gpu_model_runner.py 208 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] = {}
508
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512
        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(
513
514
                self.max_num_tokens, dtype=torch.int32, device=self.device
            )
515

516
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518
519
520
        self.uniform_decode_query_len = (
            1
            if not self.speculative_config
            else 1 + self.speculative_config.num_speculative_tokens
        )
521
522
523
524

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

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529
530
531
532
533
        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

537
538
539
540
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),
547
548
            dtype=torch.int64,
            device="cpu",
549
550
            pin_memory=self.pin_memory,
        )
551

552
553
554
        # Ephemeral state transferred between execute_model() and sample_tokens().
        self.execute_model_state: ExecuteModelState | None = None

555
556
557
558
    def reset_mm_cache(self) -> None:
        if self.mm_budget:
            self.mm_budget.reset_cache()

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563
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566
567
568
    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
571
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573
574
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576
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578
    ) -> CpuGpuBuffer:
        return CpuGpuBuffer(
            *size,
            dtype=dtype,
            device=self.device,
            pin_memory=self.pin_memory,
            with_numpy=numpy,
        )
579

580
581
582
    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
587
        num_reqs = self.input_batch.num_reqs
        pooling_params = self.input_batch.get_pooling_params()
588
589
590

        token_type_id_requests = dict[int, Any]()
        for i, param in enumerate(pooling_params):
591
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595
            if (
                param.extra_kwargs is not None
                and (token_types := param.extra_kwargs.get("compressed_token_type_ids"))
                is not None
            ):
596
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600
                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]
602
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609
        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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611
            device=self.device
        )
612
613
        return model_kwargs

614
    def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
615
616
        """
        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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623
        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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629
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631
        # 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

632
        if self.reorder_batch_threshold is not None:
633
634
635
            # NOTE(lucas): currently no backend supports the custom masking
            #  required for DCP with q_len > 1, so we assert here. Remove this
            #  assert once the custom mask is support is added to FA3.
636
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638
639
            if (
                self.dcp_world_size > 1
                and envs.VLLM_ATTENTION_BACKEND != "FLASH_ATTN_MLA"
            ):
640
                assert self.reorder_batch_threshold == 1, (
641
                    "DCP not support reorder_batch_threshold > 1 now."
642
                )
643
644
645
            reorder_batch_to_split_decodes_and_prefills(
                self.input_batch,
                scheduler_output,
646
647
                decode_threshold=self.reorder_batch_threshold,
            )
648

649
650
    # Note: used for model runner override.
    def _init_device_properties(self) -> None:
651
        """Initialize attributes from torch.cuda.get_device_properties"""
652
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654
655
656
657
658
        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()

659
    def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
660
661
662
663
664
665
        """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.

666
667
        The SamplingMetadata is updated and copied to the GPU if there is a
        new/resumed/paused/finished request in the batch.
668
669
        """
        # Remove finished requests from the cached states.
670
671
        for req_id in scheduler_output.finished_req_ids:
            self.requests.pop(req_id, None)
672
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674
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676
677
678
        # 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:
679
            self.input_batch.remove_request(req_id)
680
681

        # Free the cached encoder outputs.
682
683
        for mm_hash in scheduler_output.free_encoder_mm_hashes:
            self.encoder_cache.pop(mm_hash, None)
684

685
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688
689
690
691
692
693
694
695
696
697
        # 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:
698
            self.input_batch.remove_request(req_id)
699

700
        reqs_to_add: list[CachedRequestState] = []
701
        # Add new requests to the cached states.
702
703
704
        for new_req_data in scheduler_output.scheduled_new_reqs:
            req_id = new_req_data.req_id
            sampling_params = new_req_data.sampling_params
705
            pooling_params = new_req_data.pooling_params
706

707
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709
710
            if (
                sampling_params
                and sampling_params.sampling_type == SamplingType.RANDOM_SEED
            ):
711
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715
                generator = torch.Generator(device=self.device)
                generator.manual_seed(sampling_params.seed)
            else:
                generator = None

716
717
            if self.is_pooling_model:
                assert pooling_params is not None
718
719
                task = pooling_params.task
                assert task is not None, "You did not set `task` in the API"
720

721
                model = cast(VllmModelForPooling, self.get_model())
722
                to_update = model.pooler.get_pooling_updates(task)
723
724
                to_update.apply(pooling_params)

725
            req_state = CachedRequestState(
726
                req_id=req_id,
727
                prompt_token_ids=new_req_data.prompt_token_ids,
728
                prompt_embeds=new_req_data.prompt_embeds,
729
                mm_features=new_req_data.mm_features,
730
                sampling_params=sampling_params,
731
                pooling_params=pooling_params,
732
                generator=generator,
733
734
                block_ids=new_req_data.block_ids,
                num_computed_tokens=new_req_data.num_computed_tokens,
735
                output_token_ids=[],
736
                lora_request=new_req_data.lora_request,
737
            )
738
739
            self.requests[req_id] = req_state

740
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
741
            if self.uses_mrope:
742
                self._init_mrope_positions(req_state)
743

744
            reqs_to_add.append(req_state)
745

746
        # Update the states of the running/resumed requests.
747
        is_last_rank = get_pp_group().is_last_rank
748
749
        req_data = scheduler_output.scheduled_cached_reqs
        for i, req_id in enumerate(req_data.req_ids):
750
            req_state = self.requests[req_id]
751
752
            num_computed_tokens = req_data.num_computed_tokens[i]
            new_block_ids = req_data.new_block_ids[i]
753
            resumed_from_preemption = req_id in req_data.resumed_req_ids
754
            num_output_tokens = req_data.num_output_tokens[i]
755

756
            # Update the cached states.
757

758
            req_state.num_computed_tokens = num_computed_tokens
759
            req_index = self.input_batch.req_id_to_index.get(req_id)
760
761
762
763
764
765
766
767

            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.
768
769
770
                num_new_tokens = (
                    num_computed_tokens + len(new_token_ids) - req_state.num_tokens
                )
771
772
773
774
                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:
775
                    req_state.output_token_ids.extend(new_token_ids[-num_new_tokens:])
776
777
778
779
780
            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:
781
782
783
784
                    end_idx = (
                        self.input_batch.num_prompt_tokens[req_index]
                        + num_output_tokens
                    )
785
786
                    self.input_batch.num_tokens[req_index] = end_idx
                    self.input_batch.num_tokens_no_spec[req_index] = end_idx
787

788
            # Update the block IDs.
789
            if not resumed_from_preemption:
790
791
                if new_block_ids is not None:
                    # Append the new blocks to the existing block IDs.
792
                    for block_ids, new_ids in zip(req_state.block_ids, new_block_ids):
793
                        block_ids.extend(new_ids)
794
            else:
795
                assert req_index is None
796
                assert new_block_ids is not None
797
798
                # The request is resumed from preemption.
                # Replace the existing block IDs with the new ones.
799
                req_state.block_ids = new_block_ids
800
801
802
803
804

            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.
805
806
807
808
809
810
811

                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:]

812
                reqs_to_add.append(req_state)
813
814
815
                continue

            # Update the persistent batch.
816
            self.input_batch.num_computed_tokens_cpu[req_index] = num_computed_tokens
817
            if new_block_ids is not None:
818
                self.input_batch.block_table.append_row(new_block_ids, req_index)
819
820
821
822
823
824
825

            # 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)
826
                self.input_batch.token_ids_cpu[
827
828
829
                    req_index, start_token_index:end_token_index
                ] = new_token_ids
                self.input_batch.num_tokens_no_spec[req_index] = end_token_index
830
                self.input_batch.num_tokens[req_index] = end_token_index
831

832
            # Add spec_token_ids to token_ids_cpu.
833
            spec_token_ids = scheduler_output.scheduled_spec_decode_tokens.get(
834
                req_id, []
835
            )
836
837
838
839
840
            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[
841
842
                    req_index, start_index:end_token_index
                ] = spec_token_ids
843
844
                # NOTE(woosuk): `num_tokens` here may include spec tokens.
                self.input_batch.num_tokens[req_index] += num_spec_tokens
845
846
847
848
849
850
851

            # 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
852

853
854
        # Add the new or resumed requests to the persistent batch.
        # The smaller empty indices are filled first.
855
856
        for request in reqs_to_add:
            self.input_batch.add_request(request)
857

858
859
860
861
862
863
        # 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()
864

865
    def _update_states_after_model_execute(
866
867
        self, output_token_ids: torch.Tensor
    ) -> None:
868
869
870
871
872
873
874
875
876
877
878
879
        """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.
880
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882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
        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()
        )
900
901
902
        for i, num_tokens in enumerate(num_accepted_tokens):
            self.input_batch.num_accepted_tokens_cpu[i] = num_tokens

903
    def _init_mrope_positions(self, req_state: CachedRequestState):
904
905
        model = self.get_model()
        assert supports_mrope(model), "M-RoPE support is not implemented."
906
907

        req_state.mrope_positions, req_state.mrope_position_delta = (
908
            model.get_mrope_input_positions(
909
                req_state.prompt_token_ids,
910
                req_state.mm_features,
911
            )
912
        )
913

914
    def _extract_mm_kwargs(
915
        self,
916
917
        scheduler_output: "SchedulerOutput",
    ) -> BatchedTensorInputs:
918
        if not scheduler_output or not self.is_multimodal_raw_input_only_model:
919
            return {}
920

921
922
        mm_kwargs = list[MultiModalKwargsItem]()
        for req in scheduler_output.scheduled_new_reqs:
923
924
925
            for feature in req.mm_features:
                if feature.data is not None:
                    mm_kwargs.append(feature.data)
926

927
        # Input all modalities at once
928
        model = cast(SupportsMultiModal, self.model)
929
930
        mm_kwargs_combined: BatchedTensorInputs = {}
        for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
931
932
933
934
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
935
            multimodal_cpu_fields=model.multimodal_cpu_fields,
936
937
        ):
            mm_kwargs_combined.update(mm_kwargs_group)
938

939
        return mm_kwargs_combined
940

941
    def _dummy_mm_kwargs(self, num_seqs: int) -> BatchedTensorInputs:
942
        if not self.is_multimodal_raw_input_only_model:
943
            return {}
944

945
946
947
948
949
        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)
950

951
952
953
    def _get_cumsum_and_arange(
        self,
        num_tokens: np.ndarray,
954
        cumsum_dtype: np.dtype | None = None,
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
    ) -> 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

971
972
973
    def _prepare_input_ids(
        self, total_num_scheduled_tokens: int, cu_num_tokens: np.ndarray
    ) -> None:
974
        """Prepare the input IDs for the current batch.
975

976
977
978
979
980
981
982
        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)
983
984
985
            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)
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
            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)
1004
                indices_match &= prev_index == flattened_index
1005
1006
1007
1008
1009
1010
                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)
1011
1012
1013
            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)
1014
1015
        if num_commmon_tokens == 0:
            # No requests in common with the previous iteration
1016
            # So input_ids.cpu will have all the input ids.
1017
1018
1019
1020
1021
1022
1023
            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_(
1024
1025
1026
                self.input_batch.prev_sampled_token_ids[:num_commmon_tokens, 0],
                non_blocking=True,
            )
1027
1028
            if self.enable_prompt_embeds:
                self.is_token_ids.gpu[:num_commmon_tokens] = True
1029
            return
1030
        # Upload the index tensors asynchronously so the scatter can be non-blocking.
1031
1032
1033
        input_ids_index_tensor = torch.tensor(
            flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1034
        prev_common_req_indices_tensor = torch.tensor(
1035
1036
            prev_common_req_indices, dtype=torch.int64, pin_memory=self.pin_memory
        ).to(self.device, non_blocking=True)
1037
1038
1039
1040
        self.input_ids.gpu.scatter_(
            dim=0,
            index=input_ids_index_tensor,
            src=self.input_batch.prev_sampled_token_ids[
1041
1042
1043
                prev_common_req_indices_tensor, 0
            ],
        )
1044

1045
1046
    def _get_encoder_seq_lens(
        self,
1047
        scheduled_encoder_inputs: dict[str, list[int]],
1048
1049
        kv_cache_spec: KVCacheSpec,
        num_reqs: int,
1050
    ) -> np.ndarray | None:
1051
1052
1053
1054
1055
1056
        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)
1057
        for req_id in scheduled_encoder_inputs:
1058
1059
1060
1061
1062
            req_index = self.input_batch.req_id_to_index[req_id]
            encoder_seq_lens[req_index] = self.max_encoder_len

        return encoder_seq_lens

1063
    def _prepare_inputs(
1064
1065
1066
1067
        self,
        scheduler_output: "SchedulerOutput",
        num_scheduled_tokens: np.ndarray,
        max_num_scheduled_tokens: int,
1068
1069
    ) -> tuple[
        torch.Tensor,
1070
1071
1072
        SpecDecodeMetadata | None,
        UBatchSlices | None,
        torch.Tensor | None,
1073
    ]:
1074
1075
        """
        :return: tuple[
1076
            logits_indices, spec_decode_metadata,
1077
            ubatch_slices, num_tokens_across_dp,
1078
1079
        ]
        """
1080
1081
1082
1083
1084
1085
1086
        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.
1087
        self.input_batch.block_table.commit_block_table(num_reqs)
1088
1089
1090

        # Get request indices.
        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
1091
        req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens)
1092

1093
1094
        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
        # arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
1095
        cu_num_tokens, arange = self._get_cumsum_and_arange(num_scheduled_tokens)
1096
1097

        # Get positions.
1098
        positions_np = self.positions.np[:total_num_scheduled_tokens]
1099
1100
1101
1102
1103
        np.add(
            self.input_batch.num_computed_tokens_cpu[req_indices],
            arange,
            out=positions_np,
        )
1104

1105
1106
        # Calculate M-RoPE positions.
        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1107
        if self.uses_mrope:
1108
1109
            self._calc_mrope_positions(scheduler_output)

1110
1111
1112
1113
        # 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.
1114
1115
1116
        token_indices = (
            positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]
        )
1117
        token_indices_tensor = torch.from_numpy(token_indices)
1118

1119
1120
1121
        # 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.
1122
1123
1124
1125
1126
1127
        torch.index_select(
            self.input_batch.token_ids_cpu_tensor.flatten(),
            0,
            token_indices_tensor,
            out=self.input_ids.cpu[:total_num_scheduled_tokens],
        )
1128
        if self.enable_prompt_embeds:
1129
            is_token_ids = self.input_batch.is_token_ids_tensor.flatten()
1130
1131
1132
1133
            torch.index_select(
                is_token_ids,
                0,
                token_indices_tensor,
1134
1135
                out=self.is_token_ids.cpu[:total_num_scheduled_tokens],
            )
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168

        # 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:
1169
1170
1171
                    self.inputs_embeds.cpu[
                        output_idx : output_idx + actual_num_sched
                    ].copy_(req_embeds[start_pos:actual_end])
1172
1173

                output_idx += num_sched
1174

1175
1176
        self.input_batch.block_table.compute_slot_mapping(req_indices, positions_np)
        self.input_batch.block_table.commit_slot_mapping(total_num_scheduled_tokens)
1177
1178

        # Prepare the attention metadata.
1179
        self.query_start_loc.np[0] = 0
1180
        self.query_start_loc.np[1 : num_reqs + 1] = cu_num_tokens
1181
1182
        # Note: pad query_start_loc to be non-decreasing, as kernels
        # like FlashAttention requires that
1183
        self.query_start_loc.np[num_reqs + 1 :].fill(cu_num_tokens[-1])
1184
        self.query_start_loc.copy_to_gpu()
1185
        query_start_loc = self.query_start_loc.gpu[: num_reqs + 1]
1186

1187
        num_tokens_unpadded = scheduler_output.total_num_scheduled_tokens
1188
        num_tokens_padded = self._get_num_input_tokens(num_tokens_unpadded)
1189
1190
1191
        uniform_decode = (
            max_num_scheduled_tokens == self.uniform_decode_query_len
        ) and (total_num_scheduled_tokens == num_reqs * max_num_scheduled_tokens)
1192
1193
1194
1195
1196
1197
1198

        # 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

1199
        ubatch_slices, num_tokens_across_dp = coordinate_batch_across_dp(
1200
1201
1202
1203
1204
1205
1206
            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,
1207
        )
1208

1209
        self.seq_lens.np[:num_reqs] = (
1210
1211
            self.input_batch.num_computed_tokens_cpu[:num_reqs] + num_scheduled_tokens
        )
1212
        # Fill unused with 0 for full cuda graph mode.
1213
1214
        self.seq_lens.np[num_reqs:].fill(0)
        self.seq_lens.copy_to_gpu()
1215

1216
        num_tokens = [self.requests[r].num_tokens for r in self.input_batch.req_ids]
1217
1218
1219
1220
1221
1222
1223
        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)
1224
1225
1226
        self.discard_request_indices.np[: self.num_discarded_requests] = (
            discard_request_indices
        )
1227
1228
1229

        self.discard_request_indices.copy_to_gpu(self.num_discarded_requests)

1230
        # Copy the tensors to the GPU.
1231
1232
        self._prepare_input_ids(total_num_scheduled_tokens, cu_num_tokens)

1233
        if self.uses_mrope:
1234
            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
1235
1236
            self.mrope_positions.gpu[:, :total_num_scheduled_tokens].copy_(
                self.mrope_positions.cpu[:, :total_num_scheduled_tokens],
1237
1238
                non_blocking=True,
            )
1239
1240
        else:
            # Common case (1D positions)
1241
            self.positions.copy_to_gpu(total_num_scheduled_tokens)
1242

1243
        use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
1244
1245
1246
1247
1248
1249
1250
        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
1251
            num_draft_tokens = None
1252
            spec_decode_metadata = None
1253
            num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
1254
1255
1256
1257
1258
        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)
1259
1260
1261
            # 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)
1262
1263
1264
1265
            for (
                req_id,
                draft_token_ids,
            ) in scheduler_output.scheduled_spec_decode_tokens.items():
1266
1267
                req_idx = self.input_batch.req_id_to_index[req_id]
                num_draft_tokens[req_idx] = len(draft_token_ids)
1268
1269
1270
1271
1272
1273
1274
1275
                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
                )
1276
            spec_decode_metadata = self._calc_spec_decode_metadata(
1277
1278
                num_draft_tokens, cu_num_tokens
            )
1279
            logits_indices = spec_decode_metadata.logits_indices
1280
            num_sampled_tokens = num_draft_tokens + 1
1281
            # For DECODE only cuda graph of some attention backends (e.g., GDN).
1282
            self.num_decode_draft_tokens.np[:num_reqs] = num_decode_draft_tokens
1283
1284
            self.num_decode_draft_tokens.np[num_reqs:].fill(-1)
            self.num_decode_draft_tokens.copy_to_gpu()
1285

1286
1287
1288
1289
1290
        # Hot-Swap lora model
        if self.lora_config:
            assert (
                np.sum(num_sampled_tokens)
                <= self.vllm_config.scheduler_config.max_num_batched_tokens
1291
            )
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
            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
1319
        num_logits_indices = None
1320
1321
1322
1323
1324
1325
        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
                )
1326

1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
        # 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)

1337
1338
1339
        attn_metadata: PerLayerAttnMetadata = {}
        if ubatch_slices is not None:
            attn_metadata = [dict() for _ in range(len(ubatch_slices))]
1340

1341
1342
        # Used in the below loop
        query_start_loc = self.query_start_loc.gpu[: num_reqs + 1]
1343
        query_start_loc_cpu = self.query_start_loc.cpu[: num_reqs + 1]
1344
        seq_lens = self.seq_lens.gpu[:num_reqs]
1345
        seq_lens_cpu = self.seq_lens.cpu[:num_reqs]
1346
1347
1348
        num_computed_tokens_cpu = self.input_batch.num_computed_tokens_cpu_tensor[
            :num_reqs
        ]
1349
1350
1351
        dcp_local_seq_lens = (
            self.dcp_local_seq_lens.gpu[:num_reqs] if self.dcp_world_size > 1 else None
        )
1352
        spec_decode_common_attn_metadata = None
1353
1354
1355
1356
1357
1358
1359
1360
1361

        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()

1362
1363
        if use_spec_decode:
            self.num_accepted_tokens.np[:num_reqs] = (
1364
1365
                self.input_batch.num_accepted_tokens_cpu[:num_reqs]
            )
1366
1367
            self.num_accepted_tokens.np[num_reqs:].fill(1)
            self.num_accepted_tokens.copy_to_gpu()
1368

1369
1370
        # Prepare the attention metadata for each KV cache group and make layers
        # in the same group share the same metadata.
1371
        for kv_cache_gid, kv_cache_group in enumerate(
1372
1373
            self.kv_cache_config.kv_cache_groups
        ):
1374
            encoder_seq_lens = self._get_encoder_seq_lens(
1375
1376
1377
                scheduled_encoder_inputs or {},
                kv_cache_group.kv_cache_spec,
                num_reqs,
1378
            )
1379

1380
            if isinstance(kv_cache_group.kv_cache_spec, EncoderOnlyAttentionSpec):
1381
1382
1383
1384
1385
                # 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,
1386
1387
1388
                    device=self.device,
                )
                slot_mapping = torch.zeros(
1389
                    (total_num_scheduled_tokens,),
1390
1391
1392
                    dtype=torch.int64,
                    device=self.device,
                )
1393
            else:
1394
                blk_table = self.input_batch.block_table[kv_cache_gid]
1395
                blk_table_tensor = blk_table.get_device_tensor(num_reqs)
1396
                slot_mapping = blk_table.slot_mapping.gpu[:total_num_scheduled_tokens]
1397
1398
1399

                # Fill unused with -1. Needed for reshape_and_cache in full cuda
                # graph mode.
1400
                blk_table.slot_mapping.gpu[total_num_scheduled_tokens:].fill_(-1)
1401

1402
            common_attn_metadata = CommonAttentionMetadata(
1403
1404
1405
1406
1407
                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,
1408
1409
1410
                num_reqs=num_reqs,
                num_actual_tokens=total_num_scheduled_tokens,
                max_query_len=max_num_scheduled_tokens,
1411
                max_seq_len=max_seq_len,
1412
1413
                block_table_tensor=blk_table_tensor,
                slot_mapping=slot_mapping,
1414
                logits_indices_padded=logits_indices_padded,
1415
                num_logits_indices=num_logits_indices,
1416
                causal=True,
1417
                encoder_seq_lens=encoder_seq_lens,
1418
                dcp_local_seq_lens=dcp_local_seq_lens,
1419
1420
            )

1421
            if self.speculative_config and spec_decode_common_attn_metadata is None:
1422
                if isinstance(self.drafter, EagleProposer):
1423
                    if self.drafter.attn_layer_names[0] in kv_cache_group.layer_names:
1424
1425
1426
                        spec_decode_common_attn_metadata = common_attn_metadata
                else:
                    spec_decode_common_attn_metadata = common_attn_metadata
1427

1428
1429
1430
1431
1432
1433
            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
                )
1434
                builder = attn_group.get_metadata_builder()
1435

1436
                extra_attn_metadata_args = {}
1437
                if use_spec_decode and isinstance(builder, GDNAttentionMetadataBuilder):
1438
                    extra_attn_metadata_args = dict(
1439
1440
1441
1442
                        num_accepted_tokens=self.num_accepted_tokens.gpu[:num_reqs],
                        num_decode_draft_tokens_cpu=self.num_decode_draft_tokens.cpu[
                            :num_reqs
                        ],
1443
1444
                    )

1445
1446
                if ubatch_slices is not None:
                    common_attn_metadata_list = split_attn_metadata(
1447
1448
                        ubatch_slices, common_attn_metadata
                    )
1449
                    for ubid, common_attn_metadata in enumerate(
1450
1451
                        common_attn_metadata_list
                    ):
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
                        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:
1463
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1465
1466
                            assert type(attn_metadata) is list
                            attn_metadata[ubid][layer_name] = attn_metadata_i
                else:
                    assert isinstance(attn_metadata, dict)
1467
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1470
1471
1472
1473
1474
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1476
                    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,
                        )
1477
1478
                    for layer_name in attn_group.layer_names:
                        attn_metadata[layer_name] = attn_metadata_i
1479

1480
        return attn_metadata, spec_decode_common_attn_metadata
1481

1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
    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
        """
1492

1493
1494
1495
1496
1497
1498
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1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
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1514
        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
1515

1516
1517
1518
1519
    def _compute_cascade_attn_prefix_len(
        self,
        num_scheduled_tokens: np.ndarray,
        num_common_prefix_blocks: int,
1520
1521
        kv_cache_spec: KVCacheSpec,
        attn_metadata_builder: AttentionMetadataBuilder,
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
    ) -> 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.
        """
1540

1541
        common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
1542
1543
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1545
1546
1547
1548
1549
1550
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1553
1554
1555
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1561
1562
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1575
1576
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1578
        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]
1579
        # Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
1580
1581
1582
1583
1584
1585
1586
        # 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(
1587
1588
            common_prefix_len, self.input_batch.num_computed_tokens_cpu[:num_reqs].min()
        )
1589
        # common_prefix_len should be a multiple of the block size.
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
        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
        )
1601
1602
        assert isinstance(kv_cache_spec, AttentionSpec)
        use_cascade = attn_metadata_builder.use_cascade_attention(
1603
1604
1605
            common_prefix_len=common_prefix_len,
            query_lens=num_scheduled_tokens,
            num_query_heads=self.num_query_heads,
1606
            num_kv_heads=kv_cache_spec.num_kv_heads,
1607
            use_alibi=self.use_alibi,
1608
            use_sliding_window=use_sliding_window,
1609
            use_local_attention=use_local_attention,
1610
            num_sms=self.num_sms,
1611
            dcp_world_size=self.dcp_world_size,
1612
1613
1614
        )
        return common_prefix_len if use_cascade else 0

1615
1616
    def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
        mrope_pos_ptr = 0
1617
        for index, req_id in enumerate(self.input_batch.req_ids):
1618
1619
1620
            req = self.requests[req_id]
            assert req.mrope_positions is not None

1621
1622
            num_computed_tokens = self.input_batch.num_computed_tokens_cpu[index]
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1623
            num_prompt_tokens = length_from_prompt_token_ids_or_embeds(
1624
1625
                req.prompt_token_ids, req.prompt_embeds
            )
1626
1627

            if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
1628
1629
                prompt_part_len = max(0, num_prompt_tokens - num_computed_tokens)
                completion_part_len = max(0, num_scheduled_tokens - prompt_part_len)
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
            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

1643
1644
1645
                self.mrope_positions.cpu[:, dst_start:dst_end] = req.mrope_positions[
                    :, src_start:src_end
                ]
1646
1647
1648
1649
1650
1651
1652
                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

1653
                MRotaryEmbedding.get_next_input_positions_tensor(
1654
                    out=self.mrope_positions.np,
1655
1656
1657
1658
1659
                    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,
                )
1660
1661
1662

                mrope_pos_ptr += completion_part_len

1663
1664
    def _calc_spec_decode_metadata(
        self,
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
        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
1681
1682
1683
1684

        # 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(
1685
1686
            num_sampled_tokens, cumsum_dtype=np.int32
        )
1687
        # Step 2. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
1688
        logits_indices = np.repeat(
1689
1690
            cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens
        )
1691
        # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
1692
1693
1694
1695
1696
1697
        logits_indices += arange

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

        # Compute the draft logits indices.
1698
1699
1700
        # 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(
1701
1702
            num_draft_tokens, cumsum_dtype=np.int32
        )
1703
1704
        # [0, 0, 0, 5, 5, 9]
        target_logits_indices = np.repeat(
1705
1706
            cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens
        )
1707
1708
1709
1710
1711
        # [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(
1712
1713
            self.device, non_blocking=True
        )
1714
1715
1716
        cu_num_sampled_tokens = torch.from_numpy(cu_num_sampled_tokens).to(
            self.device, non_blocking=True
        )
1717
1718
1719
        logits_indices = torch.from_numpy(logits_indices).to(
            self.device, non_blocking=True
        )
1720
        target_logits_indices = torch.from_numpy(target_logits_indices).to(
1721
1722
            self.device, non_blocking=True
        )
1723
        bonus_logits_indices = torch.from_numpy(bonus_logits_indices).to(
1724
1725
            self.device, non_blocking=True
        )
1726

1727
1728
        # Compute the draft token ids.
        # draft_token_indices:      [  1,   2,   3, 105, 106, 208]
1729
        draft_token_ids = self.input_ids.gpu[logits_indices]
1730
1731
        draft_token_ids = draft_token_ids[target_logits_indices + 1]

1732
        return SpecDecodeMetadata(
1733
1734
1735
            draft_token_ids=draft_token_ids,
            num_draft_tokens=num_draft_tokens.tolist(),
            cu_num_draft_tokens=cu_num_draft_tokens,
1736
            cu_num_sampled_tokens=cu_num_sampled_tokens,
1737
1738
1739
1740
1741
            target_logits_indices=target_logits_indices,
            bonus_logits_indices=bonus_logits_indices,
            logits_indices=logits_indices,
        )

1742
1743
1744
1745
1746
1747
1748
    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
1749
        self.kv_sharing_fast_prefill_logits_indices[:num_logits].copy_(logits_indices)
1750
1751
1752
1753
1754
        # 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_(
1755
1756
1757
1758
1759
1760
            logits_indices[-1].item()
        )
        if (
            self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
            and num_logits <= self.cudagraph_batch_sizes[-1]
        ):
1761
1762
1763
1764
1765
            # 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
1766
1767
1768
        logits_indices_padded = self.kv_sharing_fast_prefill_logits_indices[
            :num_logits_padded
        ]
1769
1770
        return logits_indices_padded

1771
1772
1773
1774
1775
1776
1777
1778
    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
1779
                inputs.
1780
1781
1782
1783
1784
1785

        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
        """
1786
1787
        scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
        if not scheduled_encoder_inputs:
1788
            return [], []
1789
        # Batch the multi-modal inputs.
1790
        mm_kwargs = list[MultiModalKwargsItem]()
1791
1792
        # list of tuple (mm_hash, position_info)
        mm_hashes_pos = list[tuple[str, PlaceholderRange]]()
1793
1794
        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
            req_state = self.requests[req_id]
1795
1796

            for mm_input_id in encoder_input_ids:
1797
1798
1799
1800
                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))
1801

1802
1803
1804
1805
1806
        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(
1807
1808
            scheduler_output
        )
1809
1810
1811
1812

        if not mm_kwargs:
            return

1813
1814
1815
1816
1817
1818
1819
        # 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.
1820
        model = cast(SupportsMultiModal, self.model)
1821
        encoder_outputs = []
1822
        for modality, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
1823
1824
1825
1826
            mm_kwargs,
            device=self.device,
            pin_memory=self.pin_memory,
            merge_by_field_config=model.merge_by_field_config,
1827
            multimodal_cpu_fields=model.multimodal_cpu_fields,
1828
        ):
1829
1830
1831
            curr_group_outputs = []

            # EVS-related change.
1832
            # (ekhvedchenia): Temporary hack to limit peak memory usage when
1833
            # processing multimodal data. This solves the issue with scheduler
1834
1835
1836
1837
            # 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)
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
1852
1853
            # 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,
1854
                            multimodal_cpu_fields=model.multimodal_cpu_fields,
1855
                        )
1856
                    )
1857

1858
                    micro_batch_outputs = model.embed_multimodal(
1859
1860
                        **micro_batch_mm_inputs
                    )
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870

                    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.
1871
                curr_group_outputs = model.embed_multimodal(**mm_kwargs_group)
1872

1873
1874
            sanity_check_mm_encoder_outputs(
                curr_group_outputs,
1875
                expected_num_items=num_items,
1876
            )
1877
            encoder_outputs.extend(curr_group_outputs)
1878

1879
1880
1881
        # 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(
1882
1883
1884
                output,
                is_embed=pos_info.is_embed,
            )
1885
1886
            logger.debug("Finish execute for mm hash %s", mm_hash)
            self.maybe_save_ec_to_connector(self.encoder_cache, mm_hash)
1887
1888

    def _gather_mm_embeddings(
1889
1890
        self,
        scheduler_output: "SchedulerOutput",
1891
        shift_computed_tokens: int = 0,
1892
1893
1894
1895
1896
1897
1898
1899
    ) -> 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
1900
        should_sync_mrope_positions = False
1901

1902
        for req_id in self.input_batch.req_ids:
1903
1904
            mm_embeds_req: list[torch.Tensor] = []

1905
            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
1906
            req_state = self.requests[req_id]
1907
            num_computed_tokens = req_state.num_computed_tokens + shift_computed_tokens
1908

1909
1910
            for mm_feature in req_state.mm_features:
                pos_info = mm_feature.mm_position
1911
1912
                start_pos = pos_info.offset
                num_encoder_tokens = pos_info.length
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928

                # 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,
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                    num_encoder_tokens,
                )
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                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
            # any requests with sampling params that that require output ids.
            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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Cyrus Leung committed
2935
                    indices.append(offset + tokens.shape[0] - 1)
2936
                    offset += num_draft + 1
2937
                indices = torch.tensor(indices, device=self.device)
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                hidden_states = sample_hidden_states[indices]

2940
            draft_token_ids = self.drafter.propose(
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                target_hidden_states=hidden_states,
                sampling_metadata=sampling_metadata,
            )
2944
        elif self.speculative_config.use_eagle():
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            assert isinstance(self.drafter, EagleProposer)
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            if self.speculative_config.disable_padded_drafter_batch:
                # When padded-batch is disabled, the sampled_token_ids should be
                # the cpu-side list[list[int]] of valid sampled tokens for each
                # request, with invalid requests having empty lists.
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                assert isinstance(sampled_token_ids, list), (
                    "sampled_token_ids should be a python list when"
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                    "padded-batch is disabled."
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                )
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                next_token_ids = self.drafter.prepare_next_token_ids_cpu(
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                    sampled_token_ids,
                    self.requests,
                    self.input_batch,
                    scheduler_output.num_scheduled_tokens,
                )
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            else:
                # When using padded-batch, the sampled_token_ids should be
                # the gpu tensor of sampled tokens for each request, of shape
                # (num_reqs, num_spec_tokens + 1) with rejected tokens having
                # value -1.
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                assert isinstance(sampled_token_ids, torch.Tensor), (
                    "sampled_token_ids should be a torch.Tensor when"
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                    "padded-batch is enabled."
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                )
                next_token_ids, valid_sampled_tokens_count = (
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                    self.drafter.prepare_next_token_ids_padded(
                        common_attn_metadata,
                        sampled_token_ids,
                        self.requests,
                        self.input_batch,
                        self.discard_request_indices.gpu,
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                        self.num_discarded_requests,
2978
                    )
2979
                )
Jiayi Yao's avatar
Jiayi Yao committed
2980

2981
            if spec_decode_metadata is None:
2982
                token_indices_to_sample = None
2983
                # input_ids can be None for multimodal models.
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                target_token_ids = self.input_ids.gpu[:num_scheduled_tokens]
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                target_positions = self._get_positions(num_scheduled_tokens)
2986
                if self.use_aux_hidden_state_outputs:
Wentao Ye's avatar
Wentao Ye committed
2987
                    assert aux_hidden_states is not None
2988
                    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)
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                if self.use_aux_hidden_state_outputs:
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Wentao Ye committed
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                    assert aux_hidden_states is not None
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                    target_hidden_states = torch.cat(
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                        [h[token_indices] for h in aux_hidden_states], dim=-1
                    )
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                else:
                    target_hidden_states = hidden_states[token_indices]
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            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,
3037
            )
3038

3039
        return draft_token_ids
3040

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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}"
3047
            )
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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)
        )
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        if self.parallel_config.enable_eplb:
            self.eplb_state = EplbState(self.parallel_config, self.device)
            eplb_models = 0
3071
        with DeviceMemoryProfiler() as m:
3072
            time_before_load = time.perf_counter()
3073
            model_loader = get_model_loader(self.load_config)
3074
            self.model = model_loader.load_model(
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                vllm_config=self.vllm_config, model_config=self.model_config
            )
3077
            if self.lora_config:
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                self.model = self.load_lora_model(
                    self.model, self.vllm_config, self.device
                )
3081
            if hasattr(self, "drafter"):
3082
                logger.info_once("Loading drafter model...")
3083
                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

3115
            if self.use_aux_hidden_state_outputs:
3116
                if not supports_eagle3(self.get_model()):
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3118
                    raise RuntimeError(
                        "Model does not support EAGLE3 interface but "
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3120
                        "aux_hidden_state_outputs was requested"
                    )
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3130
3131
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3133

                # 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)
3134
            time_after_load = time.perf_counter()
3135
        self.model_memory_usage = m.consumed_memory
3136
        logger.info_once(
3137
            "Model loading took %.4f GiB memory and %.6f seconds",
3138
3139
            self.model_memory_usage / GiB_bytes,
            time_after_load - time_before_load,
3140
            scope="local",
3141
        )
3142
        prepare_communication_buffer_for_model(self.model)
3143
        self.is_multimodal_pruning_enabled = (
3144
            supports_multimodal_pruning(self.get_model())
3145
3146
            and self.model_config.multimodal_config.is_multimodal_pruning_enabled()
        )
3147

3148
        if is_mixture_of_experts(self.model) and self.parallel_config.enable_eplb:
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            logger.info_once("EPLB is enabled for model %s.", self.model_config.model)
            global_expert_load = (
                global_expert_loads[eplb_models] if global_expert_loads else None
            )
            old_global_expert_indices = (
                old_global_expert_indices_per_model[eplb_models]
                if old_global_expert_indices_per_model
                else None
            )
            assert self.eplb_state is not None
            self.eplb_state.add_model(
3160
                self.model,
3161
                self.model_config,
3162
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3164
                global_expert_load,
                old_global_expert_indices,
                rank_mapping,
3165
3166
            )

3167
        if (
3168
3169
            self.vllm_config.compilation_config.mode
            == CompilationMode.STOCK_TORCH_COMPILE
3170
            and supports_dynamo()
3171
        ):
3172
            backend = self.vllm_config.compilation_config.init_backend(self.vllm_config)
3173
            compilation_counter.stock_torch_compile_count += 1
3174
            self.model.compile(fullgraph=True, backend=backend)
3175
            return
3176
        # for other compilation modes, cudagraph behavior is controlled by
3177
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3179
        # CudagraphWraper and CudagraphDispatcher of vllm.

        # wrap the model with full cudagraph wrapper if needed.
3180
3181
3182
3183
3184
3185
3186
        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
            )
3187
3188
        elif self.parallel_config.enable_dbo:
            if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
3189
3190
3191
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.FULL, self.device
                )
3192
            else:
3193
3194
3195
                self.model = UBatchWrapper(
                    self.model, self.vllm_config, CUDAGraphMode.NONE, self.device
                )
3196

3197
    def _get_eagle3_aux_layers_from_config(self) -> tuple[int, ...] | None:
3198
3199
3200
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
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3214
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3220
        """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

3221
    def reload_weights(self) -> None:
3222
        assert getattr(self, "model", None) is not None, (
3223
            "Cannot reload weights before model is loaded."
3224
        )
3225
3226
        model_loader = get_model_loader(self.load_config)
        logger.info("Reloading weights inplace...")
3227
        model_loader.load_weights(self.get_model(), model_config=self.model_config)
3228

3229
3230
3231
3232
3233
    def save_tensorized_model(
        self,
        tensorizer_config: "TensorizerConfig",
    ) -> None:
        TensorizerLoader.save_model(
3234
            self.get_model(),
3235
            tensorizer_config=tensorizer_config,
3236
            model_config=self.model_config,
3237
3238
        )

3239
3240
3241
    def _get_prompt_logprobs_dict(
        self,
        hidden_states: torch.Tensor,
3242
        num_scheduled_tokens: dict[str, int],
3243
    ) -> dict[str, LogprobsTensors | None]:
3244
3245
3246
3247
        num_prompt_logprobs_dict = self.input_batch.num_prompt_logprobs
        if not num_prompt_logprobs_dict:
            return {}

3248
        in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
3249
        prompt_logprobs_dict: dict[str, LogprobsTensors | None] = {}
3250
3251
3252
3253
3254

        # 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():
3255
            num_tokens = num_scheduled_tokens[req_id]
3256
3257
3258

            # Get metadata for this request.
            request = self.requests[req_id]
3259
3260
3261
3262
            if request.prompt_token_ids is None:
                # Prompt logprobs is incompatible with prompt embeddings
                continue

3263
3264
            num_prompt_tokens = len(request.prompt_token_ids)
            prompt_token_ids = torch.tensor(request.prompt_token_ids).to(
3265
3266
                self.device, non_blocking=True
            )
3267

3268
3269
3270
3271
3272
3273
            # 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(
3274
3275
                    num_prompt_tokens - 1, num_prompt_logprobs + 1
                )
3276
3277
                in_progress_dict[req_id] = logprobs_tensors

3278
            # Determine number of logits to retrieve.
3279
3280
            start_idx = request.num_computed_tokens
            start_tok = start_idx + 1
3281
            num_remaining_tokens = num_prompt_tokens - start_tok
3282
            if num_tokens <= num_remaining_tokens:
3283
                # This is a chunk, more tokens remain.
3284
3285
3286
                # 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.
3287
3288
3289
3290
3291
                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)
3292
3293
3294
3295
3296
3297
3298
                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
3299
3300
3301
3302
3303

            # 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]
3304
            offset = self.query_start_loc.np[req_idx].item()
3305
            prompt_hidden_states = hidden_states[offset : offset + num_logits]
3306
            logits = self.model.compute_logits(prompt_hidden_states)
3307
3308
3309
3310

            # 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.
3311
            tgt_token_ids = prompt_token_ids[start_tok : start_tok + num_logits]
3312
3313

            # Compute prompt logprobs.
3314
3315
            logprobs = self.sampler.compute_logprobs(logits)
            token_ids, logprobs, ranks = self.sampler.gather_logprobs(
3316
3317
                logprobs, num_prompt_logprobs, tgt_token_ids
            )
3318
3319

            # Transfer GPU->CPU async.
3320
3321
            chunk_slice = slice(start_idx, start_idx + num_logits)
            logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
3322
3323
3324
                token_ids, non_blocking=True
            )
            logprobs_tensors.logprobs[chunk_slice].copy_(logprobs, non_blocking=True)
3325
            logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
3326
3327
                ranks, non_blocking=True
            )
3328
3329
3330
3331
3332

        # 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]
3333
            del in_progress_dict[req_id]
3334
3335

        # Must synchronize the non-blocking GPU->CPU transfers.
3336
        if prompt_logprobs_dict:
3337
            self._sync_device()
3338
3339
3340

        return prompt_logprobs_dict

3341
3342
    def _get_nans_in_logits(
        self,
3343
        logits: torch.Tensor | None,
3344
3345
3346
3347
3348
3349
3350
3351
3352
3353
3354
    ) -> 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])
3355
3356
3357
                    if num_nans_for_index is not None and req_index < logits.shape[0]
                    else 0
                )
3358
3359
3360
3361
            return num_nans_in_logits
        except IndexError:
            return {}

3362
3363
3364
3365
3366
3367
    @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
3368
         - during DP rank dummy run
3369
3370
3371
3372
3373
3374
3375
3376
3377
3378
3379
        """
        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(
3380
                    self.input_ids.gpu,
3381
3382
                    low=0,
                    high=self.model_config.get_vocab_size(),
3383
3384
                    dtype=input_ids.dtype,
                )
3385

3386
            logger.debug_once("Randomizing dummy data for DP Rank")
3387
            input_ids.copy_(rand_input_ids()[: input_ids.size(0)], non_blocking=True)
3388
3389
3390
            yield
            input_ids.fill_(0)

3391
3392
3393
3394
3395
3396
    def _get_mm_dummy_batch(
        self,
        modality: str,
        max_items_per_batch: int,
    ) -> BatchedTensorInputs:
        """Dummy data for profiling and precompiling multimodal models."""
3397
3398
        assert self.mm_budget is not None

3399
3400
        dummy_decoder_data = self.mm_registry.get_decoder_dummy_data(
            model_config=self.model_config,
3401
            seq_len=self.max_model_len,
3402
            mm_counts={modality: 1},
3403
            cache=self.mm_budget.cache,
3404
3405
3406
3407
        )
        dummy_mm_data = dummy_decoder_data.multi_modal_data

        # Result in the maximum GPU consumption of the model
3408
3409
        dummy_mm_item = dummy_mm_data[modality][0]
        dummy_mm_items = [dummy_mm_item] * max_items_per_batch
3410

3411
        model = cast(SupportsMultiModal, self.model)
3412
3413
3414
3415
3416
3417
3418
        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,
3419
                multimodal_cpu_fields=model.multimodal_cpu_fields,
3420
3421
            )
        )
3422

3423
3424
3425
3426
    @torch.inference_mode()
    def _dummy_run(
        self,
        num_tokens: int,
3427
        cudagraph_runtime_mode: CUDAGraphMode | None = None,
3428
3429
        force_attention: bool = False,
        uniform_decode: bool = False,
3430
        allow_microbatching: bool = True,
3431
3432
        skip_eplb: bool = False,
        is_profile: bool = False,
3433
        create_mixed_batch: bool = False,
3434
        remove_lora: bool = True,
3435
        activate_lora: bool = False,
3436
    ) -> tuple[torch.Tensor, torch.Tensor]:
3437
3438
3439
3440
3441
3442
3443
        """
        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.
3444
                - if not set will determine the cudagraph mode based on using
3445
                    the self.cudagraph_dispatcher.
3446
3447
3448
3449
                - CUDAGraphMode.NONE: No cudagraph, for warm up and profile run
                - CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
                - CUDAGraphMode.FULL: Full cudagraph, attention metadata is
                    needed.
3450
            force_attention: If True, always create attention metadata. Used to
3451
3452
3453
3454
                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.
3455
3456
            create_mixed_batch: If True, create a mixed batch with both decode
                (1 token) and prefill (multiple tokens) requests.
3457
            remove_lora: If False, dummy LoRAs are not destroyed after the run
3458
            activate_lora: If False, dummy_run is performed without LoRAs.
3459
        """
3460
3461
3462
3463
        assert (
            cudagraph_runtime_mode is None
            or cudagraph_runtime_mode.valid_runtime_modes()
        )
3464

3465
        # If cudagraph_mode.decode_mode() == FULL and
3466
        # cudagraph_mode.separate_routine(). This means that we are using
3467
3468
3469
3470
3471
3472
3473
3474
3475
3476
3477
        # 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.
3478
        max_query_len = self.uniform_decode_query_len if uniform_decode else num_tokens
3479

3480
3481
3482
3483
3484
        # 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
3485
3486
3487
3488
        if create_mixed_batch:
            assert not uniform_decode
            # Create mixed batch:
            # first half decode tokens, second half one prefill
3489
            num_decode_tokens = min(max_num_reqs - 1, num_tokens // 2)
3490
3491
3492
3493
            num_prefill_tokens = num_tokens - num_decode_tokens
            num_reqs = num_decode_tokens + 1

            # Create decode requests (1 token each) followed by prefill request
3494
            num_scheduled_tokens_list = [1] * num_decode_tokens + [num_prefill_tokens]
3495
3496
3497
            # Note: Overriding max_query_len to be the prefill tokens
            max_query_len = num_prefill_tokens
        elif uniform_decode:
3498
            assert not create_mixed_batch
3499
            num_reqs = min(max_num_reqs, cdiv(num_tokens, max_query_len))
3500
3501
            num_scheduled_tokens_list = [max_query_len] * num_reqs
            if num_tokens % max_query_len != 0:
3502
                num_scheduled_tokens_list[-1] = num_tokens % max_query_len
3503
3504
3505
3506
3507
3508
        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

3509
3510
        assert sum(num_scheduled_tokens_list) == num_tokens
        assert len(num_scheduled_tokens_list) == num_reqs
3511
        num_scheduled_tokens = np.array(num_scheduled_tokens_list, dtype=np.int32)
3512
        total_num_scheduled_tokens = int(num_scheduled_tokens.sum())
3513
        num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
3514

3515
3516
3517
        # Disable DP padding when running eager
        allow_dp_padding = self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE

3518
3519
        # We currently only microbatch if the number of tokens is
        # over a certain threshold.
3520
        ubatch_slices, num_tokens_across_dp = coordinate_batch_across_dp(
3521
3522
3523
3524
3525
3526
3527
            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,
3528
3529
3530
        )
        num_tokens_after_padding = num_tokens
        if num_tokens_across_dp is not None:
3531
3532
            dp_rank = self.parallel_config.data_parallel_rank
            num_tokens_after_padding = int(num_tokens_across_dp[dp_rank])
3533

3534
        attn_metadata: PerLayerAttnMetadata | None = None
3535
3536
3537

        # If force_attention is True, we always capture attention. Otherwise,
        # it only happens for cudagraph_runtime_mode=FULL.
3538
        if force_attention or cudagraph_runtime_mode == CUDAGraphMode.FULL:
3539
3540
3541
3542
3543
3544
            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:
3545
                seq_lens = max_query_len
3546
            self.seq_lens.np[:num_reqs] = seq_lens
3547
3548
            self.seq_lens.np[num_reqs:] = 0
            self.seq_lens.copy_to_gpu()
3549

3550
3551
            cum_num_tokens, _ = self._get_cumsum_and_arange(num_scheduled_tokens)
            self.query_start_loc.np[1 : num_reqs + 1] = cum_num_tokens
3552
3553
            self.query_start_loc.copy_to_gpu()

3554
3555
3556
3557
3558
3559
3560
            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,
            )
3561

3562
        with self.maybe_dummy_run_with_lora(
3563
3564
3565
3566
3567
            self.lora_config,
            num_scheduled_tokens,
            num_sampled_tokens,
            activate_lora,
            remove_lora,
3568
        ):
3569
3570
3571
            # 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)
3572
            if self.supports_mm_inputs and not self.model_config.is_encoder_decoder:
3573
                input_ids = None
3574
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_after_padding]
3575
                model_kwargs = {
3576
                    **model_kwargs,
3577
3578
                    **self._dummy_mm_kwargs(num_reqs),
                }
3579
3580
            elif self.enable_prompt_embeds:
                input_ids = None
3581
3582
                inputs_embeds = self.inputs_embeds.gpu[:num_tokens_after_padding]
                model_kwargs = self._init_model_kwargs(num_tokens_after_padding)
3583
            else:
3584
                input_ids = self.input_ids.gpu[:num_tokens_after_padding]
3585
                inputs_embeds = None
3586

3587
            if self.uses_mrope:
3588
                positions = self.mrope_positions.gpu[:, :num_tokens_after_padding]
3589
            else:
3590
                positions = self.positions.gpu[:num_tokens_after_padding]
3591
3592
3593
3594
3595
3596
3597
3598
3599

            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,
3600
3601
3602
                            device=self.device,
                        )
                    )
3603
3604

                intermediate_tensors = self.sync_and_slice_intermediate_tensors(
3605
                    num_tokens_after_padding, None, False
3606
                )
3607
3608

            # filter out the valid batch descriptor
3609
3610
3611
3612
3613
            _cg_mode, batch_descriptor = (
                self.cudagraph_dispatcher.dispatch(
                    BatchDescriptor(
                        num_tokens=num_tokens_after_padding,
                        uniform_decode=uniform_decode,
3614
                        has_lora=activate_lora and self.lora_config is not None,
3615
3616
3617
3618
3619
                    )
                )
                if not is_profile
                else (CUDAGraphMode.NONE, None)
            )
3620
3621
3622
            if cudagraph_runtime_mode is not None:
                # we allow forcing NONE when the dispatcher disagrees to support
                # warm ups for cudagraph capture
3623
3624
3625
3626
                assert (
                    cudagraph_runtime_mode == CUDAGraphMode.NONE
                    or cudagraph_runtime_mode == _cg_mode
                ), (
3627
                    f"Cudagraph runtime mode mismatch at dummy_run. "
3628
3629
                    f"Expected {_cg_mode}, but got {cudagraph_runtime_mode}."
                )
3630
3631
            else:
                cudagraph_runtime_mode = _cg_mode
3632

3633
            if ubatch_slices is not None:
3634
3635
3636
3637
3638
3639
3640
                # 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

3641
3642
3643
            with (
                self.maybe_randomize_inputs(input_ids),
                set_forward_context(
3644
3645
                    attn_metadata,
                    self.vllm_config,
3646
                    num_tokens=num_tokens_after_padding,
3647
3648
                    num_tokens_across_dp=num_tokens_across_dp,
                    cudagraph_runtime_mode=cudagraph_runtime_mode,
3649
                    batch_descriptor=batch_descriptor,
3650
3651
3652
                    ubatch_slices=ubatch_slices,
                ),
            ):
3653
                outputs = self.model(
3654
3655
3656
3657
                    input_ids=input_ids,
                    positions=positions,
                    intermediate_tensors=intermediate_tensors,
                    inputs_embeds=inputs_embeds,
3658
                    **model_kwargs,
3659
                )
3660

3661
3662
3663
3664
            if self.use_aux_hidden_state_outputs:
                hidden_states, _ = outputs
            else:
                hidden_states = outputs
3665

3666
            if self.speculative_config and self.speculative_config.use_eagle():
3667
                assert isinstance(self.drafter, EagleProposer)
3668
3669
3670
3671
                use_cudagraphs = (
                    cudagraph_runtime_mode == CUDAGraphMode.PIECEWISE
                    and not self.speculative_config.enforce_eager
                )
3672
3673
3674
3675
3676
3677
3678
3679
3680
3681
3682
3683

                # 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,
                )
3684

3685
3686
3687
3688
3689
3690
3691
3692
3693
3694
        # 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)

3695
        logit_indices = np.cumsum(num_scheduled_tokens) - 1
3696
3697
3698
3699
        logit_indices_device = torch.from_numpy(logit_indices).to(
            self.device, non_blocking=True
        )
        return hidden_states, hidden_states[logit_indices_device]
3700
3701
3702
3703
3704
3705

    @torch.inference_mode()
    def _dummy_sampler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
3706
3707
3708
3709
        # 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)
3710

3711
        logits = self.model.compute_logits(hidden_states)
3712
3713
        num_reqs = logits.size(0)

3714
        dummy_tensors = lambda v: torch.full((num_reqs,), v, device=self.device)
3715
3716
3717
3718
3719
3720
3721
3722
3723
3724
3725
3726
3727
3728
3729

        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)],
3730
            spec_token_ids=[[] for _ in range(num_reqs)],
3731
3732
            allowed_token_ids_mask=None,
            bad_words_token_ids={},
3733
            logitsprocs=LogitsProcessors(),
3734
        )
3735
        try:
3736
3737
3738
            sampler_output = self.sampler(
                logits=logits, sampling_metadata=dummy_metadata
            )
3739
        except RuntimeError as e:
3740
            if "out of memory" in str(e):
3741
3742
3743
3744
                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 "
3745
3746
                    "initializing the engine."
                ) from e
3747
3748
            else:
                raise e
3749
        if self.speculative_config:
3750
3751
            draft_token_ids = [[0] for _ in range(num_reqs)]
            dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
3752
3753
                draft_token_ids, self.device
            )
3754
3755
3756
3757
3758
3759

            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
3760
3761
3762
3763
3764
            logits = torch.randn(
                num_tokens + num_reqs,
                logits.shape[-1],
                device=self.device,
                dtype=logits.dtype,
3765
            )
3766
3767
3768
            self.rejection_sampler(
                dummy_spec_decode_metadata,
                draft_probs,
3769
                logits,
3770
3771
                dummy_metadata,
            )
3772
        return sampler_output
3773

3774
    def _dummy_pooler_run_task(
3775
3776
        self,
        hidden_states: torch.Tensor,
3777
3778
        task: PoolingTask,
    ) -> PoolerOutput:
3779
3780
3781
3782
3783
3784
3785
3786
3787
3788
3789
        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

3790
        dummy_prompt_lens = torch.tensor(
3791
3792
            num_scheduled_tokens_list,
            device="cpu",
3793
        )
3794
3795
3796
        dummy_token_ids = torch.zeros(
            (num_reqs, req_num_tokens), dtype=torch.int32, device=self.device
        )
3797

3798
        model = cast(VllmModelForPooling, self.get_model())
3799
        dummy_pooling_params = PoolingParams(task=task)
3800
        dummy_pooling_params.verify(task=task, model_config=self.model_config)
3801
        to_update = model.pooler.get_pooling_updates(task)
3802
3803
        to_update.apply(dummy_pooling_params)

3804
        dummy_metadata = PoolingMetadata(
3805
3806
3807
3808
            prompt_lens=dummy_prompt_lens,
            prompt_token_ids=dummy_token_ids,
            pooling_params=[dummy_pooling_params] * num_reqs,
        )
3809

3810
3811
3812
        dummy_metadata.build_pooling_cursor(
            num_scheduled_tokens_list, device=hidden_states.device
        )
3813

3814
        try:
3815
3816
3817
            return model.pooler(
                hidden_states=hidden_states, pooling_metadata=dummy_metadata
            )
3818
        except RuntimeError as e:
3819
            if "out of memory" in str(e):
3820
                raise RuntimeError(
3821
3822
3823
                    "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 "
3824
3825
                    "initializing the engine."
                ) from e
3826
3827
            else:
                raise e
3828
3829
3830
3831
3832
3833
3834

    @torch.inference_mode()
    def _dummy_pooler_run(
        self,
        hidden_states: torch.Tensor,
    ) -> PoolerOutput:
        # Find the task that has the largest output for subsequent steps
3835
3836
3837
        supported_pooling_tasks = self.get_supported_pooling_tasks()

        if not supported_pooling_tasks:
3838
            if self.scheduler_config.enable_chunked_prefill:
3839
3840
3841
3842
3843
3844
3845
3846
3847
3848
3849
3850
3851
3852
3853
3854
                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."
                )

3855
        output_size = dict[PoolingTask, float]()
3856
        for task in supported_pooling_tasks:
3857
3858
            # Run a full batch with each task to ensure none of them OOMs
            output = self._dummy_pooler_run_task(hidden_states, task)
3859
            output_size[task] = sum(o.nbytes for o in output)
3860
3861
3862
3863
            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)
3864

3865
    def profile_run(self) -> None:
3866
        # Profile with multimodal encoder & encoder cache.
3867
        if self.supports_mm_inputs:
3868
            if self.model_config.multimodal_config.skip_mm_profiling:
3869
                logger.info(
3870
                    "Skipping memory profiling for multimodal encoder and "
3871
3872
                    "encoder cache."
                )
3873
3874
3875
3876
3877
3878
3879
3880
            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.
3881
                    dummy_modality = mm_budget.get_modality_with_max_tokens()
3882
3883
3884
                    max_mm_items_per_batch = mm_budget.max_items_per_batch_by_modality[
                        dummy_modality
                    ]
3885
3886
3887
3888
3889
3890
3891
3892
3893

                    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,
                    )
3894

3895
3896
3897
3898
3899
                    # Create dummy batch of multimodal inputs.
                    batched_dummy_mm_inputs = self._get_mm_dummy_batch(
                        dummy_modality,
                        max_mm_items_per_batch,
                    )
3900

3901
                    # Run multimodal encoder.
3902
                    dummy_encoder_outputs = self.model.embed_multimodal(
3903
3904
                        **batched_dummy_mm_inputs
                    )
3905

3906
3907
3908
3909
                    sanity_check_mm_encoder_outputs(
                        dummy_encoder_outputs,
                        expected_num_items=max_mm_items_per_batch,
                    )
3910

3911
3912
3913
3914
3915
3916
3917
3918
3919
3920
                    # NOTE: This happens when encoder cache needs to store
                    # the embeddings that encoder outputs are scattered onto.
                    # In this case we create dummy embeddings of size
                    # (encode_budget, hidden_size) and scatter encoder
                    # output into it.
                    encoder_output_shape = dummy_encoder_outputs[0].shape
                    if encoder_output_shape[0] < encoder_budget:
                        expanded_outputs = []
                        for output in dummy_encoder_outputs:
                            expanded = output.new_zeros(
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                                (encoder_budget, encoder_output_shape[-1])
                            )
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                            num_tokens = output.shape[0]
                            expanded[:num_tokens].copy_(output)
                            expanded_outputs.append(expanded)

                        dummy_encoder_outputs = expanded_outputs

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                    # Cache the dummy encoder outputs.
3930
                    self.encoder_cache["tmp"] = dict(enumerate(dummy_encoder_outputs))
3931

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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()
3946
        gc.collect()
3947

3948
    def capture_model(self) -> int:
3949
        if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
3950
            logger.warning(
3951
                "Skipping CUDA graph capture. To turn on CUDA graph capture, "
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                "ensure `cudagraph_mode` was not manually set to `NONE`"
            )
3954
            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()
3974
                    gc.collect()
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        # Trigger CUDA graph capture for specific shapes.
        # Capture the large shapes first so that the smaller shapes
        # can reuse the memory pool allocated for the large shapes.
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        set_cudagraph_capturing_enabled(True)
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        with freeze_gc(), graph_capture(device=self.device):
3981
            start_free_gpu_memory = torch.cuda.mem_get_info()[0]
3982
            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,
                )
4004

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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,
                )
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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
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        # we may do lazy capturing in future that still allows capturing
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        # after here.
        set_cudagraph_capturing_enabled(False)
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        end_time = time.perf_counter()
        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),
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            scope="local",
4047
        )
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        return cuda_graph_size
4049

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    def _capture_cudagraphs(
        self,
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        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,
                ),
            )
4071

4072
        # We skip EPLB here since we don't want to record dummy metrics
4073
        for num_tokens, activate_lora in compilation_cases:
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            # 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)
4116

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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,
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        ) -> 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]
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                key = (full_cls_name, layer_kv_cache_spec)
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                attn_backends[key] = AttentionGroupKey(
                    attn_backend, layer_kv_cache_spec
                )
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                attn_backend_layers[key].append(layer_name)
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            return (
                {attn_backends[k]: v for k, v in attn_backend_layers.items()},
                set(group_key.attn_backend for group_key in attn_backends.values()),
            )
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        def create_attn_groups(
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            attn_backends_map: dict[AttentionGroupKey, list[str]],
4165
            kv_cache_group_id: int,
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        ) -> list[AttentionGroup]:
            attn_groups: list[AttentionGroup] = []
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            for (attn_backend, kv_cache_spec), layer_names in attn_backends_map.items():
4169
                attn_group = AttentionGroup(
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                    attn_backend,
4171
                    layer_names,
4172
                    kv_cache_spec,
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                    kv_cache_group_id,
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                )

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                attn_groups.append(attn_group)
            return attn_groups

4179
        attention_backend_maps = []
4180
        attention_backend_list = []
4181
        for kv_cache_group_spec in kv_cache_config.kv_cache_groups:
4182
            attn_backends = get_attn_backends_for_group(kv_cache_group_spec)
4183
            attention_backend_maps.append(attn_backends[0])
4184
            attention_backend_list.append(attn_backends[1])
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4186

        # Resolve cudagraph_mode before actually initialize metadata_builders
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        self._check_and_update_cudagraph_mode(
            attention_backend_list, kv_cache_config.kv_cache_groups
        )
4190

4191
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        for i, attn_backend_map in enumerate(attention_backend_maps):
            self.attn_groups.append(create_attn_groups(attn_backend_map, i))
4193

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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
4212
        # Calculate reorder batch threshold (if needed)
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        # Note (tdoublep): do this *after* constructing builders,
        # because some of them change the threshold at init time.
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        self.calculate_reorder_batch_threshold()

4217
    def _check_and_update_cudagraph_mode(
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        self,
        attention_backends: list[set[type[AttentionBackend]]],
        kv_cache_groups: list[KVCacheGroupSpec],
4221
    ) -> None:
4222
        """
4223
        Resolve the cudagraph_mode when there are multiple attention
4224
        groups with potential conflicting CUDA graph support.
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4226
4227
        Then initialize the cudagraph_dispatcher based on the resolved
        cudagraph_mode.
        """
4228
        min_cg_support = AttentionCGSupport.ALWAYS
4229
        min_cg_backend_name = None
4230

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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__
4243
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4245
        # Flexible resolve the cudagraph mode
        cudagraph_mode = self.compilation_config.cudagraph_mode
        # check cudagraph for mixed batch is supported
4246
4247
4248
4249
4250
4251
        if (
            cudagraph_mode.mixed_mode() == CUDAGraphMode.FULL
            and min_cg_support != AttentionCGSupport.ALWAYS
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
4252
                f"with {min_cg_backend_name} backend (support: "
4253
4254
                f"{min_cg_support})"
            )
4255
4256
            if min_cg_support == AttentionCGSupport.NEVER:
                # if not supported any full cudagraphs, just raise it.
4257
4258
                msg += (
                    "; please try cudagraph_mode=PIECEWISE, and "
4259
                    "make sure compilation mode is VLLM_COMPILE"
4260
                )
4261
4262
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4264
4265
                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"
4266
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4267
                    CUDAGraphMode.FULL_AND_PIECEWISE
4268
                )
4269
4270
            else:
                msg += "; setting cudagraph_mode=FULL_DECODE_ONLY"
4271
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4272
                    CUDAGraphMode.FULL_DECODE_ONLY
4273
                )
4274
4275
            logger.warning(msg)

4276
        # check that if we are doing decode full-cudagraphs it is supported
4277
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4280
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4282
        if (
            cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
            and min_cg_support == AttentionCGSupport.NEVER
        ):
            msg = (
                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
4283
                f"with {min_cg_backend_name} backend (support: "
4284
4285
                f"{min_cg_support})"
            )
4286
            if self.compilation_config.mode == CompilationMode.VLLM_COMPILE and (
4287
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4291
                self.compilation_config.splitting_ops_contain_attention()
                or self.compilation_config.use_inductor_graph_partition
            ):
                msg += (
                    "; setting cudagraph_mode=PIECEWISE because "
4292
                    "attention is compiled piecewise"
4293
4294
                )
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4295
                    CUDAGraphMode.PIECEWISE
4296
                )
4297
            else:
4298
4299
                msg += (
                    "; setting cudagraph_mode=NONE because "
4300
                    "attention is not compiled piecewise"
4301
4302
                )
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4303
                    CUDAGraphMode.NONE
4304
                )
4305
4306
            logger.warning(msg)

4307
4308
        # check that if we are doing spec-decode + decode full-cudagraphs it is
        # supported
4309
4310
4311
4312
4313
4314
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4316
        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 "
4317
                f"{min_cg_backend_name} (support: {min_cg_support})"
4318
            )
4319
4320
            if self.compilation_config.splitting_ops_contain_attention():
                msg += "; setting cudagraph_mode=PIECEWISE"
4321
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4322
                    CUDAGraphMode.PIECEWISE
4323
                )
4324
4325
            else:
                msg += "; setting cudagraph_mode=NONE"
4326
                cudagraph_mode = self.compilation_config.cudagraph_mode = (
4327
                    CUDAGraphMode.NONE
4328
                )
4329
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4332
            logger.warning(msg)

        # double check that we can support full cudagraph if they are requested
        # even after automatic downgrades
4333
4334
4335
4336
4337
4338
        if (
            cudagraph_mode.has_full_cudagraphs()
            and min_cg_support == AttentionCGSupport.NEVER
        ):
            raise ValueError(
                f"CUDAGraphMode.{cudagraph_mode.name} is not "
4339
                f"supported with {min_cg_backend_name} backend ("
4340
4341
                f"support:{min_cg_support}) "
                "; please try cudagraph_mode=PIECEWISE, "
4342
                "and make sure compilation mode is VLLM_COMPILE"
4343
            )
4344

4345
4346
        # Trigger cudagraph dispatching keys initialization after
        # resolved cudagraph mode.
4347
        self.cudagraph_dispatcher.initialize_cudagraph_keys(
4348
4349
            self.compilation_config.cudagraph_mode, self.uniform_decode_query_len
        )
4350

4351
4352
    def calculate_reorder_batch_threshold(self) -> None:
        """
4353
4354
4355
4356
        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.
4357
        """
4358
4359
4360
4361
4362
4363
        min_none_high = lambda a, b: a if b is None else b if a is None else min(a, b)

        reorder_batch_thresholds = [
            group.get_metadata_builder().reorder_batch_threshold
            for group in self._attn_group_iterator()
        ]
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4368
        # 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
4369
        self.reorder_batch_threshold = reduce(min_none_high, reorder_batch_thresholds)
4370

4371
4372
4373
    @staticmethod
    def select_common_block_size(
        kv_manager_block_size: int, attn_groups: list[AttentionGroup]
4374
4375
    ) -> int:
        """
4376
4377
4378
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4380
        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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4383
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4385
4386

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

        Returns:
4387
            The selected block size
4388
4389

        Raises:
4390
            ValueError: If no valid block size found
4391
4392
        """

4393
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        def block_size_is_supported(
            backends: list[type[AttentionBackend]], block_size: int
        ) -> bool:
            """
            Check if the block size is supported by all backends.
            """
            for backend in backends:
                is_supported = False
4401
                for supported_size in backend.supported_kernel_block_sizes:
4402
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4431
                    if isinstance(supported_size, int):
                        if block_size == supported_size:
                            is_supported = True
                    elif isinstance(supported_size, MultipleOf):
                        if block_size % supported_size.base == 0:
                            is_supported = True
                    else:
                        raise ValueError(f"Unknown supported size: {supported_size}")
                if not is_supported:
                    return False
            return True

        backends = [group.backend for group in attn_groups]

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

        # Case 2: otherwise, the block_size must be an `int`-format supported size of
        # at least one backend. Iterate over all `int`-format supported sizes in
        # descending order and return the first one that is supported by all backends.
        # Simple proof:
        # If the supported size b is in MultipleOf(x_i) format for all attention
        # backends i, and b a factor of kv_manager_block_size, then
        # kv_manager_block_size also satisfies MultipleOf(x_i) for all i. We will
        # return kv_manager_block_size in case 1.
        all_int_supported_sizes = set(
            supported_size
            for backend in backends
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            for supported_size in backend.supported_kernel_block_sizes
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            if isinstance(supported_size, int)
        )
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        for supported_size in sorted(all_int_supported_sizes, reverse=True):
            if kv_manager_block_size % supported_size != 0:
                continue
            if block_size_is_supported(backends, supported_size):
                return supported_size
        raise ValueError(f"No common block size for {kv_manager_block_size}. ")
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    def may_reinitialize_input_batch(
        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
    ) -> None:
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        """
        Re-initialize the input batch if the block sizes are different from
        `[self.cache_config.block_size]`. This usually happens when there
        are multiple KV cache groups.

        Args:
            kv_cache_config: The KV cache configuration.
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            kernel_block_sizes: The kernel block sizes for each KV cache group.
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        """
        block_sizes = [
            kv_cache_group.kv_cache_spec.block_size
            for kv_cache_group in kv_cache_config.kv_cache_groups
4458
            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.
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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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4645
                elif isinstance(kv_cache_spec, MambaSpec):
4646
                    has_mamba = True
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                    raw_tensor = kv_cache_raw_tensors[layer_name]
                    state_tensors = []
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                    storage_offset_bytes = 0
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                    for shape, dtype in zip(kv_cache_spec.shapes, kv_cache_spec.dtypes):
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                        dtype_size = get_dtype_size(dtype)
                        num_element_per_page = (
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                            kv_cache_spec.page_size_bytes // dtype_size
                        )
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                        target_shape = (num_blocks, *shape)
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                        stride = torch.empty(target_shape).stride()
                        target_stride = (num_element_per_page, *stride[1:])
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                        assert storage_offset_bytes % dtype_size == 0
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                        tensor = torch.as_strided(
                            raw_tensor.view(dtype),
                            size=target_shape,
                            stride=target_stride,
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                            storage_offset=storage_offset_bytes // dtype_size,
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                        )
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4665
                        state_tensors.append(tensor)
4666
                        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

4677
    def _update_hybrid_attention_mamba_layout(
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        self, kv_caches: dict[str, torch.Tensor]
    ) -> None:
4680
        """
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        Update the layout of attention layers from (2, num_blocks, ...) to
        (num_blocks, 2, ...).
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4684

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

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        for group in self._kv_cache_spec_attn_group_iterator():
            kv_cache_spec = group.kv_cache_spec
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            for layer_name in group.layer_names:
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                kv_cache = kv_caches[layer_name]
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                if isinstance(kv_cache_spec, AttentionSpec) and kv_cache.shape[0] == 2:
                    assert kv_cache.shape[1] != 2, (
                        "Fail to determine whether the layout is "
                        "(2, num_blocks, ...) or (num_blocks, 2, ...) for "
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                        f"a tensor of shape {kv_cache.shape}"
4697
                    )
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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:]),
                    )
4703

4704
    def initialize_kv_cache_tensors(
4705
        self, kv_cache_config: KVCacheConfig, kernel_block_sizes: list[int]
4706
    ) -> dict[str, torch.Tensor]:
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        """
        Initialize the memory buffer for KV cache.

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

4714
        Returns:
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            Dict[str, torch.Tensor]: A map between layer names to their
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            corresponding memory buffer for KV cache.
        """
        # Initialize the memory buffer for KV cache
        kv_cache_raw_tensors = self._allocate_kv_cache_tensors(kv_cache_config)
        # Change the memory buffer to the desired shape
4721
        kv_caches = self._reshape_kv_cache_tensors(
4722
            kv_cache_config, kv_cache_raw_tensors, kernel_block_sizes
4723
        )
4724

4725
        # Set up cross-layer KV cache sharing
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        for layer_name, target_layer_name in self.shared_kv_cache_layers.items():
            logger.debug("%s reuses KV cache of %s", layer_name, target_layer_name)
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            kv_caches[layer_name] = kv_caches[target_layer_name]

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

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

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

        if self.cache_config.kv_sharing_fast_prefill:
            # In You Only Cache Once (https://arxiv.org/abs/2405.05254) or other
            # similar KV sharing setups, only the layers that generate KV caches
            # are involved in the prefill phase, enabling prefill to early exit.
4762
            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:
4765
                    self.kv_sharing_fast_prefill_eligible_layers.add(layer_name)
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                else:
                    break
4768

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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
        """
4776
        kv_cache_config = deepcopy(kv_cache_config)
4777
        self.kv_cache_config = kv_cache_config
4778
        self.may_add_encoder_only_layers_to_kv_cache_config()
4779
        self.maybe_add_kv_sharing_layers_to_kv_cache_groups(kv_cache_config)
4780
        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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4790

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

4791
        # 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
4803
        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
4807

4808
        if self.dcp_world_size > 1:
4809
            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."
                )
4817

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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
4823
        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:
4827
                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)
            )
4843

4844
    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
4845
        """
4846
        Generates the KVCacheSpec by parsing the kv cache format from each
4847
4848
        Attention module in the static forward context.
        Returns:
4849
            KVCacheSpec: A dictionary mapping layer names to their KV cache
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            format. Layers that do not need KV cache are not included.
        """
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4853
        if has_ec_transfer() and get_ec_transfer().is_producer:
            return {}
4854
        kv_cache_spec: dict[str, KVCacheSpec] = {}
4855
        attn_layers = get_layers_from_vllm_config(self.vllm_config, AttentionLayerBase)
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4856
        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
4872

4873
        return kv_cache_spec
4874

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4875
    def _to_list(self, sampled_token_ids: torch.Tensor) -> list[np.ndarray]:
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        # This is a short term mitigation for issue mentioned in
        # https://github.com/vllm-project/vllm/issues/22754.
        # `tolist` would trigger a cuda wise stream sync, which
        # would block other copy ops from other cuda streams.
        # A cuda event sync would avoid such a situation. Since
        # this is in the critical path of every single model
        # forward loop, this has caused perf issue for a disagg
        # setup.
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        pinned = self.sampled_token_ids_pinned_cpu[: sampled_token_ids.shape[0]]
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        pinned.copy_(sampled_token_ids, non_blocking=True)
        self.transfer_event.record()
        self.transfer_event.synchronize()
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        return [row for row in pinned.numpy()]