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 (
    cdiv,
    check_use_alibi,
    length_from_prompt_token_ids_or_embeds,
    round_up,
)
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from vllm.utils.jsontree import json_map_leaves
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from vllm.utils.mem_constants import GiB_bytes
from vllm.utils.mem_utils import DeviceMemoryProfiler
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from vllm.utils.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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        # TODO(woosuk): Provide an option to tune the max cudagraph batch size.
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        # The convention is different.
        # self.cudagraph_batch_sizes sorts in ascending order.
        # The batch sizes in the config are in descending order.
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        if (
            self.compilation_config.cudagraph_capture_sizes
            and self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
        ):
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            self.cudagraph_batch_sizes = list(
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                reversed(self.compilation_config.cudagraph_capture_sizes)
            )
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        # Cache the device properties.
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        self._init_device_properties()
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        # Persistent buffers for CUDA graphs.
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        self.input_ids = self._make_buffer(self.max_num_tokens, dtype=torch.int32)
        self.positions = self._make_buffer(self.max_num_tokens, dtype=torch.int64)
        self.query_start_loc = self._make_buffer(
            self.max_num_reqs + 1, dtype=torch.int32
        )
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        self.seq_lens = self._make_buffer(self.max_num_reqs, dtype=torch.int32)
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        if self.dcp_world_size > 1:
            self.dcp_local_seq_lens = self._make_buffer(
                self.max_num_reqs, dtype=torch.int32
            )
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        # Because inputs_embeds may be bfloat16 and we don't need a numpy
        # version of this tensor, avoid a RuntimeError by not creating a
        # numpy buffer.
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        self.inputs_embeds = self._make_buffer(
            self.max_num_tokens, self.hidden_size, dtype=self.dtype, numpy=False
        )
        self.is_token_ids = self._make_buffer(self.max_num_tokens, dtype=torch.bool)
        self.discard_request_indices = self._make_buffer(
            self.max_num_reqs, dtype=torch.int64
        )
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        self.num_discarded_requests = 0

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

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

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

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

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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]

520
    def _make_buffer(
521
        self, *size: int | torch.SymInt, dtype: torch.dtype, numpy: bool = True
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    ) -> CpuGpuBuffer:
        return CpuGpuBuffer(
            *size,
            dtype=dtype,
            device=self.device,
            pin_memory=self.pin_memory,
            with_numpy=numpy,
        )
530

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

534
        if not self.is_pooling_model:
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            return model_kwargs

537
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        num_reqs = self.input_batch.num_reqs
        pooling_params = self.input_batch.get_pooling_params()
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541

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

        if len(token_type_id_requests) == 0:
            return model_kwargs

552
        seq_lens = self.seq_lens.gpu[:num_reqs]
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        token_type_ids = []

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

        model_kwargs["token_type_ids"] = torch.concat(token_type_ids).to(
561
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            device=self.device
        )
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        return model_kwargs

565
    def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
566
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        """
        Update the order of requests in the batch based on the attention
568
        backend's needs. For example, some attention backends (namely MLA) may
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        want to separate requests based on if the attention computation will be
        compute-bound or memory-bound.

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

583
        if self.reorder_batch_threshold is not None:
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            # NOTE(lucas): currently no backend supports the custom masking
            #  required for DCP with q_len > 1, so we assert here. Remove this
            #  assert once the custom mask is support is added to FA3.
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            if (
                self.dcp_world_size > 1
                and envs.VLLM_ATTENTION_BACKEND != "FLASH_ATTN_MLA"
            ):
591
                assert self.reorder_batch_threshold == 1, (
592
                    "DCP not support reorder_batch_threshold > 1 now."
593
                )
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            reorder_batch_to_split_decodes_and_prefills(
                self.input_batch,
                scheduler_output,
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                decode_threshold=self.reorder_batch_threshold,
            )
599

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    # Note: used for model runner override.
    def _init_device_properties(self) -> None:
602
        """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()

610
    def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
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        """Update the cached states and the persistent batch with the scheduler
        output.

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

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        The SamplingMetadata is updated and copied to the GPU if there is a
        new/resumed/paused/finished request in the batch.
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        """
        # Remove finished requests from the cached states.
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        for req_id in scheduler_output.finished_req_ids:
            self.requests.pop(req_id, None)
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        # 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:
630
            self.input_batch.remove_request(req_id)
631
632

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

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        # Remove the unscheduled requests from the persistent batch.
        # NOTE(woosuk): The unscheduled requests are either preempted requests
        # or running requests that are not scheduled in this step. We remove
        # them from the persistent batch but keep their cached states since
        # they will be scheduled again sometime in the future.
        scheduled_req_ids = scheduler_output.num_scheduled_tokens.keys()
        cached_req_ids = self.input_batch.req_id_to_index.keys()
        unscheduled_req_ids = cached_req_ids - scheduled_req_ids
        # NOTE(woosuk): The persistent batch optimization assumes that
        # consecutive batches contain mostly the same requests. If batches
        # have low request overlap (e.g., alternating between two distinct
        # sets of requests), this optimization becomes very inefficient.
        for req_id in unscheduled_req_ids:
649
            self.input_batch.remove_request(req_id)
650

651
        reqs_to_add: list[CachedRequestState] = []
652
        # Add new requests to the cached states.
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655
        for new_req_data in scheduler_output.scheduled_new_reqs:
            req_id = new_req_data.req_id
            sampling_params = new_req_data.sampling_params
656
            pooling_params = new_req_data.pooling_params
657

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

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

672
                model = cast(VllmModelForPooling, self.get_model())
673
                to_update = model.pooler.get_pooling_updates(task)
674
675
                to_update.apply(pooling_params)

676
            req_state = CachedRequestState(
677
                req_id=req_id,
678
                prompt_token_ids=new_req_data.prompt_token_ids,
679
                prompt_embeds=new_req_data.prompt_embeds,
680
                mm_features=new_req_data.mm_features,
681
                sampling_params=sampling_params,
682
                pooling_params=pooling_params,
683
                generator=generator,
684
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                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
686
                output_token_ids=[],
687
                lora_request=new_req_data.lora_request,
688
            )
689
690
            self.requests[req_id] = req_state

691
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
692
            if self.uses_mrope:
693
                self._init_mrope_positions(req_state)
694

695
            reqs_to_add.append(req_state)
696

697
        # Update the states of the running/resumed requests.
698
        is_last_rank = get_pp_group().is_last_rank
699
700
        req_data = scheduler_output.scheduled_cached_reqs
        for i, req_id in enumerate(req_data.req_ids):
701
            req_state = self.requests[req_id]
702
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            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]
705
            num_output_tokens = req_data.num_output_tokens[i]
706

707
            # Update the cached states.
708

709
            req_state.num_computed_tokens = num_computed_tokens
710
            req_index = self.input_batch.req_id_to_index.get(req_id)
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            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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                num_new_tokens = (
                    num_computed_tokens + len(new_token_ids) - req_state.num_tokens
                )
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                if num_new_tokens == 1:
                    # Avoid slicing list in most common case.
                    req_state.output_token_ids.append(new_token_ids[-1])
                elif num_new_tokens > 0:
726
                    req_state.output_token_ids.extend(new_token_ids[-num_new_tokens:])
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            elif num_output_tokens < len(req_state.output_token_ids):
                # Some output tokens were discarded due to a sync-KV-load
                # failure. Align the cached state.
                del req_state.output_token_ids[num_output_tokens:]
                if req_index is not None:
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                    end_idx = (
                        self.input_batch.num_prompt_tokens[req_index]
                        + num_output_tokens
                    )
736
737
                    self.input_batch.num_tokens[req_index] = end_idx
                    self.input_batch.num_tokens_no_spec[req_index] = end_idx
738

739
            # Update the block IDs.
740
            if not resumed_from_preemption:
741
742
                if new_block_ids is not None:
                    # Append the new blocks to the existing block IDs.
743
                    for block_ids, new_ids in zip(req_state.block_ids, new_block_ids):
744
                        block_ids.extend(new_ids)
745
            else:
746
                assert req_index is None
747
                assert new_block_ids is not None
748
749
                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
750
                req_state.block_ids = new_block_ids
751

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757
                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:]
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761
            if req_index is None:
                # The request is not in the persistent batch.
                # The request was either preempted and resumed later, or was not
                # scheduled in the previous step and needs to be added again.
762
                reqs_to_add.append(req_state)
763
764
765
                continue

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

            # For the last rank, we don't need to update the token_ids_cpu
            # because the sampled tokens are already cached.
            if not is_last_rank:
                # Add new_token_ids to token_ids_cpu.
                start_token_index = num_computed_tokens
                end_token_index = num_computed_tokens + len(new_token_ids)
776
                self.input_batch.token_ids_cpu[
777
778
779
                    req_index, start_token_index:end_token_index
                ] = new_token_ids
                self.input_batch.num_tokens_no_spec[req_index] = end_token_index
780
                self.input_batch.num_tokens[req_index] = end_token_index
781

782
            # Add spec_token_ids to token_ids_cpu.
783
            spec_token_ids = scheduler_output.scheduled_spec_decode_tokens.get(
784
                req_id, []
785
            )
786
787
788
789
790
            if spec_token_ids:
                num_spec_tokens = len(spec_token_ids)
                start_index = self.input_batch.num_tokens_no_spec[req_index]
                end_token_index = start_index + num_spec_tokens
                self.input_batch.token_ids_cpu[
791
792
                    req_index, start_index:end_token_index
                ] = spec_token_ids
793
794
                # NOTE(woosuk): `num_tokens` here may include spec tokens.
                self.input_batch.num_tokens[req_index] += num_spec_tokens
795
796
797
798
799
800
801

            # 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
802

803
804
        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
805
806
        for request in reqs_to_add:
            self.input_batch.add_request(request)
807

808
809
810
811
812
813
        # 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()
814

815
    def _update_states_after_model_execute(
816
817
        self, output_token_ids: torch.Tensor
    ) -> None:
818
819
820
821
822
823
824
825
826
827
828
829
        """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.
830
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834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
        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()
        )
850
851
852
        for i, num_tokens in enumerate(num_accepted_tokens):
            self.input_batch.num_accepted_tokens_cpu[i] = num_tokens

853
854
855
856
857
858
    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
859
860
861
862
        for mm_feature in req_state.mm_features:
            mm_item = mm_feature.data
            if mm_item is None:
                continue
863
864
865
866
867
868
869
870
871
872
873
874
            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

875
876
877
878
879
880
881
882
883
884
885
        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,
886
            )
887
        )
888

889
    def _extract_mm_kwargs(
890
        self,
891
892
        scheduler_output: "SchedulerOutput",
    ) -> BatchedTensorInputs:
893
        if not scheduler_output or not self.is_multimodal_raw_input_only_model:
894
            return {}
895

896
897
        mm_kwargs = list[MultiModalKwargsItem]()
        for req in scheduler_output.scheduled_new_reqs:
898
899
900
            for feature in req.mm_features:
                if feature.data is not None:
                    mm_kwargs.append(feature.data)
901

902
        # Input all modalities at once
903
        model = cast(SupportsMultiModal, self.model)
904
905
        mm_kwargs_combined: BatchedTensorInputs = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
906
907
908
909
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
910
911
        ):
            mm_kwargs_combined.update(mm_kwargs_group)
912

913
        return mm_kwargs_combined
914

915
    def _dummy_mm_kwargs(self, num_seqs: int) -> BatchedTensorInputs:
916
        if not self.is_multimodal_raw_input_only_model:
917
            return {}
918

919
920
921
922
923
        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)
924

925
926
927
    def _get_cumsum_and_arange(
        self,
        num_tokens: np.ndarray,
928
        cumsum_dtype: np.dtype | None = None,
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
    ) -> 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

945
946
947
    def _prepare_input_ids(
        self, total_num_scheduled_tokens: int, cu_num_tokens: np.ndarray
    ) -> None:
948
        """Prepare the input IDs for the current batch.
949

950
951
952
953
954
955
956
        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)
957
958
959
            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)
960
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962
963
964
965
966
967
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969
970
971
972
973
974
975
976
977
            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)
978
                indices_match &= prev_index == flattened_index
979
980
981
982
983
984
                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)
985
986
987
            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)
988
989
        if num_commmon_tokens == 0:
            # No requests in common with the previous iteration
990
            # So input_ids.cpu will have all the input ids.
991
992
993
994
995
996
997
            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_(
998
999
1000
                self.input_batch.prev_sampled_token_ids[:num_commmon_tokens, 0],
                non_blocking=True,
            )
1001
1002
            if self.enable_prompt_embeds:
                self.is_token_ids.gpu[:num_commmon_tokens] = True
1003
            return
1004
        # Upload the index tensors asynchronously so the scatter can be non-blocking.
1005
1006
1007
        input_ids_index_tensor = torch.tensor(
            flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1008
        prev_common_req_indices_tensor = torch.tensor(
1009
1010
            prev_common_req_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1011
1012
1013
1014
        self.input_ids.gpu.scatter_(
            dim=0,
            index=input_ids_index_tensor,
            src=self.input_batch.prev_sampled_token_ids[
1015
1016
1017
                prev_common_req_indices_tensor, 0
            ],
        )
1018

1019
1020
1021
1022
1023
    def _get_encoder_seq_lens(
        self,
        scheduler_output: "SchedulerOutput",
        kv_cache_spec: KVCacheSpec,
        num_reqs: int,
1024
    ) -> np.ndarray | None:
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
        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

1037
    def _prepare_inputs(
1038
        self, scheduler_output: "SchedulerOutput"
1039
1040
1041
    ) -> tuple[
        PerLayerAttnMetadata,
        torch.Tensor,
1042
        SpecDecodeMetadata | None,
1043
        np.ndarray,
1044
        CommonAttentionMetadata | None,
1045
        int,
1046
1047
        UBatchSlices | None,
        torch.Tensor | None,
1048
1049
        bool,
    ]:
1050
1051
1052
        """
        :return: tuple[
            attn_metadata: layer-to-attention_metadata mapping,
1053
1054
1055
            logits_indices, spec_decode_metadata,
            num_scheduled_tokens, spec_decode_common_attn_metadata,
            max_num_scheduled_tokens, use_cascade_attn
1056
1057
        ]
        """
1058
1059
1060
1061
1062
1063
1064
        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.
1065
        self.input_batch.block_table.commit_block_table(num_reqs)
1066
1067

        # Get the number of scheduled tokens for each request.
1068
1069
1070
1071
        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)
1072
1073
1074

        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
1075
        req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens)
1076

1077
1078
        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
        # arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
1079
        cu_num_tokens, arange = self._get_cumsum_and_arange(num_scheduled_tokens)
1080
1081

        # Get positions.
1082
        positions_np = self.positions.np[:total_num_scheduled_tokens]
1083
1084
1085
1086
1087
        np.add(
            self.input_batch.num_computed_tokens_cpu[req_indices],
            arange,
            out=positions_np,
        )
1088

1089
1090
        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1091
        if self.uses_mrope:
1092
1093
            self._calc_mrope_positions(scheduler_output)

1094
1095
1096
1097
        # 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.
1098
1099
1100
        token_indices = (
            positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]
        )
1101
        token_indices_tensor = torch.from_numpy(token_indices)
1102

1103
1104
1105
        # 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.
1106
1107
1108
1109
1110
1111
        torch.index_select(
            self.input_batch.token_ids_cpu_tensor.flatten(),
            0,
            token_indices_tensor,
            out=self.input_ids.cpu[:total_num_scheduled_tokens],
        )
1112
1113
1114
1115
1116
1117
        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,
1118
1119
                out=self.is_token_ids.cpu[:total_num_scheduled_tokens],
            )
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
1149
1150
1151
1152

        # 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:
1153
1154
1155
                    self.inputs_embeds.cpu[
                        output_idx : output_idx + actual_num_sched
                    ].copy_(req_embeds[start_pos:actual_end])
1156
1157

                output_idx += num_sched
1158

1159
1160
        self.input_batch.block_table.compute_slot_mapping(req_indices, positions_np)
        self.input_batch.block_table.commit_slot_mapping(total_num_scheduled_tokens)
1161
1162

        # Prepare the attention metadata.
1163
        self.query_start_loc.np[0] = 0
1164
        self.query_start_loc.np[1 : num_reqs + 1] = cu_num_tokens
1165
1166
        # Note: pad query_start_loc to be non-decreasing, as kernels
        # like FlashAttention requires that
1167
        self.query_start_loc.np[num_reqs + 1 :].fill(cu_num_tokens[-1])
1168
        self.query_start_loc.copy_to_gpu()
1169
        query_start_loc = self.query_start_loc.gpu[: num_reqs + 1]
1170

1171
        num_tokens_unpadded = scheduler_output.total_num_scheduled_tokens
1172
        num_tokens_padded = self._get_num_input_tokens(num_tokens_unpadded)
1173
1174
1175
        uniform_decode = (
            max_num_scheduled_tokens == self.uniform_decode_query_len
        ) and (total_num_scheduled_tokens == num_reqs * max_num_scheduled_tokens)
1176
1177
1178
1179
1180
1181
1182

        # 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

1183
        ubatch_slices, num_tokens_across_dp = coordinate_batch_across_dp(
1184
1185
1186
1187
1188
1189
1190
            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,
1191
        )
1192

1193
        self.seq_lens.np[:num_reqs] = (
1194
1195
            self.input_batch.num_computed_tokens_cpu[:num_reqs] + num_scheduled_tokens
        )
1196
        # Fill unused with 0 for full cuda graph mode.
1197
1198
1199
1200
        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()
1201

1202
        num_tokens = [self.requests[r].num_tokens for r in self.input_batch.req_ids]
1203
1204
1205
1206
1207
1208
1209
        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)
1210
1211
1212
        self.discard_request_indices.np[: self.num_discarded_requests] = (
            discard_request_indices
        )
1213
1214
1215

        self.discard_request_indices.copy_to_gpu(self.num_discarded_requests)

1216
        # Copy the tensors to the GPU.
1217
1218
        self._prepare_input_ids(total_num_scheduled_tokens, cu_num_tokens)

1219
        if self.uses_mrope:
1220
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1221
1222
            self.mrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.mrope_positions.cpu[:, :total_num_scheduled_tokens],
1223
1224
                non_blocking=True,
            )
1225
1226
        else:
            # Common case (1D positions)
1227
            self.positions.copy_to_gpu(total_num_scheduled_tokens)
1228

1229
        use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
1230
1231
1232
1233
1234
1235
1236
        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
1237
            num_draft_tokens = None
1238
1239
1240
1241
1242
1243
            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)
1244
1245
1246
            # 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)
1247
1248
1249
1250
            for (
                req_id,
                draft_token_ids,
            ) in scheduler_output.scheduled_spec_decode_tokens.items():
1251
1252
                req_idx = self.input_batch.req_id_to_index[req_id]
                num_draft_tokens[req_idx] = len(draft_token_ids)
1253
1254
1255
1256
1257
1258
1259
1260
                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
                )
1261
            spec_decode_metadata = self._calc_spec_decode_metadata(
1262
1263
                num_draft_tokens, cu_num_tokens
            )
1264
            logits_indices = spec_decode_metadata.logits_indices
1265
1266

            # For DECODE only cuda graph of some attention backends (e.g., GDN).
1267
            self.num_decode_draft_tokens.np[:num_reqs] = num_decode_draft_tokens
1268
1269
            self.num_decode_draft_tokens.np[num_reqs:].fill(-1)
            self.num_decode_draft_tokens.copy_to_gpu()
1270
1271
1272

        logits_indices_padded = None
        if self.cache_config.kv_sharing_fast_prefill:
1273
            logits_indices_padded = self._prepare_kv_sharing_fast_prefill(
1274
1275
                logits_indices
            )
1276

1277
1278
1279
        attn_metadata: PerLayerAttnMetadata = {}
        if ubatch_slices is not None:
            attn_metadata = [dict() for _ in range(len(ubatch_slices))]
1280
        use_cascade_attn = False
1281

1282
        # Used in the below loop.
1283
        query_start_loc_cpu = self.query_start_loc.cpu[: num_reqs + 1]
1284
        seq_lens_cpu = self.seq_lens.cpu[:num_reqs]
1285
1286
1287
        num_computed_tokens_cpu = self.input_batch.num_computed_tokens_cpu_tensor[
            :num_reqs
        ]
1288
        spec_decode_common_attn_metadata = None
1289
1290
        if use_spec_decode:
            self.num_accepted_tokens.np[:num_reqs] = (
1291
1292
                self.input_batch.num_accepted_tokens_cpu[:num_reqs]
            )
1293
1294
            self.num_accepted_tokens.np[num_reqs:].fill(1)
            self.num_accepted_tokens.copy_to_gpu()
1295

1296
1297
1298
        # 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(
1299
1300
            self.kv_cache_config.kv_cache_groups
        ):
1301
            encoder_seq_lens = self._get_encoder_seq_lens(
1302
1303
                scheduler_output, kv_cache_group_spec.kv_cache_spec, num_reqs
            )
1304

1305
            if isinstance(kv_cache_group_spec.kv_cache_spec, EncoderOnlyAttentionSpec):
1306
1307
1308
1309
1310
                # 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,
1311
1312
1313
                    device=self.device,
                )
                slot_mapping = torch.zeros(
1314
                    (total_num_scheduled_tokens,),
1315
1316
1317
                    dtype=torch.int64,
                    device=self.device,
                )
1318
1319
1320
                num_common_prefix_blocks = 0
            else:
                blk_table = self.input_batch.block_table[kv_cache_group_id]
1321
                blk_table_tensor = blk_table.get_device_tensor(num_reqs)
1322
                slot_mapping = blk_table.slot_mapping.gpu[:total_num_scheduled_tokens]
1323
1324
1325

                # Fill unused with -1. Needed for reshape_and_cache in full cuda
                # graph mode.
1326
1327
1328
1329
                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
                ]
1330

1331
            common_attn_metadata = CommonAttentionMetadata(
1332
1333
1334
1335
1336
                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,
1337
1338
1339
                num_reqs=num_reqs,
                num_actual_tokens=total_num_scheduled_tokens,
                max_query_len=max_num_scheduled_tokens,
1340
                max_seq_len=max_seq_len,
1341
1342
                block_table_tensor=blk_table_tensor,
                slot_mapping=slot_mapping,
1343
1344
                logits_indices_padded=logits_indices_padded,
                num_logits_indices=logits_indices.size(0),
1345
                causal=True,
1346
                encoder_seq_lens=encoder_seq_lens,
1347
1348
1349
                dcp_local_seq_lens=self.dcp_local_seq_lens.gpu[:num_reqs]
                if self.dcp_world_size > 1
                else None,
1350
1351
            )

1352
            if self.speculative_config and spec_decode_common_attn_metadata is None:
1353
                if isinstance(self.drafter, EagleProposer):
1354
1355
1356
1357
                    if (
                        self.drafter.attn_layer_names[0]
                        in kv_cache_group_spec.layer_names
                    ):
1358
1359
1360
                        spec_decode_common_attn_metadata = common_attn_metadata
                else:
                    spec_decode_common_attn_metadata = common_attn_metadata
1361

1362
1363
1364
            for attn_group in self.attn_groups[kv_cache_group_id]:
                # Prepare for cascade attention if enabled & beneficial.
                common_prefix_len = 0
1365
                builder = attn_group.get_metadata_builder()
1366
1367
1368
                if self.cascade_attn_enabled:
                    common_prefix_len = self._compute_cascade_attn_prefix_len(
                        num_scheduled_tokens,
1369
                        num_common_prefix_blocks,
1370
                        attn_group.kv_cache_spec,
1371
1372
                        builder,
                    )
1373

1374
                extra_attn_metadata_args = {}
1375
                if use_spec_decode and isinstance(builder, GDNAttentionMetadataBuilder):
1376
                    extra_attn_metadata_args = dict(
1377
1378
1379
1380
                        num_accepted_tokens=self.num_accepted_tokens.gpu[:num_reqs],
                        num_decode_draft_tokens_cpu=self.num_decode_draft_tokens.cpu[
                            :num_reqs
                        ],
1381
1382
                    )

1383
1384
                if ubatch_slices is not None:
                    common_attn_metadata_list = split_attn_metadata(
1385
1386
                        ubatch_slices, common_attn_metadata
                    )
1387
                    for ubid, common_attn_metadata in enumerate(
1388
1389
1390
1391
1392
1393
1394
1395
                        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,
                        )
1396
1397
1398
1399
1400
1401
1402
1403
                        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,
1404
1405
1406
                        **extra_attn_metadata_args,
                    )
                    use_cascade_attn |= getattr(attn_metadata_i, "use_cascade", False)
1407
1408
                    for layer_name in attn_group.layer_names:
                        attn_metadata[layer_name] = attn_metadata_i
1409

1410
1411
1412
1413
        # disable cascade attention when DBO
        if ubatch_slices is not None:
            use_cascade_attn = False

1414
1415
1416
1417
        # Hot-Swap lora model
        if self.lora_config:
            self.set_active_loras(self.input_batch, num_scheduled_tokens)

1418
1419
1420
1421
1422
1423
1424
1425
        return (
            attn_metadata,
            logits_indices,
            spec_decode_metadata,
            num_scheduled_tokens,
            spec_decode_common_attn_metadata,
            max_num_scheduled_tokens,
            ubatch_slices,
1426
            num_tokens_across_dp,
1427
1428
            use_cascade_attn,
        )
1429

1430
1431
1432
1433
    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
        num_common_prefix_blocks: int,
1434
1435
        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
    ) -> 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.
        """
1454
        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
        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]
1492
        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
1493
1494
1495
1496
1497
1498
1499
        # 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(
1500
1501
            common_prefix_len, self.input_batch.num_computed_tokens_cpu[:num_reqs].min()
        )
1502
        # common_prefix_len should be a multiple of the block size.
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
        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
        )
1514
1515
        assert isinstance(kv_cache_spec, AttentionSpec)
        use_cascade = attn_metadata_builder.use_cascade_attention(
1516
1517
1518
            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
1519
            num_kv_heads=kv_cache_spec.num_kv_heads,
1520
            use_alibi=self.use_alibi,
1521
            use_sliding_window=use_sliding_window,
1522
            use_local_attention=use_local_attention,
1523
            num_sms=self.num_sms,
1524
            dcp_world_size=self.dcp_world_size,
1525
1526
1527
        )
        return common_prefix_len if use_cascade else 0

1528
1529
    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
1530
        for index, req_id in enumerate(self.input_batch.req_ids):
1531
1532
1533
            req = self.requests[req_id]
            assert req.mrope_positions is not None

1534
1535
            num_computed_tokens = self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1536
            num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
1537
1538
                req.prompt_token_ids, req.prompt_embeds
            )
1539
1540

            if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
1541
1542
                prompt_part_len = max(0, num_prompt_tokens - num_computed_tokens)
                completion_part_len = max(0, num_scheduled_tokens - prompt_part_len)
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
            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

1556
1557
1558
                self.mrope_positions.cpu[:, dst_start:dst_end] = req.mrope_positions[
                    :, src_start:src_end
                ]
1559
1560
1561
1562
1563
1564
1565
                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

1566
                MRotaryEmbedding.get_next_input_positions_tensor(
1567
                    out=self.mrope_positions.np,
1568
1569
1570
1571
1572
                    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,
                )
1573
1574
1575

                mrope_pos_ptr += completion_part_len

1576
1577
    def _calc_spec_decode_metadata(
        self,
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
        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
1594
1595
1596
1597

        # 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(
1598
1599
            num_sampled_tokens, cumsum_dtype=np.int32
        )
1600
        # Step 2. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
1601
        logits_indices = np.repeat(
1602
1603
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens
        )
1604
        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
1605
1606
1607
1608
1609
1610
        logits_indices += arange

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

        # Compute the draft logits indices.
1611
1612
1613
        # 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(
1614
1615
            num_draft_tokens, cumsum_dtype=np.int32
        )
1616
1617
        # [0, 0, 0, 5, 5, 9]
        target_logits_indices = np.repeat(
1618
1619
            cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens
        )
1620
1621
1622
1623
1624
        # [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(
1625
1626
            self.device, non_blocking=True
        )
1627
1628
1629
        cu_num_sampled_tokens = torch.from_numpy(cu_num_sampled_tokens).to(
            self.device, non_blocking=True
        )
1630
1631
1632
        logits_indices = torch.from_numpy(logits_indices).to(
            self.device, non_blocking=True
        )
1633
        target_logits_indices = torch.from_numpy(target_logits_indices).to(
1634
1635
            self.device, non_blocking=True
        )
1636
        bonus_logits_indices = torch.from_numpy(bonus_logits_indices).to(
1637
1638
            self.device, non_blocking=True
        )
1639

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

1645
        return SpecDecodeMetadata(
1646
1647
1648
            draft_token_ids=draft_token_ids,
            num_draft_tokens=num_draft_tokens.tolist(),
            cu_num_draft_tokens=cu_num_draft_tokens,
1649
            cu_num_sampled_tokens=cu_num_sampled_tokens,
1650
1651
1652
1653
1654
            target_logits_indices=target_logits_indices,
            bonus_logits_indices=bonus_logits_indices,
            logits_indices=logits_indices,
        )

1655
1656
1657
1658
1659
1660
1661
    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
1662
        self.kv_sharing_fast_prefill_logits_indices[:num_logits].copy_(logits_indices)
1663
1664
1665
1666
1667
        # 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_(
1668
1669
1670
1671
1672
1673
            logits_indices[-1].item()
        )
        if (
            self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
            and num_logits <= self.cudagraph_batch_sizes[-1]
        ):
1674
1675
1676
1677
1678
            # 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
1679
1680
1681
        logits_indices_padded = self.kv_sharing_fast_prefill_logits_indices[
            :num_logits_padded
        ]
1682
1683
        return logits_indices_padded

1684
1685
1686
1687
1688
1689
1690
1691
    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
1692
                inputs.
1693
1694
1695
1696
1697
1698

        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
        """
1699
1700
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
1701
            return [], []
1702
        # Batch the multi-modal inputs.
1703
        mm_kwargs = list[MultiModalKwargsItem]()
1704
1705
        # list of tuple (mm_hash, position_info)
        mm_hashes_pos = list[tuple[str, PlaceholderRange]]()
1706
1707
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
1708
1709

            for mm_input_id in encoder_input_ids:
1710
1711
1712
1713
                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))
1714

1715
1716
1717
1718
1719
        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(
1720
1721
            scheduler_output
        )
1722
1723
1724
1725

        if not mm_kwargs:
            return

1726
1727
1728
1729
1730
1731
1732
        # 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.
1733
        model = cast(SupportsMultiModal, self.model)
1734
        encoder_outputs = []
1735
        for modality, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
1736
1737
1738
1739
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
1740
        ):
1741
1742
1743
            curr_group_outputs = []

            # EVS-related change.
1744
            # (ekhvedchenia): Temporary hack to limit peak memory usage when
1745
            # processing multimodal data. This solves the issue with scheduler
1746
1747
1748
1749
            # 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)
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
            # 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,
                        )
1767
                    )
1768
1769

                    micro_batch_outputs = model.get_multimodal_embeddings(
1770
1771
                        **micro_batch_mm_inputs
                    )
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781

                    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.
1782
                curr_group_outputs = model.get_multimodal_embeddings(**mm_kwargs_group)
1783

1784
1785
            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
1786
                expected_num_items=num_items,
1787
            )
1788
            encoder_outputs.extend(curr_group_outputs)
1789

1790
1791
1792
        # 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(
1793
1794
1795
1796
1797
                output,
                is_embed=pos_info.is_embed,
            )

    def _gather_mm_embeddings(
1798
1799
        self,
        scheduler_output: "SchedulerOutput",
1800
        shift_computed_tokens: int = 0,
1801
1802
1803
1804
1805
1806
1807
1808
    ) -> 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
1809
        should_sync_mrope_positions = False
1810

1811
        for req_id in self.input_batch.req_ids:
1812
1813
            mm_embeds_req: list[torch.Tensor] = []

1814
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1815
            req_state = self.requests[req_id]
1816
            num_computed_tokens = req_state.num_computed_tokens + shift_computed_tokens
1817

1818
1819
            for mm_feature in req_state.mm_features:
                pos_info = mm_feature.mm_position
1820
1821
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
1822
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837

                # 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,
1838
1839
                    num_encoder_tokens,
                )
1840
                assert start_idx < end_idx
1841

1842
                mm_hash = mm_feature.identifier
1843
                encoder_output = self.encoder_cache.get(mm_hash, None)
1844
                assert encoder_output is not None, f"Encoder cache miss for {mm_hash}."
1845
1846
1847
1848

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

1849
                req_start_pos = req_start_idx + start_pos - num_computed_tokens
1850
1851
1852
                is_mm_embed[req_start_pos + start_idx : req_start_pos + end_idx] = (
                    True if is_embed is None else is_embed
                )
1853

1854
1855
1856
1857
                mm_embeds_item = gather_mm_placeholders(
                    encoder_output[start_idx:end_idx],
                    is_embed=is_embed,
                )
1858
1859
1860
                mm_embeds_req.append(mm_embeds_item)

            if self.is_multimodal_pruning_enabled and self.uses_mrope:
1861
                assert req_state.mrope_positions is not None
1862
1863
1864
1865
1866
1867
1868
                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,
1869
1870
                    )
                )
1871
1872
1873
1874
                req_state.mrope_positions.copy_(new_mrope_positions)
                req_state.mrope_position_delta = new_delta

            mm_embeds.extend(mm_embeds_req)
1875
1876
1877
            req_start_idx += num_scheduled_tokens

        is_mm_embed = self.is_mm_embed.copy_to_gpu(total_num_scheduled_tokens)
1878
1879
1880

        if should_sync_mrope_positions:
            self._calc_mrope_positions(scheduler_output)
1881
            self.mrope_positions.copy_to_gpu(total_num_scheduled_tokens)
1882

1883
        return mm_embeds, is_mm_embed
1884

1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
    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
1901
        model = cast(SupportsMultiModal, self.model)
1902
1903
        encoder_features = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
1904
1905
1906
1907
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
1908
1909
1910
1911
1912
1913
1914
1915
        ):
            # 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

1916
    def get_model(self) -> nn.Module:
1917
        # get raw model out of the cudagraph wrapper.
1918
        if isinstance(self.model, (CUDAGraphWrapper, UBatchWrapper)):
1919
            return self.model.unwrap()
1920
1921
        return self.model

1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
    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

1937
1938
1939
1940
1941
    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
        model = self.get_model()
        if not is_pooling_model(model):
            return []

1942
1943
        supported_tasks = list(model.pooler.get_supported_tasks())

1944
1945
1946
1947
1948
        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")
1949

1950
1951
            logger.debug_once(
                "Chunked prefill is not supported with "
1952
1953
                "token_embed and token_classify tasks "
                "which using ALL pooling. "
1954
1955
1956
                "Please turn off chunked prefill by "
                "`--no-enable-chunked-prefill` before using it."
            )
1957
1958
1959
1960
1961

        if "score" in supported_tasks:
            num_labels = getattr(self.model_config.hf_config, "num_labels", 0)
            if num_labels != 1:
                supported_tasks.remove("score")
1962
                logger.debug_once("Score API is only enabled for num_labels == 1.")
1963
1964

        return supported_tasks
1965

1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
    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)

1976
    def sync_and_slice_intermediate_tensors(
1977
1978
1979
1980
1981
        self,
        num_tokens: int,
        intermediate_tensors: IntermediateTensors,
        sync_self: bool,
    ) -> IntermediateTensors:
1982
1983
1984
        assert self.intermediate_tensors is not None

        tp = self.vllm_config.parallel_config.tensor_parallel_size
1985
        is_rs = is_residual_scattered_for_sp(self.vllm_config, num_tokens)
1986
1987
1988
1989
1990
1991

        # 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():
1992
                is_scattered = k == "residual" and is_rs
1993
                copy_len = num_tokens // tp if is_scattered else num_tokens
1994
                self.intermediate_tensors[k][:copy_len].copy_(
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
                    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:
2008
2009
2010
2011
2012
2013
2014
        """
        Step for the EPLB (Expert Parallelism Load Balancing) state.
        """
        if not self.parallel_config.enable_eplb:
            return

        assert self.eplb_state is not None
2015
2016
        model = self.get_model()
        assert is_mixture_of_experts(model)
2017
        self.eplb_state.step(
2018
            model,
2019
2020
            is_dummy,
            is_profile,
2021
            log_stats=self.parallel_config.eplb_config.log_balancedness,
2022
2023
        )

2024
2025
2026
2027
    # 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)
2028
2029
    @staticmethod
    def pad_out_ubatch_slice(ubatch_slices: UBatchSlices, num_total_tokens: int):
2030
2031
2032
2033
2034
2035
        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
        )
2036

2037
2038
2039
2040
2041
2042
    def _pool(
        self,
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
        num_scheduled_tokens_np: np.ndarray,
    ) -> ModelRunnerOutput:
2043
2044
2045
        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"
        )
2046

2047
        hidden_states = hidden_states[:num_scheduled_tokens]
2048
        pooling_metadata = self.input_batch.get_pooling_metadata()
2049
2050
2051
2052
        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]
2053

2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
        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()
2064

2065
        pooler_output: list[torch.Tensor | None] = []
2066
        for raw_output, seq_len, prompt_len in zip(
2067
2068
            raw_pooler_output, seq_lens_cpu, pooling_metadata.prompt_lens
        ):
2069
            output = raw_output if seq_len == prompt_len else None
2070
            pooler_output.append(output)
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080

        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,
        )

2081
    def _get_num_input_tokens(self, num_scheduled_tokens: int) -> int:
2082
2083
2084
2085
2086
2087
        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]
        ):
2088
2089
2090
2091
2092
2093
2094
2095
            # 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
2096
2097
2098
2099
        if (
            self.compilation_config.pass_config.enable_sequence_parallelism
            and tp_size > 1
        ):
2100
2101
2102
            return round_up(num_scheduled_tokens, tp_size)
        return num_scheduled_tokens

2103
    def _preprocess(
2104
2105
        self,
        scheduler_output: "SchedulerOutput",
2106
        num_input_tokens: int,  # Padded
2107
        intermediate_tensors: IntermediateTensors | None = None,
2108
2109
    ) -> tuple[
        int,
2110
2111
        torch.Tensor | None,
        torch.Tensor | None,
2112
        torch.Tensor,
2113
        IntermediateTensors | None,
2114
2115
        dict[str, Any],
    ]:
2116
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
2117
        is_first_rank = get_pp_group().is_first_rank
2118

2119
2120
        # _prepare_inputs may reorder the batch, so we must gather multi
        # modal outputs after that to ensure the correct order
2121
2122
        if (
            self.supports_mm_inputs
2123
            and is_first_rank
2124
2125
            and not self.model_config.is_encoder_decoder
        ):
2126
2127
            # Run the multimodal encoder if any.
            self._execute_mm_encoder(scheduler_output)
2128
            mm_embeds, is_mm_embed = self._gather_mm_embeddings(scheduler_output)
2129

2130
2131
2132
            # 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.
2133
            inputs_embeds_scheduled = self.model.get_input_embeddings(
2134
2135
2136
                self.input_ids.gpu[:num_scheduled_tokens],
                multimodal_embeddings=mm_embeds,
                is_multimodal=is_mm_embed,
2137
            )
2138

2139
            # TODO(woosuk): Avoid the copy. Optimize.
2140
            self.inputs_embeds.gpu[:num_scheduled_tokens].copy_(inputs_embeds_scheduled)
2141

2142
            input_ids = None
2143
            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
2144
2145
2146
2147
            model_kwargs = {
                **self._init_model_kwargs(num_scheduled_tokens),
                **self._extract_mm_kwargs(scheduler_output),
            }
2148
        elif self.enable_prompt_embeds and is_first_rank:
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
            # 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).
2161
2162
2163
            token_ids_idx = (
                self.is_token_ids.gpu[:num_scheduled_tokens]
                .nonzero(as_tuple=False)
2164
                .squeeze(1)
2165
            )
2166
2167
2168
            # Some tokens ids may need to become embeds
            if token_ids_idx.numel() > 0:
                token_ids = self.input_ids.gpu[token_ids_idx]
2169
                tokens_to_embeds = self.model.get_input_embeddings(input_ids=token_ids)
2170
2171
2172
2173
2174
                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
2175
        else:
2176
2177
2178
2179
            # 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.
2180
            input_ids = self.input_ids.gpu[:num_input_tokens]
2181
            inputs_embeds = None
2182
            model_kwargs = self._init_model_kwargs(num_input_tokens)
2183
        if self.uses_mrope:
2184
            positions = self.mrope_positions.gpu[:, :num_input_tokens]
2185
        else:
2186
            positions = self.positions.gpu[:num_input_tokens]
2187

2188
        if is_first_rank:
2189
2190
            intermediate_tensors = None
        else:
2191
            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
2192
2193
                num_input_tokens, intermediate_tensors, True
            )
2194

2195
2196
2197
2198
        if (
            self.model_config.is_encoder_decoder
            and scheduler_output.scheduled_encoder_inputs
        ):
2199
2200
2201
            encoder_inputs = self._extract_encoder_inputs(scheduler_output)
            model_kwargs.update(encoder_inputs)

2202
2203
2204
2205
2206
2207
2208
2209
        return (
            num_scheduled_tokens,
            input_ids,
            inputs_embeds,
            positions,
            intermediate_tensors,
            model_kwargs,
        )
2210

2211
    def _sample(
2212
        self,
2213
2214
        logits: torch.Tensor | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
2215
    ) -> SamplerOutput:
2216
        # Sample the next token and get logprobs if needed.
2217
        sampling_metadata = self.input_batch.sampling_metadata
2218
        if spec_decode_metadata is None:
2219
2220
2221
            # 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()
2222
            return self.sampler(
2223
2224
2225
                logits=logits,
                sampling_metadata=sampling_metadata,
            )
2226

2227
        sampler_output = self.rejection_sampler(
2228
2229
            spec_decode_metadata,
            None,  # draft_probs
2230
            logits,
2231
2232
            sampling_metadata,
        )
2233
        self._update_states_after_model_execute(sampler_output.sampled_token_ids)
2234
2235
2236
        return sampler_output

    def _bookkeeping_sync(
2237
2238
2239
        self,
        scheduler_output: "SchedulerOutput",
        sampler_output: SamplerOutput,
2240
        logits: torch.Tensor | None,
2241
2242
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
2243
        spec_decode_metadata: SpecDecodeMetadata | None,
2244
    ) -> tuple[
2245
        dict[str, int],
2246
        LogprobsLists | None,
2247
        list[list[int]],
2248
        dict[str, LogprobsTensors | None],
2249
2250
2251
        list[str],
        dict[str, int],
        list[int],
2252
    ]:
2253
2254
2255
2256
        num_nans_in_logits = {}
        if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
            num_nans_in_logits = self._get_nans_in_logits(logits)

2257
2258
2259
        discard_sampled_tokens_req_indices = self.discard_request_indices.np[
            : self.num_discarded_requests
        ]
2260
2261
2262
2263
        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)
2264

2265
2266
2267
        # 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()
2268
        req_id_to_index_output_copy = self.input_batch.req_id_to_index.copy()
2269
2270

        num_sampled_tokens = sampler_output.sampled_token_ids.shape[0]
2271
        sampled_token_ids = sampler_output.sampled_token_ids
2272
        invalid_req_indices = []
2273
2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
        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:
2287
                valid_sampled_token_ids[int(i)].clear()
2288
        else:
2289
            valid_sampled_token_ids = []
2290
            invalid_req_indices = discard_sampled_tokens_req_indices.tolist()
2291
2292
2293
2294
2295
2296
            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.
2297
            self.input_batch.prev_sampled_token_ids = sampled_token_ids
2298
2299
2300
2301
2302
            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
            }
2303

2304
2305
2306
2307
2308
        # 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.
2309
        req_ids = self.input_batch.req_ids
2310
2311
2312
2313
        logprobs_tensors = sampler_output.logprobs_tensors
        cu_num_accepted_tokens = (
            [0] if spec_decode_metadata and logprobs_tensors else None
        )
2314
2315
        for req_idx in range(num_sampled_tokens):
            if self.use_async_scheduling:
2316
                sampled_ids = [-1] if req_idx not in invalid_req_indices_set else None
2317
2318
            else:
                sampled_ids = valid_sampled_token_ids[req_idx]
2319
2320
2321
2322
2323
            if not sampled_ids:
                continue

            start_idx = self.input_batch.num_tokens_no_spec[req_idx]
            end_idx = start_idx + len(sampled_ids)
2324
2325
2326
2327
            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}"
2328
            )
2329

2330
2331
            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
2332
2333
            self.input_batch.num_tokens_no_spec[req_idx] = end_idx
            self.input_batch.num_tokens[req_idx] = end_idx
2334

2335
            req_id = req_ids[req_idx]
2336
2337
2338
            req_state = self.requests[req_id]
            req_state.output_token_ids.extend(sampled_ids)

2339
2340
2341
2342
2343
2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
2357
            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,
        )

2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
        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,
        )

2368
2369
2370
2371
2372
2373
2374
2375
2376
2377
2378
2379
2380
2381
2382
    @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()

2383
2384
    def _model_forward(
        self,
2385
2386
2387
2388
        input_ids: torch.Tensor | None = None,
        positions: torch.Tensor | None = None,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
2389
2390
2391
2392
2393
        **model_kwargs: dict[str, Any],
    ) -> Any:
        """Helper method to call the model forward pass.

        This method can be overridden by subclasses for model execution.
2394
        Motivation: We can inspect only this method versus
2395
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
        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,
        )

2415
2416
2417
2418
    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
2419
2420
        intermediate_tensors: IntermediateTensors | None = None,
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput | IntermediateTensors:
2421
        with record_function_or_nullcontext("Preprocess"):
2422
2423
2424
2425
2426
2427
2428
2429
2430
            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(
2431
2432
                        scheduler_output, self.vllm_config
                    )
2433
2434
2435
2436
                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 "
2437
2438
                        "it when the requests need prompt logprobs"
                    )
2439

2440
                # Prepare the decoder inputs.
2441
2442
2443
2444
2445
2446
2447
2448
                (
                    attn_metadata,
                    logits_indices,
                    spec_decode_metadata,
                    num_scheduled_tokens_np,
                    spec_decode_common_attn_metadata,
                    max_query_len,
                    ubatch_slices,
2449
                    num_tokens_across_dp,
2450
2451
                    use_cascade_attn,
                ) = self._prepare_inputs(scheduler_output)
2452

2453
            dp_rank = self.parallel_config.data_parallel_rank
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            if ubatch_slices:
                assert num_tokens_across_dp is not None
2456
                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:
2459
                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(
2473
                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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2486
            )
            cudagraph_runtime_mode, batch_descriptor = (
                self.cudagraph_dispatcher.dispatch(batch_descriptor, use_cascade_attn)
            )
2487

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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]
            )
2495
            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(
2504
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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,
2510
                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,
        ):
2515
            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:
2525
                # True when EAGLE 3 is used.
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                hidden_states, aux_hidden_states = model_output
            else:
2528
                # 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
2539

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                if self.is_pooling_model:
2541
                    # Return the pooling output.
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                    output = self._pool(
                        hidden_states, num_scheduled_tokens, num_scheduled_tokens_np
                    )
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                    output.kv_connector_output = kv_connector_output
                    return output
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2548

                sample_hidden_states = hidden_states[logits_indices]
2549
                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:
2555
                    all_gather_tensors = {
2556
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                        "residual": not is_residual_scattered_for_sp(
                            self.vllm_config, num_input_tokens
                        )
2559
                    }
2560
                    get_pp_group().send_tensor_dict(
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                        hidden_states.tensors,
                        all_gather_group=get_tp_group(),
2563
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                        all_gather_tensors=all_gather_tensors,
                    )
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                    logits = None
                else:
                    sample_hidden_states = hidden_states[logits_indices]
2568
                    logits = self.model.compute_logits(sample_hidden_states)
2569
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2571
2572
2573

                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)
2583
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2586

        with record_function_or_nullcontext("Sample"):
            sampler_output = self._sample(logits, spec_decode_metadata)

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2600
        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,
                )

2601
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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
        )
2606
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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
2609
2610
2611
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2613
        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
        ):
2614
            effective_drafter_max_model_len = (
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                self.speculative_config.draft_model_config.max_model_len
            )
2617
        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
        )
2622
        if use_padded_batch_for_eagle and input_fits_in_drafter:
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2624
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2626
            # 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
2638
2639
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2641
            ) = self._bookkeeping_sync(
                scheduler_output,
                sampler_output,
                logits,
                hidden_states,
                num_scheduled_tokens,
2642
                spec_decode_metadata,
2643
            )
2644

2645
2646
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        if (
            self.speculative_config
            and not use_padded_batch_for_eagle
            and input_fits_in_drafter
        ):
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2652
            # 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)
2653

2654
2655
        with record_function_or_nullcontext("EPLB"):
            self.eplb_step()
2656

2657
2658
2659
        output = ModelRunnerOutput(
            req_ids=req_ids_output_copy,
            req_id_to_index=req_id_to_index_output_copy,
2660
2661
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2663
            sampled_token_ids=valid_sampled_token_ids,
            logprobs=logprobs_lists,
            prompt_logprobs_dict=prompt_logprobs_dict,
            pooler_output=[],
2664
            kv_connector_output=kv_connector_output,
2665
2666
2667
            num_nans_in_logits=num_nans_in_logits,
        )

2668
2669
2670
        if not self.use_async_scheduling:
            return output

2671
        async_output = AsyncGPUModelRunnerOutput(
2672
            model_runner_output=output,
2673
            sampled_token_ids=sampler_output.sampled_token_ids,
2674
2675
2676
2677
            invalid_req_indices=invalid_req_indices,
            async_output_copy_stream=self.async_output_copy_stream,
        )

2678
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2680
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2682
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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

2687
    def take_draft_token_ids(self) -> DraftTokenIds | None:
2688
2689
2690
2691
2692
2693
2694
2695
2696
2697
        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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2700
    def propose_draft_token_ids(
        self,
        scheduler_output: "SchedulerOutput",
2701
        sampled_token_ids: torch.Tensor | list[list[int]],
2702
2703
2704
        sampling_metadata: SamplingMetadata,
        hidden_states: torch.Tensor,
        sample_hidden_states: torch.Tensor,
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2706
        aux_hidden_states: list[torch.Tensor] | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
2707
        common_attn_metadata: CommonAttentionMetadata,
2708
    ) -> list[list[int]] | torch.Tensor:
2709
2710
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
        if self.speculative_config.method == "ngram":
2711
            assert isinstance(sampled_token_ids, list)
2712
            assert isinstance(self.drafter, NgramProposer)
2713
            draft_token_ids = self.drafter.propose(
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2715
                sampled_token_ids,
                self.input_batch.req_ids,
2716
2717
                self.input_batch.num_tokens_no_spec,
                self.input_batch.token_ids_cpu,
2718
2719
                self.input_batch.spec_decode_unsupported_reqs,
            )
2720
        elif self.speculative_config.method == "medusa":
2721
            assert isinstance(sampled_token_ids, list)
2722
            assert isinstance(self.drafter, MedusaProposer)
2723

2724
2725
            if sample_hidden_states.shape[0] == len(sampled_token_ids):
                # The input to the target model does not include draft tokens.
2726
2727
2728
2729
                hidden_states = sample_hidden_states
            else:
                indices = []
                offset = 0
Wentao Ye's avatar
Wentao Ye committed
2730
                assert spec_decode_metadata is not None
2731
                for num_draft, tokens in zip(
2732
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                    spec_decode_metadata.num_draft_tokens, sampled_token_ids
                ):
2734
2735
                    indices.append(offset + len(tokens) - 1)
                    offset += num_draft + 1
2736
                indices = torch.tensor(indices, device=self.device)
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2738
                hidden_states = sample_hidden_states[indices]

2739
            draft_token_ids = self.drafter.propose(
2740
2741
2742
                target_hidden_states=hidden_states,
                sampling_metadata=sampling_metadata,
            )
2743
        elif self.speculative_config.use_eagle():
2744
            assert isinstance(self.drafter, EagleProposer)
2745
2746
2747
2748
2749

            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.
2750
2751
                assert isinstance(sampled_token_ids, list), (
                    "sampled_token_ids should be a python list when"
2752
                    "padded-batch is disabled."
2753
                )
2754
                next_token_ids = self.drafter.prepare_next_token_ids_cpu(
2755
2756
2757
2758
2759
                    sampled_token_ids,
                    self.requests,
                    self.input_batch,
                    scheduler_output.num_scheduled_tokens,
                )
2760
2761
2762
2763
2764
            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.
2765
2766
                assert isinstance(sampled_token_ids, torch.Tensor), (
                    "sampled_token_ids should be a torch.Tensor when"
2767
                    "padded-batch is enabled."
2768
2769
                )
                next_token_ids, valid_sampled_tokens_count = (
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2771
2772
2773
2774
2775
                    self.drafter.prepare_next_token_ids_padded(
                        common_attn_metadata,
                        sampled_token_ids,
                        self.requests,
                        self.input_batch,
                        self.discard_request_indices.gpu,
2776
                        self.num_discarded_requests,
2777
                    )
2778
                )
Jiayi Yao's avatar
Jiayi Yao committed
2779

2780
            if spec_decode_metadata is None:
2781
                token_indices_to_sample = None
2782
                # input_ids can be None for multimodal models.
2783
                target_token_ids = self.input_ids.gpu[:num_scheduled_tokens]
2784
                target_positions = self._get_positions(num_scheduled_tokens)
2785
                if self.use_aux_hidden_state_outputs:
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Wentao Ye committed
2786
                    assert aux_hidden_states is not None
2787
                    target_hidden_states = torch.cat(
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2789
                        [h[:num_scheduled_tokens] for h in aux_hidden_states], dim=-1
                    )
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2791
                else:
                    target_hidden_states = hidden_states[:num_scheduled_tokens]
2792
            else:
2793
2794
                if self.speculative_config.disable_padded_drafter_batch:
                    token_indices_to_sample = None
2795
2796
2797
2798
2799
                    common_attn_metadata, token_indices = self.drafter.prepare_inputs(
                        common_attn_metadata,
                        sampled_token_ids,
                        spec_decode_metadata.num_draft_tokens,
                    )
2800
                else:
2801
                    common_attn_metadata, token_indices, token_indices_to_sample = (
2802
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2804
                        self.drafter.prepare_inputs_padded(
                            common_attn_metadata,
                            spec_decode_metadata,
2805
2806
2807
                            valid_sampled_tokens_count,
                        )
                    )
2808

2809
                target_token_ids = self.input_ids.gpu[token_indices]
2810
                target_positions = self._get_positions(token_indices)
2811
                if self.use_aux_hidden_state_outputs:
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Wentao Ye committed
2812
                    assert aux_hidden_states is not None
2813
                    target_hidden_states = torch.cat(
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2815
                        [h[token_indices] for h in aux_hidden_states], dim=-1
                    )
2816
2817
                else:
                    target_hidden_states = hidden_states[token_indices]
2818

2819
            if self.supports_mm_inputs:
2820
2821
2822
2823
2824
2825
                mm_embed_inputs = self._gather_mm_embeddings(
                    scheduler_output,
                    shift_computed_tokens=1,
                )
            else:
                mm_embed_inputs = None
2826

2827
            draft_token_ids = self.drafter.propose(
2828
2829
2830
2831
                target_token_ids=target_token_ids,
                target_positions=target_positions,
                target_hidden_states=target_hidden_states,
                next_token_ids=next_token_ids,
2832
                last_token_indices=token_indices_to_sample,
2833
                sampling_metadata=sampling_metadata,
2834
                common_attn_metadata=common_attn_metadata,
2835
                mm_embed_inputs=mm_embed_inputs,
2836
            )
2837

2838
        return draft_token_ids
2839

2840
2841
2842
    def update_config(self, overrides: dict[str, Any]) -> None:
        allowed_config_names = {"load_config", "model_config"}
        for config_name, config_overrides in overrides.items():
2843
2844
            assert config_name in allowed_config_names, (
                f"Config `{config_name}` not supported. "
2845
                f"Allowed configs: {allowed_config_names}"
2846
            )
2847
2848
2849
2850
            config = getattr(self, config_name)
            new_config = update_config(config, config_overrides)
            setattr(self, config_name, new_config)

2851
2852
2853
2854
2855
    def load_model(self, eep_scale_up: bool = False) -> None:
        """
        Args:
            eep_scale_up: the model loading is for elastic EP scale up.
        """
2856
        logger.info("Starting to load model %s...", self.model_config.model)
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
2918
2919
2920
2921
        logger.info(
            "Model loading took %.4f GiB and %.6f seconds",
            self.model_memory_usage / GiB_bytes,
            time_after_load - time_before_load,
        )
2922
        prepare_communication_buffer_for_model(self.model)
2923

2924
        self.is_multimodal_pruning_enabled = (
2925
            supports_multimodal_pruning(self.get_model())
2926
2927
            and self.model_config.multimodal_config.is_multimodal_pruning_enabled()
        )
2928

2929
2930
        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)
2931
2932
2933
2934
            self.eplb_state = EplbState.build(
                self.model,
                self.device,
                self.parallel_config,
2935
2936
2937
                global_expert_load,
                old_global_expert_indices,
                rank_mapping,
2938
2939
            )

2940
        if (
2941
2942
            self.vllm_config.compilation_config.mode
            == CompilationMode.STOCK_TORCH_COMPILE
2943
            and supports_dynamo()
2944
        ):
2945
            backend = self.vllm_config.compilation_config.init_backend(self.vllm_config)
2946
            compilation_counter.stock_torch_compile_count += 1
2947
            self.model.compile(fullgraph=True, backend=backend)
2948
            return
2949
        # for other compilation modes, cudagraph behavior is controlled by
2950
2951
2952
        # CudagraphWraper and CudagraphDispatcher of vllm.

        # wrap the model with full cudagraph wrapper if needed.
2953
2954
2955
2956
2957
2958
2959
        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
            )
2960
2961
        elif self.parallel_config.enable_dbo:
            if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
2962
2963
2964
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.FULL, self.device
                )
2965
            else:
2966
2967
2968
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.NONE, self.device
                )
2969

2970
    def _get_eagle3_aux_layers_from_config(self) -> tuple[int, ...] | None:
2971
2972
2973
2974
2975
2976
2977
2978
2979
2980
2981
2982
2983
2984
2985
2986
2987
2988
2989
2990
2991
2992
2993
        """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

2994
    def reload_weights(self) -> None:
2995
        assert getattr(self, "model", None) is not None, (
2996
            "Cannot reload weights before model is loaded."
2997
        )
2998
2999
        model_loader = get_model_loader(self.load_config)
        logger.info("Reloading weights inplace...")
3000
        model_loader.load_weights(self.get_model(), model_config=self.model_config)
3001

3002
3003
3004
3005
3006
    def save_tensorized_model(
        self,
        tensorizer_config: "TensorizerConfig",
    ) -> None:
        TensorizerLoader.save_model(
3007
            self.get_model(),
3008
            tensorizer_config=tensorizer_config,
3009
            model_config=self.model_config,
3010
3011
        )

3012
3013
3014
    def _get_prompt_logprobs_dict(
        self,
        hidden_states: torch.Tensor,
3015
        num_scheduled_tokens: dict[str, int],
3016
    ) -> dict[str, LogprobsTensors | None]:
3017
3018
3019
3020
        num_prompt_logprobs_dict = self.input_batch.num_prompt_logprobs
        if not num_prompt_logprobs_dict:
            return {}

3021
        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
3022
        prompt_logprobs_dict: dict[str, LogprobsTensors | None] = {}
3023
3024
3025
3026
3027

        # 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():
3028
            num_tokens = num_scheduled_tokens[req_id]
3029
3030
3031

            # Get metadata for this request.
            request = self.requests[req_id]
3032
3033
3034
3035
            if request.prompt_token_ids is None:
                # Prompt logprobs is incompatible with prompt embeddings
                continue

3036
3037
            num_prompt_tokens = len(request.prompt_token_ids)
            prompt_token_ids = torch.tensor(request.prompt_token_ids).to(
3038
3039
                self.device, non_blocking=True
            )
3040

3041
3042
3043
3044
3045
3046
            # 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(
3047
3048
                    num_prompt_tokens - 1, num_prompt_logprobs + 1
                )
3049
3050
                in_progress_dict[req_id] = logprobs_tensors

3051
            # Determine number of logits to retrieve.
3052
3053
            start_idx = request.num_computed_tokens
            start_tok = start_idx + 1
3054
            num_remaining_tokens = num_prompt_tokens - start_tok
3055
            if num_tokens <= num_remaining_tokens:
3056
                # This is a chunk, more tokens remain.
3057
3058
3059
                # 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.
3060
3061
3062
3063
3064
                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)
3065
3066
3067
3068
3069
3070
3071
                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
3072
3073
3074
3075
3076

            # 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]
3077
            offset = self.query_start_loc.np[req_idx].item()
3078
            prompt_hidden_states = hidden_states[offset : offset + num_logits]
3079
            logits = self.model.compute_logits(prompt_hidden_states)
3080
3081
3082
3083

            # 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.
3084
            tgt_token_ids = prompt_token_ids[start_tok : start_tok + num_logits]
3085
3086

            # Compute prompt logprobs.
3087
3088
            logprobs = self.sampler.compute_logprobs(logits)
            token_ids, logprobs, ranks = self.sampler.gather_logprobs(
3089
3090
                logprobs, num_prompt_logprobs, tgt_token_ids
            )
3091
3092

            # Transfer GPU->CPU async.
3093
3094
            chunk_slice = slice(start_idx, start_idx + num_logits)
            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
3095
3096
3097
                token_ids, non_blocking=True
            )
            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs, non_blocking=True)
3098
            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
3099
3100
                ranks, non_blocking=True
            )
3101
3102
3103
3104
3105

        # 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]
3106
            del in_progress_dict[req_id]
3107
3108

        # Must synchronize the non-blocking GPU->CPU transfers.
3109
        if prompt_logprobs_dict:
3110
            self._sync_device()
3111
3112
3113

        return prompt_logprobs_dict

3114
3115
    def _get_nans_in_logits(
        self,
3116
        logits: torch.Tensor | None,
3117
3118
3119
3120
3121
3122
3123
3124
3125
3126
3127
    ) -> 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])
3128
3129
3130
                    if num_nans_for_index is not None and req_index < logits.shape[0]
                    else 0
                )
3131
3132
3133
3134
            return num_nans_in_logits
        except IndexError:
            return {}

3135
3136
3137
3138
3139
3140
    @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
3141
         - during DP rank dummy run
3142
3143
3144
3145
3146
3147
3148
3149
3150
3151
3152
        """
        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(
3153
                    self.input_ids.gpu,
3154
3155
                    low=0,
                    high=self.model_config.get_vocab_size(),
3156
3157
                    dtype=input_ids.dtype,
                )
3158

3159
            logger.debug_once("Randomizing dummy data for DP Rank")
3160
            input_ids.copy_(rand_input_ids()[: input_ids.size(0)], non_blocking=True)
3161
3162
3163
            yield
            input_ids.fill_(0)

3164
3165
3166
3167
3168
3169
    def _get_mm_dummy_batch(
        self,
        modality: str,
        max_items_per_batch: int,
    ) -> BatchedTensorInputs:
        """Dummy data for profiling and precompiling multimodal models."""
3170
3171
        assert self.mm_budget is not None

3172
3173
        dummy_decoder_data = self.mm_registry.get_decoder_dummy_data(
            model_config=self.model_config,
3174
            seq_len=self.max_model_len,
3175
            mm_counts={modality: 1},
3176
            cache=self.mm_budget.cache,
3177
3178
3179
3180
        )
        dummy_mm_data = dummy_decoder_data.multi_modal_data

        # Result in the maximum GPU consumption of the model
3181
3182
        dummy_mm_item = dummy_mm_data[modality][0]
        dummy_mm_items = [dummy_mm_item] * max_items_per_batch
3183

3184
        model = cast(SupportsMultiModal, self.model)
3185
3186
3187
3188
3189
3190
3191
3192
3193
        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,
            )
        )
3194

3195
3196
3197
3198
    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
3199
        cudagraph_runtime_mode: CUDAGraphMode | None = None,
3200
3201
        force_attention: bool = False,
        uniform_decode: bool = False,
3202
        allow_microbatching: bool = True,
3203
3204
        skip_eplb: bool = False,
        is_profile: bool = False,
3205
        create_mixed_batch: bool = False,
3206
        remove_lora: bool = True,
3207
        activate_lora: bool = False,
3208
    ) -> tuple[torch.Tensor, torch.Tensor]:
3209
3210
3211
3212
3213
3214
3215
        """
        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.
3216
                - if not set will determine the cudagraph mode based on using
3217
                    the self.cudagraph_dispatcher.
3218
3219
3220
3221
                - CUDAGraphMode.NONE: No cudagraph, for warm up and profile run
                - CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
                - CUDAGraphMode.FULL: Full cudagraph, attention metadata is
                    needed.
3222
            force_attention: If True, always create attention metadata. Used to
3223
3224
3225
3226
                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.
3227
3228
            create_mixed_batch: If True, create a mixed batch with both decode
                (1 token) and prefill (multiple tokens) requests.
3229
            remove_lora: If False, dummy LoRAs are not destroyed after the run
3230
            activate_lora: If False, dummy_run is performed without LoRAs.
3231
        """
3232
3233
3234
3235
        assert (
            cudagraph_runtime_mode is None
            or cudagraph_runtime_mode.valid_runtime_modes()
        )
3236

3237
        # If cudagraph_mode.decode_mode() == FULL and
3238
        # cudagraph_mode.separate_routine(). This means that we are using
3239
3240
3241
3242
3243
3244
3245
3246
3247
3248
3249
        # 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.
3250
        max_query_len = self.uniform_decode_query_len if uniform_decode else num_tokens
3251

3252
3253
3254
3255
3256
        # 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
3257
3258
3259
3260
        if create_mixed_batch:
            assert not uniform_decode
            # Create mixed batch:
            # first half decode tokens, second half one prefill
3261
            num_decode_tokens = min(max_num_reqs - 1, num_tokens // 2)
3262
3263
3264
3265
            num_prefill_tokens = num_tokens - num_decode_tokens
            num_reqs = num_decode_tokens + 1

            # Create decode requests (1 token each) followed by prefill request
3266
            num_scheduled_tokens_list = [1] * num_decode_tokens + [num_prefill_tokens]
3267
3268
3269
            # Note: Overriding max_query_len to be the prefill tokens
            max_query_len = num_prefill_tokens
        elif uniform_decode:
3270
            assert not create_mixed_batch
3271
            num_reqs = min(max_num_reqs, cdiv(num_tokens, max_query_len))
3272
3273
            num_scheduled_tokens_list = [max_query_len] * num_reqs
            if num_tokens % max_query_len != 0:
3274
                num_scheduled_tokens_list[-1] = num_tokens % max_query_len
3275
3276
3277
3278
3279
3280
        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

3281
3282
        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs
3283
        num_scheduled_tokens = np.array(num_scheduled_tokens_list, dtype=np.int32)
3284
        total_num_scheduled_tokens = int(num_scheduled_tokens.sum())
3285

3286
3287
3288
        # Disable DP padding when running eager
        allow_dp_padding = self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE

3289
3290
        # We currently only microbatch if the number of tokens is
        # over a certain threshold.
3291
        ubatch_slices, num_tokens_across_dp = coordinate_batch_across_dp(
3292
3293
3294
3295
3296
3297
3298
            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,
3299
3300
3301
        )
        num_tokens_after_padding = num_tokens
        if num_tokens_across_dp is not None:
3302
3303
            dp_rank = self.parallel_config.data_parallel_rank
            num_tokens_after_padding = int(num_tokens_across_dp[dp_rank])
3304

3305
        attn_metadata: PerLayerAttnMetadata | None = None
3306
3307
3308

        # If force_attention is True, we always capture attention. Otherwise,
        # it only happens for cudagraph_runtime_mode=FULL.
3309
        if force_attention or cudagraph_runtime_mode == CUDAGraphMode.FULL:
3310
            attn_metadata = {}
3311
3312
            if ubatch_slices is not None:
                attn_metadata = [dict() for _ in range(len(ubatch_slices))]
3313

3314
3315
3316
3317
3318
3319
            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:
3320
                seq_lens = max_query_len
3321
            self.seq_lens.np[:num_reqs] = seq_lens
3322
3323
            self.seq_lens.np[num_reqs:] = 0
            self.seq_lens.copy_to_gpu()
3324

3325
3326
            cum_num_tokens, _ = self._get_cumsum_and_arange(num_scheduled_tokens)
            self.query_start_loc.np[1 : num_reqs + 1] = cum_num_tokens
3327
3328
            self.query_start_loc.copy_to_gpu()

3329
            for kv_cache_group_id, kv_cache_group_spec in enumerate(
3330
3331
                self.kv_cache_config.kv_cache_groups
            ):
3332
                common_attn_metadata = CommonAttentionMetadata(
3333
3334
                    query_start_loc=self.query_start_loc.gpu[: num_reqs + 1],
                    query_start_loc_cpu=self.query_start_loc.cpu[: num_reqs + 1],
3335
3336
                    seq_lens=self.seq_lens.gpu[:num_reqs],
                    seq_lens_cpu=self.seq_lens.cpu[:num_reqs],
3337
3338
3339
                    num_computed_tokens_cpu=self.input_batch.num_computed_tokens_cpu_tensor[
                        :num_reqs
                    ],
3340
3341
                    num_reqs=num_reqs,
                    num_actual_tokens=num_tokens,
3342
                    max_query_len=max_query_len,
3343
                    max_seq_len=self.max_model_len,
3344
3345
3346
                    block_table_tensor=self.input_batch.block_table[
                        kv_cache_group_id
                    ].get_device_tensor(num_reqs),
3347
                    slot_mapping=self.input_batch.block_table[
3348
3349
3350
                        kv_cache_group_id
                    ].slot_mapping.gpu[:num_tokens],
                    causal=True,
3351
3352
3353
                    dcp_local_seq_lens=self.dcp_local_seq_lens.gpu[:num_reqs]
                    if self.dcp_world_size > 1
                    else None,
3354
                )
3355
                for attn_group in self.attn_groups[kv_cache_group_id]:
3356
3357
                    if ubatch_slices is not None:
                        common_attn_metadata_list = split_attn_metadata(
3358
3359
                            ubatch_slices, common_attn_metadata
                        )
3360
                        for ubid, common_attn_metadata in enumerate(
3361
3362
                            common_attn_metadata_list
                        ):
3363
                            assert common_attn_metadata.max_query_len == 1
3364
3365
3366
                            attn_metadata_i = attn_group.get_metadata_builder(
                                ubatch_id=ubid
                            ).build_for_cudagraph_capture(common_attn_metadata)
3367
                            for layer_name in attn_group.layer_names:
3368
                                assert type(attn_metadata) is list
3369
                                attn_metadata[ubid][layer_name] = attn_metadata_i
3370
3371
                    else:
                        assert type(attn_metadata) is dict
3372
3373
                        metadata_builder = attn_group.get_metadata_builder()
                        attn_metadata_i = metadata_builder.build_for_cudagraph_capture(
3374
3375
                            common_attn_metadata
                        )
3376
                        for layer_name in attn_group.layer_names:
3377
                            attn_metadata[layer_name] = attn_metadata_i
3378

3379
        with self.maybe_dummy_run_with_lora(
3380
            self.lora_config, num_scheduled_tokens, activate_lora, remove_lora
3381
        ):
3382
3383
3384
            # 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)
3385
            if self.supports_mm_inputs and not self.model_config.is_encoder_decoder:
3386
                input_ids = None
3387
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_after_padding]
3388
                model_kwargs = {
3389
                    **model_kwargs,
3390
3391
                    **self._dummy_mm_kwargs(num_reqs),
                }
3392
3393
            elif self.enable_prompt_embeds:
                input_ids = None
3394
3395
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_after_padding]
                model_kwargs = self._init_model_kwargs(num_tokens_after_padding)
3396
            else:
3397
                input_ids = self.input_ids.gpu[:num_tokens_after_padding]
3398
                inputs_embeds = None
3399

3400
            if self.uses_mrope:
3401
                positions = self.mrope_positions.gpu[:, :num_tokens_after_padding]
3402
            else:
3403
                positions = self.positions.gpu[:num_tokens_after_padding]
3404
3405
3406
3407
3408
3409
3410
3411
3412

            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,
3413
3414
3415
                            device=self.device,
                        )
                    )
3416
3417

                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
3418
                    num_tokens_after_padding, None, False
3419
                )
3420
3421

            # filter out the valid batch descriptor
3422
3423
3424
3425
3426
            _cg_mode, batch_descriptor = (
                self.cudagraph_dispatcher.dispatch(
                    BatchDescriptor(
                        num_tokens=num_tokens_after_padding,
                        uniform_decode=uniform_decode,
3427
                        has_lora=activate_lora and self.lora_config is not None,
3428
3429
3430
3431
3432
                    )
                )
                if not is_profile
                else (CUDAGraphMode.NONE, None)
            )
3433
3434
3435
            if cudagraph_runtime_mode is not None:
                # we allow forcing NONE when the dispatcher disagrees to support
                # warm ups for cudagraph capture
3436
3437
3438
3439
                assert (
                    cudagraph_runtime_mode == CUDAGraphMode.NONE
                    or cudagraph_runtime_mode == _cg_mode
                ), (
3440
                    f"Cudagraph runtime mode mismatch at dummy_run. "
3441
3442
                    f"Expected {_cg_mode}, but got {cudagraph_runtime_mode}."
                )
3443
3444
            else:
                cudagraph_runtime_mode = _cg_mode
3445

3446
            if ubatch_slices is not None:
3447
3448
3449
3450
3451
3452
3453
                # 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

3454
3455
3456
            with (
                self.maybe_randomize_inputs(input_ids),
                set_forward_context(
3457
3458
                    attn_metadata,
                    self.vllm_config,
3459
                    num_tokens=num_tokens_after_padding,
3460
3461
                    num_tokens_across_dp=num_tokens_across_dp,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
3462
                    batch_descriptor=batch_descriptor,
3463
3464
3465
                    ubatch_slices=ubatch_slices,
                ),
            ):
3466
                outputs = self.model(
3467
3468
3469
3470
                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
3471
                    **model_kwargs,
3472
                )
3473

3474
3475
3476
3477
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
3478

3479
            if self.speculative_config and self.speculative_config.use_eagle():
3480
                assert isinstance(self.drafter, EagleProposer)
3481
3482
3483
3484
                use_cudagraphs = (
                    cudagraph_runtime_mode == CUDAGraphMode.PIECEWISE
                    and not self.speculative_config.enforce_eager
                )
3485
                self.drafter.dummy_run(num_tokens, use_cudagraphs=use_cudagraphs)
3486

3487
3488
3489
3490
3491
3492
3493
3494
3495
3496
        # 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)

3497
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
3498
        return hidden_states, hidden_states[logit_indices]
3499
3500
3501
3502
3503
3504

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
3505
3506
3507
3508
        # 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)
3509

3510
        logits = self.model.compute_logits(hidden_states)
3511
3512
        num_reqs = logits.size(0)

3513
        dummy_tensors = lambda v: torch.full((num_reqs,), v, device=self.device)
3514
3515
3516
3517
3518
3519
3520
3521
3522
3523
3524
3525
3526
3527
3528

        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)],
3529
            spec_token_ids=[[] for _ in range(num_reqs)],
3530
3531
            allowed_token_ids_mask=None,
            bad_words_token_ids={},
3532
            logitsprocs=LogitsProcessors(),
3533
        )
3534
        try:
3535
3536
3537
            sampler_output = self.sampler(
                logits=logits, sampling_metadata=dummy_metadata
            )
3538
        except RuntimeError as e:
3539
            if "out of memory" in str(e):
3540
3541
3542
3543
                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 "
3544
3545
                    "initializing the engine."
                ) from e
3546
3547
            else:
                raise e
3548
        if self.speculative_config:
3549
3550
            draft_token_ids = [[0] for _ in range(num_reqs)]
            dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
3551
3552
                draft_token_ids, self.device
            )
3553
3554
3555
3556
3557
3558

            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
3559
3560
3561
3562
3563
            logits = torch.randn(
                num_tokens + num_reqs,
                logits.shape[-1],
                device=self.device,
                dtype=logits.dtype,
3564
            )
3565
3566
3567
            self.rejection_sampler(
                dummy_spec_decode_metadata,
                draft_probs,
3568
                logits,
3569
3570
                dummy_metadata,
            )
3571
        return sampler_output
3572

3573
    def _dummy_pooler_run_task(
3574
3575
        self,
        hidden_states: torch.Tensor,
3576
3577
        task: PoolingTask,
    ) -> PoolerOutput:
3578
3579
3580
3581
3582
3583
3584
3585
3586
3587
3588
        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

3589
        dummy_prompt_lens = torch.tensor(
3590
3591
            num_scheduled_tokens_list,
            device="cpu",
3592
        )
3593
3594
3595
        dummy_token_ids = torch.zeros(
            (num_reqs, req_num_tokens), dtype=torch.int32, device=self.device
        )
3596

3597
        model = cast(VllmModelForPooling, self.get_model())
3598
        dummy_pooling_params = PoolingParams(task=task)
3599
        dummy_pooling_params.verify(task=task, model_config=self.model_config)
3600
        to_update = model.pooler.get_pooling_updates(task)
3601
3602
        to_update.apply(dummy_pooling_params)

3603
        dummy_metadata = PoolingMetadata(
3604
3605
3606
3607
            prompt_lens=dummy_prompt_lens,
            prompt_token_ids=dummy_token_ids,
            pooling_params=[dummy_pooling_params] * num_reqs,
        )
3608

3609
3610
3611
        dummy_metadata.build_pooling_cursor(
            num_scheduled_tokens_list, device=hidden_states.device
        )
3612

3613
        try:
3614
3615
3616
            return model.pooler(
                hidden_states=hidden_states, pooling_metadata=dummy_metadata
            )
3617
        except RuntimeError as e:
3618
            if "out of memory" in str(e):
3619
                raise RuntimeError(
3620
3621
3622
                    "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 "
3623
3624
                    "initializing the engine."
                ) from e
3625
3626
            else:
                raise e
3627
3628
3629
3630
3631
3632
3633

    @torch.inference_mode()
    def _dummy_pooler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> PoolerOutput:
        # Find the task that has the largest output for subsequent steps
3634
3635
3636
3637
3638
3639
3640
3641
3642
3643
3644
3645
3646
3647
3648
3649
3650
3651
3652
3653
        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."
                )

3654
        output_size = dict[PoolingTask, float]()
3655
        for task in supported_pooling_tasks:
3656
3657
            # Run a full batch with each task to ensure none of them OOMs
            output = self._dummy_pooler_run_task(hidden_states, task)
3658
            output_size[task] = sum(o.nbytes for o in output)
3659
3660
3661
3662
            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)
3663

3664
    def profile_run(self) -> None:
3665
        # Profile with multimodal encoder & encoder cache.
3666
        if self.supports_mm_inputs:
3667
            if self.model_config.multimodal_config.skip_mm_profiling:
3668
                logger.info(
3669
                    "Skipping memory profiling for multimodal encoder and "
3670
3671
                    "encoder cache."
                )
3672
3673
3674
3675
3676
3677
3678
3679
            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.
3680
                    dummy_modality = mm_budget.get_modality_with_max_tokens()
3681
3682
3683
                    max_mm_items_per_batch = mm_budget.max_items_per_batch_by_modality[
                        dummy_modality
                    ]
3684
3685
3686
3687
3688
3689
3690
3691
3692

                    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,
                    )
3693

3694
3695
3696
3697
3698
                    # Create dummy batch of multimodal inputs.
                    batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                        dummy_modality,
                        max_mm_items_per_batch,
                    )
3699

3700
                    # Run multimodal encoder.
3701
3702
3703
                    dummy_encoder_outputs = self.model.get_multimodal_embeddings(
                        **batched_dummy_mm_inputs
                    )
3704

3705
3706
3707
3708
                    sanity_check_mm_encoder_outputs(
                        dummy_encoder_outputs,
                        expected_num_items=max_mm_items_per_batch,
                    )
3709

3710
3711
3712
3713
3714
3715
3716
3717
3718
3719
                    # 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(
3720
3721
                                (encoder_budget, encoder_output_shape[-1])
                            )
3722
3723
3724
3725
3726
3727
                            num_tokens = output.shape[0]
                            expanded[:num_tokens].copy_(output)
                            expanded_outputs.append(expanded)

                        dummy_encoder_outputs = expanded_outputs

3728
                    # Cache the dummy encoder outputs.
3729
                    self.encoder_cache["tmp"] = dict(enumerate(dummy_encoder_outputs))
3730

3731
        # Add `is_profile` here to pre-allocate communication buffers
3732
3733
3734
        hidden_states, last_hidden_states = self._dummy_run(
            self.max_num_tokens, is_profile=True
        )
3735
        if get_pp_group().is_last_rank:
3736
3737
3738
3739
            if self.is_pooling_model:
                output = self._dummy_pooler_run(hidden_states)
            else:
                output = self._dummy_sampler_run(last_hidden_states)
3740
        else:
3741
            output = None
3742
        self._sync_device()
3743
        del hidden_states, output
3744
        self.encoder_cache.clear()
3745
        gc.collect()
3746

3747
    def capture_model(self) -> int:
3748
        if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
3749
            logger.warning(
3750
                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
3751
3752
                "ensure `cudagraph_mode` was not manually set to `NONE`"
            )
3753
            return 0
3754

3755
3756
        compilation_counter.num_gpu_runner_capture_triggers += 1

3757
3758
        start_time = time.perf_counter()

3759
3760
3761
3762
3763
3764
3765
3766
3767
3768
3769
3770
3771
3772
        @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()
3773
                    gc.collect()
3774

3775
3776
3777
        # 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.
3778
        set_cudagraph_capturing_enabled(True)
3779
        with freeze_gc(), graph_capture(device=self.device):
3780
            start_free_gpu_memory = torch.cuda.mem_get_info()[0]
3781
            cudagraph_mode = self.compilation_config.cudagraph_mode
3782
            assert cudagraph_mode is not None
3783
3784
3785
3786
3787
3788
3789
3790
3791

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

3792
3793
3794
            if cudagraph_mode.mixed_mode() != CUDAGraphMode.NONE:
                cudagraph_runtime_mode = cudagraph_mode.mixed_mode()

3795
3796
3797
                compilation_cases = list(
                    product(reversed(self.cudagraph_batch_sizes), lora_cases)
                )
3798
3799
3800
                self._capture_cudagraphs(
                    compilation_cases,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
3801
3802
                    uniform_decode=False,
                )
3803

3804
3805
            # Capture full cudagraph for uniform decode batches if we
            # don't already have full mixed prefill-decode cudagraphs.
3806
3807
3808
3809
3810
3811
3812
            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
                )
3813
                decode_cudagraph_batch_sizes = [
3814
3815
                    x
                    for x in self.cudagraph_batch_sizes
3816
                    if max_num_tokens >= x >= self.uniform_decode_query_len
3817
                ]
3818
3819
3820
                compilation_cases_decode = list(
                    product(reversed(decode_cudagraph_batch_sizes), lora_cases)
                )
3821
3822
3823
                self._capture_cudagraphs(
                    compilation_cases=compilation_cases_decode,
                    cudagraph_runtime_mode=CUDAGraphMode.FULL,
3824
3825
                    uniform_decode=True,
                )
3826

3827
3828
3829
            torch.cuda.synchronize()
            end_free_gpu_memory = torch.cuda.mem_get_info()[0]

3830
3831
3832
        # 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
3833
        # we may do lazy capturing in future that still allows capturing
3834
3835
        # after here.
        set_cudagraph_capturing_enabled(False)
3836
3837
3838
3839
3840

        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.
3841
3842
3843
3844
3845
        logger.info(
            "Graph capturing finished in %.0f secs, took %.2f GiB",
            elapsed_time,
            cuda_graph_size / (1 << 30),
        )
3846
        return cuda_graph_size
3847

3848
3849
    def _capture_cudagraphs(
        self,
3850
        compilation_cases: list[tuple[int, bool]],
3851
3852
3853
3854
3855
3856
3857
        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}"
3858
3859
3860
3861
3862
3863
3864
3865

        # 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",
3866
3867
3868
                    cudagraph_runtime_mode.name,
                ),
            )
3869

3870
        # We skip EPLB here since we don't want to record dummy metrics
3871
        for num_tokens, activate_lora in compilation_cases:
3872
            # We currently only capture ubatched graphs when its a FULL
3873
3874
3875
            # 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
3876
3877
3878
3879
            allow_microbatching = (
                self.parallel_config.enable_dbo
                and cudagraph_runtime_mode == CUDAGraphMode.FULL
                and uniform_decode
3880
3881
3882
3883
3884
                and check_ubatch_thresholds(
                    config=self.vllm_config.parallel_config,
                    num_tokens=num_tokens,
                    uniform_decode=uniform_decode,
                )
3885
            )
3886

3887
3888
3889
3890
3891
3892
            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.
3893
3894
3895
3896
3897
3898
3899
3900
3901
                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,
3902
                    activate_lora=activate_lora,
3903
3904
3905
3906
3907
3908
3909
3910
                )
            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,
3911
                activate_lora=activate_lora,
3912
            )
3913
        self.maybe_remove_all_loras(self.lora_config)
3914

3915
3916
3917
3918
    def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize the attention backends and attention metadata builders.
        """
3919
        assert len(self.attn_groups) == 0, "Attention backends are already initialized"
3920

3921
3922
3923
3924
3925
3926
        class AttentionGroupKey(NamedTuple):
            attn_backend: type[AttentionBackend]
            kv_cache_spec: KVCacheSpec

        def get_attn_backends_for_group(
            kv_cache_group_spec: KVCacheGroupSpec,
3927
        ) -> tuple[dict[AttentionGroupKey, list[str]], set[type[AttentionBackend]]]:
3928
            layers = get_layers_from_vllm_config(
3929
3930
                self.vllm_config, AttentionLayerBase, kv_cache_group_spec.layer_names
            )
3931
3932
            attn_backends = {}
            attn_backend_layers = defaultdict(list)
3933
            # Dedupe based on full class name; this is a bit safer than
3934
3935
3936
3937
            # 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"
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            )
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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
        )
4120

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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)
        """
4126
        for group in self._attn_group_iterator():
4127
            attn_metadata_builder_i = group.get_metadata_builder()
4128

4129
4130
            # check that if any backends reorder batches; that the reordering
            # is compatible (e.g., decode threshold is the same)
4131
            reorder_batch_threshold_i = attn_metadata_builder_i.reorder_batch_threshold
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4133
            if reorder_batch_threshold_i is not None:
                if self.reorder_batch_threshold is not None:
4134
                    if reorder_batch_threshold_i != self.reorder_batch_threshold:
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                        raise ValueError(
                            f"Attention backend reorders decodes with "
                            f"threshold {reorder_batch_threshold_i} but other "
                            f"backend uses threshold "
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4140
                            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)

4224
    def may_reinitialize_input_batch(self, kv_cache_config: KVCacheConfig) -> None:
4225
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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
4236
            if not isinstance(kv_cache_group.kv_cache_spec, EncoderOnlyAttentionSpec)
4237
        ]
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4240
4241
4242
4243
4244

        # 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
        ]:
4245
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4247
            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
4248
4249
                "for more details."
            )
4250
4251
            self.input_batch = InputBatch(
                max_num_reqs=self.max_num_reqs,
4252
                max_model_len=max(self.max_model_len, self.max_encoder_len),
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4254
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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,
4258
                kernel_block_sizes=kernel_block_sizes,
4259
                is_spec_decode=bool(self.vllm_config.speculative_config),
4260
                logitsprocs=self.input_batch.logitsprocs,
4261
                logitsprocs_need_output_token_ids=self.input_batch.logitsprocs_need_output_token_ids,
4262
                is_pooling_model=self.is_pooling_model,
4263
4264
                num_speculative_tokens=(
                    self.vllm_config.speculative_config.num_speculative_tokens
4265
4266
4267
                    if self.vllm_config.speculative_config
                    else 0
                ),
4268
4269
            )

4270
    def _allocate_kv_cache_tensors(
4271
4272
        self, kv_cache_config: KVCacheConfig
    ) -> dict[str, torch.Tensor]:
4273
        """
4274
4275
4276
        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.

4277
        Args:
4278
            kv_cache_config: The KV cache config
4279
        Returns:
4280
            dict[str, torch.Tensor]: A map between layer names to their
4281
            corresponding memory buffer for KV cache.
4282
        """
4283
4284
        kv_cache_raw_tensors: dict[str, torch.Tensor] = {}
        for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
4285
4286
4287
            tensor = torch.zeros(
                kv_cache_tensor.size, dtype=torch.int8, device=self.device
            )
4288
4289
4290
4291
4292
            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:
4293
4294
4295
4296
            for layer_name in group.layer_names:
                if layer_name in self.runner_only_attn_layers:
                    continue
                layer_names.add(layer_name)
4297
4298
4299
        assert layer_names == set(kv_cache_raw_tensors.keys()), (
            "Some layers are not correctly initialized"
        )
4300
4301
        return kv_cache_raw_tensors

4302
4303
4304
    def _attn_group_iterator(self) -> Iterator[AttentionGroup]:
        return itertools.chain.from_iterable(self.attn_groups)

4305
    def _kv_cache_spec_attn_group_iterator(self) -> Iterator[AttentionGroup]:
4306
4307
        if not self.kv_cache_config.kv_cache_groups:
            return
4308
4309
        for attn_groups in self.attn_groups:
            yield from attn_groups
4310

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4328
    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
        ):
4329
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4331
4332
4333
4334
            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):
4335
                continue
4336
            elif isinstance(kv_cache_spec, AttentionSpec):
4337
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4339
4340
4341
4342
4343
4344
4345
                # 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)
4346
            elif isinstance(kv_cache_spec, MambaSpec):
4347
4348
                # This is likely Mamba or other non-attention cache,
                # no splitting.
4349
                kernel_block_sizes.append(kv_cache_spec.block_size)
4350
4351
4352
4353
4354
4355
            else:
                raise NotImplementedError(
                    f"unknown kv cache spec {kv_cache_group.kv_cache_spec}"
                )
        return kernel_block_sizes

4356
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4360
    def _reshape_kv_cache_tensors(
        self,
        kv_cache_config: KVCacheConfig,
        kv_cache_raw_tensors: dict[str, torch.Tensor],
    ) -> dict[str, torch.Tensor]:
4361
        """
4362
        Reshape the KV cache tensors to the desired shape and dtype.
4363

4364
        Args:
4365
4366
            kv_cache_config: The KV cache config
            kv_cache_raw_tensors: The KV cache buffer of each layer, with
4367
                correct size but uninitialized shape.
4368
        Returns:
4369
            Dict[str, torch.Tensor]: A map between layer names to their
4370
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            corresponding memory buffer for KV cache.
        """
4372
        kv_caches: dict[str, torch.Tensor] = {}
4373
        has_attn, has_mamba = False, False
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4375
        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
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4377
            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
4382
                num_blocks = raw_tensor.numel() // kv_cache_spec.page_size_bytes
4383
                if isinstance(kv_cache_spec, AttentionSpec):
4384
                    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

4393
                    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,
                    )
4400
                    dtype = kv_cache_spec.dtype
4401
                    try:
4402
                        kv_cache_stride_order = attn_backend.get_kv_cache_stride_order()  # noqa: E501
4403
                        assert len(kv_cache_stride_order) == len(kv_cache_shape)
4404
                    except (AttributeError, NotImplementedError):
4405
                        kv_cache_stride_order = tuple(range(len(kv_cache_shape)))
4406
4407
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4410
                    # 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.
4411
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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))
                    ]
4419
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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()