gpu_model_runner.py 207 KB
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import gc
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import itertools
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import time
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from collections import defaultdict
from collections.abc import Iterator
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from contextlib import contextmanager
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from copy import deepcopy
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from functools import reduce
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from itertools import product
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from typing import TYPE_CHECKING, Any, NamedTuple, TypeAlias, cast
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import numpy as np
import torch
import torch.distributed
import torch.nn as nn
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from tqdm import tqdm
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import vllm.envs as envs
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from vllm.attention import Attention, AttentionType
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from vllm.attention.backends.abstract import (
    AttentionBackend,
    AttentionMetadata,
    MultipleOf,
)
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from vllm.compilation.counter import compilation_counter
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from vllm.compilation.cuda_graph import CUDAGraphWrapper
from vllm.compilation.monitor import set_cudagraph_capturing_enabled
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from vllm.config import (
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    CompilationMode,
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    CUDAGraphMode,
    VllmConfig,
    get_layers_from_vllm_config,
    update_config,
)
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from vllm.distributed.ec_transfer import get_ec_transfer, has_ec_transfer
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from vllm.distributed.eplb.eplb_state import EplbState
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from vllm.distributed.kv_transfer import get_kv_transfer_group, has_kv_transfer_group
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from vllm.distributed.kv_transfer.kv_connector.utils import copy_kv_blocks
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from vllm.distributed.parallel_state import (
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    get_dcp_group,
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    get_pp_group,
    get_tp_group,
    graph_capture,
    is_global_first_rank,
    prepare_communication_buffer_for_model,
)
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from vllm.forward_context import BatchDescriptor, set_forward_context
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from vllm.logger import init_logger
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from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
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from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
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from vllm.model_executor.model_loader import TensorizerLoader, get_model_loader
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from vllm.model_executor.models.interfaces import (
    SupportsMultiModal,
    is_mixture_of_experts,
    supports_eagle3,
    supports_mrope,
    supports_multimodal_pruning,
    supports_transcription,
)
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from vllm.model_executor.models.interfaces_base import (
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    VllmModelForPooling,
    is_pooling_model,
    is_text_generation_model,
)
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (
    BatchedTensorInputs,
    MultiModalKwargsItem,
    PlaceholderRange,
)
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from vllm.multimodal.utils import group_mm_kwargs_by_modality
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from vllm.pooling_params import PoolingParams
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from vllm.sampling_params import SamplingType
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from vllm.sequence import IntermediateTensors
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from vllm.tasks import GenerationTask, PoolingTask, SupportedTask
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from vllm.utils import length_from_prompt_token_ids_or_embeds
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from vllm.utils.jsontree import json_map_leaves
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from vllm.utils.math_utils import cdiv, round_up
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from vllm.utils.mem_constants import GiB_bytes
from vllm.utils.mem_utils import DeviceMemoryProfiler
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from vllm.utils.platform_utils import is_pin_memory_available
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from vllm.utils.torch_utils import (
    get_dtype_size,
    kv_cache_dtype_str_to_dtype,
    supports_dynamo,
)
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from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadataBuilder
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from vllm.v1.attention.backends.utils import (
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    AttentionCGSupport,
    AttentionMetadataBuilder,
    CommonAttentionMetadata,
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    create_fast_prefill_custom_backend,
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    get_dcp_local_seq_lens,
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    reorder_batch_to_split_decodes_and_prefills,
    split_attn_metadata,
)
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from vllm.v1.cudagraph_dispatcher import CudagraphDispatcher
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from vllm.v1.kv_cache_interface import (
    AttentionSpec,
    ChunkedLocalAttentionSpec,
    CrossAttentionSpec,
    EncoderOnlyAttentionSpec,
    FullAttentionSpec,
    KVCacheConfig,
    KVCacheGroupSpec,
    KVCacheSpec,
    MambaSpec,
    SlidingWindowSpec,
    UniformTypeKVCacheSpecs,
)
from vllm.v1.outputs import (
    EMPTY_MODEL_RUNNER_OUTPUT,
    AsyncModelRunnerOutput,
    DraftTokenIds,
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    ECConnectorOutput,
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    KVConnectorOutput,
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    LogprobsLists,
    LogprobsTensors,
    ModelRunnerOutput,
    PoolerOutput,
    SamplerOutput,
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    make_empty_encoder_model_runner_output,
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)
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from vllm.v1.pool.metadata import PoolingMetadata
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from vllm.v1.sample.logits_processor import LogitsProcessors, build_logitsprocs
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from vllm.v1.sample.metadata import SamplingMetadata
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from vllm.v1.sample.rejection_sampler import RejectionSampler
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from vllm.v1.sample.sampler import Sampler
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from vllm.v1.spec_decode.eagle import EagleProposer
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from vllm.v1.spec_decode.medusa import MedusaProposer
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from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
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from vllm.v1.spec_decode.ngram_proposer import NgramProposer
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from vllm.v1.spec_decode.suffix_decoding import SuffixDecodingProposer
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from vllm.v1.structured_output.utils import apply_grammar_bitmask
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from vllm.v1.utils import CpuGpuBuffer, record_function_or_nullcontext
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from vllm.v1.worker.dp_utils import coordinate_batch_across_dp
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from vllm.v1.worker.ec_connector_model_runner_mixin import ECConnectorModelRunnerMixin
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from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
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from vllm.v1.worker.gpu_ubatch_wrapper import UBatchWrapper
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from vllm.v1.worker.kv_connector_model_runner_mixin import KVConnectorModelRunnerMixin
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from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
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from vllm.v1.worker.ubatch_utils import (
    UBatchSlice,
    UBatchSlices,
    check_ubatch_thresholds,
)
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from vllm.v1.worker.utils import is_residual_scattered_for_sp
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from .utils import (
    AttentionGroup,
    MultiModalBudget,
    add_kv_sharing_layers_to_kv_cache_groups,
    bind_kv_cache,
    gather_mm_placeholders,
    sanity_check_mm_encoder_outputs,
    scatter_mm_placeholders,
)
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if TYPE_CHECKING:
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    from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
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    from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput
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logger = init_logger(__name__)

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

        # Event on the copy stream so we can synchronize the non-blocking copy.
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        self.async_copy_ready_event = torch.cuda.Event()
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        # Keep a reference to the device tensor to avoid it being
        # deallocated until we finish copying it to the host.
        self._sampled_token_ids = sampled_token_ids
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        self._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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        valid_sampled_token_ids: list[np.ndarray] = [
            row for row in self.sampled_token_ids_cpu.numpy()
        ]
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        for i in self._invalid_req_indices:
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            valid_sampled_token_ids[i] = np.array([])
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        output = self._model_runner_output
        output.sampled_token_ids = valid_sampled_token_ids
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        if self._logprobs_tensors_cpu:
            # NOTE(nick): this will need to be updated to use cu_num_accepted_tokens
            # for async sched + spec decode + logprobs compatibility.
            output.logprobs = self._logprobs_tensors_cpu.tolists()
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        return output


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

    scheduler_output: "SchedulerOutput"
    logits: torch.Tensor
    spec_decode_metadata: SpecDecodeMetadata | None
    spec_decode_common_attn_metadata: CommonAttentionMetadata | None
    hidden_states: torch.Tensor
    sample_hidden_states: torch.Tensor
    aux_hidden_states: list[torch.Tensor] | None
    kv_connector_output: KVConnectorOutput | None
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    ec_connector_output: ECConnectorOutput | None
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class GPUModelRunner(
    LoRAModelRunnerMixin, KVConnectorModelRunnerMixin, ECConnectorModelRunnerMixin
):
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    def __init__(
        self,
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        vllm_config: VllmConfig,
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        device: torch.device,
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    ):
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        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        self.cache_config = vllm_config.cache_config
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        self.compilation_config = vllm_config.compilation_config
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        self.lora_config = vllm_config.lora_config
        self.load_config = vllm_config.load_config
        self.parallel_config = vllm_config.parallel_config
        self.scheduler_config = vllm_config.scheduler_config
        self.speculative_config = vllm_config.speculative_config
        self.observability_config = vllm_config.observability_config
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        from vllm.model_executor.models.utils import set_cpu_offload_max_bytes
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        set_cpu_offload_max_bytes(int(self.cache_config.cpu_offload_gb * 1024**3))
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        model_config = self.model_config
        cache_config = self.cache_config
        scheduler_config = self.scheduler_config
        parallel_config = self.parallel_config
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        self.device = device
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        self.pin_memory = is_pin_memory_available()
        self.dtype = self.model_config.dtype
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        self.kv_cache_dtype = kv_cache_dtype_str_to_dtype(
            cache_config.cache_dtype, self.model_config
        )
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        self.is_pooling_model = model_config.runner_type == "pooling"
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        self.enable_prompt_embeds = model_config.enable_prompt_embeds
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        self.is_multimodal_raw_input_only_model = (
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            model_config.is_multimodal_raw_input_only_model
        )
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        # This will be overridden in load_model()
        self.is_multimodal_pruning_enabled = False
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        self.max_model_len = model_config.max_model_len
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        # Always set to false after the first forward pass
        self.calculate_kv_scales = self.cache_config.calculate_kv_scales
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        self.dcp_world_size = self.parallel_config.decode_context_parallel_size
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        self.dcp_rank = 0 if self.dcp_world_size <= 1 else get_dcp_group().rank_in_group
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        self.max_num_tokens = scheduler_config.max_num_batched_tokens
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        self.max_num_reqs = scheduler_config.max_num_seqs
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        # Broadcast PP output for external_launcher (torchrun)
        # to make sure we are synced across pp ranks
        # TODO: Support overlapping mirco-batches
        # https://github.com/vllm-project/vllm/issues/18019
        self.broadcast_pp_output = (
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            self.parallel_config.distributed_executor_backend == "external_launcher"
            and len(get_pp_group().ranks) > 0
        )
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        # Model-related.
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        self.num_query_heads = model_config.get_num_attention_heads(parallel_config)
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        self.hidden_size = model_config.get_hidden_size()
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        self.attention_chunk_size = model_config.attention_chunk_size
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        # Only relevant for models using ALiBi (e.g, MPT)
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        self.use_alibi = model_config.uses_alibi
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        self.cascade_attn_enabled = not self.model_config.disable_cascade_attn
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        # Multi-modal data support
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        self.mm_registry = MULTIMODAL_REGISTRY
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        self.uses_mrope = model_config.uses_mrope
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        self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs(
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            model_config
        )
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        if self.model_config.is_encoder_decoder:
            # Maximum length of the encoder input, only for encoder-decoder
            # models.
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            self.max_encoder_len = scheduler_config.max_num_encoder_input_tokens
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        else:
            self.max_encoder_len = 0

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

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

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

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        # mm_hash ->  encoder_output
        self.encoder_cache: dict[str, torch.Tensor] = {}
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        self.use_aux_hidden_state_outputs = False
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        # Set up speculative decoding.
        # NOTE(Jiayi): currently we put the entire draft model on
        # the last PP rank. This is not ideal if there are many
        # layers in the draft model.
        if self.speculative_config and get_pp_group().is_last_rank:
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            self.drafter: (
                NgramProposer | SuffixDecodingProposer | EagleProposer | MedusaProposer
            )
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            if self.speculative_config.method == "ngram":
                self.drafter = NgramProposer(self.vllm_config)
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            elif self.speculative_config.method == "suffix":
                self.drafter = SuffixDecodingProposer(self.vllm_config)
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            elif self.speculative_config.use_eagle():
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                self.drafter = EagleProposer(self.vllm_config, self.device, self)
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                if self.speculative_config.method == "eagle3":
                    self.use_aux_hidden_state_outputs = True
            elif self.speculative_config.method == "medusa":
                self.drafter = MedusaProposer(
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                    vllm_config=self.vllm_config, device=self.device
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                )
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            else:
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                raise ValueError(
                    "Unknown speculative decoding method: "
                    f"{self.speculative_config.method}"
                )
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            self.rejection_sampler = RejectionSampler(self.sampler)
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        # Request states.
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        self.requests: dict[str, CachedRequestState] = {}
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        self.comm_stream = torch.cuda.Stream()
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        # Input Batch
        # NOTE(Chen): Ideally, we should initialize the input batch inside
        # `initialize_kv_cache` based on the kv cache config. However, as in
        # https://github.com/vllm-project/vllm/pull/18298, due to some unknown
        # reasons, we have to initialize the input batch before `load_model`,
        # quantization + weight offloading will fail otherwise. As a temporary
        # solution, we initialize the input batch here, and re-initialize it
        # in `initialize_kv_cache` if the block_sizes here is different from
        # the block_sizes in the kv cache config.
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        custom_logitsprocs = model_config.logits_processors
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        self.input_batch = InputBatch(
            max_num_reqs=self.max_num_reqs,
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            # We need to use the encoder length for encoder-decoer
            # because of KV cache for cross-attention.
            max_model_len=max(self.max_model_len, self.max_encoder_len),
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            max_num_batched_tokens=self.max_num_tokens,
            device=self.device,
            pin_memory=self.pin_memory,
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            vocab_size=self.model_config.get_vocab_size(),
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            block_sizes=[self.cache_config.block_size],
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            kernel_block_sizes=[self.cache_config.block_size],
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            is_spec_decode=bool(self.vllm_config.speculative_config),
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            logitsprocs=build_logitsprocs(
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                self.vllm_config,
                self.device,
                self.pin_memory,
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                self.is_pooling_model,
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                custom_logitsprocs,
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            ),
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            # We currently don't know whether a particular custom logits processor
            # uses output token ids so we set this conservatively.
            logitsprocs_need_output_token_ids=bool(custom_logitsprocs),
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            is_pooling_model=self.is_pooling_model,
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            dcp_kv_cache_interleave_size=self.parallel_config.dcp_kv_cache_interleave_size,
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        )
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        self.use_async_scheduling = self.scheduler_config.async_scheduling
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        # Separate cuda stream for overlapping transfer of sampled token ids from
        # GPU to CPU when async scheduling is enabled.
        self.async_output_copy_stream: torch.cuda.Stream | None = None
        # cuda event to synchronize use of reused CPU tensors between steps
        # when async scheduling is enabled.
        self.prepare_inputs_event: torch.cuda.Event | None = None
        if self.use_async_scheduling:
            self.async_output_copy_stream = torch.cuda.Stream()
            self.prepare_inputs_event = torch.cuda.Event()
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        # self.cudagraph_batch_sizes sorts in ascending order.
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        if (
            self.compilation_config.cudagraph_capture_sizes
            and self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
        ):
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            self.cudagraph_batch_sizes = sorted(
                self.compilation_config.cudagraph_capture_sizes
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            )
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        # Cache the device properties.
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        self._init_device_properties()
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        # Persistent buffers for CUDA graphs.
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        self.input_ids = self._make_buffer(self.max_num_tokens, dtype=torch.int32)
        self.positions = self._make_buffer(self.max_num_tokens, dtype=torch.int64)
        self.query_start_loc = self._make_buffer(
            self.max_num_reqs + 1, dtype=torch.int32
        )
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        self.seq_lens = self._make_buffer(self.max_num_reqs, dtype=torch.int32)
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        if self.dcp_world_size > 1:
            self.dcp_local_seq_lens = self._make_buffer(
                self.max_num_reqs, dtype=torch.int32
            )
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        # Because inputs_embeds may be bfloat16 and we don't need a numpy
        # version of this tensor, avoid a RuntimeError by not creating a
        # numpy buffer.
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        self.inputs_embeds = self._make_buffer(
            self.max_num_tokens, self.hidden_size, dtype=self.dtype, numpy=False
        )
        self.is_token_ids = self._make_buffer(self.max_num_tokens, dtype=torch.bool)
        self.discard_request_indices = self._make_buffer(
            self.max_num_reqs, dtype=torch.int64
        )
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        self.num_discarded_requests = 0

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        self.num_decode_draft_tokens = self._make_buffer(
            self.max_num_reqs, dtype=torch.int32
        )
        self.num_accepted_tokens = self._make_buffer(
            self.max_num_reqs, dtype=torch.int64
        )
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        # Only relevant for multimodal models
        if self.supports_mm_inputs:
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            self.is_mm_embed = self._make_buffer(self.max_num_tokens, dtype=torch.bool)
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        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
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        if self.uses_mrope:
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            # NOTE: `mrope_positions` is implemented with one additional dummy
            # position on purpose to make it non-contiguous so that it can work
            # with torch compile.
            # See detailed explanation in https://github.com/vllm-project/vllm/pull/12128#discussion_r1926431923
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            # NOTE: When M-RoPE is enabled, position ids are 3D regardless of
            # the modality of inputs. For text-only inputs, each dimension has
            # identical position IDs, making M-RoPE functionally equivalent to
            # 1D-RoPE.
            # See page 5 of https://arxiv.org/abs/2409.12191
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            self.mrope_positions = self._make_buffer(
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                (3, self.max_num_tokens + 1), dtype=torch.int64
            )
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        # None in the first PP rank. The rest are set after load_model.
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        self.intermediate_tensors: IntermediateTensors | None = None
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        # OPTIMIZATION: Cache the tensors rather than creating them every step.
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        # Keep in int64 to avoid overflow with long context
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        self.arange_np = np.arange(
            max(self.max_num_reqs + 1, self.max_model_len, self.max_num_tokens),
            dtype=np.int64,
        )
502

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        # Layer pairings for cross-layer KV sharing.
        # If an Attention layer `layer_name` is in the keys of this dict, it
        # means this layer will perform attention using the keys and values
        # from the KV cache of `shared_kv_cache_layers[layer_name]`.
        self.shared_kv_cache_layers: dict[str, str] = {}
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        self.kv_sharing_fast_prefill_eligible_layers: set[str] = set()

        self.kv_sharing_fast_prefill_logits_indices = None
        if self.cache_config.kv_sharing_fast_prefill:
            self.kv_sharing_fast_prefill_logits_indices = torch.zeros(
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                self.max_num_tokens, dtype=torch.int32, device=self.device
            )
515

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        self.uniform_decode_query_len = (
            1
            if not self.speculative_config
            else 1 + self.speculative_config.num_speculative_tokens
        )
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524

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

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531
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        self.mm_budget = (
            MultiModalBudget(
                self.model_config,
                self.scheduler_config,
                self.mm_registry,
            )
            if self.supports_mm_inputs
            else None
        )
534

535
        self.reorder_batch_threshold: int | None = None
536

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541
        # 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()

542
        # Cached outputs.
543
        self._draft_token_ids: list[list[int]] | torch.Tensor | None = None
544
545
        self.transfer_event = torch.cuda.Event()
        self.sampled_token_ids_pinned_cpu = torch.empty(
546
            (self.max_num_reqs, 1),
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548
            dtype=torch.int64,
            device="cpu",
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550
            pin_memory=self.pin_memory,
        )
551

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554
        # Ephemeral state transferred between execute_model() and sample_tokens().
        self.execute_model_state: ExecuteModelState | None = None

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

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

569
    def _make_buffer(
570
        self, *size: int | torch.SymInt, dtype: torch.dtype, numpy: bool = True
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    ) -> CpuGpuBuffer:
        return CpuGpuBuffer(
            *size,
            dtype=dtype,
            device=self.device,
            pin_memory=self.pin_memory,
            with_numpy=numpy,
        )
579

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

583
        if not self.is_pooling_model:
584
585
            return model_kwargs

586
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        num_reqs = self.input_batch.num_reqs
        pooling_params = self.input_batch.get_pooling_params()
588
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590

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

        if len(token_type_id_requests) == 0:
            return model_kwargs

601
        seq_lens = self.seq_lens.gpu[:num_reqs]
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        token_type_ids = []

        for i in range(num_reqs):
            pos = token_type_id_requests.get(i, seq_lens[i])
            ids = (torch.arange(seq_lens[i]) >= pos).int()
            token_type_ids.append(ids)

        model_kwargs["token_type_ids"] = torch.concat(token_type_ids).to(
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            device=self.device
        )
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        return model_kwargs

614
    def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
615
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        """
        Update the order of requests in the batch based on the attention
617
        backend's needs. For example, some attention backends (namely MLA) may
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        want to separate requests based on if the attention computation will be
        compute-bound or memory-bound.

        Args:
            scheduler_output: The scheduler output.
        """
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        # Attention free models have zero kv_cache_goups, however models
        # like Mamba are also attention free but use the kv_cache for
        # keeping its internal state. This is why we check the number
        # of kv_cache groups instead of solely checking
        # for self.model_config.is_attention_free.
        if len(self.kv_cache_config.kv_cache_groups) == 0:
            return

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        if self.reorder_batch_threshold is not None:
            reorder_batch_to_split_decodes_and_prefills(
                self.input_batch,
                scheduler_output,
636
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                decode_threshold=self.reorder_batch_threshold,
            )
638

639
640
    # Note: used for model runner override.
    def _init_device_properties(self) -> None:
641
        """Initialize attributes from torch.cuda.get_device_properties"""
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648
        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()

649
    def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
650
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655
        """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.

656
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        The SamplingMetadata is updated and copied to the GPU if there is a
        new/resumed/paused/finished request in the batch.
658
659
        """
        # Remove finished requests from the cached states.
660
661
        for req_id in scheduler_output.finished_req_ids:
            self.requests.pop(req_id, None)
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664
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        # Remove the finished requests from the persistent batch.
        # NOTE(woosuk): There could be an edge case where finished_req_ids and
        # scheduled_req_ids overlap. This happens when a request is aborted and
        # then resubmitted with the same ID. In this case, we treat them as two
        # distinct requests - clearing the cached states for the first request
        # and handling the second as a new request.
        for req_id in scheduler_output.finished_req_ids:
669
            self.input_batch.remove_request(req_id)
670
671

        # Free the cached encoder outputs.
672
673
        for mm_hash in scheduler_output.free_encoder_mm_hashes:
            self.encoder_cache.pop(mm_hash, None)
674

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686
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        # Remove the unscheduled requests from the persistent batch.
        # NOTE(woosuk): The unscheduled requests are either preempted requests
        # or running requests that are not scheduled in this step. We remove
        # them from the persistent batch but keep their cached states since
        # they will be scheduled again sometime in the future.
        scheduled_req_ids = scheduler_output.num_scheduled_tokens.keys()
        cached_req_ids = self.input_batch.req_id_to_index.keys()
        unscheduled_req_ids = cached_req_ids - scheduled_req_ids
        # NOTE(woosuk): The persistent batch optimization assumes that
        # consecutive batches contain mostly the same requests. If batches
        # have low request overlap (e.g., alternating between two distinct
        # sets of requests), this optimization becomes very inefficient.
        for req_id in unscheduled_req_ids:
688
            self.input_batch.remove_request(req_id)
689

690
        reqs_to_add: list[CachedRequestState] = []
691
        # Add new requests to the cached states.
692
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694
        for new_req_data in scheduler_output.scheduled_new_reqs:
            req_id = new_req_data.req_id
            sampling_params = new_req_data.sampling_params
695
            pooling_params = new_req_data.pooling_params
696

697
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699
700
            if (
                sampling_params
                and sampling_params.sampling_type == SamplingType.RANDOM_SEED
            ):
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705
                generator = torch.Generator(device=self.device)
                generator.manual_seed(sampling_params.seed)
            else:
                generator = None

706
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            if self.is_pooling_model:
                assert pooling_params is not None
708
709
                task = pooling_params.task
                assert task is not None, "You did not set `task` in the API"
710

711
                model = cast(VllmModelForPooling, self.get_model())
712
                to_update = model.pooler.get_pooling_updates(task)
713
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                to_update.apply(pooling_params)

715
            req_state = CachedRequestState(
716
                req_id=req_id,
717
                prompt_token_ids=new_req_data.prompt_token_ids,
718
                prompt_embeds=new_req_data.prompt_embeds,
719
                mm_features=new_req_data.mm_features,
720
                sampling_params=sampling_params,
721
                pooling_params=pooling_params,
722
                generator=generator,
723
724
                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
725
                output_token_ids=[],
726
                lora_request=new_req_data.lora_request,
727
            )
728
729
            self.requests[req_id] = req_state

730
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
731
            if self.uses_mrope:
732
                self._init_mrope_positions(req_state)
733

734
            reqs_to_add.append(req_state)
735

736
        # Update the states of the running/resumed requests.
737
        is_last_rank = get_pp_group().is_last_rank
738
739
        req_data = scheduler_output.scheduled_cached_reqs
        for i, req_id in enumerate(req_data.req_ids):
740
            req_state = self.requests[req_id]
741
742
            num_computed_tokens = req_data.num_computed_tokens[i]
            new_block_ids = req_data.new_block_ids[i]
743
            resumed_from_preemption = req_id in req_data.resumed_req_ids
744
            num_output_tokens = req_data.num_output_tokens[i]
745

746
            # Update the cached states.
747

748
            req_state.num_computed_tokens = num_computed_tokens
749
            req_index = self.input_batch.req_id_to_index.get(req_id)
750
751
752
753
754
755
756
757

            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.
758
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760
                num_new_tokens = (
                    num_computed_tokens + len(new_token_ids) - req_state.num_tokens
                )
761
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763
764
                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:
765
                    req_state.output_token_ids.extend(new_token_ids[-num_new_tokens:])
766
767
768
769
770
            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:
771
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773
774
                    end_idx = (
                        self.input_batch.num_prompt_tokens[req_index]
                        + num_output_tokens
                    )
775
776
                    self.input_batch.num_tokens[req_index] = end_idx
                    self.input_batch.num_tokens_no_spec[req_index] = end_idx
777

778
            # Update the block IDs.
779
            if not resumed_from_preemption:
780
781
                if new_block_ids is not None:
                    # Append the new blocks to the existing block IDs.
782
                    for block_ids, new_ids in zip(req_state.block_ids, new_block_ids):
783
                        block_ids.extend(new_ids)
784
            else:
785
                assert req_index is None
786
                assert new_block_ids is not None
787
788
                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
789
                req_state.block_ids = new_block_ids
790
791
792
793
794

            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.
795
796
797
798
799
800
801

                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:]

802
                reqs_to_add.append(req_state)
803
804
805
                continue

            # Update the persistent batch.
806
            self.input_batch.num_computed_tokens_cpu[req_index] = num_computed_tokens
807
            if new_block_ids is not None:
808
                self.input_batch.block_table.append_row(new_block_ids, req_index)
809
810
811
812
813
814
815

            # 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)
816
                self.input_batch.token_ids_cpu[
817
818
819
                    req_index, start_token_index:end_token_index
                ] = new_token_ids
                self.input_batch.num_tokens_no_spec[req_index] = end_token_index
820
                self.input_batch.num_tokens[req_index] = end_token_index
821

822
            # Add spec_token_ids to token_ids_cpu.
823
            spec_token_ids = scheduler_output.scheduled_spec_decode_tokens.get(
824
                req_id, []
825
            )
826
827
828
829
830
            if spec_token_ids:
                num_spec_tokens = len(spec_token_ids)
                start_index = self.input_batch.num_tokens_no_spec[req_index]
                end_token_index = start_index + num_spec_tokens
                self.input_batch.token_ids_cpu[
831
832
                    req_index, start_index:end_token_index
                ] = spec_token_ids
833
834
                # NOTE(woosuk): `num_tokens` here may include spec tokens.
                self.input_batch.num_tokens[req_index] += num_spec_tokens
835
836
837
838
839
840
841

            # When speculative decoding is used with structured output,
            # the scheduler can drop draft tokens that do not
            # conform to the schema. This can result in
            # scheduler_output.scheduled_spec_decode_tokens being empty,
            # even when speculative decoding is enabled.
            self.input_batch.spec_token_ids[req_index] = spec_token_ids
842

843
844
        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
845
846
        for request in reqs_to_add:
            self.input_batch.add_request(request)
847

848
849
850
851
852
853
        # 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()
854

855
    def _update_states_after_model_execute(
856
857
        self, output_token_ids: torch.Tensor
    ) -> None:
858
859
860
861
862
863
864
865
866
867
868
869
        """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.
870
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873
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875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
        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()
        )
890
891
892
        for i, num_tokens in enumerate(num_accepted_tokens):
            self.input_batch.num_accepted_tokens_cpu[i] = num_tokens

893
    def _init_mrope_positions(self, req_state: CachedRequestState):
894
895
        model = self.get_model()
        assert supports_mrope(model), "M-RoPE support is not implemented."
896
897

        req_state.mrope_positions, req_state.mrope_position_delta = (
898
            model.get_mrope_input_positions(
899
                req_state.prompt_token_ids,
900
                req_state.mm_features,
901
            )
902
        )
903

904
    def _extract_mm_kwargs(
905
        self,
906
907
        scheduler_output: "SchedulerOutput",
    ) -> BatchedTensorInputs:
908
        if not scheduler_output or not self.is_multimodal_raw_input_only_model:
909
            return {}
910

911
912
        mm_kwargs = list[MultiModalKwargsItem]()
        for req in scheduler_output.scheduled_new_reqs:
913
914
915
            for feature in req.mm_features:
                if feature.data is not None:
                    mm_kwargs.append(feature.data)
916

917
        # Input all modalities at once
918
        model = cast(SupportsMultiModal, self.model)
919
920
        mm_kwargs_combined: BatchedTensorInputs = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
921
922
923
924
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
925
            multimodal_cpu_fields=model.multimodal_cpu_fields,
926
927
        ):
            mm_kwargs_combined.update(mm_kwargs_group)
928

929
        return mm_kwargs_combined
930

931
    def _dummy_mm_kwargs(self, num_seqs: int) -> BatchedTensorInputs:
932
        if not self.is_multimodal_raw_input_only_model:
933
            return {}
934

935
936
937
938
939
        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)
940

941
942
943
    def _get_cumsum_and_arange(
        self,
        num_tokens: np.ndarray,
944
        cumsum_dtype: np.dtype | None = None,
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
    ) -> 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

961
962
963
    def _prepare_input_ids(
        self, total_num_scheduled_tokens: int, cu_num_tokens: np.ndarray
    ) -> None:
964
        """Prepare the input IDs for the current batch.
965

966
967
968
969
970
971
972
        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)
973
974
975
            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)
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
            return

        # Async scheduling case, where some decode requests from the previous
        # iteration won't have entries in input_ids_cpu and need to be copied
        # on the GPU from prev_sampled_token_ids.
        prev_req_id_to_index = self.input_batch.prev_req_id_to_index
        assert prev_req_id_to_index is not None
        flattened_indices = []
        prev_common_req_indices = []
        indices_match = True
        max_flattened_index = -1
        for req_id, cur_index in self.input_batch.req_id_to_index.items():
            if (prev_index := prev_req_id_to_index.get(req_id)) is not None:
                prev_common_req_indices.append(prev_index)
                # We need to compute the flattened input_ids index of the
                # last token in each common request.
                flattened_index = cu_num_tokens[cur_index].item() - 1
                flattened_indices.append(flattened_index)
994
                indices_match &= prev_index == flattened_index
995
996
997
998
999
1000
                max_flattened_index = max(max_flattened_index, flattened_index)
        num_commmon_tokens = len(flattened_indices)
        if num_commmon_tokens < total_num_scheduled_tokens:
            # If not all requests are decodes from the last iteration,
            # We need to copy the input_ids_cpu to the GPU first.
            self.input_ids.copy_to_gpu(total_num_scheduled_tokens)
1001
1002
1003
            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)
1004
1005
        if num_commmon_tokens == 0:
            # No requests in common with the previous iteration
1006
            # So input_ids.cpu will have all the input ids.
1007
1008
1009
1010
1011
1012
1013
            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_(
1014
1015
1016
                self.input_batch.prev_sampled_token_ids[:num_commmon_tokens, 0],
                non_blocking=True,
            )
1017
1018
            if self.enable_prompt_embeds:
                self.is_token_ids.gpu[:num_commmon_tokens] = True
1019
            return
1020
        # Upload the index tensors asynchronously so the scatter can be non-blocking.
1021
1022
1023
        input_ids_index_tensor = torch.tensor(
            flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1024
        prev_common_req_indices_tensor = torch.tensor(
1025
1026
            prev_common_req_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1027
1028
1029
1030
        self.input_ids.gpu.scatter_(
            dim=0,
            index=input_ids_index_tensor,
            src=self.input_batch.prev_sampled_token_ids[
1031
1032
1033
                prev_common_req_indices_tensor, 0
            ],
        )
1034

1035
1036
    def _get_encoder_seq_lens(
        self,
1037
        scheduled_encoder_inputs: dict[str, list[int]],
1038
1039
        kv_cache_spec: KVCacheSpec,
        num_reqs: int,
1040
    ) -> np.ndarray | None:
1041
1042
1043
1044
1045
1046
        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)
1047
        for req_id in scheduled_encoder_inputs:
1048
1049
1050
1051
1052
            req_index = self.input_batch.req_id_to_index[req_id]
            encoder_seq_lens[req_index] = self.max_encoder_len

        return encoder_seq_lens

1053
    def _prepare_inputs(
1054
1055
1056
1057
        self,
        scheduler_output: "SchedulerOutput",
        num_scheduled_tokens: np.ndarray,
        max_num_scheduled_tokens: int,
1058
1059
    ) -> tuple[
        torch.Tensor,
1060
1061
1062
        SpecDecodeMetadata | None,
        UBatchSlices | None,
        torch.Tensor | None,
1063
    ]:
1064
1065
        """
        :return: tuple[
1066
            logits_indices, spec_decode_metadata,
1067
            ubatch_slices, num_tokens_across_dp,
1068
1069
        ]
        """
1070
1071
1072
1073
1074
1075
1076
        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.
1077
        self.input_batch.block_table.commit_block_table(num_reqs)
1078
1079
1080

        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
1081
        req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens)
1082

1083
1084
        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
        # arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
1085
        cu_num_tokens, arange = self._get_cumsum_and_arange(num_scheduled_tokens)
1086
1087

        # Get positions.
1088
        positions_np = self.positions.np[:total_num_scheduled_tokens]
1089
1090
1091
1092
1093
        np.add(
            self.input_batch.num_computed_tokens_cpu[req_indices],
            arange,
            out=positions_np,
        )
1094

1095
1096
        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1097
        if self.uses_mrope:
1098
1099
            self._calc_mrope_positions(scheduler_output)

1100
1101
1102
1103
        # 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.
1104
1105
1106
        token_indices = (
            positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]
        )
1107
        token_indices_tensor = torch.from_numpy(token_indices)
1108

1109
1110
1111
        # 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.
1112
1113
1114
1115
1116
1117
        torch.index_select(
            self.input_batch.token_ids_cpu_tensor.flatten(),
            0,
            token_indices_tensor,
            out=self.input_ids.cpu[:total_num_scheduled_tokens],
        )
1118
        if self.enable_prompt_embeds:
1119
            is_token_ids = self.input_batch.is_token_ids_tensor.flatten()
1120
1121
1122
1123
            torch.index_select(
                is_token_ids,
                0,
                token_indices_tensor,
1124
1125
                out=self.is_token_ids.cpu[:total_num_scheduled_tokens],
            )
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158

        # 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:
1159
1160
1161
                    self.inputs_embeds.cpu[
                        output_idx : output_idx + actual_num_sched
                    ].copy_(req_embeds[start_pos:actual_end])
1162
1163

                output_idx += num_sched
1164

1165
1166
        self.input_batch.block_table.compute_slot_mapping(req_indices, positions_np)
        self.input_batch.block_table.commit_slot_mapping(total_num_scheduled_tokens)
1167
1168

        # Prepare the attention metadata.
1169
        self.query_start_loc.np[0] = 0
1170
        self.query_start_loc.np[1 : num_reqs + 1] = cu_num_tokens
1171
1172
        # Note: pad query_start_loc to be non-decreasing, as kernels
        # like FlashAttention requires that
1173
        self.query_start_loc.np[num_reqs + 1 :].fill(cu_num_tokens[-1])
1174
        self.query_start_loc.copy_to_gpu()
1175
        query_start_loc = self.query_start_loc.gpu[: num_reqs + 1]
1176

1177
        num_tokens_unpadded = scheduler_output.total_num_scheduled_tokens
1178
        num_tokens_padded = self._get_num_input_tokens(num_tokens_unpadded)
1179
1180
1181
        uniform_decode = (
            max_num_scheduled_tokens == self.uniform_decode_query_len
        ) and (total_num_scheduled_tokens == num_reqs * max_num_scheduled_tokens)
1182
1183
1184
1185
1186
1187
1188

        # 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

1189
        ubatch_slices, num_tokens_across_dp = coordinate_batch_across_dp(
1190
1191
1192
1193
1194
1195
1196
            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,
1197
        )
1198

1199
        self.seq_lens.np[:num_reqs] = (
1200
1201
            self.input_batch.num_computed_tokens_cpu[:num_reqs] + num_scheduled_tokens
        )
1202
        # Fill unused with 0 for full cuda graph mode.
1203
1204
        self.seq_lens.np[num_reqs:].fill(0)
        self.seq_lens.copy_to_gpu()
1205

1206
        num_tokens = [self.requests[r].num_tokens for r in self.input_batch.req_ids]
1207
1208
1209
1210
1211
1212
1213
        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)
1214
1215
1216
        self.discard_request_indices.np[: self.num_discarded_requests] = (
            discard_request_indices
        )
1217
1218
1219

        self.discard_request_indices.copy_to_gpu(self.num_discarded_requests)

1220
        # Copy the tensors to the GPU.
1221
1222
        self._prepare_input_ids(total_num_scheduled_tokens, cu_num_tokens)

1223
        if self.uses_mrope:
1224
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1225
1226
            self.mrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.mrope_positions.cpu[:, :total_num_scheduled_tokens],
1227
1228
                non_blocking=True,
            )
1229
1230
        else:
            # Common case (1D positions)
1231
            self.positions.copy_to_gpu(total_num_scheduled_tokens)
1232

1233
        use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
1234
1235
1236
1237
1238
1239
1240
        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
1241
            num_draft_tokens = None
1242
            spec_decode_metadata = None
1243
            num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
1244
1245
1246
1247
1248
        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)
1249
1250
1251
            # 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)
1252
1253
1254
1255
            for (
                req_id,
                draft_token_ids,
            ) in scheduler_output.scheduled_spec_decode_tokens.items():
1256
1257
                req_idx = self.input_batch.req_id_to_index[req_id]
                num_draft_tokens[req_idx] = len(draft_token_ids)
1258
1259
1260
1261
1262
1263
1264
1265
                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
                )
1266
            spec_decode_metadata = self._calc_spec_decode_metadata(
1267
1268
                num_draft_tokens, cu_num_tokens
            )
1269
            logits_indices = spec_decode_metadata.logits_indices
1270
            num_sampled_tokens = num_draft_tokens + 1
1271
            # For DECODE only cuda graph of some attention backends (e.g., GDN).
1272
            self.num_decode_draft_tokens.np[:num_reqs] = num_decode_draft_tokens
1273
1274
            self.num_decode_draft_tokens.np[num_reqs:].fill(-1)
            self.num_decode_draft_tokens.copy_to_gpu()
1275

1276
1277
1278
1279
1280
        # Hot-Swap lora model
        if self.lora_config:
            assert (
                np.sum(num_sampled_tokens)
                <= self.vllm_config.scheduler_config.max_num_batched_tokens
1281
            )
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
            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
1309
        num_logits_indices = None
1310
1311
1312
1313
1314
1315
        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
                )
1316

1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
        # update seq_lens of decode reqs under DCP.
        if self.dcp_world_size > 1:
            self.dcp_local_seq_lens.cpu[:num_reqs] = get_dcp_local_seq_lens(
                self.seq_lens.cpu[:num_reqs],
                self.dcp_world_size,
                self.dcp_rank,
                self.parallel_config.dcp_kv_cache_interleave_size,
            )
            self.dcp_local_seq_lens.copy_to_gpu(num_reqs)

1327
1328
1329
        attn_metadata: PerLayerAttnMetadata = {}
        if ubatch_slices is not None:
            attn_metadata = [dict() for _ in range(len(ubatch_slices))]
1330

1331
1332
        # Used in the below loop
        query_start_loc = self.query_start_loc.gpu[: num_reqs + 1]
1333
        query_start_loc_cpu = self.query_start_loc.cpu[: num_reqs + 1]
1334
        seq_lens = self.seq_lens.gpu[:num_reqs]
1335
        seq_lens_cpu = self.seq_lens.cpu[:num_reqs]
1336
1337
1338
        num_computed_tokens_cpu = self.input_batch.num_computed_tokens_cpu_tensor[
            :num_reqs
        ]
1339
1340
1341
        dcp_local_seq_lens = (
            self.dcp_local_seq_lens.gpu[:num_reqs] if self.dcp_world_size > 1 else None
        )
1342
        spec_decode_common_attn_metadata = None
1343
1344
1345
1346
1347
1348
1349
1350
1351

        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()

1352
1353
        if use_spec_decode:
            self.num_accepted_tokens.np[:num_reqs] = (
1354
1355
                self.input_batch.num_accepted_tokens_cpu[:num_reqs]
            )
1356
1357
            self.num_accepted_tokens.np[num_reqs:].fill(1)
            self.num_accepted_tokens.copy_to_gpu()
1358

1359
1360
        # Prepare the attention metadata for each KV cache group and make layers
        # in the same group share the same metadata.
1361
        for kv_cache_gid, kv_cache_group in enumerate(
1362
1363
            self.kv_cache_config.kv_cache_groups
        ):
1364
            encoder_seq_lens = self._get_encoder_seq_lens(
1365
1366
1367
                scheduled_encoder_inputs or {},
                kv_cache_group.kv_cache_spec,
                num_reqs,
1368
            )
1369

1370
            if isinstance(kv_cache_group.kv_cache_spec, EncoderOnlyAttentionSpec):
1371
1372
1373
1374
1375
                # 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,
1376
1377
1378
                    device=self.device,
                )
                slot_mapping = torch.zeros(
1379
                    (total_num_scheduled_tokens,),
1380
1381
1382
                    dtype=torch.int64,
                    device=self.device,
                )
1383
            else:
1384
                blk_table = self.input_batch.block_table[kv_cache_gid]
1385
                blk_table_tensor = blk_table.get_device_tensor(num_reqs)
1386
                slot_mapping = blk_table.slot_mapping.gpu[:total_num_scheduled_tokens]
1387
1388
1389

                # Fill unused with -1. Needed for reshape_and_cache in full cuda
                # graph mode.
1390
                blk_table.slot_mapping.gpu[total_num_scheduled_tokens:].fill_(-1)
1391

1392
            common_attn_metadata = CommonAttentionMetadata(
1393
1394
1395
1396
1397
                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,
1398
1399
1400
                num_reqs=num_reqs,
                num_actual_tokens=total_num_scheduled_tokens,
                max_query_len=max_num_scheduled_tokens,
1401
                max_seq_len=max_seq_len,
1402
1403
                block_table_tensor=blk_table_tensor,
                slot_mapping=slot_mapping,
1404
                logits_indices_padded=logits_indices_padded,
1405
                num_logits_indices=num_logits_indices,
1406
                causal=True,
1407
                encoder_seq_lens=encoder_seq_lens,
1408
                dcp_local_seq_lens=dcp_local_seq_lens,
1409
1410
            )

1411
            if self.speculative_config and spec_decode_common_attn_metadata is None:
1412
                if isinstance(self.drafter, EagleProposer):
1413
                    if self.drafter.attn_layer_names[0] in kv_cache_group.layer_names:
1414
1415
1416
                        spec_decode_common_attn_metadata = common_attn_metadata
                else:
                    spec_decode_common_attn_metadata = common_attn_metadata
1417

1418
1419
1420
1421
1422
1423
            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
                )
1424
                builder = attn_group.get_metadata_builder()
1425

1426
                extra_attn_metadata_args = {}
1427
                if use_spec_decode and isinstance(builder, GDNAttentionMetadataBuilder):
1428
                    extra_attn_metadata_args = dict(
1429
1430
1431
1432
                        num_accepted_tokens=self.num_accepted_tokens.gpu[:num_reqs],
                        num_decode_draft_tokens_cpu=self.num_decode_draft_tokens.cpu[
                            :num_reqs
                        ],
1433
1434
                    )

1435
1436
                if ubatch_slices is not None:
                    common_attn_metadata_list = split_attn_metadata(
1437
1438
                        ubatch_slices, common_attn_metadata
                    )
1439
                    for ubid, common_attn_metadata in enumerate(
1440
1441
                        common_attn_metadata_list
                    ):
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
                        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:
1453
1454
1455
1456
                            assert type(attn_metadata) is list
                            attn_metadata[ubid][layer_name] = attn_metadata_i
                else:
                    assert isinstance(attn_metadata, dict)
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
                    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,
                        )
1467
1468
                    for layer_name in attn_group.layer_names:
                        attn_metadata[layer_name] = attn_metadata_i
1469

1470
        return attn_metadata, spec_decode_common_attn_metadata
1471

1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
    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
        """
1482

1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
        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
1505

1506
1507
1508
1509
    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
        num_common_prefix_blocks: int,
1510
1511
        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
    ) -> 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.
        """
1530

1531
        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
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1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
        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]
1569
        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
1570
1571
1572
1573
1574
1575
1576
        # 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(
1577
1578
            common_prefix_len, self.input_batch.num_computed_tokens_cpu[:num_reqs].min()
        )
1579
        # common_prefix_len should be a multiple of the block size.
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
        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
        )
1591
1592
        assert isinstance(kv_cache_spec, AttentionSpec)
        use_cascade = attn_metadata_builder.use_cascade_attention(
1593
1594
1595
            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
1596
            num_kv_heads=kv_cache_spec.num_kv_heads,
1597
            use_alibi=self.use_alibi,
1598
            use_sliding_window=use_sliding_window,
1599
            use_local_attention=use_local_attention,
1600
            num_sms=self.num_sms,
1601
            dcp_world_size=self.dcp_world_size,
1602
1603
1604
        )
        return common_prefix_len if use_cascade else 0

1605
1606
    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
1607
        for index, req_id in enumerate(self.input_batch.req_ids):
1608
1609
1610
            req = self.requests[req_id]
            assert req.mrope_positions is not None

1611
1612
            num_computed_tokens = self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1613
            num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
1614
1615
                req.prompt_token_ids, req.prompt_embeds
            )
1616
1617

            if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
1618
1619
                prompt_part_len = max(0, num_prompt_tokens - num_computed_tokens)
                completion_part_len = max(0, num_scheduled_tokens - prompt_part_len)
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
            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

1633
1634
1635
                self.mrope_positions.cpu[:, dst_start:dst_end] = req.mrope_positions[
                    :, src_start:src_end
                ]
1636
1637
1638
1639
1640
1641
1642
                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

1643
                MRotaryEmbedding.get_next_input_positions_tensor(
1644
                    out=self.mrope_positions.np,
1645
1646
1647
1648
1649
                    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,
                )
1650
1651
1652

                mrope_pos_ptr += completion_part_len

1653
1654
    def _calc_spec_decode_metadata(
        self,
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
        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
1671
1672
1673
1674

        # 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(
1675
1676
            num_sampled_tokens, cumsum_dtype=np.int32
        )
1677
        # Step 2. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
1678
        logits_indices = np.repeat(
1679
1680
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens
        )
1681
        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
1682
1683
1684
1685
1686
1687
        logits_indices += arange

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

        # Compute the draft logits indices.
1688
1689
1690
        # 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(
1691
1692
            num_draft_tokens, cumsum_dtype=np.int32
        )
1693
1694
        # [0, 0, 0, 5, 5, 9]
        target_logits_indices = np.repeat(
1695
1696
            cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens
        )
1697
1698
1699
1700
1701
        # [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(
1702
1703
            self.device, non_blocking=True
        )
1704
1705
1706
        cu_num_sampled_tokens = torch.from_numpy(cu_num_sampled_tokens).to(
            self.device, non_blocking=True
        )
1707
1708
1709
        logits_indices = torch.from_numpy(logits_indices).to(
            self.device, non_blocking=True
        )
1710
        target_logits_indices = torch.from_numpy(target_logits_indices).to(
1711
1712
            self.device, non_blocking=True
        )
1713
        bonus_logits_indices = torch.from_numpy(bonus_logits_indices).to(
1714
1715
            self.device, non_blocking=True
        )
1716

1717
1718
        # Compute the draft token ids.
        # draft_token_indices:      [  1,   2,   3, 105, 106, 208]
1719
        draft_token_ids = self.input_ids.gpu[logits_indices]
1720
1721
        draft_token_ids = draft_token_ids[target_logits_indices + 1]

1722
        return SpecDecodeMetadata(
1723
1724
1725
            draft_token_ids=draft_token_ids,
            num_draft_tokens=num_draft_tokens.tolist(),
            cu_num_draft_tokens=cu_num_draft_tokens,
1726
            cu_num_sampled_tokens=cu_num_sampled_tokens,
1727
1728
1729
1730
1731
            target_logits_indices=target_logits_indices,
            bonus_logits_indices=bonus_logits_indices,
            logits_indices=logits_indices,
        )

1732
1733
1734
1735
1736
1737
1738
    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
1739
        self.kv_sharing_fast_prefill_logits_indices[:num_logits].copy_(logits_indices)
1740
1741
1742
1743
1744
        # 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_(
1745
1746
1747
1748
1749
1750
            logits_indices[-1].item()
        )
        if (
            self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
            and num_logits <= self.cudagraph_batch_sizes[-1]
        ):
1751
1752
1753
1754
1755
            # 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
1756
1757
1758
        logits_indices_padded = self.kv_sharing_fast_prefill_logits_indices[
            :num_logits_padded
        ]
1759
1760
        return logits_indices_padded

1761
1762
1763
1764
1765
1766
1767
1768
    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
1769
                inputs.
1770
1771
1772
1773
1774
1775

        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
        """
1776
1777
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
1778
            return [], []
1779
        # Batch the multi-modal inputs.
1780
        mm_kwargs = list[MultiModalKwargsItem]()
1781
1782
        # list of tuple (mm_hash, position_info)
        mm_hashes_pos = list[tuple[str, PlaceholderRange]]()
1783
1784
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
1785
1786

            for mm_input_id in encoder_input_ids:
1787
1788
1789
1790
                mm_feature = req_state.mm_features[mm_input_id]
                mm_hash = mm_feature.identifier
                mm_kwargs.append(mm_feature.data)
                mm_hashes_pos.append((mm_hash, mm_feature.mm_position))
1791

1792
1793
1794
1795
1796
        return mm_kwargs, mm_hashes_pos

    def _execute_mm_encoder(self, scheduler_output: "SchedulerOutput"):
        # Batch the multi-modal inputs using the helper method.
        mm_kwargs, mm_hashes_pos = self._batch_mm_kwargs_from_scheduler(
1797
1798
            scheduler_output
        )
1799
1800
1801
1802

        if not mm_kwargs:
            return

1803
1804
1805
1806
1807
1808
1809
        # 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.
1810
        model = cast(SupportsMultiModal, self.model)
1811
        encoder_outputs = []
1812
        for modality, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
1813
1814
1815
1816
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
1817
            multimodal_cpu_fields=model.multimodal_cpu_fields,
1818
        ):
1819
1820
1821
            curr_group_outputs = []

            # EVS-related change.
1822
            # (ekhvedchenia): Temporary hack to limit peak memory usage when
1823
            # processing multimodal data. This solves the issue with scheduler
1824
1825
1826
1827
            # 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)
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
            # 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,
1844
                            multimodal_cpu_fields=model.multimodal_cpu_fields,
1845
                        )
1846
                    )
1847

1848
                    micro_batch_outputs = model.embed_multimodal(
1849
1850
                        **micro_batch_mm_inputs
                    )
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860

                    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.
1861
                curr_group_outputs = model.embed_multimodal(**mm_kwargs_group)
1862

1863
1864
            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
1865
                expected_num_items=num_items,
1866
            )
1867
            encoder_outputs.extend(curr_group_outputs)
1868

1869
1870
1871
        # 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(
1872
1873
1874
                output,
                is_embed=pos_info.is_embed,
            )
1875
1876
            logger.debug("Finish execute for mm hash %s", mm_hash)
            self.maybe_save_ec_to_connector(self.encoder_cache, mm_hash)
1877
1878

    def _gather_mm_embeddings(
1879
1880
        self,
        scheduler_output: "SchedulerOutput",
1881
        shift_computed_tokens: int = 0,
1882
1883
1884
1885
1886
1887
1888
1889
    ) -> 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
1890
        should_sync_mrope_positions = False
1891

1892
        for req_id in self.input_batch.req_ids:
1893
1894
            mm_embeds_req: list[torch.Tensor] = []

1895
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1896
            req_state = self.requests[req_id]
1897
            num_computed_tokens = req_state.num_computed_tokens + shift_computed_tokens
1898

1899
1900
            for mm_feature in req_state.mm_features:
                pos_info = mm_feature.mm_position
1901
1902
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918

                # 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,
1919
1920
                    num_encoder_tokens,
                )
1921
                assert start_idx < end_idx
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                mm_hash = mm_feature.identifier
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                encoder_output = self.encoder_cache.get(mm_hash, None)
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                assert encoder_output is not None, f"Encoder cache miss for {mm_hash}."
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                if (is_embed := pos_info.is_embed) is not None:
                    is_embed = is_embed[start_idx:end_idx]

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                req_start_pos = req_start_idx + start_pos - num_computed_tokens
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                is_mm_embed[req_start_pos + start_idx : req_start_pos + end_idx] = (
                    True if is_embed is None else is_embed
                )
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                mm_embeds_item = gather_mm_placeholders(
                    encoder_output[start_idx:end_idx],
                    is_embed=is_embed,
                )
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                mm_embeds_req.append(mm_embeds_item)

            if self.is_multimodal_pruning_enabled and self.uses_mrope:
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                assert req_state.mrope_positions is not None
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                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,
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                    )
                )
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                req_state.mrope_positions.copy_(new_mrope_positions)
                req_state.mrope_position_delta = new_delta

            mm_embeds.extend(mm_embeds_req)
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            req_start_idx += num_scheduled_tokens

        is_mm_embed = self.is_mm_embed.copy_to_gpu(total_num_scheduled_tokens)
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        if should_sync_mrope_positions:
            self._calc_mrope_positions(scheduler_output)
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            self.mrope_positions.copy_to_gpu(total_num_scheduled_tokens)
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        return mm_embeds, is_mm_embed
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    def _extract_encoder_inputs(
        self,
        scheduler_output: "SchedulerOutput",
    ) -> dict[str, torch.Tensor]:
        """Extract encoder inputs for encoder-decoder models.

        This method extracts multimodal input features from scheduled encoder
        inputs and formats them for the encoder-decoder model forward pass.
        """
        # Batch the multi-modal inputs using the helper method.
        mm_kwargs, _ = self._batch_mm_kwargs_from_scheduler(scheduler_output)

        if not mm_kwargs:
            return {}

        # Group MM kwargs by modality and extract features
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        model = cast(SupportsMultiModal, self.model)
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        encoder_features = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
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            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
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            multimodal_cpu_fields=model.multimodal_cpu_fields,
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        ):
            # Add the grouped features to encoder_features dict
            # This allows the model to receive them as kwargs (e.g.,
            # input_features=...)
            encoder_features.update(mm_kwargs_group)

        return encoder_features

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

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    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

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

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

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        if self.scheduler_config.enable_chunked_prefill:
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            if "token_embed" in supported_tasks:
                supported_tasks.remove("token_embed")
            if "token_classify" in supported_tasks:
                supported_tasks.remove("token_classify")
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            logger.debug_once(
                "Chunked prefill is not supported with "
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                "token_embed and token_classify tasks "
                "which using ALL pooling. "
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                "Please turn off chunked prefill by "
                "`--no-enable-chunked-prefill` before using it."
            )
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        if "score" in supported_tasks:
            num_labels = getattr(self.model_config.hf_config, "num_labels", 0)
            if num_labels != 1:
                supported_tasks.remove("score")
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                logger.debug_once("Score API is only enabled for num_labels == 1.")
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        return supported_tasks
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    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)

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    def sync_and_slice_intermediate_tensors(
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        self,
        num_tokens: int,
        intermediate_tensors: IntermediateTensors,
        sync_self: bool,
    ) -> IntermediateTensors:
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        assert self.intermediate_tensors is not None

        tp = self.vllm_config.parallel_config.tensor_parallel_size
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        is_rs = is_residual_scattered_for_sp(self.vllm_config, num_tokens)
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        # 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():
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                is_scattered = k == "residual" and is_rs
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                copy_len = num_tokens // tp if is_scattered else num_tokens
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                self.intermediate_tensors[k][:copy_len].copy_(
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                    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:
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        """
        Step for the EPLB (Expert Parallelism Load Balancing) state.
        """
        if not self.parallel_config.enable_eplb:
            return

        assert self.eplb_state is not None
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        model = self.get_model()
        assert is_mixture_of_experts(model)
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        self.eplb_state.step(
            is_dummy,
            is_profile,
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            log_stats=self.parallel_config.eplb_config.log_balancedness,
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        )

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    # 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)
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    @staticmethod
    def pad_out_ubatch_slice(ubatch_slices: UBatchSlices, num_total_tokens: int):
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        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
        )
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    def _pool(
        self,
        hidden_states: torch.Tensor,
        num_scheduled_tokens: int,
        num_scheduled_tokens_np: np.ndarray,
    ) -> ModelRunnerOutput:
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        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"
        )
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        hidden_states = hidden_states[:num_scheduled_tokens]
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        pooling_metadata = self.input_batch.get_pooling_metadata()
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        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]
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        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()
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        pooler_output: list[torch.Tensor | None] = []
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        for raw_output, seq_len, prompt_len in zip(
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            raw_pooler_output, seq_lens_cpu, pooling_metadata.prompt_lens
        ):
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            output = raw_output if seq_len == prompt_len else None
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            pooler_output.append(output)
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        return ModelRunnerOutput(
            req_ids=self.input_batch.req_ids,
            req_id_to_index=self.input_batch.req_id_to_index,
            sampled_token_ids=[],
            logprobs=None,
            prompt_logprobs_dict={},
            pooler_output=pooler_output,
        )

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    def _get_num_input_tokens(self, num_scheduled_tokens: int) -> int:
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        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]
        ):
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            # 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
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        if (
            self.compilation_config.pass_config.enable_sequence_parallelism
            and tp_size > 1
        ):
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            return round_up(num_scheduled_tokens, tp_size)
        return num_scheduled_tokens

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    def _preprocess(
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        self,
        scheduler_output: "SchedulerOutput",
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        num_input_tokens: int,  # Padded
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        intermediate_tensors: IntermediateTensors | None = None,
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    ) -> tuple[
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        torch.Tensor | None,
        torch.Tensor | None,
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        torch.Tensor,
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        IntermediateTensors | None,
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        dict[str, Any],
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        ECConnectorOutput | None,
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    ]:
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        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
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        is_first_rank = get_pp_group().is_first_rank
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        # _prepare_inputs may reorder the batch, so we must gather multi
        # modal outputs after that to ensure the correct order
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        ec_connector_output = None

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        if (
            self.supports_mm_inputs
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            and is_first_rank
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            and not self.model_config.is_encoder_decoder
        ):
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            # Run the multimodal encoder if any.
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            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)
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            # 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.
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            inputs_embeds_scheduled = self.model.embed_input_ids(
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                self.input_ids.gpu[:num_scheduled_tokens],
                multimodal_embeddings=mm_embeds,
                is_multimodal=is_mm_embed,
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            )
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            # TODO(woosuk): Avoid the copy. Optimize.
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            self.inputs_embeds.gpu[:num_scheduled_tokens].copy_(inputs_embeds_scheduled)
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            input_ids = None
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            inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
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            model_kwargs = {
                **self._init_model_kwargs(num_scheduled_tokens),
                **self._extract_mm_kwargs(scheduler_output),
            }
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        elif self.enable_prompt_embeds and is_first_rank:
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            # 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).
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            token_ids_idx = (
                self.is_token_ids.gpu[:num_scheduled_tokens]
                .nonzero(as_tuple=False)
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                .squeeze(1)
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            )
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            # Some tokens ids may need to become embeds
            if token_ids_idx.numel() > 0:
                token_ids = self.input_ids.gpu[token_ids_idx]
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                tokens_to_embeds = self.model.embed_input_ids(input_ids=token_ids)
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                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
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        else:
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            # 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.
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            input_ids = self.input_ids.gpu[:num_input_tokens]
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            inputs_embeds = None
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            model_kwargs = self._init_model_kwargs(num_input_tokens)
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        if self.uses_mrope:
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            positions = self.mrope_positions.gpu[:, :num_input_tokens]
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        else:
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            positions = self.positions.gpu[:num_input_tokens]
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        if is_first_rank:
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            intermediate_tensors = None
        else:
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            intermediate_tensors = self.sync_and_slice_intermediate_tensors(
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                num_input_tokens, intermediate_tensors, True
            )
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        if (
            self.model_config.is_encoder_decoder
            and scheduler_output.scheduled_encoder_inputs
        ):
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            encoder_inputs = self._extract_encoder_inputs(scheduler_output)
            model_kwargs.update(encoder_inputs)

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

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

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        discard_sampled_tokens_req_indices = self.discard_request_indices.np[
            : self.num_discarded_requests
        ]
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        for i in discard_sampled_tokens_req_indices:
            gen = self.input_batch.generators.get(int(i))
            if gen is not None:
                gen.set_offset(gen.get_offset() - 4)
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        # Copy some objects so they don't get modified after returning.
        # This is important when using async scheduling.
        req_ids_output_copy = self.input_batch.req_ids.copy()
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        req_id_to_index_output_copy = self.input_batch.req_id_to_index.copy()
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        num_sampled_tokens = sampler_output.sampled_token_ids.shape[0]
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        sampled_token_ids = sampler_output.sampled_token_ids
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        invalid_req_indices = []
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        valid_sampled_token_ids: list[np.ndarray]
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        if not self.use_async_scheduling:
            # Get the valid generated tokens.
            max_gen_len = sampled_token_ids.shape[-1]
            if max_gen_len == 1:
                # No spec decode tokens.
                valid_sampled_token_ids = self._to_list(sampled_token_ids)
            else:
                # Includes spec decode tokens.
                valid_sampled_token_ids = self.rejection_sampler.parse_output(
                    sampled_token_ids,
                    self.input_batch.vocab_size,
                )
            # Mask out the sampled tokens that should not be sampled.
            for i in discard_sampled_tokens_req_indices:
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                valid_sampled_token_ids[int(i)] = np.array([])
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        else:
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            valid_sampled_token_ids = []
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            invalid_req_indices = discard_sampled_tokens_req_indices.tolist()
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            invalid_req_indices_set = set(invalid_req_indices)
            assert sampled_token_ids.shape[-1] == 1

            # Cache the sampled tokens on the GPU and avoid CPU sync.
            # These will be copied into input_ids in the next step
            # when preparing inputs.
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            self.input_batch.prev_sampled_token_ids = sampled_token_ids
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            self.input_batch.prev_req_id_to_index = {
                req_id: i
                for i, req_id in enumerate(self.input_batch.req_ids)
                if i not in invalid_req_indices_set
            }
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        # Cache the sampled tokens in the model runner, so that the scheduler
        # doesn't need to send them back.
        # NOTE(woosuk): As an exception, when using PP, the scheduler sends
        # the sampled tokens back, because there's no direct communication
        # between the first-stage worker and the last-stage worker.
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        req_ids = self.input_batch.req_ids
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        logprobs_tensors = sampler_output.logprobs_tensors
        cu_num_accepted_tokens = (
            [0] if spec_decode_metadata and logprobs_tensors else None
        )
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        for req_idx in range(num_sampled_tokens):
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            sampled_ids: np.ndarray | None
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            if self.use_async_scheduling:
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                sampled_ids = (
                    np.array([-1]) if req_idx not in invalid_req_indices_set else None
                )
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            else:
                sampled_ids = valid_sampled_token_ids[req_idx]
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            num_sampled_ids: int = (
                sampled_ids.shape[0] if sampled_ids is not None else 0
            )
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            if cu_num_accepted_tokens is not None:
                cu_num_accepted_tokens.append(
                    cu_num_accepted_tokens[-1] + num_sampled_ids
                )

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            if sampled_ids is None or num_sampled_ids == 0:
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                continue

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

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

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

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        return (
            num_nans_in_logits,
            logprobs_lists,
            valid_sampled_token_ids,
            prompt_logprobs_dict,
            req_ids_output_copy,
            req_id_to_index_output_copy,
            invalid_req_indices,
        )

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    @contextmanager
    def synchronize_input_prep(self):
        if self.prepare_inputs_event is None:
            yield
            return

        # Ensure prior step has finished with reused CPU tensors.
        # This is required in the async scheduling case because
        # the CPU->GPU transfer happens async.
        self.prepare_inputs_event.synchronize()
        try:
            yield
        finally:
            self.prepare_inputs_event.record()

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

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

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

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

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    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
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        intermediate_tensors: IntermediateTensors | None = None,
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    ) -> ModelRunnerOutput | IntermediateTensors | None:
        if self.execute_model_state is not None:
            raise RuntimeError(
                "State error: sample_tokens() must be called "
                "after execute_model() returns None."
            )
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
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        with record_function_or_nullcontext("gpu_model_runner: preprocess"):
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            with self.synchronize_input_prep():
                # Update persistent batch states.
                self._update_states(scheduler_output)

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

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                if not num_scheduled_tokens:
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                    if not has_kv_transfer_group():
                        # Return empty ModelRunnerOutput if no work to do.
                        return EMPTY_MODEL_RUNNER_OUTPUT
                    return self.kv_connector_no_forward(
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                        scheduler_output, self.vllm_config
                    )
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                if self.cache_config.kv_sharing_fast_prefill:
                    assert not self.input_batch.num_prompt_logprobs, (
                        "--kv-sharing-fast-prefill produces incorrect "
                        "logprobs for prompt tokens, tokens, please disable "
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                        "it when the requests need prompt logprobs"
                    )
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                num_reqs = self.input_batch.num_reqs
                req_ids = self.input_batch.req_ids
                tokens = [scheduler_output.num_scheduled_tokens[i] for i in req_ids]
                num_scheduled_tokens_np = np.array(tokens, dtype=np.int32)
                max_num_scheduled_tokens = int(num_scheduled_tokens_np.max())

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

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

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

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

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

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

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

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

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

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

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

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

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        def propose_draft_token_ids(
            sampled_token_ids: torch.Tensor | list[np.ndarray],
        ) -> None:
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            assert spec_decode_common_attn_metadata is not None
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            with record_function_or_nullcontext("gpu_model_runner: draft"):
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                self._draft_token_ids = self.propose_draft_token_ids(
                    scheduler_output,
                    sampled_token_ids,
                    self.input_batch.sampling_metadata,
                    hidden_states,
                    sample_hidden_states,
                    aux_hidden_states,
                    spec_decode_metadata,
                    spec_decode_common_attn_metadata,
                )

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

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        with record_function_or_nullcontext("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,
            )
        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 propose_draft_token_ids(
        self,
        scheduler_output: "SchedulerOutput",
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        sampled_token_ids: torch.Tensor | list[np.ndarray],
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        sampling_metadata: SamplingMetadata,
        hidden_states: torch.Tensor,
        sample_hidden_states: torch.Tensor,
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        aux_hidden_states: list[torch.Tensor] | None,
        spec_decode_metadata: SpecDecodeMetadata | None,
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        common_attn_metadata: CommonAttentionMetadata,
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    ) -> torch.Tensor | list[list[int]]:
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        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
        if self.speculative_config.method == "ngram":
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            assert isinstance(sampled_token_ids, list)
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            assert isinstance(self.drafter, NgramProposer)
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            draft_token_ids = self.drafter.propose(
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                sampled_token_ids,
                self.input_batch.req_ids,
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                self.input_batch.num_tokens_no_spec,
                self.input_batch.token_ids_cpu,
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                self.input_batch.spec_decode_unsupported_reqs,
            )
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        elif self.speculative_config.method == "suffix":
            assert isinstance(sampled_token_ids, list)
            assert isinstance(self.drafter, SuffixDecodingProposer)
            draft_token_ids = self.drafter.propose(self.input_batch, sampled_token_ids)
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        elif self.speculative_config.method == "medusa":
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            assert isinstance(sampled_token_ids, list)
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            assert isinstance(self.drafter, MedusaProposer)
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            if sample_hidden_states.shape[0] == len(sampled_token_ids):
                # The input to the target model does not include draft tokens.
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                hidden_states = sample_hidden_states
            else:
                indices = []
                offset = 0
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                assert spec_decode_metadata is not None, (
                    "No spec decode metadata for medusa"
                )
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                for num_draft, tokens in zip(
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                    spec_decode_metadata.num_draft_tokens, sampled_token_ids
                ):
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                    indices.append(offset + tokens.shape[0] - 1)
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                    offset += num_draft + 1
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                indices = torch.tensor(indices, device=self.device)
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                hidden_states = sample_hidden_states[indices]

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            draft_token_ids = self.drafter.propose(
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                target_hidden_states=hidden_states,
                sampling_metadata=sampling_metadata,
            )
2934
        elif self.speculative_config.use_eagle():
2935
            assert isinstance(self.drafter, EagleProposer)
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            if self.speculative_config.disable_padded_drafter_batch:
                # When padded-batch is disabled, the sampled_token_ids should be
                # the cpu-side list[list[int]] of valid sampled tokens for each
                # request, with invalid requests having empty lists.
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                assert isinstance(sampled_token_ids, list), (
                    "sampled_token_ids should be a python list when"
2943
                    "padded-batch is disabled."
2944
                )
2945
                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"
2958
                    "padded-batch is enabled."
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                )
                next_token_ids, valid_sampled_tokens_count = (
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                    self.drafter.prepare_next_token_ids_padded(
                        common_attn_metadata,
                        sampled_token_ids,
                        self.requests,
                        self.input_batch,
                        self.discard_request_indices.gpu,
2967
                        self.num_discarded_requests,
2968
                    )
2969
                )
Jiayi Yao's avatar
Jiayi Yao committed
2970

2971
            if spec_decode_metadata is None:
2972
                token_indices_to_sample = None
2973
                # input_ids can be None for multimodal models.
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                target_token_ids = self.input_ids.gpu[:num_scheduled_tokens]
2975
                target_positions = self._get_positions(num_scheduled_tokens)
2976
                if self.use_aux_hidden_state_outputs:
Wentao Ye's avatar
Wentao Ye committed
2977
                    assert aux_hidden_states is not None
2978
                    target_hidden_states = torch.cat(
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                        [h[:num_scheduled_tokens] for h in aux_hidden_states], dim=-1
                    )
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                else:
                    target_hidden_states = hidden_states[:num_scheduled_tokens]
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            else:
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                if self.speculative_config.disable_padded_drafter_batch:
                    token_indices_to_sample = None
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                    common_attn_metadata, token_indices = self.drafter.prepare_inputs(
                        common_attn_metadata,
                        sampled_token_ids,
                        spec_decode_metadata.num_draft_tokens,
                    )
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                else:
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                    common_attn_metadata, token_indices, token_indices_to_sample = (
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                        self.drafter.prepare_inputs_padded(
                            common_attn_metadata,
                            spec_decode_metadata,
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                            valid_sampled_tokens_count,
                        )
                    )
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                target_token_ids = self.input_ids.gpu[token_indices]
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                target_positions = self._get_positions(token_indices)
3002
                if self.use_aux_hidden_state_outputs:
Wentao Ye's avatar
Wentao Ye committed
3003
                    assert aux_hidden_states is not None
3004
                    target_hidden_states = torch.cat(
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                        [h[token_indices] for h in aux_hidden_states], dim=-1
                    )
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                else:
                    target_hidden_states = hidden_states[token_indices]
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            if self.supports_mm_inputs:
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                mm_embed_inputs = self._gather_mm_embeddings(
                    scheduler_output,
                    shift_computed_tokens=1,
                )
            else:
                mm_embed_inputs = None
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            draft_token_ids = self.drafter.propose(
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                target_token_ids=target_token_ids,
                target_positions=target_positions,
                target_hidden_states=target_hidden_states,
                next_token_ids=next_token_ids,
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                last_token_indices=token_indices_to_sample,
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                sampling_metadata=sampling_metadata,
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                common_attn_metadata=common_attn_metadata,
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                mm_embed_inputs=mm_embed_inputs,
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            )
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        return draft_token_ids
3030

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

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    def load_model(self, eep_scale_up: bool = False) -> None:
        """
        Args:
            eep_scale_up: the model loading is for elastic EP scale up.
        """
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        logger.info_once(
            "Starting to load model %s...",
            self.model_config.model,
            scope="global",
        )
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        global_expert_loads, old_global_expert_indices_per_model, rank_mapping = (
            EplbState.get_eep_state(self.parallel_config)
            if eep_scale_up
            else (None, None, None)
        )
3057

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        if self.parallel_config.enable_eplb:
            self.eplb_state = EplbState(self.parallel_config, self.device)
            eplb_models = 0
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        with DeviceMemoryProfiler() as m:
3062
            time_before_load = time.perf_counter()
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            model_loader = get_model_loader(self.load_config)
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            self.model = model_loader.load_model(
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                vllm_config=self.vllm_config, model_config=self.model_config
            )
3067
            if self.lora_config:
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                self.model = self.load_lora_model(
                    self.model, self.vllm_config, self.device
                )
3071
            if hasattr(self, "drafter"):
3072
                logger.info_once("Loading drafter model...")
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                self.drafter.load_model(self.model)
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                if (
                    hasattr(self.drafter, "model")
                    and is_mixture_of_experts(self.drafter.model)
                    and self.parallel_config.enable_eplb
                ):
                    logger.info_once(
                        "EPLB is enabled for drafter model %s.",
                        self.vllm_config.speculative_config.draft_model_config.model,
                    )

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

3105
            if self.use_aux_hidden_state_outputs:
3106
                if not supports_eagle3(self.get_model()):
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                    raise RuntimeError(
                        "Model does not support EAGLE3 interface but "
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3110
                        "aux_hidden_state_outputs was requested"
                    )
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3119
3120
3121
3122
3123

                # 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)
3124
            time_after_load = time.perf_counter()
3125
        self.model_memory_usage = m.consumed_memory
3126
        logger.info_once(
3127
            "Model loading took %.4f GiB memory and %.6f seconds",
3128
3129
            self.model_memory_usage / GiB_bytes,
            time_after_load - time_before_load,
3130
            scope="local",
3131
        )
3132
        prepare_communication_buffer_for_model(self.model)
3133
        self.is_multimodal_pruning_enabled = (
3134
            supports_multimodal_pruning(self.get_model())
3135
3136
            and self.model_config.multimodal_config.is_multimodal_pruning_enabled()
        )
3137

3138
        if is_mixture_of_experts(self.model) and self.parallel_config.enable_eplb:
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3142
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3145
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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(
3150
                self.model,
3151
                self.model_config,
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3153
3154
                global_expert_load,
                old_global_expert_indices,
                rank_mapping,
3155
3156
            )

3157
        if (
3158
3159
            self.vllm_config.compilation_config.mode
            == CompilationMode.STOCK_TORCH_COMPILE
3160
            and supports_dynamo()
3161
        ):
3162
            backend = self.vllm_config.compilation_config.init_backend(self.vllm_config)
3163
            compilation_counter.stock_torch_compile_count += 1
3164
            self.model.compile(fullgraph=True, backend=backend)
3165
            return
3166
        # for other compilation modes, cudagraph behavior is controlled by
3167
3168
3169
        # CudagraphWraper and CudagraphDispatcher of vllm.

        # wrap the model with full cudagraph wrapper if needed.
3170
3171
3172
3173
3174
3175
3176
        if (
            self.compilation_config.cudagraph_mode.has_full_cudagraphs()
            and not self.parallel_config.enable_dbo
        ):
            self.model = CUDAGraphWrapper(
                self.model, self.vllm_config, runtime_mode=CUDAGraphMode.FULL
            )
3177
3178
        elif self.parallel_config.enable_dbo:
            if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
3179
3180
3181
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.FULL, self.device
                )
3182
            else:
3183
3184
3185
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.NONE, self.device
                )
3186

3187
    def _get_eagle3_aux_layers_from_config(self) -> tuple[int, ...] | None:
3188
3189
3190
3191
3192
3193
3194
3195
3196
3197
3198
3199
3200
3201
3202
3203
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3210
        """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

3211
    def reload_weights(self) -> None:
3212
        assert getattr(self, "model", None) is not None, (
3213
            "Cannot reload weights before model is loaded."
3214
        )
3215
3216
        model_loader = get_model_loader(self.load_config)
        logger.info("Reloading weights inplace...")
3217
        model_loader.load_weights(self.get_model(), model_config=self.model_config)
3218

3219
3220
3221
3222
3223
    def save_tensorized_model(
        self,
        tensorizer_config: "TensorizerConfig",
    ) -> None:
        TensorizerLoader.save_model(
3224
            self.get_model(),
3225
            tensorizer_config=tensorizer_config,
3226
            model_config=self.model_config,
3227
3228
        )

3229
3230
3231
    def _get_prompt_logprobs_dict(
        self,
        hidden_states: torch.Tensor,
3232
        num_scheduled_tokens: dict[str, int],
3233
    ) -> dict[str, LogprobsTensors | None]:
3234
3235
3236
3237
        num_prompt_logprobs_dict = self.input_batch.num_prompt_logprobs
        if not num_prompt_logprobs_dict:
            return {}

3238
        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
3239
        prompt_logprobs_dict: dict[str, LogprobsTensors | None] = {}
3240
3241
3242
3243
3244

        # 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():
3245
            num_tokens = num_scheduled_tokens[req_id]
3246
3247
3248

            # Get metadata for this request.
            request = self.requests[req_id]
3249
3250
3251
3252
            if request.prompt_token_ids is None:
                # Prompt logprobs is incompatible with prompt embeddings
                continue

3253
3254
            num_prompt_tokens = len(request.prompt_token_ids)
            prompt_token_ids = torch.tensor(request.prompt_token_ids).to(
3255
3256
                self.device, non_blocking=True
            )
3257

3258
3259
3260
3261
3262
3263
            # 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(
3264
3265
                    num_prompt_tokens - 1, num_prompt_logprobs + 1
                )
3266
3267
                in_progress_dict[req_id] = logprobs_tensors

3268
            # Determine number of logits to retrieve.
3269
3270
            start_idx = request.num_computed_tokens
            start_tok = start_idx + 1
3271
            num_remaining_tokens = num_prompt_tokens - start_tok
3272
            if num_tokens <= num_remaining_tokens:
3273
                # This is a chunk, more tokens remain.
3274
3275
3276
                # 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.
3277
3278
3279
3280
3281
                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)
3282
3283
3284
3285
3286
3287
3288
                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
3289
3290
3291
3292
3293

            # 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]
3294
            offset = self.query_start_loc.np[req_idx].item()
3295
            prompt_hidden_states = hidden_states[offset : offset + num_logits]
3296
            logits = self.model.compute_logits(prompt_hidden_states)
3297
3298
3299
3300

            # 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.
3301
            tgt_token_ids = prompt_token_ids[start_tok : start_tok + num_logits]
3302
3303

            # Compute prompt logprobs.
3304
3305
            logprobs = self.sampler.compute_logprobs(logits)
            token_ids, logprobs, ranks = self.sampler.gather_logprobs(
3306
3307
                logprobs, num_prompt_logprobs, tgt_token_ids
            )
3308
3309

            # Transfer GPU->CPU async.
3310
3311
            chunk_slice = slice(start_idx, start_idx + num_logits)
            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
3312
3313
3314
                token_ids, non_blocking=True
            )
            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs, non_blocking=True)
3315
            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
3316
3317
                ranks, non_blocking=True
            )
3318
3319
3320
3321
3322

        # 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]
3323
            del in_progress_dict[req_id]
3324
3325

        # Must synchronize the non-blocking GPU->CPU transfers.
3326
        if prompt_logprobs_dict:
3327
            self._sync_device()
3328
3329
3330

        return prompt_logprobs_dict

3331
3332
    def _get_nans_in_logits(
        self,
3333
        logits: torch.Tensor | None,
3334
3335
3336
3337
3338
3339
3340
3341
3342
3343
3344
    ) -> 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])
3345
3346
3347
                    if num_nans_for_index is not None and req_index < logits.shape[0]
                    else 0
                )
3348
3349
3350
3351
            return num_nans_in_logits
        except IndexError:
            return {}

3352
3353
3354
3355
3356
3357
    @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
3358
         - during DP rank dummy run
3359
3360
3361
3362
3363
3364
3365
3366
3367
3368
3369
        """
        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(
3370
                    self.input_ids.gpu,
3371
3372
                    low=0,
                    high=self.model_config.get_vocab_size(),
3373
3374
                    dtype=input_ids.dtype,
                )
3375

3376
            logger.debug_once("Randomizing dummy data for DP Rank")
3377
            input_ids.copy_(rand_input_ids()[: input_ids.size(0)], non_blocking=True)
3378
3379
3380
            yield
            input_ids.fill_(0)

3381
3382
3383
3384
3385
3386
    def _get_mm_dummy_batch(
        self,
        modality: str,
        max_items_per_batch: int,
    ) -> BatchedTensorInputs:
        """Dummy data for profiling and precompiling multimodal models."""
3387
3388
        assert self.mm_budget is not None

3389
3390
        dummy_decoder_data = self.mm_registry.get_decoder_dummy_data(
            model_config=self.model_config,
3391
            seq_len=self.max_model_len,
3392
            mm_counts={modality: 1},
3393
            cache=self.mm_budget.cache,
3394
3395
3396
3397
        )
        dummy_mm_data = dummy_decoder_data.multi_modal_data

        # Result in the maximum GPU consumption of the model
3398
3399
        dummy_mm_item = dummy_mm_data[modality][0]
        dummy_mm_items = [dummy_mm_item] * max_items_per_batch
3400

3401
        model = cast(SupportsMultiModal, self.model)
3402
3403
3404
3405
3406
3407
3408
        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,
3409
                multimodal_cpu_fields=model.multimodal_cpu_fields,
3410
3411
            )
        )
3412

3413
3414
3415
3416
    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
3417
        cudagraph_runtime_mode: CUDAGraphMode | None = None,
3418
3419
        force_attention: bool = False,
        uniform_decode: bool = False,
3420
        allow_microbatching: bool = True,
3421
3422
        skip_eplb: bool = False,
        is_profile: bool = False,
3423
        create_mixed_batch: bool = False,
3424
        remove_lora: bool = True,
3425
        activate_lora: bool = False,
3426
    ) -> tuple[torch.Tensor, torch.Tensor]:
3427
3428
3429
3430
3431
3432
3433
        """
        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.
3434
                - if not set will determine the cudagraph mode based on using
3435
                    the self.cudagraph_dispatcher.
3436
3437
3438
3439
                - CUDAGraphMode.NONE: No cudagraph, for warm up and profile run
                - CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
                - CUDAGraphMode.FULL: Full cudagraph, attention metadata is
                    needed.
3440
            force_attention: If True, always create attention metadata. Used to
3441
3442
3443
3444
                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.
3445
3446
            create_mixed_batch: If True, create a mixed batch with both decode
                (1 token) and prefill (multiple tokens) requests.
3447
            remove_lora: If False, dummy LoRAs are not destroyed after the run
3448
            activate_lora: If False, dummy_run is performed without LoRAs.
3449
        """
3450
3451
3452
3453
        assert (
            cudagraph_runtime_mode is None
            or cudagraph_runtime_mode.valid_runtime_modes()
        )
3454

3455
        # If cudagraph_mode.decode_mode() == FULL and
3456
        # cudagraph_mode.separate_routine(). This means that we are using
3457
3458
3459
3460
3461
3462
3463
3464
3465
3466
3467
        # 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.
3468
        max_query_len = self.uniform_decode_query_len if uniform_decode else num_tokens
3469

3470
3471
3472
3473
3474
        # 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
3475
3476
3477
3478
        if create_mixed_batch:
            assert not uniform_decode
            # Create mixed batch:
            # first half decode tokens, second half one prefill
3479
            num_decode_tokens = min(max_num_reqs - 1, num_tokens // 2)
3480
3481
3482
3483
            num_prefill_tokens = num_tokens - num_decode_tokens
            num_reqs = num_decode_tokens + 1

            # Create decode requests (1 token each) followed by prefill request
3484
            num_scheduled_tokens_list = [1] * num_decode_tokens + [num_prefill_tokens]
3485
3486
3487
            # Note: Overriding max_query_len to be the prefill tokens
            max_query_len = num_prefill_tokens
        elif uniform_decode:
3488
            assert not create_mixed_batch
3489
            num_reqs = min(max_num_reqs, cdiv(num_tokens, max_query_len))
3490
3491
            num_scheduled_tokens_list = [max_query_len] * num_reqs
            if num_tokens % max_query_len != 0:
3492
                num_scheduled_tokens_list[-1] = num_tokens % max_query_len
3493
3494
3495
3496
3497
3498
        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

3499
3500
        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs
3501
        num_scheduled_tokens = np.array(num_scheduled_tokens_list, dtype=np.int32)
3502
        total_num_scheduled_tokens = int(num_scheduled_tokens.sum())
3503
        num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
3504

3505
3506
3507
        # Disable DP padding when running eager
        allow_dp_padding = self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE

3508
3509
        # We currently only microbatch if the number of tokens is
        # over a certain threshold.
3510
        ubatch_slices, num_tokens_across_dp = coordinate_batch_across_dp(
3511
3512
3513
3514
3515
3516
3517
            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,
3518
3519
3520
        )
        num_tokens_after_padding = num_tokens
        if num_tokens_across_dp is not None:
3521
3522
            dp_rank = self.parallel_config.data_parallel_rank
            num_tokens_after_padding = int(num_tokens_across_dp[dp_rank])
3523

3524
        attn_metadata: PerLayerAttnMetadata | None = None
3525
3526
3527

        # If force_attention is True, we always capture attention. Otherwise,
        # it only happens for cudagraph_runtime_mode=FULL.
3528
        if force_attention or cudagraph_runtime_mode == CUDAGraphMode.FULL:
3529
3530
3531
3532
3533
3534
            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:
3535
                seq_lens = max_query_len
3536
            self.seq_lens.np[:num_reqs] = seq_lens
3537
3538
            self.seq_lens.np[num_reqs:] = 0
            self.seq_lens.copy_to_gpu()
3539

3540
3541
            cum_num_tokens, _ = self._get_cumsum_and_arange(num_scheduled_tokens)
            self.query_start_loc.np[1 : num_reqs + 1] = cum_num_tokens
3542
3543
            self.query_start_loc.copy_to_gpu()

3544
3545
3546
3547
3548
3549
3550
            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,
            )
3551

3552
        with self.maybe_dummy_run_with_lora(
3553
3554
3555
3556
3557
            self.lora_config,
            num_scheduled_tokens,
            num_sampled_tokens,
            activate_lora,
            remove_lora,
3558
        ):
3559
3560
3561
            # 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)
3562
            if self.supports_mm_inputs and not self.model_config.is_encoder_decoder:
3563
                input_ids = None
3564
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_after_padding]
3565
                model_kwargs = {
3566
                    **model_kwargs,
3567
3568
                    **self._dummy_mm_kwargs(num_reqs),
                }
3569
3570
            elif self.enable_prompt_embeds:
                input_ids = None
3571
3572
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_after_padding]
                model_kwargs = self._init_model_kwargs(num_tokens_after_padding)
3573
            else:
3574
                input_ids = self.input_ids.gpu[:num_tokens_after_padding]
3575
                inputs_embeds = None
3576

3577
            if self.uses_mrope:
3578
                positions = self.mrope_positions.gpu[:, :num_tokens_after_padding]
3579
            else:
3580
                positions = self.positions.gpu[:num_tokens_after_padding]
3581
3582
3583
3584
3585
3586
3587
3588
3589

            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,
3590
3591
3592
                            device=self.device,
                        )
                    )
3593
3594

                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
3595
                    num_tokens_after_padding, None, False
3596
                )
3597
3598

            # filter out the valid batch descriptor
3599
3600
3601
3602
3603
            _cg_mode, batch_descriptor = (
                self.cudagraph_dispatcher.dispatch(
                    BatchDescriptor(
                        num_tokens=num_tokens_after_padding,
                        uniform_decode=uniform_decode,
3604
                        has_lora=activate_lora and self.lora_config is not None,
3605
3606
3607
3608
3609
                    )
                )
                if not is_profile
                else (CUDAGraphMode.NONE, None)
            )
3610
3611
3612
            if cudagraph_runtime_mode is not None:
                # we allow forcing NONE when the dispatcher disagrees to support
                # warm ups for cudagraph capture
3613
3614
3615
3616
                assert (
                    cudagraph_runtime_mode == CUDAGraphMode.NONE
                    or cudagraph_runtime_mode == _cg_mode
                ), (
3617
                    f"Cudagraph runtime mode mismatch at dummy_run. "
3618
3619
                    f"Expected {_cg_mode}, but got {cudagraph_runtime_mode}."
                )
3620
3621
            else:
                cudagraph_runtime_mode = _cg_mode
3622

3623
            if ubatch_slices is not None:
3624
3625
3626
3627
3628
3629
3630
                # 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

3631
3632
3633
            with (
                self.maybe_randomize_inputs(input_ids),
                set_forward_context(
3634
3635
                    attn_metadata,
                    self.vllm_config,
3636
                    num_tokens=num_tokens_after_padding,
3637
3638
                    num_tokens_across_dp=num_tokens_across_dp,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
3639
                    batch_descriptor=batch_descriptor,
3640
3641
3642
                    ubatch_slices=ubatch_slices,
                ),
            ):
3643
                outputs = self.model(
3644
3645
3646
3647
                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
3648
                    **model_kwargs,
3649
                )
3650

3651
3652
3653
3654
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
3655

3656
            if self.speculative_config and self.speculative_config.use_eagle():
3657
                assert isinstance(self.drafter, EagleProposer)
3658
3659
3660
3661
                use_cudagraphs = (
                    cudagraph_runtime_mode == CUDAGraphMode.PIECEWISE
                    and not self.speculative_config.enforce_eager
                )
3662
3663
3664
3665
3666
3667
3668
3669
3670
3671
3672
3673

                # 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,
                )
3674

3675
3676
3677
3678
3679
3680
3681
3682
3683
3684
        # 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)

3685
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
3686
3687
3688
3689
        logit_indices_device = torch.from_numpy(logit_indices).to(
            self.device, non_blocking=True
        )
        return hidden_states, hidden_states[logit_indices_device]
3690
3691
3692
3693
3694
3695

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
3696
3697
3698
3699
        # 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)
3700

3701
        logits = self.model.compute_logits(hidden_states)
3702
3703
        num_reqs = logits.size(0)

3704
        dummy_tensors = lambda v: torch.full((num_reqs,), v, device=self.device)
3705
3706
3707
3708
3709
3710
3711
3712
3713
3714
3715
3716
3717
3718
3719

        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)],
3720
            spec_token_ids=[[] for _ in range(num_reqs)],
3721
3722
            allowed_token_ids_mask=None,
            bad_words_token_ids={},
3723
            logitsprocs=LogitsProcessors(),
3724
        )
3725
        try:
3726
3727
3728
            sampler_output = self.sampler(
                logits=logits, sampling_metadata=dummy_metadata
            )
3729
        except RuntimeError as e:
3730
            if "out of memory" in str(e):
3731
3732
3733
3734
                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 "
3735
3736
                    "initializing the engine."
                ) from e
3737
3738
            else:
                raise e
3739
        if self.speculative_config:
3740
3741
            draft_token_ids = [[0] for _ in range(num_reqs)]
            dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
3742
3743
                draft_token_ids, self.device
            )
3744
3745
3746
3747
3748
3749

            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
3750
3751
3752
3753
3754
            logits = torch.randn(
                num_tokens + num_reqs,
                logits.shape[-1],
                device=self.device,
                dtype=logits.dtype,
3755
            )
3756
3757
3758
            self.rejection_sampler(
                dummy_spec_decode_metadata,
                draft_probs,
3759
                logits,
3760
3761
                dummy_metadata,
            )
3762
        return sampler_output
3763

3764
    def _dummy_pooler_run_task(
3765
3766
        self,
        hidden_states: torch.Tensor,
3767
3768
        task: PoolingTask,
    ) -> PoolerOutput:
3769
3770
3771
3772
3773
3774
3775
3776
3777
3778
3779
        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

3780
        dummy_prompt_lens = torch.tensor(
3781
3782
            num_scheduled_tokens_list,
            device="cpu",
3783
        )
3784
3785
3786
        dummy_token_ids = torch.zeros(
            (num_reqs, req_num_tokens), dtype=torch.int32, device=self.device
        )
3787

3788
        model = cast(VllmModelForPooling, self.get_model())
3789
        dummy_pooling_params = PoolingParams(task=task)
3790
        dummy_pooling_params.verify(task=task, model_config=self.model_config)
3791
        to_update = model.pooler.get_pooling_updates(task)
3792
3793
        to_update.apply(dummy_pooling_params)

3794
        dummy_metadata = PoolingMetadata(
3795
3796
3797
3798
            prompt_lens=dummy_prompt_lens,
            prompt_token_ids=dummy_token_ids,
            pooling_params=[dummy_pooling_params] * num_reqs,
        )
3799

3800
3801
3802
        dummy_metadata.build_pooling_cursor(
            num_scheduled_tokens_list, device=hidden_states.device
        )
3803

3804
        try:
3805
3806
3807
            return model.pooler(
                hidden_states=hidden_states, pooling_metadata=dummy_metadata
            )
3808
        except RuntimeError as e:
3809
            if "out of memory" in str(e):
3810
                raise RuntimeError(
3811
3812
3813
                    "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 "
3814
3815
                    "initializing the engine."
                ) from e
3816
3817
            else:
                raise e
3818
3819
3820
3821
3822
3823
3824

    @torch.inference_mode()
    def _dummy_pooler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> PoolerOutput:
        # Find the task that has the largest output for subsequent steps
3825
3826
3827
        supported_pooling_tasks = self.get_supported_pooling_tasks()

        if not supported_pooling_tasks:
3828
            if self.scheduler_config.enable_chunked_prefill:
3829
3830
3831
3832
3833
3834
3835
3836
3837
3838
3839
3840
3841
3842
3843
3844
                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."
                )

3845
        output_size = dict[PoolingTask, float]()
3846
        for task in supported_pooling_tasks:
3847
3848
            # Run a full batch with each task to ensure none of them OOMs
            output = self._dummy_pooler_run_task(hidden_states, task)
3849
            output_size[task] = sum(o.nbytes for o in output)
3850
3851
3852
3853
            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)
3854

3855
    def profile_run(self) -> None:
3856
        # Profile with multimodal encoder & encoder cache.
3857
        if self.supports_mm_inputs:
3858
            if self.model_config.multimodal_config.skip_mm_profiling:
3859
                logger.info(
3860
                    "Skipping memory profiling for multimodal encoder and "
3861
3862
                    "encoder cache."
                )
3863
3864
3865
3866
3867
3868
3869
3870
            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.
3871
                    dummy_modality = mm_budget.get_modality_with_max_tokens()
3872
3873
3874
                    max_mm_items_per_batch = mm_budget.max_items_per_batch_by_modality[
                        dummy_modality
                    ]
3875
3876
3877
3878
3879
3880
3881
3882
3883

                    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,
                    )
3884

3885
3886
3887
3888
3889
                    # Create dummy batch of multimodal inputs.
                    batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                        dummy_modality,
                        max_mm_items_per_batch,
                    )
3890

3891
                    # Run multimodal encoder.
3892
                    dummy_encoder_outputs = self.model.embed_multimodal(
3893
3894
                        **batched_dummy_mm_inputs
                    )
3895

3896
3897
3898
3899
                    sanity_check_mm_encoder_outputs(
                        dummy_encoder_outputs,
                        expected_num_items=max_mm_items_per_batch,
                    )
3900

3901
3902
3903
3904
3905
3906
3907
3908
3909
3910
                    # 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(
3911
3912
                                (encoder_budget, encoder_output_shape[-1])
                            )
3913
3914
3915
3916
3917
3918
                            num_tokens = output.shape[0]
                            expanded[:num_tokens].copy_(output)
                            expanded_outputs.append(expanded)

                        dummy_encoder_outputs = expanded_outputs

3919
                    # Cache the dummy encoder outputs.
3920
                    self.encoder_cache["tmp"] = dict(enumerate(dummy_encoder_outputs))
3921

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        # Add `is_profile` here to pre-allocate communication buffers
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        hidden_states, last_hidden_states = self._dummy_run(
            self.max_num_tokens, is_profile=True
        )
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        if get_pp_group().is_last_rank:
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            if self.is_pooling_model:
                output = self._dummy_pooler_run(hidden_states)
            else:
                output = self._dummy_sampler_run(last_hidden_states)
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        else:
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            output = None
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        self._sync_device()
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        del hidden_states, output
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        self.encoder_cache.clear()
3936
        gc.collect()
3937

3938
    def capture_model(self) -> int:
3939
        if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
3940
            logger.warning(
3941
                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
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3943
                "ensure `cudagraph_mode` was not manually set to `NONE`"
            )
3944
            return 0
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        compilation_counter.num_gpu_runner_capture_triggers += 1

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        start_time = time.perf_counter()

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        @contextmanager
        def freeze_gc():
            # Optimize garbage collection during CUDA graph capture.
            # Clean up, then freeze all remaining objects from being included
            # in future collections.
            gc.collect()
            should_freeze = not envs.VLLM_ENABLE_CUDAGRAPH_GC
            if should_freeze:
                gc.freeze()
            try:
                yield
            finally:
                if should_freeze:
                    gc.unfreeze()
3964
                    gc.collect()
3965

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        # Trigger CUDA graph capture for specific shapes.
        # Capture the large shapes first so that the smaller shapes
        # can reuse the memory pool allocated for the large shapes.
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        set_cudagraph_capturing_enabled(True)
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        with freeze_gc(), graph_capture(device=self.device):
3971
            start_free_gpu_memory = torch.cuda.mem_get_info()[0]
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            cudagraph_mode = self.compilation_config.cudagraph_mode
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            assert cudagraph_mode is not None
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            if self.lora_config:
                if self.compilation_config.cudagraph_specialize_lora:
                    lora_cases = [True, False]
                else:
                    lora_cases = [True]
            else:
                lora_cases = [False]

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            if cudagraph_mode.mixed_mode() != CUDAGraphMode.NONE:
                cudagraph_runtime_mode = cudagraph_mode.mixed_mode()
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                # make sure we capture the largest batch size first
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                compilation_cases = list(
                    product(reversed(self.cudagraph_batch_sizes), lora_cases)
                )
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                self._capture_cudagraphs(
                    compilation_cases,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
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                    uniform_decode=False,
                )
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            # Capture full cudagraph for uniform decode batches if we
            # don't already have full mixed prefill-decode cudagraphs.
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            if (
                cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
                and cudagraph_mode.separate_routine()
            ):
                max_num_tokens = (
                    self.scheduler_config.max_num_seqs * self.uniform_decode_query_len
                )
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                decode_cudagraph_batch_sizes = [
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                    x
                    for x in self.cudagraph_batch_sizes
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                    if max_num_tokens >= x >= self.uniform_decode_query_len
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                ]
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                compilation_cases_decode = list(
                    product(reversed(decode_cudagraph_batch_sizes), lora_cases)
                )
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                self._capture_cudagraphs(
                    compilation_cases=compilation_cases_decode,
                    cudagraph_runtime_mode=CUDAGraphMode.FULL,
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                    uniform_decode=True,
                )
4017

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

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        # 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
4024
        # we may do lazy capturing in future that still allows capturing
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        # after here.
        set_cudagraph_capturing_enabled(False)
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        end_time = time.perf_counter()
        elapsed_time = end_time - start_time
        cuda_graph_size = start_free_gpu_memory - end_free_gpu_memory
        # This usually takes 5~20 seconds.
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        logger.info_once(
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            "Graph capturing finished in %.0f secs, took %.2f GiB",
            elapsed_time,
            cuda_graph_size / (1 << 30),
4036
            scope="local",
4037
        )
4038
        return cuda_graph_size
4039

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    def _capture_cudagraphs(
        self,
4042
        compilation_cases: list[tuple[int, bool]],
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        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}"
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        # Only rank 0 should print progress bar during capture
        if is_global_first_rank():
            compilation_cases = tqdm(
                compilation_cases,
                disable=not self.load_config.use_tqdm_on_load,
                desc="Capturing CUDA graphs ({}, {})".format(
                    "decode" if uniform_decode else "mixed prefill-decode",
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                    cudagraph_runtime_mode.name,
                ),
            )
4061

4062
        # We skip EPLB here since we don't want to record dummy metrics
4063
        for num_tokens, activate_lora in compilation_cases:
4064
            # We currently only capture ubatched graphs when its a FULL
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            # 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
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            allow_microbatching = (
                self.parallel_config.enable_dbo
                and cudagraph_runtime_mode == CUDAGraphMode.FULL
                and uniform_decode
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                and check_ubatch_thresholds(
                    config=self.vllm_config.parallel_config,
                    num_tokens=num_tokens,
                    uniform_decode=uniform_decode,
                )
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            )
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            for _ in range(self.compilation_config.cudagraph_num_of_warmups):
                # Use CUDAGraphRuntimeStyle.NONE (default) for warmup.
                # But be careful, warm up with `NONE`is orthogonal to
                # if we want to warm up attention or not. This is
                # different from the case where `FULL` implies capture
                # attention while `PIECEWISE` implies no attention.
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                force_attention = cudagraph_runtime_mode == CUDAGraphMode.FULL
                self._dummy_run(
                    num_tokens,
                    cudagraph_runtime_mode=CUDAGraphMode.NONE,
                    force_attention=force_attention,
                    uniform_decode=uniform_decode,
                    allow_microbatching=allow_microbatching,
                    skip_eplb=True,
                    remove_lora=False,
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                    activate_lora=activate_lora,
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                )
            self._dummy_run(
                num_tokens,
                cudagraph_runtime_mode=cudagraph_runtime_mode,
                uniform_decode=uniform_decode,
                allow_microbatching=allow_microbatching,
                skip_eplb=True,
                remove_lora=False,
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                activate_lora=activate_lora,
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            )
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        self.maybe_remove_all_loras(self.lora_config)
4106

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    def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None:
        """
        Initialize the attention backends and attention metadata builders.
        """
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        assert len(self.attn_groups) == 0, "Attention backends are already initialized"
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        class AttentionGroupKey(NamedTuple):
            attn_backend: type[AttentionBackend]
            kv_cache_spec: KVCacheSpec

        def get_attn_backends_for_group(
            kv_cache_group_spec: KVCacheGroupSpec,
4119
        ) -> tuple[dict[AttentionGroupKey, list[str]], set[type[AttentionBackend]]]:
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            layers = get_layers_from_vllm_config(
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                self.vllm_config, AttentionLayerBase, kv_cache_group_spec.layer_names
            )
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            attn_backends = {}
            attn_backend_layers = defaultdict(list)
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            # Dedupe based on full class name; this is a bit safer than
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            # using the class itself as the key because when we create dynamic
            # attention backend subclasses (e.g. ChunkedLocalAttention) unless
            # they are cached correctly, there will be different objects per
            # layer.
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            for layer_name in kv_cache_group_spec.layer_names:
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                attn_backend = layers[layer_name].get_attn_backend()
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                if layer_name in self.kv_sharing_fast_prefill_eligible_layers:
                    attn_backend = create_fast_prefill_custom_backend(
                        "FastPrefill",
                        attn_backend,
                    )

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                full_cls_name = attn_backend.full_cls_name()
                layer_kv_cache_spec = kv_cache_group_spec.kv_cache_spec
                if isinstance(layer_kv_cache_spec, UniformTypeKVCacheSpecs):
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                    layer_kv_cache_spec = layer_kv_cache_spec.kv_cache_specs[layer_name]
4143
                key = (full_cls_name, layer_kv_cache_spec)
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                attn_backends[key] = AttentionGroupKey(
                    attn_backend, layer_kv_cache_spec
                )
4147
                attn_backend_layers[key].append(layer_name)
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            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()),
            )
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        def create_attn_groups(
4154
            attn_backends_map: dict[AttentionGroupKey, list[str]],
4155
            kv_cache_group_id: int,
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        ) -> list[AttentionGroup]:
            attn_groups: list[AttentionGroup] = []
4158
            for (attn_backend, kv_cache_spec), layer_names in attn_backends_map.items():
4159
                attn_group = AttentionGroup(
4160
                    attn_backend,
4161
                    layer_names,
4162
                    kv_cache_spec,
4163
                    kv_cache_group_id,
4164
4165
                )

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                attn_groups.append(attn_group)
            return attn_groups

4169
        attention_backend_maps = []
4170
        attention_backend_list = []
4171
        for kv_cache_group_spec in kv_cache_config.kv_cache_groups:
4172
            attn_backends = get_attn_backends_for_group(kv_cache_group_spec)
4173
            attention_backend_maps.append(attn_backends[0])
4174
            attention_backend_list.append(attn_backends[1])
4175
4176

        # Resolve cudagraph_mode before actually initialize metadata_builders
4177
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        self._check_and_update_cudagraph_mode(
            attention_backend_list, kv_cache_config.kv_cache_groups
        )
4180

4181
4182
        for i, attn_backend_map in enumerate(attention_backend_maps):
            self.attn_groups.append(create_attn_groups(attn_backend_map, i))
4183

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    def initialize_metadata_builders(
        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
    ) -> None:
        """
        Create the metadata builders for all KV cache groups and attn groups.
        """
        for kv_cache_group_id in range(len(kv_cache_config.kv_cache_groups)):
            for attn_group in self.attn_groups[kv_cache_group_id]:
                attn_group.create_metadata_builders(
                    self.vllm_config,
                    self.device,
                    kernel_block_sizes[kv_cache_group_id]
                    if kv_cache_group_id < len(kernel_block_sizes)
                    else None,
                    num_metadata_builders=1
                    if not self.parallel_config.enable_dbo
                    else 2,
                )
co63oc's avatar
co63oc committed
4202
        # Calculate reorder batch threshold (if needed)
4203
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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()

4207
    def _check_and_update_cudagraph_mode(
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4210
        self,
        attention_backends: list[set[type[AttentionBackend]]],
        kv_cache_groups: list[KVCacheGroupSpec],
4211
    ) -> None:
4212
        """
4213
        Resolve the cudagraph_mode when there are multiple attention
4214
        groups with potential conflicting CUDA graph support.
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4216
4217
        Then initialize the cudagraph_dispatcher based on the resolved
        cudagraph_mode.
        """
4218
        min_cg_support = AttentionCGSupport.ALWAYS
4219
        min_cg_backend_name = None
4220

4221
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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__
4233
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4235
        # Flexible resolve the cudagraph mode
        cudagraph_mode = self.compilation_config.cudagraph_mode
        # check cudagraph for mixed batch is supported
4236
4237
4238
4239
4240
4241
        if (
            cudagraph_mode.mixed_mode() == CUDAGraphMode.FULL
            and min_cg_support != AttentionCGSupport.ALWAYS
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
4242
                f"with {min_cg_backend_name} backend (support: "
4243
4244
                f"{min_cg_support})"
            )
4245
4246
            if min_cg_support == AttentionCGSupport.NEVER:
                # if not supported any full cudagraphs, just raise it.
4247
4248
                msg += (
                    "; please try cudagraph_mode=PIECEWISE, and "
4249
                    "make sure compilation mode is VLLM_COMPILE"
4250
                )
4251
4252
4253
4254
4255
                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"
4256
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4257
                    CUDAGraphMode.FULL_AND_PIECEWISE
4258
                )
4259
4260
            else:
                msg += "; setting cudagraph_mode=FULL_DECODE_ONLY"
4261
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4262
                    CUDAGraphMode.FULL_DECODE_ONLY
4263
                )
4264
4265
            logger.warning(msg)

4266
        # check that if we are doing decode full-cudagraphs it is supported
4267
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4269
4270
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4272
        if (
            cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
            and min_cg_support == AttentionCGSupport.NEVER
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
4273
                f"with {min_cg_backend_name} backend (support: "
4274
4275
                f"{min_cg_support})"
            )
4276
            if self.compilation_config.mode == CompilationMode.VLLM_COMPILE and (
4277
4278
4279
4280
4281
                self.compilation_config.splitting_ops_contain_attention()
                or self.compilation_config.use_inductor_graph_partition
            ):
                msg += (
                    "; setting cudagraph_mode=PIECEWISE because "
4282
                    "attention is compiled piecewise"
4283
4284
                )
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4285
                    CUDAGraphMode.PIECEWISE
4286
                )
4287
            else:
4288
4289
                msg += (
                    "; setting cudagraph_mode=NONE because "
4290
                    "attention is not compiled piecewise"
4291
4292
                )
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4293
                    CUDAGraphMode.NONE
4294
                )
4295
4296
            logger.warning(msg)

4297
4298
        # check that if we are doing spec-decode + decode full-cudagraphs it is
        # supported
4299
4300
4301
4302
4303
4304
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4306
        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 "
4307
                f"{min_cg_backend_name} (support: {min_cg_support})"
4308
            )
4309
4310
            if self.compilation_config.splitting_ops_contain_attention():
                msg += "; setting cudagraph_mode=PIECEWISE"
4311
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4312
                    CUDAGraphMode.PIECEWISE
4313
                )
4314
4315
            else:
                msg += "; setting cudagraph_mode=NONE"
4316
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4317
                    CUDAGraphMode.NONE
4318
                )
4319
4320
4321
4322
            logger.warning(msg)

        # double check that we can support full cudagraph if they are requested
        # even after automatic downgrades
4323
4324
4325
4326
4327
4328
        if (
            cudagraph_mode.has_full_cudagraphs()
            and min_cg_support == AttentionCGSupport.NEVER
        ):
            raise ValueError(
                f"CUDAGraphMode.{cudagraph_mode.name} is not "
4329
                f"supported with {min_cg_backend_name} backend ("
4330
4331
                f"support:{min_cg_support}) "
                "; please try cudagraph_mode=PIECEWISE, "
4332
                "and make sure compilation mode is VLLM_COMPILE"
4333
            )
4334

4335
4336
        # Trigger cudagraph dispatching keys initialization after
        # resolved cudagraph mode.
4337
        self.cudagraph_dispatcher.initialize_cudagraph_keys(
4338
4339
            self.compilation_config.cudagraph_mode, self.uniform_decode_query_len
        )
4340

4341
4342
    def calculate_reorder_batch_threshold(self) -> None:
        """
4343
4344
4345
4346
        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.
4347
        """
4348
4349
4350
4351
4352
4353
        min_none_high = lambda a, b: a if b is None else b if a is None else min(a, b)

        reorder_batch_thresholds = [
            group.get_metadata_builder().reorder_batch_threshold
            for group in self._attn_group_iterator()
        ]
4354
4355
4356
4357
4358
        # 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
4359
        self.reorder_batch_threshold = reduce(min_none_high, reorder_batch_thresholds)
4360

4361
4362
4363
    @staticmethod
    def select_common_block_size(
        kv_manager_block_size: int, attn_groups: list[AttentionGroup]
4364
4365
    ) -> int:
        """
4366
4367
4368
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4370
        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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4373
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4375
4376

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

        Returns:
4377
            The selected block size
4378
4379

        Raises:
4380
            ValueError: If no valid block size found
4381
4382
        """

4383
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4390
        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
4391
                for supported_size in backend.supported_kernel_block_sizes:
4392
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4418
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4420
4421
                    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
4422
            for supported_size in backend.supported_kernel_block_sizes
4423
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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)
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        ]
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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.vllm_config.speculative_config.num_speculative_tokens
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                    if self.vllm_config.speculative_config
                    else 0
                ),
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            )

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

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

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

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

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

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    def _reshape_kv_cache_tensors(
        self,
        kv_cache_config: KVCacheConfig,
        kv_cache_raw_tensors: dict[str, torch.Tensor],
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        kernel_block_sizes: list[int],
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    ) -> dict[str, torch.Tensor]:
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        """
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        Reshape the KV cache tensors to the desired shape and dtype.
4571

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        Args:
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            kv_cache_config: The KV cache config
            kv_cache_raw_tensors: The KV cache buffer of each layer, with
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                correct size but uninitialized shape.
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            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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        kv_caches: dict[str, torch.Tensor] = {}
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        has_attn, has_mamba = False, False
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        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
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            attn_backend = group.backend
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            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]
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            for layer_name in group.layer_names:
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                if layer_name in self.runner_only_attn_layers:
                    continue
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                raw_tensor = kv_cache_raw_tensors[layer_name]
                assert raw_tensor.numel() % kv_cache_spec.page_size_bytes == 0
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                num_blocks = raw_tensor.numel() // kv_cache_spec.page_size_bytes
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                if isinstance(kv_cache_spec, AttentionSpec):
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                    has_attn = True
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                    num_blocks_per_kv_block = (
                        kv_cache_spec.block_size // kernel_block_size
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                    )
                    kernel_num_blocks = num_blocks * num_blocks_per_kv_block

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                    kv_cache_shape = attn_backend.get_kv_cache_shape(
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                        kernel_num_blocks,
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                        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,
                    )
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                    dtype = kv_cache_spec.dtype
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                    try:
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                        kv_cache_stride_order = attn_backend.get_kv_cache_stride_order()
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                        assert len(kv_cache_stride_order) == len(kv_cache_shape)
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                    except (AttributeError, NotImplementedError):
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                        kv_cache_stride_order = tuple(range(len(kv_cache_shape)))
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                    # The allocation respects the backend-defined stride order
                    # to ensure the semantic remains consistent for each
                    # backend. We first obtain the generic kv cache shape and
                    # then permute it according to the stride order which could
                    # result in a non-contiguous tensor.
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                    kv_cache_shape = tuple(
                        kv_cache_shape[i] for i in kv_cache_stride_order
                    )
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                    # Maintain original KV shape view.
                    inv_order = [
                        kv_cache_stride_order.index(i)
                        for i in range(len(kv_cache_stride_order))
                    ]
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                    kv_caches[layer_name] = (
                        kv_cache_raw_tensors[layer_name]
                        .view(dtype)
                        .view(kv_cache_shape)
                        .permute(*inv_order)
                    )
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4635
                elif isinstance(kv_cache_spec, MambaSpec):
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                    has_mamba = True
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                    raw_tensor = kv_cache_raw_tensors[layer_name]
                    state_tensors = []
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                    storage_offset_bytes = 0
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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,
4653
                            storage_offset=storage_offset_bytes // dtype_size,
4654
                        )
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4655
                        state_tensors.append(tensor)
4656
                        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

4667
    def _update_hybrid_attention_mamba_layout(
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        self, kv_caches: dict[str, torch.Tensor]
    ) -> None:
4670
        """
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        Update the layout of attention layers from (2, num_blocks, ...) to
        (num_blocks, 2, ...).
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4674

        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 "
4686
                        f"a tensor of shape {kv_cache.shape}"
4687
                    )
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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:]),
                    )
4693

4694
    def initialize_kv_cache_tensors(
4695
        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
4696
    ) -> dict[str, torch.Tensor]:
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4701
        """
        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.

4704
        Returns:
4705
            Dict[str, torch.Tensor]: A map between layer names to their
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            corresponding memory buffer for KV cache.
        """
        # Initialize the memory buffer for KV cache
        kv_cache_raw_tensors = self._allocate_kv_cache_tensors(kv_cache_config)
        # Change the memory buffer to the desired shape
4711
        kv_caches = self._reshape_kv_cache_tensors(
4712
            kv_cache_config, kv_cache_raw_tensors, kernel_block_sizes
4713
        )
4714

4715
        # Set up cross-layer KV cache sharing
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4717
        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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4751
        """
        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.
4752
            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:
4755
                    self.kv_sharing_fast_prefill_eligible_layers.add(layer_name)
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                else:
                    break
4758

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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
        """
4766
        kv_cache_config = deepcopy(kv_cache_config)
4767
        self.kv_cache_config = kv_cache_config
4768
        self.may_add_encoder_only_layers_to_kv_cache_config()
4769
        self.maybe_add_kv_sharing_layers_to_kv_cache_groups(kv_cache_config)
4770
        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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4780

        # create metadata builders
        self.initialize_metadata_builders(kv_cache_config, kernel_block_sizes)

4781
        # 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)

Robert Shaw's avatar
Robert Shaw committed
4793
        if has_kv_transfer_group():
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            kv_transfer_group = get_kv_transfer_group()
            kv_transfer_group.register_kv_caches(kv_caches)
            kv_transfer_group.set_host_xfer_buffer_ops(copy_kv_blocks)
Robert Shaw's avatar
Robert Shaw committed
4797

4798
        if self.dcp_world_size > 1:
4799
            layers = get_layers_from_vllm_config(self.vllm_config, AttentionLayerBase)
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            for layer in layers.values():
                assert layer.impl.need_to_return_lse_for_decode, (
                    "DCP requires attention impls to return"
                    " the softmax lse for decode, but the impl "
                    f"{layer.impl.__class__.__name__} "
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                    "does not return the softmax lse for decode."
                )
4807

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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
4813
        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:
4817
                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)
            )
4833

4834
    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
4835
        """
4836
        Generates the KVCacheSpec by parsing the kv cache format from each
4837
4838
        Attention module in the static forward context.
        Returns:
4839
            KVCacheSpec: A dictionary mapping layer names to their KV cache
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4841
            format. Layers that do not need KV cache are not included.
        """
4842
4843
        if has_ec_transfer() and get_ec_transfer().is_producer:
            return {}
4844
        kv_cache_spec: dict[str, KVCacheSpec] = {}
4845
        attn_layers = get_layers_from_vllm_config(self.vllm_config, AttentionLayerBase)
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4846
        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
4862

4863
        return kv_cache_spec
4864

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4865
    def _to_list(self, sampled_token_ids: torch.Tensor) -> list[np.ndarray]:
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        # This is a short term mitigation for issue mentioned in
        # https://github.com/vllm-project/vllm/issues/22754.
        # `tolist` would trigger a cuda wise stream sync, which
        # would block other copy ops from other cuda streams.
        # A cuda event sync would avoid such a situation. Since
        # this is in the critical path of every single model
        # forward loop, this has caused perf issue for a disagg
        # setup.
4874
        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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4878
        return [row for row in pinned.numpy()]