gpu_model_runner.py 220 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
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from collections.abc import Iterator, Sequence
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from contextlib import contextmanager
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from copy import copy, 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 (
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    SupportsMRoPE,
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    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.logits_processor.interface import LogitsProcessor
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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.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:
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            valid_sampled_token_ids = self.sampled_token_ids_cpu.tolist()
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        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].clear()
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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
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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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        # Initialize in initialize_kv_cache_tensors
        self.cross_layers_kv_cache: torch.Tensor | None = None
        self.cross_layers_attn_backend: type[AttentionBackend] | None = None
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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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        logits_processors = model_config.logits_processors
        custom_logitsprocs: Sequence[str | type[LogitsProcessor]] = (
            tuple(logits_processors) if logits_processors is not None else ()
        )
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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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            cp_kv_cache_interleave_size=self.parallel_config.cp_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.
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        self.prepare_inputs_event: torch.Event | None = None
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        if self.use_async_scheduling:
            self.async_output_copy_stream = torch.cuda.Stream()
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            self.prepare_inputs_event = torch.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
506
            self.mrope_positions = self._make_buffer(
507
508
                (3, self.max_num_tokens + 1), dtype=torch.int64
            )
509

510
        # None in the first PP rank. The rest are set after load_model.
511
        self.intermediate_tensors: IntermediateTensors | None = None
512

513
        # OPTIMIZATION: Cache the tensors rather than creating them every step.
514
        # Keep in int64 to avoid overflow with long context
515
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517
518
        self.arange_np = np.arange(
            max(self.max_num_reqs + 1, self.max_model_len, self.max_num_tokens),
            dtype=np.int64,
        )
519

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524
        # 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] = {}
525
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529
        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(
530
531
                self.max_num_tokens, dtype=torch.int32, device=self.device
            )
532

533
        self.uniform_decode_query_len = 1 + self.num_spec_tokens
534
535
536
537

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

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541
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543
544
545
546
        self.mm_budget = (
            MultiModalBudget(
                self.model_config,
                self.scheduler_config,
                self.mm_registry,
            )
            if self.supports_mm_inputs
            else None
        )
547

548
        self.reorder_batch_threshold: int | None = None
549

550
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553
554
        # 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()

555
        # Cached outputs.
556
        self._draft_token_ids: list[list[int]] | torch.Tensor | None = None
557
        self.transfer_event = torch.Event()
558
        self.sampled_token_ids_pinned_cpu = torch.empty(
559
            (self.max_num_reqs, 1),
560
561
            dtype=torch.int64,
            device="cpu",
562
563
            pin_memory=self.pin_memory,
        )
564

565
566
        # Pre-allocated tensor for copying valid sampled token counts to CPU,
        # with dedicated stream for overlapping and event for coordination.
567
        self.valid_sampled_token_count_event: torch.Event | None = None
568
569
        self.valid_sampled_token_count_copy_stream: torch.cuda.Stream | None = None
        if self.use_async_scheduling and self.num_spec_tokens:
570
            self.valid_sampled_token_count_event = torch.Event()
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573
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577
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            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,
        )

579
580
        # Ephemeral state transferred between execute_model() and sample_tokens().
        self.execute_model_state: ExecuteModelState | None = None
581
        self.kv_connector_output: KVConnectorOutput | None = None
582

583
584
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586
    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]

597
    def _make_buffer(
598
        self, *size: int | torch.SymInt, dtype: torch.dtype, numpy: bool = True
599
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606
    ) -> CpuGpuBuffer:
        return CpuGpuBuffer(
            *size,
            dtype=dtype,
            device=self.device,
            pin_memory=self.pin_memory,
            with_numpy=numpy,
        )
607

608
609
610
    def _init_model_kwargs(self, num_tokens: int):
        model_kwargs = dict[str, Any]()

611
        if not self.is_pooling_model:
612
613
            return model_kwargs

614
615
        num_reqs = self.input_batch.num_reqs
        pooling_params = self.input_batch.get_pooling_params()
616
617
618

        token_type_id_requests = dict[int, Any]()
        for i, param in enumerate(pooling_params):
619
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621
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623
            if (
                param.extra_kwargs is not None
                and (token_types := param.extra_kwargs.get("compressed_token_type_ids"))
                is not None
            ):
624
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628
                token_type_id_requests[i] = token_types

        if len(token_type_id_requests) == 0:
            return model_kwargs

629
        seq_lens = self.seq_lens.gpu[:num_reqs]
630
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635
636
637
        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(
638
639
            device=self.device
        )
640
641
        return model_kwargs

642
    def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
643
644
        """
        Update the order of requests in the batch based on the attention
645
        backend's needs. For example, some attention backends (namely MLA) may
646
647
648
649
650
651
        want to separate requests based on if the attention computation will be
        compute-bound or memory-bound.

        Args:
            scheduler_output: The scheduler output.
        """
652
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658
659
        # 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

660
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662
663
        if self.reorder_batch_threshold is not None:
            reorder_batch_to_split_decodes_and_prefills(
                self.input_batch,
                scheduler_output,
664
665
                decode_threshold=self.reorder_batch_threshold,
            )
666

667
668
    # Note: used for model runner override.
    def _init_device_properties(self) -> None:
669
        """Initialize attributes from torch.cuda.get_device_properties"""
670
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672
673
674
675
676
        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()

677
    def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
678
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680
681
682
683
        """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.

684
685
        The SamplingMetadata is updated and copied to the GPU if there is a
        new/resumed/paused/finished request in the batch.
686
687
        """
        # Remove finished requests from the cached states.
688
689
        for req_id in scheduler_output.finished_req_ids:
            self.requests.pop(req_id, None)
690
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692
693
694
695
696
        # 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:
697
            self.input_batch.remove_request(req_id)
698
699

        # Free the cached encoder outputs.
700
701
        for mm_hash in scheduler_output.free_encoder_mm_hashes:
            self.encoder_cache.pop(mm_hash, None)
702

703
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705
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708
709
710
711
712
713
714
715
        # 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:
716
            self.input_batch.remove_request(req_id)
717

718
        reqs_to_add: list[CachedRequestState] = []
719
        # Add new requests to the cached states.
720
721
722
        for new_req_data in scheduler_output.scheduled_new_reqs:
            req_id = new_req_data.req_id
            sampling_params = new_req_data.sampling_params
723
            pooling_params = new_req_data.pooling_params
724

725
726
727
728
            if (
                sampling_params
                and sampling_params.sampling_type == SamplingType.RANDOM_SEED
            ):
729
730
731
732
733
                generator = torch.Generator(device=self.device)
                generator.manual_seed(sampling_params.seed)
            else:
                generator = None

734
735
            if self.is_pooling_model:
                assert pooling_params is not None
736
737
                task = pooling_params.task
                assert task is not None, "You did not set `task` in the API"
738

739
                model = cast(VllmModelForPooling, self.get_model())
740
                to_update = model.pooler.get_pooling_updates(task)
741
742
                to_update.apply(pooling_params)

743
            req_state = CachedRequestState(
744
                req_id=req_id,
745
                prompt_token_ids=new_req_data.prompt_token_ids,
746
                prompt_embeds=new_req_data.prompt_embeds,
747
                mm_features=new_req_data.mm_features,
748
                sampling_params=sampling_params,
749
                pooling_params=pooling_params,
750
                generator=generator,
751
752
                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
753
                output_token_ids=[],
754
                lora_request=new_req_data.lora_request,
755
            )
756
757
            self.requests[req_id] = req_state

758
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
759
            if self.uses_mrope:
760
                self._init_mrope_positions(req_state)
761

762
            reqs_to_add.append(req_state)
763

764
        # Update the states of the running/resumed requests.
765
        is_last_rank = get_pp_group().is_last_rank
766
        req_data = scheduler_output.scheduled_cached_reqs
767
768
769
770
771

        # 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()

772
        for i, req_id in enumerate(req_data.req_ids):
773
            req_state = self.requests[req_id]
774
775
            num_computed_tokens = req_data.num_computed_tokens[i]
            new_block_ids = req_data.new_block_ids[i]
776
            resumed_from_preemption = req_id in req_data.resumed_req_ids
777
            num_output_tokens = req_data.num_output_tokens[i]
778
            req_index = self.input_batch.req_id_to_index.get(req_id)
779

780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
            # 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)
803

804
            # Update the cached states.
805
            req_state.num_computed_tokens = num_computed_tokens
806
807
808
809
810
811
812
813

            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.
814
815
816
                num_new_tokens = (
                    num_computed_tokens + len(new_token_ids) - req_state.num_tokens
                )
817
818
819
820
                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:
821
                    req_state.output_token_ids.extend(new_token_ids[-num_new_tokens:])
822
823
824
825
826
            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:
827
828
829
830
                    end_idx = (
                        self.input_batch.num_prompt_tokens[req_index]
                        + num_output_tokens
                    )
831
832
                    self.input_batch.num_tokens[req_index] = end_idx
                    self.input_batch.num_tokens_no_spec[req_index] = end_idx
833

834
            # Update the block IDs.
835
            if not resumed_from_preemption:
836
837
                if new_block_ids is not None:
                    # Append the new blocks to the existing block IDs.
838
                    for block_ids, new_ids in zip(req_state.block_ids, new_block_ids):
839
                        block_ids.extend(new_ids)
840
            else:
841
                assert req_index is None
842
                assert new_block_ids is not None
843
844
                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
845
                req_state.block_ids = new_block_ids
846
847
848
849
850

            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.
851
852
853
854
855
856
857

                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:]

858
                reqs_to_add.append(req_state)
859
860
861
                continue

            # Update the persistent batch.
862
            self.input_batch.num_computed_tokens_cpu[req_index] = num_computed_tokens
863
            if new_block_ids is not None:
864
                self.input_batch.block_table.append_row(new_block_ids, req_index)
865
866
867
868
869
870
871

            # 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)
872
                self.input_batch.token_ids_cpu[
873
874
875
                    req_index, start_token_index:end_token_index
                ] = new_token_ids
                self.input_batch.num_tokens_no_spec[req_index] = end_token_index
876
                self.input_batch.num_tokens[req_index] = end_token_index
877

878
            # Add spec_token_ids to token_ids_cpu.
879
            spec_token_ids = scheduler_output.scheduled_spec_decode_tokens.get(
880
                req_id, []
881
            )
882
883
884
885
886
            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:
887
888
889
                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[
890
891
                    req_index, start_index:end_token_index
                ] = spec_token_ids
892
893
                # NOTE(woosuk): `num_tokens` here may include spec tokens.
                self.input_batch.num_tokens[req_index] += num_spec_tokens
894
895
896
897
898
899

            # 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.
900
901
            self.input_batch.spec_token_ids[req_index].clear()
            self.input_batch.spec_token_ids[req_index].extend(spec_token_ids)
902

903
904
905
906
907
908
909
910
911
            # 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)
912
913
        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
914
915
        for request in reqs_to_add:
            self.input_batch.add_request(request)
916

917
918
919
920
921
922
        # 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()
923

924
    def _update_states_after_model_execute(
925
926
        self, output_token_ids: torch.Tensor
    ) -> None:
927
928
929
930
931
932
933
934
935
936
937
938
        """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.
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
        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()
        )
959
960
961
        for i, num_tokens in enumerate(num_accepted_tokens):
            self.input_batch.num_accepted_tokens_cpu[i] = num_tokens

962
    def _init_mrope_positions(self, req_state: CachedRequestState):
963
964
        model = self.get_model()
        assert supports_mrope(model), "M-RoPE support is not implemented."
965
966
967
968
        assert req_state.prompt_token_ids is not None, (
            "M-RoPE requires prompt_token_ids to be available."
        )
        mrope_model = cast(SupportsMRoPE, model)
969
970

        req_state.mrope_positions, req_state.mrope_position_delta = (
971
            mrope_model.get_mrope_input_positions(
972
                req_state.prompt_token_ids,
973
                req_state.mm_features,
974
            )
975
        )
976

977
    def _extract_mm_kwargs(
978
        self,
979
980
        scheduler_output: "SchedulerOutput",
    ) -> BatchedTensorInputs:
981
        if not scheduler_output or not self.is_multimodal_raw_input_only_model:
982
            return {}
983

984
985
        mm_kwargs = list[MultiModalKwargsItem]()
        for req in scheduler_output.scheduled_new_reqs:
986
987
988
            for feature in req.mm_features:
                if feature.data is not None:
                    mm_kwargs.append(feature.data)
989

990
        # Input all modalities at once
991
        model = cast(SupportsMultiModal, self.model)
992
993
        mm_kwargs_combined: BatchedTensorInputs = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
994
995
996
997
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
998
            multimodal_cpu_fields=model.multimodal_cpu_fields,
999
1000
        ):
            mm_kwargs_combined.update(mm_kwargs_group)
1001

1002
        return mm_kwargs_combined
1003

1004
    def _dummy_mm_kwargs(self, num_seqs: int) -> BatchedTensorInputs:
1005
        if not self.is_multimodal_raw_input_only_model:
1006
            return {}
1007

1008
1009
1010
1011
1012
        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)
1013

1014
1015
1016
    def _get_cumsum_and_arange(
        self,
        num_tokens: np.ndarray,
1017
        cumsum_dtype: np.dtype | None = None,
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
    ) -> 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

1034
    def _prepare_input_ids(
1035
1036
1037
1038
        self,
        scheduler_output: "SchedulerOutput",
        total_num_scheduled_tokens: int,
        cu_num_tokens: np.ndarray,
1039
    ) -> None:
1040
        """Prepare the input IDs for the current batch.
1041

1042
1043
1044
1045
1046
1047
1048
        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)
1049
1050
1051
            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)
1052
1053
1054
1055
1056
1057
1058
            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
1059
1060
1061
1062
        sample_flattened_indices: list[int] = []
        spec_flattened_indices: list[int] = []
        prev_common_req_indices: list[int] = []
        prev_draft_token_indices: list[int] = []
1063
1064
        indices_match = True
        max_flattened_index = -1
1065
1066
1067
        total_num_spec_tokens = 0
        scheduled_spec_tokens = scheduler_output.scheduled_spec_decode_tokens

1068
1069
1070
1071
1072
        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.
1073
1074
                draft_len = len(scheduled_spec_tokens.get(req_id, ()))
                total_num_spec_tokens += draft_len
1075
                flattened_index = cu_num_tokens[cur_index].item() - 1
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
                # 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))
1091
                indices_match &= prev_index == flattened_index
1092
                max_flattened_index = max(max_flattened_index, flattened_index)
1093
1094
1095
        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:
1096
1097
1098
            # 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)
1099
1100
1101
            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)
1102
1103
        if num_commmon_tokens == 0:
            # No requests in common with the previous iteration
1104
            # So input_ids.cpu will have all the input ids.
1105
1106
1107
1108
1109
1110
1111
            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_(
1112
1113
1114
                self.input_batch.prev_sampled_token_ids[:num_commmon_tokens, 0],
                non_blocking=True,
            )
1115
1116
            if self.enable_prompt_embeds:
                self.is_token_ids.gpu[:num_commmon_tokens] = True
1117
            return
1118
        # Upload the index tensors asynchronously so the scatter can be non-blocking.
1119
1120
        sampled_tokens_index_tensor = torch.tensor(
            sample_flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
1121
        ).to(self.device, non_blocking=True)
1122
        prev_common_req_indices_tensor = torch.tensor(
1123
1124
            prev_common_req_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1125
1126
        self.input_ids.gpu.scatter_(
            dim=0,
1127
            index=sampled_tokens_index_tensor,
1128
            src=self.input_batch.prev_sampled_token_ids[
1129
1130
1131
                prev_common_req_indices_tensor, 0
            ],
        )
1132

1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
        # 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],
        )

1156
1157
    def _get_encoder_seq_lens(
        self,
1158
        scheduled_encoder_inputs: dict[str, list[int]],
1159
1160
        kv_cache_spec: KVCacheSpec,
        num_reqs: int,
1161
    ) -> np.ndarray | None:
1162
1163
1164
1165
1166
1167
        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)
1168
        for req_id in scheduled_encoder_inputs:
1169
1170
1171
1172
1173
            req_index = self.input_batch.req_id_to_index[req_id]
            encoder_seq_lens[req_index] = self.max_encoder_len

        return encoder_seq_lens

1174
    def _prepare_inputs(
1175
1176
1177
1178
        self,
        scheduler_output: "SchedulerOutput",
        num_scheduled_tokens: np.ndarray,
        max_num_scheduled_tokens: int,
1179
1180
    ) -> tuple[
        torch.Tensor,
1181
1182
1183
        SpecDecodeMetadata | None,
        UBatchSlices | None,
        torch.Tensor | None,
1184
    ]:
1185
1186
        """
        :return: tuple[
1187
            logits_indices, spec_decode_metadata,
1188
            ubatch_slices, num_tokens_across_dp,
1189
1190
        ]
        """
1191
1192
1193
1194
1195
1196
1197
        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.
1198
        self.input_batch.block_table.commit_block_table(num_reqs)
1199
1200
1201

        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
1202
        req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens)
1203

1204
1205
        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
        # arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
1206
        cu_num_tokens, arange = self._get_cumsum_and_arange(num_scheduled_tokens)
1207
1208

        # Get positions.
1209
        positions_np = self.positions.np[:total_num_scheduled_tokens]
1210
1211
1212
1213
1214
        np.add(
            self.input_batch.num_computed_tokens_cpu[req_indices],
            arange,
            out=positions_np,
        )
1215

1216
1217
        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1218
        if self.uses_mrope:
1219
1220
            self._calc_mrope_positions(scheduler_output)

1221
1222
1223
1224
        # 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.
1225
1226
1227
        token_indices = (
            positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]
        )
1228
        token_indices_tensor = torch.from_numpy(token_indices)
1229

1230
1231
1232
        # 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.
1233
1234
1235
1236
1237
1238
        torch.index_select(
            self.input_batch.token_ids_cpu_tensor.flatten(),
            0,
            token_indices_tensor,
            out=self.input_ids.cpu[:total_num_scheduled_tokens],
        )
1239
        if self.enable_prompt_embeds:
1240
            is_token_ids = self.input_batch.is_token_ids_tensor.flatten()
1241
1242
1243
1244
            torch.index_select(
                is_token_ids,
                0,
                token_indices_tensor,
1245
1246
                out=self.is_token_ids.cpu[:total_num_scheduled_tokens],
            )
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279

        # 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:
1280
1281
1282
                    self.inputs_embeds.cpu[
                        output_idx : output_idx + actual_num_sched
                    ].copy_(req_embeds[start_pos:actual_end])
1283
1284

                output_idx += num_sched
1285

1286
1287
        self.input_batch.block_table.compute_slot_mapping(req_indices, positions_np)
        self.input_batch.block_table.commit_slot_mapping(total_num_scheduled_tokens)
1288
1289

        # Prepare the attention metadata.
1290
        self.query_start_loc.np[0] = 0
1291
        self.query_start_loc.np[1 : num_reqs + 1] = cu_num_tokens
1292
1293
        # Note: pad query_start_loc to be non-decreasing, as kernels
        # like FlashAttention requires that
1294
        self.query_start_loc.np[num_reqs + 1 :].fill(cu_num_tokens[-1])
1295
        self.query_start_loc.copy_to_gpu()
1296
        query_start_loc = self.query_start_loc.gpu[: num_reqs + 1]
1297

1298
        num_tokens_unpadded = scheduler_output.total_num_scheduled_tokens
1299
        num_tokens_padded = self._get_num_input_tokens(num_tokens_unpadded)
1300
1301
1302
        uniform_decode = (
            max_num_scheduled_tokens == self.uniform_decode_query_len
        ) and (total_num_scheduled_tokens == num_reqs * max_num_scheduled_tokens)
1303
1304
1305
1306
1307
1308
1309

        # 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

1310
        ubatch_slices, num_tokens_across_dp = coordinate_batch_across_dp(
1311
1312
1313
1314
1315
1316
1317
            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,
1318
        )
1319

1320
        self.seq_lens.np[:num_reqs] = (
1321
1322
            self.input_batch.num_computed_tokens_cpu[:num_reqs] + num_scheduled_tokens
        )
1323
        # Fill unused with 0 for full cuda graph mode.
1324
1325
        self.seq_lens.np[num_reqs:].fill(0)
        self.seq_lens.copy_to_gpu()
1326

1327
        num_tokens = [self.requests[r].num_tokens for r in self.input_batch.req_ids]
1328
1329
1330
1331
1332
1333
1334
        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)
1335
1336
1337
        self.discard_request_indices.np[: self.num_discarded_requests] = (
            discard_request_indices
        )
1338
1339
1340

        self.discard_request_indices.copy_to_gpu(self.num_discarded_requests)

1341
        # Copy the tensors to the GPU.
1342
1343
1344
1345
1346
        self._prepare_input_ids(
            scheduler_output,
            total_num_scheduled_tokens,
            cu_num_tokens,
        )
1347

1348
        if self.uses_mrope:
1349
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1350
1351
            self.mrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.mrope_positions.cpu[:, :total_num_scheduled_tokens],
1352
1353
                non_blocking=True,
            )
1354
1355
        else:
            # Common case (1D positions)
1356
            self.positions.copy_to_gpu(total_num_scheduled_tokens)
1357

1358
        use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
1359
1360
1361
1362
1363
1364
1365
        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
1366
            num_draft_tokens = None
1367
            spec_decode_metadata = None
1368
            num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
1369
1370
1371
1372
1373
        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)
1374
1375
1376
            # 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)
1377
1378
1379
1380
            for (
                req_id,
                draft_token_ids,
            ) in scheduler_output.scheduled_spec_decode_tokens.items():
1381
1382
                req_idx = self.input_batch.req_id_to_index[req_id]
                num_draft_tokens[req_idx] = len(draft_token_ids)
1383
1384
1385
1386
1387
1388
1389
1390
                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
                )
1391
            spec_decode_metadata = self._calc_spec_decode_metadata(
1392
1393
                num_draft_tokens, cu_num_tokens
            )
1394
            logits_indices = spec_decode_metadata.logits_indices
1395
            num_sampled_tokens = num_draft_tokens + 1
1396
            # For DECODE only cuda graph of some attention backends (e.g., GDN).
1397
            self.num_decode_draft_tokens.np[:num_reqs] = num_decode_draft_tokens
1398
1399
            self.num_decode_draft_tokens.np[num_reqs:].fill(-1)
            self.num_decode_draft_tokens.copy_to_gpu()
1400

1401
1402
1403
1404
1405
        # Hot-Swap lora model
        if self.lora_config:
            assert (
                np.sum(num_sampled_tokens)
                <= self.vllm_config.scheduler_config.max_num_batched_tokens
1406
            )
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
            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
1434
        num_logits_indices = None
1435
1436
1437
1438
1439
1440
        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
                )
1441

1442
1443
1444
1445
1446
1447
        # 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,
1448
                self.parallel_config.cp_kv_cache_interleave_size,
1449
1450
1451
            )
            self.dcp_local_seq_lens.copy_to_gpu(num_reqs)

1452
1453
1454
        attn_metadata: PerLayerAttnMetadata = {}
        if ubatch_slices is not None:
            attn_metadata = [dict() for _ in range(len(ubatch_slices))]
1455

1456
1457
        # Used in the below loop
        query_start_loc = self.query_start_loc.gpu[: num_reqs + 1]
1458
        query_start_loc_cpu = self.query_start_loc.cpu[: num_reqs + 1]
1459
        seq_lens = self.seq_lens.gpu[:num_reqs]
1460
        seq_lens_cpu = self.seq_lens.cpu[:num_reqs]
1461
1462
1463
        num_computed_tokens_cpu = self.input_batch.num_computed_tokens_cpu_tensor[
            :num_reqs
        ]
1464
1465
1466
1467
1468
1469

        dcp_local_seq_lens, dcp_local_seq_lens_cpu = None, None
        if self.dcp_world_size > 1:
            dcp_local_seq_lens = self.dcp_local_seq_lens.gpu[:num_reqs]
            dcp_local_seq_lens_cpu = self.dcp_local_seq_lens.cpu[:num_reqs]

1470
        spec_decode_common_attn_metadata = None
1471
1472
1473
1474
1475
1476
1477
1478
1479

        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()

1480
1481
        if use_spec_decode:
            self.num_accepted_tokens.np[:num_reqs] = (
1482
1483
                self.input_batch.num_accepted_tokens_cpu[:num_reqs]
            )
1484
1485
            self.num_accepted_tokens.np[num_reqs:].fill(1)
            self.num_accepted_tokens.copy_to_gpu()
1486

1487
1488
        # Prepare the attention metadata for each KV cache group and make layers
        # in the same group share the same metadata.
1489
        for kv_cache_gid, kv_cache_group in enumerate(
1490
1491
            self.kv_cache_config.kv_cache_groups
        ):
1492
            encoder_seq_lens = self._get_encoder_seq_lens(
1493
1494
1495
                scheduled_encoder_inputs or {},
                kv_cache_group.kv_cache_spec,
                num_reqs,
1496
            )
1497

1498
            if isinstance(kv_cache_group.kv_cache_spec, EncoderOnlyAttentionSpec):
1499
1500
1501
1502
1503
                # 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,
1504
1505
1506
                    device=self.device,
                )
                slot_mapping = torch.zeros(
1507
                    (total_num_scheduled_tokens,),
1508
1509
1510
                    dtype=torch.int64,
                    device=self.device,
                )
1511
            else:
1512
                blk_table = self.input_batch.block_table[kv_cache_gid]
1513
                blk_table_tensor = blk_table.get_device_tensor(num_reqs)
1514
                slot_mapping = blk_table.slot_mapping.gpu[:total_num_scheduled_tokens]
1515
1516
1517

                # Fill unused with -1. Needed for reshape_and_cache in full cuda
                # graph mode.
1518
                blk_table.slot_mapping.gpu[total_num_scheduled_tokens:].fill_(-1)
1519

1520
            common_attn_metadata = CommonAttentionMetadata(
1521
1522
1523
1524
1525
                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,
1526
1527
1528
                num_reqs=num_reqs,
                num_actual_tokens=total_num_scheduled_tokens,
                max_query_len=max_num_scheduled_tokens,
1529
                max_seq_len=max_seq_len,
1530
1531
                block_table_tensor=blk_table_tensor,
                slot_mapping=slot_mapping,
1532
                logits_indices_padded=logits_indices_padded,
1533
                num_logits_indices=num_logits_indices,
1534
                causal=True,
1535
                encoder_seq_lens=encoder_seq_lens,
1536
                dcp_local_seq_lens=dcp_local_seq_lens,
1537
                dcp_local_seq_lens_cpu=dcp_local_seq_lens_cpu,
1538
1539
            )

1540
            if self.speculative_config and spec_decode_common_attn_metadata is None:
1541
                if isinstance(self.drafter, EagleProposer):
1542
                    if self.drafter.attn_layer_names[0] in kv_cache_group.layer_names:
1543
1544
1545
                        spec_decode_common_attn_metadata = common_attn_metadata
                else:
                    spec_decode_common_attn_metadata = common_attn_metadata
1546

1547
1548
1549
1550
1551
1552
            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
                )
1553
                builder = attn_group.get_metadata_builder()
1554

1555
                extra_attn_metadata_args = {}
1556
                if use_spec_decode and isinstance(builder, GDNAttentionMetadataBuilder):
1557
                    extra_attn_metadata_args = dict(
1558
1559
1560
1561
                        num_accepted_tokens=self.num_accepted_tokens.gpu[:num_reqs],
                        num_decode_draft_tokens_cpu=self.num_decode_draft_tokens.cpu[
                            :num_reqs
                        ],
1562
1563
                    )

1564
1565
                if ubatch_slices is not None:
                    common_attn_metadata_list = split_attn_metadata(
1566
1567
                        ubatch_slices, common_attn_metadata
                    )
1568
                    for ubid, common_attn_metadata in enumerate(
1569
1570
                        common_attn_metadata_list
                    ):
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
                        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:
1582
1583
1584
1585
                            assert type(attn_metadata) is list
                            attn_metadata[ubid][layer_name] = attn_metadata_i
                else:
                    assert isinstance(attn_metadata, dict)
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
                    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,
                        )
1596
1597
                    for layer_name in attn_group.layer_names:
                        attn_metadata[layer_name] = attn_metadata_i
1598

1599
        return attn_metadata, spec_decode_common_attn_metadata
1600

1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
    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
        """
1611

1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
        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
1634

1635
1636
1637
1638
    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
        num_common_prefix_blocks: int,
1639
1640
        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
    ) -> 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.
        """
1659

1660
        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
        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]
1698
        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
1699
1700
1701
1702
1703
1704
1705
        # 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(
1706
1707
            common_prefix_len, self.input_batch.num_computed_tokens_cpu[:num_reqs].min()
        )
1708
        # common_prefix_len should be a multiple of the block size.
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
        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
        )
1720
1721
        assert isinstance(kv_cache_spec, AttentionSpec)
        use_cascade = attn_metadata_builder.use_cascade_attention(
1722
1723
1724
            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
1725
            num_kv_heads=kv_cache_spec.num_kv_heads,
1726
            use_alibi=self.use_alibi,
1727
            use_sliding_window=use_sliding_window,
1728
            use_local_attention=use_local_attention,
1729
            num_sms=self.num_sms,
1730
            dcp_world_size=self.dcp_world_size,
1731
1732
1733
        )
        return common_prefix_len if use_cascade else 0

1734
1735
    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
1736
        for index, req_id in enumerate(self.input_batch.req_ids):
1737
1738
1739
            req = self.requests[req_id]
            assert req.mrope_positions is not None

1740
1741
            num_computed_tokens = self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1742
            num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
1743
1744
                req.prompt_token_ids, req.prompt_embeds
            )
1745
1746

            if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
1747
1748
                prompt_part_len = max(0, num_prompt_tokens - num_computed_tokens)
                completion_part_len = max(0, num_scheduled_tokens - prompt_part_len)
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
            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

1762
1763
1764
                self.mrope_positions.cpu[:, dst_start:dst_end] = req.mrope_positions[
                    :, src_start:src_end
                ]
1765
1766
1767
1768
1769
1770
1771
                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

1772
                assert req.mrope_position_delta is not None
1773
                MRotaryEmbedding.get_next_input_positions_tensor(
1774
                    out=self.mrope_positions.np,
1775
1776
1777
1778
1779
                    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,
                )
1780
1781
1782

                mrope_pos_ptr += completion_part_len

1783
1784
    def _calc_spec_decode_metadata(
        self,
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
        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
1801
1802
1803
1804

        # 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(
1805
1806
            num_sampled_tokens, cumsum_dtype=np.int32
        )
1807
        # Step 2. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
1808
        logits_indices = np.repeat(
1809
1810
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens
        )
1811
        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
1812
1813
1814
1815
1816
1817
        logits_indices += arange

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

        # Compute the draft logits indices.
1818
1819
1820
        # 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(
1821
1822
            num_draft_tokens, cumsum_dtype=np.int32
        )
1823
1824
        # [0, 0, 0, 5, 5, 9]
        target_logits_indices = np.repeat(
1825
1826
            cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens
        )
1827
1828
1829
1830
1831
        # [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(
1832
1833
            self.device, non_blocking=True
        )
1834
1835
1836
        cu_num_sampled_tokens = torch.from_numpy(cu_num_sampled_tokens).to(
            self.device, non_blocking=True
        )
1837
1838
1839
        logits_indices = torch.from_numpy(logits_indices).to(
            self.device, non_blocking=True
        )
1840
        target_logits_indices = torch.from_numpy(target_logits_indices).to(
1841
1842
            self.device, non_blocking=True
        )
1843
        bonus_logits_indices = torch.from_numpy(bonus_logits_indices).to(
1844
1845
            self.device, non_blocking=True
        )
1846

1847
1848
        # Compute the draft token ids.
        # draft_token_indices:      [  1,   2,   3, 105, 106, 208]
1849
        draft_token_ids = self.input_ids.gpu[logits_indices]
1850
1851
        draft_token_ids = draft_token_ids[target_logits_indices + 1]

1852
        return SpecDecodeMetadata(
1853
1854
1855
            draft_token_ids=draft_token_ids,
            num_draft_tokens=num_draft_tokens.tolist(),
            cu_num_draft_tokens=cu_num_draft_tokens,
1856
            cu_num_sampled_tokens=cu_num_sampled_tokens,
1857
1858
1859
1860
1861
            target_logits_indices=target_logits_indices,
            bonus_logits_indices=bonus_logits_indices,
            logits_indices=logits_indices,
        )

1862
1863
1864
1865
1866
1867
1868
    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
1869
        self.kv_sharing_fast_prefill_logits_indices[:num_logits].copy_(logits_indices)
1870
1871
1872
1873
1874
        # 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_(
1875
1876
1877
1878
1879
1880
            logits_indices[-1].item()
        )
        if (
            self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
            and num_logits <= self.cudagraph_batch_sizes[-1]
        ):
1881
1882
1883
1884
1885
            # 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
1886
1887
1888
        logits_indices_padded = self.kv_sharing_fast_prefill_logits_indices[
            :num_logits_padded
        ]
1889
1890
        return logits_indices_padded

1891
1892
1893
1894
1895
1896
1897
1898
    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
1899
                inputs.
1900
1901
1902
1903
1904
1905

        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
        """
1906
1907
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
1908
            return [], []
1909
        # Batch the multi-modal inputs.
1910
        mm_kwargs = list[MultiModalKwargsItem]()
1911
1912
        # list of tuple (mm_hash, position_info)
        mm_hashes_pos = list[tuple[str, PlaceholderRange]]()
1913
1914
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
1915
1916

            for mm_input_id in encoder_input_ids:
1917
                mm_feature = req_state.mm_features[mm_input_id]
1918
1919
                if mm_feature.data is None:
                    continue
1920
1921
1922
                mm_hash = mm_feature.identifier
                mm_kwargs.append(mm_feature.data)
                mm_hashes_pos.append((mm_hash, mm_feature.mm_position))
1923

1924
1925
        return mm_kwargs, mm_hashes_pos

1926
1927
1928
    def _execute_mm_encoder(
        self, scheduler_output: "SchedulerOutput"
    ) -> list[torch.Tensor]:
1929
1930
        # Batch the multi-modal inputs using the helper method.
        mm_kwargs, mm_hashes_pos = self._batch_mm_kwargs_from_scheduler(
1931
1932
            scheduler_output
        )
1933
1934

        if not mm_kwargs:
1935
            return []
1936

1937
1938
1939
1940
1941
1942
1943
        # 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.
1944
        model = cast(SupportsMultiModal, self.model)
1945
        encoder_outputs: list[torch.Tensor] = []
1946
        for modality, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
1947
1948
1949
1950
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
1951
            multimodal_cpu_fields=model.multimodal_cpu_fields,
1952
        ):
1953
            curr_group_outputs: list[torch.Tensor] = []
1954
1955

            # EVS-related change.
1956
            # (ekhvedchenia): Temporary hack to limit peak memory usage when
1957
            # processing multimodal data. This solves the issue with scheduler
1958
1959
1960
1961
            # 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)
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
            # 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,
1978
                            multimodal_cpu_fields=model.multimodal_cpu_fields,
1979
                        )
1980
                    )
1981

1982
                    micro_batch_outputs = model.embed_multimodal(
1983
1984
                        **micro_batch_mm_inputs
                    )
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994

                    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.
1995
                curr_group_outputs = model.embed_multimodal(**mm_kwargs_group)  # type: ignore[assignment]
1996

1997
1998
            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
1999
                expected_num_items=num_items,
2000
            )
2001
            encoder_outputs.extend(curr_group_outputs)
2002

2003
2004
2005
        # 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(
2006
2007
2008
                output,
                is_embed=pos_info.is_embed,
            )
2009
2010
            logger.debug("Finish execute for mm hash %s", mm_hash)
            self.maybe_save_ec_to_connector(self.encoder_cache, mm_hash)
2011

2012
2013
        return encoder_outputs

2014
    def _gather_mm_embeddings(
2015
2016
        self,
        scheduler_output: "SchedulerOutput",
2017
        shift_computed_tokens: int = 0,
2018
2019
2020
2021
2022
2023
2024
2025
    ) -> 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
2026
        should_sync_mrope_positions = False
2027

2028
        for req_id in self.input_batch.req_ids:
2029
2030
            mm_embeds_req: list[torch.Tensor] = []

2031
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
2032
            req_state = self.requests[req_id]
2033
            num_computed_tokens = req_state.num_computed_tokens + shift_computed_tokens
2034

2035
2036
            for mm_feature in req_state.mm_features:
                pos_info = mm_feature.mm_position
2037
2038
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054

                # 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,
2055
2056
                    num_encoder_tokens,
                )
2057
                assert start_idx < end_idx
2058

2059
                mm_hash = mm_feature.identifier
2060
                encoder_output = self.encoder_cache.get(mm_hash, None)
2061
                assert encoder_output is not None, f"Encoder cache miss for {mm_hash}."
2062
2063
2064
2065

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

2066
                req_start_pos = req_start_idx + start_pos - num_computed_tokens
2067
2068
2069
                is_mm_embed[req_start_pos + start_idx : req_start_pos + end_idx] = (
                    True if is_embed is None else is_embed
                )
2070

2071
2072
2073
2074
                mm_embeds_item = gather_mm_placeholders(
                    encoder_output[start_idx:end_idx],
                    is_embed=is_embed,
                )
2075
2076
2077
                mm_embeds_req.append(mm_embeds_item)

            if self.is_multimodal_pruning_enabled and self.uses_mrope:
2078
                assert req_state.mrope_positions is not None
2079
2080
2081
2082
2083
2084
2085
                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,
2086
2087
                    )
                )
2088
2089
2090
2091
                req_state.mrope_positions.copy_(new_mrope_positions)
                req_state.mrope_position_delta = new_delta

            mm_embeds.extend(mm_embeds_req)
2092
2093
2094
            req_start_idx += num_scheduled_tokens

        is_mm_embed = self.is_mm_embed.copy_to_gpu(total_num_scheduled_tokens)
2095
2096
2097

        if should_sync_mrope_positions:
            self._calc_mrope_positions(scheduler_output)
2098
            self.mrope_positions.copy_to_gpu(total_num_scheduled_tokens)
2099

2100
        return mm_embeds, is_mm_embed
2101

2102
    def get_model(self) -> nn.Module:
2103
        # get raw model out of the cudagraph wrapper.
2104
        if isinstance(self.model, (CUDAGraphWrapper, UBatchWrapper)):
2105
            return self.model.unwrap()
2106
2107
        return self.model

2108
2109
2110
2111
2112
2113
2114
2115
2116
2117
2118
2119
2120
2121
2122
    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

2123
2124
2125
2126
2127
    def get_supported_pooling_tasks(self) -> list[PoolingTask]:
        model = self.get_model()
        if not is_pooling_model(model):
            return []

2128
2129
        supported_tasks = list(model.pooler.get_supported_tasks())

2130
        if self.scheduler_config.enable_chunked_prefill:
2131
2132
2133
2134
            if "token_embed" in supported_tasks:
                supported_tasks.remove("token_embed")
            if "token_classify" in supported_tasks:
                supported_tasks.remove("token_classify")
2135

2136
2137
            logger.debug_once(
                "Chunked prefill is not supported with "
2138
2139
                "token_embed and token_classify tasks "
                "which using ALL pooling. "
2140
2141
2142
                "Please turn off chunked prefill by "
                "`--no-enable-chunked-prefill` before using it."
            )
2143
2144
2145
2146
2147

        if "score" in supported_tasks:
            num_labels = getattr(self.model_config.hf_config, "num_labels", 0)
            if num_labels != 1:
                supported_tasks.remove("score")
2148
                logger.debug_once("Score API is only enabled for num_labels == 1.")
2149
2150

        return supported_tasks
2151

2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
    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)

2162
    def sync_and_slice_intermediate_tensors(
2163
2164
        self,
        num_tokens: int,
2165
        intermediate_tensors: IntermediateTensors | None,
2166
2167
        sync_self: bool,
    ) -> IntermediateTensors:
2168
2169
2170
        assert self.intermediate_tensors is not None

        tp = self.vllm_config.parallel_config.tensor_parallel_size
2171
        is_rs = is_residual_scattered_for_sp(self.vllm_config, num_tokens)
2172
2173
2174
2175
2176
2177

        # 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():
2178
                is_scattered = k == "residual" and is_rs
2179
                copy_len = num_tokens // tp if is_scattered else num_tokens
2180
                self.intermediate_tensors[k][:copy_len].copy_(
2181
2182
2183
2184
2185
2186
2187
2188
2189
2190
2191
2192
2193
                    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:
2194
2195
2196
2197
2198
2199
2200
        """
        Step for the EPLB (Expert Parallelism Load Balancing) state.
        """
        if not self.parallel_config.enable_eplb:
            return

        assert self.eplb_state is not None
2201
2202
        model = self.get_model()
        assert is_mixture_of_experts(model)
2203
2204
2205
        self.eplb_state.step(
            is_dummy,
            is_profile,
2206
            log_stats=self.parallel_config.eplb_config.log_balancedness,
2207
2208
        )

2209
2210
2211
2212
    # 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)
2213
2214
    @staticmethod
    def pad_out_ubatch_slice(ubatch_slices: UBatchSlices, num_total_tokens: int):
2215
2216
2217
2218
2219
2220
        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
        )
2221

2222
2223
2224
2225
2226
2227
    def _pool(
        self,
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
        num_scheduled_tokens_np: np.ndarray,
    ) -> ModelRunnerOutput:
2228
2229
2230
        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"
        )
2231

2232
        hidden_states = hidden_states[:num_scheduled_tokens]
2233
        pooling_metadata = self.input_batch.get_pooling_metadata()
2234
2235
2236
2237
        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]
2238

2239
2240
2241
2242
2243
2244
2245
2246
2247
2248
        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()
2249

2250
        pooler_output: list[torch.Tensor | None] = []
2251
        for raw_output, seq_len, prompt_len in zip(
2252
2253
            raw_pooler_output, seq_lens_cpu, pooling_metadata.prompt_lens
        ):
2254
            output = raw_output if seq_len == prompt_len else None
2255
            pooler_output.append(output)
2256
2257
2258
2259
2260
2261
2262
2263
2264
2265

        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,
        )

2266
    def _get_num_input_tokens(self, num_scheduled_tokens: int) -> int:
2267
2268
2269
2270
2271
2272
        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]
        ):
2273
2274
2275
2276
2277
2278
2279
2280
            # 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
2281
2282
2283
2284
        if (
            self.compilation_config.pass_config.enable_sequence_parallelism
            and tp_size > 1
        ):
2285
2286
2287
            return round_up(num_scheduled_tokens, tp_size)
        return num_scheduled_tokens

2288
    def _preprocess(
2289
2290
        self,
        scheduler_output: "SchedulerOutput",
2291
        num_input_tokens: int,  # Padded
2292
        intermediate_tensors: IntermediateTensors | None = None,
2293
    ) -> tuple[
2294
2295
        torch.Tensor | None,
        torch.Tensor | None,
2296
        torch.Tensor,
2297
        IntermediateTensors | None,
2298
        dict[str, Any],
2299
        ECConnectorOutput | None,
2300
    ]:
2301
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
2302
        is_first_rank = get_pp_group().is_first_rank
2303

2304
2305
        # _prepare_inputs may reorder the batch, so we must gather multi
        # modal outputs after that to ensure the correct order
2306
2307
        ec_connector_output = None

2308
2309
        if (
            self.supports_mm_inputs
2310
            and is_first_rank
2311
2312
            and not self.model_config.is_encoder_decoder
        ):
2313
            # Run the multimodal encoder if any.
2314
2315
2316
2317
2318
2319
            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)
2320

2321
2322
2323
            # 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.
2324
            inputs_embeds_scheduled = self.model.embed_input_ids(
2325
2326
2327
                self.input_ids.gpu[:num_scheduled_tokens],
                multimodal_embeddings=mm_embeds,
                is_multimodal=is_mm_embed,
2328
            )
2329

2330
            # TODO(woosuk): Avoid the copy. Optimize.
2331
            self.inputs_embeds.gpu[:num_scheduled_tokens].copy_(inputs_embeds_scheduled)
2332

2333
            input_ids = None
2334
            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
2335
2336
2337
2338
            model_kwargs = {
                **self._init_model_kwargs(num_scheduled_tokens),
                **self._extract_mm_kwargs(scheduler_output),
            }
2339
        elif self.enable_prompt_embeds and is_first_rank:
2340
2341
2342
2343
2344
2345
2346
2347
2348
2349
2350
2351
            # 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).
2352
2353
2354
            token_ids_idx = (
                self.is_token_ids.gpu[:num_scheduled_tokens]
                .nonzero(as_tuple=False)
2355
                .squeeze(1)
2356
            )
2357
2358
2359
            # Some tokens ids may need to become embeds
            if token_ids_idx.numel() > 0:
                token_ids = self.input_ids.gpu[token_ids_idx]
2360
                tokens_to_embeds = self.model.embed_input_ids(input_ids=token_ids)
2361
2362
2363
2364
2365
                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
2366
        else:
2367
2368
2369
2370
            # 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.
2371
            input_ids = self.input_ids.gpu[:num_input_tokens]
2372
            inputs_embeds = None
2373
            model_kwargs = self._init_model_kwargs(num_input_tokens)
2374
        if self.uses_mrope:
2375
            positions = self.mrope_positions.gpu[:, :num_input_tokens]
2376
        else:
2377
            positions = self.positions.gpu[:num_input_tokens]
2378

2379
        if is_first_rank:
2380
2381
            intermediate_tensors = None
        else:
2382
            assert intermediate_tensors is not None
2383
            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
2384
2385
                num_input_tokens, intermediate_tensors, True
            )
2386

2387
2388
2389
2390
        if (
            self.model_config.is_encoder_decoder
            and scheduler_output.scheduled_encoder_inputs
        ):
2391
2392
2393
2394
2395
2396
2397
            # Run the encoder, just like we do with other multimodal inputs.
            # For an encoder-decoder model, our processing here is a bit
            # simpler, because the outputs are just passed to the decoder.
            # We are not doing any prompt replacement. We also will only
            # ever have a single encoder input.
            encoder_outputs = self._execute_mm_encoder(scheduler_output)
            model_kwargs.update({"encoder_outputs": encoder_outputs})
2398

2399
2400
2401
2402
2403
2404
        return (
            input_ids,
            inputs_embeds,
            positions,
            intermediate_tensors,
            model_kwargs,
2405
            ec_connector_output,
2406
        )
2407

2408
    def _sample(
2409
        self,
2410
2411
        logits: torch.Tensor | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
2412
    ) -> SamplerOutput:
2413
        # Sample the next token and get logprobs if needed.
2414
        sampling_metadata = self.input_batch.sampling_metadata
2415
        if spec_decode_metadata is None:
2416
2417
2418
            # 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()
2419
            return self.sampler(
2420
2421
2422
                logits=logits,
                sampling_metadata=sampling_metadata,
            )
2423

2424
        sampler_output = self.rejection_sampler(
2425
2426
            spec_decode_metadata,
            None,  # draft_probs
2427
            logits,
2428
2429
            sampling_metadata,
        )
2430
        self._update_states_after_model_execute(sampler_output.sampled_token_ids)
2431
2432
2433
        return sampler_output

    def _bookkeeping_sync(
2434
2435
2436
        self,
        scheduler_output: "SchedulerOutput",
        sampler_output: SamplerOutput,
2437
        logits: torch.Tensor | None,
2438
2439
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
2440
        spec_decode_metadata: SpecDecodeMetadata | None,
2441
    ) -> tuple[
2442
        dict[str, int],
2443
        LogprobsLists | None,
2444
        list[list[int]],
2445
        dict[str, LogprobsTensors | None],
2446
2447
2448
        list[str],
        dict[str, int],
        list[int],
2449
    ]:
2450
2451
2452
2453
        num_nans_in_logits = {}
        if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
            num_nans_in_logits = self._get_nans_in_logits(logits)

2454
2455
2456
        discard_sampled_tokens_req_indices = self.discard_request_indices.np[
            : self.num_discarded_requests
        ]
2457
2458
2459
2460
        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)
2461

2462
2463
2464
        # 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()
2465
        req_id_to_index_output_copy = self.input_batch.req_id_to_index.copy()
2466
2467

        num_sampled_tokens = sampler_output.sampled_token_ids.shape[0]
2468
        sampled_token_ids = sampler_output.sampled_token_ids
2469
        invalid_req_indices = []
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
2480
2481
2482
2483
        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:
2484
                valid_sampled_token_ids[int(i)].clear()
2485
        else:
2486
            valid_sampled_token_ids = []
2487
            invalid_req_indices = discard_sampled_tokens_req_indices.tolist()
2488
2489
2490
2491
2492
            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.
2493
2494
2495
2496
            # 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
2497
2498
2499
2500
2501
            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
            }
2502

2503
2504
2505
2506
2507
        # 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.
2508
        req_ids = self.input_batch.req_ids
2509
2510
2511
2512
        logprobs_tensors = sampler_output.logprobs_tensors
        cu_num_accepted_tokens = (
            [0] if spec_decode_metadata and logprobs_tensors else None
        )
2513
2514
        for req_idx in range(num_sampled_tokens):
            if self.use_async_scheduling:
2515
                sampled_ids = [-1] if req_idx not in invalid_req_indices_set else None
2516
2517
            else:
                sampled_ids = valid_sampled_token_ids[req_idx]
2518

2519
            num_sampled_ids: int = len(sampled_ids) if sampled_ids else 0
2520
2521
2522
2523
2524
2525

            if cu_num_accepted_tokens is not None:
                cu_num_accepted_tokens.append(
                    cu_num_accepted_tokens[-1] + num_sampled_ids
                )

2526
            if not sampled_ids:
2527
2528
2529
                continue

            start_idx = self.input_batch.num_tokens_no_spec[req_idx]
2530
            end_idx = start_idx + num_sampled_ids
2531
2532
2533
2534
            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}"
2535
            )
2536

2537
2538
            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
2539
2540
            self.input_batch.num_tokens_no_spec[req_idx] = end_idx
            self.input_batch.num_tokens[req_idx] = end_idx
2541

2542
            req_id = req_ids[req_idx]
2543
2544
2545
            req_state = self.requests[req_id]
            req_state.output_token_ids.extend(sampled_ids)

2546
2547
        logprobs_lists = (
            logprobs_tensors.tolists(cu_num_accepted_tokens)
2548
            if not self.use_async_scheduling and logprobs_tensors is not None
2549
2550
2551
2552
2553
2554
2555
2556
2557
            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,
        )

2558
2559
2560
2561
2562
2563
2564
2565
2566
2567
        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,
        )

2568
2569
2570
2571
2572
2573
2574
2575
2576
2577
2578
2579
2580
2581
2582
    @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()

2583
2584
    def _model_forward(
        self,
2585
2586
2587
2588
        input_ids: torch.Tensor | None = None,
        positions: torch.Tensor | None = None,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
2589
2590
2591
2592
2593
        **model_kwargs: dict[str, Any],
    ) -> Any:
        """Helper method to call the model forward pass.

        This method can be overridden by subclasses for model execution.
2594
        Motivation: We can inspect only this method versus
2595
2596
2597
2598
2599
2600
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2614
        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,
        )

2615
2616
2617
2618
    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
2619
        intermediate_tensors: IntermediateTensors | None = None,
2620
2621
2622
2623
2624
2625
    ) -> 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."
            )
2626
2627
2628
2629
2630
2631
2632
2633
2634
2635
2636
2637
2638
2639
2640

        # 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)

2641
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
2642
        with record_function_or_nullcontext("gpu_model_runner: preprocess"):
2643
2644
2645
2646
            with self.synchronize_input_prep():
                # Update persistent batch states.
                self._update_states(scheduler_output)

2647
2648
2649
2650
2651
2652
2653
2654
                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)

2655
                if not num_scheduled_tokens:
2656
2657
2658
2659
2660
2661
2662
2663
2664
2665
2666
2667
                    if (
                        self.parallel_config.distributed_executor_backend
                        == "external_launcher"
                        and self.parallel_config.data_parallel_size > 1
                    ):
                        # this is a corner case when both external launcher
                        # and DP are enabled, num_scheduled_tokens could be
                        # 0, and has_unfinished_requests in the outer loop
                        # returns True. before returning early here we call
                        # dummy run to ensure coordinate_batch_across_dp
                        # is called into to avoid out of sync issues.
                        self._dummy_run(1)
2668
2669
2670
2671
                    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(
2672
2673
                        scheduler_output, self.vllm_config
                    )
2674
2675
2676
2677
                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 "
2678
2679
                        "it when the requests need prompt logprobs"
                    )
2680

2681
2682
2683
2684
2685
2686
                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())

2687
2688
2689
2690
                (
                    logits_indices,
                    spec_decode_metadata,
                    ubatch_slices,
2691
                    num_tokens_across_dp,
2692
2693
2694
2695
2696
2697
2698
2699
2700
2701
2702
2703
2704
2705
2706
2707
2708
2709
2710
2711
2712
2713
2714
2715
2716
2717
2718
2719
2720
2721
2722
                ) = 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,
                    )
                )
2723

2724
2725
2726
2727
2728
2729
2730
2731
2732
2733
2734
                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
                    )
2735

2736
2737
2738
2739
2740
2741
2742
2743
2744
2745
                (
                    input_ids,
                    inputs_embeds,
                    positions,
                    intermediate_tensors,
                    model_kwargs,
                    ec_connector_output,
                ) = self._preprocess(
                    scheduler_output, num_input_tokens, intermediate_tensors
                )
2746

2747
2748
2749
            uniform_decode = (
                max_num_scheduled_tokens == self.uniform_decode_query_len
            ) and (num_scheduled_tokens == num_reqs * max_num_scheduled_tokens)
2750
            batch_desc = BatchDescriptor(
2751
2752
2753
                num_tokens=num_input_tokens,
                uniform_decode=uniform_decode,
                has_lora=len(self.input_batch.lora_id_to_lora_request) > 0,
2754
2755
            )
            cudagraph_runtime_mode, batch_descriptor = (
2756
                self.cudagraph_dispatcher.dispatch(
2757
                    batch_desc,
2758
2759
                    use_cascade_attn=cascade_attn_prefix_lens is not None,
                )
2760
            )
2761

2762
        # Set cudagraph mode to none if calc_kv_scales is true.
2763
2764
2765
2766
2767
2768
        # 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
2769

2770
2771
        # Run the model.
        # Use persistent buffers for CUDA graphs.
2772
2773
        with (
            set_forward_context(
2774
2775
2776
2777
2778
2779
                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,
2780
                ubatch_slices=ubatch_slices,
2781
            ),
2782
            record_function_or_nullcontext("gpu_model_runner: forward"),
2783
2784
            self.maybe_get_kv_connector_output(scheduler_output) as kv_connector_output,
        ):
2785
            model_output = self._model_forward(
2786
2787
2788
2789
2790
2791
2792
                input_ids=input_ids,
                positions=positions,
                intermediate_tensors=intermediate_tensors,
                inputs_embeds=inputs_embeds,
                **model_kwargs,
            )

2793
        with record_function_or_nullcontext("gpu_model_runner: postprocess"):
2794
            if self.use_aux_hidden_state_outputs:
2795
                # True when EAGLE 3 is used.
2796
2797
                hidden_states, aux_hidden_states = model_output
            else:
2798
                # Common case.
2799
2800
2801
                hidden_states = model_output
                aux_hidden_states = None

2802
2803
2804
2805
2806
            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)
2807
                    hidden_states.kv_connector_output = kv_connector_output
2808
                    self.kv_connector_output = kv_connector_output
2809
                    return hidden_states
2810

2811
                if self.is_pooling_model:
2812
                    # Return the pooling output.
2813
2814
2815
                    output = self._pool(
                        hidden_states, num_scheduled_tokens, num_scheduled_tokens_np
                    )
2816
2817
                    output.kv_connector_output = kv_connector_output
                    return output
2818
2819

                sample_hidden_states = hidden_states[logits_indices]
2820
                logits = self.model.compute_logits(sample_hidden_states)
2821
2822
2823
2824
            else:
                # Rare case.
                assert not self.is_pooling_model

2825
                sample_hidden_states = hidden_states[logits_indices]
2826
                if not get_pp_group().is_last_rank:
2827
                    all_gather_tensors = {
2828
2829
2830
                        "residual": not is_residual_scattered_for_sp(
                            self.vllm_config, num_input_tokens
                        )
2831
                    }
2832
                    get_pp_group().send_tensor_dict(
2833
2834
                        hidden_states.tensors,
                        all_gather_group=get_tp_group(),
2835
2836
                        all_gather_tensors=all_gather_tensors,
                    )
2837
2838
                    logits = None
                else:
2839
                    logits = self.model.compute_logits(sample_hidden_states)
2840

2841
                model_output_broadcast_data: dict[str, Any] = {}
2842
2843
2844
                if logits is not None:
                    model_output_broadcast_data["logits"] = logits.contiguous()

2845
                broadcasted = get_pp_group().broadcast_tensor_dict(
2846
2847
                    model_output_broadcast_data, src=len(get_pp_group().ranks) - 1
                )
2848
2849
                assert broadcasted is not None
                logits = broadcasted["logits"]
2850

2851
2852
2853
2854
2855
2856
2857
2858
        self.execute_model_state = ExecuteModelState(
            scheduler_output,
            logits,
            spec_decode_metadata,
            spec_decode_common_attn_metadata,
            hidden_states,
            sample_hidden_states,
            aux_hidden_states,
2859
            ec_connector_output,
2860
        )
2861
        self.kv_connector_output = kv_connector_output
2862
2863
2864
2865
2866
2867
        return None

    @torch.inference_mode
    def sample_tokens(
        self, grammar_output: "GrammarOutput | None"
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput | IntermediateTensors:
2868
2869
2870
        kv_connector_output = self.kv_connector_output
        self.kv_connector_output = None

2871
2872
        if self.execute_model_state is None:
            # Nothing to do (PP non-final rank case), output isn't used.
2873
            if not kv_connector_output:
2874
                return None  # type: ignore[return-value]
2875
2876
2877
2878
2879
2880
2881
2882
2883

            # In case of PP with kv transfer, we need to pass through the
            # kv_connector_output
            if kv_connector_output.is_empty():
                return EMPTY_MODEL_RUNNER_OUTPUT

            output = copy(EMPTY_MODEL_RUNNER_OUTPUT)
            output.kv_connector_output = kv_connector_output
            return output
2884
2885
2886
2887
2888
2889
2890
2891
2892
2893

        # Unpack ephemeral state.
        (
            scheduler_output,
            logits,
            spec_decode_metadata,
            spec_decode_common_attn_metadata,
            hidden_states,
            sample_hidden_states,
            aux_hidden_states,
2894
            ec_connector_output,
2895
2896
2897
2898
2899
2900
2901
2902
2903
        ) = 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
            )
2904

2905
        with record_function_or_nullcontext("gpu_model_runner: sample"):
2906
2907
            sampler_output = self._sample(logits, spec_decode_metadata)

2908
2909
        self.input_batch.prev_sampled_token_ids = None

2910
        def propose_draft_token_ids(sampled_token_ids):
2911
            assert spec_decode_common_attn_metadata is not None
2912
            with record_function_or_nullcontext("gpu_model_runner: draft"):
2913
2914
2915
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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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        spec_config = self.speculative_config
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        use_padded_batch_for_eagle = (
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            spec_config is not None
            and spec_config.use_eagle()
            and not spec_config.disable_padded_drafter_batch
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        )
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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 (
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            spec_config is not None
            and spec_config.draft_model_config is not None
            and spec_config.draft_model_config.max_model_len is not None
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        ):
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            effective_drafter_max_model_len = (
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                spec_config.draft_model_config.max_model_len
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            )
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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:
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            assert self.speculative_config is not None
            assert isinstance(self.drafter, EagleProposer)
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            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:
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                assert spec_decode_common_attn_metadata is not None
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                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",
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        sampled_token_ids: torch.Tensor | list[list[int]],
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        sampling_metadata: SamplingMetadata,
        hidden_states: torch.Tensor,
        sample_hidden_states: torch.Tensor,
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        aux_hidden_states: list[torch.Tensor] | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
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        common_attn_metadata: CommonAttentionMetadata,
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    ) -> list[list[int]] | torch.Tensor:
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        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
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        spec_config = self.speculative_config
        assert spec_config is not None
        if spec_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 spec_config.method == "suffix":
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            assert isinstance(sampled_token_ids, list)
            assert isinstance(self.drafter, SuffixDecodingProposer)
            draft_token_ids = self.drafter.propose(self.input_batch, sampled_token_ids)
3108
        elif spec_config.method == "medusa":
3109
            assert isinstance(sampled_token_ids, list)
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            assert isinstance(self.drafter, MedusaProposer)
3111

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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
                ):
3124
                    indices.append(offset + len(tokens) - 1)
3125
                    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]

3129
            draft_token_ids = self.drafter.propose(
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                target_hidden_states=hidden_states,
                sampling_metadata=sampling_metadata,
            )
3133
        elif spec_config.use_eagle():
3134
            assert isinstance(self.drafter, EagleProposer)
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3136
            if spec_config.disable_padded_drafter_batch:
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                # 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"
3142
                    "padded-batch is disabled."
3143
                )
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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"
3157
                    "padded-batch is enabled."
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3159
                )
                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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                    )
3168
                )
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                self._copy_valid_sampled_token_count(
                    next_token_ids, valid_sampled_tokens_count
                )
Jiayi Yao's avatar
Jiayi Yao committed
3172

3173
            if spec_decode_metadata is None:
3174
                token_indices_to_sample = None
3175
                # input_ids can be None for multimodal models.
3176
                target_token_ids = self.input_ids.gpu[:num_scheduled_tokens]
3177
                target_positions = self._get_positions(num_scheduled_tokens)
3178
                if self.use_aux_hidden_state_outputs:
Wentao Ye's avatar
Wentao Ye committed
3179
                    assert aux_hidden_states is not None
3180
                    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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3184
                else:
                    target_hidden_states = hidden_states[:num_scheduled_tokens]
3185
            else:
3186
                if spec_config.disable_padded_drafter_batch:
3187
                    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,
                    )
3193
                else:
3194
                    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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3200
                            valid_sampled_tokens_count,
                        )
                    )
3201

3202
                target_token_ids = self.input_ids.gpu[token_indices]
3203
                target_positions = self._get_positions(token_indices)
3204
                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
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                    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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3212
            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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3220
            draft_token_ids = self.drafter.propose(
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3224
                target_token_ids=target_token_ids,
                target_positions=target_positions,
                target_hidden_states=target_hidden_states,
                next_token_ids=next_token_ids,
3225
                last_token_indices=token_indices_to_sample,
3226
                sampling_metadata=sampling_metadata,
3227
                common_attn_metadata=common_attn_metadata,
3228
                mm_embed_inputs=mm_embed_inputs,
3229
            )
3230

3231
        return draft_token_ids
3232

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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():
3236
3237
            assert config_name in allowed_config_names, (
                f"Config `{config_name}` not supported. "
3238
                f"Allowed configs: {allowed_config_names}"
3239
            )
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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.
        """
3249
3250
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3253
        logger.info_once(
            "Starting to load model %s...",
            self.model_config.model,
            scope="global",
        )
3254
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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)
        )
3259

3260
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3262
        if self.parallel_config.enable_eplb:
            self.eplb_state = EplbState(self.parallel_config, self.device)
            eplb_models = 0
3263
        with DeviceMemoryProfiler() as m:
3264
            time_before_load = time.perf_counter()
3265
            model_loader = get_model_loader(self.load_config)
3266
            self.model = model_loader.load_model(
3267
3268
                vllm_config=self.vllm_config, model_config=self.model_config
            )
3269
            if self.lora_config:
3270
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3272
                self.model = self.load_lora_model(
                    self.model, self.vllm_config, self.device
                )
3273
            if hasattr(self, "drafter"):
3274
                logger.info_once("Loading drafter model...")
3275
                self.drafter.load_model(self.model)
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3277
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3280
                if (
                    hasattr(self.drafter, "model")
                    and is_mixture_of_experts(self.drafter.model)
                    and self.parallel_config.enable_eplb
                ):
3281
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                    spec_config = self.vllm_config.speculative_config
                    assert spec_config is not None
                    assert spec_config.draft_model_config is not None
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3285
                    logger.info_once(
                        "EPLB is enabled for drafter model %s.",
3286
                        spec_config.draft_model_config.model,
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                    )

                    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,
3303
                        spec_config.draft_model_config,
3304
3305
3306
3307
3308
3309
                        global_expert_load,
                        old_global_expert_indices,
                        rank_mapping,
                    )
                    eplb_models += 1

3310
            if self.use_aux_hidden_state_outputs:
3311
                if not supports_eagle3(self.get_model()):
3312
3313
                    raise RuntimeError(
                        "Model does not support EAGLE3 interface but "
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3315
                        "aux_hidden_state_outputs was requested"
                    )
3316
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3321
3322
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3324
3325
3326
3327
3328

                # 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)
3329
            time_after_load = time.perf_counter()
3330
        self.model_memory_usage = m.consumed_memory
3331
        logger.info_once(
3332
            "Model loading took %.4f GiB memory and %.6f seconds",
3333
3334
            self.model_memory_usage / GiB_bytes,
            time_after_load - time_before_load,
3335
            scope="local",
3336
        )
3337
        prepare_communication_buffer_for_model(self.model)
3338
        mm_config = self.model_config.multimodal_config
3339
        self.is_multimodal_pruning_enabled = (
3340
            supports_multimodal_pruning(self.get_model())
3341
3342
            and mm_config is not None
            and mm_config.is_multimodal_pruning_enabled()
3343
        )
3344

3345
        if is_mixture_of_experts(self.model) and self.parallel_config.enable_eplb:
3346
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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(
3357
                self.model,
3358
                self.model_config,
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3361
                global_expert_load,
                old_global_expert_indices,
                rank_mapping,
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3363
            )

3364
        if (
3365
3366
            self.vllm_config.compilation_config.mode
            == CompilationMode.STOCK_TORCH_COMPILE
3367
            and supports_dynamo()
3368
        ):
3369
            backend = self.vllm_config.compilation_config.init_backend(self.vllm_config)
3370
            compilation_counter.stock_torch_compile_count += 1
3371
            self.model.compile(fullgraph=True, backend=backend)
3372
            return
3373
        # for other compilation modes, cudagraph behavior is controlled by
3374
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        # CudagraphWraper and CudagraphDispatcher of vllm.

        # wrap the model with full cudagraph wrapper if needed.
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        cudagraph_mode = self.compilation_config.cudagraph_mode
        assert cudagraph_mode is not None
        if cudagraph_mode.has_full_cudagraphs() and not self.parallel_config.enable_dbo:
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            self.model = CUDAGraphWrapper(
                self.model, self.vllm_config, runtime_mode=CUDAGraphMode.FULL
            )
3383
        elif self.parallel_config.enable_dbo:
3384
            if cudagraph_mode.has_full_cudagraphs():
3385
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3387
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.FULL, self.device
                )
3388
            else:
3389
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3391
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.NONE, self.device
                )
3392

3393
    def _get_eagle3_aux_layers_from_config(self) -> tuple[int, ...] | None:
3394
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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

3417
    def reload_weights(self) -> None:
3418
        assert getattr(self, "model", None) is not None, (
3419
            "Cannot reload weights before model is loaded."
3420
        )
3421
3422
        model_loader = get_model_loader(self.load_config)
        logger.info("Reloading weights inplace...")
3423
        model_loader.load_weights(self.get_model(), model_config=self.model_config)
3424

3425
3426
3427
3428
3429
    def save_tensorized_model(
        self,
        tensorizer_config: "TensorizerConfig",
    ) -> None:
        TensorizerLoader.save_model(
3430
            self.get_model(),
3431
            tensorizer_config=tensorizer_config,
3432
            model_config=self.model_config,
3433
3434
        )

3435
3436
3437
    def _get_prompt_logprobs_dict(
        self,
        hidden_states: torch.Tensor,
3438
        num_scheduled_tokens: dict[str, int],
3439
    ) -> dict[str, LogprobsTensors | None]:
3440
3441
3442
3443
        num_prompt_logprobs_dict = self.input_batch.num_prompt_logprobs
        if not num_prompt_logprobs_dict:
            return {}

3444
        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
3445
        prompt_logprobs_dict: dict[str, LogprobsTensors | None] = {}
3446
3447
3448
3449
3450

        # 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():
3451
            num_tokens = num_scheduled_tokens[req_id]
3452
3453
3454

            # Get metadata for this request.
            request = self.requests[req_id]
3455
3456
3457
3458
            if request.prompt_token_ids is None:
                # Prompt logprobs is incompatible with prompt embeddings
                continue

3459
3460
            num_prompt_tokens = len(request.prompt_token_ids)
            prompt_token_ids = torch.tensor(request.prompt_token_ids).to(
3461
3462
                self.device, non_blocking=True
            )
3463

3464
3465
3466
3467
3468
3469
            # 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(
3470
3471
                    num_prompt_tokens - 1, num_prompt_logprobs + 1
                )
3472
3473
                in_progress_dict[req_id] = logprobs_tensors

3474
            # Determine number of logits to retrieve.
3475
3476
            start_idx = request.num_computed_tokens
            start_tok = start_idx + 1
3477
            num_remaining_tokens = num_prompt_tokens - start_tok
3478
            if num_tokens <= num_remaining_tokens:
3479
                # This is a chunk, more tokens remain.
3480
3481
3482
                # 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.
3483
3484
3485
3486
3487
                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)
3488
3489
3490
3491
3492
3493
3494
                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
3495
3496
3497
3498
3499

            # 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]
3500
            offset = self.query_start_loc.np[req_idx].item()
3501
            prompt_hidden_states = hidden_states[offset : offset + num_logits]
3502
            logits = self.model.compute_logits(prompt_hidden_states)
3503
3504
3505
3506

            # 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.
3507
            tgt_token_ids = prompt_token_ids[start_tok : start_tok + num_logits]
3508
3509

            # Compute prompt logprobs.
3510
3511
            logprobs = self.sampler.compute_logprobs(logits)
            token_ids, logprobs, ranks = self.sampler.gather_logprobs(
3512
3513
                logprobs, num_prompt_logprobs, tgt_token_ids
            )
3514
3515

            # Transfer GPU->CPU async.
3516
3517
            chunk_slice = slice(start_idx, start_idx + num_logits)
            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
3518
3519
3520
                token_ids, non_blocking=True
            )
            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs, non_blocking=True)
3521
            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
3522
3523
                ranks, non_blocking=True
            )
3524
3525
3526
3527
3528

        # 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]
3529
            del in_progress_dict[req_id]
3530
3531

        # Must synchronize the non-blocking GPU->CPU transfers.
3532
        if prompt_logprobs_dict:
3533
            self._sync_device()
3534
3535
3536

        return prompt_logprobs_dict

3537
3538
    def _get_nans_in_logits(
        self,
3539
        logits: torch.Tensor | None,
3540
3541
3542
3543
3544
3545
3546
3547
3548
3549
3550
    ) -> 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])
3551
3552
3553
                    if num_nans_for_index is not None and req_index < logits.shape[0]
                    else 0
                )
3554
3555
3556
3557
            return num_nans_in_logits
        except IndexError:
            return {}

3558
3559
3560
3561
3562
3563
    @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
3564
         - during DP rank dummy run
3565
3566
3567
3568
3569
3570
3571
3572
3573
3574
3575
        """
        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(
3576
                    self.input_ids.gpu,
3577
3578
                    low=0,
                    high=self.model_config.get_vocab_size(),
3579
3580
                    dtype=input_ids.dtype,
                )
3581

3582
            logger.debug_once("Randomizing dummy data for DP Rank")
3583
            input_ids.copy_(rand_input_ids()[: input_ids.size(0)], non_blocking=True)
3584
3585
3586
            yield
            input_ids.fill_(0)

3587
3588
3589
3590
3591
3592
    def _get_mm_dummy_batch(
        self,
        modality: str,
        max_items_per_batch: int,
    ) -> BatchedTensorInputs:
        """Dummy data for profiling and precompiling multimodal models."""
3593
3594
        assert self.mm_budget is not None

3595
3596
        dummy_decoder_data = self.mm_registry.get_decoder_dummy_data(
            model_config=self.model_config,
3597
            seq_len=self.max_model_len,
3598
            mm_counts={modality: 1},
3599
            cache=self.mm_budget.cache,
3600
3601
3602
3603
        )
        dummy_mm_data = dummy_decoder_data.multi_modal_data

        # Result in the maximum GPU consumption of the model
3604
3605
        dummy_mm_item = dummy_mm_data[modality][0]
        dummy_mm_items = [dummy_mm_item] * max_items_per_batch
3606

3607
        model = cast(SupportsMultiModal, self.model)
3608
3609
3610
3611
3612
3613
3614
        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,
3615
                multimodal_cpu_fields=model.multimodal_cpu_fields,
3616
3617
            )
        )
3618

3619
3620
3621
3622
    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
3623
        cudagraph_runtime_mode: CUDAGraphMode | None = None,
3624
3625
        force_attention: bool = False,
        uniform_decode: bool = False,
3626
        allow_microbatching: bool = True,
3627
3628
        skip_eplb: bool = False,
        is_profile: bool = False,
3629
        create_mixed_batch: bool = False,
3630
        remove_lora: bool = True,
3631
        activate_lora: bool = False,
3632
    ) -> tuple[torch.Tensor, torch.Tensor]:
3633
3634
3635
3636
3637
3638
3639
        """
        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.
3640
                - if not set will determine the cudagraph mode based on using
3641
                    the self.cudagraph_dispatcher.
3642
3643
3644
3645
                - CUDAGraphMode.NONE: No cudagraph, for warm up and profile run
                - CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
                - CUDAGraphMode.FULL: Full cudagraph, attention metadata is
                    needed.
3646
            force_attention: If True, always create attention metadata. Used to
3647
3648
3649
3650
                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.
3651
3652
            create_mixed_batch: If True, create a mixed batch with both decode
                (1 token) and prefill (multiple tokens) requests.
3653
            remove_lora: If False, dummy LoRAs are not destroyed after the run
3654
            activate_lora: If False, dummy_run is performed without LoRAs.
3655
        """
3656
3657
3658
3659
        assert (
            cudagraph_runtime_mode is None
            or cudagraph_runtime_mode.valid_runtime_modes()
        )
3660

3661
        # If cudagraph_mode.decode_mode() == FULL and
3662
        # cudagraph_mode.separate_routine(). This means that we are using
3663
3664
3665
3666
3667
3668
3669
3670
3671
3672
3673
        # 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.
3674
        max_query_len = self.uniform_decode_query_len if uniform_decode else num_tokens
3675

3676
3677
3678
3679
3680
        # 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
3681
3682
3683
3684
        if create_mixed_batch:
            assert not uniform_decode
            # Create mixed batch:
            # first half decode tokens, second half one prefill
3685
            num_decode_tokens = min(max_num_reqs - 1, num_tokens // 2)
3686
3687
3688
3689
            num_prefill_tokens = num_tokens - num_decode_tokens
            num_reqs = num_decode_tokens + 1

            # Create decode requests (1 token each) followed by prefill request
3690
            num_scheduled_tokens_list = [1] * num_decode_tokens + [num_prefill_tokens]
3691
3692
3693
            # Note: Overriding max_query_len to be the prefill tokens
            max_query_len = num_prefill_tokens
        elif uniform_decode:
3694
            assert not create_mixed_batch
3695
            num_reqs = min(max_num_reqs, cdiv(num_tokens, max_query_len))
3696
3697
            num_scheduled_tokens_list = [max_query_len] * num_reqs
            if num_tokens % max_query_len != 0:
3698
                num_scheduled_tokens_list[-1] = num_tokens % max_query_len
3699
3700
3701
3702
3703
3704
        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

3705
3706
        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs
3707
        num_scheduled_tokens = np.array(num_scheduled_tokens_list, dtype=np.int32)
3708
        total_num_scheduled_tokens = int(num_scheduled_tokens.sum())
3709
        num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
3710

3711
3712
3713
        # Disable DP padding when running eager
        allow_dp_padding = self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE

3714
3715
        # We currently only microbatch if the number of tokens is
        # over a certain threshold.
3716
        ubatch_slices, num_tokens_across_dp = coordinate_batch_across_dp(
3717
3718
3719
3720
3721
3722
3723
            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,
3724
3725
3726
        )
        num_tokens_after_padding = num_tokens
        if num_tokens_across_dp is not None:
3727
3728
            dp_rank = self.parallel_config.data_parallel_rank
            num_tokens_after_padding = int(num_tokens_across_dp[dp_rank])
3729

3730
        attn_metadata: PerLayerAttnMetadata | None = None
3731
3732
3733

        # If force_attention is True, we always capture attention. Otherwise,
        # it only happens for cudagraph_runtime_mode=FULL.
3734
        if force_attention or cudagraph_runtime_mode == CUDAGraphMode.FULL:
3735
3736
3737
3738
3739
3740
            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:
3741
                seq_lens = max_query_len  # type: ignore[assignment]
3742
            self.seq_lens.np[:num_reqs] = seq_lens
3743
3744
            self.seq_lens.np[num_reqs:] = 0
            self.seq_lens.copy_to_gpu()
3745

3746
3747
            cum_num_tokens, _ = self._get_cumsum_and_arange(num_scheduled_tokens)
            self.query_start_loc.np[1 : num_reqs + 1] = cum_num_tokens
3748
3749
            self.query_start_loc.copy_to_gpu()

3750
3751
3752
3753
3754
3755
3756
            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,
            )
3757

3758
        with self.maybe_dummy_run_with_lora(
3759
3760
3761
3762
3763
            self.lora_config,
            num_scheduled_tokens,
            num_sampled_tokens,
            activate_lora,
            remove_lora,
3764
        ):
3765
3766
3767
            # 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)
3768
            if self.supports_mm_inputs and not self.model_config.is_encoder_decoder:
3769
                input_ids = None
3770
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_after_padding]
3771
                model_kwargs = {
3772
                    **model_kwargs,
3773
3774
                    **self._dummy_mm_kwargs(num_reqs),
                }
3775
3776
            elif self.enable_prompt_embeds:
                input_ids = None
3777
3778
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_after_padding]
                model_kwargs = self._init_model_kwargs(num_tokens_after_padding)
3779
            else:
3780
                input_ids = self.input_ids.gpu[:num_tokens_after_padding]
3781
                inputs_embeds = None
3782

3783
            if self.uses_mrope:
3784
                positions = self.mrope_positions.gpu[:, :num_tokens_after_padding]
3785
            else:
3786
                positions = self.positions.gpu[:num_tokens_after_padding]
3787
3788
3789
3790
3791
3792
3793
3794
3795

            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,
3796
3797
3798
                            device=self.device,
                        )
                    )
3799
3800

                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
3801
                    num_tokens_after_padding, None, False
3802
                )
3803
3804

            # filter out the valid batch descriptor
3805
3806
3807
3808
3809
            _cg_mode, batch_descriptor = (
                self.cudagraph_dispatcher.dispatch(
                    BatchDescriptor(
                        num_tokens=num_tokens_after_padding,
                        uniform_decode=uniform_decode,
3810
                        has_lora=activate_lora and self.lora_config is not None,
3811
3812
3813
3814
3815
                    )
                )
                if not is_profile
                else (CUDAGraphMode.NONE, None)
            )
3816
3817
3818
            if cudagraph_runtime_mode is not None:
                # we allow forcing NONE when the dispatcher disagrees to support
                # warm ups for cudagraph capture
3819
3820
3821
3822
                assert (
                    cudagraph_runtime_mode == CUDAGraphMode.NONE
                    or cudagraph_runtime_mode == _cg_mode
                ), (
3823
                    f"Cudagraph runtime mode mismatch at dummy_run. "
3824
3825
                    f"Expected {_cg_mode}, but got {cudagraph_runtime_mode}."
                )
3826
3827
            else:
                cudagraph_runtime_mode = _cg_mode
3828

3829
            if ubatch_slices is not None:
3830
3831
3832
3833
3834
3835
3836
                # 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

3837
3838
3839
            with (
                self.maybe_randomize_inputs(input_ids),
                set_forward_context(
3840
3841
                    attn_metadata,
                    self.vllm_config,
3842
                    num_tokens=num_tokens_after_padding,
3843
3844
                    num_tokens_across_dp=num_tokens_across_dp,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
3845
                    batch_descriptor=batch_descriptor,
3846
3847
3848
                    ubatch_slices=ubatch_slices,
                ),
            ):
3849
                outputs = self.model(
3850
3851
3852
3853
                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
3854
                    **model_kwargs,
3855
                )
3856

3857
3858
3859
3860
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
3861

3862
            if self.speculative_config and self.speculative_config.use_eagle():
3863
                assert isinstance(self.drafter, EagleProposer)
3864
3865
3866
3867
                use_cudagraphs = (
                    cudagraph_runtime_mode == CUDAGraphMode.PIECEWISE
                    and not self.speculative_config.enforce_eager
                )
3868
3869
3870
3871
3872
3873
3874
3875
3876
3877
3878
3879

                # 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,
                )
3880

3881
3882
3883
3884
3885
3886
3887
3888
3889
3890
        # 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)

3891
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
3892
3893
3894
3895
        logit_indices_device = torch.from_numpy(logit_indices).to(
            self.device, non_blocking=True
        )
        return hidden_states, hidden_states[logit_indices_device]
3896
3897
3898
3899
3900
3901

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
3902
3903
3904
3905
        # 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)
3906

3907
        logits = self.model.compute_logits(hidden_states)
3908
3909
        num_reqs = logits.size(0)

3910
        dummy_tensors = lambda v: torch.full((num_reqs,), v, device=self.device)
3911
3912
3913
3914
3915
3916
3917
3918
3919
3920
3921
3922
3923
3924
3925

        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)],
3926
            spec_token_ids=[[] for _ in range(num_reqs)],
3927
3928
            allowed_token_ids_mask=None,
            bad_words_token_ids={},
3929
            logitsprocs=LogitsProcessors(),
3930
        )
3931
        try:
3932
3933
3934
            sampler_output = self.sampler(
                logits=logits, sampling_metadata=dummy_metadata
            )
3935
        except RuntimeError as e:
3936
            if "out of memory" in str(e):
3937
3938
3939
3940
                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 "
3941
3942
                    "initializing the engine."
                ) from e
3943
3944
            else:
                raise e
3945
        if self.speculative_config:
3946
3947
            draft_token_ids = [[0] for _ in range(num_reqs)]
            dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
3948
3949
                draft_token_ids, self.device
            )
3950
3951
3952
3953
3954
3955

            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
3956
3957
3958
3959
3960
            logits = torch.randn(
                num_tokens + num_reqs,
                logits.shape[-1],
                device=self.device,
                dtype=logits.dtype,
3961
            )
3962
3963
3964
            self.rejection_sampler(
                dummy_spec_decode_metadata,
                draft_probs,
3965
                logits,
3966
3967
                dummy_metadata,
            )
3968
        return sampler_output
3969

3970
    def _dummy_pooler_run_task(
3971
3972
        self,
        hidden_states: torch.Tensor,
3973
3974
        task: PoolingTask,
    ) -> PoolerOutput:
3975
3976
3977
3978
3979
3980
3981
3982
3983
3984
3985
        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

3986
        dummy_prompt_lens = torch.tensor(
3987
3988
            num_scheduled_tokens_list,
            device="cpu",
3989
        )
3990
3991
3992
        dummy_token_ids = torch.zeros(
            (num_reqs, req_num_tokens), dtype=torch.int32, device=self.device
        )
3993

3994
        model = cast(VllmModelForPooling, self.get_model())
3995
        dummy_pooling_params = PoolingParams(task=task)
3996
        dummy_pooling_params.verify(task=task, model_config=self.model_config)
3997
        to_update = model.pooler.get_pooling_updates(task)
3998
3999
        to_update.apply(dummy_pooling_params)

4000
        dummy_metadata = PoolingMetadata(
4001
4002
4003
4004
            prompt_lens=dummy_prompt_lens,
            prompt_token_ids=dummy_token_ids,
            pooling_params=[dummy_pooling_params] * num_reqs,
        )
4005

4006
4007
4008
        dummy_metadata.build_pooling_cursor(
            num_scheduled_tokens_list, device=hidden_states.device
        )
4009

4010
        try:
4011
4012
4013
            return model.pooler(
                hidden_states=hidden_states, pooling_metadata=dummy_metadata
            )
4014
        except RuntimeError as e:
4015
            if "out of memory" in str(e):
4016
                raise RuntimeError(
4017
4018
4019
                    "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 "
4020
4021
                    "initializing the engine."
                ) from e
4022
4023
            else:
                raise e
4024
4025
4026
4027
4028
4029
4030

    @torch.inference_mode()
    def _dummy_pooler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> PoolerOutput:
        # Find the task that has the largest output for subsequent steps
4031
4032
4033
        supported_pooling_tasks = self.get_supported_pooling_tasks()

        if not supported_pooling_tasks:
4034
            if self.scheduler_config.enable_chunked_prefill:
4035
4036
4037
4038
4039
4040
4041
4042
4043
4044
4045
4046
4047
4048
4049
4050
                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."
                )

4051
        output_size = dict[PoolingTask, float]()
4052
        for task in supported_pooling_tasks:
4053
4054
            # Run a full batch with each task to ensure none of them OOMs
            output = self._dummy_pooler_run_task(hidden_states, task)
4055
            output_size[task] = sum(o.nbytes for o in output)
4056
4057
4058
4059
            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)
4060

4061
    def profile_run(self) -> None:
4062
        # Profile with multimodal encoder & encoder cache.
4063
        if self.supports_mm_inputs:
4064
4065
            mm_config = self.model_config.multimodal_config
            if mm_config is not None and mm_config.skip_mm_profiling:
4066
                logger.info(
4067
                    "Skipping memory profiling for multimodal encoder and "
4068
4069
                    "encoder cache."
                )
4070
4071
4072
4073
4074
4075
4076
4077
            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.
4078
                    dummy_modality = mm_budget.get_modality_with_max_tokens()
4079
4080
4081
                    max_mm_items_per_batch = mm_budget.max_items_per_batch_by_modality[
                        dummy_modality
                    ]
4082
4083
4084
4085
4086
4087
4088
4089
4090

                    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,
                    )
4091

4092
4093
4094
4095
4096
                    # Create dummy batch of multimodal inputs.
                    batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                        dummy_modality,
                        max_mm_items_per_batch,
                    )
4097

4098
                    # Run multimodal encoder.
4099
                    dummy_encoder_outputs = self.model.embed_multimodal(
4100
4101
                        **batched_dummy_mm_inputs
                    )
4102

4103
4104
4105
4106
                    sanity_check_mm_encoder_outputs(
                        dummy_encoder_outputs,
                        expected_num_items=max_mm_items_per_batch,
                    )
4107

4108
4109
4110
4111
4112
4113
4114
4115
4116
4117
                    # 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(
4118
4119
                                (encoder_budget, encoder_output_shape[-1])
                            )
4120
4121
4122
4123
4124
4125
                            num_tokens = output.shape[0]
                            expanded[:num_tokens].copy_(output)
                            expanded_outputs.append(expanded)

                        dummy_encoder_outputs = expanded_outputs

4126
                    # Cache the dummy encoder outputs.
4127
                    self.encoder_cache["tmp"] = dict(enumerate(dummy_encoder_outputs))
4128

4129
        # Add `is_profile` here to pre-allocate communication buffers
4130
4131
4132
        hidden_states, last_hidden_states = self._dummy_run(
            self.max_num_tokens, is_profile=True
        )
4133
        if get_pp_group().is_last_rank:
4134
4135
4136
4137
            if self.is_pooling_model:
                output = self._dummy_pooler_run(hidden_states)
            else:
                output = self._dummy_sampler_run(last_hidden_states)
4138
        else:
4139
            output = None
4140
        self._sync_device()
4141
        del hidden_states, output
4142
        self.encoder_cache.clear()
4143
        gc.collect()
4144

4145
    def capture_model(self) -> int:
4146
        if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
4147
            logger.warning(
4148
                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
4149
4150
                "ensure `cudagraph_mode` was not manually set to `NONE`"
            )
4151
            return 0
4152

4153
4154
        compilation_counter.num_gpu_runner_capture_triggers += 1

4155
4156
        start_time = time.perf_counter()

4157
4158
4159
4160
4161
4162
4163
4164
4165
4166
4167
4168
4169
4170
        @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()
4171
                    gc.collect()
4172

4173
4174
4175
        # 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.
4176
        set_cudagraph_capturing_enabled(True)
4177
        with freeze_gc(), graph_capture(device=self.device):
4178
            start_free_gpu_memory = torch.cuda.mem_get_info()[0]
4179
            cudagraph_mode = self.compilation_config.cudagraph_mode
4180
            assert cudagraph_mode is not None
4181
4182
4183
4184
4185
4186
4187
4188
4189

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

4190
4191
            if cudagraph_mode.mixed_mode() != CUDAGraphMode.NONE:
                cudagraph_runtime_mode = cudagraph_mode.mixed_mode()
4192
                # make sure we capture the largest batch size first
4193
4194
4195
                compilation_cases = list(
                    product(reversed(self.cudagraph_batch_sizes), lora_cases)
                )
4196
4197
4198
                self._capture_cudagraphs(
                    compilation_cases,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
4199
4200
                    uniform_decode=False,
                )
4201

4202
4203
            # Capture full cudagraph for uniform decode batches if we
            # don't already have full mixed prefill-decode cudagraphs.
4204
4205
4206
4207
4208
4209
4210
            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
                )
4211
                decode_cudagraph_batch_sizes = [
4212
4213
                    x
                    for x in self.cudagraph_batch_sizes
4214
                    if max_num_tokens >= x >= self.uniform_decode_query_len
4215
                ]
4216
4217
4218
                compilation_cases_decode = list(
                    product(reversed(decode_cudagraph_batch_sizes), lora_cases)
                )
4219
4220
4221
                self._capture_cudagraphs(
                    compilation_cases=compilation_cases_decode,
                    cudagraph_runtime_mode=CUDAGraphMode.FULL,
4222
4223
                    uniform_decode=True,
                )
4224

4225
4226
4227
            torch.cuda.synchronize()
            end_free_gpu_memory = torch.cuda.mem_get_info()[0]

4228
4229
4230
        # 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
4231
        # we may do lazy capturing in future that still allows capturing
4232
4233
        # after here.
        set_cudagraph_capturing_enabled(False)
4234
4235
4236
4237
4238

        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.
4239
        logger.info_once(
4240
4241
4242
            "Graph capturing finished in %.0f secs, took %.2f GiB",
            elapsed_time,
            cuda_graph_size / (1 << 30),
4243
            scope="local",
4244
        )
4245
        return cuda_graph_size
4246

4247
4248
    def _capture_cudagraphs(
        self,
4249
        compilation_cases: list[tuple[int, bool]],
4250
4251
4252
4253
4254
4255
4256
        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}"
4257
4258
4259
4260
4261
4262
4263
4264

        # 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",
4265
4266
4267
                    cudagraph_runtime_mode.name,
                ),
            )
4268

4269
        # We skip EPLB here since we don't want to record dummy metrics
4270
        for num_tokens, activate_lora in compilation_cases:
4271
            # We currently only capture ubatched graphs when its a FULL
4272
4273
4274
            # 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
4275
4276
4277
4278
            allow_microbatching = (
                self.parallel_config.enable_dbo
                and cudagraph_runtime_mode == CUDAGraphMode.FULL
                and uniform_decode
4279
4280
4281
4282
4283
                and check_ubatch_thresholds(
                    config=self.vllm_config.parallel_config,
                    num_tokens=num_tokens,
                    uniform_decode=uniform_decode,
                )
4284
            )
4285

4286
4287
4288
4289
4290
4291
            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.
4292
4293
4294
4295
4296
4297
4298
4299
4300
                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,
4301
                    activate_lora=activate_lora,
4302
4303
4304
4305
4306
4307
4308
4309
                )
            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,
4310
                activate_lora=activate_lora,
4311
            )
4312
        self.maybe_remove_all_loras(self.lora_config)
4313

4314
4315
4316
4317
    def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize the attention backends and attention metadata builders.
        """
4318
        assert len(self.attn_groups) == 0, "Attention backends are already initialized"
4319

4320
4321
4322
4323
4324
4325
        class AttentionGroupKey(NamedTuple):
            attn_backend: type[AttentionBackend]
            kv_cache_spec: KVCacheSpec

        def get_attn_backends_for_group(
            kv_cache_group_spec: KVCacheGroupSpec,
4326
        ) -> tuple[dict[AttentionGroupKey, list[str]], set[type[AttentionBackend]]]:
4327
            layer_type = cast(type[Any], AttentionLayerBase)
4328
            layers = get_layers_from_vllm_config(
4329
                self.vllm_config, layer_type, kv_cache_group_spec.layer_names
4330
            )
4331
4332
            attn_backends = {}
            attn_backend_layers = defaultdict(list)
4333
            # Dedupe based on full class name; this is a bit safer than
4334
4335
4336
4337
            # 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.
4338
            for layer_name in kv_cache_group_spec.layer_names:
4339
                attn_backend = layers[layer_name].get_attn_backend()
4340
4341
4342
4343

                if layer_name in self.kv_sharing_fast_prefill_eligible_layers:
                    attn_backend = create_fast_prefill_custom_backend(
                        "FastPrefill",
4344
                        attn_backend,  # type: ignore[arg-type]
4345
4346
                    )

4347
4348
4349
                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):
4350
                    layer_kv_cache_spec = layer_kv_cache_spec.kv_cache_specs[layer_name]
4351
                key = (full_cls_name, layer_kv_cache_spec)
4352
4353
4354
                attn_backends[key] = AttentionGroupKey(
                    attn_backend, layer_kv_cache_spec
                )
4355
                attn_backend_layers[key].append(layer_name)
4356
4357
4358
4359
            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()),
            )
4360
4361

        def create_attn_groups(
4362
            attn_backends_map: dict[AttentionGroupKey, list[str]],
4363
            kv_cache_group_id: int,
4364
4365
        ) -> list[AttentionGroup]:
            attn_groups: list[AttentionGroup] = []
4366
            for (attn_backend, kv_cache_spec), layer_names in attn_backends_map.items():
4367
                attn_group = AttentionGroup(
4368
                    attn_backend,
4369
                    layer_names,
4370
                    kv_cache_spec,
4371
                    kv_cache_group_id,
4372
4373
                )

4374
4375
4376
                attn_groups.append(attn_group)
            return attn_groups

4377
        attention_backend_maps = []
4378
        attention_backend_list = []
4379
        for kv_cache_group_spec in kv_cache_config.kv_cache_groups:
4380
            attn_backends = get_attn_backends_for_group(kv_cache_group_spec)
4381
            attention_backend_maps.append(attn_backends[0])
4382
            attention_backend_list.append(attn_backends[1])
4383
4384

        # Resolve cudagraph_mode before actually initialize metadata_builders
4385
4386
4387
        self._check_and_update_cudagraph_mode(
            attention_backend_list, kv_cache_config.kv_cache_groups
        )
4388

4389
4390
        for i, attn_backend_map in enumerate(attention_backend_maps):
            self.attn_groups.append(create_attn_groups(attn_backend_map, i))
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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,
                )
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        # Calculate reorder batch threshold (if needed)
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        # 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()

4415
    def _check_and_update_cudagraph_mode(
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        self,
        attention_backends: list[set[type[AttentionBackend]]],
        kv_cache_groups: list[KVCacheGroupSpec],
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    ) -> None:
4420
        """
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        Resolve the cudagraph_mode when there are multiple attention
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        groups with potential conflicting CUDA graph support.
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        Then initialize the cudagraph_dispatcher based on the resolved
        cudagraph_mode.
        """
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        min_cg_support = AttentionCGSupport.ALWAYS
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        min_cg_backend_name = None
4428

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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
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        assert cudagraph_mode is not None
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        # check cudagraph for mixed batch is supported
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        if (
            cudagraph_mode.mixed_mode() == CUDAGraphMode.FULL
            and min_cg_support != AttentionCGSupport.ALWAYS
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
4451
                f"with {min_cg_backend_name} backend (support: "
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                f"{min_cg_support})"
            )
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            if min_cg_support == AttentionCGSupport.NEVER:
                # if not supported any full cudagraphs, just raise it.
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                msg += (
                    "; please try cudagraph_mode=PIECEWISE, and "
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                    "make sure compilation mode is VLLM_COMPILE"
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                )
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                raise ValueError(msg)

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

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

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

        # double check that we can support full cudagraph if they are requested
        # even after automatic downgrades
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        if (
            cudagraph_mode.has_full_cudagraphs()
            and min_cg_support == AttentionCGSupport.NEVER
        ):
            raise ValueError(
                f"CUDAGraphMode.{cudagraph_mode.name} is not "
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                f"supported with {min_cg_backend_name} backend ("
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                f"support:{min_cg_support}) "
                "; please try cudagraph_mode=PIECEWISE, "
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                "and make sure compilation mode is VLLM_COMPILE"
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            )
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        # 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
            )
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            capture_sizes = self.compilation_config.cudagraph_capture_sizes
            self.cudagraph_batch_sizes = (
                capture_sizes if capture_sizes is not None else []
            )
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        # Trigger cudagraph dispatching keys initialization after
        # resolved cudagraph mode.
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        cudagraph_mode = self.compilation_config.cudagraph_mode
        assert cudagraph_mode is not None
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        self.cudagraph_dispatcher.initialize_cudagraph_keys(
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            cudagraph_mode, self.uniform_decode_query_len
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        )
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    def calculate_reorder_batch_threshold(self) -> None:
        """
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        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.
4577
        """
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        min_none_high = lambda a, b: a if b is None else b if a is None else min(a, b)

4580
        reorder_batch_thresholds: list[int | None] = [
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            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
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        self.reorder_batch_threshold = reduce(min_none_high, reorder_batch_thresholds)  # type: ignore[assignment]
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    @staticmethod
    def select_common_block_size(
        kv_manager_block_size: int, attn_groups: list[AttentionGroup]
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    ) -> 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.
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        Args:
            kv_manager_block_size: Block size of KV cache
            attn_groups: List of attention groups

        Returns:
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            The selected block size
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        Raises:
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            ValueError: If no valid block size found
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        """

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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
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                for supported_size in backend.get_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
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            for supported_size in backend.get_supported_kernel_block_sizes()
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            if isinstance(supported_size, int)
        )
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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}. ")
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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.
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        """
        block_sizes = [
            kv_cache_group.kv_cache_spec.block_size
            for kv_cache_group in kv_cache_config.kv_cache_groups
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            if not isinstance(kv_cache_group.kv_cache_spec, EncoderOnlyAttentionSpec)
4679
        ]
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        if block_sizes != [self.cache_config.block_size] or kernel_block_sizes != [
            self.cache_config.block_size
        ]:
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            assert self.cache_config.cpu_offload_gb == 0, (
                "Cannot re-initialize the input batch when CPU weight "
                "offloading is enabled. See https://github.com/vllm-project/vllm/pull/18298 "  # noqa: E501
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                "for more details."
            )
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            self.input_batch = InputBatch(
                max_num_reqs=self.max_num_reqs,
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                max_model_len=max(self.max_model_len, self.max_encoder_len),
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                max_num_batched_tokens=self.max_num_tokens,
                device=self.device,
                pin_memory=self.pin_memory,
                vocab_size=self.model_config.get_vocab_size(),
                block_sizes=block_sizes,
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                kernel_block_sizes=kernel_block_sizes,
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                is_spec_decode=bool(self.vllm_config.speculative_config),
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                logitsprocs=self.input_batch.logitsprocs,
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                logitsprocs_need_output_token_ids=self.input_batch.logitsprocs_need_output_token_ids,
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                is_pooling_model=self.is_pooling_model,
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                num_speculative_tokens=self.num_spec_tokens,
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            )

4705
    def _allocate_kv_cache_tensors(
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        self, kv_cache_config: KVCacheConfig
    ) -> dict[str, torch.Tensor]:
4708
        """
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        Initializes the KV cache buffer with the correct size. The buffer needs
        to be reshaped to the desired shape before being used by the models.

4712
        Args:
4713
            kv_cache_config: The KV cache config
4714
        Returns:
4715
            dict[str, torch.Tensor]: A map between layer names to their
4716
            corresponding memory buffer for KV cache.
4717
        """
4718
4719
        kv_cache_raw_tensors: dict[str, torch.Tensor] = {}
        for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
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            tensor = torch.zeros(
                kv_cache_tensor.size, dtype=torch.int8, device=self.device
            )
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            for layer_name in kv_cache_tensor.shared_by:
                kv_cache_raw_tensors[layer_name] = tensor

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

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

4740
    def _kv_cache_spec_attn_group_iterator(self) -> Iterator[AttentionGroup]:
4741
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        if not self.kv_cache_config.kv_cache_groups:
            return
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        for attn_groups in self.attn_groups:
            yield from attn_groups
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    def _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 = []
4761
        for kv_cache_gid, kv_cache_group in enumerate(kv_cache_config.kv_cache_groups):
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            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):
4768
                continue
4769
            elif isinstance(kv_cache_spec, AttentionSpec):
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4772
                # This is an attention backend that supports virtual
                # block splitting. Get the supported block sizes from
                # all backends in the group.
4773
                attn_groups = self.attn_groups[kv_cache_gid]
4774
                kv_manager_block_size = kv_cache_group.kv_cache_spec.block_size
4775
                selected_kernel_size = self.select_common_block_size(
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                    kv_manager_block_size, attn_groups
                )
                kernel_block_sizes.append(selected_kernel_size)
4779
            elif isinstance(kv_cache_spec, MambaSpec):
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                # This is likely Mamba or other non-attention cache,
                # no splitting.
4782
                kernel_block_sizes.append(kv_cache_spec.block_size)
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4788
            else:
                raise NotImplementedError(
                    f"unknown kv cache spec {kv_cache_group.kv_cache_spec}"
                )
        return kernel_block_sizes

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4792
    def _reshape_kv_cache_tensors(
        self,
        kv_cache_config: KVCacheConfig,
        kv_cache_raw_tensors: dict[str, torch.Tensor],
4793
        kernel_block_sizes: list[int],
4794
    ) -> dict[str, torch.Tensor]:
4795
        """
4796
        Reshape the KV cache tensors to the desired shape and dtype.
4797

4798
        Args:
4799
4800
            kv_cache_config: The KV cache config
            kv_cache_raw_tensors: The KV cache buffer of each layer, with
4801
                correct size but uninitialized shape.
4802
            kernel_block_sizes: The kernel block sizes for each KV cache group.
4803
        Returns:
4804
            Dict[str, torch.Tensor]: A map between layer names to their
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            corresponding memory buffer for KV cache.
        """
4807
        kv_caches: dict[str, torch.Tensor] = {}
4808
        has_attn, has_mamba = False, False
4809
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        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
4811
            attn_backend = group.backend
4812
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4815
            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]
4816
            for layer_name in group.layer_names:
4817
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                if layer_name in self.runner_only_attn_layers:
                    continue
4819
4820
                raw_tensor = kv_cache_raw_tensors[layer_name]
                assert raw_tensor.numel() % kv_cache_spec.page_size_bytes == 0
4821
                num_blocks = raw_tensor.numel() // kv_cache_spec.page_size_bytes
4822
                if isinstance(kv_cache_spec, AttentionSpec):
4823
                    has_attn = True
4824
4825
                    num_blocks_per_kv_block = (
                        kv_cache_spec.block_size // kernel_block_size
4826
4827
4828
                    )
                    kernel_num_blocks = num_blocks * num_blocks_per_kv_block

4829
                    kv_cache_shape = attn_backend.get_kv_cache_shape(
4830
                        kernel_num_blocks,
4831
                        kernel_block_size,
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                        kv_cache_spec.num_kv_heads,
                        kv_cache_spec.head_size,
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                        cache_dtype_str=self.cache_config.cache_dtype,
                    )
4836
                    dtype = kv_cache_spec.dtype
4837
                    try:
4838
                        kv_cache_stride_order = attn_backend.get_kv_cache_stride_order()
4839
                        assert len(kv_cache_stride_order) == len(kv_cache_shape)
4840
                    except (AttributeError, NotImplementedError):
4841
                        kv_cache_stride_order = tuple(range(len(kv_cache_shape)))
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4846
                    # The allocation respects the backend-defined stride order
                    # to ensure the semantic remains consistent for each
                    # backend. We first obtain the generic kv cache shape and
                    # then permute it according to the stride order which could
                    # result in a non-contiguous tensor.
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                    kv_cache_shape = tuple(
                        kv_cache_shape[i] for i in kv_cache_stride_order
                    )
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                    # Maintain original KV shape view.
                    inv_order = [
                        kv_cache_stride_order.index(i)
                        for i in range(len(kv_cache_stride_order))
                    ]
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                    kv_caches[layer_name] = (
                        kv_cache_raw_tensors[layer_name]
                        .view(dtype)
                        .view(kv_cache_shape)
                        .permute(*inv_order)
                    )
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Chen Zhang committed
4861
                elif isinstance(kv_cache_spec, MambaSpec):
4862
                    has_mamba = True
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                    raw_tensor = kv_cache_raw_tensors[layer_name]
                    state_tensors = []
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                    storage_offset_bytes = 0
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                    for shape, dtype in zip(kv_cache_spec.shapes, kv_cache_spec.dtypes):
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                        dtype_size = get_dtype_size(dtype)
                        num_element_per_page = (
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                            kv_cache_spec.page_size_bytes // dtype_size
                        )
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                        target_shape = (num_blocks, *shape)
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                        stride = torch.empty(target_shape).stride()
                        target_stride = (num_element_per_page, *stride[1:])
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                        assert storage_offset_bytes % dtype_size == 0
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                        tensor = torch.as_strided(
                            raw_tensor.view(dtype),
                            size=target_shape,
                            stride=target_stride,
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                            storage_offset=storage_offset_bytes // dtype_size,
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                        )
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                        state_tensors.append(tensor)
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                        storage_offset_bytes += stride[0] * dtype_size
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                    kv_caches[layer_name] = state_tensors
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                else:
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                    raise NotImplementedError
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        if has_attn and has_mamba:
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            self._update_hybrid_attention_mamba_layout(kv_caches)
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        return kv_caches

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    def _update_hybrid_attention_mamba_layout(
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        self, kv_caches: dict[str, torch.Tensor]
    ) -> None:
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        """
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        Update the layout of attention layers from (2, num_blocks, ...) to
        (num_blocks, 2, ...).
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        Args:
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            kv_caches: The KV cache buffer of each layer.
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        """

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        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
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            for layer_name in group.layer_names:
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                kv_cache = kv_caches[layer_name]
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                if isinstance(kv_cache_spec, AttentionSpec) and kv_cache.shape[0] == 2:
                    assert kv_cache.shape[1] != 2, (
                        "Fail to determine whether the layout is "
                        "(2, num_blocks, ...) or (num_blocks, 2, ...) for "
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                        f"a tensor of shape {kv_cache.shape}"
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                    )
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                    hidden_size = kv_cache.shape[2:].numel()
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                    kv_cache.as_strided_(
                        size=kv_cache.shape,
                        stride=(hidden_size, 2 * hidden_size, *kv_cache.stride()[2:]),
                    )
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    def initialize_kv_cache_tensors(
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        self, kv_cache_config: KVCacheConfig, 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.
        """
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        # Try creating KV caches optimized for kv-connector transfers
        cache_dtype = self.cache_config.cache_dtype
        if self.use_uniform_kv_cache(self.attn_groups, cache_dtype):
            kv_caches, cross_layers_kv_cache, attn_backend = (
                self.allocate_uniform_kv_caches(
                    kv_cache_config,
                    self.attn_groups,
                    cache_dtype,
                    self.device,
                    kernel_block_sizes,
                )
            )
            self.cross_layers_kv_cache = cross_layers_kv_cache
            self.cross_layers_attn_backend = attn_backend
        else:
            # Fallback to the general case
            # Initialize the memory buffer for KV cache
            kv_cache_raw_tensors = self._allocate_kv_cache_tensors(kv_cache_config)

            # Change the memory buffer to the desired shape
            kv_caches = self._reshape_kv_cache_tensors(
                kv_cache_config, kv_cache_raw_tensors, kernel_block_sizes
            )
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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()
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            if self.cross_layers_kv_cache is not None:
                assert self.cross_layers_attn_backend is not None
                kv_transfer_group.register_cross_layers_kv_cache(
                    self.cross_layers_kv_cache, self.cross_layers_attn_backend
                )
            else:
                kv_transfer_group.register_kv_caches(kv_caches)
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            kv_transfer_group.set_host_xfer_buffer_ops(copy_kv_blocks)
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        if self.dcp_world_size > 1:
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            layer_type = cast(type[Any], AttentionLayerBase)
            layers = get_layers_from_vllm_config(self.vllm_config, layer_type)
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            for layer in layers.values():
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                layer_impl = getattr(layer, "impl", None)
                if layer_impl is None:
                    continue
                assert layer_impl.need_to_return_lse_for_decode, (
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                    "DCP requires attention impls to return"
                    " the softmax lse for decode, but the impl "
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                    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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        layer_type = cast(type[Any], AttentionLayerBase)
        attn_layers = get_layers_from_vllm_config(self.vllm_config, layer_type)
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        for layer_name, attn_module in attn_layers.items():
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            if isinstance(attn_module, Attention) and (
                kv_tgt_layer := attn_module.kv_sharing_target_layer_name
            ):
                # The layer doesn't need its own KV cache and will use that of
                # the target layer. We skip creating a KVCacheSpec for it, so
                # that KV cache management logic will act as this layer does
                # not exist, and doesn't allocate KV cache for the layer. This
                # enables the memory saving of cross-layer kv sharing, allowing
                # a given amount of memory to accommodate longer context lengths
                # or enable more requests to be processed simultaneously.
                self.shared_kv_cache_layers[layer_name] = kv_tgt_layer
                continue
            # Skip modules that don't need KV cache (eg encoder-only attention)
            if spec := attn_module.get_kv_cache_spec(self.vllm_config):
                kv_cache_spec[layer_name] = spec
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        return kv_cache_spec
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    def _to_list(self, sampled_token_ids: torch.Tensor) -> list[list[int]]:
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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 pinned.tolist()