gpu_model_runner.py 217 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 functools import reduce
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from itertools import product
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from typing import TYPE_CHECKING, Any, NamedTuple, TypeAlias, cast
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import numpy as np
import torch
import torch.distributed
import torch.nn as nn
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from tqdm import tqdm
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import vllm.envs as envs
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from vllm.attention import Attention, AttentionType
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from vllm.attention.backends.abstract import (
    AttentionBackend,
    AttentionMetadata,
    MultipleOf,
)
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from vllm.compilation.counter import compilation_counter
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from vllm.compilation.cuda_graph import CUDAGraphWrapper
from vllm.compilation.monitor import set_cudagraph_capturing_enabled
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from vllm.config import (
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    CompilationMode,
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    CUDAGraphMode,
    VllmConfig,
    get_layers_from_vllm_config,
    update_config,
)
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from vllm.distributed.ec_transfer import get_ec_transfer, has_ec_transfer
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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_dcp_group,
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    get_pp_group,
    get_tp_group,
    graph_capture,
    is_global_first_rank,
    prepare_communication_buffer_for_model,
)
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from vllm.forward_context import BatchDescriptor, set_forward_context
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from vllm.logger import init_logger
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from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
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from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
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from vllm.model_executor.model_loader import TensorizerLoader, get_model_loader
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from vllm.model_executor.models.interfaces import (
    SupportsMultiModal,
    is_mixture_of_experts,
    supports_eagle3,
    supports_mrope,
    supports_multimodal_pruning,
    supports_transcription,
)
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from vllm.model_executor.models.interfaces_base import (
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    VllmModelForPooling,
    is_pooling_model,
    is_text_generation_model,
)
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (
    BatchedTensorInputs,
    MultiModalKwargsItem,
    PlaceholderRange,
)
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from vllm.multimodal.utils import group_mm_kwargs_by_modality
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from vllm.pooling_params import PoolingParams
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from vllm.sampling_params import SamplingType
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from vllm.sequence import IntermediateTensors
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from vllm.tasks import GenerationTask, PoolingTask, SupportedTask
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from vllm.utils import length_from_prompt_token_ids_or_embeds
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from vllm.utils.jsontree import json_map_leaves
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from vllm.utils.math_utils import cdiv, round_up
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from vllm.utils.mem_constants import GiB_bytes
from vllm.utils.mem_utils import DeviceMemoryProfiler
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from vllm.utils.platform_utils import is_pin_memory_available
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from vllm.utils.torch_utils import (
    get_dtype_size,
    kv_cache_dtype_str_to_dtype,
    supports_dynamo,
)
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from vllm.v1.attention.backends.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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    get_dcp_local_seq_lens,
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    reorder_batch_to_split_decodes_and_prefills,
    split_attn_metadata,
)
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from vllm.v1.cudagraph_dispatcher import CudagraphDispatcher
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from vllm.v1.kv_cache_interface import (
    AttentionSpec,
    ChunkedLocalAttentionSpec,
    CrossAttentionSpec,
    EncoderOnlyAttentionSpec,
    FullAttentionSpec,
    KVCacheConfig,
    KVCacheGroupSpec,
    KVCacheSpec,
    MambaSpec,
    SlidingWindowSpec,
    UniformTypeKVCacheSpecs,
)
from vllm.v1.outputs import (
    EMPTY_MODEL_RUNNER_OUTPUT,
    AsyncModelRunnerOutput,
    DraftTokenIds,
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    ECConnectorOutput,
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    KVConnectorOutput,
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    LogprobsLists,
    LogprobsTensors,
    ModelRunnerOutput,
    PoolerOutput,
    SamplerOutput,
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    make_empty_encoder_model_runner_output,
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)
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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.spec_decode.suffix_decoding import SuffixDecodingProposer
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from vllm.v1.structured_output.utils import apply_grammar_bitmask
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from vllm.v1.utils import CpuGpuBuffer, record_function_or_nullcontext
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from vllm.v1.worker.dp_utils import coordinate_batch_across_dp
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from vllm.v1.worker.ec_connector_model_runner_mixin import ECConnectorModelRunnerMixin
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from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
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from vllm.v1.worker.gpu_ubatch_wrapper import UBatchWrapper
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from vllm.v1.worker.kv_connector_model_runner_mixin import KVConnectorModelRunnerMixin
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from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
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from vllm.v1.worker.ubatch_utils import (
    UBatchSlice,
    UBatchSlices,
    check_ubatch_thresholds,
)
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from vllm.v1.worker.utils import is_residual_scattered_for_sp
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from .utils import (
    AttentionGroup,
    MultiModalBudget,
    add_kv_sharing_layers_to_kv_cache_groups,
    bind_kv_cache,
    gather_mm_placeholders,
    sanity_check_mm_encoder_outputs,
    scatter_mm_placeholders,
)
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if TYPE_CHECKING:
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    from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
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    from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput
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logger = init_logger(__name__)

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AttnMetadataDict: TypeAlias = dict[str, AttentionMetadata]
# list when ubatching is enabled
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PerLayerAttnMetadata: TypeAlias = list[AttnMetadataDict] | AttnMetadataDict
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# Wrapper for ModelRunnerOutput to support overlapped execution.
class AsyncGPUModelRunnerOutput(AsyncModelRunnerOutput):
    def __init__(
        self,
        model_runner_output: ModelRunnerOutput,
        sampled_token_ids: torch.Tensor,
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        logprobs_tensors: torch.Tensor | None,
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        invalid_req_indices: list[int],
        async_output_copy_stream: torch.cuda.Stream,
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        vocab_size: int,
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    ):
        self._model_runner_output = model_runner_output
        self._invalid_req_indices = invalid_req_indices

        # Event on the copy stream so we can synchronize the non-blocking copy.
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        self.async_copy_ready_event = torch.cuda.Event()
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        # Keep a reference to the device tensor to avoid it being
        # deallocated until we finish copying it to the host.
        self._sampled_token_ids = sampled_token_ids
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        self.vocab_size = vocab_size
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        self._logprobs_tensors = logprobs_tensors
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        # Initiate the copy on a separate stream, but do not synchronize it.
        default_stream = torch.cuda.current_stream()
        with torch.cuda.stream(async_output_copy_stream):
            async_output_copy_stream.wait_stream(default_stream)
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            self.sampled_token_ids_cpu = self._sampled_token_ids.to(
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                "cpu", non_blocking=True
            )
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            self._logprobs_tensors_cpu = (
                self._logprobs_tensors.to_cpu_nonblocking()
                if self._logprobs_tensors
                else None
            )
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            self.async_copy_ready_event.record()
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    def get_output(self) -> ModelRunnerOutput:
        """Copy the device tensors to the host and return a ModelRunnerOutput.
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        This function blocks until the copy is finished.
        """
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        self.async_copy_ready_event.synchronize()
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        # Release the device tensors once the copy has completed.
        del self._logprobs_tensors
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        del self._sampled_token_ids
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        max_gen_len = self.sampled_token_ids_cpu.shape[-1]
        if max_gen_len == 1:
            valid_sampled_token_ids: list[np.ndarray] = [
                row for row in self.sampled_token_ids_cpu.numpy()
            ]
        else:
            valid_sampled_token_ids = RejectionSampler.parse_output(
                self.sampled_token_ids_cpu,
                self.vocab_size,
            )
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        for i in self._invalid_req_indices:
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            valid_sampled_token_ids[i] = np.array([])
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        output = self._model_runner_output
        output.sampled_token_ids = valid_sampled_token_ids
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        if self._logprobs_tensors_cpu:
            # NOTE(nick): this will need to be updated to use cu_num_accepted_tokens
            # for async sched + spec decode + logprobs compatibility.
            output.logprobs = self._logprobs_tensors_cpu.tolists()
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        return output


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class ExecuteModelState(NamedTuple):
    """Ephemeral cached state transferred between execute_model() and
    sample_tokens(), after execute_model() returns None."""

    scheduler_output: "SchedulerOutput"
    logits: torch.Tensor
    spec_decode_metadata: SpecDecodeMetadata | None
    spec_decode_common_attn_metadata: CommonAttentionMetadata | None
    hidden_states: torch.Tensor
    sample_hidden_states: torch.Tensor
    aux_hidden_states: list[torch.Tensor] | None
    kv_connector_output: KVConnectorOutput | None
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    ec_connector_output: ECConnectorOutput | None
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class GPUModelRunner(
    LoRAModelRunnerMixin, KVConnectorModelRunnerMixin, ECConnectorModelRunnerMixin
):
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    def __init__(
        self,
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        vllm_config: VllmConfig,
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        device: torch.device,
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    ):
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        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        self.cache_config = vllm_config.cache_config
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        self.compilation_config = vllm_config.compilation_config
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        self.lora_config = vllm_config.lora_config
        self.load_config = vllm_config.load_config
        self.parallel_config = vllm_config.parallel_config
        self.scheduler_config = vllm_config.scheduler_config
        self.speculative_config = vllm_config.speculative_config
        self.observability_config = vllm_config.observability_config
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        from vllm.model_executor.models.utils import set_cpu_offload_max_bytes
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        set_cpu_offload_max_bytes(int(self.cache_config.cpu_offload_gb * 1024**3))
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        model_config = self.model_config
        cache_config = self.cache_config
        scheduler_config = self.scheduler_config
        parallel_config = self.parallel_config
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        self.device = device
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        self.pin_memory = is_pin_memory_available()
        self.dtype = self.model_config.dtype
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        self.kv_cache_dtype = kv_cache_dtype_str_to_dtype(
            cache_config.cache_dtype, self.model_config
        )
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        self.is_pooling_model = model_config.runner_type == "pooling"
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        self.enable_prompt_embeds = model_config.enable_prompt_embeds
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        self.is_multimodal_raw_input_only_model = (
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            model_config.is_multimodal_raw_input_only_model
        )
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        # This will be overridden in load_model()
        self.is_multimodal_pruning_enabled = False
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        self.max_model_len = model_config.max_model_len
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        # Always set to false after the first forward pass
        self.calculate_kv_scales = self.cache_config.calculate_kv_scales
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        self.dcp_world_size = self.parallel_config.decode_context_parallel_size
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        self.dcp_rank = 0 if self.dcp_world_size <= 1 else get_dcp_group().rank_in_group
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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)
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        self.use_alibi = model_config.uses_alibi
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        self.cascade_attn_enabled = not self.model_config.disable_cascade_attn
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        # Multi-modal data support
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        self.mm_registry = MULTIMODAL_REGISTRY
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        self.uses_mrope = model_config.uses_mrope
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        self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
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            model_config
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        if self.model_config.is_encoder_decoder:
            # Maximum length of the encoder input, only for encoder-decoder
            # models.
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            self.max_encoder_len = scheduler_config.max_num_encoder_input_tokens
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        else:
            self.max_encoder_len = 0

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

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

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

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        # mm_hash ->  encoder_output
        self.encoder_cache: dict[str, torch.Tensor] = {}
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        self.use_aux_hidden_state_outputs = False
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        # Set up speculative decoding.
        # NOTE(Jiayi): currently we put the entire draft model on
        # the last PP rank. This is not ideal if there are many
        # layers in the draft model.
        if self.speculative_config and get_pp_group().is_last_rank:
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            self.drafter: (
                NgramProposer | SuffixDecodingProposer | EagleProposer | MedusaProposer
            )
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            if self.speculative_config.method == "ngram":
                self.drafter = NgramProposer(self.vllm_config)
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            elif self.speculative_config.method == "suffix":
                self.drafter = SuffixDecodingProposer(self.vllm_config)
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            elif self.speculative_config.use_eagle():
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                self.drafter = EagleProposer(self.vllm_config, self.device, self)
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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
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                )
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            else:
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                raise ValueError(
                    "Unknown speculative decoding method: "
                    f"{self.speculative_config.method}"
                )
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            self.rejection_sampler = RejectionSampler(self.sampler)
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        self.num_spec_tokens = 0
        if self.speculative_config:
            self.num_spec_tokens = self.speculative_config.num_speculative_tokens

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        # Request states.
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        self.requests: dict[str, CachedRequestState] = {}
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        self.comm_stream = torch.cuda.Stream()
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        # Input Batch
        # NOTE(Chen): Ideally, we should initialize the input batch inside
        # `initialize_kv_cache` based on the kv cache config. However, as in
        # https://github.com/vllm-project/vllm/pull/18298, due to some unknown
        # reasons, we have to initialize the input batch before `load_model`,
        # quantization + weight offloading will fail otherwise. As a temporary
        # solution, we initialize the input batch here, and re-initialize it
        # in `initialize_kv_cache` if the block_sizes here is different from
        # the block_sizes in the kv cache config.
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        custom_logitsprocs = model_config.logits_processors
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        self.input_batch = InputBatch(
            max_num_reqs=self.max_num_reqs,
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            # We need to use the encoder length for encoder-decoer
            # because of KV cache for cross-attention.
            max_model_len=max(self.max_model_len, self.max_encoder_len),
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            max_num_batched_tokens=self.max_num_tokens,
            device=self.device,
            pin_memory=self.pin_memory,
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            vocab_size=self.model_config.get_vocab_size(),
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            block_sizes=[self.cache_config.block_size],
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            kernel_block_sizes=[self.cache_config.block_size],
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            is_spec_decode=bool(self.vllm_config.speculative_config),
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            logitsprocs=build_logitsprocs(
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                self.vllm_config,
                self.device,
                self.pin_memory,
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                self.is_pooling_model,
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                custom_logitsprocs,
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            ),
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            # We currently don't know whether a particular custom logits processor
            # uses output token ids so we set this conservatively.
            logitsprocs_need_output_token_ids=bool(custom_logitsprocs),
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            is_pooling_model=self.is_pooling_model,
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            dcp_kv_cache_interleave_size=self.parallel_config.dcp_kv_cache_interleave_size,
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        )
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        self.use_async_scheduling = self.scheduler_config.async_scheduling
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        # Separate cuda stream for overlapping transfer of sampled token ids from
        # GPU to CPU when async scheduling is enabled.
        self.async_output_copy_stream: torch.cuda.Stream | None = None
        # cuda event to synchronize use of reused CPU tensors between steps
        # when async scheduling is enabled.
        self.prepare_inputs_event: torch.cuda.Event | None = None
        if self.use_async_scheduling:
            self.async_output_copy_stream = torch.cuda.Stream()
            self.prepare_inputs_event = torch.cuda.Event()
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        # self.cudagraph_batch_sizes sorts in ascending order.
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        if (
            self.compilation_config.cudagraph_capture_sizes
            and self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
        ):
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            self.cudagraph_batch_sizes = sorted(
                self.compilation_config.cudagraph_capture_sizes
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            )
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        # Cache the device properties.
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        self._init_device_properties()
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        # Persistent buffers for CUDA graphs.
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        self.input_ids = self._make_buffer(self.max_num_tokens, dtype=torch.int32)
        self.positions = self._make_buffer(self.max_num_tokens, dtype=torch.int64)
        self.query_start_loc = self._make_buffer(
            self.max_num_reqs + 1, dtype=torch.int32
        )
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        self.seq_lens = self._make_buffer(self.max_num_reqs, dtype=torch.int32)
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        if self.dcp_world_size > 1:
            self.dcp_local_seq_lens = self._make_buffer(
                self.max_num_reqs, dtype=torch.int32
            )
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        # Because inputs_embeds may be bfloat16 and we don't need a numpy
        # version of this tensor, avoid a RuntimeError by not creating a
        # numpy buffer.
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        self.inputs_embeds = self._make_buffer(
            self.max_num_tokens, self.hidden_size, dtype=self.dtype, numpy=False
        )
        self.is_token_ids = self._make_buffer(self.max_num_tokens, dtype=torch.bool)
        self.discard_request_indices = self._make_buffer(
            self.max_num_reqs, dtype=torch.int64
        )
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        self.num_discarded_requests = 0

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        self.num_decode_draft_tokens = self._make_buffer(
            self.max_num_reqs, dtype=torch.int32
        )
        self.num_accepted_tokens = self._make_buffer(
            self.max_num_reqs, dtype=torch.int64
        )
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        # Only relevant for multimodal models
        if self.supports_mm_inputs:
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            self.is_mm_embed = self._make_buffer(self.max_num_tokens, dtype=torch.bool)
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        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
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        if self.uses_mrope:
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            # NOTE: `mrope_positions` is implemented with one additional dummy
            # position on purpose to make it non-contiguous so that it can work
            # with torch compile.
            # See detailed explanation in https://github.com/vllm-project/vllm/pull/12128#discussion_r1926431923
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            # NOTE: When M-RoPE is enabled, position ids are 3D regardless of
            # the modality of inputs. For text-only inputs, each dimension has
            # identical position IDs, making M-RoPE functionally equivalent to
            # 1D-RoPE.
            # See page 5 of https://arxiv.org/abs/2409.12191
501
            self.mrope_positions = self._make_buffer(
502
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                (3, self.max_num_tokens + 1), dtype=torch.int64
            )
504

505
        # None in the first PP rank. The rest are set after load_model.
506
        self.intermediate_tensors: IntermediateTensors | None = None
507

508
        # OPTIMIZATION: Cache the tensors rather than creating them every step.
509
        # Keep in int64 to avoid overflow with long context
510
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512
513
        self.arange_np = np.arange(
            max(self.max_num_reqs + 1, self.max_model_len, self.max_num_tokens),
            dtype=np.int64,
        )
514

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519
        # 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] = {}
520
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522
523
524
        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(
525
526
                self.max_num_tokens, dtype=torch.int32, device=self.device
            )
527

528
        self.uniform_decode_query_len = 1 + self.num_spec_tokens
529
530
531
532

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

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537
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539
540
541
        self.mm_budget = (
            MultiModalBudget(
                self.model_config,
                self.scheduler_config,
                self.mm_registry,
            )
            if self.supports_mm_inputs
            else None
        )
542

543
        self.reorder_batch_threshold: int | None = None
544

545
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549
        # 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()

550
        # Cached outputs.
551
        self._draft_token_ids: list[list[int]] | torch.Tensor | None = None
552
553
        self.transfer_event = torch.cuda.Event()
        self.sampled_token_ids_pinned_cpu = torch.empty(
554
            (self.max_num_reqs, 1),
555
556
            dtype=torch.int64,
            device="cpu",
557
558
            pin_memory=self.pin_memory,
        )
559

560
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572
573
        # Pre-allocated tensor for copying valid sampled token counts to CPU,
        # with dedicated stream for overlapping and event for coordination.
        self.valid_sampled_token_count_event: torch.cuda.Event | None = None
        self.valid_sampled_token_count_copy_stream: torch.cuda.Stream | None = None
        if self.use_async_scheduling and self.num_spec_tokens:
            self.valid_sampled_token_count_event = torch.cuda.Event()
            self.valid_sampled_token_count_copy_stream = torch.cuda.Stream()
        self.valid_sampled_token_count_cpu = torch.empty(
            self.max_num_reqs,
            dtype=torch.int64,
            device="cpu",
            pin_memory=self.pin_memory,
        )

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576
        # Ephemeral state transferred between execute_model() and sample_tokens().
        self.execute_model_state: ExecuteModelState | None = None

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579
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    def reset_mm_cache(self) -> None:
        if self.mm_budget:
            self.mm_budget.reset_cache()

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

591
    def _make_buffer(
592
        self, *size: int | torch.SymInt, dtype: torch.dtype, numpy: bool = True
593
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600
    ) -> CpuGpuBuffer:
        return CpuGpuBuffer(
            *size,
            dtype=dtype,
            device=self.device,
            pin_memory=self.pin_memory,
            with_numpy=numpy,
        )
601

602
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604
    def _init_model_kwargs(self, num_tokens: int):
        model_kwargs = dict[str, Any]()

605
        if not self.is_pooling_model:
606
607
            return model_kwargs

608
609
        num_reqs = self.input_batch.num_reqs
        pooling_params = self.input_batch.get_pooling_params()
610
611
612

        token_type_id_requests = dict[int, Any]()
        for i, param in enumerate(pooling_params):
613
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617
            if (
                param.extra_kwargs is not None
                and (token_types := param.extra_kwargs.get("compressed_token_type_ids"))
                is not None
            ):
618
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621
622
                token_type_id_requests[i] = token_types

        if len(token_type_id_requests) == 0:
            return model_kwargs

623
        seq_lens = self.seq_lens.gpu[:num_reqs]
624
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629
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631
        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(
632
633
            device=self.device
        )
634
635
        return model_kwargs

636
    def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
637
638
        """
        Update the order of requests in the batch based on the attention
639
        backend's needs. For example, some attention backends (namely MLA) may
640
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644
645
        want to separate requests based on if the attention computation will be
        compute-bound or memory-bound.

        Args:
            scheduler_output: The scheduler output.
        """
646
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652
653
        # 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

654
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656
657
        if self.reorder_batch_threshold is not None:
            reorder_batch_to_split_decodes_and_prefills(
                self.input_batch,
                scheduler_output,
658
659
                decode_threshold=self.reorder_batch_threshold,
            )
660

661
662
    # Note: used for model runner override.
    def _init_device_properties(self) -> None:
663
        """Initialize attributes from torch.cuda.get_device_properties"""
664
665
666
667
668
669
670
        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()

671
    def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
672
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675
676
677
        """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.

678
679
        The SamplingMetadata is updated and copied to the GPU if there is a
        new/resumed/paused/finished request in the batch.
680
681
        """
        # Remove finished requests from the cached states.
682
683
        for req_id in scheduler_output.finished_req_ids:
            self.requests.pop(req_id, None)
684
685
686
687
688
689
690
        # 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:
691
            self.input_batch.remove_request(req_id)
692
693

        # Free the cached encoder outputs.
694
695
        for mm_hash in scheduler_output.free_encoder_mm_hashes:
            self.encoder_cache.pop(mm_hash, None)
696

697
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702
703
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708
709
        # 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:
710
            self.input_batch.remove_request(req_id)
711

712
        reqs_to_add: list[CachedRequestState] = []
713
        # Add new requests to the cached states.
714
715
716
        for new_req_data in scheduler_output.scheduled_new_reqs:
            req_id = new_req_data.req_id
            sampling_params = new_req_data.sampling_params
717
            pooling_params = new_req_data.pooling_params
718

719
720
721
722
            if (
                sampling_params
                and sampling_params.sampling_type == SamplingType.RANDOM_SEED
            ):
723
724
725
726
727
                generator = torch.Generator(device=self.device)
                generator.manual_seed(sampling_params.seed)
            else:
                generator = None

728
729
            if self.is_pooling_model:
                assert pooling_params is not None
730
731
                task = pooling_params.task
                assert task is not None, "You did not set `task` in the API"
732

733
                model = cast(VllmModelForPooling, self.get_model())
734
                to_update = model.pooler.get_pooling_updates(task)
735
736
                to_update.apply(pooling_params)

737
            req_state = CachedRequestState(
738
                req_id=req_id,
739
                prompt_token_ids=new_req_data.prompt_token_ids,
740
                prompt_embeds=new_req_data.prompt_embeds,
741
                mm_features=new_req_data.mm_features,
742
                sampling_params=sampling_params,
743
                pooling_params=pooling_params,
744
                generator=generator,
745
746
                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
747
                output_token_ids=[],
748
                lora_request=new_req_data.lora_request,
749
            )
750
751
            self.requests[req_id] = req_state

752
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
753
            if self.uses_mrope:
754
                self._init_mrope_positions(req_state)
755

756
            reqs_to_add.append(req_state)
757

758
        # Update the states of the running/resumed requests.
759
        is_last_rank = get_pp_group().is_last_rank
760
        req_data = scheduler_output.scheduled_cached_reqs
761
762
763
764
765

        # Wait until valid_sampled_tokens_count is copied to cpu,
        # then use it to update actual num_computed_tokens of each request.
        valid_sampled_token_count = self._get_valid_sampled_token_count()

766
        for i, req_id in enumerate(req_data.req_ids):
767
            req_state = self.requests[req_id]
768
769
            num_computed_tokens = req_data.num_computed_tokens[i]
            new_block_ids = req_data.new_block_ids[i]
770
            resumed_from_preemption = req_id in req_data.resumed_req_ids
771
            num_output_tokens = req_data.num_output_tokens[i]
772
            req_index = self.input_batch.req_id_to_index.get(req_id)
773

774
775
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782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
            # prev_num_draft_len is used in async scheduling mode with
            # spec decode. it indicates if need to update num_computed_tokens
            # of the request. for example:
            # fist step: num_computed_tokens = 0, spec_tokens = [],
            # prev_num_draft_len = 0.
            # second step: num_computed_tokens = 100(prompt lenth),
            # spec_tokens = [a,b], prev_num_draft_len = 0.
            # third step: num_computed_tokens = 100 + 2, spec_tokens = [c,d],
            # prev_num_draft_len = 2.
            # num_computed_tokens in first step and second step does't contain
            # the spec tokens length, but in third step it contains the
            # spec tokens length. we only need to update num_computed_tokens
            # when prev_num_draft_len > 0.
            if req_state.prev_num_draft_len:
                if req_index is None:
                    req_state.prev_num_draft_len = 0
                else:
                    assert self.input_batch.prev_req_id_to_index is not None
                    prev_req_index = self.input_batch.prev_req_id_to_index[req_id]
                    num_accepted = valid_sampled_token_count[prev_req_index] - 1
                    num_rejected = req_state.prev_num_draft_len - num_accepted
                    num_computed_tokens -= num_rejected
                    req_state.output_token_ids.extend([-1] * num_accepted)
797

798
            # Update the cached states.
799
            req_state.num_computed_tokens = num_computed_tokens
800
801
802
803
804
805
806
807

            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.
808
809
810
                num_new_tokens = (
                    num_computed_tokens + len(new_token_ids) - req_state.num_tokens
                )
811
812
813
814
                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:
815
                    req_state.output_token_ids.extend(new_token_ids[-num_new_tokens:])
816
817
818
819
820
            elif num_output_tokens < len(req_state.output_token_ids):
                # Some output tokens were discarded due to a sync-KV-load
                # failure. Align the cached state.
                del req_state.output_token_ids[num_output_tokens:]
                if req_index is not None:
821
822
823
824
                    end_idx = (
                        self.input_batch.num_prompt_tokens[req_index]
                        + num_output_tokens
                    )
825
826
                    self.input_batch.num_tokens[req_index] = end_idx
                    self.input_batch.num_tokens_no_spec[req_index] = end_idx
827

828
            # Update the block IDs.
829
            if not resumed_from_preemption:
830
831
                if new_block_ids is not None:
                    # Append the new blocks to the existing block IDs.
832
                    for block_ids, new_ids in zip(req_state.block_ids, new_block_ids):
833
                        block_ids.extend(new_ids)
834
            else:
835
                assert req_index is None
836
                assert new_block_ids is not None
837
838
                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
839
                req_state.block_ids = new_block_ids
840
841
842
843
844

            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.
845
846
847
848
849
850
851

                if self.use_async_scheduling and num_output_tokens > 0:
                    # We must recover the output token ids for resumed requests in the
                    # async scheduling case, so that correct input_ids are obtained.
                    resumed_token_ids = req_data.all_token_ids[req_id]
                    req_state.output_token_ids = resumed_token_ids[-num_output_tokens:]

852
                reqs_to_add.append(req_state)
853
854
855
                continue

            # Update the persistent batch.
856
            self.input_batch.num_computed_tokens_cpu[req_index] = num_computed_tokens
857
            if new_block_ids is not None:
858
                self.input_batch.block_table.append_row(new_block_ids, req_index)
859
860
861
862
863
864
865

            # 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)
866
                self.input_batch.token_ids_cpu[
867
868
869
                    req_index, start_token_index:end_token_index
                ] = new_token_ids
                self.input_batch.num_tokens_no_spec[req_index] = end_token_index
870
                self.input_batch.num_tokens[req_index] = end_token_index
871

872
            # Add spec_token_ids to token_ids_cpu.
873
            spec_token_ids = scheduler_output.scheduled_spec_decode_tokens.get(
874
                req_id, []
875
            )
876
877
878
879
880
            num_spec_tokens = len(spec_token_ids)
            # For async scheduling, token_ids_cpu assigned from
            # spec_token_ids are placeholders and will be overwritten in
            # _prepare_input_ids.
            if num_spec_tokens:
881
882
883
                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[
884
885
                    req_index, start_index:end_token_index
                ] = spec_token_ids
886
887
                # NOTE(woosuk): `num_tokens` here may include spec tokens.
                self.input_batch.num_tokens[req_index] += num_spec_tokens
888
889
890
891
892
893
894

            # When speculative decoding is used with structured output,
            # the scheduler can drop draft tokens that do not
            # conform to the schema. This can result in
            # scheduler_output.scheduled_spec_decode_tokens being empty,
            # even when speculative decoding is enabled.
            self.input_batch.spec_token_ids[req_index] = spec_token_ids
895

896
897
898
899
900
901
902
903
904
            # there are no draft tokens with async scheduling,
            # we clear the spec_decoding info in scheduler_output and
            # use normal sampling but rejection_sampling.
            if self.use_async_scheduling:
                req_state.prev_num_draft_len = num_spec_tokens
                if num_spec_tokens and self._draft_token_ids is None:
                    scheduler_output.total_num_scheduled_tokens -= num_spec_tokens
                    scheduler_output.num_scheduled_tokens[req_id] -= num_spec_tokens
                    scheduler_output.scheduled_spec_decode_tokens.pop(req_id, None)
905
906
        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
907
908
        for request in reqs_to_add:
            self.input_batch.add_request(request)
909

910
911
912
913
914
915
        # 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()
916

917
    def _update_states_after_model_execute(
918
919
        self, output_token_ids: torch.Tensor
    ) -> None:
920
921
922
923
924
925
926
927
928
929
930
931
        """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.
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
        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()
        )
952
953
954
        for i, num_tokens in enumerate(num_accepted_tokens):
            self.input_batch.num_accepted_tokens_cpu[i] = num_tokens

955
    def _init_mrope_positions(self, req_state: CachedRequestState):
956
957
        model = self.get_model()
        assert supports_mrope(model), "M-RoPE support is not implemented."
958
959

        req_state.mrope_positions, req_state.mrope_position_delta = (
960
            model.get_mrope_input_positions(
961
                req_state.prompt_token_ids,
962
                req_state.mm_features,
963
            )
964
        )
965

966
    def _extract_mm_kwargs(
967
        self,
968
969
        scheduler_output: "SchedulerOutput",
    ) -> BatchedTensorInputs:
970
        if not scheduler_output or not self.is_multimodal_raw_input_only_model:
971
            return {}
972

973
974
        mm_kwargs = list[MultiModalKwargsItem]()
        for req in scheduler_output.scheduled_new_reqs:
975
976
977
            for feature in req.mm_features:
                if feature.data is not None:
                    mm_kwargs.append(feature.data)
978

979
        # Input all modalities at once
980
        model = cast(SupportsMultiModal, self.model)
981
982
        mm_kwargs_combined: BatchedTensorInputs = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
983
984
985
986
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
987
            multimodal_cpu_fields=model.multimodal_cpu_fields,
988
989
        ):
            mm_kwargs_combined.update(mm_kwargs_group)
990

991
        return mm_kwargs_combined
992

993
    def _dummy_mm_kwargs(self, num_seqs: int) -> BatchedTensorInputs:
994
        if not self.is_multimodal_raw_input_only_model:
995
            return {}
996

997
998
999
1000
1001
        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)
1002

1003
1004
1005
    def _get_cumsum_and_arange(
        self,
        num_tokens: np.ndarray,
1006
        cumsum_dtype: np.dtype | None = None,
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
    ) -> 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

1023
    def _prepare_input_ids(
1024
1025
1026
1027
        self,
        scheduler_output: "SchedulerOutput",
        total_num_scheduled_tokens: int,
        cu_num_tokens: np.ndarray,
1028
    ) -> None:
1029
        """Prepare the input IDs for the current batch.
1030

1031
1032
1033
1034
1035
1036
1037
        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)
1038
1039
1040
            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)
1041
1042
1043
1044
1045
1046
1047
            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
1048
1049
1050
1051
        sample_flattened_indices: list[int] = []
        spec_flattened_indices: list[int] = []
        prev_common_req_indices: list[int] = []
        prev_draft_token_indices: list[int] = []
1052
1053
        indices_match = True
        max_flattened_index = -1
1054
1055
1056
        total_num_spec_tokens = 0
        scheduled_spec_tokens = scheduler_output.scheduled_spec_decode_tokens

1057
1058
1059
1060
1061
        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.
1062
1063
                draft_len = len(scheduled_spec_tokens.get(req_id, ()))
                total_num_spec_tokens += draft_len
1064
                flattened_index = cu_num_tokens[cur_index].item() - 1
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
                # example: cu_num_tokens = [2, 5, 8], draft_tokens = [1, 2, 2]
                # sample_flattened_indices = [0, 2, 5]
                # spec_flattened_indices = [1,   3, 4,    6, 7]
                sample_flattened_indices.append(flattened_index - draft_len)
                spec_flattened_indices.extend(
                    range(flattened_index - draft_len + 1, flattened_index + 1)
                )
                start = prev_index * self.num_spec_tokens
                # prev_draft_token_indices is used to find which draft_tokens_id
                # should be copied to input_ids
                # example: prev draft_tokens_id [[1,2], [3,4], [5, 6]]
                # flatten draft_tokens_id [1,2,3,4,5,6]
                # draft_len of each request [1, 2, 1]
                # then prev_draft_token_indices is [0,   2, 3,   4]
                prev_draft_token_indices.extend(range(start, start + draft_len))
1080
                indices_match &= prev_index == flattened_index
1081
                max_flattened_index = max(max_flattened_index, flattened_index)
1082
1083
1084
        num_commmon_tokens = len(sample_flattened_indices)
        total_without_spec = total_num_scheduled_tokens - total_num_spec_tokens
        if num_commmon_tokens < total_without_spec:
1085
1086
1087
            # 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)
1088
1089
1090
            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)
1091
1092
        if num_commmon_tokens == 0:
            # No requests in common with the previous iteration
1093
            # So input_ids.cpu will have all the input ids.
1094
1095
1096
1097
1098
1099
1100
            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_(
1101
1102
1103
                self.input_batch.prev_sampled_token_ids[:num_commmon_tokens, 0],
                non_blocking=True,
            )
1104
1105
            if self.enable_prompt_embeds:
                self.is_token_ids.gpu[:num_commmon_tokens] = True
1106
            return
1107
        # Upload the index tensors asynchronously so the scatter can be non-blocking.
1108
1109
        sampled_tokens_index_tensor = torch.tensor(
            sample_flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
1110
        ).to(self.device, non_blocking=True)
1111
        prev_common_req_indices_tensor = torch.tensor(
1112
1113
            prev_common_req_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1114
1115
        self.input_ids.gpu.scatter_(
            dim=0,
1116
            index=sampled_tokens_index_tensor,
1117
            src=self.input_batch.prev_sampled_token_ids[
1118
1119
1120
                prev_common_req_indices_tensor, 0
            ],
        )
1121

1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
        # Scatter the draft tokens after the sampled tokens are scattered.
        if self._draft_token_ids is None or not spec_flattened_indices:
            return

        assert isinstance(self._draft_token_ids, torch.Tensor)
        draft_tokens_index_tensor = torch.tensor(
            spec_flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
        prev_draft_token_indices_tensor = torch.tensor(
            prev_draft_token_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)

        # because input_ids dtype is torch.int32,
        # so convert draft_token_ids to torch.int32 here.
        draft_token_ids = self._draft_token_ids.to(dtype=torch.int32)
        self._draft_token_ids = None

        self.input_ids.gpu.scatter_(
            dim=0,
            index=draft_tokens_index_tensor,
            src=draft_token_ids.flatten()[prev_draft_token_indices_tensor],
        )

1145
1146
    def _get_encoder_seq_lens(
        self,
1147
        scheduled_encoder_inputs: dict[str, list[int]],
1148
1149
        kv_cache_spec: KVCacheSpec,
        num_reqs: int,
1150
    ) -> np.ndarray | None:
1151
1152
1153
1154
1155
1156
        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)
1157
        for req_id in scheduled_encoder_inputs:
1158
1159
1160
1161
1162
            req_index = self.input_batch.req_id_to_index[req_id]
            encoder_seq_lens[req_index] = self.max_encoder_len

        return encoder_seq_lens

1163
    def _prepare_inputs(
1164
1165
1166
1167
        self,
        scheduler_output: "SchedulerOutput",
        num_scheduled_tokens: np.ndarray,
        max_num_scheduled_tokens: int,
1168
1169
    ) -> tuple[
        torch.Tensor,
1170
1171
1172
        SpecDecodeMetadata | None,
        UBatchSlices | None,
        torch.Tensor | None,
1173
    ]:
1174
1175
        """
        :return: tuple[
1176
            logits_indices, spec_decode_metadata,
1177
            ubatch_slices, num_tokens_across_dp,
1178
1179
        ]
        """
1180
1181
1182
1183
1184
1185
1186
        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.
1187
        self.input_batch.block_table.commit_block_table(num_reqs)
1188
1189
1190

        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
1191
        req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens)
1192

1193
1194
        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
        # arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
1195
        cu_num_tokens, arange = self._get_cumsum_and_arange(num_scheduled_tokens)
1196
1197

        # Get positions.
1198
        positions_np = self.positions.np[:total_num_scheduled_tokens]
1199
1200
1201
1202
1203
        np.add(
            self.input_batch.num_computed_tokens_cpu[req_indices],
            arange,
            out=positions_np,
        )
1204

1205
1206
        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1207
        if self.uses_mrope:
1208
1209
            self._calc_mrope_positions(scheduler_output)

1210
1211
1212
1213
        # 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.
1214
1215
1216
        token_indices = (
            positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]
        )
1217
        token_indices_tensor = torch.from_numpy(token_indices)
1218

1219
1220
1221
        # 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.
1222
1223
1224
1225
1226
1227
        torch.index_select(
            self.input_batch.token_ids_cpu_tensor.flatten(),
            0,
            token_indices_tensor,
            out=self.input_ids.cpu[:total_num_scheduled_tokens],
        )
1228
        if self.enable_prompt_embeds:
1229
            is_token_ids = self.input_batch.is_token_ids_tensor.flatten()
1230
1231
1232
1233
            torch.index_select(
                is_token_ids,
                0,
                token_indices_tensor,
1234
1235
                out=self.is_token_ids.cpu[:total_num_scheduled_tokens],
            )
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268

        # 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:
1269
1270
1271
                    self.inputs_embeds.cpu[
                        output_idx : output_idx + actual_num_sched
                    ].copy_(req_embeds[start_pos:actual_end])
1272
1273

                output_idx += num_sched
1274

1275
1276
        self.input_batch.block_table.compute_slot_mapping(req_indices, positions_np)
        self.input_batch.block_table.commit_slot_mapping(total_num_scheduled_tokens)
1277
1278

        # Prepare the attention metadata.
1279
        self.query_start_loc.np[0] = 0
1280
        self.query_start_loc.np[1 : num_reqs + 1] = cu_num_tokens
1281
1282
        # Note: pad query_start_loc to be non-decreasing, as kernels
        # like FlashAttention requires that
1283
        self.query_start_loc.np[num_reqs + 1 :].fill(cu_num_tokens[-1])
1284
        self.query_start_loc.copy_to_gpu()
1285
        query_start_loc = self.query_start_loc.gpu[: num_reqs + 1]
1286

1287
        num_tokens_unpadded = scheduler_output.total_num_scheduled_tokens
1288
        num_tokens_padded = self._get_num_input_tokens(num_tokens_unpadded)
1289
1290
1291
        uniform_decode = (
            max_num_scheduled_tokens == self.uniform_decode_query_len
        ) and (total_num_scheduled_tokens == num_reqs * max_num_scheduled_tokens)
1292
1293
1294
1295
1296
1297
1298

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

1299
        ubatch_slices, num_tokens_across_dp = coordinate_batch_across_dp(
1300
1301
1302
1303
1304
1305
1306
            num_tokens_unpadded=num_tokens_unpadded,
            parallel_config=self.parallel_config,
            allow_microbatching=True,
            allow_dp_padding=allow_dp_padding,
            num_tokens_padded=num_tokens_padded,
            uniform_decode=uniform_decode,
            num_scheduled_tokens_per_request=num_scheduled_tokens,
1307
        )
1308

1309
        self.seq_lens.np[:num_reqs] = (
1310
1311
            self.input_batch.num_computed_tokens_cpu[:num_reqs] + num_scheduled_tokens
        )
1312
        # Fill unused with 0 for full cuda graph mode.
1313
1314
        self.seq_lens.np[num_reqs:].fill(0)
        self.seq_lens.copy_to_gpu()
1315

1316
        num_tokens = [self.requests[r].num_tokens for r in self.input_batch.req_ids]
1317
1318
1319
1320
1321
1322
1323
        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)
1324
1325
1326
        self.discard_request_indices.np[: self.num_discarded_requests] = (
            discard_request_indices
        )
1327
1328
1329

        self.discard_request_indices.copy_to_gpu(self.num_discarded_requests)

1330
        # Copy the tensors to the GPU.
1331
1332
1333
1334
1335
        self._prepare_input_ids(
            scheduler_output,
            total_num_scheduled_tokens,
            cu_num_tokens,
        )
1336

1337
        if self.uses_mrope:
1338
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1339
1340
            self.mrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.mrope_positions.cpu[:, :total_num_scheduled_tokens],
1341
1342
                non_blocking=True,
            )
1343
1344
        else:
            # Common case (1D positions)
1345
            self.positions.copy_to_gpu(total_num_scheduled_tokens)
1346

1347
        use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
1348
1349
1350
1351
1352
1353
1354
        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
1355
            num_draft_tokens = None
1356
            spec_decode_metadata = None
1357
            num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
1358
1359
1360
1361
1362
        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)
1363
1364
1365
            # 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)
1366
1367
1368
1369
            for (
                req_id,
                draft_token_ids,
            ) in scheduler_output.scheduled_spec_decode_tokens.items():
1370
1371
                req_idx = self.input_batch.req_id_to_index[req_id]
                num_draft_tokens[req_idx] = len(draft_token_ids)
1372
1373
1374
1375
1376
1377
1378
1379
                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
                )
1380
            spec_decode_metadata = self._calc_spec_decode_metadata(
1381
1382
                num_draft_tokens, cu_num_tokens
            )
1383
            logits_indices = spec_decode_metadata.logits_indices
1384
            num_sampled_tokens = num_draft_tokens + 1
1385
            # For DECODE only cuda graph of some attention backends (e.g., GDN).
1386
            self.num_decode_draft_tokens.np[:num_reqs] = num_decode_draft_tokens
1387
1388
            self.num_decode_draft_tokens.np[num_reqs:].fill(-1)
            self.num_decode_draft_tokens.copy_to_gpu()
1389

1390
1391
1392
1393
1394
        # Hot-Swap lora model
        if self.lora_config:
            assert (
                np.sum(num_sampled_tokens)
                <= self.vllm_config.scheduler_config.max_num_batched_tokens
1395
            )
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
            self.set_active_loras(
                self.input_batch, num_scheduled_tokens, num_sampled_tokens
            )

        return (
            logits_indices,
            spec_decode_metadata,
            ubatch_slices,
            num_tokens_across_dp,
        )

    def _build_attention_metadata(
        self,
        total_num_scheduled_tokens: int,
        max_num_scheduled_tokens: int,
        num_reqs: int,
        ubatch_slices: UBatchSlices | None = None,
        logits_indices: torch.Tensor | None = None,
        use_spec_decode: bool = False,
        for_cudagraph_capture: bool = False,
        scheduled_encoder_inputs: dict[str, list[int]] | None = None,
        cascade_attn_prefix_lens: list[list[int]] | None = None,
    ) -> tuple[PerLayerAttnMetadata, CommonAttentionMetadata | None]:
        """
        :return: tuple[attn_metadata, spec_decode_common_attn_metadata]
        """
        logits_indices_padded = None
1423
        num_logits_indices = None
1424
1425
1426
1427
1428
1429
        if logits_indices is not None:
            num_logits_indices = logits_indices.size(0)
            if self.cache_config.kv_sharing_fast_prefill:
                logits_indices_padded = self._prepare_kv_sharing_fast_prefill(
                    logits_indices
                )
1430

1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
        # update seq_lens of decode reqs under DCP.
        if self.dcp_world_size > 1:
            self.dcp_local_seq_lens.cpu[:num_reqs] = get_dcp_local_seq_lens(
                self.seq_lens.cpu[:num_reqs],
                self.dcp_world_size,
                self.dcp_rank,
                self.parallel_config.dcp_kv_cache_interleave_size,
            )
            self.dcp_local_seq_lens.copy_to_gpu(num_reqs)

1441
1442
1443
        attn_metadata: PerLayerAttnMetadata = {}
        if ubatch_slices is not None:
            attn_metadata = [dict() for _ in range(len(ubatch_slices))]
1444

1445
1446
        # Used in the below loop
        query_start_loc = self.query_start_loc.gpu[: num_reqs + 1]
1447
        query_start_loc_cpu = self.query_start_loc.cpu[: num_reqs + 1]
1448
        seq_lens = self.seq_lens.gpu[:num_reqs]
1449
        seq_lens_cpu = self.seq_lens.cpu[:num_reqs]
1450
1451
1452
        num_computed_tokens_cpu = self.input_batch.num_computed_tokens_cpu_tensor[
            :num_reqs
        ]
1453
1454
1455
        dcp_local_seq_lens = (
            self.dcp_local_seq_lens.gpu[:num_reqs] if self.dcp_world_size > 1 else None
        )
1456
        spec_decode_common_attn_metadata = None
1457
1458
1459
1460
1461
1462
1463
1464
1465

        if for_cudagraph_capture:
            # For some attention backends (e.g. FA) with sliding window models we need
            # to make sure the backend see a max_seq_len that is larger to the sliding
            # window size when capturing to make sure the correct kernel is selected.
            max_seq_len = self.max_model_len
        else:
            max_seq_len = self.seq_lens.np[:num_reqs].max().item()

1466
1467
        if use_spec_decode:
            self.num_accepted_tokens.np[:num_reqs] = (
1468
1469
                self.input_batch.num_accepted_tokens_cpu[:num_reqs]
            )
1470
1471
            self.num_accepted_tokens.np[num_reqs:].fill(1)
            self.num_accepted_tokens.copy_to_gpu()
1472

1473
1474
        # Prepare the attention metadata for each KV cache group and make layers
        # in the same group share the same metadata.
1475
        for kv_cache_gid, kv_cache_group in enumerate(
1476
1477
            self.kv_cache_config.kv_cache_groups
        ):
1478
            encoder_seq_lens = self._get_encoder_seq_lens(
1479
1480
1481
                scheduled_encoder_inputs or {},
                kv_cache_group.kv_cache_spec,
                num_reqs,
1482
            )
1483

1484
            if isinstance(kv_cache_group.kv_cache_spec, EncoderOnlyAttentionSpec):
1485
1486
1487
1488
1489
                # 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,
1490
1491
1492
                    device=self.device,
                )
                slot_mapping = torch.zeros(
1493
                    (total_num_scheduled_tokens,),
1494
1495
1496
                    dtype=torch.int64,
                    device=self.device,
                )
1497
            else:
1498
                blk_table = self.input_batch.block_table[kv_cache_gid]
1499
                blk_table_tensor = blk_table.get_device_tensor(num_reqs)
1500
                slot_mapping = blk_table.slot_mapping.gpu[:total_num_scheduled_tokens]
1501
1502
1503

                # Fill unused with -1. Needed for reshape_and_cache in full cuda
                # graph mode.
1504
                blk_table.slot_mapping.gpu[total_num_scheduled_tokens:].fill_(-1)
1505

1506
            common_attn_metadata = CommonAttentionMetadata(
1507
1508
1509
1510
1511
                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,
1512
1513
1514
                num_reqs=num_reqs,
                num_actual_tokens=total_num_scheduled_tokens,
                max_query_len=max_num_scheduled_tokens,
1515
                max_seq_len=max_seq_len,
1516
1517
                block_table_tensor=blk_table_tensor,
                slot_mapping=slot_mapping,
1518
                logits_indices_padded=logits_indices_padded,
1519
                num_logits_indices=num_logits_indices,
1520
                causal=True,
1521
                encoder_seq_lens=encoder_seq_lens,
1522
                dcp_local_seq_lens=dcp_local_seq_lens,
1523
1524
            )

1525
            if self.speculative_config and spec_decode_common_attn_metadata is None:
1526
                if isinstance(self.drafter, EagleProposer):
1527
                    if self.drafter.attn_layer_names[0] in kv_cache_group.layer_names:
1528
1529
1530
                        spec_decode_common_attn_metadata = common_attn_metadata
                else:
                    spec_decode_common_attn_metadata = common_attn_metadata
1531

1532
1533
1534
1535
1536
1537
            for attn_gid, attn_group in enumerate(self.attn_groups[kv_cache_gid]):
                cascade_attn_prefix_len = (
                    cascade_attn_prefix_lens[kv_cache_gid][attn_gid]
                    if cascade_attn_prefix_lens
                    else 0
                )
1538
                builder = attn_group.get_metadata_builder()
1539

1540
                extra_attn_metadata_args = {}
1541
                if use_spec_decode and isinstance(builder, GDNAttentionMetadataBuilder):
1542
                    extra_attn_metadata_args = dict(
1543
1544
1545
1546
                        num_accepted_tokens=self.num_accepted_tokens.gpu[:num_reqs],
                        num_decode_draft_tokens_cpu=self.num_decode_draft_tokens.cpu[
                            :num_reqs
                        ],
1547
1548
                    )

1549
1550
                if ubatch_slices is not None:
                    common_attn_metadata_list = split_attn_metadata(
1551
1552
                        ubatch_slices, common_attn_metadata
                    )
1553
                    for ubid, common_attn_metadata in enumerate(
1554
1555
                        common_attn_metadata_list
                    ):
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
                        builder = attn_group.get_metadata_builder(ubatch_id=ubid)
                        if for_cudagraph_capture:
                            attn_metadata_i = builder.build_for_cudagraph_capture(
                                common_attn_metadata
                            )
                        else:
                            attn_metadata_i = builder.build(
                                common_prefix_len=cascade_attn_prefix_len,
                                common_attn_metadata=common_attn_metadata,
                            )
                        for layer_name in kv_cache_group.layer_names:
1567
1568
1569
1570
                            assert type(attn_metadata) is list
                            attn_metadata[ubid][layer_name] = attn_metadata_i
                else:
                    assert isinstance(attn_metadata, dict)
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
                    if for_cudagraph_capture:
                        attn_metadata_i = builder.build_for_cudagraph_capture(
                            common_attn_metadata
                        )
                    else:
                        attn_metadata_i = builder.build(
                            common_prefix_len=cascade_attn_prefix_len,
                            common_attn_metadata=common_attn_metadata,
                            **extra_attn_metadata_args,
                        )
1581
1582
                    for layer_name in attn_group.layer_names:
                        attn_metadata[layer_name] = attn_metadata_i
1583

1584
        return attn_metadata, spec_decode_common_attn_metadata
1585

1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
    def _compute_cascade_attn_prefix_lens(
        self,
        num_scheduled_tokens: np.ndarray,
        num_common_prefix_blocks: list[int],
    ) -> list[list[int]] | None:
        """
        :return: Optional[cascade_attn_prefix_lens]
            cascade_attn_prefix_lens is 2D: ``[kv_cache_group_id][attn_group_idx]``,
            None if we should not use cascade attention
        """
1596

1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
        use_cascade_attn = False
        num_kv_cache_groups = len(self.kv_cache_config.kv_cache_groups)
        cascade_attn_prefix_lens: list[list[int]] = [
            [] for _ in range(num_kv_cache_groups)
        ]

        for kv_cache_gid in range(num_kv_cache_groups):
            for attn_group in self.attn_groups[kv_cache_gid]:
                if isinstance(attn_group.kv_cache_spec, EncoderOnlyAttentionSpec):
                    cascade_attn_prefix_len = 0
                else:
                    # 0 if cascade attention should not be used
                    cascade_attn_prefix_len = self._compute_cascade_attn_prefix_len(
                        num_scheduled_tokens,
                        num_common_prefix_blocks[kv_cache_gid],
                        attn_group.kv_cache_spec,
                        attn_group.get_metadata_builder(),
                    )
                cascade_attn_prefix_lens[kv_cache_gid].append(cascade_attn_prefix_len)
                use_cascade_attn |= cascade_attn_prefix_len > 0

        return cascade_attn_prefix_lens if use_cascade_attn else None
1619

1620
1621
1622
1623
    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
        num_common_prefix_blocks: int,
1624
1625
        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
    ) -> 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.
        """
1644

1645
        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
        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]
1683
        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
1684
1685
1686
1687
1688
1689
1690
        # 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(
1691
1692
            common_prefix_len, self.input_batch.num_computed_tokens_cpu[:num_reqs].min()
        )
1693
        # common_prefix_len should be a multiple of the block size.
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
        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
        )
1705
1706
        assert isinstance(kv_cache_spec, AttentionSpec)
        use_cascade = attn_metadata_builder.use_cascade_attention(
1707
1708
1709
            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
1710
            num_kv_heads=kv_cache_spec.num_kv_heads,
1711
            use_alibi=self.use_alibi,
1712
            use_sliding_window=use_sliding_window,
1713
            use_local_attention=use_local_attention,
1714
            num_sms=self.num_sms,
1715
            dcp_world_size=self.dcp_world_size,
1716
1717
1718
        )
        return common_prefix_len if use_cascade else 0

1719
1720
    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
1721
        for index, req_id in enumerate(self.input_batch.req_ids):
1722
1723
1724
            req = self.requests[req_id]
            assert req.mrope_positions is not None

1725
1726
            num_computed_tokens = self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1727
            num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
1728
1729
                req.prompt_token_ids, req.prompt_embeds
            )
1730
1731

            if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
1732
1733
                prompt_part_len = max(0, num_prompt_tokens - num_computed_tokens)
                completion_part_len = max(0, num_scheduled_tokens - prompt_part_len)
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
            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

1747
1748
1749
                self.mrope_positions.cpu[:, dst_start:dst_end] = req.mrope_positions[
                    :, src_start:src_end
                ]
1750
1751
1752
1753
1754
1755
1756
                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

1757
                MRotaryEmbedding.get_next_input_positions_tensor(
1758
                    out=self.mrope_positions.np,
1759
1760
1761
1762
1763
                    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,
                )
1764
1765
1766

                mrope_pos_ptr += completion_part_len

1767
1768
    def _calc_spec_decode_metadata(
        self,
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
        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
1785
1786
1787
1788

        # 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(
1789
1790
            num_sampled_tokens, cumsum_dtype=np.int32
        )
1791
        # Step 2. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
1792
        logits_indices = np.repeat(
1793
1794
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens
        )
1795
        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
1796
1797
1798
1799
1800
1801
        logits_indices += arange

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

        # Compute the draft logits indices.
1802
1803
1804
        # 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(
1805
1806
            num_draft_tokens, cumsum_dtype=np.int32
        )
1807
1808
        # [0, 0, 0, 5, 5, 9]
        target_logits_indices = np.repeat(
1809
1810
            cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens
        )
1811
1812
1813
1814
1815
        # [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(
1816
1817
            self.device, non_blocking=True
        )
1818
1819
1820
        cu_num_sampled_tokens = torch.from_numpy(cu_num_sampled_tokens).to(
            self.device, non_blocking=True
        )
1821
1822
1823
        logits_indices = torch.from_numpy(logits_indices).to(
            self.device, non_blocking=True
        )
1824
        target_logits_indices = torch.from_numpy(target_logits_indices).to(
1825
1826
            self.device, non_blocking=True
        )
1827
        bonus_logits_indices = torch.from_numpy(bonus_logits_indices).to(
1828
1829
            self.device, non_blocking=True
        )
1830

1831
1832
        # Compute the draft token ids.
        # draft_token_indices:      [  1,   2,   3, 105, 106, 208]
1833
        draft_token_ids = self.input_ids.gpu[logits_indices]
1834
1835
        draft_token_ids = draft_token_ids[target_logits_indices + 1]

1836
        return SpecDecodeMetadata(
1837
1838
1839
            draft_token_ids=draft_token_ids,
            num_draft_tokens=num_draft_tokens.tolist(),
            cu_num_draft_tokens=cu_num_draft_tokens,
1840
            cu_num_sampled_tokens=cu_num_sampled_tokens,
1841
1842
1843
1844
1845
            target_logits_indices=target_logits_indices,
            bonus_logits_indices=bonus_logits_indices,
            logits_indices=logits_indices,
        )

1846
1847
1848
1849
1850
1851
1852
    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
1853
        self.kv_sharing_fast_prefill_logits_indices[:num_logits].copy_(logits_indices)
1854
1855
1856
1857
1858
        # 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_(
1859
1860
1861
1862
1863
1864
            logits_indices[-1].item()
        )
        if (
            self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
            and num_logits <= self.cudagraph_batch_sizes[-1]
        ):
1865
1866
1867
1868
1869
            # 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
1870
1871
1872
        logits_indices_padded = self.kv_sharing_fast_prefill_logits_indices[
            :num_logits_padded
        ]
1873
1874
        return logits_indices_padded

1875
1876
1877
1878
1879
1880
1881
1882
    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
1883
                inputs.
1884
1885
1886
1887
1888
1889

        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
        """
1890
1891
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
1892
            return [], []
1893
        # Batch the multi-modal inputs.
1894
        mm_kwargs = list[MultiModalKwargsItem]()
1895
1896
        # list of tuple (mm_hash, position_info)
        mm_hashes_pos = list[tuple[str, PlaceholderRange]]()
1897
1898
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
1899
1900

            for mm_input_id in encoder_input_ids:
1901
1902
1903
1904
                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))
1905

1906
1907
1908
1909
1910
        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(
1911
1912
            scheduler_output
        )
1913
1914
1915
1916

        if not mm_kwargs:
            return

1917
1918
1919
1920
1921
1922
1923
        # 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.
1924
        model = cast(SupportsMultiModal, self.model)
1925
        encoder_outputs = []
1926
        for modality, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
1927
1928
1929
1930
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
1931
            multimodal_cpu_fields=model.multimodal_cpu_fields,
1932
        ):
1933
1934
1935
            curr_group_outputs = []

            # EVS-related change.
1936
            # (ekhvedchenia): Temporary hack to limit peak memory usage when
1937
            # processing multimodal data. This solves the issue with scheduler
1938
1939
1940
1941
            # 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)
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
            # TODO(ywang96): Fix memory profiling to take EVS into account and
            # remove this hack.
            if (
                self.is_multimodal_pruning_enabled
                and modality == "video"
                and num_items > 1
            ):
                for video_mm_kwargs_item in filter(
                    lambda item: item.modality == "video", mm_kwargs
                ):
                    _, _, micro_batch_mm_inputs = next(
                        group_mm_kwargs_by_modality(
                            [video_mm_kwargs_item],
                            device=self.device,
                            pin_memory=self.pin_memory,
                            merge_by_field_config=model.merge_by_field_config,
1958
                            multimodal_cpu_fields=model.multimodal_cpu_fields,
1959
                        )
1960
                    )
1961

1962
                    micro_batch_outputs = model.embed_multimodal(
1963
1964
                        **micro_batch_mm_inputs
                    )
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974

                    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.
1975
                curr_group_outputs = model.embed_multimodal(**mm_kwargs_group)
1976

1977
1978
            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
1979
                expected_num_items=num_items,
1980
            )
1981
            encoder_outputs.extend(curr_group_outputs)
1982

1983
1984
1985
        # 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(
1986
1987
1988
                output,
                is_embed=pos_info.is_embed,
            )
1989
1990
            logger.debug("Finish execute for mm hash %s", mm_hash)
            self.maybe_save_ec_to_connector(self.encoder_cache, mm_hash)
1991
1992

    def _gather_mm_embeddings(
1993
1994
        self,
        scheduler_output: "SchedulerOutput",
1995
        shift_computed_tokens: int = 0,
1996
1997
1998
1999
2000
2001
2002
2003
    ) -> 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
2004
        should_sync_mrope_positions = False
2005

2006
        for req_id in self.input_batch.req_ids:
2007
2008
            mm_embeds_req: list[torch.Tensor] = []

2009
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
2010
            req_state = self.requests[req_id]
2011
            num_computed_tokens = req_state.num_computed_tokens + shift_computed_tokens
2012

2013
2014
            for mm_feature in req_state.mm_features:
                pos_info = mm_feature.mm_position
2015
2016
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032

                # 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,
2033
2034
                    num_encoder_tokens,
                )
2035
                assert start_idx < end_idx
2036

2037
                mm_hash = mm_feature.identifier
2038
                encoder_output = self.encoder_cache.get(mm_hash, None)
2039
                assert encoder_output is not None, f"Encoder cache miss for {mm_hash}."
2040
2041
2042
2043

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

2044
                req_start_pos = req_start_idx + start_pos - num_computed_tokens
2045
2046
2047
                is_mm_embed[req_start_pos + start_idx : req_start_pos + end_idx] = (
                    True if is_embed is None else is_embed
                )
2048

2049
2050
2051
2052
                mm_embeds_item = gather_mm_placeholders(
                    encoder_output[start_idx:end_idx],
                    is_embed=is_embed,
                )
2053
2054
2055
                mm_embeds_req.append(mm_embeds_item)

            if self.is_multimodal_pruning_enabled and self.uses_mrope:
2056
                assert req_state.mrope_positions is not None
2057
2058
2059
2060
2061
2062
2063
                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,
2064
2065
                    )
                )
2066
2067
2068
2069
                req_state.mrope_positions.copy_(new_mrope_positions)
                req_state.mrope_position_delta = new_delta

            mm_embeds.extend(mm_embeds_req)
2070
2071
2072
            req_start_idx += num_scheduled_tokens

        is_mm_embed = self.is_mm_embed.copy_to_gpu(total_num_scheduled_tokens)
2073
2074
2075

        if should_sync_mrope_positions:
            self._calc_mrope_positions(scheduler_output)
2076
            self.mrope_positions.copy_to_gpu(total_num_scheduled_tokens)
2077

2078
        return mm_embeds, is_mm_embed
2079

2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
    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
2096
        model = cast(SupportsMultiModal, self.model)
2097
2098
        encoder_features = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
2099
2100
2101
2102
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
2103
            multimodal_cpu_fields=model.multimodal_cpu_fields,
2104
2105
2106
2107
2108
2109
2110
2111
        ):
            # 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

2112
    def get_model(self) -> nn.Module:
2113
        # get raw model out of the cudagraph wrapper.
2114
        if isinstance(self.model, (CUDAGraphWrapper, UBatchWrapper)):
2115
            return self.model.unwrap()
2116
2117
        return self.model

2118
2119
2120
2121
2122
2123
2124
2125
2126
2127
2128
2129
2130
2131
2132
    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

2133
2134
2135
2136
2137
    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
        model = self.get_model()
        if not is_pooling_model(model):
            return []

2138
2139
        supported_tasks = list(model.pooler.get_supported_tasks())

2140
        if self.scheduler_config.enable_chunked_prefill:
2141
2142
2143
2144
            if "token_embed" in supported_tasks:
                supported_tasks.remove("token_embed")
            if "token_classify" in supported_tasks:
                supported_tasks.remove("token_classify")
2145

2146
2147
            logger.debug_once(
                "Chunked prefill is not supported with "
2148
2149
                "token_embed and token_classify tasks "
                "which using ALL pooling. "
2150
2151
2152
                "Please turn off chunked prefill by "
                "`--no-enable-chunked-prefill` before using it."
            )
2153
2154
2155
2156
2157

        if "score" in supported_tasks:
            num_labels = getattr(self.model_config.hf_config, "num_labels", 0)
            if num_labels != 1:
                supported_tasks.remove("score")
2158
                logger.debug_once("Score API is only enabled for num_labels == 1.")
2159
2160

        return supported_tasks
2161

2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
    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)

2172
    def sync_and_slice_intermediate_tensors(
2173
2174
2175
2176
2177
        self,
        num_tokens: int,
        intermediate_tensors: IntermediateTensors,
        sync_self: bool,
    ) -> IntermediateTensors:
2178
2179
2180
        assert self.intermediate_tensors is not None

        tp = self.vllm_config.parallel_config.tensor_parallel_size
2181
        is_rs = is_residual_scattered_for_sp(self.vllm_config, num_tokens)
2182
2183
2184
2185
2186
2187

        # 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():
2188
                is_scattered = k == "residual" and is_rs
2189
                copy_len = num_tokens // tp if is_scattered else num_tokens
2190
                self.intermediate_tensors[k][:copy_len].copy_(
2191
2192
2193
2194
2195
2196
2197
2198
2199
2200
2201
2202
2203
                    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:
2204
2205
2206
2207
2208
2209
2210
        """
        Step for the EPLB (Expert Parallelism Load Balancing) state.
        """
        if not self.parallel_config.enable_eplb:
            return

        assert self.eplb_state is not None
2211
2212
        model = self.get_model()
        assert is_mixture_of_experts(model)
2213
2214
2215
        self.eplb_state.step(
            is_dummy,
            is_profile,
2216
            log_stats=self.parallel_config.eplb_config.log_balancedness,
2217
2218
        )

2219
2220
2221
2222
    # 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)
2223
2224
    @staticmethod
    def pad_out_ubatch_slice(ubatch_slices: UBatchSlices, num_total_tokens: int):
2225
2226
2227
2228
2229
2230
        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
        )
2231

2232
2233
2234
2235
2236
2237
    def _pool(
        self,
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
        num_scheduled_tokens_np: np.ndarray,
    ) -> ModelRunnerOutput:
2238
2239
2240
        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"
        )
2241

2242
        hidden_states = hidden_states[:num_scheduled_tokens]
2243
        pooling_metadata = self.input_batch.get_pooling_metadata()
2244
2245
2246
2247
        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]
2248

2249
2250
2251
2252
2253
2254
2255
2256
2257
2258
        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()
2259

2260
        pooler_output: list[torch.Tensor | None] = []
2261
        for raw_output, seq_len, prompt_len in zip(
2262
2263
            raw_pooler_output, seq_lens_cpu, pooling_metadata.prompt_lens
        ):
2264
            output = raw_output if seq_len == prompt_len else None
2265
            pooler_output.append(output)
2266
2267
2268
2269
2270
2271
2272
2273
2274
2275

        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,
        )

2276
    def _get_num_input_tokens(self, num_scheduled_tokens: int) -> int:
2277
2278
2279
2280
2281
2282
        if (
            self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
            and hasattr(self, "cudagraph_batch_sizes")
            and self.cudagraph_batch_sizes
            and num_scheduled_tokens <= self.cudagraph_batch_sizes[-1]
        ):
2283
2284
2285
2286
2287
2288
2289
2290
            # 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
2291
2292
2293
2294
        if (
            self.compilation_config.pass_config.enable_sequence_parallelism
            and tp_size > 1
        ):
2295
2296
2297
            return round_up(num_scheduled_tokens, tp_size)
        return num_scheduled_tokens

2298
    def _preprocess(
2299
2300
        self,
        scheduler_output: "SchedulerOutput",
2301
        num_input_tokens: int,  # Padded
2302
        intermediate_tensors: IntermediateTensors | None = None,
2303
    ) -> tuple[
2304
2305
        torch.Tensor | None,
        torch.Tensor | None,
2306
        torch.Tensor,
2307
        IntermediateTensors | None,
2308
        dict[str, Any],
2309
        ECConnectorOutput | None,
2310
    ]:
2311
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
2312
        is_first_rank = get_pp_group().is_first_rank
2313

2314
2315
        # _prepare_inputs may reorder the batch, so we must gather multi
        # modal outputs after that to ensure the correct order
2316
2317
        ec_connector_output = None

2318
2319
        if (
            self.supports_mm_inputs
2320
            and is_first_rank
2321
2322
            and not self.model_config.is_encoder_decoder
        ):
2323
            # Run the multimodal encoder if any.
2324
2325
2326
2327
2328
2329
            with self.maybe_get_ec_connector_output(
                scheduler_output,
                encoder_cache=self.encoder_cache,
            ) as ec_connector_output:
                self._execute_mm_encoder(scheduler_output)
                mm_embeds, is_mm_embed = self._gather_mm_embeddings(scheduler_output)
2330

2331
2332
2333
            # 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.
2334
            inputs_embeds_scheduled = self.model.embed_input_ids(
2335
2336
2337
                self.input_ids.gpu[:num_scheduled_tokens],
                multimodal_embeddings=mm_embeds,
                is_multimodal=is_mm_embed,
2338
            )
2339

2340
            # TODO(woosuk): Avoid the copy. Optimize.
2341
            self.inputs_embeds.gpu[:num_scheduled_tokens].copy_(inputs_embeds_scheduled)
2342

2343
            input_ids = None
2344
            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
2345
2346
2347
2348
            model_kwargs = {
                **self._init_model_kwargs(num_scheduled_tokens),
                **self._extract_mm_kwargs(scheduler_output),
            }
2349
        elif self.enable_prompt_embeds and is_first_rank:
2350
2351
2352
2353
2354
2355
2356
2357
2358
2359
2360
2361
            # 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).
2362
2363
2364
            token_ids_idx = (
                self.is_token_ids.gpu[:num_scheduled_tokens]
                .nonzero(as_tuple=False)
2365
                .squeeze(1)
2366
            )
2367
2368
2369
            # Some tokens ids may need to become embeds
            if token_ids_idx.numel() > 0:
                token_ids = self.input_ids.gpu[token_ids_idx]
2370
                tokens_to_embeds = self.model.embed_input_ids(input_ids=token_ids)
2371
2372
2373
2374
2375
                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
2376
        else:
2377
2378
2379
2380
            # 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.
2381
            input_ids = self.input_ids.gpu[:num_input_tokens]
2382
            inputs_embeds = None
2383
            model_kwargs = self._init_model_kwargs(num_input_tokens)
2384
        if self.uses_mrope:
2385
            positions = self.mrope_positions.gpu[:, :num_input_tokens]
2386
        else:
2387
            positions = self.positions.gpu[:num_input_tokens]
2388

2389
        if is_first_rank:
2390
2391
            intermediate_tensors = None
        else:
2392
            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
2393
2394
                num_input_tokens, intermediate_tensors, True
            )
2395

2396
2397
2398
2399
        if (
            self.model_config.is_encoder_decoder
            and scheduler_output.scheduled_encoder_inputs
        ):
2400
2401
2402
            encoder_inputs = self._extract_encoder_inputs(scheduler_output)
            model_kwargs.update(encoder_inputs)

2403
2404
2405
2406
2407
2408
        return (
            input_ids,
            inputs_embeds,
            positions,
            intermediate_tensors,
            model_kwargs,
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            ec_connector_output,
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        )
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    def _sample(
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        self,
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        logits: torch.Tensor | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
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    ) -> SamplerOutput:
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        # Sample the next token and get logprobs if needed.
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        sampling_metadata = self.input_batch.sampling_metadata
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        if spec_decode_metadata is None:
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            # Update output token ids with tokens sampled in last step
            # if async scheduling and required by current sampling params.
            self.input_batch.update_async_output_token_ids()
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            return self.sampler(
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                logits=logits,
                sampling_metadata=sampling_metadata,
            )
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        sampler_output = self.rejection_sampler(
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            spec_decode_metadata,
            None,  # draft_probs
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            logits,
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            sampling_metadata,
        )
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        self._update_states_after_model_execute(sampler_output.sampled_token_ids)
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        return sampler_output

    def _bookkeeping_sync(
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        self,
        scheduler_output: "SchedulerOutput",
        sampler_output: SamplerOutput,
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        logits: torch.Tensor | None,
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        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
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        spec_decode_metadata: SpecDecodeMetadata | None,
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    ) -> tuple[
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        dict[str, int],
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        LogprobsLists | None,
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        list[np.ndarray],
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        dict[str, LogprobsTensors | None],
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        list[str],
        dict[str, int],
        list[int],
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    ]:
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        num_nans_in_logits = {}
        if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
            num_nans_in_logits = self._get_nans_in_logits(logits)

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        discard_sampled_tokens_req_indices = self.discard_request_indices.np[
            : self.num_discarded_requests
        ]
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        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)
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        # 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()
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        req_id_to_index_output_copy = self.input_batch.req_id_to_index.copy()
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        num_sampled_tokens = sampler_output.sampled_token_ids.shape[0]
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        sampled_token_ids = sampler_output.sampled_token_ids
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        invalid_req_indices = []
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        valid_sampled_token_ids: list[np.ndarray]
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        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:
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                valid_sampled_token_ids[int(i)] = np.array([])
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        else:
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            valid_sampled_token_ids = []
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            invalid_req_indices = discard_sampled_tokens_req_indices.tolist()
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            invalid_req_indices_set = set(invalid_req_indices)

            # 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.
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            # With spec decoding, this is done in propose_draft_token_ids().
            if self.input_batch.prev_sampled_token_ids is None:
                assert sampled_token_ids.shape[-1] == 1
                self.input_batch.prev_sampled_token_ids = sampled_token_ids
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            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
            }
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        # 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.
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        req_ids = self.input_batch.req_ids
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        logprobs_tensors = sampler_output.logprobs_tensors
        cu_num_accepted_tokens = (
            [0] if spec_decode_metadata and logprobs_tensors else None
        )
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        for req_idx in range(num_sampled_tokens):
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            sampled_ids: np.ndarray | None
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            if self.use_async_scheduling:
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                sampled_ids = (
                    np.array([-1]) if req_idx not in invalid_req_indices_set else None
                )
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            else:
                sampled_ids = valid_sampled_token_ids[req_idx]
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            num_sampled_ids: int = (
                sampled_ids.shape[0] if sampled_ids is not None else 0
            )
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            if cu_num_accepted_tokens is not None:
                cu_num_accepted_tokens.append(
                    cu_num_accepted_tokens[-1] + num_sampled_ids
                )

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            if sampled_ids is None or num_sampled_ids == 0:
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                continue

            start_idx = self.input_batch.num_tokens_no_spec[req_idx]
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            end_idx = start_idx + num_sampled_ids
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            assert end_idx <= self.max_model_len, (
                "Sampled token IDs exceed the max model length. "
                f"Total number of tokens: {end_idx} > max_model_len: "
                f"{self.max_model_len}"
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            )
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            self.input_batch.token_ids_cpu[req_idx, start_idx:end_idx] = sampled_ids
            self.input_batch.is_token_ids[req_idx, start_idx:end_idx] = True
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            self.input_batch.num_tokens_no_spec[req_idx] = end_idx
            self.input_batch.num_tokens[req_idx] = end_idx
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            req_id = req_ids[req_idx]
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            req_state = self.requests[req_id]
            req_state.output_token_ids.extend(sampled_ids)

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        logprobs_lists = (
            logprobs_tensors.tolists(cu_num_accepted_tokens)
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            if not self.use_async_scheduling and logprobs_tensors is not None
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            else None
        )

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

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        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,
        )

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    @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,
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        input_ids: torch.Tensor | None = None,
        positions: torch.Tensor | None = None,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
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        **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",
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        intermediate_tensors: IntermediateTensors | None = None,
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    ) -> ModelRunnerOutput | IntermediateTensors | None:
        if self.execute_model_state is not None:
            raise RuntimeError(
                "State error: sample_tokens() must be called "
                "after execute_model() returns None."
            )
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        # self._draft_token_ids is None when `input_fits_in_drafter=False`
        # and there is no draft tokens scheduled. so it need to update the
        # spec_decoding info in scheduler_output with async_scheduling.
        # use deepcopy to avoid the modification has influence on the
        # scheduler_output in engine core process.
        # TODO(Ronald1995): deepcopy is expensive when there is a large
        # number of requests, optimize it later.
        if (
            self.use_async_scheduling
            and self.num_spec_tokens
            and self._draft_token_ids is None
        ):
            scheduler_output = deepcopy(scheduler_output)

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        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
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        with record_function_or_nullcontext("gpu_model_runner: preprocess"):
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            with self.synchronize_input_prep():
                # Update persistent batch states.
                self._update_states(scheduler_output)

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                if has_ec_transfer() and get_ec_transfer().is_producer:
                    with self.maybe_get_ec_connector_output(
                        scheduler_output,
                        encoder_cache=self.encoder_cache,
                    ) as ec_connector_output:
                        self._execute_mm_encoder(scheduler_output)
                        return make_empty_encoder_model_runner_output(scheduler_output)

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                if not num_scheduled_tokens:
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                    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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                num_reqs = self.input_batch.num_reqs
                req_ids = self.input_batch.req_ids
                tokens = [scheduler_output.num_scheduled_tokens[i] for i in req_ids]
                num_scheduled_tokens_np = np.array(tokens, dtype=np.int32)
                max_num_scheduled_tokens = int(num_scheduled_tokens_np.max())

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                (
                    logits_indices,
                    spec_decode_metadata,
                    ubatch_slices,
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                    num_tokens_across_dp,
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                ) = self._prepare_inputs(
                    scheduler_output, num_scheduled_tokens_np, max_num_scheduled_tokens
                )

                cascade_attn_prefix_lens = None
                # Disable cascade attention when using microbatching (DBO)
                if self.cascade_attn_enabled and ubatch_slices is None:
                    # Pre-compute cascade attention prefix lengths
                    # NOTE: Must be AFTER _prepare_inputs uses self.input_batch state
                    cascade_attn_prefix_lens = self._compute_cascade_attn_prefix_lens(
                        num_scheduled_tokens_np,
                        scheduler_output.num_common_prefix_blocks,
                    )

                # TODO(lucas): move cudagraph dispatching here:
                #   https://github.com/vllm-project/vllm/issues/23789

                total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
                use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
                attn_metadata, spec_decode_common_attn_metadata = (
                    self._build_attention_metadata(
                        total_num_scheduled_tokens=total_num_scheduled_tokens,
                        max_num_scheduled_tokens=max_num_scheduled_tokens,
                        num_reqs=num_reqs,
                        ubatch_slices=ubatch_slices,
                        logits_indices=logits_indices,
                        use_spec_decode=use_spec_decode,
                        scheduled_encoder_inputs=scheduler_output.scheduled_encoder_inputs,
                        cascade_attn_prefix_lens=cascade_attn_prefix_lens,
                    )
                )
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                dp_rank = self.parallel_config.data_parallel_rank
                if ubatch_slices:
                    assert num_tokens_across_dp is not None
                    num_input_tokens = int(num_tokens_across_dp[dp_rank].item())
                    self.pad_out_ubatch_slice(ubatch_slices, num_input_tokens)
                elif num_tokens_across_dp is not None:
                    num_input_tokens = int(num_tokens_across_dp[dp_rank].item())
                else:
                    num_input_tokens = self._get_num_input_tokens(
                        scheduler_output.total_num_scheduled_tokens
                    )
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                (
                    input_ids,
                    inputs_embeds,
                    positions,
                    intermediate_tensors,
                    model_kwargs,
                    ec_connector_output,
                ) = self._preprocess(
                    scheduler_output, num_input_tokens, intermediate_tensors
                )
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            uniform_decode = (
                max_num_scheduled_tokens == self.uniform_decode_query_len
            ) and (num_scheduled_tokens == num_reqs * max_num_scheduled_tokens)
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            batch_descriptor = BatchDescriptor(
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                num_tokens=num_input_tokens,
                uniform_decode=uniform_decode,
                has_lora=len(self.input_batch.lora_id_to_lora_request) > 0,
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            )
            cudagraph_runtime_mode, batch_descriptor = (
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                self.cudagraph_dispatcher.dispatch(
                    batch_descriptor,
                    use_cascade_attn=cascade_attn_prefix_lens is not None,
                )
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            )
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        # Set cudagraph mode to none if calc_kv_scales is true.
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        # KV scales calculation involves dynamic operations that are incompatible
        # with CUDA graph capture.
        if self.calculate_kv_scales:
            cudagraph_runtime_mode = CUDAGraphMode.NONE
            # Mark KV scales as calculated after the first forward pass
            self.calculate_kv_scales = False
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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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            ),
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            record_function_or_nullcontext("gpu_model_runner: forward"),
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            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,
            )

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        with record_function_or_nullcontext("gpu_model_runner: postprocess"):
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            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

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                sample_hidden_states = hidden_states[logits_indices]
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                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:
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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"]

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        self.execute_model_state = ExecuteModelState(
            scheduler_output,
            logits,
            spec_decode_metadata,
            spec_decode_common_attn_metadata,
            hidden_states,
            sample_hidden_states,
            aux_hidden_states,
            kv_connector_output,
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            ec_connector_output,
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        )
        return None

    @torch.inference_mode
    def sample_tokens(
        self, grammar_output: "GrammarOutput | None"
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput | IntermediateTensors:
        if self.execute_model_state is None:
            # Nothing to do (PP non-final rank case), output isn't used.
            return None  # noqa

        # Unpack ephemeral state.
        (
            scheduler_output,
            logits,
            spec_decode_metadata,
            spec_decode_common_attn_metadata,
            hidden_states,
            sample_hidden_states,
            aux_hidden_states,
            kv_connector_output,
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            ec_connector_output,
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        ) = self.execute_model_state
        # Clear ephemeral state.
        self.execute_model_state = None

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

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        self.input_batch.prev_sampled_token_ids = None

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        def propose_draft_token_ids(
            sampled_token_ids: torch.Tensor | list[np.ndarray],
        ) -> None:
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            assert spec_decode_common_attn_metadata is not None
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            with record_function_or_nullcontext("gpu_model_runner: draft"):
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                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.num_spec_tokens
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            <= effective_drafter_max_model_len
        )
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        if use_padded_batch_for_eagle:
            sampled_token_ids = sampler_output.sampled_token_ids
            if input_fits_in_drafter:
                # 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(sampled_token_ids)
            elif self.valid_sampled_token_count_event is not None:
                next_token_ids, valid_sampled_tokens_count = (
                    self.drafter.prepare_next_token_ids_padded(
                        spec_decode_common_attn_metadata,
                        sampled_token_ids,
                        self.requests,
                        self.input_batch,
                        self.discard_request_indices.gpu,
                        self.num_discarded_requests,
                    )
                )
                self._copy_valid_sampled_token_count(
                    next_token_ids, valid_sampled_tokens_count
                )
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        with record_function_or_nullcontext("gpu_model_runner: bookkeep"):
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            (
                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,
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                scheduler_output.total_num_scheduled_tokens,
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                spec_decode_metadata,
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            )
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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("gpu_model_runner: eplb"):
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            self.eplb_step()
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        with record_function_or_nullcontext("gpu_model_runner: ModelRunnerOutput"):
            output = ModelRunnerOutput(
                req_ids=req_ids_output_copy,
                req_id_to_index=req_id_to_index_output_copy,
                sampled_token_ids=valid_sampled_token_ids,
                logprobs=logprobs_lists,
                prompt_logprobs_dict=prompt_logprobs_dict,
                pooler_output=[],
                kv_connector_output=kv_connector_output,
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                ec_connector_output=ec_connector_output
                if self.supports_mm_inputs
                else None,
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                num_nans_in_logits=num_nans_in_logits,
            )
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        if not self.use_async_scheduling:
            return output
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        with record_function_or_nullcontext(
            "gpu_model_runner: AsyncGPUModelRunnerOutput"
        ):
            async_output = AsyncGPUModelRunnerOutput(
                model_runner_output=output,
                sampled_token_ids=sampler_output.sampled_token_ids,
                logprobs_tensors=sampler_output.logprobs_tensors,
                invalid_req_indices=invalid_req_indices,
                async_output_copy_stream=self.async_output_copy_stream,
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                vocab_size=self.input_batch.vocab_size,
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            )
        with record_function_or_nullcontext(
            "gpu_model_runner: set_async_sampled_token_ids"
        ):
            # Save ref of sampled_token_ids CPU tensor if the batch contains
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            # any requests with sampling params that require output ids.
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            self.input_batch.set_async_sampled_token_ids(
                async_output.sampled_token_ids_cpu,
                async_output.async_copy_ready_event,
            )
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        return async_output

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    def take_draft_token_ids(self) -> DraftTokenIds | None:
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        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 _copy_valid_sampled_token_count(
        self, next_token_ids: torch.Tensor, valid_sampled_tokens_count: torch.Tensor
    ) -> None:
        if self.valid_sampled_token_count_event is None:
            return

        default_stream = torch.cuda.current_stream()
        # Initialize a new stream to overlap the copy operation with
        # prepare_input of draft model.
        with torch.cuda.stream(self.valid_sampled_token_count_copy_stream):
            self.valid_sampled_token_count_copy_stream.wait_stream(default_stream)  # type: ignore
            counts = valid_sampled_tokens_count
            counts_cpu = self.valid_sampled_token_count_cpu
            counts_cpu[: counts.shape[0]].copy_(counts, non_blocking=True)
            self.valid_sampled_token_count_event.record()

        self.input_batch.prev_sampled_token_ids = next_token_ids.unsqueeze(1)

    def _get_valid_sampled_token_count(self) -> list[int]:
        # Wait until valid_sampled_tokens_count is copied to cpu,
        prev_sampled_token_ids = self.input_batch.prev_sampled_token_ids
        if (
            self.valid_sampled_token_count_event is None
            or prev_sampled_token_ids is None
        ):
            return []

        counts_cpu = self.valid_sampled_token_count_cpu
        self.valid_sampled_token_count_event.synchronize()
        return counts_cpu[: prev_sampled_token_ids.shape[0]].tolist()

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    def propose_draft_token_ids(
        self,
        scheduler_output: "SchedulerOutput",
Cyrus Leung's avatar
Cyrus Leung committed
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        sampled_token_ids: torch.Tensor | list[np.ndarray],
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        sampling_metadata: SamplingMetadata,
        hidden_states: torch.Tensor,
        sample_hidden_states: torch.Tensor,
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        aux_hidden_states: list[torch.Tensor] | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
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        common_attn_metadata: CommonAttentionMetadata,
Cyrus Leung's avatar
Cyrus Leung committed
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    ) -> torch.Tensor | list[list[int]]:
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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 == "suffix":
            assert isinstance(sampled_token_ids, list)
            assert isinstance(self.drafter, SuffixDecodingProposer)
            draft_token_ids = self.drafter.propose(self.input_batch, sampled_token_ids)
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        elif self.speculative_config.method == "medusa":
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            assert isinstance(sampled_token_ids, list)
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            assert isinstance(self.drafter, MedusaProposer)
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            if sample_hidden_states.shape[0] == len(sampled_token_ids):
                # The input to the target model does not include draft tokens.
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                hidden_states = sample_hidden_states
            else:
                indices = []
                offset = 0
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                assert spec_decode_metadata is not None, (
                    "No spec decode metadata for medusa"
                )
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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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Cyrus Leung committed
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                    indices.append(offset + tokens.shape[0] - 1)
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                    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"
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                    "padded-batch is disabled."
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                )
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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"
3138
                    "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,
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                    )
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                )
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                self._copy_valid_sampled_token_count(
                    next_token_ids, valid_sampled_tokens_count
                )
Jiayi Yao's avatar
Jiayi Yao committed
3153

3154
            if spec_decode_metadata is None:
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                token_indices_to_sample = None
3156
                # 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)
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                if self.use_aux_hidden_state_outputs:
Wentao Ye's avatar
Wentao Ye committed
3160
                    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)
3185
                if self.use_aux_hidden_state_outputs:
Wentao Ye's avatar
Wentao Ye committed
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                    assert aux_hidden_states is not None
3187
                    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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3193
            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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            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,
3206
                last_token_indices=token_indices_to_sample,
3207
                sampling_metadata=sampling_metadata,
3208
                common_attn_metadata=common_attn_metadata,
3209
                mm_embed_inputs=mm_embed_inputs,
3210
            )
3211

3212
        return draft_token_ids
3213

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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():
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            assert config_name in allowed_config_names, (
                f"Config `{config_name}` not supported. "
3219
                f"Allowed configs: {allowed_config_names}"
3220
            )
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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.
        """
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        logger.info_once(
            "Starting to load model %s...",
            self.model_config.model,
            scope="global",
        )
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        global_expert_loads, old_global_expert_indices_per_model, rank_mapping = (
            EplbState.get_eep_state(self.parallel_config)
            if eep_scale_up
            else (None, None, None)
        )
3240

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        if self.parallel_config.enable_eplb:
            self.eplb_state = EplbState(self.parallel_config, self.device)
            eplb_models = 0
3244
        with DeviceMemoryProfiler() as m:
3245
            time_before_load = time.perf_counter()
3246
            model_loader = get_model_loader(self.load_config)
3247
            self.model = model_loader.load_model(
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3249
                vllm_config=self.vllm_config, model_config=self.model_config
            )
3250
            if self.lora_config:
3251
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                self.model = self.load_lora_model(
                    self.model, self.vllm_config, self.device
                )
3254
            if hasattr(self, "drafter"):
3255
                logger.info_once("Loading drafter model...")
3256
                self.drafter.load_model(self.model)
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                if (
                    hasattr(self.drafter, "model")
                    and is_mixture_of_experts(self.drafter.model)
                    and self.parallel_config.enable_eplb
                ):
                    logger.info_once(
                        "EPLB is enabled for drafter model %s.",
                        self.vllm_config.speculative_config.draft_model_config.model,
                    )

                    global_expert_load = (
                        global_expert_loads[eplb_models]
                        if global_expert_loads
                        else None
                    )
                    old_global_expert_indices = (
                        old_global_expert_indices_per_model[eplb_models]
                        if old_global_expert_indices_per_model
                        else None
                    )
                    if self.eplb_state is None:
                        self.eplb_state = EplbState(self.parallel_config, self.device)
                    self.eplb_state.add_model(
                        self.drafter.model,
                        self.vllm_config.speculative_config.draft_model_config,
                        global_expert_load,
                        old_global_expert_indices,
                        rank_mapping,
                    )
                    eplb_models += 1

3288
            if self.use_aux_hidden_state_outputs:
3289
                if not supports_eagle3(self.get_model()):
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                    raise RuntimeError(
                        "Model does not support EAGLE3 interface but "
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                        "aux_hidden_state_outputs was requested"
                    )
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                # 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)
3307
            time_after_load = time.perf_counter()
3308
        self.model_memory_usage = m.consumed_memory
3309
        logger.info_once(
3310
            "Model loading took %.4f GiB memory and %.6f seconds",
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3312
            self.model_memory_usage / GiB_bytes,
            time_after_load - time_before_load,
3313
            scope="local",
3314
        )
3315
        prepare_communication_buffer_for_model(self.model)
3316
        self.is_multimodal_pruning_enabled = (
3317
            supports_multimodal_pruning(self.get_model())
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3319
            and self.model_config.multimodal_config.is_multimodal_pruning_enabled()
        )
3320

3321
        if is_mixture_of_experts(self.model) and self.parallel_config.enable_eplb:
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            logger.info_once("EPLB is enabled for model %s.", self.model_config.model)
            global_expert_load = (
                global_expert_loads[eplb_models] if global_expert_loads else None
            )
            old_global_expert_indices = (
                old_global_expert_indices_per_model[eplb_models]
                if old_global_expert_indices_per_model
                else None
            )
            assert self.eplb_state is not None
            self.eplb_state.add_model(
3333
                self.model,
3334
                self.model_config,
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                global_expert_load,
                old_global_expert_indices,
                rank_mapping,
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3339
            )

3340
        if (
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3342
            self.vllm_config.compilation_config.mode
            == CompilationMode.STOCK_TORCH_COMPILE
3343
            and supports_dynamo()
3344
        ):
3345
            backend = self.vllm_config.compilation_config.init_backend(self.vllm_config)
3346
            compilation_counter.stock_torch_compile_count += 1
3347
            self.model.compile(fullgraph=True, backend=backend)
3348
            return
3349
        # for other compilation modes, cudagraph behavior is controlled by
3350
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        # CudagraphWraper and CudagraphDispatcher of vllm.

        # wrap the model with full cudagraph wrapper if needed.
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        if (
            self.compilation_config.cudagraph_mode.has_full_cudagraphs()
            and not self.parallel_config.enable_dbo
        ):
            self.model = CUDAGraphWrapper(
                self.model, self.vllm_config, runtime_mode=CUDAGraphMode.FULL
            )
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3361
        elif self.parallel_config.enable_dbo:
            if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
3362
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                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.FULL, self.device
                )
3365
            else:
3366
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                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.NONE, self.device
                )
3369

3370
    def _get_eagle3_aux_layers_from_config(self) -> tuple[int, ...] | None:
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        """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

3394
    def reload_weights(self) -> None:
3395
        assert getattr(self, "model", None) is not None, (
3396
            "Cannot reload weights before model is loaded."
3397
        )
3398
3399
        model_loader = get_model_loader(self.load_config)
        logger.info("Reloading weights inplace...")
3400
        model_loader.load_weights(self.get_model(), model_config=self.model_config)
3401

3402
3403
3404
3405
3406
    def save_tensorized_model(
        self,
        tensorizer_config: "TensorizerConfig",
    ) -> None:
        TensorizerLoader.save_model(
3407
            self.get_model(),
3408
            tensorizer_config=tensorizer_config,
3409
            model_config=self.model_config,
3410
3411
        )

3412
3413
3414
    def _get_prompt_logprobs_dict(
        self,
        hidden_states: torch.Tensor,
3415
        num_scheduled_tokens: dict[str, int],
3416
    ) -> dict[str, LogprobsTensors | None]:
3417
3418
3419
3420
        num_prompt_logprobs_dict = self.input_batch.num_prompt_logprobs
        if not num_prompt_logprobs_dict:
            return {}

3421
        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
3422
        prompt_logprobs_dict: dict[str, LogprobsTensors | None] = {}
3423
3424
3425
3426
3427

        # 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():
3428
            num_tokens = num_scheduled_tokens[req_id]
3429
3430
3431

            # Get metadata for this request.
            request = self.requests[req_id]
3432
3433
3434
3435
            if request.prompt_token_ids is None:
                # Prompt logprobs is incompatible with prompt embeddings
                continue

3436
3437
            num_prompt_tokens = len(request.prompt_token_ids)
            prompt_token_ids = torch.tensor(request.prompt_token_ids).to(
3438
3439
                self.device, non_blocking=True
            )
3440

3441
3442
3443
3444
3445
3446
            # 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(
3447
3448
                    num_prompt_tokens - 1, num_prompt_logprobs + 1
                )
3449
3450
                in_progress_dict[req_id] = logprobs_tensors

3451
            # Determine number of logits to retrieve.
3452
3453
            start_idx = request.num_computed_tokens
            start_tok = start_idx + 1
3454
            num_remaining_tokens = num_prompt_tokens - start_tok
3455
            if num_tokens <= num_remaining_tokens:
3456
                # This is a chunk, more tokens remain.
3457
3458
3459
                # 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.
3460
3461
3462
3463
3464
                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)
3465
3466
3467
3468
3469
3470
3471
                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
3472
3473
3474
3475
3476

            # 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]
3477
            offset = self.query_start_loc.np[req_idx].item()
3478
            prompt_hidden_states = hidden_states[offset : offset + num_logits]
3479
            logits = self.model.compute_logits(prompt_hidden_states)
3480
3481
3482
3483

            # 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.
3484
            tgt_token_ids = prompt_token_ids[start_tok : start_tok + num_logits]
3485
3486

            # Compute prompt logprobs.
3487
3488
            logprobs = self.sampler.compute_logprobs(logits)
            token_ids, logprobs, ranks = self.sampler.gather_logprobs(
3489
3490
                logprobs, num_prompt_logprobs, tgt_token_ids
            )
3491
3492

            # Transfer GPU->CPU async.
3493
3494
            chunk_slice = slice(start_idx, start_idx + num_logits)
            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
3495
3496
3497
                token_ids, non_blocking=True
            )
            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs, non_blocking=True)
3498
            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
3499
3500
                ranks, non_blocking=True
            )
3501
3502
3503
3504
3505

        # 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]
3506
            del in_progress_dict[req_id]
3507
3508

        # Must synchronize the non-blocking GPU->CPU transfers.
3509
        if prompt_logprobs_dict:
3510
            self._sync_device()
3511
3512
3513

        return prompt_logprobs_dict

3514
3515
    def _get_nans_in_logits(
        self,
3516
        logits: torch.Tensor | None,
3517
3518
3519
3520
3521
3522
3523
3524
3525
3526
3527
    ) -> 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])
3528
3529
3530
                    if num_nans_for_index is not None and req_index < logits.shape[0]
                    else 0
                )
3531
3532
3533
3534
            return num_nans_in_logits
        except IndexError:
            return {}

3535
3536
3537
3538
3539
3540
    @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
3541
         - during DP rank dummy run
3542
3543
3544
3545
3546
3547
3548
3549
3550
3551
3552
        """
        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(
3553
                    self.input_ids.gpu,
3554
3555
                    low=0,
                    high=self.model_config.get_vocab_size(),
3556
3557
                    dtype=input_ids.dtype,
                )
3558

3559
            logger.debug_once("Randomizing dummy data for DP Rank")
3560
            input_ids.copy_(rand_input_ids()[: input_ids.size(0)], non_blocking=True)
3561
3562
3563
            yield
            input_ids.fill_(0)

3564
3565
3566
3567
3568
3569
    def _get_mm_dummy_batch(
        self,
        modality: str,
        max_items_per_batch: int,
    ) -> BatchedTensorInputs:
        """Dummy data for profiling and precompiling multimodal models."""
3570
3571
        assert self.mm_budget is not None

3572
3573
        dummy_decoder_data = self.mm_registry.get_decoder_dummy_data(
            model_config=self.model_config,
3574
            seq_len=self.max_model_len,
3575
            mm_counts={modality: 1},
3576
            cache=self.mm_budget.cache,
3577
3578
3579
3580
        )
        dummy_mm_data = dummy_decoder_data.multi_modal_data

        # Result in the maximum GPU consumption of the model
3581
3582
        dummy_mm_item = dummy_mm_data[modality][0]
        dummy_mm_items = [dummy_mm_item] * max_items_per_batch
3583

3584
        model = cast(SupportsMultiModal, self.model)
3585
3586
3587
3588
3589
3590
3591
        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,
3592
                multimodal_cpu_fields=model.multimodal_cpu_fields,
3593
3594
            )
        )
3595

3596
3597
3598
3599
    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
3600
        cudagraph_runtime_mode: CUDAGraphMode | None = None,
3601
3602
        force_attention: bool = False,
        uniform_decode: bool = False,
3603
        allow_microbatching: bool = True,
3604
3605
        skip_eplb: bool = False,
        is_profile: bool = False,
3606
        create_mixed_batch: bool = False,
3607
        remove_lora: bool = True,
3608
        activate_lora: bool = False,
3609
    ) -> tuple[torch.Tensor, torch.Tensor]:
3610
3611
3612
3613
3614
3615
3616
        """
        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.
3617
                - if not set will determine the cudagraph mode based on using
3618
                    the self.cudagraph_dispatcher.
3619
3620
3621
3622
                - CUDAGraphMode.NONE: No cudagraph, for warm up and profile run
                - CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
                - CUDAGraphMode.FULL: Full cudagraph, attention metadata is
                    needed.
3623
            force_attention: If True, always create attention metadata. Used to
3624
3625
3626
3627
                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.
3628
3629
            create_mixed_batch: If True, create a mixed batch with both decode
                (1 token) and prefill (multiple tokens) requests.
3630
            remove_lora: If False, dummy LoRAs are not destroyed after the run
3631
            activate_lora: If False, dummy_run is performed without LoRAs.
3632
        """
3633
3634
3635
3636
        assert (
            cudagraph_runtime_mode is None
            or cudagraph_runtime_mode.valid_runtime_modes()
        )
3637

3638
        # If cudagraph_mode.decode_mode() == FULL and
3639
        # cudagraph_mode.separate_routine(). This means that we are using
3640
3641
3642
3643
3644
3645
3646
3647
3648
3649
3650
        # 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.
3651
        max_query_len = self.uniform_decode_query_len if uniform_decode else num_tokens
3652

3653
3654
3655
3656
3657
        # 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
3658
3659
3660
3661
        if create_mixed_batch:
            assert not uniform_decode
            # Create mixed batch:
            # first half decode tokens, second half one prefill
3662
            num_decode_tokens = min(max_num_reqs - 1, num_tokens // 2)
3663
3664
3665
3666
            num_prefill_tokens = num_tokens - num_decode_tokens
            num_reqs = num_decode_tokens + 1

            # Create decode requests (1 token each) followed by prefill request
3667
            num_scheduled_tokens_list = [1] * num_decode_tokens + [num_prefill_tokens]
3668
3669
3670
            # Note: Overriding max_query_len to be the prefill tokens
            max_query_len = num_prefill_tokens
        elif uniform_decode:
3671
            assert not create_mixed_batch
3672
            num_reqs = min(max_num_reqs, cdiv(num_tokens, max_query_len))
3673
3674
            num_scheduled_tokens_list = [max_query_len] * num_reqs
            if num_tokens % max_query_len != 0:
3675
                num_scheduled_tokens_list[-1] = num_tokens % max_query_len
3676
3677
3678
3679
3680
3681
        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

3682
3683
        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs
3684
        num_scheduled_tokens = np.array(num_scheduled_tokens_list, dtype=np.int32)
3685
        total_num_scheduled_tokens = int(num_scheduled_tokens.sum())
3686
        num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
3687

3688
3689
3690
        # Disable DP padding when running eager
        allow_dp_padding = self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE

3691
3692
        # We currently only microbatch if the number of tokens is
        # over a certain threshold.
3693
        ubatch_slices, num_tokens_across_dp = coordinate_batch_across_dp(
3694
3695
3696
3697
3698
3699
3700
            num_tokens_unpadded=total_num_scheduled_tokens,
            parallel_config=self.vllm_config.parallel_config,
            allow_microbatching=allow_microbatching,
            allow_dp_padding=allow_dp_padding,
            num_tokens_padded=total_num_scheduled_tokens,
            uniform_decode=uniform_decode,
            num_scheduled_tokens_per_request=num_scheduled_tokens,
3701
3702
3703
        )
        num_tokens_after_padding = num_tokens
        if num_tokens_across_dp is not None:
3704
3705
            dp_rank = self.parallel_config.data_parallel_rank
            num_tokens_after_padding = int(num_tokens_across_dp[dp_rank])
3706

3707
        attn_metadata: PerLayerAttnMetadata | None = None
3708
3709
3710

        # If force_attention is True, we always capture attention. Otherwise,
        # it only happens for cudagraph_runtime_mode=FULL.
3711
        if force_attention or cudagraph_runtime_mode == CUDAGraphMode.FULL:
3712
3713
3714
3715
3716
3717
            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:
3718
                seq_lens = max_query_len  # type: ignore[assignment]
3719
            self.seq_lens.np[:num_reqs] = seq_lens
3720
3721
            self.seq_lens.np[num_reqs:] = 0
            self.seq_lens.copy_to_gpu()
3722

3723
3724
            cum_num_tokens, _ = self._get_cumsum_and_arange(num_scheduled_tokens)
            self.query_start_loc.np[1 : num_reqs + 1] = cum_num_tokens
3725
3726
            self.query_start_loc.copy_to_gpu()

3727
3728
3729
3730
3731
3732
3733
            attn_metadata, _ = self._build_attention_metadata(
                total_num_scheduled_tokens=num_tokens,
                max_num_scheduled_tokens=max_query_len,
                num_reqs=num_reqs,
                ubatch_slices=ubatch_slices,
                for_cudagraph_capture=True,
            )
3734

3735
        with self.maybe_dummy_run_with_lora(
3736
3737
3738
3739
3740
            self.lora_config,
            num_scheduled_tokens,
            num_sampled_tokens,
            activate_lora,
            remove_lora,
3741
        ):
3742
3743
3744
            # 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)
3745
            if self.supports_mm_inputs and not self.model_config.is_encoder_decoder:
3746
                input_ids = None
3747
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_after_padding]
3748
                model_kwargs = {
3749
                    **model_kwargs,
3750
3751
                    **self._dummy_mm_kwargs(num_reqs),
                }
3752
3753
            elif self.enable_prompt_embeds:
                input_ids = None
3754
3755
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_after_padding]
                model_kwargs = self._init_model_kwargs(num_tokens_after_padding)
3756
            else:
3757
                input_ids = self.input_ids.gpu[:num_tokens_after_padding]
3758
                inputs_embeds = None
3759

3760
            if self.uses_mrope:
3761
                positions = self.mrope_positions.gpu[:, :num_tokens_after_padding]
3762
            else:
3763
                positions = self.positions.gpu[:num_tokens_after_padding]
3764
3765
3766
3767
3768
3769
3770
3771
3772

            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,
3773
3774
3775
                            device=self.device,
                        )
                    )
3776
3777

                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
3778
                    num_tokens_after_padding, None, False
3779
                )
3780
3781

            # filter out the valid batch descriptor
3782
3783
3784
3785
3786
            _cg_mode, batch_descriptor = (
                self.cudagraph_dispatcher.dispatch(
                    BatchDescriptor(
                        num_tokens=num_tokens_after_padding,
                        uniform_decode=uniform_decode,
3787
                        has_lora=activate_lora and self.lora_config is not None,
3788
3789
3790
3791
3792
                    )
                )
                if not is_profile
                else (CUDAGraphMode.NONE, None)
            )
3793
3794
3795
            if cudagraph_runtime_mode is not None:
                # we allow forcing NONE when the dispatcher disagrees to support
                # warm ups for cudagraph capture
3796
3797
3798
3799
                assert (
                    cudagraph_runtime_mode == CUDAGraphMode.NONE
                    or cudagraph_runtime_mode == _cg_mode
                ), (
3800
                    f"Cudagraph runtime mode mismatch at dummy_run. "
3801
3802
                    f"Expected {_cg_mode}, but got {cudagraph_runtime_mode}."
                )
3803
3804
            else:
                cudagraph_runtime_mode = _cg_mode
3805

3806
            if ubatch_slices is not None:
3807
3808
3809
3810
3811
3812
3813
                # 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

3814
3815
3816
            with (
                self.maybe_randomize_inputs(input_ids),
                set_forward_context(
3817
3818
                    attn_metadata,
                    self.vllm_config,
3819
                    num_tokens=num_tokens_after_padding,
3820
3821
                    num_tokens_across_dp=num_tokens_across_dp,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
3822
                    batch_descriptor=batch_descriptor,
3823
3824
3825
                    ubatch_slices=ubatch_slices,
                ),
            ):
3826
                outputs = self.model(
3827
3828
3829
3830
                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
3831
                    **model_kwargs,
3832
                )
3833

3834
3835
3836
3837
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
3838

3839
            if self.speculative_config and self.speculative_config.use_eagle():
3840
                assert isinstance(self.drafter, EagleProposer)
3841
3842
3843
3844
                use_cudagraphs = (
                    cudagraph_runtime_mode == CUDAGraphMode.PIECEWISE
                    and not self.speculative_config.enforce_eager
                )
3845
3846
3847
3848
3849
3850
3851
3852
3853
3854
3855
3856

                # Note(gnovack) - We need to disable cudagraphs for one of the two
                # lora cases when cudagraph_specialize_lora is enabled. This is a
                # short term mitigation for issue mentioned in
                # https://github.com/vllm-project/vllm/issues/28334
                if self.compilation_config.cudagraph_specialize_lora and activate_lora:
                    use_cudagraphs = False

                self.drafter.dummy_run(
                    num_tokens,
                    use_cudagraphs=use_cudagraphs,
                )
3857

3858
3859
3860
3861
3862
3863
3864
3865
3866
3867
        # 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)

3868
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
3869
3870
3871
3872
        logit_indices_device = torch.from_numpy(logit_indices).to(
            self.device, non_blocking=True
        )
        return hidden_states, hidden_states[logit_indices_device]
3873
3874
3875
3876
3877
3878

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
3879
3880
3881
3882
        # 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)
3883

3884
        logits = self.model.compute_logits(hidden_states)
3885
3886
        num_reqs = logits.size(0)

3887
        dummy_tensors = lambda v: torch.full((num_reqs,), v, device=self.device)
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        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)],
3903
            spec_token_ids=[[] for _ in range(num_reqs)],
3904
3905
            allowed_token_ids_mask=None,
            bad_words_token_ids={},
3906
            logitsprocs=LogitsProcessors(),
3907
        )
3908
        try:
3909
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3911
            sampler_output = self.sampler(
                logits=logits, sampling_metadata=dummy_metadata
            )
3912
        except RuntimeError as e:
3913
            if "out of memory" in str(e):
3914
3915
3916
3917
                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 "
3918
3919
                    "initializing the engine."
                ) from e
3920
3921
            else:
                raise e
3922
        if self.speculative_config:
3923
3924
            draft_token_ids = [[0] for _ in range(num_reqs)]
            dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
3925
3926
                draft_token_ids, self.device
            )
3927
3928
3929
3930
3931
3932

            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
3933
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            logits = torch.randn(
                num_tokens + num_reqs,
                logits.shape[-1],
                device=self.device,
                dtype=logits.dtype,
3938
            )
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            self.rejection_sampler(
                dummy_spec_decode_metadata,
                draft_probs,
3942
                logits,
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3944
                dummy_metadata,
            )
3945
        return sampler_output
3946

3947
    def _dummy_pooler_run_task(
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        self,
        hidden_states: torch.Tensor,
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        task: PoolingTask,
    ) -> PoolerOutput:
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        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

3963
        dummy_prompt_lens = torch.tensor(
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            num_scheduled_tokens_list,
            device="cpu",
3966
        )
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        dummy_token_ids = torch.zeros(
            (num_reqs, req_num_tokens), dtype=torch.int32, device=self.device
        )
3970

3971
        model = cast(VllmModelForPooling, self.get_model())
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        dummy_pooling_params = PoolingParams(task=task)
3973
        dummy_pooling_params.verify(task=task, model_config=self.model_config)
3974
        to_update = model.pooler.get_pooling_updates(task)
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        to_update.apply(dummy_pooling_params)

3977
        dummy_metadata = PoolingMetadata(
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            prompt_lens=dummy_prompt_lens,
            prompt_token_ids=dummy_token_ids,
            pooling_params=[dummy_pooling_params] * num_reqs,
        )
3982

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        dummy_metadata.build_pooling_cursor(
            num_scheduled_tokens_list, device=hidden_states.device
        )
3986

3987
        try:
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            return model.pooler(
                hidden_states=hidden_states, pooling_metadata=dummy_metadata
            )
3991
        except RuntimeError as e:
3992
            if "out of memory" in str(e):
3993
                raise RuntimeError(
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                    "CUDA out of memory occurred when warming up pooler "
                    f"({task=}) with {num_reqs} dummy requests. Please try "
                    "lowering `max_num_seqs` or `gpu_memory_utilization` when "
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                    "initializing the engine."
                ) from e
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4000
            else:
                raise e
4001
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4006
4007

    @torch.inference_mode()
    def _dummy_pooler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> PoolerOutput:
        # Find the task that has the largest output for subsequent steps
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4010
        supported_pooling_tasks = self.get_supported_pooling_tasks()

        if not supported_pooling_tasks:
4011
            if self.scheduler_config.enable_chunked_prefill:
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4022
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                raise RuntimeError(
                    f"Model {self.model_config.model} does not support "
                    "any pooling tasks with chunked prefill enabled. "
                    "Please add --no-enable-chunked-prefill to your "
                    "config or CLI args. See "
                    "https://docs.vllm.ai/en/latest/models/pooling_models.html "
                    "to learn more."
                )
            else:
                raise RuntimeError(
                    f"Model {self.model_config.model} does not support "
                    "any pooling tasks. See "
                    "https://docs.vllm.ai/en/latest/models/pooling_models.html "
                    "to learn more."
                )

4028
        output_size = dict[PoolingTask, float]()
4029
        for task in supported_pooling_tasks:
4030
4031
            # Run a full batch with each task to ensure none of them OOMs
            output = self._dummy_pooler_run_task(hidden_states, task)
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            output_size[task] = sum(o.nbytes for o in output)
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            del output  # Allow GC

        max_task = max(output_size.items(), key=lambda x: x[1])[0]
        return self._dummy_pooler_run_task(hidden_states, max_task)
4037

4038
    def profile_run(self) -> None:
4039
        # Profile with multimodal encoder & encoder cache.
4040
        if self.supports_mm_inputs:
4041
            if self.model_config.multimodal_config.skip_mm_profiling:
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                logger.info(
4043
                    "Skipping memory profiling for multimodal encoder and "
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                    "encoder cache."
                )
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            else:
                mm_budget = self.mm_budget
                assert mm_budget is not None

                if (encoder_budget := mm_budget.get_encoder_budget()) > 0:
                    # NOTE: Currently model is profiled with a single non-text
                    # modality with the max possible input tokens even when
                    # it supports multiple.
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                    dummy_modality = mm_budget.get_modality_with_max_tokens()
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                    max_mm_items_per_batch = mm_budget.max_items_per_batch_by_modality[
                        dummy_modality
                    ]
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                    logger.info(
                        "Encoder cache will be initialized with a budget of "
                        "%s tokens, and profiled with %s %s items of the "
                        "maximum feature size.",
                        encoder_budget,
                        max_mm_items_per_batch,
                        dummy_modality,
                    )
4067

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                    # Create dummy batch of multimodal inputs.
                    batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                        dummy_modality,
                        max_mm_items_per_batch,
                    )
4073

4074
                    # Run multimodal encoder.
4075
                    dummy_encoder_outputs = self.model.embed_multimodal(
4076
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                        **batched_dummy_mm_inputs
                    )
4078

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                    sanity_check_mm_encoder_outputs(
                        dummy_encoder_outputs,
                        expected_num_items=max_mm_items_per_batch,
                    )
4083

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                    # 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(
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                                (encoder_budget, encoder_output_shape[-1])
                            )
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                            num_tokens = output.shape[0]
                            expanded[:num_tokens].copy_(output)
                            expanded_outputs.append(expanded)

                        dummy_encoder_outputs = expanded_outputs

4102
                    # Cache the dummy encoder outputs.
4103
                    self.encoder_cache["tmp"] = dict(enumerate(dummy_encoder_outputs))
4104

4105
        # Add `is_profile` here to pre-allocate communication buffers
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        hidden_states, last_hidden_states = self._dummy_run(
            self.max_num_tokens, is_profile=True
        )
4109
        if get_pp_group().is_last_rank:
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            if self.is_pooling_model:
                output = self._dummy_pooler_run(hidden_states)
            else:
                output = self._dummy_sampler_run(last_hidden_states)
4114
        else:
4115
            output = None
4116
        self._sync_device()
4117
        del hidden_states, output
4118
        self.encoder_cache.clear()
4119
        gc.collect()
4120

4121
    def capture_model(self) -> int:
4122
        if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
4123
            logger.warning(
4124
                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
4125
4126
                "ensure `cudagraph_mode` was not manually set to `NONE`"
            )
4127
            return 0
4128

4129
4130
        compilation_counter.num_gpu_runner_capture_triggers += 1

4131
4132
        start_time = time.perf_counter()

4133
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        @contextmanager
        def freeze_gc():
            # Optimize garbage collection during CUDA graph capture.
            # Clean up, then freeze all remaining objects from being included
            # in future collections.
            gc.collect()
            should_freeze = not envs.VLLM_ENABLE_CUDAGRAPH_GC
            if should_freeze:
                gc.freeze()
            try:
                yield
            finally:
                if should_freeze:
                    gc.unfreeze()
4147
                    gc.collect()
4148

4149
4150
4151
        # 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.
4152
        set_cudagraph_capturing_enabled(True)
4153
        with freeze_gc(), graph_capture(device=self.device):
4154
            start_free_gpu_memory = torch.cuda.mem_get_info()[0]
4155
            cudagraph_mode = self.compilation_config.cudagraph_mode
4156
            assert cudagraph_mode is not None
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            if self.lora_config:
                if self.compilation_config.cudagraph_specialize_lora:
                    lora_cases = [True, False]
                else:
                    lora_cases = [True]
            else:
                lora_cases = [False]

4166
4167
            if cudagraph_mode.mixed_mode() != CUDAGraphMode.NONE:
                cudagraph_runtime_mode = cudagraph_mode.mixed_mode()
4168
                # make sure we capture the largest batch size first
4169
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                compilation_cases = list(
                    product(reversed(self.cudagraph_batch_sizes), lora_cases)
                )
4172
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                self._capture_cudagraphs(
                    compilation_cases,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
4175
4176
                    uniform_decode=False,
                )
4177

4178
4179
            # Capture full cudagraph for uniform decode batches if we
            # don't already have full mixed prefill-decode cudagraphs.
4180
4181
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4183
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4186
            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
                )
4187
                decode_cudagraph_batch_sizes = [
4188
4189
                    x
                    for x in self.cudagraph_batch_sizes
4190
                    if max_num_tokens >= x >= self.uniform_decode_query_len
4191
                ]
4192
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4194
                compilation_cases_decode = list(
                    product(reversed(decode_cudagraph_batch_sizes), lora_cases)
                )
4195
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                self._capture_cudagraphs(
                    compilation_cases=compilation_cases_decode,
                    cudagraph_runtime_mode=CUDAGraphMode.FULL,
4198
4199
                    uniform_decode=True,
                )
4200

4201
4202
4203
            torch.cuda.synchronize()
            end_free_gpu_memory = torch.cuda.mem_get_info()[0]

4204
4205
4206
        # 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
4207
        # we may do lazy capturing in future that still allows capturing
4208
4209
        # after here.
        set_cudagraph_capturing_enabled(False)
4210
4211
4212
4213
4214

        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.
4215
        logger.info_once(
4216
4217
4218
            "Graph capturing finished in %.0f secs, took %.2f GiB",
            elapsed_time,
            cuda_graph_size / (1 << 30),
4219
            scope="local",
4220
        )
4221
        return cuda_graph_size
4222

4223
4224
    def _capture_cudagraphs(
        self,
4225
        compilation_cases: list[tuple[int, bool]],
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4228
4229
4230
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4232
        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}"
4233
4234
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4237
4238
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4240

        # 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",
4241
4242
4243
                    cudagraph_runtime_mode.name,
                ),
            )
4244

4245
        # We skip EPLB here since we don't want to record dummy metrics
4246
        for num_tokens, activate_lora in compilation_cases:
4247
            # We currently only capture ubatched graphs when its a FULL
4248
4249
4250
            # 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
4251
4252
4253
4254
            allow_microbatching = (
                self.parallel_config.enable_dbo
                and cudagraph_runtime_mode == CUDAGraphMode.FULL
                and uniform_decode
4255
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4259
                and check_ubatch_thresholds(
                    config=self.vllm_config.parallel_config,
                    num_tokens=num_tokens,
                    uniform_decode=uniform_decode,
                )
4260
            )
4261

4262
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            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.
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                force_attention = cudagraph_runtime_mode == CUDAGraphMode.FULL
                self._dummy_run(
                    num_tokens,
                    cudagraph_runtime_mode=CUDAGraphMode.NONE,
                    force_attention=force_attention,
                    uniform_decode=uniform_decode,
                    allow_microbatching=allow_microbatching,
                    skip_eplb=True,
                    remove_lora=False,
4277
                    activate_lora=activate_lora,
4278
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4280
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4285
                )
            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,
4286
                activate_lora=activate_lora,
4287
            )
4288
        self.maybe_remove_all_loras(self.lora_config)
4289

4290
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4293
    def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize the attention backends and attention metadata builders.
        """
4294
        assert len(self.attn_groups) == 0, "Attention backends are already initialized"
4295

4296
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4301
        class AttentionGroupKey(NamedTuple):
            attn_backend: type[AttentionBackend]
            kv_cache_spec: KVCacheSpec

        def get_attn_backends_for_group(
            kv_cache_group_spec: KVCacheGroupSpec,
4302
        ) -> tuple[dict[AttentionGroupKey, list[str]], set[type[AttentionBackend]]]:
4303
            layers = get_layers_from_vllm_config(
4304
4305
                self.vllm_config, AttentionLayerBase, kv_cache_group_spec.layer_names
            )
4306
4307
            attn_backends = {}
            attn_backend_layers = defaultdict(list)
4308
            # Dedupe based on full class name; this is a bit safer than
4309
4310
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4312
            # 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.
4313
            for layer_name in kv_cache_group_spec.layer_names:
4314
                attn_backend = layers[layer_name].get_attn_backend()
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4317
4318
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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,
                    )

4322
4323
4324
                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):
4325
                    layer_kv_cache_spec = layer_kv_cache_spec.kv_cache_specs[layer_name]
4326
                key = (full_cls_name, layer_kv_cache_spec)
4327
4328
4329
                attn_backends[key] = AttentionGroupKey(
                    attn_backend, layer_kv_cache_spec
                )
4330
                attn_backend_layers[key].append(layer_name)
4331
4332
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4334
            return (
                {attn_backends[k]: v for k, v in attn_backend_layers.items()},
                set(group_key.attn_backend for group_key in attn_backends.values()),
            )
4335
4336

        def create_attn_groups(
4337
            attn_backends_map: dict[AttentionGroupKey, list[str]],
4338
            kv_cache_group_id: int,
4339
4340
        ) -> list[AttentionGroup]:
            attn_groups: list[AttentionGroup] = []
4341
            for (attn_backend, kv_cache_spec), layer_names in attn_backends_map.items():
4342
                attn_group = AttentionGroup(
4343
                    attn_backend,
4344
                    layer_names,
4345
                    kv_cache_spec,
4346
                    kv_cache_group_id,
4347
4348
                )

4349
4350
4351
                attn_groups.append(attn_group)
            return attn_groups

4352
        attention_backend_maps = []
4353
        attention_backend_list = []
4354
        for kv_cache_group_spec in kv_cache_config.kv_cache_groups:
4355
            attn_backends = get_attn_backends_for_group(kv_cache_group_spec)
4356
            attention_backend_maps.append(attn_backends[0])
4357
            attention_backend_list.append(attn_backends[1])
4358
4359

        # Resolve cudagraph_mode before actually initialize metadata_builders
4360
4361
4362
        self._check_and_update_cudagraph_mode(
            attention_backend_list, kv_cache_config.kv_cache_groups
        )
4363

4364
4365
        for i, attn_backend_map in enumerate(attention_backend_maps):
            self.attn_groups.append(create_attn_groups(attn_backend_map, i))
4366

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    def initialize_metadata_builders(
        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
    ) -> None:
        """
        Create the metadata builders for all KV cache groups and attn groups.
        """
        for kv_cache_group_id in range(len(kv_cache_config.kv_cache_groups)):
            for attn_group in self.attn_groups[kv_cache_group_id]:
                attn_group.create_metadata_builders(
                    self.vllm_config,
                    self.device,
                    kernel_block_sizes[kv_cache_group_id]
                    if kv_cache_group_id < len(kernel_block_sizes)
                    else None,
                    num_metadata_builders=1
                    if not self.parallel_config.enable_dbo
                    else 2,
                )
co63oc's avatar
co63oc committed
4385
        # Calculate reorder batch threshold (if needed)
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4387
        # Note (tdoublep): do this *after* constructing builders,
        # because some of them change the threshold at init time.
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        self.calculate_reorder_batch_threshold()

4390
    def _check_and_update_cudagraph_mode(
4391
4392
4393
        self,
        attention_backends: list[set[type[AttentionBackend]]],
        kv_cache_groups: list[KVCacheGroupSpec],
4394
    ) -> None:
4395
        """
4396
        Resolve the cudagraph_mode when there are multiple attention
4397
        groups with potential conflicting CUDA graph support.
4398
4399
4400
        Then initialize the cudagraph_dispatcher based on the resolved
        cudagraph_mode.
        """
4401
        min_cg_support = AttentionCGSupport.ALWAYS
4402
        min_cg_backend_name = None
4403

4404
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        for attn_backend_set, kv_cache_group in zip(
            attention_backends, kv_cache_groups
        ):
            for attn_backend in attn_backend_set:
                builder_cls = attn_backend.get_builder_cls()

                cg_support = builder_cls.get_cudagraph_support(
                    self.vllm_config, kv_cache_group.kv_cache_spec
                )
                if cg_support.value < min_cg_support.value:
                    min_cg_support = cg_support
                    min_cg_backend_name = attn_backend.__name__
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        # Flexible resolve the cudagraph mode
        cudagraph_mode = self.compilation_config.cudagraph_mode
        # check cudagraph for mixed batch is supported
4419
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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 "
4425
                f"with {min_cg_backend_name} backend (support: "
4426
4427
                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 "
4432
                    "make sure compilation mode is VLLM_COMPILE"
4433
                )
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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"
4439
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4440
                    CUDAGraphMode.FULL_AND_PIECEWISE
4441
                )
4442
4443
            else:
                msg += "; setting cudagraph_mode=FULL_DECODE_ONLY"
4444
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4445
                    CUDAGraphMode.FULL_DECODE_ONLY
4446
                )
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            logger.warning(msg)

4449
        # 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 "
4456
                f"with {min_cg_backend_name} backend (support: "
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4458
                f"{min_cg_support})"
            )
4459
            if self.compilation_config.mode == CompilationMode.VLLM_COMPILE and (
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                self.compilation_config.splitting_ops_contain_attention()
                or self.compilation_config.use_inductor_graph_partition
            ):
                msg += (
                    "; setting cudagraph_mode=PIECEWISE because "
4465
                    "attention is compiled piecewise"
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                )
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4468
                    CUDAGraphMode.PIECEWISE
4469
                )
4470
            else:
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                msg += (
                    "; setting cudagraph_mode=NONE because "
4473
                    "attention is not compiled piecewise"
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4475
                )
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4476
                    CUDAGraphMode.NONE
4477
                )
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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 "
4490
                f"{min_cg_backend_name} (support: {min_cg_support})"
4491
            )
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4493
            if self.compilation_config.splitting_ops_contain_attention():
                msg += "; setting cudagraph_mode=PIECEWISE"
4494
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
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                    CUDAGraphMode.PIECEWISE
4496
                )
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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
4501
                )
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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 "
4512
                f"supported with {min_cg_backend_name} backend ("
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                f"support:{min_cg_support}) "
                "; please try cudagraph_mode=PIECEWISE, "
4515
                "and make sure compilation mode is VLLM_COMPILE"
4516
            )
4517

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        # if we have dedicated decode cudagraphs, and spec-decode is enabled,
        # we need to adjust the cudagraph sizes to be a multiple of the uniform
        # decode query length to avoid: https://github.com/vllm-project/vllm/issues/28207
        # temp-fix: https://github.com/vllm-project/vllm/issues/28207#issuecomment-3504004536
        # Will be removed in the near future when we have seperate cudagraph capture
        # sizes for decode and mixed prefill-decode.
        if (
            cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
            and cudagraph_mode.separate_routine()
            and self.uniform_decode_query_len > 1
        ):
            self.compilation_config.adjust_cudagraph_sizes_for_spec_decode(
                self.uniform_decode_query_len, self.parallel_config.tensor_parallel_size
            )
            self.cudagraph_batch_sizes = self.compilation_config.cudagraph_capture_sizes

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4535
        # Trigger cudagraph dispatching keys initialization after
        # resolved cudagraph mode.
4536
        self.cudagraph_dispatcher.initialize_cudagraph_keys(
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            self.compilation_config.cudagraph_mode, self.uniform_decode_query_len
        )
4539

4540
4541
    def calculate_reorder_batch_threshold(self) -> None:
        """
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4544
4545
        Choose the minimum reorder batch threshold from all attention groups.
        Backends should be able to support lower threshold then what they request
        just may have a performance penalty due to that backend treating decodes
        as prefills.
4546
        """
4547
4548
4549
4550
4551
4552
        min_none_high = lambda a, b: a if b is None else b if a is None else min(a, b)

        reorder_batch_thresholds = [
            group.get_metadata_builder().reorder_batch_threshold
            for group in self._attn_group_iterator()
        ]
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        # If there are no attention groups (attention-free model) or no backend
        # reports a threshold, leave reordering disabled.
        if len(reorder_batch_thresholds) == 0:
            self.reorder_batch_threshold = None
            return
4558
        self.reorder_batch_threshold = reduce(min_none_high, reorder_batch_thresholds)
4559

4560
4561
4562
    @staticmethod
    def select_common_block_size(
        kv_manager_block_size: int, attn_groups: list[AttentionGroup]
4563
4564
    ) -> int:
        """
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        Select a block size that is supported by all backends and is a factor of
        kv_manager_block_size.

        If kv_manager_block_size is supported by all backends, return it directly.
        Otherwise, return the max supported size.
4570
4571
4572
4573
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4575

        Args:
            kv_manager_block_size: Block size of KV cache
            attn_groups: List of attention groups

        Returns:
4576
            The selected block size
4577
4578

        Raises:
4579
            ValueError: If no valid block size found
4580
4581
        """

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        def block_size_is_supported(
            backends: list[type[AttentionBackend]], block_size: int
        ) -> bool:
            """
            Check if the block size is supported by all backends.
            """
            for backend in backends:
                is_supported = False
4590
                for supported_size in backend.supported_kernel_block_sizes:
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                    if isinstance(supported_size, int):
                        if block_size == supported_size:
                            is_supported = True
                    elif isinstance(supported_size, MultipleOf):
                        if block_size % supported_size.base == 0:
                            is_supported = True
                    else:
                        raise ValueError(f"Unknown supported size: {supported_size}")
                if not is_supported:
                    return False
            return True

        backends = [group.backend for group in attn_groups]

        # Case 1: if the block_size of kv cache manager is supported by all backends,
        # return it directly
        if block_size_is_supported(backends, kv_manager_block_size):
            return kv_manager_block_size

        # Case 2: otherwise, the block_size must be an `int`-format supported size of
        # at least one backend. Iterate over all `int`-format supported sizes in
        # descending order and return the first one that is supported by all backends.
        # Simple proof:
        # If the supported size b is in MultipleOf(x_i) format for all attention
        # backends i, and b a factor of kv_manager_block_size, then
        # kv_manager_block_size also satisfies MultipleOf(x_i) for all i. We will
        # return kv_manager_block_size in case 1.
        all_int_supported_sizes = set(
            supported_size
            for backend in backends
4621
            for supported_size in backend.supported_kernel_block_sizes
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4623
            if isinstance(supported_size, int)
        )
4624

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        for supported_size in sorted(all_int_supported_sizes, reverse=True):
            if kv_manager_block_size % supported_size != 0:
                continue
            if block_size_is_supported(backends, supported_size):
                return supported_size
        raise ValueError(f"No common block size for {kv_manager_block_size}. ")
4631

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    def may_reinitialize_input_batch(
        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
    ) -> 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.
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            kernel_block_sizes: The kernel block sizes for each KV cache group.
4643
4644
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4646
        """
        block_sizes = [
            kv_cache_group.kv_cache_spec.block_size
            for kv_cache_group in kv_cache_config.kv_cache_groups
4647
            if not isinstance(kv_cache_group.kv_cache_spec, EncoderOnlyAttentionSpec)
4648
        ]
4649
4650
4651
4652

        if block_sizes != [self.cache_config.block_size] or kernel_block_sizes != [
            self.cache_config.block_size
        ]:
4653
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4655
            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,
4660
                max_model_len=max(self.max_model_len, self.max_encoder_len),
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4665
                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,
4666
                kernel_block_sizes=kernel_block_sizes,
4667
                is_spec_decode=bool(self.vllm_config.speculative_config),
4668
                logitsprocs=self.input_batch.logitsprocs,
4669
                logitsprocs_need_output_token_ids=self.input_batch.logitsprocs_need_output_token_ids,
4670
                is_pooling_model=self.is_pooling_model,
4671
                num_speculative_tokens=self.num_spec_tokens,
4672
4673
            )

4674
    def _allocate_kv_cache_tensors(
4675
4676
        self, kv_cache_config: KVCacheConfig
    ) -> dict[str, torch.Tensor]:
4677
        """
4678
4679
4680
        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.

4681
        Args:
4682
            kv_cache_config: The KV cache config
4683
        Returns:
4684
            dict[str, torch.Tensor]: A map between layer names to their
4685
            corresponding memory buffer for KV cache.
4686
        """
4687
4688
        kv_cache_raw_tensors: dict[str, torch.Tensor] = {}
        for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
4689
4690
4691
            tensor = torch.zeros(
                kv_cache_tensor.size, dtype=torch.int8, device=self.device
            )
4692
4693
4694
4695
4696
            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:
4697
4698
4699
4700
            for layer_name in group.layer_names:
                if layer_name in self.runner_only_attn_layers:
                    continue
                layer_names.add(layer_name)
4701
4702
4703
        assert layer_names == set(kv_cache_raw_tensors.keys()), (
            "Some layers are not correctly initialized"
        )
4704
4705
        return kv_cache_raw_tensors

4706
4707
4708
    def _attn_group_iterator(self) -> Iterator[AttentionGroup]:
        return itertools.chain.from_iterable(self.attn_groups)

4709
    def _kv_cache_spec_attn_group_iterator(self) -> Iterator[AttentionGroup]:
4710
4711
        if not self.kv_cache_config.kv_cache_groups:
            return
4712
4713
        for attn_groups in self.attn_groups:
            yield from attn_groups
4714

4715
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4729
    def _prepare_kernel_block_sizes(self, kv_cache_config: KVCacheConfig) -> list[int]:
        """
        Generate kernel_block_sizes that matches each block_size.

        For attention backends that support virtual block splitting,
        use the supported block sizes from the backend.
        For other backends (like Mamba), use the same block size (no splitting).

        Args:
            kv_cache_config: The KV cache configuration.

        Returns:
            list[int]: List of kernel block sizes for each cache group.
        """
        kernel_block_sizes = []
4730
        for kv_cache_gid, kv_cache_group in enumerate(kv_cache_config.kv_cache_groups):
4731
4732
4733
4734
4735
4736
            kv_cache_spec = kv_cache_group.kv_cache_spec
            if isinstance(kv_cache_spec, UniformTypeKVCacheSpecs):
                # All layers in the UniformTypeKVCacheSpecs have the same type,
                # Pick an arbitrary one to dispatch.
                kv_cache_spec = next(iter(kv_cache_spec.kv_cache_specs.values()))
            if isinstance(kv_cache_spec, EncoderOnlyAttentionSpec):
4737
                continue
4738
            elif isinstance(kv_cache_spec, AttentionSpec):
4739
4740
4741
                # This is an attention backend that supports virtual
                # block splitting. Get the supported block sizes from
                # all backends in the group.
4742
                attn_groups = self.attn_groups[kv_cache_gid]
4743
                kv_manager_block_size = kv_cache_group.kv_cache_spec.block_size
4744
                selected_kernel_size = self.select_common_block_size(
4745
4746
4747
                    kv_manager_block_size, attn_groups
                )
                kernel_block_sizes.append(selected_kernel_size)
4748
            elif isinstance(kv_cache_spec, MambaSpec):
4749
4750
                # This is likely Mamba or other non-attention cache,
                # no splitting.
4751
                kernel_block_sizes.append(kv_cache_spec.block_size)
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4753
4754
4755
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4757
            else:
                raise NotImplementedError(
                    f"unknown kv cache spec {kv_cache_group.kv_cache_spec}"
                )
        return kernel_block_sizes

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4761
    def _reshape_kv_cache_tensors(
        self,
        kv_cache_config: KVCacheConfig,
        kv_cache_raw_tensors: dict[str, torch.Tensor],
4762
        kernel_block_sizes: list[int],
4763
    ) -> dict[str, torch.Tensor]:
4764
        """
4765
        Reshape the KV cache tensors to the desired shape and dtype.
4766

4767
        Args:
4768
4769
            kv_cache_config: The KV cache config
            kv_cache_raw_tensors: The KV cache buffer of each layer, with
4770
                correct size but uninitialized shape.
4771
            kernel_block_sizes: The kernel block sizes for each KV cache group.
4772
        Returns:
4773
            Dict[str, torch.Tensor]: A map between layer names to their
4774
4775
            corresponding memory buffer for KV cache.
        """
4776
        kv_caches: dict[str, torch.Tensor] = {}
4777
        has_attn, has_mamba = False, False
4778
4779
        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
4780
            attn_backend = group.backend
4781
4782
4783
4784
            if group.kv_cache_group_id == len(kernel_block_sizes):
                # There may be a last group for layers without kv cache.
                continue
            kernel_block_size = kernel_block_sizes[group.kv_cache_group_id]
4785
            for layer_name in group.layer_names:
4786
4787
                if layer_name in self.runner_only_attn_layers:
                    continue
4788
4789
                raw_tensor = kv_cache_raw_tensors[layer_name]
                assert raw_tensor.numel() % kv_cache_spec.page_size_bytes == 0
4790
                num_blocks = raw_tensor.numel() // kv_cache_spec.page_size_bytes
4791
                if isinstance(kv_cache_spec, AttentionSpec):
4792
                    has_attn = True
4793
4794
                    num_blocks_per_kv_block = (
                        kv_cache_spec.block_size // kernel_block_size
4795
4796
4797
                    )
                    kernel_num_blocks = num_blocks * num_blocks_per_kv_block

4798
                    kv_cache_shape = attn_backend.get_kv_cache_shape(
4799
                        kernel_num_blocks,
4800
                        kernel_block_size,
4801
4802
                        kv_cache_spec.num_kv_heads,
                        kv_cache_spec.head_size,
4803
4804
                        cache_dtype_str=self.cache_config.cache_dtype,
                    )
4805
                    dtype = kv_cache_spec.dtype
4806
                    try:
4807
                        kv_cache_stride_order = attn_backend.get_kv_cache_stride_order()
4808
                        assert len(kv_cache_stride_order) == len(kv_cache_shape)
4809
                    except (AttributeError, NotImplementedError):
4810
                        kv_cache_stride_order = tuple(range(len(kv_cache_shape)))
4811
4812
4813
4814
4815
                    # 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.
4816
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4818
                    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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4830
                elif isinstance(kv_cache_spec, MambaSpec):
4831
                    has_mamba = True
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                    raw_tensor = kv_cache_raw_tensors[layer_name]
                    state_tensors = []
4834
                    storage_offset_bytes = 0
4835
                    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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4839
                            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:])
4843
                        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,
4848
                            storage_offset=storage_offset_bytes // dtype_size,
4849
                        )
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                        state_tensors.append(tensor)
4851
                        storage_offset_bytes += stride[0] * dtype_size
4852
4853

                    kv_caches[layer_name] = state_tensors
4854
                else:
4855
                    raise NotImplementedError
4856
4857

        if has_attn and has_mamba:
4858
            self._update_hybrid_attention_mamba_layout(kv_caches)
4859

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4861
        return kv_caches

4862
    def _update_hybrid_attention_mamba_layout(
4863
4864
        self, kv_caches: dict[str, torch.Tensor]
    ) -> None:
4865
        """
4866
4867
        Update the layout of attention layers from (2, num_blocks, ...) to
        (num_blocks, 2, ...).
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4869

        Args:
4870
            kv_caches: The KV cache buffer of each layer.
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4872
        """

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

        Args:
            kv_cache_config: The KV cache config
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            kernel_block_sizes: The kernel block sizes for each KV cache group.

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

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

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

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

        if self.cache_config.kv_sharing_fast_prefill:
            # In You Only Cache Once (https://arxiv.org/abs/2405.05254) or other
            # similar KV sharing setups, only the layers that generate KV caches
            # are involved in the prefill phase, enabling prefill to early exit.
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            attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention)
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            for layer_name in reversed(attn_layers):
                if layer_name in self.shared_kv_cache_layers:
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                    self.kv_sharing_fast_prefill_eligible_layers.add(layer_name)
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                else:
                    break
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    def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize KV cache based on `kv_cache_config`.
        Args:
            kv_cache_config: Configuration for the KV cache, including the KV
            cache size of each layer
        """
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        kv_cache_config = deepcopy(kv_cache_config)
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        self.kv_cache_config = kv_cache_config
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        self.may_add_encoder_only_layers_to_kv_cache_config()
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        self.maybe_add_kv_sharing_layers_to_kv_cache_groups(kv_cache_config)
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        self.initialize_attn_backend(kv_cache_config)
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        # The kernel block size for all KV cache groups. For example, if
        # kv_cache_manager uses block_size 256 for a given group, but the attention
        # backends for that group only supports block_size 64, we will return
        # kernel_block_size 64 and split the 256-token-block to 4 blocks with 64
        # tokens each.
        kernel_block_sizes = self._prepare_kernel_block_sizes(kv_cache_config)
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        # create metadata builders
        self.initialize_metadata_builders(kv_cache_config, kernel_block_sizes)

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        # Reinitialize need to after initialize_attn_backend
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        self.may_reinitialize_input_batch(kv_cache_config, kernel_block_sizes)
        kv_caches = self.initialize_kv_cache_tensors(
            kv_cache_config, kernel_block_sizes
        )
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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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            layers = get_layers_from_vllm_config(self.vllm_config, AttentionLayerBase)
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            for layer in layers.values():
                assert layer.impl.need_to_return_lse_for_decode, (
                    "DCP requires attention impls to return"
                    " the softmax lse for decode, but the impl "
                    f"{layer.impl.__class__.__name__} "
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                    "does not return the softmax lse for decode."
                )
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    def may_add_encoder_only_layers_to_kv_cache_config(self) -> None:
        """
        Add encoder-only layers to the KV cache config.
        """
        block_size = self.vllm_config.cache_config.block_size
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        encoder_only_attn_specs: dict[AttentionSpec, list[str]] = defaultdict(list)
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        attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention)
        for layer_name, attn_module in attn_layers.items():
            if attn_module.attn_type == AttentionType.ENCODER_ONLY:
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                attn_spec: AttentionSpec = EncoderOnlyAttentionSpec(
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                    block_size=block_size,
                    num_kv_heads=attn_module.num_kv_heads,
                    head_size=attn_module.head_size,
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                    dtype=self.kv_cache_dtype,
                )
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                encoder_only_attn_specs[attn_spec].append(layer_name)
                self.runner_only_attn_layers.add(layer_name)
        if len(encoder_only_attn_specs) > 0:
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            assert len(encoder_only_attn_specs) == 1, (
                "Only support one encoder-only attention spec now"
            )
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            spec, layer_names = encoder_only_attn_specs.popitem()
            self.kv_cache_config.kv_cache_groups.append(
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                KVCacheGroupSpec(layer_names=layer_names, kv_cache_spec=spec)
            )
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    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
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        """
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        Generates the KVCacheSpec by parsing the kv cache format from each
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        Attention module in the static forward context.
        Returns:
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            KVCacheSpec: A dictionary mapping layer names to their KV cache
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            format. Layers that do not need KV cache are not included.
        """
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        if has_ec_transfer() and get_ec_transfer().is_producer:
            return {}
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        kv_cache_spec: dict[str, KVCacheSpec] = {}
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        attn_layers = get_layers_from_vllm_config(self.vllm_config, AttentionLayerBase)
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        for layer_name, attn_module in attn_layers.items():
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            if isinstance(attn_module, Attention) and (
                kv_tgt_layer := attn_module.kv_sharing_target_layer_name
            ):
                # The layer doesn't need its own KV cache and will use that of
                # the target layer. We skip creating a KVCacheSpec for it, so
                # that KV cache management logic will act as this layer does
                # not exist, and doesn't allocate KV cache for the layer. This
                # enables the memory saving of cross-layer kv sharing, allowing
                # a given amount of memory to accommodate longer context lengths
                # or enable more requests to be processed simultaneously.
                self.shared_kv_cache_layers[layer_name] = kv_tgt_layer
                continue
            # Skip modules that don't need KV cache (eg encoder-only attention)
            if spec := attn_module.get_kv_cache_spec(self.vllm_config):
                kv_cache_spec[layer_name] = spec
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        return kv_cache_spec
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    def _to_list(self, sampled_token_ids: torch.Tensor) -> list[np.ndarray]:
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        # 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()
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        return [row for row in pinned.numpy()]