gpu_model_runner.py 196 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
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from vllm.config import (
    CompilationLevel,
    CUDAGraphMode,
    VllmConfig,
    get_layers_from_vllm_config,
    update_config,
)
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from vllm.distributed.eplb.eplb_state import EplbState
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from vllm.distributed.kv_transfer import get_kv_transfer_group, has_kv_transfer_group
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from vllm.distributed.kv_transfer.kv_connector.utils import copy_kv_blocks
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from vllm.distributed.parallel_state import (
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    get_pp_group,
    get_tp_group,
    graph_capture,
    is_global_first_rank,
    prepare_communication_buffer_for_model,
)
from vllm.forward_context import BatchDescriptor, DPMetadata, set_forward_context
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from vllm.logger import init_logger
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from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
from vllm.model_executor.layers.mamba.abstract import MambaBase
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from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
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from vllm.model_executor.model_loader import TensorizerLoader, get_model_loader
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from vllm.model_executor.models.deepseek_v2 import DeepseekV32IndexerCache
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from vllm.model_executor.models.interfaces import (
    SupportsMultiModal,
    is_mixture_of_experts,
    supports_eagle3,
    supports_mrope,
    supports_multimodal_pruning,
    supports_transcription,
)
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from vllm.model_executor.models.interfaces_base import (
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    VllmModelForPooling,
    is_pooling_model,
    is_text_generation_model,
)
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (
    BatchedTensorInputs,
    MultiModalKwargsItem,
    PlaceholderRange,
)
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from vllm.multimodal.utils import group_mm_kwargs_by_modality
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from vllm.pooling_params import PoolingParams
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from vllm.sampling_params import SamplingType
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from vllm.sequence import IntermediateTensors
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from vllm.tasks import GenerationTask, PoolingTask, SupportedTask
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from vllm.utils import (
    STR_DTYPE_TO_TORCH_DTYPE,
    DeviceMemoryProfiler,
    GiB_bytes,
    cdiv,
    check_use_alibi,
    get_dtype_size,
    is_pin_memory_available,
    length_from_prompt_token_ids_or_embeds,
    round_up,
    supports_dynamo,
)
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from vllm.utils.jsontree import json_map_leaves
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from vllm.v1.attention.backends.flash_attn import AttentionMetadata
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from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadataBuilder
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from vllm.v1.attention.backends.utils import (
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    AttentionCGSupport,
    AttentionMetadataBuilder,
    CommonAttentionMetadata,
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    create_fast_prefill_custom_backend,
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    reorder_batch_to_split_decodes_and_prefills,
    split_attn_metadata,
)
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from vllm.v1.cudagraph_dispatcher import CudagraphDispatcher
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from vllm.v1.kv_cache_interface import (
    AttentionSpec,
    ChunkedLocalAttentionSpec,
    CrossAttentionSpec,
    EncoderOnlyAttentionSpec,
    FullAttentionSpec,
    KVCacheConfig,
    KVCacheGroupSpec,
    KVCacheSpec,
    MambaSpec,
    MLAAttentionSpec,
    SlidingWindowSpec,
    UniformTypeKVCacheSpecs,
)
from vllm.v1.outputs import (
    EMPTY_MODEL_RUNNER_OUTPUT,
    AsyncModelRunnerOutput,
    DraftTokenIds,
    LogprobsLists,
    LogprobsTensors,
    ModelRunnerOutput,
    PoolerOutput,
    SamplerOutput,
)
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from vllm.v1.pool.metadata import PoolingMetadata
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from vllm.v1.sample.logits_processor import LogitsProcessors, build_logitsprocs
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from vllm.v1.sample.metadata import SamplingMetadata
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from vllm.v1.sample.rejection_sampler import RejectionSampler
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from vllm.v1.sample.sampler import Sampler
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from vllm.v1.spec_decode.eagle import EagleProposer
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from vllm.v1.spec_decode.medusa import MedusaProposer
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from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
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from vllm.v1.spec_decode.ngram_proposer import NgramProposer
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from vllm.v1.structured_output.utils import apply_grammar_bitmask
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from vllm.v1.utils import CpuGpuBuffer, record_function_or_nullcontext
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from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
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from vllm.v1.worker.gpu_ubatch_wrapper import UBatchWrapper
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from vllm.v1.worker.kv_connector_model_runner_mixin import KVConnectorModelRunnerMixin
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from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
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from vllm.v1.worker.ubatch_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
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PerLayerAttnMetadata: TypeAlias = Union[list[AttnMetadataDict], AttnMetadataDict]
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# Wrapper for ModelRunnerOutput to support overlapped execution.
class AsyncGPUModelRunnerOutput(AsyncModelRunnerOutput):
    def __init__(
        self,
        model_runner_output: ModelRunnerOutput,
        sampled_token_ids: torch.Tensor,
        invalid_req_indices: list[int],
        async_output_copy_stream: torch.cuda.Stream,
    ):
        self._model_runner_output = model_runner_output
        self._invalid_req_indices = invalid_req_indices

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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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        # Cached outputs.
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        self._draft_token_ids: Optional[Union[list[list[int]], torch.Tensor]] = None
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        self.transfer_event = torch.cuda.Event()
        self.sampled_token_ids_pinned_cpu = torch.empty(
            (self.max_model_len, 1),
            dtype=torch.int64,
            device="cpu",
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            pin_memory=self.pin_memory,
        )
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    def _get_positions(self, num_tokens: Any):
        if isinstance(num_tokens, int):
            if self.uses_mrope:
                return self.mrope_positions.gpu[:, :num_tokens]
            return self.positions.gpu[:num_tokens]
        else:
            if self.uses_mrope:
                return self.mrope_positions.gpu[:, num_tokens]
            return self.positions.gpu[num_tokens]

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

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

525
        if not self.is_pooling_model:
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            return model_kwargs

528
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        num_reqs = self.input_batch.num_reqs
        pooling_params = self.input_batch.get_pooling_params()
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532

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

        if len(token_type_id_requests) == 0:
            return model_kwargs

543
        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(
552
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            device=self.device
        )
554
555
        return model_kwargs

556
    def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
557
558
        """
        Update the order of requests in the batch based on the attention
559
        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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573
        # 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

574
        if self.reorder_batch_threshold is not None:
575
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577
            # 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.
578
            if self.dcp_world_size > 1:
579
                assert self.reorder_batch_threshold == 1, (
580
                    "DCP not support reorder_batch_threshold > 1 now."
581
                )
582
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584
            reorder_batch_to_split_decodes_and_prefills(
                self.input_batch,
                scheduler_output,
585
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                decode_threshold=self.reorder_batch_threshold,
            )
587

588
589
    # Note: used for model runner override.
    def _init_device_properties(self) -> None:
590
        """Initialize attributes from torch.cuda.get_device_properties"""
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        self.device_properties = torch.cuda.get_device_properties(self.device)
        self.num_sms = self.device_properties.multi_processor_count

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

598
    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.
607
608
        """
        # Remove finished requests from the cached states.
609
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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:
618
            self.input_batch.remove_request(req_id)
619
620

        # Free the cached encoder outputs.
621
622
        for mm_hash in scheduler_output.free_encoder_mm_hashes:
            self.encoder_cache.pop(mm_hash, None)
623

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636
        # 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:
637
            self.input_batch.remove_request(req_id)
638

639
        reqs_to_add: list[CachedRequestState] = []
640
        # Add new requests to the cached states.
641
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643
        for new_req_data in scheduler_output.scheduled_new_reqs:
            req_id = new_req_data.req_id
            sampling_params = new_req_data.sampling_params
644
            pooling_params = new_req_data.pooling_params
645

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            if (
                sampling_params
                and sampling_params.sampling_type == SamplingType.RANDOM_SEED
            ):
650
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654
                generator = torch.Generator(device=self.device)
                generator.manual_seed(sampling_params.seed)
            else:
                generator = None

655
656
            if self.is_pooling_model:
                assert pooling_params is not None
657
658
                task = pooling_params.task
                assert task is not None, "You did not set `task` in the API"
659

660
                model = cast(VllmModelForPooling, self.get_model())
661
                to_update = model.pooler.get_pooling_updates(task)
662
663
                to_update.apply(pooling_params)

664
            req_state = CachedRequestState(
665
                req_id=req_id,
666
                prompt_token_ids=new_req_data.prompt_token_ids,
667
                prompt_embeds=new_req_data.prompt_embeds,
668
                mm_features=new_req_data.mm_features,
669
                sampling_params=sampling_params,
670
                pooling_params=pooling_params,
671
                generator=generator,
672
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                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
674
                output_token_ids=[],
675
                lora_request=new_req_data.lora_request,
676
            )
677
678
            self.requests[req_id] = req_state

679
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
680
            if self.uses_mrope:
681
                self._init_mrope_positions(req_state)
682

683
            reqs_to_add.append(req_state)
684

685
        # Update the states of the running/resumed requests.
686
        is_last_rank = get_pp_group().is_last_rank
687
688
        req_data = scheduler_output.scheduled_cached_reqs
        for i, req_id in enumerate(req_data.req_ids):
689
            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]
693
            num_output_tokens = req_data.num_output_tokens[i]
694

695
            # Update the cached states.
696

697
            req_state.num_computed_tokens = num_computed_tokens
698
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            if not is_last_rank:
                # When using PP, the scheduler sends the sampled tokens back,
                # because there's no direct communication between the first-
                # stage worker and the last-stage worker.
                new_token_ids = req_data.new_token_ids[i]
                # Add the sampled token(s) from the previous step (if any).
                # This doesn't include "unverified" tokens like spec tokens.
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                num_new_tokens = (
                    num_computed_tokens + len(new_token_ids) - req_state.num_tokens
                )
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                if num_new_tokens == 1:
                    # Avoid slicing list in most common case.
                    req_state.output_token_ids.append(new_token_ids[-1])
                elif num_new_tokens > 0:
713
                    req_state.output_token_ids.extend(new_token_ids[-num_new_tokens:])
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            elif num_output_tokens < len(req_state.output_token_ids):
                # Some output tokens were discarded due to a sync-KV-load
                # failure. Align the cached state.
                del req_state.output_token_ids[num_output_tokens:]

                req_index = self.input_batch.req_id_to_index.get(req_id)
                if req_index is not None:
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723
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725
                    old_end_idx = self.input_batch.num_tokens_no_spec[req_index]
                    end_idx = (
                        self.input_batch.num_prompt_tokens[req_index]
                        + num_output_tokens
                    )
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                    self.input_batch.num_tokens[req_index] = end_idx
                    self.input_batch.num_tokens_no_spec[req_index] = end_idx
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                    self.input_batch.is_token_ids[req_index, end_idx:old_end_idx] = (
                        False
                    )
731

732
            # Update the block IDs.
733
            if not resumed_from_preemption:
734
735
                if new_block_ids is not None:
                    # Append the new blocks to the existing block IDs.
736
                    for block_ids, new_ids in zip(req_state.block_ids, new_block_ids):
737
                        block_ids.extend(new_ids)
738
            else:
739
                assert new_block_ids is not None
740
741
                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
742
                req_state.block_ids = new_block_ids
743
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746
747
748

            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.
749
                reqs_to_add.append(req_state)
750
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752
                continue

            # Update the persistent batch.
753
            self.input_batch.num_computed_tokens_cpu[req_index] = num_computed_tokens
754
            if new_block_ids is not None:
755
                self.input_batch.block_table.append_row(new_block_ids, req_index)
756
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761
762

            # 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)
763
                self.input_batch.token_ids_cpu[
764
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766
                    req_index, start_token_index:end_token_index
                ] = new_token_ids
                self.input_batch.num_tokens_no_spec[req_index] = end_token_index
767
                self.input_batch.num_tokens[req_index] = end_token_index
768

769
            # Add spec_token_ids to token_ids_cpu.
770
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772
            spec_token_ids = scheduler_output.scheduled_spec_decode_tokens.get(
                req_id, ()
            )
773
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            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[
778
779
                    req_index, start_index:end_token_index
                ] = spec_token_ids
780
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782
                # NOTE(woosuk): `num_tokens` here may include spec tokens.
                self.input_batch.num_tokens[req_index] += num_spec_tokens

783
784
        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
785
786
        for request in reqs_to_add:
            self.input_batch.add_request(request)
787

788
789
790
791
792
793
        # 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()
794

795
    def _update_states_after_model_execute(
796
797
        self, output_token_ids: torch.Tensor
    ) -> None:
798
799
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802
803
804
805
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808
809
        """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.
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819
820
821
822
823
824
825
826
827
828
829
        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()
        )
830
831
832
        for i, num_tokens in enumerate(num_accepted_tokens):
            self.input_batch.num_accepted_tokens_cpu[i] = num_tokens

833
834
835
836
837
838
    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
839
840
841
842
        for mm_feature in req_state.mm_features:
            mm_item = mm_feature.data
            if mm_item is None:
                continue
843
844
845
846
847
848
849
850
851
852
853
854
            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

855
        if supports_mrope(self.model):
856
            req_state.mrope_positions, req_state.mrope_position_delta = (
857
858
859
860
861
862
863
864
865
                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,
                )
866
            )
867
        else:
868
            req_state.mrope_positions, req_state.mrope_position_delta = (
869
870
871
872
873
874
875
876
877
                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,
                )
878
            )
879

880
    def _extract_mm_kwargs(
881
        self,
882
883
        scheduler_output: "SchedulerOutput",
    ) -> BatchedTensorInputs:
884
        if not scheduler_output or not self.is_multimodal_raw_input_only_model:
885
            return {}
886

887
888
        mm_kwargs = list[MultiModalKwargsItem]()
        for req in scheduler_output.scheduled_new_reqs:
889
890
891
            for feature in req.mm_features:
                if feature.data is not None:
                    mm_kwargs.append(feature.data)
892

893
        # Input all modalities at once
894
        model = cast(SupportsMultiModal, self.model)
895
896
        mm_kwargs_combined: BatchedTensorInputs = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
897
898
899
900
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
901
902
        ):
            mm_kwargs_combined.update(mm_kwargs_group)
903

904
        return mm_kwargs_combined
905

906
    def _dummy_mm_kwargs(self, num_seqs: int) -> BatchedTensorInputs:
907
        if not self.is_multimodal_raw_input_only_model:
908
            return {}
909

910
911
912
913
914
        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)
915

916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
    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

936
937
938
    def _prepare_input_ids(
        self, total_num_scheduled_tokens: int, cu_num_tokens: np.ndarray
    ) -> None:
939
        """Prepare the input IDs for the current batch.
940

941
942
943
944
945
946
947
        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)
948
949
950
            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)
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
            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)
969
                indices_match &= prev_index == flattened_index
970
971
972
973
974
975
                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)
976
977
978
            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)
979
980
981
982
983
984
985
986
987
988
        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_(
989
990
991
                self.input_batch.prev_sampled_token_ids[:num_commmon_tokens, 0],
                non_blocking=True,
            )
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993
            if self.enable_prompt_embeds:
                self.is_token_ids.gpu[:num_commmon_tokens] = True
994
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996
            return
        # Upload the index tensors asynchronously
        # so the scatter can be non-blocking.
997
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999
        input_ids_index_tensor = torch.tensor(
            flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1000
        prev_common_req_indices_tensor = torch.tensor(
1001
1002
            prev_common_req_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1003
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1006
        self.input_ids.gpu.scatter_(
            dim=0,
            index=input_ids_index_tensor,
            src=self.input_batch.prev_sampled_token_ids[
1007
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1009
                prev_common_req_indices_tensor, 0
            ],
        )
1010

1011
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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

1029
    def _prepare_inputs(
1030
        self, scheduler_output: "SchedulerOutput"
1031
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1033
1034
1035
1036
1037
1038
1039
1040
1041
    ) -> tuple[
        PerLayerAttnMetadata,
        torch.Tensor,
        Optional[SpecDecodeMetadata],
        np.ndarray,
        Optional[CommonAttentionMetadata],
        int,
        Optional[UBatchSlices],
        Optional[torch.Tensor],
        bool,
    ]:
1042
1043
1044
        """
        :return: tuple[
            attn_metadata: layer-to-attention_metadata mapping,
1045
1046
1047
            logits_indices, spec_decode_metadata,
            num_scheduled_tokens, spec_decode_common_attn_metadata,
            max_num_scheduled_tokens, use_cascade_attn
1048
1049
        ]
        """
1050
1051
1052
1053
1054
1055
1056
        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.
1057
        self.input_batch.block_table.commit_block_table(num_reqs)
1058
1059

        # Get the number of scheduled tokens for each request.
1060
1061
1062
1063
        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)
1064
1065
1066

        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
1067
        req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens)
1068

1069
1070
        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
        # arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
1071
        cu_num_tokens, arange = self._get_cumsum_and_arange(num_scheduled_tokens)
1072
1073

        # Get positions.
1074
        positions_np = self.positions.np[:total_num_scheduled_tokens]
1075
1076
1077
1078
1079
        np.add(
            self.input_batch.num_computed_tokens_cpu[req_indices],
            arange,
            out=positions_np,
        )
1080

1081
1082
        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1083
        if self.uses_mrope:
1084
1085
            self._calc_mrope_positions(scheduler_output)

1086
1087
1088
1089
        # 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.
1090
1091
1092
        token_indices = (
            positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]
        )
1093
        token_indices_tensor = torch.from_numpy(token_indices)
1094

1095
1096
1097
        # 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.
1098
1099
1100
1101
1102
1103
        torch.index_select(
            self.input_batch.token_ids_cpu_tensor.flatten(),
            0,
            token_indices_tensor,
            out=self.input_ids.cpu[:total_num_scheduled_tokens],
        )
1104
1105
1106
1107
1108
1109
        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,
1110
1111
                out=self.is_token_ids.cpu[:total_num_scheduled_tokens],
            )
1112
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1130
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1132
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1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144

        # 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:
1145
1146
1147
                    self.inputs_embeds.cpu[
                        output_idx : output_idx + actual_num_sched
                    ].copy_(req_embeds[start_pos:actual_end])
1148
1149

                output_idx += num_sched
1150

1151
1152
        self.input_batch.block_table.compute_slot_mapping(req_indices, positions_np)
        self.input_batch.block_table.commit_slot_mapping(total_num_scheduled_tokens)
1153
1154

        # Prepare the attention metadata.
1155
        self.query_start_loc.np[0] = 0
1156
        self.query_start_loc.np[1 : num_reqs + 1] = cu_num_tokens
1157
1158
        # Note: pad query_start_loc to be non-decreasing, as kernels
        # like FlashAttention requires that
1159
        self.query_start_loc.np[num_reqs + 1 :].fill(cu_num_tokens[-1])
1160
        self.query_start_loc.copy_to_gpu()
1161
        query_start_loc = self.query_start_loc.gpu[: num_reqs + 1]
1162

1163
1164
        num_tokens_unpadded = scheduler_output.total_num_scheduled_tokens
        num_tokens_padded = num_tokens_unpadded + self.get_local_padding(
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
            num_tokens_unpadded
        )
        uniform_decode = (
            max_num_scheduled_tokens == self.uniform_decode_query_len
        ) and (total_num_scheduled_tokens == num_reqs * max_num_scheduled_tokens)
        ubatch_slices, num_tokens_after_padding = ubatch_split(
            num_scheduled_tokens,
            num_tokens_unpadded,
            num_tokens_padded,
            uniform_decode=uniform_decode,
            vllm_config=self.vllm_config,
        )
1177

1178
        self.seq_lens.np[:num_reqs] = (
1179
1180
            self.input_batch.num_computed_tokens_cpu[:num_reqs] + num_scheduled_tokens
        )
1181
        # Fill unused with 0 for full cuda graph mode.
1182
1183
1184
1185
        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()
1186

1187
        num_tokens = [self.requests[r].num_tokens for r in self.input_batch.req_ids]
1188
1189
1190
1191
1192
1193
1194
        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)
1195
1196
1197
        self.discard_request_indices.np[: self.num_discarded_requests] = (
            discard_request_indices
        )
1198
1199
1200

        self.discard_request_indices.copy_to_gpu(self.num_discarded_requests)

1201
        # Copy the tensors to the GPU.
1202
1203
        self._prepare_input_ids(total_num_scheduled_tokens, cu_num_tokens)

1204
        if self.uses_mrope:
1205
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1206
1207
            self.mrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.mrope_positions.cpu[:, :total_num_scheduled_tokens],
1208
1209
                non_blocking=True,
            )
1210
1211
        else:
            # Common case (1D positions)
1212
            self.positions.copy_to_gpu(total_num_scheduled_tokens)
1213

1214
        use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
1215
1216
1217
1218
1219
1220
1221
        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
1222
            num_draft_tokens = None
1223
1224
1225
1226
1227
1228
            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)
1229
1230
1231
            # 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)
1232
1233
1234
1235
            for (
                req_id,
                draft_token_ids,
            ) in scheduler_output.scheduled_spec_decode_tokens.items():
1236
1237
                req_idx = self.input_batch.req_id_to_index[req_id]
                num_draft_tokens[req_idx] = len(draft_token_ids)
1238
1239
1240
1241
1242
1243
1244
1245
                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
                )
1246
            spec_decode_metadata = self._calc_spec_decode_metadata(
1247
1248
                num_draft_tokens, cu_num_tokens
            )
1249
            logits_indices = spec_decode_metadata.logits_indices
1250
1251

            # For DECODE only cuda graph of some attention backends (e.g., GDN).
1252
            self.num_decode_draft_tokens.np[:num_reqs] = num_decode_draft_tokens
1253
1254
            self.num_decode_draft_tokens.np[num_reqs:].fill(-1)
            self.num_decode_draft_tokens.copy_to_gpu()
1255
1256
1257

        logits_indices_padded = None
        if self.cache_config.kv_sharing_fast_prefill:
1258
            logits_indices_padded = self._prepare_kv_sharing_fast_prefill(
1259
1260
                logits_indices
            )
1261

1262
1263
1264
        attn_metadata: PerLayerAttnMetadata = {}
        if ubatch_slices is not None:
            attn_metadata = [dict() for _ in range(len(ubatch_slices))]
1265
        use_cascade_attn = False
1266

1267
        # Used in the below loop.
1268
        query_start_loc_cpu = self.query_start_loc.cpu[: num_reqs + 1]
1269
        seq_lens_cpu = self.seq_lens.cpu[:num_reqs]
1270
1271
1272
        num_computed_tokens_cpu = self.input_batch.num_computed_tokens_cpu_tensor[
            :num_reqs
        ]
1273
        spec_decode_common_attn_metadata = None
1274
1275
        if use_spec_decode:
            self.num_accepted_tokens.np[:num_reqs] = (
1276
1277
                self.input_batch.num_accepted_tokens_cpu[:num_reqs]
            )
1278
1279
            self.num_accepted_tokens.np[num_reqs:].fill(1)
            self.num_accepted_tokens.copy_to_gpu()
1280

1281
1282
1283
        # 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(
1284
1285
            self.kv_cache_config.kv_cache_groups
        ):
1286
            encoder_seq_lens = self._get_encoder_seq_lens(
1287
1288
                scheduler_output, kv_cache_group_spec.kv_cache_spec, num_reqs
            )
1289

1290
            if isinstance(kv_cache_group_spec.kv_cache_spec, EncoderOnlyAttentionSpec):
1291
1292
1293
1294
1295
                # 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,
1296
1297
1298
                    device=self.device,
                )
                slot_mapping = torch.zeros(
1299
                    (total_num_scheduled_tokens,),
1300
1301
1302
                    dtype=torch.int64,
                    device=self.device,
                )
1303
1304
1305
                num_common_prefix_blocks = 0
            else:
                blk_table = self.input_batch.block_table[kv_cache_group_id]
1306
                blk_table_tensor = blk_table.get_device_tensor(num_reqs)
1307
                slot_mapping = blk_table.slot_mapping.gpu[:total_num_scheduled_tokens]
1308
1309
1310

                # Fill unused with -1. Needed for reshape_and_cache in full cuda
                # graph mode.
1311
1312
1313
1314
                blk_table.slot_mapping.gpu[total_num_scheduled_tokens:].fill_(-1)
                num_common_prefix_blocks = scheduler_output.num_common_prefix_blocks[
                    kv_cache_group_id
                ]
1315

1316
            common_attn_metadata = CommonAttentionMetadata(
1317
1318
1319
1320
1321
                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,
1322
1323
1324
                num_reqs=num_reqs,
                num_actual_tokens=total_num_scheduled_tokens,
                max_query_len=max_num_scheduled_tokens,
1325
                max_seq_len=max_seq_len,
1326
1327
                block_table_tensor=blk_table_tensor,
                slot_mapping=slot_mapping,
1328
1329
                logits_indices_padded=logits_indices_padded,
                num_logits_indices=logits_indices.size(0),
1330
                causal=True,
1331
                encoder_seq_lens=encoder_seq_lens,
1332
1333
            )

1334
            if self.speculative_config and spec_decode_common_attn_metadata is None:
1335
                if isinstance(self.drafter, EagleProposer):
1336
1337
1338
1339
                    if (
                        self.drafter.attn_layer_names[0]
                        in kv_cache_group_spec.layer_names
                    ):
1340
1341
1342
                        spec_decode_common_attn_metadata = common_attn_metadata
                else:
                    spec_decode_common_attn_metadata = common_attn_metadata
1343

1344
1345
1346
            for attn_group in self.attn_groups[kv_cache_group_id]:
                # Prepare for cascade attention if enabled & beneficial.
                common_prefix_len = 0
1347
                builder = attn_group.get_metadata_builder()
1348
1349
1350
                if self.cascade_attn_enabled:
                    common_prefix_len = self._compute_cascade_attn_prefix_len(
                        num_scheduled_tokens,
1351
                        num_common_prefix_blocks,
1352
                        attn_group.kv_cache_spec,
1353
1354
                        builder,
                    )
1355

1356
                extra_attn_metadata_args = {}
1357
                if use_spec_decode and isinstance(builder, GDNAttentionMetadataBuilder):
1358
                    extra_attn_metadata_args = dict(
1359
1360
1361
1362
                        num_accepted_tokens=self.num_accepted_tokens.gpu[:num_reqs],
                        num_decode_draft_tokens_cpu=self.num_decode_draft_tokens.cpu[
                            :num_reqs
                        ],
1363
1364
                    )

1365
1366
                if ubatch_slices is not None:
                    common_attn_metadata_list = split_attn_metadata(
1367
1368
                        ubatch_slices, common_attn_metadata
                    )
1369
                    for ubid, common_attn_metadata in enumerate(
1370
1371
1372
1373
1374
1375
1376
1377
                        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,
                        )
1378
1379
1380
1381
1382
1383
1384
1385
                        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,
1386
1387
1388
                        **extra_attn_metadata_args,
                    )
                    use_cascade_attn |= getattr(attn_metadata_i, "use_cascade", False)
1389
1390
                    for layer_name in attn_group.layer_names:
                        attn_metadata[layer_name] = attn_metadata_i
1391

1392
1393
1394
1395
        # disable cascade attention when DBO
        if ubatch_slices is not None:
            use_cascade_attn = False

1396
1397
1398
1399
        # Hot-Swap lora model
        if self.lora_config:
            self.set_active_loras(self.input_batch, num_scheduled_tokens)

1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
        return (
            attn_metadata,
            logits_indices,
            spec_decode_metadata,
            num_scheduled_tokens,
            spec_decode_common_attn_metadata,
            max_num_scheduled_tokens,
            ubatch_slices,
            num_tokens_after_padding,
            use_cascade_attn,
        )
1411

1412
1413
1414
1415
    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
        num_common_prefix_blocks: int,
1416
1417
        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
    ) -> 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.
        """
1436
        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
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1457
1458
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1461
1462
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1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
        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]
1474
        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
1475
1476
1477
1478
1479
1480
1481
        # 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(
1482
1483
            common_prefix_len, self.input_batch.num_computed_tokens_cpu[:num_reqs].min()
        )
1484
        # common_prefix_len should be a multiple of the block size.
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
        common_prefix_len = (
            common_prefix_len // kv_cache_spec.block_size * kv_cache_spec.block_size
        )
        use_sliding_window = isinstance(kv_cache_spec, SlidingWindowSpec) or (
            isinstance(kv_cache_spec, FullAttentionSpec)
            and kv_cache_spec.sliding_window is not None
        )
        use_local_attention = isinstance(kv_cache_spec, ChunkedLocalAttentionSpec) or (
            isinstance(kv_cache_spec, FullAttentionSpec)
            and kv_cache_spec.attention_chunk_size is not None
        )
1496
1497
        assert isinstance(kv_cache_spec, AttentionSpec)
        use_cascade = attn_metadata_builder.use_cascade_attention(
1498
1499
1500
            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
1501
            num_kv_heads=kv_cache_spec.num_kv_heads,
1502
            use_alibi=self.use_alibi,
1503
            use_sliding_window=use_sliding_window,
1504
            use_local_attention=use_local_attention,
1505
1506
1507
1508
            num_sms=self.num_sms,
        )
        return common_prefix_len if use_cascade else 0

1509
1510
    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
1511
        for index, req_id in enumerate(self.input_batch.req_ids):
1512
1513
1514
            req = self.requests[req_id]
            assert req.mrope_positions is not None

1515
1516
            num_computed_tokens = self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1517
            num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
1518
1519
                req.prompt_token_ids, req.prompt_embeds
            )
1520
1521

            if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
1522
1523
                prompt_part_len = max(0, num_prompt_tokens - num_computed_tokens)
                completion_part_len = max(0, num_scheduled_tokens - prompt_part_len)
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
            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

1537
1538
1539
                self.mrope_positions.cpu[:, dst_start:dst_end] = req.mrope_positions[
                    :, src_start:src_end
                ]
1540
1541
1542
1543
1544
1545
1546
                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

1547
                MRotaryEmbedding.get_next_input_positions_tensor(
1548
                    out=self.mrope_positions.np,
1549
1550
1551
1552
1553
                    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,
                )
1554
1555
1556

                mrope_pos_ptr += completion_part_len

1557
1558
    def _calc_spec_decode_metadata(
        self,
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
        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
1575
1576
1577
1578

        # 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(
1579
1580
            num_sampled_tokens, cumsum_dtype=np.int32
        )
1581
        # Step 2. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
1582
        logits_indices = np.repeat(
1583
1584
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens
        )
1585
        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
1586
1587
1588
1589
1590
1591
        logits_indices += arange

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

        # Compute the draft logits indices.
1592
1593
1594
        # 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(
1595
1596
            num_draft_tokens, cumsum_dtype=np.int32
        )
1597
1598
        # [0, 0, 0, 5, 5, 9]
        target_logits_indices = np.repeat(
1599
1600
            cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens
        )
1601
1602
1603
1604
1605
        # [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(
1606
1607
1608
1609
1610
            self.device, non_blocking=True
        )
        logits_indices = torch.from_numpy(logits_indices).to(
            self.device, non_blocking=True
        )
1611
        target_logits_indices = torch.from_numpy(target_logits_indices).to(
1612
1613
            self.device, non_blocking=True
        )
1614
        bonus_logits_indices = torch.from_numpy(bonus_logits_indices).to(
1615
1616
            self.device, non_blocking=True
        )
1617

1618
1619
        # Compute the draft token ids.
        # draft_token_indices:      [  1,   2,   3, 105, 106, 208]
1620
        draft_token_ids = self.input_ids.gpu[logits_indices]
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
        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

1633
1634
1635
1636
1637
1638
1639
    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
1640
        self.kv_sharing_fast_prefill_logits_indices[:num_logits].copy_(logits_indices)
1641
1642
1643
1644
1645
        # 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_(
1646
1647
1648
1649
1650
1651
            logits_indices[-1].item()
        )
        if (
            self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
            and num_logits <= self.cudagraph_batch_sizes[-1]
        ):
1652
1653
1654
1655
1656
            # 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
1657
1658
1659
        logits_indices_padded = self.kv_sharing_fast_prefill_logits_indices[
            :num_logits_padded
        ]
1660
1661
        return logits_indices_padded

1662
1663
1664
1665
1666
1667
1668
1669
    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
1670
                inputs.
1671
1672
1673
1674
1675
1676

        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
        """
1677
1678
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
1679
            return [], []
1680
        # Batch the multi-modal inputs.
1681
        mm_kwargs = list[MultiModalKwargsItem]()
1682
1683
        # list of tuple (mm_hash, position_info)
        mm_hashes_pos = list[tuple[str, PlaceholderRange]]()
1684
1685
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
1686
1687

            for mm_input_id in encoder_input_ids:
1688
1689
1690
1691
                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))
1692

1693
1694
1695
1696
1697
        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(
1698
1699
            scheduler_output
        )
1700
1701
1702
1703

        if not mm_kwargs:
            return

1704
1705
1706
1707
1708
1709
1710
        # 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.
1711
        model = cast(SupportsMultiModal, self.model)
1712
        encoder_outputs = []
1713
        for modality, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
1714
1715
1716
1717
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
1718
        ):
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
            # (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(
1731
1732
1733
                        (k, v[i : i + micro_batch_size])
                        for k, v in mm_kwargs_group.items()
                    )
1734
1735

                    micro_batch_outputs = model.get_multimodal_embeddings(
1736
1737
                        **micro_batch_mm_inputs
                    )
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747

                    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.
1748
                curr_group_outputs = model.get_multimodal_embeddings(**mm_kwargs_group)
1749

1750
1751
            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
1752
                expected_num_items=num_items,
1753
            )
1754
            encoder_outputs.extend(curr_group_outputs)
1755

1756
1757
1758
        # 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(
1759
1760
1761
1762
1763
                output,
                is_embed=pos_info.is_embed,
            )

    def _gather_mm_embeddings(
1764
1765
        self,
        scheduler_output: "SchedulerOutput",
1766
        shift_computed_tokens: int = 0,
1767
1768
1769
1770
1771
1772
1773
1774
    ) -> tuple[list[torch.Tensor], torch.Tensor]:
        total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens

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

        req_start_idx = 0
1775
        should_sync_mrope_positions = False
1776

1777
        for req_id in self.input_batch.req_ids:
1778
1779
            mm_embeds_req: list[torch.Tensor] = []

1780
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1781
            req_state = self.requests[req_id]
1782
            num_computed_tokens = req_state.num_computed_tokens + shift_computed_tokens
1783

1784
1785
            for mm_feature in req_state.mm_features:
                pos_info = mm_feature.mm_position
1786
1787
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803

                # 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,
1804
1805
                    num_encoder_tokens,
                )
1806
                assert start_idx < end_idx
1807

1808
                mm_hash = mm_feature.identifier
1809
                encoder_output = self.encoder_cache.get(mm_hash, None)
1810
                assert encoder_output is not None, f"Encoder cache miss for {mm_hash}."
1811
1812
1813
1814

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

1815
                req_start_pos = req_start_idx + start_pos - num_computed_tokens
1816
1817
1818
                is_mm_embed[req_start_pos + start_idx : req_start_pos + end_idx] = (
                    True if is_embed is None else is_embed
                )
1819

1820
1821
1822
1823
                mm_embeds_item = gather_mm_placeholders(
                    encoder_output[start_idx:end_idx],
                    is_embed=is_embed,
                )
1824
1825
1826
                mm_embeds_req.append(mm_embeds_item)

            if self.is_multimodal_pruning_enabled and self.uses_mrope:
1827
                assert req_state.mrope_positions is not None
1828
1829
1830
1831
1832
1833
1834
                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,
1835
1836
                    )
                )
1837
1838
1839
1840
                req_state.mrope_positions.copy_(new_mrope_positions)
                req_state.mrope_position_delta = new_delta

            mm_embeds.extend(mm_embeds_req)
1841
1842
1843
            req_start_idx += num_scheduled_tokens

        is_mm_embed = self.is_mm_embed.copy_to_gpu(total_num_scheduled_tokens)
1844
1845
1846

        if should_sync_mrope_positions:
            self._calc_mrope_positions(scheduler_output)
1847
            self.mrope_positions.copy_to_gpu(total_num_scheduled_tokens)
1848

1849
        return mm_embeds, is_mm_embed
1850

1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
    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
1867
        model = cast(SupportsMultiModal, self.model)
1868
1869
        encoder_features = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
1870
1871
1872
1873
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
1874
1875
1876
1877
1878
1879
1880
1881
        ):
            # 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

1882
    def get_model(self) -> nn.Module:
1883
        # get raw model out of the cudagraph wrapper.
1884
        if isinstance(self.model, (CUDAGraphWrapper, UBatchWrapper)):
1885
            return self.model.unwrap()
1886
1887
        return self.model

1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
    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

1903
1904
1905
1906
1907
    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
        model = self.get_model()
        if not is_pooling_model(model):
            return []

1908
1909
        supported_tasks = list(model.pooler.get_supported_tasks())

1910
1911
1912
1913
        if (
            self.scheduler_config.chunked_prefill_enabled
            and "encode" in supported_tasks
        ):
1914
1915
            supported_tasks.remove("encode")

1916
1917
1918
1919
1920
1921
            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."
            )
1922
1923
1924
1925
1926

        if "score" in supported_tasks:
            num_labels = getattr(self.model_config.hf_config, "num_labels", 0)
            if num_labels != 1:
                supported_tasks.remove("score")
1927
                logger.debug_once("Score API is only enabled for num_labels == 1.")
1928
1929

        return supported_tasks
1930

1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
    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)

1941
    def sync_and_slice_intermediate_tensors(
1942
1943
1944
1945
1946
        self,
        num_tokens: int,
        intermediate_tensors: IntermediateTensors,
        sync_self: bool,
    ) -> IntermediateTensors:
1947
1948
1949
        assert self.intermediate_tensors is not None

        tp = self.vllm_config.parallel_config.tensor_parallel_size
1950
        is_rs = is_residual_scattered_for_sp(self.vllm_config, num_tokens)
1951
1952
1953
1954
1955
1956

        # 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():
1957
                is_scattered = k == "residual" and is_rs
1958
                copy_len = num_tokens // tp if is_scattered else num_tokens
1959
                self.intermediate_tensors[k][:copy_len].copy_(
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
                    v[:copy_len], non_blocking=True
                )

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

    def eplb_step(self, is_dummy: bool = False, is_profile: bool = False) -> None:
1973
1974
1975
1976
1977
1978
1979
        """
        Step for the EPLB (Expert Parallelism Load Balancing) state.
        """
        if not self.parallel_config.enable_eplb:
            return

        assert self.eplb_state is not None
1980
1981
        model = self.get_model()
        assert is_mixture_of_experts(model)
1982
        self.eplb_state.step(
1983
            model,
1984
1985
            is_dummy,
            is_profile,
1986
            log_stats=self.parallel_config.eplb_config.log_balancedness,
1987
1988
        )

1989
    def get_dp_padding(self, num_tokens: int) -> tuple[int, Optional[torch.Tensor]]:
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
        """
        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.
        ]
        """
2001
2002
        dp_size = self.vllm_config.parallel_config.data_parallel_size
        dp_rank = self.vllm_config.parallel_config.data_parallel_rank
2003
2004
2005
2006
2007
2008
2009
2010
2011

        # 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:
2012
            # Early exit.
2013
            return 0, None
2014
2015

        num_tokens_across_dp = DPMetadata.num_tokens_across_dp(
2016
2017
            num_tokens, dp_size, dp_rank
        )
2018
        max_tokens_across_dp_cpu = torch.max(num_tokens_across_dp).item()
2019
2020
2021
        num_tokens_after_padding = torch.tensor(
            [max_tokens_across_dp_cpu] * dp_size, device="cpu", dtype=torch.int32
        )
2022
        return max_tokens_across_dp_cpu - num_tokens, num_tokens_after_padding
2023

2024
2025
2026
    def get_local_padding(self, num_tokens_unpadded: int) -> int:
        num_tokens_padded = num_tokens_unpadded

2027
2028
2029
2030
        if (
            self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
            and num_tokens_unpadded <= self.cudagraph_batch_sizes[-1]
        ):
2031
2032
            # Use piecewise CUDA graphs.
            # Add padding to the batch size.
2033
            num_tokens_padded = self.vllm_config.pad_for_cudagraph(num_tokens_unpadded)
2034
2035
2036
2037
2038
        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
2039
2040
2041
2042
            if (
                self.vllm_config.compilation_config.pass_config.enable_sequence_parallelism
                and tp_size > 1
            ):
2043
2044
2045
2046
2047
2048
2049
2050
2051
                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)
2052
2053
2054
2055
2056
2057
2058
    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
        )
2059

2060
2061
2062
2063
2064
2065
    def _pool(
        self,
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
        num_scheduled_tokens_np: np.ndarray,
    ) -> ModelRunnerOutput:
2066
2067
2068
        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"
        )
2069

2070
        hidden_states = hidden_states[:num_scheduled_tokens]
2071
        pooling_metadata = self.input_batch.get_pooling_metadata()
2072
2073
2074
2075
        pooling_metadata.build_pooling_cursor(
            num_scheduled_tokens_np.tolist(), device=hidden_states.device
        )
        seq_lens_cpu = self.seq_lens.cpu[: self.input_batch.num_reqs]
2076

2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
        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()
2087
2088
2089

        pooler_output: list[Optional[torch.Tensor]] = []
        for raw_output, seq_len, prompt_len in zip(
2090
2091
            raw_pooler_output, seq_lens_cpu, pooling_metadata.prompt_lens
        ):
2092
            output = raw_output if seq_len == prompt_len else None
2093
            pooler_output.append(output)
2094
2095
2096
2097
2098
2099
2100
2101
2102
2103

        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,
        )

2104
    def _get_num_input_tokens(self, num_scheduled_tokens: int) -> int:
2105
2106
2107
2108
2109
2110
2111
        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]
        ):
2112
2113
2114
2115
2116
2117
2118
2119
            # 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
2120
2121
2122
2123
        if (
            self.compilation_config.pass_config.enable_sequence_parallelism
            and tp_size > 1
        ):
2124
2125
2126
            return round_up(num_scheduled_tokens, tp_size)
        return num_scheduled_tokens

2127
    def _preprocess(
2128
2129
        self,
        scheduler_output: "SchedulerOutput",
2130
        intermediate_tensors: Optional[IntermediateTensors] = None,
2131
2132
        ubatch_slices: Optional[UBatchSlices] = None,
        num_tokens_after_padding: Optional[torch.Tensor] = None,
2133
2134
2135
2136
2137
2138
2139
2140
2141
2142
    ) -> tuple[
        int,
        int,
        Optional[torch.Tensor],
        Optional[torch.Tensor],
        Optional[torch.Tensor],
        torch.Tensor,
        Optional[IntermediateTensors],
        dict[str, Any],
    ]:
2143
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
2144
2145
2146
2147
2148
2149
        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)
2150
            num_pad, num_tokens_after_padding = self.get_dp_padding(num_input_tokens)
2151
            num_input_tokens += num_pad
2152

2153
2154
        # _prepare_inputs may reorder the batch, so we must gather multi
        # modal outputs after that to ensure the correct order
2155
2156
2157
2158
2159
        if (
            self.supports_mm_inputs
            and get_pp_group().is_first_rank
            and not self.model_config.is_encoder_decoder
        ):
2160
2161
            # Run the multimodal encoder if any.
            self._execute_mm_encoder(scheduler_output)
2162
            mm_embeds, is_mm_embed = self._gather_mm_embeddings(scheduler_output)
2163

2164
2165
2166
            # 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.
2167
            inputs_embeds_scheduled = self.model.get_input_embeddings(
2168
2169
2170
                self.input_ids.gpu[:num_scheduled_tokens],
                multimodal_embeddings=mm_embeds,
                is_multimodal=is_mm_embed,
2171
            )
2172

2173
            # TODO(woosuk): Avoid the copy. Optimize.
2174
            self.inputs_embeds.gpu[:num_scheduled_tokens].copy_(inputs_embeds_scheduled)
2175

2176
            input_ids = None
2177
            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
2178
2179
2180
2181
            model_kwargs = {
                **self._init_model_kwargs(num_scheduled_tokens),
                **self._extract_mm_kwargs(scheduler_output),
            }
2182
        elif self.enable_prompt_embeds and get_pp_group().is_first_rank:
2183
2184
2185
2186
2187
2188
2189
2190
2191
2192
2193
2194
            # 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).
2195
2196
2197
            token_ids_idx = (
                self.is_token_ids.gpu[:num_scheduled_tokens]
                .nonzero(as_tuple=False)
2198
                .squeeze(1)
2199
            )
2200
2201
2202
            # Some tokens ids may need to become embeds
            if token_ids_idx.numel() > 0:
                token_ids = self.input_ids.gpu[token_ids_idx]
2203
                tokens_to_embeds = self.model.get_input_embeddings(input_ids=token_ids)
2204
2205
2206
2207
2208
                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
2209
        else:
2210
2211
2212
2213
            # 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.
2214
            input_ids = self.input_ids.gpu[:num_input_tokens]
2215
            inputs_embeds = None
2216
            model_kwargs = self._init_model_kwargs(num_input_tokens)
2217
        if self.uses_mrope:
2218
            positions = self.mrope_positions.gpu[:, :num_input_tokens]
2219
        else:
2220
            positions = self.positions.gpu[:num_input_tokens]
2221

2222
2223
2224
        if get_pp_group().is_first_rank:
            intermediate_tensors = None
        else:
2225
            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
2226
2227
                num_input_tokens, intermediate_tensors, True
            )
2228

2229
2230
2231
2232
        if (
            self.model_config.is_encoder_decoder
            and scheduler_output.scheduled_encoder_inputs
        ):
2233
2234
2235
            encoder_inputs = self._extract_encoder_inputs(scheduler_output)
            model_kwargs.update(encoder_inputs)

2236
2237
2238
        return (
            num_scheduled_tokens,
            num_input_tokens,
2239
            num_tokens_after_padding,
2240
2241
2242
2243
2244
2245
            input_ids,
            inputs_embeds,
            positions,
            intermediate_tensors,
            model_kwargs,
        )
2246

2247
    def _sample(
2248
2249
2250
        self,
        logits: Optional[torch.Tensor],
        spec_decode_metadata: Optional[SpecDecodeMetadata],
2251
    ) -> SamplerOutput:
2252
        # Sample the next token and get logprobs if needed.
2253
        sampling_metadata = self.input_batch.sampling_metadata
2254
        if spec_decode_metadata is None:
2255
            sampler_output = self.sampler(
2256
2257
2258
2259
                logits=logits,
                sampling_metadata=sampling_metadata,
            )
        else:
2260
2261
2262
2263
            # 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.
2264
            assert logits is not None
2265
            bonus_logits = logits[spec_decode_metadata.bonus_logits_indices]
2266
            sampler_output = self.sampler(
2267
                logits=bonus_logits,
2268
2269
2270
                sampling_metadata=sampling_metadata,
            )
            bonus_token_ids = sampler_output.sampled_token_ids
2271

2272
2273
2274
            # 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.
2275
            target_logits = logits[spec_decode_metadata.target_logits_indices]
2276
            output_token_ids = self.rejection_sampler(
2277
                spec_decode_metadata,
2278
                None,  # draft_probs
2279
                target_logits,
2280
                bonus_token_ids,
2281
2282
                sampling_metadata,
            )
2283
            sampler_output.sampled_token_ids = output_token_ids
2284
            self._update_states_after_model_execute(output_token_ids)
2285

2286
2287
2288
        return sampler_output

    def _bookkeeping_sync(
2289
2290
2291
2292
2293
2294
        self,
        scheduler_output: "SchedulerOutput",
        sampler_output: SamplerOutput,
        logits: Optional[torch.Tensor],
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
2295
    ) -> tuple[
2296
2297
2298
2299
2300
2301
2302
        dict[str, int],
        Optional[LogprobsLists],
        list[list[int]],
        dict[str, Optional[LogprobsTensors]],
        list[str],
        dict[str, int],
        list[int],
2303
    ]:
2304
2305
2306
2307
        num_nans_in_logits = {}
        if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
            num_nans_in_logits = self._get_nans_in_logits(logits)

2308
2309
2310
        discard_sampled_tokens_req_indices = self.discard_request_indices.np[
            : self.num_discarded_requests
        ]
2311
2312
2313
2314
        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)
2315

2316
2317
2318
        # 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()
2319
        req_id_to_index_output_copy = self.input_batch.req_id_to_index.copy()
2320

2321
2322
        # NOTE: GPU -> CPU Sync happens here.
        # Move as many CPU operations as possible before this sync point.
2323
        logprobs_tensors = sampler_output.logprobs_tensors
2324
2325
2326
        logprobs_lists = (
            logprobs_tensors.tolists() if logprobs_tensors is not None else None
        )
2327
2328
2329

        # Compute prompt logprobs if needed.
        prompt_logprobs_dict = self._get_prompt_logprobs_dict(
2330
            hidden_states[:num_scheduled_tokens],
2331
            scheduler_output.num_scheduled_tokens,
2332
2333
        )

2334
        num_sampled_tokens = sampler_output.sampled_token_ids.shape[0]
2335
        sampled_token_ids = sampler_output.sampled_token_ids
2336
        invalid_req_indices = []
2337
2338
2339
2340
2341
2342
2343
2344
2345
2346
2347
2348
2349
2350
        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:
2351
                valid_sampled_token_ids[int(i)].clear()
2352
        else:
2353
            valid_sampled_token_ids = []
2354
            invalid_req_indices = discard_sampled_tokens_req_indices.tolist()
2355
2356
2357
2358
2359
2360
            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.
2361
2362
            self.input_batch.prev_sampled_token_ids = sampled_token_ids
            self.input_batch.prev_sampled_token_ids_invalid_indices = (
2363
                invalid_req_indices_set
2364
            )
2365
2366
2367
2368
2369
            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
            }
2370

2371
2372
2373
2374
2375
        # 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.
2376
        req_ids = self.input_batch.req_ids
2377
2378
        for req_idx in range(num_sampled_tokens):
            if self.use_async_scheduling:
2379
                sampled_ids = [-1] if req_idx not in invalid_req_indices_set else None
2380
2381
            else:
                sampled_ids = valid_sampled_token_ids[req_idx]
2382
2383
2384
2385
2386
            if not sampled_ids:
                continue

            start_idx = self.input_batch.num_tokens_no_spec[req_idx]
            end_idx = start_idx + len(sampled_ids)
2387
2388
2389
            assert end_idx <= self.max_model_len + 1, (
                "Sampled token IDs exceed the max model length + 1. "
                f"Total number of tokens: {end_idx} > max_model_len + 1: "
2390
2391
                f"{self.max_model_len + 1}"
            )
2392
2393
2394
2395
2396
2397
2398
2399
2400
2401
2402
2403

            n_tokens_cache = len(sampled_ids)

            # Sampled token IDs exceed the max model length by 1. This is
            # legitimate as we can still sample 1 last token when the context
            # length equals the max model length. Note that we do not need to
            # cache this token ID as the sequence finishes after this step.
            # Additionally, the buffers token_ids_cpu and is_token_ids are of
            # size max model length only.
            if end_idx == self.max_model_len + 1:
                n_tokens_cache -= 1

2404
2405
2406
2407
2408
2409
            self.input_batch.token_ids_cpu[
                req_idx, start_idx : (start_idx + n_tokens_cache)
            ] = sampled_ids[:n_tokens_cache]
            self.input_batch.is_token_ids[
                req_idx, start_idx : (start_idx + n_tokens_cache)
            ] = True
2410

2411
2412
            self.input_batch.num_tokens_no_spec[req_idx] = end_idx
            self.input_batch.num_tokens[req_idx] = end_idx
2413

2414
            req_id = req_ids[req_idx]
2415
2416
2417
            req_state = self.requests[req_id]
            req_state.output_token_ids.extend(sampled_ids)

2418
2419
2420
2421
2422
2423
2424
2425
2426
2427
        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,
        )

2428
2429
2430
2431
2432
2433
2434
2435
2436
2437
2438
2439
2440
2441
2442
    @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()

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    def _model_forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        positions: Optional[torch.Tensor] = None,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        **model_kwargs: dict[str, Any],
    ) -> Any:
        """Helper method to call the model forward pass.

        This method can be overridden by subclasses for model execution.
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        Motivation: We can inspect only this method versus
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        the whole execute_model, which has additional logic.

        Args:
            input_ids: Input token IDs
            positions: Token positions
            intermediate_tensors: Tensors from previous pipeline stages
            inputs_embeds: Input embeddings (alternative to input_ids)
            **model_kwargs: Additional model arguments

        Returns:
            Model output tensor
        """
        return self.model(
            input_ids=input_ids,
            positions=positions,
            intermediate_tensors=intermediate_tensors,
            inputs_embeds=inputs_embeds,
            **model_kwargs,
        )

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    @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"):
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            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(
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                        scheduler_output, self.vllm_config
                    )
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                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 "
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                        "it when the requests need prompt logprobs"
                    )
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                # Prepare the decoder inputs.
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                (
                    attn_metadata,
                    logits_indices,
                    spec_decode_metadata,
                    num_scheduled_tokens_np,
                    spec_decode_common_attn_metadata,
                    max_query_len,
                    ubatch_slices,
                    num_tokens_after_padding,
                    use_cascade_attn,
                ) = self._prepare_inputs(scheduler_output)
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            (
                num_scheduled_tokens,
                num_input_tokens,
                num_tokens_across_dp,
                input_ids,
                inputs_embeds,
                positions,
                intermediate_tensors,
                model_kwargs,
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            ) = self._preprocess(
                scheduler_output,
                intermediate_tensors,
                ubatch_slices,
                num_tokens_after_padding,
            )

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

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        # 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

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

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

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

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

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

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

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

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

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        with record_function_or_nullcontext("Bookkeep"):
            (
                num_nans_in_logits,
                logprobs_lists,
                valid_sampled_token_ids,
                prompt_logprobs_dict,
                req_ids_output_copy,
                req_id_to_index_output_copy,
                invalid_req_indices,
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            ) = self._bookkeeping_sync(
                scheduler_output,
                sampler_output,
                logits,
                hidden_states,
                num_scheduled_tokens,
            )
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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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2753
        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(
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                sampled_token_ids,
                self.input_batch.req_ids,
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                self.input_batch.num_tokens_no_spec,
                self.input_batch.token_ids_cpu,
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                self.input_batch.spec_decode_unsupported_reqs,
            )
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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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2778
                assert spec_decode_metadata is not None
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                for num_draft, tokens in zip(
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                    spec_decode_metadata.num_draft_tokens, sampled_token_ids
                ):
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                    indices.append(offset + len(tokens) - 1)
                    offset += num_draft + 1
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                indices = torch.tensor(indices, device=self.device)
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                hidden_states = sample_hidden_states[indices]

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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.
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                assert isinstance(sampled_token_ids, list), (
                    "sampled_token_ids should be a python list when"
2800
                    "padded-batch is disabled."
2801
                )
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                next_token_ids = self.drafter.prepare_next_token_ids_cpu(
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                    sampled_token_ids,
                    self.requests,
                    self.input_batch,
                    scheduler_output.num_scheduled_tokens,
                )
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            else:
                # When using padded-batch, the sampled_token_ids should be
                # the gpu tensor of sampled tokens for each request, of shape
                # (num_reqs, num_spec_tokens + 1) with rejected tokens having
                # value -1.
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                assert isinstance(sampled_token_ids, torch.Tensor), (
                    "sampled_token_ids should be a torch.Tensor when"
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                    "padded-batch is enabled."
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                )
                next_token_ids, valid_sampled_tokens_count = (
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                    self.drafter.prepare_next_token_ids_padded(
                        common_attn_metadata,
                        sampled_token_ids,
                        self.requests,
                        self.input_batch,
                        self.discard_request_indices.gpu,
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                        self.num_discarded_requests,
2825
                    )
2826
                )
Jiayi Yao's avatar
Jiayi Yao committed
2827

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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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                target_positions = self._get_positions(num_scheduled_tokens)
2833
                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(
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                        [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
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                    common_attn_metadata, token_indices = self.drafter.prepare_inputs(
                        common_attn_metadata,
                        sampled_token_ids,
                        spec_decode_metadata.num_draft_tokens,
                    )
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                else:
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                    common_attn_metadata, token_indices, token_indices_to_sample = (
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                        self.drafter.prepare_inputs_padded(
                            common_attn_metadata,
                            spec_decode_metadata,
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                            valid_sampled_tokens_count,
                        )
                    )
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                target_token_ids = self.input_ids.gpu[token_indices]
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                target_positions = self._get_positions(token_indices)
2859
                if self.use_aux_hidden_state_outputs:
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2860
                    assert aux_hidden_states is not None
2861
                    target_hidden_states = torch.cat(
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                        [h[token_indices] for h in aux_hidden_states], dim=-1
                    )
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                else:
                    target_hidden_states = hidden_states[token_indices]
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            if self.supports_mm_inputs:
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                mm_embed_inputs = self._gather_mm_embeddings(
                    scheduler_output,
                    shift_computed_tokens=1,
                )
            else:
                mm_embed_inputs = None
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2875
            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,
2880
                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_embed_inputs=mm_embed_inputs,
2884
            )
2885

2886
        return draft_token_ids
2887

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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():
2891
2892
            assert config_name in allowed_config_names, (
                f"Config `{config_name}` not supported. "
2893
                f"Allowed configs: {allowed_config_names}"
2894
            )
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            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.
        """
2904
        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
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            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
            )
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            num_local_physical_experts = int(num_local_physical_experts.item())
            new_ep_size = get_ep_group().world_size
2914
            global_expert_load, old_global_expert_indices = EplbState.recv_state()
2915
            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
            )
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            rank_mapping = {
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                old_ep_rank: old_ep_rank for old_ep_rank in range(old_ep_size)
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            }
        else:
            global_expert_load = None
            old_global_expert_indices = None
            rank_mapping = None

2931
        with DeviceMemoryProfiler() as m:
2932
            time_before_load = time.perf_counter()
2933
            model_loader = get_model_loader(self.load_config)
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            logger.info("Loading model from scratch...")
            self.model = model_loader.load_model(
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                vllm_config=self.vllm_config, model_config=self.model_config
            )
2938
            if self.lora_config:
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                self.model = self.load_lora_model(
                    self.model, self.vllm_config, self.device
                )
2942
2943
2944
            if hasattr(self, "drafter"):
                logger.info("Loading drafter model...")
                self.drafter.load_model(self.model)
2945
            if self.use_aux_hidden_state_outputs:
2946
                if not supports_eagle3(self.model):
2947
2948
                    raise RuntimeError(
                        "Model does not support EAGLE3 interface but "
2949
2950
                        "aux_hidden_state_outputs was requested"
                    )
2951
2952
2953
2954
2955
2956
2957
2958
2959
2960
2961
2962
2963

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

                self.model.set_aux_hidden_state_layers(aux_layers)
2964
            time_after_load = time.perf_counter()
2965
        self.model_memory_usage = m.consumed_memory
2966
2967
2968
2969
2970
        logger.info(
            "Model loading took %.4f GiB and %.6f seconds",
            self.model_memory_usage / GiB_bytes,
            time_after_load - time_before_load,
        )
2971
        prepare_communication_buffer_for_model(self.model)
2972

2973
2974
2975
2976
        self.is_multimodal_pruning_enabled = (
            supports_multimodal_pruning(self.model)
            and self.model_config.multimodal_config.is_multimodal_pruning_enabled()
        )
2977

2978
2979
        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)
2980
2981
2982
2983
            self.eplb_state = EplbState.build(
                self.model,
                self.device,
                self.parallel_config,
2984
2985
2986
                global_expert_load,
                old_global_expert_indices,
                rank_mapping,
2987
2988
            )

2989
        if (
2990
2991
            self.vllm_config.compilation_config.level == CompilationLevel.DYNAMO_AS_IS
            and supports_dynamo()
2992
        ):
2993
            backend = self.vllm_config.compilation_config.init_backend(self.vllm_config)
2994
            compilation_counter.dynamo_as_is_count += 1
2995
            self.model.compile(fullgraph=True, backend=backend)
2996
2997
2998
2999
3000
            return
        # for other compilation levels, cudagraph behavior is controlled by
        # CudagraphWraper and CudagraphDispatcher of vllm.

        # wrap the model with full cudagraph wrapper if needed.
3001
3002
3003
3004
3005
3006
3007
        if (
            self.compilation_config.cudagraph_mode.has_full_cudagraphs()
            and not self.parallel_config.enable_dbo
        ):
            self.model = CUDAGraphWrapper(
                self.model, self.vllm_config, runtime_mode=CUDAGraphMode.FULL
            )
3008
3009
        elif self.parallel_config.enable_dbo:
            if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
3010
3011
3012
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.FULL, self.device
                )
3013
            else:
3014
3015
3016
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.NONE, self.device
                )
3017

3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3032
3033
3034
3035
3036
3037
3038
3039
3040
3041
    def _get_eagle3_aux_layers_from_config(self) -> Optional[tuple[int, ...]]:
        """Extract Eagle3 auxiliary layer indices from speculative config.

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

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

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

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

        return None

3042
    def reload_weights(self) -> None:
3043
        assert getattr(self, "model", None) is not None, (
3044
            "Cannot reload weights before model is loaded."
3045
        )
3046
3047
        model_loader = get_model_loader(self.load_config)
        logger.info("Reloading weights inplace...")
3048
        model_loader.load_weights(self.get_model(), model_config=self.model_config)
3049

3050
3051
3052
3053
3054
    def save_tensorized_model(
        self,
        tensorizer_config: "TensorizerConfig",
    ) -> None:
        TensorizerLoader.save_model(
3055
            self.get_model(),
3056
            tensorizer_config=tensorizer_config,
3057
            model_config=self.model_config,
3058
3059
        )

3060
3061
3062
    def _get_prompt_logprobs_dict(
        self,
        hidden_states: torch.Tensor,
3063
        num_scheduled_tokens: dict[str, int],
3064
    ) -> dict[str, Optional[LogprobsTensors]]:
3065
3066
3067
3068
        num_prompt_logprobs_dict = self.input_batch.num_prompt_logprobs
        if not num_prompt_logprobs_dict:
            return {}

3069
        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
3070
        prompt_logprobs_dict: dict[str, Optional[LogprobsTensors]] = {}
3071
3072
3073
3074
3075

        # 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():
3076
            num_tokens = num_scheduled_tokens[req_id]
3077
3078
3079

            # Get metadata for this request.
            request = self.requests[req_id]
3080
3081
3082
3083
            if request.prompt_token_ids is None:
                # Prompt logprobs is incompatible with prompt embeddings
                continue

3084
3085
            num_prompt_tokens = len(request.prompt_token_ids)
            prompt_token_ids = torch.tensor(request.prompt_token_ids).to(
3086
3087
                self.device, non_blocking=True
            )
3088

3089
3090
3091
3092
3093
3094
            # 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(
3095
3096
                    num_prompt_tokens - 1, num_prompt_logprobs + 1
                )
3097
3098
                in_progress_dict[req_id] = logprobs_tensors

3099
            # Determine number of logits to retrieve.
3100
3101
            start_idx = request.num_computed_tokens
            start_tok = start_idx + 1
3102
            num_remaining_tokens = num_prompt_tokens - start_tok
3103
            if num_tokens <= num_remaining_tokens:
3104
                # This is a chunk, more tokens remain.
3105
3106
3107
                # 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.
3108
3109
3110
3111
3112
                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)
3113
3114
3115
3116
3117
3118
3119
                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
3120
3121
3122
3123
3124

            # 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]
3125
            offset = self.query_start_loc.np[req_idx].item()
3126
            prompt_hidden_states = hidden_states[offset : offset + num_logits]
3127
            logits = self.model.compute_logits(prompt_hidden_states)
3128
3129
3130
3131

            # 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.
3132
            tgt_token_ids = prompt_token_ids[start_tok : start_tok + num_logits]
3133
3134

            # Compute prompt logprobs.
3135
3136
            logprobs = self.sampler.compute_logprobs(logits)
            token_ids, logprobs, ranks = self.sampler.gather_logprobs(
3137
3138
                logprobs, num_prompt_logprobs, tgt_token_ids
            )
3139
3140

            # Transfer GPU->CPU async.
3141
3142
            chunk_slice = slice(start_idx, start_idx + num_logits)
            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
3143
3144
3145
                token_ids, non_blocking=True
            )
            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs, non_blocking=True)
3146
            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
3147
3148
                ranks, non_blocking=True
            )
3149
3150
3151
3152
3153

        # 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]
3154
            del in_progress_dict[req_id]
3155
3156

        # Must synchronize the non-blocking GPU->CPU transfers.
3157
        if prompt_logprobs_dict:
3158
            self._sync_device()
3159
3160
3161

        return prompt_logprobs_dict

3162
3163
3164
3165
3166
3167
3168
3169
3170
3171
3172
3173
3174
3175
    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])
3176
3177
3178
                    if num_nans_for_index is not None and req_index < logits.shape[0]
                    else 0
                )
3179
3180
3181
3182
            return num_nans_in_logits
        except IndexError:
            return {}

3183
3184
3185
3186
3187
3188
    @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
3189
         - during DP rank dummy run
3190
3191
3192
3193
3194
3195
3196
3197
3198
3199
3200
        """
        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(
3201
                    self.input_ids.gpu,
3202
3203
                    low=0,
                    high=self.model_config.get_vocab_size(),
3204
3205
                    dtype=input_ids.dtype,
                )
3206

3207
            logger.debug_once("Randomizing dummy data for DP Rank")
3208
            input_ids.copy_(rand_input_ids()[: input_ids.size(0)], non_blocking=True)
3209
3210
3211
            yield
            input_ids.fill_(0)

3212
3213
3214
3215
3216
3217
    def _get_mm_dummy_batch(
        self,
        modality: str,
        max_items_per_batch: int,
    ) -> BatchedTensorInputs:
        """Dummy data for profiling and precompiling multimodal models."""
3218
3219
        assert self.mm_budget is not None

3220
3221
        dummy_decoder_data = self.mm_registry.get_decoder_dummy_data(
            model_config=self.model_config,
3222
            seq_len=self.max_model_len,
3223
            mm_counts={modality: 1},
3224
            cache=self.mm_budget.cache,
3225
3226
3227
3228
        )
        dummy_mm_data = dummy_decoder_data.multi_modal_data

        # Result in the maximum GPU consumption of the model
3229
3230
        dummy_mm_item = dummy_mm_data[modality][0]
        dummy_mm_items = [dummy_mm_item] * max_items_per_batch
3231

3232
        model = cast(SupportsMultiModal, self.model)
3233
3234
3235
3236
3237
3238
3239
3240
3241
        return next(
            mm_kwargs_group
            for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
                dummy_mm_items,
                device=self.device,
                pin_memory=self.pin_memory,
                merge_by_field_config=model.merge_by_field_config,
            )
        )
3242

3243
3244
3245
3246
    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
3247
        cudagraph_runtime_mode: Optional[CUDAGraphMode] = None,
3248
3249
        force_attention: bool = False,
        uniform_decode: bool = False,
3250
        allow_microbatching: bool = True,
3251
3252
        skip_eplb: bool = False,
        is_profile: bool = False,
3253
        create_mixed_batch: bool = False,
3254
        remove_lora: bool = True,
3255
    ) -> tuple[torch.Tensor, torch.Tensor]:
3256
3257
3258
3259
3260
3261
3262
        """
        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.
3263
                - if not set will determine the cudagraph mode based on using
3264
                    the self.cudagraph_dispatcher.
3265
3266
3267
3268
                - CUDAGraphMode.NONE: No cudagraph, for warm up and profile run
                - CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
                - CUDAGraphMode.FULL: Full cudagraph, attention metadata is
                    needed.
3269
            force_attention: If True, always create attention metadata. Used to
3270
3271
3272
3273
                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.
3274
3275
            create_mixed_batch: If True, create a mixed batch with both decode
                (1 token) and prefill (multiple tokens) requests.
3276
            remove_lora: If False, dummy LoRAs are not destroyed after the run
3277
        """
3278
3279
3280
3281
        assert (
            cudagraph_runtime_mode is None
            or cudagraph_runtime_mode.valid_runtime_modes()
        )
3282

3283
        # If cudagraph_mode.decode_mode() == FULL and
3284
        # cudagraph_mode.separate_routine(). This means that we are using
3285
3286
3287
3288
3289
3290
3291
3292
3293
3294
3295
        # 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.
3296
        max_query_len = self.uniform_decode_query_len if uniform_decode else num_tokens
3297

3298
3299
3300
3301
3302
        # 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
3303
3304
3305
3306
        if create_mixed_batch:
            assert not uniform_decode
            # Create mixed batch:
            # first half decode tokens, second half one prefill
3307
            num_decode_tokens = min(max_num_reqs - 1, num_tokens // 2)
3308
3309
3310
3311
            num_prefill_tokens = num_tokens - num_decode_tokens
            num_reqs = num_decode_tokens + 1

            # Create decode requests (1 token each) followed by prefill request
3312
            num_scheduled_tokens_list = [1] * num_decode_tokens + [num_prefill_tokens]
3313
3314
3315
            # Note: Overriding max_query_len to be the prefill tokens
            max_query_len = num_prefill_tokens
        elif uniform_decode:
3316
            assert not create_mixed_batch
3317
            num_reqs = min(max_num_reqs, cdiv(num_tokens, max_query_len))
3318
3319
            num_scheduled_tokens_list = [max_query_len] * num_reqs
            if num_tokens % max_query_len != 0:
3320
                num_scheduled_tokens_list[-1] = num_tokens % max_query_len
3321
3322
3323
3324
3325
3326
        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

3327
3328
        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs
3329
        num_scheduled_tokens = np.array(num_scheduled_tokens_list, dtype=np.int32)
3330
        total_num_scheduled_tokens = int(num_scheduled_tokens.sum())
3331

3332
        ubatch_slices = None
3333
3334
        num_tokens_after_padding = None

3335
3336
        # We currently only microbatch if the number of tokens is
        # over a certain threshold.
3337
        if self.parallel_config.enable_dbo and allow_microbatching:
3338
            ubatch_slices, ubatch_num_tokens_after_padding = ubatch_split(
3339
3340
3341
                num_scheduled_tokens,
                total_num_scheduled_tokens,
                total_num_scheduled_tokens,
3342
3343
                uniform_decode=uniform_decode,
                vllm_config=self.vllm_config,
3344
            )
3345
3346
3347
3348
3349
3350
            # Currently when DBO is enabled `ubatch_split` returns
            # the num_tokens_after_padding for a single ubatch, but we have 2
            # TODO(sage,lucas): this is cruft that should be addressed in the
            # padding refactor.
            if ubatch_num_tokens_after_padding is not None:
                num_tokens_after_padding = ubatch_num_tokens_after_padding * 2
3351
3352
3353
3354
3355
3356
3357
3358
3359
3360
3361

        # 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())
3362
3363

        attn_metadata: Optional[PerLayerAttnMetadata] = None
3364
3365
3366

        # If force_attention is True, we always capture attention. Otherwise,
        # it only happens for cudagraph_runtime_mode=FULL.
3367
        if force_attention or cudagraph_runtime_mode == CUDAGraphMode.FULL:
3368
            attn_metadata = {}
3369
3370
            if ubatch_slices is not None:
                attn_metadata = [dict() for _ in range(len(ubatch_slices))]
3371

3372
3373
3374
3375
3376
3377
            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:
3378
                seq_lens = max_query_len
3379
            self.seq_lens.np[:num_reqs] = seq_lens
3380
3381
            self.seq_lens.np[num_reqs:] = 0
            self.seq_lens.copy_to_gpu()
3382

3383
3384
            cum_num_tokens, _ = self._get_cumsum_and_arange(num_scheduled_tokens)
            self.query_start_loc.np[1 : num_reqs + 1] = cum_num_tokens
3385
3386
            self.query_start_loc.copy_to_gpu()

3387
            for kv_cache_group_id, kv_cache_group_spec in enumerate(
3388
3389
                self.kv_cache_config.kv_cache_groups
            ):
3390
                common_attn_metadata = CommonAttentionMetadata(
3391
3392
                    query_start_loc=self.query_start_loc.gpu[: num_reqs + 1],
                    query_start_loc_cpu=self.query_start_loc.cpu[: num_reqs + 1],
3393
3394
                    seq_lens=self.seq_lens.gpu[:num_reqs],
                    seq_lens_cpu=self.seq_lens.cpu[:num_reqs],
3395
3396
3397
                    num_computed_tokens_cpu=self.input_batch.num_computed_tokens_cpu_tensor[
                        :num_reqs
                    ],
3398
3399
                    num_reqs=num_reqs,
                    num_actual_tokens=num_tokens,
3400
                    max_query_len=max_query_len,
3401
                    max_seq_len=self.max_model_len,
3402
3403
3404
                    block_table_tensor=self.input_batch.block_table[
                        kv_cache_group_id
                    ].get_device_tensor(num_reqs),
3405
                    slot_mapping=self.input_batch.block_table[
3406
3407
3408
3409
                        kv_cache_group_id
                    ].slot_mapping.gpu[:num_tokens],
                    causal=True,
                )
3410
                for attn_group in self.attn_groups[kv_cache_group_id]:
3411
3412
                    if ubatch_slices is not None:
                        common_attn_metadata_list = split_attn_metadata(
3413
3414
                            ubatch_slices, common_attn_metadata
                        )
3415
                        for ubid, common_attn_metadata in enumerate(
3416
3417
                            common_attn_metadata_list
                        ):
3418
                            assert common_attn_metadata.max_query_len == 1
3419
3420
3421
                            attn_metadata_i = attn_group.get_metadata_builder(
                                ubatch_id=ubid
                            ).build_for_cudagraph_capture(common_attn_metadata)
3422
                            for layer_name in attn_group.layer_names:
3423
                                assert type(attn_metadata) is list
3424
                                attn_metadata[ubid][layer_name] = attn_metadata_i
3425
3426
                    else:
                        assert type(attn_metadata) is dict
3427
3428
                        metadata_builder = attn_group.get_metadata_builder()
                        attn_metadata_i = metadata_builder.build_for_cudagraph_capture(
3429
3430
                            common_attn_metadata
                        )
3431
                        for layer_name in attn_group.layer_names:
3432
                            attn_metadata[layer_name] = attn_metadata_i
3433

3434
3435
3436
        with self.maybe_dummy_run_with_lora(
            self.lora_config, num_scheduled_tokens, remove_lora
        ):
3437
3438
3439
            # Make sure padding doesn't exceed max_num_tokens
            assert num_tokens_after_padding <= self.max_num_tokens
            model_kwargs = self._init_model_kwargs(num_tokens_after_padding)
3440
            if self.supports_mm_inputs and not self.model_config.is_encoder_decoder:
3441
                input_ids = None
3442
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_after_padding]
3443
                model_kwargs = {
3444
                    **model_kwargs,
3445
3446
                    **self._dummy_mm_kwargs(num_reqs),
                }
3447
3448
            elif self.enable_prompt_embeds:
                input_ids = None
3449
3450
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_after_padding]
                model_kwargs = self._init_model_kwargs(num_tokens_after_padding)
3451
            else:
3452
                input_ids = self.input_ids.gpu[:num_tokens_after_padding]
3453
                inputs_embeds = None
3454

3455
            if self.uses_mrope:
3456
                positions = self.mrope_positions.gpu[:, :num_tokens_after_padding]
3457
            else:
3458
                positions = self.positions.gpu[:num_tokens_after_padding]
3459
3460
3461
3462
3463
3464
3465
3466
3467

            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,
3468
3469
3470
                            device=self.device,
                        )
                    )
3471
3472

                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
3473
                    num_tokens_after_padding, None, False
3474
                )
3475
3476

            # filter out the valid batch descriptor
3477
3478
3479
3480
3481
3482
3483
3484
3485
3486
            _cg_mode, batch_descriptor = (
                self.cudagraph_dispatcher.dispatch(
                    BatchDescriptor(
                        num_tokens=num_tokens_after_padding,
                        uniform_decode=uniform_decode,
                    )
                )
                if not is_profile
                else (CUDAGraphMode.NONE, None)
            )
3487
3488
3489
            if cudagraph_runtime_mode is not None:
                # we allow forcing NONE when the dispatcher disagrees to support
                # warm ups for cudagraph capture
3490
3491
3492
3493
                assert (
                    cudagraph_runtime_mode == CUDAGraphMode.NONE
                    or cudagraph_runtime_mode == _cg_mode
                ), (
3494
                    f"Cudagraph runtime mode mismatch at dummy_run. "
3495
3496
                    f"Expected {_cg_mode}, but got {cudagraph_runtime_mode}."
                )
3497
3498
            else:
                cudagraph_runtime_mode = _cg_mode
3499

3500
            if ubatch_slices is not None:
3501
3502
3503
3504
3505
3506
3507
                # Adjust values to reflect a single ubatch.
                # TODO(sage,lucas): this is cruft that should be addressed in
                #  the padding refactor.
                num_tokens_after_padding = ubatch_slices[0].num_tokens
                if num_tokens_across_dp is not None:
                    num_tokens_across_dp[:] = num_tokens_after_padding

3508
3509
3510
            with (
                self.maybe_randomize_inputs(input_ids),
                set_forward_context(
3511
3512
                    attn_metadata,
                    self.vllm_config,
3513
                    num_tokens=num_tokens_after_padding,
3514
3515
                    num_tokens_across_dp=num_tokens_across_dp,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
3516
                    batch_descriptor=batch_descriptor,
3517
3518
3519
                    ubatch_slices=ubatch_slices,
                ),
            ):
3520
                outputs = self.model(
3521
3522
3523
3524
                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
3525
                    **model_kwargs,
3526
                )
3527

3528
3529
3530
3531
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
3532

3533
            if self.speculative_config and self.speculative_config.use_eagle():
3534
3535
3536
                assert isinstance(self.drafter, EagleProposer)
                self.drafter.dummy_run(num_tokens)

3537
3538
3539
3540
3541
3542
3543
3544
3545
3546
        # 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)

3547
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
3548
        return hidden_states, hidden_states[logit_indices]
3549
3550
3551
3552
3553
3554

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
3555
3556
3557
3558
        # 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)
3559

3560
        logits = self.model.compute_logits(hidden_states)
3561
3562
        num_reqs = logits.size(0)

3563
        dummy_tensors = lambda v: torch.full((num_reqs,), v, device=self.device)
3564
3565
3566
3567
3568
3569
3570
3571
3572
3573
3574
3575
3576
3577
3578
3579
3580

        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={},
3581
            logitsprocs=LogitsProcessors(),
3582
        )
3583
        try:
3584
3585
3586
            sampler_output = self.sampler(
                logits=logits, sampling_metadata=dummy_metadata
            )
3587
        except RuntimeError as e:
3588
            if "out of memory" in str(e):
3589
3590
3591
3592
                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 "
3593
3594
                    "initializing the engine."
                ) from e
3595
3596
            else:
                raise e
3597
        if self.speculative_config:
3598
3599
            draft_token_ids = [[0] for _ in range(num_reqs)]
            dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
3600
3601
                draft_token_ids, self.device
            )
3602
3603
3604
3605
3606
3607

            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
3608
3609
3610
            target_logits = torch.randn(
                num_tokens, logits.shape[-1], device=self.device, dtype=logits.dtype
            )
3611
3612
3613
            # 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.
3614
3615
3616
            bonus_token_ids = torch.zeros(
                num_reqs, device=self.device, dtype=torch.int32
            )
3617
3618
3619
3620
3621
3622
3623
            self.rejection_sampler(
                dummy_spec_decode_metadata,
                draft_probs,
                target_logits,
                bonus_token_ids,
                dummy_metadata,
            )
3624
        return sampler_output
3625

3626
    def _dummy_pooler_run_task(
3627
3628
        self,
        hidden_states: torch.Tensor,
3629
3630
        task: PoolingTask,
    ) -> PoolerOutput:
3631
3632
3633
3634
3635
3636
3637
3638
3639
3640
3641
        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

3642
        dummy_prompt_lens = torch.tensor(
3643
3644
            num_scheduled_tokens_list,
            device="cpu",
3645
        )
3646
3647
3648
        dummy_token_ids = torch.zeros(
            (num_reqs, req_num_tokens), dtype=torch.int32, device=self.device
        )
3649

3650
        model = cast(VllmModelForPooling, self.get_model())
3651
        dummy_pooling_params = PoolingParams(task=task)
3652
        dummy_pooling_params.verify(task=task, model_config=self.model_config)
3653
        to_update = model.pooler.get_pooling_updates(task)
3654
3655
        to_update.apply(dummy_pooling_params)

3656
        dummy_metadata = PoolingMetadata(
3657
3658
3659
3660
            prompt_lens=dummy_prompt_lens,
            prompt_token_ids=dummy_token_ids,
            pooling_params=[dummy_pooling_params] * num_reqs,
        )
3661

3662
3663
3664
        dummy_metadata.build_pooling_cursor(
            num_scheduled_tokens_list, device=hidden_states.device
        )
3665

3666
        try:
3667
3668
3669
            return model.pooler(
                hidden_states=hidden_states, pooling_metadata=dummy_metadata
            )
3670
        except RuntimeError as e:
3671
            if "out of memory" in str(e):
3672
                raise RuntimeError(
3673
3674
3675
                    "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 "
3676
3677
                    "initializing the engine."
                ) from e
3678
3679
            else:
                raise e
3680
3681
3682
3683
3684
3685
3686
3687
3688
3689
3690

    @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)
3691
            output_size[task] = sum(o.nbytes for o in output)
3692
3693
3694
3695
            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)
3696

3697
    def profile_run(self) -> None:
3698
        # Profile with multimodal encoder & encoder cache.
3699
        if self.supports_mm_inputs:
3700
            if self.model_config.multimodal_config.skip_mm_profiling:
3701
                logger.info(
3702
                    "Skipping memory profiling for multimodal encoder and "
3703
3704
                    "encoder cache."
                )
3705
3706
3707
3708
3709
3710
3711
3712
            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.
3713
                    dummy_modality = mm_budget.get_modality_with_max_tokens()
3714
3715
3716
                    max_mm_items_per_batch = mm_budget.max_items_per_batch_by_modality[
                        dummy_modality
                    ]
3717
3718
3719
3720
3721
3722
3723
3724
3725

                    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,
                    )
3726

3727
3728
3729
3730
3731
                    # Create dummy batch of multimodal inputs.
                    batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                        dummy_modality,
                        max_mm_items_per_batch,
                    )
3732

3733
                    # Run multimodal encoder.
3734
3735
3736
                    dummy_encoder_outputs = self.model.get_multimodal_embeddings(
                        **batched_dummy_mm_inputs
                    )
3737

3738
3739
3740
3741
                    sanity_check_mm_encoder_outputs(
                        dummy_encoder_outputs,
                        expected_num_items=max_mm_items_per_batch,
                    )
3742

3743
3744
3745
3746
3747
3748
3749
3750
3751
3752
                    # NOTE: This happens when encoder cache needs to store
                    # the embeddings that encoder outputs are scattered onto.
                    # In this case we create dummy embeddings of size
                    # (encode_budget, hidden_size) and scatter encoder
                    # output into it.
                    encoder_output_shape = dummy_encoder_outputs[0].shape
                    if encoder_output_shape[0] < encoder_budget:
                        expanded_outputs = []
                        for output in dummy_encoder_outputs:
                            expanded = output.new_zeros(
3753
3754
                                (encoder_budget, encoder_output_shape[-1])
                            )
3755
3756
3757
3758
3759
3760
                            num_tokens = output.shape[0]
                            expanded[:num_tokens].copy_(output)
                            expanded_outputs.append(expanded)

                        dummy_encoder_outputs = expanded_outputs

3761
                    # Cache the dummy encoder outputs.
3762
                    self.encoder_cache["tmp"] = dict(enumerate(dummy_encoder_outputs))
3763

3764
        # Add `is_profile` here to pre-allocate communication buffers
3765
3766
3767
        hidden_states, last_hidden_states = self._dummy_run(
            self.max_num_tokens, is_profile=True
        )
3768
        if get_pp_group().is_last_rank:
3769
3770
3771
3772
            if self.is_pooling_model:
                output = self._dummy_pooler_run(hidden_states)
            else:
                output = self._dummy_sampler_run(last_hidden_states)
3773
        else:
3774
            output = None
3775
        self._sync_device()
3776
        del hidden_states, output
3777
        self.encoder_cache.clear()
3778
        gc.collect()
3779

3780
    def capture_model(self) -> int:
3781
        if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
3782
            logger.warning(
3783
                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
3784
3785
                "ensure `cudagraph_mode` was not manually set to `NONE`"
            )
3786
            return 0
3787
3788
        else:
            self.initialize_cudagraph_capture()
3789

3790
3791
        compilation_counter.num_gpu_runner_capture_triggers += 1

3792
3793
        start_time = time.perf_counter()

3794
3795
3796
3797
3798
3799
3800
3801
3802
3803
3804
3805
3806
3807
        @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()
3808
                    gc.collect()
3809

3810
3811
3812
        # 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.
3813
        set_cudagraph_capturing_enabled(True)
3814
        with freeze_gc(), graph_capture(device=self.device):
3815
            start_free_gpu_memory = torch.cuda.mem_get_info()[0]
3816
            cudagraph_mode = self.compilation_config.cudagraph_mode
3817
            assert cudagraph_mode is not None
3818
3819
3820
3821
3822
3823
3824
            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,
3825
3826
                    uniform_decode=False,
                )
3827

3828
3829
            # Capture full cudagraph for uniform decode batches if we
            # don't already have full mixed prefill-decode cudagraphs.
3830
3831
3832
3833
3834
3835
3836
            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
                )
3837
                decode_cudagraph_batch_sizes = [
3838
3839
3840
                    x
                    for x in self.cudagraph_batch_sizes
                    if x <= max_num_tokens and x >= self.uniform_decode_query_len
3841
                ]
3842
                compilation_cases_decode = list(reversed(decode_cudagraph_batch_sizes))
3843
3844
3845
                self._capture_cudagraphs(
                    compilation_cases=compilation_cases_decode,
                    cudagraph_runtime_mode=CUDAGraphMode.FULL,
3846
3847
                    uniform_decode=True,
                )
3848

3849
3850
3851
            torch.cuda.synchronize()
            end_free_gpu_memory = torch.cuda.mem_get_info()[0]

3852
3853
3854
        # 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
3855
        # we may do lazy capturing in future that still allows capturing
3856
3857
        # after here.
        set_cudagraph_capturing_enabled(False)
3858
3859
3860
3861
3862

        end_time = time.perf_counter()
        elapsed_time = end_time - start_time
        cuda_graph_size = start_free_gpu_memory - end_free_gpu_memory
        # This usually takes 5~20 seconds.
3863
3864
3865
3866
3867
        logger.info(
            "Graph capturing finished in %.0f secs, took %.2f GiB",
            elapsed_time,
            cuda_graph_size / (1 << 30),
        )
3868
        return cuda_graph_size
3869

3870
3871
3872
3873
3874
3875
3876
3877
3878
3879
    def _capture_cudagraphs(
        self,
        compilation_cases: list[int],
        cudagraph_runtime_mode: CUDAGraphMode,
        uniform_decode: bool,
    ):
        assert (
            cudagraph_runtime_mode != CUDAGraphMode.NONE
            and cudagraph_runtime_mode.valid_runtime_modes()
        ), f"Invalid cudagraph runtime mode: {cudagraph_runtime_mode}"
3880
3881
3882
3883
3884
3885
3886
3887

        # 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",
3888
3889
3890
                    cudagraph_runtime_mode.name,
                ),
            )
3891

3892
3893
3894
        # 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
3895
3896
3897
            # cudagraph, a uniform decode batch, and the number of tokens
            # is above the threshold. Otherwise we just capture a non-ubatched
            # version of the graph
3898
3899
3900
3901
            allow_microbatching = (
                self.parallel_config.enable_dbo
                and cudagraph_runtime_mode == CUDAGraphMode.FULL
                and uniform_decode
3902
3903
3904
3905
3906
                and check_ubatch_thresholds(
                    config=self.vllm_config.parallel_config,
                    num_tokens=num_tokens,
                    uniform_decode=uniform_decode,
                )
3907
            )
3908

3909
3910
3911
3912
3913
3914
            for _ in range(self.compilation_config.cudagraph_num_of_warmups):
                # Use CUDAGraphRuntimeStyle.NONE (default) for warmup.
                # But be careful, warm up with `NONE`is orthogonal to
                # if we want to warm up attention or not. This is
                # different from the case where `FULL` implies capture
                # attention while `PIECEWISE` implies no attention.
3915
3916
3917
3918
3919
3920
3921
3922
3923
3924
3925
3926
3927
3928
3929
3930
3931
3932
                force_attention = cudagraph_runtime_mode == CUDAGraphMode.FULL
                self._dummy_run(
                    num_tokens,
                    cudagraph_runtime_mode=CUDAGraphMode.NONE,
                    force_attention=force_attention,
                    uniform_decode=uniform_decode,
                    allow_microbatching=allow_microbatching,
                    skip_eplb=True,
                    remove_lora=False,
                )
            self._dummy_run(
                num_tokens,
                cudagraph_runtime_mode=cudagraph_runtime_mode,
                uniform_decode=uniform_decode,
                allow_microbatching=allow_microbatching,
                skip_eplb=True,
                remove_lora=False,
            )
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        self.maybe_remove_all_loras(self.lora_config)
3934

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    def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize the attention backends and attention metadata builders.
        """
3939
        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(
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                self.vllm_config, AttentionLayerBase, kv_cache_group_spec.layer_names
            )
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            attn_backends = {}
            attn_backend_layers = defaultdict(list)
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            # Dedupe based on full class name; this is a bit safer than
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            # using the class itself as the key because when we create dynamic
            # attention backend subclasses (e.g. ChunkedLocalAttention) unless
            # they are cached correctly, there will be different objects per
            # layer.
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            for layer_name in kv_cache_group_spec.layer_names:
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                attn_backend = layers[layer_name].get_attn_backend()
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                if layer_name in self.kv_sharing_fast_prefill_eligible_layers:
                    attn_backend = create_fast_prefill_custom_backend(
                        "FastPrefill",
                        attn_backend,
                    )

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

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                attn_groups.append(attn_group)
            return attn_groups

        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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        # 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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        """
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        Resolve the cudagraph_mode when there are multiple attention
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        backends with potential conflicting CUDA graph support.
        Then initialize the cudagraph_dispatcher based on the resolved
        cudagraph_mode.
        """
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        min_cg_support = AttentionCGSupport.ALWAYS
        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
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        if (
            cudagraph_mode.mixed_mode() == CUDAGraphMode.FULL
            and min_cg_support != AttentionCGSupport.ALWAYS
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
                f"with {min_cg_builder_name} backend (support: "
                f"{min_cg_support})"
            )
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            if min_cg_support == AttentionCGSupport.NEVER:
                # if not supported any full cudagraphs, just raise it.
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                msg += (
                    "; please try cudagraph_mode=PIECEWISE, and "
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                    "make sure compilation level is piecewise"
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                )
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                raise ValueError(msg)

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

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

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

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

4150
    def may_reinitialize_input_batch(self, kv_cache_config: KVCacheConfig) -> None:
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        """
        Re-initialize the input batch if the block sizes are different from
        `[self.cache_config.block_size]`. This usually happens when there
        are multiple KV cache groups.

        Args:
            kv_cache_config: The KV cache configuration.
        """
        block_sizes = [
            kv_cache_group.kv_cache_spec.block_size
            for kv_cache_group in kv_cache_config.kv_cache_groups
        ]
        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
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                "for more details."
            )
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            self.input_batch = InputBatch(
                max_num_reqs=self.max_num_reqs,
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                max_model_len=max(self.max_model_len, self.max_encoder_len),
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                max_num_batched_tokens=self.max_num_tokens,
                device=self.device,
                pin_memory=self.pin_memory,
                vocab_size=self.model_config.get_vocab_size(),
                block_sizes=block_sizes,
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                is_spec_decode=bool(self.vllm_config.speculative_config),
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                logitsprocs=self.input_batch.logitsprocs,
                is_pooling_model=self.is_pooling_model,
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                num_speculative_tokens=(
                    self.vllm_config.speculative_config.num_speculative_tokens
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                    if self.vllm_config.speculative_config
                    else 0
                ),
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            )

4187
    def _allocate_kv_cache_tensors(
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        self, kv_cache_config: KVCacheConfig
    ) -> dict[str, torch.Tensor]:
4190
        """
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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.

4194
        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.
4199
        """
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        kv_cache_raw_tensors: dict[str, torch.Tensor] = {}
        for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
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            tensor = torch.zeros(
                kv_cache_tensor.size, dtype=torch.int8, device=self.device
            )
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            for layer_name in kv_cache_tensor.shared_by:
                kv_cache_raw_tensors[layer_name] = tensor

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

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

4222
    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
4227

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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]:
4233
        """
4234
        Reshape the KV cache tensors to the desired shape and dtype.
4235

4236
        Args:
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            kv_cache_config: The KV cache config
            kv_cache_raw_tensors: The KV cache buffer of each layer, with
4239
                correct size but uninitialized shape.
4240
        Returns:
4241
            Dict[str, torch.Tensor]: A map between layer names to their
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            corresponding memory buffer for KV cache.
        """
4244
        kv_caches: dict[str, torch.Tensor] = {}
4245
        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
4254
                num_blocks = raw_tensor.numel() // kv_cache_spec.page_size_bytes
4255
                if isinstance(kv_cache_spec, AttentionSpec):
4256
                    has_attn = True
4257
                    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,
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                        cache_dtype_str=self.cache_config.cache_dtype,
                    )
4264
                    dtype = kv_cache_spec.dtype
4265
                    try:
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                        kv_cache_stride_order = attn_backend.get_kv_cache_stride_order()
                        assert len(kv_cache_stride_order) == len(kv_cache_shape)
4268
                    except (AttributeError, NotImplementedError):
4269
                        kv_cache_stride_order = tuple(range(len(kv_cache_shape)))
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                    # The allocation respects the backend-defined stride order
                    # to ensure the semantic remains consistent for each
                    # backend. We first obtain the generic kv cache shape and
                    # then permute it according to the stride order which could
                    # result in a non-contiguous tensor.
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                    kv_cache_shape = tuple(
                        kv_cache_shape[i] for i in kv_cache_stride_order
                    )
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                    # Maintain original KV shape view.
                    inv_order = [
                        kv_cache_stride_order.index(i)
                        for i in range(len(kv_cache_stride_order))
                    ]
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                    kv_caches[layer_name] = (
                        kv_cache_raw_tensors[layer_name]
                        .view(dtype)
                        .view(kv_cache_shape)
                        .permute(*inv_order)
                    )
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4289
                elif isinstance(kv_cache_spec, MambaSpec):
4290
                    has_mamba = True
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                    raw_tensor = kv_cache_raw_tensors[layer_name]
                    state_tensors = []
4293
                    storage_offset_bytes = 0
4294
                    for shape, dtype in zip(kv_cache_spec.shapes, kv_cache_spec.dtypes):
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                        dtype_size = get_dtype_size(dtype)
                        num_element_per_page = (
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                            kv_cache_spec.page_size_bytes // dtype_size
                        )
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                        target_shape = (num_blocks, *shape)
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                        stride = torch.empty(target_shape).stride()
                        target_stride = (num_element_per_page, *stride[1:])
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                        assert storage_offset_bytes % dtype_size == 0
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                        tensor = torch.as_strided(
                            raw_tensor.view(dtype),
                            size=target_shape,
                            stride=target_stride,
4307
                            storage_offset=storage_offset_bytes // dtype_size,
4308
                        )
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                        state_tensors.append(tensor)
4310
                        storage_offset_bytes += stride[0] * dtype_size
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4312

                    kv_caches[layer_name] = state_tensors
4313
                else:
4314
                    raise NotImplementedError
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4316

        if has_attn and has_mamba:
4317
            self._update_hybrid_attention_mamba_layout(kv_caches)
4318

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        return kv_caches

4321
    def _update_hybrid_attention_mamba_layout(
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        self, kv_caches: dict[str, torch.Tensor]
    ) -> None:
4324
        """
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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
4334
            for layer_name in group.layer_names:
4335
                kv_cache = kv_caches[layer_name]
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                if isinstance(kv_cache_spec, AttentionSpec) and kv_cache.shape[0] == 2:
                    assert kv_cache.shape[1] != 2, (
                        "Fail to determine whether the layout is "
                        "(2, num_blocks, ...) or (num_blocks, 2, ...) for "
4340
                        f"a tensor of shape {kv_cache.shape}"
4341
                    )
4342
                    hidden_size = kv_cache.shape[2:].numel()
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                    kv_cache.as_strided_(
                        size=kv_cache.shape,
                        stride=(hidden_size, 2 * hidden_size, *kv_cache.stride()[2:]),
                    )
4347

4348
    def initialize_kv_cache_tensors(
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        self, kv_cache_config: KVCacheConfig
    ) -> dict[str, torch.Tensor]:
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        """
        Initialize the memory buffer for KV cache.

        Args:
            kv_cache_config: The KV cache config
        Returns:
4357
            Dict[str, torch.Tensor]: A map between layer names to their
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            corresponding memory buffer for KV cache.
        """
        # Initialize the memory buffer for KV cache
        kv_cache_raw_tensors = self._allocate_kv_cache_tensors(kv_cache_config)
        # Change the memory buffer to the desired shape
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        kv_caches = self._reshape_kv_cache_tensors(
            kv_cache_config, kv_cache_raw_tensors
        )
4366

4367
        # Set up cross-layer KV cache sharing
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        for layer_name, target_layer_name in self.shared_kv_cache_layers.items():
            logger.debug("%s reuses KV cache of %s", layer_name, target_layer_name)
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            kv_caches[layer_name] = kv_caches[target_layer_name]

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

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

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

        if self.cache_config.kv_sharing_fast_prefill:
            # In You Only Cache Once (https://arxiv.org/abs/2405.05254) or other
            # similar KV sharing setups, only the layers that generate KV caches
            # are involved in the prefill phase, enabling prefill to early exit.
4404
            attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention)
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            for layer_name in reversed(attn_layers):
                if layer_name in self.shared_kv_cache_layers:
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                    self.kv_sharing_fast_prefill_eligible_layers.add(layer_name)
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                else:
                    break
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    def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize KV cache based on `kv_cache_config`.
        Args:
            kv_cache_config: Configuration for the KV cache, including the KV
            cache size of each layer
        """
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        kv_cache_config = deepcopy(kv_cache_config)
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        self.kv_cache_config = kv_cache_config
        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():
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            kv_transfer_group = get_kv_transfer_group()
            kv_transfer_group.register_kv_caches(kv_caches)
            kv_transfer_group.set_host_xfer_buffer_ops(copy_kv_blocks)
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        if self.dcp_world_size > 1:
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            layer_names = self.attn_groups[0][0].layer_names
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            layers = get_layers_from_vllm_config(
                self.vllm_config, AttentionLayerBase, layer_names
            )
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            for layer in layers.values():
                assert layer.impl.need_to_return_lse_for_decode, (
                    "DCP requires attention impls to return"
                    " the softmax lse for decode, but the impl "
                    f"{layer.impl.__class__.__name__} "
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                    "does not return the softmax lse for decode."
                )
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    def may_add_encoder_only_layers_to_kv_cache_config(self) -> None:
        """
        Add encoder-only layers to the KV cache config.
        """
        block_size = self.vllm_config.cache_config.block_size
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        encoder_only_attn_specs: dict[AttentionSpec, list[str]] = defaultdict(list)
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        attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention)
        for layer_name, attn_module in attn_layers.items():
            if attn_module.attn_type == AttentionType.ENCODER_ONLY:
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                attn_spec: AttentionSpec = EncoderOnlyAttentionSpec(
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                    block_size=block_size,
                    num_kv_heads=attn_module.num_kv_heads,
                    head_size=attn_module.head_size,
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                    dtype=self.kv_cache_dtype,
                )
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                encoder_only_attn_specs[attn_spec].append(layer_name)
                self.runner_only_attn_layers.add(layer_name)
        if len(encoder_only_attn_specs) > 0:
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            assert len(encoder_only_attn_specs) == 1, (
                "Only support one encoder-only attention spec now"
            )
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            spec, layer_names = encoder_only_attn_specs.popitem()
            self.kv_cache_config.kv_cache_groups.append(
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                KVCacheGroupSpec(layer_names=layer_names, kv_cache_spec=spec)
            )
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    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
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        """
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        Generates the KVCacheSpec by parsing the kv cache format from each
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        Attention module in the static forward context.
        Returns:
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            KVCacheSpec: A dictionary mapping layer names to their KV cache
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            format. Layers that do not need KV cache are not included.
        """

        block_size = self.vllm_config.cache_config.block_size
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        use_mla = self.vllm_config.model_config.use_mla
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        cache_dtype_str = self.vllm_config.cache_config.cache_dtype
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        kv_cache_spec: dict[str, KVCacheSpec] = {}
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        attn_layers = get_layers_from_vllm_config(self.vllm_config, 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:
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                # 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:
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                    assert not use_mla, "MLA is not supported for slidingwindow"
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                    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,
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                        sliding_window=attn_module.sliding_window,
                    )
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                elif use_mla:
                    kv_cache_spec[layer_name] = MLAAttentionSpec(
                        block_size=block_size,
                        num_kv_heads=attn_module.num_kv_heads,
                        head_size=attn_module.head_size,
                        dtype=self.kv_cache_dtype,
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                        cache_dtype_str=cache_dtype_str,
                    )
                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,
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                        attention_chunk_size=self.attention_chunk_size,
                    )
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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,
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                        dtype=self.kv_cache_dtype,
                    )
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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,
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                    dtype=self.kv_cache_dtype,
                )
            elif attn_module.attn_type in (
                AttentionType.ENCODER,
                AttentionType.ENCODER_ONLY,
            ):
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                # encoder-only attention does not need KV cache.
                continue
            else:
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                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(
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                    "Mamba with speculative decoding is not supported yet."
                )
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            mamba_block_size = self.vllm_config.cache_config.mamba_block_size
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            page_size_padded = self.vllm_config.cache_config.mamba_page_size_padded
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            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=mamba_block_size,
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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
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                        if self.speculative_config
                        else 0
                    ),
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                )
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        ds_indexer_layers = get_layers_from_vllm_config(
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            self.vllm_config, DeepseekV32IndexerCache
        )
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        for layer_name, ds_indexer_module in ds_indexer_layers.items():
            kv_cache_spec[layer_name] = ds_indexer_module.get_kv_cache_spec()
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        return kv_cache_spec
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    def _to_list(self, sampled_token_ids: torch.Tensor) -> list[list[int]]:
        # This is a short term mitigation for issue mentioned in
        # https://github.com/vllm-project/vllm/issues/22754.
        # `tolist` would trigger a cuda wise stream sync, which
        # would block other copy ops from other cuda streams.
        # A cuda event sync would avoid such a situation. Since
        # this is in the critical path of every single model
        # forward loop, this has caused perf issue for a disagg
        # setup.
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        pinned = self.sampled_token_ids_pinned_cpu[: sampled_token_ids.shape[0]]
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        pinned.copy_(sampled_token_ids, non_blocking=True)
        self.transfer_event.record()
        self.transfer_event.synchronize()
        return pinned.tolist()