gpu_model_runner.py 185 KB
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import gc
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import itertools
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import time
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from collections import defaultdict
from collections.abc import Iterator
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from contextlib import contextmanager
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from copy import deepcopy
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from typing import TYPE_CHECKING, Any, NamedTuple, Optional, Union, cast
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import numpy as np
import torch
import torch.distributed
import torch.nn as nn
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from tqdm import tqdm
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from typing_extensions import TypeAlias
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import vllm.envs as envs
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from vllm.attention import Attention, AttentionType
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from vllm.attention.backends.abstract import AttentionBackend
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from vllm.attention.layers.chunked_local_attention import ChunkedLocalAttention
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from vllm.compilation.counter import compilation_counter
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from vllm.compilation.cuda_graph import CUDAGraphWrapper
from vllm.compilation.monitor import set_cudagraph_capturing_enabled
from vllm.config import (CompilationLevel, CUDAGraphMode, VllmConfig,
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                         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,
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    prepare_communication_buffer_for_model)
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from vllm.forward_context import (BatchDescriptor, DPMetadata,
                                  set_forward_context)
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from vllm.logger import init_logger
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from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
from vllm.model_executor.layers.mamba.abstract import MambaBase
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from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
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from vllm.model_executor.model_loader import TensorizerLoader, get_model_loader
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from vllm.model_executor.models.interfaces import (is_mixture_of_experts,
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                                                   supports_eagle3,
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                                                   supports_mrope,
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                                                   supports_transcription)
from vllm.model_executor.models.interfaces_base import (
    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,
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                                    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, PoolerOutput
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from vllm.tasks import GenerationTask, PoolingTask, SupportedTask
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from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, DeviceMemoryProfiler,
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                        GiB_bytes, cdiv, check_use_alibi, get_dtype_size,
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                        is_pin_memory_available,
                        length_from_prompt_token_ids_or_embeds, round_up,
                        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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# yapf conflicts with isort for this block
# yapf: disable
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from vllm.v1.kv_cache_interface import (AttentionSpec,
                                        ChunkedLocalAttentionSpec,
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                                        CrossAttentionSpec,
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                                        EncoderOnlyAttentionSpec,
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                                        FullAttentionSpec, KVCacheConfig,
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                                        KVCacheGroupSpec, KVCacheSpec,
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                                        MambaSpec, SlidingWindowSpec,
                                        UniformTypeKVCacheSpecs)
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# yapf: enable
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from vllm.v1.outputs import (EMPTY_MODEL_RUNNER_OUTPUT, AsyncModelRunnerOutput,
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                             DraftTokenIds, LogprobsLists, LogprobsTensors,
                             ModelRunnerOutput, 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.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 (
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    KVConnectorModelRunnerMixin)
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from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
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from vllm.v1.worker.ubatch_splitting import (check_ubatch_thresholds,
                                             ubatch_split)
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from vllm.v1.worker.ubatch_utils import UBatchSlice, UBatchSlices
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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
PerLayerAttnMetadata: TypeAlias = Union[list[AttnMetadataDict],
                                        AttnMetadataDict]

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# Wrapper for ModelRunnerOutput to support overlapped execution.
class AsyncGPUModelRunnerOutput(AsyncModelRunnerOutput):

    def __init__(
        self,
        model_runner_output: ModelRunnerOutput,
        sampled_token_ids: torch.Tensor,
        invalid_req_indices: list[int],
        async_output_copy_stream: torch.cuda.Stream,
    ):
        self._model_runner_output = model_runner_output
        self._invalid_req_indices = invalid_req_indices

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

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

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

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

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

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

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


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class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
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    def __init__(
        self,
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        vllm_config: VllmConfig,
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        device: torch.device,
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    ):
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        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        self.cache_config = vllm_config.cache_config
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        self.compilation_config = vllm_config.compilation_config
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        self.lora_config = vllm_config.lora_config
        self.load_config = vllm_config.load_config
        self.parallel_config = vllm_config.parallel_config
        self.scheduler_config = vllm_config.scheduler_config
        self.speculative_config = vllm_config.speculative_config
        self.observability_config = vllm_config.observability_config
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        from vllm.model_executor.models.utils import set_cpu_offload_max_bytes
        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
        if cache_config.cache_dtype == "auto":
            self.kv_cache_dtype = self.dtype
        else:
            self.kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[
                cache_config.cache_dtype]

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        self.is_pooling_model = (model_config.runner_type == 'pooling')
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        self.enable_prompt_embeds = model_config.enable_prompt_embeds
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        self.is_multimodal_raw_input_only_model = (
            model_config.is_multimodal_raw_input_only_model)

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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 = (
            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(
            model_config)
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        if self.model_config.is_encoder_decoder:
            # Maximum length of the encoder input, only for encoder-decoder
            # models.
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            self.max_encoder_len = scheduler_config.\
                            max_num_encoder_input_tokens
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        else:
            self.max_encoder_len = 0

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

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

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

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        # mm_hash ->  encoder_output
        self.encoder_cache: dict[str, torch.Tensor] = {}
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        self.use_aux_hidden_state_outputs = False
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        # Set up speculative decoding.
        # NOTE(Jiayi): currently we put the entire draft model on
        # the last PP rank. This is not ideal if there are many
        # layers in the draft model.
        if self.speculative_config and get_pp_group().is_last_rank:
            if self.speculative_config.method == "ngram":
                self.drafter = NgramProposer(self.vllm_config)
            elif self.speculative_config.use_eagle():
                self.drafter = EagleProposer(self.vllm_config, self.device,
                                             self)  # type: ignore
                if self.speculative_config.method == "eagle3":
                    self.use_aux_hidden_state_outputs = True
            elif self.speculative_config.method == "medusa":
                self.drafter = MedusaProposer(
                    vllm_config=self.vllm_config,
                    device=self.device)  # type: ignore
            else:
                raise ValueError("Unknown speculative decoding method: "
                                 f"{self.speculative_config.method}")
            self.rejection_sampler = RejectionSampler()
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        # Request states.
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        self.requests: dict[str, CachedRequestState] = {}
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        self.comm_stream = torch.cuda.Stream()
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        # Input Batch
        # NOTE(Chen): Ideally, we should initialize the input batch inside
        # `initialize_kv_cache` based on the kv cache config. However, as in
        # https://github.com/vllm-project/vllm/pull/18298, due to some unknown
        # reasons, we have to initialize the input batch before `load_model`,
        # quantization + weight offloading will fail otherwise. As a temporary
        # solution, we initialize the input batch here, and re-initialize it
        # in `initialize_kv_cache` if the block_sizes here is different from
        # the block_sizes in the kv cache config.
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        self.input_batch = InputBatch(
            max_num_reqs=self.max_num_reqs,
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            # We need to use the encoder length for encoder-decoer
            # because of KV cache for cross-attention.
            max_model_len=max(self.max_model_len, self.max_encoder_len),
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            max_num_batched_tokens=self.max_num_tokens,
            device=self.device,
            pin_memory=self.pin_memory,
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            vocab_size=self.model_config.get_vocab_size(),
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            block_sizes=[self.cache_config.block_size],
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            is_spec_decode=bool(self.vllm_config.speculative_config),
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            logitsprocs=build_logitsprocs(
                self.vllm_config, self.device, self.pin_memory,
                self.is_pooling_model,
                self.vllm_config.model_config.logits_processors),
            is_pooling_model=self.is_pooling_model,
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        )
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        self.use_async_scheduling = self.scheduler_config.async_scheduling
        self.async_output_copy_stream = torch.cuda.Stream() if \
            self.use_async_scheduling else None

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        # TODO(woosuk): Provide an option to tune the max cudagraph batch size.
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        # The convention is different.
        # self.cudagraph_batch_sizes sorts in ascending order.
        # The batch sizes in the config are in descending order.
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        if self.compilation_config.cudagraph_capture_sizes and \
                self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE:
            self.cudagraph_batch_sizes = list(
                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)
        self.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.
        self.inputs_embeds = self._make_buffer(self.max_num_tokens,
                                               self.hidden_size,
                                               dtype=self.dtype,
                                               numpy=False)
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        self.is_token_ids = self._make_buffer(self.max_num_tokens,
                                              dtype=torch.bool)
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        self.discard_request_indices = self._make_buffer(self.max_num_reqs,
                                                         dtype=torch.int64)
        self.num_discarded_requests = 0

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        self.num_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 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(
                (3, self.max_num_tokens + 1), dtype=torch.int64)
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        # CUDA event to synchronize use of reused CPU tensors between steps
        # when async scheduling is enabled.
        self.prepare_inputs_event: Optional[torch.cuda.Event] = None
        if self.use_async_scheduling:
            self.prepare_inputs_event = torch.cuda.Event()
            # Start in a completed state.
            self.prepare_inputs_event.record(torch.cuda.default_stream())

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        # None in the first PP rank. The rest are set after load_model.
        self.intermediate_tensors: Optional[IntermediateTensors] = None
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        # OPTIMIZATION: Cache the tensors rather than creating them every step.
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        # Keep in int64 to avoid overflow with long context
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        self.arange_np = np.arange(max(self.max_num_reqs + 1,
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                                       self.max_model_len,
                                       self.max_num_tokens),
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                                   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(
                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

        # Cudagraph dispatcher for runtime cudagraph dispatching.
        self.cudagraph_dispatcher = CudagraphDispatcher(self.vllm_config)

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        self.mm_budget = MultiModalBudget(
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            self.model_config,
            self.scheduler_config,
            self.mm_registry,
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        ) if self.supports_mm_inputs else None
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        self.reorder_batch_threshold: Optional[int] = None

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        # Attention layers that are only in the KVCacheConfig of the runner
        # (e.g., KV sharing, encoder-only attention), but not in the
        # KVCacheConfig of the scheduler.
        self.runner_only_attn_layers: set[str] = set()

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        # Cached outputs.
        self._draft_token_ids: Optional[Union[list[list[int]],
                                              torch.Tensor]] = None
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        self.transfer_event = torch.cuda.Event()
        self.sampled_token_ids_pinned_cpu = torch.empty(
            (self.max_model_len, 1),
            dtype=torch.int64,
            device="cpu",
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            pin_memory=self.pin_memory)
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    def _make_buffer(self,
                     *size: Union[int, torch.SymInt],
                     dtype: torch.dtype,
                     numpy: bool = True) -> CpuGpuBuffer:
        return CpuGpuBuffer(*size,
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                            dtype=dtype,
                            device=self.device,
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                            pin_memory=self.pin_memory,
                            with_numpy=numpy)
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    def _init_model_kwargs(self, num_tokens: int):
        model_kwargs = dict[str, Any]()

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        if not self.is_pooling_model:
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            return model_kwargs

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

        if len(token_type_id_requests) == 0:
            return model_kwargs

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        seq_lens = self.seq_lens.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(
            device=self.device)
        return model_kwargs

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

        Args:
            scheduler_output: The scheduler output.
        """
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        # Attention free models have zero kv_cache_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

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        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:
                assert self.reorder_batch_threshold == 1, \
                    "DCP not support reorder_batch_threshold > 1 now."
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            reorder_batch_to_split_decodes_and_prefills(
                self.input_batch,
                scheduler_output,
                decode_threshold=self.reorder_batch_threshold)
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    # Note: used for model runner override.
    def _init_device_properties(self) -> None:
        """Initialize attributes from torch.cuda.get_device_properties
        """
        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()

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    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:
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            self.input_batch.remove_request(req_id)
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        # Free the cached encoder outputs.
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        for mm_hash in scheduler_output.free_encoder_mm_hashes:
            self.encoder_cache.pop(mm_hash, None)
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        # Remove the unscheduled requests from the persistent batch.
        # NOTE(woosuk): The unscheduled requests are either preempted requests
        # or running requests that are not scheduled in this step. We remove
        # them from the persistent batch but keep their cached states since
        # they will be scheduled again sometime in the future.
        scheduled_req_ids = scheduler_output.num_scheduled_tokens.keys()
        cached_req_ids = self.input_batch.req_id_to_index.keys()
        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:
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            self.input_batch.remove_request(req_id)
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        reqs_to_add: list[CachedRequestState] = []
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        # Add new requests to the cached states.
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        for new_req_data in scheduler_output.scheduled_new_reqs:
            req_id = new_req_data.req_id
            sampling_params = new_req_data.sampling_params
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            pooling_params = new_req_data.pooling_params
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            if sampling_params and \
                sampling_params.sampling_type == SamplingType.RANDOM_SEED:
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                generator = torch.Generator(device=self.device)
                generator.manual_seed(sampling_params.seed)
            else:
                generator = None

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

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            req_state = CachedRequestState(
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                req_id=req_id,
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                prompt_token_ids=new_req_data.prompt_token_ids,
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                prompt_embeds=new_req_data.prompt_embeds,
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                mm_features=new_req_data.mm_features,
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                sampling_params=sampling_params,
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                pooling_params=pooling_params,
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                generator=generator,
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                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
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                output_token_ids=[],
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                lora_request=new_req_data.lora_request,
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            )
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            self.requests[req_id] = req_state

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            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
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            if self.uses_mrope:
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                self._init_mrope_positions(req_state)
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            reqs_to_add.append(req_state)
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        # Update the states of the running/resumed requests.
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        is_last_rank = get_pp_group().is_last_rank
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        req_data = scheduler_output.scheduled_cached_reqs
        for i, req_id in enumerate(req_data.req_ids):
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            req_state = self.requests[req_id]
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            num_computed_tokens = req_data.num_computed_tokens[i]
            new_block_ids = req_data.new_block_ids[i]
            resumed_from_preemption = req_data.resumed_from_preemption[i]
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            # Update the cached states.
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            req_state.num_computed_tokens = num_computed_tokens
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            if not is_last_rank:
                # 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.
                num_new_tokens = (num_computed_tokens + len(new_token_ids) -
                                  req_state.num_tokens)
                if num_new_tokens == 1:
                    # Avoid slicing list in most common case.
                    req_state.output_token_ids.append(new_token_ids[-1])
                elif num_new_tokens > 0:
                    req_state.output_token_ids.extend(
                        new_token_ids[-num_new_tokens:])

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            # Update the block IDs.
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            if not resumed_from_preemption:
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                if new_block_ids is not None:
                    # Append the new blocks to the existing block IDs.
                    for block_ids, new_ids in zip(req_state.block_ids,
                                                  new_block_ids):
                        block_ids.extend(new_ids)
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            else:
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                assert new_block_ids is not None
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                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
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                req_state.block_ids = new_block_ids
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            req_index = self.input_batch.req_id_to_index.get(req_id)
            if req_index is None:
                # The request is not in the persistent batch.
                # The request was either preempted and resumed later, or was not
                # scheduled in the previous step and needs to be added again.
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                reqs_to_add.append(req_state)
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                continue

            # Update the persistent batch.
            self.input_batch.num_computed_tokens_cpu[req_index] = (
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                num_computed_tokens)
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            if new_block_ids is not None:
                self.input_batch.block_table.append_row(
                    new_block_ids, req_index)
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            # For the last rank, we don't need to update the token_ids_cpu
            # because the sampled tokens are already cached.
            if not is_last_rank:
                # Add new_token_ids to token_ids_cpu.
                start_token_index = num_computed_tokens
                end_token_index = num_computed_tokens + len(new_token_ids)
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                self.input_batch.token_ids_cpu[
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                    req_index,
                    start_token_index:end_token_index] = new_token_ids
                self.input_batch.num_tokens_no_spec[
                    req_index] = end_token_index
                self.input_batch.num_tokens[req_index] = end_token_index
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            # Add spec_token_ids to token_ids_cpu.
            spec_token_ids = (
                scheduler_output.scheduled_spec_decode_tokens.get(req_id, ()))
            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[
                    req_index, start_index:end_token_index] = spec_token_ids
                # NOTE(woosuk): `num_tokens` here may include spec tokens.
                self.input_batch.num_tokens[req_index] += num_spec_tokens

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        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
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        for request in reqs_to_add:
            self.input_batch.add_request(request)
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        # Condense the batched states if there are gaps left by removed requests
        self.input_batch.condense()
        # Allow attention backend to reorder the batch, potentially
        self._may_reorder_batch(scheduler_output)
        # Refresh batch metadata with any pending updates.
        self.input_batch.refresh_metadata()
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    def _update_states_after_model_execute(
            self, output_token_ids: torch.Tensor) -> None:
        """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.
        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()
        for i, num_tokens in enumerate(num_accepted_tokens):
            self.input_batch.num_accepted_tokens_cpu[i] = num_tokens

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    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
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        for mm_feature in req_state.mm_features:
            mm_item = mm_feature.data
            if mm_item is None:
                continue
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            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

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        if supports_mrope(self.model):
            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,
                )
        else:
            req_state.mrope_positions, req_state.mrope_position_delta = \
                MRotaryEmbedding.get_input_positions_tensor(
                    req_state.prompt_token_ids,
                    hf_config=self.model_config.hf_config,
                    image_grid_thw=image_grid_thw,
                    video_grid_thw=video_grid_thw,
                    second_per_grid_ts=second_per_grid_ts,
                    audio_feature_lengths=audio_feature_lengths,
                    use_audio_in_video=use_audio_in_video,
                )
764

765
    def _extract_mm_kwargs(
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        self,
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        scheduler_output: "SchedulerOutput",
    ) -> BatchedTensorInputs:
769
        if not scheduler_output or not self.is_multimodal_raw_input_only_model:
770
            return {}
771

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        mm_kwargs = list[MultiModalKwargsItem]()
        for req in scheduler_output.scheduled_new_reqs:
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            for feature in req.mm_features:
                if feature.data is not None:
                    mm_kwargs.append(feature.data)
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        # Input all modalities at once
        mm_kwargs_combined: BatchedTensorInputs = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
                mm_kwargs,
                device=self.device,
                pin_memory=self.pin_memory,
        ):
            mm_kwargs_combined.update(mm_kwargs_group)
786

787
        return mm_kwargs_combined
788

789
    def _dummy_mm_kwargs(self, num_seqs: int) -> BatchedTensorInputs:
790
        if not self.is_multimodal_raw_input_only_model:
791
            return {}
792

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        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)
798

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    def _get_cumsum_and_arange(
        self,
        num_tokens: np.ndarray,
        cumsum_dtype: Optional[np.dtype] = None,
    ) -> tuple[np.ndarray, np.ndarray]:
        """Get the cumulative sum and batched arange of the given array.
        # E.g., [2, 5, 3] -> ([2, 7, 10], [0, 1, 0, 1, 2, 3, 4, 0, 1, 2])
        # Equivalent to but faster than:
        # np.concatenate([np.arange(n) for n in num_tokens])
        """
        # Step 1. [2, 5, 3] -> [2, 7, 10]
        cu_num_tokens = np.cumsum(num_tokens, dtype=cumsum_dtype)
        total_num_tokens = cu_num_tokens[-1]
        # Step 2. [2, 7, 10] -> [0, 0, 2, 2, 2, 2, 2, 7, 7, 7]
        cumsums_offsets = np.repeat(cu_num_tokens - num_tokens, num_tokens)
        # Step 3. [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
        arange = self.arange_np[:total_num_tokens] - cumsums_offsets

        return cu_num_tokens, arange

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    def _prepare_input_ids(self, total_num_scheduled_tokens: int,
                           cu_num_tokens: np.ndarray) -> None:
        """Prepare the input IDs for the current batch.
        
        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)
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            self.inputs_embeds.copy_to_gpu(total_num_scheduled_tokens)
            self.is_token_ids.copy_to_gpu(total_num_scheduled_tokens)
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            return

        # Async scheduling case, where some decode requests from the previous
        # iteration won't have entries in input_ids_cpu and need to be copied
        # on the GPU from prev_sampled_token_ids.
        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)
                indices_match &= (prev_index == flattened_index)
                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)
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            self.inputs_embeds.copy_to_gpu(total_num_scheduled_tokens)
            self.is_token_ids.copy_to_gpu(total_num_scheduled_tokens)
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        if num_commmon_tokens == 0:
            # No requests in common with the previous iteration
            # So input_ids_cpu will have all the input ids.
            return
        if indices_match and max_flattened_index == (num_commmon_tokens - 1):
            # Common-case optimization: the batch is unchanged
            # and no reordering happened.
            # The indices are both the same permutation of 0..N-1 so
            # we can copy directly using a single slice.
            self.input_ids.gpu[:num_commmon_tokens].copy_(
                self.input_batch.prev_sampled_token_ids[:num_commmon_tokens,
                                                        0],
                non_blocking=True)
872
            self.is_token_ids.gpu[:num_commmon_tokens] = True
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            return
        # Upload the index tensors asynchronously
        # so the scatter can be non-blocking.
        input_ids_index_tensor = torch.tensor(flattened_indices,
                                              dtype=torch.int64,
                                              pin_memory=self.pin_memory).to(
                                                  self.device,
                                                  non_blocking=True)
        prev_common_req_indices_tensor = torch.tensor(
            prev_common_req_indices,
            dtype=torch.int64,
            pin_memory=self.pin_memory).to(self.device, non_blocking=True)
        self.input_ids.gpu.scatter_(
            dim=0,
            index=input_ids_index_tensor,
            src=self.input_batch.prev_sampled_token_ids[
                prev_common_req_indices_tensor, 0])

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    def _get_encoder_seq_lens(
        self,
        scheduler_output: "SchedulerOutput",
        kv_cache_spec: KVCacheSpec,
        num_reqs: int,
    ) -> Optional[np.ndarray]:
        if not isinstance(kv_cache_spec, CrossAttentionSpec):
            return None

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

        return encoder_seq_lens

909
    def _prepare_inputs(
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        self, scheduler_output: "SchedulerOutput"
    ) -> tuple[PerLayerAttnMetadata, torch.Tensor,
               Optional[SpecDecodeMetadata], np.ndarray,
               Optional[CommonAttentionMetadata], int, Optional[UBatchSlices],
               Optional[torch.Tensor]]:
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        """
        :return: tuple[
            attn_metadata: layer-to-attention_metadata mapping,
            logits_indices, spec_decode_metadata
        ]
        """
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        total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
        assert total_num_scheduled_tokens > 0
        num_reqs = self.input_batch.num_reqs
        assert num_reqs > 0

        # OPTIMIZATION: Start copying the block table first.
        # This way, we can overlap the copy with the following CPU operations.
928
        self.input_batch.block_table.commit_block_table(num_reqs)
929
930

        # Get the number of scheduled tokens for each request.
931
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        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)
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937

        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
938
939
        req_indices = np.repeat(self.arange_np[:num_reqs],
                                num_scheduled_tokens)
940

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        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
        # arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
        cu_num_tokens, arange = self._get_cumsum_and_arange(
            num_scheduled_tokens)
945
946

        # Get positions.
947
        positions_np = self.positions.np[:total_num_scheduled_tokens]
948
949
950
951
        np.add(self.input_batch.num_computed_tokens_cpu[req_indices],
               arange,
               out=positions_np)

952
953
        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
954
        if self.uses_mrope:
955
956
            self._calc_mrope_positions(scheduler_output)

957
958
959
960
        # 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.
961
962
        token_indices = (positions_np +
                         req_indices * self.input_batch.token_ids_cpu.shape[1])
963
        token_indices_tensor = torch.from_numpy(token_indices)
964

965
966
967
968
        # 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.
        torch.index_select(self.input_batch.token_ids_cpu_tensor.flatten(),
969
                           0,
970
                           token_indices_tensor,
971
                           out=self.input_ids.cpu[:total_num_scheduled_tokens])
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
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992
993
994
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996
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998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
        is_token_ids = self.input_batch.is_token_ids.flatten()
        torch.index_select(
            is_token_ids,
            0,
            token_indices_tensor,
            out=self.is_token_ids.cpu[:total_num_scheduled_tokens])

        # 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:
                    self.inputs_embeds.cpu[output_idx:output_idx +
                                           actual_num_sched].copy_(
                                               req_embeds[start_pos:actual_end]
                                           )

                output_idx += num_sched
1017

1018
1019
1020
1021
        self.input_batch.block_table.compute_slot_mapping(
            req_indices, positions_np)
        self.input_batch.block_table.commit_slot_mapping(
            total_num_scheduled_tokens)
1022
1023

        # Prepare the attention metadata.
1024
1025
        self.query_start_loc.np[0] = 0
        self.query_start_loc.np[1:num_reqs + 1] = cu_num_tokens
1026
1027
        # Note: pad query_start_loc to be non-decreasing, as kernels
        # like FlashAttention requires that
1028
1029
1030
        self.query_start_loc.np[num_reqs + 1:].fill(cu_num_tokens[-1])
        self.query_start_loc.copy_to_gpu()
        query_start_loc = self.query_start_loc.gpu[:num_reqs + 1]
1031

1032
1033
1034
1035
        num_tokens_unpadded = scheduler_output.total_num_scheduled_tokens
        num_tokens_padded = num_tokens_unpadded + self.get_local_padding(
            num_tokens_unpadded)
        ubatch_slices, num_tokens_after_padding = \
1036
            ubatch_split(num_scheduled_tokens,
1037
1038
1039
1040
                         num_tokens_unpadded,
                         num_tokens_padded,
                         self.vllm_config)

1041
        self.seq_lens.np[:num_reqs] = (
1042
1043
            self.input_batch.num_computed_tokens_cpu[:num_reqs] +
            num_scheduled_tokens)
1044
        # Fill unused with 0 for full cuda graph mode.
1045
1046
1047
1048
        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()
1049

1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
        num_tokens = [
            self.requests[r].num_tokens for r in self.input_batch.req_ids
        ]
        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)
        self.discard_request_indices.np[:self.num_discarded_requests] = (
            discard_request_indices)

        self.discard_request_indices.copy_to_gpu(self.num_discarded_requests)

1065
        # Copy the tensors to the GPU.
1066
1067
        self._prepare_input_ids(total_num_scheduled_tokens, cu_num_tokens)

1068
        if self.uses_mrope:
1069
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1070
1071
            self.mrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.mrope_positions.cpu[:, :total_num_scheduled_tokens],
1072
1073
1074
                non_blocking=True)
        else:
            # Common case (1D positions)
1075
            self.positions.copy_to_gpu(total_num_scheduled_tokens)
1076

1077
1078
1079
1080
1081
1082
1083
1084
1085
        use_spec_decode = len(
            scheduler_output.scheduled_spec_decode_tokens) > 0
        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
1086
            num_draft_tokens = None
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
            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)
            for req_id, draft_token_ids in (
                    scheduler_output.scheduled_spec_decode_tokens.items()):
                req_idx = self.input_batch.req_id_to_index[req_id]
                num_draft_tokens[req_idx] = len(draft_token_ids)

            spec_decode_metadata = self._calc_spec_decode_metadata(
                num_draft_tokens, cu_num_tokens)
            logits_indices = spec_decode_metadata.logits_indices
1101
1102
1103
            self.num_draft_tokens.np[:num_reqs] = num_draft_tokens
            self.num_draft_tokens.np[num_reqs:].fill(0)
            self.num_draft_tokens.copy_to_gpu()
1104
1105
1106

        logits_indices_padded = None
        if self.cache_config.kv_sharing_fast_prefill:
1107
            logits_indices_padded = self._prepare_kv_sharing_fast_prefill(
1108
1109
                logits_indices)

1110
1111
1112
        attn_metadata: PerLayerAttnMetadata = {}
        if ubatch_slices is not None:
            attn_metadata = [dict() for _ in range(len(ubatch_slices))]
1113

1114
        # Used in the below loop.
1115
1116
        query_start_loc_cpu = self.query_start_loc.cpu[:num_reqs + 1]
        seq_lens_cpu = self.seq_lens.cpu[:num_reqs]
1117
1118
1119
        num_computed_tokens_cpu = (
            self.input_batch.num_computed_tokens_cpu_tensor[:num_reqs])
        spec_decode_common_attn_metadata = None
1120
1121
1122
1123
1124
        if use_spec_decode:
            self.num_accepted_tokens.np[:num_reqs] = (
                self.input_batch.num_accepted_tokens_cpu[:num_reqs])
            self.num_accepted_tokens.np[num_reqs:].fill(1)
            self.num_accepted_tokens.copy_to_gpu()
1125

1126
1127
1128
1129
        # 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(
                self.kv_cache_config.kv_cache_groups):
1130
1131
            encoder_seq_lens = self._get_encoder_seq_lens(
                scheduler_output, kv_cache_group_spec.kv_cache_spec, num_reqs)
1132

1133
1134
1135
1136
1137
1138
1139
            if isinstance(kv_cache_group_spec.kv_cache_spec,
                          EncoderOnlyAttentionSpec):
                # 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,
1140
1141
1142
1143
1144
1145
1146
                    device=self.device,
                )
                slot_mapping = torch.zeros(
                    (total_num_scheduled_tokens, ),
                    dtype=torch.int64,
                    device=self.device,
                )
1147
1148
1149
                num_common_prefix_blocks = 0
            else:
                blk_table = self.input_batch.block_table[kv_cache_group_id]
1150
1151
1152
                blk_table_tensor = blk_table.get_device_tensor(num_reqs)
                slot_mapping = blk_table.slot_mapping.gpu[:
                                                          total_num_scheduled_tokens]
1153
1154
1155

                # Fill unused with -1. Needed for reshape_and_cache in full cuda
                # graph mode.
1156
1157
                blk_table.slot_mapping.gpu[total_num_scheduled_tokens:].fill_(
                    -1)
1158
1159
1160
                num_common_prefix_blocks = (
                    scheduler_output.
                    num_common_prefix_blocks[kv_cache_group_id])
1161

1162
            common_attn_metadata = CommonAttentionMetadata(
1163
1164
1165
1166
1167
                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,
1168
1169
1170
                num_reqs=num_reqs,
                num_actual_tokens=total_num_scheduled_tokens,
                max_query_len=max_num_scheduled_tokens,
1171
                max_seq_len=max_seq_len,
1172
1173
                block_table_tensor=blk_table_tensor,
                slot_mapping=slot_mapping,
1174
1175
                logits_indices_padded=logits_indices_padded,
                num_logits_indices=logits_indices.size(0),
1176
                causal=True,
1177
                encoder_seq_lens=encoder_seq_lens,
1178
1179
            )

1180
1181
1182
1183
1184
1185
1186
1187
            if (self.speculative_config
                    and spec_decode_common_attn_metadata is None):
                if isinstance(self.drafter, EagleProposer):
                    if (self.drafter.attn_layer_names[0]
                            in kv_cache_group_spec.layer_names):
                        spec_decode_common_attn_metadata = common_attn_metadata
                else:
                    spec_decode_common_attn_metadata = common_attn_metadata
1188

1189
1190
1191
            for attn_group in self.attn_groups[kv_cache_group_id]:
                # Prepare for cascade attention if enabled & beneficial.
                common_prefix_len = 0
1192
                builder = attn_group.get_metadata_builder()
1193
1194
1195
                if self.cascade_attn_enabled:
                    common_prefix_len = self._compute_cascade_attn_prefix_len(
                        num_scheduled_tokens,
1196
                        num_common_prefix_blocks,
1197
                        attn_group.kv_cache_spec,
1198
1199
                        builder,
                    )
1200

1201
1202
1203
1204
1205
1206
1207
1208
1209
                extra_attn_metadata_args = {}
                if use_spec_decode and isinstance(builder,
                                                  GDNAttentionMetadataBuilder):
                    extra_attn_metadata_args = dict(
                        num_accepted_tokens=self.num_accepted_tokens.
                        gpu[:num_reqs],
                        num_draft_tokens=self.num_draft_tokens.gpu[:num_reqs],
                    )

1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
                if ubatch_slices is not None:
                    common_attn_metadata_list = split_attn_metadata(
                        ubatch_slices, common_attn_metadata)
                    for ubid, common_attn_metadata in enumerate(
                            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))
                        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,
                        **extra_attn_metadata_args)
                    for layer_name in attn_group.layer_names:
                        attn_metadata[layer_name] = attn_metadata_i
1230

1231
1232
1233
1234
        # Hot-Swap lora model
        if self.lora_config:
            self.set_active_loras(self.input_batch, num_scheduled_tokens)

1235
1236
        return (attn_metadata, logits_indices, spec_decode_metadata,
                num_scheduled_tokens, spec_decode_common_attn_metadata,
1237
1238
                max_num_scheduled_tokens, ubatch_slices,
                num_tokens_after_padding)
1239

1240
1241
1242
1243
    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
        num_common_prefix_blocks: int,
1244
1245
        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
    ) -> 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.
        """
1264
        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
        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]
1302
        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
        # 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(
            common_prefix_len,
            self.input_batch.num_computed_tokens_cpu[:num_reqs].min())
        # common_prefix_len should be a multiple of the block size.
1313
1314
1315
1316
1317
        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))
1318
1319
1320
1321
        use_local_attention = (
            isinstance(kv_cache_spec, ChunkedLocalAttentionSpec)
            or (isinstance(kv_cache_spec, FullAttentionSpec)
                and kv_cache_spec.attention_chunk_size is not None))
1322
1323
        assert isinstance(kv_cache_spec, AttentionSpec)
        use_cascade = attn_metadata_builder.use_cascade_attention(
1324
1325
1326
            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
1327
            num_kv_heads=kv_cache_spec.num_kv_heads,
1328
            use_alibi=self.use_alibi,
1329
            use_sliding_window=use_sliding_window,
1330
            use_local_attention=use_local_attention,
1331
1332
1333
1334
            num_sms=self.num_sms,
        )
        return common_prefix_len if use_cascade else 0

1335
1336
    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
1337
        for index, req_id in enumerate(self.input_batch.req_ids):
1338
1339
1340
1341
1342
1343
1344
            req = self.requests[req_id]
            assert req.mrope_positions is not None

            num_computed_tokens = \
                self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = \
                scheduler_output.num_scheduled_tokens[req_id]
1345
1346
            num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
                req.prompt_token_ids, req.prompt_embeds)
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365

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

            assert num_scheduled_tokens == prompt_part_len + completion_part_len

            if prompt_part_len > 0:
                # prompt's 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

1366
1367
                self.mrope_positions.cpu[:, dst_start:dst_end] = (
                    req.mrope_positions[:, src_start:src_end])
1368
1369
1370
1371
1372
1373
1374
                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

1375
                MRotaryEmbedding.get_next_input_positions_tensor(
1376
                    out=self.mrope_positions.np,
1377
1378
1379
1380
1381
                    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,
                )
1382
1383
1384

                mrope_pos_ptr += completion_part_len

1385
1386
    def _calc_spec_decode_metadata(
        self,
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
        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
1403
1404
1405
1406
1407
1408

        # 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(
            num_sampled_tokens, cumsum_dtype=np.int32)
        # Step 2. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
1409
1410
        logits_indices = np.repeat(
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens)
1411
        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
1412
1413
1414
1415
1416
1417
        logits_indices += arange

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

        # Compute the draft logits indices.
1418
1419
1420
1421
        # 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(
            num_draft_tokens, cumsum_dtype=np.int32)
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
        # [0, 0, 0, 5, 5, 9]
        target_logits_indices = np.repeat(
            cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens)
        # [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(
            self.device, non_blocking=True)
        logits_indices = torch.from_numpy(logits_indices).to(self.device,
                                                             non_blocking=True)
        target_logits_indices = torch.from_numpy(target_logits_indices).to(
            self.device, non_blocking=True)
        bonus_logits_indices = torch.from_numpy(bonus_logits_indices).to(
1436
1437
            self.device, non_blocking=True)

1438
1439
        # Compute the draft token ids.
        # draft_token_indices:      [  1,   2,   3, 105, 106, 208]
1440
        draft_token_ids = self.input_ids.gpu[logits_indices]
1441
1442
1443
1444
1445
1446
1447
1448
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1450
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1452
        draft_token_ids = draft_token_ids[target_logits_indices + 1]

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

1453
1454
1455
1456
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    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
        self.kv_sharing_fast_prefill_logits_indices[:num_logits].copy_(
            logits_indices)
        # 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_(
            logits_indices[-1].item())
        if (self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
                and num_logits <= self.cudagraph_batch_sizes[-1]):
            # 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
        logits_indices_padded = (
            self.kv_sharing_fast_prefill_logits_indices[:num_logits_padded])
        return logits_indices_padded

1479
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1486
    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
1487
                inputs.
1488
1489
1490
1491
1492
1493

        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
        """
1494
1495
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
1496
            return [], []
1497
        # Batch the multi-modal inputs.
1498
        mm_kwargs = list[MultiModalKwargsItem]()
1499
1500
        # list of tuple (mm_hash, position_info)
        mm_hashes_pos = list[tuple[str, PlaceholderRange]]()
1501
1502
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
1503
1504

            for mm_input_id in encoder_input_ids:
1505
1506
1507
1508
                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))
1509

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1517
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1519
        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(
            scheduler_output)

        if not mm_kwargs:
            return

1520
1521
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1524
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1526
1527
        # 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.
        encoder_outputs = []
1528
1529
        for _, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
                mm_kwargs,
1530
                device=self.device,
1531
1532
                pin_memory=self.pin_memory,
        ):
1533
1534
1535
1536
1537
1538
1539
1540
            # 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.
            curr_group_outputs = self.model.get_multimodal_embeddings(
1541
                **mm_kwargs_group)
1542

1543
1544
            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
1545
                expected_num_items=num_items,
1546
1547
            )

1548
1549
            for output in curr_group_outputs:
                encoder_outputs.append(output)
1550

1551
1552
1553
        # 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(
1554
1555
1556
1557
1558
                output,
                is_embed=pos_info.is_embed,
            )

    def _gather_mm_embeddings(
1559
1560
        self,
        scheduler_output: "SchedulerOutput",
1561
        shift_computed_tokens: int = 0,
1562
    ) -> list[torch.Tensor]:
1563
        mm_embeds: list[torch.Tensor] = []
1564
        for req_id in self.input_batch.req_ids:
1565
1566
1567
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[
                req_id]
            req_state = self.requests[req_id]
1568
1569
            num_computed_tokens = \
                req_state.num_computed_tokens + shift_computed_tokens
1570
1571
            for mm_feature in req_state.mm_features:
                pos_info = mm_feature.mm_position
1572
1573
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589

                # 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,
1590
1591
                    num_encoder_tokens,
                )
1592
                assert start_idx < end_idx
1593

1594
                mm_hash = mm_feature.identifier
1595
1596
1597
                encoder_output = self.encoder_cache.get(mm_hash, None)
                assert encoder_output is not None,\
                    f"Encoder cache miss for {mm_hash}."
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607

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

                mm_embeds_item = gather_mm_placeholders(
                    encoder_output[start_idx:end_idx],
                    is_embed=is_embed,
                )
                mm_embeds.append(mm_embeds_item)
        return mm_embeds
1608

1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
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1628
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1630
1631
1632
1633
1634
1635
1636
1637
    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
        encoder_features = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
                mm_kwargs,
                device=self.device,
                pin_memory=self.pin_memory,
        ):
            # 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

1638
    def get_model(self) -> nn.Module:
1639
        # get raw model out of the cudagraph wrapper.
1640
        if isinstance(self.model, (CUDAGraphWrapper, UBatchWrapper)):
1641
            return self.model.unwrap()
1642
1643
        return self.model

1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
    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

1659
1660
1661
1662
1663
    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
        model = self.get_model()
        if not is_pooling_model(model):
            return []

1664
1665
1666
1667
1668
1669
        supported_tasks = list(model.pooler.get_supported_tasks())

        if (self.scheduler_config.chunked_prefill_enabled
                and "encode" in supported_tasks):
            supported_tasks.remove("encode")

1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
            logger.debug_once("Chunked prefill is not supported with "
                              "encode task which using ALL pooling. "
                              "Please turn off chunked prefill by "
                              "`--no-enable-chunked-prefill` before using it.")

        if "score" in supported_tasks:
            num_labels = getattr(self.model_config.hf_config, "num_labels", 0)
            if num_labels != 1:
                supported_tasks.remove("score")
                logger.debug_once(
                    "Score API is only enabled for num_labels == 1.")
1681
1682

        return supported_tasks
1683

1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
    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)

1694
1695
1696
1697
1698
1699
1700
    def sync_and_slice_intermediate_tensors(
            self, num_tokens: int, intermediate_tensors: IntermediateTensors,
            sync_self: bool) -> IntermediateTensors:

        assert self.intermediate_tensors is not None

        tp = self.vllm_config.parallel_config.tensor_parallel_size
1701
        is_rs = is_residual_scattered_for_sp(self.vllm_config, num_tokens)
1702
1703
1704
1705
1706
1707

        # 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():
1708
                is_scattered = k == "residual" and is_rs
1709
1710
1711
1712
1713
1714
1715
                copy_len = num_tokens // tp if is_scattered else \
                    num_tokens
                self.intermediate_tensors[k][:copy_len].copy_(
                    v[:copy_len], non_blocking=True)

        return IntermediateTensors({
            k:
1716
1717
            v[:num_tokens //
              tp] if k == "residual" and is_rs else v[:num_tokens]
1718
1719
1720
            for k, v in self.intermediate_tensors.items()
        })

1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
    def eplb_step(self,
                  is_dummy: bool = False,
                  is_profile: bool = False) -> None:
        """
        Step for the EPLB (Expert Parallelism Load Balancing) state.
        """
        if not self.parallel_config.enable_eplb:
            return

        assert self.eplb_state is not None
1731
1732
        model = self.get_model()
        assert is_mixture_of_experts(model)
1733
        self.eplb_state.step(
1734
            model,
1735
1736
            is_dummy,
            is_profile,
1737
            log_stats=self.parallel_config.eplb_config.log_balancedness,
1738
1739
        )

1740
1741
    def get_dp_padding(self,
                       num_tokens: int) -> tuple[int, Optional[torch.Tensor]]:
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
        """
        Determines the total number of tokens that each rank will run.
        All ranks will be padded out so that they run with the same number
        of tokens

        Returns: tuple[
            num_pad_tokens: The number of tokens that will be added to the batch
            num_tokens_after_padding: A tensor containing the total number of
            tokens for each DP rank including padding.
        ]
        """
1753
1754
        dp_size = self.vllm_config.parallel_config.data_parallel_size
        dp_rank = self.vllm_config.parallel_config.data_parallel_rank
1755
1756
1757
1758
1759
1760
1761
1762
1763

        # For DP: Don't pad when setting enforce_eager.
        # 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.
        #
        # TODO(tms) : There are many cases where padding is enabled for
        # prefills, causing unnecessary and excessive padding of activations.

        if dp_size == 1 or self.vllm_config.model_config.enforce_eager:
1764
            # Early exit.
1765
            return 0, None
1766
1767
1768
1769

        num_tokens_across_dp = DPMetadata.num_tokens_across_dp(
            num_tokens, dp_size, dp_rank)
        max_tokens_across_dp_cpu = torch.max(num_tokens_across_dp).item()
1770
1771
1772
1773
1774
        num_tokens_after_padding = torch.tensor([max_tokens_across_dp_cpu] *
                                                dp_size,
                                                device="cpu",
                                                dtype=torch.int32)
        return max_tokens_across_dp_cpu - num_tokens, num_tokens_after_padding
1775

1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
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1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
    def get_local_padding(self, num_tokens_unpadded: int) -> int:

        num_tokens_padded = num_tokens_unpadded

        if (self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
                and num_tokens_unpadded <= self.cudagraph_batch_sizes[-1]):
            # Use piecewise CUDA graphs.
            # Add padding to the batch size.
            num_tokens_padded = self.vllm_config.pad_for_cudagraph(
                num_tokens_unpadded)
        else:
            # 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
            if self.vllm_config.compilation_config.pass_config. \
                enable_sequence_parallelism and tp_size > 1:
                num_tokens_padded = round_up(num_tokens_unpadded, tp_size)

        num_pad_tokens = num_tokens_padded - num_tokens_unpadded
        return num_pad_tokens

    # 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)
    def pad_out_ubatch_slice(self, ubatch_slices: UBatchSlices,
                             num_total_tokens: int):
        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)

1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
    def _pool(
        self,
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
        num_scheduled_tokens_np: np.ndarray,
    ) -> ModelRunnerOutput:
        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"

1820
        hidden_states = hidden_states[:num_scheduled_tokens]
1821
        pooling_metadata = self.input_batch.get_pooling_metadata()
1822
1823
        pooling_metadata.build_pooling_cursor(num_scheduled_tokens_np.tolist(),
                                              device=hidden_states.device)
1824
        seq_lens_cpu = self.seq_lens.cpu[:self.input_batch.num_reqs]
1825

1826
        # Pooling models D2H & synchronize occurs in pooler.py:build_output
1827
        raw_pooler_output = self.model.pooler(
1828
            hidden_states=hidden_states, pooling_metadata=pooling_metadata)
1829
1830
1831

        pooler_output: list[Optional[torch.Tensor]] = []
        for raw_output, seq_len, prompt_len in zip(
1832
                raw_pooler_output, seq_lens_cpu, pooling_metadata.prompt_lens):
1833

1834
1835
            output = raw_output.data if seq_len == prompt_len else None
            pooler_output.append(output)
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845

        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,
        )

1846
1847
1848
1849
1850
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
    def _get_num_input_tokens(self, num_scheduled_tokens: int) -> int:
        if (self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
                and not envs.VLLM_DISABLE_PAD_FOR_CUDAGRAPH
                and hasattr(self, "cudagraph_batch_sizes")
                and self.cudagraph_batch_sizes
                and num_scheduled_tokens <= self.cudagraph_batch_sizes[-1]):
            # 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
        if (self.compilation_config.pass_config.enable_sequence_parallelism
                and tp_size > 1):
            return round_up(num_scheduled_tokens, tp_size)
        return num_scheduled_tokens

1865
    def _preprocess(
1866
1867
        self,
        scheduler_output: "SchedulerOutput",
1868
        intermediate_tensors: Optional[IntermediateTensors] = None,
1869
1870
        ubatch_slices: Optional[UBatchSlices] = None,
        num_tokens_after_padding: Optional[torch.Tensor] = None,
1871
1872
1873
    ) -> tuple[int, int, Optional[torch.Tensor], Optional[torch.Tensor],
               Optional[torch.Tensor], torch.Tensor,
               Optional[IntermediateTensors], dict[str, Any]]:
1874

1875
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
1876
1877
1878
1879
1880
1881
1882
1883
1884
        if ubatch_slices:
            assert num_tokens_after_padding is not None
            num_input_tokens = int(num_tokens_after_padding[0].item() * 2)
            self.pad_out_ubatch_slice(ubatch_slices, num_input_tokens)
        elif ubatch_slices is None:
            num_input_tokens = self._get_num_input_tokens(num_scheduled_tokens)
            num_pad, num_tokens_after_padding = self.get_dp_padding(
                num_input_tokens)
            num_input_tokens += num_pad
1885

1886
1887
        # _prepare_inputs may reorder the batch, so we must gather multi
        # modal outputs after that to ensure the correct order
1888
1889
        if (self.supports_mm_inputs and get_pp_group().is_first_rank
                and not self.model_config.is_encoder_decoder):
1890
1891
1892
1893
            # Run the multimodal encoder if any.
            self._execute_mm_encoder(scheduler_output)
            mm_embeds = self._gather_mm_embeddings(scheduler_output)

1894
1895
1896
            # 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.
1897
            inputs_embeds_scheduled = self.model.get_input_embeddings(
1898
                input_ids=self.input_ids.gpu[:num_scheduled_tokens],
1899
1900
                multimodal_embeddings=mm_embeds or None,
            )
1901

1902
            # TODO(woosuk): Avoid the copy. Optimize.
1903
            self.inputs_embeds.gpu[:num_scheduled_tokens].copy_(
1904
1905
                inputs_embeds_scheduled)

1906
            input_ids = None
1907
            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
1908
1909
1910
1911
            model_kwargs = {
                **self._init_model_kwargs(num_scheduled_tokens),
                **self._extract_mm_kwargs(scheduler_output),
            }
1912
        elif self.enable_prompt_embeds and get_pp_group().is_first_rank:
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
            # 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).
            token_ids_idx = self.is_token_ids.gpu[:num_scheduled_tokens] \
                .nonzero(as_tuple=False) \
                .squeeze(1)
            # Some tokens ids may need to become embeds
            if token_ids_idx.numel() > 0:
                token_ids = self.input_ids.gpu[token_ids_idx]
                tokens_to_embeds = self.model.get_input_embeddings(
                    input_ids=token_ids)
                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
1938
        else:
1939
1940
1941
1942
            # 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.
1943
            input_ids = self.input_ids.gpu[:num_input_tokens]
1944
            inputs_embeds = None
1945
            model_kwargs = self._init_model_kwargs(num_input_tokens)
1946
        if self.uses_mrope:
1947
            positions = self.mrope_positions.gpu[:, :num_input_tokens]
1948
        else:
1949
            positions = self.positions.gpu[:num_input_tokens]
1950

1951
1952
1953
        if get_pp_group().is_first_rank:
            intermediate_tensors = None
        else:
1954
1955
            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
                num_input_tokens, intermediate_tensors, True)
1956

1957
1958
1959
1960
1961
        if (self.model_config.is_encoder_decoder
                and scheduler_output.scheduled_encoder_inputs):
            encoder_inputs = self._extract_encoder_inputs(scheduler_output)
            model_kwargs.update(encoder_inputs)

1962
1963
1964
        return (
            num_scheduled_tokens,
            num_input_tokens,
1965
            num_tokens_after_padding,
1966
1967
1968
1969
1970
1971
            input_ids,
            inputs_embeds,
            positions,
            intermediate_tensors,
            model_kwargs,
        )
1972

1973
1974
1975
1976
    def _sample(
            self, logits: Optional[torch.Tensor],
            spec_decode_metadata: Optional[SpecDecodeMetadata]
    ) -> SamplerOutput:
1977
        # Sample the next token and get logprobs if needed.
1978
        sampling_metadata = self.input_batch.sampling_metadata
1979
        if spec_decode_metadata is None:
1980
            sampler_output = self.sampler(
1981
1982
1983
1984
                logits=logits,
                sampling_metadata=sampling_metadata,
            )
        else:
1985
1986
1987
1988
            # When indexing with a tensor (bonus_logits_indices), PyTorch
            # creates a new tensor with separate storage from the original
            # logits tensor. This means any in-place operations on bonus_logits
            # won't affect the original logits tensor.
1989
            assert logits is not None
1990
            bonus_logits = logits[spec_decode_metadata.bonus_logits_indices]
1991
            sampler_output = self.sampler(
1992
                logits=bonus_logits,
1993
1994
1995
                sampling_metadata=sampling_metadata,
            )
            bonus_token_ids = sampler_output.sampled_token_ids
1996

1997
1998
1999
            # Just like `bonus_logits`, `target_logits` is a new tensor with
            # separate storage from the original `logits` tensor. Therefore,
            # it is safe to update `target_logits` in place.
2000
            target_logits = logits[spec_decode_metadata.target_logits_indices]
2001
            output_token_ids = self.rejection_sampler(
2002
                spec_decode_metadata,
2003
                None,  # draft_probs
2004
                target_logits,
2005
                bonus_token_ids,
2006
2007
                sampling_metadata,
            )
2008
            sampler_output.sampled_token_ids = output_token_ids
2009
            self._update_states_after_model_execute(output_token_ids)
2010

2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
        return sampler_output

    def _bookkeeping_sync(
        self, scheduler_output: "SchedulerOutput",
        sampler_output: SamplerOutput, logits: Optional[torch.Tensor],
        hidden_states: torch.Tensor, num_scheduled_tokens: int
    ) -> tuple[
            dict[str, int],
            Optional[LogprobsLists],
            list[list[int]],
            dict[str, Optional[LogprobsTensors]],
            list[str],
            dict[str, int],
            list[int],
    ]:
2026
2027
2028
2029
        num_nans_in_logits = {}
        if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
            num_nans_in_logits = self._get_nans_in_logits(logits)

2030
2031
2032
2033
2034
2035
        discard_sampled_tokens_req_indices = \
            self.discard_request_indices.np[:self.num_discarded_requests]
        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)
2036

2037
2038
2039
2040
2041
2042
        # 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()
        req_id_to_index_output_copy = \
            self.input_batch.req_id_to_index.copy()

2043
2044
        # NOTE: GPU -> CPU Sync happens here.
        # Move as many CPU operations as possible before this sync point.
2045
2046
2047
2048
2049
2050
        logprobs_tensors = sampler_output.logprobs_tensors
        logprobs_lists = logprobs_tensors.tolists() \
            if logprobs_tensors is not None else None

        # Compute prompt logprobs if needed.
        prompt_logprobs_dict = self._get_prompt_logprobs_dict(
2051
            hidden_states[:num_scheduled_tokens],
2052
            scheduler_output.num_scheduled_tokens,
2053
2054
        )

2055
        num_sampled_tokens = sampler_output.sampled_token_ids.shape[0]
2056
        sampled_token_ids = sampler_output.sampled_token_ids
2057
        invalid_req_indices = []
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
        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:
2072
                valid_sampled_token_ids[int(i)].clear()
2073
        else:
2074
            valid_sampled_token_ids = []
2075
            invalid_req_indices = discard_sampled_tokens_req_indices.tolist()
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
            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.
            self.input_batch.prev_sampled_token_ids = \
                sampled_token_ids
            self.input_batch.prev_sampled_token_ids_invalid_indices = \
                invalid_req_indices_set
            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
            }
2091

2092
2093
2094
2095
2096
        # 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.
2097
        req_ids = self.input_batch.req_ids
2098
2099
2100
2101
2102
2103
        for req_idx in range(num_sampled_tokens):
            if self.use_async_scheduling:
                sampled_ids = [-1] if \
                    req_idx not in invalid_req_indices_set else None
            else:
                sampled_ids = valid_sampled_token_ids[req_idx]
2104
2105
2106
2107
2108
2109
2110
2111
2112
2113
2114
2115
            if not sampled_ids:
                continue

            start_idx = self.input_batch.num_tokens_no_spec[req_idx]
            end_idx = start_idx + len(sampled_ids)
            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}")

            self.input_batch.token_ids_cpu[req_idx,
                                           start_idx:end_idx] = sampled_ids
2116
            self.input_batch.is_token_ids[req_idx, start_idx:end_idx] = True
2117
2118
            self.input_batch.num_tokens_no_spec[req_idx] = end_idx
            self.input_batch.num_tokens[req_idx] = end_idx
2119

2120
            req_id = req_ids[req_idx]
2121
2122
2123
            req_state = self.requests[req_id]
            req_state.output_token_ids.extend(sampled_ids)

2124
2125
2126
2127
2128
2129
2130
2131
2132
2133
        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,
        )

2134
2135
2136
2137
2138
2139
2140
2141
2142
2143
2144
2145
2146
2147
2148
    @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()

2149
2150
2151
2152
2153
2154
2155
    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
        intermediate_tensors: Optional[IntermediateTensors] = None,
    ) -> Union[ModelRunnerOutput, AsyncModelRunnerOutput, IntermediateTensors]:
        with record_function_or_nullcontext("Preprocess"):
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
            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(
                        scheduler_output, self.vllm_config)
                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 "
                        "it when the requests need prompt logprobs")

2172
2173
2174
                # Prepare the decoder inputs.
                (attn_metadata, logits_indices, spec_decode_metadata,
                 num_scheduled_tokens_np, spec_decode_common_attn_metadata,
2175
2176
                 max_query_len, ubatch_slices, num_tokens_after_padding
                 ) = self._prepare_inputs(scheduler_output)
2177

2178
2179
2180
2181
2182
2183
2184
2185
2186
            (
                num_scheduled_tokens,
                num_input_tokens,
                num_tokens_across_dp,
                input_ids,
                inputs_embeds,
                positions,
                intermediate_tensors,
                model_kwargs,
2187
2188
2189
            ) = self._preprocess(scheduler_output, intermediate_tensors,
                                 ubatch_slices, num_tokens_after_padding)

2190
2191
2192
2193
2194
2195
2196
2197
2198
            uniform_decode = (max_query_len
                              == self.uniform_decode_query_len) and (
                                  num_scheduled_tokens
                                  == self.input_batch.num_reqs * max_query_len)
            batch_descriptor = BatchDescriptor(num_tokens=num_input_tokens,
                                               uniform_decode=uniform_decode)
            cudagraph_runtime_mode, batch_descriptor = \
                self.cudagraph_dispatcher.dispatch(batch_descriptor)

2199
2200
2201
2202
2203
        # This is currently to get around the assert in the DPMetadata
        # where it wants `num_tokens_across_dp` to align with `num_tokens`
        if ubatch_slices is not None:
            num_input_tokens = ubatch_slices[0].num_tokens

2204
2205
2206
2207
2208
2209
2210
2211
2212
        # Run the model.
        # Use persistent buffers for CUDA graphs.
        with (set_forward_context(
                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,
2213
                ubatch_slices=ubatch_slices,
2214
2215
2216
2217
2218
2219
2220
2221
2222
2223
2224
2225
2226
        ), record_function_or_nullcontext("Forward"),
              self.maybe_get_kv_connector_output(scheduler_output) as
              kv_connector_output):
            model_output = self.model(
                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:
2227
                # True when EAGLE 3 is used.
2228
2229
                hidden_states, aux_hidden_states = model_output
            else:
2230
                # Common case.
2231
2232
2233
                hidden_states = model_output
                aux_hidden_states = None

2234
2235
2236
2237
2238
            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)
2239
2240
                    hidden_states.kv_connector_output = kv_connector_output
                    return hidden_states
2241

2242
                if self.is_pooling_model:
2243
2244
2245
2246
2247
                    # Return the pooling output.
                    output = self._pool(hidden_states, num_scheduled_tokens,
                                        num_scheduled_tokens_np)
                    output.kv_connector_output = kv_connector_output
                    return output
2248
2249

                sample_hidden_states = hidden_states[logits_indices]
2250
                logits = self.model.compute_logits(sample_hidden_states)
2251
2252
2253
2254
2255
            else:
                # Rare case.
                assert not self.is_pooling_model

                if not get_pp_group().is_last_rank:
2256
2257
2258
2259
2260
                    all_gather_tensors = {
                        "residual":
                        not is_residual_scattered_for_sp(
                            self.vllm_config, num_input_tokens)
                    }
2261
                    get_pp_group().send_tensor_dict(
2262
2263
2264
                        hidden_states.tensors,
                        all_gather_group=get_tp_group(),
                        all_gather_tensors=all_gather_tensors)
2265
2266
2267
                    logits = None
                else:
                    sample_hidden_states = hidden_states[logits_indices]
2268
                    logits = self.model.compute_logits(sample_hidden_states)
2269
2270
2271
2272
2273

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

2274
2275
2276
2277
2278
2279
2280
2281
                model_output_broadcast_data = get_pp_group(
                ).broadcast_tensor_dict(model_output_broadcast_data,
                                        src=len(get_pp_group().ranks) - 1)
                assert model_output_broadcast_data is not None
                logits = model_output_broadcast_data["logits"]

            # Apply structured output bitmasks if present
            if scheduler_output.grammar_bitmask is not None:
2282
2283
                apply_grammar_bitmask(scheduler_output, self.input_batch,
                                      logits, self.device)
2284
2285
2286
2287

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

2288
2289
2290
2291
2292
2293
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
2305
2306
2307
2308
2309
        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,
                )

        use_padded_batch_for_eagle = self.speculative_config and \
            self.speculative_config.use_eagle() and \
            not self.speculative_config.disable_padded_drafter_batch
        if use_padded_batch_for_eagle:
            # 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)

2310
2311
2312
2313
2314
2315
2316
2317
2318
2319
2320
2321
2322
        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,
            ) = self._bookkeeping_sync(scheduler_output, sampler_output,
                                       logits, hidden_states,
                                       num_scheduled_tokens)

2323
2324
2325
2326
        if self.speculative_config and not use_padded_batch_for_eagle:
            # 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)
2327

2328
2329
        with record_function_or_nullcontext("EPLB"):
            self.eplb_step()
2330

2331
2332
2333
        output = ModelRunnerOutput(
            req_ids=req_ids_output_copy,
            req_id_to_index=req_id_to_index_output_copy,
2334
2335
2336
2337
            sampled_token_ids=valid_sampled_token_ids,
            logprobs=logprobs_lists,
            prompt_logprobs_dict=prompt_logprobs_dict,
            pooler_output=[],
2338
            kv_connector_output=kv_connector_output,
2339
2340
2341
            num_nans_in_logits=num_nans_in_logits,
        )

2342
2343
2344
2345
2346
        if not self.use_async_scheduling:
            return output

        return AsyncGPUModelRunnerOutput(
            model_runner_output=output,
2347
            sampled_token_ids=sampler_output.sampled_token_ids,
2348
2349
2350
2351
            invalid_req_indices=invalid_req_indices,
            async_output_copy_stream=self.async_output_copy_stream,
        )

2352
2353
2354
2355
2356
2357
2358
2359
2360
2361
2362
    def take_draft_token_ids(self) -> Optional[DraftTokenIds]:
        if self._draft_token_ids is None:
            return None
        req_ids = self.input_batch.req_ids
        if isinstance(self._draft_token_ids, torch.Tensor):
            draft_token_ids = self._draft_token_ids.tolist()
        else:
            draft_token_ids = self._draft_token_ids
        self._draft_token_ids = None
        return DraftTokenIds(req_ids, draft_token_ids)

2363
2364
2365
    def propose_draft_token_ids(
        self,
        scheduler_output: "SchedulerOutput",
2366
        sampled_token_ids: Union[torch.Tensor, list[list[int]]],
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        sampling_metadata: SamplingMetadata,
        hidden_states: torch.Tensor,
        sample_hidden_states: torch.Tensor,
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        aux_hidden_states: Optional[list[torch.Tensor]],
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        spec_decode_metadata: Optional[SpecDecodeMetadata],
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        common_attn_metadata: CommonAttentionMetadata,
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    ) -> Union[list[list[int]], torch.Tensor]:
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        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
        if self.speculative_config.method == "ngram":
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            assert isinstance(sampled_token_ids, list)
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            assert isinstance(self.drafter, NgramProposer)
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            draft_token_ids = self.propose_ngram_draft_token_ids(
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                sampled_token_ids)
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        elif self.speculative_config.method == "medusa":
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            assert isinstance(sampled_token_ids, list)
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            assert isinstance(self.drafter, MedusaProposer)
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            if sample_hidden_states.shape[0] == len(sampled_token_ids):
                # The input to the target model does not include draft tokens.
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                hidden_states = sample_hidden_states
            else:
                indices = []
                offset = 0
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                assert spec_decode_metadata is not None
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                for num_draft, tokens in zip(
                        spec_decode_metadata.num_draft_tokens,
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                        sampled_token_ids):
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                    indices.append(offset + len(tokens) - 1)
                    offset += num_draft + 1
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                indices = torch.tensor(indices, device=self.device)
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                hidden_states = sample_hidden_states[indices]

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            draft_token_ids = self.drafter.propose(
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                target_hidden_states=hidden_states,
                sampling_metadata=sampling_metadata,
            )
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        elif self.speculative_config.use_eagle():
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            assert isinstance(self.drafter, EagleProposer)
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            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.
                assert isinstance(sampled_token_ids, list), \
                    "sampled_token_ids should be a python list when" \
                    "padded-batch is disabled."
                next_token_ids = self.drafter.prepare_next_token_ids_cpu(
                    sampled_token_ids, self.requests, self.input_batch,
                    scheduler_output.num_scheduled_tokens)
            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.
                assert isinstance(sampled_token_ids, torch.Tensor), \
                    "sampled_token_ids should be a torch.Tensor when" \
                    "padded-batch is enabled."
                next_token_ids, valid_sampled_tokens_count = \
                    self.drafter.prepare_next_token_ids_padded(
                        common_attn_metadata,
                        sampled_token_ids,
                        self.requests,
                        self.input_batch,
                        self.discard_request_indices.gpu,
                        self.num_discarded_requests
                    )
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            if spec_decode_metadata is None:
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                token_indices_to_sample = None
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                # input_ids can be None for multimodal models.
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                target_token_ids = self.input_ids.gpu[:num_scheduled_tokens]
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                # TODO(woosuk): Support M-RoPE.
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                target_positions = self.positions.gpu[:num_scheduled_tokens]
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                if self.use_aux_hidden_state_outputs:
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                    assert aux_hidden_states is not None
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                    target_hidden_states = torch.cat(
                        [h[:num_scheduled_tokens] for h in aux_hidden_states],
                        dim=-1)
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                else:
                    target_hidden_states = hidden_states[:num_scheduled_tokens]
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            else:
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                if self.speculative_config.disable_padded_drafter_batch:
                    token_indices_to_sample = None
                    common_attn_metadata, token_indices =\
                        self.drafter.prepare_inputs(
                            common_attn_metadata,
                            sampled_token_ids,
                            spec_decode_metadata.num_draft_tokens)
                else:
                    common_attn_metadata, token_indices, \
                        token_indices_to_sample =\
                        self.drafter.prepare_inputs_padded(
                            common_attn_metadata,
                            spec_decode_metadata,
                            valid_sampled_tokens_count)
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                target_token_ids = self.input_ids.gpu[token_indices]
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                # TODO(woosuk): Support M-RoPE.
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                target_positions = self.positions.gpu[token_indices]
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                if self.use_aux_hidden_state_outputs:
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                    assert aux_hidden_states is not None
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                    target_hidden_states = torch.cat(
                        [h[token_indices] for h in aux_hidden_states], dim=-1)
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                else:
                    target_hidden_states = hidden_states[token_indices]
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            mm_embeds = None
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            if self.supports_mm_inputs:
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                mm_embeds = self._gather_mm_embeddings(scheduler_output,
                                                       shift_computed_tokens=1)

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            draft_token_ids = self.drafter.propose(
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                target_token_ids=target_token_ids,
                target_positions=target_positions,
                target_hidden_states=target_hidden_states,
                next_token_ids=next_token_ids,
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                last_token_indices=token_indices_to_sample,
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                sampling_metadata=sampling_metadata,
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                common_attn_metadata=common_attn_metadata,
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                mm_embeds=mm_embeds,
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            )
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        return draft_token_ids
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    def propose_ngram_draft_token_ids(
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        self,
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        sampled_token_ids: list[list[int]],
    ) -> list[list[int]]:
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        # TODO(woosuk): Optimize.
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        req_ids = self.input_batch.req_ids
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        draft_token_ids: list[list[int]] = []
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        for i, sampled_ids in enumerate(sampled_token_ids):
            num_sampled_ids = len(sampled_ids)
            if not num_sampled_ids:
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                # Skip speculative decoding.
                draft_token_ids.append([])
                continue

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            # Skip requests that require sampling parameters that are not
            # supported with speculative decoding.
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            req_id = req_ids[i]
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            if req_id in self.input_batch.spec_decode_unsupported_reqs:
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                draft_token_ids.append([])
                continue

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            num_tokens = self.input_batch.num_tokens_no_spec[i]
            if num_tokens >= self.max_model_len:
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                # Skip requests that have already reached the max model length.
                draft_token_ids.append([])
                continue

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            drafter_output = self.drafter.propose(
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                self.input_batch.token_ids_cpu[i, :num_tokens])
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            if drafter_output is None or len(drafter_output) == 0:
                draft_token_ids.append([])
            else:
                draft_token_ids.append(drafter_output.tolist())
        return draft_token_ids

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

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    def load_model(self, eep_scale_up: bool = False) -> None:
        """
        Args:
            eep_scale_up: the model loading is for elastic EP scale up.
        """
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        logger.info("Starting to load model %s...", self.model_config.model)
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        if eep_scale_up:
            from vllm.distributed.parallel_state import get_ep_group
            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)
            num_local_physical_experts = int(num_local_physical_experts.item())
            new_ep_size = get_ep_group().world_size
            global_expert_load, old_global_expert_indices = (
                EplbState.recv_state())
            num_logical_experts = global_expert_load.shape[1]
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            self.parallel_config.eplb_config.num_redundant_experts = (
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                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
            rank_mapping = {
                old_ep_rank: old_ep_rank
                for old_ep_rank in range(old_ep_size)
            }
        else:
            global_expert_load = None
            old_global_expert_indices = None
            rank_mapping = None

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        with DeviceMemoryProfiler() as m:
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            time_before_load = time.perf_counter()
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            model_loader = get_model_loader(self.load_config)
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            logger.info("Loading model from scratch...")
            self.model = model_loader.load_model(
                vllm_config=self.vllm_config, model_config=self.model_config)
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            if self.lora_config:
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                self.model = self.load_lora_model(self.model, self.vllm_config,
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                                                  self.device)
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            if hasattr(self, "drafter"):
                logger.info("Loading drafter model...")
                self.drafter.load_model(self.model)
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            if self.use_aux_hidden_state_outputs:
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                if supports_eagle3(self.model):
                    self.model.set_aux_hidden_state_layers(
                        self.model.get_eagle3_aux_hidden_state_layers())
                else:
                    raise RuntimeError(
                        "Model does not support EAGLE3 interface but "
                        "aux_hidden_state_outputs was requested")
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            time_after_load = time.perf_counter()
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        self.model_memory_usage = m.consumed_memory
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        logger.info("Model loading took %.4f GiB and %.6f seconds",
                    self.model_memory_usage / GiB_bytes,
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                    time_after_load - time_before_load)
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        prepare_communication_buffer_for_model(self.model)
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        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)
            self.eplb_state = EplbState.build(
                self.model,
                self.device,
                self.parallel_config,
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                global_expert_load,
                old_global_expert_indices,
                rank_mapping,
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            )

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        if (
            self.vllm_config.compilation_config.level == \
                CompilationLevel.DYNAMO_AS_IS and supports_dynamo()
        ):
            backend = self.vllm_config.compilation_config.init_backend(
                self.vllm_config)
            compilation_counter.dynamo_as_is_count += 1
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            self.model.compile(fullgraph=True, backend=backend)
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            return
        # for other compilation levels, cudagraph behavior is controlled by
        # CudagraphWraper and CudagraphDispatcher of vllm.

        # wrap the model with full cudagraph wrapper if needed.
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        if self.compilation_config.cudagraph_mode.has_full_cudagraphs() \
            and not self.parallel_config.enable_dbo:
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            self.model = CUDAGraphWrapper(self.model,
                                          self.vllm_config,
                                          runtime_mode=CUDAGraphMode.FULL)
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        elif self.parallel_config.enable_dbo:
            if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
                self.model = UBatchWrapper(self.model, self.vllm_config,
                                           CUDAGraphMode.FULL, self.device)
            else:
                self.model = UBatchWrapper(self.model, self.vllm_config,
                                           CUDAGraphMode.NONE, self.device)
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    def reload_weights(self) -> None:
        assert getattr(self, "model", None) is not None, \
            "Cannot reload weights before model is loaded."
        model_loader = get_model_loader(self.load_config)
        logger.info("Reloading weights inplace...")
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        model = self.get_model()
        model_loader.load_weights(model, model_config=self.model_config)
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    def save_tensorized_model(
        self,
        tensorizer_config: "TensorizerConfig",
    ) -> None:
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        model = self.get_model()
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        TensorizerLoader.save_model(
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            model,
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            tensorizer_config=tensorizer_config,
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            model_config=self.model_config,
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        )

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    def _get_prompt_logprobs_dict(
        self,
        hidden_states: torch.Tensor,
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        num_scheduled_tokens: dict[str, int],
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    ) -> dict[str, Optional[LogprobsTensors]]:
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        num_prompt_logprobs_dict = self.input_batch.num_prompt_logprobs
        if not num_prompt_logprobs_dict:
            return {}

2662
        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
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        prompt_logprobs_dict: dict[str, Optional[LogprobsTensors]] = {}
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        # Since prompt logprobs are a rare feature, prioritize simple,
        # maintainable loop over optimal performance.
        completed_prefill_reqs = []
        for req_id, num_prompt_logprobs in num_prompt_logprobs_dict.items():
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            num_tokens = num_scheduled_tokens[req_id]
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            # Get metadata for this request.
            request = self.requests[req_id]
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            if request.prompt_token_ids is None:
                # Prompt logprobs is incompatible with prompt embeddings
                continue

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            num_prompt_tokens = len(request.prompt_token_ids)
            prompt_token_ids = torch.tensor(request.prompt_token_ids).to(
                self.device, non_blocking=True)

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

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            # Determine number of logits to retrieve.
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            start_idx = request.num_computed_tokens
            start_tok = start_idx + 1
2693
            num_remaining_tokens = num_prompt_tokens - start_tok
2694
            if num_tokens <= num_remaining_tokens:
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                # This is a chunk, more tokens remain.
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                # In the == case, there are no more prompt logprobs to produce
                # but we want to defer returning them to the next step where we
                # have new generated tokens to return.
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                num_logits = num_tokens
            else:
                # This is the last chunk of prompt tokens to return.
                num_logits = num_remaining_tokens
                completed_prefill_reqs.append(req_id)
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                prompt_logprobs_dict[req_id] = logprobs_tensors

            if num_logits <= 0:
                # This can happen for the final chunk if we prefilled exactly
                # (num_prompt_tokens - 1) tokens for this request in the prior
                # step. There are no more prompt logprobs to produce.
                continue
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            # Get the logits corresponding to this req's prompt tokens.
            # If this is a partial request (i.e. chunked prefill),
            # then there is prompt logprob generated for each index.
            req_idx = self.input_batch.req_id_to_index[req_id]
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            offset = self.query_start_loc.np[req_idx].item()
2717
            prompt_hidden_states = hidden_states[offset:offset + num_logits]
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            logits = self.model.compute_logits(prompt_hidden_states)
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            # Get the "target" tokens for each index. For prompt at index i,
            # the token at prompt index i+1 is the "sampled" token we want
            # to gather the logprob for.
            tgt_token_ids = prompt_token_ids[start_tok:start_tok + num_logits]

            # Compute prompt logprobs.
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            logprobs = self.sampler.compute_logprobs(logits)
            token_ids, logprobs, ranks = self.sampler.gather_logprobs(
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                logprobs, num_prompt_logprobs, tgt_token_ids)

            # Transfer GPU->CPU async.
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            chunk_slice = slice(start_idx, start_idx + num_logits)
            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
                token_ids, non_blocking=True)
            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs,
                                                         non_blocking=True)
            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
                ranks, non_blocking=True)
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        # Remove requests that have completed prefill from the batch
        # num_prompt_logprobs_dict.
        for req_id in completed_prefill_reqs:
            del num_prompt_logprobs_dict[req_id]
2743
            del in_progress_dict[req_id]
2744
2745

        # Must synchronize the non-blocking GPU->CPU transfers.
2746
        if prompt_logprobs_dict:
2747
            self._sync_device()
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2750

        return prompt_logprobs_dict

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    def _get_nans_in_logits(
        self,
        logits: Optional[torch.Tensor],
    ) -> dict[str, int]:
        try:
            if logits is None:
                return {req_id: 0 for req_id in self.input_batch.req_ids}

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

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    @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
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         - during DP rank dummy run
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        """
        dp_size = self.vllm_config.parallel_config.data_parallel_size
        randomize_inputs = envs.VLLM_RANDOMIZE_DP_DUMMY_INPUTS and dp_size > 1
        if not randomize_inputs:
            yield
        else:
            import functools

            @functools.cache
            def rand_input_ids() -> torch.Tensor:
                return torch.randint_like(
2789
                    self.input_ids.gpu,
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                    low=0,
                    high=self.model_config.get_vocab_size(),
                    dtype=input_ids.dtype)

2794
            logger.debug_once("Randomizing dummy data for DP Rank")
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            input_ids.copy_(rand_input_ids()[:input_ids.size(0)],
                            non_blocking=True)
            yield
            input_ids.fill_(0)

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    def _get_mm_dummy_batch(
        self,
        modality: str,
        max_items_per_batch: int,
    ) -> BatchedTensorInputs:
        """Dummy data for profiling and precompiling multimodal models."""
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        assert self.mm_budget is not None

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2809
        dummy_decoder_data = self.mm_registry.get_decoder_dummy_data(
            model_config=self.model_config,
2810
            seq_len=self.max_model_len,
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            mm_counts={modality: 1},
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            cache=self.mm_budget.cache,
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        )
        dummy_mm_data = dummy_decoder_data.multi_modal_data

        # Result in the maximum GPU consumption of the model
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        dummy_mm_item = dummy_mm_data[modality][0]
        dummy_mm_items = [dummy_mm_item] * max_items_per_batch
2819

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        return next(mm_kwargs_group
                    for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
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                        dummy_mm_items,
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                        device=self.device,
                        pin_memory=self.pin_memory,
                    ))
2826

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    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
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        cudagraph_runtime_mode: Optional[CUDAGraphMode] = None,
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        force_attention: bool = False,
        uniform_decode: bool = False,
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        allow_microbatching: bool = True,
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        skip_eplb: bool = False,
        is_profile: bool = False,
2837
        create_mixed_batch: bool = False,
2838
        remove_lora: bool = True,
2839
    ) -> tuple[torch.Tensor, torch.Tensor]:
2840
2841
2842
2843
2844
2845
2846
        """
        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.
2847
2848
                - if not set will determine the cudagraph mode based on using 
                    the self.cudagraph_dispatcher.
2849
2850
2851
2852
                - CUDAGraphMode.NONE: No cudagraph, for warm up and profile run
                - CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
                - CUDAGraphMode.FULL: Full cudagraph, attention metadata is
                    needed.
2853
            force_attention: If True, always create attention metadata. Used to
2854
2855
2856
2857
                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.
2858
2859
            create_mixed_batch: If True, create a mixed batch with both decode
                (1 token) and prefill (multiple tokens) requests.
2860
            remove_lora: If False, dummy LoRAs are not destroyed after the run
2861
        """
2862
        assert cudagraph_runtime_mode is None or cudagraph_runtime_mode in {
2863
2864
            CUDAGraphMode.NONE, CUDAGraphMode.PIECEWISE, CUDAGraphMode.FULL
        }
2865

2866
        # If cudagraph_mode.decode_mode() == FULL and
2867
        # cudagraph_mode.separate_routine(). This means that we are using
2868
2869
2870
2871
2872
2873
2874
2875
2876
2877
2878
2879
2880
2881
        # 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.
        max_query_len = self.uniform_decode_query_len if uniform_decode else \
                                                                num_tokens

2882
2883
2884
2885
2886
        # 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
2887
2888
2889
2890
2891
2892
2893
2894
2895
2896
2897
2898
2899
2900
2901
        if create_mixed_batch:
            assert not uniform_decode
            # Create mixed batch:
            # first half decode tokens, second half one prefill
            num_decode_tokens = num_tokens // 2
            num_prefill_tokens = num_tokens - num_decode_tokens
            num_reqs = num_decode_tokens + 1

            # Create decode requests (1 token each) followed by prefill request
            num_scheduled_tokens_list = [1] * num_decode_tokens + [
                num_prefill_tokens
            ]
            # Note: Overriding max_query_len to be the prefill tokens
            max_query_len = num_prefill_tokens
        elif uniform_decode:
2902
2903
            assert not create_mixed_batch
            num_reqs = cdiv(num_tokens, max_query_len)
2904
2905
            num_scheduled_tokens_list = [max_query_len] * num_reqs
            if num_tokens % max_query_len != 0:
2906
                num_scheduled_tokens_list[-1] = num_tokens % max_query_len
2907
2908
2909
2910
2911
2912
        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

2913
2914
2915
2916
        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs
        num_scheduled_tokens = np.array(num_scheduled_tokens_list,
                                        dtype=np.int32)
2917
        total_num_scheduled_tokens = int(num_scheduled_tokens.sum())
2918

2919
        ubatch_slices = None
2920
2921
        num_tokens_after_padding = None

2922
2923
        # We currently only microbatch if the number of tokens is
        # over a certain threshold.
2924
2925
2926
2927
2928
2929
2930
2931
2932
2933
2934
2935
2936
2937
2938
2939
2940
2941
        if self.parallel_config.enable_dbo and allow_microbatching:
            ubatch_slices, num_tokens_after_padding = ubatch_split(
                num_scheduled_tokens,
                total_num_scheduled_tokens,
                total_num_scheduled_tokens,
                self.vllm_config,
            )

        # If we failed to microbatch, currently need to resynchronize
        # TODO(lucas,sage): we should be able to avoid this second sync by
        #  refactoring `get_dp_padding_ubatch` and `get_dp_padding` into
        #  a single `coordinate_batch_across_dp` function.
        if num_tokens_after_padding is None:
            num_pad, num_tokens_across_dp = self.get_dp_padding(num_tokens)
            num_tokens_after_padding = num_tokens + num_pad
        else:
            num_tokens_across_dp = num_tokens_after_padding
            num_tokens_after_padding = int(num_tokens_after_padding[0].item())
2942
2943

        attn_metadata: Optional[PerLayerAttnMetadata] = None
2944
2945
2946

        # If force_attention is True, we always capture attention. Otherwise,
        # it only happens for cudagraph_runtime_mode=FULL.
2947
        if force_attention or cudagraph_runtime_mode == CUDAGraphMode.FULL:
2948
            attn_metadata = {}
2949
2950
            if ubatch_slices is not None:
                attn_metadata = [dict() for _ in range(len(ubatch_slices))]
2951

2952
2953
2954
2955
2956
2957
            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:
2958
                seq_lens = max_query_len
2959
            self.seq_lens.np[:num_reqs] = seq_lens
2960
2961
            self.seq_lens.np[num_reqs:] = 0
            self.seq_lens.copy_to_gpu()
2962

2963
2964
2965
2966
2967
            cum_num_tokens, _ = self._get_cumsum_and_arange(
                num_scheduled_tokens)
            self.query_start_loc.np[1:num_reqs + 1] = cum_num_tokens
            self.query_start_loc.copy_to_gpu()

2968
2969
            for kv_cache_group_id, kv_cache_group_spec in enumerate(
                    self.kv_cache_config.kv_cache_groups):
2970
                common_attn_metadata = CommonAttentionMetadata(
2971
2972
                    query_start_loc=self.query_start_loc.gpu[:num_reqs + 1],
                    query_start_loc_cpu=self.query_start_loc.cpu[:num_reqs +
2973
                                                                 1],
2974
2975
                    seq_lens=self.seq_lens.gpu[:num_reqs],
                    seq_lens_cpu=self.seq_lens.cpu[:num_reqs],
2976
2977
2978
2979
                    num_computed_tokens_cpu=self.input_batch.
                    num_computed_tokens_cpu_tensor[:num_reqs],
                    num_reqs=num_reqs,
                    num_actual_tokens=num_tokens,
2980
                    max_query_len=max_query_len,
2981
                    max_seq_len=self.max_model_len,
2982
2983
2984
2985
                    block_table_tensor=self.input_batch.
                    block_table[kv_cache_group_id].get_device_tensor(num_reqs),
                    slot_mapping=self.input_batch.block_table[
                        kv_cache_group_id].slot_mapping.gpu[:num_tokens],
2986
                    causal=True)
2987
                for attn_group in self.attn_groups[kv_cache_group_id]:
2988
2989
2990
2991
2992
2993
2994
2995
2996
2997
2998
2999
3000
3001
3002
3003
3004
3005
3006
                    if ubatch_slices is not None:
                        common_attn_metadata_list = split_attn_metadata(
                            ubatch_slices, common_attn_metadata)
                        for ubid, common_attn_metadata in enumerate(
                                common_attn_metadata_list):
                            assert common_attn_metadata.max_query_len == 1
                            attn_metadata_i = (attn_group\
                                               .get_metadata_builder(ubatch_id=ubid)\
                                               .build_for_cudagraph_capture(common_attn_metadata))
                            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 type(attn_metadata) is dict
                        attn_metadata_i = attn_group.get_metadata_builder()\
                            .build_for_cudagraph_capture(common_attn_metadata)
                        for layer_name in kv_cache_group_spec.layer_names:
                            attn_metadata[layer_name] = attn_metadata_i
3007

3008
        with self.maybe_dummy_run_with_lora(self.lora_config,
3009
                                            num_scheduled_tokens, remove_lora):
3010
3011
3012
            model_kwargs = self._init_model_kwargs(num_tokens)
            if (self.supports_mm_inputs
                    and not self.model_config.is_encoder_decoder):
3013
                input_ids = None
3014
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens]
3015
                model_kwargs = {
3016
                    **model_kwargs,
3017
3018
                    **self._dummy_mm_kwargs(num_reqs),
                }
3019
3020
3021
3022
            elif self.enable_prompt_embeds:
                input_ids = None
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens]
                model_kwargs = self._init_model_kwargs(num_tokens)
3023
            else:
3024
                input_ids = self.input_ids.gpu[:num_tokens]
3025
                inputs_embeds = None
3026

3027
            if self.uses_mrope:
3028
                positions = self.mrope_positions.gpu[:, :num_tokens]
3029
            else:
3030
                positions = self.positions.gpu[:num_tokens]
3031
3032
3033
3034
3035
3036
3037
3038
3039
3040

            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,
                            device=self.device))
3041
3042
3043

                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
                    num_tokens, None, False)
3044
3045
3046
3047
3048
3049
3050
3051
3052
3053

            # filter out the valid batch descriptor
            _cg_mode, batch_descriptor = self.cudagraph_dispatcher.dispatch(
                BatchDescriptor(num_tokens=num_tokens,
                                uniform_decode=uniform_decode))
            if cudagraph_runtime_mode is not None:
                # we allow forcing NONE when the dispatcher disagrees to support
                # warm ups for cudagraph capture
                assert cudagraph_runtime_mode == CUDAGraphMode.NONE or \
                    cudagraph_runtime_mode == _cg_mode, (
3054
3055
                    f"Cudagraph runtime mode mismatch at dummy_run. "
                    f"Expected {_cg_mode}, but got {cudagraph_runtime_mode}.")
3056
3057
            else:
                cudagraph_runtime_mode = _cg_mode
3058

3059
3060
            if ubatch_slices is not None:
                num_tokens = num_tokens // 2
3061
            with self.maybe_randomize_inputs(input_ids), set_forward_context(
3062
3063
                    attn_metadata,
                    self.vllm_config,
3064
                    num_tokens=num_tokens_after_padding,
3065
3066
                    num_tokens_across_dp=num_tokens_across_dp,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
3067
3068
                    batch_descriptor=batch_descriptor,
                    ubatch_slices=ubatch_slices):
3069
                outputs = self.model(
3070
3071
3072
3073
                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
3074
                    **model_kwargs,
3075
                )
3076

3077
3078
3079
3080
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
3081

3082
            if self.speculative_config and self.speculative_config.use_eagle():
3083
3084
3085
                assert isinstance(self.drafter, EagleProposer)
                self.drafter.dummy_run(num_tokens)

3086
3087
3088
3089
3090
3091
3092
3093
3094
3095
        # 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)

3096
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
3097
        return hidden_states, hidden_states[logit_indices]
3098
3099
3100
3101
3102
3103

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
3104
3105
3106
3107
        # 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)
3108

3109
        logits = self.model.compute_logits(hidden_states)
3110
3111
3112
3113
3114
3115
3116
3117
3118
3119
3120
3121
3122
3123
3124
3125
3126
3127
3128
3129
3130
        num_reqs = logits.size(0)

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

        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)],
            allowed_token_ids_mask=None,
            bad_words_token_ids={},
3131
            logitsprocs=LogitsProcessors(),
3132
        )
3133
        try:
3134
3135
            sampler_output = self.sampler(logits=logits,
                                          sampling_metadata=dummy_metadata)
3136
3137
3138
3139
3140
3141
3142
3143
3144
        except RuntimeError as e:
            if 'out of memory' in str(e):
                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 "
                    "initializing the engine.") from e
            else:
                raise e
3145
        if self.speculative_config:
3146
3147
3148
3149
3150
3151
3152
3153
3154
3155
3156
3157
3158
3159
3160
3161
3162
3163
3164
3165
3166
3167
3168
3169
3170
3171
            draft_token_ids = [[0] for _ in range(num_reqs)]
            dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
                draft_token_ids, self.device)

            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
            target_logits = torch.randn(num_tokens,
                                        logits.shape[-1],
                                        device=self.device,
                                        dtype=logits.dtype)
            # NOTE(woosuk): Here, we should use int32 because the sampler uses
            # int32 for bonus_token_ids. If the dtype mismatches, re-compilation
            # will occur at runtime.
            bonus_token_ids = torch.zeros(num_reqs,
                                          device=self.device,
                                          dtype=torch.int32)
            self.rejection_sampler(
                dummy_spec_decode_metadata,
                draft_probs,
                target_logits,
                bonus_token_ids,
                dummy_metadata,
            )
3172
        return sampler_output
3173

3174
    def _dummy_pooler_run_task(
3175
3176
        self,
        hidden_states: torch.Tensor,
3177
3178
        task: PoolingTask,
    ) -> PoolerOutput:
3179
3180
3181
3182
3183
3184
3185
3186
3187
3188
3189
        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

3190
        dummy_prompt_lens = torch.tensor(
3191
3192
            num_scheduled_tokens_list,
            device="cpu",
3193
3194
3195
3196
        )
        dummy_token_ids = torch.zeros((num_reqs, req_num_tokens),
                                      dtype=torch.int32,
                                      device=self.device)
3197

3198
        model = cast(VllmModelForPooling, self.get_model())
3199
        dummy_pooling_params = PoolingParams(task=task)
3200
        dummy_pooling_params.verify(task=task, model_config=self.model_config)
3201
        to_update = model.pooler.get_pooling_updates(task)
3202
3203
        to_update.apply(dummy_pooling_params)

3204
        dummy_metadata = PoolingMetadata(
3205
3206
3207
3208
            prompt_lens=dummy_prompt_lens,
            prompt_token_ids=dummy_token_ids,
            pooling_params=[dummy_pooling_params] * num_reqs,
        )
3209

3210
3211
3212
        dummy_metadata.build_pooling_cursor(num_scheduled_tokens_list,
                                            device=hidden_states.device)

3213
        try:
3214
            return model.pooler(hidden_states=hidden_states,
3215
                                pooling_metadata=dummy_metadata)
3216
3217
3218
        except RuntimeError as e:
            if 'out of memory' in str(e):
                raise RuntimeError(
3219
3220
3221
                    "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 "
3222
3223
3224
                    "initializing the engine.") from e
            else:
                raise e
3225
3226
3227
3228
3229
3230
3231
3232
3233
3234
3235
3236
3237
3238
3239
3240

    @torch.inference_mode()
    def _dummy_pooler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> PoolerOutput:
        # Find the task that has the largest output for subsequent steps
        output_size = dict[PoolingTask, float]()
        for task in self.get_supported_pooling_tasks():
            # Run a full batch with each task to ensure none of them OOMs
            output = self._dummy_pooler_run_task(hidden_states, task)
            output_size[task] = output.get_data_nbytes()
            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)
3241

3242
    def profile_run(self) -> None:
3243
        # Profile with multimodal encoder & encoder cache.
3244
        if self.supports_mm_inputs:
3245
            if self.model_config.multimodal_config.skip_mm_profiling:
3246
                logger.info(
3247
3248
3249
3250
3251
3252
3253
3254
3255
3256
                    "Skipping memory profiling for multimodal encoder and "
                    "encoder cache.")
            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.
3257
3258
3259
                    dummy_modality = mm_budget.get_modality_with_max_tokens()
                    max_mm_items_per_batch = mm_budget \
                        .max_items_per_batch_by_modality[dummy_modality]
3260
3261
3262
3263
3264
3265
3266
3267
3268

                    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,
                    )
3269

3270
3271
3272
3273
3274
                    # Create dummy batch of multimodal inputs.
                    batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                        dummy_modality,
                        max_mm_items_per_batch,
                    )
3275

3276
3277
3278
3279
                    # Run multimodal encoder.
                    dummy_encoder_outputs = \
                        self.model.get_multimodal_embeddings(
                        **batched_dummy_mm_inputs)
3280

3281
3282
3283
3284
                    sanity_check_mm_encoder_outputs(
                        dummy_encoder_outputs,
                        expected_num_items=max_mm_items_per_batch,
                    )
3285

3286
3287
3288
                    # Cache the dummy encoder outputs.
                    self.encoder_cache["tmp"] = dict(
                        enumerate(dummy_encoder_outputs))
3289

3290
        # Add `is_profile` here to pre-allocate communication buffers
3291
        hidden_states, last_hidden_states \
3292
            = self._dummy_run(self.max_num_tokens, is_profile=True)
3293
        if get_pp_group().is_last_rank:
3294
3295
3296
3297
            if self.is_pooling_model:
                output = self._dummy_pooler_run(hidden_states)
            else:
                output = self._dummy_sampler_run(last_hidden_states)
3298
        else:
3299
            output = None
3300
        self._sync_device()
3301
        del hidden_states, output
3302
        self.encoder_cache.clear()
3303
        gc.collect()
3304

3305
    def capture_model(self) -> int:
3306
        if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
3307
            logger.warning(
3308
                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
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                "ensure `cudagraph_mode` was not manually set to `NONE`")
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            return 0
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        else:
            self.initialize_cudagraph_capture()
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        compilation_counter.num_gpu_runner_capture_triggers += 1

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        start_time = time.perf_counter()
        start_free_gpu_memory = torch.cuda.mem_get_info()[0]

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        @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()
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                    gc.collect()
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        # 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.
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        set_cudagraph_capturing_enabled(True)
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        with freeze_gc(), graph_capture(device=self.device):
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            cudagraph_mode = self.compilation_config.cudagraph_mode
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            assert cudagraph_mode is not None
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            if cudagraph_mode.mixed_mode() != CUDAGraphMode.NONE:
                cudagraph_runtime_mode = cudagraph_mode.mixed_mode()

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

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            # Capture full cudagraph for uniform decode batches if we
            # don't already have full mixed prefill-decode cudagraphs.
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            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
                decode_cudagraph_batch_sizes = [
                    x for x in self.cudagraph_batch_sizes if
                    x <= max_num_tokens and x >= self.uniform_decode_query_len
                ]
                compilation_cases_decode = list(
                    reversed(decode_cudagraph_batch_sizes))
                self._capture_cudagraphs(
                    compilation_cases=compilation_cases_decode,
                    cudagraph_runtime_mode=CUDAGraphMode.FULL,
                    uniform_decode=True)

        # 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
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        # we may do lazy capturing in future that still allows capturing
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        # after here.
        set_cudagraph_capturing_enabled(False)
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        end_time = time.perf_counter()
        end_free_gpu_memory = torch.cuda.mem_get_info()[0]
        elapsed_time = end_time - start_time
        cuda_graph_size = start_free_gpu_memory - end_free_gpu_memory
        # This usually takes 5~20 seconds.
        logger.info("Graph capturing finished in %.0f secs, took %.2f GiB",
                    elapsed_time, cuda_graph_size / (1 << 30))
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        return cuda_graph_size
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    def _capture_cudagraphs(self, compilation_cases: list[int],
                            cudagraph_runtime_mode: CUDAGraphMode,
                            uniform_decode: bool):
        assert cudagraph_runtime_mode != CUDAGraphMode.NONE and \
            cudagraph_runtime_mode in [CUDAGraphMode.FULL,
                                        CUDAGraphMode.PIECEWISE]

        # 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",
                    cudagraph_runtime_mode.name))
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        # We skip EPLB here since we don't want to record dummy metrics
        for num_tokens in compilation_cases:
            # We currently only capture ubatched graphs when its a FULL
            # cudagraph and for uniform decode batches.
            capture_ubatched_graph = self.parallel_config.enable_dbo \
                and cudagraph_runtime_mode == CUDAGraphMode.FULL \
                and uniform_decode \
                and check_ubatch_thresholds(
                    config=self.vllm_config.parallel_config,
                    num_tokens=num_tokens,
                    uniform_decode=uniform_decode,
                )

            # Currently we capture both microbatched and non-microbatched
            # graphs when capture_ubatched_graph is True, this is because
            # occasionally we will be forced out of microbatching due to other
            # DP ranks not microbatching (usually caused by an empty second
            # microbatch; once we resolve this, we can remove the
            # non-microbatched graph capture).
            allow_microbatching_options = [True, False] if \
                capture_ubatched_graph else [False]
            for allow_microbatching in allow_microbatching_options:
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                for _ in range(
                        self.compilation_config.cudagraph_num_of_warmups):
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                    # 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.
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                    force_attention = (
                        cudagraph_runtime_mode == CUDAGraphMode.FULL)
                    self._dummy_run(num_tokens,
                                    cudagraph_runtime_mode=CUDAGraphMode.NONE,
                                    force_attention=force_attention,
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                                    uniform_decode=uniform_decode,
                                    allow_microbatching=allow_microbatching,
                                    skip_eplb=True,
                                    remove_lora=False)
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                self._dummy_run(num_tokens,
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                                cudagraph_runtime_mode=cudagraph_runtime_mode,
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                                uniform_decode=uniform_decode,
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                                allow_microbatching=allow_microbatching,
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                                skip_eplb=True,
                                remove_lora=False)
        self.maybe_remove_all_loras(self.lora_config)
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    def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize the attention backends and attention metadata builders.
        """
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        assert len(self.attn_groups) == 0, \
            "Attention backends are already initialized"

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        class AttentionGroupKey(NamedTuple):
            attn_backend: type[AttentionBackend]
            kv_cache_spec: KVCacheSpec

        def get_attn_backends_for_group(
            kv_cache_group_spec: KVCacheGroupSpec,
        ) -> dict[AttentionGroupKey, list[str]]:
            layers = get_layers_from_vllm_config(
                self.vllm_config, AttentionLayerBase,
                kv_cache_group_spec.layer_names)
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            attn_backends = {}
            attn_backend_layers = defaultdict(list)
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            # Dedupe based on full class name; this is a bit safer than
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            # using the class itself as the key because when we create dynamic
            # attention backend subclasses (e.g. ChunkedLocalAttention) unless
            # they are cached correctly, there will be different objects per
            # layer.
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            for layer_name in kv_cache_group_spec.layer_names:
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                attn_backend = layers[layer_name].get_attn_backend()
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                if layer_name in self.kv_sharing_fast_prefill_eligible_layers:
                    attn_backend = create_fast_prefill_custom_backend(
                        "FastPrefill",
                        attn_backend,
                    )

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                full_cls_name = attn_backend.full_cls_name()
                layer_kv_cache_spec = kv_cache_group_spec.kv_cache_spec
                if isinstance(layer_kv_cache_spec, UniformTypeKVCacheSpecs):
                    layer_kv_cache_spec = layer_kv_cache_spec.kv_cache_specs[
                        layer_name]
                key = (full_cls_name, layer_kv_cache_spec)
                attn_backends[key] = AttentionGroupKey(attn_backend,
                                                       layer_kv_cache_spec)
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                attn_backend_layers[key].append(layer_name)
            return {
                attn_backends[k]: v
                for k, v in attn_backend_layers.items()
            }

        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
                    if not self.parallel_config.enable_dbo else 2,
                )

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                attn_groups.append(attn_group)
            return attn_groups

        for kv_cache_group_spec in kv_cache_config.kv_cache_groups:
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            attn_backends = get_attn_backends_for_group(kv_cache_group_spec)
            self.attn_groups.append(create_attn_groups(attn_backends))
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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 initialize_cudagraph_capture(self) -> None:
        min_cg_support = AttentionCGSupport.ALWAYS
        min_cg_builder_name = None

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

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

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

        # double check that we can support full cudagraph if they are requested
        # even after automatic downgrades
        if cudagraph_mode.has_full_cudagraphs() \
            and min_cg_support == AttentionCGSupport.NEVER:
            raise ValueError(f"CUDAGraphMode.{cudagraph_mode.name} is not "
                             f"supported with {min_cg_builder_name} backend ("
                             f"support:{min_cg_support}) "
                             "; please try cudagraph_mode=PIECEWISE, "
                             "and make sure compilation level is piecewise")

        # Trigger cudagraph dispatching keys initialization here (after
        # initializing attn backends).
        self.cudagraph_dispatcher.initialize_cudagraph_keys(
            self.compilation_config.cudagraph_mode,
            self.uniform_decode_query_len)

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    def calculate_reorder_batch_threshold(self) -> None:
        """
        Check that if any backends reorder batches; that the reordering
        is compatible (e.g., decode threshold is the same)
        """
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        for group in self._attn_group_iterator():
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            attn_metadata_builder_i = group.get_metadata_builder()
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            # check that if any backends reorder batches; that the reordering
            # is compatible (e.g., decode threshold is the same)
            reorder_batch_threshold_i = (
                attn_metadata_builder_i.reorder_batch_threshold)
            if reorder_batch_threshold_i is not None:
                if self.reorder_batch_threshold is not None:
                    if reorder_batch_threshold_i != \
                        self.reorder_batch_threshold:
                        raise ValueError(
                            f"Attention backend reorders decodes with "
                            f"threshold {reorder_batch_threshold_i} but other "
                            f"backend uses threshold "
                            f"{self.reorder_batch_threshold}")
                else:
                    self.reorder_batch_threshold = reorder_batch_threshold_i

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    def may_reinitialize_input_batch(self,
                                     kv_cache_config: KVCacheConfig) -> None:
        """
        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
        ]
        if block_sizes != [self.cache_config.block_size]:
            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
                "for more details.")
            self.input_batch = InputBatch(
                max_num_reqs=self.max_num_reqs,
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                max_model_len=max(self.max_model_len, self.max_encoder_len),
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                max_num_batched_tokens=self.max_num_tokens,
                device=self.device,
                pin_memory=self.pin_memory,
                vocab_size=self.model_config.get_vocab_size(),
                block_sizes=block_sizes,
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                is_spec_decode=bool(self.vllm_config.speculative_config),
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                logitsprocs=self.input_batch.logitsprocs,
                is_pooling_model=self.is_pooling_model,
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                num_speculative_tokens=(
                    self.vllm_config.speculative_config.num_speculative_tokens
                    if self.vllm_config.speculative_config else 0),
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            )

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

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

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

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

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    def _kv_cache_spec_attn_group_iterator(self) -> Iterator[AttentionGroup]:
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        if not self.kv_cache_config.kv_cache_groups:
            return
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        for attn_groups in self.attn_groups:
            yield from attn_groups
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    def _reshape_kv_cache_tensors(
        self,
        kv_cache_config: KVCacheConfig,
        kv_cache_raw_tensors: dict[str, torch.Tensor],
    ) -> dict[str, torch.Tensor]:
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        """
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        Reshape the KV cache tensors to the desired shape and dtype.
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        Args:
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            kv_cache_config: The KV cache config
            kv_cache_raw_tensors: The KV cache buffer of each layer, with
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                correct size but uninitialized shape.
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        Returns:
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            Dict[str, torch.Tensor]: A map between layer names to their
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            corresponding memory buffer for KV cache.
        """
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        kv_caches: dict[str, torch.Tensor] = {}
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        has_attn, has_mamba = False, False
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        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
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            attn_backend = group.backend
            for layer_name in group.layer_names:
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                if layer_name in self.runner_only_attn_layers:
                    continue
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                raw_tensor = kv_cache_raw_tensors[layer_name]
                assert raw_tensor.numel() % kv_cache_spec.page_size_bytes == 0
                num_blocks = (raw_tensor.numel() //
                              kv_cache_spec.page_size_bytes)
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                if isinstance(kv_cache_spec, AttentionSpec):
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                    has_attn = True
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                    kv_cache_shape = attn_backend.get_kv_cache_shape(
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                        num_blocks, kv_cache_spec.block_size,
                        kv_cache_spec.num_kv_heads, kv_cache_spec.head_size)
                    dtype = kv_cache_spec.dtype
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                    try:
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                        kv_cache_stride_order = \
                            attn_backend.get_kv_cache_stride_order()
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                        assert len(kv_cache_stride_order) == len(
                            kv_cache_shape)
                    except (AttributeError, NotImplementedError):
                        kv_cache_stride_order = tuple(
                            range(len(kv_cache_shape)))
                    # 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.
                    kv_cache_shape = tuple(kv_cache_shape[i]
                                           for i in kv_cache_stride_order)
                    # Maintain original KV shape view.
                    inv_order = [
                        kv_cache_stride_order.index(i)
                        for i in range(len(kv_cache_stride_order))
                    ]
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                    kv_caches[layer_name] = kv_cache_raw_tensors[
                        layer_name].view(dtype).view(kv_cache_shape).permute(
                            *inv_order)
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                elif isinstance(kv_cache_spec, MambaSpec):
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                    has_mamba = True
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                    raw_tensor = kv_cache_raw_tensors[layer_name]
                    state_tensors = []
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                    storage_offset_bytes = 0
                    for (shape, dtype) in zip(kv_cache_spec.shapes,
                                              kv_cache_spec.dtypes):
                        dtype_size = get_dtype_size(dtype)
                        num_element_per_page = (
                            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(
            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]
                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 " \
                        f"a tensor of shape {kv_cache.shape}"
                    hidden_size = kv_cache.shape[2:].numel()
                    kv_cache.as_strided_(size=kv_cache.shape,
                                         stride=(hidden_size, 2 * hidden_size,
                                                 *kv_cache.stride()[2:]))
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    def initialize_kv_cache_tensors(
            self, kv_cache_config: KVCacheConfig) -> dict[str, torch.Tensor]:
        """
        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
        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
        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)
            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
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        bind_kv_cache(kv_caches,
                      self.compilation_config.static_forward_context,
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                      self.kv_caches, num_attn_module)
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        return kv_caches

    def maybe_add_kv_sharing_layers_to_kv_cache_groups(
            self, kv_cache_config: KVCacheConfig) -> None:
        """
        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)
            for layer_name in reversed(attn_layers):
                if layer_name in self.shared_kv_cache_layers:
                    self.kv_sharing_fast_prefill_eligible_layers.add(
                        layer_name)
                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
        self.may_reinitialize_input_batch(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)
        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():
            get_kv_transfer_group().register_kv_caches(kv_caches)
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            if self.device.type == 'xpu':
                get_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
            layers = get_layers_from_vllm_config(self.vllm_config,
                                                 AttentionLayerBase,
                                                 layer_names)
            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__} "
                    "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
        use_mla = self.vllm_config.model_config.use_mla
        encoder_only_attn_specs: dict[AttentionSpec,
                                      list[str]] = defaultdict(list)
        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,
                    dtype=self.kv_cache_dtype,
                    use_mla=use_mla)
                encoder_only_attn_specs[attn_spec].append(layer_name)
                self.runner_only_attn_layers.add(layer_name)
        if len(encoder_only_attn_specs) > 0:
            assert len(
                encoder_only_attn_specs
            ) == 1, "Only support one encoder-only attention spec now"
            spec, layer_names = encoder_only_attn_specs.popitem()
            self.kv_cache_config.kv_cache_groups.append(
                KVCacheGroupSpec(layer_names=layer_names, kv_cache_spec=spec))

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    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
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        """
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        Generates the KVCacheSpec by parsing the kv cache format from each
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        Attention module in the static forward context.
        Returns:
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            KVCacheSpec: A dictionary mapping layer names to their KV cache
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            format. Layers that do not need KV cache are not included.
        """

        block_size = self.vllm_config.cache_config.block_size
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        use_mla = self.vllm_config.model_config.use_mla
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        kv_cache_spec: dict[str, KVCacheSpec] = {}
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        attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention)
        for layer_name, attn_module in attn_layers.items():
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            if (kv_tgt_layer :=
                    attn_module.kv_sharing_target_layer_name) is not None:
                # The layer doesn't need its own KV cache and will use that of
                # the target layer. We skip creating a KVCacheSpec for it, so
                # that KV cache management logic will act as this layer does
                # not exist, and doesn't allocate KV cache for the layer. This
                # enables the memory saving of cross-layer kv sharing, allowing
                # a given amount of memory to accommodate longer context lengths
                # or enable more requests to be processed simultaneously.
                self.shared_kv_cache_layers[layer_name] = kv_tgt_layer
                continue

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            # TODO(lucas): move the attention specs into the model layers like
            # the attention backends
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            if attn_module.attn_type == AttentionType.DECODER:
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                if attn_module.sliding_window is not None:
                    kv_cache_spec[layer_name] = SlidingWindowSpec(
                        block_size=block_size,
                        num_kv_heads=attn_module.num_kv_heads,
                        head_size=attn_module.head_size,
                        dtype=self.kv_cache_dtype,
                        sliding_window=attn_module.sliding_window,
                        use_mla=use_mla)
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                elif self.attention_chunk_size is not None \
                        and isinstance(attn_module, ChunkedLocalAttention):
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                    kv_cache_spec[layer_name] = ChunkedLocalAttentionSpec(
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                        block_size=block_size,
                        num_kv_heads=attn_module.num_kv_heads,
                        head_size=attn_module.head_size,
                        dtype=self.kv_cache_dtype,
                        attention_chunk_size=self.attention_chunk_size,
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                        use_mla=use_mla)
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                else:
                    kv_cache_spec[layer_name] = FullAttentionSpec(
                        block_size=block_size,
                        num_kv_heads=attn_module.num_kv_heads,
                        head_size=attn_module.head_size,
                        dtype=self.kv_cache_dtype,
                        use_mla=use_mla)
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            elif attn_module.attn_type == AttentionType.ENCODER_DECODER:
                kv_cache_spec[layer_name] = CrossAttentionSpec(
                    block_size=block_size,
                    num_kv_heads=attn_module.num_kv_heads,
                    head_size=attn_module.head_size,
                    dtype=self.kv_cache_dtype,
                    use_mla=use_mla)
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            elif attn_module.attn_type in (AttentionType.ENCODER,
                                           AttentionType.ENCODER_ONLY):
                # encoder-only attention does not need KV cache.
                continue
            else:
                raise ValueError(
                    f"Unknown attention type: {attn_module.attn_type}")

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        mamba_layers = get_layers_from_vllm_config(self.vllm_config, MambaBase)
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        if len(mamba_layers) > 0:
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            if (self.vllm_config.speculative_config is not None
                    and self.vllm_config.model_config.hf_config.model_type
                    not in ["qwen3_next"]):
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                raise NotImplementedError(
                    "Mamba with speculative decoding is not supported yet.")
            if self.vllm_config.cache_config.enable_prefix_caching:
                raise NotImplementedError(
                    "Prefix caching is not supported for Mamba yet.")
            max_model_len = self.vllm_config.model_config.max_model_len
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            page_size_padded = (
                self.vllm_config.cache_config.mamba_page_size_padded)
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            # Set block_size to max_model_len, so that mamba model will always
            # have only one block in the KV cache.
            for layer_name, mamba_module in mamba_layers.items():
                kv_cache_spec[layer_name] = MambaSpec(
                    shapes=mamba_module.get_state_shape(),
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                    dtypes=mamba_module.get_state_dtype(),
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                    block_size=max_model_len,
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                    page_size_padded=page_size_padded,
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                    mamba_type=mamba_module.mamba_type,
                    num_speculative_blocks=(
                        self.speculative_config.num_speculative_tokens
                        if self.speculative_config else 0),
                )
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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.
        pinned = self.sampled_token_ids_pinned_cpu[:sampled_token_ids.shape[0]]
        pinned.copy_(sampled_token_ids, non_blocking=True)
        self.transfer_event.record()
        self.transfer_event.synchronize()
        return pinned.tolist()