gpu_model_runner.py 190 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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# yapf conflicts with isort for this block
# yapf: disable
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from vllm.model_executor.models.interfaces import (SupportsMultiModal,
                                                   is_mixture_of_experts,
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                                                   supports_eagle3,
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                                                   supports_mrope,
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                                                   supports_multimodal_pruning,
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                                                   supports_transcription)
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# yapf: enable
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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
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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.utils.jsontree import json_map_leaves
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from vllm.v1.attention.backends.flash_attn import AttentionMetadata
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from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadataBuilder
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from vllm.v1.attention.backends.utils import (
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    AttentionCGSupport, AttentionMetadataBuilder, CommonAttentionMetadata,
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    create_fast_prefill_custom_backend,
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    reorder_batch_to_split_decodes_and_prefills, split_attn_metadata)
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from vllm.v1.cudagraph_dispatcher import CudagraphDispatcher
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# 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,
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                             ModelRunnerOutput, PoolerOutput, SamplerOutput)
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from vllm.v1.pool.metadata import PoolingMetadata
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from vllm.v1.sample.logits_processor import LogitsProcessors, build_logitsprocs
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from vllm.v1.sample.metadata import SamplingMetadata
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from vllm.v1.sample.rejection_sampler import RejectionSampler
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from vllm.v1.sample.sampler import Sampler
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from vllm.v1.spec_decode.eagle import EagleProposer
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from vllm.v1.spec_decode.medusa import MedusaProposer
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from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
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from vllm.v1.spec_decode.ngram_proposer import NgramProposer
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from vllm.v1.structured_output.utils import apply_grammar_bitmask
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from vllm.v1.utils import CpuGpuBuffer, record_function_or_nullcontext
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from vllm.v1.worker.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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        # This will be overridden in load_model()
        self.is_multimodal_pruning_enabled = False
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        self.max_model_len = model_config.max_model_len
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        self.dcp_world_size = self.parallel_config.decode_context_parallel_size
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        self.max_num_tokens = scheduler_config.max_num_batched_tokens
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        self.max_num_reqs = scheduler_config.max_num_seqs
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        # Broadcast PP output for external_launcher (torchrun)
        # to make sure we are synced across pp ranks
        # TODO: Support overlapping mirco-batches
        # https://github.com/vllm-project/vllm/issues/18019
        self.broadcast_pp_output = (
            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_decode_draft_tokens = self._make_buffer(self.max_num_reqs,
                                                         dtype=torch.int32)
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        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.
648
                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,
                )
771

772
    def _extract_mm_kwargs(
773
        self,
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        scheduler_output: "SchedulerOutput",
    ) -> BatchedTensorInputs:
776
        if not scheduler_output or not self.is_multimodal_raw_input_only_model:
777
            return {}
778

779
780
        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)
784

785
        # Input all modalities at once
786
        model = cast(SupportsMultiModal, self.model)
787
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        mm_kwargs_combined: BatchedTensorInputs = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
                mm_kwargs,
                device=self.device,
                pin_memory=self.pin_memory,
792
                merge_by_field_config=model.merge_by_field_config,
793
794
        ):
            mm_kwargs_combined.update(mm_kwargs_group)
795

796
        return mm_kwargs_combined
797

798
    def _dummy_mm_kwargs(self, num_seqs: int) -> BatchedTensorInputs:
799
        if not self.is_multimodal_raw_input_only_model:
800
            return {}
801

802
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806
        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)
807

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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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            if self.enable_prompt_embeds:
                self.inputs_embeds.copy_to_gpu(total_num_scheduled_tokens)
                self.is_token_ids.copy_to_gpu(total_num_scheduled_tokens)
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            return

        # Async scheduling case, where some decode requests from the previous
        # iteration won't have entries in input_ids_cpu and need to be copied
        # on the GPU from prev_sampled_token_ids.
        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)
867
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            if self.enable_prompt_embeds:
                self.inputs_embeds.copy_to_gpu(total_num_scheduled_tokens)
                self.is_token_ids.copy_to_gpu(total_num_scheduled_tokens)
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        if num_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)
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            if self.enable_prompt_embeds:
                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

921
    def _prepare_inputs(
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        self, scheduler_output: "SchedulerOutput"
    ) -> tuple[PerLayerAttnMetadata, torch.Tensor,
               Optional[SpecDecodeMetadata], np.ndarray,
               Optional[CommonAttentionMetadata], int, Optional[UBatchSlices],
926
               Optional[torch.Tensor], bool]:
927
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929
        """
        :return: tuple[
            attn_metadata: layer-to-attention_metadata mapping,
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            logits_indices, spec_decode_metadata,
            num_scheduled_tokens, spec_decode_common_attn_metadata,
            max_num_scheduled_tokens, use_cascade_attn
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        ]
        """
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941
        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.
942
        self.input_batch.block_table.commit_block_table(num_reqs)
943
944

        # Get the number of scheduled tokens for each request.
945
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948
        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)
949
950
951

        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
952
953
        req_indices = np.repeat(self.arange_np[:num_reqs],
                                num_scheduled_tokens)
954

955
956
957
958
        # 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)
959
960

        # Get positions.
961
        positions_np = self.positions.np[:total_num_scheduled_tokens]
962
963
964
965
        np.add(self.input_batch.num_computed_tokens_cpu[req_indices],
               arange,
               out=positions_np)

966
967
        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
968
        if self.uses_mrope:
969
970
            self._calc_mrope_positions(scheduler_output)

971
972
973
974
        # 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.
975
976
        token_indices = (positions_np +
                         req_indices * self.input_batch.token_ids_cpu.shape[1])
977
        token_indices_tensor = torch.from_numpy(token_indices)
978

979
980
981
982
        # 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(),
983
                           0,
984
                           token_indices_tensor,
985
                           out=self.input_ids.cpu[:total_num_scheduled_tokens])
986
987
988
989
990
991
992
        if self.enable_prompt_embeds:
            is_token_ids = self.input_batch.is_token_ids.flatten()
            torch.index_select(
                is_token_ids,
                0,
                token_indices_tensor,
                out=self.is_token_ids.cpu[:total_num_scheduled_tokens])
993
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999
1000
1001
1002
1003
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1005
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1007
1008
1009
1010
1011
1012
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1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031

        # 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
1032

1033
1034
1035
1036
        self.input_batch.block_table.compute_slot_mapping(
            req_indices, positions_np)
        self.input_batch.block_table.commit_slot_mapping(
            total_num_scheduled_tokens)
1037
1038

        # Prepare the attention metadata.
1039
1040
        self.query_start_loc.np[0] = 0
        self.query_start_loc.np[1:num_reqs + 1] = cu_num_tokens
1041
1042
        # Note: pad query_start_loc to be non-decreasing, as kernels
        # like FlashAttention requires that
1043
1044
1045
        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]
1046

1047
1048
1049
        num_tokens_unpadded = scheduler_output.total_num_scheduled_tokens
        num_tokens_padded = num_tokens_unpadded + self.get_local_padding(
            num_tokens_unpadded)
1050
1051
1052
        uniform_decode = \
            (max_num_scheduled_tokens == self.uniform_decode_query_len) and \
            (total_num_scheduled_tokens == num_reqs * max_num_scheduled_tokens)
1053
        ubatch_slices, num_tokens_after_padding = \
1054
            ubatch_split(num_scheduled_tokens,
1055
1056
                         num_tokens_unpadded,
                         num_tokens_padded,
1057
1058
                         uniform_decode=uniform_decode,
                         vllm_config=self.vllm_config)
1059

1060
        self.seq_lens.np[:num_reqs] = (
1061
1062
            self.input_batch.num_computed_tokens_cpu[:num_reqs] +
            num_scheduled_tokens)
1063
        # Fill unused with 0 for full cuda graph mode.
1064
1065
1066
1067
        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()
1068

1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
        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)

1084
        # Copy the tensors to the GPU.
1085
1086
        self._prepare_input_ids(total_num_scheduled_tokens, cu_num_tokens)

1087
        if self.uses_mrope:
1088
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1089
1090
            self.mrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.mrope_positions.cpu[:, :total_num_scheduled_tokens],
1091
1092
1093
                non_blocking=True)
        else:
            # Common case (1D positions)
1094
            self.positions.copy_to_gpu(total_num_scheduled_tokens)
1095

1096
1097
1098
1099
1100
1101
1102
1103
1104
        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
1105
            num_draft_tokens = None
1106
1107
1108
1109
1110
1111
            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)
1112
1113
1114
            # For chunked prefills, use -1 as mask rather than 0, as guided
            # decoding may rollback speculative tokens.
            num_decode_draft_tokens = np.full(num_reqs, -1, dtype=np.int32)
1115
1116
1117
1118
            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)
1119
1120
1121
                num_decode_draft_tokens[req_idx] = (len(draft_token_ids) if (
                    self.input_batch.num_computed_tokens_cpu[req_idx]
                    >= self.input_batch.num_prompt_tokens[req_idx]) else -1)
1122
1123
1124
            spec_decode_metadata = self._calc_spec_decode_metadata(
                num_draft_tokens, cu_num_tokens)
            logits_indices = spec_decode_metadata.logits_indices
1125
1126
1127
1128
1129
1130

            # For DECODE only cuda graph of some attention backends (e.g., GDN).
            self.num_decode_draft_tokens.np[:
                                            num_reqs] = num_decode_draft_tokens
            self.num_decode_draft_tokens.np[num_reqs:].fill(-1)
            self.num_decode_draft_tokens.copy_to_gpu()
1131
1132
1133

        logits_indices_padded = None
        if self.cache_config.kv_sharing_fast_prefill:
1134
            logits_indices_padded = self._prepare_kv_sharing_fast_prefill(
1135
1136
                logits_indices)

1137
1138
1139
        attn_metadata: PerLayerAttnMetadata = {}
        if ubatch_slices is not None:
            attn_metadata = [dict() for _ in range(len(ubatch_slices))]
1140
        use_cascade_attn = False
1141

1142
        # Used in the below loop.
1143
1144
        query_start_loc_cpu = self.query_start_loc.cpu[:num_reqs + 1]
        seq_lens_cpu = self.seq_lens.cpu[:num_reqs]
1145
1146
1147
        num_computed_tokens_cpu = (
            self.input_batch.num_computed_tokens_cpu_tensor[:num_reqs])
        spec_decode_common_attn_metadata = None
1148
1149
1150
1151
1152
        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()
1153

1154
1155
1156
1157
        # 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):
1158
1159
            encoder_seq_lens = self._get_encoder_seq_lens(
                scheduler_output, kv_cache_group_spec.kv_cache_spec, num_reqs)
1160

1161
1162
1163
1164
1165
1166
1167
            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,
1168
1169
1170
1171
1172
1173
1174
                    device=self.device,
                )
                slot_mapping = torch.zeros(
                    (total_num_scheduled_tokens, ),
                    dtype=torch.int64,
                    device=self.device,
                )
1175
1176
1177
                num_common_prefix_blocks = 0
            else:
                blk_table = self.input_batch.block_table[kv_cache_group_id]
1178
1179
1180
                blk_table_tensor = blk_table.get_device_tensor(num_reqs)
                slot_mapping = blk_table.slot_mapping.gpu[:
                                                          total_num_scheduled_tokens]
1181
1182
1183

                # Fill unused with -1. Needed for reshape_and_cache in full cuda
                # graph mode.
1184
1185
                blk_table.slot_mapping.gpu[total_num_scheduled_tokens:].fill_(
                    -1)
1186
1187
1188
                num_common_prefix_blocks = (
                    scheduler_output.
                    num_common_prefix_blocks[kv_cache_group_id])
1189

1190
            common_attn_metadata = CommonAttentionMetadata(
1191
1192
1193
1194
1195
                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,
1196
1197
1198
                num_reqs=num_reqs,
                num_actual_tokens=total_num_scheduled_tokens,
                max_query_len=max_num_scheduled_tokens,
1199
                max_seq_len=max_seq_len,
1200
1201
                block_table_tensor=blk_table_tensor,
                slot_mapping=slot_mapping,
1202
1203
                logits_indices_padded=logits_indices_padded,
                num_logits_indices=logits_indices.size(0),
1204
                causal=True,
1205
                encoder_seq_lens=encoder_seq_lens,
1206
1207
            )

1208
1209
1210
1211
1212
1213
1214
1215
            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
1216

1217
1218
1219
            for attn_group in self.attn_groups[kv_cache_group_id]:
                # Prepare for cascade attention if enabled & beneficial.
                common_prefix_len = 0
1220
                builder = attn_group.get_metadata_builder()
1221
1222
1223
                if self.cascade_attn_enabled:
                    common_prefix_len = self._compute_cascade_attn_prefix_len(
                        num_scheduled_tokens,
1224
                        num_common_prefix_blocks,
1225
                        attn_group.kv_cache_spec,
1226
1227
                        builder,
                    )
1228

1229
1230
1231
1232
1233
1234
                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],
1235
1236
                        num_decode_draft_tokens_cpu=self.
                        num_decode_draft_tokens.cpu[:num_reqs],
1237
1238
                    )

1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
                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)
1257
1258
                    use_cascade_attn |= getattr(attn_metadata_i, "use_cascade",
                                                False)
1259
1260
                    for layer_name in attn_group.layer_names:
                        attn_metadata[layer_name] = attn_metadata_i
1261

1262
1263
1264
1265
        # disable cascade attention when DBO
        if ubatch_slices is not None:
            use_cascade_attn = False

1266
1267
1268
1269
        # Hot-Swap lora model
        if self.lora_config:
            self.set_active_loras(self.input_batch, num_scheduled_tokens)

1270
1271
        return (attn_metadata, logits_indices, spec_decode_metadata,
                num_scheduled_tokens, spec_decode_common_attn_metadata,
1272
                max_num_scheduled_tokens, ubatch_slices,
1273
                num_tokens_after_padding, use_cascade_attn)
1274

1275
1276
1277
1278
    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
        num_common_prefix_blocks: int,
1279
1280
        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
    ) -> 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.
        """
1299
        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
        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]
1337
        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
        # 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.
1348
1349
1350
1351
1352
        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))
1353
1354
1355
1356
        use_local_attention = (
            isinstance(kv_cache_spec, ChunkedLocalAttentionSpec)
            or (isinstance(kv_cache_spec, FullAttentionSpec)
                and kv_cache_spec.attention_chunk_size is not None))
1357
1358
        assert isinstance(kv_cache_spec, AttentionSpec)
        use_cascade = attn_metadata_builder.use_cascade_attention(
1359
1360
1361
            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
1362
            num_kv_heads=kv_cache_spec.num_kv_heads,
1363
            use_alibi=self.use_alibi,
1364
            use_sliding_window=use_sliding_window,
1365
            use_local_attention=use_local_attention,
1366
1367
1368
1369
            num_sms=self.num_sms,
        )
        return common_prefix_len if use_cascade else 0

1370
1371
    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
1372
        for index, req_id in enumerate(self.input_batch.req_ids):
1373
1374
1375
1376
1377
1378
1379
            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]
1380
1381
            num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
                req.prompt_token_ids, req.prompt_embeds)
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400

            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

1401
1402
                self.mrope_positions.cpu[:, dst_start:dst_end] = (
                    req.mrope_positions[:, src_start:src_end])
1403
1404
1405
1406
1407
1408
1409
                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

1410
                MRotaryEmbedding.get_next_input_positions_tensor(
1411
                    out=self.mrope_positions.np,
1412
1413
1414
1415
1416
                    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,
                )
1417
1418
1419

                mrope_pos_ptr += completion_part_len

1420
1421
    def _calc_spec_decode_metadata(
        self,
1422
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        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
1438
1439
1440
1441
1442
1443

        # 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]
1444
1445
        logits_indices = np.repeat(
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens)
1446
        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
1447
1448
1449
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1452
        logits_indices += arange

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

        # Compute the draft logits indices.
1453
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        # 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)
1457
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1464
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1466
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1470
        # [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(
1471
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            self.device, non_blocking=True)

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1474
        # Compute the draft token ids.
        # draft_token_indices:      [  1,   2,   3, 105, 106, 208]
1475
        draft_token_ids = self.input_ids.gpu[logits_indices]
1476
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        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

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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

1514
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1521
    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
1522
                inputs.
1523
1524
1525
1526
1527
1528

        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
        """
1529
1530
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
1531
            return [], []
1532
        # Batch the multi-modal inputs.
1533
        mm_kwargs = list[MultiModalKwargsItem]()
1534
1535
        # list of tuple (mm_hash, position_info)
        mm_hashes_pos = list[tuple[str, PlaceholderRange]]()
1536
1537
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
1538
1539

            for mm_input_id in encoder_input_ids:
1540
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1543
                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))
1544

1545
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1547
1548
1549
1550
1551
1552
1553
1554
        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

1555
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1561
        # 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.
1562
        model = cast(SupportsMultiModal, self.model)
1563
        encoder_outputs = []
1564
        for modality, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
1565
                mm_kwargs,
1566
                device=self.device,
1567
                pin_memory=self.pin_memory,
1568
                merge_by_field_config=model.merge_by_field_config,
1569
        ):
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            # (ekhvedchenia): Temporary hack to limit peak memory usage when
            # processing multimodal data.This solves the issue with scheduler
            # putting too many video samples into a single batch. Scheduler
            # uses pruned vision tokens count to compare it versus compute
            # budget which is incorrect (Either input media size or non-pruned
            # output vision tokens count should be considered)
            curr_group_outputs = []

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

                    micro_batch_outputs = model.get_multimodal_embeddings(
                        **micro_batch_mm_inputs)

                    curr_group_outputs.extend(micro_batch_outputs)
            else:
                # Run the encoder.
                # `curr_group_outputs` is either of the following:
                # 1. A tensor of shape (num_items, feature_size, hidden_size)
                # in case feature_size is fixed across all multimodal items.
                # 2. A list or tuple (length: num_items) of tensors,
                # each of shape (feature_size, hidden_size) in case the feature
                # size is dynamic depending on the input multimodal items.
                curr_group_outputs = model.get_multimodal_embeddings(
                    **mm_kwargs_group)
1599

1600
1601
            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
1602
                expected_num_items=num_items,
1603
            )
1604
            encoder_outputs.extend(curr_group_outputs)
1605

1606
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1608
        # 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(
1609
1610
1611
1612
1613
                output,
                is_embed=pos_info.is_embed,
            )

    def _gather_mm_embeddings(
1614
1615
        self,
        scheduler_output: "SchedulerOutput",
1616
        shift_computed_tokens: int = 0,
1617
    ) -> list[torch.Tensor]:
1618
        should_sync_mrope_positions = False
1619
        mm_embeds: list[torch.Tensor] = []
1620
        for req_id in self.input_batch.req_ids:
1621
1622
            mm_embeds_req: list[torch.Tensor] = []

1623
1624
1625
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[
                req_id]
            req_state = self.requests[req_id]
1626
1627
            num_computed_tokens = \
                req_state.num_computed_tokens + shift_computed_tokens
1628
1629
            for mm_feature in req_state.mm_features:
                pos_info = mm_feature.mm_position
1630
1631
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
1632
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1640
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1643
1644
1645
1646
1647

                # 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,
1648
1649
                    num_encoder_tokens,
                )
1650
                assert start_idx < end_idx
1651

1652
                mm_hash = mm_feature.identifier
1653
1654
1655
                encoder_output = self.encoder_cache.get(mm_hash, None)
                assert encoder_output is not None,\
                    f"Encoder cache miss for {mm_hash}."
1656
1657
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1659
1660
1661
1662
1663

                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,
                )
1664
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1685
                mm_embeds_req.append(mm_embeds_item)

            if self.is_multimodal_pruning_enabled and self.uses_mrope:
                should_sync_mrope_positions = True
                mm_embeds_req, new_mrope_positions, new_delta = (
                    self.model.recompute_mrope_positions(
                        input_ids=req_state.prompt_token_ids,
                        multimodal_embeddings=mm_embeds_req,
                        mrope_positions=req_state.mrope_positions,
                        num_computed_tokens=req_state.num_computed_tokens,
                    ))
                assert req_state.mrope_positions is not None
                req_state.mrope_positions.copy_(new_mrope_positions)
                req_state.mrope_position_delta = new_delta

            mm_embeds.extend(mm_embeds_req)

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

1686
        return mm_embeds
1687

1688
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1700
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1703
    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
1704
        model = cast(SupportsMultiModal, self.model)
1705
1706
1707
1708
1709
        encoder_features = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
                mm_kwargs,
                device=self.device,
                pin_memory=self.pin_memory,
1710
                merge_by_field_config=model.merge_by_field_config,
1711
1712
1713
1714
1715
1716
1717
1718
        ):
            # 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

1719
    def get_model(self) -> nn.Module:
1720
        # get raw model out of the cudagraph wrapper.
1721
        if isinstance(self.model, (CUDAGraphWrapper, UBatchWrapper)):
1722
            return self.model.unwrap()
1723
1724
        return self.model

1725
1726
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1731
1732
1733
1734
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1736
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1738
1739
    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

1740
1741
1742
1743
1744
    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
        model = self.get_model()
        if not is_pooling_model(model):
            return []

1745
1746
1747
1748
1749
1750
        supported_tasks = list(model.pooler.get_supported_tasks())

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

1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
            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.")
1762
1763

        return supported_tasks
1764

1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
    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)

1775
1776
1777
1778
1779
1780
1781
    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
1782
        is_rs = is_residual_scattered_for_sp(self.vllm_config, num_tokens)
1783
1784
1785
1786
1787
1788

        # 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():
1789
                is_scattered = k == "residual" and is_rs
1790
1791
1792
1793
1794
1795
1796
                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:
1797
1798
            v[:num_tokens //
              tp] if k == "residual" and is_rs else v[:num_tokens]
1799
1800
1801
            for k, v in self.intermediate_tensors.items()
        })

1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
    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
1812
1813
        model = self.get_model()
        assert is_mixture_of_experts(model)
1814
        self.eplb_state.step(
1815
            model,
1816
1817
            is_dummy,
            is_profile,
1818
            log_stats=self.parallel_config.eplb_config.log_balancedness,
1819
1820
        )

1821
1822
    def get_dp_padding(self,
                       num_tokens: int) -> tuple[int, Optional[torch.Tensor]]:
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
        """
        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.
        ]
        """
1834
1835
        dp_size = self.vllm_config.parallel_config.data_parallel_size
        dp_rank = self.vllm_config.parallel_config.data_parallel_rank
1836
1837
1838
1839
1840
1841
1842
1843
1844

        # 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:
1845
            # Early exit.
1846
            return 0, None
1847
1848
1849
1850

        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()
1851
1852
1853
1854
1855
        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
1856

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1889
    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)

1890
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1900
    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"

1901
        hidden_states = hidden_states[:num_scheduled_tokens]
1902
        pooling_metadata = self.input_batch.get_pooling_metadata()
1903
1904
        pooling_metadata.build_pooling_cursor(num_scheduled_tokens_np.tolist(),
                                              device=hidden_states.device)
1905
        seq_lens_cpu = self.seq_lens.cpu[:self.input_batch.num_reqs]
1906

1907
1908
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1910
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1912
1913
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1915
1916
        model = cast(VllmModelForPooling, self.model)
        raw_pooler_output: PoolerOutput = model.pooler(
            hidden_states=hidden_states,
            pooling_metadata=pooling_metadata,
        )
        raw_pooler_output = json_map_leaves(
            lambda x: x.to("cpu", non_blocking=True),
            raw_pooler_output,
        )
        self._sync_device()
1917
1918
1919

        pooler_output: list[Optional[torch.Tensor]] = []
        for raw_output, seq_len, prompt_len in zip(
1920
                raw_pooler_output, seq_lens_cpu, pooling_metadata.prompt_lens):
1921

1922
            output = raw_output if seq_len == prompt_len else None
1923
            pooler_output.append(output)
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933

        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,
        )

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1951
1952
    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

1953
    def _preprocess(
1954
1955
        self,
        scheduler_output: "SchedulerOutput",
1956
        intermediate_tensors: Optional[IntermediateTensors] = None,
1957
1958
        ubatch_slices: Optional[UBatchSlices] = None,
        num_tokens_after_padding: Optional[torch.Tensor] = None,
1959
1960
1961
    ) -> tuple[int, int, Optional[torch.Tensor], Optional[torch.Tensor],
               Optional[torch.Tensor], torch.Tensor,
               Optional[IntermediateTensors], dict[str, Any]]:
1962

1963
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
1964
1965
1966
1967
1968
1969
1970
1971
1972
        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
1973

1974
1975
        # _prepare_inputs may reorder the batch, so we must gather multi
        # modal outputs after that to ensure the correct order
1976
1977
        if (self.supports_mm_inputs and get_pp_group().is_first_rank
                and not self.model_config.is_encoder_decoder):
1978
1979
1980
1981
            # Run the multimodal encoder if any.
            self._execute_mm_encoder(scheduler_output)
            mm_embeds = self._gather_mm_embeddings(scheduler_output)

1982
1983
1984
            # 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.
1985
            inputs_embeds_scheduled = self.model.get_input_embeddings(
1986
                input_ids=self.input_ids.gpu[:num_scheduled_tokens],
1987
1988
                multimodal_embeddings=mm_embeds or None,
            )
1989

1990
            # TODO(woosuk): Avoid the copy. Optimize.
1991
            self.inputs_embeds.gpu[:num_scheduled_tokens].copy_(
1992
1993
                inputs_embeds_scheduled)

1994
            input_ids = None
1995
            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
1996
1997
1998
1999
            model_kwargs = {
                **self._init_model_kwargs(num_scheduled_tokens),
                **self._extract_mm_kwargs(scheduler_output),
            }
2000
        elif self.enable_prompt_embeds and get_pp_group().is_first_rank:
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
            # 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
2026
        else:
2027
2028
2029
2030
            # 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.
2031
            input_ids = self.input_ids.gpu[:num_input_tokens]
2032
            inputs_embeds = None
2033
            model_kwargs = self._init_model_kwargs(num_input_tokens)
2034
        if self.uses_mrope:
2035
            positions = self.mrope_positions.gpu[:, :num_input_tokens]
2036
        else:
2037
            positions = self.positions.gpu[:num_input_tokens]
2038

2039
2040
2041
        if get_pp_group().is_first_rank:
            intermediate_tensors = None
        else:
2042
2043
            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
                num_input_tokens, intermediate_tensors, True)
2044

2045
2046
2047
2048
2049
        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)

2050
2051
2052
        return (
            num_scheduled_tokens,
            num_input_tokens,
2053
            num_tokens_after_padding,
2054
2055
2056
2057
2058
2059
            input_ids,
            inputs_embeds,
            positions,
            intermediate_tensors,
            model_kwargs,
        )
2060

2061
2062
2063
2064
    def _sample(
            self, logits: Optional[torch.Tensor],
            spec_decode_metadata: Optional[SpecDecodeMetadata]
    ) -> SamplerOutput:
2065
        # Sample the next token and get logprobs if needed.
2066
        sampling_metadata = self.input_batch.sampling_metadata
2067
        if spec_decode_metadata is None:
2068
            sampler_output = self.sampler(
2069
2070
2071
2072
                logits=logits,
                sampling_metadata=sampling_metadata,
            )
        else:
2073
2074
2075
2076
            # 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.
2077
            assert logits is not None
2078
            bonus_logits = logits[spec_decode_metadata.bonus_logits_indices]
2079
            sampler_output = self.sampler(
2080
                logits=bonus_logits,
2081
2082
2083
                sampling_metadata=sampling_metadata,
            )
            bonus_token_ids = sampler_output.sampled_token_ids
2084

2085
2086
2087
            # 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.
2088
            target_logits = logits[spec_decode_metadata.target_logits_indices]
2089
            output_token_ids = self.rejection_sampler(
2090
                spec_decode_metadata,
2091
                None,  # draft_probs
2092
                target_logits,
2093
                bonus_token_ids,
2094
2095
                sampling_metadata,
            )
2096
            sampler_output.sampled_token_ids = output_token_ids
2097
            self._update_states_after_model_execute(output_token_ids)
2098

2099
2100
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
2112
2113
        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],
    ]:
2114
2115
2116
2117
        num_nans_in_logits = {}
        if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
            num_nans_in_logits = self._get_nans_in_logits(logits)

2118
2119
2120
2121
2122
2123
        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)
2124

2125
2126
2127
2128
2129
2130
        # 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()

2131
2132
        # NOTE: GPU -> CPU Sync happens here.
        # Move as many CPU operations as possible before this sync point.
2133
2134
2135
2136
2137
2138
        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(
2139
            hidden_states[:num_scheduled_tokens],
2140
            scheduler_output.num_scheduled_tokens,
2141
2142
        )

2143
        num_sampled_tokens = sampler_output.sampled_token_ids.shape[0]
2144
        sampled_token_ids = sampler_output.sampled_token_ids
2145
        invalid_req_indices = []
2146
2147
2148
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
        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:
2160
                valid_sampled_token_ids[int(i)].clear()
2161
        else:
2162
            valid_sampled_token_ids = []
2163
            invalid_req_indices = discard_sampled_tokens_req_indices.tolist()
2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
2175
2176
2177
2178
            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
            }
2179

2180
2181
2182
2183
2184
        # 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.
2185
        req_ids = self.input_batch.req_ids
2186
2187
2188
2189
2190
2191
        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]
2192
2193
2194
2195
2196
2197
2198
2199
2200
2201
2202
2203
            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
2204
            self.input_batch.is_token_ids[req_idx, start_idx:end_idx] = True
2205
2206
            self.input_batch.num_tokens_no_spec[req_idx] = end_idx
            self.input_batch.num_tokens[req_idx] = end_idx
2207

2208
            req_id = req_ids[req_idx]
2209
2210
2211
            req_state = self.requests[req_id]
            req_state.output_token_ids.extend(sampled_ids)

2212
2213
2214
2215
2216
2217
2218
2219
2220
2221
        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,
        )

2222
2223
2224
2225
2226
2227
2228
2229
2230
2231
2232
2233
2234
2235
2236
    @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()

2237
2238
2239
2240
2241
2242
2243
    @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"):
2244
2245
2246
2247
2248
2249
2250
2251
2252
2253
2254
2255
2256
2257
2258
2259
            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")

2260
2261
2262
                # Prepare the decoder inputs.
                (attn_metadata, logits_indices, spec_decode_metadata,
                 num_scheduled_tokens_np, spec_decode_common_attn_metadata,
2263
2264
                 max_query_len, ubatch_slices, num_tokens_after_padding,
                 use_cascade_attn) = self._prepare_inputs(scheduler_output)
2265

2266
2267
2268
2269
2270
2271
2272
2273
2274
            (
                num_scheduled_tokens,
                num_input_tokens,
                num_tokens_across_dp,
                input_ids,
                inputs_embeds,
                positions,
                intermediate_tensors,
                model_kwargs,
2275
2276
2277
            ) = self._preprocess(scheduler_output, intermediate_tensors,
                                 ubatch_slices, num_tokens_after_padding)

2278
2279
2280
2281
2282
2283
2284
            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 = \
2285
2286
                self.cudagraph_dispatcher.dispatch(batch_descriptor,
                                                   use_cascade_attn)
2287

2288
2289
2290
2291
2292
        # 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

2293
2294
2295
2296
2297
2298
2299
2300
2301
        # 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,
2302
                ubatch_slices=ubatch_slices,
2303
2304
2305
2306
2307
2308
2309
2310
2311
2312
2313
2314
2315
        ), 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:
2316
                # True when EAGLE 3 is used.
2317
2318
                hidden_states, aux_hidden_states = model_output
            else:
2319
                # Common case.
2320
2321
2322
                hidden_states = model_output
                aux_hidden_states = None

2323
2324
2325
2326
2327
            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)
2328
2329
                    hidden_states.kv_connector_output = kv_connector_output
                    return hidden_states
2330

2331
                if self.is_pooling_model:
2332
2333
2334
2335
2336
                    # 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
2337
2338

                sample_hidden_states = hidden_states[logits_indices]
2339
                logits = self.model.compute_logits(sample_hidden_states)
2340
2341
2342
2343
2344
            else:
                # Rare case.
                assert not self.is_pooling_model

                if not get_pp_group().is_last_rank:
2345
2346
2347
2348
2349
                    all_gather_tensors = {
                        "residual":
                        not is_residual_scattered_for_sp(
                            self.vllm_config, num_input_tokens)
                    }
2350
                    get_pp_group().send_tensor_dict(
2351
2352
2353
                        hidden_states.tensors,
                        all_gather_group=get_tp_group(),
                        all_gather_tensors=all_gather_tensors)
2354
2355
2356
                    logits = None
                else:
                    sample_hidden_states = hidden_states[logits_indices]
2357
                    logits = self.model.compute_logits(sample_hidden_states)
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                model_output_broadcast_data = {}
                if logits is not None:
                    model_output_broadcast_data["logits"] = logits.contiguous()

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

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

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

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

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

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        if (self.speculative_config and not use_padded_batch_for_eagle
                and input_fits_in_drafter):
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            # ngram and other speculative decoding methods use the sampled
            # tokens on the CPU, so they are run after bookkeeping.
            propose_draft_token_ids(valid_sampled_token_ids)
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        with record_function_or_nullcontext("EPLB"):
            self.eplb_step()
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        output = ModelRunnerOutput(
            req_ids=req_ids_output_copy,
            req_id_to_index=req_id_to_index_output_copy,
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            sampled_token_ids=valid_sampled_token_ids,
            logprobs=logprobs_lists,
            prompt_logprobs_dict=prompt_logprobs_dict,
            pooler_output=[],
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            kv_connector_output=kv_connector_output,
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            num_nans_in_logits=num_nans_in_logits,
        )

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        if not self.use_async_scheduling:
            return output

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

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

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    def propose_draft_token_ids(
        self,
        scheduler_output: "SchedulerOutput",
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        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.drafter.propose(
                sampled_token_ids, self.input_batch.req_ids,
                self.input_batch.num_tokens_no_spec,
                self.input_batch.token_ids_cpu,
                self.input_batch.spec_decode_unsupported_reqs)
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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 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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        self.is_multimodal_pruning_enabled = (supports_multimodal_pruning(
            self.model) and self.model_config.multimodal_config.
                                              is_multimodal_pruning_enabled())

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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_loader.load_weights(self.get_model(),
                                  model_config=self.model_config)
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    def save_tensorized_model(
        self,
        tensorizer_config: "TensorizerConfig",
    ) -> None:
        TensorizerLoader.save_model(
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            self.get_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 {}

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

            if num_logits <= 0:
                # This can happen for the final chunk if we prefilled exactly
                # (num_prompt_tokens - 1) tokens for this request in the prior
                # step. There are no more prompt logprobs to produce.
                continue
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            # Get the logits corresponding to this req's prompt tokens.
            # If this is a partial request (i.e. chunked prefill),
            # then there is prompt logprob generated for each index.
            req_idx = self.input_batch.req_id_to_index[req_id]
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            offset = self.query_start_loc.np[req_idx].item()
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            prompt_hidden_states = hidden_states[offset:offset + num_logits]
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            logits = self.model.compute_logits(prompt_hidden_states)
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            # Get the "target" tokens for each index. For prompt at index i,
            # the token at prompt index i+1 is the "sampled" token we want
            # to gather the logprob for.
            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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2814
2815
2816

        # 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]
2817
            del in_progress_dict[req_id]
2818
2819

        # Must synchronize the non-blocking GPU->CPU transfers.
2820
        if prompt_logprobs_dict:
2821
            self._sync_device()
2822
2823
2824

        return prompt_logprobs_dict

2825
2826
2827
2828
2829
2830
2831
2832
2833
2834
2835
2836
2837
2838
2839
2840
2841
2842
2843
2844
    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 {}

2845
2846
2847
2848
2849
2850
    @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
2851
         - during DP rank dummy run
2852
2853
2854
2855
2856
2857
2858
2859
2860
2861
2862
        """
        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(
2863
                    self.input_ids.gpu,
2864
2865
2866
2867
                    low=0,
                    high=self.model_config.get_vocab_size(),
                    dtype=input_ids.dtype)

2868
            logger.debug_once("Randomizing dummy data for DP Rank")
2869
2870
2871
2872
2873
            input_ids.copy_(rand_input_ids()[:input_ids.size(0)],
                            non_blocking=True)
            yield
            input_ids.fill_(0)

2874
2875
2876
2877
2878
2879
    def _get_mm_dummy_batch(
        self,
        modality: str,
        max_items_per_batch: int,
    ) -> BatchedTensorInputs:
        """Dummy data for profiling and precompiling multimodal models."""
2880
2881
        assert self.mm_budget is not None

2882
2883
        dummy_decoder_data = self.mm_registry.get_decoder_dummy_data(
            model_config=self.model_config,
2884
            seq_len=self.max_model_len,
2885
            mm_counts={modality: 1},
2886
            cache=self.mm_budget.cache,
2887
2888
2889
2890
        )
        dummy_mm_data = dummy_decoder_data.multi_modal_data

        # Result in the maximum GPU consumption of the model
2891
2892
        dummy_mm_item = dummy_mm_data[modality][0]
        dummy_mm_items = [dummy_mm_item] * max_items_per_batch
2893

2894
        model = cast(SupportsMultiModal, self.model)
2895
2896
        return next(mm_kwargs_group
                    for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
2897
                        dummy_mm_items,
2898
2899
                        device=self.device,
                        pin_memory=self.pin_memory,
2900
                        merge_by_field_config=model.merge_by_field_config,
2901
                    ))
2902

2903
2904
2905
2906
    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
2907
        cudagraph_runtime_mode: Optional[CUDAGraphMode] = None,
2908
2909
        force_attention: bool = False,
        uniform_decode: bool = False,
2910
        allow_microbatching: bool = True,
2911
2912
        skip_eplb: bool = False,
        is_profile: bool = False,
2913
        create_mixed_batch: bool = False,
2914
        remove_lora: bool = True,
2915
    ) -> tuple[torch.Tensor, torch.Tensor]:
2916
2917
2918
2919
2920
2921
2922
        """
        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.
2923
                - if not set will determine the cudagraph mode based on using
2924
                    the self.cudagraph_dispatcher.
2925
2926
2927
2928
                - CUDAGraphMode.NONE: No cudagraph, for warm up and profile run
                - CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
                - CUDAGraphMode.FULL: Full cudagraph, attention metadata is
                    needed.
2929
            force_attention: If True, always create attention metadata. Used to
2930
2931
2932
2933
                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.
2934
2935
            create_mixed_batch: If True, create a mixed batch with both decode
                (1 token) and prefill (multiple tokens) requests.
2936
            remove_lora: If False, dummy LoRAs are not destroyed after the run
2937
        """
2938
2939
        assert cudagraph_runtime_mode is None or \
            cudagraph_runtime_mode.valid_runtime_modes()
2940

2941
        # If cudagraph_mode.decode_mode() == FULL and
2942
        # cudagraph_mode.separate_routine(). This means that we are using
2943
2944
2945
2946
2947
2948
2949
2950
2951
2952
2953
2954
2955
2956
        # 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

2957
2958
2959
2960
2961
        # 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
2962
2963
2964
2965
2966
2967
2968
2969
2970
2971
2972
2973
2974
2975
2976
        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:
2977
2978
            assert not create_mixed_batch
            num_reqs = cdiv(num_tokens, max_query_len)
2979
2980
            num_scheduled_tokens_list = [max_query_len] * num_reqs
            if num_tokens % max_query_len != 0:
2981
                num_scheduled_tokens_list[-1] = num_tokens % max_query_len
2982
2983
2984
2985
2986
2987
        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

2988
2989
2990
2991
        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)
2992
        total_num_scheduled_tokens = int(num_scheduled_tokens.sum())
2993

2994
        ubatch_slices = None
2995
2996
        num_tokens_after_padding = None

2997
2998
        # We currently only microbatch if the number of tokens is
        # over a certain threshold.
2999
3000
3001
3002
3003
        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,
3004
3005
                uniform_decode=uniform_decode,
                vllm_config=self.vllm_config,
3006
3007
3008
3009
3010
3011
3012
3013
3014
3015
3016
3017
            )

        # 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())
3018
3019

        attn_metadata: Optional[PerLayerAttnMetadata] = None
3020
3021
3022

        # If force_attention is True, we always capture attention. Otherwise,
        # it only happens for cudagraph_runtime_mode=FULL.
3023
        if force_attention or cudagraph_runtime_mode == CUDAGraphMode.FULL:
3024
            attn_metadata = {}
3025
3026
            if ubatch_slices is not None:
                attn_metadata = [dict() for _ in range(len(ubatch_slices))]
3027

3028
3029
3030
3031
3032
3033
            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:
3034
                seq_lens = max_query_len
3035
            self.seq_lens.np[:num_reqs] = seq_lens
3036
3037
            self.seq_lens.np[num_reqs:] = 0
            self.seq_lens.copy_to_gpu()
3038

3039
3040
3041
3042
3043
            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()

3044
3045
            for kv_cache_group_id, kv_cache_group_spec in enumerate(
                    self.kv_cache_config.kv_cache_groups):
3046
                common_attn_metadata = CommonAttentionMetadata(
3047
3048
                    query_start_loc=self.query_start_loc.gpu[:num_reqs + 1],
                    query_start_loc_cpu=self.query_start_loc.cpu[:num_reqs +
3049
                                                                 1],
3050
3051
                    seq_lens=self.seq_lens.gpu[:num_reqs],
                    seq_lens_cpu=self.seq_lens.cpu[:num_reqs],
3052
3053
3054
3055
                    num_computed_tokens_cpu=self.input_batch.
                    num_computed_tokens_cpu_tensor[:num_reqs],
                    num_reqs=num_reqs,
                    num_actual_tokens=num_tokens,
3056
                    max_query_len=max_query_len,
3057
                    max_seq_len=self.max_model_len,
3058
3059
3060
3061
                    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],
3062
                    causal=True)
3063
                for attn_group in self.attn_groups[kv_cache_group_id]:
3064
3065
3066
3067
3068
3069
3070
3071
3072
3073
3074
3075
3076
3077
3078
3079
3080
3081
3082
                    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
3083

3084
        with self.maybe_dummy_run_with_lora(self.lora_config,
3085
                                            num_scheduled_tokens, remove_lora):
3086
3087
3088
            model_kwargs = self._init_model_kwargs(num_tokens)
            if (self.supports_mm_inputs
                    and not self.model_config.is_encoder_decoder):
3089
                input_ids = None
3090
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens]
3091
                model_kwargs = {
3092
                    **model_kwargs,
3093
3094
                    **self._dummy_mm_kwargs(num_reqs),
                }
3095
3096
3097
3098
            elif self.enable_prompt_embeds:
                input_ids = None
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens]
                model_kwargs = self._init_model_kwargs(num_tokens)
3099
            else:
3100
                input_ids = self.input_ids.gpu[:num_tokens]
3101
                inputs_embeds = None
3102

3103
            if self.uses_mrope:
3104
                positions = self.mrope_positions.gpu[:, :num_tokens]
3105
            else:
3106
                positions = self.positions.gpu[:num_tokens]
3107
3108
3109
3110
3111
3112
3113
3114
3115
3116

            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))
3117
3118
3119

                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
                    num_tokens, None, False)
3120
3121
3122
3123

            # filter out the valid batch descriptor
            _cg_mode, batch_descriptor = self.cudagraph_dispatcher.dispatch(
                BatchDescriptor(num_tokens=num_tokens,
3124
3125
                                uniform_decode=uniform_decode)) \
                if not is_profile else (CUDAGraphMode.NONE, None)
3126
3127
3128
3129
3130
            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, (
3131
3132
                    f"Cudagraph runtime mode mismatch at dummy_run. "
                    f"Expected {_cg_mode}, but got {cudagraph_runtime_mode}.")
3133
3134
            else:
                cudagraph_runtime_mode = _cg_mode
3135

3136
3137
            if ubatch_slices is not None:
                num_tokens = num_tokens // 2
3138
            with self.maybe_randomize_inputs(input_ids), set_forward_context(
3139
3140
                    attn_metadata,
                    self.vllm_config,
3141
                    num_tokens=num_tokens_after_padding,
3142
3143
                    num_tokens_across_dp=num_tokens_across_dp,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
3144
3145
                    batch_descriptor=batch_descriptor,
                    ubatch_slices=ubatch_slices):
3146
                outputs = self.model(
3147
3148
3149
3150
                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
3151
                    **model_kwargs,
3152
                )
3153

3154
3155
3156
3157
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
3158

3159
            if self.speculative_config and self.speculative_config.use_eagle():
3160
3161
3162
                assert isinstance(self.drafter, EagleProposer)
                self.drafter.dummy_run(num_tokens)

3163
3164
3165
3166
3167
3168
3169
3170
3171
3172
        # 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)

3173
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
3174
        return hidden_states, hidden_states[logit_indices]
3175
3176
3177
3178
3179
3180

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
3181
3182
3183
3184
        # 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)
3185

3186
        logits = self.model.compute_logits(hidden_states)
3187
3188
3189
3190
3191
3192
3193
3194
3195
3196
3197
3198
3199
3200
3201
3202
3203
3204
3205
3206
3207
        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={},
3208
            logitsprocs=LogitsProcessors(),
3209
        )
3210
        try:
3211
3212
            sampler_output = self.sampler(logits=logits,
                                          sampling_metadata=dummy_metadata)
3213
3214
3215
3216
3217
3218
3219
3220
3221
        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
3222
        if self.speculative_config:
3223
3224
3225
3226
3227
3228
3229
3230
3231
3232
3233
3234
3235
3236
3237
3238
3239
3240
3241
3242
3243
3244
3245
3246
3247
3248
            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,
            )
3249
        return sampler_output
3250

3251
    def _dummy_pooler_run_task(
3252
3253
        self,
        hidden_states: torch.Tensor,
3254
3255
        task: PoolingTask,
    ) -> PoolerOutput:
3256
3257
3258
3259
3260
3261
3262
3263
3264
3265
3266
        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

3267
        dummy_prompt_lens = torch.tensor(
3268
3269
            num_scheduled_tokens_list,
            device="cpu",
3270
3271
3272
3273
        )
        dummy_token_ids = torch.zeros((num_reqs, req_num_tokens),
                                      dtype=torch.int32,
                                      device=self.device)
3274

3275
        model = cast(VllmModelForPooling, self.get_model())
3276
        dummy_pooling_params = PoolingParams(task=task)
3277
        dummy_pooling_params.verify(task=task, model_config=self.model_config)
3278
        to_update = model.pooler.get_pooling_updates(task)
3279
3280
        to_update.apply(dummy_pooling_params)

3281
        dummy_metadata = PoolingMetadata(
3282
3283
3284
3285
            prompt_lens=dummy_prompt_lens,
            prompt_token_ids=dummy_token_ids,
            pooling_params=[dummy_pooling_params] * num_reqs,
        )
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        dummy_metadata.build_pooling_cursor(num_scheduled_tokens_list,
                                            device=hidden_states.device)

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        try:
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            return model.pooler(hidden_states=hidden_states,
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                                pooling_metadata=dummy_metadata)
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        except RuntimeError as e:
            if 'out of memory' in str(e):
                raise RuntimeError(
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                    "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 "
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                    "initializing the engine.") from e
            else:
                raise e
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    @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)
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            output_size[task] = sum(o.nbytes for o in output)
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            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)
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    def profile_run(self) -> None:
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        # Profile with multimodal encoder & encoder cache.
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        if self.supports_mm_inputs:
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            if self.model_config.multimodal_config.skip_mm_profiling:
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                logger.info(
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                    "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.
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                    dummy_modality = mm_budget.get_modality_with_max_tokens()
                    max_mm_items_per_batch = mm_budget \
                        .max_items_per_batch_by_modality[dummy_modality]
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                    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,
                    )
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                    # Create dummy batch of multimodal inputs.
                    batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                        dummy_modality,
                        max_mm_items_per_batch,
                    )
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                    # Run multimodal encoder.
                    dummy_encoder_outputs = \
                        self.model.get_multimodal_embeddings(
                        **batched_dummy_mm_inputs)
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                    sanity_check_mm_encoder_outputs(
                        dummy_encoder_outputs,
                        expected_num_items=max_mm_items_per_batch,
                    )
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                    # Cache the dummy encoder outputs.
                    self.encoder_cache["tmp"] = dict(
                        enumerate(dummy_encoder_outputs))
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        # Add `is_profile` here to pre-allocate communication buffers
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        hidden_states, last_hidden_states \
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            = self._dummy_run(self.max_num_tokens, is_profile=True)
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        if get_pp_group().is_last_rank:
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            if self.is_pooling_model:
                output = self._dummy_pooler_run(hidden_states)
            else:
                output = self._dummy_sampler_run(last_hidden_states)
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        else:
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            output = None
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        self._sync_device()
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        del hidden_states, output
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        self.encoder_cache.clear()
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        gc.collect()
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    def capture_model(self) -> int:
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        if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
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            logger.warning(
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                "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 \
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            cudagraph_runtime_mode.valid_runtime_modes(), \
            f"Invalid cudagraph runtime mode: {cudagraph_runtime_mode}"
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        # 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's avatar
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:
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        """
        Resolve the cudagraph_mode when there are multiple attention 
        backends with potential conflicting CUDA graph support.
        Then initialize the cudagraph_dispatcher based on the resolved
        cudagraph_mode.
        """
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        min_cg_support = AttentionCGSupport.ALWAYS
        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()