hidden_states = model_output
                aux_hidden_states = None

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            if not self.broadcast_pp_output:
                # Common case.
                if not get_pp_group().is_last_rank:
                    # Return the intermediate tensors.
                    assert isinstance(hidden_states, IntermediateTensors)
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                    hidden_states.kv_connector_output = kv_connector_output
                    return hidden_states
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                if self.is_pooling_model:
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                    # Return the pooling output.
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                    output = self._pool(
                        hidden_states, num_scheduled_tokens, num_scheduled_tokens_np
                    )
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                    output.kv_connector_output = kv_connector_output
                    return output
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                sample_hidden_states = hidden_states[logits_indices]
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                logits = self.model.compute_logits(sample_hidden_states)
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            else:
                # Rare case.
                assert not self.is_pooling_model

                if not get_pp_group().is_last_rank:
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                    all_gather_tensors = {
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                        "residual": not is_residual_scattered_for_sp(
                            self.vllm_config, num_input_tokens
                        )
2543
                    }
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                    get_pp_group().send_tensor_dict(
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                        hidden_states.tensors,
                        all_gather_group=get_tp_group(),
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                        all_gather_tensors=all_gather_tensors,
                    )
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                    logits = None
                else:
                    sample_hidden_states = hidden_states[logits_indices]
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                    logits = self.model.compute_logits(sample_hidden_states)
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                model_output_broadcast_data = {}
                if logits is not None:
                    model_output_broadcast_data["logits"] = logits.contiguous()

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

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

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

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        use_padded_batch_for_eagle = (
            self.speculative_config
            and self.speculative_config.use_eagle()
            and not self.speculative_config.disable_padded_drafter_batch
        )
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        effective_drafter_max_model_len = self.max_model_len
        if effective_drafter_max_model_len is None:
            effective_drafter_max_model_len = self.model_config.max_model_len
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        if (
            self.speculative_config
            and self.speculative_config.draft_model_config is not None
            and self.speculative_config.draft_model_config.max_model_len is not None
        ):
2600
            effective_drafter_max_model_len = (
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                self.speculative_config.draft_model_config.max_model_len
            )
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        input_fits_in_drafter = spec_decode_common_attn_metadata and (
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            spec_decode_common_attn_metadata.max_seq_len
            + self.speculative_config.num_speculative_tokens
            <= effective_drafter_max_model_len
        )
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        if use_padded_batch_for_eagle and input_fits_in_drafter:
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            # EAGLE speculative decoding can use the GPU sampled tokens
            # as inputs, and does not need to wait for bookkeeping to finish.
            propose_draft_token_ids(sampler_output.sampled_token_ids)

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        with record_function_or_nullcontext("Bookkeep"):
            (
                num_nans_in_logits,
                logprobs_lists,
                valid_sampled_token_ids,
                prompt_logprobs_dict,
                req_ids_output_copy,
                req_id_to_index_output_copy,
                invalid_req_indices,
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            ) = self._bookkeeping_sync(
                scheduler_output,
                sampler_output,
                logits,
                hidden_states,
                num_scheduled_tokens,
            )
2629

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        if (
            self.speculative_config
            and not use_padded_batch_for_eagle
            and input_fits_in_drafter
        ):
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            # ngram and other speculative decoding methods use the sampled
            # tokens on the CPU, so they are run after bookkeeping.
            propose_draft_token_ids(valid_sampled_token_ids)
2638

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        with record_function_or_nullcontext("EPLB"):
            self.eplb_step()
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        output = ModelRunnerOutput(
            req_ids=req_ids_output_copy,
            req_id_to_index=req_id_to_index_output_copy,
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            sampled_token_ids=valid_sampled_token_ids,
            logprobs=logprobs_lists,
            prompt_logprobs_dict=prompt_logprobs_dict,
            pooler_output=[],
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            kv_connector_output=kv_connector_output,
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            num_nans_in_logits=num_nans_in_logits,
        )

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        if not self.use_async_scheduling:
            return output

        return AsyncGPUModelRunnerOutput(
            model_runner_output=output,
2658
            sampled_token_ids=sampler_output.sampled_token_ids,
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            invalid_req_indices=invalid_req_indices,
            async_output_copy_stream=self.async_output_copy_stream,
        )

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    def take_draft_token_ids(self) -> Optional[DraftTokenIds]:
        if self._draft_token_ids is None:
            return None
        req_ids = self.input_batch.req_ids
        if isinstance(self._draft_token_ids, torch.Tensor):
            draft_token_ids = self._draft_token_ids.tolist()
        else:
            draft_token_ids = self._draft_token_ids
        self._draft_token_ids = None
        return DraftTokenIds(req_ids, draft_token_ids)

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    def propose_draft_token_ids(
        self,
        scheduler_output: "SchedulerOutput",
2677
        sampled_token_ids: Union[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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2681
        aux_hidden_states: Optional[list[torch.Tensor]],
2682
        spec_decode_metadata: Optional[SpecDecodeMetadata],
2683
        common_attn_metadata: CommonAttentionMetadata,
2684
    ) -> Union[list[list[int]], torch.Tensor]:
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        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
        if self.speculative_config.method == "ngram":
2687
            assert isinstance(sampled_token_ids, list)
2688
            assert isinstance(self.drafter, NgramProposer)
2689
            draft_token_ids = self.drafter.propose(
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                sampled_token_ids,
                self.input_batch.req_ids,
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                self.input_batch.num_tokens_no_spec,
                self.input_batch.token_ids_cpu,
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                self.input_batch.spec_decode_unsupported_reqs,
            )
2696
        elif self.speculative_config.method == "medusa":
2697
            assert isinstance(sampled_token_ids, list)
2698
            assert isinstance(self.drafter, MedusaProposer)
2699

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            if sample_hidden_states.shape[0] == len(sampled_token_ids):
                # The input to the target model does not include draft tokens.
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                hidden_states = sample_hidden_states
            else:
                indices = []
                offset = 0
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2706
                assert spec_decode_metadata is not None
2707
                for num_draft, tokens in zip(
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                    spec_decode_metadata.num_draft_tokens, sampled_token_ids
                ):
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                    indices.append(offset + len(tokens) - 1)
                    offset += num_draft + 1
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                indices = torch.tensor(indices, device=self.device)
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                hidden_states = sample_hidden_states[indices]

2715
            draft_token_ids = self.drafter.propose(
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                target_hidden_states=hidden_states,
                sampling_metadata=sampling_metadata,
            )
2719
        elif self.speculative_config.use_eagle():
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            assert isinstance(self.drafter, EagleProposer)
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2723
2724
2725

            if self.speculative_config.disable_padded_drafter_batch:
                # When padded-batch is disabled, the sampled_token_ids should be
                # the cpu-side list[list[int]] of valid sampled tokens for each
                # request, with invalid requests having empty lists.
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                assert isinstance(sampled_token_ids, list), (
                    "sampled_token_ids should be a python list when"
2728
                    "padded-batch is disabled."
2729
                )
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                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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2740
            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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2742
                assert isinstance(sampled_token_ids, torch.Tensor), (
                    "sampled_token_ids should be a torch.Tensor when"
2743
                    "padded-batch is enabled."
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2745
                )
                next_token_ids, valid_sampled_tokens_count = (
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                    self.drafter.prepare_next_token_ids_padded(
                        common_attn_metadata,
                        sampled_token_ids,
                        self.requests,
                        self.input_batch,
                        self.discard_request_indices.gpu,
2752
                        self.num_discarded_requests,
2753
                    )
2754
                )
Jiayi Yao's avatar
Jiayi Yao committed
2755

2756
            if spec_decode_metadata is None:
2757
                token_indices_to_sample = None
2758
                # input_ids can be None for multimodal models.
2759
                target_token_ids = self.input_ids.gpu[:num_scheduled_tokens]
2760
                target_positions = self._get_positions(num_scheduled_tokens)
2761
                if self.use_aux_hidden_state_outputs:
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2762
                    assert aux_hidden_states is not None
2763
                    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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2767
                else:
                    target_hidden_states = hidden_states[:num_scheduled_tokens]
2768
            else:
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2770
                if self.speculative_config.disable_padded_drafter_batch:
                    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,
                    )
2776
                else:
2777
                    common_attn_metadata, token_indices, token_indices_to_sample = (
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                        self.drafter.prepare_inputs_padded(
                            common_attn_metadata,
                            spec_decode_metadata,
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2782
2783
                            valid_sampled_tokens_count,
                        )
                    )
2784

2785
                target_token_ids = self.input_ids.gpu[token_indices]
2786
                target_positions = self._get_positions(token_indices)
2787
                if self.use_aux_hidden_state_outputs:
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2788
                    assert aux_hidden_states is not None
2789
                    target_hidden_states = torch.cat(
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                        [h[token_indices] for h in aux_hidden_states], dim=-1
                    )
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                else:
                    target_hidden_states = hidden_states[token_indices]
2794

2795
            if self.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
2802

2803
            draft_token_ids = self.drafter.propose(
2804
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2807
                target_token_ids=target_token_ids,
                target_positions=target_positions,
                target_hidden_states=target_hidden_states,
                next_token_ids=next_token_ids,
2808
                last_token_indices=token_indices_to_sample,
2809
                sampling_metadata=sampling_metadata,
2810
                common_attn_metadata=common_attn_metadata,
2811
                mm_embed_inputs=mm_embed_inputs,
2812
            )
2813

2814
        return draft_token_ids
2815

2816
2817
2818
    def update_config(self, overrides: dict[str, Any]) -> None:
        allowed_config_names = {"load_config", "model_config"}
        for config_name, config_overrides in overrides.items():
2819
2820
            assert config_name in allowed_config_names, (
                f"Config `{config_name}` not supported. "
2821
                f"Allowed configs: {allowed_config_names}"
2822
            )
2823
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2825
2826
            config = getattr(self, config_name)
            new_config = update_config(config, config_overrides)
            setattr(self, config_name, new_config)

2827
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2831
    def load_model(self, eep_scale_up: bool = False) -> None:
        """
        Args:
            eep_scale_up: the model loading is for elastic EP scale up.
        """
2832
        logger.info("Starting to load model %s...", self.model_config.model)
2833
2834
        if eep_scale_up:
            from vllm.distributed.parallel_state import get_ep_group
2835
2836
2837
2838
2839

            num_local_physical_experts = torch.empty(1, dtype=torch.int32, device="cpu")
            torch.distributed.broadcast(
                num_local_physical_experts, group=get_ep_group().cpu_group, group_src=0
            )
2840
2841
            num_local_physical_experts = int(num_local_physical_experts.item())
            new_ep_size = get_ep_group().world_size
2842
            global_expert_load, old_global_expert_indices = EplbState.recv_state()
2843
            num_logical_experts = global_expert_load.shape[1]
2844
            self.parallel_config.eplb_config.num_redundant_experts = (
2845
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2847
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2849
2850
                num_local_physical_experts * new_ep_size - num_logical_experts
            )
            assert old_global_expert_indices.shape[1] % num_local_physical_experts == 0
            old_ep_size = (
                old_global_expert_indices.shape[1] // num_local_physical_experts
            )
2851
            rank_mapping = {
2852
                old_ep_rank: old_ep_rank for old_ep_rank in range(old_ep_size)
2853
2854
2855
2856
2857
2858
            }
        else:
            global_expert_load = None
            old_global_expert_indices = None
            rank_mapping = None

2859
        with DeviceMemoryProfiler() as m:
2860
            time_before_load = time.perf_counter()
2861
            model_loader = get_model_loader(self.load_config)
2862
2863
            logger.info("Loading model from scratch...")
            self.model = model_loader.load_model(
2864
2865
                vllm_config=self.vllm_config, model_config=self.model_config
            )
2866
            if self.lora_config:
2867
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2869
                self.model = self.load_lora_model(
                    self.model, self.vllm_config, self.device
                )
2870
2871
2872
            if hasattr(self, "drafter"):
                logger.info("Loading drafter model...")
                self.drafter.load_model(self.model)
2873
            if self.use_aux_hidden_state_outputs:
2874
                if not supports_eagle3(self.model):
2875
2876
                    raise RuntimeError(
                        "Model does not support EAGLE3 interface but "
2877
2878
                        "aux_hidden_state_outputs was requested"
                    )
2879
2880
2881
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2883
2884
2885
2886
2887
2888
2889
2890
2891

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

                self.model.set_aux_hidden_state_layers(aux_layers)
2892
            time_after_load = time.perf_counter()
2893
        self.model_memory_usage = m.consumed_memory
2894
2895
2896
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2898
        logger.info(
            "Model loading took %.4f GiB and %.6f seconds",
            self.model_memory_usage / GiB_bytes,
            time_after_load - time_before_load,
        )
2899
        prepare_communication_buffer_for_model(self.model)
2900

2901
2902
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2904
        self.is_multimodal_pruning_enabled = (
            supports_multimodal_pruning(self.model)
            and self.model_config.multimodal_config.is_multimodal_pruning_enabled()
        )
2905

2906
2907
        if is_mixture_of_experts(self.model) and self.parallel_config.enable_eplb:
            logger.info("EPLB is enabled for model %s.", self.model_config.model)
2908
2909
2910
2911
            self.eplb_state = EplbState.build(
                self.model,
                self.device,
                self.parallel_config,
2912
2913
2914
                global_expert_load,
                old_global_expert_indices,
                rank_mapping,
2915
2916
            )

2917
        if (
2918
2919
            self.vllm_config.compilation_config.level == CompilationLevel.DYNAMO_AS_IS
            and supports_dynamo()
2920
        ):
2921
            backend = self.vllm_config.compilation_config.init_backend(self.vllm_config)
2922
            compilation_counter.dynamo_as_is_count += 1
2923
            self.model.compile(fullgraph=True, backend=backend)
2924
2925
2926
2927
2928
            return
        # for other compilation levels, cudagraph behavior is controlled by
        # CudagraphWraper and CudagraphDispatcher of vllm.

        # wrap the model with full cudagraph wrapper if needed.
2929
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2931
2932
2933
2934
2935
        if (
            self.compilation_config.cudagraph_mode.has_full_cudagraphs()
            and not self.parallel_config.enable_dbo
        ):
            self.model = CUDAGraphWrapper(
                self.model, self.vllm_config, runtime_mode=CUDAGraphMode.FULL
            )
2936
2937
        elif self.parallel_config.enable_dbo:
            if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
2938
2939
2940
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.FULL, self.device
                )
2941
            else:
2942
2943
2944
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.NONE, self.device
                )
2945

2946
2947
2948
2949
2950
2951
2952
2953
2954
2955
2956
2957
2958
2959
2960
2961
2962
2963
2964
2965
2966
2967
2968
2969
    def _get_eagle3_aux_layers_from_config(self) -> Optional[tuple[int, ...]]:
        """Extract Eagle3 auxiliary layer indices from speculative config.

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

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

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

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

        return None

2970
    def reload_weights(self) -> None:
2971
        assert getattr(self, "model", None) is not None, (
2972
            "Cannot reload weights before model is loaded."
2973
        )
2974
2975
        model_loader = get_model_loader(self.load_config)
        logger.info("Reloading weights inplace...")
2976
        model_loader.load_weights(self.get_model(), model_config=self.model_config)
2977

2978
2979
2980
2981
2982
    def save_tensorized_model(
        self,
        tensorizer_config: "TensorizerConfig",
    ) -> None:
        TensorizerLoader.save_model(
2983
            self.get_model(),
2984
            tensorizer_config=tensorizer_config,
2985
            model_config=self.model_config,
2986
2987
        )

2988
2989
2990
    def _get_prompt_logprobs_dict(
        self,
        hidden_states: torch.Tensor,
2991
        num_scheduled_tokens: dict[str, int],
2992
    ) -> dict[str, Optional[LogprobsTensors]]:
2993
2994
2995
2996
        num_prompt_logprobs_dict = self.input_batch.num_prompt_logprobs
        if not num_prompt_logprobs_dict:
            return {}

2997
        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
2998
        prompt_logprobs_dict: dict[str, Optional[LogprobsTensors]] = {}
2999
3000
3001
3002
3003

        # 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():
3004
            num_tokens = num_scheduled_tokens[req_id]
3005
3006
3007

            # Get metadata for this request.
            request = self.requests[req_id]
3008
3009
3010
3011
            if request.prompt_token_ids is None:
                # Prompt logprobs is incompatible with prompt embeddings
                continue

3012
3013
            num_prompt_tokens = len(request.prompt_token_ids)
            prompt_token_ids = torch.tensor(request.prompt_token_ids).to(
3014
3015
                self.device, non_blocking=True
            )
3016

3017
3018
3019
3020
3021
3022
            # 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(
3023
3024
                    num_prompt_tokens - 1, num_prompt_logprobs + 1
                )
3025
3026
                in_progress_dict[req_id] = logprobs_tensors

3027
            # Determine number of logits to retrieve.
3028
3029
            start_idx = request.num_computed_tokens
            start_tok = start_idx + 1
3030
            num_remaining_tokens = num_prompt_tokens - start_tok
3031
            if num_tokens <= num_remaining_tokens:
3032
                # This is a chunk, more tokens remain.
3033
3034
3035
                # 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.
3036
3037
3038
3039
3040
                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)
3041
3042
3043
3044
3045
3046
3047
                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
3048
3049
3050
3051
3052

            # 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]
3053
            offset = self.query_start_loc.np[req_idx].item()
3054
            prompt_hidden_states = hidden_states[offset : offset + num_logits]
3055
            logits = self.model.compute_logits(prompt_hidden_states)
3056
3057
3058
3059

            # 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.
3060
            tgt_token_ids = prompt_token_ids[start_tok : start_tok + num_logits]
3061
3062

            # Compute prompt logprobs.
3063
3064
            logprobs = self.sampler.compute_logprobs(logits)
            token_ids, logprobs, ranks = self.sampler.gather_logprobs(
3065
3066
                logprobs, num_prompt_logprobs, tgt_token_ids
            )
3067
3068

            # Transfer GPU->CPU async.
3069
3070
            chunk_slice = slice(start_idx, start_idx + num_logits)
            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
3071
3072
3073
                token_ids, non_blocking=True
            )
            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs, non_blocking=True)
3074
            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
3075
3076
                ranks, non_blocking=True
            )
3077
3078
3079
3080
3081

        # 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]
3082
            del in_progress_dict[req_id]
3083
3084

        # Must synchronize the non-blocking GPU->CPU transfers.
3085
        if prompt_logprobs_dict:
3086
            self._sync_device()
3087
3088
3089

        return prompt_logprobs_dict

3090
3091
3092
3093
3094
3095
3096
3097
3098
3099
3100
3101
3102
3103
    def _get_nans_in_logits(
        self,
        logits: Optional[torch.Tensor],
    ) -> dict[str, int]:
        try:
            if logits is None:
                return {req_id: 0 for req_id in self.input_batch.req_ids}

            num_nans_in_logits = {}
            num_nans_for_index = logits.isnan().sum(dim=-1).cpu().numpy()
            for req_id in self.input_batch.req_ids:
                req_index = self.input_batch.req_id_to_index[req_id]
                num_nans_in_logits[req_id] = (
                    int(num_nans_for_index[req_index])
3104
3105
3106
                    if num_nans_for_index is not None and req_index < logits.shape[0]
                    else 0
                )
3107
3108
3109
3110
            return num_nans_in_logits
        except IndexError:
            return {}

3111
3112
3113
3114
3115
3116
    @contextmanager
    def maybe_randomize_inputs(self, input_ids: torch.Tensor):
        """
        Randomize input_ids if VLLM_RANDOMIZE_DP_DUMMY_INPUTS is set.
        This is to help balance expert-selection
         - during profile_run
3117
         - during DP rank dummy run
3118
3119
3120
3121
3122
3123
3124
3125
3126
3127
3128
        """
        dp_size = self.vllm_config.parallel_config.data_parallel_size
        randomize_inputs = envs.VLLM_RANDOMIZE_DP_DUMMY_INPUTS and dp_size > 1
        if not randomize_inputs:
            yield
        else:
            import functools

            @functools.cache
            def rand_input_ids() -> torch.Tensor:
                return torch.randint_like(
3129
                    self.input_ids.gpu,
3130
3131
                    low=0,
                    high=self.model_config.get_vocab_size(),
3132
3133
                    dtype=input_ids.dtype,
                )
3134

3135
            logger.debug_once("Randomizing dummy data for DP Rank")
3136
            input_ids.copy_(rand_input_ids()[: input_ids.size(0)], non_blocking=True)
3137
3138
3139
            yield
            input_ids.fill_(0)

3140
3141
3142
3143
3144
3145
    def _get_mm_dummy_batch(
        self,
        modality: str,
        max_items_per_batch: int,
    ) -> BatchedTensorInputs:
        """Dummy data for profiling and precompiling multimodal models."""
3146
3147
        assert self.mm_budget is not None

3148
3149
        dummy_decoder_data = self.mm_registry.get_decoder_dummy_data(
            model_config=self.model_config,
3150
            seq_len=self.max_model_len,
3151
            mm_counts={modality: 1},
3152
            cache=self.mm_budget.cache,
3153
3154
3155
3156
        )
        dummy_mm_data = dummy_decoder_data.multi_modal_data

        # Result in the maximum GPU consumption of the model
3157
3158
        dummy_mm_item = dummy_mm_data[modality][0]
        dummy_mm_items = [dummy_mm_item] * max_items_per_batch
3159

3160
        model = cast(SupportsMultiModal, self.model)
3161
3162
3163
3164
3165
3166
3167
3168
3169
        return next(
            mm_kwargs_group
            for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
                dummy_mm_items,
                device=self.device,
                pin_memory=self.pin_memory,
                merge_by_field_config=model.merge_by_field_config,
            )
        )
3170

3171
3172
3173
3174
    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
3175
        cudagraph_runtime_mode: Optional[CUDAGraphMode] = None,
3176
3177
        force_attention: bool = False,
        uniform_decode: bool = False,
3178
        allow_microbatching: bool = True,
3179
3180
        skip_eplb: bool = False,
        is_profile: bool = False,
3181
        create_mixed_batch: bool = False,
3182
        remove_lora: bool = True,
3183
    ) -> tuple[torch.Tensor, torch.Tensor]:
3184
3185
3186
3187
3188
3189
3190
        """
        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.
3191
                - if not set will determine the cudagraph mode based on using
3192
                    the self.cudagraph_dispatcher.
3193
3194
3195
3196
                - CUDAGraphMode.NONE: No cudagraph, for warm up and profile run
                - CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
                - CUDAGraphMode.FULL: Full cudagraph, attention metadata is
                    needed.
3197
            force_attention: If True, always create attention metadata. Used to
3198
3199
3200
3201
                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.
3202
3203
            create_mixed_batch: If True, create a mixed batch with both decode
                (1 token) and prefill (multiple tokens) requests.
3204
            remove_lora: If False, dummy LoRAs are not destroyed after the run
3205
        """
3206
3207
3208
3209
        assert (
            cudagraph_runtime_mode is None
            or cudagraph_runtime_mode.valid_runtime_modes()
        )
3210

3211
        # If cudagraph_mode.decode_mode() == FULL and
3212
        # cudagraph_mode.separate_routine(). This means that we are using
3213
3214
3215
3216
3217
3218
3219
3220
3221
3222
3223
        # 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.
3224
        max_query_len = self.uniform_decode_query_len if uniform_decode else num_tokens
3225

3226
3227
3228
3229
3230
        # Set num_scheduled_tokens based on num_tokens and max_num_seqs
        # for dummy run with LoRA so that the num_reqs collectively
        # has num_tokens in total.
        assert num_tokens <= self.scheduler_config.max_num_batched_tokens
        max_num_reqs = self.scheduler_config.max_num_seqs
3231
3232
3233
3234
        if create_mixed_batch:
            assert not uniform_decode
            # Create mixed batch:
            # first half decode tokens, second half one prefill
3235
            num_decode_tokens = min(max_num_reqs - 1, num_tokens // 2)
3236
3237
3238
3239
            num_prefill_tokens = num_tokens - num_decode_tokens
            num_reqs = num_decode_tokens + 1

            # Create decode requests (1 token each) followed by prefill request
3240
            num_scheduled_tokens_list = [1] * num_decode_tokens + [num_prefill_tokens]
3241
3242
3243
            # Note: Overriding max_query_len to be the prefill tokens
            max_query_len = num_prefill_tokens
        elif uniform_decode:
3244
            assert not create_mixed_batch
3245
            num_reqs = min(max_num_reqs, cdiv(num_tokens, max_query_len))
3246
3247
            num_scheduled_tokens_list = [max_query_len] * num_reqs
            if num_tokens % max_query_len != 0:
3248
                num_scheduled_tokens_list[-1] = num_tokens % max_query_len
3249
3250
3251
3252
3253
3254
        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

3255
3256
        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs
3257
        num_scheduled_tokens = np.array(num_scheduled_tokens_list, dtype=np.int32)
3258
        total_num_scheduled_tokens = int(num_scheduled_tokens.sum())
3259

3260
3261
        # We currently only microbatch if the number of tokens is
        # over a certain threshold.
3262
3263
3264
3265
3266
3267
3268
3269
3270
3271
3272
        ubatch_slices, num_tokens_across_dp = coordinate_batch_across_dp(
            num_scheduled_tokens,
            total_num_scheduled_tokens,
            total_num_scheduled_tokens,
            self.vllm_config.parallel_config,
            allow_microbatching,
            uniform_decode,
        )
        num_tokens_after_padding = num_tokens
        if num_tokens_across_dp is not None:
            num_tokens_after_padding = int(num_tokens_across_dp[0])
3273
3274

        attn_metadata: Optional[PerLayerAttnMetadata] = None
3275
3276
3277

        # If force_attention is True, we always capture attention. Otherwise,
        # it only happens for cudagraph_runtime_mode=FULL.
3278
        if force_attention or cudagraph_runtime_mode == CUDAGraphMode.FULL:
3279
            attn_metadata = {}
3280
3281
            if ubatch_slices is not None:
                attn_metadata = [dict() for _ in range(len(ubatch_slices))]
3282

3283
3284
3285
3286
3287
3288
            if create_mixed_batch:
                # In the mixed batch mode (used for FI warmup), we use
                # shorter sequence lengths to run faster.
                # TODO(luka) better system for describing dummy batches
                seq_lens = [1] * num_decode_tokens + [num_prefill_tokens + 1]
            else:
3289
                seq_lens = max_query_len
3290
            self.seq_lens.np[:num_reqs] = seq_lens
3291
3292
            self.seq_lens.np[num_reqs:] = 0
            self.seq_lens.copy_to_gpu()
3293

3294
3295
            cum_num_tokens, _ = self._get_cumsum_and_arange(num_scheduled_tokens)
            self.query_start_loc.np[1 : num_reqs + 1] = cum_num_tokens
3296
3297
            self.query_start_loc.copy_to_gpu()

3298
            for kv_cache_group_id, kv_cache_group_spec in enumerate(
3299
3300
                self.kv_cache_config.kv_cache_groups
            ):
3301
                common_attn_metadata = CommonAttentionMetadata(
3302
3303
                    query_start_loc=self.query_start_loc.gpu[: num_reqs + 1],
                    query_start_loc_cpu=self.query_start_loc.cpu[: num_reqs + 1],
3304
3305
                    seq_lens=self.seq_lens.gpu[:num_reqs],
                    seq_lens_cpu=self.seq_lens.cpu[:num_reqs],
3306
3307
3308
                    num_computed_tokens_cpu=self.input_batch.num_computed_tokens_cpu_tensor[
                        :num_reqs
                    ],
3309
3310
                    num_reqs=num_reqs,
                    num_actual_tokens=num_tokens,
3311
                    max_query_len=max_query_len,
3312
                    max_seq_len=self.max_model_len,
3313
3314
3315
                    block_table_tensor=self.input_batch.block_table[
                        kv_cache_group_id
                    ].get_device_tensor(num_reqs),
3316
                    slot_mapping=self.input_batch.block_table[
3317
3318
3319
                        kv_cache_group_id
                    ].slot_mapping.gpu[:num_tokens],
                    causal=True,
3320
3321
3322
                    dcp_local_seq_lens=self.dcp_local_seq_lens.gpu[:num_reqs]
                    if self.dcp_world_size > 1
                    else None,
3323
                )
3324
                for attn_group in self.attn_groups[kv_cache_group_id]:
3325
3326
                    if ubatch_slices is not None:
                        common_attn_metadata_list = split_attn_metadata(
3327
3328
                            ubatch_slices, common_attn_metadata
                        )
3329
                        for ubid, common_attn_metadata in enumerate(
3330
3331
                            common_attn_metadata_list
                        ):
3332
                            assert common_attn_metadata.max_query_len == 1
3333
3334
3335
                            attn_metadata_i = attn_group.get_metadata_builder(
                                ubatch_id=ubid
                            ).build_for_cudagraph_capture(common_attn_metadata)
3336
                            for layer_name in attn_group.layer_names:
3337
                                assert type(attn_metadata) is list
3338
                                attn_metadata[ubid][layer_name] = attn_metadata_i
3339
3340
                    else:
                        assert type(attn_metadata) is dict
3341
3342
                        metadata_builder = attn_group.get_metadata_builder()
                        attn_metadata_i = metadata_builder.build_for_cudagraph_capture(
3343
3344
                            common_attn_metadata
                        )
3345
                        for layer_name in attn_group.layer_names:
3346
                            attn_metadata[layer_name] = attn_metadata_i
3347

3348
3349
3350
        with self.maybe_dummy_run_with_lora(
            self.lora_config, num_scheduled_tokens, remove_lora
        ):
3351
3352
3353
            # Make sure padding doesn't exceed max_num_tokens
            assert num_tokens_after_padding <= self.max_num_tokens
            model_kwargs = self._init_model_kwargs(num_tokens_after_padding)
3354
            if self.supports_mm_inputs and not self.model_config.is_encoder_decoder:
3355
                input_ids = None
3356
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_after_padding]
3357
                model_kwargs = {
3358
                    **model_kwargs,
3359
3360
                    **self._dummy_mm_kwargs(num_reqs),
                }
3361
3362
            elif self.enable_prompt_embeds:
                input_ids = None
3363
3364
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_after_padding]
                model_kwargs = self._init_model_kwargs(num_tokens_after_padding)
3365
            else:
3366
                input_ids = self.input_ids.gpu[:num_tokens_after_padding]
3367
                inputs_embeds = None
3368

3369
            if self.uses_mrope:
3370
                positions = self.mrope_positions.gpu[:, :num_tokens_after_padding]
3371
            else:
3372
                positions = self.positions.gpu[:num_tokens_after_padding]
3373
3374
3375
3376
3377
3378
3379
3380
3381

            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,
3382
3383
3384
                            device=self.device,
                        )
                    )
3385
3386

                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
3387
                    num_tokens_after_padding, None, False
3388
                )
3389
3390

            # filter out the valid batch descriptor
3391
3392
3393
3394
3395
3396
3397
3398
3399
3400
            _cg_mode, batch_descriptor = (
                self.cudagraph_dispatcher.dispatch(
                    BatchDescriptor(
                        num_tokens=num_tokens_after_padding,
                        uniform_decode=uniform_decode,
                    )
                )
                if not is_profile
                else (CUDAGraphMode.NONE, None)
            )
3401
3402
3403
            if cudagraph_runtime_mode is not None:
                # we allow forcing NONE when the dispatcher disagrees to support
                # warm ups for cudagraph capture
3404
3405
3406
3407
                assert (
                    cudagraph_runtime_mode == CUDAGraphMode.NONE
                    or cudagraph_runtime_mode == _cg_mode
                ), (
3408
                    f"Cudagraph runtime mode mismatch at dummy_run. "
3409
3410
                    f"Expected {_cg_mode}, but got {cudagraph_runtime_mode}."
                )
3411
3412
            else:
                cudagraph_runtime_mode = _cg_mode
3413

3414
            if ubatch_slices is not None:
3415
3416
3417
3418
3419
3420
3421
                # Adjust values to reflect a single ubatch.
                # TODO(sage,lucas): this is cruft that should be addressed in
                #  the padding refactor.
                num_tokens_after_padding = ubatch_slices[0].num_tokens
                if num_tokens_across_dp is not None:
                    num_tokens_across_dp[:] = num_tokens_after_padding

3422
3423
3424
            with (
                self.maybe_randomize_inputs(input_ids),
                set_forward_context(
3425
3426
                    attn_metadata,
                    self.vllm_config,
3427
                    num_tokens=num_tokens_after_padding,
3428
3429
                    num_tokens_across_dp=num_tokens_across_dp,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
3430
                    batch_descriptor=batch_descriptor,
3431
3432
3433
                    ubatch_slices=ubatch_slices,
                ),
            ):
3434
                outputs = self.model(
3435
3436
3437
3438
                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
3439
                    **model_kwargs,
3440
                )
3441

3442
3443
3444
3445
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
3446

3447
            if self.speculative_config and self.speculative_config.use_eagle():
3448
3449
3450
                assert isinstance(self.drafter, EagleProposer)
                self.drafter.dummy_run(num_tokens)

3451
3452
3453
3454
3455
3456
3457
3458
3459
3460
        # 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)

3461
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
3462
        return hidden_states, hidden_states[logit_indices]
3463
3464
3465
3466
3467
3468

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
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3472
        # The dummy hidden states may contain special values,
        # like `inf` or `nan`.
        # To avoid breaking the sampler, we use a random tensor here instead.
        hidden_states = torch.rand_like(hidden_states)
3473

3474
        logits = self.model.compute_logits(hidden_states)
3475
3476
        num_reqs = logits.size(0)

3477
        dummy_tensors = lambda v: torch.full((num_reqs,), v, device=self.device)
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3479
3480
3481
3482
3483
3484
3485
3486
3487
3488
3489
3490
3491
3492

        dummy_metadata = SamplingMetadata(
            temperature=dummy_tensors(0.5),
            all_greedy=False,
            all_random=False,
            top_p=dummy_tensors(0.9),
            top_k=dummy_tensors(logits.size(1) - 1),
            generators={},
            max_num_logprobs=None,
            no_penalties=True,
            prompt_token_ids=None,
            frequency_penalties=dummy_tensors(0.1),
            presence_penalties=dummy_tensors(0.1),
            repetition_penalties=dummy_tensors(0.1),
            output_token_ids=[[] for _ in range(num_reqs)],
3493
            spec_token_ids=[[] for _ in range(num_reqs)],
3494
3495
            allowed_token_ids_mask=None,
            bad_words_token_ids={},
3496
            logitsprocs=LogitsProcessors(),
3497
        )
3498
        try:
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3501
            sampler_output = self.sampler(
                logits=logits, sampling_metadata=dummy_metadata
            )
3502
        except RuntimeError as e:
3503
            if "out of memory" in str(e):
3504
3505
3506
3507
                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 "
3508
3509
                    "initializing the engine."
                ) from e
3510
3511
            else:
                raise e
3512
        if self.speculative_config:
3513
3514
            draft_token_ids = [[0] for _ in range(num_reqs)]
            dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
3515
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                draft_token_ids, self.device
            )
3517
3518
3519
3520
3521
3522

            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
3523
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3525
            target_logits = torch.randn(
                num_tokens, logits.shape[-1], device=self.device, dtype=logits.dtype
            )
3526
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3528
            # NOTE(woosuk): Here, we should use int32 because the sampler uses
            # int32 for bonus_token_ids. If the dtype mismatches, re-compilation
            # will occur at runtime.
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            bonus_token_ids = torch.zeros(
                num_reqs, device=self.device, dtype=torch.int32
            )
3532
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3534
3535
3536
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3538
            self.rejection_sampler(
                dummy_spec_decode_metadata,
                draft_probs,
                target_logits,
                bonus_token_ids,
                dummy_metadata,
            )
3539
        return sampler_output
3540

3541
    def _dummy_pooler_run_task(
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        self,
        hidden_states: torch.Tensor,
3544
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        task: PoolingTask,
    ) -> PoolerOutput:
3546
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3548
3549
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3551
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3553
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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
        num_scheduled_tokens_list = [min_tokens_per_req] * num_reqs
        num_scheduled_tokens_list[-1] += num_tokens % num_reqs
        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs

        req_num_tokens = num_tokens // num_reqs

3557
        dummy_prompt_lens = torch.tensor(
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3559
            num_scheduled_tokens_list,
            device="cpu",
3560
        )
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        dummy_token_ids = torch.zeros(
            (num_reqs, req_num_tokens), dtype=torch.int32, device=self.device
        )
3564

3565
        model = cast(VllmModelForPooling, self.get_model())
3566
        dummy_pooling_params = PoolingParams(task=task)
3567
        dummy_pooling_params.verify(task=task, model_config=self.model_config)
3568
        to_update = model.pooler.get_pooling_updates(task)
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        to_update.apply(dummy_pooling_params)

3571
        dummy_metadata = PoolingMetadata(
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            prompt_lens=dummy_prompt_lens,
            prompt_token_ids=dummy_token_ids,
            pooling_params=[dummy_pooling_params] * num_reqs,
        )
3576

3577
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        dummy_metadata.build_pooling_cursor(
            num_scheduled_tokens_list, device=hidden_states.device
        )
3580

3581
        try:
3582
3583
3584
            return model.pooler(
                hidden_states=hidden_states, pooling_metadata=dummy_metadata
            )
3585
        except RuntimeError as e:
3586
            if "out of memory" in str(e):
3587
                raise RuntimeError(
3588
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3590
                    "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 "
3591
3592
                    "initializing the engine."
                ) from e
3593
3594
            else:
                raise e
3595
3596
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3598
3599
3600
3601
3602
3603
3604
3605

    @torch.inference_mode()
    def _dummy_pooler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> PoolerOutput:
        # Find the task that has the largest output for subsequent steps
        output_size = dict[PoolingTask, float]()
        for task in self.get_supported_pooling_tasks():
            # Run a full batch with each task to ensure none of them OOMs
            output = self._dummy_pooler_run_task(hidden_states, task)
3606
            output_size[task] = sum(o.nbytes for o in output)
3607
3608
3609
3610
            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)
3611

3612
    def profile_run(self) -> None:
3613
        # Profile with multimodal encoder & encoder cache.
3614
        if self.supports_mm_inputs:
3615
            if self.model_config.multimodal_config.skip_mm_profiling:
3616
                logger.info(
3617
                    "Skipping memory profiling for multimodal encoder and "
3618
3619
                    "encoder cache."
                )
3620
3621
3622
3623
3624
3625
3626
3627
            else:
                mm_budget = self.mm_budget
                assert mm_budget is not None

                if (encoder_budget := mm_budget.get_encoder_budget()) > 0:
                    # NOTE: Currently model is profiled with a single non-text
                    # modality with the max possible input tokens even when
                    # it supports multiple.
3628
                    dummy_modality = mm_budget.get_modality_with_max_tokens()
3629
3630
3631
                    max_mm_items_per_batch = mm_budget.max_items_per_batch_by_modality[
                        dummy_modality
                    ]
3632
3633
3634
3635
3636
3637
3638
3639
3640

                    logger.info(
                        "Encoder cache will be initialized with a budget of "
                        "%s tokens, and profiled with %s %s items of the "
                        "maximum feature size.",
                        encoder_budget,
                        max_mm_items_per_batch,
                        dummy_modality,
                    )
3641

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                    # Create dummy batch of multimodal inputs.
                    batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                        dummy_modality,
                        max_mm_items_per_batch,
                    )
3647

3648
                    # Run multimodal encoder.
3649
3650
3651
                    dummy_encoder_outputs = self.model.get_multimodal_embeddings(
                        **batched_dummy_mm_inputs
                    )
3652

3653
3654
3655
3656
                    sanity_check_mm_encoder_outputs(
                        dummy_encoder_outputs,
                        expected_num_items=max_mm_items_per_batch,
                    )
3657

3658
3659
3660
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3662
3663
3664
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3666
3667
                    # NOTE: This happens when encoder cache needs to store
                    # the embeddings that encoder outputs are scattered onto.
                    # In this case we create dummy embeddings of size
                    # (encode_budget, hidden_size) and scatter encoder
                    # output into it.
                    encoder_output_shape = dummy_encoder_outputs[0].shape
                    if encoder_output_shape[0] < encoder_budget:
                        expanded_outputs = []
                        for output in dummy_encoder_outputs:
                            expanded = output.new_zeros(
3668
3669
                                (encoder_budget, encoder_output_shape[-1])
                            )
3670
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3672
3673
3674
3675
                            num_tokens = output.shape[0]
                            expanded[:num_tokens].copy_(output)
                            expanded_outputs.append(expanded)

                        dummy_encoder_outputs = expanded_outputs

3676
                    # Cache the dummy encoder outputs.
3677
                    self.encoder_cache["tmp"] = dict(enumerate(dummy_encoder_outputs))
3678

3679
        # Add `is_profile` here to pre-allocate communication buffers
3680
3681
3682
        hidden_states, last_hidden_states = self._dummy_run(
            self.max_num_tokens, is_profile=True
        )
3683
        if get_pp_group().is_last_rank:
3684
3685
3686
3687
            if self.is_pooling_model:
                output = self._dummy_pooler_run(hidden_states)
            else:
                output = self._dummy_sampler_run(last_hidden_states)
3688
        else:
3689
            output = None
3690
        self._sync_device()
3691
        del hidden_states, output
3692
        self.encoder_cache.clear()
3693
        gc.collect()
3694

3695
    def capture_model(self) -> int:
3696
        if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
3697
            logger.warning(
3698
                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
3699
3700
                "ensure `cudagraph_mode` was not manually set to `NONE`"
            )
3701
            return 0
3702
3703
        else:
            self.initialize_cudagraph_capture()
3704

3705
3706
        compilation_counter.num_gpu_runner_capture_triggers += 1

3707
3708
        start_time = time.perf_counter()

3709
3710
3711
3712
3713
3714
3715
3716
3717
3718
3719
3720
3721
3722
        @contextmanager
        def freeze_gc():
            # Optimize garbage collection during CUDA graph capture.
            # Clean up, then freeze all remaining objects from being included
            # in future collections.
            gc.collect()
            should_freeze = not envs.VLLM_ENABLE_CUDAGRAPH_GC
            if should_freeze:
                gc.freeze()
            try:
                yield
            finally:
                if should_freeze:
                    gc.unfreeze()
3723
                    gc.collect()
3724

3725
3726
3727
        # 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.
3728
        set_cudagraph_capturing_enabled(True)
3729
        with freeze_gc(), graph_capture(device=self.device):
3730
            start_free_gpu_memory = torch.cuda.mem_get_info()[0]
3731
            cudagraph_mode = self.compilation_config.cudagraph_mode
3732
            assert cudagraph_mode is not None
3733
3734
3735
3736
3737
3738
3739
            if cudagraph_mode.mixed_mode() != CUDAGraphMode.NONE:
                cudagraph_runtime_mode = cudagraph_mode.mixed_mode()

                compilation_cases = list(reversed(self.cudagraph_batch_sizes))
                self._capture_cudagraphs(
                    compilation_cases,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
3740
3741
                    uniform_decode=False,
                )
3742

3743
3744
            # Capture full cudagraph for uniform decode batches if we
            # don't already have full mixed prefill-decode cudagraphs.
3745
3746
3747
3748
3749
3750
3751
            if (
                cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
                and cudagraph_mode.separate_routine()
            ):
                max_num_tokens = (
                    self.scheduler_config.max_num_seqs * self.uniform_decode_query_len
                )
3752
                decode_cudagraph_batch_sizes = [
3753
3754
                    x
                    for x in self.cudagraph_batch_sizes
3755
                    if max_num_tokens >= x >= self.uniform_decode_query_len
3756
                ]
3757
                compilation_cases_decode = list(reversed(decode_cudagraph_batch_sizes))
3758
3759
3760
                self._capture_cudagraphs(
                    compilation_cases=compilation_cases_decode,
                    cudagraph_runtime_mode=CUDAGraphMode.FULL,
3761
3762
                    uniform_decode=True,
                )
3763

3764
3765
3766
            torch.cuda.synchronize()
            end_free_gpu_memory = torch.cuda.mem_get_info()[0]

3767
3768
3769
        # 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
3770
        # we may do lazy capturing in future that still allows capturing
3771
3772
        # after here.
        set_cudagraph_capturing_enabled(False)
3773
3774
3775
3776
3777

        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.
3778
3779
3780
3781
3782
        logger.info(
            "Graph capturing finished in %.0f secs, took %.2f GiB",
            elapsed_time,
            cuda_graph_size / (1 << 30),
        )
3783
        return cuda_graph_size
3784

3785
3786
3787
3788
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3790
3791
3792
3793
3794
    def _capture_cudagraphs(
        self,
        compilation_cases: list[int],
        cudagraph_runtime_mode: CUDAGraphMode,
        uniform_decode: bool,
    ):
        assert (
            cudagraph_runtime_mode != CUDAGraphMode.NONE
            and cudagraph_runtime_mode.valid_runtime_modes()
        ), f"Invalid cudagraph runtime mode: {cudagraph_runtime_mode}"
3795
3796
3797
3798
3799
3800
3801
3802

        # Only rank 0 should print progress bar during capture
        if is_global_first_rank():
            compilation_cases = tqdm(
                compilation_cases,
                disable=not self.load_config.use_tqdm_on_load,
                desc="Capturing CUDA graphs ({}, {})".format(
                    "decode" if uniform_decode else "mixed prefill-decode",
3803
3804
3805
                    cudagraph_runtime_mode.name,
                ),
            )
3806

3807
3808
3809
        # We skip EPLB here since we don't want to record dummy metrics
        for num_tokens in compilation_cases:
            # We currently only capture ubatched graphs when its a FULL
3810
3811
3812
            # 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
3813
3814
3815
3816
            allow_microbatching = (
                self.parallel_config.enable_dbo
                and cudagraph_runtime_mode == CUDAGraphMode.FULL
                and uniform_decode
3817
3818
3819
3820
3821
                and check_ubatch_thresholds(
                    config=self.vllm_config.parallel_config,
                    num_tokens=num_tokens,
                    uniform_decode=uniform_decode,
                )
3822
            )
3823

3824
3825
3826
3827
3828
3829
            for _ in range(self.compilation_config.cudagraph_num_of_warmups):
                # Use CUDAGraphRuntimeStyle.NONE (default) for warmup.
                # But be careful, warm up with `NONE`is orthogonal to
                # if we want to warm up attention or not. This is
                # different from the case where `FULL` implies capture
                # attention while `PIECEWISE` implies no attention.
3830
3831
3832
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3835
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3837
3838
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3840
3841
3842
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3844
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3846
3847
                force_attention = cudagraph_runtime_mode == CUDAGraphMode.FULL
                self._dummy_run(
                    num_tokens,
                    cudagraph_runtime_mode=CUDAGraphMode.NONE,
                    force_attention=force_attention,
                    uniform_decode=uniform_decode,
                    allow_microbatching=allow_microbatching,
                    skip_eplb=True,
                    remove_lora=False,
                )
            self._dummy_run(
                num_tokens,
                cudagraph_runtime_mode=cudagraph_runtime_mode,
                uniform_decode=uniform_decode,
                allow_microbatching=allow_microbatching,
                skip_eplb=True,
                remove_lora=False,
            )
3848
        self.maybe_remove_all_loras(self.lora_config)
3849

3850
3851
3852
3853
    def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize the attention backends and attention metadata builders.
        """
3854
        assert len(self.attn_groups) == 0, "Attention backends are already initialized"
3855

3856
3857
3858
3859
3860
3861
3862
3863
        class AttentionGroupKey(NamedTuple):
            attn_backend: type[AttentionBackend]
            kv_cache_spec: KVCacheSpec

        def get_attn_backends_for_group(
            kv_cache_group_spec: KVCacheGroupSpec,
        ) -> dict[AttentionGroupKey, list[str]]:
            layers = get_layers_from_vllm_config(
3864
3865
                self.vllm_config, AttentionLayerBase, kv_cache_group_spec.layer_names
            )
3866
3867
            attn_backends = {}
            attn_backend_layers = defaultdict(list)
3868
            # Dedupe based on full class name; this is a bit safer than
3869
3870
3871
3872
            # 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.
3873
            for layer_name in kv_cache_group_spec.layer_names:
3874
                attn_backend = layers[layer_name].get_attn_backend()
3875
3876
3877
3878
3879
3880
3881

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

3882
3883
3884
                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):
3885
                    layer_kv_cache_spec = layer_kv_cache_spec.kv_cache_specs[layer_name]
3886
                key = (full_cls_name, layer_kv_cache_spec)
3887
3888
3889
                attn_backends[key] = AttentionGroupKey(
                    attn_backend, layer_kv_cache_spec
                )
3890
                attn_backend_layers[key].append(layer_name)
3891
            return {attn_backends[k]: v for k, v in attn_backend_layers.items()}
3892
3893

        def create_attn_groups(
3894
            attn_backends_map: dict[AttentionGroupKey, list[str]],
3895
3896
        ) -> list[AttentionGroup]:
            attn_groups: list[AttentionGroup] = []
3897
            for (attn_backend, kv_cache_spec), layer_names in attn_backends_map.items():
3898
3899
                attn_group = AttentionGroup.create_with_metadata_builders(
                    attn_backend,
3900
                    layer_names,
3901
                    kv_cache_spec,
3902
3903
                    self.vllm_config,
                    self.device,
3904
                    num_metadata_builders=1
3905
3906
                    if not self.parallel_config.enable_dbo
                    else 2,
3907
3908
                )

3909
3910
3911
3912
                attn_groups.append(attn_group)
            return attn_groups

        for kv_cache_group_spec in kv_cache_config.kv_cache_groups:
3913
3914
            attn_backends = get_attn_backends_for_group(kv_cache_group_spec)
            self.attn_groups.append(create_attn_groups(attn_backends))
3915

co63oc's avatar
co63oc committed
3916
        # Calculate reorder batch threshold (if needed)
3917
3918
        self.calculate_reorder_batch_threshold()

3919
    def initialize_cudagraph_capture(self) -> None:
3920
        """
3921
        Resolve the cudagraph_mode when there are multiple attention
3922
3923
3924
3925
        backends with potential conflicting CUDA graph support.
        Then initialize the cudagraph_dispatcher based on the resolved
        cudagraph_mode.
        """
3926
3927
3928
3929
        min_cg_support = AttentionCGSupport.ALWAYS
        min_cg_builder_name = None

        for attn_group in self._attn_group_iterator():
3930
            builder = attn_group.get_metadata_builder()
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            if builder.cudagraph_support.value < min_cg_support.value:
                min_cg_support = builder.cudagraph_support
                min_cg_builder_name = builder.__class__.__name__
        # Flexible resolve the cudagraph mode
        cudagraph_mode = self.compilation_config.cudagraph_mode
        # check cudagraph for mixed batch is supported
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        if (
            cudagraph_mode.mixed_mode() == CUDAGraphMode.FULL
            and min_cg_support != AttentionCGSupport.ALWAYS
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
                f"with {min_cg_builder_name} backend (support: "
                f"{min_cg_support})"
            )
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            if min_cg_support == AttentionCGSupport.NEVER:
                # if not supported any full cudagraphs, just raise it.
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                msg += (
                    "; please try cudagraph_mode=PIECEWISE, and "
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                    "make sure compilation level is piecewise"
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                )
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                raise ValueError(msg)

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

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

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

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

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

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

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

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

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

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

        return compatible_sizes if return_all else [max(compatible_sizes)]

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

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

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

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

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

        common_supported_sizes = set.intersection(*all_backend_supports)

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

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

        return max(common_supported_sizes)

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

        Args:
            kv_cache_config: The KV cache configuration.
        """
        block_sizes = [
            kv_cache_group.kv_cache_spec.block_size
            for kv_cache_group in kv_cache_config.kv_cache_groups
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            if not isinstance(kv_cache_group.kv_cache_spec, EncoderOnlyAttentionSpec)
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        ]
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        # Generate kernel_block_sizes that matches each block_size
        kernel_block_sizes = self._prepare_kernel_block_sizes(kv_cache_config)

        if block_sizes != [self.cache_config.block_size] or kernel_block_sizes != [
            self.cache_config.block_size
        ]:
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            assert self.cache_config.cpu_offload_gb == 0, (
                "Cannot re-initialize the input batch when CPU weight "
                "offloading is enabled. See https://github.com/vllm-project/vllm/pull/18298 "  # noqa: E501
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                "for more details."
            )
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            self.input_batch = InputBatch(
                max_num_reqs=self.max_num_reqs,
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                max_model_len=max(self.max_model_len, self.max_encoder_len),
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                max_num_batched_tokens=self.max_num_tokens,
                device=self.device,
                pin_memory=self.pin_memory,
                vocab_size=self.model_config.get_vocab_size(),
                block_sizes=block_sizes,
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                kernel_block_sizes=kernel_block_sizes,
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                is_spec_decode=bool(self.vllm_config.speculative_config),
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                logitsprocs=self.input_batch.logitsprocs,
                is_pooling_model=self.is_pooling_model,
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                num_speculative_tokens=(
                    self.vllm_config.speculative_config.num_speculative_tokens
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                    if self.vllm_config.speculative_config
                    else 0
                ),
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            )

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

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

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

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

        Args:
            kv_cache_config: The KV cache configuration.

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

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    def _reshape_kv_cache_tensors(
        self,
        kv_cache_config: KVCacheConfig,
        kv_cache_raw_tensors: dict[str, torch.Tensor],
    ) -> dict[str, torch.Tensor]:
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        """
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        Reshape the KV cache tensors to the desired shape and dtype.
4278

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        Args:
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            kv_cache_config: The KV cache config
            kv_cache_raw_tensors: The KV cache buffer of each layer, with
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                correct size but uninitialized shape.
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        Returns:
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            Dict[str, torch.Tensor]: A map between layer names to their
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            corresponding memory buffer for KV cache.
        """
4287
        kv_caches: dict[str, torch.Tensor] = {}
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        has_attn, has_mamba = False, False
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        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
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            attn_backend = group.backend
            for layer_name in group.layer_names:
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                if layer_name in self.runner_only_attn_layers:
                    continue
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                raw_tensor = kv_cache_raw_tensors[layer_name]
                assert raw_tensor.numel() % kv_cache_spec.page_size_bytes == 0
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                num_blocks = raw_tensor.numel() // kv_cache_spec.page_size_bytes
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                if isinstance(kv_cache_spec, AttentionSpec):
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                    has_attn = True
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                    kv_manager_block_size = kv_cache_spec.block_size
                    kernel_size_list = self._find_compatible_block_sizes(
                        kv_manager_block_size, attn_backend, return_all=False
                    )
                    kernel_size = kernel_size_list[0]
                    num_blocks_per_kv_block = kv_manager_block_size // kernel_size
                    kernel_num_blocks = num_blocks * num_blocks_per_kv_block

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                    kv_cache_shape = attn_backend.get_kv_cache_shape(
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                        kernel_num_blocks,
                        kernel_size,
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                        kv_cache_spec.num_kv_heads,
                        kv_cache_spec.head_size,
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                        cache_dtype_str=self.cache_config.cache_dtype,
                    )
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                    dtype = kv_cache_spec.dtype
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                    try:
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                        kv_cache_stride_order = attn_backend.get_kv_cache_stride_order()  # noqa: E501
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                        assert len(kv_cache_stride_order) == len(kv_cache_shape)
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                    except (AttributeError, NotImplementedError):
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                        kv_cache_stride_order = tuple(range(len(kv_cache_shape)))
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                    # The allocation respects the backend-defined stride order
                    # to ensure the semantic remains consistent for each
                    # backend. We first obtain the generic kv cache shape and
                    # then permute it according to the stride order which could
                    # result in a non-contiguous tensor.
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                    kv_cache_shape = tuple(
                        kv_cache_shape[i] for i in kv_cache_stride_order
                    )
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                    # Maintain original KV shape view.
                    inv_order = [
                        kv_cache_stride_order.index(i)
                        for i in range(len(kv_cache_stride_order))
                    ]
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                    kv_caches[layer_name] = (
                        kv_cache_raw_tensors[layer_name]
                        .view(dtype)
                        .view(kv_cache_shape)
                        .permute(*inv_order)
                    )
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                elif isinstance(kv_cache_spec, MambaSpec):
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                    has_mamba = True
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                    raw_tensor = kv_cache_raw_tensors[layer_name]
                    state_tensors = []
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                    storage_offset_bytes = 0
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                    for shape, dtype in zip(kv_cache_spec.shapes, kv_cache_spec.dtypes):
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                        dtype_size = get_dtype_size(dtype)
                        num_element_per_page = (
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                            kv_cache_spec.page_size_bytes // dtype_size
                        )
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                        target_shape = (num_blocks, *shape)
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                        stride = torch.empty(target_shape).stride()
                        target_stride = (num_element_per_page, *stride[1:])
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                        assert storage_offset_bytes % dtype_size == 0
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                        tensor = torch.as_strided(
                            raw_tensor.view(dtype),
                            size=target_shape,
                            stride=target_stride,
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                            storage_offset=storage_offset_bytes // dtype_size,
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                        )
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                        state_tensors.append(tensor)
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                        storage_offset_bytes += stride[0] * dtype_size
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                    kv_caches[layer_name] = state_tensors
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                else:
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                    raise NotImplementedError
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        if has_attn and has_mamba:
4368
            self._update_hybrid_attention_mamba_layout(kv_caches)
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        return kv_caches

4372
    def _update_hybrid_attention_mamba_layout(
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        self, kv_caches: dict[str, torch.Tensor]
    ) -> None:
4375
        """
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        Update the layout of attention layers from (2, num_blocks, ...) to
        (num_blocks, 2, ...).
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        Args:
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            kv_caches: The KV cache buffer of each layer.
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        """

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        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
4385
            for layer_name in group.layer_names:
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                kv_cache = kv_caches[layer_name]
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                if isinstance(kv_cache_spec, AttentionSpec) and kv_cache.shape[0] == 2:
                    assert kv_cache.shape[1] != 2, (
                        "Fail to determine whether the layout is "
                        "(2, num_blocks, ...) or (num_blocks, 2, ...) for "
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                        f"a tensor of shape {kv_cache.shape}"
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                    )
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                    hidden_size = kv_cache.shape[2:].numel()
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                    kv_cache.as_strided_(
                        size=kv_cache.shape,
                        stride=(hidden_size, 2 * hidden_size, *kv_cache.stride()[2:]),
                    )
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    def initialize_kv_cache_tensors(
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        self, kv_cache_config: KVCacheConfig
    ) -> dict[str, torch.Tensor]:
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        """
        Initialize the memory buffer for KV cache.

        Args:
            kv_cache_config: The KV cache config
        Returns:
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            Dict[str, torch.Tensor]: A map between layer names to their
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            corresponding memory buffer for KV cache.
        """
        # Initialize the memory buffer for KV cache
        kv_cache_raw_tensors = self._allocate_kv_cache_tensors(kv_cache_config)
        # Change the memory buffer to the desired shape
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        kv_caches = self._reshape_kv_cache_tensors(
            kv_cache_config, kv_cache_raw_tensors
        )
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        # Set up cross-layer KV cache sharing
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        for layer_name, target_layer_name in self.shared_kv_cache_layers.items():
            logger.debug("%s reuses KV cache of %s", layer_name, target_layer_name)
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            kv_caches[layer_name] = kv_caches[target_layer_name]

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

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

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

        if self.cache_config.kv_sharing_fast_prefill:
            # In You Only Cache Once (https://arxiv.org/abs/2405.05254) or other
            # similar KV sharing setups, only the layers that generate KV caches
            # are involved in the prefill phase, enabling prefill to early exit.
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            attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention)
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            for layer_name in reversed(attn_layers):
                if layer_name in self.shared_kv_cache_layers:
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                    self.kv_sharing_fast_prefill_eligible_layers.add(layer_name)
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                else:
                    break
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    def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize KV cache based on `kv_cache_config`.
        Args:
            kv_cache_config: Configuration for the KV cache, including the KV
            cache size of each layer
        """
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        kv_cache_config = deepcopy(kv_cache_config)
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        self.kv_cache_config = kv_cache_config
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        self.may_add_encoder_only_layers_to_kv_cache_config()
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        self.maybe_add_kv_sharing_layers_to_kv_cache_groups(kv_cache_config)
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        self.initialize_attn_backend(kv_cache_config)
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        # Reinitialize need to after initialize_attn_backend
        self.may_reinitialize_input_batch(kv_cache_config)
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        kv_caches = self.initialize_kv_cache_tensors(kv_cache_config)

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

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        if has_kv_transfer_group():
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            kv_transfer_group = get_kv_transfer_group()
            kv_transfer_group.register_kv_caches(kv_caches)
            kv_transfer_group.set_host_xfer_buffer_ops(copy_kv_blocks)
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        if self.dcp_world_size > 1:
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            layer_names = self.attn_groups[0][0].layer_names
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            layers = get_layers_from_vllm_config(
                self.vllm_config, AttentionLayerBase, layer_names
            )
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            for layer in layers.values():
                assert layer.impl.need_to_return_lse_for_decode, (
                    "DCP requires attention impls to return"
                    " the softmax lse for decode, but the impl "
                    f"{layer.impl.__class__.__name__} "
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                    "does not return the softmax lse for decode."
                )
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    def may_add_encoder_only_layers_to_kv_cache_config(self) -> None:
        """
        Add encoder-only layers to the KV cache config.
        """
        block_size = self.vllm_config.cache_config.block_size
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        encoder_only_attn_specs: dict[AttentionSpec, list[str]] = defaultdict(list)
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        attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention)
        for layer_name, attn_module in attn_layers.items():
            if attn_module.attn_type == AttentionType.ENCODER_ONLY:
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                attn_spec: AttentionSpec = EncoderOnlyAttentionSpec(
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                    block_size=block_size,
                    num_kv_heads=attn_module.num_kv_heads,
                    head_size=attn_module.head_size,
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                    dtype=self.kv_cache_dtype,
                )
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                encoder_only_attn_specs[attn_spec].append(layer_name)
                self.runner_only_attn_layers.add(layer_name)
        if len(encoder_only_attn_specs) > 0:
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            assert len(encoder_only_attn_specs) == 1, (
                "Only support one encoder-only attention spec now"
            )
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            spec, layer_names = encoder_only_attn_specs.popitem()
            self.kv_cache_config.kv_cache_groups.append(
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                KVCacheGroupSpec(layer_names=layer_names, kv_cache_spec=spec)
            )
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    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
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        """
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        Generates the KVCacheSpec by parsing the kv cache format from each
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        Attention module in the static forward context.
        Returns:
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            KVCacheSpec: A dictionary mapping layer names to their KV cache
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            format. Layers that do not need KV cache are not included.
        """

        block_size = self.vllm_config.cache_config.block_size
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        use_mla = self.vllm_config.model_config.use_mla
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        cache_dtype_str = self.vllm_config.cache_config.cache_dtype
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        kv_cache_spec: dict[str, KVCacheSpec] = {}
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        attn_layers = get_layers_from_vllm_config(self.vllm_config, AttentionLayerBase)
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        for layer_name, attn_module in attn_layers.items():
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            if isinstance(attn_module, Attention):
                if (
                    kv_tgt_layer := attn_module.kv_sharing_target_layer_name
                ) is not None:
                    # The layer doesn't need its own KV cache and will use that of
                    # the target layer. We skip creating a KVCacheSpec for it, so
                    # that KV cache management logic will act as this layer does
                    # not exist, and doesn't allocate KV cache for the layer. This
                    # enables the memory saving of cross-layer kv sharing, allowing
                    # a given amount of memory to accommodate longer context lengths
                    # or enable more requests to be processed simultaneously.
                    self.shared_kv_cache_layers[layer_name] = kv_tgt_layer
                    continue
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                # TODO(lucas): move the attention specs into the model layers like
                # the attention backends
                if attn_module.attn_type == AttentionType.DECODER:
                    if attn_module.sliding_window is not None:
                        assert not use_mla, "MLA is not supported for slidingwindow"
                        kv_cache_spec[layer_name] = SlidingWindowSpec(
                            block_size=block_size,
                            num_kv_heads=attn_module.num_kv_heads,
                            head_size=attn_module.head_size,
                            dtype=self.kv_cache_dtype,
                            sliding_window=attn_module.sliding_window,
                        )
                    elif self.attention_chunk_size is not None and isinstance(
                        attn_module, ChunkedLocalAttention
                    ):
                        kv_cache_spec[layer_name] = ChunkedLocalAttentionSpec(
                            block_size=block_size,
                            num_kv_heads=attn_module.num_kv_heads,
                            head_size=attn_module.head_size,
                            dtype=self.kv_cache_dtype,
                            attention_chunk_size=self.attention_chunk_size,
                        )
                    else:
                        kv_cache_spec[layer_name] = FullAttentionSpec(
                            block_size=block_size,
                            num_kv_heads=attn_module.num_kv_heads,
                            head_size=attn_module.head_size,
                            dtype=self.kv_cache_dtype,
                        )
                elif attn_module.attn_type == AttentionType.ENCODER_DECODER:
                    kv_cache_spec[layer_name] = CrossAttentionSpec(
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                        block_size=block_size,
                        num_kv_heads=attn_module.num_kv_heads,
                        head_size=attn_module.head_size,
                        dtype=self.kv_cache_dtype,
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                    )
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                elif attn_module.attn_type in (
                    AttentionType.ENCODER,
                    AttentionType.ENCODER_ONLY,
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                ):
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                    # encoder-only attention does not need KV cache.
                    continue
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                else:
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                    raise ValueError(f"Unknown attention type: {attn_module.attn_type}")

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