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

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

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

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

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

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


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class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
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    def __init__(
        self,
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        vllm_config: VllmConfig,
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        device: torch.device,
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    ):
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        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        self.cache_config = vllm_config.cache_config
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        self.compilation_config = vllm_config.compilation_config
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        self.lora_config = vllm_config.lora_config
        self.load_config = vllm_config.load_config
        self.parallel_config = vllm_config.parallel_config
        self.scheduler_config = vllm_config.scheduler_config
        self.speculative_config = vllm_config.speculative_config
        self.observability_config = vllm_config.observability_config
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        from vllm.model_executor.models.utils import set_cpu_offload_max_bytes
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        set_cpu_offload_max_bytes(int(self.cache_config.cpu_offload_gb * 1024**3))
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        model_config = self.model_config
        cache_config = self.cache_config
        scheduler_config = self.scheduler_config
        parallel_config = self.parallel_config
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        self.device = device
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        self.pin_memory = is_pin_memory_available()
        self.dtype = self.model_config.dtype
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        self.kv_cache_dtype = kv_cache_dtype_str_to_dtype(
            cache_config.cache_dtype, self.model_config
        )
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        self.is_pooling_model = model_config.runner_type == "pooling"
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        self.enable_prompt_embeds = model_config.enable_prompt_embeds
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        self.is_multimodal_raw_input_only_model = (
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            model_config.is_multimodal_raw_input_only_model
        )
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        # This will be overridden in load_model()
        self.is_multimodal_pruning_enabled = False
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        self.max_model_len = model_config.max_model_len
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        self.dcp_world_size = self.parallel_config.decode_context_parallel_size
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        self.max_num_tokens = scheduler_config.max_num_batched_tokens
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        self.max_num_reqs = scheduler_config.max_num_seqs
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        # Broadcast PP output for external_launcher (torchrun)
        # to make sure we are synced across pp ranks
        # TODO: Support overlapping mirco-batches
        # https://github.com/vllm-project/vllm/issues/18019
        self.broadcast_pp_output = (
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            self.parallel_config.distributed_executor_backend == "external_launcher"
            and len(get_pp_group().ranks) > 0
        )
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        # Model-related.
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        self.num_query_heads = model_config.get_num_attention_heads(parallel_config)
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        self.hidden_size = model_config.get_hidden_size()
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        self.attention_chunk_size = model_config.attention_chunk_size
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        # Only relevant for models using ALiBi (e.g, MPT)
        self.use_alibi = check_use_alibi(model_config)
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        self.cascade_attn_enabled = not self.model_config.disable_cascade_attn
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        # Multi-modal data support
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        self.mm_registry = MULTIMODAL_REGISTRY
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        self.uses_mrope = model_config.uses_mrope
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        self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
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            model_config
        )
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        if self.model_config.is_encoder_decoder:
            # Maximum length of the encoder input, only for encoder-decoder
            # models.
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            self.max_encoder_len = scheduler_config.max_num_encoder_input_tokens
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        else:
            self.max_encoder_len = 0

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

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

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

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        # mm_hash ->  encoder_output
        self.encoder_cache: dict[str, torch.Tensor] = {}
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        self.use_aux_hidden_state_outputs = False
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        # Set up speculative decoding.
        # NOTE(Jiayi): currently we put the entire draft model on
        # the last PP rank. This is not ideal if there are many
        # layers in the draft model.
        if self.speculative_config and get_pp_group().is_last_rank:
            if self.speculative_config.method == "ngram":
                self.drafter = NgramProposer(self.vllm_config)
            elif self.speculative_config.use_eagle():
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                self.drafter = EagleProposer(self.vllm_config, self.device, self)  # type: ignore
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                if self.speculative_config.method == "eagle3":
                    self.use_aux_hidden_state_outputs = True
            elif self.speculative_config.method == "medusa":
                self.drafter = MedusaProposer(
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                    vllm_config=self.vllm_config, device=self.device
                )  # type: ignore
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            else:
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                raise ValueError(
                    "Unknown speculative decoding method: "
                    f"{self.speculative_config.method}"
                )
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            self.rejection_sampler = RejectionSampler(self.sampler)
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        # Request states.
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        self.requests: dict[str, CachedRequestState] = {}
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        self.comm_stream = torch.cuda.Stream()
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        # Input Batch
        # NOTE(Chen): Ideally, we should initialize the input batch inside
        # `initialize_kv_cache` based on the kv cache config. However, as in
        # https://github.com/vllm-project/vllm/pull/18298, due to some unknown
        # reasons, we have to initialize the input batch before `load_model`,
        # quantization + weight offloading will fail otherwise. As a temporary
        # solution, we initialize the input batch here, and re-initialize it
        # in `initialize_kv_cache` if the block_sizes here is different from
        # the block_sizes in the kv cache config.
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        custom_logitsprocs = model_config.logits_processors
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        self.input_batch = InputBatch(
            max_num_reqs=self.max_num_reqs,
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            # We need to use the encoder length for encoder-decoer
            # because of KV cache for cross-attention.
            max_model_len=max(self.max_model_len, self.max_encoder_len),
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            max_num_batched_tokens=self.max_num_tokens,
            device=self.device,
            pin_memory=self.pin_memory,
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            vocab_size=self.model_config.get_vocab_size(),
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            block_sizes=[self.cache_config.block_size],
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            kernel_block_sizes=[self.cache_config.block_size],
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            is_spec_decode=bool(self.vllm_config.speculative_config),
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            logitsprocs=build_logitsprocs(
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                self.vllm_config,
                self.device,
                self.pin_memory,
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                self.is_pooling_model,
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                custom_logitsprocs,
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            ),
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            # We currently don't know whether a particular custom logits processor
            # uses output token ids so we set this conservatively.
            logitsprocs_need_output_token_ids=bool(custom_logitsprocs),
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            is_pooling_model=self.is_pooling_model,
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        )
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        self.use_async_scheduling = self.scheduler_config.async_scheduling
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        # Separate cuda stream for overlapping transfer of sampled token ids from
        # GPU to CPU when async scheduling is enabled.
        self.async_output_copy_stream: torch.cuda.Stream | None = None
        # cuda event to synchronize use of reused CPU tensors between steps
        # when async scheduling is enabled.
        self.prepare_inputs_event: torch.cuda.Event | None = None
        if self.use_async_scheduling:
            self.async_output_copy_stream = torch.cuda.Stream()
            self.prepare_inputs_event = torch.cuda.Event()
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        # self.cudagraph_batch_sizes sorts in ascending 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 = sorted(
                self.compilation_config.cudagraph_capture_sizes
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            )
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        # Cache the device properties.
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        self._init_device_properties()
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        # Persistent buffers for CUDA graphs.
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        self.input_ids = self._make_buffer(self.max_num_tokens, dtype=torch.int32)
        self.positions = self._make_buffer(self.max_num_tokens, dtype=torch.int64)
        self.query_start_loc = self._make_buffer(
            self.max_num_reqs + 1, dtype=torch.int32
        )
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        self.seq_lens = self._make_buffer(self.max_num_reqs, dtype=torch.int32)
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        if self.dcp_world_size > 1:
            self.dcp_local_seq_lens = self._make_buffer(
                self.max_num_reqs, dtype=torch.int32
            )
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        # Because inputs_embeds may be bfloat16 and we don't need a numpy
        # version of this tensor, avoid a RuntimeError by not creating a
        # numpy buffer.
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        self.inputs_embeds = self._make_buffer(
            self.max_num_tokens, self.hidden_size, dtype=self.dtype, numpy=False
        )
        self.is_token_ids = self._make_buffer(self.max_num_tokens, dtype=torch.bool)
        self.discard_request_indices = self._make_buffer(
            self.max_num_reqs, dtype=torch.int64
        )
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        self.num_discarded_requests = 0

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        self.num_decode_draft_tokens = self._make_buffer(
            self.max_num_reqs, dtype=torch.int32
        )
        self.num_accepted_tokens = self._make_buffer(
            self.max_num_reqs, dtype=torch.int64
        )
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        # Only relevant for multimodal models
        if self.supports_mm_inputs:
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            self.is_mm_embed = self._make_buffer(self.max_num_tokens, dtype=torch.bool)
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        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
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        if self.uses_mrope:
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            # NOTE: `mrope_positions` is implemented with one additional dummy
            # position on purpose to make it non-contiguous so that it can work
            # with torch compile.
            # See detailed explanation in https://github.com/vllm-project/vllm/pull/12128#discussion_r1926431923
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            # NOTE: When M-RoPE is enabled, position ids are 3D regardless of
            # the modality of inputs. For text-only inputs, each dimension has
            # identical position IDs, making M-RoPE functionally equivalent to
            # 1D-RoPE.
            # See page 5 of https://arxiv.org/abs/2409.12191
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            self.mrope_positions = self._make_buffer(
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                (3, self.max_num_tokens + 1), dtype=torch.int64
            )
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        # None in the first PP rank. The rest are set after load_model.
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        self.intermediate_tensors: IntermediateTensors | None = None
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        # OPTIMIZATION: Cache the tensors rather than creating them every step.
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        # Keep in int64 to avoid overflow with long context
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        self.arange_np = np.arange(
            max(self.max_num_reqs + 1, self.max_model_len, self.max_num_tokens),
            dtype=np.int64,
        )
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        # Layer pairings for cross-layer KV sharing.
        # If an Attention layer `layer_name` is in the keys of this dict, it
        # means this layer will perform attention using the keys and values
        # from the KV cache of `shared_kv_cache_layers[layer_name]`.
        self.shared_kv_cache_layers: dict[str, str] = {}
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        self.kv_sharing_fast_prefill_eligible_layers: set[str] = set()

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

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

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        # Cached outputs.
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        self._draft_token_ids: list[list[int]] | torch.Tensor | None = None
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        self.transfer_event = torch.cuda.Event()
        self.sampled_token_ids_pinned_cpu = torch.empty(
            (self.max_model_len, 1),
            dtype=torch.int64,
            device="cpu",
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            pin_memory=self.pin_memory,
        )
501

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

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

516
    def _make_buffer(
517
        self, *size: int | torch.SymInt, dtype: torch.dtype, numpy: bool = True
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525
    ) -> CpuGpuBuffer:
        return CpuGpuBuffer(
            *size,
            dtype=dtype,
            device=self.device,
            pin_memory=self.pin_memory,
            with_numpy=numpy,
        )
526

527
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529
    def _init_model_kwargs(self, num_tokens: int):
        model_kwargs = dict[str, Any]()

530
        if not self.is_pooling_model:
531
532
            return model_kwargs

533
534
        num_reqs = self.input_batch.num_reqs
        pooling_params = self.input_batch.get_pooling_params()
535
536
537

        token_type_id_requests = dict[int, Any]()
        for i, param in enumerate(pooling_params):
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540
541
542
            if (
                param.extra_kwargs is not None
                and (token_types := param.extra_kwargs.get("compressed_token_type_ids"))
                is not None
            ):
543
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547
                token_type_id_requests[i] = token_types

        if len(token_type_id_requests) == 0:
            return model_kwargs

548
        seq_lens = self.seq_lens.gpu[:num_reqs]
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551
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556
        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(
557
558
            device=self.device
        )
559
560
        return model_kwargs

561
    def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
562
563
        """
        Update the order of requests in the batch based on the attention
564
        backend's needs. For example, some attention backends (namely MLA) may
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570
        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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578
        # 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

579
        if self.reorder_batch_threshold is not None:
580
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582
            # 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.
583
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            if (
                self.dcp_world_size > 1
                and envs.VLLM_ATTENTION_BACKEND != "FLASH_ATTN_MLA"
            ):
587
                assert self.reorder_batch_threshold == 1, (
588
                    "DCP not support reorder_batch_threshold > 1 now."
589
                )
590
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592
            reorder_batch_to_split_decodes_and_prefills(
                self.input_batch,
                scheduler_output,
593
594
                decode_threshold=self.reorder_batch_threshold,
            )
595

596
597
    # Note: used for model runner override.
    def _init_device_properties(self) -> None:
598
        """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()

606
    def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
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612
        """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.

613
614
        The SamplingMetadata is updated and copied to the GPU if there is a
        new/resumed/paused/finished request in the batch.
615
616
        """
        # Remove finished requests from the cached states.
617
618
        for req_id in scheduler_output.finished_req_ids:
            self.requests.pop(req_id, None)
619
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623
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625
        # 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:
626
            self.input_batch.remove_request(req_id)
627
628

        # Free the cached encoder outputs.
629
630
        for mm_hash in scheduler_output.free_encoder_mm_hashes:
            self.encoder_cache.pop(mm_hash, None)
631

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643
644
        # 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:
645
            self.input_batch.remove_request(req_id)
646

647
        reqs_to_add: list[CachedRequestState] = []
648
        # Add new requests to the cached states.
649
650
651
        for new_req_data in scheduler_output.scheduled_new_reqs:
            req_id = new_req_data.req_id
            sampling_params = new_req_data.sampling_params
652
            pooling_params = new_req_data.pooling_params
653

654
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657
            if (
                sampling_params
                and sampling_params.sampling_type == SamplingType.RANDOM_SEED
            ):
658
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662
                generator = torch.Generator(device=self.device)
                generator.manual_seed(sampling_params.seed)
            else:
                generator = None

663
664
            if self.is_pooling_model:
                assert pooling_params is not None
665
666
                task = pooling_params.task
                assert task is not None, "You did not set `task` in the API"
667

668
                model = cast(VllmModelForPooling, self.get_model())
669
                to_update = model.pooler.get_pooling_updates(task)
670
671
                to_update.apply(pooling_params)

672
            req_state = CachedRequestState(
673
                req_id=req_id,
674
                prompt_token_ids=new_req_data.prompt_token_ids,
675
                prompt_embeds=new_req_data.prompt_embeds,
676
                mm_features=new_req_data.mm_features,
677
                sampling_params=sampling_params,
678
                pooling_params=pooling_params,
679
                generator=generator,
680
681
                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
682
                output_token_ids=[],
683
                lora_request=new_req_data.lora_request,
684
            )
685
686
            self.requests[req_id] = req_state

687
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
688
            if self.uses_mrope:
689
                self._init_mrope_positions(req_state)
690

691
            reqs_to_add.append(req_state)
692

693
        # Update the states of the running/resumed requests.
694
        is_last_rank = get_pp_group().is_last_rank
695
696
        req_data = scheduler_output.scheduled_cached_reqs
        for i, req_id in enumerate(req_data.req_ids):
697
            req_state = self.requests[req_id]
698
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700
            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]
701
            num_output_tokens = req_data.num_output_tokens[i]
702

703
            # Update the cached states.
704

705
            req_state.num_computed_tokens = num_computed_tokens
706
            req_index = self.input_batch.req_id_to_index.get(req_id)
707
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709
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711
712
713
714

            if not is_last_rank:
                # When using PP, the scheduler sends the sampled tokens back,
                # because there's no direct communication between the first-
                # stage worker and the last-stage worker.
                new_token_ids = req_data.new_token_ids[i]
                # Add the sampled token(s) from the previous step (if any).
                # This doesn't include "unverified" tokens like spec tokens.
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717
                num_new_tokens = (
                    num_computed_tokens + len(new_token_ids) - req_state.num_tokens
                )
718
719
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721
                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:
722
                    req_state.output_token_ids.extend(new_token_ids[-num_new_tokens:])
723
724
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726
727
            elif num_output_tokens < len(req_state.output_token_ids):
                # Some output tokens were discarded due to a sync-KV-load
                # failure. Align the cached state.
                del req_state.output_token_ids[num_output_tokens:]
                if req_index is not None:
728
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731
                    end_idx = (
                        self.input_batch.num_prompt_tokens[req_index]
                        + num_output_tokens
                    )
732
733
                    self.input_batch.num_tokens[req_index] = end_idx
                    self.input_batch.num_tokens_no_spec[req_index] = end_idx
734

735
            # Update the block IDs.
736
            if not resumed_from_preemption:
737
738
                if new_block_ids is not None:
                    # Append the new blocks to the existing block IDs.
739
                    for block_ids, new_ids in zip(req_state.block_ids, new_block_ids):
740
                        block_ids.extend(new_ids)
741
            else:
742
                assert req_index is None
743
                assert new_block_ids is not None
744
745
                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
746
                req_state.block_ids = new_block_ids
747

748
749
750
751
752
753
                if self.use_async_scheduling and num_output_tokens > 0:
                    # We must recover the output token ids for resumed requests in the
                    # async scheduling case, so that correct input_ids are obtained.
                    resumed_token_ids = req_data.resumed_req_token_ids[i]
                    assert resumed_token_ids is not None
                    req_state.output_token_ids = resumed_token_ids[-num_output_tokens:]
754
755
756
757
            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.
758
                reqs_to_add.append(req_state)
759
760
761
                continue

            # Update the persistent batch.
762
            self.input_batch.num_computed_tokens_cpu[req_index] = num_computed_tokens
763
            if new_block_ids is not None:
764
                self.input_batch.block_table.append_row(new_block_ids, req_index)
765
766
767
768
769
770
771

            # 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)
772
                self.input_batch.token_ids_cpu[
773
774
775
                    req_index, start_token_index:end_token_index
                ] = new_token_ids
                self.input_batch.num_tokens_no_spec[req_index] = end_token_index
776
                self.input_batch.num_tokens[req_index] = end_token_index
777

778
            # Add spec_token_ids to token_ids_cpu.
779
            spec_token_ids = scheduler_output.scheduled_spec_decode_tokens.get(
780
                req_id, []
781
            )
782
783
784
785
786
            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[
787
788
                    req_index, start_index:end_token_index
                ] = spec_token_ids
789
790
                # NOTE(woosuk): `num_tokens` here may include spec tokens.
                self.input_batch.num_tokens[req_index] += num_spec_tokens
791
792
793
794
795
796
797

            # When speculative decoding is used with structured output,
            # the scheduler can drop draft tokens that do not
            # conform to the schema. This can result in
            # scheduler_output.scheduled_spec_decode_tokens being empty,
            # even when speculative decoding is enabled.
            self.input_batch.spec_token_ids[req_index] = spec_token_ids
798

799
800
        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
801
802
        for request in reqs_to_add:
            self.input_batch.add_request(request)
803

804
805
806
807
808
809
        # 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()
810

811
    def _update_states_after_model_execute(
812
813
        self, output_token_ids: torch.Tensor
    ) -> None:
814
815
816
817
818
819
820
821
822
823
824
825
        """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.
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
        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()
        )
846
847
848
        for i, num_tokens in enumerate(num_accepted_tokens):
            self.input_batch.num_accepted_tokens_cpu[i] = num_tokens

849
850
851
852
853
854
    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
855
856
857
858
        for mm_feature in req_state.mm_features:
            mm_item = mm_feature.data
            if mm_item is None:
                continue
859
860
861
862
863
864
865
866
867
868
869
870
            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

871
872
873
874
875
876
877
878
879
880
881
        assert supports_mrope(self.get_model()), "M-RoPE support is not implemented."

        req_state.mrope_positions, req_state.mrope_position_delta = (
            self.model.get_mrope_input_positions(
                req_state.prompt_token_ids,
                hf_config=self.model_config.hf_config,
                image_grid_thw=image_grid_thw,
                video_grid_thw=video_grid_thw,
                second_per_grid_ts=second_per_grid_ts,
                audio_feature_lengths=audio_feature_lengths,
                use_audio_in_video=use_audio_in_video,
882
            )
883
        )
884

885
    def _extract_mm_kwargs(
886
        self,
887
888
        scheduler_output: "SchedulerOutput",
    ) -> BatchedTensorInputs:
889
        if not scheduler_output or not self.is_multimodal_raw_input_only_model:
890
            return {}
891

892
893
        mm_kwargs = list[MultiModalKwargsItem]()
        for req in scheduler_output.scheduled_new_reqs:
894
895
896
            for feature in req.mm_features:
                if feature.data is not None:
                    mm_kwargs.append(feature.data)
897

898
        # Input all modalities at once
899
        model = cast(SupportsMultiModal, self.model)
900
901
        mm_kwargs_combined: BatchedTensorInputs = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
902
903
904
905
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
906
907
        ):
            mm_kwargs_combined.update(mm_kwargs_group)
908

909
        return mm_kwargs_combined
910

911
    def _dummy_mm_kwargs(self, num_seqs: int) -> BatchedTensorInputs:
912
        if not self.is_multimodal_raw_input_only_model:
913
            return {}
914

915
916
917
918
919
        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)
920

921
922
923
    def _get_cumsum_and_arange(
        self,
        num_tokens: np.ndarray,
924
        cumsum_dtype: np.dtype | None = None,
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
    ) -> 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

941
942
943
    def _prepare_input_ids(
        self, total_num_scheduled_tokens: int, cu_num_tokens: np.ndarray
    ) -> None:
944
        """Prepare the input IDs for the current batch.
945

946
947
948
949
950
951
952
        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)
953
954
955
            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)
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
            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)
974
                indices_match &= prev_index == flattened_index
975
976
977
978
979
980
                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)
981
982
983
            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)
984
985
        if num_commmon_tokens == 0:
            # No requests in common with the previous iteration
986
            # So input_ids.cpu will have all the input ids.
987
988
989
990
991
992
993
            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_(
994
995
996
                self.input_batch.prev_sampled_token_ids[:num_commmon_tokens, 0],
                non_blocking=True,
            )
997
998
            if self.enable_prompt_embeds:
                self.is_token_ids.gpu[:num_commmon_tokens] = True
999
            return
1000
        # Upload the index tensors asynchronously so the scatter can be non-blocking.
1001
1002
1003
        input_ids_index_tensor = torch.tensor(
            flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1004
        prev_common_req_indices_tensor = torch.tensor(
1005
1006
            prev_common_req_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1007
1008
1009
1010
        self.input_ids.gpu.scatter_(
            dim=0,
            index=input_ids_index_tensor,
            src=self.input_batch.prev_sampled_token_ids[
1011
1012
1013
                prev_common_req_indices_tensor, 0
            ],
        )
1014

1015
1016
1017
1018
1019
    def _get_encoder_seq_lens(
        self,
        scheduler_output: "SchedulerOutput",
        kv_cache_spec: KVCacheSpec,
        num_reqs: int,
1020
    ) -> np.ndarray | None:
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
        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

1033
    def _prepare_inputs(
1034
        self, scheduler_output: "SchedulerOutput"
1035
1036
1037
    ) -> tuple[
        PerLayerAttnMetadata,
        torch.Tensor,
1038
        SpecDecodeMetadata | None,
1039
        np.ndarray,
1040
        CommonAttentionMetadata | None,
1041
        int,
1042
1043
        UBatchSlices | None,
        torch.Tensor | None,
1044
1045
        bool,
    ]:
1046
1047
1048
        """
        :return: tuple[
            attn_metadata: layer-to-attention_metadata mapping,
1049
1050
1051
            logits_indices, spec_decode_metadata,
            num_scheduled_tokens, spec_decode_common_attn_metadata,
            max_num_scheduled_tokens, use_cascade_attn
1052
1053
        ]
        """
1054
1055
1056
1057
1058
1059
1060
        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.
1061
        self.input_batch.block_table.commit_block_table(num_reqs)
1062
1063

        # Get the number of scheduled tokens for each request.
1064
1065
1066
1067
        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)
1068
1069
1070

        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
1071
        req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens)
1072

1073
1074
        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
        # arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
1075
        cu_num_tokens, arange = self._get_cumsum_and_arange(num_scheduled_tokens)
1076
1077

        # Get positions.
1078
        positions_np = self.positions.np[:total_num_scheduled_tokens]
1079
1080
1081
1082
1083
        np.add(
            self.input_batch.num_computed_tokens_cpu[req_indices],
            arange,
            out=positions_np,
        )
1084

1085
1086
        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1087
        if self.uses_mrope:
1088
1089
            self._calc_mrope_positions(scheduler_output)

1090
1091
1092
1093
        # 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.
1094
1095
1096
        token_indices = (
            positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]
        )
1097
        token_indices_tensor = torch.from_numpy(token_indices)
1098

1099
1100
1101
        # 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.
1102
1103
1104
1105
1106
1107
        torch.index_select(
            self.input_batch.token_ids_cpu_tensor.flatten(),
            0,
            token_indices_tensor,
            out=self.input_ids.cpu[:total_num_scheduled_tokens],
        )
1108
1109
1110
1111
1112
1113
        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,
1114
1115
                out=self.is_token_ids.cpu[:total_num_scheduled_tokens],
            )
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148

        # 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:
1149
1150
1151
                    self.inputs_embeds.cpu[
                        output_idx : output_idx + actual_num_sched
                    ].copy_(req_embeds[start_pos:actual_end])
1152
1153

                output_idx += num_sched
1154

1155
1156
        self.input_batch.block_table.compute_slot_mapping(req_indices, positions_np)
        self.input_batch.block_table.commit_slot_mapping(total_num_scheduled_tokens)
1157
1158

        # Prepare the attention metadata.
1159
        self.query_start_loc.np[0] = 0
1160
        self.query_start_loc.np[1 : num_reqs + 1] = cu_num_tokens
1161
1162
        # Note: pad query_start_loc to be non-decreasing, as kernels
        # like FlashAttention requires that
1163
        self.query_start_loc.np[num_reqs + 1 :].fill(cu_num_tokens[-1])
1164
        self.query_start_loc.copy_to_gpu()
1165
        query_start_loc = self.query_start_loc.gpu[: num_reqs + 1]
1166

1167
        num_tokens_unpadded = scheduler_output.total_num_scheduled_tokens
1168
        num_tokens_padded = self._get_num_input_tokens(num_tokens_unpadded)
1169
1170
1171
        uniform_decode = (
            max_num_scheduled_tokens == self.uniform_decode_query_len
        ) and (total_num_scheduled_tokens == num_reqs * max_num_scheduled_tokens)
1172
1173
1174
1175
1176
1177
1178

        # Disable DP padding when running eager to avoid excessive padding when
        # running prefills. This lets us set enforce_eager on the prefiller in
        # a P/D setup and still use CUDA graphs (enabled by this padding) on the
        # decoder.
        allow_dp_padding = self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE

1179
        ubatch_slices, num_tokens_across_dp = coordinate_batch_across_dp(
1180
1181
1182
1183
1184
1185
1186
            num_tokens_unpadded=num_tokens_unpadded,
            parallel_config=self.parallel_config,
            allow_microbatching=True,
            allow_dp_padding=allow_dp_padding,
            num_tokens_padded=num_tokens_padded,
            uniform_decode=uniform_decode,
            num_scheduled_tokens_per_request=num_scheduled_tokens,
1187
        )
1188

1189
        self.seq_lens.np[:num_reqs] = (
1190
1191
            self.input_batch.num_computed_tokens_cpu[:num_reqs] + num_scheduled_tokens
        )
1192
        # Fill unused with 0 for full cuda graph mode.
1193
1194
1195
1196
        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()
1197

1198
        num_tokens = [self.requests[r].num_tokens for r in self.input_batch.req_ids]
1199
1200
1201
1202
1203
1204
1205
        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)
1206
1207
1208
        self.discard_request_indices.np[: self.num_discarded_requests] = (
            discard_request_indices
        )
1209
1210
1211

        self.discard_request_indices.copy_to_gpu(self.num_discarded_requests)

1212
        # Copy the tensors to the GPU.
1213
1214
        self._prepare_input_ids(total_num_scheduled_tokens, cu_num_tokens)

1215
        if self.uses_mrope:
1216
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1217
1218
            self.mrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.mrope_positions.cpu[:, :total_num_scheduled_tokens],
1219
1220
                non_blocking=True,
            )
1221
1222
        else:
            # Common case (1D positions)
1223
            self.positions.copy_to_gpu(total_num_scheduled_tokens)
1224

1225
        use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
1226
1227
1228
1229
1230
1231
1232
        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
1233
            num_draft_tokens = None
1234
1235
1236
1237
1238
1239
            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)
1240
1241
1242
            # 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)
1243
1244
1245
1246
            for (
                req_id,
                draft_token_ids,
            ) in scheduler_output.scheduled_spec_decode_tokens.items():
1247
1248
                req_idx = self.input_batch.req_id_to_index[req_id]
                num_draft_tokens[req_idx] = len(draft_token_ids)
1249
1250
1251
1252
1253
1254
1255
1256
                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
                )
1257
            spec_decode_metadata = self._calc_spec_decode_metadata(
1258
1259
                num_draft_tokens, cu_num_tokens
            )
1260
            logits_indices = spec_decode_metadata.logits_indices
1261
1262

            # For DECODE only cuda graph of some attention backends (e.g., GDN).
1263
            self.num_decode_draft_tokens.np[:num_reqs] = num_decode_draft_tokens
1264
1265
            self.num_decode_draft_tokens.np[num_reqs:].fill(-1)
            self.num_decode_draft_tokens.copy_to_gpu()
1266
1267
1268

        logits_indices_padded = None
        if self.cache_config.kv_sharing_fast_prefill:
1269
            logits_indices_padded = self._prepare_kv_sharing_fast_prefill(
1270
1271
                logits_indices
            )
1272

1273
1274
1275
        attn_metadata: PerLayerAttnMetadata = {}
        if ubatch_slices is not None:
            attn_metadata = [dict() for _ in range(len(ubatch_slices))]
1276
        use_cascade_attn = False
1277

1278
        # Used in the below loop.
1279
        query_start_loc_cpu = self.query_start_loc.cpu[: num_reqs + 1]
1280
        seq_lens_cpu = self.seq_lens.cpu[:num_reqs]
1281
1282
1283
        num_computed_tokens_cpu = self.input_batch.num_computed_tokens_cpu_tensor[
            :num_reqs
        ]
1284
        spec_decode_common_attn_metadata = None
1285
1286
        if use_spec_decode:
            self.num_accepted_tokens.np[:num_reqs] = (
1287
1288
                self.input_batch.num_accepted_tokens_cpu[:num_reqs]
            )
1289
1290
            self.num_accepted_tokens.np[num_reqs:].fill(1)
            self.num_accepted_tokens.copy_to_gpu()
1291

1292
1293
1294
        # 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(
1295
1296
            self.kv_cache_config.kv_cache_groups
        ):
1297
            encoder_seq_lens = self._get_encoder_seq_lens(
1298
1299
                scheduler_output, kv_cache_group_spec.kv_cache_spec, num_reqs
            )
1300

1301
            if isinstance(kv_cache_group_spec.kv_cache_spec, EncoderOnlyAttentionSpec):
1302
1303
1304
1305
1306
                # 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,
1307
1308
1309
                    device=self.device,
                )
                slot_mapping = torch.zeros(
1310
                    (total_num_scheduled_tokens,),
1311
1312
1313
                    dtype=torch.int64,
                    device=self.device,
                )
1314
1315
1316
                num_common_prefix_blocks = 0
            else:
                blk_table = self.input_batch.block_table[kv_cache_group_id]
1317
                blk_table_tensor = blk_table.get_device_tensor(num_reqs)
1318
                slot_mapping = blk_table.slot_mapping.gpu[:total_num_scheduled_tokens]
1319
1320
1321

                # Fill unused with -1. Needed for reshape_and_cache in full cuda
                # graph mode.
1322
1323
1324
1325
                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
                ]
1326

1327
            common_attn_metadata = CommonAttentionMetadata(
1328
1329
1330
1331
1332
                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,
1333
1334
1335
                num_reqs=num_reqs,
                num_actual_tokens=total_num_scheduled_tokens,
                max_query_len=max_num_scheduled_tokens,
1336
                max_seq_len=max_seq_len,
1337
1338
                block_table_tensor=blk_table_tensor,
                slot_mapping=slot_mapping,
1339
1340
                logits_indices_padded=logits_indices_padded,
                num_logits_indices=logits_indices.size(0),
1341
                causal=True,
1342
                encoder_seq_lens=encoder_seq_lens,
1343
1344
1345
                dcp_local_seq_lens=self.dcp_local_seq_lens.gpu[:num_reqs]
                if self.dcp_world_size > 1
                else None,
1346
1347
            )

1348
            if self.speculative_config and spec_decode_common_attn_metadata is None:
1349
                if isinstance(self.drafter, EagleProposer):
1350
1351
1352
1353
                    if (
                        self.drafter.attn_layer_names[0]
                        in kv_cache_group_spec.layer_names
                    ):
1354
1355
1356
                        spec_decode_common_attn_metadata = common_attn_metadata
                else:
                    spec_decode_common_attn_metadata = common_attn_metadata
1357

1358
1359
1360
            for attn_group in self.attn_groups[kv_cache_group_id]:
                # Prepare for cascade attention if enabled & beneficial.
                common_prefix_len = 0
1361
                builder = attn_group.get_metadata_builder()
1362
1363
1364
                if self.cascade_attn_enabled:
                    common_prefix_len = self._compute_cascade_attn_prefix_len(
                        num_scheduled_tokens,
1365
                        num_common_prefix_blocks,
1366
                        attn_group.kv_cache_spec,
1367
1368
                        builder,
                    )
1369

1370
                extra_attn_metadata_args = {}
1371
                if use_spec_decode and isinstance(builder, GDNAttentionMetadataBuilder):
1372
                    extra_attn_metadata_args = dict(
1373
1374
1375
1376
                        num_accepted_tokens=self.num_accepted_tokens.gpu[:num_reqs],
                        num_decode_draft_tokens_cpu=self.num_decode_draft_tokens.cpu[
                            :num_reqs
                        ],
1377
1378
                    )

1379
1380
                if ubatch_slices is not None:
                    common_attn_metadata_list = split_attn_metadata(
1381
1382
                        ubatch_slices, common_attn_metadata
                    )
1383
                    for ubid, common_attn_metadata in enumerate(
1384
1385
1386
1387
1388
1389
1390
1391
                        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,
                        )
1392
1393
1394
1395
1396
1397
1398
1399
                        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,
1400
1401
1402
                        **extra_attn_metadata_args,
                    )
                    use_cascade_attn |= getattr(attn_metadata_i, "use_cascade", False)
1403
1404
                    for layer_name in attn_group.layer_names:
                        attn_metadata[layer_name] = attn_metadata_i
1405

1406
1407
1408
1409
        # disable cascade attention when DBO
        if ubatch_slices is not None:
            use_cascade_attn = False

1410
1411
1412
1413
        # Hot-Swap lora model
        if self.lora_config:
            self.set_active_loras(self.input_batch, num_scheduled_tokens)

1414
1415
1416
1417
1418
1419
1420
1421
        return (
            attn_metadata,
            logits_indices,
            spec_decode_metadata,
            num_scheduled_tokens,
            spec_decode_common_attn_metadata,
            max_num_scheduled_tokens,
            ubatch_slices,
1422
            num_tokens_across_dp,
1423
1424
            use_cascade_attn,
        )
1425

1426
1427
1428
1429
    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
        num_common_prefix_blocks: int,
1430
1431
        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
    ) -> 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.
        """
1450
        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
        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]
1488
        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
1489
1490
1491
1492
1493
1494
1495
        # 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(
1496
1497
            common_prefix_len, self.input_batch.num_computed_tokens_cpu[:num_reqs].min()
        )
1498
        # common_prefix_len should be a multiple of the block size.
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
        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
        )
1510
1511
        assert isinstance(kv_cache_spec, AttentionSpec)
        use_cascade = attn_metadata_builder.use_cascade_attention(
1512
1513
1514
            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
1515
            num_kv_heads=kv_cache_spec.num_kv_heads,
1516
            use_alibi=self.use_alibi,
1517
            use_sliding_window=use_sliding_window,
1518
            use_local_attention=use_local_attention,
1519
            num_sms=self.num_sms,
1520
            dcp_world_size=self.dcp_world_size,
1521
1522
1523
        )
        return common_prefix_len if use_cascade else 0

1524
1525
    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
1526
        for index, req_id in enumerate(self.input_batch.req_ids):
1527
1528
1529
            req = self.requests[req_id]
            assert req.mrope_positions is not None

1530
1531
            num_computed_tokens = self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1532
            num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
1533
1534
                req.prompt_token_ids, req.prompt_embeds
            )
1535
1536

            if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
1537
1538
                prompt_part_len = max(0, num_prompt_tokens - num_computed_tokens)
                completion_part_len = max(0, num_scheduled_tokens - prompt_part_len)
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
            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

1552
1553
1554
                self.mrope_positions.cpu[:, dst_start:dst_end] = req.mrope_positions[
                    :, src_start:src_end
                ]
1555
1556
1557
1558
1559
1560
1561
                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

1562
                MRotaryEmbedding.get_next_input_positions_tensor(
1563
                    out=self.mrope_positions.np,
1564
1565
1566
1567
1568
                    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,
                )
1569
1570
1571

                mrope_pos_ptr += completion_part_len

1572
1573
    def _calc_spec_decode_metadata(
        self,
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
        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
1590
1591
1592
1593

        # 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(
1594
1595
            num_sampled_tokens, cumsum_dtype=np.int32
        )
1596
        # Step 2. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
1597
        logits_indices = np.repeat(
1598
1599
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens
        )
1600
        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
1601
1602
1603
1604
1605
1606
        logits_indices += arange

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

        # Compute the draft logits indices.
1607
1608
1609
        # 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(
1610
1611
            num_draft_tokens, cumsum_dtype=np.int32
        )
1612
1613
        # [0, 0, 0, 5, 5, 9]
        target_logits_indices = np.repeat(
1614
1615
            cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens
        )
1616
1617
1618
1619
1620
        # [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(
1621
1622
            self.device, non_blocking=True
        )
1623
1624
1625
        cu_num_sampled_tokens = torch.from_numpy(cu_num_sampled_tokens).to(
            self.device, non_blocking=True
        )
1626
1627
1628
        logits_indices = torch.from_numpy(logits_indices).to(
            self.device, non_blocking=True
        )
1629
        target_logits_indices = torch.from_numpy(target_logits_indices).to(
1630
1631
            self.device, non_blocking=True
        )
1632
        bonus_logits_indices = torch.from_numpy(bonus_logits_indices).to(
1633
1634
            self.device, non_blocking=True
        )
1635

1636
1637
        # Compute the draft token ids.
        # draft_token_indices:      [  1,   2,   3, 105, 106, 208]
1638
        draft_token_ids = self.input_ids.gpu[logits_indices]
1639
1640
        draft_token_ids = draft_token_ids[target_logits_indices + 1]

1641
        return SpecDecodeMetadata(
1642
1643
1644
            draft_token_ids=draft_token_ids,
            num_draft_tokens=num_draft_tokens.tolist(),
            cu_num_draft_tokens=cu_num_draft_tokens,
1645
            cu_num_sampled_tokens=cu_num_sampled_tokens,
1646
1647
1648
1649
1650
            target_logits_indices=target_logits_indices,
            bonus_logits_indices=bonus_logits_indices,
            logits_indices=logits_indices,
        )

1651
1652
1653
1654
1655
1656
1657
    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
1658
        self.kv_sharing_fast_prefill_logits_indices[:num_logits].copy_(logits_indices)
1659
1660
1661
1662
1663
        # 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_(
1664
1665
1666
1667
1668
1669
            logits_indices[-1].item()
        )
        if (
            self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
            and num_logits <= self.cudagraph_batch_sizes[-1]
        ):
1670
1671
1672
1673
1674
            # 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
1675
1676
1677
        logits_indices_padded = self.kv_sharing_fast_prefill_logits_indices[
            :num_logits_padded
        ]
1678
1679
        return logits_indices_padded

1680
1681
1682
1683
1684
1685
1686
1687
    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
1688
                inputs.
1689
1690
1691
1692
1693
1694

        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
        """
1695
1696
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
1697
            return [], []
1698
        # Batch the multi-modal inputs.
1699
        mm_kwargs = list[MultiModalKwargsItem]()
1700
1701
        # list of tuple (mm_hash, position_info)
        mm_hashes_pos = list[tuple[str, PlaceholderRange]]()
1702
1703
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
1704
1705

            for mm_input_id in encoder_input_ids:
1706
1707
1708
1709
                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))
1710

1711
1712
1713
1714
1715
        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(
1716
1717
            scheduler_output
        )
1718
1719
1720
1721

        if not mm_kwargs:
            return

1722
1723
1724
1725
1726
1727
1728
        # 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.
1729
        model = cast(SupportsMultiModal, self.model)
1730
        encoder_outputs = []
1731
        for modality, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
1732
1733
1734
1735
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
1736
        ):
1737
1738
1739
            curr_group_outputs = []

            # EVS-related change.
1740
            # (ekhvedchenia): Temporary hack to limit peak memory usage when
1741
            # processing multimodal data. This solves the issue with scheduler
1742
1743
1744
1745
            # 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)
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
            # TODO(ywang96): Fix memory profiling to take EVS into account and
            # remove this hack.
            if (
                self.is_multimodal_pruning_enabled
                and modality == "video"
                and num_items > 1
            ):
                for video_mm_kwargs_item in filter(
                    lambda item: item.modality == "video", mm_kwargs
                ):
                    _, _, micro_batch_mm_inputs = next(
                        group_mm_kwargs_by_modality(
                            [video_mm_kwargs_item],
                            device=self.device,
                            pin_memory=self.pin_memory,
                            merge_by_field_config=model.merge_by_field_config,
                        )
1763
                    )
1764
1765

                    micro_batch_outputs = model.get_multimodal_embeddings(
1766
1767
                        **micro_batch_mm_inputs
                    )
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777

                    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.
1778
                curr_group_outputs = model.get_multimodal_embeddings(**mm_kwargs_group)
1779

1780
1781
            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
1782
                expected_num_items=num_items,
1783
            )
1784
            encoder_outputs.extend(curr_group_outputs)
1785

1786
1787
1788
        # 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(
1789
1790
1791
1792
1793
                output,
                is_embed=pos_info.is_embed,
            )

    def _gather_mm_embeddings(
1794
1795
        self,
        scheduler_output: "SchedulerOutput",
1796
        shift_computed_tokens: int = 0,
1797
1798
1799
1800
1801
1802
1803
1804
    ) -> 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
1805
        should_sync_mrope_positions = False
1806

1807
        for req_id in self.input_batch.req_ids:
1808
1809
            mm_embeds_req: list[torch.Tensor] = []

1810
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1811
            req_state = self.requests[req_id]
1812
            num_computed_tokens = req_state.num_computed_tokens + shift_computed_tokens
1813

1814
1815
            for mm_feature in req_state.mm_features:
                pos_info = mm_feature.mm_position
1816
1817
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
1818
1819
1820
1821
1822
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833

                # 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,
1834
1835
                    num_encoder_tokens,
                )
1836
                assert start_idx < end_idx
1837

1838
                mm_hash = mm_feature.identifier
1839
                encoder_output = self.encoder_cache.get(mm_hash, None)
1840
                assert encoder_output is not None, f"Encoder cache miss for {mm_hash}."
1841
1842
1843
1844

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

1845
                req_start_pos = req_start_idx + start_pos - num_computed_tokens
1846
1847
1848
                is_mm_embed[req_start_pos + start_idx : req_start_pos + end_idx] = (
                    True if is_embed is None else is_embed
                )
1849

1850
1851
1852
1853
                mm_embeds_item = gather_mm_placeholders(
                    encoder_output[start_idx:end_idx],
                    is_embed=is_embed,
                )
1854
1855
1856
                mm_embeds_req.append(mm_embeds_item)

            if self.is_multimodal_pruning_enabled and self.uses_mrope:
1857
                assert req_state.mrope_positions is not None
1858
1859
1860
1861
1862
1863
1864
                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,
1865
1866
                    )
                )
1867
1868
1869
1870
                req_state.mrope_positions.copy_(new_mrope_positions)
                req_state.mrope_position_delta = new_delta

            mm_embeds.extend(mm_embeds_req)
1871
1872
1873
            req_start_idx += num_scheduled_tokens

        is_mm_embed = self.is_mm_embed.copy_to_gpu(total_num_scheduled_tokens)
1874
1875
1876

        if should_sync_mrope_positions:
            self._calc_mrope_positions(scheduler_output)
1877
            self.mrope_positions.copy_to_gpu(total_num_scheduled_tokens)
1878

1879
        return mm_embeds, is_mm_embed
1880

1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
    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
1897
        model = cast(SupportsMultiModal, self.model)
1898
1899
        encoder_features = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
1900
1901
1902
1903
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
1904
1905
1906
1907
1908
1909
1910
1911
        ):
            # 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

1912
    def get_model(self) -> nn.Module:
1913
        # get raw model out of the cudagraph wrapper.
1914
        if isinstance(self.model, (CUDAGraphWrapper, UBatchWrapper)):
1915
            return self.model.unwrap()
1916
1917
        return self.model

1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
    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

1933
1934
1935
1936
1937
    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
        model = self.get_model()
        if not is_pooling_model(model):
            return []

1938
1939
        supported_tasks = list(model.pooler.get_supported_tasks())

1940
1941
1942
1943
1944
        if self.scheduler_config.chunked_prefill_enabled:
            if "token_embed" in supported_tasks:
                supported_tasks.remove("token_embed")
            if "token_classify" in supported_tasks:
                supported_tasks.remove("token_classify")
1945

1946
1947
            logger.debug_once(
                "Chunked prefill is not supported with "
1948
1949
                "token_embed and token_classify tasks "
                "which using ALL pooling. "
1950
1951
1952
                "Please turn off chunked prefill by "
                "`--no-enable-chunked-prefill` before using it."
            )
1953
1954
1955
1956
1957

        if "score" in supported_tasks:
            num_labels = getattr(self.model_config.hf_config, "num_labels", 0)
            if num_labels != 1:
                supported_tasks.remove("score")
1958
                logger.debug_once("Score API is only enabled for num_labels == 1.")
1959
1960

        return supported_tasks
1961

1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
    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)

1972
    def sync_and_slice_intermediate_tensors(
1973
1974
1975
1976
1977
        self,
        num_tokens: int,
        intermediate_tensors: IntermediateTensors,
        sync_self: bool,
    ) -> IntermediateTensors:
1978
1979
1980
        assert self.intermediate_tensors is not None

        tp = self.vllm_config.parallel_config.tensor_parallel_size
1981
        is_rs = is_residual_scattered_for_sp(self.vllm_config, num_tokens)
1982
1983
1984
1985
1986
1987

        # 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():
1988
                is_scattered = k == "residual" and is_rs
1989
                copy_len = num_tokens // tp if is_scattered else num_tokens
1990
                self.intermediate_tensors[k][:copy_len].copy_(
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
                    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:
2004
2005
2006
2007
2008
2009
2010
        """
        Step for the EPLB (Expert Parallelism Load Balancing) state.
        """
        if not self.parallel_config.enable_eplb:
            return

        assert self.eplb_state is not None
2011
2012
        model = self.get_model()
        assert is_mixture_of_experts(model)
2013
        self.eplb_state.step(
2014
            model,
2015
2016
            is_dummy,
            is_profile,
2017
            log_stats=self.parallel_config.eplb_config.log_balancedness,
2018
2019
        )

2020
2021
2022
2023
    # 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)
2024
2025
    @staticmethod
    def pad_out_ubatch_slice(ubatch_slices: UBatchSlices, num_total_tokens: int):
2026
2027
2028
2029
2030
2031
        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
        )
2032

2033
2034
2035
2036
2037
2038
    def _pool(
        self,
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
        num_scheduled_tokens_np: np.ndarray,
    ) -> ModelRunnerOutput:
2039
2040
2041
        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"
        )
2042

2043
        hidden_states = hidden_states[:num_scheduled_tokens]
2044
        pooling_metadata = self.input_batch.get_pooling_metadata()
2045
2046
2047
2048
        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]
2049

2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
        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()
2060

2061
        pooler_output: list[torch.Tensor | None] = []
2062
        for raw_output, seq_len, prompt_len in zip(
2063
2064
            raw_pooler_output, seq_lens_cpu, pooling_metadata.prompt_lens
        ):
2065
            output = raw_output if seq_len == prompt_len else None
2066
            pooler_output.append(output)
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076

        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,
        )

2077
    def _get_num_input_tokens(self, num_scheduled_tokens: int) -> int:
2078
2079
2080
2081
2082
2083
        if (
            self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
            and hasattr(self, "cudagraph_batch_sizes")
            and self.cudagraph_batch_sizes
            and num_scheduled_tokens <= self.cudagraph_batch_sizes[-1]
        ):
2084
2085
2086
2087
2088
2089
2090
2091
            # 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
2092
2093
2094
2095
        if (
            self.compilation_config.pass_config.enable_sequence_parallelism
            and tp_size > 1
        ):
2096
2097
2098
            return round_up(num_scheduled_tokens, tp_size)
        return num_scheduled_tokens

2099
    def _preprocess(
2100
2101
        self,
        scheduler_output: "SchedulerOutput",
2102
        num_input_tokens: int,  # Padded
2103
        intermediate_tensors: IntermediateTensors | None = None,
2104
2105
    ) -> tuple[
        int,
2106
2107
        torch.Tensor | None,
        torch.Tensor | None,
2108
        torch.Tensor,
2109
        IntermediateTensors | None,
2110
2111
        dict[str, Any],
    ]:
2112
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
2113
        is_first_rank = get_pp_group().is_first_rank
2114

2115
2116
        # _prepare_inputs may reorder the batch, so we must gather multi
        # modal outputs after that to ensure the correct order
2117
2118
        if (
            self.supports_mm_inputs
2119
            and is_first_rank
2120
2121
            and not self.model_config.is_encoder_decoder
        ):
2122
2123
            # Run the multimodal encoder if any.
            self._execute_mm_encoder(scheduler_output)
2124
            mm_embeds, is_mm_embed = self._gather_mm_embeddings(scheduler_output)
2125

2126
2127
2128
            # 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.
2129
            inputs_embeds_scheduled = self.model.get_input_embeddings(
2130
2131
2132
                self.input_ids.gpu[:num_scheduled_tokens],
                multimodal_embeddings=mm_embeds,
                is_multimodal=is_mm_embed,
2133
            )
2134

2135
            # TODO(woosuk): Avoid the copy. Optimize.
2136
            self.inputs_embeds.gpu[:num_scheduled_tokens].copy_(inputs_embeds_scheduled)
2137

2138
            input_ids = None
2139
            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
2140
2141
2142
2143
            model_kwargs = {
                **self._init_model_kwargs(num_scheduled_tokens),
                **self._extract_mm_kwargs(scheduler_output),
            }
2144
        elif self.enable_prompt_embeds and is_first_rank:
2145
2146
2147
2148
2149
2150
2151
2152
2153
2154
2155
2156
            # 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).
2157
2158
2159
            token_ids_idx = (
                self.is_token_ids.gpu[:num_scheduled_tokens]
                .nonzero(as_tuple=False)
2160
                .squeeze(1)
2161
            )
2162
2163
2164
            # Some tokens ids may need to become embeds
            if token_ids_idx.numel() > 0:
                token_ids = self.input_ids.gpu[token_ids_idx]
2165
                tokens_to_embeds = self.model.get_input_embeddings(input_ids=token_ids)
2166
2167
2168
2169
2170
                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
2171
        else:
2172
2173
2174
2175
            # 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.
2176
            input_ids = self.input_ids.gpu[:num_input_tokens]
2177
            inputs_embeds = None
2178
            model_kwargs = self._init_model_kwargs(num_input_tokens)
2179
        if self.uses_mrope:
2180
            positions = self.mrope_positions.gpu[:, :num_input_tokens]
2181
        else:
2182
            positions = self.positions.gpu[:num_input_tokens]
2183

2184
        if is_first_rank:
2185
2186
            intermediate_tensors = None
        else:
2187
            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
2188
2189
                num_input_tokens, intermediate_tensors, True
            )
2190

2191
2192
2193
2194
        if (
            self.model_config.is_encoder_decoder
            and scheduler_output.scheduled_encoder_inputs
        ):
2195
2196
2197
            encoder_inputs = self._extract_encoder_inputs(scheduler_output)
            model_kwargs.update(encoder_inputs)

2198
2199
2200
2201
2202
2203
2204
2205
        return (
            num_scheduled_tokens,
            input_ids,
            inputs_embeds,
            positions,
            intermediate_tensors,
            model_kwargs,
        )
2206

2207
    def _sample(
2208
        self,
2209
2210
        logits: torch.Tensor | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
2211
    ) -> SamplerOutput:
2212
        # Sample the next token and get logprobs if needed.
2213
        sampling_metadata = self.input_batch.sampling_metadata
2214
        if spec_decode_metadata is None:
2215
2216
2217
            # Update output token ids with tokens sampled in last step
            # if async scheduling and required by current sampling params.
            self.input_batch.update_async_output_token_ids()
2218
            return self.sampler(
2219
2220
2221
                logits=logits,
                sampling_metadata=sampling_metadata,
            )
2222

2223
        sampler_output = self.rejection_sampler(
2224
2225
            spec_decode_metadata,
            None,  # draft_probs
2226
            logits,
2227
2228
            sampling_metadata,
        )
2229
        self._update_states_after_model_execute(sampler_output.sampled_token_ids)
2230
2231
2232
        return sampler_output

    def _bookkeeping_sync(
2233
2234
2235
        self,
        scheduler_output: "SchedulerOutput",
        sampler_output: SamplerOutput,
2236
        logits: torch.Tensor | None,
2237
2238
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
2239
        spec_decode_metadata: SpecDecodeMetadata | None,
2240
    ) -> tuple[
2241
        dict[str, int],
2242
        LogprobsLists | None,
2243
        list[list[int]],
2244
        dict[str, LogprobsTensors | None],
2245
2246
2247
        list[str],
        dict[str, int],
        list[int],
2248
    ]:
2249
2250
2251
2252
        num_nans_in_logits = {}
        if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
            num_nans_in_logits = self._get_nans_in_logits(logits)

2253
2254
2255
        discard_sampled_tokens_req_indices = self.discard_request_indices.np[
            : self.num_discarded_requests
        ]
2256
2257
2258
2259
        for i in discard_sampled_tokens_req_indices:
            gen = self.input_batch.generators.get(int(i))
            if gen is not None:
                gen.set_offset(gen.get_offset() - 4)
2260

2261
2262
2263
        # Copy some objects so they don't get modified after returning.
        # This is important when using async scheduling.
        req_ids_output_copy = self.input_batch.req_ids.copy()
2264
        req_id_to_index_output_copy = self.input_batch.req_id_to_index.copy()
2265
2266

        num_sampled_tokens = sampler_output.sampled_token_ids.shape[0]
2267
        sampled_token_ids = sampler_output.sampled_token_ids
2268
        invalid_req_indices = []
2269
2270
2271
2272
2273
2274
2275
2276
2277
2278
2279
2280
2281
2282
        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:
2283
                valid_sampled_token_ids[int(i)].clear()
2284
        else:
2285
            valid_sampled_token_ids = []
2286
            invalid_req_indices = discard_sampled_tokens_req_indices.tolist()
2287
2288
2289
2290
2291
2292
            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.
2293
            self.input_batch.prev_sampled_token_ids = sampled_token_ids
2294
2295
2296
2297
2298
            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
            }
2299

2300
2301
2302
2303
2304
        # 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.
2305
        req_ids = self.input_batch.req_ids
2306
2307
2308
2309
        logprobs_tensors = sampler_output.logprobs_tensors
        cu_num_accepted_tokens = (
            [0] if spec_decode_metadata and logprobs_tensors else None
        )
2310
2311
        for req_idx in range(num_sampled_tokens):
            if self.use_async_scheduling:
2312
                sampled_ids = [-1] if req_idx not in invalid_req_indices_set else None
2313
2314
            else:
                sampled_ids = valid_sampled_token_ids[req_idx]
2315
2316
2317
2318
2319
            if not sampled_ids:
                continue

            start_idx = self.input_batch.num_tokens_no_spec[req_idx]
            end_idx = start_idx + len(sampled_ids)
2320
2321
2322
2323
            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}"
2324
            )
2325

2326
2327
            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
2328
2329
            self.input_batch.num_tokens_no_spec[req_idx] = end_idx
            self.input_batch.num_tokens[req_idx] = end_idx
2330

2331
            req_id = req_ids[req_idx]
2332
2333
2334
            req_state = self.requests[req_id]
            req_state.output_token_ids.extend(sampled_ids)

2335
2336
2337
2338
2339
2340
2341
2342
2343
2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
            if cu_num_accepted_tokens is not None:
                cu_num_accepted_tokens.append(
                    cu_num_accepted_tokens[-1] + len(sampled_ids)
                )

        # NOTE: GPU -> CPU Sync happens here.
        # Move as many CPU operations as possible before this sync point.
        logprobs_lists = (
            logprobs_tensors.tolists(cu_num_accepted_tokens)
            if logprobs_tensors is not None
            else None
        )

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

2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
        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,
        )

2364
2365
2366
2367
2368
2369
2370
2371
2372
2373
2374
2375
2376
2377
2378
    @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()

2379
2380
    def _model_forward(
        self,
2381
2382
2383
2384
        input_ids: torch.Tensor | None = None,
        positions: torch.Tensor | None = None,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
2385
2386
2387
2388
2389
        **model_kwargs: dict[str, Any],
    ) -> Any:
        """Helper method to call the model forward pass.

        This method can be overridden by subclasses for model execution.
2390
        Motivation: We can inspect only this method versus
2391
2392
2393
2394
2395
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
        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,
        )

2411
2412
2413
2414
    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
2415
2416
        intermediate_tensors: IntermediateTensors | None = None,
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput | IntermediateTensors:
2417
        with record_function_or_nullcontext("Preprocess"):
2418
2419
2420
2421
2422
2423
2424
2425
2426
            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(
2427
2428
                        scheduler_output, self.vllm_config
                    )
2429
2430
2431
2432
                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 "
2433
2434
                        "it when the requests need prompt logprobs"
                    )
2435

2436
                # Prepare the decoder inputs.
2437
2438
2439
2440
2441
2442
2443
2444
                (
                    attn_metadata,
                    logits_indices,
                    spec_decode_metadata,
                    num_scheduled_tokens_np,
                    spec_decode_common_attn_metadata,
                    max_query_len,
                    ubatch_slices,
2445
                    num_tokens_across_dp,
2446
2447
                    use_cascade_attn,
                ) = self._prepare_inputs(scheduler_output)
2448

2449
            dp_rank = self.parallel_config.data_parallel_rank
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            if ubatch_slices:
                assert num_tokens_across_dp is not None
2452
                num_input_tokens = int(num_tokens_across_dp[dp_rank].item())
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                self.pad_out_ubatch_slice(ubatch_slices, num_input_tokens)
            elif num_tokens_across_dp is not None:
2455
                num_input_tokens = int(num_tokens_across_dp[dp_rank].item())
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            else:
                num_input_tokens = self._get_num_input_tokens(
                    scheduler_output.total_num_scheduled_tokens
                )

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            (
                num_scheduled_tokens,
                input_ids,
                inputs_embeds,
                positions,
                intermediate_tensors,
                model_kwargs,
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            ) = self._preprocess(
2469
                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(
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                num_tokens=num_input_tokens,
                uniform_decode=uniform_decode,
                has_lora=len(self.input_batch.lora_id_to_lora_request) > 0,
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            )
            cudagraph_runtime_mode, batch_descriptor = (
                self.cudagraph_dispatcher.dispatch(batch_descriptor, use_cascade_attn)
            )
2483

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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]
            )
2491
            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(
2500
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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,
2506
                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,
        ):
2511
            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:
2521
                # True when EAGLE 3 is used.
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                hidden_states, aux_hidden_states = model_output
            else:
2524
                # 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
2535

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                if self.is_pooling_model:
2537
                    # 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
2543
2544

                sample_hidden_states = hidden_states[logits_indices]
2545
                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:
2551
                    all_gather_tensors = {
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                        "residual": not is_residual_scattered_for_sp(
                            self.vllm_config, num_input_tokens
                        )
2555
                    }
2556
                    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]
2564
                    logits = self.model.compute_logits(sample_hidden_states)
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2569

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

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

            # Apply structured output bitmasks if present
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            if scheduler_output.structured_output_request_ids:
                apply_grammar_bitmask(scheduler_output, self.input_batch, logits)
2579
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2582

        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
2605
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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
        ):
2610
            effective_drafter_max_model_len = (
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                self.speculative_config.draft_model_config.max_model_len
            )
2613
        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
        )
2618
        if use_padded_batch_for_eagle and input_fits_in_drafter:
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2622
            # 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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2637
            ) = self._bookkeeping_sync(
                scheduler_output,
                sampler_output,
                logits,
                hidden_states,
                num_scheduled_tokens,
2638
                spec_decode_metadata,
2639
            )
2640

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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)
2649

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2651
        with record_function_or_nullcontext("EPLB"):
            self.eplb_step()
2652

2653
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2655
        output = ModelRunnerOutput(
            req_ids=req_ids_output_copy,
            req_id_to_index=req_id_to_index_output_copy,
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2659
            sampled_token_ids=valid_sampled_token_ids,
            logprobs=logprobs_lists,
            prompt_logprobs_dict=prompt_logprobs_dict,
            pooler_output=[],
2660
            kv_connector_output=kv_connector_output,
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2662
2663
            num_nans_in_logits=num_nans_in_logits,
        )

2664
2665
2666
        if not self.use_async_scheduling:
            return output

2667
        async_output = AsyncGPUModelRunnerOutput(
2668
            model_runner_output=output,
2669
            sampled_token_ids=sampler_output.sampled_token_ids,
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2672
2673
            invalid_req_indices=invalid_req_indices,
            async_output_copy_stream=self.async_output_copy_stream,
        )

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

        return async_output

2683
    def take_draft_token_ids(self) -> DraftTokenIds | None:
2684
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2686
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2688
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2690
2691
2692
2693
        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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2696
    def propose_draft_token_ids(
        self,
        scheduler_output: "SchedulerOutput",
2697
        sampled_token_ids: torch.Tensor | list[list[int]],
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2699
2700
        sampling_metadata: SamplingMetadata,
        hidden_states: torch.Tensor,
        sample_hidden_states: torch.Tensor,
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2702
        aux_hidden_states: list[torch.Tensor] | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
2703
        common_attn_metadata: CommonAttentionMetadata,
2704
    ) -> list[list[int]] | torch.Tensor:
2705
2706
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
        if self.speculative_config.method == "ngram":
2707
            assert isinstance(sampled_token_ids, list)
2708
            assert isinstance(self.drafter, NgramProposer)
2709
            draft_token_ids = self.drafter.propose(
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2711
                sampled_token_ids,
                self.input_batch.req_ids,
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2713
                self.input_batch.num_tokens_no_spec,
                self.input_batch.token_ids_cpu,
2714
2715
                self.input_batch.spec_decode_unsupported_reqs,
            )
2716
        elif self.speculative_config.method == "medusa":
2717
            assert isinstance(sampled_token_ids, list)
2718
            assert isinstance(self.drafter, MedusaProposer)
2719

2720
2721
            if sample_hidden_states.shape[0] == len(sampled_token_ids):
                # The input to the target model does not include draft tokens.
2722
2723
2724
2725
                hidden_states = sample_hidden_states
            else:
                indices = []
                offset = 0
Wentao Ye's avatar
Wentao Ye committed
2726
                assert spec_decode_metadata is not None
2727
                for num_draft, tokens in zip(
2728
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                    spec_decode_metadata.num_draft_tokens, sampled_token_ids
                ):
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2731
                    indices.append(offset + len(tokens) - 1)
                    offset += num_draft + 1
2732
                indices = torch.tensor(indices, device=self.device)
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2734
                hidden_states = sample_hidden_states[indices]

2735
            draft_token_ids = self.drafter.propose(
2736
2737
2738
                target_hidden_states=hidden_states,
                sampling_metadata=sampling_metadata,
            )
2739
        elif self.speculative_config.use_eagle():
2740
            assert isinstance(self.drafter, EagleProposer)
2741
2742
2743
2744
2745

            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.
2746
2747
                assert isinstance(sampled_token_ids, list), (
                    "sampled_token_ids should be a python list when"
2748
                    "padded-batch is disabled."
2749
                )
2750
                next_token_ids = self.drafter.prepare_next_token_ids_cpu(
2751
2752
2753
2754
2755
                    sampled_token_ids,
                    self.requests,
                    self.input_batch,
                    scheduler_output.num_scheduled_tokens,
                )
2756
2757
2758
2759
2760
            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.
2761
2762
                assert isinstance(sampled_token_ids, torch.Tensor), (
                    "sampled_token_ids should be a torch.Tensor when"
2763
                    "padded-batch is enabled."
2764
2765
                )
                next_token_ids, valid_sampled_tokens_count = (
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2767
2768
2769
2770
2771
                    self.drafter.prepare_next_token_ids_padded(
                        common_attn_metadata,
                        sampled_token_ids,
                        self.requests,
                        self.input_batch,
                        self.discard_request_indices.gpu,
2772
                        self.num_discarded_requests,
2773
                    )
2774
                )
Jiayi Yao's avatar
Jiayi Yao committed
2775

2776
            if spec_decode_metadata is None:
2777
                token_indices_to_sample = None
2778
                # input_ids can be None for multimodal models.
2779
                target_token_ids = self.input_ids.gpu[:num_scheduled_tokens]
2780
                target_positions = self._get_positions(num_scheduled_tokens)
2781
                if self.use_aux_hidden_state_outputs:
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2782
                    assert aux_hidden_states is not None
2783
                    target_hidden_states = torch.cat(
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2785
                        [h[:num_scheduled_tokens] for h in aux_hidden_states], dim=-1
                    )
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2787
                else:
                    target_hidden_states = hidden_states[:num_scheduled_tokens]
2788
            else:
2789
2790
                if self.speculative_config.disable_padded_drafter_batch:
                    token_indices_to_sample = None
2791
2792
2793
2794
2795
                    common_attn_metadata, token_indices = self.drafter.prepare_inputs(
                        common_attn_metadata,
                        sampled_token_ids,
                        spec_decode_metadata.num_draft_tokens,
                    )
2796
                else:
2797
                    common_attn_metadata, token_indices, token_indices_to_sample = (
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2800
                        self.drafter.prepare_inputs_padded(
                            common_attn_metadata,
                            spec_decode_metadata,
2801
2802
2803
                            valid_sampled_tokens_count,
                        )
                    )
2804

2805
                target_token_ids = self.input_ids.gpu[token_indices]
2806
                target_positions = self._get_positions(token_indices)
2807
                if self.use_aux_hidden_state_outputs:
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2808
                    assert aux_hidden_states is not None
2809
                    target_hidden_states = torch.cat(
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2811
                        [h[token_indices] for h in aux_hidden_states], dim=-1
                    )
2812
2813
                else:
                    target_hidden_states = hidden_states[token_indices]
2814

2815
            if self.supports_mm_inputs:
2816
2817
2818
2819
2820
2821
                mm_embed_inputs = self._gather_mm_embeddings(
                    scheduler_output,
                    shift_computed_tokens=1,
                )
            else:
                mm_embed_inputs = None
2822

2823
            draft_token_ids = self.drafter.propose(
2824
2825
2826
2827
                target_token_ids=target_token_ids,
                target_positions=target_positions,
                target_hidden_states=target_hidden_states,
                next_token_ids=next_token_ids,
2828
                last_token_indices=token_indices_to_sample,
2829
                sampling_metadata=sampling_metadata,
2830
                common_attn_metadata=common_attn_metadata,
2831
                mm_embed_inputs=mm_embed_inputs,
2832
            )
2833

2834
        return draft_token_ids
2835

2836
2837
2838
    def update_config(self, overrides: dict[str, Any]) -> None:
        allowed_config_names = {"load_config", "model_config"}
        for config_name, config_overrides in overrides.items():
2839
2840
            assert config_name in allowed_config_names, (
                f"Config `{config_name}` not supported. "
2841
                f"Allowed configs: {allowed_config_names}"
2842
            )
2843
2844
2845
2846
            config = getattr(self, config_name)
            new_config = update_config(config, config_overrides)
            setattr(self, config_name, new_config)

2847
2848
2849
2850
2851
    def load_model(self, eep_scale_up: bool = False) -> None:
        """
        Args:
            eep_scale_up: the model loading is for elastic EP scale up.
        """
2852
2853
2854
2855
2856
        logger.info_once(
            "Starting to load model %s...",
            self.model_config.model,
            scope="global",
        )
2857
2858
        if eep_scale_up:
            from vllm.distributed.parallel_state import get_ep_group
2859
2860
2861
2862
2863

            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
            )
2864
2865
            num_local_physical_experts = int(num_local_physical_experts.item())
            new_ep_size = get_ep_group().world_size
2866
            global_expert_load, old_global_expert_indices = EplbState.recv_state()
2867
            num_logical_experts = global_expert_load.shape[1]
2868
            self.parallel_config.eplb_config.num_redundant_experts = (
2869
2870
2871
2872
2873
2874
                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
            )
2875
            rank_mapping = {
2876
                old_ep_rank: old_ep_rank for old_ep_rank in range(old_ep_size)
2877
2878
2879
2880
2881
2882
            }
        else:
            global_expert_load = None
            old_global_expert_indices = None
            rank_mapping = None

2883
        with DeviceMemoryProfiler() as m:
2884
            time_before_load = time.perf_counter()
2885
            model_loader = get_model_loader(self.load_config)
2886
            self.model = model_loader.load_model(
2887
2888
                vllm_config=self.vllm_config, model_config=self.model_config
            )
2889
            if self.lora_config:
2890
2891
2892
                self.model = self.load_lora_model(
                    self.model, self.vllm_config, self.device
                )
2893
2894
2895
            if hasattr(self, "drafter"):
                logger.info("Loading drafter model...")
                self.drafter.load_model(self.model)
2896
            if self.use_aux_hidden_state_outputs:
2897
                if not supports_eagle3(self.get_model()):
2898
2899
                    raise RuntimeError(
                        "Model does not support EAGLE3 interface but "
2900
2901
                        "aux_hidden_state_outputs was requested"
                    )
2902
2903
2904
2905
2906
2907
2908
2909
2910
2911
2912
2913
2914

                # 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)
2915
            time_after_load = time.perf_counter()
2916
        self.model_memory_usage = m.consumed_memory
2917
        logger.info_once(
2918
2919
2920
            "Model loading took %.4f GiB and %.6f seconds",
            self.model_memory_usage / GiB_bytes,
            time_after_load - time_before_load,
2921
            scope="local",
2922
        )
2923
        prepare_communication_buffer_for_model(self.model)
2924

2925
        self.is_multimodal_pruning_enabled = (
2926
            supports_multimodal_pruning(self.get_model())
2927
2928
            and self.model_config.multimodal_config.is_multimodal_pruning_enabled()
        )
2929

2930
2931
        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)
2932
2933
2934
2935
            self.eplb_state = EplbState.build(
                self.model,
                self.device,
                self.parallel_config,
2936
2937
2938
                global_expert_load,
                old_global_expert_indices,
                rank_mapping,
2939
2940
            )

2941
        if (
2942
2943
            self.vllm_config.compilation_config.mode
            == CompilationMode.STOCK_TORCH_COMPILE
2944
            and supports_dynamo()
2945
        ):
2946
            backend = self.vllm_config.compilation_config.init_backend(self.vllm_config)
2947
            compilation_counter.stock_torch_compile_count += 1
2948
            self.model.compile(fullgraph=True, backend=backend)
2949
            return
2950
        # for other compilation modes, cudagraph behavior is controlled by
2951
2952
2953
        # CudagraphWraper and CudagraphDispatcher of vllm.

        # wrap the model with full cudagraph wrapper if needed.
2954
2955
2956
2957
2958
2959
2960
        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
            )
2961
2962
        elif self.parallel_config.enable_dbo:
            if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
2963
2964
2965
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.FULL, self.device
                )
2966
            else:
2967
2968
2969
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.NONE, self.device
                )
2970

2971
    def _get_eagle3_aux_layers_from_config(self) -> tuple[int, ...] | None:
2972
2973
2974
2975
2976
2977
2978
2979
2980
2981
2982
2983
2984
2985
2986
2987
2988
2989
2990
2991
2992
2993
2994
        """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

2995
    def reload_weights(self) -> None:
2996
        assert getattr(self, "model", None) is not None, (
2997
            "Cannot reload weights before model is loaded."
2998
        )
2999
3000
        model_loader = get_model_loader(self.load_config)
        logger.info("Reloading weights inplace...")
3001
        model_loader.load_weights(self.get_model(), model_config=self.model_config)
3002

3003
3004
3005
3006
3007
    def save_tensorized_model(
        self,
        tensorizer_config: "TensorizerConfig",
    ) -> None:
        TensorizerLoader.save_model(
3008
            self.get_model(),
3009
            tensorizer_config=tensorizer_config,
3010
            model_config=self.model_config,
3011
3012
        )

3013
3014
3015
    def _get_prompt_logprobs_dict(
        self,
        hidden_states: torch.Tensor,
3016
        num_scheduled_tokens: dict[str, int],
3017
    ) -> dict[str, LogprobsTensors | None]:
3018
3019
3020
3021
        num_prompt_logprobs_dict = self.input_batch.num_prompt_logprobs
        if not num_prompt_logprobs_dict:
            return {}

3022
        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
3023
        prompt_logprobs_dict: dict[str, LogprobsTensors | None] = {}
3024
3025
3026
3027
3028

        # 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():
3029
            num_tokens = num_scheduled_tokens[req_id]
3030
3031
3032

            # Get metadata for this request.
            request = self.requests[req_id]
3033
3034
3035
3036
            if request.prompt_token_ids is None:
                # Prompt logprobs is incompatible with prompt embeddings
                continue

3037
3038
            num_prompt_tokens = len(request.prompt_token_ids)
            prompt_token_ids = torch.tensor(request.prompt_token_ids).to(
3039
3040
                self.device, non_blocking=True
            )
3041

3042
3043
3044
3045
3046
3047
            # 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(
3048
3049
                    num_prompt_tokens - 1, num_prompt_logprobs + 1
                )
3050
3051
                in_progress_dict[req_id] = logprobs_tensors

3052
            # Determine number of logits to retrieve.
3053
3054
            start_idx = request.num_computed_tokens
            start_tok = start_idx + 1
3055
            num_remaining_tokens = num_prompt_tokens - start_tok
3056
            if num_tokens <= num_remaining_tokens:
3057
                # This is a chunk, more tokens remain.
3058
3059
3060
                # 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.
3061
3062
3063
3064
3065
                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)
3066
3067
3068
3069
3070
3071
3072
                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
3073
3074
3075
3076
3077

            # 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]
3078
            offset = self.query_start_loc.np[req_idx].item()
3079
            prompt_hidden_states = hidden_states[offset : offset + num_logits]
3080
            logits = self.model.compute_logits(prompt_hidden_states)
3081
3082
3083
3084

            # 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.
3085
            tgt_token_ids = prompt_token_ids[start_tok : start_tok + num_logits]
3086
3087

            # Compute prompt logprobs.
3088
3089
            logprobs = self.sampler.compute_logprobs(logits)
            token_ids, logprobs, ranks = self.sampler.gather_logprobs(
3090
3091
                logprobs, num_prompt_logprobs, tgt_token_ids
            )
3092
3093

            # Transfer GPU->CPU async.
3094
3095
            chunk_slice = slice(start_idx, start_idx + num_logits)
            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
3096
3097
3098
                token_ids, non_blocking=True
            )
            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs, non_blocking=True)
3099
            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
3100
3101
                ranks, non_blocking=True
            )
3102
3103
3104
3105
3106

        # 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]
3107
            del in_progress_dict[req_id]
3108
3109

        # Must synchronize the non-blocking GPU->CPU transfers.
3110
        if prompt_logprobs_dict:
3111
            self._sync_device()
3112
3113
3114

        return prompt_logprobs_dict

3115
3116
    def _get_nans_in_logits(
        self,
3117
        logits: torch.Tensor | None,
3118
3119
3120
3121
3122
3123
3124
3125
3126
3127
3128
    ) -> 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])
3129
3130
3131
                    if num_nans_for_index is not None and req_index < logits.shape[0]
                    else 0
                )
3132
3133
3134
3135
            return num_nans_in_logits
        except IndexError:
            return {}

3136
3137
3138
3139
3140
3141
    @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
3142
         - during DP rank dummy run
3143
3144
3145
3146
3147
3148
3149
3150
3151
3152
3153
        """
        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(
3154
                    self.input_ids.gpu,
3155
3156
                    low=0,
                    high=self.model_config.get_vocab_size(),
3157
3158
                    dtype=input_ids.dtype,
                )
3159

3160
            logger.debug_once("Randomizing dummy data for DP Rank")
3161
            input_ids.copy_(rand_input_ids()[: input_ids.size(0)], non_blocking=True)
3162
3163
3164
            yield
            input_ids.fill_(0)

3165
3166
3167
3168
3169
3170
    def _get_mm_dummy_batch(
        self,
        modality: str,
        max_items_per_batch: int,
    ) -> BatchedTensorInputs:
        """Dummy data for profiling and precompiling multimodal models."""
3171
3172
        assert self.mm_budget is not None

3173
3174
        dummy_decoder_data = self.mm_registry.get_decoder_dummy_data(
            model_config=self.model_config,
3175
            seq_len=self.max_model_len,
3176
            mm_counts={modality: 1},
3177
            cache=self.mm_budget.cache,
3178
3179
3180
3181
        )
        dummy_mm_data = dummy_decoder_data.multi_modal_data

        # Result in the maximum GPU consumption of the model
3182
3183
        dummy_mm_item = dummy_mm_data[modality][0]
        dummy_mm_items = [dummy_mm_item] * max_items_per_batch
3184

3185
        model = cast(SupportsMultiModal, self.model)
3186
3187
3188
3189
3190
3191
3192
3193
3194
        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,
            )
        )
3195

3196
3197
3198
3199
    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
3200
        cudagraph_runtime_mode: CUDAGraphMode | None = None,
3201
3202
        force_attention: bool = False,
        uniform_decode: bool = False,
3203
        allow_microbatching: bool = True,
3204
3205
        skip_eplb: bool = False,
        is_profile: bool = False,
3206
        create_mixed_batch: bool = False,
3207
        remove_lora: bool = True,
3208
        activate_lora: bool = False,
3209
    ) -> tuple[torch.Tensor, torch.Tensor]:
3210
3211
3212
3213
3214
3215
3216
        """
        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.
3217
                - if not set will determine the cudagraph mode based on using
3218
                    the self.cudagraph_dispatcher.
3219
3220
3221
3222
                - CUDAGraphMode.NONE: No cudagraph, for warm up and profile run
                - CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
                - CUDAGraphMode.FULL: Full cudagraph, attention metadata is
                    needed.
3223
            force_attention: If True, always create attention metadata. Used to
3224
3225
3226
3227
                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.
3228
3229
            create_mixed_batch: If True, create a mixed batch with both decode
                (1 token) and prefill (multiple tokens) requests.
3230
            remove_lora: If False, dummy LoRAs are not destroyed after the run
3231
            activate_lora: If False, dummy_run is performed without LoRAs.
3232
        """
3233
3234
3235
3236
        assert (
            cudagraph_runtime_mode is None
            or cudagraph_runtime_mode.valid_runtime_modes()
        )
3237

3238
        # If cudagraph_mode.decode_mode() == FULL and
3239
        # cudagraph_mode.separate_routine(). This means that we are using
3240
3241
3242
3243
3244
3245
3246
3247
3248
3249
3250
        # 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.
3251
        max_query_len = self.uniform_decode_query_len if uniform_decode else num_tokens
3252

3253
3254
3255
3256
3257
        # 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
3258
3259
3260
3261
        if create_mixed_batch:
            assert not uniform_decode
            # Create mixed batch:
            # first half decode tokens, second half one prefill
3262
            num_decode_tokens = min(max_num_reqs - 1, num_tokens // 2)
3263
3264
3265
3266
            num_prefill_tokens = num_tokens - num_decode_tokens
            num_reqs = num_decode_tokens + 1

            # Create decode requests (1 token each) followed by prefill request
3267
            num_scheduled_tokens_list = [1] * num_decode_tokens + [num_prefill_tokens]
3268
3269
3270
            # Note: Overriding max_query_len to be the prefill tokens
            max_query_len = num_prefill_tokens
        elif uniform_decode:
3271
            assert not create_mixed_batch
3272
            num_reqs = min(max_num_reqs, cdiv(num_tokens, max_query_len))
3273
3274
            num_scheduled_tokens_list = [max_query_len] * num_reqs
            if num_tokens % max_query_len != 0:
3275
                num_scheduled_tokens_list[-1] = num_tokens % max_query_len
3276
3277
3278
3279
3280
3281
        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

3282
3283
        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs
3284
        num_scheduled_tokens = np.array(num_scheduled_tokens_list, dtype=np.int32)
3285
        total_num_scheduled_tokens = int(num_scheduled_tokens.sum())
3286

3287
3288
3289
        # Disable DP padding when running eager
        allow_dp_padding = self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE

3290
3291
        # We currently only microbatch if the number of tokens is
        # over a certain threshold.
3292
        ubatch_slices, num_tokens_across_dp = coordinate_batch_across_dp(
3293
3294
3295
3296
3297
3298
3299
            num_tokens_unpadded=total_num_scheduled_tokens,
            parallel_config=self.vllm_config.parallel_config,
            allow_microbatching=allow_microbatching,
            allow_dp_padding=allow_dp_padding,
            num_tokens_padded=total_num_scheduled_tokens,
            uniform_decode=uniform_decode,
            num_scheduled_tokens_per_request=num_scheduled_tokens,
3300
3301
3302
        )
        num_tokens_after_padding = num_tokens
        if num_tokens_across_dp is not None:
3303
3304
            dp_rank = self.parallel_config.data_parallel_rank
            num_tokens_after_padding = int(num_tokens_across_dp[dp_rank])
3305

3306
        attn_metadata: PerLayerAttnMetadata | None = None
3307
3308
3309

        # If force_attention is True, we always capture attention. Otherwise,
        # it only happens for cudagraph_runtime_mode=FULL.
3310
        if force_attention or cudagraph_runtime_mode == CUDAGraphMode.FULL:
3311
            attn_metadata = {}
3312
3313
            if ubatch_slices is not None:
                attn_metadata = [dict() for _ in range(len(ubatch_slices))]
3314

3315
3316
3317
3318
3319
3320
            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:
3321
                seq_lens = max_query_len
3322
            self.seq_lens.np[:num_reqs] = seq_lens
3323
3324
            self.seq_lens.np[num_reqs:] = 0
            self.seq_lens.copy_to_gpu()
3325

3326
3327
            cum_num_tokens, _ = self._get_cumsum_and_arange(num_scheduled_tokens)
            self.query_start_loc.np[1 : num_reqs + 1] = cum_num_tokens
3328
3329
            self.query_start_loc.copy_to_gpu()

3330
            for kv_cache_group_id, kv_cache_group_spec in enumerate(
3331
3332
                self.kv_cache_config.kv_cache_groups
            ):
3333
                common_attn_metadata = CommonAttentionMetadata(
3334
3335
                    query_start_loc=self.query_start_loc.gpu[: num_reqs + 1],
                    query_start_loc_cpu=self.query_start_loc.cpu[: num_reqs + 1],
3336
3337
                    seq_lens=self.seq_lens.gpu[:num_reqs],
                    seq_lens_cpu=self.seq_lens.cpu[:num_reqs],
3338
3339
3340
                    num_computed_tokens_cpu=self.input_batch.num_computed_tokens_cpu_tensor[
                        :num_reqs
                    ],
3341
3342
                    num_reqs=num_reqs,
                    num_actual_tokens=num_tokens,
3343
                    max_query_len=max_query_len,
3344
                    max_seq_len=self.max_model_len,
3345
3346
3347
                    block_table_tensor=self.input_batch.block_table[
                        kv_cache_group_id
                    ].get_device_tensor(num_reqs),
3348
                    slot_mapping=self.input_batch.block_table[
3349
3350
3351
                        kv_cache_group_id
                    ].slot_mapping.gpu[:num_tokens],
                    causal=True,
3352
3353
3354
                    dcp_local_seq_lens=self.dcp_local_seq_lens.gpu[:num_reqs]
                    if self.dcp_world_size > 1
                    else None,
3355
                )
3356
                for attn_group in self.attn_groups[kv_cache_group_id]:
3357
3358
                    if ubatch_slices is not None:
                        common_attn_metadata_list = split_attn_metadata(
3359
3360
                            ubatch_slices, common_attn_metadata
                        )
3361
                        for ubid, common_attn_metadata in enumerate(
3362
3363
                            common_attn_metadata_list
                        ):
3364
                            assert common_attn_metadata.max_query_len == 1
3365
3366
3367
                            attn_metadata_i = attn_group.get_metadata_builder(
                                ubatch_id=ubid
                            ).build_for_cudagraph_capture(common_attn_metadata)
3368
                            for layer_name in attn_group.layer_names:
3369
                                assert type(attn_metadata) is list
3370
                                attn_metadata[ubid][layer_name] = attn_metadata_i
3371
3372
                    else:
                        assert type(attn_metadata) is dict
3373
3374
                        metadata_builder = attn_group.get_metadata_builder()
                        attn_metadata_i = metadata_builder.build_for_cudagraph_capture(
3375
3376
                            common_attn_metadata
                        )
3377
                        for layer_name in attn_group.layer_names:
3378
                            attn_metadata[layer_name] = attn_metadata_i
3379

3380
        with self.maybe_dummy_run_with_lora(
3381
            self.lora_config, num_scheduled_tokens, activate_lora, remove_lora
3382
        ):
3383
3384
3385
            # 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)
3386
            if self.supports_mm_inputs and not self.model_config.is_encoder_decoder:
3387
                input_ids = None
3388
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_after_padding]
3389
                model_kwargs = {
3390
                    **model_kwargs,
3391
3392
                    **self._dummy_mm_kwargs(num_reqs),
                }
3393
3394
            elif self.enable_prompt_embeds:
                input_ids = None
3395
3396
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_after_padding]
                model_kwargs = self._init_model_kwargs(num_tokens_after_padding)
3397
            else:
3398
                input_ids = self.input_ids.gpu[:num_tokens_after_padding]
3399
                inputs_embeds = None
3400

3401
            if self.uses_mrope:
3402
                positions = self.mrope_positions.gpu[:, :num_tokens_after_padding]
3403
            else:
3404
                positions = self.positions.gpu[:num_tokens_after_padding]
3405
3406
3407
3408
3409
3410
3411
3412
3413

            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,
3414
3415
3416
                            device=self.device,
                        )
                    )
3417
3418

                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
3419
                    num_tokens_after_padding, None, False
3420
                )
3421
3422

            # filter out the valid batch descriptor
3423
3424
3425
3426
3427
            _cg_mode, batch_descriptor = (
                self.cudagraph_dispatcher.dispatch(
                    BatchDescriptor(
                        num_tokens=num_tokens_after_padding,
                        uniform_decode=uniform_decode,
3428
                        has_lora=activate_lora and self.lora_config is not None,
3429
3430
3431
3432
3433
                    )
                )
                if not is_profile
                else (CUDAGraphMode.NONE, None)
            )
3434
3435
3436
            if cudagraph_runtime_mode is not None:
                # we allow forcing NONE when the dispatcher disagrees to support
                # warm ups for cudagraph capture
3437
3438
3439
3440
                assert (
                    cudagraph_runtime_mode == CUDAGraphMode.NONE
                    or cudagraph_runtime_mode == _cg_mode
                ), (
3441
                    f"Cudagraph runtime mode mismatch at dummy_run. "
3442
3443
                    f"Expected {_cg_mode}, but got {cudagraph_runtime_mode}."
                )
3444
3445
            else:
                cudagraph_runtime_mode = _cg_mode
3446

3447
            if ubatch_slices is not None:
3448
3449
3450
3451
3452
3453
3454
                # 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

3455
3456
3457
            with (
                self.maybe_randomize_inputs(input_ids),
                set_forward_context(
3458
3459
                    attn_metadata,
                    self.vllm_config,
3460
                    num_tokens=num_tokens_after_padding,
3461
3462
                    num_tokens_across_dp=num_tokens_across_dp,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
3463
                    batch_descriptor=batch_descriptor,
3464
3465
3466
                    ubatch_slices=ubatch_slices,
                ),
            ):
3467
                outputs = self.model(
3468
3469
3470
3471
                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
3472
                    **model_kwargs,
3473
                )
3474

3475
3476
3477
3478
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
3479

3480
            if self.speculative_config and self.speculative_config.use_eagle():
3481
                assert isinstance(self.drafter, EagleProposer)
3482
3483
3484
3485
                use_cudagraphs = (
                    cudagraph_runtime_mode == CUDAGraphMode.PIECEWISE
                    and not self.speculative_config.enforce_eager
                )
3486
                self.drafter.dummy_run(num_tokens, use_cudagraphs=use_cudagraphs)
3487

3488
3489
3490
3491
3492
3493
3494
3495
3496
3497
        # 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)

3498
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
3499
3500
3501
3502
        logit_indices_device = torch.from_numpy(logit_indices).to(
            self.device, non_blocking=True
        )
        return hidden_states, hidden_states[logit_indices_device]
3503
3504
3505
3506
3507
3508

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
3509
3510
3511
3512
        # 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)
3513

3514
        logits = self.model.compute_logits(hidden_states)
3515
3516
        num_reqs = logits.size(0)

3517
        dummy_tensors = lambda v: torch.full((num_reqs,), v, device=self.device)
3518
3519
3520
3521
3522
3523
3524
3525
3526
3527
3528
3529
3530
3531
3532

        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)],
3533
            spec_token_ids=[[] for _ in range(num_reqs)],
3534
3535
            allowed_token_ids_mask=None,
            bad_words_token_ids={},
3536
            logitsprocs=LogitsProcessors(),
3537
        )
3538
        try:
3539
3540
3541
            sampler_output = self.sampler(
                logits=logits, sampling_metadata=dummy_metadata
            )
3542
        except RuntimeError as e:
3543
            if "out of memory" in str(e):
3544
3545
3546
3547
                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 "
3548
3549
                    "initializing the engine."
                ) from e
3550
3551
            else:
                raise e
3552
        if self.speculative_config:
3553
3554
            draft_token_ids = [[0] for _ in range(num_reqs)]
            dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
3555
3556
                draft_token_ids, self.device
            )
3557
3558
3559
3560
3561
3562

            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
3563
3564
3565
3566
3567
            logits = torch.randn(
                num_tokens + num_reqs,
                logits.shape[-1],
                device=self.device,
                dtype=logits.dtype,
3568
            )
3569
3570
3571
            self.rejection_sampler(
                dummy_spec_decode_metadata,
                draft_probs,
3572
                logits,
3573
3574
                dummy_metadata,
            )
3575
        return sampler_output
3576

3577
    def _dummy_pooler_run_task(
3578
3579
        self,
        hidden_states: torch.Tensor,
3580
3581
        task: PoolingTask,
    ) -> PoolerOutput:
3582
3583
3584
3585
3586
3587
3588
3589
3590
3591
3592
        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

3593
        dummy_prompt_lens = torch.tensor(
3594
3595
            num_scheduled_tokens_list,
            device="cpu",
3596
        )
3597
3598
3599
        dummy_token_ids = torch.zeros(
            (num_reqs, req_num_tokens), dtype=torch.int32, device=self.device
        )
3600

3601
        model = cast(VllmModelForPooling, self.get_model())
3602
        dummy_pooling_params = PoolingParams(task=task)
3603
        dummy_pooling_params.verify(task=task, model_config=self.model_config)
3604
        to_update = model.pooler.get_pooling_updates(task)
3605
3606
        to_update.apply(dummy_pooling_params)

3607
        dummy_metadata = PoolingMetadata(
3608
3609
3610
3611
            prompt_lens=dummy_prompt_lens,
            prompt_token_ids=dummy_token_ids,
            pooling_params=[dummy_pooling_params] * num_reqs,
        )
3612

3613
3614
3615
        dummy_metadata.build_pooling_cursor(
            num_scheduled_tokens_list, device=hidden_states.device
        )
3616

3617
        try:
3618
3619
3620
            return model.pooler(
                hidden_states=hidden_states, pooling_metadata=dummy_metadata
            )
3621
        except RuntimeError as e:
3622
            if "out of memory" in str(e):
3623
                raise RuntimeError(
3624
3625
3626
                    "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 "
3627
3628
                    "initializing the engine."
                ) from e
3629
3630
            else:
                raise e
3631
3632
3633
3634
3635
3636
3637

    @torch.inference_mode()
    def _dummy_pooler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> PoolerOutput:
        # Find the task that has the largest output for subsequent steps
3638
3639
3640
3641
3642
3643
3644
3645
3646
3647
3648
3649
3650
3651
3652
3653
3654
3655
3656
3657
        supported_pooling_tasks = self.get_supported_pooling_tasks()

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

3658
        output_size = dict[PoolingTask, float]()
3659
        for task in supported_pooling_tasks:
3660
3661
            # Run a full batch with each task to ensure none of them OOMs
            output = self._dummy_pooler_run_task(hidden_states, task)
3662
            output_size[task] = sum(o.nbytes for o in output)
3663
3664
3665
3666
            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)
3667

3668
    def profile_run(self) -> None:
3669
        # Profile with multimodal encoder & encoder cache.
3670
        if self.supports_mm_inputs:
3671
            if self.model_config.multimodal_config.skip_mm_profiling:
3672
                logger.info(
3673
                    "Skipping memory profiling for multimodal encoder and "
3674
3675
                    "encoder cache."
                )
3676
3677
3678
3679
3680
3681
3682
3683
            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.
3684
                    dummy_modality = mm_budget.get_modality_with_max_tokens()
3685
3686
3687
                    max_mm_items_per_batch = mm_budget.max_items_per_batch_by_modality[
                        dummy_modality
                    ]
3688
3689
3690
3691
3692
3693
3694
3695
3696

                    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,
                    )
3697

3698
3699
3700
3701
3702
                    # Create dummy batch of multimodal inputs.
                    batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                        dummy_modality,
                        max_mm_items_per_batch,
                    )
3703

3704
                    # Run multimodal encoder.
3705
3706
3707
                    dummy_encoder_outputs = self.model.get_multimodal_embeddings(
                        **batched_dummy_mm_inputs
                    )
3708

3709
3710
3711
3712
                    sanity_check_mm_encoder_outputs(
                        dummy_encoder_outputs,
                        expected_num_items=max_mm_items_per_batch,
                    )
3713

3714
3715
3716
3717
3718
3719
3720
3721
3722
3723
                    # 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(
3724
3725
                                (encoder_budget, encoder_output_shape[-1])
                            )
3726
3727
3728
3729
3730
3731
                            num_tokens = output.shape[0]
                            expanded[:num_tokens].copy_(output)
                            expanded_outputs.append(expanded)

                        dummy_encoder_outputs = expanded_outputs

3732
                    # Cache the dummy encoder outputs.
3733
                    self.encoder_cache["tmp"] = dict(enumerate(dummy_encoder_outputs))
3734

3735
        # Add `is_profile` here to pre-allocate communication buffers
3736
3737
3738
        hidden_states, last_hidden_states = self._dummy_run(
            self.max_num_tokens, is_profile=True
        )
3739
        if get_pp_group().is_last_rank:
3740
3741
3742
3743
            if self.is_pooling_model:
                output = self._dummy_pooler_run(hidden_states)
            else:
                output = self._dummy_sampler_run(last_hidden_states)
3744
        else:
3745
            output = None
3746
        self._sync_device()
3747
        del hidden_states, output
3748
        self.encoder_cache.clear()
3749
        gc.collect()
3750

3751
    def capture_model(self) -> int:
3752
        if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
3753
            logger.warning(
3754
                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
3755
3756
                "ensure `cudagraph_mode` was not manually set to `NONE`"
            )
3757
            return 0
3758

3759
3760
        compilation_counter.num_gpu_runner_capture_triggers += 1

3761
3762
        start_time = time.perf_counter()

3763
3764
3765
3766
3767
3768
3769
3770
3771
3772
3773
3774
3775
3776
        @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()
3777
                    gc.collect()
3778

3779
3780
3781
        # 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.
3782
        set_cudagraph_capturing_enabled(True)
3783
        with freeze_gc(), graph_capture(device=self.device):
3784
            start_free_gpu_memory = torch.cuda.mem_get_info()[0]
3785
            cudagraph_mode = self.compilation_config.cudagraph_mode
3786
            assert cudagraph_mode is not None
3787
3788
3789
3790
3791
3792
3793
3794
3795

            if self.lora_config:
                if self.compilation_config.cudagraph_specialize_lora:
                    lora_cases = [True, False]
                else:
                    lora_cases = [True]
            else:
                lora_cases = [False]

3796
3797
            if cudagraph_mode.mixed_mode() != CUDAGraphMode.NONE:
                cudagraph_runtime_mode = cudagraph_mode.mixed_mode()
3798
                # make sure we capture the largest batch size first
3799
3800
3801
                compilation_cases = list(
                    product(reversed(self.cudagraph_batch_sizes), lora_cases)
                )
3802
3803
3804
                self._capture_cudagraphs(
                    compilation_cases,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
3805
3806
                    uniform_decode=False,
                )
3807

3808
3809
            # Capture full cudagraph for uniform decode batches if we
            # don't already have full mixed prefill-decode cudagraphs.
3810
3811
3812
3813
3814
3815
3816
            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
                )
3817
                decode_cudagraph_batch_sizes = [
3818
3819
                    x
                    for x in self.cudagraph_batch_sizes
3820
                    if max_num_tokens >= x >= self.uniform_decode_query_len
3821
                ]
3822
3823
3824
                compilation_cases_decode = list(
                    product(reversed(decode_cudagraph_batch_sizes), lora_cases)
                )
3825
3826
3827
                self._capture_cudagraphs(
                    compilation_cases=compilation_cases_decode,
                    cudagraph_runtime_mode=CUDAGraphMode.FULL,
3828
3829
                    uniform_decode=True,
                )
3830

3831
3832
3833
            torch.cuda.synchronize()
            end_free_gpu_memory = torch.cuda.mem_get_info()[0]

3834
3835
3836
        # 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
3837
        # we may do lazy capturing in future that still allows capturing
3838
3839
        # after here.
        set_cudagraph_capturing_enabled(False)
3840
3841
3842
3843
3844

        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.
3845
        logger.info_once(
3846
3847
3848
            "Graph capturing finished in %.0f secs, took %.2f GiB",
            elapsed_time,
            cuda_graph_size / (1 << 30),
3849
            scope="local",
3850
        )
3851
        return cuda_graph_size
3852

3853
3854
    def _capture_cudagraphs(
        self,
3855
        compilation_cases: list[tuple[int, bool]],
3856
3857
3858
3859
3860
3861
3862
        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}"
3863
3864
3865
3866
3867
3868
3869
3870

        # 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",
3871
3872
3873
                    cudagraph_runtime_mode.name,
                ),
            )
3874

3875
        # We skip EPLB here since we don't want to record dummy metrics
3876
        for num_tokens, activate_lora in compilation_cases:
3877
            # We currently only capture ubatched graphs when its a FULL
3878
3879
3880
            # 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
3881
3882
3883
3884
            allow_microbatching = (
                self.parallel_config.enable_dbo
                and cudagraph_runtime_mode == CUDAGraphMode.FULL
                and uniform_decode
3885
3886
3887
3888
3889
                and check_ubatch_thresholds(
                    config=self.vllm_config.parallel_config,
                    num_tokens=num_tokens,
                    uniform_decode=uniform_decode,
                )
3890
            )
3891

3892
3893
3894
3895
3896
3897
            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.
3898
3899
3900
3901
3902
3903
3904
3905
3906
                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,
3907
                    activate_lora=activate_lora,
3908
3909
3910
3911
3912
3913
3914
3915
                )
            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,
3916
                activate_lora=activate_lora,
3917
            )
3918
        self.maybe_remove_all_loras(self.lora_config)
3919

3920
3921
3922
3923
    def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize the attention backends and attention metadata builders.
        """
3924
        assert len(self.attn_groups) == 0, "Attention backends are already initialized"
3925

3926
3927
3928
3929
3930
3931
        class AttentionGroupKey(NamedTuple):
            attn_backend: type[AttentionBackend]
            kv_cache_spec: KVCacheSpec

        def get_attn_backends_for_group(
            kv_cache_group_spec: KVCacheGroupSpec,
3932
        ) -> tuple[dict[AttentionGroupKey, list[str]], set[type[AttentionBackend]]]:
3933
            layers = get_layers_from_vllm_config(
3934
3935
                self.vllm_config, AttentionLayerBase, kv_cache_group_spec.layer_names
            )
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            attn_backends = {}
            attn_backend_layers = defaultdict(list)
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            # Dedupe based on full class name; this is a bit safer than
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            # 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.
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            for layer_name in kv_cache_group_spec.layer_names:
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                attn_backend = layers[layer_name].get_attn_backend()
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                if layer_name in self.kv_sharing_fast_prefill_eligible_layers:
                    attn_backend = create_fast_prefill_custom_backend(
                        "FastPrefill",
                        attn_backend,
                    )

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

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                attn_groups.append(attn_group)
            return attn_groups

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        attention_backend_maps = []
        attention_backend_set: set[type[AttentionBackend]] = set()
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        for kv_cache_group_spec in kv_cache_config.kv_cache_groups:
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            attn_backends = get_attn_backends_for_group(kv_cache_group_spec)
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            attention_backend_maps.append(attn_backends[0])
            attention_backend_set.update(attn_backends[1])

        # Resolve cudagraph_mode before actually initialize metadata_builders
        self._check_and_update_cudagraph_mode(attention_backend_set)

        for attn_backends_map in attention_backend_maps:
            self.attn_groups.append(create_attn_groups(attn_backends_map))
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co63oc committed
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        # Calculate reorder batch threshold (if needed)
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        self.calculate_reorder_batch_threshold()

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    def _check_and_update_cudagraph_mode(
        self, attention_backends: set[type[AttentionBackend]]
    ) -> None:
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        """
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        Resolve the cudagraph_mode when there are multiple attention
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        backends with potential conflicting CUDA graph support.
        Then initialize the cudagraph_dispatcher based on the resolved
        cudagraph_mode.
        """
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        min_cg_support = AttentionCGSupport.ALWAYS
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        min_cg_backend_name = None
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        for attn_backend in attention_backends:
            builder_cls = attn_backend.get_builder_cls()
            if builder_cls.cudagraph_support.value < min_cg_support.value:
                min_cg_support = builder_cls.cudagraph_support
                min_cg_backend_name = attn_backend.__name__
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        # 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 "
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                f"with {min_cg_backend_name} backend (support: "
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                f"{min_cg_support})"
            )
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            if min_cg_support == AttentionCGSupport.NEVER:
                # if not supported any full cudagraphs, just raise it.
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                msg += (
                    "; please try cudagraph_mode=PIECEWISE, and "
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                    "make sure compilation mode is VLLM_COMPILE"
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                )
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                raise ValueError(msg)

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

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

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        # check that if we are doing spec-decode + decode full-cudagraphs it is
        # supported
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        if (
            cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
            and self.uniform_decode_query_len > 1
            and min_cg_support.value < AttentionCGSupport.UNIFORM_BATCH.value
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported"
                f" with spec-decode for attention backend "
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                f"{min_cg_backend_name} (support: {min_cg_support})"
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            )
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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 "
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                f"supported with {min_cg_backend_name} backend ("
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                f"support:{min_cg_support}) "
                "; please try cudagraph_mode=PIECEWISE, "
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                "and make sure compilation mode is VLLM_COMPILE"
4118
            )
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        # Trigger cudagraph dispatching keys initialization after
        # resolved cudagraph mode.
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        self.cudagraph_dispatcher.initialize_cudagraph_keys(
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            self.compilation_config.cudagraph_mode, self.uniform_decode_query_len
        )
4125

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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)
        """
4131
        for group in self._attn_group_iterator():
4132
            attn_metadata_builder_i = group.get_metadata_builder()
4133

4134
4135
            # check that if any backends reorder batches; that the reordering
            # is compatible (e.g., decode threshold is the same)
4136
            reorder_batch_threshold_i = attn_metadata_builder_i.reorder_batch_threshold
4137
4138
            if reorder_batch_threshold_i is not None:
                if self.reorder_batch_threshold is not None:
4139
                    if reorder_batch_threshold_i != self.reorder_batch_threshold:
4140
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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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4145
                            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)

4229
    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
4241
            if not isinstance(kv_cache_group.kv_cache_spec, EncoderOnlyAttentionSpec)
4242
        ]
4243
4244
4245
4246
4247
4248
4249

        # 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
        ]:
4250
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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
4253
4254
                "for more details."
            )
4255
4256
            self.input_batch = InputBatch(
                max_num_reqs=self.max_num_reqs,
4257
                max_model_len=max(self.max_model_len, self.max_encoder_len),
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4260
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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,
4263
                kernel_block_sizes=kernel_block_sizes,
4264
                is_spec_decode=bool(self.vllm_config.speculative_config),
4265
                logitsprocs=self.input_batch.logitsprocs,
4266
                logitsprocs_need_output_token_ids=self.input_batch.logitsprocs_need_output_token_ids,
4267
                is_pooling_model=self.is_pooling_model,
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4269
                num_speculative_tokens=(
                    self.vllm_config.speculative_config.num_speculative_tokens
4270
4271
4272
                    if self.vllm_config.speculative_config
                    else 0
                ),
4273
4274
            )

4275
    def _allocate_kv_cache_tensors(
4276
4277
        self, kv_cache_config: KVCacheConfig
    ) -> dict[str, torch.Tensor]:
4278
        """
4279
4280
4281
        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.

4282
        Args:
4283
            kv_cache_config: The KV cache config
4284
        Returns:
4285
            dict[str, torch.Tensor]: A map between layer names to their
4286
            corresponding memory buffer for KV cache.
4287
        """
4288
4289
        kv_cache_raw_tensors: dict[str, torch.Tensor] = {}
        for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
4290
4291
4292
            tensor = torch.zeros(
                kv_cache_tensor.size, dtype=torch.int8, device=self.device
            )
4293
4294
4295
4296
4297
            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:
4298
4299
4300
4301
            for layer_name in group.layer_names:
                if layer_name in self.runner_only_attn_layers:
                    continue
                layer_names.add(layer_name)
4302
4303
4304
        assert layer_names == set(kv_cache_raw_tensors.keys()), (
            "Some layers are not correctly initialized"
        )
4305
4306
        return kv_cache_raw_tensors

4307
4308
4309
    def _attn_group_iterator(self) -> Iterator[AttentionGroup]:
        return itertools.chain.from_iterable(self.attn_groups)

4310
    def _kv_cache_spec_attn_group_iterator(self) -> Iterator[AttentionGroup]:
4311
4312
        if not self.kv_cache_config.kv_cache_groups:
            return
4313
4314
        for attn_groups in self.attn_groups:
            yield from attn_groups
4315

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4333
    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
        ):
4334
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4336
4337
4338
4339
            kv_cache_spec = kv_cache_group.kv_cache_spec
            if isinstance(kv_cache_spec, UniformTypeKVCacheSpecs):
                # All layers in the UniformTypeKVCacheSpecs have the same type,
                # Pick an arbitrary one to dispatch.
                kv_cache_spec = next(iter(kv_cache_spec.kv_cache_specs.values()))
            if isinstance(kv_cache_spec, EncoderOnlyAttentionSpec):
4340
                continue
4341
            elif isinstance(kv_cache_spec, AttentionSpec):
4342
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4344
4345
4346
4347
4348
4349
4350
                # 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)
4351
            elif isinstance(kv_cache_spec, MambaSpec):
4352
4353
                # This is likely Mamba or other non-attention cache,
                # no splitting.
4354
                kernel_block_sizes.append(kv_cache_spec.block_size)
4355
4356
4357
4358
4359
4360
            else:
                raise NotImplementedError(
                    f"unknown kv cache spec {kv_cache_group.kv_cache_spec}"
                )
        return kernel_block_sizes

4361
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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]:
4366
        """
4367
        Reshape the KV cache tensors to the desired shape and dtype.
4368

4369
        Args:
4370
4371
            kv_cache_config: The KV cache config
            kv_cache_raw_tensors: The KV cache buffer of each layer, with
4372
                correct size but uninitialized shape.
4373
        Returns:
4374
            Dict[str, torch.Tensor]: A map between layer names to their
4375
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            corresponding memory buffer for KV cache.
        """
4377
        kv_caches: dict[str, torch.Tensor] = {}
4378
        has_attn, has_mamba = False, False
4379
4380
        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
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4382
            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
4387
                num_blocks = raw_tensor.numel() // kv_cache_spec.page_size_bytes
4388
                if isinstance(kv_cache_spec, AttentionSpec):
4389
                    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

4398
                    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,
                    )
4405
                    dtype = kv_cache_spec.dtype
4406
                    try:
4407
                        kv_cache_stride_order = attn_backend.get_kv_cache_stride_order()  # noqa: E501
4408
                        assert len(kv_cache_stride_order) == len(kv_cache_shape)
4409
                    except (AttributeError, NotImplementedError):
4410
                        kv_cache_stride_order = tuple(range(len(kv_cache_shape)))
4411
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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:
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            self._update_hybrid_attention_mamba_layout(kv_caches)
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        return kv_caches

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

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

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

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

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

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

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

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

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

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        kv_cache_spec: dict[str, KVCacheSpec] = {}
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        attn_layers = get_layers_from_vllm_config(self.vllm_config, AttentionLayerBase)
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        for layer_name, attn_module in attn_layers.items():
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            if isinstance(attn_module, Attention) and (
                kv_tgt_layer := attn_module.kv_sharing_target_layer_name
            ):
                # The layer doesn't need its own KV cache and will use that of
                # the target layer. We skip creating a KVCacheSpec for it, so
                # that KV cache management logic will act as this layer does
                # not exist, and doesn't allocate KV cache for the layer. This
                # enables the memory saving of cross-layer kv sharing, allowing
                # a given amount of memory to accommodate longer context lengths
                # or enable more requests to be processed simultaneously.
                self.shared_kv_cache_layers[layer_name] = kv_tgt_layer
                continue
            # Skip modules that don't need KV cache (eg encoder-only attention)
            if spec := attn_module.get_kv_cache_spec(self.vllm_config):
                kv_cache_spec[layer_name] = spec
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        return kv_cache_spec
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    def _to_list(self, sampled_token_ids: torch.Tensor) -> list[list[int]]:
        # This is a short term mitigation for issue mentioned in
        # https://github.com/vllm-project/vllm/issues/22754.
        # `tolist` would trigger a cuda wise stream sync, which
        # would block other copy ops from other cuda streams.
        # A cuda event sync would avoid such a situation. Since
        # this is in the critical path of every single model
        # forward loop, this has caused perf issue for a disagg
        # setup.
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        pinned = self.sampled_token_ids_pinned_cpu[: sampled_token_ids.shape[0]]
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        pinned.copy_(sampled_token_ids, non_blocking=True)
        self.transfer_event.record()
        self.transfer_event.synchronize()
        return pinned.tolist()